[ { "path": ".gitignore", "content": ".Rproj.user\n.Rhistory\n.RData\n.Ruserdata\n.httr-oauth\nSQF 2012.csv\n" }, { "path": "0-Practise/day1.R", "content": "# Day 1\n\nlibrary(ISLR)\ndata('Default')\nstr(Default)\nLR1 = glm(default ~ ., family='binomial', data=Default)\nsummary(LR1) #leave income\nLR2 = glm(default ~ student + balance, family='binomial', data=Default)\nsummary(LR2)\n#\nrange(Default$balance)\n\nndata3 = Default[c(1,60,700),]\npredict(LR2,newdata=ndata3, type='response' )\n\n#mtcars\nstr(mtcars)\n\n\n\n\n\n\n#vectors, arrays, matrix, list, factor, dataframe\n\nx = 1:5\nx1 = c('a','b')\nm1 = matrix(1:24, nrow=6)\nm1\nlist1 = list(x, x1, m1)\nlist1\nclass(women)\nwomen\nstr(women)\n?women\nwomen\nhead(women)\ntail(women,n=3)\nhead(women, n=3)\nnames(women)\nsummary(women)\ndim(women)\ndata()\nlibrary(MASS)\nx = women$height\nx\nplot(x)\nmean(x)\nsd(x) ; var(x)\nmax(x)\nmedian(x)\nx\nsort(x, decreasing = T)\ntable(x)\nquantile(x)\nx\nseq(0,1,.1)\nquantile(x, c(.1, .5, .8))\nquantile(x,seq(0,1,.1) )\nsummary(x)\nmin(x); max(x)\nboxplot(x)\nabline(h= c(min(x), max(x),mean(x)+1, median(x)), col=1:5, lwd=4)\n#\n\n\n# LM\nhead(women)\nnames(women)\nmodel1 = lm(weight ~ height, data=women)\nplot(women)\n?lm\n#options(scipen=999)\nsummary(model1)\nmodel1\n#y = mx + c\ny = 3.45 * x + - 87\nwomen$height\nfitted(model1)\ncbind(women, fitted(model1))\nresiduals(model1)\ncbind(women, fitted(model1), residuals(model1), diff= fitted(model1) - women$weight)\nsqrt(sum(residuals(model1)^2)/nrow(women))\ncbind(women, fitted(model1))\nrange(women$height)\nnew1= data.frame(height=c(57, 60.5,70))\np1=predict(model1, newdata = new1)\ncbind(new1, p1)\n\n#mtcars----\nnames(mtcars)\n?mtcars\nmtmodel_1 = lm(mpg ~ wt, data=mtcars )\nmtmodel_2 = lm(mpg ~ wt + disp, data=mtcars )\nmtmodel_3 = lm(mpg ~ wt + disp + cyl, data=mtcars )\nmtmodel_4 = lm(mpg ~ ., data=mtcars )\nsummary(mtmodel_1) #.745\nsummary(mtmodel_2) #.766\nsummary(mtmodel_3) #.766\nsummary(mtmodel_4) #.807\nAIC(mtmodel_1, mtmodel_2,mtmodel_3,mtmodel_4)\nsummary(mtmodel_4) #.807\nstep(lm(mpg ~ ., data=mtcars ))\nmtmodel_5= lm(mpg ~ wt + qsec + am, data=mtcars)\nsummary(mtmodel_5) #Adjusted R-squared: 0.834 \n\n\n#\n\nattendance = 1:20\nmarks = 1:20\nsummary(lm(marks ~ attendance))\ncbind(attendance, marks)\ncor(attendance, marks)\n#\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#y = mx + c\nx\ny = 3.45 * x + - 87\nx\nhead(women)\n(y = 4.45 * 58 - 87)\nplot(women)\nabline(model1, col='red', lwd=4)\nabline(v=64) ; abline(h=150)\n\n\n\nx2 = floor(runif(1000, 50, 100))\nx2\nx2a= sort(x2)\nx2a[1000/2]\nmedian(x2)\nsort(x)\n\nt1= table(x2)\nsort(t1, decreasing = T)\n\nx1 = rep(10,10)\nx1\nsd(x1)\n\n\n\n\ndim(mtcars)\nmtlogmodel = glm(am ~ hp + wt, family='binomial', data=mtcars)\nsummary(mtlogmodel)\np1=predict(mtlogmodel, newdata=mtcars, type='response')\np2= round(p1, 3)\np3 = ifelse(p2<0.5,0,1)\ncbind(mtcars$am, mtcars$hp, mtcars$wt, p2,p3, truefalse= mtcars$am == p3)\n\n\n" }, { "path": "0-Practise/day2.R", "content": "# Day 3 - Online batch of MA\n\n#attach function of R\nwomen\nnames(women)\nheight\nattach(women)\nheight\nweight\nwomen$height\n\n#List\ng <- \"My First List\" #scalar\nh <- c(25, 26, 18, 39) # numeric vector\nj <- matrix(1:10, nrow=5) #matrix\nk <- c(\"one\", \"two\", \"three\") # character vector\nmylist <- list(title=g, ages=h, j, k, women)\nmylist\n\nmylist[[2]]\nmylist[[5]]\n\n\n# plot\nplot(x=height, y=weight, type='b', lty=5, pch=11, fg='red', bg='green', col.axis='purple', cex=1.5, cex.axis=2)\ntitle(main='Henry Harvin', sub=' MA Course')\n" }, { "path": "0-Practise/day3.R", "content": "attach(mtcars)\nplot(wt, mpg)\nabline(lm(mpg~wt))\ntitle(\"Regression of MPG on Weight\")\ndetach(mtcars)\n\ndose <- c(20, 30, 40, 45, 60)\ndrugA <- c(16, 20, 27, 40, 60)\ndrugB <- c(15, 18, 25, 31, 40)\nplot(dose, drugA, type=\"l\")\nplot(dose, drugA, type=\"b\")\n\npar(no.readonly = T)\nopar <- par(no.readonly=TRUE)\nplot(dose, drugA, type=\"b\")\npar(lty=2, pch=17)\nplot(dose, drugA, type=\"b\")\nplot(dose, drugB, type=\"b\")\npar(opar)\nplot(dose, drugA, type=\"b\")\nplot(dose, drugA, type=\"b\",fg='red', col='purple', col.axis='green')\n\nlibrary(RColorBrewer)\nn <- 7\nmycolors <- brewer.pal(n, \"Set1\")\nbarplot(rep(1,n), col=mycolors)\nbarplot(rep(1,n), col=1:7)\n\nn <- 10\nmycolors <- rainbow(n)\npie(rep(1, n), labels=mycolors, col=mycolors)\nmygrays <- gray(0:n/n)\npie(rep(1, n), labels=mygrays, col=mygrays)\n" }, { "path": "0-Practise/first.R", "content": "# First File in R\nx1 <- c(1, 5, 4, 9, 0) # <- is assignment x to have value 1,5,4,9,0\n#control + enter\nx2 = c(1, 5, 4, 9, 0)\nx1\nx2\n\nx = c(1,2,3,4,5,6,7,8,9,10)\nx\nx = 1:100\nx\nx = runif(100, 50, 200)\n?runif\nx\nx = rnorm(100, mean=50, sd=10)\nx\ntrunc(x)\nround(x,1)\nfloor(x)\nceiling(x)\nhist(x)\nas.integer(x)\nplot(density(x))\nabline(v=50)\n?runif\nhead(x)\nclass(x);mode(x)\ntypeof(x)\nsummary(x)\n\n\n# Types of Data Structures in R\n\n\n" }, { "path": "0-Practise/htmlimport.R", "content": "#Installing the web scraping package rvest\n#install.packages(\"rvest\")\nlibrary(rvest)\n#Specifying the url for desired website to be scrapped\nurl <- 'http://pgdbablog.wordpress.com/2015/12/10/pre-semester-at-iim-calcutta/'\n\n\n#Reading the HTML code from the website\nwebpage <- read_html(url)\n\n#Know about the selector gadget\nvignette(\"selectorgadget\")\n\n#Using CSS selectors to scrap the post date\npost_date_html <- html_nodes(webpage,'.entry-date')\n\npost_date_html <- html_nodes(webpage,'.published , .entry-title')\n#Converting the post date to text\npost_date <- html_text(post_date_html)\n\n#Verify the date captured\npost_date\n\n\n\nurl=\"www.imdb.com\"\n\n#Using the CSS selector (using ‘www.imdb.com’ website in this example)\nrating_html=html_nodes(webpage,'.imdb-rating') #’.imdb-rating’ is taken from CSS selector\n\n#Converting the rating data to text\nrating <- html_text(rating_html)\n\n#Check the rating captured\nrating\n\nhtml <- read_html(\"http://www.imdb.com/title/tt1490017/\")\ncast <- html_nodes(html, \"#titleCast .itemprop\")\nlength(cast)\n#> [1] 30\ncast[1:2]\n\n\nhtml <- read_html(\"http://www.imdb.com/title/tt1490017/\")\ncast <- html_nodes(html, \".quicklink\")\nlength(cast)\n#> [1] 15\n\nhtml_text(cast)\n\n\n\nurl=\"https://www.timeshighereducation.com/world-university-rankings/2019/world-ranking#!/page/0/length/-1/sort_by/rank/sort_order/asc/cols/stats\"\ntypeof(url)\nlength(url)\nlinks= \".stats_female_male_ratio , .stats_pc_intl_students , .stats_student_staff_ratio , .stats_number_students , .ranking-institution-title\"\n\nhtml <- read_html(\"https://www.timeshighereducation.com/world-university-rankings/2019/world-ranking#!/page/0/length/-1/sort_by/rank/sort_order/asc/cols/stats\")\ncast <- html_nodes(html, \".stats_female_male_ratio\")\nlength(cast)\n#> [1] 30\ncast[1:2]\ncast\nranks <- html_nodes(url, \".ranking-institution-title\")\n\n\n\n\nlibrary(rvest)\n\nURL <- \"https://www.soccerstats.com/latest.asp?league=netherlands\" #Feed page\n\nWS <- read_html (URL) #reads webpage into WS variable\n\nURLs <- WS %>% html_nodes (\"a:nth-child(1)\") %>% html_attr(\"href\") %>% as.character() # Get the CSS nodes & extract the URLs \n\nURLs <- paste0(\"http://www.soccerstats.com/\",URLs) \n\noversdf <- data.frame(URLs=URLs)\n\nrownames(oversdf) #returns a vector of row names in the overs data.frame:\n\nURLs <-subset(oversdf, grepl(\"pmatch\", oversdf$URLs),stringsAsFactors = FALSE)\n\nwrite.csv(URLs,file=paste(getwd(),\"/sportURLs.csv\",sep=\"\"),row.names=FALSE)\n\nCatcher1 <- data.frame(FMatch=character(),TotalGoals=character (),stringsAsFactors = FALSE)\n\n##################################\n#start of workaround\nn<-nrow(URLs)\nURLs2<-character()\nfor (i in 1:n) {\n URLs2[i]<-as.character(URLs[i,1])\n}\n\n\nlibrary(dplyr)\nlibrary(rvest)\nweb = read_html(\"https://news.google.com/?hl=en-IN&gl=IN&ceid=IN:en\")\n\nweb %>% html_nodes(\".VDXfz\") %>% html_text()\n\nlibrary(rvest)\nlibrary(purr)\nurl_base =html(\"https://www.cochranelibrary.com/cdsr/table-of-contents/2018/11\")\n#map_df(1:4)\n\npage = read_html(url_base)\npage %>% data.frame(paper = html_text(html_nodes(\".search-result-doi\"))) %>% df4\n\n\nurl2 = \"http://www.espncricinfo.com/india/content/player/28081.html\"\nlibrary(rvest)\nlibrary(curl)\nmsd = read_html(url2)\nmsd\n\nmsd2 <- msd %>% html_nodes(\"table\") %>% .[1] %>% html_table(fill=T)\nmsd2\nstr(msd2)\n\n#-----\nurl3 = \"https://www.timeshighereducation.com/world-university-rankings/2019/world-ranking#!/page/0/length/-1/sort_by/rank/sort_order/asc/cols/stats\"\nthe = read_html(url3)\nthe\n\nthe3 <- the %>% html_nodes(\"table\") %>% .[1] %>% html_table(fill=T)\nthe3\nstr(msd2)\n\nhtml_" }, { "path": "0-Practise/import2.R", "content": "#web scrapping\n#https://www.analyticsvidhya.com/blog/2017/03/beginners-guide-on-web-scraping-in-r-using-rvest-with-hands-on-knowledge/\n\n\n#Loading the rvest package\nlibrary('rvest')\n\n#Specifying the url for desired website to be scrapped\nurl <- 'http://www.imdb.com/search/title?count=100&release_date=2016,2016&title_type=feature'\n\n#Reading the HTML code from the website\nwebpage <- read_html(url)\nwebpage\n\n#Using CSS selectors to scrap the rankings section\nrank_data_html <- html_nodes(webpage,'.text-primary')\n\n#Converting the ranking data to text\nrank_data <- html_text(rank_data_html)\n\n#Let's have a look at the rankings\nhead(rank_data)\n\n#Data-Preprocessing: Converting rankings to numerical\nrank_data<-as.numeric(rank_data)\n\n#Let's have another look at the rankings\nhead(rank_data)\n\n#[1] 1 2 3 4 5 6\n\n\n#Using CSS selectors to scrap the title section\ntitle_data_html <- html_nodes(webpage,'.lister-item-header a')\n\n#Converting the title data to text\ntitle_data <- html_text(title_data_html)\n\n#Let's have a look at the title\nhead(title_data)\n\n#[1] \"Sing\" \"Moana\" \"Moonlight\" \"Hacksaw Ridge\"\n#[5] \"Passengers\" \"Trolls\"\n\n#Using CSS selectors to scrap the description section\ndescription_data_html <- html_nodes(webpage,'.ratings-bar+ .text-muted')\n\n#Converting the description data to text\ndescription_data <- html_text(description_data_html)\n\n#Let's have a look at the description data\nhead(description_data)\n\n#Data-Preprocessing: removing '\\n'\ndescription_data<-gsub(\"\\n\",\"\",description_data)\n\n#Let's have another look at the description data \nhead(description_data)\n" }, { "path": "0-Practise/practise.R", "content": "#List\nx; m1; a1; df1\ng =\"My First List\"\nh = c(25, 26,18,39)\nj = matrix(1:10,nrow=2)\nk = c('one','two','three')\nmylist = list(title=g, ages=h, j, h)\nmylist2 = list(k, mylist)\nmylist2\nmylist\nmylist[1]\nmylist[2]\nmylist[[2]]\nmylist[['ages']]\nmylist$ages\n\n#List end\n\n\n#Factors\ndf1\n# category type - ordered or unordered\n#gender, course, color - unordered\n#grades, division, position,likertscale, ratings\nsummary(df1)\n(grades = sample(c('A', 'B', 'C'),size=10, replace=T, prob=c(.4,.3,.3)))\n\ndf1$grades = grades\ndf1\nsummary(df1)\ndf1$gender = factor(df1$gender)\nsummary(df1)\ndf1$grades =factor(df1$grades, ordered=T)\ndf1$grades\naggregate(df1$age, by=list(df1$grades), mean)\naggregate(df1$age, by=list(df1$gender), mean)\naggregate(df1$age, by=list(df1$course), mean)\n(df1$grades =factor(df1$grades, ordered=T, levels=c('C','B','A')))\n\n(division = sample(c('Excellent', 'Very Good', 'Sat'),size=10, replace=T, prob=c(.4,.3,.3)))\ndivision\nsummary(division)\nFdivision = factor(division)\nsummary(Fdivision)\nFdivision2 = factor(division, ordered=T, levels=c('Sat', 'Very Good', 'Excellent'))\nsummary(Fdivision2)\nFdivision2\nFdivision3 = factor(division, ordered=T)\nsummary(Fdivision3)\nFdivision3\n#factors end\n\n\n#Data Frame\n(rollno = 1:10)\n(sname = paste('Student',1:10,sep='-'))\n(age = floor(runif(10, 20, 30)))\n(gender = c(rep('M',5),rep('F',5)))\n(course = sample(c('Engg','Medical','MBA'), 10, replace=T, prob=c(.3, .4, .3)))\ntable(course)\n(married = sample(c(TRUE, FALSE), 10, replace=T))\ntable(married)\nrollno; sname; age ; gender; course; married\n\n(df1 = data.frame(rollno, sname, age , gender, course, married))\ndf1[1:2,3:4]\ndf1$sname\ndf1[df1$married==T, ]\ndf1[df1$course=='Engg' & df1$age > 25, ]\ndf1[df1$married==T & df1$course=='Engg' & df1$age > 25, c('sname') ]\n?aggregate\naggregate(df1$age, by=list(df1$gender), FUN=mean)\naggregate(df1$age, by=list(df1$course), FUN=mean)\naggregate(df1$age, by=list(df1$course, df1$gender), FUN=mean)\n\n\ndf1\nsummary(df1)\n\n#DF end\n\n\n\n\n\n\n\n\n#Array\n?array\narray(data = NA, dim = length(data), dimnames = NULL)\n#Coys - 5, Products-3, Locations-4\nceiling(3.2);\n\n(salesfig = floor(runif(60, 70, 100)))\n(a1 = array(data = salesfig, dim = c(4,3,5), dimnames = list(paste('Loc',1:4),paste('Prod',1:3),paste('Coy',1:5))))\napply(a1,1, sum)# sum locationwise\napply(a1,2, sum)\napply(a1,3, sum)\n(ma1 = apply(a1,c(1,3), sum))\ncolSums(ma1)\nrowSums(ma1)\n\napply(a1,c(2,3), sum)\n\n\n\n\n#arrayend\n\n\n#Matrix\n#row x columns\n?rnorm\nset.seed(1234)\n(x = trunc(runif(24,100,500)))\n(m1 = matrix(data=x, nrow=4,dimnames = list(c('delhi','mumbai','noida','chennai'),paste('Prod',1:6,sep=\"-\"))))\ncolMeans(m1);rowMeans(m1)\ncolSums(m1); rowSums(m1)\npie(x=rowMeans(m1))\nbarplot(rowMeans(m1)) # barplot for locations\nbarplot(colMeans(m1)) #barlplot for products\nbarplot(colMeans(m1), horiz = T)\nbarplot(colMeans(m1), horiz = T, col=1:6)\nm1\n#Subset a Matrix\nm1[ , 1:2]\nm1[ ,c(1,4)]\nm1[c(1,3) ,c(1,4)]\nm1[c('delhi','mumbai'),c('Prod-3')]\nm1[m1 > 300]\nm1\nm1[c('delhi'),]\nsd(m1[c('delhi'),])\nsum(m1[c('delhi','mumbai'),c('Prod-3','Prod-4')])\n\n#m end\n\n(m2 = matrix(data=x, nrow=4, byrow = T))\n(m3= matrix(x, ncol=4 ))\n(m4 = matrix(c(1,2,3,4), nrow=2, ncol=4,byrow=T))\nm1\n\n\n\n\n# Vectors\nsnames = string (single char, multiple char)\nmarks1 = numeric(integer, decimal)\nmarried - true or F\ngender = categories\nsnames = c('student1', \"student2\", 'student3')\nsnames\nclass(snames)\nmarks = c(10, 20 ,30)\nmarks\nclass(marks)\nmarried = c(TRUE, FALSE, TRUE)\nmarried\nclass(married)\nsnames; marks; married\n(age = c(30,35,26))\nage = c(30,35,26)\nage\n?class\n\n#subsetting a Vector\n(x1 = 1:100)\n(x2 = seq(50,100,3))\n?seq\nx1\nx1[10]\nx1[20:30]\nx2[2:5]\nx1[x1 > 50]\nx2\nx2 > 70\nx2[(x2 > 70) | (x2 < 60)]\nx2[c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE,TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE)]\nx2\n#operations on the vector\nx3 = c('A','B','C')\nx3[c(1,3)]\nx3[c(TRUE, FALSE, TRUE)]\nx3[(x3=='A') | (x3=='C')]\n\nx2[(x2 > 60) & (x2 < 70)]\nx2[x2==69]\nx2==69\n\nx2\nlength(x2)\nlength(x1)\nmean(x2)\nsum(x2)/length(x2)\nsum(x2)\nmedian(x2)\nx2\n\nset.seed(1)\n(x4 = trunc(runif(20,5,100)))\nmedian(x4)\n(shirtcolors = c(\"red\",'blue','green','blue','green', 'blue'))\nmean(shirtcolors)\ntable(shirtcolors)\n\nsort(x4)\n\nmtcars$mpg\nmean(mtcars$mpg)\nwomen\ndata()\n?mtcars\n\nsnames\n\nx = rnorm(1000000, 50,10)\nhead(x)\nmean(x)\nhist(x)\n" }, { "path": "0-Practise/practise2.R", "content": "#misc practise\n\nx = 1:5\ndata.entry(x)\n" }, { "path": "0-Practise/rough.R", "content": "# Rough Work\n?cat\n?dput\n?dget\n?dump\n?write\n?write.table\n?save\n?detach\n?attach\n?dir\n?ls\n?rm\n?attr\n?attributes\n\ndata1 = c(3, 60, 19, 9, 4 , 5)\n\nlabels1 = c('Building training sets', 'Cleaning and Organising Data', 'Collecting Data sets', 'Mining data for patterns', 'Refining Algorithms', 'Others')\npie(data1, labels= pielabels)\n\nbarplot(data1)\npie(data1,labels=NA, clockwise=TRUE,\n col=rainbow(6), border=\"white\", radius=1.2,\n cex=0.8, main=\"Average Time Spent by Data Scientists\")\nlegend(\"bottomright\",legend=pielabels,bty=\"n\", # horiz = T,\n fill=rainbow(6))\nbarplot(data1, col=rainbow(6), names.arg=pielabels, \n cex.names = 1, horiz=T, angle=90, \n main=\"Average Time Spent by Data Scientists\"\n)\ntext(1:6, data1, labels= pielabels)\n?barplot\n\n\n\nbrowsers<-read.table(\"browsers.txt\",header=TRUE)\nbrowsers<-data1\nbrowsers\npielabels <- sprintf(\"%s = %3.1f%s\", labels1, browsers, \"%\")\npielabels\n?sprintf\nlibrary(RColorBrewer)\npie(browsers,\n labels=NA,\n clockwise=TRUE,\n col=brewer.pal(6,\"Set1\"),\n border=\"white\",\n radius=1,\n cex=0.8,\n main=\"Percentage Share of Internet Browser usage\")\nlegend(\"bottomleft\",legend=labels1,bty=\"n\",\n fill=brewer.pal(6,\"Set1\"))\n\n\n?strde\nstates\n" }, { "path": "0-Practise/vector.R", "content": "#Data Structure - Vectors\n\nx = c(1,5,7,8,4)\nx2 <- c(2,5,7,8,4)\nx\nx2\nx4 = c('M','F','M','F','M')\nx4\n(x5 = 1:100)\n(x6 = seq(1,100,by=3))\n\nmarks = rnorm(60, mean=60, sd=10)\nmarks\nplot(density(marks))\nmatrix(marks, ncol=6)\n\n\n\n\n\n\n" }, { "path": "00-toc.R", "content": "# Table of Contents\n\n#Folder Name - Topic\n\nlms " }, { "path": "02-lms/1-ds.R", "content": "# Data Structures\n\n#vectors----\nv1 = 1:100 #create vector from 1 to 100\nv2 = c(1,4,5,10)\nclass(v1)\nclass(v2)\nv3 = c('a','Dhiraj','Ashish')\nv3 #print the vector\nclass(v3)\nv4 = c(TRUE, FALSE, T, F, T)\nclass(v4)\n\n#summary on vectors\nmean(v1)\nmedian(v1)\nsd(v1)\nvar(v1)\nhist(v1)\nhist(women$height)\nv2\nv2[v2>=5]\n\nx = rnorm(60, mean=60, sd=10)\nx\nplot(x)\nhist(x)\nplot(density(x))\nabline(v=60)\n#rectangles and density together\nhist(x, freq=F)\nlines(density(x))\n\nhist(x, breaks=10, col=1:10)\nlength(x)\nsd(x)\n\n?sample\nx1 = LETTERS[5:20]\nx1\n\nset.seed(1234)\ny1 = sample(x1)\ny1\n\nset.seed(53)\n(y2= sample(x1, size=5))\n\n(gender=sample(c('M','F'), size=1000000, replace=TRUE, prob=c(.3,.7)))\n(t1=table(gender))\nprop.table(t1)\npie(t1)\nbarplot(t1, col=1:2, horiz=T)\n#\n\n\n#matrix----\n(m1 = matrix(1:24, nrow=4))\n(m2 = matrix(1:24, nrow=4, byrow=T))\n(m3 = matrix(1:24, ncol=4, byrow=T))\n(x=trunc(runif(60,60,100)))\n#round, trunc, ceiling, floor\nplot(density(x))\n(m4 = matrix(x, ncol=6))\ncolSums(m4)\nrowSums(m4)\nrowMeans(m4)\ncolMeans(m4)\nm4[m4> 67 & m4 < 86]\nm4[8:10, ]\nm4[8:10, c(1,3,5) ]\nrowSums(m4[8:10, c(1,3,5) ])\nm4[ ,c(1,5,6) ]\n#\n#array----\n\n\n#data.frame\n#rollno, name, gender, course, marks1, marks2,grades\n(rollno = 1:60)\n(name=paste('student1',1:60,sep='-'))\n(gender=sample(c('Male','Female'), size=60, replace=T,prob=c(.3,.7)))\n(course=sample(c('BBA','MBA','FPM'), size=60, replace=T, prob=c(.4,.2,.1)))\n(marks1 = ceiling(rnorm(60, mean=65, sd=7)))\n(marks2 = ceiling(rnorm(60, mean=60, sd=11)))\n(grades = sample(c('A','B','C'), size=60, replace=T))\nstudents = data.frame(rollno, name,gender, course, marks1, marks2, grades, stringsAsFactors = F)\nclass(students )\n\nsummary(students)\nstudents[, c('name')]\nstudents[students$gender == 'Male', c('rollno','gender','marks1') ]\nstudents[students$gender == 'Male' & students$grades == 'C', c('rollno','gender','marks1') ]\nstudents[students$marks1 > 55 | students$marks1 < 75, c('name','marks1') ]\nstudents$gender\nt1=table(students$gender)\nbarplot(table(students$course), ylim=c(0,50), col=1:3)\ntext(1:3,table(students$course)+5,table(students$course) )\nstr(students)\nnrow(students)\nnames(students)\ndim(students)\nhead(students)\ntail(students)\nhead(students,n=7)\n#\n#avg marks scored by each gender in marks1\n#gender, marks1\naggregate(students$marks1, by=list(students$gender), FUN=mean)\naggregate(students$marks2, by=list(students$course), FUN=max)\naggregate(students$marks2, by=list(students$course, students$gender), FUN=mean)\n\n#dplyr\nlibrary(dplyr)\nstudents %>% group_by(gender) %>% summarise(mean(marks1))\nstudents %>% group_by(course,gender) %>% summarise(meanmarks1= mean(marks1), min(marks2),max(marks2)) %>% arrange(desc(meanmarks1))\nstudents %>% arrange(desc(marks1)) %>% filter(gender=='Male') %>% head(n=3)\nstudents %>% sample_frac(.1) %>% arrange(course) %>% select(name, gender)\nstudents %>% sample_n(2) \nstudents %>% arrange(desc(course), gender, marks1)\nstudents %>% arrange(course, grades,marks1) %>% select(course, grades, marks1) %>% filter(course=='BBA')\n#\nstudents %>% group_by(course, gender) %>% arrange(marks1) %>% top_n(n=1)\n\n\n#factor\nnames(students)\nstudents$gender = factor(students$gender)\nsummary(students$gender)\nsummary(students$course)\nstudents$course = factor(students$course,ordered=T)\nsummary(students$course)\nstudents$course = factor(students$course,ordered=T, levels=c('FPM','MBA','BBA'))\nsummary(students$course)\nstudents$grades\n# C, A, B\nstudents$grades = factor(students$grades, ordered=T, levels=c('C','A','B'))\nstudents$grades\ntable(students$grades)\nbarplot(table(students$grades))\n\n#\n\nstudents\nwrite.csv(students,'./data/iimtrichy.csv' )\nstudents2 = read.csv('./data/iimtrichy.csv')\nhead(students2)\nstudents3 = read.csv(file.choose())\nhead(students3)\nstudents3 = students3[,-1]\nhead(students3)\n#\n\n\n\n\n\n\n#extra commands\nname[1:10]\nname[15:20]\nname[c(15,20,37)]\nname[-c(1:10)]\nrev(name)\nname[60:1]\nname[-c(1:10, 35:40)]\n\n\nmtcars\nplot(women)\nwomen\ninstall.packages('car')\n?women\n?mean\nx = 1:100\nx\ny = seq(1,100, by=2)\ny = seq(1,100, by=2)\n\n" }, { "path": "02-lms/fms.txt", "content": "The Faculty of Management Studies focuses on management education more than just business management. The commitment is thought leadership with a deep understanding of business. The approach to pedagogy combines fieldwork, case studies and instrumented feedback with a strong emphasis on concepts and theory. The intent is to encourage intellectual curiosity and open minds to the adventure of ideas.\n\nBut much about the school is not just about what is taught within its confines. FMS has the unique privilege to be part of one of the premier universities world – the University of Delhi - with some of the finest departments in Economics, Law, Sociology, Commerce and Operations Research. Our collaborative approach involves inputs from various departments which gives the benefit of a much wider view and deeper understanding. This is indicated by the success of our alumni who are in positions of leadership in industries & governments across the world. The network of more than 10000 alumni gives students unmatched access to information, mentors and careers.\n\nFMS rewards initiative, novelty and thinking outside the box. Our students are individualists of enormous intellectual energy with a talent for collaboration and teamwork. We are a diverse lot, possessing flair and dynamism that develops in a metropolis like Delhi and thrives on challenges both inside and outside the classroom.\n\nCorporate recruiters value our graduates for their intellectual abilities, their collaborative mind-set, their individuality and their ability to hit the ground running.\n\nWe invite you to discover FMS for yourself." }, { "path": "02-lms/importcsv.R", "content": "#import from csv\n\ndf1 = read.csv('pdpu.csv')\ndf1\nhead(df1)\nnames(df1)\n#avg marks gender wise\naggregate(cbind(df1$marks1,df1$marks2), by=list(df1$gender), FUN=mean)\nt1=table(df1$gender)\ntable(df1$batch)\nbarplot(table(df1$gender), col=1:2)\nbarplot(t1, col=c('red','green'))\npie(t1)\n\n\nwrite.csv(df1,'pdpu2.csv')\nwrite.csv(t1,'pdpu3.csv')\n\n\nmtcars\nstr(mtcars)\ndf2 = mtcars\ndf3=rbind(df2,mtcars)\ndim(df3)\n" }, { "path": "03-wksp1/1a1-start.R", "content": "#initial commands\n\n# assign\nx1 = 3 #press control + enter to run the line\nx2 <- 3 # same \n#which is better\nx1\nx2\ny\nls() #variables in env\nwomen\n\n?AirPassengers\ndata() # datasets available for use\n\nlibrary() # libraries currently loaded\n\n?mean #help\nhelp(mean)\n??mean #search through other sources\nx=0:10\nx\nx <- c(0:10, 50)\nx\nxm <- mean(x)\nxm\nmean(x, trim = 0.10)\nx=c(1,1,1,1,5,5,5,5,7,7)\nmean(x)\nmean(x, trim=.3)\nx=c(1,5,5)\nmean(x)\nc(mean(x), mean(x, trim = 0.10))\n\n1:10\n1:10000000\nx=c(1,34,5)\nx\n?c\nversion #version of R\n\nSys.Date() # todays date\n\ngetwd() # working directory\n\nmethods(class='matrix') #methods available for a class of object\n\nplot(10:100) #basic command to plot\nplot(women)\n" }, { "path": "03-wksp1/1a3-packages1.R", "content": "# Packages installation\n\n#List avl packages\nlibrary()\n\n\n#Total Avl Packages\nnrow(available.packages())\n\n#Install Package amap\ninstall.packages('amap')\n\n#Load package\nlibrary(amap)\n\n#Find functions in package\nlibrary(help=amap)\n\n#Help wrt a package\nhelp(package='amap') #see on right side pane\n\n\n#Unload---\ninstall.packages('tm')\n\nlibrary(tm)\nlibrary(VIM)\nsearch()\ndetach('package:tm', unload=TRUE)\ndetach(package:VIM, unload = T) \nsearch()\n\n\n#----------------- Part I Over\n#Detach Multiple Packages\n(detpkg = c('plyr','tm'))\nlibrary('plyr') #load lib\nlibrary('tm') #load lib\nsearch() #check if loaded\nVectorize(detach)(name=paste0(\"package:\", detpkg), unload=TRUE, character.only=TRUE) #code to detach\nsearch() # confirm if removed\n#specify the argument unload=TRUE; otherwise, R removes the package from the search path but doesn’t unload it.\n\n#Detach from memory all packages\nrm(list = ls(all = TRUE))\nsessionInfo()\n\n\n#Remove Packages ----- uninstall \nremove.packages(\"tm\")\nrequire('tm')# check if unistalled\n\n\n\n#multiple packages\n\n#Function\nipak <- function(pkg){\n new.pkg <- pkg[!(pkg %in% installed.packages()[, \"Package\"])]\n if (length(new.pkg)) \n install.packages(new.pkg, dependencies = TRUE)\n sapply(pkg, require, character.only = TRUE)\n}\npackagelist <- c('NLP','tm', 'lubridate')\nipak(packagelist)\n\n# package lists\npkg1 = c('dplyr','plyr', 'data.table','xlsx', 'Hmisc','rJava','ggplot2','lattice','gsheet','curl','stringr', 'syuzhet','e10171', 'catools','caret', 'olsrr' ,'swirl','sqldf','XML','VIM', 'outliers','car','MASS','DMwR','rvest')\npkg2 = c('forecast','rpart', 'rpart.plot', 'partykit','strucchange', 'didrooRFM')\npkg3 = c('zoo', 'astsa','lubridate','timeSeries','tseries','xts')\npkg4 = c('arules' , 'arulesViz')\npkg5 = c('twitterR','ROAuth','RGtk2','RTextTools','wordcloud')\ninstall.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\n\n\nlibrary(lubridate)\n\n#multiple load \neasypackages::libraries(pkg1)\n\n\n#easypackages\n#install.packages('easypackages')\nlibrary(easypackages)\n\n#Install Multiple Packages\npackages(\"plyr\", \"psych\", \"tm\")\nlibraries(\"plyr\", \"psych\", \"tm\")\n\n\n\n#lubripack\ninstall.packages('lubripack') #NA for some versions\n#install older version of R packages\n#https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages\n#https://rdrr.io/github/Espanta/lubripack/\n\nrequire(devtools)\ninstall_github(\"Espanta/lubripack\")\nlibrary(lubripack)\nlubripack(\"plyr\", \"psych\", \"tm\", \"quantmod\")\n\n\n\npack <- available.packages()\npack[\"ggplot2\",\"Depends\"]\npack[\"ggplot2\",\"Imports\"]\npack[\"data.table\",\"Depends\"]\n\npackrat:::recursivePackageDependencies(\"ggplot2\",lib.loc = .libPaths()[1])\ntools::dependsOnPkgs('ggplot2')\ntools::dependsOnPkgs('dplyr')\ntools::dependsOnPkgs(\"ggplot2\",installed=available.packages())\nlibrary(rusk)\n#Remove Package\nremove.packages('quantmod')\nlibrary(quantmod)\n\n\n\n#This will remove all/Detach all packages\nlibrary(mise)\nsearch()\n#mise(vars = TRUE, figs = TRUE, console = TRUE, pkgs = FALSE)\nmise(pkgs=T)\nsearch()\n\n\n\n# list all packages where an update is available\nold.packages()\n\n# update all available packages\nupdate.packages()\n\n# update, without prompts for permission/clarification\nupdate.packages(ask = FALSE)\n" }, { "path": "03-wksp1/1b2-ds.R", "content": "# Data Structures in R\n\n#control+enter when you are in the line to execute\n# Vectors-----\nc(2,4,6)\nseq(2,3,.5)\nseq(by=.5, from=2,to=3)\nrep(1:3,times=4)\nrep(c(3,6,7,2),each=4)\nrep(c(3,6,7,2), times=4)\n\n?rep\n\nx=1:10 #create seq of nos from 1 to 10\nx\n(x1 <- 1:20)\n\n(x1=1:30)\n(x2=c(1,2,13,4,5))\nclass(x2)\n\n(x3=c('a',\"ABC\"))\nclass(x3)\n(x3=letters[1:10])\nclass(x3)\nLETTERS[1:26]\n(x3b = c('a',\"Henry\",4))\nclass(x3b)\n\n(x4=c(T,FALSE,TRUE,T,F))\nclass(x4)\nclass(c(3,5))\nx5a = c(3,5.5)\nclass(x5a)\nas.integer(x5a)\n\nx5=c(3L,5L)\nclass(x5)\nx5a = c(3,5)\nclass(x5a)\n(x5b = c(1, 'a',T, 4L))\nclass(x5b)\n#blank variable ?\n\n#access elements\n?seq\n(x6 = seq(0,100,by=3))\nseq(0,100,3)\nseq(to=100,from=0,by=3)\nseq(1,5,2)\n?seq\n#[1] 0 2 4 6 8 10\nls() #variables in my environment\nx6\nlength(x6)\nx6[1:5]\nx6[10:20]\nx6[ seq(1,length(x6), 2)]\nx6[3] # access 3rd element\n#[1] 4\nx6[c(2, 4)] # access 2nd and 4th element\nx6[-1] # access all but 1st element\nx6[-c(1:10, 15:20)]\nx6[c(2, -4)] # cannot mix positive and negative integers\n#Error in x[c(2, -4)] : only 0's may be mixed with negative subscripts\nx6[c(2.4, 3.54)] # real numbers are truncated to integers\nx6[c(2,3)]\n\nx6[-c(1,5,20)]\nx6\nx6[x6 > 30]\n\nx6[x6 > 30 & x6 < 40] # 31-39\n#or |\nlength(x6)\nx6[-(length(x6)-1)]\n(x7 = c(x6, x2))\n\n\n#modify\nx6\nset.seed(1234)\n(x6 = sample(1:50))\n(x6b = sort(sample(1:50)))\nsort(x6)\nsort(x6[-c(1,2)])\nsort(x6, decreasing=T)\nx6\nrev(x6)\n\nseq(-3, 10, by=.2)\nx6[-c(1:12)]\nx6\nx6[x6> 30 & x6 < 40]\n(x = -3:2)\nx6\nx6[2:10] <- 99; x6 # modify 2nd element\nx6[x6 > 30 & x6 < 40] = 999\nx6\n\n\nx6\nx7 = x6[1:4]; x7 # truncate x to first 4 elements\n\n1:5\n#equal partitions within a range\n(x = seq(1,5, length.out = 15))\nx\nx = NULL\nx\n#NULL\nx[4]\n#NULL\n?distribution\n?rnorm\n(x = rnorm(100))\nplot(density(x))\nabline(v=c(-3,0,3))\nmean(x)\n(x1 = rnorm(100, mean=50, sd=5))\nplot(density(x1))\nabline(v=mean(x1),h=0.04)\nhist(x1, breaks=7)\nhist(x1)\nhist(x1, freq=F)\nlines(density(x1), col=2)\nsummary(x1)\nquantile(x1)\nquantile(x1, seq(0,1,.25))\nquantile(x1,c(.1, .5, .8))\nquantile(x1,seq(0,1,.01))\nstem(x1)\n\n#Matrix-----\n100:111\nlength(100:111)\nmatrix(1,ncol=3, nrow=4)\n(m1 = matrix(100:111, nrow=4))\n(m2 = matrix(100:111, ncol=3, byrow=T))\n\nx=101:124\nlength(x)\nmatrix(x, ncol=6)\nclass(m1)\nattributes(m1)\ndim(m1)\nm1\n\n# access elements of matrix\nm1[1,]\nm1[,1]\nm1[,1, drop=F]\nm1[,-1] #remove 1st column\nm1[1,2:3]\nm1[c(1,3),]\nm1[,-c(1,3), drop=F]\nm1[m1> 105 & m1 < 108]\n\n#names of cols and rows\nm1\n\npaste(\"C\",\"D\",sep=\"-\")\npaste(\"C\",1:100,sep=\"-\")\npaste(\"C\",1:3,sep='')\n(colnames(m1) = paste('C',1:3, sep=''))\nm1\n(rownames(m1) = paste(\"R\",1:4, sep=''))\nm1\nattributes(m1)\nm1[,c('C1','C3')]\nm1[,c(1,3)]\n#Vector to Matrix\n(m3 = 1:24)\nm3\ndim(m3)= c(6,4)\nm3\n\n#access elements\nm2\nm2[1,] #first row\nm2[c(1,3,4),] #1st,3rd,4th row\n\nm2[,1] #first col\nm2[,2:3] # 2nd to 3rd coln\n\nm2[c(1,2),c(2,3)]\nm2[,]\nm2[-2,] # exclude 2nd row\nm2\nm2[1:5] # matrix is like vector\nm2\nm2[c(TRUE,F,T,F),c(F, T, T)] #logical indexing\nm2[m2 > 5 & m2 < 10]\n\nm1\nm1[1:2,1:2]\nm1[c('R1','R2'),c('C1','C2')]\nm1[1:2,]\nm1[c(T,T,F,F),]\nm1\n\n#modify Vector\nm2\nm2[2,2]\nm2[2,2] = 10\nm2\nm2[,2] = 10\nm2\nm2[m2> 107] = 9999\nm2\nrbind(m2, c(50,60,70))\nrbind(m2,m2)\nm2\ncbind(m2, c(55,65,75,85))\nm2m2= cbind(m2,m2)\nm2m2\nm2\ncbind(m2,m2)\nrbind(m2,m2)\n#row and col wise summary\n\nm1\ncolSums(m1)\nrowSums(m1)\ncolMeans(m1)\nrowMeans(m1)\n\nt(m1) # transpose\nm1\nsweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN=\"+\" ) #rowise\nsweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN=\"*\" ) #colwise\n\n#addmargins\nm1\n?addmargins\naddmargins(m1,margin=1,sum) #colwise function\naddmargins(m1,1,sd) #colwise function\n\naddmargins(m1,2,mean) #rowwise function\naddmargins(m1,c(1,2),mean) #row & col wise function\n?addmargins\n(M1sum= addmargins(m1,c(1,2),list(list(mean,sum,max, min), list(var,sd, max, min)))) #row & col wise function\nround(M1sum,0)\n\n#Array-----\nlength(100:123)\n4*3*2\n#2 coys, 3 products, 4 locations sold qty\n(a1 = array(100:123, dim=c(4,3,2)))\n(loc = paste('loc', 1:4,sep='-'))\n(product = paste('p', 1:3,sep='@'))\n(coy = paste('coy', 1:2,sep='%'))\ndimnames(a1) = list(loc, product, coy)\na1\napply(a1,1, sum) #locationwise\napply(a1,2, sum) #productwise\napply(a1,c(1,2), sum) #product-location wise\napply(a1,c(2,3), sum) #product-coy wise\napply(a1,c(1,3), sum) #coy-location\napply(a1,3, sum) #coywise\nsum(a1) #total\n\n\n#DataFrame----\n#create Vectors to be combined into DF\n(rollno = 1:30)\n(sname = paste('student',1:30,sep=''))\n(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))\n(marks1 = floor(rnorm(30,mean= 50,sd=10)))\n(marks2 = ceiling(rnorm(30,40,5)))\n(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))\nrollno; sname; gender\nmarks1 ; marks2; course\n\n#create DF\ndf1= data.frame(rollno, sname, gender, marks1, marks2, course, stringsAsFactors = F)\nstr(df1) #structure of DF\nhead(df1) #top 6 rows\nhead(df1,n=3) #top 3 rows\ntail(df1) #last 6 rows\nclass(df1) # DF\nsummary(df1) #summary\nnrow(df1) \ndim(df1)\nlength(df1)\ndf1$course\ndf1$gender = factor(df1$gender)\ndf1$course = factor(df1$course)\n#df1$sname = as.character(df1$sname)\nstr(df1)\nsummary(df1)\nboxplot(marks1 ~ gender + course, data=df1)\n\ndf1 #full data\ndf1$gender # one column\nhead(df1[ , c(2,4)]) #multiple columns\ndf1[1:10 ,] #select rows, all columns\ndf1[1:5,1:4]\n#as per conditionis\ndf1[ marks1 > 50 & gender=='F', c('rollno', 'sname','gender', 'marks1')]\ndf1[ marks1 > 50 & gender=='F', c(1,2)]\ndf1[ marks1 > 50 | gender=='F', ]\n\nnames(df1) # names of columns\ndim(df1) #Dimensions\n\naggregate(df1$marks1, by=list(df1$gender), FUN=sum)\naggregate(marks1 ~ gender, data=df1, FUN=max)\naggregate(cbind(marks1, marks2) ~ gender, data=df1, FUN=max)\n\n\n(df2 = aggregate(cbind(marks1,marks2) ~ gender + course, data=df1, FUN=mean))\ndf2\n\ndf1\n\n\n\n\n\n#List -----\ng =\"My First List\"\nh = c(25, 26,18,39)\nj = matrix(1:10,nrow=2)\nk = c('one','two','three')\nmylist = list(title=g, ages=h, j, h)\nmylist\nmylist[2]\nmylist[[2]]\nmylist[['ages']]\nmylist$ages\n\n\n\n\n\n#Factor -----\n\n(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))\nsummary(grades)\ntable(grades)\n(gradesFactor = factor(grades))\nsummary(gradesFactor)\n\n(gradesFactorOrdered = factor(grades, ordered=T))\nsummary(gradesFactorOrdered)\n\n(gradesFactorOrderedLevels = factor(grades, ordered=T, levels=c('D','C','B','A')))\nsummary(gradesFactorOrderedLevels)\ngradesFactor\ngradesFactorOrdered\ngradesFactorOrderedLevels\npie(c(10,15,17))\npie(summary(gradesFactorOrderedLevels))\nbarplot(summary(gradesFactorOrderedLevels), col=1:4)\n\nclass(grades)\nclass(gradesFactorOrdered)\nclass(gradesFactorOrderedLevels)\n\n\n\n# Object Properties\n#vector\nv1= 1:100\nclass(v1) ; typeof(v1)\nv2=letters[1:10]\nclass(v2) ; typeof(v2)\nlength(v2)\nsummary(v1)\n\n#matrix\nm1= matrix(1:24,nrow=6)\nclass(m1)\nsummary(m1)\ndim(m1)\nstr(m1)\n\n#Array\na1 =array(1:24, dim=c(4,3,2))\nclass(a1)\nstr(a1)\ndim(a1)\nsummary(a1)\n\n\n\n#DF\n#data() #built in datasets\ndf1= iris \nstr(df1)\nsummary(df1)\nclass(df1); dim(df1)\nnrow(df1) ; names(df1) ;NROW(df1)\ncolnames(df1)\nrownames(df1)\n\n#list\nlist1 = list(v1,m1,a1,df1)\nstr(list1)\n\n#Statistical Description\nlibrary(Hmisc)\ndescribe(df1)\n\n\n#Next Topics\nx= c(123.2234, 33333.544, 43243.8442)\nfloor(x)\nceiling(x)\ntrunc(x)\nround(x,-2)\nround(x, digits = 5)\n" }, { "path": "03-wksp1/1b3-factor.R", "content": "\n\n(grades = sample(c(LETTERS[1:4]), size=30, replace=T, prob=c(.4,.2,.3,.1 )))\nsummary(grades)\n(gradesF = factor(grades))\nsummary(gradesF)\ntable(grades)\ntable(gradesF)\nclass(gradesF)\n(gradesFO = factor(grades, ordered=T))\n(gradesFO1 = factor(grades, ordered=T, levels=c('B','C','A','D')))\nsummary(gradesFO1)\n\n(marks = ceiling(rnorm(30, mean=60, sd=5)))\n(gender = factor(sample(c('M', 'F'), size=30, replace=T)))\n(student1 = data.frame(marks, gender, gradesFO1))\nboxplot( marks ~ gradesFO1, data=student1) \nboxplot( marks ~ gradesFO1 + gender, data=student1) \n\nboxplot(marks)\nsummary(marks)\nabline(h = summary(marks))\nquantile(marks)\n" }, { "path": "03-wksp1/1d2-basicstats.R", "content": "# Basic Stats\nx = ceiling(rnorm(10000, mean=60, sd=20))\nmean(x)\nmedian(x)\n#there is no mode function for mode stats\ntable(x)\nsort(table(x), decreasing=T)\n\n#mode\nlibrary(modeest)\nmlv(x,method='shorth')\n\n#quantile\nquantile(x)\nquantile(x,seq(.1,1,by=.1)) #decile\nquantile(x,seq(.01,1,by=.01)) #percentile\n\nlibrary(e1071) # load e1071 \n\nplot(density(x)) #density plot\ne1071::skewness(x) # apply the skewness \nkurtosis(x)\n\nsd(x); var(x)\ncov(women$weight, women$height)\ncor(women$height, women$height)\n\nstem(x)\n\n#Freq Table\nlibrary(fdth) #fast way of creating FT\nftable1 = fdt(x)\nftable1\n" }, { "path": "03-wksp1/1d2-dm-student1.R", "content": "# Data Manipulation : Academic Data\n\n#Method1 : gsheet\nlibrary(gsheet)\nurl= \"https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2051155174\"\nstudent1 = as.data.frame(gsheet2tbl(url))\n\n#Method2 : read.csv\nstudent2 = read.csv('./data/student1.csv')\nnames(student2)\n\n#save the imported data object into a new object\nstudent=student1 # or student2\nstr(student)\nnames(student)\ndf1= student\nlm(df1$btechmarks ~ df1$attnd, data=df1)\n\n(colnames = names(df1))\n#attributes(df1) = NULL\n#df1 = as.data.frame(df1)\nattributes(df1)\nattr(df1, which='spec') = NULL\n#names(df1) = colnames\nstr(df1)\n#class of each column\nclass(df1$gender)\nsapply(df1, class)\n\n# convert character to factor\nfactorcols = c('gender', 'cat', 'class12', 'batch','batchyr', 'br', 'city', 'finalgrade', 'btechfinal')\n\ndf1[factorcols] = lapply(df1[factorcols] ,factor)\nsapply(df1, class)\nstr(df1)\n\n\n# Now do summarisation\n#attach(df1)\nnames(df1)\ntable(df1$gender)\n#combine it in single command \n(l1= lapply(df1[factorcols],factor,ordered=TRUE)) #ordering is not necessary for all\nsapply(l1,table)\n\ntable(df1$gender)\ntable(df1$batchyr)\nsapply(df1[factorcols],table)\n\n\nsapply(lapply(df1[factorcols],factor,ordered=TRUE), table) #Method1\n\n\nsapply(df1[factorcols], table) #Method2\nsapply(df1[c('gender', 'cat', 'class12')], table)\n?lapply\nstr(df1)\n# Numeric Cols\nsapply(df1, is.numeric)\n(numcols = sapply(df1, is.numeric))\nclass(numcols)\n(numcols = names(df1[numcols]))\n#remove rollno\n(numcols = numcols[-1])\nhead(df1[numcols])\ncolSums(df1[numcols])\ncolSums(df1[numcols],dims=1)\ncolMeans(df1[numcols],dims=1)\nnumcols[c(1,3)]\ncolMeans(df1[c('age','java', 'cpp')])\nnames(df1)\ncolMeans(df1[numcols[c(1:3)]])\n\nnames(df1)\n#Look for other summarisation and grouping\naggregate(df1[numcols], by=list(df1$br), FUN=mean)\naggregate(cbind(sem1, sem2) ~ br + gender, data=student, FUN=mean)\n\naggregate(java + cbnst ~ br, data=df1, FUN=mean) #sum of java & cbnst wrt branch\naggregate( cbnst ~ gender, data=df1, FUN=mean)\naggregate( cbnst ~ gender + br, data=df1, FUN=mean)\n\n\n\n#using dplyr package\nlibrary(dplyr)\n\n#Top 2 students from each Branch---- \ndf1 %>% select(br, sname, btechmarks) %>% group_by(br) %>% arrange(desc(btechmarks)) %>% top_n(n=2)\n\n# Average Fees Paid by Batch Yr----\ndf1 %>% group_by(batchyr) %>% select(batchyr, feepaid) %>% summarize(mean_fees = mean(feepaid, na.rm = TRUE))\n\nnames(df1)\n# Avg BTech Marks and Min Marks in Java : Group by Gender, Granch and Final Grade ----\ndf1 %>% filter(finalgrade == 'A') %>% group_by(gender, br, finalgrade) %>% summarize(btechmks = mean(btechmarks, na.rm = TRUE), javamin = min(java, na.rm = TRUE))\n\n#Count by Gender ----\ndf1 %>% group_by(gender) %>% tally()\n\n#Filter by Final Grade = B : select only few columns ----\ndf1 %>% filter(finalgrade == 'B') %>% select(rollno, sname, finalgrade)\n\n#sample : select few rows on randowm basis ----\ndf1 %>% sample_frac(0.2, replace = TRUE) %>% select(rollno, sname) # % of Total\ndf1 %>% sample_frac(0.2, replace = F) %>% select(rollno, sname) # % of total : replace should be False to have non repeated rows\ndf1 %>% sample_n(3, replace = F) %>% select(rollno, sname) # select only 3 rows\n\n#few rows : 10 to 15\nslice(df1, 10:15)\n\nlibrary(tidyr) #Reshape\ndf1long <- df1 %>% select(sname, java, cbnst)\ndf1long <- df1 %>% select(sname, java, cbnst) %>% tidyr::gather(key = subject, value = marks, java, cbnst) \n\nhead(df1long)\ndistinct(df1long)\n\ntidyr::unite(df1, 'rollnoname', c(rollno, sname), sep=\"-\") %>% select(1:5)%>% head\n\n\n# data\nstr(df1$dob)\nas.Date(\"5-Apr-91\", '%d-%b-%y')\ndf1$dob = as.Date(df1$dob, '%d-%b-%y')\ndf1 %>% select(sname, dob) %>% tidyr::separate(dob, c(\"y\", \"m\", \"d\"))\n\n\n# Rows\ndf1 %>% slice(1:n():5)\nslice(df1, 1:5)\n\ndf1 %>% slice(1:n())\ndf1 %>% slice(1:10)\nslice(df1, n()-10: n()) #different way\n\nnames(df1)\n#defaults to the last variable in the tbl\ndf1 %>% group_by(gender) %>% top_n(2) #Rank not data on last column\n# top_n(n = 5, wt = x)\ndf1 %>% group_by(gender) %>% top_n(2,wt=class10) #Rank not data wrt class10 marks\n\ndf1 %>% top_n(-2, wt=class10) %>% select(rollno, sname, class10, cgpa) #bottom 2 ranks - see 3 values\ndf1 %>% group_by(gender) %>% tally(java) #total count\ndf1 %>% select(finalgrade, btechmarks, sname) %>% top_n(1, btechmarks)\n\n#Selecting Columns\ndf1 %>% select(1:5)\ndf1 %>% select(contains('java'))\ndf1 %>% select(starts_with('btech'))\ndf1 %>% select(ends_with('s'))\ndf1 %>% select(everything())\ndf1 %>% select(finalgrade, everything()) %>% select(1:5) %>% select(-2)\nnames(df1)\n\n\n# Summarise\ndf1 %>% summarise(avgjava = mean(java))\ndf1 %>% group_by(gender) %>% summarise(avgjava = mean(java),sumcbnst = sum(cbnst))\ndf1 %>% group_by(br, batch) %>% select(numcols) %>% summarise_each( funs(mean))\n\ndf1 %>% group_by(cat, gender) %>% count(class12, finalgrade)\n\n\n\n\n#Summary Functions\nv1 = df1$btechmarks\nv1\nv1 %>% first\nv1 %>% last\nv1 %>% sd\nv1 %>% mean\nv1 %>% n_distinct()\n\n\n# Combine Data Sets\n\n(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))\n(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))\n\ndplyr::left_join(a, b, by = \"x1\")\n#Join matching rows from b to a.\n\ndplyr::right_join(a, b, by = \"x1\")\n#Join matching rows from a to b.\n\ndplyr::inner_join(a, b, by = \"x1\")\n#Join data. Retain only rows in both sets.\n\ndplyr::full_join(a, b, by = \"x1\")\n#Join data. Retain all values, all rows.\n\ndplyr::semi_join(a, b, by = \"x1\")\n#All rows in a that have a match in b.\n\ncbind(a,b)\n\ndplyr::anti_join(a, b, by = \"x1\")\n#All rows in a that do not have a match in b.\n\n#-----\ny = data.frame(x1=c('A','B','C'), x2=c(1,2,3))\nz = data.frame(x1=c('B','C','D'), x2=c(2,3,4))\ncbind(y,z)\n\ndplyr::intersect(y, z) # B&C\n#Rows that appear in both y and z.\n\ndplyr::union(y, z)\n#Rows that appear in either or both y and z.\n\ndplyr::setdiff(y, z)\n#Rows that appear in y but not z.\n\n#Bind\ndplyr::bind_rows(y, z)\n#Append z to y as new rows.\ndplyr::bind_cols(y, z)\n#Append z to y as new columns.\n#Caution: matches rows by position\n\n\n" }, { "path": "03-wksp1/1d3-dencoCase.R", "content": "# Case Study - Denco \n#Manufacturing Firm with sales data of partnum and customer with region wise sales\n\n# Should know - import, table, dplyr, aggregate etc\n\n#read file : Method1\nsales1 = read.csv(\"./data/denco.csv\")\nstr(sales1) #see if data is loaded and check its structure\nhead(sales1)\n#read file : Method2 : when location is not in project folder\nsales2 = read.csv(file.choose())\nstr(sales2)\nhead(sales2)\n\n#read file: Method3\n#install.packages('gsheet')\nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907\"\nsales3 = as.data.frame(gsheet2tbl(url))\nstr(sales3)\nhead(sales3)\n\n\n# whichever you read the data, store it in sales object\nsales = sales1 # keeping a backuph\nhead(sales) #top 6 rows\nstr(sales) # structure\nclass(sales) # class type - Df\ndim(sales) # rows & columns\nsummary(sales) # summary- colname, type\nnames(sales) # col names\nhead(sales) # first 6 values\ntail(sales) # last 6 values\n\n# Some other functions\nunique(sales$custname) # unique customer names\nlength(unique(sales$custname)) # no of unique customers\nlength(unique(sales$region )) # no of distinct regions\n\n#Information Required----\n# 1. Who are the most loyal Customers - What to do : Improve repeated sales, Target customers with low sales Volumes\n# 2. Which customers contribute the most to their revenue : How do I retain these customers & target incentives\n# 3a. What part numbers bring in to significant portion of revenue : Maximise revenue from high value parts\n# 3b. What parts have the highest profit margin : What parts are driving profits & what parts need to build further\n\n\n#dplyr\nnames(sales)\nlibrary(dplyr) #use this library to do summarisation\n\n#Case-1 : Loyal Customers----\n# Finding Frequency and sort them in descending order\nnames(sales)\n(t1=table(sales$custname)) # freq table for all customers\nclass(t1); length(t1)\nhead(t1) # top 6 but not sorted\nt2= sort(t1,decreasing=T )\nhead(t2)\n#Ans: CHIZ BROS INC most loyal with 253 times purchase\n\n#other methods through dplyr\nlibrary(dplyr)\nsales %>% count(custname, sort=TRUE) %>% head(n=5)\n#Ans: CHIZ BROS INC most loyal with 253 times purchase\n\nsales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)\n#Ans: CHIZ BROS INC most loyal with 253 times purchase\n\n\n#Case-2 : Customer- Highest Revenue---\nsales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=5)\n#Ans2: Triumph Insulation gave max revenue\n\n# save this object and then analyse\nsalesrevenue = sales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\n\n#dply uses tibble format and does not show all rows/cols by default\noptions(tibble.width = Inf) # displays all columns.\noptions(tibble.print_max = Inf) # to show all the rows.\nsalesrevenue[1:5,] # first 5 rows\n#Ans2: here also Triump insulation gives max revenue = 35592K\n\n#Case3 : Most Profitable PartsNums ----\n# Look for freq, revenue and/ or profit margin\nhead(sales)\n# Summarise by Part Num for frequency\nsales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)\n#Ans3a: Part Num- 764821000 was purchased max times - 122\n\n# Summarise Partnum for Profit(margin) : sum(profit)\nnames(sales)\nsales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()\n#Ans 3b: Part Num - 733648000 gave max margin profit - 11003367\n\n# Summarise Partnum for revenue\n#(practise it )\n\n\n#Case : Extra ----\n#Find Regions wise sales\n\n(salesregionrevenue = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue)))\n\n#Some Graphs related to Data Summarised\npie(x = salesregionrevenue$Revenue, labels=unique(sales$region))\nbarplot(salesregionrevenue$Revenue, col=1:4)\n\n# Filter Data----\n#Rows where revenue > 700000 : show only custname, revenue\nsales %>% filter(revenue >= 700000 ) %>% select(custname, revenue)\n\n#select 1% rows on random basis\nsales %>% sample_frac(.01) %>% select(custname, revenue)\n\n#select 10 rows on random basis and sort them regionwise\nsales %>% sample_n(10) %>% select(custname, region) %>% arrange(region)\nsample_n(sales, 10) # similar method\n\n#End of session for Denco Case - Manufacturing Firm\n#Descriptive Analysis\n#Loyal Customers, Customer giving max revenue\n#Profitable Partnums - Freq, Revenue and Margin\n\n" }, { "path": "03-wksp1/1d4-DA-dencoCase.R", "content": "# Case Study - Denco \n\n#read file : Method1\nsales1 = read.csv(\"./data/denco.csv\")\nstr(sales1)\n\n#read file : Method2\nsales2 = read.csv(file.choose())\nstr(sales2)\n\n#read file: Method3\n#install.packages('gsheet')\nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907\"\nsales3 = as.data.frame(gsheet2tbl(url))\nstr(sales3)\n\n\n#using gsheet\nlibrary(gsheet)\ndenco2 = as.data.frame(gsheet2tbl(url))\nstr(denco2)\n\n#head(sales1,n=7)\n#names(sales1)\n\n# whichever you read the data, store it in sales object\nsales = sales1 # keeping a backup\nstr(sales)\nclass(sales)\nstr(sales)\n?summary\nsummary(sales)\n\nstr(sales)\ndim(sales) #dimensios of DF rows & colnum\nunique(sales$custname)\nlength(unique(sales$custname))\nlength(unique(sales$region ))\n\n# aggregation\naggregate(sales$revenue , by=list(sales$custname), FUN=sum)\ndf1 = aggregate(sales$revenue , by=list(sales$custname), FUN=sum)\nhead(df1)\nstr(df1)\n\ndf1=df1[order(df1$x, decreasing=TRUE),]\nhead(df1,10)\n\nhead(df1[order(df1$x, decreasing=TRUE),], 5)\n\naggregate(sales$revenue, by=list(sales$region), FUN=mean)\ndf2= aggregate(formula=revenue ~ region, data=sales, FUN=sum)\ndf2[order(df2$revenue, decreasing=F),]\n\n\n#Aggregate Formula\n(df2 = aggregate(revenue ~ custname + region, data=sales, FUN=sum))\nhead(df2[order(df2$revenue,decreasing=T),],10)\n\n#List\nlist1= tapply(sales$revenue, sales$custname, FUN=sum)\nhead(list1)\nlist1\nhead(sort(list1, decreasing=T))\nsummary(df3)\nstr(df3)\n\n#dplyr\nnames(sales)\n\nlibrary(dplyr)\n\nsales %>% filter(margin > 10000) %>% arrange(region, desc(revenue))\nfilter(sales, margin > 1000000)\n\nsales %>% filter(region == '01-East' & revenue > 400000) %>% select(partnum, region, revenue)\n\nnames(sales)\nsales %>% group_by(custname) %>% \n summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\n\n\nlibrary(data.table)\ndt1 = as.data.table(sales)\ndt2 = dt1[, sum(revenue), by=custname]\nnames(dt2)\ndt1[, dt1[, sum(revenue), by=custname]]\n#dt1[, order( decreasing = T)]\n\n# Select\nlibrary(sqldf)\ndf5 =sqldf(\"Select custname, sum(revenue) from sales Group By custname order by sum(revenue) desc \")\nhead(df5)\n\n# Frequency --------\nnames(sales)\nt1=table(sales$custname)\nclass(t1)\nlength(t1)\nhead(t1)\nt2= sort(t1,decreasing=T )\nhead(t2)\nhead(sort(table(sales$custname), decreasing=T), n=10)\ntail(sort(table(sales$custname), decreasing=T), n=10)\n\n#xtab\nhead(sort(xtabs(~ custname, sales), decreasing=T))\n\n#\nlibrary(dplyr)\nsales %>% dplyr::count(custname, sort=TRUE)\n\nsales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))\n\n\n\n#plyr\ndf2a= plyr::count(sales, c('custname'))\nstr(df2a); names(df2a)\nhead(df2a[order(-df2a$freq),])\n\n# Summarise by Part Num\n\ndf3a= aggregate(sales$revenue, by=list(sales$partnum), FUN=sum)\nhead(df3a)\nstr(df3a)\ndf3a[order(-df3a$x),][1:6,]\nhead(df3a[order(-df3a$x),])\n\ndf3b = aggregate(revenue ~ partnum, data=sales, FUN=sum)\nhead(df3b)\nhead(df3b[order(df3b$revenue, decreasing=T),])\n\nsales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))\n\n\n# which parts have highest Profit : partno - sum(profit)\nnames(sales)\ndf4a = aggregate(margin ~ partnum, data=sales, FUN=sum)\naggregate(margin ~ partnum, data=sales, FUN=sum)\nhead(df4a[order(df4a$margin, decreasing = T),])\n\nsales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()\n\n" }, { "path": "03-wksp1/1e-graphs-basic.R", "content": "# Combined Plots\n#plot, histogram, pie, boxplot, linechart, correlation plot\n\n#plot\nwomen\nplot(women)\n?plot\nplot(women, type='p', pch=16, col='red')\nplot(women, type='l')\nplot(women, type='b')\nplot(women, type='b', pch=18, lty=1, col=2, lwd=4)\nplot(women, xlim=c(30,100), ylim=c(min(women$weight)-10, 200), pch=10)\n\n#more features with plot\nplot(x=women$weight, y=women$height, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')\ntitle(main='Main Title', sub='Sub Title')\n#see cheat sheet on base graphs\nmtcars$cyl\n#plot(x=mtcars$wt, y=mtcars$mpg, col=mtcars$gear, pch=c(4,6,8), cex=c(1,2))\n#as.numeric(levels(as.factor(mtcars$cyl)))\nplot(women)\nabline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)\n\n\n#boxplot\nboxplot(women$height)\nabline(h=c(58, 62,65,68,72))\n\n#draw lines on plot for number summary\nsummary(women)\nquantile(women$height)\nquantile(women$height, seq(0,1,.1))\nquantile(women$height, seq(0,1,.01))\nstem(women$height)\nboxplot(women$height, col='green')\nabline(h=quantile(women$height))\ntext(1, quantile(women$height), labels=c('min','1Q','median','3Q','max'))\n\n#histogram\nhist(women$height)\nhist(women$height, breaks=10)\nhist(women$height, breaks=5, col=1:5)\n\n#histogram2\n(x = rnorm(100,50,10))\nhist(x)\n\nhist(x, freq=F, col=1:10)\nlines(density(x))\n\n#density plot : shape of data\nplot(density(x), col='red')\n\n#pie\ngender= sample(c('M','F'), size=100, replace=T)\ntable(gender)\npie(table(gender))\nx = c(10,20,40,50)\npie(x)\nxlabels = c('A ','B ','C ','D ')\nx/sum(x)\n(labels2 = paste(xlabels, round(x/sum(x),2) * 100 , sep='-'))\n(labels3 = paste0(labels2,\"%%\"))\n(labels2 = paste0(xlabels, round(x/sum(x),2) * 100, '%'))\n\npie(x, labels=labels2)\nx\n#barplot\nbarplot(x,col=1:4)\nbarplot(x,col=1:4, horiz = T)\n\n#correlation plot\npairs(women)\ncor(women$height,women$weight)\ncov(women$height, women$weight)\nhead(mtcars)\n?mtcars\n\ncor(mtcars)\nnames(mtcars)\npairs(mtcars)\npairs(mtcars[1:4])\noptions(digits=4)\npairs(mtcars[c('mpg', 'wt','hp')])\n" }, { "path": "03-wksp1/1e2-graphs.R", "content": "# Combined Plots\n\n#plot, histogram, pie, boxplot, linechart, correlation plot\n\n\n#plot\nwomen\n?women\nstr(women)\nplot(women)\nplot(x=women$height, y=women$weight)\n?plot\nplot(women, type='p', pch=17)\nplot(women, type='l')\nplot(women, type='b', pch=18, lty=2, col=2)\nplot(women, xlim=c(30,100), ylim=c(min(women$weight)-10, 200), pch=10)\ndata()\n\n#more features with plot\nplot(y=women$height, x=women$weight, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')\ntitle(main='Main Title- PDU', sub='Sub Title')\n#see cheat sheet on base graphs\n\nplot(women)\nabline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)\nabline(h = c(130, 150), col='green') \nabline(v=c(62, 66, 70), col='blue')\nabline(v=women$height, col='purple')\n\n#boxplot\nboxplot(women$height)\nboxplot(df$marks1)\n\nabline(h=c(58, 62,65,68,72))\n\n#draw lines on plot for number summary\nsummary(women)\nquantile(women$height)\nboxplot(women$height, col='green')\nabline(h=quantile(women$height))\n\n\n#histogram\nhist(women$height)\nhist(women$height, breaks=10)\nhist(women$height, breaks=5, col=1:5)\nhist(df$marks2, breaks=3)\n#histogram2\n?rnorm\nx = rnorm(n=100000,mean=50,sd=10)\nhist(x)\n\nhist(x, freq=F, col=1:5)\nlines(density(x))\n\n#density plot : shape of data\nplot(density(x), col='red')\n\n\n#pie\nx = c(10,20,40,50)\npie(x)\nxlabels = c('A','B','C','D')\npie(x, labels=xlabels)\npie(x, labels=paste(round(x/sum(x) * 100,0),'%') )\n\nx\n#barplot\nbarplot(x,col=1:4)\nbarplot(x,col=1:4, horiz = T)\n\n#correlation plot\npairs(women)\ncor(women$height,women$weight)\n\nnames(mtcars)\ncor(mtcars)\npairs(mtcars)\noptions(digits=4)\npairs(mtcars[1:4])\n" }, { "path": "03-wksp1/1e3-advgraphs.R", "content": "#Advanced Graphs\n\n\nlibrary(corrgram)\ncor(mtcars[1:4])\ncorrgram(mtcars[1:4], order=TRUE, lower.panel=panel.shade,\n upper.panel=panel.pie, text.panel=panel.txt,\n main=\"Car Milage Data in PC2/PC1 Order\")\n\n\n#alternative of box plot\n\nboxplot(mpg ~ cyl, data=mtcars)\n\n\nlibrary(corrplot)\nrelationship=cor(mtcars)\nrelationship\ncorrplot(relationship)\ncorrplot(relationship, type=\"upper\")\n\n#-----\n\nlibrary(vioplot)\nx1 <- mtcars$mpg[mtcars$cyl==4]\nx2 <- mtcars$mpg[mtcars$cyl==6]\nx3 <- mtcars$mpg[mtcars$cyl==8]\nx1; x2; x3\nvioplot(x1, x2, x3, names=c(\"4 cyl\", \"6 cyl\", \"8 cyl\"), \n col=\"gold\")\ntitle(\"Violin Plots of Miles Per Gallon\")\nabline(h=c(15,20))\n" }, { "path": "03-wksp1/1f-SLR-women.R", "content": "#topics ----\n#factors, env, import/export. package install\n#rep, recode, split, partition, subset, loops, cast & melt\n#missing values. duplicates, apply\n#graphs - bar, multiple line, pie, box, corrgram\n# predict weight for certain height\n\nhead(women)\ndim(women)\nfit = lm(weight ~ height,data = women)\nsummary(fit)\nrange(women$height)\n(ndata = data.frame(height = c(60.5, 70.5, 71.5)))\n(predictedwt = predict(fit, newdata = ndata))\ncbind(ndata, predictedwt)\n\nresid(fit)\nfitted(fit)\ncbind(women, fitted(fit), resid(fit))\n#assumptions\n\nplot(fit)\n\n\n" }, { "path": "03-wksp1/1h1-dplyr.R", "content": "#dplyr - mtcars\nlibrary(dplyr)\n#library(tidyverse)\n#Filter----\n\nfilter(mtcars, cyl == 8)\nfilter(mtcars, cyl < 6)\n\n# Multiple criteria\nfilter(mtcars, cyl < 6 & vs == 1)\nfilter(mtcars, cyl < 6 | vs == 1)\n\n# Multiple arguments are equivalent to and\nfilter(mtcars, cyl < 6, vs == 1)\n\n\nfilter(mtcars, row_number() == 1L)\nfilter(mtcars, row_number() == n())\nfilter(mtcars, between(row_number(), 5, n()-2))\n\n\n\n#mutate----\nmutate(mtcars, displ_l = disp / 61.0237) #keeps other col\ntransmute(mtcars, displ_l = disp / 61.0237) #removes other cols\nmutate(mtcars, cyl = NULL) #do not display cyl\n\n\n#slice-----\nslice(mtcars, 1L)\nslice(mtcars, n())\nslice(mtcars, 5:n())\nslice(mtcars, c(2,4,5,10))\n\n(by_cyl <- group_by(mtcars, cyl)) # ???\nslice(by_cyl, 1:2)\n\n#structure----\ntbl_df(mtcars) # convert to tbl class\nglimpse(mtcars) # dense summary of tbl data\nView(mtcars) # spreasheet like form base pacakge\n\nmtcars %>% group_by(am) \n#nothing - just separation\n\nmtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt), min(wt))\n\n\n#summarise----\nsummarise(mtcars, mean(disp)) \nsummarise(group_by(mtcars, cyl), mean(disp)) \nsummarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))\n\n\n#summarise_all\nmtcars %>% group_by(am, gear) %>% summarise_all(mean)\nmtcars %>% group_by(am, gear)%>% summarise_all(c(\"min\", \"max\"))\nmtcars %>% group_by(am, gear)%>% summarise_all(funs(med = median))\n\n\n\n#without Group\nmtcars %>% summarise(mean(mpg), max(wt))\nmtcars %>% summarise_all(mean)\nmtcars %>% select(wt, gear)%>% summarise_all(c(\"min\", \"max\"))\nmtcars %>% summarise_all(funs(med = median))\n\n\n\n#summarise if : \nmtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)\nstr(iris) #Species is a factor\niris %>% summarise_all(mean)\n\niris %>% summarise_if(is.numeric, mean, na.rm = TRUE)\n\n#specific columns\nmtcars %>% summarise_at(c(\"mpg\", \"wt\"), mean, na.rm = TRUE)\n\n\n#------------------------------------\n#unsorted----\ndplyr::tbl_df(iris) #all rows not displayed\nprint(dplyr::tbl_df(mtcars), n=20) #display more columns and rows\n#print(dplyr::tbl_df(mtcars), width=11)\ntbl_df(mtcars) %>% print(n = Inf) #all rows\ntbl_df(mtcars) %>% print(width = Inf)\ntbl_df(mtcars) %>% as.data.frame(mtcars)\n\nglimpse(mtcars)\ndf = mtcars\nrow.names(df) = NULL #remove rownames\ndf %>% select(mpg)\n#head(mtcars)\nselect(mtcars, mpg, vs)\nmtcars %>% dplyr::select(vs, mpg, wt)\nmtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)\nmtcars\n\nnames(mtcars)\nfilter(mtcars, mpg > 23 | wt < 2)\nmtcars %>% filter(mpg > 23 & wt > 2)\nmtcars %>% select(mpg, wt) %>% filter(mpg > 23) \nmtcars %>% \n \nfilter(iris, Sepal.Length > 7)\nfilter(mtcars, cyl == 4)\n\n#distinct rows\ndistinct(mtcars)\n(df3 = data.frame(a=c(2,2,3),b=c(2,2,1)))\ndistinct(df3)\n\n#sampling\nsample_frac(mtcars, 0.2, replace=F)\nsample_n(mtcars, 2, replace=F)\n#%>% select(mpg)\nslice(mtcars,10:14)\nsort(mtcars$mpg, decreasing = T)\ntop_n(mtcars,-2, mpg) #least 2 mpg\n\nselect(mtcars, mpg) %>% arrange(desc(mpg))\n\n#Columns\nselect(mtcars, mpg, wt)\nselect(mtcars, contains('a'))\nnames(mtcars)\nselect(mtcars, contains ='vs')\nselect(mtcars, everything())\n\ndf= mtcars\ndf$names = rownames(mtcars)\nhead(df)\ndf %>% select(1:5,12) %>% arrange(mpg)\n\nmtcars %>% group_by(cyl, am) %>% summarise_all(mean)\n\n(df4 = data.frame(marks=c(1,2,2,3,7,1,100)))\ncbind(df4, dplyr::mutate_all(df4, funs(min_rank)))\n\n#shift the columns\nmtcars %>% lead() \nmtcars %>% lag()\nmtcars %>% summarise(n())\n\nselect(mtcars, mpg2 = mpg)\n\ndf = mtcars[1:4]\nnames(df) = c('MPG','C1','C2','C3')\ndf= rename(df, C5=C1)\nnames(df)\ndf\n\ndf = women\nrename(df, HeightWomen = height)\ndf %>% mutate(height2 = height + 2, weight2 = weight + 4)\n#does not show orginal columns\ndf %>% transmute(height2 = height + 2, weight2 = weight + 4)\n\nlibrary(nycflights13)\ndata(flights)\nhead(flights)\ndestinations <- group_by(flights, dest)\ndestinations\nsummarise(destinations, planes = n_distinct(tailnum), flights = n())\n\n\nselect(iris, -ends_with(\"Width\")) %>% head\nvars <- c(\"Petal.Length\", \"Petal.Width1\")\nselect(iris, from=1, to=n()) \nfilter(mtcars, row_number() == n())\nfilter(mtcars, between(row_number(), 5, n()))\n\nmtcars %>% group_by(cyl) %>% filter(1:3)\ngroup_by( mtcars, cyl ) %>% integer_filter(1:2)\n?integer_filter\n\n#error ???????\n# Select odd\nmtcars %>% slice(from = 1, to = n(), by = 2)\n# Select even\nslice(mtcars, from = 2, to = n(), by = 2)\n# Select first 10\nslice(mtcars, from = 1, to = 10)\n# Select last 10\nslice(mtcars, n()-10: n())\nslice(mtcars, 1:4)\nmtcars\n\ndf <- tibble(\n g1 = c(1, 1, 2, 2, 2),\n g2 = c(1, 2, 1, 2, 1),\n a = sample(5), \n b = sample(5)\n)\ndf\ndf %>% slice(n()-2:n())\n\nvar1 <- quo(letters[1:5])\nvar1\nquo(toupper(!!var1))\n\n# Here we capture `letters[1:5]` as an expression:\nquo(toupper(letters[1:5]))\n#> ~toupper(letters[1:5])\n\n# Here we capture the value of `letters[1:5]`\nquo(toupper(!!letters[1:5]))\n#> ~toupper(c(\"a\", \"b\", \"c\", \"d\", \"e\"))\nquo(toupper(UQ(letters[1:5])))\n#> ~toupper(c(\"a\", \"b\", \"c\", \"d\", \"e\"))\n#\ntoupper(letters[1:5])\nquote(toupper(letters[1:5]))\n\nhead(mtcars)\nslice(mtcars, 1:5)\nslice(mtcars, 1) #rowno missing, first row\nslice(mtcars, 1L)\n\ntail(mtcars,n=5)\nslice(mtcars, n()-5:n())\nslice(mtcars, n()) #last row\nslice(mtcars, n() - 1) #2nd last row\n\nmtcars %>% top_n(2)\nmtcars %>% top_n(-2)\nmtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)\ndim(mtcars)\nbind_rows(mtcars, mtcars)\nbind_cols(mtcars,mtcars)\ngtable_combine(list(mtcars, mtcars))\ndim_desc(mtcars)\n\n# combine applies the same coercion rules\nf1 <- factor(\"a\")\nf2 <- factor(\"b\")\nc(f1, f2)\nunlist(list(f1, f2))\n\ngtable_combine(f1, f2)\ngtable_combine(list(f1, f2))\n\nslice( mtcars, c(1L,3L,2L,7L)) \n\nby_cyl <- mtcars %>% group_by(cyl)\n# Select first row in each group\nmtcars %>% slice(1)\nby_cyl %>% slice(1)\n# Select last row in each group\nmtcars %>% slice(n())\nby_cyl %>% slice(n())\n# Rows not present in group silently ignored\nmtcars %>% slice(10)\nby_cyl %>% slice(10)\n\n# Select arbitrary rows\nmtcars %>% slice(1:9)\nby_cyl %>% slice(1:3)\n\nmtcars %>% slice(c(1, 3, 9))\nby_cyl %>% slice(c(1, 3, 5))\n\n# Select even rows\nmtcars %>% slice(seq(2, n(), by = 2))\nby_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())\n\n# Drop first row in each group\nmtcars %>% group_by(cyl, am) %>% slice(1)\nby_cyl %>% slice(1)\n\n# Returns all values\nby_cyl %>% slice()\n\n\n\n\ndf <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))\ndf %>% top_n(2)\n\n# Negative values select bottom from group. Note that we get more\n# than 2 values here because there's a tie: top_n() either takes\n# all rows with a value, or none.\ndf %>% top_n(-2)\n\n\n\n\n" }, { "path": "03-wksp1/1h2-freqtable.R", "content": "# Frequency Distribution\n\n#Discrete Cat Data\n(attend = c('A','P','P','A','P','A'))\ntable(attend)\ncbind(table(attend)) # A & P on left side\n\n#Discrete Numeric Data\n\ncolor=c('Blue','Green','Magenta','Green','Black','Blue','Black')\nx2 = table(color)\nx2\nx2a = cbind(x2)\nx2a\nhist(x2a) # not relevant \nbarplot(x2a) # not good\nbarplot(x2a, beside=T) # Better\nunique(color) ; length(unique(color))\nbarplot(x2a, beside=T, col=rainbow(length(unique(color))))\npie(x2a)\n\n#Continuous Data \nset.seed(1234)\nx3 = runif(100,0,150) # 0 to 150 marks range, 100 values \nx3\nx3 = ceiling(x3) #round to higher value\nx3\nrange(x3)\n# Divide range into step of 15 ie 10 levels\nbreaks = seq(0,150,by=15)\nbreaks\nlength(breaks)\nx3\n#x3[1] = 60; x3[2] = 75\nx3.cut = cut(x3, breaks)\nx3.cut\ntable(x3.cut)\ncbind(table(x3.cut)) #see it vertically\n\n#give intervals a character values a, b..\n(x3.cut = cut(x3, breaks, labels=letters[1:10]))\n#(x3.cut = cut(x3, breaks, labels=letters[1:length(breaks)-1]))\nx3.cut\n\n(x3a = table(x3.cut))\n(x3b = cbind(x3a))\n\n#plot these freq Table: which is better\nhist(x3b)\npie(x3b)\nbarplot(x3b, beside=T)\nbarplot(x3b, beside=T, names.arg =rownames(x3b))\nplot(x3b)\n\n\n# and so..on like previous eg\n\n\n" }, { "path": "03-wksp1/2a-importExport.R", "content": "# Read Data into R Environment\n\n#CSV Files----\n#Read from CSV file in PC\nhead(iris)\nwrite.csv(iris, \"./data/iris.csv\", row.names=F)\nread1 = read.csv(file=\"./data/iris.csv\", header = TRUE,sep = \",\")\nread1 = read.csv(file=\"./data/dhiraj.csv\", header = TRUE,sep = \",\")\n\nstr(read1)\nclass(read1)\nhead(read1)\nread2 = read.table(file=\"./data/iris.csv\", header = TRUE,sep = \",\")\nstr(read2); class(read2)\nhead(read2)\nread3 = read.delim(file=\"./data/iris.csv\", header = TRUE,sep = \",\")\nstr(read3) ; class(read3)\nhead(read3)\n#difference is use of specify delimeter(read.csv takes default as comma)\n#or location is different from Project Folders, or want to search for the file\nread4 = read.csv(file=file.choose())\nstr(read4)\nhead(read4)\n\n\n# From URL : Read CSV from Web----\nread_web1 = read.csv('http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat')\nhead(read_web1)\nlibrary(data.table)\nread_web2 = fread(\"http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv\")\nhead(read_web2)\nclass(read_web2)\n\n#Text file from Web-----\nread_txt = read.table(\"https://s3.amazonaws.com/assets.datacamp.com/blog_assets/test.txt\", header = FALSE)\nhead(read_txt)\n\n#Google Sheets-----\nlibrary(gsheet) #install it\ninstall.packages('gsheet')\nlibrary(gsheet)\nurl_gsheet = \"https://docs.google.com/spreadsheets/d/1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k/edit#gid=107865534\"\ndf_gsheet = as.data.frame(gsheet2tbl(url_gsheet))\nhead(df_gsheet)\n\n#Excel----\n#Create a excel file with data in 2 sheets\n# first row contains variable names\n#C:\\Program Files\\Java\\jre1.8.0_191\nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_191')\n#library(rjava)\nlibrary(xlsx)\ndf_excel1 = read.xlsx( \"./data/myexcel.xlsx\", 1)\ndf_excel1\n# read in the worksheet named mysheet\ndf_excel2a = read.xlsx(\"./data/myexcel.xlsx\", sheetName = \"bowlers\")\ndf_excel2a\ndf_excel2b = read.xlsx( \"./data/myexcel.xlsx\", sheetIndex = 2)\ndf_excel2b\n" }, { "path": "03-wksp1/2b-SLR-salesarea.R", "content": "#Simple Linear Regression - Case Study\n# Regression : Areas vs Sales\n#Given data of area and sales, predict value for sales for specific areas eg : (1.5,2,3,4,5)\n#manual way of doing regression see online URL\n\n#Create/ Import Data-----\n#Method1 : creating data from Vectors\n#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y\nX = c(1.7,1.6,2.8,5.6,1.3,2.2,1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )\nY = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )\ndf1 = data.frame(X,Y)\nhead(df1)\n\n#2nd method of importing data\n#import from ggsheet #pickup the correct url\nlibrary(gsheet)\narea1 = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519\"\ndf2 = as.data.frame(gsheet::gsheet2tbl(area1))\nstr(df2)\nhead(df2)\n\n#Third method of importing data from csv\ndf3 = read.csv('./data/slr1.csv')\nstr(df3)\n\n#Method4 for importing from CSV file with choose location\ndf4 = read.csv(file.choose())\nstr(df4)\n\n# Use Vector Data or method used to import data\n#make one of the DF active\ndf = df1\ndf\n#simple stats\nmean(df$X); mean(df$Y)\nsum(df$X); sum(df$Y)\nsd(df$X) ; var(df$Y)\ncov(df$X,df$Y); cov(df$Y,df$X)\ncor(df$X,df$Y) ; cor(df$Y,df$X)\n#cor.test(df$X,df$Y)\n\n#some plots to understand pattern\nplot(df$X, df$Y) #simple command to plot : Next with features\nplot(y=df$Y, x=df$X,xlab='Area in sqft', ylab='Sales Amount', type='p', ylim=c(0, max(df$Y)+1), main='Plot of Area Vs Sales', xlim=c(0,max(df$X)+ 1), col='red',pch=17)\n?plot\nabline(lm(Y ~ X,data=df1), lty=1, lwd=2, col='green') # with regression line\nabline(v=c(3,5, min(df$X), max(df$X)),h=c(6,10, min(df$Y), max(df$Y)), col=c('red','blue','green','yellow')) # few straight lines at x & y axis\nrange(df$X)\n#Model\nfit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X\nfit1\nsummary(fit1)\n\n\n#few extras\nnames(fit1) # attributes(fit1)\nsystem.time(lm(Y ~ X, data=df)) #time taken to compute linear regression\ncoef(fit1) # Coefficients of Equation Y = mX + C\nfitted(fit1) # predicted values for all X in orginal data\npredict(fit1, newdata=data.frame(X))\nresiduals(fit1) # diff between actual and predicted values - residuals\n(R= df$Y - fitted(fit1))\nplot(density(residuals(fit1)))\n#residuals should be less :Diff of Y actual - Y predicted\n#abline(h=coef(fit1)[1])\n\nsummary(fit1) # summary statistics of Linear Model(LM)\n#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP\n\nnames(fit1) #output variables names of LM\nnames(summary(fit1))\n#u can select them to see output\n\nsummary(fit1)$r.squared\ncoef(fit1)[2] # slope or beta\nfitted(fit1)\n#combine the data with Ypredicted, errors\ncbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))\n\n#Mathematical Equation and predictions\n(Y = 0.9645 + 1.6699 * 4) # Predict Y for X=4\n#predict for area = 4\n#using equation\nsummary(fit1)\ncoef(fit1)\n(Y = coef(fit1)[1] + coef(fit1)[2] * 4)\n#using model\nrange(df$X)\n(new1 = data.frame(X=c(4,5,3,2)))\npredicted2= predict(fit1, newdata= new1)\ncbind(new1, predicted2)\n#prediction can only be interpolated not extrapolated\nrange(df$X) #min to max value of X: area\n\n#select new data given in the case\n(new1 = data.frame(X=c(1.5,2,3,4,5)))\n#sample data for X for prediction, should be between the range of X values\npredict(fit1, newdata= new1) # Predict Function for 4 values of X\n#columnbind with input and predicted values\ncbind(new1, predictedY = predict(fit1, newdata= new1) )\n\nlibrary(forecast) #install it first\n?accuracy(fit1)\n#RMSE is generally used and should be least when selecting models\n\nanova(fit1)\nsummary(fit1)$r.squared # R2 from Model\nsummary(fit1)$sigma #Residual Std Error SD along the LM Line\n\n#---------------------------------------#Assumptions--------\n#Assumption : Graphical Analysis : IMP STEP\nplot(fit1)\npar(mfrow=c(2,2))\nplot(fit1)\npar(mfrow=c(1,1))\n\nplot(fit1, which=1)\n# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y\nplot(df)\nabline(h=0)\nplot(residuals(fit1))\n\n#Auto Collinearity : relation between successive values of Y\ncar::durbinWatsonTest(fit1)\n?car::durbinWatsonTest\n#pvalue > 0 : Do not reject Ho. that means there is no autocorrelation\n\n#Normality of residuals\nresid(fit1)\n#qqplot(fitted(fit1),resid(fit1) )\nplot(fit1, which=2)\n#points to be around the straight line\n\n#Equal Variance : 4th Assumption : homoscedasticity\nplot(fit1, which=1)\n#no funnel shape to show hetero-cedasticity\n\n#Outlier Analysis\nplot(fit1, which=4)\n#abline(h=c(.5))\n#no value of cooks distance > .5 : no data to be removed\n#outlier values can affect the model\n\n#see all diagnostic plots together\npar(mfrow=c(2,2))\n#multiple frames per row : rowwise filling, 2 rows, 2 columns\nplot(fit1)\npar(mfrow=c(1,1))\n\n#SUMMARY ------\nsummary(fit1)$r.squared # 90% variation in Y explained by X\nsummary(fit1)\n#F Stats pvalue < 0.05: Model exists : At least 1 indep variable has strong relationship with Dependent variable (Y)\n#pvalue of Coef (X) < 0.05 : Significant X\n\n\n#End of Simple Linear Regression\n#Do different SLR on different data sets\n#Learn what to do if there are violations of assumptions\n\n\n\n\ndf # dataset being used for LM\nfit= lm(Y ~ X, data=df) #model creation\nsummary(fit) #summary of linear model\nplot(fit) #diagnostic plots\npredict(fit, newdata=data.frame(X=mean(df$X)))\n#Multiple R2 explains the variation, model fitness\n\n" }, { "path": "03-wksp1/2b-allmodels.R", "content": "# All models\nlibrary(dplyr)\n\n#Linear Regression\nhead(women)\nhead(mtcars)\n#predict weight for new height\nplot(women)\nplot(women, ylim=c(0, 160), xlim=c(0,90))\nfit1 = lm(weight ~ height, data=women)\nsummary(fit1)\nrange(women$height)\n(ndata1=data.frame(height=c(59.5, 62.5)))\n(predicted1 = predict(fit1, newdata= ndata1))\ncbind(ndata1, predicted1)\nplot(fit1) #check for assumptions\npar(mfrow=c(2,2))\nplot(fit1)\npar(mfrow=c(1,1))\n\n\n#Logistic Regression\ndata2 = read.csv(\"https://stats.idre.ucla.edu/stat/data/binary.csv\")\nhead(data2)\nstr(data2)\nsummary(data2)\ndata2$rank = factor(data2$rank)\ndata2$admit = factor(data2$admit)\ntable(data2$rank, data2$admit) #2 way table\nxtabs(~admit + rank, data = data2)\n#create Logistic Model\nfit2 <- glm(admit ~ gre + gpa + rank,data=data2,family=\"binomial\")\nsummary(fit2)\n(ndata2 = sample_n(data2, 3)) #pick up sample rows\n#Predict admit for input data\n(predicted2=predict(fit2,newdata=ndata2, type=\"response\"))\n(predictedclass2=ifelse(predicted2 > .5, 1,0))\ncbind(ndata2, predicted2, predictedclass2)\n\n#Data Partition\n\n\n\n\n#Decision Tree - Classification \nurl3 = 'https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_csv.csv'\ndata3a = read.csv(url3)\nhead(data3a)\nnames(data3a)\ndata3 = data3a[,c(2,3,5,6,7)] #select few columns only\nhead(data3)\n\n#install & load libraries\nlibrary(rpart)\nlibrary(rpart.plot)\n\n#Decision Tree\nfit3 = rpart(survived ~ ., data = data3, method = 'class')\nfit3\nrpart.plot(fit3, extra = 106, cex=.8,nn=T) #plot\nprintcp(fit3) #select complexity parameter\nprunetree3 = prune(fit3, cp=.014) #make tree smaller\nrpart.plot(prunetree3, cex=.8,nn=T)\nprunetree3\ntable(data3$survived)\n#Predict class category or probabilities\n(ndata3 = sample_n(data3,4))\npredicted3 = predict(prunetree3, newdata=ndata3, type='class')\npredicted3b= predict(prunetree3, newdata=ndata3, type='prob')\ncbind(ndata3, predicted3, predicted3b)\n\n#similarly Regression Tree can be made\n\n\n\n#Clustering----\n#install.packages(\"amap\")\n##Read the data in the file\nurl4 = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'\nlibrary(gsheet)\ndata4 = as.data.frame(gsheet2tbl(url4))\nhead(data4)\nsummary(data4)\nstr(data4)\nnrow(data4)\n###Verify the data\ncolnames(data4)\napply(data4, 2, FUN= class) #are all numeric\nfit4 = kmeans(data4[,-1],centers=3)\nfit4$centers # group means\nfit4$size #how rows in which cluster\nfit4$withinss #math difference within each cluster; Which is more cohesive gp?\nfit4$cluster\ntable(fit4$cluster)\ncluster2 = data4[ fit4$cluster == 2,]\nhead(cluster2)\ncluster2[-1] %>% summarise_all(mean)\nwrite.csv(cluster2, file = \"./data/data4cluster2.csv\")\n\n\n## Association Rules - Groceries data set----\nlibrary(arules) #install first\nlibrary(arulesViz) #install first\nlibrary(datasets) # no need to install, just load it reqd for Groceries\ndata('Groceries')\nGroceries\narules::LIST(Groceries[1:6]) #different format\n#Find Frequent Itemset\nfrequentItems = eclat(Groceries,parameter=list(supp = 0.01,minlen=3, maxlen=5)) \ninspect(frequentItems[1:10])\nfrequentItems\ninspect(sort(frequentItems,by=\"count\",decreasing=TRUE)[1:25])\n\n#Support is : support(A&B) = n(A&B)/ N\n#Plot the Frequency Plot\nitemFrequencyPlot(Groceries,topN = 15,type=\"absolute\")\nitemFrequencyPlot(Groceries, topN = 10, type='relative')\nabline(h=0.15)\n\n# Create rules and the relationship between items\n#parameters are min filter conditions \nrules = apriori(Groceries, parameter = list(supp = 0.001, conf = 0.5, minlen=2))\nrules\ninspect (rules[1:5])\n#Sort Rules by confidence, lift and see the data\nrulesc <- sort (rules, by=\"confidence\", decreasing=TRUE)\ninspect(rulesc[1:5])\n#similary it can be done for lift and support\n#which items have strong confidence and lift \n\n#How To Control The Number Of Rules in Output ?\n#maxlen, minlen, supp, conf\n\n#subset -----\n#legend to condition commands \n# lhs - means left hand side, or antecendent\n# rhs - mean right hand side, or consequent\n# items - items, that make up itemsets\n# %in% - matches any\n# %ain% - matches all\n# %pin% - matches partially\n# default - no restrictions applied\n# & - additional restrictions on lift, confidence etc.\n#Find what factors influenced an event ‘X’ :\n#Find out what events were influenced by a given event\nsubset1a = subset(rules, subset=rhs %in% \"whole milk\")\ninspect(subset1a[1:10])\nsubset1b = subset(rules, subset=rhs %in% 'bottled beer' )\ninspect(subset1b) #no such rule with beer on rhs, change some parameters to such rules \n#inspect(rules)\n#Items in : all or any\nsubset2a = subset(rules, subset=lhs %ain% c('baking powder','soda') )\ninspect(subset2a) #all items in 1 rule\nsubset2b = subset(rules, subset=lhs %in% c('baking powder','soda') )\ninspect(subset2b[1:5]) #any of the items in the rule\n#rhs- beer, confidence , sort by lift\nsubset3a = subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)\ninspect(subset3a)\nsubset4 = subset(rules, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' ) # lhs- beer, rhs- milk\ninspect(subset4)\n\n#Visualizing The Rules -----\nplot(subset1[1:10]) \nplot(subset1[1:10], measure=c(\"support\", \"lift\"), shading=\"confidence\")\n\n#\n\n#install.packages('wordcloud2')\nlibrary(wordcloud2)\n\ndf = data.frame(word=c('cbap','cmap','iim','imt','calcutta'),freq=c(20,23,15,10,13))\ndf\nwordcloud2(df)\n\n\nhead(demoFreq)\nwordcloud2(demoFreq, size = 2, color = \"random-light\", backgroundColor = \"grey\")\nnames(demoFreq)\n" }, { "path": "03-wksp1/2b2-SLM-women.R", "content": "# Regression Analysis\n# Simple Linear with 1 IV and 1 DV\n\ndata(women)\nwomen\nnames(women)\nstr(women)\n\ncov(women$height, women$weight)\n#69 : which show positive relationship between height and weight\n\ncor(women$height, women$weight)\n#0.995 : which shows Strong and Positive relationship betw height & weight\n0.995^2 # also equal R^2 value in this case (Simple Linear Regression)\n\nplot(x=women$height, y=women$weight, type='b')\nabline(lm(weight ~ height, data=women), col='red')\nnames(women) #x- IV, y -DV\nfit1 = lm(weight ~ height, data=women) # creating a model\nsummary(fit1) #summary of the Model\n\n#Ho: (F Test) : No relationship between Y and any X\n#Ha: There is relationship between Y and at least one X\n# p < 0.05 Reject Ho in favour of Ha\n\nattributes(fit1) #output of model\n\n#coefficients\ncoef(fit1)\n#p values for b0 & b1 are significant as it is < 0.05\n#pvalue1 = 1.71e-09 < 0.05\n#pvalue2 = 1.09e-14 < 0.05\n\ncoef(fit1)\n#Y = -87 + 3.4 * X # no extrapolations\n(Y = -87 + 3.4 * 20) # weight cannot be negative\nrange(women$height)\n(Y = -87 + 3.4 * 58)\n(Y = -87 + 3.4 * 61.5)\n(Y = -87 + 3.4 * 72)\nrange(women$height)\nwomen\n\n\n#R^2\n(summary(fit1))$r.squared #0.991\n(summary(fit1))$adj.r.squared #0.9903\n#99% of variation in weight is explained by Height\n#Good Linear Model for Prediction\n\n\n#FStats : p value\n(summary(fit1))$fstatistic \n#pvalue : 1.09e-14 < 0.05 : Model exists\n#There is at least 1 IV which explains variation in Y (DV)\n\n\n#Prediction : for height = 65, 66\n\n(new1 = data.frame(height=c(65,66,66.5)))\nnew1\n(p1=predict(fit1, newdata = new1))\ncbind(new1, p1)\n#136.7333 140.1833\n\n(new2 = data.frame(height=c(60,69)))\n(p2=predict(fit1, newdata = new2))\ncbind(new2, p2)\n\n?predict\npredict(fit1, newdata = new1, interval='confidence')\npredict(fit1, newdata = new1, interval='prediction')\n\nwomen\nwomen$weight\nfitted(fit1) # predicted values of all original Xs\npredict(fit1, newdata = data.frame(women$height)) # same as above for single X\n\nnew3 = data.frame(women$height) # DF using height of original data\ncbind(women, fitted(fit1), residuals(fit1)) # compare women, predicted, errors\nresiduals(fit1) # diff between actual and predicted values of weight\nsummary(fit1)\nsummary(residuals(fit1))\n\nplot(fit1)\ncor(fitted(fit1), women$weight)\nsummary(fit1)\n" }, { "path": "03-wksp1/2b3-SLM-women-A.R", "content": "# Simple Linear Regression : Built in Data Set Women\n# Check for assumptions of Regression in the data Set\nwomen\n?women\nstr(women)\nfit = lm(weight ~ height, data=women)\n?plot\n#Initial Checks\ncor(women$height, women$weight)\n#there is Strong and Positive Relationship between height and weight\nplot(women$height, women$weight)\npar(mfrow=c(1,1))\nplot(fit, which=2)\nplot(women)\n\nstr(women)\nhead(women)\nwomen[,2]\nfitted(fit)\ncbind(women, fitted(fit), predicted=3.45 * women$height - 87)\ncbind(women, fitted(fit), residue=fitted(fit)-women$weight, resid(fit))\ny = 3.4 * height - 87\nplot(residuals(fit))\nhist(women$height, breaks=4)\nhist(residuals(fit))\n\nhist(residuals(fit), freq=F)\nlines(density(residuals(fit)))\n\n#Prediction\nndata = data.frame(height=52.5)\npredict(fit, newdata=ndata, type='response')\n\n\n#Assumptions Regression\n#Linearity----\n#Linearity of the data. The relationship between the predictor (x) and the outcome (y) is assumed to be linear.\n# component + residual plot \nplot(women$height, fitted(fit))\nplot(residuals(fit) ~ fitted(fit))\nplot(fit, which=1)\n#residuals should be randomly distributed and not increase or decrease \n\n\n#Normality----\n#Normality of residuals. The residual errors are assumed to be normally distributed.\nplot(density(resid(fit)))\nplot(fit, which=2)\n\n#Homoscedasticity----\n#Homogeneity of residuals variance. The residuals are assumed to have a constant variance (homoscedasticity - opposite of heteroscedasticity)\nplot(fit, which=3)\n#No funnel shape, random distribution of residuals\nplot(fit, which=4)\n\n#Auto-Correlation----\n#Independence of residuals error terms. (Not dependent on previous values)\ndurbinWatsonTest(fit)\n#pvalue < 0; Ho that there is no correlation (r2=0) is accepted\n\n#Outliers\nplot(fit, which=4)\n#potential outliers are highlighted 1, 14, 15 row\nwomen[c(1,14,15),]\n\n#Lets remove these values and then find R2\nfit2 = lm(weight ~ height, data=women[-c(1,14,15),])\nsummary(fit2)\n(summary(fit))$r.squared\n(summary(fit2))$r.squared\nAIC(fit, fit2) #lower value of AIC is better\n\n\n#Potential Problems\n#Non-linearity of the outcome - predictor relationships\n#Heteroscedasticity: Non-constant variance of error terms.\n#Auto Collinearity, Multi-collinearity\n#Presence of influential values in the data that can be:\n#Outliers: extreme values in the outcome (y) variable\n#High-leverage points: extreme values in the predictors (x) variable\n\n#All these assumptions and potential problems can be checked by producing some diagnostic plots visualizing the residual errors.\n\nplot(fit1)\n" }, { "path": "03-wksp1/2b4-LM-cars.R", "content": "\n#http://r-statistics.co/Linear-Regression.html\nhead(cars) \nfit2=lm(dist ~ speed, data=cars)\nsummary(fit2)\npredict(fit2, newdata=data.frame(speed=c(5,8)))\nplot(fit2)\nnrow(cars)\n\nindex = sample(1:nrow(cars), size=0.8 * nrow(cars))\nindex\nlength(index)\nlength(unique(index))\ntraindata = cars[index,]\ntraindata\ntestdata = cars[-index,]\ntestdata\nnrow(traindata) + nrow(testdata)\n\nmodel1 = lm(dist ~ speed, data = traindata)\ncoef(model1)\n(P1 = predict(model1, newdata = testdata))\ndf_test = cbind(testdata, P1)\ncor(df_test$dist, P1)\n(error = testdata$dist - P1)\n\nAIC(model1)\nBIC(model1)\n(MAPE_error = mean(abs(error)/testdata$dist))\n\nlibrary(forecast)\naccuracy(model1)\naccuracy(error)\n\n#https://www.ritchieng.com/machine-learning-evaluate-linear-regression-model/\n#https://www.guru99.com/r-decision-trees.html\n#http://www.sthda.com/english/articles/35-statistical-machine-learning-essentials/141-cart-model-decision-tree-essentials/\n " }, { "path": "03-wksp1/2b4-SLR-women.R", "content": "\nfit = lm(weight ~ height, data=women)\nsummary(fit)\nrange(women$height)\n(ndata = data.frame(height= c(58.5, 60.7)))\n(p = predict(fit, newdata = ndata))\ncbind(ndata, p)\nplot(fit)\n\nsum((fitted(fit) - women$weight)^2)\n" }, { "path": "03-wksp1/2c3-MLM-salespromotion.R", "content": "#Multiple Linear Regression : DV vs more than 1 IVs\n#sales Qty vs price & promotion\n#Predict Sales Qty from Price and Promotion of the Product\n\n\n#Omni Store\n#creating data using Vector\nsales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)\nprice = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)\npromotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)\n#Create a DF from 3 variables\nomni1 = data.frame(sales,price,promotion)\nhead(omni1)\n\n#2nd Method : from CSV file\n#omni2 = read.csv(file.choose())\n\n#3rd Method : from gsheet \nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231\"\nomni3 = as.data.frame(gsheet::gsheet2tbl(url))\nhead(omni3)\n#Make one of data frames active\nomni = omni1\nhead(omni)\nstr(omni)\nnrow(omni)\ndim(omni)\n#MLR Create Multiple Linear Regression\n# we want to see how Sales Qty depend on Price and Promotion Values\nfit2 = lm(sales ~ price + promotion, data=omni)\n\n# summary statistics of model IMP STEP\nsummary(fit2)\n#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals\n#F Stats pvalue = 2.86e-10 < 0.05 : Model Exists\n#At least 1 IV can be used to predict sales\nnames(summary(fit2))\nsummary(fit2)$adj.r.squared # Adjt R2 here > .6 \n#>74% of variation in sales is explained by price and promotion\n\n#coefficients b1, b2\ncoef(fit2)\nsummary(fit2)\n#price : -53 , pvalue = 9.2e-09 < 0.05 *** : Significant\n#keeping promotion constant, if price is increased by 1 unit, salesqty decreases by 53 units\n#promotion : +3.6 , pvalue = 9.82e-06 < 0.05 ***: Significant\n#keeping price constant, if promotion is increased by 1 unit, salesqty increases by 53 units\n\nfitted(fit2)\nomni$sales\nresiduals(fit2)\nsummary(residuals(fit2))\nsummary(fit2)\n#Predict SalesQty for new combination of Values----\n\n#create a dataframe of new sample values\nrange(omni$price) ; range(omni$promotion)\n(ndata2 = data.frame(price=c(60,70), promotion=c(300,400)))\np2sales = predict(fit2, newdata=ndata2)\ncbind(ndata2, p2sales)\n\n#Assumptions\npar(mfrow=c(2,2))\nplot(fit2)\npar(mfrow=c(1,1))\n\nplot(fit2,which=1) # no pattern, equal variance\nplot(fit2,2) # Residuals are normally distributed\nplot(fit2,3) # No hetero-scedascity\nplot(fit2,4) # tells outliers which affect model\nomni[c(11,14,15),]\n\nfit3 = lm(sales ~ price + promotion, data=omni[-c(11,14,15),])\nplot(fit3,4)\nsummary(fit3)\n\n#End of Multiple Linear Regression\n\n#when variables are large, select only significant variables\n#Model with higher R2 to be selected\n#other measures of model selection : AIC, BIC, RMSE\n#Dataset can be divided into train(70%) and test(30%) set to check the accuracy\n\n#create model with t\n\n\n\n\n\n#questions\nfit2\nsummary(fit2)\nhead(omni)\ncbind(omni, predict(fit2, newdata = data.frame(omni$price, omni$promotion)))\ncbind(omni, fitted(fit2))\ncbind(omni, fitted(fit2), omni$sales - fitted(fit2), residuals(fit2))\n" }, { "path": "03-wksp1/2c4-MLM-mtcars1.R", "content": "#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf\n#install.packages('olsrr')\nlibrary(olsrr) #install it first\n\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nsummary(model)\n#model <- lm(mpg ~ hp + wt , data = mtcars)\n#summary(model)\nk <- ols_step_all_possible(model)\n#plot(k)\nk\nsummary(lm(mpg ~ wt, data=mtcars))\nsummary(lm(mpg ~ wt+ hp, data=mtcars))\nsummary(lm(mpg ~ hp + wt + qsec, data=mtcars))\n\n\n#library(olsrr)\nfit = lm(mpg ~ disp + hp + wt + qsec, data = train)\nk = ols_step_all_possible(fit)\nplot(k)\nk\nsummary(lm(mpg ~ wt, data= train))\nsummary(lm(mpg ~ wt + hp, data= train))\nfinalmodel = lm(mpg ~ wt + hp, data= train)\nlibrary(gvlma)\ngvmodel = gvlma(finalmodel)\ngvmodel\n\nfinalmodel = lm(mpg ~ wt + hp, data= train)\n(predictedvalues = predict(finalmodel, ndata=test))\ncbind(test$mpg, predictedvalues)\n\n" }, { "path": "03-wksp1/2d1-missingvalues.R", "content": "# Missing values\n\nx = c(NA, 1, NA, 2,3, NA)\nis.na(x)\nsum(is.na(x))\nsum(c(T,F,T,F,F))\nmean(x)\n?mean\nmean(x, na.rm=T)\nx\nx[is.na(x)]\nx[is.na(x)] = mean(x, na.rm=T)\nx\nx1 = c(4,6,8,9)\nlength(x1[x1 >= 6])\nsum(x1 >= 6)\nx1 >= 6\n\nx2 = rnorm(100000, mean=50, sd=5)\nlength(x2)\nposn=sample(100000, size=30)\nx2[posn] = NA\nsum(is.na(x2))\n\n#install this library\nlibrary(VIM)\n?sleep\n\nhead(sleep) #first few rows of sleep\ndim(sleep) #dimensions of sleep data\ncomplete.cases(sleep) # which row have complete data in T/ F\nsum(complete.cases(sleep)) # no of rows have which no missing data\nsum(!complete.cases(sleep)) # no of rows which have missing data\nsleep[complete.cases(sleep),] #rows which are complete\nsleep[!complete.cases(sleep),] #rows which have missing values\ncolSums(is.na(sleep)) #which column how many data missing\nrowSums(is.na(sleep)) #which row how many data missing\n\n\n#use mice package\nlibrary(mice)\n" }, { "path": "03-wksp1/2d3-datapartition.R", "content": "#partition the data into train and test set\nmtcars\nnrow(mtcars)\n#train-70%, test-30%\nsample(x=1:32, size=.7 * 32)\nindex = sample(x=1:nrow(mtcars), size=.7 * nrow(mtcars), replace=F)\nindex\ntrain= mtcars[index,]\ntest= mtcars[-index,]\nnrow(train)\nnrow(test)\nnrow(train) + nrow(test)\n\n\n\n\n\n#-------\ndata(mtcars)\n\n## 75% of the sample size\nsmp_size <- floor(0.75 * nrow(mtcars))\n\n## set the seed to make your partition reproducible\nset.seed(123)\ntrain_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)\n\ntrain <- mtcars[train_ind, ]\ntest <- mtcars[-train_ind, ]\n\n# -----\nrequire(caTools)\nset.seed(101) \nsample = sample.split(mtcars$am, SplitRatio = .75)\nsample\ntrain = subset(mtcars, sample == TRUE)\ntest = subset(mtcars, sample == FALSE)\ntrain; test\ntable(train$am); table(test$am)\n\nmtcars$id <- 1:nrow(mtcars)\ntrain <- mtcars %>% dplyr::sample_frac(.75)\ntest <- dplyr::anti_join(mtcars, train, by = 'id')\n\nlibrary(caret)\nintrain<-createDataPartition(y=factor(mtcars$am),p=0.7,list=FALSE)\nintrain\ntraining<-mtcars[intrain,]\ntesting<-mtcars[-intrain,]\ntraining \ntesting\ntable(training$am)\ntable(testing$am)\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "03-wksp1/2e1-logR-purchase.R", "content": "# Logistic Regression : Predict Purchase\n\n\n# Import the dataset\ndf1 = read.csv('./data/logr2.csv')\nhead(df1)\n\nurl=\"https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=120271978\"\nlibrary(gsheet)\ndf2 = as.data.frame(gsheet2tbl(url))\nhead(df2)\n\ndataset=df2 #or df2 if data is imported from google sheets\nhead(dataset)\nstr(dataset)\nsummary(dataset)\ndim(dataset)\nView(dataset)\ndataset$gender = factor(dataset$gender)\n\n\n# Split the dataset into the Training set and Test set\n#install.packages('caTools')\nlibrary(caTools)\nset.seed(2000)\nsplit = sample.split(dataset$purchased, SplitRatio = 0.75)\ntraining_set = subset(dataset, split == TRUE)\ntest_set = subset(dataset, split == FALSE)\n\ndim(dataset); dim(training_set); dim(test_set)\nnames(dataset)\n\n\n# Logisitic Model on Training Set\nlogitmodel1 = glm(purchased ~ gender + age + salary, family = binomial, data = training_set)\nsummary(logitmodel1)\n\n# gender not insignificant dropped here\nlogitmodel2 = glm(purchased ~ age + salary, family = binomial, data = training_set)\nsummary(logitmodel2)\n\n#summary(logitmodel2)$coefficient # they are in log terms\nhead(training_set)\n#predict on sample data\ntest_set2 = data.frame(age=c(40,65), gender=c('Male', 'Female'), salary=c(40000, 50000))\ntest_set2\n(prob_pred2 = predict(logitmodel1, type = 'response', newdata = test_set2))\ncbind(test_set2, prob_pred2)\n#age=65 person likely to purchase\n\n# Predicting the Test set results from testset\nhead(test_set)\nprob_pred = predict(logitmodel1, type = 'response', newdata = test_set)\nsummary(prob_pred)\nhead(cbind(test_set,prob_pred ),10)\n\n#if prob > 0.5 make it 1, else 0\ny_pred = ifelse(prob_pred > 0.5, 1, 0)\nhead(cbind(test_set$purchased, y_pred),100)\n\n# Making the Confusion Matrix\ncm = table(test_set[,5], y_pred)\ncm\nlibrary(caret)\ncaret::confusionMatrix(cm)\n\nnames(dataset)\n\n\n" }, { "path": "03-wksp1/2e2-LOGR-adult.R", "content": "#Logistic Regression : Binary Cls : 0 or 1\n\n#Case Study : predict if an individual will earn more than $50K using logistic regression based on demographic variables available in the adult data.\n\n#Steps\n# Import the data\n# Check for class bias\n# Create training and test samples\n# Compute information value to find out important variables\n# Build logit models and predict on test data\n# Do model diagnostics\n\n#Data Import ----\n#from URL\ninputData <- read.csv(\"http://rstatistics.net/wp-content/uploads/2015/09/adult.csv\")\nhead(inputData)\nstr(inputData)\nnames(inputData)\nremoveColumns = c('FNLWGT','EDUCATION')\n\ndata= inputData[,-which(names(inputData) %in% removeColumns)]\nnames(data)\n\ndata$ABOVE50K = factor(data$ABOVE50K)\n\n#train and test sets\nlibrary(caret)\nIndex <- createDataPartition(y=data$ABOVE50K, p=0.70, list=FALSE)\nhead(Index)\nnrow(data)\ntrainData = data[Index ,]\ntestData = data[-Index, ]\ntable(data$ABOVE50K); prop.table(table(data$ABOVE50K))\nsummary(trainData$ABOVE50K); summary(testData$ABOVE50K)\nnrow(trainData) ; nrow(testData); nrow(trainData) + nrow(testData)\nprop.table(table(trainData$ABOVE50K))\nprop.table(table(testData$ABOVE50K))\nstr(testData)\n\n\n#Logistic Regression on selected columns\nnames(data)\nlogitMod <- glm(ABOVE50K ~ RELATIONSHIP + AGE + CAPITALGAIN + OCCUPATION , data=trainData, family='binomial')\nsummary(logitMod)\nAIC(logitMod)\n\n#Check the probabilities predicted for test data - 2 methods\npredicted <- plogis(predict(logitMod, testData)) \nhead(predicted)\npredicted <- predict(logitMod, testData, type=\"response\")\nhead(predicted)\n\n#what should be the cutoff value between 0 and 1 to categorise them into 0 or 1, so that accuracy is high (correct splitting)\n\n#cutoff value\nlibrary(InformationValue)\n(optCutOff <- optimalCutoff(testData$ABOVE50K, predicted)[1] )\n\n#Confusion Matrix\n(cm1= confusionMatrix(testData$ABOVE50K, predicted, threshold = optCutOff))\n#0 classified as 0, 1 classified as 1\n\n\n\n#diagnostics\n\ncar::vif(logitMod)\n#all X variables in the model to have VIF below 4.\n\nmisClassError(testData$ABOVE50K, predicted, threshold = optCutOff)\n#Misclassification error is the percentage mismatch of predcited vs actuals, irrespective of 1’s or 0’s. The lower the misclassification error, the better is your model.\n\n\n\n\nROC\n#Receiver Operating Characteristics Curve traces the percentage of true positives accurately predicted by a given logit model as the prediction probability cutoff is lowered from 1 to 0. For a good model, as the cutoff is lowered, it should mark more of actual 1’s as positives and lesser of actual 0’s as 1’s. So for a good model, the curve should rise steeply, indicating that the TPR (Y-Axis) increases faster than the FPR (X-Axis) as the cutoff score decreases. Greater the area under the ROC curve, better the predictive ability of the model.\n\nplotROC(testData$ABOVE50K, predicted)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#http://r-statistics.co/Logistic-Regression-With-R.html\n#https://rpubs.com/H_Zhu/235617\n\n\n" }, { "path": "03-wksp1/2e3-LOGR-gre.R", "content": "#Logistic Regresion : GRE\n#https://stats.idre.ucla.edu/r/dae/logit-regression/\n#A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable\n\n\ninputData <- read.csv(\"https://stats.idre.ucla.edu/stat/data/binary.csv\")\n## view the first few rows of the data\nhead(inputData)\ndim(inputData)\ninputData\nsummary(inputData)\nsapply(inputData, sd)\nstr(inputData)\ndata= inputData # make a copy for futher analysis\n\ndata$rank = factor(data$rank)\ndata$admit = factor(data$admit)\nstr(data)\n## 2way contingency table of cat outcome and predictors we want\n## to make sure there are not 0 cells\ntable(data$rank, data$admit)\nxtabs(~admit + rank, data = data)\nxtabs(~ gear + cyl + am , data=mtcars)\n#create Logistic Model\nmylogit <- glm(admit ~ gre + gpa + rank, data = data, family = \"binomial\")\n\nsummary(mylogit)\n#gre,gpa, rank are statistically significant, \n#For every one unit change in gre, the log odds of admission (versus non-admission) increases by 0.002.\n#For a one unit increase in gpa, the log odds of being admitted to graduate school increases by 0.804.\n#The indicator variables for rank have a slightly different interpretation. For example, having attended an undergraduate institution with rank of 2, versus an institution with a rank of 1, changes the log odds of admission by -0.675.\n\n## odds ratios only\nexp(coef(mylogit))\nlibrary(dplyr)\n(ndata = sample_n(data, 3))\n#Predict admit for input data\n(prob=predict(mylogit,newdata=ndata, type=c(\"response\")))\nprob\ncbind(ndata, prob)\n\n\n#cutoff value\nlibrary(InformationValue)\n(optCutOff <- optimalCutoff(data$admit, prob)[1] ) #.46\nconfusionMatrix(data$admit, prob, threshold = optCutOff)\n(accuracy = (247+38)/ (sum(247+38+89+26))) # .715\nconfusionMatrix(data$admit, prob, threshold = .7)\n(accuracy = (272+2)/ (sum(272+2+125+1))) #.685\n\nconfusionMatrix(data$admit, prob, threshold = .2)\n\n\n## view data frame\nlibrary(dplyr)\nsample_n(data,size=1)\n(newdata1 = data.frame(gre=450, gpa=3.7, rank=factor(3) ))\n(newdata1$admitPredicted <- predict(mylogit, newdata = newdata1, type = \"response\"))\n(newdata1$admitClass = ifelse(newdata1$admitPredicted > .46,1,0))\nnewdata1 #b=not admitted to institute\n\n#End of Logistic Regression\n#also check for assumptions of residues, VIF, Multi-collinearity\n#Parition the data into train and test\n\n\n\nlibrary(caret)\nIndex <- createDataPartition(y=data$admit, p=0.70, list=FALSE)\nhead(Index)\nnrow(data)\ntrainData = data[Index ,]\ntestData = data[-Index, ]\ntable(data$admit); prop.table(table(data$admit))\nsummary(trainData$admit); summary(testData$admit)\nnrow(trainData) ; nrow(testData); nrow(trainData) + nrow(testData)\nprop.table(table(trainData$admit))\nprop.table(table(testData$admit))\n#same promotion of admit in test and train\nstr(testData)\n#now construct a model with train and then test on testdata\n\n" }, { "path": "03-wksp1/3b1-DT-CART-carseats.R", "content": "# Decision Trees : - regression tree\n#install and load this library\nlibrary(ISLR)\ndata(Carseats)\n?Carseats\ndata = Carseats\nhead(data)\n\n#Libraries for Decision Tree\nlibrary(rpart)\nlibrary(rpart.plot)\n\n#Model\ntree1 = rpart(Sales ~ . , data=data, method='anova' )\ntree1\nrpart.plot(tree1, cex=.8,nn=T)\n\n#this is large tree, so prune it: check cp\nprintcp(tree1)\n#cp value should be chosen such that xerror is least\nprunetree = prune(tree1, cp=0.03)\nprunetree\n#here we have selected a different value to simplify the tree\nmean(data$Sales)\nprunetree\nrpart.plot(prunetree, nn=T)\n#Interpretation\n#if ShelveLoc=Good, and Price >= 109.5, sales predicted is 9.2\n\n#improve the plot\nrpart.plot(prunetree, nn=T, cex=.8, type=4)\n#read this document to improve the plot\n#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf\n#http://www.milbo.org/rpart-plot/prp.pdf\nlibrary(dplyr)\n#Predict for test value\n(testdata = sample_n(data,2))\n(predictedSales=predict(prunetree, newdata=testdata, type='vector'))\ncbind(testdata, predictedSales)\n#next line will show error because we have to predict numerical value instead of class/ category, so type of response reqd is vector not class\n(predict(prunetree, newdata=testdata, type='class'))\n#ERROR - use vector\n\n#see online help here\n#https://www.datacamp.com/community/tutorials/decision-trees-R\n\n" }, { "path": "03-wksp1/3b2-DT-CART-R-sales.R", "content": "# CART Models - HH Case Study - Regression\nlibrary(rpart)\nlibrary(rpart.plot)\nlibrary(forecast)\n\nlibrary(gsheet)\nurl='https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=1941519952'\ndata = as.data.frame(gsheet2tbl(url))\nstr(data)\n\n# Summarize the dataset\nsummary(data)\nnames(data)\n# Random Sampling\nset.seed(777) # To ensure reproducibility\nIndex = sample(x = 1:nrow(data), size = 0.7*nrow(data))\nIndex\n\n# Create Train dataset\ntrain= data[Index, ]\nnrow(train)\n\n# Create Test dataset\ntest = data[-Index, ]\nnrow(test)\nnrow(test) + nrow(train)\n\n########################### Modeling #################################\n\ntrainModel = rpart(Annual_Sales ~ . , data = train[,-1], method = \"anova\")\ntrainModel\nmean(train$Annual_Sales)\n\n# Plot the Regression Tree\nrpart.plot(trainModel, type = 4,fallen.leaves = T, cex = 1.0, nn=T)\n\n#cp selection\nprintcp(trainModel)\ntrainModel_prune = prune(trainModel, cp=0.01)\nrpart.plot(trainModel_prune)\n\n#Predict and check accuracy\npredictSales_test = predict(trainModel_prune, newdata = test, type = \"vector\")\npredictSales_test #vector to print values of sales predicted\n\nlibrary(forecast)\n# Validate RMSE and MAPE calculation with a function in R\nModelAccuarcy = accuracy(predictSales_test, test$Annual_Sales)\nModelAccuarcy\n#RMSE should be as less as possible\n\n" }, { "path": "03-wksp1/3b3-DT-CART-titanic.R", "content": "# Decision Tree - Classification\n#we want predict for combination of input variables, is a person likely to servive or not\n\n#import data from online site\npath = 'https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_csv.csv'\ntitanic <- read.csv(path)\nhead(titanic)\nnames(titanic)\ndata = titanic[,c(2,3,5,6,7)] #select few columns only\nhead(data)\ndim(data)\n#load libraries\nlibrary(rpart)\nlibrary(rpart.plot)\nstr(data)\n#Decision Tree\nnames(data)\nfit <- rpart(survived ~ ., data = data, method = 'class')\nfit\nrpart.plot(fit, extra = 104, cex=.8,nn=T) #plot\n\nprintcp(fit) #select complexity parameter\nprunetree2 = prune(fit, cp=.014)\nrpart.plot(prunetree2, cex=.8,nn=T, extra=104)\nprunetree2\nnrow(data)\ntable(data$survived)\n#Predict class category or probabilities\n(testdata = sample_n(data,2))\npredict(prunetree2, newdata=testdata, type='class')\npredict(prunetree2, newdata=testdata, type='prob')\n\n#Use decision trees for predicting\n#customer is likely to buy a product or not with probabilities\n#customer is likely to default on payment or not with probabilities\n#Student is likely to get selected, cricket team likely to win etc\n\n#Imp steps\n#select columns for prediction\n#load libraries, create model\n#prune the tree with cp value\n#plot the graph\n#predict for new cases" }, { "path": "03-wksp1/3b4-DT-CART-R-loan.R", "content": "\n\n#Decision Tree\n#(https://rpubs.com/fabiorocha5150/decisiontreemodel)\n\nurl='https://raw.githubusercontent.com/fabiorcampos/Bank-Marketing/master/data/bank-full.csv'\ndata= read.table(url,sep=';', header = T )\nhead(data)\nstr(data)\nnames(data)\ndf1 = data\ndf1\nstr(df1)\nhist(df1$age)\nhist(df1$balance)\ntable(df1$marital)\nbarplot(table(df1$marital), col=1:3, beside=T)\nbarplot(table(df1$loan), col=1:3, beside=T)\n" }, { "path": "03-wksp1/3b5-DT-loanapproved1.R", "content": "# Decision Tree # loanapproved = age + job + house + credit\nloanapproved = sample(x=c('Yes','No'), size=50, replace=T)\nage = runif(50,30,60)\nhouse = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.4,.6))\njob = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.6,.4))\ncredit = ceiling(rnorm(50,100, 10))\nloan = data.frame(loanapproved, age,job,house, credit)\nhead(loan)\nctree = rpart(loanapproved ~ ., data=loan)\nctree\nrpart.plot(ctree, cex=1)\nndata = data.frame(age=c(45,55), house=c(\"No\",'Yes'), job=c(\"Yes\",'No'), credit=c(90,100))\nndata\n(p1=predict(ctree, newdata=ndata,type='class'))\n\n(p2=predict(ctree, newdata=ndata,type='prob'))\n(p3=predict(ctree, newdata=ndata,type='matrix'))\ncbind(ndata, p1, p2, p3)\n\n\n\n\nloan_tree = rpart(loanapproved ~ ., data=loan, method='class', control=rpart.control(minsplit=5, cp=-1))\nloan_tree\nrpart.plot(loan_tree)\nrpart.plot(loan_tree, type = 4,fallen.leaves = T, cex = 0.6)\nrpart.plot(loan_tree,type=2, extra=104, cex=1, tweak=1, under=T, shadow=c('brown', 'green','red'), nn=T)\n\nprintcp(loan_tree)\n\n\n\n\n\nplot(loan_tree)\ntext(loan_tree)" }, { "path": "03-wksp1/3b5-DT-rpart-iris.R", "content": "#CART Regression Tree\n\n#Load Libraries\nlibrary(rpart) #does only binary splits; CART\nlibrary(rpart.plot)\n\n#DataSet\nstr(iris)\nhead(iris)\n\ntable(iris$Species)\n\nlibrary(dplyr)\nsample_n(iris, 3)\nsample_frac(iris, .1)\n# Classification Tree\nsummary(iris)\nset.seed(1234)\n#Predict Species\nctree = rpart(Species ~ ., method='class', data=iris)\nctree\nrpart.plot(ctree, main='Classification Tree', nn=T, type=4, cex=1,extra=104)\nprintcp(ctree)\nctreeprune = prune(ctree, cp=0.44)\nctreeprune\nrpart.plot(ctreeprune, main='Classification Tree', nn=T, type=4, extra=104)\n# Regression Tree - Predict Continuous Value Length\n#Predict Sepal.Length\nrtree = rpart(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, method=\"anova\", data=iris )\nrtree\nrpart.plot(rtree, main='Regression Tree', nn=T, type=2, cex=1)\n\n#another way to plot\nplot(rtree, uniform=TRUE, main=\"Regression Tree for Sepal Length\")\ntext(rtree, use.n=TRUE, cex = 1)\nrtree\n\n#CP\nprintcp(rtree)\nrsq.rpart(rtree) #r2 on anova only\n\nrtreeprune = prune(rtree, cp=0.023)\nrpart.plot(rtreeprune, main='Pruned Regression Tree', nn=T, type=2, cex=1)\n\n\n\n#https://analytics4all.org/2016/11/23/r-decision-trees-regression/\n\n\n#Predict : Class : Category of Flower\nstr(iris)\ntestData1 = data.frame ( Sepal.Length = 5, Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)\npredict(ctree, testData1, method = \"class\") #setosa\n\n\n#Predict : Continous Value : Length\n\ntestData2 = data.frame (Species = 'setosa', Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)\npredict(rtree, testData2, method = \"anova\") #5.17\n\n" }, { "path": "03-wksp1/3d1-DT-CHAID-usvote.R", "content": "#CHAID - dataset USvote #multisplit\n# require(rsample) # for dataset and splitting also loads broom and tidyr\n#install.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\nlibrary(CHAID) #library for performing CHAID decision tree\n\n#Dataset\ndata(USvote) #from lib CHAID\n?USvote\n\n#Quick CHAID analysis\nset.seed(101)\nsample1 = USvote[sample(1:nrow(USvote), 1000),]\nhead(sample1)\nstr(sample1)\nchaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 10, minsplit=20, minprob=0))\n\n?chaid\nprint(chaidModel)\nplot(chaidModel)\nsample1 %>% filter(marstat=='married') %>% tally()\n#sample1 %>% filter(ager=='18-24') %>% tally\ndim(sample1)\nchaidModel2 <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=20, minprob=0, maxheight=2))\nplot(chaidModel2) \n\n#Summary\n#More than 2 splits\n#used in Marketing / Business \n#Net Promoter Score, Feedback, Where all variables are categories" }, { "path": "03-wksp1/3e1-clust-customer.R", "content": "# HH MA example - customer\n\n#install.packages(\"amap\")\nlibrary(amap)\n##Read the data in the file\nurl = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'\nlibrary(gsheet)\ndata = as.data.frame(gsheet2tbl(url))\nstr(data)\nhead(data)\n\nnames(data)\nsummary(data)\n###Verify the data\ncolnames(data)\nclass(data$Age)\napply(data, 2, FUN= class) #are all numeric\ndim(data)\nhead(data)\nsummary(data)\n###Run the kmeans algorithm to generate the clusters\n#?amap::Kmeans\nnames(data)\n\nk1 <- amap::Kmeans(data[,-1],centers=3, iter.max = 200,nstart = 1, method = c(\"euclidean\"))\n\nk1$centers # group means\n###Fetch size/n of obs for the groups\nk1$size\n###Fetch sum of squared for the groups\nk1$withinss\n###Fetch the cluster for each obs\n#k1$cluster\nk1$cluster\nk1$centers\nk1$cluster[9000:9800]\ntable(k1$cluster)\nk1$size\ndata_clus_2 <- data[ k1$cluster == 2,]\n(data_clus_2)\nmean(data_clus_2$Age)\ndata_clus_2$Cust_id\n\n# Write CSV\nwrite.csv(data_clus_2[,1], file = \"./data/data_clus_2.csv\")\n" }, { "path": "03-wksp1/3e1-clustering.R", "content": "#Clustering\n#sample data, iris, no of clusters\nlibrary(cluster)\nlibrary(fpc)\nlibrary(dplyr)\n\n#sample Data\nmarks = data.frame(sub1=c(0,1,2,4,5,4,6,7),sub2=c(0,1,2,3,3,4,5,5))\nmarks\n#with 1 column\nmarks[,1]\nkm12 = kmeans(marks[,1], center=3)\n#analyse output\nkm12\n\nkm12$cluster #which row is assigned to which cluster No\nkm12$size #size of each cluster\nkm12$centers #average of each variable for each cluster\nkm12$iter #no cycles, optimal cluster was found\n\nmarks %>% mutate(cluster = km12$cluster) %>% group_by(cluster) %>% summarise(mean(sub1))\n\n#Distances\nkm12$withinss\nkm12$tot.withinss #sum of withinss\nkm12$betweenss\nkm12$tot.withinss + km12$betweenss\nkm12$totss\nkm12$iter\n\n#now do it for both the columns\nkm23 = kmeans(marks, center=3)\nkm23\nkm23$centers\n\nkm23$betweenss\nkm23$tot.withinss\nkm23$withinss\nkm23$betweenss/ (km23$betweenss + km23$tot.withinss)\nkm23$betweenss; km23$betweenss/ (km23$totss) #same as below\nkm23 #within cluster sum of sq by cluster\n# should be high for good clustering\n\n#plots\nplot(marks,col=km23$cluster,cex=1.5)\npoints(km23$center,col=1:2,pch=8,cex=2) #center point\n#see the change in columns : use PCA\nplotcluster(marks, km23$cluster)\nclusplot(marks, clus=km23$cluster, color=TRUE, shade=TRUE, labels=2, lines=1, plotchar=T, span=T,main=\"Cluster Plot\")\n?clusplot\n\n#iris dataset\n\n#Plot1----\nlibrary(cluster)\nlibrary(fpc)\n\ndata(iris)\nhead(iris)\ndata = iris[, -5] # without known classification \n# Kmeans cluster analysis\niriskm1 = kmeans(data, centers=3)\nplotcluster(data, iriskm1$cluster)\n\n# More complex : PCA method: 2 dim\nclusplot(data, iriskm1$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)\niris[c(16,19),]\n\n# another method\nwith(iris, pairs(data, col=c(1:3)[iriskm1$cluster])) \nwith(iris, pairs(data, col=c(1:3)[iriskm1$cluster])) \n\n\n#Finding optimal No of Clusters\niris\ntable(iris$Species)\ndata = iris[-5] #remove target column\nhead(data)\n\nkm1= kmeans(data,centers=1)\nkm1$tot.withinss; km1$withinss\n\nkm2= kmeans(data,centers=2)\nkm2$tot.withinss; km2$withinss\n\nkm3= kmeans(data,centers=3)\nkm3$tot.withinss ; km3$withinss\n\n\nkm4= kmeans(data,centers=4)\nkm4$tot.withinss; km4$withinss\n\nkm5= kmeans(data,centers=5)\nkm5$tot.withinss;km5$withinss\n\nkm1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km5$tot.withinss ; km5$tot.withinss\ndata[km5$cluster==4,]\n\n#Selecting the number of clusters\nlibrary(NbClust)\nnc = NbClust(data, distance=\"euclidean\",min.nc=2, max.nc=15, method=\"average\") \n#avg - distance Dij between two clusters Ci and Cj is the mean of the distances between the pair of points x and y\n\nkiris = kmeans(data, centers=3)\nkiris$centers\nirisclusters = cbind(iris$Species, data, kiris$cluster)\nhead(irisclusters)\n\nlibrary(dplyr)\niris %>% group_by(Species) %>% summarise_all(mean)\nkiris$centers\n#setsoa - Cluster1, etc\n#grouping may not be perfect, but close to orginal classification\n#this way we can do Customer, Product Segmentation\n#Other Clustering Techniques - Hierarchical etc\n\n\n\n\n\n#Scaling in Clusters: marks of different max\nset.seed(1234); marks50 = ceiling(runif(100, 1,50)) \nset.seed(1234); marks500 = ceiling(runif(100, 250,500)) \n\nstudents1= data.frame(marks50, marks500)\nhead(students1)\n\nskm1 = kmeans(students1, centers=3)\nskm1$centers\nskm1$withinss\n\n#scaling gives equal importance to all variables\n#avoid obtaining clusters that are dominated by variables having the largest amount of variation\n\nstudents2 = scale(students1)\nhead(students2)\nskm2 = kmeans(students2, centers=3)\nskm2$centers\nskm2$withinss\n\npar(mfrow=c(1,2))\nplot(students1$marks50, students1$marks500, col=skm1$cluster)\nplot(students1$marks50, students1$marks500, col=skm2$cluster)\n#better cluster in 2nd case\npar(mfrow=c(1,1))\n" }, { "path": "03-wksp1/3e2-clust-samplecase.R", "content": "# Clustering\n\nset.seed(1234)\nsubject1 = trunc(rnorm(30, mean=60, sd=15))\nrange(subject1)\nsubject1\nmarks = data.frame(subject1)\nhead(marks)\n\nk2 = kmeans(marks, centers=2)\nk2\nk2$size\nlength(marks[k2$cluster==1,])\nmarks[k2$cluster==2,]\nmean(marks[k2$cluster==1,])\nmean(marks[k2$cluster==2,])\nk2$centers\n\n\n\nk2a = kmeans(marks, centers=c(50,70))\nk2a\nk2a$centers\n" }, { "path": "03-wksp1/3e3-clust-segmentation.R", "content": "# HH MA example - customer\n\n#install.packages(\"amap\")\nlibrary(amap)\n##Read the data in the file\nurl = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'\nlibrary(gsheet)\ndata = as.data.frame(gsheet2tbl(url))\nstr(data)\nhead(data)\ndim(data)\nnames(data)\nsummary(data)\nstr(data)\n###Verify the data\ncolnames(data)\nclass(data$Age)\napply(data, 2, FUN= class) #are all numeric\ndim(data)\nhead(data)\nsummary(data)\n###Run the kmeans algorithm to generate the clusters\n#?amap::Kmeans\nnames(data)\n\nk1 <- amap::Kmeans(data[,-1],centers=3, iter.max = 200)\n?Kmeans\nk1$centers # group means\n###Fetch size/n of obs for the groups\nattributes(k1)\nk1$size\n###Fetch sum of squared for the groups\nk1$withinss\n###Fetch the cluster for each obs\n#k1$cluster\nk1$cluster\nk1$centers\nk1$cluster[9000:9800]\ntable(k1$cluster)\nk1$size\ndata_clus_2 <- data[ k1$cluster == 2,]\n(data_clus_2)\nmean(data_clus_2$Age)\ndata_clus_2$Cust_id\n\n# Write CSV\nwrite.csv(data_clus_2[,1], file = \"./data/data_clus_2.csv\")\n" }, { "path": "03-wksp1/3e4-clust-noOfclusters.R", "content": "#Optimal Number of Clusters in data\n#Reduce total within ss\niris\nhead(iris)\ntable(iris$Species)\n\ndata = iris[-5]\nhead(data)\n\nkm1= kmeans(data,centers=1)\nkm1$withinss\nkm1$tot.withinss\n\nkm2= kmeans(data,centers=2)\nkm2$tot.withinss\nkm2$withinss\n\nkm3= kmeans(data,centers=3)\nkm3$tot.withinss\n\nkm4= kmeans(data,centers=4)\nkm4$tot.withinss\n\nkm5= kmeans(data,centers=5)\nkm5$tot.withinss\n\nkm1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km4$tot.withinss ; km5$tot.withinss\n\n#Selecting the number of clusters\nlibrary(NbClust)\nnc = NbClust(data, distance=\"euclidean\",min.nc=2, max.nc=15, method=\"average\")\nnc\n\n?NbClust\nkiris = kmeans(data, centers=3)\nkiris$centers\n\ncbind(iris$Species, data, kiris$cluster)\naggregate(cbind(Sepal.Length, Sepal.Width) ~ Species, data=iris, mean)\n\nnames(mtcars)\ndata2 = mtcars[c('mpg','disp','hp','wt')]\nhead(data2)\nnc = NbClust(data2, distance=\"euclidean\",min.nc=2, max.nc=15, method=\"average\")\ndet(as.matrix(mtcars))\n?na.action\nkm3= kmeans(data,centers=3)\nkm3$tot.withinss\n\ncbind(km1$tot.withinss, km2$tot.withinss, km3$tot.withinss, km4$tot.withinss,km5$tot.withinss)\n\n#we select no clusters at elbow point\n#adding more clusters does not significantly reduce total withinss\n" }, { "path": "03-wksp1/4b1-AR-groceries.R", "content": "# Association Rules - Groceries data set ####\n\nlibrary(arules) #install first\nlibrary(arulesViz) #install first\nlibrary(datasets) # no need to install, just load it reqd for Groceries\ndata('Groceries')\nGroceries\n\n#Structure of Groceries\nstr(Groceries)\nGroceries\narules::LIST(Groceries[1:6]) #another view\narules::inspect(Groceries[1:5])\n\n#Find Frequent Itemset\nfrequentItems = eclat (Groceries, parameter = list(supp = 0.006, minlen= 4, maxlen = 5)) \ninspect(frequentItems[1:4])\nfrequentItems\ninspect(frequentItems[10:200])\n#inspect(frequentItems[100:122])\n#Descending Sort frequent items by count : 1 to 25 itemsets\ninspect(sort (frequentItems, by=\"count\", decreasing=TRUE)[1:25])\ninspect(sort (frequentItems, by=\"count\", decreasing=F)[1:25])\n\n#Support is : support(A&B) = n(A&B)/ N\n#Plot the Frequency Plot\nitemFrequencyPlot(Groceries,topN = 15,type=\"absolute\")\nitemFrequencyPlot(Groceries, topN = 10, type='relative')\nabline(h=0.15)\n\n# Create rules and the relationship between items\n#parameters are min filter conditions \nrules = apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5, minlen=2))\nrules\ninspect (rules[1:5])\n#Sort Rules by confidence, lift and see the data\nrulesc <- sort (rules, by=\"confidence\", decreasing=TRUE)\ninspect(rulesc[1:5])\nrulesl <- sort (rules, by=\"lift\", decreasing=TRUE)\ninspect (rulesl[1:5])\n#which items have strong confidence and lift \n\n#How To Control The Number Of Rules in Output ?\n#maxlen, minlen, supp, conf\nrules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=3)) \ninspect(rules2[1:15])\n\n#Find what factors influenced an event ‘X’\nrules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.8), appearance = list (default=\"lhs\",rhs=\"whole milk\"), control = list (verbose=F))\ninspect(rules3[1:5])\ninspect(rules3)\n\n#Find out what events were influenced by a given event\nsubset1 = subset(rules2, subset=rhs %in% \"whole milk\")\ninspect(subset1)\nsubset1 = subset(rules2, subset=rhs %in% 'bottled beer' )\ninspect(subset1)\ninspect(rules2)\nsubset2 = subset(rules2, subset=lhs %ain% c('baking powder','soda') )\ninspect(subset2)\nsubset2a = subset(rules2, subset=lhs %in% c('baking powder','soda') )\ninspect(subset2a)\n\n\nsubset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)\ninspect(subset3)\nsubset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )\ninspect(subset4)\n\n#Visualizing The Rules -----\nplot(subset1) \nplot(subset1, measure=c(\"support\", \"lift\"), shading=\"confidence\")\n\n#\n\n\nrules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default=\"rhs\",lhs=c('tropical fruit','herbs')), control = list (verbose=F))\ninspect(rules4[1:5])\ninspect(rules4)\n\n" }, { "path": "03-wksp1/4b2-AR-samplecase.R", "content": "# Association Rule - Simple Example Case\n# read this pdf for help\n#https://cran.r-project.org/web/packages/arules/arules.pdf\n\n#libraries\nlibrary(arules)\nlibrary(arulesViz)\n\n#Create Data\n\n#Method3 Use: ----\n#Data in the form of list\nitemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))\nitemlist\nlength(itemlist)\n## set transaction names\nnames(itemlist) <- paste(\"Tr\",c(1:9), sep = \"\")\nitemlist\n## coerce into transactions\ntdata3 <- as(itemlist, \"transactions\")\ntdata3\nsummary(tdata3)\n\ntdata=tdata3\n\n#Data ready - Perform AR ----\n## analyze transactions\nsummary(tdata)\nitemlist\nimage(tdata)\n\n#Analysis\nfreqitems = eclat(tdata) #default support=.1\nfreqitems = eclat(tdata, parameter = list(minlen=1, supp=.1, maxlen=2 ))\n\nfreqitems\ninspect(freqitems)\n\nsupport(items(freqitems[1:2]), transactions=tdata)\ninspect(freqitems[1])\ninspect(items(freqitems[1]))\n\nitemFrequencyPlot(tdata,topN = 5,type=\"absolute\")\nitemFrequencyPlot(tdata,topN = 5,type=\"relative\", horiz=T)\nwrite.csv(as.data.frame(inspect(freqitems)),'freqitems1.csv')\n\n\n#Construct the Rules\nrules = apriori(tdata, parameter = list(supp = 0.2, conf = 0.5, minlen=2))\nitemFrequencyPlot(items(rules))\n\ninspect(rules[1:5])\ninspect(rules)\nwrite.csv(as.data.frame(inspect(rules)),'rules1.csv')\n#sort rules by support\nrules_s = sort(rules, by=\"support\", decreasing=TRUE )\ninspect(rules_s)\ninspect(rules_s[1:5]) #itemsset having high support\n\n#sort rules by confidence\nrules_c = sort(rules, by=\"confidence\", decreasing=TRUE )\ninspect(rules_c)\ninspect(rules_c[1:5]) #itemsset having high confidence\n\n#sort rules by lift\ninspect(head(rules, n = 3, by =\"lift\"))\nrules_l = sort(rules, by=\"lift\", decreasing=TRUE )\ninspect(rules_l)\ninspect(rules_l[1:5]) #itemsset having high confidence\n\n#Quality Data of Rules\nquality(rules_c) \n\n#Redundant Rules\ninspect(rules)\n(redundant = which(is.redundant(rules)))\ninspect(rules[c(8,9,10,11,12,14,14)])\n\ninspect(rules[redundant])\ninspect(rules)\nwrite.csv(as(rules,\"data.frame\"), file='./data/rulesR.csv')\n\n#Remove Redundant Rules\nrulesNR <- rules[-redundant] \nis.redundant(rulesNR)\nsum(is.redundant(rulesNR)) #ok now\ninspect(rulesNR)\n\n#Rules with LHS and RHS: single or combination\nrules2= rulesNR\ninspect(rules2)\nrules2.lhs1 <- subset(rules2, lhs %in% c(\"I1\", \"I5\"))\ninspect(rules2.lhs1)\n\nrules2.rhs1 <- subset(rules2, rhs %in% c(\"I3\"))\ninspect(rules2.rhs1)\n\nrules2.lhsrhs1 = subset(rules2, lhs %in% c(\"I1\") & rhs %in% c(\"I3\"))\ninspect(rules2.lhsrhs1)\n\nrules2.lhsrhs2 = subset(rules2, lhs %in% c(\"I1\") | rhs %in% c(\"I3\"))\ninspect(rules2.lhsrhs2)\n\n\n\n# Rules as DF: original rules\nrules_DF <- as(rules,\"data.frame\")\nrules_DF\nstr(rules_DF)\nwrite.csv(rules_DF, './data/myrules1.csv')\n\n#Visualisation\nplot(rules)\n" }, { "path": "03-wksp1/4b3-AR-groceries-subset.R", "content": "#AR - Groceries - Subset\n\n#Subsetting rules and itemsets\nrules <- apriori(Groceries, parameter = list(support=.001, confidence=.7, maxlen=5, target='rules' ))\nrules\n#target='rules' # to mine for rules)\n\ninspect(sort(rules, by=\"confidence\", decreasing = T)[1:5])\n\n#subset conditions\n#rhs should be ‘bottled beer’\n#confidence should be above .7\n#results should be sorted by lift\n\n#Subset1----\ninspect(sort(subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7), by = 'lift', decreasing = T))\n\n#people buying “liquor” and “red wine” are almost certain to buy “bottled beer” (9 times out of 10), but not “canned beer”\n\n# Another rule with different parameters\n#subset2----\ncanned_rules <- apriori(Groceries,parameter = list(support=.001, confidence=.01, maxlen=5, target='rules' ))\n\ninspect(subset(canned_rules, subset=lhs %ain% c(\"liquor\", \"red/blush wine\") & rhs %in% 'canned beer' ))\n#no rule found or it could be significant low % less than 1 in 100 people would do\n\n#legend to condition commands \n# lhs - means left hand side, or antecendent\n# rhs - mean right hand side, or consequent\n# items - items, that make up itemsets\n# %in% - matches any\n# %ain% - matches all\n# %pin% - matches partially\n# default - no restrictions applied\n# & - additional restrictions on lift, confidence etc.\n\n#either side lhs or rhs\n#“whole milk” and “yogurt” must be present and rule’s confidence must be higher than .95\n#subset3----\ninspect(subset(rules, subset=items %ain% c(\"whole milk\",\"yogurt\") & confidence >.95))\n\n#whole milk” and “yogurt” must be present in lhs and rule’s confidence must be higher than .9\ninspect(subset(rules, subset=lhs %ain% c(\"whole milk\",\"yogurt\") & confidence >.9))\n\n#Subset4----\n#“Bread” must be present in lhs: any type of “bread” – “white bread”, “brown bread” – both qualify. “Whole milk” must be present in rhs “as is”. confidence of the rule must be higher than .9\n\ninspect(subset(rules, subset= lhs %pin% \"bread\" & rhs %in% \"whole milk\" & confidence > .9))\n\n\n#Subset5----\n#what we can expect at rhs with confidence higher than .7 if we have both “flour” and “whole milk” on the lhs\ninspect(subset(rules, subset= lhs %ain% c(\"flour\",\"whole milk\") & confidence>.7))\n\n\n\n#Let’s consider case “Bottled beer Vs. Canned beer” and prove that people tend to buy either one or the other, and rarely do they buy both, qualifying these two as substitute products.\nrules <- apriori(Groceries, parameter = list(support=.001, conf = .01, minlen=2, maxlen=2, target='rules'))\n\n#Let’s only look at the rules where “beer” is present at both left- and right-hand-side of the rule and add chiSquared p-value to prove statistical significance of our findings:\n\ninspect(subset(rules, lhs %pin% 'beer' & rhs %pin% 'beer'))\n\ncrossTable(Groceries)['canned beer','bottled beer']\n\n#the probability of a consecutive purchase (confidence) is pretty small: ~3%\n#this is despite both bottled beer and canned beer being pretty popular purchases\neclat(Groceries, maxlen=1)\nsingleitems = eclat (Groceries, parameter = list(supp = 0.05, maxlen = 2)) \ninspect(singleitems)\ninspect(sort (singleitems, by=\"count\", decreasing=TRUE)[1:25])\n\ncrossTable(Groceries)['canned beer','canned beer']\ncrossTable(Groceries)['bottled beer','bottled beer']\n\ncrossTable(Groceries)['bottled beer','canned beer']\ncrossTable(Groceries)['canned beer','bottled beer']\n\nquality(rules)$chi <- interestMeasure(rules, measure='chi', significance=T, Groceries)\nquality(rules)$chi[4785:4786]\n#All these figures, combined with statistically significant lift below 1 (chi ~ 1e-6) tells us that “bottled beer” and “canned beer” do behave as substitutes.\n\n" }, { "path": "03-wksp1/4b5-AR-finproducts.R", "content": "# AR data for Finance\n\nlibrary(\"arules\")\n\nset.seed(101)\ntransactionID = sample(1:500, 1000, replace=T)\ntransactionID\n\nfinproducts = c('Mutual Funds', 'NPS', 'Savings Account', 'PPF', 'FD', 'Bonds', 'Stocks', 'General Insurance', 'NRI Banking', 'Car Insurance', 'Debit Card', 'Credit Card', 'Mobile Banking')\nlength(finproducts)\nitem = sample(finproducts,1000, replace=T)\nitem\norders = data.frame(transactionID, item)\nhead(orders)\n\nwrite.csv(orders, \"./data/fintransactions.csv\")\n\nordertrans <- arules::read.transactions(\n file = \"./data/fintransactions.csv\", format = \"single\",\n sep = \",\", cols=c(\"transactionID\",\"item\"), rm.duplicates = T\n)\nordertrans\ninspect(ordertrans[1:5])\nordertrans\nsummary(ordertrans)\nstr(ordertrans)\nhead(ordertrans)\n\n\n#find frequent item set\nfrequentItems <- eclat (ordertrans, parameter = list(supp = 0.005, minlen= 2, maxlen = 5)) \ninspect(frequentItems[1:10])\ninspect(sort (frequentItems, by=\"count\", decreasing=TRUE)[1:15])\n#support(A&B) = n(A&B)/ N\n\nfrequentItems\n\nitemFrequencyPlot (ordertrans,topN = 15,type=\"absolute\")\nitemFrequencyPlot(ordertrans, topN = 10, type='relative')\nabline(h=0.15)\n\n#Create Rules\nrules1 <- arules::apriori(ordertrans, parameter = list(supp = 0.005, conf = 0.5))\nrules1\nwrite.csv(inspect(rules1, './data/rules.csv'))\ninspect(rules1[1:5])\n\nrules1L = sort (rules1, by=\"lift\", decreasing=TRUE)\ninspect (rules1L[1:5])\n\n\n#How To Control The Number Of Rules in Output ?\nrules2 = apriori (ordertrans, parameter = list (supp = 0.005, conf = 0.5, minlen=3, maxlen=5)) # minlen = 3 & maxlen limits the elements in a rule to 3 & 5\ninspect(rules2)\n\nrules3 = apriori(ordertrans) #use default values no rules\n\n\n#Find what factors influenced an event ‘X’\n\n\nrules4 <- apriori(data=ordertrans, parameter=list (supp=0.001,conf = 0.08), appearance = list (default=\"lhs\",rhs=\"Credit Card\"), control = list (verbose=F)) #verbose=F no log\ninspect(rules4[1:15])\n\n#Find out what events were influenced by a given event\nrules5 <- apriori (data=ordertrans, parameter=list (supp=0.001,conf = 0.05,minlen=2), appearance = list (default=\"rhs\",lhs=\"General Insurance\"), control = list (verbose=F)) \ninspect(rules5)\n\ninspect( subset( rules5, subset = rhs %pin% c(\"Bonds\") ))\ninspect( subset( rules5, subset = rhs %pin% c(\"FD\") ))\n\n\n#Control lhs and rhs\nrules6 <- apriori(ordertrans, parameter=list(support =0.00001, confidence =0.05, minlen=2, maxlen=5), appearance = list(lhs=c(\"Debit Card\"), rhs=c(\"Credit Card\"), default=\"none\"))\ninspect(rules6)\n\n# commands for subset \n# lhs - means left hand side, or antecendent\n# rhs - mean right hand side, or consequent\n# items - items, that make up itemsets\n# %in% - matches any\n# %ain% - matches all\n# %pin% - matches partially\n# default - no restrictions applied\n# & - additional restrictions on lift, confidence etc.\n\ninspect(subset(rules1, subset=items %ain% c(\"Mobile Banking\") & confidence >.95))\ninspect(subset(rules1, subset=lhs %ain% c(\"Mobile Banking\") & confidence >.9))\n\ninspect(subset(rules1, subset= lhs %pin% \"Debit Card\" & rhs %in% \"Savings Account\" & confidence > .7))\n\n#Export Rules into a table\nlibrary(data.table)\nrules_dt <- data.table( lhs = labels( lhs(rules2) ), rhs = labels( rhs(rules2) ), \n quality(rules2) )[ order(-lift), ]\nrules_dt\nDT::datatable(rules_dt) # wrapper for datatables\nwrite.csv(rules_dt, './data/arulesfin.csv')\n\n\n#Visualizing The Rules -----\nlibrary(arulesViz)\ninspect(rules1)\nplot(rules1, measure=c(\"support\", \"lift\"), shading=\"confidence\")\nplot(rules1, measure=c(\"support\", \"confidence\"), shading=\"lift\")\n\nplot(rules1,method=\"graph\",engine='interactive', shading=\"confidence\") \n\n" }, { "path": "03-wksp1/4e1-twitter1.R", "content": "#Twitter 1 - Configure Tweets and Download them\n#@dupadhyaya #Working using my Keys\n#Load libraries\nlibrary(\"curl\")\nlibrary(\"twitteR\")\nlibrary(\"ROAuth\")\nlibrary(\"syuzhet\") #library for sentiment analysis - comparison\n\ndownload.file(url=\"http://curl.haxx.se/ca/cacert.pem\",destfile=\"cacert.pem\")\n\n#https://apps.twitter.com/\n#different for each account\nconsumerKey=\"uRDuync3BziwQnor1MZFBKp0x\"\nconsumerSecret=\"t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV\"\nAccessToken=\"14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW\"\nAccessTokenSecret=\"3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz\"\n\n#Common for all accounts except the keys\n\ncred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize')\n\ncred$handshake(cainfo=\"cacert.pem\") # it will take you to browser: authorise, copy key and paste in R Studio at Console. Once it stores, it move to R prompt\nsave(cred, file=\"twitter authentication.Rdata\") # store this to avoid asking again\n#Load saved authentication cert\nload(\"twitter authentication.Rdata\")\n#registerTwitterOAuth(cred)\n\nsetup_twitter_oauth(consumerKey, consumerSecret, AccessToken, AccessTokenSecret)\n\n#type 1 : Yes \nsearch.string <- \"#businessanalytics\"\n#search.string <- \"#marketinganalytics\"\n\nno.of.tweets <- 100\n\ntweets <- searchTwitter(search.string, n=no.of.tweets,lang=\"en\")\ntweets\ntweets[1:10]\n#Another Topics\nsearch.string <- \"#indvsaus\"\n#search.string <- \"#asiacup\"\n\nno.of.tweets <- 100\n\ntweets <- searchTwitter(search.string, n=no.of.tweets,lang=\"en\")\ntweets[1:5]\n\n#My Tweets : will change if you use your own account\nhomeTimeline(n=15) #my tweets\nmentions(n=15) # my tags\nmentions(n=5)\n\n#for user - \n(tweets = userTimeline(\"riddheishad\", n=10))\nuserTimeline(\"PrabhanshRai\", n=5)\n\n#------------------------------------\n?userTimeline\ntweets = userTimeline(\"realDonaldTrump\", n=100)\n#english\ntweets[1:5]\nn.tweet <- length(tweets)\nn.tweet\ntweets.df = twListToDF(tweets) \nhead(tweets.df)\nsummary(tweets.df)\n\n#Remove hashtags & unnecessary characters\ntweets.df2 <- gsub(\"http.*\",\"\",tweets.df$text)\ntweets.df2 <- gsub(\"https.*\",\"\",tweets.df2)\ntweets.df2 <- gsub(\"#.*\",\"\",tweets.df2)\ntweets.df2 <- gsub(\"@.*\",\"\",tweets.df2)\n\nhead(tweets.df2)\n#-----\nlibrary(\"syuzhet\") #library for sentiment analysis - comparison\nword.df <- as.vector(tweets.df2)\nemotion.df <- get_nrc_sentiment(word.df)\nemotion.df2 <- cbind(tweets.df2, emotion.df) \nhead(emotion.df2)\n\n#-----\nsent.value <- get_sentiment(word.df)\nmost.positive <- word.df[sent.value == max(sent.value)]\nmost.positive\nmost.negative<- word.df[sent.value <= min(sent.value)] \nmost.negative\nsent.value\n\n#-----\npositive.tweets <- word.df[sent.value > 0]\nhead(positive.tweets)\nnegative.tweets <- word.df[sent.value < 0] \nhead(negative.tweets)\nneutral.tweets <- word.df[sent.value == 0]\nhead(neutral.tweets)\n#----\n# Alternate way to classify as Positive, Negative or Neutral tweets\ncategory_senti <- ifelse(sent.value < 0, \"Negative\", ifelse(sent.value > 0, \"Positive\", \"Neutral\"))\nhead(category_senti)\ncategory_senti2 <- cbind(tweets,category_senti,sent.value) \nhead(category_senti2)\n\n#----\ntable(category_senti)\ntweets[13]\n" }, { "path": "03-wksp1/4e2-wordcloud.R", "content": "# Word Cloud\n\n##http://dni-institute.in/blogs/colorful-word-cloud-using-r/\n# tm for text mining\n# SnowballC for text stemming\n# wordcloud for generating word cloud images\n# RCurl and XML packages to download and parse web pages\n# RColorBrewer for color palettes\n\nlibrary(wordcloud)\nlibrary(RColorBrewer)\nlibrary(SnowballC)\nlibrary(RCurl)\nlibrary(XML)\nlibrary(tm)\n\nsource('http://www.sthda.com/upload/rquery_wordcloud.r')\n#or\n#source('./TM/rquery_wordcloud.R')\nfilePath <- \"http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt\"\n#filePath2 <- \"./data/martin-luther-king-i-have-a-dream-speech.txt\"\n\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\")\n#res<-rquery.wordcloud(filePath2, type =\"file\", lang = \"english\")\n\n" }, { "path": "03-wksp1/4e3-worldcloud2.R", "content": "# World Cloud 2\nlibrary(wordcloud)\nlibrary(RColorBrewer)\nlibrary(SnowballC)\nlibrary(RCurl)\nlibrary(XML)\nlibrary(tm)\n\n# Read the text file from file\n#text = readLines(file.choose())\ntext = readLines(con= file(\"ximb.txt\"))\ntext\ntext[1]\n# Load the data as a corpus\ndocs = Corpus(VectorSource(text))\ndocs\n\n#Text transformation\ntoSpace <- content_transformer(function (x , pattern ) gsub(pattern, \" \", x))\ndocs <- tm_map(docs, toSpace, \"/\")\ndocs <- tm_map(docs, toSpace, \"@\")\ndocs <- tm_map(docs, toSpace, \"\\\\|\")\ndocs\n#Cleaning Text\n# Convert the text to lower case\ndocs <- tm_map(docs, content_transformer(tolower))\n# Remove numbers\ndocs <- tm_map(docs, removeNumbers)\n# Remove english common stopwords\ndocs <- tm_map(docs, removeWords, stopwords(\"english\"))\n# Remove your own stop word\n# specify your stopwords as a character vector\ndocs <- tm_map(docs, removeWords, c(\"blabla1\", \"blabla2\")) \ndocs\n# Remove punctuations\ndocs <- tm_map(docs, removePunctuation)\n# Eliminate extra white spaces\ndocs <- tm_map(docs, stripWhitespace)\n# Text stemming\ndocs <- tm_map(docs, stemDocument)\n\n\n#Document Matrix\ndtm <- TermDocumentMatrix(docs)\nm <- as.matrix(dtm)\nv <- sort(rowSums(m),decreasing=TRUE)\nd <- data.frame(word = names(v),freq=v)\nhead(d, 10)\n\n#Generate Word Cloud\nset.seed(1234)\nwordcloud(words = d$word, freq = d$freq, min.freq = 1, max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, \"Dark2\"))\n\nfindFreqTerms(dtm, lowfreq = 4)\nfindAssocs(dtm, terms = \"freedom\", corlimit = 0.3)\nhead(d, 10)\n\n#Plot Freq\nbarplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,\n col =\"lightblue\", main =\"Most frequent words\",\n ylab = \"Word frequencies\")\n\n" }, { "path": "03-wksp1/4e5-wordcloud3.R", "content": "# World Cloud\n#http://stat.ethz.ch/R-manual/R-devel/library/base/html/strsplit.html\n#https://stackoverflow.com/questions/4350440/split-a-column-of-a-data-frame-to-multiple-columns\n\nlibrary(stringr)\nlibrary(wordcloud)\nlibrary(RColorBrewer)\nlibrary(tm)\nlibrary(SnowballC)\nlibrary(RCurl)\nlibrary(XML)\n\n\n\n#How to use strsplit\nstrsplit('IIT-Gawahati', \"-\")\nstrsplit('IIT Gawahati', \" \")\n\nlibrary(gsheet)\nurl = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=0'\n#check for dashtype, they are not always same\ndf1 = as.data.frame(gsheet2tbl(url))\ndf1\nhead(df1)\ndf1$wordexplanation\nbase::strsplit(df1$wordexplanation,'-') #output as list need in DF\n\n#Different Methods split and put in dataframe objects\n\n#Stringr - str_split\nstringr::str_split('IIT-Guwahati', \"-\")\n\n#Method\n(out1 = strsplit(as.character('IIT - Guwahati'),'-')) #if the word was not character\nhead(df1)\nout = strsplit(df1$wordexplanation,'-') \nhead(out)\n\nt(sapply(out[1:5], '['))\ndf2=data.frame(t(sapply(out[1:200], '[')))\n\nhead(df2)\ndf= cbind(df1,df2)\nhead(df)\nnames(df)[c(2,3)] = c('word','explanation') #rename columns\nhead(df)\n\n# create random frequencies for all words in a col\ndf$freq = floor(runif(100, 1,100))\nhead(df)\n\n#Method for word cloud\nlibrary(wordcloud)\nwc1 = wordcloud(df$word, df$freq, random.order=T)\nwc2 = wordcloud(df$word, df$freq, random.order=F, colors = topo.colors(10))\nwc3 = wordcloud(df$word, df$freq, scale=c(4,.1), min.freq=2, max.words=50, random.order=F)\nwc4 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F)\n\n\n#------\n\nlibrary(RColorBrewer)\nlibrary(tm)\npal <- brewer.pal(8,\"Dark2\")\nwc5 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F, colors=pal)\n\n\n#load the function rquery.wordcloud\nsource('http://www.sthda.com/upload/rquery_wordcloud.r')\n\nres <- rquery.wordcloud(filePath, type =\"file\", lang = \"english\",min.freq = 1, max.words = 200)\n\n# Reds color palette\nres <- rquery.wordcloud(filePath, type =\"file\", lang = \"english\",colorPalette = \"Reds\")\n# RdBu color palette\nres <- rquery.wordcloud(filePath, type =\"file\", lang = \"english\", colorPalette = \"RdBu\")\n# use unique color\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\", colorPalette = \"black\")\n\n\ntdm <- res$tdm\nfreqTable <- res$freqTable\n# Show the top10 words and their frequency\nhead(freqTable, 10)\n\n# Bar plot of the frequency for the top10\nbarplot(freqTable[1:10,]$freq, las = 2, \n names.arg = freqTable[1:10,]$word,\n col =\"lightblue\", main =\"Most frequent words\",\n ylab = \"Word frequencies\")\n\nfindFreqTerms(tdm, lowfreq = 4)\n\nfindAssocs(tdm, terms = \"freedom\", corlimit = 0.3)\n\nurl = \"http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-software\"\nrquery.wordcloud(x=url, type=\"url\")\n\n" }, { "path": "03-wksp1/4f2-quantmod1.R", "content": "#https://ntguardian.wordpress.com/2017/03/27/introduction-stock-market-data-r-1/\n#Stock Analysis \n\n# Get quantmod\nif (!require(\"quantmod\")) {\n install.packages(\"quantmod\")\n library(quantmod)\n}\n\nstart <- as.Date(\"2017-01-01\")\nend <- as.Date(\"2018-10-01\")\n\n# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the quantmod function getSymbols, and pass a string as a first argument to identify the desired ticker symbol, pass 'yahoo' to src for Yahoo! Finance, and from and to specify date ranges\n# The default behavior for getSymbols is to load data directly into the global environment, with the object being named after the loaded ticker symbol. This feature may become deprecated in the future, but we exploit it now.\n\ngetSymbols(\"AAPL\", src = \"yahoo\", from = start, to = end)\n\n# What is AAPL?\nclass(AAPL)\nhead(AAPL)\ntail(AAPL)\nplot(AAPL[, \"AAPL.Close\"], main = \"AAPL\")\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\")\n\n\n# Let's get data for Microsoft (MSFT) and Google (GOOG) (actually, Google isheld by a holding company called Alphabet, Inc., which is the company traded on the exchange and uses the ticker symbol GOOG).\ngetSymbols(c(\"MSFT\", \"GOOG\"), src = \"yahoo\", from = start, to = end)\n\n# Create an xts object (xts is loaded with quantmod) that contains closing prices for AAPL, MSFT, and GOOG\nstocks = as.xts(data.frame(AAPL = AAPL[, \"AAPL.Close\"], MSFT = MSFT[, \"MSFT.Close\"], GOOG = GOOG[, \"GOOG.Close\"]))\nhead(stocks)\ntail(stocks)\n\n# Create a plot showing all series as lines; must use as.zoo to use the zoo method for plot, which allows for multiple series to be plotted on same plot\nplot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Price\")\nlegend(\"right\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\nplot(as.zoo(stocks[, c(\"AAPL.Close\", \"MSFT.Close\")]), screens = 1, lty = 1:2, xlab = \"Date\", ylab = \"Price\")\npar(new = TRUE)\nplot(as.zoo(stocks[, \"GOOG.Close\"]), screens = 1, lty = 3, xaxt = \"n\", yaxt = \"n\", xlab = \"\", ylab = \"\")\naxis(4)\nmtext(\"Price\", side = 4, line = 3)\nlegend(\"topleft\", c(\"AAPL (left)\", \"MSFT (left)\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\n\n# Get pipe operator!\nif (!require(\"magrittr\")) {\n install.packages(\"magrittr\")\n library(magrittr)\n}\nstock_return = apply(stocks, 1, function(x) {x / stocks[1,]}) %>% t %>% as.xts\n\nhead(stock_return)\n\nplot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Return\")\nlegend(\"topleft\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\nstock_change = stocks %>% log %>% diff\nhead(stock_change)\n\nplot(as.zoo(stock_change), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Log Difference\")\nlegend(\"topleft\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5) \n\n\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\")\naddSMA(n = 20)\n\n\n#------\nstart = as.Date(\"2010-01-01\")\ngetSymbols(c(\"AAPL\", \"MSFT\", \"GOOG\"), src = \"yahoo\", from = start, to = end)\n# The subset argument allows specifying the date range to view in the chart.\n# This uses xts style subsetting. Here, we r using the idiom\n# 'YYYY-MM-DD/YYYY-MM-DD', where the date on the left-hand side of the / is\n# the start date, and the date on the right-hand side is the end date. If\n# either is left blank, either the earliest date or latest date in the series is used (as appropriate). This method can be used for any xts object, say, AAPL\n\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\", subset = \"2016-01-04/\")\naddSMA(n = 20)\n\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\", subset = \"2016-01-04/\")\naddSMA(n = c(20, 50, 200))\n\n" }, { "path": "03-wksp1/4f3-indianstocks.R", "content": "# Indian Stocks\n\nlibrary(quantmod)\nstart <- as.Date(\"2017-01-01\")\nend <- as.Date(\"2018-10-01\")\ngetSymbols(\"SBIN.NS\", src = \"yahoo\", from = start, to = end)\n\n# What is SBI?\nclass(SBIN.NS)\nhead(SBIN.NS)\ntail(SBIN.NS)\nplot(SBIN.NS[, \"SBIN.NS.Close\"], main = \"SBIN.NS\")\ncandleChart(SBIN.NS, up.col = \"black\", dn.col = \"red\", theme = \"white\")\n\n\n#ICICIBANK.NS\n#TATAMOTORS.NS\ngetSymbols(c(\"ICICIBANK.NS\", \"TATAMOTORS.NS\"), src = \"yahoo\", from = start, to = end)\n\nstocks = as.xts(data.frame(SBIN = SBIN.NS[, \"SBIN.NS.Close\"]))\nstocks\nstocks = as.xts(data.frame(SBIN = SBIN.NS[, \"SBIN.NS.Close\"], ICICI = ICICIBANK.NS[, \"ICICIBANK.NS.Close\"], TATAMOTORS = TATAMOTORS.NS[, \"TATAMOTORS.NS.Close\"]))\nhead(stocks)\n\nplot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Price\")\nlegend(\"right\", c(\"SBIN\", \"ICICI\", \"TATATMOTORS\"), lty = 1:3, cex = 0.5)\n\n" }, { "path": "03-wksp1/5-wordcloud2-New.R", "content": "#wordcloud2\n\n#install.packages('wordcloud2')\nlibrary(wordcloud2)\n\n?wordcloud2\n\ndf = data.frame(word=c('mdi','iim','imt'),freq=c(20,23,15))\ndf\nwordcloud2(df)\n\n\nhead(demoFreq)\nwordcloud2(demoFreq, size = 2, color = \"random-light\", backgroundColor = \"grey\")\nnames(demoFreq)\nwordcloud2(demoFreq, size = 2, minRotation = -pi/2, maxRotation = -pi/2)\nwordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = -pi/6, rotateRatio = 1)\nwordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = pi/6, rotateRatio = 0.9)\n\nwordcloud2(demoFreqC, size = 2, color = \"random-light\", backgroundColor = \"grey\")\nwordcloud2(demoFreqC, size = 2, minRotation = -pi/6, maxRotation = -pi/6, rotateRatio = 1)\n\n# Color Vector\n\ncolorVec = rep(c('red', 'skyblue'), length.out=nrow(demoFreq))\nwordcloud2(demoFreq, color = colorVec, fontWeight = \"bold\")\n\nwordcloud2(demoFreq,\n color = ifelse(demoFreq[, 2] > 20, 'red', 'skyblue'))\n" }, { "path": "03-wksp1/5b-LP-marketingspend.R", "content": "#LP in R : Marketing Spend\n#https://analyticsprofile.com/business-analytics/how-to-optimise-digital-marketing-spend-using-linear-programming-in-r/\n " }, { "path": "03-wksp1/5c2-LP-marketingspend-case.R", "content": "# LP - Marketing Spend\n## Code to solve LP\n\n#install.packages(\"linprog\")\nlibrary(linprog)\n\nMax_ROI = c(0.07, 0.03, 0.15, 0.12, 0.05) #Objective Function\n\nContraint_Vector = c(5000, -500, 0, -200, 0, 0, -300, 900, -100, 2500) #Constraints\n\n#Decision variables under constraints \nDecision_Var <- rbind(\n c(1,1,1,1,1), \n c(-1, 0, 0, 0, 0), \n c(-0.05, 0.95, -0.05, -0.05, -0.05), \n c(-0, -1, 0, 0, 0), \n c(0.5, 0.5, -0.5, -0.5, 0.5), \n c(0, 0, 1, -2.5, 0), \n c(0, 0, 0, -1, 0), \n c(0, 0, 0, 1, 0), \n c(0, 0, 0, 0, -1),\n c(2, 0.3, 1.8, 0.9, 2)\n)\nDecision_Var\nsolveLP(Max_ROI, Contraint_Vector, Decision_Var, TRUE)\n\n" }, { "path": "03-wksp1/5d-wordcloud2.R", "content": "##http://www.sthda.com/english/wiki/text-mining-and-word-cloud-fundamentals-in-r-5-simple-steps-you-should-know\n\n#https://www.r-graph-gallery.com/196-the-wordcloud2-library/\n\n# library #install this\nlibrary(wordcloud2) \n\n# have a look to the example dataset\nhead(demoFreq)\nwordcloud2(demoFreq, size=1)\n?wordcloud2\n#create your set of words and freq\ndf = data.frame(word=c('cbap','cmap','iim','imt','calcutta'),freq=c(20,23,15,10,13))\ndf\nwordcloud2(df)\n\n\n\n# Gives a proposed palette\nwordcloud2(demoFreq, size=1.6, color='random-dark')\n\n# or a vector of colors. vector must be same length than input data\nwordcloud2(demoFreq, size=1.6, color=rep_len( c(\"green\",\"blue\"), nrow(demoFreq) ) )\n\n# Change the background color\nwordcloud2(demoFreq, size=1.6, color='random-light', backgroundColor=\"black\")\n\n# Change the shape:\nwordcloud2(demoFreq, size = 0.7, shape = 'star')\nhead(demoFreq)\n?wordcloud2\n# Change the shape using your image\nwordcloud2(demoFreq, figPath = \"india.jpg\", size = 1.5, color = \"skyblue\", backgroundColor=\"black\")\n" }, { "path": "03-wksp1/5d2-LP-tpt.R", "content": "# LP - Transportation Problem \n#https://docs.google.com/spreadsheets/d/1G6-iPDoD_i4THQAHwBeOLeiTfuqn7a6Q7MrOg9v1C5U/edit#gid=166724984\n#https://cran.r-project.org/web/packages/lpSolve/lpSolve.pdf\nlibrary(lpSolve)\n\n(costs <- matrix (c(3,1,5,2,5,4),ncol=2))\nrow.signs <- c(\"<\", \"<\", \"<\")\nrow.rhs <- c(45, 60, 35)\ncol.signs <- rep (\">\", 2)\ncol.rhs <- c(50, 60)\n#edit(costs)\n# Run\ntptproblem = lp.transport (costs, \"min\", row.signs, row.rhs, col.signs, col.rhs)\n## Not run: Success: the objective function is 7790\ntptproblem$solution\n#lp.transport (costs, \"min\", row.signs, row.rhs, col.signs, col.rhs)$solution\n\n#Eg2\n(cost <- matrix(c(3,1,5,2,5,4),ncol=2))\nlp.transport(cost, \"min\" , rep(\"<\",3) , c(45, 60, 35) , rep(\">=\",2) , c(50, 60) )$solution\nlp.transport(cost, \"min\" , rep(\"<\",3) , c(45, 60, 35) , rep(\">=\",2) , c(50, 60) )\n?lp.transport\n\nlibrary(lpSolve)\nx <- matrix(c(.91,.32,.86,.14,.59,.36,.67,.34,.87,.56,.10,.09),ncol=3,byrow=T)\nx\nlp.transport(x, \"min\" , rep(\"==\",4) , rep(1,4) , rep(\">=\",3) , rep(1,3)) \nlp.transport(x, \"min\" , rep(\"==\",4) , rep(1,4) , rep(\">=\",3) , rep(1,3) )$solution\nlp.transport(x, \"min\" , rep(\"==\",4) , rep(1,4) , rep(\">=\",3) , rep(1,3) )$object\n" }, { "path": "03-wksp1/5e2-LP-machassign.R", "content": "#----------------------------------------------#\n#Another Method\n#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf\n#https://ecreee.wikischolars.columbia.edu/file/view/lpSolveAPI+Tutorial.Rmd\n\nlibrary(lpSolveAPI)\n\n#First we create an empty model x.\n?make.lp\n#max: x + y - 50\nlprec <- make.lp(0, 2)\nlprec\nset.objfn(lprec, c(1, 1))\nlprec\n#maximise it\nlp.control(lprec, sense=\"max\")\nlprec\n\n#set.type(lprec, c(1,2), type = c(\"integer\"))\nlprec\n#50x + 24y <= 2400\nadd.constraint(lprec, c(50,24), \"<=\", 2400)\nlprec\n#30x + 33y <= 2100\nadd.constraint(lprec, c(30,33), \"<=\", 2100)\nlprec\n#x >= 45\n#add.constraint(lprec, c(1,0), \">=\", 45)\n#lprec\n#y >= 5\n#add.constraint(lprec, c(0,1), \">=\", 5)\n#lprec\n#50 >= x + y\nadd.constraint(lprec, c(1,1), \">=\", 50)\nlprec\n\nset.bounds(lprec, lower = c(45, 5), columns = c(1, 2))\nlprec\n\n#set.bounds(lprec, upper = 48.98, columns = 4)\n#RowNames <- c(\"MachineA\", \"MachineB\",\"InitalA\", \"InitalB\",\"TotalInitial\")\nRowNames <- c(\"MachineA\", \"MachineB\",\"TotalInitial\")\nColNames <- c(\"ProductX\", \"ProductY\")\ndimnames(lprec) <- list(RowNames, ColNames)\nlprec\nsolve(lprec) #[1] 0 ok\n#get.dual.solution(lprec)\nget.objective(lprec)\nget.variables(lprec)\nget.constraints(lprec)\nplot(lprec)\nprint(lprec)\n" }, { "path": "03-wksp1/5e5-LP-farmer1.R", "content": "#Farmer Problem in LP\n#A farmer plans to plant two crops, A and B. The cost of cultivating crop A is $40/acre, whereas the cost of crop B is $60/acre. The farmer has a maximum of $7400 available for land cultivation. Each acre of crop A requires 20 labor-hours and each acre of crop B requires 25 labor-hours. The farmer has a maximum of 3300 labor-hours available. If she expects to make a profit of $150/acre on crop A and $200/acre on crop B, how many acres of each crop should she plant in order to maximize her profit?\n \nlibrary(lpSolveAPI)\n\n#First we create an empty model x.\n?make.lp\n#two variables ie. crops A & B: find which crop to be grown how many acres to max profit\nlprecF1 <- make.lp(0, 2)\nlprecF1\n#Profit :: 150A + 200B\nset.objfn(lprecF1, c(150, 200))\nlprecF1\n#Change from min to max problem\nlp.control(lprecF1, sense=\"max\")\nlprecF1\n\n#answer required in integer or real no for A & B: default Real\nlprecF1\n#1st Constraint : Budget Avl\n#40x + 60y <= 7400\nadd.constraint(lprecF1, c(40,60), \"<=\", 7400)\nlprecF1\n#2nd constraint : Labour Hours Avl\n#20x + 25y <= 3300\nadd.constraint(lprecF1, c(20,25), \"<=\", 3300)\nlprecF1\n#set lower limits : A & B > 0\nset.bounds(lprecF1, lower = c(0, 0), columns = c(1, 2))\nlprecF1\n#upper bounds can also be set only for 1 or more columns\n#set.bounds(lprec, upper = c(200), columns = 2)\nColNames <- c(\"CropA\", \"CropB\")\nRowNames <- c(\"Budget\", \"Labor\")\ndimnames(lprecF1) <- list(RowNames, ColNames)\nlprecF1\nsolve(lprecF1) #if 0 then solution found\n#get.dual.solution(lprec)\nget.objective(lprecF1) # profit achieved\nget.variables(lprecF1) #how much of each crop A & B\n150* 65 + 200 * 80\nget.constraints(lprecF1) #constraints of budget & labor used\nplot(lprecF1) # print graphical output : only when type is real\n#if type is integer, the plot will not work\nprint(lprecF1) #see the model\n\n\n#add more constraints like water\n#35x + 40y <= 10000\nadd.constraint(lprecF1, c(5,10), \"<=\", 1000)\nlprecF1\ndelete.constraint(lprecF1, 3)\nsolve(lprecF1) #if 0 then solution found\nget.objective(lprecF1) # profit achieved\nget.variables(lprecF1) #how much of each crop A & B\n\n#setting integer value\nset.type(lprecF1, c(1,2), type = c(\"integer\"))\nlprecF1\nsolve(lprecF1) #if 0 then solution found\nget.objective(lprecF1) # profit achieved\nget.variables(lprecF1) #how much of each crop A & B\n\n\n#http://lpsolve.sourceforge.net/5.5/R.htm\n?lp\n?lp.assign\n?lp.object\n\n?lp.transport\n?print.lp\n" }, { "path": "03-wksp1/6b1-dates.R", "content": "#Data Format in R \n#When we import data into R, dates and times are usually stored as character or factor by default due to symbols such as “-”, “:” and “/”\n\ncdate1='15/August/1947'\ncdate1\ncdate2 = c('15-Aug-1947', \"26-Jan-1950\", \"01-Oct-2018\")\ncdate2\n\nclass(cdate1) ; class(cdate2)\n#convert to Dates\n#Default Format of Date in R\n?as.Date()\n\n#See/ Convert Date format\nas.Date('2018-12-01')\n#Default Format : Year-Month-Date : %Y-%m-%d\n(date3= as.Date('2018-12-01',format='%Y-%m-%d'))\nclass(date3)\n\n#Format Types ----\n#%Y: 4-digit year (1982),%y: 2-digit year (82)\n#%m: 2-digit month (01)\n#%B: month (January), %b: abbreviated month (Jan) \n#%d: 2-digit day of the month (13)\n#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)\n\n#convert cdate1 cdate2 to date formats\ncdate1 #\"15/August/1947\ndate1 = as.Date(cdate1, format='%d/%B/%Y')\ndate1\nclass(date1)\ndate1 - 1:15\n#\ncdate2 #\"15-Aug-1947\" \"26-Jan-1950\"\ndate2 = as.Date(cdate2, format='%d-%b-%Y')\ndate2\n#\n\n#one more practise on date format\ndate3 =as.Date('30Apr18',format(\"%d%b%y\"))\nclass(date3)\n\n#Date to Characters\nclass(date3)\n(cdate2 = as.character(date3))\nclass(cdate2)\n\n#excel date columns\n#01 Jan 2018, 01/Jan/18, 01-Jan 2018, 2018-Jan-01, 2018 01 jan\n#this is wrong\n#The “Date” class means dates are stored as the number of days since January 1, 1970, with negative values for earlier dates. We can use the as.numeric function to view the raw values.\n\ndate1\nclass(date1)\nas.numeric(date1)\ndate2\nas.numeric(date2)\nas.Date('1970-01-01') \nas.numeric(Sys.Date())\nSys.Date() - as.Date('1970-01-01') \n\n#Sequence of Dates\n#Create Sequence of Dates\n\n#Next day after certain date\nas.Date('2018-12-01') + 1\n#Sequence of Dates\nas.Date('2018-12-01') + 0:14\nas.Date('2018-12-01') + 0:33\n\n#alternative Dates\nseq(1,30,2)\nas.Date('2018-12-09') + seq(0,29,7)\n\n\n#system date\nSys.Date()\n#next 15 days after todays date\nSys.Date() + 1:15\n\n##increment/ decrement dates \n(startdt = as.Date(\"2018-12-01\", format=\"%Y-%m-%d\"))\n(next20days = startdt + 1:20) # start : 20 days course\nstartdt - 1\n(prev20days = startdt - c(0:19)) # before : 20 days course\nrev(prev20days)\n\n\n\n#Data Arithmetic\n# date Arithmetic and subsets\n\n#start course on YYYY-MM-DD for 30 days\n(course1 = as.Date(\"2018-12-01\") + 0:29)\n\n#Difference in dates\ncourse1\nlength(course1) #no of days\nmin(course1) # first date\nmax(course1) # last date\nrange(course1) # start to end\nmean(course1) # center of the course period\nmedian(course1) # middle date\n\n#sd(course1) #no meaning\n\n#subset / select dates\ncourse1\n#1st and 5th dates\ncourse1[c(1,5)] # 1st & 5th date \n#1st and 5th dates\ncourse1[1] ; course1[5]\n\n#duration from 8th date to first date\n(duration1 = course1[8] - course1[1])\n#total duration\n(duration2 = max(course1) - min(course1)+ 1)\n\n#duration since independence\n(independencedays = Sys.Date() - as.Date('15-08-1947', '%d-%m-%Y'))\nindependencedays/365 #years\n\nas.character(Sys.Date(), format=\"%Y--%m--/%d %A\")\ncourse1\nas.character(course1, format=\"%A\")\npaste(course1 , as.character(course1, format=\"%A\"), sep=\":: \")\n\n#day on which you were born\ndob = \"07/03/1996\"\ndob_date = as.Date(dob,format='%d/%m/%Y')\ndob_date\nas.character(dob_date, format=\"%A\")\n\n" }, { "path": "03-wksp1/6b1-ts-data.R", "content": "# create a time series data\n\n#first create a vector of numerical values\n# 36 observations \nset.seed(1234)\n(sales = round(runif(36, 0,100)))\nlength(sales)\n\n#This data can be daily, weekly, monthly, quarter, yearly data\n#create yearly time series : start year 1980\n\n#Yearly----\n(ysales = ts(sales, frequency = 1))\n\n(yearlysales = ts(sales, start=c(1980), frequency=1))\nplot(yearlysales)\n(yearlysales1 = ts(sales, start=c(1980,3), frequency=1)) \n# 3rd yr from 1980\nplot(yearlysales)\n\n#find the year when sales was > 50\nyearlysales1[ yearlysales1 > 50]\nclass(yearlysales1)\nmethods(class=ts)\nyearlysales1\n(w1= window(yearlysales1, start=1983, end=1990))\nplot(w1)\n\n\n\n#Quarterly -----\n12/4 # freq=4\n(qtrsales = ts(sales, start=c(1980), frequency=4))\nplot(qtrsales)\n#list data from Qtr3 1980 to 1985\nwindow(qtrsales, start=c(1980, 3), end=c(1985, 2))\n\n\n#Monthly -----\n12/12 # freq=12 start month=Apr/ 1990\n(monsales = ts(sales, start=c(1990,4), frequency=12))\nplot(monsales)\nwindow(monsales, start=c(1991, 3))\n\n#create data from Feb 2000 to Nov 2002\n(monsales1 = ts(sales, start=c(2000,2), end=c(2003,3), frequency=12)) #recycling of elements beyond given sales value\nmonsales1\nstr(monsales1)\nlength(monsales1)\n\n#see subset of sales data : May 2000 to Aug 2001\nwindow(monsales1, start=c(2000, 5), end=c(2001, 8))\n\n#Monthly TS\nsales2 = ceiling(rnorm(365, mean=100, sd=10))\nsales2\n#YYYY,day\n(dailysales = ts(sales2, start=c(2017,10), frequency=365))\nwindow(dailysales, start=c(2017,50), end=c(2017,100))\nmean(window(dailysales, start=c(2017,50), end=c(2017,100)))\nhead(sales2)\nplot(dailysales)\nclass(dailysales)\n\n#quarterly\nsales3 = floor(rnorm(16, mean=200, sd = 12))\n(qtrsales = ts(sales3, start = c(2018,1), frequency = 4))\nplot(qtrsales)\n\n#weekly\n\n" }, { "path": "03-wksp1/6c2-dates-lubridate.R", "content": "#Package Lubridate https://data.library.virginia.edu/working-with-dates-and-time-in-r-using-the-lubridate-package/\n# Date Functions - Deal with Date & Time\n\n#lubridate provides a series of functions that are a permutation of the letters “m”, “d” and “y” to represent the ordering of month, day and year. For example, if our data has a column of dates such as May 11, 1996, our dates are ordered month-day-year. Therefore we would use the mdy function to transform the column to a date object. If our dates were in the order of, say, year-month-day, we would use the ymd function. lubridate provides functions for every permutation of “m”, “d”, “y”.\n \n \n#Eg1\nlibrary(lubridate)\n(date8a = lubridate::ymd(\"20110604\"))\n(date8 = ymd(\"20110604\"))\nclass(date8)\ndate8 + 1:10\n#date in different format seq\nmdy(\"06-04-2011\") #mon-date-year\ndmy(\"04/06/2011\") #date-mon-year\n\n#Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.\n\nbegin = c(\"May 11, 1996\", \"September 12, 2001\", \"July 1, 1988\")\nend = c(\"7/8/97\",\"10/23/02\",\"1/4/91\")\nclass(begin) ## [1] \"character\"\nclass(end) ## [1] \"character\"\n\n(begin = mdy(begin))\n## [1] \"1996-05-11\" \"2001-09-12\" \"1988-07-01\"\nclass(begin)\n\n(end = mdy(end))\n## [1] \"1997-07-08\" \"2002-10-23\" \"1991-01-04\"\n\nclass(begin) ; class(end)## [1] \"Date\"\n\n\n#If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.\n\nbegin1 = c(\"May 11, 1996 12:05\", \"September 12, 2001 1:00\", \"July 1, 1988 3:32\")\nend1 = c(\"7/8/97 8:00\",\"10/23/02: 12:00\",\"1/4/91 2:05\")\n(begin1a = mdy_hm(begin1))\n(end1a = mdy_hm(end1))\n\nclass(begin1a) ; class(end1a) ## [1] \"POSIXct\" \"POSIXt\"\nbegin1a ; as.numeric(begin1a)\nend1a ; as.numeric(end1a)\n\n#class is now “POSIXct”. “POSIXct” represents the number of seconds since the beginning of 1970. If a date is before 1970, the number of seconds is negative. \n\n#Notice also the the letters “UTC” have been appended to the date-times. UTC is short for Universal Coordinated Time. it’s basically the time standard by which the world regulates clocks. If we prefer we can specify a time zone when formatting dates by using the tz argument. Here’s how we can specify the Eastern Time Zone in the United States when formatting our dates.\n\n(begin1b = mdy_hm(begin1, tz = \"US/Eastern\"))\n(begin1c = mdy_hm(begin1, tz = \"Asia/Calcutta\"))\n\n#use the OlsonNames function to see a character vector of all time zone names\nOlsonNames()\nSys.timezone()\n\n#read in times without dates using the functions ms, hm, or hms, where again “h”, “m”, and “s” stand for “hours”, “minutes”, and “seconds”\n\ntime1 = c(\"1:13\", \"0:58\", \"1:01\")\ntime2 = c(\"12:23:11\", \"09:45:31\", \"12:05:22\")\ntime3 = c(\"2:14\", \"2:16\", \"3:35\")\n\n(time1a <- ms(time1)) ## [1] \"1M 13S\" \"58S\" \"1M 1S\"\n(time2a <- hms(time2)) ## [1] \"12H 23M 11S\" \"9H 45M 31S\" \"12H 5M 22S\"\n(time3a <- hm(time3)) ## [1] \"2H 14M 0S\" \"2H 16M 0S\" \"3H 35M 0S\"\n\n\n\narrive <- ymd_hms(\"2018-04-27 18:40:15\", tz = \"Asia/Calcutta\")\narrive\nleave <- ymd_hms(\"2018-04-29 22:00:00\", tz = \"Asia/Calcutta\")\nleave\n\nleave - arrive # Time Difference\n\n#Setting and Extracting information\n#Functions\n\n#Eg\nsecond(arrive)\nsecond(arrive) = 25 #change\nsecond(arrive)\narrive\nminute(arrive)\nhour(arrive)\nday(arrive)\nwday(arrive)\nwday(arrive)\nwday(arrive, label = TRUE)\n\nweek(arrive)\n\nmonth(arrive)\nyear(arrive)\ntz(arrive)\n\n#Time Intervals\n#save an interval of time as an Interval class object\n(student1 <- interval(arrive, leave))\n(student2 <- arrive %--% leave)\n(student3 = interval(ymd(20180320, tz = \"Asia/Calcutta\"),\n ymd(20180327, tz = \"Asia/Calcutta\")))\n\n(courseperiod = interval(ymd(20180421, tz = \"Asia/Calcutta\"),\n ymd(20180425, tz = \"Asia/Calcutta\")))\n\nint_overlaps(student1, courseperiod)\n#TRUE\nint_overlaps(student3, courseperiod)\n\nsetdiff(student2, courseperiod)\nsetdiff(student3, courseperiod)\n\n?setdiff\n#2017-07-05 IST--2017-07-15 IST\n\n#Other Functions\n#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.\nint_start(courseperiod)\nint_end(courseperiod)\nint_flip(courseperiod)\nint_aligns(student1, courseperiod) #share end point\n\ndates = now() + days(1:10)\ndates\nint_diff(dates)\n\n# Years Betw ----------\nref_date <- as.Date('20/04/08',format='%d/%m/%y')\ntoday <- as.Date(Sys.Date(), format='%d/%m/%y')\nyear(arrive) = 2015\narrive\narrive2 = as.Date(arrive, format='%d/%m/%y')\n(yrsbetw = year(today)-year(ref_date))\n(yrsbetw = year(today)-year(arrive2))\n\n" }, { "path": "03-wksp1/6d-TS-airpassengers.R", "content": "# Time Series Case Study - Decomposition\n\n#https://rpubs.com/emb90/137525\n# Data Set - AirPassengers\nx=c(9.23221232,5.3430000)\nx\noptions(digits=3)\nx\n\n?AirPassengers\nhead(AirPassengers)\nAirPassengers\nstr(AirPassengers)\nclass(AirPassengers)\n\n#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns\n# TS data components : Level + Irregular + Seasonal\n\n#stl(x, s.window, t.window = ) # command to do decomp\nstl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time\n# save it in an object\n\nplot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model\n#no cyclic here - only seasonal, trend, irregular\n#s.window - specifies seasonal effects to be identical across years\n#can handle on additive models\n\nstl1 = stl(AirPassengers, s.window = 'periodic')\nplot(stl1) # actual data, seasonal, long term trends, remainder/ irregular\n\nclass(stl1)\n\nstl1$time.series\n#(df = stl1$time.series)\n#df = as.data.frame(df)\n#write.csv(df, './data/airpsng.csv')\n\n\n#Additive Model Y = Trend + Seasonal + Irregular\n#sales increase by 300 qty in month of Nov\n#Multiplicative Model Y = Trend * Seasonal * Irregular\n#sales increase by 10% in month of Nov\n\n\n#dataset\nAirPassengers\nclass(AirPassengers)\n\n# Plot\nplot(AirPassengers)\n#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested\n\n#stabilise the plot\nLogAirPassengers = log(AirPassengers) # make it additive because stl handles only additive models\n\n# YA = T + S + I : \n#YM= T * S * I : take log of this\n# log(YM) = log(T) + log(S) +log(I)\n\nplot(LogAirPassengers) #stabilises variation due to multiplication\n#looks like additive : no increase of seasonsal component now over years\nplot(AirPassengers)\n(m1 = matrix(1:2, nrow=1, byrow = F))\nlayout(m1)\nplot(AirPassengers); plot(LogAirPassengers) # see again the change\n\n#STL\n?stl\nfit = stl(LogAirPassengers, s.window = 'periodic' )\n#Seasonal components constrainted to be same across years : periodic\n\nplot(fit)\nfit$time.series #decompose the data into S, T, R/I \n\n#december of all months same value for seasonal\n#this was after taking log : so take antilog\n#toprow = actual data with all series\nexp(fit$time.series)\n\nhead(exp(fit$time.series),n=20) # first 20 values see them\n\n# df= exp(fit$time.series)\n# names(df) = c('S','T','I')\n# head(AirPassengers)\n# head(cbind(AirPassengers, df))\n\n#Various Plots - Monthwise, quarter, \nlayout(matrix(1,nrow=1))\n#Avg of each month\nAirPassengers\nstl1 = stl(AirPassengers, s.window = 'periodic')\nmonthplot(AirPassengers) #max traffic in Jun/ Jul across years\nmonthplot(LogAirPassengers)\nmonthplot(stl1, choice='seasonal') # less in winters, more in summers\nmonthplot(stl1, choice='trend') #slight increase from Jan to Dec\n#trend increasing for each month, highest passengers in Jul\nmonthplot(stl1, choice='remainder') # irregular components\n?monthplot\n\n# see combined plots\n(m2 = matrix(1:3, nrow=3, byrow = T))\nlayout(m2) # change layout of plots\nmonthplot(stl1, choice='seasonal')\nmonthplot(stl1, choice='trend')\nmonthplot(stl1, choice='remainder')\n\n\n# Practise with different methods - Self Practise\n\n#Decompose another way\nAP.decompM = decompose(AirPassengers, type = \"multiplicative\")\nplot(AP.decompM)\n\nlibrary(forecast) #install the library\n# Forecast # adjust for multiplicative model\n?ets\nfit2b = ets(AirPassengers, model='MAM')\nfit2b\ntail(AirPassengers)\n(f2b=forecast(fit2b, 12))\nhead(f2b)$mean\n\nplot(f2b)\n" }, { "path": "03-wksp1/6d-ts-components-airp.R", "content": "# Time Series Case Study - Decomposition\n\n#https://rpubs.com/emb90/137525\n# Data Set - AirPassengers\nx=c(9.23221232,5.3430000)\nx\noptions(digits=2)\nx\n\n?AirPassengers\nhead(AirPassengers)\nAirPassengers\nstr(AirPassengers)\nclass(AirPassengers)\n\n#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns\n# TS data components : Level + Irregular + Seasonal\n\n#stl(x, s.window, t.window = ) # command to do decomp\nstl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time\n# save it in an object\n\nplot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model\n#no cyclic here - only seasonal, trend, irregual\n#s.window - specifies seasonal effects to be identical across years\n#can handle on additive models\n\nstl1 = stl(AirPassengers, s.window = 'periodic')\nplot(stl1) # actual data, seasonal, long term trends, remainder/ irregular\n\nclass(stl1)\n\nstl1$time.series\n#(df = stl1$time.series)\n#df = as.data.frame(df)\n#write.csv(df, './data/airpsng.csv')\n\n\n#Additive Model Y = Trend + Seasonal + Irregular\n#sales increase by 300 qty in month of Nov\n#Multiplicative Model Y = Trend * Seasonal * Irregular\n#sales increase by 10% in month of Nov\n\n\n#dataset\nAirPassengers\nclass(AirPassengers)\n\n# Plot\nplot(AirPassengers)\n#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested\n\n#stabilise the plot\nLogAirPassengers = log(AirPassengers) # make it additive because stl handles only additive models\n\n# YA = T + S + I : \n#YM= T * S * I : take log of this\n# log(YM) = log(T) + log(S) +log(I)\n\nplot(LogAirPassengers) #stabilises variation due to multiplication\n#looks like additive : no increase of seasonsal component now over years\n\n(m1 = matrix(1:2, nrow=1, byrow = F))\nlayout(m1)\nplot(AirPassengers); plot(LogAirPassengers) # see again the change\n\n\n#STL\nfit = stl(LogAirPassengers, s.window = 'periodic' )\n#Seasonal components constrainted to be same across years : periodic\n\nplot(fit)\nfit$time.series #decompose the data into S, T, R/I \n\n#december of all months same value for seasonal\n#this was after taking log : so take antilog\n#toprow = actual data with all series\nexp(fit$time.series)\n\nhead(exp(fit$time.series),n=20) # first 20 values see them\n\n# df= exp(fit$time.series)\n# names(df) = c('S','T','I')\n# head(AirPassengers)\n# head(cbind(AirPassengers, df))\n\n#Various Plots - Monthwise, quarter, \nlayout(matrix(1,nrow=1))\n#Avg of each month\nmonthplot(AirPassengers) #max traffic in Jun/ Jul across years\nmonthplot(fit, choice='seasonal') # less in winters, more in summers\nmonthplot(fit, choice='trend') #slight increase from Jan to Dec\n#trend increasing for each month, highest passengers in Jul\nmonthplot(fit, choice='remainder') # irregular components\n\n# see combined plots\n(m2 = matrix(1:3, nrow=3, byrow = T))\nlayout(m2) # change layout of plots\nmonthplot(fit, choice='seasonal')\nmonthplot(fit, choice='trend')\nmonthplot(fit, choice='remainder')\n\n\n# Practise with different methods - Self Practise\n\n#Decompose another way\nAP.decompM = decompose(AirPassengers, type = \"multiplicative\")\nplot(AP.decompM)\n\nlibrary(forecast)\n# Forecast # adjust for multiplicative model\nfit2b = ets(AirPassengers, model='MAM')\nfit2b\n(f2b=forecast(fit2b, 12))\nhead(f2b)$mean\n\n" }, { "path": "03-wksp1/6d-ts-johnson.R", "content": "#Johnson Case - TS\n#time series analysis\n#plot, decompose, forecast, \nJohnsonJohnson\n?JohnsonJohnson\n\nmonthplot(JohnsonJohnson)\nquarters.Date(Sys.Date())\n\nstl_jj = stl(JohnsonJohnson, s.window = 'periodic')\nplot(stl_jj)\nplot(JohnsonJohnson)\nmonthplot(stl_jj, choice='seasonal')\nmonthplot(stl_jj, choice='trend')\nmonthplot(stl_jj, choice='remainder')\n\nlibrary(forecast)\nets_jj = ets(JohnsonJohnson, model = \"MAM\")\nforecast(ets_jj,h=3)\nplot(forecast(ets_jj,h=3))\n\n\n#---\nrequire(stats); require(graphics)\nJJ <- log10(JohnsonJohnson)\nplot(JohnsonJohnson)\nplot(JJ)\n\n## This example gives a possible-non-convergence warning on some\n## platforms, but does seem to converge on x86 Linux and Windows.\n(fit <- StructTS(JJ, type = \"BSM\"))\ntsdiag(fit)\nsm <- tsSmooth(fit)\nplot(cbind(JJ, sm[, 1], sm[, 3]-0.5), plot.type = \"single\",\n col = c(\"black\", \"green\", \"blue\"))\nabline(h = -0.5, col = \"grey60\")\n\nmonthplot(fit)" }, { "path": "03-wksp1/6d-ts-xts-data.R", "content": "# xts - create object and export data\n\nlibrary(xts)\n\n#create matrix : 1 col for 1 share\n(stockprices = matrix(c(100,103, 105, 205, 210, 207, 530, 500, 535), ncol=3, byrow = F))\nstockprices\n\n(cdtindex = c('25-09-2018', '27-09-2018', '28-09-2018'))\n\n#convert to date format\n(dtindex = as.Date(cdtindex, format=\"%d-%m-%Y\"))\n#now we have index + matrix for xts object\n\n(ts_xts = xts(x=stockprices, order.by=dtindex))\n#this series is not continuous can have missing dates\n\nts_xts\ncolnames(ts_xts) = c(\"SBI\", \"ICICI\", 'HDFC')\nts_xts\n#-----\n#matrix data extract\ncoredata(ts_xts)\n#extract dates\nindex(ts_xts)\n\n\n#write to csv file\nwrite.zoo(ts_xts, \"./data/zoodata.csv\")" }, { "path": "03-wksp1/6e-TS-auto-arima-johnson.R", "content": "#Times Series Analysis \n# is the price of Johnson and Johnson shares change over time\n# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year\n# Can you forecast what future share prices will be and to what degree of accuracy\n\n#dataset - Johnson\n#Quarterly earnings per Johnson Shares\n#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA\n\nJohnsonJohnson\n\nlibrary(forecast)\n\n#ets auto select best predicton model\n?ets\n#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time\n\nfit1 = ets(JohnsonJohnson)\nfit1\n#alpha - trend\n#beta = seasonal\n#gamma - irregular\n\nJohnsonJohnson\nhead(JohnsonJohnson)\ntail(JohnsonJohnson)\n\n(f1= forecast(fit1,h=10)) # \n?forecast.ets\nplot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3) # linetype for forecast area\n#shaded portion is confidence intervals area\n\npar(mfrow=c(1,1))\n\n# ARIMA Forecasting : \n#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.\n\n#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg\n\nf2 = auto.arima(JohnsonJohnson)\nsummary(f2)\ntail(JohnsonJohnson) # last few values\nforecast(f2,h=5)\nplot(forecast(f2,h=5))\n\n#ARIMA Forecasting : compare two datasets\nlibrary(tseries)\nplot(JohnsonJohnson)\nndiffs(JohnsonJohnson)\nplot(diff(JohnsonJohnson))\n\nplot(Nile)\nplot(diff(Nile))\nndiffs(Nile)\n\n#-----\ndjj = diff(JohnsonJohnson)\nplot(djj)\n\ndnile = diff(Nile)\nplot(dnile)\n\n#----\nadf.test(djj)\n#if pv < 0.05 accept Alt Hypothesis that series is stationary\n\n#Model Selection \n#parameters p, d , q\n# d = no of diffs applied to make the series stationary\n#https://people.duke.edu/~rnau/arimrule.htm\nAcf(dnile)\n#Trail off to zero : Zero after lag ; 0,1(p)\n#Zero after lag q : Trails off to zero ; 1(q), 0 \n#Trails off to zero : Trial off to zero : 0,0\n#Nile - 1 large auto correlation at lag 1 : \n#Nile - pacf trails off to zero as the lags gets bigger\n?arima\nPacf(dnile)\n\nfit3 = arima(Nile, order=c(0,1,1)) # p,d,q\nfit3\n\n(fit3b = arima(Nile, order=c(1,1,1)))\n\n\n#Model Test\nqqnorm(fit3$residuals) # residuals ND\nqqline(fit3$residuals)\n\n#auto correl = 0 : check\nBox.test(fit3$residuals, type='Ljung-Box')\n#Test auto corr : H0= r=0 (True)\n\n#Forecast\nforecast(fit3,4)\n\nNile\n#Auto ARIMA\nplot(Nile)\n\nlibrary(forecast)\n#forecast::auto.arima()\nfit4 = auto.arima(Nile)\nfit4\n\nforecast(fit4,5)\nplot(forecast(fit4,5))\n\n" }, { "path": "03-wksp1/6g-ts-TTR-ma.R", "content": "#Time Series - SMA\nlibrary(TTR)\n\nlibrary(forecast)\n#MA\n#https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/ma\nhead(wineind)\n?wineind\nclass(wineind)\nstr(wineind)\nplot(wineind)\nsm <- ma(wineind,order=3)\n?ma\nsm\nhead(wineind,n=3)\nhead(sm, n=3)\nsum(head(wineind,n=3)) / 3\n\nlines(sm,col=\"red\")\nsmF <- ma(wineind,order=3, centre=F)\nhead(smF)\n\n\n#centered\nx = ts(runif(25,50,80))\nx\nforecast::ma(x, order=2, centre=T)\n\nplot(wineind)\ncsm <- ma(wineind,order=12, centre=T)\nlines(csm,col=\"red\")\n\n\n\n#method1\n#https://rpubs.com/ajaydecis/ts5\nkings <- scan(\"http://robjhyndman.com/tsdldata/misc/kings.dat\",skip=3)\nkings\nkingstimeseries <- ts(kings)\nkingstimeseries\nplot.ts(kingstimeseries)\n#install.packages(\"TTR\")\nlibrary(\"TTR\")\n(kingstimeseriesSMA8 <- SMA(kingstimeseries,n=8))\n?SMA\nplot.ts(kingstimeseriesSMA8)\n\nlibrary(forecast)\nfit <- HoltWinters(kingstimeseries, beta=FALSE, gamma=FALSE)\nfit1<- ets(kingstimeseries)\n?ets\nforecast(fit, 3)\nplot(forecast(fit, 3)) \n\nforecast(fit1, 3)\nplot(forecast(fit1, 3)) \n\n\n\n\n#Method2\n#https://cran.r-project.org/web/packages/smooth/vignettes/sma.html\nrequire(smooth)\nrequire(Mcomp)\nsma(M3$N2457$x, h=18, silent=FALSE)\nsma(M3$N2568$x, h=18)\n" }, { "path": "03-wksp1/8-fa-quandl.R", "content": "# Finance Stock Analysis\n#Stock Download\n\nlibrary(Quandl)\n#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW\n\nQuandl.api_key(\"4D8hkYAV4WEkcTmD9LMW\")\n\n## Download the data Set\nICICI = Quandl(\"NSE/ICICIBANK\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nICICI\nHDFC = Quandl(\"NSE/HDFCBANK\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nHDFC\nPNB= Quandl(\"NSE/PNB\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nSBI=Quandl(\"NSE/SBIN\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\n\n## Add another (\"Stock\") column in Datasets using cbind command\nhead(ICICI)\n## Paste the stock name in stock column\n\nICICI$Stock = \"ICICI\"\nPNB$Stock = \"PNB\"\nSBI$Stock = \"SBI\"\nSBI\n\nallstocks = rbind(ICICI, PNB, SBI)\nstr(allstocks)\nallstocks$Stock = factor(allstocks$Stock)\nstr(allstocks)\nnames(allstocks)\nnames(allstocks)[c(7,8)] = c('Qty','Turnover')\nnames(allstocks)\n\n\n#Using Aggregations\nnames(allstocks)\ndim(allstocks)\naggregate(allstocks$Close, by=list(allstocks$Stock), mean)\naggregate(cbind(Close, Open) ~ Stock , data= allstocks, mean)\n#https://www.statmethods.net/input/dates.html\naggregate(allstocks['Close'], by=list(allstocks$Date), mean)\naggregate(allstocks['Close'], by=list(format(allstocks$Date,\"%d\")), mean)\naggregate(allstocks['Close'], by=list(format(allstocks$Date,\"%b\")), mean)\naggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,\"%m\")), mean)\naggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,\"%Y\")), mean)\n\n#Library to store data in xlsx files\nSys.setenv(JAVA_HOME=\"C:\\\\Program Files\\\\Java\\\\jre1.8.0_191\")\nlibrary(xlsx) #needs rJava\n\n#using Dplyr Package to do Data Manipulation\nlibrary(dplyr)\nnames(allstocks)\n#select Columns\n(df1 <- allstocks %>% select(Stock, Open, Close))\ndf1 = as.data.frame(df1)\nwrite.xlsx(df1, './data/iitgfa.xlsx', sheetName = \"IITG12\", append = T)\n\n\n#Groupby\nallstocks %>% group_by(Stock) #nothing summarised\n\n#summarise\nallstocks %>% summarise(mean(Open), max(High))\n\nallstocks %>% group_by(Stock) %>% summarise_all(mean)\n#store this data into DF\n(df2 <- allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean) )\nwrite.csv(df2, './data/iitgfa.csv')\n\n#http://www.sthda.com/english/wiki/writing-data-from-r-to-excel-files-xls-xlsx#using-xlsx-package\n\n\n#write.xlsx(USArrests, file = \"myworkbook.xlsx\", #check with this file sheetName = \"USA-ARRESTS\", append = FALSE)\ndf2 = as.data.frame(df2)\nwrite.xlsx(df2, './data/iitgfa.xlsx', sheetName = \"IITG2\", append = T)\n\n\noptions(dplyr.print_max = 1e9) #print all rows\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)\n\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))\n\n#sample using dplyr\nallstocks %>% sample_n(10)\n\nallstocks %>% sample_frac(.05)\nallstocks %>% group_by(Stock) %>% sample_n(2)\n\nallstocks %>% group_by(Stock) %>% tally(Qty) %>% top_n(1)\nallstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)\n\n\n# Linear Modeling\nnames(ICICI)\ndf= cbind(ICICI[2],SBI[2] )\nnames(df) = c('icici','sbi')\nhead(df)\nplot(df)\nfit = lm(icici ~ sbi, data=df)\nsummary(fit)\nnew1 = data.frame(sbi=200)\n(p1=predict(fit,new=new1, interval='confidence' ))\ncbind(new1, p1)\nplot(x=df$sbi, y=residuals(fit)) #Linearity\nqqnorm(residuals(fit))\nqqline(residuals(fit))\n\nlibrary(car)\n#Multiple LM\ndf1 = cbind(ICICI[2],SBI[2],PNB[2] )\nnames(df1) = c('icici','sbi','pnb')\nhead(df1)\n#No Plots\npairs(df1)\nfit1 = lm(icici ~ sbi + pnb, data=df1)\nsummary(fit1)\nsummary(fit1)$r.squared\nsummary(fit1)$adj.r.squared\nnew2= data.frame(sbi=c(200,300), pnb=c(250,350))\np2=predict(fit1, new=new2, interval='confidence')\ncbind(new2, p2)\n\ncrPlots(fit1)\nvif(fit1)# variance inflation factors \nsqrt(vif(fit1)) > 2 # problem? Use only 1 variable\ndurbinWatsonTest(fit1)\ncar::outlierTest(fit1) # Bonferonni p-value for most extreme obs\ncar::qqPlot(fit1, main=\"QQ Plot\")\nlibrary(gvlma)\ngvmodel <- gvlma(fit1) \nsummary(gvmodel)\n\nav.Plots(fit1)\n?car::av.Plots\ncar::influencePlot(fit1,\tid.method=\"identify\", main=\"Influence Plot\", sub=\"Circle size is proportial to Cook's Distance\" )\ncar::ncvTest(fit1)\ncar::spreadLevelPlot(fit1)\n" }, { "path": "03-wksp1/8-fa-quandl2.R", "content": "# Finance Stock Analysis\n\n#Install Packages \n#pckgs<-c(\"Quandl\",\"Sweep\",\"tidyverse\")\n#install.packages(pckgs,dependencies = TRUE)\n\nlibrary(Quandl)\n# library(tidyverse)\n# library(ggplot2)\n# library(stringr)\n# library(plyr)\n# library(stringr)\n# library(gridExtra)\n\n#Quandl(Code=“NSE/—”,collapse=“—”,start_date=“—-”,type=“…”)\n#ICICI SBI PNB price band of Rs 200 to Rs 500. \n#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW\n\nQuandl.api_key(\"4D8hkYAV4WEkcTmD9LMW\")\n\n## Download the data Set\nICICI = Quandl(\"NSE/ICICIBANK\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nPNB= Quandl(\"NSE/PNB\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nSBI=Quandl(\"NSE/SBIN\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\n\n## Add another (\"Stock\") column in Datasets using cbind command\nhead(ICICI)\n## Paste the stock name in stock column\n\nICICI$Stock = \"ICICI\"\nPNB$Stock = \"PNB\"\nSBI$Stock = \"SBI\"\nSBI\n#combine them\nallstocks = rbind(ICICI, PNB, SBI)\nstr(allstocks)\nallstocks$Stock = factor(allstocks$Stock)\nstr(allstocks)\nnames(allstocks)\nnames(allstocks)[c(7,8)] = c('Qty','Turnover')\nnames(allstocks)\n\n\n#Using Aggregations\nnames(allstocks)\naggregate(allstocks$Close, by=list(allstocks$Stock), mean)\naggregate(cbind(Close, Open) ~ Stock , data= allstocks, mean)\n\n#https://www.statmethods.net/input/dates.html\naggregate(allstocks['Close'], by=list(allstocks$Date), mean)\naggregate(allstocks['Close'], by=list(format(allstocks$Date,\"%d\")), mean)\naggregate(allstocks['Close'], by=list(format(allstocks$Date,\"%b\")), mean)\naggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,\"%m\")), mean)\naggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,\"%Y\")), mean)\n\n#using Dplyr Package to do Data Manipulation\nlibrary(dplyr)\nnames(allstocks)\n#select Columns\nallstocks %>% select(Stock, Open, Close)\n\n#Groupby\nallstocks %>% group_by(Stock) #nothing summarised\n\n#summarise\nallstocks %>% summarise(mean(Open), max(High))\n\nallstocks %>% group_by(Stock) %>% summarise_all(mean)\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)\noptions(dplyr.print_max = 1e9)\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)\n\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))\n\n#sample using dplyr\nallstocks %>% sample_n(10)\nallstocks %>% sample_frac(.05)\nallstocks %>% group_by(Stock) %>% sample_n(2)\n\nallstocks %>% group_by(Stock) %>% tally(Qty) %>% top_n(1)\nallstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)\n\n\n# Linear Modeling\nnames(ICICI)\ndf= cbind(ICICI[2],SBI[2] )\nnames(df) = c('icici','sbi')\nhead(df)\nplot(df)\nfit = lm(icici ~ sbi, data=df)\nsummary(fit)\nnew1 = data.frame(sbi=200)\n(p1=predict(fit,new=new1, interval='confidence' ))\ncbind(new1, p1)\nplot(x=df$sbi, y=residuals(fit)) #Linearity\nqqnorm(residuals(fit))\nqqline(residuals(fit))\n\nlibrary(car)\n#Multiple LM\ndf1 = cbind(ICICI[2],SBI[2],PNB[2] )\nnames(df1) = c('icici','sbi','pnb')\nhead(df1)\n#No Plots\npairs(df1)\nfit1 = lm(icici ~ sbi + pnb, data=df1)\nsummary(fit1)\nsummary(fit1)$r.squared\nsummary(fit1)$adj.r.squared\nnew2= data.frame(sbi=c(200,300), pnb=c(250,350))\np2=predict(fit1, new=new2, interval='confidence')\ncbind(new2, p2)\n\ncrPlots(fit1)\nvif(fit1)# variance inflation factors \nsqrt(vif(fit1)) > 2 # problem? Use only 1 variable\ndurbinWatsonTest(fit1)\ncar::outlierTest(fit1) # Bonferonni p-value for most extreme obs\ncar::qqPlot(fit1, main=\"QQ Plot\")\nlibrary(gvlma)\ngvmodel <- gvlma(fit1) \nsummary(gvmodel)\n\nav.Plots(fit1)\n?car::av.Plots\ncar::influencePlot(fit1,\tid.method=\"identify\", main=\"Influence Plot\", sub=\"Circle size is proportial to Cook's Distance\" )\ncar::ncvTest(fit1)\ncar::spreadLevelPlot(fit1)\n" }, { "path": "03-wksp1/8-fa-quantmod.R", "content": "#https://ntguardian.wordpress.com/2017/03/27/introduction-stock-market-data-r-1/\n#Stock Analysis \n\n# Get quantmod\nif (!require(\"quantmod\")) {\n install.packages(\"quantmod\")\n library(quantmod)\n}\n\nstart <- as.Date(\"2018-01-01\")\nend <- as.Date(\"2018-12-09\")\n\n# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the\n# quantmod function getSymbols, and pass a string as a first argument to\n# identify the desired ticker symbol, pass 'yahoo' to src for Yahoo!\n# Finance, and from and to specify date ranges\n\n# The default behavior for getSymbols is to load data directly into the\n# global environment, with the object being named after the loaded ticker\n# symbol. This feature may become deprecated in the future, but we exploit\n# it now.\n\ngetSymbols(\"AAPL\", src = \"yahoo\", from = start, to = end)\n\n# What is AAPL?\nclass(AAPL)\nhead(AAPL)\ntail(AAPL)\nplot(AAPL[, \"AAPL.Close\"], main = \"AAPL\")\ncandleChart(AAPL[1:10,], up.col = \"black\", dn.col = \"red\", theme = \"white\")\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\")\n#30-Nov-2018\t180.29\t180.33\t177.03\t178.58\t178.58\t3,94,83,800\n\n# Let's get data for Microsoft (MSFT) and Google (GOOG) (actually, Google is\n# held by a holding company called Alphabet, Inc., which is the company\n# traded on the exchange and uses the ticker symbol GOOG).\ngetSymbols(c(\"MSFT\", \"GOOG\"), src = \"yahoo\", from = start, to = end)\nMSFT\n# Create an xts object (xts is loaded with quantmod) that contains closing\n# prices for AAPL, MSFT, and GOOG\nstocks = as.xts(data.frame(AAPL = AAPL[, \"AAPL.Close\"], MSFT = MSFT[, \"MSFT.Close\"], GOOG = GOOG[, \"GOOG.Close\"]))\nhead(stocks)\nclass(stocks)\n\n# Create a plot showing all series as lines; must use as.zoo to use the zoo\n# method for plot, which allows for multiple series to be plotted on same\n# plot\nplot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Price\")\nlegend(\"right\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\nplot(as.zoo(stocks[, c(\"AAPL.Close\", \"MSFT.Close\")]), screens = 1, lty = 1:2, xlab = \"Date\", ylab = \"Price\")\npar(new = TRUE)\nplot(as.zoo(stocks[, \"GOOG.Close\"]), screens = 1, lty = 3, xaxt = \"n\", yaxt = \"n\", xlab = \"\", ylab = \"\")\naxis(4)\nmtext(\"Price\", side = 4, line = 3)\nlegend(\"topleft\", c(\"AAPL (left)\", \"MSFT (left)\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\n\n# Get pipe operator!\nif (!require(\"magrittr\")) {\n install.packages(\"magrittr\")\n library(magrittr)\n}\nstock_return = apply(stocks, 1, function(x) {x / stocks[1,]}) %>% \n t %>% as.xts\n\nhead(stock_return)\n\nplot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Return\")\nlegend(\"topleft\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\nstock_change = stocks %>% log %>% diff\nhead(stock_change)\n\nplot(as.zoo(stock_change), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Log Difference\")\nlegend(\"topleft\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5) \n\n\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\")\naddSMA(n = 20)\n\n\n\nstart = as.Date(\"2010-01-01\")\ngetSymbols(c(\"AAPL\", \"MSFT\", \"GOOG\"), src = \"yahoo\", from = start, to = end)\n# The subset argument allows specifying the date range to view in the chart.\n# This uses xts style subsetting. Here, I'm using the idiom\n# 'YYYY-MM-DD/YYYY-MM-DD', where the date on the left-hand side of the / is\n# the start date, and the date on the right-hand side is the end date. If\n# either is left blank, either the earliest date or latest date in the\n# series is used (as appropriate). This method can be used for any xts\n# object, say, AAPL\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\", subset = \"2016-01-04/\")\naddSMA(n = 20)\n\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\", subset = \"2016-01-04/\")\naddSMA(n = c(20, 50, 200))\n" }, { "path": "03-wksp1/8-quantmod-I-stocks.R", "content": "#Indian Stocks\n\n# Indian Stocks\n#stocks2\n\n# Get quantmod\nif (!require(\"quantmod\")) {\n install.packages(\"quantmod\")\n library(quantmod)\n}\n\nstart <- as.Date(\"2018-01-01\")\nend <- as.Date(\"2018-12-09\")\ngetSymbols(\"SBIN.NS\", src = \"yahoo\", from = start, to = end)\n# What is SBI?\nclass(SBIN.NS)\nhead(SBIN.NS)\ntail(SBIN.NS)\nplot(SBIN.NS[, \"SBIN.NS.Close\"], main = \"SBIN.NS\")\ncandleChart(SBIN.NS, up.col = \"black\", dn.col = \"red\", theme = \"white\")\n\n\n#ICICIBANK.NS\n#TATAMOTORS.NS\ngetSymbols(c(\"ICICIBANK.NS\", \"TATAMOTORS.NS\"), src = \"yahoo\", from = start, to = end)\n\nstocks = as.xts(data.frame(SBIN = SBIN.NS[, \"SBIN.NS.Close\"]))\nstocks\nstocks = as.xts(data.frame(SBIN = SBIN.NS[, \"SBIN.NS.Close\"], ICICI = ICICIBANK.NS[, \"ICICIBANK.NS.Close\"], TATAMOTORS = TATAMOTORS.NS[, \"TATAMOTORS.NS.Close\"]))\nhead(stocks)\n\nplot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Price\")\nlegend(\"right\", c(\"SBIN\", \"ICICI\", \"TATATMOTORS\"), lty = 1:3, cex = 0.5)\n\n" }, { "path": "03-wksp1/zz-practise.R", "content": "# Practise Exercise - XIMB\n\n#Create a 100 row DF with following Variables\n#gender, spl, age, experience, grade, placement\nhead(students)\nstr(students)\n#\n\n\n(gender = sample(c('M','F'), size=100, replace=T, prob=c(.6,.4)))\n(spl = sample(c('Marketing','Finance','HR'), size=100, replace=T, prob=c(.3,.4,.3)))\n(age = round(runif(100, 21, 30),2))\n(experience = round(rnorm(100, 4, 1),2))\n(grade = sample(c('A','B','C','D'), size=100, replace=T, prob=c(.4,.3,.2,.1)))\n(placement = sample(c('yes','no'), size=100, replace=T, prob=c(.7,.3)))\n\nstudents = data.frame(gender, spl, age, experience, grade, placement)\nstr(students)\nsummary(students)\n\n#summarise the data in various ways using dplyr\nlibrary(dplyr)\nnames(students)\nstudents %>% group_by(placement, gender) %>% summarise(mean(experience), max(experience), mean(age))\n\nstudents %>% filter(spl=='Marketing') %>% summarise(mean(age), mean(experience))\nstudents %>% filter(spl=='Marketing') %>% group_by(spl) %>% summarise(mean(age), mean(experience))\nstudents %>% filter(spl!='Marketing') %>% group_by(spl) %>% summarise(mean(age), mean(experience))\nstudents %>% filter(spl=='Marketing'& gender=='F' & grade=='A') %>% group_by(spl) %>% summarise(mean(age), mean(experience))\n\n\n#draw few graphs to understand the distribution of data\n\nhist(students$age)\nt1=table(students$placement)\nbarplot(t1, col=1:2)\nboxplot(students$age)\npairs(students[,c('age','experience')])\npie(t1)\n\npar(mfrow=c(2,2))\npie(table(students$gender))\npie(table(students$placement))\npie(table(students$grade))\npie(table(students$spl))\npar(mfrow=c(1,1))\ntable(students$gender, students$placement, students$spl)\n\n\n\n#find students having largest experience in each spl for each gender\n\nstudents %>% group_by(spl,gender) %>% arrange(spl, gender,experience) %>% top_n(1, experience) \n\n\n\n#how many were placed : draw pie plot\n\n\n\nwrite.csv(students, './data/ximb.csv')\n\nstudents2 = read.csv('./data/ximb.csv')\nhead(students2)\n\n\n#clustering\nkm3 = kmeans(students[,c('age','experience')], centers=3)\nkm3\nkm3$centers\nplot(students[,c('age','experience')], col=km3$cluster)\n\n#decision tree\nlibrary(rpart)\nlibrary(rpart.plot)\n\ntree = rpart(placement ~ . , data=students)\ntree\nrpart.plot(tree, nn=T, cex=1)\nprintcp(tree)\nprune(tree, cp=.03)\n\nndata = sample_n(students, 5)\nndata\npredict(tree, newdata= ndata, type='class')\npredict(tree, newdata= ndata, type='prob')\n\n\n#logistic Regression\nlogitmodel1 = glm(placement ~ . , data =students, family='binomial')\nsummary(logitmodel1)\nlogitmodel1a = glm(placement ~ age , data =students, family='binomial')\nsummary(logitmodel1a)\nlogitmodel2 = glm(placement ~ age + gender , data =students, family='binomial')\nsummary(logitmodel2)\n\n#linear regression\nlinear1 = lm(age ~ . , data=students)\nsummary(linear1)\n\n\n#iimkpv\n\n#vectors\nx = 1:100\nclass(x)\nx = c(1,3,5.5)\nclass(x)\nx = LETTERS[1:10]\nclass(x)\nx = c(T,F,T,F,F)\nclass(x)\n#matrix\nm = matrix(1:24, ncol=6)\nm\nclass(m)\n#dataframes\nhead(mtcars)\nclass(mtcars)\n\n#datastructures\n#filter, sort, delete, missingvalues, stats operations\ncolMeans(mtcars)\ncolMeans(m)\n?colMeans\n\n#manipulation / summarisation\nlibrary(dplyr)\nnames(mtcars)\nmtcars %>% group_by(cyl) %>% summarise(mean(mpg), max(wt), n())\n\nplot(y=mtcars$mpg, x=mtcars$wt)\nabline(lm(mpg ~ wt, data=mtcars))\n#visualisation\n\n#Modeling\nfit = lm(mpg ~ wt + hp, data=mtcars)\nsummary(fit)\n#linear regression, logistic regression, decision tree, clustering, market basket analysis\n\n#Time Series \n#stock data, sales data\n#Financial Analytics, Business \n\n#Dates are handled in the R\n#create TS objects in R\n#manipulate, Plot, Forecasting\n#decomp\n\n# Decision Trees\n\n#types - Regression & Classification\niris\nhead(iris)\n\nlibrary(rpart)\nlibrary(rpart.plot)\n#regression tree\n#DV - Sepal.Length\nrtree = rpart(Sepal.Length ~ . , data= iris, method='anova')\nrtree\nrpart.plot(rtree, nn=T, cex=.8, fallen.leaves = T)\n#classification tree\n#DV - Species\nctree = rpart(Species ~ . ,data = iris, method='class')\nctree\nrpart.plot(ctree, cex=.8, nn=T, extra=104)\nprintcp(ctree)\nctreeprune1 = prune(ctree, cp=.44)\nctreeprune1\nrpart.plot(ctreeprune1, cex=.8, nn=T, extra=104)\n(ndata = sample_n(iris,3))\npredict(ctree,newdata=ndata, type='prob')\n\n\nlibrary(gsheet)\nurl= \"https://docs.google.com/spreadsheets/d/1FTlwpywOynI9dXbgewKCf4WhBc2PwoQmHzlqnAEisuA\"\ndf = as.data.frame(gsheet2tbl(url))\nhead(df)\n\nurl2= \"https://docs.google.com/spreadsheets/d/1FTlwpywOynI9dXbgewKCf4WhBc2PwoQmHzlqnAEisuA/edit#gid=1123010023\"\ndf2 = as.data.frame(gsheet2tbl(url2))\nhead(df2)\n\n\ndf3 = read.csv('./data/iimc1.csv')\nhead(df3)\n\n" }, { "path": "04-wksp2/Graph-matrixplots.R", "content": "# Matrix Plots\n# Multiple Series on one Graph\n\ntable(iris$Species) # is data.frame with 'Species' factor\niS <- iris$Species == \"setosa\"\niV <- iris$Species == \"versicolor\"\niS;iV\nop <- par(bg = \"bisque\")\nmatplot(c(1, 8), c(0, 4.5), type = \"n\", xlab = \"Length\", \n ylab = \"Width\", main = \"Petal and Sepal Dimensions in Iris Blossoms\")\nmatpoints(iris[iS,c(1,3)], iris[iS,c(2,4)], pch = \"sS\", \n col = c(2,4))\nmatpoints(iris[iV,c(1,3)], iris[iV,c(2,4)], pch = \"vV\", \n col = c(2,4))\nlegend(1, 4, c(\" Setosa Petals\", \" Setosa Sepals\",\n \"Versicolor Petals\", \"Versicolor Sepals\"),\n pch = \"sSvV\", col = rep(c(2,4), 2))\n\n# Example 2\n\na <- rnorm(100)\nb <- 2*a + 3\nc <- 3*a + 2\na;b;c\nmatplot(a, cbind(b,c), pch=1:2, col=c(2,4))\nlegend(\"bottomright\", inset=.05, legend=c(\"b\", \"c\"), \n pch=1:2, col=c(2,4), horiz=F)\n\n\n\n# Example 3\nx = 1:10\nA = c(15, 36, 54, 60, 68, 71, 73, 75, 78, 78)\nB = c(20, 49, 58, 69, 75, 80, 83, 86, 88, 89)\nC = c(24, 58, 68, 75, 83, 90, 93, 93, 95, 96)\nPerformance = data.frame(A,B,C)\nmatplot(x,Performance, type=\"o\", pch=c(1,2,3), col=c(\"red\",\"green\",\"blue\"))\nlegend(\"bottomright\", inset=.05, legend=c(\"A\", \"B\",'C'),\n pch=1:3, col=c(\"red\",\"green\",\"blue\"), horiz=F)\n" }, { "path": "04-wksp2/LMtrainTest.R", "content": "#partition the data into train and test set\nmtcars\nlibrary(caret)\nnrow(mtcars)\nindex = sample(x=1:nrow(mtcars), size=.7 * nrow(mtcars))\nindex\ntrain= mtcars[index,]\ntest= mtcars[-index,]\nnrow(train)\nnrow(test)\nnrow(train) + nrow(test)\n\nlibrary(olsrr)\nfit = lm(mpg ~ disp + hp + wt + qsec, data = train)\nk = ols_step_all_possible(fit)\nplot(k)\nk\nsummary(lm(mpg ~ wt, data= train))\nsummary(lm(mpg ~ wt + hp, data= train))\n\nlibrary(gvlma)\ngvmodel = gvlma(finalmodel)\ngvmodel\n\nfinalmodel = lm(mpg ~ wt + hp, data= train)\n(predictedvalues = predict(finalmodel, ndata=test))\ncbind(test$mpg, predictedvalues)\n" }, { "path": "04-wksp2/Links_DAR", "content": "\nClassification\nhttp://dataaspirant.com/2017/01/30/how-decision-tree-algorithm-works/\n\n#Clustering\nhttps://www.statmethods.net/advstats/cluster.html\n\nhttp://www.sthda.com/english/articles/25-cluster-analysis-in-r-practical-guide/111-types-of-clustering-methods-overview-and-quick-start-r-code/\n \nhttps://rstudio-pubs-static.s3.amazonaws.com/33876_1d7794d9a86647ca90c4f182df93f0e8.html\n" }, { "path": "04-wksp2/Links_DAR.R", "content": "" }, { "path": "04-wksp2/TS-arima-johnson.R", "content": "#Times Series Analysis \n# is the price of Johnson and Johnson shares change over time\n# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year\n# Can you forecast what future share prices will be and to what degree of accuracy\n\n#dataset - Johnson\n#Quarterly earnings per Johnson Shares\n#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA\n\nJohnsonJohnson\n\nlibrary(forecast)\n\n#ets auto select best predicton model\n?ets\n#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time\n\nfit1 = ets(JohnsonJohnson)\nfit1\n#alpha - trend\n#beta = seasonal\n#gamma - irregular\n\nJohnsonJohnson\nhead(JohnsonJohnson)\ntail(JohnsonJohnson)\n\n(f1= forecast(fit1,h=10)) # \n?forecast.ets\nplot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3) # linetype for forecast area\n#shaded portion is confidence intervals area\n\npar(mfrow=c(1,1))\n\n# ARIMA Forecasting : \n#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.\n\n#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg\n\nf2 = auto.arima(JohnsonJohnson)\nsummary(f2)\ntail(JohnsonJohnson) # last few values\nforecast(f2,h=5)\n\n\n#ARIMA Forecasting : compare two datasets\nlibrary(tseries)\nplot(JohnsonJohnson)\nndiffs(JohnsonJohnson)\nplot(diff(JohnsonJohnson))\n\nplot(Nile)\nplot(diff(Nile))\nndiffs(Nile)\n\n#-----\ndjj = diff(JohnsonJohnson)\nplot(djj)\n\ndnile = diff(Nile)\nplot(dnile)\n\n#----\nadf.test(djj)\n#if pv < 0.05 accept Alt Hypothesis that series is stationary\n\n#Model Selection \n#parameters p, d , q\n# d = no of diffs applied to make the series stationary\n#https://people.duke.edu/~rnau/arimrule.htm\nAcf(dnile)\n#Trail off to zero : Zero after lag ; 0,1(p)\n#Zero after lag q : Trails off to zero ; 1(q), 0 \n#Trails off to zero : Trial off to zero : 0,0\n#Nile - 1 large auto correlation at lag 1 : \n#Nile - pacf trails off to zero as the lags gets bigger\n?arima\nPacf(dnile)\n\nfit3 = arima(Nile, order=c(0,1,1)) # p,d,q\nfit3\n\n(fit3b = arima(Nile, order=c(1,1,1)))\n\n\n#Model Test\nqqnorm(fit3$residuals) # residuals ND\nqqline(fit3$residuals)\n\n#auto correl = 0 : check\nBox.test(fit3$residuals, type='Ljung-Box')\n#Test auto corr : H0= r=0 (True)\n\n#Forecast\nforecast(fit3,4)\n\nNile\n#Auto ARIMA\nlibrary(forecast)\n#forecast::auto.arima()\nfit4 = auto.arima(Nile)\nfit4\n\nforecast(fit4,5)\nplot(forecast(fit4,5))\n\n" }, { "path": "04-wksp2/TS-components-airpassengers.R", "content": "# Time Series Case Study - Decomposition\n\n#https://rpubs.com/emb90/137525\n# Data Set - AirPassengers\nx=c(9.23221232,5.3430000)\nx\noptions(digits=2)\nx\n\n?AirPassengers\nhead(AirPassengers)\nAirPassengers\nstr(AirPassengers)\nclass(AirPassengers)\n\n#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns\n# TS data components : Level + Irregular + Seasonal\n\n#stl(x, s.window, t.window = ) # command to do decomp\nstl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time\n# save it in an object\nAirPassengers[1]\nplot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model\n#no cyclic here - only seasonal, trend, irregual\n#s.window - specifies seasonal effects to be identical across years\n#can handle on additive models\n\nstl1 = stl(AirPassengers, s.window = 'periodic')\nplot(stl1) # actual data, seasonal, long term trends, remainder/ irregular\n\nclass(stl1)\n\nstl1$time.series\n#(df = stl1$time.series)\n#df = as.data.frame(df)\n#write.csv(df, './data/airpsng.csv')\n\n\n#Additive Model Y = Trend + Seasonal + Irregular\n#sales increase by 300 qty in month of Nov\n#Multiplicative Model Y = Trend * Seasonal * Irregular\n#sales increase by 10% in month of Nov\n\n\n#dataset\nAirPassengers\nclass(AirPassengers)\n\n# Plot\nplot(AirPassengers)\n#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested\n\n#stabilise the plot\nLogAirPassengers = log(AirPassengers) # make it additive because stl handles only additive models\n\n# YA = T + S + I : \n#YM= T * S * I : take log of this\n# log(YM) = log(T) + log(S) +log(I)\n\nplot(LogAirPassengers) #stabilises variation due to multiplication\n#looks like additive : no increase of seasonsal component now over years\n\n(m1 = matrix(1:2, nrow=1, byrow = F))\nlayout(m1)\nplot(AirPassengers); plot(LogAirPassengers) # see again the change\n\n\n#STL\nfit = stl(LogAirPassengers, s.window = 'periodic' )\n#Seasonal components constrainted to be same across years : periodic\n\nplot(fit)\nfit$time.series #decompose the data into S, T, R/I \n\n#december of all months same value for seasonal\n#this was after taking log : so take antilog\n#toprow = actual data with all series\nexp(fit$time.series)\n\nhead(exp(fit$time.series),n=20) # first 20 values see them\n\n# df= exp(fit$time.series)\n# names(df) = c('S','T','I')\n# head(AirPassengers)\n# head(cbind(AirPassengers, df))\n\n#Various Plots - Monthwise, quarter, \nlayout(matrix(1,nrow=1))\n#Avg of each month\nmonthplot(AirPassengers) #max traffic in Jun/ Jul across years\n\nmonthplot(fit, choice='seasonal') # less in winters, more in summers\nmonthplot(fit, choice='trend') #slight increase from Jan to Dec\n#trend increasing for each month, highest passengers in Jul\nmonthplot(fit, choice='remainder') # irregular components\n\n# see combined plots\n(m2 = matrix(1:3, nrow=3, byrow = T))\nlayout(m2) # change layout of plots\nmonthplot(fit, choice='seasonal')\nmonthplot(fit, choice='trend')\nmonthplot(fit, choice='remainder')\n\n\n# Practise with different methods - Self Practise\n\n#Decompose another way\nAP.decompM = decompose(AirPassengers, type = \"multiplicative\")\nplot(AP.decompM)\n\nlibrary(forecast)\n# Forecast # adjust for multiplicative model\nfit2b = ets(AirPassengers, model='MAM')\nfit2b\n(f2b=forecast(fit2b, 12))\nhead(f2b)$mean\n\n" }, { "path": "04-wksp2/TS-data.R", "content": "#ts9\n# save a numeric vector containing 72 monthly observations # from Jan 2009 to Dec 2014 as a time series object\nmyvector = round(runif(6*12)*100,0)\nmyvector\nmyts <- ts(myvector, start=c(2009, 1), end=c(2014, 12), frequency=12)\nmyts\n# subset the time series (June 2014 to December 2014)\nmyts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))\nmyts2\n# plot series\nplot(myts)\n\n\n#Eg2\nsales= c(18,33,41,7,34,35,24,25,24,21,25,20, 22,31,40,29,25,21,22,54,31,25,26,35)\ntsales = ts(sales, start=c(2003,1), frequency=12)\ntsales\nplot(tsales)\nplot(tsales, type='o', pch=19)\nstart(tsales)\nend(tsales)\nfrequency(tsales)\ntsales.subset = window(tsales, start=c(2003,5), end=c(2004,6))\ntsales.subset\n\n#Yearly Series\n\nmyvector = round(runif(50) * 100,0)\nmyvector\nmyts <- ts(myvector, start=c(2009,2), end=c(2014,11), frequency=3)\nmyts\nmyts2 <- window(myts, start=c(2010), end=c(2013))\nmyts2\n\n\n# quarterly \n\nmyvector = round(runif(50) * 100,0)\nmyvector\nmyts <- ts(myvector, start=c(2009,1), end=c(2014,11), frequency=3)\nmyts\nstr(myts)\nplot(myts, type='o', pch=19)\ntext()\n\nplot.ts(myts, type='o', pch=19, xy.labels=F)\n\naxis(1, myts$Date, format(myts$Date, \"%b %d\"), cex.axis = .7)\n\n\nmyts <- ts(myvector, start=c(2009,2), end=c(2014,9), frequency=3)\nmyts\nstr(myts)\n\n\nmyts2 <- window(myts, start=c(2010), end=c(2013))\nmyts2\nplot(myts, type='o', pch=19)\nstr(myts2)\n\n\n\nmyts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))\nplot(myts,type='o', pch=19)\ntext()\nplot(myts2,type='o', pch=19)\n\n\nlibrary(zoo)\nx.Date <- as.Date(paste(rep(2003:2004, each = 12), rep(1:12, 2), 1, sep = \"-\"))\nx.Date\n?zoo\nx <- zoo(rnorm(24), x.Date)\nx\nplot(x)\n\n\n\n#If we want different tick locations, we can suppress the default axis plotting and add our own:\n \nplot(x, xaxt = \"n\")\naxis(1, at = time(x), labels = FALSE)\n#Or combine them:\n \nplot(x)\naxis(1, at = time(x), labels = FALSE)\n\n## weekly ticks\nplot(x)\ntimes <- time(x)\nticks <- seq(times[1], times[length(times)], by = \"weeks\")\naxis(1, at = ticks, labels = FALSE, tcl = -0.3)\n\n\nlibrary(zoo)\nlibrary(lattice)\n\n# create test data\nz <- zooreg(1:83, start = as.Date(\"2009-04-01\"), deltat = 7)\n\nxyplot(z)\n\n\n\n\n# Time series plots with good X axis labels\nlibrary(zoo)\n# data\ntoday = Sys.Date()\ndates = as.Date((today-500):today)\ndates\nz = zoo (100+cumsum(rnorm(501)), dates)\nz\n# method1 : default X axis labels do not look good\n?plot.zoo\nplot(z)\n?plot.ts\nplot(ts(z))\ntime(z)\n# method 2 : Lattice\nlibrary(lattice)\n?xyplot.zoo\nxyplot(z)\nxyplot(z, lwd=2, col=\"tomato\")\n\n# method 3 : XTS\nlibrary(xts)\n?plot.xts\nplot(as.xts(z))\nplot(as.xts(z), auto.grid=F, major.format=\"%b %y\", las=2)\n\n# method 4 : Base graph\ntimeline = time(z)\nsummary(timeline)\nindex = seq(from=1, to=length(timeline), 90) # put ticks every 90 days\nplot(z, xaxt=\"n\")\naxis(side=1, at=timeline[index], label=format(timeline[index], \"%b %y\"), cex.axis=0.8)\n\n# method 5 : ggplot\nlibrary(ggplot2)\nlibrary(scales)\n?date_breaks\ndf = data.frame(date=as.POSIXct(time(z)), value=as.numeric(z))\nhead(df)\n# default plot\nggplot(df, aes(x=date, y=value)) + geom_line()\n# formatted\nggplot(df, aes(x=date, y=value)) + geom_line() + \n scale_x_datetime(labels=date_format(\"%b '%y\"))\n# custom breaks\nggplot(df, aes(x=date, y=value)) + geom_line() + \n scale_x_datetime(labels=date_format(\"%b '%y\"), breaks=date_breaks(\"3 months\"))\n" }, { "path": "04-wksp2/TS-dates.R", "content": "#Handling and creating Dates in R\n\n#date in yyyy-mm-dd\ndates1a = c('2018-21-4', '2018-29-4')\nclass(dates1a)\n?as.Date\nas.Date(dates1a) # error\ndates1a\ndates1b = as.Date(dates1a, format='%Y-%d-%m')\nclass(dates1b)\ndates1b\ndates1b + 1\nas.Date('2018-04-29') + 1\n\n#default format %Y-%m-%d\ndates2a = c('2018-4-21')\nclass(dates2a) # character so far\n(dates2b = as.Date(dates2a)) # convert to date\nclass(dates2b) # date now\nformat(dates2b, format=('%d-%m-%Y')) # print in different format\nformat(dates2b, format=('%d / %B - %Y')) # print in different format\ndates2b\n\n\n#Format Types ----\n#%Y: 4-digit year (1982),%y: 2-digit year (82)\n#%m: 2-digit month (01)\n#%B: month (January), %b: abbreviated month (Jan)\n#, %d: 2-digit day of the month (13)\n#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)\n\nformat(dates2b, format=\"%A %d %m %y\") # another format\n\nseminardates = c('09-07-1993', '08-08-1994', '04-11-1994')\nclass(seminardates)\nseminardates1 = as.Date(seminardates, '%d-%m-%Y')\nformat(seminardates1, '%A')\n\nformat(as.Date('18-04-1994', '%d-%m-%Y'), '%A')\n\n#System Date\nSys.Date()\nformat(Sys.Date(), format=\"%A: %d %B\")\n\n#increment/ decrement dates \ndates2b\n(course = dates2b + 0:11) # start : 11 days course\n\ncat(format(course, format=\"%B-%d\"))\nmonths(course)\nweekdays(course)\nquarters(course)\npaste(quarters(course),'2018',sep='-')\n\n\n#Date to character\ndates2b; class(dates2b)\n(dtoc = as.character(dates2b))\nclass(dtoc)\n\n#one more practise on date format\ndates3a =as.Date('30Apr18',format(\"%d%b%y\"))\nclass(dates3a)\n\n#Difference in dates\ncourse\nmin(course) # first date\nmax(course) # last date\nrange(course) # start to end\nmean(course) # avg date ?\nmedian(course) # middle date\ncourse\ncourse[c(1,5)] # 1st & 5th date \ncourse[1] ; course[9]\n(duration = course[8] - course[1])\n\n#Date of Birth\ndob = as.Date('14-08-1994', '%d-%m-%Y')\ndob\ndob1= dob + seq(1,1000,30) # create 1000 dates\nlength(dob1)\nSys.Date()\nas.numeric((Sys.Date() - dob)/365) #: Years\n#par(mfrow=c(1,1))\nboxplot(as.numeric((Sys.Date() - dob1)/365))# : Years\nas.numeric((Sys.Date() - dob1)/365)# : Years\nmean(as.numeric((Sys.Date() - dob1)/365))# : Years\n(duration2 = max(course) - min(course))\n\n#Sequence of Dates----\nseq(1,10,2)\n#seq(from, to, by, length.out = NULL, along.with = NULL, ...)\n?seq\n#by years : from start date to end date\nseq(as.Date(\"2016/1/1\"), as.Date(\"2018/5/1\"), \"years\")\nseq(as.Date(\"2016/1/1\"), as.Date(\"2018/5/1\"), \"months\")\nseq(as.Date(\"2017/1/1\"), by = \"days\", length.out = 6)\n\n(students = c('ashish','chirag'))\n\n## by month : 6 months\nseq(as.Date(\"2017/1/1\"), by = \"month\", length.out = 6)\nseq(as.Date(\"2017/1/1\"), by = \"month\", along.with = c(3,2,43,5,6,7)) # \nseq(as.Date(\"2017/1/1\"), by = \"month\", along.with = students) # \nseq(as.Date(\"2017/1/1\"), by = \"quarter\", along.with = students) # \n\nseq(as.Date(\"2017/1/1\"), by = \"month\", length.out = length(c(3,2,43))) \n\n\n## quarters\nseq(as.Date(\"2017/1/1\"), as.Date(\"2018/1/1\"), by = \"quarter\")\n\n#Find 7th of all months between two dates\n(dateseqF = seq(as.Date(\"2017/1/7\"), as.Date(\"2018/1/7\"), by = \"1 month\")) # order\n# dates in opposite order\n(dateseq = seq(as.Date(\"2018/1/7\"), as.Date(\"2017/1/7\"), by = \"-2 month\")) # reverse order\n# then reverse them\nrev(dateseq)# correct the order\nformat(rev(dateseq), \"%A : %d-%b\")\n\n#another Sequence : using another package\nchron::seq.dates(\"01/01/2017\", \"12/31/2017\", by = \"months\")\nchron::seq.dates(\"01/01/2017\", \"12/31/2017\", by = \"weeks\")\n\n" }, { "path": "04-wksp2/TS-lubridate.R", "content": "#Package Lubridate\n\n#functions which allow you to specify the order in which year, month and day components appear in date\n\n\nlibrary(lubridate)\nymd(\"20170601\")\nmdy(\"06-01-2017\")\ndmy(\"01/06/2017\")\ndmy(\"01-06-2017\")\n\nSys.timezone(location=T)\n#282- Asia/Kolkata\n('Asia/Kolkatta')\n\n(arrive <- ymd_hms(\"2018-04-09 14:00:00\", tz = \"Asia/Calcutta\"))\n(leave <- ymd_hms(\"2018-04-14 13:00:00\", tz = \"Asia/Calcutta\"))\n(leave2 <- ymd_hms(\"2018-04-14 13:00:00\", tz = \"GMT\"))\nleave - arrive\nleave2- arrive\n#Check the seconds for arrival\nsecond(arrive)\n#change seconds in arrival data\nsecond(arrive) <- 25\narrive\n\n#Check which date/day/month I arrived\nmday(arrive) # arrived on 9 of the month\nwday(arrive) ## in no start from Sun-1, Mon-2 ..\nwday(arrive, label = TRUE) # arrived on Mon\nmonth(arrive)\nmonth(arrive, label=T, abbr=T)\nmonth(arrive, label=T, abbr=F)\nyear(arrive)\n\n\n#practise upto here \n#Students to look up other practise exercises for Data below\n\n\nmeeting <- ymd_hms(\"2011-07-01 09:00:00\", tz = \"Pacific/Auckland\")\n## \"2011-07-01 09:00:00 NZST\"\nwith_tz(meeting, \"America/Chicago\")\n## \"2011-06-30 16:00:00 CDT\"\n\nmistake <- force_tz(meeting, \"America/Chicago\")\n## \"2011-07-01 09:00:00 CDT\"\nwith_tz(mistake, \"Pacific/Auckland\")\n## \"2011-07-02 02:00:00 NZST\"\n## \nauckland <- interval(arrive, leave)\nauckland\n## 2011-06-04 12:00:00 NZST--2011-08-10 14:00:00 NZST\nauckland <- arrive %--% leave\n## 2011-06-04 12:00:00 NZST--2011-08-10 14:00:00 NZST\nauckland\ntdiff = arrive - leave\ntdiff\n\njsm <- interval(ymd(20110720, tz = \"Pacific/Auckland\"), ymd(20110831, tz = \"Pacific/Auckland\"))\njsm\nint_overlaps(jsm, auckland) ## TRUE\nsetdiff(auckland, jsm) ## 2011-06-04 12:00:00 NZST–2011-07-20 NZST\n\nminutes(2) # period\n## 2 minutes\ndminutes(2) # duration\n## 120s (~2 minutes)\ndhours(2)\nseconds(2)\ndyears(2)\nleap_year(2011)\n## FALSE\nymd(20110101) + dyears(1)\n## \"2012-01-01 UTC\"\nymd(20110101) + years(1)\n## \"2012-01-01 UTC\"\n\nleap_year(2012)\n## TRUE\nymd(20120101) + dyears(1)\n## \"2012-12-31 UTC\"\nymd(20120101) + years(1)\n## \"2013-01-01 UTC\"\n\nmeetings <- meeting + weeks(0:5)\nmeetings\n## [1] \"2011-07-01 09:00:00 NZST\" \"2011-07-08 09:00:00 NZST\"\n## [3] \"2011-07-15 09:00:00 NZST\" \"2011-07-22 09:00:00 NZST\"\n## [5] \"2011-07-29 09:00:00 NZST\" \"2011-08-05 09:00:00 NZST\"\n\nperiods = meeting + days(1:5)\nperiods\n\n\nsecond(arrive)\n## 0\nsecond(arrive) <- 25\narrive\n## \"2017-06-01 12:00:25 IST\"\nsecond(arrive) <- 0\nwday(arrive)\n## 5\nwday(arrive, label = TRUE)\n## Thurs\n\n\n\nmeeting <- ymd_hms(\"2017-07-01 09:00:00\", tz = \"Asia/Calcutta\")\nmeeting\n\nwith_tz(meeting, \"America/Chicago\")\nmistake <- force_tz(meeting, \"America/Chicago\")\nwith_tz(mistake, \"Asia/Calcutta\")\nmistake\n\n\nudit <- interval(arrive, leave)\nudit\nnakul <- arrive %--% leave\nnakul\n\ndhiraj <- interval(ymd(20170702, tz = \"Asia/Calcutta\"),\n ymd(20170704, tz = \"Asia/Calcutta\"))\ndhiraj\nudit\n#Will visit overlap with and his travels\nint_overlaps(udit, dhiraj) ## TRUE\n\n#For what part of Dhiraj's' visit will Udit will be there?\nsetdiff(udit, dhiraj)\nsetdiff(dhiraj, udit)\n\nminutes(2) # period\n## 2 minutes\ndminutes(2) # duration\n## 120s (~2 minutes)\neminutes(2)\n\n\nleap_year(2011)\n## FALSE\nymd(20110101) + dyears(1)\n## \"2012-01-01 UTC\"\nymd(20110101) + years(1)\n## \"2012-01-01 UTC\"\n\nleap_year(c(2012, 2017))\n## TRUE FALSE\nymd(20170101) + dyears(1)\n## \"2018-01-01\"\nymd(20170101) + years(1)\n## \"2018-01-01\"\n## \n\nmeeting <- ymd_hms(\"2017-07-01 09:00:00\", tz = \"Asia/Calcutta\")\nmeetings <- meeting + weeks(0:5)\nmeetings\n\nfriend = interval(ymd(20170705, tz = \"Asia/Calcutta\"),\n ymd(20170715, tz = \"Asia/Calcutta\"))\nfriend\nmypd = interval(ymd(20170702, tz = \"Asia/Calcutta\"),\n ymd(20170803, tz = \"Asia/Calcutta\"))\nmypd\nint_overlaps(friend, mypd)\n\n#For what part of Dhiraj's' visit will Udit will be there?\nsetdiff(friend, mypd)\n\n\n?interval\nmypd\nmeetings %within% mypd\n\nmeeting <- ymd_hms(\"2017-07-01 09:00:00\", tz = \"Asia/Calcutta\")\nmeetings <- meeting + weeks(0:5)\nmeetings\nmeetings %within% mypd\n\nmypd / edays(1)\nmypd / ddays(1)\nmypd / ddays(2)\nmypd / dminutes(1)\nmypd / dseconds(1)\nmypd / months(1)\nmypd / years(1)\n\n\nas.period(mypd %% months(1))\nas.period(mypd)\n\nlast_day <- function(date) {\n ceiling_date(date, \"month\") - days(1)\n}\n# try last_day(ymd(20000101) + months(0:11))\n\nlast_day(ymd(20000101))\nlast_day(ymd(20000101) + months(0:11))\n" }, { "path": "04-wksp2/TS-movavg-Nile.R", "content": "# TS - Simple Moving Average\n#SMA\n# technique to get an overall idea of the trends in a data set; it is an average of any subset of numbers. The moving average is extremely useful for forecasting long-term trends\n\n\nNile # river flow from year 1871 to 1970\n\nplot(Nile)\n\nlibrary(forecast) # for forecasting\nopar = par(no.readonly = T) # save current parameters\npar(mfrow=c(2,2))\n(ylim = range(Nile))\nplot(Nile, main='Original TS')\nhead(Nile)\nma(Nile,3)\nhead(Nile,4)\nhead(ma(Nile,3))\n(1120+1160+963)/3\nmean(Nile[1:3]);mean(Nile[2:4]); mean(Nile[3:5]); mean(Nile[4:6])\nma(Nile,3)[1:10]\n\nplot(ma(Nile,3), main='SMA k=3', ylim=ylim)\nplot(ma(Nile,7), main='SMA k=7', ylim=ylim)\nplot(ma(Nile,15),main='SMA k=15', ylim=ylim)\npar(opar)\nplot(ma(Nile,3), main='SMA k=3', ylim=ylim)\n\n\n\n\n#mfrow mfcol\nmtcars\nnames(mtcars)\nplot(mtcars$mpg)\npar(mfrow=c(1,2))\nplot(mtcars$mpg)\nplot(mtcars$disp)\n\npar(mfrow=c(2,1))\nplot(mtcars$mpg)\nplot(mtcars$disp)\n\npar(mfrow=c(2,2))\nplot(mtcars$mpg)\nbarplot(table(mtcars$cyl))\npie(table(mtcars$am))\nhist(mtcars$disp)\n" }, { "path": "04-wksp2/TS-movavg.R", "content": "# Mov Average\n#https://www.otexts.org/fpp/6/2\n\nadmission = c(100,110,115,130,150,145,160,125,123,134)\nlength(admission)\nadmyr = ts(admission, start=2001)\nadmyr\n(100+110+115)/3\nforecast::ma(admyr, order=3)\nplot(admyr, main=\"Yearly Admissions\", ylab=\"Admissions\", xlab=\"Year\")\nlines(ma(admyr,3),col=\"red\")\n\nadmyr1 <- window(admyr,start=2005)\nadmyr1\n\nma4 <- ma(admyr1, order=4, centre=FALSE)\nma4\nma2x4 <- ma(admyr1, order=4, centre=TRUE)\nma2x4\n\n\n\n\nabbshares <- scan(file=\"\")\n#type the data in console windows\nlibrary(pracma)\nplot(abbshares, type = \"l\", col = 1, ylim = c(15, 30),\n main = \"Types of moving averages\", sub = \"Mid 2011--Mid 2012\",\n xlab = \"Days\", ylab = \"ABB Shares Price (in USD)\")\ny <- pracma::movavg(abbshares, 50, \"s\"); lines(y, col = 2)\ny <- pracma::movavg(abbshares, 50, \"t\"); lines(y, col = 3)\ny <- pracma::movavg(abbshares, 50, \"w\"); lines(y, col = 4)\ny <- pracma::movavg(abbshares, 50, \"m\"); lines(y, col = 5)\ny <- pracma::movavg(abbshares, 50, \"e\"); lines(y, col = 6)\ny <- pracma::movavg(abbshares, 50, \"r\"); lines(y, col = 7)\ngrid()\nlegend(120, 29, c(\"original data\", \"simple\", \"triangular\", \"weighted\",\n \"modified\", \"exponential\", \"running\"),\n col = 1:7, lty = 1, lwd = 1, box.col = \"gray\", bg = \"white\")\n\n#x\nelecs = read.csv(file='./data/elect.csv')\nelecs1 = elecs$Sales\nelectsales = ts(elecs1, start= 1989)\nelectsales\n\nforecast::ma(elecsales, order=3)\nforecast::ma(elecsales, order=5)\n" }, { "path": "04-wksp2/assocrule1.R", "content": "# Association Rules - Groceries data set ####\n\nlibrary(arules) #install first\nlibrary(arulesViz) #install first\nlibrary(datasets) # no need to install, just load it reqd for Groceries\ndata('Groceries')\nGroceries\n\n#Structure of Groceries\nstr(Groceries)\nGroceries\ninspect(Groceries[1:5]) #view\nLIST(Groceries[1:6]) #another view\n\n#Find Frequent Itemset\nfrequentItems = eclat (Groceries)\ninspect(frequentItems)\n\nfrequentItems = eclat (Groceries, parameter = list(supp = 0.01, minlen= 3, maxlen = 5)) \ninspect(frequentItems[1:6])\nfrequentItems\ninspect(frequentItems[10:32])\n#inspect(frequentItems[100:122])\n#Descending Sort frequent items by count : 1 to 25 itemsets\ninspect(sort (frequentItems, by=\"count\", decreasing=TRUE)[1:25])\ninspect(sort (frequentItems, by=\"count\", decreasing=F)[1:25])\n\n#Support is : support(A&B) = n(A&B)/ N\n#Plot the Frequency Plot\nitemFrequencyPlot(Groceries,topN = 15,type=\"absolute\")\nitemFrequencyPlot(Groceries, topN = 10, type='relative')\nabline(h=0.2)\n\n# Create rules and the relationship between items\n#parameters are min filter conditions \nrules = apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5, minlen=2))\nrules\ninspect (rules[1:5])\n#Sort Rules by confidence, lift and see the data\nrulesc <- sort (rules, by=\"confidence\", decreasing=TRUE)\ninspect(rulesc[1:5])\nrulesl <- sort (rules, by=\"lift\", decreasing=TRUE)\ninspect (rulesl[1:5])\n#which items have strong confidence and lift \n\n#How To Control The Number Of Rules in Output ?\n#maxlen, minlen, supp, conf\nrules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=3)) \ninspect(rules2[1:5])\n\n# Are there any duplicate/ Redundant Rules \n#https://rdrr.io/cran/arules/man/is.redundant.html\n\nsum(is.redundant(rules2))\n(redundant = which(is.redundant(rules2)))\ninspect(rules2[redundant])\n#inspect(subset(rules2, subset=lhs %ain% c('citrus fruit','rice') & rhs %in% 'whole milk' ))\n#remove it\nrulesNR = rules2[-redundant] \nis.redundant(rulesNR)\nsum(is.redundant(rulesNR)) #ok now\n\n\n\n#Find what factors influenced an event ‘X’\nrules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.8), appearance = list (default=\"lhs\",rhs=\"whole milk\"), control = list (verbose=F))\n#verbose - output display T or F\ninspect(rules3[1:5])\ninspect(rules3)\n\n#Find out what events were influenced by a given event\nrules3b = apriori (data=Groceries, parameter=list (supp=0.002, conf = 0.7), appearance = list (default=\"lhs\",rhs=\"whole milk\"))\ninspect(rules3b[1:15])\n\n#using subset\nsubset1 = subset(rules2, subset=rhs %in% 'bottled beer' )\ninspect(subset1)\ninspect(rules2)\nsubset2 = subset(rules2, subset=lhs %ain% c('baking powder','soda') )\ninspect(subset2)\nsubset2a = subset(rules2, subset=lhs %in% c('baking powder','soda') )\ninspect(subset2a)\nsubset2b = subset(rules2, subset=lhs %ain% c('bottled beer','liquor') )\ninspect(subset2b)\nsubset2c = subset(rules2, subset=rhs %ain% c('whole milk') )\ninspect(subset2c)\n\n\nsubset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)\ninspect(subset3)\nsubset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )\ninspect(subset4)\n\n#Visualizing The Rules -----\nplot(subset1[1:10]) \nplot(subset1[1:10], measure=c(\"support\", \"lift\"), shading=\"confidence\")\n\n#\n\nrules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default=\"rhs\",lhs=c('tropical fruit','herbs')), control = list (verbose=F))\ninspect(rules4[1:5])\ninspect(rules4)\n\n" }, { "path": "04-wksp2/assocrule2.R", "content": "#AR - Groceries - Subset\n\n#Subsetting rules and itemsets\nrules <- apriori(Groceries, parameter = list(support=.001, confidence=.7, maxlen=5, target='rules' ))\n#target='rules' # to mine for rules)\n\ninspect(sort(rules, by=\"confidence\", decreasing = T)[1:5])\n\n#subset conditions\n#rhs should be ‘bottled beer’\n#confidence should be above .7\n#results should be sorted by lift\n\n#Subset1----\ninspect(sort(subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7), by = 'lift', decreasing = T))\n#people buying “liquor” and “red wine” are almost certain to buy “bottled beer” (9 times out of 10), but not “canned beer”\n\n\n# Another rule with different parameters\n#subset2----\ncanned_rules <- apriori(Groceries,parameter = list(support=.001,\n confidence=.01, maxlen=5, target='rules' ))\ninspect(subset(canned_rules, subset=lhs %ain% c(\"liquor\", \"red/blush wine\") & rhs %in% 'canned beer' ))\n#no rule found or it could be significant low % less than 1 in 100 people would do\n\n# \n# lhs - means left hand side, or antecendent\n# rhs - mean right hand side, or consequent\n# items - items, that make up itemsets\n# %in% - matches any\n# %ain% - matches all\n# %pin% - matches partially\n# default - no restrictions applied\n# & - additional restrictions on lift, confidence etc.\n\n#either side lhs or rhs\n#“whole milk” and “yogurt” must be present and rule’s confidence must be higher than .95\n#subset3----\ninspect(subset(rules, subset=items %ain% c(\"whole milk\",\"yogurt\") & confidence >.95))\n\n#whole milk” and “yogurt” must be present in lhs and rule’s confidence must be higher than .9\ninspect(subset(rules, subset=lhs %ain% c(\"whole milk\",\"yogurt\") & confidence >.9))\n\n#Subset4----\n#“Bread” must be present in lhs: any type of “bread” – “white bread”, “brown bread” – both qualify. “Whole milk” must be present in rhs “as is”. confidence of the rule must be higher than .9\n\ninspect(subset(rules, subset= lhs %pin% \"bread\" & rhs %in% \"whole milk\" & confidence > .9))\n\n\n#Subset5----\n#what we can expect at rhs with confidence higher than .7 if we have both “flour” and “whole milk” on the lhs\ninspect(subset(rules, subset= lhs %ain% c(\"flour\",\"whole milk\") & confidence>.7))\n\n\n\n#Let’s consider case “Bottled beer Vs. Canned beer” and prove that people tend to buy either one or the other, and rarely do they buy both, qualifying these two as substitute products.\nrules <- apriori(Groceries, parameter = list(support=.001,\n conf = .01, minlen=2, maxlen=2, target='rules'))\n\n#Let’s only look at the rules where “beer” is present at both left- and right-hand-side of the rule and add chiSquared p-value to prove statistical significance of our findings:\n\ninspect(subset(rules, lhs %pin% 'beer' & rhs %pin% 'beer'))\ncrossTable(Groceries)['canned beer','bottled beer']\n\n#the probability of a consecutive purchase (confidence) is pretty small: ~3%\n#this is despite both bottled beer and canned beer being pretty popular purchases\n\ncrossTable(Groceries)['canned beer','canned beer']\ncrossTable(Groceries)['bottled beer','bottled beer']\n\nquality(rules)$chi <- interestMeasure(rules, measure='chi', significance=T, Groceries)\nquality(rules)$chi[4785:4786]\n#All these figures, combined with statistically significant lift below 1 (chi ~ 1e-6) tells us that “bottled beer” and “canned beer” do behave as substitutes." }, { "path": "04-wksp2/assocrule3.R", "content": "# Association Rule - Simple Example Case\n# read this pdf for help\n#https://cran.r-project.org/web/packages/arules/arules.pdf\n#libraries\nlibrary(arules)\nlibrary(arulesViz)\n#Create Data\n\n#Method3 Use: ----\n#Data in the form of list\nitemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))\nitemlist\nlength(itemlist)\n## set transaction names\nnames(itemlist) <- paste(\"Tr\",c(1:9), sep = \"\")\nitemlist\n## coerce into transactions\ntdata3 <- as(itemlist, \"transactions\")\ntdata3\nGroceries\nsummary(tdata3)\n\ntdata=tdata3\n\n#Data ready - Perform AR ----\n## analyze transactions\nsummary(tdata)\nitemlist\nimage(tdata)\n\n#Analysis\nfreqitems = eclat(tdata) #default support=.1\ninspect(freqitems)\nfreqitems = eclat(tdata, parameter = list(minlen=1, supp=.1, maxlen=2 ))\ninspect(freqitems)\n\nfreqitems\ninspect(freqitems)\n\nsupport(items(freqitems[1:2]), transactions=tdata)\ninspect(freqitems[1])\ninspect(items(freqitems[1]))\n\nitemFrequencyPlot(tdata,topN = 5,type=\"absolute\")\nitemFrequencyPlot(tdata,topN = 5,type=\"relative\", horiz=T)\nwrite.csv(as.data.frame(inspect(freqitems)),'freqitems1.csv')\n\n\n#Construct the Rules\nrules = apriori(tdata, parameter = list(supp = 0.2, conf = 0.5, minlen=2))\nitemFrequencyPlot(items(rules))\n\ninspect(rules[1:5])\ninspect(rules)\nwrite.csv(as.data.frame(inspect(rules)),'rules1.csv')\n#sort rules by support\nrules_s = sort(rules, by=\"support\", decreasing=TRUE )\ninspect(rules_s)\ninspect(rules_s[1:5]) #itemsset having high support\n\n#sort rules by confidence\nrules_c = sort(rules, by=\"confidence\", decreasing=TRUE )\ninspect(rules_c)\ninspect(rules_c[1:5]) #itemsset having high confidence\n\n#sort rules by lift\ninspect(head(rules, n = 3, by =\"lift\"))\nrules_l = sort(rules, by=\"lift\", decreasing=TRUE )\ninspect(rules_l)\ninspect(rules_l[1:5]) #itemsset having high confidence\n\n#Quality Data of Rules\nquality(rules_c) \n\n#Redundant Rules\ninspect(rules)\n(redundant = which(is.redundant(rules)))\ninspect(rules[c(8,9,10,11,12,14,14)])\ninspect(rules[redundant])\ninspect(rules)\nwrite.csv(as(rules,\"data.frame\"), file='./data/rulesR.csv')\n\n#Remove Redundant Rules\nrulesNR <- rules[-redundant] \nis.redundant(rulesNR)\nsum(is.redundant(rulesNR)) #ok now\ninspect(rulesNR)\n\n#Rules with LHS and RHS: single or combination\nrules2= rulesNR\ninspect(rules2)\nrules2.lhs1 <- subset(rules2, lhs %in% c(\"I1\", \"I5\"))\ninspect(rules2.lhs1)\n\nrules2.rhs1 <- subset(rules2, rhs %in% c(\"I3\"))\ninspect(rules2.rhs1)\n\nrules2.lhsrhs1 = subset(rules2, lhs %in% c(\"I1\") & rhs %in% c(\"I3\"))\ninspect(rules2.lhsrhs1)\n\nrules2.lhsrhs2 = subset(rules2, lhs %in% c(\"I1\") | rhs %in% c(\"I3\"))\ninspect(rules2.lhsrhs2)\n\n\n\n# Rules as DF: original rules\nrules_DF <- as(rules,\"data.frame\")\nrules_DF\nstr(rules_DF)\nwrite.csv(rules_DF, './data/myrules1.csv')\n\n#Visualisation\nplot(rules)\n" }, { "path": "04-wksp2/decisiontree1.R", "content": "#Decision Tree\n\nlibrary(rpart)\nlibrary(rpart.plot)\n\neducation=factor(c(3,2,2,3,2,2,3,2,2,2))\nmarried=factor(c('S','M','S','M','M','M','S','S','M','S'))\nincome=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)\nbuy=factor(c(0,0,0,0,1,0,0,1,0,1))\n(data= data.frame(buy, education, married, income))\nhead(data)\n\nxtabs( ~ buy + education, data=data)\n\ntable(data$buy, data$education)\n\ndtree1 = rpart(buy ~ education + married + income, data=data)\ndtree1\ndtree1 = rpart(buy ~ education + married , data=data, minsplit=4, minbucket=2,cp=-1)\ntable(data$buy)\ndtree1\nrpart.plot(dtree1, nn=T)\nprintcp(dtree1)\n\nhead(data)\n(ndata1 = data.frame(education=factor(c(2,3)), married=factor(c('S','M')), income=c(110,120)))\n(p1=predict(dtree1, newdata=ndata1, type='class'))\n\n(p2=predict(dtree1, newdata=ndata1, type='prob'))\ncbind(ndata1, p1,p2)\n\n" }, { "path": "04-wksp2/decisiontree2.R", "content": "\nmarry=sample(c('Yes',\"No\"), size=100, replace=T)\nselfGender=sample(c('M',\"F\"), size=100, replace=T)\nselfAge=ceiling(runif(100, 23,30))\nselfEdn=sample(c(1,2,3,4), size=100, replace=T, prob=c(.1,.3,.4,.2))\nspouseAge=ceiling(rnorm(100, mean=26,sd=5))\nspouseEdn=sample(c(1,2,3,4), size=100, replace=T, prob=c(.1,.3,.4,.2))\nspouseSiblings=sample(c(0,1,2,3), size=100, replace=T, prob=c(.2,.3,.3,.2))\nspouseCity=sample(c('Rural',\"Urban\"), size=100, replace=T)\nspouseSalary= ceiling(runif(100, 0,100000))\n\ndf = data.frame(marry, selfGender, selfAge, selfEdn, spouseAge, spouseEdn, spouseSiblings, spouseCity, spouseSalary )\n\nhead(df)\n\nlibrary(rpart)\n\ndtree1 = rpart(marry ~ . , data=df)\ndtree1\nlibrary(rpart.plot)\nrpart.plot(dtree1, cex=.8, nn=T)\nprintcp(dtree1)\nrpart.plot(prune(dtree1, cp=.03))\ndf1=df[spouseAge>= 25 & spouseSalary >= 90e+3, c('spouseSalary','spouseAge', 'marry')]\ntable(df1$marry)\n\ndtree2 = rpart(marry ~ . , data=df, cp=-1)\ndtree2\nrpart.plot(dtree2)\n\nprintcp(dtree2)\nptree2 = prune(dtree2, cp=.027)\nrpart.plot(ptree2)\n\n#PRedict\nndata1 = data.frame(selfGender=\"M\", selfAge=27, selfEdn=3, spouseAge=29, spouseEdn=4, spouseSiblings=4, spouseCity='Rural', spouseSalary=45000 )\nhead(df)\nndata1\npredict(dtree1, newdata= ndata1, type=\"prob\")\n" }, { "path": "04-wksp2/decisiontree3.R", "content": "# Decision Trees : - regression tree\n\nlibrary(ISLR) #download this\ndata(Carseats)\ndata = Carseats\nhead(data)\n?Carseats\n\n#Libraries for Decision Tree\nlibrary(rpart)\nlibrary(rpart.plot)\n\n#Model\ntree1 = rpart(Sales ~ . , data=data, method='anova' )\ntree1\nrpart.plot(tree1, cex=.8)\n\n#this is large tree, so prune it: check cp\nprintcp(tree1)\n#cp value should be chosen such that xerror is least\nprunetree = prune(tree1, cp=0.024)\n#here we have selected a different value to simplify the tree\n\nprunetree\nmean(data$Sales)\nlength(data[data$ShelveLoc==\"Bad\" | data$ShelveLoc==\"Medium\", c(\"Sales\")])\nrpart.plot(prunetree, nn=T)\n#Interpretation\n#if ShelveLoc=Good, and Price >= 109.5, sales predicted is 9.2\n\n#improve the plot\nrpart.plot(prunetree, nn=T, cex=.8, type=4)\n#read this document to improve the plot\n#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf\n#http://www.milbo.org/rpart-plot/prp.pdf\n\n#Predict for test value\n(testdata = sample_n(data,2))\n(testdata2 = sample_frac(data,.2))\n\n(predictedSales=predict(prunetree, newdata=testdata, type='vector'))\ncbind(testdata, predictedSales)\n#next line will show error because we have to predict numerical value instead of class/ category, so type of response reqd is vector not class\n(predict(prunetree, newdata=testdata, type='class'))\n\n\n#see online help here\n#https://www.datacamp.com/community/tutorials/decision-trees-R\n\nrpart.plot(prunetree)\nprp(prunetree, digits=4)\nrpart.plot(prunetree, digits=5,cex=.8)\n" }, { "path": "04-wksp2/decisiontree4.R", "content": "# CART Models - HH Case Study - Regression\nlibrary(rpart)\nlibrary(rpart.plot)\nlibrary(forecast)\n\nlibrary(gsheet)\nurl='https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=1941519952'\ndata = as.data.frame(gsheet2tbl(url))\nstr(data)\n\n# Summarize the dataset\nsummary(data)\nnames(data)\n# Random Sampling\nset.seed(777) # To ensure reproducibility\nIndex = sample(x = 1:nrow(data), size = 0.7*nrow(data))\nIndex\n\n# Create Train dataset\ntrain= data[Index, ]\nnrow(train)\n\n# Create Test dataset\ntest = data[-Index, ]\nnrow(test)\nnrow(test) + nrow(train)\n\n########################### Modeling #################################\n\ntrainModel = rpart(Annual_Sales ~ . , data = train[,-1], method = \"anova\")\ntrainModel\nmean(train$Annual_Sales)\n\n# Plot the Regression Tree\nrpart.plot(trainModel, type = 4,fallen.leaves = T, cex = 1.0, nn=T)\n\n#cp selection\nprintcp(trainModel)\ntrainModel_prune = prune(trainModel, cp=0.01)\nrpart.plot(trainModel_prune)\n\n#Predict and check accuracy\npredictSales_test = predict(trainModel_prune, newdata = test, type = \"vector\")\npredictSales_test #vector to print values of sales predicted\n\nlibrary(forecast)\n# Validate RMSE and MAPE calculation with a function in R\nModelAccuarcy = accuracy(predictSales_test, test$Annual_Sales)\nModelAccuarcy\n#RMSE should be as less as possible\n\n" }, { "path": "04-wksp2/decisiontree5.R", "content": "#CHAID - dataset USvote #multisplit\n# require(rsample) # for dataset and splitting also loads broom and tidyr\n#install.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\nlibrary(CHAID) #library for performing CHAID decision tree\n\n#Dataset\ndata(USvote) #from lib CHAID\n?USvote\n\n#Quick CHAID analysis\nset.seed(101)\nsample1 = USvote[sample(1:nrow(USvote), 1000),]\nchaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))\nprint(chaidModel)\nplot(chaidModel)\n\n" }, { "path": "04-wksp2/decisiontree5CHAID.R", "content": "#CHAID - dataset USvote #multisplit\n# require(rsample) # for dataset and splitting also loads broom and tidyr\n#install.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\nlibrary(CHAID) #library for performing CHAID decision tree\n\n#Dataset\ndata(USvote) #from lib CHAID\n?USvote\n\n#Quick CHAID analysis\nset.seed(101)\nsample1 = USvote[sample(1:nrow(USvote), 1000),]\nchaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))\nprint(chaidModel)\nplot(chaidModel)\n\n" }, { "path": "04-wksp2/df.R", "content": "#data frame\n\n#rollno, name, batch, gender, marks1, marks2\n\n(rollno = 1:30)\n(name = paste('student',1:30,sep='-'))\n(batch = sample(c(2016, 2017,2018), size=30, replace=T))\ntable(batch)\n(gender = sample(c('M', 'F'), size=30, replace=T))\ntable(gender)\n(marks1 = rnorm(30, mean=65, sd=7))\n(marks2 = rnorm(30, mean=60, sd=10))\n\ndf = data.frame(rollno, name, batch, gender, marks1, marks2, stringsAsFactors = F)\ndf\nsummary(df)\nstr(df)\ndf$gender = factor(df$gender)\nstr(df)\ndf$batch = factor(df$batch, ordered=T, levels=c('2016','2017','2018'))\nstr(df)\ndf\n\nsummary(df)\n\ndf$rollno = as.character(df$rollno)\nsummary(df)\n\nstr(df) #structure\nhead(df) #first 6 rows\ntail(df)\nhead(df, n=2)\ndim(df)\nnrow(df) #no of rows\nncol(df) #no of cols\nnames(df) #names of cols\ndf[1:2,1:4]\ndf[ c(1,3), c(1,4,5,6)]\ndf[ df$gender=='M', 3:5 ] # Males ; Display 3 to 5 th col\ndf[ df$gender=='M' & df$marks1 > 70, 3:5 ] # Males ; Display 3 to 5 th col\n\nlength(df)\nnrow(df[df$gender=='M',])\n\n#sort\nsort(df$marks1)\norder(df$marks1)\ndf$marks1\ndf[ order(df$marks1), ]\nsort(df$marks1)\nrev(sort(df$marks1))\ndf[order(gender, batch), ]\ndf[order(gender, - batch), ]\n\n\n#summary/ aggregating\naggregate(df$marks1, by=list(df$batch), FUN=mean)\naggregate(marks1 ~ batch, data=df, FUN=mean)\naggregate(cbind(marks1, marks2) ~ batch, data=df, FUN=mean)\naggregate(cbind(marks1, marks2) ~ batch + gender, data=df, FUN=mean)\ndf2=aggregate(cbind(marks1, marks2) ~ batch + gender, data=df, FUN=mean)\ndf2\n\naggregate(cbind(marks1, marks2) ~ batch + gender, data=df, FUN=max)\n\n\ndf\nwrite.csv(df, \"./data/bitsgoa.csv\")\n\ndf3 = read.csv(\"./data/bitsgoa.csv\")\nhead(df3)\ndf3 = df3[,-1]\nhead(df3)\n" }, { "path": "04-wksp2/environ.R", "content": "# Environment\n\n#objects in memory\nls()\n\n#create an object\nx = 1:5\nls() #check\ny=100:200\nls() #check\n\n#remove one\nrm(x) #remove x from environ\n\nls() #check\n#remove all\nrm(list = ls())\n\nls() #check\n\n " }, { "path": "04-wksp2/freqtable.R", "content": "# Frequency Distribution\n\nduration = faithful$eruptions \nrange(duration)\nbreaks = seq(1.5, 5.5, by=0.5)# half-integer sequence \nbreaks \nduration.cut = cut(duration, breaks, right=FALSE)\n#left side not closed [1.5-2.0) ie 1.5 to 1.9999\n#value of 2 will go into [2-2.5) interval\nduration.cut\nduration.freq = table(duration.cut)\nduration.freq\ncbind(duration.freq) \n\n\n# Relative Freq\nduration.relfreq = duration.freq / nrow(faithful)\nduration.relfreq\n\nold = options(digits=1) \nduration.relfreq \noptions(old) # restore\nduration.relfreq \ncbind(duration.freq, duration.relfreq) \n\n# Cumulative freq\nduration.cumfreq = cumsum(duration.freq)\n\n#graphically-----\n#using histogram\nh = hist(faithful$eruptions)\nh$breaks\nh$counts\n#compare with\ncbind(duration.freq) \nh$mids\n\n\n#right=T\nduration.cut.R = cut(duration, breaks, right=T)\n##value of 2 will go into (1.5-2] interval\nduration.cut.R\nduration.freq.R = table(duration.cut.R)\nduration.freq.R\ncbind(duration.freq.R) \nh$counts #same\n\n" }, { "path": "04-wksp2/lm-salesarea.R", "content": "#Simple Linear - Sales vs Area\n\n#import from ggsheet #pickup the correct url\nlibrary(gsheet)\narea = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519\"\ndf = as.data.frame(gsheet::gsheet2tbl(area1))\nstr(df)\nhead(df)\n\n#some stats\nmean(df$X); mean(df$Y)\nsd(df$X) ; var(df$Y)\ncor(df$X,df$Y) \n#cor.test(df$X,df$Y)\n\nplot(df$X, df$Y) #simple command to plot : Next with features\nabline(lm(df$Y ~ df$X), lty=3, lwd=4, col='green') # with regression line\n\n#Model\nfit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X\n\nnames(fit1)\nsystem.time(lm(Y ~ X, data=df)) #time taken for LM\ncoef(fit1) # Coefficients of Equation Y = mX + C\nresiduals(fit1) # Diff of Y actual - Y predicted\n\nsummary(fit1) # summary statistics of Linear Model(LM)\n#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP\n\n#combine the data with Ypredicted, errors\ncbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))\n\n# Predictions----\n(Y = 0.9645 + 1.6699 * 4) # Predict Y for X=4\n\n#Predict for X=4\nnew1 = data.frame(X=c(4))\npredict(fit1, newdata= new1)\n\n#Predict for more than 1 input\nnew1 = data.frame(X=c(1.5,2,3,4,5)) #sample data for X for prediction, should be between the range of X values\nnew1 #prediction data should be in form of Data Frame\n\npredict(fit1, newdata= new1) # Predict Function for 4 values of X\n\n#fitted(fit1) is equal to predict function using original X values\ncbind(new1, predictedY = predict(fit1, newdata= new1) )\n\n#Accuracy of Model\nlibrary(forecast)\naccuracy(fit1) #RMSE should be least\n\n\n#Assumption : Graphical Analysis : IMP STEP\nplot(fit1, which=1)\nplot(fit1)\n\n# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y\n\nabline(h=0)\nplot(residuals(fit1))\n\n#Auto Collinearity : relation between successive values of Y\ncar::durbinWatsonTest(fit1)\n#pvalue > 0 : Do not reject Ho that there is no correlation\n\n\n#Normality of residuals\nhist(residuals(fit1)) #distribution of Residuals\n\nhist(residuals(fit1), freq=F)\nlines(density(residuals(fit1)))\n\n#histogram values to show how hist rectangle are created\n(h=hist(residuals(fit1)))\nnames(h)\ncbind(h$breaks, h$counts)\n\n#Normality Plot to check assumptions of LM\nqqnorm(residuals(fit1))\nqqline(residuals(fit1))\n\n#Normality Plot can also be used with Std Values also\nresiduals(fit1)\nrstandard(fit1)\nsales.stdres = rstandard(fit1)\nqqnorm(sales.stdres, ylab=\"Standardized Residuals\", xlab=\"Normal Scores\", main=\"Checking normality of Residuals\") \nqqline(sales.stdres)\n\nstem(residuals(fit1))\n\n#Equal Variance : 4th Assumption : homoscedasticity\nplot(y=residuals(fit1), x=df$X)\nabline(h=0) #Variance across all x values is almost constant\n\n\n#Outlier Analysis\ndf\ndf[14,]\ndf$Y\nresiduals(fit1)\nboxplot( df$Y)\nboxplot( fitted(fit1))\n\nboxplot( df$Y, fitted(fit1), residuals(fit1), names=c('Y','predictY', 'Residuals'))\nabline(h=c(4.1, 5.97, -1.87))\n\nboxplot(residuals(fit1), names=c('Residuals'))\n#identify(rep(1, length(residuals(fit1))), residuals(fit1), labels = seq_along(residuals(fit1)))\n\ncar::outlierTest(fit1)\ndf[14,]\n\ncar::outlierTest(lm(Y ~ X, data=df[-c(14,12),]))\n\nfit1\n" }, { "path": "04-wksp2/lm-salesqty.R", "content": "#Multiple Linear Regression \n#Linear Modeling : DV vs more than 1 IVs\n#sales Qty vs price & promotion\n\n#Omni Store\n\n#Method : gsheet or else download the sheet as csv and then import \nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231\"\ndf = as.data.frame(gsheet::gsheet2tbl(url))\nhead(df)\n\n#MLR Create Multiple Linear Regression\n# we want to see how Sales Qty depend on Price and Promotion Values\nfit2 = lm(sqty ~ price + promotion, data=df)\nsummary(fit2)\n\n# summary statistics of model IMP STEP\n#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals\n#coefficients b1, b2\n#Predicted Values----\n#create a dataframe of new sample values\n(ndata1 = data.frame(price=c(60,70), promotion=c(300,400)))\n\n#cbind the values\npredict(fit2, newdata=ndata1)\ncbind(ndata1, predictedSqty = predict(fit2, newdata=ndata1))\n\n#end of LM\n#check for assumptions of Linear Regression\nplot(fit2)\n\n" }, { "path": "04-wksp2/lm-women-simple.R", "content": "# Simple Linear Regression : Built in Data Set Women\n# Predit Weight of heights of women - 60.5, 75.5\n\n#Check the Data set\nwomen \nstr(women)\n\ncov(women$height, women$weight) #direction of relationship\n#69 : which show positive relationship between height and weight\n\ncor(women$height, women$weight) #strength and direction of relationship\n#0.995 : which shows Strong and Positive relationship betw height & weight\n0.995^2 # also equal R^2 value in this case (Simple Linear Regression)\n\nplot(x=women$height, y=women$weight)\nabline(lm(weight ~ height, data=women), col='red')\n\nnames(women) #x- IV, y -DV\nfit1 = lm(weight ~ height, data=women) # creating a model\nsummary(fit1) #summary of the Model\n\n\n#Predict\nrange(women$height) #values to be within this range\n(ndata = data.frame(height = c(60.5, 75.5)))\npredictedwt = predict(fit1, newdata = ndata)\ncbind(ndata, predictedwt)\n\n#End : Simple way to predict in SLM\n\n#Next task if check for assumptions\nplot(fit1)\n" }, { "path": "04-wksp2/lm.R", "content": "#BITS LM\n\n#simple linear regression\n#how Y depends on X\n#women\nwomen\nstr(women)\ncov(women$height, women$weight)\ncor(women$height, women$weight)\nplot(women$height, women$weight)\n#create Linear Model\nfit1 = lm(weight~ height, data=women)\nsummary(fit1)\n#weight = -87 + 3.45 * height\nfitted(fit1)\ncbind(women, fitted(fit1), residuals(fit1))\nnames(fit1)\n(ndata1 = data.frame(height=c(62.5,63.5)))\npredict(fit1, newdata=ndata1)\ncbind(ndata1, predictWt=predict(fit1, newdata=ndata1))\n\n#\n\n\n\n#area vs sales\n\n\n#Multiple Linear Regression\n#mtcars : mpg vs wt, hp\nnames(mtcars)\n\nfit2 = lm(mpg ~ wt + hp, data=mtcars)\nsummary(fit2)\nrange(mtcars$wt) ; range(mtcars$hp)\nndata2 = data.frame(wt=c(2,2,3), hp=c(57, 250,300))\npredict(fit2, newdata=ndata2)\ncbind(ndata2, predictMPG=predict(fit2, newdata=ndata2))\n#\nnames(mtcars)\nfit2a = lm(mpg ~ wt + hp + vs + am + gear, data=mtcars)\nsummary(fit2a)\n\n\n\n\n#sales vs price & promotion\n\n\n" }, { "path": "04-wksp2/logR.R", "content": "#Logistic Regression\n\n#probability of Y based on single or multiple variables\n#chance of default based on X1, X2\n\n" }, { "path": "04-wksp2/logr-gre.R", "content": "#Logistic Regression : Predict Probability of Selection \n\ndf = read.csv(\"https://stats.idre.ucla.edu/stat/data/binary.csv\")\n\n## view the first few rows of the data\nhead(df)\nsum(is.na(df))\n\n## two-way contingency table of categorical outcome and predictors we want to make sure there are no empty cells\nxtabs(~admit + rank, data = df)\n\nstr(df)\n#convert rank into factors\ndf$rank = factor(df$rank)\ndf$admit = factor(df$admit)\n\nfit3 = glm(admit ~ gre + gpa + rank, data=df,family=\"binomial\")\nsummary(fit3)\n\n#predict probabilities of original values\n(prob=predict(fit3,type=c(\"response\")))\nhead(cbind(df, prob))\n\n#Test with new data\n(newdata1 = data.frame(gre = mean(df$gre), gpa = mean(df$gpa), rank = factor(1)))\ncbind(newdata1, predictProb=predict(fit3, newdata = newdata1, type=\"response\"))\n#if prob > 0.5, we say select = 1\n\n#another set of data for prediction\nrange(df$gre); range(df$gpa);levels(df$rank)\n(newdata2 = data.frame(gre = c(200, 300, 400, 500), gpa = c(2.5, 3, 3.3, 3.75), rank = factor(c(1,2,3,4))))\nstr(newdata2)\nnewdata2b = cbind(newdata2, predictProb2=predict(fit3, newdata = newdata2, type = \"response\"))\nnewdata2b\n\n\n#this way you predict Probabilites \n" }, { "path": "04-wksp2/matrix.R", "content": "#matrices\n?matrix\n\n(m1 = matrix(1:12, nrow=4))\nmarks3 = floor(runif(30, 50,90))\nmarks3\nm2 = matrix(marks3, nrow=6)\nm2\n\n(m3 = matrix(marks3, nrow=6, byrow = T))\n(m4 = matrix(marks3, ncol=5, byrow = T))\n#summary\ncolSums(m4)\nrowSums(m4)\ncolMeans(m4)\nrowMeans(m4)\nsum(m4)\nm4 [ m4 > 70]\nm4\nm4[1,]\nm4[,2]\nm4[3,5]\nm4[1:2, 3:5]\nm4\n" }, { "path": "04-wksp2/missingvalues.R", "content": "# Missing Values\n# Create, Detect, Replace, Remove, Impute, Visualise\nlibrary(VIM)\n\n(v1 = c(1,2,NA,NA,5))\nis.na(v1)\nv1=NULL\nv1 # remove all values\n(v1 = c(1,2,NA,NA,5,6,7,NA))\nsum(is.na(v1)) # sum TRUE values ie missing values\nmean(v1) # cannot calculate if NA values in vector\nmean(v1, na.rm=T) # remove and calc\n(v1a = na.omit(v1)) # omit missing values and put in new vector\nv1a\nmean(v1a)\nsum(v1a) # now you can sum on non NA values\n?na.omit\n\nanyNA(v1) # is there any NA value\nanyNA(v1a)\n?anyNA\n\n#impute missing values of vector with mean value of non NA values\nv1\nis.na(v1)\nv1[c(F,F,T,T,F,F,F,T)]\nv1[is.na(v1)]\nv1[is.na(v1)] = mean(v1, na.rm=T)\nv1\nis.na(v1)\n\n#Use VIM package to load sleep dataset with NA values in rows & col\nlibrary(VIM)\ndata(sleep, package='VIM')\nsummary(sleep)# see NA values in cols\nstr(sleep)\nhead(sleep)\ndim(sleep)\ncomplete.cases(sleep) #all complete sirows without any missing values\nsum(complete.cases(sleep)) # count non NA rows/ complete 42\nsum(!complete.cases(sleep)) #count NA rows 20\nmean(complete.cases(sleep)) #42/62\n# total cases = 62, complete=42 \ndf2 = sleep[complete.cases(sleep),] # list rows which are complete\ndf2\nsleep[!complete.cases(sleep),] # list rows which have NA values\n\n\n# Particular Column\nis.na(sleep$Dream)\nsum(is.na(sleep$Dream))\nmean(is.na(sleep$Dream)) # 12/62\n\n# How many missing values\nsum(is.na(sleep)) #total in data frame\nnames(sleep)# check columname\ncolSums(is.na(sleep)) #NA values in each column\nv2 = colSums(is.na(sleep))\nv2\nv2[v2> 10]\nrowSums(is.na(sleep)) #NA values in each row\nhead(sleep)\nas.data.table(sleep) #data.table format\nis.na(sleep[1:3,]) # first 3 rows\nhead(sleep,n=3)\n\n#Tabulate\nlibrary(mice)\nmice::md.pattern(sleep)\n#42 rows without any missing values # 2 rows with 1 NA NonD\n\n#Visualisation\nVIM::aggr(sleep, prop=F, numbers=T)\n#NonD max NA values \nVIM::aggr(sleep, prop=T) # Proportion\nVIM::matrixplot(sleep) # rowno and missing values in red\nVIM::marginmatrix(sleep)\nVIM::marginplot(sleep[c('Gest','Dream')], pch=c(20), col=c(\"darkgray\", \"red\", \"blue\")) # finding relationship between NA value\n\n\n#Exploration\nx = as.data.frame(abs(is.na(sleep)))\nhead(sleep, na=5)\nhead(x, n=5)\ny = x[which(apply(x, 2, sum) > 0)]\ncor(y) #Dream - NonD 0.9\ncor(sleep, y, use=\"pairwise.complete.obs\")\n#ignore NA, \n#\n\n#Listwise Deletion\noptions(digits=3)\nna.omit(sleep)\ncor(na.omit(sleep))\ncor(sleep, use=\"complete.obs\")\n\nfit = lm(Dream ~ Span + Gest, data= na.omit(sleep))\nsummary(fit)\n\n\n#imputations Packages - mice, Amelia, mi : mice, with, pool\n\nimp = mice::mice(sleep, seed=1234)\n?mice\n\n# explore more\n" }, { "path": "04-wksp2/packages1.R", "content": "#Packages\n\n#List avl packages\nlibrary()\n\n#Total Avl Packages - CRAN Site\nnrow(available.packages())\n\n#Install Package amap\ninstall.packages('amap')\n\n#Load package\nlibrary(amap)\nlibrary(dplyr)\n\n#check if loaded \nsearch()\n\n#Find functions in package\nlibrary(help=amap)\n\n#Help wrt a package\nhelp(package='amap') #see on right side pane\n\n#Packages avl for loading\n.packages(all.available = T) #avl for loading\n\n\n#Unload---\nlibrary(tm) #load tm package\nlibrary(VIM) #load VIM package\nsearch() #check if loaded\ndetach('package:tm', unload=TRUE)\n\nsearch.packages(\"package:tm\")\n?search\ndetach(package:VIM, unload = T) \nsearch()\n\n\n\n#Detach from memory all packages\nrm(list = ls(all = TRUE))\nsearch()\nsessionInfo()\n\n#Remove Packages ----- uninstall \nremove.packages(\"tm\")\nrequire('tm')# check if unistalled\n\nfind.package(\"tm\")\n" }, { "path": "04-wksp2/packages2.R", "content": "# Install multiple Packages\n\n#Install packages for Data Analytics Course\n\n\npackages1 = c('rJava','xlsx','dplyr')\nlist.of.packages <- packages1\nnew.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,\"Package\"])]\nnew.packages\nif(length(new.packages)) install.packages(new.packages)\n#if length is 0,all are installed, else missing packages will be installed\n\n#another list\npackages2 = c('plyr', 'psych', 'tm', 'quantmod')\n\n#another list\npackages3 = c('lubridate')\n\n#another list\npackages4 = c('dplyr','plyr','data.table','tidyr','gsheet','car','ggplot2','ISLR','amap','arules','arulesViz','forecast','rattle','CHAID','rpart','rpart.plot','ts','zoo','twitterR','ROAuth','RGtk2','partykit','strucchange','didrooRFM','curl','syuzhet','stringr','RTextTools','e10171','wordcloud','caret','catools','olsrr','xlsr','rJava')\t\t\t\t\t\n\n#amend below scripts to use packages list1 to list4\nlist.of.packages <- packages4\nnew.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,\"Package\"])]\n\nnew.packages\n\nif(length(new.packages)) install.packages(new.packages)\n\n" }, { "path": "04-wksp2/stats2.R", "content": "#Stats \n\n#Package fBasics\n\nlibrary(fBasics)\n#skewness\treturns value of skewness,\nskewness(mtcars)\nplot(density(mtcars$cyl))\nplot(density(mtcars$mpg))\n\n#kurtosis\treturns value of kurtosis,\nkurtosis(mtcars)\nplot(density(mtcars$qsec))\n\n#basicStats\tcomputes an overview of basic statistical values,\nbasicStats(mtcars)\n\n#rowStats\tcalculates row statistics,\nrowStats(mtcars,FUN=mean)\n\n#colStats\tcalculates column statistics,\ncolStats(mtcars, FUN=median)\n\n#rowAvgs\tcalculates row means,\nrowAvgs(mtcars)\n\n#colAvgs\tcalculates column means,\ncolAvgs(mtcars)\n\n#rowVars\tcalculates row variances,\nrowVars(mtcars)\n\n#colVars\tcalculates column variances,\ncolVars(mtcars)\n\n#rowStdevs\tcalculates row standard deviations,\nrowStdevs(mtcars)\n\n#colStdevs\tcalculates column standard deviations,\ncolStdevs(mtcars)\n\n#rowSkewness\tcalculates row skewness,\n\n#colSkewness\tcalculates column skewness,\n\n#rowKurtosis\tcalculates row kurtosis,\n\n#colKurtosis\tcalculates column kurtosis,\nrowCumsums\tcalculates row cumulated Sums,\ncolCumsums\tcalculates column cumulated Sums." }, { "path": "04-wksp2/twitter.R", "content": "#Twitter 1 - Configure Tweets and Download them\n#@dupadhyaya #Working using my Keys\n#Load libraries\nlibrary(\"curl\")\nlibrary(\"twitteR\")\nlibrary(\"ROAuth\")\nlibrary(\"syuzhet\") #library for sentiment analysis - comparison\n\ndownload.file(url=\"http://curl.haxx.se/ca/cacert.pem\",destfile=\"cacert.pem\")\n\n#https://apps.twitter.com/\n#different for each account\nconsumerKey=\"uRDuync3BziwQnor1MZFBKp0x\"\nconsumerSecret=\"t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV\"\nAccessToken=\"14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW\"\nAccessTokenSecret=\"3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz\"\n\n#Common for all accounts except the keys\n\ncred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize')\n\ncred$handshake(cainfo=\"cacert.pem\") # it will take you to browser: authorise, copy key and paste in R Studio at Console. Once it stores, it move to R prompt\nsave(cred, file=\"twitter authentication.Rdata\") # store this to avoid asking again\n#Load saved authentication cert\nload(\"twitter authentication.Rdata\")\n#registerTwitterOAuth(cred)\n\nsetup_twitter_oauth(consumerKey, consumerSecret, AccessToken, AccessTokenSecret)\n#type 1 : Yes \nsearch.string <- \"#businessanalytics\"\nno.of.tweets <- 100\n\ntweets <- searchTwitter(search.string, n=no.of.tweets,lang=\"en\")\ntweets\ntweets[1:10]\n#Another Topics\nsearch.string <- \"#indvseng\"\nno.of.tweets <- 100\n\ntweets <- searchTwitter(search.string, n=no.of.tweets,lang=\"en\")\ntweets[1:5]\n\n#My Tweets : will change if you use your own account\nhomeTimeline(n=30) #my tweets\nmentions(n=30) # my tags\n\ntweets <- userTimeline(\"sarthak231297\", n=10)\ntweets\n\n#------------------------------------\n?userTimeline\ntweets <- userTimeline(\"realDonaldTrump\", n=100)\n#english\ntweets[1:5]\nn.tweet <- length(tweets)\nn.tweet\ntweets.df <- twListToDF(tweets) \nhead(tweets.df)\nsummary(tweets.df)\n\n#Remove hashtags & unnecessary characters\ntweets.df2 <- gsub(\"http.*\",\"\",tweets.df$text)\ntweets.df2 <- gsub(\"https.*\",\"\",tweets.df2)\ntweets.df2 <- gsub(\"#.*\",\"\",tweets.df2)\ntweets.df2 <- gsub(\"@.*\",\"\",tweets.df2)\n\nhead(tweets.df2)\n#-----\nlibrary(\"syuzhet\") #library for sentiment analysis - comparison\nword.df <- as.vector(tweets.df2)\nemotion.df <- get_nrc_sentiment(word.df)\nemotion.df2 <- cbind(tweets.df2, emotion.df) \nhead(emotion.df2)\n\n#-----\nsent.value <- get_sentiment(word.df)\nmost.positive <- word.df[sent.value == max(sent.value)]\nmost.positive\nmost.negative<- word.df[sent.value <= min(sent.value)] \nmost.negative\nsent.value\n\n#-----\npositive.tweets <- word.df[sent.value > 0]\nhead(positive.tweets)\nnegative.tweets <- word.df[sent.value < 0] \nhead(negative.tweets)\nneutral.tweets <- word.df[sent.value == 0]\nhead(neutral.tweets)\n#----\n# Alternate way to classify as Positive, Negative or Neutral tweets\ncategory_senti <- ifelse(sent.value < 0, \"Negative\", ifelse(sent.value > 0, \"Positive\", \"Neutral\"))\nhead(category_senti)\ncategory_senti2 <- cbind(tweets,category_senti,sent.value) \nhead(category_senti2)\n\n#----\ntable(category_senti)\n\n" }, { "path": "04-wksp2/vectors.R", "content": "#Vector\n\nx = c(1,2,3)\nx1 = 1:10000000\nlength(x1)\nx\nx1\n\nx2 = seq(10, 100, 2)\nseq(from = 1, to = 1, by = ((to - from)/(length.out - 1)),\n length.out = NULL, along.with = NULL, ...)\nx2\nx3 = seq(from=10, to=100, by=3)\nx3\n?seq\n\n\n#numeric vector----\n\n(marks = rnorm(n=30, mean=60, sd=10))\nmean(marks)\nmedian(marks)\nmode(marks) #no mode\nsd(marks)\nvar(marks)\nsummary(marks)\nrange(marks)\n\n#Properties of DS\nlength(marks)\nstr(marks)\nclass(marks)\n#\nhist(marks)\nplot(density(marks))\n\n#character Vector ----\n\n(names = c('Ram','Shyam', 'Robin'))\nnames\nlength(names)\nmean(names)\nclass(names)\nsummary(names)\n#\ngender = c('M','F','M')\nsummary(gender)\ngenderF = factor(gender)\nsummary(genderF)\n\n(grades = c('A','B','C','D','A','D','A'))\ngradesF = factor(grades, ordered=T)\nsummary(gradesF)\ngradesF\n\ngradesF1 = factor(grades, ordered=T, levels=c('D','B','A','C'))\nsummary(gradesF1)\ngradesF1\ntable(gradesF1)\ntable(gender)\n\nbarplot(table(gradesF1))\npie(table(gradesF1))\nmean(gradesF1)\n\n\n#logical Vector -----\n\n(married = c(TRUE, FALSE, T, F, T, F,T,T))\nmarried\nsum(married)\ntable(married)\nclass(married)\nsummary(married)\n#\n\n#subset of marks\nmarks\ntrunc(marks); round(marks,1); floor(marks); ceiling(marks)\nmarks1 = trunc(marks)\nmarks1\nmarks1[1]\nmarks1[18]\nmarks1[1:5]\nmarks1[-c(10:15)]\nmarks1[c(1,5,10,30)]\nmean(marks1[c(1,5,10,30)])\nmarks1[marks1 > 60]\nmarks1 > 60\nmarks1[marks1 > 60 & marks1 < 80]\n\n#\nset.seed(1234)\ngender2 = sample(c('M', 'F'), size=1000000, replace=T, prob=c(.6,.4))\ngender2\ntable(gender2)#M-600087\nprop.table(table(gender2))\n" }, { "path": "04-wksp2/wordcloud1.R", "content": "# World Cloud 2\n\n# Read the text file from file\ntext <- readLines(file.choose())\ntext\n\n# Load the data as a corpus\ndocs = Corpus(VectorSource(text))\ndocs\n\n#Text transformation\ntoSpace <- content_transformer(function (x , pattern ) gsub(pattern, \" \", x))\ndocs <- tm_map(docs, toSpace, \"/\")\ndocs <- tm_map(docs, toSpace, \"@\")\ndocs <- tm_map(docs, toSpace, \"\\\\|\")\n\n#Cleaning Text\n# Convert the text to lower case\ndocs <- tm_map(docs, content_transformer(tolower))\n# Remove numbers\ndocs <- tm_map(docs, removeNumbers)\n# Remove english common stopwords\ndocs <- tm_map(docs, removeWords, stopwords(\"english\"))\n# Remove your own stop word\n# specify your stopwords as a character vector\ndocs <- tm_map(docs, removeWords, c(\"blabla1\", \"blabla2\")) \n# Remove punctuations\ndocs <- tm_map(docs, removePunctuation)\n# Eliminate extra white spaces\ndocs <- tm_map(docs, stripWhitespace)\n# Text stemming\ndocs <- tm_map(docs, stemDocument)\n\n\n#Document Matrix\ndtm <- TermDocumentMatrix(docs)\nm <- as.matrix(dtm)\nv <- sort(rowSums(m),decreasing=TRUE)\nd <- data.frame(word = names(v),freq=v)\nhead(d, 10)\n\n#Generate Word Cloud\nset.seed(1234)\nwordcloud(words = d$word, freq = d$freq, min.freq = 1,\n max.words=200, random.order=FALSE, rot.per=0.35, \n colors=brewer.pal(8, \"Dark2\"))\n\nfindFreqTerms(dtm, lowfreq = 4)\nfindAssocs(dtm, terms = \"freedom\", corlimit = 0.3)\nhead(d, 10)\n\n#Plot Freq\nbarplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,\n col =\"lightblue\", main =\"Most frequent words\",\n ylab = \"Word frequencies\")\n\n" }, { "path": "04-wksp2/wordcloud2.R", "content": "#word cloud\n\n# Load\nlibrary(\"tm\")\nlibrary(\"SnowballC\")\nlibrary(\"wordcloud\")\nlibrary(\"RColorBrewer\")\n\n\ntext <- readLines(file.choose())\ntext\n\n# Read the text file from internet\nfilePath <- \"http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt\"\ntext2 <- readLines(filePath)\ntext2\n\ntext=text2\n\n# Load the data as a corpus\ndocs <- Corpus(VectorSource(text))\ninspect(docs)\n\ntoSpace <- content_transformer(function (x , pattern ) gsub(pattern, \" \", x))\ndocs <- tm_map(docs, toSpace, \"/\")\ndocs <- tm_map(docs, toSpace, \"@\")\ndocs <- tm_map(docs, toSpace, \"\\\\|\")\n\n# Convert the text to lower case\ndocs <- tm_map(docs, content_transformer(tolower))\n# Remove numbers\ndocs <- tm_map(docs, removeNumbers)\n# Remove english common stopwords\ndocs <- tm_map(docs, removeWords, stopwords(\"english\"))\n# Remove your own stop word\n# specify your stopwords as a character vector\ndocs <- tm_map(docs, removeWords, c(\"blabla1\", \"blabla2\")) \n# Remove punctuations\ndocs <- tm_map(docs, removePunctuation)\n# Eliminate extra white spaces\ndocs <- tm_map(docs, stripWhitespace)\n# Text stemming\n# docs <- tm_map(docs, stemDocument)\n\ndtm <- TermDocumentMatrix(docs)\nm <- as.matrix(dtm)\nv <- sort(rowSums(m),decreasing=TRUE)\nd <- data.frame(word = names(v),freq=v)\nhead(d, 10)\n\nset.seed(1234)\nwordcloud(words = d$word, freq = d$freq, min.freq = 1, max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, \"Dark2\"))\n" }, { "path": "10a-setup/11a-start.R", "content": "# R Environment\n\n# The operators <- and = assign into the environment in which they are # evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.\n\n#Control + Enter to execute Line by line\nx1 = 1:5\nx1\nx2 <- 1:5\nx2\n(x3 = 1:5) # assign and print to screen\n\n" }, { "path": "10a-setup/11b-gettingstarted.R", "content": "# Getting Started- Basics/Shortcuts in R\n# under construction\n\n# For Help\n\n?mean\n\nsessionInfo()\nlibrary(dplyr)\nsessionInfo()\n\ndetach(\"package:dplyr\", unload=TRUE)\nsessionInfo()\n\nlibrary(arules)\n\napriori()\n" }, { "path": "10a-setup/15a-envrm.R", "content": "# Workspace & Environment\n\n# Identify the directory\ngetwd()\n\n# List all the objects in directory\ndir()\n\n# How to run the code-- Press Control Key and then Press Enter\n\n# \"#\" is used to comment a particular section in R\n#comment\n#comment with bookmarks\n#topic2----\n# To clear the console--- Press Control and then L Key\n\ngetwd() # print the current working directory\nsetwd(\"E:\\\\rWork\\\\rProjects\\\\analytics1\") # set working directory\ngetwd()\nsetwd(\"E:\\\\rWork\\\\rProjects\\\\analytics\") # set working directory\n\nls() # list the objects in the current workspace\n\nx=1:10\nls()\n\n# To Clear the enviornment list or unload the objects\nremove(list = ls())\n\n\nrm(x) # remove an object from workspace\nrm(a,b) # remove multiple object from workspace\nrm(list=ls(all=TRUE)) # clear workspace\nls()\n\n#--------------------------------------------------\n\n" }, { "path": "10a-setup/15b-renv.R", "content": "# Sys Env\n\n## whether HOST is set will be shell-dependent e.g. Solaris' csh does not.\nSys.getenv(c(\"R_HOME\", \"R_PAPERSIZE\", \"R_PRINTCMD\", \"HOST\",\"JAVA_HOME\"))\n\nnames(s <- Sys.getenv()) # all settings (the values could be very long)\nhead(s, 12)# using the Dlist print() method\n\n## Language and Locale settings -- but rather use Sys.getlocale()\nSys.getlocale()\n\n#version of R\nversion\n\n" }, { "path": "10a-setup/15e-rjava.R", "content": "#rJava Settings\n\n\n#install java https://java.com/en/download/win10.jsp\n\n\n\nlibrary(xlsx)\nlibrary(rJava)\n\nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_181') # for 64-bit\n\nlibrary(xlsx)\nlibrary(rJava)\n" }, { "path": "10a-setup/16a-pathconfig.R", "content": "# Lib paths\n\n.libPaths()\n\nfile.exists(\"~/.Rprofile\")\nfile.edit(\"~/.Rprofile\")\n# Add these lines to Rprofile\n# .First = function() cat(\"\\n Welcome Dhiraj to R \")\n# .Last = function() cat(\"\\n Thanks for working in R\")\n# R_LIBS=\"E:/rWork/rLibrary\"\n# .libPaths()\nfile.edit(\"~/.Renviron\")\n" }, { "path": "10a-setup/17a-rstudio.R", "content": "# rstudio\n\n#Tools -> Global Options\n#code -> editing -> Soft Wrap\n\n# themes\n\n#fonts\n\n#colors\n\n#size of Fonts\n" }, { "path": "10a-setup/18a-processtime.R", "content": "# Execution Time\nproc.time()\ng <- rnorm(100000)\nh <- rep(NA, 100000)\n\n# Start the clock!\nptm <- proc.time()\n\n# Loop through the vector, adding one\nfor (i in 1:100000){\n h[i] <- g[i] + 1\n}\n\n# Stop the clock\nproc.time() - ptm\n\n\n#M2\n(ptm <- proc.time())\nh <- g + 1\nproc.time()\n(proc.time() - ptm)\n\n#user system elapsed \n#0.00 0.02 0.01\n?proc.time\n\n# M3 system.time\nquickadd <- function(g){ \n return(g+1)\n}\n\nslowadd <- function(g){\n h <- rep(NA, length(g))\n for (i in 1:length(g)){\n h[i] <- g[i] + 1\n }\n return(h)\n}\n\nsystem.time(a <- slowadd(g))\n#user system elapsed \n#0.34 0.00 0.34\n\nsystem.time(a <- quickadd(g))\n#user system elapsed \n#0 0 0\n#\n#M5\n\n\n# M3\nsystem.time(x < 1:100000000)\n\n\nsystem.time({\n # Do something that takes time\n x <- 1:100000\n for (i in seq_along(x)) x[i] <- x[i]+1\n})\n\n#> user system elapsed \n#> 0.144 0.002 0.153\n#The output means it took 0.153 seconds to run\n# the block of code.\n\n\n#M5\nsleep_for_a_minute <- function() { Sys.sleep(10) }\n\nstart_time <- Sys.time()\nsleep_for_a_minute()\nend_time <- Sys.time()\n\nend_time - start_time\n# Time difference of 1.000327 mins\n# \n# \n# Benchmark ----\nlibrary(\"benchmarkme\")\nlibrary(\"pryr\")\nSys.info()\nmethod1 = function(n) {\n vec = NULL # Or vec = c()\n for(i in seq_len(n))\n vec = c(vec, i)\n vec\n}\nmethod2 = function(n) {\n vec = numeric(n)\n for(i in seq_len(n))\n vec[i] = i\n vec\n}\nmethod3 = function(n) seq_len(n)\nn=100\nmethod3(100)\n\nlibrary(\"compiler\")\nlibrary(\"memoise\")\nlibrary(\"microbenchmark\")\nmicrobenchmark(times = 100, unit = \"s\", \n method1(n=5000), \n method2(n=5000), \n method3(n=5000))\n\nmessage(\"dhiraj\")\n" }, { "path": "10a-setup/21a-floorceiling1.R", "content": "# R Tips1\n\n#round----\nround(14.5378, digits=2) # 14.54\nround(14.5378, digits=1) # 14.5\nround(14.5378) #15\n\n#Significant----\nsignif(pi, digits=3) #3.14\nsignif(pi, digits=5) #3.14\n\n#floor----\nfloor(3.14) #3\nfloor(3.56) #3\n\n#ceiling----\nceiling(3.14) #4\nceiling(3.56) #3\n\npi\n#truncate----\ntrunc(3.141593) #integer\ntrunc(314.1) #integer\n\n\n#log----\nlogb(8, base=2) #3\nlogb(100, base=10) #3\nlog(100,base=2)\nlog(1000,2)\n\nlog2(100)\n2^log2(100)\n\nlog(100,base=10)\nlog(1000,base=10)\nlog10(1000)\n10^log(1000, base=10) #antilog\n\n#natural log -----\n#to the base e=2.71\nlog(5) #without base is natural log\nlog(5, base=2.71)\nlog(5, base=exp(1))\n\n#antilog of natural log\nexp(1.614)\n2.71^log(5)\n2.71^1.614\n" }, { "path": "10a-setup/21b-options.R", "content": "#Options\n#Allow the user to set and examine a variety of global options which affect the way in which R computes and displays its results.\n#https://stat.ethz.ch/R-manual/R-devel/library/base/html/options.html\n\n\noptions() #what options avl\nop = options() #save options to reset later\n\ngetOption(x='width')\ngetOption(x='digits')\n(x=pi)\n#set options\noptions(digits=3)\nx #3.14 instead of 3.14153\ngetOption(x='digits')\n\n#reset options\noptions(op)\nx\n\n#\noptions('scipen')\n#integer. A penalty to be applied when deciding to print numeric values in fixed or exponential notation. Positive values bias towards fixed and negative towards scientific notation\n\n(x1=123.123456789123456789)\noptions(scipen = 999) #turn off\nx1\noptions(scipen = 0) #turn on\nx1\n\n(x2 = c(1.810032e+09, 4.12345, 5) )\noptions(\"scipen\"=-100, \"digits\"=4)\nx2 #scientific\noptions(\"scipen\"=100, \"digits\"=4)\nx2 #min 4 digits shown with no scientific\noptions(\"scipen\"=100, \"digits\"=3)\nx2 #min 3 digits are shown\n\n\n\noptions(scipen = 5)\nx1\noptions(scipen = -4)\nx1\n\nformat(1810032000, scientific = FALSE)\nformat(1810032000, scientific = T)\n\n\n\n\n#graphical parameters-----\npar()\n#parameters which can be changed\npar(no.readonly = T)\n\nstop('test it')\n" }, { "path": "10a-setup/24a-github.R", "content": "# github" }, { "path": "10a-setup/25a-help.R", "content": "# Misc Commands\n#https://www.r-project.org/help.html\n\n#library for help-----\nlibrary(swirl) # for online help\nswirl()\n\n#datasets-----\ndata() # to see in built data sets\n\n#? for help----\n?mtcars\n?mean\n\n#help----\nhelp(package=\"MASS\")\nhelp(rlm, package=\"MASS\") # function in MASS package\n\nhelp(package='dplyr')\n\n#example on functions----\nexample(lm)\nexample(mean)\n\n\n#vignette----\n#Many packages include vignettes, which are discursive documents meant to illustrate and explain facilities in the package\nbrowseVignettes()\n#window opens in browser\nbrowseVignettes(package=\"survival\") #browser\nvignette(package='survival') # text form\nvignette(\"timedep\")\nvignette(\"timedep\", package=\"survival\")\nvignette(package='dplyr')\nbrowseVignettes(package=\"dplyr\") #browser\n\n\n#demo-----\n#extended code demonstrations (“demos”). \n#demo() lists all demos for all packages in your library,\n#demo(package=\"package-name\")\ndemo(package='survival') #no demo\ndemo(package='stats') #avl demos\ndemo(\"nlm\", package=\"stats\")\ndemo(graphics)\n\n#apropos-----\n#apropos() function searches for objects, including functions, directly accessible in the current R session that have names that include a specified character string.\napropos('lm')\n\n#help.search()----\n#help.search() function scans the documentation for packages installed in your library.\nhelp.search(\"^lm\")\n\n#help.start()-----\n#help.start() starts and displays a hypertext based version of R’s online documentation in your default browser \nhelp.start()\n\n\n\n#TeachingDemos library\n#https://www.rdocumentation.org/packages/TeachingDemos/versions/2.10\n" }, { "path": "10a-setup/51c-deletefiles.R", "content": "# Delete Files from command\n\nwrite.csv(mtcars, 'mcars.csv')\nfile.exists('mcars.csv')\nunlink('mcars.csv')\nfile.exists('mcars.csv')\n\nwrite.csv(mtcars, './data/mcars2.csv')\nfile.exists('./data/mcars2.csv')\nunlink('./data/mcars2.csv')\nfile.exists('./data/mcars2.csv')\n\n#fileremove----\nwrite.csv(mtcars, 'mcars3.csv')\nfile.exists('mcars3.csv')\nfile.remove('mcars3.csv')\nfile.exists('mcars3.csv')\n\n\n" }, { "path": "10a-setup/help.R", "content": "# Misc Commands\n\nlibrary(swirl) # for online help\nswirl()\n\ndata() # to see in built data sets\n\nmean(mtcars$mgp)\n?mean\n\n??mean\n" }, { "path": "10a-setup/pathconfig.R", "content": "# Lib paths\n\n.libPaths()\nfile.exists(\"~/.Rprofile\")\nfile.edit(\"~/.Rprofile\")\n# Add these lines to Rprofile\n# .First = function() cat(\"\\n Welcome Dhiraj to R \")\n# .Last = function() cat(\"\\n Thanks for working in R\")\n# R_LIBS=\"E:/rWork/rLibrary\"\n# .libPaths()\nfile.edit(\"~/.Renviron\")\n" }, { "path": "10d-excel/student1.R", "content": "# R to Excel \n# Data Analysis in Excel\n\n\nrollno = paste('S',1:30,sep='-')\nname = paste0('Student',1:30, ' - surname')\nname\ngender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3))\ncourse = sample(c('BTech','MTech','Phd'), size=30, prob=c(.5,.3,.2), replace=T)\nmarks = floor(rnorm(30,mean=50,sd=20))\nage = floor(runif(30, 20,40))\nmarried = sample(c(TRUE, FALSE), size=30, replace=T, prob=c(.2, .8))\n\ndf = data.frame(rollno, name, gender, age, course, marks, married)\ndf\n\n#subset the data\nsample(nrow(df),3)\ndf1= df[sample(nrow(df), 3), ]\ndf1\n(df2 = subset(df, age >= 20 & age < 27, select=c(rollno, name)))\n(df2b = subset(df, marks >= 55 & marks < 70, select=c(name, marks)))\n\n#dplyr\nlibrary(dplyr)\n(df3= sample_n(df, 10)) # in numbers\n(df4 = sample_frac(df, .5)) #in %\n\ndf5= df[ which(gender=='F' | age > 25),]\n\n#Save this data for export\nwrite.csv(df, './data/students3.csv')\n\nlibrary(xlsx) #rJava also to be installed at this stage\n\nSys.setenv(JAVA_HOME=\"C:\\\\Program Files\\\\Java\\\\jre1.8.0_144\")\nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_181') # for 64-bit\n\n\nlibrary(rJava)\nlibrary(xlsx) #needs rJava\n\nwrite.xlsx(df, './data/student3a.xlsx', sheetName = \"IITG1\", row.names = F, append=T)\nwrite.xlsx(df2, './data/student3a.xlsx', sheetName = \"IITG2\", row.names = F, append=T)\n\n\n#Open the xls file in Excel\n\n" }, { "path": "10e-impexp/14a-readcsv.R", "content": "# Read csv\n\n# How to read CSV File\n\n#read.csv(file, header = TRUE, sep = \",\", quote = \"\\\"\", dec = \".\", fill = TRUE, comment.char= \"\")\n\nread1 = read.csv(file=\"./data/iris.csv\",header = TRUE,sep = \",\")\nstr(read1)\nclass(read1)\n\nread2 = read.table(file=\"./data/iris.csv\",header = TRUE,sep = \",\")\nstr(read2)\nclass(read2)\n\nread3 = read.delim(file=\"./data/iris.csv\",header = TRUE,sep = \",\")\nstr(read3)\nclass(read3)\n" }, { "path": "10e-impexp/14b-readcsv.R", "content": "# reading from file\n\n#used generally when u don't want to path to the file\n#or location is different from Project Folders, or want to search for the file\nread1 = read.csv(file=file.choose())\nstr(read1)\n" }, { "path": "10e-impexp/14c-importweb.R", "content": "# read from Internet \n\n#read.csv(url(\"http://some.where.net/data/foo.csv\"))\n#url is optional\n\ndf2 = read.csv('http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat')\ndf2\n\nread.csv(url(\"http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv\"))\n\ndf3= read.csv(\"http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv\")\ndf3\n\n\n#using library\nlibrary(data.table)\nad <- fread(\"http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv\")\nhead(ad)\n\n\n#library(readr)\n\n\n#Using Curl package\nlibrary(RCurl)\n" }, { "path": "10e-impexp/14d-importweb.R", "content": "#Loading the rvest package\nlibrary('rvest')\n\n#Specifying the url for desired website to be scrapped\nlego_movie <- read_html(\"http://www.imdb.com/title/tt1490017/\")\n\n#Reading the HTML code from the website\nlego_movie %>%\n html_node(\"strong span\") %>%\n html_text() %>%\n as.numeric()\n#> [1] 7.9\n\nlego_movie %>%\n html_nodes(\"#titleCast .itemprop span\") %>%\n html_text()\n\n\n\n#Table in webpage\nlibrary(XML)\nairline = \"http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines\"\n\nairline.table = XML::readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)\n\nairline.table\n\nchess = \"http://ratings.fide.com/top.phtml?list=men\"\nchess.table = XML::readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)\nchess.table[,1:5]\nnames(chess.table)\n?readHTMLTable\n" }, { "path": "10e-impexp/14e-readothers.R", "content": "# reading data from other formats \n# txt, \n# spss, sas\n\n\n" }, { "path": "10e-impexp/15b-datawrangling.R", "content": "drinks = data.frame(ingredient=c('amulya', 'pepsi',\n 'maaza','vodka'), sweetness=c(7,9,4,2), fizziness=c(8,1,8,1),\n type=c('energy', 'health','cold','hard'))\n \ndist()\n\nurl <- \"http://www.worldatlas.com/articles/largest-cities-in-europe-by-population.html\"\ntbls_xml <- readHTMLTable(url)\nlibrary(RCurl)\nlibrary(rjson)\nlibrary(XML)\nlibrary(rvest)\nlibrary(plyr)\ntbls_xml\ntbls_read <- url %>% html_nodes(\"table\") %>% html_table(fill = TRUE)\ntbls <- html_nodes(url, \"table\")\n####################\n# #\n# Exercise 1 #\n# #\n####################\n\nurl <- getURL(\"https://raw.githubusercontent.com/VasTsak/r-exercises-dw/master/part-1/data.csv\")\n\n####################\n# #\n# Exercise 2 #\n# #\n####################\n\ncsv_file <- read.csv(text = url)\n\n####################\n# #\n# Exercise 3 #\n# #\n####################\n\nurl <- getURL(\"https://raw.githubusercontent.com/VasTsak/r-exercises-dw/master/part-2/data.txt\")\n\ntxt_file <- read.table(text = url)\ntxt_file\n####################\n# #\n# Exercise 4 #\n# #\n####################\n\nurl <- getURL(\"https://raw.githubusercontent.com/VasTsak/r-exercises-dw/master/part-2/data.json\")\n\njson_file <- fromJSON(url)\n\n####################\n# #\n# Exercise 5 #\n# #\n####################\n\nurl <- getURL(\"https://raw.githubusercontent.com/VasTsak/r-exercises-dw/master/part-2/data.xml\")\nxml_file <- ldply(xmlToList(url), data.frame)\n\n####################\n# #\n# Exercise 6 #\n# #\n####################\n\nurl <- read_html(\"http://www.worldatlas.com/articles/largest-cities-in-europe-by-population.html\")\n\n####################\n# #\n# Exercise 7 #\n# #\n####################\n\ntbls <- html_nodes(url, \"table\")\ntbls\n####################\n# #\n# Exercise 8 #\n# #\n####################\n\ntbls_read <- url %>%\n html_nodes(\"table\") %>%\n html_table(fill = TRUE)\ntbls_read\n####################\n# #\n# Exercise 9 #\n# #\n####################\n\nurl <- \"http://www.worldatlas.com/articles/largest-cities-in-europe-by-population.html\"\ntbls_xml <- readHTMLTable(url)\n\n####################\n# #\n# Exercise 10 #\n# #\n####################\n\ndf_pop <- htmltab(doc = url, which = \"//th[text() = 'Rank']/ancestor::table\")\n" }, { "path": "10e-impexp/20a-importgg.R", "content": "#Importing from ggsheet\n#install.packages('gsheet')\nlibrary(gsheet)\n\nregr1 = \"https://docs.google.com/spreadsheets/d/1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k/edit#gid=107865534\"\n\nlogr1 = \"https://docs.google.com/spreadsheets/d/1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k/edit#gid=560796239\"\n\ndf1 = as.data.frame(gsheet2tbl(regr1))\ndf2 = as.data.frame(gsheet2tbl(logr1))\nstr(df)\ndf2\nsummary(df1)\nsummary(df2)\n\n\n\n#\ndocurl = \"https://docs.google.com/spreadsheets/d/\"\nsheeturl = paste0(\"1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k\",\"/edit#gid=\")\nsheetname = \"560796239\"\nfullurl = paste0(docurl, sheeturl, sheetname)\nfullurl\ndf = as.data.frame(gsheet::gsheet2tbl(fullurl))\nsummary(df)\nstr(df)\n" }, { "path": "10e-impexp/21b-googlesheet1.R", "content": "#R and Google Sheets\n#https://cran.r-project.org/web/packages/googlesheets/vignettes/basic-usage.html\n\nlibrary(googlesheets)\nlibrary(tidyverse)\n\n#(my_sheets <- gs_ls())\n#gs_title(), gs_key(), gs_url(), and gs_gs() return a registered sheet as a googlesheet object, which is the first argument to practically every function in this package. Likewise, almost every function returns a freshly registered googlesheet object, ready to be stored or piped into the next command.\n\n#this will take you to R and require ur approval\n(my_sheets <- gs_ls())\nmy_sheets %>% glimpse()\n?gs_ls\ngs_ls(\"googleRdata\") #see if the sheet exists\nmygs = gs_title(\"googleRdata\") #Register the sheet\nmygs # details of registered sheet\nmygs %>% gs_browse() #go browser and see the online file\nmygs %>% gs_browse(ws=2)\nmygs %>% gs_browse(ws='test')\n\n# read from online sheet\ntestdata <- mygs %>% gs_read(ws = \"test\")\nstr(testdata)\nhead(testdata)\n\n#read only certain cells\nmygs %>% gs_read(ws = 2, range = \"A1:D8\")\nmygs %>% gs_read(ws = \"test\", range = cell_rows(1:4))\nmygs %>% gs_read(ws = \"test\", range = cell_rows(10:15), col_names = FALSE)\nmygs %>% gs_read(ws = \"test\", range = cell_cols(1:4))\nmygs %>% gs_read(ws = \"test\", range = cell_limits(c(1, 4), c(5, NA)))\n\n\n#Add a new worksheet to an existing Google Sheet\nmygs <- mygs %>% gs_ws_new(ws_title = \"mtcars\", input = head(mtcars), trim = TRUE, verbose = FALSE)\nmymtcars <- mygs %>% gs_read(ws = 'mtcars')\nmymtcars\n\n\n#Rename or delete worksheets\n#gs_ws_delete() and gs_ws_rename() to delete the mtcars worksheet and rename the iris worksheets, respectively:\n\n#re-register sheet / refresh\nmygs <- mygs %>% gs_gs()\n#be careful \nmygs <- mygs %>% gs_ws_delete(ws = 'test2') \n\n#data from R object to google sheets\niris_ss <- mygs %>% gs_ws_new(ws_title = \"iris\", input = iris,trim = TRUE, verbose = FALSE)\niris_ss %>% gs_read(ws = 'iris')\n\n#my data\ntrain_ss <- mygs %>% gs_ws_new(ws_title = \"train1\", input = data,trim = TRUE, verbose = FALSE)\n\n\n#Make new Sheets from local delimited files or Excel workbooks\niris %>% head(5) %>% write.csv(\"iris.csv\", row.names = FALSE)\niris_ss <- gs_upload(\"iris.csv\")\n\n\n#New sheet\nfoo <- gs_new(\"foo\") %>% \n gs_ws_rename(from = \"Sheet1\", to = \"edit_cells\") %>% \n gs_ws_new(\"add_row\")\n#two sheets created one renamed\nfoo\n" }, { "path": "10e-impexp/22b-ggsheets2.R", "content": "#https://googlesheets4.tidyverse.org/\n\n\n#install.packages(\"devtools\")\nlibrary(devtools)\ndevtools::install_github(\"tidyverse/googlesheets4\")\nlibrary(googledrive)\nlibrary(googlesheets4)\n#give authorisations\n(qsthe <- drive_get(\"AccreditionQSTHE\"))\nqsthe\nsheets_get(qsthe)\nsheet1 = sheets_get(\"1M-sAZqHHyuzzwW7vUwXoCw2JVwUiWki7LdFCgAyOccc\")\nsheet1$spreadsheet_id\nsheet1$sheets\nsheet2 = read_sheet(\"1M-sAZqHHyuzzwW7vUwXoCw2JVwUiWki7LdFCgAyOccc\", sheet = 2)\nsheet2\nstr(sheet2)\n(df <- sheets_cells(\"1M-sAZqHHyuzzwW7vUwXoCw2JVwUiWki7LdFCgAyOccc\", range = \"A1:E7\"))\n" }, { "path": "10e-impexp/31a-export.R", "content": "#export data\n\nmtcars\nwrite.csv(mtcars, file='./data/mtcars.csv')\n\n#list files\ndir('./data')\nlist.files('./data')\n\nfile.exists(\"./data/mtcars.csv\")\nfile.exists('./data/dhiraj.csv')\ndf= read.csv('./data/mtcars.csv')\nstr(df)\n\n#Add arguments at saving\nnames(mtcars)\nhead(mtcars)\n#Do not create columns for row names\nwrite.csv(mtcars, file='./data/mtcars1.csv', row.names = F)\n\n#help\n?write.csv\n" }, { "path": "10e-impexp/32c-writecsv.R", "content": "# Write to csv file\n\n" }, { "path": "12a-packages1/21b-installpackages.R", "content": "#Install packages for Data Analytics Course\n\n\npackages1 = c('rJava','xlsx','dplyr')\npackages2 = c('plyr', 'psych', 'tm', 'quantmod')\npackages3 = c('lubridate')\npackages4 = c('dplyr','plyr','data.table','tidyr','gsheet','car','ggplot2','ISLR','amap','arules','arulesViz','forecast','rattle','CHAID','rpart','rpart.plot','ts','zoo','twitterR','ROAuth','RGtk2','partykit','strucchange','didrooRFM','curl','syuzhet','stringr','RTextTools','e10171','wordcloud','caret','catools','olsrr','xlsr','rJava')\t\t\t\t\t\n\n\nlist.of.packages <- packages4\nnew.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,\"Package\"])]\nnew.packages\nif(length(new.packages)) install.packages(new.packages)\n" }, { "path": "12a-packages1/21e-installFmGit.R", "content": "#install packages from Git Hub\n\n#lubripack\ninstall.packages('lubripack') #NA for some versions\n#install older version of R packages\n#https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages\n#https://rdrr.io/github/Espanta/lubripack/\n\ninstall.packages('devtools') #install library for download from git\n\nrequire(devtools) #small diff from library\n#does not give error if package not avl\n\ninstall_github(\"Espanta/lubripack\")\n\n#this package installes multiple pacakges\nlibrary(lubripack)\nlubripack(\"plyr\", \"psych\", \"tm\", \"quantmod\",'olsrr','lattice','caret','rpart','arules','arulesViz','Matrix','outliers')\n#if already installed it will just inform TRUE\n\n" }, { "path": "12a-packages1/21g-packages1.R", "content": "# Packages installation\n#https://www.rstudio.com/products/rpackages/\n\n#List avl packages\nlibrary()\n\n#Total Avl Packages\nnrow(available.packages())\n\n#Install Package amap\ninstall.packages('amap')\n\n#Load package\nlibrary(amap)\nlibrary(dplyr)\n\n#Find functions in package\nlibrary(help=amap)\n\n#Help wrt a package\nhelp(package='amap') #see on right side pane\n\n\n#Unload---\ninstall.packages('tm')\n\nlibrary(tm)\nlibrary(VIM)\nsearch()\ndetach('package:tm', unload=TRUE)\ndetach(package:VIM, unload = T) \nsearch()\n\n\n\n#Detach Multiple Packages\n(detpkg = c('plyr','tm'))\nlibrary('plyr') #load lib\nlibrary('tm') #load lib\nsearch() #check if loaded\nVectorize(detach)(name=paste0(\"package:\", detpkg), unload=TRUE, character.only=TRUE) #code to detach\nsearch() # confirm if removed\n#specify the argument unload=TRUE; otherwise, R removes the package from the search path but doesn’t unload it.\n\n#Detach from memory all packages\nrm(list = ls(all = TRUE))\nsessionInfo()\n\n\n#Remove Packages ----- uninstall \nremove.packages(\"tm\")\nrequire('tm')# check if unistalled\n\n\n\n#multiple packages\n\n#Function\nipak <- function(pkg){\n new.pkg <- pkg[!(pkg %in% installed.packages()[, \"Package\"])]\n if (length(new.pkg)) \n install.packages(new.pkg, dependencies = TRUE)\n sapply(pkg, require, character.only = TRUE)\n}\npackagelist <- c('NLP','tm', 'lubridate')\nipak(packagelist)\n\n# package lists\npkg1 = c('dplyr','plyr', 'data.table','xlsx', 'Hmisc','rJava','ggplot2','lattice','gsheet','curl','stringr', 'syuzhet','e10171', 'catools','caret', 'olsrr' ,'swirl','sqldf','XML','VIM', 'outliers','car','MASS','DMwR','rvest')\npkg2 = c('forecast','rpart', 'rpart.plot', 'partykit','strucchange', 'didrooRFM')\npkg3 = c('zoo', 'astsa','lubridate','timeSeries','tseries','xts')\npkg4 = c('arules' , 'arulesViz')\npkg5 = c('twitterR','ROAuth','RGtk2','RTextTools','wordcloud')\ninstall.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\n\n\nlibrary(lubridate)\n\n#multiple load \neasypackages::libraries(pkg1)\n\n\n#easypackages\n#install.packages('easypackages')\nlibrary(easypackages)\n\n#Install Multiple Packages\npackages(\"plyr\", \"psych\", \"tm\")\nlibraries(\"plyr\", \"psych\", \"tm\")\n\n\n\n\n\n\npack <- available.packages()\npack[\"ggplot2\",\"Depends\"]\npack[\"ggplot2\",\"Imports\"]\npack[\"data.table\",\"Depends\"]\n\npackrat:::recursivePackageDependencies(\"ggplot2\",lib.loc = .libPaths()[1])\ntools::dependsOnPkgs('ggplot2')\ntools::dependsOnPkgs('dplyr')\ntools::dependsOnPkgs(\"ggplot2\",installed=available.packages())\nlibrary(rusk)\n#Remove Package\nremove.packages('quantmod')\nlibrary(quantmod)\n\n\n\n#Detach all mice package\nlibrary(mise)\nsearch()\n#mise(vars = TRUE, figs = TRUE, console = TRUE, pkgs = FALSE)\nmise(pkgs=T)\nsearch()\n\n\n\n# list all packages where an update is available\nold.packages()\n\n# update all available packages\nupdate.packages()\n\n# update, without prompts for permission/clarification\nupdate.packages(ask = FALSE)\n" }, { "path": "12a-packages1/31b-datasets.R", "content": "# Data Sets\n#https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html\n\n#built in datasets in base\nmtcars\nwomen\ndata() # available Data Sets with loaded libraries\n\n#package having datasets\nlibrary(datasets) # no need to install it, just load\ndata(package='datasets')\n\n#Datasets in particular Package\ndata(package = \"plyr\")\ndata(package =\"MASS\")\n\n\n#find the names of data sets in all installed packages, you can use the following. .packages(TRUE) gives all packages available in the library location path lib.loc. Since the data sets in the base and stats packages have been moved to the datasets package, we need to account for that by taking them out with setdiff().\n\n\n## names of all packages sans base and stats\npkgs <- setdiff(.packages(TRUE), c(\"base\", \"stats\"))\npkgs\n\n## get the names of all the data sets\ndsets <- data(package = pkgs)$result[, \"Item\"]\n## look at the first few in our result\nhead(dsets)\n\n\n#data frame containing the name, class, dimensions, and title of each data set found in a package.\nvcdExtra::datasets(\"plyr\")\nvcdExtra::datasets(c(\"plyr\", \"dplyr\"))\nvcdExtra::datasets(\"datasets\")\n\n\n" }, { "path": "12a-packages1/31c-datasets.R", "content": "#Datasets\n\n?datasets # Using R's built in data sets\ndata()\n\nlibrary(help=datasets)\n\ndata(mtcars) \ncars <- mtcars \nlevels(mtcars$gear)\n\n# Viewing data set\nmtcars #Console Window \nView(mtcars)#new tab \nhead(mtcars,10) #top 10 records\nhead(mtcars) # top 6\ntail(mtcars) # last 6 \n\nstr(mtcars) #structure of dataset \n\nnames(mtcars) # column names of DF\n\n" }, { "path": "12a-packages1/41-purrr1.R", "content": "#purrr\n#https://www.weirdfishes.blog/blog/practical-purrr/\n\ndf = mtcars\n\nlibrary(purrr)\nlibrary(dplyr)\n\n#functions\n\nmu = c(50,60, 70)\n#3 lists of ND with different means, n=10, sd=1\nmu %>% map(rnorm, n=10)\nsigma = c(2,3,4)\n#3 lists of 10 nos with different means and sd\nmap2(mu, sigma, rnorm, n=10)\n\nn = c(10,20,30)\n#ND lists of different values of n, mu, sd\nlist(n, mu, sigma ) %>% pmap(rnorm)\n\n#different functions with different arguments\nf <- c(\"runif\", \"rnorm\", \"rpois\")\nparam <- list(\n list(min = -1, max = 1), \n list(sd = 5), \n list(lambda = 10)\n)\n#To handle this case, you can use invoke_map():\ninvoke_map(f, param, n = 5)\ninvoke_map(f, param, n = 5) %>% str()\n" }, { "path": "12a-packages1/42-purr2.R", "content": "#purrr\n\ndf = mtcars\n\nlibrary(purrr)\nlibrary(dplyr)\n\ndf %>% split(.$cyl)\n\ndf %>% split(.$cyl) %>% walk(print)\n\n\ndf %>% split(.$am) %>% mean(mpg)\n\nlibrary(ggplot2)\nplots <- mtcars %>% split(.$cyl) %>% map(~ggplot(., aes(mpg, wt)) + geom_point())\nplots$`4`\n\npaths <- stringr::str_c(names(plots), \".pdf\")\npaths\npwalk(list(paths, plots), ggsave, path = tempdir())\ntempdir()\n\n\n#keep\niris %>% keep(is.factor) %>% str()\niris %>% discard(is.factor) %>% str()\n\n#some\nx <- list(1:5, letters, list(10))\nx %>% some(is_character)\nx %>% some(is_numeric)\nx %>% every(is_numeric)\nx %>% every(is_vector)\n\n#detect\nx <- sample(10)\nx\n\nx %>% detect(~ . > 5)\nx %>% detect_index(~ . > 5)\n\n#take elements from the start or end of a vector while a predicate is true:\nx %>% head_while(~ . > 5)\nx %>% tail_while(~ . > 5)\n\n#accumulate\nx <- sample(10)\nx\nx %>% accumulate(`+`)\nx %>% accumulate(`*`)\nx %>% accumulate(`/`)\nx %>% accumulate(`-`)\n#https://r4ds.had.co.nz/iteration.html\n" }, { "path": "12a-packages1/43-purrr3.R", "content": "#purrr3\n\nlibrary(purrr)\nlibrary(dplyr)\n\ncar_data <- transform(aggregate(. ~ cyl, data = subset(mtcars, hp > 100), FUN = function(x) round(mean(x, 2))), kpl = mpg*0.4251)\ncar_data\n\n#If we don’t want to save intermediate results, composing multiple functions becomes super import\n\nmtcars %>% subset(hp > 100) %>% aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>% transform(kpl = mpg %>% multiply_by(0.4251))\n#error on multiply_by\n\ndevtools::install_github(\"jennybc/repurrrsive\")\n\nghr <- repurrrsive::gh_repos\nhead(ghr)\n\nmap_dbl(ghr, length)\nmap_dbl(ghr, ~length(.x))\nmap(1:3, ~runif(2, max = .x))\n\nmap(1:3, runif, min = 3, max = 6)\nmap(1:3, function(n) { runif(n, min = 3, max = 6) })\n\nmap(ghr, ~map(.x, length))\n\nghr %>% map_chr(~map_chr(.x, ~.x$owner$login)[1]) %>% set_names(ghr, .) %>% pluck(\"jennybc\")\n\n#map2_xxx() (analogous to map_xxx())\n# pmap() (with which you can map over howerver many elements you want)\n# lmap() (for mapping with funcions that take and return lists)\n# imap() (for mapping over a list and its names, just like we did above)\n# map_at()/map_if() (functions that allow you to filter which elements get mapped)\n\n" }, { "path": "12a-packages1/44-purrr4.R", "content": "#purrr4\n\n#purrr is designed to help with “functional programming”, which you can take broadly as trying to use functions (preferably “pure” ones) to accomplish most of your complex and repetitive tasks (don’t copy and paste more then 3 times - H. Wickham)\n\n\nlibrary(purrr)\nlibrary(dplyr)\n\nmap(1:3, ~runif(2, max = .x))\n\nshades <- colors()[1:10]\n\nfor (i in seq_along(shades)){ print(shades[i])}\na <- lapply(shades, print)\na\nb <- map(shades, print)\n\n#general\n#map(\"Lists to apply function to\",\"Function to apply across lists\",\"Additional parameters\")\n\nmap(mtcars, mean, na.rm = T)\nmtcars_means <- map(mtcars, mean, na.rm = T)\nclass(mtcars_means)\n\n#map_TYPE returns an object of class TYPE, e.g.\n#map_lgl returns logical objects\n#map_df returns data frames, etc.\ndf1= map_df(mtcars, mean, na.rm = T)\nclass(df1)\ndf1\n\n#map can also be extended to deal with multiple input lists\n#map applies a function over one list.\n#map2 applies a function over combinations of two lists in the form\n#map2(list1, list2, ~function(.x,.y), ...)\nmap2_chr(c('one','two','red','blue'), c('fish'), paste)\n\n\nz <- 10\nfoo <- function(x,y) {\n z <- x + y\n return(z) }\n\nfoo(x = 2,y = 3)\nz #not affected\n\na <- 10\nz =0\nfoo <- function(x,y) {\n z <- x + y + a\n return(z) }\nfoo(x = 2, y = 3)\na; z\n#recommend you avoid using global variables inside functions: it\n#“anonymous” functions in :make anonymous functions with ~\nmap2_chr(c('one','two','red','blue'), c('fish'), ~paste(.x,.y))\n\ncvfoo <- function(x){ sd(x) / mean(x) }\nmap(mtcars, cvfoo)\n#MULTIPLE LISTS USING PMAP\n\nlibrary(ggplot2)\nlibrary(dplyr)\ndmonds <- diamonds %>% slice(1:4)\n\npmap_foo <- function(list1, list2 , list3){\n paste0(\"Diamond #\", list1, \" sold for $\", list2,\" and was \", list3, \" carats\") }\n\npmap(list(list1 = 1:nrow(dmonds), list2 = dmonds$price, list3 = dmonds$carat), pmap_foo)\n" }, { "path": "12a-packages1/45-purrr5.R", "content": "#purrr5\n#https://adv-r.hadley.nz/functionals.html\n\nlibrary(purrr)\nlibrary(dplyr)\n\ntriple <- function(x) x * 3\nmap(1:3, triple)\n\nmap_chr(mtcars, typeof)\nmap_lgl(mtcars, is.double)\n\nn_unique <- function(x) length(unique(x))\nmap_int(mtcars, n_unique)\nmap_dbl(mtcars, mean)\n\npair <- function(x) c(x, x)\nmap_dbl(1:2, pair)\nmap_dbl(1:2, as.character)\n#in either case, it’s often useful to switch back to map(), because map() can accept any type of output. That allows you to see the problematic output, and figure out what to do with it.\nmap(1:2, pair)\n\n#anonymous functions\nmap_dbl(mtcars, function(x) length(unique(x)))\nmap_dbl(mtcars, ~ length(unique(.x)))\nmap_dbl(mtcars, ~ mean(.x))\n#This works because all purrr functions translate formulas, created by ~ (pronounced “twiddle”), into functions. \nx <- map(1:3, ~ runif(2))\nstr(x)\n\nx <- list(1:5, c(1:10, NA))\nmap_dbl(x, ~ mean(.x, na.rm = TRUE))\nmap_dbl(x, mean, na.rm = TRUE)\n\ntrims <- c(0, 0.1, 0.2, 0.5)\nx <- rcauchy(1000)\nmap_dbl(trims, mean, x = x)\n\n\n\nby_cyl <- split(mtcars, mtcars$cyl)\nby_cyl\nby_cyl %>% map(~ lm(mpg ~ wt, data = .x)) %>% map(coef) %>% map_dbl(2)\n#base\nby_cyl %>% lapply(function(data) lm(mpg ~ wt, data = data)) %>% lapply(coef) %>% vapply(function(x) x[[2]], double(1))\nmodels <- lapply(by_cyl, function(data) lm(mpg ~ wt, data = data))\nvapply(models, function(x) coef(x)[[2]], double(1))\n\n\n\n#modify - same format as input\ndf <- data.frame( x = 1:3, y = 6:4 )\nmap(df, ~ .x * 2)\nmodify(df, ~ .x * 2) #temp\ndf <- modify(df, ~ .x * 2)\ndf\n\n\n#\tList\tAtomic\tSame type\tNothing\n# One argument\tmap()\tmap_lgl(), …\tmodify()\twalk()\n# Two arguments\tmap2()\tmap2_lgl(), …\tmodify2()\twalk2()\n# One argument + index\timap()\timap_lgl(), …\timodify()\tiwalk()\n# N arguments\tpmap()\tpmap_lgl(), …\t—\tpwalk()" }, { "path": "12a-packages1/51-plyr1.R", "content": "# split - apply - combine\n\n#https://vita.had.co.nz/papers/plyr.pdf\na*ply(.data, .margins, .fun, ..., .progress = \"none\")\nd*ply(.data, .variables, .fun, ..., .progress = \"none\")\nl*ply(.data, .fun, ..., .progress = \"none\")\n#a = array, d = data frame, l = list, and _ means the output is discarded.\n#The first argument is the .data which will be split up, processed and recombined. The second argument, .variables or .margins, describes how to split up the input into pieces. The third argument, .fun, is the processing function, and is applied to each piece in turn. All further arguments are passed on to the processing function. If you omit .fun the individual pieces\n\n# input\\output array dataframe list discarded\n# array aaply adply alply a_ply\n# dataframe daply ddply dlply d_ply\n# list laply ldply llply l_ply\n#.progress argument controls display of a progress bar,\n#all arguments start with “.”. This prevents name clashes with the arguments of the processing function, and helps to visually delineate arguments that control the repetition from arguments that control the individual steps. Some functions in base R use all uppercase argument names for this purpose,\n\nshape <- function(x) if (is.vector(x)) length(x) else dim(x)\nshape(mtcars)\nx <- array(1:24, 2:4)\nlibrary(plyr)\nshape(aaply(x, 2, function(y) 0))\nshape(aaply(x, 2, function(y) rep(1,5)))\nshape(aaply(x, 2, function(y) matrix(0,nrow=5,ncol=6)))\nshape(aaply(x, 1, function(y) matrix(0,nrow=5,ncol=6)))\n" }, { "path": "12a-packages1/61-splitapplycombine1.R", "content": "#plyr, dplyr, \n\n#http://krlmlr.github.io/pdlyr/vignettes/pdlyr.html\n\n#https://coolbutuseless.bitbucket.io/2018/03/03/split-apply-combine-my-search-for-a-replacement-for-group_by---do/#split-apply-combine---prehistoric-times---split-lapply-do.callrbind-...\n\n\n#plyr::ddply(mtcars, 'cyl', do(mean(.))\ncomplex_func <- function(x) { mean(x)} \nresult_df <- mtcars %>% group_by(cyl) %>% do(complex_func(.)) %>% ungroup() \n " }, { "path": "12a-packages1/62-splitapplycombine2.R", "content": "#split apply combine\n\ndf = mtcars\nbaseball\nlibrary(plyr)\nbaseball.1 <- ddply(baseball, .(id), transform, cyear = year - min(year) +1)\nhead(baseball.1)\n\nddply(df, .(cyl), filter, mpg > mean(mpg)) \nddply(.data = df,.variables = c('cyl'), .fun = function(p){ summarize(p, m1 = mean(mpg))} )\ndf %>% dplyr::group_by(cyl) %>% dplyr::summarize(meanMPG = mean(mpg))\nddply(df, .(cyl), filter, mpg >= min(mpg))\nddply(df, .(cyl), filter, mpg >= max(mpg))\n" }, { "path": "12a-packages1/71-broom1.R", "content": "\nlibrary(tidyr)\nlibrary(dplyr)\nlibrary(broom)\nmdply(expand.grid(mean = 1:5, sd = 1:5), as.data.frame(rnorm), n = 10)\nmtcars %>% group_by(cyl) %>% summarise(hp_mean = list(tidy(lm(hp ~ 1, data = .))))\n\napply_lm <- function(df){ tidy(lm(data = df, hp ~ 1)) }\nd <- mtcars %>% group_by(cyl) %>% nest() %>% mutate(hp_mean = purrr::map(.f = apply_lm, . = data)) \nd\nd %>% unnest(hp_mean)\n\nmtcars %>% group_by(cyl) %>% dplyr::group_split() %>% map_dfr(~lm(hp ~ 1, data = .) %>% tidy())\n\n#https://data-se.netlify.com/2018/12/10/new-split-apply-combine-variant-in-dplyr-group-split/\nlibrary(dplyr)\n\n\nlibrary(magrittr)\nlibrary(dplyr)\nmtcars %>% filter(wt > 2) %$% cor.test(hp, mpg)\n\nall_letters <- c(letters, LETTERS) %>% sort %T>% write.csv(file = \"all_letters.csv\")\nread.csv(\"all_letters.csv\") %>% head()\n\n#%T>% (tee operator) allows you to forward a value into a side-effect-producing function while keeping the original lhs value intact. In other words: the tee operator works like %>%, except the return values is lhs itself, and not the result of the rhs function/expression.\n" }, { "path": "12a-packages1/packages1.R", "content": "# Packages installation\n\n#List avl packages\nlibrary()\n\n\n#Total Avl Packages\nnrow(available.packages())\n\n#Install Package amap\ninstall.packages('amap')\n\n#Load package\nlibrary(amap)\n\n#Find functions in package\nlibrary(help=amap)\n\n#Help wrt a package\nhelp(package='amap') #see on right side pane\n\n\n#Unload---\ninstall.packages('tm')\n\nlibrary(tm)\nlibrary(VIM)\nsearch()\ndetach('package:tm', unload=TRUE)\ndetach(package:VIM, unload = T) \nsearch()\n\n\n\n#Detach Multiple Packages\n(detpkg = c('plyr','tm'))\nlibrary('plyr') #load lib\nlibrary('tm') #load lib\nsearch() #check if loaded\nVectorize(detach)(name=paste0(\"package:\", detpkg), unload=TRUE, character.only=TRUE) #code to detach\nsearch() # confirm if removed\n#specify the argument unload=TRUE; otherwise, R removes the package from the search path but doesn’t unload it.\n\n#Detach from memory all packages\nrm(list = ls(all = TRUE))\nsessionInfo()\n\n\n#Remove Packages ----- uninstall \nremove.packages(\"tm\")\nrequire('tm')# check if unistalled\n\n\n\n#multiple packages\n\n#Function\nipak <- function(pkg){\n new.pkg <- pkg[!(pkg %in% installed.packages()[, \"Package\"])]\n if (length(new.pkg)) \n install.packages(new.pkg, dependencies = TRUE)\n sapply(pkg, require, character.only = TRUE)\n}\npackagelist <- c('NLP','tm', 'lubridate')\nipak(packagelist)\n\n# package lists\npkg1 = c('dplyr','plyr', 'data.table','xlsx', 'Hmisc','rJava','ggplot2','lattice','gsheet','curl','stringr', 'syuzhet','e10171', 'catools','caret', 'olsrr' ,'swirl','sqldf','XML','VIM', 'outliers','car','MASS','DMwR','rvest')\npkg2 = c('forecast','rpart', 'rpart.plot', 'partykit','strucchange', 'didrooRFM')\npkg3 = c('zoo', 'astsa','lubridate','timeSeries','tseries','xts')\npkg4 = c('arules' , 'arulesViz')\npkg5 = c('twitterR','ROAuth','RGtk2','RTextTools','wordcloud')\ninstall.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\n\n\nlibrary(lubridate)\n\n#multiple load \neasypackages::libraries(pkg1)\n\n\n#easypackages\n#install.packages('easypackages')\nlibrary(easypackages)\n\n#Install Multiple Packages\npackages(\"plyr\", \"psych\", \"tm\")\nlibraries(\"plyr\", \"psych\", \"tm\")\n\n\n\n#lubripack\ninstall.packages('lubripack') #NA for some versions\n#install older version of R packages\n#https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages\n#https://rdrr.io/github/Espanta/lubripack/\n\nrequire(devtools)\ninstall_github(\"Espanta/lubripack\")\nlibrary(lubripack)\nlubripack(\"plyr\", \"psych\", \"tm\", \"quantmod\")\n\n\n\npack <- available.packages()\npack[\"ggplot2\",\"Depends\"]\npack[\"ggplot2\",\"Imports\"]\npack[\"data.table\",\"Depends\"]\n\npackrat:::recursivePackageDependencies(\"ggplot2\",lib.loc = .libPaths()[1])\ntools::dependsOnPkgs('ggplot2')\ntools::dependsOnPkgs('dplyr')\ntools::dependsOnPkgs(\"ggplot2\",installed=available.packages())\nlibrary(rusk)\n#Remove Package\nremove.packages('quantmod')\nlibrary(quantmod)\n\n\n\n#Detach all mice package\nlibrary(mise)\nsearch()\n#mise(vars = TRUE, figs = TRUE, console = TRUE, pkgs = FALSE)\nmise(pkgs=T)\nsearch()\n\n\n\n# list all packages where an update is available\nold.packages()\n\n# update all available packages\nupdate.packages()\n\n# update, without prompts for permission/clarification\nupdate.packages(ask = FALSE)" }, { "path": "13a-Packages2/10a-fBasics.R", "content": "#Package fBasics\n#http://math.furman.edu/~dcs/courses/math47/R/library/fBasics/html/015A-BasicStatistics.html\n\nlibrary(fBasics)\n#skewness\treturns value of skewness,\nskewness(mtcars)\n\n#kurtosis\treturns value of kurtosis,\nkurtosis(mtcars)\n\n#basicStats\tcomputes an overview of basic statistical values,\nbasicStats(mtcars)\n\n#rowStats\tcalculates row statistics,\nrowStats(mtcars,FUN=mean)\n\n#colStats\tcalculates column statistics,\ncolStats(mtcars, FUN=median)\n\n#rowAvgs\tcalculates row means,\nrowAvgs(mtcars)\n\n#colAvgs\tcalculates column means,\ncolAvgs(mtcars)\n\n#rowVars\tcalculates row variances,\nrowVars(mtcars)\n\n#colVars\tcalculates column variances,\n\n#rowStdevs\tcalculates row standard deviations,\n\n#colStdevs\tcalculates column standard deviations,\n\n#rowSkewness\tcalculates row skewness,\n\n#colSkewness\tcalculates column skewness,\n\n#rowKurtosis\tcalculates row kurtosis,\n\n#colKurtosis\tcalculates column kurtosis,\nrowCumsums\tcalculates row cumulated Sums,\ncolCumsums\tcalculates column cumulated Sums." }, { "path": "13a-Packages2/11a-pysch.R", "content": "#Descriptive Values of data set\n\nlibrary(psych)\ndescribe(mtcars)\n" }, { "path": "15a-DS/0FileList.R", "content": "# List of Files in this folder\n\n# Data Structures in R\n# Use Help\n# Packages\n# PathConfig\n" }, { "path": "15a-DS/10a-TOC", "content": "#Files in this folder related\n\n# Data Structures in R\n# Use Help\n# Packages\n# PathConfig" }, { "path": "15a-DS/13b-ds-blank.R", "content": "# Data Structures in R\n\n#Vectors----\n\n\n\n\n#Matrix----\n\n\n\n\n\n#Arrays----\n\n\n\n\n\n\n\n#List----\n\n#Factors----\n\n#DataFrame----\n\n" }, { "path": "15a-DS/14b-Basic_R_v01.R", "content": "\n\n#BASICS ABOUT R =================================\n\nx<-10+20 # Simple Math\nx\n\n\n10+20 -> x # \"<-\" Assignment Operator\nx # Print the x value\ny = 1:10 # Assign values\ny\n1:250 # Prints range of numbers \nprint(\"My First Program\") # Prints data in console\n\n\n#R objects =================================\n\nx <- 1:1 # put the numbers 1-10 in the variable X - Assignment operator (= or <-)\n1:10 -> x1 # you can also assign the same\nx1\ny <- c(11,12,13,14,15) # concate nate function , puts the numbers into y\na<- b<- c <-3 # multiple assignments\nx+y # adds corresponding elements in x & y\nx*2 # Multiplication by each element with 2\n\na\nb\nc\n\n# DATA TYPES ============================================\n\n# Numeric -------------------------------------------\n1.7 # Decimal mark is a dot\noptions(scipen=999)\n7.8e3 # One can use scientific notation\n-1/0 # Infinity is marked as inf or -inf \n\n4/6799 # Formatting numbers\nformat(4/6799, sci = TRUE) # Formatting numbers\nformat(4/6799, digits = 2) # Formatting numbers\n\nround(pi, 2) # Rounding\n\n\n# Logical -----------------------------------------------\n\n2 == 5 #is 2 equal to 5?\n\n(2 != 2) # ! means negation\n!(2 == 2) # ! means negation\n\n(2 == 2) & (5 == 5) # & - logical conjunction \n(2 == 2) | (5 != 5) # | - logical alternative \n\n# Character ------------------------------------------------------\n\n\"Meeting in with R\" # strings should be enclosed with ' or \"\npaste(\"I learn R\", 'and use R') # Concatenation of strings\n\n\n# Special --------------------------------------------------\n\n1/0 # Inf, -Inf\n-1/0 # Inf, -Inf\nlog(-1) # Not a number\nNA # NA - Missing values\nNULL # NULL\n\n\n# Factor --------------------------------------------------\nf1 = c(\"girl\", \"boy\", \"boy\", \"girl\", \"boy\")\nf1\nclass(f1)\nf <- factor(c(\"girl\", \"boy\", \"boy\", \"girl\", \"boy\" , \"child\")) \nf\nclass(f)\n# Categorical variables\n\n# DATA STRUCTURES ===========================================\n\n# Vectors # set of objects of the same type, one dimensional --------------------------------------------\n\nmy_vector <- c(-1, 2, 6, 6.7, 2, 0.45, 2, 4) # defined with a c() function (c for concatenate), vectors are vertical) # Numeric vector\n\nmode(my_vector)\ntypeof(my_vector)\nclass(my_vector)\nis.numeric(my_vector)\n\n\n\nis.character(my_vector)\n\nis.data.frame(my_vector)\n\nx5 <- c(5, 4, 2, 3, 1,6,8,9) #concatenation\nx7 <- c(3, 4, 2)\nx8 <- c(\"HA\", \"DL\",\"MH\",\"AP\") # Character Vector\n\nx7+x5 # sum of vectors\ndata <- c(x5,x7) # concatenation of vectors\ndata\n\n(my_vector2 <- 2 * my_vector) # Computations with numeric vectors\nmy_vector * c(2, 3) \nmy_vector + c(2, 3, 4)\n\n# Character vector\n(my_char_vector <- c(4, 2, \"Hello, world!\"))\n\nmode(my_char_vector)\ntypeof(my_char_vector)\nclass(my_char_vector)\nis.numeric(my_char_vector)\n\n# Maths operations\nsqrt(my_vector) # NaN (not a number) produced as a resulting of squarting negative values\nlog(my_vector) # etc.\nsum(my_vector) # sum\nprod(my_vector) # product\n\n\n# Useful functions\nlength(my_vector) # vector's length\nunique(my_vector) # distinct values of the vector\nhead(my_vector, 3) # displays first 3 elements of my_vector\ntail(my_vector, 2) # displays last 2 elements of my_vector\nsort(my_vector) # sorts my_vector\norder(my_vector) # returns the order of my_vector\nrev(my_vector) # reverts the order of elements in my_vector\nwhich.max(my_vector) # return position of the max value\nwhich.min(my_vector) # return position of the min value\nrank(my_vector) # rank elements\n\n#()[]\n\n# Indexing\nmy_vector[3]\nmy_vector[-2]\nmy_vector[1:4]\nmy_vector[-(1:7)]\nmy_vector[-c(2,4,7,5)]\nmy_vector[my_vector > 3]\nwhich(my_vector > 3)\nmy_vector[my_vector > 0 & my_vector < 4]\nmy_vector[my_vector %in% 5:7]\nmatch(my_vector, 1:3)\nmy_vector[c(rank(my_vector)) == 2]\nmy_vector[order(my_vector)]\n\nmy_vector2 = c(\"a\", \"b\")\nmy_vector = c(1,2)\n# Combining two vectors\nc(my_vector, my_vector2)\ncbind(my_vector, my_char_vector)\nrbind(my_vector, my_char_vector)\ncbind(my_char_vector,my_vector)\nrbind(my_char_vector,my_vector)\n\n# Sequences\n4:7\nseq(0, 1, length.out = 16)\nt =seq(1, 9, by = 2)\nt\nseq(1, 8, by = 2) \nseq(9, 1, by = -2) \nseq(17) \n\nall(seq(17) == 1:17)\n\n1:10 -> x1\nx1\n\nrep(1:4, 2)\nrep(1:4, each = 2) \nrep(1:4, c(2, 1, 2, 3))\nrep(1:4, each = 2, len = 10) \n\n\nrep(1:4, each = 2, times = 3)\n\n\n# Matrix --------------mxn--------------------------------\n\n# Defined with a matrix() function with 3 arguments: \n#vector of values, number of rows, number of columns\n\n(my_matrix <- matrix(c(4, 2, 7, 9), nrow=2, ncol=2))\n(my_matrix <- matrix(c(1, 2, 3, 4), 4, 1))\n(my_matrix <- matrix(c(1, 2, 3, 4), 2, 2, byrow=TRUE))\n\n#transpose\nmyMat_Transposed <- t(my_matrix)\n\n\nmy_matrix[1, 2]\nmy_matrix[, 1]\nmy_matrix[1, ]\n\ndim(my_matrix) # Dimesnion of matrix\ndet(my_matrix) # Determinant\neigen(my_matrix) # Eigenvalues/Eigenvectors\n(t(my_matrix) %*% my_matrix) # Transpose and multiply\n\nrowSums(my_matrix) # Sum of rows\ncolSums(my_matrix) # Sum of colums\nrowMeans(my_matrix) # Avgs of rows\ncolMeans(my_matrix) # Avgs of colums\n\n\n# Array -------------------------------------------------\n\n# Defined with a array() function with 3 arguments: \n#vector of values, dimensions, dimension names\n\nour_dim_names <- list(dimension1 = c(\"A\", \"B\", \"C\", \"D\"), \n dimension2 = c(\"a\", \"b\", \"c\"),\n dimension3 = c(\"1\", \"2\"))\n\n(my_array <- array(data = 1:24, dim = c(4,3,2), \n dimnames = our_dim_names))\n\ndim(my_array)\n\n# Indexing\nmy_array[3, 2, 1]\nmy_array[3, 2, ]\nmy_array[3, , ]\n\n\n# List ------------------------------------------------\n\n# Set of objects that can have different types # Defined with the list() function\n(my_list <- list(name = c(\"Analytics\", \"Labs\"), \n age = 3, \n club = \"Bulls\", \n matrix = my_matrix))\n\n(my_list2 <- list(c(\"Analytics\", \"Labs\"), \n 3, \"Bulls\",\n my_matrix))\n\n\n\nmode(my_list)\n\ntypeof(my_list)\nclass(my_list)\n\n# Indexing\ndim(list)\nmy_list$name\nmy_list[1]\nmy_list[[1]]\nmy_list[[1]][2]\nnames(my_list2) #lists all names of the list\n\n\n# Data frame ---------------------------------------------\n\n# table with the same type within a \n#column and different types between columns \n# defined with a data.frame() function\n\nmy_df <- data.frame(id = c(1, 2, 3), \n name = c(\"Analytics\", \"a\", \"b\"), \n Goals = c(50, 49, 25))\n\nmy_df \n\ndim(my_df)\n\nstr(my_df)\nsummary(my_df)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Workspace & Environment -----------------------------------------------------\n\ngetwd() # Get working directory\nsetwd(\"C:/\") # set working directory\nls() # List elements of the environment\n\nrm(x) # remove an object from workspace\nrm(a,b) # remove multiple object from workspace\nrm(list=ls()) # clear workspace\n\n# Ctrl - L ... Clear Console\n\n\n# Installing & managing packages -------------------------------------------------------------------------------------------\n\n\ninstall.packages(\"ggplot2\") # TO install packages # download packages from CRAN and install in R\nlibrary(\"ggplot2\") # make package available ; often used for loading in scripts\nrequire(\"ggplot2\") # prefered for loading in functions; may be better\nlibrary(help=\"ggplot2\") # brings up documentation in editor window\nupdate.packages(\"ggplot2\") # check for pacakge updates; do it regularly\ndetach(\"package:ggplot2\", unload=TRUE) # Unload/Remove packages # by default, all loaded packges are unloaded when R quits.\nremove.packages(\"ggplot2\") # TO permanantly remove it. \n\n#Datasets -----------------------------------------------------------------------------------------------------------------------\n?datasets # Using R's built in data sets\n\nlibrary(help=datasets)\nlibrary(datasets)\n\ndata(mtcars) # Loading mtcars data set\ncars <-mtcars # Save the data into workspace\ndetach(package:datasets) # To remove the datasets package\n\n# Viewing data set\nmtcars # Total data set in console\nView(mtcars) # Viewing dataset in spreadsheet\nhead(mtcars,10) # Viewing top-10 observations (default: top-6)\ntail(mtcars) # Viewing bottom 10 observations\nstr(mtcars) # Viewing data dictionary\nnames(mtcars) # Viewing column names\n\nv1 = mtcars$disp\nnewvar <- mtcars$disp + mtcars$hp\nv1 <- mtcars$mpg # Assigning single variable from mtcars data to v1\nv2 <- mtcars$cyl\nv3 <- mtcars$disp\nv4 <- mtcars$hp\n\nmtcars1<-rbind(v1,v2,v3,v4) # Combined as rows #Horizontal joins\nhead(mtcars1)\nmtcars2<-cbind(v1,v2,v3,v4) # Combined as columns # Vertical joins\n\n# DATA IMPORT =================================================================\n\nsetwd(\"E:\\\\HHges - Mkt Anyts\\\\Marketing Analytics\\\\R Programming\")\n\n\n\n\n# Import from csv -------------------------------------------------------------\n\n# Specify file\nfile <- paste0(path,\"\\\\\", \"Car_data_cf.csv\")\ndata_cf <- read.csv(file)\n\n\n# DATA EXPORT =================================================================\n\n# Export to csv ---------------------------------------------------------------\n\nfile <- paste0(path, \"MyRClassDemo.csv\") # Specify file\nwrite.csv(cars, file) # Export file\ndir(path)\n\n# Export to RData file --------------------------------------------------------\n\npath <- getwd() # Save entire environment\nsave.image(file = paste0(path, \"environment.RData\")) \nsave(fpe, file = paste0(path, \"data.RData\")) # Save single R objects\nload(file = paste0(path, \"data.RData\")) # Load RData file(s)\n\n# Let's Import and build a function -----------------------------------\n\nsetwd(\"E:\\\\HHges - Mkt Anyts\\\\Marketing Analytics\\\\R Programming\")\nmydata<-read.csv(\"sample_file.csv\")\nView(mydata)\nstr(mydata)\nmodelname <- mydata$Age\nmodelname\n# user written function for creating descriptive statistics\nmystats <- function(x) {\n nmiss<-sum(is.na(x))\n a <- x[!is.na(x)]\n m <- mean(a)\n n <- length(a)\n s <- sd(a)\n min <- min(a)\n p1<-quantile(a,0.01)\n p5<-quantile(a,0.05)\n p10<-quantile(a,0.10)\n q1<-quantile(a,0.25)\n q2<-quantile(a,0.5)\n q3<-quantile(a,0.75)\n p90<-quantile(a,0.90)\n p95<-quantile(a,0.95)\n p99<-quantile(a,0.99)\n max <- max(a)\n UC <- m+3*s\n LC <- m-3*s\n outlier_flag<- max>UC | min971169.4] <- 971169.4\nvars <- c(\"Age\"\t,\t\"Sales\" )\nstr(mydata[vars])\nstats<-t(data.frame(apply(mydata[vars], 2, mystats)))\nView(stats)\n\n# Writing Summary stats to external file\n\nwrite.csv(stats, file = \"stats.csv\")\n\n\n## Missing value treatment\nmydata <- mydata[!is.na(mydata$Cust_id),] # dropping obs where Gender=missing\ninstall.packages(\"Hmisc\")\nrequire(Hmisc)\n?apply\nmydata1<-data.frame(apply(mydata[vars],2, function(x) impute(x, mean))) #Imputing missings with mean for IV's\n\n\n\nmydata$Location\naggdata1 <- aggregate(x=mydata[,3],\nby = list( mydata$Location),\n FUN = mean,\n na.rm = TRUE)\naggdata1\n\n\n#*************************************/\n\ninstall.packages(\"swirl\")\nlibrary(swirl)\n\n\n#**********************************/\n\n\n######################################## Loops & Advanced R #####################\n\n#initialize a variable\nN <- 10\n\n#check if this variable * 5 is > 40\nif (N * 5 > 40){\n print(\"This is easy!\")\n} else {\n print (\"It's not easy!\")\n}\n\n\n\n#initialize a vector\ny <- c(99,45,34,65,76,23)\n\n#print the first 4 numbers of this vector\nfor(i in 1:4){\n print (y[i])\n}\n\n\n\nfor (i in seq(0, 10, 2)) print(i)\n\n\n#initialize a condition\nAge <- 12\n\n#check if age is less than 17\nwhile(Age < 17){\n print(Age)\n Age <- Age + 1 #Once the loop is executed, this code breaks the loop\n}\n\n# Creating Histogram\n\ncars\nhead(cars)\ncars$dist\n\nhist(cars$speed)\nplot(cars$dist)\nplot(cars$speed)\n\nhist(cars$dist, col = \"Grey\", border = \"green\")\n\ndata()\n\ndata(mtcars)\n\ndata(iris)\n\nhead(iris, 5)\n\nhead(mtcars, 5)\n\nrm(iris)\n\nmtcars\n\nstr(mtcars)\n\n# Summary of the data\nsummary(mtcars$mpg)\nhist(mtcars$wt)\nhist(mtcars$hp, col = \"green\", main = \"Histogram of HP\")\n\nmtcars\n\nstr(mtcars)\n\n\n#Creating new variable basis the already existing ####\nstr(iris)\nIris1 <- transform(iris,\n sumx = Sepal.Length + Sepal.Width,\n meanx = (Sepal.Width + Sepal.Length) / 2)\n\niris <- transform(iris,\n sumx = Sepal.Length - Sepal.Width,\n meanx = (Sepal.Width + Sepal.Length) / 2)\nrm(Iris)\n\n# Converting from one data type to another ####\na <- c(1, 2, 3)\na\nis.numeric(a)\nis.vector(a)\na <- as.character(a)\na\nis.numeric(a)\nis.vector(a)\nis.character(a)\n\n\n# Transposing a dataset ####\ncars\nt(cars)\n\n\n# Aggregating data ####\noptions(digits = 3)\nattach(mtcars)\nsummary(mtcars)\nsummary(mtcars$disp)\nhead(mtcars)\n?aggregate\naggdata <- aggregate(mtcars,\n by = list(cyl, gear),\n FUN = mean,\n na.rm = TRUE)\naggdata\ndetach(mtcars)\n\nmtcars\n\n\n\n\n# one way table ####\nlibrary(vcd)\nArthritis\ntable(Arthritis$Improved)\nmytable <- with(Arthritis, table(Improved))\nmytable # frequencies\nprop.table(mytable) # proportions\noptions(digits = 3)\nprop.table(mytable) * 100 # percentages\naddmargins(mytable)\n\n# two way table ####\nmytable <- xtabs( ~ Treatment + Improved, data = Arthritis)\nmytable # frequencies\n\n#Two way table using CrossTable ####\ninstall.packages(\"gmodels\")\nlibrary(gmodels)\nCrossTable(Arthritis$Treatment, Arthritis$Improved)\n" }, { "path": "15a-DS/14b-objectsmethods.R", "content": "\n#Methods to handle objects\nx = 1:5\nmethods(class=class(x))\n\nmethods(class=class(mtcars))\n\n\n#seq\n1:5\nrev(x)\nmatch(5, x)\n\n\n#all combinations\n(df=expand.grid(x=c('A','B','C','D'), y=1:3))\n\nchoose(6,4)\n\nweighted.mean(c(1,2), c(3,4))\n(1*3 + 2*4)/(3+4)\n\ndf2 = data.frame(x=c(1,2), y=c(3,4), z=c(5,6))\nstars(df2[,1:2])\nstars(df2[,1:3])\n\nfilled.contour((df2))\n?image\nimage(df2)\n\ndf2 = mtcars[1:5,1:8]\n" }, { "path": "15a-DS/14c-ds1.R", "content": "# Data Structures in R\n\n#Vectors----\nv1 = c('A', 'B','C') #create a vector\nv1 #print the vector\nclass(v1) #print the class of vector\n(v2 = c(1,2,3))\nv2\nclass(v2)\n(v3 = c(TRUE, FALSE, TRUE))\nclass(v3)\nV3a <- c(T,F,T)\nv4 = 100:200\nlength(v4)\nv4\nv5 = seq(1,10,2)\nv4; v5\nv4\nv4[-c(1,3,4)]\nv4[v4 > 150]\nv7 = c('A','C')\nv1\nv1[!v1 %in% v7]\nv1\nv1['A'] #wrong/ error\nv1[1]\nv1\n\n\nv4(v8 = 10:15)\n\nnames(v8) = c('E','F','G','H','I','J')\nv8\nv8['G']\nv8[c('G','I')]\n\nv8[3:5]\n\nstr(v8)\nstr(v1)\nclass(v8)\nclass(v1)\n\n#Dataframe----\nrollno=c(1,2,3)\nname=c('Rohit','Lalit', 'Hitesh')\ncourse=c('MBA','BBA','MCA')\ndept=c('Dept1','Dept1','Dept2')\nmarks=floor(rnorm(3,50,10))\n?rnorm\nrnorm(3,50,10) \nstudents = data.frame(rollno, name, course, dept, marks)\nstudents\nclass(students)\nstr(students)\nsummary(students) \n\n\ndf = data.frame(rollno=c(1,2,3), name=c('Rohit','Lalit', 'Hitesh'), course=c('MBA','BBA','MCA'), dept=c('Dept1','Dept1','Dept2'),marks=floor(runif(3,50,100)))\ndf\nclass(df)\n#df = fix(df)\ndf\nstr(df)\nclass(df)\nsummary(df)\n\n#List\nlistL1 = list('dhiraj', v1, students, mymatrix, myarray)\nlistL1\n\n\n#?ls\nls()\nrm(list=ls())\nls()\n\n\n(mylist1 = list( 1, df, v4))\n\n?matrix\n'a':'Z'\nmym = matrix(c('a','b',2,'d'), ncol=2)\nmym\n1:24\n(mymatrix = matrix(1:24,ncol=6,byrow=T))\n#(mymatrix = matrix(11:34,nrow=6,byrow=T))\nlength(11:35)\nmymatrix[,1]\nmymatrix[2:4,3:4]\nmymatrix[5,3]\nmymatrix\n?matrix\n(mymatrix = matrix(1:24,ncol=4, byrow=TRUE))\nrows1= c('R_1','R_2')\n1:6\nrn = paste('R',1:6, sep='-')\ncn = paste('C',1:4, sep='-')\ndimnames(mymatrix) = list(c(rn), c(cn))\nmymatrix\n?dimnames\ndimnames(mymatrix) = list(c(paste('R',1:6, sep='_')), c(paste('C',1:4,sep='')))\nmymatrix\npaste('C',1:4, sep='')\nmymatrix[c('R-1'),]\nmymatrix[,c('C-3')]\nmymatrix[c('R-1','R-3'),]\nmymatrix[c(1,3),]\n\nmymatrix\ncolSums(mymatrix)\nrowSums(mymatrix)\nrowMeans(mymatrix)\ncolMeans(mymatrix)\n\n#--\n(myarray = array(101:124, dim=c(4,3,2)))\n\n(myarray = array(1:24, dim=c(3,2,4)))\n\n\n(myarray = array(1:24, dim=c(4,3,2), dimnames = list(c('S1','S2','S3','S4'), c('Sub1','Sub2','Sub3'), c('Dept1','Dept2'))))\nmyarray\napply(myarray,3,sum)\napply(myarray,2,mean)\napply(myarray,3,sum)\napply(myarray,c(2,3),mean)\napply(myarray,c(1,3),sd)\napply(myarray,c(1,2),max)\n\n\n\n# Factors\nname = c('S1','S2','S3','S4','S5')\ncourse = c('PHD', 'MTECH', 'BTECH','BTECH','PHD')\ngender = c('M', 'F', 'M', 'F','M')\ngrades = c('A','B','C','A','F')\nmarks = c(runif(5, 50, 100))\ndf = data.frame(name, course, gender, grades, marks)\ndf\nstr(df)\ndf[1:2,]\ndf[,1:3]\ndf$name = as.character(df$name)\nstr(df)\ndf$grades = factor(df$grades, ordered=F, levels=c('A','B','C','D','E','F'))\nstr(df)\ndf$grades\ndf$gender = factor(df$gender, ordered=T, levels=c('M','F'))\n\ndf$gender\n#x\n\n\n\n#\n\nstudents\ndf\ndf=fix(df)\ndf[3:4,1:2]\ndf\ndf$course\ndf$name\ndf$gender = c('M','F','M','M')\ndf\nstr(df)\ndf$gender = factor(df$gender)\nstr(df)\ndf$grades = c('A', 'B', 'A', 'C')\ndf\nstr(df)\ndf$grades = factor(df$grades, ordered=T)\nstr(df)\ndf$grades\ndf$grades = factor(df$grades, ordered=T, levels=c('C','B','A'))\ndf$grades\n\n\n\ntable(df$course,df$gender)\nmymatrix\napply(mymatrix, 1, sum) \napply(mymatrix, 2, sum) \napply(mymatrix, 2, sd) \nmyarray\napply(myarray, 1, sum) \ndf\n?apply\ndf\ntapply(df$marks, df$gender, mean)\n?tapply\ndf\ndf$gender = NULL\ndf\ndf[df$dept == 'Dept1',]\ndf[df$marks >= 80,]\n\n\n\n\n\n" }, { "path": "15a-DS/15a-objects.R", "content": "# Objects\n# \n\nm1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),seq_len(length.out=10)), byrow=F, ncol =4)\ncolnames(m1) = c('sub1','sub2','sub3','sub4')\nrownames(m1) = paste('R',1:10,sep='')\n\na1= array(1:24, dim=c(4,3,2), dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) )\na1\n\ndf1 = data.frame(sub1=10:1, sub2=5, sub3=rep(c(5,6),5), sub4=seq_len(length.out=10))\ndf1\n\n# Lists\n\nlist1 = list(sub1=10:1, sub2=rep(5,3), sub3=rep(c(5,6),4),sub4=seq_len(length.out=10))\nlist1\n\nlist2 = list(num=1:10, vec=c(1:5, 4:5, 6:8, NA, 9, 12, 17), lg=log(1:5))\nlist2\n\n#Data Frame df3\nnewnum = c(2:5, 4:5, 6:8, 9,17)\nfac1 = factor(c(rep(\"A\", 3), rep(\"B\", 3), rep(\"C\", 3), rep(\"D\",2)))\nfac2 = gl(n=2, k=1, length=11, labels = month.abb[1:2])\nnewnum\nfac2\nfac1\ndf3 = data.frame(response = newnum, pred1 = fac1, pred2 = fac2)\ndf3\n#rm(list=ls())\n\nstudent1 <- readRDS(\"student1.rds\")\n# Dataframe student\nstr(student1)\ns1 = student1[,c('br', 'city','java','dbms', 'dwm','vlsi', 'cpp', 'cbnst')]\nstr(s1)\nstudent1[,c(15:22)]\n" }, { "path": "15a-DS/16b-datatypes.R", "content": "# R Programming\n?mean\nx <- c(0:10, 50)\nx\nx[c(seq(1,12,2))]\n?c\nseq(2,100,2)\n\n?seq\nxm <- mean(x)\nxm\nmean(x, trim = 0.10)\n\n# Scalar\n(x <- 1)\nx = 1\n# Vector\nc(1:5)\nx = c(1,2,3,4,10,5,5,3,2,5,7,7,8,9)\nmean(x); median(x)\nsort(x)\n\ntable(x)\n\nlibrary(modeest)\nmlv(x, method = \"lientz\", bw = 0.2)\n\nratings =c(rep('Good',5), rep('Poor',3))\nratings\ntable(ratings)\nbarplot(table(ratings))\n\n\n\nrollno = c(10,22,44,23)\nsales = runif(12, 50, 100)\nsales\nplot(sales, type='b')\n?plot\n\n?runif\n\n\nstr(x)\ny = c('abhinav', 'Rishi', 'Shreya')\nstr(y)\nz = c(TRUE, FALSE, TRUE, FALSE)\nstr(z)\n\n\nabinav=c(1,3,6,8,6,3,100,rep(NA,5))\nabinav\nstr(abinav)\nis.na(abinav)\nanyNA(abinav)\nmean(abinav,na.rm=T)\n\nmean(x=abinav)\nmean(x=abinav,trim=0.4, na.rm=T)\nx = c(TRUE, FALSE, TRUE)\nmean(x)\nmean(c(1,0,1))\n\n#Categorical Data\nproduct = c('maggi', 'topramen', 'hakka')\nproduct\nshirtcolor = c('green', 'red', 'blue', 'black')\nshirtcolor\nrating = c('Excellent', 'Very Good', 'Good', 'Average', 'Poor' )\nrating\nstr(rating)\nperformance = c( 1, 2, 3, 4, 5)\nstr(performance)\n\ndecision = c('buy', 'sell')\ndecisionTF = c(TRUE, FALSE )\nstr(decision); str(decisionTF)\ngrades = c('A','C','B','D','E','B')\ngrades\ngrades1 = factor(grades, ordered=T, levels=c('E','D','A','B','C'))\ngrades1\nitemssold = c(10, 20, 30, 25)\n\n\n#Nominal Data\nproduct\n\n#Ordinal Data\nperformance\n#need to create order\nperformance=c(1,2,5,2,5,2,5,2,5)\nlength(performance)\nperformance\nstr(performance)\nperformance[9] = 100\nperformance\n\nperformance1 = factor(performance, ordered=TRUE, levels=c(4,2,5,1,3,8,100))\nperformance1[1] = 100\n\ntable(performance1)\nperformance1\n\n\n\nrating\nrev(rating)\nrating1 = factor(rating, ordered=T, levels=rev(rating))\nrating1\n\ngrades\ngrades1 = factor(grades, ordered=T, levels=c('F','E','D','C','B', 'A'))\ngrades1\nsummary(grades1)\n\n# -----\n#vectors, factors\n#list matrix\n?matrix\nmdat <- matrix(c(1,2,3,11,12,13), nrow = 2, ncol = 3)\nmdat[1,1] ='dhiraj' \nmdat \n(x = 1:24) \n(matx = matrix(x, nrow=6, byrow=TRUE)) \n?data.frame\n\n\n\n(salemon = month.abb[1:12])\nsalemon = factor(salemon, ordered=T,levels=month.abb[1:12])\nsalemon\n(sales1 = runif(12, min=50, max=100))\nsalemon; sales1\n(adamt = runif(12, min=12, max=25))\n(salesmonthwise = data.frame(salemon, sales, adamt))\nrownames(salesmonthwise) = month.abb[1:12]\nsalesmonthwise\ncolnames(salesmonthwise) = c('col1','col2','col3')\n\nsalesmonthwise[1:5,1:3]\n?head\nhead(salesmonthwise,n=3)\nhead(salesmonthwise[c(1,3)],n=3)\nhead(salesmonthwise[c('col1','col2')],n=3)\nsalesmonthwise[c('Jan'),c('col1','col2')]\nsalesmonthwise[salesmonthwise$col3 < 20,c(1,2,3)]\n\ncolSums(salesmonthwise[-1])\nrowSums(salesmonthwise[-1])\n\n# Day3\n\n# Data Properties\nset.seed(10)\n?rnorm\nsales1 = rnorm(100, mean=50, sd=10)\nsales1\nrange(sales1)\nhist(sales1)\nplot(density(sales1))\nabline(v=50)\n\nset.seed(1)\nsales3 = runif(10,1,5)\nsales3\nmean(sales3)\nset.seed(1)\nsales4 = runif(10,1,5)\nsales4\nmean(sales4)\n\n\nnames = c('A', 'B', 'C')\n?sample\nv1= sample(x=names, size=4, replace = FALSE, prob = c(.2,.3,.5))\nv1\ntable(v1)\n\n\nsales2 = rnorm(100, mean=60, sd=12)\nsales2\nplot(density(sales2))\nabline(v=60)\nsales3= c(1,5,2,5,2,3,6,7,8,4,5)\nsales3\n#\n\n\nshirtcolor = c('green', 'red', 'blue', 'black')\nsellitems = sample(shirtcolor, 20, replace=T, prob=c(.4,.3,.2,.1)) \nsellitems\nsummary(sellitems)\ntable(sellitems)\n\ngrades = c('A','C','B', 'D','E', 'B', 'A', 'B', 'A', 'C','F')\ngrades1 = factor(grades, ordered=T, levels=c('F','E','D','C','B', 'A'))\ngrades1\n\n\n#Location / Central Tendency\nmean(sales1)\nmean(sales2)\nmean(sellitems)\nmean(grades)\nmean(sales3)\n\n#Middle Values\nmedian(sales1)\nmedian(sales2)\nmedian(sales3)\nsort(sales3)\nquantile(sales3)\n\nmedian(sellitems)\nmedian(grades1)\n\n\n#Mode\nmode(sales1) # error\nlibrary(modeest)\nmlv(sales1, method='mfv')\nmlv(sales2, method='mfv')\nmlv(sales3, method='mfv')\nmlv(sellitems, method='mfv')\nmlv(grades1, method='mfv')\ntable(sale3)\ntable(sellitems)\ntable(grades1)\nwhich.max(table(grades1))\n\n\n\n# Graphical Way\nmean(sales1)\nhist(sales1)\nplot(density(sales1))\nabline(v=50)\n\nsales2 = rnorm(100, mean=60, sd=12)\n\nsales2\nrange(sales2)\nhist(sales2, prob=T)\n?hist\nlines(density(sales2), col='red')\nabline(v=60, col=\"green\")\nabline(h=.02, col=\"red\")\n\n# Combine Plots\nplot(density(sales1), col='green', ylim=c(0,0.05))\nlines(density(sales2), col=\"blue\", lwd=2)\nabline(v=c(50,60), col=c('green', 'blue'), lwd=3)\n" }, { "path": "15a-DS/16c-basicDT.R", "content": "#Basic Data Types\n\n#Numeric ----\n#Decimal values are called numerics in R. It is the default computational data type. If we assign a decimal value to a variable x as follows, x will be of numeric type.\n\nx = 10.5 # assign a decimal value \nx # print the value of x [1] 10.5 \nclass(x) # print the class name of x [1] \"numeric\"\n\n#Furthermore, even if we assign an integer to a variable k, it is still being saved as a numeric value.\n\n(k = 1) #[1] 1 \nclass(k) # print the class name of k [1] \"numeric\"\nis.integer(k) # is k an integer? FALSE\n\n#INTEGER----\n#invoke as.integer function \n\n(y = as.integer(3) ) # print the value of y [1] 3 \nclass(y) # print the class name of y \n[1] \"integer\" \nis.integer(y) # is y an integer? [1] TRUE\n\n#coerce a numeric value into an integer with the same as.integer function.\nas.integer(3.14) # coerce a numeric value [1] 3\n\n#And we can parse a string for decimal values in much the same way.\nas.integer(\"5.27\") # coerce a decimal string [1] 5\n\nas.integer(TRUE) # the numeric value of TRUE [1] 1 \nas.integer(FALSE) # the numeric value of FALSE [1] 0\n\n#CHARACTER----\n#A character object is used to represent string values in R. We convert objects into character values with the as.character() function:\n \n(x = as.character(3.14) ) # print the character string [1] \"3.14\" \nclass(x) # print the class name of x [1] \"character\"\n\nfname = \"Joe\"; lname =\"Smith\" \npaste(fname, lname) #concatenate character values\n\n\n\n#LOGICAL----\n#A logical value is often created via comparison between variables.\nx = 1; y = 2 # sample values \nz = x > y # is x larger than y? \nz # print the logical value [1] FALSE \nclass(z) # print the class name of z [1] \"logical\"\n\n#Standard logical operations are \"&\" (and), \"|\" (or), and \"!\" (negation).\nu = TRUE; v = FALSE \nu & v # u AND v [1] FALSE \nu | v # u OR v [1] TRUE \n!u # negation of u [1] FALSE\n\n\n#COMPLEX\n#A complex value in R is defined via the pure imaginary value i.\n(z = 1 + 2i) # create a complex number, print the value of z 1+2i \nclass(z) # print the class name of z [1] \"complex\"\n" }, { "path": "15a-DS/16d-ds1.R", "content": "# Data Structures in R\n\n#Vectors----\nv1 = c('A', 'B','C') #create a vector\nv1 #print the vector\nclass(v1) #print the class of vector\n(v2 = c(1,2,3))\nv2\nclass(v2)\n(v3 = c(TRUE, FALSE, TRUE))\nclass(v3)\nV3a <- c(T,F,T)\nv4 = 100:200\nlength(v4)\nv4\nv5 = seq(1,10,2)\nv4; v5\nv4\nv4[-c(1,3,4)]\nv4[v4 > 150]\nv7 = c('A','C')\nv1\nv1[!v1 %in% v7]\nv1\nv1['A'] #wrong/ error\nv1[1]\nv1\n\n\nv4(v8 = 10:15)\n\nnames(v8) = c('E','F','G','H','I','J')\nv8\nv8['G']\nv8[c('G','I')]\n\nv8[3:5]\n\nstr(v8)\nstr(v1)\nclass(v8)\nclass(v1)\n\n#Dataframe----\nrollno=c(1,2,3)\nname=c('Rohit','Lalit', 'Hitesh')\ncourse=c('MBA','BBA','MCA')\ndept=c('Dept1','Dept1','Dept2')\nmarks=floor(rnorm(3,50,10))\n?rnorm\nrnorm(3,50,10) \nstudents = data.frame(rollno, name, course, dept, marks)\nstudents\nclass(students)\nstr(students)\nsummary(students) \n\n\ndf = data.frame(rollno=c(1,2,3), name=c('Rohit','Lalit', 'Hitesh'), course=c('MBA','BBA','MCA'), dept=c('Dept1','Dept1','Dept2'),marks=floor(runif(3,50,100)))\ndf\nclass(df)\n#df = fix(df)\ndf\nstr(df)\nclass(df)\nsummary(df)\n\n#List\nlistL1 = list('dhiraj', v1, students, mymatrix, myarray)\nlistL1\n\n\n#?ls\nls()\nrm(list=ls())\nls()\n\n\n(mylist1 = list( 1, df, v4))\n\n?matrix\n'a':'Z'\nmym = matrix(c('a','b',2,'d'), ncol=2)\nmym\n1:24\n(mymatrix = matrix(1:24,ncol=6,byrow=T))\n#(mymatrix = matrix(11:34,nrow=6,byrow=T))\nlength(11:35)\nmymatrix[,1]\nmymatrix[2:4,3:4]\nmymatrix[5,3]\nmymatrix\n?matrix\n(mymatrix = matrix(1:24,ncol=4, byrow=TRUE))\nrows1= c('R_1','R_2')\n1:6\nrn = paste('R',1:6, sep='-')\ncn = paste('C',1:4, sep='-')\ndimnames(mymatrix) = list(c(rn), c(cn))\nmymatrix\n?dimnames\ndimnames(mymatrix) = list(c(paste('R',1:6, sep='_')), c(paste('C',1:4,sep='')))\nmymatrix\npaste('C',1:4, sep='')\nmymatrix[c('R-1'),]\nmymatrix[,c('C-3')]\nmymatrix[c('R-1','R-3'),]\nmymatrix[c(1,3),]\n\nmymatrix\ncolSums(mymatrix)\nrowSums(mymatrix)\nrowMeans(mymatrix)\ncolMeans(mymatrix)\n\n#--\n(myarray = array(101:124, dim=c(4,3,2)))\n\n(myarray = array(1:24, dim=c(4,3,2), dimnames = list(c('S1','S2','S3','S4'), c('Sub1','Sub2','Sub3'), c('Dept1','Dept2'))))\nmyarray\napply(myarray,1,sum)\napply(myarray,2,mean)\napply(myarray,3,sum)\napply(myarray,c(2,3),mean)\napply(myarray,c(1,3),sd)\napply(myarray,c(1,2),max)\n\n\n\n# Factors\nname = c('S1','S2','S3','S4','S5')\ncourse = c('PHD', 'MTECH', 'BTECH','BTECH','PHD')\ngender = c('M', 'F', 'M', 'F','M')\ngrades = c('A','B','C','A','F')\nmarks = c(runif(5, 50, 100))\ndf = data.frame(name, course, gender, grades, marks)\ndf\nstr(df)\ndf[1:2,]\ndf[,1:3]\ndf$name = as.character(df$name)\nstr(df)\ndf$grades = factor(df$grades, ordered=F, levels=c('A','B','C','D','E','F'))\nstr(df)\ndf$grades\ndf$gender = factor(df$gender, ordered=T, levels=c('M','F'))\n\ndf$gender\n#x\n\n\n\n#\n\nstudents\ndf\ndf=fix(df)\ndf[3:4,1:2]\ndf\ndf$course\ndf$name\ndf$gender = c('M','F','M','M')\ndf\nstr(df)\ndf$gender = factor(df$gender)\nstr(df)\ndf$grades = c('A', 'B', 'A', 'C')\ndf\nstr(df)\ndf$grades = factor(df$grades, ordered=T)\nstr(df)\ndf$grades\ndf$grades = factor(df$grades, ordered=T, levels=c('C','B','A'))\ndf$grades\n\n\n\ntable(df$course,df$gender)\nmymatrix\napply(mymatrix, 1, sum) \napply(mymatrix, 2, sum) \napply(mymatrix, 2, sd) \nmyarray\napply(myarray, 1, sum) \ndf\n?apply\ndf\ntapply(df$marks, df$gender, mean)\n?tapply\ndf\ndf$gender = NULL\ndf\ndf[df$dept == 'Dept1',]\ndf[df$marks >= 80,]\n\n\n\n\n\n" }, { "path": "15a-DS/20a-vectors.R", "content": "# Vectors\n\n#sequence of data elements of the same basic type\n\n#Scalar\nx1 = 1\nx2 <- 2\nx1\nx2\n(x3 = 3) #assign and print\n\n#Vector----\n# Combining vectors with c()----\nx = c(1, 5, 4, 9, 0)\ntypeof(x) #[1] \"double\"\nlength(x) #[1] 5\n\n#Vector using : operator\nx = 1:7; x #[1] 1 2 3 4 5 6 7\ny <- 2:-2; y #[1] 2 1 0 -1 -2\n\n#Vector using seq command\nseq(1, 3, by=0.2) # specify step size\n#[1] 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0\nseq(1, 5, length.out=4) # specify length of the vector 4parts from 5\n#[1] 1.00 2.33 3.667 5.00\n\nseq(1,5, along.with = c(1,2,13,15,6,7,8,20))\nx=c(1,2,13,15,6,7,8,20,30,60); length(x)\nx1=110:150\nlength(x1)\nseq(0,1, length.out = length(x1))\nseq(0,1, along.with = x1)\n\nseq(0,1, length.out = 60)\n\n#Character/ String ----\n(x3=c('S1','S2',\"S3\"))\nclass(x3)\n#Numeric ----\n(x4=1:10)\nclass(x4)\n#Logical----\n(x5=c(TRUE, FALSE, TRUE))\nclass(x4)\n(x5b=c(F, T, T, F))\nclass(x5b)\n\n\n\n#Vector Coercian ----\n#if even 1 character, whole vector converted to character\nx = c(1, 5.4, TRUE, \"hello\")\nx #[1] \"1\" \"5.4\" \"TRUE\" \"hello\"\ntypeof(x) #[1] \"character\"\n\n\n#Vector Recycling ----\n#If two vectors are of unequal length, the shorter one will be recycled in order to match the longer vector. Recycle values of u\n\nu = c(10, 20, 30) \nv = c(1, 2, 3, 4, 5, 6, 7, 8, 9) \nu + v \n#[1] 11 22 33 14 25 36 17 28 39\n#1+10, 2+20, 3+30, 4+10, 5+20, 6+30, 7+10 ...\n\n#Vector Index ----\nu[1]\n\nu[-3] #negative index :strip the member whose position has the same absolute value as the negative index.\n\n#Numeric Index Vector ----\ns = c(\"aa\", \"bb\", \"cc\", \"dd\", \"ee\") \ns[c(2, 3)] \ns[-c(4,5)]\n\n#cannot mix + & -\ns[c(-2,3)]\n\n#Duplicate Index\ns[c(2,3,2)]\n\n#Range Index\ns[c(2:5)]\ns[2:5]\n#Out of Range Index\ns[c(10)]\n\n#Logical Index Vector\n#Its members are TRUE if the corresponding members in the original vector are to be included in the slice, and FALSE if otherwise.\ns = c(\"aa\", \"bb\", \"cc\", \"dd\", \"ee\") #retrieve 2nd & 4th member\nL = c(FALSE, TRUE, FALSE, TRUE, FALSE) \ns[L] \n#[1] \"bb\" \"dd\"\n\nx = 1:10\nx[x<5] #elements satisfying condition\n\n\n#Named Vector Members ----\n#We can assign names to vector members.\n(v = c(\"Mary\", \"Sue\") )\nnames(v) = c(\"First\", \"Last\") #Name the members\nv \n#Retrieve the first member by its name.\nv[\"First\"] #[1] \"Mary\"\n#everse the order with a character string index vector.\nv[c(\"Last\", \"First\")] \n\n\n#DELETE Vector----\n(x = 1:10)\nx = NULL #delete\nx\n\n#Vector ARITHMETIC----\n# Create two vectors.\nv1 = c(3,8,4,5,0,11)\nv2 = c(4,11,0,8,1,2)\n\n(add.result = v1+v2) # Vector addition.\n(sub.result = v1-v2) # Vector subtraction.\n(multi.result = v1*v2) # Vector multiplication.\n(divi.result = v1/v2) # Vector division.\n(v1/2)\n\n\n#Sort\nv = c(3,8,4,5,0,11, -9, 304)\nsort(v)\nsort(v, decreasing=T)\n\n\n\n#READ values\nnums = scan() #type the numbers with space\nnums\nnames = scan(what='character')\nnames\n\n\n#Missing Values\nx = c(1,4,7,NA,12,19,15,21,20)\nmean(x)\nmean(x, na.rm=T)\nis.na(x)\nx[!is.na(x)] #non missing values\n" }, { "path": "15a-DS/20b-vectors2.R", "content": "# Vectors 2\n\nx <- vector()\n# with a length and type\nvector(\"character\", length = 10)" }, { "path": "15a-DS/20c-vectors.R", "content": "#Datatypes: string- name, location, department\n# Number--- age, salary, weight, height\n# Boolean/Logical---- TRUE/FALSE\n\n\n\nsnames = c('student1', \"student2\", 'student3')\nclass(snames)\nmarks = c(10, 20 ,30,50 ,60,40)\nmarks\nclass(marks)\nmarried = c(TRUE, FALSE, TRUE)\nmarried\nclass(married)\nsnames; marks; married\n(age = c(30,35,26))\nage = c(30,35,26)\nage\n\n\n\n#subsetting a Vector\n(x1 = 1:100)\n(x2 = seq(50,100,3))\n?seq\n?class\nx3 = seq(from= 100, to= 200, by=2)\n\nx1\nx1[10]\nx1[20:30]\nx2[2:5]\nx1[x1 > 50]\nx2\nx2 > 70\nx2[(x2 > 70) | (x2 < 60)]\nx2[c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE,TRUE, TRUE, \n TRUE, TRUE, TRUE, TRUE, TRUE)]\nx2\n#operations on the vector\nx3 = c('A','B','C')\nx3[c(1,3)]\nx3[c(TRUE, FALSE, TRUE)]\nx3[(x3=='A') | (x3=='C')]\n\nx2[(x2 > 60) & (x2 < 70)]\nx2[x2==69]\nx2==69\n\nx2\nlength(x2)\nlength(x1)\nmean(x2)\nsum(x2)/length(x2)\nsum(x2)\nmedian(x2)\nx2\n\nset.seed(1)\n\nX5 <- sample(1:10,50,replace = TRUE )\n(x4 = trunc(runif(20,5,100)))\nmedian(x4)\n(shirtcolors = c(\"red\",'blue','green','blue','green', 'blue'))\nmean(shirtcolors)\ntable(shirtcolors)\n\nsort(x4)\n\n\n\n\n\ntest_matrix <- matrix(data=marks,nrow = 3, byrow = TRUE)\n\ntest_list <- list(snames, marks, married)\n\nclass(test_list)\n\nqt <- c(\"Time\", 24, \"October\", TRUE, 3.33) \n\nclass(qt)\n\n" }, { "path": "15a-DS/20d-vectorfunctions.R", "content": "# Functions for Vectors\n\nx = 1:10\n\n#Confirmation Functions\nis.numeric(x)\nis.character(x)\nis.complex(x)\nis.integer(x)\nis.factor(x)\nis.date(x)\nis.logical(x) \n\n#Change the DAta type of Objects \nas.numeric(x)\nas.character(x)\nas.complex(x)\nas.integer(x)\nas.factor(x)\nas.Date(x)\nas.logical()\n" }, { "path": "15a-DS/20f-vectors.R", "content": "# Vectors\n\n#sequence of data elements of the same basic type\n\n#Scalar\n\n#Vector----\nx = c(1, 5, 4, 9, 0)\ntypeof(x) #[1] \"double\"\nlength(x) #[1] 5\n\n#Vector using : operator\nx = 1:7; x #[1] 1 2 3 4 5 6 7\ny <- 2:-2; y #[1] 2 1 0 -1 -2\n\n#Vector using seq command\nseq(1, 3, by=0.2) # specify step size\n#[1] 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0\nseq(1, 5, length.out=4) # specify length of the vector 4parts from 5\n#[1] 1.00 2.33 3.667 5.00\n\n\n#Character/ String ----\n\n#Numeric ----\n\n\n#Logical----\n\n\n\n\n\n\n# Combining vectors with c()----\n\n\n\n\n#Vector Coercian ----\nx = c(1, 5.4, TRUE, \"hello\")\nx #[1] \"1\" \"5.4\" \"TRUE\" \"hello\"\ntypeof(x) #[1] \"character\"\n\n\n#Vector Recycling ----\n#If two vectors are of unequal length, the shorter one will be recycled in order to match the longer vector. Recycle values of u\n\nu = c(10, 20, 30) \nv = c(1, 2, 3, 4, 5, 6, 7, 8, 9) \nu + v \n#[1] 11 22 33 14 25 36 17 28 39\n#1+10, 2+20, 3+30, 4+10, 5+20, 6+30, 7+10 ...\n\n#Vector Index ----\nu[1]\n\nu[-3] #negative index :strip the member whose position has the same absolute value as the negative index.\n\n#Numeric Index Vector ----\ns = c(\"aa\", \"bb\", \"cc\", \"dd\", \"ee\") \ns[c(2, 3)] \ns[-c(4,5)]\n\n#cannot mix + & -\ns[c(-2,3)]\n\n#Duplicate Index\ns[c(2,3,2)]\n\n#Range Index\ns[c(2:5)]\ns[2:5]\n#Out of Range Index\ns[c(10)]\n\n#Logical Index Vector\n#Its members are TRUE if the corresponding members in the original vector are to be included in the slice, and FALSE if otherwise.\ns = c(\"aa\", \"bb\", \"cc\", \"dd\", \"ee\") #retrieve 2nd & 4th member\nL = c(FALSE, TRUE, FALSE, TRUE, FALSE) \ns[L] \n#[1] \"bb\" \"dd\"\n\nx = 1:10\nx[x<5] #elements satisfying condition\n\n\n#Named Vector Members ----\n#We can assign names to vector members.\n(v = c(\"Mary\", \"Sue\") )\nnames(v) = c(\"First\", \"Last\") #Name the members\nv \n#Retrieve the first member by its name.\nv[\"First\"] #[1] \"Mary\"\n#everse the order with a character string index vector.\nv[c(\"Last\", \"First\")] \n\n\n#DELETE Vector----\n(x = 1:10)\nx = NULL #delete\nx\n\n\n\n#Vector ARITHMETIC----\n# Create two vectors.\nv1 = c(3,8,4,5,0,11)\nv2 = c(4,11,0,8,1,2)\n\n(add.result = v1+v2) # Vector addition.\n(sub.result = v1-v2) # Vector subtraction.\n(multi.result = v1*v2) # Vector multiplication.\n(divi.result = v1/v2) # Vector division.\n(v1/2)\n\n\n#Sort\nv = c(3,8,4,5,0,11, -9, 304)\nsort(v)\nsort(v, decreasing=T)\n\n\n\n#READ values\nnums = scan() #type the numbers with space\nnums\nnames = scan(what='character')\nnames\n\n\n#Missing Values\nx = c(1,4,7,NA,12,19,15,21,20)\nmean(x)\nmean(x, na.rm=T)\nis.na(x)\nx[!is.na(x)] #non missing values\n" }, { "path": "15a-DS/20g-valuegenerate.R", "content": "# Value Generation\n\n#seq\n\n\n#rep\n\n#rnorm\n\n#runif\n\n#sample\n\n#months\n\n#alphabets\n" }, { "path": "15a-DS/20h-vectors2.R", "content": "# Vectors 2\n\nx <- vector()\n# with a length and type\nvector(\"character\", length = 10)" }, { "path": "15a-DS/25a-matrices.R", "content": "# Matrices\nix(1:12, ncol=3, byrow=T))\n\nclass(m1)\n#[1] \"matrix\"\nattributes(m1)\n#$dim\n#[1] 4 3\ndim(m1)\n#[1] 4 3\n\n#names of cols and rows\nm1\n(colnames(m1) = paste('C',1:3, sep=''))\n(rownames(m1) = paste('R',1:4, sep=''))\nm1\n\n#Vector to Matrix\n(m3 = 1:24)\ndim(m3)= c(6,4)\nm3\n\n#access elements\nm2\nm2[1,] #first row\nm2[c(1,3,4),] #1st,3rd,4th row\n\nm2[,1] #first col\nm2[,2:3] # 2nd to 3rd coln\n\nm2[c(1,2),c(2,3)]\nm2[,]\nm2[-2,] # exclude 2nd row\nm2[1:5] # matrix is like vector\nm2[c(TRUE,FALSE),c(2,3)] #logical indexing\nm2[m2 > 5]\n\n#modify Vector\nm2[2,2]\nm2[2,2] = 10\nm2\nm2[m2> 10] = 99\nm2\nrbind(m2, c(50,60,70))\ncbind(m2, c(55,65,75,85))\n\n#row and col wise summary\nm1\ncolSums(m1); rowSums(m1)\ncolMeans(m1); rowMeans(m1)\n\nt(m1) # transpose\nm1\nsweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN=\"+\" ) #rowise\nsweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN=\"*\" ) #colwise\n\n#addmargins\nm1\naddmargins(m1,1,sum) #colwise function\naddmargins(m1,2,mean) #rowwise function\naddmargins(m1,c(1,2),mean) #row & col wise function\naddmargins(m1,c(1,2),list(list(mean,sum), list(var,sd))) #row & col wise function\n" }, { "path": "15a-DS/25c-matrices.R", "content": "#matrix\n\nlibrary(matlib)\n\nA <- matrix( c(5, 1, 0,\n 3,-1, 2,\n 4, 0,-1), nrow=3, byrow=TRUE)\nA\ndet(A)\ninv(A)\ndiag(A)\nt(A)\nA * 2\nsolve(A)\n" }, { "path": "15a-DS/25d-matrices.R", "content": "# Matrix\n#col or rownames, sweep, addmargins\n(m1 = matrix(1:12, nrow=4))\n\nclass(m1)\nattributes(m1)\n\n#names of cols and rows\nm1\n(colnames(m1) = paste('C',1:3, sep=''))\n(rownames(m1) = paste('R',1:4, sep=''))\nm1\n\n\n#row and col wise summary\nm1\ncolSums(m1); rowSums(m1)\ncolMeans(m1); rowMeans(m1)\n\nt(m1) # transpose\nm1\nsweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN=\"+\" ) #rowise\nsweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN=\"*\" ) #colwise\n\n#addmargins\nm1\naddmargins(m1,1,sum) #colwise function\naddmargins(m1,2,mean) #rowwise function\naddmargins(m1,c(1,2),mean) #row & col wise function\naddmargins(m1,c(1,2),list(list(mean,sum), list(var,sd))) #row & col wise function\n\n" }, { "path": "15a-DS/25e-matrices.R", "content": "# Matrices" }, { "path": "15a-DS/27a-arrays.R", "content": "# Arrays\n# 2 states ; Each State has 3 districts : Each District has 4 cities\nstate = c('state1', 'state2')\ndistrict = c('district1','district2', 'district3')\ncity = c('city1', 'city2', 'city3', 'city4')\npincode = 101:124\ncitylist = array(pincode, c(4,3,2) , dimnames = list(city, district ,state))\ncitylist\n\n#state1 district1 city1 101\n#state1 district1 city2 102\nstate = paste('state',1:4,sep='')\nstate2= rep(state,6)\n\n\ncolnames(citylist)\nrownames(citylist)\ndimnames(citylist)\nlength(citylist)\nnrow(citylist)\nncol(citylist)\ndim(citylist)\nclass(citylist)\n\ncitylist[3,2,1]\ncitylist[1,1,1] # city1, district1, state1, \ncitylist[1,,] # city1 of all states & district\ncitylist[,,1] # State1\n\napply(citylist, c(1), sum) # citywise\napply(citylist, c(2), sum) # districtwise\napply(citylist, c(3), sum) # statewise\napply(citylist, c(2,3), sum) # all cities - district & statewise\napply(citylist, c(1,2,3), length) # statewise\n\n?xtabs\n\n## This is already a contingency table in array form.\nDF <- as.data.frame(UCBAdmissions)\n\n## Now 'DF' is a data frame with a grid of the factors and the counts\n## in variable 'Freq'.\nDF\n## Nice for taking margins ...\nxtabs(Freq ~ Gender + Admit, DF)\nxtabs( ~ Gender + Admit )\n## And for testing independence ...\nsummary(xtabs(Freq ~ ., DF))\n" }, { "path": "15a-DS/27b-arrays.R", "content": "# Arrays\n# 2 states ; Each State has 3 districts : Each District has 4 cities\nstate = c('state1', 'state2')\ndistrict = c('district1','district2', 'district3')\ncity = c('city1', 'city2', 'city3', 'city4')\npincode = 101:124\ncitylist = array(pincode, c(4,3,2) , dimnames = list(city, district ,state))\ncitylist\ncolnames(citylist)\nrownames(citylist)\ndimnames(citylist)\nlength(citylist)\nnrow(citylist)\nncol(citylist)\ndim(citylist)\nclass(citylist)\n\ncitylist[3,2,1]\ncitylist[1,1,1] # city1, district1, state1, \ncitylist[1,,] # city1 of all states & district\ncitylist[,,1] # State1\n\napply(citylist, c(1), sum) # citywise\napply(citylist, c(2), sum) # districtwise\napply(citylist, c(3), sum) # statewise\napply(citylist, c(2,3), sum) # all cities - district & statewise\napply(citylist, c(1,2,3), length) # statewise\n\n" }, { "path": "15a-DS/27d-arrays.R", "content": "# Arrays\n# 2 states ; Each State has 3 districts : Each District has 4 cities\nstate = c('state1', 'state2')\ndistrict = c('district1','district2', 'district3')\ncity = c('city1', 'city2', 'city3', 'city4')\npincode = 101:124\ncitylist = array(pincode, c(4,3,2) , dimnames = list(city, district ,state))\ncitylist\n\nstate1 district1 city1 101\nstate1 district1 city2 102\nstate = paste('state',1:4,sep='')\nstate2= rep(state,6)\n\n\ncolnames(citylist)\nrownames(citylist)\ndimnames(citylist)\nlength(citylist)\nnrow(citylist)\nncol(citylist)\ndim(citylist)\nclass(citylist)\n\ncitylist[3,2,1]\ncitylist[1,1,1] # city1, district1, state1, \ncitylist[1,,] # city1 of all states & district\ncitylist[,,1] # State1\n\napply(citylist, c(1), sum) # citywise\napply(citylist, c(2), sum) # districtwise\napply(citylist, c(3), sum) # statewise\napply(citylist, c(2,3), sum) # all cities - district & statewise\napply(citylist, c(1,2,3), length) # statewise\n\n?xtabs\n\n## This is already a contingency table in array form.\nDF <- as.data.frame(UCBAdmissions)\n\n## Now 'DF' is a data frame with a grid of the factors and the counts\n## in variable 'Freq'.\nDF\n## Nice for taking margins ...\nxtabs(Freq ~ Gender + Admit, DF)\nxtabs( ~ Gender + Admit )\n## And for testing independence ...\nsummary(xtabs(Freq ~ ., DF))\n" }, { "path": "15a-DS/30c-basicdatatypes.R", "content": "#Basic Data Types\n\n#Numeric ----\n#Decimal values are called numerics in R. It is the default computational data type. If we assign a decimal value to a variable x as follows, x will be of numeric type.\n\nx = 10.5 # assign a decimal value \nx # print the value of x [1] 10.5 \nclass(x) # print the class name of x [1] \"numeric\"\n\n#Furthermore, even if we assign an integer to a variable k, it is still being saved as a numeric value.\n\n(k = 1) #[1] 1 \nclass(k) # print the class name of k [1] \"numeric\"\nis.integer(k) # is k an integer? FALSE\n\n#INTEGER----\n#invoke as.integer function \n\n(y = as.integer(3) ) # print the value of y [1] 3 \nclass(y) # print the class name of y \n[1] \"integer\" \nis.integer(y) # is y an integer? [1] TRUE\n\n#coerce a numeric value into an integer with the same as.integer function.\nas.integer(3.14) # coerce a numeric value [1] 3\n\n#And we can parse a string for decimal values in much the same way.\nas.integer(\"5.27\") # coerce a decimal string [1] 5\n\nas.integer(TRUE) # the numeric value of TRUE [1] 1 \nas.integer(FALSE) # the numeric value of FALSE [1] 0\n\n#CHARACTER----\n#A character object is used to represent string values in R. We convert objects into character values with the as.character() function:\n \n(x = as.character(3.14) ) # print the character string [1] \"3.14\" \nclass(x) # print the class name of x [1] \"character\"\n\nfname = \"Joe\"; lname =\"Smith\" \npaste(fname, lname) #concatenate character values\n\n\n\n#LOGICAL----\n#A logical value is often created via comparison between variables.\nx = 1; y = 2 # sample values \nz = x > y # is x larger than y? \nz # print the logical value [1] FALSE \nclass(z) # print the class name of z [1] \"logical\"\n\n#Standard logical operations are \"&\" (and), \"|\" (or), and \"!\" (negation).\nu = TRUE; v = FALSE \nu & v # u AND v [1] FALSE \nu | v # u OR v [1] TRUE \n!u # negation of u [1] FALSE\n\n\n#COMPLEX\n#A complex value in R is defined via the pure imaginary value i.\n(z = 1 + 2i) # create a complex number, print the value of z 1+2i \nclass(z) # print the class name of z [1] \"complex\"\n" }, { "path": "15a-DS/30d-ds1.R", "content": "# data strucutres \n\n#Vectors----\nv1 = c(1,2,3,4,5,4)\nclass(v1)\nv1\nv1[v1==4]=7\nv1\n?class\nv2= c('A','B','C','D','e')\nv2\nclass(v2)\nv3 =c(TRUE,FALSE,T,F)\nclass(v3)\nv2[3]\nv3[4]\nv4= c(1,2, 'A')\nv4\nv5 = c(T,2,F,'A')\nv5\nclass(v4)\nv6=c('A', 2,'B')\nv6\nv1\nset.seed(100)\nv7 = rnorm(100, mean=60, sd=15)\nv7\nmean(v7)\nsd(v7)\n?rnorm\nhist(v7)\nhist(v7, breaks=15, col=1:15)\nhist(v7,freq=F, col='yellow', main='Pune Univ', sub='MBA' , xlab='Marks', ylab='Density of Counts', xlim=c(0, 100), ylim=c(0,0.03))\nlines(density(v7), col='red', lty=4, lwd=3)\n\n#title(main=' Pune University', sub='MBA')\n\n#Matrix----\nlength(100:123)\n(m1 =matrix(100:123, ncol=6, byrow = T))\n(m2 =matrix(c(100:123), ncol=4, byrow = T))\nm2[1 , ]\nm2[ , 2]\nm2[1:2 , ]\nm2\nm2[, c(1,4)]\nm2[c(2,6), c(1,4)] # 2 & 6th row, 1st & 4th col\nm2[2:6, 1:4]\ncolSums(m2)\ncolMeans(m2)\nrowSums(m2)\nrowMeans(m2)\nmean(m2[,2])\n\n#x\n\n\n\n#Array----\nlength(100:123)\n4*3*2\n(a1 = array(100:123, dim=c(4,3,2)))\n?array\n(loc = paste('loc', 1:4,sep='-'))\n(product = paste('p', 1:3,sep='@'))\n(coy = paste('coy', 1:2,sep='%'))\ndimnames(a1) = list(loc, product, coy)\na1\napply(a1,1, sum)\napply(a1,2, sum)\napply(a1,c(1,2), sum)\napply(a1,c(2,3), sum)\napply(a1,c(1,3), sum)\n\napply(a1,3, sum)\nsum(a1)\n\n#Data Frame----\n(rollno = 1:30)\n(sname = paste('student',1:30,sep=''))\n(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))\n(marks = floor(rnorm(30, 50,10)))\n(marks2 = ceiling(rnorm(30,40,5)))\n(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))\n\ndf1= data.frame(rollno, sname, gender, marks, marks2, course)\nhead(df1,n=3)\nhead(df1,3)\n?head\ntail(df1)\nstr(df1)\nclass(df1)\nsummary(df1)\ndf1\ndf1$gender\ndf1$marks\ndf1[ , c(2,4)]\ndf1[1:10 ,]\ndf1[ marks > 50 & gender=='F', c('rollno', 'sname')]\ndf1[ marks > 50 & gender=='F', c(1,2)]\ndf1[ marks > 50 | gender=='F', ]\nnames(df1) # names of columns\ndim(df1) #Dimensions\n\naggregate(df1$marks, by=list(df1$gender), FUN=max)\ndf2 = aggregate(cbind(marks,marks2) ~ gender + course, data=df1, FUN=max)\ndf2\npie(x=df2$marks)\nbarplot(df2$marks, col=1:2)\n?aggregate\n\n#using dplyr package\nlibrary(dplyr)\n\n\n#\n\n\n#List----\n\n#Factors----\n\n(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))\ntable(grades)\nclass(grades)\ngradesF = factor(grades)\ngrades\ngradesF\ngradesF1 = factor(grades, ordered=T)\ngradesF1\ngradesF2 = factor(grades, ordered=T, levels=c('D','C','B','A'))\ngradesF2\nx = 1:4\nsort(x)\nrev(x)\n\n\n\n\n?list\n(v1 = 1:30)\n(m1 = matrix(1:24, nrow=6))\n(a1 = array(1:24, dim=c(4,3,2)))\n(df1 = data.frame(rollno=c(1:5), sname=c(paste('S',1:5, sep='-'))))\nclass(v1); class(m1); class(a1); class(df1)\nL1 = list(v1, m1, a1, df1) # List\nL1\nL1[1]; L1[2] ; L1[3]; L1[4]\n(v2 = c('a','b'))\nL2 = list(v2, L1)\nL2\n\n" }, { "path": "15a-DS/30e-datatypes.R", "content": "# R Programming\n?mean\nx <- c(0:10, 50)\nx\nx[c(seq(1,12,2))]\n?c\nseq(2,100,2)\n\n?seq\nxm <- mean(x)\nxm\nmean(x, trim = 0.10)\n\n# Scalar\n(x <- 1)\nx = 1\n# Vector\nc(1:5)\nx = c(1,2,3,4,10,5,5,3,2,5,7,7,8,9)\nmean(x); median(x)\nsort(x)\n\ntable(x)\n\nlibrary(modeest)\nmlv(x, method = \"lientz\", bw = 0.2)\n\nratings =c(rep('Good',5), rep('Poor',3))\nratings\ntable(ratings)\nbarplot(table(ratings))\n\n\n\nrollno = c(10,22,44,23)\nsales = runif(12, 50, 100)\nsales\nplot(sales, type='b')\n?plot\n\n?runif\n\n\nstr(x)\ny = c('abhinav', 'Rishi', 'Shreya')\nstr(y)\nz = c(TRUE, FALSE, TRUE, FALSE)\nstr(z)\n\n\nabinav=c(1,3,6,8,6,3,100,rep(NA,5))\nabinav\nstr(abinav)\nis.na(abinav)\nanyNA(abinav)\nmean(abinav,na.rm=T)\n\nmean(x=abinav)\nmean(x=abinav,trim=0.4, na.rm=T)\nx = c(TRUE, FALSE, TRUE)\nmean(x)\nmean(c(1,0,1))\n\n#Categorical Data\nproduct = c('maggi', 'topramen', 'hakka')\nproduct\nshirtcolor = c('green', 'red', 'blue', 'black')\nshirtcolor\nrating = c('Excellent', 'Very Good', 'Good', 'Average', 'Poor' )\nrating\nstr(rating)\nperformance = c( 1, 2, 3, 4, 5)\nstr(performance)\n\ndecision = c('buy', 'sell')\ndecisionTF = c(TRUE, FALSE )\nstr(decision); str(decisionTF)\ngrades = c('A','C','B','D','E','B')\ngrades\ngrades1 = factor(grades, ordered=T, levels=c('E','D','A','B','C'))\ngrades1\nitemssold = c(10, 20, 30, 25)\n\n\n#Nominal Data\nproduct\n\n#Ordinal Data\nperformance\n#need to create order\nperformance=c(1,2,5,2,5,2,5,2,5)\nlength(performance)\nperformance\nstr(performance)\nperformance[9] = 100\nperformance\n\nperformance1 = factor(performance, ordered=TRUE, levels=c(4,2,5,1,3,8,100))\nperformance1[1] = 100\n\ntable(performance1)\nperformance1\n\n\n\nrating\nrev(rating)\nrating1 = factor(rating, ordered=T, levels=rev(rating))\nrating1\n\ngrades\ngrades1 = factor(grades, ordered=T, levels=c('F','E','D','C','B', 'A'))\ngrades1\nsummary(grades1)\n\n# -----\n#vectors, factors\n#list matrix\n?matrix\nmdat <- matrix(c(1,2,3,11,12,13), nrow = 2, ncol = 3)\nmdat[1,1] ='dhiraj' \nmdat \n(x = 1:24) \n(matx = matrix(x, nrow=6, byrow=TRUE)) \n?data.frame\n\n\n\n(salemon = month.abb[1:12])\nsalemon = factor(salemon, ordered=T,levels=month.abb[1:12])\nsalemon\n(sales1 = runif(12, min=50, max=100))\nsalemon; sales1\n(adamt = runif(12, min=12, max=25))\n(salesmonthwise = data.frame(salemon, sales, adamt))\nrownames(salesmonthwise) = month.abb[1:12]\nsalesmonthwise\ncolnames(salesmonthwise) = c('col1','col2','col3')\n\nsalesmonthwise[1:5,1:3]\n?head\nhead(salesmonthwise,n=3)\nhead(salesmonthwise[c(1,3)],n=3)\nhead(salesmonthwise[c('col1','col2')],n=3)\nsalesmonthwise[c('Jan'),c('col1','col2')]\nsalesmonthwise[salesmonthwise$col3 < 20,c(1,2,3)]\n\ncolSums(salesmonthwise[-1])\nrowSums(salesmonthwise[-1])\n\n# Day3\n\n# Data Properties\nset.seed(10)\n?rnorm\nsales1 = rnorm(100, mean=50, sd=10)\nsales1\nrange(sales1)\nhist(sales1)\nplot(density(sales1))\nabline(v=50)\n\nset.seed(1)\nsales3 = runif(10,1,5)\nsales3\nmean(sales3)\nset.seed(1)\nsales4 = runif(10,1,5)\nsales4\nmean(sales4)\n\n\nnames = c('A', 'B', 'C')\n?sample\nv1= sample(x=names, size=4, replace = FALSE, prob = c(.2,.3,.5))\nv1\ntable(v1)\n\n\nsales2 = rnorm(100, mean=60, sd=12)\nsales2\nplot(density(sales2))\nabline(v=60)\nsales3= c(1,5,2,5,2,3,6,7,8,4,5)\nsales3\n#\n\n\nshirtcolor = c('green', 'red', 'blue', 'black')\nsellitems = sample(shirtcolor, 20, replace=T, prob=c(.4,.3,.2,.1)) \nsellitems\nsummary(sellitems)\ntable(sellitems)\n\ngrades = c('A','C','B', 'D','E', 'B', 'A', 'B', 'A', 'C','F')\ngrades1 = factor(grades, ordered=T, levels=c('F','E','D','C','B', 'A'))\ngrades1\n\n\n#Location / Central Tendency\nmean(sales1)\nmean(sales2)\nmean(sellitems)\nmean(grades)\nmean(sales3)\n\n#Middle Values\nmedian(sales1)\nmedian(sales2)\nmedian(sales3)\nsort(sales3)\nquantile(sales3)\n\nmedian(sellitems)\nmedian(grades1)\n\n\n#Mode\nmode(sales1) # error\nlibrary(modeest)\nmlv(sales1, method='mfv')\nmlv(sales2, method='mfv')\nmlv(sales3, method='mfv')\nmlv(sellitems, method='mfv')\nmlv(grades1, method='mfv')\ntable(sale3)\ntable(sellitems)\ntable(grades1)\nwhich.max(table(grades1))\n\n\n\n# Graphical Way\nmean(sales1)\nhist(sales1)\nplot(density(sales1))\nabline(v=50)\n\nsales2 = rnorm(100, mean=60, sd=12)\n\nsales2\nrange(sales2)\nhist(sales2, prob=T)\n?hist\nlines(density(sales2), col='red')\nabline(v=60, col=\"green\")\nabline(h=.02, col=\"red\")\n\n# Combine Plots\nplot(density(sales1), col='green', ylim=c(0,0.05))\nlines(density(sales2), col=\"blue\", lwd=2)\nabline(v=c(50,60), col=c('green', 'blue'), lwd=3)\n" }, { "path": "15a-DS/33b-df.R", "content": "# Data Frames\n#data frame\n#(rollno= c('MBA-01', 'MBA-02'))\n(rollno= paste('MBA',1:60,sep='-'))\n\n#(name = c('Student1', 'Student2'))\n(name= paste('Student',1:60,sep='-'))\n\nset.seed(1234)\n(gender = sample(c('M','F'),size=60, replace=T, prob=c(.4, .6)))\ntable(gender)\nprop.table(table(gender))\n\n(marks = rnorm(60, mean=60, sd=15))\n(grades= sample(c('Excellent','Good','Poor'), size=60, replace = T, prob=c(.2, .3, .5)))\ntable(grades)\ngrades = factor(grades, levels=c('Poor','Good','Excellent'), ordered=T)\ngrades\nsummary(grades)\n\n\ndf = data.frame(rollno, name, gender,marks, grades, stringsAsFactors = F)\ndf\nhead(df, n=3); tail(df)\nstr(df)\nsummary(df)\ndf$gender =factor(df$gender)\nstr(df)\nsummary(df)\ndim(df)\nclass(df)\n\naggregate(df$marks, by=list(df$gender), FUN=sum)\naggregate(df$marks, by=list(df$gender), FUN=mean)\naggregate(marks ~ gender, data=df, FUN=mean)\naggregate(marks ~ gender + grades, data=df, FUN=mean)\n\ndf$marks2 = runif(60,50,100)\nhead(df)\n\naggregate(cbind(marks, marks2) ~ gender , data=df, FUN=mean)\n\n\ndf\ndf[ df$gender=='M',]\ndf[ df$gender=='M', c(1,2,4)]\ndf[ df$gender=='M', c('rollno', 'gender')]\n\n\n" }, { "path": "15a-DS/33c-df.R", "content": "# Data Frames" }, { "path": "15a-DS/35a-lists.R", "content": "# Lists\n# \ng =\"My First List\"\nh = c(25, 26,18,39)\nj = matrix(1:10,nrow=2)\nk = c('one','two','three')\nmylist = list(title=g, ages=h, j, h)\nmylist\nmylist[2]\nmylist[[2]]\nmylist[['ages']]\nmylist$ages\n" }, { "path": "15a-DS/35b-lists.R", "content": "# Lists\n\ng =\"My First List\"\nh = c(25, 26,18,39)\nj = matrix(1:10,nrow=2)\nk = c('one','two','three')\nmylist = list(title=g, ages=h, j, h)\nmylist\nmylist[2]\nmylist[[2]]\nmylist[['ages']]\nmylist$ages\n\n" }, { "path": "15a-DS/35e-lists.R", "content": "# Lists\n# \ng =\"My First List\"\nh = c(25, 26,18,39)\nj = matrix(1:10,nrow=2)\nk = c('one','two','three')\nmylist = list(title=g, ages=h, j, h)\nmylist\nmylist[2]\nmylist[[2]]\nmylist[['ages']]\nmylist$ages\n" }, { "path": "15a-DS/38a-factors.R", "content": "# Factors\n# \n#nominal\ndiabetes = c('Type1','Type2','Type1','Type1')\ngender = c( 1,2,1,1,2) # 1-M, 2-F\n#ordinal\nclsposn = c(1 , 2 , 3 , 1)\nstatus = c('Poor','Improved', 'Excellent', 'Improved')\n\ndiabetes ; gender ; clsposn ; status\nclass(diabetes) ; class(gender) ; class(clsposn) ; class(status)\n\n# order of values don't matter\nfdiabetes = factor(diabetes)\nclass(fdiabetes)\nfdiabetes\nsummary(fdiabetes)\nsummary(diabetes)\n\n# add another Type; converts into character\nfdiabetes = c(fdiabetes, 'Type3')\nfdiabetes\nclass(fdiabetes)\n\n# Add another Level Properly\nfdiabetes = factor(diabetes)\nlevels(fdiabetes)\nfdiabetes = factor(fdiabetes, levels = c(levels(fdiabetes), 'Type3'))\nfdiabetes\nlevels(fdiabetes) = c(levels(fdiabetes),'Type4')\nfdiabetes\nlevels(fdiabetes)[5] = 'Type5'\nfdiabetes\nsummary(fdiabetes)\nfdiabetes[4] = c('Type3')\nfdiabetes\nfdiabetes[5] = c('Type6') # Invalid as Type6 is not a level\nfdiabetes\nfdiabetes[1] = c('Type6') # Invalid changing an existing value to non existent level\nfdiabetes\n#NA created\n#\nsum (is.na (fdiabetes)) \ntable (fdiabetes, exclude=NULL)\ntable (fdiabetes, exclude=NA)\n\nis.na(fdiabetes)\n# Remove Null Values\nfdiabetes = na.omit(fdiabetes)\nfdiabetes\nfdiabetes[1] = c('Type4')\nsummary(fdiabetes)\n\n#Drop unused Levels\ndroplevels.factor(fdiabetes)\nfdiabetes\n\nlevels(fdiabetes) = factor(fdiabetes)\nfdiabetes\n\n\ntable(fdiabetes)\ndroplevels(table(fdiabetes))\n# 1-M, 2-F\ngender = c( 1,2,1,1,2)\nfgender = factor(gender, levels=c(1,2),\n labels=c('M', 'F') )\ngender ; fgender\n\nhead(airquality)\naq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9]))\naq\nsummary(airquality$Month)\nsummary(aq$Month)\naq <- subset(aq, Month != \"Jul\")\nsummary(aq$Month)\ntable( aq $Month)\ntable(droplevels(aq)$Month)\ndroplevels(aq)$Month\n\n\n# Nominal - Integer Values\ngender = c( 1,2,1,1,2) # 1-M, 2-F\nfgender = factor(gender)\nfgender\nsummary(fgender)\n\nfgender2 = factor(gender, levels=c(1,2), labels=c('M','F'))\nfgender2\nsummary(fgender2)\n\n\n\n#ordinal\nclsposn = c(1 , 2 , 3 , 1)\nclass(clsposn)\nsummary(clsposn)\nmode(clsposn)\nfclsposn = factor(clsposn)\nfclsposn\n\nsummary(fclsposn)\nlevels(fclsposn)\n\nfclsposn2 = factor(clsposn, levels=c(1,2,3,4,5), ordered=T, \n labels=c('First','Second','Third','Fourth', 'Fifth'))\nfclsposn2\nsummary(fclsposn2)\nlevels(fclsposn2)\n\n\nfclsposn3 = factor(fclsposn2,levels(fclsposn2)[c(5,3,4,2,1)])\nfclsposn3\nprint(levels(fclsposn3))\n\nfclsposn2\nfclsposn4 <- factor(fclsposn2, levels=rev(levels(fclsposn2)))\nfclsposn4\n\nclass(fclsposn4)\n\n\nstatus = c('Poor','Improved', 'Excellent', 'Improved')\n\nfstatus = factor(status)\nfstatus\n# unordered Factors can be releveled - Making Poor first\nfstatus2 = relevel(fstatus, 'Poor')\nfstatus2\n\nfstatus3 = factor(status, ordered=T, \n levels=c('Poor', 'Improved', 'Excellent') )\nfstatus3\nsummary(fstatus3)\nplot(fstatus3)\nplot(status)\nplot(table(status))\n\n\nlibrary(gdata)\nf <- factor(c(\"A\", \"B\", \"C\", \"D\"))[1:3]\nf\ndrop.levels(f)\n\nl <- list(f=f, i=1:3, c=c(\"A\", \"B\", \"D\"))\nl\ndrop.levels(l)\n\ndf <- as.data.frame(l)\ndf\nstr(df)\nstr(drop.levels(df, reorder = F))\n" }, { "path": "15a-DS/38b-factors.R", "content": "# Factors\n# \n#nominal\ndiabetes = c('Type1','Type2','Type1','Type1')\ngender = c( 1,2,1,1,2) # 1-M, 2-F\n#ordinal\nclsposn = c(1 , 2 , 3 , 1)\nstatus = c('Poor','Improved', 'Excellent', 'Improved')\n\ndiabetes ; gender ; clsposn ; status\nclass(diabetes) ; class(gender) ; class(clsposn) ; class(status)\n\n# order of values don't matter\nfdiabetes = factor(diabetes)\nclass(fdiabetes)\nfdiabetes\nsummary(fdiabetes)\nsummary(diabetes)\n\n# add another Type; converts into character\nfdiabetes = c(fdiabetes, 'Type3')\nfdiabetes\nclass(fdiabetes)\n\n# Add another Level Properly\nfdiabetes = factor(diabetes)\nlevels(fdiabetes)\nfdiabetes = factor(fdiabetes, levels = c(levels(fdiabetes), 'Type3'))\nfdiabetes\nlevels(fdiabetes) = c(levels(fdiabetes),'Type4')\nfdiabetes\nlevels(fdiabetes)[5] = 'Type5'\nfdiabetes\nsummary(fdiabetes)\nfdiabetes[4] = c('Type3')\nfdiabetes\nfdiabetes[5] = c('Type6') # Invalid as Type6 is not a level\nfdiabetes\nfdiabetes[1] = c('Type6') # Invalid changing an existing value to non existent level\nfdiabetes\n#NA created\n#\nsum (is.na (fdiabetes)) \ntable (fdiabetes, exclude=NULL)\ntable (fdiabetes, exclude=NA)\n\nis.na(fdiabetes)\n# Remove Null Values\nfdiabetes = na.omit(fdiabetes)\nfdiabetes\nfdiabetes[1] = c('Type4')\nsummary(fdiabetes)\n\n#Drop unused Levels\ndroplevels.factor(fdiabetes)\nfdiabetes\n\nlevels(fdiabetes) = factor(fdiabetes)\nfdiabetes\n\n\ntable(fdiabetes)\ndroplevels(table(fdiabetes))\n# 1-M, 2-F\ngender = c( 1,2,1,1,2)\nfgender = factor(gender, levels=c(1,2),\n labels=c('M', 'F') )\ngender ; fgender\n\nhead(airquality)\naq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9]))\naq\nsummary(airquality$Month)\nsummary(aq$Month)\naq <- subset(aq, Month != \"Jul\")\nsummary(aq$Month)\ntable( aq $Month)\ntable(droplevels(aq)$Month)\ndroplevels(aq)$Month\n\n\n# Nominal - Integer Values\ngender = c( 1,2,1,1,2) # 1-M, 2-F\nfgender = factor(gender)\nfgender\nsummary(fgender)\n\nfgender2 = factor(gender, levels=c(1,2), labels=c('M','F'))\nfgender2\nsummary(fgender2)\n\n\n\n#ordinal\nclsposn = c(1 , 2 , 3 , 1)\nclass(clsposn)\nsummary(clsposn)\nmode(clsposn)\nfclsposn = factor(clsposn)\nfclsposn\n\nsummary(fclsposn)\nlevels(fclsposn)\n\nfclsposn2 = factor(clsposn, levels=c(1,2,3,4,5), ordered=T, \n labels=c('First','Second','Third','Fourth', 'Fifth'))\nfclsposn2\nsummary(fclsposn2)\nlevels(fclsposn2)\n\n\nfclsposn3 = factor(fclsposn2,levels(fclsposn2)[c(5,3,4,2,1)])\nfclsposn3\nprint(levels(fclsposn3))\n\nfclsposn2\nfclsposn4 <- factor(fclsposn2, levels=rev(levels(fclsposn2)))\nfclsposn4\n\nclass(fclsposn4)\n\n\nstatus = c('Poor','Improved', 'Excellent', 'Improved')\n\nfstatus = factor(status)\nfstatus\n# unordered Factors can be releveled - Making Poor first\nfstatus2 = relevel(fstatus, 'Poor')\nfstatus2\n\nfstatus3 = factor(status, ordered=T, \n levels=c('Poor', 'Improved', 'Excellent') )\nfstatus3\nsummary(fstatus3)\nplot(fstatus3)\nplot(status)\nplot(table(status))\n\n\nlibrary(gdata)\nf <- factor(c(\"A\", \"B\", \"C\", \"D\"))[1:3]\nf\ndrop.levels(f)\n\nl <- list(f=f, i=1:3, c=c(\"A\", \"B\", \"D\"))\nl\ndrop.levels(l)\n\ndf <- as.data.frame(l)\ndf\nstr(df)\nstr(drop.levels(df, reorder = F))\n" }, { "path": "15a-DS/38c-factors.R", "content": "#Factor -----\n\n(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))\ntable(grades)\n(gradesFactor = factor(grades))\n(gradesFactorOrdered = factor(grades, ordered=T))\n(gradesFactorOrderedLevels = factor(grades, ordered=T, levels=c('D','C','B','A')))\nclass(grades)\nclass(gradesFactorOrdered)\nclass(gradesFactorOrderedLevels)\n" }, { "path": "15a-DS/38e-factors.R", "content": "# Factors\n# \n#nominal\ndiabetes = c('Type1','Type2','Type1','Type1')\ngender = c( 1,2,1,1,2) # 1-M, 2-F\n#ordinal\nclsposn = c(1 , 2 , 3 , 1)\nstatus = c('Poor','Improved', 'Excellent', 'Improved')\n\ndiabetes ; gender ; clsposn ; status\nclass(diabetes) ; class(gender) ; class(clsposn) ; class(status)\n\n# order of values don't matter\nfdiabetes = factor(diabetes)\nclass(fdiabetes)\nfdiabetes\nsummary(fdiabetes)\nsummary(diabetes)\n\n# add another Type; converts into character\nfdiabetes = c(fdiabetes, 'Type3')\nfdiabetes\nclass(fdiabetes)\n\n# Add another Level Properly\nfdiabetes = factor(diabetes)\nlevels(fdiabetes)\nfdiabetes = factor(fdiabetes, levels = c(levels(fdiabetes), 'Type3'))\nfdiabetes\nlevels(fdiabetes) = c(levels(fdiabetes),'Type4')\nfdiabetes\nlevels(fdiabetes)[5] = 'Type5'\nfdiabetes\nsummary(fdiabetes)\nfdiabetes[4] = c('Type3')\nfdiabetes\nfdiabetes[5] = c('Type6') # Invalid as Type6 is not a level\nfdiabetes\nfdiabetes[1] = c('Type6') # Invalid changing an existing value to non existent level\nfdiabetes\n#NA created\n#\nsum (is.na (fdiabetes)) \ntable (fdiabetes, exclude=NULL)\ntable (fdiabetes, exclude=NA)\n\nis.na(fdiabetes)\n# Remove Null Values\nfdiabetes = na.omit(fdiabetes)\nfdiabetes\nfdiabetes[1] = c('Type4')\nsummary(fdiabetes)\n\n#Drop unused Levels\ndroplevels.factor(fdiabetes)\nfdiabetes\n\nlevels(fdiabetes) = factor(fdiabetes)\nfdiabetes\n\n\ntable(fdiabetes)\ndroplevels(table(fdiabetes))\n# 1-M, 2-F\ngender = c( 1,2,1,1,2)\nfgender = factor(gender, levels=c(1,2),\n labels=c('M', 'F') )\ngender ; fgender\n\nhead(airquality)\naq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9]))\naq\nsummary(airquality$Month)\nsummary(aq$Month)\naq <- subset(aq, Month != \"Jul\")\nsummary(aq$Month)\ntable( aq $Month)\ntable(droplevels(aq)$Month)\ndroplevels(aq)$Month\n\n\n# Nominal - Integer Values\ngender = c( 1,2,1,1,2) # 1-M, 2-F\nfgender = factor(gender)\nfgender\nsummary(fgender)\n\nfgender2 = factor(gender, levels=c(1,2), labels=c('M','F'))\nfgender2\nsummary(fgender2)\n\n\n\n#ordinal\nclsposn = c(1 , 2 , 3 , 1)\nclass(clsposn)\nsummary(clsposn)\nmode(clsposn)\nfclsposn = factor(clsposn)\nfclsposn\n\nsummary(fclsposn)\nlevels(fclsposn)\n\nfclsposn2 = factor(clsposn, levels=c(1,2,3,4,5), ordered=T, \n labels=c('First','Second','Third','Fourth', 'Fifth'))\nfclsposn2\nsummary(fclsposn2)\nlevels(fclsposn2)\n\n\nfclsposn3 = factor(fclsposn2,levels(fclsposn2)[c(5,3,4,2,1)])\nfclsposn3\nprint(levels(fclsposn3))\n\nfclsposn2\nfclsposn4 <- factor(fclsposn2, levels=rev(levels(fclsposn2)))\nfclsposn4\n\nclass(fclsposn4)\n\n\nstatus = c('Poor','Improved', 'Excellent', 'Improved')\n\nfstatus = factor(status)\nfstatus\n# unordered Factors can be releveled - Making Poor first\nfstatus2 = relevel(fstatus, 'Poor')\nfstatus2\n\nfstatus3 = factor(status, ordered=T, \n levels=c('Poor', 'Improved', 'Excellent') )\nfstatus3\nsummary(fstatus3)\nplot(fstatus3)\nplot(status)\nplot(table(status))\n\n\nlibrary(gdata)\nf <- factor(c(\"A\", \"B\", \"C\", \"D\"))[1:3]\nf\ndrop.levels(f)\n\nl <- list(f=f, i=1:3, c=c(\"A\", \"B\", \"D\"))\nl\ndrop.levels(l)\n\ndf <- as.data.frame(l)\ndf\nstr(df)\nstr(drop.levels(df, reorder = F))\n" }, { "path": "15b-DM/21b-rep.R", "content": "# Value generation with : repeat\n#rep\n?rep\n#rep(x, ...)\n#rep.int(x, times)\n#rep_len(x, length.out)\n#times -repeat each element/ vector\n#length.out - length of the output vector\n#each - x is repeated each times\n\n#1, 10 times\nrep(1,times=10)\n#1,2,3 : 5 times\nrep(c(1,2,3),times=5)\n#1- 4 times, 2-4 times, 3 - 4 times\nrep(c(1,2,3),each=4)\n#(1-4times : 2-times...) - 5 times\nrep(1:3, each=4, times=5)\n#length of vector 20: 1,2,3,4,5 - 5 times \n(y1=rep(c(1,2,3,4,5),length.out=20, times=3))\nlength(y1)\n\nrep(1:3, each=4, len=20) #len is abbreviation of length.out \n\n#1-4times, 2-3 times, 3-2 times\nrep(1:3, times=c(4,3,2))\n" }, { "path": "15b-DM/21c-seq.R", "content": "# Value Generation with seq\n\n#seq\nseq(from = 1, to = 1, by = ((to - from)/(length.out - 1)),\n length.out = NULL, along.with = NULL)\n#by - number: increment of the sequence.\n#length.out - desired length of the sequence. A non-negative number, which for seq and seq.int will be rounded up if fractional.\n# along.with - take the length from the length of this argument.\n(v1 = c(8,9,10))\nseq(1, 50, by=3)\nseq(1, by=3, along.with=v1)\nseq(from=0, to=50, length.out=5) #divide range 0 to 50 into 5 equal parts\n\n\n\n#seq_along : no in seq as per another vector\n(a <- c(8, 9, 10))\n(b <- c(9, 10))\n(c <- 10)\n\n#seq_along----\nseq_along(a)\n# [1] 1 2 3\nseq_along(b)\n# [1] 1 2\nseq_along(c)\n# [1] 1\n\nx= c(8,9,10)\n#seq_along(x) takes a vector for x, and it creates a sequence upto the count of elements in the vector.\n#seq_along(x) is the same as seq_len(length(x))\nseq_along(x)\nseq_len(length(x))\n\n#seq_len----\n#seq_len(y) takes numeric for y, it creates a sequence upto the number y\n\n#seq_len(x) is the same as seq_len(x[1])\nseq_len(x)\nseq_len(x[1]) #no in seq upto first position of vector\n#When using seq_len, it will give you:\n#A warning, if x has more than 1 element.\n#An error, if x[1] is not numeric.\n#An error, if x is a frame.\n\n\n#sample\n\n#months\nmonth.abb[1:5]\nmonth.name[1:5]\n\n#alphabets\nLETTERS[1:10]\nletters[5:15]\n" }, { "path": "15b-DM/21g-replicate.R", "content": "# Replicates\n\n?replicate\n\n# 5 values/rows, 4 columns\nset.seed(1234)\n(m1=cbind(rnorm(5), rnorm(5),rnorm(5),rnorm(5)))\nset.seed(1234)\n(m2=replicate(4, rnorm(5)))\nsum(m1)\nsum(m2)\n" }, { "path": "15b-DM/21g-seqdates.R", "content": "#Value Generation - year/months\n\nmonth.abb[1:12]\nmonth.name[1:10]\nmonth\nseq.Date(from='2018/01/07', to='2018/01/07')\nmonth[4]\n\nseq(from, to, by, length.out = NULL, along.with = NULL, ...)\n#from / to - start date(reqd), end date(Optional)\n#by\tincrement of the sequence. Optional. See ‘Details’.\n#length.out\t- integer, optional. Desired length of the sequence.\n#along.with\t- take the length from the length of this argument.\n\n## first days of years\nseq(as.Date(\"2000/1/1\"), as.Date(\"2018/7/1\"), \"years\")\n## by month\nseq(as.Date(\"2018/1/1\"), by = \"month\", length.out = 7)\n## quarters\nseq(as.Date(\"2018/1/1\"), as.Date(\"2018/7/1\"), by = \"quarter\")\nseq(as.Date(\"2018/7/1\"), as.Date(\"2018/7/22\"), by = \"day\")\n" }, { "path": "15b-DM/22b-letters.R", "content": "# Value generation - letters\n\n#LETTERS and letters (for uppercase and lowercase respectively).\nletters[1:4]\nLETTERS[seq( from = 1, to = 10 )]\nletters[seq( from = 1, to = 10 )]\nletters[1:10]\nletters[c(1,5,10,26)]\n" }, { "path": "15b-DM/25b-interval.R", "content": "# Find Interval\n\ntable<- data.frame(population=c(100, 300, 5000, 2000, 900, 2500), \n habitat=c(1,2,3,4,5,6))\ntable\ntable$size <- findInterval(table$population, c(0, 500, 1000, 2000, 3000, 5000), rightmost.closed = TRUE)\ntable\n" }, { "path": "15b-DM/25c-midpoint.R", "content": "# Mid Point\n\n\ndf<- data.frame(lon1=c(-4.568,-4.3980), \n lat1=c(59.235,56.369), \n lon2=c(-5.123,-4.698), \n lat2=c(60.258,59.197) )\ndf\ndf$midlong <- apply(df[,c(1,3)], 1, mean) \ndf$midlat <- apply(df[,c(2,4)], 1, mean) \ndf \n\n\n\n#\nlibrary(geosphere) \n#library(midpoint)\n\n(p1 <- matrix(c(df$lon1,df$lat1), ncol=2 ) )\n(p2 <- matrix(c(df$lon2,df$lat2), ncol=2 ) )\n\n(midpoint <- as.data.frame(midPoint(p1,p2) ) )\n" }, { "path": "15b-DM/27b-recode-car.R", "content": "# Recode\n# \n#using car package : recode\n(marks = ceiling(runif(10,40,100)))\n#The lo keyword tells recode to start the range at the lowest value.\n#The hi keyword tells recode to end the range at the highest value.\n\n(markscode <- car::recode(marks, \"lo:70=1; 71:80=2;else = 3\"))\n\n\n#now code 1 to 6\n(markscode = car::recode(markscode, \"1=6\"))\n\n# straight method\nmarkscode[markscode==6] = 5\nmarkscode\n\n" }, { "path": "15b-DM/29b-subset.R", "content": "# Subset\n#subset( ) function is the easiest way to select variables and observations.\n\nmtcars\n# using subset function \nnewdata = subset(mtcars, mpg >=20 & mpg < 30, select=c(mpg, disp))\nnewdata\n#we select cars with mpg >= 20 and < 10 \n#we keep variables mpg and disp\n\n# using subset function (part 2)\nnames(mtcars)\nhead(mtcars)\nnewdata = subset(mtcars, cyl == 6 & disp > 150,select=mpg:wt)\nnewdata\n\nnewdata2 = mtcars[ order(mtcars$mpg), c('mpg','wt')]\nhead(newdata2)\n\nsubset(airquality, select = Ozone:Wind)\nsubset(airquality, Temp > 80, select = c(Ozone, Temp))\nsubset(airquality, Temp > 80, select = - Temp)\n\nsubset(airquality, Month == 8 & Temp > 90) # better\nairquality[airquality$Month == 8 & airquality$Temp > 90, ]\n\n" }, { "path": "15b-DM/29c-split1.R", "content": "# Splitting Data Frames\n\nmtcars\n# display first 2 cols, split by course\nsplit(mtcars[c(1,2)], mtcars$am)\n\n\n" }, { "path": "15b-DM/29d-splitdata.R", "content": "# Split data\n#partitioning data(train/test) is different from split - \nmtcars\nnames(mtcars)\ndf=mtcars[,c('mpg','cyl','disp','hp','wt','gear','am')]\n\n#Case-1----\nnames(df)\n\n#1a-split by column values\nsplit(df, df$am)\n\n#1b- split -no of rows randomly selected rows between 1 to 5\nsplit(df, sample(1:5, nrow(df), replace=T))\n#sample() will assign equal probability to each group.\n\n\n#Case-2----\n#split into bins , 4 here\nnrow(df)\nstopifnot(nrow(df) %% 4 == 0)\ndf2 <- df[order(runif(nrow(df))), ]\n(bins <- rep(1:4, nrow(df2) / 4))\nsplit(df2, bins)\n\n#Case-3----\n#library(githubinstall)\n#githubinstall(\"minDiff\")\nlibrary(minDiff)\n#split into groups with criteria avg values of mpg, wt\nnames(df)\ndf3 <- create_groups(df, criteria_scale = c(\"mpg\",'wt'), sets_n = 4, repetitions = 10)\ndf3\ndf3[order(df3$newSet),]\n\n#now split the data and find mean values\n(groups = split(df3, df3$newSet))\nsapply(groups, FUN=function(i) sapply(i[, c('mpg','wt')], mean))\nsapply(groups, function(x) colMeans(x[c('mpg','wt')]))\n\n#rejoin the groups\nlibrary(dplyr)\nbind_rows(groups) %>% group_by(newSet) %>% summarise_all(funs(mean))\n\n#bind the splitted lists\nlibrary(data.table)\nrbindlist(groups)\n\n\n#Case-4----\nset.seed(1234)\nsplit(df, sample(rep(1:2, 16)))\n\n\n\n#Case-5 ----\nlibrary(plyr)\ndlply(df, \"am\", identity)\n#spliting by multiple columns values\ndlply(df,c( \"am\",'cyl'), identity)\n\ndlply(df, c(\"cyl\", \"am\"))\n\n\n#Case-6----\n#slow\ndf %>% group_by(am) %>% do(data = (.)) %>% select(data) %>% map(identity) \n" }, { "path": "15b-DM/29e-partitiondata.R", "content": "# Data Set Partition\n\n#random basis\n\ndf = mtcars\nnrow(df)\n\nsample(x=1:32, size=10, replace=F)\nsample(x=32, size=10, replace=F)\n\nindexnos = sample(x=1:32, size=10, replace=F)\ntrain1= df[indexnos,]\n\n#other indexnos\ntest1 = df[-indexnos]\ntrain1 \ntest1\n\n#----\n## 75% of the sample size\nsmp_size <- floor(0.75 * nrow(df))\n\n## set the seed to make your partition reproducible\nset.seed(123)\nseq_len(nrow(df)) #generate a seq of number upto no of rows\nindexnos = sample(seq_len(nrow(df)), size = smp_size)\n\ntrain2 = df[indexnos, ]\ntest2 = df[-indexnos, ]\ntrain2\ntest2\n\n#Using Package\nrequire(caTools)\nset.seed(121) \n#sample.split - preserves relative ratios in each set\ngender = sample(c('M','F'), size=100, replace=T, prob=c(.6,.4))\nmarks = floor(rnorm(100,mean=50,sd=10))\nstudents = data.frame(gender, marks)\ntable(students$gender)\nprop.table(table(students$gender))\n\nsamples = sample.split(students$gender, SplitRatio = 0.75)\ntable(samples)\nset1 = subset(students, samples == TRUE)\ntable(tra$gender)\nprop.table(table(set1$gender))\n\nset2 = subset(students, samples == FALSE)\nprop.table(table(set2$gender))\n\n#proportion is almost same\n\n\n#Method\nlibrary(caret)\ntable(df$am)\nprop.table(table(df$am))\n?createDataPartition\ntrainnos = createDataPartition(y=df$am , p=0.7,list=FALSE)\ntrainnos\ntrain4 = df[trainnos,]\ntest4 = df[-trainnos,]\ntrain4\ntest4\nprop.table(table(train4$am))\nprop.table(table(test4$am))\n\n\n\nlibrary(dplyr)\nset.seed(275) #to get repeatable data\n\ndata.train <- sample_frac(Default, 0.7)\n\ntrain_index <- as.numeric(rownames(data.train))\ndata.test <- Default[-train.index, ]\n" }, { "path": "15b-DM/31b-rowcol1.R", "content": "#Row Column Summary\n# array dimnames = list(row.names, col.names, matrix.names)\n\n#Eg10\na4 = array(1:24, dim=c(4,3,2), dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) )\na4\n#, , s1 d1 d2 d3 c1 1 5 9 c2 2 6 10 c3 3 7 11 c4 4 8 12\n#, , s2 d1 d2 d3 c1 13 17 21 c2 14 18 22 c3 15 19 23 c4 16 20 24\nx = a4\n\n\nrowMeans(x,dims=1)\nrowMeans(x,dims=2)\n\nrowSums(x,dims=1)\nrowSums(x,dims=2)\n\ncolMeans(x,dims=1)\ncolMeans(x,dims=2)\n\ncolSums(x,dims=1)\ncolSums(x,dims=2)\n\n# Data Frame and Matrix\ndf1 = data.frame(sub1=10:1, sub2=5, sub3=rep(c(5,6),5), sub4=seq_len(length.out=10))\ndf1\nx = df1\n\ndata = c\nm1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),seq_len(length.out=10)), byrow=F, ncol =4)\ncolnames(m1) = c('sub1','sub2','sub3','sub4')\nrownames(m1) = paste('R',1:10,sep='')\n\nx=m1\n\nrowMeans(x,dims=1)\nrowMeans(x,dims=2)\n\nrowSums(x,dims=1)\nrowSums(x,dims=2)\n\ncolMeans(x,dims=1)\ncolMeans(x,dims=2)\ncolMeans(x,dims=0)\n\ncolSums(x,dims=1)\ncolSums(x,dims=2)\n\ncol\n\n\n" }, { "path": "15b-DM/33b-sortorder.R", "content": "#Sort Order and Rank\n\nset.seed(123)\n#Vector\n(marks = ceiling(runif(11,5,10)))\n\nsort(marks)\nsort(marks, decreasing = TRUE)\nrev(sort(marks))\n\norder(marks) #index values\nmarks[order(marks)] #this is marks\nmarks[order(-marks)] #this is marks\n\n\n#DF\n(df1=mtcars)\ndf[order(mtcars$mpg),c(1:5)]\ndf[order(mtcars$mpg, decreasing=T),c(1:5)]\ndf[order(mtcars$cyl, -mtcars$mpg),c('cyl','mpg','wt','hp')]\n\n#rank\nrank(marks)\n" }, { "path": "15b-DM/33c-order.R", "content": "#Sort Order Rank\n" }, { "path": "15b-DM/33c-sortorderrank.R", "content": "# Sort Order Rank\n\nset.seed(123)\n(marks = ceiling(runif(11,5,10)))\n\n# sort\nsort(marks)\n\nsort(marks, decreasing = TRUE)\nsort(-marks) # wrong\nrev(sort(marks))\n\n# NA values and sort later ----\n\n# order\norder(marks) # indices\nmarks\nmarks[c(6,7,10)]\nmarks[order(marks)] # similar to sort(x)\norder(marks, decreasing = T)\norder(-marks) # decreasing, this works\n\nlength(marks)\nmarks2 = marks\n#insert missing value\nmarks2[5] = NA # check with NA Value : display, position\nmarks2\norder(marks2) # NA is last\norder(marks2, na.last = NA) # removed\nlength(marks) # length is same\nis.na(marks2) # logical vector if NA\nanyNA(marks2) # is there is any NA\n\norder(marks2, na.last = NA) # 5 removed\norder(marks2, na.last = FALSE) # 5 is first\norder(marks2, na.last = TRUE) # 5 is last\nlength(order(marks2, na.last = TRUE)) # 5 is last\n\nlength(order(marks2, na.last = NA)) # 5 is not there\n\n# rank handles tied values\n?rank\nmarks # 10-3 times, 8 & - 2 times\nsort(marks)\ntable(marks)\n\n#[1] 6 6 7 8 8 9 9 10 10 10 10\n# 1 2 3 4 5 6 7 8 8 9 10 \nrank(marks) # ties method = avg, first, last, random , max, min\n?rank\ntable(marks) # check freq\nmarks\nrank(marks, ties.method='first') # 2-6s - 1,2\n#6-1, 6-2, 7-3, 8-4, 8-5, 9-6, 9-7, 10-8, 10-9, 10-10, 10-11\nrank(marks, ties.method='average') # default 2-6's - 5\n#all 6s : (1+2)/2 = 3/2=1.5 ; (8+9+10+11)/4= 37/3 = 9.5\nrank(marks, ties.method='max') # 2 6's - 7\n#Two 6 -1,2 -> 2 (max): Four 10s - 8,9,10,11 -> 11\nrank(marks, ties.method='random') # put them in unique order\nsort(rank(marks, ties.method='random'))\n\ntable(marks)\nmarks\n\nmarks2 # Now check with NA values\n\nsort(marks2) #NA value missing\nlength(marks2)\nlength(sort(marks2)) # 1 less\n\nmarks2\n\n# NA values - sort and rank\nsort(marks2, na.last=TRUE) # NA last - 11\nsort(marks2, na.last=FALSE) # NA first - 11\nsort(marks2, na.last='keep') # Nothing . not shown - 10\nlength(sort(marks2, na.last='keep'))\n\nmarks2\nlength(marks2)\nrank(marks2, na.last=TRUE) # No effect - avg; NA - largest 11\nlength(rank(marks2, na.last=TRUE)) # No effect\n\nrank(marks2, na.last=FALSE) # No effect - Avg; NA - smallest 1\nrank(marks2, na.last='keep') # NA shown as NA\n\n\nmean(marks2) # does not work with NA values\nmean(marks2, na.rm=T) # add na.rm=T\n" }, { "path": "15b-DM/33d-rank.R", "content": "#Sort Order Rank\n" }, { "path": "15b-DM/34b-castmelt1.R", "content": "# Data Frame\n\n# Wide Format to Long Format \nrollno = c(17010,17045,17012,17087,17057,17056,17032,17084,17078,17018,17013)\nlname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni')\nname = c('Achal','Apoorva','Goldie','Hitesh','Kaustav','Meena','Rashmi','Shruti','Shubham','Vijay','Lalit')\n\ncourse = c('PGDDS','PGDDS','PGDDS','PGDDS','MSCDS','PGDDS','MSCDS','PGDDS','PGDDS','PGDDS','PGDDS')\nstats = ceiling(runif(11,50,100))\nexcel = ceiling(runif(11,50,100))\nrpgm = ceiling(runif(11,50,100))\n\nstudents = data.frame(rollno, name, course, stats, excel, rpgm, stringsAsFactors = F)\nstudents\nstudents$course = factor(students$course)\nstr(students)\n# data ready\nhead(students)\n\nlibrary(reshape2)\nstudents2 <- melt(students, id.vars=c(\"rollno\",'name','course'),\n measure.vars=c(\"stats\", \"excel\", \"rpgm\"),\n variable.name=\"subject\", value.name=\"marks\")\nstudents2\n\n\n# Long to Short\nstudents3 = students2\nhead(students3)\nstudents4 = dcast(students3, rollno + name + course ~ subject, value.var='marks')\nstudents4 \n\nstudents5 = dcast(students3, course ~ subject, value.var = 'marks', mean)\nstudents5\n\nstudents6 = dcast(students3, name ~ subject, value.var = 'marks', mean)\nstudents6 # name to factor\n\nstudents7 = dcast(students3, rollno + name ~ course + subject, value.var='marks')\nstudents7 # not meaninful here\n\n?dcast\n\n\n?cast\n\n\n\n# Examples\n#Air quality example\nnames(airquality) <- tolower(names(airquality))\nhead(airquality)\naqm <- melt(airquality, id=c(\"month\", \"day\"), na.rm=TRUE)\nhead(aqm)\n\nacast(aqm, day ~ month ~ variable)\nacast(aqm, month ~ variable, mean)\nacast(aqm, month ~ variable, mean, margins = TRUE)\ndcast(aqm, month ~ variable, mean, margins = c(\"month\", \"variable\"))\n\nlibrary(plyr) # needed to access . function\nacast(aqm, variable ~ month, mean, subset = .(variable == \"ozone\"))\nacast(aqm, variable ~ month, mean, subset = .(month == 5))\n\n#Chick weight example\nnames(ChickWeight) <- tolower(names(ChickWeight))\nchick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE)\n\ndcast(chick_m, time ~ variable, mean) # average effect of time\ndcast(chick_m, diet ~ variable, mean) # average effect of diet\nacast(chick_m, diet ~ time, mean) # average effect of diet & time\n\n# How many chicks at each time? - checking for balance\nacast(chick_m, time ~ diet, length)\nacast(chick_m, chick ~ time, mean)\nacast(chick_m, chick ~ time, mean, subset = .(time < 10 & chick < 20))\n\nacast(chick_m, time ~ diet, length)\n\ndcast(chick_m, diet + chick ~ time)\nacast(chick_m, diet + chick ~ time)\nacast(chick_m, chick ~ time ~ diet)\nacast(chick_m, diet + chick ~ time, length, margins=\"diet\")\nacast(chick_m, diet + chick ~ time, length, drop = FALSE)\n\n#Tips example\ndcast(melt(tips), sex ~ smoker, mean, subset = .(variable == \"total_bill\"))\n\nff_d <- melt(french_fries, id=1:4, na.rm=TRUE)\nacast(ff_d, subject ~ time, length)\nacast(ff_d, subject ~ time, length, fill=0)\ndcast(ff_d, treatment ~ variable, mean, margins = TRUE)\ndcast(ff_d, treatment + subject ~ variable, mean, margins=\"treatment\")\nif (require(\"lattice\")) {\n lattice::xyplot(`1` ~ `2` | variable, dcast(ff_d, ... ~ rep), aspect=\"iso\")\n}\n" }, { "path": "15b-DM/34c-castmelt2.R", "content": "# Cast and Melt\n#https://www.r-statistics.com/2012/01/aggregation-and-restructuring-data-from-r-in-action/\n \n(rollno = rep(c(10,11,12,13),4))\nsname = rep(c('Achal','Apoorva','Goldie','Hitesh'),4)\n(examunit = rep(c('U1', 'U2', 'U3', 'U4'),each= 4))\n#(examunit = rep(c(1,2,3,4), each=4))\nset.seed(1234)\n(rpgm = ceiling(rnorm(4*4,60,10)))\nset.seed(1234)\n(sql = ceiling(rnorm(4*4,65,10)))\ndf1 = data.frame(rollno, sname, examunit, rpgm, sql)\ndf1 # 16 rows, 4 students, 4 Units, 2 subjects(cols)\n\n# Melt the data : wide to Long (more rows)\n\nmd = reshape::melt(df1, id=c('rollno', 'sname', 'examunit'))\nhead(md)\nmd\nmd[md$rollno==13,]\nsummary(md)\n\n# Cast\n# rollno + sname -> subjectwise mean\nreshape::cast(md, rollno + sname ~ variable, mean)\nreshape::cast(md, rollno + sname ~ variable, sum)\n\n\n\n# unitwise mean\nreshape::cast(md, examunit ~ variable)\nreshape::cast(md, examunit ~ variable, sum)\n\nhead(md)\n# unitwse mean per student\n# cat(names(md))\n# aggregate(md[-2], by=list(rollno,exam), value , FUN=mean )\n# ?aggregate\ndf1[df1$rollno==10,]\n# rollno -> subjectwise summary mean\nreshape::cast(md, rollno ~ variable, mean)\n# rollno -> unitwise summary (all subjects)\nreshape::cast(md, rollno ~ examunit, mean) # correct\n# alongwith names\nreshape::cast(md, rollno + sname ~ examunit, mean) # correct\n# rollno -> unitwise summary all subject\nreshape::cast(md, rollno ~ examunit, mean, value='value') # this is also correct\n# rollno - subject - units....\nreshape::cast(md, rollno + variable ~ examunit, FUN=mean, value='value')\n\n# initial data -> melt -> cast -> initial \nreshape::cast(md, rollno + sname + examunit ~ variable, sum)\nlength(md); nrow(md)\nhead(md)\n# Without aggregation ----\nreshape::cast(md, rollno + sname + examunit ~ variable)\nreshape::cast(md, rollno + sname + variable ~ examunit)\nreshape::cast(md, rollno + sname ~ variable ~ examunit) # list type\n\nlibrary(reshape)\n# Better way to do it\nmd2 = reshape::melt(df1, id.vars=c(\"rollno\",'sname', 'examunit'),\n measure.vars=c(\"rpgm\", \"sql\"),\n variable.name=\"subject\", value.name=\"marks\")\nstr(md2)\nhead(md2) # with proper labeling\n\nreshape::cast(md2, rollno + sname ~ subject, mean)\nreshape::cast(md2, rollno + sname ~ subject, sum) # for all units\nreshape::cast(md2, examunit ~ subject, mean, value='marks') # for all students\nreshape::cast(md2, examunit ~ subject, sum, value='marks') # for all students\n\n" }, { "path": "15b-DM/37a-mtcars-subset.R", "content": "#Subset - mtcars\n\n# Subset\n#subset( ) function is the easiest way to select variables and observations.\n\nmtcars\n# using subset function \n(newdata = subset(mtcars, mpg >=20 & mpg < 30))\n(newdata = subset(mtcars, mpg >=20 & mpg < 30, select=c(mpg, disp)))\n(newdata <- subset(mtcars, cyl == 6 & disp > 150,select=mpg:wt))\n" }, { "path": "15b-DM/37b-duplicates1.R", "content": "# Find Duplicates\n\nx <- c(9:20, 1:5, 3:7, 0:8)\n## extract unique elements\nx\n\n(xu <- x[!duplicated(x)])\n\n## similar, same elements but different order:\n(xu2 <- x[!duplicated(x, fromLast = TRUE)])\n\n## xu == unique(x) but unique(x) is more efficient\nstopifnot(identical(xu, unique(x)),\n identical(xu2, unique(x, fromLast = TRUE)))\n\nduplicated(iris)[140:143]\n\nduplicated(iris3, MARGIN = c(1, 3))\nanyDuplicated(iris) ## 143\n\nanyDuplicated(x)\nanyDuplicated(x, fromLast = TRUE)\n\n\nsubset(df, !duplicated(subset(df, select=c(allrl, RAW.PVAL))))" }, { "path": "15b-DM/37c-unique.R", "content": "# Unique" }, { "path": "15b-DM/38b-scale1.R", "content": "# Scale\n#standardization of the data\n#scale is generic function whose default method centers and/or scales the columns of a numeric matrix.\n#https://t4tutorials.com/decimal-scale-normalization-in-data-mining/\n\nx = c(1,3,5,7,9,2,4,6,8,10)\n(x1 = scale(x, scale=F, center=F))\nas.vector(x1)\n(xc = scale(x, scale=F, center=T))\nas.vector(xc)\n(xc = scale(x, scale=F, center=5.5))\nxc1 = scale(x, scale=F, center=4)\nas.vector(xc1)\n\n(xs = scale(x, scale=T, center=F))\nas.vector(round(xs,2))\n(xs1 = (x - mean(x)/ sd(x)))\n(rmse = sqrt(sum(x^2)/(length(x)-1)))\nround((xs2 = (x/ rmse)),2)\n(xs3 = (x))\n\n(xsc = scale(x, scale=T, center=T))\nas.vector(round(xsc,2))\n\ncbind(x, round(x1,2), round(xc,2), round(xs,2),round(xsc,2))\nx\n(x2 = x - mean(x))\nround((x2 - mean(x2))/ sd(x2),2)\n\n\nas.vector(xs)\nround((zscore = (x - mean(x))/ sd(x)),2)\n\nas.vector(round(xsc,2))\n\n(xc= scale(x, scale=F, center=4))\ncbind(x,-4,xc)\n(xs = scale(x, scale=T, center=F))\nx\n(xs = scale(x, scale=T, center=F))\n(xsm = sqrt(sum(x^2)/(length(x)-1)))\n\ncbind(x,round(xs,2), round(xsm,2))\n\n\nround(scale(x,center=min(x),scale=diff(range(x)))[,1],2)\nround((x - min(x))/ (max(x) - min(x)),2)\n(xC = x - mean(x))\nround((xS = xC / sqrt(sum(xC^2)/(length(xC)-1))),2)\n\n#Scale by SD - if there are 2 columns\n\nscale(x, center = FALSE, scale = apply(x, 2, sd, na.rm = TRUE))\n\ndata(women)\nwomen\nscale(women)\n#Scale by SD - if there are 2 columns\nscale(women, center = TRUE, scale = apply(women, 2, sd, na.rm = TRUE))\n\n(ws = scale(women, center = T, scale = c(max(women$height), max(women$weight))))\ncbind(women, round(ws,2))\n\n(ws2 = scale(women, center = T, scale = c(100, 200)))\ncbind(women, round(ws2,2))\n\n\n\n\n\n\n\n\n\nx\n\nset.seed(1234)\nx = rnorm(100,50,10)\nscale(x)\nhist(x)\n\nhist(scale(x))\n\nscale(x, center=T)\nscale(x, center=2)\nplot(scale(x, center=2)) # irrelevant\n\n(marks1 = ceiling(runif(10,50,100)))\n(marks2 = ceiling(runif(10,50,150)))\nscale(marks1)\nscale(marks1)[,1]\nround(scale(marks1)[,1],2)\nround(scale(marks2)[,1],2)\n\nround(scale(marks1)[,1],2)\n\nround(scale(marks1, center=F)[,1],2)\nround(scale(marks2, center=F)[,1],2) \n\nround(scale(marks1, center=c(0,1))[,1],2)\n\n\n\n\n#\ns = sort(rexp(100))\ns\nrange01 <- function(x){(x-min(x))/(max(x)-min(x))}\nrange01(s)\n\n\n#\nlibrary(\"scales\")\ns=marks1\nrescale(s)\n\nrescale(marks1)\nrescale(s, to=c(0,10))\nrescale(s, from=c(0, max(s)))\n\nx=marks1\nround(scale(x,center=min(x),scale=diff(range(x)))[,1],3)\n\n\n\n# Example Data\nx = sample(-100:100, 50)\n\n#Normalized Data\nnormalized = (x-min(x))/(max(x)-min(x))\n\n# Histogram of example data and normalized data\npar(mfrow=c(1,2))\nhist(x, breaks=10, xlab=\"Data\", col=\"lightblue\", main=\"\")\nhist(normalized, breaks=10, xlab=\"Normalized Data\", col=\"lightblue\", main=\"\")\npar(mfrow=c(1,1))\n\n\nx <- matrix(1:10, ncol = 2)\nx\n(centered.x <- scale(x, scale = FALSE))\ncentered.scaled.x <- scale(x, center = TRUE, scale = TRUE)\ncov(centered.scaled.x)\n" }, { "path": "15b-DM/41b-randnos1.R", "content": "# Random Numbers\n\n\n#Case ----\nrandu$x\ny <- qunif(ppoints(length(randu$x)))\nqqplot(randu$x,y)\nrandu\n" }, { "path": "15b-DM/41c-randnos.R", "content": "# Random Numbers\n\n#sample\nsample(x=1:10, size=3)\nsample(x=c(1.5, 2.5, 3, 6.7), size=2)\n\nsample(x=1:10, size=15) #error size is more than sample size\nsample(x=1:10, size=15, replace=T) \nsample.int(n=100, size=30, replace=F) #nos between 1:n\n\n\n#sample from vector values\nmonth.abb[1:12]\nsample(x=month.abb[1:12], size=3, replace=T)\nsample(x=c('M','F'), size=10, replace=T)\n" }, { "path": "15b-DM/42b-normdist.R", "content": "# Normal Distribution Data\n\nrnorm(10)\n?rnorm\n#rnorm(n, mean = 0, sd = 1)\n\n(x=rnorm(10, mean=5, sd=2))\n" }, { "path": "15b-DM/45b-forloop1.R", "content": "# For Loop\n\nfor ( i in 1:10)\n{ print(i)\n}\n\nprint(1:10)\n\n\n# controlling loop\n#\nfor (i in 1:10)\n{ \n if ( i==3)\n {\n next\n }\n print(i)\n}\n# 3 was not printed\n\n\nx = 0\nfor (i in 1:10)\n{\n x = x + i\n print(c(i,x))\n}\n\nx = 1\nfor (v in c(3, 4, 7, 2))\n{\n x = x*v\n}\nx\n\nx = c(1,2,3)\nfor (v in c(4:6))\n{\n print(c(x, v))\n}\nx\n\n#create vector elements\nx <- NULL\nfor(i in 1:5){\n x[i] <- i * 2\n}\nx\n#substitute 2 and 3 position with zero values\nfor(i in 2:3){\n x[i] <- 0\n}\nx\n\n\n\n\nx = c(1,2,3)\nfor (v in c(4:6))\n{\n x = x * v\n print(x)\n}\nx\n\n#-----------\ny = 0\nfor (x in c(1,2,3)) {\n for (v in c(4:6))\n {\n y[x] = x * v\n print(paste(x,v, x))\n }\n}\nx;v; y\n\n# ---------\nx = 0\nfor (i in seq(4))\n{\n for (j in seq(i))\n {\n x = x+i*j\n print(paste(i,j,x))\n }\n}\nx\n1*1 + 2*1 + 2*2 + 3*1 + 3*2 + 3*3 + 4*1 + 4*2 + 4*3 + 4*4 #65\n\n1*1 + 1*2 + 1*3 + 1*4\n2*1 + 2*2 + 2*3 + 2*4\n3*1 + 3*2 + 3*3 + 3*4\n4*1 + 4*2 + 4*3 + 4*4\n\n# ---------\n\n#http://dept.stat.lsa.umich.edu/~kshedden/Courses/Stat600/Notes/R_introduction.pdf\n\nx <- c(2,5,3,9,8,11,6);\ncount <- 0 ;\nfor (val in x) \n{ if(val %% 2 == 0)\n count = count+1 ; } \nprint(count)\n" }, { "path": "15b-DM/45e-ifelse2.R", "content": "#if else\n<<<<<<< HEAD\nas.numeric(TRUE)\n\nas.numeric(FALSE)\n1==1\n1< 1\n\ntoCheck = 1\nif ( toCheck ==1) \n{ \n print(\"hello\")\n}\ntocheck=2\ntocheck\ncheck.bool <- function(x)\n{if ( x ==1) \n { \n print(\"hello\")\n } else\n { \n print(\"No is not 1\")\n }\n}\ncheck.bool(1)\ncheck.bool(0)\n\n\n\n# ifelse\n\nifelse(1 == 1, 'Yes', 'No')\n\n\n# Compound Test\n#\na = c(1,1,0,1)\nb = c(2,1,0,1)\nifelse( a== 1 & b == 1, 'Yes', 'No')\n# check each element of a with corr element of b\nifelse( a== 1 && b == 1, 'Yes', 'No')\n# only first element checked\n=======\n\nvar <- c(\"Private\", \"Private\", \"?\", \"Private\")\ndf <- data.frame(var)\ndf$var[which(df$var == \"?\")] = \"Private\"\ndf\n\n\n#\nL3 <- LETTERS[1:3]\ndat1 <- data.frame(x=rep(1,6),y=rep(1:3,2), fac=sample(L3, 6, replace=TRUE))\ndat1\n\ndat1[dat1$x==1 & dat1$y==1 , 1:2] <- NA\ndat1\n\n# Run from start\ncond <- with(dat1, x==1 & y==1)\ncond\nis.na(dat1$x) <- cond\nis.na(dat1$y) <- cond\ndat1\n\n\n#\n\ntable<- data.frame(population=c(100, 300, 5000, 2000, 900, 2500), \n habitat=c(1,2,3,4,5,6))\ntable1 = table\n## Solution 1 \ntable$size[table$population<500]<-1\ntable$size[table$population>=500 & table$population<1000]<-2\ntable$size[table$population>=1000 & table$population<2000]<-3\ntable$size[table$population>=2000 & table$population<3000]<-4\ntable$size[table$population>=3000 & table$population<=5000]<-5\ntable\n\n\n## Solution 2 Nested ifelse\ntable = table1\ntable$size1<-ifelse(table$population<500,1,\n ifelse(table$population>=500 & table$population<1000,2,\n ifelse(table$population>=1000 & table$population<2000,3,\n ifelse(table$population>=2000 & table$population<3000,4,5\n ))))\n\ntable\n\n#\n\ntable<- data.frame(population=c(100, 300, 5000, 2000, 900, 2500), \n habitat=c(1,2,3,4,5,6))\ntable\ntable$size <- findInterval(table$population, c(0, 500, 1000, 2000, 3000, 5000), rightmost.closed = TRUE)\ntable\n#>>>>>>> 50c058778f06851438b41e2cc02aecbdfb90c903\n\nx=c(1,NA,2,3)\ndplyr::if_else(x%%2==0, \"Multiple of 2\", \"Not a multiple of 2\", \"Missing\")\n\n\n#sqldf package----\ndf=data.frame(k=c(2,NA,3,4,5))\n#library(sqldf)\nsqldf::sqldf(\n \"SELECT *,\n CASE WHEN (k%2)=0 THEN 'Multiple of 2'\n WHEN k is NULL THEN 'Missing'\n ELSE 'Not a multiple of 2'\n END AS T\n FROM df\"\n)\n" }, { "path": "15b-DM/45v-switch1.R", "content": "#Switch\n\nuse.switch <- function(x)\n{\n switch(x,'a' = 'First','b' = 'Second','c' = 'Third','other')\n}\n\nuse.switch('a')\nuse.switch('b')\nuse.switch('other')\nuse.switch('6')\nuse.switch(6) # nothing returned\n\nswitch(2, 'Red', 'Blue', 'Green')\ncolors = c('Red', 'Blue', 'Green')\nswitch(2,colors) # does not work.. elements not vectorised\n\n#collist = as.list(colors)\n#collist\n#switch(2, collist)\n\n#-----\n\nxlist = c(\"A\",\"B\", \"C\")\nvalue = \"B\"\n?alist\n(myList <- alist(print(xlist[1]), print(xlist[2]), print(xlist[3]), print(\"Other !!!\")))\n(myList <- setNames(myList, c(xlist, ''))) # give names to list, last is blank\n\n?do.call\ndo.call(switch, c(EXPR = value, myList))\n#Examples:\nvalue = \"D\"\ndo.call(switch, c(EXPR = value, myList))\n#[1] \"Other !!!\"\nvalue = \"A\"\ndo.call(switch, c(EXPR = value, myList))\n#[1] \"A\"\nvalue = \"C\"\ndo.call(switch, c(EXPR = value, myList))\n#[1] \"C\"\nvalue = \"B\"\ndo.call(switch, c(EXPR = value, myList))\n#[1] \"B\"\n" }, { "path": "15b-DM/46b-withoutapply.R", "content": "# Before Apply\n#https://csgillespie.github.io/efficientR/programming.html#the-apply-family\n\n(m1 = matrix(1:20, nrow=4))\n\n#mean of each row: manually\nmean(m1[1,]); mean(m1[2,]); mean(m1[3,]); mean(m1[4,])\n#mean of each row: for loop\nfor (i in 1:nrow(m1)) {\n print(mean(m1[i,]))\n}\n#apply command\napply(m1,1,mean)\n\n#apply for columns\napply(m1, MARGIN = 2, mean)\n\n\n# with apply\napply(m1, 2, mean)\n\n# mean using addmargins\naddmargins(m1,1,mean)\naddmargins(m1,2,mean)\naddmargins(m1,c(1,2),mean)\naddmargins(m1)\n\n\n#using for loop\nfor( i in 1:4) {\n print(mean(m1[i,]))\n}\n\n\nnrow(m1)\nncol(m1)\n\nfor( i in 1:nrow(m1)) {\n print(paste0('Mean of row', i , ' is ', mean(m1[i,])))\n}\n\nfor( i in 1:ncol(m1)) {\n print(paste0('Mean of Col', i , ' is ', mean(m1[,i])))\n}\n\n# Better way to do\napply(m1, 1, mean) # row means\napply(m1, 2, mean) # col means\n\n" }, { "path": "15b-DM/46c-applyForCompare.R", "content": "#apply LAPPLY SAPPLY\n\n\n#lapply - When you want to apply a function to each element of a list in turn and get a list back.\n\n#This is the workhorse of many of the other *apply functions. \n(x <- list(a = 1, b = 1:3, c = 10:100) )\nlapply(x, FUN = length) \n\nlapply(x, FUN = sum) \n\n\n#sapply ------\n#sapply - When you want to apply a function to each element of a list in turn,\n#but you want a vector back, rather than a list.\n\n#If you find yourself typing unlist(lapply(...)), stop and consider sapply.\n\nx <- list(a = 1, b = 1:3, c = 10:100)\n#Compare with above; a named vector, not a list \nsapply(x, FUN = length) \n#a b c \n#1 3 91\n\nsapply(x, FUN = sum) \n#a b c \n#1 6 5005 \n\n#In more advanced uses of sapply it will attempt to coerce the result to a multi-dimensional array, if appropriate. For example, if our function returns vectors of the same length, \n#sapply will use them as columns of a matrix:\n \nsapply(1:5,function(x) rnorm(3,x))\n#If our function returns a 2 dimensional matrix, \n#sapply will do essentially the same thing,\n#treating each returned matrix as a single long vector:\n\nfunc2 = function(x){\n rnorm(3,x)\n}\nfunc2(1)\n\nsapply(1:5, func2)\n\nfor (i in 1:5) {\nprint(matrix(i,2,2))\n}\n\nmatrix(5, 2,2)\n\nsapply(1:5,function(x) matrix(x,2,2))\n#Unless we specify simplify = \"array\", in which case it will use\n#the individual matrices to build a multi-dimensional array:\n \nsapply(1:5,function(x) matrix(x,2,2), simplify = \"array\")\n#Each of these behaviors is of course contingent on our function returning \n#vectors or matrices of the same length or dimension.\n\n\n\nsummary <- function(x) {\n c(mean(x, na.rm = TRUE),\n median(x, na.rm = TRUE),\n sd(x, na.rm = TRUE),\n mad(x, na.rm = TRUE),\n IQR(x, na.rm = TRUE))\n}\n\nsummary <- function(x) {\n funs <- c(mean, median, sd, mad, IQR)\n lapply(funs, function(f) f(x, na.rm = TRUE))\n}\n\nlapply(mtcars, function(x) length(unique(x)))\nFilter(function(x) !is.numeric(x), mtcars)\nintegrate(function(x) sin(x) ^ 2, 0, pi)\n\nformals(function(x = 4) g(x) + h(x))\nenvironment(function(x = 4) g(x) + h(x))\n" }, { "path": "15b-DM/46d-applyfamily.R", "content": "# Apply Family\n" }, { "path": "15b-DM/46e-applytype.R", "content": "# Apply Functions\n\n#Data Structure\n# vector\n(v1 = 1:20)\n\n#Array ----\n(a1 = array(1:24, dim=c(4,3,2), \n dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) ))\n\n#Matrix ----\nseq_len(length.out=5)\nm1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),\n seq_len(length.out=10) ), byrow=F, ncol =4)\ncolnames(m1) = c('sub1','sub2','sub3','sub4')\nrownames(m1) = paste('R',1:10,sep='')\nm1\n\n#DataFrame ----\n(df1 = data.frame(sub1=10:1, sub2=5, \n sub3=rep(c(5,6),5), \n sub4=seq_len(length.out=10)))\n\n#List ----\n(list1 = list(sub1=10:1, sub2=rep(5,3), \n sub3=rep(c(5,6),4),\n sub4=seq_len(length.out=10)))\n\n#print objects\nv1\na1\nm1\ndf1\nlist1\n\n?melt\n#apply --------\n?apply\n#apply(X, MARGIN, FUN, ...)\n#X - array or matrix MARGIN=1 (Rows) ; 2 (Columns)\n#FUN = mean, median, UDF, .... ... Others eg. na.rm=T\n#Applies a function over the margins\n\n#array\n(X=a1)\napply(X, 1, sum) # dimension=1 : c\napply(X, 1, mean) # dimension=1 : c\nX\napply(X, 2, sum) # d\napply(X, 3, sum) # s\napply(X,c(1,2), sum) # c & d\napply(X,c(1,2,3), sqrt) # original : c d s \napply(X,c(1,2,3), sum)\napply(X,c(2,3), sum) # sum over 2 & 3rd dim : d & s\n\n\nclass(apply(X, 1, sum)) # Integer Vector\naddmargins(X,1, sum) # row margin=1\naddmargins(X,2, sum) # col margin=1\naddmargins(X,3, sum) # sum s1 + s2\naddmargins(X,c(1,2), sum) # row & col\naddmargins(X,c(1,3), sum) # sum rows of s1 & s2\naddmargins(X,c(2,3), sum) # sum cols of s1 & s2\nclass(X)\nm1\n(m2=matrix(1:12,nrow=3))\n\naddmargins(m2,c(1,2), \n FUN=list(list(sum,sd), \n list(mean,var,IQR, sum))) # row & col\nhead(mtcars)\n\n#https://www.r-bloggers.com/r-tutorial-on-the-apply-family-of-functions/\n \napply(X, 2, sum) # dim2 : d\n#d1 d2 d3 68 100 132\n\napply(X, 3, sum) # dim3 s\n#s1 s2 78 222\n\napply(X,c(3,2), sum) # s & d : see switch of col & row\n# d1 d2 d3 \n#s1 10 26 42 \n#s2 58 74 90\n\napply(X,c(2,3), sum) # d & s\n# s1 s2 \n#d1 10 58 \n#d2 26 74 \n#d3 42 90\n\napply(X,c(1,2), sum) # c & d\n# d1 d2 d3 \n#c1 14 22 30 \n#c2 16 24 32 \n#c3 18 26 34 \n#c4 20 28 36\n\napply(X,c(1,3), sum) # c & s\n# s1 s2 \n#c1 15 51 \n#c2 18 54 \n#c3 21 57 \n#c4 24 60\n\n##matrix------- 2 dim\n(X=m1)\napply(X, 1, sum) # R's\n#R1 R2 R3 R4 R5 R6 R7 R8 R9 R10\n#21 22 21 22 21 22 21 22 21 22\n\napply(X,2,sum) # sub's\n#sub1 sub2 sub3 sub4\n#55 50 55 55\n\napply(X,3,sum) # no 3rd dim\n#Error in if (d2 == 0L) { : missing value where TRUE/FALSE needed\n\napply(X,c(3,2), sum) #Error\napply(X,c(2,3), sum) #Error\n\napply(X,c(1,2), sum) # original matrix\n#sub1 sub2 sub3 sub4\n#[1,] 10 5 5 1\n\napply(X,c(1,3), sum) #error no 3rd dim\n\napply(X,sum) # error - margins not specified\n\n#dataframe -------------\n# this acts like a matrix as all values are numeric\n(X=df1)\napply(X, 1, sum)\n#[1] 21 22 21 22 21 22 21 22 21 22\n\napply(X,2,sum)\n#sub1 sub2 sub3 sub4 55 50 55 55\n\napply(X,3,sum)# Error in if (d2 == 0L) { : missing value where TRUE/FALSE needed\napply(X,c(3,2), sum) # Error\napply(X,c(2,3), sum) # Error\n\napply(X,c(1,2), sum) # original DF\n#sub1 sub2 sub3 sub4\n#[1,] 10 5 5 1\n\napply(X,c(1,3), sum) #error\n\napply(X,sum) #error no dime\n\n#NA Values\n\nm1[5] = NA\n# use other function also\nX=m1\napply(X,1,mean)\n#R1 R2 R3 R4 R5 R6 R7 R8 R9 R10\n#5.25 5.50 5.25 5.50 NA 5.50 5.25 5.50 5.25 5.50\n\napply(X,1,mean,na.rm=T) # better\n#R1 R2 R3 R4 R5 R6 R7 R8 R9 R10\n#5.25 5.50 5.25 5.50 5.00 5.50 5.25 5.50 5.25 5.50\n\napply(X,2,mean)\n#sub1 sub2 sub3 sub4\n#NA 5.0 5.5 5.5\n\napply(X,2,mean,na.rm=T) # better\n#sub1 sub2 sub3 sub4\n#5.44 5.00 5.50 5.50\n \n\n\n?lapply # apply over list or vector\n#lapply ---------\n#lapply(X, FUN, na.rm=T, ...)\n(X=list1)\nlapply(X,FUN=mean, na.rm=T)\ndim(list1) # list does not have any dimensions\napply(list1, FUN=sum) # this will give error for apply\n\n#Egs: NA Values\nX[[1]][5] = NA\nlapply(X,FUN=mean, na.rm=F)\n\n\nlapply(X,FUN=mean, na.rm=T)\n\n#Unlist - output as vector\nunlist(lapply(X,FUN=mean, na.rm=T))\n\n# simply output of list apply function\n# sapply ----- \n?sapply # returns a vector, matrix or array if appropriate\n#like lapply - simplify output\nsapply(list1,mean) # for list\nsapply(df1, mean)\nsapply(m1, mean)\na1\nsapply(a1, sum)\nsapply(a1, sum, simplify = 'array')\n\nsapply(v1, summary) # not relevant here\n\n# mapply -----\n# mapply - function to multiple list or vector args\n?mapply(a1, sum)\nmapply(rep, times=1:4, x = 1:4) # list\nrep(1:4,times=1:4)\nmapply(rep,1:5,4) # matrix 1 2 3 4\n\nmapply(FUN=rep, each=1, x = v1, y=v1)\nlist1\nrep(v1,each=1)\nrep(m1,each=1)\nrep(list1,each=1)\n\ne1 = 1:3\ne2 = 6:8\ne3 = 10:12\n#function : x * y\n#1st elements : a = e1 : FUN to each elements\n#2nd elements : b = e2: takes this as arguments\n#3rd elements : c = e3: takes this as arguments\n# arguments are recycled if required\nmapply(FUN=function(x,y) x+y, e1, e2 )\ne1+e2\nmapply(FUN=function(x,y,z) x+y+z, e1, e2 , e3)\ne1+e2+e3\n\nmapply(FUN=function(x,y,z) mean(x*y)/ z, e1, e2 , e3)\n\n\n\nmapply(FUN=mean, x = list(a1,m1))\nmean(a1) ; mean(m1)\n\n\nmapply(function(x, y) x + y,\n x=c(a = 1, b = 2, c = 3), # names from first\n y=c(A = 10, B = 0, C = -10))\n\nmapply(function(x, y) x ^ y, USE.NAMES = F, # no names\n x=c(a =2, b = 3, c = 4), \n y=c(A =2, B = 3, C = 4))\n\n\n# vapply ------\n?vapply # similar to sapply - pre spcfd return type & better speed\n\nvapply(X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE)\nvapply(df1, FUN=fivenum,\n c(Min. = 0, \"1st Qu.\" = 0, Median = 0, \"3rd Qu.\" = 0, Max. = 0))\nvapply(df1, FUN=mean, FUN.VALUE=c(mean=0))\nvapply(1:5, identity)\n## [1] 1 2 3 4 5\nvapply(integer(), identity)\n## integer(0\n\n\n\n#tapply\n(df1a = df1)\ndf1a$course = sample(c('MSC','PG'),10, prob=c(.7,.3), replace=T)\ndf1a\ntapply(X=df1a$sub1, INDEX=df1a$course, mean)\ndf1a$gender = sample(c('M','F'),10, prob=c(.6,.4), replace=T)\ntapply(X=df1a$sub1, INDEX=df1a$gender, mean)\n# 2 indices\ntapply(X=df1a$sub1, INDEX=list(df1a$gender,df1a$course), mean)\n# 3 indices\ndf1a$hostel = sample(c('Y','N'),10, prob=c(.5,.5), replace=T)\ntapply(X=df1a$sub1, INDEX=list(df1a$hostel), mean)\ntapply(X=df1a$sub1, INDEX=list(df1a$hostel,df1a$gender), mean)\ntapply(X=df1a$sub1, INDEX=list(df1a$hostel,df1a$course), mean)\n\ntapply(X=df1a$sub1, INDEX=list(df1a$hostel, df1a$gender,df1a$course), sum)\ntapply(X=df1a$sub1, INDEX=list(df1a$hostel, df1a$gender,df1a$course), length)\n\ndf1a\n\n?aggregate\naggregate(x, by, FUN, ..., simplify = TRUE, drop = TRUE)\naggregate(formula, data, FUN,\n subset, na.action = na.omit)\n#Aggregate --------\ncat(names(mtcars))\naggregate(mtcars[c('disp', 'hp', 'wt')],\n by=list(mtcars$cyl,mtcars$gear),\n FUN=mean, na.rm=TRUE, simplify = T)\naggregate(mtcars[c('disp', 'hp', 'wt')],\n by=list(Cylinder=mtcars$cyl,Gears=mtcars$gear),\n FUN=mean, na.rm=TRUE, simplify = T)[,1:3]\n\naggregate(mtcars[c('cyl', 'mpg','wt')],\n by=list(mtcars$cyl),\n FUN=mean)\naggregate(cyl ~ . , data= mtcars, FUN='mean') \n# formula variables ~ groupingvars\naggregate(. ~ cyl , data= mtcars, FUN='mean') \naggregate(. ~ cyl + am , data= mtcars, FUN='mean')\naggregate(. ~ cyl + am , data= mtcars, subset=(wt < 15), FUN='mean')\n\naggregate(am ~ cyl , data= mtcars, FUN='mean') #' FUN in single quote\n# cyl ~ all variables\naggregate(cyl ~ mpg + wt, data= mtcars, FUN='mean',\n subset=(wt > 4)) \naggregate(cyl ~ mpg + wt,\n data=subset(mtcars,wt>=5 & gear==c(3,4)), FUN='mean')\nsummary(mtcars$cyl)\n\nhead(df1a)\n# some colns are non-numeric, exclude them\naggregate(x=df1a[c('sub1', 'sub2', 'sub3')], by=list(df1a$course), FUN=mean)\n(dftemp = aggregate(x=df1a[c('sub1', 'sub2', 'sub3')], \n by=list(df1a$course, df1a$hostel, df1a$gender), FUN=mean))\ndftemp[order(dftemp$Group.1, dftemp$Group.2, dftemp$Group.3),]\n\n(dftemp = aggregate(x=df1a[c('sub1', 'sub2', 'sub3')], \n by=list(gpcourse = df1a$course, gphostel=df1a$hostel,gpgender= df1a$gender), FUN=mean))\ndftemp[order(dftemp$gpcourse, dftemp$gpgender, \n dftemp$gphostel),]\ndftemp[order(dftemp$gpcourse, dftemp$gpgender, \n dftemp$gphostel),][,c('gphostel', 'sub1')]\ndftemp[order(dftemp$gpcourse, dftemp$gpgender, \n dftemp$gphostel),][,c(3,2,1,4,5,6)]\n" }, { "path": "15b-DM/46f-while1.R", "content": "# while loop\n\nx = 1\nwhile (x <= 5)\n{\n print(x)\n x = x + 1\n}\n" }, { "path": "15b-DM/47b-apply1.R", "content": "#apply\n# https://davetang.org/muse/2013/05/22/using-aggregate-and-apply-in-r/\n#apply functions over array margins\n#apply(X, MARGIN, FUN, ...)\n#make up some dataframe\ndf <- data.frame(first = c(1:10), second = c(11:20))\ndf\napply(df , 2, mean) #col\napply(df, 1, mean) # row\n\n\ns1 = student1[c(17:23)]\napply(student, 1, mean) # not all are numeric\napply(s1,1,mean)\napply(s1,2,mean)\n\n\na4 = array(1:24, dim=c(4,3,2), dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) )\na4\n\nm1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),seq_len(length.out=10)), byrow=F, ncol =4)\ncolnames(m1) = c('sub1','sub2','sub3','sub4')\nrownames(m1) = paste('R',1:10,sep='')\n\ndf1 = data.frame(sub1=10:1, sub2=5, sub3=rep(c(5,6),5), sub4=seq_len(length.out=10))\ndf1\n\nX=m1\napply(X,1,sum)\napply(X,2,sum)\napply(X,3,sum)\napply(X,c(3,2), sum)\napply(X,c(2,3), sum)\n\napply(X,c(1,2), sum)\napply(X,c(1,3), sum)\napply(X,0,sum)\n\napply(X,sum)\napply(X,0,sum)\napply(X,4,sum)\napply(X,c(1,2),sum)\napply(X,c(1,2,3),sum)\n\n# NA Values\nm1[5] = NA\nX=m1\nm1\napply(X,1,mean)\napply(X,1,mean,na.rm=T)\napply(X,2,mean)\napply(X,2,mean,na.rm=T)\n" }, { "path": "15b-DM/47c-apply.R", "content": "# Apply Commands\n\n# apply Functions\n# #apply ----\n#create matrix####\nm1 = matrix(1:10,nrow=5)\nm1\n\n# create sample data of Matrix : Use ND data : 3 cols, 30 rows\nm2 <- matrix(data=cbind(rnorm(30, 0), rnorm(30, 2), rnorm(30, 5)), nrow=30, ncol=3)\nm2\n\n# apply is used for structured data - matrix, dataframe with same number of coln & rows\n# https://www.r-bloggers.com/using-apply-sapply-lapply-in-r/\n# apply Mean on Rowise on all 3 Column\nm1\napply(m1, 1, mean)\napply(m1, 2, mean)\n# apply function on particular values : count number of negative values\napply(m2, 2, function(x) length(x[x<0]))\nm2\napply(m2, 2, function(x) is.numeric(x))\napply(m2, 1, function(x) is.character(x))\napply(m2, 2, function(x) is.vector(x))\n\napply(m2, 2, function(x) mean(x[x>0]))\n\n# sapply\nsapply(1:3, function(x) x^2)\n\nsapply(m[1,], function(x) x^2)\nsapply(m[,2], function(x) x^2)\n\n# lapply - return list rather than a vector\nlapply(1:3, function(x) x^2)\nunlist(lapply(1:3, function(x) x^2))\n\nmean(m[,1])\nmean(m[,3])\n\n#https://www.r-bloggers.com/the-apply-command-101/\n\n# tricks\nsapply(1:3, function(x) mean(m[,x]))\n# dataset name pass as y \nsapply(1:3, function(x, y) mean(y[,x]), y=m)\n\n# Means of various colns\nmeanset = c( mean(m[,1]), mean(m[,2]), mean(m[,3]))\nmeanset\n\n# fiven no summary five number summary (minimum, lower-hinge, median, upper-hinge, maximum) for the input data\n\nsum1 = apply(m,2,fivenum)\nsum1\n?fivenum\nmean(m[,2])\nsum2 = apply(m,2,summary)\nsum2\n\n# rowmeans\nrowMeans(m)\ncolMeans(m)\n\n# trimming data\nmean(m[,1])\nmean(m[,1],trim=0.5)\n\n\na1 = array(1:(3*4*2), dim=c(3,4,2))\na1\n#5 rows, 4 cols, 3 matrices\napply(a1, 1, sum)\napply(a1, 2, sum)\napply(a1, c(3), sum)\n\n\napply(a1, c(1,2), sum)\napply(a1, c(2,3), sum)\napply(a1, c(1,2,3), sum)\napply(a1, c(1,3), sum)\n\n" }, { "path": "15b-DM/47d-apply1.R", "content": "#apply APPLY\n\n#apply - When you want to apply a function to the rows or columns of a matrix (and higher-dimensional analogues); not generally advisable for data frames as it will coerce to a matrix first.\n\n# Two dimensional matrix\n(M <- matrix(seq(1,16), 4, 4))\n\n# apply min to rows\napply(M, 1, min)\n#[1] 1 2 3 4\n\n# apply max to columns\napply(M, 2, max)\n[1] 4 8 12 16\n\n# 3 dimensional array\n(M <- array( seq(32), dim = c(4,4,2)))\n\n# Apply sum across each M[*, , ] - i.e Sum across 2nd and 3rd dimension\napply(M, 1, sum)\n# Result is one-dimensional\n#[1] 120 128 136 144\napply(M, 2, sum)\n\napply(M, 3, sum)\n\n# Apply sum across each M[*, *, ] - i.e Sum across 3rd dimension\napply(M, c(1,2), sum)\n# Result is two-dimensional\n#[,1] [,2] [,3] [,4]\n#[1,] 18 26 34 42\n#[2,] 20 28 36 44\n#[3,] 22 30 38 46\n#[4,] 24 32 40 48\n" }, { "path": "15b-DM/47h-tapply1.R", "content": "# apply TAPPLY\n\n#tapply - For when you want to apply a function to subsets of a vector \n#and the subsets are defined by some other vector, usually a factor.\n\n#The black sheep of the *apply family, of sorts. \n#The help file's use of the phrase \"ragged array\" can be a bit confusing, \n#but it is actually quite simple.\n\n\n(x <- 1:20)\n#A factor (of the same length!) defining groups:\n(y <- factor(rep(letters[1:5], each = 4)))\n#Add up the values in x within each subgroup defined by y:\ncbind(x, y)\n\ntapply(x, y, sum) \n#a b c d e \n#10 26 42 58 74 \n\n#More complex examples can be handled where the subgroups are \n#defined by the unique combinations of a list of several factors.\n#tapply is similar in spirit to the split-apply-combine\n#functions that are common in R (aggregate, by, ave, ddply, etc.)\n#Hence its black sheep status.\n\ntapply(iris$Sepal.Length, iris$Species, mean)\ntapply(mtcars$mpg, mtcars$cyl, sd)\n" }, { "path": "15b-DM/47j-lapply1.R", "content": "#lapply\n#lapply = apply a function over a list\n#for reproducibility\n#write function to sample 10 numbers\n#from a Poisson distribution according to lambda\nf <- function(l){\n rpois(10, l)\n}\nf(10)\nf(1)\nset.seed(123)\n#save into draws\ndraws <- lapply(1:5,f)\ndraws\n\n\nlist1 = list(sub1=10:1, sub2=rep(5,3), sub3=rep(c(5,6),4),sub4=seq_len(length.out=10))\nlist1\nX=list1\nlapply(X,FUN=mean, na.rm=T)\nX[[1]][5] = NA\nlapply(X,FUN=mean, na.rm=F)\nlapply(X,FUN=mean, na.rm=T)\nunlist(lapply(X,FUN=mean, na.rm=T))\n\nlapply(a1, mean)\n" }, { "path": "15b-DM/47m-mapply1.R", "content": "# Mapply\n\n# vector\n(v1 = 1:20)\n(v2 = 12:15)\n# array\n#List ----\n(list1 = list(sub1=10:1, sub2=rep(5,3), \n sub3=rep(c(5,6),4),\n sub4=seq_len(length.out=10)))\nmapply(v1,v2,sum)\n\n# mapply -----\n# mapply - function to multiple list or vector args\n#mapply(a1, sum)\n\n# one way of writing - creating a list \n#1 -1 ; 2-2,2 ; 3- 3,3,3 ; 4-4,4,4,4\nmapply(rep, times=1:4, x = 1:4) # list\nrep(1:4,times=1:4)\nmapply(rep,1:5,4) # matrix 1 2 3 4\n# 1 2 3 4 5\n# 1 2 3 4 5\n# 1 2 3 4 5\n\nmapply(FUN=rep, each=1, x = v1, y=v1)\n#list1\nrep(v1,each=1)\nrep(m1,each=1)\nrep(list1,each=1)\n\ne1 = 1:3\ne2 = 6:8\ne3 = 10:12\n#function : x * y\n#1st elements : a = e1 : FUN to each elements\n#2nd elements : b = e2: takes this as arguments\n#3rd elements : c = e3: takes this as arguments\n# arguments are recycled if required\nmapply(FUN=function(x,y) x+y, e1, e2 )\ne1+e2\nmapply(FUN=function(x,y,z) x+y+z, e1, e2 , e3)\ne1+e2+e3\n\nmapply(FUN=function(x,y,z) mean(x*y)/ z, e1, e2 , e3)\n\n\n\nmapply(FUN=mean, x = list(a1,m1))\nmean(a1) ; mean(m1)\n\n\nmapply(function(x, y) x + y,\n x=c(a = 1, b = 2, c = 3), # names from first\n y=c(A = 10, B = 0, C = -10))\n\nmapply(function(x, y) x ^ y, USE.NAMES = F, # no names\n x=c(a =2, b = 3, c = 4), \n y=c(A =2, B = 3, C = 4))" }, { "path": "15b-DM/47n-mapply2.R", "content": "# Apply - mapply\n\n#‘multivariate’ apply. Its purpose is to be able to vectorize arguments \n#to a function\n#that is not usually accepting vectors as arguments.\n\n# Create a 4x4 matrix\nQ1 <- matrix(c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4)),4,4)\n\n# Print `Q1`\n(Q1)\n\n# Or use `mapply()`\n(Q2 <- mapply(rep,1:4,5))\n#same length of 4 translates to matrix\n# Print `Q2`\nprint(Q2)\n\n\n# List -----\nlist(rep(1, 4), rep(2, 3), rep(3, 2), rep(4, 1))\nmapply(rep, 1:4, 4:1)\n# different length translates to list\n\n# ------\n#https://leemendelowitz.github.io/blog/mapply.html\nnoise <- function(n, mean, std) {\n rnorm(n, mean, std)\n}\nnoise(5, 1, 2) # 5 random normally distributed values with mean, sd\n#noise function is not vectorized. mapply gives us a way\nmapply(noise, 1:5, 1:5, 2) # pairs (1,1), (2,2)\n\n#http://www.studytrails.com/r/core/control_structures_r_apply_functions_2/\n\n2^3 ; 4^2 #= 8 and 16\nmapply(function(x,y) x^y, x=c(2,4), y=c(3,2))\n#call a function FUN over vectors or lists one index at a time. \n#arguments x and y are recycled if they are of different lengths\nmapply(function(x,y){x^y},c(a=2,b=3),c(A=3,B=4))\nmapply(function(x,y){x^y},c(a=2,b=3),c(A=3,B=4),USE.NAMES=FALSE)\n\nmapply(function(x,y,z,k){(x+k)^(y+z)},x= c(a=2,b=3),\n y=c(A=3,B=4),MoreArgs=list(z=1,k=2))\n#values z and k are 1 and 2 respectively. So the first evaluation of function gives (2+2)^(3+1) and the second gives (3+2)^(4+1)\n" }, { "path": "15b-DM/47o-rapply.R", "content": "#apply RAPPLY\n\n#rapply - For when you want to apply a function to each element \n#of a nested list structure, recursively.\n\n#rapply is best illustrated with a user-defined function to apply:\n#Append ! to string, otherwise increment\n\nmyFun <- function(x){\n if (is.character(x)){\n return(paste(x,\"!\",sep=\"\")) # add character\n }\n else{\n return(x + 1) #inc by 1\n }\n}\n\n#A nested list structure\n(l <- list(a = list(a1 = \"Boo\", b1 = 2, c1 = \"Eeek\"), b = 3, c = \"Yikes\", \n d = list(a2 = 1, b2 = list(a3 = \"Hey\", b3 = 5))))\n\n\n#Result is named vector, coerced to character \nrapply(l,myFun)\n\n#Result is a nested list like l, with values altered\nrapply(l, myFun, how = \"replace\")\n" }, { "path": "15b-DM/47on-eapply.R", "content": "# eapply\n# like lapply except that rather than applying a function to \n# every element in a list, it applies a function to every \n# element in an environment. For example if you want to find\n# a list of user defined functions in the global environment:\n\n\nA<-c(1,3,5,7,9)\nB<-c(0,3,6,9,12)\nC<-list(x=1, y=2)\nD<-function(x){x+1}\nls()\nlsf.str()\n\neapply(.GlobalEnv, is.function)\n#$D - TRUE being function , $A- FALSE\n#if you are building a lot of packages or \n#create a lot of environments it may come in handy.\nunlist(eapply(.GlobalEnv, is.vector))\n" }, { "path": "15b-DM/47p-sapply1.R", "content": "#lapply sapply\n\nlapply(1:3, function(x) x^2)\nunlist(lapply(1:3, function(x) x^2))\n\n#Passing simplify=FALSE to sapply will also give you a list:\n \nsapply(1:3, function(x) x^2, simplify=F)\nsapply(1:3, function(x) x^2)\n\nm = matrix(1:12, ncol=3)\n\nmean(m[,c(1,2,3)])\nfor (i in 1:3) print(mean(m[,c(i)]))\n\nsapply(1:3, function(x) mean(m[,x]))\n\nsapply(c(1,2,3), function(x) mean(m[,x]))\n\nsapply(1:3, function(x, y) mean(y[,x]), y=m)\n\n\nsapply(vec, f)\n#input is a vector. \n#output is a vector/matrix, where element i is f(vec[i]), giving you a matrix if f has a multi-element output\nlapply(vec, f)\n#: same as sapply, but output is a list?\napply(matrix, 1/2, f): \n#input is a matrix. output is a vector, \n#where element i is f(row/col i of the matrix)\n\ntapply(vector, grouping, f)\n#: output is a matrix/array, where an element in the matrix/array is the value of f at a grouping g of the vector, and g gets pushed to the row/col names\n\nby(dataframe, grouping, f)\n#: let g be a grouping. apply f to each column of the group/dataframe. pretty print the grouping and the value of f at each column.\n\naggregate(matrix, grouping, f)\n#: similar to by, but instead of pretty printing the output, aggregate sticks everything into a dataframe.\n#https://stackoverflow.com/questions/3505701/grouping-functions-tapply-by-aggregate-and-the-apply-family\n\n\n?Map\nMap(sum, 1:5, 1:5, 1:5)\nMap(sum, 1:5, 1:5, 1:5, simplify2array=F)\nMap(sum, 1:5, 11:15, 21:25, simplify=T)\n\n" }, { "path": "15b-DM/47q-sapply2.R", "content": "# SAPPLY\n\n#List\nlist2 = list(num=1:10, vec=c(1:5, 4:5, 6:8, NA, 9, 12, 17), lg=log(1:5))\nlist2\n\nsapply(list2, FUN=mean, na.rm=T)\n\n# DF\nstr(student1)\nsapply(draws, mean)\ndim(student1)\ns1 = student1[,c('java','dbms', 'dwm','vlsi', 'cpp', 'cbnst')]\nsapply(s1,mean)\nmode(sapply(s1,mean))\nclass(sapply(s1,mean))\n\nmode(s1)\nclass(s1)\nstr(s1)\n\n#List\nl <- list(a = 1:100, b = 11:20)\nl\nl.mean <- sapply(l, mean)\nl.mean\n\n#difference with lapply?\n#lapply always returns a list. \n#sapply (if it can) simplifies the results\nlapply(draws,mean)\n#get same result as sapply\nunlist(lapply(draws,mean))\n" }, { "path": "15b-DM/47s-tapply2.R", "content": "#tapply\n\n#Data Frame df3\nnewnum = c(2:5, 4:5, 6:8, 9,17)\nfac1 = factor(c(rep(\"A\", 3), rep(\"B\", 3), rep(\"C\", 3), rep(\"D\",2)))\nfac2 = gl(n=2, k=1, length=11, labels = month.abb[1:2])\nnewnum\nfac2\nfac1\ndf3 = data.frame(response = newnum, pred1 = fac1, pred2 = fac2)\ndf3\nX = df3\ntapply(X=newnum, INDEX= fac1, FUN=NULL)\ntapply(X=newnum, INDEX = fac1, FUN = sum)\ntapply(X=newnum, INDEX = list(fac1,fac2), FUN = sum)\ntapply(X=newnum, INDEX=fac1, FUN=median)\n\nmode(tapply(X=newnum, INDEX=fac1, FUN=median, simplify=F))\n\n?tapply\nwith(df3, tapply(response, INDEX=pred1, FUN=sum) )" }, { "path": "15b-DM/47t-vapply1.R", "content": "#apply - VAPPLY\n\n#vapply - When you want to use sapply but perhaps need to squeeze some more speed out of your code.\n\n#For vapply, you basically give R an example of what sort of thing \n#your function will return, which can save some time \n#coercing returned values to fit in a single atomic vector.\n\n(x <- list(a = 1, b = 1:3, c = 10:100))\n\n#Note that since the advantage here is mainly speed, this\n# example is only for illustration. We're telling R that\n# everything returned by length() should be an integer of \n# length 1. \nvapply(x, FUN = length, FUN.VALUE = 0L) \n#a b c \n#1 3 91" }, { "path": "15b-DM/49b-replicate1.R", "content": "#replicate\n#\n?replicate\nreplicate(n, expr, simplify = \"array\")\nreplicate(3,4)\n#[1] 4 4 4\nreplicate(4,3)\n#[1] 3 3 3 3\nx = c(1:5)\nreplicate(3,x)\nreplicate(x,3) # Invalid length\n?replicate\nrnorm(5)\nreplicate(4, rnorm(5))\nj=0\n(x<-unlist(replicate(5, for(i in 1:5){\n print(paste(' Hello Dhiraj: i- ' , i,sep=': '))\n })))\n\nreplicate(5, 'Hello')\nreplicate(5, mean(1:5))\n\nseq(1,5,1)\nreplicate(5, seq(1,5,1))\n\n\nrep(1:4,len=20)\nreplicate(1:4, length=20)\n?replicate\n\n(x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE)))\n\n# compute the list mean for each list element\nlapply(x, mean)\n\n# median and quartiles for each list element\n1:3/4\nlapply(x, quantile, probs = 1:3/4)\n\nsapply(x, quantile) # on list items\n\n(i39 <- sapply(3:9, seq) ) # list of vectors\n\n\nsapply(i39, fivenum)\n\nvapply(i39, fivenum,\n c(Min. = 0, \"1st Qu.\" = 0, Median = 0, \"3rd Qu.\" = 0, Max. = 0))\n\n## sapply(*, \"array\") -- artificial example\n(v <- structure(10*(5:8), names = LETTERS[1:4]))\n\nf2 <- function(x, y) outer(rep(x, length.out = 3), y)\n(a2 <- sapply(v, f2, y = 2*(1:5), simplify = \"array\"))\na.2 <- vapply(v, f2, outer(1:3, 1:5), y = 2*(1:5))\nstopifnot(dim(a2) == c(3,5,4), all.equal(a2, a.2),\n identical(dimnames(a2), list(NULL,NULL,LETTERS[1:4])))\n\nhist(replicate(100, mean(rexp(10))))\n\n\n\n\n\n\n\n## use of replicate() with parameters:\nfoo <- function(x = 1, y = 2) c(x, y)\n\n# does not work: bar <- function(n, ...) replicate(n, foo(...))\nbar <- function(n, x) replicate(n, foo(x = x))\nbar(5, x = 3)\n\n\n\ndata1 = c(3, 60, 19, 9, 4 , 5)\n\nlabels1 = c('Building training sets', 'Cleaning and Organising Data', 'Collecting Data sets', 'Mining data for patterns', 'Refining Algorithms', 'Others')\npie(data1, labels= labels1)\n\nbarplot(data1)\npie(data1,labels=NA, clockwise=TRUE,\n col=rainbow(6), border=\"white\", radius=1.2,\n cex=0.8, main=\"Average Time Spent by Data Scientists\")\nlegend(\"bottomright\",legend=pielabels,bty=\"n\", # horiz = T,\n fill=rainbow(6))\nbarplot(data1, col=rainbow(6), names.arg=pielabels, \n cex.names = 1, horiz=T, angle=90, \n main=\"Average Time Spent by Data Scientists\"\n)\ntext(1:6, data1, labels= labels1)\n?barplot\n\n\n\nbrowsers<-read.table(\"browsers.txt\",header=TRUE)\nbrowsers<-data1\nbrowsers\npielabels <- sprintf(\"%s = %3.1f%s\", labels1, browsers, \"%\")\npielabels\n?sprintf\nlibrary(RColorBrewer)\npie(browsers,\n labels=NA,\n clockwise=TRUE,\n col=brewer.pal(6,\"Set1\"),\n border=\"white\",\n radius=1,\n cex=0.8,\n main=\"Percentage Share of Internet Browser usage\")\nlegend(\"bottomleft\",legend=labels1,bty=\"n\",\n fill=brewer.pal(6,\"Set1\"))\n" }, { "path": "15b-DM/49c-replicate.R", "content": "#replicate\n#\n?replicate\nreplicate(n, expr, simplify = \"array\")\n\nreplicate(4, rnorm(5))\n\n\nmy.fun = function() {\n \n for (i in 1:1000) {\n ...\n for (j in 1:20) {\n ...\n }\n }\n \n return(output)\n \n}\n\nrep(1:4,len=20)\nreplicate(1:4,len=20)\n" }, { "path": "15b-DM/49e-by.R", "content": "# Apply Family - by\n# used for DF split by factors\nmtcars\n# we can take am, gear or carb as factors\nby(data, INDICES, FUN, ..., simplify = TRUE)\nsplit(mtcars, mtcars$am)\nmtcars$am; mtcars$mpg\nby(mtcars$mpg,mtcars$am, FUN=mean )\n\n# wrapper for tapply\nby(warpbreaks[, 1:2], warpbreaks[,\"tension\"], summary)\nby(warpbreaks[, 1:2], warpbreaks[,\"tension\"], mean) # will not work\n\n\n# what works and what not\nby(iris$Sepal.Width , iris$Species , summary )\nby(iris, iris$Species, length)\nby(x=mtcars, mtcars$am, function(x) mean(x)) # will not work\nby(iris, iris$Species, mean) # will not work\nby(iris$Sepal.Width, iris$Species, mean)\n\n\n# DF or matrix\ndf=data.frame(a=c(1:15),b=c(1,1,2,2,2,2,3,4,4,4,5,5,6,7,7))\n#sum of all values of a grouped by values of b. \n#That is, sum of all values of a where b=1, sum of all values of a where b is 2 and so on\nby(df,factor(df$b),sum) # by(df,df$b,sum) also works\ndf\ndf2=data.frame(a=c(1:15),k=c(1:15),b=c(1,1,2,2,2,2,3,4,4,4,5,5,6,7,7))\nby(df2,factor(df2$b),sum)\ndf2\n\n?by\n# mtcars\nbo1 = by(data= mtcars[,'mpg'], INDICES=mtcars[,'cyl'], summary)\n#bo2 = by(data= mtcars[,'mpg'], INDICES=mtcars[,'cyl'], summary, simplify=T)\nstr(bo1)\n#str(bo2);\nby(data= mtcars[,'mpg'], INDICES=mtcars[,'cyl'], max)\nby(data= mtcars[,'disp'], INDICES=mtcars[,c('vs','am')], mean)\n\nby(data= mtcars[,c('disp','wt')], INDICES=mtcars[,c('vs','am')], mean) # not work\naggregate(mtcars[,c('disp','wt')], mtcars[,c('vs','am')], mean)\n\nby(data= mtcars[,c('disp','wt')], INDICES=mtcars[,c('vs','am')], summary)\n" }, { "path": "15b-DM/49f-by.R", "content": "#by - data frame - matrix form\n\n?by\n#by(data, INDICES, FUN, ..., simplify = TRUE)\n\nby(mtcars, INDICES=mtcars[,'am'], FUN=summary, simplify=T)\nby(mtcars, INDICES=mtcars[,c('am','cyl')], FUN=summary, simplify=T)\n\nby(mtcars, INDICES=mtcars[,c('am','cyl')],FUN=colMeans, simplify=T)\nby(mtcars, INDICES=mtcars[,'am'], FUN=colMeans, simplify=T)\n\n" }, { "path": "15b-DM/49g-bywith.R", "content": "#with by\n#functions that can help write simpler and more efficient code.\n\n#by function, as stated in the documentation can be though, \n#as a \"wrapper\" for tapply. The power of by arises when we want to compute a task that tapply can't handle. One example is this code:\n\n(ct <- tapply(iris$Sepal.Width , iris$Species , summary ))\n(cb <- by(iris$Sepal.Width , iris$Species , summary ))\n\n\n#ct and cb, we \"essentially\" have the same results and the only differences are in how they are shown and \n#the different class attributes, respectively by for cb and array for ct.\n\ntapply(iris, iris$Species, summary )\n#Error in tapply(iris, iris$Species, summary) : \n# arguments must have same length\n#we want to calculate the summary of all variable in iris along the factor Species\":\n#but R just can't do that because it does not know how to handle.\n\n#by function R dispatch a specific method for data frame class and\n#then let the summary function works even if the length of the first argument (and the type too) are different.\n\n#Sums the 1st elements, the 2nd elements, etc. \nmapply(sum, 1:5, 1:5, 1:5) \n[1] 3 6 9 12 15\n#To do rep(1,4), rep(2,3), etc.\nmapply(rep, 1:4, 4:1) \n\n\n# object of class by that along Species (say, for each of them) computes the summary of each variable.\n# \n# Note that if the first argument is a data frame, \n# the dispatched function must have a method for that class of objects. \n# For example is we use this code with the mean\n#function we will have this code that has no sense at all:\n\n#if the first argument is a data frame, the dispatched function \n#must have a method for that class of objects.\n#For example is we use this code with the mean function we will have this code that has no sense at all:\n \nby(iris, iris$Species, mean)\n# cannot have mean of all variables\nsplit(iris, iris$Species)\n\n(bywork <- by(iris, iris$Species, summary ))\n\nby(iris, iris$Species, length)\n\niris\n# with----------------------\n\nrm(list=ls())\n?with # temp opening data\nwith(data=mtcars, mean(mpg)) # list, df\n\nwith(data=mtcars,c(mean(mpg), mean(wt))) # list, df\n# within : modifies the data \ndf2 = within(data=mtcars, c(mean(hp), mean(wt)))\ndf2\nwith(data=mtcars, aggregate(by=list(cyl), FUN=mean))\nby(x=mtcars, mtcars$am, function(x) mean(x))\n\n\nload('./data/du3.RData')\nstr(df2)\nby(df2, df2$course, FUN=summary)\nby(df2[,'sql'], df2$gender, FUN=function(x) mean(x))\n\nnames(df2)\n#with ------\n#applys an expression to a dataset. I\n# with(data, expression)\n# example applying a t-test to a data frame mydata \nwith(mydata, t.test(y ~ group))\n\n\n\n\n#by -----\n#by( ) function applys a function to each level of a factor or factors. It is similar to BY processing in SAS.\n\n# by(data, factorlist, function)\n# example obtain variable means separately for\n# each level of byvar in data frame mydata \nby(mydata, mydata$byvar, function(x) mean(x))\n" }, { "path": "15b-DM/51b-myfunc.R", "content": "x = C(0.90,0.95,0.975,0.99,0.995)\nlog2 <-\nfunction(x) log(x,base=3)\ncummedian = function(x) {\n tmp = seq_along(x)\n for (i in 1:length(tmp)) tmp[i] = median(x[1:i])\n print(tmp)\n }\ncummedian(df1)\n\ninterest = function(p,r,t=1) return((p * r * t) / 100 )\n\nmapply(function(p,r,t=1) return((p * r * t) / 100 ),p,r)\n\n\ncl2 = c(0.90,0.95,0.975,0.99,0.995)\nqnorm(cl2)\n\n\n\n\n\n\nt.single = function(obs.mean, mu, SD, n) {\n t.obt = (obs.mean - mu) / (SD / sqrt(n))\n p.value = pt(abs(t.obt), df=n-1, lower.tail=F)\n print(c(t.obt = t.obt, p.value = p.value))\n warning(\"P-value is one-sided. Double for two-sided.\")\n}\n\nttest <- function(sample_mean, pop_mean, sample_sd, n) {\n t <- (sample_mean - pop_mean) / (sample_sd/sqrt(n))\n}\n\n\n# Blank Function\nmy.fun = function() {\n for (i in 1:1000) {\n ...\n for (j in 1:20) {\n ...\n }\n }\n return(output)\n}\n" }, { "path": "15b-DM/51c-functions1.R", "content": "#Functions\n\n# Commands \nargs()\nfunction()\nformals()\nlsf.str()\nbody()\n\n# Create function\nx=2 ; y = 3\n(sumofnos = x + y)\n\n(x1 = c(1:10))\n(sumofnos = sum(x1))\n\nsumofnos = sum(x1)\n\nfsum1 <- function(x1) {\n sumofnos = sum(x1)/length(x1)\n}\nfsum1\nfsum1(1:10)\n\n# Code - Extract Function\n# run above function lines\nfsum1(x1) # no output\n(sumout = fsum1(x1)) # output seen : value assigned to a variable\n\n# print output from function call\nfsum2 <- function(x1) {\n sumofnos = sum(x1)\n return(sumofnos)\n}\n\nfsum2(x1) # output seen\n\nx1=1:10 ; y1=100:200\n\nsum2a <- function(x3, y3) {\n sumofnos = sum(x3) * mean(y3)\n return(sumofnos)\n }\nsum2a(x1, y1)\n\n\n\n\n\n\n\n\n# another way.. printing values inside a function\nfsum3 <- function(x1) {\n sumofnos = sum(x1)\n print(sumofnos) # this is for printing\n print(paste('Sum of object is ',sumofnos, sep=' xxxx '))\n cat(paste('Sum of my object is ',sumofnos, sep='- '))\n }\nfsum3(x1)\n\n# Check structure of function\nbody(fsum3)\nbody(fsum1)\nargs('fsum3')\n2**2; 2^2\nargs(bxp) # args of built in function\nbody(bxp) # \n# create a new function\n\n# Area of Circle\nareacircle = pi * r^2\n\nareacircle <- function(constant=pi, r) {\n areacircle = constant * r^2\n return(areacircle)\n }\n# pi is constant neednot be givien\nareacircle( ,4)\nareacircle(r=4)\n\nareacircle(r=4, constant=4)\nbody(areacircle)\nargs(areacircle)\n\nlibrary(plyr)\n# Structure of functions\nlsf.str()\nsearch()\nls()\nlsf.str(pos=-1) # relate to functions\nlsf.str(pos=1) # relate to functions\nlsf.str(pos=2) # relate to 2nd package in order found in search()\nlsf.str(pos=4)\n\nsave(areacircle, fsum1, file='./data/dudata1.Rdata')\n# Clear Invironment\nrm(list=ls())\nload('./data/dudata1.RData') # Rdata RData are same in windows\nls()\nlsf.str()\nsearch()\nlsf.str(pos=1)\n\n\n# R Commands and objects name into character strings\nargs(areacircle)\n(ch1 = deparse(args(areacircle)))\nstr(ch1)\n\n# make result of function but donot print\nareacircle(r=6)\n\nareacircle2 <- function(constant=3.14, r) {\n areacircle = constant * r^2\n invisible(areacircle)\n}\nareacircle2(r=5) # no output shown\n(ch = areacircle2(r=5)) # assign and print\n\n# Print command and options\npi\nprint(pi)\nprint(pi, digits = 2)\n?print\nsname = c('Achal Kumar','Apoorva Karn',\n 'Goldie Sahni','Hitesh Mann',\n 'Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni')\nsname\nprint(sname)\nprint(sname, quote=F)\n" }, { "path": "15b-DM/53b-cbindrbind1.R", "content": "# cbind rbind\n\ncbind() # easier than matrix command\n# retain row and column names \nmarks1 = ceiling(runif(11, 4, 10))\nmarks2 = ceiling(runif(11, 4, 10))\n\ncbind(marks1, marks2)\n(m1 = matrix(c(marks1, marks2), ncol=2))\n# rownames gone\n\n(m2 = as.matrix(cbind(marks1, marks2)))\nstr(m2) #??\nm2[[1]]\nm2[1]\nm2[,1]\nm2$marks\n\n#(m3 = cbind(marks1, marks2))\n#(m3 = as.numeric(cbind(marks1, marks2))\n\n\n#(m3 = as.matrix(as.numeric(cbind(marks1, marks2)) , ncol=2))\n\n#str(m3)\n\n# DF from cbind and rbind\n# \n(df1= cbind.data.frame(marks1, marks2))\nstr(df1)\n\n(df2=rbind.data.frame(marks1,marks2))\n" }, { "path": "15b-DM/53c-joinDFs.R", "content": "#Join Data Frame\n\n#same size\nwomen1 = women\nwomen2 = women\ncbind(women1, women2)\ndata.frame(women, women)\n\nrbind(women1, women2)\n" }, { "path": "15b-DM/53c-merge1.R", "content": "#Merge\n\n?merge\n\ndf2= merge(mtcars, mtcars)\ndf2\ndim(mtcars)\ndim(df2)\ndf3= merge(mtcars, mtcars, by.x = names(mtcars))\ndim(df3)\n\n# merge2\n\nrollno = c(17010,17045,17012,17087,17057,17056,17032,17084,17078,17018,17013)\nsname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni')\ncourse = c('PGDDS','PGDDS','PGDDS','PGDDS','MSCDS','PGDDS','MSCDS','PGDDS','PGDDS','PGDDS','PGDDS')\ndf1 = data.frame(rollno, sname, course)\n\ngender = c('M','F','M','M','M','F','M','F','M','M','M')\nexp = c(3,3.5,14,5,0,1,1,15,1,3,8) # Experience\nhostel = c(FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,TRUE)\ndf2 = data.frame(rollno, gender, exp, hostel)\ndf1; df2\nmerge(df1,df2)\n\n\n# Other joins\n# DF of marks of subjec1\nID = c(17010,17045,17012,1)\nSEX = c('M','F','M','T')\nSUB1 = c(50,60, 70,80)\n(df3 = data.frame(ID, SEX, SUB1))\nmerge(df1, df3) # incorrect\nmerge(df1, df3, all=F) \n\nmerge(df1, df3, by.x = c('rollno'), by.y=c('ID'))\n\n# use two columns to join\ndf1$gender = gender\nmerge(df1, df3, by.x = c('rollno','gender'), by.y=c('ID','SEX'))\ndf3\n\nmerge(df1, df3, by.x = c('rollno'), by.y=c('ID'), all=TRUE)\nmerge(df1, df3, by.x = c('rollno'), by.y=c('ID'), all=FALSE)\nmerge(df1, df3, by.x = c('rollno'), by.y=c('ID'), all.x=TRUE)\nmerge(df1, df3, by.x = c('rollno'), by.y=c('ID'), all.y=TRUE)\n" }, { "path": "15b-DM/54b-combination.R", "content": "# Combination of Elements\n#combinations of the elements of x taken m at a time\ncombn(x, m, FUN = NULL, simplify = TRUE, ...)\n\ncombn(letters[1:4], 2)\n\n(m <- combn(10, 5, min)) # minimum value in each combination\n\nmm <- combn(15, 6, function(x) matrix(x, 2, 3))\n\nstopifnot(round(choose(10, 5)) == length(m),\n c(2,3, round(choose(15, 6))) == dim(mm))\n\n## Different way of encoding points:\ncombn(c(1,1,1,1,2,2,2,3,3,4), 3, tabulate, nbins = 4)\n\n## Compute support points and (scaled) probabilities for a\n## Multivariate-Hypergeometric(n = 3, N = c(4,3,2,1)) p.f.:\n# table.mat(t(combn(c(1,1,1,1,2,2,2,3,3,4), 3, tabulate, nbins = 4)))\n\n## Assuring the identity\nfor(n in 1:7)\n for(m in 0:n) stopifnot(is.array(cc <- combn(n, m)),\n dim(cc) == c(m, choose(n, m)))\n\n#data frame from all combinations of the supplied vectors or factors.\nexpand.grid(..., KEEP.OUT.ATTRS = TRUE, stringsAsFactors = TRUE)\n\nexpand.grid(height = seq(60, 80, 5), \n weight = seq(100, 300, 50),\n sex = c(\"Male\",\"Female\"))\n\nx <- seq(0, 10, length.out = 100)\ny <- seq(-1, 1, length.out = 20)\nd1 <- expand.grid(x = x, y = y)\nd1\n(d2 <- expand.grid(x = x, y = y, KEEP.OUT.ATTRS = FALSE))\nobject.size(d1) - object.size(d2)\n##-> 5992 or 8832 (on 32- / 64-bit platform)\n\n\n# Unique Combinations\nx = 1:10\ny = letters[1:5]\n(df= expand.grid(X=x, Y=y)) \ndf[order(df$X), ]\n" }, { "path": "15b-DM/54d-expandgrid.R", "content": "# Expand Grid\nexpand.grid(height = seq(60, 80, 5), weight = seq(100, 300, 50),\n sex = c(\"Male\",\"Female\"))\n\nx <- seq(0, 10, length.out = 100)\ny <- seq(-1, 1, length.out = 20)\nd1 <- expand.grid(x = x, y = y)\nd1\nd2 <- expand.grid(x = x, y = y, KEEP.OUT.ATTRS = FALSE)\nd2\nobject.size(d1) - object.size(d2)\n##-> 5992 or 8832 (on 32- / 64-bit platform)\n" }, { "path": "15b-DM/55b-sweep1.R", "content": "#Sweep\n\n#method signature is similar to 'apply' \n#though 'sweep' requires one more parameter, 'STATS'.\n#'sweep' returns an array of the same shape as the input array, \n#whereas the result returned by 'apply' depends on the function passed in.\n#https://stackoverflow.com/questions/3444889/how-to-use-the-r-function-sweep\n# e.g., use 'sweep' to express a given matrix in terms of distance from \n# the respective column mean\n\n# create some data:\n(M = matrix( 1:12, ncol=3))\n\nM+1\nM+c(1,2)\nM\nM+c(1,2,3)\ncbind(M, M+c(1,2,3))\n\n\n\n# calculate column-wise mean for M\n(dx = colMeans(M))\ncolSums(M) # and not colsums\n\n# now 'sweep' that summary statistic from M\nsweep(M, 2, dx, FUN=\"-\")\nM ; dx\n\n# [,1] [,2] [,3]\n#[1,] -1.5 -1.5 -1.5\n#[2,] -0.5 -0.5 -0.5\n#[3,] 0.5 0.5 0.5\n#[4,] 1.5 1.5 1.5\nsweep(M, 1, dx, FUN=\"-\") # Warning - no recycle, less nos 3 and 4\n(dy = rowMeans(M))\nsweep(M, 1, dy, FUN=\"-\")\n\nM+1\nM+c(1,2)\nM\nM+c(1,2,3)\ncbind(M, M+c(1,2,3))\n" }, { "path": "15b-DM/55d-sweep2.R", "content": "#sweep\n\n#The basic idea is to sweep through an \n#array row- or column-wise and return a modified array.\n\n#you have a matrix and want to standardize it column-wise:\n\n(dataPoints <- matrix(4:15, nrow = 4))\n\n# Find means per column with `apply()`\n(dataPoints_means <- apply(dataPoints, 2, mean))\n\n# Find standard deviation with `apply()`\n(dataPoints_sdev <- apply(dataPoints, 2, sd))\n\n# Center the points \n(dataPoints_Trans1 <- sweep(dataPoints, 2, dataPoints_means,\"-\"))\nprint(dataPoints_Trans1)\n## [,1] [,2] [,3]\n## [1,] -1.5 -1.5 -1.5\n## [2,] -0.5 -0.5 -0.5\n## [3,] 0.5 0.5 0.5\n## [4,] 1.5 1.5 1.5\n# Return the result\ndataPoints_Trans1\n## [,1] [,2] [,3]\n## [1,] -1.5 -1.5 -1.5\n## [2,] -0.5 -0.5 -0.5\n## [3,] 0.5 0.5 0.5\n## [4,] 1.5 1.5 1.5\n# Normalize\n(dataPoints_Trans2 <- sweep(dataPoints_Trans1, 2, dataPoints_sdev, \"/\"))\n\n# Return the result\ndataPoints_Trans2\n## [,1] [,2] [,3]\n## [1,] -1.1618950 -1.1618950 -1.1618950\n## [2,] -0.3872983 -0.3872983 -0.3872983\n## [3,] 0.3872983 0.3872983 0.3872983\n## [4,] 1.1618950 1.1618950 1.1618950\n\n#NB: for this simple example the same result can of course be achieved more easily by\napply(dataPoints, 2, scale)\n\n\n?attitude\ndim(attitude)\nhead(attitude)\n(med.att <- apply(attitude, 2, median))\n?sweep(data.matrix(attitude), 2, med.att) \n# subtract the column medians\n\n## More sweeping:\n#Create a array of values 1 to 24 with 3 dimension - 4,3,2\n(A <- array(1:24, dim = 4:2))\n\n## no warnings in normal use\n#Subtract 5 from each row value\nsweep(A, 1, 5)\n(A.min <- apply(A, 1, min)) # == 1:4\nsweep(A, 1, A.min)\napply(A, 1:2, median)\nsweep(A, 1:2, apply(A, 1:2, median))\n\n## warnings when mismatch\nsweep(A, 1, 1:3) # STATS does not recycle\nsweep(A, 1, 6:1) # STATS is longer\n\n## exact recycling:\nsweep(A, 1, 1:2) # no warning\nsweep(A, 1, as.array(1:2)) # warning\nas.array(1,2)\n" }, { "path": "15b-DM/56b-outer1.R", "content": "# Outer\n\nA<-c(1,3,5,7,9)\nB<-c(0,3,6,9,12)\n?pmax # parallel max\n#(1,0)->1 ; (3,3)-> 3 ; (5,6)->6\n\nmapply(FUN=pmax, A, B)\n#[1] 1 3 6 9 12\n\nouter(A,B, pmax) # every combination of every element\n# only useful for linear algebra type things. \n# However, it can be used much like mapply to apply a\n# function to two vectors of inputs. \n# The difference is that mapply will apply the function \n# to the first two elements and then the second two etc, \n# whereas outer will apply the function to every combination\n# of one element \n# from the first vector and one from the second. For example:" }, { "path": "15b-DM/56c-outer2.R", "content": "#Outer\n#\n?outer\nouter(1:4, 4:6)\n1:4 %o% 4:6\nouter(1:4, 4:6, FUN=paste)\nx=1:4 ; y=4:6 ; z=c(10,11,12)\nx;y;z\nouter(x,y, FUN='*')\nouter(x,y, FUN=paste)\nouter(x,y, FUN='+')\nouter(x,y, FUN='-')\n#(Y= matrix(-y,ncol=3,nrow=4,dimnames=list(NULL,c('y1','y2','y3'))))\n#(X=matrix(x,ncol=3,nrow=4,byrow=F,dimnames=list(NULL,c('x1','x2','x3'))))\nx;y\n#cbind(Y,X) \nouter(x,-y, FUN=paste)\nouter(x,y, FUN='-')\nouter(x,y, FUN='^')\nouter(x,rep('^',3),y, FUN=paste0)\n\nouter(x,y, FUN='/') # what is this\nz = \n" }, { "path": "15b-DM/57b-stack1.R", "content": "# Stack\n# \nclassA = c(10,14,15)\nclassB = c(20,25,27)\nclassC = c(13,15,17)\nclassmarks = data.frame(classA, classB, classC)\n(s1 = stack(classmarks))\n\n(s2= stack(classmarks, select=c(\"classA\", \"classC\")))\nunstack(s2)\n" }, { "path": "15b-DM/58-DF-common.R", "content": "#find common between DF\n#http://www.cookbook-r.com/Manipulating_data/Comparing_data_frames/\n\ndfA <- data.frame(Subject=c(1,1,2,2), Response=c(\"X\",\"X\",\"X\",\"X\"))\ndfA\n\ndfB <- data.frame(Subject=c(1,2,3), Response=c(\"X\",\"Y\",\"X\"))\ndfB\n\ndfC <- data.frame(Subject=c(1,2,3), Response=c(\"Z\",\"Y\",\"Z\"))\ndfC\n\ndfA$Coder <- \"A\"\ndfB$Coder <- \"B\"\ndfC$Coder <- \"C\"\ndfA\ndf <- rbind(dfA, dfB, dfC) # Stick them together\ndf\ndf <- df[,c(\"Coder\", \"Subject\", \"Response\")] # Reorder the columns to look nice\ndf\n\n# Find the rows which have duplicates in a different group.\ndupRows <- dupsBetweenGroups(df, \"Coder\")\ndupRows\ncbind(df, dup=dupRows)\n#find the rows that are unique within each group:\ncbind(df, unique=!dupRows)\n#Store the results in df\ndfDup <- cbind(df, dup=dupRows)\ndfDup\ndfA1 <- subset(dfDup, Coder==\"A\", select=-Coder)\ndfA1\n\n\n#2 df \n# 1 with few unique rows\n# 2 table with multiple rows of same data\n# common column is not same but part of it is same\n\na <-c('a','b','c','d','e')\nb <-c('a','b','d','e', NA)\na[!(a %in% b)]\na[(a %in% b)]\n\nab <- data.frame(a,b)\nab$a[!(ab$a %in% ab$b)]\nab$a[(ab$a %in% ab$b)]\n\n#\n\nset.seed(111) # so can replicate results\nx <- matrix(runif(500), ncol=2)\nx\ny <- matrix(runif(2000), ncol=2)\ny\nlibrary(Hmisc)\nw <- find.matches(x, y, maxmatch=5, tol=c(.02,.03))\n?find.matches\nhead(x)\nhead(y)\nw\nw$matches[1:5,]\nw$distance[1:5,]\n# Find first x with 3 or more y-matches\nnum.match <- apply(w$matches, 1, function(x)sum(x > 0))\nj <- ((1:length(num.match))[num.match > 2])[1]\nx[j,]\ny[w$matches[j,],]\n\n\nsummary(w)\n\n\n#\nlibrary(plyr)\n# count the occurrences of each id in the baseball dataframe, then get the subset with a freq >25\nhead(baseball)\nlongterm <- subset(count(baseball, \"id\"), freq > 25)\nlongterm\n# Select only rows from these longterm players from the baseball dataframe\n# (match would default to match on shared column names, but here was explicitly set \"id\")\nbb_longterm <- match_df(baseball, longterm, on=\"id\")\nbb_longterm[1:5,]\n\n#-------------\n\ndupsbetween\n#https://rdrr.io/github/vapniks/mergeutils/man/dupsBetweenGroups.html\n\ninstall.packages(\"remotes\")\nremotes::install_github(\"vapniks/mergeutils\", force=T)\nlibrary(mergeutils)\ndupsBetweenDFs(dfs, by = NULL, matchall = FALSE)\ndupsBetweenDFs(mtcars, mtcars, by=wt)\n\n" }, { "path": "15b-DM/58-df-matching1.R", "content": "#how to rows in 1st DF from column values in 2nd DF\n\nlibrary(gsheet)\nlibrary(dplyr)\n\nurl1 = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=2077624779'\n\ndf1 = data.frame(gsheet2tbl(url1))\nhead(df1)\n\nurl2 = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=943100206'\ndf2 = data.frame(gsheet2tbl(url2))\nhead(df2)\nnames(df1); names(df2)\nhead(df2)\ndf2$stock\ndf1[which(df2$stock %in% df1$stockname), ]\ndf1\n\nsqldf(\"select * from df1 where stockname LIKE '%HDFC%'\")\nsqldf(\"select stock from df2\")\n\nsqldf(\"select * from df1 where stockname in (select stock from df2)\")\nsqldf(\"select * from df1 where stockname like in (select stock from df2)\")\n\ndf1[(sapply(df1$stockname, grepl, df2$stock) %>% rowSums() %>% as.logical()) * (1:nrow(x)), ]\n# not working\n\nlibrary(dplyr)\nlibrary(fuzzyjoin)\nhead(df2)\nregex_join( mutate_if(df1, is.factor, as.character), mutate_if(df2, is.factor, as.character), by = c(\"stockname\" = \"stock\") )\n#https://stackoverflow.com/questions/45638726/how-to-query-a-dataframe-using-a-column-of-other-dataframe-in-r\n\n#\nlibrary(magrittr) # To use the pipe, \"%>%\"\ndf1 %>% data.table::setDT() # To return the result as a table easily\nm2 = df1[(sapply(df2$stock, grepl, df1$stockname) %>% rowSums() %>% as.logical()) * (1:nrow(df1)), ]\nas.data.frame(m2)\ndf2$stock\n\n\n#sqldf\nsqldf(\"select df1.stockname, df2.stock FROM df1 JOIN df2 on df1.stockname LIKE '%' || df2.stock || '%'\")\n\n#-----\nlibrary(data.table)\nmtcars[rownames(mtcars) %like% \"Merc\", ]\niris[iris$Species %like% \"osa\", ]\n\n\n#------\nmtcars[grep(\"Merc\", rownames(mtcars)), ]\niris[grep(\"osa\", iris$Species), ]\n\n\n#-----\nlibrary(stringr)\nlibrary(dplyr)\n\nCO2 %>% filter(str_detect(Treatment, \"non\"))\n\n\n#-----\nrequire(sqldf)\ndf <- data.frame(name = c('bob','robert','peter'),id=c(1,2,3))\ndf\nsqldf(\"select * from df where name LIKE '%er%'\")\n\n" }, { "path": "15b-DM/58-df2.R", "content": "\nt1 <- read.table(text = \"ID1 Name1 Type1 Name2\n1 A1 T1 B1\n2 A2 T2 B2\n3 A3 T1 B3_a\n4 A4 T3 B4_a\", header = T)\n\nt1\n\nt2 <- read.table(text = \"NameBank TypeBank\nA1 T1\nA2 T2\nA3 T1\nA4 T3\nB1 T1\nB2 T4\nB3 T2\nB4 T3\", header = T)\n\nt2\nt1;t2\n\nlibrary(dplyr)\n#charmatch which seeks matches for the elements of its first argument among those of its second.:\nt2[1,]\nt1[1,]\nchrs <- charmatch(t2$NameBank, t1$Name2)\nchrs\ncbind(\n t1[chrs[!is.na(chrs)],],\n t2[which(!is.na(chrs)),]\n)\n\nt1$Type2 <- NA_character_\nt1\nlibrary(stringr)\nfor( row in seq_len( nrow( t2 ) ) ) {\n t1$Type2[ substr( t1$Name2, 0, nchar( t2$NameBank[row] ) ) == t2$NameBank[row] ] <- t2$TypeBank[row]\n}\n" }, { "path": "15b-DM/58-hmisc.R", "content": "\nlibrary(Hmisc)\nfind.matches(x, y, tol=rep(0, ncol(y)), scale=tol, maxmatch=10)\n\n?find.matches\n\ny <- rbind(c(.1, .2),c(.11, .22), c(.3, .4), c(.31, .41), c(.32, 5))\nx <- rbind(c(.09,.21), c(.29,.39))\ny\nx\nw <- find.matches(x, y, maxmatch=5, tol=c(.05,.05))\nw\nw$matches\nw$distance\nx;y\nw1 <- find.matches(x, y, tol=c(.1,.01))\nw1\n\nfind.matches(y, x, maxmatch=5, tol=c(.05,.05))\n\n\n# NOT RUN {\nagrep(\"lasy\", \"1 lazy 2\")\nagrep(\"lasy\", c(\" 1 lazy 2\", \"1 lasy 2\"), max = list(sub = 0))\nagrep(\"laysy\", c(\"1 lazy\", \"1\", \"1 LAZY\"), max = 2)\nagrep(\"laysy\", c(\"1 lazy\", \"1\", \"1 LAZY\"), max = 2, value = TRUE)\nagrep(\"laysy\", c(\"1 lazy\", \"1\", \"1 LAZY\"), max = 2, ignore.case = TRUE)\n# }\n\nagrep(\"dhiraj\", 'Dhiraj Upadhyaya')\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ'))\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ'), ignore.case = T)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ'), max.distance = 0)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 0)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 1)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 2)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 3)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 6)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 3, value=T)\n#max expressed at absolute nos\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = .1, value=T)\n#expressed as decimal\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = (del=2), value=T)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = (cost=3), value=T)\nagrep(\"dhiraj\", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = (inser=3), value=T)\n\n\n\nlibrary(RecordLinkage)\n\nClosestMatch2 = function(string, stringVector){\n distance = levenshteinSim(string, stringVector);\n stringVector[distance == max(distance)]\n}\nword <- 'test'\nwords <- c('Teest','teeeest','New York City','yeast','text','Test')\nClosestMatch2(word,words)\n" }, { "path": "15b-DM/58-pmatchchar.R", "content": "#partial matching\n#https://astrostatistics.psu.edu/su07/R/html/base/html/pmatch.html\n\npmatch(\"m\", c(\"mean\", \"median\", \"mode\")) # returns NA\npmatch(\"med\", c(\"mean\", \"median\", \"mode\")) # returns 2\n\npmatch(c(\"\", \"ab\", \"ab\"), c(\"abc\", \"ab\"), dup = FALSE)\npmatch(c(\"\", \"ab\", \"ab\"), c(\"abc\", \"ab\"), dup = TRUE)\n## compare\ncharmatch(c(\"\", \"ab\", \"ab\"), c(\"abc\", \"ab\"))\n\ncharmatch(\"\", \"\") # returns 1\ncharmatch(\"m\", c(\"mean\", \"median\", \"mode\")) # returns 0\ncharmatch(\"med\", c(\"mean\", \"median\", \"mode\")) # returns 2\n\n" }, { "path": "15b-DM/61c-missing1.R", "content": "# Missing Values\n# Create, Detect, Replace, Remove, Impute, Visualise\nlibrary(VIM)\n\n(v1 = c(1,2,NA,NA,5))\nis.na(v1)\nv1=NULL\nv1 # remove all values\n(v1 = c(1,2,NA,NA,5,6,7,NA))\nsum(is.na(v1)) # sum TRUE values ie missing values\nmean(v1) # cannot calculate if NA values in vector\nmean(v1, na.rm=T) # remove and calc\n(v1a = na.omit(v1)) # omit missing values and put in new vector\nv1a\nmean(v1a)\nsum(v1a) # now you can sum on non NA values\n?na.omit\n\nanyNA(v1) # is there any NA value\nanyNA(v1a)\n?anyNA\n#impute missing values of vector with mean value of non NA values\nv1\nmean(v1, na.rm=T) # this value to be used\nv1[is.na(v1)]\nv1[is.na(v1)] = mean(v1, na.rm=T)\nv1\n\n#Use VIM package to load sleep dataset with NA values in rows & col\nlibrary(VIM)\ndata(sleep, package='VIM')\nsummary(sleep)# see NA values in cols\nhead(sleep)\ndim(sleep)\ncomplete.cases(sleep) #all complete rows without any missing values\nsum(complete.cases(sleep)) # count non NA rows/ complete 42\nsum(!complete.cases(sleep)) #count NA rows 20\nmean(complete.cases(sleep)) #42/62\n# total cases = 62, complete=42 \nsleep[complete.cases(sleep),] # list rows which are complete\nsleep[!complete.cases(sleep),] # list rows which have NA values\n\n# Particular Column\nis.na(sleep$Dream)\nsum(is.na(sleep$Dream))\nmean(is.na(sleep$Dream)) # 12/62\n\n# How many missing values\nsum(is.na(sleep)) #total in data frame\nnames(sleep)# check columname\ncolSums(is.na(sleep)) #NA values in each column\nrowSums(is.na(sleep)) #NA values in each row\nhead(sleep)\nas.data.table(sleep) #data.table format\nis.na(sleep[1:3,]) # first 3 rows\nhead(sleep,n=3)\n\n#Tabulate\nlibrary(mice)\nmice::md.pattern(sleep)\n#42 rows without any missing values # 2 rows with 1 NA NonD\n\n#Visualisation\nVIM::aggr(sleep, prop=F, numbers=T)\n#NonD max NA values \nVIM::aggr(sleep, prop=T) # Proportion\nVIM::matrixplot(sleep) # rowno and missing values in red\nVIM::marginmatrix(sleep)\nVIM::marginplot(sleep[c('Gest','Dream')], pch=c(20), col=c(\"darkgray\", \"red\", \"blue\")) # finding relationship between NA value\n\n\n#Exploration\nx = as.data.frame(abs(is.na(sleep)))\nhead(sleep, na=5)\nhead(x, n=5)\ny = x[which(apply(x, 2, sum) > 0)]\ncor(y) #Dream - NonD 0.9\ncor(sleep, y, use=\"pairwise.complete.obs\")\n#ignore NA, \n#\n\n#Listwise Deletion\noptions(digits=3)\nna.omit(sleep)\ncor(na.omit(sleep))\ncor(sleep, use=\"complete.obs\")\n\nfit = lm(Dream ~ Span + Gest, data= na.omit(sleep))\nsummary(fit)\n\n\n#imputations Packages - mice, Amelia, mi : mice, with, pool\n\nimp = mice::mice(sleep, seed=1234)\n?mice\n\n# explore more\n" }, { "path": "15b-DM/61c-missing2.R", "content": "#Missing Values - Visualisation \n\nlibrary(VIM)\n#Visualisation\nVIM::aggr(sleep, prop=F, numbers=T) #Total nos\n#NonD max NA values \nVIM::aggr(sleep, prop=T) # Proportion\nVIM::matrixplot(sleep) # rowno and missing values in red\n#The continuous variable is rescaled and represented by grayscale colors. Lighter colors indicate lower values and darker colors suggest larger values\nnames(sleep)\nVIM::barMiss(sleep)\nVIM::histMiss(sleep[,3:8])\nsleep$BodyWgt\n#Click in in the left margin to switch to the previous variable or in the right margin to switch to the next variable.\n#To regain use of the VIM GUI and the R console, click anywhere else in the graphics window.\nVIM::spineMiss(sleep[c('Gest','Dream')])\nVIM::marginmatrix(sleep)\nVIM::scattmatrixMiss(sleep)\nVIM::marginplot(sleep[c('Gest','Dream')], pch=c(20), col=c(\"darkgray\", \"red\", \"blue\")) # finding relationship between NA value\n\n\n\n(shadow = as.data.frame(abs(is.na(sleep))))\n\n\n#Exploration\nx = as.data.frame(abs(is.na(sleep)))\nhead(sleep, na=5)\nhead(x, n=5)\ny = x[which(apply(x, 2, sum) > 0)]\ncor(y) #Dream - NonD 0.9\ncor(sleep, y, use=\"pairwise.complete.obs\")\n#ignore NA, \n#\n\n#Listwise Deletion\noptions(digits=3)\nna.omit(sleep)\ncor(na.omit(sleep))\ncor(sleep, use=\"complete.obs\")\n\nfit = lm(Dream ~ Span + Gest, data= na.omit(sleep))\nsummary(fit)\n\n\n#imputations Packages - mice, Amelia, mi : mice, with, pool\n\nimp = mice::mice(sleep, seed=1234)\nimp\n?mice\n\n# explore more\n" }, { "path": "15b-DM/61c-missing3.R", "content": "# Missing values\n\nx = c(NA, 1, NA, 2)\nis.na(x)\nmean(x)\nmean(x, na.rm=T)\nx[is.na(x)] = mean(x, na.rm=T)\nx\nsum(is.na(x))\n\n\nx2 = rnorm(100000, mean=50, sd=5)\nlength(x2)\nposn=sample(100000, size=30)\nx2[posn] = NA\nsum(is.na(x2))\n\n\nlibrary(VIM)\n\nhead(sleep)\ncomplete.cases(sleep)\nsleep[complete.cases(sleep),]\nsleep[!complete.cases(sleep),]\ncolSums(is.na(sleep))\nrowSums(is.na(sleep))\n " }, { "path": "15b-DM/62b-outlier.R", "content": "#Outliers\n#libaries - outliers,car\neasypackages::libraries('outliers','car')\n\n#Outlier\nx = c(50:70,99, 150,150)\nmean(x)\nmean(x[x < 90])\nmean(50:70)\n\n#Box Plot\nset.seed(482)\ny = rnorm(100)\nboxplot(y)\nstripchart(y, vertical = TRUE, method = \"jitter\", add = TRUE, pch = 20, col = 'blue', jitter=.05)\n?stripchart\n(pts = identify(rep(1, length(y)), y, labels = seq_along(y)))\n#Click on outlier value and press FINISH button on Plot tab\n#click on line along dashed lines\ny[pts]\n\n\n#Case\n# Inject outliers into data.\nhead(cars)\ncars1 <- cars[1:30, ]\ncars_outliers <- data.frame(speed=c(19,19,20,20,20), dist=c(190, 186, 210, 220, 218))\ncars2 <- rbind(cars1, cars_outliers)\ntail(cars2)\n\n# Plot of data with outliers.\npar(mfrow=c(1, 2))\nplot(cars2$speed, cars2$dist, xlim=c(0, 28), ylim=c(0, 230), main=\"With Outliers\", xlab=\"speed\", ylab=\"dist\", pch=\"*\", col=\"red\", cex=2)\nabline(lm(dist ~ speed, data=cars2), col=\"blue\", lwd=3, lty=2)\n\n# Plot of original data without outliers. Note the change in slope (angle) of best fit line.\nplot(cars1$speed, cars1$dist, xlim=c(0, 28), ylim=c(0, 230), main=\"Outliers removed \\n A much better fit!\", xlab=\"speed\", ylab=\"dist\", pch=\"*\", col=\"red\", cex=2)\nabline(lm(dist ~ speed, data=cars1), col=\"blue\", lwd=3, lty=2)\n#Notice the change in slope of the best fit line after removing the outliers. Had we used the outliers to train the model(left chart), our predictions would be exagerated (high error) for larger values of speed because of the larger slope.\n\n#-------------------\n#Now proceed with Outlier Analsis with Univariate, Bivariate, Multivariate\n\n\n#Univariate Analysis\n\n#add some extreme values to MPG variable\nx = c(mtcars$mpg, c(50,55,90))\n(outlier_values = boxplot.stats(x)$out) # outlier values.\nboxplot(x, main=\"MPG\", boxwex=0.1)\nmtext(paste(\"Outliers: \", paste(outlier_values, collapse=\", \")), cex=0.6)\n\n#outlier package\noutliers::outlier(x)\noutliers::scores(x,type=\"z\", prob=0.95)\n\n#Bivariate Analysis\n#Visualize in box-plot of the X and Y, for categorical X’s\n#What is the inference? The change in the level of boxes suggests that GEAR seem to have an less impact in MPG than CYL. Any outliers in respective categorical level show up as dots outside the whiskers of the boxplot.\n\n#For continuous variable (convert to categorical if needed.)\npar(mfrow=c(1,2))\nboxplot(mpg ~ factor(gear), data=mtcars, main=\"Boxplot for MPG vs GEAR\")\nboxplot(mpg ~ factor(cyl), data=mtcars, main=\"Boxplot for MPG vs CYL\")\n#one outlier in CYL-8 boxplot with MPG\n\npar(mfrow=c(1,1))\nboxplot(mpg ~ factor(cyl), data=mtcars, main=\"Boxplot for MPG vs CYL\")\n#merge strip chart by add=T\nstripchart(mpg ~ factor(cyl), vertical = TRUE, data = mtcars, method = \"jitter\", add = TRUE, pch = 20, col = 'blue')\n\n\n#Multivariate Analysis (LM)\nmod = lm(mpg ~ ., data=mtcars)\n(cooksd = cooks.distance(mod))\ncooksd[cooksd > 4 * mean(cooksd)]\n\n#Influence measures- In general use, those observations that have a cook’s distance greater than 4 times the mean may be classified as influential. This is not a hard boundary.\n\nplot(cooksd, pch=\"*\", cex=2, main=\"Influential Obs by Cooks distance\") # plot cook's distance\nabline(h = 4*mean(cooksd, na.rm=T), col=\"red\") # add cutoff line\ntext(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>4*mean(cooksd, na.rm=T),names(cooksd),\"\"), col=\"red\") # add labels\n#rows affected\n\n(influential <- names(cooksd)[(cooksd > 4*mean(cooksd, na.rm=T))])\nmtcars[influential, ]\ncolMeans(mtcars)\n#disp & hp very high in Ford, qsec very high & hp very low in Merc 230\n\n#Using Diagnostic Plot in LM\nplot(mod,which=4)\n\n#using outlier test - most extreme outlier\ncar::outlierTest(mod)\n\n#outlier package-----\noutliers::outlier(mtcars)\ncolMeans(mtcars)\n#scores\n#Compute the normalised scores based on “z”, “t”, “chisq” etc\n#Find out observations that lie beyond a given percentile based on a given score.\n\noutliers::scores(mtcars,type=\"z\", prob=0.95)\n#prob=.95 within 95% interval\n\n\n#Methods to Resolve----\n#Imputing\n#Capping\nx = mtcars$mpg\noutliers::outlier(x)\n(qnt = quantile(x, probs=c(.25, .75), na.rm = T))\n(caps = quantile(x, probs=c(.05, .95), na.rm = T))\n(H = 1.5 * IQR(x, na.rm = T))\nboxplot(x,ylim=c(0,45))\ntext(1, 39,\"Outlier\")\n#which values is more than 1.5 IQR\nx[x > (qnt[2] + H)] #replace with 95% value\nx[x < (qnt[1] - H)] <- caps[1]\nx[x > (qnt[2] + H)] <- caps[2]\noutliers::outlier(x) #next may come up\nx[x > (qnt[2] + H)] # but here not it is < 1.5 IQR\n\n#Prediction\n\n\n#Advanced methods/ packages in R for outliers\n\n" }, { "path": "15b-DM/62c-outlier2.R", "content": "#Outliers Advance\n\n#http://www.rdatamining.com/examples/outlier-detection\n\n\nlibrary(DMwR)\n# remove \"Species\", which is a categorical column\niris2 <- iris[,1:4]\noutlier.scores <- lofactor(iris2, k=5)\nplot(density(outlier.scores))\n\n# pick top 5 as outliers\noutliers <- order(outlier.scores, decreasing=T)[1:5]\n# who are outliers\nprint(outliers)\n#[1] 42 107 23 110 63\n\n\n#Visualize Outliers with Plots\n#Next, we show outliers with a biplot of the first two principal components.\n\nn <- nrow(iris2)\nlabels <- 1:n\nlabels[-outliers] <- \".\"\nbiplot(prcomp(iris2), cex=.8, xlabs=labels)\n\n\n#Visualization of outliers in a biplot of the 1st two principal components\n#We can also show outliers with a pairs plot as below, where outliers are labeled with \"+\" in red.\n\npch <- rep(\".\", n)\npch[outliers] <- \"+\"\ncol <- rep(\"black\", n)\ncol[outliers] <- \"red\"\npairs(iris2, pch=pch, col=col)\n\n#Parallel Computation of LOF Scores\n#Package Rlof provides function lof(), a parallel implementation of the LOF algorithm. Its usage is similar to the above lofactor(), but lof() has two additional features of supporting multiple values of k and several choices of distance metrics. Below is an example of lof(). \n#how many friends u have\n\nlibrary(Rlof)\noutlier.scores <- lof(iris2, k=5)\n# try with different number of neighbors (k = 5,6,7,8,9 and 10)\noutlier.scores <- lof(iris2, k=c(5:10))\noutlier.scores\nplot(density(outlier.scores))\n# pick top 10 as outliers\noutliers <- order(outlier.scores, decreasing=T)[1:10]\n# who are outliers\nprint(outliers)\n#[1] 42 192 342 492 642 792 107 23 257 407\niris2[outliers,]\niris2\n\n\n\n#--------------------------------------------\n\n#https://gist.github.com/revodavid/162943fcc49af20f0a978f9dfa5aa162\n\nlibrary(VIM)\nlibrary(ggplot2)\nlibrary(ggthemes)\nlibrary(OutliersO3)\nknitr::opts_chunk$set(warning=FALSE, message=FALSE)\nupdate_geom_defaults(\"bar\", list(fill = \"grey70\", colour = \"grey40\"))\ns1 <- O3prep(stackloss)\nO3s1 <- O3plotT(s1)\nO3s1$gO3\n\ns2 =O3prep(mtcars)\nO3s2 = O3plotT(s2)\nO3s2$gO3\n" }, { "path": "15c-Summary/22b-aggregate.R", "content": "# Aggregate\n\nstudent1 <- readRDS(\"./0-Rdata/student1.rds\")\nstr(student1)\ndim(student1)\nnames(student1)\nsummary(student1)\nstudent1$br\nstudent1submarks = student1[c(\"java\",\"dbms\", \"dwm\",\"vlsi\",\"cpp\",\"cbnst\")]\n\nstr(student1submarks)\n\n# for each gp of btechmarks, average other columns\naggregate(student1submarks, list(student1$btechmarks), mean, na.rm=T)\n\n# Use dot notation \n# Grouping variables of java and dbms\naggregate(. ~ java + dbms, student1submarks, mean)\n\n# Find the means of the numeric variables with the grouping var\nhead(aggregate(. ~ student1$btechmarks, student1submarks, mean))\n\n# aggregate(formula, data, FUN) # use aggregate.formula for a one-to-one aggregation of “airquality” by the mean of “Ozone” to the grouping variable “Day“.\nhead(aggregate(java ~ dbms, student1submarks, mean))\nstudent1submarks$java[student1submarks$dbms==31]\nstudent1submarks$java[student1submarks$dbms==40]\naggregate(dbms ~ java, student1submarks, mean)\n\n\naggregate(student1submarks, list(student1$btechmarks), mean, na.rm=T)\nstr(student1submarks)\naggregate(student1submarks[c(2,3,4)], list(student1$btechmarks), mean, na.rm=T)\n\naggregate(student1submarks$java, list(dbms=student1submarks$dbms), mean, na.rm=T)\naggregate(student1submarks$java, list(dbms=student1submarks$dbms), sd, na.rm=T)\n\naggregate(cbind(java, dbms) ~ student1$btechmarks, student1submarks, mean)\n\nhead(aggregate(student1$btechmarks ~ ., student1submarks, mean)) # bteck last\nhead(aggregate(. ~ student1$btechmarks, student1submarks, mean)) # btech first\n\nlm(java ~ dbms+vlsi, data = student1submarks)\n\n\n\n\n" }, { "path": "15c-Summary/22c-aggregate2.R", "content": "#aggregate2\n\n?ChickWeight\ndata = ChickWeight\nstr(ChickWeight)\ndim(data)\nunique(data$Chick)\nunique(data$Diet)\n\nlibrary(ggplot2)\nggplot(data=data, aes(x=Time, y=weight, group=Chick, colour=Chick)) +\n geom_line() +\n geom_point()\n#mean wt depending on diet\naggregate(data$weight, list(diet=data$Diet), mean)\n\n#aggregate on time\naggregate(data$weight, list(time=data$Time), mean)\n\naggregate(data$weight, list(time=data$Time, diet=data$Diet), mean)\n\n#to see the weights over time across different diets\nggplot(data) + geom_line(aes(x=Time, y=weight, colour=Chick)) +\n facet_wrap(~Diet) +\n guides(col=guide_legend(ncol=3))\n\n\n\n#Eg1\nvalues <- data.frame(value = c(\"a\", \"a\", \"a\", \"a\", \n \"a\", \"b\", \"b\", \"b\", \"c\", \n \"c\", \"c\", \"c\"))\nnr.of.appearances <- aggregate(x = values, \n by = list(unique.values = values$value), FUN = length)\nnr.of.appearances\n\n\n#Eg2\ndates <- data.frame(date = as.Date(\"2016-01-01\", format = \"%Y-%m-%d\") + 0:729)\ndates\nlast.day <- aggregate(x = dates[\"date\"],by = list(month = substr(dates$date, 1, 7)), FUN = max)\nlast.day \n\n\noptions(digits=3)\nattach(mtcars)\naggregate(mtcars, by=list(cylinder= cyl,gears = gear), FUN=mean, na.rm=TRUE)\n\n" }, { "path": "15c-Summary/23b-freqdistr1.R", "content": "# Frequency Distribution\n\n#Discrete Cat Data\nattend = c('A','P','P','A','P','A')\ntable(attend)\ncbind(table(attend)) # A & P on left side\n\n#Discrete Numeric Data\n\ncolor=c('Blue','Green','Magenta','Green','Black','Blue','Black')\nx2 = table(color)\nx2\nx2a = cbind(x2)\nx2a\nhist(x2a) # not relevant \nbarplot(x2a) # not good\nbarplot(x2a, beside=T) # Better\nunique(color) ; length(unique(color))\nbarplot(x2a, beside=T, col=rainbow(length(unique(color))))\npie(x2a)\n\n#Continuous Data \nset.seed(1234)\nx3 = runif(100,0,150) # 0 to 150 marks range, 100 values \nx3\nx3 = ceiling(x3) #round to higher value\nx3\nrange(x3)\n# Divide range into step of 15 ie 10 levels\nbreaks = seq(0,150,by=15)\nbreaks\nlength(breaks)\nx3\n#x3[1] = 60; x3[2] = 75\nx3.cut = cut(x3, breaks)\nx3.cut\nx3.cut = cut(x3, breaks, labels=letters[1:10])\nx3.cut\nx3.cut = cut(x3, breaks, labels=letters[1:length(breaks)-1])\nx3.cut\n\n?cut\nx3a = table(x3.cut)\nx3a\nx3b = cbind(x3a)\nx3b\nhist(x3b)\npie(x3b)\nbarplot(x3b, beside=T)\nbarplot(x3b, beside=T, names.arg =rownames(x3b))\nplot(x3b)\n\n#2----\n# Frequency 2\n# Relative Frequency\n#http://www.r-tutor.com/elementary-statistics/qualitative-data/relative-frequency-distribution-qualitative-data\n\n\n#3----\n#FD3\n\n\n\n\n\n# cum relative Freq Graph\nduration = faithful$eruptions \nbreaks = seq(1.5, 5.5, by=0.5) \nduration.cut = cut(duration, breaks, right=FALSE) #closed on left\nduration.freq = table(duration.cut) \nduration.cumfreq = cumsum(duration.freq) \nduration.cumfreq\nduration.cumrelfreq = duration.cumfreq / nrow(faithful)\n\ncumrelfreq0 = c(0, duration.cumrelfreq) \ncumrelfreq0\nplot(breaks, cumrelfreq0, \n main=\"Old Faithful Eruptions\", # main title \n xlab=\"Duration minutes\", \n ylab=\"Cumulative eruption proportion\") \nlines(breaks, cumrelfreq0) # join the points\n\n\n#Eg3\nFn = ecdf(duration) \n?ecdf\nplot(Fn, \n main=\"Old Faithful Eruptions\", \n xlab=\"Duration minutes\", \n ylab=\"Cumulative eruption proportion\")\n\n\n# Table of Value - Mean Median Mode\n\n# Observation Data -------------\nmarks = c(43, 60, 37, 48, 65, 48, 57, 78, 31, 59, 50)\nmarks1 = scan() \n# copy column of values from Excel \n#and paste & enter after the last value\nmarks1\n\nmean(marks) # 52.36\nmedian(marks) # 50\n\nestimate_mode <- function(x) {\n d <- density(x)\n d$x[which.max(d$y)]\n}\nestimate_mode(marks)\n#50.89\n\n# Quantile, Percentile, --------\nquantile(marks)\nsort(marks)\nquantile(marks, c(0,.1,.2,.25, .4, .5, .75, .9, 1))\nquantile(marks, c(0,.09,.18,.27, .36, .45, .55, .64, .73, .82, .91, 1))\nIQR(marks)\nquantile(marks, c(.25,.75))\n(iqr = quantile(marks, .75) - quantile(marks,.25))\n\nsummary(marks)\n\n\n# Discrete Data\n\n#marks2 = scan()\n\nmarks2a = c(3.5, 4.5, 5.5, 6.5, 7.5,8.5, 9.5)\nfreq2 = c(3,7,22,60,85,32,8)\ncbind(marks2a, freq) # see how it looks - excel\ndf2 = data.frame(marks2a, freq2)\ndf2\ndf2$marksfreq = df2$marks2a * df2$freq2\ndf2\nsum(df2$marksfreq)\nmeanmarks2a = sum(df2$marksfreq)/ sum(df2$freq2)\nmeanmarks2a\nmidfreq = sum(freq2)/2\ndf2$cumfreq = cumsum(df2$freq2)\ndf2\n# Median\ndf2$marks2a[df2$cumfreq > midfreq] # X which are more than mid value\n# use min to select least value\nmedian2 = min(df2$marks2a[df2$cumfreq > midfreq]) # \nmedian2\n\n#mode\nmax(df2$freq2)\nwhich(df2$freq2 == max(df2$freq2)) # index No\ndf2$marks2[5]\n\nmode2 = df2$marks2[ which(df2$freq2 == max(df2$freq2))]\nmode2\n\n# real data is not like this - it is observations\n#create data from the table given\nx2a = rep(marks2a, freq2)\nx2a # total obsverations\ntable(x2a)\ncbind(table(x2a)) # Looks like the orginal table\nmean(x2a)\nmedian(x2a)\nestimate_mode(x2a)\n\n\n# Discrete Data - Cat Labels -------------\n?scan\n#marks2b = scan(what = character())\nmarks2b =c (\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\") \nfreq2\nx2b = rep(marks2b, freq2)\nx2b\ntable(x2b)\ncbind(table(x2b)) # looks similar\n\nmean(x2b) # will not work becoz data is not numeric\n# Median - E\nmedian(x2b) # mid value if arranged in series\nlength(x2b)\nmid = ceiling(length(x2b)/2) \nmid\nx2b[mid]\norder(x2b)[mid]\nx2b[order(x2b)[mid]]\n\n# Mode - max freq\n\nestimate_mode(x2b) # does not work\ndensity(x2b) # because u cannot calc density \n\n#simples \ntable(x2b)\nrownames(table(x2b))\ncolnames(table(x2b)) # does not work\n\nmax(table(x2b))\nmode2b1 = rownames(table(x2b))[which(table(x2b) == max(table(x2b)))]\nmode2b1\n\n#Method3\nfreq <- tapply(x2b,x2b,length)\ntable(x2b)\nnames(freq)[which.max(freq)]\n\n\n# continuous Data\n\n#class = scan(what='character')\nclass3 =c(\"0-10\",\"10-20\",\"20-30\",\"30-40\",\"40-50\",\"50-60\", \"60-70\")\nclass3\n#freq3 = scan()\nfreq3 = c(3, 7, 22, 60, 85, 32, 8)\nas.numeric(unlist(strsplit(class3,\"-\"))) # long method\nmidx3 = c(5,15,25,35,45,55,65)\ncbind(class3, midx3, freq3) #midx3 is also character\ndf3 = data.frame(class3, midx3, freq3)\ndf3\ndf3$midxfreq = df3$midx3 * df3$freq3\ndf3\n\n# and so..on like previous eg\n\n# create observation data from contiuous series\nx3 = rep(midx3, freq3)\nx3\nsum(x3)\ntable(x3)\ncbind(table(x3)) # looks like original table\n\nmean(x3)\nmedian(x3)\nestimate_mode(x3)\n" }, { "path": "15c-Summary/23c-freqdistr2.R", "content": "#Freq Distribution2\n\ncolor= sample(c('Red','Blue','Green','Black'), size=100, prob=c(.3,.2,.1,.4), replace=T)\ntable(color)\n\ncbind(table(color))\ntransform(table(color))\n\nhist(color) #error as values are not numeric\n\nbarplot(cbind(table(color)), beside=T)\n\n#Continuous\n#Continuous Data \nset.seed(1234)\nmarks = ceiling(runif(100,0,150)) # 0 to 150 marks range, 100 values \nrange(marks)\nh=hist(marks)\nplot(h)\nh$counts\nh$mids\ncbind(h$mids, h$counts) #Mid Value & count\n\n\n# Divide range into step of 15 ie 10 levels\nbreaks = seq(0,150,by=15)\nmarks.cut = cut(marks, breaks, right=FALSE)\nmarks.table = table(marks.cut)\ntransform(marks.table)\n\n#Cumulative Freq\nmarks.cumfreq = cumsum(marks.table)\nplot(marks.cumfreq, type='b')\n\n#Package\nlibrary(plyr)\ncount(color)\ncount(marks.cut)\n\n" }, { "path": "15c-Summary/23d-freqdistr3.R", "content": "#Frequency Distribution 3\n\nmarks = ceiling(runif(100,0,150)) # 0 to 150 marks\n#Package----\nlibrary(fdth)\n#https://cran.r-project.org/web/packages/fdth/fdth.pdf\n(tb = fdt(marks))\n# Histograms\nplot(tb) # Absolute frequency histogram\nplot(tb, main='My title')\nplot(tb, x.round=3, col='darkgreen')\nplot(tb, xlas=2)\nplot(tb, x.round=3, xlas=2, xlab=NULL)\nplot(tb, v=TRUE, cex=.8,x.round=3,xlas=2, xlab=NULL, col=rainbow(11))\nplot(tb, type='fh') # Absolute frequency histogram\nplot(tb, type='rfh') # Relative frequency histogram\nplot(tb, type='rfph') # Relative frequency (%) histogram\nplot(tb, type='cdh') # Cumulative density histogram\nplot(tb, type='cfh') # Cumulative frequency histogram\nplot(tb, type='cfph') # Cumulative frequency (%) histogram\n# Polygons\nplot(tb, type='fp') # Absolute frequency polygon\nplot(tb, type='rfp') # Relative frequency polygon\nplot(tb, type='rfpp') # Relative frequency (%) polygon\nplot(tb, type='cdp') # Cumulative density polygon\nplot(tb, type='cfp') # Cumulative frequency polygon\nplot(tb, type='cfpp') # Cumulative frequency (%) polygon\n\n# Density\nplot(tb, type='d') # Density\n# Summary\ntb\nsummary(tb) # the same\nprint(tb) # the same\nshow(tb) # the same\n\nsummary(tb, format=TRUE) # It can not be what you want to publications!\nsummary(tb, format=TRUE,\n pattern='%.2f') # Huumm ..., good, but ... Can it be better?\nsummary(tb, col=c(1:2, 4, 6),format=TRUE, pattern='%.2f') # Yes, it can!\n\nx=marks\nrange(x) # To know x\nsummary(fdt(x, start=1, end=9, h=1),col=c(1:2, 4, 6), format=TRUE, pattern='%d') # Is it nice now?\n\n# The fdt.object\ntb[['table']] # Stores the feq. dist. table (fdt)\ntb[['breaks']] # Stores the breaks of fdt\ntb[['breaks']]['start'] # Stores the left value of the first class\ntb[['breaks']]['end'] # Stores the right value of the last class\ntb[['breaks']]['h'] # Stores the class interval\nas.logical(tb[['breaks']]['right']) # Stores the right option\n\n# Theoretical curve and fdt\ny <- rnorm(1e5, mean=5, sd=1)\ntb <- fdt(y, k=100)\nplot(tb, type='d', # density\n col=heat.colors(100))\ncurve(dnorm(x,mean=5, sd=1),n=1e3, add=TRUE, lwd=4)\n\n\n\n#\n#" }, { "path": "15c-Summary/23f-FD.R", "content": "# FD\n\n#Eg2 https://cnx.org/contents/yTlmgmzh@4/Frequency-Relative-Frequency-a\n# Entering the data : 20 records\nhours.worked = c(5, 6, 3, 3, 2, 4, 7, 5, 2, 3, 5, 6, \n 5, 4, 4, 3, 5, 2, 5, 3)\n\n# A general frequency table\ntable(hours.worked)\n## hours.worked\n## 2 3 4 5 6 7 \n## 3 5 3 6 2 1\n\n# Relative frequency table\nlength(hours.worked)\ntable(hours.worked)/length(hours.worked)\n## hours.worked\n## 2 3 4 5 6 7 \n## 0.15 0.25 0.15 0.30 0.10 0.05\n\n# To get cumulative frequencies, we need to put\n# the hours into different intervals\nx = table(cut(hours.worked, breaks = c(1:7)))\nx\n# Cumulative frequencies\ncumsum(x)\n## (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] \n## 3 8 11 17 19 20\n\n# Cumulative relative frequencies\ncumsum(x)/length(hours.worked)\n## (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] \n## 0.15 0.40 0.55 0.85 0.95 1.00\n\n\n" }, { "path": "15c-Summary/23f-freqdistr.R", "content": "# freq dist\n\n#Eg1\nlibrary(MASS) # load the MASS package \nschool = painters$School # the painter schools \nschool.freq = table(school) # apply the table function\nschool\nschool.freq\nhead(painters)\nnrow(painters) # no of rows/ records\nschool.relfreq = school.freq / nrow(painters)\nschool.relfreq\ncbind(school.relfreq) \nround(cbind(school.relfreq),2)\nschool2 = data.frame(round(cbind(school.relfreq),2))\nhist(school2$school.relfreq)\n\n#Cumulative Relative Frequency\nschool2$cum = cumsum(as.numeric(school2$school.relfreq))\nschool2\nplot(school2$cum, type='l')\n\n\n" }, { "path": "15c-Summary/24b-freqdistr4.R", "content": "# FD with DF\nlibrary(fdth)\n\nmdf <- data.frame(X1=rep(LETTERS[1:4], 25), X2=as.factor(rep(1:10, 10)),Y1=c(NA, NA, rnorm(96, 10, 1), NA, NA), Y2=rnorm(100, 60, 4), Y3=rnorm(100, 50, 4), Y4=rnorm(100, 40, 4))\nmdf\n(tb <- fdt(mdf))\n# Histograms\nplot(tb, v=TRUE)\nplot(tb, col=rainbow(8))\nplot(tb, type='fh')\ndev.off()\npar(mar=c(1,1,1,1))\n#graphics.off()\nplot(tb, type='rfh')\nplot(tb,type='rfph')\nplot(tb,\n type='cdh')\nplot(tb,\n type='cfh')\nplot(tb,\n type='cfph')\n# Poligons\nplot(tb,\n v=TRUE,\n type='fp')\nplot(tb,\n type='rfp')\nplot(tb,\n type='rfpp')\nplot(tb,\n type='cdp')\nplot(tb,\n type='cfp')\nplot(tb,\n type='cfpp')\n" }, { "path": "15c-Summary/24f-freqdistr5.R", "content": "# FD - categories\n\nlibrary(fdth)\n## Categorical\nx <- sample(x=letters[1:5], size=5e2,rep=TRUE)\nx\n(fdt.c <- fdt_cat(x))\n(fdt.c <- fdt_cat(x, sort=FALSE))\n\n\n#----\n# Data.frame: multivariated with two categorical\n##================================================\nmdf <- data.frame(c1=sample(LETTERS[1:3], 1e2, rep=TRUE), c2=as.factor(sample(1:10, 1e2, rep=TRUE)), n1=c(NA, NA, rnorm(96, 10, 1), NA, NA), n2=rnorm(100, 60, 4),n3=rnorm(100, 50, 4))\nhead(mdf)\n(fdt.c <- fdt_cat(mdf))\n(fdt.c <- fdt_cat(mdf, dec=FALSE))\n(fdt.c <- fdt_cat(mdf,sort=FALSE))\n(fdt.c <- fdt_cat(mdf, by='c1'))#error\n\n\n#----\nx <- matrix(sample(x=letters[1:10], size=100, rep=TRUE), nc=2, dimnames=list(NULL, c('c1', 'c2')))\nx\nhead(x)\n(fdt.c <- fdt_cat(x))\n\n" }, { "path": "15c-Summary/25g-freqdistr6.R", "content": "# Freq Distr\nlibrary(freqdist)\n\n# Create Sample Data Frame\ndata <- c(1,2,3,4,4)\ndata <- as.data.frame(data)\n# Run Frequency Distribution Command on Data Frame\nfreqdist(data)\n" }, { "path": "15c-Summary/31c-rowsums1.R", "content": "# Row and Column Sums / Means\n\n?rowSums\nM = matrix(1:12, nrow=4)\nM\nrowSums(M)\ncbind(M,rowSums(M))\naddmargins(M)\naddmargins(M,2)\nmargin.table(M,1)\ncbind(M,margin.table(M,1))\n\ncolSums(M)\ncbind(M,colSums(M)) # Check ?\nrbind(M,colSums(M))\naddmargins(M, 1)\nmargin.table(M,2)\nrbind(M,margin.table(M,2))\n\n# Means\nrowMeans(M)\ncbind(M, rowMeans(M))\naddmargins(M,2,FUN=list(list(mean, sum, sd, var, median)))\naddmargins(M,c(1,2),FUN=list(list(mean, sum, sd),\n list(var, median)))\n\n?colMeans(M)\nrbind(M,colMeans(M))\naddmargins(M,1, FUN=list(mean))\n\n(hostel = matrix(rep(c(TRUE, FALSE), 12), nrow=3))\nrowSums(hostel)\ncolSums(hostel)\nrowMeans(hostel)\ncolMeans(hostel)\n\n#data frame\ndf = as.data.frame(M)\nclass(df)\nrowSums(df)\n\n\n?addmargins\n\n?rowsum\n#rowsum - Colsum - M/ F based on gp variable\nrowsum(x=mtcars, group=mtcars$cyl, reorder = T)\n\nrowsum(x=mtcars, group=mtcars$hp, reorder = T)\n# cyl - 4,6,8 ; cyl - sum \n\n?colsum # NA\n\ncolSums()\n\ncolMeans()\n\nrowMeans()\n\nrowSums()\n\nrownames(x, do.NULL = TRUE, prefix = \"row\")\nrownames(x) <- value\n\nrow.names()\n\ncolnames(x) <- value\n?colnames(mtcars,do.NULL = T, prefix='col')\ncolnames(mtcars)\nx = mtcars\n\n\n\nm0 <- matrix(NA, 4, 0)\nrownames(m0)\n\nm2 <- cbind(1, 1:4)\ncolnames(m2) = colnames(m2, do.NULL = FALSE, prefix='col')\ncolnames(m2)\n\ncolnames(m2) = NULL\ncolnames(m2)\n# do.NULL - if F & names are empty, names are created\n# if T, names are not empty, names are not created\ncolnames(m2) = colnames(m2, do.NULL = FALSE, prefix='sub')\ncolnames(m2)\nm2\n(m2 = cbind(m2, 1:4))\nis.na(colnames(m2))\ncolnames(m2)[3] = NULL\n\ncolnames(m2)\ncolnames(m2) = colnames(m2, do.NULL = TRUE, prefix='subject')\ncolnames(m2)\n\n\ncolnames(m2) <- c(\"x\",\"Y\")\ncolnames(m2)\n\nrownames(m2)\nrownames(m2) <- rownames(m2, do.NULL = FALSE, prefix = \"Obs.\")\nm2\n\n?setNames\nx= setNames(data.frame(matrix(ncol = 3, nrow = 0)), \n c(\"name\", \"age\", \"gender\"))\nstr(x)\n\n" }, { "path": "15c-Summary/32b-addmargin1.R", "content": "# Add Margins\n\nmarks = matrix(1:12, nrow=3)\nmarks\naddmargins(marks)\naddmargins(marks, FUN=sd)\naddmargins(marks, FUN=list(sum, sd))\naddmargins(marks, FUN=list(list(sum, sd), list(var, median)))\naddmargins(marks, FUN=list(SUM=sum, StdDev = sd)) # no spaces\n\n\n\nset.seed(1)\nx <- sample( 1:7, 20, replace = TRUE)\ny <- sample( 1:7, 20, replace = TRUE)\ntable(x,y)\ntx <- addmargins( table(x, y) )\nprint(tx, zero.print = \".\")\n\n\n\n\nyears <- sample(c(\"2015\", \"2016\", \"2017\"), 177, replace = TRUE)\nmatches <- sample(c(\"test\", \"oneday\"), 177, replace = TRUE)\nteams <- sample(c(\"india\", \"pakistan\", \"england\", \"australia\"), 177, replace = TRUE)\ndfcricket = data.frame(years, matches, teams)\ndfcricket\n(A <- table(years, matches, teams))\n(B = table(dfcricket)) # this also works\naddmargins(A)\naddmargins(B)\n\nftable(A)\nftable(addmargins(A))\n\n# Non-commutative functions - note differences between resulting tables:\nftable(addmargins(A, c(1, 3),\n FUN = list(Sum = sum, list(Min = min, Max = max))))\nftable(addmargins(A, c(3, 1),\n FUN = list(list(Min = min, Max = max), Sum = sum)))\n\n# Weird function needed to return the N when computing percentages\nsqsm <- function(x) sum(x)^2/100 # user defd function\n\n(C <- table(teams, matches))\napply(C, 2, sum)/100\nsweep(C, 2, apply(C, 2, sum)/100, \"/\")\n?sweep\nround(sweep(C, 2, apply(C, 2, sum)/100, \"/\"), 1)\n\n\nround(sweep(addmargins(C, 1, list(list(All = sum, N = sqsm))), 2,\n apply(C, 2, sum)/100, \"/\"), 1)\nround(sweep(addmargins(C, 2, list(list(All = sum, N = sqsm))), 1,\n apply(C, 1, sum)/100, \"/\"), 1)\n\n# A total over Bee requires formation of the Bee-margin first:\nmC <- addmargins(C, 2, FUN = list(list(Total = sum)))\nround(ftable(sweep(addmargins(mC, 1, list(list(All = sum, N = sqsm))), 2,\n apply(mC, 2, sum)/100, \"/\")), 1)\n\n\n# Example 2\n\n\n(M = matrix( 1:12, ncol=3))\naddmargins(M)\naddmargins(M,margin = 1)\naddmargins(M, FUN=mean)\naddmargins(M, FUN=list(list(mean,sum), list(sd, median)))\n\n\n" }, { "path": "15c-Summary/32c-margintable1.R", "content": "# Margin Tables\n#14 Sep 17\nparties1 = c('bjp', 'congress', 'sp', 'aap')\nlength(parties1)\nstates1 = c('up', 'delhi','haryana', 'punjab','uk')\nlength(states1)\n\n\n(col1 = rep(parties1, each=5))\nlength(col1)\n\n(col2 = rep(states1, times=4 ))\nlength(col2)\ncbind(col1,col2)\nparty = factor(col1)\nstate = factor(col2)\n#xtabs(col1 ~ col2)\nset.seed(1111)\n(mps = ceiling(runif(20,25,50)))\nsum(mps)\n(df1 = data.frame(party, state, mps))\ntable(df1$party)\ntable(df1) # not relevant here...\nftable(df1)\nstr(col1)\n#tabulate(df1$col1) # one coln factor type\n\n#table(df1)\ntable(df1$party)\ntable(df1$state)\ntable(df1[,c('party', 'state')])\ntable(df1[,c('party', 'state')], dnn=list('Political Parties', \n 'States of India'))\n\nxtabs(mps ~ party + state, data=df1) #formula method\nxtabs(mps ~ state + party , data=df1) #formula method\n\nstatesel = c('up', 'delhi') # vector of state names (few)\nxtabs(mps ~ party + state, data=df1, subset= state %in% statesel)\nxtabs(mps ~ party + state, data=df1, \n subset=c(TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE,\n TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE ))\n\nstate %in% statesel\nxtabs(mps ~ party + state, data=df1, subset= state %in% statesel,\n drop.unused.levels = T)\n\npartysel = c('bjp', 'aap')\nxtabs(mps ~ party + state, data=df1, subset= party %in% partysel)\nxtabs(mps ~ party + state, data=df1, subset= party %in% partysel,\n drop.unused.levels = T)\n\nxtabs(mps ~ party + state, data=df1, subset= state %in% statesel & \n party %in% partysel, drop.unused.levels = T)\n\n\n\nftable(df1) # where they fought and won\nftable(df1$state, df1$party, df1$mps) # change order\nftable(party ~ state, data = df1) # formula method\nftable(party ~ mps, data = df1) # formula method\nftable(df1, row.vars = 3, col.vars=1) # specify rows and cols to use\nftable(df1, row.vars = 2, col.vars=1) # specify rows and cols to use\n\n\n# Margins -----------\nmatresults = xtabs(mps ~ party + state, data=df1)\nmatresults # rows - parties, col- states\n?margin.table\nstr(matresults)\nmargin.table(matresults, margin=NULL) # total MPS\nmargin.table(matresults, margin=1) # total MPS in party Rows\nmatresults # rows - parties, col- states\n\nmargin.table(matresults, margin=2) # total MPS in states Cols\n\n# if matresults was an array : dimension=3\nmargin.table(matresults, margin=3) # error here\n\n#Prop Table----\nprop.table(matresults, margin=NULL) # proportion of total\nround(prop.table(matresults, margin=NULL),2) # proportion of total\nsum(round(prop.table(matresults, margin=NULL),2)) # proportion of total\n# total to 1\n\n(p1 = round(prop.table(matresults, margin=1),2)) \n# proportion of total Row\nrowSums(p1) # equal to 1 : rowise sum\naddmargins(p1, 2, sum)\ncolSums(p1) # not equal to 1 \n\n(p2 = round(prop.table(matresults, margin=2),2)) \n# proportion of total Col\nrowSums(p2) # not equal to 1 \ncolSums(p2) # equal to 1 colwise sum\naddmargins(p2,1,sum)\np2\n\naddmargins(p2)\naddmargins(p2,1) # \naddmargins(p2,2) # \n\n" }, { "path": "15c-Summary/32d-proptable.R", "content": "#prop.table\n" }, { "path": "15c-Summary/32d-tableprop2.R", "content": "# Proportion Table\n\n(M = matrix( 1:12, ncol=3))\nprop.table(M)\nround(prop.table(M),2)\nround(prop.table(M,1),2)\nround(prop.table(M,2),2)\n\naddmargins(prop.table(M))\naddmargins(round(prop.table(M),2))\naddmargins(round(prop.table(M),2),1)\naddmargins(round(prop.table(M),2),2)\naddmargins(round(prop.table(M),2),3) # no 3rd dim\n\nmargin.table(round(prop.table(M),2))\nmargin.table(round(prop.table(M),2),1) # summary of row - 4\nmargin.table(round(prop.table(M),2),2) #summary of col - 3\n\n\n\n" }, { "path": "15c-Summary/35b-crosstab.R", "content": "# Crosstab\n#http://rstudio-pubs-static.s3.amazonaws.com/6975_c4943349b6174f448104a5513fed59a9.html\n\nID <- seq(1:177)\nAge <- sample(c(\"0-15\", \"16-29\", \"30-44\", \"45-64\", \"65+\"), 177, replace = TRUE)\nSex <- sample(c(\"Male\", \"Female\"), 177, replace = TRUE)\nCountry <- sample(c(\"England\", \"Wales\", \"Scotland\", \"N. Ireland\"), 177, replace = TRUE)\nHealth <- sample(c(\"Poor\", \"Average\", \"Good\"), 177, replace = TRUE)\nSurvey <- data.frame(Age, Sex, Country, Health)\nhead(Survey)\n\nlibrary(catspec)\n?ctab\nftable(Titanic)\nstr(Titanic)\nctab(Titanic)\nctab(Titanic,type='r')\nctab(Titanic,type=c('r','n'))\nctab(Titanic,type=c(\"n\",\"c\",\"t\",\"r\"),style=\"w\")\nmytab<-ftable(Titanic,row.vars=c(1,3),type=\"r\")\nmytab\nctab(mytab)\nnewtab = ctab(mytab, type='r')\nnewtab\nsummary(newtab)\n\n# Data Frame ------------\n# \nlibrary(survival)\ndata(logan)\nattach(logan)\nstr(logan)\nclass(logan) #\"data.frame\"\nhead(logan)\nctab(occupation)\nctab(occupation,addmargins=TRUE)\nctab(occupation,style=\"w\",type=\"c\")\nctab(occupation,style=\"l\",type=\"n\")\nctab(occupation,style=\"l\",type=\"c\")\n\nz<-ctab(occupation,addmargins=TRUE,style=\"l\")\nz\nprint(z,addmargins=FALSE,dec.places=5)\nsummary(z)\n" }, { "path": "15c-Summary/99a-Pskim.R", "content": "# Data description ----\n#http://cmdlinetips.com/2018/03/skimr-a-r-package-to-skim-summary-data-effortlessly/\n\n# install.packages(\"devtools\")\ndevtools::install_github(\"ropenscilabs/skimr\")\n\nlibrary(gapminder)\nlibrary(dplyr)\nlibrary(skimr)\n\nx = seq(1000)\ny = rnorm(1000,mean=3)\ndf = data.frame(x,y)\nskim(df)\n\nskim(gapminder, lifeExp, gdpPercap) \nskim(mtcars)\n\nfuns <- list(\n min = min,\n max = max,\n mean = mean\n)\nskim_with(numeric = funs, append = FALSE)\n\ngapminder %>% \n filter(year==2007) %>%\n select(continent,lifeExp,gdpPercap)%>%\n group_by(continent) %>%\n skim()\n\n# Restore defaults\nskim_with_defaults()\n" }, { "path": "15c-Summary/99a-studentdata1.R", "content": "# Initialise Data\n# \n\n#Roll No\nrollno = seq(1,100)\nrollno\n\n#Subjects\nsubjects = data.frame(subid= c('STATS','MATHS','EXCEL', 'RPGM', 'PYTHON'),\n maxmks=c(150,150,100,100,100))\nsubjects\n\n#Date of Birth - Sequence\ndob= seq(as.Date(\"1994/1/1\"), by = \"week\", length.out = 100)\nlength(dob)\n\n#Course Names\ncourse = c('BSCDS','MSCDS', 'PGDDS', 'BTCSDA')\ncourse\n\n#Allot Courses to all Students in groups\nscourse = rep(x=c('BSCDS','MSCDS', 'PGDDS', 'BTCSDA'), c(20,30,15,35) )\nlength(scourse)\ntable(scourse)\n\n#Gender\nset.seed(1234)\ngender = sample(c('M','F'), size=100, replace=TRUE, prob=c(0.8,0.2))\ntable(gender)\n\n# Data frame for Master Data\nstudent = data.frame(rollno=rollno, name='XYZ', dob=dob,\n gender= gender, course=scourse, batch=c(2016,2017))\nstudent\nstr(student)\n\nsubscore = data.frame(expand.grid(rollno = student$rollno, \n subid = subjects$subid), score=0)\nstr(subscore)\ntable(subscore$subid)\nlength(subscore$rollno) # 100 students x 5 subjects = 500 rows\nhead(subscore)\n\n\n#subscore$score[subscore$score >= 0 & subscore$subid %in% c('STATS','MATHS')] = round(rnorm(100, mean=110, sd=10))\nsubjects$subid\n#STATS (110,10), MATHS(100,20), EXCEL(80,15), RPGM(70,12), PYTHON(75,10)\nsubscore$score[subscore$score >= 0 & subscore$subid == c('STATS') ] =\n round(rnorm(100, mean=110, sd=10))\nsubscore$score[subscore$score >= 0 & subscore$subid == c('MATHS') ] =\n round(rnorm(100, mean=100, sd=20))\nsubscore$score[subscore$score >= 0 & subscore$subid == c('EXCEL') ] =\n round(rnorm(100, mean=80, sd=15))\nsubscore$score[subscore$score >= 0 & subscore$subid == c('RPGM') ] =\n round(rnorm(100, mean=70, sd=12))\nsubscore$score[subscore$score >= 0 & subscore$subid == c('PYTHON') ] =\n round(rnorm(100, mean=75, sd=10))\n\nsummary(subscore)\naggregate(subscore$score, by=list(subscore$subid), mean)\nhead(subscore,70)\n\n\nlibrary(reshape2)\n\ndfcast = dcast(subscore, rollno ~ subid) # Good Function\nmelt(dfcast) # Reverse back to original\n\ntotal = merge(student, dfcast, by='rollno')\ntotal\noptions(digits=4)\nhead(total)\naggregate(total[c(7:11)], by=list(total$course, total$batch), FUN=mean)\naggregate(total[c(7:11)], by=list(total$course), FUN=mean)\naggregate(total[c(7:11)], by=list(total$batch), FUN=mean)\naggregate(total[c(7:11)], by=list(total$gender), FUN=mean)\nstr(total)\nlibrary(lubridate)\ntotal$age = as.period(interval(total$dob, Sys.Date()), unit='year')$year\nstr(total)\naggregate(total[c(7:11)], by=list(total$age), FUN=mean)\nwrite.csv(total, file='studentdata.csv', row.names = F)\n?write.csv\n# Clustering\n# \n# \nmydata = total\n\nlibrary(ggplot2)\nggplot(mydata, aes(STATS, RPGM, color=gender)) + geom_point()\nset.seed(20)\nsdataCluster <- kmeans(mydata[, 7:11], 5, nstart = 20)\nsdataCluster \nsdata$cluster = sdataCluster$cluster\nhead(sdata[c(1,7:13)])\n\nstr(mydata)\nmydata2 = mydata[-c(1:6)]\nstr(mydata2)\n# Prepare Data\nmydata2 <- na.omit(mydata2) # listwise deletion of missing\nhead(mydata2)\nmydata3 <- scale(mydata2) # standardize variables\nhead(mydata3)\nmydata = mydata3\n\n# No of Clusters\n# Determine number of clusters\nwss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))\nfor (i in 2:15) wss[i] <- sum(kmeans(mydata, \n centers=i)$withinss)\nplot(1:15, wss, type=\"b\", xlab=\"Number of Clusters\",\n ylab=\"Within groups sum of squares\")\n\n# K-Means Cluster Analysis\nfit <- kmeans(mydata, 7) # 7 cluster solution\n# get cluster means \naggregate(mydata,by=list(fit$cluster),FUN=mean)\n# append cluster assignment\nmydata <- data.frame(mydata, fit$cluster)\nhead(mydata)\n\n\n\n# Ward Hierarchical Clustering\nd <- dist(mydata, method = \"euclidean\") # distance matrix\nfit <- hclust(d, method=\"ward.D\") \nplot(fit) # display dendogram\ngroups <- cutree(fit, k=7) # cut tree into 5 clusters\n# draw dendogram with red borders around the 5 clusters \nrect.hclust(fit, k=7, border=\"red\")\nhead(mydata)\n" }, { "path": "15c-Summary/descriptive.R", "content": "#descriptive \n\nlibrary(mosaic)\nfavstats (~ mtcars$mpg)\nfavstats(~ mtcars$mpg, na.rm = TRUE)\n" }, { "path": "16a-tidyverse/20a-dplyr.R", "content": "# Data Manipulation through dplyr package\n# understand iris, mtcars, women datasets and dplyr package\n\nlibrary(dplyr)\ndplyr::tbl_df(iris)\nprint(dplyr::tbl_df(mtcars), n=15) #display more columns and rows\n#print(dplyr::tbl_df(mtcars), width=5)\ntbl_df(iris) %>% print(n = Inf)\ntbl_df(iris) %>% print(width = Inf)\ntbl_df(iris) %>% as.data.frame(iris)\n\nlibrary(dplyr)\n# glimpse\nglimpse(iris)\nglimpse(mtcars)\nstr(iris)\nView(iris)\n?mtcars\ndata()\nmtcars['mpg']\ndf = mtcars\nrow.names(df) = NULL\ndf %>% select(mpg)\nhead(mtcars)\nselect(mtcars, mpg, vs)\nmtcars %>% dplyr::select(vs, mpg, wt)\nmtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)\nmtcars\n\nnames(mtcars)\nfilter(mtcars, mpg > 23 | wt < 2)\nmtcars %>% filter(mpg > 23 & wt > 2)\nmtcars %>% select(mpg, wt) %>% filter(mpg > 23) \nmtcars %>% \n \nfilter(iris, Sepal.Length > 7)\nfilter(mtcars, cyl == 4)\ndistinct(mtcars)\ndf = data.frame(a=c(2,2),b=c(2,2))\ndf\ndistinct(df)\n\nsample_frac(mtcars, 0.2, replace=T)\nsample_n(mtcars, 60, replace=T) %>% select(mpg)\nslice(mtcars,10:14)\ntop_n(mtcars,-2, mpg)\n\nselect(mtcars, mpg) %>% arrange(desc(mpg))\n#Columns\nselect(mtcars, mpg, wt)\nselect(mtcars, contains('a'))\nnames(mtcars)\nselect(mtcars, contains ='vs')\nselect(mtcars, everything())\n\nmtcars %>% group_by(cyl, am) %>% summarise_all(mean)\n\ndf = data.frame(marks=c(1,2,3,7,1))\ncbind(df, dplyr::mutate_each(df, funs(min_rank)))\n\nmtcars %>% lead() %>% lag()\ndplyr::n(mtcars)\nselect(mtcars, mpg2 = mpg)\n\ndf = mtcars[1:4]\nnames(df) = c('MPG','C1','C2','C3')\ndf= rename(df, C5=C1)\nnames(df)\ndf\nrename(df, marks2 = marks)\ndf %>% mutate(marks2 = marks + 2, marks3 = marks + 4)\ndf %>% transmute(marks2 = marks + 2, marks3 = marks + 4)\n\nlibrary(nycflights13)\ndata(flights)\n\n\ndestinations <- group_by(flights, dest)\ndestinations\nsummarise(destinations,\n planes = n_distinct(tailnum),\n flights = n()\n)\n\n\nselect(iris, -ends_with(\"Width\")) %>% head\nvars <- c(\"Petal.Length\", \"Petal.Width1\")\nselect(iris, from=1, to=n()) \nfilter(mtcars, row_number() == n())\nfilter(mtcars, between(row_number(), 5, n()))\n\nmtcars %>% group_by(cyl) %>% filter(1:3)\n> mtcars %.% group_by(cyl) %.% filter(sample(n(), 10))\ngroup_by( mtcars, cyl ) %>% integer_filter(1:2)\n?integer_filter\n\n\n# Select odd\nmtcars %>% slice(from = 1, to = n(), by = 2)\n# Select even\nslice(mtcars, from = 2, to = n(), by = 2)\n# Select first 10\nslice(mtcars, from = 1, to = 10)\n# Select last 10\nslice(mtcars, n()-10: n())\nslice(mtcars, 1:4)\nmtcars\n\ndf <- tibble(\n g1 = c(1, 1, 2, 2, 2),\n g2 = c(1, 2, 1, 2, 1),\n a = sample(5), \n b = sample(5)\n)\ndf\ndf %>% slice(n()-2:n())\n\nvar1 <- quo(letters[1:5])\nvar1\nquo(toupper(!!var1))\n\n# Here we capture `letters[1:5]` as an expression:\nquo(toupper(letters[1:5]))\n#> ~toupper(letters[1:5])\n\n# Here we capture the value of `letters[1:5]`\nquo(toupper(!!letters[1:5]))\n#> ~toupper(c(\"a\", \"b\", \"c\", \"d\", \"e\"))\nquo(toupper(UQ(letters[1:5])))\n#> ~toupper(c(\"a\", \"b\", \"c\", \"d\", \"e\"))\n#\ntoupper(letters[1:5])\nquote(toupper(letters[1:5]))\n\nhead(mtcars)\nslice(mtcars, 1:5)\nslice(mtcars, 1) #rowno missing, first row\nslice(mtcars, 1L)\n\ntail(mtcars,n=5)\nslice(mtcars, n()-5:n())\nslice(mtcars, n()) #last row\nslice(mtcars, n() - 1) #2nd last row\n\nmtcars %>% top_n(2)\nmtcars %>% top_n(-2)\nmtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)\ndim(mtcars)\nbind_rows(mtcars, mtcars)\nbind_cols(mtcars,mtcars)\ngtable_combine(list(mtcars, mtcars))\ndim_desc(mtcars)\n\n# combine applies the same coercion rules\nf1 <- factor(\"a\")\nf2 <- factor(\"b\")\nc(f1, f2)\nunlist(list(f1, f2))\n\ngtable_combine(f1, f2)\ngtable_combine(list(f1, f2))\n\nslice( mtcars, c(1L,3L,2L,7L)) \n\nby_cyl <- mtcars %>% group_by(cyl)\n# Select first row in each group\nmtcars %>% slice(1)\nby_cyl %>% slice(1)\n# Select last row in each group\nmtcars %>% slice(n())\nby_cyl %>% slice(n())\n# Rows not present in group silently ignored\nmtcars %>% slice(10)\nby_cyl %>% slice(10)\n\n# Select arbitrary rows\nmtcars %>% slice(1:9)\nby_cyl %>% slice(1:3)\n\nmtcars %>% slice(c(1, 3, 9))\nby_cyl %>% slice(c(1, 3, 5))\n\n# Select even rows\nmtcars %>% slice(seq(2, n(), by = 2))\nby_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())\n\n# Drop first row in each group\nmtcars %>% group_by(cyl, am) %>% slice(1)\nby_cyl %>% slice(1)\n\n# Returns all values\nby_cyl %>% slice()\n\n\n\n\ndf <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))\ndf %>% top_n(2)\n\n# Negative values select bottom from group. Note that we get more\n# than 2 values here because there's a tie: top_n() either takes\n# all rows with a value, or none.\ndf %>% top_n(-2)\n\n\n\n#Default dataset\n#\nDefault %>% group_by(balance) %>% arrange(desc(balance)) %>% top_n(5, balance)\nDefault %>% group_by(balance) %>% arrange(balance) %>% top_n(5, balance)\n\nDefault %>% group_by(balance) %>% arrange(balance) %>% filter(row_number() %in% c(1:3,n()-5:n()))" }, { "path": "16a-tidyverse/21a-dplyr-select.R", "content": "# Select only selected variabels\n\niris4 = iris\nhead(iris4)\nhead(iris <- tbl_df(iris)) # so it prints a little nicer\nselect(iris, starts_with(\"Petal\")) # column name starts with\nselect(iris, ends_with(\"Width\"))\nselect(iris, contains(\"etal\"))\nselect(iris, matches(\".t.\"))\nselect(iris, Petal.Length, Petal.Width)\nvars <- c(\"Petal.Length\", \"Petal.Width\")\nselect(iris, one_of(vars))\n\ndf <- as.data.frame(matrix(runif(100), nrow = 10))\ndf\ndf <- tbl_df(df[c(3, 4, 7, 1, 9, 8, 5, 2, 6, 10)])\ndf\nselect(df, V4:V6)\nselect(df, num_range(\"V\", 4:6))\n\n\n#select-----\n\n" }, { "path": "16a-tidyverse/21b-dplyr-slice1.R", "content": "# Slice\n# does not work with DBMS\n\nlibrary(dplyr)\nslice(mtcars, 1L)\nslice(mtcars, n())\nslice(mtcars, 5:n())\nslice(mtcars, c(2,4,5,10))\n\nmtcars\n(by_cyl <- group_by(mtcars, cyl)) # ???\nnrow(by_cyl)\n\nslice(by_cyl, 1:2)\n" }, { "path": "16a-tidyverse/21c-dplyr-mutate1.R", "content": "# Mutate : add new, Add new without old\n#Mutate adds new variables and preserves existing; \n#transmute drops existing variables.\n\nlibrary(dplyr)\n\nmutate(mtcars, displ_l = disp / 61.0237)\ntransmute(mtcars, displ_l = disp / 61.0237)\n\nmutate(mtcars, cyl = NULL)\n" }, { "path": "16a-tidyverse/21d-dplyr-summarise.R", "content": "# summarise\n#Summarise multiple values to a single value.\n\nlibrary(dplyr)\n\nsummarise(mtcars, mean(disp))\nsummarise(group_by(mtcars, cyl), mean(disp))\nsummarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))\n" }, { "path": "16a-tidyverse/21e-dplyr-filter1.R", "content": "# Filter\n\nlibrary(dplyr)\n\nfilter(mtcars, cyl == 8)\nfilter(mtcars, cyl < 6)\n\n# Multiple criteria\nfilter(mtcars, cyl < 6 & vs == 1)\nfilter(mtcars, cyl < 6 | vs == 1)\n\n# Multiple arguments are equivalent to and\nfilter(mtcars, cyl < 6, vs == 1)\n\n\nfilter(mtcars, row_number() == 1L)\nfilter(mtcars, row_number() == n())\nfilter(mtcars, between(row_number(), 5, n()))\n" }, { "path": "16a-tidyverse/21f-dplyr-str.R", "content": "# Data Manipulation using tidyr\n#\nsalesd = sales\nlibrary(dplyr)\ntbl_df(salesd) # convert to tbl class\n\nglimpse(salesd) # dense summary of tbl data\n\nView(salesd) # spreasheet like format\n\nsalesd %>% group_by(daywk) # no summary till formula\n\nsalesd %>% group_by(daywk) %>% summarise(avg=mean(cakes)) %>% arrange(avg)\n\nsummarise_each(salesd, funs(mean))\ncount(salesd, cookies)\n\n#Grouping # need to use function to do some action\ngroup_by(salesd, daywk)\nsalesd %>% group_by(daywk) %>% summarise(avg = mean(cookies))\n\n\n#Reshaping\n#spread\nhead(tidyr::separate(data= salesd, col=sdate, into= c('y','m','d'), sep='-'))\nhead(tidyr::unite(data=salesd[,c('sdate','daywk')] , col='New Column',sep='::'), remove=T)\nnames(salesd)\nhead(tidyr::spread(salesd[1:5,c('sdate','pies','daywk')],sdate, pies, drop=T, fill='NoSale' ))\nhead(tidyr::spread(salesd[1:5,c('sdate','pies')],sdate, pies, drop=T, fill='NoSale' ))\n\n\n\ndplyr::arrange(salesd, daywk)\nhead(salesd)\n\n\n#Filter\ndplyr::filter(salesd, daywk == 'Thursday')\n\ndplyr::filter(salesd, cakes > 100 & pies < 40)\n\n#mutate\nmutate(salesd, incpies = pies * 1.5) %>% filter(incpies > 90)\n" }, { "path": "16a-tidyverse/21g-dplyr-arrange.R", "content": "#arrange\n\nlibrary(dplyr)\n# sort in particular order\narrange(mtcars, +cyl, -disp)\narrange(mtcars, desc(disp))\n" }, { "path": "16a-tidyverse/22b-dplyr-seperate1.R", "content": "#Seperate using dplry\n#http://cmdlinetips.com/2018/03/how-to-split-text-in-a-column-in-data-frame-in-r/\n\n#’s separate function is the best option to separate a column or split a column of text the way you want. Let us see some simple examples of using tidyr’s separate function.\n\nlibrary(dplyr)\nlibrary(tidyr)\ndf <- data.frame(file_name = c(\"1_jan_2018.csv\",\n \"2_feb_2018.csv\", \n \"3_mar_2018.csv\"))\n\ndf\ndf %>% separate(file_name, c(\"ID\", \"Month\",\"Year\", \"rest\"))\n#tidyr automatically found that the delimiters are underscore and dot and separted the single column to four columns with the names specified\n\ndf %>% separate(file_name,c(\"ID\", \"Month\",\"Year\"))\n#want only part of text in a column.\n\ndf %>% separate(file_name, \n c(\"ID\", \"Month\",\"Year\"),\n extra='drop')\n\ndf %>% tidyr::separate(file_name, \n c(\"ID\"), extra='drop')\n\ndf %>% tidyr::separate(file_name, c(\"ID\", \"Month\",\"Year\"),extra='drop') %>% select('Month')\n\n\ndf %>% tidyr::separate(file_name, c(\"ID\", \"Month\",\"Year\"), extra='drop') %>%\n tidyr::unite('ID_Month',c('ID','Month'))\n" }, { "path": "16a-tidyverse/22b-group.R", "content": "# dplyr - group by\n\nlibrary(dplyr)\n\nmtcars %>% group_by(am) \n#nothing - just separation\n\nmtcars %>% group_by(am) %>% summarise(mean(mpg))\n\n#subgroups\nmtcars %>% group_by(am, gear) %>% summarise(mean(mpg))\n\n#subgroups\nmtcars %>% group_by(am, gear) %>% summarise(mean(mpg)) %>% ungroup()\n" }, { "path": "16a-tidyverse/22c-summarise.R", "content": "#Summarise with Group\n\nlibrary(dplyr)\nhead(mtcars)\nmtcars %>% group_by(am) \n#nothing - just separation\n\nmtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt))\n\n#summarise_all\nmtcars %>% group_by(am, gear) %>% summarise_all(mean)\nmtcars %>% group_by(am, gear)%>% summarise_all(c(\"min\", \"max\"))\nmtcars %>% group_by(am, gear)%>% summarise_all(funs(med = median))\n\n\n\n#without Group\nmtcars %>% summarise(mean(mpg), max(wt))\nmtcars %>% summarise_all(mean)\nmtcars %>% select(wt, gear)%>% summarise_all(c(\"min\", \"max\"))\nmtcars %>% summarise_all(funs(med = median))\n\n\n\n\n\nmtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)\niris %>% summarise_if(is.numeric, mean, na.rm = TRUE)\n\n#specific columns\nmtcars %>% summarise_at(c(\"mpg\", \"wt\"), mean, na.rm = TRUE)\n\n" }, { "path": "16a-tidyverse/22g-tibble-rownames.R", "content": "# dplyr row names\nlibrary(dplyr)\n\nhead(iris)\niris %>% head()\n\nhead(mtcars)\nmtcars[1:2]\nmtcars %>% select(mpg,wt)\ndf = mtcars\ndf2 = mtcars\nhead(df)\ndf = tibble::rownames_to_column(df, var='cars')\nnames(df)\ndf %>% select(mpg,wt,cars)\n\n#has\ntibble::has_rownames(mtcars)\ntibble::has_rownames(df)\n\n#remove rownames \n#see without rownames\nhead(df2[1:5])\ntibble::remove_rownames(df2)\n\n\n\n#rowid as column\ntibble::rowid_to_column(df, var = \"rowid\")\n\n#column to rownames\nhead(df[1:5])\ntibble::column_to_rownames(df, var = \"cars\")\n" }, { "path": "16a-tidyverse/25b-magrittr.R", "content": "# Pipe Symbols in tidyverse\n\nlibrary(magrittr)\n#\nlibrary(magrittr)\n\ncar_data <- \n mtcars %>%\n subset(hp > 100) %>%\n aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%\n transform(kpl = mpg %>% multiply_by(0.4251)) %>%\n print\n\n#not a good alternative\ncar_data <- \n transform(aggregate(. ~ cyl, \n data = subset(mtcars, hp > 100), \n FUN = function(x) round(mean(x, 2))), \n kpl = mpg*0.4251)\ncar_data\n\n#anonymous functions\nhead(car_data,1)\ntail(car_data,1)\ncar_data %>%\n (function(x) {\n if (nrow(x) > 2) \n rbind(head(x, 1), tail(x, 1))\n else x\n })\n\n#shorthand notation of above\ncar_data %>%\n{ \n if (nrow(.) > 0)\n rbind(head(., 1), tail(., 1))\n else .\n}\n\n#useful but less used\n1:10 %>% (substitute(f(), list(f = sum)))\n\n#tee----\n#returns the left-hand side value, and not the result of the right-hand side operation.\nset.seed(1234)\nrnorm(200) %>%\n matrix(ncol = 2) %T>%\n plot %>% # plot usually does not return anything. \n colSums\nset.seed(1234)\nx=rnorm(200) \nm=matrix(x, ncol=2) #tee applied after this to skip 1 function for print\nplot(m)\ncolSums(m)\n\n\n\n#$operator-----\niris %>%\n subset(Sepal.Length > mean(Sepal.Length)) %$%\n cor(Sepal.Length, Sepal.Width)\n\ndata.frame(z = rnorm(100)) %$% \n ts.plot(z)\n\n\n\n#%<>%----\n#shorthand notation for expressions like foo <- foo %>% bar %>% baz, which boils down to foo %<>% bar %>% baz\n\nmpgwt <- mtcars %>% filter(mpg > 25) %>% select(mpg, wt) %>% print\nmpgwt <- mpgwt %>% transform(mpg = mpg * 2) %>% print\nmpgwt %<>% transform(mpg = mpg * 2) %>% print\n\n#aliases ------\nset.seed(1234)\nrnorm(1000) %>%\n multiply_by(5) %>%\n add(5) %>%\n { \n cat(\"Mean:\", mean(.), \n \"Variance:\", var(.), \"\\n\")\n head(.)\n }\n\n#alternative\nset.seed(1234)\nrnorm(100) %>% `*`(5) %>% `+`(5) %>% \n{\n cat(\"Mean:\", mean(.), \"Variance:\", var(.), \"\\n\")\n head(.)\n}\n" }, { "path": "16a-tidyverse/26c-tidyr-DSR1.R", "content": "# tidyr \n\nlibrary(tidyr)\n#devtools::install_github(\"garrettgman/DSR\")\nlibrary(DSR)\nhead(table1)\nhead(table2)\nhead(table3)\nhead(table4)\nhead(table5)\nhead(table6)\n\n\ntable1$cases\n\n#spread----\nlibrary(tidyr)\nhead(table2)\nspread(table2, type, count)\n?spread\n\n\n#gather-----\nhead(table4)\ngather(table4, \"year\", \"cases\", 2:3)\nhead(table4)\ngather(table4, \"year\", \"population\", 2:3)\n\ngather(table4, \"year\", \"population\", -1)\n\n?gather\n#seperate----\n#turns a single character column into multiple columns by splitting the values of the column wherever a separator character appears.\n?separate\nhead(table3)\nseparate(table3, rate, into = c(\"cases\", \"population\"))\n# specify character\nseparate(table3, rate, into = c(\"cases\", \"population\"), sep = \"/\")\n#integer position\nhead(table3)\nseparate(table3, year, into = c(\"century\", \"year\"), sep = 2)\n\n\n#unite----\nhead(table6)\nunite(table6, \"new\", century, year, sep = \"\")\n?unite\n" }, { "path": "16a-tidyverse/26d-tidyr-DSR-who.R", "content": "# tidyr \n\nlibrary(tidyr)\n#devtools::install_github(\"garrettgman/DSR\")\nlibrary(DSR)\ndata(who)\n#View(who)\n#who data set in the DSR package contains cases of tuberculosis (TB) reported between 1995 and 2013 sorted by country, age, and gender. \n\ndim(who) #60 columns\nnames(who)\nhead(who[1:10])\n\n\n#gather-----\nhead(names(who),10)\nwho1 <- gather(who, \"code\", \"value\", 5:60)\nhead(who1,10)\n\n#seperate----\n#default sep _\nwho2 <- separate(who1, code, c(\"new\", \"var\", \"sexage\"))\nhead(who2)\ntable(who2$sexage)\n\n#second pass will split sexage after the first character to create a sex column and an age column.\n#split after 1st character : f014 - f and 014\nwho3 <- separate(who2, sexage, c(\"sex\", \"age\"), sep = 1)\nhead(who3)\n\n\n#spread----\n#move the rel, ep, sn, and sp keys into their own column names with spread().\nhead(names(who3))\nhead(who3[c('var')])\ntable(who3$var)\nwho4 <- spread(who3, var, value)\nhead(who4)\n\ndim(who4)\nnames(who4)\n" }, { "path": "16a-tidyverse/31b-plyr1.R", "content": "#Plyr Package\n\n\nlibrary(plyr)\n#make some example data\ndd = data.frame(matrix(rnorm(216),72,3), \n c(rep(\"A\",24),rep(\"B\",24),rep(\"C\",24)),\n c(rep(\"J\",36),rep(\"K\",36)))\ncolnames(dd) <- c(\"v1\", \"v2\", \"v3\", \"dim1\", \"dim2\")\ndd\nhead(dd)\naggregate(dd$v1, by=list(dd$dim1, dd$dim2), FUN=mean)\naggregate(v1 ~ dim1 + dim2 , data=dd, FUN=mean)\n\naggregate(cbind(v1, v2) ~ dim1 + dim2 , data=dd, FUN=mean)\n\n#ddply is the plyr function\n\nplyr::ddply(dd, c(\"dim1\",\"dim2\"), function(df) mean(df$v1))\n\nddply(dd, c(\"dim1\",\"dim2\"), function(df)c(mean(df$v1), mean(df$v2), mean(df$v3), sd(df$v1), sd(df$v2), sd(df$v3)))\n\n\n\n\n#\nset.seed(1)\nd <- data.frame(year = rep(2000:2002, each = 3),\n count = round(runif(9, 0, 20)))\nprint(d)\n\nlibrary(plyr)\nddply(d, \"year\", function(x) {\n mean.count <- mean(x$count)\n sd.count <- sd(x$count)\n cv <- sd.count/mean.count\n data.frame(cv.count = cv)\n})\n\n\nddply(d, \"year\", summarise, mean.count = mean(count)) #mean of years\nddply(d, \"year\", transform, total.count = sum(count)) # yearwise sum also\nd\ncumsum(d$count)\n\n\nddply(d, \"year\", mutate, mu = mean(count), sigma = sd(count),\n cv = sigma/mu)\n\npar(mfrow = c(1, 3), mar = c(2, 2, 1, 1), oma = c(3, 3, 0, 0))\nd_ply(d, \"year\", transform, plot(count, main = unique(year), type = \"o\"))\nmtext(\"count\", side = 1, outer = TRUE, line = 1)\nmtext(\"frequency\", side = 2, outer = TRUE, line = 1)\n\n#----\nbaseball.dat <- subset(baseball, year > 2000) # data from the plyr package\nhead(baseball.dat)\nx <- ddply(baseball.dat, c(\"year\", \"team\"), summarize, homeruns = sum(hr))\nhead(x)\n\n#http://seananderson.ca/2013/12/01/plyr/\n\n\n#Speed\nsystem.time(ddply(baseball, \"id\", summarize, length(year)))\nsystem.time(tapply(baseball$year, baseball$id,function(x) length(x)))\n\n#alternative\nlibrary(data.table)\ndt <- data.table(baseball, key = \"id\")\nsystem.time(dt[, length(year), by = list(id)])\n\n\n#http://philogrammer.com/2016-12-03/aggregte/\nlibrary(plyr)\naggregate(price ~ cut, diamonds, each(mean, median))\n" }, { "path": "16a-tidyverse/32b-plyr-mutate.R", "content": "#Mutate\n\nlibrary(plyr)\ndf=mtcars\n\n#Case-ddply?----\nddply(df, .(am), mutate, mgp2 = mpg*2 + 1)\nddply(df, .(mpg), mutate, mgp2 = mpg*2 + 1)\nbase2\n" }, { "path": "16a-tidyverse/33d-dplyr-joins.R", "content": "# DM - dplyr - joins\n\n# Combine Data Sets\n\n(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))\n(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))\n\ndplyr::left_join(a, b, by = \"x1\")\n#Join matching rows from b to a.\n\ndplyr::right_join(a, b, by = \"x1\")\n#Join matching rows from a to b.\n\ndplyr::inner_join(a, b, by = \"x1\")\n#Join data. Retain only rows in both sets.\n\ndplyr::full_join(a, b, by = \"x1\")\n#Join data. Retain all values, all rows.\n\ndplyr::semi_join(a, b, by = \"x1\")\n#All rows in a that have a match in b.\n\ncbind(a,b)\n\ndplyr::anti_join(a, b, by = \"x1\")\n#All rows in a that do not have a match in b.\n\n#-----\ny = data.frame(x1=c('A','B','C'), x2=c(1,2,3))\nz = data.frame(x1=c('B','C','D'), x2=c(2,3,4))\ncbind(y,z)\n\ndplyr::intersect(y, z) # B&C\n#Rows that appear in both y and z.\n\ndplyr::union(y, z)\n#Rows that appear in either or both y and z.\n\ndplyr::setdiff(y, z)\n#Rows that appear in y but not z.\n\n#Bind\ndplyr::bind_rows(y, z)\n#Append z to y as new rows.\ndplyr::bind_cols(y, z)\n#Append z to y as new columns.\n#Caution: matches rows by position\n\n\n" }, { "path": "16a-tidyverse/33f-dplyr-split.R", "content": "# data\nstr(df1$dob)\nas.Date(\"5-Apr-91\", '%d-%b-%y')\ndf1$dob = as.Date(df1$dob, '%d-%b-%y')\ndf1 %>% select(sname, dob) %>% tidyr::separate(dob, c(\"y\", \"m\", \"d\"))\n\n\nlibrary(tidyr) #Reshape\ndf2 = df1\ndf2$name = rownames(df1) # col a column which is unique name\nhead(df2)\ndf2long <- df2 %>% select(name, cyl, carb) %>% tidyr::gather(key = name, value = name, cyl, carb) \nhead(df1long)\ndistinct(df1long)\n\ntidyr::unite(df1, 'rollnoname', c(rollno, sname), sep=\"-\") %>% select(1:5)%>% head\n\n" }, { "path": "16a-tidyverse/plyr-ddply-gpsum.R", "content": "# Group Summaries\n\n\n\n#Case- ----\n#dplyr\ndf = mtcars[,c('cyl','am','hp','wt','mpg')]\n\n# Note the use of the '.' function to allow\n# group and sex to be used without quoting\nddply(df, .(cyl, am), summarize, mean = round(mean(wt), 2),sd = round(sd(hp), 2))\n\n#Case-\n# An example using a formula for .variables\nddply(df[1:15,], ~ am, nrow)\n\n# Applying two functions; nrow and ncol\nddply(df, .(am), c(\"nrow\", \"ncol\"))\n\nbase2 <- ddply(baseball, .(id), mutate,\n career_year = year - min(year) + 1\n)" }, { "path": "16a-tidyverse/tidyr1.R", "content": "# Data Manipulation using tidyr\n#\nsalesd = sales\nlibrary(dplyr)\ntbl_df(salesd) # convert to tbl class\n\nglimpse(salesd) # dense summary of tbl data\n\nView(salesd) # spreasheet like format\n\nsalesd %>% group_by(daywk) # no summary till formula\n\nsalesd %>% group_by(daywk) %>% summarise(avg=mean(cakes)) %>% arrange(avg)\n\nsummarise_each(salesd, funs(mean))\ncount(salesd, cookies)\n\n#Grouping # need to use function to do some action\ngroup_by(salesd, daywk)\nsalesd %>% group_by(daywk) %>% summarise(avg = mean(cookies))\n\n\n#Reshaping\n#spread\nhead(tidyr::separate(data= salesd, col=sdate, into= c('y','m','d'), sep='-'))\nhead(tidyr::unite(data=salesd[,c('sdate','daywk')] , col='New Column',sep='::'), remove=T)\nnames(salesd)\nhead(tidyr::spread(salesd[1:5,c('sdate','pies','daywk')],sdate, pies, drop=T, fill='NoSale' ))\nhead(tidyr::spread(salesd[1:5,c('sdate','pies')],sdate, pies, drop=T, fill='NoSale' ))\n\n\n\ndplyr::arrange(salesd, daywk)\nhead(salesd)\n\n\n#Filter\ndplyr::filter(salesd, daywk == 'Thursday')\n\ndplyr::filter(salesd, cakes > 100 & pies < 40)\n\n#mutate\nmutate(salesd, incpies = pies * 1.5) %>% filter(incpies > 90)\n" }, { "path": "16a-tidyverse/zz-dplyr1.R", "content": "# Data Manipulation through dplyr\n\nlibrary(dplyr)\ndplyr::tbl_df(iris)\nprint(dplyr::tbl_df(mtcars), n=15)\n#print(dplyr::tbl_df(mtcars), width=5)\ntbl_df(iris) %>% print(n = Inf)\ntbl_df(iris) %>% print(width = Inf)\ntbl_df(iris) %>% as.data.frame(iris)\n\nlibrary(dplyr)\n# glimpse\nglimpse(iris)\nglimpse(mtcars)\nstr(iris)\nView(iris)\n?mtcars\ndata()\nmtcars['mpg']\ndf = mtcars\nrow.names(df) = NULL\ndf %>% select(mpg)\nhead(mtcars)\nselect(mtcars, mpg, vs)\nmtcars %>% dplyr::select(vs, mpg, wt)\nmtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)\nmtcars\n\nnames(mtcars)\nfilter(mtcars, mpg > 23 | wt < 2)\nmtcars %>% filter(mpg > 23 & wt > 2)\nmtcars %>% select(mpg, wt) %>% filter(mpg > 23) \nmtcars %>% \n\nfilter(iris, Sepal.Length > 7)\nfilter(mtcars, cyl == 4)\ndistinct(mtcars)\ndf = data.frame(a=c(2,2),b=c(2,2))\ndf\ndistinct(df)\n\nsample_frac(mtcars, 0.2, replace=T)\nsample_n(mtcars, 60, replace=T) %>% select(mpg)\nslice(mtcars,10:14)\ntop_n(mtcars,-2, mpg)\n\nselect(mtcars, mpg) %>% arrange(desc(mpg))\n#Columns\nselect(mtcars, mpg, wt)\nselect(mtcars, contains('a'))\nnames(mtcars)\nselect(mtcars, contains ='vs')\nselect(mtcars, everything())\n\nmtcars %>% group_by(cyl, am) %>% summarise_all(mean)\n\ndf = data.frame(marks=c(1,2,3,7,1))\ncbind(df, dplyr::mutate_each(df, funs(min_rank)))\n\nmtcars %>% lead() %>% lag()\ndplyr::n(mtcars)\nselect(mtcars, mpg2 = mpg)\n\ndf = mtcars[1:4]\nnames(df) = c('MPG','C1','C2','C3')\ndf= rename(df, C5=C1)\nnames(df)\ndf\nrename(df, marks2 = marks)\ndf %>% mutate(marks2 = marks + 2, marks3 = marks + 4)\ndf %>% transmute(marks2 = marks + 2, marks3 = marks + 4)\n\nlibrary(nycflights13)\ndata(flights)\n\n\ndestinations <- group_by(flights, dest)\ndestinations\nsummarise(destinations,\n planes = n_distinct(tailnum),\n flights = n()\n)\n\n\nselect(iris, -ends_with(\"Width\")) %>% head\nvars <- c(\"Petal.Length\", \"Petal.Width1\")\nselect(iris, from=1, to=n()) \nfilter(mtcars, row_number() == n())\nfilter(mtcars, between(row_number(), 5, n()))\n\nmtcars %>% group_by(cyl) %>% filter(1:3)\n> mtcars %.% group_by(cyl) %.% filter(sample(n(), 10))\ngroup_by( mtcars, cyl ) %>% integer_filter(1:2)\n?integer_filter\n\n\n# Select odd\nmtcars %>% slice(from = 1, to = n(), by = 2)\n# Select even\nslice(mtcars, from = 2, to = n(), by = 2)\n# Select first 10\nslice(mtcars, from = 1, to = 10)\n# Select last 10\nslice(mtcars, n()-10: n())\nslice(mtcars, 1:4)\nmtcars\n\ndf <- tibble(\n g1 = c(1, 1, 2, 2, 2),\n g2 = c(1, 2, 1, 2, 1),\n a = sample(5), \n b = sample(5)\n)\ndf\ndf %>% slice(n()-2:n())\n\nvar1 <- quo(letters[1:5])\nvar1\nquo(toupper(!!var1))\n\n# Here we capture `letters[1:5]` as an expression:\nquo(toupper(letters[1:5]))\n#> ~toupper(letters[1:5])\n\n# Here we capture the value of `letters[1:5]`\nquo(toupper(!!letters[1:5]))\n#> ~toupper(c(\"a\", \"b\", \"c\", \"d\", \"e\"))\nquo(toupper(UQ(letters[1:5])))\n#> ~toupper(c(\"a\", \"b\", \"c\", \"d\", \"e\"))\n#\ntoupper(letters[1:5])\nquote(toupper(letters[1:5]))\n\nhead(mtcars)\nslice(mtcars, 1:5)\nslice(mtcars, 1) #rowno missing, first row\nslice(mtcars, 1L)\n\ntail(mtcars,n=5)\nslice(mtcars, n()-5:n())\nslice(mtcars, n()) #last row\nslice(mtcars, n() - 1) #2nd last row\n\nmtcars %>% top_n(2)\nmtcars %>% top_n(-2)\nmtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)\ndim(mtcars)\nbind_rows(mtcars, mtcars)\nbind_cols(mtcars,mtcars)\ngtable_combine(list(mtcars, mtcars))\ndim_desc(mtcars)\n\n# combine applies the same coercion rules\nf1 <- factor(\"a\")\nf2 <- factor(\"b\")\nc(f1, f2)\nunlist(list(f1, f2))\n\ngtable_combine(f1, f2)\ngtable_combine(list(f1, f2))\n\nslice( mtcars, c(1L,3L,2L,7L)) \n\nby_cyl <- mtcars %>% group_by(cyl)\n# Select first row in each group\nmtcars %>% slice(1)\nby_cyl %>% slice(1)\n# Select last row in each group\nmtcars %>% slice(n())\nby_cyl %>% slice(n())\n# Rows not present in group silently ignored\nmtcars %>% slice(10)\nby_cyl %>% slice(10)\n\n# Select arbitrary rows\nmtcars %>% slice(1:9)\nby_cyl %>% slice(1:3)\n\nmtcars %>% slice(c(1, 3, 9))\nby_cyl %>% slice(c(1, 3, 5))\n\n# Select even rows\nmtcars %>% slice(seq(2, n(), by = 2))\nby_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())\n\n# Drop first row in each group\nmtcars %>% group_by(cyl, am) %>% slice(1)\nby_cyl %>% slice(1)\n\n# Returns all values\nby_cyl %>% slice()\n\n\n\n\ndf <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))\ndf %>% top_n(2)\n\n# Negative values select bottom from group. Note that we get more\n# than 2 values here because there's a tie: top_n() either takes\n# all rows with a value, or none.\ndf %>% top_n(-2)\n\n\n\n#Default dataset\n#\nDefault %>% group_by(balance) %>% arrange(desc(balance)) %>% top_n(5, balance)\nDefault %>% group_by(balance) %>% arrange(balance) %>% top_n(5, balance)\n\nDefault %>% group_by(balance) %>% arrange(balance) %>% filter(row_number() %in% c(1:3,n()-5:n()))" }, { "path": "16a-tidyverse/zz-tidy-dataformating.R", "content": "#http://garrettgman.github.io/tidying/\n\n" }, { "path": "16b-DT/0-DTsummary.R", "content": "#data Table \n#types of techniquest/ links/ commands\n\n#(http://brooksandrew.github.io/simpleblog/articles/advanced-data-table/#method-1-in-line )\n" }, { "path": "16b-DT/1-dt1.R", "content": "#data.table\n\nlibrary(data.table)\nDTmtcars = data.table(mtcars)\n\nstr(DTmtcars)\n\n#group by\n\nDTmtcars[ , mean(mpg), by=gear]\nDTmtcars[ , .(MeanMPG = mean(mpg)), by=list(gear,cyl)]\n\n#count of cars in the category\nDTmtcars[ , .(MeanMPG = mean(mpg), n=.N), by=list(gear,cyl)]\n\nDTmtcars[ , .SD[, mean(mpg)], by=gear]\nDTmtcars[ , .SD[, mean(mpg),by=gear], by=cyl]\nDTmtcars[ , .SD[, .(mean(mpg), n=.N),by=gear], by=cyl]\nDTmtcars[ , .SD[, .(mean(mpg), n=.N),by=gear], by=list(cyl,am)]\n\n\n#sort\nDTmtcars\nDTmtcars[ sort(mpg),mean(mpg) , by=cyl]\n\n\n\nDTmtcars[,`:=`(avg_mpg_cyl=mean(mpg), Ncyl=.N), by=cyl]\nDTmtcars[,.(avg_mpg_cyl=mean(mpg), Ncyl=.N), by=cyl]\n\nDTmtcars\nDTmtcars[,.(Ncylgear=.N, avg_mpg_cyl_gear=mean(mpg)), by=.(cyl, gear)]\n\nsetkey(DTmtcars, cyl, gear) \nDTmtcars\n\n\n#inrease no of rows\ndt <- data.table(mtcars)\ndt <- dt[sample(1:.N, 100000, replace=T), ] # increase # of rows in mtcars\ndt$gear <- sample(1:300, nrow(dt), replace=T) # adding in more cateogries\ndt\n\nuid <- unique(dt$gear)\nsystem.time(dt[, dt[!gear %in% (uid[.GRP]), mean(mpg), by=cyl] , by=gear][order(cyl, gear)])\ndt[, dt[!gear %in% (uid[.GRP]), mean(mpg), by=cyl] , by=gear][order(cyl, gear)]\nmtcars\n#????\nDTmtcars[,{\n vbar = sum(hp)\n n = .N\n .SD[,.(n, .N, sum_in_gear_cyl=sum(hp), sum_in_cyl=vbar), by=gear]\n} , by=cyl]\n" }, { "path": "16b-DT/2-DT.R", "content": "#Data.table\n\n#combining tables\n\nlibrary(data.table)\n(rollno = paste('S',10:20,sep=''))\n(sname = paste('Student',1:11,sep=''))\nstudents = data.table(rollno, sname)\nstudents\n\n(rollno2 = paste('S',10:15,sep=''))\n(sub1 = round(rnorm(6, mean=60, sd=10)))\n(marks = data.table(rollno2, sub1))\n\n#join\nstudents[ marks, on= .(rollno = rollno2)]\n\n#roll ?\nstudents[ marks, on= .(rollno = rollno2), roll=T]\n\n#cbind\ncbind(students, students)\nrbind(students, students)\n\n\n#reshape\n(rollno3 = paste('S',11:20,sep=''))\n(sname3 = paste('Student',1:10,sep=''))\n(sub1 = round(rnorm(10, mean=60, sd=10)))\n(sub2 = round(rnorm(10, mean=70, sd=5)))\n\nstudents3 = data.table(rollno3, sname3, sub1, sub2)\nstudents3\n#melt\nDTmelt <- melt(students3, id.vars= c('rollno3', 'sname3'), measure.vars = c('sub1','sub2'), variable.name='subject', value.name = 'marks')\nDTmelt\n\n#wide/ spread\ndcast(DTmelt, formula = rollno3 + sname3 ~ subject, value.var = 'marks')\n" }, { "path": "16b-DT/3-DT.R", "content": "#data.table - subset data\n\nlibrary(data.table)\nDTmtcars = data.table(mtcars)\n\nDTmtcars\n\nDTmtcars[ , .SD, mpg > 25]\n\n#sum groupwise for mpg > 25\n\n#first row\nDTmtcars[ , .SD[1], mpg > 25]\nDTmtcars[ , .SD[.N], mpg > 25]\n#last row\n\n#condition based\nDTmtcars[ mpg > 20 & am == 1, ]\n\nDTmtcars[ mpg > 20, .(sumHP=sum(hp)), by='gear']\n\n#nested groups\nDTmtcars[ mpg > 20, .(sumHP=sum(hp)), by=c('gear', 'cyl')]\nDTmtcars[ mpg > 20, .(sumHP=sum(hp), meanMPG = mean(mpg)), by=c('gear', 'cyl')]\n\n#group and sort\nDTmtcars[ mpg > 20, .(sumHP=sum(hp)), by=gear]\nDTmtcars[ mpg > 20, .(sumHP=sum(hp)), keyby= .(gear)]\n\nDTmtcars[ mpg > 20, .(sumHP=sum(hp)), keyby= .(gear)][ order(sumHP),]\nDTmtcars[ mpg > 20, .(sumHP=sum(hp)), keyby= .(gear)][ order(-sumHP),]\n\n\n\nDTmtcars[order(-rank(gear), mpg)]\n\nDTmtcars[order(-rank(gear), cyl), .(sumHP=sum(hp)), by=c('gear','cyl')]\n\nDTmtcars[am == 0, tx :='Auto']\nDTmtcars\nDTmtcars[am == 1, tx1 := 'Manual']\n\nDTmtcars[ , tx := ifelse(am==0, 'Auto', 'Manual')]\nDTmtcars\n\n\nDTmtcars[ , mpg]\nDTmtcars[ , mpg, ] #like vector\nDTmtcars[ , c(1), ] #column \n\nDTmtcars[ , .(mpg,hp) ] #working\n\nDTmtcars[am==1 , .(mpg,hp) ] #working\n\nsetorder(DTmtcars, cyl,-gear)\nDTmtcars[ , .(cyl, gear, mpg)]\n\n#group Min/ Max/ Optima\nDTmtcars[ , .SD[which.max(mpg)], by=cyl]\nDTmtcars[ , .SD[which(mpg > 25)], by=cyl]\nDTmtcars[ , .SD[which.min(mpg)], by=cyl]\nDTmtcars[ , .SD[which.min(mpg)], by=c('cyl','gear')]\n" }, { "path": "17a-Stats/10-statslinks.R", "content": "#Stats Links\n#https://ugoproto.github.io/ugo_r_doc/A_Hands-on_Introduction_to_Statistics_with_R,_Notes/\n " }, { "path": "17a-Stats/10a-distributions.R", "content": "# Distribution\n#http://www.stat.umn.edu/geyer/old/5101/rlook.html\n\n?Distributions\n# The functions for the density/mass function, cumulative distribution function, quantile function and random variate generation are named in the form dxxx, pxxx, qxxx and rxxx respectively.\n#\n#Every distribution that R handles has four functions. There is a root name, for example, the root name for the normal distribution is norm. This root is prefixed by one of the letters\n\n# p for \"probability\", the cumulative distribution function (c. d. f.)\n# q for \"quantile\", the inverse c. d. f.\n# d for \"density\", the density function (p. f. or p. d. f.)\n# r for \"random\", a random variable having the specified distribution\n\n# For the beta distribution see dbeta.\n# \n# For the binomial (including Bernoulli) distribution see dbinom.\n# \n# For the Cauchy distribution see dcauchy.\n# \n# For the chi-squared distribution see dchisq.\n# \n# For the exponential distribution see dexp.\n# \n# For the F distribution see df.\n# \n# For the gamma distribution see dgamma.\n# \n# For the geometric distribution see dgeom. (This is also a special case of the negative binomial.)\n# \n# For the hypergeometric distribution see dhyper.\n# \n# For the log-normal distribution see dlnorm.\n# \n# For the multinomial distribution see dmultinom.\n# \n# For the negative binomial distribution see dnbinom.\n# \n# For the normal distribution see dnorm.\n# \n# For the Poisson distribution see dpois.\n# \n# For the Student's t distribution see dt.\n# \n# For the uniform distribution see dunif.\n# \n# For the Weibull distribution see dweibull.\n#\n# For less common distributions of test statistics see pbirthday, dsignrank, ptukey and dwilcox (and see the ‘See Also’ section of cor.test)." }, { "path": "17a-Stats/11a-normal.R", "content": "#Normal Distribution\n\n#pnorm is the R function that calculates the c. d. f.\n\n#F(x) = P(X <= x)\n#where X is normal. Optional arguments described on the on-line documentation specify the parameters of the particular normal distribution.\n#Both of the R commands in the box below do exactly the same thing.\n\n\npnorm(27.4, mean=50, sd=20)\npnorm(27.4, 50, 20)\n\n#what is the probability of x being < 27.4 from distribution having mean 50 and sd 20\n#They look up P(X < 27.4) when X is normal with mean 50 and standard deviation 20.\n\n\n#Question: Suppose widgit weights produced at Acme Widgit Works have weights that are normally distributed with mean 17.46 grams and variance 375.67 grams. What is the probability that a randomly chosen widgit weighs more then 19 grams?\n \n#Question Rephrased: What is P(X > 19) when X has the N(17.46, 375.67) distribution?\n \n# Caution: R wants the s. d. as the parameter, not the variance. We'll need to take a square root!\n\n#Answer:\n1 - pnorm(19, mean=17.46, sd=sqrt(375.67))\n\n" }, { "path": "17a-Stats/11b-normalq.R", "content": "#Normal - inverse - qnorm\n\n#qnorm is the R function that calculates the inverse c. d. f. F-1 of the normal distribution The c. d. f. and the inverse c. d. f. are related by\n\n#p = F(x) :x = F-1(p)\n#So given a number p between zero and one, qnorm looks up the p-th quantile of the normal distribution. As with pnorm, optional arguments specify the mean and standard deviation of the distribution\n#Example :Question: Suppose IQ scores are normally distributed with mean 100 and standard deviation 15. What is the 95th percentile of the distribution of IQ scores?\n#Question Rephrased: What is F-1(0.95) when X has the N(100, 152) distribution?\nqnorm(0.95, mean=100, sd=15)\n" }, { "path": "17a-Stats/11c-normald.R", "content": "#Normal Distribution - Density\n\n#Density\n#dnorm is the R function that calculates the p. d. f. f of the normal distribution. As with pnorm and qnorm, optional arguments specify the mean and standard deviation of the distribution.\n\n#There's not much need for this function in doing calculations, because you need to do integrals to use any p. d. f., and R doesn't do integrals. In fact, there's not much use for the \"d\" function for any continuous distribution (discrete distributions are entirely another matter, for them the \"d\" functions are very useful, see the section about dbinom).\n\n#For an example of the use of pnorm, see the following section.\n\n#Random Variates\n#rnorm is the R function that simulates random variates having a specified normal distribution. As with pnorm, qnorm, and dnorm, optional arguments specify the mean and standard deviation of the distribution.\n\nx <- rnorm(1000, mean=100, sd=15)\nhist(x, probability=TRUE)\nxx <- seq(min(x), max(x), length=100)\nlines(xx, dnorm(xx, mean=100, sd=15))\n\n#This generates 1000 i. i. d. normal random numbers (first line), plots their histogram (second line), and graphs the p. d. f. of the same normal distribution (third and forth lines)." }, { "path": "17a-Stats/12a-binomial.R", "content": "# The Binomial Distribtion\n#http://www.stat.umn.edu/geyer/old/5101/rlook.html\n\n#dbinom is the R function that calculates the p. f. of the binomial distribution. Optional arguments described on the on-line documentation specify the parameters of the particular binomial distribution.\n\n#Both of the R commands in the box below do exactly the same thing.\n\ndbinom(27, size=100, prob=0.25)\ndbinom(27, 100, 0.25)\n\n\n#They look up P(X = 27) when X is has the Bin(100, 0.25) distribution\n#Example Question: Suppose widgits produced at Acme Widgit Works have probability 0.005 of being defective. Suppose widgits are shipped in cartons containing 25 widgits. What is the probability that a randomly chosen carton contains exactly one defective widgit?\n \n#Question Rephrased: What is P(X = 1) when X has the Bin(25, 0.005) distribution?\ndbinom(1, 25, 0.005)\n\n \n Answer:\n \n " }, { "path": "17a-Stats/13a-mean.R", "content": "#mean\n\nmtcars\nhead(mtcars)\nmean(mtcars$mpg)\n\nx=c(1:10,NA,100)\nx\nmean(x)\nmean(x, na.rm=T)\nmean(x, na.rm=T, trim=.1)\nmean(2:10)\n" }, { "path": "17a-Stats/14a-median.R", "content": "#Median\n\nhead(mtcars)\nmedian(mtcars$mpg)\n\nx=c(1:10,NA,100)\nx\nmedian(x)\nmedian(x, na.rm=T)\nmedian(x, na.rm=T, trim=.1)\nmedian(2:10)\n2:10\n" }, { "path": "17a-Stats/15a-mode.R", "content": "#mode\n\nmtcars\nhead(mtcars)\n(t1= table(mtcars$cyl))\n\nmax(table(mtcars$cyl))\n\nwhich.max(table(mtcars$cyl))\n\nnames(t1)[which(t1==max(t1))]\n\nsubset(mtcars, cyl==8)\nsubset(mtcars, cyl==names(t1)[which(t1==max(t1))])\n\n\n#two modes\n#Testing if there are two or more numbers with same frequncy\nx <- c(8,2,7,1,2,9,8,2,10,9,8)\nsort(x)\n## [1] 1 2 2 2 7 8 8 8 9 9 10\n#Mode\ntable(x)\nnames(table(x))[table(x)==max(table(x))]\n## [1] \"2\" \"8\"\n" }, { "path": "17a-Stats/15b-mode.R", "content": "# Mode with Package\n#https://cran.r-project.org/web/packages/modeest/modeest.pdf\n\nlibrary(modeest)\n\nmlv(mtcars$cyl,method='shorth')\nmlv(mtcars$cyl, method = \"discrete\")\n\n#M <- mlv(x, method = \"discrete\")\n#plot(M)\n#table(mtcars$cyl)\n" }, { "path": "17a-Stats/16a-range.R", "content": "#Range\n\nrange(mtcars$mpg)\n\nmin(mtcars$mpg)\nmax(mtcars$mpg)\n" }, { "path": "17a-Stats/17a-sd.R", "content": "#Standard Deviation\n\nsd(mtcars$mpg)\n\nvar(mtcars$mpg)\nsqrt(var(mtcars$mpg))\n\n" }, { "path": "17a-Stats/18a-covariance.R", "content": "#Covariance\n\ncov(mtcars$mpg, mtcars$wt)\n\ncov(mtcars$wt, mtcars$mpg)\n\n#direction of relationship. magnitude has no value\n" }, { "path": "17a-Stats/19a-correlation.R", "content": "#Correlation\n\ncor(mtcars$mpg, mtcars$wt)\n\ncor(mtcars$wt, mtcars$mpg)\n\n#strength and direction of relationship\n" }, { "path": "17a-Stats/20a-coev.R", "content": "#Coefficient of Variance\n#Measures of dispersion—such as range, variance, standard deviation, and coefficient of variation\n\nsd(mtcars$mpg) / mean(mtcars$mpg)\n\nlibrary(sjstats)\ncv(mtcars$mpg)\n" }, { "path": "17a-Stats/37a-sample1.R", "content": "# Sample Methods\n\n#Base Package\nletters[1:5]\n(L1 = letters[1:26])\n(L2 = LETTERS[1:26])\n(M1 = month.abb[1:12])\n(M2 = month.name[1:12])\n\nsample(L1, 2)\nsample(L1, 30) #error\nsample(L1, 30, replace=T) \nsample(M1)\nsample(M1, 5)\n\nset.seed(10)\nsample(M1,5)\nset.seed(10)\nsample(M1,5)\nsample(M1,5) # diff output w/o same seeding\n\n(grades= LETTERS[1:5])\n(sgrades = sample(grades, 20, replace=T, prob=c(0.4,0.2,0.2,0.1,0.1)))\ntable(sgrades)\n\n\n\nprop.table(table(sgrades))\n(sgrades = sample(grades, 20, replace=T, prob=c(0.4,0.2,0.2,0.1,0.1)))\ntable(sgrades)\nprop.table(table(sgrades))\n\n\n#Different Concept\nsample(2, nrow(mtcars), replace=T, prob=c(0.8,0.2))\n\n#Dplyr\nlibrary(dplyr)\ndim(mtcars)\nsample_frac(mtcars, 0.2)\nsample_n(mtcars,5)\n\n\n\n\nlibrary(caTools)\nset.seed(1000)\nsplit = sample.split(mtcars$mpg, SplitRatio = 0.80)\ntrain = subset(mtcars, split==TRUE)\ntest = subset(mtcars, split==FALSE)\nhead(train)\nhead(test)\n" }, { "path": "17a-Stats/40a-missing1.R", "content": "# Missing Values\n# Create, Detect, Replace, Remove, Impute, Visualise\nv1 = 1:5\nv1\n(v1 = c(1,2,NA,NA,5))\nis.na(v1)\nv1=NULL\nv1 # remove all values\n\n(v1 = c(1,2,NA,NA,5))\nsum(c(TRUE, FALSE,TRUE))\nsum(is.na(v1)) # sum TRUE values ie missing values\n\nmean(v1) # cannot calculate if NA values in vector\nmean(v1, na.rm=T) # remove and calc\n(1+2+5)/3\n\n(v1a = na.omit(v1)) # omit missing values and put in new vector\nsum(v1a) # now you can sum on non NA values\n?na.omit\n\nanyNA(v1) # is there any NA value\n?anyNA\n#impute missing values of vector with mean value of non NA values\nv1\nmean(v1, na.rm=T) # this value to be used\nv1[is.na(v1)]\nv1[c(FALSE,F,T,T,F)]\nv1\nis.na(v1)\nv1[is.na(v1)] = mean(v1, na.rm=T)\nv1\n\n#Use VIM package to load sleep dataset with NA values in rows & col\nlibrary(VIM)\ndata(sleep, package='VIM')\nsummary(sleep)# see NA values in cols\nhead(sleep)\ndim(sleep)\ncomplete.cases(sleep) #all complete rows without any missing values\nsum(complete.cases(sleep)) # count non NA rows/ complete 42\nsum(!complete.cases(sleep)) #count NA rows 20\nmean(complete.cases(sleep)) #42/62\n\n# total cases = 62, complete=42 \nsleep[complete.cases(sleep),] # list rows which are complete\nsleep[!complete.cases(sleep),] # list rows which have NA values\n\n# Particular Column\nis.na(sleep$Dream)\nhead(sleep)\nsum(is.na(sleep$Dream))\nmean(is.na(sleep$Dream)) # 12/62\n\n# How many missing values\nsum(is.na(sleep)) #total in data frame\n\nsummary(sleep)\n\nnames(sleep)# check columname\ncolSums(is.na(sleep)) #NA values in each column\nrowSums(is.na(sleep)) #NA values in each row\nhead(sleep)\nas.data.table(sleep) #data.table format\nis.na(sleep[1:3,]) # first 3 rows\nhead(sleep,n=3)\n\n#Tabulate\nlibrary(mice)\nmice::md.pattern(sleep)\n#42 rows without any missing values # 2 rows with 1 NA NonD\n\n#Visualisation\nVIM::aggr(sleep, prop=F, numbers=T)\n#NonD max NA values \nVIM::aggr(sleep, prop=T) # Proportion\nVIM::matrixplot(sleep) # rowno and missing values in red\nVIM::marginmatrix(sleep)\nVIM::marginplot(sleep[c('Gest','Dream')], pch=c(20), col=c(\"darkgray\", \"red\", \"blue\")) # finding relationship between NA value\n\n\n#Exploration\nx = as.data.frame(abs(is.na(sleep)))\nhead(sleep, na=5)\nhead(x, n=5)\ny = x[which(apply(x, 2, sum) > 0)]\ncor(y) #Dream - NonD 0.9\ncor(sleep, y, use=\"pairwise.complete.obs\")\n#ignore NA, \n#\n\n#Listwise Deletion\noptions(digits=3)\nna.omit(sleep)\ncor(na.omit(sleep))\ncor(sleep, use=\"complete.obs\")\n\nfit = lm(Dream ~ Span + Gest, data= na.omit(sleep))\nsummary(fit)\n\n\n#imputations Packages - mice, Amelia, mi : mice, with, pool\n\nimp = mice::mice(sleep, seed=1234)\n?mice\n \n# explore more\n" }, { "path": "17a-Stats/40b-missing1.R", "content": "#misssing data\n\n#NA\nz = c(1, 2, NA, 8 , 3, NA, 3)\nz\nis.na(z)\n\nx = c('A',NA,'B')\nis.na(x)\n\n# NULL\nx1 = c(1, NULL, 3)\nx1\nis.null(x1)\n\n#NULL cannot be part of vector\nx= 1/0\ny = x + 2\nis.nan(x)\nis.nan(y)\n \n " }, { "path": "17a-Stats/40c-missing2.R", "content": "# Missing Values\n\ndf = read.csv(file='./data/dsstudents2.csv')\ndf\nstr(df)\nsummary(df)\nis.na(df)\nanyNA(df)\ntable(is.na(df))\nall(is.na(df))\nall(is.na(df)==F)\nall(is.na(df)==T)\nall(is.na(df)==F & is.na(df)==T )\ndf\ncomplete.cases(df)\n!complete.cases(df)\nsum(complete.cases(df))\nsum(!complete.cases(df))\n\ndf[complete.cases(df),][1:2]\nmean(df$excel)\nmean(df$excel, na.rm=T)\nmean(df$excel, na.rm=T, trim=.2)\nmean(df$excel, na.rm=T, trim=.4)\ncat(df$excel, trim=.2)\n\ndf$excel\nna.omit(df$excel)\nna.omit(df)[1:2]\n\nis.na(df)\ncolSums(is.na(df)) # apply to numeric M, DF\n#there is no colsums (small S)\napply(df,2, function(x) sum(is.na(x)))\napply(is.na(df), 2, sum)\n\nfunModeling::df_status(df,2)\nVIM::aggr(df)\nAmelia::missmap(df)\nlength(which(is.na(df['excel']==T))) # particular col\nsum(is.na(df$excel))\nmean(df$excel,na.rm=T)\ndf$excel\ndf$excel[is.na(df$excel)] = mean(df$excel,na.rm=T)\ndf$excel\nma\n\n\nlibrary(mice)\nlibrary(purrr)\nlibrary(dplyr)\n# map_df with purrr\nx=purrr::map_df(df, function(x) sum(is.na(x)))\nx\nx[5:14]\n\n#dplyr::describe(df)\nna_count <-sapply(df, \n function(y) sum(length(which(is.na(y)))))\n#should give you a list with the counts for each column.\n\n(na_count <- data.frame(na_count))\n" }, { "path": "17a-Stats/40d-missing3.R", "content": "# NA Values\n\nis.finite(10^(305:310))\n#[1] TRUE TRUE TRUE TRUE FALSE FALSE\n\n2 / 0\n#[1] Inf\n\n4 - Inf\n#[1] -Inf\n\n#divide any real number by infinity, you get zero.\n#But what if you divide infinity by infinity?\nInf / Inf\n#[1] NaN\n\n# Removing bad values - NA, NaN infiite\n#NULL, NA, or NaN values.\n\napply(df, 2, function(x) any(is.na(x)))\n\n#Will test the condition by column.\n\n#In the case that you would like to test for both conditions,\n#you can add the pipe operator, |.\n\nany(is.na(x) | is.infinite(x))\n\n#It reads, \"Does any member of the variable x have \n#the value NA or -Inf or Inf?\"\n\n#if you want to learn the names of the column with these values,\n#assign the output of our apply function above to a variable, \n#and subset the column names by that new variable:\n \nindx <- apply(df, 2, function(x) any(is.na(x) | is.infinite(x)))\ncolnames[indx]\n\n\n\n# Missing Values Plot\nv1 = ceiling(rnorm(100,60,20))\nv1[sample(1:40, 10, replace=F)] = NA\nv1\nx=v1\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(dplyr)\n\nggplot_missing <- function(x){\n x %>% is.na %>% melt %>% ggplot(data = ., aes(x = X2, y = X1)) +\n geom_raster(aes(fill = value)) + scale_fill_grey(name = \"\",\n labels = c(\"Present\",\"Missing\")) + theme_minimal() + \n theme(axis.text.x = element_text(angle=45, vjust=0.5)) + \n labs(x = \"Variables in Dataset\", y = \"Rows / observations\")\n}\n#Let’s test it out\nggplot_missing(x)\n\n\n#Function\t Inf\t –Inf\tNaN\t NA\n#is.finite()\tFALSE\tFALSE\tFALSE\tFALSE\n#is.infinite()TRUE\tTRUE\tFALSE\tFALSE\n#is.nan()\t FALSE\tFALSE\tTRUE\tFALSE\n#is.na()\t FALSE\tFALSE\tTRUE\tTRUE\n#R actually considers NaN to be numeric, \n#so you can use NaN in calculations. The outcome of those calculations is always NaN,\n\nNaN + 4\n#[1] NaN" }, { "path": "17a-Stats/42a-outlier1.R", "content": "# Outlier Analysis\n#outlier not predicted well in by fitted regression model\n\nmarks1 = c(1:80, 100)\nmarks2 = c(1:80,100, 1000)\nsummary(marks1)\nsummary(marks2)\n\npar(mfrow=c(1,2)) # multiple frames per row\nboxplot(marks1)\n(q1= quantile(marks1, c(.0,.25,.50,.75,1)))\nabline(h=q1, col=1:6)\ntext(1, q1+2, labels=c('min','1Q','median','3Q','max'), col=1:5)\ntitle(main='Marks1')\n\nboxplot(marks2)\n(q2= quantile(marks2, c(.0,.25,.50,.75,1)))\nabline(h=q2, col=1:6)\ntext(1, q2+2, labels=c('min','1Q','median','3Q','max'), col=1:5)\ntitle(main='Marks2')\n\nmean(marks1); mean(marks2) # diff in means due to outlier\n; mean(marks2, trim=.1)# trim extreme values\nmedian(marks1); median(marks2) # not much diff\n\n# when you have large values how to identify outlier rows\n#Identify---\nset.seed(482)\n(y = c(rnorm(100,50,10), 150, 170,200))\nboxplot(y)\nidentify(rep(1, length(y)), y, labels = seq_along(y))\ny[c(101,102,103)]\n\n# How do outlier effect Regressions : Identify them\n\nnames(mtcars)\nmtcarslm = lm(mpg ~ wt + cyl + hp + am + gear, data=mtcars)\nboxplot(mtcars)\nmtv1 = c('hp', 'disp') # few variables only\nboxplot(mtcars[mtv1])\nboxplot(mtcars[c(-mtv1)])\nix= match(mtv1,names(mtcars))\nboxplot(mtcars[,-ix])\n?match\nlibrary(car)\ncar::outlierTest(mtcarslm)\n\ncar::influencePlot(mtcarslm)\n\n\n\n#Extras -----\nlibrary(outliers)\nset.seed(1234)\nx = rnorm(10)\nchisq.out.test(x)\nchisq.out.test(x,opposite=TRUE)\n\ncar::outlierTest(fit, labels=names(rstud))\ncar::outlierTest(lm(prestige~income+education, data=Duncan))\n\n#car::avPlots(mtcarslm, ask=F, id.method='identify')\n\nlibrary(outliers)\n#https://cran.r-project.org/web/packages/outliers/outliers.pdf\nset.seed(1234)\ny=rnorm(100)\noutlier(y)\noutlier(y,opposite=TRUE)\ny=rm.outlier(y, fill = FALSE, median = FALSE, opposite = FALSE)\nrm.outlier(y, fill = FALSE, median = FALSE, opposite = FALSE)\n#scores(y, type = c(\"z\"), prob = NA, lim = NA)\n\nset.seed(1234)\ny=rnorm(100)\ndim(y) <- c(20,5) # convert to matrix\ny\noutlier(y) #in each column\noutlier(y,opposite=TRUE)\napply(y, c(2), range)\nboxplot(y)\nscores(y, type = c(\"z\", \"t\", \"chisq\", \"iqr\", \"mad\"), prob = NA, lim = NA)\n\n\n\n#Univariate\nurl <- \"http://rstatistics.net/wp-content/uploads/2015/09/ozone.csv\" \n# alternate source: https://raw.githubusercontent.com/selva86/datasets/master/ozone.csv\ninputData <- read.csv(url) # import data\noutlier_values <- boxplot.stats(inputData$pressure_height)$out \noutlier_values\nboxplot(inputData$pressure_height, main=\"Pressure Height\", boxwex=0.1)\nmtext(paste(\"Outliers: \", paste(outlier_values, collapse=\", \")), cex=0.6)\n\n\n\n\n\nfit2 = lm(weight ~ height + I(height^2), data=women)\npar(mfrow=c(2,2)) ;plot(fit2)\npar(mfrow=c(1,1)) ; plot(fit2, which=4)\n?plot\nfit3 = lm(weight ~ height + I(height^2), data=women[-c(13,15),])\nfit3\npar(mfrow=c(1,1)) ; plot(fit3, which=4)\n\n\n\n#Bivariate\nurl <- \"http://rstatistics.net/wp-content/uploads/2015/09/ozone.csv\"\nozone <- read.csv(url)\n# For categorical variable\nboxplot(ozone_reading ~ Month, data=ozone, main=\"Ozone reading across months\") # clear pattern is noticeable.\nboxplot(ozone_reading ~ Day_of_week, data=ozone, main=\"Ozone reading for days of week\") # this may not be significant, as day of week variable is a subset of the month var.\n\n\n\n\n\nx <- ozone$pressure_height\nqnt <- quantile(x, probs=c(.25, .75), na.rm = T)\ncaps <- quantile(x, probs=c(.05, .95), na.rm = T)\nH <- 1.5 * IQR(x, na.rm = T)\nx[x < (qnt[1] - H)] <- caps[1]\nx[x > (qnt[2] + H)] <- caps[2]\n\n\n\n#----\n\nlibrary(DMwR)\nsampledata = mtcars[1:3]\noutlier.scores = lofactor(sampledata, k=2)\noutlier.scores\nplot(density(outlier.scores))\norder(outlier.scores, decreasing=T)[1:5] # rownows\nsampledata[c(6,4,25,3,28),]\n\nmarks = c(1:80,100, 1000)\nmarks2 = c(1:80, 100)\nmarks\nboxplot(marks2)\nsummary(marks)\nboxplot(marks)\noutlier.scores2 = lofactor(marks,1)\noutlier.scores2\nplot(density(outlier.scores2))\norder(outlier.scores2, decreasing=T)[1:2] # rownows\nmarks[c(82,81)]\nmarks\n" }, { "path": "17a-Stats/43a-outliers1.R", "content": "#Outliers\n#http://rstudio-pubs-static.s3.amazonaws.com/7901_72d4736f412e4414b1aeffbd1d5c69e9.html\n\n(data= df[11:14]) # numeric values\ncat(names(data))\n\n(sds <- apply(data, 2, sd, na.rm = TRUE))\n\n\nfindOutlier <- function(data, cutoff = 2) {\n ## Calculate the sd\n sds <- apply(data, 2, sd, na.rm = TRUE)\n ## Identify the cells with value greater than cutoff * sd (column wise)\n result <- mapply(function(d, s) {\n which(d > cutoff * s)\n }, data, sds)\n result\n}\n\noutliers <- findOutlier(data)\noutliers\n" }, { "path": "17a-Stats/45a-sampling.R", "content": "#sampling\nn=10\nset.seed(1)\n\na=rep(0:1,5)\nb=rep(0,10)\nc=rep(1,10)\ndataset=matrix(cbind(a,b,c),nrow=10,ncol=3)\ndataset\nsample(dataset, n, replace=T)\nmysample <- sample(length(dataset), n, replace=T)\nmysample\n" }, { "path": "17a-Stats/55a-traintest1.R", "content": "# data into train and test set\n\n#Case-1---\ndf = mtcars\ndim(df) # 32 11\n\n#Sample Indexes\nset.seed(1234)\nindexes = sample(1:nrow(df), size=0.2*nrow(df))\n# Split data\ntest = df[indexes,]\ndim(test) # 6 11\ntrain = df[-indexes,]\ndim(train) # 26 11\n\n#Case-2----\n## 75% of the sample size\n(smp_size <- floor(0.75 * nrow(df)))\n\n## set the seed to make your partition reproducible\nset.seed(1234)\ntrain_ind <- sample(seq_len(nrow(df)), size = smp_size)\n\ntrain <- df[train_ind, ]\ntest <- df[-train_ind, ]\ntrain\ntest\n\n#Case-3---\nrequire(caTools)\nset.seed(1234) \nsample = sample.split(df$mpg, SplitRatio = .75)\ntrain3 = subset(df, sample == TRUE)\ntest3 = subset(df, sample == FALSE)\ndim(train3)\ndim(test3)\n\n#Case-4----\nbound <- floor((nrow(df)/4)*3) #define % of training and test set\n\ndf <- df[sample(nrow(df)), ] #sample rows \ndf.train4 <- df[1:bound, ] #get training set\ndf.test4 <- df[(bound+1):nrow(df), ] #get test set\ndim(df.train4)\ndim(df.test4)\n\n#Case-5----\nlibrary(dplyr)\ndf5 = df\nrownames(df5) = NULL #as they were numeric\nhead(df5)\ntrain5 <-sample_frac(df5, 0.7)\nsid <- as.numeric(rownames(train5)) \nsid\ntest5 <- df5[-sid,]\ndim(train5); dim(test5)\n\n#Case-6----\ndf6 = df\ndf6$id <- 1:nrow(df6) # create id var\ntrain6 <- df6 %>% dplyr::sample_frac(.75)\ntest6 <- dplyr::anti_join(df6, train, by = 'id')\ndim(train6); dim(test6)\n\n#Case-7 ----\nset.seed(1234)\nsplit7=sample.split(df$mpg,SplitRatio=2/3)\nset.seed(1234)\nsplit7a=sample.split(df,SplitRatio=2/3)\nsplit7a\ntrain7=subset(df,split7==TRUE)\ntest7=subset(df,split7==FALSE)\ndim(train7); dim(test7)\ntrain7a=subset(df,split7a==TRUE)\ntest7a=subset(df,split7a==FALSE)\ndim(train7a); dim(test7a)\n\n\n\n\nlibrary(ISLR)\ndf = Default\nnames(df)\n\nlibrary(dplyr)\ndf <- data.frame(x = c(NA, \"a.b\", \"a.d\", \"b.c\"))\ndf %>% separate(x, c(\"A\", \"B\"))" }, { "path": "17a-Stats/60a-kurtosis.R", "content": "# kurtosis\n\n# kurtosis describes the tail shape of the data distribution.\n#Normal distribution - 0 kurtosis and thus the standard tail shape. It is said to be mesokurtic. \n#Negative kurtosis would indicate a thin-tailed data distribution, and is said to be platykurtic. \n#Positive kurtosis would indicate a fat-tailed distribution, and is said to be leptokurtic\nlibrary(e1071) # load e1071 \nduration = faithful$eruptions # eruption durations \nhist(duration)\nplot(density(duration))\nkurtosis(duration) # apply the kurtosis function \n#[1] -1.5116\n\n#Answer :The kurtosis of eruption duration is -1.5116, which indicates that eruption duration distribution is platykurtic. This is consistent with the fact that its histogram is not bell-shaped.\n\n" }, { "path": "17a-Stats/60b-kurtosis.R", "content": "#Kurtosis using fBasics\n\nlibrary(fBasics)\n\n#rowKurtosis\tcalculates row kurtosis,\nrowKurtosis(mtcars)\n\n#colKurtosis\tcalculates column kurtosis,\ncolKurtosis(mtcars)\n" }, { "path": "17a-Stats/64a-skewness.R", "content": "#skewness\n#the skewness is a measure of symmetry. As a rule, negative skewness indicates that the mean of the data values is less than the median, and the data distribution is left-skewed. Positive skewness would indicate that the mean of the data values is larger than the median, and the data distribution is right-skewed.\n\nlibrary(e1071) # load e1071 \nduration = faithful$eruptions # eruption durations \nplot(density(duration))\ne1071::skewness(duration) # apply the skewness \nfunction #[1] -0.41355\n\n#Answer :The skewness of eruption duration is -0.41355. It indicates that the eruption duration distribution is skewed towards the left.\n" }, { "path": "17a-Stats/64b-skewness.R", "content": "#Skewness\n\nlibrary(PerformanceAnalytics)\nskew <- PerformanceAnalytics::skewness(mtcars$mpg)\nskew # Right Skewed\nhist(mtcars$mpg)\nplot(density(mtcars$mpg))\nabline(v=c(mean(mtcars$mpg), median(mtcars$mpg)), col=2:3, lwd=2:3)\n\n " }, { "path": "17a-Stats/ave1.R", "content": "#AVE\n\n# ave is tapply's friendly cousin. It returns results\n# in a form that you can plug straight back into your data frame\n\n(dfr <- data.frame(a=1:20, f=rep(LETTERS[1:5], each=4)))\n(means <- tapply(dfr$a, dfr$f, mean))\n## A B C D E \n## 2.5 6.5 10.5 14.5 18.5 \n\n## great, but putting it back in the data frame is another line:\n\n(dfr$m <- means[dfr$f])\ndfr\n\n#-----------\n(dfr$m2 <- ave(dfr$a, dfr$f, FUN=mean)) # NB argument name FUN is needed!\ndfr\n\n#---------\n#There is nothing in the base package that works like ave\n#for whole data frames (as by is like tapply for data frames). \n#But you can fudge it:\n \ndfr$foo <- ave(1:nrow(dfr), dfr$f, FUN=function(x) {\n x <- dfr[x,]\n sum(x$m*x$m2)\n })\ndfr\n" }, { "path": "17a-Stats/interactions.R", "content": "# Margin Tables \n\n# Election Results\nparties1 = c('bjp', 'congress', 'sp', 'aap')\nstates1 = c('up', 'delhi','haryana', 'punjab','uk')\n\n# Factor interactions\n(comb = interaction(parties, states))\ncomb\nmps = ceiling(runif(16,30,50))\ncbind(comb, mps)\n\n?gl\nparties2= gl(n=4, k=1, length=4, labels=parties1 )\nstates2= gl(n=5, k=1, length=4, labels=states1 )\n# Longer object to multiple of shorter object length : keep length=4\n# order not reqed, only once k=1, \n(gl(n=4, k=2, length=8, labels=parties )) # random order, twice rep\n\ncomb2= interaction(parties2, states2, sep='-')\ndf1 = data.frame(comb2, mps)\nstr(df1)\ndf1 # some combination may be missing\n\n# not very good\nparties3= gl(n=4, k=5, length=20, labels=parties1 )\nstates3= gl(n=5, k=4, length=20, labels=states1 )\ncbind(parties3, states3)\n(df2 = data.frame(parties3, states3))\ntable(df2)\n" }, { "path": "17a-Stats/mean1.R", "content": "# Mean\n\nset.seed(1234)\nx = rnorm(100, 50, 10) # 100 random nos mean-50, Sd-10\nx = ceiling(x)\nx\nmeanx = sum(x) / length(x)\nmeanx\nmean(x)\nsummary(x)\n\n# Trimming Values\nx\nset.seed(1234)\nx = ceiling(runif(100,50,100)) # Extreme values\nx\nmean(x)\nmean(x, trim = 1)\n\n# NA values - Remove\nx[c(1,20,30)] = NA\nx\nlength(na.omit(x)) #3 values\nmean(x) # error\nmean(x, na.rm=T)\n\n# Trim\nx <- c(0:10, 50)\nx\nxm <- mean(x)\nc(xm, mean(x, trim = 0.10)) # with & without Trim\n" }, { "path": "17a-Stats/mean2.R", "content": "# Summary1\n\nlibrary(MASS) # load the MASS package \nschool = painters$School # the painter schools \nc_school = school == \"C\" # the logical index vector\nc_school\n#which school has the highest mean composition score\nc_painters = painters[c_school, ] # child data set\nc_painters\nmean(c_painters$Composition) \n\ntapply(painters$Composition, painters$School, mean)\n" }, { "path": "17a-Stats/mean3.R", "content": "#mean3\n\nset.seed(1234)\nx = ceiling(runif(100,50,100)) # 100 uniform dist nos\nx\nmean(x)\n\n#sampling mean\n(s1 = sample(x,10))\nmean(s1)\n?sample\n\n(s2= sample(x,10, replace=T))\nmean(s2)\n\nfor i in 1:10 {\n \n}" }, { "path": "17a-Stats/meandev.R", "content": "# Mean Deviation\nx = c(3, 6, 6, 7, 8, 11, 15, 16)\n(mx = mean(x))\n(mdev = x - mx)\nabs(mdev)\n(mabsdev = sum(abs(mdev)))/ length(x)\nmad(x) # Direct from Formula\n\n\n\n#Marks (out of 25): 7, 4, 10, 9, 15, 12, 7, 9, 7\n# Calculate MD about Mean, Median, Mode\n#http://www.emathzone.com/tutorials/basic-statistics/mean-deviation-and-its-coefficient.html\n\nx = c(7, 4, 10, 9, 15, 12, 7, 9, 7)\nmean(x)\nmedian(x)\nlibrary(modeest)\nmd = mlv(x, method = \"mfv\")\n\n(MDmean = sum(abs(x - mean(x)))/ length(x))\n(CoeffMDMean = MDmean/ mean(x) )\n\n(MDmedian = sum(abs(x - median(x)))/ length(x))\n(CoeffMDabtMedian = MDmedian/ median(x) )\n\nmd$M\n(MDmode = sum(abs(x - md$M))/ length(x))\n(CoeffMDabtMode = MDmode/ md$M)\n\n\n" }, { "path": "17a-Stats/meanwt1.R", "content": "#mean Weighted\n\nwt = c(5, 5, 4, 1 )\nrwt = wt/ sum(wt)\npie(rwt)\nrwt # relative weight of each subject\nx = c(3.7,3.3,3.5,2.8) #marks of subject\n(xm = weighted.mean(x, rwt)) # Grade Point Average\n\n(xm2 = sum(x * rwt))\n" }, { "path": "17a-Stats/median1.R", "content": "#Median" }, { "path": "17a-Stats/mode1.R", "content": "#mode1\n\n\nx = c(2,3,4,4,5,5)\nlibrary(modeest)\nm =mlv(x, method = \"mfv\")\nm\ngetmode(x)\n\nfivenum(x)\n" }, { "path": "17a-Stats/normal_height.R", "content": "# creating normal plot\n# Men's heights are normally distributed with a population mean of 69.0 inches\n# and a population standard deviation of 2.8 inches\n\npopulation_mean <- 69\npopulation_sd <- 2.8\nsd_to_fill <- 1\nlower_bound <- population_mean - population_sd * sd_to_fill\nupper_bound <- population_mean + population_sd * sd_to_fill\n\n# Generates equally spaced values within 4 standard deviations of the mean\n# This is used to connect the points on the curve so the more points the better\nx <- seq(-4, 4, length = 1000) * population_sd + population_mean\n\n# Returns the height of the probably distribution at each of those points\ny <- dnorm(x, population_mean, population_sd)\n\n# Generate the plot, where:\n# - type: the type of plot to be drawn where \"n\" means do not plot the points\n# - xlab: the title of the x axis\n# - ylab: the title of the y axis\n# - main: the overall title for the plot\n# - axes: when false it suppresses the axis automatically generated by the high level plotting function so that we can create custom axis\nplot(x, y, type=\"n\", xlab = \"Height (inches)\", ylab = \"\", main = \"Distribution of Men's Heights\", axes = FALSE)\n\n# Connect all of the points with each other to form the bell curve\nlines(x, y)\n\n# Returns a vector of boolean values representing whether the x value is between the two bounds then\n# filters the values so that only the ones within the bounds are returned\nbounds_filter <- x >= lower_bound & x <= upper_bound\nx_within_bounds <- x[bounds_filter]\ny_within_bounds <- y[bounds_filter]\n\n# We want the filled in area to extend all the way down to the y axis which is why these two lines are necessary\n# It makes the first point in the polygon (lower_bound, 0) and the last point (upper_bound, 0)\nx_polygon <- c(lower_bound, x_within_bounds, upper_bound)\ny_polygon <- c(0, y_within_bounds, 0)\n\npolygon(x_polygon, y_polygon, col = \"red\")\n\n# Now determine the probability that someone falls between the two bounds so we can display it above the curve\n# Remember that pnorm returns the probability that a normally distributed random number will be less than the given number\nprobability_within_bounds <- pnorm(upper_bound, population_mean, population_sd) - pnorm(lower_bound, population_mean, population_sd)\n\n# Concatenate the various values so we can display it on the curve\ntext <- paste(\"p(\", lower_bound, \"< height <\", upper_bound, \") =\", signif(probability_within_bounds, digits = 3))\n\n# Display the text on the plot. The default \"side\" parameter is 3, representing the top of the plot.\nmtext(text)\n\n# Add an axis to the current plot, where:\n# - side: which side of the plot the axis should be drawn on where 1 represents the bottom\n# - at: the points at which the tick-marks are to be drawn\n# - pos: the coordinate at which the axis line is to be drawn\nsd_axis_bounds = 5\naxis_bounds <- seq(-sd_axis_bounds * population_sd + population_mean, sd_axis_bounds * population_sd + population_mean, by = population_sd)\naxis(side = 1, at = axis_bounds, pos = 0)\n\n" }, { "path": "17a-Stats/normality.R", "content": "# Normality\n\n#https://stats.stackexchange.com/questions/52293/r-qqplot-how-to-see-whether-data-are-normally-distributed\n\n#Method1 -----------\nlibrary(moments)\nlibrary(nortest)\nlibrary(e1071)\n\n\n\n\n\nset.seed(777)\nx <- rnorm(250,10,1)\n\n# skewness and kurtosis, they should be around (0,3)\nskewness(x)\nkurtosis(x)\n\nplot(density(x))\n# Shapiro-Wilks test\nshapiro.test(x)\n\n# Kolmogorov-Smirnov test\nks.test(x,\"pnorm\",mean(x),sqrt(var(x)))\n\n# Anderson-Darling test\nad.test(x)\n\n# qq-plot: you should observe a good fit of the straight line\nqqnorm(x)\nqqline(x)\n\n# p-plot: you should observe a good fit of the straight line\nprobplot(x, qdist=qnorm)\n\n# fitted normal density\nf.den <- function(t) dnorm(t,mean(x),sqrt(var(x)))\ncurve(f.den,xlim=c(6,14))\nhist(x,prob=T,add=T)\n\n# Method 2\nlibrary(car)\n\nqqPlot(lm(prestige ~ income + education + type, data=Duncan), \n envelope=.99)" }, { "path": "17a-Stats/normality2.R", "content": "#Normality Test\n\nqqnorm(CO2$uptake)\nqqline(CO2$uptake)\n\n\nggplot2::qplot(sample=CO2$uptake,\n data=CO2)\nggplot2::qplot(sample=CO2$uptake,\n data=CO2, color=factor(Treatment))\n\nlibrary(car)\ncar::qqPlot(lm(prestige ~ income + education + type,\n data=Duncan), envelope=.99)\n\n\nshapiro.test(CO2$uptake)\n\nnortest::ad.test(CO2$uptake)\n\nmvoutlier::aq.plot(CO2[ c('uptake', 'conc')])\nmvoutlier::aq.plot(CO2[ c('uptake')])\n" }, { "path": "17a-Stats/outlier2.R", "content": "# Outliers 2\n\n#http://mantra.edina.ac.uk/practicals/MANTRA-R-Practical-Intro.pdf\n\n#different methods for checking apparent outliers\nwith(riverquality,which(Temp<3))# the result is given as just the row number\nwhich(riverquality$Temp<3)\nriverquality$Temp<3 # prints the entire column\n#as a set of TRUE, FALSE or NA responses for each cell, and involves counting\n#which value meets the TRUE criterion. \n" }, { "path": "17a-Stats/outliers1.R", "content": "# Outliers\n# Outliers\n#http://r-statistics.co/Outlier-Treatment-With-R.html\n\nlibrary(outliers)\nx = c(1:10,45,46,47,99,100,101)\nchisq.out.test(x)\noutlier(x)\n# Inject outliers into data.\ncars1 <- cars[1:30, ] # original data\ncars_outliers <- data.frame(speed=c(19,19,20,20,20), dist=c(190, 186, 210, 220, 218)) # introduce outliers.\ncars2 <- rbind(cars1, cars_outliers) # data with outliers.\ncars2\noutlier(cars2)\n# Plot of data with outliers.\npar(mfrow=c(1, 2))\nplot(cars2$speed, cars2$dist, xlim=c(0, 28), ylim=c(0, 230), main=\"With Outliers\", xlab=\"speed\", ylab=\"dist\", pch=\"*\", col=\"red\", cex=2)\nabline(lm(dist ~ speed, data=cars2), col=\"blue\", lwd=3, lty=2)\n\n# Plot of original data without outliers. Note the change in slope (angle) of best fit line.\nplot(cars1$speed, cars1$dist, xlim=c(0, 28), ylim=c(0, 230), main=\"Outliers removed \\n A much better fit!\", xlab=\"speed\", ylab=\"dist\", pch=\"*\", col=\"red\", cex=2)\nabline(lm(dist ~ speed, data=cars1), col=\"blue\", lwd=3, lty=2)\n\n# Detect outliers Univariate approach ---------------------------------------\n" }, { "path": "17a-Stats/poiss1.R", "content": "# Poission Distribution\n\n?poisson\n# 12 cars crossing a bridge per minute on average, find the probability of having \n# 17 or more cars crossing the bridge in a particular minute.\nppois(16, lambda=12) # lower tail sixteen or less cars \nppois(16, lambda=12, lower=FALSE) # upper tail seventeen or more cars \n\n?ppois\n# Q1: 1 yr - 18 accidents, P(X=2) Exact\nlambda = 18/12 # accidents / month\nq=2\nppois(q, lambda, lower.tail = T)\n\n# at least 1 accident P(X>=1)\n1 - ppois(q=0, lambda)\nppois(q=0, lambda, lower.tail = F)\n\n\n#probability that there are more than 2 accidents in a particular month\nppois(q=2, lambda, lower.tail = F) # right side\n#1 - (P(0) + P(1) + P(2))\n(ppois(q=c(0,1,2,3,4,5), lambda, lower.tail = T)) # cumulative\n1-ppois(q=c(2), lambda, lower.tail = T)\n#Table gives us cumulative probability.\n\n# mean=3 ; P(X = 4) = P(X ≤ 4) − P(X ≤ 3) = 0.815 − 0.647 = 0.168\nlambda=3\nppois(q=4, lambda, lower.tail = T) - ppois(q=3, lambda, lower.tail = T)\n#P(X=4 exactly)\n\n#Q: Patients = 6/hr, lambda=3/30min ; X=4\nlambda=6/2; q=4\nppois(q,lambda) - ppois(q-1,lambda) #0.1680314\n\n#Q : Outrages = 4/hr ; X=3, x>=3\nlambda=4 ; q=3\nppois(q,lambda) - ppois(q-1,lambda) #0.195\nppois(q,lambda, lower.tail = F) #0.566\n\n#Q : Trucks : exactly 5\nlambda= 3 * 2 ; q=5\nppois(q,lambda) - ppois(q-1,lambda) #0.160\n\n#Q: Messages arrive at a switching centre at random and \n#at an average rate of 1.2 per second.\n#(a) Find the probability of 5 messages arriving in a 2-sec interval.\nlambda = 1.2*2 ; q=5\nppois(q,lambda, lower.tail = T) - ppois(q-1,lambda, lower.tail = T) #0.06\nexp(-lambda) * (lambda)^q / factorial(q)\n#0.060\n\n\n#Q three or more will fail within ten years?\n(lambda = 5000000 * (10^-6))\nppois(q=2, lambda, lower.tail = F) #0.875348\n1- ppois(q=2, lambda, lower.tail = T) #0.875348\n#0.875" }, { "path": "17a-Stats/quantile1.R", "content": "# Quantile\n\nset.seed(1234)\nx = ceiling(rnorm(100,50,10))\nx\n#Quartile\nquantile(x , c(0.25, 0.5, 0.75))\nsort(x)\nquantile(x)\nIQR(x)\nQ1 = quantile(x , 0.25)\n(Q2 = quantile(x , 0.5))\nQ3 = quantile(x , 0.75)\n(iqr = Q3 - Q1)\n#Q2 - 46.5\n\n#Decile\nquantile(x, seq(.1,1,by=0.1))\n#50 = 46.5\n\n#Percentile\nquantile(x, seq(.01,1,by=0.01))\n# P50 = 46.5\n\n# Summary\nsummary(x)\n\nfivenum(x)\n\nsummary(x)\n\nplot(density(x))\nhist(x)\n" }, { "path": "18a-HypoTests/20b-distributions.R", "content": "# Table Values in R\n?distributions\n#http://www.stat.umn.edu/geyer/old/5101/rlook.html\n#https://cran.r-project.org/doc/contrib/Ricci-distributions-en.pdf\n#https://stats.idre.ucla.edu/r/modules/probabilities-and-distributions/\n #\n\n#z-----\n?dnorm\nrnorm\npnorm\nqnorm\n\n\n\n\n#t----\n?dt\nrt\npt\nqt\n\n\n\n#chisq----\n\n\n\n\n" }, { "path": "18a-HypoTests/23b-ztest-bsda.R", "content": "# Hypothesis Test in R\n\n#Z Test\n#https://www.rdocumentation.org/packages/BSDA/versions/1.2.0/topics/z.test\n#https://www.investopedia.com/terms/z/z-test.asp\n\n\nlibrary(BSDA)\n\n#z.test(x, y = NULL, alternative = \"two.sided\", mu = 0, sigma.x = NULL, sigma.y = NULL, conf.level = 0.95)\n\n\n" }, { "path": "18a-HypoTests/25c-tdistribution.R", "content": "# t distribution\n#Plot of Student Distribution\n?qt\n#Density, distribution function, quantile function and random generation \n#for the t distribution with df degrees of freedom \n\n#Usage\ndt(x, df, ncp, log = FALSE)\npt(q, df, ncp, lower.tail = TRUE, log.p = FALSE)\nqt(p, df, ncp, lower.tail = TRUE, log.p = FALSE)\nrt(n, df, ncp)\n\n#Arguments\n#x, q\t vector of quantiles.\n#p\t vector of probabilities.\n#n\t number of observations. If length(n) > 1, the length is taken to be the number required.\n#df degrees of freedom (> 0, maybe non-integer). df = Inf is allowed.\n#ncp\tnon-centrality parameter delta; currently except for rt(), only for abs(ncp) <= 37.62. If omitted, use the central t distribution.\n#log, log.p\tlogical; if TRUE, probabilities p are given as log(p).\n#lower.tail\tlogical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x].\n\ndata_vector <- c(63, 75, 84, 58, 52, 96, 63, 55, 76, 83)\ndata_vector\n(dfs <- length(data_vector)-1) #dof\ndfs\nx_ <- seq(-8,8,0.1) # x axis points\nx_\ny <- dt(x_,dfs) # y axis points density (t) func\ny\n# sign level=0.05, p = 1 -0.05 = 0.95 : 95% of values (area) from left \nt.val <- qt(1-0.05,df=dfs)\nt.val # on x axis\n\nplot(x_,y,type='l',lwd=3,col='blue',xlab='x',\n ylab='Density',main='Student distribution \\n 9 Degree of Freedom')\n\nabline(v=0)\ntext(0,0.1, labels=' Center Line', srt=90)\n\nabline(v=t.val,lwd=2,col='red')\ntext(t.val,0.2, labels='Line at tval', srt=90, col='red')\ndt(t.val,dfs) # point where t.val line touches the plot\nmtext(1,at=t.val,text=round(t.val,2))\npoints(t.val,dt(t.val,dfs),lwd=3,col='red')\ntext(t.val,dt(t.val,dfs), labels= ' Intersect')\n\n" }, { "path": "18a-HypoTests/26b-ttestindep.R", "content": "#t-test Independent : Difference Between Means\n#two-sample t-test\n\n#links\n#http://www.sthda.com/english/wiki/unpaired-two-samples-t-test-in-r#r-function-to-compute-unpaired-two-samples-t-test\n#https://ww2.coastal.edu/kingw/statistics/R-tutorials/independent-t.html\n#http://www.r-tutor.com/elementary-statistics/inference-about-two-populations/population-mean-between-two-independent-samples\n\n\n#Criteria\n#simple random sampling.\n#The samples are independent.\n#Each population is at least 20 times larger than its respective sample.\n#The sampling distribution is approximately normal, which is generally the case if any of the following conditions apply.\n# The population distribution is normal.\n# The population data are symmetric, unimodal, without outliers, and the sample size is 15 or less.\n# The population data are slightly skewed, unimodal, without outliers, and the sample size is 16 to 40.\n# The sample size is greater than 40, without outliers.\n\n#http://stattrek.com/hypothesis-test/difference-in-means.aspx?Tutorial=AP\n#Hypothesis #H0: μ1 = μ2 Ha: μ1 ≠ μ2\n(class1 = sample(60:100,25))\n(class2 = sample(60:100,30))\n\n#determine whether the difference between means found in the sample is significantly \n#different from the hypothesized difference between means ?t.test\n#t.test(x, y , alternative = c(\"two.sided\", \"less\", \"greater\"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95, ...)\n\n(ttest2s1 = t.test(x=class1, y=class2))\n\nttest2s1$statistic ; ttest2s1$p.value ;ttest2s1$conf.int\nnames(ttest2s1)\n\n(n1 = length(class1)); (n2=length(class2))\nqt(1-.05/2,df=min(n1,n2))\n\n#Equal Variance\n(ttest2s1 = t.test(x=class1, y=class2, var.equal = T))\n\n#Unequal Variance\n(ttest2s1 = t.test(x=class1, y=class2, var.equal = F))\n\n\n# Smokers\nnonsmokers = c(18,22,21,17,20,17,23,20,22,21)\nsmokers = c(16,20,14,21,20,18,13,15,17,21)\n\n#sample size < 30\nt.test(nonsmokers,smokers) # 2 sample\nt.test(nonsmokers, mu=20) #1 sample\n(n1 = length(nonsmokers))\n(n2 = length(smokers))\ndfs = n1 + n2 -2 \nqt(1-.05/2,df=dfs)\nqt(.05/2,df=dfs)\nt.test(nonsmokers,smokers)\n#2.5 > absolute(2.1)\n# t calc > t-table : pval < 0.03 : Reject Ho\n# Diff in means != 0\n\npar(mfrow=c(1,2)) # set graphics window to plot side by side\nplot(density(nonsmokers), main=\"nonsmokers\")\nplot(density(smokers), main=\"smokers\")\npar(mfrow=c(1,1))\nboxplot(nonsmokers, smokers, ylab=\"Scores on Digit Span Task\", # plot and label y-axis\n names=c(\"nonsmokers\",\"smokers\"), # group names on x-axis\n main=\"Digit Span Performance by\\n Smoking Status\") # main title\n\n# 1 Tail, Equal variance\nt.test(nonsmokers, smokers, alternative=\"greater\", var.equal=T)\n\n\n# Formula Method\nscores = c(nonsmokers, smokers)\ngroups = c(\"nonsmokers\",\"smokers\") # Make sure these are in the right order!\ngroups = rep(groups, times=c(10,10)) # 10 nonsmokers, 10 smokers\nmj.data = data.frame(groups, scores)\nmj.data\nby(scores, groups, mean)\nboxplot(scores ~ groups) \nt.test(scores ~ groups, data=mj.data) \n\n#\nwilcox.test(nonsmokers, smokers)\n\n# One Tail Tests ---------\n#Null hypothesis: μ1 - μ2 >= 7 \n#Alternative hypothesis: μ1 - μ2 < 7\n" }, { "path": "18a-HypoTests/26c-ttestpaired.R", "content": "# t test - paired\n\nbefore = c(50,60,55,70,60)\nafter = c(55,64,61,74,58)\nt.test(x=before, y=after, paired=T)\n#Ho: Diff = 0 , Ha: Diff != 0\n# Manual Calculation\n(d = before-after)\nn = length(before)\n(dmean = mean(d))\n(tcalc = (dmean - 0)/(sd(d)/sqrt(n-1)))\nqt(1-0.05/2,df=length(before)-1)\nqt(0.05/2,df=length(before)-1)\n#tcalc lies between qt values : Accept Ho : True diff = 0\nt.test(x=before, y=after, paired=T,alternative='two.sided', mu=0)\n\n# Ho: d = 3\nt.test(x=before, y=after, paired=T,alternative='two.sided', mu=3)\n\n\n\n# Example 2 : Training\nbefore= c(95,89,76,92,91,53, 67, 88, 75, 85, 90, 85, 87, 85, 85, 68, 81, 84, 71, 46, 75, 80)\nafter = c(90,85,73,90,90,53,68,90,78,89,95,83,83,83,82,65,79,83,60,47,77,83)\n# Did training help in scores\n# Matched Pair : Same People - before and after training scores\nt.test(x=before, y=after, paired=T,mu=0,alternative='two.sided')\n# Accept Ho: No significant changes\n#Manually\nd = before - after\nn = length(before)\ndmean = mean(d)\n(d.sd = sqrt(sum((d - dmean)^2)/(n-1)))\n(se = d.sd/sqrt(n))\nmu=0\n(tcalc = (mean(d) - mu)/ se)\nt.test(before, after, paired=T, mu=0, alternative='two.sided')\nqt(1-0.05/2,df=n-1); qt(0.05/2,df=n-1)\n#tcalc lies between two extremes (not in rejection) : Accept Ho\n# p value > 0.05; p-value (0.205) is greater than the signif level (0.05), we cannot reject Ho.\n# conf Interval : mean of diff lies betw conf intervals\n\nmeanx= 70\nsdx = 10\nnx=10\nx1 = rnorm(n=n,mean=meanx, sd=sdx)\nx1\nmean(x1)\nsd(x1)\nx2 = rnorm(10)\nx2 = sdx*(x2-mean(x2)) /sd(x2)+70\nmean(x2)\nsd(x2)\nhelp(distributions)\n" }, { "path": "18a-HypoTests/27b-TTS1-case1.R", "content": "# Hypothesis 1 sample Test\n\n# Q : 1 sample ------------------------------------------------------------\n#A random sample of 900 members has a mean 3.4 cm. \n#Can it be reasonably regarded as sample from a\n#large population of mean 3.25 cm and SD 2.61 cms\n#Ho : μ = 3.25 ; Ha : μ ≠ 3.25\nlibrary(TeachingDemos) \nza= qnorm(1-.05/2)\nx=rnorm(1000,mean=3.25,sd=2.61)\nplot(density(x))\nz.test(3.4,mu=3.25,sd=2.61,n=900,conf.level = .975,\n alternative=\"two.sided\")\n\nzc = (3.4 - 3.25)/(2.61/sqrt(900))\nabline(v=c(za,-za))\nabline(v=zc, col='red')\n#zc (calc) lies betw -za & za. Do not reject Ho\n\n#One Sample z-test\n#data: 3.4\n#z = 1.7241, n = 900.000, Std. Dev. = 2.610, Std. Dev. of the sample\n#mean = 0.087, p-value = 0.08468\n#alternative hypothesis: true mean is not equal to 3.25\n#95 percent confidence interval: 3.229483 3.570517\n#sample estimates: mean of 3.4 3.4 \n\n#[ |Z* = 1.724| < Zα/2 = 1.9599 : Accept Ho ]\n#Sample mean(3.4) is falling in between the 95% Confidence Interval calculated : [3.229483, 3.570517]\n\n\n\n# Q2 ----------------------------------------------------------------------\n#Manufacturer claims that the average mileage of scooters of his company is 40 km/litre.\n#A random sample of 38 scooters of the company showed an average mileage of 42 km/litre. Test the claim of the manufacturer on the assumption that the mileage of scooter is normally distributed with SD of 2 km/litre\n#Ho : μ = 40 Ha : μ ≠ 40\nqnorm(1-.05/2) #[1] 1.959964\nz.test(42,mu=40,sd=2,n=38,conf.level = .95,\n alternative=\"two.sided\")\n#Sample mean(42) is falling in between the 95% Confidence Interval calculated : [41.36, 42.63]\n#z = 6.1644, n = 38.00000, Std. Dev. = 2.00000, Std. Dev. of the sample mean = 0.32444, p-value = 7.074e-10\n\nza2 = qnorm(1-.05/2, mean=40,sd=2,lower.tail=T)\nza1 = qnorm(.05/2, mean=40,sd=2,lower.tail=T)\nza1; za2\nx = rnorm(1000,mean=40,sd=2)\nplot(density(x))\nabline(v=c(40,42))\n\nmtext(side=1,at=40,text='Mean at 40',col='green')\ntext(x=42,y=.1,labels='Sample Mean at 42', col='purple', \n srt=90, offset=0.5)\n?text\nabline(v=c(za1,za2), col='red')\ntext(x=za1-.5,y=0.1,labels='Left Bound', srt=90)\ntext(x=za2-.5,y=0.1,labels='Right Bound', srt=90)\n\n# Sample mean lies between left and right bound\n\n#One Sample z-test\n#data: 42\n#z = 6.1644, n = 38.00000, Std. Dev. = 2.00000, Std. Dev. of the sample mean = 0.32444, p-value = 7.074e-10\n#alternative hypothesis: true mean is not equal to 40\n#95 percent confidence interval: 41.3641 42.6359\n#sample estimates: mean of 42 \n\n\n# Conclusion[ |Z* = 6.61 | < Zα/2 = 1.9599 : Accept Ho ]\n#Sample mean(42) is falling in between the 95% Confidence Interval calculated : [41.36, 42.63]\n\n\n# Q3 ----------------------------------------------------------------------\n\n\n\n#A stenographer claims that she can type at the rate of 120 words per min. \n#Can we reject her claim on the basis of 100 trials in which she demonstrates a mean of 116 words with a SD of 15 words ? Use 5% level of significance\n#Ho : μ = 120 Ha : μ ≠ 120\nqnorm(1-.05/2)#[1] 1.959964\nz.test(116,mu=120,sd=15,n=100,conf.level = .95,alternative=\"two.sided\")\n?z.test\n\n#[ |Z* =-2.667 | > Zα/2 = 1.9599 : Reject Ho ]\n# use x as vector of values instead of one mean value\nx = c(117,120,110,123,125, 114, 118,123, 117, 125)\nlength(x); mean(x) ; sd(x)\nz.test(x,mu=120,sd=sd(x),n=length(x),conf.level = .95,alternative=\"two.sided\")\n\n# Q4 ----------------------------------------------------------------------\n\n\n#Mean life of a sample of 400 fluorescent bulbs produced by a company is found to be 1570 hours with a SD of 150 h. Test the hypothesis that the mean life of the bulbs produced by the company is 1600 against the alternative hypothesis that it is greater than 1600 hours at 1% levels of significance\n#Ho : μ = 1600 Ha : μ > 1600 : One Tail, Left Tail\nqnorm(1-.01) #[1] 2.326348\n\nz.test(x=1570,mu=1600,sd=150,n=400,conf.level = .95,alternative=\"greater\")\n\n#[ abs |Z* =-4 | > Zα/2 = 2.33 : Reject Ho ]\n\n#Sample mean(1570) is not falling in between the 95% Confidence Interval calculated : [1557.664, Inf ]\n# Ha Mean is higher than 1600\n\n\n" }, { "path": "18a-HypoTests/28b-TTS2-case1.R", "content": "# Hypothesis Tests - t for n < 30\n\n# Q1 ----------------------------------------------------------------------\n\n#Random Sample of size 20 from a normal population has mean 42 and SD = 5. Test the Hypothesis that the population mean is 45. Use 5% level of significance\n#Ho : μ = 45 Ha : μ ≠ 45 : Two Tail\nqt(.975,df=19) #[1] 2.093024\n#Calculate Z* since ND and SD is given.\n\nz.test(42,mu=45,alternative=\"two.sided\",n=20,sd=5)\n \n# t* > tα/2 : -2.683 > 2.093 : Reject Ho\n\n\n# Q4 ----------------------------------------------------------------------\n\n#The average breaking strength of steel rods is specified to be 18.5 thousand per kg. For this a sample for 14 rods was tested. The mean and SD obtained were 17.85 and 1.955 respectively. Test the significance of the deviation.\n#Ho : μ = 18.5 Ha : μ ≠ 18.5 (Two Tailed t test) \n\n#n=14, x̅ = 17.85 , s=1.955 , μ = 18.5 \n\n# Result : Ho accepted\n\n\n# Q5 ----------------------------------------------------------------------\n#The nine items of a sample had the following items. 45,47,50,52,48,47,49,53,51 .\n# Does the mean of nine items differ significantly from the assumed population mean of 47.5\n#N = 9 (N<30) we use t-test. Ho : μ = 47.5 Ha : μ ≠ 47.5 (Two Tailed t test) \nqt(.975,df=8) #[1] 2.306004\nx = c(45,47,50,52,48,47,49,53,51)\nt.test(x, alternative=\"two.sided\",mu=47.5,conf.level = .95)\nt.test(x,mu=47.5,conf.level = .95)\nt.test(x,mu=47.5)\nqt(p=.95+.05/2,df=length(x)-1)\nmean(x)\n#|t* = 1.8452 | < tα/2 : Do not reject Ho\n#Sample Mean between confidence interval\n\n\n# Q6 ----------------------------------------------------------------------\n\n\n#A drug manufacturer has installed a machine which automatically fills 5 gm of drug in each phial. A random sample of fills was taken and it was found to contain 5.02 gm on an average in phial. The sd of sample was 0.002 gms. SD of sample was 0.002 gms, Test at 5% level of significance if the adjustment in the machine is in order.\n#Ho : μ = 5 Ha : μ ≠ 5 (Two Tailed t test) \n\n# Solve this????\n\n\n#P value > 0.05 (significance level) : Do not reject Ho\n\n\n# Q7 ----------------------------------------------------------------------\n\n#Lifetime of Electric bulbs for a random sample of 10 from a large consignment gave the following data\n#4.2 , 4.6, 3.9, 4.1, 5.2, 3.8, 3.9, 4.3, 4.4, 5.6 (in thousands 1000s)\n#Can we accept the hypothesis that the average lifetime of bulb is 4000 hours\n#Ho : μ = 4000 Ha : μ ≠ 4000 (Two Tailed t test) \nx = c(4.2 , 4.6, 3.9, 4.1, 5.2, 3.8, 3.9, 4.3, 4.4, 5.6)\nt.test(x*1000, alternative=\"two.sided\",mu=4000)\n# t* < t(table) : for df=9 at alpha=0.05 : Ho accepted\n\n\n# Q8 ----------------------------------------------------------------------\n#Machine is designed to produce insulating washers for electrical devices of average thickness of 0.025 cm. A random sample of 10 washers was found to have a mean thickness of 0.024 cm with a SD of 0.002 cm. Test the significance of the deviation at 5%\n#Ho : μ = 0.025 Ha : μ ≠ 0.025 (Two Tailed t test) \n# Solve This ????\n\n#Result :Ho accepted\n" }, { "path": "18a-HypoTests/28c-TTS1-case3.R", "content": "# Hypothesis Tests - 2 Sample : Dependent Samples : Paired t-tests---------------------------------------------\noptions(digits=2)\n# Check for Difference Between Two Population Means: Different Hypothesis\n#l. Ho: mu1 = mu2 Ha : mu1 != mu2 \n#2. Ho: mu1 >= mu2 Ha : mu1 < mu2 \n#3. Ho: mu1 <= mu2 Ha : mu1 > mu2 \n# Independent Samples Drawn from Different Population-----------------------------------------------------\n\n# Dependent Samples -------------------------------------------------------\n# Drawn from Same Population\n#There are many experimental settings where each subject in the study is in both the\n#treatment and control group\n\n\n\n#Sample size < 30 and/or Population SD unknown- t tests ####\n#Dependent Samples \n# Cases (a) Sample Size are equal n1 = n1 (b) two sample are not independent but same observations are paired together\n# Drug Given to same set of patients; Measurements done before & after the drug\n# Training Given to same set of Students; Performance Measures before & after training\n# or students performance in 2 subjects\n#Ho : mu1 = mu2 ie the process/ drug / training / performance is not effective \n#Ha : mu1 != mu2 ie Effect is noticed\n#di = yi = xi # difference in observations of ith observation \n#compute dbar = sum(di)/n # mean difference\n#compute Pooled Variance Sv^2 = 1/(n-1)* sum((di - dbar)^2)\n\n# Q7.47\n#To test whether a course in statistics improved performance a similar test was given\n#to 12 particaipants.\nx = c(44,40,61,52,32,44,70,41,67,72,53,72)\ny = c(53,38,69,57,46,39,73,48,73,74,60,78)\nx;y\n#Using R formula for t test\nt.test(x,y,alternative='two.sided',paired=T) # check ?t.test\nttable = qt(1-0.05/2,length(x)) ; ttable # Table value of t (critical value)\nta = ttable\n#Ho: x1bar = x2bar\n#Ha: x1bar != x2bar\nn=length(x)\nd = y -x\nd ; d^2\nsum(d^2)\ndbar = mean(d); dbar\nS= sqrt((1/(n-1))* sum((d - dbar)^2)) ; S # SD is Pooled SD since the sample are dependent \ntcalc = abs(dbar)/ (S / sqrt(n)) ; tcalc\nif(abs(tcalc) > ta) paste(\"Ho is rejected, Difference Significant \",\";Course Improved Performance\" ,abs(tcalc) ,'>',ta) else paste(\"Failed to Reject Ho ; No Difference in Mean Values \" ,abs(tcalc) ,'<',ta )\n\n\n\n# Question 2 @ 7.48 -------------------------------------------------------\n\n# You are given marks obtained by 11 students in two tests one before\n# and one after special coaching. Do the data reveal that special coaching is effective\n#In either of these situations we can’t use two-sample t-tests since the independence assumption is not valid. Instead we need to\n#use a paired t-test. This can be done using the option paired =TRUE.\nx=c(23,20,19,21,18,20,18,17,23,16,19)\ny=c(24,19,22,18,20,22,20,20,23,20,17)\nt.test(x,y,alternative='two.sided',paired=T)\n# p < 0.3 : Ho is accepted\n\n\n\n# Question @ 7.49 ---------------------------------------------------------------\n#A certain stimulus when administered to each of the 12 patients\n#result in the following increase of BP\nd= x = c(5,2,8,-1,3,0,-2,1,5,0,4,6) # these are values of d = y-x\n#Can it be concluded that the stimulus when will in General\n#Calculate dbar, S (SD)\ndbar = mean(d)\nn = length(d)\nS= sqrt((1/(n-1))* sum((d - dbar)^2)) ; S # SD is Pooled SD since the sample are dependent \ntcalc = dbar / (S / sqrt(n)) ;tcalc\nttable = qt(.975,n-1) ; ta=ttable\nidentical(tcalc, ttable) # checking for equality\ncompare::compare(1, 2, ignoreNames=T) # compare equality\nif(abs(tcalc) > ta) paste(\"Ho is rejected\" ,abs(tcalc) ,'>',ta) else paste(\"Failed to Reject Ho : Accept Ho ::\" ,abs(tcalc) ,'<',ta )\n# Ho is rejected: There is significant different by stimulus on BP\n\n\n\n# Question 3 --------------------------------------------------------------\n\n#Ex. A study was performed to test whether cars get better mileage on premium gas than\n#on regular gas. Each of 10 cars was first filled with either regular or premium gas,\n#decided by a coin toss, and the mileage for that tank was recorded. The mileage was\n#recorded again for the same cars using the other kind of gasoline. We use a paired ttest\n#to determine whether cars get significantly better mileage with premium gas\nreg = c(16, 20, 21, 22, 23, 22, 27, 25, 27, 28)\nprem = c(19, 22, 24, 24, 25, 25, 26, 26, 28, 32)\nt.test(prem,reg,alternative=\"greater\", paired=TRUE)\nqt(1-0.05/2,length(reg))\n# The results show that the t-statistic is equal to 4.47 > 2.2 and the p-value is 0.00075. Since\n#the p-value is very low, we reject the null hypothesis. There is strong evidence of a\n#mean increase in gas mileage between regular and premium gasoline.\n\n\n\n# Question : Independent --------------------------------------------------\n\n#Ex. 6 subjects were given a drug (treatment group) and an additional 6 subjects a\n#placebo (control group). Their reaction time to a stimulus was measured (in ms). We\n#want to perform a two-sample t-test for comparing the means of the treatment and\n#control groups.\n#Let mu1 be the mean of the population taking medicine and mu2 the mean of the untreated\n#population. Here the hypothesis of interest can be expressed as:\n#H0: mu1- mu2=0\n#Ha: mu1- mu2<0\n#Here we will need to include the data for the treatment group in x and the data for the\n#control group in y. We will also need to include the options alternative=\"less\", mu=0.\n#Finally, we need to decide whether or not the standard deviations are the same in both groups" }, { "path": "18a-HypoTests/28e-TT-sample1i.R", "content": "# Hypothesis Tests - 2 Sample ---------------------------------------------\noptions(digits=2)\n# Check for Difference Between Two Population Means: Different Hypothesis\n#l. Ho: mu1 = mu2 Ha : mu1 != mu2 \n#2. Ho: mu1 >= mu2 Ha : mu1 < mu2 \n#3. Ho: mu1 <= mu2 Ha : mu1 > mu2 \n#http://www.stat.columbia.edu/~martin/W2024/R2.pdf\n\n# Dependent Samples -------------------------------------------------------\n# Drawn from Same Population\n\n# Independent Samples -----------------------------------------------------\n# Drawn from Different Population\n\n\n#Sample size < 30 and/or Population SD unknown ####\n#Independent Samples\n\n#Q1: (7.43) Two salesman A and B are working in certain district. \n#From a sample survey conducted by the head office, the following result were obtained.\n#State whether there is any significant difference in the average sales between the two salesman\n#Salesman A : n1=20 (No of Sales), x1bar = 170 (Average Sales), sd1 = 20 (Std Dev)\n#Salesman B : n2=18 (No of Sales), x2bar = 205 (Average Sales), sd2 = 25 (Std Dev)\n# No of samples each Data < 30 : Use t test (as Populations are different)\n#t.test to be use in r, but sample values are not given, only summary\nn1=20;x1bar = 170; sd1 = 20 ; # Salesman A\nn2=18;x2bar = 205; sd2 = 25 ; #Salesman B\n#Ho: x1bar = x2bar\n#Ha: x1bar != x2bar\ndf1=n1-1 ; df2=n2-1\npaste(\"Salesman A -\",n1,x1bar,sd1,df1)\npaste(\"Salesman B -\",n2,x2bar,sd2,df2)\n\ndfc = df1 + df2\nalpha = 0.05 # assume if not given\ncl = 1 - alpha/2 # confidence level since it is two tailed\n\n\nttable = qt(cl, dfc ) # table value for conf level/alpha & degree of freedom\n#Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(n1+n2-2)) # pooled SD\nSv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(df1+df2)) # pooled SD\npaste(\"combined df=\",dfc,\"alpha=\",alpha, \"cl=\",cl, 'S pooled =',Sv )\n\ntcalc = (x1bar - x2bar)/ (Sv * sqrt(1/n1 + 1/n2)) # calculated value of t \n(170-205)/(23*sqrt((1/19+1/17)))\n\n\npaste(' t-test =', tcalc,' ttable = ', ttable)\nta=ttable # to make it atomic\n#round(ta,digits=4); round(ttable,digits=4)\nif(abs(tcalc) > ta) paste(\"Ho is rejected\" ,abs(tcalc) ,'>',ta) else paste(\"Failed to Reject Ho\" ,abs(tcalc) ,'<',ta )\n\n\n# Question2 ---------------------------------------------------------------\n\n# A statistics teacher wants to compare his two classes to see if they performed any differently on\n# the tests he gave that semester. Class A had 25 students with an average score of 70, standard\n# deviation 15. Class B had 20 students with an average score of 74, standard deviation 25. Using\n# alpha 0.05, did these two classes perform differently on the tests?\nn1=25; x1bar=70; sd1=15;\nn1=20; x2bar=74; sd2=25;\n\n#Ho: x1bar = x2bar\n#Ha: x1bar != x2bar\ndf1=n1-1 ; df2=n2-1\npaste(\"Class A -\",n1,x1bar,sd1,df1)\npaste(\"Class B -\",n2,x2bar,sd2,df2)\n\ndfc = df1 + df2\nalpha = 0.05 # assume if not given\ncl = 1 - alpha/2 # confidence level since it is two tailed\n\nttable = qt(cl, dfc ) # table value for conf level/alpha & degree of freedom\n#Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(n1+n2-2)) # pooled SD\nSv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(df1+df2)) # pooled SD\npaste(\"combined df=\",dfc,\"alpha=\",alpha, \"cl=\",cl )\n\ntcalc = (x1bar - x2bar)/ (Sv * sqrt(1/n1 + 1/n2)) # calculated value of t \n(70-74)/(20.34*sqrt((1/25+1/19)))\npaste('S pooled =',Sv,' t-test =', tcalc,' ttable = ', ttable)\nta=ttable # to make it atomic\n#round(ta,digits=4); round(ttable,digits=4)\nif(abs(tcalc) > ta) paste(\"Ho is rejected\" ,abs(tcalc) ,'>',ta) else paste(\"Failed to Reject Ho\" ,abs(tcalc) ,'<',ta )\n\n#-----------------\n\n\n# Question 3 --------------------------------------------------------------\n\n# @7.45 : Below are the given gain of weiths in lbs of lions on two diet X and Y\n#Teat at 5% significance level whether the two dietes differ significantly in increasing weight \nx1 = x = c(25,32,30,32,24,14,32)\nx2 = y = c(24,34,22,30,42,31,40,30,32,35)\n# Step 1 : Frame Hypothesis #Ho: x1bar = x2bar ; Ha: x1bar != x2bar\n\nx1bar = mean(x1); x2bar = mean(x2)\nn1 = length(x1) ; n2 = length(x2)\npaste('x1bar= ',x1bar,' x2bar= ',x2bar,' Sample size x= ',n1,' Sample Size y= ',n2)\nsum((x1 - x1bar)^2); sum((x2 - x2bar)^2)\n#sd1 = sd(x1) ; sd2 = sd(x2) #for testing\nsd1a = sqrt(sum((x1 - x1bar)^2)/n1); sd2a = sqrt(sum((x2 - x2bar)^2)/n2) # manually calc sd\npaste(\"X : SD - Using R=\", sd1,' X-Calc= ',sd1a,\"Y : SD - Using R=\", sd2,' Y-Calc= ',sd2a)\ndf1=n1-1 ; df2=n2-1\npaste(\"Diet X -\",n1,x1bar,sd1,df1)\npaste(\"Diet Y -\",n2,x2bar,sd2,df2)\n\n\ndfc = df1 + df2\nalpha = 0.05 # assume if not given\ncl = 1 - alpha/2 # confidence level since it is two tailed\npaste(\"combined df=\",dfc,\"alpha=\",alpha, \"cl=\",cl )\n\nttable = qt(cl, dfc ) # table value for conf level/alpha & degree of freedom\n#Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(n1+n2-2)) # pooled SD\nSv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(df1+df2)) # pooled SD\nsqrt(((df1 * sd1^2) + (df2 * sd2^2))/(df1+df2))\n\ntcalc = (x1bar - x2bar)/ (Sv * sqrt(1/n1 + 1/n2)) # calculated value of t \n(27-32)/(6.4*sqrt((1/6+1/9)))\n\npaste('S pooled =',Sv,' t-test =', tcalc,' ttable = ', ttable)\nta=ttable # to make it atomic\n#round(ta,digits=4); round(ttable,digits=4)\nif(abs(tcalc) > ta) paste(\"Ho is rejected\" ,abs(tcalc) ,'>',ta) else paste(\"Failed to Reject Ho : Accept Ho ::\" ,abs(tcalc) ,'<',ta )\n\nt.test(x1,x2,alternative='two.sided',paired=F,var.equal=T)\n\n\n\n\n" }, { "path": "18a-HypoTests/31b-chisqdistr.R", "content": "# Chi Square Distribution\nx <- rchisq(100, 5) \nhist(x, prob=TRUE, ylim=c(0,0.2)) \ncurve( dchisq(x, df=1), col='blue', add=TRUE) \ncurve( dchisq(x, df=5), col='green', add=TRUE) \ncurve( dchisq(x, df=10), col='red', add=TRUE ) \n\nx <- rchisq(100, 5) \nhist(x, prob=TRUE, ylim=c(0,0.2)) \nfor (i in 1:10) {\n curve( dchisq(x, df=i), col=sample(rainbow(10)), lwd=1, add=TRUE) \n}\n?curve\n\n\n\n# Example 2\n\nx <- rchisq(100, 5)\nhist(x, prob=TRUE, ylim=c(0,0.2))\ncurve( dchisq(x, df=5), col='green', add=TRUE)\ncurve( dchisq(x, df=10), col='red', add=TRUE )\n\n#It may be easier to compare the therotical curve to a density estimate rather than the histogram:\nlines( density(x), col='orange')\n\n\n#Here is one way to dynamically change the df to see how the fit compares:\n \ntmpfun <- function(x,df=1) {\n hist(x, prob=TRUE)\n curve( dchisq(x, df=df), col='blue', add=TRUE )\n }\n\nTeachingDemos::tkexamp( tmpfun(x), list( df=list('slider', from=1, to=15)))\n\n\n#Another approach that may give a better feel for the fit is the hanging rootogram:\n \nlibrary(vcd)\n\ntmp <- hist(x, plot=FALSE)\nrootogram( tmp$counts, diff(pchisq( tmp$breaks, df=5 )*length(x) ) )\nrootogram( tmp$counts, diff(pchisq( tmp$breaks, df=10 )*length(x) ) )\n\ntmpfun2 <- function(x, df=1) {\n tmp <- hist(x, plot=FALSE)\n rootogram( tmp$counts, diff(pchisq(tmp$breaks, df=df)*length(x) ) )\n}\n\nTeachingDemos::tkexamp( tmpfun2(x), list( df=list('slider', from=1, to=15)))\n" }, { "path": "18a-HypoTests/32b-HT-chisq1.R", "content": "# Chi Square Test \n# About Chi Square Test\n# https://stat.ethz.ch/R-manual/R-devel/library/stats/html/chisq.test.html\n\n# Goodness of Fit : Discrepancy between observed & Expected ####\n# Q1 @ 7.65 ---------------------------------------------------------------\n\n#The following table gives the number of aircraft accidents that \n#occured during the various days of the week. Find whether\n#accidents are uniformly distributed over the week. \n#Given value of chi2a at 5% sign for 6 df = 12.59\nchi2a = 12.59 # Given using the table\n?chisq.test\n# days starting from Sun to Sat\nx = accidents = c(14,16,8,12,11,9,14)\nx\nqchisq(.95, df=length(x)-1) # Using R here alpha/2 is not done\npaste('Samples -', length(x) ,'Sum -', sum(x),'Mean ', mean(x))\n#chi2 = sum((Oi - Ei)^2/Ei)\nEi=mean(x) ; Ei # Expected value is mean of the week\nOi = x # Observed values\nchi2 = sum((Oi - Ei)^2/Ei)\nchi2\nif(abs(chi2) > chi2a) paste(\"Ho is rejected\" ,abs(chi2) ,'>',chi2a) else paste(\"Failed to Reject Ho : Accept Ho ::\" ,abs(chi2) ,'<',chi2a )\n# There is not difference in O & E values of accidents with alpha=5%\n\nchisq.test(x)\n# df=6, X2= 4 which < given Chi2, p value > .5 : Accept Ho\n\n\n# Q2 : @7.68 --------------------------------------------------------------\n# The theory predicts that the proportion of beans in the four groups A, B, C, D\n#should be 11:4:3:2. In an experiment it was observed that the number of fours groups\n# A,B,C,D are 1070,430,330,170. Does the result of the experiment support theory\n# chi2 for 3 df at 5% level = 7.815\nOi = c(1070,430,330,170) # Observed Values\nei = c(11,4,3,2) # Expected Proportion\ntotalei = sum(ei) # for relative frequencies\ntotalOi = sum(Oi); total # total Values ; \nEi= ei/totalei * total ; Ei # Expected Values based on ratios \nOi ; Ei\nchi2 = sum((Oi - Ei)^2/Ei); chi2\nchi2a = 7.815 # Given for 3 df at 5% level = 7.815\nqchisq(.95, df=length(Oi)-1) # Using R here alpha/2 is not done\n\nif(abs(chi2) > chi2a) paste(\"Ho is rejected\" ,abs(chi2) ,'>',chi2a) else paste(\"Failed to Reject Ho : Accept Ho ::\" ,abs(chi2) ,'<',chi2a )\n# There is not difference in O & E values with alpha=5%\n# Experiment does not support theory \n\nchisq.test(Ei,Oi)\n# df=9, chi2= 10 which is > given table Chi2(7.8- Given), p value < .5 : Reject Ho\n" }, { "path": "18a-HypoTests/32c-HT-chisq2.R", "content": "# Chi squared Tests 2\n# http://stattrek.com/chi-square-test/independence.aspx?Tutorial=AP\n\n# Q1 ----------------------------------------------------------------------\n\n#Ho: Gender and voting preferences are independent. \n#Ha: Gender and voting preferences are not independent.\n\n#Formulate an analysis plan. For this analysis, the significance level is 0.05.\n\n#Analyze sample data. Applying the chi-square test for independence to sample data, \n#we compute the degrees of freedom, the expected frequency counts, and the chi-square test statistic. \n#Based on the chi-square statistic and the degrees of freedom, we determine the P-value.\n# Find Chisq (table values from R command alpha=.05 ie CL = .95 & df = 7)\nqchisq(.95, df=7)\n#dchisq(x, df, ncp = 0, log = FALSE)\n#pchisq(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)\n#qchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)\n#rchisq(n, df, ncp = 0)\n?chisq.test\n?Chisquare\nchisq.test(x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)), \n rescale.p = FALSE, simulate.p.value = FALSE, B = 2000)\n\n\n# Chi Squared\n\n# Q1 ----------------------------------------------------------------------\n#http://www.r-tutor.com/elementary-statistics/goodness-fit/chi-squared-test-independence\n\n#In the built-in data set survey, the Smoke column records the students smoking habit, \n#while the Exer column records their exercise level. \n#The allowed values in Smoke are \"Heavy\", \"Regul\" (regularly), \"Occas\" (occasionally) and \"Never\". \n#As for Exer, they are \"Freq\" (frequently), \"Some\" and \"None\".\n#We can tally the students smoking habit against the exercise level with the table function in R. \n#The result is called the contingency table of the two variables.\nlibrary(MASS) # load the MASS package \ntbl = table(survey$Smoke, survey$Exer) \ntbl # the contingency table \n#Test the hypothesis whether the students smoking habit is independent of their exercise level at .05 significance level.\n#We apply the chisq.test function to the contingency table tbl, and found the p-value to be 0.4828.\nchisq.test(tbl)\n#as the p-value 0.4828 is greater than the .05 significance level, we do not reject the null hypothesis that the smoking habit is independent of the exercise level of the students.\n\n#The warning message found in the solution above is due to the small cell values in the contingency table. To avoid such warning, we combine the second and third columns of tbl, and save it in a new table named ctbl. Then we apply the chisq.test function against ctbl instead.\n\nctbl = cbind(tbl[,\"Freq\"], tbl[,\"None\"] + tbl[,\"Some\"]) \nctbl \nchisq.test(ctbl)\n\n\n# Q2 ----------------------------------------------------------------------\n\n#http://courses.statistics.com/software/R/Rchisq.htm\n\n# q3 ----------------------------------------------------------------------\n\n#http://www.statisticshowto.com/what-is-a-chi-square-statistic/\n \n# Q3 ----------------------------------------------------------------------\n\n#https://ww2.coastal.edu/kingw/statistics/R-tutorials/independ.html\n\nrow1 = c(91,90,51) # or col1 = c(91,150,109)\nrow2 = c(150,200,155) # and col2 = c(90,200,198)\nrow3 = c(109,198,172) # and col3 = c(51,155,172)\ndata.table = rbind(row1,row2,row3) # and data.table = cbind(col1,col2,col3)\ndata.table\nchisq.test(data.table)\n\n#If the data are available in an electronic document, like this one, it can be entered into R using the scan() function.\nls() # list all current objects\nrm(list=ls()) # Clean up a bit first!\nthe.data = scan() # input from scan\n#1: 91 90 51\n#4: 150 200 155\n#7: 109 198 172\n#10: \n# Read 9 items\nthe.data # scan produces a vector\n#[1] 91 90 51 150 200 155 109 198 172\ndata.matrix = matrix(the.data, byrow=T, nrow=3)\ndata.matrix # which we convert to a matrix\nchisq.test(data.matrix)$statistic # keeping the output brief\n\n#X-squared 25.08597\n\ndimnames(data.matrix) = list(Age=c(\"lt.45\",\"45.to.59\",\"ge.60\"), Freq=c(\"Monthly\",\"Occasionally\",\"Never\"))\n\n\n\n# Q5 ----------------------------------------------------------------------\n\n#http://stattrek.com/chi-square-test/independence.aspx\n#Ho: Gender and voting preferences are independent. \n#Ha: Gender and voting preferences are not independent.\nmale=c(200,150,50)\nfemale=c(250,300,50)\nM = as.table(rbind(male,female))\ndimnames(M) <- list(gender = c(\"M\", \"F\"),\n party = c(\"BJP\",\"Congress\", \"Independent\"))\n\nXcalc = chisq.test(M) # prints test summary\nXcalc\nXtable = qchisq(.95, df=(2-1)*(3-1)) ; Xtable\nXcalc$observed # observed counts (same as M)\nXcalc$expected # expected counts under the null\nXcalc$residuals # Pearson residuals\nXcalc$stdres # standardized residuals\n# X-squared = 16.204 > Xtable= 5.991465, then Reject Ho\n\n\n#http://web.pdx.edu/~newsomj/da1/ho_chisq.pdf\n" }, { "path": "18a-HypoTests/33b-HT-chisq.R", "content": "#Chi Square Test\n\n#In the built-in data set survey, the Smoke column records the students smoking habit, while the Exer column records their exercise level. The allowed values in Smoke are \"Heavy\", \"Regul\" (regularly), \"Occas\" (occasionally) and \"Never\". As for Exer, they are \"Freq\" (frequently), \"Some\" and \"None\".\n\n#We can tally the students smoking habit against the exercise level with the table function in R. The result is called the contingency table of the two variables.\n\nlibrary(MASS) # load the MASS package \ntbl = table(survey$Smoke, survey$Exer) \ntbl # the contingency table \n\n#Test the hypothesis whether the students smoking habit is independent of their exercise level at .05 significance level.\n\n#We apply the chisq.test function to the contingency table tbl, and found the p-value to be 0.4828.\n\nchisq.test(tbl) \n#As the p-value 0.4828 is greater than the .05 significance level, we do not reject the null hypothesis that the smoking habit is independent of the exercise level of the students.\nctbl = cbind(tbl[,\"Freq\"], tbl[,\"None\"] + tbl[,\"Some\"]) \nctbl " }, { "path": "18a-HypoTests/33d-chisqtest1.R", "content": "# Chi Squared Tests\n# Goodness of Fit\n#Links------\n#http://www.r-tutor.com/elementary-statistics/goodness-fit/multinomial-goodness-fit\n#http://math.hws.edu/javamath/ryan/ChiSquare.html\n\n\n#The test is applied when you have one categorical variable from a single population. It is used to determine \n#whether sample data are consistent with a hypothesized distribution.\n\n# When to use \n#1 Simple Random Sampling\n#2 Variable is categorical - Count of grades A, B,C ; Colors etc\n#3 min freq 5\n\n# Hypothesis -----------\n\n#Ho: data consistent with a specified distribution. (Observed as per prob)\n#Ha: data are not consistent with a specified distribution.\n\n#Interpret results -----------\n# If P-value < significance level (0.05), we cannot accept the null hypothesis.\n\n\n# School Grades\ngrades = c(50,45,5) # grades - cat variable\nexpgrades = c(0.3, 0.6,0.1) # theoretical distribution\nchisq.test(grades) # exp calc from grades itself not correct\n(ctest = chisq.test(x=grades, p=expgrades))\nctest$statistic\n(q= qchisq(1-.05,df=2))\nctest$method\n\nx <- rchisq(1000, 5) \nhist(x, prob=TRUE, ylim=c(0,0.2)) \ncurve( dchisq(x, df=2), col='blue', add=TRUE)\nabline(v=c(q,ctest$statistic), col=c('green','red'))\ntext(ctest$statistic-1, 0+0.1,srt=90, cex=0.75,\n labels = 'Test Statistics right of Table Line')\n\n\n# Data from Table\nHairEyeColor # Margin Table of Hair vs Eye Colors : Count\n(eye = margin.table(HairEyeColor,2))\neye\n(ctest2 = chisq.test(x=eye, p=c(0.5,0.25,0.15,0.1)))\nctest2\nqchisq(1-.05,df=3)\nctest2$p.value # < 0.05\nctest2$statistic # > chisq table value \n# Reject Ho : Data is not expected\n\n# Data from Data Frame\n" }, { "path": "18a-HypoTests/33e-chisqtest2.R", "content": "# Chi Square Test------------\n\n#two categorical variables from a single population.\n#Used to determine whether there is a significant association between the two variables.\n\n#egs ------\n#determine whether gender is related to voting preference.\n# Smoking habits related to gender\n\n# Hypothesis --------\n#Ho: Variable A and Variable B are independent. \n#Ha: Variable A and Variable B are not independent.\n\n# Interpret Values\n#If the sample findings are unlikely, given the null hypothesis, \n#the researcher rejects the null hypothesis. Typically, this involves \n#comparing the P-value to the significance level, \n#and rejecting the null hypothesis when the P-value is less than the significance level.\n\n\n\n#Test of Independence -----------\n# When to use ------------\n#1 Sampling method is simple random sampling.\n#2 Variables under study are each categorical.\n#3 Sample data are displayed in a contingency table, \n# the expected frequency count for each cell of the table is at least 5.\n\n\n# Link1 ------------\n#https://ww2.coastal.edu/kingw/statistics/R-tutorials/independ.html\n#http://stattrek.com/chi-square-test/independence.aspx?Tutorial=AP\n#http://www.r-tutor.com/elementary-statistics/goodness-fit/chi-squared-test-independence\n\n# Enter Data -----------\nrow1 = c(91,90,51) # or col1 = c(91,150,109)\nrow2 = c(150,200,155) # and col2 = c(90,200,198)\nrow3 = c(109,198,172) # and col3 = c(51,155,172)\ndata.table = rbind(row1,row2,row3) # and data.table = cbind(col1,col2,col3)\ndata.table\n\nchisq.test(data.table)\n\n\n\n\n# Data From Data Table -----------\nUCBAdmissions \nftable(UCBAdmissions, row.vars=c(\"Admit\"))\nstr(UCBAdmissions)\ndeptB = UCBAdmissions[,,2] # all rows, all cols, but only of layer 2\ndeptB\n\nchisq.test(deptB)\n#Yate's correction for continuity is applied by default when a 2x2 table is entered into the chi square procedure.\n\nchisq.test(deptB)$expected\nchisq.test(deptB)$resid\nchisq.test(deptB)$statistic\n\n\n\n#https://ww2.coastal.edu/kingw/statistics/R-tutorials/independ.html\n# Data From Data Frame ----------\n# The data frame \"survey\" in the \"MASS\" package contains responses from 237 statistics\n# students to a series of questions, some of which resulted in categorical answers. \n# For one item, the students were asked to fold their arms, and the recorded result was \n# which arm was on top: right, left, or neither. \n# Let's see how this variable relates to the sex of the student. \n#Ho: Folding Habits is not related to Gender (Independent)\n#Ha : Folding Habits relate to Gender (Not Independent) Males do it this way, F do it this\n\n\ndata(survey, package=\"MASS\")\nstr(survey)# Interpret the results\n# p-value > 0.05 : Accept Ho : Sex and Folding are Independent (not related)\n\ntable(survey$Sex, survey$Fold)\n(ct3 = chisq.test(x=survey$Sex, y=survey$Fold)) \nct3$observed; ct3$expected\n\nrbind(ct3$observed, ct3$expected)\nct3$residuals\n\n\n# Example Elections\n#the.data = scan() # Manually entering data\n(result = matrix(c(200,150,50,250,300,50), byrow=T, ncol=3))\ndimnames(result) = list(gender=c('male','female'), party=c('bjp','cong','aap') )\nresult\n#Ho : Gender and Party preference is independent\n#Ha : Gender and Party Preference is related (not independent)\n(ct4 = chisq.test(x=result))\nct4$observed; ct4$expected\nct4$statistic\nct4$p.value\nqchisq(1-.05,df=(2-1)*(3-1))\n#pvalue < 0.005 : Reject Ho : Gender -Party are related\n#calc > table : Rejection Region\n\nct4$residuals\n#Standardized residual = (observed count – expected count) / √expected count\n" }, { "path": "18a-HypoTests/34b-goodnessfit.R", "content": "\n\n\n# Chi Sq - Goodness of Fit\n#From the library Mass, select the survey dataset. \nlibrary(MASS)\ndata(survey)\nstr(survey)\n\n#What are the levels of Smokers. \nlevels(survey$Smoke)\n\n\n#Create frequency distribution using table command.\nsmoke.freq = table(survey$Smoke)\nround(prop.table(smoke.freq)* 100,2)\n\n\n\n#Percentage distribution of a given sample data is as follows. \n#Heavy Never Occas Regul \n#4.5% 79.5% 8.5% 7.5%\n#Carryout a Chi Square Test of Goodness of of Fit. \n#Determine whether the sample data in survey supports Distribution at .05 significance level\n# Ho: Sample Data follows the distribution\n# Ha: Sample Data does not fit the distribution\n(smoke.prob = round(prop.table(smoke.freq),3))\nsum(smoke.prob)\nsmoke.prob = c(.045, .795, .085, .075)\nsum(smoke.prob)\n\nsmoke.prob\nchisq.test(smoke.freq, p=smoke.prob) \n\n#As the p-value 0.991 is greater than the .05 significance level,\n# we do not reject the null hypothesis that the sample data in survey\n# supports the campus-wide smoking statistics.\n#" }, { "path": "18a-HypoTests/datadistr.R", "content": "# Distribution of Data\ndata2 = c(3,5,7,5,3,2,6,8,5,6,9,4,5,7,3,4)\ndata2\nlength(data2)\nsummary(data2)\nstr(data2)\ntable(data2)\nnames(table(data2))\nhist(data2, labels=T, ylim=c(0,8),main='Histogram of Data'\n ,ylab='Frequency', xlab='Size of Data'\n , col='gray')\n#breaks\t :the n+1 cell boundaries \n#(= breaks if that was a vector). These are the nominal breaks, not with the boundary fuzz.\nhist(x, breaks = \"Sturges\",\n freq = NULL, probability = !freq,\n include.lowest = TRUE, right = TRUE,\n density = NULL, angle = 45, col = NULL, border = NULL,\n main = paste(\"Histogram of\" , xname),\n xlim = range(breaks), ylim = NULL,\n xlab = xname, ylab,\n axes = TRUE, plot = TRUE, labels = FALSE,\n nclass = NULL, warn.unused = TRUE)\n\nhist(data2, label=T, ylim=c(0,8), breaks=3)\nhist(data2, label=T, ylim=c(0,8), breaks=7)\nhist(data2, label=T, ylim=c(0,8), breaks=2:9) # same\nhist(data2, label=T, ylim=c(0,8), breaks=c(2,3,4,5,6,7,8,9)) # same\nhist(data2, label=T, ylim=c(0,8), breaks=c(2,4,5,6,9)) # different\n#unequal width range; convert to density\nhist(data2, label=T,breaks=c(2,4,5,6,9)) # different\n?hist\nhist(data2, label=T, ylim=c(0,0.3), breaks=c(2,4,5,6,9))\n\n# Density\ntable(data2)\ndf2 = data.frame(t(table(data2)))\ndf2\ndf2$pf = round(df2$Freq/sum(df2$Freq),2)\ndf2$cpf = round(cumsum(df2$pf),2)\ndf2\ndens = density(data2)\nnames(dens)\nstr(dens)\nhist(data2, freq=F, labels=T)\nlines(density(data2), lty=2, col='red')\n#lines(density(data2), type='rectangular')\n\nabline(h=df2$pf,col='green')\n#text(2:9,df2$pf,labels=df2$pf,col='blue', cex=.5)\n#text(dens$x ,dens$y,labels=df2$pf,col='blue', cex=.5)\n\n?lines\n\nplot(density(data2))\n\nplot(density(data2), main=' Density Plot', xlab=' Size of Class')\n\nhist(data2, freq=F, ylim=c(0,0.3))\nlines(density(data2), lty=2)\n" }, { "path": "18a-HypoTests/htestnd1.R", "content": "# Hypo Test on ND data\n#https://www.r-bloggers.com/hypothesis-testing-on-normally-distributed-data-in-r/\n#interested in knowing if the average value of a certain parameter differs significantly from \n#a given value within a well defined confidence level: \n#Ho : U = Uo\n#Ha : U != Uo\n#mutually Exclusive Hypothesis\n#First, we should check what kind of distribution our data follows. \n#Roughly speaking, if the number of samples is > 30 we can plot \n#a histogram to get a visual grasp of the distribution and then \n#run a few simple function, \n#to assess the skewness and kurtosis of the distribution. \n\nThe code below can be used to perform a z-test under the following\n# assumptions:\n# 1. The data is normally distributed\n# 2. Samples are iid\n# \n# Remember that:\n# 1.Low pvalue: strong empirical evidence against h0\n# 2.High pvalue: little or 'no' empirical evidence against h0\n#\n# Generally, as a rule of thumb pvalue < 0.05 can be considered low \n# however it depends on many factors and I'd rather not dive into \n# this :)\n\n# Data to perform the test on\ndata_vector <- c(63, 75, 84, 58, 52, 96, 63, 55, 76, 83)\n\n# Left tail test\n# H0: mu >= mu0\n# H1: mu < mu0\nt.test.left <- function(data, mu0, alpha)\n{\n t.stat <- (mean(data) - mu0) / (sqrt(var(data) / length(data)))\n dof <- length(data) - 1\n t.critical <- qt(alpha, df= dof) #Es alpha 0.05 -> -1.64 (df=Inf)\n p.value <- pt(t.stat, df= dof)\n \n if(t.stat <= t.critical)\n {\n print(\"Reject H0\")\n }\n else\n {\n print(\"Accept H0\")\n }\n print('T statistic')\n print(t.stat)\n print('T critical value')\n print(t.critical)\n print('P value')\n print(p.value)\n print(\"#####################\")\n \n return(t.stat)\n}\n\nt.test.left(data_vector, 73, 0.05)\n\n# Right tail test\n# H0: mu <= mu0\n# H1: mu > mu0\nt.test.right <- function(data, mu0, alpha)\n{\n t.stat <- (mean(data) - mu0) / (sqrt(var(data) / length(data)))\n dof <- length(data) - 1\n t.critical <- qt(1-alpha, df= dof) #Es alpha 0.05 -> 1.64 (df=Inf)\n p.value <- 1 - pt(t.stat, df= dof)\n \n if(t.stat >= t.critical)\n {\n print(\"Reject H0\")\n }\n else\n {\n print(\"Accept H0\")\n }\n print('T statistic')\n print(t.stat)\n print('T critical value')\n print(t.critical)\n print('P value')\n print(p.value)\n print(\"#####################\")\n \n return(t.stat)\n}\n\nt.test.right(data_vector, 73, 0.05)\n\n\n# Two tail z test\n# H0: mu = mu0\n# H1: mu != mu0\nt.test.twoTails <- function(data, mu0, alpha)\n{\n t.stat <- abs((mean(data) - mu0)) / (sqrt(var(data) / length(data)))\n dof <- length(data) - 1\n t.critical <- qt(1-alpha/2, df= dof) #Es alpha 0.05 -> -1.9599 (df=Inf)\n p.value <- 2*(1-pt(t.stat, df= dof))\n \n if(t.stat >= t.critical)\n {\n print(\"Reject H0\")\n }\n else\n {\n print(\"Accept H0\")\n }\n print('T statistic')\n print(t.stat)\n print('T critical values')\n print(c(-t.critical,t.critical))\n print('P value')\n print(p.value)\n print(\"#####################\")\n \n return(t.stat)\n}\n\nt.test.twoTails(data_vector, 73, 0.05)\n\n#Plot of Student Distribution\ndfs <- length(data_vector)-1\nx_ <- seq(-8,8,0.1)\ny <- dt(x_,dfs)\nt.val <- qt(1-0.05,df=dfs)\nplot(x_,y,type='l',lwd=3,col='blue',xlab='x',\n ylab='Density',main='Student distribution \\n 9 Degree of Freedom')\nabline(v=0)\nabline(v=t.val,lwd=2,col='red')\npoints(t.val,dt(t.val,dfs),lwd=3,col='red')\n" }, { "path": "18a-HypoTests/randomdistr.R", "content": "# Random Numbers\n\n#uniformly distributed (flat) random numbers, use runif(). \n#By default, its range is from 0 to 1.\nrunif(1)\n\n# Get a vector of 4 numbers\nrunif(4)\n\n# Get a vector of 3 numbers from 0 to 100\nrunif(3, min=0, max=100)\n\n\n# Get 3 integers from 0 to 100\n# Use max=101 because it will never actually equal 101\nfloor(runif(3, min=0, max=101))\n\n# This will do the same thing\nsample(1:100, 3, replace=TRUE)\n\n# To generate integers WITHOUT replacement:\nsample(1:100, 3, replace=FALSE)\n\n\n\n#Generate a random number between 5.0 and 7.5\n\nx1 <- runif(1, 5.0, 7.5)\nx1\n\nx2 <- runif(10, 5.0, 7.5)\nx2\n\n\n\nx3 <- sample(1:10, 1)\nx3\n\nx4 <- sample(1:10, 5, replace=T)\nx4\n\nx5 <- sample(1:40, 6, replace=F)\nx5\n\nsample(state.name, 10)\n\nsample(state.name, 52) # error\n\n\n?runif\n?sample\n?.Random.seed\n\n\n\n# Random Numbers - Normal Distribution\nrnorm(4)\n#> [1] -2.3308287 -0.9073857 -0.7638332 -0.2193786\n\n# Use a different mean and standard deviation\nrnorm(4, mean=50, sd=10)\n#> [1] 59.20927 40.12440 44.58840 41.97056\n\n# To check that the distribution looks right, make a histogram of the numbers\nx <- rnorm(400, mean=50, sd=10)\nhist(x)\n\n\n# Sample\n#Another approach to randomization is the sample function,\n#which pulls elements from an object (such as a vector) of defined values or, alternatively, can be specified to select cases from a string of integers. The function also has the option of specifying whether replacement will be used or not.\n\n?sample\nsample(x, size, replace = FALSE, prob = NULL)\n#If x has length 1, is numeric (in the sense of is.numeric)\n#and x >= 1, sampling via sample takes place from 1:x.\n\n\nRandomSample <- sort(sample(99, 100, replace=TRUE))\nRandomSample\n\nRandomSample <- sort(sample(1:99, 100, replace=TRUE))\nRandomSample\n\n# same Random nos with set.seed\nset.seed(100)\nRandomSample <- sort(sample(99, 100, replace=TRUE))\nRandomSample\nset.seed(100)\nRandomSample <- sort(sample(99, 100, replace=TRUE))\nRandomSample\n\n# Conditions in Sample\n# sample()'s surprise -- example\nx <- 1:10\nsample(x[x > 8]) # length 2\nsample(x[x > 9]) # oops -- length 10!\nsample(x[x > 10]) # length 0\n\n# Resample\nresample <- function(x) x[sample.int(length(x))]\nresample(x[x > 8]) # length 2\nresample(x[x > 8]) # length 2\n\nresample(x[x > 9]) # length 1\nresample(x[x > 10]) # length 0\n\n\n# the list of party-goers\ndwarves <- c(\"Fíli\", \"Kíli\", \"Balin\", \"Dwalin\", \"Óin\", \"Glóin\", \"Bifur\", \"Bofur\", \"Bombur\", \"Ori\", \"Nori\", \"Dori\", \"Thorin\") \n# draw a sorted sample of 5 without replacement\nParty <- sort(sample(dwarves, 5))\n# print the names\nParty\n" }, { "path": "18a-HypoTests/shadeareainplot.R", "content": "x_mean <- c(1, 2, 3, 4)\ny_mean <- c(1, 1, 1, 1)\n\ny_max <- c(4, 4, 4, 4)\ny_min <- c(-4, -4, -4, -4)\n\n\nx_shade <- c(2, 3, 4)\n\ny_max_shade <- c(4, 4, 4)\ny_min_shade <- c(-4, -4, -4)\n\nplot(x=rep(x_mean, 3), y=c(y_mean, y_max, y_min), bty='n', type=\"n\" )\nrect(2,-4,4,4,col = rgb(0.5,0.5,0.5,1/4))\narrows(x0=x_mean, y0=y_min, x1=x_mean, y1=y_max, length=0)\npoints( x=x_mean, y=y_mean, pch=16)\n\n\n\n\n# ------------\n\nxx <- c(1:50)\nyy <- rnorm(50)\nn <- 50\nhline <- 0\nplot (yy ~ xx, type=\"n\", axes=FALSE, ann=FALSE)\ntext(x=xx,y=min(yy)+max(yy),labels='a')\npolygon(c(xx[1], xx, xx[n]), c(min(yy), yy, min(yy)), \n col=rgb(1, 0, 0,0.5), border=NA)\n\n\n#------normal plot\nx=seq(-4,4,length=200)\nx\ny=dnorm(x)\nplot(x,y,type=\"l\", lwd=2, col=\"blue\")\nx=seq(-1,1,length=100)\ny=dnorm(x)\npolygon(c(-1,x,1),c(0,y,0),col=\"gray\")\nabline(v=c(-1,1))\n\n\n#--------------\n\npar.default <- par(no.readonly=TRUE)\npar(mar=c(5,6,4,2)+0.1)\npar(pty=\"s\")\n\n# create data\nx <- 1:100\nExp <- 50 + x*0.1\nCon.High <- Exp + 5 + rnorm(100)\nCon.Low <- Exp - 5 + rnorm(100)\nOne.Run <- Exp + rnorm(100,0,2)\n\n# create plot\nplot(Exp~x,type=\"l\",\n xlab= \"Time Step\",\n ylab=\"Populatioh Size (N)\",\n lty=\"dashed\",\n ylim=range(One.Run,Exp,Con.High,Con.Low),\n cex.lab=1.5,las=1,cex.axis=1.5)\n\n# add polygon and overlplot the lines\npolygon(c(x,rev(x)),c(Con.Low,rev(Con.High)),col=\"thistle\",border=NA)\nlines(x,Exp,lty=\"solid\",col=\"purple\",lwd=1.5)\nlines(x,One.Run)\n\n\npar(par.default)\nrm(list=ls())\n\n#-------------\n\n# Create data for the area to shade\ncord.x <- c(-3,seq(-3,-1,0.01),-1) \ncord.y <- c(0,dnorm(seq(-3,-1,0.01)),0) \n\n# Make a curve\ncurve(dnorm(x,0,1), xlim=c(-3,3), main='Standard Normal') \n\n# Add the shaded area.\npolygon(cord.x,cord.y,col='skyblue')\n" }, { "path": "18a-HypoTests/tests1.R", "content": "\n#H0: no significant difference\n#H1: significant difference\n\n\n\n\nt.test(A,B)\n#reject null hypothesis\n?t.test\nt.test(A,B, alternative='two.sided', conf.level = 0.95)\nqt(df=9,p=.05)\n\nt.test(A,B, alternative='less', conf.level = 0.95)\nqt(df=9,p=.05)\nt.test(A,B, alternative='greater', conf.level = 0.95)\nqt(df=9,p=.05)\n\n(t= (500 - 560)/ sqrt(100^2/10 + 121^2/10))\nn1=10 ; n2=10; s1=100; s2=121\n(PV = ((n2-1)*s1 + (n2-1)*s2))/(n1 + n2 -2) \n(PS = sqrt(PV))\n(t= (500 - 560)/ (Sv * sqrt(1/n1 + 1/n2)))\n\ndf= n1+n2-1\nqt(df=n1+n2-1, p=0.975)\n" }, { "path": "19a-sum-cases/31b-DA-dencoCase.R", "content": "# Case Study - Denco \n\n#read file : Method1\nsales1 = read.csv(\"./data/denco.csv\")\nstr(sales1)\nhead(sales1)\n#read file : Method2\nsales2 = read.csv(file.choose())\nstr(sales2)\n\n#read file: Method3\n#install.packages('gsheet')\nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907\"\nsales3 = as.data.frame(gsheet2tbl(url))\nstr(sales3)\n\n\n#using gsheet\nlibrary(gsheet)\ndenco2 = as.data.frame(gsheet2tbl(url))\nstr(denco2)\n\n#head(sales1,n=7)\n#names(sales1)\n\n# whichever you read the data, store it in sales object\nsales = sales1 # keeping a backup\nstr(sales)\nclass(sales)\nstr(sales)\n?summary\nsummary(sales)\n\nstr(sales)\ndim(sales) #dimensios of DF rows & colnum\nunique(sales$custname)\nlength(unique(sales$custname))\nlength(unique(sales$region ))\n\n# aggregation\naggregate(sales$revenue , by=list(sales$custname), FUN=sum)\ndf1 = aggregate(sales$revenue , by=list(sales$custname), FUN=sum)\nhead(df1)\nstr(df1)\n\ndf1=df1[order(df1$x, decreasing=TRUE),]\nhead(df1,10)\n\nhead(df1[order(df1$x, decreasing=TRUE),], 5)\n\naggregate(sales$revenue, by=list(sales$region), FUN=mean)\ndf2= aggregate(formula=revenue ~ region, data=sales, FUN=sum)\ndf2[order(df2$revenue, decreasing=F),]\n\n\n#Aggregate Formula\n(df2 = aggregate(revenue ~ custname + region, data=sales, FUN=sum))\nhead(df2[order(df2$revenue,decreasing=T),],10)\n\n#List\nlist1= tapply(sales$revenue, sales$custname, FUN=sum)\nhead(list1)\nlist1\nhead(sort(list1, decreasing=T))\nsummary(df3)\nstr(df3)\n\n#dplyr\nnames(sales)\n\nlibrary(dplyr)\n\nsales %>% filter(margin > 10000) %>% arrange(region, desc(revenue))\nfilter(sales, margin > 1000000)\n\nsales %>% filter(region == '01-East' & revenue > 400000) %>% select(partnum, region, revenue)\n\nnames(sales)\nsales %>% group_by(custname) %>% \n summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\n\n\nlibrary(data.table)\ndt1 = as.data.table(sales)\ndt2 = dt1[, sum(revenue), by=custname]\nnames(dt2)\ndt1[, dt1[, sum(revenue), by=custname]]\n#dt1[, order( decreasing = T)]\n\n# Select\nlibrary(sqldf)\ndf5 =sqldf(\"Select custname, sum(revenue) from sales Group By custname order by sum(revenue) desc \")\nhead(df5)\n\n# Frequency --------\nnames(sales)\nt1=table(sales$custname)\nclass(t1)\nlength(t1)\nhead(t1)\nt2= sort(t1,decreasing=T )\nhead(t2)\nhead(sort(table(sales$custname), decreasing=T), n=10)\ntail(sort(table(sales$custname), decreasing=T), n=10)\n\n#xtab\nhead(sort(xtabs(~ custname, sales), decreasing=T))\n\n#\nlibrary(dplyr)\nsales %>% dplyr::count(custname, sort=TRUE)\n\nsales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))\n\n\n\n#plyr\ndf2a= plyr::count(sales, c('custname'))\nstr(df2a); names(df2a)\nhead(df2a[order(-df2a$freq),])\n\n# Summarise by Part Num\n\ndf3a= aggregate(sales$revenue, by=list(sales$partnum), FUN=sum)\nhead(df3a)\nstr(df3a)\ndf3a[order(-df3a$x),][1:6,]\nhead(df3a[order(-df3a$x),])\n\ndf3b = aggregate(revenue ~ partnum, data=sales, FUN=sum)\nhead(df3b)\nhead(df3b[order(df3b$revenue, decreasing=T),])\n\nsales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))\n\n\n# which parts have highest Profit : partno - sum(profit)\nnames(sales)\ndf4a = aggregate(margin ~ partnum, data=sales, FUN=sum)\naggregate(margin ~ partnum, data=sales, FUN=sum)\nhead(df4a[order(df4a$margin, decreasing = T),])\n\nsales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()\n\n" }, { "path": "19a-sum-cases/31c-DA-dencoCase2.R", "content": "# Case Study - Denco \n\n#read file : Method1\nsales1 = read.csv(\"./data/denco.csv\")\nstr(sales1)\n\n#read file : Method2\nsales2 = read.csv(file.choose())\nstr(sales2)\n\n# whichever you read the data, store it in sales object\nsales = sales1 # keeping a backuph\nhead(sales) #top 6 rows\nstr(sales)\nclass(sales)\ndim(sales)\nsummary(sales)\nnames(sales)\ntail(sales)\n\nunique(sales$custname)\nlength(unique(sales$custname))\nlength(unique(sales$region ))\n#dplyr\nnames(sales)\nlibrary(dplyr)\n\nnames(sales)\nsales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=3)\n#Triumph Insulation gave highest Revenue\n\ndf3 = sales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\noptions(tibble.width = Inf) # displays all columns.\noptions(tibble.print_max = Inf) # to show all the rows.\ndf3[15:60,]\n\n# Frequency --------\nnames(sales)\nt1=table(sales$custname)\nt1\nclass(t1); length(t1) ;head(t1)\nt2= sort(t1,decreasing=T )\nhead(t2)\n\n\nlibrary(dplyr)\nsales %>% count(custname, sort=TRUE)\nsales %>% count(custname, sort=TRUE) %>% head(n=5)\n\n\nsales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)\n# Summarise by Part Num\nsales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)\n# which parts have highest Profit : partno - sum(profit)\nnames(sales)\nsales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()\n\ndf4 = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\npie(x = df4$Revenue)\nbarplot(df4$Revenue, col=1:4)\n\nsales %>% filter(revenue >= 7771000 ) %>% select(custname, revenue)\nsales[, 1:2]\n\nsales %>% sample_frac(.01) \nsales %>% sample_n(5)\nsample_n(sales, 5)\n\n\n" }, { "path": "19a-sum-cases/31d-dsum-denco.R", "content": "# Case Study - Denco \n#Manufacturing Firm with sales data of partnum and customer with region wise sales\n\n# Should know - import, table, dplyr, aggregate etc\n\n#read file : Method1\nsales1 = read.csv(\"./data/denco.csv\")\nstr(sales1) #see if data is loaded and check its structure\n\n#read file : Method2 : when location is not in project folder\nsales2 = read.csv(file.choose())\nstr(sales2)\n\n# whichever you read the data, store it in sales object\nsales = sales1 # keeping a backuph\nhead(sales) #top 6 rows\nstr(sales) # structure\nclass(sales) # class type - Df\ndim(sales) # rows & columns\nsummary(sales) # summary- colname, type\nnames(sales) # col names\nhead(sales) # first 6 values\ntail(sales) # last 6 values\n\n# Some other functions\nunique(sales$custname) # unique customer names\nlength(unique(sales$custname)) # no of unique customers\nlength(unique(sales$region )) # no of distinct regions\n\n#Information Required----\n# 1. Who are the most loyal Customers - What to do : Improve repeated sales, Target customers with low sales Volumes\n# 2. Which customers contribute the most to their revenue : How do I retain these customers & target incentives\n# 3a. What part numbers bring in to significant portion of revenue : Maximise revenue from high value parts\n# 3b. What parts have the highest profit margin : What parts are driving profits & what parts need to build further\n\n\n#dplyr\nnames(sales)\nlibrary(dplyr) #use this library to do summarisation\n\n#Case-1 : Loyal Customers----\n# Finding Frequency and sort them in descending order\nnames(sales)\n(t1=table(sales$custname)) # freq table for all customers\nclass(t1); length(t1)\nhead(t1) # top 6 but not sorted\nt2= sort(t1,decreasing=T )\nhead(t2)\n#Ans: CHIZ BROS INC most loyal with 253 times purchase\n\n#other methods through dplyr\nlibrary(dplyr)\nsales %>% count(custname, sort=TRUE) %>% head(n=5)\n#Ans: CHIZ BROS INC most loyal with 253 times purchase\n\nsales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)\n#Ans: CHIZ BROS INC most loyal with 253 times purchase\n\n\n#Case-2 : Customer- Highest Revenue---\nsales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=3)\n#Ans2: Triumph Insulation gave max revenue\n\n# save this object and then analyse\nsalesrevenue = sales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\n\n#dply uses tibble format and does not show all rows/cols by default\noptions(tibble.width = Inf) # displays all columns.\noptions(tibble.print_max = Inf) # to show all the rows.\nsalesrevenue[1:5,] # first 5 rows\n#Ans2: here also Triump insulation gives max revenue = 35592K\n\n#Case3 : Most Profitable PartsNums ----\n# Look for freq, revenue and/ or profit margin\n\n# Summarise by Part Num for frequency\nsales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)\n#Ans3a: Part Num- 764821000 was purchased max times - 122\n\n# Summarise Partnum for Profit(margin) : sum(profit)\nnames(sales)\nsales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()\n#Ans 3b: Part Num - 733648000 gave max margin profit - 11003367\n\n# Summarise Partnum for revenue\n#(practise it )\n\n\n#Case : Extra ----\n#Find Regions wise sales\n\nsalesregionrevenue = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\n\n#Some Graphs related to Data Summarised\npie(x = salesregionrevenue$Revenue, labels=unique(sales$region))\nbarplot(salesregionrevenue$Revenue, col=1:4)\n\n# Filter Data----\n#Rows where revenue > 700000 : show only custname, revenue\nsales %>% filter(revenue >= 700000 ) %>% select(custname, revenue)\n\n#select 1% rows on random basis\nsales %>% sample_frac(.01) %>% select(custname, revenue)\n\n#select 10 rows on random basis and sort them regionwise\nsales %>% sample_n(10) %>% select(custname, region) %>% arrange(region)\nsample_n(sales, 10) # similar method\n\n#End of session for Denco Case - Manufacturing Firm\n#Descriptive Analysis\n#Loyal Customers, Customer giving max revenue\n#Profitable Partnums - Freq, Revenue and Margin\n\n" }, { "path": "19a-sum-cases/31e-dencoCase2.R", "content": "# Case Study - Denco \n\n#read file : Method1\nsales1 = read.csv(\"./data/denco.csv\")\nstr(sales1)\n\n#read file : Method2\nsales2 = read.csv(file.choose())\nstr(sales2)\n\n# whichever you read the data, store it in sales object\nsales = sales1 # keeping a backuph\nhead(sales) #top 6 rows\nstr(sales)\nclass(sales)\ndim(sales)\nsummary(sales)\nnames(sales)\ntail(sales)\n\nunique(sales$custname)\nlength(unique(sales$custname))\nlength(unique(sales$region ))\n#dplyr\nnames(sales)\nlibrary(dplyr)\n\nnames(sales)\nsales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=3)\n#Triumph Insulation gave highest Revenue\n\ndf3 = sales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\noptions(tibble.width = Inf) # displays all columns.\noptions(tibble.print_max = Inf) # to show all the rows.\ndf3[15:60,]\n\n# Frequency --------\nnames(sales)\nt1=table(sales$custname)\nt1\nclass(t1); length(t1) ;head(t1)\nt2= sort(t1,decreasing=T )\n\nlibrary(dplyr)\nsales %>% count(custname, sort=TRUE) %>% head(n=5)\nsales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)\n# Summarise by Part Num\nsales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)\n# which parts have highest Profit : partno - sum(profit)\nnames(sales)\nsales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()\n\ndf4 = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\npie(x = df4$Revenue)\nbarplot(df4$Revenue, col=1:4)\n\nsales %>% filter(revenue >= 7771000 ) %>% select(custname, revenue)\nsales[, 1:2]\n\nsales %>% sample_frac(.01) \nsales %>% sample_n(5)\nsample_n(sales, 5)\n\n\n" }, { "path": "19a-sum-cases/31f-dencoCase.R", "content": "# Case Study - Denco \n\n#read file : Method1\nsales1 = read.csv(\"./data/denco.csv\")\nhead(sales1)\nstr(sales1)\n\n#read file : Method2\nsales2 = read.csv(file.choose())\nstr(sales2)\nhead(sales2)\n\n#read file: Method3\n#install.packages('gsheet')\nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=216113907\"\nsales3 = as.data.frame(gsheet2tbl(url))\nstr(sales3)\nhead(sales3)\nhead(sales2)\nhead(sales1)\n\n\n#head(sales1,n=7)\n#names(sales1)\n\n# whichever you read the data, store it in sales object\nsales = sales1 # keeping a backup\nstr(sales)\nclass(sales)\nsummary(sales)\n\nsales$region = factor(sales$region)\nsummary(sales)\n\nstr(sales)\ndim(sales) #dimensios of DF rows & colnum\nunique(sales$custname)\nlength(unique(sales$custname)) #1268\nlength(unique(sales$region ))\n#custname #revenue(sum)\n\nt1= table(sales$custname)\n\n\n\noptions(digits=7)\n\n# aggregation\naggregate(sales$revenue , by=list(sales$custname), FUN=sum) #1268 rows\nhead(aggregate(revenue ~ custname, data=sales, FUN=sum)) #1268 rows\ndf1= aggregate(sales$revenue , by=list(sales$custname), FUN=sum)\nhead(df1)\nstr(df1)\n\n#head(df1[order(df1$x, decreasing=TRUE),], 5)\ndf1=df1[ order(df1$x, decreasing=TRUE), ] # order and save as original DF\nhead(df1,n=10)\n#Triumph insulation - max revenue\n\n#Region wise revenue\ndf2= aggregate(formula=revenue ~ region, data=sales, FUN=mean)\nhead(df2)\nhead(df2[order(df2$revenue, decreasing=FALSE),], 6)\n#China region gave least mean revenue\n\n#Aggregate Formula\n(df2 = aggregate(revenue ~ custname + region, data=sales, FUN=sum))\nhead(df2[order(df2$revenue,decreasing=T),],10)\n\n#List\nlist1= tapply(sales$revenue, sales$custname, FUN=sum)\nhead(list1)\nlist1\nhead(sort(list1, decreasing=T))\nsummary(df3)\nstr(df3)\n\n#dplyr----\nnames(sales) #names of variables or columns\nlibrary(dplyr)\nsales[sales$margin > 1000000, ] #base functionality\nfilter(sales, margin > 1000000)\nsales %>% filter(margin > 1000000)\nsales %>% filter(margin > 1000000) %>% filter(revenue > 2000000) \n\nsales %>% filter(margin > 100000) %>% arrange(region, desc(revenue))\n#custname - region(asc) - revenue(desc) for margins > 100000\nfilter(sales, margin > 1000000)\n\nsales %>% filter(region == '01-East' & revenue > 400000) %>% select(partnum, region, revenue)\n\nsales %>% select(custname, region, margin) %>% arrange(desc(margin)) %>% head(n=10)\n\nnames(sales)\ndf5 = (sales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)))\nsales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=5)\nstr(df5)\nas.data.frame(df5)\n\nsales %>% group_by(region) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))\n\n\n\nlibrary(data.table)\ndt1 = as.data.table(sales)\ndt2 = dt1[, sum(revenue), by=custname]\nnames(dt2)\ndt1[, dt1[, sum(revenue), by=custname]]\n#dt1[, order( decreasing = T)]\n\n# Select\nlibrary(sqldf)\ndf5 =sqldf(\"Select custname, sum(revenue) from sales Group By custname order by sum(revenue) desc \")\nhead(df5)\n\n# Frequency --------\nnames(sales)\nt1=table(sales$custname)\nclass(t1)\nlength(t1)\nhead(t1)\nt2= sort(t1,decreasing=T )\nhead(t2)\nhead(sort(table(sales$custname), decreasing=T), n=10)\ntail(sort(table(sales$custname), decreasing=T), n=10)\n\n#xtab\nhead(sort(xtabs(~ custname, sales), decreasing=T))\n\n#\nlibrary(dplyr)\nsales %>% count(custname, sort=TRUE)\nsales %>% group_by(custname) %>% summarise(n = n()) %>% arrange(desc(n))\n# chiz bros have been most loyal\n\n\n#plyr\ndf2a= plyr::count(sales, c('custname'))\nstr(df2a); names(df2a)\nhead(df2a[order(-df2a$freq),])\n\n# Summarise by Part Num\n\ndf3a= aggregate(sales$revenue, by=list(sales$partnum), FUN=sum)\nhead(df3a)\nstr(df3a)\ndf3a[order(-df3a$x),][1:6,]\nhead(df3a[order(-df3a$x),])\n\ndf3b = aggregate(revenue ~ partnum, data=sales, FUN=sum)\nhead(df3b)\nhead(df3b[order(df3b$revenue, decreasing=T),])\n\nsales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))\n\n\n# which parts gave highest Profit margin : partno - sum(profit)\nnames(sales)\ndf4a = aggregate(margin ~ partnum, data=sales, FUN=sum)\naggregate(margin ~ partnum, data=sales, FUN=sum)\nhead(df4a[order(df4a$margin, decreasing = T),])\n\nsales %>% group_by(partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()\n\n\n# Graphs\nboxplot(sales$revenue)\ndf6 <- sales %>% group_by(region) %>% summarise(Revenue = sum(revenue))\ndf6 = as.data.frame(df6)\ndf6\npie(df6$Revenue, labels = df6$region)\nbarplot(df6$Revenue, col=1:6, names.arg = df6$region)\n" }, { "path": "19a-sum-cases/33c-basicDM-mtcars.R", "content": "# Basic Data Manipulation in mtcars\n# Adding Columns, Deleting them, Removing them, Selecting them\n\ndf3 = mtcars\nhead(df3)\n\n#adding variables\ndf3$mpgplus = df3$mpg + 2\nhead(df3)\n\n#selected variables\nvarnames = c('mpg', 'wt', 'cyl')\ndf3[varnames]\n#columns to be from any of these\n(selected = names(df3) %in% varnames)\ndf3[selected]\n\n#other than selected columns\ndf3[-selected]\n\n#remove the column\ndf3$mpgplus = NULL\nhead(df3) \n" }, { "path": "19a-sum-cases/33c-dplyr-mtcars.R", "content": "# Descriptive Analysis : mtcars using : dplyr\n\ndf1 = mtcars # make a copy and do analysis on df\nhead(df1)\nstr(df)\n#what ways we can have summary\n\n# convert relevant cols to factor (can be ordered also)\nfactorcols = c('cyl','vs', 'am', 'gear', 'carb')\n\ndf1[factorcols] = lapply(df1[factorcols] ,factor, ordered=T)\nsapply(df1, class)\nstr(df1)\nunique(abbreviate(rownames(df1)))\n\n# Now do summarisation\n#attach(df1)\nnames(df1)\ntable(df1$cyl) #cylinder wise summary\ntable(df1$am) #transmission wise\n\n#combine it in single command \nsapply(df1[factorcols],table)\n\n\n# Find Numeric Cols and numerical summaries on them\nsapply(df1, is.numeric)\n(numcols = sapply(df1, is.numeric))\nclass(numcols)\n(numcols = names(df1[numcols]))\nhead(df1[numcols])\n\ncolSums(df1[numcols])\ncolSums(df1[numcols],dims=1)\ncolMeans(df1[numcols],dims=1)\n\ncolMeans(df1[c('mpg','disp', 'hp')])\ncolMeans(df1[numcols[c(1:3)]])\n\n#Look for other summarisation and grouping\naggregate(df1[numcols], by=list(df1$cyl), FUN=mean)\naggregate(hp + disp ~ am, data=df1, FUN=mean) #sum of 2 cols wrt 3 col\naggregate(hp + disp ~ am + cyl, data=df1, FUN=mean) #sum of 2 cols wrt 2 col\n\n\n#---------------------------------------------------------\n#using dplyr package\nlibrary(dplyr)\nlibrary(tibble)\n\n\n#Two ways of using dplyr\ndf1 %>% slice(1:4) #pipe it\nslice(df1, 1:4) # use object inside command\n\n#dplyr does not display rownames when do manipulation , so\ndf1 %>% tbl_df()\ndf1 %>% add_rownames() #depreciated, will not be used in future\ndf1 %>% tibble::rownames_to_column()\n\n# add column with abbreviated names\nlength(unique(abbreviate(rownames(df1))))\ndf1$name = abbreviate(rownames(df1))\nhead(df1) \n\n#all rows having numeric col values > 3\ndf1 %>% filter_if(is.numeric, all_vars(. >= 3.5))\n\n#Top mileage from each cyl type, what is its hp\ndf1 %>% select(cyl, mpg, hp, name) %>% group_by(cyl) %>% arrange(desc(mpg)) %>% top_n(n=1)\n\n# Average Mileage & disp by cyl\ndf1 %>% group_by(cyl) %>% select(cyl, mpg) %>% summarize(mean_mpg = mean(mpg, na.rm = TRUE))\n\nhead(df1)\n#groupby cyl,gear, carb: avg mpg, min disp\ndf1 %>% filter(am == 0) %>% group_by(cyl, gear,carb) %>% summarize(mpg_avg = mean(mpg, na.rm = TRUE), disp_min = min(disp, na.rm = TRUE))\n\n#Count\ndf1 %>% group_by(cyl) %>% tally()\ndf1 %>% group_by(cyl, am) %>% tally()\n\n#Filter\ndf1 %>% filter(cyl == 4 & mpg > 25) %>% select(name, mpg, cyl, wt)\n\n#sample\ndf1 %>% sample_frac(0.2, replace = TRUE) %>% select(name, mpg, cyl) #with replacement\ndf1 %>% sample_frac(0.2, replace = F) %>% select(name, mpg, cyl) #w/o replacement\ndf1 %>% sample_n(3, replace = F) %>% select(name, mpg, cyl) #numbers instead for %\n\n#few rows\nslice(df1, 10:15)\n\n# Rows \nhead(df1)\ndf1 %>% slice(1:5)\nslice(df1, 1:5)\ndf1 %>% slice(-5) # -5th row\ndf1 %>% slice(-(5:10)) # -5th to 10th\ndf1 %>% slice(1:(n()-10)) \ndf1 %>% slice(seq(1,n(),3)) #every 3rd\n\n\n\nnames(df1)\n#defaults to the last variable in the table\ndf1 %>% group_by(cyl) %>% top_n(2) #Rank not data qsec\n# top_n(n = 5, wt = x)\ndf1 %>% group_by(cyl) %>% top_n(2,wt=mpg) #Rank not data wrt mpg\n#21mpg for 6 are 2 rows\n\ndf1 %>% top_n(-2, wt=mpg) %>% select(name, mpg,wt) #bottom 2 ranks - see 3 values\n\ndf1 %>% group_by(cyl) %>% tally() #total count\n\ndf1 %>% select(name,mpg, cyl) %>% top_n(1, mpg)\n\n#Selecting Columns\nnames(df1)\ndf1 %>% select(1:5)\ndf1 %>% select(contains('s')) %>% head()\ndf1 %>% select(starts_with('d')) %>% head()\ndf1 %>% select(ends_with('s')) %>% head()\ndf1 %>% select(everything()) %>% head()\n#first col is wt\ndf1 %>% select(wt, everything()) %>% select(1:5) %>% select(-2) %>% head()\nnames(df1)\n\n\n# Summarise\ndf1 %>% summarise(mpg_ave = mean(mpg))\ndf1 %>% group_by(cyl) %>% summarise(mean_ave = mean(mpg),wt_sum = sum(wt))\ndf1 %>% group_by(cyl, am) %>% select(numcols) %>% summarise_each( funs(mean))\nfactorcols\ndf1 %>% group_by(cyl, vs) %>% count(am, gear)\n\n\n\n#Summary Functions\nv1 = df1$mpg\nv1\nv1 %>% first\nv1 %>% last\nv1 %>% sd\nv1 %>% mean\nv1 %>% n_distinct()\n\n\n\n" }, { "path": "19a-sum-cases/33f-DA-bakerydata1.R", "content": "# Data for Graph and Data Manipulation\n\nsales1 = read.csv('./data/bakery.csv')\nsales1\nstr(sales1)\nsales1$sdate = as.Date(sales1$sdate, '%d-%m-%y')\nstr(sales1)\n\nhead(sales1)\n\n#library(timeDate)\n#weekdays(sales1$sdate) # date should not be factor\n#sales1$daywk = as.character(sales$wkday)\nunique(sales1$daywk)\nsweekdays = c('Monday','Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday')\n\n\nhead(sales1)\nsales1$daywk = factor(sales1$daywk, ordered=T, levels=sweekdays)\nstr(sales1)\nhead(sales1)\n\nsales= sales1 # make a copy for analysis\n\nstr(sales)\nhead(sales)\nsave(sales,file='./data/MA.RData')\n?save\n" }, { "path": "19a-sum-cases/34b-sales1.R", "content": "# Data Visualisation and Summarisation\n\nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1JkMVHbbWlxofMy27X-TdBPfy5R6YvrPw_EExARA8Bno\"\nsales1 = as.data.frame(gsheet2tbl(url))\nsales1\nsales= sales1 # save a copy of original data\n\nhead(sales1)\n\n#missing values in 3 columns\nsales1$revporder = floor(sales1$revenue/sales1$orders)\nhead(sales1)\n\nsales1$revpsalespers = floor(sales1$revenue/sales1$salespers)\nhead(sales1)\n\n\nlibrary(dplyr)\nsales1 <- mutate(sales1, orderspsalespers = trunc(orders/salespers))\nhead(sales1)\n\n#data columns ready for summarisation and visualisation\n\nsummary(sales1)\nsales1$group = factor(sales1$group)\n\n# Distribution of Data\nboxplot(sales1$revenue)\nhead(sales1)\nboxplot(sales1$orders, sales1$salespers,names=c('Orders','Salespersons'), col=4:5)\n\n?boxplot\n\n# Summarise\n\naggregate(sales1$revenue, by=list(sales1$group), FUN=sum)\n\naggregate(revenue ~ group, data=sales1, sum)\n\ndf1=aggregate(revenue ~ group, data=sales1, sum)\npie(x=df1$revenue, labels=df1$group)\nlabels1 = paste(df1$group, 'Sum-', df1$revenue, round(df1$revenue/ sum(df1$revenue),2) * 100 , '% ' , sep=' ')\n\npie(x=df1$revenue, labels=labels1, col = rainbow(length(df1$group)))\nlegend(\"topright\", c(\"Group1\",\"Group2\",\"Group3\"), cex = 0.8,\n fill= rainbow(length(df1$group)))\ntitle(main='Revenue of Sales : Groupwise')\n\n#----\naggregate(cbind(revenue, orders) ~ group, data=sales1, FUN=sum)\ndf2= aggregate(cbind(salespers, orders) ~ group, data=sales1, FUN=sum)\n\n#simple barpot\nbarplot(height=sales1$revenue, names.arg = sales1$product, col=1:length(sales1$product))\n\n\ndf2\ndf2matrix = matrix(c(df2$salespers, df2$orders), byrow = F, ncol=2)\ndf2matrix\nt(df2matrix)\nbarplot(t(df2matrix))\nbarplot(t(df2matrix), beside = T, col=1:2, ylab='Numbers', ylim=c(0,max(df2matrix) *1.5), names.arg = df2$group)\nlegend('topright', c('salespers','orders'), cex = 1, fill = 1:2)\nbarplot(t(df2matrix), horiz = T, col=c('green','yellow'))\n\n#http://www.sthda.com/english/wiki/ggplot2-barplots-quick-start-guide-r-software-and-data-visualization#barplot-of-counts\n\n\n\naggregate(cbind(revenue, orders) ~ group, data=sales1, FUN = function(x) c(mn = mean(x), n = length(x) ))\n\naggregate(cbind(revenue, orders) ~ group + cat, data=sales1, FUN = function(x) c(mn = mean(x), n = length(x) ))\n\n\n#functions specify to column type\n\n\n# Trend Line and Line Chart\nplot(sales1$revporder, xlab=\"Products\", type='o', col='red', xaxt='n')\nmtext(text=sales1$product, side=1, at=1:length(sales$product), line= 1, col=1:length(sales$product))\nlines(sales1$orderspsalespers, type='o', col='green')\ntext(1:length(sales1$product), sales1$revenue, sales1$revenue)\ntext(1, 80, 'hellow')\n" }, { "path": "19a-sum-cases/34c-sales2.R", "content": "# Data Summarisation : Sales Data\n\n# Create a DataFrame of 50 rows with following columns and properties\n\n#product company qtysold priceitem\n\n#this will create all combination of product and coys\ndf=expand.grid(product = c('noodles', 'biscuits', 'wafers', 'chocolates'), coy = c('coy1','coy2','coy3'))\ndf\n#assign random values for qtysold and price for each product/ coy\ndf$qty = floor(runif(12,50,100))\ndf$price = floor(rnorm(12,70,15))\nhead(df)\n\n# Do descriptive Analysis and draw some graphs\n# use aggregate or functions from dplyr package\nlibrary(dplyr)\n\n#Total Sales Qty : Product wise\naggregate(df$qty, by=list(df$product), sum)\naggregate(qty ~ product, data=df, sum)\ndf %>% group_by(product) %>% summarise(TotalQty= sum(qty))\n\n#Total Sales Qty : Coy wise\ndf %>% group_by(coy) %>% summarise(TotalQty= sum(qty))\n\n\n#Total Revenue : Product and Coy Wise\ndf %>% mutate(TotalSale = qty * price)\ndf %>% mutate(TotalSale = qty * price) %>% group_by(coy) %>% summarise(TotalRevenue= sum(TotalSale))\ndf %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale))\n\n\n#Draw comparison plot : Bar and Pie Charts\nsum1 <- df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue))\nsum1\nbarplot(sum1$TotalRevenue, names.arg = sum1$product, col=1:nrow(sum1))\n\nsum1\nplabels <- paste(sum1$product, round(sum1$TotalRevenue/sum(sum1$TotalRevenue)* 100, 0) , \"%\" , sep=' ') \nplabels\npie(sum1$TotalRevenue, labels = plabels, col=1:nrow(sum1))\n\n#Which product generates max revenue\nsum1a <- df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue)) \nsum1a %>% top_n(1)\n\n#Which coy generated min Revenue\nsum2a <- df %>% mutate(TotalSale = qty * price) %>% group_by(coy) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue)) \nsum2a %>% top_n(-1)\nsum2a\n\n\n" }, { "path": "19a-sum-cases/35b-DA-student1.R", "content": "# Data Manipulation : Academic Data\n\n#Method1 : gsheet\nlibrary(gsheet)\nurl= \"https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2051155174\"\nstudent1 = as.data.frame(gsheet2tbl(url))\n\n#Method2 : read.csv\nstudent2 = read.csv('./data/student1.csv')\nnames(student2)\n\n#save the imported data object into a new object\nstudent=student1 # or student2\nstr(student)\ndf1= student\n(colnames = names(df1))\n#attributes(df1) = NULL\n#df1 = as.data.frame(df1)\nattributes(df1)\nattr(df1, which='spec') = NULL\n#names(df1) = colnames\nstr(df1)\n\n#class of each column\nclass(df1$gender)\nsapply(df1, class)\n\n# convert character to factor\nfactorcols = c('gender', 'cat', 'class12', 'batch','batchyr', 'br', 'city', 'finalgrade', 'btechfinal')\n\ndf1[factorcols] = lapply(df1[factorcols] ,factor)\nsapply(df1, class)\nstr(df1)\n\n\n# Now do summarisation\n#attach(df1)\nnames(df1)\ntable(df1$gender)\n#combine it in single command \n(l1= lapply(df1[factorcols],factor,ordered=TRUE))\nsapply(l1,table)\n\ntable(df1$gender)\ntable(df1$batchyr)\nsapply(df1[factorcols],table)\n\n\nsapply(lapply(df1[factorcols],factor,ordered=TRUE), table) #Method1\n\n\nsapply(df1[factorcols], table) #Method2\nsapply(df1[c('gender', 'cat', 'class12')], table)\n?lapply\nstr(df1)\n# Numeric Cols\nsapply(df1, is.numeric)\n(numcols = sapply(df1, is.numeric))\nclass(numcols)\n(numcols = names(df1[numcols]))\n#remove rollno\n(numcols = numcols[-1])\nhead(df1[numcols])\ncolSums(df1[numcols])\ncolSums(df1[numcols],dims=1)\ncolMeans(df1[numcols],dims=1)\nnumcols[c(1,3)]\ncolMeans(df1[c('age','java', 'cpp')])\nnames(df1)\ncolMeans(df1[numcols[c(1:3)]])\n\nnames(df1)\n#Look for other summarisation and grouping\naggregate(df1[numcols], by=list(df1$br), FUN=mean)\naggregate(java + cbnst ~ br, data=df1, FUN=mean) #sum of java & cbnst wrt branch\naggregate( cbnst ~ gender, data=df1, FUN=mean)\naggregate( cbnst ~ gender + br, data=df1, FUN=mean)\n\n\n\n#using dplyr package\nlibrary(dplyr)\n\n#Top 2 students from each Branch \ndf1 %>% select(br, sname, btechmarks) %>% group_by(br) %>% arrange(desc(btechmarks)) %>% top_n(n=2)\n\n# Average Fees Paid by Batch Yr\ndf1 %>% group_by(batchyr) %>% select(batchyr, feepaid) %>% summarize(mean_fees = mean(feepaid, na.rm = TRUE))\n\nnames(df1)\ndf1 %>% filter(finalgrade == 'A') %>% group_by(gender, br, finalgrade) %>% summarize(btechmks = mean(btechmarks, na.rm = TRUE), javamin = min(java, na.rm = TRUE))\n\n#Count\ndf1 %>% group_by(gender) %>% tally()\n\n#Filter\ndf1 %>% filter(finalgrade == 'B') %>% select(rollno, sname, finalgrade)\n\n#sample\ndf1 %>% sample_frac(0.2, replace = TRUE) %>% select(rollno, sname)\ndf1 %>% sample_frac(0.2, replace = F) %>% select(rollno, sname)\ndf1 %>% sample_n(3, replace = F) %>% select(rollno, sname)\n\n#few rows\nslice(df1, 10:15)\n\nlibrary(tidyr) #Reshape\ndf1long <- df1 %>% select(sname, java, cbnst) %>% tidyr::gather(key = subject, value = marks, java, cbnst) \nhead(df1long)\ndistinct(df1long)\n\ntidyr::unite(df1, 'rollnoname', c(rollno, sname), sep=\"-\") %>% select(1:5)%>% head\n\n\n# data\nstr(df1$dob)\nas.Date(\"5-Apr-91\", '%d-%b-%y')\ndf1$dob = as.Date(df1$dob, '%d-%b-%y')\ndf1 %>% select(sname, dob) %>% tidyr::separate(dob, c(\"y\", \"m\", \"d\"))\n\n\n# Rows\ndf1 %>% slice(1:n():5)\nslice(df1, 1:5)\n\ndf1 %>% slice(1:n())\ndf1 %>% slice(1:10)\nslice(df1, n()-10: n()) #different way\n\nnames(df1)\n#defaults to the last variable in the tbl\ndf1 %>% group_by(gender) %>% top_n(2) #Rank not data\n# top_n(n = 5, wt = x)\ndf1 %>% group_by(gender) %>% top_n(2,wt=class10) #Rank not data wrt class10 marks\n\ndf1 %>% top_n(-2, wt=class10) %>% select(rollno, sname, class10, cgpa) #bottom 2 ranks - see 3 values\ndf1 %>% group_by(gender) %>% tally(java) #total count\ndf1 %>% select(finalgrade, btechmarks, sname) %>% top_n(1, btechmarks)\n\n#Selecting Columns\ndf1 %>% select(1:5)\ndf1 %>% select(contains('java'))\ndf1 %>% select(starts_with('btech'))\ndf1 %>% select(ends_with('s'))\ndf1 %>% select(everything())\ndf1 %>% select(finalgrade, everything()) %>% select(1:5) %>% select(-2)\nnames(df1)\n\n\n# Summarise\ndf1 %>% summarise(avgjava = mean(java))\ndf1 %>% group_by(gender) %>% summarise(avgjava = mean(java),sumcbnst = sum(cbnst))\ndf1 %>% group_by(br, batch) %>% select(numcols) %>% summarise_each( funs(mean))\n\ndf1 %>% group_by(cat, gender) %>% count(class12, finalgrade)\n\n\n\n\n#Summary Functions\nv1 = df1$btechmarks\nv1\nv1 %>% first\nv1 %>% last\nv1 %>% sd\nv1 %>% mean\nv1 %>% n_distinct()\n\n\n# Combine Data Sets\n\n(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))\n(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))\n\ndplyr::left_join(a, b, by = \"x1\")\n#Join matching rows from b to a.\n\ndplyr::right_join(a, b, by = \"x1\")\n#Join matching rows from a to b.\n\ndplyr::inner_join(a, b, by = \"x1\")\n#Join data. Retain only rows in both sets.\n\ndplyr::full_join(a, b, by = \"x1\")\n#Join data. Retain all values, all rows.\n\ndplyr::semi_join(a, b, by = \"x1\")\n#All rows in a that have a match in b.\n\ncbind(a,b)\n\ndplyr::anti_join(a, b, by = \"x1\")\n#All rows in a that do not have a match in b.\n\n#-----\ny = data.frame(x1=c('A','B','C'), x2=c(1,2,3))\nz = data.frame(x1=c('B','C','D'), x2=c(2,3,4))\ncbind(y,z)\n\ndplyr::intersect(y, z) # B&C\n#Rows that appear in both y and z.\n\ndplyr::union(y, z)\n#Rows that appear in either or both y and z.\n\ndplyr::setdiff(y, z)\n#Rows that appear in y but not z.\n\n#Bind\ndplyr::bind_rows(y, z)\n#Append z to y as new rows.\ndplyr::bind_cols(y, z)\n#Append z to y as new columns.\n#Caution: matches rows by position\n\n\n" }, { "path": "19a-sum-cases/35c-dm-student1.R", "content": "# Data Manipulation : Academic Data\n\n#Method1 : gsheet\nlibrary(gsheet)\nurl= \"https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2051155174\"\nstudent1 = as.data.frame(gsheet2tbl(url))\n\n#Method2 : read.csv\nstudent2 = read.csv('./data/student1.csv')\nnames(student2)\n\n#save the imported data object into a new object\nstudent=student1 # or student2\nstr(student)\nnames(student)\ndf1= student\nlm(df1$btechmarks ~ df1$attnd, data=df1)\n\n(colnames = names(df1))\n#attributes(df1) = NULL\n#df1 = as.data.frame(df1)\nattributes(df1)\nattr(df1, which='spec') = NULL\n#names(df1) = colnames\nstr(df1)\n#class of each column\nclass(df1$gender)\nsapply(df1, class)\n\n# convert character to factor\nfactorcols = c('gender', 'cat', 'class12', 'batch','batchyr', 'br', 'city', 'finalgrade', 'btechfinal')\n\ndf1[factorcols] = lapply(df1[factorcols] ,factor)\nsapply(df1, class)\nstr(df1)\n\n\n# Now do summarisation\n#attach(df1)\nnames(df1)\ntable(df1$gender)\n#combine it in single command \n(l1= lapply(df1[factorcols],factor,ordered=TRUE)) #ordering is not necessary for all\nsapply(l1,table)\n\ntable(df1$gender)\ntable(df1$batchyr)\nsapply(df1[factorcols],table)\n\n\nsapply(lapply(df1[factorcols],factor,ordered=TRUE), table) #Method1\n\n\nsapply(df1[factorcols], table) #Method2\nsapply(df1[c('gender', 'cat', 'class12')], table)\n?lapply\nstr(df1)\n# Numeric Cols\nsapply(df1, is.numeric)\n(numcols = sapply(df1, is.numeric))\nclass(numcols)\n(numcols = names(df1[numcols]))\n#remove rollno\n(numcols = numcols[-1])\nhead(df1[numcols])\ncolSums(df1[numcols])\ncolSums(df1[numcols],dims=1)\ncolMeans(df1[numcols],dims=1)\nnumcols[c(1,3)]\ncolMeans(df1[c('age','java', 'cpp')])\nnames(df1)\ncolMeans(df1[numcols[c(1:3)]])\n\nnames(df1)\n#Look for other summarisation and grouping\naggregate(df1[numcols], by=list(df1$br), FUN=mean)\naggregate(cbind(sem1, sem2) ~ br + gender, data=student, FUN=mean)\n\naggregate(java + cbnst ~ br, data=df1, FUN=mean) #sum of java & cbnst wrt branch\naggregate( cbnst ~ gender, data=df1, FUN=mean)\naggregate( cbnst ~ gender + br, data=df1, FUN=mean)\n\n\n\n#using dplyr package\nlibrary(dplyr)\n\n#Top 2 students from each Branch---- \ndf1 %>% select(br, sname, btechmarks) %>% group_by(br) %>% arrange(desc(btechmarks)) %>% top_n(n=2)\n\n# Average Fees Paid by Batch Yr----\ndf1 %>% group_by(batchyr) %>% select(batchyr, feepaid) %>% summarize(mean_fees = mean(feepaid, na.rm = TRUE))\n\nnames(df1)\n# Avg BTech Marks and Min Marks in Java : Group by Gender, Granch and Final Grade ----\ndf1 %>% filter(finalgrade == 'A') %>% group_by(gender, br, finalgrade) %>% summarize(btechmks = mean(btechmarks, na.rm = TRUE), javamin = min(java, na.rm = TRUE))\n\n#Count by Gender ----\ndf1 %>% group_by(gender) %>% tally()\n\n#Filter by Final Grade = B : select only few columns ----\ndf1 %>% filter(finalgrade == 'B') %>% select(rollno, sname, finalgrade)\n\n#sample : select few rows on randowm basis ----\ndf1 %>% sample_frac(0.2, replace = TRUE) %>% select(rollno, sname) # % of Total\ndf1 %>% sample_frac(0.2, replace = F) %>% select(rollno, sname) # % of total : replace should be False to have non repeated rows\ndf1 %>% sample_n(3, replace = F) %>% select(rollno, sname) # select only 3 rows\n\n#few rows : 10 to 15\nslice(df1, 10:15)\n\nlibrary(tidyr) #Reshape\ndf1long <- df1 %>% select(sname, java, cbnst)\ndf1long <- df1 %>% select(sname, java, cbnst) %>% tidyr::gather(key = subject, value = marks, java, cbnst) \n\nhead(df1long)\ndistinct(df1long)\n\ntidyr::unite(df1, 'rollnoname', c(rollno, sname), sep=\"-\") %>% select(1:5)%>% head\n\n\n# data\nstr(df1$dob)\nas.Date(\"5-Apr-91\", '%d-%b-%y')\ndf1$dob = as.Date(df1$dob, '%d-%b-%y')\ndf1 %>% select(sname, dob) %>% tidyr::separate(dob, c(\"y\", \"m\", \"d\"))\n\n\n# Rows\ndf1 %>% slice(1:n():5)\nslice(df1, 1:5)\n\ndf1 %>% slice(1:n())\ndf1 %>% slice(1:10)\nslice(df1, n()-10: n()) #different way\n\nnames(df1)\n#defaults to the last variable in the tbl\ndf1 %>% group_by(gender) %>% top_n(2) #Rank not data on last column\n# top_n(n = 5, wt = x)\ndf1 %>% group_by(gender) %>% top_n(2,wt=class10) #Rank not data wrt class10 marks\n\ndf1 %>% top_n(-2, wt=class10) %>% select(rollno, sname, class10, cgpa) #bottom 2 ranks - see 3 values\ndf1 %>% group_by(gender) %>% tally(java) #total count\ndf1 %>% select(finalgrade, btechmarks, sname) %>% top_n(1, btechmarks)\n\n#Selecting Columns\ndf1 %>% select(1:5)\ndf1 %>% select(contains('java'))\ndf1 %>% select(starts_with('btech'))\ndf1 %>% select(ends_with('s'))\ndf1 %>% select(everything())\ndf1 %>% select(finalgrade, everything()) %>% select(1:5) %>% select(-2)\nnames(df1)\n\n\n# Summarise\ndf1 %>% summarise(avgjava = mean(java))\ndf1 %>% group_by(gender) %>% summarise(avgjava = mean(java),sumcbnst = sum(cbnst))\ndf1 %>% group_by(br, batch) %>% select(numcols) %>% summarise_each( funs(mean))\n\ndf1 %>% group_by(cat, gender) %>% count(class12, finalgrade)\n\n\n\n\n#Summary Functions\nv1 = df1$btechmarks\nv1\nv1 %>% first\nv1 %>% last\nv1 %>% sd\nv1 %>% mean\nv1 %>% n_distinct()\n\n\n# Combine Data Sets\n\n(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))\n(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))\n\ndplyr::left_join(a, b, by = \"x1\")\n#Join matching rows from b to a.\n\ndplyr::right_join(a, b, by = \"x1\")\n#Join matching rows from a to b.\n\ndplyr::inner_join(a, b, by = \"x1\")\n#Join data. Retain only rows in both sets.\n\ndplyr::full_join(a, b, by = \"x1\")\n#Join data. Retain all values, all rows.\n\ndplyr::semi_join(a, b, by = \"x1\")\n#All rows in a that have a match in b.\n\ncbind(a,b)\n\ndplyr::anti_join(a, b, by = \"x1\")\n#All rows in a that do not have a match in b.\n\n#-----\ny = data.frame(x1=c('A','B','C'), x2=c(1,2,3))\nz = data.frame(x1=c('B','C','D'), x2=c(2,3,4))\ncbind(y,z)\n\ndplyr::intersect(y, z) # B&C\n#Rows that appear in both y and z.\n\ndplyr::union(y, z)\n#Rows that appear in either or both y and z.\n\ndplyr::setdiff(y, z)\n#Rows that appear in y but not z.\n\n#Bind\ndplyr::bind_rows(y, z)\n#Append z to y as new rows.\ndplyr::bind_cols(y, z)\n#Append z to y as new columns.\n#Caution: matches rows by position\n\n\n" }, { "path": "19a-sum-cases/36b-dsum-Case1.R", "content": "# Data Visualisation and Summarisation\n\nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1JkMVHbbWlxofMy27X-TdBPfy5R6YvrPw_EExARA8Bno\"\nsales1 = as.data.frame(gsheet2tbl(url))\nsales1\nsales= sales1 # save a copy of original data\n\nhead(sales1)\n\n#missing values in 3 columns\nsales1$revporder = floor(sales1$revenue/sales1$orders)\nhead(sales1)\n\nsales1$revpsalespers = floor(sales1$revenue/sales1$salespers)\nhead(sales1)\n\n\nlibrary(dplyr)\nsales1 <- mutate(sales1, orderspsalespers = trunc(orders/salespers))\nhead(sales1)\n\n#data columns ready for summarisation and visualisation\n\nsummary(sales1)\nsales1$group = factor(sales1$group)\n\n# Distribution of Data\nboxplot(sales1$revenue)\nhead(sales1)\nboxplot(sales1$orders, sales1$salespers,names=c('Orders','Salespersons'), col=4:5)\n\n?boxplot\n\n# Summarise\n\naggregate(sales1$revenue, by=list(sales1$group), FUN=sum)\n\naggregate(revenue ~ group, data=sales1, sum)\n\ndf1=aggregate(revenue ~ group, data=sales1, sum)\npie(x=df1$revenue, labels=df1$group)\nlabels1 = paste(df1$group, 'Sum-', df1$revenue, round(df1$revenue/ sum(df1$revenue),2) * 100 , '% ' , sep=' ')\n\npie(x=df1$revenue, labels=labels1, col = rainbow(length(df1$group)))\nlegend(\"topright\", c(\"Group1\",\"Group2\",\"Group3\"), cex = 0.8,\n fill= rainbow(length(df1$group)))\ntitle(main='Revenue of Sales : Groupwise')\n\n#----\naggregate(cbind(revenue, orders) ~ group, data=sales1, FUN=sum)\ndf2= aggregate(cbind(salespers, orders) ~ group, data=sales1, FUN=sum)\n\n#simple barpot\nbarplot(height=sales1$revenue, names.arg = sales1$product, col=1:length(sales1$product))\n\n\ndf2\ndf2matrix = matrix(c(df2$salespers, df2$orders), byrow = F, ncol=2)\ndf2matrix\nt(df2matrix)\nbarplot(t(df2matrix))\nbarplot(t(df2matrix), beside = T, col=1:2, ylab='Numbers', ylim=c(0,max(df2matrix) *1.5), names.arg = df2$group)\nlegend('topright', c('salespers','orders'), cex = 1, fill = 1:2)\nbarplot(t(df2matrix), horiz = T, col=c('green','yellow'))\n\n#http://www.sthda.com/english/wiki/ggplot2-barplots-quick-start-guide-r-software-and-data-visualization#barplot-of-counts\n\n\n\naggregate(cbind(revenue, orders) ~ group, data=sales1, FUN = function(x) c(mn = mean(x), n = length(x) ))\n\naggregate(cbind(revenue, orders) ~ group + cat, data=sales1, FUN = function(x) c(mn = mean(x), n = length(x) ))\n\n\n#functions specify to column type\n\n\n# Trend Line and Line Chart\nplot(sales1$revporder, xlab=\"Products\", type='o', col='red', xaxt='n')\nmtext(text=sales1$product, side=1, at=1:length(sales$product), line= 1, col=1:length(sales$product))\nlines(sales1$orderspsalespers, type='o', col='green')\ntext(1:length(sales1$product), sales1$revenue, sales1$revenue)\ntext(1, 80, 'hellow')\n" }, { "path": "19a-sum-cases/36c-dsum-Case2.R", "content": "# Data Summarisation : Sales Data\n\n# Create a DF of 50 rows with following columns and properties\n#product company qtysold priceitem\n\n#this will create all combination of product and coys\ndf=expand.grid(product = c('noodles', 'biscuits', 'wafers', 'chocolates'), coy = c('coy1','coy2','coy3'))\ndf\n#assign random values for qtysold and price for each product/ coy\ndf$qty = floor(runif(12,50,100))\ndf$price = floor(rnorm(12,70,15))\nhead(df)\n\n# Do descriptive Analysis and draw some graphs\n# use aggregate or functions from dplyr package\nlibrary(dplyr)\n\n#Total Sales Qty : Product wise\naggregate(df$qty, by=list(df$product), sum)\naggregate(qty ~ product, data=df, sum)\ndf %>% group_by(product) %>% summarise(TotalQty= sum(qty))\n\n#Total Sales Qty : Coy wise\ndf %>% group_by(coy) %>% summarise(TotalQty= sum(qty))\n\n\n#Total Revenue : Product and Coy Wise\ndf %>% mutate(TotalSale = qty * price)\ndf %>% mutate(TotalSale = qty * price) %>% group_by(coy) %>% summarise(TotalRevenue= sum(TotalSale))\ndf %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale))\n\n\n#Draw comparison plot : Bar and Pie Charts\nsum1 <- df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue))\nsum1\nbarplot(sum1$TotalRevenue, names.arg = sum1$product, col=1:nrow(sum1))\n\nsum1\nplabels <- paste(sum1$product, round(sum1$TotalRevenue/sum(sum1$TotalRevenue)* 100, 0) , \"%\" , sep=' ') \nplabels\npie(sum1$TotalRevenue, labels = plabels, col=1:nrow(sum1))\n\n#Which product generates max revenue\nsum1a <- df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue)) \nsum1a %>% top_n(1)\n\n#Which coy generated min Revenue\nsum2a <- df %>% mutate(TotalSale = qty * price) %>% group_by(coy) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue)) \nsum2a %>% top_n(-1)\nsum2a\n\n" }, { "path": "19a-sum-cases/36f-DSA-case2.R", "content": "# Descriptive Analysis in R\n\ncoys = c('Nestle', 'Dabur', 'Patanjali', 'Amul' )\n#nestle - noodles, chocolate, coffee\nnestle = c('noodles', 'chocolate', 'coffee')\n#dabur - toothpaste, chyawanprash\ndabur = c('toothpaste', 'chyawanprash')\n#patanjali - honey, chyawanprash, coffee, toothpaste\npatanjali = c('honey', 'chyawanprash', 'coffee', 'toothpaste')\n#amul - butter, icecream\namul = c('butter', 'icecream')\n(products = c(nestle, dabur, patanjali, amul))\nlength(products)\n(type = c('T1','T2','T2','T3','T1','T1','T1','T2','T3','T1','T1'))\ncoys = rep(coys, times=c(3,2,4,2))\ncoys\ncbind(products, coys,type)\n#sales in Rs Crores\nset.seed(1234)\n(y2016 = sample.int(100, 11, replace = F))\nset.seed(1234)\n(y2017 = sample.int(120, 11, replace = F))\nset.seed(1234)\n(commission = round(rnorm(11,mean=.02, sd=.003),3))\n(ratings=sample(c('Excellent', 'Good','Poor'), size=11, replace=T))\nfmcg = data.frame(coys, products, type, y2016, y2017, commission, ratings, stringsAsFactors = F)\nfmcg \nsummary(fmcg)\n\n#Category columns\nfmcg$coys = factor(fmcg$coys, ordered=T)\nfmcg$type = factor(fmcg$type)\nfmcg$products = factor(fmcg$products)\nfmcg$ratings = factor(fmcg$ratings, ordered=T, levels=c('Poor','Good','Excellent'))\nsummary(fmcg)\n\n#products of each coy\ntable(fmcg$products)\ntable(fmcg$products,fmcg$coys )\n\n\n#summary\n#how much is each coy selling \naggregate(fmcg$y2016, by=list(coys), FUN=sum)\naggregate(y2016 ~ coys, data=fmcg, FUN=sum)\naggregate((y2016 + y2017) ~ coys, data=fmcg, FUN=sum)\naggregate(cbind(y2016,y2017) ~ coys, data=fmcg, FUN=sum)\naggregate(cbind(y2016,y2017) ~ coys + type, data=fmcg, FUN=sum)\n(yearcoywise = aggregate(cbind(y2016,y2017) ~ coys, data=fmcg, FUN=sum))\nstr(yearcoywise)\n#margin sums: works on arrays\nrownames(yearcoywise) = yearcoywise$coys\nyearcoywise = yearcoywise[-1]\nyearcoywise\n\n#add.margins\naddmargins(as.matrix(yearcoywise),1, FUN=sum) #\naddmargins(as.matrix(yearcoywise),2, FUN=sum) #\naddmargins(as.matrix(yearcoywise),c(1,2), FUN=sum) \naddmargins(as.matrix(yearcoywise),c(1,2), FUN=list(list(sum, mean,min), list(mean,max))) \n\n#margin.table\nmargin.table(as.matrix(yearcoywise),1)\nmargin.table(as.matrix(yearcoywise),2)\n\n#prop.table : for both years : proportional sale\nprop.table(as.matrix(yearcoywise)) \n#wrt to each coy\nprop.table(as.matrix(yearcoywise),1) \n\n#wrt to each year\nprop.table(as.matrix(yearcoywise),2) \n(pt2=prop.table(as.matrix(yearcoywise),2)) \n\n#addmargins#total column\naddmargins(pt2,1, FUN=sum)\n\n#add values data eg. Taxes in 2016,2017 were 20%, 30%\nyearcoywise\n#calculate taxes from each coy\n(taxes= sweep(yearcoywise, MARGIN=1, STATS=c(.2, .3), FUN='*'))\ntaxes[which.max(taxes$y2016),]\n\n#graphs\npie(taxes$y2016)\npie(taxes$y2016, labels=rownames(taxes))\npie(taxes$y2016, labels=rownames(taxes))\npercvalues = round(taxes$y2016/sum(taxes$y2016),2) * 100\npie(taxes$y2016, labels=paste(rownames(taxes), percvalues, sep=' - '))\n\n#barpot\nbarplot(height=taxes$y2016, col=1:nrow(taxes))\nbarplot(height=taxes$y2016, col=1:nrow(taxes), names.arg = row.names(taxes))\n\n#Stacked Bar Graph\nbarplot(as.matrix(taxes), beside = F, col=rainbow(nrow(taxes))) \nlegend(\"top\", row.names(taxes), cex=1.0, bty=\"n\", fill=rainbow(nrow(taxes)))\n\n#Besides\nbarplot(as.matrix(taxes), beside = T, col=rainbow(nrow(taxes)), ylab='Amount in Lakh Rs', xlab='Year') \ntitle(main='Tax Collections from FMCG Coy', sub = 'Product Wise')\nlegend(\"topleft\", row.names(t(taxes)), cex=1.0, bty=\"n\", fill=rainbow(nrow(taxes)))\n\n#different format\nbarplot(t(as.matrix(taxes)), beside = T, ylab='Amount in Lakh Rs', xlab='Year', col=2:3) \ntitle(main='Tax Collections from FMCG Coy', sub = 'Coy - Year')\nlegend(\"topleft\", row.names(t(taxes)), cex=1.0, bty=\"n\", fill=2:3)\n\n#Reshape the data to display in different format\nlibrary(reshape2)\nyearcoywise2a = data.frame(cbind(yearcoywise), coys=row.names(yearcoywise))\nyearcoywise2a\n\nyearcoywise2b <- melt(yearcoywise2a, id.vars ='coys')\nyearcoywise2b\n\n\n#Line Chart\nyear = 2010:2018\nnestle = ceiling(runif(9,50,100))\ndabur = floor(runif(9,55,95))\nyearwisesales = data.frame(year, nestle, dabur)\nyearwisesales\n\n\nplot(yearwisesales$year,yearwisesales$nestle, type=\"l\", col=\"green\", lwd=5, xlab=\"Year\", ylab=\"Sales\")\nlines(yearwisesales$year,yearwisesales$dabur, col=\"red\", lwd=2)\ntitle(\"Change in Sales over Years - Nestle and Dabur\")\nlegend(\"topright\",c(\"Nestle\",\"Dabur\"), lwd=c(5,2), col=c(\"green\",\"red\"))\n\n#End of Descriptive Analysis\n\n\n" }, { "path": "19a-sum-cases/37b-dsum-iris1.R", "content": "# Descriptive Stats and Graphs\n# http://www.sthda.com/english/wiki/descriptive-statistics-and-graphics\n\n\n#Import your data into R----\n\n# Store the data in the variable my_data\nmy_data <- iris\n\n#Check your data----\n# Print the first 6 rows\nhead(my_data, 6)\n\n#R functions for computing descriptive statistics----\n\n#Descriptive statistics for a single group----\n\n#Measure of central tendency: mean, median, mode----\n# Compute the mean value\nmean(my_data$Sepal.Length)\n\n# Compute the median value\nmedian(my_data$Sepal.Length)\n\n# Compute the mode\n#install.packages(\"modeest\")\nrequire(modeest)\nmfv(my_data$Sepal.Length)\n\n\n#Measure of variablity----\n\n#Range: minimum & maximum----\n# Compute the minimum value\nmin(my_data$Sepal.Length)\n\n# Compute the maximum value\nmax(my_data$Sepal.Length)\n\n# Range\nrange(my_data$Sepal.Length)\n\n#Interquartile range---\nsort(my_data$Sepal.Length)\nquantile(my_data$Sepal.Length)\n\n#By default, the function returns the minimum, the maximum and three quartiles (the 0.25, 0.50 and 0.75 quartiles).\nquantile(my_data$Sepal.Length, seq(0, 1, 0.1))\nIQR(my_data$Sepal.Length)\n\n#Variance and standard deviation----\n\n# Compute the variance\nvar(my_data$Sepal.Length)\n# Compute the standard deviation =\n# square root of th variance\nsqrt(var(my_data$Sepal.Length))\nsd(my_data$Sepal.Length)\n\n#Median absolute deviation----\n# Compute the median\nmedian(my_data$Sepal.Length)\n# Compute the median absolute deviation : Dev wrt median value\nmad(my_data$Sepal.Length)\n\n#Which measure to use?----\n\n# Range. It’s not often used because it’s very sensitive to outliers.\n# Interquartile range. It’s pretty robust to outliers. It’s used a lot in combination with the median.\n# Variance. It’s completely uninterpretable because it doesn’t use the same units as the data. It’s almost never used except as a mathematical tool\n# Standard deviation. This is the square root of the variance. It’s expressed in the same units as the data. The standard deviation is often used in the situation where the mean is the measure of central tendency.\n# Median absolute deviation. It’s a robust way to estimate the standard deviation, for data with outliers. It’s not used very often.\n# In summary, the IQR and the standard deviation are the two most common measures used to report the variability of the data.\n\n\n\n\n#Computing an overall summary of a variable and an entire data frame----\n\n#summary() function----\nsummary(my_data$Sepal.Length)\nsummary(my_data, digits = 1)\n\n# summary() is automatically applied to each column. The format of the result depends on the type of the data contained in the column. For example:\n# If the column is a numeric variable, mean, median, min, max and quartiles are returned.\n# If the column is a factor variable, the number of observations in each group is returned.\n\n\n\n#sapply() function----\n\n# Compute the mean of each column\nsapply(my_data[, -5], mean)\n\n# Compute quartiles\nsapply(my_data[, -5], quantile)\n\n\n#stat.desc() function----\n#install.packages(\"pastecs\")\n\n# Compute descriptive statistics\nlibrary(pastecs)\nres <- stat.desc(my_data[, -5])\nround(res, 2)\n\n#Case of missing values----\nmean(my_data$Sepal.Length, na.rm = TRUE)\n\n#Graphical display of distributions----\n\n#Installation and loading ggpubr----\n# Install\nif(!require(devtools)) install.packages(\"devtools\")\ndevtools::install_github(\"kassambara/ggpubr\")\n#or install.packages(\"ggpubr\")\n\nlibrary(ggpubr)\n\n#Box plots----\nggboxplot(my_data, y = \"Sepal.Length\", width = 0.5)\n\n#Histogram----\n#Histograms show the number of observations that fall within specified divisions (i.e., bins).\ngghistogram(my_data, x = \"Sepal.Length\", bins = 9, add = \"mean\")\n\n\n#Empirical cumulative distribution function (ECDF)----\n#ECDF is the fraction of data smaller than or equal to x.\nggecdf(my_data, x = \"Sepal.Length\")\n\n\n#Q-Q plots----\nggqqplot(my_data, x = \"Sepal.Length\")\n\n\n#Descriptive statistics by groups----\n\n#Frequency tables----\nlibrary(dplyr)\ngroup_by(my_data, Species) %>% \n summarise(count = n(), mean = mean(Sepal.Length, na.rm = TRUE),\n sd = sd(Sepal.Length, na.rm = TRUE)\n )\n\nlibrary(\"ggpubr\")\n# Box plot colored by groups: Species\nggboxplot(my_data, x = \"Species\", y = \"Sepal.Length\",\n color = \"Species\", palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"))\n\n# Stripchart colored by groups: Species\nggstripchart(my_data, x = \"Species\", y = \"Sepal.Length\",\n color = \"Species\", palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), add = \"mean_sd\")\n\n\n\n\n" }, { "path": "19a-sum-cases/38b-dsum-haireyecolor1.R", "content": "#Descriptive Stats - Hair/ Eye Color\n\n# Hair/eye color data\nstr(HairEyeColor)\nHairEyeColor\napply(HairEyeColor, c(1,2), sum)\n\ndf <- as.data.frame(HairEyeColor)\nhair_eye_col <- df[rep(row.names(df), df$Freq), 1:3]\nrownames(hair_eye_col) <- 1:nrow(hair_eye_col)\nhead(hair_eye_col)\n\n#Simple frequency distribution: one categorical variable----\n# hair/eye variables\nHair <- hair_eye_col$Hair\nEye <- hair_eye_col$Eye\n# Frequency distribution of hair color\ntable(Hair)\ntable(Eye)\n\n#Two-way contingency table: Two categorical variables----\n# Compute table and convert as data frame\ndf <- as.data.frame(table(Hair))\ndf\n\n# Visualize using bar plot\nlibrary(ggpubr)\nggbarplot(df, x = \"Hair\", y = \"Freq\")\n\n#Multiway tables: More than two categorical variables----\ntbl2 <- table(Hair , Eye)\ntbl2\n#function xtabs(), which will create cross tabulation of data frames with a formula interface.\nxtabs(~ Hair + Eye, data = hair_eye_col)\n\n#Compute table margins and relative frequency----\n\ndf <- as.data.frame(tbl2)\nhead(df)\n\n\n# Visualize using bar plot\nlibrary(ggpubr)\nggbarplot(df, x = \"Hair\", y = \"Freq\",\n color = \"Eye\", \n palette = c(\"brown\", \"blue\", \"gold\", \"green\"))\n\n# position dodge\nggbarplot(df, x = \"Hair\", y = \"Freq\",\n color = \"Eye\", position = position_dodge(),\n palette = c(\"brown\", \"blue\", \"gold\", \"green\"))\n\n\n#Multiway tables: More than two categorical variables\n#Hair and Eye color distributions by sex using xtabs():\nxtabs(~Hair + Eye + Sex, data = hair_eye_col)\n\n#ftable when you have more than two variables:\nftable(Sex + Hair ~ Eye, data = hair_eye_col)\n\n\n#Compute table margins and relative frequency\n#The function margin.table() and prop.table() can be used to compute table margins and relative frequencies, respectively.\n#Format of the functions: margin.table(x, margin = NULL), prop.table(x, margin = NULL)\n#x: table; margin: index number (1 for rows and 2 for columns)\n\n#compute table margins:\nHair <- hair_eye_col$Hair\nEye <- hair_eye_col$Eye\n# Hair/Eye color table\nhe.tbl <- table(Hair, Eye)\nhe.tbl\n\n# Margin of rows\nmargin.table(he.tbl, 1)\n\n# Margin of columns\nmargin.table(he.tbl, 2)\n\n# Frequencies relative to row total\nprop.table(he.tbl, 1)\n\n# Table of percentages\nround(prop.table(he.tbl, 1), 2)*100\n\n#express the frequencies relative to the grand total, use this:\nhe.tbl/sum(he.tbl)" }, { "path": "19a-sum-cases/42b-case-sum-graphs.R", "content": "# Descriptive Stats and Graphs\n# http://www.sthda.com/english/wiki/descriptive-statistics-and-graphics\n\n\n#Import your data into R----\n\n# Store the data in the variable my_data\nmy_data <- iris\n\n#Check your data----\n# Print the first 6 rows\nhead(my_data, 6)\n\n#R functions for computing descriptive statistics----\n\n#Descriptive statistics for a single group----\n\n#Measure of central tendency: mean, median, mode----\n# Compute the mean value\nmean(my_data$Sepal.Length)\n\n# Compute the median value\nmedian(my_data$Sepal.Length)\n\n# Compute the mode\n#install.packages(\"modeest\")\nrequire(modeest)\nmfv(my_data$Sepal.Length)\n\n\n#Measure of variablity----\n\n#Range: minimum & maximum----\n# Compute the minimum value\nmin(my_data$Sepal.Length)\n\n# Compute the maximum value\nmax(my_data$Sepal.Length)\n\n# Range\nrange(my_data$Sepal.Length)\n\n#Interquartile range---\nsort(my_data$Sepal.Length)\nquantile(my_data$Sepal.Length)\n\n#By default, the function returns the minimum, the maximum and three quartiles (the 0.25, 0.50 and 0.75 quartiles).\nquantile(my_data$Sepal.Length, seq(0, 1, 0.1))\nIQR(my_data$Sepal.Length)\n\n#Variance and standard deviation----\n\n# Compute the variance\nvar(my_data$Sepal.Length)\n# Compute the standard deviation =\n# square root of th variance\nsqrt(var(my_data$Sepal.Length))\nsd(my_data$Sepal.Length)\n\n#Median absolute deviation----\n# Compute the median\nmedian(my_data$Sepal.Length)\n# Compute the median absolute deviation : Dev wrt median value\nmad(my_data$Sepal.Length)\n\n#Which measure to use?----\n\n# Range. It’s not often used because it’s very sensitive to outliers.\n# Interquartile range. It’s pretty robust to outliers. It’s used a lot in combination with the median.\n# Variance. It’s completely uninterpretable because it doesn’t use the same units as the data. It’s almost never used except as a mathematical tool\n# Standard deviation. This is the square root of the variance. It’s expressed in the same units as the data. The standard deviation is often used in the situation where the mean is the measure of central tendency.\n# Median absolute deviation. It’s a robust way to estimate the standard deviation, for data with outliers. It’s not used very often.\n# In summary, the IQR and the standard deviation are the two most common measures used to report the variability of the data.\n\n\n\n\n#Computing an overall summary of a variable and an entire data frame----\n\n#summary() function----\nsummary(my_data$Sepal.Length)\nsummary(my_data, digits = 1)\n\n# summary() is automatically applied to each column. The format of the result depends on the type of the data contained in the column. For example:\n# If the column is a numeric variable, mean, median, min, max and quartiles are returned.\n# If the column is a factor variable, the number of observations in each group is returned.\n\n\n\n#sapply() function----\n\n# Compute the mean of each column\nsapply(my_data[, -5], mean)\n\n# Compute quartiles\nsapply(my_data[, -5], quantile)\n\n\n#stat.desc() function----\n#install.packages(\"pastecs\")\n\n# Compute descriptive statistics\nlibrary(pastecs)\nres <- stat.desc(my_data[, -5])\nround(res, 2)\n\n#Case of missing values----\nmean(my_data$Sepal.Length, na.rm = TRUE)\n\n#Graphical display of distributions----\n\n#Installation and loading ggpubr----\n# Install\nif(!require(devtools)) install.packages(\"devtools\")\ndevtools::install_github(\"kassambara/ggpubr\")\n#or install.packages(\"ggpubr\")\n\nlibrary(ggpubr)\n\n#Box plots----\nggboxplot(my_data, y = \"Sepal.Length\", width = 0.5)\n\n#Histogram----\n#Histograms show the number of observations that fall within specified divisions (i.e., bins).\ngghistogram(my_data, x = \"Sepal.Length\", bins = 9, add = \"mean\")\n\n\n#Empirical cumulative distribution function (ECDF)----\n#ECDF is the fraction of data smaller than or equal to x.\nggecdf(my_data, x = \"Sepal.Length\")\n\n\n#Q-Q plots----\nggqqplot(my_data, x = \"Sepal.Length\")\n\n\n#Descriptive statistics by groups----\n\n#Frequency tables----\nlibrary(dplyr)\ngroup_by(my_data, Species) %>% \n summarise(count = n(), mean = mean(Sepal.Length, na.rm = TRUE),\n sd = sd(Sepal.Length, na.rm = TRUE)\n )\n\nlibrary(\"ggpubr\")\n# Box plot colored by groups: Species\nggboxplot(my_data, x = \"Species\", y = \"Sepal.Length\",\n color = \"Species\", palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"))\n\n# Stripchart colored by groups: Species\nggstripchart(my_data, x = \"Species\", y = \"Sepal.Length\",\n color = \"Species\",\n palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n add = \"mean_sd\")\n\n#Create some data----\n\n# Hair/eye color data\nstr(HairEyeColor)\nHairEyeColor\napply(HairEyeColor, c(1,2), sum)\n\ndf <- as.data.frame(HairEyeColor)\nhair_eye_col <- df[rep(row.names(df), df$Freq), 1:3]\nrownames(hair_eye_col) <- 1:nrow(hair_eye_col)\nhead(hair_eye_col)\n\n#Simple frequency distribution: one categorical variable----\n# hair/eye variables\nHair <- hair_eye_col$Hair\nEye <- hair_eye_col$Eye\n# Frequency distribution of hair color\ntable(Hair)\ntable(Eye)\n\n#Two-way contingency table: Two categorical variables----\n# Compute table and convert as data frame\ndf <- as.data.frame(table(Hair))\ndf\n\n# Visualize using bar plot\nlibrary(ggpubr)\nggbarplot(df, x = \"Hair\", y = \"Freq\")\n\n#Multiway tables: More than two categorical variables----\ntbl2 <- table(Hair , Eye)\ntbl2\n#function xtabs(), which will create cross tabulation of data frames with a formula interface.\nxtabs(~ Hair + Eye, data = hair_eye_col)\n\n#Compute table margins and relative frequency----\n\ndf <- as.data.frame(tbl2)\nhead(df)\n\n\n# Visualize using bar plot\nlibrary(ggpubr)\nggbarplot(df, x = \"Hair\", y = \"Freq\",\n color = \"Eye\", \n palette = c(\"brown\", \"blue\", \"gold\", \"green\"))\n\n# position dodge\nggbarplot(df, x = \"Hair\", y = \"Freq\",\n color = \"Eye\", position = position_dodge(),\n palette = c(\"brown\", \"blue\", \"gold\", \"green\"))\n\n\n#Multiway tables: More than two categorical variables\n#Hair and Eye color distributions by sex using xtabs():\nxtabs(~Hair + Eye + Sex, data = hair_eye_col)\n\n#ftable when you have more than two variables:\nftable(Sex + Hair ~ Eye, data = hair_eye_col)\n\n\n#Compute table margins and relative frequency\n#The function margin.table() and prop.table() can be used to compute table margins and relative frequencies, respectively.\n#Format of the functions: margin.table(x, margin = NULL), prop.table(x, margin = NULL)\n#x: table; margin: index number (1 for rows and 2 for columns)\n\n#compute table margins:\nHair <- hair_eye_col$Hair\nEye <- hair_eye_col$Eye\n# Hair/Eye color table\nhe.tbl <- table(Hair, Eye)\nhe.tbl\n\n# Margin of rows\nmargin.table(he.tbl, 1)\n\n# Margin of columns\nmargin.table(he.tbl, 2)\n\n# Frequencies relative to row total\nprop.table(he.tbl, 1)\n\n# Table of percentages\nround(prop.table(he.tbl, 1), 2)*100\n\n#express the frequencies relative to the grand total, use this:\nhe.tbl/sum(he.tbl)\n" }, { "path": "19a-sum-cases/dataexplore.R", "content": "# tabulations \n# http://r-exercises.com/2016/04/20/data-exploration-with-tables-exercises/\n\n#Exercise 1 Basic tabulation of categorical data:\n# This is the first dataset to explore:\nGender <- c(\"Female\",\"Female\",\"Male\",\"Male\")\nRestaurant <- c(\"Yes\",\"No\",\"Yes\",\"No\")\nCount <- c(220, 780, 400, 600)\nDiningSurvey <- data.frame(Gender, Restaurant, Count)\nDiningSurvey\n# Using the above data, and the table() function, compare the Gender and Restaurant variables in the above dataset.\ntable(DiningSurvey$Gender)\n\n\n# Exercise 2 The table() function modified with a logical vector:\n# Use the table() function, and logical vector of “Count > 650”, to summarize the DiningSurvey data.\n\ntable(DiningSurvey$Count > 650)\n\n# Exercise 3 : The useNA & is.na arguments find missing values:\ntable(DiningSurvey$Restaurant, useNA = 'always')\n\n# First append the “DiningSurvey” dataset with missing values:\nDiningSurvey$Restaurant <- c(\"Yes\", \"No\", \"Yes\", NA)\n# Apply the “useNA” argument to find missing Restaurant data.\n\nDiningSurvey\n# Next, apply the “is.na()” argument to find missing Restaurant data by Gender.\ntable(DiningSurvey$Gender, is.na(DiningSurvey$Restaurant))\n\n\n# Exercise 4 : The “exclude =” parameter excludes columns of data:\n# Exclude one of the dataset’s Genders with the “exclude” argument.\ntable(DiningSurvey$Gender, exclude = 'Male')\n\n# Exercise 5 The “margin.table()” function requires data in array form, and generates tables of marginal frequencies. The margin.table() function summarizes arrays within a given index:\n# First, generate array format data:\n\nRentalUnits <- matrix(c(45,37,34,10,15,12,24,18,19),ncol=3,byrow=TRUE)\ncolnames(RentalUnits) <- c(\"Section1\",\"Section2\",\"Section3\")\nrownames(RentalUnits) <- c(\"Rented\",\"Vacant\",\"Reserved\")\nRentalUnits <- as.table(RentalUnits)\nRentalUnits\n# Using the above dataset, and the margin.table() function, find the amount of Occupancy summed over Sections.\n\nmargin.table(RentalUnits,1)\n\n#Next, find the amount of Units summed by Section.\nmargin.table(RentalUnits, 2)\n\n# Exercise 6 : The prop.table() function creates tables of proportions within the dataset:\n# With the “RentalUnits” data table, use the “prop.table()” function to create a basic table of proportions.\n# Next, find row percentages, and column percentages.\nprop.table(RentalUnits)\nprop.table(RentalUnits, 1)\nprop.table(RentalUnits, 2)\n\n\n# Exercise 7 : The ftable() function generates multidimensional n-way tables, or “flat” contingency tables:\n# Use the ftable() function to summarize the dataset, “RentalUnits”.\nftable(RentalUnits)\n\n# Exercise 8 : The “summary()” function performs an independence test of factors:\n# Use “summary()” to perform a Chi-Square Test of Independence, of the “RentalUnits” variables.\nsummary(RentalUnits)\n\n# Exercise 9 : “as.data.frame()” summarizes frequencies of data arrays.\n# Use “as.data.frame()” to list frequencies within the “RentalUnits” array.\nas.data.frame(RentalUnits)\n\n# Exercise 10 : The “addmargins()” function creates arbitrary margins on multivariate arrays:\n# Use “addmargins()” to append “RentalUnits” with sums.\naddmargins(RentalUnits)\n# Next, summarize columns with “RentalUnits”.\naddmargins(RentalUnits,1)\n# Next, summarize rows with “RentalUnits”.\naddmargins(RentalUnits,2)\n# Finally, combine “addmargins()” and “prop.table()” to summarize proportions within “RentalUnits”. What is statistically inferred about sales of rental units by section?\naddmargins(prop.table(RentalUnits, 2))\n" }, { "path": "19c-mtcars/10b-datastructures.R", "content": "# Data Structures in Slides\n\n?seq\n#control+enter when you are in the line to execute\n#\n# Vectors-----\n(x=1:10)\n(x=c(1,2,3,4,5,10))\n(x=letters[1:10])\n(x=c(T,F,T,T))\n\n#access elements\n(x = seq(0,10,by=2))\n#[1] 0 2 4 6 8 10\nx[3] # access 3rd element\n#[1] 4\nx[c(2, 4)] # access 2nd and 4th element\n#[1] 2 6\nx[-1] # access all but 1st element\n#[1] 2 4 6 8 10\nx[c(2, -4)] # cannot mix positive and negative integers\n#Error in x[c(2, -4)] : only 0's may be mixed with negative subscripts\nx[c(2.4, 3.54)] # real numbers are truncated to integers\n#[1] 2 4\n\n#modify\n(x = -3:2)\n#[1] -3 -2 -1 0 1 2\nx[2] <- 0; x # modify 2nd element\n#[1] -3 0 -1 0 1 2\nx[x<0] = 5; x # modify elements less than 0\n#[1] 5 0 5 0 1 2\nx = x[1:4]; x # truncate x to first 4 elements\n#[1] 5 0 5 0\n\n#delete vector\n(x = seq(1,5, length.out = 10))\n#[1] 1.000 1.444 1.889 2.333 2.778 3.222 3.667 4.111 4.556 5.000\nx = NULL\nx\n#NULL\nx[4]\n#NULL\n\n\n\n\n#Matrix-----\n(m1 = matrix(1:12, nrow=4))\n(m2 = matrix(1:12, ncol=3, byrow=T))\n\nclass(m1)\n#[1] \"matrix\"\nattributes(m1)\n#$dim\n#[1] 4 3\ndim(m1)\n#[1] 4 3\n\n#names of cols and rows\nm1\n(colnames(m1) = paste('C',1:3, sep=''))\n(rownames(m1) = paste('R',1:4, sep=''))\nm1\n\n#Vector to Matrix\n(m3 = 1:24)\ndim(m3)= c(6,4)\nm3\n\n#access elements\nm2\nm2[1,] #first row\nm2[c(1,3,4),] #1st,3rd,4th row\n\nm2[,1] #first col\nm2[,2:3] # 2nd to 3rd coln\n\nm2[c(1,2),c(2,3)]\nm2[,]\nm2[-2,] # exclude 2nd row\nm2[1:5] # matrix is like vector\nm2[c(TRUE,FALSE),c(2,3)] #logical indexing\nm2[m2 > 5]\n\n#modify Vector\nm2[2,2]\nm2[2,2] = 10\nm2\nm2[m2> 10] = 99\nm2\nrbind(m2, c(50,60,70))\ncbind(m2, c(55,65,75,85))\n\n#row and col wise summary\nm1\ncolSums(m1); rowSums(m1)\ncolMeans(m1); rowMeans(m1)\n\nt(m1) # transpose\nm1\nsweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN=\"+\" ) #rowise\nsweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN=\"*\" ) #colwise\n\n#addmargins\nm1\naddmargins(m1,1,sum) #colwise function\naddmargins(m1,2,mean) #rowwise function\naddmargins(m1,c(1,2),mean) #row & col wise function\naddmargins(m1,c(1,2),list(list(mean,sum), list(var,sd))) #row & col wise function\n\n\n#Array-----\nlength(100:123)\n4*3*2\n#2 coys, 3 products, 4 locations sold qty\n(a1 = array(100:123, dim=c(4,3,2)))\n(loc = paste('loc', 1:4,sep='-'))\n(product = paste('p', 1:3,sep='@'))\n(coy = paste('coy', 1:2,sep='%'))\ndimnames(a1) = list(loc, product, coy)\na1\napply(a1,1, sum) #locationwise\napply(a1,2, sum) #productwise\napply(a1,c(1,2), sum) #product-location wise\napply(a1,c(2,3), sum) #product-coy wise\napply(a1,c(1,3), sum) #coy-location\napply(a1,3, sum) #coywise\nsum(a1) #total\n\n\n#DataFrame----\n#create Vectors to be combined into DF\n(rollno = 1:30)\n(sname = paste('student',1:30,sep=''))\n(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))\n(marks = floor(rnorm(30, mean=50,sd=10)))\nplot(density(marks)); abline(v=50)\n(marks2 = ceiling(rnorm(30,40,5)))\nplot(density(marks2)); abline(v=c(40,50))\n(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))\n#create DF\ndf1= data.frame(rollno, sname, gender, marks, marks2, course)\nstr(df1) #structure of DF\nhead(df1) #top 6 rows\nhead(df1,n=3) #top 3 rows\ntail(df1) #last 6 rows\nclass(df1) # DF\nsummary(df1) #summary\n\ndf1 #full data\ndf1[,c('course')]\ndf1$course\ndf1$gender # one column\ndf1[ , c(2,4)] #multiple columns\ndf1[1:10 ,] #select rows, all columns\n#as per conditionis\nnames(df1)\ndf1[ marks > 50 & gender=='F', c('rollno', 'sname','marks')]\ndf1[ marks < 50 & gender=='F', c(1,2,3,4)]\ndf1[ marks > 50 & gender=='F', ]\n\nnames(df1) # names of columns\ndim(df1) #Dimensions\n\naggregate(df1$marks, by=list(df1$gender), FUN=min)\n\n(df2 = aggregate(cbind(marks,marks2) ~ gender + course, data=df1, FUN=max))\n\n\n\n\n#List -----\ng =\"My First List\"\nh = c(25, 26,18,39)\nj = matrix(1:10,nrow=2)\nk = c('one','two','three')\nmylist = list(title=g, ages=h, j, h)\nmylist\nmylist[2]\nmylist[[2]]\nmylist[['ages']]\nmylist$ages\n\n\n\n\n\n#Factor -----\n\n(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))\ntable(grades)\n(gradesFactor = factor(grades))\n(gradesFactorOrdered = factor(grades, ordered=T))\n(gradesFactorOrderedLevels = factor(grades, ordered=T, levels=c('D','C','B','A')))\nclass(grades)\nclass(gradesFactorOrdered)\nclass(gradesFactorOrderedLevels)\n\n\n\n# Object Properties\n#vector\nv1= 1:100\nclass(v1) ; typeof(v1)\nv2=letters[1:10]\nclass(v2) ; typeof(v2)\nlength(v2)\nsummary(v1)\n\n#matrix\nm1= matrix(1:24,nrow=6)\nclass(m1)\nsummary(m1)\ndim(m1)\nstr(m1)\n\n#Array\na1 =array(1:24, dim=c(4,3,2))\nclass(a1)\nstr(a1)\ndim(a1)\nsummary(a1)\n\n\n\n#DF\n#data() #built in datasets\ndf1= iris \nstr(df1)\nsummary(df1)\nclass(df1); dim(df1)\nnrow(df1) ; names(df1) ;NROW(df1)\ncolnames(df1)\nrownames(df1)\n\n#list\nlist1 = list(v1,m1,a1,df1)\nstr(list1)\n\n#Statistical Description\nlibrary(Hmisc)\n\ndescribe(df1)\n" }, { "path": "19c-mtcars/11b-mtcars.R", "content": "#mtcars data set - base R\n\n?mtcars #help on dataset\n\nmtcars #print dataset\nstr(mtcars) #structure of dataset\n#can be used a matrix\ndim(mtcars)#dimensions of dataset\nclass(mtcars)#class\ntypeof(mtcars) #internal storage mode - list\nmode(mtcars) #Get/set the type or storage mode of object- list\nattributes(mtcars) #col and rownames\nnames(mtcars) #colnames\ncolnames(mtcars) #colnames\nrownames(mtcars) #rownames\n\nhead(mtcars) #top 6 rows \ntail(mtcars) #bottom 6 rows\nhead(mtcars,n=3) #top 3 rows\ndim(mtcars) #dimensions of DF 32 rows, 11 columns\nNROW(mtcars)\nlength(mtcars) #variables nos\nmtcars$mpg\nlength(mtcars$mpg) #length of the vector\nsummary(mtcars)\nncol(mtcars) #matrix\nnrow(mtcars)\nView(mtcars) #View DF in seperate tab like excel\nmtcars[1] #print 1st column\nmtcars$mpg[1] #1 in vectors print 1st position value in the vector\n\nx = c(1,4,2, 20:300)\nx\nx[274:280]\n" }, { "path": "19c-mtcars/11c-mtcars-filter.R", "content": "#mtcars - filtering data, missing values, split data\n\nmtcars # print dataset\nhead(mtcars)\ntail(mtcars)\nhead(mtcars,n=3)\ntail(mtcars,n=3)\n\n#filter data----\n#selected columns\n#mtcars$mpg[1:10] #vector\nhead(mtcars[1:3]) #DF\nhead(mtcars[,1:3]) #DF\n(mtcars[1:6,1:3])\nnames(mtcars)[c(1,5,6,7)]\nhead(mtcars[c('mpg','cyl','am')])\nhead(mtcars[,c(1,5,6,7)])\nhead(mtcars[,-1]) #mpg excluded\nhead(mtcars[,-c(1:5)]) #excluded 1st to 5th\n\n#selected rows----\nhead(mtcars)\nmtcars[1:5,]\nmtcars['Mazda RX4',]\nmtcars[mtcars$mpg > 25,]\nmtcars[mtcars$mpg <= 25,]\nnrow(mtcars[mtcars$mpg > 25,]) ;nrow(mtcars[mtcars$mpg <= 25,])\ndim(mtcars)\nmtcars[mtcars$mpg > 25 & mtcars$gear==4, 1:5]\nmtcars[grep(\"Mazda\", rownames(mtcars)),] #match name in rownames or a column\nhead(mtcars)\n?grep\nrownames(mtcars)\n?toupper\ngrep(\"^Merc\",rownames(mtcars))\nmtcars[c(8,9,10,11,12,13,14,15),1:5]\n#rows with names starting with Merc\nrownames(mtcars)\nmtcars[grep(\"^Merc\",rownames(mtcars)),]\nmtcars[grep(\"0$\",rownames(mtcars)),]\n\nmtcars[-1,1:5]\nmtcars[-c(1:20),-c(1:5)]\n\n#combine rows and columns-----\nmtcars[1:5, 1:3]\nmtcars[mtcars$mpg > 25 & mtcars$gear==4,1:3]\n\n\n#subset----\n#\t am\t Transmission (0 = automatic, 1 = manual)\nmtcars[mtcars$am == 1,]\nsubset(mtcars, am==1)\nsubset(mtcars, am==1 & hp > 66, select=c(4,8:11))\nnames(mtcars)\nsubset(mtcars, cyl==4 | cyl==8, select=-c(mpg:am))\n\n\n#filter----\nvars1= c('mpg', 'disp', 'hp','drat','wt','qsec')\nvars1\nmtcars[vars1]\n\n\n#which----\nmtcars[which(mtcars$cyl==4),1:5]\n\n#unique----\nx=c(1,3,4,6,6)\nunique(x)\nlength(unique(x))\nduplicated(x)\nx[duplicated(x)]\n\nlength(unique(mtcars$mpg)) #unique value of mpg\nlength(mtcars$mpg)\nunique(mtcars$mpg)\nmtcars$mpg\nunique(mtcars$cyl)\nduplicated(mtcars[,c('cyl')])\nmtcars$cyl[duplicated(mtcars[,c('cyl')])]\n\n#missing values----\n#complete cases- rows which are complete\n\n(x1=c(1,2,3,4,5,6,1,2,3,4,5))\nduplicated(x1)\n\n#missing values in a vector\n(xna=c(1,NA,5,NA,10,5,NA))\nxna\nmean(xna) #mean not calculated\nmean(xna, na.rm=T)\nis.na(xna)\nsum(is.na(xna)) #count missing values\n\n#missing values in a df\ndf_mtcars = mtcars\nhead(df_mtcars)\ndim(df_mtcars)\nsum(is.na(df_mtcars))\ndf_mtcars$disp\n#put missing values in DF for a condition\ndf_mtcars[df_mtcars$mpg > 25, c('disp') ] = NA\ndf_mtcars[df_mtcars$mpg > 25, c('mpg','disp') ]\ndf_mtcars$disp\n\nanyNA(df_mtcars)\n\ncolSums(is.na(df_mtcars)) #disp has 6\ncomplete.cases(df_mtcars)\nsum(complete.cases(df_mtcars)) # 26 row complete\nsum(!complete.cases(df_mtcars)) # 6 row incomplete\n\n\n#replace missing values with mean of the column\nmean(df_mtcars$disp, na.rm=T) #mean of disp column\ndf_mtcars[is.na(df_mtcars$disp),] #display rows with missing values in disp column \ndf_mtcars[is.na(df_mtcars$disp),c('disp')] = mean(df_mtcars$disp, na.rm=T) \nanyNA(df_mtcars)\nsum(is.na(df_mtcars))\n\n#split data----\nhead(mtcars)\ntable(mtcars$am)\n(vars1=c('mpg','cyl','gear','wt','disp','am'))\n(names(mtcars))\nmtcars[vars1]\nsplit(mtcars[vars1], mtcars$am) #split on am 0 or 1\nsplit(mtcars, mtcars$am) #split on am 0 or 1\n\n\n(df_split_am= split(mtcars[vars1], mtcars$am)) #split on am\n\ndf_split_am[[1]] #am=0\ndf_split_am[[2]] #am=1\n\ntable(mtcars$cyl)\n(df_split_cyl= split(mtcars[vars1], mtcars$cyl)) #split on cyl\nunique(mtcars$cyl)\n\ndf_split_cyl[[1]]\ndf_split_cyl[[2]]\ndf_split_cyl[[3]]\ndfs22 = as.data.frame(df_split_am[[2]])\nstr(dfs22)\ndfs22\n\n\n\ndf2 = mtcars[vars1]\nhead(df2)\ndf2$am = as.character(df2$am)\nhead(df2)\nstr(df2)\nsplit(df2, df2$am)\n\nsplit(mtcars[vars1], list(mtcars$am,mtcars$cyl))\nsplit(mtcars[vars1], list(mtcars$am,mtcars$wt))\n\nstr(mtcars)\nrange(df_mtcars$mpg)\n(mpgcode <- car::recode(df_mtcars$mpg, \"lo:15=1; 15.1:25=2;else = 3\"))\n" }, { "path": "19c-mtcars/11d-mtcars-descp.R", "content": "#mtcars - descriptive values\n\nhead(mtcars)\n\n#check for data type\nstr(mtcars)\nlapply(mtcars, class)\nunlist(lapply(mtcars, class)) \n#all numeric, can also be used as matrix\n\n#sums & means of columns\nhead(mtcars)\ncolSums(mtcars)\nrowSums(mtcars)\ncolMeans(mtcars)\nrowMeans(mtcars)\n\n#groupwise\naggregate(mtcars$mpg, by=list(mtcars$cyl), FUN=mean)\naggregate(mtcars$mpg, by=list(mtcars$cyl), FUN=min)\n?aggregate\naggregate(mpg ~ cyl, data=mtcars, FUN=mean)\naggregate(mpg ~ cyl + am, data=mtcars, FUN=mean)\naggregate(cbind(mpg,wt) ~ cyl + am, data=mtcars, FUN=sum)\naggregate(. ~ am, data=mtcars, FUN=length)\n\naggregate(cbind(mpg,wt) ~ cyl + am, data=mtcars, FUN=function(x) c(meann = mean(x), medn = median(x) ) )\n\n\n#another way\nx.mean <- aggregate(cbind(mpg,wt) ~ cyl+am, mtcars, FUN=mean)\nx.mean\nx.max <- aggregate(cbind(mpg,wt) ~ cyl+am, mtcars, FUN=max)\nx.max\nmerge(x.mean, x.max, by = c(\"cyl\", \"am\"))\n" }, { "path": "19c-mtcars/11f-mtcars-loops.R", "content": "# mtcars - loops and apply\n\nnames(mtcars)\nvars1= c('mpg', 'disp', 'hp','drat','wt','qsec')\nvars2 = c('gear','cyl', 'vs', 'am','gear','carb')\n\npaste('mtcars','mpg',sep='$')\nnoquote(paste('mtcars','mpg',sep='$'))\n\nlength(mtcars)\nmean(mtcars[,1]); mean(mtcars[,2]); mean(mtcars[,3]) ;mean(mtcars[,4]); mean(mtcars[,5])\n\n#using indices\nfor(i in 1:5) {\n print(mean(mtcars[,i]))\n}\n\nvars1\n#using vector of names\nfor(i in vars1) {\n print(mean(mtcars[,i]))\n}\n\n?lapply\nlapply(mtcars[vars1], mean)\nlapply(mtcars[vars1],quantile)\nsapply(mtcars[vars1], mean)\nsapply(mtcars[vars1], quantile)\nquantile(mtcars$mpg, c(.1,.3,.4,.8))\n\napply(mtcars,1,FUN=mean) #rowsise mean\napply(mtcars,2,FUN=mean) #colsise mean\n\n#ifelse\nifelse(mtcars$mpg > 25,'High', 'Low')\ncbind(mtcars$mpg, ifelse(mtcars$mpg > 30,'High', 'Low'))\n\n#frequency Table\ntable(mtcars$cyl)\ntable(mtcars$gear, mtcars$cyl)\n(t1= table(mtcars$gear, mtcars$cyl, dnn=list('gears','cylinder') ))\nt1\n\n#add.margin----\naddmargins(t1) #sum all sides rows and columns\naddmargins(t1,FUN=mean)\naddmargins(t1,FUN=list(list(mean,sum), list(sd, median)))\n\n#margin.table----\nt1\nmargin.table(t1)\n?margin.table\nmargin.table(t1,margin=1)\nmargin.table(t1,margin=2)\n#prop.table\nt1\nprop.table(t1)\n1/32\naddmargins(prop.table(t1, 1))\naddmargins(prop.table(t1, 2))\n\n#freq distribution\nmtcars$mpg\nrange(mtcars$mpg)\nbreaks = seq(from=floor(min(mtcars$mpg))-5,to=ceiling(max(mtcars$mpg))+ 5,by=5) #seq(5,35,5)\nbreaks\nclass(breaks)\nmtcars$mpg\n(mpg.cut = cut(mtcars$mpg, breaks)) #cut/place into interval\n?cut\n(t2a =table(mpg.cut))\nmtcars$mpg\n(t2b =cbind(table(mpg.cut))) #transpose\n#(t2b_new = t2b[-3])\n\nhist(mtcars$mpg)\nbreaks\n(breaks1 = c(5,10,20,22,30,40, max(mtcars$mpg)))\nhist(mtcars$mpg,breaks=breaks)\nhist(mtcars$mpg,breaks=breaks1)\n\nbarplot(t2b, beside = T,col=1:6, names.arg = rownames(t2b))\n\n\n#----\n\n(mpg.cut2 = cut(mtcars$mpg, breaks, labels=LETTERS[1:6]))\n\n(mpg.cut2 = cut(mtcars$mpg, breaks, labels=LETTERS[1:6], levels=LETTERS[1:6], ordered=T))\ntable(mpg.cut2)\n(freqdist2= cbind(table(mpg.cut2))) \n\n#Creating discontinous intervals using cut command\n#mridula\n\n" }, { "path": "19c-mtcars/11g-mtcars-sort.R", "content": "#sort, rank, order, sample\n\nhead(mtcars)\n\n#sort by mpg\nsort(mtcars$mpg) # one column\nsort(mtcars$mpg, decreasing = T) # one column\n\nmtcars[order(mtcars$mpg),][1:5] #Sort DF by mpg\n\nmtcars[order(mtcars$cyl, mtcars$mpg), c('cyl','mpg','wt')] #Sort DF by cyl, mpg\n\nmtcars[order(mtcars$cyl, -mtcars$mpg), c('cyl','mpg','wt')] # desc mpg\n\nrank(mtcars$mpg, ties.method = 'min')\nx=c(1,2,36,3)\nrank(x)\n\nrorder =cbind(mtcars$mpg, rank(mtcars$mpg, ties.method = 'min'))\n\nrorder[order(rorder[,2]),1:2]\n" }, { "path": "19c-mtcars/11h-mtcars-dplyr.R", "content": "#dplyr - mtcars\nlibrary(dplyr)\n\n#Filter----\n\nfilter(mtcars, cyl == 8)\nfilter(mtcars, cyl < 6)\n\n# Multiple criteria\nfilter(mtcars, cyl < 6 & vs == 1)\nfilter(mtcars, cyl < 6 | vs == 1)\n\n# Multiple arguments are equivalent to and\nfilter(mtcars, cyl < 6, vs == 1)\n\n\nfilter(mtcars, row_number() == 1L)\nfilter(mtcars, row_number() == n())\nfilter(mtcars, between(row_number(), 5, n()-2))\n\n\n\n#mutate----\nmutate(mtcars, displ_l = disp / 61.0237) #keeps other col\ntransmute(mtcars, displ_l = disp / 61.0237) #removes other cols\nmutate(mtcars, cyl = NULL) #do not display cyl\n\n\n#slice-----\nslice(mtcars, 1L)\nslice(mtcars, n())\nslice(mtcars, 5:n())\nslice(mtcars, c(2,4,5,10))\n\n(by_cyl <- group_by(mtcars, cyl)) # ???\nslice(by_cyl, 1:2)\n\n#structure----\ntbl_df(mtcars) # convert to tbl class\nglimpse(mtcars) # dense summary of tbl data\nView(mtcars) # spreasheet like form base pacakge\n\n\n\n#rownames----\ndf = tibble::rownames_to_column(df, var='cars')\ndf2 = mtcars\nnames(df)\n\n#has\ntibble::has_rownames(mtcars)\ntibble::has_rownames(df)\n\n#remove rownames \n#see without rownames\nhead(df2[1:5])\ntibble::remove_rownames(df2)\n\n#rowid as column\ntibble::rowid_to_column(df, var = \"rowid\")\n\n#column to rownames\nhead(df[1:5])\ntibble::column_to_rownames(df, var = \"cars\")\n\nmtcars %>% group_by(am) \n#nothing - just separation\n\nmtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt))\n\n\n#summarise----\nsummarise(mtcars, mean(disp)) \nsummarise(group_by(mtcars, cyl), mean(disp)) \nsummarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))\n\n\n#summarise_all\nmtcars %>% group_by(am, gear) %>% summarise_all(mean)\nmtcars %>% group_by(am, gear)%>% summarise_all(c(\"min\", \"max\"))\nmtcars %>% group_by(am, gear)%>% summarise_all(funs(med = median))\n\n\n\n#without Group\nmtcars %>% summarise(mean(mpg), max(wt))\nmtcars %>% summarise_all(mean)\nmtcars %>% select(wt, gear)%>% summarise_all(c(\"min\", \"max\"))\nmtcars %>% summarise_all(funs(med = median))\n\n\n\n\n\nmtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)\niris %>% summarise_if(is.numeric, mean, na.rm = TRUE)\n\n#specific columns\nmtcars %>% summarise_at(c(\"mpg\", \"wt\"), mean, na.rm = TRUE)\n\n\n#------------------------------------\n#unsortd----\ndplyr::tbl_df(iris)\nprint(dplyr::tbl_df(mtcars), n=20) #display more columns and rows\n#print(dplyr::tbl_df(mtcars), width=11)\ntbl_df(mtcars) %>% print(n = Inf)\ntbl_df(mtcars) %>% print(width = Inf)\ntbl_df(mtcars) %>% as.data.frame(mtcars)\n\nglimpse(mtcars)\ndf = mtcars\nrow.names(df) = NULL\ndf %>% select(mpg)\n#head(mtcars)\nselect(mtcars, mpg, vs)\nmtcars %>% dplyr::select(vs, mpg, wt)\nmtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)\nmtcars\n\nnames(mtcars)\nfilter(mtcars, mpg > 23 | wt < 2)\nmtcars %>% filter(mpg > 23 & wt > 2)\nmtcars %>% select(mpg, wt) %>% filter(mpg > 23) \nmtcars %>% \n \n filter(iris, Sepal.Length > 7)\nfilter(mtcars, cyl == 4)\ndistinct(mtcars)\ndf = data.frame(a=c(2,2),b=c(2,2))\ndf\ndistinct(df)\n\nsample_frac(mtcars, 0.2, replace=T)\nsample_n(mtcars, 60, replace=T) %>% select(mpg)\nslice(mtcars,10:14)\ntop_n(mtcars,-2, mpg)\n\nselect(mtcars, mpg) %>% arrange(desc(mpg))\n#Columns\nselect(mtcars, mpg, wt)\nselect(mtcars, contains('a'))\nnames(mtcars)\nselect(mtcars, contains ='vs')\nselect(mtcars, everything())\n\nmtcars %>% group_by(cyl, am) %>% summarise_all(mean)\n\ndf = data.frame(marks=c(1,2,3,7,1))\ncbind(df, dplyr::mutate_each(df, funs(min_rank)))\n\nmtcars %>% lead() %>% lag()\ndplyr::n(mtcars)\nselect(mtcars, mpg2 = mpg)\n\ndf = mtcars[1:4]\nnames(df) = c('MPG','C1','C2','C3')\ndf= rename(df, C5=C1)\nnames(df)\ndf\nrename(df, marks2 = marks)\ndf %>% mutate(marks2 = marks + 2, marks3 = marks + 4)\ndf %>% transmute(marks2 = marks + 2, marks3 = marks + 4)\n\nlibrary(nycflights13)\ndata(flights)\n\n\ndestinations <- group_by(flights, dest)\ndestinations\nsummarise(destinations,\n planes = n_distinct(tailnum),\n flights = n()\n)\n\n\nselect(iris, -ends_with(\"Width\")) %>% head\nvars <- c(\"Petal.Length\", \"Petal.Width1\")\nselect(iris, from=1, to=n()) \nfilter(mtcars, row_number() == n())\nfilter(mtcars, between(row_number(), 5, n()))\n\nmtcars %>% group_by(cyl) %>% filter(1:3)\n> mtcars %.% group_by(cyl) %.% filter(sample(n(), 10))\ngroup_by( mtcars, cyl ) %>% integer_filter(1:2)\n?integer_filter\n\n\n# Select odd\nmtcars %>% slice(from = 1, to = n(), by = 2)\n# Select even\nslice(mtcars, from = 2, to = n(), by = 2)\n# Select first 10\nslice(mtcars, from = 1, to = 10)\n# Select last 10\nslice(mtcars, n()-10: n())\nslice(mtcars, 1:4)\nmtcars\n\ndf <- tibble(\n g1 = c(1, 1, 2, 2, 2),\n g2 = c(1, 2, 1, 2, 1),\n a = sample(5), \n b = sample(5)\n)\ndf\ndf %>% slice(n()-2:n())\n\nvar1 <- quo(letters[1:5])\nvar1\nquo(toupper(!!var1))\n\n# Here we capture `letters[1:5]` as an expression:\nquo(toupper(letters[1:5]))\n#> ~toupper(letters[1:5])\n\n# Here we capture the value of `letters[1:5]`\nquo(toupper(!!letters[1:5]))\n#> ~toupper(c(\"a\", \"b\", \"c\", \"d\", \"e\"))\nquo(toupper(UQ(letters[1:5])))\n#> ~toupper(c(\"a\", \"b\", \"c\", \"d\", \"e\"))\n#\ntoupper(letters[1:5])\nquote(toupper(letters[1:5]))\n\nhead(mtcars)\nslice(mtcars, 1:5)\nslice(mtcars, 1) #rowno missing, first row\nslice(mtcars, 1L)\n\ntail(mtcars,n=5)\nslice(mtcars, n()-5:n())\nslice(mtcars, n()) #last row\nslice(mtcars, n() - 1) #2nd last row\n\nmtcars %>% top_n(2)\nmtcars %>% top_n(-2)\nmtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)\ndim(mtcars)\nbind_rows(mtcars, mtcars)\nbind_cols(mtcars,mtcars)\ngtable_combine(list(mtcars, mtcars))\ndim_desc(mtcars)\n\n# combine applies the same coercion rules\nf1 <- factor(\"a\")\nf2 <- factor(\"b\")\nc(f1, f2)\nunlist(list(f1, f2))\n\ngtable_combine(f1, f2)\ngtable_combine(list(f1, f2))\n\nslice( mtcars, c(1L,3L,2L,7L)) \n\nby_cyl <- mtcars %>% group_by(cyl)\n# Select first row in each group\nmtcars %>% slice(1)\nby_cyl %>% slice(1)\n# Select last row in each group\nmtcars %>% slice(n())\nby_cyl %>% slice(n())\n# Rows not present in group silently ignored\nmtcars %>% slice(10)\nby_cyl %>% slice(10)\n\n# Select arbitrary rows\nmtcars %>% slice(1:9)\nby_cyl %>% slice(1:3)\n\nmtcars %>% slice(c(1, 3, 9))\nby_cyl %>% slice(c(1, 3, 5))\n\n# Select even rows\nmtcars %>% slice(seq(2, n(), by = 2))\nby_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())\n\n# Drop first row in each group\nmtcars %>% group_by(cyl, am) %>% slice(1)\nby_cyl %>% slice(1)\n\n# Returns all values\nby_cyl %>% slice()\n\n\n\n\ndf <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))\ndf %>% top_n(2)\n\n# Negative values select bottom from group. Note that we get more\n# than 2 values here because there's a tie: top_n() either takes\n# all rows with a value, or none.\ndf %>% top_n(-2)\n\n\n\n" }, { "path": "19c-mtcars/12d-mtcars-graph1.R", "content": "#mtcars graphs\n\nhead(mtcars)\n\n#plot - scatter\nplot(mtcars$mpg)\nplot(x=mtcars$wt,y=mtcars$mpg,pch=17,cex=2,col='red' )\n\n#histogram\nhist(mtcars$mpg)\nhist(mtcars$mpg, breaks=5, col=1:5)\n\n#----\ncars_auto = subset(mtcars, am == 0)\ncars_manu = subset(mtcars, am == 1)\npar(mfrow = c(2, 1)) #2 row,1 col\nhist(cars_auto$mpg, main = \"Distribution mpg - automatic transmission\", xlab = \"mpg\")\n\nabline(v = mean(cars_auto$mpg), col = \"red\")\nhist(cars_manu$mpg, main = \"Distribution mpg - manual transmission\", xlab = \"mpg\")\nabline(v = mean(cars_manu$mpg), col = \"red\")\npar(mfrow=c(1,1))\n\n\n#pie----\nt1=table(mtcars$cyl)\npie(t1)\n\n#barplot\n(count1 = table(mtcars$cyl))\nbarplot(count1, col=2:4)\n\n# Grouped Bar Plot\n(counts <- table(mtcars$vs, mtcars$gear))\nbarplot(counts)\nbarplot(counts, beside=T,col=1:2)\n\nbarplot(counts, main=\"Car Distribution by Gears and VS\",\n xlab=\"Number of Gears\", col=c(\"darkblue\",\"red\"),\n legend = rownames(counts), beside=TRUE)\n\n#boxplot\nboxplot(mtcars$mpg)\n\nboxplot(mpg ~ cyl, data=mtcars, col=5:7)\nboxplot(mpg~cyl*am, data=mtcars, notch=TRUE, \n col=(c(\"gold\",\"darkgreen\")),\n main=\"MT Cars\", xlab=\"cyl & transmission\")\n#4.1 = cyl=4, am=1\n\n\n\n#line chart\nplot(mtcars$wt, mtcars$mpg, type='b')\ndf=mtcars[order(mtcars$wt),]\nplot(df$wt, df$mpg[cyl==4], type='b', col='red', lwd=2)\nlines(df$wt, df$hp, type='b', col='green')\n\n\n\n# Basic Scatterplot Matrix\npairs(~mpg+disp+drat+wt,data=mtcars, \n main=\"Simple Scatterplot Matrix\")\n\n\n# 3D Scatterplot\nlibrary(scatterplot3d)\nscatterplot3d(mtcars$wt,mtcars$disp,mtcars$mpg, main=\"3D Scatterplot\")\n\n\nlibrary(vioplot)\nx1 <- mtcars$mpg[mtcars$cyl==4]\nx2 <- mtcars$mpg[mtcars$cyl==6]\nx3 <- mtcars$mpg[mtcars$cyl==8]\nvioplot(x1, x2, x3, names=c(\"4 cyl\", \"6 cyl\", \"8 cyl\"), \n col=\"gold\")\ntitle(\"Violin Plots of Miles Per Gallon\")\n\n\n#dotplot----\ndotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,\n main=\"Gas Milage for Car Models\", \n xlab=\"Miles Per Gallon\")\n\n#correllogram----\nlibrary(corrplot)\nres=cor(mtcars)\nres\ncorrplot(res, type = \"upper\", order = \"hclust\", \n tl.col = \"black\", tl.srt = 45)\n\nlibrary(corrgram)\ncor(mtcars[1:5])\ncorrgram(mtcars[1:5], order=TRUE, lower.panel=panel.shade,\n upper.panel=panel.pie, text.panel=panel.txt)\n\n\n\nlibrary(\"PerformanceAnalytics\")\nmy_data <- mtcars[, c(1,3,4,5,6,7)]\nchart.Correlation(my_data, histogram=TRUE, pch=19)\n\n\n#heatmap-----\n# Get some colors\ncol<- colorRampPalette(c(\"blue\", \"white\", \"red\"))(20)\nheatmap(x = res, col = col, symm = TRUE)\n\n#level plot\n\nz <- cor(mtcars)\nrequire(lattice) #install first\nlevelplot(z)\n\n\n\n\n\n#https://rstudio-pubs-static.s3.amazonaws.com/45786_b5e54c2d3c824f51abd2d559506cf81f.html\n" }, { "path": "19c-mtcars/12e-mtcars-graph2.R", "content": "#Advance plot with ggplot2\n\nlibrary(ggplot2)\n#use color brewer as default discrete colors\nscale_colour_discrete <- function(...) scale_color_brewer(palette=\"Set1\", ...)\nscale_fill_discrete <- function(...) scale_fill_brewer(palette=\"Set1\", ...)\n\ndata('mtcars')\ndf=mtcars\n# create factors with value labels\ndf$gear <- factor(df$gear,levels=c(3,4,5),\n labels=c(\"3gears\",\"4gears\",\"5gears\"))\ndf$am <- factor(df$am,levels=c(0,1),\n labels=c(\"Automatic\",\"Manual\"))\ndf$cyl <- factor(df$cyl,levels=c(4,6,8),\n labels=c(\"4cyl\",\"6cyl\",\"8cyl\"))\nhead(df)\nstr(df)\n\n\n#density Plot\nlibrary(ggplot2)\nqplot(mpg, data=df, geom=\"density\", fill=gear, alpha=I(.5),\n main=\"Distribution of Gas Milage\", xlab=\"Miles Per Gallon\", ylab=\"Density\")\n\n\nqplot(hp, mpg, data=df, shape=am, color=am,\n facets=gear~cyl, size=I(3),\n xlab=\"Horsepower\", ylab=\"Miles per Gallon\")\n\nggplot(df, aes(wt, mpg, color = cyl)) +\n geom_point() + geom_smooth(method = \"lm\", formula = y~x) +\n labs(title = \"Regression of MPG on Weight\", x = \"Weight\", y = \"Miles per Gallon\")\n\n\nqplot(gear, mpg, data=df, geom=c(\"boxplot\", \"jitter\"),\n fill=gear, main=\"Mileage by Gear Number\",\n xlab=\"\", ylab=\"Miles per Gallon\")\n" }, { "path": "19c-mtcars/12e-mtcars-summarise-dplyr.R", "content": "#tidyverse\n\nlibrary(dplyr)\n#Options to print increased no of rows and columns\n#options(dplyr.print_max = 1e9)\n#options(tibble.print_max = n, tibble.print_min = 3)\n#: if there are more than n rows, print only the first m rows. Use \noptions(tibble.print_max = Inf) #to always show all rows.\noptions(tibble.width = Inf)\n\n#group by cyl, calculate avg hp and wt\nmtcars %>% group_by(cyl) %>% summarise(mean(hp), mean(wt))\n#group by cyl, am\nmtcars %>% group_by(cyl,am) %>% summarise(mean(hp), mean(wt))\n\n#mean and count of all columns when gp by cyl, am\nmtcars %>%group_by(cyl, am) %>% summarise_all(funs(mean, n()))\n\n#selected columns\nmtcars %>% select(cyl, am, mpg, wt, hp, qsec) %>% group_by(cyl, am) %>% summarise_all(funs(mean, max, min))\n\n#sample \nsample_n(mtcars, 2)\nsample_frac(mtcars, .2)\nmtcars %>% sample_frac(.2)\n\n#\n" }, { "path": "19c-mtcars/12f-diag-ggplot2-mtcars.R", "content": "#Diagnostics plots using ggplot2 for mtcars\n#(https://rpubs.com/therimalaya/43190)\n\n\n#-----\n#Using the mtcars datasets, a linear model is fitted with mpg as response and cyl, disp, hp, drat and wt has predictor variable\n\nlm.model = lm(mpg~cyl+disp+hp+drat+wt, data=mtcars)\ndiagPlts = diagPlot(lm.model)\n#To display the plots in a grid, some packages mentioned above should be installed.\nlbry = c(\"grid\", \"gridExtra\")\nlapply(lbry, require, character.only=TRUE, warn.conflicts = FALSE, quietly = TRUE)\n\n#Thus the plot obtained is,\ndo.call(grid.arrange, c(diagPlts, main=\"Diagnostic Plots\", ncol=1))\n\nplot(lm.model) #one by one\npar(mfrow=c(2,2)) # 4 plots in 1 space\nplot(lm.model)\npar(mfrow=c(1,1)) # Reset\n\n#--------------------\nmodel=lm.model\np1<-ggplot(model, aes(.fitted, .resid))+geom_point()\np1<-p1+stat_smooth(method=\"loess\")+geom_hline(yintercept=0, col=\"red\", linetype=\"dashed\")\np1<-p1+xlab(\"Fitted values\")+ylab(\"Residuals\")\np1<-p1+ggtitle(\"Residual vs Fitted Plot\")+theme_bw()\np1\n#-----\n#error in this code\n\np2<-ggplot(model, aes(qqnorm(.stdresid)[[1]], .stdresid))+geom_point(na.rm = TRUE)\np2<-p2+geom_abline(aes(qqline(.stdresid)))+xlab(\"Theoretical Quantiles\")+ylab(\"Standardized Residuals\")\np2<-p2+ggtitle(\"Normal Q-Q\")+theme_bw()\np2\n\n#------\n\np3<-ggplot(model, aes(.fitted, sqrt(abs(.stdresid))))+geom_point(na.rm=TRUE)\np3<-p3+stat_smooth(method=\"loess\", na.rm = TRUE)+xlab(\"Fitted Value\")\np3<-p3+ylab(expression(sqrt(\"|Standardized residuals|\")))\np3<-p3+ggtitle(\"Scale-Location\")+theme_bw()\np3\n#------\n\np4<-ggplot(model, aes(seq_along(.cooksd), .cooksd))+geom_bar(stat=\"identity\", position=\"identity\")\np4<-p4+xlab(\"Obs. Number\")+ylab(\"Cook's distance\")\np4<-p4+ggtitle(\"Cook's distance\")+theme_bw()\np4\n\n#-----\n\np5<-ggplot(model, aes(.hat, .stdresid))+geom_point(aes(size=.cooksd), na.rm=TRUE)\np5<-p5+stat_smooth(method=\"loess\", na.rm=TRUE)\np5<-p5+xlab(\"Leverage\")+ylab(\"Standardized Residuals\")\np5<-p5+ggtitle(\"Residual vs Leverage Plot\")\np5<-p5+scale_size_continuous(\"Cook's Distance\", range=c(1,5))\np5<-p5+theme_bw()+theme(legend.position=\"bottom\")\np5\n\n#-----\n\np6<-ggplot(model, aes(.hat, .cooksd))+geom_point(na.rm=TRUE)+stat_smooth(method=\"loess\", na.rm=TRUE)\np6<-p6+xlab(\"Leverage hii\")+ylab(\"Cook's Distance\")\np6<-p6+ggtitle(\"Cook's dist vs Leverage hii/(1-hii)\")\np6<-p6+geom_abline(slope=seq(0,3,0.5), color=\"gray\", linetype=\"dashed\")\np6<-p6+theme_bw()\np6\n\n\n#------------------------\n\nrequire(ggplot2)\ndiagPlot<-function(model){\n p1<-ggplot(model, aes(.fitted, .resid))+geom_point()\n p1<-p1+stat_smooth(method=\"loess\")+geom_hline(yintercept=0, col=\"red\", linetype=\"dashed\")\n p1<-p1+xlab(\"Fitted values\")+ylab(\"Residuals\")\n p1<-p1+ggtitle(\"Residual vs Fitted Plot\")+theme_bw()\n \n p2<-ggplot(model, aes(qqnorm(.stdresid)[[1]], .stdresid))+geom_point(na.rm = TRUE)\n p2<-p2+geom_abline(aes(qqline(.stdresid)))+xlab(\"Theoretical Quantiles\")+ylab(\"Standardized Residuals\")\n p2<-p2+ggtitle(\"Normal Q-Q\")+theme_bw()\n \n p3<-ggplot(model, aes(.fitted, sqrt(abs(.stdresid))))+geom_point(na.rm=TRUE)\n p3<-p3+stat_smooth(method=\"loess\", na.rm = TRUE)+xlab(\"Fitted Value\")\n p3<-p3+ylab(expression(sqrt(\"|Standardized residuals|\")))\n p3<-p3+ggtitle(\"Scale-Location\")+theme_bw()\n \n p4<-ggplot(model, aes(seq_along(.cooksd), .cooksd))+geom_bar(stat=\"identity\", position=\"identity\")\n p4<-p4+xlab(\"Obs. Number\")+ylab(\"Cook's distance\")\n p4<-p4+ggtitle(\"Cook's distance\")+theme_bw()\n \n p5<-ggplot(model, aes(.hat, .stdresid))+geom_point(aes(size=.cooksd), na.rm=TRUE)\n p5<-p5+stat_smooth(method=\"loess\", na.rm=TRUE)\n p5<-p5+xlab(\"Leverage\")+ylab(\"Standardized Residuals\")\n p5<-p5+ggtitle(\"Residual vs Leverage Plot\")\n p5<-p5+scale_size_continuous(\"Cook's Distance\", range=c(1,5))\n p5<-p5+theme_bw()+theme(legend.position=\"bottom\")\n \n p6<-ggplot(model, aes(.hat, .cooksd))+geom_point(na.rm=TRUE)+stat_smooth(method=\"loess\", na.rm=TRUE)\n p6<-p6+xlab(\"Leverage hii\")+ylab(\"Cook's Distance\")\n p6<-p6+ggtitle(\"Cook's dist vs Leverage hii/(1-hii)\")\n p6<-p6+geom_abline(slope=seq(0,3,0.5), color=\"gray\", linetype=\"dashed\")\n p6<-p6+theme_bw()\n \n return(list(rvfPlot=p1, qqPlot=p2, sclLocPlot=p3, cdPlot=p4, rvlevPlot=p5, cvlPlot=p6))\n}\n" }, { "path": "19c-mtcars/12f-ggplot2-mtcars.R", "content": "#ggplot2 with mtcars\n#(https://rpubs.com/BillB/217355)\n\nlibrary(ggplot2)\nggplot(mtcars, aes(mpg)) + geom_histogram(binwidth = 4) + xlab('Miles per Gallon') + ylab('Number of Cars') + ggtitle('Distribution of Cars by Mileage')\n\nggplot(mtcars, aes(cyl)) + geom_histogram(binwidth=1) + xlab('Cylinders') + ylab('Number of Cars') + ggtitle('Distribution of Cars by Cylinders')\n\nggplot(mtcars, aes(hp)) + geom_histogram(binwidth=20) + xlab('horsepower') + ylab('Number of Cars') + ggtitle('Distribution of Cars by Horsepower')\n#HP vs MPG\nggplot(mtcars, aes(hp, mpg)) + geom_point() \n#with prediction line \nggplot(mtcars, aes(hp, mpg)) + geom_point() +\n geom_smooth(method = \"lm\", se = T) +\n ylab(\"Miles per Gallon\") +\n xlab(\"No. of Horsepower\") +\n ggtitle(\"Impact of Number of Horsepower on MPG\")\n\n#apply smoothing since mpg unlikely to hit zero\nggplot(mtcars, aes(hp, mpg)) +\n stat_smooth() + geom_point() +\n ylab(\"Miles per Gallon\") +\n xlab (\"No. of Horsepower\") +\n ggtitle(\"Impact of Number of Horsepower on MPG\")\n\nqplot(cyl, mpg, data = mtcars, colour = cyl, geom = \"point\", \n ylab = \"Miles per Gallon\", xlab = \"No. of Cylinders\",\n main = \"Impact of Number of Cylinders on MPG\") \n\n#lm----\n#linear regression to fit the data to a line. We use geom_smooth with the linear model method.\nggplot(mtcars, aes(cyl, mpg)) + geom_point() +\n geom_smooth(method = \"lm\", se = FALSE) +\n ylab(\"Miles per Gallon\") + xlab(\"No. of Cylinders\") +\n ggtitle(\"Impact of Number of Cylinders on MPG\")\n" }, { "path": "19c-mtcars/13b-mtcars-lm1.R", "content": "#Multiple Linear Regression - mtcars\n\nmtcars # mtcars data set\ndim(mtcars); names(mtcars)\n\n#predict mpg for following data wt=c(2.5,3.5),cyl=c(4,6)\n(ndata=data.frame(wt=c(2.5,3.5),cyl=c(4,6)))\n(cbind(ndata, data.frame(mpg=c('?','?'))))\n\n#Linear Model\nlmfit1 = lm(mpg ~ wt + cyl, data=mtcars)\n\nsummary(lmfit1)\n\n#if p value of F Stats < .05 : Linear Model exists with at least one X\n\n#both are significant variables\nattributes(summary(lmfit1)) # to use output values\n\n#adjusted R2\nsummary(lmfit1)$adj.r.squared\n#adjs R2 = .819\n#82% of variation in Y are explained by two X's in the model\n\n\n#head(mtcars[c('wt','cyl')])\n(ndata=data.frame(wt=c(2.5,3.5),cyl=c(4,6)))\n\n#predict for above wt and cyl combination\npredict(lmfit1,newdata=ndata, type='response')\n#for wt = 2.5 and cyl =4, mpg predicted is 25.67\n\ncbind(ndata, predicted=predict(lmfit1,newdata=ndata, type='response'))\n\n#how to select from large number of variables\n#how to select the best model - from variable combination\n\n" }, { "path": "19c-mtcars/13c-mtcars-lm2.R", "content": "#Multiple Linear Regression - mtcars\n\nmtcars # mtcars data set\ndim(mtcars); names(mtcars)\n\nmtcarsfit1 = lm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am +gear + carb , data = mtcars)\nsummary(mtcarsfit1)\n#only wt seems to be significant\n\n#select all variables with .\nmtcarsfit2 = lm(mpg ~ . , data = mtcars)\nsummary(mtcarsfit2)\n#still the same\n\n#select random variables and try to test the model\nmtcarsfit3 = lm(mpg ~ wt + hp + cyl, data=mtcars)\nsummary(mtcarsfit3)\n#hp not significant\nmtcarsfit3b = lm(mpg ~ wt + cyl, data=mtcars)\nsummary(mtcarsfit3b)\n#adjs R2 = .819\nhead(mtcars[c('wt','cyl')])\nndata=data.frame(wt=c(2.5,3.5),cyl=c(4,6))\npredict(mtcarsfit3b,newdata=ndata, type='response')\n\n#Faster method for model selection\n#there are large variables, we need a method to select variables, not possible to do one by one elimination\n\nlibrary(MASS)\n(model1a = lm(mpg ~ ., data=mtcars)) #full model\n#(model1b = lm(mpg ~ 1, data=mtcars)) #wihout any variables\n\nsummary(model1a)\n#summary(model1b)\n\n#step method to select best model\n#works on AIC which should be less\nstepboth = stepAIC(model1a, direction='both')\n\n# select combination of variable where AIC is minimum\n#least AIC with wt, qsec, am\nfit1= lm(mpg ~ wt + qsec + am, data=mtcars)\nsummary(fit1)\n\n\n#Step AIC helps is select the variables from large number of vars\n#variable combination which gives least AIC is selected\n\n" }, { "path": "19c-mtcars/13e-mtcars-lm3.R", "content": "#mtcars linear regression\n#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf\n#install.packages('olsrr')\nlibrary(olsrr)\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nk <- ols_step_all_possible(model)\nplot(k)\nk\n\n#create model and check which model is better\n(m1 = lm(mpg ~ wt, data=mtcars))\nsummary(m1)\n(m2 = lm(mpg ~ hp + wt + qsec , data=mtcars))\nsummary(m2)\n\nanova(m1, m2) #if p value is < 0.05 reject Ho\n#Ho : Model m1, is better : m1 should be subset of m2\n\nAIC(m1, m2)\nAIC(m1)\n#m2 AIC < m2 AIC\n\n#what is AIC\n#alkaline information criteria : way to check which model is better\n" }, { "path": "19c-mtcars/14b-mtcars-logr.R", "content": "#Logistic Regression : mtcars\n#http://www.r-tutor.com/elementary-statistics/logistic-regression/significance-test-logistic-regression\n\nhead(mtcars)\ntable(mtcars$am)\ndf = mtcars\ndf$am = factor(df$am)\n\n#requirement : estimate the probability of a vehicle being fitted with a manual transmission if it has a 120hp engine and weights 2800 lbs.\n(ndata = data.frame(hp=120, wt=2.8))\ncbind(ndata, data.frame(probTx=c('?'))) #0 or 1\n\n#create Model\nam.glm = glm(am ~ hp + wt, data=df, family=binomial)\n\nsummary(am.glm)\n#hp & wt are significant as pvalues < 0.05\n\n#test data\n(ndata = data.frame(hp=120, wt=2.8))\n\n#predict\npredict(am.glm, newdata=ndata, type=\"response\") \n\ncbind(ndata, prob=predict(am.glm, newdata=ndata, type=\"response\") )\n\n#For an automobile with 120hp engine and 2800 lbs weight, the probability of it being fitted with a manual transmission is about 64%.\n\n" }, { "path": "19c-mtcars/15b-mtcars-DT-class.R", "content": "#Classification - mtcars\n\nlibrary(rpart)\nlibrary(rpart.plot)\n\ndf = mtcars\ndf$am = factor(df$am)\n#predict likelihood of car being auto or manual using DT\n\nfit.class = rpart(am ~ wt + cyl + mpg + hp + gear, data=df, minsplit=7, cp=-1)\n#fit.class = rpart(am ~ wt + cyl + mpg + hp + gear, data=df, cp=-1)\n\n#minsplit - no of observation to create a split\nfit.class\nprintcp(fit.class)\n\nrpart.plot(fit.class, cex=1, nn=T)\n\n(prune1 = prune(fit.class, cp=0))\nrpart.plot(prune1)\n\nlibrary(dplyr)\n(ndata = sample_n(df,5))\n(ndata = ndata[c('am','wt','cyl','mpg','hp','gear')])\n\npredict(fit.class, newdata=ndata, type='prob')\npredict(fit.class, newdata=ndata, type='class')\n\ncbind(ndata, predicted=predict(fit.class, newdata=ndata, type='class') )\n\nfit2.class = rpart(am ~ wt + cyl + mpg + hp + gear, data=df, minsplit=7)\nfit2.class\nrpart.plot(fit2.class)\n\n\n" }, { "path": "19c-mtcars/15c-mtcars-DT-anova.R", "content": "#Classification - Regression Tree- mtcars\n\nlibrary(rpart)\nlibrary(rpart.plot)\n\ndf = mtcars\nmean(df$mpg)\n\n#predict likelihood of car mileage using DT\n\nfit.anova = rpart(mpg ~ wt + cyl + am + hp + gear, data=df, minsplit=7, cp=-1, method='anova')\nfit.anova\n\n\nrpart.plot(fit.anova, cex=1, nn=T)\n\nlibrary(dplyr)\nndata = sample_n(df,5)\n(ndata = ndata[c('am','wt','cyl','mpg','hp','gear')])\nndata\n\npredict(fit.anova, newdata=ndata, type='vector')\n\ncbind(ndata, predicted=predict(fit.anova, newdata=ndata, type='vector') )\n\n# Prune\nprintcp(fit.anova)\nptree2 = prune(fit.anova, cp=0.01)\n#ideally .0051, but for testing select \nptree2\nrpart.plot(ptree2,nn=T, cex=.8, roundint = F)\n" }, { "path": "19c-mtcars/16b-mtcars-cluster1.R", "content": "#Clustering on mtcars\n\nhead(mtcars)\n\n#kmeans - select only few column to group them\ndf = mtcars[, c('mpg', 'cyl', 'wt')]\nhead(df)\nkm3 = kmeans(df, centers=3)\nkm3\nkm3$centers #average of each cluster\nkm3$size #how many into each cluster\n\n\n#Plotting Clusters\nlibrary(cluster)\nlibrary(fpc)\n\nplotcluster(df, km3$cluster)\n\n#Plot2: PCA\nclusplot(df, km3$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)\n\n\n#2nd Set\n#kmeans - select only few column to group them\n\ndf = mtcars[, c('mpg', 'cyl', 'wt')]\nkm2 <- kmeans(df, centers=2)\nkm2\nkm2$centers #average of each cluster\nkm2$size #how many into each cluster\n\ncbind(df, mtcars$am, km2$cluster)\n\n\n\ndf = mtcars[, c('mpg', 'cyl', 'wt')]\nkm4 <- kmeans(df, centers=4)\nkm4\nkm4$centers #average of each cluster\nkm4$size #how many into each cluster\ncbind(df, mtcars$gear, km4$cluster)\ntable(mtcars$gear)\ntable(mtcars$cyl)\ntable(mtcars$carb)\n\nsummary(mtcars)\n\n\n\n" }, { "path": "19c-mtcars/16c-mtcars-cluster2.R", "content": "#Clustering on mtcars\n\n#https://rstudio-pubs-static.s3.amazonaws.com/111803_f6ec030f9a544fb2a058170f8047eb67.html#/4\n\n#kmeans - select only few column to group them\ndf = mtcars[, c('mpg', 'cyl', 'wt')]\nkm_fit1 <- kmeans(df, centers=3)\nkm_fit1\nkm_fit1$centers #average of each cluster\nkm_fit1$size #how many into each cluster\n\n#\nkmeanPlot <- par(mar = c(5.1, 4.1, 0, 1))\nplot(df, col = km_fit1$cluster,\n pch = 20, cex = 3)\npoints(km_fit1$centers, pch = 15, cex = 4, lwd = 4, col='pink')\n\n#Interactive Clustering at\n#https://mikedamour.shinyapps.io/ShinyProject/\n\nvar1='cyl'\nvar2='gear'\n(vars=sample(x=c('cyl','gear','vs','am','mpg','disp'), replace=F, size=2))\ndata1 = mtcars[c(vars)]\ncluster1 = kmeans(data1,3)\nplot(data1,col=cluster1$cluster, cex=cluster1$cluster)\npoints(cluster1$centers, pch = 15, cex = 2, col='violet')\n\ncluster1 = kmeans(data1,4)\nplot(data1,col=cluster1$cluster, cex=cluster1$cluster)\npoints(cluster1$centers, pch = 12, cex = 2, col='black')\n\n\n\n\n" }, { "path": "19c-mtcars/22f-tidyr-mtcars.R", "content": "#tidyr - mtcars\n#https://datascienceplus.com/data-manipulation-with-tidyr/\n\n# gather – converts wide data to longer format. It is analogous to the melt function from reshape2.\n# spread – converts long data to wider format. It is analogous to the cast function from reshape2.\n# unite – combines two or more columns into a single column.\n# separate – splits one column into two or more columns.\n\n\nlibrary(tidyr)\nlibrary(dplyr)\nhead(mtcars)\n\n#include the names of the cars in a column called car for easier manipulation.\nmtcars$car <- rownames(mtcars)\nmtcars <- mtcars[, c(12, 1:11)]\nhead(mtcars)\nnames(mtcars)\n\n#gather\nmtcarsNew <- mtcars %>% gather(attribute, value, -car)\ndim(mtcarsNew)\nhead(mtcarsNew)\ntail(mtcarsNew)\n\n\nmtcarsNew <- mtcars %>% gather(attribute, value, mpg:gear)\nhead(mtcarsNew)\ntail(mtcarsNew)\n\n\n#spread\nhead(mtcarsNew)\nmtcarsSpread <- mtcarsNew %>% spread(attribute, value)\nhead(mtcarsSpread)\n\n#unite -----\n#test data\nset.seed(1)\ndate<- as.Date('2016-01-01') + 0:14\nhour <- sample(1:24, 15)\nmin <- sample(1:60, 15)\nsecond <- sample(1:60, 15)\nevent <- sample(letters, 15)\ndata <- data.frame(date, hour, min, second, event)\ndata\nhead(data)\n#\ndataNew <- data %>%\n unite(datehour, date, hour, sep = ' ') %>%\n unite(datetime, datehour, min, second, sep = ':')\ndataNew\nhead(dataNew)\n\n#separate-----\ndata1 <- dataNew %>% \n separate(datetime, c('date', 'time'), sep = ' ') %>% \n separate(time, c('hour', 'min', 'second'), sep = ':')\nhead(data1)\n" }, { "path": "19c-mtcars/mtcars-clust1.R", "content": "#Clustering on mtcars\n\n#https://rstudio-pubs-static.s3.amazonaws.com/111803_f6ec030f9a544fb2a058170f8047eb67.html#/4\n\nselectedData <- mtcars[, c('mpg', 'cyl', 'wt')]\nclusters <- kmeans(selectedData, 3)\nkmeanPlot <- par(mar = c(5.1, 4.1, 0, 1))\nplot(selectedData,\n col = clusters$cluster,\n pch = 20, cex = 3)\npoints(clusters$centers, pch = 15, cex = 4, lwd = 4, col='pink')\n\n#Interactive Clustering at\n#https://mikedamour.shinyapps.io/ShinyProject/\n\nvar1=.'cyl'\nvar2='gear'\n(vars=sample(x=c('cyl','gear','vs','am','mpg','disp'), replace=F, size=2))\ndata1 = mtcars[c(vars)]\npar(mfrow=c(1,2))\ncluster1 = kmeans(data1,3)\nplot(data1,col=cluster1$cluster, cex=cluster1$cluster)\npoints(cluster1$centers, pch = 15, cex = 2, col='violet')\n\ncluster1 = kmeans(data1,4)\nplot(data1,col=cluster1$cluster, cex=cluster1$cluster)\npoints(cluster1$centers, pch = 12, cex = 2, col='black')\n\n\n\n" }, { "path": "19c-mtcars/s1.R", "content": "mtcars[1:2]\nmtcars$cyl2 <- with(mtcars, cyl +1)\nmtcars$variable <- with(mtcars, disp * 2)\ncor(mtcars[,c(\"am\",\"carb\",\"cyl\")], use=\"complete\")\nnormalityTest(~mpg, test=\"shapiro.test\", data=mtcars)\nRegModel.1 <- lm(mpg~am+carb, data=mtcars)\nsummary(RegModel.1)\nRegModel.2 <- lm(mpg~am+carb+cyl+disp+drat, data=mtcars)\nsummary(RegModel.2)\nBoxplot( ~ mpg, data=mtcars, id.method=\"y\")\n\n" }, { "path": "19d-iris/sumgraph1.R", "content": "# Descriptive Stats and Graphs\n# http://www.sthda.com/english/wiki/descriptive-statistics-and-graphics\n\n\n#Import your data into R----\n\n# Store the data in the variable my_data\nmy_data <- iris\n\n#Check your data----\n# Print the first 6 rows\nhead(my_data, 6)\n\n#R functions for computing descriptive statistics----\n\n#Descriptive statistics for a single group----\n\n#Measure of central tendency: mean, median, mode----\n# Compute the mean value\nmean(my_data$Sepal.Length)\n\n# Compute the median value\nmedian(my_data$Sepal.Length)\n\n# Compute the mode\n#install.packages(\"modeest\")\nrequire(modeest)\nmfv(my_data$Sepal.Length)\n\n\n#Measure of variablity----\n\n#Range: minimum & maximum----\n# Compute the minimum value\nmin(my_data$Sepal.Length)\n\n# Compute the maximum value\nmax(my_data$Sepal.Length)\n\n# Range\nrange(my_data$Sepal.Length)\n\n#Interquartile range---\nsort(my_data$Sepal.Length)\nquantile(my_data$Sepal.Length)\n\n#By default, the function returns the minimum, the maximum and three quartiles (the 0.25, 0.50 and 0.75 quartiles).\nquantile(my_data$Sepal.Length, seq(0, 1, 0.1))\nIQR(my_data$Sepal.Length)\n\n#Variance and standard deviation----\n\n# Compute the variance\nvar(my_data$Sepal.Length)\n# Compute the standard deviation =\n# square root of th variance\nsqrt(var(my_data$Sepal.Length))\nsd(my_data$Sepal.Length)\n\n#Median absolute deviation----\n# Compute the median\nmedian(my_data$Sepal.Length)\n# Compute the median absolute deviation : Dev wrt median value\nmad(my_data$Sepal.Length)\n\n#Which measure to use?----\n\n# Range. It’s not often used because it’s very sensitive to outliers.\n# Interquartile range. It’s pretty robust to outliers. It’s used a lot in combination with the median.\n# Variance. It’s completely uninterpretable because it doesn’t use the same units as the data. It’s almost never used except as a mathematical tool\n# Standard deviation. This is the square root of the variance. It’s expressed in the same units as the data. The standard deviation is often used in the situation where the mean is the measure of central tendency.\n# Median absolute deviation. It’s a robust way to estimate the standard deviation, for data with outliers. It’s not used very often.\n# In summary, the IQR and the standard deviation are the two most common measures used to report the variability of the data.\n\n\n\n\n#Computing an overall summary of a variable and an entire data frame----\n\n#summary() function----\nsummary(my_data$Sepal.Length)\nsummary(my_data, digits = 1)\n\n# summary() is automatically applied to each column. The format of the result depends on the type of the data contained in the column. For example:\n# If the column is a numeric variable, mean, median, min, max and quartiles are returned.\n# If the column is a factor variable, the number of observations in each group is returned.\n\n\n\n#sapply() function----\n\n# Compute the mean of each column\nsapply(my_data[, -5], mean)\n\n# Compute quartiles\nsapply(my_data[, -5], quantile)\n\n\n#stat.desc() function----\n#install.packages(\"pastecs\")\n\n# Compute descriptive statistics\nlibrary(pastecs)\nres <- stat.desc(my_data[, -5])\nround(res, 2)\n\n#Case of missing values----\nmean(my_data$Sepal.Length, na.rm = TRUE)\n\n#Graphical display of distributions----\n\n#Installation and loading ggpubr----\n# Install\nif(!require(devtools)) install.packages(\"devtools\")\ndevtools::install_github(\"kassambara/ggpubr\")\n#or install.packages(\"ggpubr\")\n\nlibrary(ggpubr)\n\n#Box plots----\nggboxplot(my_data, y = \"Sepal.Length\", width = 0.5)\n\n#Histogram----\n#Histograms show the number of observations that fall within specified divisions (i.e., bins).\ngghistogram(my_data, x = \"Sepal.Length\", bins = 9, add = \"mean\")\n\n\n#Empirical cumulative distribution function (ECDF)----\n#ECDF is the fraction of data smaller than or equal to x.\nggecdf(my_data, x = \"Sepal.Length\")\n\n\n#Q-Q plots----\nggqqplot(my_data, x = \"Sepal.Length\")\n\n\n#Descriptive statistics by groups----\n\n#Frequency tables----\nlibrary(dplyr)\ngroup_by(my_data, Species) %>% \n summarise(count = n(), mean = mean(Sepal.Length, na.rm = TRUE),\n sd = sd(Sepal.Length, na.rm = TRUE)\n )\n\nlibrary(\"ggpubr\")\n# Box plot colored by groups: Species\nggboxplot(my_data, x = \"Species\", y = \"Sepal.Length\",\n color = \"Species\", palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"))\n\n# Stripchart colored by groups: Species\nggstripchart(my_data, x = \"Species\", y = \"Sepal.Length\",\n color = \"Species\",\n palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n add = \"mean_sd\")\n\n#Create some data----\n\n# Hair/eye color data\nstr(HairEyeColor)\nHairEyeColor\napply(HairEyeColor, c(1,2), sum)\n\ndf <- as.data.frame(HairEyeColor)\nhair_eye_col <- df[rep(row.names(df), df$Freq), 1:3]\nrownames(hair_eye_col) <- 1:nrow(hair_eye_col)\nhead(hair_eye_col)\n\n#Simple frequency distribution: one categorical variable----\n# hair/eye variables\nHair <- hair_eye_col$Hair\nEye <- hair_eye_col$Eye\n# Frequency distribution of hair color\ntable(Hair)\ntable(Eye)\n\n#Two-way contingency table: Two categorical variables----\n# Compute table and convert as data frame\ndf <- as.data.frame(table(Hair))\ndf\n\n# Visualize using bar plot\nlibrary(ggpubr)\nggbarplot(df, x = \"Hair\", y = \"Freq\")\n\n#Multiway tables: More than two categorical variables----\ntbl2 <- table(Hair , Eye)\ntbl2\n#function xtabs(), which will create cross tabulation of data frames with a formula interface.\nxtabs(~ Hair + Eye, data = hair_eye_col)\n\n#Compute table margins and relative frequency----\n\ndf <- as.data.frame(tbl2)\nhead(df)\n\n\n# Visualize using bar plot\nlibrary(ggpubr)\nggbarplot(df, x = \"Hair\", y = \"Freq\",\n color = \"Eye\", \n palette = c(\"brown\", \"blue\", \"gold\", \"green\"))\n\n# position dodge\nggbarplot(df, x = \"Hair\", y = \"Freq\",\n color = \"Eye\", position = position_dodge(),\n palette = c(\"brown\", \"blue\", \"gold\", \"green\"))\n\n\n#Multiway tables: More than two categorical variables\n#Hair and Eye color distributions by sex using xtabs():\nxtabs(~Hair + Eye + Sex, data = hair_eye_col)\n\n#ftable when you have more than two variables:\nftable(Sex + Hair ~ Eye, data = hair_eye_col)\n\n\n#Compute table margins and relative frequency\n#The function margin.table() and prop.table() can be used to compute table margins and relative frequencies, respectively.\n#Format of the functions: margin.table(x, margin = NULL), prop.table(x, margin = NULL)\n#x: table; margin: index number (1 for rows and 2 for columns)\n\n#compute table margins:\nHair <- hair_eye_col$Hair\nEye <- hair_eye_col$Eye\n# Hair/Eye color table\nhe.tbl <- table(Hair, Eye)\nhe.tbl\n\n# Margin of rows\nmargin.table(he.tbl, 1)\n\n# Margin of columns\nmargin.table(he.tbl, 2)\n\n# Frequencies relative to row total\nprop.table(he.tbl, 1)\n\n# Table of percentages\nround(prop.table(he.tbl, 1), 2)*100\n\n#express the frequencies relative to the grand total, use this:\nhe.tbl/sum(he.tbl)\n" }, { "path": "20a-BasicGraphs/10a-graphs.R", "content": "# Combined Plots\n\n#plot, histogram, pie, boxplot, linechart, correlation plot\n\n\n#plot\nwomen\nplot(women)\nplot(women, type='p', pch=17)\nplot(women, type='l')\nplot(women, type='b', pch=18, lty=2, col=2)\nplot(women, xlim=c(30,100), ylim=c(min(women$weight)-10, 200), pch=10)\n\n#more features with plot\nplot(x=women$weight, y=women$height, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')\ntitle(main='Main Title', sub='Sub Title')\n#see cheat sheet on base graphs\n\nplot(women)\nabline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)\n\n\n#boxplot\nboxplot(women$height)\nabline(h=c(58, 62,65,68,72))\n\n#draw lines on plot for number summary\nsummary(women)\nquantile(women$height)\nboxplot(women$height, col='green')\nabline(h=quantile(women$height))\n\n\n#histogram\nhist(women$height)\nhist(women$height, breaks=10)\nhist(women$height, breaks=5, col=1:5)\n\n#histogram2\nx = rnorm(100,50,100)\nhist(x)\n\nhist(x, freq=F, col=1:5)\nlines(density(x))\n\n#density plot : shape of data\nplot(density(x), col='red')\n\n\n#pie\nx = c(10,20,40,50)\npie(x)\nxlabels = c('A','B','C','D')\npie(x, labels=xlabels)\n\n#barplot\nbarplot(x,col=1:4)\nbarplot(x,col=1:4, horiz = T)\n\n#correlation plot\npairs(women)\ncor(women$height,women$weight)\n\nhead(mtcars)\ncor(mtcars)\npairs(mtcars)\noptions(digits=4)\npairs(mtcars[1:4])\n\n\n" }, { "path": "20a-BasicGraphs/10b-graphs.R", "content": "#More Graphs\n\n# Simple Dotplot\ndotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,\n main=\"Gas Milage for Car Models\", \n xlab=\"Miles Per Gallon\")\n\n# Stacked Bar Plot with Colors and Legend\ncounts <- table(mtcars$vs, mtcars$gear)\nbarplot(counts, main=\"Car Distribution by Gears and VS\",\n xlab=\"Number of Gears\", col=c(\"darkblue\",\"red\"),\n legend = rownames(counts))\n\n# Grouped Bar Plot\ncounts <- table(mtcars$vs, mtcars$gear)\nbarplot(counts, main=\"Car Distribution by Gears and VS\",\n xlab=\"Number of Gears\", col=c(\"darkblue\",\"red\"),\n legend = rownames(counts), beside=TRUE)\n\n\n#scatterplot\n# Basic Scatterplot Matrix\npairs(~mpg+disp+drat+wt,data=mtcars, \n main=\"Simple Scatterplot Matrix\")\n\nlibrary(car) \nscatterplot(mpg ~ wt, data=mtcars)\nscatterplot(mpg ~ wt | factor(cyl), data=mtcars)\n\n\n#Violin, \n\n# Violin Plots----\nlibrary(vioplot)\nx1 <- mtcars$mpg[mtcars$cyl==4]\nx2 <- mtcars$mpg[mtcars$cyl==6]\nx3 <- mtcars$mpg[mtcars$cyl==8]\nvioplot(x1, x2, x3, names=c(\"4 cyl\", \"6 cyl\", \"8 cyl\"), \n col=\"gold\")\ntitle(\"Violin Plots of Miles Per Gallon\")\n\n# Pie Chart with Percentages\nslices <- c(10, 12, 4, 16, 8) \nlbls <- c(\"US\", \"UK\", \"Australia\", \"Germany\", \"France\")\npct <- round(slices/sum(slices)*100)\nlbls <- paste(lbls, pct) # add percents to labels \nlbls <- paste(lbls,\"%\",sep=\"\") # ad % to labels \npie(slices,labels = lbls, col=rainbow(length(lbls)),\n main=\"Pie Chart of Countries\")\n\n\n# 3D Exploded Pie Chart\nlibrary(plotrix)\nslices <- c(10, 12, 4, 16, 8) \nlbls <- c(\"US\", \"UK\", \"Australia\", \"Germany\", \"France\")\npie3D(slices,labels=lbls,explode=0.1,\n main=\"Pie Chart of Countries \")\n\n\n\n# Boxplot of MPG by Car Cylinders \nboxplot(mpg~cyl,data=mtcars, main=\"Car Milage Data\", \n xlab=\"Number of Cylinders\", ylab=\"Miles Per Gallon\")\n\n\nboxplot(mpg ~ cyl * am, data=mtcars, notch=TRUE, \n col=(c(\"gold\",\"darkgreen\")),\n main=\"MTCars MPG\", xlab=\"cyl & vs\")\n\n" }, { "path": "20a-BasicGraphs/12b-graphs2.R", "content": "\n\nwomen\nplot(women)\nplot(x=women$weight, y=women$height, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')\ntitle(main='Main Title', sub='Sub Title')\n\nboxplot(women$height)\nabline(h=c(58, 62,65,68,72))\n?boxplot\nsummary(women)\n\nquantile(women$height)\n\nhist(women$height, breaks=10)\n\nx = rnorm(100,50,100)\nhist(x)\nhist(x, freq=F)\nlines(density(x))\nplot(density(x), col='red')\n\n#pie\nx = c(10,20,40,50)\npie(x)\nxlabels = c('A','B','C','D')\npie(x, labels=xlabels)\n\nbarplot(x,col=1:4)\nbarplot(x,col=1:4, horiz = T)\n\npairs(women)\nhead(mtcars)\npairs(mtcars)\ncor(women$height,women$weight)\noptions(digits=4)\nplot(women)\nabline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)\n\n\n\nmosaic::qdist(\"norm\", p = 0.95, mean = 0, sd = 1)\n" }, { "path": "20a-BasicGraphs/12d-title1.R", "content": "#titles\n\ntitle(main=\"main title\", sub=\"sub-title\", \n xlab=\"x-axis label\", ylab=\"y-axis label\")\n\n# Add a red title and a blue subtitle. Make x and y \n# labels 25% smaller than the default and green. \nplot(x=1)\ntitle(main=\"My Title\", col.main=\"red\", \n sub=\"My Sub-title\", col.sub=\"blue\", \n xlab=\"My X label\", ylab=\"My Y label\",\n col.lab=\"green\", cex.lab=0.75)\n" }, { "path": "20a-BasicGraphs/12e-text.R", "content": "#text in graphs\n\n#Text can be added to graphs using the text( ) and mtext( ) functions. text( ) places text within the graph while mtext( ) places text in one of the four margins.\n\ntext(location, \"text to place\", pos, ...)\nmtext(\"text to place\", side, line=n, ...)\n\n# Example of labeling points\nattach(mtcars)\nplot(wt, mpg, main=\"Milage vs. Car Weight\", \n xlab=\"Weight\", ylab=\"Mileage\", pch=18, col=\"blue\")\ntext(wt, mpg, row.names(mtcars), cex=0.6, pos=4, col=\"red\")\n" }, { "path": "20a-BasicGraphs/12f-abline.R", "content": "#abline\n\nabline(h=yvalues, v=xvalues)\n\nOther graphical parameters (such as line type, color, and width) can also be specified in the abline( ) function.\n\n# add solid horizontal lines at y=1,5,7 \nabline(h=c(1,5,7))\n# add dashed blue verical lines at x = 1,3,5,7,9\nabline(v=seq(1,10,2),lty=2,col=\"blue\")\n\nNote: You can also use the grid( ) function to add reference lines.\n" }, { "path": "20a-BasicGraphs/12g-legend.R", "content": "#Legend\n\ncounts <- table(mtcars$gear)\nbarplot(counts, main=\"Car Distribution\", xlab=\"Number of Gears\")\nbarplot(counts, beside=T, legend=T, \n main=\"Car Distribution\", xlab=\"Number of Gears\",\n args.legend = list(x='right',bty='n'))\npalette() # current settings\n# 3 series in this example 3,4,5\npalette(gray(seq(0.5,1,len=3)))\npalette()\npalette('default')\npalette(gray(seq(0.5,1,len=3)))\nbarplot(counts, beside=T, legend=T, col=palette(),\n args.legend = list(x='right',bty='n'))\n#Other Method\nbarplot(counts, beside=T, col=palette())\nlegend(x= 'topright',legend = rownames(counts), fill=palette(),\n title='No of Gears')\n?legend\nlegend(x, y = NULL, legend, fill = NULL, col = par(\"col\"),\n border = \"black\", lty, lwd, pch,\n angle = 45, density = NULL, bty = \"o\", bg = par(\"bg\"),\n box.lwd = par(\"lwd\"), box.lty = par(\"lty\"), box.col = par(\"fg\"),\n pt.bg = NA, cex = 1, pt.cex = cex, pt.lwd = lwd,\n xjust = 0, yjust = 1, x.intersp = 1, y.intersp = 1,\n adj = c(0, 0.5), text.width = NULL, text.col = par(\"col\"),\n text.font = NULL, merge = do.lines && has.pch, trace = FALSE,\n plot = TRUE, ncol = 1, horiz = FALSE, title = NULL,\n inset = 0, xpd, title.col = text.col, title.adj = 0.5,\n seg.len = 2)\n\n# Other Color Palettes - rainbow, heat.colors, terrain.colors, \n# topo.colors, cm.colors, gray\npalette(c('red','blue','green'))\nbarplot(counts, beside=T, col=palette())\nlegend(x= 2, y=15,legend = rownames(counts), fill=palette(),\n title='No of Gears')\n# When plot with lines/ points , col=par('col'); pch= plotting symbol\n#bty = border n,o, # \n\n#links\n#(http://www.sthda.com/english/wiki/add-legends-to-plots-in-r-software-the-easiest-way)\n" }, { "path": "20a-BasicGraphs/12k-tick.R", "content": "#tick marks\n\n# Add minor tick marks\nlibrary(Hmisc)\nminor.tick(nx=n, ny=n, tick.ratio=n)\n\n\nnx is the number of minor tick marks to place between x-axis major tick marks.\nny does the same for the y-axis. tick.ratio is the size of the minor tick mark relative to the major tick mark. The length of the major tick mark is retrieved from par(\"tck\").\n" }, { "path": "20a-BasicGraphs/12m-axis1.R", "content": "#axis Labels and titles in R\n\nplot(faithful, main = \"Eruptions of Old Faithful\",\n xlab = \"Eruption time (min)\",\n ylab = \"Waiting time to next eruption (min)\")\n\n# Specify axis options within plot() \nplot(x, y, main=\"title\", sub=\"subtitle\",\n xlab=\"X-axis label\", ylab=\"y-axix label\",\n xlim=c(xmin, xmax), ylim=c(ymin, ymax))\n\n\n\n#axis\n\naxis(side, at=, labels=, pos=, lty=, col=, las=, tck=, ...)\n\n# side\tan integer indicating the side of the graph to draw the axis (1=bottom, 2=left, 3=top, 4=right)\n# at\ta numeric vector indicating where tic marks should be drawn\n# labels\ta character vector of labels to be placed at the tickmarks \n# (if NULL, the at values will be used)\n# pos\tthe coordinate at which the axis line is to be drawn. \n# (i.e., the value on the other axis where it crosses)\n# lty\tline type\n# col\tthe line and tick mark color\n# las\tlabels are parallel (=0) or perpendicular(=2) to axis\n# tck\tlength of tick mark as fraction of plotting region (negative number is outside graph, positive number is inside, 0 suppresses ticks, 1 creates gridlines) default is -0.01\n# (...)\n\n\n# A Silly Axis Example\n\n# specify the data \nx <- c(1:10); y <- x; z <- 10/x\n\n# create extra margin room on the right for an axis \npar(mar=c(5, 4, 4, 8) + 0.1)\n\n# plot x vs. y \nplot(x, y,type=\"b\", pch=21, col=\"red\", \n yaxt=\"n\", lty=3, xlab=\"\", ylab=\"\")\n\n# add x vs. 1/x \nlines(x, z, type=\"b\", pch=22, col=\"blue\", lty=2)\n\n# draw an axis on the left \naxis(2, at=x,labels=x, col.axis=\"red\", las=2)\n\n# draw an axis on the right, with smaller text and ticks \naxis(4, at=z,labels=round(z,digits=2),\n col.axis=\"blue\", las=2, cex.axis=0.7, tck=-.01)\n\n# add a title for the right axis \nmtext(\"y=1/x\", side=4, line=3, cex.lab=1,las=2, col=\"blue\")\n\n# add a main title and bottom and left axis labels \ntitle(\"An Example of Creative Axes\", xlab=\"X values\",\n ylab=\"Y=X\")" }, { "path": "20a-BasicGraphs/13e-multipleplots1.R", "content": "#multiple plots\n\n# 4 figures arranged in 2 rows and 2 columns\nattach(mtcars)\npar(mfrow=c(2,2))\nplot(wt,mpg, main=\"Scatterplot of wt vs. mpg\")\nplot(wt,disp, main=\"Scatterplot of wt vs disp\")\nhist(wt, main=\"Histogram of wt\")\nboxplot(wt, main=\"Boxplot of wt\")\n\n\n# 3 figures arranged in 3 rows and 1 column\nattach(mtcars)\npar(mfrow=c(3,1)) \nhist(wt)\nhist(mpg)\n\nhist(disp)\n\n\n# One figure in row 1 and two figures in row 2\nattach(mtcars)\nlayout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))\nhist(wt)\nhist(mpg)\nhist(disp)\n\n\n# One figure in row 1 and two figures in row 2\n# row 1 is 1/3 the height of row 2\n# column 2 is 1/4 the width of the column 1 \nattach(mtcars)\nlayout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE), \n widths=c(3,1), heights=c(1,2))\nhist(wt)\nhist(mpg)\nhist(disp)\n\n\n\n# Add boxplots to a scatterplot\npar(fig=c(0,0.8,0,0.8), new=TRUE)\nplot(mtcars$wt, mtcars$mpg, xlab=\"Car Weight\",\n ylab=\"Miles Per Gallon\")\npar(fig=c(0,0.8,0.55,1), new=TRUE)\nboxplot(mtcars$wt, horizontal=TRUE, axes=FALSE)\npar(fig=c(0.65,1,0,0.8),new=TRUE)\nboxplot(mtcars$mpg, axes=FALSE)\nmtext(\"Enhanced Scatterplot\", side=3, outer=TRUE, line=-3)\n\n" }, { "path": "20a-BasicGraphs/13f-multipleplots.R", "content": "#Multiple Plots\n#https://stackoverflow.com/questions/2564258/plot-two-graphs-in-same-plot-in-r\n\nx= rnorm(100, 50,10)\ny= rnorm(100, 70, 15)\nx1= runif(100, 50,100)\nx2= runif(100, 70, 120)\ny1 = runif(100, 100,200)\ny2 = runif(100, 100,200)\n\n\n#plot\n#plot(first thing to plot)\n#plot(Second thing to plotm, add=T)\n\n\n#same x coord\nmatplot(x, cbind(y1,y2),type=\"l\",col=c(\"red\",\"green\"),lty=c(1,1))\n\n\n#Diff x coordinates\nx1 <- seq(-2, 2, 0.05)\nx2 <- seq(-3, 3, 0.05)\ny1 <- pnorm(x1)\ny2 <- pnorm(x2,1,1)\n\nplot(x1,y1,ylim=range(c(y1,y2)),xlim=range(c(x1,x2)), type=\"l\",col=\"red\")\nlines(x2,y2,col=\"green\")\n\n\n#next to each other\npar(mfrow=c(1,2))\nplot(x)\nplot(y) \npar(mfrow=c(1,1))\n\n\n#\nplot(x1, y1,col='red')\npoints(x2,y2,col='blue')\nlines(x2,y2,col='green')\n\n\n#----\nkx = matrix( c(21,50,80,41), nrow=2 )\nx\ny = matrix( c(1,2,1,2), nrow=2 )\ny\nplot(x, y, col=c(\"red\",\"blue\"))\n\n#ggplot\nrequire(ggplot2)\ndf= data.frame(x, y1,y2)\n\nggplot(df, aes(x,y1)) + # basic graphical object\n geom_line(colour=\"red\") # first layer\n\nggplot(df, aes(x)) + # basic graphical object\n geom_line(aes(y=y1), colour=\"red\") + # first layer\n geom_line(aes(y=y2), colour=\"green\") # second layer\n" }, { "path": "20a-BasicGraphs/13g-subplot.R", "content": "#subplot\n#https://www.programiz.com/r-programming/subplot\n#put two or more graphs in a single plot.\npar()\n#mfrow can be used to specify the number of subplot we need\nmax.temp <- c(20, 25, 20, 27, 33, 22, 28)\nmax.temp # a vector used for plotting\n\n\npar(mfrow=c(1,2)) # set the plotting area into a 1*2 array\n\nbarplot(max.temp, main=\"Barplot\")\npie(max.temp, main=\"Piechart\", radius=1)\n#same phenomenon can be achieved with the graphical parameter mfcol.\n#only difference between the two is that, mfrow fills in the subplot region row wise while mfcol fills it column wise.\n\nTemperature <- airquality$Temp\nOzone <- airquality$Ozone\n#rowfirst\npar(mfrow=c(2,2))\n#par(mfcol=c(2,2))\n\n\nhist(Temperature)\nboxplot(Temperature, horizontal=TRUE)\nhist(Ozone)\nboxplot(Ozone, horizontal=TRUE)\n\n#more control using fig\n# make labels and margins smaller\npar(cex=0.7, mai=c(0.1,0.1,0.2,0.1))\n\nTemperature <- airquality$Temp\n\n# define area for the histogram\npar(fig=c(0.1,0.7,0.3,0.9))\nhist(Temperature)\n\n# define area for the boxplot\npar(fig=c(0.8,1,0,1), new=TRUE)\nboxplot(Temperature)\n\n# define area for the stripchart\npar(fig=c(0.1,0.67,0.1,0.25), new=TRUE)\nstripchart(Temperature, method=\"jitter\")\n" }, { "path": "20a-BasicGraphs/15a-graphdata1.R", "content": "# Data for graphs Plot using Business Data\n\n# Data\nset.seed(100)\nsales = floor(rnorm(100, 80, 20))\n\nset.seed(101)\ncoy = sample(c('coy1','coy2','coy3'), size=100, replace=T) \ncoy = factor(coy)\n\nset.seed(102)\nadtv = ceiling(rnorm(100,10,5))\n\nset.seed(103)\nadweb = ceiling(rnorm(100,7,2))\n\nset.seed(104)\nloc = sample(c('delhi','noida'), size=100, replace=T) \n\nset.seed(105)\nsalesman = factor(sample(c(1,2,3), size=100, replace=T))\n\nset.seed(106)\nproduct = factor(sample(c('TV','Friz','Car'), size=100, replace=T))\n\ndf1 = data.frame(coy,sales,adtv,adweb,loc,product,salesman)\nhead(df1)\nstr(df1)\n\nsave(df1, file='./data/badata.Rdata')\n\n" }, { "path": "20a-BasicGraphs/15b-graph1.R", "content": "# Plot using Business Data\ndf1\n#use df1 : load(file='./data/ba.Rdata')\n# Data\nload(file='./data/badata.Rdata')\ndf1\nattach(df1)\n#scatter Plot\nplot(adtv, sales)\nplot(x=adtv, y=adweb, pch=15, type='p', col='yellow')\n\nabline(lm(adweb ~ adtv))\n\ncar::scatterplot(sales ~ coy, data=df1, legend.columns=T)\ncar::scatterplot(sales ~ adtv, data=df1, legend.columns=T)\n\ncar::scatterplotMatrix(~ sales + adtv + adweb, data=df1)\nplot(sales, adtv)\nplot(sales, adweb)\nplot(adtv, adweb)\npairs( ~ sales + adtv + adweb, data=df1)\n\n\n\n#Corrgrams\nlibrary(corrgram)\nnames(df1)\ncor(df1[c('sales','adtv','adweb')])\ncorrgram::corrgram(df1[c('sales','adtv','adweb')])\nnvars = c('sales', 'adtv', 'adweb')\ncorrgram::corrgram(df1[nvars], order=T, lower.panel = panel.shade, upper.panel = panel.pie, diag.panel = panel.minmax, text.panel = panel.txt, main='Corrgram Plot')\n\ncorrgram::corrgram(df1[nvars], order=T, panel = panel.ellipse, diag.panel = panel.minmax, text.panel = panel.txt, main='Corrgram Plot - Ellipse')\n#color - magnitude, direction = +/1, pie- value, min/ max values\ncorrgram::corrgram(df1[nvars], order=T, lower.panel = panel.pts, upper.panel = panel.conf, diag.panel=panel.density, main='Corrgram Plot - Ellipse')\n\ncorrgram::corrgram(df1[nvars], order=T, lower.panel = corrgram::panel.ellipse, upper.panel = panel.bar, diag.panel=panel.minmax, col.regions=colorRampPalette(c('red','blue','green')), main='Corrgram Plot')\n\ncorrgram::corrgram(df1[nvars], order=T, upper.panel=panel.cor)\n\n\n#Mosaic Plot\nlibrary(vcd)\ndata(Titanic)\nvcd::mosaic(Titanic, shade=T, legend=T)\nnames(df1)\nXS= structable(~ coy + loc + product + salesman, data=df1)\ndf3= as.table(XS) ; is.array(XS) \ndf3\nvcd::mosaic(df3,color='skyblue', shade=T, main=\"Main\", sub='Sub Title', xlab='Product')\nvcd::assoc(df3)\n\n\n# Plots\nmean(sales)\nplot(sales, pch=18)\nplot(density(sales))\nhist(sales, bin=10)\nlibrary(ggplot2)\nplot(x=Aadv1, y=Asales1)\n#ggplot(data=df1 + aes(x=df1$Aadv1, y=df1$Acoy1)) + geom_point()\nggplot(data=mtcars) + aes(x=wt, y=mpg) + geom_point()\nnames(df1)\np1 <- ggplot(df1, aes(sales, adtv)) +\n geom_point(data = df1[coy == 'coy1',]) \np1\n\n\n\nqplot(df1$sales, geom=\"histogram\", bins=12)\n?qplot\nnames(df1)\nggplot(data=df1) + aes(y=sales, x=adtv, + geom_point(binwidth=5, colour=\"black\", fill=\"white\") ) #not working\ndf1\n\n\n#Histogram\nhist(df1$sales)\n\nhist(df1$sales, freq=F, col=1:12, breaks=5)\n?hist\nlines(density(df1$sales), lwd=2, col='red')\n\nstr(df1)\n#box plots\nboxplot(df1$sales)\nboxplot(df1$sales ~ df1$coy + df1$loc)\n\nboxplot(df1$sales ~ df1$coy, varwidth=T, col=1:3)\n\n\n\n#Dot Chart\ndotchart(df1$sales) #not effective here\ndotchart(df1$sales, group=df1$coy, color=df1$coy, pch=10:13) \n\n\n\n#add another column and convert to dataframe\nnames(df1)\nhead(df1)\naggregate(df1$sales, by=list(coy), FUN=mean)\n(adf1 = aggregate(formula = sales ~ coy, data=df1, FUN=mean))\n\npie(adf1$sales, label=adf1$coy)\nbarplot(adf1$sales,names.arg=adf1$coy)\nabline(h=adf1[adf1$coy=='coy1',]$sales)\n\nbarplot(adf1$sales,names.arg=adf1$coy, horiz = T)\nabline(v=adf1[adf1$coy=='coy1',]$sales)\n\nbarplot(adf1$sales,names.arg=adf1$coy, beside=T, legend=rownames(adf1), col=1:3)\nabline(v=adf1[adf1$coy=='coy1',]$sales)\n\n\n\n" }, { "path": "20a-BasicGraphs/21b-plot-hist1.R", "content": "# Plotting with Factors\n\n#create a data frame\nmarks = floor(rnorm(50, 50,10))\ngender1 = sample(c('M','F'), 50, replace=T, prob=c(.4,.6))\ngender1\ngender2 = factor(gender1,ordered=T, level=c('M','F'))\nstudent1 = data.frame(marks, gender1)\nstudent2 = data.frame(marks, gender2)\nstr(student1)\nstr(student2)\nboxplot(student1) #marks & gender1\nboxplot(marks ~ gender1, data=student1) # better format\nboxplot(student1$marks ~ student1$gender1)\n\nboxplot(marks ~ gender2, data=student2) # see the order of printing M then F\n\n#add another column\nmstatus = sample(c('Married','Single'), 50, replace=T, prob=c(.6,.4))\nmstatus = factor(mstatus)\nstudent3 = data.frame(marks, gender2, mstatus)\nboxplot( marks ~ gender2 + mstatus, data=student3)\n\n# use ggplot to enhance the graph\nhist(student3$marks)\n# can we fill with colors for gender or married in histogram\nlibrary(ggplot2)\nggplot(student3) #nothing\nnames(student3)\n?barplot\n(t1= table(student3$gender2))\n(t2= table(student3$gender2, student3$mstatus))\n\nbarplot(height=t1, col=1:2)\nbarplot(height=t1, col=4:5, horiz=T)\nbarplot(height=t1, col=6:7, besides=T, horiz=T)\n\nbarplot(height=t2, col=3:4)\nbarplot(height=t2, col=6:9, beside=T)\n\n#set ylim to fit legend\nbarplot(height=t2, col=6:9, beside=T, ylim=c(0,max(t2) * 1.3))\ntitle(main='Bar Plot of Married and Gender Status', sub='See the distibution')\n\nlegend(\"topright\", legend = c(\"M-Single\", \"M-Married\",\"F-Single\", \"F-Married\"), fill = 6:9, horiz = T)\n\n\n#Using GGplot2\nlibrary(ggplot2)\n\nggplot(students3, aes(x=gender2, fill=mstatus)) + geom_bar(position='stack') + labs(title='Distribution of Gender & Married status')\n\n#dodge postion . side wise\nggplot(students3, aes(x=gender2, fill=mstatus)) + geom_bar(position='dodge') + labs(title='Distribution of Gender & Married status')\n\n#fill postion . utilised full column length\nggplot(students3, aes(x=gender2, fill=mstatus)) + geom_bar(position='fill') + labs(title='Distribution of Gender & Married status')\nggplot(students3, aes(x=mstatus, fill=gender2)) + geom_bar(position='fill') + labs(title='Distribution of Gender & Married status')\n\n\n\n#Histogram and distribution\n#http://ggplot2.tidyverse.org/reference/geom_bar.html\n\nhist(student3$marks)\nggplot(student3, aes(x=marks)) + geom_histogram(bins=15)\ng1a = ggplot(student3, aes(x=marks))\ng1b = geom_histogram(bins=15)\ng1a + g1b\n\n\ng2a = ggplot(student3, aes(x=marks))\ng2b = geom_bar()\ng2a + g2b\n\ng2c = geom_bar(aes(fill = gender2))\ng2a + g2c\n\ng2d = geom_bar(aes(fill = mstatus))\ng2a + g2d\n\ng2e = geom_bar(aes(fill = mstatus)) + theme(legend.position = \"top\")\ng2a + g2e\n\ng2e = geom_bar(aes(fill = mstatus), position = position_stack(reverse = TRUE)) + coord_flip() + theme(legend.position = \"top\")\n\n fill=gender2)) + geom_bar(position='stack', binwidth = 1.3) + scale_x_continuous('marks') + scale_y_continuous('Frequency')\n\n\n\ndata(Salaries, package='car')\nSalaries\n" }, { "path": "20a-BasicGraphs/21c-plot.R", "content": "#Plot\n#https://www.programiz.com/r-programming/plot-function\n#simplest case, we can pass in a vector and we will get a scatter plot of magnitude vs index. But generally, we pass in two vectors \n#and a scatter plot of these points are plotted.\nplot(c(1,2),c(3,5))\nplot(1)\nx <- seq(-pi,pi,0.1)\n\nplot(x, sin(x))\n\nplot(x, sin(x),\n main=\"The Sine Function\",\n ylab=\"sin(x)\")\n#options\n#\"p\" - points\n# \"l\" - lines\n# \"b\" - both points and lines\n# \"c\" - empty points joined by lines\n# \"o\" - overplotted points and lines\n# \"s\" and \"S\" - stair steps\n# \"h\" - histogram-like vertical lines\n# \"n\" - does not produce any points or lines\nplot(x, sin(x),\n main=\"The Sine Function\",\n ylab=\"sin(x)\",\n type=\"l\",\n col=\"blue\")\n#Overlaying plot\nplot(x, sin(x),\n main=\"Overlaying Graphs\",\n ylab=\"\",\n type=\"l\",\n col=\"blue\")\n\nlines(x,cos(x), col=\"red\")\n\nlegend(\"topleft\",\n c(\"sin(x)\",\"cos(x)\"),\n fill=c(\"blue\",\"red\")\n)\n" }, { "path": "20a-BasicGraphs/23b-line.R", "content": "#Line Charts\n\nx <- c(1:5); y <- x # create some data \npar(pch=22, col=\"red\") # plotting symbol and color \npar(mfrow=c(2,4)) # all plots on one page \nopts = c(\"p\",\"l\",\"o\",\"b\",\"c\",\"s\",\"S\",\"h\") \nfor(i in 1:length(opts)){ \n heading = paste(\"type=\",opts[i]) \n plot(x, y, type=\"n\", main=heading) \n lines(x, y, type=opts[i]) \n}\n" }, { "path": "20a-BasicGraphs/23c-lines2.R", "content": "#multiple lines plot\n\n#base\n# Create the data for the chart. rainfall two years\nyear1 <- c(7,12,28,3,41)\nnames(year1) = month.abb[6:10]\nyear2 <- c(14,7,6,19,3)\n\n# store in file(file = \"line_chart_2_lines.jpg\")\n# Plot the bar chart.\nplot(year1,type = \"o\",col = \"red\", xlab = \"Month/Year\", ylab = \"Rain fall\", main = \"Rain fall chart\")\nlines(year2, type = \"o\", col = \"blue\")\nlegend(\"topleft\", col=c('red','blue'), pch=1, legend=c('year1','year2')) # legend()\n#dev.off() # Save the file.\n?legend\n\n\n\n\n\n#wide format\ndat <- matrix(runif(40,1,20),ncol=4) # make data\ndat\nmatplot(dat, type = c(\"b\"),pch=1,col = 1:4) #plot\nlegend(\"topleft\", legend = 1:4, col=1:4, pch=1) # optional legend\n\n\n#ggplot\n# dummy data\nset.seed(45)\ndf <- data.frame(x=rep(1:5, 9), val=sample(1:100, 45), \n variable=rep(paste0(\"category\", 1:9), each=5))\n# plot\ndf\nggplot(data = df, aes(x=x, y=val)) + geom_line(aes(colour=variable))\n\n\n#lattice\nlibrary(lattice)\nxyplot(val~x,type=c('l','p'),groups= variable,data=df,auto.key=T)\n" }, { "path": "20a-BasicGraphs/24b-histogram.R", "content": "#Histogram\n\n#Histograms\n#You can create histograms with the function hist(x) where x is a numeric vector of values to be plotted. The option freq=FALSE plots probability densities instead of frequencies. The option breaks= controls the number of bins.\n?hist\nhist(x, breaks = \"Sturges\",\n freq = NULL, probability = !freq,\n include.lowest = TRUE, right = TRUE,\n density = NULL, angle = 45, col = NULL, border = NULL,\n main = paste(\"Histogram of\" , xname),\n xlim = range(breaks), ylim = NULL,\n xlab = xname, ylab,\n axes = TRUE, plot = TRUE, labels = FALSE,\n nclass = NULL, warn.unused = TRUE, ...)\n# Simple Histogram\nhist(mtcars$mpg)\n\n# Colored Histogram with Different Number of Bins\nhist(mtcars$mpg, breaks=12, col=\"red\")\n# 12 bars\nhist(mtcars$mpg, breaks=7, xlim=c(10,40), ylim=c(0,20) ,\n col=rainbow(7), labels=T, \n main=paste('Histogram of MPG'))\n\n# Add a Normal Curve \nx <- mtcars$mpg \nh<-hist(x, breaks=10, col=\"lightblue\", xlab=\"Miles per Gallon\", \n xaxt='n', labels=T , main=\"Histogram with Normal Curve\") \nx\naxis(1, at=h$mids,labels=h$mids,line=.25, col.axis=\"red\", cex.axis=0.7, las=1)\n# add a title for the x axis \nmtext(\"Interval of 2 mpg\", side=1, line=2, \n cex.lab=1,las=1, col=\"blue\", at=30)\n# another mtext\nmtext(\"Interval of 2 mpg\", side=1, line=4, at=17,\n cex.lab=1,las=1, col=\"green\")\n\nxfit<-seq(min(x),max(x),length=40) \nxfit\nyfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) \nyfit\nh$breaks ; h$mids ; h$counts; \nh$mids[1:2] ; length(x); diff(h$mids[1:2]); diff(h$mids[1:2]) * length(x)\nyfit <- yfit*diff(h$mids[1:2])*length(x) \nyfit\nlines(xfit, yfit, col=\"blue\", lwd=2)\n\n# Air Passengers\nstr(AirPassengers)\nhead(AirPassengers)\nhist(AirPassengers)\nhist(AirPassengers, main=\"Histogram for Air Passengers\", \n xlab=\"Passengers\", border=\"blue\", col=\"green\", \n xlim=c(100,700),las=2, breaks=5)\nhist(AirPassengers, breaks=c(100, 300, 500, 700))\nhist(AirPassengers, breaks=c(100, seq(200,700, 150))) # Prob automatically\n#start at 100 on the x-axis, and from values 200 to 700, make the bins 150 wide\n\nhist(AirPassengers, main=\"Histogram for Air Passengers\", \n xlab=\"Passengers\",border=\"blue\", col=\"green\", \n xlim=c(100,700), las=1,breaks=5,prob = TRUE)\nlines(density(AirPassengers)) \n#Get a density curve to go along with your AirPassengers histogram\nhist.info = hist(AirPassengers)\nsummary(hist.info)\nhist.info\nhist(AirPassengers, freq=F)\nhist2 = hist(AirPassengers, plot=F)\nhist2$density\nsum(hist2$density)\nsum(hist2$density * diff(hist2$breaks))\n#curve(dnorm(AirPassengers, mean=mean(AirPassengers), sd=sd(AirPassengers)),\n#add=TRUE, col='darkblue', lwd=2)\n\n# Faithfull--------------\ncolors = c(\"red\", \"yellow\", \"green\", \"violet\", \"orange\", \n \"blue\", \"pink\", \"cyan\") \nhist(faithful$eruptions # apply the hist function \n ,right=FALSE # intervals closed on the left \n ,col=colors # set the color palette \n ,main=\"Old Faithful Eruptions\" # the main title \n ,xlab=\"Duration minutes\") # x-axis label\n\n\n\n\n# Kernel Density Plot\nd <- density(mtcars$mpg) # returns the density data \nplot(d) # plots the results\n\n# Filled Density Plot\nd <- density(mtcars$mpg)\nplot(d, main=\"Kernel Density of Miles Per Gallon\")\npolygon(d, col=\"red\", border=\"blue\")\n\n# Compare MPG distributions for cars with \n# 4,6, or 8 cylinders\nlibrary(sm)\nattach(mtcars)\n\n# create value labels \ncyl.f <- factor(cyl, levels= c(4,6,8),\n labels = c(\"4 cylinder\", \"6 cylinder\", \"8 cylinder\")) \n\n# plot densities \nsm.density.compare(mpg, cyl, xlab=\"Miles Per Gallon\")\ntitle(main=\"MPG Distribution by Car Cylinders\")\n\n# add legend via mouse click\ncolfill<-c(2:(2+length(levels(cyl.f)))) \nlegend(locator(1), levels(cyl.f), fill=colfill)\n\n" }, { "path": "20a-BasicGraphs/24c-histogram2.R", "content": "# Histogram2\n#https://www.programiz.com/r-programming/histogram\nstr(airquality)\nairquality$Temp\nTemperature = airquality$Temp\nhist(Temperature)\n# 9 cells equally spaced; Height of cell = no of observations falling in the cell\n# main title, xlab, ylab, xlim, ylim, col, freq\n# histogram with added parameters\n\nhist(Temperature,\n main=\"Maximum daily temperature \",\n xlab=\"Temperature in deg F\",\n xlim=c(50,100),\n col=\"darkmagenta\",\n freq=FALSE\n)\n# yaxis labeled density instead of freq. total area =1\n# return value of hist()\nh = hist(Temperature)\nh\n#breaks-places where the breaks occur,\n#counts-the number of observations falling in that cell,\n#density-the density of cells, mids-the midpoints of cells,\n#xname-the x argument name and\n#equidist-a logical value indicating if the breaks are equally spaced or not.\n#\nh <- hist(Temperature,ylim=c(0,40))\ntext(h$mids,h$counts,labels=h$counts, adj=c(0.5, -0.5))\n\n#Define no of breaks\nhist(Temperature, breaks=4, main=\"With breaks=4\")\nhist(Temperature, breaks=20, main=\"With breaks=20\")\n\n#With manual Breaks\nhist(Temperature,\n main=\"Maximum daily temperature\",\n xlab=\"Temperature in degrees Fahrenheit\",\n xlim=c(50,100),\n col=\"chocolate\",\n border=\"brown\",\n breaks=c(55,60,70,75,80,100)\n)\n\n" }, { "path": "20a-BasicGraphs/25b-barplot.R", "content": "# Bar Plot\n# Simple Bar Plot \ncounts <- table(mtcars$gear)\nbarplot(counts, main=\"Car Distribution\", xlab=\"Number of Gears\")\n#which cars have max type of gears\n\n# Simple Horizontal Bar Plot with Added Labels \ncounts <- table(mtcars$gear)\ncounts\nbarplot(counts, main=\"Car Distribution\", horiz=TRUE,\n names.arg=c(\"3 Gears\", \"4 Gears\", \"5 Gears\"))\n\n# Stacked Bar Plot with Colors and Legend\ncounts <- table(mtcars$vs, mtcars$gear)\ncounts\nbarplot(counts, main=\"Car Distribution by Gears and VS\",\n xlab=\"Number of Gears\", col=c(\"darkblue\",\"red\"),\n legend = rownames(counts))\n#what is distribution of Engine Type for each gear type\n#which type of engines are max in gear3\n\n# Grouped Bar Plot\ncounts <- table(mtcars$vs, mtcars$gear)\ncounts\nbarplot(counts, main=\"Car Distribution by Gears and VS\",\n xlab=\"Number of Gears\", col=c(\"darkblue\",\"red\"),\n legend = rownames(counts), beside=TRUE)\n\n# Fitting Labels \npar(las=2) # make label text perpendicular to axis\npar(mar=c(5,8,4,2)) # increase y-axis margin.\n\ncounts <- table(mtcars$gear)\nbarplot(counts, main=\"Car Distribution\", horiz=TRUE, names.arg=c(\"3 Gears\", \"4 Gears\", \"5 Gears\"), cex.names=0.8)\n\n" }, { "path": "20a-BasicGraphs/25c-barplot2.R", "content": "#Bar Plot2\n#\nheights1 = c(5, 5, 4.5, 4, 4, 4, 4.5, 2, 4, 4) # Your data\nmydata <- matrix(heights1, ncol = 2, byrow = TRUE,\n dimnames = list(c(\"Spanish\", \"English\", \"Hindi\", \n \"Arabic\", \"Body Lang\"), c(\"Reading/Writing\", \"Speaking\")))\n#matrix( , , , list(rowname,s colnames))\ncolors <- c(\"darkblue\", \"red\") # Define the colors you're using\n\nbarplot(t(mydata), beside = TRUE, col = colors, \n ylim = c(0, 6), axes = FALSE,\n xlab = \"Language starting with mostly used\",\n main = \"Languages (Verbal & Non-verbal)\")\naxis(2, at = 0:5, labels = 0:5)\nlegend(\"topright\", colnames(mydata), fill = colors, bty = \"n\")\nlegend(\"topleft\", c(\"reading/Writing\", \"Speaking\"), col=c(\"darkblue\",\"red\"), lwd=10)\n\n#2\n\n##Create data\nx = replicate(2, sample(letters[1:2], 100, rep=T))\napply(x, 2, table)\n\n##Create plot\nbarplot(matrix(c(5, 3, 8, 9), nr=2), beside=TRUE, \n col=c(\"aquamarine3\", \"coral\"), \n names.arg=LETTERS[1:2], \n ylim=range(0,12))\n\n##Add text at coordinates: x=2, y=6\n##Use trial and error to place them \ntext(2, 6, \"A label\")\ntext(5, 10, \"Another label\")\n\n#3\ntt = structure(c(7L, 13L, 24L, 30L, 30L, 38L, 35L, 45L, 37L, \n 43L, 38L, 59L, 33L, 45L, 37L, 58L), .Dim = c(2L, 8L), \n .Dimnames = structure(list(param = c(\"A\",\"B\"), \n xvar = c(\"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\")), \n .Names = c(\"param\", \"xvar\")), class = \"table\")\ntt\nbb <- barplot(tt, col=c(\"grey60\",\"grey80\"))\ntext(bb,tt[1,]-4,labels=tt[1,],cex=.8)\ntext(bb,colSums(tt)-4,labels=tt[2,],cex=0.8)\nstr(tt)\n\nlibrary(ggplot2)\nggplot(as.data.frame(tt),aes(x=factor(xvar),y=Freq,fill=param)) +\n geom_bar(stat=\"identity\",position=\"stack\")+\n geom_text(aes(label=Freq),position=\"stack\",vjust=1)+\n scale_fill_manual(values=c(\"grey60\",\"grey80\"))+\n theme_bw()\n\n\n#4\nstudents = c(10,5,15,\n 25,20,30,\n 12,14,17)\nstusummary = matrix(students, byrow=T,ncol=3, \n dimnames = list(c('DS','ANM', 'LAW'), \n c('sem1','sem3','sem5') ) )\nstusummary\nbarplot(stusummary) \nbarplot(stusummary, beside=T , col=rainbow(nrow(stusummary)) \n ,legend=rownames(stusummary)) \n\n#\nbarplot(stusummary, beside=T , \n col=rainbow(nrow(stusummary)) )\n\nlegend('top', legend=rownames(stusummary), horiz=T,\n fill=rainbow(nrow(stusummary)))\n\n#Invert \nbarplot(t(stusummary))\nbarplot(t(stusummary), beside=T)\nbarplot(stusummary, beside=T , \n col=rainbow(nrow(t(stusummary))) )\nlegend('top', legend=colnames(t(stusummary)), horiz=T,\n fill=rainbow(ncol(t(stusummary))))\n\n\n#4\nmax.temp <- c(22, 27, 26, 24, 23, 26, 28)\nbarplot(max.temp)\n# barchart with added parameters\n\nbarplot(max.temp,\n main = \"Maximum Temperatures in a Week\",\n xlab = \"Degree Celsius\",\n ylab = \"Day\",\n names.arg = c(\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\"),\n col = \"darkred\",\n horiz = TRUE)\n\nbarplot(max.temp,\n main = \"Maximum Temperatures in a Week\",\n ylab = \"Degree Celsius\",\n xlab = \"Day\",\n names.arg = c(\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\"),\n col = \"yellow\",\n horiz = F)\n?barplot\nmax.temp2 <- c(20, 25, 20, 27, 33, 22, 28)\ntemp = matrix(c(max.temp, max.temp2), byrow=F, ncol=2)\ncolnames(temp) = c('Jan', 'Feb')\ntemp\nbarplot(temp, beside=T, xlab='Days', ylab='Degree C', cex.names = 0.6,\n cex.axis = 0.5, ylim=range(0, max(temp)+5),names.arg = rep(c(\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\"),2)\n )\ntitle(main='Avg Day Temp of Month',sub='Jan & Feb')\ntext(x=c(1:7,9:15)+.5, temp+1, temp, col='red', cex=0.5)\naxis(1, at= c(4, 12), pos=-3, labels=c('Jan','Feb') , lwd=0)\n?axis\n" }, { "path": "20a-BasicGraphs/26b-boxplot.R", "content": "# Box Plots\n\n# Boxplot of MPG by Car Cylinders \nboxplot(mpg~cyl,data=mtcars, main=\"Car Milage Data\", \n xlab=\"Number of Cylinders\", ylab=\"Miles Per Gallon\")\n\n# Notched Boxplot of Tooth Growth Against 2 Crossed Factors\n# boxes colored for ease of interpretation \nboxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, \n col=(c(\"gold\",\"darkgreen\")),\n main=\"Tooth Growth\", xlab=\"Suppliment and Dose\")\n#In the notched boxplot, if two boxes' notches do not overlap this is ‘strong evidence’ their medians differ \n\n# Notched Boxplot of Tooth Growth Against 2 Crossed Factors\n# boxes colored for ease of interpretation \nboxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, \n col=(c(\"gold\",\"darkgreen\")),\n main=\"Tooth Growth\", xlab=\"Suppliment and Dose\")\n\n\n\n\n\n\n\n\n# BoxPlot 3 -------------\ndata <- c(0.4, 0.7, 0.75, 0.82, 0.9)\ndata\nendaxis <- c(0, 1) # endpoints of axis\ndatamm <- c(min(data), max(data))\nboxplot(data, horizontal = TRUE, range = 0, ylim = endaxis,\n axes = FALSE, col = \"grey\", add = FALSE)\narrows(endaxis, 1, datamm, 1, code = 1, angle = 90, length = 0.1)\nvaluelabels <- c(endaxis[1], round(fivenum(data)[2], digits = 2) ,\n round(fivenum(data)[4], digits = 2), endaxis[2] ) \n\n# Boxplot 4 ----------\n\nboxplot(data)\nabline(h = min(data), col = \"Blue\")\nmtext(4,at = min(data), text='Min' , las=2)\n\nabline(h = max(data), col = \"Yellow\")\ntext(x=1.2, y=max(data), labels= 'Max', col='Yellow')\n\nabline(h = median(data), col = \"Green\")\ntext(x=.8, y=median(data), labels= 'Median', col='Green')\n\nabline(h = mean(data), col = \"Violet\")\ntext(x=.8, y=mean(data), labels= 'Mean', col='Violet')\n\nabline(h = quantile(data, c(0.25, 0.75)), col = \"Red\")\ntext(x=1.5, y=quantile(data, c(0.25, 0.75)), \n labels= c('1QR','3QR'), col='Red')\n\n\ndata <- c(0.4, 0.7, 0.75, 0.82, 0.9)\nboxplot(data)\naxis(4, at=summary(data), labels = names(summary(data)), \nlas=2, line = -3, cex.lab=.5,col=\"blue\")\ntext(x=1.25, y=summary(data), labels = summary(data), col='red', cex=.5)\n\ntext(x=1, y=c(min(data)-.01, max(data)+.01), labels = c('Outliers - Low', 'Outliers - High'), col='red', cex=.5)\n\nboxplot(mtcars$mpg)\naxis(4, at=summary(mtcars$mpg), labels = names(summary(mtcars$mpg)), las=2, line = -3, cex.lab=.5,col=\"blue\")\ntext(x=1.25, y=summary(mtcars$mpg), labels = summary(mtcars$mpg), col='red', cex=.5)\ntext(x=1, y=c(min(mtcars$mpg)-.01, max(mtcars$mpg)+.01), labels = c('Outliers - Low', 'Outliers - High'), col='red', cex=.5)\n \n " }, { "path": "20a-BasicGraphs/26c-boxplot2.R", "content": "# Box Plot2\n#his function takes in any number of numeric vectors, \n#drawing a boxplot for each vector\n#\nstr(airquality)\nboxplot(airquality$Ozone)\n#median is more dispersed. We can also notice two outliers at the higher extreme.\n#Additional Para - main, xlab, ylab, col, horiz=T, notch=T\nboxplot(airquality$Ozone,\n main = \"Mean ozone in parts per billion \",\n xlab = \"Parts Per Billion\",\n ylab = \"Ozone\",\n col = \"orange\",\n border = \"brown\",\n horizontal = TRUE,\n notch = TRUE\n)\n\n#Return Value of boxplot\nb <- boxplot(airquality$Ozone)\nb\n#n-the number of observation the boxplot is drawn with (notice that NA's are not taken into account)\n#conf-upper/lower extremes of the notch, out-value of the outliers\n#group-a vector of the same length as out whose elements indicate to which group the outlier belongs and\n#names-a vector of names for the group\n\n#Multiple Box Plots\n#Ozone, Temp\nozone = airquality$Ozone\ntemp = airquality$Temp\n#generate normal distribution with the same mean and standard deviation and plot them side by side for comparison.\nozone_norm <- rnorm(200,mean=mean(ozone, na.rm=TRUE), sd=sd(ozone, na.rm=TRUE))\ntemp_norm <- rnorm(200,mean=mean(temp, na.rm=TRUE), sd=sd(temp, na.rm=TRUE))\nboxplot(ozone, ozone_norm, temp, temp_norm,\n main = \"Multiple boxplots for comparision\",\n at = c(1,2,4,5),\n names = c(\"ozone\", \"normal\", \"temp\", \"normal\"),\n las = 2,\n col = c(\"orange\",\"red\"),\n border = \"brown\",\n horizontal = TRUE,\n notch = TRUE\n)\n\n#Boxplot with Formula\nboxplot(Temp~Month,\n data=airquality,\n main=\"Different boxplots for each month\",\n xlab=\"Month Number\",\n ylab=\"Degree Fahrenheit\",\n col=\"orange\",\n border=\"brown\"\n)\n#month number 7 (July) is relatively hotter than the rest.\n#" }, { "path": "20a-BasicGraphs/26d-boxplot2.R", "content": "#understanding box plot\n#https://www.shmoop.com/basic-statistics-probability/box-whisker-plots.html\n\n\n#eg1----\nmarks = c(53,65,68,69,70,72,72,79,83,84,85,87,89,90,94)\nquantile(marks)\nsort(marks)\nboxplot(marks, horizontal = T)\n\n\n#eg2----\n#mtcars\n\n(mileage = mtcars$mpg)\n\nboxplot(mileage)\n\n\nboxplot(mileage, ylim=c(0,45))\nquantile(mileage)\nIQR(mileage)\nrange(mileage)\n\n#quartiles\nabline(h=quantile(mileage), col='green')\ntext(1-.3, quantile(mileage)+.2, labels=c('Q0','Q1','Q2','Q3','Q4'), col='green')\n\nquantile(mileage)[c(2,4)]\n(LL = quantile(mileage)[c(2)] - IQR(mileage))\n(UL = quantile(mileage)[c(4)] + IQR(mileage))\nabline(h=c(LL, UL), col='red', lwd=2)\ntext(1+.3, c(LL, UL)+.2, labels=c('Q1-IQR', 'Q3+IQR'), col='red')\ntext(1,0,label='beyond this value - consider them outliers', col='red')\n#beyond this value - consider them outliers\n\n#min/max\nabline(h=range(mileage)+.1, col='blue', lwd=2)\ntext(1+.4, range(mileage)+.3, labels=c('min', 'max'), col='blue')\n\n#mean/ median\nabline(h=c(mean(mileage)+.1, median(mileage)+.1), col='brown', lwd=2)\ntext(c(1+.3,1), c(mean(mileage)-.2, median(mileage)+.2), labels=c(paste('MEAN',round(mean(mileage))), paste('MEDIAN',round(median(mileage),2))), col='brown')\n\n# eg2------\n\nmileage = c(rnorm(100, mean=20, sd=5), rnorm(100,mean=30,sd=5), rnorm(1000,mean=35,sd=5))\nplot(density(mileage))\ne1071::skewness(mileage)\n\n#left skewed\n\nboxplot(mileage, ylim=c(0,60), notch=T, col='yellow', main='Box Plot')\nrug(jitter(mileage, amount = 0.01), side = 2, col = \"light blue\")\nquantile(mileage)\nIQR(mileage)\nrange(mileage)\n\n#quartiles\nabline(h=quantile(mileage), col='green')\ntext(1-.3, quantile(mileage)+.2, labels=c('Q0','Q1','Q2','Q3','Q4'), col='green')\n\nquantile(mileage)[c(2,4)]\n(LL = quantile(mileage)[c(2)] - IQR(mileage))\n(UL = quantile(mileage)[c(4)] + IQR(mileage))\nabline(h=c(LL, UL), col='red', lwd=2)\ntext(1+.3, c(LL, UL)+.2, labels=c('Q1-IQR', 'Q3+IQR'), col='red')\ntext(1,0,label='beyond this value - consider them outliers', col='red')\n#beyond this value - consider them outliers\n\n#min/max\nabline(h=range(mileage)+.1, col='blue', lwd=2)\ntext(1+.4, range(mileage)+.3, labels=c('min', 'max'), col='blue')\n\n#mean/ median\nabline(h=c(mean(mileage)+.1, median(mileage)+.1), col='brown', lwd=2)\ntext(c(1+.3,1), c(mean(mileage)-.2, median(mileage)+.2), labels=c(paste('MEAN',round(mean(mileage))), paste('MEDIAN',round(median(mileage),2))), col='brown')\n\nhist(mileage, freq=F)\nrug(jitter(mileage)) \nlines(density(mileage))\n" }, { "path": "20a-BasicGraphs/27b-pie.R", "content": "#Pie\npar()\n# Simple Pie Chart\nslices <- c(10, 12,4, 16, 8)\nlbls <- c(\"US\", \"UK\", \"Australia\", \"Germany\", \"France\")\npie(slices, labels = lbls, main=\"Pie Chart of Countries\")\n\n# Pie Chart with Percentages\nslices <- c(10, 12, 4, 16, 8) \nlbls <- c(\"US\", \"UK\", \"Australia\", \"Germany\", \"France\")\npct <- round(slices/sum(slices)*100)\nlbls <- paste(lbls, pct) # add percents to labels \nlbls <- paste(lbls,\"%\",sep=\"\") # ad % to labels \npie(slices,labels = lbls, col=rainbow(length(lbls)),\n main=\"Pie Chart of Countries\")\n\n# 3D Exploded Pie Chart\nlibrary(plotrix)\nslices <- c(10, 12, 4, 16, 8) \nlbls <- c(\"US\", \"UK\", \"Australia\", \"Germany\", \"France\")\npie3D(slices,labels=lbls,explode=0.1,\n main=\"Pie Chart of Countries \")\n\n# Pie Chart from data frame with Appended Sample Sizes\nmytable <- table(iris$Species)\nlbls <- paste(names(mytable), \"\\n\", mytable, sep=\"\")\npie(mytable, labels = lbls, \n main=\"Pie Chart of Species\\n (with sample sizes)\")\n\n" }, { "path": "20a-BasicGraphs/27c-pie2.R", "content": "# Pie2\nexpenditure=c(600,300,150,100,200)\nnames(expenditure)=c('Housing','Food','Cloths','Entertainment',\n 'Other') \nexpenditure\npie(expenditure)\n\npie(expenditure,\n labels=as.character(expenditure),\n main=\"Monthly Expenditure Breakdown\",\n col=c(\"red\",\"orange\",\"yellow\",\"blue\",\"green\"),\n border=\"brown\",\n clockwise=TRUE\n)\n" }, { "path": "20a-BasicGraphs/29b-corrgram1.R", "content": "#correloggram\n\n# First Correlogram Example\n\nlibrary(corrgram)\ncor(mtcars[1:4])\ncorrgram(mtcars[1:4], order=TRUE, lower.panel=panel.shade,\n upper.panel=panel.pie, text.panel=panel.txt,\n main=\"Car Milage Data in PC2/PC1 Order\")\ncor(mtcars[1:5])\ncorrgram(mtcars[1:5], order=TRUE, lower.panel=panel.shade,\n upper.panel=panel.pie, text.panel=panel.txt)\n\n\n\n#https://www.statmethods.net/advgraphs/correlograms.html\n\n\n# Second Correlogram Example\nlibrary(corrgram)\ncorrgram(mtcars[1:5], order=TRUE, lower.panel=panel.ellipse, upper.panel=panel.pts, text.panel=panel.txt,\n diag.panel=panel.minmax)\n\n" }, { "path": "20a-BasicGraphs/32b-freqdistr.R", "content": "# Frequency Distribution\n\nduration = faithful$eruptions \nrange(duration)\nbreaks = seq(1.5, 5.5, by=0.5)# half-integer sequence \nbreaks \nduration.cut = cut(duration, breaks, right=FALSE)\n#left side not closed [1.5-2.0) ie 1.5 to 1.9999\n#value of 2 will go into [2-2.5) interval\nduration.cut\nduration.freq = table(duration.cut)\nduration.freq\ncbind(duration.freq) \n\n\n# Relative Freq\nduration.relfreq = duration.freq / nrow(faithful)\nduration.relfreq\n\nold = options(digits=1) \nduration.relfreq \noptions(old) # restore\nduration.relfreq \ncbind(duration.freq, duration.relfreq) \n\n# Cumulative freq\nduration.cumfreq = cumsum(duration.freq)\n\n#graphically-----\n#using histogram\nh = hist(faithful$eruptions)\nh$breaks\nh$counts\n#compare with\ncbind(duration.freq) \nh$mids\n\n\n#right=T\nduration.cut.R = cut(duration, breaks, right=T)\n##value of 2 will go into (1.5-2] interval\nduration.cut.R\nduration.freq.R = table(duration.cut.R)\nduration.freq.R\ncbind(duration.freq.R) \nh$counts #same\n\n" }, { "path": "20a-BasicGraphs/33b-dotplot.R", "content": "# Simple Dotplot\ndotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,\n main=\"Gas Milage for Car Models\", \n xlab=\"Miles Per Gallon\")\n\n# Dotplot: Grouped Sorted and Colored\n# Sort by mpg, group and color by cylinder \nx <- mtcars[order(mtcars$mpg),] # sort by mpg\nx$cyl <- factor(x$cyl) # it must be a factor\nx$color[x$cyl==4] <- \"red\"\nx$color[x$cyl==6] <- \"blue\"\nx$color[x$cyl==8] <- \"darkgreen\"\t\ndotchart(x$mpg,labels=row.names(x),cex=.7,groups= x$cyl,\n main=\"Gas Milage for Car Models\\ngrouped by cylinder\",\n xlab=\"Miles Per Gallon\", gcolor=\"black\", color=x$color)\n" }, { "path": "20a-BasicGraphs/33b-matrixplots.R", "content": "# Matrix Plots\n# Multiple Series on one Graph\n\ntable(iris$Species) # is data.frame with 'Species' factor\niS <- iris$Species == \"setosa\"\niV <- iris$Species == \"versicolor\"\niS;iV\nop <- par(bg = \"bisque\")\nmatplot(c(1, 8), c(0, 4.5), type = \"n\", xlab = \"Length\", \n ylab = \"Width\", main = \"Petal and Sepal Dimensions in Iris Blossoms\")\nmatpoints(iris[iS,c(1,3)], iris[iS,c(2,4)], pch = \"sS\", \n col = c(2,4))\nmatpoints(iris[iV,c(1,3)], iris[iV,c(2,4)], pch = \"vV\", \n col = c(2,4))\nlegend(1, 4, c(\" Setosa Petals\", \" Setosa Sepals\",\n \"Versicolor Petals\", \"Versicolor Sepals\"),\n pch = \"sSvV\", col = rep(c(2,4), 2))\n\n# Example 2\n\na <- rnorm(100)\nb <- 2*a + 3\nc <- 3*a + 2\na;b;c\nmatplot(a, cbind(b,c), pch=1:2, col=c(2,4))\nlegend(\"bottomright\", inset=.05, legend=c(\"b\", \"c\"), \n pch=1:2, col=c(2,4), horiz=F)\n\n\n\n# Example 3\nx = 1:10\nA = c(15, 36, 54, 60, 68, 71, 73, 75, 78, 78)\nB = c(20, 49, 58, 69, 75, 80, 83, 86, 88, 89)\nC = c(24, 58, 68, 75, 83, 90, 93, 93, 95, 96)\nPerformance = data.frame(A,B,C)\nmatplot(x,Performance, type=\"o\", pch=c(1,2,3), col=c(\"red\",\"green\",\"blue\"))\nlegend(\"bottomright\", inset=.05, legend=c(\"A\", \"B\",'C'),\n pch=1:3, col=c(\"red\",\"green\",\"blue\"), horiz=F)\n" }, { "path": "20a-BasicGraphs/37b-scatter.R", "content": "#Scatter Plot\n\n#The basic function is plot(x, y), where x and y are numeric vectors denoting the (x,y) points to plot.\n\n# Simple Scatterplot\nattach(mtcars)\nplot(wt, mpg, main=\"Scatterplot Example\", \n xlab=\"Car Weight \", ylab=\"Miles Per Gallon \", pch=19)\n# Add fit lines\nabline(lm(mpg~wt), col=\"red\") # regression line (y~x) \nlines(lowess(wt,mpg), col=\"blue\") # lowess line (x,y)\n\n# Enhanced Scatterplot of MPG vs. Weight \n# by Number of Car Cylinders \nlibrary(car) \nscatterplot(mpg ~ wt | cyl, data=mtcars, \n xlab=\"Weight of Car\", ylab=\"Miles Per Gallon\", \n main=\"Enhanced Scatter Plot\", \n labels=row.names(mtcars))\n\n\n# Basic Scatterplot Matrix\npairs(~mpg+disp+drat+wt,data=mtcars, \n main=\"Simple Scatterplot Matrix\")\n\n# Scatterplot Matrices from the lattice Package \nlibrary(lattice)\nsplom(mtcars[c(1,3,5,6)], groups=cyl, data=mtcars,\n panel=panel.superpose, \n key=list(title=\"Three Cylinder Options\",\n columns=3,\n points=list(pch=super.sym$pch[1:3],\n col=super.sym$col[1:3]),\n text=list(c(\"4 Cylinder\",\"6 Cylinder\",\"8 Cylinder\"))))\n\n\n# Scatterplot Matrices from the car Package\nlibrary(car)\nscatterplot.matrix(~mpg+disp+drat+wt|cyl, data=mtcars,\n main=\"Three Cylinder Options\")\n\n\n# Scatterplot Matrices from the glus Package \nlibrary(gclus)\ndta <- mtcars[c(1,3,5,6)] # get data \ndta.r <- abs(cor(dta)) # get correlations\ndta.col <- dmat.color(dta.r) # get colors\n# reorder variables so those with highest correlation\n# are closest to the diagonal\ndta.o <- order.single(dta.r) \ncpairs(dta, dta.o, panel.colors=dta.col, gap=.5,\n main=\"Variables Ordered and Colored by Correlation\" )\n\n# High Density Scatterplot with Binning\nlibrary(hexbin)\nx <- rnorm(1000)\ny <- rnorm(1000)\nbin<-hexbin(x, y, xbins=50) \nplot(bin, main=\"Hexagonal Binning\")\n\n# High Density Scatterplot with Color Transparency \npdf(\"c:/scatterplot.pdf\") \nx <- rnorm(1000)\ny <- rnorm(1000) \nplot(x,y, main=\"PDF Scatterplot Example\", col=rgb(0,100,0,50,maxColorValue=255), pch=16)\ndev.off()\n\n\n# 3D Scatterplot\nlibrary(scatterplot3d)\nattach(mtcars)\nscatterplot3d(wt,disp,mpg, main=\"3D Scatterplot\")\n\n\n# 3D Scatterplot with Coloring and Vertical Drop Lines\nlibrary(scatterplot3d) \nattach(mtcars) \nscatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,\n type=\"h\", main=\"3D Scatterplot\")\n\n\n# 3D Scatterplot with Coloring and Vertical Lines\n# and Regression Plane \nlibrary(scatterplot3d) \nattach(mtcars) \ns3d <-scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,\n type=\"h\", main=\"3D Scatterplot\")\nfit <- lm(mpg ~ wt+disp) \ns3d$plane3d(fit)\n\n# Spinning 3d Scatterplot\nlibrary(rgl)\n\nplot3d(wt, disp, mpg, col=\"red\", size=3)\n\n# Another Spinning 3d Scatterplot\nlibrary(Rcmdr)\nattach(mtcars)\nscatter3d(wt, disp, mpg)\n\n" }, { "path": "20a-BasicGraphs/42b-intplots1.R", "content": "# Interactive Plots\nload('./data/badata.Rdata')\n\nhead(df1)\nplot(x=df1$adtv, y=df1$sales)\nidentify(x=df1$adtv, y=df1$sales, labels=row.names(df1))\n#click on the points and finish\ncoords = locator(type='l')\ncoords\n\n\n\n#gobi\nlibrary(rggobi)\n" }, { "path": "20a-BasicGraphs/43b-mosaic.R", "content": "#Mosaic Plots\n#For extended mosaic plots, use mosaic(x, condvar=, data=) where x is a table or formula, condvar= is an optional conditioning variable, and data= specifies a data frame or a table. Include shade=TRUE to color the figure, and legend=TRUE to display a legend for the Pearson residuals.\n\n# Mosaic Plot ----------\nlibrary(vcd)\nmosaic(HairEyeColor, shade=TRUE, legend=TRUE)\n\n\n#Association Plots -------------\n#To produce an extended association plot use assoc(x, row_vars, col_vars)\n#where x is a contingency table, row_vars is a vector of integers \n#giving the indices of the variables to be used for the rows, and \n#col_vars is a vector of integers giving the indices of the variables\n#to be used for the columns of the association plot.\n\n# Association Plot Example\nlibrary(vcd)\nassoc(HairEyeColor, shade=TRUE)\n" }, { "path": "20a-BasicGraphs/43c-corrplot.R", "content": "# some more advance graphs\n\n#install corrplot\nlibrary(corrplot)\nrelationship=cor(mtcars)\nrelationship\ncorrplot(relationship, type = \"upper\", order = \"hclust\", tl.col = \"black\", tl.srt = 45)\n\n#install library\nlibrary(\"PerformanceAnalytics\")\nmy_data <- mtcars[, c(1,3,4,5,6,7)]\nchart.Correlation(my_data, histogram=TRUE, pch=19)\n\n\n# Get some colors\ncol<- colorRampPalette(c(\"blue\", \"white\", \"red\"))(20)\nheatmap(x = res, col = col, symm = TRUE)\n\n" }, { "path": "20a-BasicGraphs/43c-ggally.R", "content": "\ndata(trees)\ntrees\n\nlibrary(GGally)\nggpairs(data=trees, columns=1:3, title=\"trees data\")\nfit_1 <- lm(Volume ~ Girth, data = trees) \nsummary(fit_1) \n" }, { "path": "20a-BasicGraphs/44b-textplots.R", "content": "# Text Plots\n\nlibrary('txtplot')\ntxtplot(cars[,1], cars[,2], xlab = \"speed\", ylab = \"distance\")\n" }, { "path": "20a-BasicGraphs/45b-violinplot.R", "content": "# Violin Plots\nlibrary(vioplot)\nx1 <- mtcars$mpg[mtcars$cyl==4]\nx2 <- mtcars$mpg[mtcars$cyl==6]\nx3 <- mtcars$mpg[mtcars$cyl==8]\nx1\nx2\nx3\nvioplot(x1, x2, x3, names=c(\"4 cyl\", \"6 cyl\", \"8 cyl\"), \n col=\"gold\")\ntitle(\"Violin Plots of Miles Per Gallon\")\n" }, { "path": "20a-BasicGraphs/ria2g1.R", "content": "# ----------------------------------------------------#\n# Getting started with graphs #\n# requires that the Hmisc and RColorBrewer packages #\n# have been installed #\n# install.packages(c(\"Hmisc\", \"RColorBrewer\")) #\n#-----------------------------------------------------#\n\n#par(ask=TRUE)\nopar <- par(no.readonly=TRUE) # make a copy of current settings\n\nattach(mtcars) # be sure to execute this line\n\nplot(wt, mpg)\nabline(lm(mpg~wt))\ntitle(\"Regression of MPG on Weight\")\n\n# Input data for drug example\ndose <- c(20, 30, 40, 45, 60)\ndrugA <- c(16, 20, 27, 40, 60)\ndrugB <- c(15, 18, 25, 31, 40)\n\nplot(dose, drugA, type=\"b\")\n\nopar <- par(no.readonly=TRUE) # make a copy of current settings\npar(lty=2, pch=17) # change line type and symbol\nplot(dose, drugA, type=\"b\") # generate a plot\npar(opar) # restore the original settings \n\nplot(dose, drugA, type=\"b\", lty=3, lwd=3, pch=15, cex=2)\n\n\n# choosing colors\nlibrary(RColorBrewer)\nn <- 7\nmycolors <- brewer.pal(n, \"Set1\")\nbarplot(rep(1,n), col=mycolors)\n\nn <- 10\nmycolors <- rainbow(n)\npie(rep(1, n), labels=mycolors, col=mycolors)\nmygrays <- gray(0:n/n)\npie(rep(1, n), labels=mygrays, col=mygrays)\n\n\n# Listing 3.1 - Using graphical parameters to control graph appearance\t\ndose <- c(20, 30, 40, 45, 60)\ndrugA <- c(16, 20, 27, 40, 60)\ndrugB <- c(15, 18, 25, 31, 40)\nopar <- par(no.readonly=TRUE)\npar(pin=c(2, 3))\npar(lwd=2, cex=1.5)\npar(cex.axis=.75, font.axis=3)\nplot(dose, drugA, type=\"b\", pch=19, lty=2, col=\"red\")\nplot(dose, drugB, type=\"b\", pch=23, lty=6, col=\"blue\", bg=\"green\")\npar(opar)\t\t\t\t\n\n\n# Adding text, lines, and symbols\nplot(dose, drugA, type=\"b\", \n col=\"red\", lty=2, pch=2, lwd=2,\n main=\"Clinical Trials for Drug A\", \n sub=\"This is hypothetical data\", \n xlab=\"Dosage\", ylab=\"Drug Response\",\n xlim=c(0, 60), ylim=c(0, 70))\n\n\n# Listing 3.2 - An Example of Custom Axes\nx <- c(1:10)\ny <- x\nz <- 10/x\nopar <- par(no.readonly=TRUE)\npar(mar=c(5, 4, 4, 8) + 0.1)\nplot(x, y, type=\"b\",\n pch=21, col=\"red\",\n yaxt=\"n\", lty=3, ann=FALSE)\nlines(x, z, type=\"b\", pch=22, col=\"blue\", lty=2)\naxis(2, at=x, labels=x, col.axis=\"red\", las=2)\naxis(4, at=z, labels=round(z, digits=2),\n col.axis=\"blue\", las=2, cex.axis=0.7, tck=-.01)\nmtext(\"y=1/x\", side=4, line=3, cex.lab=1, las=2, col=\"blue\")\ntitle(\"An Example of Creative Axes\",\n xlab=\"X values\",\n ylab=\"Y=X\")\npar(opar)\n\n\n# Listing 3.3 - Comparing Drug A and Drug B response by dose\ndose <- c(20, 30, 40, 45, 60)\ndrugA <- c(16, 20, 27, 40, 60)\ndrugB <- c(15, 18, 25, 31, 40)\nopar <- par(no.readonly=TRUE)\npar(lwd=2, cex=1.5, font.lab=2)\nplot(dose, drugA, type=\"b\",\n pch=15, lty=1, col=\"red\", ylim=c(0, 60),\n main=\"Drug A vs. Drug B\",\n xlab=\"Drug Dosage\", ylab=\"Drug Response\")\nlines(dose, drugB, type=\"b\",\n pch=17, lty=2, col=\"blue\")\nabline(h=c(30), lwd=1.5, lty=2, col=\"gray\")\nlibrary(Hmisc)\nminor.tick(nx=3, ny=3, tick.ratio=0.5)\nlegend(\"topleft\", inset=.05, title=\"Drug Type\", c(\"A\",\"B\"),\n lty=c(1, 2), pch=c(15, 17), col=c(\"red\", \"blue\"))\npar(opar)\n\n\n# Example of labeling points\nattach(mtcars)\nplot(wt, mpg,\n main=\"Mileage vs. Car Weight\",\n xlab=\"Weight\", ylab=\"Mileage\",\n pch=18, col=\"blue\")\ntext(wt, mpg,\n row.names(mtcars),\n cex=0.6, pos=4, col=\"red\")\ndetach(mtcars)\n\n\n# View font families \nopar <- par(no.readonly=TRUE)\npar(cex=1.5)\nplot(1:7,1:7,type=\"n\")\ntext(3,3,\"Example of default text\")\ntext(4,4,family=\"mono\",\"Example of mono-spaced text\")\ntext(5,5,family=\"serif\",\"Example of serif text\")\npar(opar)\n\n\n# Combining graphs\nattach(mtcars)\nopar <- par(no.readonly=TRUE)\npar(mfrow=c(2,2))\nplot(wt,mpg, main=\"Scatterplot of wt vs. mpg\")\nplot(wt,disp, main=\"Scatterplot of wt vs. disp\")\nhist(wt, main=\"Histogram of wt\")\nboxplot(wt, main=\"Boxplot of wt\")\npar(opar)\ndetach(mtcars)\n\nattach(mtcars)\nopar <- par(no.readonly=TRUE)\npar(mfrow=c(3,1))\nhist(wt)\nhist(mpg)\nhist(disp)\npar(opar)\ndetach(mtcars)\n\nattach(mtcars)\nlayout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))\nhist(wt)\nhist(mpg)\nhist(disp)\ndetach(mtcars)\n\n\nattach(mtcars)\nlayout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE),\n widths=c(3, 1), heights=c(1, 2))\nhist(wt)\nhist(mpg)\nhist(disp)\ndetach(mtcars)\n\n\n# Listing 3.4 - Fine placement of figures in a graph\nopar <- par(no.readonly=TRUE)\npar(fig=c(0, 0.8, 0, 0.8))\nplot(mtcars$mpg, mtcars$wt,\n xlab=\"Miles Per Gallon\",\n ylab=\"Car Weight\")\npar(fig=c(0, 0.8, 0.55, 1), new=TRUE)\nboxplot(mtcars$mpg, horizontal=TRUE, axes=FALSE)\npar(fig=c(0.65, 1, 0, 0.8), new=TRUE)\nboxplot(mtcars$wt, axes=FALSE)\nmtext(\"Enhanced Scatterplot\", side=3, outer=TRUE, line=-3)\npar(opar)" }, { "path": "20a-BasicGraphs/ria2g2.R", "content": "#---------------------------------------------------------------#\n# Basic graphs - 6\n# https://github.com/kabacoff/RiA2/blob/master/Ch06%20Basic%20Graphs.R #\n# requires packages vcd, plotrix, sm, vioplot to be installed #\n# install.packages(c(\"vcd\", \"plotrix\", \"sm\", \"vioplot\")) #\n#---------------------------------------------------------------#\n\n#par(ask=TRUE)\nopar <- par(no.readonly=TRUE) # save original parameter settings\n\nlibrary(vcd)\ncounts <- table(Arthritis$Improved)\ncounts\n\n\n# Listing 6.1 - Simple bar plot\n# vertical barplot\nbarplot(counts, \n main=\"Simple Bar Plot\",\n xlab=\"Improvement\", ylab=\"Frequency\")\n# horizontal bar plot \nbarplot(counts, \n main=\"Horizontal Bar Plot\", \n xlab=\"Frequency\", ylab=\"Improvement\", \n horiz=TRUE)\n\n\n# obtain 2-way frequency table\nlibrary(vcd)\ncounts <- table(Arthritis$Improved, Arthritis$Treatment)\ncounts\n\n\n# Listing 6.2 - Stacked and grouped bar plots \n# stacked barplot\nbarplot(counts, \n main=\"Stacked Bar Plot\",\n xlab=\"Treatment\", ylab=\"Frequency\", \n col=c(\"red\", \"yellow\",\"green\"), \n legend=rownames(counts)) \n\n# grouped barplot \nbarplot(counts, \n main=\"Grouped Bar Plot\", \n xlab=\"Treatment\", ylab=\"Frequency\",\n col=c(\"red\", \"yellow\", \"green\"),\n legend=rownames(counts), beside=TRUE)\n\n\n# Listing 6.3 - Bar plot for sorted mean values\nstates <- data.frame(state.region, state.x77)\nmeans <- aggregate(states$Illiteracy, by=list(state.region), FUN=mean)\nmeans\n\nmeans <- means[order(means$x),] \nmeans\n\nbarplot(means$x, names.arg=means$Group.1) \ntitle(\"Mean Illiteracy Rate\") \n\n\n# Listing 6.4 - Fitting labels in bar plots\npar(las=2) # set label text perpendicular to the axis\npar(mar=c(5,8,4,2)) # increase the y-axis margin\ncounts <- table(Arthritis$Improved) # get the data for the bars\n\n# produce the graph\nbarplot(counts, \n main=\"Treatment Outcome\", horiz=TRUE, cex.names=0.8,\n names.arg=c(\"No Improvement\", \"Some Improvement\", \"Marked Improvement\")\n)\npar(opar)\n\n\n# Spinograms\nlibrary(vcd)\nattach(Arthritis)\ncounts <- table(Treatment,Improved)\nspine(counts, main=\"Spinogram Example\")\ndetach(Arthritis)\n\n\n# Listing 6.5 - Pie charts\npar(mfrow=c(2,2)) \nslices <- c(10, 12,4, 16, 8) \nlbls <- c(\"US\", \"UK\", \"Australia\", \"Germany\", \"France\")\n\npie(slices, labels = lbls, \n main=\"Simple Pie Chart\")\n\npct <- round(slices/sum(slices)*100) \nlbls <- paste(lbls, pct) \nlbls <- paste(lbls,\"%\",sep=\"\")\npie(slices,labels = lbls, col=rainbow(length(lbls)),\n main=\"Pie Chart with Percentages\")\n\nlibrary(plotrix) \npie3D(slices, labels=lbls,explode=0.1,\n main=\"3D Pie Chart \")\n\nmytable <- table(state.region) \nlbls <- paste(names(mytable), \"\\n\", mytable, sep=\"\")\npie(mytable, labels = lbls, \n main=\"Pie Chart from a dataframe\\n (with sample sizes)\")\n\npar(opar)\n\n\n# Fan plots\nlibrary(plotrix)\nslices <- c(10, 12,4, 16, 8) \nlbls <- c(\"US\", \"UK\", \"Australia\", \"Germany\", \"France\") \nfan.plot(slices, labels = lbls, main=\"Fan Plot\")\n\n\n# Listing 6.6 - Histograms\n# simple histogram 1\nhist(mtcars$mpg)\n\n# colored histogram with specified number of bins \nhist(mtcars$mpg, \n breaks=12, \n col=\"red\", \n xlab=\"Miles Per Gallon\", \n main=\"Colored histogram with 12 bins\")\n\n# colored histogram with rug plot, frame, and specified number of bins \nhist(mtcars$mpg, \n freq=FALSE, \n breaks=12, \n col=\"red\", \n xlab=\"Miles Per Gallon\", \n main=\"Histogram, rug plot, density curve\") \nrug(jitter(mtcars$mpg)) \nlines(density(mtcars$mpg), col=\"blue\", lwd=2)\n\n# histogram with superimposed normal curve (Thanks to Peter Dalgaard) \nx <- mtcars$mpg \nh<-hist(x, \n breaks=12, \n col=\"red\", \n xlab=\"Miles Per Gallon\", \n main=\"Histogram with normal curve and box\") \nxfit<-seq(min(x),max(x),length=40) \nyfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) \nyfit <- yfit*diff(h$mids[1:2])*length(x) \nlines(xfit, yfit, col=\"blue\", lwd=2)\nbox()\n\n\n# Listing 6.7 - Kernel density plot\nd <- density(mtcars$mpg) # returns the density data \nplot(d) # plots the results \n\nd <- density(mtcars$mpg) \nplot(d, main=\"Kernel Density of Miles Per Gallon\") \npolygon(d, col=\"red\", border=\"blue\") \nrug(mtcars$mpg, col=\"brown\") \n\n\n# Listing 6.8 - Comparing kernel density plots\npar(lwd=2) \nlibrary(sm)\nattach(mtcars)\n\n# create value labels \ncyl.f <- factor(cyl, levels= c(4, 6, 8), \n labels = c(\"4 cylinder\", \"6 cylinder\", \"8 cylinder\")) \n\n# plot densities \nsm.density.compare(mpg, cyl, xlab=\"Miles Per Gallon\") \ntitle(main=\"MPG Distribution by Car Cylinders\")\n\n# add legend via mouse click\ncolfill<-c(2:(2+length(levels(cyl.f)))) \ncat(\"Use mouse to place legend...\",\"\\n\\n\")\nlegend(locator(1), levels(cyl.f), fill=colfill) \ndetach(mtcars)\npar(lwd=1)\n\n\n# parallel box plots\nboxplot(mpg~cyl,data=mtcars,\n main=\"Car Milage Data\", \n xlab=\"Number of Cylinders\", \n ylab=\"Miles Per Gallon\")\n\n\n# notched box plots\nboxplot(mpg~cyl,data=mtcars, \n notch=TRUE, \n varwidth=TRUE,\n col=\"red\",\n main=\"Car Mileage Data\", \n xlab=\"Number of Cylinders\", \n ylab=\"Miles Per Gallon\")\n\n\n# Listing 6.9 - Box plots for two crossed factors\n# create a factor for number of cylinders\nmtcars$cyl.f <- factor(mtcars$cyl,\n levels=c(4,6,8),\n labels=c(\"4\",\"6\",\"8\"))\n\n# create a factor for transmission type\nmtcars$am.f <- factor(mtcars$am, \n levels=c(0,1), \n labels=c(\"auto\",\"standard\"))\n\n# generate boxplot\nboxplot(mpg ~ am.f *cyl.f, \n data=mtcars, \n varwidth=TRUE,\n col=c(\"gold\", \"darkgreen\"),\n main=\"MPG Distribution by Auto Type\", \n xlab=\"Auto Type\")\n\n\n# Listing 6.10 - Violin plots\n\nlibrary(vioplot)\nx1 <- mtcars$mpg[mtcars$cyl==4] \nx2 <- mtcars$mpg[mtcars$cyl==6]\nx3 <- mtcars$mpg[mtcars$cyl==8]\nvioplot(x1, x2, x3, \n names=c(\"4 cyl\", \"6 cyl\", \"8 cyl\"), \n col=\"gold\")\ntitle(\"Violin Plots of Miles Per Gallon\")\n\n\n# dot chart\ndotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,\n main=\"Gas Mileage for Car Models\", \n xlab=\"Miles Per Gallon\")\n\n\n# Listing 6.11 - Dot plot grouped, sorted, and colored\nx <- mtcars[order(mtcars$mpg),] \nx$cyl <- factor(x$cyl) \nx$color[x$cyl==4] <- \"red\" \nx$color[x$cyl==6] <- \"blue\"\nx$color[x$cyl==8] <- \"darkgreen\" \ndotchart(x$mpg,\n labels = row.names(x), \n cex=.7, \n pch=19, \n groups = x$cyl, \n gcolor = \"black\",\n color = x$color,\n main = \"Gas Mileage for Car Models\\ngrouped by cylinder\",\n xlab = \"Miles Per Gallon\")" }, { "path": "20a-BasicGraphs/ria2g3.R", "content": "#---------------------------------------------------------------------#\n# R in Action (2nd ed): Chapter 11 #\n# Intermediate graphs #\n# requires packages car, scatterplot3d, gclus, hexbin, IDPmisc, Hmisc, # \n# corrgram, vcd, rlg to be installed #\n# install.packages(c(\"car\", \"scatterplot3d\", \"gclus\", \"hexbin\", \"IDPmisc\", \"Hmisc\", #\n# \"corrgram\", \"vcd\", \"rld\")) #\n#------------------------------------------------------#\n\n#par(ask=TRUE)\nopar <- par(no.readonly=TRUE) # record current settings\n\n# Listing 11.1 - A scatter plot with best fit lines\nattach(mtcars) \nplot(wt, mpg, \n main=\"Basic Scatterplot of MPG vs. Weight\", \n xlab=\"Car Weight (lbs/1000)\", \n ylab=\"Miles Per Gallon \", pch=19)\nabline(lm(mpg ~ wt), col=\"red\", lwd=2, lty=1) \nlines(lowess(wt, mpg), col=\"blue\", lwd=2, lty=2)\ndetach(mtcars)\n\n# Scatter plot with fit lines by group\nlibrary(car) \nscatterplot(mpg ~ wt | cyl, data=mtcars, lwd=2,\n main=\"Scatter Plot of MPG vs. Weight by # Cylinders\", \n xlab=\"Weight of Car (lbs/1000)\", \n ylab=\"Miles Per Gallon\", id.method=\"identify\",\n legend.plot=TRUE, labels=row.names(mtcars), \n boxplots=\"xy\")\n\n\n# Scatter-plot matrices\npairs(~ mpg + disp + drat + wt, data=mtcars, \n main=\"Basic Scatterplot Matrix\")\n\nlibrary(car)\nlibrary(car)\nscatterplotMatrix(~ mpg + disp + drat + wt, data=mtcars,\n spread=FALSE, smoother.args=list(lty=2),\n main=\"Scatter Plot Matrix via car Package\")\n\n\n# high density scatterplots\nset.seed(1234)\nn <- 10000\nc1 <- matrix(rnorm(n, mean=0, sd=.5), ncol=2)\nc2 <- matrix(rnorm(n, mean=3, sd=2), ncol=2)\nmydata <- rbind(c1, c2)\nmydata <- as.data.frame(mydata)\nnames(mydata) <- c(\"x\", \"y\")\n\nwith(mydata,\n plot(x, y, pch=19, main=\"Scatter Plot with 10000 Observations\"))\n\nwith(mydata,\n smoothScatter(x, y, main=\"Scatter Plot colored by Smoothed Densities\"))\n\nlibrary(hexbin)\nwith(mydata, {\n bin <- hexbin(x, y, xbins=50)\n plot(bin, main=\"Hexagonal Binning with 10,000 Observations\")\n})\n\n\n# 3-D Scatterplots\nlibrary(scatterplot3d)\nattach(mtcars)\nscatterplot3d(wt, disp, mpg,\n main=\"Basic 3D Scatter Plot\")\n\nscatterplot3d(wt, disp, mpg,\n pch=16,\n highlight.3d=TRUE,\n type=\"h\",\n main=\"3D Scatter Plot with Vertical Lines\")\n\ns3d <-scatterplot3d(wt, disp, mpg,\n pch=16,\n highlight.3d=TRUE,\n type=\"h\",\n main=\"3D Scatter Plot with Vertical Lines and Regression Plane\")\nfit <- lm(mpg ~ wt+disp)\ns3d$plane3d(fit)\ndetach(mtcars)\n\n# spinning 3D plot\nlibrary(rgl)\nattach(mtcars)\nplot3d(wt, disp, mpg, col=\"red\", size=5)\n\n# alternative\nlibrary(car)\nwith(mtcars,\n scatter3d(wt, disp, mpg))\n\n\n# bubble plots\nattach(mtcars)\nr <- sqrt(disp/pi)\nsymbols(wt, mpg, circle=r, inches=0.30,\n fg=\"white\", bg=\"lightblue\",\n main=\"Bubble Plot with point size proportional to displacement\",\n ylab=\"Miles Per Gallon\",\n xlab=\"Weight of Car (lbs/1000)\")\ntext(wt, mpg, rownames(mtcars), cex=0.6)\ndetach(mtcars)\n\n\n# Listing 11.2 - Creating side by side scatter and line plots\nopar <- par(no.readonly=TRUE)\npar(mfrow=c(1,2))\nt1 <- subset(Orange, Tree==1)\nplot(t1$age, t1$circumference,\n xlab=\"Age (days)\",\n ylab=\"Circumference (mm)\",\n main=\"Orange Tree 1 Growth\")\nplot(t1$age, t1$circumference,\n xlab=\"Age (days)\",\n ylab=\"Circumference (mm)\",\n main=\"Orange Tree 1 Growth\",\n type=\"b\")\npar(opar)\n\n\n# Listing 11.3 - Line chart displaying the growth of 5 Orange trees over time\nOrange$Tree <- as.numeric(Orange$Tree)\nntrees <- max(Orange$Tree)\nxrange <- range(Orange$age)\nyrange <- range(Orange$circumference)\nplot(xrange, yrange,\n type=\"n\",\n xlab=\"Age (days)\",\n ylab=\"Circumference (mm)\"\n)\ncolors <- rainbow(ntrees)\nlinetype <- c(1:ntrees)\nplotchar <- seq(18, 18+ntrees, 1)\nfor (i in 1:ntrees) {\n tree <- subset(Orange, Tree==i)\n lines(tree$age, tree$circumference,\n type=\"b\",\n lwd=2,\n lty=linetype[i],\n col=colors[i],\n pch=plotchar[i]\n )\n}\ntitle(\"Tree Growth\", \"example of line plot\")\nlegend(xrange[1], yrange[2],\n 1:ntrees,\n cex=0.8,\n col=colors,\n pch=plotchar,\n lty=linetype,\n title=\"Tree\"\n) \n\n\n# Correlograms\noptions(digits=2)\ncor(mtcars)\n\nlibrary(corrgram)\ncorrgram(mtcars, order=TRUE, lower.panel=panel.shade,\n upper.panel=panel.pie, text.panel=panel.txt,\n main=\"Corrgram of mtcars intercorrelations\")\n\ncorrgram(mtcars, order=TRUE, lower.panel=panel.ellipse,\n upper.panel=panel.pts, text.panel=panel.txt,\n diag.panel=panel.minmax,\n main=\"Corrgram of mtcars data using scatter plots\n and ellipses\")\n\ncols <- colorRampPalette(c(\"darkgoldenrod4\", \"burlywood1\",\n \"darkkhaki\", \"darkgreen\"))\ncorrgram(mtcars, order=TRUE, col.regions=cols,\n lower.panel=panel.shade,\n upper.panel=panel.conf, text.panel=panel.txt,\n main=\"A Corrgram (or Horse) of a Different Color\")\n\n\n# Mosaic Plots\nftable(Titanic)\nlibrary(vcd)\nmosaic(Titanic, shade=TRUE, legend=TRUE)\n\nlibrary(vcd)\nmosaic(~Class+Sex+Age+Survived, data=Titanic, shade=TRUE, legend=TRUE)\n\n\n# type= options in the plot() and lines() functions\nx <- c(1:5)\ny <- c(1:5)\npar(mfrow=c(2,4))\ntypes <- c(\"p\", \"l\", \"o\", \"b\", \"c\", \"s\", \"S\", \"h\")\nfor (i in types){\n plottitle <- paste(\"type=\", i)\n plot(x,y,type=i, col=\"red\", lwd=2, cex=1, main=plottitle)\n}\n" }, { "path": "20a-BasicGraphs/ria3g3.R", "content": "#----------------------------------------------------------#\n# R in Action (2nd ed): Chapter 19 #\n# Advanced graphics with ggplot2 #\n# requires packages ggplot2, RColorBrewer, gridExtra, #\n# and car (for datasets) #\n# install.packages(c(\"ggplot2\", \"gridExtra\", # \n# \"RColorBrewer\", \"car\")) #\n#----------------------------------------------------------#\n\n#par(ask=TRUE)\n\n# Basic scatterplot\nlibrary(ggplot2)\nggplot(data=mtcars, aes(x=wt, y=mpg)) +\n geom_point() +\n labs(title=\"Automobile Data\", x=\"Weight\", y=\"Miles Per Gallon\")\n\n\n# Scatter plot with additional options\nlibrary(ggplot2)\nggplot(data=mtcars, aes(x=wt, y=mpg)) +\n geom_point(pch=17, color=\"blue\", size=2) +\n geom_smooth(method=\"lm\", color=\"red\", linetype=2) +\n labs(title=\"Automobile Data\", x=\"Weight\", y=\"Miles Per Gallon\")\n\n\n# Scatter plot with faceting and grouping\ndata(mtcars)\nmtcars$am <- factor(mtcars$am, levels=c(0,1),\n labels=c(\"Automatic\", \"Manual\"))\nmtcars$vs <- factor(mtcars$vs, levels=c(0,1),\n labels=c(\"V-Engine\", \"Straight Engine\"))\nmtcars$cyl <- factor(mtcars$cyl)\n\n\nlibrary(ggplot2)\nggplot(data=mtcars, aes(x=hp, y=mpg,\n shape=cyl, color=cyl)) +\n geom_point(size=3) +\n facet_grid(am~vs) +\n labs(title=\"Automobile Data by Engine Type\",\n x=\"Horsepower\", y=\"Miles Per Gallon\")\n\n# Using geoms\ndata(singer, package=\"lattice\")\nggplot(singer, aes(x=height)) + geom_histogram()\n\nggplot(singer, aes(x=voice.part, y=height)) + geom_boxplot()\n\ndata(Salaries, package=\"car\")\nlibrary(ggplot2)\nggplot(Salaries, aes(x=rank, y=salary)) +\n geom_boxplot(fill=\"cornflowerblue\",\n color=\"black\", notch=TRUE)+\n geom_point(position=\"jitter\", color=\"blue\", alpha=.5)+\n geom_rug(side=\"l\", color=\"black\")\n\n\n# Grouping\nlibrary(ggplot2)\ndata(singer, package=\"lattice\")\nggplot(singer, aes(x=voice.part, y=height)) +\n geom_violin(fill=\"lightblue\") +\n geom_boxplot(fill=\"lightgreen\", width=.2)\n\ndata(Salaries, package=\"car\")\nlibrary(ggplot2)\nggplot(data=Salaries, aes(x=salary, fill=rank)) +\n geom_density(alpha=.3)\n\nggplot(Salaries, aes(x=yrs.since.phd, y=salary, color=rank,\n shape=sex)) + geom_point()\n\nggplot(Salaries, aes(x=rank, fill=sex)) +\n geom_bar(position=\"stack\") + labs(title='position=\"stack\"')\n\nggplot(Salaries, aes(x=rank, fill=sex)) +\n geom_bar(position=\"dodge\") + labs(title='position=\"dodge\"')\n\nggplot(Salaries, aes(x=rank, fill=sex)) +\n geom_bar(position=\"fill\") + labs(title='position=\"fill\"')\n\n\n# Placing options\nggplot(Salaries, aes(x=rank, fill=sex))+ geom_bar()\n\nggplot(Salaries, aes(x=rank)) + geom_bar(fill=\"red\")\n\nggplot(Salaries, aes(x=rank, fill=\"red\")) + geom_bar()\n\n\n# Faceting\ndata(singer, package=\"lattice\")\nlibrary(ggplot2)\nggplot(data=singer, aes(x=height)) +\n geom_histogram() +\n facet_wrap(~voice.part, nrow=4)\n\nlibrary(ggplot2)\nggplot(Salaries, aes(x=yrs.since.phd, y=salary, color=rank,\n shape=rank)) + geom_point() + facet_grid(.~sex)\n\ndata(singer, package=\"lattice\")\nlibrary(ggplot2)\nggplot(data=singer, aes(x=height, fill=voice.part)) +\n geom_density() +\n facet_grid(voice.part~.)\n\n\n# Adding smoothed lines\ndata(Salaries, package=\"car\")\nlibrary(ggplot2)\nggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) +\n geom_smooth() + geom_point()\n\nggplot(data=Salaries, aes(x=yrs.since.phd, y=salary,\n linetype=sex, shape=sex, color=sex)) +\n geom_smooth(method=lm, formula=y~poly(x,2),\n se=FALSE, size=1) +\n geom_point(size=2)\n\n\n# Modifying axes\ndata(Salaries,package=\"car\")\nlibrary(ggplot2)\nggplot(data=Salaries, aes(x=rank, y=salary, fill=sex)) +\n geom_boxplot() +\n scale_x_discrete(breaks=c(\"AsstProf\", \"AssocProf\", \"Prof\"),\n labels=c(\"Assistant\\nProfessor\",\n \"Associate\\nProfessor\",\n \"Full\\nProfessor\")) +\n scale_y_continuous(breaks=c(50000, 100000, 150000, 200000),\n labels=c(\"$50K\", \"$100K\", \"$150K\", \"$200K\")) +\n labs(title=\"Faculty Salary by Rank and Sex\", x=\"\", y=\"\")\n\n\n# Legends\ndata(Salaries,package=\"car\")\nlibrary(ggplot2)\nggplot(data=Salaries, aes(x=rank, y=salary, fill=sex)) +\n geom_boxplot() +\n scale_x_discrete(breaks=c(\"AsstProf\", \"AssocProf\", \"Prof\"),\n labels=c(\"Assistant\\nProfessor\",\n \"Associate\\nProfessor\",\n \"Full\\nProfessor\")) +\n scale_y_continuous(breaks=c(50000, 100000, 150000, 200000),\n labels=c(\"$50K\", \"$100K\", \"$150K\", \"$200K\")) +\n labs(title=\"Faculty Salary by Rank and Gender\",\n x=\"\", y=\"\", fill=\"Gender\") +\n theme(legend.position=c(.1,.8))\n\n\n# Scales\nggplot(mtcars, aes(x=wt, y=mpg, size=disp)) +\n geom_point(shape=21, color=\"black\", fill=\"cornsilk\") +\n labs(x=\"Weight\", y=\"Miles Per Gallon\",\n title=\"Bubble Chart\", size=\"Engine\\nDisplacement\")\n\ndata(Salaries, package=\"car\")\nggplot(data=Salaries, aes(x=yrs.since.phd, y=salary, color=rank)) +\n scale_color_manual(values=c(\"orange\", \"olivedrab\", \"navy\")) +\n geom_point(size=2)\n\nggplot(data=Salaries, aes(x=yrs.since.phd, y=salary, color=rank)) +\n scale_color_brewer(palette=\"Set1\") + geom_point(size=2)\n\nlibrary(RColorBrewer)\ndisplay.brewer.all()\n\n\n# Themes\ndata(Salaries, package=\"car\")\nlibrary(ggplot2)\nmytheme <- theme(plot.title=element_text(face=\"bold.italic\",\n size=\"14\", color=\"brown\"),\n axis.title=element_text(face=\"bold.italic\",\n size=10, color=\"brown\"),\n axis.text=element_text(face=\"bold\", size=9,\n color=\"darkblue\"),\n panel.background=element_rect(fill=\"white\",\n color=\"darkblue\"),\n panel.grid.major.y=element_line(color=\"grey\",\n linetype=1),\n panel.grid.minor.y=element_line(color=\"grey\",\n linetype=2),\n panel.grid.minor.x=element_blank(),\n legend.position=\"top\")\n\nggplot(Salaries, aes(x=rank, y=salary, fill=sex)) +\n geom_boxplot() +\n labs(title=\"Salary by Rank and Sex\", \n x=\"Rank\", y=\"Salary\") +\n mytheme\n\n\n# Multiple graphs per page\ndata(Salaries, package=\"car\")\nlibrary(ggplot2)\np1 <- ggplot(data=Salaries, aes(x=rank)) + geom_bar()\np2 <- ggplot(data=Salaries, aes(x=sex)) + geom_bar()\np3 <- ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) + geom_point()\n\nlibrary(gridExtra)\ngrid.arrange(p1, p2, p3, ncol=3)\n\n\n# Saving graphs\nggplot(data=mtcars, aes(x=mpg)) + geom_histogram()\nggsave(file=\"mygraph.pdf\")" }, { "path": "20d-AdvGraphs/cowplot1.R", "content": "# Cow Plot\nlibrary(cowplot)\n\n#adding xlim and ylim to align axis.\np1 = qplot(x = 1, y = mpg, data = mtcars, xlab = \"\", geom = 'boxplot') + \n coord_flip() +\n ylim(min(mtcars$mpg),max(mtcars$mpg))\n\np2 = qplot(x = mpg, data = mtcars, geom = 'histogram')+\n xlim(min(mtcars$mpg),max(mtcars$mpg))\n\n#result\nplot_grid(p1, p2, labels = c(\"A\", \"B\"), align = \"v\",ncol = 1)\n" }, { "path": "20d-AdvGraphs/donut.R", "content": "# Doughnut\n\n# load GGplot2\nlibrary(ggplot2)\n\n# Create test data.\ndat = data.frame(count=c(10, 60, 30), category=c(\"A\", \"B\", \"C\"))\n\n# Add addition columns, needed for drawing with geom_rect.\ndat$fraction = dat$count / sum(dat$count)\ndat = dat[order(dat$fraction), ]\ndat$ymax = cumsum(dat$fraction)\ndat$ymin = c(0, head(dat$ymax, n=-1))\n\n# Make the plot\np1 = ggplot(dat, aes(fill=category, ymax=ymax, ymin=ymin, xmax=4, xmin=3)) +\n geom_rect() +\n coord_polar(theta=\"y\") +\n xlim(c(0, 4)) +\n theme(panel.grid=element_blank()) +\n theme(axis.text=element_blank()) +\n theme(axis.ticks=element_blank()) +\n annotate(\"text\", x = 0, y = 0, label = \"My Ring plot !\") +\n labs(title=\"\")\np1\n" }, { "path": "20d-AdvGraphs/donut2.R", "content": "# Donut 2\n# \nlibrary(ggplot2)\n\n# Create test data.\ndat = data.frame(count=c(10, 60, 30), category=c(\"A\", \"B\", \"C\"))\n\n# Add addition columns, needed for drawing with geom_rect.\ndat$fraction = dat$count / sum(dat$count)\ndat = dat[order(dat$fraction), ]\ndat$ymax = cumsum(dat$fraction)\ndat$ymin = c(0, head(dat$ymax, n=-1))\n\np1 = ggplot(dat, aes(fill=category, ymax=ymax, ymin=ymin, xmax=4, xmin=3)) +\n geom_rect() +\n coord_polar(theta=\"y\") +\n xlim(c(0, 4)) +\n labs(title=\"Basic ring plot\")\n\np2 = ggplot(dat, aes(fill=category, ymax=ymax, ymin=ymin, xmax=4, xmin=3)) +\n geom_rect(colour=\"grey30\") +\n coord_polar(theta=\"y\") +\n xlim(c(0, 4)) +\n theme_bw() +\n theme(panel.grid=element_blank()) +\n theme(axis.text=element_blank()) +\n theme(axis.ticks=element_blank()) +\n labs(title=\"Customized ring plot\")\n\n\nlibrary(gridExtra)\n#png(\"ring_plots_1.png\", height=4, width=8, units=\"in\", res=120)\ngrid.arrange(p1, p2, nrow=1)\n#dev.off()\n" }, { "path": "20d-AdvGraphs/esquisse.R", "content": "#adv web based graphs\n#https://towardsdatascience.com/tableau-esque-drag-and-drop-gui-visualization-in-r-901ee9f2fe3f\n\nlibrary(esquisse)\n#loading tidyverse to read input\nlibrary(tidyverse)\n# loading itunesr for retrieving itunes review data that we will use in this analysis\nlibrary(itunesr)\n#loading the magical esquisse library\nlibrary(esquisse)\n# Flipkart Reviews\nreviews <- getReviews(742044692,'in',1)\nreviews\n#converting Rating to numeric type\nreviews$Rating <- as.numeric(reviews$Rating)\n#let us say we want to see if there's any correlation between rating and review length\nreviews$len <- nchar(reviews$Review)\n#let the magic begin\nesquisse::esquisser(reviews)\n\nesquisse::esquisser(mtcars)\n\n\n#copied code\nggplot(data = reviews) +\n aes(x = len, y = Rating) +\n geom_point(color = '#e31a1c') +\n labs(title = 'Ratings vs Reviews Length',\n x = 'Length of Review',\n y = 'Rating associated with Review',\n caption = 'Data Source: App Store',\n subtitle = 'Flipkart iOS App Reviews') +\n theme_linedraw()\n\nggplot(data = mtcars) +\n aes(x = mpg, y = hp) +\n geom_point(color = '#e31a1c') +\n labs(title = 'MPG vs HP',\n x = 'MPG',\n y = 'HP',\n caption = 'mtcars dataset',\n subtitle = 'R Programming') +\n theme_linedraw()\n" }, { "path": "20d-AdvGraphs/lattice.R", "content": "# Lattice\n# \n# http://www.statmethods.net/advgraphs/trellis.html\n# \n" }, { "path": "20d-AdvGraphs/lattice1.R", "content": "# Lattice Package - improves base graphs\nlibrary(lattice)\nhead(df1)\nnames(df1)\nlattice::densityplot(~ df1$sales)\nlattice::densityplot(~ df1$sales | df1$coy)\nlattice::densityplot(~ df1$sales | df1$coy, layout=c(1,3))\n#lattice::densityplot(df1$loc ~ df1$sales | df1$coy, layout=c(1,3))\nstr(df1)\nlattice::bwplot(df1$loc ~ df1$sales | df1$coy, layout=c(1,3))\nlattice::xyplot(df1$adtv ~ df1$adweb | df1$loc )\nlattice::cloud(df1$adweb ~ df1$salesman * df1$loc )\nlattice::dotplot(df1$sales ~ df1$adtv | df1$coy, layout=c(1,3) )\nlattice::splom(df1[c(sales, adtv, coy),] )\n\n#formula Method\n#\n" }, { "path": "20d-AdvGraphs/survey.R", "content": "\nlibrary(survey)\ndata(api)\ndstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, \n fpc = ~fpc)\nopar<-par(mfrow=c(1,3))\nsvyhist(~enroll, dstrat, main=\"Survey weighted\",col=\"purple\",ylim=c(0,1.3e-3))\nhist(apistrat$enroll, main=\"Sample unweighted\",col=\"purple\",prob=TRUE,ylim=c(0,1.3e-3))\nhist(apipop$enroll, main=\"Population\",col=\"purple\",prob=TRUE,ylim=c(0,1.3e-3))\n\npar(mfrow=c(1,1))\nsvyboxplot(enroll~stype,dstrat)\nsvyboxplot(enroll~1,dstrat)\npar(opar)\n" }, { "path": "20d-AdvGraphs/symbols.R", "content": "require(stats); require(grDevices)\nx <- 1:10\ny <- sort(10*runif(10))\nz <- runif(10)\nz3 <- cbind(z, 2*runif(10), runif(10))\nx;y;z\nsymbols(x, y, thermometers = cbind(.5, 1, z), inches = .5, fg = 1:10)\nsymbols(x, y, thermometers = z3, inches = FALSE)\ntext(x, y, apply(format(round(z3, digits = 2)), 1, paste, collapse = \",\"),\n adj = c(-.2,0), cex = .75, col = \"purple\", xpd = NA)\nsymbols(x, y , circles=z, inches=.2)\n\n## Note that example(trees) shows more sensible plots!\nN <- nrow(trees)\nwith(trees, {\n ## Girth is diameter in inches\n symbols(Height, Volume, circles = Girth/24, inches = FALSE,\n main = \"Trees' Girth\") # xlab and ylab automatically\n ## Colours too:\n op <- palette(rainbow(N, end = 0.9))\n symbols(Height, Volume, circles = Girth/16, inches = FALSE, bg = 1:N,\n fg = \"gray30\", main = \"symbols(*, circles = Girth/16, bg = 1:N)\")\n palette(op)\n})\n\nhead(trees)\nwith(trees, symbols(Height, Volume, circles = Girth/24, inches = FALSE,\n main = \"Trees' Girth\") )\nop <- palette(rainbow(N, end = 0.9)) \nwith(trees, symbols(Height, Volume, circles = Girth/16, inches = FALSE, bg = 1:N, fg = \"gray30\", main = \"symbols(*, circles = Girth/16, bg = 1:N)\"))\n" }, { "path": "20d-AdvGraphs/vtree1.R", "content": "#Vtree\n#https://cran.r-project.org/web/packages/vtree/vtree.pdf\n\nlibrary(vtree)\n#other packages needed\npack = c('vtree','glue','tibble','purrr','XML','colorspace','stringi', 'lazyeval','Rcpp','V8','DiagrammeRsvg','rsvg')\n\n#Function\nipak <- function(pkg){\n new.pkg <- pkg[!(pkg %in% installed.packages()[, \"Package\"])]\n if (length(new.pkg)) \n install.packages(new.pkg, dependencies = TRUE)\n sapply(pkg, require, character.only = TRUE)\n}\nipak(pack)\n#packages loaded\n\nFakeData\n\n# A single-level hierarchy\nvtree(FakeData,\"Severity\")\nstr(FakeData)\n# A two-level hierarchy\nvtree(FakeData,\"Severity Sex\")\n# A two-level hierarchy with pruning of some values of Severity\nvtree(FakeData,\"Severity Sex\",prune=list(\"Severity\"=c(\"Moderate\",\"NA\")))\n\n# Rename some nodes\nvtree(FakeData,\"Severity Sex\",labelnode=list(Sex=(c(\"Male\"=\"M\",\"Female\"=\"F\"))))\n# Rename a variable\nvtree(FakeData,\"Severity Sex\",labelvar=c(Severity=\"How bad?\"))\n# Show legend. Put labels on the same line as counts and percentages\nvtree(FakeData,\"Severity Sex Viral\",sameline=TRUE,showlegend=TRUE)\n# Using the summary parameter to list ID numbers (truncated to 40 characters) in specific\nvtree(FakeData,\"Severity Sex\",summary=\"id \\nid = %list% %var=Severity% %trunc=40%\")\n\n\ngrVizToPNG(g, width = NULL, height = NULL, folder = \".\")\n\n\n#mtcars\nvtree(mtcars,\"am\")\nvtree(mtcars,\"am gear\")\nvtree(mtcars,\"am gear cyl\")\n\nvtree(mtcars,\"am\", horiz = F)\n#vtree(mtcars,\"am\", summary)\n\nvtree(mtcars,\"am gear\",horiz=FALSE,plain=TRUE)\nvtree(mtcars,\"am gear\",showlegend=TRUE,shownodelabels=FALSE)\n\nvtree(mtcars,\"am gear\",sameline=TRUE)\nvtree(mtcars,\"am gear\",sameline=TRUE,showvarnames=FALSE)\nvtree(mtcars,\"am gear\", splitwidth=15, lsplitwidth=8)\n\nvtree(FakeData,\"Severity Sex\",prune=list(Severity=c(\"Mild\",\"Moderate\")))\n\nvtree(FakeData,\"Severity Sex\",prunebelow=list(Severity=c(\"Mild\",\"Moderate\")))\n\nvtree(FakeData,\"Severity Sex\",horiz=T,labelvar=c(Severity=\"Severity on day 1\"),labelnode=list(Sex=c(Male=\"M\",Female=\"F\")))\n\nvtree(FakeData,\"Severity\",horiz=T, text=list(Severity=c(Mild=\"*Includes first-time visits*\")))\n\n\n#Displaying summary statistics in nodes\nvtree(FakeData,\"Severity\",summary=\"Score %mean%\",horiz=T)\nvtree(mtcars,\"cyl\",summary=\"mpg %mean%\",horiz=T)\nvtree(mtcars,\"cyl am\",summary=\"mpg %mean%\",horiz=T)\n\nvtree(FakeData,\"Severity\",horiz=FALSE,showvarnames=FALSE, summary=c( \"Score \\nScore: mean (SD)\\n %mean% (%SD%)\", \"Pre \\n\\nPre: range\\n %min%, %max%\"))\n\nvtree(mtcars,\"cyl\",horiz=F,showvarnames=FALSE, summary=c( \"mpg \\n mpg: mean (SD)\\n %mean% (%SD%)\", \"wt \\n\\n wt: range\\n %min%, %max%\"))\n\nvtree(FakeData,\"Severity\",summary=\"Viral \\nViral %npct%\",horiz=T,showvarnames=FALSE)\nstr(FakeData)\nvtree(mtcars,\"gear\",summary=\"am \\nam %npct%\",horiz=FALSE,showvarnames=FALSE)\ntable(mtcars$am)\ntable(mtcars$am, mtcars$gear)\n#T=1, F=0:TRUE / FALSE\n\n#Venn Diagram\nvtree(FakeData,\"Ind1 Ind2 Ind3 Ind4\",Venn=TRUE)\n\nsequence <- with(FakeData,paste(Ind1,Ind2,Ind3,Ind4))\nsequence\nvtree(FakeData,\"Ind1 Ind2 Ind3 Ind4\",Venn=TRUE,seq=TRUE,\n palette=c(Ind1=1,Ind2=2,Ind3=3,Ind4=4))\n" }, { "path": "20f-ggplots/circbarplot.R", "content": "# Circular Bar Plot\n\nlibrary(ggplot2)\n\n# make data\ndata=data.frame(group=c(\"A \",\"B \",\"C \",\"D \") , value=c(33,62,56,67) )\n\n# Usual bar plot :\nggplot(data, aes(x = group, y = value ,fill = group )) + \n geom_bar(width = 0.85, stat=\"identity\")\n\n# Circular one\nggplot(data, aes(x = group, y = value ,fill = group)) + \n geom_bar(width = 0.85, stat=\"identity\") + \n \n # To use a polar plot and not a basic barplot\n coord_polar(theta = \"y\") + \n \n #Remove useless labels of axis\n xlab(\"\") + ylab(\"\") +\n \n #Increase ylim to avoid having a complete circle\n ylim(c(0,75)) + \n \n #Add group labels close to the bars :\n geom_text(data = data, hjust = 1, size = 3, aes(x = group, y = 0, label = group)) +\n \n #Remove useless legend, y axis ticks and y axis text\n theme(legend.position = \"none\" , axis.text.y = element_blank() , axis.ticks = element_blank())\n" }, { "path": "20f-ggplots/gg-bar1.R", "content": "#81 BARPLOT WITH VARIABLE WIDTH\nlibrary(ggplot2)\n\n# make data\ndata=data.frame(group=c(\"A \",\"B \",\"C \",\"D \") , value=c(33,62,56,67) , number_of_obs=c(100,500,459,342))\n\n# Calculate the future positions on the x axis of each bar (left border, central position, right border)\ndata$right=cumsum(data$number_of_obs) + 30*c(0:(nrow(data)-1))\ndata$left=data$right - data$number_of_obs \n\n# Plot\nggplot(data, aes(ymin = 0)) + \n geom_rect(aes(xmin = left, xmax = right, ymax = value, colour = group, fill = group)) +\n xlab(\"number of obs\") + ylab(\"value\")\n\n" }, { "path": "20f-ggplots/gg-bar2.R", "content": "#bar plot in ggplot2\n#mtcars\n\nlibrary(ggplot2)\nlibrary(dplyr)\nmtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar()\n\nmtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl))) \n\n\nmtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)))\n\nmtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position='dodge')\n\nmtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position = position_stack(reverse = TRUE))\n\nmtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position = position_stack(reverse = TRUE)) + coord_flip() + theme(legend.position = \"top\")\n\nmtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity')\n \nmtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity', aes(fill=gear)) + geom_text(aes(label = n), vjust = -0.3) \n\nlibrary(ggpubr)\nmtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity', aes(fill=factor(gear))) + geom_text(aes(label = n), vjust = -0.3) + theme_pubclean() \n\n\nggplot(data=mtcars, aes(x=factor(gear), fill=factor(cyl))) + geom_bar(aes(y = (..count..)/sum(..count..)))\n\nggplot(data=mtcars, aes(x=factor(gear))) + geom_bar(aes(y = ..prop.., group = 1, fill=factor(gear)))\n\nggplot(data=mtcars, aes(x=factor(gear))) + geom_bar(aes(y = ..prop.., group = 1, fill=factor(gear))) + facet_wrap(~cyl)\n\n#not correctly drawn. use proportions\nmtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(gear))) + geom_bar(stat='identity') + geom_text(aes(label = n), vjust = -0.3) + coord_polar(\"y\", start = 0) + ggpubr::fill_palette(\"jco\")+ theme_void() #http://www.sthda.com/english/articles/32-r-graphics-essentials/133-plot-one-variable-frequency-graph-density-distribution-and-more/\n\n\nmtcars %>% count(gear) %>% mutate(gearProp = n / nrow(mtcars)) %>% ggplot(., aes(x=gear, y=gearProp)) + geom_bar(stat='identity')\nmtcars %>% select(gear) %>% table() %>% prop.table() %>% as.data.frame() -> y\nclass(y)\nggplot(mtcars, aes(x = gear)) + geom_bar(aes(y = (..count..)/sum(..count..)))\nggplot(mtcars, aes(gear)) + geom_bar(aes(y = (..count..)/sum(..count..))) + scale_y_continuous(labels=scales::percent) + ylab(\"relative frequencies\")\n\nggplot(mtcars, aes(gear, group=am)) + geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat=\"count\") + scale_y_continuous(labels=scales::percent) + ylab(\"relative frequencies\") + facet_grid(~am) + scale_fill_manual(values = c(\"red\", \"green\", \"yellow\"))\n\nggplot(mtcars, aes(x=gear, group=am)) + geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat=\"count\") + geom_text(aes( label = scales::percent(round(..prop..,3)), y= ..prop.. ), stat= \"count\", vjust = -.5) + labs(y = \"Percent\", fill=\"Transmission\") + facet_grid(~am) + scale_y_continuous(labels = scales::percent)\n\n\nggplot(mtcars, aes(x = gear, fill = factor(cyl))) + geom_bar(stat = \"bin\")\n#x should be continuous\n\nmy3cols <- c(\"#E7B800\", \"#2E9FDF\", \"#FC4E07\")\nggplot(mtcars, aes(x=cyl, y=mpg , group=gear)) + geom_dotplot(aes(color = gear, fill = gear), binaxis = 'y', stackdir = 'center') + scale_color_manual(values = my3cols) + scale_fill_manual(values = my3cols)\n\n\n\n# Outside bars\nmtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n)) + geom_bar(stat=\"identity\", fill=\"steelblue\")+ geom_text(aes(label=n), vjust=-0.3, size=3.5)+ theme_minimal()\n# Inside bars\nmtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n)) + geom_bar(stat=\"identity\", fill=\"steelblue\")+ geom_text(aes(label=n), vjust=1.6, size=3.5)+ theme_minimal()\n\n#x should be continuous\nggplot(mtcars, aes(x=cyl))+ geom_bar(stat=\"bin\", fill=\"steelblue\")+ theme_minimal()\n\n\nmtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n, colour=factor(gear))) + geom_bar(stat=\"identity\", fill=\"white\")+ geom_text(aes(label=n), vjust=1.6, size=3.5) ->p\np\n# Use custom color palettes\np+scale_color_manual(values=c(\"#999999\", \"#E69F00\", \"#56B4E9\"))\n# Use brewer color palettes\np+scale_color_brewer(palette=\"Dark2\")\n# Use grey scale\np + scale_color_grey() + theme_classic()\n\nmtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n, fill=factor(gear))) + geom_bar(stat=\"identity\")+ geom_text(aes(label=n), vjust=1.6, size=3.5) -> q\nq\n# Use custom color palettes\nq+scale_fill_manual(values=c(\"#999999\", \"#E69F00\", \"#56B4E9\"))\n# use brewer color palettes\nq+scale_fill_brewer(palette=\"Dark2\")\n# Use grey scale\nq + scale_fill_grey()\nq + scale_fill_manual(values=c(\"#999999\", \"#E69F00\", \"#56B4E9\"))+\n theme_minimal()\n\n#legend position\n# Change bar fill colors to blues\nq + scale_fill_brewer(palette=\"Blues\")\nq + theme(legend.position=\"top\")\nq + theme(legend.position=\"bottom\")\n# Remove legend\nq + theme(legend.position=\"none\")\n\n\n\n#multiple groups\n# Stacked barplot with multiple groups\nmtcars %>% group_by(gear, cyl) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(cyl))) + geom_bar(stat=\"identity\")\n# Use position=position_dodge()\nmtcars %>% group_by(gear, cyl) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(cyl))) + geom_bar(stat=\"identity\", position=position_dodge())\n\n#Paired\nmtcars %>% group_by(gear, vs) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(vs))) + geom_bar(stat=\"identity\", position=position_dodge()) + geom_text(aes(label=n), vjust=1.6, color=\"white\", position = position_dodge(0.9), size=3.5)+\n scale_fill_brewer(palette=\"Paired\")+ theme_minimal()\n\nlibrary(dplyr)\nmtcars %>% group_by(gear, vs) %>% count() %>% arrange(gear,desc(vs)) -> df\ndf\nlibrary(plyr)\n# Calculate the cumulative sum of len for each dose\ndf1 <- ddply(df, c('gear'), transform, label_ypos=cumsum(n))\nhead(df1)\nlibrary(ggplot2)\ndf1 %>% ggplot(., aes(x=gear, y=n, fill=factor(vs))) + geom_bar(stat=\"identity\") + geom_text(aes(y=label_ypos, label=n), vjust=1.6, color=\"white\", size=3) + scale_fill_brewer(palette=\"Paired\")+ theme_minimal()\n\n" }, { "path": "20f-ggplots/gg-box2.R", "content": "\n\ndf <- data.frame(State = LETTERS[1:3],\n Y = sample(1:10, 30, replace = TRUE),\n X = rep(1:10, 3))\ndf\nlibrary(ggplot2)\n\n# You can plot geom_histogram or bar (pre-counted stats)\nggplot(df, aes(X, Y)) +\n geom_bar(stat = \"identity\", position = \"dodge\") +\n facet_grid(State ~ .)\n# Or you can plot similar figure with geom_rect\nggplot(df) +\n geom_rect(aes(xmin = X - 0.4, xmax = X + 0.4, ymin = 0, ymax = Y)) +\n facet_grid(State ~ .)\n\nggplot(df) +\n geom_rect(aes(xmin = 0, xmax = Y, ymin = X - 0.4, ymax = X + 0.4)) +\n geom_boxplot(aes(X, Y)) +\n coord_flip() +\n facet_grid(State ~ .)\n\nggplot(df) +\n geom_rect(aes(xmin = 0, xmax = Y, ymin = X - 0.4, ymax = X + 0.4),\n fill = \"blue\", color = \"black\") +\n geom_boxplot(aes(X, Y), alpha = 0.7, fill = \"salmon2\") +\n coord_flip() +\n facet_grid(State ~ .) +\n theme_classic() +\n scale_y_continuous(breaks = 1:max(df$X))\n\n\nggplot(iris, aes(x = Sepal.Width)) + \n geom_histogram(binwidth = 0.05) +\n geom_boxplot(aes(x = 3, y = Sepal.Width))\n\nlibrary(gridExtra)\n\na <- ggplot(iris, aes(x = Sepal.Width)) + \n geom_histogram(binwidth = 0.05) \n\nb <- ggplot(iris, aes(x = \"\", y = Sepal.Width)) + \n geom_boxplot() + \n coord_flip()\n\ngrid.arrange(a,b,nrow=2)\n\na <- ggplot(mtcars, aes(x = mpg)) + geom_histogram(binwidth = 0.1) \n\nb <- ggplot(mtcars, aes(x = \"\", y = mpg)) + geom_boxplot() + coord_flip()\n\ngrid.arrange(a,b,nrow=2)\n\n\nmy3cols <- c(\"#E7B800\", \"#2E9FDF\", \"#FC4E07\")\nggplot(mtcars, aes(x=cyl, y=mpg , group=gear)) + geom_dotplot(aes(color = gear, fill = gear), binaxis = 'y', stackdir = 'center') + scale_color_manual(values = my3cols) + scale_fill_manual(values = my3cols)" }, { "path": "20f-ggplots/gg-boxhist.R", "content": "# ggplot\n#https://stackoverflow.com/questions/4551582/combination-boxplot-and-histogram-using-ggplot2\n# box and histogram together\n\nrequire(ggplot2)\nrequire(grid)\n\nfig1 <- ggplot(data = mtcars, aes(x = 1, y = mpg)) +\n geom_boxplot( ) + coord_flip() +\n scale_y_continuous(expand = c(0,0), limit = c(10, 35))\nfig1\nfig2 <- ggplot(data = mtcars, aes(x = mpg)) +\n geom_histogram(binwidth = 1) +\n scale_x_continuous(expand = c(0,0), limit = c(10, 35))\n\ngrid.draw(rbind(ggplotGrob(fig1), ggplotGrob(fig2), size = \"first\"))\n\n" }, { "path": "20f-ggplots/gg-boxplot1.R", "content": "# ggplot\n\nmarks1 = runif(100, 50,100)\nmarks2 = rnorm(100, 60,10)\nmarks3 = rnorm(100, 70,15)\ndf = data.frame(marks1, marks2, marks3)\n\nhead(stack(df))\n\nlibrary(ggplot2)\nggplot(stack(df[,c(1,2,3)]), aes(x = ind, y = values)) + geom_boxplot() + theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust=1)) +\n labs(title = \"Boxplots of columns\") + labs(x = \"\", y = \"Values\") + \n scale_y_continuous(breaks = seq(1, 10, by = 1))\n\n" }, { "path": "20f-ggplots/gg-heatmap.R", "content": "#ggplot - Heatmap Graph\n" }, { "path": "20f-ggplots/gg-hist1.R", "content": "\n\nlibrary(ggplot2)\nhistogram <- ggplot(data=iris, aes(x=Sepal.Width))\nhistogram + geom_histogram(binwidth=0.2, color=\"black\", aes(fill=Species)) + \n xlab(\"Sepal Width\") + ylab(\"Frequency\") + ggtitle(\"Histogram of Sepal Width\")\n\nggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 3, aes(fill=factor(cyl)))\n\n\nlibrary(ggplot2)\ndensity <- ggplot(data=iris, aes(x=Sepal.Width))\ndensity + geom_histogram(binwidth=0.2, color=\"black\", fill=\"steelblue\", aes(y=..density..)) + geom_density(stat=\"density\", alpha=I(0.2), fill=\"blue\") +\n xlab(\"Sepal Width\") + ylab(\"Density\") + ggtitle(\"Histogram & Density Curve\")\n\nggplot(mtcars, aes(x=mpg, fill=factor(cyl))) + geom_histogram(binwidth =5, aes(y=..density..)) + geom_density(stat=\"density\", alpha=I(.2), fill=\"blue\") \n\n\nggplot(aes(x = price ) , data = diamonds) + \n geom_histogram(aes(fill=cut), binwidth=1500, colour=\"grey20\", lwd=0.2) + stat_bin(binwidth=1500, geom=\"text\", colour=\"white\", size=3.5, aes(label=..count.., group=cut), position=position_stack(vjust=0.5)) + scale_x_continuous(breaks=seq(0,max(diamonds$price), 1500))\n\nggplot(data=mtcars, aes(x=mpg)) + geom_histogram(aes(fill=factor(cyl)),binwidth=5, lwd=.5) + stat_bin(binwidth=5, geom=\"text\", colour=\"black\", size=4, aes(label=..count.., group=factor(cyl)), position=position_stack(vjust=0.5))\n\n\nggplot(aes(x = price ), data = diamonds) + geom_histogram(aes(fill = cut ), binwidth=1500, colour=\"grey20\", lwd=0.2) + stat_bin(binwidth=1500, geom=\"text\", colour=\"red\", size=3.5, aes(label=..count.., group=cut, y=0.8*(..count..))) + scale_x_continuous(breaks=seq(0,max(diamonds$price), 1500))\n\n\nlibrary(ggplot2)\ns <- ggplot(mpg, aes(manufacturer, fill = class))\nnrow(mpg)\ns + geom_bar(position = \"stack\") + theme(axis.text.x = element_text(angle=90, vjust=1)) + geom_text(stat='count', aes(label=..count..), position = position_stack(vjust = 0.5),size=4)\n\nggplot(mpg, aes(manufacturer, fill=class)) + geom_bar(position='stack') + geom_text(stat='count', aes(label=..count..), position=position_stack(vjust=0.5))+ theme(axis.text.x = element_text(angle=90, vjust=1)) \n\n\nggplot(mtcars, aes(mpg, fill=gear)) + geom_bar(position='stack') + geom_text(stat='count', aes(label=..count..), position=position_stack(vjust=0.5)) + theme(axis.text.x = element_text(angle=90, vjust=1)) \n\n\nggplot(data=mtcars, aes(mpg, fill=factor(gear))) + geom_histogram(breaks=seq(20,35,3)) + stat_bin(geom='text', aes(label=..count.., y=1*(..count..)), position= position_stack(vjust=0.5), breaks=seq(20,35,3))\n" }, { "path": "20f-ggplots/gg-legend1.R", "content": "#ggplot scale\n\nlibrary(ggplot2)\nV1<-rnorm(10)\nV2<-rnorm(10)\nV3<-rnorm(10)\nDF<-data.frame(V1,V2,V3)\nDF \n\nggplot(DF,aes(x=V1,y=V2,size=V3))+\n geom_point(fill='red',shape=21)+\n theme_bw()+\n scale_size(range=c(1,5))+\n theme(legend.justification=c(1,0), legend.position=c(1,0))\n\n#Option 2: Use facet_wrap(...) if you can. This way you only get one legend for the set of plots.\n\n#Option 3: Use color and size, and then hide the size legend altogether.\n\nggplot(DF,aes(x=V1,y=V2,size=V3, color=V3))+\n geom_point(shape=20)+\n theme_bw()+\n scale_size(range=c(2,10), guide=\"none\")+\n# scale_color_gradient(high=\"green\", low=\"yellow\", guide=\"none\") \nscale_color_gradient(high=\"#ff0000\", low=\"#ffffcc\") \n\n\n#-----\nlibrary(ggplot2)\nbasic=ggplot(mtcars, aes(wt, mpg, colour = factor(cyl), shape = factor(vs) )) + geom_point()\nbasic\n\nbasic+labs( colour = \"Gp-1 : Type of Eng\", shape = \"Gp-2 : Cylinders\")\n\n#do not display 1 legend\nbasic + guides(shape=FALSE)\n\n#1: no legend\nbasic + theme(legend.position = \"none\")\n\n#2: around the plot\nbasic + theme(legend.position = \"bottom\")\n\n#3: inside the plot\nbasic + theme(\n legend.position = c(.95, .95),\n legend.justification = c(\"right\", \"top\"),\n legend.box.just = \"right\",\n legend.margin = margin(6, 6, 6, 6)\n)\n\n# custom box around legend\nbasic + theme(\n legend.box.background = element_rect(color=\"red\", size=1),\n legend.box.margin = margin(116, 6, 6, 6)\n)\n\n# custom the key\nbasic + theme(legend.key = element_rect(fill = \"white\", colour = \"black\"))\n\n# custom the text\nbasic + theme(legend.text = element_text(size = 8, colour = \"red\"))\n\n# custom the title\nbasic + theme(legend.title = element_text(face = \"bold\"))\n\n\n#-----\nlibrary(ggplot2)\nlibrary(grid)\nlibrary(gridExtra)\ndsamp <- diamonds[sample(nrow(diamonds), 1000), ]\n(d <- ggplot(dsamp, aes(carat, price)) +\n geom_point(aes(colour = clarity)) +\n theme(legend.position = c(0.06, 0.75))\n)\n\nlibrary(lemon)\n\nreposition_legend(d, 'top left')\n\n#multiple legends\nd2 <- d + aes(shape=cut) + \n theme(legend.box.background = element_rect(fill='#fffafa'),\n legend.background = element_blank())\nreposition_legend(d2, 'left')\n\nreposition_legend(d + theme_classic(), 'top left')\n\nreposition_legend(d + theme_bw(), 'top left', x=0.002, y=1-0.002)\n\nreposition_legend(d + theme_bw(), 'top left', offset=0.002)\n\nd2 <- d + facet_grid(.~cut)\ngtable_show_names(d2)\nreposition_legend(d2, 'top left', panel = 'panel-3-1')\nreposition_legend(d + facet_wrap(~cut, ncol=3), 'top left', panel='panel-3-2')\n\nd3 <- d + facet_wrap(~cut, ncol=3) + scale_color_discrete(guide=guide_legend(ncol=3))\nreposition_legend(d3, 'center', panel='panel-3-2')\n#https://cran.r-project.org/web/packages/lemon/vignettes/legends.html\n" }, { "path": "20f-ggplots/gg-line.R", "content": "#ggplot - Line Graph\n#http://www.cookbook-r.com/Graphs/Bar_and_line_graphs_(ggplot2)/\n\ndat <- data.frame(\n time = factor(c(\"Lunch\",\"Dinner\"), levels=c(\"Lunch\",\"Dinner\")),\n total_bill = c(14.89, 17.23)\n)\ndat\n\n# Load the ggplot2 package\nlibrary(ggplot2)\n\n# Basic line graph\nggplot(data=dat, aes(x=time, y=total_bill, group=1)) + geom_line()\n## This would have the same result as above\nggplot(data=dat, aes(x=time, y=total_bill)) + geom_line(aes(group=1))\n\n# Add points\nggplot(data=dat, aes(x=time, y=total_bill, group=1)) + geom_line() + geom_point()\n\n# Change color of both line and points\n# Change line type and point type, and use thicker line and larger points\n# Change points to circles with white fill\nggplot(data=dat, aes(x=time, y=total_bill, group=1)) + geom_line(colour=\"red\", linetype=\"dashed\", size=1.5) + geom_point(colour=\"red\", size=4, shape=21, fill=\"white\")\n\n#---------------\n# Change the y-range to go from 0 to the maximum value in the total_bill column, and change axis labels\nggplot(data=dat, aes(x=time, y=total_bill, group=1)) + geom_line() +\n geom_point() + expand_limits(y=0) + xlab(\"Time of day\") + ylab(\"Total bill\") + ggtitle(\"Average bill for 2 people\")\n\n\n#------\ndat1 <- data.frame( sex = factor(c(\"Female\",\"Female\",\"Male\",\"Male\")), time = factor(c(\"Lunch\",\"Dinner\",\"Lunch\",\"Dinner\"), levels=c(\"Lunch\",\"Dinner\")), total_bill = c(13.53, 16.81, 16.24, 17.42))\ndat1\n#time: x-axis, sex: color fill/ line color, total_bill: y-axis.\n\n# Basic line graph with points\nggplot(data=dat1, aes(x=time, y=total_bill, group=sex)) + geom_line() + geom_point()\n\n# Map sex to color\nggplot(data=dat1, aes(x=time, y=total_bill, group=sex, colour=sex)) + geom_line() + geom_point()\n\n# Map sex to different point shape, and use larger points\nggplot(data=dat1, aes(x=time, y=total_bill, group=sex, shape=sex)) + geom_line() + geom_point()\n\n\n# Use thicker lines and larger points, and hollow white-filled points\nggplot(data=dat1, aes(x=time, y=total_bill, group=sex, shape=sex)) + geom_line(size=1.5) + geom_point(size=3, fill=\"white\") + scale_shape_manual(values=c(22,21))\n\nggplot(data=dat1, aes(x=sex, y=total_bill, group=time, shape=time, color=time)) + geom_line() + geom_point()\n\n\n#--- final graph\n# A line graph\nggplot(data=dat1, aes(x=time, y=total_bill, group=sex, shape=sex, colour=sex)) + \n geom_line(aes(linetype=sex), size=1) + # Set linetype by sex\n geom_point(size=3, fill=\"white\") + # Use larger points, fill with white\n expand_limits(y=0) + # Set y range to include 0\n scale_colour_hue(name=\"Sex of payer\", # Set legend title\n l=30) + # Use darker colors (lightness=30)\n scale_shape_manual(name=\"Sex of payer\",\n values=c(22,21)) + # Use points with a fill color\n scale_linetype_discrete(name=\"Sex of payer\") +\n xlab(\"Time of day\") + ylab(\"Total bill\") + # Set axis labels\n ggtitle(\"Average bill for 2 people\") + # Set title\n theme_bw() +\n theme(legend.position=c(.7, .4)) # Position legend inside\n# This must go after theme_bw\n\n#----------------------- With a numeric x-axis\nWhen the variable on the x-axis is numeric, it is sometimes useful to treat it as continuous, and sometimes useful to treat it as categorical. In this data set, the dose is a numeric variable with values 0.5, 1.0, and 2.0. It might be useful to treat these values as equal categories when making a graph.\ndatn <- read.table(header=TRUE, text='\nsupp dose length\n OJ 0.5 13.23\n OJ 1.0 22.70\n OJ 2.0 26.06\n VC 0.5 7.98\n VC 1.0 16.77\n VC 2.0 26.14\n')\ndatn\n#With x-axis treated as continuous: A simple graph might put dose on the x-axis as a numeric value. It is possible to make a line graph this way, but not a bar graph.\n\nggplot(data=datn, aes(x=dose, y=length, group=supp, colour=supp)) + geom_line() + geom_point()\n\n#----------With x-axis treated as categorical\n#If you wish to treat it as a categorical variable instead of a numeric one, it must be converted to a factor. This can be done by modifying the data frame, or by changing the specification of the graph.\n\n# Copy the data frame and convert dose to a factor\ndatn2 <- datn\ndatn2$dose <- factor(datn2$dose)\nggplot(data=datn2, aes(x=dose, y=length, group=supp, colour=supp)) + geom_line() + geom_point()\n\n# Use the original data frame, but put factor() directly in the plot specification\nggplot(data=datn, aes(x=factor(dose), y=length, group=supp, colour=supp)) + geom_line() + geom_point()\n\n#bar # Use the original data frame, but put factor() directly in the plot specification\nggplot(data=datn, aes(x=factor(dose), y=length, fill=supp)) + geom_bar(stat=\"identity\", position=position_dodge())\n" }, { "path": "20f-ggplots/gg-slope.R", "content": "#ggplot - Slope Graph\n\nlibrary(ggplot2)\nlibrary(scales) #label the points nicely by putting commas in the numbers.\n\nmonths <-24\nyear1 <-c(200,175, 190)\nyear3 <-c(150,100, 90)\ngroup <-c(\"Group C\", \"Group B\", \"Group A\")\na <- data.frame(year1,year3,group)\na\n\n#Labels\nl11<-paste(a$group,comma_format()(round(a$year1/(3600*24*30.5))),sep=\"\\n\")\nl13<-paste(a$group,comma_format()(round(a$year3/(3600*24*30.5))),sep=\"\\n\")\nl11\nl13\na\n\n#slope\np <- ggplot(a) + geom_segment(aes(x=0, xend=months, y=year1, yend=year3), size=.75)\np\n\n#blank themes\np<-p + theme(panel.background = element_blank())\np<-p + theme(panel.grid=element_blank())\np<-p + theme(axis.ticks=element_blank())\np<-p + theme(axis.text=element_blank())\np<-p + theme(panel.border=element_blank())\np\n\n#Set the axis labels and limits\np<-p + xlab(\"\") + ylab(\"Amount Used\")\np<-p + theme(axis.title.y=theme_text(vjust=3))\np<-p + xlim((0-12),(months+12))\np<-p + ylim(0,(1.2*(max(a$year3,a$year1))))\np\n\n\n#Label the slope\np<-p + geom_text(label=l13, y=a$year3, x=rep.int(months,length(a)),hjust=-0.2,size=3.5)\np<-p + geom_text(label=l11, y=a$year1, x=rep.int( 0,length(a)),hjust=1.2,size=3.5)\np\n\n#Label the columns\np<-p + geom_text(label=\"Year 1\", x=0, y=(1.1*(max(a$year3,a$year1))),hjust= 1.2,size=5)\np<-p + geom_text(label=\"Year 3\", x=months,y=(1.1*(max(a$year3,a$year1))),hjust=-0.1,size=5)\np\n\n\n\n\n#links\n#https://rpubs.com/walkerke/slopegraph\n\n" }, { "path": "20f-ggplots/gg-slope2.R", "content": "#ggplot - slope graph\n#https://rdrr.io/github/leeper/slopegraph/man/ggslopegraph.html\n#https://github.com/leeper/slopegraph\n#https://rdrr.io/github/leeper/slopegraph/man/\nrequire(\"ggplot2\")\nlibrary(ggslopegraph) #na from CRAN\ninstall.packages(\"remotes\")\nremotes::install_github(\"leeper/slopegraph\", force=T)\n\n#---------------\nlibrary(\"slopegraph\")\ndata(cancer)\n## Tufte's Cancer Graph (to the correct scale)\ndata(cancer)\ncancer\nggslopegraph(cancer, col.lines = 'gray', xlabels = c('5 Year','10 Year','15 Year','20 Year'))\n\n## Tufte's GDP Graph\ndata(gdp)\nggslopegraph(gdp, col.line='gray', xlabels = c('1970','1979'), main = 'Current Receipts of Goverment\\nas a Percentage of Gross Domestic Product') + \n theme_bw()\n\n## Ranking of U.S. State populations\ndata(states)\nggslopegraph(states, main = 'Relative Rank of U.S. State Populations, 1790-1870', yrev = TRUE)\n\ncls <- rep(\"black\", nrow(states))\ncls[rownames(states) == \"South Carolina\"] <- \"red\"\ncls[rownames(states) == \"Tennessee\"] <- \"blue\"\nggslopegraph(states, main = 'Relative Rank of U.S. State Populations, 1790-1870', yrev = TRUE, col.lines = cls, col.lab = cls)\n\n## ranking of U.S. Bachelors Degrees fields\ndata(bachelors)\nbachelors[] <- lapply(bachelors, function(x) rank(x))\nnames(bachelors) <- substring(names(bachelors), 3, 7)\nggslopegraph(bachelors, offset.x = 0, xlim = c(1, 25), col.num = NA, labpos.left = NULL)\n\n\n#\nslopegraph(gdp, col.lines = 'gray', col.lab = \"black\", xlabels = c('1970','1979'), main = 'Current Receipts of Goverment as a Percentage of Gross Domestic Product')\n\n\n#\ndata(states)\ncols <- `[<-`(rep(\"black\", 37), 7, \"red\")\nslopegraph(states, xlim = c(-1, 12), ylim = c(37,0), offset.x = 0.06, col.lines = cols, col.lab = cols, main = 'Relative Rank of U.S. State Populations, 1790-1870')\n\n\n#---\ndata(states)\ncols <- `[<-`(rep(\"black\", 37), 7, \"red\")\nggslopegraph(states, offset.x = 0.06, yrev = TRUE,\n col.lines = cols, col.lab = cols, \n main = 'Relative Rank of U.S. State Populations, 1790-1870') +\n theme_bw() \n\n\n\n\n#Slope Graph 2----------------------\n# basic graph\nggslopegraph2(cancer2, Year, Survival, Type, title = \"Estimates of Percent Survival Rates\")\n\n# with further customization\nggslopegraph2(cancer2, Year, Survival, Type, title = \"Estimates of Percent Survival Rates\", linecolor = \"black\", linethickness = 1, subtitle = NULL, caption = NULL)\n\n# demonstrating linecolor recyclng\nggslopegraph2(cancer2, Year, Survival, Type, \ntitle = \"Estimates of Percent Survival Rates\", subtitle = \"Based on: Edward Tufte, Beautiful Evidence, 174, 176.\", caption = \"DU\", linecolor = c(\"black\", \"red\", \"grey\"), linethickness = .5)\n#https://rdrr.io/github/leeper/slopegraph/man/ggslopegraph2.html\n\n#---- segment\ndata(gdp)\ngdp\nhead(segmentize(gdp))\nsegmentize(gdp)\n" }, { "path": "20f-ggplots/ggp2.R", "content": "# Graph 1\nlibrary(ggplot2)\nsales\nsalesg = sales\nggplot(data = salesg)+ aes(x=sdate,y=pies) +\n geom_point() + labs( title ='Sales data of Pies', x='Date', y='Sales')\n# not working\nsalesg\n#Stepwise\ng1= ggplot(data = salesg, aes(x=sdate,y=pies)) \ng2= g1+ geom_point()\ng3= g2 + labs( title ='Sales data of Pies', x='Date', y='Sales')\ng3 #run\n\ng1= ggplot(data = salesg, aes(x=sdate,y=pies)) \ng2= geom_point()\ng3= labs( title ='Sales data of Pies', x='Date', y='Sales')\ng=g1+g2+g3 #run\ng\n\n\n#Part 2\ng2b = geom_point(pch=17, color='blue', size=2)\ng3b = geom_smooth(method='lm', color='red', linetype=3)\ngA = g1 + g2b + g3b\ngA\n\nnames(salesg)\n#Facegrid\nf1 = ggplot(salesg, aes(x=sdate, y=pies, shape=daywk, col=daywk))\nf2 = geom_point(size=3)\nf= f1+f2\nf\n\n\n" }, { "path": "20f-ggplots/ggplot-DU1.R", "content": "#ggplot\nnames(df1)\nrange(df1$sales)\n\n#Histogram\nggplot(df1, aes(x = df1$sales)) + geom_histogram()\n\ng1= ggplot(df1, aes(x = df1$sales))\ng2=geom_histogram()\n(g1+g2)\ng2=geom_bar(color='green')\n(g1+g2)\n\ng2=ggplot(df1, aes(y = df1$Asales1, x=Acoy1)) + geom_boxplot(color='blue') \ng2\n\ng2=ggplot(df1, aes(y = df1$Asales1, x=Acoy1)) + geom_violin(color='yellow', fill='green')\ng2\n\ng1= ggplot(df1, aes(x = df1$Asales1))\ng2=geom_density(color='green')\n(g1+g2)\n\ng2=geom_rug(color='green')\n(g1+g2)\n\nggplot_build(g1)$data\n\nx\n# Overlay\nh1= ggplot(df1, aes(y = df1$Asales1, x=df1$Acoy1))\nh2 = geom_boxplot(fill='blue', color='green', notch = T)\nh3 = geom_point(position='jitter', color='red', alpha=.5)\nh4 = geom_rug(sides=\"trbl\", color='black')\n(h1+h2)\n(h1+h2+h3)\n(h1+h2+h3+h4)\n(h1+h3)\n\n#Overlay2\nf1 = ggplot(df1, aes(y = df1$Asales1, x=df1$Acoy1))\nf2 = geom_violin(fill='lightblue', color='green')\nf3 = geom_boxplot(fill='lightgreen', width=.5)\n(f1+f2)\n(f1+f2+f3)\n\n\n#Grouping\ne1= ggplot(data=df1, aes(x=Asales1, fill=Acoy1))\ne2 = geom_density(alpha=.3)\n(e1+e2)\n\nnames(df1)\nd1 = ggplot(data=df1, aes(y=Asales1, x=Asalesman1, color=Acoy1, shape=Aloc1 ))\nd2 = geom_point() \n(d1 + d2)\n\n#Grouped Bar\naggregate(df1$Asales1, by=list(Acoy1,Aloc1), FUN=NROW)\n(df2= aggregate(Asales1 ~ Acoy1 + Aloc1, FUN=NROW))\nc1 = ggplot(data=df1, aes( x=Acoy1, fill=Aloc1)) + geom_bar(position = 'stack') + labs(title='position=stack')\n(c1) #stack on top\nc2 = ggplot(data=df1, aes(x=Acoy1, fill=Aloc1)) + geom_bar(position = 'dodge') + labs(title='position=dodge/ beside')\n(c2) #side\n\nc3 = ggplot(data=df1, aes(x=Acoy1, fill=Aloc1)) + geom_bar(position = 'fill') + labs(title='position=fill')\n(c3) #full\n\n\n#Faceting\nb1 = ggplot(data=df1, aes(x=Asales1))\nb1\nb2 = geom_histogram(bins=10) \nb3 = facet_wrap(~ Acoy1, nrow=1)\n(b1 + b2 + b3)\n\nggplot(df1, aes(x=Aadv1, y=Asales1, color=Aloc1, shape=factor(Asalesman1))) + geom_point() + facet_grid(.~ Aloc1)\n\nsummary(df1)\nz1 = ggplot(data=df1, aes(x=Aadv1, fill=Aloc1))\nz2 = geom_density()\nz3 = facet_wrap(Acoy1 ~ Asalesman1)\n(z1+z2)\n(z1+z2+z3)\n\n# Smooth Lines\ny1 = ggplot(data=df1, aes(x=Aadv1, y=Asales1)) + geom_smooth() + geom_point()\ny2 = ggplot(data=df1, aes(x=Aadv1, y=Asales1, linetype=Asalesman1))\n(y3 = y2 + geom_smooth() + geom_point())\n\n#axis \nq1 = ggplot(data=df1, aes(x=Acoy1, y=Asales1, fill=Aloc1))\n(q2 = q1 + geom_boxplot())\n\nq3x= scale_x_discrete(breaks=c('coy1','coy2','coy3'), labels=c(\"Company1\", \"Company2\", \"Company3\"))\nq3y= scale_y_continuous(breaks=c(50,60,70,100,150), labels=c(\"50K\", \"60K\", \"70K\", \"100K\", \"150K\"))\nq4 = labs(title=\"Sales Details of 3 Companies Citywise\", x=\"\",y=\"\")\n(q2+q3x)\n(q2+q3x+q3y)\n(q2+q3x+q3y + q4)\n\n#Legends\nq4b = labs(title=\"Sales Details of 3 Companies Citywise\", x=\"\",y=\"\", fill=Acoy1)\nq5 = theme(legend.position = c(.5,.8))\n(q2+q3x+q3y+q4b+q5)\n\n# Scales\nr1 = ggplot(data=df1, aes(x=Aadv1, y=Asales1, size=Aadv2))\nr2 = geom_point(shape=21, color='red', fill='green')\nr3 = labs(x='Adv1 Amount', y=\"Sales Amount\", title=\"Sales vs Ad1 Amount - Ad2/ Salesman Shape\")\n(r1+r2+r3)\nnames(df1)\nr1a = ggplot(data=df1, aes(x=Aadv1, y=Asales1, size=Asalesman1)) #discrete\n(r1a + r2 + r3)\n#warning\nggplot(data=df1, aes(x=Aadv1, y=Asales1, color=Asalesman1)) + scale_color_manual(values=c('red','blue','yellow')) + geom_point(size=2)\n\nggplot(data=df1, aes(x=Aadv1, y=Asales1, color=Asalesman1)) + scale_color_brewer(palette=\"Set3\") + geom_point(size=3)\n#preset colors\n\n\n#Multiple Graphs\n\n(df3 = aggregate(Asales1 ~ Acoy1, data=df1, FUN=sum))\n\nq4a= ggplot(data=df2, aes(x=\"\", y=Asales1, fill=Acoy1))\nq4b = geom_bar(width=1,stat='identity')\n(q4a + q4b)\n(q4a + coord_polar(\"y\", start=0))\n\nlibrary(gridExtra)\nq1= ggplot(data=df1, aes(x=Aadv1)) + geom_bar()\nq2= ggplot(data=df1, aes(x=Aadv2)) + geom_bar()\nq3= ggplot(data=df1, aes(x=Asales1, y=Aadv1)) + geom_point()\nq4= ggplot(data=df2, aes(x=\"\", y=Asales1, fill=Acoy1)) + geom_bar(width=1,stat='identity')\n\ngridExtra::grid.arrange(q1, q2, q3, ncol=3)\ngridExtra::grid.arrange(q1, q2, q3, q4, nrow=2, ncol=2)\n\n\nggplot(data=df1, aes(x=Aadv1, y=Asales1, linetype=Asalesman1, shape=Acoy1)) + geom_smooth() + geom_point()\n\n\n#Plot\nggplot(df1, aes(x = df1$Asales1, y=df1$Aadv1)) + geom_point()\ny1 = ggplot(df1, aes(y = df1$Asales1, x=df1$Aadv1)) \ny2= geom_point(size=3, pch=17, color='blue')\n\n\nggplot(df1, aes(y = df1$Asales1, x=df1$Aadv1)) + geom_point(size=3, pch=17, color='blue') + facet_grid(Adv2 ~ Acoy1)\n\n\n\np1 <- ggplot(df1, aes(Aadv1, Aadv2)) +\n geom_point(data = df1[df1$Asales1 > 50,]) \np1\n\np2 <- ggplot(df1, aes(Aadv1, Aadv2)) +\n geom_point(data = df1[df1$Asales1 > 50,], aes(size=Asales1)) \np2\nnames(df1)\np3 <- ggplot(df1, aes(Aadv1, Aadv2)) + geom_point(data = df1[Asalesman1 ==1,]) \np3\n\np4 <- ggplot(df1, aes(Aadv1, Aadv2)) +\n geom_point(data = df1[Asales1 > 70,], aes(color=Asalesman1)) \np4\n" }, { "path": "20f-ggplots/ggplot3.R", "content": "# ggplot\n# \n#http://www.r-graph-gallery.com/209-the-options-of-barplot/\n" }, { "path": "20f-ggplots/ggplot5.R", "content": "\nlibrary(ggplot2)\ndat<-rnorm(80)\ndat<-data.frame(dat)\np<-ggplot(dat, aes(x=dat))+geom_histogram()\np<-p+geom_rug(sides=\"b\", colour=\"blue\")\np\n\n\np + geom_rug(sides = \"b\", aes(y = 0), position = \"jitter\", colour = \"blue\")\n#using some more obvious data:\n \ndat <- c(rep(1, 50), rep(2, 50))\ndat <- data.frame(dat)\n\n" }, { "path": "20f-ggplots/ggplot6.R", "content": "# GG Plot\n#\ndata(mtcars)\nnames(mtcars)\nhist(mtcars$mpg)\n\nlibrary(ggplot2)\nggplot(mtcars, aes(x = mtcars$mpg)) # nothing\nggplot(mtcars, aes(x = mtcars$mpg)) + geom_histogram()\n\nhousing = read.csv('./data/landdata-states.csv')\n# Complex\nhead(housing[,c('Home.Value', 'Date')])\nplot(Home.Value ~ Date, data=subset(housing, State='MA'))\n\npoints(Home.Value ~ Date, data=subset(housing, State='TX'), col='red')\nlegend(1975, 400000, c('MA', 'TX'), title='State', col=c('black', 'red'), pch=c(1,1))\n\nggplot(subset(housing, State %in% c('MA', 'TX')), \n aes(x=Date, y=Home.Value, color=State)) + geom_point()\n\nggplot(subset(mtcars, gear %in% c(3,4)), aes(x=cyl, y=gear, col=factor(am))) + geom_point()\n\nhelp.search(\"geom_\", package = \"ggplot2\")\ndata(\"airquality\")\nstr(airquality)\nstr(faithful)\nstr(sleep)\nsleep\nAirPassengers\nhousing\nstr(housing)\nhp2001Q1 = subset(housing, Date == 2001.25)\ndim(hp2001Q1)\n\nggplot(hp2001Q1, aes(y=Structure.Cost, x=Land.Value)) + geom_point()\n#Eg1\nhp2001Q1 <- subset(housing, Date == 2001.25)\nggplot(hp2001Q1, aes(y = Structure.Cost, x = Land.Value)) +\n geom_point()\nggplot(hp2001Q1, aes(y =Structure.Cost, x = log(Land.Value))) + geom_point()\n\n#Eg2\nggplot(mtcars, aes(y = mpg, x = wt)) +\n geom_point()\n\n#Eg1\nhp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))\np1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))\np1 + geom_point(aes(color = Home.Value)) +\n geom_line(aes(y = pred.SC))\n\n#Eg1\np1 +\n geom_point( aes(color = Home.Value )) +\n geom_smooth()\n\n#Eg1\np1 +\n geom_text(aes( label=State), size=3)\n\n#Eg2 : ggrepel\nlibrary(ggrepel)\np1 +\n geom_point() +\n geom_text_repel( aes(label=State), size=3)\n\np1 +\n geom_point (aes (size=2),\n color='red')\n\n#Eg\np1 +\n geom_point(aes (color = Home.Value, shape=region))\n \n#Eg2 : R in Action @19.2\nlibrary(ggplot2)\nggplot(data = mtcars, aes(x=wt, y=mpg)) +\n# geom_point() +\n#geom_point(pch=17, col='blue', size=2)\ngeom_smooth(method='lm', color='red', linetype=2) +\n labs(title = \"Automobile Data\", x=\"Weight\", y=\"Miles per Gallon\")\n\n\n#Eg3: RinA : Grouping\nmydata=mtcars\nmydata$am = factor(mydata$am, levels=c(0,1), labels=c('Automatic', 'Manual'))\nmydata$vs = factor(mydata$vs, levels=c(0,1), labels=c('V-Engine', 'Straight Engine'))\nlevels(mydata$cyl)\nmydata$cyl = factor(mydata$cyl)\n\nstr(mydata)\nggplot(data=mtcars, aes(x=hp, y=mpg, shape=cyl, col=cyl)) + scale_shape_identity() + geom_point(size=1) + facet_grid(am~vs) + labs(title=' Automobile Data by Engine Type', x ='Horsepower', y='Miles per Gallon')\n\n\n#Eg4 RiA : Singer\ndata(singer, package='lattice')\n#histogram\nggplot(singer, aes(x =height)) + geom_histogram()\n#boxplot\nggplot(singer, aes(x=voice.part, y=height)) + geom_boxplot()\n\n#Eg5a: RiA : Salaries\ndata(Salaries, package='carData')\nSalaries\nggplot(Salaries, aes(x=rank, y=salary)) +\n geom_boxplot( fill='cornflowerblue', color='black', notch=T) +\n geom_point(position='jitter', color='blue', alpha=.5) +\n geom_rug(sides='l', color='black')\n\n#Interpretations\n#1- salaries of 3 ranks differ from each other (No overlap in the box plot notches)\n#2 variance in salaries increases with greater rank, with larger range of salaries for full professors\n# 3 : salaries of 3 professors is so large to make them outlier's (3 black dots)\n\n?singer\nhead(singer)\ntable(singer)\nstr(singer)\ndata('Salaries', package='car')\nstr(Salaries)\nhead(Salaries)\n?Salaries\n\n\n\n\n#Eg1\ndata(Salaries, package='carData' )\nggplot( data=Salaries, aes(x=rank, y=salary, fill=sex)) +\n geom_boxplot() +\n scale_x_discrete( breaks=c('AsstProf', 'AssocProf', 'Prof'), labels= c('Assistant \\n Professor', 'Associate \\n Professor', 'Full \\n Professor' )) +\n scale_y_continuous(breaks = c(5000, 10000, 15000, 20000), labels =c('Rs 50K', 'Rs 100k', 'Rs 150K', 'Rs 200K')) +\n# labs(title= 'Faculty Salary by Rank and Sex', x=' Salaries', y=' Ranks')\n\n#Eg1\nlabs(title= 'Faculty Salary by Rank and Sex', y=' Salaries', x=' Ranks', fill='Gender') +\ntheme(legend.position =c(.1, .8))\n \nggplot( data=Salaries, aes(x=rank, y=salary, fill=sex)) +\n geom_boxplot() +\n scale_x_discrete( breaks=c('AsstProf', 'AssocProf', 'Prof'), labels= c('Assistant \\n Professor', 'Associate \\n Professor', 'Full \\n Professor' )) +\n scale_y_continuous(breaks = c(5000, 10000, 15000, 20000), labels =c('Rs 50K', 'Rs 100k', 'Rs 150K', 'Rs 200K')) +\n labs(title= 'Faculty Salary by Rank and Sex', y=' Salaries', x=' Ranks', fill='Gender') +\n theme(legend.position =c(.1, .8))\n\n\n\n#Eg1a\nggplot(mtcars, aes(x=wt, y=mpg, size=disp)) +\n geom_point(shape=21, color='black', fill='cornsilk') +\n labs(x='Weight', y='Miles per Gallon' , title='Bubble Chart', size='Engine \\n Displacement')\n\n\nEg1b\ndata(Salaries, package='carData')\nggplot(data=Salaries, aes(x=yrs.since.phd, y = salary, color=rank)) +\n #scale_color_manual(values=c('orange', 'olivedrab', 'navy')) +\n scale_color_brewer(palette='Set1') + \n geom_point(size=2)\n \n\nmytheme <- theme(plot.title=element_text(face=\"bold.italic\",\n size=\"14\", color=\"brown\"),\n axis.title=element_text(face=\"bold.italic\",\n size=10, color=\"brown\"),\n axis.text=element_text(face=\"bold\", size=9,\n color=\"darkblue\"),\n panel.background=element_rect(fill=\"white\",\n color=\"darkblue\"),\n panel.grid.major.y=element_line(color=\"grey\",\n linetype=1),\n panel.grid.minor.y=element_line(color=\"grey\",\n linetype=2),\n panel.grid.minor.x=element_blank(),\n legend.position=\"top\")\n\nggplot(Salaries, aes(x=rank, y=salary, fill=sex)) +\n geom_boxplot() +\n labs(title=\"Salary by Rank and Sex\", \n x=\"Rank\", y=\"Salary\") +\n mytheme\n\n#Eg1\ndata(Salaries, package=\"car\")\np1 <- ggplot(data=Salaries, aes(x=rank)) +\n geom_bar()\np2 <- ggplot(data=Salaries, aes(x=sex)) +\n geom_bar()\np3 <- ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) +\n geom_point()\nlibrary(gridExtra)\ngrid.arrange(p1, p2, p3, ncol=3)\n\n\n#Eg1\nggplot(data=mtcars, aes(x=mpg)) + geom_histogram()\nggsave(file=\"mygraph.pdf\")\n\n\n\nlibrary(playwith)\nlibrary(lattice)\nplaywith(\n xyplot(mpg~wt|factor(cyl)*factor(am),\n data=mtcars, subscripts=TRUE,\n type=c(\"r\", \"p\"))\n)\n" }, { "path": "20f-ggplots/ggplot7.R", "content": "\n#ggplot bar\n\nlibrary(ggplot2)\n\n(p1 <- ggplot(mtcars, aes(x=cyl)) + geom_bar())\n\nlibrary(dplyr)\n(p2 <- mtcars %>% group_by(cyl) %>% tally %>% ggplot(., aes(x=cyl, y=n)) + geom_bar(stat='identity'))\n\nlibrary(gridExtra)\ngrid.arrange(p1,p2, ncol=2)\n\n\nmtcars %>% group_by(cyl) %>% tally %>% ggplot(., aes(x=factor(cyl), y=n)) + geom_bar(stat='identity') + labs(title=\"Main Title\", x='Cylinders', y='Nos') + coord_flip()\n\nggplot(mtcars, aes(x=wt, y=mpg, color=cyl)) + geom_point() + geom_smooth()\n\nggplot(mtcars, aes(x=wt, y=mpg, color=cyl)) + geom_point() + geom_smooth() + theme(legend.position=\"none\") + labs(title=\"legend.position='none'\") # remove legend\n\n\nggplot(mtcars, aes(x=cyl)) + geom_bar(fill='darkgoldenrod2') +\n theme(panel.background = element_rect(fill = 'steelblue'),\n panel.grid.major = element_line(colour = \"firebrick\", size=2),\n panel.grid.minor = element_line(colour = \"blue\", size=1))\n\n\nggplot(mtcars, aes(x=cyl)) + geom_bar(fill=\"firebrick\") + theme(plot.background=element_rect(fill=\"steelblue\"), plot.margin = unit(c(2, 4, 1, 3), \"cm\")) # top, right, bottom, left\n" }, { "path": "20f-ggplots/twoaxis-gg.R", "content": "#two axis - ggplot2\n\nlibrary (ggplot2)\nlibrary(tidyverse)\n\n(year = seq(2016,2020))\n(students = c(1000,1200, 1500, 1300,2000))\n(fac = c(100,120, 150, 130,180))\n\nlibrary(ggplot2)\n(df = data.frame(year, students, fac))\ng= ggplot() + geom_bar(data=df, aes(x=year,y=fac,fill=year, alpha=.4) , stat='identity') + geom_line(data=df, aes(x=year, y=students/10 ), colour='red') + scale_y_continuous(sec.axis=sec_axis(~.*10, name=\"students\")) + guides(fill='none', alpha='none')\n\ng + theme(axis.text.y.right=element_text(colour=\"red\"),\n axis.title.y.right=element_text(colour=\"red\"),\n axis.text.y=element_text(colour=\"#346D9D\"),\n axis.title.y=element_text(colour=\"#346D9D\"),\n axis.title.x=element_blank(),\n legend.position=\"bottom\")\n" }, { "path": "20g-Network/NetSciX 2016 Workshop.R", "content": "\n##======================================================##\n## ##\n## NetSciX School of Code Workshop ##\n## Network analysis with R and igraph ##\n## Wroclaw, Poland, January 10 2016 ##\n## ##\n## Katya Ognyanova, katya@ognyanova.net ##\n## www.kateto.net/netscix2016 ##\n## ##\n##======================================================##\n\n\n# Handouts and example data: bitly.com/netscix2016 \n# Online version of the tutoiral: kateto.net/netscix2016\n\n# CONTENTS\n# 1. A quick R introduction/refresher\n# 2. Networks in igraph\n# 3. Reading network data from files\n# 4. Turning networks into igraph objects\n# 5. Plotting networks with igraph\n# 6. Network and node descriptives\n# 7. Distances and paths\n# 8. Subgroups and communities\n# 9. Assortativity and Homophily\n\n\n# Install the package \"igraph\" if you don't have its latest version (1.0.1) \n# The package (www.igraph.org) is maintained by Gabor Csardi and Tamas Nepusz.\n\n install.packages(\"igraph\") \n\n\n# ================ 1. A quick R introduction/reminder ================\n\n\n# You can assign a value to an object using assign(), \"<-\", or \"=\".\n\nx <- 3 # Assignment\nx # Evaluate the expression and print result\n\ny <- 4 # Assignment\ny + 5 # Evaluation, y remains 4\n\nz <- x + 17*y # Assignment\nz # Evaluation\n\nrm(z) # Remove z: deletes the object.\nz # Error!\n\n\n# ------->> Value comparisons: --------\n\n# Comparisons return boolean values: TRUE or FALSE (often abbreviated to T and F)\n \n2==2 # Equality\n2!=2 # Inequality\nx <= y # less than or equal: \"<\", \">\", and \">=\" also work\n\n\n# ------->> Special constants -------- \n\n# NA, NULL, Inf, -Inf, NaN\n\n# NA - missing or undefined data\n5 + NA # When used in an expression, the result is generally NA\nis.na(5+NA) # Check if missing\n\n# NULL - an empty object, e.g. a null/empty list\n10 + NULL # use returns an empty object (length zero)\nis.null(NULL) # check if NULL\n\n# Inf and -Inf represent positive and negative infinity\n# They can be returned by mathematical operations like division of a number by zero:\n\n5/0\nis.finite(5/0) # Check if a number is finite\n\n# NaN (Not a Number) - the result of an operation that cannot be reasonably defined \n0/0\nis.nan(0/0)\n\n\n# ------->> Vectors -------- \n\nv1 <- c(1, 5, 11, 33) # Numeric vector, length 4\nv2 <- c(\"hello\",\"world\") # Character vector, length 2 (a vector of strings)\nv3 <- c(TRUE, TRUE, FALSE) # Logical vector, same as c(T, T, F)\n\n# Combining different types of elements in one vector will coerce the elements \n# to the least restrictive type:\n\nv4 <- c(v1,v2,v3,\"boo\") \t# All elements turn into strings\n\n# Other ways to create vectors:\nv <- 1:7 # same as c(1,2,3,4,5,6,7) \nv <- rep(0, 77) # repeat zero 77 times: v is a vector of 77 zeroes\nv <- rep(1:3, times=2) # Repeat 1,2,3 twice \nv <- rep(1:10, each=2) # Repeat each element twice \nv <- seq(10,20,2) # sequence: numbers between 10 and 20, in jumps of 2 \n\nlength(v) # check the length of the vector\n\nv1 <- 1:5 # 1,2,3,4,5\nv2 <- rep(1,5) # 1,1,1,1,1 \n\n# Element-wise operations:\nv1 + v2 # Element-wise addition\nv1 + 1 # Add 1 to each element\nv1 * 2 # Multiply each element by 2\nv1 + c(1,7) # This doesn't work: (1,7) is a vector of different length\n\n# Mathematical operations:\nsum(v1) # The sum of all elements\nmean(v1) # The average of all elements\nsd(v1) # The standard deviation\ncor(v1,v1*5) # Correlation between v1 and v1*5 \n\n# Logical operations:\nv1 > 2 # Each element is compared to 2, returns logical vector\nv1==v2 # Are corresponding elements equivalent, returns logical vector.\nv1!=v2 # Are corresponding elements *not* equivalent? Same as !(v1==v2)\n(v1>2) | (v2>0) # | is the boolean OR, returns a vector.\n(v1>2) & (v2>0) # & is the boolean AND, returns a vector.\n(v1>2) || (v2>0) # || is the boolean OR, returns a single value\n(v1>2) && (v2>0) # && is the boolean AND, ditto\n\n# Vector elements\nv1[3] # third element of v1\nv1[2:4] # elements 2, 3, 4 of v1\nv1[c(1,3)] # elements 1 and 3 - note that your indexes are a vector\nv1[c(T,T,F,F,F)] # elements 1 and 2 - only the ones that are TRUE\nv1[v1>3] # v1>3 is a logical vector TRUE for elements >3\n\n# NOTE: If you are used to languages indexing from 0, R will surprise you by indexing from 1.\n\n# To add more elements to a vector, simply assign them values.\nv1[6:10] <- 6:10\n\n# We can also directly assign the vector a length:\nlength(v1) <- 15 # the last 5 elements are added as missing data: NA\n\n\n# ------->> Factors --------\n\n# Factors are used to store categorical data.\n\neye.col.v <- c(\"brown\", \"green\", \"brown\", \"blue\", \"blue\", \"blue\") #vector\neye.col.f <- factor(c(\"brown\", \"green\", \"brown\", \"blue\", \"blue\", \"blue\")) #factor\neye.col.v\neye.col.f\n\n# R will identify the different levels of the factor - e.g. all distinct values. \n# The data is stored internally as integers - each number corresponding to a factor level.\n\nlevels(eye.col.f) # The levels (distinct values) of the factor (categorical variable)\n\nas.numeric(eye.col.f) # The factor as numeric values: 1 is blue, 2 is brown, 3 is green\nas.numeric(eye.col.v) # The character vector, however, can not be coerced to numeric\n\nas.character(eye.col.f) \nas.character(eye.col.v) \n\n# ------->> Matrces & Arrays -------- \n\n# A matrix is a vector with dimensions:\nm <- rep(1, 20) # A vector of 20 elements, all 1\ndim(m) <- c(5,4) # Dimensions set to 5 & 4, so m is now a 5x4 matrix\n\n# Create a matrix using matrix():\nm <- matrix(data=1, nrow=5, ncol=4) # same matrix as above, 5x4, full of 1s\nm <- matrix(1,5,4) \t\t\t # same matrix as above\ndim(m) # What are the dimensions of m?\n\n# Create a matrix by combining vectors:\nm <- cbind(1:5, 5:1, 5:9) # Bind 3 vectors as columns, 5x3 matrix\nm <- rbind(1:5, 5:1, 5:9) # Bind 3 vectors as rows, 3x5 matrix\n\nm <- matrix(1:10,10,10)\n\n# Select matrix elements: \nm[2,3] # Matrix m, row 2, column 3 - a single cell\nm[2,] # The whole second row of m as a vector\nm[,2] # The whole second column of m as a vector\nm[1:2,4:6] # submatrix: rows 1 and 2, columns 4, 5 and 6\nm[-1,] # all rows *except* the first one\n\nm[1,]==m[,1] # Are elements in row 1 equivalent to corresponding elements from column 1? \nm>3 # A logical matrix: TRUE for m elements >3, FALSE otherwise\nm[m>3] # Selects only TRUE elements - that is ones greater than 3\n\n\nt(m) # Transpose m \nm <- t(m) # Assign m the transposed m\nm %*% t(m) # %*% does matrix multiplication\nm * m # * does element-wise multiplication\n\n# Arrays: more than 2 dimensions\n# Created with the array() function:\n\na <- array(data=1:18,dim=c(3,3,2)) # 3d with dimensions 3x3x2\na <- array(1:18,c(3,3,2)) # the same array\n\n\n# ------->> Lists -------- \n\n# Lists are collections of objects (e.g. of strings, vectors, matrices, other lists, etc.)\n\nl1 <- list(boo=v1,foo=v2,moo=v3,zoo=\"Animals!\") # A list with four components\nl2 <- list(v1,v2,v3,\"Animals!\")\n\nl3 <- list()\nl4 <- NULL\n\nl1[\"boo\"] # Access boo: this returns a list.\nl1[[\"boo\"]] # Access boo: this returns the numeric vector\nl1[[1]] # Returns the first component of the list, equivalent to above.\nl1$boo # Named elements can be accessed using the $ operator - equivalent to [[]]\n\n# Add more elements to a list:\nl3[[1]] <- 11 # add an element to the empty list l3\nl4[[3]] <- c(22, 23) # add a vector as element 3 in the empty list l4. \n # Since we added element 3, elements 1 & 2 will be generated and empty (NULL)\nl1[[5]] <- \"More elements!\" # The list l1 had 4 elements, we're adding a 5th here.\nl1[[8]] <- 1:11 # We added an 8th element, but not 6th or 7th. Those will be created empty (NULL)\nl1$Something <- \"A thing\" # Adds a ninth element - \"A thing\", named \"Something\"\n\n\n# ------->> Data Frames -------- \n\n# The data frame is a special kind of list used for storing dataset tables.\n# Think of rows as cases, columns as variables. Each column is a vector or factor.\n\n# Creating a dataframe:\n\ndfr1 <- data.frame( ID=1:4,\n FirstName=c(\"John\",\"Jim\",\"Jane\",\"Jill\"),\n Female=c(F,F,T,T), \n Age=c(22,33,44,55) )\n\ndfr1$FirstName # Access the second column of dfr1. \n# Notice that R thinks this is a categorical variable \n# and so it's treating it like a factor, not a character vector.\n\n# Let's get rid of the factor by telling R to treat FirstName as a vector:\ndfr1$FirstName <- as.vector(dfr1$FirstName)\n\n# Alternatively, you can tell R you don't like factors from the start using stringsAsFactors=FALSE\ndfr2 <- data.frame(FirstName=c(\"John\",\"Jim\",\"Jane\",\"Jill\"), stringsAsFactors=FALSE)\ndfr2$FirstName # Success: not a factor.\n\n# Access elements of the data frame\ndfr1[1,] # First row, all columns\ndfr1[,1] # First column, all rows\ndfr1$Age # Age column, all rows\ndfr1[1:2,3:4] # Rows 1 and 2, columns 3 and 4 - the gender and age of John & Jim\ndfr1[c(1,3),] # Rows 1 and 3, all columns\n\n# Find the names of everyone over the age of 30 in the data\ndfr1[dfr1$Age>30,2]\n\n# Find the average age of all females in the data:\nmean ( dfr1[dfr1$Female==TRUE,4] )\n\n\n\n# ------->> Flow Control --------\n\n# if (condition) expr1 else expr2\nx <- 5; y <- 10\nif (x==0) y <- 0 else y <- y/x # \ny\n\n# for (variable in sequence) expr\nASum <- 0; AProd <- 1\nfor (i in 1:x) \n{\n ASum <- ASum + i\n AProd <- AProd * i\n}\nASum # equivalent to sum(1:x)\nAProd # equivalemt to prod(1:x)\n\n# while (condintion) expr\nwhile (x > 0) {print(x); x <- x-1;}\n\n# repeat expr, use break to exit the loop\nrepeat { print(x); x <- x+1; if (x>10) break}\n\n\n\n# ------->> R plots and colors --------\n\n# In most R functions, you can use named colors, hex, or rgb values:\n# (In the simple base plot chart below x and y are point coordiantes, pch \n# is the point symbol shape, cex is the point size, and col is the color.\n# to see the parameters for ploting in base R, check out ?par\nplot(x=1:10, y=rep(5,10), pch=19, cex=5, col=\"dark red\")\npoints(x=1:10, y=rep(6, 10), pch=19, cex=5, col=\"#557799\")\npoints(x=1:10, y=rep(4, 10), pch=19, cex=5, col=rgb(.25, .5, .3))\n\n# You may notice that rgb here ranges from 0 to 1. While this is the R default,\n# you can also set it for the 0-255 range: \nrgb(10, 100, 100, maxColorValue=255) \n\n# We can also set the opacity/transparency using the parameter 'alpha' (range 0-1):\nplot(x=1:5, y=rep(5,5), pch=19, cex=16, col=rgb(.25, .5, .3, alpha=.5), xlim=c(0,6)) \n\n# If we have a hex color representation, we can set the transparency alpha \n# using 'adjustcolor' from package 'grDevices'. For fun, let's also set the\n# the plot background to gray using the par() function for graphical parameters.\npar(bg=\"black\")\ncol.tr <- grDevices::adjustcolor(\"#557799\", alpha=0.7)\nplot(x=1:5, y=rep(5,5), pch=19, cex=20, col=col.tr, xlim=c(0,6)) \npar(bg=\"white\")\n\n# If you plan on using the built-in color names, here's what they are: \ncolors()\ngrep(\"blue\", colors(), value=T)\n\n# In many cases, we need a number of contrasting colors, or multiple shades of a color.\n# R comes with some predefined palette function that can generate those for us.\npal1 <- heat.colors(5, alpha=1) # generate 5 colors from the heat palette, opaque\npal2 <- rainbow(5, alpha=.5) # generate 5 colors from the heat palette, semi-transparent\nplot(x=1:10, y=1:10, pch=19, cex=10, col=pal1)\nplot(x=10:1, y=1:10, pch=19, cex=10, col=pal2)\n\n# We can also generate our own gradients using colorRampPalette.\n# Note that colorRampPalette returns a *function* that we can use \n# to generate as many colors from that palette as we need.\n\npalf <- colorRampPalette(c(\"gray70\", \"red\")) \nplot(x=10:1, y=1:10, pch=19, cex=10, col=palf(100)) \n\n# To add transparency to colorRampPalette, you need to add a parameter `alpha=TRUE`:\npalf <- colorRampPalette(c(rgb(1,1,1, .2),rgb(.8,0,0, .7)), alpha=TRUE)\nplot(x=10:1, y=1:10, pch=19, cex=10, col=palf(10)) \n\n\n# ------->> R troubleshooting --------\n\n# While I generate many (and often very creative) errors in R, there are three\n# simple things that will most often go wrong for me. Those include: \n\n# 1) Capitalization. R is case sensitive - a graph vertex named \"Jack\" is not the same\n# as one named \"jack\". The function \"rowSums\" won't work as \"rowsums\" or \"RowSums\".\n#\n# 2) Object class. While many functions are willing to take anything you throw\n# at them, some will still surprisingly require character vector or a factor instead of\n# a numeric vector, or a matrix instead of a data frame. Functions will also occasionally\n# return results in an unexpected formats.\n#\n# 3) Package namespaces. Occasionally problems will arise when different packages\n# contain functions with the same name. R may warn you about this by saying something\n# like \"The following object(s) are masked from 'package:igraph'\" as you load a package.\n# One way to deal with this is to call functions from a package explicitly using '::'.\n# For instance, if function 'blah' is present in packages A and B, you can call\n# A::blah and B::blah. In other cases the problem is more complicated, and you may\n# have to load packages in certain order, or not use them together at all.\n# For example (and pertinent to this workshop), igraph and statnet packages cause\n# some problems when loaded at the same time. It is best to detach one before loading\n# the other.\n\n library(igraph) # load a package\n detach(package:igraph) # detach a package\n\n# For more advanced troubleshooting, check out try(), tryCatch(), and debug().\n?tryCatch\n\n\n\n# ================ 2. Networks in igraph ================\n\nrm(list = ls()) # Remove all the objects we created so far.\n\nlibrary(igraph) # Load the igraph package\n\n\n# ------->> Create networks --------\n\ng1 <- graph( edges=c(1,2, 2,3, 3,1), n=3, directed=F ) # an undirected graph with 3 edges\n# The numbers are interpreted as vertex IDs, so the edges are 1-->2, 2-->3, 3-->1\nplot(g1) # A simple plot of the network - we'll talk more about plots later\nclass(g1)\ng1\n\ng2 <- graph( edges=c(1,2, 2,3, 3,1), n=10 ) # now with 10 vertices, and directed by default\nplot(g2) \ng2\n\ng3 <- graph( c(\"John\", \"Jim\", \"Jim\", \"Jill\", \"Jill\", \"John\")) # named vertices\n# When the edge list has vertex names, the number of nodes is not needed\nplot(g3)\ng3\n\ng4 <- graph( c(\"John\", \"Jim\", \"Jim\", \"Jack\", \"Jim\", \"Jack\", \"John\", \"John\"), \n isolates=c(\"Jesse\", \"Janis\", \"Jennifer\", \"Justin\") ) \n# In named graphs we can specify isolates by providing a list of their names.\n\nplot(g4, edge.arrow.size=.5, vertex.color=\"gold\", vertex.size=15, \n vertex.frame.color=\"gray\", vertex.label.color=\"black\", \n vertex.label.cex=1.5, vertex.label.dist=2, edge.curved=0.2) \n\n# Small graphs can also be generated with a description of this kind:\n# '-' for undirected tie, \"+-' or \"-+\" for directed ties pointing left & right, \n# \"++\" for a symmetric tie, and \":\" for sets of vertices\n\nplot(graph_from_literal(a---b, b---c)) # the number of dashes doesn't matter\nplot(graph_from_literal(a--+b, b+--c))\nplot(graph_from_literal(a+-+b, b+-+c)) \nplot(graph_from_literal(a:b:c---c:d:e))\n\ngl <- graph_from_literal(a-b-c-d-e-f, a-g-h-b, h-e:f:i, j)\nplot(gl)\n\n\n\n# ------->> Edge, vertex, and network attributes --------\n\n# Access vertices and edges:\nE(g4) # The edges of the object\nV(g4) # The vertices of the object\n\n\n# You can also manipulate the network matrix directly:\ng4[]\ng4[1,]\ng4[3,3] <- 10\ng4[5,7] <- 10\n\n# Add attributes to the network, vertices, or edges:\nV(g4)$name # automatically generated when we created the network.\nV(g4)$gender <- c(\"male\", \"male\", \"male\", \"male\", \"female\", \"female\", \"male\")\nE(g4)$type <- \"email\" # Edge attribute, assign \"email\" to all edges\nE(g4)$weight <- 10 # Edge weight, setting all existing edges to 10\n\n# Examine attributes\nedge_attr(g4)\nvertex_attr(g4)\ngraph_attr(g4)\n\n# Another way to set attributes\n# (you can similarly use set_edge_attr(), set_vertex_attr(), etc.)\ng4 <- set_graph_attr(g4, \"name\", \"Email Network\")\ng4 <- set_graph_attr(g4, \"something\", \"A thing\")\n\ngraph_attr_names(g4)\ngraph_attr(g4, \"name\")\ngraph_attr(g4)\n\ng4 <- delete_graph_attr(g4, \"something\")\ngraph_attr(g4)\n\nplot(g4, edge.arrow.size=.5, vertex.label.color=\"black\", vertex.label.dist=1.5,\n vertex.color=c( \"pink\", \"skyblue\")[1+(V(g4)$gender==\"male\")] ) \n\n# g4 has two edges going from Jim to Jack, and a loop from John to himself.\n# We can simplify our graph to remove loops & multiple edges between the same nodes.\n# Use 'edge.attr.comb' to indicate how edge attributes are to be combined - possible \n# options include \"sum\", \"mean\", \"prod\" (product), min, max, first/last (selects \n# the first/last edge's attribute). Option \"ignore\" says the attribute should be \n# disregarded and dropped.\n\ng4s <- simplify( g4, remove.multiple = T, remove.loops = F, \n edge.attr.comb=list(weight=\"sum\", type=\"ignore\") )\nplot(g4s, vertex.label.dist=1.5)\ng4s\n\n# Let's take a look at the description of the igraph object.\n# Those will typically start with up to four letters:\n# 1. D or U, for a directed or undirected graph\n# 2. N for a named graph (where nodes have a name attribute)\n# 3. W for a weighted graph (where edges have a weight attribute)\n# 4. B for a bipartite (two-mode) graph (where nodes have a type attribute)\n#\n# The two numbers that follow refer to the number of nodes and edges in the graph. \n# The description also lists graph, node & edge attributes, for example:\n# (g/c) - graph-level character attribute\n# (v/c) - vertex-level character attribute\n# (e/n) - edge-level numeric attribute\n\n\n# ------->> Specific graphs and graph models --------\n\n# Empty graph\neg <- make_empty_graph(40)\nplot(eg, vertex.size=10, vertex.label=NA)\n\n# Full graph\nfg <- make_full_graph(40)\nplot(fg, vertex.size=10, vertex.label=NA)\n\n# Star graph \nst <- make_star(40)\nplot(st, vertex.size=10, vertex.label=NA) \n\n# Tree graph\ntr <- make_tree(40, children = 3, mode = \"undirected\")\nplot(tr, vertex.size=10, vertex.label=NA) \n\n# Ring graph\nrn <- make_ring(40)\nplot(rn, vertex.size=10, vertex.label=NA)\n\n# Erdos-Renyi random graph \n# ('n' is number of nodes, 'm' is the number of edges)\ner <- sample_gnm(n=100, m=40) \nplot(er, vertex.size=6, vertex.label=NA) \n\n# Watts-Strogatz small-world graph\n# Creates a lattice with 'dim' dimensions of 'size' nodes each, and rewires edges \n# randomly with probability 'p'. You can allow 'loops' and 'multiple' edges.\n# The neighborhood in which edges are connected is 'nei'.\nsw <- sample_smallworld(dim=2, size=10, nei=1, p=0.1)\nplot(sw, vertex.size=6, vertex.label=NA, layout=layout_in_circle)\n \n# Barabasi-Albert preferential attachment model for scale-free graphs\n# 'n' is number of nodes, 'power' is the power of attachment (1 is linear)\n# 'm' is the number of edges added on each time step \n ba <- sample_pa(n=100, power=1, m=1, directed=F)\n plot(ba, vertex.size=6, vertex.label=NA)\n \n#igraph can also give you some notable historical graphs. For instance:\n zach <- graph(\"Zachary\") # the Zachary carate club\n plot(zach, vertex.size=10, vertex.label=NA)\n \n # Rewiring a graph\n # 'each_edge()' is a rewiring method that changes the edge endpoints\n # uniformly randomly with a probability 'prob'.\n rn.rewired <- rewire(rn, each_edge(prob=0.1))\n plot(rn.rewired, vertex.size=10, vertex.label=NA)\n \n # Rewire to connect vertices to other vertices at a certain distance. \n rn.neigh = connect.neighborhood(rn, 5)\n plot(rn.neigh, vertex.size=8, vertex.label=NA) \n \n \n # Combine graphs (disjoint union, assuming separate vertex sets): %du%\n plot(rn, vertex.size=10, vertex.label=NA) \n plot(tr, vertex.size=10, vertex.label=NA) \n plot(rn %du% tr, vertex.size=10, vertex.label=NA) \n\n \n \n# ================ 3. Reading network data from files ================\n\n \nrm(list = ls()) # clear the workspace again\n\n# Download the archive with the data files from http://bitly.com/netscix2016 \n \n# Set the working directory to the folder containing the workshop files:\nsetwd(\"C:/DOCS/Conferences/2016-NetSciX/NetSciX Workshop\") \n \n# DATASET 1: edgelist \n\nnodes <- read.csv(\"Dataset1-Media-Example-NODES.csv\", header=T, as.is=T)\nlinks <- read.csv(\"Dataset1-Media-Example-EDGES.csv\", header=T, as.is=T)\n\n# Examine the data:\nhead(nodes)\nhead(links)\nnrow(nodes); length(unique(nodes$id))\nnrow(links); nrow(unique(links[,c(\"from\", \"to\")]))\n\n# Collapse multiple links of the same type between the same two nodes\n# by summing their weights, using aggregate() by \"from\", \"to\", & \"type\":\n# (we don't use \"simplify()\" here so as not to collapse different link types)\nlinks <- aggregate(links[,3], links[,-3], sum)\nlinks <- links[order(links$from, links$to),]\ncolnames(links)[4] <- \"weight\"\nrownames(links) <- NULL\n\n\n# DATASET 2: matrix \n\nnodes2 <- read.csv(\"Dataset2-Media-User-Example-NODES.csv\", header=T, as.is=T)\nlinks2 <- read.csv(\"Dataset2-Media-User-Example-EDGES.csv\", header=T, row.names=1)\n\n# Examine the data:\nhead(nodes2)\nhead(links2)\n\n# links2 is an adjacency matrix for a two-mode network:\nlinks2 <- as.matrix(links2)\ndim(links2)\ndim(nodes2)\n\n\n# ================ 4. Turning networks into igraph objects ================ \n \nlibrary(igraph)\n\n# ------->> DATASET 1 -------- \n\n# Converting the data to an igraph object:\n# The graph.data.frame function, which takes two data frames: 'd' and 'vertices'.\n# 'd' describes the edges of the network - it should start with two columns \n# containing the source and target node IDs for each network tie.\n# 'vertices' should start with a column of node IDs.\n# Any additional columns in either data frame are interpreted as attributes.\n\nnet <- graph_from_data_frame(d=links, vertices=nodes, directed=T) \n\n# Examine the resulting object:\nclass(net)\nnet \n\n# We can look at the nodes, edges, and their attributes:\nE(net)\nV(net)\nE(net)$type\nV(net)$media\n\nplot(net, edge.arrow.size=.4,vertex.label=NA)\n\n# Removing loops from the graph:\nnet <- simplify(net, remove.multiple = F, remove.loops = T) \n\n# If you need them, you can extract an edge list or a matrix from igraph networks.\nas_edgelist(net, names=T)\nas_adjacency_matrix(net, attr=\"weight\")\n\n# Or data frames describing nodes and edges:\nas_data_frame(net, what=\"edges\")\nas_data_frame(net, what=\"vertices\")\n\n\n# ------->> DATASET 2 --------\n\nhead(nodes2)\nhead(links2)\n\nnet2 <- graph_from_incidence_matrix(links2)\n\n# A built-in vertex attribute 'type' shows which mode vertices belong to.\ntable(V(net2)$type)\n\nplot(net2,vertex.label=NA)\n\n# To transform a one-mode network matrix into an igraph object,\n# use graph_from_adjacency_matrix()\n\n# We can also easily generate bipartite projections for the two-mode network:\n# (co-memberships are easy to calculate by multiplying the network matrix by\n# its transposed matrix, or using igraph's bipartite.projection function)\n\nnet2.bp <- bipartite.projection(net2)\n\n# We can calculate the projections manually as well:\n# as_incidence_matrix(net2) %*% t(as_incidence_matrix(net2))\n# t(as_incidence_matrix(net2)) %*% as_incidence_matrix(net2)\n\nplot(net2.bp$proj1, vertex.label.color=\"black\", vertex.label.dist=1,\n vertex.label=nodes2$media[!is.na(nodes2$media.type)])\n\nplot(net2.bp$proj2, vertex.label.color=\"black\", vertex.label.dist=1,\n vertex.label=nodes2$media[ is.na(nodes2$media.type)])\n\n\n# ================ 5. Plotting networks with igraph ================\n\n \n# ------->> Plot parameters in igraph --------\n\n# Plotting with igraph: node options (starting with 'vertex.') and edge options\n# (starting with 'edge.'). A list of options is included in your handout.\n?igraph.plotting\n\n# We can set the node & edge options in two ways - one is to specify\n# them in the plot() function, as we are doing below.\n\n# Plot with curved edges (edge.curved=.1) and reduce arrow size:\nplot(net, edge.arrow.size=.4, edge.curved=.1)\n\n# Set node color to orange and the border color to hex #555555\n# Replace the vertex label with the node names stored in \"media\"\nplot(net, edge.arrow.size=.2, edge.curved=0,\n vertex.color=\"orange\", vertex.frame.color=\"#555555\",\n vertex.label=V(net)$media, vertex.label.color=\"black\",\n vertex.label.cex=.7) \n\n# The second way to set attributes is to add them to the igraph object.\n\n# Generate colors based on media type:\ncolrs <- c(\"gray50\", \"tomato\", \"gold\")\nV(net)$color <- colrs[V(net)$media.type]\n\n# Set node size based on audience size:\nV(net)$size <- V(net)$audience.size*0.7\n\n# The labels are currently node IDs.\n# Setting them to NA will render no labels:\nV(net)$label.color <- \"black\"\nV(net)$label <- NA\n\n# Set edge width based on weight:\nE(net)$width <- E(net)$weight/6\n\n#change arrow size and edge color:\nE(net)$arrow.size <- .2\nE(net)$edge.color <- \"gray80\"\n\nplot(net) \n\n# We can also override the attributes explicitly in the plot:\nplot(net, edge.color=\"orange\", vertex.color=\"gray50\") \n\n\n# We can also add a legend explaining the meaning of the colors we used:\nplot(net) \nlegend(x=-1.1, y=-1.1, c(\"Newspaper\",\"Television\", \"Online News\"), pch=21,\n col=\"#777777\", pt.bg=colrs, pt.cex=2.5, bty=\"n\", ncol=1)\n\n\n# Sometimes, especially with semantic networks, we may be interested in \n# plotting only the labels of the nodes:\n\nplot(net, vertex.shape=\"none\", vertex.label=V(net)$media, \n vertex.label.font=2, vertex.label.color=\"gray40\",\n vertex.label.cex=.7, edge.color=\"gray85\")\n\n\n# Let's color the edges of the graph based on their source node color.\n# We'll get the starting node for each edge with \"ends()\".\nedge.start <- ends(net, es=E(net), names=F)[,1]\nedge.col <- V(net)$color[edge.start]\n\nplot(net, edge.color=edge.col, edge.curved=.1)\n\n\n# ------->> Network Layouts --------\n\n# Network layouts are algorithms that return coordinates for each\n# node in a network.\n\n# Let's generate a slightly larger 80-node graph.\n\nnet.bg <- sample_pa(80, 1.2) \nV(net.bg)$size <- 8\nV(net.bg)$frame.color <- \"white\"\nV(net.bg)$color <- \"orange\"\nV(net.bg)$label <- \"\" \nE(net.bg)$arrow.mode <- 0\nplot(net.bg)\n\n# You can set the layout in the plot function:\nplot(net.bg, layout=layout_randomly)\n\n# Or calculate the vertex coordinates in advance:\nl <- layout_in_circle(net.bg)\nplot(net.bg, layout=l)\n\n# l is simply a matrix of x,y coordinates (N x 2) for the N nodes in the graph. \n# You can generate your own:\nl\nl <- cbind(1:vcount(net.bg), c(1, vcount(net.bg):2))\nplot(net.bg, layout=l)\n\n# This layout is just an example and not very helpful - thankfully\n# igraph has a number of built-in layouts, including:\n\n# Randomly placed vertices\nl <- layout_randomly(net.bg)\nplot(net.bg, layout=l)\n\n# Circle layout\nl <- layout_in_circle(net.bg)\nplot(net.bg, layout=l)\n\n# 3D sphere layout\nl <- layout_on_sphere(net.bg)\nplot(net.bg, layout=l)\n\n# The Fruchterman-Reingold force-directed algorithm \n# Nice but slow, most often used in graphs smaller than ~1000 vertices. \nl <- layout_with_fr(net.bg)\nplot(net.bg, layout=l)\n\n# You will also notice that the layout is not deterministic - different runs \n# will result in slightly different configurations. Saving the layout in l\n# allows us to get the exact same result multiple times.\npar(mfrow=c(2,2), mar=c(1,1,1,1))\nplot(net.bg, layout=layout_with_fr)\nplot(net.bg, layout=layout_with_fr)\nplot(net.bg, layout=l)\nplot(net.bg, layout=l)\ndev.off()\n\n# By default, the coordinates of the plots are rescaled to the [-1,1] interval\n# for both x and y. You can change that with the parameter \"rescale=FALSE\"\n# and rescale your plot manually by multiplying the coordinates by a scalar.\n# You can use norm_coords to normalize the plot with the boundaries you want.\n\n# Get the layout coordinates:\nl <- layout_with_fr(net.bg)\n# Normalize them so that they are in the -1, 1 interval:\nl <- norm_coords(l, ymin=-1, ymax=1, xmin=-1, xmax=1)\n\npar(mfrow=c(2,2), mar=c(0,0,0,0))\nplot(net.bg, rescale=F, layout=l*0.4)\nplot(net.bg, rescale=F, layout=l*0.8)\nplot(net.bg, rescale=F, layout=l*1.2)\nplot(net.bg, rescale=F, layout=l*1.6)\ndev.off()\n\n# Another popular force-directed algorithm that produces nice results for\n# connected graphs is Kamada Kawai. Like Fruchterman Reingold, it attempts to \n# minimize the energy in a spring system.\n\nl <- layout_with_kk(net.bg)\nplot(net.bg, layout=l)\n\n# The LGL algorithm is for large connected graphs. Here you can specify a root - \n# the node that will be placed in the middle of the layout.\nplot(net.bg, layout=layout_with_lgl)\n\n# By default, igraph uses a layout called layout_nicely which selects\n# an appropriate layout algorithm based on the properties of the graph. \n\n# Check out all available layouts in igraph:\n?igraph::layout_\n\nlayouts <- grep(\"^layout_\", ls(\"package:igraph\"), value=TRUE)[-1] \n# Remove layouts that do not apply to our graph.\nlayouts <- layouts[!grepl(\"bipartite|merge|norm|sugiyama|tree\", layouts)]\n\npar(mfrow=c(3,3), mar=c(1,1,1,1))\n\nfor (layout in layouts) {\n print(layout)\n l <- do.call(layout, list(net)) \n plot(net, edge.arrow.mode=0, layout=l, main=layout) }\n\ndev.off()\n\n\n-----------------------------------\n# * TASK FOR WORKSHOP PARTICIPANTS:\n\n# Plot the Zachary karate club network with four different layouts of your choice.\n\n-----------------------------------\n\n \n\n# ------->> Improving network plots --------\n\nplot(net)\n\n# Notice that this network plot is still not too helpful.\n# We can identify the type and size of nodes, but cannot see\n# much about the structure since the links we're examining are so dense.\n# One way to approach this is to see if we can sparsify the network.\n\nhist(links$weight)\nmean(links$weight)\nsd(links$weight)\n\n# There are more sophisticated ways to extract the key edges,\n# but for the purposes of this excercise we'll only keep ones\n# that have weight higher than the mean for the network.\n\n# We can delete edges using delete_edges(net, edges)\ncut.off <- mean(links$weight) \nnet.sp <- delete_edges(net, E(net)[weight> Interactive plotting with tkplot -------- \n\n# R and igraph offer interactive plotting capabilities\n# (mostly helpful for small networks)\n\ntkid <- tkplot(net) #tkid is the id of the tkplot\nl <- tkplot.getcoords(tkid) # grab the coordinates from tkplot\ntk_close(tkid, window.close = T)\nplot(net, layout=l)\n\n\n# ------->> Heatmaps as a way to represent networks -------- \n\n# A quick reminder that there are other ways to represent a network:\n\n# Heatmap of the network matrix:\nnetm <- get.adjacency(net, attr=\"weight\", sparse=F)\ncolnames(netm) <- V(net)$media\nrownames(netm) <- V(net)$media\n\npalf <- colorRampPalette(c(\"gold\", \"dark orange\")) \nheatmap(netm[,17:1], Rowv = NA, Colv = NA, col = palf(20), \n scale=\"none\", margins=c(10,10) )\n\n\n# ------->> Plotting two-mode networks with igraph -------- \n\nhead(nodes2)\nhead(links2)\n\nnet2\nplot(net2)\n\n# This time we will make nodes look different based on their type.\nV(net2)$color <- c(\"steel blue\", \"orange\")[V(net2)$type+1]\nV(net2)$shape <- c(\"square\", \"circle\")[V(net2)$type+1]\nV(net2)$label <- \"\"\nV(net2)$label[V(net2)$type==F] <- nodes2$media[V(net2)$type==F] \nV(net2)$label.cex=.6\nV(net2)$label.font=2\n\nplot(net2, vertex.label.color=\"white\", vertex.size=(2-V(net2)$type)*8) \n\nplot(net2, vertex.label=NA, vertex.size=7, layout=layout_as_bipartite) \n\n \n# Using text as nodes:\npar(mar=c(0,0,0,0))\nplot(net2, vertex.shape=\"none\", vertex.label=nodes2$media,\n vertex.label.color=V(net2)$color, vertex.label.font=2, \n vertex.label.cex=.95, edge.color=\"gray70\", edge.width=2)\n\ndev.off()\n\n\n# ================ 6. Network and node descriptives ================\n\n\n# Density\n# The proportion of present edges from all possible ties.\nedge_density(net, loops=F)\necount(net)/(vcount(net)*(vcount(net)-1)) #for a directed network\n\n# Reciprocity\n# The proportion of reciprocated ties (for a directed network).\nreciprocity(net)\ndyad_census(net) # Mutual, asymmetric, and null node pairs\n2*dyad_census(net)$mut/ecount(net) # Calculating reciprocity\n\n# Transitivity\n# global - ratio of triangles (direction disregarded) to connected triples\n# local - ratio of triangles to connected triples each vertex is part of\ntransitivity(net, type=\"global\") # net is treated as an undirected network\ntransitivity(as.undirected(net, mode=\"collapse\")) # same as above\ntransitivity(net, type=\"local\")\ntriad_census(net) # for directed networks\n\n# Triad types (per Davis & Leinhardt):\n# \n# 003 A, B, C, empty triad.\n# 012 A->B, C \n# 102 A<->B, C \n# 021D A<-B->C \n# 021U A->B<-C \n# 021C A->B->C\n# 111D A<->B<-C\n# 111U A<->B->C\n# 030T A->B<-C, A->C\n# 030C A<-B<-C, A->C.\n# 201 A<->B<->C.\n# 120D A<-B->C, A<->C.\n# 120U A->B<-C, A<->C.\n# 120C A->B->C, A<->C.\n# 210 A->B<->C, A<->C.\n# 300 A<->B<->C, A<->C, completely connected.\n\n\n# Diameter (longest geodesic distance)\n# Note that edge weights are used by default, unless set to NA.\ndiameter(net, directed=F, weights=NA)\ndiameter(net, directed=F)\ndiam <- get_diameter(net, directed=T)\ndiam\n\n# Note: vertex sequences asked to behave as a vector produce numeric index of nodes\nclass(diam)\nas.vector(diam)\n\n# Color nodes along the diameter:\nvcol <- rep(\"gray40\", vcount(net))\nvcol[diam] <- \"gold\"\necol <- rep(\"gray80\", ecount(net))\necol[E(net, path=diam)] <- \"orange\" \n# E(net, path=diam) finds edges along a path, here 'diam'\nplot(net, vertex.color=vcol, edge.color=ecol, edge.arrow.mode=0)\n\n# Node degrees\n# 'degree' has a mode of 'in' for in-degree, 'out' for out-degree,\n# and 'all' or 'total' for total degree. \ndeg <- degree(net, mode=\"all\")\nplot(net, vertex.size=deg*3)\nhist(deg, breaks=1:vcount(net)-1, main=\"Histogram of node degree\")\n\n# Degree distribution\ndeg.dist <- degree_distribution(net, cumulative=T, mode=\"all\")\nplot( x=0:max(deg), y=1-deg.dist, pch=19, cex=1.2, col=\"orange\", \n xlab=\"Degree\", ylab=\"Cumulative Frequency\")\n\n\n# Centrality & centralization\n\n# Centrality functions (vertex level) and centralization functions (graph level).\n# The centralization functions return \"res\" - vertex centrality, \"centralization\", \n# and \"theoretical_max\" - maximum centralization score for a graph of that size.\n# The centrality functions can run on a subset of nodes (set with the \"vids\" parameter)\n\n# Degree (number of ties)\ndegree(net, mode=\"in\")\ncentr_degree(net, mode=\"in\", normalized=T)\n\n# Closeness (centrality based on distance to others in the graph)\n# Inverse of the node's average geodesic distance to others in the network\ncloseness(net, mode=\"all\", weights=NA) \ncentr_clo(net, mode=\"all\", normalized=T) \n\n# Eigenvector (centrality proportional to the sum of connection centralities)\n# Values of the first eigenvector of the graph adjacency matrix\neigen_centrality(net, directed=T, weights=NA)\ncentr_eigen(net, directed=T, normalized=T) \n\n# Betweenness (centrality based on a broker position connecting others)\n# (Number of geodesics that pass through the node or the edge)\nbetweenness(net, directed=T, weights=NA)\nedge_betweenness(net, directed=T, weights=NA)\ncentr_betw(net, directed=T, normalized=T)\n\n\n\n-----------------------------------\n# * TASK FOR WORKSHOP PARTICIPANTS:\n\n# Compute the degree, closeness, eigenvector, and betweenness centrality of\n# the actors in the Zachary karate club network. Plot the network, sizing the\n# nodes based on the different centrality types.\n\n-----------------------------------\n\n \n\n# Hubs and authorities\n\n# The hubs and authorities algorithm developed by Jon Kleinberg was initially used \n# to examine web pages. Hubs were expected to contain catalogues with a large number \n# of outgoing links; while authorities would get many incoming links from hubs, \n# presumably because of their high-quality relevant information. \n\nhs <- hub_score(net, weights=NA)$vector\nas <- authority_score(net, weights=NA)$vector\n\npar(mfrow=c(1,2))\n plot(net, vertex.size=hs*50, main=\"Hubs\")\n plot(net, vertex.size=as*30, main=\"Authorities\")\ndev.off()\n\n\n# ================ 7. Distances and paths ================\n\n\n# Average path length \n# The mean of the shortest distance between each pair of nodes in the network \n# (in both directions for directed graphs). \nmean_distance(net, directed=F)\nmean_distance(net, directed=T)\n\n# We can also find the length of all shortest paths in the graph:\ndistances(net) # with edge weights\ndistances(net, weights=NA) # ignore weights\n\n# We can extract the distances to a node or set of nodes we are interested in.\n# Here we will get the distance of every media from the New York Times.\ndist.from.NYT <- distances(net, v=V(net)[media==\"NY Times\"], to=V(net), weights=NA)\n\n# Set colors to plot the distances:\noranges <- colorRampPalette(c(\"dark red\", \"gold\"))\ncol <- oranges(max(dist.from.NYT)+1)\ncol <- col[dist.from.NYT+1]\n\nplot(net, vertex.color=col, vertex.label=dist.from.NYT, edge.arrow.size=.6, \n vertex.label.color=\"white\")\n\n# We can also find the shortest path between specific nodes.\n# Say here between MSNBC and the New York Post:\nnews.path <- shortest_paths(net, \n from = V(net)[media==\"MSNBC\"], \n to = V(net)[media==\"New York Post\"],\n output = \"both\") # both path nodes and edges\n\n# Generate edge color variable to plot the path:\necol <- rep(\"gray80\", ecount(net))\necol[unlist(news.path$epath)] <- \"orange\"\n# Generate edge width variable to plot the path:\new <- rep(2, ecount(net))\new[unlist(news.path$epath)] <- 4\n# Generate node color variable to plot the path:\nvcol <- rep(\"gray40\", vcount(net))\nvcol[unlist(news.path$vpath)] <- \"gold\"\n\nplot(net, vertex.color=vcol, edge.color=ecol, \n edge.width=ew, edge.arrow.mode=0)\n\n\n# Identify the edges going into or out of a vertex, for instance the WSJ.\n# For a single node, use 'incident()', for multiple nodes use 'incident_edges()'\ninc.edges <- incident(net, V(net)[media==\"Wall Street Journal\"], mode=\"all\")\n\n# Set colors to plot the selected edges.\necol <- rep(\"gray80\", ecount(net))\necol[inc.edges] <- \"orange\"\nvcol <- rep(\"grey40\", vcount(net))\nvcol[V(net)$media==\"Wall Street Journal\"] <- \"gold\"\nplot(net, vertex.color=vcol, edge.color=ecol)\n\n\n# We can also easily identify the immediate neighbors of a vertex, say WSJ.\n# The 'neighbors' function finds all nodes one step out from the focal actor.\n# To find the neighbors for multiple nodes, use 'adjacent_vertices()'.\n# To find node neighborhoods going more than one step out, use function 'ego()'\n# with parameter 'order' set to the number of steps out to go from the focal node(s).\n\nneigh.nodes <- neighbors(net, V(net)[media==\"Wall Street Journal\"], mode=\"out\")\n\n# Set colors to plot the neighbors:\nvcol[neigh.nodes] <- \"#ff9d00\"\nplot(net, vertex.color=vcol)\n\n# Special operators for the indexing of edge sequences: %--%, %->%, %<-%\n# E(network)[X %--% Y] selects edges between vertex sets X and Y, ignoring direction\n# E(network)[X %->% Y] selects edges from vertex sets X to vertex set Y\n# E(network)[X %->% Y] selects edges from vertex sets Y to vertex set X\n\n# For example, select edges from newspapers to online sources:\nE(net)[ V(net)[type.label==\"Newspaper\"] %->% V(net)[type.label==\"Online\"] ]\n\n# Cocitation (for a couple of nodes, how many shared nominations they have)\ncocitation(net)\n\n\n\n# ================ 8. Subgroups and communities ================\n\n# Converting 'net' to an undirected network.\n# There are several ways to do that: we can create an undirected link between any pair\n# of connected nodes (mode=\"collapse), or create an undirected link for each directed\n# one (mode=\"each\"), or create an undirected link for each symmetric link (mode=\"mutual\").\n# In cases when A -> B and B -> A are collapsed into a single undirected link, we\n# need to specify what to do with the edge attributes. Here we have said that\n# the 'weight' of links should be summed, and all other edge attributes ignored.\n\nnet.sym <- as.undirected(net, mode=\"collapse\", edge.attr.comb=list(weight=\"sum\", \"ignore\"))\n\n\n# ------->> Cliques --------\n\n # Find cliques (complete subgraphs of an undirected graph)\ncliques(net.sym) # list of cliques \nsapply(cliques(net.sym), length) # clique sizes\nlargest_cliques(net.sym) # cliques with max number of nodes\n\nvcol <- rep(\"grey80\", vcount(net.sym))\nvcol[unlist(largest_cliques(net.sym))] <- \"gold\"\nplot(net.sym, vertex.label=V(net.sym)$name, vertex.color=vcol)\n\n\n\n# ------->> Communities --------\n\n# A number of algorithms aim to detect groups that consist of densely connected nodes\n# with fewer connections across groups. \n\n# Community detection based on edge betweenness (Newman-Girvan)\n# High-betweenness edges are removed sequentially (recalculating at each step)\n# and the best partitioning of the network is selected.\nceb <- cluster_edge_betweenness(net) \ndendPlot(ceb, mode=\"hclust\")\nplot(ceb, net) \n\n# Let's examine the community detection igraph object:\nclass(ceb)\nlength(ceb) # number of communities\nmembership(ceb) # community membership for each node\ncrossing(ceb, net) # boolean vector: TRUE for edges across communities\nmodularity(ceb) # how modular the graph partitioning is\n\n# High modularity for a partitioning reflects dense connections within communities \n# and sparse connections across communities.\n\n\n# Community detection based on propagating labels\n# Assigns node labels, randomizes, and replaces each vertex's label with\n# the label that appears most frequently among neighbors. Repeated until\n# each vertex has the most common label of its neighbors.\nclp <- cluster_label_prop(net)\nplot(clp, net)\n\n# Community detection based on greedy optimization of modularity\ncfg <- cluster_fast_greedy(as.undirected(net))\nplot(cfg, as.undirected(net))\n \n# We can also plot the communities without relying on their built-in plot:\nV(net)$community <- cfg$membership\ncolrs <- adjustcolor( c(\"gray50\", \"tomato\", \"gold\", \"yellowgreen\"), alpha=.6)\nplot(net, vertex.color=colrs[V(net)$community])\n\n\n-----------------------------------\n# * TASK FOR WORKSHOP PARTICIPANTS:\n\n# Plot the results of the three different community detection algorithms \n# applied to the Zachary karate club network. \n \n-----------------------------------\n\n\n# K-core decomposition\n# The k-core is the maximal subgraph in which every node has degree of at least k\n# This also means that the (k+1)-core will be a subgraph of the k-core.\n# The result here gives the coreness of each vertex in the network.\nkc <- coreness(net, mode=\"all\")\nplot(net, vertex.size=kc*6, vertex.label=kc, vertex.color=colrs[kc])\n\n\n# ================ 9. Assortativity and Homophily ================\n\n# Assortativity (homophily)\n# The tendency of nodes to connect to others who are similar on some variable.\n# assortativity_nominal() is for categorical variables (labels)\n# assortativity() is for ordinal and above variables\n# assortativity_degree() checks assortativity in node degrees\n\nV(net)$type.label\nV(net)$media.type\n\nassortativity_nominal(net, V(net)$media.type, directed=F)\n\nassortativity(net, V(net)$audience.size, directed=F)\n\nassortativity_degree(net, directed=F)\n\n\n# ================ The End ================\n\n\n\n\n" }, { "path": "20g-Network/network1.R", "content": "# Network i graph\n#http://kateto.net/networks-r-igraph\nlibrary(igraph) # Load the igraph package\ng1 <- graph( edges=c(1,2, 2,3, 3, 1), n=3, directed=F ) \nplot(g1)\nclass(g1)\ng1\n# Now with 10 vertices, and directed by default:\ng2 <- graph( edges=c(1,2, 2,3, 3, 1), n=10 )\nplot(g2) \n\ng3 <- graph( c(\"John\", \"Jim\", \"Jim\", \"Jill\", \"Jill\", \"John\")) # named vertices\n# When the edge list has vertex names, the number of nodes is not needed\nplot(g3)\n\ng4 <- graph( c(\"John\", \"Jim\", \"Jim\", \"Jack\", \"Jim\", \"Jack\", \"John\", \"John\"), \n isolates=c(\"Jesse\", \"Janis\", \"Jennifer\", \"Justin\") ) \n# In named graphs we can specify isolates by providing a list of their names.\nplot(g4, edge.arrow.size=.5, vertex.color=\"gold\", vertex.size=15, \n vertex.frame.color=\"gray\", vertex.label.color=\"black\", \n vertex.label.cex=0.8, vertex.label.dist=2, edge.curved=0.2) \nplot(graph_from_literal(a---b, b---c)) # the number of dashes doesn't matter\nplot(graph_from_literal(a--+b, b+--c))\nplot(graph_from_literal(a+-+b, b+-+c)) \nplot(graph_from_literal(a:b:c---c:d:e))\ngl <- graph_from_literal(a-b-c-d-e-f, a-g-h-b, h-e:f:i, j)\nplot(gl)\n\n#Edge, vertex, and network attributes -------------------------\nE(g4) # The edges of the object\n## + 4/4 edges (vertex names):\n## [1] John->Jim Jim ->Jack Jim ->Jack John->John\nV(g4) # The vertices of the object\n## + 7/7 vertices, named:\n## [1] John Jim Jack Jesse Janis Jennifer Justin\ng4[]\ng4[1,] \n\nV(g4)$name # automatically generated when we created the network.\n## [1] \"John\" \"Jim\" \"Jack\" \"Jesse\" \"Janis\" \"Jennifer\"\n\n## [7] \"Justin\"\nV(g4)$gender <- c(\"male\", \"male\", \"male\", \"male\", \"female\", \"female\", \"male\")\nE(g4)$type <- \"email\" # Edge attribute, assign \"email\" to all edges\nE(g4)$weight <- 10 # Edge weight, setting all existing edges to 10\n\n#Examine attributes:\nedge_attr(g4)\n## $type\n## [1] \"email\" \"email\" \"email\" \"email\"\n## \n## $weight\n## [1] 10 10 10 10\nvertex_attr(g4)\n\ngraph_attr(g4)\n## named list()\n#Another way to set attributes (you can similarly use set_edge_attr(), set_vertex_attr(), etc.):\ng4 <- set_graph_attr(g4, \"name\", \"Email Network\")\ng4 <- set_graph_attr(g4, \"something\", \"A thing\")\ngraph_attr_names(g4)\n\ngraph_attr(g4, \"name\")\n## [1] \"Email Network\"\ngraph_attr(g4)\n## $name\n## [1] \"Email Network\"\n## $something\n## [1] \"A thing\"\ng4 <- delete_graph_attr(g4, \"something\")\ngraph_attr(g4)\n\n## $name\n## [1] \"Email Network\"\nplot(g4, edge.arrow.size=.5, vertex.label.color=\"black\", vertex.label.dist=1.5,\n vertex.color=c( \"pink\", \"skyblue\")[1+(V(g4)$gender==\"male\")] ) \n\ng4s <- simplify( g4, remove.multiple = T, remove.loops = F, \n edge.attr.comb=c(weight=\"sum\", type=\"ignore\") )\nplot(g4s, vertex.label.dist=1.5)\ng4s\n\n#2.3 Specific graphs and graph models ----------------\neg <- make_empty_graph(40)\nplot(eg, vertex.size=10, vertex.label=NA)\neg <- make_empty_graph(100)\nplot(eg, vertex.size=6, vertex.label=1:100)\n\n# Full Graph\nfg <- make_full_graph(40)\nplot(fg, vertex.size=10, vertex.label=NA)\n\n#Simple star graph\nst <- make_star(40)\nplot(st, vertex.size=10, vertex.label=NA) \n\n#Tree graph\ntr <- make_tree(40, children = 4, mode = \"undirected\")\nplot(tr, vertex.size=10, vertex.label=NA) \n\n#Ring graph\nrn <- make_ring(40)\nplot(rn, vertex.size=10, vertex.label=NA)\n\n#Erdos-Renyi random graph model (‘n’ is number of nodes, ‘m’ is the number of edges).\ner <- sample_gnm(n=100, m=40) \nplot(er, vertex.size=6, vertex.label=NA) \n" }, { "path": "20g-Network/network2.R", "content": "#Network2\n\nnodes <- read.csv(\"Dataset1-Media-Example-NODES.csv\", header=T, as.is=T)\nlinks <- read.csv(\"Dataset1-Media-Example-EDGES.csv\", header=T, as.is=T)\nhead(nodes)\nhead(links)\nnrow(nodes); length(unique(nodes$id))\nnrow(links); nrow(unique(links[,c(\"from\", \"to\")]))\nlinks <- aggregate(links[,3], links[,-3], sum)\nlinks <- links[order(links$from, links$to),]\ncolnames(links)[4] <- \"weight\"\nrownames(links) <- NULL\n\n#DATASET 2: matrix\nnodes2 <- read.csv(\"Dataset2-Media-User-Example-NODES.csv\", header=T, as.is=T)\nlinks2 <- read.csv(\"Dataset2-Media-User-Example-EDGES.csv\", header=T, row.names=1)\nhead(nodes2)\nhead(links2)\n#We can see that links2 is an adjacency matrix for a two-mode network:\nlinks2 <- as.matrix(links2)\ndim(links2)\ndim(nodes2)\n\n#Turning networks into igraph objects\nlibrary(igraph)\nnet <- graph_from_data_frame(d=links, vertices=nodes, directed=T) \nclass(net)\n## [1] \"igraph\"\nnet\n\nE(net) # The edges of the \"net\" object\nV(net) # The vertices of the \"net\" object\nE(net)$type # Edge attribute \"type\"\nV(net)$media # Vertex attribute \"media\"\n#Now that we have our igraph network object, let’s make a first attempt to plot it.\nplot(net, edge.arrow.size=.4,vertex.label=NA)\nplot(net, edge.arrow.size=.4,vertex.label=links$from, vertex.label.cex=0.5)\n\nnet <- simplify(net, remove.multiple = F, remove.loops = T) \nas_edgelist(net, names=T)\n\nas_adjacency_matrix(net, attr=\"weight\")\n#Or data frames describing nodes and edges:\nas_data_frame(net, what=\"edges\")\nas_data_frame(net, what=\"vertices\")\n\n# DataSet2\nhead(nodes2)\nhead(links2)\nnet2 <- graph_from_incidence_matrix(links2)\n\ntable(V(net2)$type)\nnet2.bp <- bipartite.projection(net2)\nas_incidence_matrix(net2) %*% t(as_incidence_matrix(net2)) \n\nt(as_incidence_matrix(net2)) %*% as_incidence_matrix(net2)\nplot(net2.bp$proj1, vertex.label.color=\"black\", vertex.label.dist=1,\n \nvertex.size=7, vertex.label=nodes2$media[!is.na(nodes2$media.type)])\nplot(net2.bp$proj2, vertex.label.color=\"black\", vertex.label.dist=1,\n \n vertex.size=7, vertex.label=nodes2$media[ is.na(nodes2$media.type)])\n" }, { "path": "20g-Network/traveltime1.R", "content": "# Timing Calculations\n#install.packages(\"ggmap\")\n#http://aakarner.github.io/blog/2015/03/17/comparing-gtfs-results-from-two-different-sources/\n\nlibrary(ggmap)\n\n#input Origin Destination (it accepts Lat Long info)\nfrom <- c(\"houston\", \"houston\", \"dallas\")\nto <- c(\"waco, texas\", \"san antonio\", \"houston\")\n\nmapdist(from, to, mode = c(\"transit\"))\nmapdist(from, to, mode = c(\"driving\"))\n\nfrom <- c(\"delhi\")\nto <- c(\"noida\")\nmapdist(from, to, mode = c('walking'))\nfrom <- c(\"haldwani\")\nto <- c(\"kathgodam\")\nmapdist(from, to, mode = c('walking'))\n\n" }, { "path": "21a-OneGday/1bubblechart.R", "content": "#bubble Chart - ggplot\n\nlibrary(ggplot2)\nlibrary(data.table)\n#http://brooksandrew.github.io/simpleblog/articles/advanced-data-table/#method-1-in-line\n\n\nDTmtcars = data.table(mtcars)\n\n#this will create new column in the Table\n#DTmtcars[, mpg_biased_mean:=mean(mpg)), by=cyl] \n#this will not\nDTmtcars[, .(mean_mpg=mean(mpg)), by=cyl] \n#mean mpg and count by cyl\nDTmtcars[, .(count=.N, mean_mpg=mean(mpg)), by=cyl] \n#group by cyl and gear and calc count & mean mpg and sort by gear, cyl\nDT1 <- DTmtcars[ order(gear,cyl) , .(count=.N, mean_mpg=round(mean(mpg),2)), by=list(gear, cyl)] \nDT1\n\n#simple point with size as count\nggplot(DT1, aes(x=factor(gear), y=factor(cyl), size=count)) + geom_point()\n#change the point \nggplot(DT1, aes(x=factor(gear), y=factor(cyl))) + geom_point(aes(size=count, colour = 'black', fill='cornsilk'))\n\nggplot(DT1, aes(x=factor(gear), y=factor(cyl))) + geom_point(aes(size=count, alpha=mean_mpg), fill='green', colour = 'black', shape=21) + scale_size_area(name='Mean MPG', max_size = 20, guide=F) + geom_text(aes(y=as.numeric(factor(cyl)) - sqrt(count)/22 , label=count, size=mean_mpg), vjust=1.2) + labs(x='Gear', y='Cylinder', title='Bubble Chart in GGplot') + scale_alpha(guide=F)+ theme(plot.subtitle=element_text(size=12, hjust=0.5, face=\"italic\", color=\"blue\"))-> g\nDT1\ng = g + labs( subtitle='Using mtcars data set and data.table Nos show the count of Gear & Cyl: See Details in the table \\n Density of Colour indicates Mileage Levels, Number Labels and size of Circle indicate the count of Cars in the category of Gear & Cyl ' ) \n\n#\ngrid.arrange(tableGrob(DT1), g, ncol=2, widths=c(.2,.8))\n\ngrid.arrange( tableGrob(DT1, theme=ttheme_default()), g, ncol = 2, widths = c(.5, 1))\n\n#no legend : + theme(legend.position=\"none\")\n\n\n#https://flowingdata.com/2010/11/23/how-to-make-bubble-charts/\n\ncrime <- read.csv(\"http://datasets.flowingdata.com/crimeRatesByState2005.tsv\", header=TRUE, sep=\"\\t\")\n\nsymbols(crime$murder, crime$burglary, circles=crime$population)\n\nradius <- sqrt( crime$population/ pi )\nsymbols(crime$murder, crime$burglary, circles=radius)\nsymbols(crime$murder, crime$burglary, circles=radius, inches=0.35, fg=\"white\", bg=\"red\", xlab=\"Murder Rate\", ylab=\"Burglary Rate\")\n\nsymbols(crime$murder, crime$burglary, circles=radius, inches=0.35, xlab=\"Murder Rate\", ylab=\"Burglary Rate\", fg='red', bg=rgb(255,255,255,50,maxColorValue=255))\nsymbols(crime$murder, crime$burglary, squares=sqrt(crime$population), inches=0.5)\ntext(crime$murder, crime$burglary, crime$state, cex=0.5)\n?text\n\n\nlibrary(ggplot2)\nqplot(murder, burglary, data = crime, label = state, colour='red', size = 50*population)+ geom_text(colour = \"black\", hjust=0,vjust=1, size=3)+ xlim(c(0,10))\n" }, { "path": "21a-OneGday/1bubblechart2.R", "content": "#bubble chart -using mtcars\n\n#base plot\n\nsymbols(mtcars$mpg, mtcars$wt, mtcars$hp, inches=.2, xlab=\"MPG\", ylab=\"Weight\",title=' Bubble Plot using mtcars')\ntext(mtcars$mpg, mtcars$wt,rownames(mtcars), cex=.5, col=mtcars$vs)\ntitle(main='Bubble Plot using mtcars', sub = 'Size is proportional to hp')\n\n#ggplot - qplot\nqplot(x=mpg, y=wt, data = mtcars, label = rownames(mtcars), colour='red', size = hp/2) + geom_text(colour = \"black\", hjust=0,vjust=1, size=3) + ggtitle('Bubble Plot using qplot', subtitle = 'Size is proportional to hp/2')\n\n#ggplot\np1 <- ggplot(data=mtcars, aes(x=mpg, y=wt)) + geom_point(aes(size=hp/2), colour = 'green', alpha=.5)\np1\np2 = p1 + geom_text(aes(x=mpg, y=wt, label=rownames(mtcars)), colour = \"black\", hjust=0,vjust=1, size=3) \np2\np3 = p2 + scale_size_area( max_size = 10, guide=F) + scale_alpha(guide=F)\np3\np4 = p3 + labs(x='MPG', y='Weight', title='Bubble Chart in GGplot using MTCARS dataset') + theme(plot.subtitle=element_text(size=12, hjust=0.5, face=\"italic\", color=\"blue\"))\np4\np5 = p4 + labs( subtitle='Size of Point is proportional to HP/2' ) \np5\n\n#Another bubble chart showing count of values\nlibrary(dplyr)\nmtcars %>% group_by(gear, cyl) %>% summarise(count=n()) -> df1\ndf1\ndf1[,1:2] <- lapply(df1[,1:2], as.factor)\n\nstr(df1)\n\ng1 <- ggplot(df1, aes(x=gear, y=cyl) ) + geom_point(aes(size=count), fill='green', colour = 'black', shape=21)\ng1\ng2 = g1 + scale_size_area(name='Count', max_size = 20, guide=F) \ng2\ng3 = g2 + geom_text(aes(y=as.numeric(cyl) - sqrt(count)/20 , label=count, size=count/2), vjust=1.5)\ng3\ng4= g3 + labs(x='Gear', y='Cylinder', title='Bubble Chart in GGplot') \ng4\ng5 = g4 + labs( subtitle='Using mtcars data set and data.table Nos show the count of Gear & Cyl: See Details in the table \\n Number Labels and size of Circle indicate the count of Cars in the category of Gear & Cyl ' ) + theme(plot.subtitle=element_text(size=12, hjust=0.5, face=\"italic\", color=\"blue\"))\ng5\ng6 = g5 + annotate(geom='text', x=2, y=3.2, label='Max cars with Gears=3 & Cylinder=8', size=4)\ng6\ng7 = g6 + geom_segment(mapping=aes(x=2, y=3.2, xend=1, yend=3), arrow=arrow(), size=1, colour=\"blue\") \ng7\n#\nlibrary(gridExtra)\ngrid.arrange(tableGrob(df1), g7, ncol=2, widths=c(.2,.8))\n\ngrid.arrange( tableGrob(DT1, theme=ttheme_default()), g7, ncol = 2, widths = c(.5, 1))\n\n\nlibrary(sp)\n\n# NOT RUN {\ndata(meuse)\nhead(meuse)\ncoordinates(meuse) <- c(\"x\", \"y\") # promote to SpatialPointsDataFrame\ndf2 = df1\ncoordinates(df2) <- c('x','y')\nbubble(df2, \"count\", maxsize = 2.5, main = \"cadmium concentrations (ppm)\", key.entries = 2^(-1:4))\nbubble(meuse, \"zinc\", main = \"zinc concentrations (ppm)\",\n key.entries = 100 * 2^(0:4))\n# }" }, { "path": "21a-OneGday/multipleplots1.R", "content": "#multiple Plots\n\nlibrary(ggplot2)\np1 <- qplot(mpg, wt, data = mtcars, colour = cyl)\np2 <- qplot(mpg, data = mtcars) + ggtitle(\"title\")\np3 <- qplot(mpg, data = mtcars, geom = \"dotplot\")\np4 <-\n p1 + facet_wrap( ~ carb, nrow = 1) + theme(legend.position = \"none\") +\n ggtitle(\"facetted plot\")\np4\n\ngrid.arrange(p1, p2, nrow = 1)\n\ngl=list(p1,p2,p3,p4)\ngrid.arrange(\n grobs = gl,\n widths = c(2, 1, 1),\n layout_matrix = rbind(c(1, 2, NA),\n c(3, 3, 4))\n)\n\nlibrary(grid)\ng <- ggplotGrob(qplot(1, 1) + theme(plot.background = element_rect(colour = \"black\")))\ng\nqplot(1:10, 1:10) +\n annotation_custom(grob = g, xmin = 1, xmax = 5, ymin = 5, ymax = 10 ) +\n annotation_custom(grob = rectGrob(gp = gpar(fill = \"white\")), xmin = 7.5, xmax = Inf,ymin = -Inf,ymax = 5 )\n\n\nlibrary(gtable)\ng2 <- ggplotGrob(p2)\ng3 <- ggplotGrob(p3)\ng <- rbind(g2, g3, size = \"first\")\ng$widths <- unit.pmax(g2$widths, g3$widths)\ngrid.newpage()\ngrid.draw(g)\n\n\nlibrary(ggpubr)\np1 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +\n geom_point()\np2 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +\n geom_point() + facet_wrap(~ cyl, ncol = 2, scales = \"free\") +\n guides(colour = \"none\") +\n theme()\n\nggarrange(p1, p2, widths = 1:2)\n\ngrid.arrange( p3, p3, p3, nrow = 1, top = \"Title of the page\",\n bottom = textGrob(\"this footnote is right-justified\", gp = gpar(fontface = 3, fontsize = 9), hjust = 1, x = 1 ))\n\n\ngrid.arrange( tableGrob(mtcars[1:4, 1:4]), p2, ncol = 2, widths = c(1.5, 1),\n clip = FALSE )\n" }, { "path": "21a-OneGday/tableGrob.R", "content": "#https://cran.r-project.org/web/packages/gridExtra/vignettes/tableGrob.html\n\nlibrary(gridExtra)\nlibrary(grid)\nd <- head(iris[,1:3])\ngrid.table(d)\n\nd[2,3] <- \"this is very wwwwwide\"\nd[1,2] <- \"this\\nis\\ntall\"\ncolnames(d) <- c(\"alpha*integral(xdx,a,infinity)\",\n \"this text\\nis high\", 'alpha/beta')\n\ntt <- ttheme_default(colhead=list(fg_params = list(parse=TRUE)))\ngrid.table(d, theme=tt)\n\n\ntt1 <- ttheme_default()\ntt2 <- ttheme_minimal()\ntt3 <- ttheme_minimal(\n core=list(bg_params = list(fill = blues9[1:4], col=NA),\n fg_params=list(fontface=3)),\n colhead=list(fg_params=list(col=\"navyblue\", fontface=4L)),\n rowhead=list(fg_params=list(col=\"orange\", fontface=3L)))\n\ngrid.arrange(\n tableGrob(iris[1:4, 1:2], theme=tt1),\n tableGrob(iris[1:4, 1:2], theme=tt2),\n tableGrob(iris[1:4, 1:2], theme=tt3),\n nrow=1)\n\n\nt1 <- ttheme_default(core=list(\n fg_params=list(fontface=c(rep(\"plain\", 4), \"bold.italic\")),\n bg_params = list(fill=c(rep(c(\"grey95\", \"grey90\"),\n length.out=4), \"#6BAED6\"),\n alpha = rep(c(1,0.5), each=5))\n))\ngrid.newpage()\ngrid.table(iris[1:5, 1:3], theme = t1)\n\n\ntt1 <- ttheme_default()\ntt2 <- ttheme_default(core=list(fg_params=list(hjust=1, x=0.9)),\n rowhead=list(fg_params=list(hjust=1, x=0.95)))\ntt3 <- ttheme_default(core=list(fg_params=list(hjust=0, x=0.1)),\n rowhead=list(fg_params=list(hjust=0, x=0)))\ngrid.arrange(\n tableGrob(mtcars[1:4, 1:2], theme=tt1),\n tableGrob(mtcars[1:4, 1:2], theme=tt2),\n tableGrob(mtcars[1:4, 1:2], theme=tt3),\n nrow=1)\n\n\ng <- g2 <- tableGrob(iris[1:4, 1:3], cols = NULL, rows=NULL)\ng2$widths <- unit(rep(1/ncol(g2), ncol(g2)), \"npc\")\ngrid.arrange(rectGrob(), rectGrob(), nrow=1)\ngrid.arrange(g, g2, nrow=1, newpage = FALSE)\n\n\nd1 <- PlantGrowth[1:3,1, drop=FALSE]\nd2 <- PlantGrowth[1:2,1:2]\n\ng1 <- tableGrob(d1)\ng2 <- tableGrob(d2)\n\nhaligned <- gtable_combine(g1,g2, along=1)\nvaligned <- gtable_combine(g1,g2, along=2)\ngrid.newpage()\ngrid.arrange(haligned, valigned, ncol=2)\n\n\nlibrary(gtable)\ng <- tableGrob(iris[1:4, 1:3], rows = NULL)\ng <- gtable_add_grob(g,\n grobs = rectGrob(gp = gpar(fill = NA, lwd = 2)),\n t = 2, b = nrow(g), l = 1, r = ncol(g))\ng <- gtable_add_grob(g,\n grobs = rectGrob(gp = gpar(fill = NA, lwd = 2)),\n t = 1, l = 1, r = ncol(g))\ngrid.draw(g)\n" }, { "path": "23a-Strings/abvn.R", "content": "# Abbreviations\n\nhead(USArrests)\nstr(USArrests)\n# rows as strings\n\n# name of States\nstates = rownames(USArrests)\nhead(states)\n\n# substr\n(s1= substr(x = states, start=1, stop=4))\n\n# Find duplicates\nduplicated(s1)\nsum(duplicated(s1), na.rm=TRUE)\nsum(duplicated(s1))\nsum(duplicated(s1), na.rm=FALSE)\n# sums only TRUE values\n\ntable(duplicated(s1))\n\nlength(s1)\nlength(unique(s1))\n\n# Better\n(states2 = abbreviate(states))\nhead(states2)\n# remove rownames for convenience\nnames(states2) = NULL\nstates2\nsum(duplicated(states2))\n\n# Change length of abbreviation to 5\n(states3 = abbreviate(states, minlength = 5))\nnames(states3) = NULL\nstates3\n\n" }, { "path": "23a-Strings/latex.R", "content": "library(Hmisc)\nd1 = data.frame(drugId = c(11, 22, 33), drugInfo = c(\"$36.\",\n \"2 for $11\", \"50% sale\"), stringsAsFactors = FALSE)\nd1$drugInfo = gsub(\"\\\\$\", \"\\\\\\\\$\", d1$drugInfo)\nd1$drugInfo = gsub(\"\\\\%\", \"\\\\\\\\%\", d1$drugInfo)\nd1\n\nlatex(d1, rowname = NULL, colheads = c(\"Drug ID\", \"Drug Price\"),\n file = \"\")\n" }, { "path": "23a-Strings/output.txt", "content": " Programming with data is fun with R" }, { "path": "23a-Strings/paste1.R", "content": "# Paste Functions\n## When passing a single vector, paste0 and paste work like as.character.\npaste0(1:12)\npaste(1:12) # same\nas.character(1:12) # same\n\n## If you pass several vectors to paste0, they are concatenated in a\n## vectorized way.\n(nth <- paste0(1:12, c(\"st\", \"nd\", \"rd\", rep(\"th\", 9))))\n\n## paste works the same, but separates each input with a space.\n## Notice that the recycling rules make every input as long as the longest input.\npaste(month.abb, \"is the\", nth, \"month of the year.\")\npaste(month.abb, letters)\n\n## You can change the separator by passing a sep argument\n## which can be multiple characters.\npaste(month.abb, \"is the\", nth, \"month of the year.\", sep = \"_*_\")\n\n## To collapse the output into a single string, pass a collapse argument.\npaste0(nth, collapse = \", \")\n\n## For inputs of length 1, use the sep argument rather than collapse\npaste(\"1st\", \"2nd\", \"3rd\", collapse = \", \") # probably not what you wanted\npaste(\"1st\", \"2nd\", \"3rd\", sep = \", \")\n\n## You can combine the sep and collapse arguments together.\npaste(month.abb, nth, sep = \": \", collapse = \"; \")\n\n## Using paste() in combination with strwrap() can be useful\n## for dealing with long strings.\n(title <- paste(strwrap(\n \"Stopping distance of cars (ft) vs. speed (mph) from Ezekiel (1930)\",\n width = 15), collapse = \"\\n\"))\nplot(dist ~ speed, cars, main = title)\n" }, { "path": "23a-Strings/setop1.R", "content": "#set Operations\n\nunion() # set union\nintersect() # Intersection\nsetdiff() # set difference\nsetequal() # eualt sets\nidentical() # exact equality\nis.element() # is element\n#%in%() # contains\nsort() # sorting\npaste(rep()) # repetition\n\n\n(first1 = c('Achal','Apoorva','Goldie','Hitesh','Kaustav','Meena','Rashmi','Shruti','Shubham','Vijay','Lalit'))\n(second = c('Kumar','Karn','Sahni','Mann','Chatterjee','Srisha','Ranjan','Sinha','Pujan','Singh','Sahni'))\n(first2 = c('Achal','Apoorva','Kaustav','Rashmi','Shruti','Vijay','Lalit','Varun','Dhiraj'))\n\n# union -------\n\nunion(first1, second) #nothing\nunion(first1, first2)\n\nset1 =c('some', 'random', 'words', 'some')\nset2 =c('some', 'many', 'none', 'few')\nunion(set1, set2)\n\nset3 = c('A','B','C','E')\nset4 = c('B','D','F','P')\nunion(set3, set4) # remove duplicates\n\n#intersect\nintersect(first1, first2) # common names\nintersect(set3,set4)\n\n#setdiff\nsetdiff(set3,set4)\nfirst1 ; first2\nsetdiff(first1, first2) # check ? what is there in 1st & not in 2\nsetdiff(first2, first1) # check ? what is there in 2nd & not in 1\n\n# set equal : same elements\nsetequal(first1, first1)\nsetequal(first1, first2)\n\nset4 = c('A','B','C','E')\nset5 = c('B','A','E','C')\nsetequal(set4, set5)\nset6 = c('B','A','E','F')\nsetequal(set4, set6)\n\n# identical : element by element\nidentical(set4,set5)\nset7 = c('A','B','C','E')\nidentical(set4, set7)\n\n# check if element is contained in ..\n\nis.element('A', set4)\nis.element('Dhiraj', first2)\nis.element('Varun', first1)\nis.element('Varun', first2)\n\n'Dhiraj' %in% first2 # %in% operator\n'A' %in% set4\n\n# sort\nsort(first1)\nsort(set5)\nsort(first1, decreasing = T)\nsort(first1, decreasing = F)\n\n\n# Repetition\n\npaste(rep('x',4), collapse='') # xxxx\npaste(rep('x',4)) # 'x' 'x' 'x' 'x'\n" }, { "path": "23a-Strings/strcmpt1.R", "content": "# String Computations\n\n# name of States\nstates = rownames(USArrests)\nhead(states)\n\n# Histogram\nnchar(states)\nhist(nchar(states))\nhist(nchar(states), main='Histogram',\n xlab='No of Characters in US State Names')\n\n\n# Find no of vowels in Names\n\n#no match = -1\n# positions of a's\nposition_a = gregexpr(pattern='a', text=states, ignore.case = T)\nposition_a\n\nstr(position_a) # positions a occured 1 3 6\nstates[1]\nhead(states,n=10)\nposition_a[c(1,7)] # 1st -1,3,5,7; 7th -1\n\n# how many a's\n(num_a = sapply(position_a, function(x) ifelse(x[1] > 0, length(x), 0)))\nx = c(1,3,0)\nsapply(x, function(x) ifelse(x[1] > 0, length(x), 0))\nx = c('dhiraj', 'upadhyaya')\n(num_a = sapply(position_a, function(x) ifelse(x[1] > 0, length(x), 0)))\nhead(position_a)\n\n# Same command - stringr\nlibrary(stringr)\nstr_count(states,'a') # only small case : no option for this\n# transform to upper/lower case and then count\nstr_count(tolower(states), 'a')\n\n# Same can be done for other vowels a, e,i,o, u\nvowels = c('a', 'e', 'i', 'o', 'u')\n# vector for storing results of positions\nnum_vowels = vector(mode='integer', length=5) # blank int vector\nnum_vowels\n# find vowels in each name\n#num_aux = str_count(tolower(states), 'e') # check for e\n#sum(num_aux)\n\nfor(j in seq_along(vowels)) {\n num_aux = str_count(tolower(states), vowels[j])\n num_vowels[j] = sum(num_aux)\n}\nnum_vowels\nnames(num_vowels) = vowels\nnum_vowels\n\n# sort\nsort(num_vowels, decreasing = T)\n\n# draw barplot\nbarplot(num_vowels, main='No of vowels in US States Names',\n border=NA, ylim=c(0,80))\n" }, { "path": "23a-Strings/string1.R", "content": "#Strings http://gastonsanchez.com/Handling_and_Processing_Strings_in_R.pdf\n# names of states\nstates = rownames(USArrests)\n# substr\nsubstr(x = states, start = 1, stop = 4)\n\n# abbreviate state names\nstates2 = abbreviate(states)\nstates2\n\n# remove vector names (for convenience)\nnames(states2) = NULL\nstates2\n\n\n# abbreviate state names with 5 letters\nabbreviate(states, minlength = 5)\n\n\n# size (in characters) of each name\nstate_chars = nchar(states)\n# longest name\nstates[which(state_chars == max(state_chars))]\n\n# get states names with 'k'\ngrep(pattern = \"k\", x = states, value = TRUE)\n\n# get states names with 'w'\ngrep(pattern = \"w\", x = states, value = TRUE)\n\n# get states names with 'w' or 'W'\ngrep(pattern = \"[wW]\", x = states, value = TRUE)\n\n# get states names with 'w'\ngrep(pattern = \"w\", x = tolower(states), value = TRUE)\n\n# get states names with 'W'\ngrep(pattern = \"W\", x = toupper(states), value = TRUE)\n\n# get states names with 'w'\ngrep(pattern = \"w\", x = states, value = TRUE, ignore.case = TRUE)\n\n# histogram\nhist(nchar(states), main = \"Histogram\",\n xlab = \"number of characters in US State names\")\n\n# position of a's\npositions_a = gregexpr(pattern = \"a\", text = states, ignore.case = TRUE)\n\n# how many a's?\nnum_a = sapply(positions_a, function(x) ifelse(x[1] > 0, length(x), 0))\nnum_a\n\n# load stringr (remember to install it first)\nlibrary(stringr)\n\n# total number of a's\nstr_count(states, \"a\")\n" }, { "path": "23a-Strings/strjoin.R", "content": "# Strings Join\nlibrary(stringr)\nstr_c(\"Letter: \", letters)\nstr_c(\"Letter\", letters, sep = \": \")\nstr_c(letters, \" is for\", \"...\")\nstr_c(letters[-26], \" comes before \", letters[-1])\n\nstr_c(letters, collapse = \"\")\nstr_c(rev(letters), collapse = \"\")\n\nstr_c(letters, collapse = \", \")\n" }, { "path": "23a-Strings/strlength.R", "content": "# String Length\n\n# name of States\nstates = rownames(USArrests)\nhead(states)\n\n# size of characters\nlength(states) # length of vector\n(state_chars = nchar(states))\n\n# Longest name\nmax(state_chars)\nstate_chars\nstates[which(state_chars == max(state_chars))]\n" }, { "path": "23a-Strings/strman1.R", "content": "# String Manipulations\n\n?paste\npaste (..., sep = \" \", collapse = NULL)\npaste0(..., collapse = NULL)\n\n#paste\n(PI = paste('The Life of ', pi))\n# default sep space ' '\n\n(IloveR = paste('I', 'love', 'R', sep='-'))\n\n# different lengths\npaste('X', 1:5, sep='.')\n\n# paste with collapse after each paste what to do ?\npaste(1:3, c('!', '?', \"+\"), sep='', collapse='')\npaste(1:3, c('!', '?', \"+\"), sep='')\n\npaste(1:3, c('!', '?', \"+\"), sep='%%', collapse='***')\npaste(1:3, c('!', '?', \"+\"), sep='%%')\n\n# after end of each character\n\npaste(1:3,c('a','b','c'), c('A','B','C'))\n# explore with sep & collapse\n\n# handle NA - use as character\n(evalue = paste(\" The value of 'e' is \", exp(1), NA))\n\n# paste0\n#eq of paste(..., sep=\"\", collapse)\npaste0('lets', 'collapse', 'all', 'these', 'words')\npaste('lets', 'collapse', 'all', 'these', 'words')\npaste('lets', 'collapse', 'all', 'these', 'words', collapse='') # check this\n\npaste('lets', 'collapse', 'all', 'these', 'words', sep='-', collapse='*') # check this\n\n" }, { "path": "23a-Strings/strman2.R", "content": "# String Manipulations\n\nnchar() # no of characters\ntolower() # convert to lowercase\ntoupper() # convert to uppercase\ncasefold() # case folding\nchartr() # character translation\nabbreviate() # abbreviation\nsubstr() # substrings of a character vector\n\n# ---" }, { "path": "23a-Strings/strman3.R", "content": "# String Manipulations2\n\n\nnchar() # no of characters\ntolower() # convert to lowercase\ntoupper() # convert to uppercase\ncasefold() # case folding\nchartr() # character translation\nabbreviate() # abbreviation\nsubstring() # substrings of a character vector\nsubstr() # substrings of a character vector\n\n\n#nchar -------------\n(c1 = c('How', 'Many', 'Characters?'))\n(nchar(c1))\nlength(c1)\n\n(c2= c('How Many Characters?')) # white spaces also counted\n\n(nchar(c2)) # white spaces also counted\nlength(c2) # no of elements\n\n#tolower\ntolower(c1)\n\ntoupper(c1)\n\ncasefold(c1)\ncasefold(c1, upper=TRUE) #opposite\nc3 = c('DHIRAJ','UPADHYAYA')\ncasefold(c3) # lower case\ncasefold(c3, upper=TRUE)\n\n\nchartr( old= 'a',new= 'A', x= c1) # find a, replace with A\nchartr('A', 'a', c3)\nchartr('RA', 'a', c3) # RA is longer than a\n\nsname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni')\nchartr('a', 'A', sname) #multiple replacements\n\n?abbreviate\nabbreviate(names.arg, minlength = 4, use.classes = TRUE,\n dot = FALSE, strict = FALSE,\n method = c(\"left.kept\", \"both.sides\"), named = TRUE)\nsname\nabbreviate(sname)\n\ncolors[1:4]\n(some_colors = colors[1:4])\n(colors1 = abbreviate(some_colors))\n\n(colors2 = abbreviate(some_colors, minlength = 5))\n(colors2 = abbreviate(some_colors, minlength = 5, method='both.sides'))\n\n(aname2 = abbreviate(sname, minlength = 5, method='both.sides'))\n\n\n# Replace by substr\n?substr\nsubstr(x, start, stop)\nsubstring(text, first, last = 1000000L)\nsubstr(x, start, stop) <- value\nsubstring(text, first, last = 1000000L) <- value\n\nsubstr('bcdef', start=2, stop=4) # extract cde\n(x= month.abb[1:6])\nsubstr(x,2,2) <- '#'\nx\n\ny= c('may', 'the', 'force', 'be', 'with', 'you')\nsubstr(y,2,3) <- \":)\"\ny\n\n# replace with recycling\nz= c('may', 'the', 'force', 'be', 'with', 'you')\nsubstr(z, 2,1) <- c('#', '@')\nsubstr(z, 2,2) <- c('#', '@')\nz\n\n# substring - similar to substr\nsubstring('ABCDEF', 2, 4)\nsubstr('ABCDEF', 2, 4)\n\nsubstring('ABCDEF', 1:6, 1:6) # each character\n\n# multiple replacement with recycling ???? check\n(text = c('more', 'emotions', 'are', 'better', 'than', 'less'))\nsubstring(text, 1:3) <- c('', 'zzz')\ntext\n(text1 = c('dhiraj','more', 'emotions', 'are', 'better', 'than', 'less'))\nsubstring(text1, 1:3) <- c('', 'yyz')\ntext1\n\n(sname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni'))\nsubstring(sname, 1:4) <- c('', 'sods')\nsname\n\n(sname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni'))\nsubstring(sname, 2:5) <- c('', 'MUIT')\nsname\n\n" }, { "path": "23a-Strings/strman4.R", "content": "# Set Operations\n\n\nunion()# set union\nintersect() #intersection\nsetdiff() #set difference\nsetequal() #equal sets\nidentical() #exact equality\nis.element() #is element\n#%in%() #contains\nsort() #sorting\npaste(rep()) #repetition\n\n# Union total, no repeats\nset1 = c('A', 'B', 'C', 'D')\nset2 = c('A', 'C', 'F' , 'G')\n\nunion(set1,set2) # no repeats\n\n#intersect common\nintersect(set1, set2)\nintersect(set2, set1)\n\n# setdiff wrt to each other\nsetdiff(set1, set2) # set1 not in set2\nsetdiff(set2, set1) # set2 not in set1\n\n#setequal\n\nsetequal(set1, set2)\nset3 = set1\nsetequal(set1, set3)\n\n#identical\nset1\nset4 = c('B','C', 'D', 'A') # same as set1 but in different order\nidentical(set1, set2)\nidentical(set1, set3)\nidentical(set1, set4) # not same element by element\nsetequal(set1, set4) # but same elements present\n\n#is.element\n# three vectors\nset10 = c(\"some\", \"stuff\", \"to\", \"play\", \"with\")\nelem1 = \"play\"\nelem2 = \"crazy\"\n\nis.element(elem1, set10)\nis.element('A', set1)\nis.element('P', set1)\n\n\n# %in% operator\nelem1 %in% set10\n'A' %in% set1\n'P' %in% set1\n\n# sort\nsort(set10)\nset4\nsort(set4)\nsort(set4, decreasing=T)\n\nset12 = c(\"today\", \"produced\", \"example\", \"beautiful\", \"1\", \"nicely\")\nsort(set12) # alpha - numbers first\nsort(set12, decreasing=T)\n\n# rep()\n\npaste(rep('x', 4), collapse='')\npaste(rep('x', 4))\n\n" }, { "path": "23a-Strings/strman5.R", "content": "#Stringr 5\nlibrary(stringr)\ndetach(\"package:stringr\", unload=TRUE)\n\n# Drawbacks\n\n# some text vector\n(text = c(\"one\", \"two\", \"three\", NA, \"five\"))\n\n# how many characters in each string?\nnchar(text)\n## [1] 3 3 5 2 4 ? NA should be left blank instead of 2\n\n\n# this works fine\npaste(\"University\", \"of\", \"California\", \"Berkeley\")\n## [1] \"University of California Berkeley\"\n# this works fine too\npaste(\"University\", \"of\", \"California\", \"Berkeley\")\n\n# this is weird\npaste(\"University\", \"of\", \"California\", \"Berkeley\", NULL)\n## [1] \"University of California Berkeley \"\n\n# this is ugly\npaste(\"University\", \"of\", \"California\", \"Berkeley\", NULL, character(0),\n \"Go Bears!\")\n## [1] \"University of California Berkeley Go Bears!\"\n\n#The objects NULL and character(0)\n#have zero length, yet when included inside paste() they are treated as an empty string \"\".\n\n#install.packages('stringr')\nlibrary(stringr)\n\n#Function Description Similar to\nstr_c() #string concatenation paste()\nstr_length() #number of characters nchar()\nstr_sub()# extracts substrings substring()\nstr_dup() #duplicates characters none\nstr_trim() #removes leading and trailing whitespace none\nstr_pad() #pads a string none\nstr_wrap() #wraps a string paragraph strwrap()\nstr_trim() #trims a string none\n\n#str_c\n# default usage\nstr_c(\"May\", \"The\", \"Force\", \"Be\", \"With\", \"You\")\n## [1] \"MayTheForceBeWithYou\"\n\n# removing zero length objects\nstr_c(\"May\", \"The\", \"Force\", NULL, \"Be\", \"With\", \"You\", character(0))\n## [1] \"MayTheForceBeWithYou\"\npaste(\"May\", \"The\", \"Force\", NULL, \"Be\", \"With\", \"You\", character(0))\n#[1] \"May The Force Be With You \"\n#: zero length arguments like\n#NULL and character(0) are silently removed by str c().\n\n# changing separator\nstr_c(\"May\", \"The\", \"Force\", \"Be\", \"With\", \"You\", sep = \"_\")\n## [1] \"May_The_Force_Be_With_You\"\n\n# synonym function 'str_join'\nstr_join(\"May\", \"The\", \"Force\", \"Be\", \"With\", \"You\", sep = \"-\")\n## [1] \"May-The-Force-Be-With-You\"\n\n#string length\n\n# some text (NA included)\nsome_text = c(\"one\", \"two\", \"three\", NA, \"five\")\n# compare 'str_length' with 'nchar'\nnchar(some_text)\n## [1] 3 3 5 2 4 check it is same\nstr_length(some_text)\n## [1] 3 3 5 NA 4\n\n# some factor\nsome_factor = factor(c(1, 1, 1, 2, 2, 2), labels = c(\"good\", \"bad\"))\nsome_factor\n## [1] good good good bad bad bad\n## Levels: good bad\n# try 'nchar' on a factor\n\nnchar(some_factor)\n## Error: ’nchar()’ requires a character vector\n# now compare it with 'str_length'\nstr_length(some_factor)\n## [1] 4 4 4 3 3 3\n\n# Extract Strings -------\nstr_sub(string, start = 1L, end = -1L)\n\n# some text\nlorem = \"Lorem Ipsum\"\n# apply 'str_sub'\nstr_sub(lorem, start = 1, end = 5)\n## [1] \"Lorem\"\n# equivalent to 'substring'\nsubstring(lorem, first = 1, last = 5)\n## [1] \"Lorem\"\n# another example\nstr_sub(\"adios\", 1:3)\n## [1] \"adios\" \"dios\" \"ios\" ???\n\n# can work with negative indices\n\n# some strings\nresto = c(\"brasserie\", \"bistrot\", \"creperie\", \"bouchon\")\n# 'str_sub' with negative positions\nstr_sub(resto, start = -4, end = -1)\n## [1] \"erie\" \"trot\" \"erie\" \"chon\"\n# compared to substring (useless)\nsubstring(resto, first = -4, last = -1)\n## [1] \"\" \"\" \"\" \"\"\n\n# extracting sequentially ???\nlorem\nseq_len(nchar(lorem))\nstr_sub(lorem, seq_len(nchar(lorem)))\n\nsubstring(lorem, seq_len(nchar(lorem)))\n\n# reverse substrings with negative positions\nstr_sub(lorem, -seq_len(nchar(lorem)))\n\nsubstring(lorem, -seq_len(nchar(lorem)))\n\n#We can use str sub() not only for extracting subtrings but also for replacing substrings:\n# replacing 'Lorem' with 'Nullam'\nlorem = \"Lorem Ipsum\"\nstr_sub(lorem, 1, 5) <- \"Nullam\"\nlorem\n\n# replacing with negative positions\nlorem = \"Lorem Ipsum\"\nstr_sub(lorem, -1) <- \"Nullam\"\nlorem\n\n# multiple replacements\nlorem = \"Lorem Ipsum\"\nstr_sub(lorem, c(1, 7), c(5, 8)) <- c(\"Nullam\", \"Enim\")\nlorem\n## [1] \"Nullam Ipsum\" \"Lorem Enimsum\"\n\n\n#Duplicates\nstr_dup(string, times)\n# default usage\nstr_dup(\"hola\", 3)\n\n# use with differetn 'times'\nstr_dup(\"adios\", 1:3)\n## [1] \"adios\" \"adiosadios\" \"adiosadiosadios\"\n\n# use with a string vector\nwords = c(\"lorem\", \"ipsum\", \"dolor\", \"sit\", \"amet\")\nstr_dup(words, 2)\n## [1] \"loremlorem\" \"ipsumipsum\" \"dolordolor\" \"sitsit\" \"ametamet\"\nstr_dup(words, 1:5)\nstr_dup(words, 1:15) # recycle\n\n\n#Padding\nstr_pad(string, width, side = \"left\", pad = \" \")\n\n# default usage\nstr_pad(\"hola\", width = 7)\n## [1] \" hola\"\n\n# pad both sides\nstr_pad(\"adios\", width = 7, side = \"both\")\n## [1] \" adios \"\n# left padding with '#'\nstr_pad(\"hashtag\", width = 8, pad = \"#\")\n## [1] \"#hashtag\"\n\n# pad both sides with '-'\nstr_pad(\"hashtag\", width = 9, side = \"both\", pad = \"-\")\n## [1] \"-hashtag-\"\n\n#string wrap\nstr_wrap(string, width = 80, indent = 0, exdent = 0)\n\n# quote (by Douglas Adams)\nsome_quote = c(\n \"I may not have gone\",\n \"where I intended to go,\",\n \"but I think I have ended up\",\n \"where I needed to be\")\n# some_quote in a single paragraph\nsome_quote = paste(some_quote, collapse = \" \")\nsome_quote\n# display paragraph with width=30\ncat(str_wrap(some_quote, width = 30))\n\n# display paragraph with first line indentation of 2\ncat(str_wrap(some_quote, width = 30, indent = 2), \"\\n\")\n\n# display paragraph with following lines indentation of 3\ncat(str_wrap(some_quote, width = 30, exdent = 3), \"\\n\")\n\n\n#Trimming with str trim() --------\nstr_trim(string, side = \"both\")\n\n# text with whitespaces\nbad_text = c(\"This\", \" example \", \"has several \", \" whitespaces \")\n\n# remove whitespaces on the left side\nstr_trim(bad_text, side = \"left\")\n\n## [1] \"This\" \"example \" \"has several \" \"whitespaces \"\n\n# remove whitespaces on the right side\n\nstr_trim(bad_text, side = \"right\")\n## [1] \"This\" \" example\" \"has several\" \" whitespaces\"\n\n# remove whitespaces on both sides\nstr_trim(bad_text, side = \"both\")\n\n#Word extraction with word() ---------\nword(string, start = 1L, end = start, sep = fixed(\" \"))\n\n# some sentence\n(change = c(\"Be the change\", \"you want to be\"))\n\n# extract first word\nword(change, 1)\n\n## [1] \"Be\" \"you\"\n# extract second word\nword(change, 2)\n## [1] \"the\" \"want\"\n\n# extract last word\nword(change, -1)\n## [1] \"change\" \"be\"\n\n# extract all but the first words\nword(change, 2, -1)\n## [1] \"the change\" \"want to be\"\n\nword(change, c(1,2), c(3,4)) # ???\n\n\n\n" }, { "path": "23a-Strings/strman6.R", "content": "# String Manipulations 6\n# Regular Expressions\n#A regular expression (a.k.a. regex) is a special text string for describing a certain amount\n#of text. This “certain amount of text” receives the formal name of pattern. Hence we say\n#that a regular expression is a pattern that describes a set of strings.\n\n# Meta Characters . \\ | ( ) [ { $ * + ?\n#escape them with a backslash \\. In R, however, we\n#need to escape them with a double backslash \\\\\n# Metacharacter Literal meaning Escape in R\n# . the period or dot \\\\.\n# $ the dollar sign \\\\$\n# * the asterisk or star \\\\*\n# + the plus sign \\\\+\n# ? the question mark \\\\?\n# | the vertical bar or pipe symbol \\\\|\n# \\ the backslash \\\\\\\\\n# ^ the caret \\\\^\n# [ the opening square bracket \\\\[\n# ] the closing square bracket \\\\]\n# { the opening curly bracket \\\\{\n# } the closing curly bracket \\\\}\n# ( the opening round bracket \\\\(\n# ) the closing round bracket \\\\)\n\n#dollar sign $ with an empty string \"\"\n# string\nmoney = \"$money\"\n# the naive but wrong way\nsub(pattern = \"$\", replacement = \"\", x = money)\n## [1] \"$money\"\n\n# the usual (in other languages) yet wrong way in R\nsub(pattern = \"\\$\", replacement = \"\", x = money)\n\n# the right way in R\nsub(pattern = \"\\\\$\", replacement = \"\", x = money)\n## [1] \"money\"\n\n# silly egs\n# dollar\nsub(\"\\\\$\", \"\", \"$Peace-Love\")\n## [1] \"Peace-Love\"\n# dot\nsub(\"\\\\.\", \"\", \"Peace.Love\")\n## [1] \"PeaceLove\"\n# plus\nsub(\"\\\\+\", \"\", \"Peace+Love\")\n## [1] \"PeaceLove\"\n# caret\nsub(\"\\\\^\", \"\", \"Peace^Love\")\n## [1] \"PeaceLove\"\n# vertical bar\nsub(\"\\\\|\", \"\", \"Peace|Love\")\n## [1] \"PeaceLove\"\n# opening round bracket\nsub(\"\\\\(\", \"\", \"Peace(Love)\")\n## [1] \"PeaceLove)\"\n\n# closing round bracket\nsub(\"\\\\)\", \"\", \"Peace(Love)\")\n## [1] \"Peace(Love\"\n# opening square bracket\nsub(\"\\\\[\", \"\", \"Peace[Love]\")\n## [1] \"PeaceLove]\"\n# closing square bracket\nsub(\"\\\\]\", \"\", \"Peace[Love]\")\n## [1] \"Peace[Love\"\n# opening curly bracket\nsub(\"\\\\{\", \"\", \"Peace{Love}\")\n\n## [1] \"PeaceLove}\"\n# closing curly bracket\nsub(\"\\\\}\", \"\", \"Peace{Love}\")\n## [1] \"Peace{Love\"\n# double backslash\nsub(\"\\\\\\\\\", \"\", \"Peace\\\\Love\")\n## [1] \"PeaceLove\"\n\n\n\n# Sequences ------------\n# Anchor Description\n# \\\\d match a digit character\n# \\\\D match a non-digit character\n# \\\\s match a space character\n# \\\\S match a non-space character\n# \\\\w match a word character\n# \\\\W match a non-word character\n# \\\\b match a word boundary\n# \\\\B match a non-(word boundary)\n# \\\\h match a horizontal space\n# \\\\H match a non-horizontal space\n# \\\\v match a vertical space\n# \\\\V match a non-vertical space\n\n# Digits and non digits\n# replace digit with '_' first\nsub(\"\\\\d\", \"_\", \"the dandelion war 2010\")\n## [1] \"the dandelion war _010\"\n\ngsub(\"\\\\d\", \"_\", \"the dandelion war 2010\")\n## [1] \"the dandelion war ____\" all digits\n\n# replace non-digit with '_'\nsub(\"\\\\D\", \"_\", \"the dandelion war 2010\")\n## [1] \"_he dandelion war 2010\"\ngsub(\"\\\\D\", \"_\", \"the dandelion war 2010\")\n## [1] \"__________________2010\"\n\n\n#Spaces and non-spaces -----\n# replace space with '_'\nsub(\"\\\\s\", \"_\", \"the dandelion war 2010\")\n## [1] \"the_dandelion war 2010\"\ngsub(\"\\\\s\", \"_\", \"the dandelion war 2010\")\n## [1] \"the_dandelion_war_2010\"\n\n# replace non-space with '_'\nsub(\"\\\\S\", \"_\", \"the dandelion war 2010\")\n## [1] \"_he dandelion war 2010\"\ngsub(\"\\\\S\", \"_\", \"the dandelion war 2010\")\n## [1] \"___ _________ ___ ____\"\n\n#Words and non-words\n# replace word with '_'\nsub(\"\\\\b\", \"_\", \"the dandelion war 2010\")\n## [1] \"_the dandelion war 2010\"\ngsub(\"\\\\b\", \"_\", \"the dandelion war 2010\")\n## [1] \"_t_h_e_ _d_a_n_d_e_l_i_o_n_ _w_a_r_ _2_0_1_0_\"\n\n\n# replace non-word with '_'\nsub(\"\\\\B\", \"_\", \"the dandelion war 2010\")\n## [1] \"t_he dandelion war 2010\"\ngsub(\"\\\\B\", \"_\", \"the dandelion war 2010\")\n## [1] \"t_he d_an_de_li_on w_ar 2_01_0\"\n\n#Word boundaries and non-word-boundaries wherever word is there\n# replace word boundary with '_'\nsub(\"\\\\w\", \"_\", \"the dandelion war 2010\")\n## [1] \"_he dandelion war 2010\"\ngsub(\"\\\\w\", \"_\", \"the dandelion war 2010\")\n## [1] \"___ _________ ___ ____\"\n\n# replace non-word-boundary with '_'\nsub(\"\\\\W\", \"_\", \"the dandelion war 2010\")\n## [1] \"the_dandelion war 2010\"\ngsub(\"\\\\W\", \"_\", \"the dandelion war 2010\")\n## [1] \"the_dandelion_war_2010\"\n\n\n\n# Character Classes ---------\n\n# Anchor Description\n# [aeiou] match any one lower case vowel\n# [AEIOU] match any one upper case vowel\n# [0123456789] match any digit\n# [0-9] match any digit (same as previous class)\n# [a-z] match any lower case ASCII letter\n# [A-Z] match any upper case ASCII letter\n# [a-zA-Z0-9] match any of the above classes\n# [^aeiou] match anything other than a lowercase vowel\n# [^0-9] match anything other than a digit\n\n# some string\ntransport = c(\"car\", \"bike\", \"plane\", \"boat\")\n# look for 'e' or 'i'\ngrep(pattern = \"[ei]\", transport, value = TRUE)\n## [1] \"bike\" \"plane\"\n\n# some numeric strings\nnumerics = c(\"123\", \"17-April\", \"I-II-III\", \"R 3.0.1\")\n# match strings with 0 or 1\ngrep(pattern = \"[01]\", numerics, value = TRUE)\n## [1] \"123\" \"17-April\" \"R 3.0.1\"\n# match any digit\ngrep(pattern = \"[0-9]\", numerics, value = TRUE)\n## [1] \"123\" \"17-April\" \"R 3.0.1\"\n# negated digit\ngrep(pattern = \"[^0-9]\", numerics, value = TRUE)\n## [1] \"17-April\" \"I-II-III\" \"R 3.0.1\"\n\n\n# POSIX Characters\n#Closely related to the regex character classes we have what is known as POSIX character\n#classes. In R, POSIX character classes are represented with expressions inside double brackets\n#[[ ]].\n# Class Description\n# [[:lower:]] Lower-case letters\n# [[:upper:]] Upper-case letters\n# [[:alpha:]] Alphabetic characters ([[:lower:]] and [[:upper:]])\n# [[:digit:]] Digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9\n# [[:alnum:]] Alphanumeric characters ([[:alpha:]] and [[:digit:]])\n# [[:blank:]] Blank characters: space and tab\n# [[:cntrl:]] Control characters\n# [[:punct:]] Punctuation characters: ! ” # % & ’ ( ) * + , - . / : ;\n# [[:space:]] Space characters: tab, newline, vertical tab, form feed,\n# carriage return, and space\n# [[:xdigit:]] Hexadecimal digits: 0-9 A B C D E F a b c d e f\n# [[:print:]] Printable characters ([[:alpha:]], [[:punct:]] and space)\n# [[:graph:]] Graphical characters ([[:alpha:]] and [[:punct:]])\n\n\n# la vie (string)\nla_vie = \"La vie en #FFC0CB (rose);\\nCes't la vie! \\ttres jolie\"\n# if you print 'la_vie'\nprint(la_vie)\n## [1] \"La vie en #FFC0CB (rose);\\nCes't la vie! \\ttres jolie\"\n# if you cat 'la_vie'\ncat(la_vie)\n## La vie en #FFC0CB (rose);\n## Ces't la vie! tres jolie\n\n\n# remove space characters\ngsub(pattern = \"[[:blank:]]\", replacement = \"\", la_vie)\n## [1] \"Lavieen#FFC0CB(rose);\\nCes'tlavie!tresjolie\"\n# remove digits\ngsub(pattern = \"[[:punct:]]\", replacement = \"\", la_vie)\n## [1] \"La vie en FFC0CB rose\\nCest la vie \\ttres jolie\"\n# remove digits\ngsub(pattern = \"[[:xdigit:]]\", replacement = \"\", la_vie)\n## [1] \"L vi n # (ros);\\ns't l vi! \\ttrs joli\"\n# remove printable characters\ngsub(pattern = \"[[:print:]]\", replacement = \"\", la_vie)\n## [1] \"\\n\\t\"\n# remove non-printable characters\ngsub(pattern = \"[^[:print:]]\", replacement = \"\", la_vie)\n## [1] \"La vie en #FFC0CB (rose);Ces't la vie! tres jolie\"\n\n\n# remove graphical characters\ngsub(pattern = \"[[:graph:]]\", replacement = \"\", la_vie)\n## [1] \" \\n \\t \"\n# remove non-graphical characters\ngsub(pattern = \"[^[:graph:]]\", replacement = \"\", la_vie)\n## [1] \"Lavieen#FFC0CB(rose);Ces'tlavie!tresjolie\"\n\n\n\n# Quantifiers ---\n#These are used when we want to match a certain number of characters that meet certain criteria\n\n# Quantifier Description\n# ? The preceding item is optional and will be matched at most once\n# * The preceding item will be matched zero or more times\n# + The preceding item will be matched one or more times\n# {n} The preceding item is matched exactly n times\n# {n,} The preceding item is matched n or more times\n# {n,m} The preceding item is matched at least n times, but not more than m times\n\n\n# people names\npeople = c(\"rori\", \"emilia\", \"matteo\", \"mehmet\", \"filipe\", \"anna\", \"tyler\",\n \"rasmus\", \"jacob\", \"youna\", \"flora\", \"adi\")\n# match 'm' at most once\ngrep(pattern = \"m?\", people, value = TRUE)\n## [1] \"rori\" \"emilia\" \"matteo\" \"mehmet\" \"filipe\" \"anna\" \"tyler\"\n## [8] \"rasmus\" \"jacob\" \"youna\" \"flora\" \"adi\"\n# match 'm' exactly once\ngrep(pattern = \"m{1}\", people, value = TRUE, perl = FALSE)\n## [1] \"emilia\" \"matteo\" \"mehmet\" \"rasmus\"\n# match 'm' zero or more times, and 't'\ngrep(pattern = \"m*t\", people, value = TRUE)\n## [1] \"matteo\" \"mehmet\" \"tyler\"\n\n# match 't' zero or more times, and 'm'\ngrep(pattern = \"t*m\", people, value = TRUE)\n## [1] \"emilia\" \"matteo\" \"mehmet\" \"rasmus\"\n# match 'm' one or more times\ngrep(pattern = \"m+\", people, value = TRUE)\n## [1] \"emilia\" \"matteo\" \"mehmet\" \"rasmus\"\n# match 'm' one or more times, and 't'\ngrep(pattern = \"m+.t\", people, value = TRUE)\n## [1] \"matteo\" \"mehmet\"\n# match 't' exactly twice\ngrep(pattern = \"t{2}\", people, value = TRUE)\n## [1] \"matteo\"\n\n\n# Reg Exp Functions \n# Function Purpose Characteristic\n# grep() finding regex matches which elements are matched (index or value)\n# grepl() finding regex matches which elements are matched (TRUE & FALSE)\n# regexpr() finding regex matches positions of the first match\n# gregexpr() finding regex matches positions of all matches\n# regexec() finding regex matches hybrid of regexpr() and gregexpr()\n# sub() replacing regex matches only first match is replaced\n# gsub() replacing regex matches all matches are replaced\n# strsplit() splitting regex matches split vector according to matches\n\n# in Stringr\n# str_detect() Detect the presence or absence of a pattern in a string\n# str_extract() Extract first piece of a string that matches a pattern\n# str_extract all() Extract all pieces of a string that match a pattern\n# str_match() Extract first matched group from a string\n# str_match all() Extract all matched groups from a string\n# str_locate() Locate the position of the first occurence of a pattern in a string\n# str_locate all() Locate the position of all occurences of a pattern in a string\n# str_replace() Replace first occurrence of a matched pattern in a string\n# str_replace all() Replace all occurrences of a matched pattern in a string\n# str_split() Split up a string into a variable number of pieces\n# str_split fixed() Split up a string into a fixed number of pieces\nstr_function(string, pattern)\n\n# Complimentary\n# Function Purpose Characteristic\n# regmatches() extract or replace matches use with data from regexpr(),\n# gregexpr() or regexec()\n# match() value matching finding positions of (first) matches\n# pmatch() partial string matching finding positions\n# charmatch() similar to pmatch() finding positions\n\n# Accessory Functions accepting regex\n# apropos() find objects by (partial) name\n# browseEnv() browse objects in environment\n# glob2rx() change wildcard or globbing pattern into Regular Expression\n# help.search() search the help system\n# list.files() list the files in a directory/folder\n" }, { "path": "23a-Strings/strman7.R", "content": "#String Manipulations 7\n\n#Pattern Finding Functions\n\n#Finding a match\ngrep(pattern, text)\ngrepl(pattern, text)\nregexpr(pattern, text)\ngregexpr(pattern, text)\nregexec(pattern, text)\n\n# some text\ntext = c(\"one word\", \"a sentence\", \"you and me\", \"three two one\")\n# pattern\npat = \"one\"\n# default usage\ngrep(pat, text)\n## [1] 1 4\n\n# with 'value' (showing matched text)\ngrep(pat, text, value = TRUE)\n## [1] \"one word\" \"three two one\"\n\n# with 'invert' (showing unmatched parts)\ngrep(pat, text, invert = TRUE)\n## [1] 2 3\n# same with 'values'\ngrep(pat, text, invert = TRUE, value = TRUE)\n## [1] \"a sentence\" \"you and me\"\n\n\n# some text\ntext = c(\"one word\", \"a sentence\", \"you and me\", \"three two one\")\n# pattern\npat = \"one\"\n# default usage\ngrepl(pat, text)\n## [1] TRUE FALSE FALSE TRUE\n\n# location \n# some text\ntext = c(\"one word\", \"a sentence\", \"you and me\", \"three two one\")\n# default usage\nregexpr(\"one\", text)\n## [1] 1 -1 -1 11\n## attr(,\"match.length\")\n## [1] 3 -1 -1 3\n## attr(,\"useBytes\")\n## [1] TRUE\n\n\n# some text\ntext = c(\"one word\", \"a sentence\", \"you and me\", \"three two one\")\n# pattern\npat = \"one\"\n# default usage\ngregexpr(pat, text)\n\n# some text\ntext = c(\"one word\", \"a sentence\", \"you and me\", \"three two one\")\n# pattern\npat = \"one\"\n# default usage\nregexec(pat, text)\n\n# handy function to extract matched term\nx = regexpr(pat, text)\nsubstring(text, x, x + attr(x, \"match.length\") - 1)\n## [1] \"one\" \"\" \"\" \"one\"\n# with NA\nregexpr(pat, c(text, NA))\n## [1] 1 -1 -1 11 NA\n## attr(,\"match.length\")\n## [1] 3 -1 -1 3 NA\n\n\n#6.2 Pattern Replacement Functions ----\nsub(pattern, replacement, text)\ngsub(pattern, replacement, text)\n\n\n# string\nRstring = c(\"The R Foundation\",\n \"for Statistical Computing\",\n \"R is FREE software\",\n \"R is a collaborative project\")\n# substitute 'R' with 'RR'\nsub(\"R\", \"RR\", Rstring)\n## [1] \"The RR Foundation\" \"for Statistical Computing\"\n## [3] \"RR is FREE software\" \"RR is a collaborative project\"\n\nRstring = c(\"The R Foundation\",\n \"for Statistical Computing\",\n \"R is FREE software\",\n \"R is a collaborative project\")\n# substitute\ngsub(\"R\", \"RR\", Rstring)\n## [1] \"The RR Foundation\" \"for Statistical Computing\"\n## [3] \"RR is FRREE software\" \"RR is a collaborative project\"\n\nstrsplit(x, split) # same as strsplit(text, pattern)\n\n# a sentence\nsentence = c(\"R is a collaborative project with many contributors\")\n# split into words\nstrsplit(sentence, \" \")\n## [[1]]\n## [1] \"R\" \"is\" \"a\" \"collaborative\"\n## [5] \"project\" \"with\" \"many\" \"contributors\"\n\n# telephone numbers\ntels = c(\"510-548-2238\", \"707-231-2440\", \"650-752-1300\")\n# split each number into its portions\nstrsplit(tels, \"-\")\n\n# some objects\nsome_objs = c(\"pen\", \"pencil\", \"marker\", \"spray\")\n# detect phones\nstr_detect(some_objs, \"pen\")\n## [1] TRUE TRUE FALSE FALSE\n# select detected macthes\nsome_objs[str_detect(some_objs, \"pen\")]\n## [1] \"pen\" \"pencil\"\n\n# some strings\nstrings = c(\"12 Jun 2002\", \" 8 September 2004 \", \"22-July-2009 \",\n \"01 01 2001\", \"date\", \"02.06.2000\",\n \"xxx-yyy-zzzz\", \"$2,600\")\n# date pattern (month as text)\ndates = \"([0-9]{1,2})[- .]([a-zA-Z]+)[- .]([0-9]{4})\"\n# detect dates\nstr_detect(strings, dates)\n## [1] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE\n\n\n# tweets about 'Paris'\nparis_tweets = c(\n \"#Paris is chock-full of cultural and culinary attractions\",\n \"Some time in #Paris along Canal St.-Martin famous by #Amelie\",\n \"While you're in #Paris, stop at cafe: http://goo.gl/yaCbW\",\n \"Paris, the city of light\")\n# hashtag pattern\nhash = \"#[a-zA-Z]{1,}\"\n# extract (first) hashtag\nstr_extract(paris_tweets, hash)\n## [1] \"#Paris\" \"#Paris\" \"#Paris\" NA\n#As you can see, the output of str extract() is a vector of same length as string. Those\n#elements that don’t match the pattern are indicated as NA. Note that str extract() only\n#matches the first pattern: it didn’t extract the hashtag \"#Amelie\"\n\n# extract (all) hashtags\nstr_extract_all(paris_tweets, \"#[a-zA-Z]{1,}\") #??? where was Paris\n\n# string vector\nstrings = c(\"12 Jun 2002\", \" 8 September 2004 \", \"22-July-2009 \",\n \"01 01 2001\", \"date\", \"02.06.2000\",\n \"xxx-yyy-zzzz\", \"$2,600\")\n# date pattern (month as text)\ndates = \"([0-9]{1,2})[- .]([a-zA-Z]+)[- .]([0-9]{4})\"\n# extract first matched group\nstr_match(strings, dates)\n\n# tweets about 'Paris'\nparis_tweets = c(\n \"#Paris is chock-full of cultural and culinary attractions\",\n \"Some time in #Paris along Canal St.-Martin famous by #Amelie\",\n \"While you're in #Paris, stop at cafe: http://goo.gl/yaCbW\",\n \"Paris, the city of light\")\n# match (all) hashtags in 'paris_tweets'\nstr_match_all(paris_tweets, \"#[a-zA-Z]{1,}\")\n\n\n# locate position of (first) hashtag\nstr_locate(paris_tweets, \"#[a-zA-Z]{1,}\")\n## start end\n## [1,] 1 6\n## [2,] 14 19\n## [3,] 17 22\n## [4,] NA NA\n\n# locate (all) hashtags in 'paris_tweets'\nstr_locate_all(paris_tweets, \"#[a-zA-Z]{1,}\")\n\n\n# city names\ncities = c(\"San Francisco\", \"Barcelona\", \"Naples\", \"Paris\")\n# replace first matched vowel\nstr_replace(cities, \"[aeiou]\", \";\")\n## [1] \"S;n Francisco\" \"B;rcelona\" \"N;ples\" \"P;ris\"\n\n#Now, suppose that we want to replace the first consonant in each name. We just need to\n#modify the pattern with a negated class:\n# replace first matched consonant\nstr_replace(cities, \"[^aeiou]\", \";\")\n## [1] \";an Francisco\" \";arcelona\" \";aples\" \";aris\"\n\n\n# city names\ncities = c(\"San Francisco\", \"Barcelona\", \"Naples\", \"Paris\")\n# replace all matched vowel\nstr_replace_all(cities, pattern = \"[aeiou]\", \";\")\n## [1] \"S;n Fr;nc;sc;\" \"B;rc;l;n;\" \"N;pl;s\" \"P;r;s\"\n\n# replace all matched consonants\nstr_replace_all(cities, pattern = \"[^aeiou]\", \";\")\n## [1] \";a;;;;a;;i;;o\" \";a;;e;o;a\" \";a;;e;\" \";a;i;\"\n\n\n# a sentence\nsentence = c(\"R is a collaborative project with many contributors\")\n# split into words\nstr_split(sentence, \" \")\n## [[1]]\n## [1] \"R\" \"is\" \"a\" \"collaborative\"\n## [5] \"project\" \"with\" \"many\" \"contributors\"\n\n# telephone numbers\ntels = c(\"510-548-2238\", \"707-231-2440\", \"650-752-1300\")\n# split each number into its portions\nstr_split(tels, \"-\")\n\n# string\nflavors = c(\"chocolate\", \"vanilla\", \"cinnamon\", \"mint\", \"lemon\")\n# split by vowels\nstr_split(flavors, \"[aeiou]\")\n\n\n# split by first vowel\nstr_split(flavors, \"[aeiou]\", n = 2)\n\n# string\nflavors = c(\"chocolate\", \"vanilla\", \"cinnamon\", \"mint\", \"lemon\")\n# split flavors into 2 pieces\nstr_split_fixed(flavors, \"n\", 2)\n\n# split favors into 3 pieces\nstr_split_fixed(flavors, \"n\", 3)\n" }, { "path": "23a-Strings/strman9.R", "content": "# String Manipulation 9\n\n# function that reverses a string by characters\nreverse_chars <- function(string)\n{\n # split string by characters\n string_split = strsplit(string, split = \"\")\n # reverse order\n rev_order = nchar(string):1\n # reversed characters\n reversed_chars = string_split[[1]][rev_order]\n # collapse reversed characters\n paste(reversed_chars, collapse=\"\")\n}\n#Let’s test our reversing function with a character and numeric vectors:\n # try 'reverse_chars'\nreverse_chars(\"abcdefg\")\n## [1] \"gfedcba\"\n# try with non-character input\nreverse_chars(12345)\n## Error: non-character argument\nreverse_chars('12345')\n\n\n# reversing a string by characters\nreverse_chars <- function(string)\n{\n string_split = strsplit(as.character(string), split = \"\")\n reversed_split = string_split[[1]][nchar(string):1]\n paste(reversed_split, collapse=\"\")\n}\n\n\n# example with one word\nreverse_chars(\"atmosphere\")\n## [1] \"erehpsomta\"\n# example with a several words\nreverse_chars(\"the big bang theory\")\n## [1] \"yroeht gnab gib eht\"\n\n# try 'reverse_chars'\nreverse_chars(\"abcdefg\")\n## [1] \"gfedcba\"\n# try with non-character input\nreverse_chars(12345)\n## [1] \"54321\"\n\n# reverse vector (by characters)\nlapply(c(\"the big bang theory\", \"atmosphere\"), reverse_chars)\n## [[1]]\n## [1] \"yroeht gnab gib eht\"\n##\n## [[2]]\n## [1] \"erehpsomta\"\n\n# function that reverses a string by words\nreverse_words <- function(string)\n{\n # split string by blank spaces\n string_split = strsplit(as.character(string), split = \" \")\n # how many split terms?\n string_length = length(string_split[[1]])\n # decide what to do\n if (string_length == 1) {\n # one word (do nothing)\n reversed_string = string_split[[1]]\n } else {\n # more than one word (collapse them)\n reversed_split = string_split[[1]][string_length:1]\n reversed_string = paste(reversed_split, collapse = \" \")\n }\n # output\n return(reversed_string)\n}\n\n\n# examples\nreverse_words(\"atmosphere\")\n## [1] \"atmosphere\"\nreverse_words(\"the big bang theory\")\n## [1] \"theory bang big the\"\n\n# reverse vector (by words)\nlapply(c(\"the big bang theory\", \"atmosphere\"), reverse_words)\n## [[1]]\n## [1] \"theory bang big the\"\n##\n## [[2]]\n## [1] \"atmosphere\"\n\n\n#Email\n# pattern\nemail_pat = \"^([a-z0-9_\\\\.-]+)@([\\\\da-z\\\\.-]+)\\\\.([a-z\\\\.]{2,6})$\"\n# string that matches\ngrepl(pattern = email_pat, x = \"gaston@abc.com\")\n## [1] TRUE\n\n# another string that matches\ngrep(pattern = email_pat, x = \"gaston.sanchez@research-center.fr\")\n## [1] 1\n# unmatched email (TLD too long)\ngrep(pattern = email_pat, x = \"gaston@abc.something\")\n## integer(0)\n\n\n# potential email addresses\nemails = c(\n \"simple@example.com\",\n \"johnsmith@email.gov\",\n \"marie.curie@college.edu\",\n \"very.common@example.com\",\n \"a.little.lengthy.but.ok@dept.example.com\",\n \"disposable.style.email.with+symbol@example.com\",\n \"not_good@email.address\")\n\n# detect pattern\nstr_detect(string=emails, pattern=email_pat)\n## [1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE\n" }, { "path": "23a-Strings/strprint1.R", "content": "# Printing Characters\nprint() # generic printing\nnoquotes() # print with no quotes\ncat() # concatenation\nformat() # special formats\ntoStrings() # convert to string\nsprintf() # print as per c format\n\n# print ------------\n\n# text string\nmystring = \" Programming with data is fun\"\n\nprint(mystring)\n# quoted by default\n\n# noquote -------------\nnoquote(mystring)\n\n(no_quote1 = noquote(c('some', 'quoted', 'text', '!%~(&=')))\n\nclass(no_quote1) # noquote\nclass(noquote) # function \n#text character\nis.character(no_quote1) # TRUE\nno_quote1[2:3] # 2nd & 3rd string\n\n\n# concatenate -----------\n?cat\ncat(... , file = \"\", sep = \" \", fill = FALSE, labels = NULL,\n append = FALSE)\n# Accepts several types of data -> character\n# default sep space\n\nmystring\ncat(mystring) # without quote\n# prints to screen or file, no line indicator[1]\n\n# 2 or more strings\ncat(mystring, 'with R')\n\n# cat with sep\ncat(mystring, 'with R', sep=' =) ')\ncat(1:10, sep=\"-\")\n#passing vectors, taken as seperate arguments\ncat(month.abb[1:4], sep='#')\n# break long strings fill=30 string width\ncat('Loooooooong strings', 'can be displayed', 'in a nice format',\n \"by using the 'fill ' argument \", fill=30)\ncat('Loooooooong strings', 'can be displayed', 'in a nice format',\n \"by using the 'fill ' argument \", fill=50)\ncat('Loooooooong strings', 'can be displayed', 'in a nice format',\n \"by using the 'fill ' argument \", fill=5) # takes min of string\n\n# output to a file\ncat(mystring, 'with R', file='strings/output.txt')\n\n# Encoding strings with format() ----------\n# preety printing\n\n#default usage\nformat(13.7)\nformat(13.1343232)\nformat(13.134323245544545)\n# upto 5 decimal places\n# arguments width, trim(padding), justify(text only), digits, scientific\n\nformat(13.7, nsmall = 3) # padding upto 3 digits\nformat(13.7, nsmall = 5)\n\nformat(c(6, 13.1), digits=2)\nformat(c(6, 13.1), digits=3) # max digits\nformat(c(6, 13.1), digits=1) # no effect\nformat(c(6, 13.1), digits=5) # no effect\nformat(c(6, 13.12345), digits=5)\n\nformat(c(6, 13.1), digits=2, nsmall=2)\n\nformat(c(6, 13.1), digits=2, nsmall=4)\nformat(c(6, 13.12345), digits=5, nsmall=3) #shorten the display, pad if reqd\n\n# pad - makes length same\n\n#justify ----------------\n\nformat(c('A', 'BB', 'CCC'), width=5, justify='centre')\n\nformat(c('A', 'BB', 'CCC'), width=6, justify='left')\n\nformat(c('A', 'BB', 'CCC'), width=3, justify='right')\n\nformat(c('A', 'BB', 'CCC'), width=2, justify='none')\n\n#digits\nformat(1/1:5, digits=2)\nformat(1/1:5, digits=5)\n\nformat( format(1/1:5, digits=2), width=6, justify='c') # center\nformat( format(1/1:5, digits=2), width=8, justify='r') # center\nformat( format(1/1:5, digits=10), width=8, justify='r') # center\n\n# big mark\nformat(123456, big.mark = ',')\nformat(123456, big.interval = ',')\n?big.mark\nformat(12345.678,big.mark=\",\",scientific=FALSE)\nformat(12345.678,big.mark=\",\",scientific=TRUE)\n\nlibrary(scales) \ncomma_format()(1000000)\n\n#sprintf - c styling\n?sprintf\nsprintf(fmt, ...)\ngettextf(fmt, ..., domain = NULL)\n\nsprintf('%f', pi)\nsprintf('%.3f', pi) # 3 decimal places\nsprintf('%1.0f', pi) # 1 integer & 0 decimal\nsprintf('%5.1f', pi) # decimal notation 1 decimals\nsprintf('%5.2f', pi) # decimal notation 2 decimals\nsprintf('%05.1f', pi) # decimal notation 1 decimals inital padding\nsprintf('%05.1f', 1234567.12345) # decimal notation 1 decimals inital padding\n\nsprintf('%+f', 1234567.12345) # decimal notation 1 decimals inital padding\n\nsprintf('% f', 1234567.12345) # prefix a space\nsprintf('%-10f', 1234567.12345) # left justified\nn1 = 12345678.12345678 ; n2=87654321.87654321\nsprintf('%-10f', n1,'%+10f', n2) # only 1 print variable\n\nsprintf('%e', 1234567.12345) # exponential format e\nsprintf('%E', 1234567.12345) # exponential format E\nsprintf('%g', 1234567.12345) # significant digits default 6\nsprintf('%.3g', 1234567.12345) # significant digits default 6\nsprintf('%.8g', 123456789123.12345) # significant digits default 6\n\n\n# Convert objects to strings\n\ntoString(17.04)\n\n#combine 2 objects to single string sepearated by commas\ntoString(c(17.04, 1978))\n\n# combine objects\ntoString(c('Bonjour', 123, TRUE, NA, log(exp(1))))\n\n# use of width max \ntoString(c('One', 'two', '3333333', 444), width=8)\ntoString(c('One', 'two', '3333333', 444), width=20)\ntoString(c('One', 'two', '3333333', 444))\n\n\n# Printing methods\n\nprint(1:5)\nas.character(1:5)\ncat(1:5, sep='-')\npaste(1:5)\npaste(1:5, collapse='')\ntoString(1:5)\nnoquote(as.character(1:5))\n\n\n# extras\nprettyNum(c(123,1234),big.mark=\",\")\n" }, { "path": "23a-Strings/strreplace1.R", "content": "\nx = '0_0_01-09-2013_0'\ngsub('_', ',',x)\ngsub('_', ',',x, fixed=T)\nsub('_', ',',x) # only 1st occurance\nlibrary(stringr)\nstr_replace_all(x, \"_\", \",\")\n\nlibrary(stringi)\nstri_replace_all(x, \",\", fixed=\"_\")\n\n\n#----\nlibrary(tidyverse)\ntext_string = \"developer\"\n\n# This works fine\nx %>% str_replace_all(c('_' =',')) \n\n# But this is not what I want\nx %>% str_replace_all(c('_' =\", \", '-' = \"/\"))\n\n\ncasefold('DHiraj Upadhyaya', upper=T)\ncasefold('DHiraj Upadhyaya', upper=F)\n\n\nchartr('a', 'A', 'This is a boring string')\nchartr(\"_\", \",\", x)\n" }, { "path": "23a-Strings/strsearch.R", "content": "# Selecting from Strings\n\n# name of States\nstates = rownames(USArrests)\nhead(states)\n\n# Select states with k\npattern1 = 'k'\ngrep(pattern = pattern1, x = states, value = T)\n\npattern1 = 'w'\ngrep(pattern = pattern1, x = states, value = T)\n\n# w or W\npattern1 = '[wW]'\ngrep(pattern = pattern1, x = states, value = T)\n\n# ignore case\npattern1 = 'w'\ngrep(pattern = pattern1, x = tolower(states), value = T)\n\npattern1 = 'W'\ngrep(pattern = pattern1, x = toupper(states), value = T)\n\npattern1 = 'w'\ngrep(pattern = pattern1, x = states, ignore.case=T, value = T)\n" }, { "path": "23a-Strings/strsplit1.R", "content": "# String - Splitting Columns\n\nlibrary(splitstackshape)\nconcat.test\n# Sample data\ntemp <- head(concat.test)\ntemp\n\n## Split the \"Likes\" column\ncSplit(temp, \"Likes\")\n\n## Split the \"Likes\" and \"Hates\" columns --\n## they have different delimiters...\ncSplit(temp, c(\"Likes\", \"Hates\"), c(\",\", \";\"))\n\n## Split \"Siblings\" into a long form...\ncSplit(temp, \"Siblings\", \",\", direction = \"long\")\n\nhead(temp)\n## Split \"Siblings\" into a long form, not removing whitespace\ncSplit(temp, \"Siblings\", \",\", direction = \"long\", stripWhite = FALSE)\ncSplit(temp, \"Siblings\", \",\", direction = \"long\", stripWhite = T)\n\n\n## Split a vector\n(y <- c(\"a_b_c\", \"a_b\", \"c_a_b\"))\ncSplit(data.frame(y), \"y\", \"_\")\n\n\n\n\n#---\nlibrary(splitstackshape)\nwordexplantion\nsplitstackshape::cSplit(df, wordexplantion, \"-\")\n\nbefore <- data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2')) \nbefore\ndo.call(rbind, stringr::str_split(before$type, '_and_'))\n\n\n#-----\nlibrary(data.table) ## v 1.9.6+ \nsetDT(before)[, paste0(\"type\", 1:2) := tstrsplit(type, \"_and_\")]\nbefore\n\n\n#before <- data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2')) \nout <- strsplit(as.character(before$type),'_and_') \ndo.call(rbind, out)\n\n\n#---\nibrary(dplyr)\nlibrary(tidyr)\n\nbefore <- data.frame(\n attr = c(1, 30 ,4 ,6 ), \n type = c('foo_and_bar', 'foo_and_bar_2')\n)\n\nbefore %>%\n separate(type, c(\"foo\", \"bar\"), \"_and_\")\n" }, { "path": "23a-Strings/strsplit2.R", "content": "#String Split\n\n(x=c('10-20','20-30','30-40','40-50'))\n(x1 = strsplit(x,\"-\"))\n(x2 = unlist(x1))\n(x3 = as.numeric(x2))\n(x4 = unique(x3))\n?strsplit\nx4\nx4[-length(x4)]\ndiff(x4)\ndiff(x4)/2\n\n(x5 = x4[-length(x4)] + diff(x4)/2)\n\n\n# Mid Point\na <- seq(0,50,10)\na\na[-length(a)] + diff(a)/2\n\n" }, { "path": "23a-Strings/strsplit3.R", "content": "#\nlibrary(splitstackshape)\n\ncampus = c('AUHR_M', 'AUHR_M')\ninstitute = c('ABS', 'ASET')\nprograms = c('BBA, MBA, MBA-HR', 'BTECH_CSE, BTECH_ME, BTECH_CE, MTECH, PHD')\ncourses = data.frame(campus,institute, programs)\ncourses\n\n## Split the \"programs\" column\ncSplit(courses, \"programs\")\n\n## they have different delimiters...\n#cSplit(temp, c(\"Likes\", \"Hates\"), c(\",\", \";\"))\n\n## Split \"Siblings\" into a long form...\ncSplit(courses, \"programs\", \",\", direction = \"long\")\n#solved\ncSplit(courses, \"programs\", \",\", direction = \"long\", stripWhite = F)\n\n\n#split a occurance of a character or string\n\n\n" }, { "path": "23a-Strings/strsplit4.R", "content": "\nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1M-sAZqHHyuzzwW7vUwXoCw2JVwUiWki7LdFCgAyOccc/edit#gid=1470469521\"\n\ndf = as.data.frame(gsheet2tbl(url))\nhead(df)\n\ndf1 = df[,2:4]\nhead(df1)\nunlist(strsplit(df1$name, \"\\n\"))[1]\ndf2= vapply(strsplit(df1$name, \"\\n\", fixed = TRUE), \"[\", \"\", 1)\nhead(df2,n=20)\nstrsplit(df2[1:20], \"United Kingdom\")\nvapply(strsplit(df1[1:20], c(\"United Kingdom\",\"United States\"), fixed = TRUE), \"[\", \"\", 1)\n\n\n\nword <- c('apple-orange-strawberry','chocolate')\nsapply(strsplit(word, \"-\"), head, 1)\n\ndf3= head(df2)\nhead(df3); names(df3)\nnames(df3)\ndf3\ndf4 = data.frame(do.call(rbind, strsplit(df3, split = \"United Kingdom\")))\ndf4\n\nword <- c('JNUIndia','IITIndia','OxfordUnited Kingdom','HarvardUSA')\nstrsplit(word, split=c('India'))\nstrsplit(word, split=c('United Kingdom'))\nstrsplit(word, split=c('India','United Kingdom'))\nstrsplit(word, split=c('India|United Kingdom|USA'))\n\nsapply(strsplit(word, split=c('India|United Kingdom|USA')),head,1)\n?sapply\nvapply(strsplit(word,split=c('India|United Kingdom|USA'), fixed = F), \"[\", 1)\n\nvapply(strsplit(word, split=c('India|United Kingdom|USA')), fixed = TRUE, \"[\", \"\", 1)\n\nword2 <- c('JNU India','IIT India','Oxford UnitedKingdom','Harvard USA')\n\nSplit <- strsplit(word,split=c('India|United Kingdom|USA') , fixed = F)\nSplit\nDates <- sapply(Split, \"[\", 1)\nRates <- sapply(Split, \"[\", 2)\nDates\nRates\n\nSplit <- strsplit(word2, split=\" \" , fixed = F)\nSplit\nDates <- sapply(Split, \"[\", 1)\nRates <- sapply(Split, \"[\", 2)\nDates\nRates\n\n\n\nsapply(strsplit(word, split=c(\"India\")), head, 1)\n?strsplit\n " }, { "path": "23a-Strings/tidyr-strseperate.R", "content": "#Strings using dplyr\n#http://tidyr.tidyverse.org/reference/separate.html\n# separate(data, col, into, sep = \"[^[:alnum:]]+\", remove = TRUE, convert = FALSE, extra = \"warn\", fill = \"warn\", ...)\n\nlibrary(dplyr)\nlibrary(tidyr)\ndf1 <- data.frame(x = c(NA, \"a.b\", \"a.d\", \"b.c\"))\ndf1\n(df1 %>% separate(x, c(\"A\", \"B\")))\n\n\n# If every row doesn't split into the same number of pieces, use\n# the extra and file arguments to control what happens\ndf2 <- data.frame(x = c(\"a\", \"a b\", \"a b c\", NA))\ndf2\ndf2 %>% separate(x, c(\"a\", \"b\"))\n#> Warning: Expected 2 pieces. Additional pieces discarded in 1 rows [3].\n#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].\n\n# The same behaviour but no warnings\ndf2 %>% separate(x, c(\"a\", \"b\"), extra = \"drop\", fill = \"right\")\n\n# Another option:\ndf2 %>% separate(x, c(\"a\", \"b\"), extra = \"merge\", fill = \"left\")\n\n# If only want to split specified number of times use extra = \"merge\"\ndf3 <- data.frame(x = c(\"x: 123\", \"y: error: 7\"))\ndf3 %>% separate(x, c(\"key\", \"value\"), \": \", extra = \"merge\")\n\n#> key value\n#> 1 x 123\n#> 2 y error: 7\n\n" }, { "path": "24a-LM/10a-lm-women2.R", "content": "# Regression Analysis\n\ndata(women)\nwomen\nstr(women)\ncov(women$height, women$weight)\n#69 : which show positive relationship between height and weight\ncor(women$height, women$weight)\n#0.995 : which shows Strong and Positive relationship betw height & weight\n0.995^2 # also equal R^2 value in this case (Simple Linear Regression)\n\nplot(x=women$height, y=women$weight)\nabline(lm(weight ~ height, data=women), col='red')\nnames(women) #x- IV, y -DV\nfit1 = lm(weight ~ height, data=women)\nsummary(fit1)\n\n#coefficients\ncoef(fit1)\n#p values for b0 & b1 are significant as it is < 0.05\n#pvalue1 = 1.71e-09 < 0.05\n#pvalue2 = 1.09e-14 < 0.05\n\n#R2\n(summary(fit1))$r.squared #0.991\n(summary(fit1))$adj.r.squared #0.9903\n#99% of variation in weight is explained by Height\n#Good Linear Model for Prediction\n\n\n#FStats : p value\n(summary(fit1))$fstatistic \n#pvalue : 1.09e-14 < 0.05 : Model exists\n#There is at least 1 IV which explains variation in Y (DV)\n\nrange(women$height)\n#Prediction : for height = 65, 66\n(new1 = data.frame(height=c(65,66)))\npredict(fit1, newdata = new1, type='response')\n#136.7333 140.1833\n\npredict(fit1, newdata = new1, interval='confidence')\npredict(fit1, newdata = new1, interval='prediction')\n\nset.seed(10)\n(df$age = floor(runif(15, 20,30)))\nset.seed(10)\n(age = floor(runif(15, 20,30)))\ndf$age == age\ncbind(df$age, age, df$age-age)\ndf$var2 = floor(runif(15, 30,40))\ndf\ndf$age2 = age\ndf[-c(1:2)]\ndf = df[-4]\ndf\n\n#y = mx + c\n#fit1= lm(formula= weight ~ height + age +var2 , data= df)\nsummary(fit1)\nabline(fit1)\ndf\ncor(df$weight, df$height)\ndf=women\ndf$predict = predict(fit1, newdata=data.frame(height=women$height))\ndf\nabline(v=60)\nabline(h=119)\n\n" }, { "path": "24a-LM/10b-lm-salesarea2.R", "content": "# SLR Area vs Sales\n#import from ggsheet\nlibrary(gsheet)\narea1 = \"https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2023826519\"\ndf2 = as.data.frame(gsheet::gsheet2tbl(area1))\nstr(df2)\ndf = df2\nfit2 = lm(Y ~ X, data=df)\nnew2 = data.frame(X=c(1.5,2,3,4,5))\nnew2\npredict(fit2, newdata= new2)\n" }, { "path": "24a-LM/10c-MLR-omni.R", "content": "#Multiple Linear Regression \n#Linear Modeling : DV vs more than 1 IVs\n#sales Qty vs price & promotion\n\n#Omni Store\n#creating data using Vector\nsales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)\nprice = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)\npromotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)\nomni1 = data.frame(sales, price, promotion)\nhead(omni1)\nstr(omni1)\n\n#2nd Method : CSV file\nomni2 = read.csv(file.choose())\n\n#3rd Method : gsheet \nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231\"\nomni3 = as.data.frame(gsheet::gsheet2tbl(url))\n\n#Make one of data frames active\nomni = omni1\nhead(omni)\n\n?lm #see help of LM\n#Simple Linear Model would look like this\nslr1 = lm(formula = sales ~ price, data=omni) # sales depend on price of item\nslr2 = lm(formula = sales ~ promotion, data=omni) # sales depend on promotion exp\nsummary(slr1)\nsummary(slr2)\n\n\n#MLR Create Multiple Linear Regression\n# we want to see how Sales Qty depend on Price and Promotion Values\nmlrmodel1 = lm(sales ~ price + promotion, omni)\n#mlrmodel1 = lm(omni, sales ~ price + promotion)\n?lm\n#how to give parameter values in different sequence, use arguments names if in different order\nmlrmodel1 = lm( data=omni, formula = sales ~ price + promotion)\n\nrange(omni$sales)\nsummary(mlrmodel1) # summary statistics IMP STEP\n#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals\n\ncoef(mlrmodel1) #coefficients b1, b2\n#anova(mlrmodel1) #seeing from anova model\n\nhead(omni)\n\nplot(y=omni$sales, x=omni$promotion)\n\n#Predicted Values----\ndim(omni)\nfitted(mlrmodel1)\ncbind(omni, fitted(mlrmodel1), residuals(mlrmodel1))\nsummary(mlrmodel1)\n\nnames(omni)\n#create a dataframe of new sample values\n(ndata1 = data.frame(price=c(60,70), promotion=c(300,400)))\nrange(omni$price); range(omni$promotion)\npredict(mlrmodel1, newdata=ndata1)\ncbind(ndata1, Predict=predict(mlrmodel1, newdata=ndata1, predict='response'))\n\n\n\n\n\n\n#R2 and Adjs R2\nnames(mlrmodel1)\nsummary(mlrmodel1)\nsummary(mlrmodel1)$r.squared\nsummary(mlrmodel1)$adj.r.squared\n\n#Manual Calculation of Adjs R2\n(r2 = summary(mlrmodel1)$r.squared)\nk = 2 # no of IVs\n(n = nrow(omni)) # sample size\n(adjr2 = 1 - ( (1 - r2) * ((n - 1)/ (n - k - 1))))\n\n\n# Fstatistics\nsummary(mlrmodel1)$fstatistic[1] # from output of model\n\n(df1 = k) ; (df2 = n-k-1) \nqf(.95, df1, df2) # from table wrt df1 & df2\n#Model Fstats > table(Fstat) \n\n# Pvalue of Model\nfstat = summary(mlrmodel1)$fstatistic \npf(fstat[1], fstat[2], fstat[3], lower.tail=FALSE) \n# this is < 0.05 : Significant\n# \n\n#Plots of the Modle\nplot(mlrmodel1,1) # no pattern, equal variance\nplot(mlrmodel1,2) # Residues are normally distributed\nplot(mlrmodel1,3)\nplot(mlrmodel1,4) # tells outliers which affect model\n\n\n# Confidence Intervals\n\n\n#Fitted values : Predicting on IVs using model\nfitted(mlrmodel1)\nresiduals(mlrmodel1) \nmlrmodel1$residuals\ncbind(omni$sales, fitted(mlrmodel1), omni$sales - fitted(mlrmodel1), residuals(mlrmodel1))\n\n#sqrt(sum((residuals(mlrmodel1)^2)))\nnames(mlrmodel1)\nsummary(mlrmodel1)\n\n\n\n\n#Diagnostics Test for Checking Assumptions \n#Should be Linear relationship between Residuals Vs Ypi, X1i, X2i\ncbind(fitted(mlrmodel1), residuals(mlrmodel1))\nplot(cbind(fitted(mlrmodel1), residuals(mlrmodel1)))\n#not quadratic\nplot(cbind(omni$price, residuals(mlrmodel1)))\nplot(cbind(omni$promotion, residuals(mlrmodel1)))\n#May indicate quadratic term of IVs\n\n\n#Train and Test Data\n\n\n# RMSE\n\n\nomni\nnames(omni)\nmlr2 = lm(sales ~ price + promotion, data= omni)\nsummary(mlr2)\nnew1=data.frame(price=60:70, promotion=400)\npredict(mlr2, newdata = new1)\ncbind(new1,predict(mlr2, newdata = new1) )\n\n\n" }, { "path": "24a-LM/10e-lm-errorplot.R", "content": "#LM - Sample Cases\n\n#Case1----\n#Different Data\nx <- c(173, 169, 176, 166, 161, 164, 160, 158, 180, 187)\ny <- c(80, 68, 72, 75, 70, 65, 62, 60, 85, 92)\nmod2 <- lm(y ~ x)\nplot(x, y, xlim=c(min(x)-5, max(x)+5), ylim=c(min(y)-10, max(y)+10))\nabline(mod2, lwd=2)\nres <- signif(residuals(mod2), 5)\npre <- predict(mod2)\nsegments(x, y, x, pre, col=\"red\")\nlibrary(calibrate)\ntextxy(x, y, res, cx=0.7)" }, { "path": "24a-LM/13b-lm-commands.R", "content": "\nfit = lm(weight ~ height, data = women)\nattributes(fit)\nattributes(summary(fit))\n" }, { "path": "24a-LM/16b-SLM-women2.R", "content": "# Regression Analysis\n# Simple Linear with 1 IV and 1 DV\n\ndata(women)\nwomen\nnames(women)\nstr(women)\n\ncov(women$height, women$weight)\n#69 : which show positive relationship between height and weight\n\ncor(women$height, women$weight)\n#0.995 : which shows Strong and Positive relationship betw height & weight\n0.995^2 # also equal R^2 value in this case (Simple Linear Regression)\n\nplot(x=women$height, y=women$weight)\nabline(lm(weight ~ height, data=women), col='red')\nnames(women) #x- IV, y -DV\nfit1 = lm(weight ~ height, data=women) # creating a model\nsummary(fit1) #summary of the Model\n\n#Ho: (F Test) : No relationship between Y and any X\n#Ha: There is relationship between Y and at least one X\n# p < 0.05 Reject Ho in favour of Ha\n\nattributes(fit1) #output of model\n\n#coefficients\ncoef(fit1)\n#p values for b0 & b1 are significant as it is < 0.05\n#pvalue1 = 1.71e-09 < 0.05\n#pvalue2 = 1.09e-14 < 0.05\n\ncoef(fit1)\n#Y = -87 + 3.4 * X # no extrapolations\n(Y = -87 + 3.4 * 20) # weight cannot be negative\n(Y = -87 + 3.4 * 58)\n(Y = -87 + 3.4 * 61.5)\n(Y = -87 + 3.4 * 72)\nrange(women$height)\nwomen\n\n\n#R^2\n(summary(fit1))$r.squared #0.991\n(summary(fit1))$adj.r.squared #0.9903\n#99% of variation in weight is explained by Height\n#Good Linear Model for Prediction\n\n\n#FStats : p value\n(summary(fit1))$fstatistic \n#pvalue : 1.09e-14 < 0.05 : Model exists\n#There is at least 1 IV which explains variation in Y (DV)\n\n\n#Prediction : for height = 65, 66\n(new1 = data.frame(height=c(65,66)))\nnew1\n(p1=predict(fit1, newdata = new1))\ncbind(new1, p1)\n#136.7333 140.1833\n\n(new2 = data.frame(height=c(60,69)))\n(p2=predict(fit1, newdata = new2))\ncbind(new2, p2)\n\n?predict\npredict(fit1, newdata = new1, interval='confidence')\npredict(fit1, newdata = new1, interval='prediction')\n\nwomen\nwomen$weight\nfitted(fit1) # predicted values of all original Xs\npredict(fit1, newdata = data.frame(women$height)) # same as above for single X\n\nnew3 = data.frame(women$height) # DF using height of original data\ncbind(women, fitted(fit1), residuals(fit1)) # compare women, predicted, errors\nresiduals(fit1) # diff between actual and predicted values of weight\nsummary(fit1)\nsummary(residuals(fit1))\n\nplot(fit1)\n\n\nx1 = c(1,2,3)\nx2 = c(4,5,6)\nx1\nx2\ncbind(x1,x2)\nrbind(x1,x2)\n" }, { "path": "24a-LM/16c-SLM-women1.R", "content": "# Simple Linear Regression : Built in Data Set Women\n# Predit Weight of heights of women - 60.5, 75.5\n\n#Check the Data set\nwomen \nstr(women)\n\ncov(women$height, women$weight)\n#69 : which show positive relationship between height and weight\n\ncor(women$height, women$weight)\n#0.995 : which shows Strong and Positive relationship betw height & weight\n0.995^2 # also equal R^2 value in this case (Simple Linear Regression)\n\nplot(x=women$height, y=women$weight)\nabline(lm(weight ~ height, data=women), col='red')\nnames(women) #x- IV, y -DV\nfit1 = lm(weight ~ height, data=women) # creating a model\nsummary(fit1) #summary of the Model\n\n\n#Predict\nfit = lm(weight ~ height, data=women)\n(ndata = data.frame(height = c(60.5, 75.5)))\npredictedwt = predict(fit, newdata = ndata)\ncbind(ndata, predictedwt)\n\n#End : Simple way to predict in SLM" }, { "path": "24a-LM/16e-SLM-women-A.R", "content": "# Simple Linear Regression : Built in Data Set Women\n# Check for assumptions of Regression in the data Set\n\nstr(women)\nfit = lm(weight ~ height, data=women)\n\n#Initial Checks\ncor(women$height, women$weight)\n#there is Strong and Positive Relationship between height and weight\nplot(women$height, women$weight)\nplot(fit)\n\n#Assumptions Regression\n#Linearity----\n#Linearity of the data. The relationship between the predictor (x) and the outcome (y) is assumed to be linear.\n# component + residual plot \nplot(women$height, fitted(fit))\nplot(residuals(fit) ~ fitted(fit))\nplot(fit, which=1)\n#residuals should be randomly distributed and not increase or decrease \n\n\n#Normality----\n#Normality of residuals. The residual errors are assumed to be normally distributed.\nplot(density(resid(fit)))\nplot(fit, which=2)\n\n#Homoscedasticity----\n#Homogeneity of residuals variance. The residuals are assumed to have a constant variance (homoscedasticity - opposite of heteroscedasticity)\nplot(fit, which=3)\n#No funnel shape, random distribution of residuals\nplot(fit, which=4)\n\n#Auto-Correlation----\n#Independence of residuals error terms. (Not dependent on previous values)\ndurbinWatsonTest(fit)\n#pvalue < 0; Ho that there is no correlation (r2=0) is accepted\n\n#Outliers\nplot(fit, which=4)\n#potential outliers are highlighted 1, 14, 15 row\nwomen[c(1,14,15),]\n\n#Lets remove these values and then find R2\nfit2 = lm(weight ~ height, data=women[-c(1,14,15),])\nsummary(fit2)\n(summary(fit))$r.squared\n(summary(fit2))$r.squared\nAIC(fit, fit2) #lower value of AIC is better\n\n\n#Potential Problems\n#Non-linearity of the outcome - predictor relationships\n#Heteroscedasticity: Non-constant variance of error terms.\n#Presence of influential values in the data that can be:\n#Outliers: extreme values in the outcome (y) variable\n#High-leverage points: extreme values in the predictors (x) variable\n\n#All these assumptions and potential problems can be checked by producing some diagnostic plots visualizing the residual errors.\n\n\n" }, { "path": "24a-LM/16f-SLM-women-V.R", "content": "#Simple Linear Regression : Validation of the Model - women\n#Is it good Model for Prediction, Are errors large, \n#Are Coefficient of variables significant\n\nstr(women)\nfit1 = lm(weight ~ height, data=women)\nsummary(fit1)\n\n#R2----\n#Coefficient of determination : between 0 and 1; higher the better\n(summary(fit1))$r.squared #0.991\n(summary(fit1))$adj.r.squared #0.9903\n#99% of variation in weight is explained by Height\n#Good Linear Model for Prediction\n\n#HypoTests----\n#Hypothesis Tests for Linear coefficient b1\n#if b1=0, no relationship between y and x's\n#coefficients\ncoef(fit1)\n#p values for b1 are significant as it's p value < 0.05 and coef > 0\nsummary(fit1)\n\n#FTest----\n#Overall Model Test\n#FStats : p value\n(summary(fit))$fstatistic \nsummary(fit)\n#p-value of FStats is 1.9e-14 << 0; Significant Model\n# Theres is atleast one IV which has significant relationship with DV\n\n#SSE\n#std sum of sq errors should be min (0)\nresiduals(fit)\nsum(residuals(fit))\n" }, { "path": "24a-LM/16f-SLM-women.R", "content": "# Regression Analysis\ndata()\ndata(women)\nwomen\nstr(women)\ncov(women$height, women$weight)\ncor(women$height, women$weight)\nplot(women)\nnames(women) #x- IV, y -DV\nplot(women, xlim=c(0,75), ylim=c(0,180))\ndf = women\ndf\nfit1 = lm(height ~ weight, data=women)\nsummary(fit1)\n\nset.seed(10)\n(df$age = floor(runif(15, 20,30)))\nset.seed(10)\n(age = floor(runif(15, 20,30)))\ndf$age == age\ncbind(df$age, age, df$age-age)\ndf$var2 = floor(runif(15, 30,40))\ndf\ndf$age2 = age\ndf[-c(1:2)]\ndf = df[-4]\ndf\n\n#y = mx + c\nfit1= lm(formula= weight ~ height + age +var2 , data= df)\nsummary(fit1)\nabline(fit1)\ndf\ncor(df$weight, df$height)\ndf=women\ndf$predict = predict(fit1, newdata=data.frame(height=women$height))\ndf\nabline(v=60)\nabline(h=119)\n\n" }, { "path": "24a-LM/16m-SLM-women2.R", "content": "#Simple Linear Regression \n#Predicting on variable only : DV\n\ndata(women)\nstr(women)\n\n#Batch of 15 women students have certain weights\n#Predict weight of next women \n\nwt = women$weight\n\n#Plot the weight\nplot(wt)\nmean(wt) #This is what we predict normally\ntext(wt+2, labels = wt, cex=.6, col='green')\nabline(h=mean(wt), col='red')\ntext(3,mean(wt)+2,label=paste('Predicited Wt', round(mean(wt))), col='red', cex=.7)\n\n#error in predicting : draw line\nsegments(x0=1:15, y0=mean(wt), x1=1:15, y=wt, col='purple')\n#Put Lables\ne = mean(wt) - wt\ntext(1:15, wt+5, labels=round(e,1), cex=.8, col='orange')\n\n#sum of errors\nsum(e) # approx zero\n\n#this is why sum of squares is taken\nsum(e^2) # SSE\n(rmse = sqrt(sum(e^2)/length(wt))) #root mean square\n# difference between predicted and actual values of DV\n# Regression tries to minimise this error by creating a model based on best fit line\n# if some indepedent variable is added there is likelihood of this error decreasing\n\n#weight not dependent on any variable - mean(wt)\nfit0 = lm(wt ~ 1)\nsummary(fit0)\n#here the intercept is 137 which is also the mean weight\nattributes(fit0)\n\n\n\n\n\n#read this http://www.learnbymarketing.com/tutorials/explaining-the-lm-summary-in-r/\n\n" }, { "path": "24a-LM/17a-LM-case1.R", "content": "# Regression Analysis\n# \nsales = read.csv('./data/bowl.csv')\nnames(sales)\nsummary(sales)\n?gsub\nhead(sales)\n#sales$bowlprice = as.numeric(gsub(\"\\\\$\", \"\", sales$bowlprice))\nsummary(sales)\nnames(sales)\nsales1 = sales[,1:2]\nnames(sales1)\ncor(sales1$bowls, sales1$bowlprice)\n\nfit1 = lm(bowls ~ bowlprice, data=sales1)\n\nsummary(fit1)\nhead(sales1)\nnew1 = data.frame(bowlprice=c(7,8,9))\npredict(fit1, newdata=new1)\n\nplot(x=sales1$bowlprice, y= sales1$bowls)\nsales2 = sales1[order(sales1$bowlprice),]\nplot(x=sales2$bowlprice, y= sales2$bowls)\nabline(fit1)\n\n\n" }, { "path": "24a-LM/17b-LM-stock1.R", "content": "# Regression on Stock Prices\n#Stock Prices\n\nYprice = c(80.13, 79.57, 79.93, 81.69, 80.82, 81.02)\nXprice = c(72.86, 72.88, 71.72, 71.54, 71, 71.78)\ndata = data.frame(Yprice, Xprice)\nplot(y=data$Yprice, x=data$Xprice)\nLinearR.lm = lm(Yprice ~ Xprice, data = data)\ncoef(LinearR.lm)\nLinearR.lm\nsummary(LinearR.lm)$r.squared\n" }, { "path": "24a-LM/18a-SLM-salesarea.R", "content": "#Simple Linear Regression : IIT Gauhati\n# Regression : Areas vs Sales\n\n#Method1 : creating data from Vectors\n#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y\nX = c(1.7,1.6,2.8,5.6,1.3,2.2, 1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )\nY = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )\n\n#create a data frame from X & Y\n#df1 =\n#first few values\n\n#2nd method of importing data\n#import from ggsheet #pickup the correct url\nlibrary(gsheet)\n\n#df2 = \n#first few values\n\n#Third method of importing data from CSV file\n#df3 =\n#first few values\n\n#Method4 for importing from CSV file with choose\n#df4 = read.csv(file.choose())\n#first few values\n\n# Use Vector Data or method used to import data\n#make one of the DF active\n#df = \n\n#Find mean, sum of X & Y, Covariance, Correlation\n\n#plot X & Y\n\n#draw abline/ prediction line\n\n\n#Linear Model fit= lm(Y~X, data=)\n\n\n#Print the Model, Summary, Coeff, Residuals\n\n\n#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP\n\n#output of variable Names\n\n#Print Slope value\n\n\n#combine the data with Ypredicted, errors\n\n# Predictions\n(Y = 0.9645 + 1.6699 * 4) # Predict Y for X=4\n(Y = coef(fit1)[1] + coef(fit1)[2] * 4) # for X=4 using values from output of LM \n\nfitted(fit1)\ncbind(df, fitted(fit1)) # combine data with predicted values\nrange(df$X) #min to max value of X: area\n\n# Create a Data Frame with sample values of X\n\n\n\n#sample data for X for prediction, should be between the range of X values\n#prediction data should be in the form of Data Frame\n\n#Predict\n\npredict(fit1, newdata= new1) # Predict Function for 4 values of X\n#fitted(fit1) is equal to predict function using original X values\n\ndf$X\npredictX = predict(fit1, newdata= data.frame(X=df$X))\ncbind(df, predictX, fitted(fit1))\n\nlibrary(forecast)\naccuracy(fit1)\n\n\nsummary(fit1)\nsummary(fit1)$sigma #Residual Std Error SD along the LM Line\n\n\n#Assumption : Graphical Analysis : IMP STEP\nplot(df$X, df$Y) # plot of Y & X\nplot(x=df$X, y=residuals(fit1)) # Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y\nabline(h=0)\nplot(residuals(fit1))\n\n#Auto Collinearity : relation between successive values of Y\ncar::durbinWatsonTest(fit1)\n#pvalue > 0 : Do not reject Ho that there is no correlation\n\n\n#Normality of residuals\nhist(residuals(fit1)) #distribution of Residuals\n\nhist(residuals(fit1), freq=F)\nlines(density(residuals(fit1)))\n\n#histogram values to show how hist rectangle are created\n(h=hist(residuals(fit1)))\nnames(h)\ncbind(h$breaks, h$counts)\n\n#Normality Plot to check assumptions of LM\nqqnorm(residuals(fit1))\nqqline(residuals(fit1))\n\n#Equal Variance : 4th Assumption : homoscedasticity\nplot(y=residuals(fit1), x=df$X)\nabline(h=0) #Variance across all x values is almost constant\n\n\n#Outlier Analysis\n\nboxplot(residuals(fit1), names=c('Residuals'))\nidentify(rep(1, length(residuals(fit1))), residuals(fit1), labels = seq_along(residuals(fit1)))\n\ncar::outlierTest(fit1)\n\ncar::outlierTest(lm(Y ~ X, data=df[-c(14,12),]))\n\n\n" }, { "path": "24a-LM/18b-SLM-salesarea.R", "content": "# Regression : Areas vs Sales\n\n#Method1 : creating data from Vectors\n#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y\nX = c(1.7,1.6,2.8,5.6,1.3,2.2, 1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )\nY = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )\ndf1 = data.frame(X,Y)\nhead(df1)\n\n#2nd method of importing data\n#import from ggsheet #pickup the correct url\nlibrary(gsheet)\narea1 = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519\"\ndf2 = as.data.frame(gsheet::gsheet2tbl(area1))\nstr(df2)\nhead(df2)\n\n#Third method of importing data\ndf3 = read.csv('./data/slr1.csv')\nstr(df3)\n\n#Method4 for importing from CSV file\ndf4 = read.csv(file.choose())\nstr(df4)\n\n# Use Vector Data or method used to import data\n#make one of the DF active\ndf = df1\n\nmean(df$X); mean(df$Y)\nsum(df$X); sum(df$Y)\nsd(df$X) ; var(df$Y)\ncov(df$X,df$Y); cov(df$Y,df$X)\ncor(df$X,df$Y) ; cor(df$Y,df$X)\n#cor.test(df$X,df$Y)\n\nplot(df$X, df$Y) #simple command to plot : Next with features\nplot(y=df$Y, x=df$X,xlab='Area in sqft', ylab='Sales Amount', type='p', ylim=c(0, max(df$Y)), main='Plot of Area Vs Sales', xlim=c(0,max(df$X)), col='red',pch=10)\n\nabline(lm(df$Y ~ df$X), lty=3, lwd=4, col='green') # with regression line\nabline(v=c(3,5),h=c(6,10), col=c('red','blue')) # few straight lines at x & y axis\n\n#Model\nfit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X\nfit1\nsummary(fit1)\n\nfitted(fit1) # predicted values for all X in orginal data\nresiduals(fit1)\n\nnames(fit1) # attributes(fit1)\nsystem.time(lm(Y ~ X, data=df))\ncoef(fit1) # Coefficients of Equation Y = mX + C\nresiduals(fit1) # Diff of Y actual - Y predicted\n#abline(h=coef(fit1)[1])\n\nsummary(fit1) # summary statistics of Linear Model(LM)\n#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP\n\nnames(fit1) #output variables names of LM\ncoef(fit1)\n#terms(fit1)\ncoef(fit1)[2] # slope or beta\nfitted(fit1)\n#combine the data with Ypredicted, errors\ncbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))\n\n# Predictions----\n(Y = 0.9645 + 1.6699 * 4) # Predict Y for X=4\n(Y = coef(fit1)[1] + coef(fit1)[2] * 4) # for X=4 using values from output of LM \ncoef(fit1)[2]\nnew1 = data.frame(X=c(4))\npredict(fit1, newdata= new1)\n\n\nfitted(fit1)\ncbind(df, fitted(fit1)) # combine data with predicted values\nrange(df$X) #min to max value of X: area\n\n\nnew1 = data.frame(X=c(1.5,2,3,4,5)) #sample data for X for prediction, should be between the range of X values\nnew1 #prediction data should be inform of Data Frame\n\npredict(fit1, newdata= new1) # Predict Function for 4 values of X\n\n\n#fitted(fit1) is equal to predict function using original X values\ncbind(new1, predictedY = predict(fit1, newdata= new1) )\n\nplot(fit1)\n\nlibrary(forecast)\naccuracy(fit1)\n\n#Variation # Errors in the model \n#Calculating R2 manually\n(SSE = sum((df1$Y - df1$predict)^2))\n(SSR = sum((df1$predict - mean(df1$Y))^2))\n#11.2 + 105.74\n(SST = sum((df1$Y - mean(df1$Y))^2))\nanova(fit1)\nqt(0.95+.025, 14-2)\n\nanova(fit1)\n(r2 = SSR/SST) # R2 from Calculation ANOVA\nsummary(fit1)$r.squared # R2 from Model\n\nsummary(fit1)\nsummary(fit1)$sigma #Residual Std Error SD along the LM Line\n\n\n#Assumption : Graphical Analysis : IMP STEP\nplot(fit1, which=1)\nplot(fit1)\n\n# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y\n\nabline(h=0)\nplot(residuals(fit1))\n\n#Auto Collinearity : relation between successive values of Y\ncar::durbinWatsonTest(fit1)\n#pvalue > 0 : Do not reject Ho that there is no correlation\n\n\n#Normality of residuals\nhist(residuals(fit1)) #distribution of Residuals\n\nhist(residuals(fit1), freq=F)\nlines(density(residuals(fit1)))\n\n#histogram values to show how hist rectangle are created\n(h=hist(residuals(fit1)))\nnames(h)\ncbind(h$breaks, h$counts)\n\n#Normality Plot to check assumptions of LM\nqqnorm(residuals(fit1))\nqqline(residuals(fit1))\n\n#Normality Plot can also be used with Std Values also\nresiduals(fit1)\nrstandard(fit1)\nsales.stdres = rstandard(fit1)\nqqnorm(sales.stdres, ylab=\"Standardized Residuals\", xlab=\"Normal Scores\", main=\"Checking normality of Residuals\") \nqqline(sales.stdres)\n\nstem(residuals(fit1))\n\n#Equal Variance : 4th Assumption : homoscedasticity\nplot(y=residuals(fit1), x=df$X)\nabline(h=0) #Variance across all x values is almost constant\n\n\n#Outlier Analysis\ndf\ndf[14,]\ndf$Y\nresiduals(fit1)\nboxplot( df$Y)\nboxplot( fitted(fit1))\n\nboxplot( df$Y, fitted(fit1), residuals(fit1), names=c('Y','predictY', 'Residuals'))\nabline(h=c(4.1, 5.97, -1.87))\n\nboxplot(residuals(fit1), names=c('Residuals'))\nidentify(rep(1, length(residuals(fit1))), residuals(fit1), labels = seq_along(residuals(fit1)))\n\ncar::outlierTest(fit1)\ndf[14,]\n\ncar::outlierTest(lm(Y ~ X, data=df[-c(14,12),]))\n\nfit1\n" }, { "path": "24a-LM/18c-SLM-salesarea.R", "content": "# Regression : Areas vs Sales\n\n#Method1 : creating data from Vectors\n#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y\nX = c(1.7,1.6,2.8,5.6,1.3,2.2, 1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )\nY = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )\ndf1 = data.frame(X,Y)\nhead(df1)\n\n#2nd method of importing data\n#import from ggsheet #pickup the correct url\nlibrary(gsheet)\narea1 = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519\"\ndf2 = as.data.frame(gsheet::gsheet2tbl(area1))\nstr(df2)\nhead(df2)\n\n#Third method of importing data\ndf3 = read.csv('./data/slr1.csv')\nstr(df3)\n\n#Method4 for importing from CSV file\ndf4 = read.csv(file.choose())\nstr(df4)\n\n# Use Vector Data or method used to import data\n#make one of the DF active\ndf = df1\n\nmean(df$X); mean(df$Y)\nsum(df$X); sum(df$Y)\nsd(df$X) ; var(df$Y)\ncov(df$X,df$Y); cov(df$Y,df$X)\ncor(df$X,df$Y) ; cor(df$Y,df$X)\n#cor.test(df$X,df$Y)\n\nplot(df$X, df$Y) #simple command to plot : Next with features\nplot(y=df$Y, x=df$X,xlab='Area in sqft', ylab='Sales Amount', type='p', ylim=c(0, max(df$Y)), main='Plot of Area Vs Sales', xlim=c(0,max(df$X)), col='red',pch=10)\n\nabline(lm(df$Y ~ df$X), lty=3, lwd=4, col='green') # with regression line\nabline(v=c(3,5),h=c(6,10), col=c('red','blue')) # few straight lines at x & y axis\n#Model\nfit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X\nfit1\nfitted(fit1)\n\nnames(fit1)\nsystem.time(lm(Y ~ X, data=df))\ncoef(fit1) # Coefficients of Equation Y = mX + C\nresiduals(fit1) # Diff of Y actual - Y predicted\n#abline(h=coef(fit1)[1])\n\nsummary(fit1) # summary statistics of Linear Model(LM)\n#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP\n\nnames(fit1) #output variables names of LM\ncoef(fit1)\n#terms(fit1)\ncoef(fit1)[2] # slope or beta\nfitted(fit1)\n#combine the data with Ypredicted, errors\ncbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))\n\n# Predictions----\n(Y = 0.9645 + 1.6699 * 4) # Predict Y for X=4\n(Y = coef(fit1)[1] + coef(fit1)[2] * 4) # for X=4 using values from output of LM \nnew1 = data.frame(X=c(4))\npredict(fit1, newdata= new1)\n\n\n\n\n\nfitted(fit1)\ncbind(df, fitted(fit1)) # combine data with predicted values\nrange(df$X) #min to max value of X: area\n\n\nnew1 = data.frame(X=c(1.5,2,3,4,5)) #sample data for X for prediction, should be between the range of X values\nnew1 #prediction data should be inform of Data Frame\n\npredict(fit1, newdata= new1) # Predict Function for 4 values of X\n\n\n#fitted(fit1) is equal to predict function using original X values\ncbind(new1, predictedY = predict(fit1, newdata= new1) )\n\nlibrary(forecast)\naccuracy(fit1)\n\n#Variation # Errors in the model \n#Calculating R2 manually\n(SSE = sum((df1$Y - df1$predict)^2))\n(SSR = sum((df1$predict - mean(df1$Y))^2))\n#11.2 + 105.74\n(SST = sum((df1$Y - mean(df1$Y))^2))\nanova(fit1)\nqt(0.95+.025, 14-2)\n\nanova(fit1)\n(r2 = SSR/SST) # R2 from Calculation ANOVA\nsummary(fit1)$r.squared # R2 from Model\n\nsummary(fit1)\nsummary(fit1)$sigma #Residual Std Error SD along the LM Line\n\n\n#Assumption : Graphical Analysis : IMP STEP\nplot(fit1, which=1)\nplot(fit1)\n\n# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y\n\nabline(h=0)\nplot(residuals(fit1))\n\n#Auto Collinearity : relation between successive values of Y\ncar::durbinWatsonTest(fit1)\n#pvalue > 0 : Do not reject Ho that there is no correlation\n\n\n#Normality of residuals\nhist(residuals(fit1)) #distribution of Residuals\n\nhist(residuals(fit1), freq=F)\nlines(density(residuals(fit1)))\n\n#histogram values to show how hist rectangle are created\n(h=hist(residuals(fit1)))\nnames(h)\ncbind(h$breaks, h$counts)\n\n#Normality Plot to check assumptions of LM\nqqnorm(residuals(fit1))\nqqline(residuals(fit1))\n\n#Normality Plot can also be used with Std Values also\nresiduals(fit1)\nrstandard(fit1)\nsales.stdres = rstandard(fit1)\nqqnorm(sales.stdres, ylab=\"Standardized Residuals\", xlab=\"Normal Scores\", main=\"Checking normality of Residuals\") \nqqline(sales.stdres)\n\nstem(residuals(fit1))\n\n#Equal Variance : 4th Assumption : homoscedasticity\nplot(y=residuals(fit1), x=df$X)\nabline(h=0) #Variance across all x values is almost constant\n\n\n#Outlier Analysis\ndf\ndf[14,]\ndf$Y\nresiduals(fit1)\nboxplot( df$Y)\nboxplot( fitted(fit1))\n\nboxplot( df$Y, fitted(fit1), residuals(fit1), names=c('Y','predictY', 'Residuals'))\nabline(h=c(4.1, 5.97, -1.87))\n\nboxplot(residuals(fit1), names=c('Residuals'))\nidentify(rep(1, length(residuals(fit1))), residuals(fit1), labels = seq_along(residuals(fit1)))\n\ncar::outlierTest(fit1)\ndf[14,]\n\ncar::outlierTest(lm(Y ~ X, data=df[-c(14,12),]))\n\nfit1\n" }, { "path": "24a-LM/23a-MLM-omni.R", "content": "#Multiple Linear Regression \n#Linear Modeling : DV vs more than 1 IVs\n#sales Qty vs price & promotion\n\n#Omni Store\n#creating data using Vector\nsales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)\nprice = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)\npromotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)\n#Create a DF from 3 variables\n#omni1 = data.frame( , , )\n\n#Check if it has been created\n\n\n#2nd Method : CSV file\n#omni2 = read.csv(file.choose())\n\n#3rd Method : gsheet \nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231\"\nomni3 = as.data.frame(gsheet::gsheet2tbl(url))\n\n#Make one of data frames active\nomni = omni1\n\n?lm #see help of LM\n#Simple Linear Model would look like this\nslr1 = lm(formula = sales ~ price, data=omni) # sales depend on price of item\nslr2 = lm(formula = sales ~ promotion, data=omni) # sales depend on promotion exp\nsummary(slr1)\nsummary(slr2)\n\n\n#MLR Create Multiple Linear Regression\n# we want to see how Sales Qty depend on Price and Promotion Values\nmlrmodel1 = lm(formula = sales ~ price + promotion, data=omni)\n#how to give parameter values in different sequence, use arguments names if in different order\nmlrmodel1 = lm( data=omni, formula = sales ~ price + promotion)\n\n# summary statistics of model IMP STEP\n\n#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals\n\n#coefficients b1, b2\n\n\n\n#Predicted Values----\n\n#create a dataframe of new sample values\n(ndata1 = data.frame(price=c(60,70), promotion=c(300,400)))\n\n#cbind the values\n\n\n\n\n\n#Plots of the Modle\nplot(mlrmodel1,1) # no pattern, equal variance\nplot(mlrmodel1,2) # Residuals are normally distributed\nplot(mlrmodel1,3)\nplot(mlrmodel1,4) # tells outliers which affect model\n\n\n\n\n\n\n\n\n" }, { "path": "24a-LM/23c-MLM-omni.R", "content": "#Multiple Linear Regression \n#Linear Modeling : DV vs more than 1 IVs\n#sales Qty vs price & promotion\n#Prerquistes - Lin Modeling, lm, output\n\n#Omni Store\n#creating data using Vector\nsqty= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)\nprice = c(59,59,59,59,59,59,59,59,59,59,59,59,79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)\npromotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)\nsales1 = data.frame(sqty, price, promotion)\nhead(sales1)\nstr(sales1)\n# find change in Sales Qty for 1 unit change in price and promotion\n\n#2nd Method : CSV file : file not in project folder\nsales2 = read.csv(file.choose())\n\n#3rd Method : gsheet library\nlibrary(gsheet)\nurl = \"https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231\"\nsales3 = as.data.frame(gsheet::gsheet2tbl(url))\nstr(sales3)\n\n#4th Method : CSV file : file in project folder\nsales4 = read.csv('./data/salesqty.csv')\nstr(sales4)\n\nsapply(list(sales1,sales2,sales3, sales4), dim)\nsapply(list(sales1,sales2,sales3, sales4), names)\n\n#Make one of data frames active\nsales = sales1\nhead(sales)\n\n?lm #see help of LM\n#Simple Linear Model would look like this\nslr1 = lm(formula = sqty ~ price, data=sales) # sales depend on price of item\nsummary(slr1)\n\nslr2 = lm(formula = sqty ~ promotion, data=sales) # sales depend on promotion exp\nsummary(slr2)\nAIC(slr1, slr2) # slr1 better with 1 IV\n\n\n#MLR Create Multiple Linear Regression\n# we want to see how Sales Qty depend on Price and Promotion Values\nmlrmodel1 = lm(sqty ~ price + promotion, sales)\n\n#how to give parameter values in different sequence, use arguments names if in different order\nmlrmodel1a = lm( data=sales, formula = sqty ~ price + promotion)\n\n\nrange(sales$sqty) # range of sales qty\nsummary(mlrmodel1) # summary statistics IMP STEP\n#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals\n\ncoef(mlrmodel1) #coefficients b1, b2\n#anova(mlrmodel1) #seeing from anova model\nattributes(mlrmodel1)\nhead(sales)\n\n#coplot(sqty ~ price | promotion, data=sales)\n\n\n#Predicted Values----\ndim(sales)\nfitted(mlrmodel1) #predicted values for various input combinations of IV\ncbind(sales, fitted(mlrmodel1), residuals(mlrmodel1))\nsummary(mlrmodel1)\ncoef(mlrmodel1)\n5837 + 59 * -53 + 200 * 3.651\nnames(omni)\nrange(sales$price); range(sales$promotion)\n#create a dataframe of new sample values\n(ndata1 = data.frame(price=c(69,98), promotion=c(500,559)))\npredict(mlrmodel1, newdata=ndata1)\ncbind(ndata1, Predict=predict(mlrmodel1, newdata=ndata1, predict='response'))\n\n\n\n#R2 and Adjs R2\nnames(mlrmodel1)\nsummary(mlrmodel1)\nsummary(mlrmodel1)$r.squared\n\n#Manual Calculation of Adjs R2\n(r2 = summary(mlrmodel1)$r.squared)\nk = 2 # no of IVs\n(n = nrow(sales)) # sample size\n(adjr2 = 1 - ( (1 - r2) * ((n - 1)/ (n - k - 1))))\n\n\n# Fstatistics\nsummary(mlrmodel1)$fstatistic[1] # from output of model\n\n(df1 = k) ; (df2 = n-k-1) \nqf(.95, df1, df2) # from table wrt df1 & df2\n#Model Fstats > table(Fstat) \n\n# Pvalue of Model\nfstat = summary(mlrmodel1)$fstatistic \npf(fstat[1], fstat[2], fstat[3], lower.tail=FALSE) \n# this is < 0.05 : Significant\n# \n\n#Plots of the Model\nplot(mlrmodel1,1) # no pattern, equal variance\nplot(mlrmodel1,2) # Residues are normally distributed\nplot(mlrmodel1,3)\nplot(mlrmodel1,4) # tells outliers which affect model\n#most significant is row 14\n\n# Confidence Intervals\n\n\n#Fitted values : Predicting on IVs using model\nfitted(mlrmodel1)\nresiduals(mlrmodel1) \nmlrmodel1$residuals\ncbind(sales$sqty, fitted(mlrmodel1), sales$sqt - fitted(mlrmodel1), residuals(mlrmodel1))\n\n#sqrt(sum((residuals(mlrmodel1)^2)))\nnames(mlrmodel1)\nsummary(mlrmodel1)\n\n\n\n\n#Diagnostics Test for Checking Assumptions \n#Should be Linear relationship between Residuals Vs Ypi, X1i, X2i\ncbind(fitted(mlrmodel1), residuals(mlrmodel1))\nplot(cbind(fitted(mlrmodel1), residuals(mlrmodel1)))\n#not quadratic\nplot(cbind(sales$price, residuals(mlrmodel1)))\nplot(cbind(sales$promotion, residuals(mlrmodel1)))\n#May indicate quadratic term of IVs\n\n\n#Train and Test Data\n#since this data is small full data is used for train and test\n#otherwise train = 70% of data\n\n# RMSE\nlibrary(Metrics)\nmlrmodel1\nrmse(sales$sqty, fitted(mlrmodel1))\n\nslr1\nrmse(sales$sqty, fitted(slr1))\nslr2\nrmse(sales$sqty, fitted(slr2))\n\nAIC(mlrmodel1, slr1, slr2)\n#Which is best prediction model - with price and promotion\n\n# Extra: Predict for other ranges\nhead(sales) ; names(sales)\nmlr2 = lm(sqty ~ price + promotion, data= sales)\nsummary(mlr2)\nnew1=data.frame(price=60:70, promotion=400)\npredict(mlr2, newdata = new1)\n#summary of IV values and predicted values\ncbind(new1,predict2 = predict(mlr2, newdata = new1) )\n\n\n" }, { "path": "24a-LM/24a-MLM-pcsales.R", "content": "#Multiple Linear Regression\n#\nsales3 = read.csv('./data/pcsales.csv', skip=1)\nsummary(sales3)\nsales3$salescapita = as.numeric(gsub(\"\\\\$\", \"\", sales3$salescapita))\nsummary(sales3)\nsales4 = sales3[-1]\nnames(sales4)\nfit2 = lm(salescapita ~ ., data=sales4)\nsummary(fit2)\n\nfit3 = lm(salescapita ~ . -unemrate, data=sales4)\nsummary(fit3)\n#this is ok\nhead(sales4)\nnew1 = data.frame(pop=8, pcsales=200, gnp=5000, unemrate=5, eduspend=5)\npredict(fit3, newdata=new1)\n" }, { "path": "24a-LM/25a-MLM-mtcars.R", "content": "#MLR using mtcars dataset\n#mtcars\nmtcars # mtcars data set\ndata() # available dataset in R through libraries\n?mtcars # help on mtcars\n\ndim(mtcars)\nnames(mtcars)\nhead(mtcars)\n?mtcars\n\nmtcarsfit1 = lm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am +gear +carb , data = mtcars)\nsummary(mtcarsfit1)\n\nmtcarsfit2 = lm(mpg ~ . , data = mtcars)\nsummary(mtcarsfit2)\n\n\n\nlibrary(MASS)\n(model1a = lm(mpg ~ ., data=mtcars))\n(model1b = lm(mpg ~ 1, data=mtcars))\n\nsummary(model1a)\nsummary(model1b)\n\n\nstepboth = stepAIC(model1a, direction='both')\nfit1= lm(mpg ~ wt + qsec + am, data=mtcars)\nsummary(fit1)\n\n\nstepfwd = stepAIC(model1b, direction='forward', scope=list(upper=model1a,lower=model1b))\n\nstepback = stepAIC(model1a, direction='backward')\n?stepAIC\n\n\n#-----\n?step # base package\n\nlm.full = lm(mpg ~ wt + cyl + gear + am +disp, data=mtcars)\nmodel.aic.backward <- step(lm.full, direction = \"backward\", trace = 1)\nsummary(model.aic.backward)\n\nlm.null = lm(mpg ~ 1, data=mtcars)\nmodel.aic.forward <- step(lm.null, direction = \"forward\", trace = 1,scope = ~ wt + cyl + gear + am +disp)\n\nsummary(model.aic.forward)\n\n\n#----\nfit1 <- lm(y ~ ., data)\nfit2 <- lm(y ~ 1, data)\nstepAIC(fit1,direction=\"backward\")\nstepAIC(fit2,direction=\"forward\",scope=list(upper=fit1,lower=fit2))\nstepAIC(fit2,direction=\"both\",scope=list(upper=fit1,lower=fit2))\n\n\n(model1a = lm(mpg ~ disp + cyl + hp + drat + wt +\n qsec + carb, data=mtcars))\n(model1b = lm(mpg ~ 1, data=mtcars))\n\n" }, { "path": "24a-LM/25c-MLM--mtcars1.R", "content": "#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf\n#install.packages('olsrr')\nlibrary(olsrr)\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nk <- ols_step_all_possible(model)\nplot(k)\nk\nsummary(lm(mpg ~ wt, data=mtcars))\nsummary(lm(mpg ~ wt+ hp, data=mtcars))\n" }, { "path": "24a-LM/25c-MLM-mtcars.R", "content": "#MLR using mtcars dataset\n#mtcars\nmtcars # mtcars data set\ndata() # available dataset in R through libraries\n?mtcars # help on mtcars\n\ndim(mtcars)\nnames(mtcars)\nhead(mtcars)\n?mtcars\n\nmtcarsfit1 = lm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am +gear +carb , data = mtcars)\nsummary(mtcarsfit1)\n\nmtcarsfit2 = lm(mpg ~ . , data = mtcars)\nsummary(mtcarsfit2)\n\n\n\nlibrary(MASS)\n(model1a = lm(mpg ~ ., data=mtcars))\n(model1b = lm(mpg ~ 1, data=mtcars))\n\nsummary(model1a)\nsummary(model1b)\n\n\nstepboth = stepAIC(model1a, direction='both')\nfit1= lm(mpg ~ wt + qsec + am, data=mtcars)\nsummary(fit1)\n\n\nstepfwd = stepAIC(model1b, direction='forward', scope=list(upper=model1a,lower=model1b))\n\nstepback = stepAIC(model1a, direction='backward')\n?stepAIC\n\n\n#-----\n?step # base package\n\nlm.full = lm(mpg ~ wt + cyl + gear + am +disp, data=mtcars)\nmodel.aic.backward <- step(lm.full, direction = \"backward\", trace = 1)\nsummary(model.aic.backward)\n\nlm.null = lm(mpg ~ 1, data=mtcars)\nmodel.aic.forward <- step(lm.null, direction = \"forward\", trace = 1,scope = ~ wt + cyl + gear + am +disp)\n\nsummary(model.aic.forward)\n\n\n#----\nfit1 <- lm(y ~ ., data)\nfit2 <- lm(y ~ 1, data)\nstepAIC(fit1,direction=\"backward\")\nstepAIC(fit2,direction=\"forward\",scope=list(upper=fit1,lower=fit2))\nstepAIC(fit2,direction=\"both\",scope=list(upper=fit1,lower=fit2))\n\n\n(model1a = lm(mpg ~ disp + cyl + hp + drat + wt +\n qsec + carb, data=mtcars))\n(model1b = lm(mpg ~ 1, data=mtcars))\n\n" }, { "path": "24a-LM/25d-MLM-mtcars-A.R", "content": "# Model Assumptions in Linear Regression\n# Diagnostics (https://www.statmethods.net/stats/rdiagnostics.html)\n\n# Assume that we are fitting a multiple linear regression\n# on the MTCARS data\nlibrary(car)\nfit <- lm(mpg~disp+hp+wt+drat, data=mtcars)\n\n# Assessing Outliers----\noutlierTest(fit) # Bonferonni p-value for most extreme obs\nqqPlot(fit, main=\"QQ Plot\") #qq plot for studentized resid \nleveragePlots(fit) # leverage plots\n\n\n# Influential Observations----\n# added variable plots \ncar::avPlots(fit)\n\n# Cook's D plot\n# identify D values > 4/(n-k-1) \ncutoff <- 4/((nrow(mtcars)-length(fit$coefficients)-2)) \nplot(fit, which=4, cook.levels=cutoff)\n\n# Influence Plot \ninfluencePlot(fit,\tid.method=\"identify\", main=\"Influence Plot\", sub=\"Circle size is proportial to Cook's Distance\" )\n\n\n# Normality of Residuals----\n# qq plot for studentized resid - \nqqPlot(fit, main=\"QQ Plot\")\n# distribution of studentized residuals - should be normal\nlibrary(MASS)\nresiduals(fit) # standardise them\nsresid <- studres(fit) \nhist(sresid, freq=FALSE, main=\"Distribution of Studentized Residuals\")\nplot(density(sresid))\n\nxfit<-seq(min(sresid),max(sresid),length=40) \nyfit<-dnorm(xfit) \nlines(xfit, yfit)\n\n#Non-constant Error Variance ---\n# Evaluate homoscedasticity. Shouldhave constant variance\n# non-constant error variance test\ncar::ncvTest(fit)\n# plot studentized residuals vs. fitted values \nspreadLevelPlot(fit)\n#spread is scattered\n\n#Multi-collinearity ----\n# Evaluate Collinearity\nvif(fit) # variance inflation factors \nsqrt(vif(fit)) > 2 # if > 2, there is aproblem?\n\n\n#Nonlinearity----\n# Evaluate Nonlinearity\n# component + residual plot \ncrPlots(fit)\n# Ceres plots \nceresPlots(fit)\n\n#Non-independence of Errors----\n# Test for Autocorrelated Errors\ndurbinWatsonTest(fit)\n\n\n#Additional Diagnostic Help----\n#The gvlma( ) function in the gvlma package, performs a global validation of linear model assumptions as well separate evaluations of skewness, kurtosis, and heteroscedasticity.\n\n# Global test of model assumptions\nlibrary(gvlma)\ngvmodel <- gvlma(fit) \nsummary(gvmodel)\n\n\n#(http://r-statistics.co/Assumptions-of-Linear-Regression.html)\n#Simple Linear\n\n\n\n\n\n#Multiple Linear\n\n\n\n#http://r-statistics.co/Assumptions-of-Linear-Regression.html\n\n\n\n\n\n\n\n\n" }, { "path": "24a-LM/26a-MLM-airquality.R", "content": "# Linear Regression - Data set - Air Quality\n\n\ndata(airquality)# to call the data\nattach(airquality)\nhead(airquality,10)# to see first 10 rows\n#attach () function makes the data available to the R search path.\n\n#Summary function gives you the range, quartiles, median and mean for numerical variables and table with frequencies for categorical variable.\nsummary(airquality)\n\n#Data visualization \n#boxplot to visualize the daily temperature for month 5, 6, 7, 8 and 9.\nmonth5=subset(airquality,Month=5)\nmonth6=subset(airquality,Month=6)\nmonth7=subset(airquality,Month=7)\nmonth8=subset(airquality,Month=8)\nmonth9=subset(airquality,Month=9)\n\npar(mfrow = c(1,2)) # 3 rows and 2 columns\nboxplot((month5$Temp~airquality$Day),main='Month 5',col=rainbow(3))\nboxplot((month6$Temp~airquality$Day),main='Month 6',col=rainbow(3))\nboxplot((month7$Temp~airquality$Day),main='Month 7',col=rainbow(3))\nboxplot((month8$Temp~airquality$Day),main='Month 8',col=rainbow(3))\nboxplot((month9$Temp~airquality$Day),main='Month 9',col=rainbow(3))\n\npar(mfrow = c(1,1)) \n#histogram to see the distribution of temperature data.\nhist(airquality$Temp,col=rainbow(2))\n\n#scatter plot to see if there is a linear pattern between the ‘temperature rise’ and other variables.\n \nplot(airquality$Temp ~ airquality$Day + airquality$Solar.R + airquality$Wind + airquality$Ozone, col='blue')\n \n#It seems that solar.R , Ozone, and wind have a linear pattern with temperature. Solar and Ozone have a positive relationship and wind has a negative one. I use Co-plot to see the effect of wind and solar radiations combined on Temperature\n\nhead(airquality[,c('Ozone','Solar.R', 'Wind')])\ncoplot(Ozone ~ Solar.R | Wind, panel=panel.smooth, airquality, col= 'green')\n\n# Data Cleaning ----\n#Missing Values\nsapply(airquality,function(x){sum(is.na(x))})\n\n#Impute Missing Values\nairquality$Ozone[is.na(airquality$Ozone)]=mean(airquality$Ozone,na.rm=T)\n\nairquality$Solar.R[is.na(airquality$Solar.R)]=mean(airquality$Solar.R,na.rm=T)\n\nsapply(airquality,function(x){sum(is.na(x))})\n\n#Correlation\nlibrary(corrplot)\n\no=corrplot(cor(airquality),method='number') \n# this method can be changed try using method=’circle’\ncorrplot(cor(airquality),method='circle') \n\n#Model\nModel_lm1=lm(Temp~.,data=airquality)\nsummary(Model_lm1)\nAIC(Model_lm1)\n\n#Best Model based on AIC\nModel_lm_best=step(Model_lm1)\nsummary(Model_lm_best)\nplot(Model_lm_best,col='blue')\n\n\n# VIF\nlibrary(fmsb)\nModel_lm1=lm(Temp~ Ozone+Solar.R+Month,data=airquality)\nVIF(lm(Month ~ Ozone+Solar.R,data=airquality))\nVIF(lm(Ozone ~ Solar.R+Month, data=airquality))\nVIF(lm(Solar.R ~ Ozone +Month, data=airquality))\n#As a general rule, VIF < 5 is acceptable (VIF = 1 means there is no multicollinearity), and VIF > 5 is not acceptable and we need to check our model.\n\n#In our example, VIF < 5 and hence there is no need of any additional verification needed." }, { "path": "24a-LM/27a-MLM-marketing.R", "content": "#MLR in R\n\n\n#http://www.sthda.com/english/articles/40-regression-analysis/168-multiple-linear-regression-in-r/\n \n " }, { "path": "24a-LM/35a-MLM-case1.R", "content": "#Set Working Directory\n\n#Read the data file\nDefaultData<-read.csv(\"./regr/MMM_raw_data_v02.csv\")\n\n# Cars Sold analysis\n\n#Check if the data is populated/imported properly\nhead(DefaultData)\ntail(DefaultData)\n\n#Check the summary of the file\nsummary(DefaultData)\n\n#Generate plot of Dependent variable\nplot(DefaultData$Unit_Sold)\n\n#Check the quantile to find out the outlier limit\nquantile(DefaultData$Unit_Sold, c(0,0.05,0.1,0.25,0.5,0.75,0.90,0.95,0.99,0.995,1))\n\n#Creating the Capped Losses column with 768 cap\nDefaultData$CappedUnitSold<-ifelse(DefaultData$Unit_Sold>768,768,DefaultData$Unit_Sold)\n\n#Check if Capped Losses column has been created properly or not\nsummary(DefaultData)\nnames(DefaultData)\n\n#Create new object deleting Losses and S.no.\nDefaultData3<-DefaultData[,-c(2)]\n\n#Check the headings of the new object\n\nnames(DefaultData3)\nhead(DefaultData3)\n\n#Generate plots to see the relation between the independent variables and the dependent variable\nplot(DefaultData3$Print_Spend,DefaultData3$CappedUnitSold)\n\nplot(DefaultData3$Radio_Spend,DefaultData3$CappedUnitSold)\nplot(DefaultData3$TV_Spend,DefaultData3$CappedUnitSold)\n\nnames(DefaultData3)\n\n\n#Install car Package for vif (Multicollinearity)\n#install.packages(\"car\")\n\n#Load the car package\nlibrary(\"car\")\n\n#Create linear function for vif\nvif_data<-lm(CappedUnitSold~\n Print_Spend+\n Radio_Spend+\n TV_Spend+\n Promotion_Dummy,data=DefaultData3)\n\n#Check Vif, vif>2 means presence of multicollinearity\nvif(vif_data)\n\n#Run Linear Regression w/o Years_Drv_Exp\nlin_r1<-lm(CappedUnitSold~\n Print_Spend+\n Radio_Spend+\n TV_Spend+\n Promotion_Dummy,data=DefaultData3)\n\n#Let's look at the results\nsummary(lin_r1)\n\nfitted(lin_r1)\nsummary(lin_r1)$r.squared #R-square Value \n\n\n##Generate the diagnostic plots\nplot(lin_r1)\n\nmeancnt1=DefaultData3\nmeancnt1$pred = 56.7185191 + \n 0.0098758*DefaultData3$Print_Spend+0.2493764*DefaultData3$Radio_Spend\n\n\n\nmeancnt1$res = meancnt1$pred - meancnt1$CappedUnitSold\nmeancnt1$abs_res = abs(meancnt1$res)\nmeancnt1$mape = 100*meancnt1$abs_res/meancnt1$CappedUnitSold;\nmeancnt1$mape\n\n# Plot Y-pred vs. Error to ensure there is no pattern\nplot(meancnt1$pred,meancnt1$res)\n\n" }, { "path": "24a-LM/37a-LM-dummy-fireplace.R", "content": "# Dummy Variables in Regression\n\nvalue = c(84.4,77.4,75.7,85.9,79.1,70.4,75.8,85.9,78.5,79.2,86.7,79.3,74.5,83.8,76.8)\narea = c(2.00,1.71,1.45,1.76,1.93,1.20,1.55,1.93,1.59,1.50,1.90,1.39,1.54,1.89,1.59)\nfireplace= c('Yes','No','No','Yes','No','Yes','Yes','Yes','Yes','Yes','Yes','Yes','No','Yes','No')\n\nfireplace = factor(fireplace)\nhouse = data.frame(value, area, fireplace)\nstr(house)\n\nmlrhouse1 = lm(value ~ area + fireplace, data=house)\nsummary(mlrhouse1)\n\n#Equation : value = 50 + 16.1 * area + 3.85 * 1 : for fireplace\n#Equation : value = 16.1 * area : for no fireplace\n\nhead(house)\npredict(mlrhouse1, newdata=head(house), predict='response')\n\n(value1 = 50 + 16.1 * 2 + 1 * 1) # Fireplace\n(value2 = 50 + 16.1 * 1.71 ) #no fireplace\n" }, { "path": "24a-LM/37b-dummy1.R", "content": "# Dummy Variables\n\ndf = mtcars\nsummary(df)\n(df$cyl = factor(df$cyl))\n(df$am = factor(df$am))\n\nM1 = lm(mpg ~ wt + cyl , data=df)\nsummary(M1)\nsummary(df$cyl)\ndata.frame(wt=c(2,3), cyl=factor(c(4,6)))\npredict(M1, newdata = data.frame(wt=c(2,3), cyl=factor(c(4,6))) )\n\n\nsummary(df)\ny4 = 33.99 + -3.2 * wt + 0\ny6 = 33.99 + -3.2 * wt + -4.2 * cyl6 #(=1)\ny8 = 33.99 + -3.2 * wt + -6.07 * cyl8 #(=1)\n\nhead(df)\ny4 = " }, { "path": "24a-LM/38c-LM-dummy1.R", "content": "# Dummy Variables\n# How categorical Values are handled in Linear Regression\n\ndf = mtcars\nsummary(df)\ndf$cyl = factor(df$cyl)\ndf$am = factor(df$am)\n\nM1 = lm(mpg ~ wt + cyl , data=df)\nsummary(M1)\n\nsummary(df$cyl)\n\ndata.frame(wt=c(2,3), cyl=factor(c(4,6)))\npredict(M1, newdata = data.frame(wt=c(2,2,2), cyl=factor(c(4,6, 8))) )\nstr(df)\n\nsummary(df)\n#Predicting in case of Dummy Variables : Manual\ny = 33.99 + -3.2 * wt + (0) * cyl4 + (-4.1) * cyl6 + (-6.071) * cyl8\nwt=2 ; cyl4=1; cyl6=0; cyl8=0 #value predicted = 27.48\ny4 = 33.99 + -3.2 * wt + 0 # this is for cyl=4\ncyl6=1\n(y6 = 33.99 + -3.2 * wt + -4.2 * cyl6) #cyl = 6 (=1)\n\ny8 = 33.99 + -3.2 * wt + -6.07 * cyl8 #cyl = 8(=1)\n\nhead(df)\n" }, { "path": "24a-LM/41c-LM-assumptions.R", "content": "# Model Assumptions in Linear Regression\n# Diagnostics (https://www.statmethods.net/stats/rdiagnostics.html)\n#(http://www.sthda.com/english/articles/39-regression-model-diagnostics/161-linear-regression-assumptions-and-diagnostics-in-r-essentials/)\n\n\n#The regression has five key assumptions:\n#1.Linear relationship.\n#Multivariate normality.\n#No or little multicollinearity.\n#No auto-correlation.\n#Homoscedasticity.\n\nbtech --> java , cpp, maths\nsales figures over 1 year\n\n\n# Assume that we are fitting a multiple linear regression\n# on the MTCARS data\nlibrary(car)\nfit <- lm(mpg~disp+hp+wt+drat, data=mtcars)\n# Assessing Outliers\noutlierTest(fit) # Bonferonni p-value for most extreme obs\nqqPlot(fit, main=\"QQ Plot\") #qq plot for studentized resid \nleveragePlots(fit) # leverage plots\n\n# Influential Observations\n# added variable plots \nav.Plots(fit)\n# Cook's D plot\n# identify D values > 4/(n-k-1) \ncutoff <- 4/((nrow(mtcars)-length(fit$coefficients)-2)) \nplot(fit, which=4, cook.levels=cutoff)\n# Influence Plot \ninfluencePlot(fit,\tid.method=\"identify\", main=\"Influence Plot\", sub=\"Circle size is proportial to Cook's Distance\" )\n\n\n# Normality of Residuals\n# qq plot for studentized resid\nqqPlot(fit, main=\"QQ Plot\")\n# distribution of studentized residuals\nlibrary(MASS)\nsresid <- studres(fit) \nhist(sresid, freq=FALSE, \n main=\"Distribution of Studentized Residuals\")\nxfit<-seq(min(sresid),max(sresid),length=40) \nyfit<-dnorm(xfit) \nlines(xfit, yfit)\n\n#Non-constant Error Variance ---\n# Evaluate homoscedasticity. Should have constant variance\n# non-constant error variance test\nncvTest(fit)\n# plot studentized residuals vs. fitted values \nspreadLevelPlot(fit)\n\n#Multi-collinearity ----\n# Evaluate Collinearity\nvif(fit) # variance inflation factors \nsqrt(vif(fit)) > 2 # problem?\n\nsummary(fit)\n#Nonlinearity----\n# Evaluate Nonlinearity\n# component + residual plot \ncrPlots(fit)\n# Ceres plots \nceresPlots(fit)\n\n#Non-independence of Errors----\n# Test for Autocorrelated Errors\ndurbinWatsonTest(fit)\n\n\n#Additional Diagnostic Help----\n#The gvlma( ) function in the gvlma package, performs a global validation of linear model assumptions as well separate evaluations of skewness, kurtosis, and heteroscedasticity.\n\n# Global test of model assumptions\nlibrary(gvlma)\ngvmodel <- gvlma(fit) \nsummary(gvmodel)\n\n#Plot command\nplot(fit)\n\n#(http://r-statistics.co/Assumptions-of-Linear-Regression.html)\n#Simple Linear\n\n#Multiple Linear\n\n#http://r-statistics.co/Assumptions-of-Linear-Regression.html\n" }, { "path": "24a-LM/42b-LM-linearity.R", "content": "#Nonlinearity\n\n# Evaluate Nonlinearity\nplot(fit,1)\n\n# component + residual plot \ncrPlots(fit)\n# Ceres plots \nceresPlots(fit)\n\n#The regression model is linear in parameters\n#An example of model equation that is linear in parameters \n#Y=a+(β1*X1)+(β2*X2^2)\n\n#Assumption 2----\n#The mean of residuals is zero\n#How to check?\n#Check the mean of the residuals. If it zero (or very close), then this assumption is held true for that model. This is default unless you explicitly make amends, such as setting the intercept term to zero.\n\nfit2 <- lm(dist ~ speed, data=cars)\nmean(fit2$residuals)\n#Since the mean of residuals is approximately zero, this assumption holds true for this model.\n" }, { "path": "24a-LM/42c-LM-normality.R", "content": "#Normality\n\n#If the error terms are non- normally distributed, confidence intervals may become too wide or narrow. Once confidence interval becomes unstable, it leads to difficulty in estimating coefficients based on minimization of least squares. Presence of non – normal distribution suggests that there are a few unusual data points which must be studied closely to make a better model.\n\n#How to check\n#QQ plot (shown below). You can also perform statistical tests of normality such as Kolmogorov-Smirnov test, Shapiro-Wilk test\n\n# Normality of Residuals\n# qq plot for studentized resid\nqqPlot(fit, main=\"QQ Plot\")\n# distribution of studentized residuals\nlibrary(MASS)\nsresid <- studres(fit) \nhist(sresid, freq=FALSE, \n main=\"Distribution of Studentized Residuals\")\nxfit<-seq(min(sresid),max(sresid),length=40) \nyfit<-dnorm(xfit) \nlines(xfit, yfit)" }, { "path": "24a-LM/42d-LM-variance.R", "content": "#Variance\n\n#The presence of non-constant variance in the error terms results in heteroskedasticity. Generally, non-constant variance arises in presence of outliers or extreme leverage values. Look like, these values get too much weight, thereby disproportionately influences the model’s performance. When this phenomenon occurs, the confidence interval for out of sample prediction tends to be unrealistically wide or narrow.\n\n#How to check\n#Look at residual vs fitted values plot. If heteroskedasticity exists, the plot would exhibit a funnel shape pattern \n\n#Non-constant Error Variance\n# Evaluate homoscedasticity\n# non-constant error variance test\nncvTest(fit)\n# plot studentized residuals vs. fitted values \nspreadLevelPlot(fit)\n\n#Homoscedasticity of residuals or equal variance\n#Top Left and bottom-Left Plots show how the residuals vary as the fitted values increase.\n\npar(mfrow=c(2,2)) # set 2 rows and 2 column plot layout\nfit3 <- lm(mpg ~ disp, data=mtcars) # linear model\nplot(fit3)\n\n#first plot (top-left), as the fitted values along x increase, the residuals decrease and then increase. This pattern is indicated by the red line, which should be approximately flat if the disturbances are homoscedastic. The plot on the bottom left also checks this, and is more convenient as the disturbance term in Y axis is standardized.\n\n#another model\n\nfit4 = lm(dist ~ speed, data=cars[1:20, ]) # linear model\nplot(fit4) #homoscedascity present\n\n#M3\nlibrary(car)\ndata(cars)\nlmMod <- lm(dist ~ speed, data=cars) # initial model\nlmtest::bptest(lmMod)\ncar::ncvTest(lmMod) \n\n#Box Transformation\n\ndistBCMod <- caret::BoxCoxTrans(cars$dist)\nprint(distBCMod)\ncars <- cbind(cars, dist_new=predict(distBCMod, cars$dist)) # append the transformed variable to cars\nhead(cars) # view the top 6 rows\nlmMod_bc <- lm(dist_new ~ speed, data=cars)\nlmtest::bptest(lmMod_bc)\n#With a p-value of 0.91, we fail to reject the null hypothesis (that variance of residuals is constant) and therefore infer that ther residuals are homoscedastic. Lets check this graphically as well.\n\nplot(lmMod_bc)\n\n#much flatter line and an evenly distributed residuals in the top-left plot. So the problem of heteroscedsticity is solved and the case is closed. If you have any question post a comment below.\n\n\n# M4----\n#https://cran.r-project.org/web/packages/olsrr/vignettes/heteroskedasticity.html\n\nlibrary(olsrr)\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nols_test_score(model)\n\n\n#Holm’s p value Adjustment\nmodel <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)\nols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'holm')\n\n#Bonferroni p value Adjustment\nmodel <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)\nols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'bonferroni')\n\n#Use independent variables of the model and perform multiple tests\nmodel <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)\nols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE)\n\n#Use independent variables of the model\nmodel <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)\nols_test_breusch_pagan(model, rhs = TRUE)\n\n#Use fitted values of the model\nmodel <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)\nols_test_breusch_pagan(model)\n\n#Use independent variables of the model\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nols_test_score(model, rhs = TRUE)\n\n#Specify variables\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nols_test_score(model, vars = c('disp', 'hp'))\n\n#F Test Use fitted values of the model\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nols_test_f(model)\n\n#Use independent variables of the model\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nols_test_f(model, rhs = TRUE)\n\n#Specify variables\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nols_test_f(model, vars = c('disp', 'hp'))\n" }, { "path": "24a-LM/42e-LM-outliers.R", "content": "#Outliers\n\n# Assessing Outliers\noutlierTest(fit) # Bonferonni p-value for most extreme obs\nqqPlot(fit, main=\"QQ Plot\") #qq plot for studentized resid \nleveragePlots(fit) # leverage plots" }, { "path": "24a-LM/42f-LM-autocorr.R", "content": "#Non-independence of Errors\n\n# Test for Autocorrelated Errors\ndurbinWatsonTest(fit)\n\n\n#No autocorrelation of residuals\n#This is applicable especially for time series data. Autocorrelation is the correlation of a time Series with lags of itself. When the residuals are autocorrelated, it means that the current value is dependent of the previous (historic) values and that there is a definite unexplained pattern in the Y variable that shows up in the disturbances.\n\npar(mfrow=c(1,1))\n#M1:Using acf plot\n# Method 1: Visualise with acf plot\nlibrary(ggplot2)\ndata(economics)\nfit1 <- lm(pce ~ pop, data=economics)\nacf(fit1$residuals) # highly autocorrelated from the picture.\n\nfit2 = lm(weight ~ height, data = women)\nacf(fit2$residuals) # no autocorrelation\n\n\n# Method 2: Runs test to test for randomness\nlawstat::runs.test(fit1$residuals)\n#=> Runs Test - Two sided # Ho: Random Ha: Relation\n#With a p-value < 0.05, we reject the null hypothesis that it is random. This means there is a definite pattern in the residuals.\n\n# Method 3: Durbin-Watson test\nlmtest::dwtest(fit1)\n#=> Durbin-Watson test\n#pvalue < 0.05; Auto Corr > 0 ie there is auto corr\n\nlmtest::dwtest(fit2)\n#pvalue < 0.05 but the dat is cross sectional\n\n#How to rectify?----\n# Add lag1 of residual as an X variable to the original model. This can be conveniently done using the slide function in DataCombine package.\n\nlibrary(DataCombine)\necon_data <- data.frame(economics, resid_mod1=fit1$residuals)\necon_data_1 <- slide(econ_data, Var=\"resid_mod1\", NewVar = \"lag1\", slideBy = -1)\necon_data_2 <- na.omit(econ_data_1)\nfit1a <- lm(pce ~ pop + lag1, data=econ_data_2)\n\nlmtest::dwtest(fit1a)\n#now p-value > 0.05 ; hence Ho accept, Auto corr=0\n" }, { "path": "24a-LM/42g-LM-influentialvariables.R", "content": "# Influential Observations\n\n# added variable plots \nav.Plots(fit)\n# Cook's D plot\n# identify D values > 4/(n-k-1) \ncutoff <- 4/((nrow(mtcars)-length(fit$coefficients)-2)) \nplot(fit, which=4, cook.levels=cutoff)\n# Influence Plot \ninfluencePlot(fit,\tid.method=\"identify\", main=\"Influence Plot\", sub=\"Circle size is proportial to Cook's Distance\" )" }, { "path": "24a-LM/42h-LM-multicollinearity.R", "content": "#Multi-collinearity\n\n# Evaluate Collinearity\nvif(fit) # variance inflation factors \nsqrt(vif(fit)) > 2 # problem?" }, { "path": "24a-LM/42j-gvlma.R", "content": "# Global test of model assumptions\n\nlibrary(gvlma)\ngvmodel <- gvlma(fit) \nsummary(gvmodel)" }, { "path": "24a-LM/43a-LM-graphs.R", "content": "# Linear Regression - Graphs for Visualise\n\n\nfit = lm(weight ~ height, data=women)\n\n#Base Package\npar(mfrow=c(2,2))\nplot(fit, ask=FALSE)\npar(mfrow=c(1,1))\n\n\nlibrary(ggfortify) #ggplotbased\nautoplot(fit)\n\n\n#Cooks Distance\n#measures howm much the Predicted value of DV changes for all the obsversations in the sample when a particular observation is excluded from the sample for estimation of regression\ncar::avPlots(fit)\ncar::influencePlot(fit)\n" }, { "path": "24a-LM/LM-all-mtcars1.R", "content": "#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf\n#install.packages('olsrr')\nlibrary(olsrr)\nmodel <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nk <- ols_step_all_possible(model)\nplot(k)\nk\nsummary(lm(mpg ~ wt, data=mtcars))\nsummary(lm(mpg ~ wt+ hp, data=mtcars))\n" }, { "path": "24a-LM/ProbDist.R", "content": "# Prob Distri\nface = seq(1:6)\n#face = 1:6 will also work.\nface\n#You will see the six die faces.\np = c(1, 1, 1, 1, 1, 1) / 6\n#This assigns a uniform distribution to the die faces; each gets a probability of 1/6\ndata.frame(face, p)\n#We see a tabular description of the distribution. \n\n#PROBABILITY SPIKE PLOT FOR ONE DIE\nplot(face, p, type=\"h\", xlim=c(0,6), ylim=c(0,1))\npoints(face, p, pch=16, cex=2)\n#We see a probability spike plot of the distribution.This resembles a probability histogram with very thin bars.\n#In the ‘points’ command, ‘pch’ may be thought of as “point character.” ‘pch=16’ gives us filled-in circular disks at the points.\n# If we do ‘pch=15’, for example, we get boxes at the points.\n#‘cex’ controls the size of the disks, boxes, etc.\npoints(face, p, pch=15, cex=3.5, col=\"red\") \n\n#----\n#SAMPLING FROM A UNIFORM DISTRIBUTION\n#Let’s roll our die.\nsample(face, size=1)\n#• It is assumed that we have a uniform distribution. As we will see later, we\n#can type ‘sample(face, size=1, prob=p)’, where p is our probability\n#distribution, uniform or otherwise.\n#> (Repeat the above command a few times. You should get a variety of results.)\n\n#• Let’s roll a pair of dice and get their sum.\n\nsum(sample(face, size=2, replace=T))\n#• We want to sample with replacement, because we want doubles to be possible.\n# The distribution of totals is not uniform!\n# > (Repeat the above command a few times. You should get a variety of results.)\nsample(face, size=100, replace=T)\n#• Our die is rolled 100 times.\n#• How do the numbers of ‘1’s, ‘2’s, etc. compare?\n#• ‘replace=T’ means that we sample with replacement. It is OK for us to get the same number more than once.\n#DESCRIBING SAMPLE RESULTS\n#• Let’s store some sample results and do a frequency table, a relative frequency\n#table, a histogram, and a pie chart.\nresults = sample(face, size=100, replace=T)\nresults\nsort(results)\ntable(results)\n#• We obtain a frequency table of our results.\ntable(results) / length(results)\n#• We obtain a relative frequency table of our results.\n#• Note: length(results) is 100 here. \n#Page R9.3\nhist(results, prob=T, breaks=seq(-0.5, 6.5, by=1), ylim=c(0,1))\n#• We see a relative frequency histogram of our results.\n#• For example, the interval from 3.5 to 4.5 corresponds to “4.”\n#• This should resemble the (theoretical) probability distribution for one die.\nlines(c(0.5,6.5), c(1/6,1/6), col=\"blue\")\n#• This adds a blue line from the point 0.5, 16 to 6.5, 16\n#The line corresponds to the (theoretical) probability distribution.\npie(table(results), face)\n#• We see a pie chart.\n#A NON-UNIFORM DISTRIBUTION\nx = c(2, 4, 8, 16)\np2 = c(1/8, 1/8, 1/4, 1/2)\n#• This assigns a non-uniform distribution to the x values.\ndata.frame(x, p2)\n#• We see a tabular description of the distribution.\nplot(x, p2, type=\"h\", xlim=c(0,20), ylim=c(0,1))\npoints(x, p2, pch=16, cex=2)\n#• We see a probability spike plot of the distribution.\nresults2 = sample(x, size=100, replace=T, prob=p2)\n#• ‘prob=p2’ indicates that p2 contains our probabilities for the x values.\nresults2\n#What do you notice?\nsort(results2)\n#What do you notice?\ntable(results2)\n#We obtain a frequency table of our results.\n#What do you notice?\ntable(results2) / length(results2)\n#We obtain a relative frequency table of our results.\nlength(results2) \nhist(results2, prob=T, breaks=seq(-0.5, 16.5, by=1), ylim=c(0,1))\n#We see a relative frequency histogram of our results.\n#For example, the interval from 3.5 to 4.5 corresponds to “4.” \n#Page R9.4\n#MEAN (OR EXPECTED VALUE) OF A DISTRIBUTION\ndata.frame(x, p2)\n#• We see a tabular description of the distribution.\n#• Let’s find the mean (or expected value) of this distribution. It’s a “zigzag.”\nthe.mean = sum(x*p2)\n#The formula is: μ, or E X( ) = x \u0001 P x( ).\n#We use ‘the.mean’ to avoid conflict with the ‘mean’ command.\nthe.mean\n#VARIANCE AND STANDARD DEVIATION OF A DISTRIBUTION\n#Let’s find the variance of this distribution.\nthe.var = sum(x^2 * p2) - the.mean^2\n#• The formula is: 2 = E X2 ( ) \u0001 μ2 = E X2 ( ) \u0001 \u0005E X( ) \u0006 \u0007\b2x2 \u0005\u0004 P x( ) \u0006 \u0007\b \u0001 μ2.\n#• ‘p2’ is the name of the probability vector; there is no squaring here!\n# • Also, 2 = E X( ) \u0001 μ2 \u0005\b = ( ) x \u0001 μ2\u0004 P x( ), so we could do:\nthe.var = sum(((x - the.mean)^2) * p2)\nthe.var\n#Let’s find the standard deviation of this distribution.\nthe.sd = sqrt(the.var)\nthe.sd\n#COMPARING SAMPLE RESULTS WITH THEORETICAL RESULTS\n#• How do our sample mean, variance, and standard deviation compare with the\n#[theoretical] mean, variance, and standard deviation for the distribution?\nresults2\n#• This was our sample of size 100 from the distribution.\nmean(results2)\n#• Compare this sample mean with the.mean, the theoretical mean of thedistribution.\nvar(results2)\n#We do want the sample variance here.\n#• Compare this sample variance with the.var, the theoretical variance of thedistribution.\nsd(results2)\n#• Compare this sample standard deviation with the.sd, the theoretical\n#standard deviation of the distribution. \n" }, { "path": "24a-LM/Simulation.R", "content": "# Simulation\nx = c(1, 3, 5)\npx = c(0.6, 0.3, 0.1)\ndraws = sample(x, size = 500, replace = TRUE, prob = px)\ndraws\nhist(draws, breaks = seq(1, 5, by = 0.25), main = \"1000 discrete draws\")\nvar(draws)\ndraws = sample(x, size = 4 * 500,replace = TRUE, prob = px)\ndraws = matrix(draws, 4)\ndrawmeans = apply(draws, 2, mean)\nhist(drawmeans, breaks = seq(1, 5, by = 0.25), main = \"1000 means of 4 draws\")\n\nvar(drawmeans)\ndrawmeans = apply(matrix(sample(x, size = 16 * 500, replace = TRUE, prob = px), 16), 2, mean)\nhist(drawmeans, breaks = seq(1, 5, by = 0.25), main = \"1000 means of 16 draws\")\nvar(drawmeans)\n\n\n-----------\n p.die <- rep(1/6,6)\nsum(p.die)\ndie <- 1:6\nsample(die, size=10, prob=p.die, replace=T)\ns <- table(sample(die, size=1000, prob=p.die, replace=T))\nlbls = sprintf(\"%0.1f%%\", s/sum(s)*100)\nbarX <- barplot(s, ylim=c(0,200))\ntext(x=barX, y=s+10, label=lbls)\n\ngenerate.sample.means <- function(n) {\n sample.means <- numeric()\n for (i in 1:1000) { \n sample.means <- append(sample.means, sum(sample(die, size=n, prob=p.die, replace=T))/n)\n }\n return (sample.means)\n}\nsample.means <- generate.sample.means(100)\nplot(density(sample.means), main=\"Distribution of sample means\",xlab=\"sample mean\", col=\"orange\")\n\n" }, { "path": "24a-LM/confusionmatrix.R", "content": "#Confusion Matrix\n#https://cran.r-project.org/web/packages/heuristica/vignettes/confusion-matrix.html\n#\nlibrary(heuristica)\nlvs <- c(\"normal\", \"abnormal\")\ntruth <- factor(rep(lvs, times = c(86, 258)),\n levels = rev(lvs))\npred <- factor(\n c(\n rep(lvs, times = c(54, 32)),\n rep(lvs, times = c(27, 231))), \n levels = rev(lvs))\n\nxtab <- table(pred, truth)\n# load Caret package for computing Confusion matrix\nlibrary(caret) \nconfusionMatrix(xtab)\n" }, { "path": "24a-LM/contrasts1.R", "content": "\n# Coding for Regression Variables\n# https://stats.idre.ucla.edu/r/modules/coding-for-categorical-variables-in-regression-models/\n# \nhsb2 <- read.csv(\"https://stats.idre.ucla.edu/stat/data/hsb2.csv\")\nstr(hsb2)\n\n# creating the factor variable\nhsb2$race.f <- factor(hsb2$race)\nis.factor(hsb2$race.f)\nsummary(lm(write ~ race.f, data = hsb2))\n\nsummary(lm(write ~ factor(race), data = hsb2))\n\n\nhsb2 <- within(hsb2, {\n race.ct <- C(race.f, treatment)\n print(attributes(race.ct))\n})\n\nhsb2 <- within(hsb2, {\n race.ch <- C(race.f, helmert)\n print(attributes(race.ch))\n})\n\nsummary(lm(write ~ race.ct, data = hsb2))\nsummary(lm(write ~ race.ch, data = hsb2))\n\nhsb2 <- within(hsb2, {\n race.ch1 <- C(race.f, helmert, 3)\n print(attributes(race.ch1))\n})\n\nsummary(lm(write ~ race.ch1, data = hsb2))\n\n(a <- contrasts(hsb2$race.f))\n\n(contrasts(hsb2$race.f) <- contr.treatment(4))\nsummary(lm(write ~ race.f, data = hsb2))\n\n(contrasts(hsb2$race.f) <- contr.treatment(4, base = 2))\nsummary(lm(write ~ race.f, data = hsb2))\n\nsummary(lm(write ~ I(race.f == 1) + I(race.f == 3) + I(race.f == 4), data = hsb2))\n\n(contrasts(hsb2$race.f) <- contr.helmert(4))\nsummary(lm(write ~ race.f, data = hsb2))\n" }, { "path": "24a-LM/dummies.R", "content": "\nlibrary(dummies)\n\n# example data\ndf1 <- data.frame(id = 1:4, year = 1991:1994)\n\ndf1 <- cbind(df1, dummy(df1$year, sep = \"_\"))\n\ndf1\n\n\n\nlibrary(mlr)\ndf <- data.frame(var = sample(c(\"A\", \"B\", \"C\"), 10, replace = TRUE))\ndf\ncreateDummyFeatures(df, cols = \"var\")\n\nyear=sample(c(1957,1958,1959,1960),size=10,replace=T, prob=c(.4,.2,.2,.2))\n(data = data.frame(year))\n \n \ndata$factor_year_1 <- factor ( with ( data, ifelse ( ( year == 1957 ), 1 , 0 ) ) )\ndata\n\nifelse(year == 1957, 1, 0)\n\n" }, { "path": "24a-LM/homosecadicity.R", "content": "set.seed(5)\n\nN = 500; b0 = 3 ; b1 = 0.4\ns2 = 5 ; g1 = 1.5 ; g2 = 0.015\n\nx = runif(N, min=0, max=100)\ny_homo = b0 + b1*x + rnorm(N, mean=0, sd=sqrt(s2 ))\ny_hetero = b0 + b1*x + rnorm(N, mean=0, sd=sqrt(exp(g1 + g2*x)))\n\nmod.homo = lm(y_homo~x)\nmod.hetero = lm(y_hetero~x)\nplot(mod.homo)\nplot(mod.hetero)\nfit=mod.homo\n# Evaluate homoscedasticity\n# non-constant error variance test\nncvTest(fit)\n# plot studentized residuals vs. fitted values \nspreadLevelPlot(fit)\n\nlibrary(gvlma)\ngvmodel <- gvlma(fit) \nsummary(gvmodel)\n" }, { "path": "24a-LM/lm-broom.R", "content": "#Broom Package for output view of LM\n#http://varianceexplained.org/r/broom-intro/" }, { "path": "24a-LM/lm-dummy1.R", "content": "#Regression with Dummy Variables\nmycars <- mtcars\nmycars$cyl <- factor(mycars$cyl)\ncontrasts(mycars$cyl)\nfit <- lm( mpg ~ wt+cyl, data=mycars, x=T )\nsummary(fit)\n?lm\nfit$contrasts\nhead(fit$x)\ncbind(mycars$mpg, fitted(fit))\n\n# 2 Dummy Variables\nmycars <- mtcars\nmycars$cyl = factor(mycars$cyl)\nmycars$am = factor(mycars$am)\ncontrasts(mycars$cyl)\ncontrasts(mycars$am)\nfit2 <- lm( mpg ~ wt + cyl + am, data=mycars, x=T )\nsummary(fit2)\nhead(fit2$x)\ncbind(mycars$mpg, fitted(fit))\n" }, { "path": "24a-LM/lm-housing.R", "content": "# Housing Rent on factors\n\nhouse = read.table(\"http://www.rossmanchance.com/iscam2/data/housing.txt\", header = T, sep = \"\\t\")\nhouse\n\nattach(house)\nnames(house)\nfit = lm(price ~ sqft + City + bedrooms + baths)\nsummary(fit)\n\npanel.cor <- function(x, y, digits = 2, prefix = \"\", cex.cor, ...) {\n usr <- par(\"usr\")\n on.exit(par(usr))\n par(usr = c(0, 1, 0, 1))\n r <- cor(x, y)\n txt <- format(c(r, 0.123456789), digits = digits)[1]\n txt <- paste0(prefix, txt)\n text(0.5, 0.5, txt)\n}\npairs(cbind(price, sqft, bedrooms, baths), lower.panel = panel.cor, pch = 18)\n\nsummary(lm(price ~ sqft))\n\nsummary(lm(price ~ bedrooms))\n\npar(mfrow = c(1, 2))\nplot(lm(price ~ sqft)$fitted, lm(price ~ sqft)$resid, xlab = \"fitted w sqft\", \n ylab = \"resid\", pch = 18)\nabline(h = 0)\nplot(lm(price ~ bedrooms)$fitted, lm(price ~ bedrooms)$resid, xlab = \"fitted w bed\", \n ylab = \"resid\", pch = 18)\nabline(h = 0)\n\npar(mfrow = c(1, 2))\nplot(lm(log(price) ~ sqft)$fitted, lm(log(price) ~ sqft)$resid , xlab = \"fitted w sqft\", \n ylab = \"resid for ln\", pch = 18)\nabline(h = 0)\nplot(lm(log(price) ~ bedrooms)$fitted, lm(log(price) ~ bedrooms)$resid, xlab = \"fitted w bed\", \n ylab = \"resid for ln\", pch = 18)\nabline(h = 0)\nsummary(lm(log(price) ~ sqft + bedrooms))\n\nsummary(lm(log(price) ~ sqft))\n\n\npredict(lm(log(price) ~ sqft), newdata = data.frame(sqft = 2000), interval = \"confidence\")\npredict(lm(log(price) ~ sqft), newdata = data.frame(sqft = 2000), interval = \"prediction\")\n\npar(mfrow=c(1,1))\nplot(sqft, log(price), pch = 18)\nsqftlm = lm(log(price) ~ sqft)\nabline(sqftlm, col = \"red\")\nnewX = seq(min(sqft), max(sqft), 1)\nprd.CI = predict(sqftlm, newdata = data.frame(sqft = newX), interval = \"confidence\", \n level = 0.95)\nlines(newX, prd.CI[, 2], col = \"blue\", lty = 2)\nlines(newX, prd.CI[, 3], col = \"blue\", lty = 2)\nprd.PI = predict(sqftlm, newdata = data.frame(sqft = newX), interval = \"prediction\", \n level = 0.95)\nlines(newX, prd.PI[, 2], col = \"green\", lty = 3)\nlines(newX, prd.PI[, 3], col = \"green\", lty = 3)\n\n" }, { "path": "24a-LM/lm-mtcars1.R", "content": "# LR with MT cars\n# Forward and Backward\n\n#Data----\nstr(mtcars)\nnames(mtcars)\ndim(mtcars)\ntable(mtcars$am)\naddmargins(table(mtcars$am))\n\n#Blank Model----\n(mtcars.lm = lm(mpg ~ 1, data=mtcars))\nsummary(mtcars.lm)\n\n#Find Best Predictor----\nadd1(mtcars.lm, mtcars, test='F')\n\n#Add Best Predictor----\nmtcars.lm = lm(mpg ~ wt, data=mtcars)\nsummary(mtcars.lm)\n\n#Next Best Predictor\nadd1(mtcars.lm, mtcars, test='F')\n\nmtcars.lm = lm(mpg ~ wt + cyl, data=mtcars)\nsummary(mtcars.lm)\n\nadd1(mtcars.lm, mtcars, test='F')\n#None to be added\n\n\n# compare models\nfit1 <- lm(mpg ~ wt, data=mtcars)\nfit2 <- lm(mpg ~ wt + cyl, mtcars)\nanova(fit1, fit2) #more,less\n#pv < 0.05, adding a variable has significant effect on R2\n\nAIC(fit1,fit2) #fit2 with wt & cyl better\n\n# Other useful functions \ncoefficients(fit) # model coefficients\nconfint(fit, level=0.95) # CIs for model parameters \nfitted(fit) # predicted values\nresiduals(fit) # residuals\nanova(fit) # anova table \nvcov(fit) # covariance matrix for model parameters \ninfluence(fit) # regression diagnostics\n\n# diagnostic plots \nlayout(matrix(c(1,2,3,4),2,2)) # optional 4 graphs/page \nplot(fit)\n\n\n\n\n\n\n#Backward\n#\nfit.full <- lm(mpg ~ wt + cyl + hp + am ,data=mtcars)\nsummary(fit.full)\ndrop1(fit.full, test='F')\ndrop1(update(fit.full, ~ . -am), test = \"F\")\ndrop1(update(fit.full, ~ . -am - hp), test = \"F\")\n\n\n#http://rstudio-pubs-static.s3.amazonaws.com/2899_a9129debf6bd47d2a0501de9c0dc583d.html\n\n# K-fold cross-validation\nlibrary(DAAG)\ncv.lm(data=mtcars, fit2, m=3, plotit=F) # 3 fold cross-validation\n#mse = 7.09\n#assess R2 shrinkage via K-fold cross-validation.\n#\n#\n# Assessing R2 shrinkage using 10-Fold Cross-Validation \n\nfit <- lm(mpg ~wt + cyl,data=mtcars) \n\nlibrary(bootstrap)\n# define functions \ntheta.fit <- function(x,y){lsfit(x,y)}\ntheta.predict <- function(fit,x){cbind(1,x)%*%fit$coef} \n\n# matrix of predictors\nX <- as.matrix(mtcars[c(\"wt\",\"cyl\")])\n# vector of predicted values\ny <- as.matrix(mydata[c(\"mpg\")]) \n\nresults <- crossval(X,y,theta.fit,theta.predict,ngroup=10)\ncor(y, fit$fitted.values)**2 # raw R2 \ncor(y,results$cv.fit)**2 # cross-validated R2\n\nnames(mtcars)\n# Stepwise Regression\nlibrary(MASS)\nfit <- lm(mpg ~ wt + cyl + hp + am ,data=mtcars)\nstep <- stepAIC(fit, direction=\"both\")\nstep$anova # display results\n\n\nAlternatively, you can perform all-subsets regression using the leaps( ) function from the leaps package. In the following code nbest indicates the number of subsets of each size to report. Here, the ten best models will be reported for each subset size (1 predictor, 2 predictors, etc.).\n\n# All Subsets Regression\nlibrary(leaps)\nattach(mydata)\nleaps<-regsubsets(mpg ~ wt + cyl + hp + am,data=mtcars,nbest=10)\n# view results \nsummary(leaps)\n# plot a table of models showing variables in each model.\n# models are ordered by the selection statistic.\nplot(leaps,scale=\"r2\")\n# plot statistic by subset size \nlibrary(car)\nsubsets(leaps, statistic=\"rsq\")\nOther options for plot( ) are bic, Cp, and adjr2.\nOther options for plotting with \nsubset( ) are bic, cp, adjr2, and rss\n\nThe relaimpo package provides measures of relative importance for each of the predictors in the model. See help(calc.relimp) for details on the four measures of relative importance provided.\n\n# Calculate Relative Importance for Each Predictor\nlibrary(relaimpo)\ncalc.relimp(fit,type=c(\"lmg\",\"last\",\"first\",\"pratt\"),rela=TRUE)\n\n# Bootstrap Measures of Relative Importance (1000 samples) \nboot <- boot.relimp(fit, b = 1000, type = c(\"lmg\", \n \"last\", \"first\", \"pratt\"), rank = TRUE, \n diff = TRUE, rela = TRUE)\nbooteval.relimp(boot) # print result\nplot(booteval.relimp(boot,sort=TRUE)) # plot result\n\n\nlibrary(rms)\nmydata = as.matrix(mtcars)\nols.full <- ols(mpg ~ mpg + wt , data = mtcars)\n\nfastbw(ols.full, rule = \"p\", sls = 0.1)\nmtcars$cylgr <- with(mtcars,interaction(cyl,gear == \"4\"))\nregular_ols <- ols(mpg ~ wt + cyl + gear + cylgr, data=mtcars)\nregular_ols\nfastbw(regular_ols, rule='p', sls=0.1)\n?ols\n\nlm.full = lm(mpg ~ wt + cyl + gear + am +disp, data=mtcars)\nmodel.aic.backward <- step(lm.full, direction = \"backward\", trace = 1)\nsummary(model.aic.backward)\nlm.null = lm(mpg ~ 1, data=mtcars)\nmodel.aic.forward <- step(lm.null, direction = \"forward\", trace = 1,\n scope = ~ wt + cyl + gear + am +disp)\nsummary(model.aic.forward)\n\nmodel.aic.both <- step(lm.null, direction =\"both\",\ntrace = 1, scope = ~ wt + cyl + gear + am +disp)\nsummary(model.aic.both)\n\n\n\n" }, { "path": "24a-LM/lm-mtcars2.R", "content": "# Multiple Regression in R\nmtcars\n(model1 = lm(mpg ~ disp + cyl + hp\n + drat + wt + qsec + carb, data=mtcars))\n\nsummary(model1)\nanova(model1)\nvcov(model1)\n\nlayout(matrix(c(1,2,3,4),2,2))\nplot(model1)\n\n(model1 = lm(mpg ~ disp + cyl + hp\n + drat + wt + qsec + carb, data=mtcars))\nsummary(model1)\n(model2 = lm(mpg ~ disp + cyl + wt , data=mtcars))\nsummary(model2)\n\nanova(model1, model2)\nanova(model2, model1)\n\n(model3 = lm(mpg ~ cyl + wt , data=mtcars))\nsummary(model3)\n\nanova(model3, model2)\n\nplot(model3)\n\nanova(model3, model1)\nanova(model1, model3)\n\n\n#Interaction Effects\n(model4 = lm(mpg ~ cyl + wt + cyl*wt, data=mtcars))\nsummary(model4)\n\nanova(model3, model4)\n\n#AIC\nlibrary(MASS)\n(model1a = lm(mpg ~ disp + cyl + hp\n + drat + wt + qsec + carb, data=mtcars))\nstep = stepAIC(model1a, direction='both')\n\nstep$anova\n\n\n#Polynomial Regression\n(model5 = lm(mpg ~ cyl + wt + I(wt^2), data=mtcars))\nsummary(model5)\n\n\n\n#LOOCV\nlibrary(ISLR)\nlibrary(boot)\nstr(Auto)\n(model1 = glm(mpg ~ horsepower, data = Auto))\nmse_loocv = cv.glm(Auto , model1)\nmse_loocv\n\npar(mfrow=c(1,1))\n#Kfold\nlibrary(DAAG)\n(model2 = lm(mpg ~ horsepower, data=Auto))\n(mse_fold = cv.lm(Auto, model2, m=3))\nsummary(mse_fold)\nn=nrow(Auto)\nsumcv = sum(mse_fold$cvpred)\n(msecalc = sqrt(n/sumcv))\nattributes(mse_fold)$ms\n" }, { "path": "24a-LM/lm-plot1.R", "content": "# Graph Practise\n?text\n\nplot(mtcars$wt, mtcars$mpg, main=' MPG vs Weight',\n xlab='Weight' , ylab='Miles per Gallon', pch=18, col='blue', las=1)\ntext(mtcars$wt, mtcars$mpg, cex=0.6, pos=4, col='red')\ntext(mtcars$wt, mtcars$mpg, rownames(mtcars), cex=0.6, pos=4, col='red')\ntext(mtcars$wt, mtcars$mpg, 1:length(mtcars), cex=0.6, pos=2, col='blue')\n\nplot(1:10, pch=10)\ntext(1:10, 1:10, 1:10)\ntext(1:10, 1:10, 1:10, pos=1, col='blue')\ntext(1:10, 1:10, 1:10, pos=2, cex=0.75, col='green')\ntext(1:10, 1:10, 1:10, pos=3, cex=1.25, col='red')\ntext(1:10, 1:10, 1:10, pos=4, cex=0.8, col='purple')\n\n\n?plot\n?text\n?legend\n\n# Data\nx <- c(1,2,3,4,5)\ny <- 2*x\n\n# Plot\nplot(x,y)\n\n# Grid\ngrid(10,10)\n\ndata(iris)\nstr(iris)\niris[iris$Species=='setosa',]\ndfsetosa = iris[iris$Species=='setosa',][1:2]\ndfsetosa\nplot(y=dfsetosa$Sepal.Length, x=dfsetosa$Sepal.Width)\nfit=lm(Sepal.Length ~ Sepal.Width, data=dfsetosa)\nsummary(fit)\nplot(fit, which=1)\n" }, { "path": "24a-LM/lm-segments1.R", "content": "#Segments\n\nplot(y=mtcars$mpg, x=mtcars$wt, xlim=range(mtcars$wt), ylim=range(mtcars$mpg))\nabline(fit)\nsegments(mtcars$wt, mtcars$mpg, mtcars$wt, fitted(fit), lty=2, lwd=2,col=1:nrow(mtcars))\ntext(mtcars$wt+.02, mtcars$mpg+.5, labels = round(residuals(fit)), cex=.8)\n\n\n\n#Covariance in R\ncov(mtcars$mpg,mtcars$wt)\ncov(mtcars$wt,mtcars$mpg)\n#Covariance manually\n\nsum( (mtcars$wt - mean(mtcars$wt)) * (mtcars$mpg - mean(mtcars$mpg)) )/ (length(mtcars$wt) - 1)\n \n\n#Diagnostic Plots\nplot(fitted(fit), residuals(fit), pch=20, xlab='Fitted', ylab='Residual')\nsdres = sd(residuals(fit))\nfor (j in -3:3)\n abline(h = j * sqrt(var(resid(fit))), col=abs(j) + 1)\n\n\n#Plot - Residuals vs Fitted values with horz lines at 3,2,1 SDEV of residuals\n\n\nix = which(abs(resid(fit)) > 2 * sdres)\ntext(fitted(fit)[ix], resid(fit)[ix], ix, pos=4)\ncbind(mtcars[ix, c('wt', 'mpg')], fit=round(fitted(fit)[ix],1), resid=round(residuals(fit)[ix],1) )\nrm(sdres, ix)\n\n#ND\nstem(residuals(fit),scale=2)\nqqnorm(residuals(fit))\nqqline(residuals(fit))\n?shapiro.test(residuals(fit))\n#Ho data is Normal Distribution; Ha - not ND\n\nshapiro.test(rnorm(100, mean = 5, sd = 3)) # ND : p > 0.05\nshapiro.test(runif(100, min = 2, max = 4)) # not ND : p < 0.05\n\n\n#hat values for Influencing Observations\nh = hatvalues(fit)\nhead(h)\nhi.lev = which( h > 3 * mean(h))\nhi.lev\nmtcars[hi.lev,]\n\nplot(mtcars$wt, h)\nplot(mtcars$wt, mtcars$mpg)\n\nfit2 = lm(mtcars$mpg ~ mtcars$wt, data=mtcars, subset = - hi.lev)\nsummary(fit2)\nround(coef(fit)); round(coef(fit2))\n\nsqrt(sum(residuals(fit)^2)/ length(residuals(fit)))\n1 - (sum(residuals(fit)^2)/ sum((mtcars$mpg - mean(mtcars$mpg))^2))\nsummary(fit)\n\n\n(pred=predict(fit, data.frame(wt = mean(mtcars$wt)), se.fit=T))\nstr(pred)\npred\nround(pred$fit + qt(c(.025, .975), pred$df) * pred$se.fit, 1)\n\npframe = data.frame(wt = seq(min(mtcars$wt), max(mtcars$wt), by=1))\npframe\npred.c = predict(fit, pframe, interval='confidence', level=.95)\nstr(pred.c)\npred.c\n\n#error\n\n#pred.p = predict(fit2,pframe, interval='prediction', level=.95)\n#pred.p\n\n#plot\nplot(pframe$wt, pred.c[, 'fit'], type='n')\nlines(pframe$wt, pred.c[,'fit'], col=1, lwd=2)\nlines(pframe$wt, pred.c[,'lwr'], col=2, lwd=2)\nlines(pframe$wt, pred.c[,'upr'], col=3, lwd=2)\n\n\nzone" }, { "path": "24a-LM/mlm-state77.R", "content": "# Multiple Regression Part 1 - 16 Jan 16\n# http://stats.stackexchange.com/questions/59250/how-to-interpret-the-output-of-the-summary-method-for-an-lm-object-in-r\n# http://feliperego.github.io/blog/2015/10/23/Interpreting-Model-Output-In-R\n\n#We will be using the dataset state.x77, which is part of the state datasets available in R. (Additional information \n#about the dataset can be obtained by running help(state.x77).)\n\n# Ex1\n# a. Load the state datasets.\n# b. Convert the state.x77 dataset to a dataframe.\n# c. Rename the Life Exp variable to Life.Exp, and HS Grad to HS.Grad. (This avoids problems with referring to these variables when specifying a model.)\ndata(state) \nstate.x77\nstr(state.x77)\nclass(state.x77)\n# convert to DF\nstate77 = data.frame(state.x77)\nclass(state77)\n# Rename\nnames(state77)\nnames(state.x77)\nnames(state77)[4] = 'Life.Exp'\nnames(state77)[6] = 'HS.Grad'\n\n# \n# Ex2\n# Suppose we wanted to enter all the variables in a first-order \n#linear regression model with Life Expectancy as the dependent\n#variable. Fit this model\n# \nstr(state77)\nmodel= lm(Life.Exp ~ . , data= state77) # . means all\nsummary(model)\n# \n# ex3\n# Suppose we wanted to remove the Income, Illiteracy, and\n#Area variables from the model in Exercise 2. \n#Use the update function to fit this model.\nstr(state77) \nmodel2 = update(model, . ~ . - Income - Illiteracy - Area)\n#model2 = lm(Life.Exp ~ Population + Murder, HS.Grad, Frost, data=state77)\nsummary(model2)\n\n# Exercise 4\n# Let’s assume that we have settled on a model that has HS.Grad and\n#Murder as predictors. Fit this model.\n#\nmodel3 = lm(Life.Exp ~ Murder+ HS.Grad, data = state77)\nsummary(model3)\n\n# Exercise 5\n# Add an interaction term to the model in Exercise 4 \n#(3 different ways).\n#\nhead(state77[,c('Murder', 'HS.Grad')])\nmodel4a = lm(Life.Exp ~ HS.Grad + Murder + HS.Grad:Murder, data=state77)\nsummary(model4a)\n\nmodel4b = lm(Life.Exp ~ HS.Grad * Murder, data=state77)\nsummary(model4b)\n\nmodel4c = lm(Life.Exp ~ (HS.Grad + Murder)^2, data=state77)\nsummary(model4c)\n\n\n# Exercise 6\n# For this and the remaining exercises in this set \n#we will use the model from Exercise 4. Obtain 95% confidence intervals for \n#the coefficients of the two predictor variables.\n\nconfint(model3, level=0.95)\n\n# Exercise 7\n# Predict the Life Expectancy for a state where 55% of the population are High School graduates, and the murder rate is 8 per 100,000.\n#\npredict(model3, data.frame(HS.Grad=55, Murder=8))\n\n# Exercise 8\n# \n# Obtain a 98% confidence interval for the mean Life Expectancy in a state where 55% \n#of the population are High School graduates, and the murder rate is 8 per 100,000.\n#\nformula(model3)\ncoefficients(model3)\n\npredict(model3, data.frame(HS.Grad=55, Murder=8), interval='confidence', level=0.98)\n# Ex9\n# Obtain a 98% confidence interval for the Life Expectancy of a person living in a state where 55% of the population are High School graduates, and the murder rate is 8 per 100,000.\n#\npredict(model3, data.frame(HS.Grad=55, Murder=8), interval='confidence')\n\n# Ex10\n# Since our model only has two predictor variables, we can generate \n#a 3D plot of our data and the fitted regression plane. Create this plot.\n\nrequire(rgl)\n# create a DF with all combinations of factor variables of HS.Grad & Murder\nplotdat = expand.grid(HS.Grad = seq(34,70,by=2), Murder=seq(1,16,by=1))\nsummary(plotdat)\nplotdat$pred1 = predict(model3, newdata = plotdat)\nplotdat$pred1\n\nwith(state77, plot3d(HS.Grad, Murder, Life.Exp, col='red', size=2\n ,xlab='High School Grad', ylab='Murder Rate', zlab=' Life Expectancy'\n ,main(\"3D Scatter Plot \"), type='p'))\n?plot3d\n\nwith(plotdat, surface3d(unique(HS.Grad), unique(Murder), pred1\n , alpha=0.5, front='line', back='line'))\n?surface3d\n\n# Misc -----------------\nplot(model3)\n#http://data.princeton.edu/R/linearModels.html\n\nrequire(broom)\nlmfit <- lm(mpg ~ wt, mtcars)\nsummary(lmfit) \ntidy(lmfit)\nis.data.frame(tidy(lmfit))" }, { "path": "24a-LM/multvariate1.R", "content": "# Linear Modeling http://data.princeton.edu/R/readingdata.html\n\nfpe <- read.table(\"http://data.princeton.edu/wws509/datasets/effort.dat\")\nnames(fpe)\nstr(fpe)\nboxplot(fpe$setting, col='lavender')\nattach(fpe)\nplot(effort, change,pch=21, bg='gold')\ntitle('Scatter Plot of change vs Effort', col.main='#3366CC')\nidentify(effort, change, row.names(fpe), ps=9)\nrow.names(fpe)\nfpe\npairs(fpe)\n\n# Linear Model\nlmfit = lm(change ~ setting + effort)\nlmfit\n(lmfit2 = lm(change ~ setting:effort))\n(lmfit3 = lm(change ~ setting * effort))\nsummary(lmfit)\nsummary(lmfit, correlation = T)\nanova(lmfit)\nplot(lmfit)\nfitted(lmfit)\ncoef(lmfit)\nresiduals(lmfit)\n# Factors & Covariates\nunique(effort)\n(effortg = cut(effort, breaks = c(-1,4,14,100)))\neffortg = cut(effort, breaks = c(-1,4,14,100), label=c('weak', 'moderate', 'strong'))\neffortg # <4, 4-15, > 15 : weak, moderate, strong\ncovfit = lm(change ~ setting + effortg)\ncovfit\noptions(contrasts=c('contr.treatment','contr.poly'))\n\nanova(covfit)\nlm(change ~ log(setting) + effort)\n\nlm(change ~ poly(setting,3) + effort)\n\nlibrary(splines)\nsetting.bs = bs(setting, knots = c(66,74,84))\nsetting.bs\n\nsetting.ns = ns(setting, df=5)\nsetting.ns\nsplinefit = lm(change ~ ns(setting, knot = median(setting)) + \n ns(effort, knot = median(effort)))\nsplinefit\n\n\n# Other Options\nX = cbind(1, effort, setting)\nX\nsolve(t(X) %*% X ) %*% t(X) %*% change\n\n# GLM\ncuse <- read.table(\"http://data.princeton.edu/wws509/datasets/cuse.dat\", header=TRUE)\ncuse\nattach(cuse)\nlrfit = glm(cbind(using, notUsing) ~ age + education + wantsMore, family = binomial)\nlrfit\n(noMore = wantsMore== 'no')\nhiEduc = education == 'high'\nhiEduc\n\nglm( cbind(using, notUsing) ~ age + hiEduc + noMore, family = binomial)\n1- pchisq(29.92,10)\n\nlrfit = glm(cbind(using, notUsing) ~ age * noMore + hiEduc, family = binomial)\nlrfit\nsummary(lrfit)\n\n# Updating Models\nlrfit0 = update(lrfit, ~. - age:noMore)\nanova(lrfit0, lrfit)\n\nanova(lrfit, test='Chisq')\n\n# Model Selection\ndrop1(lrfit, test='Chisq')\n\nadd1(lrfit, ~. ^2, test='Chisq')\nsearch = step(lrfit, ~ . ^2) # ???\nstr(cuse)\n?step\n" }, { "path": "24a-LM/plotcoef1.R", "content": "\nlibrary(car) # for the example data\ndata(Duncan) # example data\nm1 = lm(prestige ~ income + education + type, data=Duncan)\nlibrary(arm) # for coefplot()\ncoefplot(m1, vertical=FALSE, mar=c(5.5,2.5,2,2))\n\nrequire(rpart)\nmodel <- lm(Petal.Width ~ ., data = iris)\n\nplot_coeffs(model)\ncoefplot(model)\n\n\ndata(\"Mroz\", package = \"car\")\nfm <- glm(lfp ~ ., data = Mroz, family = binomial)\ncoefplot(fm)\n?coefplot\n\n\nlibrary(\"arm\")\ndata(\"Mroz\", package = \"car\")\nM1<- glm(lfp ~ ., data = Mroz, family = binomial)\nM2<- bayesglm(lfp ~ ., data = Mroz, family = binomial)\nM3<- glm(lfp ~ ., data = Mroz, family = binomial(probit))\ncoefplot(M2, xlim=c(-2, 6), intercept=TRUE)\ncoefplot(M1, add=TRUE, col.pts=\"red\", intercept=TRUE)\ncoefplot(M3, add=TRUE, col.pts=\"blue\", intercept=TRUE, offset=0.2)\n\n" }, { "path": "24a-LM/regrplot1.R", "content": "\nx <- c(173, 169, 176, 166, 161, 164, 160, 158, 180, 187)\ny <- c(80, 68, 72, 75, 70, 65, 62, 60, 85, 92) # plot scatterplot and the regression line\n\ndata(\"women\")\nx = women$height\ny = women$weight\nmod1 <- lm(y ~ x)\nplot(x, y, xlim=c(min(x)-5, max(x)+5), ylim=c(min(y)-10, max(y)+10))\nabline(mod1, lwd=2)\n\n\n# calculate residuals and predicted values\nres <- signif(residuals(mod1), 5)\npre <- predict(mod1) # plot distances between points and the regression line\nsegments(x, y, x, pre, col=\"red\")\n\n# add labels (res values) to points\nlibrary(calibrate)\ntextxy(x, y, res, cx=0.7)\n\n# Difference Data Set\nx <- c(173, 169, 176, 166, 161, 164, 160, 158, 180, 187)\ny <- c(80, 68, 72, 75, 70, 65, 62, 60, 85, 92) # plot scatterplot and the regression line\n\n\n#Women\nfit = lm(weight ~ height, women) \nplot(fit)\n\n\ny <- qunif(ppoints(length(randu$x)))\nqqplot(randu$x,y)\nrandu\n" }, { "path": "24c-NLM/nlm1.R", "content": "#Non - Linear Regression in R\n#https://www.r-bloggers.com/first-steps-with-non-linear-regression-in-r/\n\n#simulate some data\nset.seed(20160227)\nx = seq(0,50,1)\ny = ((runif(1,10,20)*x)/(runif(1,0,10)+x))+rnorm(51,0,1)\ny\n#for simple models nls find good starting values for the parameters even if it throw a warning\nm = nls(y~a*x/(b+x))\nsummary(m)\n\n#get some estimation of goodness of fit\ncor(y,predict(m))\n\n\n#plot\nplot(x,y)\nlines(x,predict(m),lty=2,col=\"red\",lwd=3)\n" }, { "path": "24c-NLM/nlm2-mtcars.R", "content": "# NLM using mtcars\n# https://stats.stackexchange.com/questions/142443/simple-non-linear-regression-problem\n#Load data----\n\ndata(mtcars)\n\n# Scatterplot with log-transformation\nplot(mpg ~ disp, data=mtcars)\nplot(log(mpg)~disp, data = mtcars, las = 1, pch = 16, xlab = \"Displacement\", ylab = \"Log(Miles/Gallon)\")\n\n# Linear regression with log-transformation\n\nlog.mod <- lm(log(mpg)~disp, data = mtcars)\n\n# Prediction intervals\n\nnewframe <- data.frame(disp = seq(min(mtcars$disp), max(mtcars$disp), length = 1000))\n\npred <- predict(log.mod, newdata = newframe, interval = \"prediction\")\npred\n# Plot prediction intervals on log scale\n\nplot(log(mpg)~disp\n , data = mtcars\n , ylim = c(2, 4)\n , las = 1\n , pch = 16\n , main = \"Log scale\"\n , xlab = \"Displacement\", ylab = \"Log(Miles/Gallon)\")\n\nlines(pred[,\"fit\"]~newframe$disp, col = \"steelblue\", lwd = 2)\nlines(pred[,\"lwr\"]~newframe$disp, lty = 2)\nlines(pred[,\"upr\"]~newframe$disp, lty = 2)\n\n# Plot prediction intervals on original scale\n\nplot(mpg~disp\n , data = mtcars\n , ylim = c(8, 38)\n , las = 1\n , pch = 16\n , main = \"Original scale\"\n , xlab = \"Displacement\", ylab = \"Miles/Gallon\")\n\nlines(exp(pred[,\"fit\"])~newframe$disp, col = \"steelblue\", lwd = 2)\nlines(exp(pred[,\"lwr\"])~newframe$disp, lty = 2)\nlines(exp(pred[,\"upr\"])~newframe$disp, lty = 2)\n\n# Quadratic regression on original scale\n\nquad.lm <- lm(mpg~poly(disp, 2), data = mtcars)\n\n# Prediction intervals\n\nnewframe <- data.frame(disp = seq(min(mtcars$disp), max(mtcars$disp), length = 1000))\n\npred <- predict(quad.lm, newdata = newframe, interval = \"prediction\")\n\n# Plot prediction intervals on log scale\n\nplot(mpg~disp\n , data = mtcars\n , ylim = c(7, 36)\n , las = 1\n , pch = 16\n , main = \"Original scale\"\n , xlab = \"Displacement\", ylab = \"Miles/Gallon\")\n\nlines(pred[,\"fit\"]~newframe$disp, col = \"steelblue\", lwd = 2)\nlines(pred[,\"lwr\"]~newframe$disp, lty = 2)\nlines(pred[,\"upr\"]~newframe$disp, lty = 2)" }, { "path": "28a-LogR/24c-LR-default.R", "content": "# Logistic Regression\n#data() # datasets available for use in R\n\n# Load the textbook R package\nlibrary(ISLR) #install the package\n?Default # data set\n# Load in the credit data\ndata(\"Default\")\n# See the properties\nstr(Default)\nclass(Default)\nhead(Default)\nnames(Default)\ndim(Default)\nsummary(Default)\n\n# How many people actual default?\n(tmp = table(Default$default))\npaste(333/10000,' people default in payment of credit card')\n#How many student default\n?table\ntable(Default$default, Default$student, dnn=c(\"default\",\"students\"))\nftable(Default$default, Default$student)\nt1= table(Default$default, Default$student, dnn=c(\"default\",\"students\"))\naddmargins(t1)\ntable(Default$student)\n\n\n?glm\n#Multiple Logistic Regression\nlogit1 = glm(default ~ income + balance + student, family='binomial', data=Default)\nsummary(logit1)\nexp(coef(logit1))\n#No Rs\n#income is not significant - remove it\n\nlogit2 = glm(default ~ balance + student, family='binomial', data=Default)\nsummary(logit2)\ncoef(logit2)\nexp(coef(logit2))\nanova(logit2, logit1)\n#if pvalue < 0.05, logit1 is better model otherwise logit2\n\n# Predict : use sample values\nhead(Default)\nseq(1, 10000,500)\nDefault[c(1,501),]\n\nndata1= Default[seq(1, 10000,500),]\nndata1\nnrow(Default[seq(1, 10000,500),])\n10000/500\n\nlibrary(dplyr)\n(ndata = (slice(Default, seq(1,n(),500))))\nndata\nslice(Default, seq(1,n(),1000)) # another way\nhead(ndata)\n\naddmargins(prop.table(table(Default$default,Default$student)))\n0.2817/0.9667; 0.0127/0.0333\n\noptions(digits=10)\n\nfitted.results = predict(logit2, newdata=ndata,type='response')\nfitted.results = round(fitted.results,4)\nhead(fitted.results)\nfitted.results\ncbind(ndata, fitted.results)\n\nndata\np2 <- ndata %>% mutate(predictnew = ifelse(fitted.results < 0.5, 'No','Yes'))\ncbind(p2,fitted.results)\nfitted.results\nifelse(fitted.results < 0.05, 0,1)\n\n\n(ndata2 = data.frame(student=c('Yes','No'), balance=mean(Default$balance), income=mean(Default$income)))\n(fitted.results2 <- predict(logit2, newdata=ndata2,type='response'))\ncbind(ndata2, fitted.results2)\n\nDefault\nxtabs( ~ default + student, data=Default)\ntable(Default$default, Default$student)\nftable(Default$default, Default$student)\n\n#Accuracy of Model\nlibrary(caret)\nset.seed(3456)\nstr(Default)\ntrainIndex <- createDataPartition(Default$default, p = .67, list = FALSE, times = 1)\n\nTrain <- Default[ trainIndex,]\nTest <- Default[-trainIndex,]\nhead(Train)\nhead(Test)\n\n# Logistic Regression Model\nmodel = glm(default ~ student, data=Default, family='binomial')\nTest$model_prob <- predict(model, Test, type = \"response\")\nhead(Test)\nTest <- Test %>% mutate(default_pred = ifelse(model_prob > .5,'Yes','No'))\nhead(Test)\nTest <- Test %>% mutate(accurate = 1*(default == default_pred))\nsum(Test$accurate)/nrow(Test) \n#96% Accuracy\n\n\n" }, { "path": "28a-LogR/24d-LR-default.R", "content": "# Logistic Regression\n\n# Load the textbook R package\nlibrary(ISLR)\n?Default\n# Load in the credit data\n#Check the structure of Data\n#Data Structures\n#\nhead(Default)\nstr(Default)\nsummary(Default)\n\nnames(Default)\n# How many people actual default?\n\n#Plot\nDefault$default\n\nplot(Default$balance, Default$income, col=Default$default)\n#Model\nlogitb = glm(default ~ balance, data=Default, family='binomial')\nsummary(logitb)\ncoef(logitb)\n\nexp(coef(logitb))\nrange(Default$balance)\nb1= logitb$coefficients[2]\nexp(b1)^1 # due to 1 unit increase in balance \nexp(b1)^100 # due to 100 unit increase in balance\nexp(b1)^1000 # due to 1000 unit increase in balance\n\n\nexp(confint(logitb))\n\n#Assessing impact of predictors on Probability of Outcome\n\n# Create test data between the range\npredict(logit, newdata = data.frame(balance=c(0,100,500,1000,1500,1800,2500)), type=c('response'))\n\nhead(Default)\n\ntop_n(Default, 5, balance)\nDefault %>% arrange(balance) %>% slice(1:5)\n#top_n(Default, 5, desc(balance))\nDefault %>% arrange(balance) %>% slice(seq(1,n(),1000))\n\n\n# Create a new dummy variable for students\n#Default$studentD <- 0 # put all zeros\n#Default$studentD[Default$student==\"Yes\"] <- 1 # replace 0 wherever yes is there\n#Multiple Logistic Regression\nlogit = glm(default ~ income + balance + student, family='binomial', data=Default)\nsummary(logit2)\nexp(coef(logit2))\n\nlogit2 = glm(default ~ balance + student, family='binomial', data=Default)\nsummary(logit)\nexp(coef(logit))\n\n\nDefault %>% group_by(student) %>% arrange(student, balance) \n\n\n\n# Predict\nhead(Default)\nndata2 = data.frame(income=c(40000), balance=c(700), student=c('Yes'))\npredict(logit, newdata=ndata2, predict='response')\n\n(ndata = (slice(Default, seq(1,n(),500))))\n\nslice(Default, seq(1,n(),1000))\nhead(ndata)\naddmargins(prop.table(table(Default$default,Default$student)))\n0.2817/0.9667; 0.0127/0.0333\noptions(digits=10)\n\nfitted.results = predict(logit, newdata=ndata,type='response')\n\nfitted.results\ncbind(ndata, fitted.results)\nndata\nndata %>% mutate(predict = ifelse(fitted.results < 0.5, 0,1))\n\nfitted.results\nifelse(fitted.results < 0.05, 0,1)\n\n\n(ndata2 = data.frame(student=c('Yes','No'), balance=mean(Default$balance), income=mean(Default$income)))\n(fitted.results2 <- predict(logit, newdata=ndata2,type='response'))\n\n\n\n#Accuracy of Model\nlibrary(caret)\nset.seed(3456)\nstr(Default)\ntrainIndex <- createDataPartition(Default$default, p = .67,\n list = FALSE, times = 1)\n\nTrain <- Default[ trainIndex,]\nTest <- Default[-trainIndex,]\nhead(Train)\nhead(Test)\n\n# Logistic Regression Model\nmodel = glm(default ~ student, data=Default, family='binomial')\nTest$model_prob <- predict(model, Test, type = \"response\")\nhead(Test)\nTest <- Test %>% mutate(default_pred = ifelse(model_prob > .5,'Yes','No'))\nhead(Test)\nTest <- Test %>% mutate(accurate = 1*(default == default_pred))\nsum(Test$accurate)/nrow(Test) \n#96% Accuracy\n\n?createDataPartition\n" }, { "path": "28a-LogR/24e-LR-default.R", "content": "# Logistic Regression\ndata() # datasets available for use in R\n\n# Load the textbook R package\nlibrary(ISLR)\n?Default # data set\n# Load in the credit data\ndata(\"Default\")\nstr(Default)\nclass(Default)\nhead(Default)\nnames(Default)\nbalance\nattach(Default)\nbalance\nstr(Default)\nDefault\ndim(Default)\n#write.csv(Default,'./data/cclogr.csv')\n#Data Structures\n\nhead(Default)\nstr(Default)\nsummary(Default)\n\nnames(Default)\n# How many people actual default?\ntmp = table(default)\ntmp\n333/9667\n\n(tmp[[2]]/tmp[[1]])*100\ntmp[[2]]/dim(Default)[1] * 100\n\n#Plot\nlibrary(ggplot2); library(gridExtra)\n## Loading required package: grid\nx = qplot(x=balance, y=income, color=default, shape=default, geom='point')+scale_shape(solid=FALSE)\ny = qplot(x=default, y=balance, fill=default, geom='boxplot')+guides(fill=FALSE)\nz = qplot(x=default, y=income, fill=default, geom='boxplot')+guides(fill=FALSE)\n# Plot\nx\ngrid.arrange(y, z, nrow=1)\n\n#Model\nlogitb = glm(default ~ balance, data=Default, family='binomial')\nsummary(logitb)\ncoef(logitb)\n\nexp(coef(logitb))\nrange(Default$balance)\nb1= logitb$coefficients[2]\nexp(b1)^1 # due to 1 unit increase in balance \nexp(b1)^100 # due to 100 unit increase in balance\nexp(b1)^1000 # due to 1000 unit increase in balance\n\n\nexp(confint(logitb))\n\n#Assessing impact of predictors on Probability of Outcome\n\n# Create test data between the range\npredict(logit, newdata = data.frame(balance=c(0,100,500,1000,1500,1800,2500)), type=c('response'))\n\nhead(Default)\n\ntop_n(Default, 5, balance)\nDefault %>% arrange(balance) %>% slice(1:5)\n#top_n(Default, 5, desc(balance))\nDefault %>% arrange(balance) %>% slice(seq(1,n(),1000))\n\n#student\nstr(Default)\nlogits = glm(default ~ student, data=Default, family='binomial')\nsummary(logits)\n\n\n# Create a new dummy variable for students\n#Default$studentD <- 0 # put all zeros\n#Default$studentD[Default$student==\"Yes\"] <- 1 # replace 0 wherever yes is there\n#head(Default)\n\n#logit <- glm(default ~ studentD, data=Default, family='binomial')\n#summary(logit)\n#predict(logit, newdata = data.frame(studentD=c(0)), type=c('response'))\n#predict(logit, newdata = data.frame(studentD=c(1)), type=c('response'))\n\n\nlogits <- glm(default ~ student, data=Default, family='binomial')\nsummary(logits)\npredict(logits, newdata = data.frame(student=c('Yes')), type=c('response'))\npredict(logits, newdata = data.frame(student=c('No')), type=c('response'))\n\n\nclass(Default)\n#Multiple Logistic Regression\nlogit1 = glm(default ~ income + balance + student, family='binomial', data=Default)\nsummary(logit1)\nexp(coef(logit1))\n\nlogit2 = glm(default ~ balance + student, family='binomial', data=Default)\nsummary(logit2)\nexp(coef(logit2))\n\n\nDefault %>% group_by(student) %>% arrange(student, balance) \n\n\n\n# Predict\nhead(Default)\n#ndata2 = data.frame(balance=c(700), student=c('Yes'))\n#ndata2\n#predict(logit2, newdata=ndata2, predict='response')\nseq(1, 10000,500)\nDefault[c(1,501),]\nDefault[seq(1, 10000,500),]\n\nlibrary(dplyr)\n(ndata = (slice(Default, seq(1,n(),500))))\nndata\nslice(Default, seq(1,n(),1000))\nhead(ndata)\naddmargins(prop.table(table(Default$default,Default$student)))\n0.2817/0.9667; 0.0127/0.0333\noptions(digits=10)\n\nfitted.results = predict(logit2, newdata=ndata,type='response')\nfitted.results\nhead(fitted.results)\nfitted.results\ncbind(ndata, fitted.results)\nndata\nndata %>% mutate(predict = ifelse(fitted.results < 0.5, 'No','Yes'))\n\nfitted.results\nifelse(fitted.results < 0.05, 0,1)\n\n\n(ndata2 = data.frame(student=c('Yes','No'), balance=mean(Default$balance), income=mean(Default$income)))\n(fitted.results2 <- predict(logit, newdata=ndata2,type='response'))\n\n\n\n#Accuracy of Model\nlibrary(caret)\nset.seed(3456)\nstr(Default)\ntrainIndex <- createDataPartition(Default$default, p = .67,\n list = FALSE, times = 1)\n\nTrain <- Default[ trainIndex,]\nTest <- Default[-trainIndex,]\nhead(Train)\nhead(Test)\n\n# Logistic Regression Model\nmodel = glm(default ~ student, data=Default, family='binomial')\nTest$model_prob <- predict(model, Test, type = \"response\")\nhead(Test)\nTest <- Test %>% mutate(default_pred = ifelse(model_prob > .5,'Yes','No'))\nhead(Test)\nTest <- Test %>% mutate(accurate = 1*(default == default_pred))\nsum(Test$accurate)/nrow(Test) \n#96% Accuracy\n\n?createDataPartition\n\nDefault\nhead(Default)\nlogr1 = glm(default ~ student + balance + income, data=Default, family='binomial')\nsummary(logr1)\nlogr2 = glm(default ~ student + balance , data=Default, family='binomial')\nsummary(logr2)\nndata3 = Default[seq(1,nrow(Default), 1000), ]\nndata3\n(p1 = predict(logr2, newdata = ndata3, type='response'))\ncbind(ndata3, p1, p2= ifelse(p1 < 0.5, 'No', 'Yes'))\n" }, { "path": "28a-LogR/24g-LR-default-accuracy.R", "content": "#Logistic Regression - German Credit\n\nlibrary(caret)\nlibrary(ISLR)\ndata(Default) ; head(Default); str(Default)\ndata = Default\ndata$default = ifelse(data$default==\"No\",0,1)\nhead(data)\n\nTrain <- createDataPartition(data$default, p=0.6, list=FALSE)\ntraining <- data[ Train, ]\ntesting <- data[ -Train, ]\n\n\nhead(training); dim(training); dim(testing)\nstr(training) ; str(training$default)\n\n#Model\nlogR1 = glm(default ~ income + balance + student, family='binomial', data=training)\npredict(logR1, newdata=testing, type=\"response\")\n\n\n\nactuals = testing$default\npreds = predict(logR1, newdata=testing, type=\"response\")\n#best threshold (or cutoff) point to be used in glm models is the point which maximises the specificity and the sensitivity. This threshold point might not give the highest prediction in your model, but it wouldn't be biased towards positives or negatives.\n\n#-------\nlibrary(InformationValue)\n(optCutOff <- optimalCutoff(actuals, preds)[1] )\n\nsummary(preds)\npred_default = ifelse(preds > optCutOff, 1, 0)\n\nlibrary(caret)\nconfusionMatrix(actuals, pred_default)\n(t1=table(testing$default, pred_default))\naccuracy1= caret::confusionMatrix(t1)\n#-------\ncutoff2 =0.5\npred_default2 = ifelse(preds > cutoff2, 1, 0)\nconfusionMatrix(actuals, pred_default2)\n(t2=table(actuals, pred_default2))\naccuracy2= caret::confusionMatrix(t2)\nt1;t2\naccuracy1; accuracy2\n(TP1 = sum(diag(t1))/ sum(t1))\n(TP2 = sum(diag(t2))/ sum(t2))\naccuracy2\n" }, { "path": "28a-LogR/26b-LR-germancredit.R", "content": "# Logisitic Regression : German Credit\n#https://www.r-bloggers.com/evaluating-logistic-regression-models/\n#\nlibrary(caret)\ndata(GermanCredit)\n?GermanCredit\nstr(GermanCredit)\nTrain <- createDataPartition(GermanCredit$Class, p=0.6, list=FALSE)\ntraining <- GermanCredit[ Train, ]\ntesting <- GermanCredit[ -Train, ]\n\n#Model\nmod_fit <- train(Class ~ Age + ForeignWorker + Property.RealEstate + Housing.Own + CreditHistory.Critical, data=training, method=\"glm\", family=\"binomial\")\n\nexp(coef(mod_fit$finalModel))\n\npredict(mod_fit, newdata=testing)\npredict(mod_fit, newdata=testing, type=\"prob\")\n\nmod_fit_one <- glm(Class ~ Age + ForeignWorker + Property.RealEstate + Housing.Own + CreditHistory.Critical, data=training, family=\"binomial\")\npredict(mod_fit_one, newdata=testing)\n\nmod_fit_two <- glm(Class ~ Age + ForeignWorker, data=training, family=\"binomial\")\n\n\nanova(mod_fit_one, mod_fit_two, test =\"Chisq\")\n\nlibrary(lmtest)\nlrtest(mod_fit_one, mod_fit_two)\n\nlibrary(pscl)\npR2(mod_fit_one) # look for 'McFadden'\nlibrary(MKmisc)\nHLgof.test(fit = fitted(mod_fit_one), obs = training$Class)\nlibrary(ResourceSelection)\nhoslem.test(training$Class, fitted(mod_fit_one), g=10)\n\nlibrary(survey)\nregTermTest(mod_fit_one, \"ForeignWorker\")\n## Wald test for ForeignWorker\n## in glm(formula = Class ~ Age + ForeignWorker + Property.RealEstate + \n## Housing.Own + CreditHistory.Critical, family = \"binomial\", \n## data = training)\n## F = 0.949388 on 1 and 594 df: p= 0.33027\nregTermTest(mod_fit_one, \"CreditHistory.Critical\")\n## Wald test for CreditHistory.Critical\n## in glm(formula = Class ~ Age + ForeignWorker + Property.RealEstate + \n## Housing.Own + CreditHistory.Critical, family = \"binomial\", \n## data = training)\n## F = 16.67828 on 1 and 594 df: p= 5.0357e-05\n#Variable ImportanceTo assess the relative importance of individual predictors in the model, we can also look at the absolute value of the t-statistic for each model parameter. This technique is utilized by the varImp function in the caret package for general and generalized linear models.\n\nvarImp(mod_fit)\n## glm variable importance\n## \n## Overall\n## CreditHistory.Critical 100.00\n## Property.RealEstate 57.53\n## Housing.Own 50.73\n## Age 22.04\n## ForeignWorker 0.00\n\nred = predict(mod_fit, newdata=testing)\naccuracy <- table(pred, testing[,\"Class\"])\nsum(diag(accuracy))/sum(accuracy)\n## [1] 0.705\npred = predict(mod_fit, newdata=testing)\nconfusionMatrix(data=pred, testing$Class)\n\nibrary(pROC)\n# Compute AUC for predicting Class with the variable CreditHistory.Critical\nf1 = roc(Class ~ CreditHistory.Critical, data=training) \nplot(f1, col=\"red\")\n## \n## Call:\n## roc.formula(formula = Class ~ CreditHistory.Critical, data = training)\n## \n## Data: CreditHistory.Critical in 180 controls (Class Bad) < 420 cases (Class Good).\n## Area under the curve: 0.5944\nlibrary(ROCR)\n# Compute AUC for predicting Class with the model\nprob <- predict(mod_fit_one, newdata=testing, type=\"response\")\npred <- prediction(prob, testing$Class)\nperf <- performance(pred, measure = \"tpr\", x.measure = \"fpr\")\nplot(perf)\nauc <- performance(pred, measure = \"auc\")\nauc <- auc@y.values[[1]]\nauc\n## [1] 0.6540625\n\nctrl <- trainControl(method = \"repeatedcv\", number = 10, savePredictions = TRUE)\n\nmod_fit <- train(Class ~ Age + ForeignWorker + Property.RealEstate + Housing.Own + \n CreditHistory.Critical, data=GermanCredit, method=\"glm\", family=\"binomial\",\n trControl = ctrl, tuneLength = 5)\n\npred = predict(mod_fit, newdata=testing)\nconfusionMatrix(data=pred, testing$Class)" }, { "path": "28a-LogR/27b-LR-gre.R", "content": "# ROCC AUC\n#https://stats.idre.ucla.edu/r/dae/logit-regression/\n\nmydata <- read.csv(\"https://stats.idre.ucla.edu/stat/data/binary.csv\")\n## view the first few rows of the data\nhead(mydata)\nsummary(mydata)\nsapply(mydata, sd)\n\n## two-way contingency table of categorical outcome and predictors we want\n## to make sure there are not 0 cells\nxtabs(~admit + rank, data = mydata)\n\n#convert rank into factors\nmydata$rank <- factor(mydata$rank)\nmylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = \"binomial\")\n\nsummary(mylogit)\n\n## CIs using profiled log-likelihood\nconfint(mylogit)\n\n## CIs using standard errors\nconfint.default(mylogit)\n\n#Rank Significance - Wald Test\nlibrary(aod)\naod::wald.test(b = coef(mylogit), Sigma = vcov(mylogit), Terms = 4:6)\nl <- cbind(0, 0, 0, 1, -1, 0)\nwald.test(b = coef(mylogit), Sigma = vcov(mylogit), L = l)\n\n#----\n## odds ratios only\nexp(coef(mylogit))\n\n## odds ratios and 95% CI\nexp(cbind(OR = coef(mylogit), confint(mylogit)))\n\n\nprob=predict(mylogit,type=c(\"response\"))\nmydata$prob=prob\n\n#Test with new data\nnewdata1 <- with(mydata, data.frame(gre = mean(gre), gpa = mean(gpa), rank = factor(1:4)))\n## view data frame\nnewdata1\nnewdata1$rankP <- predict(mylogit, newdata = newdata1, type = \"response\")\nnewdata1\nnewdata2 <- with(mydata, data.frame(gre = rep(seq(from = 200, to = 800, length.out = 100),4), gpa = mean(gpa), rank = factor(rep(1:4, each = 100))))\nnewdata3 <- cbind(newdata2, predict(mylogit, newdata = newdata2, type = \"link\", se = TRUE))\nnewdata3\nnewdata3 <- within(newdata3, {\n PredictedProb <- plogis(fit)\n LL <- plogis(fit - (1.96 * se.fit))\n UL <- plogis(fit + (1.96 * se.fit))\n})\nhead(newdata3)\nlibrary(ggplot2)\n\nggplot(newdata3, aes(x = gre, y = PredictedProb)) + geom_ribbon(aes(ymin = LL,\n ymax = UL, fill = rank), alpha = 0.2) + geom_line(aes(colour = rank), size = 1)\n## view first few rows of final dataset\nhead(newdata3)\n\n#----\nwith(mylogit, null.deviance - deviance)\nwith(mylogit, df.null - df.residual)\nwith(mylogit, pchisq(null.deviance - deviance, df.null - df.residual, lower.tail = FALSE))\nlogLik(mylogit)\n\nlibrary(pROC)\ng <- roc(admit ~ prob, data = mydata)\nplot(g) \n\n\n#best fit point ----\ncoords(g, \"best\")\nmydata$prediction=ifelse(prob>=0.3126844,1,0)\ncaret::confusionMatrix(mydata$prediction,mydata$admit)\nwith(mydata, table(admit,gre))\n\n\n#ROC Curve\n#C:\\Program Files\\Java\\jre1.8.0_144\nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_144') \nlibrary(rJava)\nlibrary(Deducer)\nmodelfit <- glm(formula=admit ~ gre + gpa, family=binomial(), data=mydata, na.action=na.omit)\nrocplot(modelfit)\n\n\n#Another way to plot ROC\nlibrary(ROCR)\npred <- prediction(prob, mydata$admit) \nperf <- performance(pred, measure = \"tpr\", x.measure = \"fpr\") \nplot(perf, col=rainbow(7), main=\"ROC curve Admissions\", xlab=\"Specificity\", ylab=\"Sensitivity\") \nabline(0, 1) #add a 45 degree line\n(auc = performance(pred, \"auc\"))\n" }, { "path": "28a-LogR/28b-LR-subscribe.R", "content": "# Logistic Regression\n\nsubscribe = read.csv('./data/subscribe.csv')\n\nsummary(subscribe)\ntable(subscribe$subyn)\nfitlr = glm(subyn ~ age, data=subscribe, family= binomial())\nsummary(fitlr)\n\ncoef(fitlr)\nexp(coef(fitlr))\nnew1 = data.frame(age=24)\npredict(fitlr, newdata=new1, type='response')\n\n" }, { "path": "28a-LogR/28c-LR-subscribe.R", "content": "# Logistic Regression\n#https://www.youtube.com/watch?v=AVx7Wc1CQ7Y\n\n# Read data file\nmydata <- read.csv(file.choose(), header = T)\nstr(mydata)\nmydata$admit <- as.factor(mydata$admit)\nmydata$rank <- as.factor(mydata$rank)\n\n# Two-way table of factor variables\nxtabs(~admit + rank, data = mydata)\n\n# Partition data - train (80%) & test (20%)\nset.seed(1234)\ndim(mydata)\nind = sample(2, nrow(mydata), replace = T, prob = c(0.8, 0.2))\nind\n?sample\n(train = mydata[ind==1,])\n(test = mydata[ind==2,])\ndim(train)\ndim(test)\n# Logistic regression model\nmymodel <- glm(admit ~ gpa + rank, data = train, family = 'binomial')\nsummary(mymodel)\n\n# Prediction\np1 <- predict(mymodel, train, type = 'response')\nhead(p1)\nhead(train)\n\n# Misclassification error - train data\npred1 <- ifelse(p1>0.5, 1, 0)\ntab1 <- table(Predicted = pred1, Actual = train$admit)\ntab1\n1 - sum(diag(tab1))/sum(tab1)\n\n# Misclassification error - test data\np2 <- predict(mymodel, test, type = 'response')\npred2 <- ifelse(p2>0.5, 1, 0)\ntab2 <- table(Predicted = pred2, Actual = test$admit)\ntab2\n1 - sum(diag(tab2))/sum(tab2)\n\n# Goodness-of-fit test\nwith(mymodel, pchisq(null.deviance - deviance, df.null-df.residual, lower.tail = F))\n" }, { "path": "28a-LogR/29b-LR-ads.R", "content": "# Logistic Regression\n# Import the dataset\n\ndataset = read.csv('./regr/Social_Network_Ads.csv')\n#dataset = dataset[, 3:5]\nView(dataset)\n# Split the dataset into the Training set and Test set\n\n#install.packages('caTools')\nlibrary(caTools)\n?caTools\nset.seed(2000)\nsplit = sample.split(dataset$Purchased, SplitRatio = 0.75)\ntraining_set = subset(dataset, split == TRUE)\ntest_set = subset(dataset, split == FALSE)\ndim(training_set); dim(test_set)\nnames(training_set)\n# Feature Scaling\n#training_set[, 1:2] = scale(training_set[, 1:2])\n#test_set[, 1:2] = scale(test_set[, 1:2])\n\n# Fitting Logistic Regression to the Training set\nclassifier = glm(formula = Purchased ~ as.factor(Gender)+Age+EstimatedSalary,family = binomial, data = training_set)\n\nsummary(classifier)\n\n# Fitting Logistic Regression to the Training set - Age being insignificant is dropped here\nclassifier = glm(formula = Purchased ~ as.factor(Gender)+EstimatedSalary, family = binomial, data = training_set)\n\nsummary(classifier)\nsummary(classifier)$coefficient\n\n\n# Predicting the Test set results\nprob_pred = predict(classifier, type = 'response', newdata = test_set)\ndf_prob_pred = as.data.frame(prob_pred)\n\nhead(df_prob_pred)\ny_pred = ifelse(prob_pred > 0.5, 1, 0)\ny_pred\n\n# Making the Confusion Matrix\ncm = table(test_set[,5], y_pred)\ncm\ncaret::confusionMatrix(cm)\n" }, { "path": "28a-LogR/31b-LR-income.R", "content": "#logistic Regression\n#https://www.guru99.com/r-generalized-linear-model.html\n\n\n\nlibrary(dplyr)\ndata_adult <-\n read.csv(\"https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/data_adult.csv\")\nglimpse(data_adult)\nstr(data_adult)\n\ncontinuous <-select_if(data_adult, is.numeric)\nsummary(continuous)\n\n# Histogram with kernel density curve\nlibrary(ggplot2)\nggplot(continuous, aes(x = hours.per.week)) +\n geom_density(alpha = .2, fill = \"#FF6666\")\n\nquantile(data_adult$hours.per.week)\ntop_one_percent <- quantile(data_adult$hours.per.week, .99)\ntop_one_percent\n\ndata_adult_drop <-data_adult %>%\n filter(hours.per.week < top_one_percent)\ndim(data_adult_drop)\n\n#std only numeric columns\ndata_adult_rescale <- data_adult_drop %>%\n mutate_if(is.numeric, funs(as.numeric(scale(.))))\nhead(data_adult_rescale)\n\n\n# Select categorical column\n(charcols <- data.frame(select_if(data_adult_rescale, is.character)))\nncol(charcols)\ndata_adult_rescale[names(charcols)]\ndata_adult_rescale[names(charcols)] = lapply(data_adult_rescale[names(charcols)],factor)\nfactor <- data.frame(select_if(data_adult_rescale, is.factor))\n\nncol(factor)\nsummary(data_adult_rescale)\nnames(factor)\nlibrary(ggplot2)\n# Create graph for each column\ngraph <- lapply(names(factor),\n function(x) \n ggplot(factor, aes(get(x))) +\n geom_bar() +\n theme(axis.text.x = element_text(angle = 90)))\ngraph\n\n\n#Feature Engg\nrecast_data <- data_adult_rescale %>%\n select(-X) %>% mutate(education = factor(ifelse(education == \"Preschool\" | education == \"10th\" | education == \"11th\" | education == \"12th\" | education == \"1st-4th\" | education == \"5th-6th\" | education == \"7th-8th\" | education == \"9th\", \"dropout\", ifelse(education == \"HS-grad\", \"HighGrad\", ifelse(education == \"Some-college\" | education == \"Assoc-acdm\" | education == \"Assoc-voc\", \"Community\", ifelse(education == \"Bachelors\", \"Bachelors\",ifelse(education == \"Masters\" | education == \"Prof-school\", \"Master\", \"PhD\")))))))\n\n\nrecast_data %>% group_by(education) %>%\n summarize(average_educ_year = mean(educational.num),\n count = n()) %>% arrange(average_educ_year)\n\n# Change level marry\nrecast_data <- recast_data %>% mutate(marital.status = factor(ifelse(marital.status == \"Never-married\" | marital.status == \"Married-spouse-absent\", \"Not_married\", ifelse(marital.status == \"Married-AF-spouse\" | marital.status == \"Married-civ-spouse\", \"Married\", ifelse(marital.status == \"Separated\" | marital.status == \"Divorced\", \"Separated\", \"Widow\")))))\n\ntable(recast_data$marital.status)\n\n# Plot gender income\nggplot(recast_data, aes(x = gender, fill = income)) +\n geom_bar(position = \"fill\") + theme_classic()\n\n# Plot origin income\nggplot(recast_data, aes(x = race, fill = income)) +\n geom_bar(position = \"fill\") +\n theme_classic() +\n theme(axis.text.x = element_text(angle = 90))\n\n# box plot gender working time\nggplot(recast_data, aes(x = gender, y = hours.per.week)) +\n geom_boxplot() + stat_summary(fun.y = mean, geom = \"point\", size = 3, color = \"steelblue\") + theme_classic()\n\n# Plot distribution working time by education\nggplot(recast_data, aes(x = hours.per.week)) +\n geom_density(aes(color = education), alpha = 0.5) +\n theme_classic()\n\nanova <- aov(hours.per.week~education, recast_data)\nsummary(anova)\n\nlibrary(ggplot2)\nggplot(recast_data, aes(x = age, y = hours.per.week)) +\n geom_point(aes(color = income),\n size = 0.5) +\n stat_smooth(method = 'lm',\n formula = y~poly(x, 2),\n se = TRUE,\n aes(color = income)) +\n theme_classic()\n\nlibrary(GGally)\n# Convert data to numeric\ncorr <- data.frame(lapply(recast_data, as.integer))\n# Plot the graph\nggcorr(corr,method = c(\"pairwise\", \"spearman\"),nbreaks = 6, hjust = 0.8,label = TRUE,label_size = 3,color = \"grey50\")\n\nset.seed(1234)\ncreate_train_test <- function(data, size = 0.8, train = TRUE) {\n n_row = nrow(data)\n total_row = size * n_row\n train_sample <- 1: total_row\n if (train == TRUE) {\n return (data[train_sample, ])\n } else {\n return (data[-train_sample, ])\n }\n}\ndata_train <- create_train_test(recast_data, 0.8, train = TRUE)\ndata_test <- create_train_test(recast_data, 0.8, train = FALSE)\ndim(data_train)\n\nformula <- income ~ .\nlogit <- glm(formula, data = data_train, family = 'binomial')\nsummary(logit)\n\nlapply(logit, class)[1:3]\nlogit$aic\n\npredict <- predict(logit, data_test, type = 'response')\n# confusion matrix\ntable_mat <- table(data_test$income, predict > 0.5)\ntable_mat\n\naccuracy_Test <- sum(diag(table_mat)) / sum(table_mat)\naccuracy_Test\n\nprecision <- function(matrix) {\n # True positive\n tp <- matrix[2, 2]\n # false positive\n fp <- matrix[1, 2]\n return (tp / (tp + fp))\n}\n\nrecall <- function(matrix) {\n # true positive\n tp <- matrix[2, 2]# false positive\n fn <- matrix[2, 1]\n return (tp / (tp + fn))\n}\n\nprec <- precision(table_mat)\nprec\nrec <- recall(table_mat)\nrec\n\nf1 <- 2 * ((prec * rec) / (prec + rec))\nf1\n\nlibrary(ROCR)\nROCRpred <- prediction(predict, data_test$income)\nROCRperf <- performance(ROCRpred, 'tpr', 'fpr')\nplot(ROCRperf, colorize = TRUE, text.adj = c(-0.2, 1.7))\n\nformula_2 <- income~age: hours.per.week + gender: hours.per.week + .\nlogit_2 <- glm(formula_2, data = data_train, family = 'binomial')\npredict_2 <- predict(logit_2, data_test, type = 'response')\ntable_mat_2 <- table(data_test$income, predict_2 > 0.5)\nprecision_2 <- precision(table_mat_2)\nrecall_2 <- recall(table_mat_2)\nf1_2 <- 2 * ((precision_2 * recall_2) / (precision_2 + recall_2))\nf1_2\n" }, { "path": "28a-LogR/31c-income.R", "content": "#Log R - income class\n##http://r-statistics.co/Logistic-Regression-With-R.html\n" }, { "path": "28a-LogR/33b-LR-purchase.R", "content": "# Logistic Regression : Predict Purchase\n\n# Import the dataset\ndf1 = read.csv('./data/logr2.csv')\nhead(df1)\n\nurl=\"https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=120271978\"\nlibrary(gsheet)\ndf2 = as.data.frame(gsheet2tbl(url))\nhead(df2)\n\ndataset=df1 #or df2 if data is imported from google sheets\nhead(dataset)\nstr(dataset)\nsummary(dataset)\ndim(dataset)\nView(dataset)\ndataset$gender = factor(dataset$gender)\n\n\n# Split the dataset into the Training set and Test set\n#install.packages('caTools')\nlibrary(caTools)\nset.seed(2000)\n\nsplit = sample.split(dataset$purchased, SplitRatio = 0.75)\ntraining_set = subset(dataset, split == TRUE)\ntest_set = subset(dataset, split == FALSE)\n\ndim(dataset); dim(training_set); dim(test_set)\nnames(dataset)\n\n\n# Logisitic Model on Training Set\nlogitmodel1 = glm(purchased ~ gender + age + salary, family = binomial, data = training_set)\nsummary(logitmodel1)\n\n# gender not insignificant dropped here\nlogitmodel2 = glm(purchased ~ age + salary, family = binomial, data = training_set)\nsummary(logitmodel2)\n\n#summary(logitmodel2)$coefficient # they are in log terms\n\n#predict on sample data\ntest_set2 = data.frame(age=c(40,65), salary=c(40000, 50000))\n(prob_pred2 = predict(logitmodel2, type = 'response', newdata = test_set2))\ncbind(test_set2, prob_pred2)\n#age=65 person likely to purchase\n\n# Predicting the Test set results from testset\nhead(test_set)\nprob_pred = predict(logitmodel2, type = 'response', newdata = test_set)\nsummary(prob_pred)\nhead(cbind(test_set,prob_pred ),10)\n\n#if prob > 0.5 make it 1, else 0\ny_pred = ifelse(prob_pred > 0.5, 1, 0)\nhead(cbind(test_set$purchased, y_pred),15)\n\n# Making the Confusion Matrix\ncm = table(test_set[,5], y_pred)\ncm\ncaret::confusionMatrix(cm)\n\n#ROCR Curve\nlibrary(ROCR)\nROCRpred <- prediction(y_pred, test_set$purchased)\nROCRperf <- performance(ROCRpred, 'tpr','fpr')\nplot(ROCRperf, colorize = TRUE, text.adj = c(-0.2,1.7))\n\n" }, { "path": "28a-LogR/45b-compareAUC.R", "content": "# ROC Area : 2 models\n#uc-r.github.i/logistic_regression#multi\n\nlibrary(tidyverse) # formating output\nlibrary(ISLR)\ndata(\"Default\")\nhead(Default)\n\ndf2 = Default\n#convert default to binary coded : No-0, Yes-1\ndf2$default = ifelse(df2$default=='No',0,1)\nhead(df2$default)\n#train Data\nset.seed(100)\nindex = sample(0:nrow(df2), .6 * nrow(df2), replace=F)\nindex\ndf2Train = df2[index,]\n\n#test Data\ndf2Test = df2[-index,]\nnrow(df2Train) ; nrow(df2Test)\n\nnames(df2)\nsummary(df2)\n#Models on Train Data\nrocmodel1 = glm(default ~ balance, data=df2Train, family='binomial')\nrocmodel2 = glm(default ~ student, data=df2Train, family='binomial')\nrocmodel3 = glm(default ~ balance + income + student, data=df2Train, family='binomial')\ntidy(rocmodel1)\ntidy(rocmodel2)\ntidy(rocmodel3)\n\n#Model Evaluation and Comparison\n#ANOVA----\nanova(rocmodel1, rocmodel3, test='Chisq') #1 < 2 for variables\n#M3 is better as pvalue < 0.05\n#McFadden : Pseudo R2 ----\nlist(M1=pscl::pR2(rocmodel1)['McFadden'],\n M2=pscl::pR2(rocmodel2)['McFadden'],\n M3=pscl::pR2(rocmodel3)['McFadden'])\n#M3 has highest value > -.4 \n\n\n#Apply models to Test Data\ntest.predicted.m1 = predict(rocmodel1, newdata= df2Test, type='response')\ntest.predicted.m2 = predict(rocmodel2, newdata= df2Test, type='response')\ntest.predicted.m3 = predict(rocmodel3, newdata= df2Test, type='response')\n\n#now compare the outputs by making confusion matrix \nlist(M1 = table(df2Test$default, test.predicted.m1 > 0.5) %>% prop.table,\n M2 = table(df2Test$default, test.predicted.m2 > 0.5) %>% prop.table,\n M3 = table(df2Test$default, test.predicted.m3 > 0.5) %>% prop.table)\n#M2 does not predict default cases (1) accurately\n#M1 & M3 are almost same\n#let us understand misclassification rate instead of accuracy\noptions(digits=4)\nhead(test.predicted.m1)\nhead(df2Test)\ndf2Test %>% \n mutate(m1.pred = ifelse(test.predicted.m1 > 0.5, 1, 0), \n m2.pred = ifelse(test.predicted.m2 > 0.5, 1, 0), \n m3.pred = ifelse(test.predicted.m3 > 0.5, 1, 0)) %>%\n summarise(m1.error = mean(default != m1.pred),\n m2.error = mean(default != m2.pred),\n m3.error = mean(default != m3.pred))\n\nhead(df2Test)\ntable(df2Test$default, test.predicted.m1 > 0.5) %>% addmargins()\ntable(df2Test$default, test.predicted.m1 > 0.5) %>% prop.table %>% addmargins()\n\n#Plots\nlibrary(ROCR)\npar(mfrow=c(1,3))\nprediction(test.predicted.m1, df2Test$default) %>% performance(measure = 'tpr', x.measure = 'fpr') %>% plot()\nprediction(test.predicted.m2, df2Test$default) %>% performance(measure = 'tpr', x.measure = 'fpr') %>% plot()\nprediction(test.predicted.m3, df2Test$default) %>% performance(measure = 'tpr', x.measure = 'fpr') %>% plot()\n\n#AUC\nprediction(test.predicted.m1, df2Test$default) %>% performance(measure='auc') %>% .@y.values\n#M1 has larger AUC : better\nprediction(test.predicted.m2, df2Test$default) %>% performance(measure='auc') %>% .@y.values\nprediction(test.predicted.m3, df2Test$default) %>% performance(measure='auc') %>% .@y.values\n#M1 & M3 are almost same\n\nmodel1 = glm(default1 ~ balance + income + student, data=dfTrain, family='binomial')\nmodel1\ncaret::varImp(model1)" }, { "path": "28a-LogR/45c-roc-default.R", "content": "\nlibrary(ISLR)\ndata(\"Default\")\nhead(Default)\n\ndf = Default\n#convert default to binary coded : No-0, Yes-1\ndf$default1 = ifelse(df$default=='No',0,1)\n#or\ndf$default2 = factor(df$default, levels = c('No','Yes'), labels=c(0,1))\nsummary(df)\n\n#plot sigmoid curve\nlibrary(tidyverse)\nlibrary(ggplot2)\ndf %>% ggplot(aes(balance, default1))+ geom_point(alpha=.15) + geom_smooth(method='glm', method.args=list(family='binomial')) + ylab(\"Prob of Default\")\n\n\nhead(df$default1)\n#train Data\nset.seed(100)\nindex = sample(0:nrow(df), .7 * nrow(df), replace=F)\nindex\ndfTrain = df[index,]\n\n#test Data\ndfTest = df[-index,]\nnrow(dfTrain) ; nrow(dfTest)\n\nnames(df)\nsummary(df)\n#Model on Train Data\nmodel1 = glm(default1 ~ balance + income + student, data=dfTrain, family='binomial')\nmodel1\ncaret::varImp(model1)\n\nexp(coef(model1))\n\n#sample case :\nbalance=1500 ; income= 40000 ; student=1\nc= as.numeric(coef(model1))\nc[1:4]\n(ex = exp( c[1] + balance * c[2] + income * c[3] + student * c[4])) #student\nex/(1+ex)\n(ex = exp( c[1] + balance * c[2] + income * c[3] + 0 * c[4])) #Non student\nex/(1+ex)\nex/(1+ex)/2\n\n\n#predict \n(new1 = data.frame(balance=1500, income= 40000, student=c('Yes','No')))\npredict(model1, newdata = new1, type='response')\n#same values as above manual calc\n#being student, prob of default decreases by 50%\n\np1 = predict(model1, newdata = dfTest, type='response')\nhead(p1)\n\n#model fitness\npscl::pR2(model1)['McFadden']\n#good fit > .3\n\nbroom::augment(model1)\nlibrary(broom)\nmodel1_data <- augment(model1) %>% mutate(index= 1:n())\nggplot(model1_data, aes(index, .std.resid, color=default1)) + geom_point(alpha=.5) + geom_abline(h=3)\nplot(model1, which=4, id.n=15)\n\ndf[c(4160,652,9539, 9874, 9523),] #influential values top5\n\n" }, { "path": "28a-LogR/45e-roc-general.R", "content": "# ROC Curve\n#https://rocr.bioinf.mpi-sb.mpg.de\n\nlibrary(ROCR)\n\npred = prediction(predictions, labels)\nperf = performacne(pred, measure='tpr', x.measure='fpr')\nplot(perf, col=rainbow(10))\n" }, { "path": "28a-LogR/45f-roc1.R", "content": "# ROC Curve\n\nset.seed(1)\nN = 2000\nP = .01\n\nrare_success = sample(c(TRUE, FALSE), N, replace=TRUE, prob=c(P,1-P))\ntable(rare_success)\n\nguess_not = rep(0,N)\ntable(guess_not)\n\nlibrary(pROC)\nplot(roc(rare_success, guess_not), print.auc=T)\n\n\nsimp_roc = function(labels, scores) {\n labels = labels[order(scores, decreasing=T)]\n data.frame(TPR=cumsum(labels)/sum(labels), FPR=cumsum(!labels)/sum(!labels), labels) }\n\nsimp_roc = simp_roc(rare_success, guess_not)\nsimp_roc\n\nwith(simp_roc, lines(1-FPR, TPR, col='blue', lty=2))\n" }, { "path": "28a-LogR/45h-roc2.R", "content": "# ROCR\n\nlibrary(ROCR)\ndata(ROCR.simple)\n\nsummary(ROCR.simple)\nclass(ROCR.simple)\nROCR.simple\n\npred = prediction(ROCR.simple$predictions, ROCR.simple$labels)\npred\nperf = performance(pred, 'tpr', 'fpr')\nplot(perf)\n" }, { "path": "28a-LogR/46c-accuracy.R", "content": "# ROC Characteristics\n\nactual = c(1,1,0,1,0,1,1,0)\n#Model1 and Model2 probability values\nM1 = c(0.75,0.8,0.4,0.25,0.45,0.35,0.65,0.15)\nM2 = c(0.9,0.85,0.15,0.45,0.05,0.45,0.7,0.2)\n(m=cbind(actual,M1,M2))\n\n#threshold values T1\nT1=0.5\npredictM1T1 = ifelse(M1 > T1,1,0)\npredictM2T1 = ifelse(M2 > T1,1,0)\n(m1 = cbind(m, predictM1T1, predictM2T1))\n#crosstable of actual vs predicted for each model\n(t111= table(m1[,1],m1[,4])) #xtable actual vs predict 1\n(t211 = table(m1[,1],m1[,5]))\n\n(ma = matrix(c('tp','fp','fn','tn'),nrow=2))\n# tp – True Positive\n# fp – False Positive\n# fn – False Negative\n# tn – True Negative\n# \n# Accuracy = (tp+tn)/Total\n(accuracy = sum(diag(t111))/ sum(t111))\n# True Positive Rate (tpr) = tp/(tp+fn)\n(tpr = t111[1,1]/sum(t111[1,1] + t111[2,2]))\n(tpr = t111[1,1]/sum(diag(t111)))\n\n# False Positive Rate (fpr) = fp/(tn+fp)\n\n# Sensitivity (Recall) = tpr\n\n# Specificity = tnr = tn/(fp+tn)\n\n\n#library caret - easier way to do: pass table to function\nlibrary(caret)\ncaret::confusionMatrix(table(m1[,1],m1[,4]))\ncaret::confusionMatrix(table(m1[,1],m1[,5]))\ncaret::confusionMatrix(t111)\ncaret::confusionMatrix(t211)\n\n\n#Different threshold T2=1\n#2nd Stage\nT2=1\npredictM1T2 = ifelse(M1 > T2,1,0)\npredictM2T2 = ifelse(M2 > T2,1,0)\n(m2 = cbind(m, predictM1T2, predictM2T2))\n#crosstable of actual vs predicted for each model\n(t122= table(m2[,1],m2[,4])) #xtable actual vs predict 1\n# no value for column 1 (predicted); actual has\n(t222 = table(m2[,1],m2[,5]))\ncaret::confusionMatrix(t122) #does not work becos no 2nd col\ncaret::confusionMatrix(table(m2[,1],m2[,4]))\n# this threshold values does not classify well so will be for value of T=0\n\n#3rd threshold values = 0.8\nT3=0.8\npredictM1T3 = ifelse(M1 > T3,1,0)\npredictM2T3 = ifelse(M2 > T3,1,0)\n(m3 = cbind(m, predictM1T3, predictM2T3))\n#crosstable of actual vs predicted for each model\n(t133= table(m3[,1],m3[,4])) #xtable actual vs predict 1\n# no value for column 1 (predicted); actual has\n(t233 = table(m3[,1],m3[,5]))\ncaret::confusionMatrix(t133) #does not work becos no 2nd col\ncaret::confusionMatrix(t233)\n# this threshold values does not classify well so will be for value of T=0\n\n#4th threshold value = 0.3\nT4=0.3\npredictM1T4 = ifelse(M1 > T4,1,0)\npredictM2T4 = ifelse(M2 > T4,1,0)\n(m4 = cbind(m, predictM1T4, predictM2T4))\n#crosstable of actual vs predicted for each model\n(t144= table(m4[,1],m4[,4])) #xtable actual vs predict 1\n# no value for column 1 (predicted); actual has\n(t244 = table(m4[,1],m4[,5]))\ncaret::confusionMatrix(t144) \ncaret::confusionMatrix(t244)\n\n#what is the right value\n" }, { "path": "28a-LogR/48b-auc1.R", "content": "# AUC Logistic Regression\n#https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/\n\n\ncategory <- c(1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0)\ncategory\nprediction <- rev(seq_along(category))\nprediction\nprediction[9:10] <- mean(prediction[9:10])\nprediction\n\nlibrary(pROC)\nroc_obj <- roc(category, prediction)\nauc(roc_obj)\n## Area under the curve: 0.825\nroc_df <- data.frame(\n TPR=rev(roc_obj$sensitivities), \n FPR=rev(1 - roc_obj$specificities), \n labels=roc_obj$response, \n scores=roc_obj$predictor)\n\nrectangle <- function(x, y, width, height, density=12, angle=-45, ...) {\n polygon(c(x,x,x+width,x+width), c(y,y+height,y+height,y), \n density=density, angle=angle, ...)\n}\n\n\nroc_df <- transform(roc_df, \n dFPR = c(diff(FPR), 0),\n dTPR = c(diff(TPR), 0))\n\n\nplot(0:10/10, 0:10/10, type='n', xlab=\"FPR\", ylab=\"TPR\")\nabline(h=0:10/10, col=\"lightblue\")\nabline(v=0:10/10, col=\"lightblue\")\n\nwith(roc_df, {\n mapply(rectangle, x=FPR, y=0, \n width=dFPR, height=TPR, col=\"green\", lwd=2)\n mapply(rectangle, x=FPR, y=TPR, \n width=dFPR, height=dTPR, col=\"blue\", lwd=2)\n \n lines(FPR, TPR, type='b', lwd=3, col=\"red\")\n})\n\n\n\nsimple_auc <- function(TPR, FPR){\n # inputs already sorted, best scores first \n dFPR <- c(diff(FPR), 0)\n dTPR <- c(diff(TPR), 0)\n sum(TPR * dFPR) + sum(dTPR * dFPR)/2\n}\n\nwith(roc_df, simple_auc(TPR, FPR))\n## [1] 0.825\n\nrank_comparison_auc <- function(labels, scores, plot_image=TRUE, ...){\n score_order <- order(scores, decreasing=TRUE)\n labels <- as.logical(labels[score_order])\n scores <- scores[score_order]\n pos_scores <- scores[labels]\n neg_scores <- scores[!labels]\n n_pos <- sum(labels)\n n_neg <- sum(!labels)\n M <- outer(sum(labels):1, 1:sum(!labels), \n function(i, j) (1 + sign(pos_scores[i] - neg_scores[j]))/2)\n \n AUC <- mean (M)\n if (plot_image){\n image(t(M[nrow(M):1,]), ...)\n library(pROC)\n with( roc(labels, scores),\n lines((1 + 1/n_neg)*((1 - specificities) - 0.5/n_neg), \n (1 + 1/n_pos)*sensitivities - 0.5/n_pos, \n col=\"blue\", lwd=2, type='b'))\n text(0.5, 0.5, sprintf(\"AUC = %0.4f\", AUC))\n }\n \n return(AUC)\n}\n\n\nrank_comparison_auc(labels=as.logical(category), scores=prediction)\n\nauc_probability <- function(labels, scores, N=1e7){\n pos <- sample(scores[labels], N, replace=TRUE)\n neg <- sample(scores[!labels], N, replace=TRUE)\n # sum( (1 + sign(pos - neg))/2)/N # does the same thing\n (sum(pos > neg) + sum(pos == neg)/2) / N # give partial credit for ties\n}\n\nauc_probability(as.logical(category), prediction)\n" }, { "path": "28a-LogR/48c-auc1.R", "content": "# AUC \n\nlibrary(pROC)\n\nauc(predictions$survived, predictions$pred)\n\n?aSAH\n\n# load the dataset\ndata(aSAH)\n\n# Gender, outcome and set\nwith(aSAH, table(gender, outcome))\n\nsummary(aSAH$s100b)\n\nhead(aSAH)\nauc(aSAH$outcome, aSAH$s100b)\n#Area under the curve: 0.7314" }, { "path": "28a-LogR/48d-auc.R", "content": "#https://stackoverflow.com/questions/4903092/calculate-auc-in-r\n\n\nlibrary(data.table)\nlibrary(mltools)\n\nactuals <- c(0, 0, 1, 1)\npreds <- c(.1, .3, .3, .9)\n\n\nauc_roc(preds, actuals) # 0.875\n\nauc_roc(preds, actuals, returnDT=TRUE)\n" }, { "path": "28a-LogR/48e-auc.R", "content": "#LR Model Checking\n#https://stats.stackexchange.com/questions/132777/what-does-auc-stand-for-and-what-is-it\n\ncls = c('P', 'P', 'N', 'P', 'P', 'P', 'N', 'N', 'P', 'N', 'P','N', 'P', 'N', 'N', 'N', 'P', 'N', 'P', 'N')\ntable(cls)\nscore = c(0.9, 0.8, 0.7, 0.6, 0.55, 0.51, 0.49, 0.43, 0.42, 0.39, 0.33, 0.31, 0.23, 0.22, 0.19, 0.15, 0.12, 0.11, 0.04, 0.01)\n\ncbind(cls, score)\npos = score[cls == 'P']\nneg = score[cls == 'N']\n\ncbind(cls, score)\nset.seed(14)\nfor( i in 1:100 ){\nrnorm(10)\nmean(rnorm(10))\nprint(mean(rnorm(10)))\n}\n\nreplicate(100, mean(rnorm(10)))\nsample(pos, size=1) > sample(neg, size=1)\n\n#create values of True and false such that pos count > neg\np = replicate(50000, sample(pos, size=1) > sample(neg, size=1))\nhead(p); table(p)\nprop.table(table(p))\nmean(p)\n#True=1, False=0 mean= 0.67926. Quite close, isn't it?\n\n\n\n# use ROCR package for drawing ROC curves and calculating AUC.\n\nlibrary('ROCR')\n\npred = prediction(score, cls)\npred\nroc = performance(pred, \"tpr\", \"fpr\")\nroc\n\nplot(roc, lwd=2, colorize=TRUE)\nlines(x=c(0, 1), y=c(0, 1), col=\"black\", lwd=1)\n\nauc = performance(pred, \"auc\")\nauc = unlist(auc@y.values)\nauc\n" }, { "path": "28a-LogR/49c-thresholdvalue.R", "content": "#http://ethen8181.github.io/machine-learning/unbalanced/unbalanced.html\n\n" }, { "path": "28a-LogR/pdpu.R", "content": "#logisitic regression\n\nlibrary(ISLR)\n?Default\ndata(Default)\nhead(Default,n=30)\nstr(Default)\nhead(Default[Default$default=='Yes',])\n\ntable(Default$student)\ntable(Default$default)\nt1=table(Default$default, Default$student)\naddmargins(t1)\naddmargins(t1, FUN = mean)\n\n\nlibrary(dplyr)\nDefault %>% group_by(student, default) %>% summarise(n())\nDefault %>% group_by(student, default) %>% summarise(mean(income), mean(balance))\n\n?glm\n\n#logistic model\nlogit1 = glm(default ~ income + balance + student, family='binomial', data=Default)\n\n#output is different from linear regression\n#predict prob from 0 to 1\nsummary(logit1)\n\n#are all IV signicant - No (income)\nlogit2 = glm(default ~ balance + student, family='binomial', data=Default)\nsummary(logit2)\n\n(ndata1 = data.frame(balance = mean(Default$balance), student=\"Yes\"))\n(p1=predict(logit2, newdata = ndata1, type='response') )\n\n(ndata1 = data.frame(balance = 1486, student=\"Yes\"))\n(p1=predict(logit2, newdata = ndata1, type='response') )\n" }, { "path": "28a-LogR/zz--logR.R", "content": "acc = read.csv(\"path to data\")\nView(acc)\n\nset.seed(1)\nindex <- sample(1:nrow(acc), round(0.75*nrow(acc)))\ntrain <- acc[index,]\ntest <- acc[-index,]\nfitTrn <- glm(isOneday~., data=train, family=binomial(link=\"logit\")) \nfitted.results <- predict(fitTrn, newdata=test, type='response')\n\nlibrary(ROCR)\np <- predict(fitTrn, newdata=test, type=\"response\")\npr <- prediction(p, test$isOneday)\nprf <- performance(pr, measure=\"tpr\", x.measure=\"fpr\")\nauc <- performance(pr, measure=\"auc\")" }, { "path": "29a-GLM/Logr-party.R", "content": "#Log Regression\n#https://visualstudiomagazine.com/articles/2016/12/01/logistic-regression-using-r.aspx\n#\nAge <- c(1,5,3,2,6,3,7,4,2,4)\nEdu <- c(4,8,7,5,7,2,5,5,3,7)\nParty <- c(\"red\",\"red\",\"red\",\"red\",\"red\", \"blue\",\"blue\",\"blue\",\"blue\",\"blue\")\nmydf <- data.frame(Age,Edu,Party)\nmydf$Party <- factor(mydf$Party, levels=c(\"red\",\"blue\"))\nmydf\n\nplot(mydf$Age, mydf$Edu, xlim=c(0,9), ylim=c(0,9),\n xaxs=\"i\", yaxs=\"i\", col=c(\"red\",\"blue\")[mydf$Party],pch=20, cex=2)\n\naxis(side=1, at=c(0:9))\naxis(side=2, at=c(0:9))\nabline(h=0:9,v=0:9, col=\"gray\", lty=3)\n\nmymodel = glm(Party ~ Age + Edu, data=mydf, family=\"binomial\")\nsummary(mymodel)\nage=1; edu=4\n(z = 3.5566 + (0.9939 * 1) + (-1.3191 * 4)) # -0.7259\n(z = 3.5566 + 0.9939 * age - 1.3191 * edu)\n(p = 1 / (1 + exp(-z))) #0.3261\ncontrasts(mydf$Party) # blue=1\n#If p > 0.5, the predicted value is the second possible value, \"blue\n\npredict(mymodel, mydf, type=\"response\")\n\n\nif (p <= 0.5) { \n cat(\"predicted party = red \\n\") \n } else {\n cat(\"predicted party = blue \\n\")\n }\n#predicted party = red\n#2nd way to predict\nb0 = mymodel$coefficients[[1]]\nb1 = mymodel$coefficients[[2]]\nb2 = mymodel$coefficients[[3]]\n(z = b0 + (b1 * 1) + (b2 * 4))\n(p = 1 / (1 + exp(-z)))\n\n#3rd way to predict\nnd = with(mydf, data.frame(Age=1, Edu=4))\nnd\nnd$pred = predict(mymodel, newdata=nd, type=\"response\")\nnd\n" }, { "path": "29a-GLM/crossfold.R", "content": "# Cross Validation using Auto dataset\n\nsplit(mtcars[,1:4] , f = rep_len(1:4, nrow(mtcars) ) )\n\nlibrary(ISLR)\nlibrary(boot)\nISLR::Auto\nattach(Auto)\n(model1 = glm(mpg ~ horsepower, data =Auto))\n(mse_loocv = cv.glm(Auto, model1))\n\n(model2 = lm(mpg ~ horsepower, data=Auto))\n(mse_fold = cv.glm(Auto, model1, K=3))\n\nlibrary(DAAG)\nmydata=women\n(fit = lm(weight ~ height, data=mydata))\nplot( mydata$height , mydata$weight)\nDAAG::CVlm(data=mydata, form.lm=formula(\n weight ~ height), m=3, plotit=\"Observed\" )\n\n\n#Eg2\nlibrary(DAAG)\nmydata=Auto\n(fit = lm(mpg ~ horsepower, data=mydata))\nplot( y=mydata$horsepower , mydata$mpg)\nnrow(Auto)\nDAAG::CVlm(data=mydata, form.lm=fit, m=3, plotit=\"Observed\" )\n\n" }, { "path": "29a-GLM/crossval1.R", "content": "# load the libraries\nlibrary(caret)\nlibrary(klaR)\n# load the iris dataset\ndata(iris)\n# define an 80%/20% train/test split of the dataset\nsplit=0.80\ntrainIndex <- createDataPartition(iris$Species, p=split, list=FALSE)\ndata_train <- iris[ trainIndex,]\ndata_test <- iris[-trainIndex,]\n# train a naive bayes model\nmodel <- NaiveBayes(Species~., data=data_train)\n# make predictions\nx_test <- data_test[,1:4]\ny_test <- data_test[,5]\npredictions <- predict(model, x_test)\n# summarize results\nconfusionMatrix(predictions$class, y_test)\n\n\n# load the library\n\n# load the library\nlibrary(caret)\n# load the iris dataset\ndata(iris)\n# define training control\ntrain_control <- trainControl(method=\"cv\", number=10)\n# fix the parameters of the algorithm\ngrid <- expand.grid(.fL=c(0), .usekernel=c(FALSE))\n# train the model\nmodel <- train(Species~., data=iris, trControl=train_control, method=\"nb\", tuneGrid=grid)\n# summarize results\nprint(model)\n\nated k-fold Cross Validation in RR\n\n# load the library\nlibrary(caret)\n# load the iris dataset\ndata(iris)\n# define training control\ntrain_control <- trainControl(method=\"repeatedcv\", number=10, repeats=3)\n# train the model\nmodel <- train(Species~., data=iris, trControl=train_control, method=\"nb\")\n# summarize results\nprint(model)\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n\n# load the library\nlibrary(caret)\n# load the iris dataset\ndata(iris)\n# define training control\ntrain_control <- trainControl(method=\"repeatedcv\", number=10, repeats=3)\n# train the model\nmodel <- train(Species~., data=iris, trControl=train_control, method=\"nb\")\n# summarize results\nprint(model)\n" }, { "path": "29a-GLM/cv-houseprices.R", "content": "#CV - houseprices\n#Eg1\nstr(houseprices)\n?houseprices\n(fit = lm(sale.price ~ ., data=houseprices))\nsummary(fit)\nattr(fit,\"ms\")\n\n(fit1 <- lm(sale.price ~ area, data=houseprices))\n(cv1 <- DAAG::CVlm(houseprices, fit1, plotit=TRUE, m=4))\nattr(cv1, \"ms\")\nanova(fit1)\nsummary(fit1)\n(673 + 1849 + 6070 + 6287)/4\n\n(fit2 <- lm(sale.price ~ bedrooms, data=houseprices))\n(cv2 <- DAAG::CVlm(houseprices, fit2, plotit=TRUE, m=4))\nattr(cv2, \"ms\")\nanova(fit2)\nsummary(fit2)\n(40 + 954 + 2781 + 4563)/4\n" }, { "path": "29a-GLM/cv-women1.R", "content": "# CV using women dataset\n\nwomen\n\nmydata=women\n\n(fit1 = lm(weight ~ height, data=mydata))\nsummary(fit1)\n(RSS <- c(crossprod(fit1$residuals)))\n(MSE <- RSS / length(fit1$residuals))\n(RMSE <- sqrt(MSE))\n(sig2 <- RSS / fit1$df.residual)\npredict(fit1)\nlibrary(DAAG)\n(fit1 = lm(weight ~ height, data=mydata))\n\n(cvfit1 = DAAG::CVlm(data=mydata, form.lm=lm(weight ~ height,\n data=mydata), m=3, plotit=\"Observed\" ))\npredict(fit1)\nattributes(cvfit1a)\n(76.9 + 8.29 + 15.9)/15\n\ncvfit1a$cvpred\nwomen$height #58 59 60 61 62 63 64 65 66 67 68 69 70 71 72\nwomen$weight #115 117 120 123 126 129 132 135 139 142 146 150 154 159 164\n\n\n# Model2\n\n(fit2 = lm(weight ~ I(height^2), data=mydata))\nsummary(fit2)\n\n(RSS <- c(crossprod(fit2$residuals)))\n(MSE <- RSS / length(fit2$residuals))\n(RMSE <- sqrt(MSE))\n(sig2 <- RSS / fit2$df.residual)\npredict(fit2)\nlibrary(DAAG)\n\n(fit2 = lm(weight ~ I(height^2), data=mydata))\nsummary(fit2)\n#(cvfit2a = DAAG::CVlm(data=mydata, form.lm=fit2 ))\n(cvfit2a = DAAG::CVlm(data=mydata, form.lm=lm(weight ~ height + I(height^2), data=mydata),m=3, dots = TRUE, seed=29, plotit=TRUE, printit=TRUE))\n(cvfit2a = DAAG::CVlm(data=mydata, form.lm=fit2, dots = TRUE, seed=29, plotit=TRUE, printit=TRUE))\n\n\n(15.4 + 1.66 + 3.88 )/3\nsummary(cvfit2a)\ncvfit2a$cvpred\n\n\ns1 = caTools::sample.split(mydata, SplitRatio = .75)\ns1\ndf=mydata\nstopifnot(nrow(df) %% N == 0)\ndf <- df[order(runif(nrow(df))), ]\nbins <- rep(1:N, nrow(df) / N)\nsplit(df, bins)\nN= nrow(df)\nsplit(df, sample(1:N, nrow(df), replace=T))\n\n\nn <- 3\nnr <- nrow(df)\nnr\nsplit1 = split(df, rep(1:ceiling(nr/n), each=n, length.out=nr))\nsplit1\nsplit1$`1`\n(fit1 = lm(weight ~ height, data=split1$`1`))\nsummary(fit1)\n\n\nCVres1= c(-0.154, 1.9,2.8, 4.69, 6.59)\nCVres1^2\nsum(CVres1^2)\nsum(CVres1^2)/5\n\n" }, { "path": "29a-GLM/cv3.R", "content": "library(gRapfa)\ndata(Wheeze)\ngRapfa::cross.validate(Data=Wheeze, K = 10, crit = NULL, beagle = TRUE, dir='')\n\nlibrary(adabag)\ndata(churn)\ntrainset = churn[1:100,]\nchurn.baggingcv = bagging.cv(churn ~ .,\n v=10, data=trainset, mfinal=10)\nchurn.baggingcv$confusion\n\nchurn.baggingcv$error\n" }, { "path": "29a-GLM/cvlm2.R", "content": "\nlibrary(caret)\n\ndata(diamonds)\n?diamonds\n\nmydata = diamonds\nmodel <- train(\n price ~ ., mydata,\n method = \"lm\",\n trControl = trainControl(\n method = \"cv\", number = 10,\n verboseIter = TRUE\n )\n)\nsummary(model)\n\nfit = lm(price ~ . , data=mydata)\nsummary(fit)\nAIC(model,fit)\n\n\n\nlibrary(caret)\n# load the iris dataset\ndata(iris)\n# define training control\ntrain_control <- trainControl(method=\"cv\", number=10)\n# fix the parameters of the algorithm\ngrid <- expand.grid(.fL=c(0), .usekernel=c(FALSE))\ngrid\n?expand.grid\n# train the model\nmodel <- train(Species~., data=iris, trControl=train_control, method=\"nb\", tuneGrid=grid)\n# summarize results\nprint(model)\n\n\nlibrary(DAAG)\nhouseprices\ncv.lm(data = DAAG::houseprices, form.lm = formula(sale.price ~ area),\n m = 3, dots = FALSE, seed = 29, plotit = c(\"Observed\",\"Residual\"),\n main=\"Small symbols show cross-validation predicted values\",\n legend.pos=\"topleft\", printit = TRUE)\nCVlm(data = DAAG::houseprices, form.lm = formula(sale.price ~ area),\n m = 3, dots = FALSE, seed = 29, plotit = c(\"Observed\",\"Residual\"),\n main=\"Small symbols show cross-validation predicted values\",\n legend.pos=\"topleft\", printit = TRUE)\n\n\n\nCVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),\n plotit=\"Observed\")\nCVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),\n plotit=\"Residual\")\nout <- CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),\n plotit=\"Observed\")\nout\nout[c(\"ms\",\"df\")]\n\nmydata=women\nstr(mydata)\n\nfit = lm(weight ~ height, data=mydata)\nplot( mydata$height , mydata$weight)\nabline(fit)\nsummary(fit)\n\nCV1 =CVlm(data=mydata, m=3, form.lm=formula(weight ~ height), plotit=\"Observed\" )\n\nCV2 = CVlm(data=mydata, m=4, form.lm=formula(weight ~ height), plotit=\"Observed\" )\n\nattributes(CV2)$ms\n" }, { "path": "29a-GLM/glm-affairs1.R", "content": "#Affairs,\n#https://github.com/marianov24/RAffairsDataAnalysis/blob/master/Affairs_George_Maria.Rmd\n\n# Loading all the required libraries\nlibrary(\"dplyr\")\nlibrary(\"ggplot2\")\nlibrary(\"car\") # Contains the scatterplotMatrix function\n#install.packages(\"boot\")\nlibrary(\"boot\") # Perform crossvalidation\n#install.packages(\"tree\")\nlibrary(\"tree\")\nlibrary(\"randomForest\")\nlibrary(pROC) # Useful for computing and plotting classifer metrics\n\n# install.packages(\"AER\")\nlibrary(AER) # Contains Affairs dataset \n\n# Loading the Affairs dataset of AER package and saving it into a local variable\ndata(\"Affairs\")\nAffairs.data <- Affairs\n\n# Displaying the first few rows of the dataset\nhead(Affairs.data)\n# Displaying the summary of the Affairs.data dataset \nsummary(Affairs.data)\n# Displaying the structure of the Affairs.data dataset \nstr(Affairs.data)\n\n# Finding the proportion of male and female respondents\nAffairs.data %>% \n group_by(gender) %>% \n summarise(total_participants = n()) %>% \n ungroup() %>%\n mutate(prop_gender = total_participants/sum(total_participants))\n\n# Plotting the frequency of participants based on gender\nggplot(Affairs.data, aes(gender)) + geom_bar(width = 0.2) +\n xlab(\"Gender\") + ylab(\"Frequency\") + \n ggtitle(\"Number of participants based on gender\")\n\n# Calculating the average age of the participants\nAffairs.data %>% \n summarise(avg_age = mean(age, na.rm = TRUE))\n\n# Plotting the frequency of participants based on occupation\nggplot(Affairs.data, aes(occupation)) + geom_bar(width = 0.2) +\n xlab(\"Occupation\") + ylab(\"Frequency\") + \n ggtitle(\"Number of participants based on occupation\") +\n scale_x_continuous(breaks=c(1, 2, 3, 4, 5, 6, 7), \n labels=c(\"Class 1\", \"Class 2\", \"Class 3\", \"Class 4\",\n \"Class 5\", \"Class 6\", \"Class 7\"))\n\n\n# Plotting the frequency of participants based on education\nggplot(Affairs.data, aes(education)) + geom_bar(width = 0.2) +\n xlab(\"Education\") + ylab(\"Frequency\") + \n ggtitle(\"Number of participants based on education\") +\n scale_x_continuous(breaks=c(9, 12, 14, 16, 17, 18, 20), \n labels=c(\"Grade School\", \"High School Graduate\", \n \"Some college\", \"College graduate\",\n \"Some graduate work\", \"Masters degree\",\n \"Advanced Degree\")) + coord_flip()\n\n\n# Creating a binary variable haveaffair denoting whether a participant have\n# an affair or not\n# If the number of affairs are greater than 0, binary variable is set to 1\nAffairs.data$haveaffair[Affairs.data$affairs > 0] <- 1\n# If the number of affairs is equal to 0, binary variable is set to 0\nAffairs.data$haveaffair[Affairs.data$affairs == 0] <- 0\n\n# Converting the binary response variable to factor datatype with labels \n# No and Yes for levels 0 and 1 respectively.\nAffairs$haveaffair <- factor(Affairs.data$haveaffair,\n levels=c(0,1),\n labels=c(\"No\",\"Yes\"))\n\n# Displaying the count of the new binary variable\ntable(Affairs$haveaffair)\n\n#Fitting a logistic regression model twith haveaffair as the response variable\n# and all other personal characteristics as predictor variables\nfit.allpredictors <- glm(haveaffair ~ gender + age + yearsmarried + children +\n religiousness + education + occupation +rating,\n data=Affairs.data,family=binomial())\n\n# Loading the bestglm package\n#install.packages(\"bestglm\")\nlibrary(\"bestglm\")\n\n# Creating a new column y, response variable to fit bestglm\nAffairs.data$y <- Affairs.data$haveaffair\n# Rearranging the columns of Affairs.data dataset to fit bestglm\nAffairs.for.bestglm <- Affairs.data[,c(\"gender\",\"age\",\"yearsmarried\",\"children\",\n \"religiousness\", \"education\",\n \"occupation\", \"rating\", \"y\")]\n\n# Using bestglm to perform subset model selection \nset.seed(1)\nfit.reduced <- bestglm(Affairs.for.bestglm, family = binomial,\n method = \"exhaustive\")\n\n# Displaying the summary statistic of the Best Model\nfit.reduced$BestModel\n\n# Displaying the summary statistics of the fitted model.\nsummary(fit.allpredictors)\n\n\n# Creating an artificial test dataset\ntestdata <- data.frame(yearsmarried=mean(Affairs.data$yearsmarried),\n religiousness=mean(Affairs.data$religiousness),\n rating=c(1, 2, 3, 4, 5))\n\n# Creating a new column prob to the test data containing the predicted \n# probabilities\ntestdata$prob <- predict(fit.reduced$BestModel, testdata, type=\"response\")\n# Displaying the test data\ntestdata\n\n# Plotting rating of marriage vs probability of having an affair\nggplot(testdata, aes(rating, prob)) + geom_point() + geom_line() +\n xlab(\"Rating\") + ylab(\"Probabilty of having an affair\") + \n ggtitle(\"Rating of marriage vs probability of having an affair\") +\n scale_x_continuous(breaks=c(1, 2, 3, 4, 5), \n labels=c(\"Very unhappy\", \"Somewhat unhappy\",\n \"Average\", \"Happier than avaerage\", \"Very happy\"))\n\n\n" }, { "path": "29a-GLM/glm-affairs2.R", "content": "#Log Regression\n#\nlibrary(AER)\ndata(Affairs)\ndata(Affairs, package='AER')\nmydata=Affairs\nsummary(mydata)\ndim(mydata)\nx=mydata$affairs\ntable(x)\nround(prop.table(table(x)),3)\nround(prop.table(table(x))*100,1)\nx=mydata$gender\nround(prop.table(table(x))*100)\n\nx=mydata$children\nround(prop.table(table(x))*100)\n\nmedian(mydata$age)\n\n# Convert No of Affairs - Yes/ No\nmydata$ynaffair[mydata$affairs > 0 ] = 1\nmydata$ynaffair[mydata$affairs == 0 ] = 0\nmydata$ynaffair = factor(mydata$ynaffair,\n levels=c(0,1), labels=c('No','Yes'))\ntable(mydata$ynaffair)\n\n# ready for Log Regr\nfit.full = glm(ynaffair ~ gender + age + yearsmarried + children + religiousness + education + occupation + rating, data=mydata, family=binomial())\nsummary(fit.full)\n\n#Modified Model\nfit.reduced = glm(ynaffair ~ age + yearsmarried + religiousness\n + rating, data=mydata, family=binomial())\nsummary(fit.reduced)\n\n#Compare\nanova(fit.reduced, fit.full, test='Chisq')\n\n\ncoef(fit.reduced)\nexp(coef(fit.reduced))\n\nexp(confint(fit.reduced))\n\n1.106^10\n\ntestdata = data.frame(rating=c(1,2,3,4,5), age=mean(mydata$age),\n yearsmarried=mean(mydata$yearsmarried), \n religiousness = mean(mydata$religiousness))\ntestdata\n\ntestdata$predict = predict(fit.reduced, newdata=testdata, type='response')\ntestdata\n\n#Age varies\ntestdata = data.frame(rating=mean(mydata$rating), age=seq(17,57,10),\n yearsmarried=mean(mydata$yearsmarried), \n religiousness = mean(mydata$religiousness))\ntestdata\n\ntestdata$predict = predict(fit.reduced, newdata=testdata, type='response')\ntestdata\n\n# Overdispersion\n\ndeviance(fit.reduced)/ df.residual(fit.reduced)\n\nfit.b = glm(ynaffair ~ age + yearsmarried + religiousness\n + rating, data=mydata, family=binomial())\nfit.qb= glm(ynaffair ~ age + yearsmarried + religiousness\n + rating, data=mydata, family=quasibinomial())\npchisq(summary(fit.qb)$dispersion * fit.b$df.residual, fit.b$df.residual, lower=F)\n\n" }, { "path": "29a-GLM/glm-cars.R", "content": "# CV - cars\n\nplot(speed~dist, data=cars, main = \"Cars\" , xlab = \"Stopping\nDistance\", ylab = \"Speed\")\n\n#Step-2\nglm.fit = glm(speed~dist, data=cars)\ndegree=1:5\ncv.error5=rep(0,5)\nfor(d in degree){ \n glm.fit = glm(speed~poly(dist, d), data=cars) \n cv.error5[d] = cv.glm(cars,glm.fit,K=5)$delta[1]\n}\nglm.fit\npar(mfrow=c(2,2))\nplot(glm.fit)\ncv.error5\nplot(cv.error5)\nlines(cv.error5)\npar(mfrow=c(1,1))\nplot(speed~dist, data=cars, main = \"Cars\" , xlab\n = \"Stopping Distance\", ylab = \"Speed\")\n" }, { "path": "29a-GLM/glm-titanic1.R", "content": "# R Bloggers Site\n#Log Regression 2\ntraining.data.raw <- read.csv('./../../rDataSets/titanic/train.csv',header=T,na.strings=c(\"\"))\n\nsapply(training.data.raw,function(x) sum(is.na(x)))\n\nsapply(training.data.raw, function(x) length(unique(x)))\n\nlibrary(Amelia)\nAmelia::missmap(training.data.raw, main = \"Missing values vs observed\")\n\ndata <- subset(training.data.raw,select=c(2,3,5,6,7,8,10,12))\n\ndata$Age[is.na(data$Age)] <- mean(data$Age,na.rm=T)\n\nis.factor(data$Sex)\nis.factor(data$Embarked)\nunique(data$Sex)\ncontrasts(data$Sex)\n\nunique(data$Embarked) \ncontrasts(data$Embarked)\n\ndata <- data[!is.na(data$Embarked),]\nrownames(data) <- NULL\n\n# Split the data in order\ntrain <- data[1:800,]\ntest <- data[801:889,]\n\nmodel <- glm(Survived ~.,family=binomial(link='logit'),data=train)\nlog(2e-16)\nexp(5.45e-06)\nsummary(model)\n\nanova(model, test=\"Chisq\")\n" }, { "path": "29a-GLM/logR1.R", "content": "#Clear All\nrm(list=ls())\n\n# Load libraries\nlibrary(datasets)\nlibrary(caTools)\nlibrary(nnet)\n\n# Load the dataset\ndataset <- iris \n#Structure of the data\nstr(dataset)\n\n#Split data into train and test\nset.seed(123)\nsplit <- sample.split(dataset$Species,SplitRatio = 0.80)\nsplit\ntrain <- subset(dataset,split==TRUE)\ntest <- subset(dataset,split==FALSE)\ndim(dataset)\ndim(train)\ndim(test)\n\n# Use logistic regression and the one-vs-all strategy to build a classification model.\nmultinomModel <- multinom(Species~.,data=train)\nsummary (multinomModel) # model summary\n\n# Predict on Test Data\n(predicted_scores <- predict (multinomModel, test, \"class\")) # predict on new data\n\npredicted_scores <- predict (multinomModel, test, \"probs\") # predict on new data\npredicted_scores\npredictions <- apply(predicted_scores, 1, which.max)\npredictions\nnames(test)[apply(predicted_scores, 1, which.max)]\npredictions[which(predictions==\"1\")] <- unique(dataset$Species)[1]\npredictions\nunique(dataset$Species)[1]\npredictions[which(predictions==\"2\")] <- unique(dataset$Species)[2]\npredictions[which(predictions==\"3\")] <- unique(dataset$Species)[3]\ntest$prediction <- predictions\ntest$prediction\ntest$Species\n# summarize accuracy\ntable(test$prediction, test$Species)\n(10 + 9 + 10)/((10 + 9 + 10 + 1))\n\n\ndf <- data.frame(a = rnorm(5), b = rnorm(5), c = rnorm(5), x = rnorm(5), y = rnorm(5), z = rnorm(5))\ndf\ndf2 = df[,4:6]\ndf2\nnames(df2)[apply(df2, 1, which.max)]\n" }, { "path": "29a-GLM/logpos1.R", "content": "# Log Regression - Poission\n#\ndata(breslow.dat, package='robust')\nnames(breslow.dat)\nmydata = breslow.dat\nsummary(mydata)\nsummary(mydata[c(6,7,8,10)])\n\nopar = par(no.readonly = TRUE)\npar(mfrow=c(1,2))\nattach(mydata)\nhist(sumY, breaks=20, xlab='Seizure Count', main='Distribution of Seizures')\nboxplot(sumY ~ Trt, xlab='Treatment', main='Group Comparisons')\npar(opar)\n\n\nfit = glm(sumY ~ Base + Age + Trt, data=mydata, family=poisson())\nsummary(fit)\n\n\ncoef(fit)\nexp(coef(fit))\n\ndeviance(fit)/ df.residual(fit)\nqcc::qcc.overdispersion.test( mydata$sumY, type='poisson')\n\nfit.od = glm(sumY ~ Base +\n Age + Trt, data = mydata,\n family = quasipoisson() )\nsummary(fit)\n" }, { "path": "29a-GLM/logr-mtcars.R", "content": "# Logistic Regression\n\n# Contingency Table for Tx of Cars\ndata(mtcars)\n?mtcars\n(t= table(mtcars$am))\n(amt = addmargins(t))\n(ppt = prop.table(t))\np1 = 19/32 #automatic Probability\n# Prob that am=Manual\n(p0a = 1 - 19/32 )\n# Odds Ratio\n(p1 = 19/13 )\n(p2 = 13/19 )\n\n#glm\nattach(mtcars)\n(base = glm(am ~ 1, data=mtcars, family = binomial()))\nsummary(base)\nanova(base)\n\n#Fit 1\n(fit1 = glm(am ~ mpg + disp + hp + wt, data\n = mtcars, family = binomial() ))\nsummary(fit1)\nanova(fit1)\n\nlayout(matrix(c(1,2,3,4), 2,2))\nplot(fit1)\n\n\n#Fit2\n(fit2 = glm(am ~ mpg + hp + wt, data\n = mtcars, family = binomial() ))\nsummary(fit2)\n\nAIC(fit1, fit2)\n\nanova(fit2)\nlayout(matrix(c(1,2,3,4), 2,2))\nplot(fit2)\n\nanova(fit1, fit2)\n\n#Fit3\n(fit3 = glm(am ~ hp + wt, data=mtcars, family = binomial()))\nsummary(fit3)\nanova(fit3)\nplot(fit3)\nanova(fit2, fit3)\nAIC(fit2,fit3)\n\n\n#Final Model\nnewdata = data.frame(wt=2, hp=150)\npredict(fit3, newdata, type='response')\n" }, { "path": "29a-GLM/logr-mtcars1.R", "content": "# Logistic Regression - mtcars\n\n(base = glm(am ~ 1, data=mtcars,family = binomial()))\nexp(-0.3795)\n(odds = exp(-0.3795))\nanova(base)\n(fit1 = glm(am ~ mpg + disp + hp + wt, data = mtcars, family = binomial() ))\nfit1\nsummary(fit1)\npar(mar=c(1,1,1,1))\nlayout(matrix(c(1,2,3,4), 2,2))\nplot(fit1)\n\n\n(fit2 = glm(am ~ mpg + hp + wt, data = mtcars,\n family = binomial() ))\nfit2\n\n\n(fit3 = glm(am ~ hp + wt, data=mtcars, family = binomial()))\n\nexp(8.124)\n(p = exp(8.124)/ (1 + exp(8.124)))\n\n\nnewdata = data.frame(wt=2.780, hp=109)\nnewdata = data.frame(wt=3.845, hp=175)\n\npredict(fit3, newdata = newdata, type='response')\nnewdata = data.frame(wt=mtcars$wt, hp=mtcars$hp)\np1=predict(fit3, newdata = newdata, type='response')\n\n\n(p1 = predict(fit3, newdata = mtcars[c('wt','hp')], type='response'))\n(p1= round(p1,2))\n(p1a =ifelse(p1 > 0.5, 1, 0))\ncbind(mtcars$am, mtcars$wt, mtcars$hp, p1, p1a)\n(xtab = table(mtcars$am, p1a))\ncaret::confusionMatrix(xtab)\n\ndf= mtcars\n#Manually calc probabilities\ncalcp = function(df, x1, x2) {\ndf$y = 18.86 + (0.0362 * df[,x1]) - (8.083 * df[,x2])\ndf$p = exp(p)/ (1 + exp(p))\n#print(p)\n#return(y)\n}\ncalcp('df', 'hp','wt')\ndf\ncbind(p1,p2)\n\n\nd <- data.frame(hp=mtcars$hp, wt=mtcars$wt)\nd\ntesting <- function (d, x1,x2) {\n d$y = 18.86 + (0.0362 * d[[x1]]) - (8.083 * df[[x2]])\n d$p = exp(d$y)/ (1 + exp(d$y))\n #return (d[[p]])\n d$p\n}\n\nprint (testing(d, 'hp','wt')) \nd\n" }, { "path": "29a-GLM/logrMaths.R", "content": "# General Logistic Regression\n# Subscribe y ~ age\nconstant = -26.52\nagecoeff = 0.78\nage = c(35,36,25,26,45, 46)\n(y = constant * 1 + agecoeff * age)\nround(y,2)\n(p = exp(y) / ((exp(y) + 1)))\nround(p,3)\nround(diff(p),3)\n\n\n# Subscribe y ~ age + woman\n# \nconstant = -26.46\nagecoeff = 0.787\nwomancoeff = -0.55\nage = c(35,36,36,36)\ngender = c('F','M','F','M')\nwoman = c(1,0,1,0)\n(y = constant * 1 + agecoeff * age + womancoeff * woman)\n\nround(y,3)\n(p = exp(y) / ((exp(y) + 1)))\nround(p,3)\nround(diff(p),3)\n" }, { "path": "29a-GLM/logreg-iris1.R", "content": "#Logistic Regression using Iris Dataset\n#06 Nov 17\n#http://rstudio-pubs-static.s3.amazonaws.com/230133_fb9cb3c35ca345d0b9e5726ed3f45a21.html\nlibrary(datasets)\nir_data<- iris\nhead(ir_data)\nstr(ir_data)\nlevels(ir_data$Species)\nsum(is.na(ir_data))\n\nir_data<-ir_data[1:100,]\nhead(ir_data)\nunique(ir_data$Species)\n\nset.seed(100)\nsamp<-sample(1:100,80)\nir_test<-ir_data[samp,]\nir_ctrl<-ir_data[-samp,]\n\nlibrary(ggplot2); library(GGally)\npairs(ir_test)\nggpairs(ir_test)\n\n# Model Fitting\ny<-ir_test$Species; x<-ir_test$Sepal.Length\nglfit<-glm(y~x, family = 'binomial')\n\nsummary(glfit)\n\nnewdata<- data.frame(x=ir_ctrl$Sepal.Length)\npredicted_val<-predict(glfit, newdata, type=\"response\")\nprediction<-data.frame(ir_ctrl$Sepal.Length, ir_ctrl$Species,predicted_val)\nprediction\n\nqplot(prediction[,1], round(prediction[,3]), col=prediction[,2], xlab = 'Sepal Length', ylab =\n 'Prediction using Logistic Reg.')\n\n\n\n\n# Other Examples on IRIS\ndata(iris)\nfit <- lm(Petal.Width ~ Petal.Length, data=iris)\nclass(fit)\nmethods(class=class(fit))\nsummary(fit) # show results\n\ncoefficients(fit) # model coefficients\npredict(fit) # fitted predictions\npredict(fit, newdata=data.frame(Petal.Length=seq(1, 2, by=0.1)))\nconfint(fit, level=0.95) # CIs for model parameters \nfitted(fit) # predicted values\nresiduals(fit) # residuals\nanova(fit) # anova table \ninfluence(fit) # regression diagnostics\n\npar(mfrow=c(2,2))\npar(mar=c(2,2,2,2))\nplot(fit)\n\n\n#Multiple Regression\nfit2 <- lm(Petal.Width ~ Petal.Length + Sepal.Length + Sepal.Width, data=iris)\nsummary(fit2) # show results\nanova(fit, fit2)\n\nfit2int <- lm(Petal.Width ~ Petal.Length + Sepal.Length + Sepal.Width + Petal.Length:Sepal.Length, data=iris)\nanova(fit2, fit2int)\n\ntail(model.matrix(~ Species, data=iris))\n(model.matrix(~ Species, data=iris))\ntail(iris)\n\nfit3 <- lm(Petal.Width ~ Petal.Length + Sepal.Length + Sepal.Width + Species, data=iris)\nsummary(fit3)\n\nanova(fit2, fit3)\n\nlibrary(xtable)\nprint(xtable(fit3), type=\"html\")\n\n\niris2 <- iris\niris2$virginica <- iris$Species == \"virginica\"\nfit4 <- glm(virginica ~ Petal.Width + Petal.Length + Sepal.Length + Sepal.Width, data=iris2, family=binomial)\n\nplot(fit4)\n\n\nfit5 <- glm(round(Petal.Width) ~ Petal.Length + Sepal.Length + Sepal.Width, data=iris2, family=poisson)\nplot(fit5)\n\nsummary(fit5)\n\n#Plotting a linear regression with confidence and prediction intervals.\n\nplot(Petal.Width ~ Petal.Length, col=c(\"black\", \"red\", \"blue\")[Species], pch=(15:17)[Species], xlab=\"Petal Length (cm)\", ylab=\"Petal Width (cm)\", data=iris)\nnewx <- data.frame(Petal.Length=seq(min(iris$Petal.Length), max(iris$Petal.Length), length.out=100))\nconf.interval <- predict(fit, newdata=newx, interval=\"confidence\")\npred.interval <- predict(fit, newdata=newx, interval=\"prediction\")\nlines(conf.interval[, \"fit\"] ~ newx[, 1], lty=1, lw=3)\nlines(conf.interval[, \"lwr\"] ~ newx[, 1], lty=2)\nlines(conf.interval[, \"upr\"] ~ newx[, 1], lty=2)\nlines(pred.interval[, \"lwr\"] ~ newx[, 1], lty=3)\nlines(pred.interval[, \"upr\"] ~ newx[, 1], lty=3)\nlegend(\"topleft\", legend=c(levels(iris$Species), \"CI\", \"PI\"), col=c(\"black\", \"red\", \"blue\", \"black\", \"black\"), pch=c(15:17, -1, -1), lty=c(-1, -1, -1, 2, 3))\n\n\n\n#Multi Class https://stackoverflow.com/questions/22009871/how-to-perform-multi-class-classification-using-svm-of-e1071-package-in-r\n#\n# install.packages( 'e1071' )\nlibrary( 'e1071' )\ndata( iris )\nmodel <- svm( iris$Species~., iris )\nsummary(model)\nres <- predict( model, newdata=iris )\nres\nhead(cbind(res, iris))\n" }, { "path": "29a-GLM/multinominal.R", "content": "#Multinominal Regression\n\nrequire(foreign)\nrequire(nnet)\nrequire(ggplot2)\nrequire(reshape2)\n#Eg1\nml <- read.dta(\"https://stats.idre.ucla.edu/stat/data/hsbdemo.dta\")\nstr(ml)\nwith(ml, table(ses, prog))\nwith(ml, do.call(rbind, tapply(write, prog, function(x) c(M = mean(x), SD = sd(x)))))\ntapply(ml$write, ml$prog, function(x) c(M = mean(x), SD = sd(x)))\n\nml$prog2 <- relevel(ml$prog, ref = \"academic\")\nunique(ml$prog2)\ntest <- multinom(prog2 ~ ses + write, data = ml) \n\nsummary(test) \n\nz <- summary(test)$coefficients/summary(test)$standard.errors\nz\n\n# 2-tailed z test\np <- (1 - pnorm(abs(z), 0, 1)) * 2\np\n\n## extract the coefficients from the model and exponentiate\nexp(coef(test))\n\nhead(pp <- fitted(test))\n\ndses <- data.frame(ses = c(\"low\", \"middle\", \"high\"), write = mean(ml$write))\npredict(test, newdata = dses, \"probs\")\n\ndwrite <- data.frame(ses = rep(c(\"low\", \"middle\", \"high\"), each = 41), write = rep(c(30:70),\n 3))\nhead(dwrite)\n\n## store the predicted probabilities for each value of ses and write\npp.write <- cbind(dwrite, predict(test, newdata = dwrite, type = \"probs\", se = TRUE))\n\n## calculate the mean probabilities within each level of ses\nby(pp.write[, 3:5], pp.write$ses, colMeans)\n\n\n## melt data set to long for ggplot2\nlpp <- melt(pp.write, id.vars = c(\"ses\", \"write\"), value.name = \"probability\")\nhead(lpp) # view first few rows\n\n\n## plot predicted probabilities across write values for each level of ses\n## facetted by program type\nggplot(lpp, aes(x = write, y = probability, colour = ses)) + geom_line() + facet_grid(variable ~\n ., scales = \"free\")\n" }, { "path": "29a-GLM/multinominal2.R", "content": "#Multinominal 2\n\n#https://www.princeton.edu/~otorres/LogitR101.pdf\n#\n# Loading the required packages\nlibrary(foreign)\nlibrary(nnet)\nlibrary(stargazer)\n\n# Getting the sample data from UCLA\n#mydata = read.dta(\"http://www.ats.ucla.edu/stat/data/hsb2.dta\")\nmydata <- read.dta(\"https://stats.idre.ucla.edu/stat/data/hsbdemo.dta\")\n\n# Checking the output (dependent) variable\ntable(mydata$ses)\n\n\n# By default the first category is the reference.\n# To change it so ‘middle’ is the reference type\nmydata$ses2 = relevel(mydata$ses, ref = \"middle\")\nunique(mydata$ses2)\n\n# Running the multinomial logit model using the multinom() function\nmulti1 = multinom(ses2 ~ science + socst + female, data=mydata)\n\nsummary(multi1)\n\nlibrary(stargazer)\nstargazer(multi1, type=\"html\", out=\"multi1.htm\")\nstargazer(multi1, type=\"text\",out=\"multi1.txt\")\nmulti1.rrr = exp(coef(multi1))\nmulti1.rrr\n\n\nlibrary(stargazer)\nstargazer(multi1, type=\"html\", coef=list(multi1.rrr), p.auto=FALSE, out=\"multi1rrr.htm\")\n\n\nallmean <- data.frame(science=rep(mean(mydata$science),2),\n socst=rep(mean(mydata$socst),2),\n female = c(\"male\",\"female\"))\nallmean\n\nallmean[, c(\"pred.prob\")] <- predict(multi1, newdata=allmean, type=\"probs\")\n\nallmean\n\nallmean[, c(\"pred.prob\")] <- predict(multi1, newdata=allmean, type=\"class\")\nallmean\n" }, { "path": "29a-GLM/multinominal3.R", "content": "#multinomial3\n#https://www.r-bloggers.com/how-to-multinomial-regression-models-in-r/\n#\nn <- 1000\ndf1 <- data.frame(x1=runif(n,0,100),x2=runif(n,0,100))\nhead(df1)\ndf1 <- transform(df1, y=1+ifelse(100 - x1 - x2 + rnorm(n,sd=10) < 0, 0,\n ifelse(100 - 2*x2 + rnorm(n,sd=10) < 0, 1, 2)), set=\"Original\")\nhead(df1)\ndf1\n# Load the neural network package and fit the model\nlibrary(nnet)\nmod <- multinom(y ~ x1 + x2, df1)\n\npredict(mod)\n\npredict(mod,df1,\"probs\")\n\n\n# Function to predict multinomial logit choice model outcomes\n# model = nnet class multinomial model\n# newdata = data frame containing new values to predict\npredictMNL <- function(model, newdata) {\n \n # Only works for neural network models\n if (is.element(\"nnet\",class(model))) {\n # Calculate the individual and cumulative probabilities\n probs <- predict(model,newdata,\"probs\")\n cum.probs <- t(apply(probs,1,cumsum))\n \n # Draw random values\n vals <- runif(nrow(newdata))\n \n # Join cumulative probabilities and random draws\n tmp <- cbind(cum.probs,vals)\n \n # For each row, get choice index.\n k <- ncol(probs)\n ids <- 1 + apply(tmp,1,function(x) length(which(x[1:k] < x[k+1])))\n \n # Return the values\n return(ids)\n }\n}\n\n\n# Make up some new data\nn <- 200\ndf <- data.frame(x1=runif(n,0,100),\n x2=runif(n,0,100),\n set=\"Model\")\ny2 <- predictMNL(mod,df)\ndf2 <- cbind(df,y=y2)\n\n" }, { "path": "29a-GLM/multinominal4.R", "content": "#multinominal 4\n#https://stats.stackexchange.com/questions/9715/how-to-set-up-and-estimate-a-multinomial-logit-model-in-r\n#\nlibrary(\"nnet\")\ndata(\"Fishing\", package = \"mlogit\")\nfishing.mu <- multinom(mode ~ income, data = Fishing)\nsum.fishing <- summary(fishing.mu) # gives a table of outcomes by covariates for coef and SE\nstr(sum.fishing)\n# now get the p values by first getting the t values\npt(abs(sum.fishing$coefficients / sum.fishing$standard.errors),\n df=nrow(Fishing)-6,lower=FALSE)\n" }, { "path": "29a-GLM/multinominal5.R", "content": "#Clear All\nrm(list=ls())\n\n# Load libraries\nlibrary(datasets)\nlibrary(caTools)\nlibrary(nnet)\n\n# Load the dataset\ndataset <- iris \n#Structure of the data\nstr(dataset)\n\n#Split data into train and test\nset.seed(123)\nsplit <- sample.split(dataset$Species,SplitRatio = 0.80)\ntrain <- subset(dataset,split==TRUE)\ntest <- subset(dataset,split==FALSE)\n\n# Use logistic regression and the one-vs-all strategy to build a classification model.\nmultinomModel <- multinom(Species~.,data=train)\nsummary (multinomModel) # model summary\n\n# Predict on Test Data\npredicted_scores <- predict (multinomModel, test, \"probs\") # predict on new data\npredictions <- apply(predicted_scores , 1, which.max)\npredictions[which(predictions==\"1\")] <- unique(dataset$Species)[1]\npredictions[which(predictions==\"2\")] <- unique(dataset$Species)[2]\npredictions[which(predictions==\"3\")] <- unique(dataset$Species)[3]\ntest$prediction <- predictions\n\n# summarize accuracy\ntable(test$prediction, test$Species)\n" }, { "path": "29a-GLM/multinominal6.R", "content": "# MUltinominal Regression\n# Students Selecting a Stream\ndept = c('an', 'law', 'ds')\nstudent = c('s',1:50,sep='')\nsex = c('M','F')\nset.seed(1234)\nmaths = ceiling(runif(50, 55,95))\nset.seed(1234)\nstream = sample(dept, 50, replace=T)\nset.seed(1234)\ngender = sample(sex, 50, replace=T)\nadmission = data.frame(stream, gender, maths )\nadmission\naggregate(admission$maths, by=list(stream,gender), mean )\naggregate(admission$maths, by=list(stream,gender), length )\naggregate(admission$maths, by=list(stream), length )\nstr(admission)\nunique(admission$stream)\n#base is an\n#\nlibrary(nnet)\nmmodel1 <- multinom( stream ~ gender + maths ,data= admission)\nsummary (mmodel1) # model summary\n" }, { "path": "29a-GLM/nls1.R", "content": "# Non Linear\n#simulate some data\nset.seed(20160227)\nx<-seq(0,50,1)\ny<-((runif(1,10,20)*x)/(runif(1,0,10)+x))+rnorm(51,0,1)\n#for simple models nls find good starting values for the parameters even if it throw a warning\nm<-nls(y~a*x/(b+x))\n#get some estimation of goodness of fit\ncor(y,predict(m))\n\n[1] 0.9496598\n\n#plot\nplot(x,y)\nlines(x,predict(m),lty=2,col=\"red\",lwd=3)\n\n\n#simulate some data, this without a priori knowledge of the parameter value\ny<-runif(1,5,15)*exp(-runif(1,0.01,0.05)*x)+rnorm(51,0,0.5)\n#visually estimate some starting parameter values\nplot(x,y)\n#from this graph set approximate starting values\na_start<-8 #param a is the y value when x=0\nb_start<-2*log(2)/a_start #b is the decay rate\n#model\nm<-nls(y~a*exp(-b*x),start=list(a=a_start,b=b_start))\n#get some estimation of goodness of fit\ncor(y,predict(m))\n\n#plot the fit\nlines(x,predict(m),col=\"red\",lty=2,lwd=3)\n\n\nlibrary(deSolve)\n#simulating some population growth from the logistic equation and estimating the parameters using nls\nlog_growth <- function(Time, State, Pars) {\n with(as.list(c(State, Pars)), {\n dN <- R*N*(1-N/K)\n return(list(c(dN)))\n })\n}\n#the parameters for the logisitc growth\npars <- c(R=0.2,K=1000)\n#the initial numbers\nN_ini <- c(N=1)\n#the time step to evaluate the ODE\ntimes <- seq(0, 50, by = 1)\n#the ODE\nout <- ode(N_ini, times, log_growth, pars)\n#add some random variation to it\nN_obs<-out[,2]+rnorm(51,0,50)\n#numbers cannot go lower than 1\nN_obs<-ifelse(N_obs<1,1,N_obs)\n#plot\nplot(times,N_obs)\n\n\n#find the parameters for the equation\nSS<-getInitial(N_obs~SSlogis(times,alpha,xmid,scale),data=data.frame(N_obs=N_obs,times=times))\nSS\n" }, { "path": "30a-CLS/cls-gen", "content": "Classification\n\nLinks\nhttps://predictoanalycto.wordpress.com/2014/05/30/classification-trees-with-r-programming-language-part-1/\nhttps://www.dezyre.com/data-science-in-r-programming-tutorial/decision-tree-tutorial\nhttp://ipub.com/id3-with-data-tree/\n\n" }, { "path": "30a-CLS/cls1M-cancer.R", "content": "\n#Wisconsin Breast Cancer\nloc='http://archive.ics.uci.edu/ml/machine-learning-databases/'\nds='breast-cancer-wisconsin/breast-cancer-wisconsin.data'\nurl = paste(loc,ds,sep='')\nbreast = read.table(url, sep=',' , header=F, na.strings='?' )\nnames(breast)\n#[1] \"V1\" \"V2\" \"V3\" \"V4\" ..\n#'data.frame': 699 obs. of 11 variables: $ V1 : int 1000025 1002945 $ V2 : int 5 5 3 6\nnames(breast) = c('ID' , 'clumpThickness', 'sizeUniformity', 'shapeUniformity', 'maginalAdhesion', 'singleEpthelialCellSize', 'bareNuclei', 'blandChromatin', 'normalNucleoli', 'mitosis', 'class')\ndf = breast[-1]\nhead(df)\ndf$class = factor(df$class, levels=c(2,4), labels = c('benign', 'malignant'))\nhead(df$class)\nset.seed(1234)\ntrain = sample(nrow(df), 0.7*nrow(df) )\nlength(train)\ndf.train = df[train, ]\nhead(df.train[,c(1,10)])\ndf.validate= df[-train, ]\nnrow(df.validate)\ntable(df.train$class)\ntable(df.validate$class)\n\n?glm\n\n#Eg1\nfit.logit = glm(class ~. , data = df.train, family = binomial() )\nsummary(fit.logit)\nprob = predict (fit.logit, df.validate, type='response')\nprob\nlogit.pred = factor(prob > .5, level=c(F,T), labels=c('benign', 'malignant') )\nlogit.pred\nlogit.perf = table(df.validate$class, logit.pred, dnn=c('Actual', 'Predicted') )\nlogit.perf\ntable(df.validate$class)\n\n\n#Eg\nlibrary(rpart)\nset.seed(1234)\nstr(df.train)\nstr(df.validate)\ndtree = rpart(class ~ ., data=df.train, method= 'class', parms = list(split='information') )\ndtree$cptable\nprint(dtree)\nplotcp(dtree)\nabline(v=1)\nsummary(dtree)\ndtree.pruned = prune(dtree, cp =.01)\ndtree.pruned\nlibrary(rpart.plot)\nprp(dtree.pruned, type=2, extra = 104, fallen.leaves=T, main='Decision Tree')\ndtree.pred = predict(dtree.pruned, df.validate, type='class')\ndtree.perf = table(df.validate$class, dtree.pred, dnn=c('Actual', 'Predicted') )\ndtree.perf\n201/210\n\n#Eg\nlibrary(party)\nfit.ctree = ctree(class ~ ., data = df.train)\nplot(fit.ctree, main='Conditional Inference Tree')\nctree.pred = predict(fit.ctree, df.validate, type='response')\nctree.perf = table(df.validate$class, ctree.pred, dnn=c('Actual', 'Predicted') )\nctree.perf\n\n#Eg\nlibrary(randomForest)\nset.seed(1234)\nfit.forest = randomForest(class ~ ., data=df.train, na.action=na.roughfix, important=T)\nfit.forest\nimportance(fit.forest, type=2)\nforest.pred = predict( fit.forest, df.validate)\nforest.perf = table(df.validate$class, forest.pred, dnn=c('Actual', 'Predicted') )\nforest.perf\n(126+73)\n\n#Eg2 : cforest() :: party package\n#Eg2 : cforest() :: party package\nlibrary(party)\ncforest(Species ~ ., data=iris, controls=cforest_control(mtry=2, mincriterion=0))\nRandom Forest using Conditional Inference Trees Number of trees: 500 Response: Species Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width Number of observations: 150\nrf <- randomForest(Species ~ ., data=iris)\ngetTree(rf, 1, labelVar=TRUE)\n\n#Eg3\ndata(mtcars)\nmtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000, keep.forest=FALSE, importance=TRUE)\nplot(mtcars.rf, log=\"y\")\nvarImpPlot(mtcars.rf)\n#Eg4\nset.seed(1)\ndata(iris)\niris.rf <- randomForest(Species ~ ., iris, proximity=TRUE, keep.forest=FALSE)\nMDSplot(iris.rf, iris$Species)\n\n#Eg1\nlibrary(e1071)\nset.seed(1234)\nfit.svm = svm(class ~., data = df.train)\nfit.svm\nsvm.pred = predict(fit.svm, na.omit(df.validate))\nsvm.perf = table(na.omit (df.validate)$class, svm.pred, dnn=c('Actual', 'Predicted') )\nsvm.perf\n\n\n#Eg2\nset.seed(1234)\ntuned = tune.svm(class ~., data = df.train, gamma = 10^ (-6:1), cost(10^(-10:10)))\n\n tuned\nfit.svm = svm(class ~., data=df.train, gamma=0.01, cost=1 )\nsvm.pred = predict(fit.svm, na.omit(df.validate))\nsvm.perf = table(na.omit (df.validate)$class, svm.pred, dnn=c('Actual', 'Predicted') )\nsvm.perf\n " }, { "path": "30a-CLS/dt-multiplemodels.R", "content": "#Classification - DT\n\n#Load Libraries\nlibrary(tidyverse)\nlibrary(caret)\nlibrary(rpart)\n\nlibrary(rpart)\n#Create Model\nmodel <- rpart(Species ~., data = iris)\npar(xpd = NA) # otherwise on some devices the text is clipped\nplot(model)\ntext(model, digits = 3)\n\n#Print the Rules\nprint(model, digits = 2)\n \n#Predict\nnewdata <- data.frame(\n Sepal.Length = 6.5, Sepal.Width = 3.0,\n Petal.Length = 5.2, Petal.Width = 2.0\n)\nmodel %>% predict(newdata, \"class\") \n\n\n#Case-2----\n# Load the data and remove NAs\ndata(\"PimaIndiansDiabetes2\", package = \"mlbench\")\nPimaIndiansDiabetes2 <- na.omit(PimaIndiansDiabetes2)\n\n# Inspect the data\nsample_n(PimaIndiansDiabetes2, 3)\n\n# Split the data into training and test set\nset.seed(123)\n\ntraining.samples <- PimaIndiansDiabetes2$diabetes %>% \n createDataPartition(p = 0.8, list = FALSE)\ntrain.data <- PimaIndiansDiabetes2[training.samples, ]\ntest.data <- PimaIndiansDiabetes2[-training.samples, ]\n\n#----\n# Build the model\nset.seed(123)\nmodel1 <- rpart(diabetes ~., data = train.data, method = \"class\")\n# Plot the trees\npar(xpd = NA) # Avoid clipping the text in some device\nplot(model1)\ntext(model1, digits = 3)\n\n#Predict\n# Make predictions on the test data\npredicted.classes <- model1 %>% \n predict(test.data, type = \"class\")\nhead(predicted.classes)\n\n# Compute model accuracy rate on test data\nmean(predicted.classes == test.data$diabetes)\n\n#caret package----\n# Fit the model on the training set\nset.seed(123)\nmodel2 <- caret::train(\n diabetes ~., data = train.data, method = \"rpart\",\n trControl = trainControl(\"cv\", number = 10),\n tuneLength = 10\n)\n# Plot model accuracy vs different values of\n# cp (complexity parameter)\nplot(model2)\n\n# Print the best tuning parameter cp that\n# maximizes the model accuracy\nmodel2$bestTune\n\n# Plot the final tree model\npar(xpd = NA) # Avoid clipping the text in some device\nplot(model2$finalModel)\ntext(model2$finalModel, digits = 3)\n\n# Decision rules in the model\nmodel2$finalModel\n\n# Make predictions on the test data\npredicted.classes <- model2 %>% predict(test.data)\n# Compute model accuracy rate on test data\nmean(predicted.classes == test.data$diabetes)\n\n\n#----\n#regression tree\n# Load the data\ndata(\"Boston\", package = \"MASS\")\n# Inspect the data\nsample_n(Boston, 3)\n# Split the data into training and test set\nset.seed(123)\ntraining.samples <- Boston$medv %>%\n createDataPartition(p = 0.8, list = FALSE)\ntrain.data <- Boston[training.samples, ]\ntest.data <- Boston[-training.samples, ]\n\n# Fit the model on the training set\nset.seed(123)\nmodel <- train(\n medv ~., data = train.data, method = \"rpart\",\n trControl = trainControl(\"cv\", number = 10),\n tuneLength = 10\n)\n# Plot model error vs different values of\n# cp (complexity parameter)\nplot(model)\n# Print the best tuning parameter cp that\n# minimize the model RMSE\nmodel$bestTune\n\n# Plot the final tree model\npar(xpd = NA) # Avoid clipping the text in some device\nplot(model$finalModel)\ntext(model$finalModel, digits = 3)\n\n# Decision rules in the model\nmodel$finalModel\n# Make predictions on the test data\npredictions <- model %>% predict(test.data)\nhead(predictions)\n# Compute the prediction error RMSE\nRMSE(predictions, test.data$medv)\n\n\n#Conditional Tree\n# Load the data\ndata(\"PimaIndiansDiabetes2\", package = \"mlbench\")\npima.data <- na.omit(PimaIndiansDiabetes2)\n\n# Split the data into training and test set\nset.seed(123)\ntraining.samples <- pima.data$diabetes %>%\n createDataPartition(p = 0.8, list = FALSE)\ntrain.data <- pima.data[training.samples, ]\ntest.data <- pima.data[-training.samples, ]\n\n#-\nlibrary(party)\nset.seed(123)\nmodel <- train(\n diabetes ~., data = train.data, method = \"ctree2\",\n trControl = trainControl(\"cv\", number = 10),\n tuneGrid = expand.grid(maxdepth = 3, mincriterion = 0.95 )\n)\nplot(model$finalModel)\nmodel$finalModel\n\n# Make predictions on the test data\npredicted.classes <- model %>% predict(test.data)\n# Compute model accuracy rate on test data\nmean(predicted.classes == test.data$diabetes)\n\n" }, { "path": "30a-CLS/giniIndex.R", "content": "#Gini Index\nstudent = c('Yes', 'Yes','Yes','No','No','No','No', 'Yes','No')\nlength(student)\nhostel = c(T,T,F,F,T,T,F,T,T)\nlength(hostel)\ntarget = c('Yes','Yes','No','Yes','No','No','No', 'Yes','No')\nlength(target)\n\n(df = data.frame(student, hostel, target))\nstr(df)\n#Gini Index\ntable(df$target)\n(gini_target = 1 - ((5/9)^2 + (4/9)^2))\n#1 - 41/81 = 59/81\n# 0.49\n\n#Gini Index\ntable(df$student, df$target)\nxtabs(~ student + target, data = df)\n(gini_NonStudent_Target = 1 - ((4/5)^2 + (1/5)^2)) #8/25\n(gini_Student_Target = 1 - ((1/4)^2 + (3/4)^2)) #3/8\ntable(df$student)\n(gini_Student = gini_target - ((5/9)* gini_NonStudent_Target + (4/9) * gini_Student_Target))\n#0.149\n\n#Gini Index - hostel\ntable(df$hosel, df$target)\nxtabs(~ hostel + target, data = df)\n(gini_NonHostel_Target = 1 - ((2/3)^2 + (1/3)^2)) # 4/9\n(gini_Hostel_Target = 1 - ((3/6)^2 + (3/6)^2)) # 1/2\ntable(df$hostel)\n(gini_Hostel = gini_target - ((3/9)* gini_NonHostel_Target + (6/9) * gini_Hostel_Target))\n#0.012\ngini_Student > gini_Hostel\n#higher Gain by Student Variable, best splitting variable\n\n#check by rpart\nlibrary(rpart)\nlibrary(rpart.plot)\n(dt_rpart1 = rpart(target ~ student + hostel, data=df))\n#no splitting\n\n(dt_rpart2 = rpart(target ~ student + hostel, data=df, cp=-1, minsplit=2))\nrpart.plot(dt_rpart2)\n#splitting by student\ndt_rpart2$variable.importance\nsummary(dt_rpart2)\n\ninfo.gain.rpart(dt_rpart) #\nrc1 = rpart.control(cp = 0.005, minsplit=2, minbucket = 1,maxcompete = 2, maxdepth = 1)\n(dt_rpart3 = rpart(target ~ student + hostel, data=df, control=rc1))\nrpart.plot(dt_rpart3)\n\n\n#randomForest\nlibrary(randomForest)\n(dt_rf = randomForest(target ~ student + hostel, data=df))\nimportance(dt_rf)\nvarImpPlot(dt_rf,type=2)\n\n\nedarf::variable_importance(dt_rf, data=df)\n\nlibrary(caret)\nhead(df)\ndt_caret = train( target ~ student + hostel, data=df,method=\"rpart\", cp=-1)\ndt_caret\n\nlibrary(CHAID)\ndt_chaid = chaid(target ~ hostel + student, data=df)\ndt_chaid\nplot(dt_chaid)\n\nlibrary(party)\ndt_party = ctree(target ~ hostel + student, data=df)\ndt_party\nplot(dt_party)\n\n#https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf\n\ndf_mtcars = rpart(am ~ ., data=mtcars)\ndf_mtcars\ndf_mtcars$variable.importance\n\n" }, { "path": "30a-CLS/rattle.R", "content": "# Rattle\n#Eg1\nloc='http://archive.ics.uci.edu/ml/machine-learning-databases/'\nds = 'pima-indians-diabetes/pima-indians-diabetes.data'\nurl = paste(loc, ds, sep='')\ndiabetes = read.table(url, sep=',' , header=F)\nnames(diabetes) = c('npregant', 'plasma', 'bp', 'triceps', 'insulin', 'bmi', 'pedigree', 'age', 'class')\ndiabetes$class = factor(diabetes$class, levels = c(0,1) , labels = c('normal', 'diabetic'))\nlibrary(rattle)\nrattle()\n\ncv = matrix(c(145, 50, 8,27, nrow=2))\ncv\nperformance(as.table(cv))\n" }, { "path": "30b-CART/10-CART-gen.R", "content": "#Basic Steps in CART\n\n#Collect Data\n#Identify if it is Classification or Regression Model\n#Write the formula : y ~ x` + x2 ....\n\n#load libraries \nlibrary(rpart)\nlibrary(rpart.plot)\nmodel= rpart(y ~ ., method='class', data= , control=rpart.control(minsplit=4, cp=0.00001))\nrpart.plot()\n\n#Set any control Parameters\ncontrol_1 = rpart.control(cp = 0.005, minsplit=10, minbucket = 5)\ncontrol_2 = rpart.control(cp = -1)\n\n#using iris dataset\nset.seed(123)\nirisclass = rpart(Species ~ ., data = iris, control = control_1)\nirisclass\nrpart.plot(irisclass)\nprintcp(irisclass)\n#--------\nirisclass2 = rpart(Species ~ ., data = iris, control = control_2)\nirisclass2\nrpart.plot(irisclass2, cex=.8)\n\n\n#----------------------\n#regression Tree-----\nnames(iris) #which is numeric column - Length\nirisregression = rpart(Sepal.Length ~ ., data = iris, method=\"anova\", control = control_1)\nirisregression\nrpart.plot(irisregression)\nprintcp(irisregression)\npruneRT = prune(irisregression, cp=.008)\nrpart.plot(pruneRT)\n" }, { "path": "30b-CART/11-cart-understandsplit.R", "content": "#Understanding Splitting and selection of variables\n\nlibrary(rpart)\nlibrary(rpart.plot)\n#---\n\n(gender = c(rep('M', 60), rep('F', 40)))\n(play = c(rep(c('Yes','No'), c(30,30)), rep(c('Yes','No'), c(20,20))))\nstudents = data.frame(gender, play) \nhead(students) \n(t1=table(students$gender, students$play))\nprop.table(t1)\n\ndtree1 = rpart(play ~ gender, data=students, control = list(cp=-1, minsplit=5))\ndtree1\nrpart.plot(dtree1)\n#no decision tree as proportion is 50%\n\n#----------------------\n(gender2 = c(rep('M', 60), rep('F', 40)))\n(play2 = c(rep(c('Yes','No'), c(40,20)), rep(c('Yes','No'), c(20,20))))\nstudents2 = data.frame(gender2, play2) \nhead(students2) \n(t2=table(students2$gender2, students2$play2))\naddmargins(t2)\nprop.table(t2) #more % of males play\nprop.table(t1)\naddmargins(prop.table(t2))\n#how many play - .6, 60% from 100% values\n\ndtree2 = rpart(play2 ~ gender2, data=students2, control = list(cp=-1, minsplit=5))\ndtree2\nrpart.plot(dtree2)\nrpart.plot(dtree2, extra=104)\nrpart.plot(dtree2, extra=104, yesno=2, left=F, xflip=T, yflip=T,faclen=3, cex=1.5)\n#explore below document\n#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf\nprop.table(t2,1) #40/60\n#majority play (at root node)\n\n#Eg3----------------------\n\n(gender3 = c(rep('M', 60), rep('F', 40)))\n(play3 = c(rep(c('Yes','No'), c(40,20)), rep(c('Yes','No'), c(20,20))))\n(married3 = c(rep(c('Md','Sg'), c(50,10)), rep(c('Md','Sg'), c(10,30))))\n\n\nstudents3 = data.frame(gender3, play3, married3) \nhead(students3) \n(t3a= table(students3$play3, students3$gender3))\n(t3b= table(students3$play3, students3$married3))\naddmargins(t3a)\naddmargins(t3b)\n\ndtree3 = rpart(play3 ~ gender3 + married3, data=students3, control = list(cp=-1, minsplit=5))\ndtree3\nrpart.plot(dtree3)\naddmargins(prop.table(table(students3$play3, students3$married3)))\n\n#decision tree as proportion of M is not 50%\ntable(students3$play3)\n#majority play (at root node)\n\n#rpart(y~x+z, data=df, parms=list(split='gini'))\n\n\n#Variable with lower Gini Index value, should be chosen as a variable that gives best split. The next step would be to take the results from the split and further partition. \ntable(students3$play3)\n\ntable(students3$married3)\n#married Variable : Gini Index\ntable(students3$play3, students3$married3)\n(gini_md_sg = 1 - (30/40)^2 - (10/40)^2)\n(gini_md_md = 1 - (10/60)^2 - (50/60)^2)\n(gini_md = gini_md_sg + gini_md_md)\n\n#gender Variable : Gini Index\ntable(students3$play3, students3$gender3)\n(gini_gd_m = 1 - (20/60)^2 - (40/60)^2)\n(gini_gd_f = 1 - (20/40)^2 - (20/40)^2)\n(gini_gd = gini_gd_m + gini_gd_f)\ngini_md < gini_gd\n#gini_md is lower hence should be selected as split variable\n\n#After Splitting : look at left tree ie. Single students\n#singles = split(students3, married3=\"Sg\")\n" }, { "path": "30b-CART/12-DT-outlook.R", "content": "#example in many books\noutlook =c('Sunny', 'Sunny', 'Overcast', 'Rain','Rain', 'Rain','Overcast', 'Sunny', 'Sunny','Rain','Sunny', 'Overcast','Overcast','Rain')\ntemperature = c( 'Hot','Hot','Hot','Mild','Cool','Cool','Cool','Mild','Cool','Mild','Mild','Mild','Hot','Mild')\nhumidity = c('Weak','Strong','Weak','Weak','Weak','Strong','Strong','Weak','Weak','Weak','Strong','Strong','Weak','Strong')\nwind = c( 'High','High','High','High','Normal','Normal','Normal','High','Normal','Normal','Normal','High','Normal','High')\nplay = c('No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No')\n\ngame = data.frame(outlook, temperature, humidity, wind, play)\nhead(game)\n\nlibrary(rpart)\nlibrary(rpart.plot)\ndt_game1 = rpart(play ~ . , data=game) \ndt_game1\n#no tree \n#cp=-1\n\ndt_game2 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=1, minbucket = 1))\ndt_game2\nrpart.plot(dt_game2,cex=.8, nn=T)\n\n#make tree smaller\ndt_game3 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=3, minbucket = 2, split = \"gini\"))\ndt_game3\nrpart.plot(dt_game3,cex=.8, nn=T)\n\n?rpart\n\n#make tree smaller\ndt_game3 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=3, minbucket = 2, split = \"information\"))\ndt_game3\nattributes(dt_game3)\nrpart.plot(dt_game3,cex=.8, nn=T)\ndt_game3$parms\ndt_game3$variable.importance\ndt_game3$method\ndt_game3$cptable\ndt_game3$control\ndt_game3$splits\ndt_game3$numresp\ndt_game3$y\ngame$play\ndt_game3$ordered\n\n\n\n\n" }, { "path": "30b-CART/CARTR_sales.R", "content": "# CART Models\n\nlibrary(rpart)\nlibrary(rpart.plot)\nlibrary(forecast)\n#install.packages(\"https://cran.r-project.org/bin/windows/contrib/3.3/RGtk2_2.20.31.zip\", repos=NULL)\n#library(RGtk2)\n#install.packages(\"rattle\")\n#library(rattle)\n\n\n# Set the working directory to folder where you have placed the Input Data\n\n\nMerSales2 = read.csv(file = \"./data/msales.csv\", skip=1, header = T)\nMerSales= MerSales2\ndim(MerSales)\n# Summarize the dataset\nsummary(MerSales)\nstr(MerSales)\n# Look at the average Annual_Sales()\nfor(i in 2:ncol(MerSales))\n{\n if(length(unique(MerSales[,i])) <= 5)\n {\n AnnualSales = aggregate(x = MerSales$sales, by = list(MerSales[,i]), FUN = mean)\n print(colnames(MerSales)[i])\n print(AnnualSales)\n print(\"*******************************************\")\n }\n}\n\naggregate(MerSales$sales, by=list(MerSales$loc), FUN=sum)\n# Make a copy of the Original Dataset\nMerSalesUncapped = MerSales\n\n# Random Sampling\nset.seed(777) # To ensure reproducibility\nIndex = sample(x = 1:nrow(MerSalesUncapped), size = 0.7*nrow(MerSalesUncapped))\nIndex\nlength(Index)\n# Create Train dataset\nMerSalesTrainUncapped = MerSalesUncapped[Index, ]\nnrow(MerSalesTrainUncapped)\nsummary(object = MerSalesTrainUncapped)\n\n# Create Test dataset\nMerSalesTestUncapped = MerSalesUncapped[-Index, ]\nnrow(MerSalesTestUncapped)\nsummary(object = MerSalesTestUncapped)\n#divide data into 2 parts - Train (70%), Test(30%)\n\n########################### Modeling ############################\n\n# Build a full model with default settings\nset.seed(123) # To ensure reproducibility of xerrors (cross validated errors while estimating complexity paramter for tree pruning)\n\nCartFullModel = rpart(sales ~ . , data = MerSalesTrainUncapped[,-1], method = \"anova\")\nCartFullModel\nsummary(object = CartFullModel)\n\n\n# Plot the Regression Tree\nrpart.plot(CartFullModel, type = 4,fallen.leaves = T,nn=T, cex = 1)\n\nmean(MerSalesTrainUncapped$sales)\n\ntitle(\"CartFullModel\") # Enlarge the plot by clicking on Zoom button in Plots Tab on R Studio\n\n# fancyRpartPlot() function to plot the same model\n# Expand the plot window in R Studio to see a presentable output\nfancyRpartPlot(model = CartFullModel, main = \"CartFullModel\", cex = 0.6) \n?fancyRpartPlot\n# The following code also produces the same output, but in a windowed form\nwindows()\nfancyRpartPlot(model = CartFullModel, main = \"CartFullModel\", cex = 0.6)\n\n\n# The CP table also shows us valueable information in terms of pruning the tree (which is explained later in the code)\n # => The complexity parameter \"CP\" specifies how the cost of a tree C(T) is penalized by the number of terminal nodes |T|.\n # Hence, Small \"CP\" results in larger trees and potential overfitting, large CP\" in small trees and potential underfitting.\n # => The \"rel error\" is 1- RSquare, similar to linear regression. This is the error on the observations used to estimate the model. The last node value of rel error suggests the R-Square of the model, if this happens to be the model\n # => The \"xerror\" is is the error on the observations from cross validation data, which happens to be a 10-Fold Cross Validation. Particpants need to note that, in order to reproduce the \"xerror\" values, they must use the same set.seed() number each time\n # => Root Node Error is given by sum((Dependent - Mean(Dependent))^2), i.e.\n # sum((InsDataTrainUncapped$Losses-mean(InsDataTrainUncapped$Losses))^2\n \nprintcp(CartFullModel)\n\n# This produces a plot which may help particpants to look for a model depending on R-Square values produced at various splits\nrsq.rpart(x = CartFullModel)\n\nfit4 = prune(CartFullModel, cp=.014)\nfit4\nrpart.plot(fit4, nn=T, cex=1)\n\n########################### Using CP to expand / Prune the tree #################################################\n# Lets change rpart.control() to specify certain attributes for tree building\nRpartControl = rpart.control(cp = 0.005)\nset.seed(123)\nnames(MerSales)\nCartModel_1 = rpart(formula = sales ~ . , \n data = MerSalesTrainUncapped[,-1], \n method = \"anova\", control = RpartControl)\n\nCartModel_1\n\nCartModel_1$where\ntrainingnodes = rownames(CartModel_1$frame) [ CartModel_1$where]\ntrainingnodes\n\nsummary(CartModel_1)\nrpart.plot(x = CartModel_1, type = 4,cex = 0.6)\nprintcp(x = CartModel_1)\nrsq.rpart(x = CartModel_1)\n\n\n\n\n####################### Some Extra pointers in R ###########################################\n\n\n# Model Evaluation Measures on test dataset using the finalized (pruned model)\n# Use predict() the get the predicted values for the testset using the finalized model\n\n# Intermediate Model: Finalize CartFullModel (Based on Tree size i.e. Depth, Variables included as well as the R-Square produced)\n# Predict on testset\nCartFullModelPredictTest = predict(CartFullModel, newdata = MerSalesTestUncapped, type = \"vector\")\nCartFullModelPredictTest[1]\nMerSales[2,'sales']\n# Calculate RMSE and MAPE manually\n# Participants can calculate RMSE and MAPE using various available functions in R, but that may not\n# communicate effectively the mathematical aspect behind the calculations\n\n# RMSE\nAct_vs_Pred = CartFullModelPredictTest - MerSalesTestUncapped$Annual_Sales # Differnce\nAct_vs_Pred_Square = Act_vs_Pred^2 # Square\nAct_vs_Pred_Square_Mean = mean(Act_vs_Pred_Square) # Mean\nAct_vs_Pred_Square_Mean_SqRoot = sqrt(Act_vs_Pred_Square_Mean) # Square Root\nAct_vs_Pred_Square_Mean_SqRoot\n\n# MAPE\nAct_vs_Pred_Abs = abs(CartFullModelPredictTest - MerSalesTestUncapped$sales) # Absolute Differnce\nAct_vs_Pred_Abs_Percent = Act_vs_Pred_Abs/MerSalesTestUncapped$sales # Percent Error\nAct_vs_Pred_Abs_Percent_Mean = mean(Act_vs_Pred_Abs_Percent)*100 # Mean\nAct_vs_Pred_Abs_Percent_Mean\nlibrary(forecast)\n# Validate RMSE and MAPE calculation with a function in R\nUncappedModelAccuarcy = forecast::accuracy(f = CartFullModelPredictTest, x = MerSalesTestUncapped$sales)\nUncappedModelAccuarcy\n\n\n\nwindows()\nfancyRpartPlot(model = CartModel_1, main = \"Final CART Regression Tree\", cex = 0.6, sub = \"Model 12\")\n" }, { "path": "30b-CART/CART_Regression Tree v01.R", "content": "# CART Models - HH Case Study\n\nlibrary(rpart)\nlibrary(rpart.plot)\nlibrary(forecast)\n#install.packages(\"https://cran.r-project.org/bin/windows/contrib/3.3/RGtk2_2.20.31.zip\", repos=NULL)\n#library(RGtk2)\n#install.packages(\"rattle\")\n#library(rattle)\n\nlibrary(gsheet)\nurl='https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=1941519952'\ndata = as.data.frame(gsheet2tbl(url))\nstr(data)\n# or Load from CSV File\nMer_Sales = read.csv(file = \"./data/Predict Merchant_Sales v01.csv\", header = T)\n\nMer_Sales = data\n\n# Summarize the dataset\nsummary(Mer_Sales)\nnames(Mer_Sales)\n# Look at the average Annual_Sales()\nfor(i in 2:ncol(Mer_Sales))\n{\n if(length(unique(Mer_Sales[,i])) <= 5)\n {\n Annual_Sales=aggregate(x=Mer_Sales$Annual_Sales,by=list(Mer_Sales[,i]),FUN=mean)\n print(colnames(Mer_Sales)[i])\n print(Annual_Sales)\n print(\"****************************************\")\n }\n}\nhead(Mer_Sales)\nsummary(Mer_Sales)\nstr(Mer_Sales)\n# Make a copy of the Original Dataset\nMer_SalesUncapped = Mer_Sales\ndim(Mer_SalesUncapped)\n# Random Sampling\nset.seed(777) # To ensure reproducibility\nIndex = sample(x = 1:nrow(Mer_SalesUncapped), size = 0.7*nrow(Mer_SalesUncapped))\nIndex\n\n# Create Train dataset\nMer_SalesTrainUncapped = Mer_SalesUncapped[Index, ]\nnrow(Mer_SalesTrainUncapped)\nsummary(Mer_SalesTrainUncapped)\n\n# Create Test dataset\nMer_SalesTestUncapped = Mer_SalesUncapped[-Index, ]\nnrow(Mer_SalesTestUncapped)\nsummary(object = Mer_SalesTestUncapped)\n\n\n########################### Modeling #################################\n\nhead(Mer_SalesTrainUncapped)\n# Build a full model with default settings\nset.seed(123) # To ensure reproducibility of xerrors (cross validated errors while estimating complexity paramter for tree pruning)\nsummary(Mer_SalesTestUncapped)\nCartFullModel = rpart(Annual_Sales ~ . , data = Mer_SalesTrainUncapped[,-1], method = \"anova\")\nCartFullModel\nmean(Mer_SalesTrainUncapped$Annual_Sales)\nsummary(object = CartFullModel)\nsummary(Mer_SalesTestUncapped[,'Annual_Sales'])\n\n\nnames(Mer_SalesTrainUncapped)\n# Plot the Regression Tree\nrpart.plot(x = CartFullModel, type = 4,fallen.leaves = T, cex = 1.0, nn=T)\ntitle(\"CartFullModel\") # Enlarge the plot by clicking on Zoom button in Plots Tab on R Studio\n\n# fancyRpartPlot() function to plot the same model\n# Expand the plot window in R Studio to see a presentable output\nfancyRpartPlot(model = CartFullModel, main = \"CartFullModel\", cex = 0.6) \n\n# The following code also produces the same output, but in a windowed form\n#windows()\n#fancyRpartPlot(model = CartFullModel, main = \"CartFullModel\", cex = 0.6)\n\n\n# The CP table also shows us valueable information in terms of pruning the tree (which is explained later in the code)\n # => The complexity parameter \"CP\" specifies how the cost of a tree C(T) is penalized by the number of terminal nodes |T|.\n # Hence, Small \"CP\" results in larger trees and potential overfitting, large CP\" in small trees and potential underfitting.\n # => The \"rel error\" is 1- RSquare, similar to linear regression. This is the error on the observations used to estimate the model. The last node value of rel error suggests the R-Square of the model, if this happens to be the model\n # => The \"xerror\" is is the error on the observations from cross validation data, which happens to be a 10-Fold Cross Validation. Particpants need to note that, in order to reproduce the \"xerror\" values, they must use the same set.seed() number each time\n # => Root Node Error is given by sum((Dependent - Mean(Dependent))^2), i.e.\n # sum((InsDataTrainUncapped$Losses-mean(InsDataTrainUncapped$Losses))^2\n \nprintcp(x = CartFullModel)\nptree = prune(CartFullModel, cp=0.027)\nrpart.plot(ptree)\nplotcp(CartFullModel)\n\n# This produces a plot which may help particpants to look for a model depending on R-Square values produced at various splits\nrsq.rpart(x = CartFullModel)\n\n#### Using CP to expand / Prune the tree ####################\n# Lets change rpart.control() to specify certain attributes for tree building\nRpartControl = rpart.control(cp = 0.027)\nset.seed(123)\nCartModel_1 = rpart(formula = Annual_Sales ~ . , \n data = Mer_SalesTrainUncapped[,-1], \n method = \"anova\", control = RpartControl)\n\nCartModel_1\nsummary(CartModel_1)\nrpart.plot(x = CartModel_1, type = 4,fallen.leaves = T, cex = 0.6)\nprintcp(x = CartModel_1)\nplotcp(CartModel_1)\nrsq.rpart(x = CartModel_1)\n\n\n\n\n####### Some Extra pointers in R #####################\n\n\n# Model Evaluation Measures on test dataset using the finalized (pruned model)\n# Use predict() the get the predicted values for the testset using the finalized model\n\n# Intermediate Model: Finalize CartFullModel (Based on Tree size i.e. Depth, Variables included as well as the R-Square produced)\n# Predict on testset\nCartFullModelPredictTest = predict(CartFullModel, newdata = Mer_SalesTestUncapped, type = \"vector\")\nCartFullModelPredictTest\n# Calculate RMSE and MAPE manually\n# Participants can calculate RMSE and MAPE using various available functions in R, but that may not\n# communicate effectively the mathematical aspect behind the calculations\n\n# RMSE\nAct_vs_Pred = CartFullModelPredictTest - Mer_SalesTestUncapped$Annual_Sales # Differnce\nAct_vs_Pred_Square = Act_vs_Pred^2 # Square\nAct_vs_Pred_Square_Mean = mean(Act_vs_Pred_Square) # Mean\nAct_vs_Pred_Square_Mean_SqRoot = sqrt(Act_vs_Pred_Square_Mean) # Square Root\nAct_vs_Pred_Square_Mean_SqRoot\n\n# MAPE\nAct_vs_Pred_Abs = abs(CartFullModelPredictTest - Mer_SalesTestUncapped$Annual_Sales) # Absolute Differnce\nAct_vs_Pred_Abs_Percent = Act_vs_Pred_Abs/Mer_SalesTestUncapped$Annual_Sales # Percent Error\nAct_vs_Pred_Abs_Percent_Mean = mean(Act_vs_Pred_Abs_Percent)*100 # Mean\nAct_vs_Pred_Abs_Percent_Mean\n\nlibrary(forecast)\n# Validate RMSE and MAPE calculation with a function in R\nUncappedModelAccuarcy = accuracy(f = CartFullModelPredictTest, x = Mer_SalesTestUncapped$Annual_Sales)\nUncappedModelAccuarcy\n\n\nLift = 100/ 400 / ((200 /\n 400) * (160/400))AIC(CartModel_1)\n#Needs library rtk\nwindows()\nfancyRpartPlot(model = CartModel_1, main = \"Final CART Regression Tree\", cex = 0.6, sub = \"Model 12\")\n" }, { "path": "30b-CART/DT-rpart-claims.R", "content": "#DT using Rpart\n#https://gormanalysis.com/decision-trees-in-r-using-rpart/\n\n#define a problem. There’s a common scam amongst motorists where a person will slam on his breaks in heavy traffic with the intention of being rear-ended. The person will then file an insurance claim for personal injury and damage to his vehicle, alleging that the other driver was at fault. Suppose we want to predict which of an insurance company’s claims are fraudulent using a decision tree\n\ntrain <- data.frame(ClaimID = c(1,2,3), RearEnd = c(TRUE, FALSE, TRUE), Fraud = c(TRUE, FALSE, TRUE))\n\ntrain\n\nlibrary(rpart) #load the rpart package\n\nmytree <- rpart(Fraud ~ RearEnd, data = train, method = \"class\")\nmytree\n#output shows only a root node. This is because rpart has some default parameters that prevented our tree from growing. Namely: minsplit and minbucket. minsplit is “the minimum number of observations that must exist in a node in order for a split to be attempted” and minbucket is “the minimum number of observations in any terminal node”. See what happens when we override these parameters.\n\nmytree <- rpart(Fraud ~ RearEnd, data = train, method = \"class\", minsplit = 2, minbucket = 1)\nmytree\n\nlibrary(rattle)\nlibrary(rpart.plot)\nlibrary(RColorBrewer)\nfancyRpartPlot(mytree)\n\n#decision tree correctly identified that if a claim involved a rear-end collision, the claim was most likely fraudulent.\n\n#By default, rpart uses gini impurty to select splits when performing classification. (If you’re unfamiliar with this metric, read this article.) You can use information gain instead by specifying it in the parms parameter.\n#https://gormanalysis.com/magic-behind-constructing-a-decision-tree/\n\nmytree <- rpart(Fraud ~ RearEnd, data = train, method = \"class\", parms = list(split = 'information'), minsplit = 2, minbucket = 1)\nmytree\n\ntrain <- data.frame(ClaimID = c(1,2,3), RearEnd = c(TRUE, FALSE, TRUE), Fraud = c(TRUE, FALSE, FALSE))\ntrain\n\nmytree <- rpart(Fraud ~ RearEnd, data = train, method = \"class\", minsplit = 2, minbucket = 1)\nmytree\nrpart.plot(mytree)\n#Once again we’re left with just a root node. Internally, rpart keeps track of something called the complexity of a tree. The complexity measure is a combination of the size of a tree and the ability of the tree to separate the classes of the target variable. If the next best split in growing a tree does not reduce the tree’s overall complexity by a certain amount, rpart will terminate the growing process. This amount is specified by the complexity parameter, cp, in the call to rpart. Setting cp to a negative amount ensures that the tree will be fully grown.\n\nmytree <- rpart(Fraud ~ RearEnd, data = train, method = \"class\", minsplit = 2, minbucket = 1, cp=-1)\n\nfancyRpartPlot(mytree)\n\n#This is not always a good idea since it will typically produce over-fitted trees, but trees can be pruned back as discussed later in this article.\n\n#You can also weight each observation for the tree’s construction by specifying the weights argument to rpart.\n\ntrain\nmytree <- rpart(Fraud ~ RearEnd, data = train, method = \"class\", minsplit = 2, minbucket = 1, weights = c(.4, .4, .2))\nmytree\nfancyRpartPlot(mytree)\n\n#One of the best ways to identify a fraudulent claim is to hire a private investigator to monitor the activities of a claimant. Since private investigators don’t work for free, the insurance company will have to strategically decide which claims to investigate. To do this, they can use a decision tree model based off some initial features of the claim. If the insurance company wants to aggressively investigate claims (i.e. investigate a lot of claims), they can train their decision tree in a manner that will penalize incorrectly labeled fraudulent claims more than it penalizes incorrectly labeled non-fraudulent claims.\n\n#To alter the default, equal penalization of mislabeled target classes set the loss component of the parms parameter to a matrix where the (i,j) element is the penalty for misclassifying an i as a j. (The loss matrix must have 0s in the diagonal). For example, consider the following training data.\n\ntrain <- data.frame(ClaimID = c(1,2,3,4,5,6,7), RearEnd = c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE), Whiplash = c(TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE),Fraud = c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE))\ntrain\n#grow our decision tree, restricting it to one split by setting the maxdepth argument to 1.\n\nmytree <- rpart(Fraud ~ RearEnd + Whiplash, data = train, method = \"class\", maxdepth = 1, minsplit = 2, minbucket = 1)\nmytree\nfancyRpartPlot(mytree)\n\n#rpart has determined that RearEnd was the best variable for identifying a fraudulent claim. BUT there was one fraudulent claim in the training set that was not the case of a rear-end collision. If the insurance company wants to identify a high percentage of fraudulent claims without worrying too much about investigating non-fraudulent claims they can set the loss matrix to penalize claims incorrectly labeled as fraudulent three times less than claims incorrectly labeled as non-fraudulent.\n\nlossmatrix <- matrix(c(0,1,3,0), byrow=TRUE, nrow=2)\nlossmatrix\n\nmytree <- rpart(Fraud ~ RearEnd + Whiplash, data = train, method = \"class\", maxdepth = 1, minsplit = 2, minbucket = 1,parms = list(loss = lossmatrix))\nfancyRpartPlot(mytree)\n#Now our model suggests that Whiplash is the best vairable to identify fraudulent claims. What I just described is known as an error measure and its up to the discretion of the insurance company to decide on it.\n#http://work.caltech.edu/library/042.html\n\n\n#Now let’s see how rpart interacts with factor variables. Suppose the insurance company hires an investigator to assess the activity level of claimants. Activity levels can be Very Active, Active, Inactive, or Very Inactive.\n\ntrain <- data.frame(ClaimID = c(1,2,3,4,5,6,7,8,9,10),RearEnd = c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE), Whiplash = c(TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE), Activity = factor(c(\"active\", \"very active\", \"very active\", \"inactive\", \"very inactive\", \"inactive\", \"very inactive\", \"active\", \"active\", \"very active\"),levels=c(\"very inactive\", \"inactive\", \"active\", \"very active\"), ordered=TRUE),Fraud = c(FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE))\ntrain\n\n#Grow a full tree\nmytree <- rpart(Fraud ~ RearEnd + Whiplash + Activity, data = train, method = \"class\", minsplit = 2, minbucket = 1, cp=-1)\n\nfancyRpartPlot(mytree)\n# view the importance of each variable in the model by calling the variable.importance attribute of the resulting rpart object. From the rpart documentation, “An overall measure of variable importance is the sum of the goodness of split measures for each split for which it was the primary variable…”\nmytree$variable.importance\n#When rpart grows a tree it performs 10-fold cross validation on the data. To see the cross validation results use the printcp() function.\nprintcp(mytree)\n#The rel error of each iteration of the tree is the fraction of mislabeled elements in the iteration relative to the fraction of mislabeled elements in the root. In this example, 50% of training cases are fraudulent. The first splitting criteria is “Is the claimant very active?”, which separates the data into a set of three cases, all of which are fraudulent and a set of seven cases of which two are fraudulent. Labeling the cases at this point would produce an error rate of 20% which is 40% of the root node error rate (i.e. it’s 60% better). The cross validation error rates and standard deviations are displayed in the columns xerror and xstd respectively.\n\n#As a rule of thumb, it’s best to prune a decision tree using the cp of smallest tree that is within one standard deviation of the tree with the smallest xerror. In this example, the best xerror is 0.4 with standard deviation .25298. So, we want the smallest tree with xerror less than 0.65298. This is the tree with cp = 0.2, so we’ll want to prune our tree with a cp slightly greater than than 0.2.\n\nmytree <- prune(mytree, cp=.21)\n\nfancyRpartPlot(mytree)\n\n#use our decision tree to predict fraudulent claims on an unseen dataset using the predict() function.\n\ntest <- data.frame(ClaimID = c(1,2,3,4,5,6,7,8,9,10),RearEnd = c(FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE), Whiplash = c(FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE), Activity = factor(c(\"inactive\", \"very active\", \"very active\", \"inactive\", \"very inactive\", \"inactive\", \"very inactive\", \"active\", \"active\", \"very active\"),levels=c(\"very inactive\", \"inactive\", \"active\", \"very active\"), ordered=TRUE))\ntest\n\ntest$FraudClass <- predict(mytree, newdata = test, type=\"class\") #Returns the predicted class\ntest$FraudProb <- round(predict(mytree, newdata = test, type=\"prob\"),2) #Returns a matrix of predicted probabilities\ntest\n" }, { "path": "30b-CART/c-dt-rpart-Case-DU1.R", "content": "# # Finding how people take decisions to buy products\n# Creating Decision Tree\n#install packages - rpart, rpart.plot, RColorBrewer\n\nlibrary(rpart)# doing classification\nlibrary(rpart.plot) # visualising the tree\n\n#Students : Gender - (Male & Female) buy a product\n#Rownames\nrollno = paste('S',1:1000, sep='')\nrollno\n\n#Variable Gender\nset.seed(100)\ngender = sample(x=c('Male','Female'), size=1000, replace=T, prob=c(0.5,0.5) )\nhead(gender)\ntable(gender)\n\n#Variable- Buy : Decision\nset.seed(100)\nbuy = sample(x=c('Buy','NotBuy'), size=1000, replace=T, prob=c(.41,.59) )\nhead(buy)\ntable(buy)\nprop.table(table(buy))\n\n#create Data Frame\nstudents1 = data.frame(gender, buy)\nrownames(students1) = rollno\nhead(students1)\n\nxtabs(~ gender + buy,data = students1)\n\n#Table\ntable(students1)\nprop.table(table(students1))\naddmargins(prop.table(table(students1)))\n(t1= table(students1$gender, students1$buy))\naddmargins(t1)\nprop.table(table(students1$gender, students1$buy))\naddmargins(prop.table(table(students1$gender, students1$buy))\n)\n\n#Model1\nhead(students1)\nfit = rpart(buy ~ gender, data= students1)\nfit\ntable(students1$buy)\nrpart.plot(fit,nn=T)\n\nfit1 = rpart(buy ~ gender, data=students1, minsplit=4, minbucket=2)\n#'minsplit' is min 4 obsv reqd to split a node\n#'minbucket' determines the minimal number of observations per leaf ('minbucket') \n\nfit1 #print(fit1)\n\ntable(students1$gender, students1$buy)\nhead(students1)\nlibrary(rpart.plot)\nrpart.plot(fit1, main='Classification Tree', nn=T, type=4, extra=104)\n\nfit1\n\npredict(fit1, newdata = data.frame(gender='Female'))\n\npredict(fit1, newdata = data.frame(gender='Female'), type=c('class'))\npredict(fit1, newdata = data.frame(gender=c('Male','Female','Male')), type='class')\n\n#---- Part -2 Add another column----\nset.seed(1234)\nmarried = sample(x=c('Married','Single'), size=1000, replace=T, prob=c(0.6,0.4) )\ntable(married)\n#new data frame\nstudents2 = data.frame(gender, married, buy)\nrownames(students2) = rollno\nhead(students2)\nstr(students2)\nprop.table(ftable(students2))\ntable(students2$buy) # Majority - Don't Buy\naddmargins(prop.table(table(students2)))\n#write.csv(students2, 'dtdata.csv')\n\n# Model2\n#library(rpart)\nnames(students2)\nhead(students2)\nfit2 = rpart(buy ~ gender + married, data=students2, minsplit=10, cp=-1)\n#fit2 = rpart(buy ~ gender + married, data=students2, minsplit=10)\n\nsummary(fit2)\nfit2\nrpart.plot(fit2,type=2,extra=104, tweak=1.2, under=T, shadow=c('brown', 'green','red'), nn=T)\nfit2\nprp(fit2)\nprp(fit2, main=\"An Example\",\n type=4, fallen=T, branch=.3, round=0, leaf.round=9,\n clip.right.labs=F, under.cex=1,\n box.palette=\"GnYlRd\",\n prefix=\"Student\\n\", branch.col=\"gray\", branch.lwd=2,\n extra=101, under=T, lt=\" < \", ge=\" >= \", cex.main=1.5)\n\nprp(fit2, branch.type=5)\nlabels(fit2)\n\n\n#Plot----\nlibrary(RColorBrewer)\nrpart.plot::rpart.plot(fit2, main='Classification Tree')\nrpart.plot::rpart.plot(fit2, extra=104, box.palette=\"GnBu\", branch.lty=3, shadow.col=\"gray\", nn=TRUE)\n\nrpart.plot::prp(fit2,fallen.leaves = F)\nprp(fit2, type=2)\n\n\n#Predict\n\npredict(fit2, newdata = data.frame(gender='Male', married='Married'), type='prob')\npredict(fit2, newdata = data.frame(gender='Male', married='Married'), type='class')\npredict(fit2, newdata = data.frame(gender='Male', married='Married'), type='vector')\n\npredict(fit2, newdata = data.frame(gender='Male', married='Married'))\n\ntestdata = data.frame(gender=c('Male','Male','Female','Female'), married=c('Married','Single','Married','Single'))\ntestdata\n\n(p1 = predict(fit2, newdata = testdata, type='vector')) #node/level \n#play=2, notplay=1\n(p2 = predict(fit2, newdata = testdata, type='class')) #factor\n(p3 = predict(fit2, newdata = testdata, type='prob')) # prob\n\ncbind(testdata, p1, p2, p3)\n#level number, class frequencies, probabilities\npredict(fit2, newdata= testdata, type = \"matrix\")\n\nhead(students2)\n\n#Parameters Setting : CP\nprintcp(fit2)\ngetOption('digits')\nplotcp(fit2)\nnames(fit2)\n\nfit2$where #which row at which node no\n\nstudents2[1:5,]\ncbind(students2, nodeno=rownames(fit2$frame) [ fit2$where])\nfit2\nrpart.plot(fit2)\npfit= prune(fit2, cp=0.02) # from cptable \npfit\nrpart.plot(pfit)\n\n#Interactive----\nnew.tree <- prp(fit2, snip=TRUE)$obj # interactively trim the tree\nprp(new.tree) # display the new tree\n#click on quit \n\n" }, { "path": "30b-CART/c-dt-rpart-iris.R", "content": "#CART Regression Tree\n\n#Load Libraries\nlibrary(rpart) #does only binary splits; CART\nlibrary(rpart.plot)\n\n#DataSet\nstr(iris)\nhead(iris)\nsummary(iris)\nnames(iris)\n# Classification Tree\nsummary(iris)\nset.seed(1234)\n#Predict Species\nctree = rpart(Species ~ ., method='class', data=iris)\nctree\nrpart.plot(ctree)\nrpart.plot(ctree, main='Classification Tree', nn=T, type=4, extra=104, cex=1.2)\n\nprintcp(ctree)\n\nctreeprune = prune(ctree, cp=0.44)\nctreeprune\nrpart.plot(ctreeprune, main='Classification Tree', nn=T, type=4, extra=104)\n\n# Regression Tree - Predict Continuous Value Length\n#Predict Sepal.Length\nrtree = rpart(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, method=\"anova\", data=iris )\nrtree\nrpart.plot(rtree, main='Regression Tree', nn=T, type=2, cex=1)\n\n#another way to plot\nplot(rtree, uniform=TRUE, main=\"Regression Tree for Sepal Length\")\ntext(rtree, use.n=TRUE, cex = 1)\n\n#CP\nprintcp(rtree)\nrsq.rpart(rtree) #r2 on anova only\n\nrtreeprune = prune(rtree, cp=0.023)\nrpart.plot(rtreeprune, main='Pruned Regression Tree', nn=T, type=2, cex=1)\n\n\n\n#https://analytics4all.org/2016/11/23/r-decision-trees-regression/\n\n\n#Predict : Class : Category of Flower\nstr(iris)\ntestData1 = data.frame ( Sepal.Length = 5, Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)\npredict(ctree, testData1, method = \"class\") #setosa\n\n\n#Predict : Continous Value : Length\n\ntestData2 = data.frame (Species = 'setosa', Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)\npredict(rtree, testData2, method = \"anova\") #5.17\n\n" }, { "path": "30b-CART/c-dt-rpart-sales1.R", "content": "# CART Models - Regression Trees - Merchent Sales\n#Predict the mean sales from the data; create decision tree\nlibrary(rpart) # CART algo\nlibrary(rpart.plot)# visualising decision tree\nlibrary(forecast)# calculating accuracy\n\nmsales = read.csv(file = \"./data/msales.csv\", skip=1, header = T)\ndim(msales)\n# Summarize the dataset\nsummary(msales)\nstr(msales)\n# See Summary values of sales\naggregate(msales$sales,by=list(msales$zone), FUN = mean)\n\n# Random Sampling : Train and Test\nset.seed(777) # To ensure reproducibility\nIndex = sample(x = 1:nrow(msales), size = 0.7*nrow(msales))\nIndex\nlength(Index)\n\n#divide data into 2 parts - Train (70%), Test(30%)\n# Create Train dataset\ntrain = msales[Index, ]\nnrow(train); dim(train)\n\n# Create Test dataset\ntest = msales[-Index, ]\nnrow(test); dim(test)\n\n#### Modeling ############\n# Build a full model with default settings\nset.seed(123)\nCartModel=rpart(sales ~ . ,data=train[,-1], method=\"anova\")\nCartModel\nsummary(CartModel)\n\n# Plot the Regression Tree\nrpart.plot(CartModel,type=4,fallen.leaves=T,nn=T, cex=1)\nmean(msales$sales)\nprintcp(CartModel)\nrsq.rpart(CartModel) #only for Regression Tree\n\nprune1 = prune(CartModel, cp=.014)\nprune1\nrpart.plot(prune1, nn=T, cex=1)\n\n#### Using CP to expand / Prune the tree ###\n#Start fresh Model\nRpartControl = rpart.control(cp = 0.005)\nset.seed(123)\nCartModel_1 = rpart(sales ~ ., data = msales[,-1], \n method = \"anova\", control = RpartControl)\nCartModel_1\n\nCartModel_1$where\nmsales[987,'sales']\nCartModel_1$frame\ntrainingnodes = rownames(CartModel_1$frame) [ CartModel_1$where]\ntrainingnodes\n\nsummary(CartModel_1)\nrpart.plot(CartModel_1, type = 4,cex = 0.6)\nprintcp(CartModel_1)\nrsq.rpart(CartModel_1)\n\n\n#Validation / Test Accuracy\nPredictTest=predict(CartModel,newdata=test,type=\"vector\")\nPredictTest1=predict(CartModel_1,newdata=test,type=\"vector\")\nPredictTest1\nlength(PredictTest)\nmsales[2,'sales']\ncbind(test$sales, PredictTest, PredictTest1)\n\n# Calculate RMSE and MAPE \nlibrary(forecast)\n# Validate RMSE and MAPE calculation with a function in R\nlength(train$sales)\nModelAccuracy = accuracy(PredictTest,test$sales)\nModelAccuracy1 = accuracy(PredictTest1,test$sales)\nrbind(ModelAccuracy,ModelAccuracy1)\n\n#select model with least error\n" }, { "path": "30b-CART/cls-cart-churn2.R", "content": "# Churn Case : CART\n\ntelecom <- read.csv(\"http://www.dataminingconsultant.com/data/churn.txt\", header=TRUE, sep=\",\")\n" }, { "path": "30b-CART/cls-rpart-plot2.R", "content": "#CART Plot\n#\n\n\n\nlibrary(rpart.plot)\ntree <- rpart(Kyphosis~., data=kyphosis)\nprp(tree, extra=7) # left graph\nlibrary(plotmo)\nplotmo(tree, type=\"prob\", nresponse=\"present\") # middle graph\n# type=\"prob\" is passed to predict()\nplotmo(tree, type=\"prob\", nresponse=\"present\", # right graph\n type2=\"image\", ngrid2=200, # type2=\"image\" for an image plot\n pt.col=ifelse(kyphosis$Kyphosis==\"present\", \"red\", \"lightblue\"))\nlabels(tree)\n" }, { "path": "30b-CART/dt-car.R", "content": "#DT - car\n\nlibrary(caret)\nlibrary(rpart.plot)\n\ndata_url <- c(\"https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data\")\ndownload.file(url = data_url, destfile = \"car.data\")\n\ncar_df <- read.csv(\"car.data\", sep = ',', header = FALSE)\n\nstr(car_df)\ndim(car_df)\nhead(car_df)\n#all are categories, no normalisation required\nset.seed(3033)\nintrain = createDataPartition(y=car_df$V7, p=0.7, list=F)\nintrain\ntraining = car_df[intrain,]\ntesting = car_df[-intrain,]\n\ndim(training)\ndim(testing)\n#---------------\nanyNA(car_df)\nsummary(car_df)\n\n#---- Training DT classifier with criterion as information gain\n#Training DT classifier with criterion as Info Gain\n\ntrctrl = trainControl(method ='repeatedcv', number = 10, repeats=3)\nset.seed(333)\ndtree_fit = train(V7 ~ ., data= training, method='rpart', parms= list(split='information'), trControl=trctrl, tuneLength = 10)\n\ndtree_fit\n\n#Plot DT\nprp(dtree_fit$finalModel, box.palette = 'Reds', tweak=1.2)\n\n\n#---- Prediction\ntesting[1,]\npredict(dtree_fit, newdata = testing[1,])\n\ntest_pred = predict(dtree_fit, newdata = testing)\ntest_pred\nconfusionMatrix(test_pred, testing$V7)\n\n#---- Gini Index\nset.seed(3333)\ndtree_fit_gini = train(V7 ~ . , data = training, method = 'rpart', \n parms = list(split='gini'), trControl=trctrl, tuneLength=10)\ndtree_fit_gini\nprp(dtree_fit_gini$finalModel, box.palette = 'Blues', tweak=1.2)\n\n#Prediction\ntest_pred_gini = predict(dtree_fit_gini, newdata=testing)\nconfusionMatrix(test_pred_gini, testing$V7)\n\n" }, { "path": "30b-CART/dt-general.R", "content": "# Decision Tree 2\n#not working\n\nrefund=c('Yes','No' ,'No', 'Yes','No','No', 'Yes','No','No','No')\nmarried=c('S','M','S','M','D','M','D','S','M','S')\nincome=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)\ncheat=c('No','No','No','No','Yes','No','No','Yes','No','Yes')\n\ntrain= data.frame(refund, married, income, cheat)\ntrain\n\nlibrary(rpart)\nfit1 = rpart(cheat ~ ., data=train,cp=0)\nfit1\n\nlibrary(rpart.plot)\nrpart.plot(fit1)\n\n" }, { "path": "30b-CART/dt-glaucoma.R", "content": "#https://www.r-project.org/conferences/useR-2009/slides/Hothorn+Zeileis.pdf\n\nlibrary(ipred)\ndata(data=GlaucomaMVF , package = \"ipred\")\n\nlibrary(\"rpart\")\nhead(GlaucomaMVF)\ng_rpart <- rpart(Class ~ ., data = GlaucomaMVF)\nprint(g_rpart)\nplot(g_rpart)\ntext(g_rpart)\n\nlibrary(\"partykit\")\ng_party <- as.party(g_rpart)\nprint(g_party, header = FALSE)\nprint(g_party, header = T)\nplot(g_party, type = \"simple\")\nplot(g_party, type = \"extended\")\n\n" }, { "path": "30b-CART/dt-ionos1.R", "content": "#Ionosphere\n#https://eight2late.wordpress.com/2016/02/16/a-gentle-introduction-to-decision-trees-using-r/\n#\n#set working directory if needed (modify path as needed)\n#setwd(“C:/Users/.....”)\n#load required libraries – rpart for classification and regression trees\nlibrary(rpart)\n#mlbench for Ionosphere dataset\nlibrary(mlbench)\n#load Ionosphere\ndata(Ionosphere)\nstr(Ionosphere)\n#set seed to ensure reproducible results\nset.seed(42)\n#split into training and test sets\nIonosphere[,'train'] <- ifelse(runif(nrow(Ionosphere))<0.8,1,0)\n#separate training and test sets\ntrainset <- Ionosphere[Ionosphere$train==1,]\ntestset <- Ionosphere[Ionosphere$train==0,]\n#get column index of train flag\ntrainColNum <- grep(\"train\",names(trainset))\n#remove train flag column from train and test sets\ntrainset <- trainset[,-trainColNum]\ntestset <- testset[,-trainColNum]\n\n\n#get column index of predicted variable in dataset\ntypeColNum <- grep(\"Class\",names(Ionosphere))\ntypeColNum\n#build model\nrpart_model <- rpart(Class~.,data = trainset, method=\"class\")\n#plot tree\nplot(rpart_model);text(rpart_model)\n\n\n#…and the moment of reckoning\nrpart_predict <- predict(rpart_model,testset[,-typeColNum],type=\"class\")\nmean(rpart_predict==testset$Class)\n#confusion matrix\ntable(pred=rpart_predict,true=testset$Class)\n\n#cost-complexity pruning\nprintcp(rpart_model)\n\n# get index of CP with lowest xerror\nopt <- which.min(rpart_model$cptable[,\"xerror\"])\n#get its value\ncp <- rpart_model$cptable[opt, \"CP\"]\ncp\n\n#prune tree\npruned_model <- prune(rpart_model,cp)\n#plot tree\nplot(pruned_model);text(pruned_model)\n" }, { "path": "30b-CART/dt-iris1.R", "content": "# Iris\n#\nset.seed(1234)\niris.rp = rpart(Species ~ ., method='class', data=iris, \n control=rpart.control(minsplit=4, cp=0.00001))\nprintcp(iris.rp)\niris.rp\n\niris.rp = rpart(Species ~ ., method='anova', data=iris, \n control=rpart.control(minsplit=4, cp=0.00001))\nprintcp(iris.rp)\n\n" }, { "path": "30b-CART/dt-kyphosis.R", "content": "#\n#https://www.statmethods.net/advstats/cart.html\n# Classification Tree with rpart\n\nlibrary(rpart)\n?kyphosis\n# grow tree \nfit <- rpart(Kyphosis ~ Age + Number + Start,\n method=\"class\", data=kyphosis)\nhead(kyphosis) #DV Kyphosis\nsummary(kyphosis)\nprintcp(fit) # display the results \nplotcp(fit) # visualize cross-validation results \nsummary(fit) # detailed summary of splits\n\nlibrary(tree)\nsummary(tree( Kyphosis ~ Age + Number + Start, data=kyphosis))\n\n\n# plot tree \nplot(fit, uniform=TRUE, main=\"Classification Tree for Kyphosis\")\ntext(fit, use.n=TRUE, all=TRUE, cex=.8)\n\n# create attractive postscript plot of tree \npost(fit, file = \"tree.ps\", \n title = \"Classification Tree for Kyphosis\")\n\n\n# prune the tree \n#pfit <- prune(fit, cp= fit$cptable[which.min(fit$cptable[,\"xerror\"]),\"CP\"])\npfit <- prune(fit, cp=0.01)\n\npfit\n\n# plot the pruned tree \nplot(pfit, uniform=TRUE, main=\"Pruned Classification Tree for Kyphosis\")\ntext(pfit, use.n=TRUE, all=TRUE, cex=.8)\npost(fit, file = \"ptree.ps\", title = \"Pruned Classification Tree for Kyphosis\")\n\n\n\n# Regression Tree Example\nlibrary(rpart)\n?cu.summary\n# grow tree \nfit <- rpart(Mileage~Price + Country + Reliability + Type, method=\"anova\", data=cu.summary)\n\nprintcp(fit) # display the results \nplotcp(fit) # visualize cross-validation results \nsummary(fit) # detailed summary of splits\n\n# create additional plots \npar(mfrow=c(1,2)) # two plots on one page \nrsq.rpart(fit) # visualize cross-validation results \t\n\n# plot tree \nplot(fit, uniform=TRUE, main=\"Regression Tree for Mileage \")\ntext(fit, use.n=TRUE, all=TRUE, cex=.8)\n\n# create attractive postcript plot of tree \npost(fit, file = \"c:/tree2.ps\", title = \"Regression Tree for Mileage \")\n\n\n# prune the tree \npfit<- prune(fit, cp=0.01160389) # from cptable \n\n# plot the pruned tree \nplot(pfit, uniform=TRUE, main=\"Pruned Regression Tree for Mileage\")\ntext(pfit, use.n=TRUE, all=TRUE, cex=.8)\npost(pfit, file = \"c:/ptree2.ps\", title = \"Pruned Regression Tree for Mileage\")\n\n\n\n# Conditional Inference Tree for Kyphosis\nlibrary(party)\nfit <- ctree(Kyphosis ~ Age + Number + Start, data=kyphosis)\nplot(fit, main=\"Conditional Inference Tree for Kyphosis\")\n\n# Conditional Inference Tree for Mileage\nlibrary(party)\nfit2 <- ctree(Mileage~Price + Country + Reliability + Type, data=na.omit(cu.summary))\n\n\n# Random Forest prediction of Kyphosis data\nlibrary(randomForest)\nfit <- randomForest(Kyphosis ~ Age + Number + Start, data=kyphosis)\nprint(fit) # view results \nimportance(fit) # importance of each predictor\n" }, { "path": "30b-CART/dt-loanapproved1.R", "content": "# Decision Tree \n# loanapproved = age + job + house + credit\n\nloanapproved = sample(x=c('Yes','No'), size=50, replace=T)\nage = runif(50,30,60)\nhouse = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.4,.6))\njob = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.6,.4))\ncredit = ceiling(rnorm(50,100, 10))\n\nloan = data.frame(loanapproved, age,job, credit)\nloan\nhead(loan)\nlibrary(rpart)\ntable(loan$loanapproved, loan$credit)\n\nloan_tree = rpart(loanapproved ~ ., data=loan, method='class', control=rpart.control(minsplit=5, cp=.09))\nloan_tree\nplot(loan_tree)\ntext(loan_tree)\n\nlibrary(rpart.plot)\nrpart.plot(loan_tree)\nrpart.plot(loan_tree, type = 4,fallen.leaves = T, cex = 0.6)\nrpart.plot(loan_tree,type=2, extra=104, cex=1, tweak=1, under=T, shadow=c('brown', 'green','red'), nn=T)\n\nprintcp(loan_tree)\n\n" }, { "path": "30b-CART/dt-rpart-du.R", "content": "# Decision Tree - DU Example\nlibrary(rpart)\nlibrary(rpart.plot)\nlibrary(caret) # variable imp\n\n#Case-1----\n#Student Data Create Data\n(rollno = paste('S',1:30, sep=''))\nset.seed(1234)\n(gender = sample(x=c('Male', 'Female'), size=30, replace=T, prob=c(0.5,0.5) ))\ntable(gender)\nset.seed(1234)\n(play = sample(x=c('Play','NotPlay'), size=30, replace=T, prob=c(17/30,13/30) ))\ntable(play)\nprop.table(table(play))\n\nstudents1 = data.frame(gender, play)\nrownames(students1) = rollno\nstudents1\ntable(students1)\n\n#Model1\nset.seed(1234)\nfit1 = rpart(play ~ gender, data=students1, minsplit=5, cp=-1)\nprint(fit1)\nvarImp(fit1)\noptions(digits=4)\nrpart.plot(fit1, nn=T)\n(t1=table(students1$play))\nprop.table(t1)\n\n(t2= table(students1$gender, students1$play))\nprop.table(t2)\nprop.table(t2,1)\n\n\n#Predict\npredict(fit1, newdata = data.frame(gender=c('Female')))\npredict(fit1, newdata = data.frame(gender=c('Male', 'Female','Male')))\npredict(fit1, newdata = data.frame(gender=c('Male', 'Female','Male')), type='class')\n\n#Case-2----\n#Create Dataset\nset.seed(1234)\ngender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.4,0.6) )\ntable(gender)\nset.seed(1234)\nmarried = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.3,0.7) )\ntable(married)\n\nset.seed(1234)\nplay = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(10/30,20/30) )\ntable(play)\n\nstudents2 = data.frame(gender, married, play)\n#rownames(students2) = rollno\nhead(students2)\nftable(students2)\n\n#Model2\nset.seed(1234)\nfit2 = rpart(play ~ gender + married, data=students2, minsplit=5, cp=-1)\nfit2\nsummary(fit2)\nvarImp(fit2)\n\n#Plot\nrpart.plot::rpart.plot(fit2, nn=T, main='Model2 : Classification Tree')\n\nrpart.plot::rpart.plot(fit2, extra=104, box.palette=\"GnBu\", branch.lty=3, shadow.col=\"gray\", nn=TRUE)\n\nrpart.plot::prp(fit2, extra=104, box.palette=\"GnBu\", branch.lty=3, shadow.col=\"gray\", nn=TRUE, fallen.leaves = T)\n\n\n#Predict\n\npredict(fit2, newdata = data.frame(gender='Male', married='Married'))\ntestdata = data.frame(gender=c( 'Male','Male','Female','Female'), married=c('Married', 'Single','Married','Single'))\ntestdata\n\n(p1 = predict(fit2, newdata = testdata, type='vector'))\n(p2 = predict(fit2, newdata = testdata, type='class'))\n(p3 = predict(fit2, newdata = testdata, type='prob'))\ncbind(testdata, p1, p2, p3)\npredict(fit2, newdata= testdata, type = \"matrix\")\n\n# Accuracy rate\n(pdata = predict(fit2, newdata=students2, type='class'))\ntable(students2$play, pdata)\nsum(students2$play==pdata)/length(pdata)\ncaret::confusionMatrix(factor(students2$play), pdata)\n\n#Location of each data point\nfit2$where\nrownames(fit2$frame) [fit2$where]\n?fit2$frame\n?rpart\npath.rpart(fit2, c(2,3), pretty = 0, print.it = TRUE)\nresiduals(fit2, type = c(\"usual\"))\n#reduce/ snip the nodes\nrpart.plot(snip.rpart(fit2, c(2,3,4,5)))\n\n#Pruning\nprintcp(fit2)\nplotcp(fit2)\nfit2a = prune(fit2, cp=0)\nfit2a\nrpart.plot(fit2a)\n\n#Case-3----\n#Create Dataset\nset.seed(1234)\ngender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.7,0.3) )\ntable(gender)\nset.seed(1234)\nmarried = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.2,0.8) )\ntable(married)\n\nset.seed(1111)\nplay = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(20/30,10/30) )\ntable(play)\nset.seed(1234)\neducation = sample(x=c('school','graduate', 'pg'), size=30, replace=T, prob=c(0.3,0.4,.3) )\ntable(education)\nset.seed(1234)\nhostel = sample(x=c(TRUE, FALSE), size=30, replace=T, prob=c(.3,.7))\ntable(hostel)\n\nset.seed(1234)\nmarks = floor(rnorm(30,70,20))\nmean(marks)\n\nstudents3 = data.frame(gender, married, education, hostel, marks,play)\nwith(students3, ftable(education, hostel, gender, married,play))\nset.seed(1234)\nfit3 = rpart(play ~ . , data=students3, minsplit=5, cp=-1)\nfit3\nvarImp(fit3) #at root level \n\nprintcp(fit3)\n(bestcp= fit3$cptable[which.min(fit3$cptable[,'xerror']),'CP'])\nfit3b = prune(fit3, cp=0)\nfit3b\n#fit3b.pruned = prune(fit3, cp=bestcp)\nrpart.plot(fit3b, nn=T, cex=.8)\nfit3b$where\npath.rpart(fit3b, nodes=c(1,2,7,13), pretty =0, print.it = TRUE)\ntestdata1= data.frame(gender='Male', married='Married',\n education='school', hostel=TRUE, marks=60)\npredict(fit3b, newdata = testdata1 )\n\n\n#Case-4----\n#Create Dataset V is numeric CV\nset.seed(1234)\ngender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.4,0.6) )\ntable(gender)\nset.seed(1234)\nmarried = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.3,0.7) )\ntable(married)\n\nset.seed(1234)\nplay = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(10/30,25/30) )\ntable(play)\n\nset.seed(1234)\nincome = ceiling(runif(30, 50,100) )\nmean(income)\n\nstudents4 = data.frame(income,gender, married, play)\nrownames(students4) = rollno\nhead(students4)\nstr(students4)\n\n#Model4\nfit4 = rpart(income ~ gender + married + play, data=students4, minsplit=5, cp=-1, method='anova')\nfit4\nrpart.plot(fit4)\n\n#complexity Parameter\nprintcp(fit4)\nrsq.rpart(fit4)\n\n#Predict\n(ndata= data.frame(gender='Male', married='Single', play='Play'))\npredict(fit4, newdata=ndata, type='vector')\nround(residuals(fit4),2)\nstudents4b = cbind(head(students4), predicted=predict(fit4, newdata=head(students4)))\ndiff = round(students4b$income - students4b$predicted,2)\ncbind(rownames(students4b), diff)\n\n#Case-5 ----\n#: Divide and conquer\n\nhead(students2)\nfit2\nrpart.plot(fit2, nn=T)\nnode1= students2\nwrite.csv(node1, \"./data/node1.csv\")\ntable(node1$play)\nprop.table(table(node1$play))\nnode1[,1:3] = lapply(node1[,c(1:3)], factor)\nnode1\nstr(node1)\n(node123=split(node0, node0$married))\n\n(node2= node123$Single)\ntable(node2$play)\nprop.table(table(node2$play))\n\n(node3= node123$Married)\n(node4 = split(node2, node2$gender)$Female)\n(node5 = split(node2, node2$gender)$Male)\nnrow(node1); nrow(node2); nrow(node3);nrow(node4);nrow(node5)\n" }, { "path": "30b-CART/dt-rpart-du1.R", "content": "# # Finding how people take decisions to buy products\n# Creating Decision Tree\n\nlibrary(rpart)\nlibrary(rplart.plot)\n\n#Students : Gender - (Male & Female) buy a product\n#Rownames\nrollno = paste('S',1:1000, sep='')\nrollno\n\n#Variable Gender\nset.seed(100)\ngender = sample(x=c('Male','Female'), size=1000, replace=T, prob=c(0.5,0.5) )\nhead(gender)\ntable(gender)\n\n#Variable- Buy : Decision\nset.seed(3000)\nbuy = sample(x=c('Buy','NotBuy'), size=1000, replace=T, prob=c(.49,.51) )\nhead(buy)\ntable(buy)\nprop.table(table(buy))\n\n#create Data Frame\nstudents1 = data.frame(gender, buy)\nrownames(students1) = rollno\nhead(students1)\n\n#Table\ntable(students1)\nprop.table(table(students1))\naddmargins(prop.table(table(students1)))\n(t1= table(students1$gender, students1$buy))\naddmargins(t1)\nprop.table(table(students1$gender, students1$buy))\naddmargins(prop.table(table(students1$gender, students1$buy))\n)\n#Model1\nfit1 = rpart(buy ~ gender, data=students1,minsplit=4, minbucket=2)\n#'minsplit' is 20 and determines the minimal number of observations per leaf ('minbucket') \nfit1 #print(fit1)\ntable(students1$gender, students1$buy)\nhead(students1)\nlibrary(rpart.plot)\nrpart.plot(fit1, main='Classification Tree', nn=T, type=4, extra=104)\n\nfit1\npredict(fit1, newdata = data.frame(gender='Female'))\npredict(fit1, newdata = data.frame(gender='Female'), type=c('class'))\npredict(fit1, newdata = data.frame(gender=c('Male','Female','Male')), type='class')\n\n#---- Part -2 Add another column\nset.seed(5000)\nmarried = sample(x=c('Married','Single'), size=1000, replace=T, prob=c(0.5,0.5) )\ntable(married)\nstudents2 = data.frame(gender, married, buy)\nrownames(students2) = rollno\nhead(students2)\nstr(students2)\nprop.table(ftable(students2))\ntable(students2$buy) # Majority - Don't Buy\naddmargins(prop.table(table(students2)))\n#write.csv(students2, 'dtdata.csv')\n\n# Model2\n#library(rpart)\nfit2 = rpart(buy ~ gender + married, data=students2, minsplit=12)\nsummary(fit2)\nfit2\nrpart.plot(fit2,type=2,extra=104, tweak=1.2, under=T, shadow=c('brown', 'green','red'), nn=T)\nfit2\nprp(fit2)\nprp(fit2, main=\"An Example\",\n type=4, fallen=T, branch=.3, round=0, leaf.round=9,\n clip.right.labs=F, under.cex=1,\n box.palette=\"GnYlRd\",\n prefix=\"Student\\n\", branch.col=\"gray\", branch.lwd=2,\n extra=101, under=T, lt=\" < \", ge=\" >= \", cex.main=1.5)\n\nprp(fit2, branch.type=5)\nlabels(fit2)\n#Interactive\n\nnew.tree <- prp(fit2, snip=TRUE)$obj # interactively trim the tree\nprp(new.tree) # display the new tree\n\n#Plot----\nlibrary(RColorBrewer)\n#library(rattle)\n\nrpart.plot::rpart.plot(fit2, main='Classification Tree')\nrpart.plot::rpart.plot(fit2, extra=104, box.palette=\"GnBu\", branch.lty=3, shadow.col=\"gray\", nn=TRUE)\n\nrpart.plot::prp(fit2,fallen.leaves = F)\nprp(fit2, type=2)\n\n\n#Predict\n#\npredict(fit2, newdata = data.frame(gender='Male', married='Married'), type='prob')\npredict(fit2, newdata = data.frame(gender='Male', married='Married'), type='class')\npredict(fit2, newdata = data.frame(gender='Male', married='Married'), type='vector')\n\npredict(fit2, newdata = data.frame(gender='Male', married='Married'))\n?predict\n\n\ntestdata = data.frame(gender=c('Male','Male','Female','Female'), married=c('Married','Single','Married','Single'))\ntestdata\n \n(p1 = predict(fit2, newdata = testdata, type='vector')) #node/level \n#play=2, notplay=1\n(p2 = predict(fit2, newdata = testdata, type='class')) #factor\n(p3 = predict(fit2, newdata = testdata, type='prob')) # prob\n\ncbind(testdata, p1, p2, p3)\n#level number, class frequencies, probabilities\npredict(fit2, newdata= testdata, type = \"matrix\")\n\nhead(students2)\n\n#Parameters Setting : CP\nprintcp(fit2)\ngetOption('digits')\nplotcp(fit2)\nnames(fit2)\n\nfit2$where #which row at which nodeno\n\nstudents2[1:5,]\ncbind(students2, nodeno=rownames(fit2$frame) [ fit2$where])\npfit= prune(fit2, cp=0.077) # from cptable \npfit\nrpart.plot(pfit)\n\n#do some changes to pfit\n\n#--------------------------------------------------------\n\n#add column with 3 classes and numeric and logical\nset.seed(105)\neducation = sample(x=c('school','graduate', 'pg'), size=1000, replace=T, prob=c(0.3,0.4,.3) )\neducation; table(education)\nset.seed(106)\nhostel = sample(x=c(TRUE, FALSE), size=1000, replace=T, prob=c(.3,.7))\ntable(hostel)\nset.seed(107)\nmarks = floor(runif(1000,50,100))\nmean(marks)\nstudents3 = data.frame(gender, married, education, hostel,marks,buy)\nwith(students3, ftable(education, hostel, gender, married,buy))\n\n# Model3\nfit3a = rpart(buy ~ ., data=students3)\nfit3a\nrpart.plot::rpart.plot(fit3a, main='Classification Tree')\n\n#Model3b : change some parameters minbucket, minsplit\nfit3b = rpart(buy ~ ., data=students3, minsplit=4, minbucket=2) #control= rpart.control(cp=0.00001))#use low cp\nfit3b\nrpart.plot::rpart.plot(fit3b, main='Classification Tree', cex=.6, type=3, extra=104, nn=T)\nrpart.plot::prp(fit3b)\n#rattle::fancyRpartPlot(model = fit3b, main = \"Final CART Regression Tree\", cex = 0.6, sub = \"Model3\")\nprp(fit3b,box.col=c(\"Grey\", \"Orange\")[fit3b$frame$yval],varlen=0,faclen=0, type=1,extra=4,under=TRUE, tweak=1.2)\n\n\n#Lets see CP\nplotcp(fit3b)\nprintcp(fit3b, digits = getOption(\"digits\") - 5)\n(bestcp= fit3b$cptable[which.min(fit3b$cptable[,'xerror']),'CP'])\n#but this is at root node only, select next better \nbestcp = 0.01\n\nprp(fit3b)\nfit3b2 = rpart(buy ~ ., data=students3, minsplit=4, minbucket=2, control= rpart.control(cp=0.001))\nfit3b2\nprp(fit3b2)\nfit3b.pruned = prune(fit3b, cp=bestcp)\nfit3b.pruned\nprp(fit3b.pruned)\n\nrpart.plot(fit3b.pruned,cex=.6, extra=101, type=1)\n\n\n#Predict Model3\nfit3b.pruned$where\nfit3b.pruned\npath.rpart(fit3b.pruned, nodes=c(1,4,10,43), pretty = 0, print.it = TRUE)\ntestdata1= data.frame(gender='Male', married='Married', education='school', hostel=TRUE, marks=60)\ntestdata1\npredict(fit3b.pruned, newdata = testdata1 )\n\n\n\n# now practise with Marketing Data\n\n" }, { "path": "30b-CART/dt-rpart-du2.R", "content": "# Decision Tree 2 : Not Working\n\nrefund=c('Yes','No' ,'No', 'Yes','No','No', 'Yes','No','No','No')\nmarried=c('S','M','S','M','D','M','D','S','M','S')\nincome=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)\ncheat=c('No','No','No','No','Yes','No','No','Yes','No','Yes')\ntrain= data.frame(refund, married, income, cheat)\ntrain\nlibrary(rpart)\nfit1 = rpart(cheat ~ ., data=train, cp=-1, minsplit=3)\nfit1\nlibrary(rpart.plot)\nrpart.plot(fit1)\n" }, { "path": "30b-CART/dt-rpart-du3.R", "content": "# Simple Example in CT\n\ntrain1 = data.frame(rollno=c(1,2,3), gender=c('M','M','F'),\n play=c('Play','NoPlay','Play'))\ntrain1\n\nlibrary(rpart)\n?rpart\nmytree1 = rpart (play ~ gender, data=train1, method='class', minsplit=2, cp=-1)\nmytree1\nrpart.plot(mytree1)\n\n#more data\ntrain2 = data.frame(rollno=c(1,2,3,4), gender=c('M','M','F','F'), play=c('Play','NoPlay','Play','Play'))\nrpart (play ~ gender, data=train2, method='class')\nrpart (play ~ gender, data=train2, method='class',\n minsplit=1, minbucket=1)\ntrain2\n\n# Part -2 : increase data points\ntrain3 = data.frame(rollno=c(1,2,3,4,5,6,7), gender=c('M','M','F','M','F','F','M'), play=c('Play','Play','NoPlay','Play','Play','NoPlay','Play'))\ntrain3\nmytree3 = rpart (play ~ gender, data=train3, method='class',\n minsplit=2, minbucket=1 )\n#method - class uses gini index\nmytree3\ntrain3\npath.rpart(mytree3, nodes=c(1,2,3), pretty = 0, print.it = TRUE)\nmytree3$where\npredict(mytree3, newdata=data.frame(gender='F'),\n type = c(\"class\"))\n\ntable(train3$gender,train3$play)\nlibrary(rattle)\nlibrary(rpart.plot)\nlibrary(RColorBrewer)\nfancyRpartPlot(mytree3)\npredict(mytree3, newdata=data.frame(gender='M'))\n\n\n# Another method to do \nmytree3a = rpart (play ~ gender, data=train3, method='class',\n minsplit=2, minbucket=1 )\nfancyRpartPlot(mytree3a)\n\n#cp usage\nmytree4 = rpart (play ~ gender, data=train3, method='class',\n minsplit=2, minbucket=1, cp=-1 )\nmytree4\nfancyRpartPlot(mytree4)\n\n# first DF\nmytree1b = rpart (play ~ gender, data=train1, method='class',\n minsplit=2, minbucket=1, cp=-1 )\nmytree1b\n\n# Add more columsn to train3\ntrain3$married = c('Married','Single','Married',\n 'Married','Married','Single','Single')\ntrain3\nmytree4b = rpart (play ~ gender + married, data=train3,\n method='class', minsplit=2, minbucket=1)\nfancyRpartPlot(mytree4b)\n\nmytree4b = rpart (play ~ gender + married, data=train3,\n method='class', minsplit=2, minbucket=1, cp=-1 )\nmytree4b\nfancyRpartPlot(mytree4b)\ntable(train3$play, train3$gender)\nmytree4b$where\ntrain3\n# Giving Weights\nmytree4c = rpart (play ~ gender + married, data=train3, method='class',\n minsplit=2, minbucket=1)\nfancyRpartPlot(mytree4c)\nmytree4c = rpart (play ~ gender, data=train3, method='class',\n weights=c(.3,.1,.1,.1,.1,.1,.2), minsplit=2, minbucket=1)\nfancyRpartPlot(mytree4c)\n\npredict(mytree4b, newdata=data.frame(gender='M',\n married='Single'))\nprintcp(mytree4b)\n" }, { "path": "30b-CART/dt-rpart-metal.R", "content": "# Decision Tree\n\nlibrary(rpart)\nraw.orig <- read.csv(file='E:/dataSets/DM/rsei212_chemical.txt', header=T, sep=\"\\t\")\nstr(raw.orig)\nhead(raw.orig)\n# Keep the dataset small and tidy\n# The Dataverse: hdl:1902.1/21235\nraw = subset(raw.orig, select=c(\"Metal\",\"OTW\",\"AirDecay\",\"Koc\"))\n\nrow.names(raw) = raw.orig$CASNumber\nraw = na.omit(raw);\nraw\nfrmla = Metal ~ OTW + AirDecay + Koc\n\n# Metal: Core Metal (CM); Metal (M); Non-Metal (NM); Core Non-Metal (CNM)\n\nfit = rpart(frmla, method=\"class\", data=raw)\n\nprintcp(fit) # display the results\nplotcp(fit) # visualize cross-validation results\nsummary(fit) # detailed summary of splits\n\n# plot tree\nplot(fit, uniform=TRUE, main=\"Classification Tree for Chemicals\")\ntext(fit, use.n=TRUE, all=TRUE, cex=.8)\n\n# tabulate some of the data\ntable(subset(raw, Koc>=190.5)$Metal)\n\n" }, { "path": "30b-CART/dt-rpart-student1.R", "content": "#Classification\n\ndata <- read.csv(\"./data/studentdata.csv\")\nhead(data)\ndata$class = sample(c('Pass','Fail'), size=100, replace=TRUE, prob=c(0.6,0.4))\nstr(data)\nhead(data[c(1:4,13)])\ndata1 = data[-c(2,3)]\nstr(data1)\nhead(data1)\nlibrary(rpart)\nset.seed(1234)\ntrain = sample(nrow(data1), 0.7*nrow(data1) )\ndata1.train = data1[train, ]\ndata1.train\ndata1.validate= data1[-train, ]\ntable(data1.train$class)\ntable(data1.validate$class)\n\n#attribute selectio\ndtree = rpart(class ~ ., data=data1.train, method= 'class', parms =\n list(split='information') )\nprint(dtree)\nsummary(dtree)\ndtree$cptable\nplotcp(dtree)\ndtree.pruned = prune(dtree, cp =.01)\nlibrary(rpart.plot)\n\nprp(dtree.pruned, type=2, extra = 104,\n fallen.leaves=T, main='Decision Tree')\ndtree.pred = predict(dtree.pruned, data1.validate, type='class')\ndtree.perf = table(data1.validate$class, dtree.pred, dnn=c('Actual', 'Predicted') )\ndtree.perf\n\n\n#Method2 : errors\n#Eg Character Value not supported\nlibrary(party)\nfit.ctree = party::ctree(class ~ ., data = data1.train)\nplot(fit.ctree)\n#Logistics\nfit.logit = glm(class ~. , data = data1.train, family = binomial() )\n" }, { "path": "30b-CART/dt-rpart-text1.R", "content": "# Text Rpart\nlibrary(rpart)\nfreen.tr <- rpart(y ~ ., freeny)\npar(xpd = TRUE)\nplot(freen.tr)\ntext(freen.tr, use.n = TRUE, all = TRUE)\nlabels(freen.tr, digits = 4, minlength = 1L, pretty, collapse = TRUE)\n" }, { "path": "30b-CART/dt-rpart-varimp1.R", "content": "#DT - Variable Imp 1\n\nlibrary(rpart)\nlibrary(rpart.plot)\nlibrary(caret)\n\n#Case-2----\n#Create Dataset\nset.seed(1234)\ngender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.4,0.6) )\ntable(gender)\ngender = factor(gender)\nset.seed(1234)\nmarried = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.3,0.7) )\ntable(married)\nmarried = factor(married)\nset.seed(1234)\nplay = factor(sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(10/30,20/30) ))\ntable(play)\n\nstudents2 = data.frame(gender, married, play)\n#rownames(students2) = rollno\nhead(students2)\nftable(students2)\n\n#Model2\nset.seed(1234)\nfit2 = rpart(play ~ gender + married, data=students2, minsplit=5, cp=-1)\nfit2 ; rpart.plot(fit2)\nsummary(fit2)\nnames(fit2)\nlibrary(caret)\nlibrary(caret)\nvarImp(fit2)\n\n\n#Plot\nrpart.plot::rpart.plot(fit2, nn=T, main='Model2 : Classification Tree')\n\nrpart.plot::rpart.plot(fit2, extra=104, box.palette=\"GnBu\", branch.lty=3, shadow.col=\"gray\", nn=TRUE)\n\nrpart.plot::prp(fit2, extra=104, box.palette=\"GnBu\", branch.lty=3, shadow.col=\"gray\", nn=TRUE, fallen.leaves = T)\n\n\n#Predict\n\npredict(fit2, newdata = data.frame(gender='Male', married='Married'))\ntestdata = data.frame(gender=c( 'Male','Male','Female','Female'), married=c('Married', 'Single','Married','Single'))\ntestdata\n\n(p1 = predict(fit2, newdata = testdata, type='vector'))\n(p2 = predict(fit2, newdata = testdata, type='class'))\n(p3 = predict(fit2, newdata = testdata, type='prob'))\ncbind(testdata, p1, p2, p3)\npredict(fit2, newdata= testdata, type = \"matrix\")\n\n\n#Variable Importance\n\nset.seed(1234)\ndata(mtcars)\nlibrary(randomForest)\nmtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000, keep.forest=FALSE,\n importance=TRUE)\nvarImpPlot(mtcars.rf)\n\n\n\nstr(students2)\nstudents2.rf <- randomForest(play ~ gender + married, data=students2, ntree=1000, keep.forest=FALSE,importance=TRUE)\n?randomForest\nvarImpPlot(students2.rf)\nimportance(students2.rf)\n\n#Mean Decrease in Accuracy the number or proportion of observations that are incorrectly classified by removing the feature (or values from the feature) in question from the model?\"\n\n\n" }, { "path": "30b-CART/dt-rpart-varimp2.R", "content": "# Decision Tree - Variable Importance - rpart\n\nlibrary(rpart)\nlibrary(rpart.plot)\nlibrary(caret)\n\n#Case-3----\n#Create Dataset\nset.seed(1234)\ngender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.7,0.3) )\ntable(gender)\nset.seed(1234)\nmarried = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.2,0.8) )\ntable(married)\n\nset.seed(1111)\nplay = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(20/30,10/30) )\ntable(play)\nset.seed(1234)\neducation = sample(x=c('school','graduate', 'pg'), size=30, replace=T, prob=c(0.3,0.4,.3) )\ntable(education)\nset.seed(1234)\nhostel = sample(x=c(TRUE, FALSE), size=30, replace=T, prob=c(.3,.7))\ntable(hostel)\n\nset.seed(1234)\nmarks = floor(rnorm(30,70,20))\nmean(marks)\n\nstudents3 = data.frame(gender, married, education, hostel, marks,play)\nwith(students3, ftable(education, hostel, gender, married,play))\nset.seed(1234)\nfit3 = rpart(play ~ . , data=students3, minsplit=5, cp=-1)\nfit3\nvarImp(fit3) #at root level \nsummary(fit3)\nrpart.plot(fit3)\n\n#Split DF - daply & split, subset\nlibrary(plyr)\nx=daply(students3, .(gender), function(x) return(x))\nx\n(split1 = split(students3, marks >=76))\nsplit1$'FALSE'\nsplit(students3, marks >=76)$'FALSE'\nsplit(students3, marks >=76)[['TRUE']]\n\ns3Left = as.data.frame(split1$'FALSE')\ns3Right = as.data.frame(split1$'TRUE')\n#left node\nfitL2 = rpart(play ~ ., data = s3Left, minsplit=5, cp=-1)\nfitL2\nrpart.plot(fitL2)\nvarImp(fitL2) # other variable are not important\nprintcp(fitL2)\n\nfitL2b = rpart(play ~ ., data = s3Left, minsplit=5, cp=.1)\nfitL2b\nvarImp(fitL2b) # other variable are not important\nrpart.plot(fitL2b)\n\n#right side\ns3Right = as.data.frame(split1$'TRUE')\nfitR2 = rpart(play ~ ., data = s3Right, minsplit=5, cp=-1)\nfitR2\nrpart.plot(fitR2)\nvarImp(fitR2) # other variable are not important\nprintcp(fitR2)\n\nfitL2b = rpart(play ~ ., data = s3Left, minsplit=5, cp=.1)\nfitL2b\nvarImp(fitL2b) # other variable are not important\nrpart.plot(fitL2b)\n\n\n\n\n#Case-4----\n#Create Dataset V is numeric CV\nset.seed(1234)\ngender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.4,0.6) )\ntable(gender)\nset.seed(1234)\nmarried = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.3,0.7) )\ntable(married)\n\nset.seed(1234)\nplay = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(10/30,25/30) )\ntable(play)\n\nset.seed(1234)\nincome = ceiling(runif(30, 50,100) )\nmean(income)\n\nstudents4 = data.frame(income,gender, married, play)\nrownames(students4) = rollno\nhead(students4)\nstr(students4)\n\n#Model4\nfit4 = rpart(income ~ gender + married + play, data=students4, minsplit=5, cp=-1, method='anova')\nfit4\nrpart.plot(fit4)" }, { "path": "30b-CART/dt-sleep.R", "content": "#Sleep Data Set\n\nlibrary(ggplot2)\ndata(\"msleep\")\n?msleep\nstr(msleep)\ndf = msleep[, c(3,4,6,10,11)]\ndf\nstr(df)\nhead(df)\n\n#Build a Regression Tree\n#Predicting Sleeping Hours based on other IVs\nlibrary(rpart)\nlibrary(rpart.plot)\nrtree1= rpart(sleep_total ~ . , data=df, method='anova')\nrtree1\nrpart.plot(rtree1, type=3, digits=3, fallen.leaves = T)\n\n#dataset could test data instead of df\np1 = predict(rtree1, df)\np1\n#Lower MAE the better\nMAE = function(actual, predicted) { mean(abs(actual - predicted))}\nMAE(df$sleep_total, p1)\n\n" }, { "path": "30b-CART/dt-tree-car1.R", "content": "library(caret)\nlibrary(rpart.plot)\n\ndata_url <- c(\"https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data\")\ndownload.file(url = data_url, destfile = \"car.data\")\n\ncar_df <- read.csv(\"car.data\", sep = ',', header = FALSE)\n\nstr(car_df)\ndim(car_df)\nhead(car_df)\n#all are categories, no normalisation required\nset.seed(3033)\nintrain = createDataPartition(y=car_df$V7, p=0.7, list=F)\nintrain\ntraining = car_df[intrain,]\ntesting = car_df[-intrain,]\n\ndim(training)\ndim(testing)\n#---------------\nanyNA(car_df)\nsummary(car_df)\n\n#---- Training DT classifier with criterion as information gain\n#Training DT classifier with criterion as Info Gain\n\ntrctrl = trainControl(method ='repeatedcv', number = 10, repeats=3)\nset.seed(333)\ndtree_fit = train(V7 ~ ., data= training, method='rpart', parms= list(split='information'), trControl=trctrl, tuneLength = 10)\n\ndtree_fit\n\n#Plot DT\nprp(dtree_fit$finalModel, box.palette = 'Reds', tweak=1.2)\n\n\n#---- Prediction\ntesting[1,]\npredict(dtree_fit, newdata = testing[1,])\n\ntest_pred = predict(dtree_fit, newdata = testing)\ntest_pred\nconfusionMatrix(test_pred, testing$V7)\n\n#---- Gini Index\nset.seed(3333)\ndtree_fit_gini = train(V7 ~ . , data = training, method = 'rpart', \n parms = list(split='gini'), trControl=trctrl, tuneLength=10)\ndtree_fit_gini\nprp(dtree_fit_gini$finalModel, box.palette = 'Blues', tweak=1.2)\n\n#Prediction\ntest_pred_gini = predict(dtree_fit_gini, newdata=testing)\nconfusionMatrix(test_pred_gini, testing$V7)\n" }, { "path": "30b-CART/dt3-eyes.R", "content": "#https://www.r-project.org/conferences/useR-2009/slides/Hothorn+Zeileis.pdf\n\n#Working\n\nlibrary(ipred)\ndata(data=GlaucomaMVF , package = \"ipred\")\nhead(GlaucomaMVF)\nsummary(GlaucomaMVF)\n\nlibrary(\"rpart\")\ng_rpart <- rpart(Class ~ ., data = GlaucomaMVF)\nprint(g_rpart)\nplot(g_rpart)\ntext(g_rpart)\n\nlibrary(\"partykit\")\ng_party <- partykit::as.party(g_rpart)\nprint(g_party, header = FALSE)\nprint(g_party, header = T)\nplot(g_party, type = \"simple\")\nplot(g_party, type = \"extended\")\n" }, { "path": "30b-CART/entropy.R", "content": "# Entropy\n\nlibrary(entropy)\n\nbuys <- c(\"no\", \"no\", \"yes\", \"yes\", \"yes\", \"no\", \"yes\", \"no\", \"yes\", \"yes\", \"yes\", \"yes\", \"yes\", \"no\")\n\nfreqs <- table(buys)/length(buys)\nfreqs\n#Same this way\nprop.table(table(buys))\n\n# calculate shannon-entropy\n-sum(freqs * log2(freqs))\n#[1] 0.940286\n\nentropy.empirical(freqs, unit=\"log2\")\n#0.940286\n" }, { "path": "30b-CART/multimodel.R", "content": "#Multiple Models\n#\n#A model with a binary response.\nlibrary(titanic)\ndata(\"Titanic\")\nstr(Titanic)\ntitanic = data.frame(Titanic)\n\nlibrary(rpart)\nlibrary(rpart.plot)\nbinary.model <- rpart(Survived∼., data=titanic, cp=.02)\n\nrpart.plot(binary.model)\nrpart.plot(binary.model, type=1)\nrpart.plot(binary.model, type=2)\nrpart.plot(binary.model, extra=1)\nrpart.plot(binary.model, extra=2)\nrpart.plot(binary.model, extra=100)\n\n\n\n\n#A model with a continuous response (an anova model).\nstr(cu.summary)\nanova.model <- rpart(Mileage∼., data=cu.summary)\nrpart.plot(anova.model, extra=0)\nrpart.plot(anova.model, extra=1)\nrpart.plot(anova.model, extra=100)\nrpart.plot(anova.model, extra=101)\n\n#Each node shows- the predicted value,\n#- the percentage of observations in the node.\n\n#A model with a multi-class response.\nmulti.class.model <- rpart(Reliability ~ ., data=cu.summary)\nrpart.plot(multi.class.model)\nrpart.plot(multi.class.model, extra=0)\n\n#Each node shows\n#- the predicted class (Much worse, worse, ..., Much better),\n#- the predicted probability of each class,\n#- the percentage of observations in the node.\n\n" }, { "path": "30b-CART/tree-houseprices.R", "content": "# tree\n# http://www.di.fc.ul.pt/~jpn/r/tree/tree.html\n# https://rpubs.com/JanpuHou/242986\nlibrary(tree)\nset.seed(1234)\n\nreal.state <- read.table(file=\"https://raw.githubusercontent.com/jbryer/CompStats/master/Data/cadata.dat\",head=TRUE)\ntree.model= tree(log(MedianHouseValue) ~ Longitude + Latitude, data=real.state)\nplot(tree.model)\ntext(tree.model, cex=.75)\n\nprice.decibels = quantile(real.state$MedianHouseValue, 0:10/10)\nprice.decibels\ncut.prices = cut(real.state$MedianHouseValue, price.decibels, include.lowest = T)\ncut.prices\nplot(real.state$Longitude, real.state$Latitude, col=grey(10:2/11) [cut.prices], pch=20, xlab='Longitude', ylab='Latitude')\npartition.tree(tree.model, ordvars = c('Longitude','Latitude'), add=T)\n?partition.tree\n\nsummary(tree.model)\n" }, { "path": "30b-CART/zz-test.R", "content": "\n\n#method = one of \"anova\", \"poisson\", \"class\" or \"exp\". \n#If method is missing then the routine tries to make an intelligent guess. If y is a survival object, then method = \"exp\" is assumed, if y has 2 columns then method = \"poisson\" is assumed, if y is a factor then method = \"class\" is assumed, otherwise method = \"anova\" is\n#x\n\n#Different Control Parameters\n#cp, minsplit, minbucket, maxdepth, maxcompete, xval,\ncontrol_1 = rpart.control(cp = 0.005, minsplit=3, minbucket = 2)\ncontrol_1 = rpart.control(cp = 0.005, minsplit=10, minbucket = 5)\n\nm1 = rpart(play ~ . , data=game, x=T, model=T, control=control_1)\nm1\nm1$frame\nrpart.plot(m1, nn=T)\nm1$model\nclass(m1$model)\n\nsummary(m1)\nprint(m1)\n\n#\nlibrary(rattle)\nlibrary(RColorBrewer)\nfancyRpartPlot(m1)\n" }, { "path": "30c-Ctree/CTREE NPS R code v01.R", "content": "###\n\n#install.packages(\"party\")\nlibrary(party) #better than partykit\n#install.packages(\"partykit\")\nlibrary(partykit)\n\n##Read the data in the file\ncust_data<-read.csv(\"./data/NPS Data Food Order v01.csv\")\nstr(cust_data)\ndim(cust_data)\nhead(cust_data)\ncust_data$NPS_Category\ncust_data$NPS_Score\n\n# Conditional Inference Tree for Default_On_Payment\nfit <- party::ctree( NPS_Category~\n Timely_delivery\t+\n No_of_calls_in_an_Order\t+\n Loyalty_Years\t+\n C_Marital_Status\t+\n Tier_City\t+\n Mode_of_Payment\t+\n No_of_Items_in_Order\t+\n Gender\t+\n Order_Mode\t+\n Score_on_Food_Quality\t+\n Score_on_Order_Logistics\t+\n Menu_Presentation_Score\t+\n Score_on_Recommendations\t+\n Score_on_Delivery_Person_Friendliness\n , \n data=cust_data )\nfit\nplot(fit)\nlength(fit)\n\n# Conditional Inference Tree for Default_On_Payment by specifying the limits\nfit <- ctree( NPS_Category~\n Timely_delivery\t+\n No_of_calls_in_an_Order\t+\n Loyalty_Years\t+\n C_Marital_Status\t+\n Tier_City\t+\n Mode_of_Payment\t+\n No_of_Items_in_Order\t+\n Gender\t+\n Order_Mode\t+\n Score_on_Food_Quality\t+\n Score_on_Order_Logistics\t+\n Menu_Presentation_Score\t+\n Score_on_Recommendations\t+\n Score_on_Delivery_Person_Friendliness\n , \n data=cust_data,\n control = ctree_control(maxdepth = 4))\n\n#plot(fit, main=\"Conditional Inference Tree for NPS \")\n\nplot(fit, gp = gpar(fontsize = 5), # font size changed to 6\n inner_panel=node_inner,\n ip_args=list(\n abbreviate = FALSE, \n id = FALSE)\n)\n\n###detailed results including splits\nsummary(fit)\n\n\npartykit:::.list.rules.party(fit)\n\n# Method2 ------\noldnames=names(cust_data)\noldnames\ndf= cust_data\n# Changing Names\nnewnames = c('custid', 'nps', 'npscat', 'dely', 'calls', 'years', 'married', 'city',\n'paymode', 'items', 'gender','mode', 'squality','sdely','smenu',\n'srecom', 'sdperson')\nlength(newnames)\nnames(df) = newnames\ndf\ntable(df$npscat)\nfit2a <- partykit::ctree(npscat ~ .- custid - nps, data=df)\n#length(fit2a) #length of tree for partykit only\nplot(fit2a, main=\"Conditional Inference Tree for NPS \")\nplot(fit2a, gp = gpar(fontsize = 5), inner_panel=node_inner,\n ip_args=list( abbreviate = FALSE,id = FALSE))\nsummary(fit2a)\nfit2b <- ctree(npscat ~ .- custid - nps, data=df, control = ctree_control(maxdepth = 4))\nplot(fit2b, type = \"simple\")\nprint(fit2b)\n\n#list.rules.party(fit2b)\nnames(df)\npredict(fit2b, newdata=df[1:10,])\n\ndf$npscat[1:10]\n" }, { "path": "30c-Ctree/ctree-KyCU.R", "content": "# Condition Tree \n\n# Conditional Inference Tree for Kyphosis\nlibrary(party)\nstr(kyphosis)\nfit <- ctree(Kyphosis ~ Age + Number + Start, \n data=kyphosis)\nplot(fit, main=\"Conditional Inference Tree for Kyphosis\")\n\n\nlibrary(partykit)\nstr(kyphosis)\nfit <- partykit::ctree(Kyphosis ~ Age + Number + Start, \n data=kyphosis)\nplot(fit, main=\"Conditional Inference Tree for Kyphosis\")\nsctest(fit, node = c(1,2,3))\n\n\n\n\n\n# Conditional Inference Tree for Mileage\nlibrary(party)\nstr(cu.summary)\nfit2 <- ctree(Mileage~Price + Country + Reliability + Type, \n data=na.omit(cu.summary))\nplot(fit2, main=\"Conditional Inference Tree for cu.summary - DV numeric\")\n\n\nlibrary(\"strucchange\")\nstrucchange::sctest(fit2, node = 1)\nstrucchange::sctest(fit2, node = c(2,7,4,9))\n\n" }, { "path": "30c-Ctree/ctree-airquality.R", "content": "#ctree using Air Quality\n#https://www.r-bloggers.com/package-party-conditional-inference-trees/\n\n#\nlibrary(party)\ndata(airquality)\nhead(airquality)\nairq <- subset(airquality, !is.na(Ozone))\nsummary(lm(Ozone ~., data=airq))\nhead(airq)\nair.ct <- party::ctree(Ozone ~ ., data = airq)\nair.ct\nplot(air.ct)\nnodes(air.ct, 1)\n\nnodes(air.ct, 5)\nnodes(air.ct, 7)\n?ctree\n\nSolar.R = c(rep(0,6))\nWind = c(rep(c(5,12),3))\nTemp = c(rep(70,6))\nMonth = Day = c(rep(0,6))\n\nnew.airq = data.frame(Solar.R, Wind, Temp, Month, Day)\nnew.airq\nsapply(airq[,-1], class)\nsapply(new.airq, class)\nclass(new.airq$Solar.R) = 'integer'\nclass(new.airq$Temp) = 'integer'\nclass(new.airq$Month) = 'integer'\nclass(new.airq$Day) = 'integer'\n\ncbind(new.airq, predict(air.ct, newdata = new.airq))\npredict(air.ct, newdata = new.airq, type = \"node\")\nairq$node = predict(air.ct, type = \"node\")\nhead(airq)\nwhere(air.ct)\n\n" }, { "path": "30c-Ctree/ctree-churn2.R", "content": "# Decision Tree - Loan Default or Churn\n# age, gender, status, \n\nlibrary(rpart)\n# Part -2 : increase data points\nage = floor(runif(50,30,60))\nage\ngender = sample(c('M','F'), size=50, replace=T)\ngender\nchurn = sample(c('Yes','No'), size=50, replace=T, prob=c(.6,.4))\nchurn\nmobilecust = data.frame(age, gender, churn)\nstr(mobilecust)\n\n\nmytree1 = rpart (churn ~ age + gender, data=mobilecust, method='class', minsplit=10, minbucket=5)\n\nsummary(mytree1)\nplotcp(mytree1)\nplot(mytree1)\nlibrary(rpart.plot)\nlibrary(RColorBrewer)\nfancyRpartPlot(mytree1)\n\n\n\n#Case2 : Larger Data\n#https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/\nchurn2 = read.csv('./data/churn.csv') \nsummary(churn2)\nsapply(churn2, function(x) sum(is.na(x)))\nchurn2 <- churn2[complete.cases(churn2), ]\nnames(churn2)\ntree2 <- ctree(Churn ~ Contract + tenure +PaperlessBilling, data=churn2)\nplot(tree2)\nfancyRpartPlot(tree2)\n" }, { "path": "30c-Ctree/ctree-clsregr-party.R", "content": "#ctree - Classification and Regression\nlibrary(party) # use this to model, can also use party but that will give different output\n\nhead(airquality)\n### regression----\nairq <- subset(airquality, !is.na(Ozone))\nairct <- party::ctree(Ozone ~ ., data = airq)\nairct \nplot(airct)\nplot(airct, type='simple')\nplot(as.simpleparty(airct)) #when partykit is used to model\n\nlibrary(\"strucchange\")\nstrucchange::sctest(airct, node = 1)\nstrucchange::sctest(airct, node = c(2,7,4,9))\n\n\n\npredict(airct)\nmean((airq$Ozone - predict(airct))^2) #error\nnodeids(airct,2)\nnodeids(airct)\nnodeids(airct, terminal=T)\nnodeids(airct, from=2, terminal=T)\nnodeids(airct,1,5)\n\nrange(airq$Ozone)\n#party\n#party::ctree(Ozone ~ ., data = airq)\nhead(iris)\nlibrary(partykit)\n### classification ---\nirisct <- partykit::ctree(Species ~ .,data = iris)\nirisct\nplot(irisct, type='simple')\nstrucchange::sctest(irisct, node = 1)\nstrucchange::sctest(irisct, node = c(2,3,4))\n#7 not significant\nnodeapply(irisct, ids = nodeids(irisct), function(n) info_node(n)$p.value)\n\n\n\n\ntable(predict(irisct), iris$Species)\n### estimated class probabilities, a list\n(tr <- predict(irisct, newdata = iris[1:10,], type = \"prob\"))\n(tr <- predict(irisct, newdata = iris, type = \"prob\"))\n\n(tr2 <- predict(irisct, newdata = iris[1:10,], type = \"response\"))\nhead(iris)\n " }, { "path": "30c-Ctree/ctree-clsregr.R", "content": "#ctree - Classification and Regression\nlibrary(partykit) # use this to model, can also use party but that will give different output\n\nhead(airquality)\n### regression----\nairq <- subset(airquality, !is.na(Ozone))\nairct <- ctree(Ozone ~ ., data = airq)\nairct \nplot(airct)\nplot(airct, type='simple')\nplot(as.simpleparty(airct)) #when partykit is used to model\n\nlibrary(\"strucchange\")\nstrucchange::sctest(airct, node = 1)\nstrucchange::sctest(airct, node = c(2,7,4,9))\n\n\n\npredict(airct)\nmean((airq$Ozone - predict(airct))^2) #error\nnodeids(airct,2)\nnodeids(airct)\nnodeids(airct, terminal=T)\nnodeids(airct, from=2, terminal=T)\nnodeids(airct,1,5)\n\nrange(airq$Ozone)\n#party\n#party::ctree(Ozone ~ ., data = airq)\nhead(iris)\nlibrary(partykit)\n### classification ---\nirisct <- partykit::ctree(Species ~ .,data = iris)\nirisct\nplot(irisct, type='simple')\nstrucchange::sctest(irisct, node = 1)\nstrucchange::sctest(irisct, node = c(2,3,4))\n#7 not significant\nnodeapply(irisct, ids = nodeids(irisct), function(n) info_node(n)$p.value)\n\n\n\n\ntable(predict(irisct), iris$Species)\n### estimated class probabilities, a list\n(tr <- predict(irisct, newdata = iris[1:10,], type = \"prob\"))\n(tr <- predict(irisct, newdata = iris, type = \"prob\"))\n\n(tr2 <- predict(irisct, newdata = iris[1:10,], type = \"response\"))\nhead(iris)\n " }, { "path": "30c-Ctree/ctree-readingskills.R", "content": "# Classification : Dataset - readingSkills\n?readingSkills\nhead(readingSkills)\nlibrary(party)\nstr(readingSkills)\nreadingSkills\n\n# Create the input data frame.\ninput.dat <- readingSkills[c(1:105),]\n?readingSkills\n\n# Give the chart file a name.\n#png(file = \"decision_tree.png\")\n\n# Create the tree.\noutput.tree <- ctree( nativeSpeaker ~ age + shoeSize + score, data = input.dat)\noutput.tree\n\n# Plot the tree.\nplot(output.tree)\n\n# Save the file.\n#dev.off()\n\n" }, { "path": "30c-Ctree/ctree2-iris.R", "content": "# Classification ####\n# Decision Treee Eg 1 ####\n#http://www.rdatamining.com/examples/decision-tree\n\nlibrary(\"party\")\nstr(iris)\n#Call function ctree to build a decision tree. The first parameter is a formula, which defines a target variable and a list of independent variables.\n\niris_ctree <- ctree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=iris)\nprint(iris_ctree)\nplot(iris_ctree)\nplot(iris_ctree,type='simple')\n\n\n\n\n# 2nd Example\nhead(iris)\ntable(iris$Species, predict(iris.ct), dnn = c(\"Actual species\", \"Predicted species\"))\nnew.iris\npredict(iris.ct, newdata = new.iris)\npredict(iris.ct, newdata = new.iris, type = \"node\")\n#https://www.r-bloggers.com/package-party-conditional-inference-trees/\n#\n\n" }, { "path": "30c-Ctree/ctreee-iris.R", "content": "# Classification - Party(Ctree) + IRIS\n#https://predictoanalycto.wordpress.com/2014/05/30/classification-trees-with-r-programming-language-part-1/\n\n#Load Libraries\n\n\nstr(iris)\n#decision tree for classifying the flowers as one of the three Species : “setosa”, “versicolor”, “virginica”\n#Attribute X - Condition - Either A or B or ...\n#partition data set into two parts: Training and Testing\n#CART, ID3, C4.5 are most widely used. ctree() uses CART algorithm.\n\nset.seed(1234)\nind <- sample(2, nrow(iris), replace = TRUE, prob = c(0.7,0.3))\nind # 70% samples rows go as Trg, \ntrainData <- iris[ind == 1,]\ntestData <- iris[ind == 2,]\nhead(trainData) ; head(testData)\n\nlibrary(party) #loads the library party\nmyFormula <- Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width\niris_tree <- ctree(myFormula, data = trainData) #ctree is a part of party package\ntable(predict(iris_tree), trainData$Species)\n\n#setosa class are correctly classified. 3 objects belonging to versicolor class are classified as virginica and 1 object belonging to virgnica class has been classified as versicolor. \n#These values can differ depending on the seed that you had set previously\n\nprint(iris_tree) #To see how plain text version of our classification tree \nplot(iris_tree) #To see the plot of our tree\n\nplot(iris_tree, type = \"simple\")\n# for a condensed decision tree n = 19 y = (0, 0.842, 0.158) means that this node contains 19 training instances\n#and they belong to versicolor with a # probability of 0.842\n\ntestPred <- predict (iris_tree, newdata = testData)\n# Predicts the class name of the testData instances\ntable(testPred, testData$Species)\n# Plots the predicted instances class against actual classes\n\n# Most of the algorithm work similarly. Most of them involve following steps:\n# \n# Find split location(attribute) in the dataset that minimizes a certain measure of node impurity.\n# Repeat the above step on rest of the attributes until no attribute is left on which split can be achieved or model provides required amount of accuracy on unseen data.\n# It is not necessary to use all the attributes of objects in making a decision tree, sometimes less number of attribute tests can also provide satisfactory results.\n# Measures of node impurity:\n# \n# Entropy = -\\sum_{i=0}^{c-1}p(i|t)\\log_{2}p(i|t)\n# Gini = 1-\\sum_{i=0}^{c-1}[p(i|t)]^{2}\n# Classification error = 1-max_{i}[p(i|t)]\n\n\n" }, { "path": "30c-Ctree/dt-ctree-playYes.R", "content": "#Classification - Decision Trees - Distances\n#ctree\n\nplay = read.csv('./data/clsplay.csv', row.names=1)\ncredit = read.csv('./data/clscredit.csv')\nplay\nstr(play)\ncredit\nnames(play)\n# Case-1 : Play\nlibrary(party) \nctc = ctree_control(minsplit = 1, minbucket = 1)\ntreeplay <- ctree(play ~ outlook + temperature + humidity + wind, data = play,controls = ctc )\n\n?ctree\ntreeplay\nplot(treeplay)\npredict(treeplay, )\n\ntable(predict(treeplay), play$play)\ntable(predict(iris_tree), trainData$Species)\n\n#working Example\nls <- data.frame(y = gl(3, 50, labels = c(\"A\", \"B\", \"C\")),\n x1 = rnorm(150) + rep(c(1, 0, 0), c(50, 50, 50)),\n x2 = runif(150))\nls\nnames(ls)\nct= ctree(y ~ x1 + x2, data = ls)\nplot(ct)\npredict(ct, ls) #predict for training data\npredict(ct, newdata = ls[c(1, 51, 101),], type = \"prob\")\npredict(ct, newdata = ls[c(1, 51, 101),], type = \"response\")\npredict(ct, newdata = ls[c(1, 51, 101),], type = \"node\")\n\n\ndata(\"airquality\", package = \"datasets\")\nairq <- subset(airquality, !is.na(Ozone))\n(airctparty <- party::ctree(Ozone ~ ., data = airq,\n controls = party::ctree_control(maxsurrogate = 3)))\nplot(airctparty)\nmean((airq$Ozone - predict(airctparty))^2)\n?ctree\n" }, { "path": "30d-CHAID/CHAID-nps2.R", "content": "###\nlibrary(\"CHAID\")\n#convert data to factors\n\n##Read the data in the file\ncust_data<-read.csv(\"./data/NPS Data Food Order v01.csv\")\nstr(cust_data)\ndim(cust_data)\nhead(cust_data)\noldnames=names(cust_data)\noldnames\ndf= cust_data\n\n\n# Changing Names\nnewnames = c('custid', 'nps', 'npscat', 'dely', 'calls', 'years', 'married', 'city', 'paymode', 'items', 'gender','mode', 'squality','sdely','smenu', 'srecom', 'sdperson')\nlength(newnames)\nnames(df) = newnames\napply(df,2, class)\ndf2 =lapply(df[,c('npscat', 'dely', 'srecom', 'years','calls')], factor)\ndf2= as.data.frame(df2)\ntable(df2$npscat)\nfit <- chaid(npscat ~ dely + srecom + years + calls, data=df2)\nplot(fit, main=\"Conditional Inference Tree for NPS \")\nplot(fit, gp = gpar(fontsize = 5), inner_panel=node_inner,\n ip_args=list( abbreviate = FALSE,id = FALSE))\nsummary(fit)\npartykit:::.list.rules.party(fit)\nnames(df2)\ncbind(predict(fit, newdata=df2[1:10,]), df2$npscat[1:10])\nnodeids(fit,1)\nnodeids(fit, from=4, TERMINAL=TRUE)\n?nodeids\n\nprint(fit)\n\ndf2\nsummary(df2$years)\nsummary(df2)\nunique(df2$years)\n" }, { "path": "30d-CHAID/CHAID-xsell1.R", "content": "# CHAID 2\n#http://dni-institute.in/blogs/reading-data-in-r/\n \n#install.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\nlibrary(CHAID)\nlibrary(help=CHAID)\n#install.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\ntermCrosssell1 <- read.csv(file=\"http://dni-institute.in/blogs/wp-content/uploads/2016/07/termCrosssell.csv\")\n\ntermCrosssell = termCrosssell1\nsave(termCrosssell,file='madata.Rdata')\nnames(termCrosssell)\n\ntable(termCrosssell$housing) #multiple classes\nsummary(termCrosssell[,c('marital','education', 'y')])\ndim(termCrosssell)\n\n\n\ndt.chaid <- chaid(y~ marital+education , \n control = chaid_control(minprob = 0.001,\n minsplit = 500, minbucket = 200), data=termCrosssell)\n\nplot(dt.chaid)\n\nplot(dt.chaid, uniform = T, compress = T, margin = 0.2, branch = 0.3)\n# Label on Decision Tree\ntext(dt.chaid)\nsummary(dt.chaid)\nprint(dt.chaid)\n\n\n\n\n# ?chaid\nhead(termCrosssell[,c('y','marital', 'education')])\nftable(termCrosssell[,c('y','education', 'marital')])\n\n#Chisquare test \n#education to y or marital to y which is more dependent\n(te1= table(termCrosssell[,c('education','y')]))\n(tm2= table(termCrosssell[,c('marital','y')]))\n#Ho: Xcell is independent of their education level (No association)\n#H1 : There is relationship : Some association\n(ce1= chisq.test(t1))\n#Ho: Xcell is independent of their marital level \n(cm2=chisq.test(t2))\nce1$statistic;cm2$statistic\nce1$p.value;cm2$p.value\n" }, { "path": "30d-CHAID/c-dt-chaid-nps.R", "content": "###\n#install.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\nlibrary(\"CHAID\")\n#convert data to factors\n\n##Read the data in the file\ncust_data<-read.csv(\"./data/nps.csv\")\nstr(cust_data)\ndim(cust_data)\nhead(cust_data)\noldnames=names(cust_data)\noldnames\ndf= cust_data\n\n\n# Changing Names\nnewnames = c('custid', 'nps', 'npscat', 'dely', 'calls', 'years', 'married', 'city', 'paymode', 'items', 'gender','mode', 'squality','sdely','smenu', 'srecom', 'sdperson')\nlength(newnames)\nnames(df) = newnames\napply(df,2, class)\ndf2 =lapply(df[,c('npscat', 'dely', 'srecom', 'years','calls')], factor)\ndf2= as.data.frame(df2)\ntable(df2$npscat)\nfit <- chaid(npscat ~ dely + srecom + years + calls, data=df2)\nplot(fit, main=\"Conditional Inference Tree for NPS \")\nplot(fit, gp = gpar(fontsize = 5), inner_panel=node_inner,\n ip_args=list( abbreviate = FALSE,id = FALSE))\nsummary(fit)\n\n\n\npartykit:::.list.rules.party(fit)\nnames(df2)\ncbind(predict(fit, newdata=df2[1:10,]), df2$npscat[1:10])\nnodeids(fit,1)\nnodeids(fit, from=4, TERMINAL=TRUE)\n?nodeids\n\nprint(fit)\n\ndf2\nsummary(df2$years)\nsummary(df2)\nunique(df2$years)\n\n" }, { "path": "30d-CHAID/c-dt-chaid-usvote1.R", "content": "#CHAID - dataset USvote\n#multisplit\n# require(rsample) # for dataset and splitting also loads broom and tidyr\nrequire(dplyr);require(ggplot2);theme_set(theme_bw()) ;require(CHAID); require(purrr) ; require(caret)\n#install.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\nlibrary(CHAID) #library for performing CHAID decision tree\n#Dataset\ndata(USvote)\n?USvote\n\n\nhead(USvote)\nstr(USvote)\nnames(USvote)\nsummary(USvote)\ndim(USvote)\n\n#Quick CHAID analysis\nset.seed(101)\nsample1 = USvote[sample(1:nrow(USvote), 1000),]\nchaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))\nprint(chaidModel)\nplot(chaidModel)\n\n\n#Train and Test Set\nrequire(caTools)\nset.seed(101) \nsample = sample.split(USvote$vote3, SplitRatio = .75)\ntrain = subset(USvote, sample == TRUE)\ntest = subset(USvote, sample == FALSE)\ndim(train); dim(test)\n\n#control Parameters\nctrl <- chaid_control(minsplit = 100, minbucket = 10, minprob = 0.001)\nctrl_1 <- chaid_control(minprob=.002, maxheight = 4, minsplit=2000, minbucket=700)\n#parameters - minsplit, minbucket, minprob, \nctrl_1 #see the control values\nchaidModel_1 <- chaid(vote3 ~ ., data = train, control = ctrl_1)\n\n#summary(chaidModel_1)\nprint(chaidModel_1)\nplot(chaidModel_1)\nplot(chaidModel_1, type='simple')\n\n#Variable Selection\nchisq.test(train$vote3, train$marstat) #least\n#this is why 1st split happens on marstat\nchisq.test(train$vote3, train$ager)\nchisq.test(train$vote3, train$empstat)\n\n\n#Plots\nplot(chaidModel_1,main = \"Testing Graphical Options\",\n gp = gpar(fontsize = 8), type = \"simple\")\n\nplot(chaidModel_1, main = \"Testing More Graphical Options\",\n gp = gpar(col = \"blue\", lty = \"solid\", lwd = 3, fontsize = 10\n )\n)\n\n\n#Predict\np1a = predict(chaidModel_1, type='response')\nhead(p1a)\n#“response”, “prob”, “quantile”, “density”, “node”\np1b = predict(chaidModel_1, type='prob')\nhead(p1b)\np1c = predict(chaidModel_1, type='node')\nhead(p1c)\n#quantile & density for numeric responses\n\n\n\n\nsum(complete.cases(train))# there are some missing values\nsum(!complete.cases(train))# there are some missing values\n\nlength(p1a); length(train$vote3)\n\n\n#Confusion Matrix\nlibrary(caret)\nconfusionMatrix(p1a, train[complete.cases(train),]$vote3)\n\n\n#Now change different parameters of chaid and predict accuracy\n#Select best model\nctrl_2 <- chaid_control(minprob=.01, minsplit=1000)\n#parameters - minsplit, minbucket, minprob, \nctrl_2 #see the control values\nchaidModel_2 <- chaid(vote3 ~ ., data = train, control = ctrl_2)\np2a = predict(chaidModel_2, type='response')\nconfusionMatrix(p2a, train[complete.cases(train),]$vote3)\n#\n\n#Compare models and select with higher accuracy\n\n\n" }, { "path": "30d-CHAID/chaid-attrition.R", "content": "# CHAID - multiple splits\n\nlibrary(CHAID)\nlibrary(rsample)\nlibrary(plyr)\nlibrary(dplyr)\n\nattrition %>% select_if(is.factor) %>% ncol\n## [1] 15\nattrition %>% select_if(is.numeric) %>% ncol\n## [1] 16\n\nattrition %>% select_if(function(col) length(unique(col)) <= 5 & is.integer(col)) %>% head\n\nattrition %>% select_if(function(col) length(unique(col)) <= 10 & is.integer(col)) %>% head\n\n\n#\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#create a data set\nincome = floor(runif(1000, 50, 100))\ngender = sample(c('M','F'), size=1000, replace=T, prob=c(.6,.4))\nmarried = sample(c('Married','Single', 'Divorce'), size=1000, replace=T, prob=c(.3,.4,.3))\nbuy = sample(c('Buy','DontBuy'), size=1000, replace=T, prob=c(.6,.4))\n\ndf = data.frame(income, gender, married, buy)\nhead(df)\nstr(df)\nfcols = c('income','gender', 'married', 'buy')\ndf[fcols] = lapply(df[fcols], factor)\nlapply(df,class)\n\ntable(df$gender, df$buy)\n\n#Run the model\nchaidmodel_1 = chaid(buy ~ gender + married + income, data=df)\n#summary(chaidmodel_1)\nprint(chaidmodel_1)\nplot(chaidmodel_1)\n\n#no plot or tree: some control parameters to be modified\n\n#Modify the parameters\nctrl_1 = chaid_control(minsplit = 10, minprob = 0.01)\nchaidmodel_2 = chaid(buy ~ incomee + gender + married, data=df, control = ctrl_1)\nplot(chaidmodel_2)\nprint(chaidmodel_2)\n\nchaidmodel = chaid(buy ~ ., data=df)\n?chaid\n\n\n#https://www.r-bloggers.com/chaid-and-r-when-you-need-explanation-may-15-2018/\nrequire(rsample)\ndata(attrition)\nstr(attrition)\nnames(attrition)\n" }, { "path": "30d-CHAID/chaid-cancer.R", "content": "#CHAID on Breast Cancer\n\ndata(\"BreastCancer\", package = \"mlbench\")\n#install.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\n\nlibrary(\"CHAID\")\nb_chaid <- chaid(Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli + Mitoses, data = BreastCancer)\nplot(b_chaid)\ndim(BreastCancer)\n\nattach(BreastCancer)\ntable(BreastCancer$class, BreastCancer$Cell.size)\ntable(table(BreastCancer$Cell.size), BreastCancer$class)\ntable(BreastCancer$Cell.size, BreastCancer$class)\n" }, { "path": "30d-CHAID/chaid-usvote.R", "content": "#CHAID1\n\n### fit tree to\n### subsample\nUSvote\nlibrary(CHAID)\nset.seed(290875)\ndata(USvote)\nattach(USvote)\n?USvote\nhead(USvote)\nstr(USvote)\nnames(USvote)\nsummary(USvote)\ndim(USvote)\nsample1 = USvote[sample(1:nrow(USvote), 1000),]\nchaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))\nprint(chaidModel)\nplot(chaidModel)\n\n\n#Train and Test Set\nrequire(caTools)\nset.seed(101) \nsample = sample.split(USvote$vote3, SplitRatio = .75)\ntrain = subset(USvote, sample == TRUE)\ntest = subset(USvote, sample == FALSE)\ndim(train); dim(test)\n\n#control Parameters\nctrl1 <- chaid_control(minsplit = 100, minbucket = 10, minprob = 0.001)\nctrl2 <- chaid_control(minprob=.002,maxheight = 4, minsplit=2000, minbucket=700)\n#parameters - minsplit, minbucket, minprob, \nctrl2 #see the control values\nchaidModel_1 <- chaid(vote3 ~ ., data = train, control = ctrl2)\n\n#summary(chaidModel_1)\nprint(chaidModel_1)\nplot(chaidModel_1)\nplot(chaidUS1, type='simple')\nchisq.test(train$vote3, train$marstat) #least\n#this is why 1st split happens on marstat\nchisq.test(train$vote3, train$ager)\nchisq.test(train$vote3, train$empstat)\n\n\n#Plots\nplot(chaidModel_1,main = \"Testing Graphical Options\",\n gp = gpar(fontsize = 8), type = \"simple\")\n\nplot(chaidModel_1, main = \"Testing More Graphical Options\",\n gp = gpar(col = \"blue\", lty = \"solid\", lwd = 3, fontsize = 10\n )\n)\n\n\n#Predict\np1 = predict(chaidModel_1)\nhead(p1)\nsum(complete.cases(train))# there are some missing values\nsum(!complete.cases(train))# there are some missing values\n\nlength(p1); length(train$vote3)\n\n\n#Confusion Matrix\nlibrary(caret)\nconfusionMatrix(p1, train[complete.cases(train),]$vote3)\n\n\n# aggregation\nunique(USvote$vote3)\nplyr::count(USvote, c('gender','vote3'))\nplyr::count(USvote, c('marstat','vote3'))\n(t1=ftable(USvote$vote3, USvote$gender))\n(t2=ftable(USvote$vote3, USvote$marstat))\nprop.table(t2)\n(t3=ftable(USvote$vote3, USvote$educr))\nprop.table(t3)\n(t4=ftable(USvote$vote3, USvote$ager))\n\nchisq.test(t1)\nchisq.test(t2)\nchisq.test(t3)\nchisq.test(t4)\n\n\nchisq.test(t3)\n\n#https://select-statistics.co.uk/blog/chaid-chi-square-automatic-interaction-detector/\n \n\n#selecting the best variable\nnames(USvote)\ndplyr::count(USvote, vote3 , gender)\nlibrary(dplyr)\nUSvote %>% count(vote3, gender)\nUSvote %>% group_by(vote3, gender) %>% summarise(number = n())\nUSvote %>% group_by(vote3, gender) %>% tally()\n\n\n(t1=ftable(USvote$vote3, USvote$gender))\n(t2=ftable(USvote$vote3, USvote$ager))\n(t3=ftable(USvote$vote3, USvote$empstat))\n(t4=ftable(USvote$vote3, USvote$educr))\n(t4=ftable(USvote$vote3, USvote$marstat))\nchisq.test(t1)\n\nlibrary(EnQuireR)\ndata(tea)\nchisq.desc(tea,c(1,2),c(3,4))\n\nchisq.desc(tea,c(1,2),c(3,4),report=TRUE)" }, { "path": "30d-CHAID/chaid2.R", "content": "#CHAID\n#\ngender = c(rep('Female',10),rep('Male',20))\ngender\nplay = c(rep('Yes',2),rep('No',8),rep('Yes',13),rep('No',7) )\nplay\ndf1 = data.frame(gender, play)\ndf1\ntable(df1)\n?chisq.test(table(df1))\nchisq.test(gender,play)\n\n\n((-1)^2 / 7) ^ 0.5\n((1)^2 / 8) ^ 0.5\n((-2)^2 / 8) ^ 0.5\n\n" }, { "path": "30d-CHAID/chaid4.R", "content": "# CHAID - Own eg\n\nset.seed(1234)\ngender = sample(c('Male', 'Female'), 200, replace=T,prob = c(0.7,0.3))\ngender\nset.seed(1234)\nplay = sample(c('Play', 'NoPlay'), 200, replace=T,prob = c(0.4,0.6))\nplay\ndf = data.frame(gender, play)\ntable(df)\n#CART - classification\nfit1 = rpart(play ~ gender, data=df)\nfit1\n# not a good plot\nplot(fit1)\ntext(fit1)\nrpart.plot(fit1)\nprop.table(table(df))\naddmargins(prop.table(table(df)))\npredict(fit1, newdata=data.frame(gender=c('Male','Female','Male')))\n\n?rpart::predict.rpart\npredict(fit1, newdata=data.frame(gender=c('Male','Female','Male')),\n type='vector')\npredict(fit1, newdata=data.frame(gender=c('Male','Female','Male')),\n type='class')\npredict(fit1, newdata=data.frame(gender=c('Male','Female','Male')), type='vector')\npredict(fit1, newdata=data.frame(gender=c('Male','Female','Male')), type='prob')\npredict(fit1, newdata=data.frame(gender=c('Male','Female','Male')), type='vector')\npredict(fit1, newdata=data.frame(gender=c('Male','Female','Male')),\n type='matrix')\n\n \nprintcp(fit1)\nprune(fit1, cp=0.74)\nprune(fit1, cp=1)\nprune(fit1, cp=0)\n\nlibrary(rattle)\nfancyRpartPlot(fit1)\nrpart.plot::prp(fit1, type=3)\nrpart.plot::prp(fit1, type=1)\nrpart.plot::prp(fit1, extra=2)\nrpart.plot::prp(fit1, extra=3)\nrpart.plot::prp(fit1, extra=4, cex=2)\n\n\n# Vary min split and see \nfit1a <- rpart(play ~ gender,data=df, \n control=rpart.control(minsplit=210,minbucket=10, cp=0))\nprp(fit1a)\n\nfit1.new = prp(fit1,snip=TRUE)$obj\n# some error coming\n\n\n#---- Chisq\nchisq.test(table(df))\n#Ho reject: Not Independent : But Dependent : Only Males Play\nlibrary(CHAID)\n?chaid\ndf.chaid= CHAID::chaid(play ~ gender, data = df)\nplot(df.chaid)\n\ndf.chaid = CHAID::chaid(play ~ gender, data=df, \n control = chaid_control(minprob = 0.001,\n minsplit=10, minbucket = 5))\nplot(df.chaid, uniform=T, compress=T, margin=0.4, branch=0.5)\n\nstr(df.chaid)\nsummary(dt.chaid)\n# prp will not work here as it is not rpart object\n\n\n\n\n\n\n\n\n\n\nlibrary(party)\nctree1 = party::ctree(play ~ gender, data=df)\nplot(ctree1)\n\nset.seed(1111)\nmarks = sample(c(60,70, 80,90), 200, replace=T,prob = c(0.3,0.4,.2,.1))\nmarks\ndf2 = data.frame(gender, play, marks)\nctree2 = party::ctree(marks ~ play + gender, data=df2)\nplot(ctree2)\ndim(df2)\nhead(df2)\nlibrary(rpart)\nlibrary(rpart.plot)\ntree2a = rpart::rpart(marks ~ play + gender, data=df2)\ntree2a\nplot(tree2a)\n\ntree2a = rpart::rpart(marks ~ play + gender, data=df2,\n control=rpart.control(minsplit=4, cp=0.001), method='anova')\ntree2a\nrpart.plot(tree2a)\nlibrary(rattle)\nfancyRpartPlot(tree2a)\ntable(df2$play)\ntable(df2$play, df2$gender)\nprp(tree2a)\n\ntree2a = rpart::rpart(play ~ marks + gender, data=df2,\n control=rpart.control(minsplit=4, cp=0.001))\ntree2a\nprp(tree2a)\nnew.tree.1 <- prp(tree2a,snip=TRUE)$obj # interactively trim the tree\nprp(new.tree.1) # display the new tree\n" }, { "path": "30d-CHAID/chisq.R", "content": "#Chi Square\nlibrary(MASS)\nstr(Cars93)\nnames(Cars93)\ncar.data = Cars93[,c('AirBags','Type')]\ntable(car.data)\ncar.data.table =table(car.data$AirBags, car.data$Type)\ncar.data.table\nchisq.test(car.data.table)\n\n\n#---- 2nd Eg\nlibrary(MASS) # load the MASS package \ntbl = table(survey$Smoke, survey$Exer) \ntbl # the contingency table \nchisq.test(tbl) \nctbl = cbind(tbl[,\"Freq\"], tbl[,\"None\"] + tbl[,\"Some\"]) \nctbl \nchisq.test(ctbl) \n" }, { "path": "30d-CHAID/chisqtest2.R", "content": "#CHisquare test \n(result = matrix(c(200,150,50,250,300,50), byrow=T, ncol=3))\ndimnames(result) = list(gender=c('male','female'),party=c('bjp','cong','aap') )\n(ct= chisq.test(result))\nsummary(ct)\nct$statistic; ct$p.value\nct$observed; ct$expected\naddmargins(result,c(1,2),sum)\naddmargins(prop.table(result,1),c(2),sum)\naddmargins(prop.table(result,2),c(1),sum)\n\nprop.table(result,2)\nprop.table(result,1)\n" }, { "path": "30d-splitcriteria/cls-entropy.R", "content": "# Entropy\n\nbuys <- c(\"no\", \"no\", \"yes\", \"yes\", \"yes\", \"no\", \"yes\", \"no\", \"yes\", \"yes\", \"yes\", \"yes\", \"yes\", \"no\")\n\ncredit <- c(\"fair\", \"excellent\", \"fair\", \"fair\", \"fair\", \"excellent\", \"excellent\", \"fair\", \"fair\", \"fair\", \"excellent\", \"excellent\", \"fair\", \"excellent\")\n\nstudent <- c(\"no\", \"no\", \"no\",\"no\", \"yes\", \"yes\", \"yes\", \"no\", \"yes\", \"yes\", \"yes\", \"no\", \"yes\", \"no\")\n\nincome <- c(\"high\", \"high\", \"high\", \"medium\", \"low\", \"low\", \"low\", \"medium\", \"low\", \"medium\", \"medium\", \"medium\", \"high\", \"medium\")\n\nage <- c(25, 27, 35, 41, 48, 42, 36, 29, 26, 45, 23, 33, 37, 44) # we change the age from categorical to numeric\n\ndf = data.frame(age, student, income, credit, buys)\ndf\n\n#M1\nfreqs <- table(buys)/length(buys)\nfreqs\nsum(freqs)\n#info(freqs) #[1] 0.940286\n\n#M2\n# calculate shannon-entropy\n-sum(freqs * log2(freqs)) #[1] 0.940286\n\n#M3\nentropy::entropy.empirical(freqs, unit=\"log2\") #[1] 0.940286\n\n6/16\n10/16\n6/8; 2/8\n" }, { "path": "30d-splitcriteria/dt-rpart-criteria.R", "content": "\n# Regression Tree Example\nlibrary(rpart)\n\n# grow tree \nfit <- rpart(Mileage~Price + Country + Reliability + Type, \n method=\"anova\", data=cu.summary)\n\nprintcp(fit) # display the results \nplotcp(fit) # visualize cross-validation results \nsummary(fit) # detailed summary of splits\n\n# create additional plots \npar(mfrow=c(1,2)) # two plots on one page \nrsq.rpart(fit) # visualize cross-validation results \t\n\npar(mfrow=c(1,1)) # two plots on one page \n\n# plot tree \nplot(fit, uniform=TRUE, \n main=\"Regression Tree for Mileage \")\ntext(fit, use.n=TRUE, all=TRUE, cex=.8)\n\n# create attractive postcript plot of tree \npost(fit, file = \"c:/tree2.ps\", \n title = \"Regression Tree for Mileage \")\n# prune the tree \npfit<- prune(fit, cp=0.01160389) # from cptable \n\n# plot the pruned tree \nplot(pfit, uniform=TRUE, \n main=\"Pruned Regression Tree for Mileage\")\ntext(pfit, use.n=TRUE, all=TRUE, cex=.8)\npost(pfit, file = \"c:/ptree2.ps\", \n title = \"Pruned Regression Tree for Mileage\")\n\n\n\n#Gini and Information Gain Method\n\nfmla = Mileage~Price + Country + Reliability + Type\ndata = cu.summary\ngini_model<-rpart(fmla,data = data, \n parms = list(split = \"gini\"),\n control = list(minsplit=10))\n\nrpart.plot(gini_model,main=\"Gini Split Criteria\")\n\ninfo_model<-rpart(fmla,data = data, \n parms = list(split = \"information\"),\n control = list(minsplit=10))\n\nrpart.plot(info_model,main=\"InfoGain Split Criteria\")\n" }, { "path": "30d-splitcriteria/splitcriteria1.R", "content": "#Decision Tree - CART - Gini Index\n# Gini Index\n\nlibrary(rpart)\nlibrary(rpart.plot)\n\ngraduate=c('Yes','No' ,'No', 'Yes','No','No', 'Yes','No','No','No')\nmarried=c('S','M','S','M','D','M','D','S','M','S')\nincome=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)\nbuy=c('No','No','No','No','Yes','No','No','Yes','No','Yes')\n(train= data.frame(graduate, married, income, buy))\n\n#uploading datat\nmytrain %>% gs_ws_new(ws_title = \"sample\", input = train, trim = TRUE, verbose = FALSE)\n\n\n#rpart(y~x+z, data=df, parms=list(split='gini'))\nlibrary(rpart)\n\nfit1a = rpart(cheat ~ married + refund, data=train, parms=list(split='gini'), minsplit=4, minbucket=2, cp=-1)\nfit1a\nrpart.plot(fit1a)\n\n#Split on Married or Refund : why : Gini index\nt1a=table(cheat, married)\naddmargins(t1a)\n#CART has only binary split, so one of labels of married has to be combined with other\n(m1 = matrix(c(4,0,3,3), byrow=F, nrow=2, dimnames=list(c('No','Yes'), c('M','SD'))))\n#Gini Index\n#Married M\n(GMM = 1 - (4/4)^2 - (0/4)^2 )\n(GMSD = 1 - (3/6)^2 - (3/6)^2)\n(Gmarried = (4/10) * GMM + (7/10)* GMSD)\n#Refund R\n(t1b = table(cheat, refund))\n(GRN = 1 - (4/7)^2 - (3/4)^2)\n(GRY = 1 - (3/3)^2 - (0/3)^2)\n(Grefund = (7/10) * GRN + (3/10)* GRY)\n(GIM = 1 - Gmarried) ; (GIR = 1 - Grefund)\n#married has lower value of GI, so married columns selected\n\n(t1b = table(cheat, income)) # not done this way\nincome2 = ifelse(income < 78, 'Yes', 'No')\n(t1b2 = table(cheat, income2)) # this way\n(GIY = 1 - (4/7)^2 - (3/4)^2)\n(GIN = 1 - (3/3)^2 - (0/3)^2)\n(Gincome = (7/10)*GIY + (3/10)*GIN)\n(GII = 1 - Gincome)\npaste(GIM, GIR, GII, sep= ' ')\nmin(GIM, GIR, GII)\n#Gini Index married column is least\n\n\nfit2a = rpart(cheat ~ ., data=train, method='class', parms=list(split='gini'), minsplit=2, minbucket=1, cp=-1)\nfit2a\nrpart.plot(fit2a)\n\n\n\nfit2b = rpart(cheat ~ ., data=train, method= 'class', parms = list(split='information'), minsplit=2, minbucket=1, cp=-1 )\nfit2b\nrpart.plot(fit2b, nn=T)\n\nfit3a = rpart(income ~ ., data=train, method= 'anova', parms = list(split='gini'), minsplit=2, minbucket=1, cp=-1 )\nfit3a\nrpart.plot(fit3a)\n\nfit3b = rpart(income ~ ., data=train, method= 'anova', parms = list(split='information'), minsplit=2, minbucket=1, cp=-1 )\nfit3b\nrpart.plot(fit3b, nn=T)\n\n\nlibrary(ineq)\nineq(cheat,type = c(\"Gini\"), na.rm = TRUE)\nGini(x, corr = FALSE, na.rm = TRUE)\n\nlibrary(ineq)\nyour_data = train\ncoeff= NULL\nfor (i in colnames(your_data[])){\n coeff= c(coeff,round(ineq(your_data[,i],type = 'Gini'),4))\n}\ncoeff\ndata_coeff = data.frame(cbind(coeff,colnames(your_data[,-1])))\ncolnames(data_coeff) = c(\"Coeff\",\"Colnames\")\ndata_coeff\nIf you want it by for each rows try this :\n \n your_new_data = as.data.frame(t(your_data[,-1]), row.names =T)\n\ncolnames(your_new_data) = your_data[,1]\n\n\n\n\n\n\n\n\n\nlibrary(lawstat)\ndata(popdata)\ngini.index(popdata[,\"pop1900\"])\ngini.index(popdata[,\"pop1972\"])\ngini.index(income)\ngini.index(married)\ngini.index(refund)\n\nlibrary(DescTools)\n\nGini(names(t1),t1)\n\nlibrary(rpart)\nfit1 = rpart(cheat ~ ., data=train)\nfit1\nlibrary(rpart.plot)\nrpart.plot(fit1)\n" }, { "path": "30d-splitcriteria/splitcriteria2.R", "content": "# Split Criteria\n\nlibrary(rpart)\nlibrary(rpart.plot)\n\n(target = rep(c(1,0),times=c(5,5)))\n(var1 = c(0,0,0,0,1,1,1,0,1,0))\n(var2 = c(34,45,54,41,47,51,29,0,65,41))\ndata = data.frame(target, var1, var2)\n\nset.seed(1234)\nfit = rpart(target ~ var1 + var2, data= data, parms=list(split='gini'), minsplit=4, minbucket=2,cp=.2)\nfit\nrpart.plot(fit)\nprintcp(fit)\n\ntable(data$target, data$var1, dnn=c('target', 'var1'))\ntable(data$target, data$var2, dnn=c('target', 'var2'))" }, { "path": "30d-splitcriteria/splitcriteria3.R", "content": "# Decision Tree - Splitting Criteria\n\n# Gini Index\n\nlibrary(rpart)\nlibrary(rpart.plot)\n\neducation=factor(c(3,2,2,3,2,2,3,2,2,2))\nmarried=factor(c('S','M','S','M','M','M','S','S','M','S'))\nincome=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)\nbuy=factor(c(0,0,0,0,1,0,0,1,0,1))\n(data= data.frame(buy, education, married, income))\n\nfit = rpart(buy ~ education + married, data=data, parms=list(split='gini'), minsplit=4, minbucket=2,cp=-1)\nfit\nrpart.plot(fit)\nprintcp(fit)\n\nfit1 = rpart(buy ~ education + married + income, data=data, parms=list(split='gini'), minsplit=4, minbucket=2,cp=.2)\nfit1\nrpart.plot(fit1)\nprintcp(fit1)\n\nfit2 = rpart(buy ~ education + married + income, data=data, parms=list(split='gini'), minsplit=4, minbucket=2,cp=-1)\nfit2\nrpart.plot(fit2)\nprintcp(fit2)\n\n\nfit2$where #which row at which node no\ndata[1:5,]\ncbind(data, nodeno=rownames(fit2$frame) [ fit2$where])\n\n\n\n" }, { "path": "31b-KNN/knn1_cancer.R", "content": "# KNN algo Analytics Vidya\n#https://www.analyticsvidhya.com/blog/2015/08/learning-concept-knn-algorithms-programming/\n#prc = read.csv(file=file.choose(), stringsAsFactors = F)\nprc = read.csv('https://raw.githubusercontent.com/dupadhyaya/Ranalytics/master/data/Prostate_Cancer.csv', stringsAsFactors=F)\nhead(prc)\nstr(prc)\nprc = prc[-1] # remove id column\nstr(prc)\ntable(prc$diagnosis_result) # table of cancer diagnosis Malignant or Benign \nprc$diagnosis = factor(prc$diagnosis_result,level=c('B','M'), labels=c('Benign','Malignant'))\nstr(prc)\nprc[1:10,c('diagnosis_result','diagnosis')]\nprc[1:10,c(1,10)]\ntable(prc$diagnosis)\nround(prop.table(table(prc$diagnosis))*100,digits=1)\n\n#Normalising Data\n\nnormalise <- function(x) {\n (x - min(x))/ (max(x) - min(x))\n}\na = normalise(prc$radius)\nprc_n = as.data.frame(lapply(prc[2:9],normalise))\nhead(prc_n)\nsummary(prc_n$radius) # data has been normalised Min is 0, Max is 1, Median 0.5, Mean may vary\nsummary(prc_n)\n\n# Divide the data into Training and Test Data\nprc_train = prc_n[1:65,] # all columns to be included\nprc_test = prc_n[66:100,]\n# Extract labels from prc\nprc_train_labels = prc[1:65,1]\nprc_test_labels = prc[66:100,1]\nprc_train_labels;prc_test_labels\n\n# Training the Model\nlibrary(class)\nprc_test_pred = knn(train= prc_train, test=prc_test, cl = prc_train_labels, k=10) # k = sqrt(obsvn=100)\nprc_test_pred\ndata.frame(prc_test_labels,prc_test_pred) # compare the output\n\n# Evaluate\nlibrary(gmodels)\nCrossTable(x=prc_test_labels, y = prc_test_pred, prop.chisq = F) # Confusion Matrix\n# B-B : 7 ; M:M- 15 - correct prediction 7 + 15 = 22 out of 35\n(accuracy = 22/35)\n\n# Improving Algorithm\n# Training the Model\nprc_test_pred1 = knn(train= prc_train, test=prc_test, cl = prc_train_labels, k=5) # k = sqrt(obsvn=100)\nprc_test_pred1\ndata.frame(prc_test_labels,prc_test_pred1) # compare the output\n\n# Evaluate\nCrossTable(x=prc_test_labels, y = prc_test_pred1, prop.chisq = F) # Confusion Matrix\n# B-B : 9 ; M:M- 15 - correct prediction 9 + 15 = 24 out of 35\n(accuracy1 = 24/35)\n\n# Training the Model\n#k is therefore just the number of neighbors \"voting\" on the test example's class.\n# if k=1, class of the training sample, k=3, mode of 3 closest samples\nprc_test_pred2 = knn(train= prc_train, test=prc_test, cl = prc_train_labels, k=3) # k = sqrt(obsvn=100)\nprc_test_pred2\ndata.frame(prc_test_labels,prc_test_pred2) # compare the output\n\n# Evaluate\nCrossTable(x=prc_test_labels, y = prc_test_pred2, prop.chisq = F ) # Confusion Matrix\n# B-B : 7 ; M:M- 15 - correct prediction 7 + 15 = 22 out of 35\n(accuracy2 = 22/35) # no more improvement\n\n\n\n" }, { "path": "31b-KNN/knn3_KKNN.R", "content": "# K neigbour - glass\n\nlibrary(kknn) #Weighted k-Nearest Neighbors\n#kknn(formula = formula(train), train, test, na.action = na.omit(), k = 7, distance = 2, kernel = \"optimal\", ykernel = NULL, scale=TRUE, contrasts = c('unordered' = \"contr.dummy\", ordered = \"contr.ordinal\"))\n#kknn.dist(learn, valid, k = 10, distance = 2)\n\n#https://cran.r-project.org/web/packages/kknn/kknn.pdf\ndata(glass)\nstr(glass)\n#A data frame with 214 observations, where the problem is to predict the type of glass in terms of\n#their oxide content (i.e. Na, Fe, K, etc). The study of classification of types of glass was motivated\n#by criminological investigation. At the scene of the crime, the glass left can be used as evidence...\n#if it is correctly identified!\n\ndata(\"ionosphere\")\nstr(ionosphere)\n# This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased\n# array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts.\n# See the paper for more details. The targets were free electrons in the ionosphere. \"Good\" radar\n# returns are those showing evidence of some type of structure in the ionosphere. \"Bad\" returns are\n# those that do not; their signals pass through the ionosphere.\n# Received signals were processed using an autocorrelation function whose arguments are the time of\n# a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Instances in\n# this database are described by 2 attributes per pulse number, corresponding to the complex values\n# returned by the function resulting from the complex electromagnetic signal.\n# A data frame with 351 observations on the following 35 variables. The first 34 continuous covariables\n# are used for the prediction. The 35th attribute is either g (\"good\") or b (\"bad\") according to\n# the definition summarized above. This is a binary classification task.\n\ndata(miete)\nstr(miete)\n\n# Spectral Clustering\n#specClust(data=miete, centers=NULL, nn = 7, method = \"symmetric\", gmax=NULL, ...)\n## S3 method for class 'specClust'\nplot(x, ...)\n" }, { "path": "31b-KNN/knn4.R", "content": "# Knn4\n#http://archive.ics.uci.edu/ml/\n#https://www3.nd.edu/~steve/computing_with_data/17_Refining_kNN/refining_knn.html" }, { "path": "31b-KNN/knn_diamonds.R", "content": "##because diamonds dataset is in ggplot2 package\nlibrary(ggplot2)\n##load data\ndata(diamonds)\n\n##store it as data frame\ndia <- data.frame(diamonds)\n\n##create a random number equal 90% of total number of rows\nran <- sample(1:nrow(dia),0.9 * nrow(dia))\n\n##the normalization function is created\nnor <-function(x) { (x -min(x))/(max(x)-min(x)) }\n\n##normalization function is created\ndia_nor <- as.data.frame(lapply(dia[,c(1,5,6,7,8,9,10)], nor))\n\n##training dataset extracted\ndia_train <- dia_nor[ran,]\n\n##test dataset extracted\ndia_test <- dia_nor[-ran,]\n##the 2nd column of training dataset because that is what we need to predict about testing dataset\n##also convert ordered factor to normal factor\ndia_target <- as.factor(dia[ran,2])\n\n##the actual values of 2nd couln of testing dataset to compaire it with values that will be predicted\n##also convert ordered factor to normal factor\ntest_target <- as.factor(dia[-ran,2])\n\n##run knn function\nlibrary(class)\npr <- knn(dia_train,dia_test,cl=dia_target,k=20)\n\n##create the confusion matrix\ntb <- table(pr,test_target)\n\n##check the accuracy\naccuracy <- function(x){sum(diag(x)/(sum(rowSums(x)))) * 100}\naccuracy(tb)\n\n#In this model we have tried to predict the “cut” variable since it is a categorical variable and we know knn works better for classification problems. we have not taken the 3rd and 4th column into consideration since they were also categorical and would cause ambiguity because other predictive variables are integers. Note that there are ways to run knn on mixture of categorical and integer variables, but we are not discussing it here. The rest of the procedure is same as the iris dataset, and in the end we get the accurate result 71% of the times." }, { "path": "31b-KNN/knn_iris.R", "content": "#KNN using iris\n\ndf <- data(iris) ##load data\nhead(iris) ## see the studcture\n##Generate random numbers that is 90% of the total number of rows in dataset.\nran <- sample(1:nrow(iris), 0.9 * nrow(iris)) \nran\n##the normalization function is created\nnor <-function(x) { (x -min(x))/(max(x)-min(x)) }\nnor\n##Run nomalization on first 4 coulumns of dataset because they are the predictors\niris_norm <- as.data.frame(lapply(iris[,c(1,2,3,4)], nor))\nhead(iris_norm)\nsummary(iris_norm)\n\n##extract training set\niris_train <- iris_norm[ran,] \nhead(iris_train)\n\n##extract testing set\niris_test <- iris_norm[-ran,] \nhead(iris_test)\n\n##extract 5th column of train dataset because it will be used as 'cl' argument in knn function.\niris_target_category <- iris[ran,5]\n\n##extract 5th column if test dataset to measure the accuracy\niris_test_category <- iris[-ran,5]\n\n##load the package class\nlibrary(class)\n\n##run knn function\npr <- knn(train=iris_train,test=iris_test,cl=iris_target_category,k=13)\n\n##create confusion matrix\ntab <- table(pr,iris_test_category)\ntab\n\n##this function divides the correct predictions by total number of predictions that tell us how accurate teh model is.\n\naccuracy <- function(x){sum(diag(x)/(sum(rowSums(x)))) * 100}\naccuracy(tab)\n\n\n#read more from here\n#https://towardsdatascience.com/k-nearest-neighbors-algorithm-with-examples-in-r-simply-explained-knn-1f2c88da405c\n\n\n#-----------------------------\n\n# Classification K knn\n\ntrain <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])\ntest <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])\ncl <- factor(c(rep(\"s\",25), rep(\"c\",25), rep(\"v\",25)))\nknn(train, test, cl, k = 3, prob=TRUE)\nattributes(.Last.value)" }, { "path": "31c-naive/naivbayes1.R", "content": "# Naive Bayes\n#https://www.r-bloggers.com/naive-bayes-classification-in-r-part-1/\n\n\nlibrary(e1071)\nmodel <- naiveBayes(am ~ ., data = mtcars)\nclass(model)\nsummary(model)\nprint(model)\n\npreds <- predict(model, newdata = mtcars)\npreds\nconf_matrix <- table(preds, mtcars$am)\n\n\n\n#----\n## Categorical data only:\ndata(HouseVotes84, package = \"mlbench\")\nhead(HouseVotes84)\nmodel <- naiveBayes(Class ~ ., data = HouseVotes84)\npredict(model, HouseVotes84[1:10,])\npredict(model, HouseVotes84[1:10,], type = \"raw\")\n\npred <- predict(model, HouseVotes84)\ntable(pred, HouseVotes84$Class)\n\n## using laplace smoothing:\nmodel <- naiveBayes(Class ~ ., data = HouseVotes84, laplace = 3)\npred <- predict(model, HouseVotes84[,-1])\ntable(pred, HouseVotes84$Class)\n\n\n## Example of using a contingency table:\ndata(Titanic)\nm <- naiveBayes(Survived ~ ., data = Titanic)\nm\npredict(m, as.data.frame(Titanic))\n\n## Example with metric predictors:\ndata(iris)\nm <- naiveBayes(Species ~ ., data = iris)\n## alternatively:\nm <- naiveBayes(iris[,-5], iris[,5])\nm\ntable(predict(m, iris), iris[,5])\n\n\n" }, { "path": "31c-naive/naivbayes2.R", "content": "# Naive Bayes \n#https://www.r-bloggers.com/understanding-naive-bayes-classifier-using-r/\n\n\n#Getting started with Naive Bayes\n#Install the package\n#install.packages(“e1071”)\n#Loading the library\nlibrary(e1071)\n?naiveBayes #The documentation also contains an example implementation of Titanic dataset\n#Next load the Titanic dataset\ndata('Titanic')\n#Save into a data frame and view it\nTitanic_df=as.data.frame(Titanic)\n\n#Creating data from table\nrepeating_sequence=rep.int(seq_len(nrow(Titanic_df)), Titanic_df$Freq) #This will repeat each combination equal to the frequency of each combination\nrep.int(c(1,2,3),c(5,6,7))\nrepeating_sequence\n\n#Create the dataset by row repetition created\nTitanic_dataset=Titanic_df[repeating_sequence,]\n#We no longer need the frequency, drop the feature\n\n#Fitting the Naive Bayes model\nNaive_Bayes_Model=naiveBayes(Survived ~., data=Titanic_dataset)\n#What does the model say? Print the model summary\nNaive_Bayes_Model\n\n\n#Prediction on the dataset\nNB_Predictions=predict(Naive_Bayes_Model,Titanic_dataset)\n#Confusion matrix to check accuracy\ntable(NB_Predictions,Titanic_dataset$Survived)\n\n\n#improvement\nGetting started with Naive Bayes in mlr\n#Install the package\n#install.packages(“mlr”)\n#Loading the library\nlibrary(mlr)\n\n#Create a classification task for learning on Titanic Dataset and specify the target feature\ntask = makeClassifTask(data = Titanic_dataset, target = \"Survived\")\n#Initialize the Naive Bayes classifier\nselected_model = makeLearner(\"classif.naiveBayes\")\n#Train the model\nNB_mlr = train(selected_model, task)\n\n#Read the model learned \nNB_mlr$learner.model\n\n\n#Predict on the dataset without passing the target feature\npredictions_mlr = as.data.frame(predict(NB_mlr, newdata = Titanic_dataset[,1:3]))\n\n##Confusion matrix to check accuracy\ntable(predictions_mlr[,1],Titanic_dataset$Survived)\nTitanic_dataset$Freq=NULL\n" }, { "path": "31d-randomforest/dt-caret-xxx.R", "content": "# Decision Tree - caret pacakage\n#The caret library is really useful because you can easily apply different models and compare their performance. It can call rpart but uses a slightly different syntax to include all features.\n\nlibrary(caret)\nlibrary(randomForest)\nlibrary(data.table)\n\nmt <- data.table(mtcars)\n\ntr <- train(x=mt[,-'hp', with=FALSE], y = mt[, hp], method='rpart')\ntr2 <- train(x=mt[,-'hp', with=FALSE], y = mt[, hp], method='rf')\n\nplot(tr$finalModel)\ntext(tr$finalModel)\n\n" }, { "path": "31d-randomforest/dt-rf-DU3.R", "content": "# Random Forests\n#https://www.r-bloggers.com/variable-importance-plot-and-variable-selection/\n\nset.seed(1)\nn=500\nlibrary(clusterGeneration)\nlibrary(mnormt)\nS=genPositiveDefMat(\"eigen\",dim=15)\nS\nS=genPositiveDefMat(\"unifcorrmat\",dim=15)\nS\nX=rmnorm(n,varcov=S$Sigma)\nX\nlibrary(corrplot)\ncorrplot(cor(X), order = \"hclust\")\n\nP=exp(Score)/(1+exp(Score))\nP=exp(S)/(1+exp(S))\n\nY=rbinom(n,size=1,prob=P)\ndf=data.frame(Y,X)\nallX=paste(\"X\",1:ncol(X),sep=\"\")\nnames(df)=c(\"Y\",allX)\n#The variable importance plot is obtained by growing some trees,\n\nrequire(randomForest)\nfit=randomForest(factor(Y)~., data=df)\n(VI_F=importance(fit))\n\n" }, { "path": "31d-randomforest/dt-rf-eg2.R", "content": "#random Forests in R\n#https://www.r-bloggers.com/how-to-implement-random-forests-in-r/\n\n# Data Source: https://archive.ics.uci.edu/ml/machine-learning-databases/car/\n\n#install.packages(\"randomForest\")\nlibrary(randomForest)\n\ndf1 = read.csv('https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data')\nhead(df1)\ndata1 = df1\nhead(data1)\nstr(data1)\nnames(data1) = c(\"BuyingPrice\", \"Maintenance\", \"NumDoors\", \"NumPersons\", \"BootSpace\" , \"Safety\" , \"Condition\")\nsummary(data1)\nhead(data1)\n\n# Split into Train and Validation sets\n# Training Set : Validation Set = 70 : 30 (random)\nset.seed(100)\ntrain <- sample(nrow(data1), 0.7*nrow(data1), replace = FALSE)\nTrainSet <- data1[train,]\nValidSet <- data1[-train,]\nsummary(TrainSet)\nsummary(ValidSet)\n\nsummary(TrainSet)\nsummary(ValidSet)\n\n# Create a Random Forest model with default parameters\nmodel1 <- randomForest(Condition ~ ., data = TrainSet, importance = TRUE)\nmodel1\n\n# Fine tuning parameters of Random Forest model\nmodel2 <- randomForest(Condition ~ ., data = TrainSet, ntree = 500, mtry = 6, importance = TRUE)\nmodel2\n\n# Predicting on train set\npredTrain <- predict(model2, TrainSet, type = \"class\")\n# Checking classification accuracy\ntable(predTrain, TrainSet$Condition)\n\n# Predicting on Validation set\npredValid <- predict(model2, ValidSet, type = \"class\")\n# Checking classification accuracy\nmean(predValid == ValidSet$Condition) \ntable(predValid,ValidSet$Condition)\n\n# To check important variables\nimportance(model2) \nvarImpPlot(model2)\n\n# Using For loop to identify the right mtry for model\na=c()\ni=5\nfor (i in 3:8) {\n model3 <- randomForest(Condition ~ ., data = TrainSet, ntree = 500, mtry = i, importance = TRUE)\n predValid <- predict(model3, ValidSet, type = \"class\")\n a[i-2] = mean(predValid == ValidSet$Condition)\n}\n\na\n\nplot(3:8,a)\n\n\n#compare with dtree\n# Compare with Decision Tree\n\n#install.packages(\"rpart\")\n#install.packages(\"caret\")\n#install.packages(\"e1071\")\n\nlibrary(rpart)\nlibrary(caret)\nlibrary(e1071)\n\nmodel_dt = train(Condition ~ ., data = TrainSet, method = \"rpart\")\nmodel_dt_1 = predict(model_dt, data = TrainSet)\ntable(model_dt_1, TrainSet$Condition)\n\nmean(model_dt_1 == TrainSet$Condition)\n\n# Running on Validation Set\nmodel_dt_vs = predict(model_dt, newdata = ValidSet)\ntable(model_dt_vs, ValidSet$Condition)\n\nmean(model_dt_vs == ValidSet$Condition)\n" }, { "path": "31d-randomforest/dt-rf-eg3.R", "content": "#https://datascienceplus.com/random-forests-in-r/ \nrequire(randomForest)\nrequire(MASS)#Package which contains the Boston housing dataset\nattach(Boston)\nset.seed(101)\n\ndim(Boston)\n\n#training Sample with 300 observations\ntrain=sample(1:nrow(Boston),300)\n?Boston #to search on the dataset\n\nBoston.rf=randomForest(medv ~ . , data = Boston , subset = train)\nBoston.rf\nplot(Boston.rf)\n\noob.err=double(13)\ntest.err=double(13)\n\n#mtry is no of Variables randomly chosen at each split\nfor(mtry in 1:13) \n{\n rf=randomForest(medv ~ . , data = Boston , subset = train,mtry=mtry,ntree=400) \n oob.err[mtry] = rf$mse[400] #Error of all Trees fitted\n \n pred<-predict(rf,Boston[-train,]) #Predictions on Test Set for each Tree\n test.err[mtry]= with(Boston[-train,], mean( (medv - pred)^2)) #Mean Squared Test Error\n \n cat(mtry,\" \") #printing the output to the console\n \n}\ntest.err\noob.err\n\nmatplot(1:mtry , cbind(oob.err,test.err), pch=19 , col=c(\"red\",\"blue\"),type=\"b\",ylab=\"Mean Squared Error\",xlab=\"Number of Predictors Considered at each Split\")\nlegend(\"topright\",legend=c(\"Out of Bag Error\",\"Test Error\"),pch=19, col=c(\"red\",\"blue\"))\n\n" }, { "path": "31d-randomforest/dt-rf-kyphosis1.R", "content": "# Random Forests\n\nRandom Forest prediction of Kyphosis data\nlibrary(randomForest)\nfit <- randomForest(Kyphosis ~ Age + Number + Start, data=kyphosis)\nprint(fit) # view results \nimportance(fit) # importance of each predictor\nhead(kyphosis)\n?kyphosis\nplot(fit)\n# Importance of each predictor.\nprint(importance(fit,type = 2)) \n\n#EWg2\nLoad the party package. It will automatically load other\n# required packages.\nlibrary(party)\nlibrary(randomForest)\n\n# Create the forest.\noutput.forest <- randomForest(nativeSpeaker ~ age + shoeSize + score, data = readingSkills)\n\n# View the forest results.\nprint(output.forest) \n# Importance of each predictor.\nprint(importance(output.forest,type = 2)) \n\n" }, { "path": "31d-weka/cls-ID3.R", "content": "# Classification - ID3 \n#https://cis.temple.edu/~giorgio/cis587/readings/id3-c45.html\n\n# basic ideas behind ID3 are that:\n# In the decision tree each node corresponds to a non-categorical attribute and each arc to a possible value of that attribute. A leaf of the tree specifies the expected value of the categorical attribute for the records described by the path from the root to that leaf. [This defines what is a Decision Tree.]\n# In the decision tree at each node should be associated the non-categorical attribute which is most informative among the attributes not yet considered in the path from the root. [This establishes what is a \"Good\" decision tree.]\n# Entropy is used to measure how informative is a node. [This defines what we mean by \"Good\". By the way, this notion was introduced by Claude Shannon in Information Theory.]\n#C4.5 is an extension of ID3 that accounts for unavailable values, continuous attribute value ranges, pruning of decision trees, rule derivation, and so on.\n\nlibrary(data.tree)\n\n# http://r.789695.n4.nabble.com/Is-there-an-ID3-implementation-in-R-td4696375.html\n\n## load RWeka \n\nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_111\\\\bin') # for 64-bit version\n\nsystem(\"java -version\")\n# This is important.. Use rJava Home instead of Windows Home\nif(Sys.getenv(\"JAVA_HOME\")!=\"\"){\n Sys.setenv(JAVA_HOME=\"\")\n}\nlibrary(rJava)\nlibrary(\"RWekajars\")\nlibrary(RWeka) # needs rJava\n## look for a package providing id3 \nWPM(\"refresh-cache\") \nWPM(\"list-packages\", \"available\") ## look for id3 \n## install package providing id3 \nWPM(\"install-package\", \"simpleEducationalLearningSchemes\") \n## load the package \nWPM(\"load-package\", \"simpleEducationalLearningSchemes\") \n## make classifier \nID3 <- make_Weka_classifier(\"weka/classifiers/trees/Id3\") \n## test it out. \nDF2 <- read.arff(system.file(\"arff\", \"contact-lenses.arff\", package = \"RWeka\")) \n.jinit(\".\")\nstr(DF2)\nID3(`contact-lenses` ~ ., data = DF2) \n\nWPM(\"load-package\", \"Decorate\") \nmake_Weka_classifier(\"Decorate\") \nWPM(\"load-package\", \"naiveBayesTree\")\n\nprint(.jclassPath())\n" }, { "path": "31d-weka/cls-c45weka.R", "content": "# C4.5 Algo https://en.wikipedia.org/wiki/C4.5_algorithm\n# C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan.[1] C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, \n# and for this reason, C4.5 is often referred to as a statistical classifier.\n# C4.5 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy.\n# At each node of the tree, C4.5 chooses the attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. The splitting criterion is the normalized information gain (difference in entropy). The attribute with the highest normalized information gain is chosen to make the decision. \n# The C4.5 algorithm then recurs on the smaller sublists.\n# C5.0 offers a number of improvements on C4.5. \n\n#https://www.r-bloggers.com/r-talks-to-weka-about-data-mining/\nsummary(iris)\nlibrary(RWeka)\niris_j48 <- J48(Species ~ ., data = iris)\niris_j48\nsummary(iris_j48)\nplot(iris_j48)\n\n# Evaluation\neval_j48 <- evaluate_Weka_classifier(iris_j48, numFolds = 10, complexity = FALSE, \n seed = 1, class = TRUE)\neval_j48\n\n# More Functions\nWOW(\"J48\")\n\nj48_control <- J48(Species ~ ., data = iris, control = Weka_control(M = 10))\nj48_control\n\n# Cost Sensitive Cls\ncsc <- CostSensitiveClassifier(Species ~ ., data = iris, control = Weka_control(`cost-matrix` = matrix(c(0, \n 10, 0, 0, 0, 0, 0, 10, 0), ncol = 3), W = \"weka.classifiers.trees.J48\", \nM = TRUE))\ncsc\n\nmatrix(c(0, 1, 0, 0, 0, 0, 0, 1, 0), ncol = 3)\neval_csc <- evaluate_Weka_classifier(csc, numFolds = 10, complexity = FALSE, \n seed = 1, class = TRUE)\neval_csc\n" }, { "path": "31d-weka/clsW-iris.R", "content": "R Cls4\nSys.getenv(\"JAVA_HOME\")\n\nSys.setenv(JAVA_HOME=\"C:\\\\Program Files\\\\Java\\\\jre1.8.0_111\")\n\n\n#Eg1\nlibrary(RWeka)\ndata(iris)\nfit <- J48(Species~., data=iris)\nsummary(fit)\npredictions <- predict(fit, iris[,1:4])\ntable(predictions, iris$Species)\n#Eg2\nlibrary(RWeka)\ndata(iris)\nfit <- PART(Species~., data=iris)\nsummary(fit)\npredictions <- predict(fit, iris[,1:4])\ntable(predictions, iris$Species)\n\n\n\n" }, { "path": "31d-weka/clsW-iris2.R", "content": "R Cls4\n#Eg1\nlibrary(RWeka)\ndata(iris)\nfit <- J48(Species~., data=iris)\nsummary(fit)\npredictions <- predict(fit, iris[,1:4])\ntable(predictions, iris$Species)\n#Eg2\nlibrary(RWeka)\ndata(iris)\nfit <- PART(Species~., data=iris)\nsummary(fit)\npredictions <- predict(fit, iris[,1:4])\ntable(predictions, iris$Species)\n" }, { "path": "31e-Case-Cancer/data-cancer.R", "content": "# Data for Classfication\n\n# dataset for Classification\n#https://github.com/kabacoff/RiA2/blob/master/Ch17%20Classification.R\n\nbreast = read.table('http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', sep=',', header=F, na.strings='?')\nnames(breast)\n\nnames(breast) <- c(\"ID\", \"clumpThickness\", \"sizeUniformity\",\n \"shapeUniformity\", \"maginalAdhesion\", \n \"singleEpithelialCellSize\", \"bareNuclei\", \n \"blandChromatin\", \"normalNucleoli\", \"mitosis\", \"class\")\nnames(breast)\n\ndf <- breast[-1]\ndf$class <- factor(df$class, levels=c(2,4), labels=c(\"benign\", \"malignant\"))\n\n\n\nset.seed(1234)\ntrain <- sample(nrow(df), 0.7*nrow(df))\ndf.train <- df[train,]\ndf.validate <- df[-train,]\ntable(df.train$class)\ntable(df.validate$class)" }, { "path": "31e-Case-Cancer/rf-cancer.R", "content": "# Random Forests\n#http://www.instituteofanalytics.com/forum/uploads/editor/ls/4kivialj5lvj.pdf\n\nlibrary(randomForest)\nset.seed(1234)\n\nfit.forest = randomForest(class ~ . , data=df.train, na.action = na.roughfix, importance=T)\nfit.forest\n\nimportance(fit.forest, type=2)\n\nforest.pred = predict(fit.forest, df.validate)\nforest.perf = table(df.validate$class, forest.pred, dnn=c('Actual', 'Predicted'))\nforest.perf\n\ncaret::confusionMatrix(forest.perf)\n\n\n#Plot Random Forests\nlibrary(reprtree)\ngetTree(fit.forest, 1, labelVar=TRUE)\n \nlibrary(reprtree)\n\ncforest(Species ~ ., data=iris, controls=cforest_control(mtry=2, mincriterion=0))\n\n\n\n#----\nlibrary(devtools)\nif(!('reprtree' %in% installed.packages())){\n install_github('araastat/reprtree')\n}\n" }, { "path": "31e-Case-Cancer/svm-cancer1.R", "content": "# Support Vector Machine\n#https://rpubs.com/ricardosc/breast-cancer\n#https://github.com/kabacoff/RiA2/blob/master/Ch17%20Classification.R\n\n#first setup the data from data-breast.R\nsource('./cls/case-cancer/data-cancer.R')\nlibrary(e1071)\nset.seed(1234)\n\nnames(df.train)\nhead(df.train)\nsummary(df.train)\nfit.svm = svm(class ~ . , df.train)\nfit.svm\nplot(fit.svm, data=df.train) # will not work\nplot(fit.svm, df.train, clumpThickness ~ sizeUniformity)\n# slice = list(Sepal.Width = 3, Sepal.Length = 4))\n#http://ugrad.stat.ubc.ca/R/library/e1071/html/plot.svm.html\n\n\n\nsvm.pred = predict(fit.svm, na.omit(df.validate))\nsvm.pred\nplot(svm.pred)\nplot(svm.pred, na.omit(df.validate))\n\n\nna.omit(df.validate)$class\n\nsvm.perf = table(na.omit(df.validate)$class, svm.pred, dnn=c('Actual','Predicted'))\nsvm.perf \n\n\n#Cannot handle missing values in IV\n#scaling done before classification model\n#better accuracy but < random forests\n# Predicted\n# Actual benign malignant\n# benign 116 4\n# malignant 3 77\n\n#Tuning\nset.see(1234)\n#tuned = tune.svm(class ~ ., data=df.train, gamma=10 ^ (-6:1), cost=10^(-10:10))\n#tuned\n\nfit.svm = svm(class ~. , df.train, gamma=0.2, cost=1)\nfit.svm\nsvm.pred = predict(fit.svm, na.omit(df.validate))\nsvm.pred\nsvm.perf = table(na.omit(df.validate)$class, svm.pred, dnn=c('Actual', 'Predicted'))\nsvm.perf\n# Predicted\n# Actual benign malignant\n# benign 116 4\n# malignant 3 77\n\ndim(df.validate)\nlibrary(caret) \nconfusionMatrix(svm.perf)\n(116+77) ; 193/220\n" }, { "path": "31e-Case-Cancer/svm-examples.R", "content": "# SVM\n\n# Solved Eg\n## a simple example\nlibrary(MASS)\ndata(cats)\nm <- svm(Sex~., data = cats)\nhead(cats) # 2dim : fix for higher dime\nplot(m, cats)\n\n## more than two variables: fix 2 dimensions\ndata(iris)\nm2 <- svm(Species~., data = iris)\nplot(m2, iris, Petal.Width ~ Petal.Length,\n slice = list(Sepal.Width = 3, Sepal.Length = 4))\n\n#small examples on SVM\nlibrary(e1071)\n\nday = c(0,1,2,3,4,5,6)\nweather = c(1,0,0,0,0,0,0)\nhappy = factor(c(T,F,F,F,F,F,F))\n\nd = data.frame(day=day, weather=weather, happy=happy)\nhead(d)\nmodel = svm(happy ~ day + weather, data = d)\n\nplot(x=d$weather, y=d$day, xlim=c(0,2))\nplot(model, d,add=T, yaxt=n, xaxt=n, xlim=c(0,2))\n\n#add another row\nd1a = data.frame(day=c(7), weather=c(1), happy=c(T))\nd2 = rbind(d, d1a)\nd2\nmodel2 = svm(happy ~ day + weather, data = d2)\nplot(x=d2$weather, y=d2$day, xlim=c(0,2))\nplot(model2, d2,add=T, yaxt=n, xaxt=n, xlim=c(0,2))\n\n#add another row\nd1b = data.frame(day=c(8), weather=c(0), happy=c(T))\nd3 = rbind(d2, d1b)\nd3\nmodel3 = svm(happy ~ day + weather, data = d3)\nplot(x=d3$weather, y=d3$day, xlim=c(0,2))\nplot(model3, d3,add=T, yaxt=n, xaxt=n, xlim=c(0,2))\n\n" }, { "path": "40a-CLUST/10-clust-packages.R", "content": "#Packages for Clustering\n\n#install.packages('easypackages')\nlibrary(easypackages)\nclustpackages = c('dplyr','amap','NbClust','gsheet', 'cluster', 'factoextra','fpc', 'devtools', 'vegan', 'kohonen','datasets','ggplot2', 'flexclust','tsne','ISLR','reshape','corrplot','fMultivar','animation','plyr','switchr')\neasypackages::packages(clustpackages)\ninstall_github(\"pablo14/clusplus\")\neasypackages::libraries(c(clustpackages,'clusplus'))\n\n " }, { "path": "40a-CLUST/16b-km-withinss.R", "content": "# Clustering - Video Lesson\n\nlibrary(cluster) #computing pam, analyzing cluster silhouettes\nlibrary(factoextra) # visualizing clusters using ggplot2 plotting system\nlibrary(NbClust) # finding the optimal number of clusters\n#Case-1: One Numeric Variable\n#libraries\nlibrary(cluster)\nlibrary(factoextra)\nlibrary(NbClust)\n#create data\nset.seed(1234)\nmarks1 = ceiling(runif(20,50,90))\ndf1 = data.frame(marks1)\n#Perform Clustering\nset.seed(1234)\nkm1 = kmeans(df1,3,nstart=5)\n#nstart option attempts multiple initial configurations and reports on the best one. For example, adding nstart=25 will generate 25 initial random centroids and choose the best one for the algorithm\n\nkm1\n(df1a = cbind(df1, cluster=km1$cluster))\ndf1a[ order(df1a$cluster), ]\n\n#Avl components\nstr(km1)\nattributes(km1)\n# Calculate distances between each point\n#stats::dist(df1$marks1, method = \"euclidean\")\nm1 = as.matrix(stats::dist(df1$marks1, method = \"euclidean\"))\n?kmeans\nm1\nrownames(m1) = df1$marks1\ncolnames(m1) = df1$marks1\nm1\n#distance between 1st and 2nd \nsqrt((diff(df1$marks1[c(1,20)]))^2)\nuseful::topright(m1)\n\n# distance calculated by Algo\n#within : inside a cluster : sum of sq differences of each point from centeriod\nkm1$withinss\n#cluster 1 : \nkm1$withinss[1]\n#calculate manually\n(c1 = df1[km1$cluster==1,])\n(m1 = km1$centers[1])\nsum((c1 - m1)^2)\n\n#sum all within cluster sum sq differences\nkm1$tot.withinss\nkm1$withinss ; sum(km1$withinss)\n\n#between each cluster centerioids\nkm1$betweenss\nkm1$centers\ndiff(km1$centers)\n(comb1 = combn(km1$centers,2))\n(diff1 = apply(comb1, 2, diff))\ndiff1^2\nsum(diff1^2)\n\nc(diff(km1$centers))\nsum(as.numeric(dist(km1$centers))^2)\n\nsum((abs(apply(combn(km1$centers,2), 2, diff))^2))\n\n#total distance : tot.within + between\nkm1$totss\nkm1$tot.withinss + km1$betweenss\n\n#Other Output of Algo\n#Centeriod\nkm1$centers\n#Size of each cluster\nkm1$size\n#Total SS Distance\nkm1$totss\n\n#Plot the Clusters\n#dim of plot should > 2\nfviz_cluster(km1, data = df1, geom = \"point\", stand = FALSE, ellipse.type = \"norm\") #Less than 2 Not work\nplot(df1$marks1, col=km1$cluster)\n\nlibrary(cluster)\nclusplot(df1, km1$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)\n#optimal number of clusters\n#Package\nset.seed(123)\nfviz_nbclust(df1, kmeans, method = \"wss\") + geom_vline(xintercept = 3, linetype = 2)\nnb <- NbClust(df1, distance = \"euclidean\", min.nc = 2, max.nc = 10, method = \"complete\", index =\"all\") #Not Working\n\n#Manual way\nset.seed(1234)\n(kmc1 = kmeans(df1,1)) ; (kmc2 = kmeans(df1,2)); (kmc3 = kmeans(df1,3)) ; (kmc4 = kmeans(df1,4));(kmc5 = kmeans(df1,5))\nkmc1$tot.withinss; kmc5$tot.withinss\n(wss = sapply(1:5, function(k){kmeans(df1, k )$tot.withinss}))\nplot(1:5, wss, type=\"b\")\nabline(v = 3, lty =2)\n\n\n\n#doing with 2 variables\n(df2=women)\nkm2 = kmeans(df2, 3)\nstr(km2)\ndf2$cluster=factor(km2$cluster)\nfviz_cluster(km2, data = df2[1:2], geom = \"point\", stand = FALSE, ellipse.type = \"norm\") # 2 cols required\n\nclusplot(df2, km2$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)\nanimation::kmeans.ani(df2[1:2],3)\n(centers=as.data.frame(km2$centers))\n\ng= ggplot(data=df2[1:3], aes(x=height, y=weight, color=cluster,size=10 )) + geom_point()\ng\ng + geom_point(data=centers, aes(x=height,y=weight, colour=unique(df2$cluster)), size=67, alpha=.3) + theme(legend.position = \"none\")\n\n" }, { "path": "40a-CLUST/17b-clust-noclusters1.R", "content": "# Clustering - Number of Clusters\nlibrary(NbClust)\n#https://www.rdocumentation.org/packages/NbClust/versions/3.0/topics/NbClust\n\nincome = ceiling(runif(100,60,100))\nage = ceiling(rnorm(100, 35,5))\n\ndf = data.frame(income, age)\nhead(df)\ndata = scale(df)\n\nfit.km3 = kmeans(data,centers=3)\nfit.km3$tot.withinss\nfit.km4 = kmeans(data,centers=4)\nfit.km4$tot.withinss\nfit.km10 = kmeans(data,centers=10)\nfit.km10$tot.withinss\n\n# Second set of data\nn = 100; g = 6 \nset.seed(g)\nd <- data.frame(x = unlist(lapply(1:g, function(i) rnorm(n/g, runif(1)*i^2))), y = unlist(lapply(1:g, function(i) rnorm(n/g, runif(1)*i^2))))\nd\nplot(d)\n\ndata=d\n\n#Selecting the number of clusters\nlibrary(NbClust)\nnc = NbClust(data, distance=\"euclidean\", \n min.nc=2, max.nc=15, method=\"average\")\npar(opar)\ntable(nc$Best.n[1,])\nbarplot(table(nc$Best.n[1,]), xlab=\"Numer of Clusters\", ylab=\"Number of Criteria\", main=\"Number of Clusters Chosen by 26 Criteria\") \n\n#Method2\nwss <- (nrow(data)-1)*sum(apply(data,2,var))\nfor (i in 2:15) wss[i] <- sum(kmeans(data, centers=i)$withinss)\nplot(1:15, wss, type=\"b\", xlab=\"Number of Clusters\",\n ylab=\"Within groups sum of squares\")\n\n\n#Method : usin PAM\nlibrary(fpc)\npamk.best <- pamk(data)\ncat(\"number of clusters estimated by optimum average silhouette width:\", pamk.best$nc, \"\\n\")\nplot(pam(data, pamk.best$nc))\n\n\n#Method we could also do:Not Working\nlibrary(fpc)\nasw <- numeric(20)\nfor(k in 2:20) asw[[k]] = pam(data, k)$silinfo$avg.width\nk.best = which.max(asw)\ncat(\"silhouette-optimal number of clusters:\", k.best, \"\\n\")\n\n#Method----\nrequire(vegan)\nfit <- cascadeKM(scale(data, center = TRUE, scale = TRUE), 1, 10, iter = 1000)\nplot(fit, sortg = TRUE, grpmts.plot = TRUE)\ncalinski.best <- as.numeric(which.max(fit$results[2,]))\ncat(\"Calinski criterion optimal number of clusters:\", calinski.best, \"\\n\")\n# 3 clusters!\n\n#Method : Bayesian\nlibrary(mclust)\n# at least 1 model and up 20.\nd_clust <- Mclust(as.matrix(data), G=1:20)\nd_clust\nm.best <- dim(d_clust$z)[2]\ncat(\"model-based optimal number of clusters:\", m.best, \"\\n\")\n# 4 clusters\nplot(d_clust)\n\n#Method----\nlibrary(apcluster)\nd.apclus <- apcluster(negDistMat(r=2), data)\ncat(\"affinity propogation optimal number of clusters:\", length(d.apclus@clusters), \"\\n\")\n# \nheatmap(d.apclus)\nplot(d.apclus, data)\n\n\n#Method\nlibrary(cluster)\nclusGap(data, kmeans, 10, B = 100, verbose = interactive())\n\n\n#Method\nlibrary(NbClust)\nnb <- NbClust(data, distance = \"euclidean\", min.nc=2, max.nc=15, method = \"kmeans\", index = \"alllong\", alphaBeale = 0.1)\n#diss=\"NULL\" remove\nhist(nb$Best.nc[1,], breaks = max(na.omit(nb$Best.nc[1,])))\n\n\n#Method\nd_dist <- dist(as.matrix(data)) # find distance matrix \nplot(hclust(d_dist)) \nclusters <- identify(hclust(d_dist))\nclusters\n\n\n#Method\nlibrary(apcluster)\napclus = apcluster(negDistMat(r=2), data)\nshow(apclus)\n\nsim = corSimMat(data, method=\"spearman\")\napclus = apcluster(s=sim)\napclus\n\n\n#Method - Weka\nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_144')\nlibrary(RWeka)\n\n# Print a list of available options for the X-Means algorithm\nWOW(\"XMeans\")\nWPM(\"refresh-cache\")\nWPM(\"list-packages\", \"installed\")\nWPM(\"list-packages\", \"available\")\nWPM(\"install-package\", 'XMeans')\nWOW(\"XMeans\")\n# Create a Weka_control object which will specify our parameters\nweka_ctrl <- Weka_control(\n I = 1000, # max no. of overall iterations\n M = 1000, # max no. of iterations in the kMeans loop\n L = 20, # min no. of clusters\n H = 150, # max no. of clusters\n D = \"weka.core.EuclideanDistance\", # dist metric Euclidean\n C = 0.4, # cutoff factor ???\n S = 12 # random number seed (for reproducibility)\n)\n\n# Run the algorithm on your data, d\nx_means <- XMeans(data, control = weka_ctrl)\n\n# Assign cluster IDs to original data set\ndata$xmeans.cluster <- x_means$class_ids\ndata$xmeans.cluster\n\n\n#Method\nset.seed(2)\nx=matrix(rnorm(50*2), ncol=2)\nhc.complete = hclust(dist(x), method=\"complete\")\nplot(hc.complete)\ncutree(hc.complete,k = 2)\ncutree(hc.complete,h = 4.9)\n\n\n#\nlibrary(pvclust)\nlibrary(MASS)\ndata(Boston)\nboston.pv <- pvclust(Boston)\nplot(boston.pv)\n" }, { "path": "40a-CLUST/17c-clust-numbers-iris.R", "content": "#Optimal Number of Clusters in data\n#Reduce total within ss\niris\ntable(iris$Species)\n\ndata = iris[-5]\nhead(data)\n\nkm1= kmeans(data,centers=1)\nkm1$tot.withinss\n\nkm2= kmeans(data,centers=2)\nkm2$tot.withinss\nkm2$withinss\n\nkm3= kmeans(data,centers=3)\nkm3$tot.withinss\n\n\nkm4= kmeans(data,centers=4)\nkm4$tot.withinss\n\nkm5= kmeans(data,centers=5)\nkm5$tot.withinss\n\nkm1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km4$tot.withinss ; km5$tot.withinss\n\n#Selecting the number of clusters\nlibrary(NbClust)\nnc = NbClust(data, distance=\"euclidean\",min.nc=2, max.nc=15, method=\"average\")\n\nkiris = kmeans(data, centers=3)\nkiris$centers\n\ncbind(iris$Species, data, kiris$cluster)\n\n\nnames(mtcars)\ndata2 = mtcars[c('mpg','disp','hp','wt')]\nhead(data2)\nnc = NbClust(data2, distance=\"euclidean\",min.nc=2, max.nc=15, method=\"average\")\ndet(as.matrix(mtcars))\n?na.action\nkm3= kmeans(data,centers=3)\nkm3$tot.withinss\n\ncbind(km1$tot.withinss, km2$tot.withinss, km3$tot.withinss, km4$tot.withinss,km5$tot.withinss)\n\n#we select no clusters at elbow point\n#adding more clusters does not significantly reduce total withinss\n" }, { "path": "40a-CLUST/17d-noc-mclust.R", "content": "#Nos of clusters\n#https://datascienceplus.com/finding-optimal-number-of-clusters/\n" }, { "path": "40a-CLUST/19b-clust-distances.R", "content": "# Various Distance in Clustering\n" }, { "path": "40a-CLUST/19c-clust-distances.R", "content": "# Clustering - Video Lesson\n\nlibrary(cluster) #computing pam, analyzing cluster silhouettes\nlibrary(factoextra) # visualizing clusters using ggplot2 plotting system\nlibrary(NbClust) # finding the optimal number of clusters\n#Case-1: One Numeric Variable\n#libraries\nlibrary(cluster)\nlibrary(factoextra)\nlibrary(NbClust)\n#create data\nset.seed(1234)\n\nincome = ceiling(runif(20,10000,300000))\nchildren = round(runif(20,0,5),0)\nrent = ceiling(rnorm(20,15000,2000))\ndf2 = data.frame(income, children, rent)\n\n#Perform Clustering\nset.seed(1234)\nkm2 = kmeans(df2,3)\nkm2\ndf2a = cbind(df2, cluster=km2$cluster)\ndf2a\ndf2a[ order(df2a$cluster), ]\n\n#Avl components\nattributes(km2)\n# Calculate distances between each point\n#stats::dist(df1$marks1, method = \"euclidean\")\nm2 = as.matrix(stats::dist(df2, method = \"euclidean\"))\nm2\n# distance calculated by Algo\n#within\nkm2$withinss\nkm2$tot.withinss\n#between\nkm2$betweenss\n#Other Output of Algo\n#Centeriod\nkm2$centers\n#Size of each cluster\nkm2$size\n#Total SS Distance\nkm2$totss\n\n#Plot the Clusters\nfviz_cluster(km2, data = df2, geom = \"point\", stand = FALSE, ellipse.type = \"norm\") #work more data reqd\nplot(df2$income, col=km2$cluster)\n\nlibrary(cluster)\nclusplot(df2, km2$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)\n\n#optimal number of clusters\n#Package\nset.seed(123)\nfviz_nbclust(df2, kmeans, method = \"wss\") + geom_vline(xintercept = 3, linetype = 2)\n#nb <- NbClust(df2, distance = \"euclidean\", min.nc = 2, max.nc = 10, method = \"complete\", index =\"all\") #Not Working\n\n#Manual way\nset.seed(1234)\n(kmc1 = kmeans(df2,1)) ; (kmc2 = kmeans(df2,2)); (kmc3 = kmeans(df2,3)) ; (kmc4 = kmeans(df2,4));(kmc5 = kmeans(df2,5))\nkmc2$tot.withinss; kmc5$tot.withinss\n(wss = sapply(1:5, function(k){kmeans(df2, k )$tot.withinss}))\nplot(1:5, wss, type=\"b\")\nabline(v = 3, lty =2)\n\n#Scale the data and then do Kmeans\ndf3 = data.frame(scale(df2))\nclass(df3)\nhead(df3)\nkm3 = kmeans(df3, 3)\nkm3\nkm3$tot.withinss\n#Plot the Clusters\nfviz_cluster(km3, data = df3, geom = \"point\", stand = FALSE, ellipse.type = \"norm\") #work more data reqd\nplot(df3$income, col=km3$cluster)\n\n#Package\nset.seed(123)\nfviz_nbclust(df3, kmeans, method = \"wss\")\n(wss3 = sapply(1:5, function(k){kmeans(df3, k )$tot.withinss}))\nplot(1:5, wss3, type=\"b\")\nabline(v = 3, lty =2)\n\nfviz_nbclust(df3, kmeans, method = \"silhouette\")\n" }, { "path": "40a-CLUST/19d-clust-scaling.R", "content": "# Scaling the data for Clustering\n\nset.seed(1234); marks10 = ceiling(runif(100, 5,10)) \nset.seed(1234); marks500 = ceiling(runif(100, 250,500)) \n\nstudents1= data.frame(marks10, marks500)\nhead(students1)\n\nkm1 = kmeans(students1, centers=3)\nkm1$centers; km1$withinss\n#scaling gives equal importance to all variables\n#avoid obtaining clusters that are dominated by variables having the largest amount of variation\n\nstudents2 = scale(students1)\nkm2 = kmeans(students2, centers=3)\nkm2$centers; km2$withinss\n\npar(mfrow=c(1,2))\nplot(students1$marks10, students1$marks500, col=km1$cluster)\nplot(students1$marks10, students1$marks500, col=km2$cluster)\npar(mfrow=c(1,1))\n" }, { "path": "40a-CLUST/20b-clust-plots.R", "content": "# Plotting in Clusters\n\n\n#Plot1----\nlibrary(cluster)\nlibrary(fpc)\n\ndata(iris)\ndata = iris[, -5] # without known classification \n# Kmeans cluster analysis\nkm1 = kmeans(data, centers=3)\nplotcluster(data, km1$cluster)\n\n#Plot2\n# More complex\nclusplot(data, km1$cluster, color=TRUE, shade=TRUE, \n labels=2, lines=0)\n\n#plot3\nwith(iris, pairs(data, col=c(1:3)[km1$cluster])) \n\n#plot4\nX <- data.frame(c1=c(0,1,2,4,5,4,6,7),c2=c(0,1,2,3,3,4,5,5))\nkm <- kmeans(X, center=2)\nplot(X,col=km$cluster)\npoints(km$center,col=1:2,pch=8,cex=1)\n\n\n\n\n\n#Plot5----\nlibrary(cluster)\nlibrary(HSAUR)\ndata(pottery)\nkm <- kmeans(pottery,3)\ndissE <- daisy(pottery) \ndE2 <- dissE^2\nsk2 <- silhouette(km$cl, dE2)\nplot(sk2)\n\n\n\n\n\nset.seed(1234); (age = ceiling(rnorm(50, 45, 10)))\nset.seed(1234);(income = ceiling(rnorm(50, 100000, 10000)))\nset.seed(1234);(children = sample(c(1,2,3), size=50, replace=T, prob=c(.4,.3,.2)))\ncustomers = data.frame(age, income, children)\nhead(customers)\n\n# No of Clusters\nlibrary(NbClust)\nnc = NbClust(customers, distance=\"euclidean\", \n min.nc=2, max.nc=15, method=\"average\")\n#without scaling\nkm1 = kmeans(customers, centers=3)\nkm1$centers\n\n\n#Fan Plot\nlibrary(ape)\nlibrary(cluster) \ndata(mtcars)\nplot(hclust(dist(mtcars)))\n\nplot(as.phylo(hclust(dist(mtcars))),type=\"fan\")\n\n\n#\n# Load data\ndata(mtcars)\n# Compute distances and hierarchical clustering\ndd <- dist(scale(mtcars), method = \"euclidean\")\nhc <- hclust(dd, method = \"ward.D2\")\nlibrary(factoextra)\nfviz_dend(hc, cex = 0.5)\n#You can use the arguments main, sub, xlab, ylab to change plot titles as follow:\n \nfviz_dend(hc, cex = 0.5, main = \"Dendrogram - ward.D2\", xlab = \"Objects\", ylab = \"Distance\", sub = \"\")\n#To draw a horizontal dendrogram, type this:\n \nfviz_dend(hc, cex = 0.5, horiz = TRUE)\n\nfviz_dend(hc, k = 4, # Cut in four groups\n cex = 0.5, # label size\n k_colors = c(\"#2E9FDF\", \"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n color_labels_by_k = TRUE, # color labels by groups\n rect = TRUE, # Add rectangle around groups\n rect_border = c(\"#2E9FDF\", \"#00AFBB\", \"#E7B800\", \"#FC4E07\"), \n rect_fill = TRUE)\n\nfviz_dend(hc, k = 4, # Cut in four groups\n cex = 0.5, # label size\n k_colors = c(\"#2E9FDF\", \"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n color_labels_by_k = TRUE, # color labels by groups\n ggtheme = theme_gray() # Change theme\n)\nfviz_dend(hc, cex = 0.5, k = 4, # Cut in four groups\n k_colors = \"jco\")\n\nfviz_dend(hc, cex = 0.5, k = 4, # Cut in four groups\n k_colors = \"jco\", horiz=T)\n\nfviz_dend(hc, k = 4, cex = 0.4, horiz = TRUE, k_colors = \"jco\", \n rect = TRUE, rect_border = \"jco\", rect_fill = TRUE)\n\nfviz_dend(hc, cex = 0.5, k = 4, \n k_colors = \"jco\", type = \"circular\")\n\nrequire(\"igraph\")\nfviz_dend(hc, k = 4, k_colors = \"jco\",\n type = \"phylogenic\", repel = TRUE)\n\nrequire(\"igraph\")\nfviz_dend(hc, k = 4, # Cut in four groups\n k_colors = \"jco\",\n type = \"phylogenic\", repel = TRUE,\n phylo_layout = \"layout.gem\")\n\n\n#zoom in dendrogram\nfviz_dend(hc)\nfviz_dend(hc, xlim = c(1, 20), ylim = c(0, 5))\nfviz_dend(hc, xlim = c(0, 10), ylim = c(-2, 5))\n\n#subtree\n# Create a plot of the whole dendrogram,\n# and extract the dendrogram data\ndend_plot <- fviz_dend(hc, k = 4, # Cut in four groups\n cex = 0.5, # label size\n k_colors = \"jco\")\ndend_plot\ndend_data <- attr(dend_plot, \"dendrogram\") # Extract dendrogram data\ndend_data\n# Cut the dendrogram at height h = 10\ndend_cuts <- cut(dend_data, h = 10)\n# Visualize the truncated version containing\n# two branches\nfviz_dend(dend_cuts$upper)\nattributes(dend_cuts)\ndend_cuts\n#Plot subtree 1\nfviz_dend(dend_cuts$lower[[1]], main = \"Subtree 1\")\n# Plot subtree 2\nfviz_dend(dend_cuts$lower[[2]], main = \"Subtree 2\")\nfviz_dend(dend_cuts$lower[[3]])\n# Plot the whole dendrogram\nprint(dend_plot)\n\nfviz_dend(dend_cuts$lower[[2]], type = \"circular\")\n\n#\npdf(\"dendrogram.pdf\", width=30, height=15) # Open a PDF\np <- fviz_dend(hc, k = 4, cex = 1, k_colors = \"jco\" ) # Do plotting\nprint(p)\ndev.off() # Close the PDF\n#http://www.sthda.com/english/articles/28-hierarchical-clustering-essentials/92-visualizing-dendrograms-ultimate-guide/\n \n#using chain operat\ndata <- scale(mtcars)\ndist.res <- dist(data)\nhc <- hclust(dist.res, method = \"ward.D2\")\ndend <- as.dendrogram(hc)\nplot(dend)\n#R code for creating a dendrogram using chaining operator:\nlibrary(dendextend)\ndend \nmtcars[1:8,] %>% # data\n scale %>% # Scale the data\n dist %>% # calculate a distance matrix, \n hclust(method = \"ward.D2\") %>% # Hierarchical clustering \n as.dendrogram %>% # Turn the object into a dendrogram.\n plot\n\n#-\nlibrary(dendextend)\n# 1. Create a customized dendrogram\nmycols <- c(\"#2E9FDF\", \"#00AFBB\", \"#E7B800\", \"#FC4E07\")\ndend <- as.dendrogram(hc) %>%\n set(\"branches_lwd\", 5) %>% # Branches line width\n set(\"branches_k_color\", mycols, k = 4) %>% # Color branches by groups\n set(\"labels_colors\", mycols, k = 4) %>% # Color labels by groups\n set(\"labels_cex\", 0.8) # Change label size\n# 2. Create plot \nfviz_dend(dend, horiz=T) \n\n#radial Plot\nlibrary(dendextend)\nlibrary(circlize)\n\n# create a dendrogram\nhc <- hclust(dist(datasets::mtcars))\ndend <- as.dendrogram(hc)\n\n# modify the dendrogram to have some colors in the branches and labels\ndend <- dend %>% \n color_branches(k=4) %>% \n color_labels\n\n# plot the radial plot\npar(mar = rep(0,4))\n# circlize_dendrogram(dend, dend_track_height = 0.8) \ncirclize_dendrogram(dend, labels_track_height = NA, dend_track_height = .2) \n#increase the size of windows \n\n\n#https://rdrr.io/cran/GOplot/man/GOCluster.html\nlibrary(GOplot)\n\n#-------\n\n#dendrogram with distance\nmat <- matrix(abs(rnorm(100)),10,10)\ndimnames(mat) <- list(letters[1:10],letters[1:10])\nma2 <- as.dist(mat)\nhc <- hclust(ma2)\ndend <- as.dendrogram(hc)\n\npar(mfrow=c(1,2))\n\nplot(dend)\n\nrequire(circlize)\nrequire(dendextend)\n\n#Get the heights for each branch\nheights <- round(get_branches_heights(dend, sort=FALSE), 1)\n\n#Get max height\nmaxHeight= max(heights)\n\n#Set label and dendrogram height for cicular dendrogram\nlabelHeight=0.1\ndendHeight=0.8\n\n#Draw the circular dendrogram\ncirclize_dendrogram(dend, facing=\"outside\", labels=TRUE, labels_track_height=labelHeight, dend_track_height=dendHeight)\n\n#Create tick co-ordinates and values for the new axis\n#We have to enure that we don't overlap the label plot region (height specified by labelHeight), nor the central region of the plot (1-(dendHeight+labelHeight))\nticks <- seq(from=(1-(dendHeight+labelHeight)), to=(1-labelHeight), length.out=5)\nvalues <- round(rev(seq(from=0, to=maxHeight, length.out=5)), 1)\n\n#Add the new axis\nrequire(plotrix)\nablineclip(h=0, v=ticks, col=\"black\", x1=1-(dendHeight+labelHeight), x2=1-labelHeight, y1=0, y2=0.04, lwd=1.5)\ntext(ticks, 0+0.08, values, cex=0.8)\ntext((1-labelHeight)-(((1-labelHeight)-(1-(dendHeight+labelHeight)))/2), 0+0.14, \"Height\", cex=0.8)\n\n\n\n#https://en.proft.me/2017/01/29/exploring-hierarchical-clustering-r/\n#Parallel coordinates are a common way of visualizing high-dimensional geometry and analyzing multivariate data. The lines show the relationship between the axes, much like scatterplots, and the patterns that the lines form indicate the relationship. \n# get nice colors\nlibrary(colorspace)\n\niris2 <- iris[,-5]\nspecies_labels <- iris[,5]\nspecies_col <- rev(rainbow_hcl(3))[as.numeric(species_labels)]\nMASS::parcoord(iris2, col = species_col, var.label = TRUE, lwd = 2)\ntitle(\"Parallel coordinates plot of the Iris data\")\nlegend(\"top\", cex = 1, legend = as.character(levels(species_labels)), fill = unique(species_col), horiz = TRUE)\n\n#We can see that the Setosa species are distinctly different from Versicolor and Virginica (they have lower petal length and width). But Versicolor and Virginica cannot easily be separated based on measurements of their sepal and petal width/length.\n" }, { "path": "40a-CLUST/20c-clust-plots2.R", "content": "#Clustering Plots\n\n### some sample data\nrequire(vegan)\ndata(dune)\n\n# kmeans\nkclus <- kmeans(dune,centers= 4, iter.max=1000, nstart=10000)\n\n# distance matrix\ndune_dist <- dist(dune)\n\n# Multidimensional scaling\ncmd <- cmdscale(dune_dist)\nhead(cmd)\n\n# plot MDS, with colors by groups from kmeans\ngroups <- levels(factor(kclus$cluster))\nordiplot(cmd, type = \"n\")\ncols <- c(\"steelblue\", \"darkred\", \"darkgreen\", \"pink\")\nfor(i in seq_along(groups)){\n points(cmd[factor(kclus$cluster) == groups[i], ], col = cols[i], pch = 16)\n}\n\n# add spider and hull\nordispider(cmd, factor(kclus$cluster), label = TRUE)\nordihull(cmd, factor(kclus$cluster), lty = \"dotted\")\n\n\n\n#Plot2----\n#install.packages(\"devtools\") ## To be able to download packages from github\nlibrary(devtools)\n#install_github(\"pablo14/clusplus\")\nlibrary(clusplus)\n\n## Create k-means model with 3 clusters\nfit_mtcars=kmeans(mtcars,3)\n\n## Call the function\nplot_clus_coord(fit_mtcars, mtcars)\n" }, { "path": "40a-CLUST/23b-km-marks1.R", "content": "#Clustering - Simple dataset - Marks in 2 subjects\n\nA=c(1,1.5,3,5,3.5,4.5,3.5)\nB=c(1,2,4,7,5,5,4.5)\nlength(A); length(B)\nmarks=data.frame(A,B)\nmarks\n?kmeans\n(c1 = kmeans(marks, centers=3)) #??\ncbind(marks, c1$cluster)\nplot(marks, pch=10,col = c1$cluster)\nc1$centers\npoints(c1$centers, col = 1:3, pch = 8, cex = 3)\n\nc1$iter #?\n\n#C1- 1, 2 : (1.3, 1.5)\n#C2- 3, 4, 5, 6, 7 : (3.9, 5.1)\n#\n#Specify Coordinates for Centers\nmcenters = marks[c(1,5),]\nmcenters\n(c2a <- kmeans(marks, centers=mcenters))\nc2a\ncbind(marks, c2a$cluster)\nmatrix(c(1,1,5,7), ncol=2)\n?matrix\n(c2b <- kmeans(marks, centers=matrix(c(1,1,5,7), ncol=2)))\n\nc2a\ncbind(marks,c2a$cluster)\nc2a$centers\naggregate(marks,by=list(c2a$cluster),FUN=mean)\nc2a\nc2a$iter\n\nlibrary(dplyr)\nmarks\nmarks %>% group_by(c2a$cluster) %>% summarise_all(funs(sum, mean, median, n()))\n\n\n# Distances\nx1=marks[1,]; x2=marks[2,]\nx1;x2\nsqrt(sum((x1-x2)^2))\nsqrt(1.25)\ndist(rbind(x1,x2))\neuc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))\nfor (i in 1:7)\n print(paste(i, round(euc.dist(marks[i,], marks[1,]),2),sep='-'))\n\nref1 = marks[1,]; ref1\nref2 = marks[4,]; ref2\n\n(d1= apply(marks,1,function(x)sqrt(sum((x-ref1)^2))))\n(d2= apply(marks,1,function(x)sqrt(sum((x-ref2)^2))))\ndf=cbind(marks, d1,d2)\ndf\n\napply(df, 1, function(x) max(which(x == min(x, na.rm = TRUE))))\ndf\napply(df[,c(3,4)],1, min)\ndf3 <-transform(df, mind1d2=apply(df[,c(3,4)],1, min, na.rm = TRUE))\ndf3\n\n\n\n\ngender = c('M','F','M')\ngender\ngenderF = factor(gender)\ngenderF\ngrades = c('A','B','C')\ngrades\ngradesF = factor(grades)\ngradesF\ngradesF1 = factor(grades, ordered=T)\ngradesF1\ngradesF2 = factor(grades, ordered=T, levels=c('C','B','A'))\ngradesF2\nmarks = rnorm(3, 50,10)\ndf = data.frame(genderF, gradesF, marks)\ndf\nstr(df)\n\n" }, { "path": "40a-CLUST/23c-km-marks2.R", "content": "#Clustering - Simple dataset - Marks in 2 subjects\n\nA=c(1,1.5,3,5,3.5,4.5,3.5)\nB=c(1,2,4,7,5,5,4.5)\nmarks=data.frame(A,B)\nmarks\n?kmeans\n(c1 = kmeans(marks,centers=3)) #??\n#no of clusters or initial distinct cluster centers\nc1$iter\ncbind(marks, c1$cluster)\nc1$centers\n\nplot(marks, pch=10,col = c1$cluster)\nc1$centers\npoints(c1$centers, col = 1:3, pch = 8, cex = 3)\n\nc1$iter #?\n\n#C1- 1, 2 : (1.3, 1.5)\n#C2- 3, 4, 5, 6, 7 : (3.9, 5.1)\n#\n#Specify Coordinates for Centers\nmcenters = marks[c(1,5),]\nmcenters\n(c2a <- kmeans(marks, centers=mcenters))\ncbind(marks, c2a$cluster)\nmatrix(c(1,1,5,7), ncol=2)\n?matrix\n(c2b <- kmeans(marks, centers=matrix(c(1,1,5,7), ncol=2)))\n\nc2a\ncbind(marks,c2a$cluster)\nc2a$centers\naggregate(marks,by=list(c2a$cluster),FUN=mean)\nc2a\nc2a$iter\n\nlibrary(dplyr)\nmarks\nmarks %>% group_by(c2a$cluster) %>% summarise_all(funs(sum, mean, median, n()))\n\n\n# Distances\nx1=marks[1,]; x2=marks[2,]\nx1;x2\nsqrt(sum((x1-x2)^2))\nsqrt(1.25)\ndist(rbind(x1,x2))\neuc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))\nfor (i in 1:7)\n print(paste(i, round(euc.dist(marks[i,], marks[1,]),2),sep='-'))\n\nref1 = marks[1,]; ref1\nref2 = marks[4,]; ref2\n\n(d1= apply(marks,1,function(x)sqrt(sum((x-ref1)^2))))\n(d2= apply(marks,1,function(x)sqrt(sum((x-ref2)^2))))\ndf=cbind(marks, d1,d2)\ndf\n\napply(df, 1, function(x) max(which(x == min(x, na.rm = TRUE))))\ndf\napply(df[,c(3,4)],1, min)\ndf3 <-transform(df, mind1d2=apply(df[,c(3,4)],1, min, na.rm = TRUE))\ndf3\n\n\n\n\ngender = c('M','F','M')\ngender\ngenderF = factor(gender)\ngenderF\ngrades = c('A','B','C')\ngrades\ngradesF = factor(grades)\ngradesF\ngradesF1 = factor(grades, ordered=T)\ngradesF1\ngradesF2 = factor(grades, ordered=T, levels=c('C','B','A'))\ngradesF2\nmarks = rnorm(3, 50,10)\ndf = data.frame(genderF, gradesF, marks)\ndf\nstr(df)\n\n" }, { "path": "40a-CLUST/23d-km-amap-marks3.R", "content": "#Clustering using amap package\n\nlibrary(amap)\nA=c(1,1.5,3,5,3.5,4.5,3.5)\nB=c(1,2,4,7,5,5,4.5)\nmarks=data.frame(A,B)\nmarks\n?amap::Kmeans\n#Kmeans(x, centers, iter.max = 10, nstart = 1, method = \"euclidean\")\n(c1a =amap::Kmeans(marks,centers= 2))\n(c2a =amap::Kmeans(marks,centers= marks[c(1,4),] ))\nanova(c1a, cs2a)\nc1a$cluster\n\nlibrary(fpc)\n?cluster.stats\ncluster.stats(c1a$cluster, c2a$cluster)\n" }, { "path": "40a-CLUST/23e-km-student2.R", "content": "# Clustering- \n\ndata <- read.csv(\"./data/studentdata.csv\")\nstr(data)\ndata1 = data[-(1:6)]\nsdata1 = data.frame(scale(data[-(1:6)]))\nstr(sdata1)\n# K-Means\nk.means.fit <- kmeans(sdata1, 5) # k = 5 clusters\nattributes(k.means.fit)\n\n# Centroids:\nk.means.fit$centers\n\n# Clusters:\nk.means.fit$cluster\n# Cluster size:\nk.means.fit$size\n\nwssplot <- function(data, nc=15, seed=1234){\n wss <- (nrow(data)-1)*sum(apply(data,2,var))\n for (i in 2:nc){\n set.seed(seed)\n wss[i] <- sum(kmeans(data, centers=i)$withinss)}\n plot(1:nc, wss, type=\"b\", xlab=\"Number of Clusters\",\n ylab=\"Within groups sum of squares\")}\n\nwssplot(sdata1, nc=6) \n\nlibrary(cluster)\nclusplot(sdata1, k.means.fit$cluster, color=TRUE, shade=TRUE,\n main='2D representation of the Cluster solution',\n labels=2, lines=0)\n# which student in which cluster\ntable(data1[,1],k.means.fit$cluster)\n\n# Hierarchical\nd <- dist(sdata1, method = \"euclidean\") # Euclidean distance matrix.\nH.fit <- hclust(d, method=\"ward.D2\")\n\nplot(H.fit) # display dendogram\ngroups <- cutree(H.fit, k=5) # cut tree into 5 clusters\n# draw dendogram with red borders around the 5 clusters\nrect.hclust(H.fit, k=5, border=\"red\") \ntable(data1[,1],groups)\n\n\n# Clustering 2 Method\n\nlibrary(NbClust)\nnb = NbClust(sdata1,\n distance='euclidean', min.nc=2,\n max.nc=15, method='average')\npar(mfrow=c(1,1))\nbarplot(nb$Best.n[1, ], xlab='No of Clusters', \n ylab='No of Criteria', main='No of Clusters Chosen by 26 Criteria')\nlibrary(flexclust)\nd = dist(sdata1)\nfit.average = hclust(d, method = 'average')\nplot(fit.average, hang=-1, cex=.8,\n main='Average Linkage Clustering')\nclusters = cutree(fit.average, k=2)\nsort(clusters)\ntable(clusters)\naggregate(data1,\n by=list(cluster=clusters), median)\naggregate(as.data.frame(sdata1),\n by=list(cluster=clusters), median)\nx=2\nplot(fit.average, hang=-1, cex=.8, main=paste( 'Average Linkage Clustering \\n', x, 'Cluster Solution'))\nrect.hclust(fit.average, k=2)\n\n\n#Pam\n#\nstr(data)\nfit.pam = pam(data[-(1:6)], k=2, stand=T)\nfit.pam\nfit.pam$medoids\nclusplot(fit.pam, main='Bivariate Cluster Plot')\nct.pam = table(data$course,\n fit.pam$clustering)\nct.pam\nrandIndex(ct.pam)\n\n" }, { "path": "40a-CLUST/24b-clust-women.R", "content": "#Clustering on women data set\n#http://dni-institute.in/blogs/k-means-clustering-algorithm-explained/\nlibrary(cluster)\nlibrary(fpc)\n\ndata(women)\ndf = women\nset.seed(101)\nwc1 = kmeans(df, centers=3, iter.max=1)\nwc1\n\nset.seed(101)\nwc2 = kmeans(df, centers=3, iter.max=5)\nwc2\n\n# Plot\nplot(df,col=wc2$cluster)\npoints(wc2$center,col=1:2,pch=8,cex=1)\n\nfpc::plotcluster(df, wc2$cluster)\n\n#plot(df, wc2)\ncluster::clusplot(df, wc2$cluster, color=TRUE, shade=TRUE, labels=3, lines=1)\npoints(wc2$centers, col = 1:2, pch = 15, cex = 5)\n\nwith(df, pairs(df, col=c(1:3)[wc2$cluster])) \n\n" }, { "path": "40a-CLUST/25b-km-iris.R", "content": "# Clustering - IRIS data set\n\nhead(iris)\nstr(iris$Species)\ntable(iris$Species)\nirisFeatures = iris[,-5]\nhead(irisFeatures)\niriskm1 = kmeans(irisFeatures,centers=3)\niriskm1\niriskm1$centers\ncolMeans(irisFeatures[iriskm1$cluster==1,])\niriskm1$iter\n(iris[iriskm1$cluster==1,])\n\niriskm1$size # no of rows in in each cluster\niriskm1$cluster # row no to clusters\nplot(irisFeatures$Sepal.Length, col=iriskm1$cluster)\nplot(irisFeatures$Sepal.Length, col=1:3)\nx= cbind(irisFeatures, cl=iriskm1$cluster, cat=iris$Species)\nhead(x)\nx\niriskm1 # understand the output\n\n#Method2 - Scaling \nirisFeatures2 = irisFeatures\nscaleiris = scale(irisFeatures2) # scaling dataset if they are in different range\nhead(scaleiris) #use this scaleiris for kmeans and plots; this becomes data\nirisKM = kmeans(scaleiris, 2)\nirisKM$cluster\n\nlibrary(fpc)\nlibrary(cluster)\nclusplot(scaleiris, irisKM$cluster, color= T, lines=0)\n# using PCA technique - finds most Principal Components\n\n#Method3\nirisKM2 = kmeans(scaleiris, 3, iter.max= 5)\nirisKM2\nclusplot(scaleiris, irisKM2$cluster, color=T, lines=0) \n\n" }, { "path": "40a-CLUST/25c-km-iris2.R", "content": "#Clustering - iris Data Set\n\n?iris\nhead(iris)\nlibrary(dplyr)\nstr(iris)\niris %>% group_by(Species) %>% summarise_all(mean)\n\nhead(iris[,1:4])\n\nirisFeatures = iris[-5]\nhead(irisFeatures)\nnames(irisFeatures)\n\niriskm1 = kmeans(irisFeatures,3)\n\niriskm1$size # no of rows in in each cluster\niriskm1$cluster # row no to clusters\n\nplot(irisFeatures$Sepal.Length, col=iriskm1$cluster)\nplot(irisFeatures$Sepal.Width, col=iriskm1$cluster)\n\nhead(irisFeatures)\niriskm1$cluster\ncbind(irisFeatures, iriskm1$cluster, iris$Species)\n\n\nkm # understand the output\n\n#Method2 - Scaling \nirisFeatures2 = irisFeatures\nscaleiris = scale(irisFeatures2) # scaling dataset if they are in different range\nhead(scaleiris) #use this scaleiris for kmeans and plots; this becomes data\nirisKM = kmeans(scaleiris, 3)\nirisKM$cluster\n\nlibrary(fpc)\nlibrary(cluster)\nclusplot(scaleiris, irisKM$cluster, color= T, lines=0)\n# using PCA technique - finds most Principal Components\n\n#Method3\nirisKM2 = kmeans(scaleiris, 3, iter.max= 5)\nirisKM2\nclusplot(scaleiris, irisKM2$cluster, color=T, lines=0) \n" }, { "path": "40a-CLUST/25f-km-iris2.R", "content": "# Clustering 1 ####\n# Types - k menas with kmeans\n# kmediods with pam() and pamk()\n#hierarchical clustering\n# density based clustering with DBScan\n#KMEANS\n\nset.seed(8953)\niris2 = iris\niris2\nhead(iris2)\niris2$Species = NULL # remove values\nstr(iris2)\n(kmeans.result = kmeans(iris2,3))\nhead(iris2)\n#check clustering against class labels Species\nkmeans.result$cluster\ntable(iris$Species,kmeans.result$cluster)\n#class setosa is distinct, other 2 are overlapping\nplot(iris2[c(\"Sepal.Length\", \"Sepal.Width\")], col = kmeans.result$cluster)\npoints(kmeans.result$centers[, c(\"Sepal.Length\", \"Sepal.Width\")],\n col = 1:3, pch = 8, cex = 2) # plot cluster centers\n" }, { "path": "40a-CLUST/26h-km-attitude.R", "content": "#Clustering - kmeans \n\nlibrary(datasets)\ndata(attitude)\nattitude\n\n# Subset the attitude data\ndat = attitude[,c(3,4)]\n\n# Plot subset data\nplot(dat, main = \"% of favourable responses to\n Learning and Privilege\", pch =20, cex =2)\n\n# Perform K-Means with 2 clusters\nset.seed(7)\nkm2 = kmeans(dat, 2, nstart=100)\nkm2$withinss\nkm2$tot.withinss\n\n# Plot results\nplot(dat, col =(km2$cluster +1) , main=\"K-Means result with 2 clusters\", pch=20, cex=2)\n\nkm3 = kmeans(dat, 3, nstart=100)\nkm3$withinss\nkm3$tot.withinss\n\n# Check for the optimal number of clusters given the data\n\nmydata <- dat\nwss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))\nfor (i in 2:15)\n wss[i] <- sum(kmeans(mydata, centers=i)$withinss)\nplot(1:15, wss, type=\"b\", xlab=\"Number of Clusters\", ylab=\"Within groups sum of squares\", main=\"Assessing the Optimal Number of Clusters with the Elbow Method\", pch=20, cex=2)\n\n\n# Perform K-Means with the optimal number of clusters identified from the Elbow method\nset.seed(7)\nkm6 = kmeans(dat, 6, nstart=100)\nkm10 = kmeans(dat, 10, nstart=100)\n\n# Examine the result of the clustering algorithm\nkm6 \nkm1$tot.withinss; km2$tot.withinss;\nkm3$tot.withinss; km4$tot.withinss\n\n# Plot results\nplot(dat, col =(km6$cluster +1) , main=\"K-Means result with 6 clusters\", pch=20, cex=2)\n\n?kmeans\n" }, { "path": "40a-CLUST/27c-clust-som1.R", "content": "# Creating Self-organising maps in R\n#https://www.shanelynn.ie/self-organising-maps-for-customer-segmentation-using-r/\n\n\n# Load the kohonen package \nrequire(kohonen)\n# Create a training data set (rows are samples, columns are variables\n# Here I am selecting a subset of my variables available in \"data\"\ndata_train <- data[, c(2,4,5,8)]\n# Change the data frame with training data to a matrix\n# Also center and scale all variables to give them equal importance during\n# the SOM training process. \ndata_train_matrix <- as.matrix(scale(data_train))\n# Create the SOM Grid - you generally have to specify the size of the \n# training grid prior to training the SOM. Hexagonal and Circular \n# topologies are possible\nsom_grid <- somgrid(xdim = 20, ydim=20, topo=\"hexagonal\")\n# Finally, train the SOM, options for the number of iterations,\n# the learning rates, and the neighbourhood are available\nsom_model <- som(data_train_matrix, \n grid=som_grid, \n rlen=500, \n alpha=c(0.05,0.01), \n keep.data = TRUE )\n\n#Training progress for SOM\nplot(som_model, type=\"changes\")" }, { "path": "40a-CLUST/33c-hc-nutrients1.R", "content": "# RinA - Hierarchical Clustering\n\n#Packages Required - flexclust,cluster, NbClust\n#Dataset - fkexclust::nutrient\n#data(nutrient, package=\"flexclust\")\n\nlibrary(flexclust)----\ndata(nutrient)\nstr(nutrient)\nhead(nutrient,4)\ndim(nutrient)\n\n#Calculating Distances\n#Distance, Dissimilarity, Proxmity\n#Euclidean Distance\nd = dist(nutrient)\nd[1:10]\nd\n(dm = as.matrix(d)[1:4, 1:4]) #see only few values\ndm[1,2]\nnutrient[1:2,]\n#dist Beef & burger\n\n#Manually Calculating\npaste('sqrt (', paste(nutrient[1,], nutrient[2,], sep=\"-\", collapse=\" ^2 + \"), \")\")\nsqrt ( (340-245)^2 + (20-21)^2 + (28-17) ^2 + (9-9) ^2 + (2.6-2.7) )\ndist(nutrient[c(1,2),])\n#manuall & by algo is same\n\n#-----\nlibrary(cluster)\n#Pairwise distances \ndaisy(nutrient[c(1,2),]) #same distance\ndaisy(nutrient)\n\n#Scaling-----\n#Scaling Data - reduce of particular variable\ndata(nutrient, package='flexclust')\n\nrow.names(nutrient) = tolower(row.names(nutrient))\nhead(nutrient)\nnutrient.scaled = scale(nutrient) #zscore\nhead(nutrient.scaled)\nd = dist(nutrient.scaled)\nhead(d)\n\n#Perform HClustering-----\nfit.average = hclust(d, method = 'average')\nplot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering')\n\n\n#Eg\n#Smaller No of Clusters\n\nlibrary(NbClust)\ndevAskNewPage(ask=T)#ask for next plot\nnc = NbClust(nutrient.scaled, distance='euclidean', min.nc=2, max.nc=15, method='average')\ndevAskNewPage(ask=F)\npar(mfrow=c(1,1))\nbarplot(nc$Best.n[1, ], xlab='No of Clusters', ylab='No of Criteria', main='No of Clusters Chosen by 26 Criteria')\n\n?NbClust\n#Eg : Final Cluster Solution, Select 5\nclusters = cutree (fit.average, k=5)\nsort(clusters)\ntable(clusters)\naggregate(nutrient, by=list(cluster=clusters), median)\n\naggregate(as.data.frame(nutrient.scaled), by=list(cluster=clusters), median)\n\nplot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering \\n5 Cluster Solution')\nrect.hclust(fit.average, k=5)\n\nlibrary(rattle)\nstr(wine)\nhead(wine)\ndf = scale(wine[-1])\ndf\n\nwssplot <- function(data, nc=15, seed=1234){\n wss <- (nrow(data)-1)*sum(apply(data,2,var))\n for (i in 2:nc){\n set.seed(seed)\n wss[i] <- sum(kmeans(data, centers=i)$withinss)}\n plot(1:nc, wss, type=\"b\", xlab=\"Number of Clusters\",\n ylab=\"Within groups sum of squares\")}\n\nwssplot(df)\n\nlibrary(NBClust)\nset.seed(1234)\ndevAskNewPage(ask=T)\nnc = NbClust(df, min.nc =2, max.nc=15, method='kmeans')\ntable(nc$Best.nc[1,])\npar(mfrow=c(1,1))\nbarplot(table(nc$Best.nc[1,]), xlab='No of Clusters', ylab='No of Criteria',\n main='No of Clusters Chosen by 26 Criteria')\n\nset.seed(1234)\nfit.km = kmeans(df,3, nstart=25)\nfit.km\nfit.km$size\n\nfit.km$centers\n\naggregate(wine[-1], by=list(cluster=fit.km$cluster), mean)\n\nct.km = table(wine$Type, fit.km$cluster)\nct.km\n\nlibrary(flexclust)\nrandIndex(ct.km)\n\nlibrary(cluster)\n?pam\nlibrary(cluster)\nset.seed(1234)\nfit.pam = pam(wine[-1], k=3, stand=T)\nfit.pam\nfit.pam$medoids\nclusplot(fit.pam, main='Bivariate Cluster Plot')\nct.pam = table(wine$Type, fit.pam$clustering)\nct.pam\nrandIndex(ct.pam)\n\n#Eg\nlibrary(fMultivar)\nset.seed(1234)\ndf = rnorm2d(1000, rho=.5)\ndf = as.data.frame(df)\ndf\nplot(df, main='Bivariate Normal Distribution with rho=0.5')\nwssplot(df)\nlibrary(NbClust)\nnc = NbClust(df, min.nc=2, max.nc=15, method='kmeans')\n\nlibrary(ggplot2)\nlibrary(cluster)\nfit = pam(df, k=2)\nfit\ndf$clustering = factor(fit$clustering)\n\npar(mfrow=c(1,1))\nggplot(data=df, aes(x=V1,y=V2, color=clustering, shape=clustering)) +\n geom_point() +\n ggtitle('Clustering of Bivariate Normal Data')\nplot(nc$All.index[ ,4], type='o',\n ylab='CCC', xlab='Number of\n clusters', col='blue')\n\n" }, { "path": "40a-CLUST/33c-hc-vegan-dune1.R", "content": "# Clustering\n#http://cc.oulu.fi/~jarioksa/opetus/metodi/sessio3.pdf\n\nlibrary(vegan) #decostand(df, \"hell\"), 3)\ndata(dune)\nstr(dune)\ndim(dune) # 30 speicies, 20 obsvn\nsummary(dune)\n?dune\n\nd = vegdist(dune)\nd\n#par(mfros=c(1,3))\n(csin = hclust(d, method='single'))\nplot(csin)\nplot(csin, hang=-1)\n(ccom = hclust(d, method='complete'))\nplot(ccom)\nplot(ccom, hang=-1)\n\ncaver = hclust(d, method='aver')\nplot(caver)\nplot(caver, hang=-1)\n#\nvegemite(dune, caver)\n#\nplot(csin, hang=-1)\nrect.hclust(csin,3)\nplot(ccom, hang=-1)\nrect.hclust(ccom,3)\nplot(caver, hang=-1)\nrect.hclust(caver,3)\n#\nc1 = cutree(ccom,3)\nc1\ntable(c1)\n\ntable(c1, cutree(csin,3))\ntable(c1, cutree(caver,3))\n#\nord = cmdscale(d)\nord\nordiplot(ord)\n\nordihull(ord, c1, lty=3)\nordispider(ord, c1, col='blue', label=T)\nordiellipse(ord, c1, col='red')\n\nordiplot(ord, dis='si')\nordiplot(ord, cutree(caver, 3))\nordiplot(ord, dis='si')\nordicluster(ord, csin)\n#\nordiplot(ord, dis='sites')\nordicluster(ord, caver)\nordiplot(ord, dis='si')\nordicluster(ord, caver, prune=2)\n\n#\n#Reordering a Dendrogram\nden = as.dendrogram(caver)\nx = scores(ord, display ='sites', choices=1)\noden = reorder(den, x)\nplot(den)\nplot(oden)\nvegemite(dune, oden)\n\ntabasco(dune, caver)\ntabasco(dune, caver, Rowv=F)\ntabasco(dune, oden, Rowv=F)\ntabasco(dune, oden, Colv=F)\n#\n# Minimum Spanning Tree\nmst = spantree(d)\nordiplot(ord, dis='si')\nlines(mst, ord)\nplot(mst, type='t')\n#\n#Cophenetic Distance\nplot(d, cophenetic(csin), asp=1)\nabline(0,1)\nplot(d, cophenetic(ccom), asp=1)\nabline(0,1)\nplot(d, cophenetic(caver), asp=1)\nabline(0,1)\n\ncor(d, cophenetic(csin))\ncor(d, cophenetic(ccom))\ncor(d, cophenetic(caver))\n\n#Intepretation of Classes\ndata(dune.env)\nstr(dune.env)\nc1 = factor(c1)\nMoist = with(dune.env, as.numeric(as.character(Moisture)))\nwith(dune.env, as.numeric(Moisture))\nboxplot(Moist ~ c1, notch = T)\nanova(lm(Moist ~ c1))\nanova( rda(Moist ~ c1))\nwith(dune.env, table(c1, Management))\n#\nlibrary(labdsv)\nconst(dune, c1)\nimportance(dune, c1)\nmod = indval(dune, as.numeric(c1))\nnames(mod)\nmod$maxcls\nmod$pval\nsummary(mod)\nsummary(mod, type='long')\n#\n#Optimisd Clustering at Given Level\nckm = kmeans(decostand(dune, 'hell'), 3)\nckm$cluster\nordiplot(ord, dis='si')\nordihull(ord, ckm$cluster, col='red')\n# Optimal no of clusters\nccas = cascadeKM(decostand(dune, 'hell'), 2, 15)\nplot(ccas, sortq=T)\n#\n#Fuzzy Clustering\nlibrary(cluster)\ncfuz = fanny(d, 3, memb.exp = 1.7)\nnames(cfuz)\nordiplot(ord, dis='si')\nordiplot(ord, dis='si', type='n')\nstars(cfuz$membership, locatio=ord, draw.segm=T, add=T, scale=F,len=0.1)\nordihull(ord, cfuz$clustering, col='blue')\n\n#\ndf = iris[-5]\ncluster = kmeans(decostand(df,'hell'), 3)\ncluster$cluster\nordiplot(cluster, dis=\"si\")\nordihull(ord, ckm$cluster, col=\"red\")\n\nccas <- cascadeKM(decostand(df, \"hell\"), 2, 15)\nplot(ccas, sortq=TRUE)\n" }, { "path": "40a-CLUST/33d-hc-protein.R", "content": "#Clustering - Protein Consumption\n\n#Study case I: EUROPEAN PROTEIN CONSUMPTION\n#25 European countries and their protein intakes (in percent) from nine major food sources (p = 9)\n\nurl = 'http://www.biz.uiowa.edu/faculty/jledolter/DataMining/protein.csv'\nfood = read.csv(url)\nhead(food)\n\n#----\n#clustering on just Red and White meat (p=2) and k=3 clusters.\nnames(food)\n\nset.seed(123) ## to fix the random starting clusters\ngrpMeat = kmeans(food[,c(\"WhiteMeat\",\"RedMeat\")], centers=3, nstart=10)\ngrpMeat\ngrpMeat$cluster\n\n## list of cluster assignments\n(o=order(grpMeat$cluster))\nfood[o,] #only white and red meat\ngrpMeat$cluster[o]\n\ndata.frame(food$Country[o],grpMeat$cluster[o])\n\n#cluster assignment on a scatter plot:\n \nplot(food$Red, food$White, type=\"n\", xlim=c(3,19), xlab=\"Red Meat\", ylab=\"White Meat\")\ntext(x=food$Red, y=food$White, labels=food$Country, col=grpMeat$cluster+1) #color as per cluster no\n\n\n#cluster on all nine protein groups and prepare the program to create seven clusters. \n\nset.seed(123)\ngrpProtein = kmeans(food[,-1], centers=7, nstart=10)\n\no=order(grpProtein$cluster)\ndata.frame(country=food$Country[o],cluster=grpProtein$cluster[o])\n\n#Plot\nlibrary(cluster)\nclusplot(food[,-1], grpProtein$cluster, main='2D representation of the Cluster solution', color=TRUE, shade=TRUE, labels=2, lines=0)\n\n# Use agnes function for computing H clust\nfoodagg=agnes(food, diss=FALSE, metric=\"euclidian\")\nplot(foodagg, main='Dendrogram') ## dendrogram\ngroups = cutree(foodagg, k=4) # cut tree into 3 clusters\nrect.hclust(foodagg, k=4, border=\"red\") \n\n\n#\nlibrary(cluster)\nclusplot(food[,-1], grpProtein$cluster, main='2D representation of the Cluster solution', color=TRUE, shade=TRUE, labels=2, lines=0)\n" }, { "path": "40a-CLUST/33f-hc-marks.R", "content": "#Hierarchical Clustering \n\n#Marks\nA=c(1,1.5,3,5,3.5,4.5,3.5)\nB=c(1,2,4,7,5,5,4.5)\nmarks=data.frame(A,B)\nmarks\n\nhc <- hclust(dist(marks), \"ave\")\nplot(hc)\nplot(hc, hang = -1)\n\n\n" }, { "path": "40a-CLUST/33g-hc-sample.R", "content": "#Hierarchical Clustering\n\nhclust()\nset.seed(123)\nmarks = ceiling(rnorm(6, mean=60, sd=10))\nm = matrix(marks, nrow=6)\nrownames(m) = paste(\"s\",1:6,sep='')\ncolnames(m) = c('sub1')\nm\n\n(d= dist(m))\nclust_h = hclust(d)\nclust_h\nplot(clust_h)\nplot(clust_h, hang=-1, cex=.8, main='Average Linkage Clustering')\nrect.hclust(clust_h, k=2)\nrect.hclust(clust_h, k=3, x=3)\nrect.hclust(clust_h, k=3, which=3)\nrect.hclust(clust_h, k=3, which=2, border=5)\nrect.hclust(clust_h, k=3, which=1, border=6)\nrect.hclust(clust_h, k=3, which=c(1,3), border=7)\nrect.hclust(clust_h, h=5, which=c(1,2), border=9)\n\nclust_h$height\nattributes(clust_h)\nnames(clust_h)\n\n?rect.hclust\n\n#----\nset.seed(123)\nx = ceiling(rnorm(20*5, 65,15))\nm2 = matrix(x, ncol=5)\nrownames(m2) = paste('s',1:20,sep='')\ncolnames(m2) = paste('sub',1:5,sep='')\nhead(m2)\n\n#------\nd2 = dist(m2)\nhead(d2)\nd2\nm2_hclust = hclust(d2)\nplot(m2_hclust)\nrect.hclust(clust_h, k=4)\n\nplot(m2_hclust, hang=-1,cex=.8)\nrect.hclust(m2_hclust, h=60, border=5)\n\nplot(m2_hclust, hang=-1,cex=.8)\nrect.hclust(clust_h, k=4, which=2, border='red')\n\nplot(m2_hclust, hang=-1,cex=.8)\nabline(h=80,col=3)\nrect.hclust(clust_h, h=40, which=c(1,2))\n\n#-----\nhca <- hclust(dist(USArrests))\nplot(hca,hang=-1)\nrect.hclust(hca, k = 3, border = \"red\")\nrect.hclust(hca, h = 50, which = c(2,3,4,7), border = 3:4)\n\nx <- rect.hclust(hca, h = 50, which = c(2,7), border = 3:4)\nx\n#------\n\nlibrary(fpc)\nplotcluster(m, clust_h2$cluster)\nplotcluster(m, clust_h2$cluster)\n#---\n# Ward Hierarchical Clustering with Bootstrapped p values\nlibrary(pvclust)\nfit <- pvclust(m2, method.hclust=\"ward.D2\", method.dist=\"euclidean\")\nplot(fit) # dendogram with p values\n# add rectangles around groups highly supported by the data\npvrect(fit, alpha=.95)\n\n#------\nlibrary(factoextra)\n\nclust_h2 = hkmeans(m, 3)\nclust_h2\nclust_h2$centers\ncbind(m, clust_h2$cluster)\n# Visualize the tree\nfviz_dend(clust_h2, cex = 0.6, palette = \"jco\", rect = TRUE, rect_border = \"jco\", rect_fill = TRUE)\n# Visualize the final clusters\nfviz_cluster(clust_h2, palette = \"jco\", repel = TRUE, ggtheme = theme_classic())\n#needs 2 variables at least\n#\n\n\n\n\n# comparing 2 cluster solutions\nlibrary(fpc)\ncluster.stats(d, fit1$cluster, fit2$cluster)\n" }, { "path": "40a-CLUST/35d-pam-iris.R", "content": "# Clustering 2 : K Mediods ####\n#http://www.rdatamining.com/docs/data-clustering-with-r\n# cluster is represented with its center in k means algo\n#but with object closest to the center of the cluster in the k mediods clustering\n# more robusst than k means in presence of outliers\n#PAM and CLARA in package cluster, pamk in fpc (cant choose k)\nlibrary(fpc)\niris2 = iris\niris2$Species = NULL # making it pure numeric DF\npamk.result = pamk(iris2)\nstr(iris2)\npamk.result$nc # no of clusters\ntable(pamk.result$pamobject$clustering,iris$Species)\n# two clusters - setso and mixture of versi& virgin\nlayout(matrix(c(1,2),1,2)) # 2 graphs per page\nplot(pamk.result$pamobject)\n# left - 2 dim clusplot of 2 clusters & lines shows distance betw clusters\n# right - silhouetters higher s more cluster\nlayout(matrix(1)) # reverse layout\n\n#pam\nlibrary(cluster)\npam.result = pam(iris2,3) # k=3\ntable(pam.result$clustering,iris$Species)\n# cluster 1 seperated from 2 & 3\n# cluster 2 mainly made of versicolor\n# cluster 3 mainly of virigina\nlayout(matrix(c(1,2),1,2))\nplot(pam.result)\nlayout(matrix(1)) # reverse layout\n# 3 Clusters identified \n\n# Hierarchical Clustering ####\nset.seed(2835)\nidx = sample(1:dim(iris)[1],40)\nidx\nirisSample = iris[idx,]\n# remove class label\nirisSample$Species = NULL\n# hierarchical clustering\nhc = hclust((dist(irisSample)),method='ave')\n# plot clusters\nplot(hc,hang=-1,labels = iris$Species[idx])\n# cut tree into 3 clusters\nrect.hclust(hc,k=3)\n# get clusterids\ngroups = cutree(hc,k=3)\n\n# Density Based Clustering\n#DBSCAN min points, reachability\nlibrary(fpc)\niris2 = iris[-5] # remove class tags\nstr(iris2)\nds = dbscan(iris2, eps=0.42, MinPts = 5)\nds\ntable(ds$cluster,iris$Species)\n# 1 to 3 identified clusters; 0 due to noise\nplot(ds,iris2)\nplot(ds,iris2[c(1,2)])\nplotcluster(iris2, ds$cluster)\n\n\n# Prediction with Clustering Model\nset.seed(435)\nidx = sample(1:nrow(iris),10)\nidx\n# remove class label\nnew.data = iris[idx,-5]\nhead(new.data)\n# add random noise\nnew.data = new.data + matrix(runif(10*4,min=0,max=0.2),nrow=10,ncol=4)\nhead(new.data)\n#label new data\npred = predict(ds,iris2, new.data)\npred\ntable(pred, iris$Species[idx]) # check cluster labels\n# 3 + 3 + 2 = 8 out of 10 objects assigned with correct classes labels\nplot(iris2[c(1,4)],col=1 + ds$cluster)\npoints(new.data[c(1,4)], pch='+',col=1+pred,cex=3)\n" }, { "path": "40a-CLUST/35e-pam-nutrient.R", "content": "# R in Action\nlibrary(flexclust)\ndata(nutrient)\nstr(nutrient)\nhead(nutrient,4)\n#Calculating Distances\n#Distance, Dissimilarity, Proxmity\n?dist\ndata(nutrient, package=\"flexclust\")\nhead(nutrient,4)\nd = dist(nutrient)\nd[1:4]\ndm = as.matrix(d)[1:4, 1:4]\ndm[1,2]\nnutrient[1,1]\n#dist Beef & burger\npaste('sqrt (', paste(nutrient[1,], nutrient[2,], sep=\"-\", collapse=\" ^2 + \"), \")\")\ndist(nutrient[c(1,2),])\ndaisy(nutrient[c(1,2),])\nlibrary(cluster)\n\n#Eg\ndata(nutrient, package='flexclust')\nrow.names(nutrient) = tolower(row.names(nutrient))\nnutrient.scaled = scale(nutrient)\nd = dist(nutrient.scaled)\nfit.average = hclust(d, method = 'average')\nplot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering')\npar(mfrow=c(1,1))\n#Eg\n#Smaller No of Clusters\nlibrary(NbClust)\ndevAskNewPage(ask=T)\nnc = NbClust(nutrient.scaled, distance='euclidean', min.nc=2, max.nc=15, method='average')\nbarplot(nc$Best.n[1, ], xlab='No of Clusters', ylab='No of Criteria', main='No of Clusters Chosen by 26 Criteria')\n\n?NbClust\n#Eg : Final Cluster Solution\nclusters = cutree (fit.average, k=5)\nsort(clusters)\ntable(clusters)\naggregate(nutrient, by=list(cluster=clusters), median)\naggregate(as.data.frame(nutrient.scaled), by=list(cluster=clusters), median)\nplot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering \\n5 Cluster Solution')\nrect.hclust(fit.average, k=5)\n\nlibrary(rattle)\nstr(wine)\nhead(wine)\ndf = scale(wine[-1])\ndf\n\nwssplot <- function(data, nc=15, seed=1234){\n wss <- (nrow(data)-1)*sum(apply(data,2,var))\n for (i in 2:nc){\n set.seed(seed)\n wss[i] <- sum(kmeans(data, centers=i)$withinss)}\n plot(1:nc, wss, type=\"b\", xlab=\"Number of Clusters\",\n ylab=\"Within groups sum of squares\")}\n\nwssplot(df)\n\nlibrary(NBClust)\nset.seed(1234)\ndevAskNewPage(ask=T)\nnc = NbClust(df, min.nc =2, max.nc=15, method='kmeans')\ntable(nc$Best.nc[1,])\npar(mfrow=c(1,1))\nbarplot(table(nc$Best.nc[1,]), xlab='No of Clusters', ylab='No of Criteria',\n main='No of Clusters Chosen by 26 Criteria')\n\nset.seed(1234)\nfit.km = kmeans(df,3, nstart=25)\nfit.km\nfit.km$size\n\nfit.km$centers\n\naggregate(wine[-1], by=list(cluster=fit.km$cluster), mean)\n\nct.km = table(wine$Type, fit.km$cluster)\nct.km\n\nlibrary(flexclust)\nrandIndex(ct.km)\n\nlibrary(cluster)\n?pam\nlibrary(cluster)\nset.seed(1234)\nfit.pam = pam(wine[-1], k=3, stand=T)\nfit.pam\nfit.pam$medoids\nclusplot(fit.pam, main='Bivariate Cluster Plot')\nct.pam = table(wine$Type, fit.pam$clustering)\nct.pam\nrandIndex(ct.pam)\n\n#Eg\nlibrary(fMultivar)\nset.seed(1234)\ndf = rnorm2d(1000, rho=.5)\ndf = as.data.frame(df)\ndf\nplot(df, main='Bivariate Normal Distribution with rho=0.5')\nwssplot(df)\nlibrary(NbClust)\nnc = NbClust(df, min.nc=2, max.nc=15, method='kmeans')\n\nlibrary(ggplot2)\nlibrary(cluster)\nfit = pam(df, k=2)\nfit\ndf$clustering = factor(fit$clustering)\n\npar(mfrow=c(1,1))\nggplot(data=df, aes(x=V1,y=V2, color=clustering, shape=clustering)) +\n geom_point() +\n ggtitle('Clustering of Bivariate Normal Data')\nplot(nc$All.index[ ,4], type='o',\n ylab='CCC', xlab='Number of\n clusters', col='blue')\n\ns" }, { "path": "40a-CLUST/40b-mixedclust1.R", "content": "#Clustering\n#https://www.listendata.com/2016/01/cluster-analysis-with-r.html\n# Loading data\ndata<-iris[,-c(5)]\n\n# To standarize the variables \ndata = scale(data) \n\n# Assessing cluster tendency\nif(!require(clustertend)) install.packages(\"clustertend\")\nlibrary(clustertend)\n# Compute Hopkins statistic for the dataset\nset.seed(123)\nhopkins(data, n = nrow(data)-1)\n#Since the H value = 0.1815 which is far below the threshold 0.5, it is highly clusterable\n\n###########################################################################\n####################### K Means clustering ################################\n###########################################################################\n\n# K-mean - Determining optimal number of clusters\n# NbClust Package : 30 indices to determine the number of clusters in a dataset\n# If index = 'all' - run 30 indices to determine the optimal no. of clusters\n# If index = \"silhouette\" - It is a measure to estimate the dissimilarity between clusters.\n# A higher silhouette width is preferred to determine the optimal number of clusters\n\nif(!require(NbClust)) install.packages(\"NbClust\")\nnb <- NbClust(data, distance = \"euclidean\", min.nc=2, max.nc=15, method = \"kmeans\", index = \"silhouette\")\nnb$All.index\nnb$Best.nc\n\n#Method II : Same Silhouette Width analysis with fpc package\nlibrary(fpc)\npamkClus <- pamk(data, krange = 2:15, criterion=\"multiasw\", ns=2, critout=TRUE)\npamkClus$nc\ncat(\"number of clusters estimated by optimum average silhouette width:\", pamkClus$nc, \"\\n\")\n\n#Method III : Scree plot to determine the number of clusters\nwss <- (nrow(data)-1)*sum(apply(data,2,var))\nfor (i in 2:15) {\n wss[i] <- sum(kmeans(data,centers=i)$withinss)\n} \nplot(1:15, wss, type=\"b\", xlab=\"Number of Clusters\",ylab=\"Within groups sum of squares\")\n\n# K-Means Cluster Analysis\nfit <- kmeans(data,pamkClus$nc)\n\n# get cluster means\naggregate(data,by=list(fit$cluster),FUN=mean)\n\n# append cluster assignment\ndata <- data.frame(data, clusterid=fit$cluster)\n\n###########################################################################\n####################### Hierarchical clustering############################\n###########################################################################\n\n# Hierarchical clustering - Determining optimal number of clusters\nlibrary(NbClust)\nres<-NbClust(data, diss=NULL, distance = \"euclidean\", min.nc=2, max.nc=6,\n method = \"ward.D2\", index = \"kl\")\nres$All.index\nres$Best.nc\n\n# Ward Hierarchical Clustering\nd <- dist(data, method = \"euclidean\")\nfit <- hclust(d, method=\"ward.D2\")\nplot(fit) # display dendogram\n\n# cluster assignment (members)\ngroups <- cutree(fit, k=2)\ndata = cbind(data,groups)\n\n# draw dendogram with red borders around the 2 clusters\nrect.hclust(fit, k=2, border=\"red\")\n\n" }, { "path": "40a-CLUST/40c-clust-dendgm.R", "content": "# Dendrograms - Hierarchical Clustering\n\n\n# Load data\ndata(USArrests)\n# Compute distances and hierarchical clustering\ndd <- dist(scale(USArrests), method = \"euclidean\")\nhc <- hclust(dd, method = \"ward.D2\")\nhc\n\n#plot(x, labels = NULL, hang = 0.1, main = \"Cluster dendrogram\", sub = NULL, xlab = NULL, ylab = \"Height\")\n\n# Default plot\nplot(hc)\n\n# Put the labels at the same height: hang = -1\nplot(hc, hang = -1, cex = 0.6)\n\n# Convert hclust into a dendrogram and plot\nhcd <- as.dendrogram(hc)\n# Default plot\nplot(hcd, type = \"rectangle\", ylab = \"Height\")\n\n# Triangle plot\nplot(hcd, type = \"triangle\", ylab = \"Height\")\n\n# Zoom in to the first dendrogram\nplot(hcd, xlim = c(1, 20), ylim = c(1,8))\n\n# Define nodePar\nnodePar <- list(lab.cex = 0.6, pch = c(NA, 19), \n cex = 0.7, col = \"blue\")\n# Customized plot; remove labels\nplot(hcd, ylab = \"Height\", nodePar = nodePar, leaflab = \"none\")\n\n# Horizontal plot\nplot(hcd, xlab = \"Height\",\n nodePar = nodePar, horiz = TRUE)\n\n# Change edge color\nplot(hcd, xlab = \"Height\", nodePar = nodePar, \n edgePar = list(col = 2:3, lwd = 2:1))\n\n# install.packages(\"ape\")\nlibrary(\"ape\")\n# Default plot\nplot(as.phylo(hc), cex = 0.6, label.offset = 0.5)\n\n\nCladogram\nplot(as.phylo(hc), type = \"cladogram\", cex = 0.6, \n label.offset = 0.5)\n\n# Unrooted\nplot(as.phylo(hc), type = \"unrooted\", cex = 0.6,\n no.margin = TRUE)\n\n# Fan\nplot(as.phylo(hc), type = \"fan\")\n\n# Radial\nplot(as.phylo(hc), type = \"radial\")\n\n# Cut the dendrogram into 4 clusters\ncolors = c(\"red\", \"blue\", \"green\", \"black\")\nclus4 = cutree(hc, 4)\nplot(as.phylo(hc), type = \"fan\", tip.color = colors[clus4],\n label.offset = 1, cex = 0.7)\n\n# Change the appearance\n# change edge and label (tip)\nplot(as.phylo(hc), type = \"cladogram\", cex = 0.6,\n edge.color = \"steelblue\", edge.width = 2, edge.lty = 2,\n tip.color = \"steelblue\")\n\ninstall.packages(\"ggdendro\")\n\nlibrary(\"ggplot2\")\nlibrary(\"ggdendro\")\n\n# Visualization using the default theme named theme_dendro()\nggdendrogram(hc)\n\n# Rotate the plot and remove default theme\nggdendrogram(hc, rotate = TRUE, theme_dendro = FALSE)\n\n# Build dendrogram object from hclust results\ndend <- as.dendrogram(hc)\n# Extract the data (for rectangular lines)\n# Type can be \"rectangle\" or \"triangle\"\ndend_data <- dendro_data(dend, type = \"rectangle\")\n# What contains dend_data\nnames(dend_data)\n\n# Extract data for line segments\nhead(dend_data$segments)\n\n# Extract data for labels\nhead(dend_data$labels)\n\n# Plot line segments and add labels\np <- ggplot(dend_data$segments) + \n geom_segment(aes(x = x, y = y, xend = xend, yend = yend))+\n geom_text(data = dend_data$labels, aes(x, y, label = label),\n hjust = 1, angle = 90, size = 3)+\n ylim(-3, 15)\nprint(p)\n\n\ndata <- scale(USArrests)\ndist.res <- dist(data)\nhc <- hclust(dist.res, method = \"ward.D2\")\ndend <- as.dendrogram(hc)\nplot(dend)\n\ndend <- USArrests[1:5,] %>% # data\n scale %>% # Scale the data\n dist %>% # calculate a distance matrix, \n hclust(method = \"ward.D2\") %>% # Hierarchical clustering \n as.dendrogram # Turn the object into a dendrogram.\nplot(dend)\n\nlibrary(dendextend)\nCreate a dendrogram and plot it\ndend <- USArrests[1:5,] %>% scale %>% \n dist %>% hclust %>% as.dendrogram\ndend %>% plot\n\n# Change the labels, and then plot:\ndend %>% set(\"labels\", c(\"a\", \"b\", \"c\", \"d\", \"e\")) %>% plot\n\n# Change color and size for labels\ndend %>% set(\"labels_col\", c(\"green\", \"blue\")) %>% # change color\n set(\"labels_cex\", 2) %>% # Change size\n plot(main = \"Change the color \\nand size\") # plot\n\n# Color labels by specifying the number of cluster (k)\ndend %>% set(\"labels_col\", value = c(\"green\", \"blue\"), k=2) %>% \n plot(main = \"Color labels \\nper cluster\")\nabline(h = 2, lty = 2)\n\n\n\n\n\n\nlibrary(pvclust)\ndata(lung) # 916 genes for 73 subjects\nset.seed(1234)\nresult <- pvclust(lung[1:100, 1:10], method.dist=\"cor\", \n method.hclust=\"average\", nboot=10)\n# Default plot of the result\nplot(result)\npvrect(result)\n\n# pvclust and dendextend\nresult %>% as.dendrogram %>% \n set(\"branches_k_color\", k = 2, value = c(\"purple\", \"orange\")) %>%\n plot\nresult %>% text\nresult %>% pvrect\n\n\n#circular -----\niris <- datasets::iris\niris2 <- iris[,-5]\nspecies_labels <- iris[,5]\nlibrary(colorspace) # get nice colors\nspecies_col <- rev(rainbow_hcl(3))[as.numeric(species_labels)]\nd_iris <- dist(iris2) # method=\"man\" # is a bit better\nhc_iris <- hclust(d_iris, method = \"complete\")\niris_species <- rev(levels(iris[,5]))\n\nlibrary(dendextend)\ndend <- as.dendrogram(hc_iris)\n# order it the closest we can to the order of the observations:\ndend <- rotate(dend, 1:150)\npar(mar = rep(0,4))\npar(mar = rep(1,4))\npar(mar =c(1,1,1,1))\ndend <- hang.dendrogram(dend,hang_height=0.1)\ncirclize_dendrogram(dend)\n" }, { "path": "40a-CLUST/43b-clust-mixedDataTypes1.R", "content": "#Clustetering - College Data\n\nset.seed(1680) # for reproducibility\n\nlibrary(dplyr) # for data cleaning\nlibrary(ISLR) # for college dataset\nlibrary(cluster) # for gower similarity and pam\nlibrary(tsne) # for t-SNE plot\nlibrary(ggplot2) # for visualization\n\ncollege_clean <- College %>% mutate(name = row.names(.), accept_rate = Accept/Apps, isElite = cut(Top10perc, breaks = c(0, 50, 100),labels = c(\"Not Elite\", \"Elite\"),include.lowest = TRUE)) %>% mutate(isElite = factor(isElite)) %>% select(name, accept_rate, Outstate, Enroll, Grad.Rate, Private, isElite)\n\nglimpse(college_clean)\n\n# Remove college name before clustering\n\ngower_dist <- daisy(college_clean[, -1],metric = \"gower\",\n type = list(logratio = 3))\ngower_dist\n# Check attributes to ensure the correct methods are being used\n# (I = interval, N = nominal)\n# Note that despite logratio being called, \n# the type remains coded as \"I\"\n\nsummary(gower_dist)\n## 301476 dissimilarities, summarized :\n## Min. 1st Qu. Median Mean 3rd Qu. Max. \n## 0.0018601 0.1034400 0.2358700 0.2314500 0.3271400 0.7773500 \n## Metric : mixed ; Types = I, I, I, I, N, N \n## Number of objects : 777\n\ngower_mat <- as.matrix(gower_dist)\ngower_mat\n\n# Output most similar pair\ncollege_clean[which(gower_mat == min(gower_mat[gower_mat != min(gower_mat)]),\n arr.ind = TRUE)[1, ], ]\n\n# Output most dissimilar pair\ncollege_clean[which(gower_mat == max(gower_mat[gower_mat != max(gower_mat)]),\n arr.ind = TRUE)[1, ], ]\n\n\n# Calculate silhouette width for many k using PAM\n\nsil_width <- c(NA)\nfor(i in 2:10){\n pam_fit <- pam(gower_dist,\n diss = TRUE,\n k = i)\n sil_width[i] <- pam_fit$silinfo$avg.width\n}\n\n# Plot sihouette width (higher is better)\nplot(1:10, sil_width,\n xlab = \"Number of clusters\",\n ylab = \"Silhouette Width\")\nlines(1:10, sil_width)\n\n\npam_fit <- pam(gower_dist, diss = TRUE, k = 3)\n\npam_results <- college_clean %>%\n dplyr::select(-name) %>%\n mutate(cluster = pam_fit$clustering) %>%\n group_by(cluster) %>%\n do(the_summary = summary(.))\n\npam_results$the_summary\n\ncollege_clean[pam_fit$medoids, ]\n\n\n#tsne_obj <- tsne(gower_dist, is_distance = TRUE)\ntsne_obj <- tsne(gower_dist)\n\ntsne_data <- tsne_obj$Y %>%\n data.frame() %>%\n setNames(c(\"X\", \"Y\")) %>%\n mutate(cluster = factor(pam_fit$clustering),\n name = college_clean$name)\n\nggplot(aes(x = X, y = Y), data = tsne_data) +\n geom_point(aes(color = cluster))\n\ntsne_data %>%\n filter(X > 15 & X < 25,\n Y > -15 & Y < -10) %>%\n left_join(college_clean, by = \"name\") %>%\n collect %>%\n .[[\"name\"]]\n" }, { "path": "40a-CLUST/45c-clustering-exist1.R", "content": "#CLustering - Does Cluster exist\n#install.packages(\"seriation\")\nlibrary(seriation)\n\nd_iris <- dist(iris[,-5])\nVAT(d_iris)\niVAT(d_iris)\n" }, { "path": "40a-CLUST/45e-clustering-animation1.R", "content": "#Clustering and Animation\n#https://www.guru99.com/r-k-means-clustering.html\n\nlibrary(dplyr)\nPATH <-\"https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/Computers.csv\"\ndata = read.csv(PATH)\ndata = read.csv(file.choose())\nnames(data)\ndim(data)\n#Remove certain columns\ndf <- data %>% select(-c(X, cd, multi, premium))\nglimpse(df)\n\nsummary(df)\n#scale and remove unscaled columns\nrescale_df <- df %>%\n mutate(price_scal = scale(price),\n hd_scal = scale(hd),\n ram_scal = scale(ram),\n screen_scal = scale(screen),\n ads_scal = scale(ads),\n trend_scal = scale(trend)) %>%\n select(-c(price, speed, hd, ram, screen, ads, trend))\n\nhead(rescale_df)\n\n#kmeans & animation\nset.seed(2345)\nlibrary(animation)\n\nkmeans.ani(rescale_df[2:5], 5)\n#see the plot area on right hand side of rstudio\n# Step 1: R randomly chooses three points\n# Step 2: Compute the Euclidean distance and draw the clusters. You have one cluster in green at the bottom left, one large cluster colored in black at the right and a red one between them.\n# Step 3: Compute the centroid, i.e. the mean of the clusters\n# Repeat until no data changes cluster\n\npc_cluster <-kmeans(rescale_df, 5)\n\n# The list pc_cluster contains seven interesting elements:\n# pc_cluster$cluster: Indicates the cluster of each observation\n# pc_cluster$centers: The cluster centres\n# pc_cluster$totss: The total sum of squares\n# pc_cluster$withinss: Within sum of square. The number of components return is equal to `k`\n# pc_cluster$tot.withinss: Sum of withinss\n# pc_clusterbetweenss: Total sum of square minus Within sum of square\n# pc_cluster$size: Number of observation within each cluster\n\nkmean_withinss <- function(k) {\n cluster <- kmeans(rescale_df, k)\n return (cluster$tot.withinss)\n}\nkmean_withinss(2)\n\n# Set maximum cluster \nmax_k <-20 \n# Run algorithm over a range of k \nwss <- sapply(2:max_k, kmean_withinss)\n\n# Create a data frame to plot the graph\nelbow <-data.frame(2:max_k, wss)\n\n# Plot the graph with gglop\nlibrary(ggplot2)\nggplot(elbow, aes(x = X2.max_k, y = wss)) +\n geom_point() +\n geom_line() +\n scale_x_continuous(breaks = seq(1, 20, by = 1))\n\n#From the graph, you can see the optimal k is seven, where the curve is starting to have a diminishing return. Once you have our optimal k, you re-run the algorithm with k equals to 7 and evaluate the clusters.\n\npc_cluster_2 <-kmeans(rescale_df, 7)\n\npc_cluster_2$cluster\npc_cluster_2$centers\npc_cluster_2$size\n\n(center <-pc_cluster_2$centers)\n\nlibrary(tidyr)\n\n# create dataset with the cluster number\n\ncluster <- c(1: 7)\n(center_df <- data.frame(cluster, center))\n\n# Reshape the data\n\ncenter_reshape <- gather(center_df, features, values, price_scal: trend_scal)\nhead(center_reshape)\n\nlibrary(RColorBrewer)\n# Create the palette\nhm.palette <-colorRampPalette(rev(brewer.pal(10, 'RdYlGn')),space='Lab')\n\n# Plot the heat map\nggplot(data = center_reshape, aes(x = features, y = cluster, fill = values)) +\n scale_y_continuous(breaks = seq(1, 7, by = 1)) +\n geom_tile() +\n coord_equal() +\n scale_fill_gradientn(colours = hm.palette(90)) +\n theme_classic()\n\n" }, { "path": "40a-CLUST/50b-clust-ma1.R", "content": "#Clustering - offer - marketing campaign\n\nlibrary(gsheet)\n?gsheet2tbl\nfullurl_1 = \"https://docs.google.com/spreadsheets/d/13tWJP6fcWNE7FObz4Cn_-_R0us7qIFialUy1m9nwBYY/edit#gid=1772535900\"\noffers = as.data.frame(gsheet::gsheet2tbl(fullurl_1))\ndim(offers)\n\n\nfullurl_2 = \"https://docs.google.com/spreadsheets/d/13tWJP6fcWNE7FObz4Cn_-_R0us7qIFialUy1m9nwBYY/edit#gid=788072611\"\ntransactions = as.data.frame(gsheet::gsheet2tbl(fullurl_2))\ndim(transactions)\n\n\n#Step-1: # Create transaction matrix (a pivot table like in Excel way!)\nlibrary(reshape)\nhead(transactions)\npivot = melt(transactions[1:2])\nhead(pivot)\n\n\npivot2 = (cast(pivot,value ~ custlastname, fill=0, fun.aggregate=function(x) length(x)))\nhead(pivot2)\n\npivot3 = cbind(offers,pivot2[-1])\nhead(pivot3)\n\n#can save the data for future use\n# write.csv(file=\"pivot.csv\",pivot) # to save your data\n\ncluster.data = pivot3[,8:length(pivot3)]\nnames(cluster.data)\ncluster.data2 = t(cluster.data) #transpose\nhead(cluster.data2)\n\n#Step-2\nlibrary(cluster)\nD=daisy(cluster.data2, metric='gower')\n\n#After the creation of a distance matrix, we implement a Ward’s hierarchical clustering procedure:\n \nH.fit = hclust(D, method=\"ward.D2\")\nplot(H.fit) # display dendrogram\ngroups = cutree(H.fit, k=4) # cut tree into 4 clusters\n# draw dendogram with red borders around the 4 clusters\nrect.hclust(H.fit, k=4, border=\"red\") \n# 2D representation of the Segmentation:\nclusplot(cluster.data2,groups, color=TRUE, shade=TRUE, labels=2, lines=0, main= 'Customer segments')\n\n?clusplot\n" }, { "path": "40a-CLUST/50c-clust-ma2.R", "content": "# Clustering For Customer Segmentation\n\nset.seed(1234); (age = ceiling(rnorm(50, 45, 10)))\nset.seed(1234);(income = ceiling(rnorm(50, 100000, 10000)))\nset.seed(1234);(children = sample(c(1,2,3), size=50, replace=T, prob=c(.4,.3,.2)))\ncustomers = data.frame(age, income, children)\nhead(customers)\n\n# No of Clusters\nlibrary(NbClust)\nnc = NbClust(customers, distance=\"euclidean\", \n min.nc=2, max.nc=15, method=\"average\")\n#without scaling\nkm1 = kmeans(customers, centers=3)\nkm1$centers\n\n#----\n#------------------------------------------------------------------------\n#plots\nlibrary(cluster)\ncluster::clusplot(customers, km1$cluster, color=TRUE, shade=TRUE, \n labels=2, lines=0)\n\n#https://blog.datascienceheroes.com/short-lesson-on-cluster-analysis/\nlibrary(clusplus)\nplot_clus_coord(km1, customers)\n\n\n\n\n\n#case1 : only age\nage1 = data.frame(age)\nclust1 = kmeans(age1, 3)\nclust1$cluster\ncombined = cbind(age1, clust1$cluster)\nplot(age1$age)\nplot(age1$age, col=clust1$cluster, pch=c(11,12,14))\n\n#case1 : only income\nincome1 = data.frame(income)\nclust1a = kmeans(income, 4)\nclust1a$cluster\n(combined = cbind(income1, clust1a$cluster))\ncombined[order(clust1a$cluster),]\nplot(income1$income, col=clust1a$cluster, pch=c(11,12,14,15))\n\n\n\n\ndf2 = data.frame(age, income)\nclust2 = kmeans(df2, 4)\nclust2$cluster\ncombined2 = cbind(df2, clust2$cluster)\nhead(combined2)\nplot(x=df2$age, y=df2$income, col=clust2$cluster, pch=c(11,12,14,17))\ndf2\ndf2[clust2$cluster==3,]\n\n\n\n(gender = sample(c(1,2), size=1000, replace=T, prob=c(.6,.4)))\ntable(gender)\nprop.table(table(gender))\n" }, { "path": "40a-CLUST/50d-clust-ma3.R", "content": "# HH MA example \n#setwd(\"\")\n#install.packages(\"amap\")\nlibrary(amap)\n##Read the data in the file\nurl = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'\nlibrary(gsheet)\ndata = as.data.frame(gsheet2tbl(url))\nstr(data)\nnames(data)\nsummary(data)\n##\n##\n##\n##\ncust_data<-read.csv(\"./data/Segmentation_Data v01.csv\")\ncust_data = data\n###Verify the data\ncolnames(cust_data)\nclass(cust_data$Age)\napply(cust_data, 2, FUN= class)\n\ndim(cust_data)\nhead(cust_data)\nsummary(cust_data)\nstr(cust_data)\n###Run the kmeans algorithm to generate the clusters\n#?amap::Kmeans\nnames(cust_data)\n\nk1<-amap::Kmeans(cust_data[,-c(1)],centers=3, iter.max = 200,nstart = 1, \n method = c(\"euclidean\"))\nk1<-amap::Kmeans(cust_data[,-c(1)],3)\n\n\nk1$centers # group means\n###Fetch size/n of obs for the groups\nk1$size\n###Fetch sum of squared for the groups\nk1$withinss\n###Fetch the cluster for each obs\n#k1$cluster\nk1$cluster\nk1$centers\ndim(cust_data)\nk1$cluster[9000:9800]\ntable(k1$cluster)\nk1$size\ndata_clus_2 <- cust_data[ k1$cluster == 2,]\n(data_clus_2)\nmean(data_clus_2$Age)\ndata_clus_2$Cust_id\n\n# Write CSV\nwrite.csv(data_clus_2[,1], file = \"./data/data_clus_2.csv\")\n\nnames(mtcars)\ncolnames(mtcars)\nrownames(mtcars)\n" }, { "path": "40a-CLUST/61b-clust-custsegm.R", "content": "#Clustering - Customer Segmentation\n#Import Data using Google Sheets\n#library - gsheet, corrplot, dplyr\n#Link : http://archive.ics.uci.edu/ml/datasets/Wholesale+customers https://subhayo.wordpress.com/2017/08/15/customer-segmentation-using-k-means-clustering/\n\neasypackages::libraries( 'dplyr', 'gsheet', 'corrplot')\n#library(dplyr);library(gsheet); library(corrplot)\n\n#import Data\nurl=\"https://docs.google.com/spreadsheets/d/1y6a6neeU-nH7zsAyDuyyy8GbPyGaxYfwpWcjIT4gInU/edit#gid=251433617\"\ndata1 = as.data.frame(gsheet2tbl(url))\n#data = read.csv(file.choose())\nhead(data1) \ncdata1 = data1\n\n#Read the input file\nw = cor(cdata1)\ncorrplot(w)\ncorrplot(w,method=\"number\")\n#check for strong cor = Grocery & Detergents\n\n#Choosing no of clusters - The elbow method looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn’t give much better modeling of the data. More precisely, if one plots the percentage of variance explained by the clusters against the number of clusters, the first clusters will add much information (explain a lot of variance), but at some point the marginal gain will drop, giving an angle in the graph. The number of clusters is chosen at this point, hence the “elbow criterion”.\n\nset.seed(123)\nk.max = 15\nhead(cdata1) #skip 1st & 2nd col\n#scale the data and put in matrix : all will be numeric\nsdata1 = as.matrix(scale(cdata1[,(3:8)]))\nhead(sdata1)\nwss = sapply(1:k.max, function(k){\n kmeans(sdata1, k, nstart=50,iter.max = 15 )$tot.withinss\n })\n\nplot(1:k.max, wss,\n type=\"b\", pch = 19, frame = FALSE, \n xlab=\"Number of clusters\",\n ylab=\"Sum of squares\")\nabline(v=c(3:5))\n#rom the above plot we can see that 3 or 5 is the optimal number of clusters, as we can see that after these numbers the curve remains less changing.\n\n#create 3 clusters\n#use scaled data sdata1\nset.seed(123)\nkm3 = kmeans(sdata1, 3)\nkm3\nset.seed(456)\n#create 5 clusters\nkm5 = kmeans(sdata1, 5)\nkm5\n\n#Cluster Analysis & Interpretation\nhead(km3$cluster)\ncluster_data = cbind(cdata1, C3= km3$cluster, C5=km5$cluster)\nhead(cluster_data)\n#join them into 1 data frame\n\n(C3sum <- cluster_data %>% select(-c(C5, Channel, Region)) %>% group_by(C3) %>% summarise_all(mean) %>% print(width=Inf, n = Inf))\n\n# Cluster 2 – highest fresh-products.\nC3sum %>% arrange(Fresh)\n\n# Cluster 3 – low spenders.\nC3sum %>% select(-1) %>% rowSums %>% cbind()\n\n# Cluster 1 – highest milk, grocery, detergents_papers spenders.\nC3sum %>% replace(is.na(.), 0) %>% mutate(Rsum = rowSums(.[-1])) %>% arrange(-Rsum)\nC3sum #find max of each column and tell its cluster no\nmax.col(C3sum,ties.method=\"first\") #column no, we want name\ncolnames(C3sum)[max.col(C3sum,ties.method=\"first\")]\n(C3sumT = t(C3sum))\ncolnames(C3sumT) = paste(\"cluster\",1:3,sep='')\nC3sumT\n#Cluster No with highest in Col...\ncbind(rownames(C3sumT), colnames(C3sumT)[max.col(C3sumT,ties.method=\"first\")])\n\n#Analyse Cluster 5\n\n(C5sum <- cluster_data %>% select(-c(C3, Channel, Region)) %>% group_by(C5) %>% summarise_all(mean) %>% print(width=Inf, n = Inf))\n\n# Cluster 3 – low spenders\nrowSums(C5sum)\ncbind(rowSums(C5sum[-1]), C5sum$C5)\nC5sum %>% replace(is.na(.), 0) %>% mutate(Rsum = rowSums(.[-1])) %>% arrange(-Rsum)\n# Cluster 1 – mediocre spenders 55681\n# Cluster 3 – low spenders 20087\n\n# Cluster 4 – highest Fresh spenders\nC5sum %>% arrange(-Fresh)\n# Cluster 4 – highest- Fresh, milk, Frozen, Delcassen\n(C5sumT = t(C5sum))\ncolnames(C5sumT) = paste(\"cluster\",1:5,sep='')\nC5sumT\n#Cluster No with highest in Col...\ncbind(rownames(C5sumT), colnames(C5sumT)[max.col(C5sumT,ties.method=\"first\")])\n\n\n#cluster 2 from km5 : save as csv file and send for more analysis \nkm5_c2 = subset(cluster_data, C5==2)\nwrite.csv(km5_c2, './data/km5_c2.csv')\n\n\n#Hierarchical Clustering\n#http://inseaddataanalytics.github.io/INSEADAnalytics/CourseSessions/Sessions45/ClusterAnalysisReading.html\n\n#Kmeans requires the user to define how many segments to create, while Hierarchical Clustering does not.\n#Hierarchial Clustering method, as we do not know for now how many segments there are in our data. Hierarchical clustering is a method that also helps us visualise how the data may be clustered together. It generates a plot called the Dendrogram which is often helpful for visualization \n\nhead\n# Run hclust algorithm\nhead(cdata1)\nhc1 = hclust(dist(cdata1))\ndim(cdata1)\n\nlibrary(ggdendro)\ndendrogram1 <- ggdendrogram(hc1, rotate = TRUE)\ndendrogram1\n# Write plot to disk\nlibrary(ggplot2)\nggsave(\"dendrogram.png\",dendrogram1, width = 16, height = 9)\n\nplot(hc1)\nplot(hc1, cex = 0.6, hang = -1)\nrect.hclust(hc1, k=3, border=\"red\") \n\n#Cut the Hcluster into 5 groups\nhc5 = cutree(hc1, k=5) # cut tree into 5 clusters\n#which data into which cluster\nhc5_data = cbind(cdata1, hc5)\nhead(hc5_data)\nhc5_data %>% group_by(hc5) %>% select(-c(1,2)) %>% summarise_all(mean)\nC5sum\n" }, { "path": "40a-CLUST/animation2.R", "content": "library(animation)\ndf = mtcars\ndata = df[c('mpg','wt')]\nstr(data)\n#kmeans.ani - function provides a demo of the k-Means cluster algorithm for data containing only two variables (columns).\nplot(scale(data))\nkm = kmeans(scale(data),3)\nplot(scale(data), col=1:km$cluster)\nplotcluster(data, km$cluster)\nplotcluster(scale(data), km$cluster)\nclusplot(data, km$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)\nclusplot(scale(data), km$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)\n\n\nkm2= kmeans.ani(scale(data), 3)\nkm2\nkm$cluster == km2$cluster\ncbind(km$cluster, km2$cluster)\nkm$tot.withinss \nkm$centers;km2$centers" }, { "path": "40a-CLUST/clust-allcustering.R", "content": "#https://github.com/mhahsler/Introduction_to_Data_Mining_R_Examples/blob/master/chap8.R\n\n\n#' This code covers chapter 8 of _\"Introduction to Data Mining\"_\n#' by Pang-Ning Tan, Michael Steinbach and Vipin Kumar.\n#' __See [table of contents](https://github.com/mhahsler/Introduction_to_Data_Mining_R_Examples#readme) for code examples for other chapters.__\n\n#' [Michael Hahsler](http://michael.hahsler.net).\n\n\n\n#' ruspini_scaled data is in package cluster. It is a very simple data set with well separated clusters.\ndata(ruspini, package=\"cluster\")\n#' Shuffle rows\nruspini <- ruspini[sample(1:nrow(ruspini)),]\nplot(ruspini)\nhead(ruspini)\n\n#' Scale each column in the data to zero mean and unit standard deviation (z-scores). This prevents one attribute with a large range to dominate the others for the distance calculation.\nruspini_scaled <- scale(ruspini)\nplot(ruspini_scaled)\n\n#' # Clustering methods\n#' ## k-means Clustering\n#'\n#' Assumes Euclidean distances. We use k=10 clusters and run the algorithm 10 times with random initialized centroids. The best result is returned.\nkm <- kmeans(ruspini_scaled, centers=4, nstart=10)\nkm\nkm$iter\nplot(ruspini_scaled, col=km$cluster)\npoints(km$centers, pch=3, cex=2) # this adds the centroids\ntext(km$centers, labels=1:4, pos=2) # this adds the cluster ID\n#' Alternative plot from package cluster (uses principal components analysis for >2 dimensions)\nlibrary(cluster)\nclusplot(ruspini_scaled, km$cluster)\n\n#' Inspect the centroids (cluster profiles)\nkm$centers\n\n#+ fig.height=3, fig.width=10\ndef.par <- par(no.readonly = TRUE) # save default, for resetting...\nlayout(t(1:4)) # 4 plots in one\nfor(i in 1:4) barplot(km$centers[i,], ylim=c(-2,2), main=paste(\"Cluster\", i))\npar(def.par) #- reset to default\n\n#' Find data for a single cluster\n#'#' All you need is to select the rows corresponding to the cluster. The next\n#' example plots all data points of cluster 1\ncluster1 <- ruspini_scaled[km$cluster==1,]\nhead(cluster1)\nplot(cluster1, xlim = c(-2,2), ylim = c(-2,2))\n\n#' Try 10 clusters\nplot(ruspini_scaled, col=kmeans(ruspini_scaled, centers=10)$cluster)\n\n#' ## Hierarchical Clustering\n#'\n#' dist defaults to method=\"Euclidean\"\nd <- dist(ruspini_scaled)\n#' We cluster using complete link\nhc <- hclust(d, method=\"complete\")\n\n#' Dendrogram\nplot(hc)\nrect.hclust(hc, k=4)\n\nplot(as.dendrogram(hc), leaflab=\"none\") # plot dendrogram without leaf labels\n\n#' More plotting options for dendrograms, including plotting\n#' parts of large dendrograms can be found [here.](https://rpubs.com/gaston/dendrograms)\n\ncluster_complete <- cutree(hc, k=4)\nplot(ruspini_scaled, col=cluster_complete)\n\n#' Try 10 clusters\nplot(ruspini_scaled, col=cutree(hc, k=10))\n\n#' Clustering with single link\nhc_single <- hclust(d, method=\"single\")\nplot(hc_single)\nrect.hclust(hc_single, k=4)\n\ncluster_single <- cutree(hc_single, k=4)\nplot(ruspini_scaled, col=cluster_single)\n\n#' Try 10 clusters\nplot(ruspini_scaled, col=cutree(hc_single, k=10))\n\n#' ## Density-based clustering with DBSCAN\n\nlibrary(dbscan)\n\n#' Parameters: minPts is often chosen as dimensionality of the data +1.\n#' Decide on epsilon using the knee in the kNN distance plot\n#' (seems to be around eps = .25).\nkNNdistplot(ruspini_scaled, k = 3)\nabline(h=.25, col=\"red\")\n\n#' run dbscan\ndb <- dbscan(ruspini_scaled, eps=.25, minPts=3)\ndb\nstr(db)\nplot(ruspini_scaled, col=db$cluster+1L)\n#' __Note:__ 0 is not a color so we add 1 to cluster (0 is black now).\n#'\n#' Alternative visualization from package dbscan\nhullplot(ruspini_scaled, db)\n\n#'\n#' Play with eps (neighborhood size) and MinPts (minimum of points needed for core cluster)\n\n#'\n#' ## Gaussian Mixture Models\nlibrary(mclust)\n\n#' Mclust uses Bayesian Information Criterion (BIC) to find the\n#' number of clusters (model selection). BIC uses the likelihood and a\n#' penalty term to guard against overfitting.\nm <- Mclust(ruspini_scaled)\nsummary(m)\nplot(m, what = \"classification\")\n\n#' Rerun with a fixed number of 4 clusters\nm <- Mclust(ruspini_scaled, G=4)\nsummary(m)\nplot(m, what = \"classification\")\n\n#' ## Spectral clustering\nlibrary(\"kernlab\")\ncluster_spec <- specc(ruspini_scaled, centers = 4)\nplot(ruspini_scaled, col=cluster_spec)\n\n\n#' # Internal Cluster Validation\n#'\n#' ## Compare the Clustering Quality\n#'\n#' Look at the within.cluster.ss and the avg.silwidth\n\n#library(fpc)\n#' Note: I do not load fpc since the NAMESPACE overwrites dbscan.\n\nfpc::cluster.stats(d, km$cluster)\n#cluster.stats(d, cluster_complete)\n#cluster.stats(d, cluster_single)\n\n#' Read `? cluster.stats` for an explanation of all the available indices.\n\nsapply(list(\n km=km$cluster,\n hc_compl=cluster_complete,\n hc_single=cluster_single),\n FUN=function(x)\n fpc::cluster.stats(d, x))[c(\"within.cluster.ss\",\"avg.silwidth\"),]\n\n#' ## Silhouette plot\nlibrary(cluster)\nplot(silhouette(km$cluster, d))\n#' __Note:__ The silhouette plot does not show correctly in R Studio if you have too many objects (bars are missing). I will work when you open a new plotting device with `windows()`, `x11()` or `quartz()`.\n#'\n\n#' ## Find Optimal Number of Clusters for k-means\nplot(ruspini_scaled)\n\nset.seed(1234)\nks <- 2:10\n\n#' ### Within Sum of Squares\n#' Use within sum of squares and look for the knee (nstart=5 repeats k-means 5 times and returns the best solution)\nWSS <- sapply(ks, FUN=function(k) {\n kmeans(ruspini_scaled, centers=k, nstart=5)$tot.withinss\n})\nplot(ks, WSS, type=\"l\")\nabline(v=4, col=\"red\", lty=2)\n\n#' ### Average Silhouette Width\n#' Use average silhouette width (look for the max)\nASW <- sapply(ks, FUN=function(k) {\n fpc::cluster.stats(d, kmeans(ruspini_scaled, centers=k, nstart=5)$cluster)$avg.silwidth\n})\nplot(ks, ASW, type=\"l\")\n\nks[which.max(ASW)]\nabline(v=ks[which.max(ASW)], col=\"red\", lty=2)\n\n#' ### Dunn Index\n#' Use Dunn index (another internal measure given by min. separation/ max. diameter)\nDI <- sapply(ks, FUN=function(k) {\n fpc::cluster.stats(d, kmeans(ruspini_scaled, centers=k, nstart=5)$cluster)$dunn\n})\nplot(ks, DI, type=\"l\")\nks[which.max(DI)]\nabline(v=ks[which.max(DI)], col=\"red\", lty=2)\n\n\n#' ### Gap Statistic\n#' Compares the change in within-cluster dispersion with that expected\n#' from a null model (see `? clusGap`).\n#' The default method is to\n#' choose the smallest k such that its value Gap(k) is not more\n#' than 1 standard error away from the first local maximum.\nlibrary(cluster)\nk <- clusGap(ruspini_scaled, FUN = kmeans, nstart = 10, K.max = 10)\nk\nplot(k)\n\n\n#' __Note:__ these methods can also be used for hierarchical clustering.\n#'\n#' There have been many other methods and indices proposed to determine\n#' the number of clusters.\n#' See, e.g., package [NbClust](https://cran.r-project.org/package=NbClust).\n#'\n\n#' ## Visualize the Distance Matrix\n#'\n#' Visualizing the unordered distance matrix does not show much structure.\n\nplot(ruspini_scaled)\nd <- dist(ruspini_scaled)\n\nlibrary(seriation)\npimage(d)\n\n#' Reorder using cluster labels\npimage(d, order=order(km$cluster))\n\n#' Use dissplot which rearranges clusters, adds cluster labels,\n#' and shows average dissimilarity in the lower half of the plot.\ndissplot(d, labels=km$cluster, options=list(main=\"k-means with k=4\"))\ndissplot(d, labels=db$cluster+1, options=list(main=\"DBSCAN\"))\n#' Spot the problem data points for DBSCAN (we use +1 so the noise is now cluster #1)\n#'\n#' Misspecified k\ndissplot(d, labels=kmeans(ruspini_scaled, centers=3)$cluster)\ndissplot(d, labels=kmeans(ruspini_scaled, centers=9)$cluster)\n\n\n#' # External Cluster Validation\n#'\n#' External cluster validation uses ground truth information. That is,\n#' the user has an idea how the data should be grouped. This could be a know\n#' class label not provided to the clustering algorithm.\n#'\n#' We use an artificial data set with known groups here. First, we need to\n#' cluster the new data. We do k-means and hierarchical clustering.\n\nlibrary(mlbench)\nshapes <- mlbench.smiley(n=500, sd1 = 0.1, sd2 = 0.05)\nplot(shapes)\n\n#' Prepare data\ntruth <- as.integer(shapes$class)\nshapes <- scale(shapes$x)\n\nplot(shapes)\n\n#' Find optimal number of Clusters for k-means\nks <- 2:20\n\n#' Use within sum of squares (look for the knee)\nWSS <- sapply(ks, FUN=function(k) {\n kmeans(shapes, centers=k, nstart=10)$tot.withinss\n})\nplot(ks, WSS, type=\"l\")\n#' looks like 6 clusters\nkm <- kmeans(shapes, centers=6, nstart = 10)\nplot(shapes, col=km$cluster)\n\n#' Hierarchical clustering: single-link because of the mouth\nd <- dist(shapes)\nhc <- hclust(d, method=\"single\")\n\n#' Find optimal number of clusters\nASW <- sapply(ks, FUN=function(k) {\n fpc::cluster.stats(d, cutree(hc, k))$avg.silwidth\n})\nplot(ks, ASW, type=\"l\")\n#' 4 clusters\nhc_4 <- cutree(hc, 4)\nplot(shapes, col=hc_4)\n\n#' Spectral Clustering\nlibrary(\"kernlab\")\nspec <- specc(shapes, centers = 4)\nplot(shapes, col=spec)\n\n\n#' Compare with ground truth with the corrected (=adjusted) Rand index (ARI),\n#' the variation of information (VI) index, entropy and purity.\n#\n#' define entropy and purity\nentropy <- function(cluster, truth) {\n k <- max(cluster, truth)\n cluster <- factor(cluster, levels = 1:k)\n truth <- factor(truth, levels = 1:k)\n m <- length(cluster)\n mi <- table(cluster)\n \n cnts <- split(truth, cluster)\n cnts <- sapply(cnts, FUN = function(n) table(n))\n p <- sweep(cnts, 1, rowSums(cnts), \"/\")\n p[is.nan(p)] <- 0\n e <- -p * log(p, 2)\n sum(rowSums(e, na.rm = TRUE) * mi/m)\n}\n\npurity <- function(cluster, truth) {\n k <- max(cluster, truth)\n cluster <- factor(cluster, levels = 1:k)\n truth <- factor(truth, levels = 1:k)\n m <- length(cluster)\n mi <- table(cluster)\n \n cnts <- split(truth, cluster)\n cnts <- sapply(cnts, FUN = function(n) table(n))\n p <- sweep(cnts, 1, rowSums(cnts), \"/\")\n p[is.nan(p)] <- 0\n \n sum(apply(p, 1, max) * mi/m)\n}\n\n#' calculate measures (for comparison we also use random \"clusterings\"\n#' with 4 and 6 clusters)\nrandom4 <- sample(1:4, nrow(shapes), replace = TRUE)\nrandom6 <- sample(1:6, nrow(shapes), replace = TRUE)\n\nr <- rbind(\n kmeans = c(\n unlist(fpc::cluster.stats(d, km$cluster, truth, compareonly = TRUE)),\n entropy = entropy(km$cluster, truth),\n purity = purity(km$cluster, truth)\n ),\n hc = c(\n unlist(fpc::cluster.stats(d, hc_4, truth, compareonly = TRUE)),\n entropy = entropy(hc_4, truth),\n purity = purity(hc_4, truth)\n ),\n spec = c(\n unlist(fpc::cluster.stats(d, spec, truth, compareonly = TRUE)),\n entropy = entropy(spec, truth),\n purity = purity(spec, truth)\n ),\n random4 = c(\n unlist(fpc::cluster.stats(d, random4, truth, compareonly = TRUE)),\n entropy = entropy(random4, truth),\n purity = purity(random4, truth)\n ),\n random6 = c(\n unlist(fpc::cluster.stats(d, random6, truth, compareonly = TRUE)),\n entropy = entropy(random6, truth),\n purity = purity(random6, truth)\n )\n)\nr\n\n#' Hierarchical clustering found the perfect clustering.\n#'\n#' Read `? cluster.stats` for an explanation of all the available indices.\n\n\n#'\n#' # Related Topics\n#' ## Outlier Removal\n#' It is often useful to remove outliers prior to clustering.\n#' A density based method to identify outlier is LOF (Local Outlier Factor).\n#' It is related to dbscan and compares the density around a point with the\n#' densities around its neighbors. The LOF value for a regular data\n#' point is 1. The larger the LOF value gets, the more likely the point is and\n#' outlier.\nlibrary(dbscan)\nlof <- lof(ruspini_scaled, k = 3)\nlof\nplot(ruspini_scaled, pch = \".\", main = \"LOF (k=3)\")\npoints(ruspini_scaled, cex = (lof-1)*3, pch = 1, col=\"red\")\n\n#' Find outliers (find the knee)\nplot(sort(lof), type = \"l\")\nabline(h = 1.3, col = \"red\")\nplot(ruspini_scaled[lof < 1.3,], main = \"Data and outliers\")\npoints(ruspini_scaled[lof >= 1.3,], col = \"grey\", pch = 4)\n\n\n#' There are many other outlier removal strategies available. See, e.g., package\n#' [outliers](https://cran.r-project.org/package=outliers).\n#'\n#' ## Clustering Tendency\n#' Most clustering algorithms will always produce a clustering, even if the\n#' data does not contain a cluster structure. It is typically good to check\n#' cluster tendency before attempting to cluster the data.\n#'\n#' We use again the smiley data.\nd_shapes <- dist(shapes)\n\nlibrary(seriation)\n#' Visual Analysis for Cluster Tendency Assessment (VAT) reorders the\n#' objects to show potential clustering tendency as a block structure\n#' (dark blocks along the main diagonal).\nVAT(d_shapes)\n\n#' iVAT uses largest distances in all possible paths between the objects\n#' instead of the distances to make the block structure better visible.\niVAT(d_shapes)\n\n#' Both plots show a strong cluster structure with 4 clusters.\n#'\n#' Compare with random data.\ndata_random <- matrix(runif(2*500), ncol=2, dimnames = list(NULL, c(\"x\", \"y\")))\nplot(data_random)\n\nd_random <- dist(data_random)\nVAT(d_random)\niVAT(d_random)\n#' There is very little structure visible." }, { "path": "40a-CLUST/clust-case-liberty.R", "content": "# Liberty Case for Clustering\n\ncustid = 1:12\ntrans = c(5,10,15,2,18,9,14,8,7,9,1,6)\ntotpurch = c(450,800,900,50, 900, 200, 500, 300, 250, 1000, 30, 700)\nincome = c(90,82,77,30, 60, 45, 82, 22, 90, 80,60, 80)\ndf = data.frame(custid, trans, totpurch, income)\napply(df, 2, FUN=length)\n#Right no of clusters\n#Centeriod for the clusters\n\n\n" }, { "path": "40a-CLUST/clust-class-differences.R", "content": "#Simple Case - classification and clustering\n#This could be done by finding the similarity between two hair lengths and keep them in the same group if the similarity is less(Difference of hair length is less). The same process could continue until all the hair length properly grouped into two categories.\n#http://dataaspirant.com/2016/09/24/classification-clustering-alogrithms/\n#sample data\nset.seed(123)\nhairlength = ceiling(c(rnorm(25, mean=12, sd=4), (rnorm(25, mean=25, sd=5))))\nhairlength\ngender = factor(rep(c('M','F'), each=25)) \ngender\nhair = data.frame(hairlength, gender)\ntable(hair$gender)\naggregate(hairlength ~ gender, data=hair, mean)\n\n#classification-----\nlibrary(rpart); library(rpart.plot)\ntree = rpart(gender ~ hairlength, data= hair)\ntree\nrpart.plot(tree)\npredict(tree, newdata = data.frame(hairlength=22), type='class')\n#\n\n#Clustering-----\nnames(hair)\ndata = hair$hairlength\nkm2 = kmeans(data, centers=2, nstart=3)\nkm2\nkm2$centers\nkm2$cluster\nplot(data, col=km2$cluster)\n#clusters do not indicate whether they relate to male or female\n\n#hierarchial clustering\nhc = hclust(dist(data))\nplot(hc)\nplot(hc, cex = 0.6)\nrect.hclust(hc, k = 2, border = 4:5)\nrect.hclust(hc, k = 3, border = 3:5)\nct = cutree(hc, k = 2)\nmean(data[ct==1])\nmean(data[ct==2])\n\n?rect.hclust\n\nlibrary(ape)\nlibrary(cluster) \nplot(as.phylo(hc),type=\"fan\")\n" }, { "path": "40a-CLUST/clust-compare.R", "content": "# compare different clustering methods using cluster.stat from the fpc package.\n\nlibrary(fpc)\n#Perform the following steps to compare clustering methods.\n\n#install.packages(\"fpc\")\n\n#You then need to use hierarchical clustering with the single method to cluster customer data and generate the object hc_single\n\nsingle_c = hclust(dist(iris[,1:4]), method=\"single\")\nplot(single_c)\n\nhc_single = cutree(single_c, k=3)\n\n#Use hierarchical clustering with the complete method to cluster customer data and generate the object hc_complete\n\ncomplete_c = hclust(dist(iris[,1:4]), method=\"complete\")\n\n(hc_complete = cutree(complete_c, k=3))\n\n#You can then use k-means clustering to cluster customer data and generate the object km\n\nset.seed(1234)\nkm = kmeans(iris[1:4], 3, iter.max=1000, algorithm=c(\"Forgy\"))\n\n#Next, retrieve the cluster validation statistics of either clustering method:\n \ncs = cluster.stats(dist(iris[1:4]), km$cluster)\ncs\n#Most often, we focus on using within.cluster.ss and avg.silwidth to validate the clustering method. The within.cluster.ss measurement stands for the within clusters sum of squares, and avg.silwidth represents the average silhouette width.\n\n#within.cluster.ss measurement shows how closely related objects are in clusters; the smaller the value, the more closely related objects are within the cluster.\n#avg.silwidth is a measurement that considers how closely related objects are within the cluster and how clusters are separated from each other. The silhouette value usually ranges from 0 to 1; a value closer to 1 suggests the data is better clustered.\ncs[c(\"within.cluster.ss\",\"avg.silwidth\")]\n\n#Finally, we can generate the cluster statistics of each clustering method and list them in a table:\n\ncluster.stats(dist(iris[1:4]), km$cluster)[c(\"within.cluster.ss\",\"avg.silwidth\")] \ncluster.stats(dist(iris[1:4]), hc_single)[c(\"within.cluster.ss\",\"avg.silwidth\")] \ncluster.stats(dist(iris[1:4]), hc_complete)[c(\"within.cluster.ss\",\"avg.silwidth\")] \n\n\nsapply(list(kmeans = km$cluster, hc_single = hc_single, hc_complete = hc_complete), function(c) cluster.stats(dist(iris[1:4]), c)[c(\"within.cluster.ss\",\"avg.silwidth\")])\n\n \n#https://en.proft.me/2017/01/29/exploring-hierarchical-clustering-r/\n" }, { "path": "40a-CLUST/clust-distance-calc.R", "content": "# Distance Calcuation\n#https://stackoverflow.com/questions/27082378/how-to-compute-distances-between-centroids-and-data-matrix-for-kmeans-algorithm\n\nmyEuclid <- function(points1, points2) {\n distanceMatrix <- matrix(NA, nrow=dim(points1)[1], ncol=dim(points2)[1])\n for(i in 1:nrow(points2)) {\n distanceMatrix[,i] <- sqrt(rowSums(t(t(points1)-points2[i,])^2))\n }\n distanceMatrix\n}\n\n\nmyKmeans <- function(x, centers, distFun, nItter=10) {\n clusterHistory <- vector(nItter, mode=\"list\")\n centerHistory <- vector(nItter, mode=\"list\")\n \n for(i in 1:nItter) {\n distsToCenters <- distFun(x, centers)\n clusters <- apply(distsToCenters, 1, which.min)\n centers <- apply(x, 2, tapply, clusters, mean)\n # Saving history\n clusterHistory[[i]] <- clusters\n centerHistory[[i]] <- centers\n }\n \n list(clusters=clusterHistory, centers=centerHistory)\n}\n\n\nmat <- matrix(rnorm(100), ncol=2)\nmat\ncenters <- mat[sample(nrow(mat), 5),]\ncenters\ntheResult <- myKmeans(mat, centers, myEuclid, 10)\ntheResult\n\ntheResult2 <- kmeans(mat, centers, 10, algorithm=\"Forgy\")\ntheResult2\n\npar(mfrow=c(2,2))\nfor(i in 1:4) {\n plot(mat, col=theResult$clusters[[i]], main=paste(\"itteration:\", i), xlab=\"x\", ylab=\"y\")\n points(theResult$centers[[i]], cex=3, pch=19, col=1:nrow(theResult$centers[[i]]))\n}\n\nfor(i in 5:8) {\n plot(mat, col=theResult$clusters[[i]], main=paste(\"itteration:\", i), xlab=\"x\", ylab=\"y\")\n points(theResult$centers[[i]], cex=3, pch=19, col=1:nrow(theResult$centers[[i]]))\n}\n\n\nfor(i in 9:10) {\n plot(mat, col=theResult$clusters[[i]], main=paste(\"itteration:\", i), xlab=\"x\", ylab=\"y\")\n points(theResult$centers[[i]], cex=3, pch=19, col=1:nrow(theResult$centers[[i]]))\n}\n\n\nmyCor <- function(points1, points2) {\n return(1 - ((cor(t(points1), t(points2))+1)/2))\n}\n\ntheResult <- myKmeans(mat, centers, myCor, 10)\n\n" }, { "path": "40a-CLUST/clust-distance2.R", "content": "#Clustering Example\n\n#Packages - library(fields)\n\n(income = c(68,72,100))\n(customers = data.frame(income))\n\n#which 2 customers are similar\n(cm =combn(income, 2))\ndiff(c(68,72))\n(d1= apply(cm, 2, diff))\nrbind(cm,d1 )\n#C1 & C2 are closer/ similar\n\n#Now use euclidean distance\nsqrt((68-72)^2); sqrt((68-100)^2); sqrt((100-72)^2)\nfields::rdist(customers)\n#Most Similar are 1 and 2 - diff is 4 units\n\n#add one more factor\n(age = c(25, 70, 28))\n(customers2 = data.frame(income, age))\n#which 2 customers are similar\n# minus sign will cancel plus values, so use euclidean distance\ndist(customers)\n#sqrt(sum((x1-x2)^2 + (y1-y2)^2))\nsqrt(sum((68 - 72)^2 + (25 - 70) ^2))\nrdist(customers2)\n\n#Most Similar now are C1 and C3\n\n\n#when scales are different is recommended to standardise values\nscale(customers2)\ncustomers2\ncolMeans(customers2)\n?scale\nx = customers$income\n\nscale1 = function(x) (x - min(x))/ (max(x) - min(x))\nscale2 = function(x) (x - mean(x))/ sd(x)\nscale1(x)\nscale2(x)\nscale(x)\nrescale(x, to=c(0,1))\nrescale(x, to=c(0,100))\napply(customers2, 2, FUN = rescale)\napply(customers, 2, FUN = rescale)\n\n\nlibrary(BBmisc)\nnormalize(x, method = \"standardize\", range = c(0, 1), margin = 1, on.constant = \"quiet\")\n" }, { "path": "40a-CLUST/clust-entropy.R", "content": "#Entropy in Clustering\n#https://www.rdocumentation.org/packages/NMF/versions/0.21.0/topics/purity\n\nlibrary(NMF)\n# NOT RUN {\n# generate a synthetic dataset with known classes: 50 features, 18 samples (5+5+8)\nn <- 50; counts <- c(5, 5, 8);\nV <- syntheticNMF(n, counts)\nV\ncl <- unlist(mapply(rep, 1:3, counts))\nhead(cl)\ncl\n# perform default NMF with rank=2\nx2 <- nmf(V, 2)\npurity(x2, cl)\nentropy(x2, cl)\n# perform default NMF with rank=2\nx3 <- nmf(V, 3)\npurity(x3, cl)\nentropy(x3, cl)\n# }" }, { "path": "40a-CLUST/clust-iterations.R", "content": "#Check how clustering changes due to No of Iterations\n\niris\nciris = iris[-5]\n\nkm3 = kmeans(ciris, 3, iter.max=3, nstart=3, trace=T)\nkm3\nkm3$iter\n?kmeans\nkm3 = kmeans(ciris, 3, iter.max = 15)\nkm3\nkm3$iter\n\nlibrary(ISLR)\ndata(package='ISLR')\nhead(Default)\ncDefault = Default[c(3:4)]\nhead(cDefault)\ndim(cDefault)\nsDefault = scale(cDefault)\nkm3_default = kmeans(sDefault, 4)\nkm3_default$centers\n" }, { "path": "40a-CLUST/clust-kselect.R", "content": "# Choosing K\n\n\n# OutL2 is the dataset we are working with\niris\nsummary(iris)\ndf = iris[-5]\nhead(df)\n# We take the first iteration\nwss <- sum(kmeans(df,centers=1)$withinss)\nwss\n\nk1 = kmeans(df,3)\nk1\n\nk1$withinss # k=3\nk1$tot.withinss # sum for all k\n#tot.withinss = sum(withinss)\n(tot.withinss = sum(k1$withinss))\n\nk1$betweenss #1-2, 1-3, 2-3\nk1$tot.withinss #as above\nk1$totss #\n#totss = tot.withinss + betweenss\n(totss = k1$tot.withinss + k1$betweenss)\n\n\nk1$size\nk1$iter\nk1$centers\n\n\n# We take iteration 2 to 15\nfor (i in 2:15) wss[i] <- sum(kmeans(df,centers=i)$withinss)\nwss\n\n# We plot the 15 withinss values. One for each k\nplot(1:15, wss, type=\"b\", xlab=\"Number of Clusters\",ylab=\"Within groups sum of squares\")\n\n\nlibrary(cluster)\nset.seed(1234)\nfit.pam2 = pam(df, k=3, stand=TRUE)\nfit.pam2$medoids\nclusplot(fit.pam2, main='Bivariate Cluster Plot')\n#plot of 1st two PC derived from PCA\nsummary(fit.pam2)\nnames(fit.pam2)\n\n\n" }, { "path": "40a-CLUST/clustering-women.R", "content": "#Clustering with Women Data set\n#http://dni-institute.in/blogs/k-means-clustering-algorithm-explained/\n" }, { "path": "40a-CLUST/clusters3.R", "content": "# Non Existent Clusters\n\nlibrary(fMultivar)\nset.seed(1234)\ndf = rnorm2d(1000, rho=.5)\ndf = as.data.frame(df)\nhead(df)\nplot(df, main='Pivariable ND with rho = 0.5')\n# seems to be no cluster\n\n#Function wssplot\nwssplot <- function(data, nc=15, seed=1234){\n wss <- (nrow(data)-1)*sum(apply(data,2,var))\n for (i in 2:nc){\n set.seed(seed)\n wss[i] <- sum(kmeans(data, centers=i)$withinss)}\n plot(1:nc, wss, type=\"b\", xlab=\"Number of Clusters\",\n ylab=\"Within groups sum of squares\")\n }\n\n#Determine no of clusters present\nlibrary(NbClust)\nwssplot(df)\n\nnc = NbClust(df, min.nc=2, max.nc=15, method='kmeans')\nnc" }, { "path": "40a-CLUST/hier-simplecase.R", "content": "#hier clust - HW\n\ndf = data.frame(x= c(1,4,8,6,3,10,7,11,13,2), y= c(5,9,15,2,12, 7,7, 4,10,13))\nnrow(df)\nrownames(df) = paste('A',1:10,sep='') \ndf \n\nhc1 = hclust(dist(df))\nattributes(hc1)\nplot(hc1, hang=-1)\n\nhc2 = hclust(dist(df), method = \"ward.D2\")\nplot(hc2, hang=-1)\n\nhc3 = hclust(dist(df), method = \"ave\")\nplot(hc3, hang=-1)\n\nplot(as.dendrogram(hc3), cex = 0.6, horiz = TRUE)\n\n# Cut tree into 4 groups\ngrp <- cutree(hc3, k = 3)\n# Number of members in each cluster\ntable(grp)\n# Get the names for the members of cluster 2\nrownames(df)[grp == 2]\n\nplot(hc3, cex = 0.6)\nrect.hclust(hc3, k = 3, border = 2:5)\n# Cut agnes() tree into 4 groups\nct = cutree(hc3, k = 2)\n\n\nplot(hc3, cex = 0.6)\nrect.hclust(hc3, k = 3, border = 2:5)\n\n#best number of clusters\nlibrary(NbClust)\nnc = NbClust(data, distance=\"euclidean\", min.nc=2, max.nc=15, method=\"average\")\n" }, { "path": "40a-CLUST/hier-usarrests.R", "content": "#Hierarchical Clustering\n#http://www.sthda.com/english/wiki/print.php?id=237\n\n#if(!require(devtools)) install.packages(\"devtools\")\ndevtools::install_github(\"kassambara/factoextra\")\n#Install cluster and dendextend packages as follow:\ninstall.packages(\"cluster\")\ninstall.packages(\"dendextend\")\n#Load the packages :\nlibrary(cluster)\nlibrary(dendextend)\nlibrary(factoextra)\n\n# Load the data set\ndata(\"USArrests\")\n\n# Remove any missing value (i.e, NA values for not available)\n# That might be present in the data\ndf <- na.omit(USArrests)\nhead(df)\n\n# View the firt 6 rows of the data\nhead(df, n = 6)\n\n#Before hierarchical clustering, we can compute some descriptive statistics:\n \ndesc_stats <- data.frame(\n Min = apply(df, 2, min), # minimum\n Med = apply(df, 2, median), # median\n Mean = apply(df, 2, mean), # mean\n SD = apply(df, 2, sd), # Standard deviation\n Max = apply(df, 2, max) # Maximum\n )\ndesc_stats <- round(desc_stats, 1)\nhead(desc_stats)\n\n#Note that the variables have a large different means and variances. This is explained by the fact that the variables are measured in different units; Murder, Rape, and Assault are measured as the number of occurrences per 100 000 people, and UrbanPop is the percentage of the state’s population that lives in an urban area.\n\n#They must be standardized (i.e., scaled) to make them comparable. Recall that, standardization consists of transforming the variables such that they have mean zero and standard deviation one. You can read more about standardization in the following article: distance measures and scaling.\n\n#As we don’t want the hierarchical clustering result to depend to an arbitrary variable unit, we start by scaling the data using the R function scale() as follow:\n \ndf <- scale(df)\nhead(df)\n#----\n#There are different functions available in R for computing hierarchical clustering. The commonly used functions are:\n \n#hclust() [in stats package] and agnes() [in cluster package] for agglomerative hierarchical clustering (HC)\n#diana() [in cluster package] for divisive HC\n\n#hclust() function is the built-in R function [in stats package] for computing hierarchical clustering.\n\n#The simplified format is: hclust(d, method = \"complete\")\n#d a dissimilarity structure as produced by the dist() function.\n#method: The agglomeration method to be used. Allowed values is one of “ward.D”, “ward.D2”, “single”, “complete”, “average”, “mcquitty”, “median” or “centroid”.\n\n#The dist() function is used to compute the Euclidean distance between observations. Finally, observations are clustered using Ward’s method.\n\n# Dissimilarity matrix\nd <- dist(df, method = \"euclidean\")\n\n# Hierarchical clustering using Ward's method\nres.hc <- hclust(d, method = \"ward.D2\" )\n\n# Plot the obtained dendrogram\nplot(res.hc, cex = 0.6, hang = -1)\n\n#The R function agnes() [in cluster package] can be also used to compute agglomerative hierarchical clustering. The R function diana() [ in cluster package ] is an example of divisive hierarchical clustering.\n\n# Agglomerative Nesting (Hierarchical Clustering)\nlibrary(cluster)\nagnes(x, metric = \"euclidean\", stand = FALSE, method = \"average\")\n\n# DIvisive ANAlysis Clustering\ndiana(x, metric = \"euclidean\", stand = FALSE)\n\n#x: data matrix or data frame or dissimilarity matrix. In case of matrix and data frame, rows are observations and columns are variables. In case of a dissimilarity matrix, x is typically the output of daisy() or dist().\n#metric: the metric to be used for calculating dissimilarities between observations. Possible values are “euclidean” and “manhattan”.\n#stand: if TRUE, then the measurements in x are standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable’s mean value and dividing by the variable’s mean absolute deviation\n#method: The clustering method. Possible values includes “average”, “single”, “complete”, “ward”.\n#The function agnes() returns an object of class “agnes” (see ?agnes.object) which has methods for the functions: print(), summary(), plot(), pltree(), as.dendrogram(), as.hclust() and cutree().\n#The function diana() returns an object of class “diana” (see ?diana.object) which has also methods for the functions: print(), summary(), plot(), pltree(), as.dendrogram(), as.hclust() and cutree().\n#Compared to other agglomerative clustering methods such as hclust(), agnes() has the following features:\n \n# It yields the agglomerative coefficient (see agnes.object) which measures the amount of clustering structure found\n#Apart from the usual tree it also provides the banner, a novel graphical display (see plot.agnes).\n\n#R code for computing agnes\nlibrary(\"cluster\")\n# Compute agnes()\nres.agnes <- agnes(df, method = \"ward\")\n# Agglomerative coefficient\nres.agnes$ac\n## [1] 0.934621\n# Plot the tree using pltree()\npltree(res.agnes, cex = 0.6, hang = -1,\n main = \"Dendrogram of agnes\") \n\n#It’s also possible to draw AGNES dendrogram using the function plot.hclust() and the function plot.dendrogram() as follow:\n \n# plot.hclust()\nplot(as.hclust(res.agnes), cex = 0.6, hang = -1)\n# plot.dendrogram()\nplot(as.dendrogram(res.agnes), cex = 0.6, horiz = TRUE)\n\n#R code for computing diana\n# Compute diana()\nres.diana <- diana(df)\n# Plot the tree\npltree(res.diana, cex = 0.6, hang = -1, main = \"Dendrogram of diana\")\n\n# Divise coefficient; amount of clustering structure found\nres.diana$dc\n## [1] 0.8514345\n\n#As for plotting AGNES dendrogram, the functions plot.hclust() and plot.dendrogram() can be used as follow:\n \n# plot.hclust()\nplot(as.hclust(res.diana), cex = 0.6, hang = -1)\n# plot.dendrogram()\nplot(as.dendrogram(res.diana), cex = 0.6, horiz = TRUE)\n\n\n\n\n\n#------------\n#Cut the dendrogram into different groups\n#The height of the cut to the dendrogram controls the number of clusters obtained. It plays the same role as the k in k-means clustering.\n\n#The function cutree() is used and it returns a vector containing the cluster number of each observation:\n#n the dendrogram displayed above, each leaf corresponds to one observation. As we move up the tree, observations that are similar to each other are combined into branches, which are themselves fused at a higher height.\n\n#The height of the fusion, provided on the vertical axis, indicates the (dis)similarity between two observations. The higher the height of the fusion, the less similar the observations are.\n\n#Note that, conclusions about the proximity of two observations can be drawn only based on the height where branches containing those two observations first are fused. We cannot use the proximity of two observations along the horizontal axis as a criteria of their similarity.\n\n#In order to identify sub-groups (i.e. clusters), we can cut the dendrogram at a certain height as described in the next section.\n#The height of the cut to the dendrogram controls the number of clusters obtained. It plays the same role as the k in k-means clustering.\n\n#The function cutree() is used and it returns a vector containing the cluster number of each observation:\n \n# Cut tree into 4 groups\ngrp <- cutree(res.hc, k = 4)\n# Number of members in each cluster\ntable(grp)\n# Get the names for the members of cluster 1\nrownames(df)[grp == 1]\n\n#It’s also possible to draw the dendrogram with a border around the 4 clusters. The argument border is used to specify the border colors for the rectangles:\n \nplot(res.hc, cex = 0.6)\nrect.hclust(res.hc, k = 4, border = 2:5)\n\n#using the function fviz_cluster() [in factoextra], we can also visualize the result in a scatter plot. Observations are represented by points in the plot, using principal components. A frame is drawn around each cluster.\n\nlibrary(factoextra)\nfviz_cluster(list(data = df, cluster = grp))\n\n\n#The function cutree() can be used also to cut the tree generated with agnes() and diana() as follow:\n\n# Cut agnes() tree into 4 groups\ncutree(res.agnes, k = 4)\n\n# Cut diana() tree into 4 groups\ncutree(as.hclust(res.diana), k = 4)\n\n#Hierarchical clustering and correlation based distance\n#The different functions for hierarchical clustering use Euclidean distance measures as default metric. It’s also possible to use correlation-based distance measures. Firstly, pairwise correlation matrix between items is computed using the function cor() which can calculate either “pearson”, “spearman” or “kendall” correlation #method. Next, the correlation matrix is converted as a distance matrix and finally clustering can be computed on the resulting distance matrix.\n\nres.cor <- cor(t(df), method = \"pearson\")\nd.cor <- as.dist(1 - res.cor)\nplot(hclust(d.cor, method = \"ward.D2\"), cex = 0.6)\n\n\n#What type of distance measures should we choose?\n#The choice of dissimilarity measures is very important, as it has a strong influence on the resulting dendrogram.\n\n#In many of the examples described above, we used Euclidean distance as the dissimilarity measure. Depending on the type of the data and the researcher questions, other dissimilarity measures might be preferred such as correlation-based distance.\n\n#Correlation-based distance considers two observations to be similar if their features are highly correlated, even though the observed values may be far apart in terms of Euclidean distance.\n\n#If we want to identify clusters of observations with the same overall profiles regardless of their magnitudes, then we should go with correlation-based distance as a dissimilarity measure. This is particularly the case in gene expression data analysis, where we might want to consider genes similar when they are “up” and “down” together. It is also the case, in marketing if we want to identify group of shoppers with the same preference in term of items, regardless of the volume of items they bought.\n\n#If Euclidean distance is chosen, then observations with high values of features will be clustered together. The same holds true for observations with low values of features.\n\n\n#Note that, when the data are standardized, there is a functional relationship between the Pearson correlation coefficient r(x,y) and the Euclidean distance.\n\n#Comparing two dendrograms\n#We’ll use the package dendextend which contains many functions for comparing two dendrograms, including: dend_diff(), tanglegram(), entanglement(), all.equal.dendrogram(), cor.dendlist().\n\n#The function tanglegram() and cor.dendlist() are described in this section.\n\n#A random subset of the dataset will be used in the following example. The function sample() is used to randomly select 10 observations among the 50 observations contained in the data set\n\n# Subset containing 10 rows\nset.seed(123)\nss <- sample(1:50, 10)\ndf <- df[ss,]\n#In the R code below, we’ll start by computing pairwise distance matrix using the function dist(). Next, hierarchical clustering (HC) is computed using two different linkage methods (“average” and “ward.D2”). Finally the results of HC are transformed as dendrograms:\n \nlibrary(dendextend)\n# Compute distance matrix\nres.dist <- dist(df, method = \"euclidean\")\n\n# Compute 2 hierarchical clusterings\nhc1 <- hclust(res.dist, method = \"average\")\nhc2 <- hclust(res.dist, method = \"ward.D2\")\n\n# Create two dendrograms\ndend1 <- as.dendrogram (hc1)\ndend2 <- as.dendrogram (hc2)\n\n# Create a list of dendrograms\ndend_list <- dendlist(dend1, dend2)\n# Tanglegram\n#The function tanglegram() plots two dendrograms, side by side, with their labels connected by lines. It can be used for visually comparing two methods of Hierarchical clustering as follow:\n\ntanglegram(dend1, dend2)\n\n#Note that “unique” nodes, with a combination of labels/items not present in the other tree, are highlighted with dashed lines.\n\n#The quality of the alignment of the two trees can be measured using the function entanglement(). The output of tanglegram() can be customized using many other options as follow:\n \ntanglegram(dend1, dend2, highlight_distinct_edges = FALSE, # Turn-off dashed lines\n common_subtrees_color_lines = FALSE, # Turn-off line colors\n common_subtrees_color_branches = TRUE, # Color common branches \n main = paste(\"entanglement =\", round(entanglement(dend_list), 2))\n )\n#Correlation matrix between a list of dendrogram\n#The function cor.dendlist() is used to compute “Baker” or “Cophenetic” correlation matrix between a list of trees.\n\n# Cophenetic correlation matrix\ncor.dendlist(dend_list, method = \"cophenetic\")\n\n# Baker correlation matrix\ncor.dendlist(dend_list, method = \"baker\")\n\n\n#The correlation between two trees can be also computed as follow:\n\n# Cophenetic correlation coefficient\ncor_cophenetic(dend1, dend2)\n## [1] 0.9646883\n# Baker correlation coefficient\ncor_bakers_gamma(dend1, dend2)\n## [1] 0.9622885\n#It’s also possible to compare simultaneously multiple dendrograms. A chaining operator %>% (available in dendextend) is used to run multiple function at the same time. It’s useful for simplifying the code:\n \n# Subset data\nset.seed(123)\nss <- sample(1:150, 10 )\n# Create multiple dendrograms by chaining\ndend1 <- df %>% dist %>% hclust(\"com\") %>% as.dendrogram\ndend2 <- df %>% dist %>% hclust(\"single\") %>% as.dendrogram\ndend3 <- df %>% dist %>% hclust(\"ave\") %>% as.dendrogram\ndend4 <- df %>% dist %>% hclust(\"centroid\") %>% as.dendrogram\n# Compute correlation matrix\ndend_list <- dendlist(\"Complete\" = dend1, \"Single\" = dend2, \"Average\" = dend3, \"Centroid\" = dend4)\ncors <- cor.dendlist(dend_list)\n# Print correlation matrix\nround(cors, 2)\n\n# Visualize the correlation matrix using corrplot package\nlibrary(corrplot)\ncorrplot(cors, \"pie\", \"lower\")\n" }, { "path": "40a-CLUST/iris.R", "content": "\n\ndata(iris)\nlibrary(dplyr)\nlibrary(tidyr)\n\n# The easy way, if your data allows, is to tidy the data, find the max in\n# each group, and join the results back in\niris_id = iris %>% mutate(id=1:nrow(.))\niris_id\niris_id %>%\n gather('attribute', 'value', Sepal.Length:Petal.Width) %>%\n group_by(id) %>%\n summarize(max_attribute=max(value)) %>%\n right_join(iris_id, by='id') %>%\n head(3)\n" }, { "path": "40a-CLUST/kmeans-bankdata.R", "content": "#kmeans - bank data\n#https://www.linkedin.com/pulse/decision-making-using-unsupervised-learning-data-r-k-means-pandian/\n\n#bank loan data and picked up two factors Age and Income \\\nset.seed(123)\nage = round(runif(100,20,70),2)\nincome = round(rnorm(100,100000,20000),2)\nbank = data.frame(age, income)\nhead(bank)\n\nbankdata = scale(bank)\nfor(i in 1:15){\n wss[i]<-sum(kmeans(bankdata,centers = i)$withinss)\n}\nplot(wss, type='b')\n#From the plot of cluster sizes we can see that K values of 4, 5, 6 and 7 might have the potential. After K=7 the sizes become too small and may be the smaller clusters may be just sub sets of some larger cluster and may not possess unique characteristics. For the reaming study we have parked at these values of K.\nabline(v=7)\nkm7 = kmeans(bankdata, centers=7)\nkm7\nlibrary(ggvis)\nbank %>% ggvis(~age, ~income)\nbankdata %>% ggvis(x = ~age, y = ~income)\nbank %>% ggvis(x = ~age, y = ~income,fill=~factor(km7$cluster)) %>% layer_points()\n\nrequire(lattice)\nxyplot(income ~age| as.factor(km7$cluster),groups= as.factor(km7$cluster),bank, pch= 20)\n\n#PCA\nlibrary(cluster)\nclusplot(bank,km7$cluster,color=TRUE,shade=TRUE,labels = 2,lines=0)\n\n#symbols\n?symbols\nkm7$tot.withinss\nhead(bankdata)\nclass(bankdata)\nbankdata[,2]\nbankdata[,1:2]\nplot(bankdata)\nkm7$withinss\nkm7$centers\n\nkm7a = kmeans(bank,7)\n\n(symboldata = as.data.frame(cbind(km7a$centers, sradius=sqrt(km7a$withinss))))\nhead(trees)\n\nwith(symboldata, symbols(age,income, circles=sradius, inches = .5))\n\nwith(bank, plot(age,income))\npar(new=TRUE)\npoints(km7a$centers, col='red', pch=18)\npar(new=TRUE)\nwith(symboldata, symbols(age,income, circles=sradius, inches = 1), axis=F, xlim=c(20,70), ylim=c(60000, 140000))\n\nwith(symboldata, symbols(age, income, circles = sradius/24, inches = FALSE,\n main = \"Trees' Girth\") )\n\n\nwith(trees, symbols(Height, Volume, circles = Girth/24, inches = FALSE,\n main = \"Trees' Girth\") )\n\n\nsymbols(x=bankdata[,1], y=bankdata[,2], circles=km7$tot.withinss,inches=0.25, fg=\"black\", bg=\"blue\", xlab=\"Age\", ylab=\"Income\",main=\"K=4\")\n\ntext(k4$age, k4$income, k4$sqrt_withinss,pos=4,col=\"dark green\",cex=1.2)\n\n\n" }, { "path": "40a-CLUST/kmeans-pcalike.R", "content": "#kmeans : PCA like technique\n\n#https://www.biostars.org/p/15669/\n\nlibrary(fpc)\nlibrary(ggplot2)\ndata=mtcars\nkclust=kmeans(data,centers=3)\nkclust$cluster <- as.factor(kclust$cluster)\nkclust\nd=dist(data, method = \"euclidean\") \nfit=cmdscale(d,eig=TRUE, k=2) # k is the number of dim\nfit\n\np = ggplot(data.frame(data), aes(fit$points[,1], fit$points[,2], color = factor(kclust$cluster))) \np <- p + theme(axis.title.y = element_text(size = rel(1), angle = 90))\np <- p + theme(axis.title.x = element_text(size = rel(1), angle = 00))\np= p + theme(axis.text=element_text(size=10,angle=90),axis.title=element_text(size=20,face=\"bold\")) + geom_point(size=4)\np= p + theme(legend.text = element_text(size = 10, colour = \"black\"))\np= p + theme(legend.title = element_text(size = 10, colour = \"black\"))\np= p + theme(legend.key.size = unit(1.5, \"cm\"))\np\n\n\n#-----\n" }, { "path": "40a-CLUST/kmeans-plots.R", "content": "#kmeans plots\n\ndf=iris\nm=as.matrix(cbind(df$Petal.Length, df$Petal.Width),ncol=2)\n\n#Now we will do the actual clustering.\n\ncl=(kmeans(m,3))\n\n#The cl object contains a number of interesting attributes associated with the model.\n\ncl$size\ncl$withinss\n\n#Next we do a bit of data formatting and preparation for subsequent calls to graph the data. Notice that we add the cluster information back to our original data frame. This is a good organization of the data and also a requirement for working with ggplot2 which is designed to use data frames.\n\ndf$cluster=factor(cl$cluster)\ncenters=as.data.frame(cl$centers)\n\n#The following graph color codes the points by cluster. We also add the centers and a semi transparent halo around the center to emphasize the place of the center… and its role in classifying the observations into clusters.\n\nlibrary(ggplot2)\ncenters\nggplot(data=df, aes(x=Petal.Length, y=Petal.Width, color=cluster )) + geom_point() + geom_point(data=centers, aes(x=V1,y=V2, color='Center')) + geom_point(data=centers, aes(x=V1,y=V2, color='Center'), size=52, alpha=.3)\n\nlibrary(sqldf)\nsqldf('select Species, cluster, count(*) from df group by Species, Cluster')\n\n\n#oultiers\ndf2 = sqldf('select * from df where (Species || cluster) in (select Species || cluster from df group by Species, Cluster having count(*) < 10)')\ndf2\n\n#sqldf('select Species || cluster from df group by Species, Cluster having count(*) < 10')\n\nlast_plot()\nlast_plot() + geom_point(data=df2, aes(x=Petal_Length, y=Petal_Width, shape=5, alpha=.7, size=4.5), legend=FALSE) \n" }, { "path": "40a-CLUST/kmeans-randomness.R", "content": "#kmeans - everytime new values of withinss\n#https://rpubs.com/mohammadshadan/273129\n# Set up 2 x 3 plotting grid\n\npar(mfrow = c(2, 3))\n\n# Set seed\nset.seed(1)\n(x = iris[,1:2])\nfor(i in 1:6) {\n # Run kmeans() on x with three clusters and one start\n km.out <- kmeans(x, centers=3, nstart=1)\n \n # Plot clusters\n plot(x, col = km.out$cluster, \n main = km.out$tot.withinss, \n xlab = \"\", ylab = \"\")\n}\nurl <- \"http://s3.amazonaws.com/assets.datacamp.com/production/course_1903/datasets/WisconsinCancer.csv\"\n\n#Download the data: wisc.df\nwisc.df <- read.csv(url)\nhead(wisc.df)\n# Convert the features of the data: wisc.data\nwisc.data <- as.matrix(wisc.df[3:32])\n\n# Set the row names of wisc.data\nrow.names(wisc.data) <- wisc.df$id\nhead(wisc.data)\n# Create diagnosis vector\ndiagnosis <- as.numeric(wisc.df$diagnosis == \"M\")\nhead(diagnosis)\ncolnames(wisc.data)\nlength(grep(\"_mean\", colnames(wisc.data)))\ncolnames(wisc.data)[(grep(\"_mean\", colnames(wisc.data)))]\ntable(diagnosis)\n\n# Execute PCA, scaling if appropriate: wisc.pr\nwisc.pr <- prcomp(wisc.data, scale=TRUE)\n\n# Look at summary of results\nsummary(wisc.pr)\n\n# Create a biplot of wisc.pr\nbiplot(wisc.pr)\n# Scatter plot observations by components 1 and 2\nplot(wisc.pr$x[, c(1, 2)], col = (diagnosis + 1), \n xlab = \"PC1\", ylab = \"PC2\")\n# Repeat for components 1 and 3\nplot(wisc.pr$x[, c(1, 3)], col = (diagnosis + 1), \n xlab = \"PC1\", ylab = \"PC3\")\n# Do additional data exploration of your choosing below (optional)\nplot(wisc.pr$x[, c(1, 4)], col = (diagnosis + 1), \n xlab = \"PC1\", ylab = \"PC3\")\n\npar(mfrow = c(1, 2))\n\n# Calculate variability of each component\npr.var <- wisc.pr$sdev^2\n\n# Variance explained by each principal component: pve\npve <- pr.var/sum(pr.var)\n\n# Plot variance explained for each principal component\nplot(pve, xlab = \"Principal Component\", \n ylab = \"Proportion of Variance Explained\", \n ylim = c(0, 1), type = \"b\")\n\n# Plot cumulative proportion of variance explained\nplot(cumsum(pve), xlab = \"Principal Component\", \n ylab = \"Cummulative Proportion of Variance Explained\", \n ylim = c(0, 1), type = \"b\")\n\nwisc.pr$rotation[rownames(wisc.pr$rotation)==\"concave.points_mean\",]\n\nwisc.pr$rotation[rownames(wisc.pr$rotation)==\"concave.points_mean\",][1]\n\nround(colMeans(wisc.data),1)\n\n# Scale the wisc.data data: data.scaled\ndata.scaled <- scale(wisc.data)\n\n# Calculate the (Euclidean) distances: data.dist\ndata.dist <- dist(data.scaled)\n\n# Create a hierarchical clustering model: wisc.hclust\nwisc.hclust <- hclust(data.dist, method=\"complete\")\nplot(wisc.hclust)\n\n# Cut tree so that it has 4 clusters: wisc.hclust.clusters\nwisc.hclust.clusters <- cutree(wisc.hclust, k=4)\n\n# Compare cluster membership to actual diagnoses\ntable(wisc.hclust.clusters, diagnosis)\n" }, { "path": "40a-CLUST/pam1.R", "content": "# Partitioning around Mediods (PAM)\n\n\nlibrary(cluster)\nset.seed(1234)\ndata(wine, package='rattle.data')\nhead(wine)\nfit.pam = pam(wine[-1,], k=3, stand=TRUE)\nfit.pam$medoids\nclusplot(fit.pam, main='Bivariate Cluster Plot')\n#plot of 1st two PC derived from PCA\nsummary(fit.pam)\nnames(fit.pam)\n\n\n\n" }, { "path": "45a-AR/11a-measures1.R", "content": "(Lift = 100/ 400 / ((200 /\n 400) * (160/400)))\n.25/.40\n0.75 * 0.9\n.75/.9\n" }, { "path": "45a-AR/12a-ar-samplecase.R", "content": "# Association Rule - Simple Example Case\n\n#libraries\nlibrary(arules)\nlibrary(arulesViz)\n\n#Create Data\n#Method1: format basket\nitemlist1 = paste('I1,I2,I5','I2,I4', 'I2,I3','I1,I2,I4','I1,I3','I2,I3','I1,I3','I1,I2,I3,I5','I1,I2,I3', sep=\"\\n\");\nitemlist1\nwrite(itemlist1, file = \"./data/itemlist1\");\ntdata1 = read.transactions(\"./data/itemlist1\", format = \"basket\", sep=\",\");\nsummary(tdata1)\ntdata1\ninspect(tdata1)\n\n#Method2\nitemlist2 <- data.frame(\n TID = c(1,1,1,2,2,3,3,4,4,4,5,5,6,6,7,7,8,8,8,8,9,9,9), \n item=c('I1','I2','I5', 'I2','I4', 'I2','I3','I1','I2','I4','I1','I3','I2','I3','I1','I3','I1','I2','I3','I5','I1','I2','I3')\n)\nitemlist2\ntdata2 <- as(split(itemlist2[,\"item\"], itemlist2[,\"TID\"]), \"transactions\")\ntdata2\ninspect(tdata2)\nsummary(tdata2)\nimage(tdata2)\n\n#Method3 Use: ----\n#Data in the form of list\nitemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))\nlength(itemlist)\n## set transaction names\nnames(itemlist) <- paste(\"Tr\",c(1:9), sep = \"\")\nitemlist\n## coerce into transactions\ntdata3 <- as(itemlist, \"transactions\")\ntdata3\nsummary(tdata3)\ninspect(tdata3)\n\n#Method4 : DF ----\nT1 = c(T,T,F,F,T); T2 = c(T,F,F,T,F); T3 = c(F,T,T,F,T)\nT4 = c(T,T,F,T,F); T5 = c(T,F,T,F,T); T6 = c(F,T,T,F,T)\nT7 = c(T,F,T,F,T); T8 = c(T,T,T,F,T); T9 = c(T,T,T,F,T)\ndf = data.frame(rbind(T1,T2,T3,T4,T5,T6,T7,T8,T9))\ncolnames(df)=c('I1','I2','I3','I4','I5')\ndf\ntdata4 = as(df, \"transactions\")\nsummary(tdata4)\ntdata4\ninspect(tdata4)\n\ntdata=tdata1\n\n#Data ready - Perform AR ----\n## analyze transactions\nsummary(tdata)\nimage(tdata)\n\n#Analysis\nfreqitems = eclat(tdata)\nfreqitems\ninspect(freqitems)\nitemFrequencyPlot(tdata,topN = 5,type=\"absolute\")\nitemFrequencyPlot(tdata,topN = 5,type=\"relative\", horiz=T)\n\n#Construct the Rules\nrules = apriori(tdata, parameter = list(supp = 0.1, conf = 0.6, minlen=2))\nitemFrequencyPlot(items(rules))\n\ninspect(rules)\n\n#sort rules by support\nrules_s = sort(rules, by=\"support\", decreasing=TRUE )\ninspect(rules_s)\ninspect(rules_s[1:5]) #itemsset having high support\n\n#sort rules by confidence\nrules_c = sort(rules, by=\"confidence\", decreasing=TRUE )\ninspect(rules_c)\ninspect(rules_c[1:5]) #itemsset having high confidence\n\n#sort rules by lift\ninspect(head(rules, n = 3, by =\"lift\"))\nrules_l = sort(rules, by=\"lift\", decreasing=TRUE )\ninspect(rules_l)\ninspect(rules_l[1:5]) #itemsset having high confidence\n\n#Quality Data of Rules\nquality(rules_c) \n\n#Redundant Rules\ninspect(rules)\n(redundant = which(is.redundant(rules)))\ninspect(rules[c(8,9,10,11,12,14,14)])\ninspect(rules[redundant])\ninspect(rules)\nwrite.csv(as(rules,\"data.frame\"), file='./data/rulesR.csv')\n\n#Remove Redundant Rules\nrulesNR <- rules[-redundant] \nis.redundant(rulesNR)\nsum(is.redundant(rulesNR)) #ok now\ninspect(rulesNR)\n\n#Rules with LHS and RHS: single or combination\nrules2= rulesNR\nrules2.lhs1 <- subset(rules2, lhs %in% c(\"I1\", \"I5\"))\ninspect(rules2.lhs1)\n\nrules2.rhs1 <- subset(rules2, rhs %in% c(\"I3\"))\ninspect(rules2.rhs1)\n\nrules2.lhsrhs1 = subset(rules2, lhs %in% c(\"I1\") & rhs %in% c(\"I3\"))\ninspect(rules2.lhsrhs1)\n\nrules2.lhsrhs2 = subset(rules2, lhs %in% c(\"I1\") | rhs %in% c(\"I3\"))\ninspect(rules2.lhsrhs2)\n\n# Rules as DF: original rules\nrules_DF <- as(rules,\"data.frame\")\nrules_DF\nstr(rules_DF)\nwrite.csv(rules_DF, './data/myrules1.csv')\n\n#Visualisation\nplot(rules)\n\n#This interactve plot will open in another window\n#use menu options to check \nplot(rules[1:5],method=\"graph\",engine='interactive', shading=\"confidence\") \n\n#plot with unified interface, the no of items contained in the rule\nplot(rules, method = \"two-key plot\")\n\n#plot with lift on the y-axis. identify all rules with high lift. \nplot(rules, measure = c(\"support\", \"lift\"), shading = \"confidence\")\n\n#Matrix Plots\nsubrules <- rules[quality(rules)$confidence > 0.8]\ninspect(subrules)\nplot(subrules, method = \"matrix\", measure = \"lift\")\nplot(subrules, method = \"matrix\", engine='3d', measure = \"lift\")\n\n#Grouped\nplot(rules, method = \"grouped\")\n#The group of most interesting rules according to lift (the default measure) are shown in the top-left corner of the plot.\nplot(subrules, method = \"graph\")\n\nplot(subrules, method = \"paracoord\", control = list(reorder = TRUE))\n\ninspect(rules[14])\nplot(rules[14], method = \"doubledecker\", data = tdata)\n\n#https://cran.r-project.org/web/packages/arulesViz/vignettes/arulesViz.pdf\n\n\n#extra\n# freq items\n\nfreqitems = eclat(tdata, parameter = list(supp = 0.001))\nfreqitems\ninspect(freqitems)\n\nfreqitems1 = eclat(tdata, parameter = list(supp = 0.1))\nfreqitems1\ninspect(freqitems1)\n\nfreqitems2 = eclat(tdata, parameter = list(supp = 0.2))\nfreqitems2\ninspect(freqitems2)\n\nfreqitems3 = eclat(tdata, parameter = list(supp = 0.3))\nfreqitems3\ninspect(freqitems3)\n\n## Create rules from the itemsets\nrules3b <- ruleInduction(freqitems3, tdata, confidence = .6)\nrules3b\ninspect(rules3b)\n\n\n#calculate confidence and lift manually\ninspect(head(rules, n = 1, by =\"lift\"))\nitemsets <- unique(generatingItemsets(rules))\nitemsets\ninspect(itemsets)\n(itemsets.df <- as(itemsets, \"data.frame\"))\n\nfrequentItemsets <- itemsets.df[with(itemsets.df, order(-support,items)),]\nnames(frequentItemsets)[1] <- \"itemset\"\nitemsets.df\nitemFrequency(items(rules))\nfreqitems = eclat(tdata)\ninspect(freqitems)\n" }, { "path": "45a-AR/12b-ar-samplecase2.R", "content": "#AR - sample Case\nlibrary(arules)\n\n#Data\n#Method1: format basket\nitemlist1 = paste('I1,I2,I5','I2,I4', 'I2,I3','I1,I2,I4','I1,I3','I2,I3','I1,I3','I1,I2,I3,I5','I1,I2,I3', sep=\"\\n\");\nitemlist1\nwrite(itemlist1, file = \"./data/itemlist1\");\ntdata1 = read.transactions(\"./data/itemlist1\", format = \"basket\", sep=\",\");\nsummary(tdata1)\ninspect(tdata1);\n\n#Modeling\nlibrary(arules)\ntdata1\ninspect(abbreviate(tdata1)) #smaller names of items\n\nfitems = eclat(tdata1, parameter = list(supp = 0.0))\ninspect(fitems)\ninspect(sort(fitems, by='support'))\nrules = apriori(tdata1, list(supp = 0.10, conf=.60, minlen=2) )\n\ninspect(rules )\ninspect(head(rules, n = 5, by =\"lift\"))\ninspect(head(rules, n = 5, by =\"support\"))\n\nrules2 = apriori(tdata1, list(supp = 0.2, conf=.8) )\ninspect(rules2)\nsupport(items(fitems), tdata1)\n\n\n#Part 2\nrules3 <- ruleInduction(fitems, tdata1, confidence = 0)\ninspect(rules3)\n\n#Other Measures\n#https://rdrr.io/cran/arules/man/interestMeasure.html\nmeasures = c('lift', 'confidence', 'support', 'conviction', 'count', 'coverage', 'doc', 'importance')\ninterestMeasure(rules,measure=measures, tdata1)\n\ninspect(fitems[is.maximal(fitems)])\n\nlength(fitems)\nLIST(fitems)\nsize(fitems)\ninspect(fitems)\nsupportingTransactions(fitems, tdata1)\nunique(fitems)\nsupport(fitems, tdata1)\nsubset(rules, lhs %in% c(\"I3\",'I1') & lift > 1)\nsubset(rules, lhs %oin% c(\"I1\",'I2') & lift > 1)\n\n#Cross Table - Good\ncrossTable(tdata1, measure = c(\"count\"), sort = FALSE)\ncrossTable(tdata1, measure = c(\"lift\"), sort = FALSE)\ncrossTable(tdata1, measure = c(\"support\"), sort = FALSE)\nDATAFRAME(fitems)\nDATAFRAME(rules)\nDATAFRAME(rules, separate = TRUE, setStart = '', itemSep = ' + ', setEnd = '')\n\nhits(tdata1, iter = 16L, tol = NULL, type = \"absolute\")\nitemFrequency(tdata1)\nis.closed(fitems)\ninspect(fitems[is.closed(fitems)])\ninspect(fitems[is.significant(rules,tdata1, method='fisher')])\n\ncoverage(rules, transactions = tdata1)\ncbind(quality(rules), coverage = coverage(rules))\n\n#aggregate(rules, by='level2')\n\n#Combine trans, rules\naComb <- c(a1, a2)\nrComb <- c(r1, r2) \nrUnion <- union(r1,r2)\n\n#Add items\ng2 <- addComplement(tdata1, c(\"I1\",\"I2\",\"I3\",\"I4\"))\ninspect(g2)\n\n## use a custom label for the complement-item\ng2=addComplement(tdata1, labels=\"I5\", complementLabels=c('No-I5'))\ninspect(g2)\n\n\n#\n## Example 1: Manual decoding\n## get code\niLabels <- itemLabels(tdata1)\nhead(iLabels)\n\n## get undecoded list and decode in a second step\nlist <- LIST(tdata1, decode = FALSE)\nlist\n\ndecode(list, itemLabels = iLabels)\ninspect(tdata1)\n\n\n\n#Random Items\n## create a random item as a matrix\nrandomItem <- sample(c(TRUE, FALSE), size=length(tdata1),replace=TRUE)\nrandomItem <- as.matrix(randomItem)\ncolnames(randomItem) <- \"random item\"\nhead(randomItem, 3)\n\n## add the random item to Groceries\ng3 <- merge(tdata1, randomItem)\nnitems(tdata1)\nnitems(g3)\ninspect(head(g3))\n\n\n#----\n#https://rdrr.io/cran/arules/f/inst/doc/arules.pdf\nrandom.transactions(c('I1','I2','I3', 'I4','I5'))\nitems5 = random.transactions(15,10, method='agrawal')\ninspect(items5)\ntrans <- random.transactions(nItems = 20, nTrans = 1000, lambda = 5, iProb = seq(0.2,0.0001, length=20))\nsummary(size(trans))\nsummary(trans)\n\n\nfi = eclat(trans)\nLIST(fi)\n\nimage(trans)\nitemMatrix(trans)\nclass(trans)\ninspect(trans[1:5])\n\nitemFrequencyPlot(trans,topN = 5,type=\"absolute\")\nitemFrequencyPlot(trans,topN = 5,type=\"relative\", horiz=T)\n\n#Construct the Rules\nrules8 = apriori(trans, parameter = list(supp = 0.02, conf = 0.7, minlen=2))\nitemFrequencyPlot(items(rules8))\n\ninspect(rules8)\n\n\n\n#\nAR in R : simple steps\n#eclat\nlibrary(arules)\nlibrary(arulesViz)\nlibrary(igraph)\nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_144')\nlibrary(iplots)\ndata <- read.transactions('file', sep=',')\ntdata1\nitemsets <- eclat(tdata, parameter = list(supp = 0.1, minlen=2, maxlen = 3))\ninspect(itemsets)\nplot(itemsets, method=\"graph\", control=list(type=\"items\"))\n" }, { "path": "45a-AR/14a-ar-datastr.R", "content": "# Data Structures for AR in R\n\ndata(\"Groceries\")\nmydata = Groceries\nstr(mydata)\ndim(mydata)\nlength (mydata)\nsize(mydata)\nLIST(mydata[1:5]) \ninspect(mydata[1:5])\nhead(mydata)\nmode(mydata)\nmydata[1:5]\nmydata\n\nsummary(rules)\n\n#Transactions to List\nLIST(mydata[1:5])\nLIST(mydata[1:5])[[1]]\nLIST(mydata[1:5])[[1]][1]\n\n#Transactions to Data Frame\nmydf = as(mydata,\"data.frame\")\nstr(mydf)\nhead(mydf$items)\n\n#Data Frame to Transactions\n\n\n\n\n\n#Measures\nquality(rules)$improvement <- interestMeasure(rules, measure = \"improvement\")\nquality(rules)\n\n\n\n#Making Rules For Continuous Data\ndiscretize (x, method=\"cluster\", categories=3) \n\n\n# Calculates affinity - the 'nxn' Jaccard Index affinity matrix\naffinity(mydata) \n\n# Adds \"Item 1\" to all transactions in transDat\ntransDat_c <- addComplement(mydata, \"meat\") \ntransDat_c\ninspect(transDat_c)\n\n# Duplicate Rules\nduplicated(rules) # find out if any rule is duplicated\n\nsum(duplicated(rules))\n" }, { "path": "45a-AR/15-ar-groceries.R", "content": "# Market Basket / Association Rule Analysis \n# Find Relationship between items\n" }, { "path": "45a-AR/15a-ar-Groceries1.R", "content": "# Association Rule - Set 3 ###\n#http://www.salemmarafi.com/code/market-basket-analysis-with-r/\n#if this, then that”.\n# The Groceries Dataset\n\n# Load the libraries\nlibrary(arules)\nlibrary(arulesViz)\nlibrary(datasets)\ndata(Groceries)\n# Load the data set : Items set structure - List Type; Cannot see properly\nGroceries\n#str(Groceries)\nhead(Groceries)\ninspect(Groceries[1:5])\n\n# Create an item frequency plot for the top 20 items\nitemFrequencyPlot(Groceries,topN=20,type=\"absolute\")\n#set the min support to 0.001 , minimum conf of 0.8 ; show top 5 rules\n# Get the rules\nrules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8))\n\n# Show the top 5 rules, but only 2 digits\noptions(digits=5)\ninspect(rules[1:5])\n#[3] if someone buys yogurt & cereals, they are 81% likely to buy whole milk too.\nsummary(rules)\n# 410 rules, Most rules are 4 items long\n# Sort Rules The first issue we see here is that the rules are not sorted. Often we will want the most relevant rules first. Lets say we wanted to have the most likely rules. We can easily sort by confidence by executing the following code.\n\nrules<-sort(rules, by=\"lift\", decreasing=TRUE)\ninspect(head(rules))\n#Rule 4/ other is perhaps excessively long. Lets say you wanted more concise rules. That is also easy to do by adding a “maxlen” parameter to your apriori function:\nrules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8,maxlen=3))\ninspect(head(rules))\n\n# Redundancies ####\nsubset.matrix <- is.subset(rules, rules)\nsubset.matrix[lower.tri(subset.matrix, diag=T)] <- NA\nredundant <- colSums(subset.matrix, na.rm=T) >= 1\nrules.pruned <- rules[!redundant]\nrules<-rules.pruned\ninspect(rules)\n# Target Items\n#Now that we know how to generate rules, limit the output, lets say we wanted to target items to generate rules. There are two types of targets we might be interested in that are illustrated with an example of “whole milk”:\n# What are customers likely to buy before buying whole milk\n#What are customers likely to buy if they purchase whole milk?\n#This essentially means we want to set either the Left Hand Side and Right Hand Side. This is not difficult to do with R!\n# 1st part\nrules<-apriori(data=Groceries, parameter=list(supp=0.001,conf = 0.08), \n appearance = list(default=\"lhs\",rhs=\"whole milk\"),\n control = list(verbose=F))\nrules<-sort(rules, decreasing=TRUE,by=\"confidence\")\ninspect(rules[1:5])\n#Likewise, we can set the left hand side to be “whole milk” and find its antecedents.\n#Note the following:\n# We set the confidence to 0.15 since we get no rules with 0.8\n#We set a minimum length of 2 to avoid empty left hand side items\nrules<-apriori(data=Groceries, parameter=list(supp=0.001,conf = 0.15,minlen=2), appearance = list(default=\"rhs\",lhs=\"whole milk\"),\n control = list(verbose=F))\nrules<-sort(rules, decreasing=TRUE,by=\"confidence\")\ninspect(rules)\n\n# Visualisation ####\n#The last step is visualization. Lets say you wanted to map out the rules in a graph. We can do that with another library called “arulesViz”.\nlibrary(arulesViz)\n# Interactive Graph - Seperate Window\ninspect(rules[1:5])\nplot(rules[1:5],method=\"graph\",engine='interactive',shading=NA)\n\n\n\n# Groceries Example 2 ####\n#http://rstatistics.net/association-mining-with-r/\nlibrary (arules)\nlibrary (arulesViz)\nlibrary (datasets)\ndata (Groceries) # Load the data set\nstr(Groceries)\n# Change to any data Frame\nmyDataFrame= Groceries\ntransDat <- as (myDataFrame, \"transactions\") # convert to 'transactions' class\ninspect (transDat) # view the observations\nlength (transDat) # get number of observations\nsize (transDat) # number of items in each observation\nLIST(transDat) # convert 'transactions' to a list, note the LIST in CAPS\n\n#Lets Apply Apriori Algorithm\n#For illustrative purpose, Lets continue to work with ‘Groceries’ dataset from ‘arules’ package.\nfrequentItems <- eclat (Groceries, parameter = list(supp = 0.07, maxlen = 15)) # calculates support for frequent items\nitemFrequencyPlot (Groceries,topN=10,type=\"absolute\") # plot frequent items\n\n#A low support and high confidence helps to extract strong relationship even for less overall co-occurrences in data.\nrules <- apriori (Groceries, parameter = list(supp = 0.001, conf = 0.5)) # Min Support as 0.001, confidence as 0.8.\n\n#quality(rules) # show the support, lift and confidence for all rules\nhead(quality(rules))\n# Show the top 5 rules, but only 2 digits\noptions (digits=2)\ninspect (rules[1:5])\n\nrules <- sort (rules, by=\"confidence\", decreasing=TRUE) # 'high-confidence' rules.\n#How To Control The Number Of Rules in Output ?\n#Adjust the maxlen and conf arguments in the apriori statement to control the number of rules generated. Use your best judgement here.\nrules <- apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, maxlen=3)) # maxlen = 3 limits the elements in a rule to 3\n\n#To get ‘strong‘ rules, increase the value of ‘conf’ parameter. To get ‘longer‘ rules, increase ‘maxlen’\n#How To Remove Redundant Rules ?\n#Use the below code to find out and filter the redundant rules.\nredundant <- which (colSums (is.subset (rules, rules)) > 1) # get redundant rules in vector\nrules <- rules[-redundant] # remove redundant rules\n\n#How to Find Rules Related To Given Item/s ? This method is the core of ‘Market basket analysis’ that is useful to make recommendations of new items to your users. \n#This can be achieved by modifying the ‘appearance’ parameter in the apriori() function. For example,\n#Find what factors influenced an event ‘X’\n#To find out what customers had purchased before buying ‘Whole Milk’. This will help you understand the patterns that led to the purchase of ‘whole milk’.\nrules <- apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.08), appearance = list (default=\"lhs\",rhs=\"whole milk\"), control = list (verbose=F)) # get rules that lead to buying 'whole milk'\n\n#Find out what events were influenced by a given event\n\n#In this case: the Customers who bought ‘Whole Milk’ also bought. In the equation, ‘whole milk’ is in LHS (left hand side). \nrules <- apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.15,minlen=2), appearance = list (default=\"rhs\",lhs=\"whole milk\"), control = list (verbose=F)) # those who bought 'milk' also bought..\n\n#Remove redundancies\n#Sort the rules, filter the redundant ones and show the Top 7 Rules.\n\nrules <- sort (rules, decreasing=TRUE,by=\"confidence\")\n\nredundant <- which (colSums(is.subset(rules, rules)) > 1) # get redundant rules in vector\n\nrules <- rules[-redundant] # remove redundant rules inspect (rules[1:7])\nrules\n#Making Rules For Continuous Data\n#If you try to make rules on continuous variables, each value will be treated as distinct item, causing undesirable explosion of rules. So, convert the continuous variables to factors, which can be easily done using discretize() function.\n#discretize (x, method=\"cluster\", categories=3) # method can make cuts in equal \"intervals\", \"frequency\", \"cluster\", \"fixed\"\n\n#Visualizing The Rules\n\n# Interactive Plot\n\nplot (rules[1:25],method=\"graph\",interactive=TRUE,shading=\"confidence\") # feel free to expand and move around the objects in this plot\n\nplot (rules, measure=c(\"support\", \"lift\"), shading=\"confidence\")\n\n\naffinity(transDat) # Calculates affinity - the 'nxn' Jaccard Index affinity matrix\ntransDat_c <- addComplement(transDat, \"Item 1\") # Adds \"Item 1\" to all transactions in transDat\nduplicated(rules) # find out if any rule is duplicated\n" }, { "path": "45a-AR/15b-ar-Groceries.R", "content": "# Association Rules - Groceries data set ####\n\nlibrary(arules) #install first\nlibrary(arulesViz) #install first\nlibrary(datasets) # no need to install, just load it reqd for Groceries\ndata('Groceries')\nGroceries\n\n#Structure of Groceries\nstr(Groceries)\nGroceries\ninspect(Groceries[1:5]) #view\nLIST(Groceries[1:6]) #another view\n\n#Find Frequent Itemset\nfrequentItems = eclat (Groceries, parameter = list(supp = 0.01, minlen= 2, maxlen = 5)) \ninspect(frequentItems)\nfrequentItems\ninspect(frequentItems[10:15])\n#inspect(frequentItems[100:122])\n#Descending Sort frequent items by count : 1 to 25 itemsets\ninspect(sort (frequentItems, by=\"count\", decreasing=TRUE)[1:25])\ninspect(sort (frequentItems, by=\"count\", decreasing=F)[1:25])\n\n#Support is : support(A&B) = n(A&B)/ N\n#Plot the Frequency Plot\nitemFrequencyPlot(Groceries,topN = 15,type=\"absolute\")\nitemFrequencyPlot(Groceries, topN = 10, type='relative')\nabline(h=0.2)\n\n# Create rules and the relationship between items\n#parameters are min filter conditions \nrules = apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5))\nrules\noptions (digits=2)\ninspect (rules[1:5])\n\n#Sort Rules by confidence, lift and see the data\nrulesc <- sort (rules, by=\"confidence\", decreasing=TRUE)\ninspect(rulesc[1:5])\nrulesl <- sort (rules, by=\"lift\", decreasing=TRUE)\ninspect (rulesl[1:5])\n#which items have strong confidence and lift \n\n#How To Control The Number Of Rules in Output ?\n#maxlen, minlen, supp, conf\nrules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=3)) \ninspect(rules2[1:5])\n\n# Are there any duplicate/ Redundant Rules \n#https://rdrr.io/cran/arules/man/is.redundant.html\n\nsum(is.redundant(rules2))\n(redundant = which(is.redundant(rules2)))\ninspect(rules2[redundant])\n#inspect(subset(rules2, subset=lhs %ain% c('citrus fruit','rice') & rhs %in% 'whole milk' ))\n#remove it\nrulesNR = rules2[-redundant] \nis.redundant(rulesNR)\nsum(is.redundant(rulesNR)) #ok now\n\n\n\n#Find what factors influenced an event ‘X’\nrules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.8), appearance = list (default=\"lhs\",rhs=\"whole milk\"), control = list (verbose=F))\ninspect(rules3[1:5])\ninspect(rules3)\n\n#Find out what events were influenced by a given event\nsubset1 = subset(rules2, appearance = list (default=\"lhs\",rhs=\"whole milk\"))\nsubset1 = subset(rules2, subset=rhs %in% 'bottled beer' )\ninspect(subset1)\ninspect(rules2)\nsubset2 = subset(rules2, subset=lhs %ain% c('baking powder','soda') )\ninspect(subset2)\nsubset2a = subset(rules2, subset=lhs %in% c('baking powder','soda') )\ninspect(subset2a)\n\n\n\n\nsubset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)\ninspect(subset3)\nsubset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )\ninspect(subset4)\n\n#Visualizing The Rules -----\nplot(subset1[1:10]) \nplot(subset1[1:10], measure=c(\"support\", \"lift\"), shading=\"confidence\")\n\n#\n\n\nrules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default=\"rhs\",lhs=c('tropical fruit','herbs')), control = list (verbose=F))\ninspect(rules4[1:5])\ninspect(rules4)\n" }, { "path": "45a-AR/16b-groceries-summary.R", "content": "#Association Rule - Analysis\n\nlibrary(arules)\nlibrary(datasets)\ndata(Groceries)\n\nsummary(Groceries)\n#there were 9,835 transactions altogether\n#169 different items were bought during the month\n#the most frequently bought item was “whole milk”: 2’513 purchases\n#there were 2159 single item baskets, the biggest basket included 32 items\n#median basket included 3 items; mean had 4.4 items.\n\ninspect(Groceries[1:5])\nitemLabels(Groceries)[1:10] # [1:10] can be dropped to show all items\n\npar(mfrow=c(1,2))\n\n#Plot - Most Frequent\nitemFrequencyPlot(Groceries,\n type=\"relative\",\n topN=10, # can be changed to the number of interest\n horiz=TRUE,\n col='steelblue3',\n xlab='',\n main='Item frequency, relative')\n\nitemFrequencyPlot(Groceries,\n type=\"absolute\",\n topN=10,\n horiz=TRUE,\n col='steelblue3',\n xlab='',\n main='Item frequency, absolute')\n\n\n#Plot - Least Frequent\npar(mar=c(2,10,2,2), mfrow=c(1,2))\ntable(unlist(LIST(Groceries)))\nsort(table(unlist(LIST(Groceries))))\n\nbarplot(sort(table(unlist(LIST(Groceries))))[1:10]/9835,\n horiz=TRUE,\n las=1,\n col='steelblue3',\n xlab='',\n main='Frequency, relative')\n\nbarplot(sort(table(unlist(LIST(Groceries))))[1:10],\n horiz=TRUE,\n las=1,\n col='steelblue3',\n xlab='',\n main='Frequency, absolute')\ncrossTable(Groceries, measure='lift',sort=T)[1:5,1:5]\n\n\n#Contingency Table\ntbl <- crossTable(Groceries)\n#tbl #too big list\ntbl[1:5,1:5]\n\ntbl['whole milk','whole milk'] #count of milk\ntbl['whole milk','flour'] #milk with flour\n#number of occasions when these two items were purchased together.\n\ntbl <- crossTable(Groceries, sort=TRUE)\ntbl[1:5,1:5]\n\n\n#Xtable with lift\ncrossTable(Groceries, measure='lift',sort=T)[1:5,1:5]\n#whole milk” goes well with all products, but “soda”.\n#judged by lift, we are on the way to claim that soda is a substitute for “whole milk” for some people: they tend to buy either one or the other, but buying them together is a relatively rare event\ncrossTable(Groceries, measure='chi')['whole milk', 'soda']\n#low p-value excludes possibility that lift less than 1 is due to chance." }, { "path": "45a-AR/16d-ar-groceries-subset.R", "content": "#AR - Groceries - Subset\n\n#Subsetting rules and itemsets\nrules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,\n maxlen=5, target='rules' ))\n#target='rules' # to mine for rules)\n\ninspect(sort(rules, by=\"confidence\", decreasing = T)[1:5])\n\n#subset conditions\n#rhs should be ‘bottled beer’\n#confidence should be above .7\n#results should be sorted by lift\n\n#Subset1----\ninspect(sort(subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7),\n by = 'lift', decreasing = T))\n#people buying “liquor” and “red wine” are almost certain to buy “bottled beer” (9 times out of 10), but not “canned beer”\n\n\n# Another rule with different parameters\n#subset2----\ncanned_rules <- apriori(Groceries,parameter = list(support=.001,\n confidence=.01, maxlen=5, target='rules' ))\ninspect(subset(canned_rules, subset=lhs %ain% c(\"liquor\", \"red/blush wine\") & rhs %in% 'canned beer' ))\n#no rule found or it could be significant low % less than 1 in 100 people would do\n\n# \n# lhs - means left hand side, or antecendent\n# rhs - mean right hand side, or consequent\n# items - items, that make up itemsets\n# %in% - matches any\n# %ain% - matches all\n# %pin% - matches partially\n# default - no restrictions applied\n# & - additional restrictions on lift, confidence etc.\n\n#either side lhs or rhs\n#“whole milk” and “yogurt” must be present and rule’s confidence must be higher than .95\n#subset3----\ninspect(subset(rules, subset=items %ain% c(\"whole milk\",\"yogurt\") & confidence >.95))\n\n#whole milk” and “yogurt” must be present in lhs and rule’s confidence must be higher than .9\ninspect(subset(rules, subset=lhs %ain% c(\"whole milk\",\"yogurt\") & confidence >.9))\n\n#Subset4----\n#“Bread” must be present in lhs: any type of “bread” – “white bread”, “brown bread” – both qualify. “Whole milk” must be present in rhs “as is”. confidence of the rule must be higher than .9\n\ninspect(subset(rules, subset= lhs %pin% \"bread\" & rhs %in% \"whole milk\" & confidence > .9))\n\n\n#Subset5----\n#what we can expect at rhs with confidence higher than .7 if we have both “flour” and “whole milk” on the lhs\ninspect(subset(rules, subset= lhs %ain% c(\"flour\",\"whole milk\") & confidence>.7))\n\n\n\n#Let’s consider case “Bottled beer Vs. Canned beer” and prove that people tend to buy either one or the other, and rarely do they buy both, qualifying these two as substitute products.\nrules <- apriori(Groceries, parameter = list(support=.001,\n conf = .01, minlen=2, maxlen=2, target='rules'))\n\n#Let’s only look at the rules where “beer” is present at both left- and right-hand-side of the rule and add chiSquared p-value to prove statistical significance of our findings:\n\ninspect(subset(rules, lhs %pin% 'beer' & rhs %pin% 'beer'))\ncrossTable(Groceries)['canned beer','bottled beer']\n\n#the probability of a consecutive purchase (confidence) is pretty small: ~3%\n#this is despite both bottled beer and canned beer being pretty popular purchases\n\ncrossTable(Groceries)['canned beer','canned beer']\ncrossTable(Groceries)['bottled beer','bottled beer']\n\nquality(rules)$chi <- interestMeasure(rules, measure='chi', significance=T, Groceries)\nquality(rules)$chi[4785:4786]\n#All these figures, combined with statistically significant lift below 1 (chi ~ 1e-6) tells us that “bottled beer” and “canned beer” do behave as substitutes." }, { "path": "45a-AR/16f-ar-groceries-vis.R", "content": "#Association Rule - Visualisation\n\nlibrary(arules)\nlibrary(datasets)\ndata(Groceries)\n\n#\nrules <- apriori(Groceries, parameter = list(support=.001,\n conf = .01, minlen=2, maxlen=2, target='rules'))\n\n#Visually mining rules. Both rules and itemsets can be visualized with the help of arulesViz library. The power of the plot() function from arulesViz library comes from interactive argument. Remember, as a general rule we want rules with both high support and high confidence.\n\nplot(rules, interactive = T)\n\n#interactive-rules-choice With the help of this function we can visually mine rules by:\n#1. Selecting rectangular area by clicking twice on the plot.\n#2. Then clicking inspect\n\n#This would produce rules found in that region, e.g.:\n\n\n\ninspectDT(rules)\n " }, { "path": "45a-AR/16f-ar-groceries-vis2.R", "content": "# AR - Visualisation\n\nlibrary(arules)\nlibrary(datasets)\ndata(Groceries)\nlibrary(arulesViz)\nrules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,\n maxlen=5, target='rules' ))\n\n\nplot(rules, measure = c(\"support\", \"lift\"), shading = \"confidence\")\nplot(rules, measure = c(\"confidence\", \"lift\"), shading = \"support\")\n\n\n#Twokey plot. Here support and confidence are used for the x and y-axes and the color of the points is used to indicate “order,” i.e., the number of items contained in the rule. Two-key plots can be produced using the unified interface by:\nplot(rules, method = \"two-key plot\")\n\n#order and support have a very strong inverse relationship, which is a known fact for association rules\n\n\n\n#Interactive\nsel <- plot(rules, measure=c(\"support\", \"lift\"), shading = \"confidence\", interactive = TRUE)\n#Inspecting individual rules by selecting them and clicking the inspect button.\n#Inspecting sets of rules by selecting a rectangular region of the plot and clicking the inspect button.\n#Zooming into a selected region (zoom in/zoom out buttons).\n#Filtering rules using the measure used for shading by clicking the filter button and selecting a cut-off point in the color key. All rules with a measure lower than the cut-off point will be filtered.\n#Returning the last selection for further analysis (end button).\n\n\n" }, { "path": "45a-AR/16f-ar-groceries-vis3.R", "content": "# AR - Visualisation - Matrix\n\nlibrary(arules)\nlibrary(datasets)\ndata(Groceries)\nlibrary(arulesViz)\nrules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,\n maxlen=5, target='rules' ))\n\n\n#Matrix-based visualization techniques organize the antecedent and consequent itemsets on the x and y-axes, respectively. A selected interest measure is displayed at the intersection of the antecedent and consequent of a given rule. If no rule is available for a antecedent/consequent combination the intersection area is left blank.\n\nsubrules <- rules[quality(rules)$confidence > 0.8]\nsubrules\ninspect(subrules)\nplot(subrules, method = \"matrix\", measure = \"lift\")\n\n# not much space for long labels in the plot, we only show numbers as labels for rows and columns and the complete itemsets are printed to the terminal for look-up\n\nplot(subrules, method = \"matrix3D\", measure = \"lift\")\n\n\n\n#Grouped matrix-based visualization\n#Matrix-based visualization is limited in the number of rules it can visualize effectively since large sets of rules typically also have large sets of unique antecedents/consequents.\nsummary(rules)\nplot(rules, method = \"grouped\")\n\nplot(rules, method = \"grouped\", control = list(k = 50))\n\nsel <- plot(rules, method = \"grouped\", interactive = TRUE)\n" }, { "path": "45a-AR/16f-ar-groceries-vis4.R", "content": "#AR - Graph Based Visualisation\n\nlibrary(arules)\nlibrary(datasets)\nlibrary(arulesViz)\n\ndata(Groceries)\n\nrules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,\n maxlen=5, target='rules' ))\n\n#visualize association rules using vertices and edges where vertices annodated with item labels represent items, and itemsets or rules are reptesented as a second set of vertices. Items are connected with itemsets/rules using arrows. For rules arrows pointing from items to rule vertices indicate LHS items and an arrow from a rule to an item indicates the RHS. Interest measures are typically added to the plot by using color or size of the vertices representing the itemsets/rules.\n\n#Graph-based visualization offers a very clear representation of rules but they tend to easily become cluttered and thus are only viable for very small sets of rule\n\nsubrules2 <- head(rules, n = 10, by = \"lift\")\n\nplot(subrules2, method = \"graph\")\n\n\n#only useful for small set of rules. To explore large sets of rules with graphs, advanced interactive features like zooming, filtering, grouping and coloring nodes are needed\n\nsaveAsGraph(head(rules, n = 1000, by = \"lift\"), file = \"rules.graphml\")\n" }, { "path": "45a-AR/16f-ar-groceries-vis5.R", "content": "# Parallel coordinates plot\n\nlibrary(arules)\nlibrary(datasets)\nlibrary(arulesViz)\n\ndata(Groceries)\n\nrules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,\n maxlen=5, target='rules' ))\nsubrules2 <- head(rules, n = 10, by = \"lift\")\nplot(subrules2, method = \"paracoord\")\n\n#The width of the arrows represents support and the intensity of the color represent confidence. It is obvious that for larger rule sets visual analysis becomes difficult since with an increasing number of rules also the number\n#of crossovers between the lines increases\n\n#reduce crossover by reorder\nplot(subrules2, method = \"paracoord\", control = list(reorder = TRUE))\n" }, { "path": "45a-AR/16k-ar-grocery-DT.R", "content": "#Association Rule - Interactive Data Table\n\nlibrary(arules)\nlibrary(datasets)\ndata(Groceries)\n\nrules <- apriori(Groceries, parameter = list(support=.002, confidence=.7,\n maxlen=5, target='rules' ))\ninspectDT(rules)\n#see on right side\n\nplotly_arules(rules)# depreciated\n#may not work" }, { "path": "45a-AR/17a-ar-transactionformat.R", "content": "# Data Formats : Conversion to Transactions\n\nlibrary(arules)\n#Matrix----\nlibrary(arules)\nm = matrix(c(1,1,1,0,0,1,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1,1,0,1,1,1,0,1,0,1), ncol = 5)\nm\ndimnames(m) <- list(paste(\"Tr\",c(1:6), sep = \"\"), c(\"a\",\"b\",\"c\",\"d\",\"e\" ))\nm\ntrans1 <- as(m, \"transactions\")\ntrans1\ninspect(trans1)\nrules1= apriori(trans1)\ninspect(rules1[1:5])\n\n#Data Frame----\ndf <- data.frame(\n age = as.factor(c(6,8,7,6,9,5)), \n grade = as.factor(c(1,3,1,1,4,1))) \n\n#note: all attributes have to be factors ####\ndf\ntrans2 <- as(df, \"transactions\") \ninspect(trans2)\nimage(trans2)\n\n\n\n\n#List-----\n\n\n\n\n#csv----\n" }, { "path": "45a-AR/17d-ar-matrix-transactions.R", "content": "# Creating Transaction Data ####\n#http://stackoverflow.com/questions/20758814/convert-data-frame-in-r-to-transactions-or-an-itemmatrix\n#example 1: creating transactions from a matrix ####\nlibrary(arules)\na_matrix <- matrix(c(1,1,1,0,0,1,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1,1,0,1,1), ncol = 5)\n#set dim names ####\na_matrix\n\ndimnames(a_matrix) <- list(\n c(\"a\",\"b\",\"c\",\"d\",\"e\"),\n paste(\"Tr\",c(1:5), sep = \"\"))\n\na_matrix\n#coerce ####\n\ntrans2 <- as(a_matrix, \"transactions\")\ntrans2\ninspect(trans2)\n#example 2: creating transactions from data.frame ####\n\na_df <- data.frame(\n age = as.factor(c(6,8,7,6,9,5)), \n grade = as.factor(c(1,3,1,1,4,1))) \n#note: all attributes have to be factors ####\n\na_df\n#coerce ####\n\ntrans3 <- as(a_df, \"transactions\") \ninspect(trans3)\nimage(trans3)\ndata2 = read.csv(\"dar2.csv\")\n\ndata2\ndata2t = as(data2,'transactions')\ninspect(data2t)\n\ndata3 = read.csv(\"dar3.csv\")\n#data3t = as(data3,'transactions')\ndata3t = read.transactions(\"dar3a.csv\", format = \"single\", sep = \",\")\nstr(data3t)\nhead(data3t)\ninspect(data3t)\nlibrary(datasets)\nitemFrequencyPlot(data3t,topN=10,type='absolute')\nrules3 = apriori(data3t, parameter=list(minlen=2))\nrules3.sorted = sort(rules3, by='lift')\ninspect(rules3.sorted)\n\n" }, { "path": "45a-AR/17e-ar-df-transcations.R", "content": "# DF to Transactions\n\n#example 2: creating transactions from data.frame ####\n\na_df <- data.frame(\n age = as.factor(c(6,8,7,6,9,5)), \n grade = as.factor(c(1,3,1,1,4,1))) \n#note: all attributes have to be factors ####\n\na_df\n#coerce ####\n\ntrans3 <- as(a_df, \"transactions\") \ninspect(trans3)\nimage(trans3)\n\n\n\n" }, { "path": "45a-AR/17f-ar-csv-transactions.R", "content": "# CSV to Transaction Format\n\ndata2 = read.csv(\"./data/dar2.csv\")\ndata2\ndata2t = as(data2,'transactions')\ninspect(data2t)\n\ndata3 = read.csv(\"dar3.csv\")\n#data3t = as(data3,'transactions')\ndata3t = read.transactions(\"dar3a.csv\", format = \"single\", sep = \",\")\nstr(data3t)\nhead(data3t)\ninspect(data3t)\nlibrary(datasets)\nitemFrequencyPlot(data3t,topN=10,type='absolute')\nrules3 = apriori(data3t, parameter=list(minlen=2))\nrules3.sorted = sort(rules3, by='lift')\ninspect(rules3.sorted)" }, { "path": "45a-AR/17f-ar-csv2-transactions.R", "content": "# AR Data Formats and Conversion\n\n# Data Format ----\n\nlibrary(\"arules\")\n\nset.seed(101)\ntransactionID = sample(1:500, 1000, replace=T)\ntransactionID\nitem = paste(\"item\", sample(1:50, 1000, replace=T),sep = \"\")\nitem\norders <- data.frame(transactionID, item)\nhead(orders)\norders [ order(orders$transactionID),]\nwrite.csv(orders, \"./data/talltransactions.csv\")\n\n\n\nordertrans <- arules::read.transactions(\n file = \"./data/talltransactions.csv\",\n format = \"single\",\n sep = \",\",\n cols=c(\"transactionID\",\"item\"),\n rm.duplicates = T\n)\nordertrans\ninspect(ordertrans[1:5])\n?read.transactions\nsummary(ordertrans)\nstr(ordertrans)\nhead(ordertrans)\n\n# Create an item frequency plot for the top 20 items\nitemFrequencyPlot(ordertrans,topN=20,type=\"absolute\")\n\n#minsupport to 0.001 , min conf of 0.8 ; show top 5 rules\nrules <- apriori(ordertrans, parameter = list(supp = 0.003, conf = 0.8))\n\n# Show the top 5 rules, but only 2 digits\noptions(digits=2)\ninspect(rules[1:5])\n\nsummary(rules)\n\nrules<-sort(rules, by=\"confidence\", decreasing=TRUE)\ninspect(head(rules))\n\n\nrules2 <- apriori(ordertrans, parameter = list(supp = 0.001, conf = 0.8,maxlen=3))\ninspect(head(rules2))\n\nlibrary(arulesViz)\n# Interactive Graph - Seperate Window\nplot(rules2,method=\"graph\",interactive=T,shading=NA)\n\n\n\n\n\n\n\n\n\n\n# DataFrame directly to transactions\n# Converting to a Matrix ####\nsummary(orders)\norders$const = TRUE\n\n# Remove duplicates\ndim(orders) #1,000 x 3\norders <- unique(orders)\ndim(orders) #979 x 3\n\n?reshape\n# Need to reshape the matrix\nordersmatprep <- reshape(data = orders,\n idvar = \"transactionID\",\n timevar = \"item\",\n direction = \"wide\")\nhead(ordersmatprep[,1:3])\n\n# Drop the transaction ID\nordermatrix <- as.matrix(ordersmatprep[,-1])\nhead(ordermatrix[,1:3])\n\n\n# Clean up the missing values to be FALSE\nordermatrix[is.na(ordermatrix)] <- FALSE\nhead(ordermatrix[,1:3])\ndim(ordermatrix)\n\n# Clean up names\ncolnames(ordermatrix)\ncolnames(ordermatrix) <- gsub(x=colnames(ordermatrix),\n pattern=\"const\\\\.\", replacement=\"\")\ncolnames(ordermatrix)\n\nordertrans2 <- as(ordermatrix,\"transactions\")\nsummary(ordertrans2)\n" }, { "path": "45a-AR/17g-ar-list-transcations.R", "content": "#List to Transactions\n\na_list <- list(\n c(\"a\",\"b\",\"c\"),\n c(\"a\",\"b\"),\n c(\"a\",\"b\",\"d\"),\n c(\"c\",\"e\"),\n c(\"a\",\"b\",\"d\",\"e\")\n)\n\n## set transaction names\nnames(a_list) <- paste(\"Tr\",c(1:5), sep = \"\")\na_list\n\n## coerce into transactions\ntrans1 <- as(a_list, \"transactions\")\ninspect(trans1)\n\n## analyze transactions\nsummary(trans1)\nimage(trans1)\ninspect(trans1)\n\n" }, { "path": "45a-AR/17h-ar-dataformats.R", "content": "# Basket Analysis - Converting into Transactions format\n# \n#\nlibrary(arules)\nlibrary(arulesViz)\n\n## convert to transaction\ntrans <- as(df, \"transactions\")\ntrans\nsummary(trans)\ninspect(trans)\n\n\n#DF---\n#creating transactions from data.frame\na_df <- data.frame(\n age = as.factor(c(6, 8, NA, 9, 16)), \n grade = as.factor(c(\"A\", \"C\", \"F\", NA, \"C\")),\n pass = c(TRUE, TRUE, FALSE, TRUE, TRUE)) \n## note: factors are translated differently to logicals and NAs are ignored\na_df\n\n## coerce ## convert to transaction\ntrans3 <- as(a_df, \"transactions\") \ninspect(trans3)\nas(trans3, \"data.frame\")\nimage(trans3)\n\n# M3 : format basket\nmy_data = paste(\"1,2\",\"1\",\"2,3\", sep=\"\\n\");\nmy_data\nwrite(my_data, file = \"./data/my_basket\");\ntrans = read.transactions(\"./data/my_basket\", format = \"basket\", sep=\",\");\ninspect(trans);\n\n#-----------------\n## example 4: creating transactions from a data.frame with \n## transaction IDs and items (by converting it into a list of transactions first) \na_df3 <- data.frame(\n TID = c(1,1,2,2,2,3), \n item=c(\"a\",\"b\",\"a\",\"b\",\"c\", \"b\")\n)\na_df3\ntrans4 <- as(split(a_df3[,\"item\"], a_df3[,\"TID\"]), \"transactions\")\ntrans4\ninspect(trans4)\nimage(trans4)\n## Note: This is very slow for large datasets. It is much faster to read transactions in this format from disk using read.transactions() with format = \"single\".\n\n\n\n\n\n#--------------------\n\n# creating transactions from a matrix\na_matrix <- matrix(c( 1,1,1,0,0,1,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1,1,0,1,1),ncol=5)\na_matrix\n## set dim names\ndimnames(a_matrix) <- list(c(\"a\",\"b\",\"c\",\"d\",\"e\"), paste(\"Tr\",c(1:5), sep = \"\"))\na_matrix\n\n## coerce\ntrans2 <- as(a_matrix, \"transactions\")\ntrans2\ninspect(trans2)\n\n" }, { "path": "45a-AR/18a-arules1.R", "content": "# Documentation on arules\n\nlibrary(arules)\n\n\n\n\n\n#----\n\n\n\n#----\n\n\n#significant\n#LHS andthe RHS depend on each other. \ndata(\"Income\")\nrules <- apriori(Income, parameter = list(support = 0.5))\nis.significant(rules, Income)\ninspect(rules[is.significant(rules, Income)])\n\n#super-subset-----\n#memory intensive- looks for each element in x which elements in y are supersets or subsets.\ndata(\"Adult\")\nset <- eclat(Adult, parameter = list(supp = 0.8))\ninspect(set)\n### find the supersets of each itemset in set\nis.superset(set, set)\nis.superset(set, set, sparse = FALSE)\nis.subset(set,set)\n\n\n#itemCoding----\ndata(\"Adult\")\n## Example 1: Manual decoding\n## get code\niLabels <- itemLabels(Adult)\nhead(iLabels)\n## get undecoded list and decode in a second step\nlist <- LIST(Adult[1:5], decode = FALSE)\nlist\n\ndecode(list, itemLabels = iLabels)\n## Example 2: Manually create an itemMatrix\ndata <- list(\n c(\"income=small\", \"age=Young\"),\n c(\"income=large\", \"age=Middle-aged\")\n)\niM <- encode(data, iLabels)\niM\ninspect(iM)\n## use the itemMatrix to create transactions\nas(iM, \"transactions\")\n## Example 3: use recode\n## select first 100 transactions and all education-related items\nsub <- Adult[1:100, itemInfo(Adult)$variables == \"education\"]\nitemLabels(sub)\nimage(sub)\n## recode to match Adult again\nsub.recoded <- recode(sub, match = Adult)\nimage(sub.recoded)\n\n#itemFrequency\ndata(\"Adult\")\nitemFrequency(Adult, type = \"relative\")\n\n\n#itemFrequencyPlot\ndata(Adult)\n## the following example compares the item frequencies\n## of people with a large income (boxes) with the average in the data set\nAdult.largeIncome <- Adult[Adult %in% \"income=large\"]\n## simple plot\nitemFrequencyPlot(Adult.largeIncome)\n## plot with the averages of the population plotted as a line\n## (for first 72 variables/items)\nitemFrequencyPlot(Adult.largeIncome[, 1:72],population = Adult[, 1:72])\n## plot lift ratio (frequency in x / frequency in population)\n## for items with a support of 20% in the population\nitemFrequencyPlot(Adult.largeIncome, population = Adult, support = 0.2,\n lift = TRUE, horiz = TRUE)\n\n\n\n\n\n#itemsetsclass-----\ndata(\"Adult\")\n## Mine frequent itemsets with Eclat.\nfsets <- eclat(Adult, parameter = list(supp = 0.5))\n## Display the 5 itemsets with the highest support.\nfsets.top5 <- sort(fsets)[1:5]\ninspect(fsets.top5)\n## Get the itemsets as a list\nas(items(fsets.top5), \"list\")\n## Get the itemsets as a binary matrix\nas(items(fsets.top5), \"matrix\")\n## Get the itemsets as a sparse matrix, a ngCMatrix from package Matrix.\n## Warning: for efficiency reasons, the ngCMatrix you get is transposed\nas(items(fsets.top5), \"ngCMatrix\")\n\n\n#LIST----\ndata(Adult)\n### default coercions (same as as(Adult[1:5], \"list\"))\nLIST(Adult[1:5])\n### coercion without item decoding: uses column n\nLIST(Adult[1:5], decode = FALSE)\n\n\n#match----\ndata(\"Adult\")\n## get unique transactions, count frequency of unique transactions\n## and plot frequency of unique transactions\nvals <- unique(Adult)\nvals\ncnts <- tabulate(match(Adult, vals))\ncnts\nplot(sort(cnts, decreasing=TRUE))\n## find all transactions which are equal to transaction 10 in Adult\nwhich(Adult %in% Adult[10])\n## for transactions we can also match directly with itemLabels.\n## Find in the first 10 transactions the ones which\n## contain age=Middle-aged (see help page for class itemMatrix)\nAdult[1:10] %in% \"age=Middle-aged\"\ninspect(Adult[1:3])\n## find all transactions which contain items that partially match \"age=\" (all here).\nAdult[1:10] %pin% \"age=\"\n## find all transactions that only include the item \"age=Middle-aged\" (none here).\nAdult[1:10] %oin% \"age=Middle-aged\"\n## find al transaction which contain both items \"age=Middle-aged\" and \"sex=Male\"\nAdult[1:10] %ain% c(\"age=Middle-aged\", \"sex=Male\")\n\n\n\n#merge----\ndata(\"Groceries\")\n## create a random item as a matrix\nrandomItem <- sample(c(TRUE, FALSE), size=length(Groceries),replace=TRUE)\nrandomItem\nrandomItem <- as.matrix(randomItem)\nrandomItem\ncolnames(randomItem) <- \"random item\"\nhead(randomItem, 3)\n## add the random item to Groceries\ng2 <- merge(Groceries, randomItem)\nnitems(Groceries)\nnitems(g2)\ninspect(head(g2,3)) #added to 3rd transaction\n\n#Mushroom----\ndata(\"Mushroom\")\ninspect(head(Mushroom,3))\nhead(LIST(Mushroom),3)\n\n#predit----\ndata(\"Adult\")\n## sample\nsmall <- sample(Adult, 500)\nhead(LIST(small))\nlarge <- sample(Adult, 5000)\nhead(LIST(large))\n\n## cluster a small sample\nd_jaccard <- dissimilarity(small)\nhead(d_jaccard)\nhc <- hclust(d_jaccard)\nhc\nl <- cutree(hc, k=4)\n\n## predict labels for a larger sample\nlabels <- predict(small, large, l)\n## plot the profile of the 1. cluster\nitemFrequencyPlot(large[labels==1, itemFrequency(large) > 0.2])\n\n\n#random.transactions----\n\n\n#read.PMML-----\nlibrary(pmml)\ndata(\"Groceries\")\nrules <- apriori(Groceries, parameter=list(support=0.001))\nrules <- head(rules, by=\"lift\")\nrules\ninspect(rules)\n### save rules as PMML\nwrite.PMML(rules, file = \"rules.xml\")\n### read rules back\nrules2 <- read.PMML(\"rules.xml\")\nrules2\n### compare rules\ninspect(rules[1])\ninspect(rules2[1])\n### clean up\nunlink(\"rules.xml\")\n\n\n#read.transactions----\n## create a demo file using basket format for the example\ndata <- paste(\n \"# this is some test data\",\n \"item1, item2\",\n \"item1\",\n \"item2, item3\",\n sep=\"\\n\")\ncat(data)\nwrite(data, file = \"demo_basket\")\n## read demo data (skip comment line)\ntr <- read.transactions(\"demo_basket\", format = \"basket\", sep=\",\", skip = 1)\ninspect(tr)\n## create a demo file using single format for the example\n## column 1 contains the transaction ID and column 2 contains one item\ndata <- paste(\n \"trans1 item1\",\n \"trans2 item1\",\n \"trans2 item2\",\n sep =\"\\n\")\ncat(data)\nwrite(data, file = \"demo_single\")\n## read demo data\ntr <- read.transactions(\"demo_single\", format = \"single\", cols = c(1,2))\ninspect(tr)\n## tidy up\nunlink(\"demo_basket\")\nunlink(\"demo_single\")\n\n# rule-induction-----\ndata(\"Adult\")\n## find all closed frequent itemsets\nclosed_is <- apriori(Adult, parameter = list(target = \"closed frequent itemsets\", support = 0.4))\nclosed_is\n## use rule induction to produce all closed association rules\nclosed_rules <- ruleInduction(closed_is, Adult,control = list(verbose = TRUE))\n## X&Y are already closed, check that X is also closed\nclosed_rules[is.element(lhs(closed_rules), items(closed_is))]\n## inspect the resulting closed rules\nsummary(closed_rules)\ninspect(head(closed_rules, by = \"lift\"))\n\n## use lattice of frequent itemsets\nec <- eclat(Adult, parameter = list(support = 0.4))\nrec <- ruleInduction(ec)\nrec\ninspect(head(rec))\n\n\n#rule-class----\ndata(\"Adult\")\n## Mine rules.\nrules <- apriori(Adult, parameter = list(support = 0.4))\n## Select a subset of rules using partial matching on the items\n## in the right-hand-side and a quality measure\nrules.sub <- subset(rules, subset = rhs %pin% \"sex\" & lift > 1.3)\n## Display the top 3 support rules\ninspect(head(rules.sub, n = 3, by = \"support\"))\n## Display the first 3 rules\ninspect(rules.sub[1:3])\n## Get labels for the first 3 rules\nlabels(rules.sub[1:3])\nlabels(rules.sub[1:3], itemSep = \" + \", setStart = \"\", setEnd=\"\",\n ruleSep = \" ---> \")\n\n#sample----\ndata(\"Adult\")\n## sample with replacement\ns <- sample(Adult, 50, replace = TRUE)\ns\n\n\n#setOperations----\ndata(\"Adult\")\n## mine some rules\nr <- apriori(Adult)\n## take 2 subsets\nr1 <- r[1:10]\nr2 <- r[6:15]\nunion(r1,r2)\nintersect(r1,r2)\nsetequal(r1,r2)\n\n\n#size----\ndata(\"Adult\")\nsummary(size(Adult))\n\n\n#sort----\ndata(\"Adult\")\n## Mine rules with APRIORI\nrules <- apriori(Adult, parameter = list(supp = 0.6))\nrules_by_lift <- sort(rules, by = \"lift\")\ninspect(head(rules))\ninspect(head(rules_by_lift))\n## A faster/less memory consuming way to get the top 5 rules according to lift\ninspect(head(rules, n = 5, by = \"lift\"))\n\n\n#subset-----\n#subset works on the rows/itemsets/rules of x\n#%in% (selectitemsets matching any given item), %ain% (select only itemsets matching all given item), %oin% (select only itemsets matching only the given item), and %pin% (%in% with partial matching)\n\ndata(\"Adult\")\nrules <- apriori(Adult)\n## select all rules with item \"marital-status=Never-married\" in\n## the right-hand-side and lift > 2\nrules.sub <- subset(rules, subset = rhs %in% \"marital-status=Never-married\" & lift > 2)\n## use partial matching for all items corresponding to the variable\n## \"marital-status\"\nrules.sub <- subset(rules, subset = rhs %pin% \"marital-status=\")\n## select only rules with items \"age=Young\" and \"workclass=Private\" in the left-hand-side\nrules.sub <- subset(rules, subset = lhs %ain% c(\"age=Young\", \"workclass=Private\"))\n\n#SunBai----\ndata(SunBai)\nsummary(SunBai)\ninspect(SunBai)\ntransactionInfo(SunBai)\n\n\n#support----\ndata(\"Income\")\n## find and some frequent itemsets\nitemsets <- eclat(Income)[1:5]\n## inspect the support returned by eclat\ninspect(itemsets)\n## count support in the database\nsupport(items(itemsets), Income)\n\n\n#supportingTransactions----\ndata <- list(\n c(\"a\",\"b\",\"c\"),\n c(\"a\",\"b\"),\n c(\"a\",\"b\",\"d\"),\n c(\"b\",\"e\"),\n c(\"b\",\"c\",\"e\"),\n c(\"a\",\"d\",\"e\"),\n c(\"a\",\"c\"),\n c(\"a\",\"b\",\"d\"),\n c(\"c\",\"e\"),\n c(\"a\",\"b\",\"d\",\"e\")\n)\ndata <- as(data, \"transactions\")\n## mine itemsets\nf <- eclat(data, parameter = list(support = .2, minlen=3))\ninspect(f)\n## find supporting Transactions\nst <- supportingTransactions(f, data)\nst\nas(st, \"list\")\n\n#tidLists-class----\n## Create transaction data set.\ndata <- list(\n c(\"a\",\"b\",\"c\"),\n c(\"a\",\"b\"),\n c(\"a\",\"b\",\"d\"),\n c(\"b\",\"e\"),\n c(\"b\",\"c\",\"e\"),\n c(\"a\",\"d\",\"e\"),\n c(\"a\",\"c\"),\n c(\"a\",\"b\",\"d\"),\n c(\"c\",\"e\"),\n c(\"a\",\"b\",\"d\",\"e\")\n)\ndata <- as(data, \"transactions\")\ndata\n## convert transactions to transaction ID lists\ntl <- as(data, \"tidLists\")\ntl\ninspect(tl)\ndim(tl)\ndimnames(tl)\n## inspect visually\nimage(tl)\n\n\n#transactions-class-----\n## example 1: creating transactions form a list\na_list <- list(\n c(\"a\",\"b\",\"c\"),\n c(\"a\",\"b\"),\n c(\"a\",\"b\",\"d\"),\n c(\"c\",\"e\"),\n c(\"a\",\"b\",\"d\",\"e\")\n)\n## set transaction names\nnames(a_list) <- paste(\"Tr\",c(1:5), sep = \"\")\na_list\n## coerce into transactions\ntrans1 <- as(a_list, \"transactions\")\n## analyze transactions\nsummary(trans1)\nimage(trans1)\n## example 2: creating transactions from a matrix\na_matrix <- matrix(c(\n 1,1,1,0,0,\n 1,1,0,0,0,\n 1,1,0,1,0,\n 0,0,1,0,1,\n 1,1,0,1,1\n), ncol = 5)\n\n## set dim names\ndimnames(a_matrix) <- list(c(\"a\",\"b\",\"c\",\"d\",\"e\"),\n paste(\"Tr\",c(1:5), sep = \"\"))\na_matrix\n## coerce\ntrans2 <- as(a_matrix, \"transactions\")\ntrans2\ninspect(trans2)\n## example 3: creating transactions from data.frame\na_df <- data.frame(\n age = as.factor(c(6, 8, NA, 9, 16)),\n grade = as.factor(c(\"A\", \"C\", \"F\", NA, \"C\")),\n pass = c(TRUE, TRUE, FALSE, TRUE, TRUE))\n## note: factors are translated differently to logicals and NAs are ignored\na_df\n## coerce\ntrans3 <- as(a_df, \"transactions\")\ninspect(trans3)\nas(trans3, \"data.frame\")\n\n## example 4: creating transactions from a data.frame with\n## transaction IDs and items (by converting it into a list of transactions first)\na_df3 <- data.frame(\n TID = c(1,1,2,2,2,3), item=c(\"a\",\"b\",\"a\",\"b\",\"c\", \"b\")\n)\na_df3\ntrans4 <- as(split(a_df3[,\"item\"], a_df3[,\"TID\"]), \"transactions\")\ntrans4\ninspect(trans4)\n## Note: This is very slow for large datasets. It is much faster to\n## read transactions in this format from disk using read.transactions()## with format = \"single\".\n## example 5: create transactions from a dataset with numeric variables## using discretization.\ndata(iris)\nirisDisc <- discretizeDF(iris)\nhead(irisDisc)\ntrans5 <- as(irisDisc, \"transactions\")\ntrans5\ninspect(head(trans5))\n\n\n#unique----\ndata(\"Adult\")\nr1 <- apriori(Adult[1:1000], parameter = list(support = 0.5))\nr2 <- apriori(Adult[1001:2000], parameter = list(support = 0.5))\n## Note that this produces a collection of rules from two sets\nr_comb <- c(r1, r2)\nr_comb <- unique(r_comb)\nr_comb\n\n\n#weclat-----\ndata(SunBai)\nSunBai\n## weights are stored in transactionInfo\ntransactionInfo(SunBai)\n## mine weighted support itemsets using transaction support in SunBai\ns <- weclat(SunBai, parameter = list(support = 0.3),\n control = list(verbose = TRUE))\ninspect(sort(s))\n## create rules using weighted support (satisfying a minimum\n## weighted confidence of 90%).\nr <- ruleInduction(s, confidence = .9)\ninspect(r)\n\n#write----\ndata(\"Epub\")\nhead(Epub)\n## write the formated transactions to screen (basket format)\nwrite(head(Epub))\n## write the formated transactions to screen (single format)\nwrite(head(Epub), format=\"single\")\n\n## write the formated result to file in CSV format\nwrite(Epub, file = \"data.csv\", format=\"single\", sep = \",\")\n## write rules in CSV format\nrules <- apriori(Epub, parameter=list(support=0.0005, conf=0.8))\nwrite(rules, file = \"data.csv\", sep = \",\")\nunlink(\"data.csv\") # tidy up delete files\n\n#[----\ndata(Adult)\nAdult\n## select first 10 transactions\nAdult[1:10]\n## select first 10 items for first 100 transactions\nAdult[1:100, 1:10]\n## select the first 100 transactions for the items containing\n## \"income\" or \"age=Young\" in their labels\nAdult[1:100, c(\"income=small\", \"income=large\" ,\"age=Young\")]\n" }, { "path": "45a-AR/20a-ar-DU1.R", "content": "# Association Rules Set 4 ####\n\nlibrary(arules)\ndf <- data.frame(\n age = as.factor(c(6, 6, 8, 8, NA, 9, 16)),\n grade = as.factor(c(\"A\", \"C\", \"C\", \"C\", \"F\", NA, \"C\")),\n pass = c(TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE)) \ndf\n## convert to transaction\ntrans3 <- as(df, \"transactions\")\nsummary(trans3)\ninspect(trans3)\n\n# MBA Example\nmydata = read.csv(\"./data/MBA.csv\")\nmydata\n# See first 10 observations\nhead(mydata, n=10)\n\n# Split data\ndt <- split(mydata$Products, mydata$ID)\ndt\n# Loading arules package\nif(!require(arules)) install.packages(\"arules\")\n\n# Convert data to transaction level\ndt2 = as(dt,\"transactions\")\ndt2\nsummary(dt2)\ninspect(dt2)\n\n# Most Frequent Items\nitemFrequency(dt2, type = \"relative\")\nitemFrequencyPlot(dt2,topN = 5)\n\n# aggregated data\nrules = apriori(dt2, parameter=list(support=0.005, confidence=0.8))\nrules = apriori(dt2, parameter=list(support=0.005, confidence=0.8, minlen = 3))\nrules = apriori(dt2, parameter=list(support=0.005, confidence=0.8, maxlen = 4))\n\n#Convert rules into data frame\nrules3 = as(rules, \"data.frame\")\nwrite(rules, \"MBArules.csv\", sep=\",\")\n\n# Show only particular product rules\ninspect( subset( rules, subset = rhs %pin% \"Product H\" ))\n\n# Show the top 10 rules\noptions(digits=2)\ninspect(rules[1:10])\n\n# Get Summary Information\nsummary(rules)\n\n# Sort by Lift\nrules<-sort(rules, by=\"lift\", decreasing=TRUE)\n\n# Remove Unnecessary Rules\nsubset.matrix <- is.subset(rules, rules)\nsubset.matrix[lower.tri(subset.matrix, diag=T)] <- NA\nredundant <- colSums(subset.matrix, na.rm=T) >= 1\nwhich(redundant)\nrules.pruned <- rules[!redundant]\nrules<-rules.pruned\n\n#Clean Rules\nrules3$rules=gsub(\"\\\\{\", \"\", rules3$rules)\nrules3$rules=gsub(\"\\\\}\", \"\", rules3$rules)\nrules3$rules=gsub(\"\\\"\", \"\", rules3$rules)\n\n#Split the rule\nif(!require(splitstackshape))library(splitstackshape)\nRules4=cSplit(rules3, \"rules\",\"=>\")\nnames(Rules4)[names(Rules4) == 'rules_1'] <- 'LHS'\nRules5=cSplit(Rules4, \"LHS\",\",\")\nRules6=subset(Rules5, select= -c(rules_2))\nnames(Rules6)[names(Rules6) == 'rules_3'] <- 'RHS'\n\n# What are customers likely to buy before they purchase \"Product A\"\nrules<-apriori(data=dt, parameter=list(supp=0.001,conf = 0.8), \n appearance = list(default=\"lhs\",rhs=\"Product A\"),\n control = list(verbose=F))\nrules<-sort(rules, decreasing=TRUE,by=\"confidence\")\ninspect(rules[1:5])\n\n# What are customers likely to buy if they purchased \"Product A\"\nrules<-apriori(data=dt, parameter=list(supp=0.001,conf = 0.8), \n appearance = list(default=\"rhs\",lhs=\"Product A\"),\n control = list(verbose=F))\nrules<-sort(rules, decreasing=TRUE,by=\"confidence\")\nrules\ninspect(rules)\n" }, { "path": "45a-AR/20b-ar-DU2.R", "content": "# My Basket \n\nlibrary(package = \"arules\")\nmypurchase = read.csv(\"./ar/my_basket1.txt\", sep=\" \")\n\n\nv = read.table(file = file('stdin'))\ntransactions = read.transactions(file = file(\"stdin\"), format = \"basket\", sep = \",\")\n#a,b\n#a,b,c\n#\n#\nmybasket = read.csv(file='./ar/my_basket1.txt')\nv = read.table(file = file('stdin'))\ntransactions = read.transactions(file = file(\"stdin\"), format = \"basket\", sep = \",\")\n" }, { "path": "45a-AR/20c-ar-DU3.R", "content": "#My Basket 3\n#\nset.seed(101)\ntransactionID = sample(1:10, 50, replace=T)\ntransactionID\nitems = sample(c('milk', 'bread', 'butter', 'eggs'),size=50, replace=T, prob=c(.4,.25,.2,.15))\nitems\norders <- data.frame(transactionID, items)\nhead(orders)\n\nwrite.csv(orders, \"./data/myitems1.csv\")\n\n\n\nordertrans <- arules::read.transactions(\n file = \"./data/myitems1.csv\",\n format = \"single\",\n sep = \",\",\n cols=c(\"transactionID\",\"items\"),\n rm.duplicates = T\n)\ninspect(ordertrans[1:5])\n?read.transactions\nsummary(ordertrans)\nstr(ordertrans)\nhead(ordertrans)\n\n# Create an item frequency plot for the top 20 items\nitemFrequencyPlot(ordertrans,topN=20,type=\"absolute\")\n\n#minsupport to 0.001 , min conf of 0.8 ; show top 5 rules\nrules <- apriori(ordertrans, parameter = list(supp = 0.001, conf = 0.8))\n\n# Show the top 5 rules, but only 2 digits\noptions(digits=2)\ninspect(rules[1:5])\n\nsummary(rules)\n\nrules<-sort(rules, by=\"confidence\", decreasing=TRUE)\ninspect(head(rules))\n\n" }, { "path": "45a-AR/22a-ar-edn.R", "content": "# Association Rule 14\n# data set having Gradution, Attendance, Assignment, Unit Test and Univ Result Results\n# Aim - to develop Association Rule \noptions(digits=2)\nedndata = read.csv(file='ar14.csv',header=T, stringsAsFactors = T)\n\n# Check Properties of Data Set \nstr(edndata)\ndim(edndata)\nnames(edndata)\n# load library\nlibrary(arules)\n# Find Association Rules with Default Settings\nrules = apriori(edndata)\ninspect(rules)\n\n# Custom Rules\nrules = apriori(edndata, parameter = list(minlen=2,supp=.005,conf=0.8),\n list(rhs=c(\"result=Poor\"),default=\"lhs\"), control=list(verbose=F))\nrules.sorted = sort(rules, by=\"lift\")\ninspect(rules.sorted)\n\n\n\n# Taking data as numerical values and then converting it into categorical values\nedndata14b = read.csv(file='ar14b.csv',header=T)\nstr(edndata14b)\nbreaks = seq(50,100,by=10)\n# Table values of Attendance\nattnd.cut = cut(edndata14b$attendance,breaks,right=F)\nattnd.freq = table(attnd.cut) ; attnd.freq\n\nedndata14b[edndata14b >= 70] = 'Good'\nedndata14b[edndata14b >= 60 & edndata14b < 70 ] = 'Avg'\nedndata14b[edndata14b < 60] = 'Poor'\nstr(edndata14b)\n#edndata14b$graduation = factor(edndata14b$graduation) # one column at a time\nedndata14b[] <- lapply( edndata14b, factor) # All Colns of DF\nstr(edndata14b)\n\nrules14b = apriori(edndata14b)\ninspect(rules14b)\nhead(edndata14b)\n# Custom Rules result = 'Poor'\narules14b = apriori(edndata14b, parameter = list(minlen=2,supp=.005,conf=0.8),\n list(rhs=c('result=Poor'),default=\"lhs\"), control=list(verbose=F))\n<<<<<<< HEAD\narules14b.sorted = sort(arules14b, by=\"lift\")\ninspect(arules14b.sorted)\nquality(arules14b.sorted)\nhead(inspect(arules14b.sorted))\n\n\n\n# Plotting Rules\n# Visualising\nlibrary(arulesViz)\nplot(arules14b)\n\nplot(arules14b, method=\"graph\", control=list(type=\"items\"))\n\nplot(arules14b, method=\"paracoord\", control=list(reorder=TRUE))\n=======\nrules.sorted = sort(rules, by=\"lift\")\ninspect(rules.sorted)\nquality(rules.sorted)\nhead(inspect(rules.sorted))\n\n>>>>>>> 0b50577bb0f052107564367a2845372e98756506\n" }, { "path": "45a-AR/22b-ar-elective.R", "content": "# Subject Choices of Electives\n#http://tallman-world.tumblr.com/post/111511141517/rhow-to-make-association-rules-on-r-all-integer\nlibrary(arules)\ndatar2 <- read.csv(\"dar3b.csv\", header = TRUE, row.names=1)\nstr(datar2)\ndatar2 <- as.matrix(datar2)\nstr(datar2)\nhead(datar2)\n#If all the columns except the row name are two-valued data (0 or 1), matrix class is easier to deal with in association rules.\n#IMP If all the columns are string data, I just convert the data.frame into transaction data without converting it into matrix data.\n#Next, I convert onsen matrix into transaction class.\ndatar2.trans <- as(datar2, \"transactions\")\nstr(datar2.trans)\nsummary(datar2.trans) # check what’s in the transaction data.\n#With inspect(), check each transaction’s items.\ninspect(datar2.trans)\n\n#want to know frequency of items, I can plot as below;\nitemFrequencyPlot(datar2.trans, ylim=c(0,1))\n#Making association rules ####\n#Now make association rules.\noptions(digits=2)\ndatar2.rule <- apriori(datar2.trans, parameter = list(maxlen=3, support=0.04, confidence=0.8, ext=TRUE))\n\n\n#apriori() is a function to make association rules.\n#maxlen means a number of rules (in this case, number of effects of an datar2) in a association rule.\n#support means how often it happens (or probability of an event). 0.04 is not so rare, not too often.\n#And confidence means how often it occurs under specific situations. If this is so small, it may be not be worth making association rules in the first place.\n#Now, check summary of the association rules made, with summary().\n#check a number of rules and how many rules each association rule has.\n#datar2.rule = sort(datar2.rule, by='confidence', decreasing=T)\ndatar2.rule = sort(datar2.rule, by='lift', decreasing=T)\n\nsummary(datar2.rule)\ninspect(sort(subset(datar2.rule, subset= (support >0.5))))\n\n#In order to inspect the contents of association rules, I can do it as below;\ninspect(datar2.rule[1:5])\n\n#If I want to extract specific association rules, I use subset() as below.\n#In this case, I want the result to have “comnskills” as the rule body and more than 1 lift.\n\nrule1 <- subset(datar2.rule, subset=(rhs %in% \"elective6\") & (lift>1.0))\nrule1\ninspect(rule1)\n\n\nrule2 <- subset(datar2.rule, subset=(rhs %in% c('elective3','elective4')) & (lift>1.5))\nrule2 = sort(rule2, by='confidence', decreasing=T)\ninspect(rule2)\n\nrule3 <- subset(datar2.rule, subset=(rhs %in% \"elective1\") & (support >0.5))\nrule3 = sort(rule3, by='support', decreasing=T)\ninspect(rule3)\n" }, { "path": "45a-AR/22d-ar-subjects.R", "content": "# Association Rule\ndataar1 = read.csv(\"dar1.csv\")\nstr(dataar1)\n?write.csv\nwrite.csv(dataar1,file='dar1w.csv',row.names = F)\n(dataar1 = dataar1[-7])\nlibrary(arules)\nstr(dataar1)\n# change to factors for Association Rule\n#dataar1 = lapply(dataar1,function(x) as.factor(x))\nrules = apriori(dataar1)\ninspect(rules)\nrules\n# more than 300 rules generated\n# Refining Rules\nrhsrule = c('java=Yes','cpp=Yes')\nrules = apriori(dataar1, parameter = list(minlen=2,supp=.05,conf=0.8),\n list(rhs=rhsrule,default=\"lhs\"), control=list(verbose=F))\nrules.sorted = sort(rules,by='lift')\ninspect(rules.sorted)\n\n# Prune Redundant Rules\nsubset.matrix = is.subset(rules.sorted,rules.sorted)\nsubset.matrix\nsubset.matrix[lower.tri(subset.matrix,diag=T)] = NA\nsubset.matrix\nredundant <- colSums(subset.matrix,na.rm=T) >=1\nsubset.matrix\nredundant\nwhich(redundant)\nrules.pruned = rules.sorted[!redundant]\ninspect(rules.pruned)\n# Visualising\nlibrary(arulesViz)\nplot(rules)\nplot.new()\nplot(rules,method='graph',control=list(type='items'))\nplot(rules,method='paracoord',control=list(reorder=T))\n\n# Using Weka Library ####\n#Using Weka \nlibrary(RWeka)\nlibrary(rattle)\nrattle()\nApriori()" }, { "path": "45a-AR/22e-ar-placement.R", "content": "# Data set - Placement Selection Criteria\n#http://tallman-world.tumblr.com/post/111511141517/rhow-to-make-association-rules-on-r-all-integer\n# About data set\nlibrary(arules)\ndatar2 <- read.csv(\"./data/dar2.csv\", header = TRUE, row.names=1)\nstr(datar2)\ndatar2 <- as.matrix(datar2)\nstr(datar2)\nhead(datar2)\n#If all the columns except the row name are two-valued data (0 or 1), matrix class is easier to deal with in association rules.\n#IMP If all the columns are string data, I just convert the data.frame into transaction data without converting it into matrix data.\n#Next, I convert onsen matrix into transaction class.\ndatar2.trans <- as(datar2, \"transactions\")\nstr(datar2.trans)\nsummary(datar2.trans) # check what’s in the transaction data.\n#With inspect(), check each transaction’s items.\ninspect(datar2.trans)\n\n#want to know frequency of items, I can plot as below;\nitemFrequencyPlot(datar2.trans, ylim=c(0,1))\n#Making association rules ####\n#Now make association rules.\noptions(digits=2)\ndatar2.rule <- apriori(datar2.trans, parameter = list(maxlen=3, support=0.04, confidence=0.8, ext=TRUE))\n\ndatar2.rule = sort(datar2.rule, by='confidence', decreasing=T)\n\nsummary(datar2.rule)\n\n#inspect\ninspect(datar2.rule[1:5])\n\n#extract specific association rules, use subset\n#subset - commnskills and lift > 1\n\nrule1 <- subset(datar2.rule, subset=(rhs %in% \"comnskills\") & (lift>1.0))\nrule1\ninspect(rule1[1:5])\n\nrule2 <- subset(datar2.rule, subset=(rhs %in% c('extraco','gender')) & (lift>1.7))\nrule2\ninspect(rule2[1:5])\n\nrule3 <- subset(datar2.rule, subset=(rhs %in% \"select\") & lhs %in% \"relocwilling\" & (lift>1.0))\nrule3\ninspect(rule3)\n" }, { "path": "45a-AR/22f-myAR1.R", "content": "# Presentation to Guide\ndataar1 = read.csv(\"./data/dar1.csv\")\nstr(dataar1)\n#?write.csv\n#write.csv(dataar1,file='dar1w.csv',row.names = F)\ndataar1 = dataar1[-7]\nlibrary(arules)\nstr(dataar1)\n# change to factors for Association Rule\n#dataar1 = lapply(dataar1,function(x) as.factor(x))\nrules = apriori(dataar1)\ninspect(rules[1:5])\nrules\n# more than 300 rules generated\n# Refining Rules\nrhsrule = c('java=Yes','cpp=Yes')\nrules = apriori(dataar1, parameter = list(minlen=2,supp=.05,conf=0.8),\n list(rhs=rhsrule,default=\"lhs\"), control=list(verbose=F))\nrules.sorted = sort(rules,by='lift')\ninspect(rules.sorted[1:5])\n\n# Prune Redundant Rules\nsum(is.redundant(rules.sorted))\n(redundant = which(is.redundant(rules.sorted)))\nrulesNR <- rules[-redundant] \ninspect(rulesNR[1:5])\nlength(rulesNR)\n\n# Visualising\nlibrary(arulesViz)\nplot(rules)\nplot.new()\nplot(rules,method='graph',control=list(type='items'))\nplot(rules,method='paracoord',control=list(reorder=T))\n\n# Using Weka Library ####\n#Using Weka \nlibrary(RWeka)\nlibrary(rattle)\nrattle()\nApriori()\n" }, { "path": "45a-AR/25a-ar-income.R", "content": "#AR - Income\n\ndata(\"Income\")\n\n## mine some rules with the consequent \"language in home=english\"\nrules <- apriori(Income, parameter = list(support = 0.5), appearance = list(rhs = \"language in home=english\", default = \"lhs\"))\n\n## for better comparison we sort the rules by confidence and add Bayado's improvement\nrules <- sort(rules, by = \"confidence\")\nquality(rules)$improvement <- interestMeasure(rules, measure = \"improvement\")\ninspect(rules)\nis.redundant(rules)\n\n## redundant rules\ninspect(rules[is.redundant(rules)])\n\n## non-redundant rules\ninspect(rules[!is.redundant(rules)])\n\n" }, { "path": "45a-AR/25b-ar-medical.R", "content": "# Onsen\n#http://tallman-world.tumblr.com/post/111511141517/rhow-to-make-association-rules-on-r-all-integer\n#“onsen_name” is a name of onsen (hot spring) and the other columns mean effects that people can get from it.\nlibrary(arules)\nonsen <- read.csv(\"./data/onsen.csv\", header = TRUE, row.names=1)\nonsen <- as.matrix(onsen)\n#If all the columns except the row name are two-valued data (0 or 1), matrix class is easier to deal with in association rules.\n#If all the columns are string data, I just convert the data.frame into transaction data without converting it into matrix data.\n#Next, I convert onsen matrix into transaction class.\nonsen\nonsen.trans <- as(onsen, \"transactions\")\nsummary(onsen.trans) # check what’s in the transaction data.\n#With inspect(), check each transaction’s items.\ninspect(onsen.trans)\n#want to know frequency of items, I can plot as below;\nitemFrequencyPlot(onsen.trans, ylim=c(0,1))\n#Making association rules ####\n#Now that I am fully prepared, I make association rules.\nonsen.rule <- apriori(onsen.trans, parameter = list(maxlen=4, support=0.04, confidence=0.55, ext=TRUE))\n\n#apriori() is a function to make association rules.\n#maxlen means a number of rules (in this case, number of effects of an onsen) in a association rule.\n#support means how often it happens (or probability of an event). 0.04 is not so rare, not too often.\n#And confidence means how often it occurs under specific situations. If this is so small, it may be worth making association rules in the first place.\n#Now, I check summary of the association rules I’ve made, with summary().\n#check a number of rules and how many rules each association rule has.\nsummary(onsen.rule)\n\n#In order to inspect the contents of association rules, I can do it as below;\ninspect(onsen.rule)\n\n#If I want to extract specific association rules, I use subset() as below.\n#In this case, I want the result to have “nerve_pain” as the rule body and more than 1 lift.\n\nnerve_pain_and_more_than_1_lift <- subset(onsen.rule, subset=(rhs %in% \"nerve_pain\") & (lift>1.0))\n" }, { "path": "45a-AR/25c-ar-titanic.R", "content": "# Association Rule Example 1 ####\n#http://www.rdatamining.com/examples/association-rules\nload(\"./data/titanic.raw.rdata\")\ntitanic1 = titanic.raw\nstr(titanic1)\n\n#write.csv(titanic.raw,file=\"titanic.csv\")\ntitanic2 = read.csv(\"./data/titanic.csv\")\nstr(titanic2)\n\ntitanic = titanic1\nhead(titanic)\n#titanic = titanic[-1] # not reqd from rdata\nhead(titanic)\nstr(titanic)\n\nlibrary(arules)\n\nrules = apriori(titanic, control=list(verbose=F))\nrules = apriori(titanic)\n\noptions(digits=3)\ninspect(rules)\nrules\ninspect(rules[1:5])\nquality(rules) = round(quality(rules), digits=3)\nquality(rules[1:5])\n\nrules = apriori(titanic, parameter = list(minlen=2,supp=.005,conf=0.8), list(rhs=c(\"Survived=No\",\"Survived=Yes\"),default=\"lhs\"), control=list(verbose=F))\n\nrules.sorted = sort(rules, by=\"lift\")\n\ninspect(rules.sorted[1:5])\n\noptions(digits=2)\n\n\n# Prune Redundant\nrules.pruned <- rules[!is.redundant(rules)]\nrules.redundant <- rules[is.redundant(rules)]\n?is.redundant\ninspect(rules.pruned)\nrules.pruned\ninspect(rules.redundant)\nrules.pruned.sorted = sort(rules.pruned, by=\"lift\")\ninspect(rules.pruned.sorted)\n\n# Visualising\nlibrary(arulesViz)\nplot(rules.pruned.sorted)\n\nplot(rules.pruned.sorted, method=\"graph\", control=list(type=\"items\"))\n\nplot(rules.pruned.sorted, method=\"paracoord\", control=list(reorder=TRUE))\n\n" }, { "path": "45a-AR/29a-ar-Adult.R", "content": "# Association Rule Adult ----\ndata(\"Adult\")\nhead(Adult)\nLIST(Adult[1:5])\nrulesA1 = apriori(Adult)\ninspect(rulesA1[1:5])\ninspect(rulesA1[1:5], ruleSep=\"---->\", itemSep=\"+\", setStart = \"\", setEnd =\"\", linebreak = FALSE)\n\n\n# Change to any data Frame\nmyDataFrame= Groceries\n#transDat <- as (myDataFrame, \"transactions\") \ntransDat\ninspect (transDat[1:10]) \ninspect (transDat[10]) \n\n?inspect\nlength (transDat)\nsize (transDat)\nLIST(transDat) \n" }, { "path": "45a-AR/29b-ar-Adult-NW.R", "content": "#my Work\nlibrary(arules)\ndata(\"AdultUCI\")\nAdultUCI\nstr(AdultUCI)\nAdult = as(AdultUCI,'transactions')\n# error here as some colns are logical or factor\ndata1 = read.csv(file.choose())\ndata1\ndata1a = as(data1,'transactions')\ndata1a\n#trans = read.transactions('data1',format='basket',sep=',')\ninspect(data1a)\n" }, { "path": "45a-AR/29c-ar-Adult-Draft.R", "content": "# Rough Work on Association Rules\nlibrary(arules) \ndata(\"AdultUCI\") \nAdultUCI$ID <- 1:nrow(AdultUCI) \ndim(AdultUCI)\nstr(AdultUCI)\nnames(AdultUCI)\nsummary(AdultUCI)\nunique(AdultUCI$`marital-status`)\nhead(AdultUCI[,c('ID','marital-status')])\nlst1 <- split(AdultUCI[,\"ID\"], AdultUCI[,\"marital-status\"]) \n#head(lst1)\nas(lst1, \"transactions\") \n#transactions in sparse format with \n# 7 transactions (rows) and \n# 48842 items (columns) \n\n" }, { "path": "45a-AR/30a-ar-Finance1.R", "content": "# AR in Financial Domain\n\nfinproducts = c('Mutual Funds', 'NPS', 'Savings Account', 'PPF', 'FD', 'Bonds', 'Stocks', 'General Insurance', 'NRI Banking', 'Car Insurance', 'Debit Card', 'Credit Card', 'Mobile Banking')\n\nfinproducts\n" }, { "path": "45a-AR/30b-ar-Finance.R", "content": "# AR data for Finance\n\nlibrary(\"arules\")\n\nset.seed(101)\ntransactionID = sample(1:500, 1000, replace=T)\ntransactionID\n\nfinproducts = c('Mutual Funds', 'NPS', 'Savings Account', 'PPF', 'FD', 'Bonds', 'Stocks', 'General Insurance', 'NRI Banking', 'Car Insurance', 'Debit Card', 'Credit Card', 'Mobile Banking')\nlength(finproducts)\nitem = sample(finproducts,1000, replace=T)\nitem\norders = data.frame(transactionID, item)\nhead(orders)\n\nwrite.csv(orders, \"./data/fintransactions.csv\")\n\nordertrans <- arules::read.transactions(\n file = \"./data/fintransactions.csv\", format = \"single\",\n sep = \",\", cols=c(\"transactionID\",\"item\"), rm.duplicates = T\n)\ninspect(ordertrans[1:5])\nordertrans\nsummary(ordertrans)\nstr(ordertrans)\nhead(ordertrans)\n\n\n#find frequent item set\nfrequentItems <- eclat (ordertrans, parameter = list(supp = 0.005, minlen= 2, maxlen = 5)) \ninspect(frequentItems[1:10])\ninspect(sort (frequentItems, by=\"count\", decreasing=TRUE)[1:15])\n#support(A&B) = n(A&B)/ N\n\nfrequentItems\ninspect(frequentItems[1:20], by='support', decreasing=F) #view by support increasing order\ninspect(frequentItems) #view all\n\nitemFrequencyPlot (ordertrans,topN = 15,type=\"absolute\")\nitemFrequencyPlot(ordertrans, topN = 10, type='relative')\nabline(h=0.15)\n\nrules1 <- arules::apriori(ordertrans, parameter = list(supp = 0.005, conf = 0.5))\nrules1\nwrite.csv(inspect(rules1, 'rules.csv'))\ninspect(rules1[1:5])\nquality(rules1) \nhead(quality(rules1))\noptions (digits=2) # set no of digits\ninspect (rules1[1:5])\nrules1C = sort (rules1, by=\"confidence\", decreasing=TRUE)\ninspect(rules1C[1:5])\nrules1L = sort (rules1, by=\"lift\", decreasing=TRUE)\ninspect (rules1L[1:5])\nrules1S = sort (rules1, by=\"support\", decreasing=TRUE)\ninspect (rules1S[1:5])\nrules1N = sort (rules1, by=\"count\", decreasing=TRUE)\ninspect (rules1N[1:5])\n\n\n#How To Control The Number Of Rules in Output ?\nrules2 <- apriori (ordertrans, parameter = list (supp = 0.005, conf = 0.5, minlen=3, maxlen=5)) # minlen = 3 & maxlen limits the elements in a rule to 3 & 5\ninspect(rules2)\n\nrules3 = apriori(ordertrans) #use default values no rules\nrules3 = apriori(ordertrans, parameter=list(support=0.002)) #use default values no rules\n\n#How To Remove Redundant Rules ?\n#A->B, C->B, A&C ->B : Then A&C -> B is redundant if value of life <= 1st two\n#A,B,C -> D; A&B -> D having same sp & conf : has lower lift\nis.redundant(rules3)\nsum(is.redundant(rules3))\n(redundant = which(is.redundant(rules3)))\ninspect(rules3[redundant])\nlength(redundant)\ninspect(rules3[redundant][1:10])\nrulesNR = rules3[-redundant] \nis.redundant(rulesNR)\nsum(is.redundant(rulesNR)) #ok now\n\n#Another method union of lhs & rhs\nredundant3 = which (colSums (is.subset (rules3, rules3)) > 1) \nredundant3\nlength(redundant3)\nrules3NR = rules3[-redundant3]\ninspect(rules3NR[1:10])\n\nitemFrequency(ordertrans)\n#Find what factors influenced an event ‘X’\nrules4 <- apriori(data=ordertrans, parameter=list (supp=0.001,conf = 0.08), appearance = list (default=\"lhs\",rhs=\"Credit Card\"), control = list (verbose=F)) #verbose=F no log\ninspect(rules4[1:15])\n\n#Find out what events were influenced by a given event\n\nrules5 <- apriori (data=ordertrans, parameter=list (supp=0.001,conf = 0.05,minlen=2), appearance = list (default=\"rhs\",lhs=\"General Insurance\"), control = list (verbose=F)) \ninspect(rules5)\ninspect( subset( rules5, subset = rhs %pin% c(\"Bonds\") ))\ninspect( subset( rules5, subset = rhs %pin% c(\"FD\") ))\n\n\n#Control lhs and rhs\nrules6 <- apriori(ordertrans, parameter=list(support =0.00001, confidence =0.05, minlen=2, maxlen=5), appearance = list(lhs=c(\"Debit Card\"), rhs=c(\"Credit Card\"), default=\"none\"))\ninspect(rules6)\n\n#Export Rules into a table\nlibrary(data.table)\nrules_dt <- data.table( lhs = labels( lhs(rules2) ), \n rhs = labels( rhs(rules2) ), \n quality(rules2) )[ order(-lift), ]\nrules_dt\nDT::datatable(rules_dt) # wrapper for datatables\nwrite.csv(rules_dt, './data/arulesfin.csv')\n\n\n#Visualizing The Rules -----\nlibrary(arulesViz)\ninspect(rules1)\nplot(rules1, measure=c(\"support\", \"lift\"), shading=\"confidence\")\nplot(rules1, measure=c(\"support\", \"confidence\"), shading=\"lift\")\n\nplot(rules1,method=\"graph\",engine='interactive', shading=\"confidence\") \n#extra window\nplot(rules1,method=\"graph\",engine='interactive',shading=NA)\ninterestMeasure(rules1, transactions=ordertrans, measure=c('support','count','lift'))\nquality(rules1)\nplot(rules1, shading=\"order\", control=list(main = \"Two-key plot\"), col=4:6)\ninspect(rules1)\nplot(rules2, shading=\"order\", control=list(main = \"Two-key plot\"), col=1:4)\ninspect(rules2)\n\nsel = plot(rules1, measure=c(\"support\", \"confidence\"), shading=\"lift\", engine='interactive') #use mouse to do selection\n#https://cran.r-project.org/web/packages/arulesViz/vignettes/arulesViz.pdf\n\n\n#Matrix Based Visualisation\nsubrules1 <- rules1[quality(rules1)$confidence > 0.8]\nsubrules1\nplot(subrules1, method=\"matrix\", measure=\"lift\")\nplot(subrules1, method=\"matrix\", measure=\"lift\", control=list(reorder=TRUE))\nplot(subrules1, method=\"matrix\", engine='3d', measure=\"lift\")\n\n# Group based\nplot(rules1, method=\"grouped\")\n\nplot(subrules1, method=\"graph\", control=list(type=\"itemsets\"))\n\nplot(subrules1, method=\"paracoord\")\nplot(rules1[1], method=\"doubledecker\", data = ordertrans)\n#The area of blocks gives the support and the height of the “yes” blocks is proportional to the confidence for the rules consisting of the antecedent items marked as “yes.”\n" }, { "path": "45a-AR/32a-ar-visual.R", "content": "\nlibrary(arules)\nlibrary(arulesViz)\ndata(Groceries)\nrules <- apriori(Groceries, parameter=list(support=0.001, confidence=0.8))\ninspect(rules[1:5])\n## Scatterplot\n## -----------\nplot(rules)\n\n## Scatterplot with custom colors\nlibrary(colorspace) # for sequential_hcl\nplot(rules, control = list(col=sequential_hcl(100)))\nplot(rules, col=sequential_hcl(100))\nplot(rules, col=grey.colors(50, alpha =.8))\n\n\n## See all control options using verbose\nplot(rules, verbose = TRUE)\n\nplot(rules, engine = \"htmlwidget\")\nplot(rules, method = \"two-key plot\")\n\n\nsubrules <- subset(rules, lift>5)\nsubrules\n## 2D matrix with shading\nplot(subrules, method=\"matrix\")\n## 3D matrix\nplot(subrules, method=\"matrix\", engine = \"3d\")\n## Matrix with two measures\nplot(subrules, method=\"matrix\", shading=c(\"lift\", \"confidence\"))\n\n\nplot(rules, method=\"grouped matrix\")\nplot(rules, method=\"grouped matrix\", col = grey.colors(10), gp_labels = gpar(col = \"blue\", cex=1, fontface=\"italic\"))\n\n\n## Graphs only work well with very few rules\nsubrules2 <- sample(subrules, 25)\n\nplot(subrules2, method=\"graph\")\n## Custom colors\nplot(subrules2, method=\"graph\", nodeCol = grey.colors(10), edgeCol = grey(.7), alpha = 1)\n## igraph layout generators can be used (see ? igraph::layout_)\nplot(subrules2, method=\"graph\", layout=igraph::in_circle())\nplot(subrules2, method=\"graph\",\n layout=igraph::with_graphopt(spring.const=5, mass=50))\n\n## Parallel coordinates plot\n## -------------------------\nplot(subrules2, method=\"paracoord\")\nplot(subrules2, method=\"paracoord\", reorder=TRUE)\n\n## Note: only works for a single rule\noneRule <- sample(rules, 1)\ninspect(oneRule)\nplot(oneRule, method=\"doubledecker\", data = Groceries)\n\n\n## Itemsets\n## --------\nitemsets <- eclat(Groceries, parameter = list(support = 0.02, minlen=2))\nplot(itemsets)\nplot(itemsets, method=\"graph\")\nplot(itemsets, method=\"paracoord\", alpha=.5, reorder=TRUE)\n\n\n## Add more quality measures to use for the scatterplot\n## ----------------------------------------------------\nquality(itemsets) <- interestMeasure(itemsets, trans=Groceries)\nhead(quality(itemsets))\nplot(itemsets, measure=c(\"support\", \"allConfidence\"), shading=\"lift\")\n\n\n\nlibrary(plotly)\ndata(Groceries)\nrules <- apriori(Groceries, parameter=list(support=0.001, confidence=0.8))\nrules\n# interactive scatter plot visualization\nplotly_arules(rules)\nplotly_arules(rules, measure = c(\"support\", \"lift\"), shading = \"confidence\")\nplotly_arules(rules, method = \"two-key plot\")\n\n# add jitter, change color and markers and add a title\nplotly_arules(rules, jitter = 10,\n marker = list(opacity = .7, size = 10, symbol = 1),\n colors = c(\"blue\", \"green\"))\n# save a plot as a html page\np <- plotly_arules(rules)\nhtmlwidgets::saveWidget(p, \"arules.html\", selfcontained = FALSE)\nbrowseURL(\"arules.html\")\n# Note: selfcontained seems to make the browser slow.\n# interactive matrix visualization\nplotly_arules(rules, method = \"matrix\")\n\n#Graph\n#\ndata(\"Groceries\")\nrules <- apriori(Groceries, parameter=list(support=0.01, confidence=0.5))\nsaveAsGraph(rules, \"rules.graphml\")\n## clean up\nunlink(\"rules.graphml\")" }, { "path": "45a-AR/33a-ar-redundant.R", "content": "# Redundant Rules\n# \n\n# Case 1: Income ----\ndata(\"Income\")\n\n## mine some rules with the consequent \"language in home=english\"\nrules <- apriori(Income, parameter = list(support = 0.5), appearance = list(rhs = \"language in home=english\", default = \"lhs\"))\ninspect(rules[1:5])\n\n## comparison :sort the rules by confidence and add Bayado's improvement\nrules2 <- sort(rules, by = \"confidence\")\nquality(rules2)$improvement <- interestMeasure(rules2, measure = \"improvement\")\ninspect(rules2)\nis.redundant(rules2)\n\n## redundant rules\ninspect(rules2[is.redundant(rules2)])\n\n## non-redundant rules\ninspect(rules2[!is.redundant(rules2)])\n\n\n\n#Case-2 :\ntransactions = Groceries\nrules<-apriori(transactions,parameter = list(support=0.001,confidence=0.8,minlen=3))\nrules\nsummary(rules)\nrules\n#Method_1\nsubset.matrix <- is.subset(rules,rules)\nsubset.matrix[lower.tri(subset.matrix,diag=T)] <- NA\nredundant <- colSums(subset.matrix,na.rm=T) >= 1\nredundant\nrules[which(redundant)]\nrules2 <- rules[!redundant]\nrules2\nrules[redundant]\n\n#Method_2\nis.redundant(rules)\ninspect(rules[!is.redundant(rules)])\n\n\n\n#Method X ----\n\nsubsetRules <- which(colSums(is.subset(rules, rules)) > 1) # get subset rules in vector\nlength(subsetRules) #> 3913\nrules <- rules[-subsetRules] # remove subset rules. \n" }, { "path": "45a-AR/33b-redundantrules.R", "content": "# Redundant Rules 2\n" }, { "path": "45a-AR/40a-ar-multilevel-Groceries.R", "content": "#Multi Level AR \n#https://rdrr.io/cran/arules/man/aggregate.html\n\ndata(Groceries)\n\nhead(itemInfo(Groceries))\n## aggregate by level2: items will become labels at level2\n## Note that the number of items is therefore reduced to 55\nGroceries_level2 <- aggregate(Groceries, by = \"level2\")\nGroceries_level2\nhead(itemInfo(Groceries_level2)) ## labels are alphabetically sorted!\n\n\n## compare orginal and aggregated transactions\ninspect(head(Groceries, 2))\ninspect(head(Groceries_level2, 2))\n\nitemLabels(Groceries)\n## create lables manually (organize items by the first letter)\nmylevels <- toupper(substr(itemLabels(Groceries), 1, 1))\nhead(mylevels)\n\nGroceries_alpha <- aggregate(Groceries, by = mylevels)\nGroceries_alpha\ninspect(head(Groceries_alpha, 2))\n\n## aggregate rules ## Note: you could also directly mine rules from aggregated transactions toget support, lift and support\nrules <- apriori(Groceries, parameter=list(supp=0.005, conf=0.5))\nrules\ninspect(rules[1])\n\nrules_level2 <- aggregate(rules, by = \"level2\")\ninspect(rules_level2[1])\n\n## mine multi-level rules:\n## (1) add aggregate items. These items are followed by a *\nGroceries_multilevel <- addAggregate(Groceries, \"level2\")\nsummary(Groceries_multilevel)\ninspect(head(Groceries_multilevel))\n\nrules <- apriori(Groceries_multilevel, \n parameter = list(support = 0.01, conf = .9))\ninspect(head(rules, by = \"lift\"))\n## this contains many spurous rules of type 'item X => aggregare of item X'\n## with a confidence of 1 and high lift.\n\n## filter spurious rules resulting from the aggregation \nrules <- filterAggregate(rules)\ninspect(head(rules, by = \"lift\"))\n\n" }, { "path": "45a-AR/43a-ar-patterns.R", "content": "#Association Rule Learning and the Apriori Algorithm\n#http://statistical-research.com/association-rule-learning-and-the-apriori-algorithm/\n#Often Association Rule Learning is used to analyze the “market-basket” for retailers. Traditionally, this simply looks at whether a person has purchased an item or not and can be seen as a binary matrix.\nlibrary(\"arules\");\n\nlibrary(\"arulesViz\");\n#The random data is generated in such a way where there is correlation and has correlated items.\npatterns = random.patterns(nItems = 100);\npatterns\nsummary(patterns);\n\ntrans = random.transactions(nItems = 100, nTrans = 150, method = \"agrawal\", patterns = patterns);\nimage(trans);\noptions(digits=2)\nitemFrequencyPlot(trans, support = 0.1, cex.names=0.8)\n#Sample Transactions from data Set ####\n#However, a transaction dataset will usually be available using the approach described in “Data Frames and Transactions“. \n#The rules can then be created using the apriori function on the transaction dataset.\nrules = apriori(trans, parameter=list(support=0.05, confidence=0.8));\n\nrules;\ninspect(rules)\nitemFrequency(trans,type='absolute')\n?itemFrequency\n#Once the rules have been created a researcher can then review and filter the rules down to a manageable subset. This can be done in a variety of ways using both graphs and by simply inspecting the rules.\n\ninspect(head(sort(rules, by=\"lift\"),3));\n\nplot(rules);\n\nhead(quality(rules));\n\nplot(rules, measure=c(\"support\",\"lift\"), shading=\"confidence\");\n\nplot(rules, shading=\"order\", control=list(main =\"Two-key plot\"));\n\n#trim down the rules to the ones that are important.\n\nsel = plot(rules, measure=c(\"support\",\"lift\"), shading=\"confidence\", interactive=TRUE);\n\nsubrules = rules[quality(rules)$confidence > 0.8];\n\nsubrules\n# Once again we can now subset the rules to get a visual. In these graphs we can see the two parts to an association rule: the antecedent (IF) and the consequent (THEN). These patterns are found by determining frequent patterns in the data and these are identified by the support and confidence. The support indicates how frequently the items appear in the dataset. The confidence indicates the number of times the IF/THEN statement on the data are true. These IF/THEN statements can be visualized by the following graph:\n\n# --------------------\nWRITE(rules, file = \"\", sep = \",\", quote = TRUE, col.names = NA)\n" }, { "path": "45a-AR/45a-ar-rulesextract.R", "content": "# Association Rules Extraction\n\n\nlibrary(arules)\nlibrary(arulesViz)\nlibrary(datasets)\n\nrules = apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8))\n\n# Show the top 5 rules, but only 2 digits\noptions(digits=5)\n\n#summary of rule\nsummary(rules)\n\n#rules with measures\ninspect(rules[1:5])\n#numerical values of measures\nquality(rules[1:5])\n\n#Levels of Rules\nstr(rhs(rules)@itemInfo)\n\n\nrulelength = length(rules)\n\nrulelabels = labels(rules)\nprint(rulelabels[1:5])\n\ncut = unlist(strsplit(rulelabels,\"=> \"))[seq(2,2*rulelength,by=2)]\nprint(cut[1:15])\n\n\n\n#RHS of rule\nrules@rhs@itemInfo$labels[rules@rhs@data@i+1]\n\n#Subset of Rules based on items\nsub_rules <- rules[3]\ninspect(sub_rules)\nsub_trans = subset(Groceries, items %in% unlist(as(lhs(sub_rules), \"list\")))\nsub_trans\ninspect(sub_trans[1:5])\n\n\nitem <- \"whole milk\"\narules::subset(rules, rhs %in% \"whole milk\")\nrules.sub <- subset(rules, lhs %in% item & size(lhs)==1)\ninspect(rules.sub)\n\n\n# rules to DF\ndf <- as(rules, 'data.frame')\nhead(df)\n#can save now\n" }, { "path": "45a-AR/99-ar-NW.R", "content": "# Interactive Association rule Analysis\n#https://www.r-bloggers.com/interactive-association-rules-exploration-app/\nlibrary('devtools')\ninstall_github('brooksandrew/Rsenal')\nlibrary('Rsenal')\n?Rsenal::arulesApp\nsource_gist(id='706a28f832a33e90283b')\n# Not Working Now - check after update\nlibrary('devtools')\nlibrary('shiny')\nlibrary('arules')\nlibrary('arulesViz')\nsource_gist(id='706a28f832a33e90283b')\n" }, { "path": "45a-AR/99-ar-OnlineSales.R", "content": "# Association Rule - Online Sales\n\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(knitr)\nlibrary(ggplot2)\nlibrary(lubridate)\nlibrary(arules)\nlibrary(arulesViz)\nlibrary(plyr)\n\n\n#retail <- read_excel('Online_retail.xlsx')\nretail1 <- readxl::read_excel('E:/rWork/rDataSets/Onlineretail.xlsx')\n\nretail <- retail[complete.cases(retail1), ]\nsummary(retail)\nretail %>% plyr::mutate(Description = as.factor(Description))\nretail %>% plyr::mutate(Country = as.factor(Country))\nretail$Date <- as.Date(retail$InvoiceDate)\nretail$Time <- format(retail$InvoiceDate,\"%H:%M:%S\")\nretail$InvoiceNo <- as.numeric(as.character(retail$InvoiceNo))\ndplyr::glimpse(retail)\n\n\nretail$Time <- as.factor(retail$Time)\nretail %>%\n ggplot(aes(x=Time)) + \n geom_histogram(stat=\"count\",fill=\"indianred\")\n\n\ndetach(\"package:plyr\", unload=TRUE)\nretail %>% \n group_by(InvoiceNo) %>% \n summarize(n_items = mean(Quantity)) %>%\n ggplot(aes(x=n_items))+\n geom_histogram(fill=\"indianred\", bins = 100000) + \n geom_rug()+\n coord_cartesian(xlim=c(0,80))\n\n\n\nretail %>%\n group_by(StockCode, Description) %>% \n summarize(count = n()) %>% \n arrange(desc(count))\n#error\nretail %>%\n ggplot(aes(x=reorder(Description,count), y=count))+\n geom_bar(stat=\"identity\",fill=\"indian red\")+\n coord_flip()\n\n\nretail_sorted <- retail[order(retail$CustomerID),]\nlibrary(plyr)\nitemList <- ddply(retail,c(\"CustomerID\",\"Date\"), \n function(df1)paste(df1$Description, \n collapse = \",\"))\nhead(itemList)\n\nitemList$CustomerID <- NULL\nitemList$Date <- NULL\ncolnames(itemList) <- c(\"items\")\nhead(itemList)\n\nwrite.csv(itemList,\"./data/market_basket.csv\", quote = FALSE, row.names = TRUE)\n\n#AR Analysis\ntr <- read.transactions('./data/market_basket.csv', format = 'basket', sep=',')\nhead(tr)\nLIST(tr[1:5])\nsummary(tr)\n\nitemFrequencyPlot(tr, topN=10, type='absolute')\n#graphics error\ndev.off()\ngraphics.off()\npar(mar=c(1,1,1,1))\n\nrules <- apriori(tr, parameter = list(supp=0.001, conf=0.8))\nrules <- sort(rules, by='confidence', decreasing = TRUE)\nsummary(rules)\n\ninspect(rules[1:10])\n\ntopRules <- rules[1:10]\nplot(topRules)\n\nplot(topRules, method=\"graph\")\n\nplot(topRules, method = \"grouped\")\n\n\n" }, { "path": "45a-AR/99-ar-basketanalysis2.R", "content": "#Market Basket Analysis2\n#https://datascienceplus.com/a-gentle-introduction-on-market-basket-analysis%E2%80%8A-%E2%80%8Aassociation-rules/\n" }, { "path": "45a-AR/99-ar-policechecks.R", "content": "# AR - Police Checks\n#http://michael.hahsler.net/research/arules_RUG_2015/demo/#slides-code-and-about-the-author\n\nif(!file.exists(\"./data/SQF 2012.csv\")) {\n download.file(\"http://michael.hahsler.net/research/arules_RUG_2015/demo/Stop-and-Frisk-2012.zip\",\"./data/Stop-and-Frisk-2012.zip\")\n unzip(\"./data/Stop-and-Frisk-2012.zip\")\n}\n\ndat <- read.csv(\"./data/SQF 2012.csv\")\ndim(dat)\n\n\ndata <- list(\n c(\"a\",\"b\",\"c\"),\n c(\"a\",\"b\"),\n c(\"a\",\"b\",\"d\"),\n c(\"b\",\"e\"),\n c(\"b\",\"c\",\"e\"),\n c(\"a\",\"d\",\"e\"),\n c(\"a\",\"c\"),\n c(\"a\",\"b\",\"d\"),\n c(\"c\",\"e\"),\n c(\"a\",\"b\",\"d\",\"e\")\n)\n\nt <- as(data, \"transactions\")\nt\n## Mine itemsets with tidLists.\nf <- eclat(data, parameter = list(support = 0, tidLists = TRUE))\n\n?tidLists\nf\ntidLists(f)\n## Get dimensions of the tidLists.\ndim(tidLists(f))\n\n## Coerce tidLists to list.\nas(tidLists(f), \"list\")\n\n## Inspect visually.\nimage(tidLists(f))\n\n##Show the Frequent itemsets and respectives supports\ninspect(f)\n" }, { "path": "45a-AR/AR-Weka", "content": "=== Run information ===\n\nScheme: weka.associations.Apriori -N 10 -T 0 -C 0.9 -D 0.05 -U 1.0 -M 0.1 -S -1.0 -c -1\nRelation: dar1w-weka.filters.unsupervised.attribute.Remove-R7\nInstances: 50\nAttributes: 6\n java\n cpp\n dbms\n dwm\n cbnst\n os\n=== Associator model (full training set) ===\n\n\nApriori\n=======\n\nMinimum support: 0.55 (27 instances)\nMinimum metric : 0.9\nNumber of cycles performed: 9\n\nGenerated sets of large itemsets:\n\nSize of set of large itemsets L(1): 6\n\nSize of set of large itemsets L(2): 6\n\nSize of set of large itemsets L(3): 3\n\nBest rules found:\n\n 1. java=Yes dbms=Yes 27 ==> cpp=Yes 27 conf:(1)\n 2. dbms=Yes dwm=Yes 27 ==> cpp=Yes 27 conf:(1)\n 3. java=Yes 29 ==> cpp=Yes 28 conf:(0.97)\n 4. java=Yes 29 ==> dwm=Yes 28 conf:(0.97)\n 5. cpp=Yes dbms=Yes 28 ==> java=Yes 27 conf:(0.96)\n 6. java=Yes cpp=Yes 28 ==> dbms=Yes 27 conf:(0.96)\n 7. java=Yes dwm=Yes 28 ==> cpp=Yes 27 conf:(0.96)\n 8. java=Yes cpp=Yes 28 ==> dwm=Yes 27 conf:(0.96)\n 9. cpp=Yes dbms=Yes 28 ==> dwm=Yes 27 conf:(0.96)\n10. java=Yes 29 ==> dbms=Yes 27 conf:(0.93)\n" }, { "path": "45a-AR/ar-case-liberty.R", "content": "# Transactions from Liberty Store\n# Create AR for the data with 33% support level & 66% conf\n\nbarley = c(1,NA, 1, NA, 1, NA, 1, NA, 1, 1, NA, 1)\ncorn = c(1, 1, NA, 1, NA, NA, NA, NA, 1, 1, 1, 1)\ngram = c(1, NA, 1,1,1,1,NA, NA, 1,1, NA, NA)\nmillet = c(1,1,NA, NA, 1, NA, 1,1,1,NA,1,1)\nrice = c(1,1,1,1,1,1,1,1,NA,1,1,1)\nwheat = c(1,1,1,1,NA,1,1,1,NA,1,1,1)\nsapply(list(barley, corn, gram, millet, rice, wheat), length)\n\nstores = data.frame(barley,corn,millet, rice, wheat)\napply(stores, 2, length)\napply(stores, 2, is.na)\ncolSums(!is.na(stores))\n\n(stores1= as.data.frame(apply(stores, 2, as.factor)))\n\nlibrary(arules)\nstores2= as(stores1, 'transactions')\ninspect(stores2)\n\nfitems1 = arules::eclat(stores2, parameter = list(support=0.33, minlen=2, maxlen=4))\ninspect(fitems1)\nrules1 = ruleInduction(fitems1, stores2, confidence=.7)\nrules1\ninspect(rules1)\n\n\n# Part 2\n\nrules2 = apriori(stores2, parameter =list(supp=0.33, conf=.7, minlen=2, maxlen=6))\ninspect(rules2)\n\n\n#Sort\n(top.support <- sort(rules2, decreasing = TRUE, na.last = NA, by = \"support\"))\ninspect(head(top.support,10))\n\ntop.confidence <- sort(rules2, decreasing = TRUE, na.last = NA, by = \"confidence\")\ninspect(head(top.confidence,10))\n\n# lhs, rhs\nrules3 <- apriori(stores2, parameter=list(supp = 0.1, conf = 0.8), appearance = list(lhs = c(\"wheat=1\", \"millet=1\"),default = \"rhs\"))\ntop.lift3 = sort(rules3, decreasing = TRUE, na.last = NA, by = \"lift\")\ninspect(top.lift3)\ninspect(head(subset(top.lift3, lhs %pin% \"wheat\"), 3))\n\nrules4 <- apriori(stores2, parameter=list(supp = 0.001, conf = 0.3), appearance = list(lhs = c(\"wheat=1\") , rhs= c('rice=1')))\ntop.lift4 = sort(rules4, decreasing = TRUE, na.last = NA, by = \"lift\")\ninspect(top.lift4)\ninspect(head(subset(top.lift4, lhs %pin% \"wheat\"), 3))\n\n\n\n\nas(rules2, \"data.frame\")\n" }, { "path": "45a-AR/ar-groceries2.R", "content": "# Association Rule - Groceries\n#http://datascienceplus.com/implementing-apriori-algorithm-in-r/\n\ndf_groceries <- read.csv(\"./data/groceries.csv\")\nstr(df_groceries)\nhead(df_groceries)\n#Implementing Market Basket Analysis using Apriori Algorithm\ndim(df_groceries)\n#read transactions\n\ndf_sorted <- df_groceries[order(df_groceries$Member_number),]\ndf_sorted\n\n#convert member number to numeric\ndf_sorted$Member_number <- as.numeric(df_sorted$Member_number)\nstr(df_sorted)\nhead(df_sorted)\n#convert item description to categorical format\ndf_sorted$itemDescription <- as.factor(df_sorted$itemDescription)\nstr(df_sorted)\n#convert dataframe to transaction format using ddply; \n\n#if(sessionInfo()['basePkgs']==\"dplyr\" | sessionInfo()['otherPkgs']==\"dplyr\"){\n# detach(package:dplyr, unload=TRUE)\n#}\nlibrary(dplyr)\n\n#group all the items that were bought together; by the same customer on the same date\nlibrary(plyr)\n?ddply\n\ndf_itemList <- ddply(df_groceries, c(\"Member_number\",\"Date\"), function(df1)paste(df1$itemDescription,collapse = \",\"))\n\n#remove member number and date\ndf_itemList$Member_number <- NULL\ndf_itemList$Date <- NULL\nstr(df_itemList)\ncolnames(df_itemList) <- c(\"itemList\")\nstr(df_itemList)\nhead(df_itemList,n=20)\ndf_itemList\n#write to csv format\nwrite.csv(df_itemList,\"./data/ItemList.csv\", row.names = TRUE)\n\n#-------------------- association rule mining algorithm : apriori ---#\n\n#load package required\nlibrary(arules)\n\n#convert csv file to basket format - transactions\ntxn = read.transactions(file=\"./data/ItemList.csv\", rm.duplicates= TRUE, format=\"basket\",sep=\",\",cols=1);\ntxn = read.transactions(file.choose(), rm.duplicates= TRUE, format=\"basket\",sep=\",\",cols=1);\ntxn\ninspect(txn)\n#remove quotes from transactions\ntxn@itemInfo$labels <- gsub(\"\\\"\",\"\",txn@itemInfo$labels)\n\n\n#run apriori algorithm\nbasket_rules <- apriori(txn,parameter = list(minlen=2,sup = 0.01, conf = 0.7, target=\"rules\"))\n#basket_rules <- apriori(txn,parameter = list(minlen=2,sup = 0.00001, conf = 0.01, target=\"rules\"),appearance = list(lhs = \"CLEMENTINES\")))\ninspect(basket_rules) # no rule\n\n#check if tm is attched; if yes then detach\nif(sessionInfo()['basePkgs']==\"tm\" | sessionInfo()['otherPkgs']==\"tm\"){\n detach(package:sentiment, unload=TRUE)\n detach(package:tm, unload=TRUE)\n}\n\n\n#view rules\ninspect(basket_rules)\n\n#convert to datframe and view; optional\ndf_basket <- as(basket_rules,\"data.frame\")\ndf_basket$confidence <- df_basket$confidence * 100\ndf_basket$support <- df_basket$support * nrow(df)\n\nwrite.csv(df_basket,\"Rules_20.csv\",row.names = FALSE)\n\n#plot the rules\nlibrary(arulesViz)\nplot(basket_rules)\n\nset.seed(8000)\nplot(basket_rules, method = \"grouped\", control = list(k = 5))\n\nplot(basket_rules[1:10,], method=\"graph\", control=list(type=\"items\"))\n\nplot(basket_rules[1:10,], method=\"paracoord\", control=list(alpha=.5, reorder=TRUE))\n\nitemFrequencyPlot(txn, topN = 5)\n\nplot(basket_rules[1:10,],measure=c(\"support\",\"lift\"),shading=\"confidence\",interactive=T)\n\n" }, { "path": "45a-AR/my_basket1.txt", "content": "'milk', 'bread'\n'bread', 'milk'\n'milk', 'butter'" }, { "path": "46a-GD/aboutSL", "content": "# Supervised Learning\nAlgo consists of a target/ outcome variable (DV) which is to be predicted from a given set of predictors (IV). Using tese set of variables, we generate a function that map inputs to desired outputs. \nThe training process continues until the model achieves a desired level of accuracy on the training data. \nEgs - Regression, Decision Treee, Random Forest, KNN, Logistic Regression\n" }, { "path": "46a-GD/gradientdescent1.R", "content": "# Gradient Descent\n# http://econometricsense.blogspot.in/2011/06/gradient-descent.html\n# Read above gradient concepts\n#http://econometricsense.blogspot.in/2011/11/gradient-descent-in-r.html\n#f(x) = x^4 - 3*x^3 + 2\n# set up a stepsize\nalpha = 0.003\n\n# set up a number of iteration\niter = 500\n\n# define the gradient of f(x) = x^4 - 3*x^3 + 2\ngradient = function(x) return((4*x^3) - (9*x^2))\n\n# randomly initialize a value to x\nset.seed(100)\nx = floor(runif(1)*10)\nx\n# create a vector to contain all xs for all steps\nx.All = vector(\"numeric\",iter) # iterate 500 times\n#vector('integer',10)\n?vector\nx.All\n# gradient descent method to find the minimum\n# Stepwise \n#x ;gradient(x); alpha*gradient(x); x - alpha*gradient(x); x = x - alpha*gradient(x)\n#x ;gradient(x); alpha*gradient(x); x - alpha*gradient(x); x = x - alpha*gradient(x)\n#...\n# these value of x are stored in blank vector\nfor(i in 1:iter){\n x = x - alpha*gradient(x)\n x.All[i] = x\n print(x) # print last value of x\n}\nhead(x.All); x\n# print result and plot all xs for every iteration\nprint(paste(\"The minimum of f(x) is \", x, sep = \"\"))\n#layout(matrix(c(1,2,3,4),2,2))\nlayout(1,1)\nplot(x.All, type = \"l\")\n\n" }, { "path": "46a-GD/gradientdescent2.R", "content": "# Multivariate Gradient Descent \n# https://www.r-bloggers.com/multivariable-gradient-descent/\n\nx <- seq(-5,5,length=100)\nx\ny <- x # similar values of x and y\n\nfun <- function(x,y)\n{\n return(x**2+y**2-2*x*y)\n}\n# Outer Product\n#x=1:9 ; x%o%x\n#outer(x,x,'*')\n\nz <- outer(x,y,fun) # Outer Product of arrays\nz\n\n?persp # Perspective Plots of surface over x-y plane\n\n\npersp(x,y,z,theta=30,phi=30,expand=0.5,col=\"lightgreen\",\n ltheta=100,xlab=\"x\",ticktype=\"detailed\",ylab=\"y\",zlab=\"z\")\n#\n#Well in this case, we need to calculate two thetas in order \n#to find the point (theta,theta1) such that f(theta,theta1) = minimum.\n#" }, { "path": "46a-GD/gradientdescent3.R", "content": "# Gradient Descent in R\n# https://www.r-bloggers.com/gradient-descent-in-r/\n\nxs <- seq(0,4,len=20) # create some values\nxs\n# define the function we want to optimize\n\nf <- function(x) {\n 1.2 * (x-2)^2 + 3.2\n}\n\n# plot the function \nplot(xs , f (xs), type=\"l\",xlab=\"x\",\n ylab=expression(1.2(x-2)^2 +3.2))\n\n# calculate the gradeint df/dx\n# derivative of the function\ngrad <- function(x){\n 1.2*2*(x-2)\n}\n\n\n# df/dx = 2.4(x-2), if x = 2 then 2.4(2-2) = 0\n# The actual solution we will approximate with gradeint descent\n# is x = 2 as depicted in the plot below\n\nlines (c (2,2), c (3,8), col=\"red\",lty=2)\ntext (2.1,7, \"Closedform solution\",col=\"red\",pos=4)\n#http://mathworld.wolfram.com/Closed-FormSolution.html\n#\n\n# gradient descent implementation\nx <- 0.1 # initialize the first guess for x-value\nxtrace <- x # store x -values for graphing purposes (initial)\nftrace <- f(x) # store y-values (function evaluated at x) for graphing purposes (initial)\nf(x)\nstepFactor <- 0.6 # learning rate 'alpha'\nfor (step in 1:100) {\n x <- x - stepFactor*grad(x) # gradient descent update\n xtrace <- c(xtrace,x) # update for graph\n ftrace <- c(ftrace,f(x)) # update for graph\n print(x)\n \n }\n\nlines ( xtrace , ftrace , type=\"b\",col=\"blue\")\ntext (0.5,6, \"Gradient Descent\",col=\"blue\",pos= 4)\n\n# print final value of x\nprint(x) # x converges to 2.0\n" }, { "path": "46a-GD/gradientdescent4.R", "content": "# gradient descent4\n#https://rstudio-pubs-static.s3.amazonaws.com/159940_4a7d620cb4e0460486c364281cdf5780.html\n\n#First, let’s generate some data to work with, let’s define our training example with 4 features:\n \nset.seed(11)\nx <- matrix(rnorm(400), ncol = 4)\nx\ny <- rnorm(100)\ny\nm <- length(y)\nm\nX<-cbind(rep(1, 100), x)\nX\ntheta<-rep(0,5)\ntheta\n#Second, set up the cost function for least square linear regression:\nlength(y) \ncompCost<-function(X, y, theta){\n m <- length(y)\n J <- sum((X%*%theta - y)^2)/(2*m)\n return(J)\n }\n#Next, set up the gradient descent function, running for iterations:\n \ngradDescent<-function(X, y, theta, alpha, num_iters){\n m <- length(y)\n J_hist <- rep(0, num_iters)\n for(i in 1:num_iters){\n \n # this is a vectorized form for the gradient of the cost function\n # X is a 100x5 matrix, theta is a 5x1 column vector, y is a 100x1 column vector\n # X transpose is a 5x100 matrix. So t(X)%*%(X%*%theta - y) is a 5x1 column vector\n theta <- theta - alpha*(1/m)*(t(X)%*%(X%*%theta - y))\n \n # this for-loop records the cost history for every iterative move of the gradient descent,\n # and it is obtained for plotting number of iterations against cost history.\n J_hist[i] <- compCost(X, y, theta)\n }\n # for a R function to return two values, we need to use a list to store them:\n results<-list(theta, J_hist)\n return(results)\n }\n#Then, let’s set a training rate alpha and number of iterations to perform gradient descent:\n \nalpha <- .1\nnum_iters <- 150\nresults <- gradDescent(X, y, theta, alpha, num_iters)\ntheta <- results[[1]]\ncost_hist <- results[[2]]\nprint(theta)\n## [,1]\n## [1,] 0.04636521\n## [2,] 0.09681053\n## [3,] 0.10089729\n## [4,] -0.11856147\n## [5,] -0.20665900\n#Finally let’s plot the cost history:\n \nplot(1:num_iters, cost_hist, type = 'l')\n" }, { "path": "46a-GD/regr1.R", "content": "# Regression\n\n#Load Train and Test datasets\n#Identify feature and response variable(s) and values must be numeric and numpy arrays\n#x_train <- input_variables_values_training_datasets\n#y_train <- target_variables_values_training_datasets\n#x_test <- input_variables_values_test_datasets\n\nx <- data.frame(cbind(x_train,y_train))\n# Train the model using the training sets and check score\nlinear <- lm(y_train ~ ., data = x)\nsummary(linear)\nlayout(matrix(c(1,2,3,4),2,2))\nplot(linear)\nhead(x_test)\n\n#Predict Output\n#newdata = data.frame(waiting=80) # wrap the parameter\n#predict(eruption.lm, newdata) # apply predict \nnewdate = data.frame()\npredicted= predict(linear,x_test) \n?predict\n" }, { "path": "47A-TS/12b-TS-add-mult.R", "content": "# TS - Log of Values\n\n#additive ZA = T + S + I (+ e)\n#multiplicative ZM = T * S +* I (+ e)\nT=4 ; S=5; I=6\n(A = T + S + I)\n(M = T * S * I)\n\n#multiplicative to additive\nlog(M)\nlog(T)+ log(S) + log(I)\n\nlog(32); 5 * log(2); log(2) + log(2) + log(2) + log(2) + log(2) \n\n" }, { "path": "47A-TS/16c-dates-split1.R", "content": "# Split Data into parts\n\n#Split date and time seperately\n#M1----\n(tdatetime = Sys.time())\n(splitTime <- sapply(strsplit(as.character(tdatetime), \" \"), \"[\", 2)) #time\n(splitDate <- sapply(strsplit(as.character(tdatetime), \" \"), \"[\", 1)) #date\n\n# M2----\n(df <- data.frame(datetime = c( \"13:11:2013 15:39\" , \"13:11:2013 16:15\" , \"13:11:2013 17:52\" ) ))\n(new <- do.call( rbind , strsplit( as.character( df$datetime ) , \" \" ) ))\n\n#M3----\nformat(as.POSIXct(tdatetime,format=\"%Y:%m:%d %H:%M:%S\"),\"%H:%M:%S\")\nformat(as.POSIXct(tdatetime,format=\"%Y:%m:%d %H:%M:%S\"),\"%Y:%m:%d\")\n\n#Time Stamp to POSIXT\ntd = 1377986400\nas.POSIXct(1377986400,origin=\"1970-01-01\")\n(td1 = as.POSIXct(as.numeric(td),origin=\"1970-01-01\"))\n\n\ndatetime2 <- as.POSIXct('4/17/2008 16:01', format='%m/%d/%Y %H:%M') \n(datetime2split <- strsplit(format(datetime2, \"%d %m %Y %H %M\"), ' ') )\n\n(x2 = as.POSIXct(td1, format='%m/%d/%Y %H:%M'))\n(y2 <- strsplit(format(x2, \"%d %m %Y %H %M\"), ' ') )\n\n#-----\n# In R, there are two basic data types around date and time in R. One is Date, which contains only the date information like 2016–01–10. Another is POSIXct, which has both date and time information like 2016–01–10 10:45:20.\n\nlibrary(dplyr)\ndateposix = seq(1377986400, 1377986420, by=1)\ndf = data.frame(dateposix)\n(df$datetime = as.POSIXct(as.numeric(df$dateposix),origin=\"1970-01-01\"))\nlibrary(lubridate)\nhead(df)\ndf$dateposix2 = format(unclass(df$datetime), scientific=F)\ndf <- df %>% mutate(year=year(datetime), month = month(datetime), day= day(datetime), hour=hour(datetime), min=minute(datetime))\nhead(format(df, scientific=F))\n\nsplit1 =strsplit(format(df$datetime, \"%d %m %Y %H %M\"), ' ') #list form\nmatrix(as.numeric(unlist(split1)), ncol=5, byrow = T) \ndo.call(rbind, split1)\nas.Date(with(df$datetime, ISOdate(Year, Month, Day)))\n\n#-----\n\ndo.call(rbind, spl) \n\n# using seperate in tidyr\nlibrary(dplyr)\nhead(df)\ndf1 = df['datetime']\nhead(df1)\ndf1 %>% separate(datetime, into=c('Date', 'Time'), sep=' ')\n(year=as.numeric (format(df1$datetime,\"%Y\")))\n(month=as.numeric (format(df1$datetime,\"%m\")))\n(day=as.numeric (format(df1$datetime,\"%d\")))\n(hour=as.numeric (format(df1$datetime,\"%H\")))\n(min =as.numeric (format(df1$datetime,\"%M\")))\ndata.frame(df1,cbind(year, month, day, hour, min))\n\n#method\n(dt = df$datetime) #vector\n(df2 = df['datetime'])\nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_144') \nlibrary(qdap)\ndf2a = colSplit(df2$datetime, c(\"Date\", \"Time\"), \",\")\ndf2a\n\n#-----\nhead(df2)\nlist <- strsplit(df2$datetime, \" \")\nlibrary(\"plyr\")\ndf <- ldply(list)\ncolnames(df2) <- c('datetime', 'date', 'time')\n\n\n\n\n#-----\nDF <- readr::read_csv(\n 'ID,Date1,Date2\n 1,\"21-03-16 8:36\",\"22-03-16 12:36\"\n 1,\"23-03-16 9:36\",\"24-03-16 01:36\"\n 1,\"22-03-16 10:36\",\"25-03-16 11:46\"\n 1,\"23-03-16 11:36\",\"28-03-16 10:16\"'\n)\nDF\n\nlibrary(dplyr)\nlibrary(lubridate)\nlibrary(hms)\nDF\nDF %>% mutate_at(vars(Date1, Date2), dmy_hm) %>% \n mutate_at(vars(Date1, Date2), funs(\"date\" = date(.), \"time\" = as.hms(.)))\n\nDF %>% mutate_at(vars(Date1, Date2), dmy_hm) %>% \n mutate_at(vars(Date1, Date2), funs(\"date\" = date(.), \"time\" = as.hms(.))) %>% select(-Date1, -Date2)\n" }, { "path": "47A-TS/16d-dates1.R", "content": "#Handling and creating Dates in R\n\n#date in yyyy-mm-dd\ndates1a = c('2018-21-4', '2018-29-4')\nclass(dates1a)\n?as.Date\nas.Date(dates1a) # error\ndates1a\ndates1b = as.Date(dates1a, format='%Y-%d-%m')\nclass(dates1b)\ndates1b\ndates1b + 1\nas.Date('2018-04-29') + 1\nas.Date('2018-09-16') + 1:15\nas.Date('2018-09-16') + seq(1,10,2)\n\n\n#default format %Y-%m-%d\ndates2a = c('2018-4-21')\nclass(dates2a) # character so far\n(dates2b = as.Date(dates2a)) # convert to date\nclass(dates2b) # date now\n\nformat(dates2b, format=('%d-%m-%Y')) # print in different format\nformat(dates2b, format=('%d / %B - %Y')) # print in different format\nformat(dates2b, format=('%d / %b - %Y')) # print in different format\n\ndates2b\n\n\n#Format Types ----\n#%Y: 4-digit year (1982),%y: 2-digit year (82)\n#%m: 2-digit month (01)\n#%B: month (January), %b: abbreviated month (Jan)\n#, %d: 2-digit day of the month (13)\n#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)\n\nformat(Sys.Date(), \"%A\")\n\nformat(Sys.Date(), format=\"%A %a %d %m %b %B %y %Y\") # another format\n\npumba = c('09-07-1993', '08-08-1994', '04-11-1994')\nclass(pumba)\npumba1 = as.Date(pumba, '%d-%m-%Y')\nformat(pumba1, '%A')\n\nformat(as.Date('18-04-1994', '%d-%m-%Y'), '%A')\n\n#System Date\nSys.Date()\nformat(Sys.Date(), format=\"%A: %d %B\")\n\n#increment/ decrement dates \nstartdt = as.Date(\"2018-13-09\", format=\"%Y-%d-%m\")\n(course = startdt + 0:20) # start : 20 days course\n\ncat(format(course, format=\"%B-%d\"))\nmonths(course)\nweekdays(course)\nquarters(course)\npaste(quarters(course),'2018',sep='-')\n\n\n#Date to character\ndates2b; class(dates2b)\n(dtoc = as.character(dates2b))\nclass(dtoc)\n\n#one more practise on date format\ndates3a =as.Date('30Apr18',format(\"%d%b%y\"))\nclass(dates3a)\n\n#Difference in dates\ncourse\nmin(course) # first date\nmax(course) # last date\nrange(course) # start to end\nmean(course) # avg date ?\nmedian(course) # middle date\ncourse\ncourse[c(1,5)] # 1st & 5th date \ncourse[1] ; course[9]\n(duration = course[8] - course[1])\n(duration = max(course) - min(course))\n(duration1 = Sys.Date() - as.Date('14-08-1994', '%d-%m-%Y'))\n\n\n#Date of Birth\ndob = as.Date('14-08-1994', '%d-%m-%Y')\ndob\n\n\ndob1= dob + seq(1,1000,30) # create 1000 dates\nlength(dob1)\nSys.Date()\nas.numeric((Sys.Date() - dob)/365) #: Years\n#par(mfrow=c(1,1))\nboxplot(as.numeric((Sys.Date() - dob1)/365))# : Years\nas.numeric((Sys.Date() - dob1)/365)# : Years\nmean(as.numeric((Sys.Date() - dob1)/365))# : Years\n(duration2 = max(course) - min(course))\n\n#Sequence of Dates----\nseq(1,10,2)\n#seq(from, to, by, length.out = NULL, along.with = NULL, ...)\n?seq\n#by years : from start date to end date\nseq(as.Date(\"2016/1/1\"), as.Date(\"2018/5/1\"), \"years\")\nseq(as.Date(\"2016/1/1\"), as.Date(\"2018/5/1\"), \"months\")\nseq(as.Date(\"2017/1/1\"), by = \"days\", length.out = 6)\n\n(students = c('ashish','chirag'))\n\n## by month : 6 months\nseq(as.Date(\"2017/1/1\"), by = \"month\", length.out = 6)\nseq(as.Date(\"2017/1/1\"), by = \"month\", along.with = c(3,2,43,5,6,7)) # \nseq(as.Date(\"2017/1/1\"), by = \"month\", along.with = students) # \nseq(as.Date(\"2017/1/1\"), by = \"quarter\", along.with = students) # \n\nseq(as.Date(\"2017/1/1\"), by = \"month\", length.out = length(c(3,2,43))) \n\n\n## quarters\nseq(as.Date(\"2017/1/1\"), as.Date(\"2018/1/1\"), by = \"quarter\")\n\n#Find 7th of all months between two dates\n(dateseqF = seq(as.Date(\"2017/1/7\"), as.Date(\"2018/1/7\"), by = \"1 month\")) # order\n# dates in opposite order\n(dateseq = seq(as.Date(\"2018/1/7\"), as.Date(\"2017/1/7\"), by = \"-2 month\")) # reverse order\n# then reverse them\nrev(dateseq)# correct the order\nformat(rev(dateseq), \"%A : %d-%b\")\n\n#another Sequence : using another package\nchron::seq.dates(\"01/01/2017\", \"12/31/2017\", by = \"months\")\nchron::seq.dates(\"01/01/2017\", \"12/31/2017\", by = \"weeks\")\n\n" }, { "path": "47A-TS/23b-TS-Case-sales.R", "content": "#TS analysis\n#https://stepupanalytics.com/time-series-forecasting-using-r/\n\nlibrary(gsheet)\n\nurl = \"https://docs.google.com/spreadsheets/d/1D9WaU_CZUyLiZv0TyjFX8pgdJ1WIOh99n37BWvwRAz4/edit#gid=1372342646\"\n\nsales = as.data.frame(gsheet2tbl(url))\nhead(sales)\n\nlibrary(forecast)\n\ndata = ts(sales[,2], start = c(2003,1), frequency = 12)\nhead(data)\ndata\nplot(data, xlab = \"Years\", ylab = \"Sales\")\n\nlibrary(tseries)\nadf.test(data, alternative = \"stationary\")\n#Null Hypo : non stationary : it is non-stationary here\n\nplot(diff(data), ylab = \"1st Order Difference of Sales\")\n\ndata1 = diff(data)\ndata1\n\nadf.test(data1, alternative = \"stationary\")\nplot(diff(data1), ylab = \"2nd Order Difference of Sales\")\n\ndata2 = diff(data1)\ndata2\n\nadf.test(data2, alternative = \"stationary\")\n\nplot(log10(data), ylab = \"Log(Sales)\")\n\ndata10 = log10(data)\ndata10\n\nadf.test(data10, alternative = \"stationary\")\n\nplot(diff(data10), ylab = \"1st Order Difference of Log(Sales)\")\ndata11 = diff(data10)\nplot(diff(data11), ylab = \"2nd Order Difference of Log(Sales)\")\ndata12 = diff(data11)\n\nadf.test(data12, alternative = \"stationary\")\n\nacf(ts(diff(diff(data))), main = \"ACF Sales\")\n\npacf(ts(diff(diff(data))), main = \"PACF Sales\")\n\nARIMAfit = arima(diff(data), c(2,2,0))\nsummary(ARIMAfit)\n\nARIMAautofit = auto.arima(diff(data), approximation = TRUE, trace = TRUE)\nsummary(ARIMAautofit)\n\nacf(ts(diff(diff(log10(data)))), main = \"ACF Log(Sales)\")\npacf(ts(diff(diff(log10(data)))), main = \"PACF Log(Sales)\")\n\nARIMAautofit2 = auto.arima(diff(log10(data)), approximation = TRUE, trace = TRUE)\nsummary(ARIMAautofit2)\n\npred = predict(ARIMAautofit, n.ahead = 36)\npred\n\npred$pred\npred$se\n\nplot(forecast(ARIMAautofit, h = 36))\n\nacf(ts(ARIMAautofit$residuals), main = \"ACF Residual Of Sales\")\npacf(ts(ARIMAautofit$residuals), main = \"PACF Residual Of Sales\")\n\nautoexp_fit = ets(data10)\nsummary(autoexp_fit)\n\npred_ets = predict(autoexp_fit, h = 36)\nplot(forecast(autoexp_fit))\nsummary(pred_ets)\nacf(autoexp_fit$residuals, lag.max = 20, main = \"ACF Residual Of Sales\")\n" }, { "path": "47A-TS/23b-lubridate1.R", "content": "# Package Lubridate\n# Date Functions - Date & Time\n\n#Eg1\nlibrary(lubridate)\ndt=lubridate::ymd(\"20110604\")\ndt + 1:10\nlubridate::mdy(\"06-04-2011\")\nlubridate::dmy(\"04/06/2011\")\n#Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.\n\n#Eg1\n#If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.\nOlsonNames()\n?Sys.timezone()\nSys.timezone()\n\narrive <- ymd_hms(\"2018-04-27 18:40:15\", tz = \"Asia/Calcutta\")\narrive\nleave <- ymd_hms(\"2018-04-29 22:00:00\", tz = \"Asia/Calcutta\")\nleave\n\nleave - arrive # Time Difference\n\n#Setting and Extracting information\n#Functions\n\n#Eg\nsecond(arrive)\nsecond(arrive) = 25 #change\nsecond(arrive)\narrive\nminute(arrive)\nhour(arrive)\nday(arrive)\nwday(arrive)\nwday(arrive)\nwday(arrive, label = TRUE)\n\nweek(arrive)\n\nmonth(arrive)\nyear(arrive)\ntz(arrive)\n\n#Time Intervals\n#save an interval of time as an Interval class object\n(student1 <- interval(arrive, leave))\n(student2 <- arrive %--% leave)\n(student3 = interval(ymd(20180320, tz = \"Asia/Calcutta\"),\n ymd(20180327, tz = \"Asia/Calcutta\")))\n\n(courseperiod = interval(ymd(20180421, tz = \"Asia/Calcutta\"),\n ymd(20180425, tz = \"Asia/Calcutta\")))\n\nint_overlaps(student1, courseperiod)\n#TRUE\nint_overlaps(student3, courseperiod)\n\nsetdiff(student2, courseperiod)\nsetdiff(student3, courseperiod)\n\n?setdiff\n#2017-07-05 IST--2017-07-15 IST\n\n#Other Functions\n#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.\nint_start(courseperiod)\nint_end(courseperiod)\nint_flip(courseperiod)\nint_aligns(student1, courseperiod) #share end point\n\ndates = now() + days(1:10)\ndates\nint_diff(dates)\n\n# Years Betw ----------\nref_date <- as.Date('20/04/08',format='%d/%m/%y')\ntoday <- as.Date(Sys.Date(), format='%d/%m/%y')\nyear(arrive) = 2015\narrive\narrive2 = as.Date(arrive, format='%d/%m/%y')\n(yrsbetw = year(today)-year(ref_date))\n(yrsbetw = year(today)-year(arrive2))\n" }, { "path": "47A-TS/24b-Data-DFtoTS.R", "content": "# Times Series \n#DF to TS\n\nLines <- \"Dates Bajaj_close Hero_close\n3/14/2013 1854.8 1669.1\n3/15/2013 1850.3 1684.45\n3/18/2013 1812.1 1690.5\n3/19/2013 1835.9 1645.6\n3/20/2013 1840 1651.15\n3/21/2013 1755.3 1623.3\n3/22/2013 1820.65 1659.6\n3/25/2013 1802.5 1617.7\n3/26/2013 1801.25 1571.85\n3/28/2013 1799.55 1542\"\nLines\n\nlibrary(zoo)\nz <- read.zoo(text = Lines, header = TRUE, format = \"%m/%d/%Y\")\nz\n\nDF <- read.table(text = Lines, header = TRUE)\nz2 <- read.zoo(DF, format = \"%m/%d/%Y\")\nz2\n\nzz <- z\ntime(zz) <- 1:nrow(zz)\nzz\n\nas.ts(z)\nas.ts(zz)\n#The first has a time index which is the number of days since the Epoch (January 1, 1970) and will have NAs for missing days and the second will have 1, 2, 3, ... as the time index and no NAs.\n\n#Monthly Series\nz.m <- as.zooreg(aggregate(z, as.yearmon, mean), freq = 12)\nas.ts(z.m)\n\n#time_ser=ts(data,frequency=4,start=c(1954,2))\n" }, { "path": "47A-TS/24b-timeseries1.R", "content": "# Time Series\n#https://rpubs.com/ryankelly/tsa6\n\nlibrary(astsa, quietly=TRUE, warn.conflicts=FALSE)\nrequire(knitr)\nlibrary(ggplot2)\nkings<-scan('http://robjhyndman.com/tsdldata/misc/kings.dat', skip=3)\nkings\n(kings = ts(kings))\n\nbirths <- scan(\"http://robjhyndman.com/tsdldata/data/nybirths.dat\")\n\n(births <- ts(births, frequency = 12, start = c(1946, 1)))\n\nplot.ts(kings) \nplot.ts(births)\n\n\ngift <- scan(\"http://robjhyndman.com/tsdldata/data/fancy.dat\")\n(gift<- ts(gift, frequency=12, start=c(1987,1)))\n\nplot.ts(gift)\n\n(logGift = log(gift))\nplot.ts(logGift)\n\n\n#Simple Moving Average\nlibrary(TTR)\n(kingsSMA3 = SMA(kings, n=3))\nplot.ts(kingsSMA3)\n\n(kingsSMA8 = SMA(kings, n=8))\nplot.ts(kingsSMA8)\n\n(birthsComp <- decompose(births))\nplot(birthsComp)\n\n(birthsSeasonAdj <- births - birthsComp$seasonal)\nplot(birthsSeasonAdj)\n\n\n\n#\nrain <- ts(scan(\"http://robjhyndman.com/tsdldata/hurst/precip1.dat\",skip=1), start=c(1813))\nhead(rain)\nrain\nplot.ts(rain)\n\nrainF <- HoltWinters(rain, beta=FALSE, gamma = FALSE)\nrainF\n\nplot(rainF)\n\n\n\n\n\n#Forecasts using Exponential Smoothing\n#Simple Exponential Smoothing\nrain <- ts(scan(\"http://robjhyndman.com/tsdldata/hurst/precip1.dat\",skip=1), start=c(1813))\nplot.ts(rain)\n\nlibrary(forecast)\n\nrainF8 <- forecast:::forecast.HoltWinters(rainF, h=8)\nforecast:::plot.forecast(rainF8)\nacf(rainF8$residuals, na.action = na.pass)\nrainF8$residuals\nBox.test(rainF8$residuals, lag=20, type='Ljung-Box')\n\nplot.ts(rainF8$residuals)\n\n\nskirts <- ts(scan(\"http://robjhyndman.com/tsdldata/roberts/skirts.dat\",skip=5), start=c(1866))\nplot.ts(skirts)\n\nskirtsF <- HoltWinters(skirts, gamma=F)\nskirtsF\nskirtsF$SSE\nplot(skirtsF)\n\n\n# Forecast into the future\nskirtsF19 <- forecast:::forecast.HoltWinters(skirtsF, h=19)\nforecast:::plot.forecast(skirtsF19)\n\ndev.off(); graphics.off()\npar(mar=c(3,2,2,2))\npar(mfrow=c(3, 1))\nacf(skirtsF19$residuals, lag.max=20, na.action = na.pass)\nBox.test(skirtsF19$residuals, lag=20, type='Ljung-Box')\n\nplot.ts(skirtsF19$residuals)\n\npar(mfrow=c(1, 1))\n#Eg2\ngiftLog <- log(gift) # take natural log\nplot.ts(giftLog)\n\n\ngiftLogF <- HoltWinters(giftLog)\ngiftLogF\n\ngiftLogF$SSE\nplot(giftLogF)\n\ngiftLogF48 <- forecast:::forecast.HoltWinters(giftLogF, h=48) # predict 48 months ahead\nforecast:::plot.forecast(giftLogF48)\n\npar(mar=c(3,2,2,2))\n\n#Test\nacf(giftLogF48$residuals, lag.max=20, na.action = na.pass)\n\nBox.test(giftLogF48$residuals, lag=20, type='Ljung-Box')\nplot.ts(giftLogF48$residuals)\n\n" }, { "path": "47A-TS/24c-timeseries2.R", "content": "# Mov Average\n#https://www.otexts.org/fpp/6/2\n\nadmission = c(100,110,115,130,150,145,160,125,123,134)\nlength(admission)\nadmyr = ts(admission, start=2001)\nadmyr\n(100+110+115)/3\nforecast::ma(admyr, order=3)\nplot(admyr, main=\"Yearly Admissions\", ylab=\"Admissions\", xlab=\"Year\")\nlines(ma(admyr,3),col=\"red\")\n\nadmyr1 <- window(admyr,start=2005)\nadmyr1\n\nma4 <- ma(admyr1, order=4, centre=FALSE)\nma4\nma2x4 <- ma(admyr1, order=4, centre=TRUE)\nma2x4\n\n\n\n\nabbshares <- scan(file=\"\")\n\nlibrary(pracma)\nplot(abbshares, type = \"l\", col = 1, ylim = c(15, 30),\n main = \"Types of moving averages\", sub = \"Mid 2011--Mid 2012\",\n xlab = \"Days\", ylab = \"ABB Shares Price (in USD)\")\ny <- pracma::movavg(abbshares, 50, \"s\"); lines(y, col = 2)\ny <- pracma::movavg(abbshares, 50, \"t\"); lines(y, col = 3)\ny <- pracma::movavg(abbshares, 50, \"w\"); lines(y, col = 4)\ny <- pracma::movavg(abbshares, 50, \"m\"); lines(y, col = 5)\ny <- pracma::movavg(abbshares, 50, \"e\"); lines(y, col = 6)\ny <- pracma::movavg(abbshares, 50, \"r\"); lines(y, col = 7)\ngrid()\nlegend(120, 29, c(\"original data\", \"simple\", \"triangular\", \"weighted\",\n \"modified\", \"exponential\", \"running\"),\n col = 1:7, lty = 1, lwd = 1, box.col = \"gray\", bg = \"white\")\n\n#x\nelecs = read.csv(file='./data/elect.csv')\nelecs1 = elecs$Sales\nelectsales = ts(elecs1, start= 1989)\nelectsales\n\nforecast::ma(elecsales, order=3)\nforecast::ma(elecsales, order=5)\n" }, { "path": "47A-TS/24f-ts-data.R", "content": "#ts9\n# save a numeric vector containing 72 monthly observations # from Jan 2009 to Dec 2014 as a time series object\nmyvector = round(runif(6*12)*100,0)\nmyvector\nmyts <- ts(myvector, start=c(2009, 1), end=c(2014, 12), frequency=12)\nmyts\n# subset the time series (June 2014 to December 2014)\nmyts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))\nmyts2\n# plot series\nplot(myts)\n\n\n#Eg2\nsales= c(18,33,41,7,34,35,24,25,24,21,25,20, 22,31,40,29,25,21,22,54,31,25,26,35)\ntsales = ts(sales, start=c(2003,1), frequency=12)\ntsales\nplot(tsales)\nplot(tsales, type='o', pch=19)\nstart(tsales)\nend(tsales)\nfrequency(tsales)\ntsales.subset = window(tsales, start=c(2003,5), end=c(2004,6))\ntsales.subset\n\n#Yearly Series\n\nmyvector = round(runif(50) * 100,0)\nmyvector\nmyts <- ts(myvector, start=c(2009,2), end=c(2014,11), frequency=3)\nmyts\nmyts2 <- window(myts, start=c(2010), end=c(2013))\nmyts2\n\n\n# quarterly \n\nmyvector = round(runif(50) * 100,0)\nmyvector\nmyts <- ts(myvector, start=c(2009,1), end=c(2014,11), frequency=3)\nmyts\nstr(myts)\nplot(myts, type='o', pch=19)\ntext()\n\nplot.ts(myts, type='o', pch=19, xy.labels=F)\n\naxis(1, myts$Date, format(myts$Date, \"%b %d\"), cex.axis = .7)\n\n\nmyts <- ts(myvector, start=c(2009,2), end=c(2014,9), frequency=3)\nmyts\nstr(myts)\n\n\nmyts2 <- window(myts, start=c(2010), end=c(2013))\nmyts2\nplot(myts, type='o', pch=19)\nstr(myts2)\n\n\n\nmyts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))\nplot(myts,type='o', pch=19)\ntext()\nplot(myts2,type='o', pch=19)\n\n\nlibrary(zoo)\nx.Date <- as.Date(paste(rep(2003:2004, each = 12), rep(1:12, 2), 1, sep = \"-\"))\nx.Date\n?zoo\nx <- zoo(rnorm(24), x.Date)\nx\nplot(x)\n\n\n\n#If we want different tick locations, we can suppress the default axis plotting and add our own:\n \nplot(x, xaxt = \"n\")\naxis(1, at = time(x), labels = FALSE)\n#Or combine them:\n \nplot(x)\naxis(1, at = time(x), labels = FALSE)\n\n## weekly ticks\nplot(x)\ntimes <- time(x)\nticks <- seq(times[1], times[length(times)], by = \"weeks\")\naxis(1, at = ticks, labels = FALSE, tcl = -0.3)\n\n\nlibrary(zoo)\nlibrary(lattice)\n\n# create test data\nz <- zooreg(1:83, start = as.Date(\"2009-04-01\"), deltat = 7)\n\nxyplot(z)\n\n\n\n\n# Time series plots with good X axis labels\nlibrary(zoo)\n# data\ntoday = Sys.Date()\ndates = as.Date((today-500):today)\ndates\nz = zoo (100+cumsum(rnorm(501)), dates)\nz\n# method1 : default X axis labels do not look good\n?plot.zoo\nplot(z)\n?plot.ts\nplot(ts(z))\ntime(z)\n# method 2 : Lattice\nlibrary(lattice)\n?xyplot.zoo\nxyplot(z)\nxyplot(z, lwd=2, col=\"tomato\")\n\n# method 3 : XTS\nlibrary(xts)\n?plot.xts\nplot(as.xts(z))\nplot(as.xts(z), auto.grid=F, major.format=\"%b %y\", las=2)\n\n# method 4 : Base graph\ntimeline = time(z)\nsummary(timeline)\nindex = seq(from=1, to=length(timeline), 90) # put ticks every 90 days\nplot(z, xaxt=\"n\")\naxis(side=1, at=timeline[index], label=format(timeline[index], \"%b %y\"), cex.axis=0.8)\n\n# method 5 : ggplot\nlibrary(ggplot2)\nlibrary(scales)\n?date_breaks\ndf = data.frame(date=as.POSIXct(time(z)), value=as.numeric(z))\nhead(df)\n# default plot\nggplot(df, aes(x=date, y=value)) + geom_line()\n# formatted\nggplot(df, aes(x=date, y=value)) + geom_line() + \n scale_x_datetime(labels=date_format(\"%b '%y\"))\n# custom breaks\nggplot(df, aes(x=date, y=value)) + geom_line() + \n scale_x_datetime(labels=date_format(\"%b '%y\"), breaks=date_breaks(\"3 months\"))\n" }, { "path": "47A-TS/26b-ts-components-airp.R", "content": "# Time Series Case Study - Decomposition\n\n#https://rpubs.com/emb90/137525\n# Data Set - AirPassengers\nx=c(9.23221232,5.3430000)\nx\noptions(digits=2)\nx\n\n?AirPassengers\nhead(AirPassengers)\nAirPassengers\nstr(AirPassengers)\nclass(AirPassengers)\n\n#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns\n# TS data components : Level + Irregular + Seasonal\n\n#stl(x, s.window, t.window = ) # command to do decomp\nstl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time\n# save it in an object\n\nplot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model\n#no cyclic here - only seasonal, trend, irregual\n#s.window - specifies seasonal effects to be identical across years\n#can handle on additive models\n\nstl1 = stl(AirPassengers, s.window = 'periodic')\nplot(stl1) # actual data, seasonal, long term trends, remainder/ irregular\n\nclass(stl1)\n\nstl1$time.series\n#(df = stl1$time.series)\n#df = as.data.frame(df)\n#write.csv(df, './data/airpsng.csv')\n\n\n#Additive Model Y = Trend + Seasonal + Irregular\n#sales increase by 300 qty in month of Nov\n#Multiplicative Model Y = Trend * Seasonal * Irregular\n#sales increase by 10% in month of Nov\n\n\n#dataset\nAirPassengers\nclass(AirPassengers)\n\n# Plot\nplot(AirPassengers)\n#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested\n\n#stabilise the plot\nLogAirPassengers = log(AirPassengers) # make it additive because stl handles only additive models\n\n# YA = T + S + I : \n#YM= T * S * I : take log of this\n# log(YM) = log(T) + log(S) +log(I)\n\nplot(LogAirPassengers) #stabilises variation due to multiplication\n#looks like additive : no increase of seasonsal component now over years\n\n(m1 = matrix(1:2, nrow=1, byrow = F))\nlayout(m1)\nplot(AirPassengers); plot(LogAirPassengers) # see again the change\n\n\n#STL\nfit = stl(LogAirPassengers, s.window = 'periodic' )\n#Seasonal components constrainted to be same across years : periodic\n\nplot(fit)\nfit$time.series #decompose the data into S, T, R/I \n\n#december of all months same value for seasonal\n#this was after taking log : so take antilog\n#toprow = actual data with all series\nexp(fit$time.series)\n\nhead(exp(fit$time.series),n=20) # first 20 values see them\n\n# df= exp(fit$time.series)\n# names(df) = c('S','T','I')\n# head(AirPassengers)\n# head(cbind(AirPassengers, df))\n\n#Various Plots - Monthwise, quarter, \nlayout(matrix(1,nrow=1))\n#Avg of each month\nmonthplot(AirPassengers) #max traffic in Jun/ Jul across years\nmonthplot(fit, choice='seasonal') # less in winters, more in summers\nmonthplot(fit, choice='trend') #slight increase from Jan to Dec\n#trend increasing for each month, highest passengers in Jul\nmonthplot(fit, choice='remainder') # irregular components\n\n# see combined plots\n(m2 = matrix(1:3, nrow=3, byrow = T))\nlayout(m2) # change layout of plots\nmonthplot(fit, choice='seasonal')\nmonthplot(fit, choice='trend')\nmonthplot(fit, choice='remainder')\n\n\n# Practise with different methods - Self Practise\n\n#Decompose another way\nAP.decompM = decompose(AirPassengers, type = \"multiplicative\")\nplot(AP.decompM)\n\nlibrary(forecast)\n# Forecast # adjust for multiplicative model\nfit2b = ets(AirPassengers, model='MAM')\nfit2b\n(f2b=forecast(fit2b, 12))\nhead(f2b)$mean\n\n" }, { "path": "47A-TS/26c-ts-components.R", "content": "# Time Series Components\n\n# TS data components : Level + Irregular + Seasonal\n# Level changing - Trend ; Irregular are variations \n#http://slideplayer.com/1507363/5/images/5/Time-Series+Components.jpg\n#http://www.grroups.com/uploads_media/4a47a0db6e60853dedfcfdf08a5ca249%20%285%29.png\n#http://images.slideplayer.com/31/9794753/slides/slide_4.jpg\n#http://rstatistics.net/wp-content/uploads/2014/09/Additive-Decomposition-of-Time-series.png\n#https://www.frontiersin.org/files/Articles/144575/fpsyg-06-00727-HTML/image_m/fpsyg-06-00727-g002.jpg\n#https://image.slidesharecdn.com/operationsmanagement-forecasting-150217234022-conversion-gate02/95/operations-management-forecasting-11-638.jpg?cb=1432630382\n\n\n" }, { "path": "47A-TS/27b-ts-johnson.R", "content": "#Times Series Analysis \n# is the price of Johnson and Johnson shares change over time\n# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year\n# Can you forecast what future share prices will be and to what degree of accuracy\n\n#dataset - Johnson\n#Quarterly earnings per Johnson Shares\n#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA\n\nJohnsonJohnson\n\nlibrary(forecast)\n\n#ets auto select best predicton model\n?ets\n#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time\n\nfit1 = ets(JohnsonJohnson)\nfit1\n#alpha - trend\n#beta = seasonal\n#gamma - irregular\n\nJohnsonJohnson\nhead(JohnsonJohnson)\ntail(JohnsonJohnson)\n\n(f1= forecast(fit1,h=10)) # \n?forecast.ets\nplot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3) # linetype for forecast area\n#shaded portion is confidence intervals area\n\npar(mfrow=c(1,1))\n\n# ARIMA Forecasting : \n#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.\n\n#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg\n\nf2 = auto.arima(JohnsonJohnson)\nsummary(f2)\ntail(JohnsonJohnson) # last few values\nforecast(f2,h=5)\n\n\n#ARIMA Forecasting : compare two datasets\nlibrary(tseries)\nplot(JohnsonJohnson)\nndiffs(JohnsonJohnson)\nplot(diff(JohnsonJohnson))\n\nplot(Nile)\nplot(diff(Nile))\nndiffs(Nile)\n\n#-----\ndjj = diff(JohnsonJohnson)\nplot(djj)\n\ndnile = diff(Nile)\nplot(dnile)\n\n#----\nadf.test(djj)\n#if pv < 0.05 accept Alt Hypothesis that series is stationary\n\n#Model Selection \n#parameters p, d , q\n# d = no of diffs applied to make the series stationary\n#https://people.duke.edu/~rnau/arimrule.htm\nAcf(dnile)\n#Trail off to zero : Zero after lag ; 0,1(p)\n#Zero after lag q : Trails off to zero ; 1(q), 0 \n#Trails off to zero : Trial off to zero : 0,0\n#Nile - 1 large auto correlation at lag 1 : \n#Nile - pacf trails off to zero as the lags gets bigger\n?arima\nPacf(dnile)\n\nfit3 = arima(Nile, order=c(0,1,1)) # p,d,q\nfit3\n\n(fit3b = arima(Nile, order=c(1,1,1)))\n\n\n#Model Test\nqqnorm(fit3$residuals) # residuals ND\nqqline(fit3$residuals)\n\n#auto correl = 0 : check\nBox.test(fit3$residuals, type='Ljung-Box')\n#Test auto corr : H0= r=0 (True)\n\n#Forecast\nforecast(fit3,4)\n\nNile\n#Auto ARIMA\nlibrary(forecast)\n#forecast::auto.arima()\nfit4 = auto.arima(Nile)\nfit4\n\nforecast(fit4,5)\nplot(forecast(fit4,5))\n\n" }, { "path": "47A-TS/27c-ts-lm-uscons.R", "content": "# Time Series - Regression\n\ndata(\"usconsumption\")\n#https://www.otexts.org/fpp/4/8\n?usconsumption\n\nstr(usconsumption)\nhead(usconsumption)\nfit.ex3 <- tslm(consumption ~ income, data=usconsumption)\nplot(usconsumption, ylab=\"% change in consumption and income\",\n plot.type=\"single\", col=1:2, xlab=\"Year\")\nlegend(\"topright\", legend=c(\"Consumption\",\"Income\"),\n lty=1, col=c(1,2), cex=.9)\nplot(consumption ~ income, data=usconsumption, \n ylab=\"% change in consumption\", xlab=\"% change in income\")\nabline(fit.ex3)\nsummary(fit.ex3)\n\n\n\nfcast <- forecast(fit.ex3, newdata=data.frame(income=c(-1,1)))\nfcast\nplot(fcast, ylab=\"% change in consumption\", xlab=\"% change in income\")\n\n?austa\nausta\npar(mfrow=c(1,1)); par(mar=c(3,2,2,2))\nfit.ex4 <- tslm(austa ~ trend)\nfit.ex4\n\n(f <- forecast(fit.ex4, h=5,level=c(80,95)))\n\nplot(f, ylab=\"International tourist arrivals to Australia (millions)\",\n xlab=\"t\")\nlines(fitted(fit.ex4),col=\"blue\")\nsummary(fit.ex4)\n\ngraphics.off()\npar(mar=c(3,5,2,1))\npar(mfrow=c(2,2))\nres3 <- ts(resid(fit.ex3),s=1970.25,f=4)\nres3\nplot.ts(res3,ylab=\"res (Consumption)\")\nabline(0,0)\nAcf(res3)\nres4 <- resid(fit.ex4)\nres4\nplot(res4,ylab=\"res (Tourism)\")\nabline(0,0)\nAcf(res4)\n\npar(mfrow=c(2,1))\n\ntslm(AirPassengers ~ guinearice)\nplot(AirPassengers)\nplot(guinearice)\n" }, { "path": "47A-TS/28c-ts-lubridate1.R", "content": "#Package Lubridate\n\n#functions which allow you to specify the order in which year, month and day components appear in date\n\n\nlibrary(lubridate)\nymd(\"20170601\")\nmdy(\"06-01-2017\")\ndmy(\"01/06/2017\")\ndmy(\"01-06-2017\")\n\nSys.timezone(location=T)\n#282- Asia/Kolkata\n('Asia/Kolkatta')\n\n(arrive <- ymd_hms(\"2018-04-09 14:00:00\", tz = \"Asia/Calcutta\"))\n(leave <- ymd_hms(\"2018-04-14 13:00:00\", tz = \"Asia/Calcutta\"))\n(leave2 <- ymd_hms(\"2018-04-14 13:00:00\", tz = \"GMT\"))\nleave - arrive\nleave2- arrive\n#Check the seconds for arrival\nsecond(arrive)\n#change seconds in arrival data\nsecond(arrive) <- 25\narrive\n\n#Check which date/day/month I arrived\nmday(arrive) # arrived on 9 of the month\nwday(arrive) ## in no start from Sun-1, Mon-2 ..\nwday(arrive, label = TRUE) # arrived on Mon\nmonth(arrive)\nmonth(arrive, label=T, abbr=T)\nmonth(arrive, label=T, abbr=F)\nyear(arrive)\n\n\n#practise upto here \n#Students to look up other practise exercises for Data below\n\n\nmeeting <- ymd_hms(\"2011-07-01 09:00:00\", tz = \"Pacific/Auckland\")\n## \"2011-07-01 09:00:00 NZST\"\nwith_tz(meeting, \"America/Chicago\")\n## \"2011-06-30 16:00:00 CDT\"\n\nmistake <- force_tz(meeting, \"America/Chicago\")\n## \"2011-07-01 09:00:00 CDT\"\nwith_tz(mistake, \"Pacific/Auckland\")\n## \"2011-07-02 02:00:00 NZST\"\n## \nauckland <- interval(arrive, leave)\nauckland\n## 2011-06-04 12:00:00 NZST--2011-08-10 14:00:00 NZST\nauckland <- arrive %--% leave\n## 2011-06-04 12:00:00 NZST--2011-08-10 14:00:00 NZST\nauckland\ntdiff = arrive - leave\ntdiff\n\njsm <- interval(ymd(20110720, tz = \"Pacific/Auckland\"), ymd(20110831, tz = \"Pacific/Auckland\"))\njsm\nint_overlaps(jsm, auckland) ## TRUE\nsetdiff(auckland, jsm) ## 2011-06-04 12:00:00 NZST–2011-07-20 NZST\n\nminutes(2) # period\n## 2 minutes\ndminutes(2) # duration\n## 120s (~2 minutes)\ndhours(2)\nseconds(2)\ndyears(2)\nleap_year(2011)\n## FALSE\nymd(20110101) + dyears(1)\n## \"2012-01-01 UTC\"\nymd(20110101) + years(1)\n## \"2012-01-01 UTC\"\n\nleap_year(2012)\n## TRUE\nymd(20120101) + dyears(1)\n## \"2012-12-31 UTC\"\nymd(20120101) + years(1)\n## \"2013-01-01 UTC\"\n\nmeetings <- meeting + weeks(0:5)\nmeetings\n## [1] \"2011-07-01 09:00:00 NZST\" \"2011-07-08 09:00:00 NZST\"\n## [3] \"2011-07-15 09:00:00 NZST\" \"2011-07-22 09:00:00 NZST\"\n## [5] \"2011-07-29 09:00:00 NZST\" \"2011-08-05 09:00:00 NZST\"\n\nperiods = meeting + days(1:5)\nperiods\n\n\nsecond(arrive)\n## 0\nsecond(arrive) <- 25\narrive\n## \"2017-06-01 12:00:25 IST\"\nsecond(arrive) <- 0\nwday(arrive)\n## 5\nwday(arrive, label = TRUE)\n## Thurs\n\n\n\nmeeting <- ymd_hms(\"2017-07-01 09:00:00\", tz = \"Asia/Calcutta\")\nmeeting\n\nwith_tz(meeting, \"America/Chicago\")\nmistake <- force_tz(meeting, \"America/Chicago\")\nwith_tz(mistake, \"Asia/Calcutta\")\nmistake\n\n\nudit <- interval(arrive, leave)\nudit\nnakul <- arrive %--% leave\nnakul\n\ndhiraj <- interval(ymd(20170702, tz = \"Asia/Calcutta\"),\n ymd(20170704, tz = \"Asia/Calcutta\"))\ndhiraj\nudit\n#Will visit overlap with and his travels\nint_overlaps(udit, dhiraj) ## TRUE\n\n#For what part of Dhiraj's' visit will Udit will be there?\nsetdiff(udit, dhiraj)\nsetdiff(dhiraj, udit)\n\nminutes(2) # period\n## 2 minutes\ndminutes(2) # duration\n## 120s (~2 minutes)\neminutes(2)\n\n\nleap_year(2011)\n## FALSE\nymd(20110101) + dyears(1)\n## \"2012-01-01 UTC\"\nymd(20110101) + years(1)\n## \"2012-01-01 UTC\"\n\nleap_year(c(2012, 2017))\n## TRUE FALSE\nymd(20170101) + dyears(1)\n## \"2018-01-01\"\nymd(20170101) + years(1)\n## \"2018-01-01\"\n## \n\nmeeting <- ymd_hms(\"2017-07-01 09:00:00\", tz = \"Asia/Calcutta\")\nmeetings <- meeting + weeks(0:5)\nmeetings\n\nfriend = interval(ymd(20170705, tz = \"Asia/Calcutta\"),\n ymd(20170715, tz = \"Asia/Calcutta\"))\nfriend\nmypd = interval(ymd(20170702, tz = \"Asia/Calcutta\"),\n ymd(20170803, tz = \"Asia/Calcutta\"))\nmypd\nint_overlaps(friend, mypd)\n\n#For what part of Dhiraj's' visit will Udit will be there?\nsetdiff(friend, mypd)\n\n\n?interval\nmypd\nmeetings %within% mypd\n\nmeeting <- ymd_hms(\"2017-07-01 09:00:00\", tz = \"Asia/Calcutta\")\nmeetings <- meeting + weeks(0:5)\nmeetings\nmeetings %within% mypd\n\nmypd / edays(1)\nmypd / ddays(1)\nmypd / ddays(2)\nmypd / dminutes(1)\nmypd / dseconds(1)\nmypd / months(1)\nmypd / years(1)\n\n\nas.period(mypd %% months(1))\nas.period(mypd)\n\nlast_day <- function(date) {\n ceiling_date(date, \"month\") - days(1)\n}\n# try last_day(ymd(20000101) + months(0:11))\n\nlast_day(ymd(20000101))\nlast_day(ymd(20000101) + months(0:11))\n" }, { "path": "47A-TS/31c-TS-airp.R", "content": "# Time Series ts1\n#https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/\n\ndata(AirPassengers)\nclass(AirPassengers)\n#This tells you that the data series is in a time series format\nstr(AirPassengers)\nhead(AirPassengers); tail(AirPassengers)\nstart(AirPassengers)\n#This is the start of the time series\nend(AirPassengers)\n#[1] 1960 12\n#This is the end of the time series\nfrequency(AirPassengers) # $[1] 12\n#The cycle of this time series is 12months in a year\nsummary(AirPassengers)\n\n\n# Sec 2 -------------------------------------------------------------------\nAirPassengers\n#The number of passengers are distributed across the spectrum\nplot(AirPassengers)\n#This will plot the time series\nabline(reg=lm(AirPassengers~time(AirPassengers)))\n# This will fit in a line\n\n\n# Sec3 --------------------------------------------------------------------\n\ncycle(AirPassengers)\n#This will print the cycle across years.\nplot(aggregate(AirPassengers,FUN=mean))\n#This will aggregate the cycles and display a year on year trend\nboxplot(AirPassengers~cycle(AirPassengers))\n#Box plot across months will give us a sense on seasonal effect\n\n\nplot(aggregate(AirPassengers,FUN=mean))\nplot(log(AirPassengers))\nabline(reg=lm(log(AirPassengers) ~ time(AirPassengers)))\n\nplot(diff (log(AirPassengers)))\nabline(h=0, col='red')\nabline(reg=lm(diff(log(AirPassengers)) ~ time(AirPassengers)))\n\n\n#Model\nacf(AirPassengers)\nacf(diff (log(AirPassengers)))\npacf(diff (log(AirPassengers)))\n\nfit = arima(log(AirPassengers), c(0,1,1), seasonal =\n list(order = c(0,1,1), period = 12) )\npred = predict(fit, n.ahead = 10 * 12)\npred1 = 2.718 * pred$pred\nts.plot(AirPassengers, pred1, log='y', lty=c(1,3))\n\n" }, { "path": "47A-TS/33b-zoo-ts.R", "content": "# Zoo Package\n#Regular and Irregular Time Series\n# normal ts commands creates date in series/ sequence\n\nset.seed(1234)\noptions(digits=2)\n(sales = round(rnorm(30,50,10),0)) #normal distributed values\n\n\nlibrary(zoo)\n?zooreg\n(z1 = zooreg(sales)) #nothing much done here only type of object changed\nclass(z1)\n\n(z2 = zooreg(sales, start=as.Date(\"2016-01-01\")))\n\nsales\nlength(2000:2017) ; length(sales)\n(z3 = zooreg(sales, start=c(2000,3), end=c(2017,3), freq=12, calendar=T))\n?zoo - used for converting ts to zoo format\n#(z3 = zoo(sales, start=c(2000,3), end=c(2017,3), freq=12, calendar=T)) # error\n\n(sales4 = zooreg(sales, start=c(2016,2), freq=12))\n\n#compare ts and zoo\n(ts(sales,start=c(2016,2), freq=12))\nas.zoo(ts(x, start=c(2016,2), freq=12))\n\nzoo(sales, seq(2016.0, 2017.5, by=1/12), freq=12)\nzoo(sales, seq(2016.3, 2017.7, by=1/12), freq=12)\nzoo(sales, c(2016.1, 2016.4, 2016.13)) #irregular TS\n\nzoo(sales, yearqtr(seq(2016.25, 2017.5, by=1/4)))\nzooreg(sales, start=yearmon(2016), end=yearmon(2017))\n\nzooreg(sales[(1:5)], end = yearmon(2017)) #only end date specified, works backwards\n(z7= zooreg(sales, end = yearmon(2018.10))) #.10 - Feb, .25 - Apr\n\nfrequency(z7)\ndelta(z7)\ncycle(z7)\ncycle(z[-(3:4)])\nlag(z, k=-1)\ndiff(z)\nx <- c(1, 2, 3, 6)\nzz <- zoo(x, x)\nzz\nzr <- as.zooreg(zz)\nlag(zz, k = -1)\nlag(zr, k = -1)\ndiff(zz)\ndiff(zr)\n\n\n\ndd <- as.Date(c(\"2016-01-01\", \"2016-01-02\", \"2000-01-03\", \"2000-01-04\"))\nzrd <- as.zooreg(zoo(sales, dd))\nzrd\nstr(zrd)\nplot(zrd)\n" }, { "path": "47A-TS/35b-LSM-beer1.R", "content": "# LSM\nlibrary(fpp)\ndata(beer)\nbeer\nstr(beer)\n\n#Model Parabola\nlbeer<-log(beer)\nlbeer\nt<-seq(1956,1995.2,length=length(beer))\nt\nt2<-t^2\nt2\nplot(lbeer)\nlm(lbeer~ t + t2)\nlines(lm(lbeer~t+t2)$fit,col='red',lwd=3)\n\n\n\n#Model2\nlbeer<-log(beer)\nt<-seq(1956,1995.2,length=length(beer))\nt2<-t^2\nsin.t<-sin(2*pi*t)\ncos.t<-cos(2*pi*t)\nplot(lbeer)\nlines(lm(lbeer~t+t2+sin.t+cos.t)$fit,col=4)\nsummary(lm(lbeer ~ t+t2+sin.t+cos.t))\n" }, { "path": "47A-TS/38b-tsplots2a.R", "content": "# Plot 2 TS together\ngraphics.off()\nplot(AirPassengers)\nplot(guinearice)\nts.plot(..., gpars = list())\nset.seed(1)\nx = ts(rnorm(20), frequency = 4, start = c(1959, 2))\ny = ts(rnorm(20), frequency = 4, start = c(1959, 2))\nts.plot(x, y, gpars = list(col = c(\"black\", \"red\")))\n\n\n#Eg2\nset.seed(1)\nxz = zoo(ts(rnorm(20), frequency = 4, start = c(1959, 2)))\nyz = zoo(ts(rnorm(20), frequency = 4, start = c(1959, 2)))\n# Basic approach\nplot(xz)\nlines(yz, col = \"red\")\n# Panels\nplot.zoo(cbind(xz, yz))\n# Overplotted\nplot.zoo(cbind(xz, yz),\n plot.type = \"single\", col = c(\"red\", \"blue\"))\n\n\n#GGPLOT\nlibrary(ggplot2)\n\ndta <- data.frame(year = 2012,\n month = rep(seq(1,9),each=10),\n day = sample(seq(1,20),90,replace=T),\n Group = sample(c('A','B'),90,replace=T),\n Value = seq(1,90)\n)\n\ndta$Date <- apply(dta[,c('year','month','day')],1,paste,sep='',collapse='-')\ndta$Date <- as.Date(dta$Date)\n\nqplot(Date,Value,data=dta,geom='line',color=Group)\n\n\n\n#Eg4\ntemp<- rnorm(365, 5, 10)\nmort<- rnorm(365, 300, 45)\npoll<- rpois(365, lambda=76)\ndate<-seq(as.Date('2011-01-01'),as.Date('2011-12-31'),by = 1)\ndf<-data.frame(date,mort,poll,temp)\ndf\nlibrary(ggplot2)\nlibrary(reshape2)\n\ndf_melt = reshape2::melt(df, id.vars = 'date')\nggplot(df_melt, aes(x = date, y = value)) + \n geom_line() + \n facet_wrap(~ variable, scales = 'free_y', ncol = 1)\n\nggplot(df_melt, aes(x = date, y = value)) + \n geom_line() + \n facet_wrap(~ variable, nrow=1, scales = 'free_y')\n\n\ndf = within(df, {\n temp[61:90] = temp[61:90] + runif(30, 30, 50)\n mort[61:90] = mort[61:90] + runif(30, 300, 500)\n})\ndf_melt = reshape2::melt(df, id.vars = 'date')\nggplot(df_melt, aes(x = date, y = value)) + \n geom_line() + \n facet_wrap(~ variable, scales = 'free_y', ncol = 1)\n\n\n\n#Eg5\nlibrary(zoo)\nz <- read.zoo(df)\n# classic graphics in separate and single plots\nplot(z)\nplot(z, screen = 1)\n# lattice graphics in separate and single plots\nlibrary(lattice)\nxyplot(z)\nxyplot(z, screen = 1)\n# ggplot2 graphics in separate and single plots\nlibrary(ggplot2)\nautoplot(z) + facet_free()\nautoplot(z, facet = NULL)\n\n# Eg6\ntseries::seqplot.ts(AirPassengers, guinearice)\ndata(USeconomic)\nx <- ts.union(log(M1), log(GNP), rs, rl)\nhead(x)\nm.ar <- ar(x, method = \"ols\", order.max = 5)\ny <- predict(m.ar, x, n.ahead = 200, se.fit = FALSE)\nseqplot.ts(x, y)\n\n#Eg7\ncomponents_airp <- decompose(AirPassengers)\ncomponents_airp\nplot(components_airp)\nts.plot(components_airp$seasonal,y=NULL,\n gpars = list(col = c(\"red\", \"blue\"), ylab = \"Seasonality\",\n main = \"Seasonality in Search Term Popularity\"))\n" }, { "path": "47A-TS/38c-tsplots3.R", "content": "# plot the graph in R\nset.seed(15)\na = c(1,2,-2,4,2,3,1,0,3,4,2,3,1)\nb = a + rnorm(length(a), sd = 0.4)\nplot(ts(b), col=\"#f44e2e\", lwd=3)\nlines(a, col=\"#27ccc0\", lwd=3)\na\nround(b,1)\n# compute using the R language\ncorr = ccf(a,b)\ncorr\n" }, { "path": "47A-TS/38f-plot-zz.R", "content": "#ts8\n#https://www.otexts.org/fpp/6/1\npar(mfrow=c(2,2))\nplot(hsales,xlab=\"Year\",ylab=\"Monthly housing sales (millions)\")\nplot(ustreas,xlab=\"Day\",ylab=\"US treasury bill contracts\")\nplot(elec,xlab=\"Year\",ylab=\"Australian monthly electricity production\")\nplot(diff(dj),xlab=\"Day\",ylab=\"Daily change in Dow Jones index\")\n" }, { "path": "47A-TS/41b-arima1.R", "content": "#ARIMA\n\nset.seed(1)\n\n# set number of observations\n(my.n <- 5000)\n\n# set model's parameters\n(my.model <- list(ar = 0.5, ma = -0.1))\n(my.sd <- 1)\n\n# simulate model\n(my.ts <- arima.sim(n = my.n, model = my.model , sd = my.sd))\n\nlibrary(ggplot2)\n\n# set df\ntemp.df <- data.frame(y = unclass(my.ts), date = Sys.Date() + 1:my.n)\nhead(temp.df, n=10)\np <- ggplot(temp.df, aes(x = date, y = y))\np <- p + geom_line(size=0.5)\nprint(p)\n\nhead(temp.df)\n# estimate arima model\nmy.arima <- arima(my.ts, order = c(1,0,1))\n\n# print result\nprint(coef(my.arima))\n\nprint(summary(my.arima))\n\n\n# read file\nmy.f <- 'data/SP500.csv'\ndf.SP500 <- read.csv(my.f, header=F, col.names = c('price'))\nhead(df.SP500)\nnames(df.SP500)\ncalc.ret <- function(P) {\n # calculates arithmetic returns from a vector of prices\n # # Args:\n # P - vector of prices (numeric)\n # # Returns: # A vector of returns\n \n my.length <- length(P)\n ret <- c(NA, P[2:my.length]/P[1:(my.length - 1)] - 1)\n return(ret)\n}\nhead(df.SP500)\n\n# set return column\n(df.SP500$ret <- calc.ret(df.SP500$price))\n\nlibrary(tseries)\nprint(adf.test(na.omit(df.SP500$ret)))\n \nprint(adf.test(df.SP500$price))\n\nlibrary(forecast)\n\n# estimate arima model with automatic identification\nmy.autoarima <- auto.arima(x = df.SP500$ret)\n\n# print result\nprint(my.autoarima)\n\n# forecast model\nprint(forecast(my.autoarima, h = 5))\n" }, { "path": "47A-TS/41c-arima2.R", "content": "#arima\nNile\nlag(Nile,1)\n?lag\nyearmon(Nile)\n\nAcf(Nile)\nacf(Nile)\npacf(Nile)\nPacf(Nile)\n\n#Ensure that the TS is stationery\nlibrary(forecast)\nlibrary(tseries)\nplot(Nile)\nndiffs(Nile)\ndNile = diff(Nile)\ndNile\nplot(dNile)\nadf.test(dNile)\n\n#Ensure a reasonable model(s) (possible values of p & q)\nAcf(dNile)\nPacf(dNile)\n\n#Fit the model\narima(ts, order=c(p,d,q))\n fit = arima(Nile, order=c(0,1,1))\n fit\naccuracy(fit)\nqqnorm(fit$residuals)\nqqline(fit$residuals)\nBox.test(fit$residuals,\n type='Ljung-Box')\n#Once final model has been chosen, it can be used to make forecast of Future Values.\nforecast(fit, 3)\nplot(forecast(fit,3), xlab='Year', ylab='Annual Flow')\n\n#eg\nlibrary(forecast)\nstr(sunspots)\nhead(sunspots)\nfit = auto.arima(sunspots)\nfit\nforecast(fit,3)\naccuracy(fit)\n\n" }, { "path": "47A-TS/41d-arima-airp.R", "content": "# Time Series Arima ts2\n# https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/\n#library(forecast)\nlibrary(tseries)\nadf.test(diff(log(AirPassengers)), alternative=\"stationary\", k=0)\n#series is stationary enough to do any kind of time series modelling.\n#Next step is to find the right parameters to be used in the ARIMA model. \n#ACF Plots\n\nacf(log(AirPassengers))\nacf(diff(log(AirPassengers)))\npacf(diff(log(AirPassengers)))\n(fit <- arima(log(AirPassengers), c(0, 1, 1),seasonal = list(order = c(0, 1, 1), period = 12)))\npred <- predict(fit, n.ahead = 10*12)\nts.plot(AirPassengers,2.718^pred$pred, log = \"y\", lty = c(1,3))\n\n" }, { "path": "47A-TS/41d-arima-jj-nile.R", "content": "#Times Series Analysis \n# is the price of Johnson and Johnson shares change over time\n# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year\n# Can you forecast what future share prices will be and to what degree of accuracy\n\n#dataset - Johnson\n#Quarterly earnings per Johnson Shares\n#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA\n\nJohnsonJohnson\n?JohnsonJohnson\nlibrary(forecast)\n\n#ets auto select best predicton model\n?ets\n#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time\n\nfit1 = ets(JohnsonJohnson)\nfit1\n#alpha - trend\n#beta = seasonal\n#gamma - irregular\n\nJohnsonJohnson\nhead(JohnsonJohnson)\ntail(JohnsonJohnson)\n\n(f1= forecast(fit1,h=10)) # \n?forecast.ets\npar(mfrow=c(1,1))\nplot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3) # linetype for forecast area\n#shaded portion is confidence intervals area\n\npar(mfrow=c(1,1))\n\n# ARIMA Forecasting : \n#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.\n\n#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg\n\nf2 = auto.arima(JohnsonJohnson)\nsummary(f2)\ntail(JohnsonJohnson) # last few values\nforecast(f2,h=5)\n\n#Auto ARIMA on Nile DataSet\n\nNile\n#Auto ARIMA\nlibrary(forecast)\n#forecast::auto.arima()\nfit4 = auto.arima(Nile)\nfit4\n\nforecast(fit4,h=5)\nplot(forecast(fit4,h=5))\n#forecasting is generally done for limited periods\n\n" }, { "path": "47A-TS/45b-TS-arima.R", "content": "#Time Series - Arima ts4\n#http://stat.ethz.ch/R-manual/R-devel/library/stats/html/arima.html\n\narima(lh, order = c(1,0,0))\narima(lh, order = c(3,0,0))\narima(lh, order = c(1,0,1))\n\narima(lh, order = c(3,0,0), method = \"CSS\")\n\narima(USAccDeaths, order = c(0,1,1), seasonal = list(order = c(0,1,1)))\narima(USAccDeaths, order = c(0,1,1), seasonal = list(order = c(0,1,1)),\n method = \"CSS\") # drops first 13 observations.\n# for a model with as few years as this, we want full ML\n\narima(LakeHuron, order = c(2,0,0), xreg = time(LakeHuron) - 1920)\n\n## presidents contains NAs\n## graphs in example(acf) suggest order 1 or 3\nrequire(graphics)\n(fit1 <- arima(presidents, c(1, 0, 0)))\nnobs(fit1)\ntsdiag(fit1)\n(fit3 <- arima(presidents, c(3, 0, 0))) # smaller AIC\ntsdiag(fit3)\nBIC(fit1, fit3)\n## compare a whole set of models; BIC() would choose the smallest\nAIC(fit1, arima(presidents, c(2,0,0)),\n arima(presidents, c(2,0,1)), # <- chosen (barely) by AIC\n fit3, arima(presidents, c(3,0,1)))\n\n## An example of ARIMA forecasting:\npredict(fit3, 3)\n\n" }, { "path": "47A-TS/52c-Case1-complete.R", "content": "#TS4 Time Series\n#http://www.statoek.wiso.uni-goettingen.de/veranstaltungen/zeitreihen/sommer03/ts_r_intro.pdf\ntui <- read.csv(\"tui.csv\", header=T, dec=\",\", sep=\";\")\ntui #dataset is stored as a matrix–object with the name tui\nstr(tui)\nplot(tui[,5],type=\"l\") # A chart with the closing values\n\nplot(tui[,5], type=\"l\",lwd=2, col=\"red\", xlab=\"time\", ylab=\"closing values\",main=\"TUI AG\", ylim=c(0,60) )\n\n#plot differences of the logarithms of the returns.\nplot(diff(log(tui[,5])),type=\"l\")\n\n# investigate distributional properties. A first\n#step would be to draw a histogram and to compare it with e.g. the density of the ND\n\nhist(diff(tui[,4]),prob=T,ylim=c(0,0.6),xlim=c(-5,5),col=\"red\")\nlines(density(diff(tui[,4])),lwd=2)\n\nmu<-mean(diff(tui[,4]))\nsigma<-sd(diff(tui[,4]))\n\nx<-seq(-4,4,length=100)\ny<-dnorm(x,mu,sigma)\nlines(x,y,lwd=2,col=\"blue\")\n\nqqnorm(diff(tui[,4]))\nabline(0,1)\n\nx<-diff(log(tui[,5]))\nks.test(x,\"pnorm\",mean(x),sd(x))\n\nshapiro.test(x)\n\n# Linear Filtering of Time Series ------------------\n# plot the closing values of the TUI shares and the averages in different colours\n#library(ts) # NA for 3.3.2\nlibrary(timeSeries)\nplot(tui[,5],type=\"l\")\ntui.1 <- filter(tui[,5],filter=rep(1/5,5))\ntui.2 <- filter(tui[,5],filter=rep(1/25,25))\ntui.3 <- filter(tui[,5],filter=rep(1/81,81))\nlines(tui.1,col=\"red\")\nlines(tui.2,col=\"purple\")\nlines(tui.3,col=\"blue\")\n\n# Decomposition of Time Series ------------------\n#http://astro.temple.edu/~buck/Econ616/Kernel/beer.csv\n\n\n\n\nbeer<-read.csv(\"beer.csv\",header=T,dec=\",\",sep=\";\")\nbeer<-ts(beer[,1],start=1956,freq=12)\nplot(stl(log(beer),s.window=\"periodic\"))\n\n#2.3 Regression analysis -------------------------------------------------\nlbeer<-log(beer)\nt<-seq(1956,1995.2,length=length(beer))\nt2<-t^2\nplot(lbeer)\nlm(lbeer~t+t2)\nlines(lm(lbeer~t+t2)$fit,col=2,lwd=2)\n\nlbeer<-log(beer)\nt<-seq(1956,1995.2,length=length(beer))\nt2<-t^2\nsin.t<-sin(2*pi*t)\ncos.t<-cos(2*pi*t)\nplot(lbeer)\nlines(lm(lbeer~t+t2+sin.t+cos.t)$fit,col=4)\n\nsummary(lm(lbeer ~ t+t2+sin.t+cos.t))\n\n\n\n# Exponential Smoothing --------------------------------------------------\n\nbeer <- read.csv(\"beer.csv\",header=T,dec=\",\",sep=\";\")\nbeer<-ts(beer[,1],start=1956,freq=12)\nHoltWinters(beer)\n# This performs the Holt–Winters procedure on the beer – dataset. As result, it\n#displays a list with e.g. the smoothing parameters (which should be α ≈ 0.076,β ≈ 0.07 and γ ≈ 0.145 in this case).\n\nplot(beer)\nlines(HoltWinters(beer)$fitted,col=\"red\") # ERROR\n\nbeer.hw<-HoltWinters(beer)\n\npredict(beer.hw,n.ahead=12)\nplot(beer,xlim=c(1956,1999))\nlines(predict(beer.hw,n.ahead=48),col=2)\n\n# ARIMA–Models-------------------\n#Analysis of Autocorrelations and Partial Autocorrelations ----------------------\n\nsim.ar<-arima.sim(list(ar=c(0.4,0.4)),n=1000)\nsim.ma<-arima.sim(list(ma=c(0.6,-0.4)),n=1000)\npar(mfrow=c(2,2))\nacf(sim.ar,main=\"ACF of AR(2) process\")\nacf(sim.ma,main=\"ACF of MA(2) process\")\npacf(sim.ar,main=\"PACF of AR(2) process\")\npacf(sim.ma,main=\"PACF of MA(2) process\")\n\n#arima(data,order=c(p,d,q))\n\ndata(LakeHuron)\nfit<-arima(LakeHuron,order=c(1,0,1))\nfit<-arima(LakeHuron,order=c(1,0,1))\ntsdiag(fit) # NOT VISIBLE\n\nBox.test(fit$residuals,lag=1)\n\n# Prediction\nfit<-arima(LakeHuron,order=c(1,0,1))\nLH.pred<-predict(fit,n.ahead=8)\n\npar(mfrow=c(1,1))\nplot(LakeHuron,xlim=c(1875,1980),ylim=c(575,584))\nLH.pred<-predict(fit,n.ahead=8)\nlines(LH.pred$pred,col=\"red\")\nlines(LH.pred$pred+2*LH.pred$se,col=\"red\",lty=3)\nlines(LH.pred$pred-2*LH.pred$se,col=\"red\",lty=3)\n\n" }, { "path": "47A-TS/53b-sales-ts.R", "content": "#-----------------------------------------Time Series Forecasting---------------------------------------#\n\n\n\n#Analytical Problem: To forecast the Sales (in thoushand units) of a Automobile company for the next 36 months\n\n#----------------------------------Preparing the environment--------------------------------------------#\n\nlist.of.packages <- c(\"forecast\", \"ggplot2\",\"MASS\",\"caTools\",\"sqldf\",\"tseries\")\nnew.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,\"Package\"])]\nif(length(new.packages)) install.packages(new.packages, repos=\"http://cran.rstudio.com/\")\nlibrary(forecast)\nlibrary(tseries)\n\n#-----------------------------Setting the working directory-------------------------------------------#\n\nPath<-\"C:/Ganesha_Accenture/Ganesha_IVY/R/Gan_B01/03Data\"\nsetwd(Path)\ngetwd()\n\ndata<-read.csv(\"./data/Sales.csv\",header = TRUE)\n#data = read.csv(file.choose())\nhead(data)\nTSdata=data#To create a backup of original data\n\n\n#-------Exploring the data-------------------#\nhead(TSdata)#\ndim(TSdata)#We have 144 time series points (at a date level) and 2 vars(Date, Sales)\nstr(TSdata)\nsummary(TSdata)\ncolSums(is.na(TSdata))\nnames(TSdata)[c(1:2)]=c(\"Date\",\"Sales\")\n\n\n#--Transformation of the date data into time series----------#\n\nTSdata=ts(TSdata[,2],start=c(2003,1),frequency=12)\nstart(TSdata)\nend(TSdata)\nfrequency(TSdata)\nStructTS(TSdata)\nTSdata\n\n#2003,1 is the start date and 12 is the frequency of the time series (monthly series)\nstr(TSdata)\n\n\n#--------------------->plotting the sales \nplot(TSdata,ylab=\"Sales\", xlab=\"Year\",main=\"Sales between 2003-2014\",col=\"grey\")\nabline(reg = lm(TSdata~time(TSdata)))\ncycle(TSdata)\nplot(aggregate(TSdata,FUN=mean))\n#This plot displays the year on year trend in the sales from 2003 \n\n#Data has both a trend and drift, i.e. time invariant mean and variance \n\n\n\n#--------------->Differencing the data to remove trend and drift\n\n\nplot(log10(TSdata),ylab=\"log(Sales)\",xlab=\"Year\",main=\"log(Sales) between 2003-2014\",col=\"grey\")\n##Differencing the data to remove trend\nplot(diff(TSdata,differences = 1),ylab=\"Diff(Sales)\",xlab=\"Year\",main=\"Diff(Sales) between 2003-2014\",col=\"grey\")\n#The differenced data continues to have unequal variance \n\n\n#--------------->Differencing+Log transformation the data to remove trend and unequal variance\n\n\nplot(diff(log10(TSdata),differences = 1),ylab=\"Diff(Sales)\",xlab=\"Year\",main=\"Diff(Log(Sales)) between 2003-2014\",col=\"grey\")\n\n#So, with Log10 and 1 order of differencing makes the series stationary\n\n#----------------->Checking the stationarity of transformed data using the Augmented Dickey-Fuller Test(ADF)\n\nLDTSdata=diff(log10(TSdata),differences = 1)\nrequire(forecast)\nadf.test(LDTSdata,alternative=\"stationary\")\n\n#Since, the p-value <0.05, hence, we reject the Ho: Series is Non-Stationary \n\nkpss.test(LDTSdata)\n#creating the ACF and PACF plot\npar(mfrow=c(1,2))\nacf(diff(log10(TSdata)),main=\"ACF plot\")#ACF PLOT -- Moving Average or q\npacf(diff(log10(TSdata)),main=\"PACF plot\")#PACF PLOT -- Auto Regressive or p\n#Running the ARIMA model\nARIMAFit=arima((log10(TSdata)),c(1,1,0))\n\n\nsummary(ARIMAFit)\n#Running the ARIMA model-R, gives the best model fit \nrequire(forecast)\nARIMAFit1=auto.arima(log10(TSdata),approximation=TRUE,trace=TRUE)\n?auto.arima\n?arima\nsummary(ARIMAFit1)\nARIMAFit1$residuals\n#Predicting the future values\npred=predict(ARIMAFit1,n.ahead=36)\npred\n\n#n.ahead is the no. of time series, we want to predict\n\n##########################################3\n##Ploting the observed data and forecasted data together\npar(mfrow=c(1,1))\nplot(TSdata,type=\"l\",xlim=c(2004,2018),ylim=c(1,1600),xlab=\"Year\",ylab=\"Sales\")\nlines(10^(pred$pred),col=\"red\")\n\n###############################################\n#plotting the +-2 standard error to range of expected error\nplot(TSdata,type=\"l\",xlim=c(2004,2018),ylim=c(1,1600),xlab = \"Year\",ylab = \"Sales\")\nlines(10^(pred$pred),col=\"red\")\nlines(10^(pred$pred+2*pred$se),col=\"blue\")\nlines(10^(pred$pred-2*pred$se),col=\"black\")\n## then forecast the result\npred = predict(ARIMAFit1, n.ahead = 36)\nwrite.csv(pred,\"predict.csv\")\n\n## then do the exponential since you had used log earlier.\nnormal_result=10^pred$pred ## you get the desired result. \nwrite.csv(normal_result,\"finalpredict.csv\", row.names = FALSE)\nplot(normal_result)\n\n#-------------------------------End of the model-----------------------------------#\n" }, { "path": "47A-TS/55b-ts-case-xxx2.R", "content": "# Aggregating TS\n\ndata = read.csv(file.choose())\nstr(data)\nhead(data)\nnames(data)\nnrow(data)\ndf= data\n\ndf$timestamp2 = as.POSIXct(df$timestamp ,origin=\"1970-01-01\")\nhead(df)\n\nlibrary(zoo)\nlibrary(xts)\nts1 = xts(df[1:3], order.by=df$timestamp2)\nclass(ts1)\n\nts1\n\n# use dplyr\nlibrary(dplyr)\nhourly_summary <- df %>% mutate(hour = as.Date(timestamp2, format=\"%Y-%m-%d %H\")) %>% group_by(house_id, household_id) %>% summarise(hoursum=sum(value),hourlsd = sd(value), hourmean=mean(value))\n\nhourly_summary\nclass(hourly_summary)\ndf %>% group_by(house_id, household_id, as.Date(timestamp2, format=\"%Y-%m-%d\") )\naggregate(value ~ house_id + household_id, data=df, FUN=sum)\n\nData$hour <- as.POSIXlt(dates)$hour # extract hour of the day\n\n" }, { "path": "47A-TS/55c-ts-case-xxxx.R", "content": "#Analytics Vidya Case\n#06 Jul 18\n\n\nsummary(rollmean2)\n#ts range : 1377986400 1380578340\nrawdata = expand.grid(c(0,1), c(0,1,2), c(1377986400:1380578340))\nnrow(rawdata)\nvalue1 = runif(15551646,10,50)\nhead(value1)\n\nrollmean1 = rollmean(value1,k=60, fill=NA, align='right')\nrollsum1 = rollsum(value1,k=60, fill=NA, align='right')\nrollsd # not avl\nlibrary(TTR)\nrollsd1 = runSD(value1,n = 60, sample = FALSE, cumulative = FALSE) \ndu1 = cbind(rawdata, value1, rollmean1, rollsd1, rollmean1 + rollsd1)\ndu2 = cbind(rawdata, value1, rollsd1)\nhead(du2,n=62)\nhead(du2)\nnames(du1) = c('house_id','household_id', 'timestamp', 'value', 'rollmean', 'rollsd', 'checkvalue')\nnames(du2) = c('house_id','household_id', 'timestamp', 'value','rollsd')\n\nhead(du2)\ndu1$alert = ifelse(du1$value > du1$checkvalue,1,0)\nhead(du1, n=70)\n\n\n#Time Series Data\ndu2ts = xts(du2, order.by= as.POSIXct(du2$timestamp ,origin=\"1970-01-01\"))\ndu2ts\nendpoints(du1ts, on = 'minute') \nendpoints(du1ts, on = 'minute', k=2) \n# Calculate the minute endpoints\nep <- endpoints(du1ts, on = \"minute\")\n\n# Now calculate the weekly mean and display the results\nperiod.apply(du1[, \"value\"], INDEX = ep, FUN = mean)\nperiod.apply(du1[, \"value\"], INDEX = ep, FUN = sd)\n\nrollapply(du1$value, width=60, FUN = sd)\nperiodicity(du1ts)\n\ndu1\n#build decision tree\nlibrary(rpart)\nlibrary(rpart.plot)" }, { "path": "47A-TS/SMA-nile.R", "content": "# TS - Simple Moving Average\n#SMA\n# technique to get an overall idea of the trends in a data set; it is an average of any subset of numbers. The moving average is extremely useful for forecasting long-term trends\n\n\nNile # river flow from year 1871 to 1970\n\nplot(Nile)\n\nlibrary(forecast) # for forecasting\nopar = par(no.readonly = T) # save current parameters\npar(mfrow=c(2,2))\n(ylim = range(Nile))\nplot(Nile, main='Original TS')\nhead(Nile)\nma(Nile,3)\nhead(Nile,4)\nhead(ma(Nile,3))\n(1120+1160+963)/3\nmean(Nile[1:3]);mean(Nile[2:4]); mean(Nile[3:5]); mean(Nile[4:6])\nma(Nile,3)[1:10]\n\nplot(ma(Nile,3), main='SMA k=3', ylim=ylim)\nplot(ma(Nile,7), main='SMA k=7', ylim=ylim)\nplot(ma(Nile,15),main='SMA k=15', ylim=ylim)\npar(opar)\nplot(ma(Nile,3), main='SMA k=3', ylim=ylim)\n\n\n\n\n#mfrow mfcol\nmtcars\nnames(mtcars)\nplot(mtcars$mpg)\npar(mfrow=c(1,2))\nplot(mtcars$mpg)\nplot(mtcars$disp)\n\npar(mfrow=c(2,1))\nplot(mtcars$mpg)\nplot(mtcars$disp)\n\npar(mfrow=c(2,2))\nplot(mtcars$mpg)\nbarplot(table(mtcars$cyl))\npie(table(mtcars$am))\nhist(mtcars$disp)\n" }, { "path": "47A-TS/TS-P-fpp.R", "content": "#House Sales\n3https://www.otexts.org/fpp/6/1\nlibrary(fpp)\nhsales\ndata(package='fpp')\npar(mfrow=c(2,2))\nplot(hsales,xlab=\"Year\",ylab=\"Monthly housing sales (millions)\")\nplot(ustreas,xlab=\"Day\",ylab=\"US treasury bill contracts\")\nplot(elec,xlab=\"Year\",ylab=\"Australian monthly electricity production\")\nplot(diff(dj),xlab=\"Day\",ylab=\"Daily change in Dow Jones index\")\npar(mfrow=c(1,1))\nfit <- stl(elecequip, s.window=5)\nplot(elecequip, col=\"gray\",\n main=\"Electrical equipment manufacturing\",\n ylab=\"New orders index\", xlab=\"\")\nlines(fit$time.series[,2],col=\"red\",ylab=\"Trend\")\n\nplot(fit)\n\nmonthplot(fit$time.series[,\"seasonal\"], main=\"\", ylab=\"Seasonal\")\n\nplot(elecequip, col=\"grey\",\n main=\"Electrical equipment manufacturing\",\n xlab=\"\", ylab=\"New orders index\")\nlines(seasadj(fit),col=\"red\",ylab=\"Seasonally adjusted\")\nma(elecsales, order=5)\nplot(elecsales, main=\"Residential electricity sales\",\n ylab=\"GWh\", xlab=\"Year\")\nlines(ma(elecsales,5),col=\"red\")\n\n\nbeer2 <- window(ausbeer,start=1992)\nma4 <- ma(beer2, order=4, centre=FALSE)\nma2x4 <- ma(beer2, order=4, centre=TRUE)\n\nplot(elecequip, ylab=\"New orders index\", col=\"gray\",\n main=\"Electrical equipment manufacturing (Euro area)\")\nlines(ma(elecequip, order=12), col=\"red\")\n\nfit <- stl(elecequip, t.window=15, s.window=\"periodic\", robust=TRUE)\nplot(fit)\n" }, { "path": "47A-TS/TS-c02.R", "content": "#Time Series with CO2 data\n#https://www.datacamp.com/community/tutorials/time-series-r\n\n#R packages to be used\nlibrary(forecast)\nlibrary(TSA)\n#ARIMA(p,d,q) process : Autoregressive Integrated Moving Average Models are time series defined by the equation: SARIMA(p,d,q)(P,D,Q) process\n\n# Data from TSA package\ndata(\"co2\")\n#A time series of 468 observations; monthly from 1959 to 1997.\nhead(co2)\nstr(co2)\n# fitting\nfit <- auto.arima(co2)\n\n# Time series plot\nplot(fc <- forecast(fit, h = 15))\n\n#%%%%----------------------\n#Boardings\ndata(\"boardings\")\n?boardings\n# fitting\nfit2 <- auto.arima(boardings[,\"log.price\"])\n\n\n# forecasting\nplot(fc2 <- forecast(fit2, h = 15))\n\n#%%%---------------------------\ndata('co2', package='datasets')\nco2\nhead(co)\nstr(co2)\n468 * 12\n" }, { "path": "47A-TS/TS-data-DU1.R", "content": "# Time Series\n\n# Create Time Series data of Finance Sales\nsales = c(18, 33, 41, 7, 34, 35, 24, 25, 24, 21, 25, 20,\n 22, 31, 40, 29, 25, 21, 22, 54, 31, 25, 26, 35)\nclass(sales)\nstr(sales)\nlength(sales)\nsales ; class(sales) # vector of sales values\n\n#put in TS without any dates : format of TS\n?ts # help\n\n#ts(data = NA, start = 1, end = numeric(), frequency = 1)\n#start - time of 1st obsv - single no / vector of 2 integers\n#frequency - no of obvsv per unit of time\n#No of obsv per unit time\n(tsales2a = ts(sales, start=1, end=12)) #only first 12 considered\n\n(ts(sales, start=1, end=12, freq=1)) #only first 12 considered\n(ts(sales, start=1, end=12, freq=2)) #\n\n(ts(sales, start=1, end=12, freq=3)) #\n(ts(sales, start=1, end=12, freq=4)) # Qtrly series\n(ts(sales, start=1, end=12, freq=5)) #\n(ts(sales, start=1, end=12, freq=6)) #\n\n(ts(sales, start=1, end=12, freq=12)) # 12 months * 12 years\n\n\n(tsales2b = ts(sales, start=1, end=6)) #only first 6\n(tsales2c = ts(sales, start=1, end=24)) #all 24 freq=1\n(tsales2d = ts(sales, start=1, end=12, frequency = 1))\n(tsales2e = ts(sales, start=1, end=12, frequency = 2)) # 1 to 12 : 1 unit divided into 2 : so 24 observations\n\n#Specify data periods in start and end\n#specify only start dates & freq\nlength(sales)\n\n(tsales3a = ts(sales, start=c(2017, 1), frequency=12)) # years 2017 to 2018, 24 values, monthwise 24 observations spread to 2 years\nsales2 = c(sales, sales)\nlength(sales2)\n(tsales3a = ts(sales2, start=c(2017, 1), frequency=12)) # years 2017 to 2018, \n\n(tsales3a = ts(sales2, start=c(2017, 1), frequency=1)) # years 2017 to 2018, \n\n\nadmission = c(1000, 900, 1200, 3000)\nts1= ts(admission, start=c(2016), end=c(2018), frequency=1)\nts1\nplot(ts1)\nadmission = c(1000, 900, 1200, 3000)\nts2= ts(admission, start=c(2016,7), end=c(2016,10), frequency=12)\nts2\nplot(ts1)\n\n\nts(sales, start=c(2017, 4), end=c(2018,3), frequency=12)\nts(sales, start=c(2017, 2), frequency=12) # different start month\nts(sales, start=c(2017, 2), frequency=4) # different start month\nplot(ts(sales, start=c(2017,1), frequency=1)) # different start month\n\n\n\n?ts\n#Specify start and end date periods\n(tsales3b = ts(sales, start=c(2016,4), end=12) ) #error start and end to be of same format\n(tsales3b = ts(sales, start=c(2016), end=c(2018))) # default freq=1 once a year 1st 3 values becomes year sales value\n# R understands that 2016 is year\n\n(tsales3c = ts(sales, start=c(2016,4), end=c(2018,4), frequency=12)) # period diveded into 24 equal intervals\ntsales3c\nplot(tsales3c)\n\n# Daily Sales Data\nsales2 = ceiling(runif(365, 50,100))\nsales2\n(dailyTSdata = ts(sales2, start=c(2017,1), frequency=365))\nplot(dailyTSdata)\nstart(dailyTSdata)\nend(dailyTSdata)\n\n#Weekly Sales Data 52 weeks in a year\nsales3 = ceiling(runif(52, 50,100))\n(weeklyTSdata = ts(sales3, start=c(2017,3), frequency=52))\nweeklyTSdata\nplot(weeklyTSdata)\nstart(weeklyTSdata); end(weeklyTSdata) ;frequency(weeklyTSdata)\n\n?ts\n#Yearly Sales Data : 2000 to 2017 \nseq(1,10,2)\n?seq\nseq(1,20, along.with = c(1:5))\nseq(1,20, along.with = sales) # 24 values\n\n(values= trunc(seq(1,20, along.with = c(2000:2017)))) # take length from 2000 to 2017\n\n(sales4 = floor(rnorm(values, mean=50, sd=10)) ) #pick up length from values\n\n(yearlyTSdata = ts(sales4, start=c(2000), end=c(2017)))\nplot(yearlyTSdata)\n\nstart(yearlyTSdata); end(yearlyTSdata) ;frequency(yearlyTSdata)\n\n#diff and lag & window in ts\ntsales3c\ntsales = tsales3c\ntsales\nrange(tsales)\ndiff(tsales)\ndiff(tsales,lag=1)\ndiff(tsales,lag=2)\ndiff(tsales,lag=6)\n\ncycle(tsales)\n\ntsales\ntsales\n#subset Time Series with range of dates\n(tsales.subset = window(tsales, start=c(2003, 5), end=c(2004, 6))) #error due to range period incorrect\n\ntsales\n(tsales.subset = window(tsales, start=c(2016, 5), end=c(2017, 8))) \n#5th month of 2016 to 8th month of 2016\n\n#Another TS with different freq\n\n\n#dataset---- \n#available for TS Analysis - understand them, they are used for TS analysis\nAirPassengers #passengers travel data - seasonal data\n?AirPassengers\nplot(AirPassengers)\nclass(AirPassengers)\nJohnsonJohnson #stock price prediction\nnhtemp # temp variations\nNile # river level variations\nsunspots #Temp of sun spots variations\nds = list(AirPassengers,JohnsonJohnson,nhtemp,Nile,sunspots)\nsapply(ds, class) # check type of class of each dataset\n\n\n#end here - do time series analysis - SMA / EXP/ ARIMA\n\n# Move to doing TS analysis - \n# other packages zoo, xts\n\n\n\n\n" }, { "path": "47A-TS/TS-fpp-seasonplot.R", "content": "# Time Series 6\n\n\n# Section 5 : fpp package -------------------------------------------------\nlibrary(fpp)\nplot(a10)\n# Intro https://www.otexts.org/fpp/using-r\n\n#Using the Workspace menu, choose \"Import Dataset\" and import the data from the tute1.csv file.\ntute1 = read.csv('tute1.csv')\nhead(tute1)\ntail(tute1)\ntute1[,2]\ntute1[,\"Sales\"]\ntute1[5,]\ntute1[1:10,3:4]\ntute1[1:20,]\n# Data into TS\ntute1 <- ts(tute1[,-1], start=1981, frequency=4)\nplot(tute1)\n\n# Plot\nseasonplot(tute1[,\"Sales\"])\nseasonplot(tute1[,\"AdBudget\"])\nseasonplot(tute1[,\"GDP\"])\nmonthplot(tute1[,\"Sales\"])\nmonthplot(tute1[,\"GDP\"])\nmonthplot(tute1[,\"AdBudget\"])\n\n#\nplot(Sales ~ AdBudget, data=tute1)\nplot(Sales ~ GDP, data=tute1)\n\n#\npairs(as.data.frame(tute1))\n#\nsummary(tute1)\n#\nhist(tute1[,\"Sales\"])\nhist(tute1[,\"AdBudget\"])\nboxplot(tute1[,\"Sales\"])\nboxplot(as.data.frame(tute1))\n#\ncor.test(tute1[,\"Sales\"],tute1[,\"AdBudget\"])\n\n\n#https://www.otexts.org/fpp/6/1\npar(mfrow=c(2,2))\nstr(hsales) ;head(hsales)\nplot(hsales,xlab=\"Year\",ylab=\"Monthly housing sales (millions)\")\nstr(ustreas)\nplot(ustreas,xlab=\"Day\",ylab=\"US treasury bill contracts\")\nstr(elec)\nplot(elec,xlab=\"Year\",ylab=\"Australian monthly electricity production\")\nstr(dj)\nplot(diff(dj),xlab=\"Day\",ylab=\"Daily change in Dow Jones index\")\n\n# Electrical Components\nfit <- stl(elecequip, s.window=5)\nplot(elecequip, col=\"gray\",\n main=\"Electrical equipment manufacturing\",\n ylab=\"New orders index\", xlab=\"\")\nlines(fit$time.series[,2],col=\"red\",ylab=\"Trend\")\nplot(fit)\n#\nmonthplot(fit$time.series[,\"seasonal\"], main=\"\", ylab=\"Seasonal\")\n\n# Seasonally Adjusted Data seasonal component is removed from the original data,\nplot(elecequip, col=\"grey\",\n main=\"Electrical equipment manufacturing\",\n xlab=\"\", ylab=\"New orders index\")\nlines(seasadj(fit),col=\"red\",ylab=\"Seasonally adjusted\")\n\n# Moving Average\nma(elecsales, order=5)\n\n# Moving Averages of Moving Averages\n# apply a moving average to a moving average. One reason for doing this is to make an even-order moving average symmetric.\nstr(ausbeer)\nbeer2 <- window(ausbeer,start=1992)\nma4 <- ma(beer2, order=4, centre=FALSE)\nma4\nma2x4 <- ma(beer2, order=4, centre=TRUE)\nma2x4\n\n# Estimating the trend-cycle with seasonal data\nplot(elecequip, ylab=\"New orders index\", col=\"gray\",\n main=\"Electrical equipment manufacturing (Euro area)\")\nlines(ma(elecequip, order=12), col=\"red\")\n\n# Classication Decomposition\n# x is the time series\n#https://www.otexts.org/fpp/6/3\n\n\n# STL Decomposition\nfit <- stl(elecequip, t.window=15, s.window=\"periodic\", robust=TRUE)\nplot(fit)\n\nx = data1\nx\nfit <- decompose(x, type=\"multiplicative\")\nplot(fit)\n\n# Forecasting with Decomposition\nfit <- stl(elecequip, t.window=15, s.window=\"periodic\", robust=TRUE)\neeadj <- seasadj(fit)\nplot(naive(eeadj), xlab=\"New orders index\",\n main=\"Naive forecasts of seasonally adjusted data\")\n\nfcast <- forecast(fit, method=\"naive\")\nplot(fcast, ylab=\"New orders index\")\n\n# Exercise\n#https://www.otexts.org/fpp/6/7" }, { "path": "47A-TS/TS-kings.R", "content": "# Time Series ts3\n#http://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html\nkings <- scan(\"http://robjhyndman.com/tsdldata/misc/kings.dat\",skip=3)\n#Read 42 items\nkings\n#To store the data in a time series object, we use the ts() function in R.\n\nkingstimeseries <- ts(kings)\nkingstimeseries\n# Sometimes the time series data set that you have may have been collected at regular intervals that were less than one year, \n#for example, monthly or quarterly. In this case, you can specify the number of times that data was collected per year by using \n#the ‘frequency’ parameter in the ts() function. For monthly time series data, you set frequency=12, while for quarterly time series data, \n#you set frequency=4.\n\n#You can also specify the first year that the data was collected, and the first interval in that year by using the ‘start’ parameter \n#in the ts() function. For example, if the first data point corresponds to the second quarter of 1986, you would set start=c(1986,2).\n\nplot.ts(kingstimeseries)\n\n\n\n# Sec2 --------------------------------------------------------------------\n#example is a data set of the number of births per month in New York city, from January 1946 to December 1959\n\nbirths <- scan(\"http://robjhyndman.com/tsdldata/data/nybirths.dat\")\n#Read 168 items\nbirthstimeseries <- ts(births, frequency=12, start=c(1946,1))\nbirthstimeseries\nplot.ts(birthstimeseries)\n\n# Sec3 --------------------------------------------------------------------\n\nsouvenir <- scan(\"http://robjhyndman.com/tsdldata/data/fancy.dat\")\n#Read 84 items\nsouvenirtimeseries <- ts(souvenir, frequency=12, start=c(1987,1))\nsouvenirtimeseries\nplot.ts(souvenirtimeseries)\n#In this case, it appears that an additive model is not appropriate for describing this time series, since the size of the seasonal fluctuations and random fluctuations seem to increase with the level of the time series. Thus, we may need to transform the time series in order to get a transformed time series that can be described using an additive model. For example, we can transform the time series by calculating the natural log of the original data:\nlogsouvenirtimeseries <- log(souvenirtimeseries)\nplot.ts(logsouvenirtimeseries)\n# size of the seasonal fluctuations and random fluctuations in the log-transformed time series seem to be roughly constant over time, and do not depend on the level of the time series. Thus, the log-transformed time series can probably be described using an additive model.\n\n\n\n# Decomposing Time Series -------------------------------------------------\n\n# Decomposing a time series means separating it into its constituent components, which are usually a trend component and an irregular component, and if it is a seasonal time series, a seasonal component.\n# http://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html#decomposing-time-series\nlibrary(\"TTR\")\nkingstimeseriesSMA3 <- SMA(kingstimeseries,n=3)\nplot.ts(kingstimeseries)\nplot.ts(kingstimeseriesSMA3)\n\nkingstimeseriesSMA8 <- SMA(kingstimeseries,n=8)\nplot.ts(kingstimeseriesSMA8)\nforecast(kingstimeseriesSMA8,3)\nplot(forecast(kingstimeseriesSMA8,3))\n\nbirthstimeseriescomponents <- decompose(birthstimeseries)\nbirthstimeseriescomponents$seasonal # get the estimated values of the seasonal component\nplot(birthstimeseriescomponents)\n\n# Seasonal Adjusting\nbirthstimeseriescomponents <- decompose(birthstimeseries)\nbirthstimeseriesseasonallyadjusted <- birthstimeseries - birthstimeseriescomponents$seasonal\n\nplot(birthstimeseriesseasonallyadjusted)\n\n\n# Forecasts using Exponential Smoothing - make short-term forecasts for time series data.----------------------------------\n\nrain <- scan(\"http://robjhyndman.com/tsdldata/hurst/precip1.dat\",skip=1)\n#Read 100 items\nrainseries <- ts(rain,start=c(1813))\nplot.ts(rainseries)\n\nrainseriesforecasts <- HoltWinters(rainseries, beta=FALSE, gamma=FALSE)\nrainseriesforecasts\nrainseriesforecasts$fitted\nplot(rainseriesforecasts)\nrainseriesforecasts$SSE\nHoltWinters(rainseries, beta=FALSE, gamma=FALSE, l.start=23.56)\n\nlibrary(\"forecast\")\n#error in code\nrainseriesforecasts2 <- forecast.HoltWinters(rainseriesforecasts, h=8)\nrainseriesforecasts2\nplot.forecast(rainseriesforecasts2)\nacf(rainseriesforecasts2$residuals, lag.max=20)\n#Box.test(rainseriesforecasts2$residuals, lag=20, type=\"Ljung-Box\")\nplot.ts(rainseriesforecasts2$residuals)\n\n" }, { "path": "47A-TS/TS-links", "content": "# Time Series \n\nLinks\n\nhttps://www.analyticsvidhya.com/blog/2018/08/auto-arima-time-series-modeling-python-r/\n\n" }, { "path": "47A-TS/TS-movag1.R", "content": "# TS Moving Averages \n# Daily to Month to Yearly Averages\n# Section 1 ---------------------------------------------------------------\n\n\n(x = 1:20)\n# 3 Year and Past Data\n?filter #Linear Filtering of TS\nmav <- function(x,n=3){filter(x,rep(1/n,n), sides=1)}\nmav(x)\n# First 2 values are NA (Previous Data)\n# 5 Year and Past Data\nmav <- function(x,n=5){filter(x,rep(1/n,n), sides=1)}\nmav(x)\n# First 4 valueas are NA\n# 3 Year and Past & Present Data : Centered Sides=2\nmav <- function(x,n=3){filter(x,rep(1/n,n), sides=2)}\nmav(x)\n# 1st and Last values are NA\n\n# 5 Year and Past & Present Data : Centered Sides=2\nmav <- function(x,n=5){filter(x,rep(1/n,n), sides=2)}\nmav(x)\n# 1st,2nd and Last 2 values are NA\n\n#https://druedin.com/2012/08/11/moving-averages-in-r/\n\n# Price of milk over consective 6 years\ndata1 <- c(30,32,42,50,60,90)\nplot(data1, type=\"l\", col=\"blue\")\ntext(1:5, data1, data1, pos=2, col=\"blue\")\n# draw line of 3 period moving average Past Periods forecast\nmav2 <- function(x,n=3){filter(x,rep(1/n,n), sides=1)}\nmav2(data1)\nlines(mav2(data1), type=\"b\", col=\"red\")\ntext(1:5, mav2(data1,3), round(mav2(data1,3),2), pos=3, col=\"red\")\n\n# if NA values\nna.omit(mav(c(4,5,4,6,NA,4), 3))\n\n# Section2 ----------------------------------------------------------------\n# Using ZOO package\nlibrary(zoo)\nrollmean(data1,3)\n\n\n# Section 3 ---------------------------------------------------------------\n\nlibrary(forecast)\n?ma\n# 3 year moving average\nma(data1,order=3)\nplot(data1)\nlines(ma(data1,order=3),col='red')\ntext(data1)\n\n\n\n# Section 4 TTR -----------------------------------------------------------\nlibrary(TTR)\n?MovingAverages\n#Simple Moving average for past 3 observations\nSMA(data1,3)\ndata(ttrc)\nstr(ttrc); dim(ttrc)\nhead(ttrc)\n#SMA over 20month \nsma.20 = SMA(ttrc[,'Close'],20)\nsma.20\n\n\n# Section 5 hydroTSM ------------------------------------------------------\n#https://www.rforge.net/doc/packages/hydroTSM/ma.html\n\nlibrary(hydroTSM)\nma(x,3)\n# Loading daily streamflows at the station Oca en Ona (Ebro River basin, Spain) ##\ndata(OcaEnOnaQts)\nx <- OcaEnOnaQts\n\n## Daily to Monthly ts\nm <- daily2monthly(x, FUN=mean, na.rm=FALSE)\nx\nm\n# Plotting the monthly values\nplot(m, xlab=\"Time\")\n\n## Plotting the annual moving average in station 'x'\nlines(ma(m, win.len=12), col=\"blue\")\n\n" }, { "path": "47A-TS/TS-nile.R", "content": "# TS Nile\n# \ndata(\"Nile\")\nstr(Nile)\nNile\n# Plot the Nile data\nplot(Nile)\n?plot\n# Plot the Nile data with xlab and ylab arguments\nplot(Nile, xlab = \"Year\", ylab = \"River Volume\", type=\"b\", \n main = \"Annual River Nile Volume at Aswan, 1871-1970\")\n# Plot the Nile data with xlab, ylab, main, and type arguments\n\n#Dataset : Nile\n#Time-Series [1:100] from 1871 to 1970: 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...\n#Time Series: Start = 1871 End = 1970 Frequency = 1 [1] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110\nNile\nlength(Nile)\nNile[29]\nNile[51:58]\nNile[c(1,98,100)]\nwhich(Nile > 1000)\nwhich(Nile > 1000) + 1870\nhist(Nile)\nplot(Nile)\n#Other Examples on Nile\n\n\n#Package : Forecast\nlibrary(forecast)\nopar = par(no.readonly=T)\npar(mfrow=c(2,2))\n#par(opar)\n#Nile Dataset\nylim = c(min(Nile), max(Nile))\nplot(Nile, main='Raw time series')\nabline(v=c(1892,1900, 1941,1961))\nplot(ma(Nile, 3), main='Simple Moving Averages (k=3)', ylim=ylim)\nplot(ma(Nile, 7), main='Simple Moving Averages (k=7)', ylim=ylim)\nplot(ma(Nile, 15), main='Simple Moving Averages (k=15)', ylim=ylim)\n\npar(opar)\n\n?stl\nstr(AirPassengers)\nAirPassengers\nplot(AirPassengers)\nAirPassengers_log = log(AirPassengers)\nplot( AirPassengers_log, ylab=\"log(AirPassengers)\" )\nfit = stl(AirPassengers_log, s.window = 'period')\nplot(fit)\n\nfit$time.series\nexp(fit$time.series)\n\npar(mfrow=c(1,1))\nlibrary(forecast)\nmonthplot(AirPassengers,xlab=\"\", ylab=\"\")\nseasonplot(AirPassengers, year.labels = 'TRUE', main='')\n\n\n#Eg1a : Dataset - nhtemp\n#Mean annual temp in degree F in a city from 1912 to 1971\n#No obvious trend, lack seasonal component\nlibrary(forecast)\nfit = ets(nhtemp, model = 'ANN')\nfit\n\nforecast(fit,1)\n\nplot(forecast(fit,1), xlab='Year', ylab=expression(paste(\"Temp (\", degree * F,\")\" ,)), main=\" New Haven Annual Mean Temp\")\naccuracy(fit)\n\n#Eg1b\n#Decompose a TS describing monthly totals (in log 1000) of passengers into additive - trend, seasonal, irregular components ; Predict future travel ; Log values to used\nlibrary(forecast)\nfit = ets(log(AirPassengers), model='AAA')\nfit\naccuracy(fit)\npred = forecast(fit, 5)\npred\n\nplot(pred, main ='Forecast for Air Travel', ylab='Log(AirPassengers)', xlab='Time')\n\npred$mean\npred$mean = exp(pred$mean)\npred$mean\n\npred$lower = exp(pred$lower)\npred$upper = exp(pred$upper)\np = cbind(pred$mean, pred$lower, pred$upper)\ndimnames(p)[[2]] = c(\"mean\", \"Lo 80\", \"Lo 95\",\"Hi 80\", \"Hi 95\")\np\n\n\n#Eg1d @15.2.3\nlibrary(forecast)\nfit =ets(JohnsonJohnson)\nfit\nplot(forecast(fit), main=\"Johnson & Johnson Forecasts\", ylab=\"Quarterly Earnings in Dolloars\", xlab=\"TIme\", flty=2)\n\n " }, { "path": "47A-TS/TS-xts.R", "content": "# Time Series - xts\n" }, { "path": "47A-TS/TS-zoo.R", "content": "# Zoo package\n#Eg1\nlibrary(zoo)\nlibrary(lattice)\n# create test data\nz <- zooreg(1:83, start = as.Date(\"2009-04-01\"), deltat = 7)\nhead(z)\nxyplot(z)\n\n\n# Time series plots with good X axis labels\nlibrary(zoo)\n# data\ntoday = Sys.Date()\ndates = as.Date((today-500):today)\nhead(dates)\nz = zoo (100+cumsum(rnorm(501)), dates)\ncumsum(rnorm(501))\nhead(z)\n\n# method1 : default X axis labels do not look good\n?plot.zoo\nplot(z)\n?plot.ts\nplot(ts(z))\n\n# method 2 : Lattice\nlibrary(lattice)\n?xyplot.zoo\nxyplot(z)\nxyplot(z, lwd=2, col=\"tomato\")\n\n# method 3 : XTS\nlibrary(xts)\n?plot.xts\nplot(as.xts(z))\nplot(as.xts(z), auto.grid=F, major.format=\"%b %y\", las=2)\n\n# method 4 : Base graph\ntimeline = time(z)\nsummary(timeline)\nindex = seq(from=1, to=length(timeline), 90) # put ticks every 90 days\nindex\nplot(z, xaxt=\"n\")\naxis(side=1, at=timeline[index], label=format(timeline[index], \"%b %y\"), cex.axis=0.8)\n\n# method 5 : ggplot\nlibrary(ggplot2)\nlibrary(scales)\n?date_breaks\ndf = data.frame(date=as.POSIXct(time(z)), value=as.numeric(z))\nhead(df)\n# default plot\nggplot(df, aes(x=date, y=value)) + geom_line()\n# formatted\nggplot(df, aes(x=date, y=value)) + geom_line() + \n scale_x_datetime(labels=date_format(\"%b '%y\"))\n# custom breaks\nggplot(df, aes(x=date, y=value)) + geom_line() + \n scale_x_datetime(labels=date_format(\"%b '%y\"), breaks=date_breaks(\"3 months\"))\n\n\n\nxx.Date <- as.Date((Sys.Date()-500):Sys.Date())\nx <- zoo(cumsum(rnorm(501)), xx.Date)\ntt=time(x)\nplot(x, xaxt =\"n\")\ntt <- time(x)\nix <- seq(1, length(tt), by=60) #every 60 days\nfmt <- \"%b-%d\" # format for axis labels\nlabs <- format(tt[ix], fmt)\naxis(side = 1, at = tt[ix], labels = labs, cex.axis = 0.7)\n\n#Eg: Set 2\n#Eg1\nx.Date <- as.Date(paste(rep(2003:2004, each = 12), rep(1:12, 2), 1, sep = \"-\"))\nhead(x.Date)\nx <- zoo(rnorm(24), x.Date)\nhead(x)\nplot(x)\n#Eg2\nplot(x, xaxt = \"n\")\naxis(1, at = time(x), labels = FALSE)\ntime(x)\n#Eg3\nplot(x)\naxis(1, at = time(x), labels = FALSE)\n#Eg4\nplot(x)\ntimes <- time(x)\nticks <- seq(times[1], times[length(times)], by = \"weeks\")\naxis(1, at = ticks, labels = FALSE, tcl = -0.3)\n" }, { "path": "47A-TS/ts-P-highfreq.R", "content": "\nlibrary(highfrequency)\n#load sample price data\ndata(\"sample_tdata\");\nhead(sample_tdata)\nts = sample_tdata$PRICE;\n\n#Previous tick aggregation to the 5-minute sampling frequency:\ntsagg5min = aggregatets(ts,on=\"minutes\",k=5);\nhead(tsagg5min);\n\n#Previous tick aggregation to the 30-second sampling frequency:\ntsagg30sec = aggregatets(ts,on=\"seconds\",k=30);\ntail(tsagg30sec);\n" }, { "path": "47A-TS/ts-P-openair.R", "content": "# Package - openair\n\nlibrary(openair)\n\n# load example data from package\ndata(mydata)\n# trend plot for nox\nTheilSen(mydata, pollutant = \"nox\")\n\ntimeAverage(mydata)\nhead(mydata)\nprint(tbl_df(mydata), n=100)\n\nmydata %>% tbl_df %>% print(n=40)\n\n\n## daily average values\ndaily <- timeAverage(mydata, avg.time = \"day\")\nhead(daily)\n## daily average values ensuring at least 75 % data capture\n## i.e. at least 18 valid hours\n# NOT RUN {\ndaily <- timeAverage(mydata, avg.time = \"day\", data.thresh = 75)\n# }\nhead(daily)\n## 2-weekly averages\n# NOT RUN {\nfortnight <- timeAverage(mydata, avg.time = \"2 week\")\n# }\nhead(fortnight)\n\nhour <- timeAverage(mydata, avg.time = \"hour\")\nhead(hour)\n\n\n#----\nhead(df)\ndf$date = df$timestamp2\nhour2 <- timeAverage(df, avg.time = \"hour\")\nhead(hour2)\n" }, { "path": "47A-TS/ts-P-padr.R", "content": "# PADR package\n\nlibrary(tidyverse)\n\nlibrary(padr)\ntop12 <- emergency %>% count(title) %>% arrange(desc(n)) %>% slice(1:12) %>% \n select(title) %>% inner_join(emergency)\ntop12\nhead(emergency)\n?emergency\n\ntop12 %>% thicken('month', col = 'month') %>% \n count(title, month) %>% ggplot(aes(month, n)) + geom_bar(aes(fill = title), \n col = \"black\", stat = 'identity', position = \"fill\")\n\nhourly_distribution <- top12 %>% thicken('hour') %>% \n count(title, time_stamp_hour) %>% \n pad(group = 'title') %>% fill_by_value(n) \n\nhourly_distribution\n\nnames(df)\ndf %>% thicken('hour') %>% sum('value') %>% pad(group='household_id')\n" }, { "path": "47A-TS/ts-beer2.R", "content": "# Australian Beer\nlibrary(ts)\n\nlibrary(gsheet)\ndf1 = gsheet2tbl('https://docs.google.com/spreadsheets/d/1RCj6uvsu242xDf1l5Qb720DSnOmjnTUG8ety3iYBG90')\nbeer=df1\n\nbeer.ts <- ts(beer, frequency = 12, start = c(1956,1), end = c(1994,12))\nbeer.ts.qtr <- aggregate(beer.ts, nfrequency=4)\nbeer.ts.yr <- aggregate(beer.ts, nfrequency=1)\nplot.ts(beer.ts[,2], main = \"Monthly Beer Production in Australia\", xlab = \"Year\", ylab = \"ML\")\n\nplot.ts(beer.ts.qtr[,2], main = \"Quarterly Beer Production in Australia\", xlab = \"Year\", ylab = \"ML\")\n\n\nseasonplot(beer.ts[,2], year.labels = TRUE, year.labels.left=TRUE, col=1:40, pch=19, main = \"Monthly Beer Production in Australia - seasonplot\", xlab = \"Month\", ylab = \"ML\")\n\n" }, { "path": "47A-TS/ts-case1.R", "content": "# Time Series Analysis\n#http://r-statistics.co/Time-Series-Analysis-With-R.html\n\n" }, { "path": "47A-TS/ts-case2.R", "content": "# TS Case3\n\n\nlibrary(astsa) # then load it (has to be done at the start of each session)\ndata() # use this command to view all the loaded data \n?astsa\n#Johnson & Johnson data set. It's included in astsa as jj\njj\n\n# data properties\njj[1] # the first element\n#[1] 0.71\njj[84] # the last element\n#[1] 11.61\njj[1:4] # the first 4 elements\n#[1] 0.71 0.63 0.85 0.44\njj[-(1:80)] # everything EXCEPT the first 80 elements\n#[1] 16.20 14.67 16.02 11.61\nlength(jj) # the number of elements \n#[1] 84\ndim(jj) # but no dimensions ...\n#NULL\nnrow(jj) # ... no rows\n#NULL\nncol(jj) # ... and no columns\n#NULL\n#-- if you want it to be a column vector (in R, a matrix), an easy way to go is:\njjm = as.matrix(jj)\nmatrix(jjm, ncol=6)\ndim(jjm) \n#[1] 84 1 \njjm\n\n\n#monthly model\noptions(digits=2) # the default is 7, but it's more than I want now\n?options # to see your options (it's the help file)\n?rnorm # we're using it on the next line\n(zardoz = ts(rnorm(48), start=c(2018,6), frequency=12)) #start from Jun 2018\n(oz = window(zardoz, start=c(2018,10), end=c(2019,10)))\n(oz = window(zardoz, start=2019, end=c(2019,10)))\ntime(jj)\ntime(oz)\ntime(zardoz)\n\ncycle(oz)\ncycle(zardoz) #position of cycle in each obsvn\n?cycle\n\n#plot\nplot(jj, ylab=\"Earnings per Share\", main=\"J & J\")\nplot(jj, type=\"o\", col=\"blue\", lty=\"dashed\")\nplot(diff(log(jj)), main=\"logged and diffed\") \n\n\nx = -5:5 # sequence of integers from -5 to 5\ny = 5*cos(x) # guess\npar(mfrow=c(3,2)) # multifigure setup: 3 rows, 2 cols\n#--- plot:\nplot(x, main=\"plot(x)\")\nplot(x, y, main=\"plot(x,y)\")\n#--- plot.ts:\nplot.ts(x, main=\"plot.ts(x)\")\nplot.ts(x, y, main=\"plot.ts(x,y)\")\n#--- ts.plot:\nts.plot(x, main=\"ts.plot(x)\")\nts.plot(ts(x), ts(y), col=1:2, main=\"ts.plot(x,y)\") # note- x and y are ts objects \n#--- the help files [? and help() are the same]:\n?plot.ts\nhelp(ts.plot)\n?par # might as well skim the graphical parameters help file while you're here\n\n\npar(mfrow=c(1,1))\nk = c(.5,1,1,1,.5) # k is the vector of weights\n(k = k/sum(k)) \n#[1] 0.125 0.250 0.250 0.250 0.125\nfjj = stats::filter(jj, sides=2, k) # ?filter for help [but you knew that already]\nplot(jj)\nlines(fjj, col=\"red\") # adds a line to the existing plot\nlines(lowess(jj), col=\"blue\", lty=\"dashed\")\n\n#... and the result:\n\n\ndljj = diff(log(jj)) # difference the logged data\nplot(dljj) # plot it (not shown)\n\nshapiro.test(dljj) # test for normality \n#Shapiro-Wilk normality test\n#data: dljj \n#W = 0.9725, p-value = 0.07211\n\n#sanity.clause(dljj) # test for sanity\n#dljj is crazy \n#SC = 45.57, p-value = 0.0003 \n\n# I couldn't fool you - there ain't no sanity clause \n\n\n\npar(mfrow=c(2,1)) # set up the graphics \nhist(dljj, prob=TRUE, 12) # histogram \nlines(density(dljj)) # smooth it - ?density for details \nqqnorm(dljj) # normal Q-Q plot \nqqline(dljj) # add a line \n\nlag1.plot(dljj, 4) # this is the astsa version of lag.plot in the stats package \n\nacf2(dljj) # astsa gives both in one swell foop ... or use acf() and pacf() individually \n\nplot(dog <- stl(log(jj), \"per\")) \n" }, { "path": "47A-TS/ts-lubridate2.R", "content": "# Package Lubridate\n# Date Functions - Date & Time\n\n#Eg1\nlibrary(lubridate)\nlubridate::ymd(\"20110604\")\nlubridate::mdy(\"06-04-2011\")\nlubridate::dmy(\"04/06/2011\")\n#Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.\n\n#Eg1\n#If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.\nOlsonNames()\nSys.timezone()\nSys.timezone(location=F)\narrive <- ymd_hms(\"2018-04-27 18:40:15\", tz = \"Asia/Calcutta\")\narrive\nleave <- ymd_hms(\"2018-04-29 22:00:00\", tz = \"Asia/Calcutta\")\nleave\n\nleave - arrive # Time Difference\n\n#Setting and Extracting information\n#Functions\n\n#Eg\nsecond(arrive)\nsecond(arrive) = 25 #change\nsecond(arrive)\narrive\nminute(arrive)\nhour(arrive)\nday(arrive)\nwday(arrive)\nwday(arrive)\nwday(arrive, label = TRUE)\n\nweek(arrive)\n\nmonth(arrive)\nyear(arrive)\ntz(arrive)\n\n#Time Intervals\n#save an interval of time as an Interval class object\n(student1 <- interval(arrive, leave))\n(student2 <- arrive %--% leave)\n(student3 = interval(ymd(20180320, tz = \"Asia/Calcutta\"),\n ymd(20180327, tz = \"Asia/Calcutta\")))\n\n(courseperiod = interval(ymd(20180421, tz = \"Asia/Calcutta\"),\n ymd(20180425, tz = \"Asia/Calcutta\")))\n\nint_overlaps(student1, courseperiod)\n#TRUE\nint_overlaps(student3, courseperiod)\n\nsetdiff(student2, courseperiod)\nsetdiff(student3, courseperiod)\n\n?setdiff\n#2017-07-05 IST--2017-07-15 IST\n\n#Other Functions\n#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.\nint_start(courseperiod)\nint_end(courseperiod)\nint_flip(courseperiod)\nint_aligns(student1, courseperiod) #share end point\n\ndates = now() + days(1:10)\ndates\nint_diff(dates)\n\n# Years Betw ----------\nref_date <- as.Date('20/04/08',format='%d/%m/%y')\ntoday <- as.Date(Sys.Date(), format='%d/%m/%y')\nyear(arrive) = 2015\narrive\narrive2 = as.Date(arrive, format='%d/%m/%y')\n(yrsbetw = year(today)-year(ref_date))\n(yrsbetw = year(today)-year(arrive2))\n" }, { "path": "47A-TS/ts-rollingvalues.R", "content": "# Rolling Time Series\n\nlibrary(zoo)\nset.seed(0)\nnSamples <- 5000\nvecDT <- rexp(nSamples, 3)\nvecTimes <- cumsum(c(0,vecDT))\nvecDrift <- c(0, rnorm(nSamples, mean = 1/nSamples, sd = 0.01))\nvecVals <- cumsum(vecDrift)\nvecZ <- zoo(vecVals, order.by = vecTimes)\nrm(vecDT, vecDrift)\n\nlibrary(xts)\nlibrary(TTR)\nx <- .xts(vecZ, vecTimes)\nxx <- na.locf(cbind(xts(, seq.POSIXt(from=start(x), to=end(x), by='sec')), x))\nx$means <- runMean(xx, n=180)\nout <- x[!is.na(x[, 1]), ]\ntail(out)\n\n\ntq_mutate_fun_options() %>%\n str()\n\n\n## $ zoo : chr [1:14] \"rollapply\" \"rollapplyr\" \"rollmax\" \"rollmax.default\" ...\n## $ xts : chr [1:27] \"apply.daily\" \"apply.monthly\" \"apply.quarterly\" \"apply.weekly\" ...\n## $ quantmod : chr [1:25] \"allReturns\" \"annualReturn\" \"ClCl\" \"dailyReturn\" ...\n## $ TTR : chr [1:61] \"adjRatios\" \"ADX\" \"ALMA\" \"aroon\" ...\n## $ PerformanceAnalytics: chr [1:7] \"Return.annualized\" \"Return.annualized.excess\" \"Return.clean\" \"Return.cumulative\" .." }, { "path": "47A-TS/ts-rollingvalues2.R", "content": "#https://www.r-bloggers.com/tidy-time-series-analysis-part-2-rolling-functions/\nlibrary(tidyquant) # Loads tidyverse, tidyquant, financial pkgs, xts/zoo\nlibrary(cranlogs) # For inspecting package downloads over time\n\nlibrary(tidyquant)\n\ncustom_stat_fun_2 <- function(x, na.rm = TRUE) {\n # x = numeric vector\n # na.rm = boolean, whether or not to remove NA's\n m <- mean(x, na.rm = na.rm)\n s <- sd(x, na.rm = na.rm)\n hi <- m + 2*s\n lo <- m - 2*s\n ret <- c(mean = m, stdev = s, hi.95 = hi, lo.95 = lo) \n return(ret)\n}\n\n\n\ntidyverse_downloads_rollstats <- tidyverse_downloads %>%\n tq_mutate(\n select = count,\n mutate_fun = rollapply, \n # rollapply args\n width = 30,\n align = \"right\",\n by.column = FALSE,\n FUN = custom_stat_fun_2,\n # FUN args\n na.rm = TRUE\n )\nclass(tidyverse_downloads)\n\ntidyverse_downloads_rollstats\nprint(tbl_df(tidyverse_downloads_rollstats), n=40)\ntq_mutate_fun_options() %>% str()\n\n#----\n# tidyverse packages (see my laptop stickers from last post) ;)\npkgs <- c(\n \"tidyr\", \"lubridate\", \"dplyr\", \n \"broom\", \"tidyquant\", \"ggplot2\", \"purrr\", \n \"stringr\", \"knitr\"\n)\n\n# Get the downloads for the individual packages\ntidyverse_downloads <- cran_downloads(\n packages = pkgs, \n from = \"2017-01-01\", \n to = \"2017-06-30\") %>%\n tibble::as_tibble() %>%\n group_by(package)\n\n# Visualize the package downloads\ntidyverse_downloads %>%\n ggplot(aes(x = date, y = count, color = package)) +\n # Data\n geom_point(alpha = 0.5) +\n facet_wrap(~ package, ncol = 3, scale = \"free_y\") +\n # Aesthetics\n labs(title = \"tidyverse packages: Daily downloads\", x = \"\",\n subtitle = \"2017-01-01 through 2017-06-30\",\n caption = \"Downloads data courtesy of cranlogs package\") +\n scale_color_tq() +\n theme_tq() +\n theme(legend.position=\"none\")\n\n# my case\nhead(data)\nhead(df)\nnames(df)\ndf2 = xts(df[1:4], order.by=df$timestamp2)\nnames(df2)\n\ncustom_stat_fun_3 <- function(x, na.rm = TRUE) {\n # x = numeric vector\n # na.rm = boolean, whether or not to remove NA's\n m <- mean(x, na.rm = na.rm)\n s <- sd(x, na.rm = na.rm)\n hi <- m + 1*s\n lo <- m - 1*s\n ret <- c(mean = m, stdev = s, hi.95 = hi, lo.95 = lo) \n return(ret)\n}\nclass(df2)\n\n\ndf2_rollstats <- df %>%\n tq_mutate(\n select = value,\n mutate_fun = rollapply, \n # rollapply args\n width = 30,\n align = \"right\",\n by.column = FALSE,\n FUN = custom_stat_fun_3,\n # FUN args\n na.rm = TRUE\n )\n\n\n#timeAverage" }, { "path": "47A-TS/ts-splitdate.R", "content": "# Split Data into parts\n\n#Split date and time seperately\n#M1----\n(tdatetime = Sys.time())\n(splitTime <- sapply(strsplit(as.character(tdatetime), \" \"), \"[\", 2)) #time\n(splitDate <- sapply(strsplit(as.character(tdatetime), \" \"), \"[\", 1)) #date\n\n# M2----\n(df <- data.frame(datetime = c( \"13:11:2013 15:39\" , \"13:11:2013 16:15\" , \"13:11:2013 17:52\" ) ))\n(new <- do.call( rbind , strsplit( as.character( df$datetime ) , \" \" ) ))\n\n#M3----\nformat(as.POSIXct(tdatetime,format=\"%Y:%m:%d %H:%M:%S\"),\"%H:%M:%S\")\nformat(as.POSIXct(tdatetime,format=\"%Y:%m:%d %H:%M:%S\"),\"%Y:%m:%d\")\n\n#Time Stamp to POSIXT\ntd = 1377986400\nas.POSIXct(1377986400,origin=\"1970-01-01\")\n(td1 = as.POSIXct(as.numeric(td),origin=\"1970-01-01\"))\n\n\ndatetime2 <- as.POSIXct('4/17/2008 16:01', format='%m/%d/%Y %H:%M') \n(datetime2split <- strsplit(format(datetime2, \"%d %m %Y %H %M\"), ' ') )\n\n(x2 = as.POSIXct(td1, format='%m/%d/%Y %H:%M'))\n(y2 <- strsplit(format(x2, \"%d %m %Y %H %M\"), ' ') )\n\n#-----\n# In R, there are two basic data types around date and time in R. One is Date, which contains only the date information like 2016–01–10. Another is POSIXct, which has both date and time information like 2016–01–10 10:45:20.\n\nlibrary(dplyr)\ndateposix = seq(1377986400, 1377986420, by=1)\ndf = data.frame(dateposix)\n(df$datetime = as.POSIXct(as.numeric(df$dateposix),origin=\"1970-01-01\"))\nlibrary(lubridate)\nhead(df)\ndf$dateposix2 = format(unclass(df$datetime), scientific=F)\ndf <- df %>% mutate(year=year(datetime), month = month(datetime), day= day(datetime), hour=hour(datetime), min=minute(datetime))\nhead(format(df, scientific=F))\n\nsplit1 =strsplit(format(df$datetime, \"%d %m %Y %H %M\"), ' ') #list form\nmatrix(as.numeric(unlist(split1)), ncol=5, byrow = T) \ndo.call(rbind, split1)\nas.Date(with(df$datetime, ISOdate(Year, Month, Day)))\n \n#-----\n\ndo.call(rbind, spl) \n\n# using seperate in tidyr\nlibrary(dplyr)\nhead(df)\ndf1 = df['datetime']\nhead(df1)\ndf1 %>% separate(datetime, into=c('Date', 'Time'), sep=' ')\n(year=as.numeric (format(df1$datetime,\"%Y\")))\n(month=as.numeric (format(df1$datetime,\"%m\")))\n(day=as.numeric (format(df1$datetime,\"%d\")))\n(hour=as.numeric (format(df1$datetime,\"%H\")))\n(min =as.numeric (format(df1$datetime,\"%M\")))\ndata.frame(df1,cbind(year, month, day, hour, min))\n" }, { "path": "47A-TS/ts-timestamp.R", "content": "help(DateTimeClasses)\nt <- Sys.time()\ntypeof(t)\nt\nprint(t)\ncat(t,\"\\n\")\n1390505301\nc <- as.POSIXct(t)\nc\ntypeof(c)\nprint(c)\n[1] \"2014-01-23 14:28:21 EST\"\n> cat(c,\"\\n\")\n1390505301\n>\n >\n > l <- as.POSIXlt(t)\n> l\n[1] \"2014-01-23 14:28:21 EST\"\n> typeof(l)\n[1] \"list\"\n> print(l)\n[1] \"2014-01-23 14:28:21 EST\"\n> cat(l,\"\\n\")\nError in cat(list(...), file, sep, fill, labels, append) :\n argument 1 (type 'list') cannot be handled by 'cat'\n> names(l)\nNULL\n> l[[1]]\n[1] 21.01023\n> l[[2]]\n[1] 28\n> l[[3]]\n[1] 14\n> l[[4]]\n[1] 23\n> l[[5]]\n[1] 0\n> l[[6]]\n[1] 114\n> l[[7]]\n[1] 4\n> l[[8]]\n[1] 22\n> l[[9]]\n[1] 0\n>\n > b <- as.POSIXct(l)\n> cat(b,\"\\n\")\n1390505301\n\nlibrary(bReeze)\ndata(winddata)\nhead(winddata)\n# format time stamp\nhead(timestamp(timestamp=winddata[,1]))\n\n\n#time stamp\nx <- 15103\nas_date(x, origin = lubridate::origin) #\"2011-05-09\"\n\ny <- 1442866615\nas_datetime(y, origin = lubridate::origin)\n#\"2015-09-21 20:16:55 UTC\"\n\nx=axis.Date(1, as.Date(15103, origin = \"1960-10-01\"))\nx\n\n\n#----\nnow <- Sys.time()\nnow\nas.numeric(now)\nnow + 10 # adds 10 seconds\nas.POSIXlt(now)\nstr(as.POSIXlt(now))\nunclass(as.POSIXlt(now))\n\n\n#----\nJan1 <- strptime(\"2018-01-01 00:00:00\", \"%Y-%m-%d %H:%M:%S\")\ndifftime(now, Jan1, unit=\"week\")\n\n\n#----\nhelp(strftime)\nt <- Sys.time()\ncat(t,\"\\n\")\n(timeStamp <- strftime(t,\"%Y-%m-%d %H:%M:%S\"))\ntypeof(timeStamp)\n\n#----\nmyData <- data.frame(time=c(\"2014-01-23 14:28:21\",\"2014-01-23 14:28:55\",\n \"2014-01-23 14:29:02\",\"2014-01-23 14:31:18\"),\n speed=c(2.0,2.2,3.4,5.5))\nmyData\nsummary(myData)\nmyData$time[1]\nmyData$time <- strptime(myData$time,\"%Y-%m-%d %H:%M:%S\")\ntypeof(myData$time)\nmyData$time[1]\nmyData$time[1][[2]]\n\nN = length(myData$time)\nN\nmyData$time[2:N] - myData$time[1:(N-1)]\n" }, { "path": "47A-TS/tsforecast-exp.R", "content": "# TS Forecasting with Exponential Smoothening\n\n# Simple exponential smoothing\nlibrary(forecast)\n?nhtemp\n#Mean annual Temp in Degree F from 1912 to 1917 : 60 observations\n# Types of Smoothening : (level, trend, seasoanal) : A- Additive, None, Z-automatic\n# Simple - Level : model- ANN\n# double - level, slope (trend) : model : AAN\n# triple - level, slope(trend), seasonal : model - AAA\npar(mfrow=c(1,1))\nplot(nhtemp)\n(fitse = ets(nhtemp, model='ANN'))\n(fitse2 = ses(nhtemp))\n\nforecast(fitse,3)\nplot(forecast(fitse,c(3)))\naccuracy(fitse)\n\n#Holt Exponential Smoothening\nTS = level + slope * t + irregular\nplot(AirPassengers)\n#log model to use additive model\nplot(log(AirPassengers))\n\n(fithe = ets(log(AirPassengers), model='AAA'))\n(pred = forecast(fithe, 5))\nplot(pred, main='Forecast for Air Travel', ylab='Log (Air Passengers)', xlab='Time')\n#since log was used, use exp to get predicted values\npred$mean\n(pred$mean = exp(pred$mean))\n(pred$lower = exp(pred$lower))\n(pred$upper = exp(pred$upper))\n(p = cbind(pred$mean, pred$lower, pred$upper))\n\n(pred$mean = exp(pred$mean))\n\n\n\n\n#Holt Winters Exponential Smoothening\nTS = level + slope * t + s(t) + irregular\n\nfit <- HoltWinters(nhtemp, beta=FALSE, gamma=FALSE)\nfit\nforecast(fit, 1)\nplot(forecast(fit, 1), xlab=\"Year\", ylab=expression(paste(\"Temperature (\", degree*F,\")\",)), main=\"New Haven Annual Mean Temperature\")\naccuracy(fit)\n# Listing 15.5 - Exponential smoothing with level, slope, and seasonal components\nfit <- HoltWinters(log(AirPassengers))\nfit\naccuracy(fit)\npred <- forecast(fit, 5)\npred\nplot(pred, main=\"Forecast for Air Travel\",\n ylab=\"Log(AirPassengers)\", xlab=\"Time\")\npred$mean <- exp(pred$mean)\npred$lower <- exp(pred$lower)\npred$upper <- exp(pred$upper)\np <- cbind(pred$mean, pred$lower, pred$upper)\ndimnames(p)[[2]] <- c(\"mean\", \"Lo 80\", \"Lo 95\", \"Hi 80\", \"Hi 95\")\np\n# Listing 15.6 - Automatic exponential forecasting with ets()\nlibrary(forecast)\nfit <- ets(JohnsonJohnson)\nfit\nplot(forecast(fit), main=\"Johnson and Johnson Forecasts\",\n ylab=\"Quarterly Earnings (Dollars)\", xlab=\"Time\")\n# Listing 15.7 - Transforming the time series and assessing stationarity\nlibrary(forecast)\nlibrary(tseries)\nplot(Nile)\nndiffs(Nile)\ndNile <- diff(Nile)\nplot(dNile)\nadf.test(dNile)\n\nlibrary(forecast)\n?auto.arima\n?arima\n" }, { "path": "48A-HTML/aboutUSL", "content": "# Unsupervised Learning\nWe do not have any target or outcome variable to predict / estimate\nIt is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.\nEgs - Apriori Algo, K-means\n" }, { "path": "48c-TM/SM-rtexttools1.R", "content": "# Text Analysis\nlibrary(RTextTools)\nlibrary(e1071)\n\n\npos_tweets = rbind(\n c('I love this car', 'positive'),\n c('This view is amazing', 'positive'),\n c('I feel great this morning', 'positive'),\n c('I am so excited about the concert', 'positive'),\n c('He is my best friend', 'positive')\n)\n\nneg_tweets = rbind(\n c('I do not like this car', 'negative'),\n c('This view is horrible', 'negative'),\n c('I feel tired this morning', 'negative'),\n c('I am not looking forward to the concert', 'negative'),\n c('He is my enemy', 'negative')\n)\n\ntest_tweets = rbind(\n c('feel happy this morning', 'positive'),\n c('larry friend', 'positive'),\n c('not like that man', 'negative'),\n c('house not great', 'negative'),\n c('your song annoying', 'negative')\n)\n\ntweets = rbind(pos_tweets, neg_tweets, test_tweets)\n\n# build dtm\nmatrix= create_matrix(tweets[,1], language=\"english\", \n removeStopwords=FALSE, removeNumbers=TRUE, \n stemWords=FALSE) \n\n# train the model\nmat = as.matrix(matrix)\nclassifier = naiveBayes(mat[1:10,], as.factor(tweets[1:10,2]) )\n\n# test the validity\npredicted = predict(classifier, mat[11:15,]); predicted\ntable(tweets[11:15, 2], predicted)\nrecall_accuracy(tweets[11:15, 2], predicted)\n\n\n# build the data to specify response variable, training set, testing set.\ncontainer = create_container(matrix, as.numeric(as.factor(tweets[,2])), trainSize=1:10, testSize=11:15,virgin=FALSE)\n\n\n\nmodels = train_models(container, algorithms=c(\"MAXENT\" , \"SVM\", \"RF\", \"BAGGING\", \"TREE\"))\n#Now, we can classify the testing set using the trained models.\n\nresults = classify_models(container, models)\n#How about the accuracy?\n \n# accuracy table\ntable(as.numeric(as.factor(tweets[11:15, 2])), results[,\"FORESTS_LABEL\"])\ntable(as.numeric(as.factor(tweets[11:15, 2])), results[,\"MAXENTROPY_LABEL\"])\n\n# recall accuracy\nrecall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,\"FORESTS_LABEL\"])\nrecall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,\"MAXENTROPY_LABEL\"])\nrecall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,\"TREE_LABEL\"])\nrecall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,\"BAGGING_LABEL\"])\nrecall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,\"SVM_LABEL\"])\n#To summarize the results (especially the validity) in a formal way:\n\n# model summary\nanalytics = create_analytics(container, results)\nsummary(analytics)\nhead(analytics@document_summary)\nanalytics@ensemble_summary\n#To cross validate the results:\n \nN=4\nset.seed(2014)\ncross_validate(container,N,\"MAXENT\")\ncross_validate(container,N,\"TREE\")\ncross_validate(container,N,\"SVM\")\ncross_validate(container,N,\"RF\")\n#The results can be found on my Rpub page. It seems that maxent reached the same recall accuracy as naive Bayes. The other methods even did a worse job. This is understandable, since we have only a very small data set. To enlarge the training set, we can get a much better results for sentiment analysis of tweets using more sophisticated methods. I will show the results with anther example.\n\n#Sentiment analysis for tweets\n#The data comes from victorneo. victorneo shows how to do sentiment analysis for tweets using Python. Here, I will demonstrate how to do it in R.\n\n#Read data:\n \n###################\n#\"load data\" : set it to your file\n###################\nsetwd(\"D:/Twitter-Sentimental-Analysis-master/\")\nhappy = readLines(\"./happy.txt\")\nsad = readLines(\"./sad.txt\")\nhappy_test = readLines(\"./happy_test.txt\")\nsad_test = readLines(\"./sad_test.txt\")\n\ntweet = c(happy, sad)\ntweet_test= c(happy_test, sad_test)\ntweet_all = c(tweet, tweet_test)\nsentiment = c(rep(\"happy\", length(happy) ), \n rep(\"sad\", length(sad)))\nsentiment_test = c(rep(\"happy\", length(happy_test) ), \n rep(\"sad\", length(sad_test)))\nsentiment_all = as.factor(c(sentiment, sentiment_test))\n\nlibrary(RTextTools)\nFirst, try naive Bayes.\n\n# naive bayes\nmat= create_matrix(tweet_all, language=\"english\", \n removeStopwords=FALSE, removeNumbers=TRUE, \n stemWords=FALSE, tm::weightTfIdf)\n\nmat = as.matrix(mat)\n\nclassifier = naiveBayes(mat[1:160,], as.factor(sentiment_all[1:160]))\npredicted = predict(classifier, mat[161:180,]); predicted\n\ntable(sentiment_test, predicted)\nrecall_accuracy(sentiment_test, predicted)\nThen, try the other methods:\n \n # the other methods\n mat= create_matrix(tweet_all, language=\"english\", \n removeStopwords=FALSE, removeNumbers=TRUE, \n stemWords=FALSE, tm::weightTfIdf)\n\ncontainer = create_container(mat, as.numeric(sentiment_all),\n trainSize=1:160, testSize=161:180,virgin=FALSE) #可以设置removeSparseTerms\n\nmodels = train_models(container, algorithms=c(\"MAXENT\",\n \"SVM\",\n #\"GLMNET\", \"BOOSTING\", \n \"SLDA\",\"BAGGING\", \n \"RF\", # \"NNET\", \n \"TREE\" \n))\n\n# test the model\nresults = classify_models(container, models)\ntable(as.numeric(as.numeric(sentiment_all[161:180])), results[,\"FORESTS_LABEL\"])\nrecall_accuracy(as.numeric(as.numeric(sentiment_all[161:180])), results[,\"FORESTS_LABEL\"])\nHere we also want to get the formal test results, including:\n \n analytics@algorithm_summary: Summary of precision, recall, f-scores, and accuracy sorted by topic code for each algorithm\nanalytics@label_summary: Summary of label (e.g. Topic) accuracy\nanalytics@document_summary: Raw summary of all data and scoring\nanalytics@ensemble_summary: Summary of ensemble precision/coverage. Uses the n variable passed into create_analytics()\nNow let’s see the results:\n \n # formal tests\n analytics = create_analytics(container, results)\nsummary(analytics)\n\nhead(analytics@algorithm_summary)\nhead(analytics@label_summary)\nhead(analytics@document_summary)\nanalytics@ensemble_summary # Ensemble Agreement\n\n# Cross Validation\nN=3\ncross_SVM = cross_validate(container,N,\"SVM\")\ncross_GLMNET = cross_validate(container,N,\"GLMNET\")\ncross_MAXENT = cross_validate(container,N,\"MAXENT\")" }, { "path": "48c-TM/TM-zz.R", "content": "#text mining\n\nlibrary(tidytext)\n\nsentiments\nget_sentiments(\"afinn\")\n?get_sentiments\n\nget_sentiments(\"bing\")\n\n\n\nlibrary(janeaustenr)\nlibrary(dplyr)\nlibrary(stringr)\n\ntidy_books <- austen_books() %>%\n group_by(book) %>%\n mutate(linenumber = row_number(),\n chapter = cumsum(str_detect(text, regex(\"^chapter [\\\\divxlc]\", ignore_case = TRUE)))) %>%\n ungroup() %>%\n unnest_tokens(word, text)\n#https://www.tidytextmining.com/tidytext.html\n\n" }, { "path": "48c-TM/downloadfile.R", "content": "# download a text Document \n\n#download.file\n#download.file can be used to download a single file as described by url from the internet and store it in destfile. The url must start with a scheme such as http://, https://,\n\n\nurl2 =\"http://52.66.73.63/docs/fms.txt\"\ndownload.file(url2, \"fms.txt\" )\ndownload.file(url2, \"./download/fms.txt\" )\n\nurl3 =\"http://52.66.73.63/docs/rowling.txt\"\ndownload.file(url3, \"./download/rowling.txt\" )\n\n\n\n\n#readtext\nlibrary(readtext)\nreadtext(\"./download/rowling.txt\")\n\n#url.show\nurl.show(\"http://52.66.73.63/docs/rowling.txt\")\n\n#file.show\nfile.show(\"./download/rowling.txt\")\n\nurl = \"http://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv\"\ndownload.file(url, \"./download/iris.csv\", quiet=TRUE)\n\n\nlibrary(httr)\nurl <- \"https://rawgit.com/yoke2/dsxref/master/iris.xlsx\"\nGET(url, write_disk(\"./download/iris.xlsx\", overwrite=TRUE))\n\n\nlibrary(downloader)\n#format is not correct\nurlforR= \"https://github.com/dupadhyaya/analytics/blob/master/15a-DS/20a-vectors.R\"\ndownload(urlforR,\"./download/vector.R\")\ndownloader::source_url(urlforR)\n" }, { "path": "48c-TM/facebook1.R", "content": "# Facebook posts\n#http://thinktostart.com/analyzing-facebook-with-r/\n#errorat authentication\n#https://www.listendata.com/2017/03/facebook-data-mining-using-r.html\n\nlibrary(devtools)\n\ninstall_github(\"Rfacebook\", \"pablobarbera\", subdir=\"Rfacebook\")\n\n#require (Rfacebook)\nlibrary(Rfacebook)\nlibrary(RCurl)\n\nrequire(\"Rfacebook\")\n#https://developers.facebook.com/apps/2208824636012038/settings/basic/\n \nfb_oauth <- fbOAuth(app_id=\"2208824636012038\", app_secret=\"xxx\",extended_permissions = TRUE)\n#Not working\n\nsave(fb_oauth, file=\"fb_oauth\")\n\nload(\"fb_oauth\")" }, { "path": "48c-TM/facebook2.R", "content": "# Facebook Analytics\n#http://thinktostart.com/analyzing-facebook-with-r/\n \n#install.packages(\"devtools\")\nlibrary(devtools)\ninstall_github(\"Rfacebook\", \"pablobarbera\", subdir=\"Rfacebook\")\n\nrequire (Rfacebook)\nfb_oauth <- fbOAuth(app_id=\"289287295005213\", app_secret=\"b624856b1b54d3146e8d9d93b5d667fa\",extended_permissions = TRUE)\n\nsave(fb_oauth, file=\"fb_oauth\")\n\nload(\"fb_oauth\")\nme <- getUsers(\"me\",token=fb_oauth)\nmy_likes <- getLikes(user=\"me\", token=fb_oauth)\n\ntoken <- 'YOUR AUTHENTICATION TOKEN'\nme <- getUsers(\"me\", token, private_info=TRUE)\n\ngetUsers(c(\"barackobama\", \"donaldtrump\"), token)\n" }, { "path": "48c-TM/fms.txt", "content": "The Faculty of Management Studies focuses on management education more than just business management. The commitment is thought leadership with a deep understanding of business. The approach to pedagogy combines fieldwork, case studies and instrumented feedback with a strong emphasis on concepts and theory. The intent is to encourage intellectual curiosity and open minds to the adventure of ideas.\n\nBut much about the school is not just about what is taught within its confines. FMS has the unique privilege to be part of one of the premier universities world – the University of Delhi - with some of the finest departments in Economics, Law, Sociology, Commerce and Operations Research. Our collaborative approach involves inputs from various departments which gives the benefit of a much wider view and deeper understanding. This is indicated by the success of our alumni who are in positions of leadership in industries & governments across the world. The network of more than 10000 alumni gives students unmatched access to information, mentors and careers.\n\nFMS rewards initiative, novelty and thinking outside the box. Our students are individualists of enormous intellectual energy with a talent for collaboration and teamwork. We are a diverse lot, possessing flair and dynamism that develops in a metropolis like Delhi and thrives on challenges both inside and outside the classroom.\n\nCorporate recruiters value our graduates for their intellectual abilities, their collaborative mind-set, their individuality and their ability to hit the ground running.\n\nWe invite you to discover FMS for yourself." }, { "path": "48c-TM/linkedin1.R", "content": "#Linkedin Posts\n\nrequire(devtools)\n#install_github(\"mpiccirilli/Rlinkedin\")\nrequire(Rlinkedin)\nin.auth <- inOAuth()\n\napp_name <- \"5231725\"\nconsumer_key <- \"818i9sxp6fbimd\"\nconsumer_secret <- \"uau6nQyST7BP9XtV\"\n\nin.auth <- inOAuth(app_name, consumer_key, consumer_secret)\nmy.connections <- getMyConnections(in.auth)\ntext <- toString(my.connections$industry)\n\n\n\nclean.text <- function(some_txt)\n{\n some_txt = gsub(\"(RT|via)((?:\\\\b\\\\W*@\\\\w+)+)\", \"\", some_txt)\n some_txt = gsub(\"@\\\\w+\", \"\", some_txt)\n some_txt = gsub(\"[[:punct:]]\", \"\", some_txt)\n some_txt = gsub(\"[[:digit:]]\", \"\", some_txt)\n some_txt = gsub(\"http\\\\w+\", \"\", some_txt)\n some_txt = gsub(\"[ \\t]{2,}\", \"\", some_txt)\n some_txt = gsub(\"^\\\\s+|\\\\s+$\", \"\", some_txt)\n some_txt = gsub(\"amp\", \"\", some_txt)\n # define \"tolower error handling\" function\n try.tolower = function(x)\n {\n y = NA\n try_error = tryCatch(tolower(x), error=function(e) e)\n if (!inherits(try_error, \"error\"))\n y = tolower(x)\n return(y)\n }\n \n some_txt = sapply(some_txt, try.tolower)\n some_txt = some_txt[some_txt != \"\"]\n names(some_txt) = NULL\n return(some_txt)\n}\n\nclean_text = clean.text(text)\ntweet_corpus = Corpus(VectorSource(clean_text))\n\ntdm = TermDocumentMatrix(tweet_corpus, control = list(removePunctuation = TRUE,stopwords = stopwords(\"english\"), removeNumbers = TRUE, tolower = TRUE))\n\n#install.packages(c(\"wordcloud\",\"tm\"),repos=\"http://cran.r-project.org\")\n\nlibrary(wordcloud)\nm = as.matrix(tdm) #we define tdm as matrix\nword_freqs = sort(rowSums(m), decreasing=TRUE) #now we get the word orders in decreasing order\ndm = data.frame(word=names(word_freqs), freq=word_freqs) #we create our data set\nwordcloud(dm$word, dm$freq, random.order=FALSE, colors=brewer.pal(8, \"Dark2\")) #and we visualize our data\n" }, { "path": "48c-TM/linkedin3.R", "content": "#Linked In posts - Note Working\n\n# Get a person's name, location, summary, # of connections, and skills & endorsements from LinkedIn\n\n# URL of the LinkedIn page \nuser_url <- \"https://www.linkedin.com/in/dhiraj-upadhyaya-4b7161a\"\n\n# since the information isn't available without being logged in, the web\n# scraper needs to log in. Provide your LinkedIn user/pw here (this isn't stored\n# anywhere as you can see, it's just used to log in during the scrape session)\nusername <- \"xxxx\"\npassword <- \"xxx\"\n\n# takes a couple seconds and might throw a warning, but ignore the warning\n(linkedin_info <- scrape_linkedin(user_url))\n\n############################\n\nlibrary(rvest)\n\nscrape_linkedin <- function(user_url) {\n linkedin_url <- \"http://linkedin.com/\"\n pgsession <- html_session(linkedin_url) \n pgform <- html_form(pgsession)[[1]]\n filled_form <- set_values(pgform,\n session_key = username, \n session_password = password)\n \n submit_form(pgsession, filled_form)\n \n pgsession <- jump_to(pgsession, user_url)\n page_html <- read_html(pgsession)\n \n name <-\n page_html %>% html_nodes(\"#name\") %>% html_text()\n \n location <-\n page_html %>% html_nodes(\"#location .locality\") %>% html_text()\n \n num_connections <-\n page_html %>% html_nodes(\".member-connections strong\") %>% html_text()\n \n summary <- \n page_html %>% html_nodes(\"#summary-item-view\") %>% html_text()\n \n skills_nodes <-\n page_html %>% html_nodes(\"#profile-skills .skill-pill\")\n \n skills <-\n lapply(skills_nodes, function(node) {\n num <- node %>% html_nodes(\".num-endorsements\") %>% html_text()\n name <- node %>% html_nodes(\".endorse-item-name-text\") %>% html_text()\n data.frame(name = name, num = num)\n })\n \n skills <- do.call(rbind, skills)\n \n \n list(\n name = name,\n location = location,\n num_connections = num_connections,\n summary = summary,\n skills = skills\n ) \n}\n\n(linkedin_info <- scrape_linkedin(user_url))\nlinkedin_info\n" }, { "path": "48c-TM/pagerank.R", "content": "#Page Rank\n\n#install.packages('igraph')\nlibrary(igraph)\n\nmygraph = graph(c('A','B',\n 'A','D',\n 'B','A',\n 'B','B',\n 'B','C',\n 'C','C',\n 'A','D',\n 'C','D',\n 'C','A'), directed=T)\nplot(mygraph, cex=1.5)\n" }, { "path": "48c-TM/rowling.txt", "content": "President Faust, members of the Harvard Corporation and the Board of Overseers, members of the faculty, proud parents, and, above all, graduates.\n\nThe first thing I would like to say is ‘thank you.’ Not only has Harvard given me an extraordinary honour, but the weeks of fear and nausea I have endured at the thought of giving this commencement address have made me lose weight. A win-win situation! Now all I have to do is take deep breaths, squint at the red banners and convince myself that I am at the world’s largest Gryffindor reunion.\nDelivering a commencement address is a great responsibility; or so I thought until I cast my mind back to my own graduation. The commencement speaker that day was the distinguished British philosopher Baroness Mary Warnock. Reflecting on her speech has helped me enormously in writing this one, because it turns out that I can’t remember a single word she said. This liberating discovery enables me to proceed without any fear that I might inadvertently influence you to abandon promising careers in business, the law or politics for the giddy delights of becoming a gay wizard.\n\nYou see? If all you remember in years to come is the ‘gay wizard’ joke, I’ve come out ahead of Baroness Mary Warnock. Achievable goals: the first step to self improvement.\n\nActually, I have wracked my mind and heart for what I ought to say to you today. I have asked myself what I wish I had known at my own graduation, and what important lessons I have learned in the 21 years that have expired between that day and this.\n\nI have come up with two answers. On this wonderful day when we are gathered together to celebrate your academic success, I have decided to talk to you about the benefits of failure. And as you stand on the threshold of what is sometimes called ‘real life’, I want to extol the crucial importance of imagination.\n\nThese may seem quixotic or paradoxical choices, but please bear with me.\n\nLooking back at the 21-year-old that I was at graduation, is a slightly uncomfortable experience for the 42-year-old that she has become. Half my lifetime ago, I was striking an uneasy balance between the ambition I had for myself, and what those closest to me expected of me.\n\nI was convinced that the only thing I wanted to do, ever, was to write novels. However, my parents, both of whom came from impoverished backgrounds and neither of whom had been to college, took the view that my overactive imagination was an amusing personal quirk that would never pay a mortgage, or secure a pension. I know that the irony strikes with the force of a cartoon anvil, now.\n\nSo they hoped that I would take a vocational degree; I wanted to study English Literature. A compromise was reached that in retrospect satisfied nobody, and I went up to study Modern Languages. Hardly had my parents’ car rounded the corner at the end of the road than I ditched German and scuttled off down the Classics corridor.\n\nI cannot remember telling my parents that I was studying Classics; they might well have found out for the first time on graduation day. Of all the subjects on this planet, I think they would have been hard put to name one less useful than Greek mythology when it came to securing the keys to an executive bathroom.\n\nI would like to make it clear, in parenthesis, that I do not blame my parents for their point of view. There is an expiry date on blaming your parents for steering you in the wrong direction; the moment you are old enough to take the wheel, responsibility lies with you. What is more, I cannot criticise my parents for hoping that I would never experience poverty. They had been poor themselves, and I have since been poor, and I quite agree with them that it is not an ennobling experience. Poverty entails fear, and stress, and sometimes depression; it means a thousand petty humiliations and hardships. Climbing out of poverty by your own efforts, that is indeed something on which to pride yourself, but poverty itself is romanticised only by fools.\n\nWhat I feared most for myself at your age was not poverty, but failure.\nAt your age, in spite of a distinct lack of motivation at university, where I had spent far too long in the coffee bar writing stories, and far too little time at lectures, I had a knack for passing examinations, and that, for years, had been the measure of success in my life and that of my peers.\n\nI am not dull enough to suppose that because you are young, gifted and well-educated, you have never known hardship or heartbreak. Talent and intelligence never yet inoculated anyone against the caprice of the Fates, and I do not for a moment suppose that everyone here has enjoyed an existence of unruffled privilege and contentment.\n\nHowever, the fact that you are graduating from Harvard suggests that you are not very well-acquainted with failure. You might be driven by a fear of failure quite as much as a desire for success. Indeed, your conception of failure might not be too far from the average person’s idea of success, so high have you already flown.\n\nUltimately, we all have to decide for ourselves what constitutes failure, but the world is quite eager to give you a set of criteria if you let it. So I think it fair to say that by any conventional measure, a mere seven years after my graduation day, I had failed on an epic scale. An exceptionally short-lived marriage had imploded, and I was jobless, a lone parent, and as poor as it is possible to be in modern Britain, without being homeless. The fears that my parents had had for me, and that I had had for myself, had both come to pass, and by every usual standard, I was the biggest failure I knew.\n\nNow, I am not going to stand here and tell you that failure is fun. That period of my life was a dark one, and I had no idea that there was going to be what the press has since represented as a kind of fairy tale resolution. I had no idea then how far the tunnel extended, and for a long time, any light at the end of it was a hope rather than a reality.\n\nSo why do I talk about the benefits of failure? Simply because failure meant a stripping away of the inessential. I stopped pretending to myself that I was anything other than what I was, and began to direct all my energy into finishing the only work that mattered to me. Had I really succeeded at anything else, I might never have found the determination to succeed in the one arena I believed I truly belonged. I was set free, because my greatest fear had been realised, and I was still alive, and I still had a daughter whom I adored, and I had an old typewriter and a big idea. And so rock bottom became the solid foundation on which I rebuilt my life.\n\nYou might never fail on the scale I did, but some failure in life is inevitable. It is impossible to live without failing at something, unless you live so cautiously that you might as well not have lived at all – in which case, you fail by default.\n\nFailure gave me an inner security that I had never attained by passing examinations. Failure taught me things about myself that I could have learned no other way. I discovered that I had a strong will, and more discipline than I had suspected; I also found out that I had friends whose value was truly above the price of rubies.\n\nThe knowledge that you have emerged wiser and stronger from setbacks means that you are, ever after, secure in your ability to survive. You will never truly know yourself, or the strength of your relationships, until both have been tested by adversity. Such knowledge is a true gift, for all that it is painfully won, and it has been worth more than any qualification I ever earned.\n" }, { "path": "48c-TM/rquery_wordcloud.R", "content": "#++++++++++++++++++++++++++++++++++\n# rquery.wordcloud() : Word cloud generator\n# - http://www.sthda.com\n#+++++++++++++++++++++++++++++++++++\n# x : character string (plain text, web url, txt file path)\n# type : specify whether x is a plain text, a web page url or a file path\n# lang : the language of the text\n# excludeWords : a vector of words to exclude from the text\n# textStemming : reduces words to their root form\n# colorPalette : the name of color palette taken from RColorBrewer package, \n# or a color name, or a color code\n# min.freq : words with frequency below min.freq will not be plotted\n# max.words : Maximum number of words to be plotted. least frequent terms dropped\n\n# value returned by the function : a list(tdm, freqTable)\nrquery.wordcloud <- function(x, type=c(\"text\", \"url\", \"file\"), \n lang=\"english\", excludeWords=NULL, \n textStemming=FALSE, colorPalette=\"Dark2\",\n min.freq=3, max.words=200)\n{ \n library(\"tm\")\n library(\"SnowballC\")\n library(\"wordcloud\")\n library(\"RColorBrewer\") \n \n if(type[1]==\"file\") text <- readLines(x)\n else if(type[1]==\"url\") text <- html_to_text(x)\n else if(type[1]==\"text\") text <- x\n \n # Load the text as a corpus\n docs <- Corpus(VectorSource(text))\n # Convert the text to lower case\n docs <- tm_map(docs, content_transformer(tolower))\n # Remove numbers\n docs <- tm_map(docs, removeNumbers)\n # Remove stopwords for the language \n docs <- tm_map(docs, removeWords, stopwords(lang))\n # Remove punctuations\n docs <- tm_map(docs, removePunctuation)\n # Eliminate extra white spaces\n docs <- tm_map(docs, stripWhitespace)\n # Remove your own stopwords\n if(!is.null(excludeWords)) \n docs <- tm_map(docs, removeWords, excludeWords) \n # Text stemming\n if(textStemming) docs <- tm_map(docs, stemDocument)\n # Create term-document matrix\n tdm <- TermDocumentMatrix(docs)\n m <- as.matrix(tdm)\n v <- sort(rowSums(m),decreasing=TRUE)\n d <- data.frame(word = names(v),freq=v)\n # check the color palette name \n if(!colorPalette %in% rownames(brewer.pal.info)) colors = colorPalette\n else colors = brewer.pal(8, colorPalette) \n # Plot the word cloud\n set.seed(1234)\n wordcloud(d$word,d$freq, min.freq=min.freq, max.words=max.words,\n random.order=FALSE, rot.per=0.35, \n use.r.layout=FALSE, colors=colors)\n \n invisible(list(tdm=tdm, freqTable = d))\n}\n\n#++++++++++++++++++++++\n# Helper function\n#++++++++++++++++++++++\n# Download and parse webpage\nhtml_to_text<-function(url){\n library(RCurl)\n library(XML)\n # download html\n html.doc <- getURL(url) \n #convert to plain text\n doc = htmlParse(html.doc, asText=TRUE)\n # \"//text()\" returns all text outside of HTML tags.\n # We also don’t want text such as style and script codes\n text <- xpathSApply(doc, \"//text()[not(ancestor::script)][not(ancestor::style)][not(ancestor::noscript)][not(ancestor::form)]\", xmlValue)\n # Format text vector into one character string\n return(paste(text, collapse = \" \"))\n}\n\n" }, { "path": "48c-TM/sentiment-tidyr1.R", "content": "# Sentiment analysis\n\nlibrary(tidytext)\nsentiments\nget_sentiments(\"afinn\")\nget_sentiments(\"bing\")\nget_sentiments(\"nrc\")\n\n#install.packages(\"janeaustenr\")\nlibrary(dplyr)\nlibrary(stringr)\n\ntidy_books <- austn_books() %>% group_by(book) %>% mutate(linenumber= row_number() )\n\n" }, { "path": "48c-TM/sentiment2.R", "content": "#Sentiment Analysis\n\n#https://cran.r-project.org/web/packages/SentimentAnalysis/SentimentAnalysis.pdf\n\nlibrary(SentimentAnalysis)\n\n## Not run:\nlibrary(tm)\n# via vector of strings\ncorpus <- c(\"Positive text\", \"Neutral but uncertain text\", \"Negative text\")\nsentiment <- analyzeSentiment(corpus)\nsentiment\ncompareToResponse(sentiment, c(+1, 0, -2))\n# via Corpus from tm package\ndata(\"crude\")\ncrude\n\nsentiment <- analyzeSentiment(crude)\nsentiment\n# via DocumentTermMatrix (with stemmed entries)\ndtm <- DocumentTermMatrix(VCorpus(VectorSource(c(\"posit posit\", \"negat neutral\"))))\nsentiment <- analyzeSentiment(dtm)\nsentiment\n\n\nd1 <- SentimentDictionary(c(\"uncertain\", \"possible\", \"likely\"))\nd2 <- SentimentDictionary(c(\"rather\", \"intend\", \"likely\"))\ncmp <- compareDictionaries(d1, d2)\n\n\ndocuments <- c(\"This is a test\", \"an one more\")\n# count words (without stopwords)\ncountWords(documents)\n# count all words (including stopwords)\ncountWords(documents, removeStopwords=FALSE)\n" }, { "path": "48c-TM/textmining-DU1.R", "content": "# Text Mining\n#Load Libraries\nlibrary(plyr)\nlibrary(ggplot2)\nlibrary(RColorBrewer)\nlibrary(wordcloud)\nlibrary(tm)\nlibrary(SnowballC)\nlibrary(NLP)\n\n#Folder of text files\n(cname = file.path(\"E:\\\\rwork\\\\rProjects\\\\analytics\\\\textdocs\"))\ndir(cname)\n#Load texts into R\ndocs = Corpus(DirSource(cname))\nsummary(docs)\ninspect(docs)\n\n#Make a copy to see all through\ndocs1 = docs\ndocs2 = tm_map(docs1, removePunctuation )\ndocs3 = tm_map(docs2, removeNumbers)\ndocs4 = tm_map(docs3, tolower)\ndocs5 = tm_map(docs4, removeWords, stopwords('english'))\ndocs6 = tm_map(docs5, removeWords, c('brothers','sisters'))\ndocs7 = tm_map(docs6, stemDocument)\ndocs8 = tm_map(docs7, stripWhitespace)\ndocs8 = Corpus(VectorSource(docs8))\ndocs9 = tm_map(docs8, PlainTextDocument)# due to error\ndocs9 = Corpus(VectorSource(docs9))\ndtm = DocumentTermMatrix(docs9)\ndtm\ntdm = TermDocumentMatrix(docs9)\ntdm\n\n\n# Frequency\nfreq = colSums(as.matrix(dtm))\nfreq\nlength(freq)\ndtms = removeSparseTerms(dtm, 0.1)\ndtms\nord = order(freq)\nord\n\nfreq[head(ord)]\nfreq[tail(ord)]\nfreq2 = sort(colSums(as.matrix(dtm)), decreasing = T)\nfreq2\nhead(freq2, 14)\nfindFreqTerms(dtm, lowfreq = 4)\nwf = data.frame(word=names(freq2), freq=freq2)\nhead(wf)\n\n# Association\ntm::findAssocs(dtms, 'student', corlimit=0.1)\ntm::findAssocs(dtms, 'learn', corlimit=0.1)\n\n#Plotting\np = ggplot2::ggplot(subset(head(wf,n=10), freq=3), aes(word, freq))\np = p + geom_bar(stat='identity')\np = p + theme(axis.text.x = element_text(angle=45, hjust=1))\np\n\n\n#Word Cloud\n#library(wordcloud)\nset.seed(142)\nwordcloud(names(freq2), scale=c(2,0.5), freq2, min.freq=2)\nwordcloud(names(freq2), freq2,scale=c(2,0.5), max.words=10)\nwordcloud(names(freq2), freq2, min.freq=3, scale=c(2,0.5),\n colors=brewer.pal(6,'Dark2'))\ndark2 = brewer.pal(6, 'Dark2')\npar(mar=c(1,1,1,1))\n#scale = c(4, 0.2) or with less words : max.words = 30\n#gsub() longwords to shorter abbreviation\nwordcloud(names(freq2), freq2, max.words=30, rot.per=0.2, \n scale=c(2,0.5), colors=dark2)\nhead(freq2)\n\ndtmss= removeSparseTerms(dtm, 0.68)\nlibrary(cluster)\nd = dist(t(dtmss), method='euclidean')\nfit = hclust(d=d, method='ward.D')\nfit\nplot(fit) # very not proper\ngroups = cutree(fit, k=5)\ngroups\nrect.hclust(fit, k=5, border='red')\n\n# Clustering\nlibrary(fpc)\nd = dist(t(dtmss), method='euclidean')\nkfit = kmeans(d, 2)\nkfit\nclusplot(as.matrix(d), kfit$cluster, color=T, shade=T, labels=2, lines=0)\n\n" }, { "path": "48c-TM/tm-worldcloud4.R", "content": "\nlibrary(wordcloud)\nlibrary(RColorBrewer)\nlibrary(SnowballC)\nlibrary(RCurl)\nlibrary(XML)\nlibrary(tm)\n\nsource('http://www.sthda.com/upload/rquery_wordcloud.r')\nfilePath <- \"http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt\"\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\")\nres\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\", min.freq = 1, max.words = 200)\n\n# Reds color palette\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\", colorPalette = \"Reds\")\n# RdBu color palette\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\", colorPalette = \"RdBu\")\n# use unique color\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\", colorPalette = \"black\")\n\n\ntdm <- res$tdm\nfreqTable <- res$freqTable\n\n\n# Show the top10 words and their frequency\nhead(freqTable, 10)\n\n# Bar plot of the frequency for the top10\nbarplot(freqTable[1:10,]$freq, las = 2, \n names.arg = freqTable[1:10,]$word,\n col =\"lightblue\", main =\"Most frequent words\",\n ylab = \"Word frequencies\")\n\nfindFreqTerms(tdm, lowfreq = 4)\n\nfindAssocs(tdm, terms = \"freedom\", corlimit = 0.3)\n\nurl = \"http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-software\"\nrquery.wordcloud(x=url, type=\"url\")\n" }, { "path": "48c-TM/twitter-hotel.R", "content": "#https://www.r-bloggers.com/mining-twitter-for-consumer-attitudes-towards-hotels/\n\n#load twitter library\nlibrary(twitteR)\n# search for all the hilton tweets\nhilton.tweets=searchTwitter('@hilton',n=1500)\nhilton.tweets=searchTwitter('@narendramodi',n=15)\n\nhilton.tweets\nlength(hilton.tweets)\nclass(hilton.tweets)\ntweet=hilton.tweets[[1]]\nclass(tweet)\ntweet$getScreenName()\ntweet$getText()\n\nlibrary(\"plyr\")\nhilton.text=laply(hilton.tweets,function(t)t$getText())\nlength(hilton.text)\nhead(hilton.text,5)\n# load list of positive and negative words for SIMPLE sentiment analysis\n\n# you would have to download the files from a website I included below - make sure you put in the directory that you will be\n# referencing\nhu.liu.pos=scan('/Users/marcinkulakowski/Downloads/r/positive-words.txt',what='character',comment.char=';')\n\nhu.liu.neg=scan('/Users/marcinkulakowski/Downloads/r/negative-words.txt',what='character',comment.char=';')\npos.words=c(hu.liu.pos,'upgrade')\nneg.words=c(hu.liu.neg,'wtf','wait','waiting','epicfail','mechanical')\n\n# sampling\nsample=c(\"You'reawesomeandIloveyou\",\"Ihateandhateandhate.Soangry.Die!\",\"Impressedandamazed:youarepeerlessinyourachievementofunparalleledmediocrity.\")\nscore.sentiment = function(sentences, pos.words, neg.words, .progress='none')\n{\n require(plyr)\n require(stringr)\n \n # we got a vector of sentences. plyr will handle a list\n # or a vector as an \"l\" for us\n # we want a simple array (\"a\") of scores back, so we use\n # \"l\" + \"a\" + \"ply\" = \"laply\":\n scores = laply(sentences, function(sentence, pos.words, neg.words) {\n \n # clean up sentences with R's regex-driven global substitute, gsub():\n sentence = gsub('[[:punct:]]', '', sentence)\n sentence = gsub('[[:cntrl:]]', '', sentence)\n sentence = gsub('\\\\d+', '', sentence)\n # and convert to lower case:\n sentence = tolower(sentence)\n \n # split into words. str_split is in the stringr package\n word.list = str_split(sentence, '\\\\s+')\n # sometimes a list() is one level of hierarchy too much\n words = unlist(word.list)\n \n # compare our words to the dictionaries of positive & negative terms\n pos.matches = match(words, pos.words)\n neg.matches = match(words, neg.words)\n \n # match() returns the position of the matched term or NA\n # we just want a TRUE/FALSE:\n pos.matches = !is.na(pos.matches)\n neg.matches = !is.na(neg.matches)\n \n # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():\n score = sum(pos.matches) - sum(neg.matches)\n \n return(score)\n }, pos.words, neg.words, .progress=.progress )\n \n scores.df = data.frame(score=scores, text=sentences)\n return(scores.df)\n}\n> result=score.sentiment(sample,pos.words,neg.words)\n> class(result)\n[1] \"data.frame\"\n> result$score\n[1] 0 0 0\n> hilton.scores=score.sentiment(hilton.text,pos.words,neg.words,.progress='text')\n> hilton.scores$hotel='Hilton'\n> hilton.scores$code='HL'\n> hist(hilton.scores$score)\n# hilton histogram\n> library(\"ggplot2\")\n> qplot(hilton.scores$score)\n# qplot hilton\n# lets search for all other major hotels\n# Intercontinental\nintercontinental.tweets=searchTwitter('@intercontinental',n=1500)\nclass(tweet)\nintercontinental.text=laply(intercontinental.tweets,function(t)t$getText())\nintercontinental.scores=score.sentiment(intercontinental.text,pos.words,neg.words,.progress='text')\nintercontinental.scores$hotel='Intercontinental'\nintercontinental.scores$code='IC'\n# Wyndham\nwyndham.tweets=searchTwitter('@wyndham',n=1500)\nclass(tweet)\nwyndham.text=laply(wyndham.tweets,function(t)t$getText())\nwyndham.scores=score.sentiment(wyndham.text,pos.words,neg.words,.progress='text')\nwyndham.scores$hotel='Wyndham'\nwyndham.scores$code='WY'\n# Marriott\nmarriott.tweets=searchTwitter('@marriott',n=1500)\nclass(tweet)\nmarriott.text=laply(marriott.tweets,function(t)t$getText())\nmarriott.scores=score.sentiment(marriott.text,pos.words,neg.words,.progress='text')\nmarriott.scores$hotel='Marriott'\nmarriott.scores$code='MI'\n# BestWestern\nbestwestern.tweets=searchTwitter('@bestwestern',n=1500)\nclass(tweet)\nbestwestern.text=laply(bestwestern.tweets,function(t)t$getText())\nbestwestern.scores=score.sentiment(bestwestern.text,pos.words,neg.words,.progress='text')\nbestwestern.scores$hotel='Bestwestern'\nbestwestern.scores$code='BW'\n# Starwood\nstarwood.tweets=searchTwitter('@starwood',n=1500)\nclass(tweet)\nstarwood.text=laply(starwood.tweets,function(t)t$getText())\nstarwood.scores=score.sentiment(starwood.text,pos.words,neg.words,.progress='text')\nstarwood.scores$hotel='Starwood'\nstarwood.scores$code='SW'\n# Hyatt\nhyatt.tweets=searchTwitter('@hyatt',n=1500)\nclass(tweet)\nhyatt.text=laply(hyatt.tweets,function(t)t$getText())\nhyatt.scores=score.sentiment(hyatt.text,pos.words,neg.words,.progress='text')\nhyatt.scores$hotel='Hyatt'\nhyatt.scores$code='HY'\n> all.scores=rbind(intercontinental.scores,wyndham.scores,hilton.scores,marriott.scores,bestwestern.scores,starwood.scores,hyatt.scores)\n# Make separate plot for each hotel\n> ggplot(data=all.scores)+#ggplotworksondata.frames,always\n geom_bar(mapping=aes(x=score,fill=hotel),binwidth=1)+\n facet_grid(hotel~.)+#makeaseparateplotforeachhotel\n theme_bw()+scale_fill_brewer()#plaindisplay,nicercolors\n# Plot\n> all.scores$very.pos=as.numeric(all.scores$score>=2)\n> all.scores$very.neg=as.numeric(all.scores$score twitter.df=ddply(all.scores,c('hotel','code'),summarise,pos.count=sum(very.pos),neg.count=sum(very.neg))\n > twitter.df$all.count=twitter.df$pos.count+twitter.df$neg.count\n > twitter.df$score=round(100*twitter.df$pos.count/twitter.df$all.count)\n > install.packages(\"doBy\")\n > library(\"doBy\")\n > orderBy(~-score,twitter.df)\n hotel code pos.count neg.count all.count score\n 1 Bestwestern BW 6 0 6 100\n 5 Starwood SW 7 0 7 100\n 6 Wyndham WY 2 0 2 100\n 3 Hyatt HY 7 1 8 88\n 2 Hilton HL 15 3 18 83\n 4 Marriott MI 13 4 17 76\n > install.packages(\"XML\")\n > library(XML)\n > acsi.url='http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Hotels'\n # scrape acsi website for scores\n > acsi.df=readHTMLTable(acsi.url,header=T,which=1,stringsAsFactors=F)\n > acsi.df=acsi.df[,c(1,18)]\n > head(acsi.df,1)\n > colnames(acsi.df)=c('hotel','score')\n > acsi.df$score=as.numeric(acsi.df$score)\n > View(acsi.df)\n\n\n" }, { "path": "48c-TM/twitter-keys.R", "content": "# Consumer Key (API Key)\tuRDuync3BziwQnor1MZFBKp0x\n# Consumer Secret (API Secret)\tt8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV\n# \n# Access Token\t14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW\n# Access Token Secret\t3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz\n" }, { "path": "48c-TM/twitter-sentiment2.R", "content": "# Tweeter Download and Analysis\n#connect all libraries\nlibrary(twitteR)\nlibrary(ROAuth)\nlibrary(plyr)\nlibrary(dplyr)\nlibrary(stringr)\nlibrary(ggplot2)\n#Certificate not Required\n#load(\"twitter authentication.Rdata\")\n#https://dataaspirant.com/2018/03/22/twitter-sentiment-analysis-using-r/\n\n\nAccessToken=\"14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW\"\nAccessTokenSecret=\"3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz\"\nConsumerKey=\"uRDuync3BziwQnor1MZFBKp0x\"\nConsumerSecret=\"t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV\"\n\n#---- Alread saved authentication \nload('twitter authentication.Rdata') #Once you launch the code first time, you can start from this line in the future (libraries should be connected)\nsetup_twitter_oauth(ConsumerKey, ConsumerSecret, AccessToken, AccessTokenSecret)\n\ntweets <- userTimeline(\"realDonaldTrump\", n=200)\n#english\ntweets[2]\nn.tweet <- length(tweets)\nn.tweet\ntweets.df <- twListToDF(tweets) \nhead(tweets.df)\nsummary(tweets.df)\n\n#Remove hashtags & unnecessary characters\ntweets.df2 <- gsub(\"http.*\",\"\",tweets.df$text)\ntweets.df2 <- gsub(\"https.*\",\"\",tweets.df2)\ntweets.df2 <- gsub(\"#.*\",\"\",tweets.df2)\ntweets.df2 <- gsub(\"@.*\",\"\",tweets.df2)\n\nhead(tweets.df2)\n#-----\nlibrary(\"syuzhet\")\nword.df <- as.vector(tweets.df2)\nemotion.df <- get_nrc_sentiment(word.df)\nemotion.df2 <- cbind(tweets.df2, emotion.df) \nhead(emotion.df2)\n\n#-----\nsent.value <- get_sentiment(word.df)\nmost.positive <- word.df[sent.value == max(sent.value)]\nmost.positive\nmost.negative<- word.df[sent.value <= min(sent.value)] \nmost.negative\nsent.value\n\n#-----\npositive.tweets <- word.df[sent.value > 0]\nhead(positive.tweets)\nnegative.tweets <- word.df[sent.value < 0] \nhead(negative.tweets)\nneutral.tweets <- word.df[sent.value == 0]\nhead(neutral.tweets)\n#----\n# Alternate way to classify as Positive, Negative or Neutral tweets\ncategory_senti <- ifelse(sent.value < 0, \"Negative\", ifelse(sent.value > 0, \"Positive\", \"Neutral\"))\nhead(category_senti)\ncategory_senti2 <- cbind(tweets,category_senti,sent.value) \nhead(category_senti2)\n\n#----\ntable(category_senti)\n\n" }, { "path": "48c-TM/twitter1-DU1.R", "content": "#Twitter 1 - Configure Tweets and Download them\n#@dupadhyaya\n#Working using my Keys\nlibrary(\"curl\")\nlibrary(\"twitteR\")\nlibrary(\"ROAuth\")\n\ndownload.file(url=\"http://curl.haxx.se/ca/cacert.pem\",destfile=\"cacert.pem\")\n\n#https://apps.twitter.com/\n#different for each account\nconsumerKey=\"uRDuync3BziwQnor1MZFBKp0x\"\nconsumerSecret=\"t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV\"\nAccessToken=\"14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW\"\nAccessTokenSecret=\"3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz\"\n\n#Common for all accounts except the keys\n\ncred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize')\n\ncred$handshake(cainfo=\"cacert.pem\") # it will take you to browser: authorise, copy key\nsave(cred, file=\"twitter authentication.Rdata\") # store this to avoid asking again\n\n#Load saved authentication cert\nload(\"twitter authentication.Rdata\")\n#registerTwitterOAuth(cred)\n\nsetup_twitter_oauth(consumerKey, consumerSecret, AccessToken, AccessTokenSecret)\n\nsearch.string <- \"#FifaWorldCup2018\"\nno.of.tweets <- 100\n\ntweets <- searchTwitter(search.string, n=no.of.tweets,lang=\"en\")\ntweets\n\n\n" }, { "path": "48c-TM/twitter1-DU2.R", "content": "# Use Existing Twitter Authentication Stored\n# Marketing/ Business Analytics\n\nlibrary(\"twitteR\")\nlibrary(\"ROAuth\")\n\n# Certificate not Required\n#load(\"twitter authentication.Rdata\")\n\nAccessToken=\"14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW\"\nAccessTokenSecret=\"3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz\"\nConsumerKey=\"uRDuync3BziwQnor1MZFBKp0x\"\nConsumerSecret=\"t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV\"\n\nload(\"twitter authentication.Rdata\")\n#registerTwitterOAuth(cred)\n\nsetup_twitter_oauth(ConsumerKey, ConsumerSecret, AccessToken, AccessTokenSecret)\n\nsearch.string <- \"#marketinganalytics\"\nno.of.tweets <- 100\n\ntweets <- searchTwitter(search.string, n=no.of.tweets,lang=\"en\")\ntweets\n\n#My Tweets : will change if you use your own account\nhomeTimeline(n=15) #my tweets\nmentions(n=15) # my tags\n" }, { "path": "48c-TM/twitter1-authen.R", "content": "# Twitter - Setting up Authentication\n\n\n" }, { "path": "48c-TM/twitter1.R", "content": "# twitter\nlibrary(twitteR)\nlibrary(ROAuth)\nlibrary(RCurl)\nlibrary(plyr)\nlibrary(stringr)\nlibrary(ggplot2)\nlibrary(digest)\nlibrary(rjson)\nlibrary(httr)\nlibrary(RJSONIO)\nlibrary(tm)\nlibrary(scales)\nlibrary(lubridate)\nlibrary(syuzhet)\n\noptions(httr_oauth_cache = T)\naccess_token='14366551-ymV6A8XM2aXcgGUYNLTBOxAuZGIRLinLb0uzzOYKe'\naccess_token_secret = 'VEmnsIvcIvfySEXD8eXKe1CLE3ZY7mMBwEIK9fB665SDe'\napi_key='L2ch59MKPheK7FsSYFPL4oFdU'\napi_secret = 'lvZyPxULK9HzI5CBgdXah5MoQFLsU2EQ6T0NND1FFBb40ovBdW'\n\n\n#setup_twitter_oauth(consumer_Key, consumer_Secret, access_token, access_secret)\nsetup_twitter_oauth(api_key,api_secret,access_token, access_token_secret)\n\ntweet = searchTwitter(\"#Demonitisation OR #Modi\",n=500,since='2006-11-08')\nview(tweet_df)\nclass(tweet)\ntweet_df = twListToDF(tweet)\ntweets.text = lapply(tweet,function(t) ....)\n\nmycorpus = Corpus(VectorSource(tweets.text))\nx = as.character(mycorpus)\nstr(mycorpus)\nmycorpus1 = tm_map(mycorpus,stripWhitespace)\nmycorpus2 = tm_map(mycorpus1,tolower)\nmycorpus3 = tm_map(mycorpus2,removewords,stopwords('english'))\nmycorpus4 = tm_map(mycorpus3,removePunctuation)\nmycorpus5 = tm_map(mycorpus4,removeNumbers)\nmycorpus6 = tm_map(mycorpus5,PlainTextDocument)\ndata_dtm1 = DocumentTermMatrix(mycorpus6)\ninspect(data_dtm1)\nfrequent = findFreqTerms(data_dtm1,lowfreq=100,highfreq=Inf)\nfrequent\n\nlibrary(wordcloud)\n\n\n\n#Sentimental Analysis\n" }, { "path": "48c-TM/twitter2.R", "content": "# Twitter 1\n\n# install.packages(\"RColorBrewer\")\n# install.packages(\"tm\")\n# install.packages(\"wordcloud\")\n# install.packages('base64enc')\n# install.packages('ROAuth')\n# install.packages('plyr')\n# install.packages('stringr')\n# install.packages(\"openssl\")\n#install.packages(\"httpuv\")\ninstall.packages('twitteR')\nlibrary(openssl)\nlibrary(httpuv)\nlibrary(RColorBrewer)\nlibrary(wordcloud)\nlibrary(tm)\nlibrary(twitteR)\nlibrary(ROAuth)\nlibrary(plyr)\nlibrary(stringr)\nlibrary(base64enc)\n\n\ndownload.file(url='http://curl.haxx.se/ca/cacert.pem', destfile='cacert.pem')\n\nreqURL = 'https://api.twitter.com/oauth/request_token'\naccessURL = 'https://api.twitter.com/oauth/access_token'\nauthURL = 'https://api.twitter.com/oauth/authorize'\n\n\n\n#Extract / error\ndubatweets1 = searchTwitter(' #businessanalytics', n=3000, lang='en')\n\n#Setup accesstoken for this session\n\n#Copy keys from Twitter Account\nconsumerKey = 'uRDuync3BziwQnor1MZFBKp0x'\nconsumerSecret = 't8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV'\nsetup_twitter_oauth(consumerKey, consumerSecret)\n\n\ncred <- ROAuth::OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL=reqURL, accessURL=accessURL, authURL=authURL) #ok\n\n#install.packages(\"openssl\")\n#install.packages(\"httpuv\")\n\n\nsetup_twitter_oauth(consumerKey, consumerSecret)\nsetup_twitter_oauth(api_key, api_secret, access_token, access_token_secret, access_token=NULL, access_secret=NULL)\n\n\ncred$handshake(cainfo = system.file('CurlSSL', 'cacert.pem', package = 'RCurl'))\n\n# goto web browser - and authorise - note the key and paste it in \n\nsave(cred, file='twitterauthentication.Rdata')\nload('twitterauthentication.Rdata') \nsetup_twitter_oauth(cred)\n\ndubatweets1 = searchTwitter(' #businessanalytics', n=3000, lang='en')\nsetup_twitter_oauth(consumerKey,consumerSecret, accessToken=consumerKey, accessTokenSecret=consumerSecret)\n" }, { "path": "48c-TM/twitteracct", "content": "ConsumerKey=\"uRDuync3BziwQnor1MZFBKp0x\"\nConsumerSecret=\"t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV\"\nAccessToken=\"14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW\"\nAccessTokenSecret=\"3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz\"\n" }, { "path": "48c-TM/wordcloud1.R", "content": "# World Cloud 2 - error\nlibrary(wordcloud)\n# Read the text file from internet\nfilePath = \"http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt\"\ntext = readLines(filePath)\ntext\n\n# Load the data as a corpus\ndocs = Corpus(VectorSource(text))\ndocs\n\n#Text transformation\ntoSpace <- content_transformer(function (x , pattern ) gsub(pattern, \" \", x))\ndocs <- tm_map(docs, toSpace, \"/\")\ndocs <- tm_map(docs, toSpace, \"@\")\ndocs <- tm_map(docs, toSpace, \"\\\\|\")\n\n#Cleaning Text\n# Convert the text to lower case\ndocs <- tm_map(docs, content_transformer(tolower))\n# Remove numbers\ndocs <- tm_map(docs, removeNumbers)\n# Remove english common stopwords\ndocs <- tm_map(docs, removeWords, stopwords(\"english\"))\n# Remove your own stop word\n# specify your stopwords as a character vector\ndocs <- tm_map(docs, removeWords, c(\"blabla1\", \"blabla2\")) \n# Remove punctuations\ndocs <- tm_map(docs, removePunctuation)\n# Eliminate extra white spaces\ndocs <- tm_map(docs, stripWhitespace)\n# Text stemming\ndocs <- tm_map(docs, stemDocument)\n\n\n#Document Matrix\ndtm <- TermDocumentMatrix(docs)\nm <- as.matrix(dtm)\nv <- sort(rowSums(m),decreasing=TRUE)\nd <- data.frame(word = names(v),freq=v)\nhead(d, 10)\n\n#Generate Word Cloud\nset.seed(1234)\nwordcloud(words = d$word, freq = d$freq, min.freq = 1,\nmax.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, \"Dark2\"))\n\n" }, { "path": "48c-TM/wordcloud2.R", "content": "# Word Cloud\n\n##http://dni-institute.in/blogs/colorful-word-cloud-using-r/\n# tm for text mining\n# SnowballC for text stemming\n# wordcloud for generating word cloud images\n# RCurl and XML packages to download and parse web pages\n# RColorBrewer for color palettes\n\nlibrary(wordcloud)\nlibrary(RColorBrewer)\nlibrary(SnowballC)\nlibrary(RCurl)\nlibrary(XML)\nlibrary(tm)\n\nsource('http://www.sthda.com/upload/rquery_wordcloud.r')\n#or\n#source('./TM/rquery_wordcloud.R')\nfilePath <- \"http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt\"\nfilePath2 <- \"./data/martin-luther-king-i-have-a-dream-speech.txt\"\n\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\")\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\")\n\n" }, { "path": "48c-TM/wordcloud3.R", "content": "# World Cloud\n#http://stat.ethz.ch/R-manual/R-devel/library/base/html/strsplit.html\n#https://stackoverflow.com/questions/4350440/split-a-column-of-a-data-frame-to-multiple-columns\n\nlibrary(stringr)\nlibrary(wordcloud)\nlibrary(RColorBrewer)\nlibrary(tm)\nlibrary(SnowballC)\nlibrary(RCurl)\nlibrary(XML)\n\n\n\n#How to use strsplit\nstrsplit('IIT-Gawahati', \"-\")\nstrsplit('IIT Gawahati', \" \")\n\nlibrary(gsheet)\nurl = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=0'\n#check for dashtype, they are not always same\ndf1 = as.data.frame(gsheet2tbl(url))\ndf1\nhead(df1)\ndf1$wordexplanation\nbase::strsplit(df1$wordexplanation,'-') #output as list need in DF\n\n#Different Methods split and put in dataframe objects\n\n#Stringr - str_split\nstringr::str_split('IIT-Guwahati', \"-\")\n\n#Method\n(out1 = strsplit(as.character('IIT - Guwahati'),'-')) #if the word was not character\nhead(df1)\nout = strsplit(df1$wordexplanation,'-') \nhead(out)\n\nt(sapply(out[1:5], '['))\ndf2=data.frame(t(sapply(out[1:200], '[')))\n\nhead(df2)\ndf= cbind(df1,df2)\nhead(df)\nnames(df)[c(2,3)] = c('word','explanation') #rename columns\nhead(df)\n\n# create random frequencies for all words in a col\ndf$freq = floor(runif(100, 1,100))\nhead(df)\n\n#Method for word cloud\nlibrary(wordcloud)\nwc1 = wordcloud(df$word, df$freq, random.order=T)\nwc2 = wordcloud(df$word, df$freq, random.order=F, colors = topo.colors(10))\nwc3 = wordcloud(df$word, df$freq, scale=c(4,.1), min.freq=2, max.words=50, random.order=F)\nwc4 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F)\n\n\n#------\n\nlibrary(RColorBrewer)\nlibrary(tm)\npal <- brewer.pal(8,\"Dark2\")\nwc5 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F, colors=pal)\n\n\n#load the function rquery.wordcloud\nsource('http://www.sthda.com/upload/rquery_wordcloud.r')\n\nres <- rquery.wordcloud(filePath, type =\"file\", lang = \"english\",min.freq = 1, max.words = 200)\n\n# Reds color palette\nres <- rquery.wordcloud(filePath, type =\"file\", lang = \"english\",colorPalette = \"Reds\")\n# RdBu color palette\nres <- rquery.wordcloud(filePath, type =\"file\", lang = \"english\", colorPalette = \"RdBu\")\n# use unique color\nres<-rquery.wordcloud(filePath, type =\"file\", lang = \"english\", colorPalette = \"black\")\n\n\ntdm <- res$tdm\nfreqTable <- res$freqTable\n# Show the top10 words and their frequency\nhead(freqTable, 10)\n\n# Bar plot of the frequency for the top10\nbarplot(freqTable[1:10,]$freq, las = 2, \n names.arg = freqTable[1:10,]$word,\n col =\"lightblue\", main =\"Most frequent words\",\n ylab = \"Word frequencies\")\n\nfindFreqTerms(tdm, lowfreq = 4)\n\nfindAssocs(tdm, terms = \"freedom\", corlimit = 0.3)\n\nurl = \"http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-software\"\nrquery.wordcloud(x=url, type=\"url\")\n" }, { "path": "48c-TM/worldcloud2.R", "content": "# World Cloud 2\n\n# Read the text file from file\ntext <- readLines(file.choose())\ntext\n\n# Load the data as a corpus\ndocs = Corpus(VectorSource(text))\ndocs\n\n#Text transformation\ntoSpace <- content_transformer(function (x , pattern ) gsub(pattern, \" \", x))\ndocs <- tm_map(docs, toSpace, \"/\")\ndocs <- tm_map(docs, toSpace, \"@\")\ndocs <- tm_map(docs, toSpace, \"\\\\|\")\n\n#Cleaning Text\n# Convert the text to lower case\ndocs <- tm_map(docs, content_transformer(tolower))\n# Remove numbers\ndocs <- tm_map(docs, removeNumbers)\n# Remove english common stopwords\ndocs <- tm_map(docs, removeWords, stopwords(\"english\"))\n# Remove your own stop word\n# specify your stopwords as a character vector\ndocs <- tm_map(docs, removeWords, c(\"blabla1\", \"blabla2\")) \n# Remove punctuations\ndocs <- tm_map(docs, removePunctuation)\n# Eliminate extra white spaces\ndocs <- tm_map(docs, stripWhitespace)\n# Text stemming\ndocs <- tm_map(docs, stemDocument)\n\n\n#Document Matrix\ndtm <- TermDocumentMatrix(docs)\nm <- as.matrix(dtm)\nv <- sort(rowSums(m),decreasing=TRUE)\nd <- data.frame(word = names(v),freq=v)\nhead(d, 10)\n\n#Generate Word Cloud\nset.seed(1234)\nwordcloud(words = d$word, freq = d$freq, min.freq = 1,\n max.words=200, random.order=FALSE, rot.per=0.35, \n colors=brewer.pal(8, \"Dark2\"))\n\nfindFreqTerms(dtm, lowfreq = 4)\nfindAssocs(dtm, terms = \"freedom\", corlimit = 0.3)\nhead(d, 10)\n\n#Plot Freq\nbarplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,\n col =\"lightblue\", main =\"Most frequent words\",\n ylab = \"Word frequencies\")\n" }, { "path": "48g-textdocs/vit.txt", "content": "VIT was established with the aim of providing quality higher education on par with international standards. It persistently seeks and adopts innovative methods to improve the quality of higher education on a consistent basis.The campus has a cosmopolitan atmosphere with students from all corners of the globe. Experienced and learned teachers are strongly encouraged to nurture the students. The global standards set at VIT in the field of teaching and research spur us on in our relentless pursuit of excellence. In fact, it has become a way of life for us. The highly motivated youngsters on the campus are a constant source of pride. Our Memoranda of Understanding with various international universities are our major strength. They provide for an exchange of students and faculty and encourage joint research projects for the mutual benefit of these universities. Many of our students, who pursue their research projects in foreign universities, bring high quality to their work and esteem to India and have done us proud. With steady steps, we continue our march forward. We look forward to meeting you here at VIT.\n\nHistory of VIT\nThe begining VIT as VEC\nOpen Gallery \nIt was established under Section 3 of the University Grants Commission (UGC) Act, 1956, and was founded in 1984 as a self-financing institution called the Vellore Engineering College. The Union Ministry of Human Resources Development conferred University status on Vellore Engineering College in 2001. The University is headed by its founder and Chancellor, Dr. G. Viswanathan, a former Parliamentarian and Minister in the Tamil Nadu Government. In recognition of his service to India in offering world class education, he was conferred an honorary doctorate by the West Virginia University, USA. Sankar Viswanathan, Sekar Viswanathan and G.V. Selvam are the Vice-Presidents; Dr. Anand A. Samuel is the Vice-Chancellor and Dr. V. Raju and Dr. S. Narayanan are the Pro-Vice-Chancellors.\n\nOur Vision\nWe at VIT will impart futuristic technical education and instil high patterns of discipline through our dedicated staff, who shall set global standards, making our students technologically superior and ethically strong, who in turn shall improve the quality of life of the human race.\n\nOur Mission\nOur mission is to educate students from all over India, including those from the local and rural areas, and from other countries, so they become enlightened individuals, improving the living standards of their families, industry and society. We will provide individual attention, world-class quality education and take care of character building.\n\nQuick Facts\nOver 2,23,000 appeared for the VIT Engineering Entrance Examination (VITEEE) in 2017\nThe institution offers 36 Undergraduate, 31 Post graduate, 4 Integrated and 3 Research Programmes.\nNAAC has re-accredited VIT with an 'A' grade.\nA 360-acre eco-friendly campus with over 50.83 lakh sq.ft. built-up space at Vellore.\nA 170 acre eco-friendly second campus at Chennai.\nOver 35,000 students, with nearly 1/3rd of them women\nStudents from 60 countries as well as from every state in India\nOver 1400 faculty and 1413 staff.\nVIT has been accredited by TCS, Wipro and Cognizant for academic collaboration and placements.\nEntered the Limca Book of Records for Campus Recruitment." }, { "path": "51a-OR-LP/15b-lpsolveAPI.R", "content": "#lpSolveAPI\n#(https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf)\n#https://www.rdocumentation.org/packages/lpSolveAPI/versions/5.5.2.0-17\n\n\n#addcolumn-----\n#Add a column to an lpSolve linear program model object\nadd.column(lprec, x, indices)\nlps.model <- make.lp(4, 0)\nlps.model\nadd.column(lps.model, c(6,2,4,9))\nlps.model\nadd.column(lps.model, c(3,1,5), indices = c(1,2,4))\nlps.model\n\n#add.constraint-----\nadd.constraint(lprec, xt, type = c(\"<=\", \"=\", \">=\"), rhs, indices, lhs)\nlps.model <- make.lp(0, 4); lps.model\nset.objfn(lps.model, rep(1, 4)); lps.model\nadd.constraint(lps.model, c(6,2,4,9), \"<=\", 50); lps.model\nadd.constraint(lps.model, c(3,1,5), 2, 75, indices = c(1,2,4)); lps.model\n\n\n#add.SOS----\n#Add A Special Ordered Set Constraint\nadd.SOS(lprec, name, type, priority, columns, weights)\n\n\n#deletecolumn----\ndelete.column(lprec, columns)\nlps.model <- make.lp(4, 0);lps.model\nx <- c(6,2,4,9)\nadd.column(lps.model, x); lps.model\ny <- c(3,1,5) ;ind <- c(1,2,4)\nadd.column(lps.model, y, ind); lps.model\ndelete.column(lps.model, 1) ; lps.model\n\n\n#deleteconstraint----\ndelete.constraint(lprec, constraints)\nlps.model <- make.lp(0, 4) ; lps.model\nset.objfn(lps.model, rep(1, 4)); lps.model\nxt <- c(6,2,4,9)\nadd.constraint(lps.model, xt, \"<=\", 50); lps.model\nyt <- c(3,1,5) ;ind <- c(1,2,4)\nadd.constraint(lps.model, yt, 2, 75, ind); lps.model\ndelete.constraint(lps.model, 1); lps.model\n\n#delete lp----\ndelete.lp(lprec)\n\n#dim ----\ndim(x) <- value\nlps.model <- make.lp(4, 0) ; lps.model\nx <- c(6,2,4,9)\nadd.column(lps.model, x); lps.model\ny <- c(3,1,5) ; ind <- c(1,2,4)\nadd.column(lps.model, y, ind) ; lps.model\ndim(lps.model)\n\n\n#dimnames.lpExtPtr -----\ndimnames(x) ; dimnames(x) <- value\n\nlps.model <- make.lp(4, 0) ; lps.model\nx <- c(6,2,4,9)\nadd.column(lps.model, x); lps.model\ny <- c(3,1,5) ; ind <- c(1,2,4)\nadd.column(lps.model, y, ind) ; lps.model\ndimnames(lps.model) <- list(c(\"alpha\", \"bravo\", \"charlie\", \"delta\"),\n c(\"whiskey\", \"tango\"))\nlps.model\n\n#get.basis-----\nget.basis(lprec, nonbasic = FALSE)\nlps.model <- make.lp(3, 3); lps.model\n## build and solve model ##\nget.basis(lps.model)\n\n#getbounds----\nget.bounds(lprec, columns = 1:n)\nlps.model <- make.lp(3, 3); lps.model\nget.bounds(lps.model)\n\n\n#getbranchmode\nget.branch.mode(lprec, columns = 1:n, as.char = TRUE)\n\nlps.model <- make.lp(3, 3); lps.model\nget.branch.mode(lps.model)\n\n#getcolumn -----\nget.column(lprec, column)\n\nlps.model <- make.lp(4, 0)\nx <- c(6,2,4,9)\nadd.column(lps.model, x); lps.model\ny <- c(3,1,5) ;ind <- c(1,2,4)\nadd.column(lps.model, y, ind); lps.model\nget.column(lps.model, 2)\n\n#getconstraint type-----\nget.constr.type(lprec, constraints = 1:m, as.char = TRUE)\nlps.model <- make.lp(0, 3); lps.model\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 15); lps.model\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 15); lps.model\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40); lps.model\nget.constr.type(lps.model)\n\n#getconstraint value-----\nget.constr.value(lprec, side = c(\"rhs\", \"lhs\"), constraints = 1:m)\n\n#getconstraints----\nget.constraints(lprec)\n\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3)); lps.model\nsolve(lps.model)\nget.constraints(lps.model)\n\n#getdualsolution----\nget.dual.solution(lprec)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3)) ; lps.model\nsolve(lps.model)\nget.dual.solution(lps.model)\n\n#get.kind ----\nget.kind(lprec, columns = 1:n)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nget.kind(lps.model)\n\n\nget.mat(lprec, i, j)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nget.mat(lps.model, 2, 2)\n\n\nget.objective(lprec)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nsolve(lps.model)\nget.objective(lps.model)\n\n\nget.primal.solution(lprec, orig = FALSE)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nsolve(lps.model)\nget.primal.solution(lps.model)\n\nget.rhs(lprec, constraints = 1:m)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nget.rhs(lps.model)\n\nget.sensitivity.obj(lprec)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nsolve(lps.model)\nget.sensitivity.obj(lps.model)\n\n\nget.sensitivity.objex(lprec)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nsolve(lps.model)\nget.sensitivity.objex(lps.model)\n\n\nget.sensitivity.rhs(lprec)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nsolve(lps.model)\nget.sensitivity.rhs(lps.model)\n\n\nget.solutioncount(lprec)\n\n\nget.total.iter(lprec)\n\n\nget.total.nodes(lprec)\n\nget.type(lprec, columns = 1:n, as.char = TRUE)\n\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nset.type(lps.model, 2, \"binary\")\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.type(lps.model, 3, \"integer\")\nset.objfn(lps.model, c(-3,-4,-3))\nget.type(lps.model)\n\nget.variables(lprec)\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nsolve(lps.model)\nget.variables(lps.model)\n\n\nguess.basis(lprec, guess)\n\nlp.control(lprec, ..., reset = FALSE)\n\n\nmake.lp(nrow = 0, ncol = 0, verbose = \"neutral\")\nlps.model <- make.lp(4, 3)\n\n\nname.lp(lprec, name)\n\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nname.lp(lps.model, \"Simple LP\")\nname.lp(lps.model)\n\n\n#plot.lpExtPtr\n#print.lpExtPtr\n\nread.lp(filename, type = c(\"lp\", \"mps\", \"freemps\"), verbose = \"neutral\", options)\n\n#resize.lp\nresize.lp(lprec, nrow, ncol)\nlps.model <- make.lp(4, 0)\nresize.lp(lps.model, 4, 2) ; lps.model\nx <- c(6,2,4,9)\nadd.column(lps.model, x); lps.model\ny <- c(3,1,5) ;ind <- c(1,2,4)\nadd.column(lps.model, y, ind); lps.model\n\n\n#row.add.mode(lprec, state)\n\n\n#select.solution(lprec, solution)\n\n#set.basis(lprec, basis, nonbasic = FALSE, default = FALSE)\n\n#set.bounds----\n\nset.bounds(lprec, lower = NULL, upper = NULL, columns = 1:n)\nlps.model <- make.lp(0, 4)\nset.bounds(lps.model, lower = rep(-1.0, 4)); lps.model\nset.bounds(lps.model, upper = 1:4);lps.model\nset.bounds(lps.model, lower = rep(0.0, 4), upper = rep(1.0, 4))\nlps.model\n\n\n#set.branch.mode(lprec, columns, modes)\n\n\n#set.branch.weights(lprec, weights)\n\n\n#set.column(lprec, column, x, indices)\n\nlps.model <- make.lp(4, 2)\nset.column(lps.model, 2, c(6,2,4,9)); lps.model\nset.column(lps.model, 1, c(3,1,5), indices = c(1,2,4)); lps.model\n\n#set.constr.type\nset.constr.type(lprec, types, constraints = 1:m)\nlps.model <- make.lp(4, 2)\nx <- c(6,2,4,9)\nset.column(lps.model, 2, x); lps.model\ny <- c(3,1,5); ind <- c(1,2,4)\nset.column(lps.model, 1, y, ind); lps.model\nset.constr.type(lps.model, rep(\"<=\", 4)) ; lps.model\n\n\nset.constr.value(lprec, rhs = NULL, lhs = NULL, constraints = 1:m)\n\n\nset.mat(lprec, i, j, value)\n\n\nlps.model <- make.lp(4, 2)\nx <- c(6,2,4,9)\nset.column(lps.model, 2, x)\ny <- c(3,1,5) ;ind <- c(1,2,4)\nset.column(lps.model, 1, y, ind)\nset.constr.type(lps.model, rep(\"<=\", 4)); lps.model\nset.mat(lps.model, 3, 2, 4.5)\nlps.model\n\n\nset.objfn(lprec, obj, indices)\nlps.model <- make.lp(2, 4)\nset.objfn(lps.model, c(1,2,3,4))\nset.objfn(lps.model, c(5,7,6), indices = c(1,2,4))\n\nset.rhs(lprec, b, constraints = 1:m)\nlps.model <- make.lp(4, 2)\nx <- c(6,2,4,9)\nset.column(lps.model, 2, x)\ny <- c(3,1,5)\nind <- c(1,2,4)\nset.column(lps.model, 1, y, ind)\nset.rhs(lps.model, c(10, 20, 40, 80))\nlps.model\n\n\nset.row(lprec, row, xt, indices)\nlps.model <- make.lp(2, 4); lps.model\nset.row(lps.model, 2, c(6,2,4,9)); lps.model\nset.row(lps.model, 1, c(3,1,5), indices = c(1,2,4)); lps.model\n\n\nset.semicont(lprec, columns, sc = TRUE)\n\n\nset.type(lprec, columns, type = c(\"integer\", \"binary\", \"real\"))\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nset.type(lps.model, 2, \"binary\")\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.type(lps.model, 3, \"integer\")\nset.objfn(lps.model, c(-3,-4,-3))\nget.type(lps.model)\nlps.model\n\n\n#solve.lpExtPtr\nlps.model <- make.lp(0, 3)\nxt <- c(6,2,4)\nadd.constraint(lps.model, xt, \"<=\", 150)\nxt <- c(1,1,6)\nadd.constraint(lps.model, xt, \">=\", 0)\nxt <- c(4,5,4)\nadd.constraint(lps.model, xt, \"=\", 40)\nset.objfn(lps.model, c(-3,-4,-3))\nsolve(lps.model)\n\n# 0: \"optimal solution found\"\n# 1: \"the model is sub-optimal\"\n# 2: \"the model is infeasible\"\n# 3: \"the model is unbounded\"\n# 4: \"the model is degenerate\"\n# 5: \"numerical failure encountered\"\n# 6: \"process aborted\"\n# 7: \"timeout\"\n# 9: \"the model was solved by presolve\"\n# 10: \"the branch and bound routine failed\"\n# 11: \"the branch and bound was stopped because of a break-at-first or break-at-value\"\n# 12: \"a feasible branch and bound solution was found\"\n# 13: \"no feasible branch and bound solution was found\"\n\n\nwrite.lp(lprec, filename, type = c(\"lp\", \"mps\", \"freemps\"),\n use.names = c(TRUE, TRUE))\n" }, { "path": "51a-OR-LP/15c-lpassign.R", "content": "#lp.assign\n#https://www.rdocumentation.org/packages/lpSolve/versions/5.6.13/topics/lp.assign\n#https://rdrr.io/cran/lpSolve/src/R/lp.assign.R\n\n\n#Interface to lp\\_solve linear/integer programming system specifically for solving assignment problems\n\nlibrary(lpSolve)\n\n#lp.assign (cost.mat, direction = \"min\", presolve = 0, compute.sens = 0)\n#This is a particular integer programming problem. All the decision variables are assumed to be integers; each row has the constraint that its entries must add up to 1 (so that there is one 1 and the remaining entries are 0) and each column has the same constraint. This is assumed to be a minimization problem.\n\n\nassign.costs <- matrix (c(2, 7, 7, 2, 7, 7, 3, 2, 7, 2, 8, 10, 1, 9, 8, 2), 4, 4)\nassign.costs\n# [,1] [,2] [,3] [,4]\n# [1,] 2 7 7 1\n# [2,] 7 7 2 9\n# [3,] 7 3 8 8\n# [4,] 2 2 10 2\n\nlp.assign (assign.costs)\n## Not run: Success: the objective function is 8\nlp.assign (assign.costs)$solution\n" }, { "path": "51a-OR-LP/21b-LP-mach-prod.R", "content": "#LP Cases - Machine A & B, Products X & Y\n#see Mindmap\n#Equations\n#50x + 24y <= 2400\n#30x + 33y <= 2100\n#x >= 45\n#y >= 5\n#max: x + y - 50\n#50 >= x + y\nlibrary(lpSolve)\nf.obj <- c(1, 1)\nf.con <- matrix (c(50, 24, 30, 33, 1, 0, 0, 1, 1, 1), ncol=2, byrow=TRUE)\nf.con\nf.dir <- c(\"<=\", \"<=\", \">=\", \">=\", \">=\")\nf.rhs <- c(2400, 2100, 45, 5,50)\ncbind(f.con, f.dir, f.rhs)\n#\nlp (\"max\", f.obj, f.con, f.dir, f.rhs)\nlp (\"max\", f.obj, f.con, f.dir, f.rhs)$solution\n#x=45, y=6.25\n\n\n#----------------------------------------------#\n#Another Method\n#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf\n#https://ecreee.wikischolars.columbia.edu/file/view/lpSolveAPI+Tutorial.Rmd\n\nlibrary(lpSolveAPI)\n\n#First we create an empty model x.\n\n#max: x + y - 50\nlprec <- make.lp(0, 2)\nlprec\nset.objfn(lprec, c(1, 1))\nlprec\n#maximise it\nlp.control(lprec, sense=\"max\")\nlprec\n\nset.type(lprec, c(1,2), type = c(\"integer\"))\nlprec\n#50x + 24y <= 2400\nadd.constraint(lprec, c(50,24), \"<=\", 2400)\nlprec\n#30x + 33y <= 2100\nadd.constraint(lprec, c(30,33), \"<=\", 2100)\nlprec\n#x >= 45\n#add.constraint(lprec, c(1,0), \">=\", 45)\n#lprec\n#y >= 5\n#add.constraint(lprec, c(0,1), \">=\", 5)\n#lprec\n#50 >= x + y\nadd.constraint(lprec, c(1,1), \">=\", 50)\nlprec\n\nset.bounds(lprec, lower = c(45, 5), columns = c(1, 2))\nlprec\n\n#set.bounds(lprec, upper = 48.98, columns = 4)\n#RowNames <- c(\"MachineA\", \"MachineB\",\"InitalA\", \"InitalB\",\"TotalInitial\")\nRowNames <- c(\"MachineA\", \"MachineB\",\"TotalInitial\")\nColNames <- c(\"ProductX\", \"ProductY\")\ndimnames(lprec) <- list(RowNames, ColNames)\nlprec\nsolve(lprec) #[1] 0 ok\n#get.dual.solution(lprec)\nget.objective(lprec)\nget.variables(lprec)\nget.constraints(lprec)\nplot(lprec)\nprint(lprec)\n" }, { "path": "51a-OR-LP/21c-LP-mach-prod.R", "content": "#----------------------------------------------#\n#Another Method\n#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf\n#https://ecreee.wikischolars.columbia.edu/file/view/lpSolveAPI+Tutorial.Rmd\n\nlibrary(lpSolveAPI)\n\n#First we create an empty model x.\n\n#max: x + y - 50\nlprec <- make.lp(0, 2)\nlprec\nset.objfn(lprec, c(1, 1))\nlprec\n#maximise it\nlp.control(lprec, sense=\"max\")\nlprec\n\nset.type(lprec, c(1,2), type = c(\"integer\"))\nlprec\n#50x + 24y <= 2400\nadd.constraint(lprec, c(50,24), \"<=\", 2400)\nlprec\n#30x + 33y <= 2100\nadd.constraint(lprec, c(30,33), \"<=\", 2100)\nlprec\n#x >= 45\n#add.constraint(lprec, c(1,0), \">=\", 45)\n#lprec\n#y >= 5\n#add.constraint(lprec, c(0,1), \">=\", 5)\n#lprec\n#50 >= x + y\nadd.constraint(lprec, c(1,1), \">=\", 50)\nlprec\n\nset.bounds(lprec, lower = c(45, 5), columns = c(1, 2))\nlprec\n\n#set.bounds(lprec, upper = 48.98, columns = 4)\n#RowNames <- c(\"MachineA\", \"MachineB\",\"InitalA\", \"InitalB\",\"TotalInitial\")\nRowNames <- c(\"MachineA\", \"MachineB\",\"TotalInitial\")\nColNames <- c(\"ProductX\", \"ProductY\")\ndimnames(lprec) <- list(RowNames, ColNames)\nlprec\nsolve(lprec) #[1] 0 ok\n#get.dual.solution(lprec)\nget.objective(lprec)\nget.variables(lprec)\nget.constraints(lprec)\nplot(lprec)\nprint(lprec)" }, { "path": "51a-OR-LP/22b-LP-case1.R", "content": "# Assignement Problem\n# Set up objective.\nlibrary(lpSolve)\nex.obj <- c( 592, 381, 273, 55, 48, 37, 23)\n# The constraint needs to be a matrix.\nex.con <- matrix (c(3534, 2356, 1767, 589, 528, 451, 304), nrow=1)\n#\n# Create the sign and the right-hand side of the constraint.#\nex.sign <- \"<=\"\nex.rhs <- 119567\n#\n# Require all seven variables to be integers. In general this\n# vector will contain the indices of the integer variables.\n#\nex.int <- 1:7\n#\n# Solve the linear program. By default print only the objective\n#\nlibrary(lpSolve)\n?lp\nlp1 = lp(\"max\", ex.obj, ex.con, ex.sign, ex.rhs, int.vec=ex.int)\n#Success: the objective function is 19979\n#lp1 - Interface to lp\\_solve linear/integer programming system\n# Solve and print the vector of optimal values.\n#\nlp(\"max\", ex.obj, ex.con, ex.sign, ex.rhs, int.vec=ex.int)$solution\n#[1] 32 2 1 0 0 0 0\n# Require only variables 1 and 3 to be integer\nlp(\"max\", ex.obj, ex.con, ex.sign, ex.rhs, int.vec=c(1, 3))$solution\n#[1] 33.00 1.25 0.00 0.00 0.00 0.00 0.00\n\n\n" }, { "path": "51a-OR-LP/22c-LP-assign-case3.R", "content": "# Assignment Problem : Minimise costs \n#lp2\n#\nlibrary(lpSolve)\nassign.costs <- matrix (c(2, 7, 7, 2, 7, 7, 3, 2, 7, 2, 8, 10, 1, 9, 8, 2), 4, 4)\nassign.costs\n\nlp.assign(assign.costs)\n## Not run: Success: the objective function is 8\nlp.assign (assign.costs)$solution\n## Not run:\n\n\n#------------------------ ------------------\n#\n# Set up problem: \n# Max = x1 + 9 x2 + x3 subject to\n# x1 + 2 x2 + 3 x3 <= 9\n# 3 x1 + 2 x2 + 2 x3 <= 15\n#\nf.obj <- c(1, 9, 1)\nf.con <- matrix (c(1, 2, 3, 3, 2, 2), nrow=2, byrow=TRUE)\nf.dir <- c(\"<=\", \"<=\")\nf.rhs <- c(9, 15)\n#\n# Now run.\n#\nlp (\"max\", f.obj, f.con, f.dir, f.rhs)\n## Not run: Success: the objective function is 40.5\nlp (\"max\", f.obj, f.con, f.dir, f.rhs)$solution\n## Not run: [1] 0.0 4.5 0.0\n\n# ----------------\n#The same problem using the dense constraint approach:\n\nf.con.d <- matrix (c(rep (1:2,each=3), \n rep (1:3, 2), t(f.con)), ncol=3)\nf.con.d\nlp(\"max\", f.obj, ,f.dir, f.rhs, dense.const=f.con.d)\n## Not run: Success: the objective function is 40.5\n\n# Get sensitivities\n#\nlp (\"max\", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$sens.coef.from\n## Not run: [1] -1e+30 2e+00 -1e+30\nlp (\"max\", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$sens.coef.to\n## Not run: [1] 4.50e+00 1.00e+30 1.35e+01\n#\n# Right now the dual values for the constraints and the variables are\n# combined, constraints coming first. So in this example...\n#\nlp (\"max\", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$duals\n## Not run: [1] 4.5 0.0 -3.5 0.0 -10.5\n#\n# ...the duals of the constraints are 4.5 and 0, and of the variables,\n# -3.5, 0.0, -10.5. Here are the lower and upper limits on these:\n#\nlp (\"max\", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$duals.from\n## Not run: [1] 0e+00 -1e+30 -1e+30 -1e+30 -6e+00\nlp (\"max\", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$duals.to\n## Not run: [1] 1.5e+01 1.0e+30 3.0e+00 1.0e+30 3.0e+00\n#\n# Run again, this time requiring that all three variables be integer\n#\nlp (\"max\", f.obj, f.con, f.dir, f.rhs, int.vec=1:3)\n## Not run: Success: the objective function is 37\nlp (\"max\", f.obj, f.con, f.dir, f.rhs, int.vec=1:3)$solution\n## Not run: [1] 1 4 0\n#\n# You can get sensitivities in the integer case, but they're harder to\n# interpret.\n#\nlp (\"max\", f.obj, f.con, f.dir, f.rhs, int.vec=1:3, compute.sens=TRUE)$duals\n## Not run: [1] 1 0 0 7 0\n#\n# Here's an example in which we want more than one solution to a problem\n# in which all variables are binary: the 8-queens problem,\n# with dense constraints.\n#\nchess.obj <- rep (1, 64)\nq8 <- make.q8 ()\nchess.dir <- rep (c(\"=\", \"<\"), c(16, 26))\nchess.rhs <- rep (1, 42)\nlp ('max', chess.obj, , chess.dir, chess.rhs, dense.const = q8,\n all.bin=TRUE, num.bin.solns=3)\n\n" }, { "path": "51a-OR-LP/22d-LP-Case-carmanufacturing.R", "content": "#LP car manufacturing\n#A car company produces 2 models, model A and model B. Long-term projections indicate an expected demand of at least 100 model A cars and 80 model B cars each day. Because of limitations on production capacity, no more than 200 model A cars and 170 model B cars can be made daily. To satisfy a shipping contract, a total of at least 200 cars much be shipped each day. If each model A car sold results in a $2000 loss, but each model B car produces a $5000 profit, how many of each type should be made daily to maximize net profits?\n\n#Problem\n#X= number of model A car Y=number of model B car\n#As we are going for the maximization the equation will be\n#-2000x+5000y=Z\n#And the constraints are\n# x>=100 {Demand of Model A}\n# y>=80 {Demand of Model B}\n# x<=200 {Model A’s Production Limit}\n# y<=170 {Model B’s Production Limit}\n# x+y>=200 {Transportation Contract}\n\nf.con<-matrix(c(1,0,0,1,1,0,0,1,1,1),nrow = 5,byrow = TRUE) #Matrix of Constraints i.e x>=100 is convered as (1,0) where in 100 is used in f.rhs\nf.con\nf.obj<-c(-2000,5000) #Objective function\nf.dir <- c(\">=\",\">=\",\"<=\", \"<=\",\">=\") #Direction\nf.rhs <- c(100,80,200,170,200) #RHS of the matrix\nlps1<-lp (\"max\", f.obj, f.con, f.dir, f.rhs,compute.sens = TRUE) #LPP Function\nlps1$solution #Solution\n\n#x and y wherein we have to produce 100 units of Model A and 170 of Model B on daily basis for a optimum production planning\n" }, { "path": "51a-OR-LP/25b-LPassign-job.R", "content": "#lpassign - Case\n#https://stackoverflow.com/questions/21149134/assignment-algorthms-in-r-lp-assign\n\n#matrix m below where each row is a student and each column is a job and 1 means the student's top choice, 2 means the second choice and so on. 9 means the student did not rank the job. There were 3 students and 4 tasks so we added a dummy student, U, of all 9s for the last row so that the number of students and tasks are the same. We assume the objective is to minimize the sum of the ranks. Below we see that the best assignment is to assign student 1 to job C, student 2 to job D, student 3 to job A and the unassigned row slurps up job B\n\nlibrary(lpSolve)\n\nm <- matrix(c(3, 2, 1, 9, 2, 3, 2, 9, 1, 9, 3, 9, 9, 1, 9, 9), 4, dimnames = list(c(1, 2, 3, \"U\"), c(\"A\", \"B\", \"C\", \"D\")))\nm\nfm <- lp.assign(m)\n\n#fm$solution contains the solution, a matrix of the same dimensions as m with 0 and 1 entries.\nfm$solution\n\nstudent <- rownames(m)\nix <- round(fm$solution %*% seq_len(ncol(m)))\njob <- colnames(m)[ifelse(ix == 0, NA, ix)]\ndata.frame(student, job)\n" }, { "path": "51a-OR-LP/30a-LP-tpt-function.R", "content": "#LP - Transport\nlibrary(lpSolve)\n\n?lp.transport" }, { "path": "51a-OR-LP/31b-LP-tpt1.R", "content": "# Transport Problem :\n# Assisn costs to each source to each destination \ncosts <- matrix (10000, 8, 5) # Give max value to all by default\ncosts # max costs denote no route, route not to be selected\n# not one by one add lower/actual costs\ncosts[4,1] <- costs[-4,5] <- 0\ncosts\ncosts[1,2] <- costs[2,3] <- costs[3,4] <- 7; \ncosts\ncosts[1,3] <- costs[2,4] <- 7.7\ncosts\ncosts[5,1] <- costs[7,3] <- 8;\ncosts\ncosts[1,4] <- 8.4; \ncosts\ncosts[6,2] <- 9\ncosts[8,4] <- 10; \ncosts[4,2:4] <- c(.7, 1.4, 2.1)\ncosts\n# Set up constraint signs and right-hand sides.\nrow.signs <- rep (\"<\", 8) # repeat the sign 8 times/ colns\nrow.rhs <- c(200, 300, 350, 200, 100, 50, 100, 150)\ncol.signs <- rep (\">\", 5) # repeat the sign 5 times/ rows\ncol.rhs <- c(250, 100, 400, 500, 200)\n# Run\n#lp.transport\n#lp.transport (cost.mat, direction=\"min\", row.signs, row.rhs, col.signs,\n# col.rhs, presolve=0, compute.sens=0, integers = 1:(nc*nr) )\n\nlp.transport(costs, \"min\", row.signs, row.rhs, col.signs, col.rhs)\n## Not run: Success: the objective function is 7790 - Min cost of tpt\nlp.transport (costs, \"min\", row.signs, row.rhs, col.signs, col.rhs)$solution\n# this is matrix how source will connect to destination\n# \n" }, { "path": "51a-OR-LP/31c-LP-tpt2.R", "content": "#http://stats.stackexchange.com/questions/136608/constrained-assignment-problem-linear-programming-genetic-algorithm-etc\n\n#As an example, suppose we have 3 shops and 2 supply centers with distances between them as x1,1x1,1 = 2.8, x1,2x1,2 = 5.4, x1,3x1,3 = 1.4, x2,1x2,1 = 4.2, x2,2x2,2 = 3.0, x2,3x2,3 = 6.3. Then\n\nlibrary(Rsymphony)\nobj <- c(2.8, 5.4, 1.4, 4.2, 3.0, 6.3, 0, 0)\nmat <- matrix(c(1,0,0,1,0,0,0,1,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,-3,0,1,0,0,0,0,-3,1), nrow = 6)\ndir <- c(\"==\", \"==\", \"==\", \"<=\", \"<=\", \"<=\")\nrhs <- c(1, 1, 1, 0, 0, 2)\nmat\nmax <- FALSE\ntypes <- \"B\"\nfac.loc <- Rsymphony_solve_LP(obj, mat, dir, rhs, types = types, max = max, write_lp = TRUE)\nfac.loc\n\ntells us that we should open both supply centers and \nsupply center 1 will supply shops 1 and 3 and supply center 2 will supply shop 2, for a total distance of 7.2.\n" }, { "path": "51a-OR-LP/31d-LP-tpt3.R", "content": "\n#Transportation Problem with Costs\n\n(costs <- matrix(c(3,1,5,2,5,4),ncol=2))\nrow.signs <- c(\"<\", \"<\", \"<\")\nrow.rhs <- c(45, 60, 35)\ncol.signs <- rep (\">\", 2)\ncol.rhs <- c(50, 60)\ncbind(costs, row.signs, row.rhs)\nrbind(costs, col.signs, col.rhs)\n\n#Model\nlp.transport (costs, \"min\", row.signs, row.rhs, col.signs, col.rhs)\n(m1=lp.transport (costs, \"min\", row.signs, row.rhs, col.signs, col.rhs)$solution)\ncolSums(m1)\nrowSums(m1)\n\n\n#Model2\n#Shorter Method\n(cost <- matrix(c(3,1,5,2,5,4),ncol=2))\n(m2=lp.transport(cost, \"min\" , rep(\"<\",3) , c(45, 60, 35) , rep(\">=\",2) , c(50, 60) )$solution)\nlp.transport(cost, \"min\" , rep(\"<\",3) , c(45, 60, 35) , rep(\">=\",2) , c(50, 60) )\n\n# Using Sweep function\nm2\ncosts\nobjvalue=sweep(m2,c(1,2),costs,\"*\" )\nsum(objvalue)\n\n" }, { "path": "51a-OR-LP/33d-proptable.R", "content": "#prop.table\n" }, { "path": "51a-OR-LP/41b-pricing.R", "content": "#LP - Pricing\n" }, { "path": "51a-OR-LP/51b-LP-marketing.R", "content": "#LP - Marketing\n\n#Final Model\n#Max : 750 * x1 + 1000 * x2\n#Subject To\n#x1 + x2 <= 10\n#x1 + 2 * x2 <= 15\n#4*x1 + 3* x2 <= 25\n#x1 >= 0\n#x2 >= 0\n\nlibrary(lpSolveAPI)\n(lprec <- make.lp(0, 2))\nset.objfn(lprec, c(750, 1000)) ; lprec\nlp.control(lprec, sense=\"max\"); lprec #maximise it\nset.type(lprec, c(1,2), type = c(\"integer\")); lprec\nadd.constraint(lprec, c(1,1), \"<=\", 10) ;lprec\nadd.constraint(lprec, c(1,2), \"<=\", 15) ;lprec\nadd.constraint(lprec, c(4,3), \"<=\", 25) ;lprec\nset.bounds(lprec, lower = c(0, 0), columns = c(1, 2));lprec\nColNames <- c(\"Notebook\", \"Desktop\")\nRowNames <- c(\"Allocated\", \"ChipsAvl\",\"AssemblyTime\")\ndimnames(lprec) <- list(RowNames, ColNames);lprec\n#Solve\nsolve(lprec) #[1] 0 ok\n#get.dual.solution(lprec)\nget.objective(lprec)\nget.variables(lprec)\nget.constraints(lprec)\nget.sensitivity.obj(lprec)\n#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf\n\nprint.lpExtPtr(lprec)\n#real constraints\nset.type(lprec, c(1,2), type = c(\"real\")); lprec\n#should be real to draw graph\nsolve(lprec)\n\nplot.lpExtPtr(lprec)\nname.lp(lprec,'Chips Allocation'); lprec\n\n" }, { "path": "51a-OR-LP/model.lp", "content": "/* Objective function */\nmax: +2000 C1 +2500 C2 +5000 C3 +3500 C4 +2000 C5 +2500 C6 +5000 C7 +3500 C8 +2000 C9 +2500 C10 +5000 C11\n +3500 C12;\n\n/* Constraints */\n+C1 +C2 +C3 +C4 <= 10;\n+C5 +C6 +C7 +C8 <= 8;\n+C9 +C10 +C11 +C12 <= 12;\n+400 C1 +300 C2 +200 C3 +500 C4 <= 5000;\n+400 C5 +300 C6 +200 C7 +500 C8 <= 4000;\n+400 C9 +300 C10 +200 C11 +500 C12 <= 8000;\n+C1 +C5 +C9 <= 18;\n+C2 +C6 +C10 <= 10;\n+C3 +C7 +C11 <= 5;\n+C4 +C8 +C12 <= 20;\n" }, { "path": "51a-OR-LP/zz-LP-clplite.R", "content": "#LP - Package clplite\n#https://github.com/yixuan/clplite\n\nlibrary('githubinstall')\ngithubinstall('clplite')\n\nlibrary(clplite)\nobj = c(1, 0, 0, 0, 2, 0, 0, -1)\nA = matrix(c(\n 3, 1, 0, -2, -1, 0, 0, -1,\n 0, 2, 1.1, 0, 0, 0, 0, 0,\n 0, 0, 1, 0, 0, 1, 0, 0,\n 0, 0, 0, 2.8, 0, 0, -1.2, 0,\n 5.6, 0, 0, 0, 1, 0, 0, 1.9\n), nrow = 5, byrow = TRUE)\nconstr_lb = c(2.5, -Inf, 4, 1.8, 3)\nconstr_ub = c(Inf, 2.1, 4, 5, 15)\nvar_lb = c(2.5, 0, 0, 0, 0.5, 0, 0, 0)\nvar_ub = c(Inf, 4.1, Inf, Inf, 4, Inf, Inf, 4.3)\n\nclp_solve(obj, A, constr_lb, constr_ub, var_lb, var_ub, max = TRUE)\nclp_solve(obj, A, constr_lb, constr_ub, var_lb, var_ub, max = FALSE)\n" }, { "path": "51a-OR-LP/zz-LP-general.R", "content": "#https://github.com/rewyllys/LinearOptimizationUsingR/blob/master/SolvingLinOptProbsUsinglpSolveAPI.doc\n\nExample 1: A Simple Airline Optimization Problem \nThe first example (Video 1 of Part 1 of Unit 8) is called the \"AirlineSimple\" situation and is presented as follows:\n \"To illustrate how linear optimization works in revenue management, let us consider a simple example: a flight from New York to Los Angeles. In this flight, there are two types of economy fares: Early Bird [i.e., discount] fares that cost $238, and Last Minute [i.e., regular] fares that cost $617.\n\"In this flight, a Boeing 757 is used that has 166 economy seats. Demand for these prices has been forecasted using analytics tools, looking at historical data and incorporating models like time series or linear regression. Clearly, forecasts have errors, and therefore, we need to assess the sensitivity of our decisions to these errors.\n\"To illustrate the use of linear optimization, we assume that demand has already been forecasted. We'll illustrate how our decisions on how many discount seats to sell vary as the demand forecasts vary. If the demand for regular seats is 50, and for discounted fares is 150, and the capacity is 166 seats, then the optimal allocation is going to be to sell the 50 seats to satisfy the regular demand, and then we allocate the remaining 116 seats to the discounted fare class. If the regular demand increases to 100 seats, then we allocate these 100 seats to these customers, and only 66 seats to discounted fare customers. Finally, if the regular demand increases to 200, then we allocate all of our capacity, 166 seats, to these customers. While this seems simple, what happens if we have 100 flights with connections in tens of fares?\"\nPart 1 of Unit 8 of \"The Analytics Edge\" has thus set up the objective in this problem as being the maximization of revenue from selling seats on this flight at either the Regular Fare, $617, or the Discount Fare, $238. The key to seeing what variables are involved in a linear optimization problem is to examine the function that encapsulates the objective; this function must involve the variables whose values are to be determined in such a way as to optimize (to maximize, in this case, or in other cases, to minimize) the whole objective function.\nIn this first example, there are two variables in the objective function: the number of seats to be sold at $617 and the number to be sold at $238. If we represent the number of seats to be sold at the Regular Fare as xR and the number to be sold at the Discount Fare as xD, then the objective function to be maximized can be written as:\n 617*xR + 238*xD\nThere are three constraints. One of them is that the aircraft used has 166 seats. The other two are that the airline forecasts the demand for Regular seats as 100 and the demand for Discount seats as 150 on a typical instance of the flight. These constraints can be written as shown next. Note that in writing out each constraint it is important to include all the variables, even if the coefficient of one or more of them is 0 in a particular constraint.\n1*xR + 1*xD <= 166\n1*xR + 0*xD <= 100\n0*xR + 1*xD <= 150\nWe see that there are three constraints, and that each involves the two variables in the objective. The interpretation of these considerations in lpSolveAPI is that we need, first, to define a model along the following lines:\n AirlineSimple <- make.lp(3,2)\nFurther, the constraints must be entered into the model as columns in an array. In this example there are two variables, so the array has two columns. In lpSolveAPI, these two columns are entered as shown next:\n set.column(AirlineSimple, 1, c(1, 1, 0))\nset.column(AirlineSimple, 2, c(1, 0, 1))\nThe inequalities are handled as follows:\n set.constr.type(AirlineSimple, c(\"<=\", \"<=\", \"<=\"))\nset.rhs(AirlineSimple, c(166, 100, 150))\nThe objective is entered into the model as follows:\n set.objfn(AirlineSimple, c(617, 238))\nSince the default sense of what is to be done with the objective function is, in lpSolveAPI, to minimize it, we need to specify that here the objective is to be maximized:\n lp.control(AirlineSimple, sense=\"max\")\nWhen we have reached this point in constructing our model, it can be helpful to have lpSolveAPI print out its understanding of the model. To do this, we simply issue, at the R prompt, the name of the model, which in this case is \"AirlineSimple\".\nAirlineSimple\nHere is what the AirlineSimple model looks like when the steps being detailed here are actually run in R:\n Model name: \n C1 C2 \nMaximize 617 238 \nR1 1 1 <= 166\nR2 1 0 <= 100\nR3 0 1 <= 150\nKind Std Std \nType Real Real \nUpper Inf Inf \nLower 0 0 \n\nIf the printed-out model looks like what we intended it to be, we are ready to tell lpSolveAPI to solve the problem:\n solve(AirlineSimple)\nThis instruction will return the value 0 if lpSolvAPI is able to find a solution. To see the details of the solution and the value of the objective, we issue the following commands:\n get.objective(AirlineSimple)\nget.variables(AirlineSimple)\nThe script provided by \"The Analytics Edge\" concludes by saying that the objective value turns out to be $77,408, and that the optimal values of the variables turn out to be: xR = 100 and xD = 66.\nBelow, between the two long rows of ###s, is the script provided in \"The Analytics Edge\". (As you might expect, in R scripts any line beginning with # is not executed by R.)\nMy recommendation on how to run this script is to open, on your computer, a copy of RStudio, which is my favorite open-source GUI for using R. You can obtain a free copy of RStudio from\nhttp://www.rstudio.com/\n To use this script, you should: (i) copy and paste it into the upper left-hand portion of the RStudio window; and then (ii) select the entire script and click on the button labelled \"Run\" in the menu bar of this upper left-hand portion.\n####################################################################################\n# R Script for the AirlineSimple Problem \n\n# The following is the R script as provided in Part 1 of Unit 8 of \n# \"The Analytics Edge\".\n\n# In this script file, we show how the airline revenue management \n# problems discussed in this lecture [i.e., in the videos in the \n# Airline Revenue Management section (Part 1) of Unit 8 of \"The \n# Analytics Edge\"] can be solved in R using the \"lpSolveAPI\" package.\n# This package allows you to solve linear optimization problems in \n# R using the lp_solve library, which is an open-source \n# optimization solver.\n\n# If you are solving larger and more complex optimization problems,\n# lp_solve might not be powerful enough to solve your problems. There \n# are other optimization packages available in R that rely on commercial\n# solvers, such as cplexAPI and Rcplex. For more information, see the\n# CRAN Optimization page: \n# http://cran.us.r-project.org/web/views/Optimization.html\n\n# First, you need to install and load the lpSolveAPI package\n\ninstall.packages(\"lpSolveAPI\")\nlibrary(lpSolveAPI)\n\n# Now let's start by solving the simple airline problem with just \n# two decision variables: the number of regular seats to offer, and \n# the number of discount seats to offer. \n\n# The first step is to create a model, which takes as arguments the\n# number of constraints in your model, and the number of decision\n# variables in your model. We have three constraints (one capacity\n# constraint and two demand constraints) and two decision variables.\n\nAirlineSimple = make.lp(3,2)\n\n# Now we need to set up the constraints and objective. The best way to \n# do this using the lpSolveAPI package is by viewing the constraints in\n# a matrix format. Our objective and three constraints are as follows:\n# max 617*R + 238*D\n# subject to 1*R + 1*D <= 166\n# 1*R + 0*D <= 100\n# 0*R + 1*D <= 150\t\n\n# So the first column in our constraint matrix is c(1,1,0), and the\n# second column in our constraint matrix is c(1,0,1). We also need to\n# indicate that these are less-than-or-equal constraints, and set the\n# right-hand-side values to c(166,100,150):\n\nset.column(AirlineSimple, 1, c(1,1,0))\nset.column(AirlineSimple, 2, c(1,0,1))\nset.constr.type(AirlineSimple, c(\"<=\",\"<=\",\"<=\"))\nset.rhs(AirlineSimple, c(166,100,150))\n\n# And our objective coefficients are c(617,238):\n\nset.objfn(AirlineSimple, c(617,238))\n\n# The default setting is minimize for the objective, so we need to \n# tell R that we are maximizing our objective:\n\nlp.control(AirlineSimple,sense='max')\n\n# If we want to take a look at our model, we can just type:\n\nAirlineSimple\n\n# You should see that there is a row called \"Lower\" in the model \n# output -- the values of zero indicate that our decision variables \n# are constrained to be non-negative, which is the default setting.\n# If you want to change the lower or upper bounds for a decision\n# variable, you can do so with the set.bounds function. (Next week \n# we will learn about integer and binary decision variables -- to \n# change a decision variable to one of these types, you can use \n# the set.type function.)\n\n# Now we are ready to solve our model:\n\nsolve(AirlineSimple)\n\n# An output of zero means that the model was successfully solved. \n# You can look at the optimal objective function value or optimal\n# decision variable values with get.objective and get.variables:\n\nget.objective(AirlineSimple)\nget.variables(AirlineSimple)\n\n# You should get an objective value of 77408 and decision variable values of 100 and 66, just as we did in the lecture.\n\n#########################################################################\n\nExample 2: An Airline Optimization Problem that Includes Connecting Flights\n\nVideo 7 of Part 1 of Unit 8 of \"The Analytics Edge\" introduces a more complex problem, that of optimizing the revenue from a flight that includes an intermediate destination. Tickets for this flight are sold for the entire flight (viz., from airport 1 to airport 3), or for either of two shorter flights: to or from the intermediate destination (viz., from airport 1 to airport 2, or from airport 2 to airport 3). \n\nHere is what the transcript of Video 7 says about this more complex situation:\n \n \"So far, we've only considered optimizing the fares for a single route. In this video, we'll change our optimization formulation to include connecting flights. Now, instead of just being able to go from JFK in New York to LAX in Los Angeles, let's suppose that the plane stops in Dallas at the Dallas Fort Worth airport. We still are just using one plane, but the passengers can now fly from New York to Dallas, Dallas to Los Angeles, or from New York to Los Angeles by just staying on the plane in Dallas.\n\n\"So how does our optimization problem change? We now have six types of seats that we can offer: the original two types, regular and discount from New York to LA, and four new types. We can sell both regular and discount seats from New York to Dallas, and regular and discount seats from Dallas to Los Angeles. We know the price of each type of ticket as well as the forecasted demand for each type of ticket. \n\n\"We also know that we have a capacity of 166 seats on our plane for each leg of the trip. There's room for 166 passengers on the plane from New York to Dallas, or the first leg of the trip. Then the passengers with a final destination of Dallas will get off the plane and the passengers flying from Dallas to LA will get on the plane. On the second leg of the trip, flying from Dallas to LA, we also have a capacity of 166 seats. So we need to remember that the passengers flying from New York to LA will take up capacity on both legs of the trip, while the other types of passengers will take up capacity on only one leg of the trip.\n\n\"What are our decisions now? They're the number of regular tickets to sell for each type, and the number of discount tickets to sell for each type. So in total, we have six decisions to make. Now, let's define our objective. As before, it's to maximize the total revenue. This is the sum of the price of the ticket times the number of seats of that type we sell, for each type of ticket. And as before, we have two types of constraints: capacity constraints and demand constraints.\n\n\"For the capacity constraints, the airline shouldn't sell more seats than the capacity of the plane, for each leg of the trip. So we need two capacity constraints here: one for the New York to Dallas leg and one for the Dallas to LA leg. Note that the New York to LA passengers have to be counted on both legs of the trip. So the first constraint accounts for all passengers that need to be on the plane when it flies from New York to Dallas, and the second constraint accounts for all passengers that need to be on the plane when it flies from Dallas to LA. \n\n\"We also need six demand constraints, one for each type of ticket. The number of seats sold should not exceed the forecasted demand for each type. And last, we can't sell a negative number of seats, so we have our non-negativity constraints to prevent the variables from being negative.\" \n\nThe remainder of the discussion of this problem, the AirlineConnecting problem, is essentially as presented in a script provided in the course, \"The Analytics Edge\". In the hope of contributing additional clarity and understanding, I have changed the names of the variables and added comments in various places.\n\nThe situation in this problem is that there are two classes of tickets, and each class can be sold for any of 3 different flights. Thus, there are six variables to be considered. We can denote these variables as follows: xRjl represents a Regular fare from JFK to LAX; xDjl represents a Discount fare from JFK to LAX; xRjd represents a Regular fare from JFK to DFW; xDjd represents a Discount fare from JFK to DFW; xRdl represents a Regular fare from DFW to LAX; and xDdl represents a Discount fare from DFW to LAX.\n\nThe objective is to maximize the revenue from this function:\n \n 428*xRjl + 190*xDjl + 642*xRjd + 224*xDjd + 512*xRdl + 190*xDdl\n\nFrom additional information provided in the statement of the problem in \"The Analytics Edge\", we have the following forecasts of demand: for Regular seats from JFK to LAX, 80; for Discount seats from JFK to LAX, 120; for Regular seats from JFK to DFW, 75; for Discount seats from JFK to DFW, 100; for Regular seats from DFW to LAX, 60; and for Discount seats from DFW to LAX, 110.\n\nThe constraints can be shown as follows: \n \n legs 1 & 2 leg 1 only leg 2 only\ncol 1 col 2 col 3 col 4 col 5 col 6 RHS\nsubject to 1*xRjl + 1*xDjl + 1*xRjd + 1*xDjd +0*xRdl + 0*xDdl <= 166\t\t\t\n1*xRjl + 1*xDjl + 0*xRjd + 0*xDjd +1*xRdl + 1*xDdl <= 166 \n1*xRjl + 0*xDjl + 0*xRjd + 0*xDjd +0*xRdl + 0*xDdl <= 80\n0*xRjl + 1*xDjl + 0*xRjd + 0*xDjd +0*xRdl + 0*xDdl <= 120\n0*xRjl + 0*xDjl + 1*xRjd + 0*xDjd +0*xRdl + 0*xDdl <= 75\n0*xRjl + 0*xDjl + 0*xRjd + 1*xDjd +0*xRdl + 0*xDdl <= 100\n0*xRjl + 0*xDjl + 0*xRjd + 0*xDjd +1*xRdl + 0*xDdl <= 60\n0*xRjl + 0*xDjl + 0*xRjd + 0*xDjd +0*xRdl + 1*xDdl <= 110\n\nThis list of constraints makes it clear that the AirlineConnecting model needs to have 8 rows and 6 columns. To build this model using lpSolveAPI, we issue the following command in R;\n\nAirlineConnecting = make.lp(8,6)\n\nTo detail the columns of this model using lpSolveAPI, we issue the following commands:\n \n set.columns(AirlineConnecting, 1, c(1,1,1,0,0,0,0,0))\nset.columns(AirlineConnecting, 2, c(1,1,0,1,0,0,0,0))\nset.columns(AirlineConnecting, 3, c(1,0,0,0,1,0,0,0))\nset.columns(AirlineConnecting, 4, c(1,0,0,0,0,1,0,0))\nset.columns(AirlineConnecting, 5, c(0,1,0,0,0,0,1,0))\nset.columns(AirlineConnecting, 6, c(0,1,0,0,0,0,0,1))\nset.constr.type(AirlineConnecting, rep(\"<=\",8))\nset.rhs(AirlineConnecting, c(166,166,80,120,75,100,60,110))\n\nFor the remaining steps in setting up the AirlineConnecting model, we issue:\n \n set.objfn(AirlineConnecting, c(428,190,642,224,512,190))\nlp.control(AirlineConnecting, sense=\"max\")\n\nTo check on the model we have created, we can type AirlineConnecting.\n\nAirline Connecting\n\nHere is how the model should look:\n Model name: \n C1 C2 C3 C4 C5 C6 \nMaximize 428 190 642 224 512 190 \nR1 1 1 1 1 0 0 <= 166\nR2 1 1 0 0 1 1 <= 166\nR3 1 0 0 0 0 0 <= 80\nR4 0 1 0 0 0 0 <= 120\nR5 0 0 1 0 0 0 <= 75\nR6 0 0 0 1 0 0 <= 100\nR7 0 0 0 0 1 0 <= 60\nR8 0 0 0 0 0 1 <= 110\nKind Std Std Std Std Std Std \nType Real Real Real Real Real Real You should get an objective function value of 120514 and decision variable values of 80, 0, 75, 11, 60, 26. \nUpper Inf Inf Inf Inf Inf Inf \nLower 0 0 0 0 0 0 \n\nThe remaining steps are to issue the following commands:\n \n solve(AirlineConnecting)\nget.objective(AirlineConnecting)\nget.variables(AirlineConnecting)\n\nYou should get an objective function value of $120,514 and decision variable values of 80, 0, 75, 11, 60, 26. \n\nBelow, between the two long rows of ###s, is the script provided in \"The Analytics Edge\" for the AirlineConnecting problem.\n########################################################################\n\n# The following commands set up and solve the more sophisticated airline\n# problem with six decision variables.\n\n# First, you need to install and load the lpSolveAPI package:\n\ninstall.packages(\"lpSolveAPI\")\nlibrary(lpSolveAPI)\n\n# We can distinguish the three different trips as: JFK-LAX, \"jl\"; \n# JFK-DFW, \"jd\"; and DFW-LAX, \"dl\".\n# The objective to be maximized is: \n# 428*Rjl + 190*Djl + 642*Rjd + 224*Djd + 512*Rdl + 190*Ddl\n\n# legs 1 & 2 leg 1 only leg 2 only\n# col 1 col 2 col 3 col 4 col 5 col 6 RHS\n# subject to 1*Rjl + 1*Djl + 1*Rjd + 1*Djd +0*Rdl + 0*Ddl <= 166 \n# 1*Rjl + 1*Djl + 0*Rjd + 0*Djd +1*Rdl + 1*Ddl <= 166 \n# 1*Rjl + 0*Djl + 0*Rjd + 0*Djd +0*Rdl + 0*Ddl <= 80\n# 0*Rjl + 1*Djl + 0*Rjd + 0*Djd +0*Rdl + 0*Ddl <= 120\n# 0*Rjl + 0*Djl + 1*Rjd + 0*Djd +0*Rdl + 0*Ddl <= 75\n# 0*Rjl + 0*Djl + 0*Rjd + 1*Djd +0*Rdl + 0*Ddl <= 100\n# 0*Rjl + 0*Djl + 0*Rjd + 0*Djd +1*Rdl + 0*Ddl <= 60\n# 0*Rjl + 0*Djl + 0*Rjd + 0*Djd +0*Rdl + 1*Ddl <= 110\n\nAirlineConnecting = make.lp(8,6)\n\nset.column(AirlineConnecting, 1, c(1,1,1,0,0,0,0,0))\nset.column(AirlineConnecting, 2, c(1,1,0,1,0,0,0,0))\nset.column(AirlineConnecting, 3, c(1,0,0,0,1,0,0,0))\nset.column(AirlineConnecting, 4, c(1,0,0,0,0,1,0,0))\nset.column(AirlineConnecting, 5, c(0,1,0,0,0,0,1,0))\nset.column(AirlineConnecting, 6, c(0,1,0,0,0,0,0,1))\nset.constr.type(AirlineConnecting, rep(\"<=\",8))\nset.rhs(AirlineConnecting, c(166,166,80,120,75,100,60,110))\n\nset.objfn(AirlineConnecting, c(428,190,642,224,512,190))\nlp.control(AirlineConnecting,sense='max')\n\n# To check on the model we have created, type AirlineConnecting.\n\nAirlineConnecting\n\nsolve(AirlineConnecting)\nget.objective(AirlineConnecting)\nget.variables(AirlineConnecting)\n\n# You should get an objective function value of 120514 and \n# decision variable values of 80, 0, 75, 11, 60, 26.\n\n# For additional examples, we direct you to the lpSolveAPI User's Guide:\n# http://www.icesi.edu.co/CRAN/web/packages/lpSolveAPI/vignettes/lpSolveAPI.pdf\n\n###########################################################################\n\nExample 3: A Problem Involving the Optimization of Beamlets of Radioactivity to Attack \t Cancerous Tumors\nPart 2 of Unit 8 of \"The Analytics Edge\" deals with the optimizing the administration of radiation therapy treatments for cancer patients. The transcript of Video 1 of Part 2 of Unit 8 says in part:\n \"Cancer can be treated using radiation therapy, where beams of high-energy photons are fired into the patient to kill cancerous cells. This is a very common form of treatment for many types of cancers, and in the United States, about half of all cancer patients undergo some form of radiation therapy. Radiation therapy has a history going back to the late 1800s. X-rays were discovered by Wilhelm Rontgen in 1895, who was later awarded the first Nobel Prize in Physics. Shortly after the discovery, X-rays started being used to treat skin cancers. This image shows an x-ray of Rontgen's wife's hand. You can see the bones in her hand as well as her wedding ring on her finger. A few years later in 1898, radium was discovered by Marie and Pierre Curie. They won the Nobel Prize for this discovery in 1911. Radium started being used to treat cancer, as well as other diseases. \n\"Later in the 1900s, the first radiation delivery machines, called linear accelerators, were developed. Then computed tomography, or CT scans, were invented in 1971. These discoveries led to the invention of Intensity Modulated Radiation Therapy, or IMRT, in the early 1980s. The invention of IMRT significantly improved the ability of radiation therapy to target cancerous cells. \n\"To reach the tumor, radiation passes through healthy tissue and therefore damages both healthy and cancerous tissue. This damage to healthy tissue can lead to undesirable side effects that reduce post-treatment quality of life. For example, blistering and burning of skin can occur because of the damage to healthy skin cells. For this reason, we want the dose to fit the tumor as closely as possible to reduce the dose to healthy tissue. This became possible with the invention of IMRT. In IMRT, the intensity profile of each beam is non-uniform. Before IMRT, each beam had to have the same intensity, so the tumor could not be targeted very well. But by using non-uniform intensity profiles, the radiation dose can better fit the tumor. Let's see what this looks like. \n\n\n\n\n\n\n\n\n\n\n\n\nIn this image, we have a person's body outlined in black, and then the target, or tumor, and two critical structures also outlined. We would like to maximize the radiation to the target, while minimizing the dose to healthy tissue, and especially to the critical structures.\"\nFrom Video 2 of Part 2 of Unit 8 of \"The Analytics Edge\" provides further discussion of how IMRT works. The transcript of this video says in part:\n \"The data are collected in the treatment-planning process, which starts from a CT scan, like the one you see here, on the right. Using a CT scan, a radiation oncologist contours, or draws outlines around the tumor and various critical structures. In [the CT] image, the oncologist would contour structures like the parotid glands, the largest of the saliva glands, and the brain.\n\"Then, each structure is discretized into voxels, or volume elements, which are typically four millimeters in dimension. The second image here shows a closer view of the brain. You can see the small squares, or voxels. Here, they're two-dimensional, but in reality they would be three-dimensional. Now, we can compute how much dose each beamlet, or piece of the beam, delivers to each voxel.\"\n\"We'll start with a small example. Suppose we have nine voxels and six beamlets. Our voxels can be categorized into three types: the tumor voxels, which are colored pink here; the spinal cord voxel, colored dark green; and other healthy tissue voxels, colored light green. So we have four tumor voxels, one spinal cord voxel, and four other healthy tissue voxels. We have two beams that are each split into three beamlets. Beam 1 is composed of beamlets 1, 2, and 3, and comes in from the right. Beam 2 is composed of beamlets 4, 5, and 6, and comes in from the top.\n\"Our objective is to minimize the total dose to healthy tissue, both to the spinal cord and to the other healthy tissue. We have two types of constraints. The first is that the dose to the tumor voxels must be at least 7 Gray, which is the unit of measure for radiation. Our second constraint is that the dose to the spinal cord voxel can't be more than 5 Gray, since we want to be careful to protect the spinal cord. \n\"We know the dose that each beamlet gives to each voxel at unit intensity. This table shows the dose that each beamlet in Beam 1 gives to the voxels. Remember that this is at unit intensity. If we double the intensity of the beamlet, we double the doses. The dose to each voxel can depend on how far the beamlet has to travel, or the type of tissue that the beamlet has to travel through. Similarly, we know the dose that each beamlet in Beam 2 gives to each voxel, again at unit intensity.\n\"The dose depends on the direction of the beam and what it travels through. Putting these tables together, we can write out our optimization problem.\"\n\n\"Our decision variables are the intensities of each beamlet. We'll call them: x1, the intensity for beamlet 1; x2, the intensity for beamlet 2; x3, the intensity for beamlet 3; etc., all the way up through x6. As we mentioned before, our objective is to minimize the total dose to the healthy tissue, including the spinal cord. So we want to minimize the total dose beamlet 1 gives to healthy tissue, which is (1 + 2)*x1, plus the total dose beamlet 2 gives to healthy tissue, which is (2 + 2.5)*x2, plus the total dose beamlet 3 gives to healthy tissue, which is 2.5*x3. Now for beamlets 4, 5, and 6, beamlet 4 just gives one dose to healthy tissue; beamlet 5, 2*x5; and then beamlet 6 gives (1 + 2 + 1)*x6. \n\"Now for our constraints. First, we need to make sure that each voxel of the tumor gets a dose of at least 7. Let's start with the tumor voxel in the top row. So 2*x1 + x5 needs to be greater than or equal to 7. For the tumor voxel in the second row, we have x2 + 2*x4, also greater than or equal to 7. For the two tumor voxels in the bottom row, we have 1.5*x3 + x4, greater than or equal to 7. And 1.5*x_3 + x5, greater than or equal to 7. Then for the spinal cord, we need to make sure that 2*x2 + 2*x5 is less than or equal to 5. And lastly, we just need to make sure that all of our decision variables are non-negative.\"\nJust below, between the long rows of ###s, is the R script that I developed to provide a linearly optimized solution to the IMRT problem using the lpSolveAPI function in R:\n######################################################################\n\n# Script for Solving the \"Analytics Edge\" IMRT Linear Optimization\n# Problem in R\n\n# In this script file, we show how the Intensity Modulated Radiation\n# Therapy problem discussed in \"The Analytics Edge\" (specifically, \n# in the videos in the IMRT part of Unit 8 of \"The Analytics Edge\")\n# can be solved in R using the \"lpSolveAPI\" package. This package \n# allows you to solve linear optimization problems in R using the \n# lp_solve library, which is an open-source optimization solver.\n\n# If you are solving larger and more complex optimization problems, \n# lp_solve might not be powerful enough to solve your problems. \n# There are other optimization packages available in R that rely \n#on commercial solvers, such as cplexAPI and Rcplex. \n# For more information, see the CRAN Optimization page: \n# http://cran.us.r-project.org/web/views/Optimization.html\n\n# First, you need to install and load the lpSolveAPI package:\n\ninstall.packages(\"lpSolveAPI\")\nlibrary(lpSolveAPI)\n\n# The IMRT problem is how to minimize the amount of radiation \n# delivered to the spinal cord and other healthy tissue while \n# ensuring that at least the minimum needed radiation reaches the \n# tumor. The target region of the human body is divided into \n# volume elements called voxels, which are typically 4mm in each \n# dimension. The first step is to create a model, which takes as \n# arguments the number of constraints in your model, and the number \n# of decision variables in your model.\n\n# In the IMRT problem, we deal with 9 voxels of body matter, arranged \n# in a 3 x 3 array. In the top row, we have voxels 1, 2, 3; in the \n# middle row, voxels 4, 5, 6; and in the bottom row, voxels 7, 8, 9. \n\n# The healthy voxels are 1, 3, 5 (spinal cord), 6, and 9. \n# The tumor voxels are 2, 4, 7, and 8.\n# The modulatable beamlets i, (i in 1:6), have intensities \n# x1, x2, . . ., x6.\n\n# For beams of UNIT intensity, the beamlets deliver varying amounts \n# of radiation, measured in Grays, to the voxels as follows. \n# beamlet 1: 1 dose to voxel 1, 2 doses to voxel 2, 2 doses to \n# voxel 3\n# beamlet 2: 1 dose to voxel 4, 2 doses to voxel 5, 2.5 doses to \n# voxel 6\n# beamlet 3: 1.5 doses to voxel 7, 1.5 doses to voxel 8, 2.5 doses to \n# voxel 9\n# beamlet 4: 1 dose to voxel 1, 2 doses to voxel 4, 1 dose to voxel 7\n# beamlet 5: 1 dose to voxel 2, 2 doses to voxel 5, 1 dose to voxel 8\n# beamlet 6: 1 dose to voxel 3, 2 doses to voxel 6, 1 doses to \n# voxel 9\n\n# Our decision variables are the intensities of each beamlet. We'll \n# call them: x1, the intensity for beamlet 1; x2, the intensity for \n# beamlet 2; x3, the intensity for beamlet 3; etc., all the way up \n# through x6. As we mentioned before, our objective is to minimize \n# the total dose to the healthy tissue, including the spinal cord. \n# Hence, we want to minimize the total dose beamlet 1 gives to \n# healthy tissue, which is (1 + 2)*x1, plus the total dose beamlet 2 \n# gives to healthy tissue, which is (2 + 2.5)*x2, plus the total dose \n# beamlet 3 gives to healthy tissue, which is 2.5*x3. \n# For beamlets 4, 5, and 6: beamlet 4 just gives one dose to healthy \n# tissue, 1*x4; beamlet 5 gives 2 doses, 2*x5; and beamlet 6 gives \n# 1 + 2 + 1 = 4 doses, 4*x6. \n\n# Next, we consider our constraints. First, we need to make sure \n# that each voxel of the tumor gets a dose of at least 7. Let's \n# start with the tumor voxel in the top row: 2*x1 + 1*x5 needs to \n# be greater than or equal to 7. For the tumor voxel in the second \n# row, we have x2 + 2*x4, which also needs to be greater than or \n# equal to 7. For the first tumor voxel in the bottom row, we need \n# 1.5*x3 + 1*x4 to be greater than or equal to 7. For the second \n# tumor voxel in the bottom row, we need 1.5*x3 + 1*x5 to be greater \n# than or equal to 7. \n\n# For the spinal cord, we need to make sure that 2*x2 + 2*x5 is less \n# than or equal to 5. \n\n# Finally, we need to make sure that all of our decision variables \n# are non-negative: x1, x2, . . ., x6 >= 0.\n\n# Writing all these considerations out in the form of inequalities, \n# we have:\n# 2*x1 + 0*x2 + 0*x3 + 0*x4 + 1*x5 + 0*x6 >= 7\n# 0*x1 + 1*x2 + 0*x3 + 2*x4 + 0*x5 + 0*x6 >= 7\n# 0*x1 + 0*x2 + 1.5*x3 + 1*x4 + 0*x5 + 0*x6 >= 7 \n# 0*x1 + 0*x2 + 1.5*x3 + 0*x4 + 1*x5 + 0*x6 >= 7\n# 0*x1 + 2*x2 + 0*x3 + 0*x4 + 2*x5 + 0*x6 <= 5\n# x1, x2, . . ., x6 >= 0 (Note: This non-negativity constraint is built into the lpSolveAPI function.)\n\nIMRT <- make.lp(5,6)\n\nset.column(IMRT, 1, c(2,0,0,0,0))\nset.column(IMRT, 2, c(0,1,0,0,2))\nset.column(IMRT, 3, c(0,0,1.5,1.5,0))\nset.column(IMRT, 4, c(0,2,1,0,0))\nset.column(IMRT, 5, c(1,0,0,1,2))\nset.column(IMRT, 6, c(0,0,0,0,0))\nset.constr.type(IMRT, c(\">=\",\">=\",\">=\",\">=\",\"<=\"))\nset.rhs(IMRT, c(7,7,7,7,5))\n\n# The objective is to minimize the total number of Grays of radiation \n# delivered to the healthy tissue: \n# (1+2)*x1 + (2+2.5)*x2 + 2.5*x3 + 1*x4 + 2*x5 + (1+2+1)*x6\n\nset.objfn(IMRT, c(3,4.5,2.5,1,2,4))\n\n# By default, the lpSolveAPI function assumes that the desired sense\n# of the objective is minimization. However, no harm is done by \n# making the sense explicit. (If the sense were maximization, we \n# would write sense=\"max\" in lp.control.)\n\nlp.control(IMRT, sense=\"min\") \n\n# To check on our model, we can type IMRT.\n\nIMRT\n\n# Model name: \n# C1 C2 C3 C4 C5 C6 \n# R1 2 0 0 0 1 0 >= 7\n# R2 0 1 0 2 0 0 >= 7\n# R3 0 0 1.5 1 0 0 >= 7\n# R4 0 0 1.5 0 1 0 >= 7\n# R5 0 2 0 0 2 0 <= 5\n# Kind Std Std Std Std Std Std \n# Type Real Real Real Real Real Real \n# Upper Inf Inf Inf Inf Inf Inf \n# Lower 0 0 0 0 0 0 \n\nsolve(IMRT)\nget.objective(IMRT)\nget.variables(IMRT)\n\n# You should get an objective value of 22.75 total Grays, and \n# beamlet intensities of 2.25, 0, 3, 3.5, 2.5, 0.\n\n######################################################################\n\nExample 4: A Linear Optimization Problem Involving the Amounts of Certain Cargoes to be \t\t Asssigned to Certain Wagons (i.e., Boxcars) of a Train\nThis problem was first presented on the World-Wide Web as \"Linear programming in R: an lpSolveAPI Example\", written by Bart and posted on July 14, 2012.iii Its URL is:\n http://www.r-bloggers.com/linear-programming-in-r-an-lpsolveapi-example/\n Bart's post indicates that an R script is available at: http://fishyoperations.com/r/linear-programming-in-r-an-lpsolveapi-example/uploads/lpsolve_example.R. However, this site appears to be no longer available on the Web.\nBart states the problem as follows: \n\"A trading company is looking for a way to maximize profit per transportation of their goods. The company has a train available with 3 wagons. When stocking the wagons they can choose between 4 types of cargo, each with its own specifications.\"\nThe following data concerning the wagons and the cargoes are available:\nTrain Wagon\nWeight Capacity \n(in tonnes)\nSpace Capacity \n(in cubic metres)\nw1\nw2\nw3\n10\n8\n12\n5000\n4000\n8000\nCargo Type\nCargo Availability \n(in tonnes)\nCargo Volume \n(in cubic metres)\nCargo Profit \n(per tonne)\nc1\nc2\nc3\nc4\n18\n10\n5\n20\n400\n300\n200\n500\n2000\n2500\n5000\n3500\n\nThe variables in this problem are more complicated than those in Examples 1 - 3 of this discussion. Here the variables consist of pairings of wagon and cargo. Since there are 3 types of wagons and 4 types of cargo, there are 12 pairings to be considered. We shall represent them by xij, where i = type of wagon and j = type of cargo: x11, x12, . . . , x14, x21, . . . , x31, . . . , x34.\nThe goal of the company is to maximize the profit from each trip by its three wagons, so the objective function is to maximize the following:\n2000*x11 + 2500*x12 + 5000*x13 + 3500*x14 +2000*x21 + 2500*x22\n+ 5000*x23 + 3500*x24 + 2000*x31 + 2500*x32 + 5000*x33 + 3500*x34\nThere are constraints due to the weight capacities of the wagons, constraints due to the space capacities of the wagons, and further constraints due to the availability of the cargoes.\nThe weight constraints can be expressed as:\nx11 + x12 + x13 + x14 <= 10\nx21 + x22 + x23 + x24 <= 8\nx31 + x32 + x33 + x34 <= 12\nThe space capacity constraints can be expressed as:\n400*x11 + 300*x12 + 200*x13 +500*x14 <= 5000\n400*x21 + 300*x22 + 200*x23 + 500*x24 <= 4000\n400*x31 + 300*x32 + 200*x33 + 500*x34 <= 8000\nThe cargo availability constraints can be expressed as:\nx11 + x21 + x31 <= 18\nx12 + x22 + x32 <= 10\nx13 + x23 + x33 <= 5\nx14 + x24 + x34 <= 20\nNext, we need to combine the statements of these constraints into columns corresponding to the 12 variables, so that we can easily see how to enter the correct values into the model when we use the set.column function:\nx11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 \n1 1 1 1 0 0 0 0 0 0 0 0 <= 10\n0 0 0 0 1 1 1 1 0 0 0 0 <= 8\n0 0 0 0 0 0 0 0 1 1 1 1 <= 12 \n400 300 200 500 0 0 0 0 0 0 0 0 <= 5000\n0 0 0 0 400 300 200 500 0 0 0 0 <= 4000\n0 0 0 0 0 0 0 0 400 300 200 500 <= 8000\n1 0 0 0 1 0 0 0 1 0 0 0 <= 18\n0 1 0 0 0 1 0 0 0 1 0 0 <= 10\n0 0 1 0 0 0 1 0 0 0 1 0 <= 5\n0 0 0 1 0 0 0 1 0 0 0 1 <= 20 \n\nNow we are ready to use the make.lp and set.column functions:\nwagoncargo <- make.lp(10, 12)\nset.column(wagoncargo, 1, c(1, 0, 0, 400, 0, 0, 1, 0, 0, 0))\nset.column(wagoncargo, 2, c(1, 0, 0, 300, 0, 0, 0, 1, 0, 0))\nset.column(wagoncargo, 3, c(1, 0, 0, 200, 0, 0, 0, 0, 1, 0))\nset.column(wagoncargo, 4, c(1, 0, 0, 500, 0, 0, 0, 0, 0, 1))\nset.column(wagoncargo, 5, c(0, 1, 0, 0, 400, 0, 1, 0, 0, 0))\nset.column(wagoncargo, 6, c(0, 1, 0, 0, 300, 0, 0, 1, 0, 0))\nset.column(wagoncargo, 7, c(0, 1, 0, 0, 200, 0, 0, 0, 1, 0))\nset.column(wagoncargo, 8, c(0, 1, 0, 0, 500, 0, 0, 0, 0, 1))\nset.column(wagoncargo, 9, c(0, 0, 1, 0, 0, 400, 1, 0, 0, 0))\nset.column(wagoncargo, 10, c(0, 0, 1, 0, 0, 300, 0, 1, 0, 0))\nset.column(wagoncargo, 11, c(0, 0, 1, 0, 0, 200, 0, 0, 1, 0))\nset.column(wagoncargo, 12, c(0, 0, 1, 0, 0, 500, 0, 0, 0, 1))\n\nNext, we set the inequalities, the right-hand side of the model, the objective function, and the lp.control function:\n\nset.constr.type(wagoncargo, rep(\"<=\", 10))\nset.rhs(wagoncargo, c(10, 8, 12, 5000, 4000, 8000, 18, 10, 5, 20))\nset.objfn(c(2000, 2500, 5000, 3500, 2000, 2500, 5000, 3500, 2000, 2500, 5000, 3500))\nlp.control(wagoncargo, sense=\"max\")\n\nTo check on whether the model looks correct, we can issue the command:\n\nwagoncargo\n\nThe remaining commands to be issued are:\n\nsolve(wagoncargo)\nget.objective(wagoncargo)\nget.variables(wagoncargo)\n\nThis finishes the discussion of Example 4 and how to solve it. Immediately below, between the long rows of ###s, is an R script for solving Example 4. Since the discussion above has been extensive, in the script below I have supplied a terser set of comments.\n\n########################################################################\n\n# We begin by installing the lpSolveAPI package and loading it into R.\n\ninstall.packages(\"lpSolveAPI\")\nlibrary(lpSolveAPI)\n\n# The variables in this problem consist of pairings of wagon and cargo. \n# Since there are 3 types of wagons and 4 types of cargo, there are 12 \n# pairings to be considered. They are represented by xij, where \n# i = type of wagon and j = type of cargo: \n# x11, x12, . . . , x14, x21, . . . , x31, . . . , x34. \n\n# The goal of the company is to maximize the profit from each trip by \n# its three wagons, so the objective function is to maximize the \n# following: \n# 2000*x11 + 2500*x12 + 5000*x13 + 3500*x14 +2000*x21 + 2500*x22 \n# + 5000*x23 + 3500*x24 + 2000*x31 + 2500*x32 + 5000*x33 + 3500*x34 \n\n# The weight constraints can be expressed as: \n# x11 + x12 + x13 + x14 <= 10 \n# x21 + x22 + x23 + x24 <= 8 \n# x31 + x32 + x33 + x34 <= 12 \n\n# The space capacity constraints can be expressed as: \n# 400*x11 + 300*x12 + 200*x13 + 500*x14 <= 5000 \n# 400*x21 + 300*x22 + 200*x23 + 500*x24 <= 4000 \n# 400*x31 + 300*x32 + 200*x33 + 500*x34 <= 8000 \n\n# The cargo availability constraints can be expressed as: \n# x11 + x21 + x31 <= 18 \n# x12 + x22 + x32 <= 10 \n# x13 + x23 + x33 <= 5 \n# x14 + x24 + x34 <= 20 \n\n# Now we are ready to use the make.lp and set.column functions: \n\nwagoncargo <- make.lp(10, 12) \nset.column(wagoncargo, 1, c(1, 0, 0, 400, 0, 0, 1, 0, 0, 0)) \nset.column(wagoncargo, 2, c(1, 0, 0, 300, 0, 0, 0, 1, 0, 0)) \nset.column(wagoncargo, 3, c(1, 0, 0, 200, 0, 0, 0, 0, 1, 0)) \nset.column(wagoncargo, 4, c(1, 0, 0, 500, 0, 0, 0, 0, 0, 1)) \nset.column(wagoncargo, 5, c(0, 1, 0, 0, 400, 0, 1, 0, 0, 0)) \nset.column(wagoncargo, 6, c(0, 1, 0, 0, 300, 0, 0, 1, 0, 0)) \nset.column(wagoncargo, 7, c(0, 1, 0, 0, 200, 0, 0, 0, 1, 0)) \nset.column(wagoncargo, 8, c(0, 1, 0, 0, 500, 0, 0, 0, 0, 1)) \nset.column(wagoncargo, 9, c(0, 0, 1, 0, 0, 400, 1, 0, 0, 0)) \nset.column(wagoncargo, 10, c(0, 0, 1, 0, 0, 300, 0, 1, 0, 0)) \nset.column(wagoncargo, 11, c(0, 0, 1, 0, 0, 200, 0, 0, 1, 0)) \nset.column(wagoncargo, 12, c(0, 0, 1, 0, 0, 500, 0, 0, 0, 1)) \n\n# Next, we set the inequalities, the right-hand side of the model, \n# the objective function, and the lp.control function: \n\nset.constr.type(wagoncargo, rep(\"<=\", 10)) \nset.rhs(wagoncargo, c(10, 8, 12, 5000, 4000, 8000, 18, 10, 5, 20)) \nset.objfn(wagoncargo, c(2000, 2500, 5000, 3500, 2000, 2500, 5000, 3500, 2000, 2500, 5000, 3500)) \n\nlp.control(wagoncargo, sense=\"max\") \n\n# To check on whether the model looks correct, we issue the command: \n\nwagoncargo \n\n# The remaining commands to be issued are: \n\nsolve(wagoncargo) \nget.objective(wagoncargo) \nget.variables(wagoncargo)\n\n# For the maximized profit per trip, you should get $107,500.\n# For the loadings of wagons with cargoes, you should get:\n# x11 = 0, x12 = 5, x13 = 5, x14 = 0; i.e., in wagon 1, 5 tonnes each \n# of cargo types 2 and 3\n# x21 = 0, x22 = 0, x23 = 0, x24 = 8; i.e., in wagon 2, 8 tonnes of \n# cargo type 4\n# x31 = 0, x32 = 0, x33 = 0, x34 = 12; i.e., in wagon 3, 12 tonnes of \n# cargo type 4\n\n########################################################################\n\nWith Example 4, this discussion of how to perfom linear optimization by using the lpSolveAPI function in R has finished. I hope the discussion and the examples have been of interest and assistance to fellow R enthusiasts.\n\n\n\nAdditional Resources on lpSolveAPI\n\nAn excellent short resource is \"lpSolveAPI Package Users Guide\"iv by Kjell Konis, the maintainer of lpSolveAPI. The URL of this guide is: \n\nhttp://www.icesi.edu.co/CRAN/web/packages/lpSolveAPI/vignettes/lpSolveAPI.pdf\n\nDetailed information about the lpSolveAPI packagev is available at:\n\nhttp://cran.cnr.berkeley.edu/web/packages/lpSolveAPI/lpSolveAPI.pdf" }, { "path": "62A-MA/CA.R", "content": "#Perceptual Maps\n#http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/113-ca-correspondence-analysis-in-r-essentials/\n\nlibrary(\"FactoMineR\")\nlibrary(\"factoextra\")\ndata(housetasks)\nhead(housetasks)\nhousetasks\n\nlibrary(\"gplots\")\n# 1. convert the data as a table\ndt <- as.table(as.matrix(housetasks))\n# 2. Graph\nballoonplot(t(dt), main =\"housetasks\", xlab =\"\", ylab=\"\",\n label = FALSE, show.margins = FALSE)\n\nchisq <- chisq.test(housetasks)\nchisq\n\n#CA(X, ncp = 5, graph = TRUE)\n\nlibrary(\"FactoMineR\")\nres.ca <- CA(housetasks, graph = FALSE)\n\nprint(res.ca)\n\n# Chi-square statistics\nchi2 <- 1944.456\n# Degree of freedom\ndf <- (nrow(housetasks) - 1) * (ncol(housetasks) - 1)\n# P-value\npval <- pchisq(chi2, df = df, lower.tail = FALSE)\npval\n\nlibrary(\"factoextra\")\neig.val <- get_eigenvalue(res.ca)\neig.val\n\nfviz_screeplot(res.ca, addlabels = TRUE, ylim = c(0, 50))\n\nfviz_screeplot(res.ca) +\n geom_hline(yintercept=33.33, linetype=2, color=\"red\")\n\n# repel= TRUE to avoid text overlapping (slow if many point)\nfviz_ca_biplot(res.ca, repel = T)\n\nfviz_ca_row(res.ca, col.row=\"steelblue\", shape.row = 15)\n\n# Color by cos2 values: quality on the factor map\nfviz_ca_row(res.ca, col.row = \"cos2\",\n gradient.cols = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), \n repel = TRUE)\n" }, { "path": "62A-MA/campaign.csv", "content": "Sales,Newspaper,TV,Online\n16850,1000,500,1500\n12010,500,500,500\n14740,2000,500,500\n13890,1000,1000,1000\n12950,1000,500,500\n15640,500,1000,1000\n14960,1000,1000,1000\n13630,500,1500,500" }, { "path": "62A-MA/data1.R", "content": "# Data\n#Students of Class IX & X (M & F) play sports\nstudent = paste('S',1:30, sep='')\nstudent\nset.seed(1234)\ngender = sample(x=c('Male','Female'), size=30, replace=T, prob=c(20/30,10/30) )\ngender\nset.seed(1234)\nclass = sample(x=c('IX','X'), size=30, replace=T, prob=c(14/30,16/30) )\nclass\nset.seed(1234)\nplay = sample(x=c('Yes','No'), size=30, replace=T, prob=c(15/30,15/30) )\nplay\nstudentsplay = data.frame(student, gender, class, play)\nstudentsplay\nt1= table(studentsplay$gender,studentsplay$play)\nprop.table(t1)\n\nstudentsplay\n\n# Pruning only on the basis of Gender\nplaymodel1 <-rpart(play ~ gender , method=\"class\", data=studentsplay,control=rpart.control(minsplit=2))\nplaymodel1\nprintcp(playmodel1)\nprp(playmodel1,box.col=c(\"Grey\", \"Orange\")[playmodel1$frame$yval],varlen=0,\n faclen=0, type=1,extra=4,under=TRUE)\n\nplaymodel2 <-rpart(play ~ class + gender, method=\"class\", data=studentsplay,control=rpart.control(minsplit=4))\nplaymodel2\nprintcp(playmodel2)\nprp(playmodel2,box.col=c(\"Grey\", \"Orange\")[playmodel2$frame$yval],varlen=0,\n faclen=0, type=1,extra=4,under=TRUE)\n\nplaymodel3 <-rpart(play ~ class + gender, method=\"class\", \n data=studentsplay,control=rpart.control(minsplit=2, cp=0.001))\nplaymodel3\nprintcp(playmodel3)\nprp(playmodel3,box.col=c(\"Grey\", \"Orange\")[playmodel3$frame$yval],varlen=0,\n faclen=0, type=1,extra=4,under=TRUE)\n\nSample_tree<-rpart(play~gender+class, method=\"class\", data=studentsplay, control=rpart.control(minsplit=2, cp=0.001))\nSample_tree\n\n" }, { "path": "62A-MA/graph1.R", "content": "# Graphs for MA\n\nsalemonth = month.abb[1:12]\nsalemonth # order of variables not correct\nsalemonth = factor(salemonth, ordered=T, levels=month.abb[1:12])\nsalemonth\nproduct1 = round(runif(12,10,100),2)\nproduct2 = round(runif(12,10,100),2)\nsales = data.frame(salemonth, product1, product2)\nstr(sales)\n# ok\nplot(salemonth, product1, data=sales, type='l', col='red')\nplot(salemonth, product2, data=sales, type='l', col='red')\npar(options)\nabline(lm(product2 ~ salemonth))\nlines(lowess(product2 ~ salemonth))\n\n\n#\npairs(sales[,1:3])\n\n#\nplot(wt ~ mpg, data=mtcars)\nplot(sales$salemonth , sales$product1, type='p', col='red', pch='17')\nbarplot(product1, data=sales, names.arg=salemonth)\nbarplot(as.matrix(sales[,c('product1', 'product2')]), beside = T, data=sales, names.arg=salemonth, legend.text = 'Legend')\n\n#\nplot(as.numeric(sales$salemonth) , sales$product1, type='l', col='red', xlab='Months', ylab='Products', ylim=c(0,100))\npar(new=TRUE)\nplot(as.numeric(sales$salemonth) , sales$product2, type='l', col='green', xlab ='Months', lwd='2', ylab=\"\", ylim=c(0,100))\n\n\n#ggplot2\nlibrary(ggplot2)\ng = ggplot(sales, aes(as.numeric(salemonth))) #g\ng = g + geom_line(aes(y=product1), color='red')\ng = g + geom_line(aes(y=product2), color='green')\ng = g + labs(x = \"Month\",y = \"Sales amount\", title = \"Sales for Different Products\")\ng\n\n#\nb = ggplot(data=sales, aes(x=salemonth, y=product1)) +\n geom_bar(stat=\"identity\")\nb\n# Two graphs do not work this way.. do some changes\nsales\nlibrary(reshape2)\nsales2 = melt(data=sales, id.vars='salemonth')\nsales2\n\nb2 = ggplot(data=sales2, aes(x=salemonth, y=value, fill=variable)) \nb2 = b2 + geom_bar(stat=\"identity\", position='dodge')\nb2 \n#ok\n\nsales2[salemonth==c('Jan','Feb'),]\nb2 = ggplot(data=sales2[salemonth==c('Jan','Feb'),], aes(x=salemonth, y=value, fill=variable)) \nb2 = b2 + geom_bar(stat=\"identity\", position='dodge')\nb2 \n\n\n\n#wide to long\n" }, { "path": "62A-MA/maregression1.R", "content": "#Campaign Analysis\n\ncampaign = read.csv(\"./data/campaign.csv\")\ncampaign\ncolSums(campaign)\nsum(campaign[,-1])\nsummary(campaign)\n(Campaignmodel<-lm(Sales~Newspaper+TV+Online, data=campaign) )\n\nsummary(Campaignmodel)\n\nplot(Campaignmodel)\n\ncbind(campaign, predict(Campaignmodel))\n\ntestdata = data.frame(Newspaper=500, TV=500, Online=2000)\ntestdata = data.frame(Newspaper=1000, TV=500, Online=2500)\n\npredict(Campaignmodel, newdata = testdata)\n9562 +(1.25*500)+(0.92*500) + (3.53*2000)\n" }, { "path": "62A-MA/pricing1.R", "content": "# Pricing Metrics\n#https://www.analyticsvidhya.com/blog/2016/07/solving-case-study-optimize-products-price-online-vendor-level-hard/\n\ndata = read.csv(file.choose())\nstr(data)\nnames(data)\ncnames = c('product','avgprice', 'unitcost', 'avgprofit', 'avgunitssold', 'incracq','incsalevol')\nnames(data) = cnames\nsummary(data)\ndim(data)\n\nlibrary(fdth)\n(tb1 = fdt(data$avgprofit))\n\n(tb2 <- fdt(data))\n\n# Histograms\nplot(tb) # Absolute frequency histogram\nplot(tb, main='My title')\n\n\n\n#-----\ndim(data)\n#250*7\nsummary(data)\n# This is the initial value of the increment vector - all zeros\nincrement <- array(0,250)\nhead(increment)\nflag = 0\nincrement_i <- increment\n#flag = 1 is a condition when the increment vector remains the same\nwhile (flag == 0) {\n print(find_rev(increment_i))\n increment_iplus1 <- incremental_new(increment_i)\n if (min(increment_iplus1 == increment_i) == 1) {flag = 1}\n increment_i <- increment_iplus1\n}\nincrement_i\nfind_rev(increment_i)\nfind_rev(increment)\nprice <- increment_i\nwrite.csv(price,\"price.csv\")\n\n#This function tries to get the next best increment vector\nincremental_new <- function(initial_increments){\n initial_rev <- find_rev(initial_increments)\n intermediate_rev <- 0\n for(i in 1:250){\n increments <- initial_increments\n if(increments[i] > -0.099) {increments[i] <- increments[i] - 0.01}\n rev <- find_rev(increments)\n if (rev > initial_rev) {final_increments <- increments\n intermediate_rev <- rev\n }\n if(increments[i] < 0.19) {increments[i] <- initial_increments[i] + 0.01}\n rev <- find_rev(increments)\n if (rev > max(initial_rev,intermediate_rev)) {final_increments <- increments}\n }\n return(final_increments)\n}\n# This function will get us the overall revenue for the given increment vector\nfind_rev <- function(increment){\n price <- data$Avg_Price_per_unit*(1+increment)\n volumes <- data$Average_units_sold*(1-(data$Increase_sale_volume*increment*10))\n multiplier <- (1-(data$Incremental_acquisition*increment*10))\n total_multiplier <- prod(multiplier)\n profit_wo_multiplier <- 0.05*(sum(price*volumes) - sum(volumes*data$Cost_per_unit))\n profit_w_multiplier <- profit_wo_multiplier*total_multiplier\n net_profit <- sum(profit_w_multiplier)\n return(net_profit)}\n" }, { "path": "62A-MA/tree1.R", "content": "# tree\n# http://www.di.fc.ul.pt/~jpn/r/tree/tree.html\n# https://rpubs.com/JanpuHou/242986\nlibrary(tree)\nset.seed(1234)\n\nreal.state <- read.table(file=\"https://raw.githubusercontent.com/jbryer/CompStats/master/Data/cadata.dat\",head=TRUE)\ntree.model= tree(log(MedianHouseValue) ~ Longitude + Latitude, data=real.state)\nplot(tree.model)\ntext(tree.model, cex=.75)\n\nprice.decibels = quantile(real.state$MedianHouseValue, 0:10/10)\nprice.decibels\ncut.prices = cut(real.state$MedianHouseValue, price.decibels, include.lowest = T)\ncut.prices\nplot(real.state$Longitude, real.state$Latitude, col=grey(10:2/11)\n [cut.prices], pch=20, xlab='Longitude', ylab='Latitude')\npartition.tree(tree.model, ordvars = c('Longitude','Latitude'), add=T)\n?partition.tree\n\nsummary(tree.model)\n" }, { "path": "62c-RFM/rfm1.R", "content": "#RFM\n\n##Creating Random Sales Data of the format CustomerId (unique to each customer), Sales.Date,Purchase.Value\n\ncustid = sample(1000:1999, replace=T,size=10000)\nsales.value = abs(round(rnorm(10000,28,13)))\nsales=data.frame(custid, sales.value)\nhead(sales)\n\nhead(700*sort(stats::runif(10000)),50)\nsales.dates <- as.Date(\"2016/1/1\") + 700*sort(stats::runif(10000))\n\nhead(sales.dates, 20)\n#generating random dates\n\nsales=cbind(sales,sales.dates)\nhead(sales,10)\nstr(sales)\nsales1 = sales\nsummary(sales)\ndifftime(Sys.Date(), Sys.Date()-3, units=\"days\")\n\nsales$recency=round(as.numeric(difftime(Sys.Date(),sales[,3],units=\"days\")) )\nhead(sales)\n\n#library(gregmisc)\nlibrary(gdata)\n\n##if you have existing sales data you need to just shape it in this format\ngdata::rename.vars(sales, from=\"sales.value\", to=\"purchase.value\" )\n\n## Creating Total Sales(Monetization),Frequency, Last Purchase date for each customer\nnames(sales)\n(salesM=aggregate(sales[,2],list(sales$custid),sum))\n\nnames(salesM)=c(\"custid\",\"monetization\")\nhead(salesM, 5)\nhead(salesM[order(salesM$monetization, decreasing=T),], 5)\n\n#Frequency\nsalesF=aggregate(sales[,2],list(sales$custid),length)\nhead(salesF)\nnames(salesF)=c(\"custid\",\"freq\")\nhead(salesF)\nhead(salesF[order(salesF$freq, decreasing=T),], 5)\n\n#Recency\nsalesR=aggregate(sales[,4],list(sales$custid),min)\nhead(salesR)\nnames(salesR)=c(\"custid\",\"recency\")\nhead(salesR[order(salesR$recency, decreasing=F),], 5)\n\n##Merging R,F,M\nsummary(sales)\ntest1=merge(salesF,salesR,\"custid\")\nhead(test1)\nsalesRFM=merge(salesM,test1,\"custid\")\n\n##Creating R,F,M levels : intervals of 5 : 1-5:A, 6-10:B\n\nsalesRFM$rankR=cut(salesRFM$recency, 5,labels=F) #rankR 1 is very recent while rankR 5 is least recent\n\nsalesRFM$rankF=cut(salesRFM$freq, 5,labels=F)#rankF 1 is least frequent while rankF 5 is most frequent\n\nsalesRFM$rankM=cut(salesRFM$monetization, 5,labels=F)#rankM 1 is lowest sales while rankM 5 is highest sales\n\nhead(salesRFM,50)\n##Looking at RFM tables\ntable(salesRFM[,5:6])\ntable(salesRFM[,6:7])\ntable(salesRFM[,5:7])\n\n#use quantiles instead of cut\n" }, { "path": "62c-RFM/rfm3.R", "content": "# RFM 2\n\nlibrary(didrooRFM)\n?didrooRFM\n#part 1\nTransNo = c('10','11','12','13')\nCustomerID = c('Cust1','Cust2', 'Cust3','Cust2')\nDateofPurch = as.Date(c('2010-11-1','2008-3-25','2017-3-25', '2016-3-25'))\nAmount = c(1000,500, 600, 700)\ncustomerData = data.frame(TransNo,CustomerID,DateofPurch,Amount)\ncustomerData\n?didrooRFM\nrfm1 = findRFM(customerData)\nrfm1$FinalScore\nrfm1[5:16]\noptions(dplyr.width = Inf)\nrfm1\nfindRFM(customerData, recencyWeight = 3, frequencyWeight = 12,\n monetoryWeight = 2)\n#\n#\n#\n#Part2\n\n\n\ndata(customerdata)\n#customerdat - \"TransNo\",\"CustomerID\",\"DateofPurch\",\"Amount\"\nTransNo = 10001:20000\nhead(sales1)\ncustomerdata = cbind(TransNo, sales1)\nhead(customerdata)\nstr(customerdata)\n#customerdat - \"TransNo\",\"CustomerID\",\"DateofPurch\",\"Amount\"\nnames(customerdata)\ndata()\ncustomerdata = customerdata[,c('TransNo','custid', 'sales.dates', 'sales.value')]\ndim(customerdata)\nhead(customerdata,100)\ndim(customerdata)\n# Create Customer Data\nlibrary(didrooRFM)\n\n\nfindRFM(customerdata, recencyWeight = 4, frequencyWeight = 4,\n monetoryWeight = 4)\n" }, { "path": "63A-FA/10-FAlinks.R", "content": "#FA Links\n#http://docplayer.net/15135763-Working-with-xts-and-quantmod.html\n" }, { "path": "63A-FA/Insurance Loss v01.R", "content": "\nsetwd(\"E:\\\\HHges - Mkt Anyts\\\\BITS Pilani Bussiness Anallytics\\\\03. Linear Regression\")\ngetwd()\n\nlibrary(dplyr)\nlibrary(car)\nfile.data <- read.csv(\"InsuranceLosses.csv\")\n\nsummary(file.data)\n\nset.seed(5)\ns1 <- sample(1:nrow(file.data), 0.7*nrow(file.data))\ndata.train <- file.data[s1,]\ndata.test <- file.data[-s1,]\n\nhead(file.data)\nnames(file.data)\nfit.train <- lm(Losses ~ Age+Years.of.Driving.Experience+\n Number.of.Vehicles+Gender+\n as.factor(Married)+Vehicle.Age+ \n Fuel_type_D+ \n Income \n , data=data.train[,-c(1)])\nlibrary(car)\nvif(fit.train)\nnames(data.train)\nfit.train <- lm(Losses ~ Years.of.Driving.Experience+\n Number.of.Vehicles+Gender+\n as.factor(Married)+Vehicle.Age+ \n Fuel_type_D+ \n Income \n , data=data.train[,-c(1,2)])\nvif(fit.train)\n\n\nsummary(fit.train)\n\nfit.final <- lm(Losses ~ \n Vehicle.Age+ \n Fuel_type_D+ \n Income \n , data=data.train[,-c(1)])\n\nsummary(fit.final)\n\nfitted(fit.final)\nsummary(fit.final)$r.squared #R-square Value \n\n\n\nmeancnt1=data.test\nmeancnt1$pred = \n 8.187 + \n -0.01376*meancnt1$Vehicle.Age+\n 0.2405*meancnt1$Fuel_type_D+\n 0.01999*meancnt1$Income\n \n\n\nmeancnt1$res = meancnt1$pred - meancnt1$Losses\nhead(meancnt1)\nmeancnt1$abs_res = abs(meancnt1$res)\nmeancnt1$mape = 100*meancnt1$abs_res/meancnt1$Losses\n(MAPE_Percentage =mean(meancnt1$mape))\n\n# Plot Y-pred vs. Error to ensure there is no pattern\nplot(meancnt1$pred,meancnt1$res)\n\n" }, { "path": "63A-FA/InsuranceLosses.csv", "content": "Policy Number,Age,Years of Driving Experience,Number of Vehicles,Gender,Married,Vehicle Age,Losses,Fuel_type_D,Income\n147805,25,8,1,0,Single,5,3500,1,174613\n122992,50,32,1,1,Single,15,3462.340629,1,172645.0315\n143824,20,1,2,0,Single,3,3462,1,172830\n174360,16,0,3,0,Single,4,3333,1,166165\n110488,35,15,1,0,Single,9,3332.771801,1,166072.5901\n190621,16,0,1,0,Single,15,3250,1,162190\n180426,25,7,4,0,Single,1,3227,1,160978\n111434,64,45,1,1,Single,13,3226.612176,1,161066.6088\n164185,25,6,1,1,Single,8,2879.176458,1,143459.8229\n142085,25,7,1,1,Married,11,2879.067137,1,143703.3569\n160115,25,8,2,0,Single,3,2879,1,143693\n161339,22,4,2,0,Single,5,2879,1,143506\n152551,52,34,1,0,Married,5,2852.18873,1,142146.4365\n157767,18,0,2,0,Single,2,2852,1,142211\n161675,66,48,1,0,Single,10,2850,1,142080\n191297,38,20,1,1,Married,3,2814.312522,1,140428.6261\n156601,19,2,2,0,Single,0,2814,1,140491\n122473,21,4,1,0,Single,0,2700,1,134575\n104242,25,8,1,0,Single,5,2600,1,129416\n188783,25,7,2,0,Single,0,2535,1,126434\n165974,63,43,1,1,Single,10,2534.63946,1,126216.973\n115780,23,5,2,0,Single,4,1953.606442,1,97452.3221\n112744,22,4,2,0,Single,1,1883.373932,1,93577.6966\n145380,21,4,2,0,Single,3,1850.996,1,92212.8\n103525,20,3,4,0,Single,0,1848.845387,1,92235.26935\n183049,17,0,3,0,Single,2,1802.444618,1,89739.2309\n115456,17,0,4,0,Single,1,1776.525239,1,88500.26195\n180372,18,0,1,0,Single,0,1772.78492,1,88276.246\n103212,22,4,2,0,Single,1,1747.297619,1,87071.88095\n190827,22,4,2,0,Single,1,1736.287451,1,86583.37255\n105766,21,3,4,0,Single,1,1731.433041,1,86171.65205\n135228,19,2,1,0,Single,5,1730.684951,1,86100.24755\n184028,19,0,4,0,Single,1,1710.178261,1,85076.91305\n108912,18,1,3,0,Single,1,1709.307889,1,85140.39445\n130887,17,0,2,0,Single,0,1679.147211,1,83524.36055\n115039,21,3,2,0,Single,1,1667.136998,1,82990.8499\n168998,21,3,4,0,Single,2,1662.706358,1,82764.3179\n177042,20,1,2,1,Single,0,1654.71703,1,82511.8515\n193787,22,4,2,0,Single,2,1641.515906,1,81594.7953\n118625,24,6,4,0,Single,5,1621.306259,1,80593.31295\n131902,22,4,1,0,Single,5,1620.536634,1,80506.8317\n164526,19,1,4,0,Single,1,1619.073617,1,80376.68085\n118298,17,0,3,0,Single,5,1610.185929,1,80309.29645\n126147,21,4,4,0,Single,5,1594.377501,1,79134.87505\n155387,17,0,2,0,Married,2,1590.157527,1,79251.87635\n187352,19,2,4,0,Single,2,1586.970936,1,78929.5468\n133730,23,6,3,0,Single,5,1582.314185,1,78796.70925\n176085,18,1,1,0,Single,1,1570.197729,1,78114.88645\n147118,17,0,4,0,Single,2,1555.951395,1,77430.56975\n159096,23,4,2,0,Single,5,1555.438202,1,77456.9101\n149211,18,1,2,0,Single,3,1555.052888,1,77212.6444\n128925,20,1,4,1,Single,3,1549.236462,1,77119.8231\n120120,17,0,3,0,Married,0,1530.056899,1,76051.84495\n175730,20,2,2,0,Single,4,1523.041103,1,75700.05515\n188722,22,3,2,1,Single,4,1511.71037,1,75048.5185\n186031,16,0,3,0,Married,1,1501.75855,1,74634.9275\n157151,17,0,2,1,Single,1,1498.958037,1,74479.90185\n109203,24,7,1,0,Single,0,1491.634513,1,74237.72565\n118210,24,5,3,1,Single,4,1490.804835,1,74013.24175\n132773,17,0,2,0,Single,0,1486.388076,1,73993.4038\n185503,20,1,3,1,Single,0,1485.928765,1,73846.43825\n177241,22,4,3,0,Single,3,1481.705551,1,73581.27755\n162984,18,0,2,0,Single,3,1481.046017,1,73690.30085\n139476,16,0,3,0,Married,3,1479.831034,1,73790.5517\n128571,22,3,4,0,Single,13,1473.7403,1,73201.015\n197370,18,0,4,0,Single,8,1440.526634,1,71747.3317\n108978,23,4,3,0,Single,2,1435.870915,1,71410.54575\n139687,21,4,2,1,Single,5,1428.120323,1,71021.01615\n182122,21,4,2,0,Single,7,1424.841406,1,70953.0703\n106191,17,0,1,0,Married,1,1401.284803,1,69515.24015\n139807,24,5,2,0,Single,12,1397.488209,1,69361.41045\n103107,22,4,2,0,Single,2,1394.085068,1,69128.2534\n189951,17,0,3,0,Single,10,1380.980203,1,68756.01015\n196688,25,7,1,0,Single,2,1380.915346,1,68583.7673\n177159,16,0,3,0,Single,15,1377.539259,1,68326.96295\n175402,20,1,2,0,Single,0,1370.142171,1,68225.10855\n167635,25,7,4,0,Single,2,1368.54557,1,68141.2785\n147525,22,4,3,0,Single,8,1363.388951,1,67953.44755\n160461,17,0,4,0,Single,4,1362.991866,1,67818.5933\n187753,24,6,3,1,Single,0,1362.170278,1,67699.5139\n139500,22,3,1,0,Single,7,1359.573132,1,67476.6566\n108695,25,8,3,1,Single,1,1355.412924,1,67404.6462\n195256,18,0,1,0,Single,2,1355.15476,1,67333.738\n190271,17,0,3,0,Single,15,1350.971873,1,67217.59365\n128195,18,0,4,0,Single,13,1350.647537,1,67129.37685\n116510,16,0,2,0,Single,2,1345.610516,1,66992.5258\n163674,24,5,3,0,Single,7,1341.042514,1,66676.1257\n158125,23,4,1,0,Single,0,1340.946549,1,66454.32745\n143467,16,0,3,0,Single,1,1326.426656,1,66036.3328\n123118,17,0,2,0,Single,8,1322.743329,1,65807.16645\n193871,17,0,2,1,Single,3,1319.454126,1,65434.7063\n177340,20,3,3,0,Single,9,1318.990738,1,65680.5369\n161198,17,0,4,0,Single,9,1317.92769,1,65328.3845\n136812,18,0,4,1,Single,1,1314.475359,1,65352.76795\n105488,25,6,2,0,Single,1,1313.38813,1,65137.4065\n200454,21,3,3,0,Single,8,1308.787234,1,65122.3617\n129585,21,4,3,0,Single,1,1305.985998,1,64807.2999\n190111,18,0,4,0,Single,2,1301.428122,1,64635.4061\n184731,18,1,3,0,Single,0,1299.059765,1,64632.98825\n174530,21,3,4,0,Married,2,1297.739892,1,64594.9946\n118982,25,8,2,0,Single,8,1297.152399,1,64601.61995\n188582,25,6,3,1,Single,0,1296.990069,1,64316.50345\n160407,20,2,3,0,Single,11,1296.816028,1,64619.8014\n109682,20,2,3,0,Single,7,1291.362186,1,64036.1093\n193169,22,3,2,0,Single,13,1291.352892,1,64109.6446\n161542,22,3,4,0,Single,2,1289.983352,1,64073.1676\n107664,23,4,3,1,Single,3,1288.521549,1,64195.07745\n164774,19,2,3,0,Single,9,1287.277428,1,63876.8714\n177869,20,1,2,0,Single,10,1286.089237,1,64038.46185\n160806,18,1,2,0,Single,1,1283.713451,1,63901.67255\n175815,20,1,2,1,Single,0,1281.070759,1,63663.53795\n123734,17,0,4,0,Single,10,1280.495256,1,63469.7628\n184927,16,0,1,0,Single,11,1278.510959,1,63654.54795\n106867,20,1,2,0,Single,12,1270.186638,1,63135.3319\n156461,22,5,3,0,Single,1,1269.443421,1,63093.17105\n141554,18,0,3,0,Single,15,1268.076011,1,63014.80055\n161657,23,6,3,0,Married,0,1267.597166,1,62880.8583\n162998,23,4,3,0,Single,9,1265.998313,1,62708.91565\n168352,25,7,1,0,Single,12,1265.254483,1,62992.72415\n187221,19,1,3,0,Single,4,1262.926707,1,62625.33535\n191874,21,2,4,1,Single,3,1262.024405,1,62834.22025\n173522,24,7,3,0,Single,15,1261.179325,1,62605.96625\n163643,17,0,4,0,Single,15,1260.120154,1,62760.0077\n194934,17,0,1,0,Single,10,1255.767314,1,62526.3657\n161998,23,5,3,0,Married,0,1250.233231,1,61936.66155\n119043,16,0,2,0,Single,4,1248.405899,1,61861.29495\n147925,17,0,3,0,Single,8,1243.405151,1,61634.25755\n162334,17,0,3,0,Single,12,1243.266651,1,61725.33255\n183420,17,0,3,0,Single,6,1242.883674,1,61776.1837\n155784,20,1,2,0,Single,7,1240.961967,1,61570.09835\n184822,22,5,3,0,Single,7,1239.894273,1,61478.71365\n124724,21,4,1,0,Single,13,1239.79541,1,61689.7705\n109426,21,2,2,0,Single,15,1238.855434,1,61646.7717\n101504,25,7,2,0,Single,12,1237.946994,1,61571.3497\n164615,18,0,2,0,Single,14,1231.570261,1,61172.51305\n151334,20,3,3,0,Single,8,1231.095742,1,61219.7871\n141795,16,0,2,0,Single,7,1229.29243,1,60971.6215\n178037,16,0,2,0,Single,6,1227.567261,1,60926.36305\n110012,18,0,2,0,Single,0,1226.265111,1,61110.25555\n113843,17,0,3,0,Single,10,1226.233875,1,60962.69375\n127761,16,0,2,0,Married,0,1225.158785,1,60940.93925\n163552,18,1,3,0,Married,2,1223.990411,1,60636.52055\n135122,25,6,2,0,Single,2,1223.159682,1,60874.9841\n186574,23,5,3,0,Single,8,1218.356458,1,60461.8229\n122102,22,4,3,0,Single,6,1216.554442,1,60345.7221\n101915,20,2,2,0,Single,4,1208.851512,1,60007.5756\n131374,25,7,1,0,Married,3,1208.666417,1,59959.32085\n158244,20,3,2,0,Single,0,1207.301401,1,60016.07005\n166321,19,2,2,0,Single,12,1207.007954,1,60023.3977\n104939,18,0,3,0,Single,2,1204.833206,1,59769.6603\n177507,18,0,1,0,Married,4,1204.444856,1,59623.2428\n113103,25,6,2,0,Single,0,1204.281553,1,59814.07765\n163906,23,5,3,0,Single,14,1204.097279,1,59784.86395\n149340,19,2,1,0,Single,10,1203.938094,1,59782.9047\n101726,20,1,2,1,Single,4,1201.951572,1,59556.5786\n105328,18,0,4,0,Single,13,1201.320791,1,59746.03955\n122908,25,8,2,0,Single,11,1200.47256,1,59822.628\n191540,25,7,1,0,Married,1,1199.944535,1,59425.22675\n112619,17,0,1,0,Single,8,1197.444316,1,59628.2158\n119061,20,2,2,0,Single,0,1196.529346,1,59329.4673\n146826,19,1,3,0,Married,5,1195.283536,1,59394.1768\n125169,23,4,2,0,Single,11,1194.817985,1,59519.89925\n186532,22,3,2,0,Married,1,1193.282553,1,59186.12765\n184808,23,4,2,0,Single,7,1191.530812,1,59324.5406\n141504,24,6,2,1,Single,0,1191.211171,1,59067.55855\n157407,18,0,2,0,Single,10,1189.577654,1,59058.8827\n103007,25,7,1,0,Single,13,1189.051543,1,58865.57715\n142018,25,7,3,0,Single,12,1188.744129,1,58971.20645\n155825,18,0,2,0,Single,6,1186.401052,1,58977.0526\n105595,20,2,2,0,Married,0,1182.802099,1,58933.10495\n188333,16,0,1,0,Single,11,1182.476864,1,58888.8432\n148397,19,1,2,0,Single,6,1178.477467,1,58393.87335\n125119,19,0,4,0,Married,1,1177.115598,1,58319.7799\n164592,22,3,4,0,Single,11,1175.319784,1,58560.9892\n104425,16,0,3,0,Single,15,1174.253909,1,58512.69545\n123534,19,2,2,0,Single,3,1173.421392,1,58336.0696\n141946,25,6,3,0,Single,8,1172.205433,1,58210.27165\n171455,17,0,2,0,Married,1,1170.318061,1,58028.90305\n142483,24,5,3,0,Single,6,1168.957156,1,58024.8578\n170929,24,6,2,1,Single,5,1167.922229,1,57858.11145\n109181,24,5,2,0,Married,5,1164.988741,1,57949.43705\n118003,20,2,3,0,Married,4,1163.722413,1,57608.12065\n100482,16,0,2,0,Single,6,1162.650269,1,57704.51345\n110063,22,5,2,0,Single,9,1162.288811,1,57619.44055\n139979,19,1,2,1,Single,0,1162.112704,1,57621.6352\n149161,22,4,4,0,Single,10,1160.872891,1,57691.64455\n109903,18,0,4,0,Single,7,1159.050397,1,57387.51985\n167570,23,5,3,0,Single,0,1157.352758,1,57328.6379\n194758,18,0,3,0,Married,4,1156.541639,1,57544.08195\n128427,25,6,2,0,Single,9,1155.585908,1,57291.2954\n111639,20,2,1,0,Single,10,1155.570126,1,57221.5063\n120016,16,0,3,0,Single,9,1154.366373,1,57301.31865\n145262,23,6,3,0,Single,7,1154.024463,1,57274.22315\n135569,24,5,2,0,Single,11,1151.642678,1,57369.1339\n132759,22,3,2,0,Married,1,1151.615653,1,57176.78265\n139785,20,3,2,0,Single,3,1151.197551,1,57148.87755\n191604,20,3,2,0,Single,0,1151.11018,1,57239.509\n117437,22,4,4,0,Single,9,1148.295076,1,57153.7538\n104479,21,2,4,0,Single,11,1147.154541,1,56934.72705\n184721,19,2,4,0,Married,0,1144.362889,1,56680.14445\n113495,20,1,2,0,Single,7,1142.6563,1,56546.815\n171797,22,4,3,1,Single,3,1141.298387,1,56656.91935\n102526,25,6,2,0,Single,10,1136.769929,1,56533.49645\n156817,16,0,2,1,Single,4,1133.909051,1,56424.45255\n159011,16,0,3,0,Single,12,1133.167261,1,56454.36305\n160916,19,1,1,1,Single,0,1131.743655,1,56072.18275\n103954,16,0,4,0,Married,4,1130.591985,1,56051.59925\n102452,16,0,4,0,Married,1,1130.276363,1,56145.81815\n195333,19,1,1,0,Single,0,1129.371234,1,55878.5617\n160246,22,3,1,0,Single,9,1128.246122,1,55917.3061\n185146,23,4,4,0,Single,6,1126.051162,1,55821.5581\n123784,19,2,4,0,Single,13,1125.801015,1,56023.05075\n174124,18,1,3,1,Single,5,1124.19002,1,56007.501\n154975,17,0,2,0,Single,13,1122.25101,1,55603.5505\n190354,16,0,2,1,Single,4,1121.535836,1,55695.7918\n150879,25,8,2,0,Single,9,1121.372912,1,55566.6456\n181952,25,7,3,0,Married,0,1121.163062,1,55468.1531\n162793,18,0,3,0,Single,14,1120.12851,1,55524.4255\n190921,16,0,4,0,Single,12,1119.340454,1,55687.0227\n114134,16,0,2,0,Single,14,1117.127223,1,55329.36115\n113119,18,0,3,0,Single,9,1113.614621,1,55333.73105\n161428,24,6,3,0,Married,2,1113.363315,1,55156.16575\n146674,16,0,4,0,Single,1,1109.628396,1,55164.4198\n141331,18,0,2,1,Single,3,1109.392633,1,54960.63165\n142978,20,3,3,0,Single,15,1109.134848,1,55233.7424\n111407,16,0,2,0,Single,6,1107.401753,1,55169.08765\n196549,20,3,3,0,Single,5,1106.820353,1,55080.01765\n195140,19,2,2,0,Single,1,1103.528284,1,54853.4142\n129514,17,0,3,1,Single,4,1102.664607,1,54594.23035\n187555,25,6,3,0,Single,6,1102.42198,1,54852.099\n181830,18,0,3,0,Single,7,1101.32202,1,54762.101\n196054,24,6,2,0,Single,15,1099.378637,1,54593.93185\n143694,20,3,2,0,Single,4,1099.181643,1,54523.08215\n133381,25,7,1,0,Single,4,1098.237869,1,54458.89345\n157217,18,0,3,0,Single,13,1096.765535,1,54496.27675\n108960,25,6,2,0,Single,3,1096.314301,1,54406.71505\n191212,24,6,2,1,Single,0,1096.206474,1,54228.3237\n155060,22,3,1,0,Single,4,1095.601585,1,54348.07925\n144397,21,2,3,0,Single,13,1095.356069,1,54392.80345\n123748,21,4,3,0,Single,15,1095.189428,1,54369.4714\n190876,23,4,2,0,Married,2,1094.928187,1,54374.40935\n114516,17,0,3,0,Single,11,1094.091621,1,54460.58105\n193022,22,4,4,0,Single,7,1093.989056,1,54440.4528\n185597,20,3,3,0,Single,12,1093.491213,1,54434.56065\n104612,21,2,3,0,Single,6,1093.141561,1,54333.07805\n143303,20,1,2,0,Single,8,1092.721727,1,54343.08635\n112005,16,0,3,0,Single,8,1092.341774,1,54166.0887\n125000,24,6,4,0,Single,6,1091.162554,1,54033.1277\n199907,20,1,3,0,Single,4,1090.679449,1,54147.97245\n192571,21,4,1,0,Single,8,1089.683475,1,54266.17375\n151336,16,0,2,0,Single,0,1087.404136,1,53989.2068\n146707,17,0,3,0,Single,9,1087.141546,1,53920.0773\n119860,23,5,1,0,Single,15,1086.924905,1,54109.24525\n167399,19,1,2,0,Single,12,1084.643825,1,53841.19125\n122159,17,0,3,0,Single,12,1084.582349,1,53848.11745\n148260,23,6,2,0,Single,7,1084.569504,1,53963.4752\n136989,23,4,2,1,Single,0,1084.380091,1,53777.00455\n168270,16,0,3,0,Single,7,1083.622202,1,53947.1101\n198247,27,7,3,0,Single,5,1080.326184,1,53455.3092\n100217,23,6,1,0,Single,13,1079.526481,1,53400.32405\n153904,21,3,4,0,Single,9,1078.616179,1,53526.80895\n159080,19,0,3,1,Single,0,1078.598204,1,53656.9102\n112682,20,3,2,0,Single,9,1078.082268,1,53479.1134\n199177,23,6,2,0,Single,8,1077.884502,1,53344.2251\n149679,21,3,3,0,Single,12,1077.083376,1,53306.1688\n113090,23,6,2,0,Single,9,1076.568147,1,53572.40735\n180572,18,1,3,0,Single,8,1073.046511,1,53076.32555\n168978,20,3,1,1,Single,3,1072.705985,1,53353.29925\n173908,18,0,2,0,Single,7,1071.895675,1,53110.78375\n104574,24,6,2,0,Single,15,1070.485093,1,53197.25465\n182178,23,5,1,0,Single,6,1070.210869,1,53309.54345\n159668,24,6,1,0,Single,12,1070.204776,1,52936.2388\n133556,23,5,1,1,Single,1,1069.470213,1,53093.51065\n138981,19,0,2,0,Single,8,1069.37391,1,53011.6955\n139616,23,5,3,0,Single,6,1068.707903,1,53030.39515\n180667,21,4,4,0,Single,7,1067.742432,1,52854.1216\n146768,24,7,3,0,Single,7,1067.514121,1,53153.70605\n169547,16,0,2,0,Single,5,1066.856405,1,52823.82025\n195112,21,4,1,0,Married,4,1066.302464,1,52732.1232\n159229,25,6,3,0,Single,9,1065.366034,1,53054.3017\n131529,17,0,2,1,Single,4,1064.583565,1,53013.17825\n145921,17,0,3,0,Single,10,1063.703625,1,52943.18125\n107139,21,4,2,0,Single,7,1061.520829,1,52493.04145\n105608,51,31,4,0,Single,2,1061.157458,1,52641.8729\n195241,20,2,2,0,Single,6,1060.415929,1,52548.79645\n111729,22,3,2,0,Single,2,1058.035625,1,52629.78125\n131315,16,0,2,0,Single,9,1057.42047,1,52410.0235\n130873,25,8,2,0,Single,9,1055.197653,1,52265.88265\n192903,25,8,3,0,Married,5,1053.630447,1,52144.52235\n158856,24,7,2,0,Single,6,1052.836142,1,52134.8071\n179570,25,8,3,0,Single,15,1052.124636,1,52373.2318\n188219,22,4,4,0,Single,1,1050.53842,1,52321.921\n113202,17,0,4,0,Single,12,1050.068538,1,51997.4269\n163260,18,0,4,0,Single,6,1049.911783,1,51999.58915\n147044,21,2,2,0,Married,5,1049.625869,1,51981.29345\n195430,23,5,3,0,Single,9,1049.170554,1,51912.5277\n112639,16,0,3,0,Single,6,1048.423276,1,51851.1638\n142707,20,1,1,0,Single,6,1048.410022,1,52122.5011\n196986,16,0,2,0,Single,6,1047.157424,1,52047.8712\n164933,20,2,2,0,Single,12,1046.414359,1,52006.71795\n190968,16,0,3,0,Single,11,1044.619269,1,51860.96345\n176455,22,3,3,0,Single,8,1042.843674,1,51621.1837\n100918,19,2,4,0,Single,7,1042.809484,1,51686.4742\n171975,18,0,3,0,Single,10,1042.184386,1,51753.2193\n122171,20,1,3,0,Single,0,1041.848121,1,51839.40605\n175375,19,2,2,0,Single,13,1041.495458,1,51513.7729\n156955,25,8,3,0,Single,14,1041.094865,1,51472.74325\n180050,24,7,3,0,Single,12,1041.064578,1,51839.2289\n163636,21,4,4,0,Single,15,1038.948205,1,51506.41025\n167470,17,0,1,1,Single,4,1037.373455,1,51384.67275\n160787,18,0,1,0,Single,14,1037.104939,1,51655.24695\n125588,25,7,1,0,Single,7,1036.422302,1,51355.1151\n152690,25,7,3,0,Single,8,1035.803278,1,51400.1639\n120755,21,2,2,0,Single,10,1034.576892,1,51214.8446\n117039,17,0,2,0,Single,8,1033.629031,1,51378.45155\n101703,25,8,4,0,Single,15,1032.654421,1,51398.72105\n117930,18,0,1,0,Single,10,1032.461873,1,51196.09365\n132985,22,4,2,0,Single,6,1030.454301,1,51144.71505\n101497,23,6,2,0,Single,12,1030.446074,1,51249.3037\n109716,33,13,2,0,Single,3,1029.987461,1,51235.37305\n132757,24,7,3,0,Single,15,1029.882891,1,51023.14455\n131655,21,4,2,0,Single,14,1029.260813,1,51247.04065\n103046,19,2,1,1,Single,3,1028.075432,1,50868.7716\n138513,18,0,2,0,Single,14,1027.582618,1,50856.1309\n103952,25,8,3,0,Single,14,1027.244394,1,51093.2197\n119795,20,2,3,0,Single,7,1027.202169,1,50761.10845\n132865,18,0,2,0,Single,5,1026.325173,1,51107.25865\n162190,16,0,2,0,Single,6,1026.310667,1,50879.53335\n189686,22,3,2,0,Married,3,1025.776072,1,50724.8036\n170574,25,8,4,0,Single,9,1025.512944,1,50956.6472\n179633,16,0,2,0,Single,11,1025.052975,1,50727.64875\n112879,25,8,3,0,Single,7,1024.033822,1,50968.6911\n154713,17,0,1,0,Single,11,1023.788951,1,50707.44755\n120401,22,5,3,0,Single,13,1023.411106,1,50846.5553\n118278,24,6,1,0,Married,0,1023.403662,1,50695.1831\n199423,21,4,3,0,Married,2,1023.205561,1,50892.27805\n153650,18,0,3,0,Single,9,1022.130812,1,50557.5406\n145428,25,8,2,1,Single,0,1021.639065,1,50800.95325\n157919,21,2,1,0,Single,7,1020.797227,1,50789.86135\n103875,17,0,3,0,Single,10,1020.479783,1,50673.98915\n129060,25,8,4,0,Single,13,1020.092166,1,50415.6083\n174992,19,1,1,0,Single,14,1018.192642,1,50489.6321\n114262,17,0,3,0,Single,14,1017.283924,1,50329.1962\n102891,42,23,2,0,Single,5,1016.827347,1,50427.36735\n158281,21,3,2,0,Single,9,1014.926338,1,50219.3169\n151957,25,7,1,0,Single,11,1014.321608,1,50371.0804\n197473,32,14,4,0,Single,2,1013.725034,1,50204.2517\n186787,53,33,2,0,Single,2,1013.473644,1,50408.6822\n166636,20,1,4,0,Single,12,1012.762359,1,50327.11795\n137449,25,6,2,0,Single,11,1008.820062,1,50131.0031\n152623,20,1,3,1,Single,4,1006.304881,1,50001.24405\n187748,20,1,1,1,Single,1,1005.793409,1,50008.67045\n171806,19,1,3,0,Single,7,1005.151383,1,49928.56915\n128905,20,3,3,0,Single,7,1003.986869,1,49730.34345\n165322,17,0,3,0,Single,10,1003.798871,1,49802.94355\n157090,17,0,3,0,Single,8,1002.27879,1,49654.9395\n135353,22,5,3,1,Single,2,1000.752305,1,49466.61525\n186577,19,0,2,0,Single,9,999.9315769,1,49503.57885\n182028,25,6,3,0,Single,0,999.9212233,1,49655.06117\n108599,21,4,3,0,Single,10,999.8730496,1,49752.65248\n125065,18,0,2,0,Single,11,998.9172356,1,49618.86178\n119841,26,6,3,0,Single,5,997.4067386,1,49598.33693\n182579,22,4,4,0,Single,13,997.2445349,1,49532.22675\n115916,51,30,2,0,Single,4,994.8093204,1,49427.46602\n189595,18,0,3,0,Single,0,994.5109291,1,49268.54646\n182354,22,4,3,1,Single,4,994.4812481,1,49262.06241\n101225,17,0,2,0,Married,1,994.0657896,1,49180.28948\n125408,24,6,3,0,Single,14,993.2333375,1,49442.66688\n145291,24,5,4,0,Single,7,989.980317,1,49254.01585\n184598,47,29,1,0,Single,1,989.827214,1,49029.3607\n115293,24,5,1,0,Married,0,989.6964115,1,49060.82058\n171994,17,0,3,0,Single,12,989.518448,1,48904.9224\n119642,24,5,2,0,Single,11,988.0390258,1,49016.95129\n152732,24,5,4,0,Single,0,987.0121712,1,49104.60856\n147113,22,5,3,0,Single,11,986.7371392,1,49006.85696\n146039,24,7,3,0,Single,8,985.8140431,1,49050.70216\n193707,24,5,3,0,Single,11,984.9714047,1,48771.57024\n196804,22,3,3,1,Single,4,984.5450866,1,48884.25433\n127572,25,7,1,0,Single,9,983.6492536,1,48867.46268\n111399,57,38,4,0,Single,1,981.6439078,1,48715.19539\n138063,21,2,2,0,Single,15,981.3483733,1,48474.41867\n129526,25,7,3,0,Single,14,981.1059191,1,48460.29596\n136782,25,6,2,0,Single,14,981.002503,1,48571.12515\n152143,24,6,3,0,Single,6,980.8848113,1,48574.24057\n142718,22,4,2,0,Single,8,980.3384425,1,48462.92213\n189426,20,1,3,0,Single,2,980.1310295,1,48667.55148\n180629,17,0,4,0,Single,11,979.0879538,1,48639.39769\n168651,24,7,4,0,Single,15,978.218947,1,48700.94735\n103237,31,13,2,0,Single,1,978.0305203,1,48392.52602\n195391,23,4,3,0,Single,15,977.7389017,1,48646.94509\n127338,19,2,3,0,Single,9,977.5152074,1,48391.76037\n127344,21,3,2,0,Single,14,977.226696,1,48533.3348\n155411,23,6,2,1,Single,3,977.006565,1,48578.32825\n141133,23,4,2,1,Single,0,976.7366225,1,48513.83113\n107600,44,27,2,0,Single,0,975.5526657,1,48261.63329\n125869,19,1,2,1,Married,2,974.0882952,1,48182.41476\n132872,31,13,3,1,Single,5,974.0050128,1,48223.25064\n138635,19,1,4,0,Single,14,973.9247055,1,48262.23528\n167680,32,12,4,0,Single,3,973.2740876,1,48126.70438\n198289,21,4,2,0,Single,11,972.7204643,1,48281.02322\n126066,19,0,3,0,Single,10,972.4744963,1,48339.72482\n113217,25,8,2,0,Married,1,971.6615958,1,48192.07979\n168613,16,0,2,0,Single,13,971.1937518,1,47994.68759\n133124,19,0,4,0,Single,10,970.5309697,1,48144.54849\n122479,37,19,3,0,Single,0,970.4087184,1,48292.43592\n119111,19,2,3,0,Single,9,970.2005878,1,48209.02939\n149732,18,1,4,0,Single,14,967.2427229,1,47900.13615\n186854,22,5,3,0,Single,7,967.0016334,1,47856.08167\n179032,21,4,4,0,Single,9,966.8027841,1,48093.13921\n172577,25,6,1,0,Single,13,966.127914,1,48001.3957\n133137,35,14,1,0,Single,5,965.3134906,1,47999.67453\n197366,19,2,4,0,Single,10,965.0542936,1,47848.71468\n103199,20,3,2,0,Single,2,964.5509037,1,47765.54519\n154884,25,7,2,0,Single,1,964.1886481,1,47858.43241\n140290,17,0,1,0,Single,13,963.5760233,1,47718.80117\n148812,17,0,1,0,Single,15,961.7886561,1,47858.43281\n180313,16,0,4,0,Single,14,961.688498,1,47881.4249\n170873,23,6,3,0,Single,12,961.5602489,1,47774.01245\n122581,16,0,4,0,Single,11,961.4443379,1,47569.2169\n156888,25,6,3,0,Single,8,960.0839428,1,47412.19714\n174931,19,0,1,0,Single,15,959.6700925,1,47640.50463\n129275,42,22,3,0,Single,3,959.66223,1,47740.1115\n103318,17,0,2,0,Single,7,959.1959551,1,47699.79776\n180875,18,0,3,0,Single,13,958.4658194,1,47509.29097\n127839,25,6,3,0,Single,12,955.6538288,1,47482.69144\n175721,25,8,2,1,Single,1,955.5054968,1,47468.27484\n162178,23,6,4,0,Single,13,953.954434,1,47149.7217\n149438,16,0,1,0,Single,13,953.3025049,1,47177.12525\n195887,22,3,3,0,Single,6,953.0572637,1,47240.86319\n118262,21,4,1,0,Single,10,953.0263777,1,47080.31889\n163533,55,35,2,1,Single,5,952.4488681,1,47115.44341\n154372,24,5,3,0,Single,12,951.3606455,1,47229.03228\n182975,16,0,1,0,Single,8,950.9628027,1,47008.14014\n130769,25,6,1,0,Single,11,950.4048689,1,47307.24345\n173602,20,3,4,1,Single,6,949.5901163,1,47175.50582\n118063,25,7,2,0,Married,3,949.3304039,1,47200.5202\n173000,52,34,3,1,Single,0,948.9842316,1,46979.21158\n137188,54,35,2,0,Single,0,948.961617,1,47126.08085\n197183,19,2,2,0,Single,15,948.6471955,1,46996.35978\n108429,23,5,1,0,Single,11,948.1599283,1,46982.99642\n142447,54,35,3,0,Single,0,948.1136788,1,47003.68394\n150580,25,7,3,0,Single,15,947.8379808,1,46957.89904\n122466,17,0,3,0,Single,14,947.6692741,1,46911.46371\n192349,54,33,4,0,Single,3,947.134347,1,47010.71735\n162980,48,29,3,1,Single,1,946.6285018,1,46745.42509\n104756,22,3,1,0,Single,11,946.1029263,1,46820.14632\n102346,20,2,2,0,Single,11,945.7624519,1,47019.1226\n181011,17,0,3,0,Single,11,945.3620797,1,46899.10399\n143099,16,0,2,0,Single,12,944.5853351,1,46795.26676\n174446,23,5,1,0,Single,6,944.4645244,1,47012.22622\n129426,46,25,2,0,Single,1,944.0640569,1,46946.20285\n178021,21,3,3,0,Single,8,943.1746391,1,46820.73196\n134930,18,0,1,0,Single,7,942.7213084,1,46637.06542\n187036,16,0,2,0,Single,11,942.5701269,1,46812.50635\n192780,19,0,3,0,Single,11,941.5145186,1,46624.72593\n123936,54,37,1,0,Single,5,941.2388843,1,46651.94422\n137285,18,1,2,0,Single,7,941.1540062,1,46658.70031\n151004,23,6,3,0,Single,9,941.1118572,1,46824.59286\n108052,20,1,1,0,Single,12,939.1788216,1,46400.94108\n171515,34,13,1,0,Single,5,939.0853092,1,46574.26546\n161468,35,17,2,0,Single,3,938.6415736,1,46536.07868\n169018,23,4,4,0,Single,15,938.5448085,1,46572.24043\n135168,16,0,3,0,Single,7,938.3568203,1,46443.84102\n191234,48,29,3,0,Single,4,938.1577435,1,46441.88718\n149230,24,6,3,0,Single,13,938.1062322,1,46537.31161\n185852,36,15,2,0,Single,3,937.8639927,1,46498.19964\n129263,40,20,3,0,Single,5,937.2636371,1,46457.18186\n139962,39,19,1,0,Single,5,937.0531376,1,46317.65688\n161120,19,0,4,0,Married,2,937.03371,1,46293.6855\n186369,16,0,3,0,Single,11,936.8815469,1,46454.07735\n116785,16,0,4,1,Single,0,936.8707277,1,46493.53639\n146514,19,1,4,0,Single,15,936.5582036,1,46555.91018\n175438,19,2,3,0,Single,12,936.441347,1,46528.06735\n189837,19,1,3,0,Single,10,936.2423902,1,46296.11951\n163026,16,0,3,0,Single,10,935.9619359,1,46248.0968\n123630,17,0,1,1,Single,5,935.8503856,1,46526.51928\n137669,24,5,2,0,Single,14,934.7447209,1,46407.23605\n188250,20,2,4,0,Single,14,932.6833827,1,46219.16914\n172164,22,5,3,0,Single,2,932.3618464,1,46206.09232\n193981,25,8,3,0,Single,10,931.7268963,1,46247.34482\n145321,21,2,1,0,Single,13,930.9972091,1,46137.86046\n105727,25,6,4,0,Single,11,930.8997836,1,46126.98918\n172899,21,4,1,0,Single,12,928.8911063,1,46073.55532\n121584,22,4,3,0,Married,5,927.8335038,1,45808.67519\n195023,18,0,4,0,Single,14,927.7630189,1,45833.15095\n137597,21,4,3,0,Single,15,927.4976597,1,45809.88299\n123069,19,0,2,0,Single,7,927.3547156,1,45924.73578\n165497,21,2,2,0,Single,12,926.8695021,1,45791.47511\n163338,24,6,3,0,Married,5,926.5767778,1,46022.83889\n174047,19,2,2,0,Single,11,925.0132734,1,45724.66367\n174281,57,36,2,0,Single,3,924.9965684,1,45783.82842\n193610,33,16,2,0,Single,2,924.3987952,1,45958.93976\n183341,17,0,4,0,Single,13,924.3242891,1,45701.21446\n156676,52,35,2,0,Single,4,924.0729718,1,45720.64859\n139662,23,4,1,0,Single,12,922.892173,1,45587.60865\n115679,17,0,4,0,Single,13,921.6483992,1,45708.41996\n185548,26,8,3,0,Single,2,921.0948551,1,45745.74276\n107699,55,37,3,0,Single,4,920.5813617,1,45782.06809\n156275,53,34,3,0,Single,5,919.846849,1,45553.34245\n187943,58,38,3,0,Single,3,919.8353211,1,45719.76606\n191190,22,5,2,0,Single,7,919.7144451,1,45739.72226\n168735,33,12,1,0,Single,4,919.4340912,1,45481.70456\n126288,20,3,4,0,Single,14,919.237615,1,45429.88075\n178951,17,0,4,1,Single,3,918.7340673,1,45521.70337\n110496,59,40,2,0,Single,2,918.2602107,1,45493.01054\n131794,21,2,2,0,Single,6,917.9892784,1,45532.46392\n132733,21,3,3,0,Single,1,917.9699854,1,45417.49927\n122086,52,32,2,0,Single,5,916.7338256,1,45540.69128\n182093,17,0,3,1,Single,10,916.4451498,1,45408.25749\n175353,20,3,3,0,Single,9,915.7145156,1,45359.72578\n123364,33,14,3,0,Single,4,915.5152136,1,45323.76068\n169562,17,0,4,0,Single,10,915.2129933,1,45193.64967\n165655,21,2,3,0,Married,9,912.3406014,1,45132.03007\n158951,23,6,1,0,Single,13,911.5321693,1,45193.60847\n194273,24,7,3,0,Single,12,910.9480966,1,45336.40483\n115431,17,0,3,0,Single,11,910.6919164,1,45185.59582\n150698,22,4,2,0,Single,14,909.7139133,1,45225.69567\n110550,19,2,3,0,Single,6,909.7073767,1,45186.36884\n198997,31,10,4,0,Single,4,909.6147219,1,45111.7361\n150643,17,0,1,0,Single,12,908.9218149,1,44857.09075\n109236,20,3,4,0,Single,15,906.6569461,1,45014.84731\n155453,58,38,2,0,Single,2,906.6119094,1,44820.59547\n156961,16,0,2,0,Single,10,906.6043671,1,45030.21836\n166897,25,6,3,0,Married,2,906.2718078,1,45014.59039\n164980,59,38,3,0,Single,1,906.1988692,1,45109.94346\n131221,24,6,2,0,Single,11,906.1379206,1,44955.89603\n112374,16,0,4,0,Married,1,905.766452,1,45059.3226\n196174,17,0,2,0,Single,8,905.5988211,1,44872.94106\n196673,18,1,1,0,Single,7,904.8401349,1,44664.00675\n157023,44,25,2,1,Single,0,904.6842247,1,44901.21124\n123071,57,39,4,0,Single,1,904.542576,1,44800.1288\n160726,17,0,3,1,Single,9,904.0967949,1,44872.83975\n190011,17,0,2,1,Single,1,903.8186993,1,44971.93497\n173130,22,4,2,1,Married,2,903.5620212,1,44736.10106\n142734,21,3,3,0,Single,12,902.9052832,1,44779.26416\n152388,25,6,3,0,Single,13,902.8980685,1,44789.90343\n151322,28,10,4,0,Single,3,901.8141232,1,44771.70616\n195381,20,1,2,0,Single,13,901.0590477,1,44472.95239\n103339,22,5,3,0,Single,9,900.8594808,1,44476.97404\n170771,32,11,2,0,Single,4,900.7508798,1,44607.54399\n119213,19,2,4,0,Single,10,900.421872,1,44573.0936\n155424,24,7,4,0,Single,12,900.3112144,1,44562.56072\n151376,22,4,4,0,Single,15,899.5962331,1,44614.81166\n118623,19,1,4,0,Single,13,899.4890292,1,44417.45146\n181506,20,3,4,0,Single,10,899.4736913,1,44374.68457\n154925,24,6,2,0,Single,5,899.4357777,1,44543.78889\n169627,20,3,2,0,Married,8,898.9884224,1,44378.42112\n116337,33,14,2,1,Single,1,898.8948057,1,44463.74029\n156489,58,38,3,0,Single,2,898.8260511,1,44733.30256\n130843,55,34,2,0,Single,5,898.5889774,1,44531.44887\n161811,21,4,4,1,Single,6,898.1259855,1,44373.29928\n112262,23,5,3,0,Single,6,898.0880446,1,44449.40223\n199569,22,3,1,0,Single,9,897.3301732,1,44287.50866\n197393,34,13,3,0,Single,2,896.7493232,1,44409.46616\n113898,45,28,2,0,Single,4,896.7392544,1,44337.96272\n128349,29,11,3,0,Single,1,896.0307292,1,44217.53646\n179151,20,1,1,0,Single,8,895.8877575,1,44250.38788\n199788,25,7,4,0,Married,4,895.8863582,1,44468.31791\n112948,18,0,3,0,Single,10,895.8679446,1,44381.39723\n151795,16,0,2,0,Single,6,895.8225724,1,44368.12862\n167134,28,7,1,0,Single,5,895.4863389,1,44267.31695\n123558,39,21,4,0,Single,0,894.1464749,1,44460.32375\n186178,21,4,2,0,Single,14,894.0514435,1,44129.57218\n133185,24,6,2,0,Single,14,892.8377124,1,44161.88562\n117460,26,5,3,0,Single,4,892.7377269,1,44039.88635\n184236,54,33,2,0,Single,1,891.7757371,1,44357.78686\n117922,21,2,1,0,Married,2,891.5187959,1,44270.9398\n185325,21,4,4,0,Single,13,890.2712621,1,43963.56311\n101176,23,4,3,0,Single,12,890.0562572,1,44040.81286\n129739,23,4,3,0,Single,9,888.8297571,1,44230.48786\n175758,60,40,3,0,Single,5,888.5431868,1,44217.15934\n120312,24,6,4,0,Single,14,888.3916178,1,44182.58089\n182418,24,7,1,0,Single,14,888.0452687,1,44027.26344\n151465,37,20,2,0,Single,0,887.8276787,1,43819.38394\n179724,25,6,4,0,Single,11,887.7569489,1,43933.84745\n193969,45,26,2,1,Single,3,887.3625199,1,44131.126\n192264,20,1,2,0,Single,12,887.0470283,1,44082.35142\n163248,22,5,3,0,Single,13,886.4901177,1,43920.50589\n190851,22,5,2,0,Single,9,886.4107121,1,43928.53561\n143662,23,6,2,0,Married,3,886.1880093,1,43922.40047\n169738,16,0,2,1,Single,6,884.8413585,1,43869.06793\n126747,18,0,3,0,Single,9,884.7424128,1,43793.12064\n127645,40,22,3,0,Single,4,884.6429806,1,43784.14903\n149280,20,2,2,0,Single,13,884.2743717,1,43795.71859\n181057,21,3,2,0,Married,4,883.5604761,1,43589.02381\n112586,17,0,3,1,Single,14,881.8866539,1,43529.3327\n113611,24,5,1,0,Single,15,880.953005,1,43680.65025\n114798,16,0,1,0,Single,15,880.5423223,1,43719.11612\n129951,22,4,3,1,Single,15,880.436309,1,43433.81545\n130541,25,8,2,0,Single,13,880.3170622,1,43578.85311\n113227,39,18,2,1,Single,1,880.3158034,1,43752.79017\n126104,50,31,3,0,Single,2,880.0825294,1,43652.12647\n105433,25,6,1,1,Single,1,879.8218612,1,43608.09306\n179079,23,4,3,0,Single,15,879.2732699,1,43385.6635\n162801,21,3,3,0,Single,6,878.9935855,1,43351.67928\n142650,61,44,4,0,Single,0,878.8911742,1,43726.55871\n184991,22,3,4,0,Single,13,878.7386362,1,43339.93181\n100180,18,1,2,0,Single,10,878.6432238,1,43354.16119\n102655,21,4,3,0,Single,12,878.0601151,1,43428.00576\n172979,36,15,1,1,Single,4,877.9333318,1,43589.66659\n142404,18,0,2,1,Single,8,877.6314114,1,43492.57057\n157853,58,40,2,0,Single,0,877.1687137,1,43611.43569\n140896,17,0,1,0,Single,15,877.0778833,1,43292.89417\n187977,48,28,3,0,Single,5,877.0190257,1,43603.95129\n147235,24,6,2,0,Single,12,876.8373128,1,43475.86564\n155073,53,32,1,1,Single,4,875.5579818,1,43483.89909\n140467,50,29,2,0,Single,1,875.2491631,1,43453.45816\n109501,22,4,1,0,Single,12,874.4650234,1,43175.25117\n161535,19,2,2,1,Single,9,874.335659,1,43283.78295\n150930,58,37,4,1,Single,4,874.0454331,1,43252.27166\n186820,22,3,2,1,Single,5,872.2668873,1,43292.34437\n128690,20,3,4,0,Single,12,872.0708925,1,43154.54463\n169504,20,1,2,1,Single,5,872.0054822,1,43067.27411\n143938,16,0,3,1,Single,3,871.983205,1,43066.16025\n167652,25,7,4,0,Single,9,871.3147058,1,43071.73529\n138407,57,39,3,1,Single,0,870.5953191,1,43157.76596\n168922,23,6,2,1,Single,7,870.4727891,1,43210.63946\n150561,19,2,3,0,Married,3,869.2425615,1,43097.12808\n137141,24,5,3,0,Single,12,868.7337283,1,43132.68642\n168794,19,2,3,0,Single,9,868.3792568,1,43141.96284\n118590,56,35,4,1,Single,0,868.2423373,1,43061.11687\n134655,24,5,2,0,Single,11,868.1959057,1,43101.79529\n123277,21,4,3,0,Single,5,867.8923141,1,42905.61571\n137207,24,5,4,0,Single,11,867.5820474,1,42904.10237\n130506,20,1,1,0,Single,13,867.5781431,1,42947.90716\n106850,19,0,2,0,Married,5,867.23165,1,43141.5825\n175631,58,39,2,0,Single,4,867.1481766,1,43136.40883\n104310,19,0,3,0,Married,6,865.4965116,1,42956.82558\n195602,33,15,2,0,Single,1,865.3294783,1,42793.47392\n166862,25,7,1,1,Single,5,865.1700498,1,42905.50249\n114055,28,10,1,1,Single,3,865.1666105,1,43031.33053\n163425,24,7,1,0,Single,6,865.1338794,1,42832.69397\n196468,33,14,2,0,Single,0,864.8282591,1,42816.41296\n146103,23,6,2,0,Single,15,864.624116,1,42903.2058\n149049,16,0,2,1,Single,8,863.5664988,1,42607.32494\n114641,23,5,2,0,Single,13,863.5322451,1,42975.61226\n178117,20,3,2,0,Single,12,862.7117483,1,42828.58742\n127638,22,3,1,0,Single,8,862.4872404,1,42778.36202\n148888,39,20,1,0,Single,0,862.1750525,1,42682.75263\n134971,57,36,1,1,Single,2,861.7717368,1,42768.58684\n176375,45,25,2,1,Single,2,861.7152966,1,42785.76483\n116098,20,2,3,1,Single,13,861.4828046,1,42742.14023\n114426,46,29,2,0,Single,1,861.3950543,1,42836.75272\n160342,20,1,3,0,Single,8,861.000057,1,42649.00285\n187193,53,33,2,0,Single,0,860.7963518,1,42604.81759\n117149,17,0,3,0,Single,12,860.4545437,1,42819.72719\n125870,24,7,3,0,Single,12,859.2409475,1,42656.04738\n158455,51,32,3,0,Single,1,858.1128834,1,42328.64417\n199174,25,7,2,1,Single,1,857.9935307,1,42517.67654\n139916,17,0,3,0,Single,8,857.9576175,1,42661.88088\n193011,24,5,1,0,Single,9,857.4740156,1,42408.70078\n149720,20,1,4,0,Single,9,857.3325509,1,42650.62755\n140546,21,3,3,0,Single,13,857.0885895,1,42633.42948\n114976,24,5,3,1,Single,0,856.298933,1,42528.94665\n101304,19,1,3,0,Married,2,856.1352159,1,42360.7608\n114113,45,28,4,0,Single,3,856.1075666,1,42384.37833\n169967,40,21,4,0,Single,2,855.9220408,1,42576.10204\n137671,19,2,2,0,Single,7,855.3519575,1,42529.59788\n105405,16,0,1,0,Single,8,855.2744104,1,42374.72052\n156075,21,4,3,0,Single,8,854.6332739,1,42241.6637\n101814,18,1,2,1,Single,4,854.6130422,1,42414.65211\n148712,17,0,2,0,Single,11,853.5472693,1,42335.36347\n172878,17,0,2,1,Single,2,853.5172334,1,42170.86167\n132270,34,15,2,0,Single,0,853.1898691,1,42285.49346\n132758,22,3,1,0,Single,11,853.178004,1,42331.9002\n161478,17,0,1,0,Single,13,852.9917769,1,42150.58885\n152189,69,50,4,0,Single,0,852.7946893,1,42243.73447\n149377,17,0,3,0,Single,7,852.6772216,1,42423.86108\n152657,55,36,2,1,Single,3,852.6038376,1,42426.19188\n163450,25,6,3,0,Single,11,851.8635648,1,42090.17824\n183501,24,5,2,0,Single,12,851.6316833,1,42121.58417\n182324,27,6,4,0,Single,5,851.2897498,1,42273.48749\n189194,21,4,4,0,Single,14,849.7975643,1,42220.87822\n138691,24,7,2,0,Single,7,849.4587334,1,41934.93667\n134822,53,35,2,0,Single,3,849.1458105,1,41940.29053\n144281,24,7,2,0,Single,8,848.20531,1,42143.2655\n156355,17,0,4,1,Single,9,848.1450321,1,42175.25161\n197902,20,1,2,0,Single,11,848.0448585,1,41994.24293\n151193,25,6,2,0,Single,8,847.9486516,1,41857.43258\n107331,17,0,1,1,Single,10,847.5286139,1,42060.4307\n163805,17,0,2,1,Married,2,847.3971276,1,41835.85638\n197720,34,15,3,0,Single,2,847.3281865,1,41859.40933\n196670,18,0,2,0,Single,9,846.0133552,1,42075.66776\n184960,25,8,2,0,Single,6,845.9031137,1,41949.15569\n152298,23,4,1,0,Single,10,845.7592784,1,41727.96392\n111024,44,24,3,0,Single,2,845.6608289,1,41850.04145\n113547,44,25,3,0,Single,3,845.4949707,1,41909.74854\n178804,24,5,3,0,Single,10,845.2984939,1,41950.9247\n179759,19,0,1,0,Single,13,844.9966518,1,41996.83259\n120756,26,6,3,0,Single,3,843.5965041,1,41757.82521\n161227,47,27,4,0,Single,4,843.3687661,1,41960.43831\n115163,63,46,2,0,Single,0,842.7744806,1,41802.72403\n110474,19,1,1,1,Single,5,842.361699,1,41616.08495\n155197,22,3,3,0,Single,13,841.8967517,1,41557.83759\n171316,25,8,3,0,Married,6,841.7800349,1,41761.00175\n196933,17,0,1,0,Single,7,841.6097425,1,41751.48713\n125427,42,22,2,0,Single,5,841.2897521,1,41551.48761\n108947,34,14,3,1,Single,0,840.4201906,1,41470.00953\n134345,39,22,4,0,Single,5,839.7674111,1,41727.37056\n103072,18,1,1,1,Married,4,839.6952451,1,41553.76226\n187192,25,6,3,0,Single,3,839.6352567,1,41611.76284\n118634,16,0,1,1,Single,2,839.4242413,1,41562.21207\n108466,20,3,4,0,Single,10,839.3059837,1,41743.29919\n126019,17,0,2,0,Married,2,839.0500314,1,41364.50157\n192376,58,41,4,0,Single,3,838.9273372,1,41593.36686\n129838,39,19,3,1,Single,0,838.8723606,1,41590.61803\n127886,25,7,2,0,Married,4,838.6878046,1,41598.39023\n183448,18,1,1,1,Single,5,838.4535579,1,41690.6779\n128138,25,8,4,0,Married,3,838.391464,1,41602.5732\n157165,19,0,3,0,Single,10,837.7497832,1,41287.48916\n150786,36,19,3,0,Single,1,837.7117701,1,41420.58851\n101731,25,6,3,0,Single,11,837.5364949,1,41409.82475\n106139,38,21,1,1,Single,0,837.4720719,1,41470.6036\n193285,16,0,2,0,Single,12,837.4157377,1,41471.78689\n110661,22,3,4,0,Single,15,837.372063,1,41569.60315\n154549,22,3,1,1,Married,1,837.3677817,1,41350.38909\n175640,21,2,2,1,Married,3,837.216986,1,41408.8493\n105249,22,5,3,0,Single,11,836.5161262,1,41386.80631\n127432,28,11,1,0,Single,4,836.2796454,1,41226.98227\n150146,44,23,2,0,Single,4,836.218815,1,41323.94075\n130871,21,3,4,0,Single,8,835.9234667,1,41467.17334\n145887,39,19,2,0,Single,0,835.8613877,1,41583.06939\n186803,19,0,1,0,Single,14,835.5043575,1,41291.21788\n168145,36,19,2,1,Single,0,834.4249675,1,41473.24838\n174033,24,6,2,0,Single,8,834.413299,1,41362.66495\n158979,22,3,2,0,Single,7,834.28166,1,41221.083\n114039,16,0,3,1,Single,4,834.1661725,1,41491.30863\n172471,41,22,3,0,Single,4,833.740771,1,41199.03855\n125401,25,7,1,0,Single,13,832.9295306,1,41206.47653\n120404,30,10,3,1,Single,3,832.7590839,1,41171.95419\n137736,20,2,4,0,Single,11,832.5978179,1,41061.8909\n170717,24,5,1,0,Single,12,831.9907195,1,41196.53598\n128154,24,6,3,0,Single,6,831.9525677,1,41040.62839\n155098,53,35,2,0,Single,4,831.8201575,1,41119.00788\n131060,23,5,3,0,Single,12,831.7622185,1,41117.11093\n121202,48,27,3,0,Single,1,831.7003993,1,41183.01997\n109587,36,17,3,1,Single,3,830.9321942,1,41267.60971\n143582,28,7,3,0,Single,3,830.5209925,1,40990.04963\n167724,39,19,3,0,Single,4,828.8621037,1,41045.10519\n149767,26,6,4,0,Single,3,828.5666144,1,41173.33072\n164011,18,0,3,0,Single,11,828.2996803,1,40954.98402\n132670,38,21,4,0,Single,3,828.1457095,1,41201.28548\n163122,36,19,4,0,Single,3,826.600291,1,40930.01455\n167700,16,0,3,1,Single,1,826.1310526,1,41063.55263\n166244,33,14,3,1,Single,1,825.9355909,1,40961.77955\n124525,24,6,4,0,Single,11,825.5370097,1,41033.85049\n129582,40,23,1,1,Single,2,825.530745,1,40696.53725\n152556,44,25,2,0,Single,3,825.4557724,1,41046.78862\n140879,19,1,3,0,Single,7,825.4040835,1,40780.20418\n127518,22,5,4,0,Single,11,825.3943013,1,41027.71507\n197555,29,10,1,1,Single,0,825.2772277,1,40860.86139\n143639,22,3,2,1,Single,3,825.2313449,1,40709.56725\n121347,21,3,4,0,Single,11,824.9399434,1,40700.99717\n126940,29,12,4,0,Single,1,824.1849697,1,40783.24849\n148188,16,0,3,0,Single,13,823.9431127,1,40935.15564\n104069,17,0,2,0,Married,3,823.7885094,1,40892.42547\n168411,67,46,3,0,Single,2,823.0331624,1,40647.65812\n180420,22,4,4,0,Married,9,822.4705539,1,40734.5277\n174925,43,22,3,0,Single,2,822.0191275,1,40825.95638\n173111,17,0,3,0,Single,12,821.9540619,1,40828.7031\n161672,25,7,3,0,Single,12,821.6369946,1,40863.84973\n160543,20,1,3,1,Single,8,821.5817084,1,40754.08542\n123905,55,35,3,1,Single,4,821.5480216,1,40537.40108\n185565,31,12,1,0,Single,4,820.9288721,1,40710.44361\n110704,24,5,2,1,Single,4,820.8498927,1,40643.49464\n156743,20,3,2,1,Single,15,820.7664852,1,40611.32426\n101900,23,5,4,1,Single,7,820.2508634,1,40575.54317\n170526,18,0,4,0,Single,10,820.2432806,1,40546.16403\n122970,21,2,3,0,Single,13,819.5961656,1,40724.80828\n151731,23,4,2,0,Single,15,819.5266661,1,40441.33331\n163505,28,10,4,0,Single,5,819.3876678,1,40697.38339\n108018,58,39,1,0,Single,0,818.0944497,1,40344.72249\n156090,31,13,1,0,Single,2,817.9641516,1,40538.20758\n192644,52,35,2,0,Single,5,817.8884176,1,40336.42088\n101100,19,2,2,0,Single,11,817.0889534,1,40418.44767\n168139,21,2,4,1,Single,0,816.5543542,1,40615.71771\n133653,53,34,4,0,Single,1,815.9700344,1,40330.50172\n194779,52,33,1,0,Single,2,815.6127623,1,40236.63812\n123300,17,0,4,0,Single,12,815.5316177,1,40414.58089\n177744,16,0,2,1,Single,14,814.827043,1,40143.35215\n108343,24,6,3,0,Single,15,814.6169377,1,40384.84689\n136629,22,3,2,0,Single,7,813.9865215,1,40118.32608\n136014,29,11,4,0,Single,1,813.7948792,1,40226.74396\n140358,20,2,3,0,Single,13,813.7886128,1,40136.43064\n112032,55,36,3,0,Single,1,813.5079191,1,40110.39596\n156994,55,38,2,0,Single,2,813.3357101,1,40183.78551\n152780,32,13,2,0,Single,0,813.2375101,1,40266.87551\n135205,27,8,2,1,Single,0,813.2008888,1,40077.04444\n180217,42,24,3,0,Single,3,812.8991652,1,40243.95826\n171799,23,5,2,0,Single,11,812.7566703,1,40250.83352\n188065,22,4,3,0,Married,3,812.6058671,1,40203.29336\n178971,54,33,4,0,Single,2,812.388498,1,40357.4249\n144509,29,12,3,0,Single,4,812.2876834,1,40088.38417\n117357,19,1,3,0,Single,13,812.2123093,1,40240.61547\n156644,53,36,2,1,Single,2,812.0970481,1,40173.85241\n128056,17,0,2,1,Single,12,811.6163512,1,40321.81756\n143659,23,5,3,1,Single,9,810.8454527,1,40257.27264\n195943,28,11,1,0,Single,5,810.8059873,1,40187.29937\n148898,22,3,2,1,Single,0,810.6958771,1,40228.79386\n113232,18,0,1,0,Single,13,810.3321729,1,40036.60865\n174454,57,37,3,0,Single,4,810.3233857,1,40024.16929\n170970,18,1,2,1,Married,3,809.9843557,1,39911.21779\n139191,22,3,2,0,Single,12,809.5177125,1,40237.88563\n186586,19,0,3,0,Single,12,809.1885008,1,40058.42504\n159367,28,8,1,1,Single,1,809.1460715,1,39931.30358\n167758,21,4,1,0,Single,8,809.0071153,1,39958.35577\n193588,66,45,2,0,Single,1,808.9727914,1,39940.63957\n122750,16,0,2,0,Single,14,808.8667038,1,40029.33519\n132928,35,18,4,1,Single,5,808.8431858,1,40014.15929\n114357,21,4,1,1,Single,7,808.7610789,1,40031.05395\n112294,24,7,2,1,Single,6,808.6426951,1,40009.13476\n116764,40,23,4,0,Single,4,808.310749,1,40135.53745\n172421,21,3,2,0,Married,2,807.7350617,1,39828.75309\n199078,29,8,3,1,Single,1,807.6592434,1,40120.96217\n138615,48,27,3,0,Single,3,807.3712285,1,39901.56143\n100303,59,40,1,0,Single,2,807.2839766,1,40111.19883\n191802,17,0,2,1,Single,11,807.2224591,1,40070.12296\n120595,22,5,4,0,Single,7,807.1660883,1,39956.30442\n108669,17,0,1,1,Married,4,806.8782918,1,40034.91459\n120355,25,7,2,0,Single,12,806.5279584,1,39932.39792\n149422,21,2,2,1,Single,2,805.8564752,1,39806.82376\n142167,25,8,3,0,Single,9,805.7545692,1,40038.72846\n135519,25,8,1,1,Married,1,805.5978863,1,39822.89432\n191329,25,8,2,1,Single,7,805.5687001,1,40073.43501\n173015,25,6,2,1,Single,5,805.4390507,1,39759.95254\n113746,46,25,1,0,Single,4,805.3226847,1,39703.13424\n188465,21,4,2,0,Single,3,804.7640707,1,39868.20354\n168182,47,29,3,0,Single,1,804.6557167,1,39776.78584\n147112,24,6,2,1,Single,13,803.8944195,1,39903.72098\n198419,64,46,1,0,Single,2,803.706458,1,39631.3229\n195236,39,20,3,0,Single,0,803.6527948,1,39756.63974\n113026,25,7,3,0,Single,15,802.9377782,1,39654.88891\n104457,20,3,3,1,Single,6,802.8991513,1,39719.95757\n142840,54,33,2,0,Single,3,802.797211,1,39667.86055\n164869,21,3,2,0,Single,12,802.6177559,1,39852.8878\n178699,18,1,1,0,Single,9,802.0179957,1,39892.89979\n122326,35,16,4,1,Single,3,801.9689769,1,39536.44885\n165231,23,6,1,0,Single,14,801.8957989,1,39540.78995\n136056,33,13,4,0,Single,1,801.5256903,1,39631.28452\n113910,21,4,3,0,Married,10,801.4144112,1,39487.72056\n124642,23,6,1,0,Single,14,801.2580357,1,39754.90179\n133739,58,37,2,0,Single,0,800.9668415,1,39534.34208\n131359,22,5,1,0,Single,6,800.9309908,1,39622.54954\n187238,43,22,4,0,Single,2,800.8715333,1,39769.57667\n188474,17,0,3,0,Single,11,800.8220074,1,39447.10037\n196799,16,0,3,1,Single,6,800.818839,1,39707.94195\n169116,31,12,1,0,Single,2,800.6210897,1,39466.05449\n134296,38,17,1,1,Single,0,800.5456109,1,39567.28055\n126133,22,3,2,0,Single,15,800.4356427,1,39454.78214\n160190,20,3,2,1,Single,15,800.4307807,1,39681.53904\n110569,21,3,2,0,Married,7,800.2390702,1,39606.95351\n148876,22,4,2,0,Married,0,800.0111715,1,39419.55858\n119863,19,1,4,0,Single,10,799.9744139,1,39692.7207\n153238,37,16,2,0,Single,4,799.820527,1,39621.02635\n175030,18,0,3,1,Single,10,799.7889888,1,39673.44944\n185238,36,16,2,1,Single,3,799.3756691,1,39466.78346\n125795,24,5,3,0,Single,12,798.1434548,1,39309.17274\n165849,21,4,3,1,Single,15,798.0314367,1,39573.57184\n101507,17,0,3,0,Single,6,797.7268973,1,39617.34487\n119945,46,25,4,1,Single,5,797.6864221,1,39411.32111\n169001,41,23,1,1,Single,0,797.4727074,1,39447.63537\n150204,28,7,3,0,Single,1,797.3344963,1,39268.72482\n132403,42,23,2,0,Single,2,797.3170676,1,39610.85338\n148429,17,0,2,0,Single,9,797.2267243,1,39358.33622\n197579,34,14,3,1,Single,4,796.8075628,1,39618.37814\n178839,48,28,2,1,Single,0,796.5701342,1,39278.50671\n188452,25,6,3,1,Single,9,796.4004917,1,39225.02459\n109133,52,31,2,0,Single,1,796.2903449,1,39500.51725\n103182,66,46,1,0,Single,1,796.0709274,1,39264.54637\n158616,25,6,2,1,Single,8,795.929798,1,39550.4899\n167265,21,4,4,0,Single,15,795.6680301,1,39372.40151\n170525,20,3,2,0,Married,6,795.5997796,1,39371.98898\n114445,57,39,1,1,Single,0,795.1639064,1,39433.19532\n160756,25,8,3,0,Single,6,795.014178,1,39480.7089\n119439,25,7,1,1,Single,2,794.6637929,1,39176.18965\n155627,23,5,3,0,Single,6,794.1462024,1,39256.31012\n149194,47,27,1,1,Single,2,793.1536372,1,39186.68186\n151623,24,7,2,1,Single,14,793.1202872,1,39059.01436\n198256,20,2,3,1,Single,6,793.0381979,1,39227.9099\n172244,16,0,4,1,Single,9,792.9503309,1,39218.51655\n182724,16,0,4,1,Single,7,792.5986484,1,39175.93242\n190965,16,0,4,1,Married,0,792.0112836,1,39248.56418\n199893,18,0,3,0,Single,11,791.9230115,1,39022.15058\n144806,16,0,2,0,Single,11,791.8835425,1,39108.17713\n135782,44,26,3,0,Single,3,791.6829438,1,39073.14719\n151382,44,24,3,0,Married,3,791.1675116,1,39217.37558\n197889,39,18,1,1,Single,3,791.135206,1,39053.7603\n169713,21,2,2,0,Married,0,791.0895425,1,38988.47713\n191964,17,0,2,0,Married,10,790.954274,1,39043.7137\n129404,25,6,2,0,Single,10,790.9371839,1,39204.8592\n185076,53,36,4,0,Single,5,790.2309083,1,38912.54542\n133031,37,19,3,0,Single,1,790.1389191,1,38957.94596\n109825,41,21,3,1,Single,1,790.0094992,1,39117.47496\n192065,29,8,3,0,Married,4,789.8858954,1,39091.29477\n155413,24,7,3,0,Single,9,789.6978699,1,38925.8935\n152224,21,2,2,0,Single,15,789.4779955,1,39174.89978\n140674,29,12,3,0,Married,2,789.2849186,1,39245.24593\n128196,16,0,1,0,Single,15,789.2221205,1,39007.10603\n101782,29,12,4,1,Single,5,788.8082877,1,39039.41439\n110074,38,17,3,1,Single,5,788.7316684,1,39115.58342\n116560,16,0,3,1,Married,4,788.6704555,1,39031.52278\n111689,21,4,3,0,Single,11,788.3187541,1,39122.93771\n120913,19,0,3,0,Married,4,788.1587277,1,38901.93639\n113670,50,32,2,0,Single,4,788.0825102,1,39118.12551\n107168,54,34,2,0,Married,1,787.9457959,1,39009.2898\n177258,23,4,4,1,Single,8,787.9110859,1,38820.5543\n117370,23,5,1,1,Single,6,787.7636887,1,38790.18444\n198426,37,19,4,0,Single,4,787.4769096,1,39167.84548\n112938,16,0,4,1,Single,14,786.1993207,1,38869.96604\n149674,69,48,3,0,Single,4,786.1841411,1,38976.20706\n104368,60,42,3,0,Single,3,785.3303755,1,38807.51878\n185050,19,2,3,0,Single,15,784.806863,1,38707.34315\n140023,38,17,2,0,Single,2,784.4685291,1,38907.42646\n197623,32,12,2,0,Single,2,784.2140182,1,38755.70091\n177965,19,2,3,0,Single,12,783.3686177,1,38758.43089\n186812,36,19,4,1,Single,3,783.342,1,38899.1\n199432,28,8,4,0,Single,2,783.2022589,1,38838.11295\n115626,19,0,2,0,Married,5,783.0149197,1,38736.74599\n177309,17,0,2,1,Single,4,782.8205413,1,38796.02707\n100236,23,4,4,0,Single,7,782.7024727,1,38557.12364\n148396,33,12,3,1,Single,2,782.658998,1,38605.9499\n141074,23,6,3,1,Single,5,782.5185902,1,38743.92951\n129946,16,0,2,0,Single,9,782.3743275,1,38772.71638\n149829,28,11,2,0,Married,2,782.3594009,1,38691.97005\n105504,18,0,4,0,Single,9,781.8003476,1,38773.01738\n133673,23,4,2,0,Single,11,781.4360444,1,38688.80222\n144753,21,4,2,0,Single,12,781.2241448,1,38524.20724\n100986,21,2,3,0,Single,15,781.0661993,1,38481.30997\n111428,24,6,2,1,Single,6,780.667958,1,38507.3979\n186105,23,6,2,0,Single,15,780.4636888,1,38742.18444\n189028,59,40,1,0,Single,2,780.2021419,1,38761.1071\n188759,21,3,2,0,Single,12,779.7358002,1,38610.79001\n137315,52,35,4,1,Single,0,779.6095191,1,38382.47596\n194931,18,0,2,0,Married,6,779.3561103,1,38623.80552\n159635,25,7,4,1,Single,8,779.3304797,1,38571.52399\n161708,33,14,1,0,Single,4,779.1418236,1,38427.09118\n140837,22,5,3,0,Single,15,778.7422888,1,38722.11444\n180669,18,0,4,0,Married,8,778.6599257,1,38396.99629\n159261,26,7,3,1,Single,0,778.5660349,1,38437.30175\n198862,23,4,4,0,Single,14,778.543894,1,38399.1947\n132293,42,23,4,1,Single,4,778.45885,1,38461.9425\n197116,23,6,2,0,Single,14,778.4199705,1,38462.99853\n133745,16,0,4,0,Single,12,778.2249222,1,38381.24611\n194831,23,4,4,0,Single,9,777.7414191,1,38377.07096\n149512,19,2,1,1,Single,9,777.5951773,1,38566.75887\n172594,30,13,4,1,Single,0,777.1630942,1,38611.15471\n123004,54,36,3,0,Single,3,777.1511103,1,38554.55552\n104924,16,0,1,0,Married,15,777.0907015,1,38440.53508\n125855,36,15,1,1,Single,1,777.0625289,1,38512.12645\n106020,25,7,1,0,Married,1,776.9765728,1,38599.82864\n177653,22,3,2,0,Single,15,776.8857949,1,38569.28975\n146482,25,6,3,1,Single,11,776.6696572,1,38424.48286\n161929,33,15,3,0,Single,4,776.5491355,1,38616.45678\n191576,30,10,2,0,Single,1,776.3322799,1,38508.614\n189726,17,0,1,1,Single,1,776.2991428,1,38291.95714\n171808,22,5,2,0,Single,10,776.2116881,1,38479.58441\n140872,22,4,4,1,Single,6,775.9261328,1,38296.30664\n107952,21,4,4,0,Single,12,775.6210576,1,38551.05288\n184678,18,1,1,0,Married,10,775.5602497,1,38539.01249\n100532,44,23,4,0,Single,1,775.2016057,1,38526.08029\n178008,35,14,2,1,Single,5,775.0018095,1,38411.09048\n171614,21,3,2,1,Single,11,774.9606361,1,38280.03181\n121061,43,26,2,0,Single,3,774.4937363,1,38161.68682\n105031,25,8,4,0,Single,15,774.2368954,1,38308.84477\n138631,20,1,4,1,Single,6,774.1602653,1,38356.01327\n169397,58,39,1,0,Single,2,773.6304312,1,38359.52156\n128758,56,39,2,0,Single,0,773.3717502,1,38123.58751\n188834,21,4,1,1,Single,14,772.9256098,1,38269.28049\n185788,16,0,3,0,Single,12,772.7090627,1,38422.45314\n132340,25,6,2,1,Single,0,772.6737478,1,38051.68739\n108345,20,1,1,0,Single,15,772.6448203,1,38321.24102\n174696,23,6,3,0,Single,15,772.2370835,1,38341.85418\n136578,21,4,1,1,Single,10,772.0719204,1,38333.59602\n104814,53,33,3,1,Single,3,772.0094708,1,38282.47354\n173422,21,3,3,0,Single,12,771.488762,1,38143.4381\n157449,23,4,1,0,Married,9,771.4163793,1,38255.81897\n161381,22,4,4,0,Single,11,771.3682045,1,38011.41022\n107370,46,27,1,0,Single,3,771.279313,1,38011.96565\n149283,17,0,3,1,Single,0,771.2105558,1,38204.52779\n149342,37,17,3,0,Single,5,770.947188,1,38287.3594\n125679,27,6,3,1,Single,5,770.667867,1,38179.39335\n127336,21,2,1,0,Married,3,770.4316087,1,38311.58044\n187752,34,16,3,1,Single,2,770.149042,1,37956.4521\n105487,22,3,3,0,Single,13,769.9601678,1,37942.00839\n197341,34,13,2,0,Single,1,769.957755,1,38051.88775\n115254,63,45,2,0,Single,0,769.8337762,1,38153.68881\n109603,17,0,4,0,Married,1,769.569766,1,37999.4883\n101763,22,3,3,0,Single,15,769.200494,1,38099.0247\n127469,45,24,2,1,Single,3,769.072315,1,37991.61575\n155860,23,5,3,1,Single,9,768.8229896,1,38240.14948\n175073,53,32,2,0,Single,4,768.5655688,1,38225.27844\n118619,18,0,3,1,Single,7,768.4793814,1,38097.96907\n125388,23,4,3,0,Single,10,768.4643137,1,38218.21569\n181059,24,5,1,1,Single,2,768.3369443,1,37851.84722\n115261,23,6,4,1,Single,9,768.3254987,1,38005.27494\n198975,16,0,2,0,Married,8,767.522167,1,38138.10835\n127388,28,11,4,0,Single,4,767.222576,1,37805.1288\n129824,30,11,3,0,Single,3,766.4775796,1,38053.87898\n157931,40,23,3,0,Married,3,766.2697688,1,37909.48844\n184387,17,0,2,0,Single,10,765.6528906,1,38027.64453\n129872,18,0,2,0,Married,6,764.4670012,1,37892.35006\n152321,21,3,3,0,Single,7,764.3609836,1,37873.04918\n152589,33,15,3,1,Single,2,764.3024432,1,37734.12216\n177143,19,1,2,0,Single,12,764.2696764,1,37672.48382\n182915,18,0,2,1,Single,7,763.8953612,1,37742.76806\n154894,19,1,1,0,Single,12,763.7298567,1,37625.49284\n158260,25,7,3,1,Single,6,763.6360013,1,37806.80007\n156819,41,20,3,1,Single,0,763.4237607,1,37913.18804\n182640,25,7,3,0,Married,10,763.3224726,1,37740.12363\n110668,20,1,4,1,Single,11,763.2407384,1,37761.03692\n153799,52,33,3,1,Single,2,763.106938,1,37596.3469\n143613,41,22,4,1,Single,3,762.8219161,1,37551.09581\n196809,24,7,4,0,Single,13,762.8124126,1,37770.62063\n104261,20,2,2,1,Single,14,762.7241919,1,37853.2096\n119333,40,19,2,0,Single,1,762.4698835,1,37895.49418\n190823,68,48,2,0,Single,3,762.3992413,1,37639.96207\n146373,34,14,2,0,Single,2,762.1941333,1,37832.70667\n195842,65,46,2,0,Single,3,762.1540821,1,37511.70411\n118790,34,14,1,0,Single,2,762.0191521,1,37755.95761\n140123,18,1,4,0,Single,15,761.9423352,1,37551.11676\n116995,17,0,2,0,Married,14,761.9294278,1,37752.47139\n155552,49,30,3,0,Single,2,761.7817369,1,37681.08685\n183875,20,3,3,1,Single,4,761.7805601,1,37811.02801\n108359,20,3,4,1,Single,7,761.1355832,1,37695.77916\n111063,25,8,2,0,Single,12,761.0834079,1,37776.1704\n161941,18,0,4,0,Married,10,760.7036261,1,37568.18131\n150727,16,0,3,1,Married,3,760.6486821,1,37666.43411\n143443,22,4,2,0,Married,14,760.2474635,1,37645.37318\n118686,23,5,3,0,Single,11,759.9971924,1,37428.85962\n147503,25,7,2,1,Single,11,759.9206549,1,37433.03275\n114217,41,20,3,0,Single,4,759.8802844,1,37440.01422\n160530,57,40,3,0,Single,1,759.7345504,1,37451.72752\n121076,17,0,2,1,Single,8,759.4798338,1,37634.99169\n109984,37,18,4,0,Single,0,759.2695883,1,37369.47942\n116415,33,14,3,1,Single,5,759.082387,1,37708.11935\n150215,22,3,3,0,Married,6,758.960996,1,37728.0498\n130774,48,30,4,0,Single,4,758.8821771,1,37543.10885\n149540,17,0,3,0,Single,15,758.6707456,1,37616.53728\n145500,31,11,4,1,Single,2,758.6592321,1,37585.96161\n183530,22,5,2,0,Single,11,758.3316514,1,37416.58257\n167956,20,3,4,0,Married,0,758.0724015,1,37481.62008\n190404,25,6,1,1,Single,10,757.8709012,1,37318.54506\n110528,29,8,2,0,Single,1,757.7647701,1,37322.23851\n179866,19,1,2,0,Married,7,757.3871116,1,37498.35558\n124566,31,11,3,1,Single,4,757.231476,1,37267.5738\n108569,17,0,2,0,Single,10,757.0065759,1,37266.3288\n105149,25,6,2,1,Single,11,756.6989314,1,37613.94657\n182799,42,22,2,0,Single,3,756.6293924,1,37498.46962\n160535,18,0,2,1,Single,6,756.4072524,1,37351.36262\n189218,19,0,4,0,Single,6,756.3925763,1,37444.62882\n166005,25,8,3,1,Married,4,756.143944,1,37341.1972\n139868,23,4,4,0,Married,11,755.8973259,1,37286.8663\n141672,19,2,2,0,Single,11,755.4257252,1,37220.28626\n126765,46,29,2,1,Single,2,755.4199753,1,37359.99877\n199620,54,35,2,0,Single,5,755.3288916,1,37558.44458\n101780,16,0,2,1,Single,3,754.5744672,1,37346.72336\n141485,21,4,2,1,Single,14,754.3540717,1,37483.70359\n124714,17,0,2,0,Single,9,754.2445733,1,37188.22867\n184258,57,39,2,1,Single,1,754.1085424,1,37287.42712\n146576,24,7,3,1,Single,7,753.9013475,1,37202.06738\n154587,55,37,3,1,Married,4,753.774245,1,37488.71225\n138710,21,2,1,1,Single,3,753.3982901,1,37318.91451\n134300,61,40,2,1,Single,3,753.3058236,1,37394.29118\n160769,21,4,2,0,Single,14,753.1638178,1,37289.19089\n136239,21,4,2,0,Single,9,753.0137799,1,37263.689\n153370,25,8,3,0,Single,8,752.9000012,1,37203.00006\n133965,44,24,3,0,Single,1,752.6678759,1,37272.3938\n189436,56,37,3,1,Single,1,752.4649834,1,37106.24917\n161230,20,3,3,1,Single,0,752.3533309,1,37327.66655\n151646,68,48,3,1,Single,2,751.6554839,1,37205.7742\n155164,22,3,3,1,Single,11,751.6107893,1,37283.53947\n116387,59,39,2,1,Single,3,751.4537726,1,37173.68863\n101152,30,13,3,1,Single,2,751.4022995,1,37128.11498\n167605,50,33,3,1,Single,3,751.3778859,1,37338.8943\n116806,20,2,2,0,Single,6,751.1068831,1,37129.34416\n104797,24,7,1,0,Single,9,750.7435809,1,37166.17905\n124202,39,18,4,0,Single,1,750.7101184,1,37189.50592\n121205,25,8,3,0,Single,11,750.7100354,1,37055.50177\n198483,22,5,1,0,Married,12,750.6949749,1,36945.74875\n173404,50,30,2,0,Single,2,750.1423091,1,36970.11546\n158402,21,3,2,1,Single,13,750.1251378,1,37218.25689\n182408,55,37,2,0,Married,5,749.5688,1,37125.44\n117642,43,22,3,1,Single,4,749.2786847,1,36903.93424\n175425,40,20,3,0,Single,0,749.0718397,1,37013.59199\n166909,17,0,3,0,Married,8,749.0647745,1,36939.23873\n130225,16,0,3,1,Single,7,749.016163,1,37027.80815\n172474,22,4,2,0,Single,12,748.3427857,1,37129.13929\n158443,53,35,1,1,Single,1,748.0892554,1,36829.46277\n102269,21,3,1,1,Single,1,747.9948959,1,37112.7448\n165057,16,0,3,0,Married,7,747.9162636,1,36918.81318\n112311,37,20,3,0,Single,4,747.8615942,1,37077.07971\n167501,28,8,1,0,Single,5,747.8135443,1,36873.67722\n167905,23,6,2,0,Single,8,747.6396837,1,36874.98419\n141901,38,21,3,0,Single,0,747.5487832,1,36808.43916\n104053,29,8,4,1,Single,4,747.5424258,1,37105.12129\n140463,25,6,1,0,Single,12,747.5273873,1,37035.36937\n192262,21,4,1,0,Single,6,747.4862966,1,36811.31483\n181490,21,2,3,1,Single,1,747.4044981,1,37021.22491\n129782,20,2,2,0,Single,6,747.1602793,1,36767.01397\n140338,45,24,2,1,Single,3,746.962177,1,37073.10885\n107194,19,1,3,0,Single,15,746.8935141,1,36854.67571\n163677,33,15,3,0,Single,4,746.8129203,1,37005.64602\n135258,57,40,1,0,Married,2,746.5266329,1,36995.33165\n115268,17,0,3,0,Married,2,746.3830767,1,36952.15384\n150373,19,0,4,0,Married,14,745.6941256,1,36684.70628\n108190,21,4,4,0,Single,15,745.3988008,1,36767.94004\n126069,49,31,3,1,Single,5,745.2307544,1,36942.53772\n145282,24,5,2,0,Single,10,745.2238905,1,37041.19453\n119478,17,0,4,1,Single,6,744.9886189,1,36711.43095\n189311,18,1,3,1,Single,7,744.7415847,1,36968.07924\n142640,20,3,2,0,Married,6,744.7098138,1,37017.49069\n180809,24,5,3,1,Single,15,744.7075008,1,36909.37504\n167598,21,4,2,0,Single,13,744.6703098,1,36799.51549\n180444,18,0,1,1,Single,6,744.3208775,1,36912.04388\n142715,22,3,3,0,Single,9,744.2801501,1,36725.00751\n115565,22,4,3,1,Single,10,743.8411092,1,36640.05546\n128105,26,5,2,1,Single,0,743.8404236,1,36694.02118\n163845,28,8,4,1,Single,4,743.7252397,1,36661.26199\n171787,37,17,3,0,Single,3,743.7041636,1,36736.20818\n112974,68,48,3,1,Single,0,743.6526726,1,36723.63363\n162671,18,0,3,0,Single,6,743.3368202,1,36870.84101\n179221,21,3,4,0,Single,7,742.9845214,1,36766.22607\n159582,55,34,3,1,Single,5,742.8816331,1,36718.08166\n100964,25,8,4,0,Single,7,742.8293205,1,36551.46603\n139614,22,3,3,0,Single,11,742.704017,1,36607.20085\n189205,19,2,1,1,Single,10,742.6331885,1,36648.65943\n170048,53,35,2,1,Single,4,742.6323394,1,36606.61697\n189638,45,28,4,1,Single,2,742.4942154,1,36561.71077\n128839,56,39,3,0,Single,1,742.4066248,1,36625.33124\n191255,34,15,2,1,Single,1,742.1126203,1,36716.63102\n113698,21,3,3,0,Single,13,742.0448449,1,36622.24225\n123406,17,0,3,0,Single,12,741.6520164,1,36585.60082\n171785,21,2,2,1,Single,10,741.6500184,1,36752.50092\n152259,24,5,2,0,Single,11,741.5099286,1,36829.49643\n176637,22,4,1,0,Married,8,741.4046857,1,36822.23429\n162610,25,6,2,0,Married,13,741.1393669,1,36670.96835\n183210,46,29,2,0,Married,5,740.3524966,1,36438.62483\n199193,16,0,2,1,Single,7,740.2205245,1,36651.02623\n167432,39,20,2,1,Single,1,740.1509003,1,36624.54502\n187973,19,2,4,1,Single,6,739.8044903,1,36497.22452\n171018,38,21,2,1,Single,4,739.7080323,1,36769.40162\n131261,53,36,3,1,Single,3,739.5009409,1,36762.04705\n118016,21,3,4,0,Single,10,739.08745,1,36643.3725\n136351,39,21,1,1,Single,4,739.0265466,1,36631.32733\n162634,29,8,1,0,Single,5,738.9508303,1,36732.54152\n158867,17,0,3,1,Single,14,738.9427143,1,36613.13572\n104486,20,3,1,0,Single,12,738.7166747,1,36579.83374\n176201,36,16,2,0,Single,3,738.4086177,1,36665.43089\n190282,58,39,2,1,Single,0,738.3166478,1,36438.83239\n192969,28,10,2,0,Single,3,738.3157306,1,36647.78653\n188569,26,5,1,0,Married,4,738.1112479,1,36689.5624\n185847,67,47,1,0,Single,0,738.0077268,1,36387.38634\n136718,21,3,4,1,Single,12,737.9319218,1,36440.59609\n162718,51,32,3,0,Single,5,737.9240961,1,36349.20481\n166649,24,6,3,0,Single,7,737.570523,1,36310.52615\n154287,49,30,3,0,Single,4,737.3769927,1,36648.84964\n100945,19,2,2,0,Single,8,737.3161446,1,36371.80723\n181199,21,4,3,1,Single,15,737.21522,1,36632.761\n101619,20,2,3,0,Married,6,736.8606984,1,36316.03492\n170495,34,16,4,1,Single,2,736.6505999,1,36496.53\n136117,18,0,3,0,Single,11,736.4336297,1,36478.68149\n147685,24,5,2,1,Single,2,736.2592586,1,36233.96293\n181095,52,31,1,0,Married,3,736.0551029,1,36211.75515\n110469,25,7,2,1,Single,4,735.9151019,1,36255.7551\n198779,16,0,4,0,Married,2,735.4850769,1,36259.25385\n153709,18,0,2,1,Single,1,735.4720889,1,36289.60445\n160854,24,6,3,0,Single,8,735.2600412,1,36552.00206\n188905,31,13,2,0,Single,3,735.2008246,1,36433.04123\n190989,34,15,1,0,Single,1,735.1929055,1,36161.64528\n153116,52,34,2,0,Married,2,735.1736164,1,36248.68082\n126897,25,8,1,0,Single,6,735.046617,1,36248.33085\n149552,46,29,2,0,Single,0,734.9538041,1,36276.69021\n149621,37,16,3,1,Single,5,734.8100542,1,36316.50271\n198391,19,1,2,1,Single,1,734.5382363,1,36317.91181\n176853,41,24,3,0,Single,2,734.4469905,1,36240.34953\n155720,58,39,3,0,Single,4,734.156536,1,36129.8268\n150250,25,7,3,1,Single,13,733.5631324,1,36209.15662\n173553,51,33,3,1,Single,4,733.5515891,1,36368.57946\n189350,22,5,1,0,Married,11,733.2995135,1,36095.97568\n191833,25,7,1,0,Married,15,733.1835635,1,36063.17818\n193784,38,21,2,1,Married,5,733.1059148,1,36444.29574\n101129,25,8,3,0,Single,13,733.0506137,1,36138.53069\n126289,16,0,3,1,Single,1,733.0239691,1,36255.19846\n170336,45,25,1,0,Single,0,732.981314,1,36169.0657\n149662,34,16,4,0,Married,2,732.911493,1,36255.57465\n107471,64,45,3,1,Single,4,732.8982638,1,36149.91319\n193717,57,37,1,1,Single,0,732.7378461,1,36313.89231\n182411,19,2,3,0,Single,10,732.5388838,1,36366.94419\n146871,24,7,2,1,Single,8,732.330998,1,36297.5499\n139399,35,15,4,0,Married,2,732.1364996,1,36387.82498\n140093,22,5,1,0,Married,13,731.936793,1,36231.83965\n146254,22,3,4,0,Single,4,731.7796272,1,36228.98136\n134194,38,19,3,0,Married,2,731.5071003,1,36349.35502\n107345,25,6,2,0,Single,14,731.4462962,1,36075.31481\n160128,20,1,2,1,Single,11,731.4175567,1,36190.87784\n170260,17,0,2,1,Single,7,731.3380755,1,36037.90378\n144971,22,5,3,1,Single,7,731.3215187,1,36239.07594\n156612,20,2,2,0,Married,3,730.946049,1,35948.30245\n194606,21,3,3,1,Single,12,730.8661549,1,36190.30775\n109970,20,3,1,0,Single,14,730.6043258,1,36322.21629\n175654,49,28,4,0,Married,3,730.5001882,1,36070.00941\n103225,30,13,4,0,Single,4,730.0730083,1,36179.65042\n138015,20,3,4,1,Single,14,729.9872438,1,36293.36219\n194129,38,17,2,0,Single,1,729.9846534,1,36016.23267\n189148,52,34,2,0,Married,3,729.7407166,1,36237.03583\n136227,35,14,2,0,Married,5,729.6449966,1,35924.24983\n145070,44,23,3,0,Married,2,729.4560945,1,36134.80473\n158743,21,4,3,0,Married,8,729.3349608,1,36000.74804\n185158,31,13,2,0,Single,3,729.2347634,1,36031.73817\n183155,23,5,2,0,Married,6,729.1836728,1,36159.18364\n102568,56,38,2,0,Single,5,728.9073294,1,36029.36647\n117065,18,0,4,1,Single,6,728.8863497,1,36084.31749\n149487,55,36,3,0,Single,2,728.8201517,1,36074.00759\n122225,30,12,3,1,Single,0,728.740211,1,36173.01055\n125666,22,4,3,1,Single,10,728.724884,1,35953.2442\n165695,50,31,1,0,Single,4,728.5124594,1,35918.62297\n107299,16,0,3,0,Married,11,728.4154246,1,36120.77123\n112346,22,5,2,1,Single,13,728.2709886,1,36011.54943\n145856,21,3,3,0,Single,12,728.2566462,1,36010.83231\n172287,51,34,2,0,Single,1,728.2463712,1,36174.31856\n196949,62,44,3,1,Single,3,728.1737695,1,35881.68848\n123654,18,0,1,1,Married,1,728.1487947,1,36178.43974\n180944,23,5,1,1,Single,11,728.0642984,1,35826.21492\n187419,24,7,3,0,Single,12,727.8987162,1,35899.93581\n137693,31,12,1,0,Single,4,727.8923199,1,35818.616\n135877,18,0,4,0,Single,12,727.6521363,1,36078.60682\n148196,19,1,3,0,Single,12,727.543379,1,35843.16895\n116954,52,32,4,1,Single,1,727.4182801,1,36096.91401\n190451,39,21,4,1,Single,1,727.4062496,1,35775.31248\n173617,18,0,3,0,Married,15,727.3539675,1,35780.69838\n137950,34,13,3,0,Married,4,727.2942665,1,36094.71333\n134430,24,5,1,0,Single,15,727.2299234,1,35836.49617\n155600,16,0,3,1,Single,7,726.9696083,1,36030.48042\n179762,20,3,1,1,Single,12,726.9287831,1,35840.43916\n106693,23,5,2,0,Single,11,726.7582512,1,35872.91256\n135565,24,6,4,0,Married,13,726.3996716,1,36068.98358\n197010,24,7,3,1,Married,3,726.2144867,1,35767.72434\n143653,48,29,3,0,Single,4,726.1445664,1,36090.22832\n187015,38,19,1,0,Single,2,726.0985559,1,35783.9278\n137374,41,22,4,1,Single,1,725.6808454,1,35760.04227\n150949,24,6,2,0,Single,8,725.5793808,1,36002.96904\n121251,21,3,2,0,Single,14,725.3238635,1,35996.19318\n175748,56,37,3,0,Single,3,725.0004017,1,35739.02009\n160684,23,5,3,1,Single,7,724.8890803,1,35807.45402\n163060,60,43,2,0,Single,3,724.8525528,1,35966.62764\n178376,22,4,1,0,Married,9,724.7811444,1,35941.05722\n129388,22,4,3,1,Single,7,724.6667315,1,35991.33658\n166429,52,31,3,0,Married,3,724.4941323,1,35938.70662\n155261,28,8,2,0,Married,3,724.4201613,1,35708.00807\n177427,21,2,3,0,Single,8,724.4198456,1,35660.99228\n189955,24,5,2,0,Single,14,724.3322862,1,35763.61431\n179435,17,0,3,0,Married,6,724.3219304,1,35968.09652\n171283,36,17,4,1,Single,3,724.1517815,1,35654.58908\n165586,33,12,4,1,Single,4,723.9313513,1,35641.56757\n117860,20,2,3,1,Single,5,723.7090932,1,35647.45466\n193669,18,0,3,0,Single,13,723.6851015,1,35687.25508\n187627,24,7,2,1,Single,8,723.358997,1,35756.94985\n199346,56,36,4,0,Single,0,723.3344189,1,35956.72095\n133604,18,1,3,0,Single,7,723.0670895,1,35902.35448\n137319,22,4,4,1,Single,13,722.9855694,1,35837.27847\n115716,21,2,2,0,Single,12,722.9591886,1,35597.95943\n149268,54,34,2,0,Single,0,722.6879625,1,35697.39813\n178291,19,1,2,0,Married,6,722.5777502,1,35790.88751\n116370,27,10,1,0,Single,0,722.4834937,1,35632.17469\n147093,17,0,2,0,Single,12,721.8671019,1,35738.3551\n183713,67,46,2,0,Single,4,721.4294983,1,35667.47492\n167244,48,27,4,0,Single,3,721.3954283,1,35469.77142\n165791,17,0,2,0,Married,1,721.3547276,1,35502.73638\n165972,70,49,3,0,Single,5,721.3353791,1,35851.76896\n154901,22,4,4,1,Single,7,721.1868745,1,35468.34373\n100968,22,4,4,0,Married,1,721.1489608,1,35513.44804\n140755,43,24,4,1,Single,1,721.0038433,1,35505.19217\n111406,17,0,3,0,Single,1,720.999411,1,35776.97055\n114072,69,48,3,0,Married,1,720.9926207,1,35779.63104\n107526,54,35,1,0,Married,0,720.563647,1,35639.18235\n197238,17,0,3,0,Single,12,720.5410613,1,35432.05307\n117823,44,25,1,0,Single,2,720.5009586,1,35592.04793\n198213,30,12,2,0,Married,0,720.3111168,1,35570.55584\n181439,60,41,1,1,Single,2,720.2641611,1,35604.20806\n106224,16,0,3,0,Married,9,720.0387756,1,35601.93878\n119432,51,30,2,1,Single,3,720.0040562,1,35626.20281\n132963,65,45,4,0,Single,3,719.882821,1,35409.14105\n171209,23,6,2,1,Single,15,719.4288199,1,35620.441\n142588,23,4,4,1,Single,15,719.4111251,1,35445.55626\n111108,60,43,4,0,Single,5,719.2340931,1,35383.70466\n120704,17,0,4,0,Single,13,718.5018398,1,35395.09199\n176273,19,2,1,1,Single,8,717.8144118,1,35646.72059\n192070,47,30,2,0,Single,0,717.6878712,1,35284.39356\n112136,17,0,3,0,Married,9,717.6696252,1,35614.48126\n155791,49,30,2,0,Single,3,717.3119185,1,35347.59593\n192033,17,0,4,0,Single,7,717.1666177,1,35268.33089\n154128,56,35,2,1,Single,5,717.0004191,1,35367.02096\n152932,26,9,3,0,Married,0,716.6627127,1,35623.13564\n124560,18,0,1,0,Single,11,716.2810493,1,35502.05247\n199655,19,0,3,0,Single,12,716.0003598,1,35577.01799\n173823,20,3,1,1,Single,9,715.9079639,1,35483.3982\n195216,21,2,3,1,Single,8,715.7363036,1,35428.81518\n181139,21,4,2,1,Single,15,715.6296193,1,35267.48097\n108149,31,13,4,1,Married,2,715.5279536,1,35288.39768\n197032,20,2,1,0,Single,14,715.4017418,1,35397.08709\n166934,39,18,4,0,Single,5,715.2978066,1,35349.89033\n153418,56,35,3,1,Single,5,715.2970434,1,35453.85217\n150351,18,0,3,0,Single,12,715.1642346,1,35239.21173\n133288,17,0,2,1,Single,12,715.003246,1,35205.1623\n125763,44,25,2,1,Single,5,714.9612771,1,35439.06386\n156248,21,4,1,0,Single,15,714.8738821,1,35482.69411\n171467,21,3,2,0,Married,8,714.1525743,1,35236.62872\n176990,25,6,2,0,Single,11,714.116311,1,35140.81555\n129660,57,39,3,0,Married,3,713.4269686,1,35134.34843\n154528,21,2,3,1,Single,4,713.340243,1,35196.01215\n109813,46,28,1,1,Single,5,713.2035955,1,35151.17978\n114007,23,4,3,1,Single,2,712.8656995,1,35257.28498\n161053,59,40,2,0,Single,1,712.7631408,1,35427.15704\n111488,58,39,3,0,Married,2,712.510153,1,35263.50765\n107649,25,8,1,0,Single,14,712.2418496,1,35315.09248\n109440,25,6,2,1,Single,13,712.1576689,1,35022.88345\n170163,29,11,2,1,Single,5,712.0506677,1,35154.53339\n163489,42,22,3,1,Single,0,711.7372416,1,35280.86208\n179465,16,0,3,0,Single,11,711.6021736,1,35345.10868\n136129,22,3,1,1,Married,5,711.319749,1,35045.98745\n199668,27,8,3,0,Single,5,710.9679541,1,35178.39771\n124204,50,31,2,0,Single,2,710.872881,1,35274.64405\n194674,47,27,2,0,Married,5,710.8321571,1,35229.60786\n153530,39,22,3,0,Married,4,710.8187758,1,34946.93879\n174898,55,37,2,1,Single,2,710.6913874,1,35267.56937\n114834,58,38,2,1,Single,3,710.6296673,1,35194.48337\n113961,38,21,2,0,Single,1,710.6181282,1,35035.90641\n191464,22,3,3,1,Single,7,710.4609954,1,35213.04977\n159161,48,30,2,1,Single,4,710.1964161,1,35305.82081\n144991,25,7,1,0,Married,10,710.145085,1,35092.25425\n117501,16,0,2,0,Single,14,709.9622245,1,35071.11123\n159180,23,5,2,0,Single,15,709.7052299,1,35272.2615\n101997,30,9,3,1,Single,4,709.0451916,1,35047.25958\n155080,24,7,4,1,Single,10,708.9236538,1,35178.18269\n195716,34,15,3,0,Married,4,708.9139951,1,35054.69976\n140722,59,42,3,0,Single,4,708.7560801,1,34880.80401\n196837,54,34,2,0,Married,1,708.6359446,1,35223.79723\n186925,43,26,3,1,Single,2,708.5914602,1,35042.57301\n161787,46,28,3,0,Single,3,708.5389958,1,34852.94979\n171296,31,13,1,1,Single,2,708.3775224,1,35112.87612\n121756,25,6,2,0,Single,12,708.2943873,1,35011.71937\n171503,18,1,2,0,Married,6,708.2637427,1,34960.18714\n172010,30,9,3,1,Single,5,708.2050607,1,35029.25304\n194685,21,4,1,0,Married,9,708.1011729,1,34819.05865\n137858,52,34,4,0,Single,1,707.9965208,1,35188.82604\n129950,16,0,4,0,Single,13,707.985618,1,34992.2809\n161298,21,2,2,1,Single,10,707.8560154,1,34938.80077\n131041,33,15,1,0,Single,2,707.7098739,1,34840.4937\n193733,44,27,2,0,Single,2,707.6464212,1,34938.32106\n161914,33,13,2,1,Single,0,707.5019734,1,35020.09867\n152174,16,0,4,0,Single,7,707.4290189,1,34943.45095\n157903,28,9,1,0,Single,5,707.3793245,1,35067.96623\n188846,19,1,2,0,Single,14,706.9268496,1,35093.34248\n193485,24,6,2,1,Married,5,706.7631618,1,35113.15809\n165061,25,6,3,0,Married,7,706.6349724,1,34879.74862\n150535,22,4,2,0,Single,11,706.4166287,1,35051.83144\n198961,44,27,2,1,Single,3,706.3421025,1,34946.10513\n139693,17,0,2,1,Single,7,706.3116363,1,35010.58182\n102630,23,4,2,0,Married,7,706.1884573,1,34863.42287\n169709,32,11,3,0,Single,3,706.1728941,1,34867.64471\n154782,46,26,3,1,Single,3,706.0207451,1,35069.03726\n174345,19,1,3,1,Single,10,705.0733831,1,34791.66916\n169185,22,3,3,1,Single,9,704.5082833,1,34835.41417\n125470,17,0,2,0,Married,6,704.3709913,1,34897.54957\n194958,20,1,2,0,Single,11,704.2772453,1,34923.86227\n199181,21,4,3,0,Married,4,704.2167743,1,34921.83872\n197594,22,3,3,0,Married,12,703.9341038,1,34974.70519\n176346,40,21,4,1,Single,5,703.8309659,1,34934.5483\n106695,18,0,2,1,Single,8,703.7133256,1,34678.66628\n130419,45,28,2,0,Married,5,703.1923045,1,34674.61523\n124975,17,0,1,0,Single,12,703.1834416,1,34647.17208\n144032,32,15,3,0,Single,3,703.1167888,1,34724.83944\n172662,19,0,1,1,Single,11,703.098246,1,34626.9123\n118154,18,0,2,0,Single,6,702.6961237,1,34845.80619\n143500,40,23,1,0,Married,4,702.6532417,1,34914.66209\n174691,18,0,2,0,Single,15,702.236446,1,34714.8223\n114082,16,0,2,0,Single,8,702.1755846,1,34512.77923\n161695,53,32,2,0,Single,4,702.1396254,1,34712.98127\n178887,50,32,3,0,Married,4,702.1098017,1,34827.49009\n183876,35,18,3,0,Married,3,702.0178521,1,34604.89261\n163307,30,10,2,1,Single,0,701.8218843,1,34564.09422\n165781,25,7,4,0,Single,15,701.7830967,1,34580.15484\n129230,51,30,2,1,Single,3,701.7325782,1,34690.62891\n195105,48,29,2,1,Married,0,701.4094598,1,34686.47299\n132521,16,0,2,0,Single,6,701.2531217,1,34649.65609\n158771,16,0,2,0,Married,11,701.2351638,1,34826.75819\n102735,23,6,2,1,Single,7,701.2232747,1,34481.16374\n157753,23,6,3,1,Single,10,701.2154637,1,34663.77319\n136927,17,0,2,1,Single,6,701.0064846,1,34651.32423\n172793,23,6,1,1,Single,3,700.8519395,1,34581.59698\n152724,23,5,4,1,Single,9,700.8223472,1,34734.11736\n113795,44,23,2,0,Married,4,700.8128781,1,34815.64391\n198246,45,26,4,0,Single,3,700.7749063,1,34802.74532\n197364,18,0,3,1,Single,12,700.5392738,1,34560.96369\n134418,16,0,4,0,Single,8,700.4517323,1,34534.58662\n195449,19,0,4,1,Married,1,700.394564,1,34657.7282\n168751,70,53,3,1,Single,2,700.3607889,1,34647.03945\n103317,26,9,2,1,Single,0,700.2866318,1,34548.33159\n170327,49,28,4,0,Single,3,700.2664529,1,34793.32265\n188774,47,29,3,1,Single,1,699.9012311,1,34445.06156\n174879,25,7,3,0,Single,13,699.7989747,1,34418.94874\n134146,36,19,1,0,Single,3,699.7955734,1,34631.77867\n106391,27,9,2,1,Single,3,699.6290908,1,34493.45454\n123075,57,36,3,0,Single,1,699.5215605,1,34485.07803\n135715,17,0,4,0,Married,14,699.4333382,1,34394.66691\n107692,67,48,2,0,Single,0,699.1471439,1,34570.3572\n119649,27,10,3,1,Single,2,699.0681192,1,34743.40596\n151238,39,19,3,0,Married,5,699.051582,1,34664.5791\n114868,18,0,4,1,Single,13,699.0305029,1,34353.52515\n115705,17,0,3,1,Single,6,698.8769957,1,34553.84979\n114814,16,0,2,1,Single,5,698.8542541,1,34718.71271\n121117,43,25,2,0,Single,4,698.6641127,1,34428.20564\n125317,62,44,3,0,Single,1,698.5960213,1,34503.80107\n140017,61,41,4,0,Married,4,698.5172302,1,34389.86151\n108319,18,1,1,1,Single,10,698.2797078,1,34694.98539\n182111,36,17,2,0,Single,3,697.7882878,1,34667.41439\n140202,23,5,1,1,Single,15,697.6382677,1,34296.91339\n116597,20,2,2,0,Married,11,697.5862202,1,34615.31101\n170803,51,30,3,0,Single,2,697.3411895,1,34543.05948\n169949,30,11,2,0,Married,3,697.2607911,1,34362.03956\n139860,36,16,4,1,Single,4,697.2179608,1,34323.89804\n157361,47,29,3,0,Single,0,697.1630111,1,34536.15056\n112732,44,27,4,0,Married,4,696.6347494,1,34331.73747\n184205,63,45,2,0,Married,2,696.6123044,1,34271.61522\n197630,42,24,2,0,Single,5,696.5284014,1,34408.42007\n101155,30,13,1,1,Single,1,696.0295489,1,34273.47745\n156502,31,12,2,1,Single,4,695.946743,1,34320.33715\n162628,22,5,2,1,Married,2,695.7030896,1,34501.15448\n155112,24,5,2,0,Married,12,695.6235802,1,34381.17901\n120073,23,4,3,0,Single,11,695.4005572,1,34412.02786\n178732,16,0,2,0,Single,13,695.3534376,1,34327.67188\n183060,18,0,2,0,Married,11,695.3406506,1,34192.03253\n152850,27,7,2,1,Single,0,695.1590822,1,34237.95411\n115872,18,0,2,1,Single,11,695.1091588,1,34384.45794\n164700,18,0,3,0,Single,14,694.7965141,1,34395.82571\n152180,64,46,3,0,Single,4,694.6043769,1,34240.21885\n177554,23,4,1,0,Married,14,694.5794472,1,34524.97236\n112350,25,7,2,0,Married,11,694.561926,1,34244.0963\n138771,33,15,1,1,Single,4,694.5450764,1,34253.25382\n113044,18,1,3,0,Married,10,694.286633,1,34224.33165\n133861,22,4,3,1,Single,12,694.2796779,1,34477.9839\n140660,24,7,4,1,Single,11,694.1400539,1,34142.0027\n101041,31,14,4,1,Single,3,694.0279665,1,34244.39833\n167595,57,39,1,0,Married,4,693.878229,1,34372.91145\n104491,49,31,3,1,Single,2,693.7368884,1,34189.84442\n198874,70,50,3,0,Single,3,693.584713,1,34139.23565\n181116,22,5,1,0,Married,10,693.4669374,1,34076.34687\n172014,57,36,3,1,Single,1,693.2775041,1,34372.87521\n199120,40,22,2,1,Single,0,693.2217985,1,34258.08993\n124285,38,20,3,0,Single,0,693.1753088,1,34239.76544\n131184,24,5,3,0,Single,14,693.1694165,1,34390.47083\n190123,21,2,3,0,Single,11,693.0582757,1,34421.91379\n143377,49,29,4,0,Single,5,692.9896657,1,34144.48329\n191555,26,8,2,0,Single,4,692.8702562,1,34414.51281\n186802,16,0,3,0,Single,2,692.758983,1,34199.94915\n165371,21,3,1,1,Married,4,692.7306542,1,34058.53271\n115774,46,26,2,0,Single,3,692.7240723,1,34196.20362\n112556,25,6,3,0,Married,15,692.7167535,1,34152.83768\n137880,18,0,2,0,Single,12,692.6939638,1,34112.69819\n134055,43,25,1,0,Married,0,692.538638,1,34296.9319\n159452,35,17,4,1,Single,2,692.5043339,1,34288.2167\n196909,24,7,2,1,Single,9,692.2309,1,34282.545\n193383,45,26,3,0,Married,1,691.986297,1,34125.31485\n120658,33,16,4,1,Single,4,691.7972533,1,34218.86267\n106125,16,0,2,0,Married,15,691.6319204,1,34323.59602\n124596,46,26,2,1,Single,4,691.5961948,1,34135.80974\n143812,17,0,1,0,Married,14,691.1435667,1,34333.17834\n120215,16,0,3,1,Married,1,691.0489419,1,34169.4471\n160398,29,8,2,1,Single,3,691.0368252,1,34145.84126\n170192,45,24,4,0,Single,4,691.0362156,1,34130.81078\n146215,27,7,2,0,Single,1,690.8500547,1,34166.50274\n163342,19,2,3,0,Married,11,690.6259334,1,34073.29667\n188180,42,24,2,1,Single,2,690.3977415,1,34148.88708\n178441,19,0,1,1,Single,8,690.3083206,1,34093.41603\n147183,20,3,1,1,Single,13,690.2847547,1,33943.23774\n162069,38,18,1,0,Married,3,690.219118,1,34115.9559\n149876,24,6,1,0,Married,6,690.1699369,1,34229.49685\n117634,16,0,2,1,Married,1,690.1442762,1,34290.21381\n164085,56,35,3,1,Single,4,690.1345826,1,33994.72913\n183961,47,26,3,1,Single,0,690.0031162,1,33933.15581\n169677,28,7,2,0,Single,3,689.9700256,1,33914.50128\n167179,70,53,3,1,Single,2,689.6666395,1,33943.33198\n173859,19,1,3,1,Single,14,689.6378418,1,34073.89209\n156380,18,0,3,0,Single,7,689.4130703,1,34230.65352\n126198,22,3,2,0,Single,13,689.3821702,1,34115.10851\n189088,19,0,3,1,Single,9,689.3640906,1,33962.20453\n172614,24,6,2,0,Single,9,689.36232,1,33932.116\n121232,43,23,1,0,Married,0,689.2935821,1,34252.67911\n159363,19,2,2,0,Single,14,689.154748,1,33953.7374\n179212,18,0,3,0,Single,6,688.9480521,1,34031.40261\n108228,17,0,2,1,Single,15,688.8547617,1,33980.73809\n145682,23,4,2,1,Single,14,688.7446412,1,33950.23206\n110198,20,3,3,1,Single,9,688.7438228,1,34200.19114\n119010,23,4,3,0,Single,12,688.7317091,1,34151.58546\n177873,25,8,4,0,Married,7,688.502446,1,34126.1223\n119290,60,41,2,0,Single,2,688.3114093,1,34072.57047\n147552,20,3,2,0,Married,8,688.2579359,1,34163.8968\n159312,44,27,1,1,Single,4,688.1702964,1,33834.51482\n190636,39,18,1,0,Single,5,688.1633261,1,33825.16631\n105095,46,27,3,1,Single,4,688.0319335,1,34157.59668\n188347,18,0,3,1,Single,6,687.9402382,1,33990.01191\n110524,19,0,2,0,Married,12,687.901036,1,33996.0518\n193936,19,1,3,1,Single,13,687.7102757,1,33877.51379\n170212,31,12,3,0,Single,0,687.4971991,1,33814.85996\n119060,30,10,3,1,Single,5,687.3164652,1,34090.82326\n177626,22,5,4,0,Single,15,687.1250602,1,34124.25301\n114333,56,39,3,1,Married,2,687.0747031,1,34080.73516\n114186,18,0,4,0,Single,11,686.9093288,1,33752.46644\n122865,19,0,2,1,Single,13,686.6814596,1,34033.07298\n173157,34,16,2,0,Married,2,686.6682312,0,33916.41156\n141182,48,27,1,1,Single,0,686.6217767,0,33840.08884\n186942,19,2,3,0,Single,12,686.5275682,0,34018.37841\n148265,20,3,3,1,Single,6,686.5084197,0,33927.42099\n131373,51,31,3,1,Single,1,686.4441136,0,34068.20568\n109243,59,39,4,1,Married,3,686.404928,0,33990.2464\n145086,23,6,3,1,Single,9,686.2576642,0,33817.88321\n144886,40,21,2,1,Married,1,686.067487,0,33842.37435\n129320,50,33,4,1,Married,0,686.0361749,0,33932.80875\n154334,18,1,3,0,Married,14,685.7656083,0,34043.28042\n169528,57,40,2,1,Single,0,685.7087943,0,34025.43972\n124217,21,3,1,0,Married,0,685.70269,0,33922.1345\n113228,16,0,2,0,Married,6,685.5676599,0,34073.383\n123935,24,5,2,0,Married,15,685.5472249,0,33919.36125\n156634,27,8,2,1,Single,3,685.5365484,0,33783.82742\n122035,26,5,3,1,Single,3,685.4844285,0,33814.22143\n178987,45,24,4,1,Single,5,685.4408486,0,33831.04243\n134245,25,8,2,1,Single,2,685.3157449,0,33699.78725\n144958,45,24,4,1,Single,0,685.1928276,0,33918.64138\n117928,24,7,3,0,Single,8,685.1143207,0,33703.71604\n168111,54,34,2,1,Single,1,685.0277859,0,33796.3893\n113488,44,27,1,0,Single,1,684.9800141,0,33746.00071\n150946,20,1,3,0,Married,14,684.9732329,0,33896.66165\n118920,19,2,2,0,Single,11,684.837681,0,33734.88405\n180703,25,6,4,0,Married,7,684.6603654,0,34023.01827\n190872,18,0,1,0,Single,9,684.6526982,0,34004.63491\n197354,35,15,3,1,Married,0,684.4578628,0,33639.89314\n174155,20,2,3,0,Single,11,684.4273231,0,33651.36616\n169530,20,1,2,0,Married,14,684.3776358,0,33759.88179\n109552,21,4,3,0,Single,14,684.2638013,0,33903.19007\n191006,35,15,2,0,Married,1,683.9467174,0,33771.33587\n127592,45,24,4,0,Married,5,683.6910719,0,33697.5536\n130243,42,21,2,1,Single,4,683.6721296,0,33592.60648\n188314,21,3,3,1,Single,3,683.5334385,0,33924.67193\n106723,20,3,1,0,Married,10,683.3311595,0,33631.55798\n137076,24,6,3,0,Single,15,683.2504772,0,33752.52386\n165138,18,0,3,1,Single,9,683.2486593,0,33719.43297\n127204,21,3,3,1,Married,2,683.1064293,0,33837.32147\n158107,38,17,3,1,Single,1,682.8144216,0,33575.72108\n160577,25,8,2,0,Single,14,682.7733509,0,33804.66755\n152280,41,23,2,0,Single,4,682.7708517,0,33673.54259\n191908,53,36,4,0,Married,4,682.7015018,0,33715.07509\n165658,23,4,3,0,Single,13,682.6715142,0,33906.57571\n158928,41,23,3,1,Single,1,682.37381,0,33776.6905\n193241,25,8,3,0,Married,9,682.3533622,0,33541.66811\n165092,47,28,4,0,Single,4,682.2925662,0,33907.62831\n165507,23,6,4,0,Single,14,682.0951784,0,33726.75892\n141877,50,30,4,1,Single,3,681.9795945,0,33889.97973\n165569,18,1,3,0,Married,11,681.9643691,0,33562.21846\n185997,19,0,3,1,Single,11,681.7601798,0,33725.00899\n166622,43,26,2,0,Married,1,681.7282118,0,33716.41059\n182444,20,2,3,1,Single,10,681.7202243,0,33629.01122\n160510,22,5,2,1,Married,1,681.6681284,0,33544.40642\n192857,23,6,2,0,Single,13,681.6024216,0,33556.12108\n192607,24,7,1,0,Single,14,681.6009788,0,33719.04894\n100079,20,3,1,0,Married,1,681.5827516,0,33621.13758\n177155,24,5,4,0,Single,7,681.5777283,0,33513.88642\n175068,20,3,2,1,Single,5,681.5311064,0,33790.55532\n135025,44,26,2,0,Married,0,681.5267918,0,33848.33959\n166556,22,3,3,0,Married,6,681.5196725,0,33580.98363\n134487,27,6,2,0,Married,3,681.3895589,0,33543.47795\n165763,54,36,3,0,Married,4,681.3273614,0,33503.36807\n185315,25,8,2,1,Married,3,681.2100708,0,33622.50354\n137894,49,32,2,0,Married,3,681.1434152,0,33756.17076\n156003,45,27,4,0,Married,1,681.0571377,0,33613.85689\n129748,22,5,1,1,Single,14,680.9239433,0,33591.19717\n194616,16,0,2,0,Married,6,680.6325471,0,33700.62736\n110987,53,33,2,0,Single,4,680.6276518,0,33738.38259\n189764,16,0,3,1,Single,6,680.6098474,0,33750.49237\n118206,26,7,4,0,Married,5,680.2711562,0,33749.55781\n146242,39,22,1,0,Married,3,680.1630093,0,33509.15047\n188206,25,8,1,0,Married,11,679.8339825,0,33691.69913\n136707,17,0,3,0,Single,9,679.790063,0,33457.50315\n107925,52,31,2,0,Married,5,679.4670175,0,33709.35088\n173702,56,36,2,0,Married,3,679.1324761,0,33481.62381\n106553,54,35,1,0,Single,1,679.0530079,0,33738.6504\n141076,19,0,2,0,Married,2,678.7030456,0,33709.15228\n152819,47,29,2,0,Single,5,678.618111,0,33366.90555\n105468,16,0,4,0,Married,5,678.4732977,0,33560.66489\n160674,58,38,4,1,Married,1,678.3193772,0,33586.96886\n135468,45,26,2,0,Single,3,678.1316539,0,33406.5827\n146413,18,0,1,1,Single,3,678.1012495,0,33602.06248\n112698,23,5,1,0,Single,6,678.0913473,0,33400.56737\n145987,69,50,2,0,Married,3,677.7201767,0,33624.00884\n124329,19,2,3,0,Married,8,677.6766583,0,33303.83292\n155135,70,49,3,0,Single,4,677.6506049,0,33512.53025\n150052,42,25,2,0,Single,5,677.604744,0,33350.2372\n102026,18,1,4,0,Married,3,677.5618141,0,33434.09071\n162882,32,13,3,0,Married,2,677.5407463,0,33369.03732\n166933,17,0,3,1,Single,11,677.3899567,0,33388.49784\n142814,23,6,3,0,Single,14,677.2536208,0,33296.68104\n116629,18,0,3,1,Single,5,676.9304876,0,33638.52438\n174475,18,0,1,0,Married,11,676.8854783,0,33578.27392\n161208,16,0,3,1,Single,14,676.7684721,0,33564.42361\n135367,58,39,1,1,Single,3,676.5249895,0,33615.24948\n124671,17,0,2,0,Single,8,676.4965432,0,33425.82716\n105815,17,0,2,1,Married,5,676.4310376,0,33236.55188\n171919,32,13,3,0,Married,5,676.3882247,0,33479.41124\n181487,62,43,2,1,Single,3,676.2909917,0,33234.54959\n124485,17,0,2,1,Single,7,676.2447027,0,33385.23514\n116102,64,44,4,0,Single,5,676.2242731,0,33314.21366\n170468,23,5,2,0,Single,11,676.0622962,0,33357.11481\n148077,20,1,3,1,Single,2,676.0452565,0,33549.26283\n158478,32,13,1,1,Single,4,675.9475429,0,33418.37715\n169898,24,5,3,0,Single,10,675.8663698,0,33444.31849\n102406,33,14,2,1,Single,2,675.8074022,0,33236.37011\n136937,29,11,1,0,Single,0,675.7091855,0,33261.45928\n148774,48,29,2,1,Single,5,675.685326,0,33251.2663\n140444,21,4,2,0,Single,3,675.247383,0,33492.36915\n115306,18,0,3,0,Single,14,675.2355254,0,33516.77627\n103733,18,0,2,0,Single,11,675.2350349,0,33524.75175\n185198,18,1,3,1,Single,11,675.1680806,0,33322.40403\n139513,58,40,2,1,Married,0,675.052376,0,33182.6188\n129989,38,17,3,1,Single,0,674.9152905,0,33532.76453\n102116,33,13,2,1,Single,4,674.8249536,0,33379.24768\n114484,16,0,2,1,Single,8,674.7125191,0,33366.62596\n119734,38,18,3,0,Single,1,674.6962598,0,33261.81299\n169401,19,1,2,1,Single,12,674.6170251,0,33256.85126\n185267,41,22,2,0,Single,1,674.064058,0,33420.2029\n169516,16,0,4,0,Married,7,673.8552929,0,33491.76465\n154675,24,7,2,0,Married,11,673.7799104,0,33378.99552\n179326,60,43,2,1,Single,1,673.7004525,0,33135.02263\n127064,23,6,2,0,Married,4,673.6614881,0,33235.07441\n133176,24,5,3,1,Single,15,673.6116548,0,33255.58274\n156319,21,3,2,0,Single,13,673.5593531,0,33121.96766\n187794,23,6,2,0,Married,14,673.3593171,0,33345.96586\n188800,43,23,1,0,Single,4,673.3541527,0,33358.70764\n132277,17,0,1,1,Single,5,673.3517116,0,33241.58558\n165502,16,0,1,0,Single,15,673.1939661,0,33179.69831\n141656,20,2,3,0,Single,15,673.1513223,0,33214.56612\n197436,25,7,3,0,Married,6,673.1208313,0,33136.04157\n165520,20,2,1,1,Single,12,672.9064735,0,33266.32368\n121973,54,34,4,1,Married,4,672.8481974,0,33352.40987\n138021,56,36,3,1,Single,3,672.7048885,0,33400.24443\n159118,20,2,2,0,Single,10,672.6785805,0,33329.92903\n152728,21,4,3,0,Married,9,672.4982295,0,33253.91148\n167797,25,8,2,0,Single,15,672.4941619,0,33040.7081\n104438,21,2,1,0,Single,14,672.0778797,0,33135.89399\n190370,38,20,2,0,Married,3,671.8734738,0,33198.67369\n140425,38,19,2,1,Single,3,671.8160061,0,33329.80031\n105932,45,26,2,0,Married,5,671.7997427,0,33036.98714\n106336,63,43,2,1,Single,5,671.7035307,0,33103.17654\n196720,23,5,3,1,Single,15,671.6230195,0,33040.15098\n156987,20,1,3,0,Single,11,671.5744658,0,33265.72329\n153354,25,8,1,1,Single,12,671.5230097,0,33245.15049\n133512,18,0,3,1,Single,7,671.1728517,0,33122.64259\n171346,17,0,2,1,Single,5,671.1452613,0,33159.26307\n173062,48,29,3,0,Married,5,670.9693545,0,32993.46773\n101564,31,12,1,0,Married,4,670.8921754,0,33165.60877\n156849,53,32,3,1,Married,1,670.6590707,0,33010.95354\n192582,49,29,1,1,Married,0,670.4684599,0,33092.423\n179338,51,34,2,1,Single,0,670.3400985,0,33228.00493\n114959,27,10,2,0,Single,0,669.8577239,0,33011.8862\n136983,16,0,3,0,Married,14,669.7758392,0,33250.79196\n121141,23,5,2,0,Single,15,669.5400051,0,33144.00026\n171120,16,0,4,1,Single,15,669.4806249,0,33054.03125\n140259,23,6,2,0,Married,9,669.3993572,0,33171.96786\n128752,44,27,3,0,Single,3,669.1662155,0,33075.31078\n136422,68,49,4,0,Married,2,669.1036894,0,33111.18447\n136254,51,32,3,0,Married,4,669.021422,0,33237.0711\n113159,22,5,1,1,Single,12,668.9781664,0,33122.90832\n103144,58,37,1,0,Married,1,668.946836,0,32872.3418\n139445,28,9,1,0,Married,2,668.780303,0,32860.01515\n150931,16,0,2,0,Married,14,668.7291417,0,33023.45709\n190315,18,0,2,0,Married,8,668.6205091,0,33104.02546\n117848,43,25,3,0,Single,2,668.5719867,0,32882.59934\n147395,18,0,3,1,Single,8,668.5541986,0,33055.70993\n197523,18,0,2,0,Married,14,668.3636007,0,33015.18004\n190606,38,19,4,0,Single,1,668.2032758,0,32890.16379\n122437,44,25,3,0,Married,5,667.6572673,0,33049.86337\n175311,25,8,2,0,Married,3,667.6514268,0,32963.57134\n130756,62,42,3,0,Single,0,667.35053,0,32814.5265\n190863,51,33,2,0,Single,4,667.3091222,0,32815.45611\n124820,40,23,2,1,Single,2,666.9432518,0,32895.16259\n122418,26,8,1,1,Single,3,666.6766088,0,32907.83044\n143896,37,19,2,0,Single,5,666.5898635,0,32853.49318\n144700,19,2,2,0,Single,15,666.5657916,0,32794.28958\n134205,30,11,4,1,Single,5,666.4330934,0,32909.65467\n103856,43,22,3,0,Single,1,666.3692559,0,32771.4628\n140800,52,31,4,1,Single,4,666.0408773,0,33046.04387\n188649,24,6,1,0,Married,15,665.9750545,0,32994.75273\n149545,29,9,1,0,Single,0,665.9491438,0,32982.45719\n160308,44,24,1,0,Married,5,665.8883025,0,33018.41513\n156680,16,0,3,1,Single,14,665.824362,0,32890.2181\n175578,28,8,2,1,Single,3,665.8071197,0,32943.35599\n108484,34,16,2,0,Single,4,665.6719734,0,33005.59867\n115371,45,27,3,1,Single,5,665.6410649,0,32744.05325\n196619,37,16,2,1,Single,1,665.5563364,0,32711.81682\n169022,54,36,3,1,Single,2,665.5022843,0,32714.11422\n153908,17,0,1,0,Single,12,665.4768153,0,32914.84077\n144251,18,0,2,0,Married,13,665.3737691,0,32698.68846\n128033,63,43,2,1,Single,0,665.2165102,0,33018.82551\n173775,37,16,1,0,Married,5,665.0776527,0,32908.88264\n184774,51,34,2,0,Single,1,664.7626221,0,33024.13111\n131066,19,1,2,0,Married,8,664.6908227,0,32914.54114\n174568,58,38,3,0,Married,1,664.6459222,0,32855.29611\n117900,22,5,1,0,Married,7,664.5934678,0,32647.67339\n196300,16,0,2,0,Married,15,664.5913205,0,32875.56603\n155029,30,12,4,0,Married,1,664.5255283,0,32738.27642\n103049,19,1,2,0,Single,11,664.5184463,0,33009.92232\n109415,28,9,3,0,Married,4,664.2565449,0,32940.82725\n122434,25,8,3,0,Single,12,664.2423112,0,32702.11556\n114318,57,36,2,0,Married,5,664.237318,0,32800.8659\n152950,23,5,2,1,Single,8,664.1069867,0,33005.34934\n145597,31,11,1,0,Married,5,664.0535613,0,32730.67807\n132714,45,24,4,0,Single,4,663.9290693,0,32738.45347\n156637,24,7,2,1,Single,8,663.9160877,0,32993.80439\n148302,17,0,2,0,Married,0,663.8869849,0,32805.34925\n126743,57,40,2,0,Single,3,663.8865517,0,32987.32759\n167466,22,3,2,0,Single,13,663.7236283,0,32872.18141\n143175,24,5,4,1,Single,6,663.6265959,0,32587.3298\n125037,16,0,1,0,Married,5,663.3734231,0,32607.67116\n171764,16,0,2,0,Single,12,663.3554871,0,32853.77436\n119469,22,4,2,1,Married,4,663.3347343,0,32885.73672\n167718,36,16,2,0,Single,4,663.29509,0,32648.7545\n194713,32,14,3,1,Single,4,663.1071537,0,32760.35769\n179975,23,4,3,0,Single,13,663.0013146,0,32907.06573\n110841,50,33,2,0,Married,3,662.9874221,0,32680.37111\n152336,58,37,2,1,Single,0,662.9831695,0,32884.15848\n149661,23,5,3,0,Single,15,662.93972,0,32638.986\n160063,52,33,2,1,Married,1,662.8956138,0,32647.78069\n179559,17,0,2,0,Single,11,662.7873582,0,32709.36791\n155435,27,6,2,0,Married,5,662.5729489,0,32539.64745\n146862,37,16,2,1,Single,1,662.5605733,0,32842.02867\n131138,49,30,3,0,Married,4,662.3350856,0,32892.75428\n164911,19,2,3,1,Single,10,662.3114573,0,32715.57287\n169435,21,4,3,0,Single,14,662.1250573,0,32549.25287\n126150,29,9,3,1,Single,3,661.6758331,0,32791.79166\n190503,37,19,2,1,Married,3,661.6002501,0,32714.01251\n121897,55,37,3,0,Married,5,661.336893,0,32684.84465\n118151,21,4,3,1,Single,15,661.3159383,0,32699.79692\n162333,33,12,1,0,Married,0,661.1792137,0,32491.96069\n146561,20,3,2,0,Single,11,661.1184216,0,32597.92108\n141276,25,8,3,0,Married,6,660.7046752,0,32754.23376\n171546,53,33,4,0,Married,2,660.4946263,0,32659.73132\n142717,31,14,3,1,Single,1,660.2775882,0,32548.87941\n196111,19,1,1,0,Single,11,660.0792756,0,32629.96378\n113208,29,10,2,1,Married,2,660.0459781,0,32403.29891\n106502,39,20,1,0,Married,4,659.9782567,0,32685.91284\n162455,19,1,2,0,Single,14,659.8311101,0,32409.55551\n187621,24,7,2,1,Married,4,659.7952018,0,32424.76009\n115898,17,0,3,0,Married,9,659.7949775,0,32449.74888\n105762,68,51,3,1,Single,2,659.7527272,0,32614.63636\n159334,19,0,2,0,Single,11,659.6831203,0,32608.15602\n116331,21,2,2,0,Married,5,659.6655382,0,32718.27691\n113416,25,6,2,1,Single,7,659.5723022,0,32720.61511\n103140,24,7,3,0,Married,3,659.249757,0,32397.48785\n110968,17,0,1,1,Single,0,659.2477512,0,32601.38756\n185133,20,3,2,1,Single,12,658.9425536,0,32375.12768\n130761,56,36,4,0,Single,1,658.5876308,0,32537.38154\n100804,19,0,3,0,Single,10,658.4688237,0,32336.44119\n188865,22,4,4,0,Single,14,658.1316668,0,32392.58334\n188331,23,4,3,1,Single,11,658.0923631,0,32557.61816\n159360,56,37,3,1,Married,1,658.0898718,0,32632.49359\n149871,67,46,1,1,Single,3,658.0763977,0,32405.81989\n182552,33,14,3,0,Married,4,657.8315098,0,32628.57549\n158364,38,20,3,0,Married,4,657.7115524,0,32341.57762\n196591,24,6,2,1,Single,11,657.7052993,0,32367.26497\n171377,33,13,4,0,Married,3,657.4021385,0,32277.10693\n157733,56,37,4,0,Single,3,657.3356664,0,32662.78332\n115844,16,0,3,0,Single,15,657.2997719,0,32636.9886\n155307,62,43,3,1,Single,4,657.0786444,0,32631.93222\n100484,57,38,3,0,Married,2,657.0095861,0,32435.47931\n176075,23,4,4,0,Married,10,656.9649014,0,32443.24507\n100969,18,0,2,0,Single,14,656.9475684,0,32575.37842\n112299,48,28,2,0,Single,3,656.8840275,0,32644.20138\n100618,42,24,3,1,Married,4,656.7647761,0,32530.23881\n133232,59,39,2,1,Single,5,656.7219971,0,32339.09986\n154022,45,24,3,1,Single,1,656.6655102,0,32554.27551\n127347,18,1,4,0,Single,15,656.5910916,0,32461.55458\n142903,35,16,3,1,Single,5,656.4117222,0,32423.58611\n127635,25,6,4,0,Married,10,656.2419141,0,32408.09571\n109613,69,49,2,0,Single,3,656.1977202,0,32466.88601\n106186,21,2,1,1,Single,13,656.1548087,0,32460.74044\n100739,28,10,3,0,Married,3,656.1247051,0,32595.23526\n173544,21,4,4,0,Single,12,655.9999696,0,32426.99848\n179788,39,18,3,0,Single,5,655.9321551,0,32462.60776\n102929,27,6,2,0,Married,3,655.695717,0,32239.78585\n138339,17,0,2,1,Single,11,655.613048,0,32293.6524\n135303,29,10,3,0,Married,3,655.4816786,0,32302.08393\n142628,50,29,4,1,Married,5,655.3008475,0,32562.04238\n109324,32,15,3,0,Married,3,655.2306971,0,32229.53486\n122265,20,2,1,0,Married,14,655.2242872,0,32319.21436\n125915,25,8,2,1,Single,9,655.1555498,0,32402.77749\n133656,22,5,1,0,Single,14,655.1327292,0,32408.63646\n186555,69,50,4,1,Married,3,655.0474775,0,32423.37388\n187173,64,46,2,0,Single,2,655.0243856,0,32387.21928\n162128,29,9,2,1,Single,0,655.0052795,0,32344.26398\n120966,26,6,3,0,Married,2,654.5872932,0,32248.36466\n188579,44,24,2,1,Single,1,654.484792,0,32414.2396\n162623,22,3,1,0,Single,15,654.294492,0,32416.7246\n162861,19,0,4,1,Single,12,654.2606407,0,32383.03204\n106966,24,7,2,1,Married,2,654.1892676,0,32287.46338\n188913,22,5,1,0,Single,8,653.8661986,0,32193.30993\n190408,49,29,4,0,Married,0,653.7769381,0,32446.84691\n169412,28,7,2,0,Single,4,653.7119739,0,32409.5987\n187837,19,1,1,0,Single,7,653.382622,0,32457.1311\n144855,36,15,3,0,Single,1,653.2984319,0,32241.9216\n104045,23,6,2,1,Married,1,653.2742442,0,32173.71221\n191846,49,32,2,1,Single,1,653.224712,0,32156.2356\n176657,36,16,4,1,Single,1,652.9423246,0,32336.11623\n134067,30,12,3,1,Single,2,652.7468437,0,32362.34219\n180064,66,48,2,1,Single,0,652.6104325,0,32228.52163\n134517,41,23,2,1,Single,1,652.4612639,0,32044.0632\n189545,50,33,2,1,Single,0,652.433378,0,32226.6689\n197814,32,11,3,1,Single,5,652.4245763,0,32242.22882\n162640,29,8,2,0,Married,0,652.1225397,0,32254.12699\n100862,59,40,2,0,Married,2,652.1079977,0,32202.39989\n177405,50,32,3,0,Married,1,651.9154879,0,32371.7744\n198629,23,5,1,0,Married,8,651.9069317,0,32230.34659\n126830,66,49,2,0,Married,3,651.8805216,0,32221.02608\n175060,16,0,3,0,Single,12,651.8411728,0,32039.05864\n141258,40,23,1,0,Single,3,651.4634054,0,32332.17027\n127084,23,4,2,0,Married,2,651.4601572,0,32154.00786\n105641,16,0,2,0,Married,13,651.4406586,0,32286.03293\n122543,20,3,1,0,Single,14,651.4197408,0,32293.98704\n125268,38,18,2,0,Single,3,651.1895254,0,32347.47627\n134118,21,2,2,1,Single,11,651.1858695,0,32174.29348\n113376,55,37,3,0,Married,0,651.1314961,0,31998.57481\n180198,24,6,3,0,Married,6,651.1247737,0,32078.23869\n102119,24,5,3,0,Married,8,650.6772051,0,32141.86026\n143763,58,37,3,1,Married,3,650.5273182,0,32146.36591\n193876,42,23,4,1,Single,4,650.1712786,0,32253.56393\n163734,49,29,3,1,Single,2,649.7087321,0,32134.43661\n156037,31,12,2,0,Married,1,649.6232377,0,32262.16189\n198585,29,8,3,1,Single,4,649.4023528,0,31883.11764\n157788,61,40,3,0,Single,0,649.0457704,0,32195.28852\n155174,23,6,1,1,Single,8,648.6773297,0,31989.86649\n196438,35,17,3,1,Married,3,648.638863,0,31948.94315\n157512,18,0,3,1,Single,12,648.5689889,0,31932.44945\n184545,63,45,2,1,Married,2,648.5628976,0,32065.14488\n185498,24,7,1,0,Single,14,648.4970615,0,32202.85308\n184700,38,20,1,0,Married,0,648.4727226,0,32026.63613\n102952,51,30,1,1,Single,0,648.3377527,0,32149.88764\n110149,36,17,3,1,Single,5,648.2724683,0,31844.62342\n154359,35,17,3,1,Single,1,648.2237088,0,32079.18544\n116478,22,5,2,0,Single,8,648.0421491,0,32011.10746\n125978,20,3,2,0,Married,11,648.0153159,0,32192.7658\n133054,18,0,2,0,Married,9,647.851471,0,31846.57355\n192887,25,6,1,1,Single,6,647.6694226,0,31915.47113\n162448,25,7,3,0,Single,12,647.6636606,0,32021.18303\n186744,21,3,2,1,Single,7,647.3982171,0,31984.91086\n171129,41,23,3,0,Married,5,647.3515386,0,31879.57693\n134255,23,6,3,0,Single,7,647.1741685,0,32055.70843\n104893,26,9,3,0,Single,4,647.088197,0,31882.40985\n128587,16,0,3,1,Single,14,647.0847879,0,32136.2394\n119401,19,0,4,0,Single,15,646.9458306,0,32127.29153\n121461,17,0,2,0,Single,15,646.9235949,0,32114.17975\n118268,45,27,3,0,Single,3,646.833763,0,32049.68815\n109456,47,26,4,1,Married,3,646.6960826,0,32005.80413\n177009,27,6,1,0,Married,3,646.6608555,0,31827.04278\n182000,39,20,2,1,Married,2,646.6507561,0,32108.53781\n157223,23,6,3,0,Single,14,646.6307467,0,32070.53734\n150615,24,5,2,0,Single,14,646.3478607,0,32059.39304\n129573,23,5,2,0,Married,7,645.5930282,0,31802.65141\n111544,23,6,2,1,Married,5,645.4350689,0,31737.75345\n130569,21,2,4,0,Married,9,645.388918,0,31832.4459\n180072,62,42,2,0,Single,1,645.3861965,0,31670.30983\n195854,16,0,1,1,Single,4,645.3645357,0,31835.22679\n156206,27,9,2,1,Married,2,645.2374919,0,31676.8746\n124872,52,31,1,1,Married,2,645.0260481,0,31851.30241\n142664,16,0,2,0,Married,6,644.9014959,0,32029.0748\n197293,48,29,2,1,Married,2,644.8848643,0,31789.24322\n160748,39,19,2,0,Single,1,644.7900113,0,32033.50057\n109646,41,20,1,0,Single,0,644.7784085,0,31642.92043\n132596,55,35,1,0,Married,1,644.72313,0,31748.1565\n199532,16,0,2,0,Single,11,644.6991757,0,31854.95879\n143527,35,14,1,1,Single,0,644.6010832,0,31651.05416\n126042,20,1,1,0,Single,12,644.5098594,0,32018.49297\n126736,20,3,4,1,Single,14,644.3614642,0,31676.07321\n192260,20,1,3,1,Single,14,644.2999437,0,31946.99719\n122640,40,22,2,0,Single,0,644.0822542,0,31850.11271\n149066,18,0,2,0,Single,14,643.8830466,0,31703.15233\n132962,26,9,2,0,Single,4,643.8323624,0,31615.61812\n160722,19,0,2,1,Single,5,643.7632811,0,31805.16406\n139945,23,4,1,0,Married,6,643.5273912,0,31963.36956\n173353,51,32,2,1,Single,0,643.4801329,0,31715.00665\n105293,49,28,3,1,Married,5,643.3409511,0,31567.04756\n188467,38,20,4,1,Married,5,643.3343761,0,31702.71881\n181560,18,0,2,0,Single,8,643.2453717,0,31850.26859\n185088,65,48,3,0,Married,3,643.1651214,0,31909.25607\n163995,45,25,2,1,Married,1,643.1581009,0,31655.90505\n161457,17,0,2,0,Married,13,643.1122901,0,31934.61451\n168636,25,6,2,0,Married,6,642.8873908,0,31831.36954\n142565,46,27,3,0,Married,0,642.879242,0,31898.9621\n178651,19,2,3,0,Single,11,642.8462797,0,31849.31399\n167850,18,0,2,1,Single,8,642.8128637,0,31807.64319\n157484,54,33,4,1,Single,2,642.790732,0,31927.5366\n198552,17,0,2,0,Single,12,642.7209895,0,31548.04948\n196221,24,6,2,1,Single,6,642.6161195,0,31533.80598\n185804,49,29,3,0,Single,5,642.3640407,0,31880.20204\n176111,29,9,1,1,Married,3,642.2300098,0,31890.50049\n112869,19,0,3,0,Married,14,642.1196909,0,31648.98455\n165268,24,7,4,0,Single,13,642.0626915,0,31868.13458\n139411,20,1,3,0,Married,5,642.0558506,0,31602.79253\n132221,68,48,2,0,Married,3,642.0489852,0,31868.44926\n129476,51,30,3,0,Married,0,641.8876964,0,31891.38482\n184119,59,40,2,0,Single,1,641.8233901,0,31759.16951\n137967,23,6,3,0,Single,9,641.8082752,0,31730.41376\n118850,23,6,1,1,Single,3,641.6075399,0,31676.377\n180908,16,0,3,0,Single,13,641.3946703,0,31599.73352\n146001,42,24,1,1,Single,3,641.3910282,0,31598.55141\n192764,30,13,4,1,Single,4,641.0927553,0,31503.63777\n185196,25,8,3,0,Married,6,641.0568299,0,31738.8415\n104826,44,27,3,1,Single,3,641.0551113,0,31534.75556\n152158,33,13,3,0,Married,2,641.0392555,0,31537.96278\n147621,35,18,3,0,Married,1,640.8854143,0,31697.27072\n166102,21,3,4,0,Married,10,640.8558315,0,31463.79158\n170631,42,23,2,0,Married,1,640.791076,0,31488.5538\n188937,40,20,1,1,Single,3,640.738802,0,31507.9401\n156254,36,17,2,0,Married,1,640.6990473,0,31477.95237\n130378,40,23,4,0,Married,4,640.6807811,0,31595.03906\n154693,20,1,2,0,Single,14,640.6504454,0,31782.52227\n198411,70,52,3,0,Married,2,640.5796391,0,31671.98196\n190032,16,0,1,1,Single,11,640.2131123,0,31785.65562\n163464,37,18,4,0,Single,2,639.9843022,0,31656.21511\n138028,21,4,2,0,Single,9,639.9576405,0,31661.88203\n192946,26,9,4,0,Single,4,639.8119066,0,31553.59533\n187735,44,23,3,0,Married,1,639.7393753,0,31522.96877\n134967,31,12,3,1,Single,1,639.7120078,0,31564.60039\n196944,38,20,3,1,Married,2,639.6023552,0,31557.11776\n192254,21,3,2,1,Single,12,639.4551566,0,31655.75783\n127602,16,0,2,1,Single,5,639.3834022,0,31725.17011\n174163,26,7,2,1,Single,0,639.34696,0,31543.348\n114006,16,0,3,0,Married,15,639.3004263,0,31408.02132\n125842,70,49,1,0,Single,2,639.261733,0,31744.08665\n196746,26,5,3,0,Married,3,639.0417596,0,31693.08798\n136526,41,24,2,1,Single,2,638.9886613,0,31622.43307\n112013,17,0,2,0,Married,13,638.9174617,0,31472.87309\n105522,63,42,3,1,Single,0,638.9009768,0,31386.04884\n143952,18,0,3,0,Married,8,638.7920458,0,31346.60229\n141263,23,6,2,0,Married,8,638.5858885,0,31410.29443\n122103,48,29,3,0,Single,3,638.5384535,0,31388.92268\n116224,28,8,3,1,Married,1,638.4408901,0,31662.04451\n137938,24,7,1,0,Single,14,638.3784062,0,31477.92031\n192531,25,8,2,0,Single,14,638.3443766,0,31541.21883\n129136,21,3,2,0,Single,15,638.2598637,0,31705.99319\n188287,39,21,4,1,Single,4,638.2435083,0,31671.17542\n167455,34,17,3,0,Single,4,638.1785514,0,31311.92757\n177551,18,0,3,0,Single,13,638.0311359,0,31627.5568\n188658,25,7,2,1,Single,7,638.02869,0,31622.4345\n100438,23,4,3,0,Single,14,637.9966337,0,31562.83169\n143194,24,5,1,0,Married,12,637.961713,0,31443.08565\n186610,42,25,2,1,Married,1,637.8821118,0,31336.10559\n131688,22,5,3,0,Married,6,637.7350752,0,31311.75376\n189559,19,2,3,0,Single,11,637.5363636,0,31478.81818\n153925,17,0,4,1,Single,14,637.2844851,0,31537.22426\n189654,31,14,2,1,Married,5,637.2550518,0,31338.75259\n183622,54,35,4,1,Single,2,637.2057499,0,31478.2875\n151745,25,8,3,1,Single,15,637.1825197,0,31369.12599\n157722,59,38,4,1,Single,1,637.1596088,0,31446.98044\n147785,16,0,2,1,Single,11,637.006292,0,31599.3146\n128152,69,48,4,0,Married,3,636.9997516,0,31272.98758\n141395,19,0,3,1,Single,5,636.9476958,0,31433.38479\n131837,26,6,1,0,Single,4,636.9060704,0,31319.30352\n198609,16,0,4,0,Single,13,636.8510539,0,31484.5527\n187541,44,27,2,1,Married,3,636.8107252,0,31370.53626\n100887,23,5,3,1,Married,3,636.7934777,0,31324.67389\n189920,32,15,1,1,Single,5,636.6797227,0,31516.98614\n111103,53,36,1,1,Single,4,636.6652396,0,31539.26198\n104884,24,5,4,0,Single,11,636.6335813,0,31400.67907\n144076,27,6,3,0,Married,1,636.5105846,0,31235.52923\n198632,22,4,3,1,Single,8,636.4555646,0,31369.77823\n140233,70,52,2,1,Single,5,636.1406818,0,31347.03409\n107217,25,6,3,1,Married,4,636.0503488,0,31541.51744\n118575,24,6,2,1,Single,11,636.0165641,0,31344.82821\n183345,36,19,2,0,Single,2,635.7843899,0,31410.2195\n183861,20,3,4,1,Single,8,635.7349774,0,31549.74887\n170165,57,39,3,0,Married,1,635.6589864,0,31471.94932\n192419,20,2,1,0,Single,12,635.5338229,0,31388.69115\n116287,23,5,3,0,Single,9,635.3993034,0,31458.96517\n109240,33,16,2,0,Single,1,635.3621555,0,31169.10778\n140139,23,4,4,1,Single,8,635.0319306,0,31344.59653\n189270,21,4,4,0,Married,9,634.9963839,0,31255.8192\n136376,48,31,4,1,Married,0,634.839158,0,31196.9579\n166848,16,0,3,1,Single,12,634.8251684,0,31303.25842\n122509,62,45,3,0,Married,0,634.7543215,0,31235.71608\n167013,61,42,3,0,Single,3,634.6854911,0,31240.27456\n162186,51,31,2,0,Married,3,634.6816208,0,31488.08104\n106351,60,40,2,0,Single,3,634.6227028,0,31297.13514\n114714,59,40,2,1,Married,3,634.5363021,0,31446.81511\n180243,24,5,2,0,Married,12,634.5101073,0,31295.50537\n182135,29,8,3,1,Married,2,634.4004256,0,31489.02128\n173377,43,25,1,0,Married,2,634.287001,0,31287.35005\n165359,18,0,3,0,Married,8,634.1466141,0,31122.33071\n116296,24,6,1,0,Married,0,633.9571627,0,31357.85814\n100609,47,30,2,1,Married,2,633.9120347,0,31237.60174\n133887,39,21,2,1,Married,3,633.8975472,0,31457.87736\n115546,16,0,3,0,Married,10,633.6359986,0,31260.79993\n133561,20,1,3,0,Single,15,633.366832,0,31408.3416\n196068,40,22,2,0,Married,1,633.2255247,0,31425.27624\n111560,25,7,3,0,Single,11,633.0663893,0,31404.31947\n100767,35,16,2,0,Married,4,633.0470208,0,31320.35104\n106584,16,0,2,0,Single,12,633.0287569,0,31440.43785\n130165,38,20,1,0,Single,3,632.8459416,0,31309.29708\n127154,46,26,3,1,Single,5,632.6400659,0,31110.0033\n124766,17,0,2,0,Single,9,632.4444462,0,31028.22231\n173764,21,3,2,0,Single,11,632.3787533,0,31132.93767\n187247,23,6,2,0,Single,10,632.2749098,0,31036.74549\n136016,19,2,2,0,Single,11,632.1200446,0,31022.00223\n127394,53,33,3,0,Single,0,631.9767168,0,31008.83584\n141683,23,5,4,0,Single,6,631.973488,0,31194.6744\n124833,17,0,3,0,Single,11,631.9304981,0,31163.52491\n170638,21,4,3,0,Married,1,631.8019488,0,31070.09744\n156140,24,7,2,0,Single,12,631.7729612,0,31013.64806\n170229,30,10,2,0,Married,0,631.7516049,0,31241.58025\n124246,51,31,3,0,Single,5,631.7062428,0,31308.31214\n197837,29,11,4,0,Single,0,631.5946979,0,31304.7349\n153571,30,11,2,0,Single,0,631.5285866,0,31031.42933\n106254,18,0,2,0,Married,12,631.3421248,0,31002.10624\n142742,44,24,2,0,Married,2,631.3342652,0,31227.71326\n182653,49,29,3,1,Married,3,631.3156236,0,31065.78118\n134326,43,22,4,0,Single,3,631.3015297,0,31013.07649\n112287,24,5,2,0,Single,13,631.206713,0,31008.33565\n179247,16,0,2,0,Single,9,631.1747614,0,31298.73807\n155991,21,2,4,0,Single,10,631.1508185,0,31042.54093\n196818,23,6,2,0,Single,11,631.0976125,0,31353.88063\n153078,26,5,4,0,Married,2,631.0840663,0,31256.20332\n128956,52,33,3,0,Married,3,630.997314,0,31037.8657\n158944,35,14,4,1,Single,4,630.8403634,0,31225.01817\n133625,20,1,4,0,Married,6,630.7948845,0,31260.74423\n195132,23,6,3,1,Single,13,630.7366806,0,31299.83403\n156007,46,28,1,0,Married,4,630.5057856,0,31096.28928\n164636,19,1,2,0,Married,11,630.4480275,0,31238.40138\n100627,19,0,3,0,Married,13,630.4318843,0,31044.59422\n118649,57,40,4,1,Single,4,630.3374905,0,30988.87453\n162072,44,24,2,1,Single,3,630.3165834,0,31086.82917\n160549,22,5,3,0,Single,9,630.2061658,0,31090.30829\n131674,34,16,4,0,Married,4,630.0057954,0,30908.28977\n116153,31,10,2,0,Single,5,629.8601463,0,31079.00732\n101987,17,0,3,0,Single,11,629.7962025,0,31020.81013\n149238,36,17,3,0,Single,2,629.5130907,0,31076.65454\n135272,40,22,1,1,Single,2,629.444886,0,31259.2443\n169084,58,40,2,0,Single,4,629.062243,0,31002.11215\n177782,17,0,3,0,Single,14,628.8984778,0,31053.92389\n145885,44,26,4,0,Single,4,628.8964673,0,31202.82337\n116818,22,3,2,0,Single,14,628.6636366,0,30932.18183\n199635,48,27,1,0,Married,5,628.6211982,0,30903.05991\n164524,23,5,2,1,Single,10,628.5541623,0,31061.70812\n172087,57,39,4,1,Married,1,628.4233997,0,30910.16999\n116640,35,18,1,0,Married,5,628.2463584,0,30819.31792\n138729,23,5,2,1,Single,13,628.1035152,0,31018.17576\n124644,22,3,3,0,Single,13,627.9072058,0,30853.36029\n166467,23,4,2,1,Single,14,627.7736206,0,31066.68103\n102985,23,4,3,0,Married,13,627.699644,0,30961.9822\n194325,21,3,4,1,Single,15,627.6955863,0,30828.77932\n103801,27,10,3,0,Married,4,627.6512931,0,30810.56466\n149436,44,23,2,0,Married,5,627.6014932,0,30931.07466\n193790,16,0,4,0,Married,10,627.5152066,0,31098.76033\n126653,54,35,4,0,Single,3,627.4470266,0,30946.35133\n148998,21,3,2,1,Single,12,627.3027977,0,31020.13989\n185444,41,21,2,1,Married,3,627.2721496,0,30772.60748\n159498,23,4,2,0,Married,12,627.2246947,0,30834.23474\n189220,54,36,2,1,Married,0,627.0833742,0,30760.16871\n145069,17,0,4,1,Single,13,627.00749,0,30994.3745\n130725,37,19,4,1,Single,4,626.9966239,0,30874.8312\n176177,20,3,3,1,Single,14,626.9501344,0,30948.50672\n103067,47,29,2,0,Married,1,626.8658744,0,30751.29372\n163646,17,0,2,0,Married,12,626.8658074,0,31115.29037\n171543,51,31,4,1,Married,5,626.778193,0,30978.90965\n179967,70,51,3,1,Single,3,626.5491835,0,31029.45918\n154432,68,48,3,1,Married,0,626.5094615,0,30877.47308\n181017,22,5,2,1,Single,9,626.4513425,0,31048.56713\n199813,18,0,2,1,Single,5,626.3859709,0,30918.29855\n175527,32,13,1,0,Married,0,626.3597611,0,30732.98806\n185185,49,28,3,1,Married,5,626.3339813,0,30842.69907\n174301,38,17,3,1,Married,0,626.2477129,0,31033.38565\n189794,26,5,4,0,Single,5,626.2195329,0,30720.97665\n173905,30,12,3,0,Single,1,626.2191271,0,30887.95636\n155538,58,41,2,0,Married,3,626.1478938,0,30913.39469\n101096,18,1,2,0,Single,7,626.0806825,0,30778.03413\n134731,32,12,4,0,Single,1,626.0486157,0,30959.43079\n114899,68,51,3,0,Single,3,626.0416517,0,31021.08259\n193943,52,33,1,0,Married,1,625.962084,0,31040.1042\n113259,38,20,1,0,Married,3,625.919221,0,30900.96105\n177539,17,0,1,0,Single,13,625.7153342,0,30723.76671\n101673,65,48,2,0,Single,3,625.6710232,0,30786.55116\n105991,17,0,2,1,Single,11,625.5870582,0,31003.35291\n186499,20,1,1,1,Single,12,625.5405704,0,30681.02852\n177382,17,0,2,0,Single,10,625.5144383,0,31030.72192\n121587,55,35,4,0,Single,4,625.4247816,0,30896.23908\n190143,47,27,2,0,Married,0,625.28844,0,30719.422\n143559,57,40,1,0,Married,3,625.2493695,0,30838.46848\n106213,24,6,3,0,Single,15,625.2450204,0,30788.25102\n158938,59,42,2,1,Married,3,625.0589683,0,30672.94842\n143877,37,18,3,1,Single,1,625.0330082,0,31021.65041\n120106,25,7,3,0,Single,8,625.0268,0,30754.34\n152480,16,0,3,1,Single,8,625.00839,0,30886.4195\n117351,46,25,2,1,Married,5,624.8275414,0,30849.37707\n125254,27,8,2,0,Married,3,624.6832837,0,30702.16419\n158826,24,7,4,0,Single,11,624.6176756,0,30998.88378\n137426,26,5,4,0,Single,5,624.4043917,0,30688.21959\n147282,19,1,3,0,Married,7,624.1928257,0,30762.64129\n122282,46,29,3,1,Married,3,624.1397563,0,30609.98782\n165514,62,41,2,1,Single,1,624.0649187,0,30653.24594\n174395,18,0,2,0,Single,15,623.9346676,0,30848.73338\n171784,23,5,1,0,Married,7,623.8842385,0,30664.21193\n188016,45,24,3,1,Single,0,623.8514651,0,30868.57326\n120339,64,45,4,0,Single,2,623.8263682,0,30766.31841\n186510,61,42,2,1,Married,3,623.7921552,0,30698.60776\n189954,19,0,4,1,Single,9,623.740872,0,30949.0436\n180832,35,15,1,0,Single,5,623.7337503,0,30983.68752\n149929,48,28,2,1,Single,1,623.6019605,0,30678.09803\n193990,49,32,2,1,Single,1,623.50911,0,30702.4555\n129584,24,7,2,1,Single,14,623.1690893,0,30786.45447\n108023,57,39,2,1,Married,4,623.0270061,0,30552.35031\n196398,37,18,4,0,Married,0,622.9318655,0,30632.59328\n177425,51,32,2,1,Single,1,622.8166758,0,30882.83379\n163177,57,36,1,0,Married,1,622.801627,0,30577.08135\n188660,21,3,3,0,Married,6,622.7737057,0,30700.68529\n109586,19,0,2,0,Single,9,622.6425648,0,30680.12824\n190277,19,1,2,0,Married,9,622.5991221,0,30846.95611\n154684,44,25,3,1,Married,3,622.2847527,0,30627.23764\n116001,20,2,3,1,Single,10,622.2253314,0,30640.26657\n108415,22,5,3,1,Single,12,622.2000691,0,30787.00345\n191111,22,5,4,0,Single,14,622.1645176,0,30588.22588\n127036,16,0,1,1,Single,15,622.0789033,0,30534.94517\n197161,20,1,2,0,Single,12,621.8172611,0,30577.86306\n145307,66,47,3,0,Married,3,621.7868996,0,30529.34498\n195906,29,10,2,1,Single,0,621.6363332,0,30738.81666\n195553,69,51,2,1,Single,5,621.628598,0,30851.4299\n115986,16,0,4,1,Single,9,621.2361814,0,30724.80907\n184789,32,11,3,0,Married,5,620.9978398,0,30685.89199\n148491,45,24,2,1,Married,3,620.9608287,0,30560.04144\n100749,26,5,2,0,Single,3,620.9459254,0,30476.29627\n106410,53,32,2,0,Married,1,620.8278988,0,30459.39494\n170007,28,10,2,1,Married,5,620.6286596,0,30576.43298\n190342,31,14,3,1,Single,5,620.6278901,0,30536.39451\n154195,45,27,1,0,Married,4,620.4772924,0,30634.86462\n150336,19,1,3,0,Single,14,620.410962,0,30451.5481\n186642,45,25,3,0,Married,3,620.3858534,0,30521.29267\n117579,24,7,4,0,Single,12,620.0075791,0,30482.37896\n122612,17,0,3,0,Single,13,619.9914755,0,30651.57378\n139817,21,4,2,0,Single,15,619.9056497,0,30790.28249\n146398,40,23,4,0,Married,3,619.8452402,0,30745.26201\n103329,17,0,4,1,Single,6,619.8070262,0,30571.35131\n123096,52,35,1,1,Married,0,619.7939909,0,30553.69955\n122060,37,20,2,0,Married,3,619.6989419,0,30718.9471\n172196,25,6,1,0,Single,12,619.623702,0,30570.1851\n192821,20,1,1,1,Single,10,619.4987572,0,30530.93786\n116004,49,32,3,1,Single,3,619.4956953,0,30649.78477\n189584,20,2,2,0,Single,13,619.4805931,0,30459.02966\n117323,46,28,2,1,Married,2,619.4605895,0,30460.02948\n197964,42,24,2,1,Married,0,619.4023233,0,30503.11617\n124243,55,34,3,1,Single,2,619.389806,0,30662.4903\n121456,21,3,1,0,Single,12,619.3364482,0,30576.82241\n136781,19,1,2,0,Married,6,619.2836967,0,30372.18484\n176702,42,23,2,1,Married,3,618.8939074,0,30688.69537\n168487,17,0,4,0,Single,12,618.5573771,0,30528.86886\n152794,30,10,4,1,Married,1,618.3818105,0,30364.09053\n148067,19,1,3,0,Married,7,618.3688318,0,30583.44159\n158813,43,25,3,1,Single,3,618.2242062,0,30495.21031\n133942,69,49,4,0,Married,5,618.1234853,0,30410.17427\n132161,24,6,3,0,Married,13,618.1098159,0,30417.4908\n151674,23,4,3,1,Single,14,617.8746458,0,30347.73229\n196341,26,6,3,0,Married,2,617.847154,0,30312.3577\n166998,53,32,2,1,Married,5,617.772548,0,30455.6274\n152308,54,35,3,1,Single,4,617.4427847,0,30650.13924\n110969,16,0,4,0,Single,12,617.3432772,0,30419.16386\n103254,35,18,3,0,Married,3,617.3324292,0,30345.62146\n151167,18,0,2,0,Married,7,617.2558574,0,30504.79287\n139039,55,38,1,0,Married,4,617.1669192,0,30274.34596\n173245,38,20,3,1,Married,3,617.1545058,0,30466.72529\n101627,31,12,2,1,Single,2,617.0991685,0,30511.95843\n147247,44,25,4,0,Married,3,616.9569873,0,30484.84937\n168212,23,4,3,1,Single,6,616.8932904,0,30470.66452\n139639,27,10,3,0,Single,5,616.4575626,0,30528.87813\n110581,58,37,2,1,Married,0,616.392507,0,30576.62535\n122083,23,6,1,0,Married,15,616.3823065,0,30388.11533\n195131,36,16,3,0,Married,0,616.2520273,0,30407.60137\n104439,53,33,2,0,Married,5,616.0916256,0,30455.58128\n122049,25,8,1,0,Single,11,616.045,0,30321.25\n115630,32,13,1,0,Single,1,616.0345533,0,30221.72767\n173358,44,23,3,0,Single,5,615.9580721,0,30360.90361\n176932,57,39,2,1,Single,3,615.6461146,0,30530.30573\n133354,22,5,2,0,Married,6,615.4151608,0,30319.75804\n186952,23,4,1,0,Single,13,615.2990801,0,30539.95401\n111509,45,27,3,0,Single,4,615.2764216,0,30353.82108\n109940,16,0,2,0,Single,11,615.1075981,0,30435.37991\n174649,24,7,1,1,Single,8,614.8820182,0,30299.10091\n148835,21,2,2,1,Single,9,614.7846154,0,30538.23077\n139524,20,2,3,1,Single,12,614.7809363,0,30404.04682\n102046,25,8,2,0,Single,6,614.7198868,0,30142.99434\n118706,36,15,4,0,Married,0,614.7065539,0,30221.3277\n130623,21,3,3,1,Married,2,614.590703,0,30219.53515\n175291,17,0,3,1,Single,11,614.3353233,0,30358.76617\n185106,23,6,3,0,Married,8,614.285921,0,30241.29605\n130894,61,41,3,1,Single,1,614.1358644,0,30135.79322\n148884,39,20,3,1,Married,4,614.0226004,0,30118.13002\n183710,56,36,4,1,Married,5,613.8447847,0,30163.23924\n195044,25,8,2,0,Single,14,613.8435182,0,30412.17591\n157189,19,1,3,0,Married,11,613.6257588,0,30435.28794\n133975,51,31,2,1,Single,5,613.5126653,0,30241.63327\n159681,46,27,2,0,Married,0,613.432478,0,30330.6239\n143297,26,7,2,0,Single,3,613.3696547,0,30144.48274\n134645,16,0,4,0,Married,11,613.3288072,0,30173.44036\n184838,67,49,2,0,Single,3,613.2362078,0,30067.81039\n191082,39,22,3,1,Married,4,613.1473579,0,30285.3679\n141928,23,4,2,1,Single,13,613.0166806,0,30351.83403\n173638,16,0,3,0,Single,14,612.9537999,0,30159.69\n129555,57,40,2,1,Married,1,612.8607726,0,30053.03863\n169737,27,7,4,1,Married,1,612.7830483,0,30093.15242\n147705,23,5,3,0,Married,11,612.7802233,0,30212.01117\n132475,38,19,3,0,Married,5,612.7651926,0,30124.25963\n126054,16,0,3,1,Married,2,612.6985693,0,30285.92847\n158515,24,5,4,0,Married,14,612.3938545,0,30404.69273\n114999,59,41,2,1,Single,2,612.2546511,0,30024.73256\n195298,55,37,2,1,Married,0,612.1433415,0,30114.16708\n157932,58,39,3,0,Married,1,612.1191026,0,30358.95513\n173790,31,11,2,1,Married,1,612.0679151,0,30046.39576\n126786,48,28,4,1,Single,4,612.0164632,0,30364.82316\n155862,43,25,1,1,Single,5,611.9370486,0,30330.85243\n120967,51,32,3,1,Single,1,611.885022,0,30380.2511\n147012,65,47,4,0,Single,1,611.5439852,0,29999.19926\n178760,70,50,3,0,Single,5,611.5310237,0,30151.55119\n192069,25,6,3,0,Married,6,611.5135423,0,30310.67712\n126308,44,24,2,0,Married,3,611.3809676,0,29981.04838\n142102,31,14,3,0,Married,3,611.2974017,0,30265.87009\n148979,58,37,2,0,Single,0,611.2208659,0,29981.0433\n191968,49,29,3,0,Single,1,611.2013614,0,30112.06807\n154310,16,0,3,0,Single,12,611.1097232,0,30336.48616\n170672,25,7,3,0,Single,11,610.8998125,0,30010.99063\n154026,19,2,2,0,Married,10,610.6187313,0,30187.93657\n178782,16,0,1,1,Single,12,610.5008673,0,30321.04337\n176052,21,3,4,1,Single,10,610.4427025,0,29930.13513\n158673,25,6,3,0,Married,4,610.3816365,0,30207.08183\n119034,16,0,3,0,Married,10,610.3039207,0,30250.19604\n140782,32,15,4,1,Married,3,610.1817019,0,30272.0851\n154115,25,6,1,0,Single,15,610.1699363,0,30292.49682\n110723,29,12,2,0,Married,1,610.0814772,0,30250.07386\n133053,41,24,1,1,Single,1,609.7857707,0,29931.28854\n195792,20,2,3,1,Single,11,609.7680208,0,30253.40104\n173688,17,0,3,1,Single,8,609.7247591,0,30235.23796\n122545,19,1,4,0,Single,12,609.613509,0,30257.67545\n159422,28,7,2,0,Single,2,609.4739476,0,30111.69738\n130607,34,15,2,0,Single,1,609.4384353,0,29936.92177\n188698,47,27,3,1,Married,3,609.2892848,0,30264.46424\n174909,31,12,2,0,Single,5,609.1627076,0,30017.13538\n145495,20,2,2,0,Single,11,608.6501064,0,30212.50532\n187711,47,28,2,1,Married,0,608.6368975,0,29936.84488\n100369,23,4,2,0,Married,11,608.2682405,0,30009.41203\n135829,18,0,4,0,Married,14,608.2213981,0,29973.06991\n117332,20,1,4,0,Single,12,607.965726,0,29995.2863\n164817,40,22,4,1,Single,0,607.8027059,0,29899.1353\n136134,23,4,2,1,Single,12,607.7779773,0,30056.89887\n155755,25,8,1,1,Single,10,607.6422879,0,30005.1144\n184049,33,14,2,1,Married,4,607.6109504,0,30112.54752\n108463,48,28,3,0,Married,4,607.5414533,0,30028.07267\n142131,53,35,3,1,Single,5,607.3659448,0,30008.29724\n156809,48,29,2,1,Married,2,607.126919,0,29976.34595\n143932,30,12,3,0,Married,0,607.0810357,0,29803.05179\n177328,16,0,4,0,Single,13,606.8939997,0,29954.69999\n111448,26,7,1,1,Married,5,606.3573767,0,30090.86884\n173137,17,0,3,0,Married,14,606.1284445,0,29803.42223\n154898,63,43,3,0,Single,0,606.0708998,0,29874.54499\n113032,20,3,1,0,Single,15,606.0372524,0,29926.86262\n119913,30,12,2,1,Single,4,605.9771739,0,29851.8587\n164982,18,1,3,0,Married,7,605.9417217,0,29773.08609\n165095,36,16,4,1,Married,2,605.7083676,0,29938.41838\n181182,22,3,2,1,Single,6,605.641365,0,29906.06825\n191335,22,4,4,0,Married,14,605.6072165,0,29898.36083\n116862,55,37,3,0,Single,1,605.5670847,0,29825.35424\n102395,39,19,4,0,Married,3,605.5566529,0,29957.83265\n179482,38,17,3,0,Married,4,605.5402007,0,29854.01004\n186428,21,3,1,1,Single,13,605.5328369,0,29858.64185\n146119,23,4,3,0,Married,11,605.1992338,0,29879.96169\n126561,22,5,2,0,Single,11,605.1604348,0,29776.02174\n148896,31,11,2,0,Married,5,605.1251804,0,30018.25902\n164683,49,29,2,0,Single,2,605.113279,0,29929.66395\n100744,29,9,3,0,Married,2,605.035559,0,29962.77795\n130233,68,48,3,0,Married,0,604.9970689,0,29838.85345\n134768,28,10,3,1,Married,2,604.9855479,0,29756.2774\n100579,20,3,2,0,Married,7,604.9244768,0,29762.22384\n100285,21,2,3,1,Single,15,604.877098,0,29828.8549\n190637,16,0,2,0,Married,12,604.7130011,0,29838.65006\n182161,24,5,1,0,Married,15,604.5968924,0,29980.84462\n116294,17,0,2,1,Single,13,604.4759269,0,30006.79635\n196553,16,0,2,1,Single,14,604.4276573,0,29832.38287\n115084,42,24,2,1,Married,2,604.4075127,0,29826.37564\n146679,31,11,2,0,Married,5,604.2948518,0,29797.74259\n107300,19,1,2,1,Single,14,604.2520723,0,29643.60362\n123070,23,4,3,0,Single,13,604.2383648,0,29780.91824\n107110,56,36,1,1,Married,2,603.7338515,0,29973.69258\n166561,44,24,3,1,Married,0,603.6616696,0,29863.08348\n149627,24,6,3,0,Married,1,603.4243667,0,29580.21834\n159319,29,8,1,1,Single,2,603.1089156,0,29668.44578\n113556,62,45,3,1,Single,0,603.1041914,0,29918.20957\n128931,58,37,2,0,Single,4,603.0673537,0,29906.36769\n163319,20,3,3,1,Married,3,603.0666933,0,29697.33467\n192126,52,34,2,0,Single,2,603.0119453,0,29833.59727\n185760,26,5,4,0,Married,2,603.0047702,0,29665.23851\n167093,32,13,2,1,Married,0,602.9203731,0,29884.01866\n158972,55,34,2,1,Married,4,602.9165094,0,29570.82547\n108478,28,8,3,0,Married,2,602.8252592,0,29793.26296\n178508,58,41,3,0,Single,2,602.7224416,0,29781.12208\n132461,34,15,2,0,Single,1,602.7205126,0,29932.02563\n188998,25,7,3,1,Single,0,602.7133477,0,29934.66739\n144736,18,0,4,1,Single,11,602.6899319,0,29869.4966\n131109,16,0,3,0,Married,15,602.4458911,0,29525.29456\n132825,27,10,2,1,Married,1,602.4381233,0,29830.90617\n120767,16,0,4,1,Single,13,602.4129398,0,29864.64699\n190767,66,46,2,1,Single,3,602.3493797,0,29531.46899\n166328,56,37,2,0,Single,1,602.3317886,0,29786.58943\n169199,33,14,3,1,Single,1,602.261663,0,29560.08315\n131294,25,7,1,0,Married,14,602.2169971,0,29597.84986\n150868,25,8,1,1,Single,12,602.1457063,0,29829.28532\n163605,40,21,1,1,Single,2,602.0505692,0,29608.52846\n168056,48,30,1,1,Married,0,601.8255275,0,29875.27638\n157859,58,40,3,1,Married,4,601.7748475,0,29664.74238\n180101,36,19,3,0,Married,5,601.6753911,0,29636.76956\n103611,23,5,1,1,Single,15,601.5725579,0,29488.6279\n121056,64,43,3,0,Single,5,601.4215068,0,29661.07534\n132110,30,9,1,1,Single,1,601.3926631,0,29722.63316\n141202,30,10,2,1,Single,5,601.052766,0,29742.6383\n147567,65,47,4,0,Married,4,601.0411263,0,29801.05632\n149939,59,38,4,1,Married,1,600.9593922,0,29547.96961\n191628,37,16,2,0,Single,2,600.8962859,0,29795.8143\n162129,18,0,3,0,Single,15,600.8702076,0,29659.51038\n118385,39,18,1,0,Married,2,600.7874896,0,29519.37448\n140016,45,27,2,1,Single,4,600.7819651,0,29467.09826\n101008,24,7,4,1,Single,1,600.6729391,0,29613.64696\n164588,35,14,4,0,Married,1,600.5539835,0,29700.69918\n198581,21,2,1,1,Single,12,600.5185664,0,29706.92832\n179803,43,26,2,1,Married,5,600.5002692,0,29464.01346\n159778,47,26,3,1,Married,1,600.4432552,0,29741.16276\n125564,51,31,1,0,Single,4,600.436007,0,29429.80035\n132075,43,23,2,1,Married,1,600.3944111,0,29534.72056\n160432,22,5,3,0,Married,11,600.3886553,0,29809.43277\n121389,57,40,3,0,Single,3,600.294342,0,29744.7171\n121313,31,13,1,0,Single,5,600.2850832,0,29621.25416\n144534,16,0,4,1,Single,15,600.0017263,0,29418.08632\n165488,62,43,1,1,Married,2,599.9778705,0,29406.89353\n171330,52,31,3,1,Married,5,599.9480653,0,29522.40327\n160235,25,7,2,0,Married,12,599.941101,0,29607.05505\n191349,23,5,2,0,Married,8,599.9407541,0,29570.03771\n190878,17,0,2,1,Single,11,599.7468055,0,29503.34028\n199990,24,6,2,1,Single,12,599.6876001,0,29497.38001\n170950,52,31,4,0,Single,0,599.6689883,0,29721.44942\n165241,23,5,3,0,Married,4,599.5931126,0,29573.65563\n139938,52,35,3,0,Married,1,599.5536436,0,29525.68218\n174078,18,1,2,1,Single,14,599.5107367,0,29724.53684\n178948,52,35,2,1,Single,1,599.4664433,0,29414.32217\n124768,32,11,3,1,Married,3,599.4292132,0,29607.46066\n171326,23,6,2,1,Single,10,599.3552457,0,29762.76229\n176110,66,47,3,0,Single,3,599.339974,0,29697.9987\n176805,22,5,3,1,Married,3,598.9419665,0,29551.09833\n100651,38,18,1,0,Married,2,598.9350854,0,29519.75427\n135264,20,2,2,0,Single,11,598.8943012,0,29491.71506\n131388,36,19,3,1,Married,3,598.7167497,0,29709.83749\n100408,33,15,1,0,Married,0,598.6892757,0,29389.46379\n109689,20,3,2,1,Single,12,598.6210114,0,29549.05057\n167577,37,19,2,1,Married,3,598.5651052,0,29500.25526\n147301,44,25,2,0,Single,0,598.4781604,0,29333.90802\n139621,62,45,3,1,Married,2,598.3740899,0,29398.7045\n183636,22,4,4,0,Married,6,598.1417857,0,29609.08929\n138353,24,7,2,1,Single,15,598.090822,0,29318.5411\n160037,31,12,2,0,Single,0,598.0787648,0,29502.93824\n195242,28,9,3,1,Single,2,598.04402,0,29625.201\n187834,41,20,1,1,Single,0,597.7945105,0,29650.72553\n117230,45,25,2,1,Single,4,597.4670941,0,29365.35471\n162445,25,6,2,1,Single,12,597.4473589,0,29363.36795\n196090,25,6,1,0,Married,9,597.4422021,0,29471.11011\n199116,20,1,3,0,Married,6,597.2813134,0,29412.06567\n131959,48,30,3,0,Married,0,597.1726794,0,29559.63397\n133035,31,11,2,1,Married,0,596.7270258,0,29452.35129\n118174,21,3,2,0,Married,3,596.6727517,0,29270.63759\n101453,57,38,1,1,Married,4,596.5321778,0,29495.60889\n157141,18,0,3,1,Single,12,596.5048263,0,29523.24132\n189475,46,27,2,1,Single,4,596.4954903,0,29502.77452\n171190,27,7,3,1,Single,5,596.4746962,0,29439.73481\n115111,23,6,4,1,Married,4,596.4062735,0,29319.31368\n189155,22,4,3,0,Married,11,596.4039365,0,29497.19683\n163555,46,27,1,0,Single,10,596.3227132,0,29348.13566\n177949,29,11,2,0,Single,2,596.1340755,0,29583.70378\n169450,49,32,4,1,Single,4,595.7744477,0,29380.72239\n169410,27,7,2,0,Single,0,595.6818148,0,29499.09074\n194224,39,22,3,1,Married,3,595.5328823,0,29569.64412\n126286,50,29,4,0,Single,0,595.5204232,0,29419.02116\n173455,24,6,3,1,Married,1,595.3860883,0,29272.30442\n131126,20,1,3,0,Married,14,595.2857875,0,29468.28938\n111131,35,17,2,1,Married,1,595.2500438,0,29376.50219\n187384,21,4,3,0,Married,0,595.2186944,0,29166.93472\n129152,18,0,4,0,Married,4,595.0163864,0,29361.81932\n170087,44,25,2,0,Single,2,594.8041445,0,29516.20723\n197705,20,2,3,1,Single,14,594.6565616,0,29341.82808\n190839,22,3,3,1,Single,4,594.6229127,0,29497.14564\n163987,19,0,2,1,Single,11,594.4983221,0,29489.91611\n178103,28,7,3,0,Single,1,594.2272725,0,29355.36363\n185742,16,0,1,1,Married,5,594.1389262,0,29266.94631\n110688,53,36,3,1,Married,5,594.1275756,0,29497.37878\n111460,37,20,1,1,Married,3,594.0556495,0,29148.78248\n135692,21,4,2,0,Single,11,593.9904687,0,29244.52344\n102839,19,1,3,0,Married,9,593.9311115,0,29324.55558\n138982,66,46,4,1,Single,4,593.9165336,0,29423.82668\n115587,52,31,3,1,Married,2,593.7741147,0,29336.70574\n120162,58,40,2,0,Single,3,593.7613643,0,29338.06822\n156416,29,9,3,0,Married,3,593.7606112,0,29280.03056\n179034,24,7,4,1,Single,14,593.7551538,0,29169.75769\n133169,19,2,3,1,Single,10,593.7213625,0,29371.06813\n151017,35,14,3,0,Married,3,593.5300039,0,29097.5002\n122164,67,49,1,0,Single,3,593.4274113,0,29313.37057\n108782,46,28,2,1,Single,1,593.3446961,0,29233.23481\n184394,23,6,2,1,Single,13,593.3395827,0,29446.97914\n132547,40,23,1,0,Married,1,593.2666445,0,29366.33223\n152164,20,3,2,1,Single,13,593.249595,0,29146.47975\n153317,20,1,2,0,Single,11,593.2050205,0,29287.25103\n159302,46,27,3,0,Married,4,593.0466566,0,29344.33283\n159655,42,25,3,1,Married,0,592.6050524,0,29361.25262\n195594,43,26,1,0,Married,2,592.2871812,0,29181.35906\n197475,62,44,2,0,Single,4,592.2771478,0,29022.85739\n185318,21,3,3,1,Single,11,592.2506615,0,29295.53308\n123888,54,35,3,0,Single,3,592.1765216,0,29285.82608\n189905,39,20,3,1,Single,4,592.1448618,0,29014.24309\n196808,21,4,3,1,Single,12,591.9987049,0,29012.93525\n100580,21,4,2,0,Married,11,591.9903556,0,29160.51778\n127270,44,26,1,0,Married,5,591.9070066,0,29032.35033\n179843,18,0,2,0,Single,12,591.6967258,0,29109.83629\n153992,21,3,2,0,Single,10,591.6375591,0,29081.87796\n111907,47,26,3,1,Single,4,591.6038049,0,29264.19025\n153057,24,6,3,0,Single,15,591.5682794,0,29214.41397\n123636,52,32,2,1,Married,3,591.5503428,0,29353.51714\n163736,33,14,2,1,Married,2,591.5242872,0,29300.21436\n104525,62,42,2,0,Single,3,591.4482438,0,29228.41219\n162809,24,5,3,1,Single,11,591.4297387,0,29082.48694\n136137,62,45,4,1,Single,5,591.4174326,0,29028.87163\n113602,49,31,2,1,Single,0,591.3840974,0,29198.20487\n166095,23,5,2,1,Single,11,591.3458088,0,29012.29044\n191243,25,7,3,0,Married,15,591.3379236,0,29024.89618\n141619,58,41,2,1,Single,2,591.3276195,0,28969.38098\n192917,42,22,1,0,Single,0,591.3249195,0,28993.24598\n126964,43,26,3,0,Married,1,591.0435319,0,29176.1766\n144221,26,9,1,1,Married,4,590.936418,0,29054.8209\n199468,34,17,1,1,Single,4,590.8513496,0,29181.56748\n108588,16,0,1,1,Single,6,590.7871142,0,29132.35571\n171774,16,0,3,1,Single,11,590.7475099,0,28960.3755\n135938,18,0,2,0,Married,10,590.7316221,0,29210.58111\n144446,51,30,1,1,Single,5,590.6407796,0,29215.03898\n168965,21,2,3,1,Married,2,590.5546604,0,29040.73302\n147105,48,30,2,1,Married,0,590.5028673,0,29043.14337\n133327,51,32,1,1,Single,0,590.2667386,0,29132.33693\n178294,18,0,3,0,Married,10,590.1640123,0,29276.20062\n124091,34,15,3,0,Single,4,590.1613276,0,28961.06638\n130163,49,31,3,0,Single,2,590.0447627,0,28913.23814\n127793,20,1,3,0,Single,11,589.8996087,0,29127.98044\n134192,60,39,2,0,Single,3,589.8219306,0,29212.09653\n114355,16,0,3,0,Single,13,589.7118387,0,29156.59194\n135290,19,2,2,1,Single,15,589.7096364,0,29066.48182\n158439,39,20,4,1,Married,4,589.6208365,0,29200.04183\n129931,17,0,2,0,Single,12,589.5833291,0,29046.16646\n100956,36,18,1,0,Single,5,589.4040255,0,29262.20128\n116831,17,0,4,1,Single,6,589.1773458,0,29209.86729\n135284,20,3,3,0,Single,14,589.1073039,0,29205.3652\n168122,37,19,1,1,Single,3,589.0525916,0,29092.62958\n174112,23,5,2,1,Single,9,588.9731914,0,29036.65957\n146829,24,5,2,1,Single,3,588.8563028,0,29086.81514\n130414,23,4,3,1,Single,15,588.7309849,0,29134.54925\n170632,20,1,3,1,Single,12,588.5465292,0,29177.32646\n147082,42,23,2,1,Married,2,588.4206179,0,28853.0309\n124947,69,51,1,0,Married,2,588.3241783,0,28868.20892\n128874,38,18,1,0,Single,3,588.097128,0,29044.8564\n103612,24,6,4,0,Married,14,588.0650147,0,28948.25074\n179821,20,3,3,1,Married,1,587.9843954,0,29082.21977\n164931,50,31,3,0,Married,1,587.8995553,0,29065.97777\n137648,50,29,1,1,Married,4,587.8910956,0,28923.55478\n154100,36,16,4,0,Married,4,587.8348869,0,28934.74435\n145789,20,2,1,0,Married,13,587.7639519,0,28896.1976\n149561,35,14,1,0,Married,0,587.7592971,0,28795.96486\n181584,30,12,3,1,Single,4,587.6479685,0,29087.39843\n161785,59,40,4,1,Married,0,587.6122574,0,29106.61287\n114480,19,0,3,1,Single,15,587.5860617,0,28858.30309\n180627,54,35,3,1,Married,5,587.5284398,0,29116.42199\n188014,28,10,3,1,Single,5,587.3366262,0,29151.83131\n147410,58,39,3,1,Single,4,587.1796237,0,29099.98119\n157495,36,18,3,0,Single,4,587.1620594,0,29109.10297\n186838,57,38,1,1,Married,2,587.121065,0,29026.05325\n136678,17,0,2,1,Married,5,587.0938381,0,29019.69191\n147654,22,5,3,1,Single,0,587.0766576,0,28803.83288\n159287,56,38,3,1,Single,4,586.9474364,0,28837.37182\n122759,18,0,2,0,Married,10,586.9042931,0,28812.21466\n160588,49,31,3,0,Married,4,586.8401022,0,29046.00511\n168461,21,3,1,0,Single,15,586.595506,0,29030.7753\n179271,23,5,1,0,Single,14,586.5806227,0,28864.03114\n181003,36,16,2,1,Single,1,586.5780957,0,28761.90479\n160796,29,10,2,1,Single,2,586.5237976,0,29084.18988\n197185,51,33,3,1,Single,0,586.5051397,0,29112.25699\n175997,25,8,2,0,Married,12,586.4575226,0,28884.87613\n151974,34,15,2,1,Single,2,586.3877207,0,28984.38604\n162214,21,2,4,1,Single,11,586.2712475,0,28731.56238\n167003,25,6,3,0,Married,7,586.1389841,0,28736.94921\n159246,39,19,2,1,Single,5,585.9087075,0,28805.43538\n158637,22,4,1,1,Single,12,585.7777296,0,28959.88648\n124690,25,7,4,1,Married,3,585.6720152,0,28983.60076\n156139,34,17,3,0,Single,2,585.611415,0,28739.57075\n119973,17,0,2,0,Married,2,585.4125944,0,28736.62972\n138629,21,3,3,1,Single,7,585.3022678,0,28782.11339\n115502,41,21,2,1,Married,5,585.2146,0,28765.73\n182091,20,2,4,1,Single,13,585.1896968,0,28793.48484\n196053,25,7,4,1,Single,9,585.0701177,0,28952.50589\n116227,40,23,3,1,Married,0,585.0351549,0,28934.75775\n135242,18,0,3,0,Married,14,584.9928807,0,28831.64404\n157795,24,6,3,1,Single,14,584.9279896,0,28794.39948\n194063,17,0,3,0,Married,8,584.7817657,0,28657.08829\n152934,64,45,2,0,Single,4,584.73356,0,28799.678\n191601,52,31,4,0,Married,3,584.728272,0,28786.4136\n157336,36,17,3,0,Married,5,584.6503363,0,29000.51682\n137741,23,5,4,0,Married,6,584.6084829,0,28705.42415\n168033,39,20,4,0,Married,2,584.5988272,0,28829.94136\n135354,17,0,2,0,Married,7,584.5653071,0,28757.26536\n137692,17,0,3,1,Single,11,584.5555815,0,28807.77908\n145233,35,14,2,1,Single,0,584.5321559,0,28694.6078\n199301,21,3,1,1,Single,15,584.4584795,0,28972.92398\n108937,47,30,2,1,Married,5,584.4515855,0,28892.57928\n199511,21,4,1,0,Married,15,584.209495,0,28813.47475\n124822,16,0,3,0,Single,7,584.1028246,0,29002.14123\n152972,25,6,1,0,Married,11,583.9925703,0,28871.62852\n143993,37,19,3,0,Single,0,583.618097,0,28923.90485\n131030,39,20,2,0,Married,5,583.6112958,0,28811.56479\n152414,45,27,2,0,Married,0,583.5034299,0,28827.1715\n111176,50,33,2,0,Married,3,583.4451347,0,28822.25674\n161526,26,6,3,1,Single,1,583.1973893,0,28563.86947\n114172,28,9,4,1,Single,3,583.1287926,0,28800.43963\n168371,46,26,2,0,Single,3,582.9595775,0,28936.97888\n111995,29,12,2,0,Single,4,582.957999,0,28799.89995\n147477,53,32,2,0,Married,2,582.7640352,0,28903.20176\n180222,38,21,3,1,Married,3,582.7183001,0,28843.91501\n149317,21,4,3,0,Married,10,582.6908244,0,28791.54122\n113284,39,22,3,0,Married,0,582.6297549,0,28821.48775\n160491,24,6,3,0,Single,11,582.4509006,0,28832.54503\n123481,20,3,3,1,Single,6,582.4014834,0,28859.07417\n158890,33,12,3,1,Married,2,582.334854,0,28527.7427\n112970,24,5,3,0,Married,15,582.2666052,0,28735.33026\n123183,40,20,3,0,Married,3,582.2109746,0,28797.54873\n143864,49,32,2,0,Married,3,582.1363851,0,28745.81926\n130340,18,0,3,1,Single,11,582.1070541,0,28848.35271\n135581,35,15,3,0,Single,2,582.0355859,0,28699.7793\n179023,25,7,4,0,Married,6,581.7991773,0,28535.95887\n152084,25,6,3,0,Married,10,581.550003,0,28562.50015\n160843,45,24,3,0,Single,10,581.4841711,0,28814.20856\n124324,67,48,2,1,Single,2,581.4427893,0,28503.13947\n158864,48,31,1,1,Single,4,581.3363152,0,28560.81576\n167778,58,40,3,0,Single,2,581.3015799,0,28710.079\n174806,16,0,3,0,Married,7,581.2386757,0,28852.93379\n121659,60,39,3,0,Married,1,581.1578264,0,28835.89132\n170443,56,38,3,0,Single,1,581.1573171,0,28833.86586\n147704,63,42,1,0,Married,0,581.0593991,0,28564.96996\n190595,61,44,3,0,Single,5,580.6063261,0,28625.31631\n147453,21,4,3,1,Single,11,580.5289052,0,28737.44526\n159762,51,34,2,1,Single,1,580.5239334,0,28607.19667\n194504,37,20,2,0,Married,2,580.4834146,0,28520.17073\n134636,66,49,3,1,Single,4,580.4705323,0,28664.52662\n176960,17,0,4,0,Single,13,580.3361641,0,28526.80821\n124134,24,7,1,0,Married,8,580.2514514,0,28591.57257\n168025,18,0,2,1,Single,14,580.2211714,0,28439.05857\n119280,35,14,3,0,Single,6,580.1694084,0,28686.47042\n155526,16,0,1,0,Single,13,580.1143576,0,28645.71788\n139891,43,26,4,0,Single,1,580.0371126,0,28616.85563\n184196,63,45,2,1,Single,4,579.7994154,0,28413.97077\n121349,21,2,2,1,Single,14,579.6378969,0,28639.89485\n155426,23,6,2,1,Single,13,579.545733,0,28522.28665\n152325,55,36,2,1,Single,3,579.5288127,0,28412.44064\n124931,58,38,4,0,Married,1,579.3588041,0,28636.94021\n180886,50,29,2,1,Married,1,579.3465404,0,28652.32702\n141828,21,2,3,0,Single,11,579.306829,0,28719.34145\n195735,66,45,2,0,Single,3,579.2662045,0,28709.31023\n107987,60,42,2,0,Single,3,579.1298354,0,28419.49177\n152066,23,5,3,0,Married,7,579.042782,0,28743.1391\n145782,36,19,4,1,Married,4,578.9294915,0,28602.47458\n157869,70,51,3,0,Single,0,578.8589413,0,28658.94707\n114765,22,3,2,0,Single,11,578.6284008,0,28338.42004\n174995,20,3,2,0,Married,10,578.3413716,0,28687.06858\n141610,53,35,2,0,Single,7,578.285181,0,28684.25905\n122447,29,12,1,0,Single,8,578.251118,0,28354.5559\n195155,25,6,3,0,Married,0,578.231489,0,28666.57445\n188360,25,8,2,0,Single,13,578.1605117,0,28658.02559\n145204,16,0,3,1,Single,15,577.8884849,0,28573.42425\n193579,70,52,2,0,Single,2,577.8240207,0,28317.20104\n132121,61,42,3,0,Married,2,577.6616085,0,28452.08043\n114889,21,2,4,0,Single,15,577.3659621,0,28438.29811\n171414,63,43,3,0,Married,1,577.3169418,0,28378.84709\n101160,61,44,1,0,Single,0,577.1904324,0,28340.52162\n197278,27,10,4,1,Single,0,576.8273667,0,28593.36834\n200435,24,6,4,0,Single,13,576.8021942,0,28334.10971\n121784,20,2,1,1,Single,6,576.7216887,0,28608.08444\n105221,17,0,1,0,Married,15,576.58074,0,28618.037\n114447,24,7,2,1,Single,15,576.5547032,0,28627.73516\n140895,45,28,1,1,Single,1,576.543884,0,28281.1942\n192060,48,30,1,0,Single,5,576.4028819,0,28330.1441\n197497,63,46,2,0,Single,5,576.3439788,0,28587.19894\n151257,23,5,2,0,Married,9,576.2774754,0,28378.87377\n129904,38,18,1,1,Married,4,576.2550331,0,28432.75166\n126080,46,29,1,0,Single,4,576.0714176,0,28600.57088\n183903,23,4,4,1,Single,11,576.0591251,0,28506.95626\n109670,34,17,1,1,Single,1,575.9114126,0,28258.57063\n112760,23,6,4,0,Single,13,575.8389248,0,28437.94624\n158104,32,13,2,0,Married,3,575.7807875,0,28481.03938\n195420,65,48,3,0,Single,1,575.7109753,0,28229.54877\n154972,43,25,1,0,Single,1,575.6892982,0,28390.46491\n110502,18,0,4,0,Married,15,575.6616867,0,28543.08434\n169037,19,1,3,1,Single,9,575.6223618,0,28401.11809\n104074,45,24,3,1,Married,0,575.5410986,0,28553.05493\n125583,25,6,1,0,Married,14,575.4669131,0,28219.34566\n191109,36,16,3,1,Married,5,575.4100503,0,28529.50252\n151532,25,6,4,0,Single,13,575.2285438,0,28490.42719\n122151,22,5,3,1,Single,13,575.2028406,0,28224.14203\n128142,42,22,2,0,Married,3,575.1646979,0,28217.2349\n111513,56,37,2,1,Married,4,575.1516927,0,28394.58464\n104417,16,0,2,0,Married,7,575.1301833,0,28438.50917\n167395,41,23,4,1,Married,4,575.1008818,0,28384.04409\n112597,17,0,4,0,Single,15,574.9427892,0,28466.13946\n169080,16,0,2,1,Married,4,574.9202244,0,28374.01122\n111217,51,34,4,0,Single,0,574.9018743,0,28295.09372\n105726,22,4,3,0,Single,7,574.8410883,0,28252.05442\n183596,48,27,1,0,Single,0,574.8216925,0,28152.08463\n185207,40,21,2,0,Married,2,574.7860207,0,28276.30104\n105644,60,40,4,0,Married,1,574.7578672,0,28151.89336\n173538,22,5,2,0,Married,13,574.688578,0,28170.4289\n129628,54,33,2,1,Single,4,574.6820178,0,28195.10089\n118391,18,0,2,0,Married,11,574.4828221,0,28165.14111\n145141,55,34,2,1,Married,1,574.4400041,0,28484.00021\n167757,52,31,4,0,Married,1,574.4374634,0,28412.87317\n126673,43,25,3,0,Married,3,574.399332,0,28503.9666\n124209,52,32,2,0,Single,6,574.3451601,0,28415.25801\n170180,31,10,3,1,Single,4,574.2567511,0,28137.83756\n169743,47,27,4,0,Single,4,574.137997,0,28417.89985\n156506,61,40,4,1,Single,4,574.0934294,0,28244.67147\n176149,53,34,2,0,Single,4,573.9830238,0,28382.15119\n130703,24,5,3,0,Single,15,573.8657603,0,28352.28802\n142072,29,9,1,1,Married,2,573.7730434,0,28278.65217\n155203,24,6,3,0,Married,12,573.7401335,0,28143.00668\n167938,42,23,3,0,Married,2,573.6219236,0,28143.09618\n131625,41,23,4,0,Single,0,573.5862881,0,28449.31441\n124841,17,0,1,1,Single,12,573.5534203,0,28159.67102\n137197,20,1,3,1,Single,14,573.5512437,0,28357.56219\n165547,31,10,2,0,Married,2,573.4375511,0,28360.87756\n176112,41,24,2,1,Single,0,573.3508955,0,28077.54478\n100015,35,15,3,1,Married,2,573.3058219,0,28209.2911\n159328,17,0,3,0,Single,15,573.3040013,0,28463.20007\n172355,59,42,4,1,Married,5,573.2609017,0,28123.04509\n180152,22,4,2,1,Single,6,573.186881,0,28180.34405\n116568,18,0,2,0,Married,9,573.1399134,0,28329.99567\n116821,47,26,3,1,Married,2,573.1388359,0,28403.9418\n139619,18,1,2,1,Single,12,573.0557924,0,28391.78962\n163830,40,22,2,1,Single,4,572.9965536,0,28222.82768\n104232,25,7,4,0,Single,8,572.8837682,0,28205.18841\n154332,56,35,2,0,Married,2,572.8256397,0,28287.28199\n120707,22,4,1,1,Single,15,572.8165632,0,28424.82816\n136771,24,7,4,0,Married,11,572.5681386,0,28406.40693\n152506,24,7,2,1,Single,9,572.3978778,0,28227.89389\n169643,32,14,2,1,Married,5,572.0640955,0,28144.20478\n107434,18,0,2,0,Married,13,571.9483062,0,28105.41531\n100210,56,35,1,1,Married,5,571.9363703,0,28088.81852\n147620,48,27,2,1,Married,1,571.8643026,0,28136.21513\n110807,16,0,1,0,Married,6,571.822653,0,28233.13265\n176267,47,29,4,0,Married,5,571.7911611,0,28293.55806\n123853,16,0,2,1,Single,12,571.7196568,0,28149.98284\n168114,27,7,3,0,Married,3,571.5633043,0,28094.16522\n190923,26,5,2,0,Married,5,571.5603593,0,28246.01797\n174123,66,48,2,1,Single,5,571.5239264,0,28315.19632\n122452,26,7,3,1,Married,3,571.4763718,0,28296.81859\n106289,50,31,2,1,Single,3,571.0809936,0,28125.04968\n121994,17,0,3,0,Single,15,571.0311774,0,28195.55887\n108934,25,7,3,0,Single,13,571.0066707,0,27968.33354\n138788,26,5,1,0,Single,10,570.8218264,0,28175.09132\n125754,25,6,1,0,Single,15,570.7708186,0,28284.54093\n116861,40,19,1,0,Married,5,570.7365692,0,28268.82846\n100110,24,5,2,1,Single,13,570.6371,0,28023.855\n197770,17,0,1,0,Married,10,570.6075147,0,28021.37574\n150352,33,12,4,0,Married,0,570.5972744,0,28229.86372\n190062,26,9,4,1,Married,2,570.5741831,0,28058.70916\n132375,30,11,1,1,Single,1,570.5547459,0,28294.7373\n104953,17,0,2,0,Married,8,570.4028307,0,28076.14154\n120894,21,2,2,0,Single,15,570.4021388,0,28047.10694\n128216,28,9,1,0,Married,0,570.3726954,0,28291.63477\n173014,65,47,1,0,Married,2,570.1182067,0,27948.91034\n130549,41,21,3,1,Married,1,570.0005691,0,28014.02846\n161680,17,0,2,0,Married,8,569.9857577,0,27914.28789\n133262,52,33,2,0,Single,3,569.9136846,0,27926.68423\n174991,17,0,3,0,Married,11,569.8105747,0,28077.52874\n116462,17,0,3,0,Single,12,569.526355,0,28087.31775\n166153,25,8,3,1,Single,12,569.2702851,0,28090.51426\n118865,25,8,3,1,Married,1,569.2346293,0,27924.73147\n176523,32,13,3,1,Single,5,569.2331006,0,28218.65503\n126266,22,4,1,1,Single,4,569.1210983,0,28092.05492\n132342,65,47,2,1,Married,2,568.9578977,0,28141.89489\n119426,16,0,3,1,Single,15,568.7389461,0,28224.94731\n199542,59,41,2,0,Single,2,568.5054218,0,27906.27109\n129852,31,12,3,0,Married,0,568.4514326,0,27835.57163\n112104,19,2,1,1,Single,7,568.2705563,0,27962.52782\n167381,47,27,2,1,Single,1,568.1048662,0,28172.24331\n156936,33,14,3,1,Married,3,568.1035644,0,27846.17822\n151393,21,4,1,1,Single,12,568.0651961,0,27884.25981\n145849,64,47,3,1,Single,4,568.0166811,0,28165.83406\n116039,28,9,3,1,Married,4,567.9869082,0,27900.34541\n190605,19,1,3,0,Single,15,567.8291037,0,28085.45519\n136626,24,5,1,1,Single,10,567.8207249,0,28142.03625\n111288,19,0,2,0,Single,13,567.8116732,0,27870.58366\n182712,51,33,2,0,Married,2,567.6471059,0,27875.3553\n160147,39,19,3,1,Married,5,567.6042812,0,28013.21406\n191739,21,2,1,0,Married,11,567.532752,0,27864.6376\n143279,23,4,4,0,Married,12,567.2643283,0,27830.21642\n137678,17,0,1,0,Single,12,567.2103512,0,27943.51756\n116735,32,11,4,0,Married,5,567.0899565,0,27754.49783\n115444,22,3,2,0,Married,10,567.0899153,0,28001.49577\n178402,22,3,1,1,Single,14,567.0537545,0,27963.68773\n156288,24,6,4,0,Married,13,567.0280937,0,27991.40469\n188975,68,48,3,1,Single,4,567.0247191,0,27819.23596\n109316,52,33,4,1,Married,0,567.0127054,0,27866.63527\n162032,17,0,4,1,Single,7,566.9732079,0,28062.6604\n142017,24,6,2,1,Single,13,566.748022,0,27959.4011\n141785,45,25,2,1,Married,5,566.3801492,0,27744.00746\n117161,26,9,4,1,Married,1,566.3665086,0,27816.32543\n118220,57,38,3,1,Single,3,566.2735358,0,27904.67679\n195858,51,30,1,0,Married,4,566.1994377,0,27848.97189\n150419,40,19,2,0,Married,0,566.1969736,0,27978.84868\n175618,56,38,2,0,Single,2,565.9103367,0,27812.51684\n166963,39,22,3,1,Married,4,565.9091424,0,27882.45712\n114755,63,44,3,0,Married,1,565.874036,0,27693.7018\n170128,20,1,2,0,Married,14,565.7705528,0,27773.52764\n156935,23,4,1,0,Single,11,565.75928,0,27991.964\n145864,40,21,2,0,Married,0,565.7537055,0,28067.68528\n157952,41,21,3,1,Married,2,565.606013,0,27752.30065\n167819,17,0,2,0,Single,15,565.5446324,0,27835.23162\n103229,32,11,4,0,Married,1,565.3811755,0,27738.05878\n187590,25,6,1,1,Single,15,565.3485155,0,28010.42578\n150749,58,39,2,1,Married,2,565.2358083,0,27684.79042\n105578,26,7,4,1,Single,3,565.0505548,0,27771.52774\n171865,21,4,1,0,Married,0,565.0137498,0,27930.68749\n112053,18,1,2,0,Single,14,564.8968135,0,27936.84068\n188861,37,19,2,1,Single,5,564.7421804,0,27980.10902\n156261,21,3,1,1,Single,14,564.7148465,0,27771.74232\n195724,21,2,3,0,Married,15,564.6980013,0,27667.90007\n142513,24,6,2,0,Married,10,564.5605181,0,27767.02591\n136482,23,6,2,0,Single,14,564.5416455,0,27638.08228\n129764,56,37,3,0,Single,7,564.435626,0,27672.7813\n115569,38,21,4,1,Married,3,564.4140951,0,27735.70476\n189714,28,10,1,0,Single,5,564.3810046,0,28015.05023\n141125,16,0,3,0,Single,11,564.343477,0,28011.17385\n131239,16,0,2,0,Single,13,564.3328789,0,27842.64395\n167489,47,28,3,0,Married,0,564.3003014,0,27761.01507\n107574,43,26,2,0,Married,4,563.9837574,0,27616.18787\n113759,25,8,3,1,Single,9,563.9122609,0,27864.61305\n182710,18,0,1,0,Single,13,563.8058374,0,27757.29187\n139651,17,0,1,0,Married,12,563.7051521,0,27755.25761\n176348,24,5,2,1,Single,11,563.6946496,0,27869.73248\n176079,57,37,4,0,Married,3,563.6273605,0,27684.36803\n123065,19,2,2,1,Married,10,563.5223583,0,27639.11792\n181986,56,35,4,1,Single,1,563.4264779,0,27822.3239\n144530,48,28,2,0,Single,2,563.4226944,0,27872.13472\n182642,26,7,1,0,Single,9,563.268251,0,27767.41255\n139777,18,0,3,1,Married,8,563.2612593,0,27568.06297\n127071,19,1,3,0,Married,8,563.1467632,0,27949.33816\n107733,37,20,4,1,Married,5,563.0655171,0,27894.27586\n151512,31,12,4,0,Married,2,563.0420952,0,27862.10476\n151315,22,4,3,0,Single,12,563.0406414,0,27581.03207\n165479,17,0,3,1,Single,14,563.0131992,0,27715.65996\n179671,42,22,3,0,Married,4,563.0070177,0,27893.35089\n130125,47,27,2,0,Single,3,562.9853303,0,27685.26652\n149897,68,50,2,0,Single,4,562.8376208,0,27587.88104\n171900,52,32,3,0,Single,1,562.8152316,0,27558.76158\n105859,25,7,4,0,Married,14,562.6906005,0,27927.53003\n174489,25,8,4,0,Married,14,562.6395892,0,27661.97946\n159112,56,38,2,1,Single,3,562.6314955,0,27803.57478\n134722,18,0,2,1,Single,11,562.6110667,0,27639.55334\n183136,25,8,3,1,Single,12,562.5552069,0,27823.76035\n135139,25,8,3,0,Single,12,562.5482144,0,27789.41072\n157731,23,4,4,0,Married,15,562.4550285,0,27917.75143\n157451,26,7,1,0,Married,1,562.2875351,0,27572.37676\n154284,20,1,2,0,Single,8,562.2078905,0,27606.39453\n150169,32,14,2,1,Single,1,562.2045212,0,27619.22606\n110799,25,7,2,1,Single,10,562.181514,0,27807.0757\n196911,66,46,3,1,Married,5,561.9063626,0,27678.31813\n139789,23,4,1,1,Single,11,561.8599305,0,27617.99653\n124248,55,35,4,1,Single,4,561.8060967,0,27505.30484\n117625,47,28,1,0,Married,1,561.796776,0,27780.8388\n154356,52,33,1,0,Married,2,561.5039553,0,27766.19777\n195753,18,0,4,1,Single,11,561.3795472,0,27709.97736\n113196,65,46,1,1,Single,2,561.2968092,0,27750.84046\n107878,26,5,2,1,Married,2,561.2020227,0,27683.10114\n143309,23,5,2,0,Single,11,561.1817658,0,27584.08829\n184024,35,14,2,1,Married,2,561.1540369,0,27690.70185\n122484,25,8,3,1,Single,11,561.1434278,0,27528.17139\n123781,16,0,2,1,Single,4,561.0042148,0,27511.21074\n124761,22,4,2,1,Single,6,560.9708858,0,27605.54429\n162367,70,50,3,0,Single,2,560.6357008,0,27655.78504\n175028,16,0,3,1,Single,13,560.4544128,0,27800.72064\n119082,57,36,2,0,Single,2,560.2708214,0,27465.54107\n152681,55,38,2,1,Married,5,560.2024583,0,27526.12292\n181358,47,26,2,0,Single,10,560.0044404,0,27598.22202\n136668,24,5,3,1,Single,13,559.9182036,0,27404.91018\n166615,17,0,3,0,Single,14,559.5988379,0,27630.9419\n127415,58,40,2,1,Single,4,559.5844128,0,27632.22064\n188005,64,44,2,0,Single,5,559.5341787,0,27544.70894\n197254,61,41,2,1,Single,5,559.3882097,0,27397.41049\n135678,19,2,1,1,Single,2,559.1728954,0,27525.64477\n161277,25,6,1,1,Single,12,559.1451267,0,27539.25634\n174811,24,7,3,1,Single,11,559.0372689,0,27628.86345\n195811,19,0,4,0,Single,12,559.0337155,0,27569.68578\n198694,17,0,4,0,Single,7,558.9765314,0,27423.82657\n124612,36,19,3,1,Single,0,558.9101034,0,27581.50517\n105842,29,9,3,1,Married,0,558.8847208,0,27431.23604\n198806,34,14,2,0,Married,5,558.5397196,0,27664.98598\n188394,40,23,2,0,Single,8,558.4147228,0,27586.73614\n112251,42,21,4,1,Married,2,558.195991,0,27343.79955\n195137,17,0,3,0,Married,14,558.1666236,0,27431.33118\n117150,51,32,3,1,Married,1,558.1007803,0,27655.03902\n128330,33,15,3,1,Single,4,558.0653071,0,27385.26536\n192900,60,42,3,0,Single,3,557.9149076,0,27440.74538\n148887,42,23,4,1,Married,5,557.8448289,0,27503.24145\n198776,57,40,1,0,Single,5,557.8158927,0,27531.79464\n119856,40,19,4,1,Single,0,557.8021951,0,27541.10976\n116433,56,35,3,0,Single,3,557.7715797,0,27329.57899\n192550,38,19,3,0,Married,1,557.7685397,0,27653.42699\n189520,17,0,3,0,Single,12,557.7324632,0,27561.62316\n148198,23,6,1,0,Single,11,557.6682868,0,27325.41434\n103872,49,29,2,0,Single,6,557.6323479,0,27365.6174\n110551,44,23,4,0,Married,0,557.5516972,0,27635.58486\n105943,17,0,1,0,Single,11,557.4409401,0,27281.04701\n196159,66,49,2,0,Married,3,557.369016,0,27554.4508\n102437,19,1,3,1,Single,12,557.0871936,0,27375.35968\n129534,18,0,3,0,Married,8,557.0582137,0,27376.91069\n194429,24,6,2,0,Married,7,557.0278671,0,27349.39336\n138686,54,35,3,0,Married,3,557.0193211,0,27566.96606\n108638,53,32,1,1,Married,4,556.9123571,0,27342.61786\n143239,21,2,2,0,Single,13,556.8865116,0,27587.32558\n140327,22,4,2,0,Single,14,556.8383931,0,27308.91966\n185347,21,3,4,0,Single,13,556.5350622,0,27519.75311\n138717,22,3,2,0,Married,4,556.4903536,0,27352.51768\n139998,22,3,3,0,Married,15,556.4574225,0,27593.87113\n199104,29,11,2,1,Single,5,556.4168189,0,27457.84095\n189659,44,24,1,1,Married,4,556.3332589,0,27373.66295\n168951,23,4,3,0,Married,11,556.176094,0,27319.8047\n145225,19,2,2,0,Married,15,555.6668504,0,27583.34252\n196413,23,4,4,1,Single,14,555.6394366,0,27277.97183\n106047,62,42,1,0,Married,2,555.6005312,0,27367.02656\n144452,18,0,2,0,Married,10,555.4625578,0,27270.12789\n175102,56,36,2,1,Single,1,555.4445444,0,27221.22722\n121865,41,21,2,1,Married,1,555.4271146,0,27512.35573\n143260,24,5,3,0,Married,11,555.2651296,0,27321.25648\n194069,21,4,2,1,Married,6,555.2635092,0,27493.17546\n169611,56,36,1,0,Single,7,555.0751738,0,27203.75869\n158110,30,12,3,0,Single,3,555.0565883,0,27154.82942\n125637,48,28,2,0,Married,1,555.037452,0,27532.8726\n116468,59,39,3,0,Married,4,554.9753541,0,27326.76771\n177648,21,4,4,1,Single,2,554.9500925,0,27308.50463\n167171,41,20,3,1,Married,0,554.9068669,0,27269.34335\n142932,60,40,1,1,Married,0,554.8941047,0,27298.70524\n179066,51,34,4,1,Single,0,554.8745914,0,27191.72957\n194123,57,38,2,0,Single,2,554.85812,0,27316.906\n125215,50,32,3,1,Married,1,554.7138947,0,27208.69474\n131971,19,1,2,0,Single,10,554.6362621,0,27352.81311\n177419,56,36,3,1,Married,5,554.6144048,0,27143.72024\n145845,69,49,1,0,Single,2,554.2172221,0,27442.86111\n115864,25,7,1,0,Married,12,554.2015344,0,27387.07672\n183489,39,20,3,0,Married,2,554.14553,0,27146.2765\n160890,52,34,2,0,Married,4,554.0828553,0,27440.14277\n123728,24,6,1,0,Married,11,553.9527603,0,27183.63802\n138683,17,0,2,0,Married,6,553.8790435,0,27370.95218\n173441,23,6,2,1,Single,9,553.862587,0,27320.12935\n183812,34,17,2,1,Married,0,553.8533345,0,27339.66673\n188283,18,0,3,1,Single,15,553.8470439,0,27422.3522\n131214,34,15,4,0,Married,4,553.7771784,0,27191.85892\n134503,19,1,1,1,Single,14,553.7473001,0,27105.36501\n181030,37,20,2,0,Married,3,553.7000692,0,27252.00346\n170576,63,42,4,0,Single,0,553.635973,0,27409.79865\n170906,22,4,2,0,Married,9,553.6027214,0,27187.13607\n137086,27,10,2,1,Married,2,553.5448463,0,27175.24232\n166103,42,23,3,0,Married,4,553.52436,0,27246.218\n112381,29,11,3,0,Single,12,553.4949769,0,27106.74885\n119956,20,3,4,1,Single,12,553.4820444,0,27275.10222\n112719,25,7,3,1,Married,9,553.462844,0,27241.1422\n179608,52,33,4,0,Single,6,553.3991282,0,27139.95641\n120427,60,43,3,0,Single,3,553.352897,0,27151.64485\n109880,19,2,3,0,Married,9,553.266107,0,27078.30535\n176414,56,38,3,1,Single,2,553.2323994,0,27443.61997\n133742,56,37,1,0,Married,0,553.1320959,0,27328.6048\n199197,34,17,2,0,Married,3,553.0695916,0,27406.47958\n185335,20,3,1,1,Married,8,553.0150379,0,27374.7519\n100019,51,31,3,1,Single,3,552.9365137,0,27167.82569\n182033,20,2,3,0,Single,13,552.7515415,0,27074.57708\n109807,31,11,4,0,Married,3,552.5747807,0,27418.73904\n190513,60,40,4,1,Single,0,552.5655449,0,27420.27725\n148948,64,45,2,0,Single,0,552.5432092,0,27416.16046\n128834,52,31,2,0,Single,6,552.3598059,0,27302.9903\n158372,33,13,3,0,Married,2,552.3288797,0,27144.44399\n103014,50,29,1,0,Single,8,552.2467274,0,27313.33637\n169382,23,5,3,0,Single,11,551.8394674,0,27378.97337\n187693,23,4,2,0,Married,5,551.8162587,0,27333.81294\n193623,52,31,3,1,Married,4,551.8101323,0,27096.50662\n105215,23,4,2,0,Single,13,551.7209522,0,27385.04761\n162281,39,22,2,0,Married,1,551.5416673,0,27271.08337\n117546,27,9,1,1,Single,4,551.5028929,0,27069.14465\n135735,24,7,2,0,Single,13,551.4259978,0,27011.29989\n100925,57,39,2,0,Single,10,551.4158376,0,27174.79188\n126988,21,2,2,1,Married,5,551.3165937,0,27289.82969\n185739,45,26,2,0,Married,3,551.2472252,0,27260.36126\n146111,19,1,3,1,Single,12,551.2024325,0,27037.12163\n132371,24,7,4,0,Married,12,551.1643552,0,27324.21776\n136059,18,0,4,1,Single,15,551.0234945,0,27198.17473\n149397,66,46,1,0,Married,0,550.8999939,0,27302.9997\n168792,18,0,3,1,Single,11,550.7666379,0,27299.3319\n159474,18,0,3,1,Single,8,550.6338249,0,26971.69125\n157387,66,47,1,0,Married,4,550.5213257,0,27226.06629\n147143,47,28,3,0,Single,7,550.5173157,0,27184.86579\n154960,29,8,3,1,Single,3,550.4771397,0,27204.85699\n165329,16,0,3,1,Single,11,550.4194392,0,27148.97196\n195258,43,22,4,1,Single,2,550.4181639,0,26974.9082\n101713,41,24,4,0,Single,1,550.4005101,0,27270.02551\n105356,30,10,2,1,Married,5,550.3847401,0,26920.23701\n192734,70,53,2,0,Married,0,550.2256006,0,27053.28003\n141027,25,6,3,1,Single,15,550.1674199,0,27292.371\n132911,27,8,1,1,Married,0,550.1011897,0,26968.05949\n160283,18,0,1,0,Single,12,550.0535565,0,26978.67783\n177806,67,49,3,0,Married,5,549.9165017,0,27052.82509\n146684,65,44,2,1,Married,2,549.8650833,0,27266.25417\n160150,17,0,4,0,Married,14,549.8274165,0,26958.37083\n131455,23,6,4,1,Single,13,549.7817678,0,27151.08839\n166657,56,39,4,1,Single,3,549.6540629,0,27209.70315\n132233,16,0,2,0,Single,14,549.6537494,0,27007.68747\n173734,16,0,3,0,Single,13,549.6470562,0,27065.35281\n197105,28,8,2,0,Single,3,549.5779838,0,27022.89919\n186538,20,2,3,1,Single,5,549.4615996,0,26934.07998\n107498,44,26,1,0,Single,7,549.1403768,0,27256.01884\n172371,18,1,2,1,Single,6,549.0982587,0,27252.91294\n134813,20,3,4,0,Married,9,548.6780026,0,27094.90013\n169698,21,2,3,0,Single,12,548.579218,0,27208.9609\n124469,51,32,3,0,Single,10,548.363274,0,26838.1637\n136724,40,20,4,1,Married,4,548.1177189,0,26818.88595\n112520,29,9,3,0,Married,1,548.0053385,0,26915.26693\n168472,23,5,2,1,Single,9,547.9896053,0,27172.48027\n118576,64,47,2,0,Married,2,547.8867472,0,26915.33736\n124228,51,30,2,0,Married,4,547.7794054,0,27073.97027\n198479,25,6,4,1,Single,14,547.6141399,0,26824.707\n117086,16,0,1,0,Married,12,547.4455297,0,26826.27649\n197190,31,14,4,1,Married,4,547.1145488,0,26934.72744\n119599,18,1,2,0,Single,15,547.0661173,0,26927.30587\n178028,16,0,1,1,Single,12,547.0137533,0,26783.68767\n143539,26,6,2,0,Single,3,547.009841,0,27042.49205\n133980,22,4,1,0,Married,12,546.9662639,0,26947.3132\n111070,22,5,4,0,Married,0,546.8840089,0,26790.20045\n164077,27,6,1,0,Single,11,546.8137954,0,27113.68977\n168057,51,31,4,0,Married,1,546.7820913,0,27044.10457\n188170,21,2,2,1,Married,9,546.7752557,0,26912.76279\n182326,50,32,3,0,Single,9,546.7070054,0,27042.35027\n163382,41,22,4,0,Married,5,546.6772185,0,27118.86093\n145800,27,8,2,1,Single,1,546.6553508,0,26987.76754\n198118,21,4,3,0,Married,15,546.6375649,0,26931.87825\n195601,18,1,1,1,Single,12,546.5943051,0,26904.71526\n172927,33,16,1,1,Married,1,546.5523699,0,26786.6185\n178723,33,15,1,0,Married,1,546.5349024,0,26776.74512\n183541,25,6,1,0,Married,3,546.4265523,0,27082.32762\n110773,26,5,3,1,Married,4,546.3187204,0,26767.93602\n148106,68,50,3,0,Single,1,546.2450947,0,26930.25474\n139720,21,3,2,1,Married,8,546.2304531,0,27060.52266\n171415,24,7,2,1,Single,10,545.9616166,0,26859.08083\n121547,69,49,2,1,Married,0,545.8851835,0,26817.25918\n180125,35,18,3,0,Single,3,545.8582689,0,26724.91345\n191403,45,28,3,1,Married,5,545.857869,0,26700.89345\n102087,41,22,3,0,Married,3,545.6882857,0,26798.41429\n182092,56,38,2,1,Married,2,545.6744992,0,26694.72496\n180764,17,0,1,0,Married,13,545.648712,0,26856.4356\n185349,20,1,2,0,Single,15,545.648396,0,26791.4198\n190826,25,8,2,0,Married,15,545.6301088,0,26714.50544\n197985,27,10,2,1,Married,5,545.6230918,0,26720.15459\n134863,55,36,2,0,Single,7,545.6146351,0,26933.73176\n102361,46,27,4,1,Single,1,545.5296984,0,27007.48492\n191229,47,26,3,0,Married,1,545.4377592,0,26927.88796\n119693,40,23,4,0,Single,1,545.415933,0,26685.79665\n139100,39,18,2,1,Married,1,545.1540724,0,26904.70362\n139632,18,1,1,0,Married,12,545.1262388,0,26834.31194\n133079,50,32,4,0,Married,0,545.097663,0,26752.88315\n145717,19,1,2,0,Single,11,545.0539578,0,26905.69789\n172312,62,44,1,1,Married,5,544.9158279,0,26804.7914\n175175,20,3,2,1,Single,12,544.9146942,0,26951.73471\n101167,30,11,4,0,Single,0,544.860822,0,27038.0411\n144976,23,5,2,0,Single,12,544.8236918,0,26751.18459\n184349,39,22,2,1,Single,4,544.7799528,0,26924.99764\n121965,41,21,3,0,Single,10,544.5625563,0,26629.12782\n114693,38,17,1,1,Single,1,544.5163493,0,26729.81747\n175145,31,14,3,0,Single,0,544.4035359,0,26906.1768\n191466,57,40,3,1,Married,2,544.3064563,0,26923.32282\n101326,21,4,3,0,Married,11,544.2942127,0,26800.71064\n184178,21,2,1,0,Married,0,544.2032325,0,26965.16163\n104625,43,26,1,0,Married,3,544.0874689,0,26795.37345\n166873,20,1,2,0,Married,7,543.9432608,0,26645.16304\n188791,17,0,2,0,Single,12,543.916028,0,26772.8014\n109793,31,14,2,0,Single,8,543.8830552,0,26683.15276\n105757,70,49,4,1,Single,4,543.7224607,0,26866.12304\n133959,17,0,2,0,Married,9,543.7158302,0,26827.79151\n180038,58,41,2,0,Single,4,543.6620257,0,26901.10129\n125998,19,0,3,1,Married,1,543.6092206,0,26714.46103\n108323,30,10,4,1,Married,3,543.4510752,0,26826.55376\n110944,23,5,3,0,Married,12,543.1251437,0,26819.25719\n185142,58,38,2,0,Married,5,543.1069812,0,26709.34906\n137923,39,22,2,1,Single,4,542.9901738,0,26906.50869\n157998,31,13,4,0,Single,3,542.9156551,0,26618.78276\n185665,57,40,4,0,Single,2,542.9116362,0,26708.58181\n125850,34,13,2,1,Single,1,542.7039232,0,26913.19616\n168801,30,10,2,1,Married,3,542.4600195,0,26559.00098\n135621,63,45,4,1,Single,0,542.1535219,0,26714.6761\n167968,19,2,3,0,Married,10,542.1328336,0,26849.64168\n126530,41,24,3,0,Single,6,542.0815196,0,26767.07598\n126732,39,20,2,1,Married,3,541.7833951,0,26508.16976\n106113,21,4,3,1,Married,4,541.7544178,0,26746.72089\n198834,48,27,4,0,Single,10,541.6842648,0,26783.21324\n146633,44,27,4,0,Single,10,541.6637242,0,26624.18621\n154231,20,3,3,0,Married,8,541.5791259,0,26798.9563\n117008,25,6,4,1,Single,10,541.5292244,0,26491.46122\n190735,32,13,3,1,Married,4,541.5014923,0,26604.07462\n117299,44,25,3,0,Married,1,541.441289,0,26797.06445\n170748,20,3,3,0,Married,7,541.3064267,0,26505.32134\n127310,32,13,2,1,Single,3,541.3010963,0,26864.05482\n113473,59,42,4,1,Married,0,541.2316688,0,26464.58344\n191263,16,0,3,1,Single,6,541.1654019,0,26744.2701\n139975,47,30,2,1,Married,3,541.0056262,0,26809.28131\n197568,16,0,2,0,Married,9,540.9575032,0,26791.87516\n172593,39,19,3,0,Single,7,540.9037321,0,26529.18661\n144487,27,8,1,0,Single,4,540.7363438,0,26612.81719\n146751,19,0,1,1,Married,12,540.6418989,0,26530.09495\n128085,55,34,1,0,Married,0,540.5405296,0,26791.02648\n157434,25,8,3,1,Married,3,540.4608782,0,26763.04391\n101104,19,1,4,0,Married,0,540.408481,0,26495.42405\n181462,43,24,2,1,Single,5,540.3638924,0,26559.19462\n170337,51,31,4,1,Single,1,540.2230723,0,26443.15362\n154586,45,26,3,1,Married,2,540.1990971,0,26586.95486\n112263,21,2,2,1,Married,10,540.0722726,0,26573.61363\n175107,54,37,3,0,Single,8,539.986628,0,26468.3314\n123081,49,30,1,1,Married,0,539.8117592,0,26542.58796\n166365,20,2,3,0,Married,8,539.7603865,0,26577.01933\n112603,48,29,3,0,Single,2,539.7408509,0,26472.04255\n144064,28,9,3,1,Married,2,539.693869,0,26597.69345\n130631,52,32,2,1,Single,3,539.6302317,0,26538.51159\n145494,39,20,3,0,Married,2,539.5619297,0,26662.09649\n100606,44,27,4,0,Married,5,539.5374638,0,26754.87319\n173771,23,6,3,1,Married,1,539.3637156,0,26758.18578\n104761,38,18,3,0,Married,1,539.3382414,0,26565.91207\n183121,16,0,2,0,Married,9,539.2670892,0,26501.35446\n163579,20,1,3,0,Single,8,539.1182634,0,26589.91317\n118826,16,0,3,0,Married,5,539.0109188,0,26484.54594\n114557,40,19,2,0,Single,0,538.9219722,0,26513.09861\n113182,51,34,2,0,Married,1,538.7582235,0,26624.91118\n108691,41,20,3,0,Single,6,538.7307087,0,26385.53544\n124536,17,0,3,0,Married,6,538.5786777,0,26695.93389\n100596,28,11,1,0,Married,4,538.4955542,0,26325.77771\n186068,25,6,3,0,Married,12,538.4290782,0,26655.45391\n133032,45,26,4,0,Single,7,538.4001789,0,26648.00895\n104456,43,23,2,0,Married,3,538.3375081,0,26446.87541\n113142,39,22,4,1,Married,4,538.2692114,0,26676.46057\n176541,33,12,4,1,Married,4,538.0784879,0,26671.9244\n132179,37,19,2,0,Single,10,538.0641605,0,26510.20803\n198526,70,53,3,0,Married,2,538.012156,0,26698.6078\n127936,57,36,3,1,Married,5,538.0067996,0,26570.33998\n175602,21,3,3,0,Single,12,537.6073349,0,26589.36675\n100792,22,4,3,1,Single,10,537.3387366,0,26326.93683\n180586,43,26,3,0,Single,0,537.3116014,0,26341.58007\n157711,28,11,1,0,Married,2,537.3045231,0,26296.22616\n123383,16,0,2,0,Single,15,537.2615494,0,26617.07747\n125723,24,6,4,0,Married,5,537.2526154,0,26650.63077\n188911,25,8,2,0,Married,10,537.1319403,0,26546.59702\n137786,17,0,4,0,Married,10,537.1256702,0,26261.28351\n152415,49,32,4,1,Single,3,537.0080753,0,26574.40377\n189679,44,24,2,0,Single,5,536.9876034,0,26460.38017\n158150,21,4,4,1,Married,2,536.9874292,0,26370.37146\n144191,22,5,3,0,Single,14,536.8686847,0,26341.43424\n198562,22,5,1,0,Married,11,536.8602461,0,26384.01231\n100664,21,4,4,1,Single,12,536.8296313,0,26414.48157\n142046,24,7,2,1,Married,4,536.7156439,0,26276.7822\n152335,30,10,3,0,Married,3,536.6853095,0,26541.26548\n146307,19,0,3,0,Single,15,536.6286554,0,26331.43277\n181262,35,16,3,0,Single,0,536.617917,0,26473.89585\n181754,23,5,2,0,Married,7,536.5691039,0,26371.4552\n152439,69,48,4,1,Married,3,536.4160863,0,26300.80432\n190221,23,6,1,1,Single,14,536.4084716,0,26443.42358\n188573,18,0,1,1,Single,12,536.4047089,0,26505.23544\n177151,67,47,3,0,Married,0,536.2712451,0,26445.56226\n137024,17,0,1,0,Married,12,536.2573482,0,26434.86741\n108856,55,34,1,0,Married,3,536.2569214,0,26446.84607\n167978,24,5,3,1,Single,15,536.0817614,0,26504.08807\n151822,70,51,4,0,Single,1,536.0607195,0,26304.03598\n178790,24,7,4,0,Single,13,535.9953989,0,26590.76995\n123743,51,31,2,1,Married,0,535.9249164,0,26423.24582\n167442,61,42,4,0,Single,2,535.7703156,0,26396.51578\n151449,28,11,1,0,Single,10,535.6739106,0,26389.69553\n183020,25,8,4,0,Married,12,535.6306016,0,26359.53008\n127015,22,5,2,1,Single,14,535.4361195,0,26293.80598\n112483,16,0,2,1,Single,15,535.3980403,0,26484.90202\n178353,29,11,4,1,Married,2,535.380846,0,26281.0423\n155567,43,22,2,0,Single,7,535.3585014,0,26278.92507\n148775,19,2,2,0,Married,12,535.3469827,0,26458.34914\n157984,47,30,2,1,Single,0,535.32901,0,26493.4505\n134676,46,29,2,0,Single,10,535.2924095,0,26459.62048\n170474,61,42,2,0,Married,4,535.2367128,0,26216.83564\n120879,24,6,2,1,Single,11,535.1427594,0,26533.13797\n165626,70,50,4,0,Single,0,535.0623087,0,26486.11544\n180726,19,1,1,0,Single,14,535.0124158,0,26450.62079\n113068,22,5,1,0,Married,9,534.9538756,0,26272.69378\n102276,24,7,2,1,Single,8,534.9424314,0,26246.12157\n189489,52,33,3,0,Single,8,534.9380745,0,26305.90373\n152873,63,46,2,0,Married,4,534.8545625,0,26413.72813\n135239,43,25,2,1,Married,0,534.8194156,0,26283.97078\n182307,25,6,3,0,Married,11,534.7978634,0,26464.89317\n133099,30,12,3,1,Single,4,534.7241487,0,26302.20744\n107293,29,9,3,0,Single,4,534.7082137,0,26461.41069\n123564,16,0,2,0,Married,11,534.6710846,0,26473.55423\n149411,16,0,1,0,Single,13,534.6193671,0,26151.96836\n136257,22,5,1,1,Single,11,534.4777431,0,26187.88716\n196912,31,12,2,1,Married,4,534.422954,0,26284.1477\n166269,21,3,1,1,Single,9,534.3700065,0,26462.50033\n182319,24,7,3,1,Single,10,534.2235319,0,26482.1766\n167589,45,26,2,1,Single,1,534.0985077,0,26473.92539\n106880,53,33,3,1,Single,0,533.8597606,0,26293.98803\n191803,55,35,3,0,Married,4,533.8138112,0,26395.69056\n138451,24,6,4,1,Single,11,533.7637776,0,26146.18888\n112510,18,0,4,1,Single,8,533.701051,0,26391.05255\n179657,64,46,4,0,Married,0,533.5487877,0,26162.43939\n132014,65,45,2,0,Single,6,533.4980434,0,26428.90217\n191533,29,8,2,1,Married,4,533.3330744,0,26415.65372\n194260,24,7,4,0,Single,12,533.3063473,0,26098.31737\n152587,36,17,3,0,Married,5,533.1888339,0,26381.4417\n149325,23,5,2,0,Married,8,533.156235,0,26164.81175\n190060,40,21,2,1,Married,1,533.0020736,0,26100.10368\n150950,59,40,3,0,Married,2,532.7518908,0,26230.59454\n127246,37,19,2,0,Single,1,532.6556978,0,26203.78489\n136633,28,8,1,0,Single,5,532.6531171,0,26078.65586\n132724,69,52,2,0,Single,0,532.616571,0,26074.82855\n142599,45,27,4,0,Single,14,532.4487946,0,26182.43973\n162958,45,26,4,1,Married,3,532.4444303,0,26141.22152\n120275,17,0,2,1,Single,10,532.4260271,0,26087.30136\n144173,61,40,4,1,Single,1,532.3122908,0,26087.61454\n177244,21,3,4,1,Single,9,532.2788507,0,26094.94254\n129393,48,29,2,1,Married,0,532.1842989,0,26374.21495\n161497,47,27,3,0,Single,1,532.1506147,0,26198.53074\n142362,54,33,4,1,Single,1,532.1328122,0,26147.64061\n194414,58,37,3,1,Single,0,532.1056554,0,26117.28277\n128963,18,0,2,1,Single,12,532.0880332,0,26182.40166\n107219,39,18,2,1,Single,5,532.0812066,0,26036.06033\n128401,22,3,2,0,Single,12,531.7350409,0,26283.75205\n126224,58,39,1,0,Single,1,531.4576965,0,26052.88483\n158546,36,18,3,1,Single,4,531.4476528,0,26021.38264\n165067,52,33,3,0,Single,5,531.4447436,0,26353.23718\n141544,65,46,2,0,Married,2,531.4259253,0,26351.29627\n132478,25,6,2,0,Single,11,531.4220299,0,26092.1015\n130757,21,3,2,1,Single,9,531.3488591,0,26240.44296\n155644,22,3,2,1,Single,11,531.3287706,0,26166.43853\n144105,18,1,3,0,Married,15,531.285276,0,26192.2638\n158292,24,5,3,0,Married,11,531.217548,0,26126.8774\n151171,58,39,4,1,Married,5,531.2165162,0,26229.82581\n138311,23,5,2,0,Single,7,531.1712975,0,26127.56488\n183957,39,22,2,1,Married,5,531.1566917,0,26209.83459\n184185,24,7,4,0,Single,13,531.1071095,0,26145.35548\n154525,18,0,3,0,Single,11,531.0883406,0,26274.41703\n173862,49,29,1,0,Single,6,531.0568631,0,26068.84316\n154398,59,41,3,1,Married,5,530.9394513,0,26109.97257\n109678,40,22,4,0,Married,1,530.8373756,0,26300.86878\n190925,43,24,3,0,Married,1,530.8330641,0,26015.65321\n163088,23,6,2,1,Single,11,530.7044789,0,26196.22395\n121210,22,3,4,1,Single,10,530.7000455,0,26161.00228\n178968,39,22,2,0,Single,1,530.6876262,0,26161.38131\n129036,64,44,1,0,Single,1,530.6500807,0,26301.50404\n102169,56,38,4,0,Single,11,530.6103621,0,26254.51811\n129570,22,4,3,0,Married,11,530.5909941,0,26181.54971\n146676,17,0,2,1,Single,10,530.5900491,0,26233.50246\n103977,23,6,1,0,Single,11,530.4131783,0,26222.65892\n122168,22,4,4,1,Single,15,530.3134314,0,26019.67157\n125027,43,23,2,0,Single,6,530.2998472,0,26004.99236\n173785,21,4,2,1,Single,12,530.298609,0,26276.93045\n133156,56,39,2,0,Single,10,530.0779528,0,26207.89764\n135846,16,0,2,0,Married,15,530.021191,0,26293.05955\n152880,44,25,3,1,Single,1,529.9217527,0,26085.08764\n195581,16,0,3,0,Single,14,529.9111881,0,26280.55941\n181771,52,32,2,0,Married,5,529.7386863,0,26085.93432\n163258,57,37,3,0,Married,1,529.7027485,0,25924.13743\n149197,44,23,2,1,Married,4,529.6163371,0,26258.81686\n135679,67,47,3,1,Single,2,529.5924504,0,26250.62252\n109545,32,13,2,0,Single,10,529.4510662,0,26111.55331\n176191,56,38,3,0,Married,0,529.4150257,0,25988.75129\n175723,19,2,3,0,Married,6,529.3968674,0,26004.84337\n133632,56,38,4,0,Single,9,529.3321479,0,26093.6074\n129048,29,9,2,1,Single,5,529.3161021,0,26261.80511\n117402,33,16,3,0,Married,5,529.1670493,0,26099.35247\n118466,21,3,3,0,Single,13,529.1282353,0,26253.41177\n110296,22,4,4,0,Married,6,529.1230508,0,25997.15254\n194646,44,24,4,1,Single,2,529.1194447,0,26065.97224\n172367,20,3,3,1,Single,6,528.9670284,0,25904.35142\n132719,58,41,2,1,Married,2,528.9243841,0,25898.21921\n112521,34,17,3,0,Single,10,528.8520586,0,26215.60293\n158096,42,24,3,0,Married,0,528.6530207,0,25999.65104\n181351,47,28,2,0,Single,10,528.6512528,0,25936.56264\n163456,50,30,3,0,Single,5,528.6100455,0,26098.50228\n192213,22,5,2,0,Married,5,528.3099659,0,26089.4983\n199417,49,28,2,0,Single,2,528.2853443,0,25886.26721\n144363,20,1,3,0,Married,10,528.2847155,0,26089.23578\n169292,22,5,2,1,Single,10,528.072594,0,26191.6297\n118020,24,7,4,0,Single,14,528.0043687,0,25872.21844\n182237,48,29,1,1,Married,5,527.9214511,0,26170.07256\n124538,19,2,2,0,Married,13,527.8022838,0,25846.11419\n117778,17,0,2,0,Married,12,527.7419482,0,25944.09741\n104552,49,28,1,1,Married,3,527.5459518,0,26025.29759\n150225,18,1,3,1,Married,14,527.4669017,0,26055.34509\n124745,48,28,3,0,Single,9,527.253295,0,26008.66475\n125727,17,0,3,0,Married,11,527.2441967,0,26063.20984\n195987,19,2,2,0,Married,10,527.15865,0,25922.9325\n112181,24,5,4,0,Married,13,527.1154549,0,25873.77275\n143670,22,3,3,0,Single,12,527.0086334,0,25838.43167\n158623,23,6,3,1,Single,8,526.9840446,0,26088.20223\n127917,18,1,1,0,Single,13,526.7659998,0,26002.29999\n127873,23,6,1,1,Single,14,526.5853106,0,25818.26553\n155764,42,22,2,1,Married,5,526.5832167,0,25904.16084\n178479,46,27,2,1,Married,5,526.5789001,0,25886.94501\n133089,21,3,2,1,Single,3,526.4446386,0,25728.23193\n182934,17,0,3,1,Single,8,526.4224252,0,26036.12126\n198734,21,2,3,1,Single,13,526.4212626,0,25801.06313\n147124,61,41,1,0,Married,4,526.2793522,0,25974.96761\n114249,45,24,2,1,Single,1,526.2110395,0,25789.55198\n147681,28,10,2,0,Single,3,526.0735068,0,25947.67534\n198788,67,49,4,0,Married,5,526.0695468,0,25897.47734\n129305,18,1,2,1,Married,0,526.0381785,0,25794.90893\n169136,47,28,2,0,Married,3,526.0220163,0,25840.10082\n158891,68,48,4,0,Single,10,525.898743,0,25844.93715\n133254,59,42,2,0,Single,11,525.6856088,0,25709.28044\n170024,20,2,2,1,Single,8,525.5917164,0,25807.58582\n196564,49,31,1,1,Single,0,525.5303261,0,26050.51631\n145235,58,38,2,1,Married,5,525.3887506,0,25707.43753\n135895,16,0,2,1,Single,2,525.3045843,0,25956.22922\n128446,23,4,3,1,Single,15,525.2903006,0,25860.51503\n192402,51,33,2,0,Single,1,525.1884374,0,25682.42187\n195481,36,15,1,0,Married,3,525.1836212,0,26059.18106\n197807,58,38,1,1,Married,3,525.1432803,0,25707.16402\n189931,25,8,2,1,Single,8,525.0689309,0,26001.44655\n176584,44,23,1,1,Married,0,525.0527842,0,25756.63921\n185458,25,8,1,0,Married,15,525.0439169,0,25814.19585\n163315,31,12,4,0,Married,2,525.0244159,0,25828.2208\n171570,40,22,3,1,Single,4,524.9501759,0,26026.5088\n125172,34,13,3,1,Married,1,524.9369061,0,25666.84531\n144666,25,8,4,0,Single,14,524.87801,0,25895.9005\n191067,20,1,4,0,Married,8,524.7778553,0,25943.89277\n103036,35,17,2,1,Single,2,524.7749293,0,25675.74647\n194970,25,7,4,0,Single,11,524.729119,0,26032.45595\n128316,44,27,3,1,Married,0,524.6173436,0,25918.86718\n175313,23,4,2,1,Married,4,524.5472718,0,25818.36359\n151701,19,2,1,0,Married,7,524.4730901,0,25659.65451\n109235,20,2,3,0,Married,15,524.4369489,0,25811.84745\n131082,19,0,3,1,Single,13,524.4048121,0,25992.24061\n162062,64,45,2,0,Single,2,524.356007,0,25945.80035\n172739,20,3,2,1,Single,10,524.2636061,0,25938.18031\n155547,31,14,3,1,Married,4,524.2253273,0,25642.26637\n113580,40,20,4,0,Single,5,524.2077031,0,25973.38516\n131556,23,4,2,0,Single,11,524.1589774,0,25612.94887\n131504,19,1,2,0,Married,12,524.1147808,0,25926.73904\n142865,35,16,4,0,Married,1,524.1141209,0,25688.70605\n131965,27,7,4,0,Single,9,524.0742274,0,25967.71137\n125784,45,27,3,0,Single,1,524.0265757,0,25966.32879\n119016,52,35,4,1,Single,5,524.0062141,0,25783.31071\n169843,63,44,1,0,Married,4,523.9833142,0,25751.16571\n166022,55,36,2,1,Single,1,523.9525796,0,25679.62898\n198039,57,40,1,1,Married,1,523.9478985,0,25724.39493\n195144,70,51,1,0,Single,6,523.5559353,0,25904.79677\n158343,26,7,2,0,Single,2,523.5247446,0,25576.23723\n105316,26,7,3,0,Married,3,523.4855829,0,25875.27915\n118275,19,0,3,1,Single,10,523.4836397,0,25669.18199\n148134,47,28,2,1,Married,1,523.3737571,0,25809.68786\n124922,23,5,1,1,Single,11,523.3410437,0,25677.05219\n187399,69,50,3,1,Married,2,523.299883,0,25843.99415\n177150,23,6,4,1,Single,15,523.2093617,0,25577.46809\n129841,16,0,2,1,Married,0,523.1735888,0,25950.67944\n150961,48,28,3,1,Single,2,523.0894728,0,25707.47364\n151091,21,2,2,0,Single,15,523.0865746,0,25607.32873\n161951,28,7,3,0,Single,9,523.084682,0,25953.2341\n178496,25,7,1,1,Married,15,523.0781246,0,25952.90623\n183078,55,34,1,1,Married,2,522.9070466,0,25620.35233\n196869,36,15,3,0,Single,1,522.8906031,0,25866.53016\n120208,38,20,1,0,Married,1,522.8312209,0,25941.56105\n119413,36,15,3,0,Single,5,522.8168614,0,25744.84307\n100870,50,31,1,0,Single,4,522.7825524,0,25655.12762\n110378,16,0,2,1,Married,9,522.6998746,0,25704.99373\n132114,33,15,1,0,Single,2,522.6579568,0,25774.89784\n160442,17,0,4,0,Married,7,522.5961596,0,25603.80798\n150186,35,14,2,1,Single,5,522.577545,0,25922.87725\n134545,24,5,4,0,Married,11,522.5298648,0,25686.49324\n148057,35,16,1,0,Single,9,522.5136507,0,25617.68254\n130647,36,17,1,0,Single,7,522.412567,0,25878.62835\n193076,37,19,3,0,Married,3,522.3375633,0,25877.87817\n102853,25,6,4,1,Married,11,522.2008189,0,25572.04095\n125126,16,0,2,1,Married,9,522.0844338,0,25821.22169\n103353,30,11,4,0,Single,5,521.9381434,0,25600.90717\n143204,17,0,1,0,Married,10,521.923239,0,25718.16195\n158306,23,6,2,0,Married,8,521.9025364,0,25877.12682\n112861,61,40,2,0,Single,8,521.8490944,0,25589.45472\n115009,26,9,2,1,Single,0,521.7207259,0,25583.0363\n117917,19,0,3,1,Married,8,521.6204512,0,25593.02256\n146889,26,6,2,1,Single,0,521.4251691,0,25748.25846\n142066,45,27,2,0,Single,1,521.4135648,0,25549.67824\n114219,43,23,2,0,Single,9,521.2527857,0,25706.63929\n115152,57,36,1,0,Single,6,521.1869239,0,25780.3462\n127403,48,27,3,1,Married,3,520.9510609,0,25706.55305\n119319,16,0,2,1,Single,8,520.8966399,0,25473.832\n196611,18,0,1,1,Married,11,520.6205018,0,25644.02509\n185342,16,0,2,0,Married,11,520.5728419,0,25719.6421\n146620,24,6,3,0,Married,8,520.4128953,0,25459.64477\n136864,59,40,1,1,Single,1,520.1692747,0,25553.46373\n155156,25,6,1,1,Single,15,520.1172483,0,25789.86242\n121299,46,29,2,1,Married,3,519.9399947,0,25558.99974\n139738,35,17,3,0,Single,2,519.9221783,0,25717.10892\n159708,61,43,3,0,Married,3,519.7360058,0,25619.80029\n159595,20,1,1,1,Single,12,519.6358893,0,25452.79447\n147006,20,2,4,1,Single,14,519.3969085,0,25676.84543\n101704,22,5,3,1,Single,14,519.3589465,0,25649.94733\n187667,21,4,2,1,Single,10,519.3587409,0,25516.93705\n139894,37,20,4,0,Single,9,519.2443029,0,25715.21515\n122843,27,6,1,0,Single,2,519.1625522,0,25745.12761\n151388,29,9,2,0,Single,10,519.1499283,0,25628.49642\n178041,45,25,3,0,Single,6,519.1243411,0,25667.21706\n103714,21,3,3,0,Single,14,519.0797905,0,25619.98953\n171700,17,0,3,0,Married,12,519.0569181,0,25682.84591\n100608,21,3,2,1,Married,8,518.8952396,0,25509.76198\n139805,43,25,3,0,Single,1,518.8539379,0,25606.6969\n152028,48,31,3,0,Single,6,518.8537848,0,25642.68924\n122422,36,18,4,0,Single,4,518.8250552,0,25646.25276\n120034,22,3,2,0,Married,8,518.8089458,0,25403.44729\n178390,21,4,2,0,Single,14,518.7770254,0,25478.85127\n188012,23,4,2,1,Single,14,518.588689,0,25400.43445\n179804,40,20,2,0,Married,2,518.4711624,0,25661.55812\n135362,18,1,2,0,Single,13,518.4613744,0,25629.06872\n120976,35,18,2,1,Married,3,518.4082923,0,25625.41462\n163324,22,5,1,1,Single,9,518.3756905,0,25612.78453\n106788,47,28,2,0,Married,1,518.3633147,0,25642.16574\n117762,60,43,4,1,Single,3,518.349276,0,25541.4638\n111152,62,44,2,0,Single,8,518.2563145,0,25466.81573\n126520,50,29,2,0,Single,11,518.1773009,0,25621.86505\n164478,28,10,2,0,Single,9,518.1463703,0,25315.31852\n125626,37,17,1,1,Married,3,518.1264247,0,25579.32124\n122980,17,0,3,1,Single,3,518.0821154,0,25523.10577\n158539,18,0,1,1,Single,13,518.0761387,0,25369.80694\n121156,59,42,3,1,Married,0,518.0649303,0,25560.24652\n145765,24,7,1,1,Single,6,518.0476289,0,25432.38145\n113027,52,32,2,1,Married,1,517.9428444,0,25446.14222\n186416,59,39,4,0,Single,7,517.8330859,0,25433.6543\n111505,61,44,3,1,Married,2,517.8114409,0,25560.57205\n173645,21,4,1,0,Single,14,517.8087863,0,25665.43932\n161718,43,26,4,0,Married,4,517.8031515,0,25370.15758\n155836,18,0,2,1,Single,15,517.8020413,0,25383.10207\n156477,24,5,3,0,Married,8,517.7877526,0,25555.38763\n158905,42,21,4,1,Married,4,517.7582409,0,25670.91205\n188013,44,27,4,1,Single,4,517.716189,0,25358.80945\n118001,46,26,1,1,Married,0,517.6533908,0,25425.66954\n134471,35,17,1,1,Married,4,517.6154431,0,25379.77216\n153906,32,12,3,1,Single,3,517.531517,0,25499.57585\n103001,20,2,4,0,Married,6,517.381102,0,25653.0551\n143561,38,20,1,0,Single,7,517.2833796,0,25334.16898\n123419,56,36,2,1,Married,0,517.2785548,0,25315.92774\n165592,22,5,3,1,Single,12,517.2495203,0,25593.47602\n171213,44,23,1,1,Single,4,517.2484855,0,25642.42428\n117962,19,0,1,1,Married,6,517.2311218,0,25606.55609\n135039,48,29,1,1,Married,2,517.2279524,0,25485.39762\n120373,53,32,1,1,Married,1,517.2034459,0,25319.1723\n171444,57,39,3,0,Single,9,517.1821163,0,25377.10582\n178888,64,43,3,0,Single,3,517.1569153,0,25595.84577\n144467,30,12,3,0,Single,7,517.1248229,0,25283.24115\n103606,41,22,1,0,Married,4,516.9890908,0,25560.45454\n166433,37,16,2,0,Single,10,516.8995385,0,25626.97693\n138096,44,24,3,1,Single,3,516.8704031,0,25636.52016\n177475,16,0,1,1,Single,9,516.8054766,0,25454.27383\n119389,54,36,1,1,Single,4,516.7950519,0,25270.7526\n180169,35,18,3,0,Single,8,516.7643522,0,25629.21761\n113517,23,6,3,1,Married,1,516.683386,0,25424.1693\n196695,18,0,2,0,Single,12,516.6607639,0,25435.0382\n189521,44,26,1,0,Single,2,516.6473229,0,25385.36615\n109556,19,1,3,0,Married,15,516.6382649,0,25390.91325\n168877,25,7,3,1,Single,14,516.6068537,0,25365.34269\n135138,20,2,3,0,Married,14,516.6017866,0,25430.08933\n152328,36,16,1,1,Married,1,516.5329848,0,25228.64924\n148679,21,4,2,1,Single,7,516.4370055,0,25250.85028\n118874,25,7,4,0,Married,7,516.216328,0,25384.8164\n182396,36,15,2,0,Single,6,516.1114568,0,25535.57284\n159663,24,6,1,1,Single,11,516.0790928,0,25208.95464\n156028,27,6,3,0,Single,7,515.9986972,0,25216.93486\n180780,51,32,4,0,Single,10,515.9404578,0,25394.02289\n142073,16,0,3,0,Single,15,515.8698595,0,25511.49298\n146273,30,13,4,1,Single,0,515.8469815,0,25308.34908\n173181,42,22,4,0,Single,0,515.8336994,0,25372.68497\n174466,61,42,4,0,Single,5,515.8096795,0,25402.48398\n100308,38,21,3,0,Single,8,515.4679101,0,25262.39551\n154480,17,0,3,0,Married,15,515.426578,0,25316.3289\n171705,29,12,2,0,Married,3,515.4020847,0,25496.10424\n126030,52,31,3,0,Married,5,515.0600561,0,25285.00281\n126031,37,20,2,0,Single,6,515.0405076,0,25339.02538\n186434,48,28,2,0,Single,8,514.9922298,0,25168.61149\n177206,37,17,2,0,Single,9,514.8116374,0,25353.58187\n148820,24,5,3,1,Single,14,514.7946934,0,25438.73467\n171677,17,0,3,0,Married,15,514.7311087,0,25420.55544\n155482,20,1,1,1,Single,12,514.7287257,0,25387.43629\n138165,53,33,2,1,Single,1,514.7171097,0,25511.85549\n196143,51,31,1,0,Married,0,514.6965606,0,25489.82803\n100032,68,47,3,0,Married,2,514.643375,0,25417.16875\n111170,57,39,3,0,Single,15,514.5581364,0,25400.90682\n149102,19,0,1,1,Single,15,514.3464743,0,25365.32372\n177191,33,13,3,0,Single,7,514.3160063,0,25200.80032\n190100,16,0,3,0,Single,12,514.2703663,0,25229.51832\n135112,51,32,4,0,Single,5,514.2654663,0,25433.27332\n112951,53,33,4,0,Married,5,514.2234708,0,25158.17354\n198747,41,24,1,0,Single,6,514.2098312,0,25369.49156\n135988,16,0,2,0,Single,13,514.1504616,0,25192.52308\n179439,67,49,4,0,Single,3,513.9254653,0,25149.27327\n110555,26,5,1,0,Single,2,513.9091147,0,25103.45574\n198408,22,3,2,0,Married,6,513.8630957,0,25322.15479\n172391,30,11,1,0,Single,7,513.8387199,0,25420.936\n142970,39,18,3,1,Married,0,513.8101821,0,25131.50911\n131869,18,0,3,1,Single,13,513.7467041,0,25143.33521\n166968,52,34,1,1,Single,6,513.7114819,0,25180.5741\n121216,19,2,2,0,Married,13,513.4584495,0,25413.92248\n158249,38,20,2,0,Married,3,513.4092655,0,25451.46328\n113051,17,0,2,0,Married,14,513.3606865,0,25084.03433\n137912,17,0,3,1,Single,9,513.3529081,0,25424.64541\n166046,55,36,3,1,Single,8,513.3494544,0,25120.47272\n123667,52,34,2,1,Married,4,513.308942,0,25395.4471\n156872,32,12,2,0,Single,7,513.061039,0,25112.05195\n147832,41,22,4,1,Single,3,513.0333792,0,25426.66896\n120901,50,31,4,1,Single,10,513.03153,0,25357.5765\n115142,25,6,1,0,Single,14,512.9049801,0,25317.24901\n109084,55,36,2,1,Married,0,512.8732401,0,25383.66201\n156019,51,34,4,0,Single,7,512.8579436,0,25275.89718\n158875,23,4,1,0,Married,14,512.8447128,0,25172.23564\n190848,19,0,3,0,Single,13,512.7732665,0,25299.66333\n155514,47,27,4,1,Single,7,512.7147153,0,25271.73577\n188991,28,8,3,0,Single,8,512.6005403,0,25291.02702\n195376,43,23,2,1,Married,1,512.4255573,0,25225.27787\n127905,23,4,3,1,Married,1,512.2381967,0,25200.90984\n133108,39,21,3,0,Single,7,512.1963882,0,25295.81941\n166014,19,0,3,0,Married,14,512.1920681,0,25311.60341\n125364,20,1,1,0,Married,6,512.1821988,0,25262.10994\n147414,52,31,1,1,Married,5,511.9886291,0,25021.43146\n189634,47,26,3,0,Married,5,511.9737592,0,25119.68796\n132480,56,39,3,0,Single,10,511.9265755,0,25059.32878\n166232,31,14,1,0,Married,2,511.7580432,0,25187.90216\n189773,16,0,1,0,Married,14,511.7329733,0,25035.64867\n160621,46,28,1,0,Married,3,511.7252723,0,25373.26362\n126059,19,0,2,0,Single,12,511.7228049,0,25166.14025\n102183,51,31,2,0,Single,5,511.6676298,0,25267.38149\n123415,38,21,3,0,Single,8,511.628823,0,25245.44115\n199205,54,35,2,0,Single,10,511.6047185,0,25354.23593\n196028,25,6,3,1,Single,13,511.5813257,0,25247.06629\n145884,17,0,4,0,Married,7,511.5249382,0,25041.24691\n175816,32,15,1,0,Single,0,511.4659889,0,25304.29945\n195518,47,28,2,1,Married,5,511.4521387,0,25170.60694\n140147,17,0,1,1,Single,12,511.3999351,0,25226.99676\n110585,50,32,4,0,Single,10,511.3851929,0,25241.25965\n166875,35,18,2,0,Single,7,511.3257622,0,24993.28811\n151488,61,44,3,0,Single,2,511.3069738,0,25263.34869\n136275,27,6,1,0,Married,5,511.235708,0,25350.7854\n128335,23,4,3,0,Married,14,511.201805,0,24963.09025\n127811,24,7,1,1,Single,15,511.193896,0,25260.6948\n153983,67,47,2,1,Single,4,511.0969097,0,25032.84549\n198827,25,6,3,1,Married,9,510.886715,0,25169.33575\n165527,35,18,2,0,Single,9,510.6502619,0,25140.5131\n101465,25,7,3,0,Single,12,510.5356987,0,25202.78494\n179573,51,31,2,1,Married,3,510.472611,0,25270.63055\n129990,56,36,2,0,Single,8,510.3591905,0,25006.95953\n122527,19,1,1,0,Married,13,510.3590398,0,25093.95199\n154645,68,48,3,0,Single,3,510.2800031,0,25298.00016\n171930,48,31,1,0,Single,2,510.2678438,0,25110.39219\n193224,28,9,2,0,Single,6,510.2597547,0,25204.98774\n134841,59,38,4,0,Single,9,510.2355171,0,24933.77586\n120156,58,41,2,0,Single,7,510.2294704,0,24950.47352\n126568,19,0,2,1,Single,14,509.9011956,0,24897.05978\n177375,68,50,2,0,Single,4,509.8965713,0,25163.82857\n142384,16,0,3,0,Married,9,509.7677548,0,25125.38774\n137959,25,6,2,0,Single,12,509.6212314,0,25261.06157\n119658,33,16,2,0,Single,9,509.6045066,0,25148.22533\n141569,57,37,4,0,Single,7,509.5510796,0,25258.55398\n194623,55,35,2,0,Single,9,509.4627758,0,25123.13879\n156485,31,14,2,1,Married,1,509.4617109,0,25101.08555\n127222,30,12,2,1,Married,2,509.4519769,0,24887.59885\n184025,63,46,2,0,Single,3,509.4245965,0,25233.22983\n128416,20,2,2,0,Single,14,509.3897629,0,24906.48815\n118796,25,7,4,1,Married,3,509.3772513,0,24986.86257\n125089,48,31,2,0,Single,8,509.3666843,0,25218.33422\n172870,56,38,3,0,Single,8,509.3638436,0,25138.19218\n127117,18,0,1,0,Married,12,509.1766552,0,25118.83276\n192220,55,34,1,0,Single,12,509.1345942,0,24995.72971\n102912,23,5,2,0,Married,13,509.1251757,0,24924.25879\n166836,29,11,1,1,Single,0,509.0594248,0,25084.97124\n105045,58,41,3,0,Single,7,508.9858737,0,25083.29369\n131219,49,29,2,1,Single,1,508.6189206,0,25142.94603\n191073,35,16,2,1,Married,5,508.5575122,0,25218.87561\n174772,16,0,2,0,Single,14,508.5325412,0,25222.62706\n128625,32,13,4,0,Single,10,508.4851291,0,24829.25646\n175646,43,22,3,0,Single,5,508.3812419,0,24884.0621\n193193,49,31,1,0,Married,3,508.3393905,0,25075.96953\n140197,19,0,4,0,Married,15,508.3261896,0,24976.30948\n145461,48,29,4,1,Married,2,508.2982535,0,25012.91268\n154426,21,3,3,0,Married,13,508.1753085,0,25090.76543\n168914,22,3,3,0,Married,3,507.9072134,0,25035.36067\n186623,52,33,2,0,Single,7,507.8694992,0,25111.47496\n163791,25,8,2,0,Married,13,507.5708927,0,25171.54464\n187137,34,17,3,0,Single,13,507.4618644,0,25167.09322\n182918,44,24,1,0,Single,10,507.3039313,0,24813.19657\n174405,18,0,3,0,Married,12,507.2981493,0,24891.90747\n198462,25,8,2,1,Single,14,507.1576806,0,25089.88403\n173426,28,8,2,0,Married,3,506.9298049,0,25114.49025\n115395,38,19,2,0,Married,0,506.922688,0,24766.1344\n161868,17,0,3,1,Married,2,506.9099905,0,24990.49953\n188060,55,34,3,0,Single,8,506.8969568,0,25075.84784\n191104,57,39,2,0,Single,2,506.7966527,0,24971.83264\n175866,43,25,2,1,Married,3,506.7637966,0,24921.18983\n181044,47,28,3,1,Married,5,506.6747288,0,24767.73644\n102155,32,14,3,1,Married,1,506.6642713,0,24881.21357\n176858,57,38,2,0,Married,5,506.5945122,0,24851.72561\n187062,16,0,2,0,Married,2,506.5618893,0,24756.09447\n131870,27,9,2,0,Single,11,506.5426469,0,24819.13235\n113593,50,33,4,0,Single,11,506.5247959,0,24948.2398\n194747,42,22,3,1,Single,5,506.4480532,0,24853.40266\n157166,17,0,3,0,Married,7,506.4295787,0,25100.47894\n165461,34,16,1,1,Married,5,506.4191291,0,24862.95646\n121451,35,14,2,1,Married,3,506.3626082,0,24745.13041\n182881,19,2,3,1,Single,11,506.3021724,0,25078.10862\n109721,21,2,2,1,Single,15,506.2082003,0,24909.41002\n174283,29,9,4,0,Single,7,505.8833102,0,24800.16551\n110141,68,51,3,0,Married,2,505.8732548,0,24961.66274\n176183,27,8,2,0,Single,10,505.7678196,0,25005.39098\n155059,66,45,4,1,Single,1,505.5540679,0,25004.7034\n180380,52,33,1,0,Single,7,505.4161096,0,24826.80548\n171454,43,22,3,1,Married,1,505.324672,0,24689.2336\n136851,70,49,3,1,Single,0,505.3069656,0,24962.34828\n180062,44,25,3,0,Married,2,505.2710024,0,25021.55012\n192290,25,8,2,0,Single,11,505.2605779,0,24704.0289\n183441,34,13,3,0,Single,8,505.2486386,0,24697.43193\n182805,46,26,4,0,Single,9,505.1093518,0,24743.46759\n145342,21,2,3,1,Single,12,504.9550731,0,24921.75366\n173255,46,27,4,1,Single,10,504.8856084,0,24721.28042\n193102,19,2,3,0,Single,15,504.8639467,0,25036.19734\n169030,21,2,2,0,Single,13,504.8449361,0,24651.24681\n116427,32,15,3,0,Married,1,504.8083705,0,24723.41853\n166434,53,34,2,0,Single,6,504.6687082,0,24676.43541\n173463,52,35,2,1,Married,1,504.5877345,0,24979.38673\n120443,38,19,3,0,Single,7,504.547965,0,24736.39825\n160006,54,37,2,0,Single,14,504.3927277,0,24806.63639\n146277,34,14,2,0,Single,10,504.3764517,0,25005.82259\n177154,57,38,2,0,Single,10,504.3286737,0,24907.43369\n165495,56,39,2,0,Single,6,504.3038973,0,24748.19487\n118922,54,33,3,0,Single,7,504.28471,0,24915.2355\n121810,37,20,4,1,Married,1,504.2440072,0,24715.20036\n192994,52,34,2,1,Single,2,504.1945897,0,24630.72949\n185260,26,6,4,0,Single,7,504.1454866,0,24782.27433\n148617,44,25,2,1,Married,0,504.0314278,0,24925.57139\n100949,38,19,2,1,Single,2,503.8319877,0,24729.59939\n149570,48,28,2,0,Single,9,503.8264386,0,24645.32193\n128399,64,46,3,1,Single,0,503.8090316,0,24848.45158\n112313,24,5,3,1,Single,11,503.7813256,0,24841.06628\n151556,21,2,4,0,Married,11,503.7421438,0,24951.10719\n106265,45,26,4,1,Single,1,503.6657596,0,24796.28798\n169892,23,4,3,0,Married,14,503.6588735,0,24967.94368\n106602,24,6,3,1,Single,14,503.5861299,0,24783.3065\n120439,32,13,2,0,Single,4,503.5517722,0,24845.58861\n143333,21,2,4,1,Single,13,503.5175373,0,24599.87687\n118078,34,15,3,0,Single,7,503.4558814,0,24914.79407\n124862,43,23,3,1,Married,0,503.3990749,0,24591.95375\n124401,40,19,1,0,Single,8,503.3720137,0,24678.60069\n144549,19,1,3,1,Single,13,503.3603026,0,24945.01513\n109715,33,15,4,0,Married,2,503.3392623,0,24786.96312\n119219,35,15,3,1,Married,2,503.1992237,0,24770.96119\n180117,16,0,3,1,Single,12,503.171863,0,24799.59315\n183239,18,1,4,1,Single,13,503.1516614,0,24658.58307\n164123,25,8,1,1,Married,6,503.1421534,0,24933.10767\n141433,36,15,3,0,Single,10,503.1180735,0,24662.90368\n103743,23,6,2,1,Single,8,503.0672471,0,24562.36236\n183996,53,34,1,1,Married,1,503.0542024,0,24780.71012\n136359,20,3,1,1,Married,3,503.0228692,0,24658.14346\n164738,18,1,3,1,Single,12,502.9435603,0,24731.17802\n124859,51,33,2,0,Single,0,502.9362785,0,24564.81393\n177437,38,21,4,1,Single,5,502.9167088,0,24809.83544\n153031,23,5,4,0,Married,14,502.8963159,0,24801.8158\n144878,48,28,4,0,Married,2,502.8923299,0,24738.6165\n175249,31,13,1,1,Single,5,502.7124322,0,24560.62161\n131412,16,0,4,0,Married,11,502.6916232,0,24811.58116\n121949,16,0,2,1,Single,11,502.645551,0,24781.27755\n153029,64,47,2,1,Single,1,502.643824,0,24827.1912\n104070,40,20,1,0,Married,3,502.6195503,0,24861.97752\n104307,16,0,1,1,Married,6,502.6132944,0,24703.66472\n183975,32,11,3,1,Single,0,502.5834979,0,24771.1749\n141436,20,3,2,0,Married,6,502.4378501,0,24675.89251\n198147,22,3,3,1,Single,7,502.4354966,0,24706.77483\n183731,24,6,2,0,Married,11,502.3428123,0,24682.14062\n191980,18,1,2,0,Married,7,502.3279723,0,24727.39862\n173338,41,21,4,1,Married,1,502.2764181,0,24597.82091\n148158,24,5,2,1,Married,6,502.240405,0,24795.02025\n109487,19,2,1,0,Married,14,502.1504835,0,24612.52418\n148649,20,1,3,1,Single,15,502.1421869,0,24822.10935\n122093,54,37,1,1,Single,5,502.1356528,0,24859.78264\n162680,32,12,1,0,Single,10,502.0650609,0,24603.25305\n181140,25,8,1,1,Single,9,501.9949382,0,24654.74691\n144536,25,7,3,0,Single,15,501.9788486,0,24776.94243\n147971,41,23,3,0,Single,8,501.5088854,0,24782.44427\n169063,63,46,4,1,Single,2,501.3705536,0,24547.52768\n176266,18,0,1,1,Single,9,501.3537223,0,24547.68612\n159876,34,15,2,0,Single,7,501.3198275,0,24593.99138\n176246,39,18,3,0,Single,6,501.2964869,0,24810.82435\n156144,20,1,3,0,Married,13,501.2273032,0,24580.36516\n164720,53,35,3,0,Married,5,501.2102467,0,24847.51234\n106220,30,12,2,1,Single,2,501.1905383,0,24696.52692\n175647,16,0,2,1,Married,13,501.1084558,0,24514.42279\n174727,37,18,4,0,Married,5,500.9940391,0,24503.70196\n181623,21,4,2,0,Single,13,500.89131,0,24620.5655\n186109,54,35,3,1,Single,3,500.8659766,0,24627.29883\n182938,47,29,2,0,Single,8,500.8359073,0,24748.79537\n195026,21,2,2,1,Married,9,500.8230195,0,24597.15098\n143836,22,5,2,1,Single,15,500.6980582,0,24821.90291\n163648,38,19,2,0,Single,7,500.6701502,0,24535.50751\n170703,22,5,4,1,Married,9,500.5121407,0,24612.60704\n162343,52,34,3,0,Single,7,500.4497483,0,24561.48742\n148213,51,31,2,0,Single,10,500.4022292,0,24697.11146\n195198,53,33,3,0,Single,5,500.3690353,0,24532.45177\n165493,22,5,3,0,Married,2,500.339043,0,24705.95215\n180162,57,40,4,1,Single,9,500.2550126,0,24698.75063\n185378,18,0,1,1,Single,8,500.2238116,0,24727.19058\n180410,44,24,4,1,Married,5,500.1520161,0,24698.60081\n186614,69,52,3,0,Single,1,500.0884252,0,24539.42126\n135690,53,32,3,0,Single,14,500.0798791,0,24796.99396\n131180,23,6,4,0,Single,13,500.059073,0,24669.95365\n180133,23,4,2,1,Married,8,500.0157464,0,24539.78732\n123531,38,19,4,1,Single,4,499.9388563,0,24458.94282\n134594,37,18,3,0,Married,1,499.8944309,0,24643.72155\n125569,26,9,2,1,Single,6,499.890252,0,24705.5126\n153049,17,0,3,0,Married,11,499.8436639,0,24488.1832\n195610,17,0,2,0,Married,12,499.7639256,0,24610.19628\n129054,34,14,4,0,Single,9,499.7077501,0,24411.38751\n190507,60,39,4,0,Married,5,499.5409298,0,24582.04649\n154000,21,4,4,0,Married,14,499.3441883,0,24578.20942\n146987,24,5,2,1,Married,6,499.1907587,0,24554.53794\n123961,16,0,3,1,Single,11,499.1825389,0,24472.12695\n156049,25,8,3,1,Single,13,499.159061,0,24527.95305\n148155,66,49,1,1,Married,5,499.0903996,0,24390.51998\n199603,29,11,4,1,Single,6,499.0814058,0,24657.07029\n122133,44,26,3,1,Married,4,499.0778914,0,24495.89457\n177116,24,7,3,0,Married,6,499.0346116,0,24699.73058\n177073,20,1,3,0,Single,14,499.032731,0,24718.63655\n124558,67,49,2,0,Married,1,499.0143263,0,24703.71632\n103861,35,17,1,0,Single,8,499.0115671,0,24583.57836\n106081,18,1,4,1,Married,9,498.9931769,0,24479.65885\n172624,45,28,1,0,Single,10,498.8552508,0,24362.76254\n148144,38,21,3,0,Single,9,498.819096,0,24446.9548\n163909,18,0,3,0,Married,9,498.7428318,0,24588.14159\n174836,55,37,3,0,Single,9,498.7307055,0,24358.53528\n185290,33,12,2,1,Single,7,498.6506277,0,24363.53139\n132621,18,0,2,1,Married,9,498.6226712,0,24649.13356\n158058,16,0,4,1,Single,7,498.5867323,0,24513.33662\n104762,23,5,3,0,Married,14,498.5825402,0,24693.12701\n109315,35,14,3,0,Married,0,498.4484834,0,24551.42417\n107342,63,42,3,0,Single,3,498.3575125,0,24450.87563\n105266,19,1,3,0,Single,14,498.2880731,0,24542.40366\n116086,53,35,2,1,Single,7,498.2544249,0,24350.72125\n163761,54,34,3,1,Married,5,498.1254997,0,24522.27499\n168471,23,6,3,1,Married,9,498.0613531,0,24453.06766\n121118,20,1,4,0,Married,14,497.9847456,0,24534.23728\n125643,41,23,3,1,Married,2,497.9610354,0,24520.05177\n119017,69,50,3,1,Single,2,497.8430728,0,24376.15364\n139852,22,4,3,0,Married,13,497.7441376,0,24420.20688\n126278,48,31,3,1,Single,2,497.7211086,0,24371.05543\n116042,18,0,3,0,Single,14,497.6852173,0,24594.26087\n150407,27,7,3,1,Single,5,497.6672225,0,24652.36113\n146939,61,41,2,1,Single,4,497.5727228,0,24393.63614\n134034,47,27,3,1,Single,4,497.5374821,0,24397.87411\n145012,21,2,3,0,Married,15,497.4716531,0,24316.58266\n155311,19,0,2,1,Married,12,497.3690476,0,24501.45238\n122071,22,5,3,0,Married,11,497.3269336,0,24602.34668\n122589,31,11,4,0,Single,6,497.3027208,0,24432.13604\n183936,41,20,3,0,Single,7,497.2943847,0,24555.71924\n138606,41,23,4,0,Single,9,497.2546463,0,24388.73232\n150525,22,5,1,1,Married,14,497.2378044,0,24354.89022\n167235,17,0,2,0,Married,13,497.1846427,0,24441.23214\n140215,25,8,4,1,Married,5,497.1707541,0,24280.53771\n126050,33,14,3,0,Single,8,497.0919139,0,24405.5957\n130244,27,10,3,1,Single,3,496.9909239,0,24425.5462\n111542,34,15,3,0,Single,10,496.9869339,0,24525.3467\n172679,25,6,1,1,Married,1,496.9800239,0,24278.0012\n142880,38,19,2,0,Single,10,496.9686527,0,24525.43264\n157886,55,37,1,1,Single,5,496.9354782,0,24279.77391\n144994,25,7,2,0,Married,6,496.8511596,0,24369.55798\n135336,37,18,3,0,Single,10,496.808397,0,24353.41985\n183785,20,1,3,0,Single,15,496.7315303,0,24360.57652\n162109,24,6,1,0,Married,13,496.7009686,0,24434.04843\n102337,41,20,1,0,Single,5,496.6251555,0,24603.25778\n119743,20,3,2,1,Single,10,496.3034295,0,24553.17148\n175141,56,35,3,0,Single,3,496.2508304,0,24411.54152\n128569,26,8,4,0,Single,9,496.2432547,0,24415.16274\n125994,38,19,2,1,Single,4,496.1258875,0,24568.29438\n126387,47,28,3,0,Single,5,496.1250167,0,24308.25084\n108976,20,2,2,1,Single,9,496.105301,0,24449.26505\n101190,22,4,3,1,Single,15,496.1007866,0,24595.03933\n136278,49,31,3,1,Single,1,496.0153048,0,24323.76524\n144906,37,20,2,0,Single,12,496.0066722,0,24232.33361\n131925,24,6,2,1,Single,14,495.9313333,0,24295.56667\n108655,53,33,3,0,Single,0,495.8811039,0,24403.0552\n175941,60,41,2,0,Single,7,495.8744707,0,24328.72354\n139715,54,33,4,0,Single,7,495.8577099,0,24343.8855\n122194,61,42,1,0,Single,12,495.8440724,0,24397.20362\n157870,47,30,2,0,Married,1,495.8239471,0,24576.19736\n109842,45,28,3,1,Single,8,495.786754,0,24247.3377\n190425,67,49,3,1,Married,0,495.6786828,0,24271.93414\n182273,40,20,2,0,Married,3,495.4931261,0,24214.65631\n131251,48,29,3,1,Single,2,495.4474053,0,24414.37027\n139932,39,19,3,0,Single,10,495.4356608,0,24298.78304\n198863,41,23,3,0,Single,6,495.3473402,0,24425.36701\n138741,23,4,3,0,Married,9,495.3208549,0,24286.04275\n115130,37,18,2,1,Married,5,495.27612,0,24173.806\n168185,16,0,2,1,Married,4,495.2585145,0,24365.92573\n110599,22,5,1,0,Married,2,495.1954386,0,24202.77193\n175429,21,2,4,1,Married,12,495.187766,0,24237.3883\n121475,26,8,3,0,Married,3,495.1714825,0,24422.57413\n128658,35,16,2,0,Married,5,495.1681692,0,24408.40846\n123516,19,0,4,1,Married,6,495.0950612,0,24238.75306\n126775,25,8,1,1,Single,10,495.0498383,0,24171.49192\n181356,48,28,2,1,Single,2,494.9337793,0,24445.68897\n189548,24,5,4,0,Married,11,494.8396278,0,24304.98139\n130638,21,4,3,1,Single,14,494.7451375,0,24490.25688\n187258,51,32,3,0,Married,4,494.7170351,0,24469.85176\n195516,20,3,3,0,Single,11,494.7122485,0,24203.61243\n100799,60,40,2,1,Married,1,494.682189,0,24469.10945\n155044,20,1,3,1,Single,13,494.6590334,0,24315.95167\n132427,43,23,2,1,Single,1,494.6508114,0,24516.54057\n184343,44,23,1,0,Single,11,494.557812,0,24396.8906\n149696,18,0,2,0,Single,11,494.5553381,0,24374.76691\n104920,59,38,2,0,Single,12,494.465162,0,24321.2581\n135709,67,48,3,0,Single,7,494.4420049,0,24202.10025\n177793,31,11,3,0,Married,4,494.3033414,0,24189.16707\n178418,43,25,4,1,Single,2,494.2415521,0,24203.0776\n102683,40,20,2,1,Married,0,494.1563874,0,24135.81937\n150113,38,20,1,0,Single,6,494.1024466,0,24503.12233\n189640,62,44,3,1,Married,4,494.0244144,0,24481.22072\n126728,68,48,2,0,Married,3,494.0108596,0,24252.54298\n170272,24,6,1,1,Married,12,493.9555063,0,24330.77532\n193964,20,1,3,1,Single,12,493.9541163,0,24250.70582\n118084,50,29,3,1,Married,4,493.8233283,0,24298.16642\n170986,20,2,1,0,Single,13,493.8180385,0,24445.90193\n187317,22,5,2,1,Married,13,493.7547271,0,24331.73636\n141248,22,5,2,1,Married,8,493.3039681,0,24177.19841\n133005,24,5,1,1,Single,13,493.2976733,0,24454.88367\n189365,45,26,1,0,Married,1,493.0585896,0,24363.92948\n192465,55,37,2,1,Married,5,492.963983,0,24216.19915\n147765,53,32,4,0,Single,12,492.9224979,0,24190.1249\n152347,30,10,2,1,Single,8,492.7189607,0,24191.94804\n117485,43,26,3,1,Single,2,492.5751319,0,24283.7566\n198418,65,47,3,0,Married,5,492.5699465,0,24396.49733\n165431,62,44,4,1,Married,2,492.556772,0,24305.8386\n168366,21,4,3,1,Married,4,492.2762627,0,24278.81314\n143895,39,19,3,1,Single,9,492.2734875,0,24312.67438\n125080,18,0,3,1,Single,13,492.2584052,0,24124.92026\n132925,49,30,4,0,Single,14,492.2416352,0,24305.08176\n109070,36,19,3,0,Single,9,492.0101209,0,24293.50605\n168567,32,14,2,0,Single,7,491.9689152,0,24121.44576\n184812,49,30,2,0,Single,10,491.907308,0,24355.3654\n137680,24,7,2,0,Married,15,491.8858119,0,24104.2906\n194350,42,21,2,1,Single,1,491.8573176,0,24254.86588\n112803,23,5,3,0,Single,14,491.6209559,0,24300.0478\n190759,59,40,3,0,Married,5,491.5911903,0,24149.55952\n128525,27,8,3,1,Single,7,491.5678067,0,24171.39034\n184004,52,35,2,0,Single,9,491.5015654,0,24085.07827\n133783,26,6,1,0,Single,6,491.4299422,0,24092.49711\n106945,21,4,2,0,Married,8,491.2875129,0,24227.37565\n102548,16,0,3,1,Single,6,491.1764037,0,24122.82019\n120100,32,15,3,0,Single,10,491.1227067,0,24238.13533\n195053,57,39,2,0,Single,9,491.049278,0,23953.4639\n176062,41,23,2,0,Single,3,491.0277254,0,24329.38627\n132881,25,8,3,1,Single,6,490.9960318,0,24349.80159\n132756,49,32,2,0,Single,10,490.9612311,0,24127.06156\n100693,47,27,1,1,Married,1,490.9507383,0,24263.53692\n146726,44,26,4,0,Single,10,490.8547467,0,24150.73734\n120151,23,6,3,1,Single,11,490.8473595,0,23988.36798\n107065,69,52,2,1,Married,0,490.8277044,0,23964.38522\n149865,56,39,3,1,Married,3,490.7600314,0,24241.00157\n192820,18,0,2,0,Married,12,490.7504061,0,23989.52031\n101681,21,2,4,1,Single,13,490.7404258,0,24122.02129\n175890,59,40,4,0,Single,2,490.6784955,0,23972.92478\n127747,53,33,2,0,Single,10,490.6586864,0,24279.93432\n117291,57,39,3,0,Single,8,490.6286884,0,24157.43442\n172823,31,10,3,0,Single,10,490.590609,0,24171.53045\n132418,53,35,2,0,Single,8,490.554657,0,24044.73285\n110024,24,6,3,1,Single,15,490.4523918,0,24161.61959\n191384,19,0,2,1,Single,6,490.4280641,0,24229.40321\n104885,33,12,2,0,Single,10,490.2322292,0,23948.61146\n153850,36,18,2,0,Married,3,490.1887384,0,24274.43692\n177448,22,4,3,1,Single,13,490.1057473,0,24214.28737\n111578,45,28,2,1,Single,1,490.0793064,0,24030.96532\n104411,56,36,2,0,Single,5,490.056154,0,23973.8077\n199401,16,0,3,1,Single,14,490.0551278,0,24299.75639\n128673,33,12,4,0,Married,0,490.0355162,0,24250.77581\n142619,59,38,1,1,Single,15,490.0252621,0,24300.26311\n139367,45,28,4,1,Married,5,490.0024758,0,24285.12379\n186288,25,6,3,0,Single,14,489.9412642,0,24136.06321\n133314,37,18,2,0,Single,15,489.9230992,0,23950.15496\n191999,17,0,4,0,Married,12,489.8625991,0,24092.12996\n106458,22,4,1,1,Married,6,489.859737,0,24239.98685\n129757,24,5,1,0,Married,7,489.8566707,0,24252.83354\n199853,19,1,2,1,Married,14,489.8100134,0,24015.50067\n148317,26,8,1,0,Single,7,489.774795,0,24228.73975\n180896,47,29,4,1,Single,5,489.70268,0,24133.134\n143868,58,40,2,0,Single,8,489.6712881,0,23942.56441\n164027,24,6,4,1,Single,12,489.5695559,0,24090.4778\n161962,23,5,1,0,Married,9,489.5382292,0,23942.91146\n168320,46,28,1,0,Single,8,489.5102333,0,24036.51167\n179057,70,52,2,1,Married,4,489.4043716,0,24014.21858\n157198,16,0,3,0,Single,15,489.3780602,0,24067.90301\n111010,21,2,4,1,Married,4,489.3396942,0,24169.98471\n128381,63,46,2,1,Single,1,489.1814974,0,24156.07487\n160812,31,13,2,1,Married,1,489.1019963,0,23929.09982\n141246,21,3,3,0,Married,12,489.0396797,0,23912.98399\n113622,31,13,4,1,Married,3,489.0028192,0,23887.14096\n108871,48,30,3,0,Single,0,488.9406356,0,23944.03178\n101872,16,0,3,1,Married,10,488.9296432,0,24143.48216\n131988,54,35,2,1,Married,0,488.9204235,0,23938.02118\n114358,21,3,3,0,Single,15,488.8615895,0,23979.07948\n188268,38,17,4,0,Single,2,488.8218131,0,24171.09066\n102492,21,2,3,1,Married,7,488.7621336,0,24146.10668\n109730,46,28,3,0,Single,8,488.755678,0,23965.7839\n174141,27,7,1,1,Married,1,488.6956097,0,23977.78049\n138819,43,22,3,0,Single,9,488.6824004,0,23950.12002\n101197,35,16,3,1,Married,1,488.6605084,0,23928.02542\n140249,18,0,2,1,Married,7,488.5919464,0,24177.59732\n110053,24,5,2,1,Single,15,488.3864875,0,24130.32438\n132739,32,11,2,0,Single,6,488.3738286,0,24164.69143\n192481,43,22,2,0,Single,9,488.3588583,0,23926.94292\n167511,55,38,1,0,Married,0,488.3571976,0,23960.85988\n181808,37,18,2,1,Married,3,488.343685,0,24046.18425\n186734,54,37,4,0,Single,7,488.2994065,0,23828.97033\n158855,19,1,1,1,Married,2,488.2883074,0,23931.41537\n117822,20,1,2,1,Single,12,488.2861253,0,24034.30627\n100178,53,33,3,0,Single,1,488.2703453,0,24024.51727\n177338,50,29,3,0,Single,15,488.2609925,0,24051.04963\n157860,52,35,2,0,Married,1,488.2366164,0,24045.83082\n121586,21,2,3,1,Married,9,488.227282,0,24147.3641\n158744,17,0,2,0,Married,15,488.2250123,0,23953.25062\n115787,28,8,4,0,Married,2,488.1996322,0,24208.98161\n132908,17,0,3,0,Married,14,488.1049567,0,24123.24784\n198848,30,11,3,0,Married,1,488.0595669,0,24163.97835\n196420,30,12,2,1,Single,10,488.0137975,0,23868.68988\n154889,16,0,2,0,Married,12,487.8985999,0,23866.93\n148740,30,11,2,1,Single,0,487.8912641,0,24045.56321\n143867,28,9,1,0,Married,2,487.8515553,0,23935.57777\n132302,54,36,1,1,Single,8,487.8492932,0,23893.46466\n195426,22,5,4,1,Single,11,487.8183517,0,23999.91759\n115868,52,32,2,0,Married,4,487.7118325,0,23826.59163\n144077,33,15,2,0,Married,1,487.6017329,0,24129.08665\n140510,35,16,2,1,Married,1,487.5793938,0,24084.96969\n185068,70,52,4,0,Single,10,487.4054935,0,23893.27468\n177964,56,35,3,0,Single,10,487.3814349,0,24026.07175\n170265,68,48,4,0,Married,3,487.3707488,0,23806.53744\n160431,40,20,2,1,Single,9,487.3630135,0,23896.15068\n155295,32,15,4,1,Married,3,487.361598,0,23956.0799\n173205,58,39,3,1,Single,1,487.3560719,0,23852.8036\n130001,28,11,3,1,Single,2,487.3538882,0,23767.69441\n115154,23,6,1,0,Single,11,487.1763139,0,23780.8157\n126611,41,20,1,0,Single,12,487.0448096,0,23870.24048\n129101,24,7,2,1,Single,14,487.0326234,0,24029.63117\n177919,29,9,4,1,Married,2,486.7930053,0,23784.65027\n193275,38,18,4,0,Single,9,486.7611141,0,23870.05571\n147945,58,39,2,0,Single,13,486.7217129,0,24054.08565\n123079,25,8,1,0,Married,8,486.7162359,0,23989.8118\n131296,37,16,3,1,Married,2,486.7029206,0,23967.14603\n108447,25,6,2,1,Married,6,486.6687182,0,23800.43591\n182490,49,28,4,1,Married,1,486.6595459,0,23915.9773\n109977,52,31,4,1,Married,2,486.6339382,0,23731.69691\n129783,16,0,3,1,Married,6,486.6290318,0,23982.45159\n123111,45,25,3,0,Single,4,486.594638,0,24043.7319\n120303,18,1,2,1,Single,12,486.5719053,0,23895.59527\n194599,26,9,2,0,Single,6,486.5580131,0,23736.90066\n131621,52,32,2,1,Married,0,486.4673105,0,23840.36553\n165213,51,31,2,0,Single,10,486.428662,0,24009.4331\n165567,48,30,2,0,Single,1,486.393953,0,23830.69765\n144117,27,10,2,0,Single,6,486.329603,0,23773.48015\n193752,52,34,3,0,Married,3,486.2978109,0,23826.89055\n103279,52,33,1,0,Single,6,486.2943804,0,23988.71902\n137040,47,26,3,0,Single,9,486.2389158,0,24036.94579\n134035,43,23,3,0,Married,2,486.206212,0,23984.3106\n191220,22,5,2,1,Married,15,486.1967145,0,23948.83573\n143784,57,37,3,0,Single,9,486.1554713,0,23887.77357\n172354,29,9,4,0,Married,2,486.1416042,0,23874.08021\n198443,58,37,2,0,Married,5,486.1067787,0,23868.33894\n128453,24,5,2,0,Single,14,486.0783301,0,23752.91651\n153986,22,4,3,1,Single,12,486.0362198,0,23937.81099\n139519,16,0,3,1,Single,7,485.9079225,0,24007.39613\n190897,18,0,3,0,Married,11,485.739117,0,23848.95585\n149615,22,3,4,1,Married,10,485.7020951,0,23717.10476\n131564,46,26,2,1,Single,8,485.6433513,0,23802.16757\n176186,50,30,3,1,Single,8,485.592403,0,23786.62015\n163045,45,28,2,0,Single,8,485.4976689,0,23852.88345\n174865,23,6,4,0,Single,13,485.4475021,0,24066.37511\n165185,22,4,1,0,Single,12,485.4291054,0,23993.45527\n144067,48,30,2,0,Single,9,485.3971298,0,23953.85649\n151489,26,6,2,1,Single,1,485.3858243,0,23752.29122\n136201,43,23,2,1,Married,1,485.269917,0,23875.49585\n171392,25,6,2,1,Single,7,485.1853442,0,23781.26721\n178213,63,42,3,0,Single,3,485.1799258,0,23849.99629\n122051,56,39,3,1,Married,0,485.0029454,0,23873.14727\n168315,63,44,2,1,Married,3,484.9096114,0,23791.48057\n140220,17,0,1,0,Single,13,484.7934584,0,23748.67292\n189674,23,4,3,1,Single,9,484.7094429,0,23684.47215\n165949,19,0,1,1,Single,9,484.6961144,0,23641.80572\n138689,23,5,3,1,Married,10,484.6273438,0,23998.36719\n156841,21,3,2,0,Married,6,484.6149446,0,23839.74723\n189276,38,18,1,1,Single,7,484.5883292,0,23687.41646\n145027,24,7,3,1,Single,12,484.5543496,0,23741.71748\n118085,25,6,2,1,Married,1,484.5439984,0,23833.19992\n112606,41,20,3,0,Single,8,484.5409196,0,23887.04598\n112431,27,8,1,0,Single,9,484.5144708,0,23679.72354\n182192,18,0,2,0,Married,5,484.4999933,0,23723.99967\n200208,21,2,2,0,Married,15,484.4608066,0,23944.04033\n119029,18,0,3,1,Single,13,484.403153,0,23972.15765\n185296,27,8,3,1,Single,6,484.3969299,0,23815.8465\n139800,38,19,2,1,Single,9,484.3743228,0,23976.71614\n125046,29,11,3,0,Married,1,484.3687625,0,24006.43813\n114559,22,3,2,0,Single,11,484.3501358,0,23876.50679\n179511,58,39,3,1,Married,0,484.3306049,0,23897.53025\n142055,49,28,4,0,Married,3,484.2915255,0,23927.57628\n152327,56,38,1,1,Single,6,484.2868664,0,23820.34332\n190969,36,19,3,0,Single,9,484.2749778,0,23816.74889\n184650,42,24,1,0,Single,6,484.2579932,0,23935.89966\n111764,20,2,2,1,Single,12,484.2490831,0,23999.45416\n152601,23,4,2,1,Single,14,484.2424026,0,23944.12013\n117693,47,27,3,1,Married,5,484.2264568,0,23653.32284\n186334,22,4,1,1,Single,10,484.2131818,0,23859.65909\n165622,46,26,2,0,Single,8,484.2038417,0,23894.19209\n118698,32,12,2,0,Single,3,484.183253,0,23956.16265\n104415,20,3,4,0,Married,14,484.181639,0,23910.08195\n103020,18,0,2,0,Married,7,484.1695927,0,23945.47964\n106808,19,2,2,1,Single,13,484.1244503,0,23689.22252\n194519,34,13,1,0,Single,9,484.1111346,0,23660.55673\n118752,42,24,2,0,Single,6,483.9802141,0,23808.01071\n199262,19,2,2,1,Married,9,483.7474678,0,23909.37339\n190873,25,6,2,0,Married,6,483.6953914,0,23938.76957\n183329,44,26,3,1,Single,7,483.656607,0,23945.83035\n104758,56,39,2,0,Single,9,483.6039055,0,23635.19528\n167948,65,45,3,0,Single,1,483.5479476,0,23913.39738\n123194,46,26,4,0,Single,0,483.4326523,0,23869.63262\n168704,50,32,2,1,Single,5,483.4007776,0,23840.03888\n111812,22,4,4,1,Single,9,483.225785,0,23880.28925\n161371,67,48,3,1,Married,5,483.1804634,0,23797.02317\n148000,54,35,4,1,Single,5,482.9441626,0,23575.20813\n119483,24,6,3,1,Single,12,482.918177,0,23776.90885\n137286,25,8,1,0,Single,12,482.8592229,0,23902.96115\n125984,24,7,1,1,Married,9,482.8298421,0,23784.49211\n185320,39,21,4,1,Single,5,482.8221754,0,23804.10877\n129117,22,3,2,1,Single,15,482.7932164,0,23822.66082\n168569,21,4,3,1,Single,14,482.7765463,0,23704.82732\n158933,18,1,4,0,Single,15,482.7386923,0,23930.93462\n191062,66,47,4,1,Single,3,482.7107033,0,23654.53517\n107080,50,30,3,0,Single,12,482.6817559,0,23861.0878\n151793,45,27,2,1,Single,8,482.675552,0,23818.7776\n196644,28,7,2,1,Single,10,482.6396475,0,23538.98238\n131996,44,24,4,0,Single,8,482.6284386,0,23765.42193\n179260,25,6,3,1,Single,11,482.6161107,0,23856.80554\n104613,25,7,2,1,Single,14,482.6127533,0,23537.63767\n121135,23,4,2,0,Single,12,482.5630144,0,23651.15072\n187245,30,11,1,0,Single,10,482.5174629,0,23799.87315\n133470,21,2,2,0,Single,10,482.433572,0,23712.6786\n131442,64,45,3,1,Single,4,482.3919225,0,23584.59613\n162225,19,2,3,0,Married,12,482.3618892,0,23763.09446\n176964,19,0,3,0,Single,13,482.2764565,0,23790.82283\n141655,31,12,1,0,Single,6,482.2712561,0,23519.56281\n168301,61,42,1,0,Married,3,482.2699173,0,23850.49587\n159198,20,1,4,1,Single,10,482.2676902,0,23715.38451\n180166,19,2,3,0,Single,13,482.2491178,0,23553.45589\n108598,25,8,3,1,Married,12,482.1907972,0,23728.53986\n152351,25,6,1,1,Married,5,481.9361346,0,23601.80673\n170974,25,8,1,0,Married,12,481.9321159,0,23726.6058\n186358,46,26,4,0,Single,9,481.9254144,0,23584.27072\n118207,35,18,2,1,Single,7,481.8868022,0,23742.34011\n167845,54,36,2,0,Single,12,481.8815938,0,23837.07969\n196724,28,9,1,1,Single,1,481.8169922,0,23609.84961\n189720,16,0,3,0,Married,6,481.8037116,0,23600.18558\n169883,47,26,4,0,Single,11,481.8013066,0,23867.06533\n130311,23,4,2,1,Married,1,481.7940738,0,23830.70369\n165197,37,20,2,0,Single,3,481.7089095,0,23739.44548\n136860,17,0,3,1,Single,13,481.6609542,0,23798.04771\n152722,17,0,1,0,Married,11,481.5460256,0,23570.30128\n160356,34,14,3,0,Single,1,481.4673179,0,23551.3659\n189904,17,0,2,1,Married,14,481.4394642,0,23855.97321\n196262,22,3,2,0,Married,14,481.4381007,0,23694.90504\n122207,26,8,1,0,Single,6,481.3734693,0,23756.67346\n139437,58,40,2,1,Single,1,481.3603897,0,23757.01949\n165045,58,41,3,0,Single,10,481.3387811,0,23864.93906\n123166,16,0,2,1,Single,13,481.335332,0,23656.7666\n171682,26,6,1,1,Single,1,481.2460799,0,23695.304\n196594,22,4,1,0,Single,12,481.2308348,0,23614.54174\n164469,25,8,3,0,Single,14,481.1921774,0,23654.60887\n131627,24,5,1,1,Single,14,481.1598112,0,23797.99056\n178913,47,28,1,0,Single,10,481.06576,0,23823.288\n160670,30,13,2,1,Married,4,481.0608707,0,23647.04354\n103284,16,0,1,1,Single,14,480.9872605,0,23718.36303\n113307,28,8,2,0,Single,9,480.7857976,0,23458.28988\n142478,59,42,3,0,Single,8,480.7611661,0,23510.05831\n113437,68,48,3,0,Single,4,480.6950282,0,23561.75141\n103588,43,24,1,1,Single,1,480.6784621,0,23516.92311\n153748,38,19,3,0,Single,9,480.5787102,0,23649.93551\n105181,46,26,4,1,Single,3,480.4791045,0,23541.95523\n135494,19,2,3,0,Single,15,480.4408846,0,23717.04423\n189757,67,50,1,1,Single,8,480.3987095,0,23531.93548\n100454,43,23,2,0,Single,5,480.2553519,0,23613.7676\n133689,70,53,1,0,Married,0,480.2444319,0,23564.2216\n144868,19,1,3,1,Single,15,480.1608244,0,23585.04122\n136948,24,5,3,0,Married,11,480.1355112,0,23619.77556\n189856,36,17,1,0,Single,8,480.0813937,0,23603.06969\n152444,20,1,2,1,Single,12,480.0008503,0,23510.04252\n162637,50,31,2,1,Single,6,479.8953476,0,23701.76738\n176790,65,44,1,0,Married,2,479.8133205,0,23790.66603\n107134,19,2,2,0,Married,10,479.8115482,0,23662.57741\n141110,21,4,1,0,Married,12,479.7951029,0,23669.75515\n144571,40,19,3,1,Single,10,479.7233194,0,23762.16597\n122863,59,39,3,1,Single,8,479.6351699,0,23762.7585\n112711,40,21,2,1,Married,5,479.4438256,0,23597.19128\n196931,17,0,1,1,Married,13,479.4391977,0,23660.95989\n120992,30,11,2,0,Married,1,479.4186448,0,23499.93224\n109596,16,0,2,1,Married,12,479.370976,0,23460.5488\n185766,48,27,3,1,Single,9,479.3608092,0,23387.04046\n141603,16,0,4,0,Single,15,479.3410737,0,23538.05369\n173574,19,2,4,1,Married,9,479.3297971,0,23662.48986\n184240,21,4,3,0,Married,9,479.3087251,0,23562.43626\n113940,56,36,3,0,Married,2,479.2886062,0,23495.43031\n163041,54,35,2,0,Single,7,479.2419774,0,23635.09887\n140088,25,6,3,1,Single,7,479.2261433,0,23515.30717\n120916,18,0,2,0,Single,14,479.1547418,0,23632.73709\n167809,49,29,3,0,Single,7,479.1289101,0,23736.44551\n144947,52,33,3,0,Married,3,479.0491104,0,23557.45552\n123896,23,6,1,0,Married,6,479.0179823,0,23671.89912\n103181,23,4,3,1,Single,2,478.9629212,0,23734.14606\n139960,43,24,2,1,Single,2,478.956515,0,23372.82575\n153449,65,45,4,0,Single,3,478.7439131,0,23627.19566\n194182,19,2,4,0,Married,15,478.6726502,0,23528.63251\n148796,47,28,1,1,Married,5,478.6587564,0,23359.93782\n176483,32,11,3,1,Single,2,478.6572702,0,23510.86351\n155734,20,1,2,1,Married,6,478.6484947,0,23701.42474\n112625,17,0,3,1,Married,2,478.5992078,0,23521.96039\n169908,53,36,3,0,Single,9,478.4915536,0,23332.57768\n101018,63,45,3,1,Single,4,478.4706675,0,23410.53338\n199343,57,38,1,1,Single,4,478.456794,0,23472.8397\n174342,35,16,1,0,Single,6,478.423885,0,23660.19425\n176878,18,1,3,0,Married,14,478.4200123,0,23559.00062\n181488,25,7,2,0,Married,11,478.4084259,0,23520.4213\n130299,42,25,3,1,Single,9,478.3853629,0,23581.26815\n197034,17,0,2,0,Single,11,478.3743453,0,23385.71727\n186036,16,0,1,0,Married,10,478.3520156,0,23450.60078\n138462,28,9,3,0,Married,2,478.3088134,0,23369.44067\n144856,22,4,2,0,Single,14,478.234253,0,23446.71265\n146919,17,0,3,0,Married,14,478.2292338,0,23630.46169\n173963,19,2,1,1,Single,11,478.2095857,0,23525.47929\n169850,69,48,1,1,Married,3,478.2047046,0,23326.23523\n120518,39,19,2,0,Single,10,478.1855179,0,23359.2759\n123772,16,0,3,0,Married,14,478.0992495,0,23548.96248\n163031,45,25,1,0,Single,5,478.0213205,0,23645.06603\n193811,45,26,3,0,Single,1,478.0020698,0,23607.10349\n128255,30,12,3,1,Single,9,477.9730572,0,23502.65286\n136685,45,24,4,0,Married,1,477.9370871,0,23406.85436\n139013,46,29,2,1,Single,3,477.8429567,0,23656.14784\n102932,25,6,3,1,Married,12,477.7597317,0,23622.98659\n141420,48,27,1,1,Single,8,477.7134309,0,23463.67155\n113620,41,21,4,1,Single,3,477.5895786,0,23495.47893\n167721,44,23,2,1,Single,1,477.4794187,0,23350.97094\n190736,17,0,4,0,Married,8,477.4649244,0,23467.24622\n159651,66,46,1,0,Single,3,477.3863378,0,23452.31689\n120390,29,8,3,0,Single,9,477.317545,0,23476.87725\n175008,32,12,3,1,Single,9,477.2962467,0,23416.81234\n166336,36,18,2,1,Single,1,477.2749712,0,23622.74856\n128035,38,19,4,0,Single,8,477.2124292,0,23356.62146\n184068,53,34,1,1,Married,4,477.1837723,0,23513.18862\n110949,49,31,2,0,Single,7,477.1025136,0,23301.12568\n193501,62,45,2,1,Married,5,477.0693649,0,23620.46825\n100377,17,0,2,0,Married,8,477.0562697,0,23464.81349\n193500,54,35,2,1,Single,2,477.0516414,0,23406.58207\n144231,42,23,3,0,Single,8,476.9330893,0,23257.65447\n190114,63,45,3,1,Single,5,476.8854492,0,23292.27246\n104980,17,0,1,0,Married,8,476.875928,0,23638.7964\n140440,55,35,3,1,Single,10,476.8177299,0,23295.8865\n183270,17,0,2,1,Married,6,476.8176623,0,23358.88312\n166146,29,10,2,0,Married,4,476.6718857,0,23242.59429\n183726,44,24,1,0,Single,9,476.6461403,0,23436.30702\n188301,38,18,2,1,Single,9,476.5783822,0,23480.91911\n176096,53,36,2,1,Married,1,476.5679194,0,23240.39597\n125668,16,0,1,0,Married,12,476.5346165,0,23526.73083\n189976,51,32,2,1,Married,1,476.4940151,0,23262.70076\n182609,47,30,2,0,Single,10,476.4459709,0,23467.29855\n174849,51,33,3,0,Single,8,476.4370649,0,23466.85325\n144713,66,48,1,1,Single,3,476.4294007,0,23224.47004\n140370,32,15,3,0,Married,4,476.3075253,0,23607.37627\n104040,54,33,2,1,Single,9,476.2559945,0,23362.79973\n157972,48,29,2,1,Single,8,476.174582,0,23497.7291\n122075,20,2,2,1,Single,10,476.0955866,0,23460.77933\n139263,24,7,3,1,Single,13,476.0368826,0,23550.84413\n158016,28,10,2,0,Married,3,475.9737206,0,23329.68603\n148072,23,6,4,0,Married,15,475.862397,0,23220.11985\n148120,16,0,3,1,Single,10,475.8312058,0,23484.56029\n178891,25,8,3,1,Single,6,475.7604088,0,23341.02044\n158351,43,24,1,0,Single,10,475.7226569,0,23424.13285\n127584,45,26,2,1,Single,10,475.6811129,0,23346.05565\n110871,55,34,1,1,Single,9,475.6477521,0,23186.38761\n188537,20,2,1,1,Single,12,475.6435642,0,23293.17821\n155349,24,7,3,1,Married,10,475.6342245,0,23292.71123\n138948,25,8,2,1,Married,13,475.6124439,0,23268.6222\n114603,16,0,2,0,Single,14,475.5995397,0,23185.97699\n149784,25,6,1,0,Married,15,475.4581044,0,23373.90522\n182243,68,51,2,1,Married,3,475.4555198,0,23271.77599\n187111,55,34,2,0,Single,7,475.419257,0,23469.96285\n197069,31,10,2,0,Single,10,475.2954651,0,23258.77326\n159776,36,15,3,0,Single,6,475.1906527,0,23448.53264\n180497,55,34,3,1,Married,3,475.1007265,0,23456.03633\n163806,46,26,2,1,Married,1,475.0563753,0,23272.81877\n199466,57,39,3,0,Single,9,475.0068252,0,23535.34126\n120285,53,35,2,0,Single,6,474.9232008,0,23266.16004\n113585,29,12,2,1,Single,8,474.8403496,0,23538.01748\n147780,31,12,3,1,Married,5,474.8034165,0,23378.17083\n129251,57,39,4,0,Single,8,474.8023125,0,23449.11563\n198453,41,21,3,0,Single,6,474.7189621,0,23390.94811\n104458,58,39,3,0,Single,9,474.5904622,0,23279.52311\n199860,20,3,3,0,Married,11,474.5805687,0,23300.02844\n101265,50,33,3,0,Single,7,474.5715389,0,23242.57695\n158325,36,16,2,1,Single,0,474.552357,0,23254.61785\n164327,43,22,2,0,Single,10,474.4684224,0,23482.42112\n117127,24,6,2,0,Single,15,474.4609259,0,23474.0463\n160730,35,16,3,0,Single,6,474.3647466,0,23328.23733\n125809,25,7,2,1,Married,6,474.2768161,0,23367.84081\n188388,34,13,4,1,Single,3,474.2729374,0,23330.64687\n140989,17,0,3,1,Single,13,474.1642879,0,23439.2144\n185134,59,42,1,1,Single,1,474.1468687,0,23467.34344\n149644,21,3,2,1,Single,13,474.1410582,0,23435.05291\n188343,31,14,2,0,Married,0,474.1045373,0,23255.22687\n176281,39,20,4,1,Single,9,474.0969887,0,23397.84944\n170432,21,4,2,0,Married,8,473.9589177,0,23438.94589\n175900,20,2,3,1,Married,10,473.9554607,0,23248.77304\n102414,38,19,3,1,Married,3,473.9448117,0,23487.24059\n168813,47,29,4,1,Single,4,473.8614676,0,23093.07338\n111236,32,15,3,1,Married,1,473.7378405,0,23173.89203\n152528,19,0,3,0,Married,11,473.6489793,0,23082.44897\n125726,23,5,1,0,Married,12,473.6257225,0,23403.28613\n146152,49,32,2,0,Single,6,473.6158634,0,23238.79317\n191569,23,6,3,0,Married,14,473.6061402,0,23462.30701\n134215,41,20,3,1,Single,6,473.4078969,0,23445.39485\n110604,58,39,2,0,Single,6,473.3205102,0,23097.02551\n149672,59,39,4,0,Married,5,473.3197121,0,23182.98561\n198250,19,1,4,0,Married,13,473.3081657,0,23450.40829\n138762,33,15,2,1,Married,3,473.2762357,0,23435.81179\n131607,28,8,2,1,Single,7,473.2256212,0,23117.28106\n105549,30,12,2,0,Single,7,473.1793042,0,23268.96521\n141593,65,45,2,0,Single,13,473.1478957,0,23406.39479\n140821,46,29,2,1,Married,0,473.0895948,0,23128.47974\n113371,52,35,3,0,Married,0,473.0839452,0,23273.19726\n125227,20,2,1,0,Married,10,473.0586347,0,23240.93174\n145945,24,6,2,0,Single,13,473.0396868,0,23193.98434\n121436,35,18,3,1,Single,7,473.0285608,0,23139.42804\n122538,40,19,2,0,Single,6,473.0012563,0,23384.06282\n197936,36,18,3,0,Married,1,472.9943625,0,23112.71813\n181088,47,30,2,1,Single,10,472.9030689,0,23405.15345\n101476,30,12,2,0,Single,10,472.8663498,0,23361.31749\n191389,50,29,4,0,Single,6,472.8194975,0,23336.97488\n190557,29,12,1,0,Single,7,472.8070804,0,23121.35402\n125273,61,43,3,0,Single,8,472.7858557,0,23275.29279\n155548,39,21,4,1,Married,0,472.7488777,0,23059.44389\n123363,58,37,3,0,Single,10,472.6673133,0,23195.36567\n177758,20,1,4,0,Married,11,472.6239431,0,23113.19716\n165177,44,27,1,1,Single,10,472.5998112,0,23042.99056\n129781,18,0,3,0,Single,14,472.598635,0,23268.93175\n179284,34,17,3,1,Single,10,472.5141741,0,23389.70871\n127847,46,26,3,0,Single,9,472.4845356,0,23424.22678\n194850,43,26,1,1,Single,10,472.460003,0,23239.00015\n190961,45,27,1,0,Single,7,472.4328688,0,23183.64344\n175296,30,11,4,0,Single,7,472.252075,0,23224.60375\n114111,23,5,4,0,Single,13,472.2269678,0,23373.34839\n162438,59,42,4,0,Single,6,472.1804482,0,23283.02241\n123968,19,0,2,1,Single,10,472.1382826,0,23169.91413\n190347,27,9,2,0,Married,2,472.0646401,0,23178.23201\n117800,52,33,3,1,Single,10,471.9688196,0,23136.44098\n177787,57,40,3,1,Single,3,471.9466109,0,23300.33055\n107360,54,36,3,1,Single,6,471.8267349,0,23209.33675\n129185,42,25,1,1,Married,2,471.8190516,0,23351.95258\n153121,28,10,2,0,Single,6,471.784096,0,23121.2048\n156988,23,4,3,0,Married,12,471.764284,0,23237.2142\n160935,19,0,2,1,Single,15,471.6957611,0,23376.78806\n102156,32,11,3,1,Single,6,471.6810004,0,23357.05002\n169405,55,38,3,1,Single,0,471.5194783,0,23266.97392\n171178,53,35,1,1,Married,5,471.5150074,0,23374.75037\n177859,33,13,2,1,Single,8,471.4780636,0,23034.90318\n117435,55,37,3,0,Single,8,471.4751538,0,23051.75769\n166669,36,16,2,1,Married,4,471.424518,0,23236.2259\n158963,22,3,3,1,Married,13,471.4209351,0,23105.04676\n181157,20,3,1,0,Married,8,471.4103848,0,23187.51924\n111432,53,33,3,0,Married,5,471.378059,0,23000.90295\n194116,47,27,3,1,Single,2,471.3673475,0,23173.36738\n178567,57,40,3,0,Married,3,471.3671809,0,23155.35905\n115124,40,19,1,1,Single,7,471.3562274,0,22974.81137\n199615,61,43,2,0,Married,3,471.323236,0,23151.1618\n195315,32,15,2,0,Single,9,471.2372417,0,23313.86209\n114531,28,8,2,1,Married,3,471.2356391,0,23135.78196\n166966,29,12,2,1,Single,15,471.1708052,0,23121.54026\n188070,43,22,3,0,Single,10,471.1239413,0,23319.19707\n138802,49,31,4,0,Single,8,471.018327,0,23088.91635\n110353,45,26,2,1,Single,9,470.9851536,0,23154.25768\n168414,31,13,3,0,Single,3,470.9101548,0,23263.50774\n158902,35,14,2,1,Single,8,470.8810033,0,23240.05017\n130624,19,0,4,1,Single,15,470.8143033,0,23229.71517\n128068,46,26,4,0,Single,8,470.7920924,0,22940.60462\n127745,18,1,1,0,Married,9,470.7737738,0,23019.68869\n171899,44,23,2,1,Married,2,470.772339,0,23122.61695\n124242,69,52,4,1,Single,3,470.723629,0,22998.18145\n190805,51,30,2,0,Single,7,470.7177821,0,23127.88911\n179235,23,6,3,0,Single,13,470.6802469,0,22961.01235\n116472,38,18,2,1,Married,1,470.6478699,0,23228.3935\n122508,59,42,4,1,Single,9,470.6478182,0,23066.39091\n133065,37,16,3,1,Single,6,470.6396289,0,22931.98145\n168263,29,9,3,0,Single,8,470.4042582,0,22941.21291\n187002,21,2,3,0,Single,15,470.4019604,0,22990.09802\n199755,35,14,2,0,Married,4,470.3827303,0,23264.13652\n196340,67,46,3,0,Married,0,470.3734647,0,23094.67324\n123616,19,0,2,0,Single,12,470.3204577,0,23264.02289\n132839,27,9,3,1,Single,10,470.2704766,0,23291.52383\n189461,22,4,1,1,Single,15,470.262049,0,23261.10245\n101066,16,0,2,0,Married,6,470.2433745,0,22919.16873\n115080,55,38,2,0,Single,10,470.179427,0,23282.97135\n170617,37,20,1,0,Married,4,470.1173279,0,23010.8664\n129116,27,6,4,0,Single,9,470.0810446,0,23063.05223\n136198,65,46,4,0,Married,3,470.0706335,0,23130.53168\n145251,23,6,2,1,Single,8,470.0655917,0,23218.27959\n197537,51,34,2,1,Single,12,470.0477801,0,23130.38901\n191545,52,34,1,0,Single,8,469.9768848,0,23070.84424\n197342,66,48,3,0,Single,4,469.964291,0,23072.21455\n173044,23,4,2,1,Married,10,469.9437156,0,23125.18578\n100325,54,35,2,0,Single,10,469.9318977,0,23281.59489\n142297,17,0,1,0,Married,15,469.8951722,0,23123.75861\n151801,40,21,3,0,Single,6,469.8517485,0,22931.58743\n114656,64,44,2,0,Single,9,469.8507048,0,22910.53524\n118798,19,2,3,1,Single,8,469.8495943,0,23132.47972\n192733,20,2,1,1,Single,14,469.8435041,0,23274.17521\n133346,23,4,4,1,Single,12,469.5534854,0,23060.67427\n176253,25,8,2,0,Single,15,469.5215724,0,23238.07862\n111714,18,0,2,0,Married,9,469.4447224,0,23202.23612\n110315,53,36,3,0,Single,6,469.4146909,0,23197.73455\n114362,57,37,4,0,Single,10,469.4075879,0,23009.3794\n102999,19,0,2,1,Married,6,469.2722573,0,22911.61287\n164206,64,47,2,0,Single,5,469.211976,0,23092.5988\n105763,18,0,2,0,Married,10,469.1917795,0,23232.58898\n107598,18,1,3,1,Single,10,469.0238476,0,23094.19238\n143641,17,0,1,1,Single,15,469.0068711,0,22942.34356\n120778,37,16,3,0,Single,7,468.9861935,0,23032.30968\n156278,40,22,2,0,Single,1,468.9365058,0,23105.82529\n140743,33,12,2,1,Single,13,468.9008097,0,23209.04049\n185817,17,0,4,1,Married,7,468.812173,0,23213.60865\n130940,49,29,4,1,Married,5,468.7989474,0,23223.94737\n112156,20,3,3,0,Single,11,468.6116157,0,22976.58079\n194371,38,19,3,0,Single,8,468.6082087,0,22976.41044\n125528,33,14,4,0,Single,10,468.5610259,0,23208.0513\n141549,56,35,2,0,Single,10,468.5555107,0,23150.77554\n116421,17,0,2,0,Single,13,468.546896,0,22828.3448\n151606,45,24,3,0,Single,10,468.5291497,0,22911.45749\n179546,25,7,4,0,Single,12,468.4496821,0,22865.48411\n155683,62,44,1,0,Married,0,468.4475546,0,23001.37773\n164081,17,0,2,1,Single,7,468.4346379,0,22954.7319\n127206,58,41,3,1,Single,6,468.4270538,0,23128.35269\n197995,51,34,3,1,Married,5,468.4252814,0,23140.26407\n170399,40,23,4,0,Single,10,468.3255721,0,22823.27861\n135648,47,26,4,0,Married,3,468.3063111,0,23093.31556\n157639,38,21,1,0,Married,4,468.29548,0,23160.774\n156582,39,19,2,0,Single,2,468.2881432,0,23152.40716\n175108,49,32,2,1,Single,8,468.2818545,0,23087.09273\n108135,51,34,4,0,Single,7,468.1876134,0,22851.38067\n119590,52,32,2,1,Married,3,468.1645654,0,23022.22827\n162919,51,33,1,0,Single,9,468.1149602,0,22949.74801\n141369,43,23,3,1,Single,0,468.0946106,0,23054.73053\n145768,46,27,2,1,Married,0,468.0371383,0,23155.85692\n121307,17,0,2,1,Married,13,468.033494,0,22947.6747\n101787,21,2,3,1,Single,15,467.9908112,0,22837.54056\n118785,20,3,3,0,Married,13,467.9868215,0,23156.34108\n137438,56,36,1,0,Single,6,467.848014,0,22938.4007\n177722,36,19,4,0,Single,9,467.8385341,0,23130.92671\n118991,34,14,4,0,Single,14,467.7716405,0,22864.58203\n136113,37,17,2,0,Single,8,467.7524182,0,22930.62091\n152982,39,21,2,0,Single,7,467.7115021,0,23151.57511\n113900,38,20,2,0,Single,9,467.7065653,0,22833.32827\n130815,21,2,3,1,Single,15,467.6318976,0,22920.59488\n192798,40,20,2,0,Married,4,467.6091343,0,22969.45672\n187871,34,14,2,1,Single,10,467.5399965,0,23107.99983\n123426,22,5,4,0,Single,15,467.4324169,0,23002.62085\n189577,44,26,3,0,Married,5,467.2539355,0,22785.69678\n126569,35,17,3,1,Single,8,467.2536567,0,23122.68284\n165328,25,7,3,1,Married,8,467.1826494,0,23131.13247\n139552,22,4,2,1,Single,11,467.1744391,0,22957.72196\n137416,21,2,4,1,Single,10,467.0681244,0,22905.40622\n188413,50,32,2,0,Single,8,466.9493141,0,22822.46571\n182624,20,1,1,0,Single,15,466.7879145,0,22994.39573\n189372,62,43,3,1,Married,4,466.7494146,0,23109.47073\n145729,47,29,1,0,Married,5,466.7029815,0,23013.14908\n191289,55,36,2,0,Married,4,466.6843585,0,22766.21793\n149155,40,23,2,0,Single,12,466.4680783,0,22979.40392\n182686,67,47,1,1,Single,0,466.4515276,0,22945.57638\n188018,36,17,3,0,Single,9,466.4344497,0,22980.72249\n114729,69,50,4,0,Married,2,466.3893452,0,22731.46726\n124963,16,0,2,0,Married,7,466.3200385,0,22731.00193\n121153,24,7,3,0,Married,11,466.2623592,0,22892.11796\n179794,46,26,2,0,Single,6,466.1547247,0,23073.73624\n131441,38,20,3,1,Single,10,466.1189789,0,22845.94895\n113082,27,6,1,0,Single,9,466.0522963,0,23100.61482\n152967,27,10,3,0,Married,0,465.9948173,0,22810.74087\n138980,40,20,3,0,Single,8,465.9272761,0,23022.36381\n136481,16,0,2,1,Married,11,465.852799,0,22988.63995\n192091,30,9,2,0,Single,9,465.8489466,0,22963.44733\n101286,53,34,4,0,Single,9,465.8402699,0,23046.0135\n189180,54,34,1,1,Single,8,465.8029002,0,22730.14501\n128818,50,29,1,1,Single,8,465.7864789,0,22983.32395\n147254,55,35,4,1,Single,0,465.783215,0,22868.16075\n117605,69,50,2,0,Single,15,465.7644331,0,22981.22166\n144790,29,8,1,1,Single,6,465.7509838,0,22720.54919\n153677,43,23,2,0,Single,6,465.7509793,0,22938.54897\n129597,22,3,4,1,Single,0,465.6766501,0,22902.83251\n150327,63,44,1,1,Single,4,465.658248,0,22924.9124\n136115,59,38,3,1,Married,3,465.6428112,0,22923.14056\n124725,49,32,2,1,Married,1,465.6171367,0,22740.85684\n110830,22,5,1,1,Single,10,465.6053779,0,22963.2689\n103287,21,2,3,0,Married,12,465.5588706,0,22773.94353\n167838,60,43,3,1,Single,2,465.5372033,0,22750.86017\n174285,51,31,4,0,Single,9,465.5054531,0,23010.27266\n183284,62,41,2,0,Single,0,465.4877164,0,23041.38582\n146253,43,22,3,0,Single,10,465.4857511,0,22998.28756\n120200,22,3,2,0,Married,9,465.4738686,0,22695.69343\n181190,27,8,2,1,Single,5,465.4325412,0,22891.62706\n161934,28,7,1,1,Single,8,465.3484514,0,22868.42257\n165059,55,38,2,0,Single,7,465.282525,0,22722.12625\n110390,19,2,2,1,Single,12,465.2822433,0,22705.11217\n117796,42,24,4,0,Single,7,465.2539004,0,22742.69502\n175217,64,43,3,0,Married,4,465.2157485,0,22735.78743\n160413,49,30,3,1,Single,8,465.2037594,0,22937.18797\n178704,18,0,4,1,Single,11,465.1659936,0,22804.29968\n167992,68,49,1,0,Single,13,465.1176686,0,22921.88343\n179621,51,30,3,0,Single,8,465.0455411,0,22822.27706\n173340,18,0,3,0,Married,11,465.0447898,0,22881.23949\n154080,61,41,2,0,Single,0,464.9369759,0,22720.84879\n151345,25,8,4,1,Married,3,464.8812183,0,22869.06092\n185968,28,10,4,0,Single,10,464.8304919,0,22992.5246\n181025,34,17,4,1,Single,3,464.8205543,0,22711.02772\n101242,47,28,3,0,Single,6,464.8176385,0,22699.88193\n142841,43,24,2,1,Single,3,464.751069,0,22693.55345\n134458,16,0,2,0,Married,15,464.6484311,0,22753.42156\n187417,48,30,4,0,Single,6,464.6320627,0,22864.60314\n185932,27,9,3,0,Single,10,464.6240989,0,22885.20495\n112199,50,31,3,0,Single,13,464.6077712,0,22882.38856\n105495,44,24,4,0,Married,2,464.5986547,0,22655.93274\n174867,41,21,3,0,Single,1,464.5602035,0,22643.01018\n134229,33,13,2,0,Single,6,464.5138413,0,22957.69207\n169280,58,37,3,0,Single,10,464.475188,0,22912.7594\n178684,52,34,3,1,Single,9,464.2904242,0,22629.52121\n153448,57,40,1,0,Single,8,464.182696,0,22815.1348\n145995,16,0,4,0,Single,13,464.1600324,0,22781.00162\n160655,49,30,2,0,Single,12,464.0981718,0,22830.90859\n132824,36,16,3,1,Single,7,464.082626,0,22605.1313\n171508,37,20,2,0,Single,12,464.0577779,0,22934.8889\n160371,22,4,2,1,Single,12,464.0568769,0,22928.84385\n130522,62,41,1,1,Single,5,463.944586,0,22708.2293\n168982,22,5,4,0,Married,12,463.934934,0,22761.7467\n103192,24,5,2,1,Single,14,463.9210844,0,22664.05422\n166553,54,37,3,0,Single,12,463.8873235,0,22959.36618\n161819,24,5,2,1,Single,15,463.8862629,0,22901.31315\n173269,26,7,2,0,Single,8,463.8537185,0,22908.68593\n148055,57,38,3,0,Single,8,463.8369854,0,22630.84927\n126999,58,38,3,0,Single,8,463.7408875,0,22959.04438\n172510,68,49,2,0,Single,10,463.7267647,0,22880.33824\n135767,40,23,2,0,Single,10,463.6257757,0,22614.28879\n156699,34,14,1,0,Single,10,463.571379,0,22623.56895\n144660,18,0,3,0,Married,15,463.5702261,0,22645.51131\n142705,21,4,3,1,Married,12,463.5494703,0,22977.47352\n191480,50,32,1,0,Single,7,463.4877059,0,22575.3853\n108733,24,7,4,0,Married,15,463.4450523,0,22810.25262\n153646,35,14,2,0,Single,7,463.3467959,0,22881.3398\n101768,18,1,1,0,Single,13,463.3050457,0,22802.25229\n142316,27,9,2,1,Single,2,463.2861116,0,22827.30558\n190133,18,0,2,0,Single,15,463.2390525,0,22928.95263\n193882,39,20,4,0,Single,7,463.1983216,0,22571.91608\n167793,18,0,2,0,Single,13,463.1947126,0,22696.73563\n108147,27,9,4,0,Single,6,463.1633708,0,22887.16854\n197005,35,14,3,1,Married,3,463.1365903,0,22773.82952\n148827,22,3,2,1,Married,15,463.1297887,0,22628.48944\n178777,33,13,1,0,Single,10,463.1202594,0,22734.01297\n133945,19,1,3,0,Married,14,463.1103208,0,22942.51604\n182064,33,14,1,1,Single,6,463.0778287,0,22777.89144\n103345,45,24,3,0,Single,7,463.0315385,0,22793.57693\n180045,46,25,3,1,Single,7,463.0235278,0,22856.17639\n186293,52,32,2,0,Single,8,462.983244,0,22857.1622\n131071,17,0,2,1,Single,12,462.9619884,0,22889.09942\n111031,18,1,1,0,Single,15,462.9537014,0,22606.68507\n109106,21,3,2,1,Single,11,462.9136625,0,22921.68313\n140849,26,9,4,0,Married,3,462.8754754,0,22657.77377\n140892,45,28,2,0,Single,6,462.7939537,0,22710.69769\n164505,63,44,3,1,Married,4,462.7926885,0,22680.63443\n123863,53,34,2,1,Single,9,462.7846202,0,22665.23101\n196396,59,38,3,0,Single,13,462.7667653,0,22706.33827\n135079,63,43,4,1,Single,7,462.7438298,0,22755.19149\n105117,52,35,4,0,Married,2,462.6800395,0,22907.00198\n186081,67,50,3,1,Married,3,462.6231671,0,22531.15836\n176721,41,20,4,1,Single,1,462.5024015,0,22670.12008\n172687,61,40,2,0,Single,5,462.5010563,0,22796.05282\n146592,49,32,2,1,Married,1,462.4542071,0,22530.71036\n110093,50,30,3,0,Single,7,462.3508888,0,22694.54444\n133081,22,3,2,0,Married,15,462.3067681,0,22579.33841\n176667,44,26,1,0,Single,8,462.2550909,0,22797.75455\n200181,51,32,3,0,Single,8,462.2527861,0,22767.63931\n132995,51,33,1,1,Single,8,462.2505406,0,22766.52703\n107940,25,6,3,1,Single,14,462.2409311,0,22905.04656\n199376,49,32,2,0,Single,6,462.1949318,0,22841.74659\n191483,25,7,3,1,Single,12,462.1866766,0,22854.33383\n183396,36,19,1,1,Single,0,462.1588562,0,22541.94281\n164002,45,26,4,0,Single,13,462.1234713,0,22890.17357\n188739,46,25,3,1,Married,1,462.1202654,0,22884.01327\n196429,43,22,1,1,Married,0,461.9718465,0,22665.59233\n164723,57,40,4,0,Single,9,461.872203,0,22721.61015\n139572,21,4,4,0,Single,15,461.8640445,0,22613.20223\n157075,48,28,3,0,Single,7,461.818948,0,22759.9474\n189575,48,30,1,0,Single,9,461.7150312,0,22571.75156\n147383,58,39,2,0,Single,6,461.662769,0,22812.13845\n157227,42,25,3,0,Married,5,461.6472663,0,22746.36332\n136928,20,3,3,1,Married,13,461.6107734,0,22746.53867\n122692,17,0,2,0,Married,12,461.5888607,0,22721.44304\n163744,56,37,2,0,Single,7,461.5670823,0,22544.35412\n125098,46,25,4,1,Single,5,461.5428561,0,22601.14281\n181731,57,40,3,0,Single,6,461.4786744,0,22580.93372\n116143,42,23,2,0,Single,8,461.4186021,0,22707.93011\n195739,27,8,2,1,Single,0,461.2807466,0,22536.03733\n131852,58,37,4,0,Single,7,461.0803128,0,22649.01564\n126573,24,7,3,0,Single,13,460.9530036,0,22804.65018\n167186,16,0,3,0,Single,13,460.9485643,0,22841.42822\n132446,16,0,2,0,Single,15,460.8457793,0,22521.28897\n182688,30,11,2,0,Single,13,460.7589211,0,22742.94606\n189765,21,3,3,0,Married,14,460.7552001,0,22474.76001\n132387,61,44,1,0,Single,3,460.7266415,0,22801.33208\n139324,21,3,3,1,Married,11,460.6325193,0,22741.62597\n135578,50,29,3,1,Single,9,460.5038004,0,22718.19002\n152775,21,4,1,0,Single,13,460.4609532,0,22759.04766\n116185,22,4,2,0,Married,7,460.4194493,0,22750.97247\n160225,47,26,2,1,Single,7,460.3342515,0,22668.71258\n199192,17,0,4,1,Married,8,460.3187601,0,22744.93801\n104653,27,8,3,0,Married,3,460.2782849,0,22606.91425\n137875,50,31,2,0,Single,9,460.0990221,0,22695.95111\n190016,39,18,2,0,Married,2,460.048472,0,22624.4236\n182356,52,31,3,1,Married,0,459.9969586,0,22782.84793\n114383,20,3,1,0,Married,7,459.9911635,0,22556.55818\n123059,36,18,1,0,Single,6,459.9861188,0,22424.30594\n196969,63,45,2,0,Single,2,459.8051617,0,22679.25809\n151706,49,31,2,1,Single,10,459.7258028,0,22611.29014\n107859,69,51,2,0,Single,5,459.7043208,0,22581.21604\n169731,16,0,3,0,Single,11,459.6553555,0,22498.76778\n157864,52,34,3,1,Single,3,459.6339659,0,22520.6983\n199592,54,33,3,1,Married,2,459.6079281,0,22690.39641\n125217,45,25,2,0,Single,11,459.5727404,0,22561.63702\n153740,58,38,3,1,Single,4,459.5673309,0,22387.36655\n173755,50,30,3,1,Single,7,459.5537069,0,22694.68535\n118695,20,2,1,1,Married,0,459.488058,0,22591.4029\n190592,37,17,1,1,Single,9,459.278839,0,22636.94195\n185811,63,46,4,1,Married,2,459.15844,0,22414.922\n189352,70,50,2,0,Single,7,459.0836812,0,22678.18406\n158694,47,28,3,0,Married,2,459.0836351,0,22482.18176\n162308,61,41,3,0,Married,2,459.0270451,0,22636.35226\n186507,22,5,2,1,Married,15,458.8803812,0,22520.01906\n182386,39,20,3,1,Married,4,458.8186227,0,22345.93114\n182906,54,34,3,1,Single,10,458.7848756,0,22389.24378\n122728,55,36,3,0,Single,7,458.7115177,0,22536.57589\n183024,69,52,2,1,Single,0,458.6602342,0,22412.01171\n147703,34,16,1,1,Single,1,458.5579608,0,22465.89804\n197931,17,0,1,1,Married,8,458.5196732,0,22494.98366\n162503,18,0,2,1,Married,9,458.5047008,0,22716.23504\n162930,47,26,3,1,Single,10,458.4858255,0,22536.29128\n100874,53,36,2,0,Single,9,458.4681505,0,22437.40753\n143053,31,12,3,0,Single,10,458.4249573,0,22493.24787\n147154,58,41,1,0,Single,10,458.3461769,0,22501.30885\n132443,21,4,4,1,Married,10,458.3320697,0,22604.60349\n199387,39,20,1,1,Single,4,458.3103986,0,22461.51993\n194620,45,27,4,1,Single,7,458.3080678,0,22326.40339\n172677,36,18,3,1,Single,11,458.3073155,0,22403.36578\n193044,42,23,1,0,Single,9,458.218025,0,22384.90125\n105180,25,8,3,1,Single,14,458.2146143,0,22529.73072\n188418,18,0,2,0,Married,9,458.1821405,0,22709.10703\n102965,20,1,3,1,Married,9,458.1800391,0,22356.00196\n167408,24,7,1,1,Married,1,458.1587158,0,22391.93579\n101259,69,48,1,1,Single,0,458.0629055,0,22672.14528\n195853,55,35,3,0,Single,9,457.9983437,0,22560.91719\n141188,39,20,3,1,Single,7,457.9628483,0,22656.14242\n156856,50,29,2,1,Single,8,457.8160532,0,22420.80266\n150318,53,33,2,0,Single,7,457.7897754,0,22512.48877\n120460,17,0,4,1,Single,10,457.7525064,0,22447.62532\n153924,43,25,1,1,Single,6,457.7243384,0,22502.21692\n147905,40,20,3,0,Single,7,457.7210762,0,22606.05381\n109305,25,7,2,0,Married,14,457.672818,0,22542.6409\n134484,27,10,1,1,Single,10,457.6599332,0,22452.99666\n139857,57,36,3,0,Single,6,457.6225089,0,22526.12545\n170981,20,2,3,1,Single,11,457.5992138,0,22668.96069\n112296,36,18,4,0,Single,10,457.5852066,0,22444.26033\n100599,16,0,3,1,Married,2,457.5623259,0,22666.1163\n111120,22,3,4,1,Single,6,457.4523172,0,22603.61586\n114281,34,16,1,1,Single,3,457.4466378,0,22340.33189\n165834,52,33,2,0,Single,8,457.3598309,0,22525.99155\n106133,30,13,2,0,Single,6,457.3564072,0,22373.82036\n155271,37,20,4,1,Single,8,457.2530171,0,22475.65086\n157568,43,23,3,0,Single,10,457.1755592,0,22329.77796\n163120,35,17,1,0,Married,3,457.1550705,0,22512.75353\n144657,16,0,1,0,Married,10,457.1441353,0,22270.20677\n116613,22,4,3,0,Single,12,457.1419101,0,22578.09551\n134668,22,4,3,0,Single,13,457.1156862,0,22419.78431\n163040,19,1,4,1,Single,15,457.1144727,0,22490.72364\n164065,43,23,2,1,Single,5,457.0761152,0,22310.80576\n173604,33,16,2,0,Single,15,457.054282,0,22576.7141\n121667,32,13,2,1,Single,9,457.0390777,0,22386.95389\n103942,62,44,2,1,Single,0,456.9286682,0,22249.43341\n162009,52,31,2,0,Single,9,456.8647913,0,22616.23957\n135237,29,9,1,0,Single,14,456.8106223,0,22348.53112\n168810,50,32,3,0,Married,1,456.8043889,0,22367.21945\n136563,19,0,1,1,Married,12,456.7314758,0,22631.57379\n102006,47,29,2,0,Single,10,456.6376307,0,22286.88154\n113268,54,33,1,0,Single,7,456.6285444,0,22432.42722\n169559,60,43,3,0,Single,3,456.5790617,0,22373.95309\n193091,22,5,4,0,Married,1,456.5629329,0,22561.14665\n157675,20,2,4,1,Single,13,456.5229362,0,22501.14681\n102744,41,20,3,0,Single,10,456.5223966,0,22597.11983\n162477,41,22,3,0,Married,2,456.4605943,0,22313.02972\n122973,27,7,2,0,Married,1,456.406225,0,22443.31125\n186902,39,22,4,0,Single,9,456.36196,0,22293.098\n158121,26,8,3,0,Single,6,456.344638,0,22406.2319\n177000,22,4,1,0,Married,8,456.2160705,0,22376.80353\n182607,50,33,2,1,Married,2,456.1775467,0,22514.87734\n132331,27,6,3,1,Single,6,456.0590024,0,22354.95012\n175095,58,38,3,1,Single,13,456.0547896,0,22283.73948\n160902,18,1,2,1,Single,2,456.0412087,0,22258.06044\n115944,16,0,3,1,Single,15,455.7646207,0,22285.23104\n144572,48,30,3,0,Single,13,455.7566006,0,22529.83003\n157569,33,12,2,0,Married,3,455.7450818,0,22205.25409\n178378,40,23,2,1,Single,6,455.7260968,0,22310.30484\n129215,51,30,4,1,Married,1,455.6210424,0,22563.05212\n115032,64,46,4,1,Married,2,455.6194336,0,22370.97168\n159560,56,38,3,0,Married,5,455.6105209,0,22498.52605\n196144,42,25,2,1,Single,8,455.5840948,0,22402.20474\n100174,67,49,2,1,Single,0,455.5473483,0,22517.36742\n183005,47,28,3,1,Married,5,455.4233876,0,22562.16938\n132910,17,0,2,1,Married,11,455.3669137,0,22230.34569\n141769,67,50,4,1,Married,0,455.3660093,0,22214.30047\n181072,56,35,4,0,Single,7,455.3487464,0,22408.43732\n163980,21,3,2,0,Single,11,455.2843485,0,22263.21743\n119279,29,8,1,0,Single,1,455.2546475,0,22175.73238\n189150,57,38,4,0,Single,13,455.1469933,0,22263.34967\n155325,57,37,2,0,Married,5,455.0825979,0,22397.1299\n188548,30,9,2,1,Single,9,455.0550828,0,22307.75414\n185449,24,5,3,1,Single,10,455.0281209,0,22472.40605\n129020,65,47,2,0,Married,2,455.0181933,0,22291.90967\n154595,64,43,1,0,Single,3,455.0087752,0,22264.43876\n116796,64,46,1,1,Single,7,454.9945793,0,22506.72897\n175734,45,26,3,1,Single,6,454.9878371,0,22343.39186\n143483,59,42,4,0,Single,8,454.9585533,0,22343.92767\n189873,26,5,4,1,Single,1,454.9421894,0,22535.10947\n172385,30,9,3,0,Single,9,454.8720816,0,22447.60408\n152524,18,1,1,0,Single,13,454.8691711,0,22196.45856\n171105,49,30,1,0,Single,10,454.8247336,0,22203.23668\n136861,33,12,1,0,Single,10,454.822674,0,22167.1337\n142474,48,28,3,0,Single,8,454.7726978,0,22170.63489\n121749,23,6,2,0,Single,14,454.7390152,0,22351.95076\n129667,69,52,2,0,Single,5,454.7246128,0,22366.23064\n143190,16,0,2,1,Married,10,454.7101392,0,22183.50696\n107853,53,35,2,0,Single,7,454.7100058,0,22303.50029\n176862,47,28,2,0,Single,8,454.691061,0,22190.55305\n138623,21,3,1,0,Married,15,454.6213775,0,22357.06888\n104049,55,37,2,0,Single,9,454.6174136,0,22390.87068\n174349,43,25,4,0,Single,9,454.5790551,0,22473.95276\n146508,42,23,1,0,Single,1,454.5588872,0,22233.94436\n119275,19,2,2,0,Single,12,454.4784827,0,22456.92414\n190665,29,9,2,1,Single,10,454.4628239,0,22289.1412\n116488,35,16,4,0,Single,9,454.4616625,0,22499.08313\n190115,35,15,3,1,Single,1,454.455568,0,22146.7784\n164033,23,5,4,1,Single,14,454.4548915,0,22340.74458\n101294,16,0,2,0,Married,11,454.3752391,0,22281.76196\n162321,56,39,2,0,Single,10,454.3229904,0,22207.14952\n160279,68,50,2,0,Single,2,454.1709307,0,22208.54654\n140237,68,47,4,0,Married,4,454.0613287,0,22424.06644\n151087,46,25,3,1,Married,1,453.9322856,0,22359.61428\n109498,40,23,2,1,Married,0,453.924572,0,22240.2286\n188650,25,8,1,0,Married,12,453.8468682,0,22157.34341\n114279,65,44,3,1,Married,4,453.8406352,0,22334.03176\n129450,25,8,3,0,Married,15,453.7812962,0,22222.06481\n160716,16,0,3,1,Single,9,453.7388068,0,22314.94034\n135659,43,26,3,0,Single,10,453.6746323,0,22444.73162\n160490,54,36,2,1,Single,10,453.621032,0,22356.0516\n153318,20,3,4,1,Single,12,453.5753348,0,22431.76674\n161937,16,0,2,0,Married,12,453.5616864,0,22303.08432\n101843,54,36,3,0,Married,1,453.5452161,0,22417.26081\n109004,47,28,3,1,Single,10,453.4353813,0,22203.76907\n167185,22,5,1,1,Married,13,453.4195793,0,22142.97897\n186995,34,16,4,0,Single,12,453.4172473,0,22431.86237\n176105,49,32,4,0,Single,8,453.3110996,0,22391.55498\n142856,28,8,3,1,Married,5,453.2883566,0,22242.41783\n179726,54,35,2,0,Single,13,453.218362,0,22459.9181\n107167,35,18,4,1,Single,9,453.1715685,0,22242.57843\n166581,34,13,4,1,Married,1,453.1583048,0,22343.91524\n147680,61,43,3,0,Single,7,453.0865876,0,22122.32938\n192363,67,46,4,0,Single,4,453.0719535,0,22198.59768\n138848,18,0,4,1,Married,7,453.060783,0,22429.03915\n163787,44,24,3,1,Single,10,453.0593091,0,22314.96546\n176178,43,23,3,0,Single,11,453.0564284,0,22108.82142\n146817,47,30,1,1,Single,7,453.0462645,0,22375.31323\n107009,33,12,3,0,Single,9,452.9667674,0,22188.33837\n164414,47,28,4,0,Married,1,452.9421807,0,22205.10904\n171660,28,11,3,0,Single,9,452.9193311,0,22084.96656\n103382,54,33,2,0,Single,9,452.9083255,0,22128.41628\n156050,49,32,3,1,Single,6,452.8799118,0,22088.99559\n189511,27,8,3,0,Married,5,452.8524893,0,22413.62447\n133195,59,41,2,0,Single,14,452.8011333,0,22052.05667\n199826,56,36,2,0,Married,3,452.7747636,0,22370.73818\n105618,50,31,2,0,Single,9,452.7632766,0,22290.16383\n114658,44,27,1,1,Single,6,452.5516079,0,22419.5804\n197812,31,14,4,1,Single,3,452.5507926,0,22336.53963\n121685,66,48,2,1,Single,2,452.5183265,0,22237.91633\n188093,51,34,4,1,Married,4,452.4733169,0,22287.66585\n111933,50,30,1,1,Single,6,452.3457636,0,22258.28818\n154763,30,11,2,0,Single,10,452.3283936,0,22030.41968\n116385,59,41,4,1,Single,6,452.3030991,0,22019.15496\n102373,63,45,3,1,Married,2,452.3014816,0,22154.07408\n172733,63,45,3,0,Married,2,452.2534672,0,22175.67336\n122660,20,1,2,0,Married,13,452.1823501,0,22268.11751\n195955,21,4,3,0,Married,13,452.1745937,0,22026.72969\n107630,25,6,2,0,Married,11,452.1485721,0,22134.42861\n123579,39,18,4,1,Married,1,452.061256,0,22195.0628\n152294,40,23,2,0,Single,9,452.0526951,0,22199.63476\n131813,16,0,2,1,Single,13,451.9667081,0,22262.33541\n169966,58,41,3,1,Married,4,451.8802037,0,22216.01019\n182863,69,51,3,0,Single,6,451.8541201,0,22345.70601\n172178,24,5,1,0,Married,14,451.8458055,0,22150.29028\n168485,49,31,2,0,Single,11,451.8193288,0,22180.96644\n111507,57,38,2,0,Single,9,451.7958416,0,22360.79208\n178822,16,0,2,1,Single,13,451.591187,0,22026.55935\n105780,23,5,3,0,Single,15,451.5880196,0,22000.40098\n161274,23,5,2,0,Single,13,451.5804951,0,22267.02476\n158326,16,0,3,1,Married,11,451.5678298,0,21986.39149\n103267,59,42,4,0,Single,10,451.5560957,0,22019.80479\n134424,35,14,4,0,Single,9,451.5186848,0,22289.93424\n132198,22,4,3,1,Single,13,451.471187,0,22353.55935\n158545,36,18,4,1,Single,14,451.4545471,0,22342.72736\n102408,62,41,3,1,Single,3,451.4395443,0,22101.97722\n175936,66,46,2,0,Single,2,451.3951911,0,22077.75956\n164129,37,17,2,0,Single,13,451.39195,0,22083.5975\n184027,56,36,3,0,Single,8,451.3842453,0,22002.21227\n151750,67,49,4,0,Single,5,451.3594441,0,22110.97221\n191520,22,4,3,1,Single,13,451.2899392,0,22226.49696\n177641,27,8,3,1,Single,2,451.2241762,0,22337.20881\n143953,57,38,2,0,Single,6,451.2019764,0,22193.09882\n167465,28,11,1,0,Single,11,450.9763993,0,22209.81997\n169169,34,17,4,0,Single,6,450.9590523,0,22229.95262\n126167,47,28,3,1,Single,8,450.932951,0,22333.64755\n137739,62,43,2,0,Married,1,450.9282755,0,22253.41378\n172249,42,22,2,0,Single,10,450.9084701,0,22134.42351\n142360,69,50,4,1,Married,0,450.8989218,0,22045.94609\n190620,16,0,1,0,Single,15,450.8608341,0,22343.04171\n193630,39,21,3,1,Single,13,450.7779733,0,22155.89867\n151980,50,29,2,0,Single,10,450.7769182,0,21940.84591\n164809,22,4,3,1,Married,8,450.753906,0,22143.6953\n136602,41,21,2,1,Single,7,450.7127007,0,21944.63504\n119996,55,37,4,0,Married,5,450.437702,0,22065.8851\n101432,58,37,2,0,Married,1,450.3063337,0,21958.31669\n102207,20,3,2,0,Single,11,450.2720017,0,22249.60009\n146047,36,18,3,1,Single,8,450.2463441,0,22047.31721\n162880,17,0,2,0,Married,7,450.1794072,0,21939.97036\n197165,28,10,3,0,Single,7,450.1408952,0,22266.04476\n163894,37,16,4,1,Single,10,450.0801777,0,22293.00889\n116527,24,6,3,1,Single,15,450.0590681,0,22247.95341\n190276,54,34,2,1,Single,1,450.0530373,0,22246.65187\n170159,28,9,3,0,Married,1,449.9609808,0,22020.04904\n189149,43,22,1,1,Single,10,449.7033597,0,22260.16799\n102667,51,30,3,0,Single,13,449.6786853,0,22039.93427\n115097,34,13,1,0,Single,10,449.6680037,0,22022.40019\n112270,49,29,4,0,Single,9,449.5227665,0,22197.13833\n120804,68,50,1,0,Married,2,449.4269416,0,22115.34708\n172083,26,5,2,0,Single,8,449.4244708,0,21895.22354\n174905,44,26,3,0,Single,6,449.3916101,0,22079.58051\n118724,23,5,3,1,Single,13,449.3817899,0,22093.0895\n142432,70,50,2,0,Married,0,449.3385602,0,22137.92801\n178907,55,36,3,1,Single,10,449.3011334,0,22122.05667\n157849,40,20,3,1,Married,2,449.2976957,0,22135.88479\n151310,17,0,1,0,Married,8,449.274253,0,22263.71265\n165859,25,6,2,1,Married,8,449.2381392,0,22072.90696\n125131,58,37,3,1,Married,4,449.230989,0,21876.54945\n172462,34,15,3,0,Married,1,449.2230127,0,21906.15064\n134145,57,36,2,1,Single,6,449.1830842,0,22255.15421\n133573,59,41,2,0,Single,7,449.1829468,0,22161.14734\n166500,26,7,4,1,Single,10,449.1761852,0,21959.80926\n151502,49,28,1,1,Single,4,449.0947929,0,21939.73965\n182983,29,10,4,0,Single,9,449.0144241,0,22113.72121\n138659,24,6,1,0,Married,15,448.9819859,0,22177.0993\n198520,25,6,4,0,Married,11,448.9265704,0,22139.32852\n136090,35,15,4,0,Single,9,448.7875087,0,22114.37544\n144395,26,5,3,0,Single,7,448.7766873,0,21929.83437\n137351,56,36,1,0,Single,9,448.7108596,0,21938.54298\n118683,24,7,3,0,Married,8,448.6384964,0,21919.92482\n136648,40,20,3,1,Single,6,448.6032097,0,22166.16049\n124375,41,22,2,0,Married,0,448.5947005,0,22206.73503\n200391,45,27,1,1,Married,3,448.5662485,0,22178.31243\n198512,63,45,2,0,Married,2,448.5640905,0,21889.20453\n186600,64,47,4,1,Single,2,448.5428067,0,22188.14034\n190560,24,7,4,1,Single,10,448.5008404,0,22194.04202\n137807,63,45,3,0,Single,8,448.4838259,0,22200.1913\n159888,56,37,2,1,Married,3,448.4698573,0,22168.49287\n199395,69,52,2,1,Single,6,448.4012457,0,22168.06229\n192229,29,12,3,0,Single,8,448.2765204,0,22118.82602\n126478,34,14,3,1,Single,9,448.2246201,0,22178.23101\n167963,39,22,2,0,Married,2,448.20283,0,21947.1415\n161213,30,11,2,0,Single,7,448.1780055,0,21937.90028\n110579,16,0,2,1,Married,8,448.1563944,0,21983.81972\n122387,21,4,2,0,Married,14,448.0424916,0,21959.12458\n178778,20,2,3,1,Single,9,448.0370161,0,22173.85081\n143777,39,19,2,0,Single,7,448.0344081,0,22056.72041\n100687,49,31,3,0,Single,10,447.9592236,0,22197.96118\n102684,51,33,2,0,Married,0,447.8181147,0,22152.90574\n154935,44,27,3,0,Married,0,447.8064522,0,21977.32261\n166290,24,5,2,1,Single,9,447.7371893,0,21860.85947\n196363,57,40,1,0,Married,5,447.7210355,0,22027.05178\n194771,43,23,2,0,Married,1,447.7196241,0,22026.98121\n100175,21,4,3,1,Married,5,447.7142831,0,21981.71416\n194535,41,23,1,0,Single,6,447.7009575,0,21983.04788\n124065,62,41,4,1,Married,1,447.6461648,0,22024.30824\n118927,56,39,1,0,Single,14,447.6154456,0,21993.77228\n185058,44,24,2,0,Single,7,447.5415679,0,21789.0784\n108266,69,49,2,1,Married,3,447.4350794,0,22127.75397\n108007,22,5,1,1,Single,14,447.3969202,0,22011.84601\n196852,60,40,2,1,Married,4,447.3291084,0,21875.45542\n198511,44,25,1,1,Single,13,447.2328034,0,22107.64017\n198567,36,15,2,0,Single,10,447.2300541,0,22048.50271\n181545,24,7,4,0,Married,11,447.220707,0,22063.03535\n127200,32,13,1,1,Single,6,447.2109481,0,22133.54741\n154848,30,9,3,1,Single,2,447.189233,0,21791.46165\n113370,21,4,2,1,Single,11,447.0450284,0,21847.25142\n192416,49,28,3,0,Married,4,447.0101645,0,21800.50822\n140484,24,7,3,1,Single,13,446.9873113,0,21878.36557\n160675,37,17,4,1,Married,3,446.9680208,0,21912.40104\n185251,69,48,3,0,Married,2,446.9364469,0,22137.82235\n144576,61,44,2,1,Single,6,446.9026627,0,22095.13314\n150005,59,41,4,1,Single,10,446.8831455,0,21836.15728\n159733,50,32,3,0,Single,9,446.8177939,0,21771.8897\n189882,37,18,4,1,Single,0,446.8091657,0,22071.45829\n140583,64,43,1,1,Married,0,446.8073503,0,22119.36752\n186749,34,15,2,0,Single,8,446.7676713,0,21892.38357\n105052,30,13,1,0,Single,1,446.7508297,0,21886.54149\n155863,50,33,4,1,Married,1,446.7192461,0,22099.96231\n123757,22,3,3,1,Married,6,446.5981693,0,22034.90847\n162467,16,0,4,0,Married,13,446.5852619,0,21743.2631\n153668,39,18,3,1,Single,8,446.5734614,0,21955.67307\n106282,48,31,3,1,Single,9,446.5730685,0,22022.65343\n196078,38,20,3,0,Single,7,446.5640427,0,21807.20214\n174624,57,36,3,1,Married,4,446.5017736,0,22067.08868\n188681,67,50,2,0,Single,6,446.4710429,0,21988.55215\n186465,30,11,2,0,Single,6,446.4560288,0,22062.80144\n113310,25,8,4,0,Single,15,446.4383494,0,21878.91747\n132984,37,19,3,1,Single,9,446.4184447,0,21765.92224\n136023,17,0,2,1,Single,12,446.4040877,0,21757.20439\n176756,30,10,3,1,Single,6,446.3542376,0,21991.71188\n119790,46,28,3,0,Single,8,446.3412369,0,22086.06185\n180183,51,32,4,1,Single,1,446.3276583,0,22075.38292\n145651,51,33,1,1,Single,8,446.3231382,0,21834.15691\n126196,22,5,2,1,Married,1,446.3145823,0,21751.72912\n174730,43,26,3,1,Married,4,446.2320038,0,21828.60019\n160884,18,1,3,0,Single,14,446.1958455,0,21809.79228\n193575,54,36,2,0,Married,1,446.1902944,0,22058.51472\n162820,43,26,3,0,Married,0,446.1783716,0,21755.91858\n155842,28,11,3,0,Single,6,446.0887616,0,22000.43808\n193942,47,27,4,1,Single,6,445.9317379,0,21760.5869\n184246,43,26,3,0,Single,8,445.9301345,0,21765.50673\n128059,64,44,3,0,Married,1,445.9037489,0,21829.18745\n116634,34,17,2,1,Single,7,445.8645954,0,21865.22977\n186998,22,4,2,0,Married,7,445.7715681,0,21699.57841\n145617,36,18,3,0,Single,3,445.7698957,0,22022.49479\n125303,47,29,3,1,Single,6,445.759839,0,21773.99195\n147114,56,38,4,0,Single,11,445.7017775,0,21742.08888\n108302,16,0,2,0,Married,8,445.6753701,0,22037.76851\n131875,20,1,3,0,Married,14,445.6734078,0,21729.67039\n149303,25,6,1,1,Married,9,445.6656637,0,21728.28319\n172397,62,42,1,0,Married,5,445.5511262,0,21744.55631\n132904,55,37,2,1,Single,8,445.5424259,0,21794.1213\n108781,16,0,3,1,Single,14,445.5253649,0,21790.26825\n188700,19,1,2,0,Married,9,445.4787861,0,21921.93931\n156161,29,11,3,0,Single,6,445.3978588,0,21882.89294\n153893,51,32,1,0,Single,7,445.3525652,0,22046.62826\n130537,58,38,4,1,Single,8,445.3099623,0,21763.49812\n133518,32,13,3,0,Single,7,445.2562954,0,21721.81477\n166443,25,7,4,1,Single,14,445.1506552,0,22002.53276\n140159,36,16,3,0,Single,9,445.1326713,0,22050.63357\n174256,52,34,4,0,Single,3,445.1230469,0,21957.15235\n116061,48,30,3,0,Single,10,445.1047033,0,21718.23517\n138111,19,2,2,1,Single,15,445.0967115,0,21796.83558\n196256,28,8,2,1,Single,9,445.0621135,0,21925.10567\n142545,22,5,2,0,Single,15,444.9877384,0,22001.38692\n119260,49,30,4,0,Single,6,444.9146431,0,21871.73216\n160951,31,11,1,1,Single,6,444.9004968,0,21942.02484\n163731,16,0,1,0,Married,15,444.7072951,0,21719.36476\n185025,49,31,2,0,Married,10,444.6162479,0,21778.8124\n109803,57,36,1,1,Married,1,444.5948574,0,21633.74287\n151458,61,42,3,0,Single,7,444.5795763,0,21644.97882\n128036,21,2,3,1,Single,6,444.5153126,0,21707.76563\n127342,33,14,2,0,Single,10,444.4745895,0,21668.72948\n108969,48,29,3,0,Single,9,444.4498458,0,21718.49229\n150018,21,2,2,1,Married,6,444.4156203,0,21792.78102\n130661,24,6,3,1,Married,8,444.4068961,0,21728.34481\n186700,16,0,2,0,Married,13,444.4002378,0,21680.01189\n114342,19,1,3,0,Married,11,444.3421521,0,21924.10761\n126963,18,0,2,0,Married,15,444.3351534,0,21901.75767\n170331,30,12,2,0,Married,8,444.3293777,0,21993.46889\n102817,16,0,3,0,Married,9,444.268167,0,21650.40835\n173549,35,18,1,0,Single,7,444.2241383,0,21877.20692\n105048,42,22,2,1,Married,0,444.2194531,0,21926.97266\n154541,48,28,4,0,Single,2,444.1549303,0,21822.74652\n126826,36,16,4,1,Single,8,444.1166015,0,21925.83008\n184395,36,19,1,0,Single,7,444.0920771,0,21751.60386\n146565,31,11,2,1,Single,10,444.075263,0,21691.76315\n184484,45,24,4,1,Single,9,444.0067488,0,21801.33744\n186875,28,10,3,0,Single,13,443.9090315,0,21640.45158\n158733,37,20,3,0,Single,14,443.8011419,0,21658.0571\n138860,22,4,4,0,Married,13,443.8005421,0,21832.02711\n101299,61,40,3,0,Single,2,443.7570469,0,21893.85235\n103522,16,0,2,1,Married,7,443.7048512,0,21960.24256\n143977,19,1,2,1,Married,9,443.6650907,0,21723.25454\n111657,39,19,3,0,Single,6,443.629057,0,21590.45285\n160831,61,41,2,1,Married,3,443.5355878,0,21619.77939\n104181,57,37,3,0,Married,0,443.5297142,0,21891.48571\n184719,34,16,2,0,Single,12,443.5268424,0,21730.34212\n125989,39,22,2,1,Single,6,443.5045498,0,21619.22749\n182982,30,11,3,1,Married,3,443.4162487,0,21793.81244\n105778,57,40,3,1,Single,10,443.3882806,0,21729.41403\n110330,16,0,1,0,Married,14,443.3265557,0,21598.32779\n167995,66,47,3,1,Married,5,443.2798699,0,21823.9935\n175808,49,30,2,1,Married,1,443.2641355,0,21866.20678\n161027,31,11,2,0,Single,13,443.2015152,0,21758.07576\n151636,58,37,1,0,Single,9,443.1218792,0,21599.09396\n149722,44,23,2,1,Single,6,443.1036083,0,21841.18042\n145404,40,22,4,1,Married,4,443.0579343,0,21577.89672\n108986,57,36,3,1,Single,10,443.0355432,0,21707.77716\n108550,22,4,1,0,Married,13,442.9296618,0,21751.48309\n129558,57,38,4,0,Single,6,442.8722012,0,21613.61006\n134092,55,38,1,0,Single,6,442.8394769,0,21749.97385\n137204,37,16,4,1,Single,9,442.8171912,0,21848.85956\n160664,20,1,4,1,Married,1,442.7993053,0,21725.96527\n148293,19,0,3,1,Married,8,442.7890589,0,21556.45295\n101350,18,0,3,1,Married,10,442.7886923,0,21762.43462\n189012,22,5,4,1,Single,13,442.7727452,0,21906.63726\n192355,16,0,3,1,Married,11,442.763682,0,21698.1841\n174157,25,7,2,0,Married,8,442.7372903,0,21763.86452\n181152,32,14,3,1,Single,6,442.700593,0,21679.02965\n117870,67,50,2,1,Single,6,442.6722795,0,21639.61398\n106926,46,29,3,0,Single,8,442.6504186,0,21823.52093\n172726,25,7,3,0,Single,14,442.6206801,0,21763.03401\n149597,43,23,4,0,Married,8,442.6114551,0,21887.57276\n195234,22,4,4,1,Single,15,442.5756581,0,21754.78291\n186287,70,49,4,1,Single,13,442.4073421,0,21724.36711\n195470,58,40,4,1,Single,3,442.4068984,0,21692.34492\n105053,66,49,4,0,Single,5,442.3922455,0,21573.61228\n115873,66,46,3,1,Single,2,442.3541467,0,21784.70734\n111019,59,38,2,1,Single,7,442.349611,0,21869.48055\n105262,36,15,2,1,Single,6,442.328608,0,21707.4304\n105130,19,0,2,1,Single,13,442.3044664,0,21872.22332\n143164,57,36,2,1,Married,3,442.2950584,0,21647.75292\n122697,56,38,2,1,Single,9,442.2016245,0,21511.08123\n123980,54,34,3,1,Single,10,442.1869973,0,21797.34987\n116445,61,43,3,0,Single,4,442.1473967,0,21547.36984\n168041,17,0,3,0,Married,12,442.1200214,0,21708.00107\n133002,41,24,3,1,Single,6,442.1138921,0,21869.69461\n130937,51,33,4,1,Single,10,442.088138,0,21566.4069\n111874,68,50,3,0,Married,5,442.0778924,0,21591.89462\n166318,23,6,2,1,Married,4,442.0467628,0,21853.33814\n114436,42,25,2,0,Single,8,442.0249542,0,21726.24771\n188597,45,26,2,1,Single,6,442.0024723,0,21659.12362\n114184,42,22,4,0,Single,7,441.994414,0,21629.7207\n153058,65,47,2,0,Married,4,441.9746791,0,21710.73396\n188645,44,26,3,1,Single,6,441.9692376,0,21738.46188\n165686,24,5,1,1,Married,14,441.8763829,0,21841.81915\n192456,44,23,3,0,Single,7,441.8691366,0,21814.45683\n197570,45,28,4,1,Single,7,441.6952086,0,21884.76043\n179584,61,44,3,1,Married,1,441.6845237,0,21801.22619\n102131,29,9,2,0,Single,9,441.6635365,0,21627.17683\n158884,49,30,2,0,Single,9,441.657568,0,21746.8784\n151066,17,0,3,1,Married,7,441.6426591,0,21521.13296\n140375,67,46,4,1,Single,0,441.6132031,0,21696.66016\n115764,64,44,3,1,Married,4,441.5664395,0,21832.32198\n148356,43,26,3,0,Single,2,441.4447936,0,21702.23968\n185725,24,6,4,1,Single,12,441.3935364,0,21691.67682\n123092,41,24,4,1,Single,1,441.3793803,0,21775.96902\n182015,67,46,3,1,Single,5,441.3103863,0,21588.51932\n193236,28,11,3,1,Single,9,441.2911224,0,21697.55612\n148654,51,34,1,0,Single,9,441.2667676,0,21793.33838\n148766,37,20,3,0,Single,6,441.2537341,0,21775.68671\n119535,39,21,1,1,Single,7,441.2336954,0,21524.68477\n174783,48,28,3,0,Married,4,441.2173704,0,21622.86852\n102222,35,18,1,0,Single,10,441.1543119,0,21667.7156\n138577,53,34,4,0,Single,6,441.1122255,0,21788.61128\n119978,63,46,2,0,Married,0,441.0022517,0,21530.11259\n116997,23,6,2,1,Single,7,440.9578964,0,21526.89482\n176184,48,30,4,0,Single,6,440.947621,0,21802.38105\n137050,53,36,4,0,Single,8,440.8848186,0,21567.24093\n158420,45,24,1,0,Single,7,440.8602363,0,21692.01182\n169473,23,6,3,0,Married,10,440.7947497,0,21523.73749\n131336,38,19,1,1,Married,5,440.7741586,0,21505.70793\n132706,31,10,2,1,Single,5,440.6938419,0,21483.6921\n154603,65,46,4,1,Married,5,440.6706032,0,21643.53016\n192847,26,8,3,0,Single,9,440.6492298,0,21657.46149\n122518,37,17,2,0,Single,10,440.5955997,0,21608.77999\n109043,25,8,1,0,Single,15,440.5874752,0,21668.37376\n121279,22,5,2,1,Married,15,440.5414769,0,21813.07385\n133723,16,0,3,1,Married,10,440.489178,0,21750.4589\n123113,20,2,2,1,Single,14,440.4829608,0,21461.14804\n132264,35,14,1,1,Single,8,440.4771322,0,21678.85661\n119519,28,9,1,0,Single,9,440.4587335,0,21653.93668\n101867,29,12,3,1,Single,6,440.450693,0,21442.53465\n192333,62,43,3,1,Single,2,440.2501085,0,21512.50543\n117910,50,30,3,0,Married,4,440.1817513,0,21506.08757\n190618,27,7,2,0,Single,6,440.158269,0,21635.91345\n161834,67,46,2,0,Married,1,440.1540283,0,21618.70142\n116684,30,9,1,0,Single,8,440.0845453,0,21727.22727\n193478,33,15,4,1,Single,8,440.0273323,0,21493.36662\n191530,57,38,3,1,Single,9,440.0203128,0,21478.01564\n109844,54,35,3,1,Single,10,439.9538341,0,21530.69171\n117458,33,12,2,0,Single,10,439.9439695,0,21727.19848\n185418,65,46,3,0,Married,1,439.8757049,0,21464.78525\n197092,29,8,1,1,Single,10,439.8534506,0,21641.67253\n191863,57,37,3,1,Single,7,439.7754556,0,21557.77278\n105127,34,17,1,0,Married,8,439.7564936,0,21613.82468\n162298,68,50,3,1,Single,0,439.728919,0,21662.44595\n126034,18,0,2,1,Single,14,439.6913022,0,21682.56511\n122673,18,0,2,1,Single,12,439.6833242,0,21699.16621\n157654,23,4,4,1,Single,12,439.6785216,0,21569.92608\n123424,55,35,3,0,Single,9,439.6770204,0,21673.85102\n173555,60,39,4,1,Single,7,439.6718132,0,21413.59066\n117599,59,42,4,0,Single,6,439.6628398,0,21661.14199\n176180,43,25,1,1,Married,4,439.646889,0,21427.34445\n177821,52,32,1,1,Married,3,439.5392875,0,21416.96438\n184605,34,14,3,1,Single,12,439.4799764,0,21517.99882\n186745,60,43,2,0,Married,5,439.4426207,0,21518.13104\n134351,18,1,2,1,Single,11,439.4230095,0,21404.15048\n119436,17,0,3,0,Married,15,439.401243,0,21596.06215\n195494,37,17,3,0,Single,5,439.3880266,0,21584.40133\n159204,42,22,3,0,Single,6,439.3203039,0,21562.0152\n130131,25,8,4,1,Married,0,439.2923396,0,21643.61698\n115228,32,12,1,0,Single,10,439.2919376,0,21407.59688\n172926,23,6,3,0,Married,10,439.2788954,0,21466.94477\n192216,39,22,3,0,Single,7,439.1429781,0,21396.14891\n112616,55,36,2,1,Single,4,439.1418913,0,21503.09457\n110295,18,0,3,0,Married,1,439.1286723,0,21716.43362\n115610,24,5,1,1,Single,11,439.1176788,0,21541.88394\n105227,59,40,3,1,Single,8,439.1101835,0,21590.50918\n118179,59,38,4,0,Single,8,439.1049171,0,21429.24586\n155716,57,37,4,0,Single,10,439.1014192,0,21376.07096\n130255,66,46,4,0,Married,4,439.094311,0,21568.71555\n124698,39,18,2,0,Married,4,438.9234708,0,21659.17354\n168568,34,17,1,0,Single,9,438.8540277,0,21708.70139\n150051,48,27,2,1,Single,10,438.8192687,0,21368.96344\n108959,51,34,4,1,Single,10,438.8067065,0,21553.33533\n101604,23,4,1,0,Married,10,438.7076785,0,21556.38393\n140373,52,33,3,0,Single,8,438.665973,0,21530.29865\n144500,29,11,4,1,Single,7,438.6081936,0,21496.40968\n184955,22,3,4,1,Married,9,438.5915609,0,21483.57805\n153488,54,34,1,1,Single,11,438.4493431,0,21491.46716\n171470,44,27,3,1,Single,10,438.3824803,0,21442.12402\n194579,31,10,2,1,Single,6,438.3801826,0,21433.00913\n113829,38,21,4,0,Single,10,438.3532057,0,21417.66029\n116924,27,9,2,1,Married,1,438.326246,0,21392.3123\n158929,23,5,4,0,Single,14,438.2569961,0,21444.84981\n155758,43,25,3,0,Single,2,438.1639396,0,21526.19698\n108690,35,17,4,0,Married,6,438.1548553,0,21354.74277\n148777,18,1,3,1,Single,14,438.128881,0,21447.44405\n150038,49,31,2,0,Married,4,438.0736832,0,21574.68416\n109389,56,35,4,1,Single,7,438.0690014,0,21520.45007\n141599,56,35,2,0,Single,7,438.0103504,0,21681.51752\n164812,48,28,3,0,Single,15,437.9751715,0,21393.75858\n145580,48,27,4,1,Single,2,437.9594676,0,21324.97338\n188556,30,12,4,0,Single,7,437.9343846,0,21325.71923\n167788,53,35,4,1,Single,8,437.9194858,0,21549.97429\n144787,33,15,2,1,Single,15,437.9020483,0,21464.10242\n166717,26,9,4,1,Single,6,437.8661847,0,21423.30924\n177844,36,15,2,0,Single,2,437.8476737,0,21691.38369\n124019,44,23,2,1,Single,7,437.8435798,0,21482.17899\n156221,48,29,1,1,Single,5,437.82145,0,21394.0725\n158355,16,0,2,0,Single,14,437.8069624,0,21633.34812\n123435,50,33,4,0,Single,6,437.7636042,0,21427.18021\n155234,35,15,2,1,Single,10,437.6261485,0,21476.30743\n172931,40,23,1,0,Single,6,437.5853059,0,21369.2653\n103126,55,38,1,1,Single,7,437.5486569,0,21610.43285\n111615,32,11,3,0,Single,8,437.5326073,0,21597.63037\n126686,22,5,1,0,Single,11,437.3787493,0,21339.93747\n113189,30,12,2,0,Married,1,437.3786203,0,21522.93102\n125285,56,39,1,0,Married,5,437.3601045,0,21529.00523\n188882,46,28,4,0,Single,6,437.3429689,0,21328.14845\n144653,27,10,3,0,Single,10,437.302042,0,21607.1021\n197638,57,39,2,0,Single,13,437.2575179,0,21651.8759\n160743,22,5,4,0,Single,12,437.2300123,0,21535.50062\n196295,22,3,4,1,Single,12,437.2083539,0,21542.4177\n157185,65,47,1,0,Single,1,437.1775426,0,21436.87713\n168348,48,30,3,0,Single,8,437.1661679,0,21574.3084\n109637,51,33,1,0,Single,8,437.1309872,0,21334.54936\n132638,51,33,3,1,Single,7,437.0706614,0,21376.53307\n171180,22,5,4,0,Married,5,437.0523196,0,21526.61598\n101674,17,0,3,1,Married,8,437.0023845,0,21391.11923\n106089,23,5,2,0,Married,9,436.8418596,0,21315.09298\n197114,53,32,3,0,Single,7,436.8340804,0,21512.70402\n144314,18,1,2,0,Single,15,436.7715984,0,21254.57992\n143897,55,34,3,0,Single,9,436.7674904,0,21281.37452\n131150,18,0,1,0,Single,11,436.7417282,0,21575.08641\n150283,25,7,2,1,Married,15,436.6690835,0,21549.45418\n190565,43,24,1,1,Married,1,436.6028495,0,21340.14248\n173116,64,46,3,1,Married,2,436.5779985,0,21572.89993\n135903,20,3,2,1,Married,10,436.5643972,0,21437.21986\n178170,41,22,3,1,Single,8,436.5459141,0,21315.29571\n157326,64,43,3,0,Single,7,436.5286065,0,21246.43033\n188178,19,2,4,1,Single,13,436.4979325,0,21489.89663\n193469,58,37,2,0,Single,10,436.4935985,0,21528.67993\n150597,59,39,2,1,Single,1,436.4761219,0,21462.8061\n197553,59,40,2,0,Married,0,436.4017589,0,21610.08795\n178501,26,8,1,0,Single,8,436.3278273,0,21585.39137\n154338,70,49,3,1,Married,0,436.314571,0,21588.72855\n183176,30,11,2,1,Single,6,436.3091977,0,21358.45989\n192288,25,6,4,1,Married,7,436.2493995,0,21366.46998\n121197,57,37,4,1,Married,3,436.2360182,0,21550.80091\n198239,54,35,2,0,Single,1,436.2093066,0,21554.46533\n160403,70,50,3,0,Married,5,436.1887136,0,21351.43568\n106991,27,10,4,0,Single,10,436.186264,0,21241.3132\n128728,26,9,1,0,Single,12,436.1834558,0,21254.17279\n134777,31,13,2,0,Single,9,436.1708524,0,21376.54262\n101165,34,16,2,1,Single,3,436.1101317,0,21399.50659\n115010,25,8,1,1,Single,12,436.0393992,0,21564.96996\n172958,41,23,2,0,Married,2,436.0292348,0,21514.46174\n131727,24,6,4,1,Single,8,436.0129371,0,21317.64686\n144306,57,40,3,0,Single,6,435.9986149,0,21262.93075\n174932,32,12,2,1,Single,10,435.997452,0,21295.8726\n112642,34,13,1,0,Single,7,435.9831882,0,21517.15941\n179055,53,35,1,0,Single,7,435.959851,0,21451.99255\n179659,18,0,4,0,Married,13,435.8457162,0,21428.28581\n137332,29,11,3,1,Married,3,435.6879662,0,21561.39831\n175948,19,1,2,0,Married,11,435.6867047,0,21301.33524\n105243,26,7,3,1,Single,2,435.6844113,0,21196.22057\n184517,18,0,4,1,Single,12,435.6660993,0,21505.30497\n111260,35,18,3,1,Married,0,435.6444132,0,21321.22066\n169509,65,48,2,0,Single,3,435.6436387,0,21480.18194\n111979,65,47,3,0,Single,5,435.4396914,0,21261.98457\n186170,18,1,2,1,Married,14,435.429934,0,21317.4967\n127806,45,24,1,1,Single,6,435.42961,0,21263.4805\n129073,45,28,2,0,Single,13,435.4216584,0,21417.08292\n150090,20,3,3,0,Married,10,435.4007981,0,21311.03991\n189680,53,34,3,0,Single,8,435.4005785,0,21521.02893\n186078,47,30,2,0,Single,12,435.3769461,0,21412.84731\n110878,16,0,4,0,Married,14,435.3721562,0,21327.60781\n149344,44,25,2,1,Single,8,435.3669469,0,21482.34735\n187285,43,24,3,1,Single,6,435.2699647,0,21405.49824\n188589,16,0,3,0,Single,14,435.2293799,0,21414.469\n188248,33,13,3,1,Married,5,435.1204689,0,21310.02345\n174077,20,2,1,1,Married,9,435.088519,0,21211.42595\n189797,54,33,2,0,Single,6,435.0601621,0,21259.00811\n104390,26,6,3,0,Married,8,435.0296062,0,21456.48031\n129744,23,5,2,1,Married,14,434.938752,0,21505.9376\n149212,34,15,1,1,Single,11,434.8411458,0,21436.05729\n193616,47,27,4,1,Married,5,434.8229191,0,21326.14596\n192592,33,16,4,0,Single,9,434.8206346,0,21511.03173\n153570,62,43,2,1,Single,0,434.7456671,0,21514.28336\n110811,21,3,3,0,Single,12,434.7249998,0,21289.24999\n193479,65,46,1,0,Married,2,434.7234839,0,21440.1742\n116697,22,5,4,0,Single,14,434.7105998,0,21270.52999\n121473,28,11,4,1,Married,1,434.7078232,0,21448.39116\n118420,37,20,4,0,Single,9,434.701813,0,21455.09065\n145776,26,6,1,0,Single,10,434.5793359,0,21259.9668\n133247,66,45,4,1,Married,1,434.5548793,0,21503.74397\n112921,54,35,1,1,Single,6,434.5258236,0,21156.29118\n110459,57,39,2,1,Single,7,434.4769865,0,21493.84933\n145730,18,1,2,0,Married,13,434.4512132,0,21258.56066\n136228,25,8,3,0,Married,11,434.4101055,0,21336.50528\n109261,19,0,2,0,Married,9,434.4062385,0,21137.31193\n187674,47,27,1,0,Single,6,434.40134,0,21221.067\n171324,16,0,2,1,Single,14,434.3302549,0,21460.51275\n140938,58,37,2,1,Single,13,434.3252095,0,21429.26048\n133866,43,26,2,0,Single,12,434.2490493,0,21503.45247\n168214,54,34,1,0,Single,9,434.1198694,0,21361.99347\n121238,33,12,2,0,Single,12,434.0823777,0,21268.11889\n182495,52,32,2,0,Married,5,433.9690892,0,21307.45446\n142525,65,45,1,1,Single,8,433.9037761,0,21312.18881\n193194,61,41,3,0,Single,4,433.8956731,0,21360.78366\n179381,22,4,3,1,Married,11,433.8649639,0,21489.2482\n151864,41,20,2,0,Single,13,433.8180465,0,21263.90233\n199233,30,10,2,0,Single,2,433.817628,0,21166.8814\n150981,54,37,2,1,Married,4,433.7988397,0,21398.94199\n188845,63,44,4,0,Single,3,433.7320858,0,21318.60429\n166710,51,30,3,0,Single,10,433.7248915,0,21239.24458\n155530,17,0,4,0,Single,11,433.6890578,0,21236.45289\n165028,31,11,3,0,Single,7,433.6557018,0,21375.78509\n118700,43,25,3,1,Single,4,433.6436742,0,21241.18371\n181126,58,39,2,1,Single,7,433.5899259,0,21267.4963\n150602,45,27,2,0,Single,10,433.4797677,0,21449.98839\n140861,57,38,3,0,Single,10,433.4630009,0,21424.15005\n157311,60,39,3,0,Married,2,433.4556152,0,21154.78076\n115952,20,2,2,1,Single,13,433.3371921,0,21438.85961\n190433,61,43,2,0,Single,1,433.3229127,0,21413.14564\n123927,59,39,3,1,Single,7,433.2388438,0,21405.94219\n128651,43,25,2,0,Single,6,433.2281911,0,21183.40956\n123822,19,0,1,1,Single,10,433.2087995,0,21095.43998\n124587,57,36,2,1,Single,8,433.1765586,0,21064.82793\n197585,40,20,2,0,Single,15,433.1540449,0,21152.70225\n186132,38,18,1,0,Single,9,433.1419791,0,21178.09896\n143651,18,0,2,1,Single,11,433.0880457,0,21348.40229\n114819,16,0,1,0,Single,11,433.080861,0,21378.04305\n142249,25,7,4,1,Married,2,433.0065501,0,21133.32751\n192359,43,23,2,1,Single,9,433.0022757,0,21352.11379\n120349,51,32,4,0,Single,10,432.9486699,0,21343.4335\n176476,17,0,2,1,Married,8,432.9213332,0,21226.06666\n182186,58,40,2,0,Married,4,432.885157,0,21200.25785\n137980,48,27,2,1,Married,2,432.8803242,0,21331.01621\n137782,23,5,3,1,Married,13,432.8053738,0,21343.26869\n104397,21,2,3,0,Married,11,432.7997392,0,21206.98696\n169020,26,5,2,1,Single,6,432.7976128,0,21114.88064\n178595,42,22,4,1,Single,9,432.7490525,0,21303.45263\n133181,60,42,3,0,Single,3,432.707755,0,21264.38775\n134522,37,17,1,0,Married,10,432.6751776,0,21354.75888\n148015,53,35,2,0,Married,3,432.6201947,0,21209.00974\n122573,51,31,4,0,Single,8,432.6065462,0,21261.32731\n108104,58,39,4,1,Single,8,432.5799167,0,21188.99584\n134754,64,44,3,0,Married,1,432.5580693,0,21110.90347\n103882,25,6,3,1,Married,15,432.5538698,0,21233.69349\n196392,66,46,4,0,Single,7,432.5229054,0,21382.14527\n120974,34,17,2,0,Married,4,432.4997239,0,21200.9862\n156370,18,0,3,0,Married,11,432.441388,0,21189.0694\n162786,24,6,2,0,Married,7,432.4344208,0,21337.72104\n146116,48,27,3,0,Single,9,432.352609,0,21344.63045\n103000,44,26,3,0,Single,6,432.3500041,0,21408.50021\n122666,37,20,2,1,Single,6,432.3490479,0,21176.4524\n127930,46,27,3,0,Single,8,432.3452389,0,21161.26195\n152204,21,3,4,1,Married,14,432.3332971,0,21051.66486\n190535,20,3,3,0,Single,14,432.3149763,0,21157.74882\n148336,29,10,4,0,Married,2,432.3005906,0,21235.02953\n111588,40,21,2,0,Married,10,432.2681353,0,21262.40677\n140083,25,8,2,0,Married,10,432.2537352,0,21148.68676\n101711,21,4,1,1,Married,6,432.2491717,0,21141.45859\n100314,33,16,4,1,Single,8,432.2238432,0,21332.19216\n184180,28,8,4,0,Single,6,432.1829193,0,21099.14597\n173508,23,6,2,1,Married,6,432.1793564,0,21108.96782\n137964,59,41,2,1,Single,10,432.1720254,0,21043.60127\n135721,17,0,2,0,Married,11,432.1548344,0,21089.74172\n115819,50,32,3,1,Single,9,432.1347485,0,21213.73743\n113134,16,0,1,1,Single,11,432.1205891,0,21312.02946\n123916,33,16,4,1,Single,6,432.1164909,0,21190.82455\n109664,45,27,1,1,Single,8,432.0837395,0,21091.18698\n133350,44,25,3,0,Single,9,432.0578807,0,21168.89404\n144257,38,17,1,0,Single,6,432.002112,0,21326.1056\n171825,19,2,2,0,Single,14,431.9491025,0,21101.45513\n113512,21,2,2,1,Married,4,431.9004518,0,21233.02259\n100936,37,17,2,1,Single,7,431.8786997,0,21350.93499\n191447,65,47,3,0,Single,7,431.8637009,0,21351.18505\n159897,20,2,3,0,Married,12,431.8164641,0,21090.82321\n184426,67,47,4,1,Single,4,431.625472,0,21104.2736\n166154,42,25,1,0,Single,9,431.5898049,0,21379.49025\n109078,40,21,3,0,Single,10,431.5806008,0,21326.03004\n146143,21,4,1,0,Married,13,431.5361637,0,21033.80819\n190585,55,34,3,1,Married,2,431.5115537,0,21053.57769\n133581,32,13,3,0,Single,8,431.5077143,0,21368.38572\n160839,39,19,2,0,Single,11,431.4657323,0,21329.28662\n123482,27,7,2,1,Single,9,431.4405981,0,21258.02991\n138119,41,22,1,0,Married,5,431.418055,0,21263.90275\n197443,53,32,2,0,Single,6,431.3881701,0,21268.40851\n162683,41,20,1,0,Single,9,431.375072,0,21190.7536\n146137,23,5,3,0,Married,15,431.3719661,0,21274.59831\n172531,26,5,3,0,Married,8,431.3532752,0,21358.66376\n181383,57,37,4,1,Single,5,431.2847917,0,21247.23959\n185606,30,9,3,1,Single,10,431.2678646,0,21023.39323\n141945,22,3,2,0,Married,7,431.2372528,0,21163.86264\n141926,59,42,4,0,Single,15,431.2316572,0,21125.58286\n193035,50,33,1,1,Single,7,431.1665527,0,21222.32764\n133136,45,25,3,0,Married,0,431.1181724,0,21323.90862\n117198,56,38,3,0,Single,8,431.1060154,0,21341.30077\n176403,46,29,4,1,Single,6,431.0993117,0,21149.96559\n173050,44,24,1,0,Single,7,431.0471708,0,21286.35854\n149898,31,11,2,1,Married,4,431.0282312,0,21123.41156\n115013,16,0,3,0,Married,13,431.0177082,0,21125.88541\n117722,19,0,1,0,Single,11,430.9655363,0,21294.27682\n107934,22,5,3,0,Married,14,430.962319,0,20968.11595\n145080,56,38,2,1,Single,8,430.9502505,0,21071.51253\n159134,18,1,2,1,Single,10,430.9324283,0,21221.62142\n183970,31,10,3,0,Single,10,430.929523,0,21004.47615\n161859,20,2,3,1,Single,7,430.9069948,0,21267.34974\n145974,49,30,3,1,Single,6,430.8618542,0,21145.09271\n198390,27,8,1,0,Single,6,430.7272458,0,21209.36229\n145959,31,12,3,1,Single,6,430.7245431,0,21030.22716\n113718,18,0,4,0,Married,11,430.7125121,0,20992.62561\n128735,31,13,3,0,Married,1,430.6421926,0,20978.10963\n191789,40,19,2,1,Married,3,430.571871,0,21132.59355\n179805,41,22,1,1,Single,8,430.5555209,0,21171.77605\n138282,48,31,3,0,Single,10,430.5422419,0,21277.1121\n105145,17,0,2,1,Married,9,430.4902428,0,20963.51214\n111706,23,6,4,0,Married,8,430.4848221,0,21252.24111\n152228,16,0,2,1,Married,14,430.4119517,0,21053.59759\n172572,38,19,2,1,Single,10,430.3666938,0,20969.33469\n171670,58,40,1,1,Single,6,430.3296192,0,21282.48096\n179398,51,30,2,0,Married,3,430.3243254,0,21079.21627\n125059,66,46,1,1,Single,4,430.2457695,0,20939.28848\n130926,70,50,3,0,Married,1,430.2231153,0,20944.15577\n198429,55,34,3,1,Single,13,430.2150373,0,21183.75187\n139939,27,9,1,1,Single,7,430.2106048,0,21101.53024\n186162,52,35,4,1,Single,7,430.1810861,0,21192.05431\n169392,25,6,2,1,Single,6,430.1482025,0,21269.41013\n148692,50,31,3,1,Single,0,430.1435472,0,21269.17736\n168957,29,9,3,0,Married,1,430.122261,0,21006.11305\n140768,27,10,1,0,Single,6,430.1199309,0,21001.99655\n177394,29,11,2,0,Single,6,429.9656904,0,20931.28452\n134701,52,34,3,0,Married,9,429.9422569,0,21166.11285\n107616,24,5,2,1,Single,12,429.933187,0,21074.65935\n140469,21,4,1,1,Single,15,429.8619791,0,21137.09896\n140516,27,10,3,0,Single,8,429.840519,0,21026.02595\n145276,36,16,1,0,Married,0,429.8250505,0,21042.25253\n136478,28,8,2,1,Single,2,429.7236818,0,21149.18409\n174791,49,30,3,1,Single,7,429.6544251,0,21005.72126\n182370,31,14,2,1,Single,13,429.6450408,0,20920.25204\n194267,32,11,3,1,Single,6,429.6147915,0,21220.73958\n101567,60,41,3,1,Single,1,429.5967528,0,20921.83764\n197277,38,18,4,0,Single,8,429.5579461,0,20988.89731\n163020,58,40,1,0,Single,9,429.506863,0,21061.34315\n161574,43,23,2,0,Single,6,429.4852152,0,21232.26076\n112612,64,43,2,1,Married,0,429.4798613,0,21253.99307\n114987,19,1,2,0,Single,12,429.4439787,0,21187.19894\n160784,35,17,2,0,Married,7,429.4151524,0,21073.75762\n177622,34,16,1,1,Married,1,429.4072773,0,20959.36387\n165901,30,9,2,1,Single,10,429.406501,0,21144.32505\n127375,26,9,4,0,Single,13,429.386194,0,21181.3097\n198181,25,6,3,0,Married,11,429.3403008,0,21130.01504\n127767,61,41,2,1,Single,5,429.3195189,0,21195.97595\n195684,16,0,3,1,Single,12,429.2779309,0,21073.89655\n170639,23,6,4,1,Single,12,429.2584884,0,21213.92442\n123228,50,31,4,0,Single,9,429.2560964,0,21143.80482\n197861,35,18,3,0,Single,6,429.2311462,0,21058.55731\n148603,33,13,3,0,Single,13,429.151349,0,21217.56745\n122069,18,0,1,1,Married,12,429.1487774,0,20936.43887\n160708,40,21,2,0,Single,9,429.1482942,0,21209.41471\n150239,21,3,3,0,Married,15,429.1453009,0,21200.26505\n194460,43,24,3,1,Single,7,429.0042771,0,20887.21386\n156341,62,44,3,0,Single,13,428.9311772,0,20962.55886\n107897,47,29,4,0,Single,7,428.8987044,0,21088.93522\n186870,45,26,3,1,Single,15,428.8885515,0,20904.42758\n196073,35,18,1,1,Single,9,428.8765231,0,20953.82616\n146923,58,39,2,1,Single,14,428.8592904,0,21030.96452\n133135,19,2,3,0,Married,12,428.8329385,0,21012.64693\n179835,24,7,1,1,Married,15,428.8144302,0,20911.72151\n169490,27,9,3,1,Single,6,428.7863104,0,21093.31552\n130984,25,8,3,1,Married,14,428.7713277,0,21002.56639\n106357,26,9,3,1,Single,7,428.7595533,0,21069.97767\n142609,40,21,2,0,Single,15,428.6185743,0,21120.92872\n196893,25,6,3,1,Married,10,428.5948527,0,20955.74264\n164860,64,46,3,0,Married,3,428.5855128,0,20884.27564\n161769,45,25,3,0,Single,15,428.5556062,0,20924.78031\n131590,46,25,2,1,Single,6,428.5202743,0,20924.01372\n124647,45,28,3,1,Single,10,428.5178983,0,21111.89492\n126842,41,24,1,1,Single,10,428.4593856,0,21117.96928\n158776,17,0,2,1,Married,6,428.437278,0,21074.8639\n190438,27,8,1,0,Single,9,428.3436991,0,21123.18496\n192140,40,19,2,1,Married,5,428.3322982,0,20879.61491\n107837,16,0,2,0,Married,11,428.3233658,0,20952.16829\n108627,19,0,2,1,Single,15,428.2658156,0,21150.29078\n162261,53,33,2,0,Single,7,428.262359,0,20916.11795\n189071,26,8,4,0,Single,10,428.2598359,0,20855.9918\n181984,42,24,3,1,Married,2,428.2211269,0,21052.05635\n180260,22,3,4,1,Single,15,428.1849528,0,20939.24764\n137017,22,5,3,1,Married,10,428.1788423,0,20994.94212\n199993,65,48,3,1,Single,3,428.1292276,0,20851.46138\n144777,50,31,1,1,Single,1,428.0443064,0,21114.21532\n144224,23,6,4,1,Married,15,428.0332128,0,20925.66064\n120469,17,0,3,1,Single,15,427.9976492,0,21013.88246\n102401,19,2,4,1,Married,14,427.8977662,0,21066.88831\n190999,66,48,2,0,Single,5,427.8914224,0,21163.57112\n199606,58,39,1,0,Married,9,427.8410376,0,20899.05188\n134992,62,42,2,0,Single,13,427.7748107,0,21027.74054\n194642,50,29,2,1,Single,8,427.7290944,0,20931.45472\n112671,40,22,4,0,Single,8,427.7195464,0,21083.97732\n130679,21,3,1,1,Married,13,427.7138738,0,20930.69369\n177512,33,16,4,1,Single,10,427.6512446,0,21094.56223\n158084,52,32,3,1,Single,10,427.6283088,0,20928.41544\n116665,57,40,4,1,Single,5,427.6201278,0,21044.00639\n157150,57,37,2,0,Married,5,427.524697,0,20804.23485\n150837,18,0,4,1,Married,7,427.4092113,0,20812.46057\n114704,27,6,3,1,Single,6,427.4058448,0,21017.29224\n161226,33,12,1,1,Married,4,427.3927161,0,20913.63581\n140244,44,25,1,1,Single,3,427.346936,0,20871.3468\n137232,27,9,3,0,Single,8,427.3192717,0,20975.96359\n102641,59,40,1,0,Single,7,427.2013464,0,20860.06732\n109196,46,25,2,0,Married,9,427.1871568,0,21129.35784\n192660,52,34,2,0,Married,0,427.1674938,0,20840.37469\n167349,44,23,1,1,Single,9,427.1032675,0,20924.16338\n148509,64,45,3,0,Single,15,427.0756419,0,20834.7821\n183580,24,5,4,1,Married,15,426.9693417,0,20799.46709\n127476,42,22,3,0,Single,6,426.9031577,0,21013.15789\n137377,44,26,1,0,Single,7,426.8421815,0,21120.10908\n175309,19,0,4,1,Single,12,426.8408152,0,20894.04076\n118408,23,5,3,0,Single,15,426.7706397,0,21042.53199\n164194,43,25,3,0,Single,7,426.7671338,0,20789.35669\n193048,24,5,2,1,Single,12,426.7631092,0,20806.15546\n105899,70,53,1,0,Single,8,426.6658746,0,20750.29373\n135309,26,7,3,1,Married,2,426.6371651,0,21093.85826\n158450,38,21,2,1,Single,10,426.5349909,0,20828.74955\n153306,22,3,3,1,Married,15,426.5257171,0,20885.28586\n162960,69,49,3,0,Single,10,426.5091944,0,20747.45972\n142490,30,12,1,1,Single,8,426.4783725,0,21118.91863\n108002,55,37,3,1,Single,9,426.4624974,0,20795.12487\n190463,58,39,2,1,Single,10,426.4276795,0,20933.38398\n196160,70,50,1,0,Single,5,426.4032579,0,20770.1629\n113291,39,18,3,1,Single,9,426.3535978,0,21023.67989\n125358,24,5,2,0,Single,12,426.3458887,0,21110.29444\n131762,29,11,3,1,Single,10,426.3416661,0,20845.08331\n125826,17,0,3,0,Married,13,426.3072964,0,21068.36482\n123025,24,5,1,1,Single,7,426.2893605,0,20997.46803\n109507,22,4,3,1,Single,11,426.2884302,0,20823.42151\n127163,44,25,2,0,Single,7,426.268402,0,21036.4201\n140699,33,14,3,0,Single,11,426.2252706,0,20963.26353\n145778,42,21,2,0,Single,10,426.2098135,0,20971.49068\n159866,16,0,1,1,Married,7,426.1706197,0,20973.53099\n171870,17,0,2,1,Married,14,426.1425635,0,21014.12818\n138985,45,26,4,0,Married,8,426.1181883,0,20799.90942\n142860,30,12,3,0,Single,9,426.0966284,0,20764.83142\n147502,38,17,2,1,Single,6,426.0835848,0,20804.17924\n139115,44,27,1,1,Single,8,426.0637585,0,20863.18793\n180549,32,15,3,0,Single,8,426.0389212,0,20888.94606\n171138,16,0,4,0,Married,15,426.0346014,0,20756.73007\n161536,59,41,1,0,Single,8,426.0073629,0,20870.36815\n134066,21,4,3,1,Married,7,426.0055263,0,20728.27632\n115938,20,2,2,1,Married,15,425.9558647,0,20903.79324\n105475,39,21,3,1,Single,7,425.9032742,0,20935.16371\n190118,36,16,2,0,Married,7,425.862971,0,20867.14855\n142113,62,42,3,1,Single,14,425.8344765,0,20816.72383\n106467,45,27,2,0,Single,6,425.7957411,0,20733.78706\n166753,33,14,2,1,Single,5,425.7547902,0,20883.73951\n159697,42,24,3,1,Married,4,425.718669,0,21000.93345\n135704,48,29,2,1,Single,9,425.7026245,0,20782.13123\n100520,30,11,1,1,Single,2,425.6230015,0,20797.15008\n112884,31,10,3,0,Single,6,425.5953663,0,20745.76832\n200000,48,27,2,1,Single,7,425.5806709,0,20990.03355\n106818,67,46,2,0,Married,1,425.5115144,0,20918.57572\n140917,55,35,2,0,Single,8,425.4967109,0,20703.83555\n165851,28,8,3,0,Single,8,425.4738051,0,20761.69026\n164199,64,44,2,1,Single,5,425.4517567,0,20849.58784\n141730,25,7,3,1,Single,11,425.4141837,0,20813.70919\n107571,26,8,1,0,Married,9,425.3481104,0,20772.40552\n166740,17,0,3,0,Single,13,425.2846908,0,20869.23454\n168573,31,14,3,1,Married,3,425.262654,0,20832.1327\n114754,23,6,4,0,Married,11,425.2231669,0,20749.15835\n178842,66,45,1,1,Married,3,425.2158617,0,20885.79309\n123972,32,12,1,1,Single,7,425.2124819,0,20920.6241\n105960,38,17,1,1,Single,6,425.1927224,0,20977.63612\n121331,43,23,3,1,Married,1,425.1740805,0,20902.70403\n100137,26,6,3,1,Married,4,425.0964309,0,20829.82155\n141815,32,12,3,0,Single,9,425.0750354,0,20978.75177\n174126,29,9,3,1,Single,9,425.0698045,0,20987.49023\n140549,32,13,4,0,Single,6,425.0154124,0,21043.77062\n142931,64,47,1,1,Single,10,424.8797525,0,20668.98763\n137641,28,9,3,0,Married,2,424.8302291,0,20649.51146\n122221,17,0,1,1,Single,12,424.7723034,0,20973.61517\n163838,23,5,2,0,Married,8,424.7652212,0,20804.26106\n106858,54,34,4,0,Married,7,424.7402596,0,20642.01298\n181704,21,3,4,1,Married,11,424.7213268,0,20884.06634\n125557,61,44,4,0,Single,2,424.6948368,0,20690.74184\n198124,29,12,3,1,Single,8,424.6311436,0,20950.55718\n193281,49,29,4,1,Single,10,424.6187252,0,20979.93626\n145491,62,42,2,1,Single,6,424.5097932,0,21017.48966\n125290,34,14,3,0,Single,9,424.4260365,0,20920.30183\n125152,54,33,3,1,Single,7,424.4244307,0,20851.22154\n150918,65,47,3,0,Single,6,424.4213686,0,20635.06843\n143677,63,44,2,1,Single,8,424.4147273,0,21011.73637\n195840,39,22,1,0,Single,6,424.411475,0,20645.57375\n139580,27,8,2,0,Married,7,424.3758894,0,20787.79447\n199702,46,27,1,1,Married,5,424.3622371,0,20753.11186\n180127,31,12,2,1,Single,7,424.2943953,0,20752.71977\n168118,43,24,2,1,Single,10,424.2836355,0,20753.18178\n170513,23,6,2,1,Single,12,424.2575249,0,20693.87625\n116991,49,28,3,0,Married,9,424.2184833,0,20795.92417\n176859,31,11,3,0,Married,8,424.0797025,0,20877.98513\n185726,27,10,2,0,Single,7,423.9501643,0,20816.50822\n109538,69,48,3,0,Married,5,423.923075,0,20634.15375\n186671,28,11,3,1,Single,15,423.8902896,0,20717.51448\n102098,46,25,3,1,Single,6,423.8582987,0,20987.91494\n117652,67,46,1,1,Single,4,423.8349611,0,20944.74806\n190278,36,15,4,0,Married,4,423.8233631,0,20871.16816\n160305,21,3,4,0,Married,11,423.8093901,0,20916.46951\n101192,68,51,2,0,Single,8,423.8090522,0,20678.45261\n122371,45,25,2,1,Married,3,423.7712298,0,20868.56149\n148117,24,5,3,1,Single,15,423.7363909,0,20916.81955\n154621,41,24,2,0,Single,8,423.7351571,0,20602.75786\n191061,40,20,1,0,Single,8,423.7222281,0,20590.11141\n104352,39,20,3,0,Married,8,423.7212376,0,20875.06188\n193020,58,38,3,0,Single,7,423.6973989,0,20799.86995\n105398,53,35,4,1,Single,14,423.6948653,0,20758.74327\n180218,17,0,1,1,Married,8,423.6904935,0,20715.52468\n101145,22,5,3,0,Single,12,423.6899364,0,20938.49682\n190303,29,11,3,0,Single,10,423.6515734,0,20909.57867\n194333,19,1,2,1,Single,12,423.6379873,0,20605.89937\n109006,47,30,3,0,Married,9,423.5838729,0,20739.19365\n122440,50,33,3,0,Single,8,423.5739827,0,20787.69914\n159893,19,2,3,1,Single,11,423.5684617,0,20868.42309\n101602,45,26,1,1,Single,6,423.4963358,0,20946.81679\n170960,27,9,3,1,Single,9,423.3146269,0,20824.73135\n166201,32,12,3,0,Single,9,423.3057527,0,20626.28764\n143444,68,49,2,0,Single,9,423.2962596,0,20626.81298\n131562,18,0,2,1,Married,8,423.2506965,0,20798.53483\n119624,64,46,2,0,Single,4,423.1791916,0,20721.95958\n119015,21,2,3,1,Married,10,423.1654373,0,20686.27187\n179617,31,11,1,1,Single,11,423.1563186,0,20581.81593\n176966,23,4,2,0,Single,11,423.1087427,0,20683.43714\n113390,47,28,4,1,Single,9,423.1020821,0,20619.10411\n199358,37,20,2,0,Married,3,423.0475688,0,20790.37844\n162826,47,29,2,1,Single,0,423.0057603,0,20826.28802\n130806,39,18,1,0,Married,13,423.0039085,0,20946.19543\n189122,63,43,1,1,Single,2,422.9930733,0,20887.65367\n105407,53,33,4,1,Single,9,422.9142045,0,20595.71023\n196484,36,19,2,1,Single,10,422.9126911,0,20834.63456\n138859,57,37,3,1,Single,10,422.8974879,0,20671.8744\n136916,62,42,4,0,Married,3,422.8597577,0,20672.98789\n104324,48,27,3,0,Married,0,422.7622226,0,20735.11113\n103583,53,32,4,1,Married,2,422.7035968,0,20838.17984\n108827,25,7,1,1,Single,7,422.6834837,0,20659.17419\n178477,48,28,4,1,Single,6,422.6596933,0,20720.98467\n151816,55,38,2,0,Single,7,422.6259844,0,20856.29922\n131193,49,30,4,0,Married,4,422.6198125,0,20746.99063\n169479,49,30,2,1,Single,8,422.5942955,0,20878.71478\n173060,20,1,2,1,Married,11,422.5476808,0,20689.38404\n183751,16,0,4,0,Married,8,422.5263359,0,20724.3168\n138712,37,16,2,0,Married,9,422.4744399,0,20768.722\n156354,45,28,3,0,Single,8,422.4733447,0,20871.66724\n172482,24,7,2,1,Married,10,422.4311103,0,20789.55552\n156228,25,6,3,0,Married,9,422.4070511,0,20884.35256\n111740,35,14,3,0,Single,11,422.3681748,0,20906.40874\n155064,41,24,4,1,Single,9,422.2574058,0,20579.87029\n185864,59,41,1,1,Single,8,422.246098,0,20821.3049\n130393,53,33,1,1,Single,6,422.243912,0,20668.1956\n110267,36,17,4,1,Married,0,422.2094254,0,20643.47127\n153290,34,17,3,1,Single,10,422.1964652,0,20594.82326\n113983,50,33,2,1,Married,0,422.1836566,0,20874.18283\n150429,17,0,1,0,Single,15,422.1703601,0,20903.51801\n196925,47,29,1,0,Married,1,422.1639047,0,20759.19524\n173850,48,31,2,0,Single,9,422.157111,0,20810.85555\n100647,51,33,1,0,Single,10,422.0508228,0,20873.54114\n176233,18,0,2,0,Married,14,422.0503089,0,20708.51545\n113063,51,33,2,1,Single,10,421.9898177,0,20707.49089\n136760,57,39,2,1,Single,5,421.8803667,0,20883.01834\n103993,45,26,3,0,Single,9,421.8538011,0,20638.69006\n130082,25,7,3,0,Married,7,421.8518345,0,20637.59173\n132671,17,0,4,1,Single,6,421.8215092,0,20646.07546\n152323,19,2,2,0,Single,11,421.8094081,0,20656.47041\n105845,59,39,3,0,Single,8,421.7638665,0,20585.19333\n134798,33,13,1,0,Single,9,421.731451,0,20586.57255\n146046,41,21,2,1,Single,6,421.7293981,0,20785.46991\n115606,59,39,4,0,Single,8,421.6409867,0,20655.04934\n140732,38,20,2,1,Married,4,421.6299652,0,20647.49826\n190369,16,0,2,1,Married,15,421.608874,0,20525.4437\n175012,31,14,4,1,Single,6,421.5918256,0,20858.59128\n197209,17,0,2,0,Married,15,421.5276772,0,20805.38386\n171095,51,30,3,1,Married,1,421.5093144,0,20593.46572\n115825,30,10,1,0,Single,9,421.4794433,0,20821.97217\n128448,45,28,3,0,Married,8,421.442929,0,20839.14645\n153152,37,19,2,1,Single,9,421.4320573,0,20839.60287\n142373,28,8,2,0,Single,10,421.3970439,0,20712.8522\n151147,34,15,3,1,Single,11,421.3343631,0,20556.71816\n195444,57,36,4,1,Single,15,421.3057238,0,20753.28619\n129325,23,4,1,1,Single,7,421.2640345,0,20603.20173\n192958,51,30,1,1,Single,8,421.1261475,0,20688.30738\n108623,70,49,4,0,Married,0,421.1059883,0,20721.29942\n121330,21,4,3,0,Single,12,421.0218123,0,20675.09062\n145156,27,8,3,1,Single,8,421.0077464,0,20722.38732\n122243,57,40,2,1,Single,9,420.9816334,0,20841.08167\n178004,62,42,2,1,Single,3,420.9783021,0,20822.91511\n126149,21,4,1,1,Single,12,420.898228,0,20810.9114\n180741,36,15,4,1,Single,7,420.8842146,0,20743.21073\n145841,39,22,4,0,Single,6,420.8534944,0,20690.67472\n131669,55,36,2,1,Single,14,420.8526954,0,20566.63477\n180852,23,6,2,0,Married,15,420.8510329,0,20445.55164\n115194,35,15,3,0,Married,0,420.7846261,0,20771.23131\n122891,23,6,1,1,Married,6,420.7564271,0,20559.82136\n141562,48,29,2,0,Single,9,420.7400793,0,20721.00397\n107695,42,21,2,1,Single,6,420.7152764,0,20448.76382\n105998,38,18,1,1,Married,5,420.6612799,0,20646.064\n183688,27,7,4,0,Married,7,420.6473521,0,20450.36761\n188699,56,37,2,1,Single,10,420.6183775,0,20598.91888\n107730,38,18,3,0,Married,8,420.5568513,0,20541.84257\n138027,37,20,2,1,Single,8,420.5444009,0,20654.22005\n112590,23,5,1,0,Married,9,420.4922713,0,20565.61357\n194671,50,32,3,0,Single,11,420.3676325,0,20697.38163\n191858,18,1,3,0,Single,13,420.3531826,0,20549.65913\n134886,53,34,2,1,Single,13,420.2826154,0,20639.13077\n181824,31,14,1,1,Single,8,420.2708203,0,20475.54102\n123320,21,2,1,0,Married,6,420.1291134,0,20766.45567\n185091,19,0,3,0,Married,11,420.1144297,0,20616.72149\n183544,32,12,2,1,Married,5,420.1064878,0,20695.32439\n159299,36,15,1,1,Single,6,420.0788009,0,20742.94005\n170077,28,7,2,0,Married,7,420.0356419,0,20774.7821\n179527,22,4,2,0,Married,12,420.0226607,0,20594.13304\n168851,26,5,3,1,Single,10,420.0175843,0,20736.87922\n195932,57,40,2,0,Single,12,419.9939116,0,20629.69558\n122420,65,46,2,0,Single,6,419.9502626,0,20694.51313\n189463,42,25,1,1,Single,9,419.8965691,0,20595.82846\n110232,22,5,4,1,Married,9,419.8804724,0,20581.02362\n151169,28,8,2,0,Single,7,419.8749439,0,20783.7472\n189381,38,21,1,1,Single,15,419.8097249,0,20434.48625\n169299,51,33,2,1,Single,6,419.7613,0,20731.065\n126815,24,6,2,1,Single,14,419.7611847,0,20740.05924\n155034,46,25,2,0,Married,4,419.7205648,0,20559.02824\n150328,39,21,4,0,Single,6,419.6725481,0,20541.62741\n197535,17,0,3,1,Single,14,419.6674977,0,20732.37489\n161064,32,12,1,1,Married,0,419.5637371,0,20516.18686\n198388,30,13,3,1,Single,10,419.5318237,0,20683.59119\n118722,36,17,1,0,Single,8,419.4122176,0,20616.61088\n104729,59,39,3,0,Single,9,419.4106329,0,20577.53165\n184100,59,40,2,0,Single,8,419.3886548,0,20496.43274\n165436,56,38,2,1,Single,8,419.3565651,0,20564.82826\n184351,52,31,3,0,Married,9,419.3280614,0,20446.40307\n115409,22,5,2,0,Married,13,419.2947294,0,20756.73647\n105170,50,30,3,1,Single,9,419.2766608,0,20544.83304\n182242,17,0,2,1,Married,14,419.2755042,0,20599.77521\n185494,49,30,4,0,Single,8,419.2073391,0,20739.36696\n160977,59,41,1,0,Single,10,419.1877776,0,20667.38888\n146470,40,20,2,0,Married,10,419.155267,0,20636.76335\n172053,34,17,4,1,Single,7,419.1292879,0,20453.4644\n161656,56,36,2,1,Single,8,419.1132254,0,20411.66127\n153719,22,4,4,1,Single,12,419.1085743,0,20626.42872\n101662,16,0,1,1,Single,13,419.0931662,0,20553.65831\n128961,38,18,3,1,Single,12,419.042823,0,20689.14115\n101374,66,49,3,1,Married,3,419.020557,0,20369.02785\n143838,17,0,3,1,Married,14,419.0158481,0,20575.79241\n182547,30,10,2,0,Married,10,419.0082341,0,20719.41171\n109143,54,34,4,1,Married,5,418.9882422,0,20558.41211\n119703,23,5,4,0,Single,13,418.9797333,0,20723.98667\n146548,45,25,2,1,Single,7,418.979255,0,20495.96275\n151567,35,14,4,0,Single,10,418.9780571,0,20597.90286\n132797,19,0,1,1,Married,8,418.9765851,0,20641.82926\n126462,18,1,2,0,Married,7,418.9755266,0,20564.77633\n188860,23,4,2,1,Single,15,418.9717831,0,20577.58916\n125697,22,4,3,0,Married,14,418.8913073,0,20455.56537\n190084,47,27,4,0,Single,15,418.8647344,0,20517.23672\n176119,65,48,4,0,Single,7,418.7576737,0,20398.88369\n184327,65,48,4,1,Single,5,418.7371198,0,20358.85599\n167862,29,11,1,1,Married,5,418.6738227,0,20362.69114\n102188,31,10,3,1,Single,9,418.6217458,0,20625.08729\n134038,48,30,2,0,Single,7,418.6160845,0,20400.80423\n132546,57,39,3,0,Single,6,418.5709616,0,20462.54808\n173069,58,39,3,0,Married,7,418.5033569,0,20377.16785\n171589,49,29,1,1,Single,6,418.456828,0,20712.8414\n111001,42,21,3,0,Single,6,418.3983242,0,20385.91621\n128021,27,10,2,1,Single,9,418.3414724,0,20510.07362\n173615,62,44,2,1,Single,8,418.2324464,0,20376.62232\n126242,25,8,4,1,Married,8,418.1871029,0,20487.35515\n157523,50,31,2,1,Single,8,418.0718072,0,20420.59036\n189903,18,0,4,0,Single,13,418.0118186,0,20619.59093\n171123,66,47,4,1,Married,4,417.996476,0,20440.8238\n147170,59,38,2,0,Single,8,417.9445843,0,20634.22922\n198892,34,14,2,0,Single,7,417.9206263,0,20547.03132\n126822,61,41,4,0,Single,6,417.9111891,0,20307.55946\n168387,67,47,4,0,Single,10,417.8562987,0,20361.81494\n130671,35,18,3,0,Single,8,417.8506231,0,20307.53116\n109313,34,15,4,1,Single,7,417.824665,0,20499.23325\n173900,68,51,3,1,Single,5,417.8022104,0,20664.11052\n193336,69,49,2,1,Single,4,417.6620295,0,20606.10148\n136216,44,25,2,1,Single,9,417.6475313,0,20294.37657\n157465,54,33,2,1,Single,8,417.620922,0,20472.0461\n185559,30,10,2,1,Single,3,417.6042699,0,20294.2135\n187927,17,0,3,0,Married,13,417.5986667,0,20428.93334\n106690,23,5,3,1,Married,7,417.5890389,0,20546.45195\n115498,27,10,2,1,Single,10,417.5322802,0,20344.61401\n164108,27,9,2,0,Married,12,417.445698,0,20665.2849\n182645,54,35,2,0,Single,9,417.4354139,0,20466.7707\n129268,45,26,3,1,Single,4,417.3547846,0,20267.73923\n166911,33,13,3,1,Single,9,417.3440064,0,20603.20032\n181735,46,29,3,0,Married,9,417.3312181,0,20650.56091\n150948,51,33,2,0,Married,9,417.3171358,0,20469.85679\n171035,17,0,3,1,Single,6,417.3126082,0,20396.63041\n128988,31,10,2,1,Single,7,417.2227443,0,20522.13722\n180220,51,34,1,0,Married,9,417.171405,0,20521.57025\n172625,45,26,2,0,Single,13,417.1290961,0,20286.45481\n157906,18,1,2,1,Single,12,417.0831495,0,20600.15748\n183719,22,5,4,1,Single,13,417.0685146,0,20370.42573\n185383,39,19,3,0,Single,10,417.0442126,0,20317.21063\n154536,37,19,1,1,Single,10,416.9465298,0,20542.32649\n121844,45,24,2,0,Single,9,416.9153157,0,20603.76579\n131995,57,39,1,0,Married,8,416.8936624,0,20435.68312\n115736,40,22,2,0,Married,1,416.8876782,0,20330.38391\n121413,30,13,1,0,Married,1,416.8568223,0,20301.84112\n119367,16,0,2,1,Married,11,416.8155548,0,20418.77774\n199557,30,13,2,0,Married,9,416.784968,0,20618.2484\n166926,58,40,3,0,Married,2,416.7597944,0,20623.98972\n138211,45,25,3,0,Single,9,416.7552524,0,20282.76262\n147194,44,27,4,1,Single,6,416.7455539,0,20531.2777\n175308,16,0,3,1,Single,11,416.6699389,0,20246.49695\n107276,43,25,2,0,Single,10,416.602557,0,20564.12785\n129766,41,21,3,1,Single,7,416.4597065,0,20423.98533\n147466,18,0,3,1,Married,8,416.4371054,0,20551.85527\n109713,58,39,1,0,Single,8,416.3768884,0,20399.84442\n141836,53,35,2,1,Married,5,416.3579204,0,20461.89602\n119170,17,0,4,1,Single,15,416.3096147,0,20466.48074\n139838,28,8,1,1,Single,6,416.2969813,0,20602.84907\n148327,43,24,4,1,Married,4,416.2711873,0,20488.55937\n155141,39,18,3,0,Single,15,416.2677296,0,20526.38648\n177938,30,10,3,1,Single,6,416.235882,0,20399.7941\n106337,24,5,4,0,Single,13,416.1952707,0,20331.76354\n177395,21,4,4,0,Married,12,416.1769399,0,20552.847\n144225,59,40,2,1,Single,8,416.0208561,0,20505.04281\n168456,45,25,2,1,Single,14,416.0056676,0,20498.28338\n119820,25,7,3,0,Married,14,415.8954178,0,20252.77089\n175740,25,6,3,0,Married,12,415.8853382,0,20592.26691\n163112,29,8,1,1,Single,9,415.8751673,0,20271.75837\n187153,39,19,1,1,Single,7,415.8200933,0,20588.00467\n198178,20,3,3,1,Single,13,415.7713599,0,20242.568\n188721,60,43,1,0,Single,9,415.7502168,0,20559.51084\n159728,32,11,2,0,Single,9,415.6980603,0,20385.90302\n139507,41,23,2,1,Single,8,415.6893802,0,20237.46901\n194942,50,30,2,1,Single,8,415.6838212,0,20217.19106\n154978,27,6,3,1,Married,3,415.6381902,0,20543.90951\n124226,24,5,1,0,Married,6,415.5792872,0,20465.96436\n169334,25,8,1,1,Married,8,415.5747896,0,20453.73948\n152552,20,3,4,1,Married,11,415.5023861,0,20546.11931\n150029,18,1,1,1,Single,12,415.4689569,0,20261.44785\n137586,43,23,2,1,Married,4,415.3391371,0,20277.95686\n155942,56,37,2,1,Single,10,415.3206391,0,20448.03196\n149558,23,4,4,1,Single,11,415.2982284,0,20541.91142\n157969,39,18,4,1,Single,14,415.2957537,0,20324.78769\n117322,41,24,3,0,Single,8,415.2278726,0,20233.39363\n199887,16,0,2,1,Single,14,415.1010704,0,20319.05352\n146847,22,4,3,1,Married,8,415.0884452,0,20186.42226\n114779,36,15,4,1,Single,0,415.0757374,0,20499.78687\n110472,39,22,2,0,Single,7,415.0274743,0,20285.37372\n134171,47,29,4,0,Single,12,415.0184049,0,20408.92025\n125873,22,3,2,0,Married,10,415.0134118,0,20538.67059\n106368,55,37,2,1,Single,7,414.9357568,0,20504.78784\n129398,26,6,2,0,Married,5,414.8684683,0,20353.42342\n153249,30,9,3,0,Single,10,414.750865,0,20293.54325\n168134,16,0,2,1,Single,11,414.7386962,0,20265.93481\n150034,22,5,4,1,Single,13,414.7337103,0,20209.68552\n151652,46,25,2,1,Married,3,414.6760995,0,20515.80498\n184505,22,5,3,1,Married,7,414.6705578,0,20428.52789\n128167,22,5,1,1,Single,15,414.6493457,0,20207.46729\n132958,61,44,1,0,Single,9,414.6447652,0,20198.23826\n149721,19,0,3,0,Married,14,414.609823,0,20186.49115\n109897,22,5,3,1,Married,9,414.6055435,0,20408.27718\n101017,53,35,3,0,Single,14,414.5835726,0,20507.17863\n170426,16,0,2,0,Married,12,414.5709368,0,20361.54684\n188725,25,7,3,1,Single,11,414.5234549,0,20329.17275\n111139,24,7,3,0,Married,15,414.5094075,0,20364.47038\n108069,53,35,3,0,Single,12,414.3733572,0,20469.66786\n134760,23,4,3,0,Single,13,414.2637215,0,20220.18608\n125623,67,49,1,0,Single,3,414.2560905,0,20119.80453\n105320,48,28,2,0,Single,6,414.2553355,0,20350.76678\n141824,39,21,4,0,Married,6,414.2517863,0,20491.58932\n196234,53,35,2,0,Married,0,414.2298226,0,20359.49113\n109561,21,2,3,0,Married,10,414.1606562,0,20404.03281\n184428,43,24,3,0,Married,7,414.133462,0,20278.6731\n174726,23,4,3,1,Married,4,414.1243237,0,20339.21619\n192977,34,14,2,0,Married,4,414.1075733,0,20314.37867\n100420,28,8,3,1,Single,6,414.0764024,0,20452.82012\n141320,28,11,3,1,Single,10,414.0625038,0,20122.12519\n142047,44,24,3,0,Married,1,414.0474787,0,20123.37394\n178406,29,8,2,0,Married,2,414.0429425,0,20342.14713\n123134,26,6,2,1,Married,4,414.0355088,0,20109.77544\n199619,59,41,3,0,Married,9,414.0135982,0,20263.67991\n179862,19,0,1,0,Married,11,413.9661157,0,20486.30579\n127195,70,52,2,0,Single,2,413.9640896,0,20410.20448\n159340,20,1,2,0,Single,14,413.9629325,0,20150.14663\n181435,63,46,3,0,Married,2,413.961804,0,20370.0902\n197130,55,37,1,1,Single,10,413.932018,0,20337.6009\n170066,21,2,4,1,Married,12,413.9059865,0,20273.29933\n134380,42,22,3,0,Married,8,413.9032242,0,20446.16121\n124845,69,48,3,1,Married,1,413.889962,0,20311.4981\n140871,24,6,1,0,Single,13,413.8328632,0,20231.64316\n130297,60,40,1,0,Married,5,413.7740071,0,20239.70036\n138505,52,32,2,0,Single,1,413.7544243,0,20249.72122\n164488,23,6,2,0,Married,8,413.6891005,0,20433.45503\n111377,70,53,4,1,Married,0,413.6092759,0,20181.4638\n121480,20,3,1,1,Single,11,413.5916968,0,20140.58484\n124703,19,2,3,1,Married,6,413.5590589,0,20274.95295\n190048,66,48,3,1,Single,5,413.5438916,0,20413.19458\n168240,48,29,3,1,Single,3,413.4234023,0,20275.17012\n143798,39,19,2,1,Single,10,413.414678,0,20444.7339\n155208,23,6,3,1,Married,11,413.4068052,0,20140.34026\n127794,35,18,3,0,Married,7,413.3484585,0,20167.42293\n165200,55,37,3,1,Single,7,413.3197159,0,20453.9858\n103670,37,18,4,0,Married,9,413.2981039,0,20361.9052\n134294,16,0,4,1,Single,9,413.2911066,0,20357.55533\n153413,17,0,1,0,Married,14,413.2819072,0,20319.09536\n192468,25,7,3,1,Single,15,413.2305429,0,20282.52715\n111331,18,1,3,0,Married,13,413.0485498,0,20348.42749\n155770,48,30,4,0,Single,10,412.9476089,0,20253.38045\n173120,50,31,4,0,Single,10,412.8486244,0,20209.43122\n191661,19,1,3,1,Single,13,412.8010801,0,20376.05401\n117832,33,14,3,1,Single,8,412.7850437,0,20160.25219\n148050,64,46,3,1,Single,10,412.7042813,0,20046.21407\n186429,34,16,3,1,Single,6,412.6961419,0,20111.8071\n197528,29,10,3,0,Single,10,412.6409725,0,20189.04863\n119594,67,48,3,0,Single,14,412.6374818,0,20368.87409\n166390,23,4,2,1,Married,15,412.5617816,0,20234.08908\n125208,22,3,4,1,Married,15,412.5535681,0,20328.67841\n105456,16,0,2,1,Single,15,412.5500186,0,20122.50093\n115201,39,21,3,0,Single,9,412.50814,0,20066.407\n136003,23,4,3,1,Married,15,412.4406058,0,20392.03029\n183774,20,3,1,0,Married,13,412.3728753,0,20335.64377\n107899,30,10,4,1,Single,7,412.3114852,0,20064.57426\n154113,20,3,4,0,Married,11,412.2869822,0,20046.34911\n190949,23,4,3,1,Single,11,412.2243603,0,20347.21802\n170149,57,39,2,0,Single,12,412.1684969,0,20392.42485\n179104,64,46,3,0,Single,0,412.164081,0,20200.20405\n166277,53,35,2,1,Single,6,412.098784,0,20013.9392\n116765,33,16,3,1,Single,7,412.0823159,0,20100.1158\n117170,70,53,2,1,Single,5,412.0798292,0,20050.99146\n167987,22,5,3,0,Married,13,412.02684,0,20399.342\n127301,27,8,1,1,Single,10,412.0192066,0,20214.96033\n130691,44,24,3,0,Single,11,412.0016654,0,20268.08327\n110638,21,3,2,0,Married,11,411.9805899,0,20164.0295\n136204,41,21,2,1,Single,10,411.9519695,0,20298.59848\n197726,39,18,3,0,Married,10,411.933519,0,20230.67595\n170153,59,41,2,1,Single,15,411.8978325,0,20021.89163\n138164,31,14,3,1,Single,7,411.8840574,0,20047.20287\n152479,45,27,2,1,Single,7,411.8218575,0,20262.09288\n136849,64,46,4,0,Married,1,411.8180828,0,20319.90414\n148910,54,36,2,1,Single,7,411.804291,0,20118.21455\n139084,58,41,4,0,Single,15,411.7883391,0,20091.41696\n199736,26,8,2,1,Single,11,411.7546684,0,20130.73342\n154992,55,36,4,0,Single,2,411.7339244,0,20310.69622\n132327,45,24,3,0,Married,7,411.7331993,0,20179.65997\n131453,70,50,3,0,Married,3,411.6984136,0,20206.92068\n166754,16,0,2,1,Married,13,411.69064,0,20369.532\n194953,38,20,3,0,Single,6,411.634984,0,20039.7492\n186462,18,0,4,1,Married,15,411.6126174,0,20039.63087\n171617,25,7,4,1,Married,15,411.6037626,0,20255.18813\n186648,59,39,1,1,Married,1,411.5706433,0,20351.53217\n198278,21,2,1,1,Single,14,411.5067461,0,20255.33731\n158172,63,46,1,0,Single,9,411.4934109,0,20012.67055\n100094,51,30,4,0,Married,10,411.4866939,0,20360.3347\n182390,19,1,3,0,Married,9,411.4666098,0,20344.33049\n146678,58,41,4,0,Single,6,411.45302,0,20075.651\n182387,34,15,4,0,Single,12,411.4021606,0,20062.10803\n121786,28,7,1,0,Single,6,411.2520222,0,20196.60111\n118394,45,25,2,1,Single,10,411.038857,0,20184.94285\n136489,43,22,3,0,Married,5,411.0143991,0,20091.71996\n196241,50,31,2,0,Married,2,411.0110495,0,20341.55248\n140731,21,3,3,0,Married,12,410.9777887,0,20261.88944\n183729,24,6,4,1,Single,11,410.966369,0,20075.31845\n132675,29,10,3,1,Single,11,410.903145,0,20016.15725\n159247,17,0,2,1,Single,12,410.8438061,0,19977.19031\n134143,57,40,4,1,Single,6,410.8316476,0,20164.58238\n144440,53,34,3,0,Single,11,410.8314787,0,20144.57394\n168830,63,46,2,0,Single,7,410.8016095,0,20225.08048\n153721,23,5,3,0,Married,10,410.7954896,0,19990.77448\n114595,47,27,2,0,Single,6,410.7950507,0,19968.75254\n114394,47,27,2,1,Single,10,410.7671135,0,20048.35567\n180810,19,1,3,1,Married,12,410.760981,0,19977.04905\n182629,39,21,3,1,Single,9,410.7412796,0,20148.06398\n150059,47,29,2,0,Single,15,410.7083923,0,20035.41962\n138688,24,6,3,0,Single,14,410.7031171,0,20135.15586\n160458,44,24,2,0,Single,9,410.6672768,0,20074.36384\n159270,53,36,3,0,Single,10,410.6653661,0,20019.26831\n177629,31,14,3,1,Single,8,410.5703012,0,19937.51506\n158865,21,3,3,1,Single,13,410.5488979,0,20278.4449\n159427,50,29,2,0,Single,9,410.5228298,0,20165.14149\n197213,35,17,2,1,Single,6,410.5218487,0,20225.09244\n129417,20,3,4,0,Married,11,410.5109323,0,20031.54662\n154652,50,29,2,1,Single,7,410.4866322,0,19947.33161\n184288,17,0,4,0,Married,15,410.4657153,0,20151.28577\n172506,35,16,3,0,Married,14,410.4520199,0,20095.601\n124750,57,36,3,1,Single,13,410.398618,0,20268.9309\n158356,40,23,3,0,Single,11,410.3906928,0,20201.53464\n141819,43,23,3,0,Single,12,410.3537401,0,20208.68701\n124480,56,39,2,1,Single,14,410.3294936,0,20227.47468\n194236,55,38,3,0,Married,1,410.2967298,0,20236.83649\n125093,43,26,1,1,Married,3,410.2660657,0,20035.30329\n172205,48,28,3,0,Single,10,410.2600858,0,20205.00429\n153564,16,0,4,1,Single,11,410.2387404,0,20242.93702\n102638,46,27,4,1,Single,12,410.1741085,0,20250.70543\n175053,22,3,2,1,Single,12,410.1476426,0,20157.38213\n136178,18,1,4,1,Married,13,410.1421679,0,20065.1084\n108252,46,27,2,0,Married,6,410.1188182,0,20148.94091\n136361,38,19,2,1,Single,8,410.1154769,0,20197.77385\n119687,20,3,1,0,Married,12,410.0568927,0,20137.84464\n196361,53,33,2,0,Married,1,410.0488244,0,20008.44122\n112151,33,16,3,1,Single,7,410.0049702,0,20104.24851\n186180,19,1,1,0,Married,1,409.9745118,0,20279.72559\n104566,18,0,3,1,Single,14,409.9402636,0,20035.01318\n191415,42,24,1,0,Married,6,409.9283918,0,20184.41959\n122683,48,29,3,1,Single,8,409.9217362,0,20100.08681\n116644,28,10,4,0,Single,7,409.919158,0,20280.9579\n164611,25,8,1,0,Married,14,409.9010323,0,20185.05162\n146071,39,20,4,0,Single,8,409.8641991,0,20255.20996\n184724,51,30,3,0,Married,0,409.8565591,0,19898.82796\n176351,22,5,4,0,Married,13,409.7724218,0,19941.62109\n138546,34,13,2,0,Single,11,409.7559707,0,20223.79854\n164769,53,36,3,1,Married,5,409.7377822,0,19996.88911\n152856,42,25,2,1,Single,9,409.6868628,0,19930.34314\n139612,27,8,2,1,Single,7,409.6832917,0,19909.16459\n121615,21,2,2,1,Married,3,409.6457061,0,20161.28531\n169772,28,7,4,1,Single,7,409.6446156,0,20233.23078\n105193,37,20,4,0,Married,3,409.6414329,0,20049.07165\n175235,54,34,3,0,Single,12,409.5840825,0,19962.20413\n154666,53,36,4,1,Single,6,409.5776595,0,20137.88298\n169157,53,32,2,0,Single,10,409.5574213,0,20182.87107\n159796,56,36,2,1,Single,8,409.5358825,0,19950.79413\n138084,42,25,1,1,Single,0,409.5283292,0,20013.41646\n115657,28,8,2,0,Married,9,409.5202285,0,19900.01143\n124927,56,39,4,0,Married,5,409.4983924,0,20069.91962\n186022,70,50,4,0,Single,7,409.4627203,0,20008.13602\n152613,26,7,1,0,Single,7,409.4059691,0,19908.29846\n141020,35,15,4,0,Single,9,409.3317426,0,20125.58713\n151323,58,37,4,0,Married,13,409.1785866,0,19979.92933\n184870,67,46,1,0,Single,15,409.1758254,0,19909.79127\n171246,31,13,2,1,Single,11,409.157673,0,20182.88365\n119640,42,25,3,1,Single,8,409.0904312,0,20244.52156\n176973,32,13,2,0,Married,9,409.0031004,0,20019.15502\n117040,22,3,4,0,Married,13,408.9283722,0,19955.41861\n155457,40,19,2,1,Single,7,408.9222735,0,19956.11368\n183321,44,24,4,0,Married,14,408.8994529,0,20221.97265\n153135,57,40,1,0,Married,3,408.8901347,0,19989.50674\n145312,36,18,3,1,Single,9,408.8804819,0,19966.0241\n104461,54,37,4,0,Married,10,408.8604562,0,19954.02281\n120503,48,27,2,0,Married,6,408.8104413,0,20236.52207\n190832,58,41,1,1,Single,7,408.7395066,0,19903.97533\n147543,50,33,1,0,Married,6,408.7089448,0,20052.44724\n101117,32,12,3,1,Single,15,408.663755,0,19940.18775\n178082,16,0,3,1,Married,15,408.6305456,0,20163.52728\n131440,17,0,3,0,Married,15,408.5475155,0,19834.37578\n120184,40,22,2,1,Single,7,408.5210693,0,20078.05347\n147136,53,36,4,1,Single,13,408.5192601,0,20081.96301\n139648,18,1,3,1,Married,14,408.4532423,0,19989.66212\n166857,68,50,1,1,Single,6,408.4515225,0,20090.57613\n102860,20,2,2,1,Married,5,408.4267315,0,19963.33658\n131408,69,51,2,0,Single,9,408.3617678,0,19971.08839\n108024,55,35,2,0,Single,13,408.31942,0,19999.971\n143774,17,0,1,1,Married,13,408.2572079,0,19918.8604\n171893,39,18,2,0,Single,15,408.2384347,0,19934.92174\n165993,27,8,3,0,Married,6,408.2288248,0,20064.44124\n112018,28,11,2,0,Married,8,408.1579098,0,19860.89549\n152233,46,28,1,1,Single,8,408.0924936,0,19830.62468\n151124,19,1,1,1,Married,14,408.0746893,0,19843.73447\n111939,46,25,3,0,Single,14,408.0652122,0,19943.26061\n131038,49,32,3,1,Single,9,408.0235502,0,20152.17751\n152236,39,18,3,0,Married,7,408.018059,0,20121.90295\n158519,37,16,3,1,Married,5,408.0070168,0,20120.35084\n139622,49,30,1,0,Married,7,407.9761934,0,20093.80967\n101168,19,0,3,1,Married,7,407.9717447,0,19927.58724\n130570,40,21,2,0,Married,4,407.9658565,0,19941.29283\n193355,42,23,3,1,Single,7,407.9470347,0,19866.35174\n183614,62,45,3,0,Married,1,407.9024019,0,19845.1201\n120179,61,44,3,1,Single,4,407.8221575,0,19881.10788\n157329,47,26,2,1,Single,6,407.8200497,0,19846.00249\n148118,69,52,1,1,Single,10,407.7675108,0,19890.37554\n118827,46,25,1,1,Single,7,407.7608046,0,20173.04023\n140714,67,47,2,0,Single,10,407.7598873,0,20146.99437\n105308,17,0,2,1,Single,11,407.7316449,0,19922.58225\n160886,16,0,2,1,Single,11,407.7216167,0,20150.08084\n106416,28,11,2,1,Married,5,407.7212568,0,19908.06284\n173747,29,8,2,0,Married,0,407.6990253,0,20036.95127\n175044,30,10,4,0,Single,11,407.6963193,0,19987.81597\n129000,43,25,3,0,Married,6,407.6469385,0,20071.34693\n108351,59,38,3,1,Single,6,407.63027,0,20051.5135\n140773,30,10,3,1,Married,1,407.5406097,0,20149.03049\n137393,32,14,1,0,Married,11,407.4961917,0,20147.80959\n179648,19,1,3,1,Married,0,407.478199,0,19956.90995\n192425,45,24,3,1,Single,7,407.4733234,0,20042.66617\n165982,56,36,2,1,Single,9,407.4627197,0,19812.13599\n121021,29,10,4,0,Single,10,407.4383768,0,20109.91884\n101955,48,27,3,1,Married,1,407.4288189,0,20032.44095\n116606,34,15,3,1,Single,10,407.3931117,0,20131.65559\n187293,29,8,1,1,Single,14,407.3893742,0,20071.46871\n155167,24,6,3,1,Single,12,407.353849,0,20098.69245\n189615,22,5,1,1,Single,7,407.3289004,0,20114.44502\n120717,56,37,4,0,Single,13,407.2254318,0,20136.27159\n199576,46,29,3,0,Married,10,407.2148587,0,19952.74294\n145693,48,29,1,1,Single,10,407.1739205,0,20104.69603\n179593,17,0,3,1,Single,11,407.1083508,0,20148.41754\n157672,38,20,3,0,Single,9,407.0604016,0,19919.02008\n172612,48,29,3,0,Married,6,407.0164062,0,19994.82031\n153885,33,13,2,1,Single,7,407.0085068,0,20019.42534\n104966,17,0,4,0,Married,9,406.9418929,0,20046.09465\n182875,67,50,1,0,Single,12,406.9366871,0,20110.83436\n157201,20,2,4,1,Married,2,406.9247402,0,20009.23701\n173922,22,5,1,1,Single,8,406.8305601,0,20041.52801\n164992,33,13,2,0,Single,12,406.8141711,0,19983.70856\n120847,54,37,2,1,Single,15,406.7887894,0,20134.43947\n184191,36,18,2,0,Married,2,406.7601439,0,19800.0072\n172063,54,37,3,0,Married,1,406.7479402,0,20031.39701\n100087,58,37,2,1,Single,5,406.7311102,0,19935.55551\n139004,48,28,2,0,Single,11,406.6455748,0,19757.27874\n116281,20,3,3,1,Married,5,406.6253826,0,19996.26913\n182924,42,24,2,1,Single,7,406.6019613,0,20058.09807\n189874,16,0,2,1,Married,10,406.556567,0,19969.82835\n191138,26,5,1,1,Single,12,406.5523388,0,20108.61694\n143874,33,15,1,1,Married,0,406.5515049,0,19821.57525\n148563,26,7,3,0,Married,10,406.5399135,0,19769.99568\n129163,67,50,1,1,Married,3,406.5367908,0,20016.83954\n101446,44,24,4,1,Single,6,406.5101689,0,19749.50845\n199465,61,40,3,0,Single,7,406.4730699,0,19723.6535\n144507,38,21,2,1,Single,6,406.4648363,0,19778.24182\n126954,42,25,3,0,Single,10,406.4183598,0,19803.91799\n106894,40,20,2,1,Married,4,406.2615853,0,19985.07927\n133894,48,30,2,1,Single,9,406.1601712,0,19818.00856\n126335,42,23,3,0,Married,4,406.1436827,0,19931.18414\n132372,64,45,2,1,Single,3,406.1312293,0,20073.56147\n129226,33,16,2,0,Single,13,406.125247,0,20095.26235\n140488,37,19,4,0,Single,7,406.0777897,0,19749.88949\n171514,22,3,1,1,Single,15,405.999425,0,19886.97125\n192686,17,0,3,1,Married,6,405.9392526,0,20088.96263\n179702,16,0,3,1,Single,15,405.773486,0,19905.6743\n106175,58,37,2,1,Married,2,405.7733647,0,19694.66824\n183669,49,30,1,0,Single,10,405.7686642,0,19985.43321\n143554,51,32,2,0,Single,10,405.7130849,0,20014.65425\n118548,47,27,3,1,Married,3,405.6995836,0,20019.97918\n193428,60,42,3,0,Single,2,405.6595645,0,19915.97823\n140129,22,4,1,1,Single,13,405.6484005,0,19755.42003\n153916,66,46,1,0,Married,1,405.6157569,0,19976.78785\n190856,19,1,2,1,Single,15,405.5768507,0,19740.84254\n107195,46,28,2,0,Single,8,405.5666572,0,19877.33286\n174792,53,34,2,1,Single,8,405.5257902,0,19834.28951\n192099,31,14,1,0,Single,8,405.4731829,0,19774.65915\n179457,28,10,2,1,Single,9,405.4427644,0,20027.13822\n142652,53,35,1,0,Single,15,405.4233286,0,20032.16643\n194543,18,0,3,0,Single,14,405.4088517,0,19789.44259\n190340,55,37,2,1,Single,14,405.3609024,0,19957.04512\n112585,51,32,2,1,Single,9,405.3250515,0,19832.25258\n155091,26,6,2,0,Married,8,405.298637,0,19961.93185\n175272,57,36,3,0,Single,12,405.2483994,0,19839.41997\n175302,49,31,3,1,Single,7,405.2084152,0,19672.42076\n127043,51,32,2,0,Married,4,405.1841261,0,19661.2063\n124603,34,14,1,1,Single,3,405.1765298,0,19944.82649\n177828,44,27,2,1,Single,6,405.1558956,0,19794.79478\n182107,49,31,2,0,Married,4,405.1173123,0,19741.86562\n195316,20,3,3,1,Married,5,405.0870183,0,19983.35092\n126073,68,47,4,0,Married,4,404.9320508,0,19919.60254\n172527,31,11,4,0,Married,4,404.8704685,0,19677.52343\n171097,52,31,1,0,Single,8,404.8567609,0,19728.83805\n180890,55,38,1,1,Single,10,404.8388241,0,19883.94121\n157577,27,7,2,0,Single,2,404.8344756,0,19806.72378\n172127,28,9,2,0,Single,13,404.7934855,0,19787.67428\n101205,31,10,3,1,Single,8,404.7788623,0,19761.94312\n145111,49,31,3,1,Single,9,404.7783083,0,19736.91542\n132374,40,23,4,1,Single,7,404.6852461,0,19983.26231\n132312,57,40,2,0,Married,6,404.6666799,0,19848.334\n116759,36,19,4,0,Single,15,404.640385,0,19997.01925\n155786,63,43,3,0,Single,10,404.6254161,0,19995.27081\n168682,27,10,4,0,Married,8,404.6099493,0,19799.49747\n129940,22,5,4,1,Single,13,404.6017735,0,19928.08868\n177910,35,18,1,1,Single,6,404.5501101,0,19824.50551\n122374,60,42,2,1,Single,11,404.5349121,0,19732.74561\n156851,33,15,3,0,Married,4,404.5052672,0,19659.26336\n129207,35,15,2,0,Married,2,404.4756817,0,19820.78409\n113255,41,23,1,0,Married,9,404.4662799,0,19959.314\n145073,40,21,4,0,Single,10,404.4660495,0,19855.30248\n133784,22,5,2,0,Married,15,404.4555197,0,19640.77599\n143671,21,4,3,0,Single,14,404.4477972,0,19719.38986\n110243,28,8,1,0,Married,7,404.4041105,0,19758.20553\n193849,40,22,2,0,Single,8,404.3905315,0,19936.52658\n137347,47,30,4,1,Single,6,404.3627944,0,19982.13972\n190392,16,0,1,0,Married,13,404.3094024,0,19674.47012\n199508,45,26,3,1,Married,0,404.2840185,0,19887.20093\n118609,38,21,1,1,Single,10,404.2819242,0,19679.09621\n118365,28,11,2,1,Married,0,404.2568287,0,19630.84144\n164810,17,0,4,1,Single,10,404.2290392,0,19656.45196\n152488,35,16,4,0,Married,15,404.2230478,0,19931.15239\n157468,22,3,3,1,Married,6,404.1804576,0,19738.02288\n157038,40,21,3,0,Married,4,404.1548976,0,19965.74488\n157353,44,27,3,0,Single,11,404.1123008,0,19849.61504\n154679,46,29,2,1,Single,10,404.0795519,0,19898.9776\n112917,63,42,3,0,Single,0,404.0128634,0,19600.64317\n176182,54,34,2,0,Single,10,403.9223957,0,19926.11979\n111946,29,8,2,1,Single,10,403.9064009,0,19743.32005\n148545,58,37,3,0,Married,9,403.8567074,0,19798.83537\n118332,61,41,3,0,Single,7,403.8071551,0,19664.35776\n136348,46,25,3,1,Single,7,403.7961705,0,19646.80853\n198932,31,13,2,0,Single,12,403.7603394,0,19700.01697\n140156,42,22,1,1,Single,7,403.70609,0,19925.3045\n155222,16,0,2,0,Single,15,403.6831755,0,19941.15878\n120604,38,20,2,1,Single,14,403.5861793,0,19873.30897\n103932,16,0,3,1,Single,9,403.5725088,0,19934.62544\n167839,50,32,4,0,Married,2,403.4675137,0,19938.37569\n132669,20,3,3,0,Married,12,403.4462956,0,19838.31478\n112487,62,43,3,0,Single,8,403.3975727,0,19721.87864\n163642,19,0,4,0,Single,13,403.3128501,0,19796.64251\n193318,29,9,1,1,Single,9,403.2553677,0,19716.76839\n111210,55,36,4,0,Single,7,403.2331793,0,19940.65897\n120482,51,33,4,0,Single,9,403.2248436,0,19706.24218\n191519,23,6,3,0,Married,13,403.2019585,0,19790.09793\n154239,21,3,4,1,Married,9,403.1786444,0,19788.93222\n185217,43,25,2,1,Single,8,403.1651617,0,19576.25809\n183977,66,49,3,1,Single,3,403.1558398,0,19763.79199\n179740,50,32,3,0,Married,8,403.1170891,0,19877.85446\n153939,23,4,2,0,Married,11,403.0817042,0,19590.08521\n138018,61,41,3,0,Single,5,403.0233863,0,19553.16932\n145898,34,16,2,0,Married,3,402.9783385,0,19578.91693\n140733,35,17,1,0,Single,10,402.9137936,0,19599.68968\n172529,21,4,3,0,Married,13,402.8665523,0,19590.32762\n111512,66,46,3,1,Single,6,402.7977956,0,19879.88978\n187197,40,21,2,1,Single,3,402.7403861,0,19857.01931\n161054,24,6,2,1,Single,14,402.7228306,0,19546.14153\n118894,21,3,3,1,Married,12,402.7099246,0,19683.49623\n183394,27,9,2,1,Single,14,402.6720946,0,19835.60473\n178657,46,29,3,0,Single,10,402.6710901,0,19903.55451\n141726,31,12,2,0,Married,11,402.6617444,0,19815.08722\n175386,16,0,3,1,Single,12,402.6410592,0,19639.05296\n125014,61,43,2,0,Single,1,402.5741106,0,19543.70553\n117425,32,15,1,0,Married,9,402.5737594,0,19818.68797\n125336,60,42,2,1,Single,7,402.556931,0,19863.84655\n129482,65,45,2,0,Single,0,402.5284304,0,19700.42152\n126976,34,15,1,1,Single,6,402.5129906,0,19546.64953\n119337,28,7,3,0,Single,15,402.4556459,0,19691.7823\n114177,54,36,3,1,Single,7,402.4294053,0,19747.47027\n198531,69,51,2,0,Married,3,402.4020588,0,19763.10294\n178623,47,30,3,0,Single,10,402.3655839,0,19860.2792\n104587,54,35,3,1,Single,9,402.3263418,0,19801.31709\n114349,32,15,1,1,Single,10,402.3212675,0,19718.06338\n171379,25,6,3,1,Married,10,402.2985092,0,19704.92546\n143665,47,27,1,0,Single,6,402.2915712,0,19893.57856\n164320,62,43,2,1,Married,2,402.2262839,0,19518.3142\n144880,31,10,1,1,Single,6,402.1973024,0,19880.86512\n127883,53,32,3,1,Single,10,402.1950357,0,19862.75179\n169959,27,8,3,0,Married,4,402.1485789,0,19753.42895\n184037,42,24,4,0,Married,6,402.0720708,0,19585.60354\n167557,29,9,3,0,Single,9,402.0454408,0,19593.27204\n148089,18,1,3,1,Married,10,402.0429849,0,19821.14925\n162335,28,9,2,1,Single,9,402.0234652,0,19728.17326\n181463,57,40,1,1,Single,15,402.0108039,0,19763.5402\n152593,42,25,2,0,Married,1,402.0085932,0,19586.42966\n142065,44,23,3,0,Single,8,402.0038533,0,19597.19267\n171399,49,28,3,0,Single,10,402.0034132,0,19710.17066\n133332,17,0,2,0,Single,15,401.972946,0,19570.6473\n129981,70,49,1,0,Married,1,401.9377187,0,19772.88594\n153086,54,36,3,0,Married,8,401.8401494,0,19874.00747\n138708,35,18,2,0,Married,9,401.7803639,0,19799.0182\n189722,22,4,3,1,Single,15,401.7802414,0,19845.01207\n180076,64,45,3,0,Single,10,401.7687185,0,19759.43593\n134916,38,21,4,1,Single,14,401.7646691,0,19834.23346\n159494,27,9,4,0,Married,7,401.657943,0,19855.89715\n158279,34,16,2,1,Single,6,401.6438973,0,19624.19487\n134499,58,40,4,1,Single,9,401.6373251,0,19771.86626\n119068,35,16,1,1,Single,10,401.6252338,0,19703.26169\n123328,65,46,2,1,Single,5,401.5321373,0,19776.60687\n193335,28,8,3,1,Single,10,401.5186952,0,19854.93476\n180665,57,36,2,1,Married,4,401.5015592,0,19497.07796\n136623,45,25,2,0,Single,10,401.4969817,0,19543.84909\n118038,39,21,3,0,Single,14,401.4792942,0,19853.96471\n100603,32,15,4,1,Single,8,401.4259936,0,19717.29968\n147345,46,26,3,1,Single,6,401.3908712,0,19750.54356\n110631,59,39,2,0,Married,6,401.3888133,0,19810.44067\n147844,53,35,3,0,Married,8,401.3823926,0,19800.11963\n124254,30,12,2,1,Married,4,401.3791781,0,19746.95891\n101967,53,33,2,0,Married,8,401.3555902,0,19726.77951\n144808,54,37,1,0,Single,13,401.34274,0,19852.137\n177825,30,9,1,0,Single,9,401.3427019,0,19523.1351\n163654,39,18,3,1,Married,3,401.3410448,0,19706.05224\n116564,52,31,4,1,Married,4,401.321637,0,19498.08185\n152171,50,30,2,1,Married,0,401.3072645,0,19737.36323\n133837,54,36,3,0,Married,9,401.2842426,0,19515.21213\n149757,41,21,3,0,Married,3,401.2398028,0,19563.99014\n126741,56,37,4,0,Single,8,401.2190964,0,19777.95482\n181968,60,43,1,0,Single,3,401.1388059,0,19674.9403\n184859,24,5,3,0,Single,15,401.1068785,0,19604.34393\n139108,59,42,4,1,Single,9,401.1018133,0,19778.09067\n150595,47,29,4,0,Married,7,401.0545251,0,19693.72626\n110879,35,18,3,0,Married,2,401.0148797,0,19667.74399\n141793,16,0,2,0,Single,13,400.9965388,0,19538.82694\n164909,41,24,3,0,Married,9,400.9269963,0,19772.34982\n118669,45,26,2,0,Single,3,400.765589,0,19504.27945\n166822,58,39,4,0,Married,2,400.762108,0,19746.1054\n154623,68,48,3,1,Married,4,400.7613756,0,19611.06878\n115299,23,4,3,0,Married,11,400.7494328,0,19517.47164\n165303,28,11,3,0,Single,7,400.6639675,0,19658.19838\n155602,23,6,3,1,Single,11,400.6522371,0,19488.61186\n117438,63,45,3,1,Single,1,400.6438918,0,19814.19459\n159465,19,1,3,1,Married,12,400.6414306,0,19454.07153\n143138,54,36,2,1,Married,1,400.6379427,0,19826.89714\n170850,16,0,4,0,Single,15,400.6040559,0,19816.2028\n146458,39,20,3,0,Single,6,400.5657359,0,19500.2868\n113784,54,36,1,1,Single,10,400.5331803,0,19648.65902\n116037,22,4,2,1,Married,12,400.5274009,0,19529.37005\n192062,28,9,2,0,Married,7,400.5056903,0,19633.28452\n181227,65,44,4,1,Single,4,400.4735077,0,19467.67539\n103906,52,34,3,0,Single,7,400.4432248,0,19567.16124\n114822,43,25,2,0,Married,2,400.4156792,0,19521.78396\n187161,40,22,3,0,Single,7,400.3701723,0,19608.50862\n107060,22,5,3,1,Single,12,400.361137,0,19446.05685\n198195,49,28,2,1,Single,10,400.3563336,0,19604.81668\n129661,25,8,2,0,Married,12,400.3345821,0,19684.72911\n141991,37,18,1,0,Married,6,400.3321226,0,19610.60613\n165360,27,8,2,0,Single,9,400.293361,0,19664.66805\n164601,60,40,3,1,Single,10,400.2435035,0,19782.17518\n151611,29,9,3,0,Single,15,400.1784244,0,19740.92122\n172850,20,2,3,1,Married,3,400.1357313,0,19747.78657\n118804,20,2,1,0,Married,14,400.110449,0,19540.52245\n126368,70,50,2,0,Single,9,400.0748994,0,19535.74497\n131459,61,42,2,0,Single,6,400.0163768,0,19406.81884\n118462,59,42,3,0,Married,10,400.0158111,0,19440.79056\n150046,44,26,1,0,Single,6,399.9851595,0,19432.25798\n144475,46,29,2,0,Single,10,399.9512129,0,19643.56065\n190152,62,45,3,1,Single,5,399.9473789,0,19585.36895\n140791,19,1,2,1,Single,13,399.9370831,0,19695.85416\n141059,26,8,2,1,Single,9,399.7696099,0,19454.4805\n107067,59,38,2,1,Single,6,399.7542272,0,19722.71136\n105783,31,14,2,0,Single,5,399.6932001,0,19538.66001\n184112,24,7,3,1,Single,13,399.6577432,0,19681.88716\n169151,35,17,4,1,Single,9,399.644485,0,19477.22425\n159653,61,43,4,0,Single,9,399.6381174,0,19386.90587\n172696,16,0,3,1,Married,10,399.6369887,0,19625.84944\n185121,36,15,2,0,Single,8,399.6292212,0,19621.46106\n183007,39,20,2,1,Single,8,399.5714797,0,19466.57399\n126982,21,3,2,1,Single,6,399.5469783,0,19685.34892\n131652,60,40,1,1,Single,1,399.5053889,0,19426.26945\n145450,48,29,4,0,Single,9,399.4958264,0,19642.79132\n101274,31,12,4,1,Single,10,399.3725433,0,19486.62717\n116605,52,32,2,1,Single,10,399.2326472,0,19410.63236\n161949,22,3,1,1,Married,0,399.165851,0,19536.29255\n133076,46,26,3,1,Single,12,399.1592176,0,19542.96088\n125095,49,29,4,1,Married,8,399.1267942,0,19736.33971\n196806,47,29,2,1,Married,3,399.0874425,0,19637.37213\n131861,28,9,3,0,Single,7,399.0786122,0,19499.93061\n120918,65,44,3,1,Single,9,399.0043001,0,19485.21501\n163870,25,6,1,1,Single,15,398.9445904,0,19429.22952\n120286,58,37,3,0,Married,7,398.9208892,0,19508.04446\n152324,68,50,1,0,Single,7,398.8494738,0,19399.47369\n178803,47,28,2,0,Married,15,398.8196598,0,19447.98299\n133098,25,6,3,0,Married,14,398.8105861,0,19551.52931\n139148,59,38,2,1,Single,14,398.7762381,0,19619.81191\n100443,60,40,4,1,Single,7,398.7462537,0,19491.31269\n159042,70,53,2,0,Single,6,398.7210364,0,19614.05182\n138558,43,25,1,0,Married,7,398.6963317,0,19358.81659\n107182,25,7,4,1,Married,14,398.6486033,0,19614.43017\n151485,50,32,3,0,Married,10,398.6416823,0,19626.08412\n163073,33,13,2,1,Single,9,398.6037432,0,19433.18716\n173004,29,12,2,0,Married,7,398.5699075,0,19581.49538\n113852,50,33,2,1,Single,4,398.5613849,0,19455.06925\n141523,70,49,2,0,Single,2,398.4667681,0,19598.33841\n169830,21,3,1,0,Married,14,398.4218218,0,19321.09109\n188811,45,24,2,0,Married,13,398.4128657,0,19716.64328\n192164,27,8,3,0,Married,4,398.4086021,0,19492.43011\n173797,21,4,2,1,Single,7,398.3864132,0,19649.32066\n173610,21,4,4,1,Married,12,398.3638732,0,19343.19366\n172940,41,23,2,0,Married,3,398.3501109,0,19328.50555\n151273,24,6,3,1,Single,14,398.3466837,0,19516.33419\n196934,36,19,4,0,Married,6,398.3014514,0,19436.07257\n192236,40,20,1,1,Single,10,398.2783895,0,19566.91948\n162638,41,24,1,1,Single,9,398.2594462,0,19457.97231\n178824,48,31,2,1,Married,1,398.2591455,0,19552.95728\n154566,34,14,2,1,Single,8,398.2315021,0,19658.57511\n145615,45,27,2,1,Single,12,398.1651222,0,19358.25611\n177789,22,5,3,0,Single,11,398.161129,0,19612.05645\n197954,22,3,3,1,Single,15,398.1277997,0,19420.38999\n134065,64,47,2,0,Single,4,398.1255067,0,19572.27534\n198396,21,4,3,1,Single,14,398.1246864,0,19485.23432\n176454,69,52,4,0,Single,12,398.1235611,0,19497.17806\n139119,37,18,2,0,Single,12,398.1223287,0,19539.11644\n181285,40,19,2,0,Married,2,398.0874134,0,19593.37067\n187312,42,25,1,0,Married,9,398.0518761,0,19687.59381\n196862,18,1,2,1,Married,2,398.0487131,0,19594.43566\n174583,27,6,4,1,Single,12,398.024417,0,19670.22085\n116173,16,0,2,1,Married,15,397.9675903,0,19500.37952\n102878,25,6,4,0,Married,10,397.9628824,0,19390.14412\n108063,19,0,2,0,Married,11,397.9584215,0,19463.92108\n128144,56,36,3,0,Married,9,397.9560458,0,19420.80229\n139361,33,12,4,0,Single,12,397.9327008,0,19371.63504\n125320,38,19,3,0,Single,6,397.9303009,0,19485.51505\n182862,33,16,1,0,Single,14,397.8918445,0,19668.59223\n160265,20,2,3,0,Single,12,397.8586159,0,19635.9308\n109477,19,0,1,1,Single,14,397.852454,0,19631.6227\n106882,52,32,3,1,Single,9,397.850668,0,19593.5334\n111556,31,13,3,1,Single,8,397.8501902,0,19536.50951\n181868,39,18,2,1,Single,10,397.8159816,0,19378.79908\n162454,54,33,3,0,Married,6,397.8113762,0,19598.56881\n100946,53,33,4,1,Single,10,397.806322,0,19312.3161\n178668,37,17,2,1,Single,12,397.7075707,0,19301.37854\n157273,31,11,3,1,Married,4,397.6946214,0,19527.73107\n198807,45,27,1,1,Single,6,397.6475864,0,19473.37932\n105239,18,0,3,0,Single,15,397.6290788,0,19469.45394\n115966,39,21,3,0,Single,8,397.6257605,0,19361.28803\n154704,54,33,2,1,Married,5,397.561892,0,19661.0946\n121996,51,32,1,0,Married,9,397.5167043,0,19436.83522\n141039,20,2,2,1,Married,7,397.4623988,0,19603.11994\n174920,27,9,1,0,Married,3,397.4175426,0,19515.87713\n154344,35,16,2,0,Single,12,397.4040398,0,19545.20199\n135507,51,32,1,1,Single,9,397.3724159,0,19315.6208\n126791,44,23,3,0,Married,8,397.3717136,0,19540.58568\n155036,57,39,3,1,Married,3,397.3381248,0,19536.90624\n184171,32,11,4,1,Married,5,397.3229693,0,19598.14847\n177612,25,6,3,1,Single,13,397.3137631,0,19337.68816\n137009,49,30,4,1,Single,6,397.2310449,0,19401.55225\n178867,67,48,1,0,Single,0,397.2069605,0,19515.34803\n111797,40,20,2,1,Single,6,397.1100067,0,19568.50034\n182616,49,32,1,0,Single,7,397.0559669,0,19371.79835\n149670,16,0,2,0,Married,8,396.9247136,0,19603.23568\n143406,30,11,1,0,Single,12,396.9147809,0,19489.73905\n117078,30,11,2,0,Single,9,396.8697556,0,19245.48778\n166756,30,10,1,0,Married,0,396.8456932,0,19269.28466\n166234,36,17,4,0,Single,13,396.7605129,0,19438.02565\n117760,53,34,4,1,Single,9,396.7072453,0,19455.36227\n185928,50,30,1,1,Single,0,396.6472629,0,19447.36315\n107047,59,39,3,1,Single,15,396.6264869,0,19527.32435\n143265,36,19,3,0,Married,10,396.6059963,0,19622.29982\n145331,50,32,4,1,Single,2,396.5585999,0,19365.93\n156172,49,30,1,0,Single,9,396.5517713,0,19389.58857\n120413,49,30,3,1,Married,1,396.5168252,0,19228.84126\n164309,18,0,2,1,Married,10,396.5146413,0,19527.73207\n194848,52,33,4,1,Single,10,396.4863563,0,19372.31782\n151565,46,26,2,1,Single,13,396.4820768,0,19239.10384\n151755,52,35,1,1,Single,8,396.4647332,0,19516.23666\n126428,30,9,1,1,Single,10,396.4566513,0,19247.83257\n101440,37,18,3,0,Single,10,396.4468939,0,19236.3447\n190540,30,13,2,1,Single,7,396.435747,0,19467.78735\n172261,35,17,2,1,Single,10,396.4333495,0,19572.66748\n198404,46,28,3,0,Married,7,396.4231609,0,19584.15805\n180229,59,38,2,0,Married,8,396.3900374,0,19257.50187\n194718,54,36,3,0,Single,9,396.3582085,0,19577.91043\n108538,48,28,4,0,Single,14,396.29863,0,19335.9315\n188176,68,50,2,1,Single,14,396.2453688,0,19574.26844\n197169,46,28,2,1,Single,9,396.236596,0,19457.8298\n126654,52,31,2,1,Single,12,396.1720549,0,19484.60275\n143943,60,42,4,1,Single,15,396.1565253,0,19442.82627\n127229,38,20,3,1,Single,9,396.1533774,0,19491.66887\n101700,25,6,3,0,Married,14,396.1469344,0,19282.34672\n137410,25,7,1,1,Married,15,396.1408555,0,19430.04278\n134377,20,3,4,1,Single,15,396.1260471,0,19252.30236\n174455,25,7,3,1,Single,15,396.0988181,0,19234.94091\n192891,64,45,3,0,Single,9,396.0848547,0,19314.24274\n160678,20,1,2,1,Married,12,396.0667996,0,19338.33998\n132860,38,17,3,0,Single,11,396.0646839,0,19387.2342\n177813,57,38,3,0,Married,9,396.0531492,0,19514.65746\n105577,54,33,2,0,Married,7,396.0368097,0,19346.84049\n152113,62,41,2,1,Single,6,396.007265,0,19223.36325\n125820,54,34,1,0,Married,9,395.9018181,0,19449.09091\n117038,59,38,4,1,Single,7,395.7263244,0,19320.31622\n196550,45,27,3,1,Single,8,395.6871017,0,19315.35509\n186937,68,51,1,1,Single,1,395.6797105,0,19319.98553\n153345,49,31,3,0,Married,6,395.6705624,0,19473.52812\n177195,25,7,3,1,Single,15,395.659444,0,19243.9722\n120281,26,5,1,1,Single,6,395.6273655,0,19571.36828\n167213,34,13,1,1,Married,2,395.6067606,0,19222.33803\n132983,38,19,2,1,Single,8,395.5648042,0,19412.24021\n164360,19,0,1,1,Married,12,395.5430993,0,19330.15497\n107935,34,16,1,1,Single,6,395.503013,0,19289.15065\n178849,26,9,4,0,Single,14,395.4993553,0,19443.96777\n132345,40,23,2,1,Married,1,395.4783752,0,19497.91876\n119004,19,2,3,1,Married,13,395.4681364,0,19205.40682\n188315,43,23,1,0,Single,11,395.4510456,0,19438.55228\n195150,46,28,1,0,Single,13,395.3882725,0,19206.41363\n114104,34,17,2,0,Single,15,395.3699722,0,19375.49861\n179303,16,0,2,0,Married,15,395.3114021,0,19476.57011\n121853,52,31,3,1,Married,4,395.2845173,0,19311.22587\n199148,65,48,2,1,Single,4,395.259523,0,19469.97615\n149191,21,2,4,1,Married,6,395.228496,0,19300.4248\n184066,39,18,2,1,Single,8,395.2145941,0,19203.72971\n141808,56,37,2,0,Single,11,395.1916986,0,19551.58493\n107555,20,1,1,0,Married,15,395.1330661,0,19474.65331\n186389,40,23,3,0,Married,8,395.1167029,0,19442.83515\n170895,63,45,3,1,Single,5,395.1008282,0,19280.04141\n117263,45,27,1,0,Married,4,395.081989,0,19505.09945\n123310,61,44,2,0,Single,12,395.0740598,0,19198.70299\n167952,27,9,3,0,Married,9,395.030539,0,19545.52695\n116127,39,20,2,0,Married,8,395.0082274,0,19201.41137\n110208,35,16,1,1,Single,7,394.9982853,0,19487.91427\n155508,45,26,2,1,Single,9,394.9737061,0,19316.68531\n126952,28,10,4,0,Single,11,394.9000223,0,19235.00112\n146311,39,20,2,0,Married,9,394.8217047,0,19366.08524\n135975,16,0,3,1,Married,10,394.7197409,0,19412.98705\n154580,57,40,3,0,Married,8,394.7196274,0,19221.98137\n129633,44,27,1,0,Married,7,394.6713476,0,19295.56738\n165140,54,37,3,1,Single,8,394.6427341,0,19440.13671\n108621,68,47,2,1,Married,1,394.5895239,0,19155.4762\n161490,54,35,3,1,Single,10,394.5028092,0,19365.14046\n145071,18,0,3,0,Married,10,394.4975892,0,19388.87946\n123037,34,14,1,0,Single,6,394.4787051,0,19417.93526\n137538,25,6,3,1,Married,11,394.4263946,0,19468.31973\n151344,59,40,2,0,Married,7,394.3574183,0,19335.87092\n158667,42,24,4,1,Single,8,394.3551484,0,19362.75742\n134629,20,3,2,0,Single,11,394.3168124,0,19380.84062\n117077,16,0,1,0,Married,14,394.2908041,0,19357.54021\n116276,25,8,1,0,Single,11,394.2728194,0,19502.64097\n199063,16,0,2,1,Married,11,394.2596954,0,19412.98477\n119064,55,37,1,0,Single,6,394.234324,0,19474.7162\n144427,55,36,2,1,Single,6,394.2260359,0,19227.3018\n177471,68,48,4,0,Married,4,394.213779,0,19382.68895\n144219,52,31,1,0,Married,0,394.2066246,0,19307.33123\n108303,19,0,2,0,Married,14,394.2036344,0,19407.18172\n116479,54,37,3,0,Married,6,394.1426351,0,19281.13176\n179256,47,26,3,1,Single,6,394.1176669,0,19334.88335\n140500,58,38,2,0,Single,8,394.0867239,0,19113.3362\n166197,35,17,1,1,Single,7,394.0789555,0,19265.94778\n125399,28,11,3,0,Married,9,394.0374154,0,19185.87077\n163990,58,38,3,0,Single,9,394.0148309,0,19361.74155\n105772,44,25,2,1,Single,10,393.9951758,0,19438.75879\n183600,16,0,4,0,Married,8,393.9599731,0,19278.99866\n115748,42,25,4,1,Married,1,393.9097482,0,19162.48741\n176367,55,36,2,1,Single,6,393.897053,0,19385.85265\n162849,58,40,2,1,Single,13,393.8860297,0,19486.30149\n122329,37,19,4,0,Married,10,393.883468,0,19146.1734\n188416,23,5,1,0,Married,14,393.8349042,0,19222.74521\n157978,18,0,4,1,Single,13,393.821824,0,19218.0912\n168530,40,19,2,0,Married,7,393.7580921,0,19225.90461\n123408,38,18,4,1,Single,10,393.7192587,0,19480.96294\n129465,40,20,2,1,Single,10,393.7172618,0,19239.86309\n189972,35,15,2,1,Single,9,393.6657261,0,19248.28631\n117846,59,38,3,0,Married,10,393.5972642,0,19208.86321\n113996,19,2,4,1,Single,12,393.5823167,0,19339.11584\n109958,26,6,2,0,Married,6,393.4216658,0,19272.08329\n108106,51,31,4,0,Married,7,393.3857401,0,19311.28701\n118116,25,7,4,1,Married,8,393.3757944,0,19254.78972\n114351,49,31,3,1,Single,8,393.3566814,0,19304.83407\n160251,46,26,1,1,Single,9,393.3384205,0,19171.92103\n173344,52,31,2,0,Single,10,393.3277765,0,19221.38883\n160525,46,27,1,0,Married,10,393.3264183,0,19172.32092\n180377,65,47,1,1,Single,7,393.3125586,0,19276.62793\n127481,36,18,1,0,Married,9,393.2955575,0,19221.77788\n145290,68,47,3,0,Single,10,393.2196126,0,19128.98063\n157822,17,0,3,0,Single,15,393.2158172,0,19400.79086\n108971,25,7,4,0,Married,11,393.198843,0,19076.94215\n159350,43,26,2,1,Married,6,393.1848298,0,19411.24149\n180849,40,19,2,1,Single,7,393.1373351,0,19062.86676\n177987,43,26,3,0,Married,10,393.0837453,0,19301.18727\n163950,31,14,4,0,Married,8,393.0277436,0,19228.38718\n170693,59,41,2,0,Married,5,393.0023037,0,19419.11519\n110486,45,24,2,0,Single,14,392.9888125,0,19131.44063\n164403,42,22,3,0,Married,8,392.980047,0,19368.00235\n177882,60,40,3,1,Married,2,392.9651895,0,19295.25948\n110437,51,31,2,1,Single,7,392.9302356,0,19274.51178\n178921,28,10,1,1,Single,10,392.8457469,0,19320.28735\n145630,41,21,4,0,Married,10,392.7729478,0,19411.64739\n160156,37,16,3,0,Married,5,392.7113111,0,19311.56556\n171500,18,0,2,0,Married,7,392.6845652,0,19047.22826\n139359,54,35,3,1,Married,2,392.6819742,0,19419.09871\n136282,62,44,4,0,Single,10,392.6550953,0,19100.75477\n163256,16,0,3,0,Married,13,392.6517604,0,19068.58802\n198620,29,12,2,0,Married,15,392.6468725,0,19302.34363\n200408,51,30,2,1,Single,6,392.6467754,0,19186.33877\n176059,42,25,3,1,Single,7,392.6355064,0,19243.77532\n198756,29,11,4,0,Married,7,392.5738636,0,19418.69318\n121352,38,17,3,0,Married,6,392.5108202,0,19302.54101\n153954,64,47,3,0,Married,10,392.4944029,0,19298.72015\n138888,50,29,3,0,Married,5,392.4860907,0,19277.30454\n120613,41,20,3,1,Single,6,392.482767,0,19105.13835\n169184,69,49,3,1,Single,1,392.4644353,0,19208.22177\n167980,33,14,2,1,Single,6,392.3951286,0,19096.75643\n103266,40,21,3,0,Single,11,392.3423001,0,19414.11501\n119773,57,37,4,0,Single,9,392.3417354,0,19286.08677\n117494,46,26,3,0,Married,15,392.3400775,0,19045.00388\n107127,22,5,3,1,Married,6,392.3148547,0,19395.74274\n186085,16,0,3,0,Single,13,392.2991853,0,19119.95927\n144222,19,2,2,0,Married,15,392.2846806,0,19304.23403\n110036,70,50,1,1,Single,6,392.2734468,0,19212.67234\n115817,18,0,3,1,Married,14,392.2099559,0,19047.4978\n184116,27,6,1,1,Single,10,392.2091444,0,19101.45722\n162310,49,29,4,1,Single,6,392.1790808,0,19248.95404\n126506,58,41,1,1,Single,7,392.0911466,0,19182.55733\n111384,49,28,3,1,Single,14,392.0745558,0,19213.72779\n138970,22,4,3,1,Married,7,392.0740512,0,19125.70256\n123723,30,9,3,0,Married,0,392.0647141,0,19310.23571\n176336,54,35,3,1,Married,4,391.9832716,0,19356.16358\n127143,69,50,1,1,Single,9,391.9819016,0,19187.09508\n123835,58,41,4,0,Married,8,391.9557976,0,19328.78988\n125802,70,53,4,0,Single,6,391.9501586,0,19125.50793\n126776,40,22,2,0,Married,7,391.9146906,0,19227.73453\n194634,66,46,2,1,Single,4,391.9022651,0,19371.11326\n188328,40,19,2,0,Married,4,391.901918,0,19137.0959\n137127,33,12,4,0,Married,10,391.8985761,0,19173.92881\n139078,42,21,3,1,Single,9,391.8918127,0,19241.59064\n158186,70,52,3,0,Single,8,391.838551,0,19254.92755\n167025,55,37,2,1,Single,6,391.7506282,0,19112.53141\n122140,49,31,3,0,Married,6,391.7092633,0,19140.46317\n162230,38,21,2,1,Married,10,391.6733379,0,19091.6669\n154305,51,34,3,0,Married,6,391.6723014,0,19104.61507\n143015,25,7,3,0,Married,14,391.6632275,0,19350.16138\n169897,39,19,3,1,Married,10,391.6516394,0,19119.58197\n166138,31,11,3,1,Married,3,391.5921212,0,19364.60606\n116966,46,28,1,0,Married,3,391.5392555,0,19284.96278\n112893,36,17,3,1,Single,10,391.514442,0,19101.7221\n141986,36,18,1,1,Single,9,391.5134925,0,19300.67463\n122575,59,41,3,1,Single,8,391.5025839,0,19133.1292\n158767,35,16,2,0,Single,15,391.4638186,0,19003.19093\n129705,37,19,1,0,Married,5,391.4331666,0,19326.65833\n190147,28,11,4,1,Single,9,391.3933857,0,19128.66929\n118147,52,35,2,1,Married,8,391.3500926,0,19005.50463\n163510,33,16,3,0,Single,6,391.3119538,0,19283.59769\n186554,45,28,4,1,Single,8,391.2879304,0,19001.39652\n191931,43,22,3,1,Married,2,391.2692388,0,18991.46194\n113056,22,5,1,1,Married,7,391.2488532,0,19145.44266\n193913,45,26,1,0,Married,9,391.2383998,0,19137.91999\n109864,52,33,1,1,Single,6,391.2338229,0,19304.69115\n143073,33,13,2,0,Single,13,391.2171737,0,19350.85869\n112484,28,7,4,0,Single,9,391.1950655,0,19160.75328\n192546,59,41,4,0,Married,8,391.0937662,0,19081.68831\n127853,42,23,2,0,Single,9,390.9589293,0,18981.94647\n134751,56,37,4,1,Married,5,390.9509227,0,19325.54614\n141578,43,23,2,1,Married,2,390.9438171,0,19132.19086\n186257,27,10,3,1,Single,9,390.9353302,0,19112.76651\n147346,55,36,1,0,Married,6,390.9282046,0,19287.41023\n101991,30,13,3,1,Single,13,390.9071442,0,19238.35721\n103917,32,14,2,0,Married,6,390.9048704,0,19120.24352\n140645,59,42,3,0,Married,12,390.902063,0,18983.10315\n101383,48,27,2,1,Single,11,390.9019909,0,19309.09955\n113127,38,18,3,0,Single,15,390.9018922,0,19223.09461\n121875,70,49,2,1,Single,7,390.8637873,0,19321.18937\n192731,49,29,3,1,Single,6,390.8618207,0,19136.09104\n134585,28,11,2,0,Married,7,390.8413478,0,19341.06739\n162392,23,6,4,1,Single,13,390.7991057,0,19102.95529\n181554,21,4,2,0,Single,11,390.7943819,0,18981.7191\n110533,44,26,4,0,Married,6,390.7695842,0,18973.47921\n140470,61,41,3,0,Single,3,390.7584802,0,19330.92401\n197751,31,14,4,0,Single,12,390.7308708,0,19145.54354\n190768,22,5,2,1,Married,10,390.7031895,0,19278.15948\n163501,19,1,1,1,Married,9,390.682358,0,19196.1179\n191631,64,45,3,0,Single,9,390.6774964,0,18979.87482\n158532,53,33,1,0,Single,10,390.6032919,0,18949.1646\n139754,17,0,2,0,Married,12,390.5690694,0,19031.45347\n164609,40,21,1,1,Single,7,390.5619161,0,19166.09581\n141306,16,0,1,1,Single,11,390.521486,0,19010.0743\n134203,22,5,2,0,Married,6,390.4995846,0,19167.97923\n155067,39,21,3,0,Married,2,390.4654393,0,19175.27197\n144656,19,2,2,0,Married,14,390.4570927,0,18997.85464\n183797,64,46,1,0,Married,4,390.4062578,0,19003.31289\n103187,53,34,1,1,Single,6,390.3979113,0,19113.89557\n111323,61,43,2,1,Single,3,390.3536993,0,19307.68497\n146771,51,30,2,0,Married,9,390.3393024,0,19279.96512\n179399,38,19,4,0,Single,10,390.3124558,0,19267.62279\n192745,62,44,2,0,Married,1,390.3009479,0,19151.0474\n167930,40,23,2,0,Married,10,390.2854666,0,19193.27333\n115929,42,25,3,0,Married,2,390.2772461,0,18997.86231\n197702,50,33,4,0,Single,14,390.2230925,0,18978.15463\n163824,45,25,2,0,Married,6,390.2215123,0,19038.07562\n171093,44,26,2,0,Married,1,390.1296231,0,18956.48116\n175149,70,50,2,1,Single,6,390.0849495,0,19020.24748\n158330,20,2,3,1,Married,0,390.052406,0,18938.6203\n160960,46,25,1,0,Married,2,390.0515833,0,19028.57917\n111926,65,44,3,0,Single,9,390.0503247,0,19137.51624\n149176,69,48,3,0,Single,3,390.0472123,0,18967.36062\n126585,16,0,4,1,Married,15,390.0462135,0,19247.31068\n106914,32,11,2,1,Married,5,390.0040602,0,19159.20301\n137624,24,7,2,1,Married,12,389.967096,0,19109.3548\n143149,52,32,2,1,Single,9,389.9110341,0,18955.55171\n157193,20,1,2,1,Married,11,389.8397239,0,19257.9862\n135757,50,31,2,1,Single,8,389.7354599,0,19089.773\n187994,18,0,1,0,Single,12,389.7294061,0,19010.47031\n160237,49,30,1,1,Single,7,389.6227552,0,19208.13776\n181789,44,26,3,0,Married,9,389.5962654,0,19085.81327\n153082,33,14,2,1,Single,8,389.5417014,0,18920.08507\n144850,48,27,2,0,Married,10,389.5266353,0,18999.33177\n127590,61,43,2,1,Single,13,389.4722314,0,18923.61157\n117862,18,0,3,1,Married,14,389.422059,0,18998.10295\n114715,50,31,4,0,Single,2,389.4203675,0,19035.01838\n104732,22,3,3,0,Married,14,389.3521158,0,19076.60579\n177720,43,22,1,0,Married,14,389.3255588,0,18873.27794\n114263,55,35,1,0,Single,8,389.3103123,0,18875.51562\n189493,26,6,3,1,Single,8,389.3048929,0,18892.24465\n187013,59,40,3,0,Single,12,389.2357428,0,19248.78714\n181558,65,48,3,0,Single,10,389.0980171,0,19108.90086\n166839,21,4,4,0,Married,11,389.0623132,0,18929.11566\n120933,53,34,2,1,Single,8,389.060504,0,19032.0252\n197635,47,27,3,1,Single,9,389.0604522,0,18910.02261\n141893,16,0,4,1,Married,11,389.0382552,0,18899.91276\n185075,25,7,4,1,Single,13,389.0232199,0,18969.161\n189429,20,1,1,1,Married,15,388.9897943,0,19232.48972\n144160,23,5,2,0,Married,7,388.9859276,0,18970.29638\n100678,51,30,3,0,Married,7,388.9625929,0,19223.12965\n141130,19,1,4,1,Single,10,388.9394613,0,19128.97307\n145159,59,41,4,0,Married,8,388.9232682,0,18972.16341\n101930,26,8,3,0,Single,14,388.9023917,0,18979.11959\n119887,48,30,1,0,Single,8,388.8524692,0,18970.62346\n171125,38,17,3,1,Single,9,388.8350024,0,19238.75012\n112569,24,7,4,1,Married,12,388.8024772,0,19010.12386\n120476,48,31,3,1,Single,3,388.7980122,0,18884.90061\n128174,53,36,4,1,Single,10,388.7396022,0,18852.98011\n145329,20,3,4,0,Married,7,388.6624743,0,19103.12372\n111279,33,16,4,1,Married,0,388.6614615,0,18866.07308\n105058,22,3,2,1,Married,3,388.6563906,0,19034.81953\n108996,61,40,1,0,Single,5,388.6544422,0,18991.72211\n105817,43,26,3,0,Married,10,388.6325127,0,18966.62564\n150460,62,44,1,0,Married,1,388.631076,0,19072.5538\n143916,56,35,3,1,Single,15,388.5680362,0,18844.40181\n124866,27,6,4,0,Married,10,388.4883752,0,19140.41876\n184129,36,15,3,1,Married,2,388.4354977,0,18979.77489\n166223,23,4,2,0,Married,11,388.4135299,0,18921.6765\n131310,58,39,1,1,Married,5,388.37178,0,19076.589\n127337,21,4,2,1,Married,14,388.349014,0,19050.4507\n129528,22,4,4,1,Married,7,388.3373806,0,19148.86903\n151981,32,12,2,1,Single,8,388.2892987,0,19161.46494\n155509,56,36,3,0,Married,8,388.2396037,0,18865.98019\n161127,41,22,3,0,Married,6,388.1827446,0,19201.13723\n162174,46,28,1,1,Single,5,388.163314,0,18976.1657\n126252,68,51,2,1,Single,4,388.1607412,0,19050.03706\n132668,46,26,1,0,Single,15,388.1507245,0,19158.53623\n178315,24,5,2,0,Single,13,388.0295722,0,18976.47861\n144692,55,36,3,0,Married,7,388.0066414,0,19007.33207\n190983,24,6,3,0,Married,12,387.9977776,0,18837.88888\n160767,50,32,2,0,Married,6,387.9552071,0,19129.76036\n105440,45,27,2,1,Single,12,387.9520813,0,19104.60407\n123112,30,9,1,0,Married,6,387.9152486,0,18974.76243\n143882,33,14,2,0,Single,10,387.8970548,0,19150.85274\n183187,36,19,2,1,Married,7,387.8513132,0,18944.56566\n167515,24,6,3,0,Single,12,387.8124404,0,18924.62202\n161182,44,27,3,0,Married,9,387.7739202,0,18822.69601\n159643,54,35,2,0,Married,8,387.7583701,0,18798.91851\n169904,42,21,2,1,Married,6,387.7468473,0,19023.34237\n137599,18,0,2,0,Married,13,387.745597,0,18893.27985\n152487,20,2,4,1,Single,12,387.73725,0,18876.8625\n177967,26,6,4,1,Married,1,387.7293836,0,18960.46918\n129311,22,5,4,1,Married,12,387.7169808,0,19106.84904\n196228,68,51,3,0,Single,1,387.7001867,0,18873.00934\n138293,27,6,2,1,Single,7,387.6816898,0,18830.08449\n185848,19,2,4,0,Married,6,387.6639715,0,18916.19858\n151715,38,21,2,0,Married,6,387.567204,0,19047.3602\n139865,28,11,2,1,Single,9,387.5613219,0,18856.0661\n101665,40,19,1,1,Single,9,387.4851589,0,18795.25795\n116079,53,32,4,1,Single,6,387.4840358,0,19090.20179\n132674,47,29,2,0,Married,9,387.4795441,0,18833.97721\n163935,30,12,2,1,Single,8,387.4681138,0,19168.40569\n120834,30,9,1,0,Married,6,387.4435732,0,19099.17866\n186295,50,30,1,1,Single,6,387.366763,0,18911.33815\n156116,46,29,1,1,Single,12,387.3513371,0,19101.56686\n166533,27,9,2,0,Married,3,387.3510246,0,19081.55123\n131600,48,30,4,1,Single,8,387.2872741,0,18887.36371\n164614,43,23,3,0,Married,8,387.2686296,0,18763.43148\n103445,61,42,2,1,Single,10,387.259968,0,18895.9984\n110085,62,42,1,0,Married,10,387.2442899,0,19064.2145\n125899,32,14,1,0,Single,14,387.2409986,0,18813.04993\n101592,19,1,1,1,Married,12,387.2362217,0,18984.81109\n187420,22,4,2,1,Married,3,387.2199773,0,18947.99887\n154443,28,7,2,1,Single,8,387.1835882,0,19076.17941\n153763,29,11,1,1,Married,0,387.1689797,0,18882.44899\n144289,24,6,3,0,Married,11,386.9850888,0,18810.25444\n103175,49,30,1,1,Married,5,386.9751075,0,18808.75538\n183226,34,13,4,1,Single,7,386.927101,0,18851.35505\n166742,25,7,3,1,Married,7,386.9087291,0,18746.43646\n137331,70,50,2,1,Married,2,386.8985782,0,19006.92891\n145758,69,51,2,1,Married,4,386.8914346,0,19101.57173\n155702,66,47,2,1,Married,1,386.8540181,0,19041.70091\n177474,25,6,3,1,Married,7,386.8409149,0,18781.04575\n110379,31,10,2,0,Single,10,386.8273102,0,19121.36551\n129879,22,3,3,1,Married,7,386.7767284,0,18767.83642\n106673,42,21,3,0,Married,8,386.7136229,0,18986.68115\n133428,62,44,2,1,Single,10,386.6502847,0,19120.51424\n150449,32,15,3,1,Single,10,386.5875011,0,18849.37506\n138538,47,26,3,0,Married,0,386.5782655,0,19050.91328\n123960,53,35,4,1,Single,10,386.473788,0,18789.6894\n145847,51,30,2,0,Single,13,386.4362948,0,19108.81474\n140998,30,13,3,1,Single,9,386.4149473,0,18916.74737\n176413,50,30,3,0,Married,12,386.4064153,0,18867.32077\n166143,46,27,2,0,Married,9,386.3281397,0,18735.40699\n148011,35,17,2,0,Married,8,386.3073455,0,18816.36728\n140697,66,47,4,0,Single,2,386.2912251,0,19023.56126\n125489,30,13,2,0,Married,9,386.2883496,0,19024.41748\n103590,47,26,3,0,Single,7,386.2420903,0,18998.10452\n186108,50,30,2,0,Married,7,386.207255,0,18766.36275\n128654,22,3,4,1,Married,5,386.1943007,0,18856.71504\n173155,26,8,2,0,Married,7,386.1919932,0,18732.59966\n191928,68,50,2,1,Single,1,386.1855164,0,18836.27582\n136647,58,38,1,1,Married,2,386.1270382,0,18982.35191\n171394,24,6,3,1,Married,11,386.0939344,0,18866.69672\n196998,54,33,1,0,Single,10,386.0425259,0,18719.1263\n126121,33,14,3,0,Married,10,386.0097307,0,18758.48654\n116758,70,52,3,0,Single,6,385.9814073,0,18748.07037\n151188,16,0,2,0,Single,11,385.9700392,0,18810.50196\n141477,22,3,3,0,Married,14,385.9403212,0,18880.01606\n135730,27,7,3,0,Married,6,385.928791,0,18788.43955\n181345,23,6,1,1,Single,13,385.8799478,0,18900.99739\n150647,53,36,2,0,Married,9,385.8222053,0,18955.11027\n187144,28,7,3,1,Single,8,385.7587634,0,18923.93817\n170804,20,2,3,1,Single,14,385.7584332,0,18978.92166\n157069,61,40,3,0,Single,8,385.7580268,0,18816.90134\n136848,38,21,1,1,Single,9,385.7485614,0,18852.42807\n115175,59,38,1,1,Single,7,385.7248952,0,18778.24476\n157058,31,11,2,0,Single,15,385.6705408,0,18685.52704\n143394,51,32,4,0,Married,1,385.5920763,0,18705.60382\n168645,34,15,3,0,Married,8,385.5605702,0,18757.02851\n187729,67,46,2,0,Single,12,385.4475519,0,18782.3776\n180355,30,10,4,0,Married,8,385.3979731,0,19024.89866\n122481,58,37,4,0,Single,9,385.3793869,0,18996.96935\n146712,24,6,2,1,Single,11,385.3673578,0,18991.36789\n125776,58,39,1,0,Married,10,385.3239242,0,18956.19621\n172292,34,15,2,0,Married,6,385.3054114,0,18864.27057\n106426,37,18,3,0,Married,9,385.3041485,0,18792.20743\n191778,65,46,1,0,Married,8,385.3041121,0,18712.20561\n154406,57,36,2,1,Single,6,385.2545372,0,18921.72686\n129431,42,22,2,0,Married,8,385.2484008,0,18855.42004\n107847,51,31,2,0,Married,0,385.2405668,0,18888.02834\n107873,24,7,2,1,Married,14,385.23888,0,18845.944\n194952,49,32,3,1,Single,15,385.2239247,0,18961.19624\n129315,63,45,3,0,Single,2,385.2021787,0,18835.10894\n186978,40,21,3,0,Single,8,385.193288,0,18923.6644\n161779,64,43,2,1,Married,4,385.1507846,0,18993.53923\n193274,48,30,2,0,Single,11,385.0728079,0,19011.6404\n192208,47,29,4,0,Single,11,385.0698403,0,18748.49202\n118382,25,6,3,1,Single,14,385.0272885,0,18697.36443\n178536,16,0,4,0,Single,15,385.0269627,0,18655.34814\n132569,70,52,1,1,Single,1,385.0208974,0,18949.04487\n145750,34,13,2,1,Single,15,384.9820021,0,19013.10011\n148788,34,16,3,0,Married,15,384.903832,0,19038.1916\n129176,23,4,2,1,Single,12,384.884809,0,18910.24045\n108864,26,9,1,0,Single,8,384.8792499,0,18914.9625\n144394,17,0,2,1,Single,14,384.8647472,0,18795.23736\n131058,68,47,3,1,Single,7,384.8580779,0,18778.9039\n170473,46,28,2,1,Single,7,384.8378553,0,18685.89277\n185239,59,42,4,0,Single,14,384.7952953,0,18699.76477\n119002,23,5,2,0,Married,13,384.7692913,0,18747.46457\n194225,65,47,3,0,Single,9,384.7250407,0,18786.25204\n199800,32,12,2,0,Married,10,384.7066176,0,18652.33088\n174975,68,49,3,1,Single,4,384.7051283,0,18823.25642\n171298,31,13,3,0,Married,8,384.679476,0,18905.9738\n142548,29,8,1,0,Married,9,384.6763948,0,18949.81974\n134360,49,28,4,0,Married,8,384.6726759,0,18684.6338\n195809,59,39,2,0,Married,8,384.6369469,0,18922.84735\n185781,25,8,4,0,Married,9,384.6180335,0,18813.90168\n153857,35,14,2,0,Married,7,384.541988,0,18766.0994\n113706,59,41,3,1,Single,6,384.5275042,0,18756.37521\n130552,43,26,2,1,Single,10,384.506558,0,18911.3279\n172588,21,4,3,1,Single,14,384.5008776,0,19006.04388\n118901,58,40,3,0,Married,6,384.4978346,0,18635.89173\n136033,47,29,2,1,Single,7,384.4920277,0,18945.60139\n128282,64,43,4,0,Single,4,384.452464,0,18810.6232\n174142,30,11,3,1,Single,4,384.3993253,0,18857.96627\n170780,38,18,2,1,Single,9,384.3948008,0,18696.74004\n174093,32,14,3,0,Single,15,384.3913073,0,18854.56537\n180479,23,5,3,1,Married,12,384.38384,0,18668.192\n114424,26,9,4,1,Single,7,384.3670391,0,18994.35196\n192845,64,43,2,0,Single,11,384.3539154,0,18986.69577\n197948,32,13,4,1,Single,7,384.2924002,0,18958.62001\n179397,59,39,4,1,Single,7,384.2764664,0,18848.82332\n171103,16,0,4,0,Married,12,384.2687037,0,18701.43519\n113793,55,36,2,1,Single,9,384.2686902,0,18983.43451\n152653,21,4,3,0,Married,13,384.1860668,0,18747.30334\n189302,58,41,3,1,Single,10,384.1628919,0,18617.1446\n168588,69,49,2,0,Single,6,384.149582,0,18613.4791\n153613,22,3,2,1,Married,6,384.1372989,0,18650.86495\n180828,63,42,3,0,Single,4,384.13711,0,18701.8555\n180327,64,45,3,1,Single,9,384.0884018,0,18666.42009\n170302,36,19,3,0,Married,8,384.0194223,0,18637.97112\n186930,70,51,3,0,Single,10,384.006876,0,18701.3438\n105909,27,8,4,0,Single,7,383.975515,0,18949.77575\n104035,45,27,4,1,Single,14,383.9529906,0,18884.64953\n146149,27,8,3,1,Single,10,383.9293078,0,18736.46539\n130279,35,14,2,1,Single,10,383.899652,0,18715.9826\n190490,69,51,3,0,Married,4,383.8839835,0,18691.19918\n108894,19,0,3,1,Married,9,383.875196,0,18902.7598\n134475,48,31,3,1,Single,7,383.8576238,0,18930.88119\n110726,31,12,3,0,Married,9,383.8345056,0,18914.72528\n111149,19,2,2,0,Married,6,383.7567704,0,18623.83852\n139464,32,12,4,1,Single,9,383.6844203,0,18619.22102\n114344,39,19,2,0,Single,10,383.6642098,0,18827.21049\n117449,37,18,2,0,Married,9,383.5760292,0,18957.80146\n138087,63,45,4,0,Single,7,383.5706642,0,18836.53321\n181021,38,21,2,0,Married,10,383.5138009,0,18876.69005\n129885,57,40,3,0,Single,15,383.4909815,0,18922.54908\n155622,23,5,2,0,Married,14,383.4600835,0,18683.00418\n153514,27,6,2,0,Married,10,383.4595893,0,18709.97947\n174635,29,10,4,0,Married,9,383.4540805,0,18598.70403\n185581,49,30,3,1,Single,14,383.4475114,0,18835.37557\n176582,45,26,1,1,Single,10,383.44538,0,18780.269\n187888,18,1,4,1,Single,11,383.4385754,0,18666.92877\n104903,32,13,3,1,Single,7,383.3799544,0,18865.99772\n123043,35,18,3,1,Married,1,383.3741104,0,18781.70552\n197243,61,43,3,1,Married,3,383.3588336,0,18758.94168\n197640,53,32,3,1,Married,0,383.3544135,0,18598.72068\n124607,53,35,2,1,Single,8,383.309022,0,18905.4511\n127717,18,0,1,1,Married,14,383.2031519,0,18757.1576\n186708,49,32,3,1,Married,11,383.1808039,0,18640.0402\n112720,24,6,1,0,Single,15,383.1709874,0,18742.54937\n184441,42,24,4,1,Single,10,383.158681,0,18784.93405\n115364,25,7,4,1,Single,14,383.1214308,0,18780.07154\n187792,43,25,1,1,Single,11,383.111618,0,18792.5809\n168948,21,2,2,1,Married,14,383.1051406,0,18761.25703\n140775,43,22,1,0,Single,8,383.0935474,0,18909.67737\n148145,69,50,1,1,Married,1,383.0844977,0,18856.22489\n165265,68,50,2,0,Single,0,383.0202439,0,18717.0122\n199074,16,0,3,1,Single,14,383.011526,0,18680.5763\n120467,53,36,3,1,Single,7,382.9838409,0,18741.19205\n151991,16,0,4,0,Married,12,382.9730687,0,18833.65344\n192266,46,29,2,0,Married,10,382.9371189,0,18870.85595\n174219,51,34,3,0,Married,9,382.9360523,0,18871.80262\n124026,28,9,2,0,Married,6,382.9229789,0,18553.14895\n190959,46,29,1,0,Married,7,382.8006063,0,18820.03032\n162340,22,3,4,1,Married,7,382.7989886,0,18832.94943\n153573,55,34,2,1,Single,8,382.7987489,0,18679.93745\n110919,58,41,2,0,Married,7,382.7712477,0,18628.56239\n161377,59,39,2,1,Married,7,382.7498593,0,18542.49297\n102840,53,36,4,0,Married,6,382.7331096,0,18876.65548\n100356,16,0,3,1,Married,14,382.6980639,0,18925.9032\n146863,17,0,2,0,Married,8,382.6898135,0,18600.49068\n155512,42,25,2,0,Married,9,382.6718535,0,18762.59268\n164603,48,30,4,1,Single,6,382.6662277,0,18926.31139\n192841,28,10,3,1,Single,11,382.6058803,0,18601.29402\n108779,30,13,2,1,Single,12,382.5545497,0,18920.72749\n174567,25,7,2,1,Single,14,382.5513738,0,18853.56869\n106897,58,39,2,0,Married,7,382.5430192,0,18563.15096\n121360,65,44,1,0,Single,12,382.5191511,0,18700.95756\n188214,16,0,1,1,Married,8,382.4581608,0,18567.90804\n128542,17,0,3,0,Married,6,382.4534923,0,18913.67462\n118587,33,14,2,0,Single,8,382.3996542,0,18565.98271\n144735,29,9,2,0,Married,9,382.3871743,0,18673.35872\n181080,45,28,3,1,Single,10,382.325365,0,18751.26825\n166748,26,9,2,1,Married,5,382.2884571,0,18752.42286\n147616,56,39,3,1,Single,10,382.263524,0,18619.1762\n161620,46,29,2,0,Married,6,382.230708,0,18794.5354\n167451,43,24,2,1,Single,7,382.2184399,0,18810.922\n184682,16,0,2,1,Married,3,382.2013599,0,18733.068\n194153,16,0,3,1,Single,12,382.1958906,0,18664.79453\n118280,25,6,3,0,Married,12,382.1901316,0,18638.50658\n126307,53,35,1,0,Married,9,382.1779015,0,18619.89508\n180455,18,0,1,0,Married,15,382.1035218,0,18728.17609\n160518,26,6,1,1,Married,9,382.1026866,0,18878.13433\n146706,59,41,2,0,Single,13,382.0857577,0,18549.28788\n181009,44,23,3,0,Single,8,382.0787055,0,18737.93528\n184210,36,15,3,0,Single,15,382.0609448,0,18505.04724\n153484,37,19,4,0,Single,9,382.0433616,0,18693.16808\n148071,55,35,2,0,Married,14,382.0276144,0,18630.38072\n185030,67,48,1,1,Married,0,381.9884749,0,18783.42375\n176012,35,16,2,0,Single,9,381.9784381,0,18508.92191\n125737,34,16,4,0,Married,8,381.9658381,0,18687.29191\n121539,43,25,3,1,Single,10,381.9142724,0,18643.71362\n101407,68,49,3,0,Single,14,381.8479586,0,18566.39793\n183184,31,12,2,1,Single,7,381.8474106,0,18646.37053\n183849,17,0,1,0,Single,12,381.8262364,0,18875.31182\n176362,26,7,3,1,Single,10,381.7875709,0,18742.37855\n126188,17,0,1,1,Married,14,381.7838871,0,18665.19436\n166042,64,45,3,0,Single,6,381.7761939,0,18709.8097\n167843,52,31,4,1,Married,7,381.7601006,0,18877.00503\n167746,58,39,1,1,Single,2,381.7477309,0,18671.38655\n103343,34,17,3,1,Single,11,381.6900721,0,18860.50361\n116581,43,22,1,0,Married,7,381.6731499,0,18500.6575\n189437,68,48,2,1,Married,2,381.6604906,0,18702.02453\n199927,49,29,3,0,Single,1,381.6267381,0,18714.33691\n172740,31,13,1,1,Single,9,381.6263384,0,18774.31692\n158057,43,22,2,1,Married,0,381.6187691,0,18703.93846\n109560,32,11,2,1,Single,10,381.610363,0,18536.51815\n102993,20,1,2,0,Single,13,381.605109,0,18689.25545\n191049,36,15,3,0,Married,9,381.5654494,0,18705.27247\n114136,44,27,2,1,Married,0,381.557682,0,18822.8841\n186896,49,31,2,1,Single,8,381.5180503,0,18643.90252\n132687,45,26,2,1,Single,6,381.4841626,0,18656.20813\n107998,68,47,3,1,Single,1,381.4821507,0,18703.10754\n180237,42,25,2,0,Married,7,381.4533716,0,18777.66858\n180370,55,36,3,0,Married,9,381.4232944,0,18866.16472\n107744,49,28,2,0,Married,14,381.410906,0,18711.5453\n144196,18,0,4,0,Married,11,381.4094542,0,18550.47271\n161232,49,32,2,0,Married,2,381.4077958,0,18554.38979\n140751,18,1,2,0,Married,15,381.394809,0,18830.74045\n167255,66,46,3,0,Single,7,381.3855125,0,18598.27563\n190045,26,8,4,1,Single,8,381.3791983,0,18508.95992\n113023,39,20,2,0,Single,2,381.372027,0,18597.60135\n154387,20,1,3,1,Married,12,381.3569653,0,18583.84827\n109528,70,51,2,0,Single,15,381.3348415,0,18487.74208\n180708,26,8,3,0,Married,6,381.3059277,0,18614.29639\n131012,51,31,3,1,Single,9,381.2959442,0,18717.79721\n122834,42,25,2,1,Single,8,381.2696849,0,18844.48425\n136951,19,2,2,1,Single,11,381.1952687,0,18816.76344\n185443,27,9,2,0,Single,8,381.18968,0,18591.484\n188935,49,31,3,1,Married,0,381.0714704,0,18597.57352\n193819,42,24,2,1,Single,9,381.0444737,0,18555.22369\n144911,41,22,1,1,Married,1,381.0411775,0,18643.05888\n171710,29,8,3,0,Single,8,381.0256228,0,18603.28114\n132712,56,36,3,0,Married,7,381.0230378,0,18816.15189\n198326,27,6,2,1,Single,10,380.9761342,0,18681.80671\n113171,35,18,4,0,Married,8,380.9647394,0,18513.23697\n118652,59,40,3,0,Married,9,380.9262192,0,18571.31096\n195634,36,15,1,1,Married,1,380.8848309,0,18549.24155\n186816,43,26,4,0,Married,3,380.882934,0,18612.1467\n141491,31,14,2,0,Married,13,380.8398167,0,18800.99084\n163337,42,21,1,1,Single,10,380.8165241,0,18712.82621\n189037,30,11,3,0,Married,15,380.7836487,0,18679.18244\n102031,50,30,3,0,Single,9,380.7686488,0,18684.43244\n181271,16,0,4,0,Married,6,380.723096,0,18585.1548\n174759,29,8,3,0,Married,7,380.684214,0,18723.2107\n168375,43,24,3,0,Married,6,380.6256736,0,18655.28368\n195882,40,23,3,1,Single,6,380.6014831,0,18725.07416\n191467,44,26,3,0,Married,9,380.5811035,0,18561.05518\n106599,32,15,3,1,Single,6,380.5640053,0,18692.20027\n138414,37,18,2,0,Married,8,380.5634077,0,18825.17039\n136810,63,44,2,0,Single,7,380.4963738,0,18769.81869\n145650,40,19,2,1,Married,6,380.4858605,0,18594.29303\n118627,25,6,2,0,Single,13,380.4447772,0,18789.23886\n187776,29,10,4,0,Married,7,380.4149657,0,18460.74829\n181090,62,45,2,1,Single,2,380.4116361,0,18511.58181\n174278,56,39,2,0,Married,10,380.4080074,0,18727.40037\n195469,43,25,2,1,Single,9,380.3847115,0,18433.23558\n158526,46,29,3,0,Married,6,380.3607881,0,18420.03941\n180685,46,27,3,0,Married,8,380.3565842,0,18725.82921\n198847,31,13,2,1,Single,7,380.3387109,0,18753.93555\n188161,46,27,3,0,Single,11,380.3042296,0,18484.21148\n153288,62,45,2,0,Single,6,380.2629278,0,18647.14639\n175464,39,22,2,1,Single,14,380.2390255,0,18677.95128\n113057,18,1,2,0,Married,14,380.1822674,0,18520.11337\n129218,33,13,4,0,Single,11,380.1495652,0,18434.47826\n101903,55,36,3,0,Single,13,380.1288578,0,18591.44289\n171548,53,34,2,1,Single,7,380.0523299,0,18429.6165\n125821,30,11,1,0,Married,10,380.0318156,0,18446.59078\n135275,17,0,3,1,Married,11,380.029545,0,18709.47725\n163103,35,17,4,1,Single,9,380.0241943,0,18525.20972\n187545,61,42,1,1,Single,8,380.0193648,0,18413.96824\n116980,22,3,1,1,Married,15,379.979143,0,18599.95715\n184965,20,1,2,0,Married,8,379.9197983,0,18755.98992\n120320,57,37,3,0,Married,10,379.8931437,0,18583.65719\n117645,51,30,3,1,Single,9,379.8887744,0,18407.43872\n148221,41,24,4,1,Single,15,379.8803505,0,18768.01753\n108958,52,31,1,1,Single,12,379.8610274,0,18766.05137\n125999,29,12,3,1,Single,6,379.8254789,0,18579.27395\n116419,43,23,1,0,Married,10,379.8147306,0,18449.73653\n105386,18,0,2,1,Single,13,379.7999706,0,18520.99853\n108917,45,28,3,1,Single,8,379.6942352,0,18557.71176\n127244,58,41,3,0,Married,0,379.6722838,0,18606.61419\n181682,59,42,3,1,Single,10,379.6260286,0,18632.30143\n141735,58,39,1,0,Married,8,379.5952303,0,18424.76152\n185447,33,13,2,0,Married,7,379.5789069,0,18665.94535\n194732,27,7,4,0,Married,3,379.5332113,0,18590.66057\n120054,50,30,3,0,Married,8,379.5283124,0,18478.41562\n110055,45,28,3,1,Single,6,379.5054224,0,18581.27112\n130290,50,30,4,0,Married,7,379.4078932,0,18588.39466\n151097,22,3,4,1,Single,7,379.3928765,0,18733.64383\n131880,59,42,3,1,Single,9,379.3433643,0,18373.16822\n199624,25,6,2,0,Single,13,379.2887008,0,18683.43504\n197785,52,33,2,0,Married,6,379.2837929,0,18712.18965\n132587,37,17,3,1,Single,10,379.273186,0,18574.6593\n158387,57,40,1,1,Single,7,379.2612055,0,18755.06028\n129418,55,34,3,0,Married,8,379.216129,0,18653.80645\n188684,44,27,1,1,Single,7,379.1966162,0,18674.83081\n148021,50,33,3,1,Single,15,379.1833775,0,18589.16888\n110718,56,37,4,1,Single,15,379.1802773,0,18466.01387\n161219,19,2,3,1,Married,9,379.1506144,0,18587.53072\n118616,50,29,4,1,Married,3,379.1346056,0,18399.73028\n177311,56,39,2,0,Married,8,379.0977842,0,18369.88921\n145469,23,6,1,0,Married,12,379.0887054,0,18380.43527\n185764,36,17,3,0,Married,9,379.0811443,0,18471.05722\n149450,47,26,2,1,Married,5,379.0807708,0,18572.03854\n135563,49,28,2,0,Married,6,379.0701103,0,18436.50552\n198414,16,0,1,0,Married,9,379.0664688,0,18591.32344\n182836,30,13,3,1,Married,3,379.0646862,0,18421.23431\n149785,38,21,4,0,Married,14,379.0386202,0,18568.93101\n127248,44,23,2,0,Married,8,379.0301602,0,18676.50801\n163333,41,23,2,0,Married,11,378.9668781,0,18746.34391\n122503,25,8,2,0,Married,14,378.9522982,0,18681.61491\n131638,16,0,3,0,Single,14,378.9476811,0,18517.38406\n171127,53,32,2,0,Single,8,378.9284189,0,18684.42095\n177005,65,46,3,1,Single,9,378.8241333,0,18425.20667\n144685,51,33,2,1,Single,7,378.7651634,0,18506.25817\n194583,36,17,2,0,Married,6,378.7564551,0,18560.82276\n170659,27,6,3,1,Single,6,378.7468718,0,18487.34359\n183217,45,25,4,0,Single,10,378.6973128,0,18529.86564\n181081,32,11,2,0,Married,14,378.6520106,0,18613.60053\n121254,47,30,2,1,Single,7,378.6471116,0,18395.35558\n126001,16,0,1,1,Married,3,378.5785956,0,18673.92978\n155077,55,36,4,0,Married,8,378.5538202,0,18454.69101\n124309,63,42,3,1,Single,10,378.4457888,0,18462.28944\n182571,21,2,1,1,Married,12,378.441003,0,18653.05015\n190916,17,0,4,0,Single,14,378.4220976,0,18327.10488\n140698,53,35,3,1,Single,6,378.397866,0,18627.8933\n154018,25,8,3,1,Married,11,378.368312,0,18420.4156\n107601,50,32,1,1,Married,3,378.3183766,0,18623.91883\n101505,54,34,2,0,Married,5,378.3054213,0,18342.27107\n199651,27,9,4,0,Single,7,378.2870801,0,18334.35401\n160933,16,0,4,1,Single,14,378.2856373,0,18331.28187\n191205,44,25,2,1,Single,8,378.2642892,0,18508.21446\n102951,39,19,3,0,Married,8,378.2457592,0,18456.28796\n144482,57,38,1,0,Married,8,378.1551984,0,18329.75992\n184801,16,0,1,1,Married,9,378.1407208,0,18321.03604\n141174,67,50,3,1,Single,4,378.1294338,0,18475.47169\n161229,38,18,2,0,Married,6,378.0871083,0,18541.35542\n192865,23,6,2,1,Married,13,378.0683314,0,18444.41657\n119211,34,13,2,0,Married,10,378.058951,0,18314.94755\n171496,57,38,2,0,Married,8,378.0352916,0,18391.76458\n143546,45,26,1,1,Single,7,377.9836825,0,18448.18413\n151496,55,38,4,0,Married,2,377.9807964,0,18396.03982\n133147,49,29,2,0,Married,6,377.9771534,0,18447.85767\n183680,38,20,4,0,Married,9,377.9346584,0,18631.73292\n124057,39,19,3,0,Married,12,377.8486606,0,18538.43303\n143261,63,46,1,0,Single,0,377.8277597,0,18429.38799\n181303,23,6,3,1,Married,12,377.7385891,0,18587.92946\n169733,31,13,2,1,Single,0,377.7105445,0,18347.52723\n135950,38,21,2,0,Married,7,377.7103177,0,18480.51589\n189035,30,9,2,1,Single,3,377.6546915,0,18381.73458\n186769,65,46,1,0,Single,8,377.6421689,0,18672.10845\n100029,45,25,3,1,Single,12,377.4992925,0,18365.96463\n103362,16,0,3,1,Married,7,377.4714749,0,18286.57375\n134244,31,12,3,1,Single,9,377.4174984,0,18585.87492\n163506,18,0,3,0,Married,12,377.3766415,0,18514.83208\n167715,65,46,3,0,Married,4,377.3416426,0,18614.08213\n145754,66,46,3,0,Married,1,377.3397888,0,18567.98944\n150042,30,10,2,1,Single,5,377.3268283,0,18466.34142\n104169,49,30,2,0,Married,1,377.2664157,0,18414.32079\n149319,63,46,1,0,Married,0,377.2200274,0,18490.00137\n145441,28,8,2,0,Married,9,377.2063695,0,18492.31848\n112680,36,17,2,1,Single,12,377.2055573,0,18295.27787\n189817,26,9,4,1,Single,9,377.1557203,0,18352.78602\n169962,55,35,4,0,Single,8,377.1115577,0,18515.57789\n174988,22,4,3,1,Married,12,377.0783636,0,18508.91818\n184497,16,0,4,1,Married,6,377.0209798,0,18291.04899\n129335,68,50,2,1,Single,10,377.0163973,0,18424.81987\n140599,37,19,1,0,Married,9,377.0143389,0,18583.71695\n109628,50,33,3,1,Single,9,377.0024966,0,18570.12483\n120466,61,43,2,0,Single,10,376.9877925,0,18649.38963\n163087,32,13,2,0,Married,6,376.9814566,0,18459.07283\n178383,39,22,3,0,Married,10,376.9462415,0,18601.31208\n153423,39,19,3,1,Single,10,376.9376247,0,18628.88124\n170004,32,14,4,1,Single,9,376.9357628,0,18453.78814\n167078,30,12,2,1,Single,13,376.9146316,0,18564.73158\n140481,26,9,2,1,Single,7,376.9055765,0,18368.27883\n128756,42,25,1,1,Married,6,376.9025625,0,18486.12813\n145797,48,29,3,0,Married,8,376.8821343,0,18454.10672\n131776,28,10,3,1,Married,1,376.851857,0,18469.59285\n139638,18,0,1,1,Single,11,376.8459815,0,18387.29908\n192512,29,12,4,0,Married,14,376.8424395,0,18634.12198\n120964,36,17,3,1,Single,8,376.8405796,0,18315.02898\n176091,26,6,2,0,Married,6,376.8354896,0,18389.77448\n189647,38,19,3,0,Single,14,376.7908524,0,18381.54262\n159386,39,22,3,0,Married,7,376.7639777,0,18405.19889\n159861,41,22,2,0,Married,12,376.743896,0,18542.1948\n144624,28,8,4,0,Married,7,376.705297,0,18605.26485\n119105,46,28,2,0,Married,9,376.7052751,0,18437.26376\n124105,64,43,4,0,Single,6,376.6981804,0,18315.90902\n165134,41,24,3,0,Married,6,376.6615546,0,18332.07773\n195573,37,19,3,0,Married,9,376.6539901,0,18268.69951\n164701,55,37,3,1,Single,9,376.6515652,0,18562.57826\n193948,54,33,2,0,Single,13,376.6252068,0,18322.26034\n169811,18,0,2,1,Single,15,376.5990356,0,18483.95178\n156774,47,28,1,1,Single,6,376.5497194,0,18270.48597\n113344,47,29,2,1,Single,9,376.5415728,0,18310.07864\n117234,42,25,3,0,Single,12,376.5347662,0,18261.73831\n181188,36,18,1,0,Married,8,376.5126581,0,18371.63291\n102171,61,44,4,1,Single,0,376.5063767,0,18443.31884\n108361,35,17,1,1,Single,11,376.4717043,0,18335.58522\n153353,49,32,3,0,Married,9,376.457237,0,18601.86185\n179499,33,14,3,1,Married,2,376.4119155,0,18303.59578\n139403,40,20,3,0,Married,14,376.384769,0,18535.23845\n136757,65,46,3,0,Married,1,376.3318364,0,18239.59182\n127128,36,15,1,1,Married,2,376.2971849,0,18410.85925\n197336,26,7,2,1,Single,8,376.2800309,0,18545.00155\n138516,20,3,2,0,Single,13,376.2787735,0,18247.93868\n192032,23,6,1,0,Single,12,376.1669213,0,18456.34607\n143894,24,6,2,0,Married,12,376.1645156,0,18523.22578\n102285,17,0,4,1,Single,9,376.1581822,0,18566.90911\n103414,49,28,2,1,Single,8,376.1090335,0,18218.45168\n166559,33,16,2,0,Married,3,376.0741809,0,18247.70905\n132644,58,41,3,1,Single,8,375.9411608,0,18283.05804\n162548,39,21,2,1,Single,7,375.8618914,0,18520.09457\n129921,59,38,2,0,Married,2,375.8090896,0,18373.45448\n124430,60,40,3,0,Single,9,375.8030948,0,18549.15474\n178767,47,27,3,1,Single,7,375.7721751,0,18348.60876\n121306,45,28,3,1,Single,10,375.7681425,0,18235.40713\n132417,26,5,4,0,Married,8,375.727011,0,18331.35055\n162119,57,36,2,1,Single,10,375.6861167,0,18412.30584\n171560,41,20,1,0,Married,6,375.6052838,0,18556.26419\n126877,53,32,2,0,Single,13,375.5798676,0,18218.99338\n136266,20,3,4,0,Married,14,375.565844,0,18293.2922\n140422,37,17,3,0,Married,9,375.5569674,0,18501.84837\n144999,64,47,2,0,Single,4,375.5132962,0,18340.66481\n115851,53,34,2,1,Single,6,375.5090453,0,18484.45227\n175968,27,6,1,1,Single,6,375.4289998,0,18294.44999\n141073,52,33,1,0,Married,8,375.4213374,0,18265.06687\n191767,48,28,3,1,Single,9,375.3919368,0,18427.59684\n140410,41,23,3,0,Married,13,375.3914082,0,18531.57041\n107436,45,26,3,0,Single,9,375.3770344,0,18433.85172\n181496,34,16,3,0,Married,7,375.3365456,0,18177.82728\n101150,24,7,3,1,Married,4,375.3185546,0,18482.92773\n170881,60,40,1,0,Single,11,375.2794805,0,18438.97403\n114508,23,4,1,1,Married,15,375.2405979,0,18532.0299\n174654,49,31,3,0,Married,9,375.2196967,0,18353.98484\n187305,57,36,1,0,Married,10,375.2039276,0,18296.19638\n163539,68,47,4,0,Married,3,375.1919183,0,18431.59592\n157802,64,43,1,1,Married,0,375.1662201,0,18396.31101\n164470,24,7,2,1,Single,15,375.1448818,0,18473.24409\n130663,32,13,3,0,Married,9,375.1443978,0,18315.21989\n127639,41,23,3,1,Married,2,375.1356238,0,18424.78119\n150804,36,17,1,0,Married,9,375.07503,0,18304.7515\n130369,36,16,3,1,Single,13,375.0535035,0,18228.67518\n191089,56,38,2,1,Single,7,374.9310319,0,18511.5516\n174779,53,35,3,0,Married,6,374.9234889,0,18275.17445\n135091,34,15,2,0,Married,9,374.8912283,0,18463.56142\n133490,38,19,4,0,Married,7,374.8623946,0,18497.11973\n105942,54,35,4,1,Single,10,374.8406799,0,18160.034\n142244,53,36,1,1,Single,10,374.7773463,0,18339.86732\n134674,28,11,1,1,Married,7,374.7148925,0,18228.74463\n165765,36,17,2,0,Married,7,374.6881469,0,18352.40735\n103240,56,35,2,0,Married,8,374.6621594,0,18204.10797\n148630,37,20,3,0,Married,0,374.6334282,0,18437.67141\n141158,48,29,4,1,Single,9,374.6222191,0,18454.11096\n169763,20,1,4,1,Married,7,374.547514,0,18263.3757\n166320,29,8,2,0,Married,8,374.5191657,0,18370.95829\n107539,18,0,2,1,Single,15,374.4713792,0,18346.56896\n145036,38,17,3,1,Single,7,374.4689304,0,18485.44652\n116286,56,39,3,0,Married,8,374.4610928,0,18139.05464\n100066,43,23,3,0,Married,7,374.440065,0,18333.00325\n124034,47,26,3,1,Married,6,374.3454105,0,18218.27053\n133655,16,0,2,0,Married,11,374.3379542,0,18501.89771\n182054,37,20,3,0,Married,8,374.3331685,0,18512.65843\n181772,29,12,1,1,Single,9,374.2554735,0,18239.77368\n133533,44,24,3,0,Married,10,374.2402912,0,18349.01456\n146238,50,30,1,0,Married,9,374.2330001,0,18246.65001\n133093,25,7,1,1,Married,13,374.1729297,0,18126.64649\n184764,35,14,3,1,Married,8,374.1665035,0,18297.32518\n125783,55,35,2,0,Single,14,374.1490041,0,18343.45021\n108815,58,41,3,0,Married,8,374.0012739,0,18168.0637\n101166,30,12,3,1,Single,7,373.9815528,0,18463.07764\n111559,20,3,2,0,Single,15,373.9408351,0,18274.04176\n147523,48,31,3,1,Married,10,373.9080561,0,18190.40281\n161882,25,7,2,1,Single,6,373.8939521,0,18301.69761\n151038,33,14,4,1,Married,9,373.8715169,0,18378.57585\n193245,61,40,3,1,Single,1,373.7628135,0,18403.14068\n120261,36,15,3,1,Married,6,373.752712,0,18126.6356\n166991,33,14,2,0,Married,7,373.7516396,0,18174.58198\n173993,27,7,3,0,Married,6,373.7460352,0,18429.30176\n169973,43,26,4,1,Single,7,373.7273369,0,18217.36685\n183297,32,13,1,1,Single,10,373.6726323,0,18453.63162\n192044,21,4,2,0,Single,15,373.6690031,0,18241.45016\n193903,32,14,2,1,Single,2,373.6270097,0,18364.35049\n119713,61,42,2,1,Single,8,373.6039557,0,18374.19779\n198612,41,20,2,0,Married,10,373.5899538,0,18164.49769\n162489,35,18,2,1,Married,7,373.5689412,0,18133.44706\n193153,38,19,1,1,Single,10,373.5628167,0,18335.14084\n179035,57,36,2,0,Married,6,373.5484093,0,18395.42047\n150568,52,32,3,1,Single,11,373.460559,0,18304.02795\n113387,39,21,2,0,Married,15,373.4394242,0,18140.97121\n151384,20,2,1,0,Married,12,373.3754632,0,18363.77316\n113962,23,5,4,0,Married,11,373.3622448,0,18151.11224\n180594,68,47,4,0,Single,6,373.3520913,0,18068.60457\n144349,50,31,3,1,Single,5,373.3074332,0,18134.37166\n143646,46,27,1,1,Single,5,373.2717697,0,18447.58849\n123680,57,38,4,0,Married,10,373.2540709,0,18402.70355\n151827,35,15,2,0,Married,6,373.2377504,0,18443.88752\n119399,25,6,3,1,Married,8,373.2290396,0,18252.45198\n196404,64,47,4,0,Single,9,373.2124307,0,18322.62154\n104251,40,22,1,1,Married,10,373.2009063,0,18366.04532\n100998,42,25,2,0,Married,8,373.1624671,0,18164.12336\n120905,59,42,2,0,Married,6,373.134439,0,18299.72195\n151911,34,13,1,1,Married,7,373.1316241,0,18157.58121\n121393,21,2,3,1,Married,10,373.1262647,0,18431.31324\n118666,25,6,3,1,Single,12,373.0683159,0,18066.4158\n116167,28,9,2,0,Married,9,373.0122733,0,18111.61367\n121508,18,0,1,1,Single,14,372.963556,0,18296.1778\n183169,32,15,1,0,Single,14,372.9603759,0,18428.0188\n188629,32,14,1,1,Single,9,372.8808668,0,18205.04334\n171818,21,3,3,1,Single,12,372.8354284,0,18081.77142\n198334,67,46,3,1,Single,5,372.8116559,0,18334.5828\n103219,57,38,4,1,Single,13,372.7775131,0,18214.87566\n177548,67,47,1,0,Single,15,372.7635182,0,18397.17591\n151397,19,1,2,0,Single,14,372.7307075,0,18244.53538\n175415,35,14,3,1,Single,9,372.7246494,0,18137.23247\n178788,25,8,4,0,Married,7,372.7214431,0,18288.07216\n154548,22,3,2,1,Married,9,372.7072207,0,18276.36104\n196227,24,6,4,1,Single,14,372.6953761,0,18337.7688\n163934,44,27,1,1,Single,8,372.644576,0,18181.2288\n177995,26,8,3,0,Married,7,372.6185169,0,18126.92585\n154223,57,38,4,0,Married,6,372.55767,0,18082.8835\n100612,43,26,2,0,Married,8,372.5381172,0,18107.90586\n178295,35,16,3,0,Married,6,372.5237849,0,18202.18925\n191272,41,20,3,1,Single,9,372.5168307,0,18346.84154\n129164,41,23,3,1,Single,9,372.5076295,0,18383.38148\n129906,70,53,3,0,Married,5,372.4882643,0,18070.41322\n181832,23,4,2,1,Married,15,372.4864791,0,18313.32396\n101618,70,49,2,0,Single,7,372.4552543,0,18063.76272\n131892,69,51,3,1,Single,3,372.3735452,0,18327.67726\n132805,69,51,2,1,Single,14,372.345364,0,18319.2682\n111320,46,28,3,0,Married,8,372.3125049,0,18019.62525\n127194,40,20,3,1,Married,6,372.2936525,0,18221.68263\n192431,34,17,2,1,Married,10,372.2736021,0,18174.68011\n176287,45,24,3,1,Married,9,372.1326209,0,18285.63105\n175579,42,25,3,1,Married,0,372.1311092,0,18107.55546\n103669,24,6,3,1,Married,12,372.0321396,0,18322.60698\n103377,20,2,2,0,Married,12,372.0202933,0,18138.01467\n155179,49,30,2,1,Married,9,371.9917979,0,18038.5899\n135717,58,37,4,1,Single,10,371.9870649,0,18161.35325\n118073,44,27,4,1,Married,7,371.9072315,0,18101.36158\n182316,67,49,3,0,Single,7,371.8820887,0,18302.10444\n190607,21,2,3,1,Married,12,371.8546013,0,18076.73007\n178380,44,25,2,0,Married,6,371.8017855,0,18069.08928\n114467,48,31,3,0,Married,7,371.7466035,0,18254.33018\n153816,25,7,3,1,Single,8,371.7248492,0,18122.24246\n173506,53,36,3,1,Married,10,371.6455345,0,18206.27673\n142009,19,1,3,0,Married,7,371.6416025,0,18286.08013\n103557,26,9,3,0,Married,9,371.6037639,0,18375.1882\n198285,54,37,2,0,Married,7,371.5487931,0,18351.43966\n151993,67,46,4,0,Single,14,371.4577215,0,18175.88608\n105450,68,47,3,1,Single,6,371.3811134,0,18211.05567\n180489,66,46,2,0,Single,13,371.3213023,0,18167.06512\n179567,69,48,2,0,Single,7,371.2506915,0,17974.53458\n115430,62,45,2,1,Single,7,371.1917185,0,18333.58593\n108843,61,40,3,1,Single,4,371.1505459,0,18279.5273\n165357,20,1,2,1,Single,13,371.0895625,0,18268.47813\n126591,49,29,4,0,Married,6,371.0670046,0,18292.35023\n152563,56,39,2,1,Married,9,371.0201511,0,18307.00756\n126352,56,38,3,1,Single,6,370.9666912,0,18176.33456\n102603,51,30,2,1,Married,8,370.9645791,0,17955.22896\n107673,35,16,2,1,Married,8,370.9385613,0,18047.92807\n139374,24,6,2,0,Married,15,370.8972654,0,18330.86327\n187121,16,0,3,1,Married,12,370.8454307,0,18070.27154\n178128,39,19,2,0,Married,10,370.8280203,0,18280.40102\n139974,59,42,4,0,Married,12,370.8265951,0,18021.32976\n133785,67,48,2,0,Single,7,370.8086612,0,18237.43306\n124247,62,43,4,1,Single,10,370.8021618,0,18324.10809\n140641,18,0,4,1,Single,12,370.7909449,0,18278.54725\n193077,26,8,4,0,Married,6,370.7876311,0,18123.38156\n136007,43,23,1,0,Single,8,370.703901,0,18126.19505\n160863,49,28,1,0,Married,13,370.6966267,0,17974.83134\n119660,24,6,1,1,Married,8,370.6644896,0,18165.22448\n145513,24,7,2,1,Married,11,370.6578777,0,17938.89389\n168063,18,0,2,1,Single,8,370.6460868,0,18113.30434\n116480,38,21,3,0,Married,9,370.6445233,0,18239.22617\n113429,49,28,3,0,Married,10,370.6164099,0,17953.8205\n117984,57,40,2,0,Married,8,370.6151907,0,18213.75954\n174843,31,11,3,1,Married,10,370.5898533,0,17936.49267\n101555,47,26,2,1,Single,10,370.577407,0,18172.87035\n124657,38,18,3,0,Married,10,370.5548054,0,18325.74027\n192280,58,41,2,0,Married,10,370.5524575,0,17951.62288\n155317,22,4,2,1,Married,8,370.5457414,0,18245.28707\n164819,32,11,2,0,Married,7,370.4949476,0,18081.74738\n148524,53,33,3,1,Single,13,370.4228264,0,18009.14132\n107635,61,44,3,0,Single,4,370.4084796,0,18229.42398\n156704,49,31,4,0,Married,9,370.397558,0,18170.8779\n199290,37,17,1,0,Married,6,370.327551,0,17991.37755\n129120,34,15,1,1,Single,4,370.307938,0,17955.3969\n179479,28,9,3,1,Single,7,370.2506663,0,18200.53332\n165122,21,2,2,1,Married,12,370.2364866,0,18102.82433\n169485,22,3,2,0,Married,14,370.2018107,0,18035.09054\n156226,55,35,3,0,Married,10,370.165043,0,18250.25215\n178705,44,23,1,1,Single,8,370.0811782,0,18161.05891\n138151,41,20,3,1,Single,11,370.0369949,0,18118.84975\n178014,50,33,3,0,Married,10,370.0097979,0,18090.4899\n188682,51,33,2,1,Single,6,369.9864994,0,18020.32497\n120475,44,26,4,0,Married,7,369.9474902,0,18209.37451\n102771,19,0,2,0,Single,14,369.9169511,0,17962.84756\n161572,19,0,1,1,Married,14,369.8911603,0,18272.55802\n156322,62,41,1,1,Single,11,369.8815672,0,18082.07836\n178300,53,35,2,1,Married,10,369.8709847,0,18236.54924\n139590,37,20,1,0,Married,9,369.8291176,0,18112.45588\n120661,36,16,3,1,Married,9,369.8083673,0,18203.41837\n107184,26,8,3,0,Married,6,369.7629582,0,18065.14791\n107011,36,16,2,0,Married,8,369.7343028,0,18229.71514\n104078,59,42,2,0,Married,4,369.7334225,0,17904.67113\n161845,61,44,3,0,Single,10,369.7127844,0,18277.63922\n127175,61,44,4,0,Married,6,369.7100602,0,18054.50301\n136536,62,42,4,1,Single,6,369.6051975,0,18072.25988\n140630,37,20,4,0,Married,10,369.5853726,0,18256.26863\n146652,51,31,1,1,Married,7,369.549543,0,18123.47715\n154495,69,48,4,1,Single,6,369.5479418,0,17897.39709\n121712,43,25,4,1,Married,1,369.5478749,0,18133.39375\n172073,34,14,2,1,Single,8,369.5443707,0,18060.21854\n110703,22,4,1,0,Married,14,369.5317511,0,18073.58756\n175674,20,1,2,0,Married,10,369.5311906,0,18037.55953\n173740,59,42,1,1,Single,9,369.5012883,0,18154.06442\n127971,21,3,3,1,Married,9,369.4871547,0,18112.35774\n136627,28,10,1,0,Married,10,369.4802741,0,18097.01371\n169975,52,31,3,0,Married,10,369.4695395,0,17948.47698\n185071,18,0,3,1,Married,11,369.4561706,0,18189.80853\n188417,27,9,3,0,Married,9,369.4463946,0,18262.31973\n194154,54,37,1,1,Married,9,369.3789377,0,18146.94689\n166113,53,32,1,1,Single,13,369.3748417,0,18197.74209\n148970,60,40,2,1,Single,14,369.3615068,0,18110.07534\n143057,52,35,4,0,Married,10,369.3236174,0,18145.18087\n122925,29,11,2,1,Married,5,369.3109119,0,18087.5456\n109988,54,37,3,0,Married,8,369.2756122,0,18116.78061\n117311,47,26,1,0,Married,7,369.2694089,0,18121.47045\n161557,16,0,2,0,Single,14,369.250598,0,18139.5299\n128924,20,2,1,1,Single,12,369.2310887,0,17887.55444\n178561,41,23,3,0,Married,6,369.2072341,0,18251.36171\n196877,56,36,1,1,Married,0,369.2066993,0,17895.33497\n145669,21,4,2,0,Married,15,369.1190717,0,18156.95359\n175844,24,6,1,1,Married,7,369.0963317,0,17865.81659\n146298,18,1,2,1,Single,14,369.0694765,0,18107.47383\n150485,34,13,3,1,Married,8,369.048159,0,18079.40795\n122776,19,1,2,1,Married,9,369.0471847,0,18168.35924\n197916,19,0,2,0,Married,13,369.0411629,0,18209.05815\n161418,49,28,3,0,Married,8,368.9977862,0,18060.88931\n131602,36,19,3,1,Married,10,368.9880854,0,18240.40427\n119576,42,21,1,0,Single,11,368.9411753,0,18004.05877\n125858,24,7,3,1,Married,8,368.9330034,0,17873.65017\n108957,40,19,3,0,Married,10,368.9068097,0,17858.34049\n121972,33,16,3,0,Single,8,368.8617356,0,18179.08678\n191057,17,0,3,0,Married,15,368.8470038,0,18143.35019\n147209,25,7,3,1,Married,8,368.7636096,0,18189.18048\n121355,61,42,1,0,Single,2,368.7234314,0,17995.17157\n120406,69,50,3,0,Single,10,368.7215463,0,18220.07732\n184934,36,17,2,1,Single,7,368.6566367,0,17950.83184\n116917,41,20,3,1,Married,8,368.6544117,0,17946.72059\n100374,40,21,2,0,Married,10,368.6516749,0,17937.58375\n172541,52,32,3,1,Single,7,368.5971401,0,18187.85701\n168054,33,12,3,1,Single,1,368.5908459,0,17947.5423\n190810,69,49,3,0,Married,5,368.5459227,0,18083.29614\n181441,18,0,4,0,Married,14,368.4986693,0,18154.93347\n160997,69,52,3,0,Married,10,368.4782178,0,18114.91089\n146256,70,51,3,0,Single,7,368.4560306,0,17935.80153\n105474,62,45,3,0,Single,8,368.4536516,0,18114.68258\n179586,52,34,2,0,Single,15,368.4395827,0,17867.97914\n149604,38,18,2,1,Married,0,368.3743589,0,18141.71795\n161441,57,36,3,0,Married,8,368.3710915,0,17921.55458\n153487,17,0,4,0,Single,14,368.3671133,0,17973.35567\n144259,27,10,3,1,Married,0,368.2570687,0,17920.85344\n140181,31,12,3,0,Married,13,368.1900985,0,17939.50493\n128006,29,8,2,0,Married,7,368.1771031,0,17858.85516\n116891,32,13,3,0,Married,5,368.1514513,0,17984.57257\n143433,66,45,2,0,Married,1,368.1462245,0,17933.31123\n158472,33,12,3,0,Married,7,368.0982266,0,18032.91133\n104300,28,7,2,1,Single,6,368.042135,0,17881.10675\n173624,42,21,2,1,Married,3,368.0245804,0,18110.22902\n180704,37,17,3,1,Married,6,367.9607088,0,17956.03544\n175548,51,30,3,0,Married,6,367.9464285,0,18045.32143\n153518,34,15,1,1,Married,7,367.9259859,0,18037.2993\n123977,25,7,3,0,Single,12,367.9006858,0,17971.03429\n118396,57,39,1,1,Single,11,367.8672027,0,18051.36014\n166958,47,28,4,0,Single,15,367.8319775,0,18108.59888\n134303,18,1,2,0,Married,14,367.7398645,0,18057.99323\n170919,32,11,2,1,Single,9,367.69469,0,17799.7345\n160218,27,10,3,1,Married,6,367.6932365,0,18002.66183\n105297,62,44,3,0,Married,8,367.6187397,0,17928.93699\n144244,27,7,3,1,Married,8,367.6105839,0,17994.5292\n162208,30,11,1,0,Married,7,367.58451,0,18170.2255\n112099,56,35,2,1,Married,6,367.5815434,0,18095.07717\n184416,52,34,2,0,Married,9,367.5651389,0,18068.25695\n182409,32,13,4,0,Married,7,367.5626878,0,18057.13439\n154293,50,33,1,1,Single,8,367.5426684,0,18036.13342\n175327,25,7,3,1,Married,12,367.5419089,0,18100.09545\n184796,35,18,4,0,Single,13,367.5393468,0,17990.96734\n192217,55,34,3,1,Single,6,367.5251252,0,18130.25626\n165117,33,12,4,0,Married,8,367.5004938,0,18001.02469\n120837,31,10,2,1,Single,8,367.4919132,0,17865.59566\n159108,31,13,2,0,Married,7,367.4895123,0,17864.47562\n160012,19,0,3,0,Married,11,367.4541349,0,18128.70675\n104836,34,16,3,1,Single,11,367.4196531,0,17919.98266\n157139,41,22,3,0,Married,10,367.407107,0,17770.35535\n112790,55,36,3,1,Single,6,367.3384677,0,17962.92339\n168444,37,19,2,1,Single,9,367.3292173,0,18053.46087\n141613,53,33,2,0,Married,7,367.3270477,0,17826.35239\n109052,27,6,3,1,Single,8,367.304245,0,18082.21225\n190239,23,5,2,1,Single,12,367.2975155,0,18105.87578\n158163,49,29,3,0,Married,9,367.2334641,0,17900.67321\n199043,22,4,3,1,Single,15,367.2309591,0,18122.54796\n171007,50,29,2,0,Married,10,367.2249058,0,17919.24529\n124527,45,24,3,0,Married,9,367.2157317,0,17860.78659\n155462,28,9,3,1,Married,10,367.2097593,0,17813.48797\n151023,19,2,3,0,Married,11,367.19389,0,17995.6945\n132158,57,37,4,0,Single,14,367.1787648,0,17903.93824\n192595,64,44,1,0,Single,12,367.1782376,0,17895.91188\n140934,27,7,1,1,Single,12,367.1256363,0,17909.28182\n110376,62,44,3,1,Single,12,367.119913,0,18092.99565\n160140,48,29,3,0,Married,10,367.1165347,0,17933.82674\n143088,33,12,3,0,Married,13,367.1027674,0,17814.13837\n171112,63,45,1,0,Married,5,367.0996594,0,17849.98297\n175857,27,7,3,1,Married,8,367.0493584,0,17759.46792\n184508,40,20,4,0,Married,9,366.9648887,0,17943.24444\n134542,47,27,2,1,Single,15,366.9579826,0,17894.89913\n111087,42,21,2,1,Single,7,366.9309369,0,17960.54685\n126039,48,31,2,0,Married,7,366.9173952,0,17994.86976\n112298,55,34,3,1,Married,2,366.86116,0,17911.058\n161364,55,38,3,0,Married,8,366.8421072,0,18120.10536\n172136,39,20,2,1,Single,14,366.7958778,0,17829.79389\n192371,22,3,3,0,Married,11,366.7344384,0,17737.72192\n188758,61,44,2,0,Single,6,366.7104914,0,17986.52457\n118329,49,30,2,0,Single,11,366.6215454,0,18061.07727\n150142,63,45,2,0,Married,4,366.5280661,0,17728.40331\n150319,43,25,1,1,Married,4,366.5270882,0,17884.35441\n199355,70,53,3,1,Single,4,366.5098281,0,17910.49141\n196983,31,11,2,0,Married,10,366.4905058,0,18101.52529\n115018,40,19,3,0,Married,6,366.487367,0,17926.36835\n146372,20,3,1,0,Single,15,366.4842282,0,17729.21141\n163481,62,44,3,1,Single,9,366.4794045,0,17725.97023\n173932,67,46,2,0,Single,10,366.4552188,0,18046.76094\n177630,53,32,2,0,Married,8,366.4283637,0,17866.41819\n159958,26,9,3,1,Single,8,366.4181839,0,17888.9092\n139160,22,5,1,0,Single,13,366.3782619,0,17930.9131\n152243,32,12,3,0,Married,9,366.3153217,0,17970.76609\n112474,39,19,4,1,Single,10,366.3004751,0,17792.02376\n140627,53,35,2,0,Married,6,366.2923626,0,17730.61813\n128670,54,37,2,1,Single,12,366.2778176,0,17768.89088\n177467,59,40,3,1,Married,6,366.2593322,0,17821.96661\n117926,26,7,4,1,Single,8,366.2268678,0,17715.34339\n107087,56,38,2,0,Married,7,366.2171734,0,17904.85867\n151902,51,31,2,1,Married,3,366.1772033,0,18005.86017\n102481,24,7,3,1,Single,11,366.1291161,0,18064.45581\n161693,49,28,3,1,Single,8,366.0757229,0,17839.78615\n181279,39,22,2,1,Single,6,366.0566454,0,17863.83227\n150607,25,7,3,1,Single,12,366.0362108,0,17873.81054\n116768,35,15,2,0,Married,9,366.0017822,0,18081.08911\n102898,18,0,4,0,Single,15,365.9026075,0,17924.13038\n154404,63,42,1,1,Single,9,365.7762262,0,18030.81131\n113737,53,32,3,1,Married,2,365.7678726,0,18011.39363\n105086,39,20,1,1,Single,6,365.7417475,0,17817.08738\n101257,70,49,1,0,Married,1,365.7364661,0,17731.82331\n185957,34,14,2,0,Married,9,365.7027876,0,17855.13938\n147439,70,53,2,0,Married,4,365.6882069,0,17962.41035\n103755,42,25,2,1,Single,7,365.6788645,0,17769.94323\n147745,37,16,3,0,Married,12,365.6708341,0,17860.54171\n158825,68,47,3,0,Married,9,365.6197951,0,17898.98976\n178136,30,9,3,0,Married,8,365.6165189,0,17739.82595\n161429,23,4,1,0,Married,12,365.5849778,0,18021.24889\n160980,41,21,3,1,Married,5,365.5685813,0,17762.42907\n138192,54,34,3,1,Married,9,365.5224138,0,18055.12069\n116935,29,11,2,0,Married,12,365.509736,0,17773.4868\n173914,40,20,2,1,Single,13,365.4732879,0,17692.6644\n168474,58,41,4,0,Married,9,365.4426038,0,17933.13019\n106507,41,24,2,0,Married,10,365.3682534,0,17953.41267\n103275,69,52,3,0,Single,12,365.3308447,0,17989.54224\n197562,58,38,2,1,Single,10,365.3096842,0,17728.48421\n153822,36,19,4,1,Married,8,365.2978239,0,17851.8912\n175957,69,52,4,1,Single,6,365.2719284,0,17924.59642\n100796,58,40,2,1,Single,8,365.2165078,0,18004.82539\n108566,41,23,3,1,Married,8,365.1680329,0,17760.40165\n186676,39,22,2,0,Married,15,365.1659112,0,17855.29556\n152382,26,9,3,1,Married,6,365.0512636,0,17928.56318\n152783,19,1,2,0,Married,13,364.9693307,0,17928.46654\n109289,16,0,3,1,Single,11,364.9645903,0,17700.22952\n149391,56,36,2,1,Single,6,364.958553,0,18035.92765\n159250,36,17,2,1,Married,10,364.9209585,0,17898.04793\n173729,34,16,4,0,Single,15,364.8843178,0,17814.21589\n193915,46,28,4,1,Married,9,364.8709313,0,17796.54657\n192514,36,18,2,1,Married,10,364.8268076,0,17919.34038\n146695,70,49,1,1,Single,9,364.8126232,0,17949.63116\n113398,41,22,4,1,Married,0,364.7726498,0,17993.63249\n147242,24,5,3,1,Married,10,364.6049834,0,18018.24917\n123110,68,51,3,0,Married,10,364.5807656,0,17947.03828\n109418,38,17,3,0,Married,8,364.5436701,0,17736.18351\n102648,25,7,1,0,Married,13,364.522933,0,17936.14665\n168180,62,45,3,1,Single,8,364.4265849,0,17795.32925\n103437,20,1,2,1,Single,14,364.4239491,0,17866.19746\n195537,38,17,3,1,Married,1,364.3623789,0,17805.11895\n113814,19,1,1,0,Married,15,364.3565246,0,17842.82623\n110639,35,15,2,0,Single,13,364.3370129,0,17958.85065\n184604,28,9,3,0,Married,6,364.2679575,0,17984.39788\n140096,25,8,1,1,Married,13,364.2571062,0,17987.85531\n102089,65,45,3,0,Single,8,364.2249733,0,17940.24867\n185338,62,41,4,0,Single,7,364.1975184,0,17637.87592\n141556,69,51,2,0,Married,10,364.1887136,0,17696.43568\n192497,46,29,2,0,Married,6,364.1707624,0,17881.53812\n167084,34,13,2,0,Married,6,364.1695599,0,17997.478\n140454,40,19,1,1,Single,10,364.1423345,0,17744.11673\n147179,26,6,4,1,Single,1,364.0440382,0,17956.20191\n145834,46,26,1,0,Single,12,364.0410413,0,17644.05207\n183606,70,49,2,0,Single,6,364.0376773,0,17922.88387\n108349,66,45,2,0,Single,14,364.0053791,0,17913.26896\n187628,60,39,1,0,Married,14,363.9823492,0,17848.11746\n159787,29,12,1,1,Single,12,363.980308,0,17895.0154\n190056,43,23,1,0,Married,10,363.9739969,0,17809.69985\n177768,32,12,3,0,Married,6,363.9676756,0,17807.38378\n122292,49,32,2,1,Married,9,363.882174,0,17848.1087\n166361,31,11,2,1,Single,9,363.8792246,0,17835.96123\n189604,39,21,3,1,Married,2,363.8399712,0,17912.99856\n105393,17,0,3,1,Single,15,363.822959,0,17646.14795\n104281,58,38,3,1,Single,10,363.8162131,0,17930.81066\n120982,67,49,4,0,Married,9,363.7838029,0,17635.19015\n192103,30,11,3,1,Single,8,363.7587341,0,17700.93671\n194157,43,25,2,0,Married,8,363.758169,0,17782.90845\n185713,27,8,1,0,Single,10,363.7386019,0,17941.9301\n167077,42,21,1,0,Married,11,363.7094092,0,17835.47046\n110601,64,44,4,0,Married,13,363.6650699,0,17781.2535\n164893,44,25,1,1,Single,7,363.644134,0,17606.2067\n113382,39,21,3,1,Single,8,363.5975285,0,17964.87643\n120444,42,22,2,0,Married,12,363.5707997,0,17785.53999\n121888,26,9,2,0,Married,7,363.5544426,0,17629.72213\n157608,34,16,2,1,Single,9,363.5457416,0,17658.28708\n133563,40,19,2,0,Married,6,363.5190657,0,17685.95329\n173398,16,0,3,1,Single,11,363.4525696,0,17893.62848\n145174,43,25,2,0,Married,7,363.4525324,0,17704.62662\n159702,34,16,3,0,Married,6,363.4212453,0,17735.06227\n159783,23,4,2,1,Married,10,363.3703599,0,17743.518\n144813,58,41,4,1,Married,2,363.323362,0,17604.1681\n116145,26,9,4,0,Married,8,363.2476661,0,17709.38331\n197644,41,23,3,0,Married,12,363.2213957,0,17578.06979\n115160,16,0,1,1,Married,9,363.1691719,0,17924.4586\n107381,39,20,2,1,Single,10,363.1498046,0,17939.49023\n177767,55,35,4,1,Married,7,363.1357921,0,17663.78961\n133384,63,45,3,1,Single,10,363.134888,0,17700.7444\n179539,21,3,3,0,Married,13,363.1280411,0,17811.40206\n104473,37,17,1,1,Married,10,363.091096,0,17936.5548\n147729,24,6,3,1,Married,12,363.089954,0,17642.4977\n118470,27,10,1,1,Single,6,362.9964994,0,17639.82497\n101398,54,35,4,1,Single,11,362.9638107,0,17770.19054\n170673,22,3,3,1,Married,2,362.9413112,0,17795.06556\n193852,22,5,1,1,Single,14,362.8274602,0,17674.37301\n169287,19,2,2,1,Married,12,362.8225461,0,17571.12731\n144816,33,16,2,1,Married,9,362.8138301,0,17910.69151\n165119,34,14,4,1,Married,4,362.8075815,0,17711.37908\n107811,37,20,3,1,Single,10,362.8027863,0,17576.13932\n136920,55,37,4,0,Married,6,362.7935448,0,17546.67724\n190943,57,38,3,1,Married,6,362.7180358,0,17808.90179\n153976,22,5,2,0,Married,11,362.70455,0,17835.2275\n141527,39,20,2,0,Married,8,362.6800565,0,17614.00283\n103517,68,50,3,1,Single,6,362.580154,0,17718.0077\n123163,43,22,2,1,Single,10,362.5033576,0,17882.16788\n176396,25,6,2,1,Married,7,362.4831808,0,17526.15904\n106579,64,45,3,1,Married,1,362.4479795,0,17805.39898\n138812,59,40,3,0,Married,6,362.4078242,0,17802.39121\n157157,24,5,2,1,Single,14,362.3409641,0,17665.04821\n127784,57,37,2,0,Single,14,362.3350871,0,17557.75436\n183901,36,15,2,0,Married,12,362.3329689,0,17732.64845\n159584,53,36,3,1,Married,6,362.3113115,0,17772.56558\n171831,70,52,1,0,Married,1,362.263205,0,17766.16025\n187117,54,33,3,1,Married,7,362.2406906,0,17601.03453\n101790,22,3,2,1,Married,10,362.2390293,0,17890.95147\n139299,65,48,3,0,Single,8,362.2361244,0,17637.80622\n117119,18,1,3,1,Married,14,362.2145736,0,17597.72868\n111606,62,45,2,1,Single,10,362.1946304,0,17658.73152\n150305,30,12,3,0,Married,6,362.174745,0,17820.73725\n168626,30,10,3,1,Married,6,362.0945447,0,17674.72724\n192503,54,34,3,1,Married,0,362.0445498,0,17889.22749\n123948,65,47,4,0,Single,10,361.9707844,0,17682.53922\n116208,19,0,3,0,Married,14,361.9234501,0,17763.17251\n108301,38,19,1,1,Married,8,361.9043137,0,17787.21569\n177493,62,41,1,1,Single,6,361.8315328,0,17565.57664\n134604,31,10,3,1,Married,7,361.8274396,0,17884.37198\n110047,53,34,3,0,Married,15,361.8216276,0,17780.08138\n150876,52,32,3,1,Married,8,361.8206825,0,17606.03413\n163339,56,39,2,0,Married,6,361.756353,0,17683.81765\n150975,19,2,2,1,Married,5,361.7308025,0,17523.54013\n143519,46,28,4,1,Single,7,361.7241882,0,17760.20941\n178118,47,30,1,1,Single,14,361.7061298,0,17783.30649\n120790,35,16,4,0,Married,9,361.6928031,0,17539.64016\n132257,59,40,2,0,Single,13,361.6928003,0,17823.64002\n154297,52,31,2,0,Married,6,361.688925,0,17580.44625\n167792,21,4,2,0,Married,13,361.6632453,0,17863.16227\n155157,26,9,4,0,Married,4,361.5741002,0,17609.70501\n197597,61,42,4,0,Married,0,361.5514503,0,17672.57252\n120204,41,20,4,0,Married,10,361.5262011,0,17844.31006\n197379,16,0,4,1,Married,12,361.4998505,0,17690.99253\n132847,36,19,2,1,Married,6,361.4984098,0,17719.92049\n163550,42,23,3,0,Married,9,361.475884,0,17839.7942\n162291,18,0,3,0,Married,15,361.4742935,0,17483.71468\n199595,64,47,4,1,Married,4,361.4699987,0,17608.49994\n102751,32,11,2,1,Married,10,361.4693117,0,17859.46559\n151421,59,40,3,1,Married,8,361.4627714,0,17594.13857\n116999,54,34,1,0,Married,10,361.4416937,0,17833.08469\n169667,37,18,3,0,Married,0,361.4324692,0,17753.62346\n156846,33,14,4,1,Single,6,361.4192118,0,17608.96059\n177435,16,0,4,0,Married,7,361.3825928,0,17858.12964\n149419,56,38,2,0,Married,10,361.2911271,0,17842.55636\n171418,43,22,1,0,Married,6,361.1706404,0,17474.53202\n153123,45,25,3,0,Married,0,361.1530109,0,17749.65055\n118235,25,7,4,0,Married,14,361.0933392,0,17584.66696\n118853,46,29,3,1,Single,10,361.0768625,0,17594.84313\n177624,22,3,3,0,Married,13,361.040788,0,17500.0394\n133531,17,0,2,1,Single,15,361.0162131,0,17801.81066\n166422,28,8,1,1,Married,7,361.0075735,0,17843.37868\n188047,28,7,3,0,Married,6,360.9738576,0,17450.69288\n193916,24,6,2,0,Married,15,360.9731498,0,17694.65749\n121840,32,14,2,1,Single,9,360.9385688,0,17552.92844\n113688,49,31,3,1,Married,5,360.8784326,0,17823.92163\n137799,50,31,1,0,Married,10,360.8718623,0,17509.59312\n153194,54,37,3,0,Married,6,360.8491918,0,17639.45959\n118612,22,4,2,1,Single,11,360.8423963,0,17760.11982\n179628,22,5,1,0,Married,12,360.8051782,0,17793.25891\n183376,26,7,2,1,Married,0,360.7666759,0,17747.3338\n160586,50,33,1,1,Single,11,360.7660378,0,17777.30189\n172968,62,41,3,0,Married,7,360.7618037,0,17808.09019\n122212,37,19,1,1,Married,1,360.7519195,0,17770.59598\n108330,22,3,3,0,Married,14,360.7239489,0,17765.19745\n146709,16,0,1,0,Married,11,360.7064431,0,17781.32216\n122274,19,1,3,0,Single,11,360.6821801,0,17569.10901\n137079,18,0,3,1,Single,14,360.5706407,0,17610.53204\n175191,28,8,2,0,Married,6,360.5535877,0,17653.67939\n118321,21,2,2,1,Single,9,360.5355759,0,17591.7788\n114441,44,24,4,1,Married,9,360.51379,0,17588.6895\n125133,43,23,3,1,Single,9,360.5060028,0,17756.30014\n108668,49,31,2,0,Single,10,360.4891664,0,17432.45832\n164655,38,21,3,0,Married,10,360.4100235,0,17811.50118\n140919,21,3,3,1,Single,15,360.4024528,0,17724.12264\n197762,62,45,2,1,Single,3,360.3568235,0,17521.84118\n136200,50,30,1,0,Married,8,360.3359183,0,17680.79592\n176968,44,24,2,0,Married,8,360.3239825,0,17532.19913\n193098,38,19,2,0,Married,4,360.2925308,0,17698.62654\n106331,24,7,3,1,Married,13,360.2741388,0,17614.70694\n107210,45,24,3,1,Married,9,360.2732696,0,17577.66348\n143264,40,22,2,1,Married,10,360.2654461,0,17697.27231\n150528,16,0,4,1,Married,6,360.2057426,0,17561.28713\n191054,16,0,2,0,Married,12,360.1461124,0,17741.30562\n167411,23,5,1,1,Single,11,360.1011196,0,17773.05598\n196853,32,12,2,1,Married,7,360.0906291,0,17649.53146\n145763,23,4,1,0,Married,14,360.0686388,0,17653.43194\n168542,70,51,3,0,Single,6,360.066833,0,17659.34165\n144597,47,26,1,1,Married,7,360.0556483,0,17490.78242\n195042,39,20,4,1,Married,8,360.0393453,0,17641.96727\n141167,35,18,3,1,Single,14,360.0315403,0,17561.57702\n182024,24,7,2,0,Single,13,359.9859003,0,17453.29502\n106292,64,44,4,1,Single,8,359.9608642,0,17403.04321\n168051,52,34,4,1,Married,14,359.9510785,0,17505.55393\n163690,41,24,3,1,Married,8,359.884876,0,17477.2438\n146211,46,26,1,1,Married,6,359.8784498,0,17679.92249\n154563,57,39,2,1,Single,14,359.8603833,0,17682.01917\n130784,40,20,3,0,Married,13,359.8486765,0,17723.43383\n114919,18,0,3,1,Single,13,359.8209941,0,17782.04971\n132929,47,29,2,1,Single,10,359.8056854,0,17395.28427\n104250,68,47,2,0,Single,9,359.7460851,0,17718.30426\n118528,47,27,1,0,Married,15,359.6943071,0,17741.71536\n119958,32,15,3,1,Married,11,359.6533563,0,17424.66782\n107487,48,29,4,1,Married,8,359.6133215,0,17456.66608\n107033,70,49,2,0,Married,5,359.5786357,0,17734.93179\n145873,30,12,1,1,Single,7,359.5554052,0,17438.77026\n154255,53,32,1,1,Married,9,359.4808954,0,17611.04477\n188976,65,46,2,0,Married,14,359.4725616,0,17740.62808\n100413,26,9,2,0,Married,8,359.4419992,0,17471.09996\n108874,48,27,3,1,Single,7,359.3704443,0,17525.52222\n102206,47,28,1,0,Married,10,359.3406363,0,17560.03182\n146816,22,3,3,0,Married,12,359.3258172,0,17558.29086\n117983,53,35,4,0,Married,8,359.3057307,0,17567.28654\n161864,40,20,1,1,Married,10,359.3019561,0,17458.09781\n133716,57,36,3,0,Married,6,359.2734733,0,17656.67367\n115291,25,6,2,1,Single,14,359.1666871,0,17680.33436\n133667,38,19,3,1,Single,9,359.1562356,0,17478.81178\n131909,38,21,3,0,Married,12,359.1558898,0,17509.79449\n152774,49,29,2,0,Married,10,359.134403,0,17381.72015\n105584,63,44,2,1,Single,4,359.1195409,0,17696.97705\n137714,36,19,1,0,Married,14,359.1130124,0,17471.65062\n160244,16,0,3,1,Married,11,359.1024007,0,17440.12004\n180369,67,48,3,0,Single,9,359.1002684,0,17635.01342\n116002,41,24,1,0,Married,10,359.0747385,0,17519.73693\n181120,57,39,2,0,Married,9,359.0615895,0,17747.07948\n169359,24,7,2,1,Married,9,359.046212,0,17490.3106\n195547,34,13,1,0,Married,10,358.9893059,0,17534.4653\n157878,31,12,3,1,Single,10,358.9696898,0,17514.48449\n165833,69,52,4,1,Single,5,358.8832187,0,17388.16094\n191637,16,0,2,1,Single,14,358.8569449,0,17479.84725\n192270,41,23,4,1,Married,9,358.8415152,0,17703.07576\n162916,51,33,1,0,Married,12,358.8084011,0,17584.42006\n167632,30,9,2,0,Single,13,358.7909877,0,17604.54939\n180328,38,20,3,0,Married,10,358.7905874,0,17543.52937\n184590,61,40,2,0,Single,0,358.7900449,0,17608.50225\n137038,62,43,4,0,Single,6,358.6892509,0,17382.46255\n165997,24,6,2,0,Married,12,358.6754086,0,17660.77043\n187108,64,46,2,1,Single,9,358.6409834,0,17612.04917\n160699,33,14,3,0,Married,4,358.6146054,0,17717.73027\n188689,40,19,1,1,Married,8,358.5975643,0,17694.87822\n105333,55,36,3,0,Married,4,358.5410346,0,17682.05173\n110383,41,21,1,1,Single,6,358.5332434,0,17336.66217\n157356,46,27,1,0,Married,7,358.521705,0,17512.08525\n146187,50,32,2,1,Single,14,358.4916028,0,17550.58014\n180452,35,15,3,0,Married,9,358.4898649,0,17397.49325\n196191,35,18,2,1,Single,11,358.4436832,0,17665.18416\n124063,16,0,2,1,Single,12,358.4103971,0,17323.51986\n104372,59,38,1,0,Married,15,358.3923987,0,17593.61994\n100544,62,44,2,0,Married,3,358.3767748,0,17505.83874\n100346,56,38,2,1,Single,10,358.3455731,0,17414.27866\n122791,53,34,2,1,Married,7,358.3340559,0,17526.7028\n160704,64,45,2,0,Single,11,358.3010608,0,17606.05304\n173129,52,33,3,0,Married,7,358.285605,0,17696.28025\n155792,39,19,2,0,Married,7,358.2851037,0,17539.25519\n134378,44,27,3,0,Single,14,358.2696999,0,17660.485\n157905,28,11,2,1,Single,13,358.2237357,0,17696.18679\n191438,17,0,2,1,Single,14,358.1998153,0,17585.99077\n111573,24,6,3,0,Single,12,358.1857548,0,17680.28774\n132370,39,20,3,1,Married,10,358.1750534,0,17309.75267\n108846,34,14,3,1,Married,10,358.1689867,0,17404.44934\n194622,29,11,4,1,Married,10,358.0808641,0,17483.04321\n104753,21,2,3,0,Single,11,358.0658029,0,17356.29015\n191015,50,30,1,1,Single,7,358.0521139,0,17478.6057\n130536,43,23,3,1,Married,7,358.0178007,0,17425.89004\n161816,50,31,1,0,Married,7,358.0054248,0,17357.27124\n172782,60,43,1,0,Single,5,357.9918615,0,17609.59308\n161449,19,1,3,1,Married,7,357.9498603,0,17372.49302\n137893,69,51,3,1,Single,6,357.8914295,0,17637.57148\n158991,16,0,1,0,Married,12,357.890352,0,17643.5176\n117004,55,38,3,0,Married,7,357.8847715,0,17517.23858\n120095,24,7,3,1,Single,15,357.8838995,0,17517.19498\n170555,37,19,2,1,Married,8,357.7547807,0,17344.73904\n181346,17,0,2,1,Married,8,357.7523531,0,17429.61766\n143609,42,25,4,1,Single,12,357.7458818,0,17542.29409\n119086,48,27,3,1,Married,15,357.7439843,0,17584.19922\n149446,50,29,1,1,Single,10,357.7424259,0,17586.1213\n103533,18,0,3,1,Single,13,357.722728,0,17400.1364\n189651,21,4,2,0,Married,13,357.6916523,0,17521.58262\n183120,20,3,2,0,Married,9,357.6863146,0,17659.31573\n157312,69,51,3,1,Single,6,357.6622667,0,17523.11334\n176955,20,2,3,1,Single,14,357.6618486,0,17581.09243\n192362,50,29,3,1,Single,12,357.6420146,0,17423.10073\n111464,17,0,3,1,Married,15,357.561568,0,17501.0784\n129317,17,0,3,1,Single,12,357.5306666,0,17624.53333\n188017,41,23,4,0,Single,10,357.4448225,0,17510.24113\n103128,44,27,4,0,Married,6,357.4247898,0,17525.23949\n133889,30,13,2,1,Married,11,357.4243453,0,17563.21727\n127894,58,40,2,0,Married,15,357.4228043,0,17290.14022\n171488,43,23,1,1,Married,7,357.4166068,0,17545.83034\n161892,28,7,3,0,Married,7,357.4069156,0,17569.34578\n120164,45,27,2,0,Married,5,357.3396368,0,17655.98184\n181132,19,2,4,1,Single,14,357.3328031,0,17333.64016\n130640,57,40,2,0,Married,13,357.3194131,0,17566.97066\n178261,18,0,3,1,Single,14,357.2899993,0,17311.49997\n152602,34,15,3,1,Married,9,357.2540341,0,17396.70171\n197174,57,39,2,1,Single,13,357.2358453,0,17461.79227\n126038,48,28,4,1,Married,4,357.2248106,0,17527.24053\n161671,26,8,3,1,Married,11,357.1884355,0,17434.42178\n119145,24,7,2,1,Married,1,357.1767153,0,17264.83577\n165421,53,35,2,0,Married,8,357.1699618,0,17381.49809\n163348,63,44,3,0,Married,8,357.0844063,0,17343.22032\n171179,65,46,1,1,Single,6,357.0767817,0,17450.83909\n131351,20,1,1,0,Married,11,357.0735933,0,17437.67967\n154403,50,33,2,1,Married,6,357.0360115,0,17570.80058\n152472,63,44,4,1,Single,6,357.0268647,0,17595.34324\n153050,47,26,2,0,Married,9,357.0108591,0,17585.54296\n114345,70,49,2,1,Single,8,356.9909506,0,17534.54753\n195353,42,22,2,0,Married,9,356.950762,0,17417.5381\n187602,35,15,2,1,Single,3,356.9179157,0,17568.89579\n118100,31,11,3,1,Single,10,356.8588871,0,17374.94436\n152452,29,8,2,1,Married,9,356.7637017,0,17550.18509\n162825,42,23,3,0,Married,7,356.6924048,0,17543.62024\n162269,45,27,4,0,Married,8,356.6566289,0,17384.83145\n176291,43,23,1,1,Single,8,356.6430284,0,17612.15142\n114359,21,2,4,1,Married,11,356.6248281,0,17549.24141\n172376,18,0,3,0,Married,15,356.6074334,0,17629.37167\n198494,47,28,1,0,Married,13,356.572264,0,17518.6132\n148023,53,35,3,1,Single,10,356.5541485,0,17312.70743\n151938,46,25,4,1,Married,9,356.5424441,0,17419.12221\n149004,45,25,1,0,Married,12,356.5330417,0,17310.65209\n134511,23,6,2,1,Single,15,356.4963621,0,17329.81811\n118035,28,7,1,1,Married,6,356.4938337,0,17568.69169\n140961,27,9,4,0,Married,10,356.4852108,0,17438.26054\n160297,37,19,3,1,Single,13,356.4652078,0,17419.26039\n136163,20,1,1,1,Married,11,356.4554345,0,17391.77173\n156179,18,0,2,0,Married,9,356.413532,0,17228.6766\n145985,52,32,3,0,Single,12,356.412001,0,17251.60005\n110838,17,0,1,0,Single,11,356.3976584,0,17498.88292\n179314,43,22,1,0,Married,8,356.3656226,0,17499.28113\n121960,67,49,2,0,Single,7,356.3458968,0,17346.29484\n154520,25,7,3,1,Single,13,356.2680382,0,17226.40191\n137494,53,36,2,1,Single,9,356.2582565,0,17521.91283\n180084,41,20,3,1,Married,4,356.2079068,0,17579.39534\n196513,32,11,3,0,Married,7,356.1896072,0,17552.48036\n179414,45,26,2,0,Married,11,356.1514895,0,17476.57448\n148448,70,50,4,0,Single,10,356.1324328,0,17470.62164\n187398,29,12,3,0,Married,7,356.1236164,0,17547.18082\n138682,55,38,1,0,Married,6,356.072465,0,17270.62325\n124520,22,4,3,1,Married,11,356.0651866,0,17580.25933\n122506,43,25,3,0,Married,8,356.0433931,0,17575.16966\n138422,17,0,3,0,Single,15,356.0351485,0,17343.75743\n182020,32,14,3,0,Married,14,355.9797147,0,17485.98574\n181395,38,20,3,1,Single,9,355.9446692,0,17271.23346\n123546,22,3,2,1,Single,15,355.9348674,0,17494.74337\n171990,61,43,1,0,Single,7,355.8451532,0,17553.25766\n181657,22,3,3,0,Married,11,355.8316015,0,17305.58008\n146076,41,23,3,0,Married,12,355.8288778,0,17369.44389\n181260,27,8,1,1,Single,7,355.8158451,0,17559.79226\n187547,59,40,2,0,Married,6,355.7809566,0,17474.04783\n159600,46,27,1,0,Married,10,355.735993,0,17459.79965\n141612,29,11,3,1,Married,2,355.727515,0,17567.37575\n163437,26,9,4,1,Married,9,355.7123012,0,17205.61506\n138093,16,0,3,0,Married,14,355.6553901,0,17335.76951\n156211,49,31,4,0,Married,9,355.5963269,0,17184.81635\n119888,34,14,1,0,Married,9,355.5928162,0,17309.64081\n159002,25,8,3,1,Single,15,355.5696848,0,17568.48424\n103760,20,3,4,1,Married,8,355.5594833,0,17192.97417\n151530,33,14,1,1,Single,9,355.4608932,0,17200.04466\n178330,66,46,3,0,Single,1,355.4420655,0,17219.10328\n192851,21,2,3,1,Married,13,355.4298125,0,17352.49063\n114377,40,21,3,0,Married,6,355.4171396,0,17233.85698\n195171,43,22,4,0,Married,8,355.3785351,0,17416.92676\n118010,30,9,4,0,Married,10,355.3730872,0,17496.65436\n150077,53,34,3,0,Married,7,355.368379,0,17229.41895\n158358,69,52,3,0,Single,8,355.337431,0,17375.87155\n161579,68,51,3,0,Single,8,355.3273666,0,17275.36833\n109945,35,14,1,1,Single,6,355.2515755,0,17180.57878\n199314,49,30,2,0,Married,7,355.1656604,0,17423.28302\n150678,57,36,4,1,Single,15,355.1653687,0,17505.26844\n159336,26,5,3,0,Married,8,355.149888,0,17467.4944\n195107,56,35,2,0,Married,9,355.1419086,0,17415.09543\n156852,22,3,1,1,Married,7,355.0916144,0,17362.58072\n155082,16,0,3,0,Married,14,355.016182,0,17449.8091\n194917,18,0,3,0,Married,14,354.9811613,0,17224.05807\n127158,55,38,1,0,Married,6,354.9675474,0,17229.37737\n102033,40,20,4,1,Single,6,354.9667235,0,17442.33618\n197856,32,13,1,0,Married,6,354.9459359,0,17174.2968\n171375,19,0,3,0,Married,11,354.9298111,0,17252.49056\n175368,66,46,3,1,Married,0,354.9292685,0,17352.46343\n177466,52,34,2,1,Single,13,354.8917762,0,17244.58881\n181743,30,11,2,1,Married,6,354.8746025,0,17385.73013\n185354,33,15,4,0,Married,13,354.8681203,0,17374.40602\n100633,67,49,1,0,Married,8,354.8619578,0,17206.09789\n111419,45,24,1,0,Married,6,354.786418,0,17278.3209\n145922,31,13,3,1,Married,7,354.7310573,0,17414.55287\n107980,47,27,2,1,Married,4,354.7019674,0,17447.09837\n149010,17,0,1,0,Married,14,354.6611912,0,17421.05956\n134142,18,1,3,0,Single,13,354.615834,0,17457.7917\n193598,57,40,2,0,Married,12,354.5973766,0,17183.86883\n114343,28,8,3,1,Married,3,354.5941095,0,17449.70548\n162468,29,11,3,0,Married,9,354.5562406,0,17503.81203\n175202,44,23,2,0,Single,15,354.5241248,0,17375.20624\n120872,40,21,3,1,Single,15,354.5028023,0,17219.14012\n141659,35,15,1,1,Single,8,354.4909509,0,17227.54755\n190731,54,36,4,1,Married,1,354.4719431,0,17132.59716\n159043,59,41,4,1,Married,0,354.4576718,0,17303.88359\n157176,65,48,4,1,Single,8,354.4444888,0,17360.22444\n110193,43,22,3,0,Married,10,354.4434427,0,17286.17214\n197124,19,1,3,1,Married,14,354.4080467,0,17477.40234\n105534,48,30,3,1,Married,5,354.4078726,0,17484.39363\n140689,19,1,2,0,Single,15,354.3986786,0,17227.93393\n109005,25,7,3,1,Married,8,354.3809089,0,17320.04545\n142498,35,17,3,1,Single,10,354.3611687,0,17196.05844\n175753,28,7,2,1,Single,14,354.2739323,0,17334.69662\n109388,28,10,2,0,Married,10,354.2663393,0,17325.31697\n185366,41,24,2,0,Married,7,354.2510202,0,17339.55101\n172970,48,30,3,1,Single,6,354.2319723,0,17404.59862\n181125,65,47,2,0,Married,3,354.2202083,0,17241.01042\n118810,54,34,3,0,Married,10,354.1951505,0,17303.75753\n113214,49,29,4,1,Married,6,354.1825345,0,17175.12673\n181457,55,37,1,1,Single,9,354.1750277,0,17495.75139\n101288,70,50,3,0,Married,1,354.158526,0,17124.9263\n102189,20,2,1,1,Married,8,354.1103301,0,17402.51651\n196910,69,51,4,0,Married,5,354.0453347,0,17364.26674\n147513,30,9,1,0,Married,6,353.9876552,0,17386.38276\n146036,54,33,2,0,Single,10,353.9874896,0,17223.37448\n145645,24,7,1,1,Single,14,353.9868016,0,17257.34008\n188545,36,16,2,1,Married,8,353.951666,0,17373.5833\n149107,18,0,2,1,Married,10,353.9067638,0,17310.33819\n191562,56,39,3,0,Married,8,353.9065494,0,17314.32747\n149006,70,49,3,1,Married,0,353.9014798,0,17379.07399\n140578,50,31,2,1,Married,15,353.8972413,0,17473.86207\n102507,42,23,2,1,Married,10,353.8887175,0,17466.43588\n105426,25,8,2,1,Married,11,353.8385538,0,17297.92769\n106813,33,14,1,1,Single,11,353.8293525,0,17195.46763\n124584,32,11,4,1,Married,3,353.7627687,0,17196.13844\n123957,61,41,4,0,Married,8,353.7379997,0,17171.89999\n123231,46,26,3,0,Married,7,353.723634,0,17414.1817\n119475,33,14,2,0,Married,6,353.7225604,0,17447.12802\n100537,22,4,3,0,Married,14,353.6721522,0,17356.60761\n171964,52,31,1,0,Married,10,353.6364025,0,17380.82013\n117988,25,6,2,0,Single,11,353.6333985,0,17180.66993\n132954,42,24,2,1,Married,9,353.6248492,0,17195.24246\n143469,49,29,3,1,Single,6,353.6232199,0,17383.161\n155225,37,19,3,1,Single,9,353.5884154,0,17448.42077\n163874,19,2,2,1,Single,15,353.5400426,0,17322.00213\n161887,64,44,3,0,Single,12,353.4922551,0,17322.61276\n125506,33,13,2,0,Married,11,353.4707712,0,17172.53856\n179587,50,30,1,0,Single,12,353.4644169,0,17387.22085\n105979,16,0,2,0,Married,10,353.4630295,0,17360.15148\n137576,38,19,3,0,Single,14,353.4471605,0,17400.35803\n162666,53,33,1,0,Married,7,353.4243914,0,17322.21957\n167409,49,29,4,1,Married,5,353.4226679,0,17185.1334\n157560,45,26,3,1,Single,11,353.4119788,0,17123.59894\n159795,26,7,3,0,Married,7,353.4056805,0,17107.28403\n159829,33,12,2,1,Single,7,353.2495832,0,17106.47916\n180505,56,39,1,0,Married,7,353.2470892,0,17202.35446\n154475,51,34,2,0,Married,6,353.2327721,0,17116.63861\n121497,39,21,2,1,Married,7,353.2253567,0,17204.26784\n100312,34,14,2,0,Married,7,353.1886871,0,17191.43436\n183209,26,6,3,1,Married,8,353.185245,0,17061.26225\n186584,69,52,2,1,Single,9,353.1683465,0,17102.41733\n133911,47,29,2,0,Married,10,353.1585726,0,17371.92863\n188342,57,36,2,0,Married,4,353.1485605,0,17147.42803\n180027,31,14,3,0,Married,8,353.0847916,0,17402.23958\n108802,34,17,3,0,Single,14,353.0514545,0,17092.57273\n105509,50,30,3,0,Married,10,353.0451445,0,17086.25723\n146365,19,2,4,1,Married,8,353.0359293,0,17304.79647\n170137,33,13,2,0,Married,8,353.0118007,0,17279.59004\n125962,16,0,3,1,Married,8,352.9864805,0,17383.32403\n178458,48,27,3,0,Married,6,352.9830599,0,17295.153\n106000,35,17,2,1,Married,6,352.9391772,0,17235.95886\n185046,22,3,3,1,Single,11,352.901898,0,17184.0949\n192198,43,25,4,0,Married,8,352.8938943,0,17325.69472\n190883,32,12,4,1,Single,7,352.8787669,0,17132.93835\n113253,16,0,2,0,Married,12,352.871164,0,17226.5582\n115037,47,26,3,1,Married,9,352.8150666,0,17327.75333\n173126,36,19,2,1,Single,14,352.7988494,0,17253.94247\n176585,47,29,2,0,Married,6,352.7070831,0,17263.35416\n182494,69,52,3,0,Married,12,352.6882863,0,17134.41432\n121925,50,31,3,1,Married,0,352.6876976,0,17170.38488\n166139,17,0,4,1,Married,15,352.6766172,0,17110.83086\n196621,26,9,2,0,Married,7,352.6742687,0,17270.71344\n131325,58,37,2,1,Single,7,352.6099119,0,17337.4956\n160642,17,0,1,1,Single,13,352.5326175,0,17154.63088\n188907,38,19,2,0,Married,7,352.5256432,0,17168.28216\n191525,38,20,2,1,Married,15,352.5064648,0,17256.32324\n169519,24,6,2,1,Married,8,352.411265,0,17128.56325\n134238,33,14,4,0,Married,8,352.3556656,0,17359.78328\n174735,49,28,3,1,Married,0,352.3441305,0,17119.20653\n131977,25,6,3,1,Single,12,352.3418282,0,17217.09141\n101179,17,0,3,0,Married,12,352.2672316,0,17239.36158\n140850,70,49,2,1,Married,1,352.2486719,0,17393.4336\n184407,47,30,4,0,Married,8,352.2256809,0,17139.28405\n125351,39,22,1,0,Married,7,352.218165,0,17340.90825\n180475,39,21,1,0,Single,8,352.1776675,0,17010.88338\n103901,30,13,2,1,Single,14,352.1600587,0,17180.00294\n132329,31,13,2,0,Married,9,352.0486787,0,17043.43394\n112191,22,4,2,1,Single,14,352.0244029,0,17026.22015\n164991,50,33,2,0,Single,12,352.0222124,0,17134.11062\n128643,42,23,3,1,Married,6,351.9808591,0,17052.04296\n193323,46,27,3,0,Married,10,351.9557039,0,17018.7852\n154044,17,0,1,1,Married,15,351.8847284,0,17197.23642\n170816,59,38,2,1,Married,10,351.8203858,0,17256.01929\n148095,36,18,3,0,Single,14,351.8171489,0,17326.85745\n144098,34,15,2,1,Single,15,351.7873354,0,17223.36677\n117379,55,36,2,1,Single,14,351.7866849,0,17203.33425\n154110,25,8,4,0,Married,8,351.7191213,0,17026.95607\n146649,20,2,1,0,Single,12,351.7077018,0,17004.38509\n133691,48,29,4,1,Single,15,351.6855186,0,17128.27593\n181502,68,49,3,1,Single,3,351.6841533,0,17031.20767\n100512,54,34,2,1,Married,11,351.6777248,0,17140.88624\n165097,34,14,2,1,Married,10,351.6458198,0,17164.29099\n159469,63,46,2,1,Single,9,351.5957984,0,17098.78992\n102488,55,38,2,1,Married,10,351.5892955,0,17366.46478\n193424,32,15,4,0,Married,8,351.5053283,0,16993.26642\n118161,62,44,1,0,Single,9,351.5052862,0,17265.26431\n144261,31,14,2,0,Married,10,351.4895098,0,17323.47549\n137905,70,49,4,0,Single,8,351.479994,0,17219.9997\n168419,33,12,2,0,Married,10,351.3479905,0,16974.39953\n191346,23,6,4,1,Married,12,351.3224314,0,17034.12157\n154802,60,39,1,1,Single,6,351.3205902,0,17341.02951\n174421,56,35,2,1,Single,9,351.3018979,0,17181.0949\n159166,24,6,4,1,Single,12,351.2954732,0,16989.77366\n194689,27,10,2,1,Married,8,351.2590205,0,17251.95103\n149669,65,45,2,1,Married,9,351.1961222,0,17347.80611\n149440,59,42,1,0,Single,11,351.1678295,0,17342.39148\n106286,50,31,3,1,Single,12,351.1635587,0,17259.17794\n131665,35,16,3,1,Married,7,351.1338432,0,17127.69216\n183216,49,29,2,0,Married,6,351.0978894,0,16995.89447\n130372,57,37,1,1,Married,1,351.0954403,0,17094.77202\n167670,59,41,3,1,Married,15,351.0509605,0,17149.54803\n122982,32,12,4,1,Married,0,351.0086366,0,16976.43183\n117203,55,36,2,1,Married,8,350.9456921,0,17345.28461\n126933,40,19,3,0,Married,7,350.8803961,0,16953.01981\n135046,53,34,4,1,Single,7,350.837945,0,17073.89725\n122145,58,41,3,0,Single,13,350.8363724,0,17163.81862\n122611,50,33,4,0,Married,11,350.8148488,0,17198.74244\n196325,32,13,2,0,Married,8,350.8046896,0,17095.23448\n130529,17,0,1,1,Single,13,350.7660344,0,17040.30172\n110031,20,2,3,1,Single,14,350.7393091,0,17107.96546\n119122,23,4,2,1,Married,13,350.6884471,0,17147.42236\n149152,36,15,4,0,Married,8,350.6783228,0,17305.91614\n118114,53,33,2,0,Married,8,350.6464345,0,17054.32173\n104521,64,45,1,0,Single,14,350.6281031,0,17116.40516\n192373,41,21,1,0,Married,9,350.5732049,0,17275.66025\n103008,52,32,1,1,Married,3,350.5612148,0,17060.06074\n129758,25,8,2,1,Single,13,350.5601193,0,17325.00597\n185743,28,9,2,0,Single,14,350.5269451,0,17092.34726\n129798,50,30,3,1,Married,10,350.5188669,0,17056.94335\n192390,45,25,4,0,Married,15,350.5132525,0,17205.66263\n121560,43,23,2,1,Single,6,350.5014551,0,16955.07276\n185829,46,27,3,1,Married,0,350.4766253,0,17183.83127\n183795,51,33,3,0,Married,8,350.4480587,0,17099.40294\n114414,57,36,3,0,Married,14,350.4226226,0,17172.13113\n138213,41,23,3,0,Single,11,350.3983156,0,17276.91578\n188884,17,0,1,1,Single,12,350.3614044,0,17184.07022\n150501,63,43,4,1,Married,0,350.3483783,0,16995.41892\n114625,61,44,3,0,Married,4,350.342034,0,17163.1017\n165315,69,51,1,1,Married,2,350.3338126,0,17126.69063\n190879,37,20,2,1,Single,15,350.2811076,0,17287.05538\n118532,61,43,3,1,Married,10,350.277242,0,17129.8621\n108278,60,41,3,0,Single,13,350.2495642,0,16925.47821\n187350,38,18,2,1,Married,7,350.2369548,0,17173.84774\n143187,45,26,3,0,Married,5,350.18656,0,16972.328\n120528,44,27,3,0,Married,7,350.1702777,0,16969.51389\n169563,28,10,4,0,Married,10,350.1622449,0,16939.11225\n170217,49,31,3,1,Married,11,350.1605282,0,17058.02641\n196584,25,7,2,1,Single,12,350.0970634,0,17013.85317\n100442,70,52,2,0,Single,0,350.0212837,0,16950.06419\n133639,35,16,2,0,Married,8,349.9969519,0,17164.8476\n132987,69,51,4,0,Single,7,349.9869437,0,17232.34719\n116099,54,35,2,1,Single,14,349.9708937,0,17185.54469\n158139,63,42,4,0,Single,14,349.9552586,0,17069.76293\n128584,55,36,1,0,Married,7,349.9393754,0,16964.96877\n168237,49,28,1,0,Single,11,349.9370395,0,16989.85198\n185934,37,16,2,1,Married,5,349.8526063,0,16999.63032\n123452,55,37,2,0,Single,6,349.693678,0,17091.6839\n176289,39,18,3,1,Single,14,349.6626186,0,16932.13093\n102432,53,36,1,0,Married,9,349.6512041,0,17114.56021\n195788,42,25,3,0,Married,8,349.6260907,0,17030.30454\n167934,33,13,3,0,Married,6,349.5705684,0,17003.52842\n142195,52,32,1,1,Single,10,349.501615,0,17160.08075\n148882,58,38,2,1,Single,10,349.4907146,0,17029.53573\n190026,21,2,2,0,Married,9,349.479746,0,17195.9873\n101950,20,1,1,1,Single,11,349.4773628,0,17206.86814\n117678,22,4,1,1,Married,13,349.4635858,0,17261.17929\n147595,51,30,3,0,Married,11,349.455222,0,17124.7611\n186736,25,8,4,1,Married,6,349.4449791,0,17053.24896\n122572,31,10,3,0,Married,10,349.4433066,0,17146.16533\n172368,24,7,2,1,Single,12,349.4374446,0,16919.87223\n172954,26,5,1,1,Married,10,349.4050007,0,16974.25004\n153576,44,23,3,1,Married,4,349.4022276,0,17014.11138\n133601,68,49,2,1,Married,2,349.3780289,0,16901.90145\n120585,47,26,3,0,Married,10,349.3442012,0,16926.21006\n185721,22,5,2,0,Single,14,349.3144355,0,17025.72178\n163227,54,33,2,0,Married,13,349.3069773,0,16985.34887\n127397,23,5,3,1,Single,13,349.2956825,0,17034.78413\n105716,20,1,3,1,Married,12,349.2639569,0,17083.19785\n105061,58,39,2,0,Married,1,349.2612861,0,16937.06431\n141230,42,22,2,0,Married,6,349.1696365,0,17063.48183\n171216,30,12,2,0,Single,15,349.1487161,0,16933.43581\n163327,35,17,1,0,Married,10,349.1377167,0,17229.88584\n109062,40,20,3,1,Married,9,349.1010083,0,16867.05042\n151723,51,32,2,1,Single,11,349.0996117,0,17203.98059\n102867,70,53,2,0,Single,14,349.0545647,0,16865.72824\n137770,63,44,2,1,Married,0,349.0543922,0,17031.71961\n168563,28,10,3,0,Married,8,349.0195329,0,17186.97665\n196430,51,32,2,1,Married,10,349.0100809,0,16896.50405\n151511,20,2,3,0,Married,14,348.9557284,0,17201.78642\n149808,34,14,3,0,Married,10,348.8530319,0,17143.6516\n105779,64,45,2,0,Married,4,348.8134872,0,17025.67436\n122469,68,49,1,0,Married,8,348.7748871,0,17058.74436\n130322,25,8,3,0,Married,12,348.7321726,0,16924.60863\n182556,24,6,3,1,Single,15,348.6990042,0,17203.95021\n102852,44,24,1,1,Married,10,348.6916777,0,16904.58389\n138868,61,43,1,0,Married,8,348.6501515,0,17194.50758\n177911,39,18,2,1,Married,10,348.6496435,0,17130.48218\n120599,29,9,2,1,Single,13,348.6479125,0,17199.39563\n109082,19,0,4,0,Married,14,348.6048979,0,17045.2449\n109980,51,31,2,0,Married,9,348.55901,0,16906.9505\n117129,19,2,3,1,Married,7,348.5274268,0,17050.37134\n168556,20,1,4,1,Married,9,348.4810314,0,17150.05157\n140466,25,7,2,1,Married,7,348.4781211,0,16841.90606\n148421,24,7,2,1,Married,7,348.4464703,0,16998.32352\n124746,29,12,2,0,Single,11,348.4339922,0,17159.69961\n100227,63,45,3,1,Single,3,348.3947227,0,16839.73614\n156605,50,29,3,1,Single,14,348.3946724,0,17052.73362\n193965,48,29,2,0,Married,6,348.3139311,0,17000.69656\n142747,54,34,2,1,Married,10,348.1976648,0,17120.88324\n178513,41,20,3,1,Married,9,348.1221337,0,17042.10669\n172107,45,25,3,0,Married,8,348.1212095,0,16908.06048\n143245,45,27,3,1,Married,9,348.0958406,0,17054.79203\n133523,67,47,3,1,Single,9,348.092367,0,16942.61835\n124111,26,5,4,1,Single,13,348.0904081,0,17008.52041\n198428,18,0,3,1,Married,14,348.0681577,0,17175.40789\n129497,39,22,4,1,Married,8,348.0553847,0,16902.76924\n165944,36,15,4,0,Married,6,348.0303265,0,17130.51633\n177289,49,30,4,0,Married,7,348.0281364,0,16930.40682\n162840,40,23,2,0,Married,7,347.9572848,0,17107.86424\n101771,56,36,2,1,Married,6,347.9234569,0,17087.17285\n155682,41,22,2,1,Single,8,347.8741141,0,16882.70571\n145809,19,0,2,0,Single,15,347.8682994,0,16899.41497\n164706,22,4,2,0,Married,11,347.8397092,0,16868.98546\n194380,38,18,2,1,Married,7,347.8272596,0,16871.36298\n112870,45,24,3,1,Married,14,347.7710942,0,16976.55471\n164342,31,10,2,0,Married,8,347.7058726,0,17007.29363\n113334,40,19,3,0,Single,13,347.6765338,0,16956.82669\n108853,44,26,2,1,Single,14,347.6735122,0,16932.67561\n119238,31,11,3,1,Married,7,347.6324753,0,17104.62377\n175486,63,46,2,0,Single,13,347.5310605,0,16780.55303\n196651,35,14,3,0,Married,7,347.5060604,0,16820.30302\n163200,32,11,2,0,Married,11,347.442301,0,16869.11505\n119095,63,43,4,1,Single,6,347.4387209,0,17005.93605\n102209,23,6,2,1,Single,11,347.3335975,0,16987.67988\n134680,48,28,4,0,Married,7,347.283444,0,16991.1722\n195939,31,10,2,0,Single,13,347.2765155,0,17113.82578\n151007,68,47,2,0,Married,3,347.2530136,0,16992.65068\n168685,31,11,4,0,Married,7,347.2452613,0,16856.26307\n132774,24,7,2,1,Married,10,347.2159474,0,16924.79737\n196210,17,0,3,1,Married,13,347.1903494,0,16974.51747\n177527,35,17,1,0,Married,7,347.1522303,0,17035.61152\n116373,63,45,4,1,Single,8,347.1100323,0,16974.50162\n192545,40,20,2,0,Married,7,347.0980232,0,17098.90116\n144803,66,47,1,0,Single,0,347.0821846,0,16764.10923\n182189,27,8,3,0,Married,9,347.0485681,0,16912.42841\n126504,58,41,2,1,Single,8,347.0430197,0,16865.15099\n186576,47,27,2,1,Single,14,347.0346467,0,17137.73234\n126838,20,3,2,0,Married,8,347.0277294,0,16905.38647\n160998,55,38,3,1,Single,10,347.0080358,0,16931.40179\n127866,33,15,1,0,Married,8,346.9535288,0,17107.67644\n173492,60,39,4,0,Married,10,346.9386523,0,17137.93262\n154346,69,50,2,0,Single,6,346.84849,0,17048.4245\n162557,28,10,3,0,Married,7,346.8444477,0,16961.22239\n124551,38,20,4,1,Married,15,346.8394825,0,17012.97413\n119496,26,6,3,1,Married,7,346.81155,0,16767.5775\n182167,58,38,3,0,Married,6,346.7603534,0,17061.01767\n177508,55,37,3,0,Married,10,346.7413411,0,16760.06706\n151694,21,4,3,1,Single,14,346.6996714,0,17013.98357\n198440,53,32,3,0,Married,6,346.6276954,0,17126.38477\n139691,26,6,2,1,Single,7,346.6259326,0,17034.29663\n146438,30,9,4,1,Single,13,346.5663868,0,17012.31934\n141729,27,8,4,0,Married,12,346.5660888,0,17124.30444\n116736,40,19,4,1,Married,13,346.5575078,0,16861.87539\n113690,19,1,2,0,Married,13,346.5476839,0,17095.3842\n133464,16,0,2,1,Married,11,346.5111224,0,16901.55612\n100122,59,39,2,0,Married,12,346.4663918,0,16919.31959\n164259,42,23,2,0,Single,11,346.4584209,0,16948.92105\n122307,56,37,1,0,Married,10,346.4563025,0,16848.81513\n196040,41,22,1,1,Married,8,346.4537728,0,17032.68864\n124424,45,26,2,1,Married,6,346.4500172,0,17066.50086\n152632,53,35,4,1,Single,10,346.4233164,0,16917.16582\n102165,23,5,1,1,Single,14,346.4052876,0,17107.26438\n115664,61,41,3,0,Single,8,346.35015,0,16946.5075\n160257,27,7,1,0,Married,9,346.3474987,0,16867.37494\n173290,27,10,3,0,Married,6,346.313464,0,16759.6732\n154758,27,9,1,1,Married,9,346.2778383,0,16860.89192\n103195,25,7,2,1,Married,7,346.2476458,0,16730.38229\n175685,51,31,3,1,Single,12,346.2398888,0,16873.99444\n177835,55,35,1,0,Married,8,346.214594,0,16817.7297\n144294,41,20,2,1,Married,6,346.2067461,0,16951.33731\n184161,26,7,1,1,Married,6,346.2011049,0,16796.05525\n129777,61,42,4,1,Single,15,346.1730892,0,16938.65446\n151534,18,0,1,1,Married,3,346.1635562,0,17061.17781\n160855,47,26,3,0,Married,10,346.1455862,0,16728.27931\n112497,36,17,3,1,Married,8,346.1351912,0,16804.75956\n128580,36,15,2,1,Married,9,346.1261774,0,16745.30887\n151099,29,11,3,1,Single,6,346.1008642,0,17038.04321\n158808,41,22,2,1,Single,10,346.0779642,0,16948.89821\n156600,70,52,3,0,Single,7,346.0747865,0,16837.73933\n181459,54,35,2,1,Married,6,346.0521767,0,16795.60884\n123990,69,49,4,1,Single,10,346.0410754,0,16956.05377\n110498,24,5,3,0,Single,11,345.9625425,0,16760.12713\n155394,24,5,2,1,Single,11,345.91391,0,17042.6955\n148988,19,0,4,1,Married,15,345.8881119,0,16923.4056\n198793,34,17,2,1,Married,10,345.8832494,0,16763.16247\n176776,45,25,3,1,Married,7,345.8649432,0,16911.24716\n131387,49,31,2,0,Married,8,345.8435284,0,16913.17642\n164405,45,28,3,1,Married,9,345.7872311,0,17054.36156\n164250,42,23,2,1,Married,6,345.775639,0,16717.78195\n100710,65,46,1,1,Single,4,345.7533158,0,16790.66579\n165897,33,16,4,1,Single,9,345.7442022,0,16757.21011\n160983,29,12,4,1,Married,10,345.7301215,0,16818.50608\n120304,49,28,4,1,Single,10,345.7293045,0,17053.46523\n180335,62,41,2,0,Single,10,345.7139435,0,16780.69718\n174547,22,4,3,1,Single,13,345.617154,0,16772.8577\n168527,50,30,3,0,Married,10,345.6061938,0,16910.30969\n159830,48,28,2,1,Single,11,345.5637767,0,16860.18884\n177284,21,4,4,0,Married,8,345.5634481,0,16864.17241\n161876,42,24,3,0,Single,10,345.5479558,0,17076.39779\n176380,35,14,4,1,Married,7,345.5422834,0,16862.11417\n200081,20,3,3,1,Married,8,345.5420496,0,16966.10248\n145396,60,42,4,1,Married,0,345.5389397,0,16955.94699\n144697,59,42,3,1,Married,7,345.4612249,0,16697.06125\n126383,52,31,2,1,Married,7,345.4512548,0,16892.56274\n106114,20,1,4,1,Single,14,345.4076428,0,16690.38214\n105472,39,20,2,1,Married,8,345.3943365,0,16885.71683\n185379,27,6,3,1,Single,6,345.3647249,0,16907.23625\n129428,36,15,3,1,Married,9,345.3383377,0,16749.91689\n178976,42,21,4,1,Married,10,345.3225416,0,16842.12708\n131335,47,27,2,0,Married,6,345.3103834,0,16851.51917\n175274,70,51,1,0,Married,2,345.2723201,0,17055.61601\n103919,58,37,1,0,Married,11,345.2673698,0,16702.36849\n198952,60,39,4,1,Married,10,345.2054142,0,16726.27071\n129289,48,31,4,0,Married,6,345.1297645,0,16989.48823\n155695,43,25,3,0,Single,8,345.1220392,0,17054.10196\n172251,44,26,4,0,Married,7,345.1190055,0,16776.95028\n179614,33,16,4,0,Married,9,345.1048901,0,16999.24451\n155148,33,13,1,0,Married,12,345.0914583,0,16941.57292\n113443,28,9,3,0,Single,12,345.0866831,0,16852.33416\n185622,16,0,4,1,Single,14,345.0605119,0,16695.0256\n157188,19,0,1,1,Married,0,345.0465518,0,16865.32759\n153802,51,30,1,0,Married,7,345.0123467,0,16708.61734\n139182,27,7,1,1,Single,14,345.0019193,0,16726.09597\n160522,47,26,3,0,Married,6,344.9483448,0,16913.41724\n139321,40,22,1,1,Married,6,344.8873218,0,16956.36609\n114187,40,23,3,1,Married,7,344.8632529,0,16796.16265\n189990,70,49,4,0,Single,8,344.8395259,0,16975.9763\n132143,58,39,1,0,Married,10,344.8105362,0,16981.52681\n118451,30,10,3,0,Married,7,344.7161475,0,16781.80738\n178669,49,28,3,1,Single,12,344.7069153,0,17026.34577\n191140,18,0,2,1,Single,15,344.6924813,0,16998.62407\n180326,61,43,2,1,Single,15,344.6696756,0,16656.48378\n139567,52,31,4,0,Single,12,344.6429943,0,16680.14972\n118368,21,3,3,1,Single,14,344.6144448,0,17018.72224\n153251,47,30,4,1,Married,7,344.5877953,0,16910.38977\n148159,38,18,2,1,Married,10,344.580356,0,16979.0178\n120741,55,36,3,0,Single,13,344.5180581,0,16725.90291\n121345,62,44,4,0,Married,4,344.5008914,0,16909.04457\n193440,25,6,2,1,Married,0,344.3833581,0,16874.16791\n125911,34,15,3,0,Married,10,344.3373564,0,16698.86782\n189329,34,16,2,1,Single,9,344.2895924,0,16732.47962\n150218,58,39,4,1,Married,5,344.2672179,0,16920.3609\n174693,39,22,2,1,Married,9,344.244867,0,16658.24335\n151362,46,28,1,1,Single,13,344.2283454,0,16972.41727\n199689,59,41,3,1,Married,6,344.1885488,0,16807.42744\n152793,51,32,2,1,Single,12,344.1671782,0,16988.35891\n142665,22,3,4,1,Single,12,344.1224663,0,16696.12332\n182957,16,0,2,0,Married,12,344.1137958,0,16888.68979\n165148,59,40,1,0,Married,10,344.0693671,0,16606.46836\n145311,22,4,4,1,Single,12,344.041623,0,16723.08115\n152104,31,10,2,1,Single,11,344.0389435,0,16933.94718\n161244,29,12,3,0,Married,8,343.9541429,0,16635.70715\n114544,56,35,3,0,Married,7,343.942022,0,16988.1011\n158579,38,21,3,0,Married,11,343.9203845,0,16777.01923\n143884,54,34,3,0,Married,7,343.9026776,0,16661.13388\n174819,19,2,2,0,Single,11,343.8728644,0,16791.64322\n130129,51,34,3,1,Single,10,343.8319521,0,16722.59761\n178649,39,18,2,0,Married,13,343.8099988,0,16864.49994\n141124,49,31,3,0,Married,8,343.8024341,0,16912.12171\n179239,48,31,3,0,Married,7,343.7715996,0,16670.57998\n127059,34,16,2,0,Married,8,343.7377114,0,16733.88557\n149574,16,0,4,0,Married,12,343.736168,0,16778.8084\n181229,23,6,3,0,Married,13,343.7103014,0,16623.51507\n151873,26,9,3,0,Married,9,343.6898797,0,16784.49399\n180156,49,29,3,1,Married,9,343.6724293,0,16620.62147\n181793,50,32,3,0,Married,12,343.6651084,0,16674.25542\n143747,17,0,2,1,Single,13,343.6331572,0,16665.65786\n124245,32,14,3,1,Single,14,343.5746726,0,16756.73363\n143346,54,37,3,0,Married,6,343.5739111,0,16626.69556\n188652,23,4,2,1,Married,14,343.544987,0,16755.24935\n155005,28,11,2,0,Married,9,343.5043559,0,16692.2178\n152927,49,28,2,0,Married,10,343.4731297,0,16645.65649\n101613,21,2,1,1,Married,6,343.4349944,0,16952.74972\n105158,23,6,1,1,Married,15,343.4044974,0,16934.22487\n131275,47,30,3,0,Single,12,343.3677913,0,16806.38957\n147912,58,40,2,1,Single,13,343.342176,0,16679.1088\n111389,52,32,2,0,Married,7,343.3311881,0,16891.55941\n195569,40,19,1,1,Married,6,343.294489,0,16865.72445\n100861,22,4,2,1,Single,12,343.2845714,0,16632.22857\n108172,54,36,2,1,Married,10,343.2754032,0,16786.77016\n194825,70,51,1,1,Single,8,343.2685172,0,16616.42586\n193643,20,1,4,1,Married,15,343.2462723,0,16579.31362\n108388,53,36,3,0,Single,15,343.2347351,0,16805.73676\n163057,44,27,3,1,Married,10,343.1851723,0,16876.25862\n188454,31,14,1,1,Single,7,343.1756364,0,16777.78182\n184771,22,3,2,0,Married,13,343.1106416,0,16889.53208\n128515,23,4,1,1,Single,14,342.9983742,0,16636.91871\n144980,60,43,2,1,Single,7,342.9824095,0,16581.12048\n188098,48,27,3,1,Married,10,342.9730674,0,16731.65337\n103168,25,8,3,0,Single,14,342.9189128,0,16665.94564\n114142,55,37,1,1,Married,11,342.9086256,0,16642.43128\n114961,36,15,3,1,Married,8,342.8995743,0,16790.97872\n181719,23,5,2,1,Married,15,342.8880303,0,16632.40152\n170515,22,4,3,1,Single,15,342.8773162,0,16938.86581\n166667,61,44,2,0,Married,2,342.8013707,0,16677.06854\n115439,67,46,2,0,Single,14,342.7898172,0,16665.49086\n136032,59,40,3,0,Married,9,342.7872945,0,16919.36473\n186656,49,28,4,0,Married,9,342.7815248,0,16735.07624\n138549,38,17,3,0,Single,15,342.7787276,0,16763.93638\n102874,64,44,1,1,Single,10,342.7594432,0,16560.97216\n150733,37,16,2,0,Single,12,342.75108,0,16652.554\n170019,63,45,4,0,Married,12,342.7440086,0,16595.20043\n155329,56,38,3,1,Married,5,342.7053956,0,16750.26978\n111222,69,48,2,1,Single,7,342.6985342,0,16810.92671\n116521,38,17,1,1,Married,0,342.69694,0,16854.847\n141044,17,0,4,1,Married,9,342.6813578,0,16721.06789\n100868,25,8,1,1,Single,12,342.6650991,0,16695.25496\n124868,34,13,4,0,Married,6,342.642589,0,16663.12945\n199850,40,19,3,1,Married,9,342.635767,0,16787.78835\n148130,47,28,1,0,Single,12,342.6143104,0,16770.71552\n191134,69,49,3,0,Single,7,342.600086,0,16794.0043\n133987,17,0,1,1,Married,1,342.5996055,0,16701.98028\n103303,40,22,3,1,Married,7,342.5927481,0,16676.63741\n195325,33,14,4,1,Married,7,342.5903152,0,16729.51576\n164331,58,40,4,0,Married,12,342.5776694,0,16678.88347\n197662,55,34,2,0,Married,6,342.5774884,0,16684.87442\n102064,68,48,3,1,Single,8,342.5345692,0,16539.72846\n111967,24,6,3,0,Single,15,342.5168582,0,16829.84291\n135019,45,27,2,1,Single,13,342.4931823,0,16800.65912\n179485,26,9,4,1,Single,6,342.4890431,0,16772.45216\n191539,23,4,3,0,Single,12,342.4540929,0,16703.70465\n181192,30,9,2,1,Married,7,342.4533934,0,16765.66967\n155596,64,44,3,0,Single,5,342.4532548,0,16720.66274\n115094,39,21,3,1,Married,8,342.4510042,0,16623.55021\n127011,19,1,2,1,Married,15,342.4184957,0,16863.92479\n154254,48,29,1,1,Married,10,342.4184844,0,16664.92422\n140047,51,32,2,0,Married,9,342.4155532,0,16566.77766\n123346,35,14,1,0,Married,10,342.3811594,0,16787.05797\n141146,64,44,2,1,Married,3,342.3560723,0,16729.80362\n107944,45,27,4,0,Married,10,342.268027,0,16746.40135\n140188,62,43,1,1,Single,1,342.2447597,0,16889.23799\n180947,35,15,2,0,Married,7,342.2152122,0,16790.76061\n137595,49,29,2,1,Married,9,342.1803999,0,16768.02\n156440,37,19,4,0,Married,9,342.0620114,0,16546.10057\n121723,57,38,2,0,Married,15,342.0250702,0,16757.25351\n111474,45,27,2,1,Married,9,341.9799628,0,16833.99814\n159188,26,6,4,1,Married,5,341.9402652,0,16621.01326\n196422,30,10,4,1,Single,10,341.9201456,0,16544.00728\n168288,23,6,2,0,Married,15,341.9096485,0,16604.48243\n185013,70,51,1,0,Single,7,341.9072784,0,16714.36392\n187466,61,42,1,0,Married,12,341.8587982,0,16802.93991\n131256,48,31,2,0,Married,9,341.8221344,0,16811.10672\n172641,65,44,2,1,Married,0,341.8161348,0,16762.80674\n111409,60,40,4,0,Single,10,341.7987715,0,16884.93858\n128356,22,5,1,0,Single,12,341.7569511,0,16699.84756\n153245,38,20,4,0,Married,10,341.7410458,0,16577.05229\n161340,51,33,2,0,Married,12,341.7148314,0,16578.74157\n115106,20,1,3,0,Married,12,341.7086908,0,16807.43454\n142696,57,38,4,0,Married,8,341.6449796,0,16680.24898\n184666,42,23,2,1,Single,10,341.5910745,0,16735.55373\n172822,48,27,3,1,Married,10,341.5685446,0,16718.42723\n172068,33,14,2,1,Married,6,341.567577,0,16781.37885\n195871,24,5,3,1,Married,7,341.551092,0,16565.5546\n127169,43,26,3,0,Married,9,341.468415,0,16611.42075\n154832,22,5,4,1,Married,9,341.4633368,0,16548.16684\n171075,69,51,3,0,Married,4,341.3515381,0,16865.57691\n198759,26,9,4,1,Married,6,341.321384,0,16728.0692\n160894,22,4,4,1,Single,14,341.2988319,0,16479.9416\n118019,35,18,4,1,Single,6,341.2750996,0,16854.75498\n148335,54,37,3,0,Married,6,341.2651472,0,16635.25736\n165484,44,25,2,0,Married,10,341.1858165,0,16728.29083\n122874,42,25,2,0,Married,8,341.1715541,0,16545.57771\n159310,47,28,3,1,Married,6,341.1694065,0,16567.47033\n126648,38,21,3,1,Single,12,341.1446327,0,16615.23164\n105742,21,3,3,0,Married,14,341.1303247,0,16726.51624\n116550,37,17,1,0,Married,12,341.0717472,0,16550.58736\n150241,45,27,1,0,Married,7,341.0716297,0,16778.58149\n157097,26,6,2,0,Single,15,341.0697115,0,16777.48558\n105748,40,19,2,0,Married,11,341.0328439,0,16778.6422\n199601,20,2,4,0,Married,11,341.0282104,0,16632.41052\n101508,41,24,4,0,Married,10,340.9718103,0,16775.59052\n140099,54,37,3,1,Married,10,340.9616183,0,16662.08092\n116898,50,30,2,1,Married,3,340.9452969,0,16589.26485\n184282,67,46,1,1,Single,10,340.8910535,0,16652.55268\n107678,34,13,2,0,Married,9,340.8726681,0,16540.63341\n168024,26,7,3,0,Married,7,340.8476184,0,16453.38092\n114228,40,21,3,0,Married,10,340.8087636,0,16839.43818\n121485,48,30,1,1,Single,8,340.8082596,0,16641.41298\n120846,69,51,2,1,Single,2,340.7535528,0,16461.67764\n150310,35,15,2,0,Married,10,340.7464028,0,16704.32014\n170309,16,0,3,0,Single,15,340.6809661,0,16632.04831\n133860,55,34,2,1,Single,15,340.6756615,0,16457.78308\n122304,40,22,3,1,Single,6,340.6393441,0,16762.96721\n188422,46,27,1,1,Married,0,340.607225,0,16585.36125\n147501,20,3,1,1,Married,8,340.5856515,0,16525.28258\n148101,19,2,4,1,Married,6,340.5823945,0,16618.11973\n175930,38,17,3,1,Married,3,340.5794941,0,16671.97471\n137276,55,37,3,1,Single,8,340.5382002,0,16817.91001\n106938,25,6,4,1,Married,7,340.5355528,0,16745.77764\n166936,68,51,2,1,Single,9,340.4844834,0,16444.22417\n115668,62,44,2,0,Married,6,340.4622361,0,16794.11181\n186367,48,31,2,0,Single,15,340.439031,0,16737.95155\n119772,43,24,3,0,Married,11,340.3817789,0,16703.08895\n102284,44,25,1,1,Single,11,340.3533667,0,16585.66834\n138994,31,11,4,1,Married,8,340.3491803,0,16682.45902\n174607,68,49,3,1,Single,9,340.3195615,0,16607.97808\n140319,65,44,2,1,Married,0,340.2747107,0,16774.73554\n130396,45,28,3,1,Single,8,340.2354144,0,16659.77072\n178650,43,23,2,1,Single,12,340.2184013,0,16436.92007\n137482,41,22,3,0,Married,11,340.180379,0,16744.01895\n162391,61,41,3,0,Single,7,340.144875,0,16622.24375\n167269,27,8,3,1,Married,14,340.095393,0,16765.76965\n189179,46,26,2,0,Married,7,340.084002,0,16444.2001\n153887,21,4,1,0,Married,13,340.0697223,0,16458.48612\n193914,26,9,3,0,Married,9,340.0277129,0,16743.38565\n119000,57,39,1,0,Married,8,340.0212605,0,16661.06303\n156649,17,0,2,0,Married,11,339.9900303,0,16601.50152\n173433,32,13,2,1,Married,9,339.9877417,0,16637.38709\n116113,40,21,2,1,Single,15,339.9531281,0,16758.65641\n149075,59,41,3,0,Married,10,339.9364265,0,16765.82133\n108241,29,12,2,1,Married,8,339.9327006,0,16774.63503\n158953,51,34,3,0,Single,13,339.9271734,0,16785.35867\n148193,50,33,3,1,Married,8,339.9036379,0,16468.1819\n132571,16,0,2,1,Married,11,339.903063,0,16434.15315\n163150,48,29,2,1,Married,14,339.8958901,0,16487.79451\n152047,45,26,2,0,Married,8,339.879647,0,16448.98235\n103888,47,30,3,0,Married,10,339.872347,0,16754.61735\n180042,33,15,3,1,Married,1,339.8535018,0,16706.67509\n190293,70,53,2,0,Married,7,339.8415865,0,16545.07933\n194715,53,32,3,0,Single,14,339.8202074,0,16584.01037\n135335,36,19,1,1,Married,6,339.811243,0,16594.56215\n122994,56,38,2,0,Single,15,339.7842178,0,16670.21089\n101548,19,2,1,0,Married,6,339.775025,0,16562.75125\n144763,61,40,3,0,Single,6,339.7156576,0,16551.78288\n181679,32,13,2,1,Single,13,339.7074083,0,16392.37042\n102500,29,12,4,0,Married,7,339.6659879,0,16541.2994\n100630,37,17,3,1,Single,9,339.6570056,0,16472.85028\n161133,25,7,4,1,Single,11,339.6139328,0,16640.69664\n199433,44,27,2,1,Married,8,339.6077025,0,16568.38513\n158931,39,19,1,1,Married,10,339.6011591,0,16393.05796\n191299,50,29,2,1,Single,8,339.5974725,0,16655.87363\n119721,49,32,3,0,Married,8,339.5879226,0,16408.39613\n113389,37,18,4,0,Single,15,339.551513,0,16435.57565\n197421,50,30,4,1,Single,11,339.4924146,0,16495.62073\n176198,62,44,3,0,Married,4,339.4783345,0,16548.91673\n180395,24,5,2,0,Single,13,339.441453,0,16461.07265\n165062,42,24,1,1,Married,4,339.3914837,0,16556.57419\n189283,49,31,2,0,Married,10,339.3651969,0,16655.25985\n128804,46,28,2,1,Married,7,339.3509979,0,16610.5499\n103071,36,17,3,1,Married,10,339.2887071,0,16407.43536\n139123,33,15,4,1,Married,9,339.2867738,0,16569.33869\n116562,66,46,3,0,Married,3,339.2291573,0,16520.45787\n107019,54,34,2,1,Married,9,339.2091481,0,16644.45741\n100798,30,13,3,0,Married,7,339.1913977,0,16758.56989\n145631,58,40,4,0,Married,10,339.1586915,0,16610.93458\n190417,61,40,4,0,Single,2,339.1459945,0,16612.29973\n149520,54,33,2,0,Married,12,339.1419475,0,16468.09738\n111723,37,20,2,1,Married,9,339.0855567,0,16599.27784\n100081,16,0,4,1,Married,15,339.0782084,0,16592.91042\n193325,30,10,3,1,Married,9,339.0187299,0,16352.9365\n166252,63,43,3,1,Single,10,339.005342,0,16416.2671\n112315,36,15,3,0,Married,10,339.0030757,0,16742.15379\n112202,44,24,3,1,Single,7,338.9907365,0,16719.53683\n109615,64,44,4,0,Single,9,338.9800943,0,16374.00472\n154215,67,46,2,0,Married,7,338.9769113,0,16563.84557\n155534,68,49,2,0,Single,3,338.9155024,0,16578.77512\n152543,16,0,3,0,Married,12,338.8692443,0,16713.46222\n102861,39,22,2,0,Married,6,338.865123,0,16705.25615\n158516,21,3,1,1,Married,11,338.8357105,0,16359.78553\n127543,25,7,2,1,Single,14,338.8142936,0,16416.71468\n145402,47,28,3,1,Married,7,338.6870138,0,16699.35069\n132503,63,43,1,1,Single,0,338.6799977,0,16422.99989\n109823,23,6,2,1,Married,10,338.6778191,0,16584.89096\n193094,43,26,3,1,Married,9,338.6659402,0,16719.29701\n178557,54,37,2,1,Married,6,338.6143252,0,16402.71626\n199224,54,34,4,0,Married,14,338.5725101,0,16605.62551\n153634,51,30,4,1,Single,8,338.5637579,0,16716.1879\n110540,33,16,4,1,Married,9,338.5552168,0,16652.76084\n127607,57,36,3,1,Married,6,338.5430311,0,16512.15156\n183604,33,16,3,0,Married,7,338.5373245,0,16492.86623\n112660,26,6,1,1,Married,12,338.521163,0,16659.05815\n105590,63,44,1,1,Married,5,338.5093334,0,16523.46667\n108410,66,46,3,0,Single,1,338.4848283,0,16606.24142\n171701,44,26,3,1,Single,13,338.4338689,0,16572.69345\n138738,24,6,3,0,Married,10,338.417008,0,16677.8504\n114275,40,19,3,1,Married,8,338.3952343,0,16485.76172\n149712,20,1,2,1,Married,13,338.3642916,0,16375.21458\n121883,60,40,3,1,Single,7,338.3607926,0,16697.03963\n114244,44,23,3,1,Single,13,338.3345866,0,16392.72933\n139947,40,20,3,1,Single,7,338.333264,0,16554.6632\n151013,57,39,3,1,Married,8,338.3037737,0,16371.18869\n147692,45,25,2,1,Married,6,338.295675,0,16493.78375\n194874,59,42,2,1,Single,14,338.2666757,0,16382.33379\n150639,69,52,3,0,Married,9,338.2597543,0,16517.98772\n121255,50,31,1,1,Married,10,338.2484618,0,16361.42309\n196424,19,0,2,1,Married,12,338.227528,0,16400.3764\n168165,16,0,2,1,Married,14,338.1644742,0,16521.22371\n186987,32,12,3,1,Married,9,338.1616198,0,16491.08099\n132518,40,20,2,0,Married,10,338.1253431,0,16405.26716\n156159,56,35,1,1,Married,6,338.0992415,0,16386.96208\n128090,48,29,3,1,Single,8,338.0449029,0,16484.24515\n113768,62,44,3,0,Married,9,337.990563,0,16578.52815\n185061,44,27,2,0,Married,6,337.9666721,0,16676.33361\n100950,19,0,3,1,Single,13,337.9628924,0,16616.14462\n111880,48,30,2,1,Married,9,337.8656877,0,16362.28439\n123749,50,31,3,1,Married,13,337.8178584,0,16457.89292\n126531,69,51,2,1,Married,2,337.7953338,0,16299.76669\n172363,64,45,2,0,Married,5,337.7526133,0,16406.63067\n124458,41,24,1,1,Married,7,337.6700037,0,16294.50019\n152931,40,22,1,0,Married,7,337.6245613,0,16406.22807\n198365,41,22,3,1,Married,0,337.6231236,0,16302.15618\n188873,49,32,1,0,Married,10,337.6150066,0,16641.75033\n193740,20,1,3,0,Married,13,337.5997169,0,16644.98585\n150702,24,5,1,1,Married,10,337.5616638,0,16390.08319\n147831,54,35,2,0,Single,13,337.541232,0,16596.0616\n138460,65,44,1,0,Married,1,337.486374,0,16612.3187\n120883,37,18,3,0,Married,7,337.4521818,0,16554.60909\n157575,41,21,4,1,Married,6,337.4332674,0,16382.66337\n116160,56,36,2,1,Married,7,337.4271396,0,16288.35698\n140875,34,17,3,1,Married,7,337.4105308,0,16498.52654\n114705,50,31,3,1,Married,8,337.4037694,0,16330.18847\n188777,43,25,2,1,Single,13,337.3980296,0,16470.90148\n198860,22,5,4,0,Married,11,337.3874253,0,16491.37127\n197244,55,34,4,1,Single,9,337.3539515,0,16628.69758\n133197,68,50,2,1,Single,15,337.3376026,0,16509.88013\n174513,51,31,3,1,Single,8,337.3336847,0,16294.68424\n132645,43,23,2,1,Married,10,337.2904711,0,16362.52356\n133972,23,6,4,1,Married,15,337.2515171,0,16394.57586\n115145,31,12,4,0,Married,9,337.2214808,0,16654.07404\n166331,26,9,3,0,Married,12,337.1969806,0,16351.84903\n175687,40,20,4,0,Married,10,337.1846081,0,16289.23041\n158234,17,0,1,0,Single,12,337.170483,0,16405.52415\n110563,22,3,2,1,Married,9,337.168717,0,16449.43585\n167677,63,45,2,0,Married,8,337.1433254,0,16405.16627\n118881,55,35,3,1,Married,7,336.9801312,0,16556.00656\n137861,33,12,3,1,Single,8,336.9777824,0,16395.88912\n198454,29,10,3,0,Married,10,336.9577303,0,16397.88652\n128538,39,18,3,1,Married,8,336.9570075,0,16248.85038\n186711,19,2,1,0,Single,13,336.9348452,0,16553.74226\n180192,49,32,3,1,Single,6,336.9093948,0,16541.46974\n101443,21,4,2,1,Married,15,336.8241458,0,16450.20729\n124294,70,53,4,1,Single,4,336.8198933,0,16324.99467\n141447,66,47,2,0,Married,9,336.8045779,0,16293.2289\n151303,60,41,2,1,Single,10,336.7476761,0,16374.38381\n106773,44,27,2,1,Married,10,336.7475878,0,16398.37939\n168964,47,30,4,0,Married,6,336.704949,0,16581.24745\n125926,36,17,4,1,Married,4,336.6827739,0,16443.1387\n135547,47,28,2,0,Married,9,336.6691002,0,16620.45501\n127992,55,38,2,1,Married,4,336.6198194,0,16516.99097\n164189,18,0,2,1,Single,12,336.609826,0,16278.4913\n198185,37,17,3,1,Married,7,336.59998,0,16437.999\n113832,17,0,3,1,Married,13,336.5965323,0,16292.82662\n120580,41,20,3,1,Married,8,336.5769978,0,16259.84989\n135388,46,25,2,0,Married,13,336.5725933,0,16610.62967\n112089,49,29,1,1,Married,6,336.5527431,0,16543.63716\n168702,19,2,3,0,Married,14,336.5321866,0,16464.60933\n102758,61,43,3,0,Single,14,336.5303485,0,16564.51743\n178563,67,50,2,1,Single,1,336.4989489,0,16473.94745\n194457,55,36,3,1,Married,13,336.4670809,0,16524.35405\n144033,22,5,4,1,Married,10,336.3712977,0,16606.56488\n193151,23,6,2,0,Married,15,336.3698196,0,16353.49098\n141558,17,0,4,1,Married,14,336.3312774,0,16499.56387\n157994,58,41,1,0,Married,6,336.3238418,0,16612.19209\n153700,69,49,4,0,Single,0,336.3024921,0,16468.12461\n162404,34,14,2,1,Married,0,336.2421645,0,16501.10823\n161015,57,39,3,0,Single,15,336.2414658,0,16453.07329\n189835,32,13,2,1,Married,10,336.2135837,0,16269.67919\n101207,41,21,2,1,Single,8,336.1982882,0,16227.91441\n165539,59,38,2,0,Married,8,336.1798645,0,16369.99323\n107460,47,28,3,0,Married,9,336.1773364,0,16545.86682\n114735,19,1,4,1,Married,11,336.0323478,0,16260.61739\n151432,20,3,2,0,Married,11,336.0320803,0,16585.60402\n142927,59,39,4,0,Married,11,336.0241915,0,16575.20958\n193311,31,14,3,1,Married,10,335.9916574,0,16311.58287\n157482,20,1,1,1,Married,8,335.9492312,0,16431.46156\n156043,62,42,1,0,Single,8,335.9417946,0,16391.08973\n185404,34,15,4,1,Married,10,335.8831424,0,16440.15712\n158191,40,21,3,1,Married,9,335.8491238,0,16238.45619\n124753,67,50,3,0,Single,14,335.8424386,0,16495.12193\n127401,63,42,3,0,Single,9,335.8138914,0,16198.69457\n180963,48,27,3,1,Married,9,335.7293722,0,16505.46861\n115076,35,14,2,1,Married,10,335.7239662,0,16317.19831\n180587,27,8,2,1,Married,9,335.7146214,0,16321.73107\n129126,53,35,2,0,Single,14,335.7056368,0,16307.28184\n196071,24,5,1,0,Married,11,335.7048594,0,16218.24297\n182720,41,24,3,1,Married,11,335.6455523,0,16195.27762\n144368,26,8,3,0,Single,14,335.6246774,0,16577.23387\n181019,16,0,1,0,Single,13,335.6097647,0,16258.48824\n140842,40,21,2,1,Married,7,335.6067935,0,16233.33968\n163512,44,27,3,1,Married,14,335.5799953,0,16462.99977\n124024,18,0,4,0,Married,15,335.5532115,0,16508.66058\n103444,56,38,1,0,Married,8,335.5032735,0,16338.16368\n177037,49,28,2,1,Married,10,335.493889,0,16257.69445\n170235,21,3,4,1,Married,7,335.4810806,0,16445.05403\n129722,56,36,3,0,Married,12,335.4308506,0,16346.54253\n113132,68,48,4,0,Single,5,335.4109417,0,16542.54709\n178416,49,28,4,1,Married,14,335.4017196,0,16481.08598\n169120,23,5,3,1,Single,14,335.3995789,0,16568.97895\n198813,64,46,1,1,Married,2,335.3879955,0,16194.39978\n175598,50,31,2,1,Married,7,335.3727761,0,16313.63881\n145479,51,32,3,0,Married,7,335.3373837,0,16561.86919\n155735,47,29,3,1,Married,9,335.3199272,0,16547.99636\n133084,26,7,1,0,Married,8,335.2827816,0,16371.13908\n145028,31,13,3,0,Married,8,335.2771126,0,16427.85563\n187712,29,9,4,0,Married,10,335.2730384,0,16406.65192\n170854,47,28,3,1,Married,7,335.2523398,0,16319.61699\n118428,56,38,2,0,Married,10,335.2309377,0,16480.54689\n144555,37,16,2,1,Married,8,335.2123869,0,16197.61935\n181713,49,31,3,1,Married,6,335.1623482,0,16192.11741\n186999,38,18,3,1,Married,10,335.13541,0,16489.7705\n158925,35,15,2,0,Married,10,335.1009955,0,16555.04978\n162994,40,23,4,1,Married,10,335.0197592,0,16474.98796\n118523,24,7,2,1,Married,13,335.0177934,0,16444.88967\n110388,49,28,1,0,Married,8,334.9851495,0,16276.25748\n154251,43,26,2,0,Single,14,334.9675153,0,16242.37577\n130127,55,34,3,1,Married,13,334.9283336,0,16151.41668\n167353,18,0,2,1,Single,13,334.9006802,0,16170.03401\n134164,22,4,1,0,Single,13,334.8882663,0,16217.41332\n164783,36,17,1,0,Married,0,334.8874633,0,16388.37316\n101733,24,6,2,0,Married,13,334.8767772,0,16189.83886\n127328,48,28,1,1,Married,10,334.7612678,0,16298.06339\n180989,39,22,1,1,Single,6,334.7391302,0,16305.95651\n138881,38,18,4,0,Married,7,334.7287652,0,16190.43826\n116929,61,40,3,0,Married,10,334.6916841,0,16464.58421\n171769,43,26,1,1,Married,9,334.6358125,0,16320.79063\n162938,28,7,2,1,Married,6,334.5390852,0,16429.95426\n125603,42,21,2,1,Single,11,334.5252695,0,16172.26348\n131853,16,0,2,1,Married,15,334.5228852,0,16422.14426\n188103,62,41,3,0,Married,9,334.48129,0,16284.0645\n120125,23,5,2,1,Married,12,334.4644319,0,16251.2216\n155877,27,8,3,1,Married,10,334.4239027,0,16158.19514\n188162,34,14,2,1,Married,10,334.4126622,0,16267.63311\n143059,42,25,3,1,Married,8,334.4120546,0,16424.60273\n175764,19,1,2,0,Married,15,334.3978505,0,16376.89253\n129154,30,11,3,1,Married,7,334.384651,0,16243.23255\n150611,51,32,4,1,Married,6,334.3324841,0,16363.62421\n103788,22,3,3,1,Married,15,334.2904507,0,16506.52254\n160497,21,4,3,1,Married,10,334.2721438,0,16313.60719\n134904,56,38,3,0,Married,8,334.2720603,0,16242.60302\n152969,59,38,3,0,Married,14,334.1823816,0,16234.11908\n172101,38,21,3,0,Married,9,334.1721313,0,16312.60657\n125466,47,28,2,1,Married,10,334.1708324,0,16167.54162\n144323,49,31,4,1,Married,2,334.1259667,0,16181.29834\n111247,20,2,2,0,Married,15,334.1035772,0,16116.17886\n166099,57,36,3,0,Single,11,334.0226029,0,16115.13015\n169276,18,0,4,1,Married,15,333.997535,0,16392.87675\n140812,34,17,3,0,Married,7,333.9880075,0,16391.40038\n141208,51,34,2,1,Married,9,333.9594722,0,16178.97361\n155691,17,0,3,1,Married,11,333.9574544,0,16345.87272\n178003,43,25,1,1,Married,8,333.9538906,0,16438.69453\n186846,45,25,2,1,Married,10,333.8893909,0,16296.46954\n191538,56,38,2,1,Married,1,333.8694586,0,16233.47293\n124098,44,27,3,1,Married,7,333.8670745,0,16470.35373\n177322,47,27,3,1,Single,6,333.8327365,0,16415.63683\n163129,16,0,2,0,Married,11,333.81286,0,16179.643\n130092,21,3,4,1,Single,12,333.8098672,0,16157.49336\n166551,16,0,3,1,Married,11,333.7185875,0,16411.92938\n166779,24,5,2,1,Single,13,333.7091493,0,16155.45747\n154357,31,11,2,1,Married,9,333.7069214,0,16284.34607\n193656,60,43,3,1,Single,10,333.6874196,0,16365.37098\n154741,55,35,1,1,Married,10,333.633504,0,16391.6752\n168790,63,45,2,0,Married,5,333.6016894,0,16122.08447\n175449,53,36,1,0,Married,7,333.5916552,0,16410.58276\n151890,20,3,3,1,Married,14,333.5899085,0,16396.49543\n135549,27,10,2,0,Married,8,333.5663511,0,16177.31756\n138591,59,41,4,1,Single,10,333.5661323,0,16436.30662\n144686,17,0,4,1,Single,12,333.51709,0,16359.8545\n172028,25,6,2,0,Married,12,333.4970862,0,16391.85431\n137892,54,34,3,1,Single,9,333.4723997,0,16300.61998\n106382,64,47,2,1,Married,5,333.4567358,0,16447.83679\n181264,67,47,4,1,Married,3,333.4236021,0,16419.18011\n145110,42,22,2,1,Married,14,333.3446093,0,16306.23047\n193147,22,3,3,0,Married,11,333.3442393,0,16288.21197\n145876,23,4,4,0,Married,11,333.2677765,0,16306.38883\n133501,42,24,1,1,Married,8,333.2618886,0,16412.09443\n109884,18,0,1,0,Married,15,333.2548377,0,16233.74189\n164400,40,19,3,0,Single,13,333.2375338,0,16118.87669\n188540,68,47,2,1,Single,6,333.1969856,0,16171.84928\n163671,45,25,2,1,Single,6,333.1953864,0,16196.76932\n136454,21,4,3,1,Married,15,333.1761337,0,16215.80669\n105226,58,39,2,0,Married,6,333.1704666,0,16143.52333\n162370,37,17,3,1,Married,7,333.1410952,0,16334.05476\n133472,69,49,3,0,Married,6,333.1220539,0,16091.1027\n125573,68,47,3,1,Married,9,333.1090304,0,16075.45152\n117724,30,11,2,0,Single,11,333.0913628,0,16199.56814\n191927,16,0,1,0,Married,6,333.0519897,0,16384.59949\n184053,36,15,2,0,Married,8,333.0432102,0,16322.16051\n106094,52,32,3,1,Married,6,333.0237772,0,16374.18886\n196576,17,0,2,1,Married,11,333.0102117,0,16082.51059\n198957,16,0,3,1,Married,8,333.0099508,0,16384.49754\n190057,41,23,3,1,Single,11,332.9836387,0,16420.18194\n120582,53,36,3,1,Married,7,332.9713842,0,16390.56921\n107641,19,2,2,1,Married,15,332.9501822,0,16083.50911\n198799,65,45,4,0,Married,9,332.9091596,0,16155.45798\n178494,40,23,2,1,Married,9,332.8686315,0,16077.43158\n142529,38,17,1,1,Married,6,332.8659118,0,16162.29559\n196218,34,15,3,1,Married,10,332.7724649,0,16060.62325\n130563,28,8,4,1,Married,9,332.7544088,0,16244.72044\n126995,59,41,3,1,Married,10,332.6400036,0,16083.00018\n100938,50,31,1,0,Married,6,332.5962019,0,16286.8101\n145801,25,6,4,1,Married,12,332.5473656,0,16321.36828\n142396,64,43,3,0,Married,9,332.5415902,0,16116.07951\n154414,59,41,2,0,Married,7,332.5312112,0,16289.56056\n178457,24,6,3,1,Married,0,332.5239841,0,16371.19921\n181352,39,19,4,0,Married,6,332.5219251,0,16185.09626\n173148,66,48,4,0,Married,8,332.5152365,0,16274.76183\n120777,54,34,3,1,Married,6,332.4973352,0,16330.86676\n179291,55,36,3,1,Married,8,332.4785656,0,16271.92828\n180878,40,20,4,0,Married,3,332.4729909,0,16027.64955\n130583,48,29,2,1,Married,9,332.4600167,0,16075.00084\n182876,52,34,2,1,Married,7,332.3828307,0,16390.14154\n110271,34,15,2,1,Married,8,332.3397089,0,16065.98544\n108514,21,3,3,1,Married,12,332.2190755,0,16251.95378\n162083,18,0,2,0,Married,15,332.2094764,0,16331.47382\n128541,30,13,2,0,Married,7,332.1763765,0,16111.81883\n191861,52,31,1,0,Married,7,332.1391406,0,16240.95703\n116100,29,8,3,1,Married,10,332.1195223,0,16275.97612\n174364,57,37,3,0,Married,8,331.9404469,0,16059.02235\n145726,44,24,2,1,Married,7,331.9379418,0,16077.89709\n145201,18,0,2,1,Single,12,331.890297,0,16318.51485\n109376,70,51,4,0,Married,10,331.8877544,0,16279.38772\n118693,58,39,3,1,Married,10,331.8867547,0,16029.33774\n159114,56,35,4,1,Single,8,331.7787397,0,16294.93699\n155719,70,51,4,0,Married,9,331.7728568,0,16288.64284\n126656,26,7,1,0,Married,13,331.7301861,0,16234.50931\n136312,47,28,2,1,Married,9,331.711632,0,16271.5816\n110278,33,16,2,1,Single,7,331.6901581,0,16156.50791\n135440,66,47,3,0,Single,6,331.6892866,0,16333.46433\n178621,35,15,4,1,Single,9,331.6762154,0,16249.81077\n169953,52,31,1,0,Married,6,331.6699986,0,16263.49993\n144833,22,5,2,0,Married,15,331.6519931,0,16065.59966\n109165,27,9,2,1,Single,13,331.6471059,0,15988.3553\n106417,52,34,2,1,Married,9,331.644446,0,16034.2223\n183463,27,9,3,1,Married,9,331.6333184,0,16268.66592\n163543,31,13,2,1,Married,8,331.6192981,0,16254.96491\n151470,22,5,4,1,Married,15,331.6047282,0,16140.23641\n136999,44,23,1,0,Married,10,331.5437249,0,16086.18625\n129258,20,1,2,0,Married,15,331.5114821,0,15991.57411\n117658,16,0,3,1,Single,12,331.4292124,0,16354.46062\n101794,19,1,4,1,Single,13,331.3962522,0,16367.81261\n161546,29,9,3,1,Single,13,331.3621786,0,16031.10893\n184798,17,0,4,0,Married,11,331.3457506,0,16256.28753\n119471,18,0,2,1,Married,14,331.3291491,0,16251.45746\n173088,57,36,1,1,Married,10,331.272518,0,16246.6259\n114991,24,6,3,0,Single,11,331.2267942,0,16166.33971\n167129,23,6,2,0,Married,12,331.2204708,0,16111.02354\n112888,37,16,2,1,Married,10,331.1874007,0,16078.37004\n125187,32,11,3,1,Married,7,331.1851862,0,16180.25931\n162740,59,38,3,1,Married,8,331.1771662,0,15963.85831\n123405,69,52,2,1,Single,10,331.1734981,0,16168.67491\n181875,56,39,4,1,Single,9,331.1692166,0,16168.46083\n104431,67,49,2,1,Single,5,331.1223796,0,16306.11898\n195401,53,36,3,0,Single,13,331.0453792,0,16118.26896\n120963,47,26,2,1,Married,9,331.0313176,0,15989.56588\n129139,46,25,1,0,Single,13,331.0279696,0,16164.39848\n145588,68,50,3,0,Single,6,331.0060826,0,15971.30413\n155517,61,44,3,0,Married,9,331.0056722,0,15979.28361\n114613,23,5,3,0,Single,13,331.0044826,0,15979.22413\n164172,22,3,2,0,Married,15,330.9809756,0,16128.04878\n118033,25,6,2,0,Married,12,330.9647165,0,15993.23583\n176817,49,28,3,0,Married,11,330.9539262,0,15971.69631\n167486,64,43,4,0,Married,2,330.9392944,0,16298.96472\n166282,52,34,4,0,Married,8,330.9141175,0,15968.70588\n151790,28,11,3,1,Married,9,330.9052159,0,15990.2608\n190272,20,1,4,1,Single,14,330.8872262,0,16088.36131\n118163,58,41,3,1,Married,9,330.8133013,0,16278.66507\n166566,31,13,1,1,Single,15,330.805852,0,16081.2926\n128711,69,50,2,1,Single,1,330.7499558,0,16103.49779\n198956,53,33,1,1,Married,9,330.7438724,0,16283.19362\n168660,67,47,2,0,Married,8,330.6806201,0,16278.03101\n179402,49,32,4,0,Single,13,330.6725427,0,16098.62714\n135956,17,0,4,0,Married,15,330.6429977,0,16244.14989\n138209,70,53,3,0,Married,9,330.5704282,0,15998.52141\n128751,61,42,2,0,Married,10,330.4881474,0,15967.40737\n136158,67,46,3,0,Married,2,330.4842953,0,16322.21477\n173527,17,0,2,1,Single,12,330.4692588,0,16180.46294\n192894,36,16,3,1,Married,10,330.4307178,0,16082.53589\n101840,37,17,3,0,Married,10,330.36069,0,16082.0345\n123725,22,3,2,1,Married,15,330.295575,0,16041.77875\n195435,22,5,2,1,Married,14,330.2577944,0,16118.88972\n127185,68,47,3,0,Married,6,330.2103463,0,16179.51732\n129254,46,26,3,0,Married,11,330.1933101,0,16233.66551\n193219,48,29,1,0,Married,12,330.1933096,0,16208.66548\n153728,58,37,1,0,Married,7,330.1871961,0,15927.35981\n179605,52,35,2,1,Single,13,330.1739026,0,16100.69513\n140894,23,5,2,1,Single,14,330.1514958,0,15965.57479\n130073,61,44,1,0,Single,6,330.1496076,0,16178.48038\n143348,24,5,4,1,Married,7,330.1472651,0,16189.36326\n103406,24,7,3,1,Married,15,330.1407156,0,15929.03578\n100901,43,23,2,0,Married,6,330.1191091,0,16258.95546\n186911,41,24,1,1,Married,10,330.0584664,0,16056.92332\n176788,35,16,2,1,Married,10,330.0194831,0,16185.97416\n101099,19,0,4,1,Married,12,329.9957677,0,16169.78839\n164953,60,43,1,1,Married,1,329.9864173,0,16103.32087\n145799,27,6,3,0,Married,9,329.9286743,0,16236.43372\n103890,50,31,2,0,Married,14,329.9110453,0,15917.55227\n188553,41,23,2,0,Single,12,329.9058702,0,16046.29351\n122330,67,47,2,1,Single,11,329.8879277,0,16009.39639\n105080,49,28,2,1,Married,9,329.8876395,0,16022.38198\n140721,56,35,2,0,Single,14,329.831764,0,15931.5882\n173593,52,34,3,0,Married,7,329.8316829,0,16201.58415\n137380,31,11,3,1,Married,9,329.8279846,0,16215.39923\n178774,22,3,3,1,Married,15,329.8254579,0,16036.2729\n118717,41,20,2,1,Married,10,329.7994814,0,16255.97407\n125732,50,32,4,1,Married,10,329.7975764,0,16287.87882\n199975,24,6,2,1,Married,14,329.7783553,0,16199.91777\n190492,42,21,3,1,Married,7,329.7768065,0,16227.84033\n102106,57,36,3,0,Single,12,329.742425,0,16120.12125\n147951,67,48,1,0,Single,6,329.7247293,0,16015.23647\n149456,52,35,1,1,Married,2,329.647359,0,16093.36795\n147849,21,4,3,0,Married,11,329.6421981,0,15935.10991\n102319,47,30,1,1,Married,6,329.6301926,0,16044.50963\n111468,56,35,3,1,Married,8,329.5878524,0,16190.39262\n140847,19,0,4,0,Single,14,329.5090874,0,16165.45437\n192640,24,6,4,1,Married,10,329.5041121,0,16114.20561\n132425,20,1,2,1,Single,15,329.4951415,0,15949.75708\n143383,44,25,3,1,Married,12,329.4730403,0,16101.65202\n126919,41,23,2,0,Single,13,329.4684763,0,16143.42382\n146153,38,21,1,0,Married,11,329.4565402,0,16166.82701\n196098,59,42,3,1,Married,8,329.4459971,0,16019.29986\n131721,22,5,3,1,Single,12,329.439408,0,15886.9704\n184554,61,44,3,1,Single,10,329.4015182,0,16049.07591\n109809,59,40,3,0,Single,14,329.3882844,0,15875.41422\n153760,51,31,3,0,Married,13,329.3468008,0,16003.34004\n166347,18,0,3,1,Married,11,329.3307815,0,16125.53908\n193780,23,5,2,0,Married,12,329.3240747,0,16079.20374\n104313,47,27,1,1,Married,10,329.280977,0,16226.04885\n139150,34,15,2,1,Married,8,329.2801738,0,15999.00869\n197615,53,35,3,1,Married,11,329.2734723,0,16116.67362\n195536,18,1,3,0,Married,15,329.2631006,0,16083.15503\n194658,54,36,3,0,Married,7,329.2370531,0,16141.85266\n109534,67,50,1,1,Married,8,329.23095,0,16142.5475\n110180,45,24,3,1,Single,9,329.2219199,0,16220.096\n129264,44,26,4,1,Married,10,329.220829,0,16149.04145\n109675,31,10,3,0,Married,15,329.2102879,0,16250.5144\n104036,22,5,2,1,Married,15,329.2043961,0,16098.21981\n178142,48,28,4,0,Married,10,329.178784,0,16024.9392\n176037,16,0,3,0,Single,13,329.155451,0,15859.77255\n176016,22,3,1,1,Married,14,329.1457386,0,15990.28693\n135610,34,13,3,0,Married,12,329.096529,0,16025.82645\n179870,43,25,1,1,Married,7,329.0893262,0,16033.46631\n182612,63,43,2,0,Married,2,329.0603972,0,15916.01986\n142332,16,0,1,1,Married,14,329.0441386,0,15928.20693\n138181,27,10,3,1,Married,0,329.0125562,0,15906.62781\n121362,61,44,1,0,Single,5,328.9980029,0,16060.90015\n185679,27,6,4,0,Married,9,328.956993,0,16066.84965\n174600,18,1,2,1,Married,11,328.9220663,0,16040.10332\n181936,33,12,2,1,Married,6,328.9200556,0,16135.00278\n192924,56,35,3,1,Married,9,328.9069773,0,15986.34887\n142275,50,29,2,1,Married,6,328.9045926,0,15856.22963\n115865,46,29,1,1,Married,8,328.9045117,0,15977.22559\n141403,25,8,1,1,Married,15,328.8843527,0,16002.21764\n113393,23,4,3,1,Married,6,328.8671672,0,16187.35836\n125907,22,3,4,0,Married,11,328.8657175,0,15950.28588\n173912,16,0,3,0,Married,12,328.7653044,0,16203.26522\n179926,31,14,3,1,Married,10,328.735011,0,16010.75055\n107595,69,52,2,0,Married,9,328.6760988,0,15895.80494\n131347,67,50,3,1,Single,9,328.6631954,0,16079.15977\n152401,19,2,2,0,Married,12,328.6549633,0,16194.74817\n135469,28,10,3,1,Single,12,328.5768603,0,15833.84302\n133498,32,11,4,1,Married,8,328.5716508,0,16142.58254\n196315,33,16,3,1,Single,10,328.569826,0,15965.4913\n191510,28,8,3,1,Married,6,328.5205018,0,16212.02509\n193862,60,41,2,1,Married,8,328.5159353,0,16181.79677\n131801,67,46,3,1,Married,5,328.4842689,0,16160.21345\n157001,20,1,2,1,Single,11,328.4752048,0,16025.76024\n184088,25,6,2,1,Married,15,328.4621503,0,15915.10752\n193212,19,0,1,0,Married,7,328.3589705,0,16198.94853\n107387,37,17,3,1,Married,9,328.3563071,0,16165.81536\n126540,52,32,4,0,Married,13,328.2961076,0,15935.80538\n178899,53,34,2,1,Single,14,328.2917116,0,16116.58558\n123566,25,7,1,1,Single,12,328.2082804,0,15941.41402\n137451,26,9,2,0,Married,6,328.180122,0,15821.0061\n119006,53,34,2,1,Married,10,328.0856883,0,16145.28442\n173308,49,28,4,0,Married,10,328.0313668,0,15862.56834\n133237,30,10,2,1,Married,10,328.0235113,0,15925.17557\n198434,60,40,1,1,Married,11,327.9731107,0,15954.65554\n156638,40,21,3,1,Married,7,327.9714218,0,15968.57109\n101048,40,23,2,1,Married,3,327.9646537,0,15911.23269\n161421,70,52,1,1,Single,7,327.935663,0,16087.78315\n132126,29,10,4,1,Married,9,327.9181678,0,15976.90839\n173480,48,28,4,1,Married,6,327.8850451,0,16015.25226\n164174,51,30,2,0,Single,14,327.7978185,0,16022.89093\n152720,25,8,2,1,Married,15,327.7078753,0,15902.39377\n111196,29,9,2,1,Married,8,327.6833589,0,15784.16795\n150286,28,8,3,1,Single,12,327.6406969,0,15946.03485\n106814,58,37,3,1,Married,6,327.6239282,0,15871.19641\n174265,58,40,2,1,Single,13,327.6220304,0,15797.10152\n110255,49,32,2,1,Married,8,327.5916895,0,16054.58448\n176160,42,24,3,1,Married,6,327.5799744,0,15914.99872\n109370,32,11,2,1,Married,9,327.5293142,0,16148.46571\n179986,45,26,2,1,Married,10,327.5076178,0,15874.38089\n137146,52,35,3,1,Married,7,327.4735937,0,15809.67969\n121036,23,4,2,0,Single,15,327.4650096,0,15812.25048\n128683,19,1,4,1,Married,7,327.4433776,0,15802.16888\n125808,55,34,4,1,Single,12,327.3929411,0,16094.64706\n147979,32,12,4,1,Married,9,327.3299205,0,15793.49603\n100360,24,7,3,1,Single,13,327.3226285,0,15982.13143\n100778,65,47,2,0,Single,7,327.3019603,0,15939.09802\n187464,39,20,1,1,Married,6,327.3002242,0,15797.01121\n194531,53,33,1,1,Married,10,327.2980775,0,15933.90388\n102995,40,22,4,1,Married,10,327.2693144,0,15908.46572\n167095,17,0,2,1,Married,6,327.145844,0,16153.2922\n185442,67,50,1,0,Single,8,327.1425875,0,15796.12938\n185432,61,41,2,1,Single,11,327.1240961,0,15898.20481\n179667,29,11,2,1,Married,7,327.1132464,0,16033.66232\n152166,54,33,3,1,Married,10,327.0209958,0,16038.04979\n186250,50,30,3,1,Single,7,327.0172654,0,16096.86327\n169309,47,26,4,0,Married,10,327.0054935,0,15850.27468\n176586,33,12,2,1,Married,6,326.9949846,0,15754.74923\n108227,46,26,4,1,Married,6,326.9483064,0,15936.41532\n119774,33,15,2,1,Married,7,326.9088065,0,15926.44033\n135502,35,17,2,1,Single,6,326.8978365,0,15839.89183\n186042,61,42,3,0,Married,8,326.8851808,0,16127.25904\n186684,22,4,1,1,Married,14,326.841169,0,15967.05845\n115634,61,43,3,1,Single,11,326.8117612,0,15935.58806\n118811,40,22,3,0,Married,15,326.8109742,0,15859.54871\n169626,28,9,3,1,Married,8,326.8085285,0,15960.42643\n164973,64,47,3,1,Single,10,326.7835962,0,15981.17981\n129188,43,23,2,1,Married,6,326.7579322,0,15996.89661\n133737,21,3,2,1,Married,13,326.7531806,0,15848.65903\n116477,44,24,1,1,Married,10,326.6636811,0,15933.18406\n172086,43,24,3,0,Married,9,326.5984595,0,15821.92298\n106720,28,9,4,1,Married,6,326.5485067,0,15867.42534\n178472,38,19,4,0,Single,11,326.5461766,0,15970.30883\n196908,43,26,1,0,Married,6,326.467776,0,15908.3888\n188456,34,16,3,1,Single,11,326.44074,0,15778.037\n149666,24,5,4,0,Married,13,326.3896388,0,16015.48194\n192834,69,50,3,1,Single,12,326.3812098,0,16114.06049\n158998,41,23,2,1,Married,14,326.3402496,0,15817.01248\n176360,38,18,2,0,Married,7,326.3390318,0,15971.95159\n161330,36,18,2,1,Married,9,326.3205155,0,15998.02578\n161385,34,14,3,0,Married,7,326.3151271,0,15771.75636\n167513,18,0,2,1,Single,13,326.2736703,0,16057.68352\n126364,52,32,4,1,Married,6,326.2572924,0,16012.86462\n183504,62,42,2,1,Single,9,326.240357,0,16002.01785\n185576,25,8,2,1,Married,12,326.2332323,0,15833.66162\n104120,19,1,3,0,Married,11,326.194937,0,15905.74685\n170338,68,50,1,0,Married,7,326.1802691,0,15978.01346\n108549,35,15,3,1,Married,6,326.1731789,0,16079.65895\n112145,59,40,3,1,Married,10,326.165019,0,15790.25095\n190098,59,42,3,1,Married,7,326.1645728,0,15999.22864\n102157,21,3,1,1,Married,9,326.0461919,0,16000.3096\n176252,26,9,3,1,Married,6,326.0453233,0,15822.26617\n159583,61,42,2,0,Married,4,326.0285606,0,15895.42803\n111842,17,0,3,0,Married,13,325.9654039,0,16025.2702\n139570,61,42,2,1,Single,6,325.964527,0,15715.22635\n111185,54,36,4,1,Married,9,325.9460374,0,15969.30187\n132864,37,17,2,1,Married,8,325.9364228,0,15888.82114\n186088,58,37,3,1,Married,10,325.9104854,0,15865.52427\n188395,25,8,2,0,Married,15,325.8331662,0,16029.65831\n172144,53,33,2,1,Single,12,325.810068,0,16070.5034\n136737,59,40,1,1,Married,8,325.8069501,0,15932.34751\n105417,66,48,1,0,Single,8,325.770291,0,15794.51455\n192620,59,41,3,1,Married,6,325.749984,0,16024.4992\n180350,43,26,2,1,Single,8,325.7362074,0,15880.81037\n173558,65,48,3,0,Married,10,325.7298449,0,16021.49225\n195847,45,25,1,1,Single,12,325.720255,0,16031.01275\n173703,20,3,4,1,Married,8,325.5778853,0,16068.89427\n117590,19,0,1,0,Single,13,325.562891,0,15997.14455\n194493,61,43,4,1,Single,1,325.5620407,0,15750.10204\n103826,35,16,2,1,Single,9,325.5331817,0,15883.65909\n153250,45,27,3,1,Single,13,325.5182642,0,16005.91321\n151804,54,35,3,0,Married,6,325.5070234,0,15995.35117\n164530,17,0,3,0,Married,11,325.5061273,0,16047.30637\n159894,19,1,2,1,Single,11,325.4653116,0,16048.26558\n194542,48,28,2,1,Married,10,325.4433647,0,15845.16824\n182479,30,11,3,1,Married,10,325.4345877,0,15790.72939\n191676,53,34,3,1,Married,1,325.4272548,0,15930.36274\n173721,40,19,1,1,Married,11,325.4188051,0,15886.94026\n152608,23,6,2,1,Married,15,325.4021154,0,15836.10577\n115554,60,41,3,0,Married,13,325.4016644,0,15949.08322\n106051,34,14,3,1,Married,6,325.3502316,0,15717.51158\n166512,28,9,3,1,Married,10,325.3412948,0,15816.06474\n174311,67,49,3,1,Single,1,325.3397826,0,15959.98913\n133149,23,6,4,0,Single,12,325.3316908,0,15802.58454\n108088,53,32,3,1,Married,3,325.2040585,0,16048.20293\n128433,40,22,2,1,Married,14,325.1315004,0,15666.57502\n191668,17,0,4,1,Married,11,325.0980665,0,15850.90333\n131450,55,38,3,1,Married,8,325.0693377,0,16023.46689\n145934,36,16,3,1,Single,7,325.0640194,0,16045.20097\n166605,67,46,2,0,Married,10,325.0409191,0,16046.04596\n124407,67,46,3,0,Single,15,325.0100078,0,15703.50039\n185180,57,37,2,0,Married,12,324.9856394,0,15780.28197\n132245,54,35,2,0,Married,10,324.9784715,0,16032.92358\n150584,66,47,1,0,Married,6,324.965049,0,15942.25245\n174755,64,46,4,1,Married,10,324.9433376,0,15693.16688\n191839,58,39,1,0,Married,11,324.9266555,0,15901.33278\n181029,47,28,3,1,Married,13,324.8769661,0,15704.84831\n109384,35,18,2,1,Married,8,324.8720717,0,15719.60359\n197656,59,41,4,1,Married,11,324.8447893,0,15887.23947\n163882,18,0,2,0,Single,11,324.8442166,0,15790.21083\n156685,25,7,4,1,Single,11,324.8406026,0,15744.03013\n194468,31,12,1,0,Married,11,324.8298627,0,15836.49314\n149804,33,14,1,1,Married,8,324.7471949,0,15724.35975\n189619,62,45,1,1,Married,2,324.736977,0,16033.84885\n127041,62,41,4,1,Married,10,324.7090949,0,15690.45475\n116905,49,30,2,1,Married,9,324.7023211,0,15829.11606\n178505,54,36,3,1,Married,0,324.6919476,0,15810.59738\n183250,50,33,2,1,Married,7,324.6840138,0,15802.20069\n198229,27,8,3,1,Married,10,324.6483153,0,15821.41577\n157276,55,37,3,1,Married,10,324.6420613,0,15754.10307\n135783,47,29,2,1,Married,9,324.6189544,0,15788.94772\n109527,60,40,3,0,Married,7,324.6184822,0,15992.92411\n188603,28,8,2,1,Married,9,324.6162633,0,15802.81317\n155175,59,38,2,1,Married,10,324.6048449,0,15813.24225\n126513,17,0,1,0,Married,6,324.5930853,0,15940.65427\n171452,42,24,1,0,Married,9,324.5803862,0,15759.01931\n164136,35,17,3,0,Married,7,324.578445,0,15746.92225\n189020,17,0,2,0,Single,11,324.5750725,0,15772.75363\n197492,55,34,3,1,Married,6,324.5377282,0,15869.88641\n173976,19,0,4,1,Single,12,324.4128197,0,15659.64099\n146571,45,28,3,0,Married,9,324.4122407,0,15682.61203\n178555,29,9,3,1,Married,9,324.3069675,0,15924.34838\n197536,31,10,2,1,Married,12,324.1953274,0,15995.76637\n102051,37,17,3,1,Single,15,324.1946037,0,15999.73019\n199940,43,26,4,0,Married,13,324.1798889,0,15862.99445\n122067,40,19,4,1,Married,1,324.1757887,0,15921.78944\n148325,26,8,3,1,Married,7,324.1735647,0,15907.67823\n199913,37,19,1,0,Married,10,324.1466938,0,15848.33469\n150986,31,12,2,1,Single,12,324.1258041,0,15913.29021\n119689,48,30,4,1,Married,0,324.0938241,0,15702.69121\n194899,48,28,2,1,Married,10,324.0911072,0,15946.55536\n155337,64,44,1,0,Married,7,324.076563,0,15790.82815\n193842,30,11,4,1,Married,6,324.0256252,0,15842.28126\n134381,45,27,3,1,Married,10,324.0121677,0,15753.60839\n188306,20,1,4,1,Married,13,324.0092688,0,15998.46344\n196507,67,46,2,1,Single,13,323.9683907,0,15603.41954\n124081,67,48,3,0,Married,6,323.9677269,0,15720.38635\n149145,28,9,2,0,Married,14,323.9466932,0,15771.33466\n121991,20,1,3,0,Married,15,323.9178715,0,15819.89358\n156408,66,47,4,0,Single,3,323.9017759,0,15931.0888\n170043,52,32,2,1,Single,13,323.8725235,0,15793.62618\n190124,36,18,2,1,Single,10,323.8457012,0,15601.28506\n133928,19,0,4,1,Married,15,323.8297945,0,15735.48973\n136896,35,17,3,1,Married,7,323.8245299,0,15714.2265\n158280,21,4,4,0,Married,13,323.8147729,0,15911.73865\n118352,64,43,2,1,Married,2,323.7629515,0,15726.14758\n107969,49,30,2,0,Married,6,323.7231033,0,15616.15517\n116926,65,44,3,1,Single,6,323.6883989,0,15859.41995\n176050,34,13,4,0,Married,6,323.6055821,0,15895.27911\n174936,61,43,2,0,Married,7,323.592949,0,15748.64745\n116763,50,29,3,1,Married,9,323.5928472,0,15871.64236\n150125,29,12,3,1,Married,7,323.5461564,0,15889.30782\n130446,58,37,2,1,Married,9,323.5138689,0,15669.69345\n105878,29,11,2,1,Married,8,323.4860547,0,15780.30274\n165073,70,51,2,0,Single,7,323.4648917,0,15575.24459\n165278,25,8,4,0,Married,11,323.4131916,0,15645.65958\n140740,52,32,1,1,Single,14,323.3784995,0,15719.92498\n177885,68,47,4,1,Married,4,323.3339868,0,15657.69934\n199644,29,8,4,0,Married,10,323.3243513,0,15760.21757\n117533,25,7,3,1,Married,8,323.3091998,0,15596.45999\n125549,42,21,2,1,Single,9,323.249291,0,15568.46455\n102726,55,35,4,0,Married,6,323.230056,0,15826.5028\n104782,29,8,4,1,Married,8,323.1856434,0,15822.28217\n195304,58,38,2,0,Married,12,323.152546,0,15878.6273\n166646,27,7,4,0,Single,13,323.1131684,0,15773.65842\n140529,17,0,3,1,Single,15,323.0925407,0,15673.62704\n193040,60,43,4,0,Single,13,323.0459727,0,15918.29864\n168868,66,47,4,0,Married,8,323.0443489,0,15775.21745\n126078,45,24,3,1,Married,8,323.0151216,0,15820.75608\n141568,65,48,2,1,Married,2,323.012883,0,15774.64415\n170123,48,27,4,1,Single,14,322.9842187,0,15851.21094\n196826,68,47,2,0,Married,6,322.9711803,0,15692.55902\n155380,18,1,1,1,Married,15,322.9551348,0,15605.75674\n142964,55,38,3,1,Married,1,322.9303903,0,15785.51952\n196833,47,28,4,0,Married,8,322.9076919,0,15778.3846\n193690,18,0,3,0,Single,13,322.8584503,0,15927.92252\n144824,54,33,3,1,Married,10,322.7617005,0,15655.08503\n187820,41,22,3,1,Married,7,322.7181033,0,15925.90517\n115369,22,3,3,0,Single,11,322.6918838,0,15611.59419\n119719,61,41,2,1,Single,9,322.6711304,0,15553.55652\n166650,32,12,2,1,Married,9,322.654642,0,15541.7321\n131576,32,13,2,1,Married,8,322.6367008,0,15626.83504\n174389,68,50,2,0,Married,15,322.596226,0,15902.8113\n152008,49,30,4,1,Married,10,322.5905582,0,15780.52791\n109326,42,21,3,1,Married,7,322.5451135,0,15642.25568\n130445,28,10,3,1,Married,4,322.5253972,0,15909.26986\n145275,19,0,2,1,Married,10,322.5251261,0,15673.25631\n152219,55,38,3,1,Married,8,322.5172508,0,15785.86254\n110692,61,40,3,1,Single,7,322.4749899,0,15739.7495\n114886,69,51,3,0,Married,8,322.4693076,0,15774.46538\n116454,68,48,2,0,Single,7,322.4341027,0,15530.70514\n145394,56,38,1,1,Married,15,322.4044828,0,15750.22414\n180729,57,39,2,1,Married,9,322.3969738,0,15728.84869\n171147,60,39,2,1,Single,3,322.3391134,0,15610.95567\n155866,45,28,2,1,Married,7,322.3281216,0,15628.40608\n164654,23,4,4,1,Single,12,322.2788795,0,15714.94398\n157957,64,43,4,1,Single,12,322.2242639,0,15901.2132\n151329,47,28,4,1,Married,9,322.213064,0,15554.6532\n126204,44,25,4,1,Married,9,322.211742,0,15645.5871\n163563,29,10,2,0,Married,7,322.1484453,0,15812.42227\n128346,33,12,2,1,Married,9,322.1399486,0,15713.99743\n133214,49,29,2,0,Married,10,322.1237309,0,15760.18655\n157222,36,17,1,1,Married,7,322.0024982,0,15747.12491\n104911,24,5,2,0,Married,14,321.9312326,0,15759.56163\n107829,58,40,2,1,Married,7,321.8637795,0,15627.18898\n127667,17,0,4,1,Married,2,321.8444221,0,15615.22111\n125718,16,0,3,0,Married,15,321.7955306,0,15654.77653\n169323,17,0,3,1,Single,12,321.7816892,0,15630.08446\n150622,44,26,3,1,Married,6,321.7725148,0,15860.62574\n162911,24,5,3,0,Married,13,321.6806677,0,15487.03339\n112771,30,9,2,1,Married,9,321.6091082,0,15740.45541\n142616,67,46,4,0,Single,5,321.5625934,0,15678.12967\n190247,28,10,4,0,Married,13,321.5327656,0,15714.63828\n142070,47,26,1,1,Married,7,321.5142862,0,15545.71431\n131803,57,40,3,0,Single,11,321.5092889,0,15710.46445\n131702,21,2,2,1,Married,6,321.5083476,0,15827.41738\n189925,52,33,1,0,Married,9,321.4892996,0,15611.46498\n122165,30,13,3,1,Married,13,321.455237,0,15627.76185\n176644,65,46,3,1,Married,5,321.4443002,0,15794.21501\n171466,36,19,2,0,Married,7,321.4183747,0,15750.91874\n172581,42,23,2,0,Married,8,321.4161214,0,15855.80607\n128278,59,41,2,1,Married,7,321.3799122,0,15751.99561\n148363,20,3,2,1,Married,15,321.3449548,0,15855.24774\n182223,34,13,3,1,Married,9,321.3345312,0,15570.72656\n176998,25,7,4,1,Married,15,321.281734,0,15673.0867\n179255,61,40,2,0,Single,1,321.2791476,0,15752.95738\n154299,20,1,3,1,Married,13,321.2749071,0,15621.74536\n196439,21,4,2,0,Single,14,321.2721691,0,15493.60846\n115406,64,46,4,0,Single,8,321.2017016,0,15613.08508\n140333,62,41,3,1,Married,5,321.1671345,0,15595.35673\n142339,62,42,1,0,Single,5,321.1319105,0,15604.59553\n104876,70,49,4,1,Married,7,321.1239141,0,15781.19571\n193111,69,51,3,0,Single,14,321.0702282,0,15572.51141\n140588,70,49,2,0,Married,2,321.0624448,0,15703.12224\n198927,38,21,1,0,Married,9,321.0614316,0,15837.07158\n192669,20,1,3,1,Married,15,321.0200474,0,15536.00237\n144829,62,41,4,0,Married,8,321.0193108,0,15483.96554\n136005,25,7,4,0,Married,13,321.0144185,0,15472.72093\n100280,42,25,4,1,Married,8,320.9891287,0,15467.45644\n188528,66,47,3,1,Single,9,320.9715915,0,15564.57958\n171291,54,37,1,1,Married,6,320.885311,0,15677.26555\n154086,70,52,1,1,Single,3,320.8710566,0,15652.55283\n133897,36,17,2,1,Married,13,320.8155551,0,15762.77776\n127719,28,7,3,1,Single,12,320.805101,0,15700.25505\n154615,44,27,3,1,Married,12,320.7584761,0,15729.92381\n165568,30,13,2,0,Married,15,320.6727996,0,15827.63998\n170070,21,4,4,1,Single,11,320.6237384,0,15489.18692\n195034,56,36,3,0,Married,11,320.5961914,0,15716.80957\n139511,31,13,1,0,Married,6,320.5519106,0,15484.59553\n149254,60,40,2,1,Single,5,320.4630531,0,15457.15266\n109311,47,28,3,1,Single,14,320.4092254,0,15786.46127\n116660,42,24,3,0,Married,8,320.3936649,0,15551.68325\n136038,39,22,4,1,Married,3,320.3874686,0,15675.37343\n162565,46,26,1,1,Married,13,320.3867101,0,15752.33551\n173047,60,39,3,1,Married,0,320.3579811,0,15760.89906\n120013,31,11,2,0,Married,9,320.3458509,0,15523.29255\n159993,28,8,2,1,Married,2,320.3349091,0,15782.74546\n194545,36,17,2,1,Married,7,320.3074776,0,15622.37388\n157421,53,35,1,1,Married,10,320.3068082,0,15643.34041\n112856,36,17,1,1,Married,13,320.2940291,0,15459.70146\n150655,17,0,3,1,Married,11,320.28926,0,15670.463\n120998,20,2,4,0,Married,14,320.2286573,0,15534.43287\n132592,23,4,3,1,Married,10,320.2235935,0,15763.17968\n147689,31,14,1,1,Married,8,320.2231634,0,15589.15817\n186397,40,23,2,0,Married,9,320.1990221,0,15559.95111\n176279,53,35,3,0,Married,7,320.1661934,0,15699.30967\n140921,44,23,2,1,Married,1,320.1570071,0,15488.85036\n115383,51,30,3,1,Single,15,320.1286042,0,15451.43021\n117664,44,27,3,1,Married,9,320.1273103,0,15598.36552\n104748,54,35,2,1,Married,8,320.1108191,0,15542.54096\n112427,47,27,2,1,Married,9,320.1014266,0,15668.07133\n158254,60,43,2,1,Single,8,320.0999341,0,15477.99671\n168515,28,10,1,1,Married,8,320.0403218,0,15686.01609\n168528,70,50,2,1,Married,9,320.0256227,0,15780.28114\n130143,31,14,2,0,Married,8,320.0112652,0,15582.56326\n102121,39,20,1,0,Married,10,320.0088665,0,15683.44333\n116323,64,43,1,1,Single,8,319.998898,0,15792.9449\n181298,65,47,2,1,Married,3,319.996219,0,15565.81095\n100994,70,53,1,0,Single,12,319.9288788,0,15560.44394\n190163,67,50,3,1,Single,8,319.9003843,0,15586.01922\n141510,44,24,1,1,Married,8,319.8835702,0,15459.17851\n195235,40,21,2,1,Married,7,319.8772113,0,15557.86057\n173146,29,11,2,0,Married,7,319.8674797,0,15411.37399\n115140,68,47,2,1,Single,6,319.8246911,0,15710.23456\n159048,18,0,2,0,Married,14,319.8078097,0,15718.39049\n190145,53,34,3,1,Married,8,319.79676,0,15562.838\n101325,25,8,1,1,Married,15,319.7637959,0,15402.1898\n196649,57,39,2,1,Married,6,319.7561592,0,15543.80796\n133706,65,48,4,1,Married,7,319.7444939,0,15615.2247\n114246,40,22,2,0,Married,15,319.7404011,0,15452.02006\n112854,60,43,3,1,Single,6,319.7258089,0,15568.29045\n163522,49,32,1,1,Married,7,319.7251844,0,15628.25922\n156503,43,26,3,1,Single,8,319.7097846,0,15430.48923\n150806,28,9,2,1,Married,13,319.5596127,0,15496.98064\n165188,36,15,3,1,Married,8,319.5458609,0,15735.29305\n122896,22,4,4,1,Married,6,319.535728,0,15582.7864\n136386,64,46,2,0,Married,6,319.5355367,0,15598.77684\n133123,19,0,1,1,Married,13,319.5264432,0,15620.32216\n191806,62,44,4,0,Married,10,319.4895725,0,15481.47863\n172595,38,17,3,0,Married,11,319.4842514,0,15398.21257\n191603,70,50,3,1,Single,9,319.4656502,0,15714.28251\n174872,59,42,2,0,Married,7,319.4358717,0,15683.79359\n196985,27,7,1,1,Single,8,319.4147025,0,15744.73513\n108849,20,1,3,1,Married,6,319.4140616,0,15661.70308\n126802,20,1,3,0,Married,13,319.3916327,0,15403.58164\n177247,27,6,2,1,Married,9,319.3783999,0,15731.92\n149802,32,14,2,1,Married,12,319.3703577,0,15401.51789\n155859,20,1,3,1,Single,14,319.3306004,0,15442.53002\n108633,49,32,2,1,Married,10,319.2959212,0,15634.79606\n108968,67,50,3,0,Married,13,319.2946602,0,15597.73301\n182492,21,4,2,1,Married,15,319.286823,0,15417.34115\n172989,21,2,3,1,Married,13,319.2840867,0,15406.20434\n135462,56,37,2,1,Married,8,319.1857115,0,15592.28558\n163160,32,13,4,1,Married,5,319.1793514,0,15511.96757\n100899,58,40,2,0,Married,7,319.1722652,0,15375.61326\n126334,16,0,3,0,Single,11,319.0940162,0,15656.70081\n101606,34,15,3,1,Married,8,319.0562067,0,15672.81034\n194569,64,44,2,0,Married,6,319.0044147,0,15532.22074\n171333,55,35,3,1,Married,10,318.9899857,0,15637.49929\n105380,18,0,1,0,Married,7,318.9302287,0,15474.51144\n164009,50,29,3,1,Married,10,318.8629879,0,15593.1494\n111357,21,4,3,0,Married,14,318.8481406,0,15374.40703\n147888,19,0,2,1,Married,13,318.7951069,0,15362.75535\n172317,40,23,3,0,Married,8,318.7812044,0,15518.06022\n160813,36,19,2,0,Married,6,318.751178,0,15454.5589\n146940,61,42,3,0,Single,8,318.7379138,0,15449.89569\n141628,22,4,1,1,Married,8,318.7108563,0,15425.54282\n184703,40,23,1,1,Married,6,318.7104773,0,15723.52387\n143969,56,35,3,1,Married,9,318.6977928,0,15499.88964\n192227,70,49,4,1,Married,7,318.638959,0,15418.94795\n123804,51,34,4,1,Married,6,318.6337262,0,15352.68631\n188142,65,45,2,1,Single,10,318.5919676,0,15659.59838\n133665,45,25,2,0,Married,7,318.5733495,0,15329.66748\n131415,27,10,3,0,Married,15,318.52301,0,15368.1505\n180991,62,42,3,0,Single,14,318.5117924,0,15581.58962\n106862,16,0,4,1,Married,7,318.5084006,0,15546.42003\n171854,60,40,3,0,Married,1,318.4869933,0,15570.34967\n145952,16,0,3,1,Married,7,318.459411,0,15460.97055\n131575,37,17,3,1,Single,11,318.4334379,0,15605.6719\n172043,16,0,3,0,Single,15,318.4263663,0,15694.31832\n181622,26,8,3,0,Married,9,318.4066722,0,15478.33361\n148897,65,46,2,0,Married,8,318.3560537,0,15344.80269\n127371,64,45,1,1,Married,1,318.332047,0,15694.60235\n127681,55,34,2,1,Married,7,318.3150965,0,15605.75483\n181334,24,6,2,1,Married,12,318.311808,0,15702.5904\n165487,29,10,3,1,Married,12,318.2893902,0,15485.46951\n109185,37,20,2,0,Married,7,318.2712128,0,15547.56064\n119842,50,32,4,1,Married,7,318.2711261,0,15382.55631\n160851,42,25,3,0,Married,8,318.220063,0,15317.00315\n199617,39,21,2,1,Married,10,318.2124455,0,15683.62228\n126470,46,29,3,1,Married,10,318.1656947,0,15527.28474\n148702,66,49,1,0,Married,7,318.1532303,0,15321.66152\n117296,61,43,2,0,Single,11,318.0824409,0,15701.12205\n174771,35,18,3,1,Married,10,318.0362048,0,15379.81024\n156940,24,7,3,0,Married,13,318.0342662,0,15628.71331\n155914,28,7,3,1,Married,8,317.9877778,0,15495.38889\n106565,45,25,1,0,Married,10,317.9583714,0,15627.91857\n137704,35,14,1,1,Married,8,317.9431993,0,15388.15997\n163777,52,34,3,1,Married,9,317.9298964,0,15581.49482\n173469,58,41,4,0,Married,11,317.8837177,0,15391.18589\n156402,52,35,2,1,Married,7,317.8069357,0,15543.34679\n155933,64,44,4,1,Married,13,317.7917793,0,15529.58897\n163701,30,10,4,0,Married,12,317.7728698,0,15519.64349\n128826,25,7,3,1,Single,13,317.7531429,0,15478.65715\n140074,22,4,3,1,Married,13,317.7358252,0,15431.79126\n116968,40,23,1,0,Single,12,317.7000496,0,15622.00248\n104716,19,1,3,1,Married,11,317.6965932,0,15659.82966\n199916,23,6,2,1,Married,14,317.6692411,0,15374.46206\n160668,26,6,3,0,Single,12,317.6423176,0,15345.11588\n186464,60,43,2,1,Single,5,317.6100662,0,15410.50331\n180493,27,7,3,1,Married,0,317.5787319,0,15648.9366\n161406,30,9,2,1,Married,9,317.5279985,0,15650.39993\n169651,16,0,2,0,Married,11,317.5033725,0,15636.16863\n105418,31,12,3,0,Married,10,317.4971253,0,15564.85627\n185143,21,2,1,0,Married,11,317.4901628,0,15604.50814\n183302,34,14,2,1,Married,15,317.4409122,0,15577.04561\n187949,69,48,2,0,Married,2,317.3049656,0,15549.24828\n121466,28,9,2,1,Married,15,317.3020942,0,15343.10471\n154397,37,16,2,1,Married,11,317.2610036,0,15625.05018\n129182,37,17,2,1,Married,9,317.2435822,0,15526.17911\n111622,51,33,1,0,Married,7,317.2362189,0,15263.81095\n121790,64,47,3,0,Married,6,317.2252761,0,15313.26381\n130345,22,3,4,1,Single,14,317.1931767,0,15431.65884\n134962,61,42,3,0,Married,10,317.1926179,0,15299.6309\n196173,39,19,2,1,Married,10,317.1897689,0,15633.48845\n165237,37,17,3,0,Married,13,317.1883999,0,15421.42\n192647,34,13,3,1,Married,10,317.1596852,0,15304.98426\n161029,52,35,3,1,Married,7,317.1256856,0,15552.28428\n106782,20,3,4,1,Married,9,317.1153563,0,15567.76782\n182889,67,46,4,0,Married,2,317.0264351,0,15331.32176\n169358,22,4,1,0,Married,14,316.9985022,0,15414.92511\n163172,49,30,2,0,Married,6,316.982423,0,15497.12115\n134220,59,41,2,1,Single,13,316.9793543,0,15478.96772\n102622,57,36,1,0,Single,11,316.9570048,0,15309.85024\n195769,58,39,3,0,Single,15,316.9003365,0,15494.01683\n101175,62,44,3,0,Single,13,316.8827303,0,15631.13652\n158349,22,4,3,1,Single,14,316.8059894,0,15301.29947\n198652,17,0,3,0,Married,14,316.7655233,0,15492.27617\n196186,28,8,4,1,Single,13,316.7052401,0,15520.26201\n148528,69,49,3,0,Single,8,316.6816588,0,15540.08294\n119343,45,24,4,1,Married,15,316.6639021,0,15386.19511\n185961,39,19,3,1,Married,14,316.6071358,0,15390.35679\n120888,41,23,2,1,Single,10,316.6019221,0,15267.09611\n175520,54,35,3,1,Single,13,316.5779989,0,15553.89995\n179961,56,35,3,1,Married,6,316.5688739,0,15357.4437\n114900,33,15,2,1,Married,4,316.562315,0,15547.11575\n144860,22,5,2,0,Single,12,316.5447298,0,15291.23649\n136845,61,44,1,0,Single,4,316.5254941,0,15422.27471\n198111,69,50,2,0,Single,15,316.5217782,0,15616.08891\n158690,47,28,3,1,Single,13,316.5179913,0,15507.89957\n154742,38,21,2,1,Married,8,316.5019388,0,15498.09694\n185535,35,15,3,0,Single,11,316.4754963,0,15383.77482\n128644,58,41,4,1,Married,9,316.4531916,0,15394.65958\n114220,27,8,2,1,Married,10,316.3652417,0,15512.26209\n147999,60,43,3,1,Married,8,316.3342789,0,15464.71395\n172836,46,28,4,1,Married,6,316.294927,0,15402.74635\n164563,28,10,1,1,Married,10,316.2639336,0,15466.19668\n103302,49,30,3,0,Single,14,316.0836756,0,15307.18378\n184313,65,48,3,1,Married,5,315.9213453,0,15244.06727\n189247,31,11,3,1,Married,7,315.9044514,0,15210.22257\n148453,30,9,2,1,Married,6,315.8990068,0,15299.95034\n147891,38,19,3,1,Married,7,315.8700753,0,15587.50377\n156978,66,47,3,0,Married,6,315.7652576,0,15300.26288\n123377,26,7,3,1,Married,8,315.7468499,0,15208.3425\n110215,21,3,2,1,Single,14,315.7115233,0,15576.57617\n162251,30,13,4,1,Married,10,315.6685673,0,15512.42837\n173726,62,44,3,1,Married,8,315.6520505,0,15268.60253\n121285,18,0,3,0,Married,11,315.6330931,0,15535.65466\n101320,20,3,2,1,Single,15,315.5982887,0,15490.91444\n115197,27,9,3,0,Married,13,315.5611003,0,15301.05502\n133507,59,38,2,1,Married,6,315.5270639,0,15212.3532\n147777,29,9,2,1,Married,9,315.51902,0,15348.951\n150505,22,5,3,1,Single,11,315.5171703,0,15311.85852\n116261,36,15,2,1,Married,9,315.4863007,0,15436.31504\n173531,28,10,3,1,Married,12,315.4713089,0,15498.56545\n102202,42,25,2,1,Married,7,315.3726493,0,15395.63247\n120132,28,11,1,0,Married,11,315.3474326,0,15379.37163\n174927,63,44,4,0,Married,13,315.2988023,0,15181.94012\n125818,37,18,3,1,Married,15,315.291104,0,15423.5552\n139637,34,15,2,0,Married,13,315.2849102,0,15463.24551\n182988,29,8,2,1,Married,12,315.2526124,0,15345.63062\n193015,69,51,4,1,Single,2,315.2465065,0,15423.32533\n153002,54,33,3,1,Married,7,315.1998983,0,15402.99492\n158937,28,7,2,1,Married,9,315.1065652,0,15447.32826\n110767,21,4,2,1,Single,13,315.0421147,0,15392.10574\n191697,55,37,3,1,Married,5,315.0349231,0,15465.74616\n174134,55,38,3,1,Single,13,315.0257709,0,15447.28855\n171724,55,38,3,1,Single,14,314.9887539,0,15472.4377\n137954,58,38,3,1,Married,8,314.9882766,0,15384.41383\n147298,21,4,3,0,Married,10,314.9871314,0,15395.35657\n128199,42,22,2,1,Married,10,314.9728176,0,15530.64088\n133468,19,1,2,1,Married,6,314.9461087,0,15365.30544\n145084,47,29,3,0,Single,12,314.8957605,0,15485.78803\n125775,39,21,4,1,Married,9,314.889906,0,15284.4953\n171234,42,23,1,0,Married,9,314.8458289,0,15176.29145\n111790,38,18,2,1,Married,7,314.8310797,0,15261.55399\n106049,26,8,1,1,Married,6,314.7922365,0,15437.61183\n157186,34,13,3,0,Married,14,314.7800995,0,15324.00498\n128519,22,4,3,1,Married,13,314.748963,0,15421.44815\n195978,31,10,4,1,Married,9,314.7164641,0,15206.82321\n158063,21,4,2,1,Married,6,314.6901717,0,15311.50859\n113778,27,10,4,1,Married,7,314.6883094,0,15467.41547\n167022,58,38,1,1,Married,10,314.6819894,0,15419.09947\n109614,37,19,1,1,Married,7,314.6814073,0,15135.07037\n141606,40,20,2,1,Married,10,314.6769002,0,15482.84501\n160387,57,40,1,0,Married,15,314.6620972,0,15457.10486\n190257,51,31,1,1,Married,15,314.6486414,0,15368.43207\n107732,16,0,3,0,Married,14,314.6315333,0,15418.57667\n184374,43,23,3,1,Married,9,314.6065556,0,15291.32778\n123352,24,5,3,1,Single,14,314.4685197,0,15319.42599\n104974,43,22,2,1,Married,8,314.461356,0,15243.0678\n129155,59,41,4,0,Married,13,314.4598733,0,15155.99367\n121031,37,17,2,1,Married,7,314.4466173,0,15249.33087\n165516,25,6,2,1,Single,15,314.4390289,0,15487.95145\n193472,19,2,2,1,Married,7,314.3495085,0,15317.47543\n159773,34,13,3,1,Married,7,314.3353919,0,15190.7696\n111562,61,43,3,1,Single,8,314.3065782,0,15165.32891\n143211,55,38,2,0,Married,7,314.2763929,0,15188.81965\n163043,52,34,3,0,Married,14,314.2609786,0,15187.04893\n181905,51,31,2,0,Married,13,314.2585416,0,15131.92708\n195382,52,31,2,1,Married,8,314.2248501,0,15349.24251\n171536,52,35,3,1,Married,9,314.2055786,0,15185.27893\n193267,52,31,1,0,Married,10,314.1997614,0,15137.98807\n175995,62,43,1,1,Married,13,314.1903441,0,15449.51721\n193809,32,11,3,1,Single,12,314.1710238,0,15402.55119\n197186,36,17,2,1,Married,6,314.1239865,0,15376.19933\n130223,58,37,2,1,Married,6,314.0996953,0,15484.98477\n162209,44,27,2,1,Married,7,314.095723,0,15404.78615\n113151,20,1,4,0,Married,6,314.0949184,0,15264.74592\n151417,51,33,4,1,Single,13,314.0866634,0,15374.33317\n161398,54,34,1,0,Married,9,314.085069,0,15136.25345\n136428,55,36,4,1,Married,7,314.0664581,0,15358.32291\n176322,50,29,2,1,Married,8,314.0491544,0,15189.45772\n122128,32,12,4,1,Married,6,314.0395774,0,15352.97887\n182777,67,46,4,0,Single,8,314.0322396,0,15110.61198\n152674,39,22,2,0,Married,14,313.9752158,0,15419.76079\n180742,52,33,3,1,Married,6,313.9598192,0,15218.99096\n199882,25,7,2,1,Single,12,313.958467,0,15391.92335\n161194,35,18,2,0,Married,8,313.9556337,0,15361.78169\n180292,21,3,3,1,Married,8,313.9542274,0,15163.71137\n124522,29,8,3,0,Single,14,313.862297,0,15206.11485\n101187,21,4,3,1,Married,12,313.8382522,0,15190.91261\n184032,43,25,3,1,Married,8,313.8178437,0,15429.89219\n149483,55,35,3,1,Married,7,313.7580238,0,15281.90119\n198522,58,40,4,0,Single,14,313.6963702,0,15469.81851\n163263,38,17,3,1,Married,6,313.5413947,0,15263.06974\n127589,69,49,3,0,Single,3,313.5289808,0,15255.44904\n175519,26,5,3,1,Married,9,313.5074495,0,15438.37248\n164624,53,33,1,1,Married,7,313.4599585,0,15117.99793\n114783,55,35,4,1,Married,7,313.4504471,0,15144.52236\n155732,63,44,2,1,Single,5,313.4314506,0,15397.57253\n160858,62,42,2,0,Single,7,313.4091523,0,15365.45762\n116386,38,21,3,1,Married,9,313.334855,0,15361.74275\n100471,58,40,2,1,Married,6,313.3115668,0,15432.57834\n153627,22,3,3,1,Married,12,313.306241,0,15394.31205\n142730,18,0,4,0,Married,11,313.2976091,0,15306.88046\n123179,43,26,3,1,Married,7,313.2618716,0,15090.09358\n187145,23,6,3,1,Married,8,313.251889,0,15136.59445\n169656,18,0,2,0,Married,14,313.2257204,0,15421.28602\n127296,29,8,2,1,Single,9,313.2083114,0,15153.41557\n152641,38,19,3,1,Married,11,313.1903744,0,15059.51872\n142477,43,25,3,1,Married,6,313.1865975,0,15091.32988\n155451,20,1,2,0,Married,10,313.162049,0,15094.10245\n106404,34,16,4,1,Married,15,313.1482523,0,15226.41262\n128261,66,47,1,0,Married,5,313.1380724,0,15449.90362\n168174,25,8,3,1,Married,15,313.1366249,0,15310.83125\n111020,18,0,2,1,Married,15,313.1189401,0,15426.94701\n157143,55,38,4,0,Single,15,313.0968221,0,15285.84111\n163400,67,47,1,0,Single,9,313.0615814,0,15085.07907\n173757,70,49,3,1,Single,6,313.0489022,0,15196.44511\n120787,68,49,1,1,Single,8,313.0465443,0,15183.32722\n135539,39,18,3,1,Married,8,313.0405547,0,15334.02774\n153328,34,17,1,1,Married,10,313.033365,0,15430.66825\n160133,43,22,1,1,Married,9,313.0289438,0,15375.44719\n127280,65,45,2,0,Married,3,313.0059872,0,15336.29936\n148479,20,1,4,0,Married,15,313.0024539,0,15336.1227\n121496,33,16,1,0,Married,9,312.9428646,0,15103.14323\n177083,36,15,3,1,Single,13,312.8123389,0,15216.61695\n121408,41,22,1,1,Married,8,312.7213527,0,15056.06764\n171489,37,20,3,1,Married,14,312.6920688,0,15118.60344\n184932,21,4,2,0,Married,15,312.6715176,0,15102.57588\n137402,35,18,4,1,Married,2,312.5287596,0,15186.43798\n192905,61,43,4,1,Single,12,312.522305,0,15206.11525\n188150,24,7,1,1,Married,13,312.4945546,0,15377.72773\n127144,33,12,2,1,Married,11,312.4806557,0,15066.03279\n108553,21,3,2,1,Single,11,312.4802515,0,15171.01258\n166043,57,36,3,1,Married,9,312.458045,0,15235.90225\n106273,31,10,2,1,Married,11,312.4465176,0,15290.32588\n107717,62,42,4,0,Married,10,312.3915256,0,15088.57628\n196562,18,0,2,1,Married,10,312.3798415,0,15363.99208\n171962,55,34,2,0,Married,14,312.3318375,0,15191.59188\n177817,32,12,1,1,Married,9,312.2857892,0,15328.28946\n162655,24,7,3,1,Single,11,312.2006784,0,15361.03392\n128836,67,46,4,0,Married,7,312.1924883,0,15016.62442\n132178,23,4,2,1,Single,14,312.1130913,0,15340.65457\n145524,30,13,3,1,Married,10,312.0818596,0,15075.09298\n180190,51,31,1,0,Married,6,312.0653155,0,15132.26578\n187532,38,19,3,0,Married,12,312.0402091,0,15360.01046\n111042,29,10,3,1,Married,7,312.0352054,0,15048.76027\n184512,46,28,2,1,Married,6,312.0185174,0,15181.92587\n175998,52,35,2,1,Married,9,311.9594323,0,15360.97162\n183546,25,7,4,0,Single,14,311.9501958,0,15350.50979\n152361,52,31,1,1,Married,10,311.9191271,0,15059.95636\n170226,26,8,3,1,Married,6,311.9054139,0,15357.2707\n158449,32,12,4,1,Married,6,311.8931682,0,15062.65841\n144598,33,14,2,0,Married,8,311.8560379,0,15385.8019\n134726,40,20,1,1,Single,13,311.8538565,0,15261.69283\n193045,63,44,3,1,Single,6,311.837761,0,15242.88805\n155782,34,13,2,1,Married,0,311.7754509,0,14999.77255\n124618,50,30,4,1,Married,6,311.735022,0,15378.7511\n113796,24,7,4,0,Married,14,311.7199146,0,15182.99573\n152501,23,5,3,1,Single,13,311.7155923,0,15044.77962\n147890,60,41,3,0,Single,5,311.6503256,0,15038.51628\n126248,23,6,3,1,Single,12,311.6227524,0,14992.13762\n153785,21,2,4,1,Single,11,311.6125588,0,15021.62794\n151767,48,28,2,1,Married,10,311.5554564,0,15302.77282\n135713,49,28,3,1,Married,9,311.4554159,0,15096.7708\n154381,60,41,2,1,Married,8,311.4301899,0,15141.5095\n107714,54,36,3,1,Single,13,311.4123886,0,15123.61943\n147658,27,9,1,1,Married,6,311.3847328,0,15351.23664\n168689,22,4,4,1,Married,11,311.3831572,0,15307.15786\n117284,33,15,3,1,Married,12,311.3746217,0,15034.73109\n134437,22,5,2,0,Married,14,311.3666598,0,15089.33299\n198272,69,50,3,0,Single,8,311.3562258,0,15197.81129\n156505,21,3,1,0,Married,13,311.353198,0,15102.6599\n129038,69,52,4,1,Married,6,311.3325728,0,15283.62864\n162873,54,36,4,1,Married,1,311.3049652,0,15122.24826\n180823,44,25,2,1,Married,9,311.2450717,0,15304.25359\n143890,31,13,3,1,Single,11,311.2100189,0,15045.50095\n159859,29,12,4,1,Married,8,311.1506004,0,15351.53002\n154373,46,29,2,1,Married,10,311.0704893,0,15201.52447\n170699,51,30,2,1,Single,14,311.0094363,0,15047.47182\n120221,27,6,2,1,Married,7,311.0017988,0,15203.08994\n181519,21,2,3,1,Married,11,310.9789476,0,15215.94738\n100681,22,3,3,1,Married,8,310.9574139,0,15147.8707\n106064,38,19,1,1,Married,7,310.9397719,0,15180.9886\n151683,33,15,3,1,Married,10,310.9373969,0,15249.86985\n135487,59,40,1,1,Married,10,310.9230046,0,14953.15023\n169265,61,43,3,1,Single,4,310.9042823,0,14996.21412\n172398,54,36,3,1,Married,7,310.8779976,0,15099.89988\n135642,55,38,2,0,Married,12,310.8686668,0,15158.43334\n162203,24,5,1,0,Single,15,310.8532989,0,15084.66495\n151645,23,4,4,0,Married,14,310.7973575,0,14955.86788\n162813,66,49,3,0,Married,1,310.7172356,0,15310.86178\n197494,20,1,2,1,Married,13,310.703127,0,15079.15635\n125221,51,30,2,1,Married,10,310.6833687,0,15025.16844\n188024,60,41,3,1,Single,9,310.6458562,0,14956.29281\n138702,34,16,1,0,Single,12,310.6430719,0,15069.1536\n151678,45,26,2,1,Married,6,310.6077188,0,15145.38594\n148258,59,41,3,1,Married,11,310.5947859,0,15293.7393\n171657,66,48,3,1,Married,6,310.5503805,0,14962.51903\n144419,37,19,2,1,Single,14,310.53615,0,15222.8075\n104143,41,24,1,1,Married,9,310.4958386,0,15282.79193\n107756,36,15,2,1,Married,8,310.4836679,0,15182.1834\n132202,49,29,3,1,Single,13,310.4775272,0,15195.87636\n161077,17,0,2,0,Married,11,310.4487937,0,15180.43969\n160131,61,44,2,0,Married,6,310.4345511,0,15173.72756\n118282,36,15,2,1,Single,11,310.4054126,0,15238.27063\n159872,25,8,4,0,Married,13,310.3989646,0,14962.94823\n147239,35,18,2,1,Married,7,310.3556518,0,15019.78259\n126210,26,9,4,0,Married,12,310.3356728,0,15134.78364\n180115,16,0,1,1,Married,15,310.3054332,0,15046.27166\n198521,22,5,3,1,Single,13,310.2679868,0,15057.39934\n130685,25,7,3,0,Married,13,310.2675403,0,15158.37702\n121820,45,28,3,1,Married,9,310.257686,0,14962.8843\n190698,51,30,3,1,Married,12,310.2260018,0,15018.30009\n150208,38,17,1,1,Married,8,310.2184717,0,15057.92359\n110844,23,6,2,1,Married,7,310.2142008,0,15310.71004\n103591,68,51,3,1,Married,6,310.2132471,0,15046.66236\n158242,23,5,2,1,Single,15,310.1527237,0,15229.63619\n167166,52,31,3,1,Married,6,310.1040789,0,14961.20395\n143538,56,38,4,1,Married,8,310.0303143,0,15024.51572\n102304,53,32,2,0,Married,10,309.9887649,0,14941.43825\n159999,57,38,2,1,Married,6,309.9708434,0,15249.54217\n170879,52,35,1,1,Married,10,309.9621785,0,15219.10893\n124188,19,0,3,1,Single,14,309.952845,0,14921.64225\n188105,16,0,2,1,Married,14,309.9269399,0,15253.347\n138287,68,47,3,0,Single,11,309.9202624,0,15135.01312\n186381,23,5,2,0,Married,14,309.907631,0,15163.38155\n138642,37,19,2,0,Married,9,309.8817858,0,15230.08929\n190232,70,51,4,0,Single,11,309.8533226,0,15204.66613\n155439,46,29,3,1,Married,10,309.8248095,0,14942.24048\n173074,65,44,3,0,Married,8,309.8096523,0,15258.48262\n166446,29,12,3,1,Married,8,309.7743793,0,14957.71897\n135552,65,44,4,0,Married,9,309.7591136,0,15002.95568\n134297,56,36,2,1,Married,6,309.7220679,0,15286.1034\n169360,44,27,4,1,Married,7,309.7076764,0,14938.38382\n172282,43,25,1,1,Married,6,309.6777545,0,15109.88773\n105915,26,6,1,1,Married,7,309.6459659,0,15089.2983\n128537,30,10,3,1,Married,6,309.6060813,0,14896.30407\n143584,43,25,3,1,Single,8,309.590637,0,14969.53185\n174983,20,2,3,0,Married,13,309.5882368,0,15001.41184\n123563,49,31,3,1,Married,6,309.5785738,0,15193.92869\n165672,17,0,3,1,Single,11,309.5589377,0,15263.94689\n181605,33,15,3,0,Married,13,309.5275766,0,15124.37883\n162297,23,6,3,1,Single,13,309.5079244,0,15039.39622\n186962,49,31,3,1,Married,14,309.4576313,0,14982.88157\n188867,37,20,3,1,Married,10,309.4043147,0,15029.21574\n195001,23,5,4,0,Married,14,309.3622537,0,15172.11269\n163282,51,30,2,0,Single,14,309.2919752,0,14903.59876\n119203,45,27,4,1,Married,7,309.2550353,0,14960.75177\n120108,55,37,1,1,Married,10,309.2396041,0,15029.98021\n157592,29,9,2,1,Single,11,309.237974,0,14979.8987\n129544,25,6,3,1,Married,14,309.2151888,0,15137.75944\n194051,69,52,3,0,Single,7,309.1605204,0,15120.02602\n192110,21,3,3,0,Married,15,309.1510858,0,15041.55429\n194459,50,29,3,1,Married,6,309.1338776,0,14916.69388\n129504,56,35,3,1,Married,6,309.1169012,0,15165.84506\n169052,48,30,1,1,Married,9,309.1141925,0,15149.70963\n110912,70,52,3,1,Single,9,309.0849575,0,15250.24788\n134097,52,35,4,0,Single,13,309.0594526,0,15188.97263\n147833,39,19,3,1,Married,7,309.0087753,0,14939.43877\n106815,25,6,2,1,Married,8,308.9951899,0,14985.7595\n141141,61,40,3,1,Married,9,308.9740656,0,15186.70328\n179725,43,25,3,1,Married,7,308.9086909,0,15182.43455\n172562,20,1,3,1,Married,14,308.9061638,0,15059.30819\n104075,69,51,1,0,Single,3,308.9019548,0,15192.09774\n189263,54,34,2,0,Married,12,308.8783677,0,14995.91839\n190025,20,3,2,1,Married,12,308.8496566,0,14912.48283\n175417,51,34,1,1,Married,8,308.8097112,0,14921.48556\n192285,61,41,3,1,Married,2,308.8055458,0,14870.27729\n153821,51,31,2,1,Single,11,308.7935312,0,15166.67656\n174299,36,16,2,1,Single,12,308.7655925,0,14912.27963\n145568,25,7,4,0,Married,15,308.7451306,0,14844.25653\n194534,25,8,1,1,Single,14,308.6653638,0,14906.26819\n173341,25,8,2,1,Married,13,308.6550336,0,15135.75168\n167665,61,41,1,1,Married,9,308.6526154,0,15105.63077\n199733,28,7,3,1,Married,11,308.6392806,0,14831.96403\n137279,33,15,3,0,Married,11,308.619471,0,15003.97355\n185162,30,12,1,1,Married,8,308.5713815,0,15060.56908\n188341,27,6,2,1,Married,10,308.5647245,0,14895.23623\n116900,40,20,4,1,Married,8,308.5336043,0,14918.68022\n145566,50,32,1,1,Married,12,308.5023481,0,15150.11741\n180717,31,11,3,1,Married,8,308.4657403,0,15096.28702\n103823,65,48,2,1,Single,3,308.462944,0,14892.1472\n158622,39,18,3,1,Single,13,308.4601596,0,15150.00798\n148014,52,32,2,0,Married,12,308.4501787,0,15042.50894\n101694,39,20,3,1,Married,7,308.4500336,0,15134.50168\n103633,27,7,4,0,Married,13,308.436658,0,15054.8329\n127083,45,26,2,1,Married,7,308.4056167,0,14882.28084\n132743,22,5,3,1,Single,15,308.3745452,0,14855.72726\n179686,46,26,3,1,Single,13,308.2932183,0,14997.66092\n127738,60,43,2,0,Married,11,308.2903275,0,15184.51638\n106957,25,7,2,1,Single,13,308.2719485,0,14942.59743\n179454,25,6,2,1,Single,14,308.2629494,0,14975.14747\n102292,65,48,2,0,Married,8,308.2452353,0,14989.26177\n144566,32,12,1,1,Married,6,308.2346622,0,14905.73311\n125452,58,38,1,1,Married,8,308.233244,0,15181.6622\n153543,29,12,2,1,Married,8,308.1962701,0,15000.81351\n116338,35,16,2,0,Single,11,308.1654928,0,15116.27464\n166672,66,47,4,1,Single,8,308.136766,0,14919.8383\n117422,27,8,2,1,Married,10,308.1027723,0,15203.13862\n134800,23,6,2,0,Married,12,308.101933,0,14866.09665\n142852,41,24,2,1,Married,12,308.0973519,0,15163.8676\n103676,49,30,3,1,Married,14,308.0832138,0,15125.16069\n172404,55,36,4,0,Married,6,308.0539618,0,14886.69809\n194617,50,32,3,1,Married,8,308.0470493,0,14821.35247\n119323,34,14,3,1,Married,10,308.0223421,0,14893.11711\n196463,17,0,4,1,Married,12,308.0193194,0,14850.96597\n192092,16,0,3,1,Single,11,308.0115413,0,15048.57707\n151043,28,8,1,1,Married,10,308.0062911,0,15030.31456\n120262,43,23,4,1,Married,7,307.9904309,0,15159.52155\n180075,33,14,3,0,Married,15,307.983331,0,15125.16655\n159016,46,29,3,1,Married,1,307.950249,0,14874.51245\n155725,24,7,3,1,Single,12,307.9414794,0,15129.07397\n169827,23,4,1,0,Married,13,307.9160669,0,15159.80335\n194998,58,40,1,1,Married,7,307.8470487,0,15000.35244\n131293,58,41,4,1,Single,12,307.8314635,0,14904.57318\n112207,45,25,1,1,Married,10,307.8064177,0,15088.32089\n194528,49,30,3,1,Married,8,307.6696858,0,15040.48429\n151098,70,52,1,1,Single,10,307.6472449,0,15111.36225\n135597,25,6,3,1,Married,13,307.6357729,0,15116.78865\n157613,29,12,4,0,Single,12,307.6140337,0,14783.70169\n133663,20,3,1,1,Married,7,307.6119306,0,15016.59653\n171124,49,30,1,0,Married,10,307.6032853,0,14817.16427\n186545,41,22,2,0,Married,15,307.5550597,0,14944.75299\n104443,22,3,3,1,Single,11,307.5161346,0,14814.80673\n102876,16,0,2,1,Married,8,307.503338,0,14895.1669\n114127,64,43,2,0,Single,6,307.4466053,0,14843.33027\n108141,52,32,1,1,Married,4,307.429253,0,15115.46265\n161391,55,36,3,1,Married,10,307.4165301,0,14858.82651\n174512,61,41,1,0,Married,12,307.4146315,0,15124.73158\n188399,68,48,4,1,Single,8,307.4119981,0,15043.59991\n191411,17,0,4,1,Married,15,307.386164,0,14879.3082\n188397,21,3,2,0,Single,15,307.3796409,0,15110.98205\n178996,41,21,2,1,Married,7,307.3442838,0,15060.21419\n146070,49,29,4,1,Married,8,307.3441817,0,14903.20909\n124623,54,35,1,1,Married,8,307.3220363,0,14959.10182\n195890,33,14,2,1,Married,8,307.2958376,0,15101.79188\n158957,43,23,2,1,Single,15,307.2698672,0,15023.49336\n154636,43,23,1,1,Married,9,307.2503882,0,15131.51941\n151503,32,13,1,0,Married,13,307.2191317,0,15143.95659\n159365,62,45,2,0,Married,9,307.2162304,0,14831.81152\n169988,53,35,1,1,Married,8,307.2012361,0,14821.06181\n157196,63,42,2,0,Single,7,307.1713191,0,15110.56596\n169880,64,47,3,1,Married,7,307.1403785,0,15048.01893\n188049,62,45,2,0,Single,12,307.1214269,0,15037.07135\n186260,69,51,2,1,Single,8,306.9941545,0,15120.70773\n186477,32,15,1,1,Married,7,306.9422816,0,15035.11408\n149890,70,51,4,1,Single,10,306.8943535,0,14901.71768\n172200,17,0,2,0,Married,13,306.8533105,0,14796.66553\n130067,22,3,1,0,Single,12,306.8513302,0,14985.56651\n164494,67,47,3,0,Married,10,306.8342585,0,14822.71293\n132009,23,4,3,1,Married,8,306.7922108,0,14869.61054\n127360,65,48,1,0,Single,11,306.7395011,0,15035.97506\n151357,46,25,1,1,Married,8,306.682273,0,15027.11365\n138587,28,10,2,1,Married,8,306.676477,0,14941.82385\n109605,32,12,2,1,Married,15,306.6404225,0,14957.02113\n154226,26,8,1,1,Single,14,306.6197955,0,14747.98978\n113797,31,12,2,1,Married,8,306.5892045,0,15020.46023\n185098,20,1,2,1,Married,9,306.5802683,0,15079.01342\n111259,68,51,1,1,Single,8,306.5707397,0,14736.53699\n168909,50,33,1,1,Married,11,306.5442945,0,14962.21473\n187381,27,6,4,0,Married,14,306.4509931,0,14761.54966\n193265,32,15,2,1,Married,6,306.3533269,0,15059.66635\n179754,68,51,2,0,Married,8,306.3404608,0,14872.02304\n104006,22,5,4,0,Married,11,306.3150769,0,14788.75385\n114423,29,12,2,1,Married,9,306.3115973,0,15112.57987\n119499,18,0,3,0,Married,15,306.3038174,0,14800.19087\n102779,67,47,1,0,Single,13,306.2881697,0,15031.40849\n109979,18,0,3,1,Single,15,306.2860105,0,15069.30053\n114435,25,6,4,1,Married,5,306.2268891,0,15094.34446\n196311,40,23,3,1,Married,15,306.1517631,0,14782.58816\n192722,28,10,2,1,Married,10,306.1515312,0,14904.57656\n154460,24,6,4,1,Single,13,306.0167496,0,14899.83748\n185844,53,34,2,1,Married,8,305.9895502,0,14934.47751\n198387,50,30,3,1,Married,7,305.9487254,0,14769.43627\n185490,54,34,3,1,Married,15,305.8906134,0,14912.53067\n189957,16,0,1,1,Married,11,305.8610846,0,14930.05423\n190669,50,33,3,0,Married,12,305.856311,0,14776.81555\n151490,25,7,2,1,Single,14,305.8109081,0,15061.54541\n167836,66,47,3,1,Married,3,305.8034818,0,14904.17409\n116739,63,45,2,0,Married,7,305.7939811,0,14915.69906\n173057,60,43,4,1,Single,7,305.7744547,0,15001.72274\n190024,58,37,4,1,Married,10,305.7425777,0,14865.12889\n152055,27,8,2,0,Single,12,305.7422196,0,14999.11098\n189565,23,6,3,1,Single,14,305.7334947,0,14799.67474\n198618,55,34,4,1,Married,14,305.7163394,0,14751.81697\n107484,68,49,2,0,Single,4,305.6524736,0,15051.62368\n110745,33,12,1,1,Married,10,305.6462484,0,15012.31242\n147104,35,16,2,0,Single,13,305.599627,0,14681.98135\n199841,21,3,4,1,Married,8,305.521177,0,14813.05885\n112234,17,0,4,1,Married,15,305.5204447,0,14823.02224\n125289,57,38,2,1,Married,8,305.4447304,0,14895.23652\n146239,42,23,3,0,Single,12,305.4003966,0,15053.01983\n132654,32,11,2,0,Single,13,305.3024049,0,14878.12025\n111781,33,15,3,0,Married,15,305.2700497,0,14865.50249\n147851,27,9,2,1,Single,14,305.2494871,0,15023.47436\n185406,62,43,2,0,Married,7,305.2483756,0,14923.41878\n155126,63,45,2,0,Married,11,305.1540949,0,14872.70475\n171878,46,27,3,1,Married,13,305.0855007,0,14679.27504\n133690,53,36,2,1,Married,7,305.0790016,0,14826.95008\n188979,46,26,2,0,Single,13,305.075432,0,14672.7716\n198008,25,7,2,0,Married,14,305.0281605,0,14782.40803\n178342,69,48,3,1,Married,3,305.0065965,0,14766.32983\n108161,18,0,2,1,Single,13,304.9882514,0,14760.41257\n193282,65,48,3,1,Single,10,304.9781505,0,14689.90753\n150335,19,1,2,0,Single,15,304.9770159,0,14937.8508\n124317,50,31,1,1,Single,13,304.9612526,0,14836.06263\n115516,54,37,2,1,Married,6,304.8658354,0,14998.29177\n108396,69,48,3,1,Single,9,304.8243163,0,14707.21582\n106548,60,42,2,1,Single,7,304.7559646,0,14728.79823\n133266,69,52,4,1,Single,7,304.7423722,0,14788.11861\n160727,60,40,2,1,Single,14,304.6880075,0,14679.40038\n175481,46,28,3,1,Married,14,304.6095956,0,14730.47978\n168584,29,10,3,1,Married,9,304.5682218,0,14654.41109\n142111,69,48,4,1,Married,9,304.5191342,0,14934.95671\n162923,29,11,4,1,Single,12,304.4703177,0,14732.51589\n106836,63,45,2,1,Single,7,304.4479143,0,14698.39572\n195717,62,43,2,1,Married,0,304.4294792,0,14699.47396\n166193,59,40,3,1,Married,10,304.4177007,0,14909.88504\n166127,16,0,2,1,Married,13,304.3596949,0,14700.98475\n131367,56,35,2,1,Married,10,304.3149226,0,14898.74613\n184879,19,1,2,0,Married,13,304.289917,0,14865.49585\n168100,24,6,3,1,Married,12,304.2591454,0,14946.95727\n150074,60,41,3,0,Single,2,304.2014304,0,14716.07152\n127268,26,9,2,0,Single,11,304.1275375,0,14955.37688\n198331,23,4,3,1,Married,7,304.0993493,0,14780.96747\n121573,25,7,2,1,Single,12,304.0808254,0,14635.04127\n108280,26,6,1,1,Married,10,304.0332799,0,14640.664\n172914,25,8,3,1,Married,7,304.0324125,0,14746.62063\n176651,54,36,2,0,Married,11,304.0070767,0,14917.35384\n123573,20,2,3,1,Single,13,303.9224718,0,14941.12359\n157900,70,50,1,1,Single,6,303.9037184,0,14988.18592\n164097,69,50,2,0,Married,9,303.8524855,0,14938.62428\n115157,64,43,3,0,Single,3,303.8452258,0,14705.26129\n111146,42,23,3,1,Married,11,303.7557202,0,14831.78601\n145739,60,39,2,0,Single,10,303.7258336,0,14666.29168\n198066,40,21,3,1,Married,7,303.6954464,0,14789.77232\n135849,69,50,3,0,Single,7,303.6715788,0,14774.57894\n187152,49,29,3,0,Single,14,303.5159812,0,14821.79906\n187863,47,29,3,1,Married,8,303.4466458,0,14583.33229\n173236,17,0,2,0,Married,11,303.444704,0,14916.2352\n111806,63,45,1,0,Single,4,303.426557,0,14890.32785\n152494,22,3,4,0,Married,11,303.4177109,0,14693.88555\n130779,52,31,2,1,Married,14,303.4051642,0,14782.25821\n114690,16,0,1,1,Married,15,303.4043979,0,14642.2199\n185733,16,0,2,1,Married,13,303.3959931,0,14944.79966\n106865,27,6,4,0,Single,11,303.3951338,0,14609.75669\n174978,27,6,3,1,Married,9,303.3747837,0,14632.73919\n144210,63,45,2,0,Single,15,303.3502155,0,14619.51078\n193260,45,24,2,1,Married,6,303.3457758,0,14817.28879\n131079,16,0,4,1,Single,13,303.3291757,0,14761.45879\n129481,67,47,2,1,Married,10,303.2492958,0,14954.46479\n112852,31,11,2,1,Married,9,303.2293178,0,14945.46589\n127718,65,45,4,1,Single,8,303.1838448,0,14712.19224\n166104,32,11,1,1,Married,9,303.1333791,0,14595.66896\n174661,38,19,1,1,Married,11,303.1201775,0,14913.00888\n143315,19,2,4,0,Married,14,303.0820897,0,14645.10449\n172701,21,3,2,1,Married,10,303.0263672,0,14789.31836\n107265,22,3,1,0,Married,12,302.9412015,0,14831.06008\n150040,21,3,4,0,Married,12,302.910467,0,14788.52335\n164713,67,49,2,0,Single,8,302.8791172,0,14886.95586\n100560,66,49,3,1,Married,2,302.877603,0,14568.88015\n143153,25,8,1,0,Single,15,302.8673506,0,14891.36753\n135883,62,41,3,0,Single,0,302.8527283,0,14759.63642\n139438,66,46,1,1,Married,9,302.8231225,0,14822.15613\n109986,28,9,3,1,Married,8,302.8204314,0,14808.02157\n134352,64,43,2,1,Single,10,302.8175023,0,14676.87512\n190788,25,8,3,1,Married,12,302.7966664,0,14556.83332\n179441,40,22,3,1,Married,8,302.787052,0,14734.3526\n149949,60,41,1,1,Single,5,302.7598559,0,14894.9928\n121023,21,4,1,1,Married,9,302.7542004,0,14715.71002\n169057,59,38,3,1,Married,7,302.7425401,0,14861.12701\n127988,50,33,3,1,Married,9,302.6707065,0,14909.53533\n196029,19,1,3,0,Single,13,302.6470886,0,14817.35443\n183911,58,41,3,1,Married,8,302.6160105,0,14832.80053\n176813,30,10,2,1,Married,8,302.5936644,0,14857.68322\n114256,54,34,2,1,Married,9,302.5911553,0,14567.55777\n143622,20,2,3,1,Married,6,302.5659292,0,14860.29646\n115404,66,47,3,1,Married,5,302.5600291,0,14652.00146\n164350,70,50,1,0,Married,2,302.4864881,0,14893.32441\n110946,48,28,2,1,Single,12,302.4315301,0,14834.57651\n174602,63,43,2,0,Married,0,302.4183973,0,14539.91987\n164748,66,46,3,1,Married,3,302.3751056,0,14792.75528\n120968,20,1,1,1,Single,12,302.3470413,0,14820.35207\n131279,28,10,3,0,Married,14,302.2831964,0,14570.15982\n175050,21,4,2,0,Single,14,302.257992,0,14858.8996\n123145,53,34,3,1,Married,9,302.2555434,0,14528.77717\n118684,41,20,3,0,Single,15,302.1979214,0,14640.89607\n118614,17,0,3,1,Single,11,302.1749564,0,14906.74782\n200235,50,32,1,1,Married,7,302.1415562,0,14768.07781\n192238,69,49,2,1,Single,10,302.1412162,0,14673.06081\n130319,39,22,4,1,Married,7,302.1335426,0,14639.67713\n186476,20,2,3,1,Single,15,302.0814942,0,14684.07471\n182999,64,43,3,1,Married,7,302.0582232,0,14773.91116\n191051,70,51,1,0,Single,15,302.0260161,0,14899.30081\n109562,54,37,3,0,Married,11,301.9860187,0,14699.30094\n132620,52,33,2,1,Married,8,301.968685,0,14580.43425\n144801,23,5,2,1,Single,13,301.8903378,0,14703.51689\n165024,45,25,2,1,Married,6,301.8893886,0,14715.46943\n189796,64,44,4,1,Single,7,301.8739204,0,14815.69602\n164286,38,20,2,0,Single,15,301.8672605,0,14590.36303\n191124,57,38,3,1,Married,7,301.8101255,0,14513.50628\n196243,64,44,2,1,Single,7,301.7878038,0,14881.39019\n112945,30,10,4,1,Married,8,301.6996352,0,14813.98176\n180600,47,30,1,1,Married,8,301.6710047,0,14744.55024\n119153,63,43,2,0,Single,11,301.6389251,0,14508.94626\n121964,37,16,3,1,Married,6,301.6203459,0,14645.0173\n191324,60,43,3,0,Married,2,301.6186751,0,14590.93376\n101147,66,46,3,0,Married,0,301.5997948,0,14849.98974\n152782,70,53,3,1,Married,10,301.5181844,0,14519.90922\n196319,39,20,2,1,Married,7,301.4943436,0,14537.71718\n105404,68,47,2,1,Single,14,301.4452901,0,14815.26451\n131862,67,50,2,1,Single,10,301.4422157,0,14559.11079\n166383,16,0,3,1,Married,15,301.2966589,0,14596.83295\n168634,16,0,3,1,Single,14,301.2494851,0,14683.47426\n172964,60,39,2,1,Single,6,301.1482711,0,14584.41356\n137112,61,42,3,0,Single,7,301.1465293,0,14482.32647\n165559,65,47,3,0,Single,6,301.0920538,0,14814.60269\n178579,19,2,2,1,Married,13,301.062505,0,14774.12525\n199618,23,5,2,1,Married,9,301.0322857,0,14517.61429\n101071,18,1,4,1,Married,11,301.015681,0,14464.78405\n166402,68,50,4,1,Married,3,300.9867581,0,14765.33791\n112655,55,35,2,0,Married,14,300.9405869,0,14473.02935\n188370,18,1,3,1,Married,7,300.9062125,0,14543.31063\n192750,26,7,2,1,Married,8,300.8567133,0,14510.83567\n175991,32,14,4,1,Married,7,300.8454963,0,14495.27482\n171438,24,7,3,0,Married,13,300.826389,0,14713.31945\n178940,53,33,1,1,Married,6,300.7818655,0,14768.09328\n137600,39,19,2,1,Married,15,300.7346324,0,14815.73162\n139101,18,0,2,1,Single,13,300.7249513,0,14560.24757\n124181,16,0,4,1,Married,13,300.6688027,0,14502.44014\n116246,21,2,2,1,Married,12,300.6285279,0,14679.4264\n123806,65,44,3,0,Single,9,300.5896711,0,14724.48356\n174989,23,4,3,1,Single,15,300.5757063,0,14572.78532\n165934,48,31,3,1,Married,9,300.5180508,0,14603.90254\n105242,32,13,4,0,Single,12,300.5163912,0,14626.81956\n148840,61,42,4,1,Married,6,300.4728614,0,14783.64307\n198858,33,15,4,1,Married,7,300.3903436,0,14595.51718\n199254,62,43,4,1,Married,9,300.3703849,0,14708.51925\n125421,50,33,1,1,Married,10,300.3399914,0,14479.99957\n127564,68,48,2,0,Single,9,300.270217,0,14742.51085\n116257,70,52,4,0,Single,2,300.2694754,0,14807.47377\n196524,56,38,1,1,Married,15,300.2605517,0,14571.02759\n128564,38,20,3,0,Married,15,300.211206,0,14723.5603\n131890,70,51,1,0,Married,8,300.1399029,0,14776.99515\n196490,68,49,3,1,Married,6,300.1130519,0,14425.6526\n196991,58,37,2,1,Married,12,300.0849615,0,14655.24808\n196730,25,8,1,1,Married,6,299.9629532,0,14657.14766\n181186,17,0,1,1,Married,15,299.9273424,0,14759.36712\n187229,64,43,1,1,Single,6,299.9230428,0,14736.15214\n148399,30,10,3,1,Married,13,299.9212102,0,14726.06051\n112751,40,19,3,1,Married,9,299.8475134,0,14679.37567\n103946,24,5,2,1,Single,12,299.8042767,0,14539.21384\n116624,55,35,3,1,Single,12,299.7912697,0,14602.56349\n102501,68,51,3,0,Married,3,299.7789219,0,14576.9461\n115611,67,46,1,1,Married,7,299.7655866,0,14408.27933\n199435,26,9,3,1,Married,8,299.7132474,0,14583.66237\n180419,40,20,3,0,Single,15,299.6729622,0,14692.64811\n198843,38,21,4,1,Single,11,299.6722412,0,14690.61206\n122599,67,50,1,0,Married,5,299.5960275,0,14494.80138\n134780,21,3,2,1,Single,14,299.5568724,0,14576.84362\n179022,52,31,4,1,Married,11,299.5390467,0,14562.95234\n116177,17,0,4,1,Married,9,299.5350945,0,14675.75473\n191401,54,36,1,1,Married,8,299.5314296,0,14437.57148\n110883,16,0,3,1,Married,14,299.5276328,0,14378.38164\n119928,62,41,1,0,Single,11,299.5254454,0,14738.27227\n111974,24,7,3,1,Married,13,299.4952697,0,14696.76349\n170763,22,3,3,1,Single,12,299.4129266,0,14547.64633\n148703,19,0,2,0,Married,14,299.3878756,0,14710.39378\n107100,28,8,2,0,Married,12,299.3629426,0,14442.14713\n162876,65,47,2,1,Married,8,299.3307887,0,14637.53944\n168587,34,16,1,1,Married,12,299.3079623,0,14566.39812\n108677,23,5,2,1,Married,12,299.3018475,0,14679.09238\n181715,68,51,3,0,Single,8,299.2517997,0,14604.58999\n169456,44,27,1,1,Married,8,299.2104772,0,14707.52386\n168611,16,0,2,1,Single,12,299.2080751,0,14681.40376\n123920,28,8,3,0,Single,12,299.2028514,0,14673.14257\n177367,18,0,3,1,Married,10,299.1970496,0,14427.85248\n127475,19,2,3,1,Married,15,299.190366,0,14668.5183\n145447,69,51,3,0,Single,6,299.1873834,0,14668.36917\n182026,24,5,2,1,Married,10,299.1868257,0,14665.34129\n125774,65,46,2,1,Single,9,299.1648586,0,14563.24293\n147959,21,3,2,1,Married,12,299.1271076,0,14494.35538\n156388,26,6,3,0,Single,12,299.0696088,0,14522.48044\n132882,16,0,2,1,Married,11,299.0554411,0,14361.77206\n196043,60,40,4,1,Married,7,299.0533046,0,14715.66523\n190864,70,53,3,0,Single,6,299.026457,0,14696.32285\n107467,65,45,2,1,Single,10,298.9834019,0,14684.1701\n135632,30,13,2,0,Married,11,298.9794918,0,14647.97459\n139249,29,8,3,1,Married,13,298.9395304,0,14360.97652\n123554,23,4,4,1,Married,14,298.9305038,0,14497.52519\n111501,26,8,1,0,Single,15,298.8722661,0,14591.61331\n154817,22,3,3,1,Single,11,298.8265379,0,14565.3269\n167588,40,22,3,0,Married,15,298.8156209,0,14529.78105\n131051,33,16,3,1,Married,15,298.7625083,0,14612.12542\n189476,56,39,2,1,Single,12,298.7437178,0,14444.18589\n129371,37,18,1,1,Married,10,298.7168796,0,14446.84398\n105919,63,45,4,0,Married,9,298.6952639,0,14621.7632\n149864,24,5,1,1,Single,12,298.6945051,0,14542.72526\n168434,19,1,3,1,Married,10,298.6623855,0,14733.11928\n162743,70,53,2,0,Married,2,298.5889495,0,14630.44748\n161487,41,24,4,1,Married,7,298.5527536,0,14332.63768\n167940,43,24,1,1,Married,8,298.5223534,0,14503.11767\n110817,59,42,2,1,Single,15,298.5194061,0,14473.97031\n183133,61,44,2,1,Single,10,298.4618592,0,14386.09296\n197349,52,34,4,1,Married,10,298.446885,0,14404.34425\n144262,46,28,4,0,Married,12,298.3104064,0,14348.52032\n113126,18,0,3,0,Married,14,298.2323025,0,14315.61513\n197380,18,0,4,0,Married,13,298.1731808,0,14433.65904\n120912,50,30,4,1,Single,12,298.1695747,0,14701.47874\n147208,40,19,3,1,Married,7,298.1310198,0,14548.55099\n188298,62,43,1,0,Married,3,298.1251488,0,14660.25744\n144672,70,53,2,0,Single,1,298.1249602,0,14607.24801\n126295,16,0,4,1,Married,11,298.0911559,0,14612.5578\n106575,35,16,2,1,Married,12,298.0254257,0,14672.27129\n124582,47,28,2,1,Married,7,297.9558558,0,14458.79279\n123401,52,31,1,1,Married,8,297.9455922,0,14630.27961\n181434,24,6,3,1,Single,15,297.8965927,0,14667.82964\n198535,20,2,2,1,Single,11,297.7994598,0,14660.97299\n114955,60,41,3,1,Single,8,297.7264273,0,14310.32137\n159645,31,11,3,1,Married,9,297.6731318,0,14371.65659\n113499,16,0,2,1,Married,9,297.6385347,0,14569.92674\n198578,68,49,2,1,Married,10,297.618713,0,14431.93565\n148852,60,43,3,0,Single,14,297.5778166,0,14346.89083\n188951,47,26,2,1,Married,6,297.5640493,0,14366.20247\n134970,69,52,3,1,Single,10,297.5445188,0,14284.22594\n187630,32,11,3,1,Married,8,297.5352388,0,14400.76194\n160027,53,36,1,1,Married,11,297.5190349,0,14556.95175\n190855,19,0,2,0,Married,11,297.5026953,0,14438.13477\n191814,52,33,2,1,Married,9,297.4858939,0,14448.2947\n138726,40,23,3,1,Married,6,297.4827401,0,14375.13701\n187775,48,31,4,1,Married,6,297.4743852,0,14464.71926\n167649,19,0,4,0,Married,15,297.4673372,0,14582.36686\n150800,28,11,1,0,Single,12,297.3944661,0,14299.72331\n181109,26,7,3,0,Married,14,297.3700277,0,14272.50139\n133913,31,13,3,1,Married,6,297.3618365,0,14298.09183\n189606,57,40,2,0,Married,11,297.269335,0,14491.46675\n181822,18,0,3,0,Married,15,297.265109,0,14357.25545\n173841,22,4,1,0,Married,12,297.2343156,0,14482.71578\n168167,52,31,3,1,Married,7,297.2306529,0,14353.53265\n129014,55,37,2,1,Married,10,297.1989156,0,14547.94578\n181349,30,11,3,0,Married,15,297.1587186,0,14430.93593\n174967,64,44,2,1,Married,5,297.0548993,0,14392.74497\n161378,61,41,3,0,Married,7,297.0394266,0,14639.97133\n153236,57,38,2,1,Single,12,297.0075424,0,14385.37712\n155171,53,34,3,1,Single,13,297.0060897,0,14514.30449\n191455,35,17,3,1,Married,7,296.9827634,0,14619.13817\n179248,37,19,3,1,Married,1,296.9675239,0,14539.3762\n143664,41,21,3,1,Married,6,296.9633824,0,14468.16912\n121161,70,50,1,0,Single,10,296.9626966,0,14606.13483\n164236,29,10,2,1,Single,13,296.9345562,0,14491.72781\n107955,22,5,2,0,Single,11,296.909497,0,14559.47485\n149488,53,33,2,1,Married,15,296.877291,0,14346.86455\n118250,64,43,3,0,Single,15,296.8730702,0,14620.65351\n170061,16,0,1,1,Married,9,296.8130956,0,14615.65478\n151476,17,0,1,1,Single,11,296.8081537,0,14346.40769\n159308,66,49,1,0,Single,12,296.8001778,0,14624.00889\n170542,63,45,4,1,Single,6,296.787742,0,14371.3871\n140702,31,13,1,1,Single,12,296.7397873,0,14532.98937\n122547,22,5,2,1,Married,11,296.7119032,0,14634.59516\n106413,60,41,2,1,Single,2,296.6977344,0,14548.88672\n199130,62,45,3,1,Married,9,296.6591367,0,14326.95684\n144119,34,17,4,1,Married,7,296.6095431,0,14359.47716\n155088,22,5,3,1,Married,13,296.5688334,0,14420.44167\n177659,49,30,2,0,Single,11,296.4003825,0,14278.01913\n119681,70,53,2,0,Married,10,296.3736051,0,14471.68026\n117061,66,46,3,1,Single,11,296.3598008,0,14339.99004\n153203,56,35,3,1,Married,12,296.3172017,0,14344.86009\n129471,22,4,1,1,Married,15,296.220441,0,14469.02205\n120331,38,17,3,1,Married,6,296.1898285,0,14417.49143\n198583,49,31,2,1,Married,7,296.1796164,0,14338.98082\n138535,22,5,2,0,Single,15,296.1718486,0,14295.59243\n118516,22,4,2,0,Married,12,296.1653493,0,14598.26747\n145803,60,40,1,1,Married,10,296.1591075,0,14427.95538\n149005,16,0,1,1,Single,15,296.1514067,0,14398.57034\n118562,36,16,2,1,Single,12,296.1230721,0,14268.15361\n193505,56,39,4,0,Single,15,296.0830274,0,14589.15137\n159509,62,44,4,0,Single,8,296.0247801,0,14297.23901\n105539,21,4,1,1,Married,7,296.0244249,0,14599.22125\n167717,63,42,1,0,Single,7,296.012821,0,14596.64105\n173173,38,17,3,1,Married,7,295.9782119,0,14581.9106\n108573,39,22,3,0,Married,14,295.9723067,0,14343.61534\n103768,24,5,4,0,Married,15,295.9290993,0,14260.45497\n168782,38,17,1,1,Married,13,295.925333,0,14512.26665\n188954,23,4,3,1,Single,14,295.918131,0,14311.90655\n124229,45,28,4,0,Single,15,295.8985641,0,14290.92821\n178092,68,48,1,0,Single,12,295.8847896,0,14458.23948\n196322,20,3,1,1,Married,14,295.8795577,0,14434.97789\n109550,31,14,4,0,Married,14,295.8537538,0,14563.68769\n176335,48,31,1,0,Married,13,295.8162876,0,14406.81438\n178800,39,20,4,1,Married,13,295.7260312,0,14240.30156\n180052,34,13,2,1,Married,14,295.7163865,0,14267.81933\n129122,59,40,2,1,Single,11,295.693528,0,14390.6764\n144776,40,21,1,1,Married,7,295.6511677,0,14250.55839\n185021,70,52,4,1,Married,5,295.6484789,0,14568.42395\n194011,70,51,2,0,Single,13,295.5957236,0,14301.78618\n176764,69,51,2,0,Single,7,295.5783635,0,14236.91818\n142663,70,49,1,0,Single,6,295.5723484,0,14282.61742\n144185,65,46,1,0,Married,14,295.5631484,0,14451.15742\n144031,25,8,3,1,Married,15,295.5071185,0,14287.35593\n115671,27,10,3,1,Married,10,295.489609,0,14374.48045\n145692,32,12,1,0,Married,12,295.4869352,0,14565.34676\n100347,40,23,4,1,Married,10,295.4246166,0,14530.23083\n111412,41,21,1,1,Married,11,295.4013748,0,14539.06874\n133641,31,12,2,1,Married,8,295.3917924,0,14364.58962\n100745,32,11,2,1,Married,8,295.3755699,0,14450.7785\n169546,27,8,3,1,Married,6,295.3055244,0,14249.27622\n197797,19,2,3,1,Married,15,295.1582098,0,14221.91049\n118578,23,6,3,1,Married,15,295.1269042,0,14230.34521\n111462,63,44,1,0,Married,7,295.0976342,0,14215.88171\n170998,29,9,3,1,Married,14,295.0696489,0,14431.48245\n108222,45,25,1,1,Married,7,295.0005078,0,14531.02539\n193937,68,51,2,1,Married,7,294.9060223,0,14472.30112\n141402,61,42,4,1,Married,8,294.8875844,0,14264.37922\n130780,55,38,3,0,Single,12,294.8744195,0,14308.72098\n191589,66,45,3,0,Married,9,294.8200829,0,14295.00415\n178088,50,30,2,0,Single,15,294.8160539,0,14378.8027\n138790,25,6,3,0,Married,14,294.7964371,0,14193.82186\n146393,22,3,2,0,Married,15,294.7767246,0,14217.83623\n196778,22,3,2,0,Married,12,294.7744672,0,14408.72336\n184850,51,31,2,0,Single,13,294.7696212,0,14222.48106\n192184,28,10,3,1,Married,11,294.7493408,0,14253.46704\n152355,24,6,3,1,Single,11,294.6843038,0,14205.21519\n116657,16,0,3,1,Single,11,294.6289145,0,14277.44573\n132918,69,51,3,1,Married,10,294.6244362,0,14391.22181\n198753,24,5,3,0,Single,15,294.6140497,0,14142.70249\n199647,50,33,2,0,Married,15,294.5488726,0,14254.44363\n149081,58,38,2,1,Married,11,294.5484416,0,14185.42208\n198204,24,5,2,1,Single,13,294.5121624,0,14394.60812\n127251,62,41,2,1,Married,9,294.4686241,0,14277.43121\n110701,63,45,3,1,Married,5,294.436679,0,14288.83395\n146319,24,7,3,1,Married,14,294.4125223,0,14397.62612\n149020,52,31,4,1,Married,14,294.3852523,0,14415.26262\n181344,63,42,3,0,Single,0,294.3530223,0,14393.65112\n168971,21,4,3,1,Married,11,294.349948,0,14238.4974\n135840,56,39,2,1,Married,8,294.1870698,0,14118.35349\n165338,24,6,1,0,Married,15,294.1784171,0,14233.92086\n162644,25,7,2,1,Single,11,294.1566306,0,14338.83153\n148365,40,20,2,1,Married,12,294.1190275,0,14441.95138\n152671,17,0,3,1,Single,11,294.0972002,0,14313.86001\n163531,19,2,1,1,Married,15,294.094824,0,14397.7412\n176827,29,9,2,1,Married,6,294.0604321,0,14303.02161\n180037,51,33,1,1,Single,12,294.0259335,0,14232.29668\n149930,59,38,2,1,Married,10,294.0112896,0,14237.56448\n146421,48,28,3,0,Married,13,293.9512088,0,14113.56044\n136691,23,4,3,0,Single,11,293.8245967,0,14216.22984\n123350,33,14,2,1,Married,12,293.7842,0,14284.21\n124164,66,46,1,0,Married,6,293.7614685,0,14170.07343\n166927,31,11,3,1,Married,13,293.741185,0,14411.05925\n119963,35,18,1,1,Married,7,293.7317934,0,14354.58967\n195317,67,47,1,1,Single,10,293.6649609,0,14350.24805\n146494,18,1,4,1,Married,12,293.6557987,0,14257.78994\n188733,16,0,4,1,Married,12,293.5591049,0,14126.95525\n173362,23,4,4,1,Single,11,293.4971294,0,14262.85647\n188640,17,0,4,1,Single,14,293.4915586,0,14153.57793\n108632,65,45,2,0,Married,10,293.4708894,0,14245.54447\n108841,65,47,3,0,Married,8,293.4360274,0,14100.80137\n111575,23,6,1,0,Married,14,293.4321973,0,14214.60987\n111243,31,14,2,0,Single,12,293.3821636,0,14129.10818\n122577,35,14,2,1,Married,9,293.3026507,0,14326.13254\n102450,21,2,1,1,Single,11,293.2690619,0,14098.4531\n141220,55,36,2,1,Married,14,293.2596459,0,14116.9823\n179615,43,25,2,1,Single,15,293.0462488,0,14296.31244\n186695,28,10,3,1,Married,7,292.9718501,0,14119.59251\n116504,68,47,3,1,Married,0,292.9110316,0,14295.55158\n112134,57,39,4,1,Single,13,292.909646,0,14366.4823\n169548,41,23,4,0,Married,11,292.866565,0,14199.32825\n145115,50,33,1,1,Married,10,292.8056952,0,14221.28476\n176235,62,44,2,1,Single,1,292.7781058,0,14126.90529\n107801,16,0,1,1,Married,12,292.7728955,0,14181.64478\n138798,45,26,4,1,Single,14,292.768699,0,14431.43495\n135723,68,49,3,1,Single,8,292.7373979,0,14251.8699\n194678,62,44,2,0,Single,6,292.7272148,0,14216.36074\n103109,22,4,4,1,Single,15,292.7197131,0,14232.98566\n101974,34,15,2,1,Married,8,292.5934558,0,14032.67279\n157636,25,6,2,1,Single,11,292.5574174,0,14357.87087\n134795,21,2,2,1,Married,12,292.5262569,0,14060.31285\n135117,61,40,2,0,Married,5,292.5180765,0,14220.90383\n107372,66,47,2,1,Married,6,292.5163962,0,14276.81981\n105765,30,9,1,1,Married,8,292.5163921,0,14153.81961\n116481,68,47,2,0,Single,6,292.4898702,0,14169.49351\n145687,65,48,3,0,Married,6,292.4787908,0,14379.93954\n176608,21,3,3,1,Single,13,292.477184,0,14061.8592\n122078,61,41,3,0,Married,8,292.4699695,0,14416.49848\n153272,58,40,2,1,Married,14,292.4367434,0,14181.83717\n151282,52,31,1,1,Married,15,292.4305792,0,14295.52896\n176879,55,38,2,1,Married,7,292.3974533,0,14255.87267\n123640,66,47,2,1,Single,12,292.3974265,0,14405.87133\n196388,49,31,1,1,Married,7,292.3818226,0,14052.09113\n198224,68,49,1,0,Single,3,292.3735058,0,14346.67529\n130282,22,5,3,1,Married,11,292.367162,0,14368.3581\n142100,22,3,4,1,Married,13,292.3054943,0,14359.27472\n162322,63,46,3,1,Married,10,292.2918382,0,14375.59191\n196964,38,18,1,0,Single,11,292.2905735,0,14284.52868\n106454,65,47,2,1,Single,14,292.2713654,0,14359.56827\n107916,19,0,3,1,Single,11,292.240232,0,14248.0116\n173363,24,5,2,1,Married,12,292.2321227,0,14411.60614\n123331,33,15,2,1,Married,15,292.2050753,0,14041.25377\n144106,35,16,2,0,Married,12,292.0929944,0,14099.64972\n199587,24,5,1,0,Married,11,292.0878142,0,14031.39071\n188570,41,24,1,1,Married,7,292.0494433,0,14089.47217\n195039,35,16,3,1,Married,6,292.0457519,0,14165.2876\n174034,62,41,4,0,Married,7,292.0239269,0,14027.19635\n139062,64,44,1,1,Single,9,292.0086982,0,14287.43491\n199695,19,1,3,0,Married,12,291.9833811,0,14366.16906\n190380,23,4,1,1,Married,12,291.9627918,0,14334.13959\n174777,28,11,4,1,Married,8,291.9148536,0,14235.74268\n176531,30,11,2,1,Single,15,291.7805945,0,14221.02973\n147110,53,32,4,1,Married,6,291.7664483,0,14379.32242\n112477,63,46,3,0,Single,7,291.7045386,0,14090.22693\n174564,34,14,3,1,Married,9,291.7016507,0,14290.08254\n159268,42,23,1,1,Married,7,291.6776414,0,14066.88207\n113487,63,46,4,1,Married,6,291.6686666,0,14103.43333\n150033,28,9,2,1,Married,9,291.6173826,0,14043.86913\n126479,37,20,3,1,Married,9,291.6092093,0,14091.46047\n136770,21,4,4,0,Married,12,291.570191,0,14167.50955\n144616,55,38,4,1,Married,10,291.5165346,0,13991.82673\n169635,38,17,3,1,Married,8,291.4424914,0,14083.12457\n127220,68,47,3,1,Married,8,291.4284143,0,14171.42072\n152002,66,49,2,0,Married,9,291.4064942,0,14332.32471\n127409,61,44,3,0,Single,0,291.3963704,0,14127.81852\n138193,70,53,3,0,Single,10,291.3569076,0,14252.84538\n193234,68,49,2,0,Married,10,291.3346239,0,14084.7312\n198995,31,10,2,1,Married,8,291.3184574,0,14137.92287\n162956,40,19,1,0,Single,12,291.3088903,0,14028.44452\n108246,17,0,1,1,Married,14,291.2674839,0,14123.3742\n174487,55,34,4,1,Married,7,291.0763148,0,13967.81574\n103750,18,0,1,1,Married,14,291.0696945,0,14015.48473\n163497,52,31,3,0,Married,11,290.9601261,0,14168.00631\n115459,50,30,3,0,Single,15,290.9001871,0,14031.00936\n137028,58,40,3,1,Married,7,290.8758858,0,13960.79429\n193097,58,41,2,0,Single,12,290.8615744,0,14070.07872\n163451,25,8,2,1,Married,10,290.85714,0,14032.857\n152138,69,50,2,0,Married,7,290.8309896,0,14317.54948\n151690,35,18,2,1,Married,11,290.8274344,0,14172.37172\n173665,68,49,2,0,Married,10,290.7900443,0,14132.50222\n133658,67,50,3,1,Married,5,290.7665179,0,14233.3259\n135733,65,44,1,0,Single,14,290.7360244,0,14264.80122\n168625,62,43,3,0,Single,12,290.7196026,0,13977.98013\n155432,63,42,4,0,Married,7,290.6463151,0,14137.31576\n175097,70,49,2,1,Single,7,290.5468122,0,14025.34061\n125295,23,4,4,1,Single,12,290.535341,0,14155.76705\n147903,25,8,3,0,Single,12,290.4448807,0,13977.24404\n177500,67,49,3,1,Married,9,290.3883404,0,13953.41702\n153102,33,12,2,1,Single,11,290.3603949,0,13956.01975\n133024,18,0,2,1,Married,13,290.262723,0,14202.13615\n143246,16,0,3,0,Married,11,290.2619311,0,14132.09656\n107052,41,21,2,0,Single,14,290.2313195,0,14241.56598\n195119,19,0,2,1,Married,14,290.1292949,0,14050.46475\n108721,20,1,4,1,Married,13,290.1191915,0,14246.95958\n191183,24,6,4,1,Married,12,290.0682866,0,14131.41433\n104725,64,46,3,0,Married,5,290.025022,0,14194.2511\n190236,44,23,1,1,Married,10,289.9684397,0,13951.42199\n151112,61,43,4,0,Single,8,289.9680308,0,14169.40154\n120839,29,8,3,1,Married,6,289.9355464,0,14249.77732\n116513,23,4,3,1,Married,13,289.8433582,0,14044.16791\n125307,20,1,3,1,Married,8,289.8156293,0,13920.78147\n196188,47,30,3,0,Single,15,289.8098663,0,13901.49332\n161738,28,9,3,1,Married,12,289.7942597,0,14200.71299\n140087,35,16,1,1,Married,10,289.7668906,0,14011.34453\n142899,17,0,3,1,Married,6,289.7260717,0,14026.30359\n196503,29,9,1,1,Married,6,289.6606846,0,14087.03423\n189660,57,38,2,0,Single,13,289.5776329,0,14138.88165\n159938,64,44,1,1,Single,14,289.5307529,0,14233.53765\n151916,25,8,2,1,Single,15,289.5292201,0,14048.46101\n151354,18,0,2,1,Married,14,289.5240989,0,14073.20495\n148927,59,42,3,1,Married,10,289.381618,0,14200.0809\n189721,53,34,1,1,Married,9,289.3514634,0,14032.57317\n142350,18,0,2,1,Married,12,289.3441822,0,14222.20911\n164381,19,2,1,0,Married,12,289.320228,0,14056.0114\n138715,43,23,1,1,Married,11,289.315993,0,14243.79965\n133600,34,13,2,1,Married,11,289.2435646,0,14120.17823\n190814,20,3,2,1,Married,15,289.2416865,0,14054.08433\n133678,64,47,2,0,Single,7,289.2225933,0,13908.12967\n154235,58,38,3,1,Married,9,289.2175028,0,14095.87514\n150679,57,38,3,1,Married,9,289.1721885,0,14138.60943\n123078,64,46,2,1,Single,7,289.1676379,0,14188.3819\n159115,30,10,4,1,Married,10,289.1312252,0,13944.56126\n145037,18,0,3,1,Married,15,289.1148939,0,13895.7447\n129656,41,22,3,1,Married,15,289.1040693,0,13961.20347\n182749,25,7,2,1,Married,13,288.9798219,0,14164.9911\n126846,39,19,2,1,Married,0,288.9363679,0,13849.8184\n154208,60,40,2,1,Married,1,288.8449569,0,14072.24785\n134512,56,38,3,0,Single,15,288.8223917,0,14074.11959\n107981,25,8,2,1,Single,15,288.7365091,0,14087.82546\n111550,63,45,3,1,Married,1,288.708758,0,14190.4379\n100151,23,5,3,1,Married,13,288.6727542,0,13926.63771\n128378,64,44,2,1,Single,4,288.6478358,0,14010.39179\n158850,63,42,3,0,Married,0,288.539194,0,13842.9597\n106872,34,17,3,1,Married,12,288.4836684,0,13844.18342\n150940,24,7,2,1,Married,11,288.4618053,0,13861.09027\n103716,33,15,2,1,Married,8,288.4368473,0,13947.84237\n114880,46,27,3,0,Single,12,288.2849049,0,14127.24525\n118395,18,0,4,1,Married,14,288.2335135,0,13826.67568\n175003,56,37,2,1,Married,8,288.2242538,0,13861.21269\n144296,17,0,4,1,Single,12,288.191003,0,13897.55015\n174049,43,23,2,0,Married,14,288.172329,0,13869.61645\n119090,25,6,2,1,Single,15,288.1330928,0,14206.65464\n122645,61,43,3,0,Married,3,288.0919694,0,14165.59847\n138167,70,51,2,1,Single,10,288.0876461,0,14008.38231\n108711,65,48,1,0,Single,3,288.0778841,0,13804.89421\n192739,61,43,2,1,Single,10,288.0075676,0,13948.37838\n164643,29,9,1,1,Married,6,287.9462439,0,14185.3122\n115954,21,2,2,1,Married,11,287.8762396,0,14097.81198\n153406,23,4,3,0,Single,14,287.8380428,0,14171.90214\n192933,24,7,2,1,Single,14,287.8092615,0,13967.46308\n167985,68,50,2,0,Married,7,287.7884577,0,13926.42289\n178158,61,40,1,1,Married,9,287.7680412,0,13902.40206\n173586,61,43,3,0,Married,7,287.7556186,0,14148.78093\n109789,19,1,2,1,Married,6,287.5581669,0,13990.90835\n182742,43,25,1,1,Married,9,287.5238284,0,13829.19142\n155769,32,13,4,1,Married,9,287.4991457,0,13856.95729\n177280,35,14,1,0,Married,13,287.4881203,0,14128.40602\n140254,21,4,4,1,Single,13,287.4566621,0,13927.83311\n127568,51,33,2,1,Married,7,287.440725,0,14065.03625\n141083,19,1,4,1,Married,15,287.4289745,0,13773.44873\n182683,33,12,1,1,Married,8,287.4214409,0,13777.07205\n127089,66,48,3,1,Married,8,287.4060986,0,13849.30493\n196387,19,0,4,0,Single,12,287.3495349,0,14160.47675\n114628,36,19,1,1,Married,6,287.3493637,0,14012.46819\n146653,61,44,3,0,Single,8,287.3031558,0,13798.15779\n191694,67,46,3,1,Single,9,287.2942512,0,13878.71256\n138443,17,0,2,1,Married,14,287.2941473,0,14003.70737\n165056,61,41,3,0,Single,15,287.231523,0,13902.57615\n129698,23,5,2,0,Married,11,287.2007063,0,13897.03532\n188282,38,17,2,1,Married,10,287.1500967,0,14008.50484\n167132,63,44,4,1,Married,10,287.1088186,0,13825.44093\n178658,44,23,4,1,Married,13,287.0759822,0,13936.79911\n186705,45,25,4,1,Married,6,287.0757361,0,13905.78681\n133272,70,53,2,0,Single,7,287.0716344,0,14096.58172\n125677,46,29,3,1,Married,8,287.0311919,0,13890.5596\n185771,50,29,2,1,Married,10,286.9764941,0,13749.82471\n185715,26,9,2,1,Married,14,286.970849,0,14130.54245\n125100,53,36,3,1,Married,9,286.9080924,0,13991.40462\n147151,64,43,3,0,Married,10,286.8998103,0,13926.99052\n198766,70,52,2,0,Married,4,286.8812378,0,13983.06189\n144164,31,11,2,1,Single,13,286.8740409,0,14105.70205\n165014,42,22,2,1,Single,11,286.818748,0,13955.9374\n135976,42,21,2,0,Married,12,286.71261,0,14075.6305\n150562,17,0,3,1,Single,12,286.6977228,0,14045.88614\n103725,69,50,3,0,Married,6,286.6598643,0,14064.99322\n167380,21,3,3,1,Married,15,286.6560119,0,13802.8006\n109839,56,35,1,1,Single,13,286.5238132,0,13778.19066\n103366,20,1,4,1,Married,11,286.4642331,0,13943.21166\n110613,58,40,3,1,Single,15,286.4599021,0,13904.99511\n192330,51,31,4,1,Married,12,286.3316671,0,13763.58336\n162373,23,6,3,1,Married,11,286.2937389,0,13740.68695\n112512,31,14,3,0,Single,12,286.2276689,0,13944.38345\n115414,31,13,3,1,Married,10,286.1992473,0,14063.96237\n200263,64,45,3,1,Single,12,286.1335231,0,13809.67616\n135970,34,16,3,0,Married,11,286.085105,0,13935.25525\n147615,65,48,2,0,Single,13,286.0536902,0,13816.68451\n156529,19,1,2,0,Married,14,286.0007918,0,13981.03959\n137135,62,41,2,1,Single,0,285.9760284,0,13707.80142\n147882,63,43,4,0,Single,7,285.9694186,0,13929.47093\n182801,19,0,2,0,Married,15,285.96304,0,13825.152\n157666,66,49,2,1,Married,3,285.9494897,0,14000.47449\n184363,24,6,2,1,Single,13,285.9482313,0,13727.41157\n126129,24,7,1,0,Married,11,285.9332715,0,14057.66358\n156361,32,14,3,0,Single,15,285.7805089,0,13788.02545\n140198,25,6,2,1,Married,13,285.7204047,0,13892.02024\n185003,60,41,1,1,Married,8,285.7046838,0,13913.23419\n173443,20,2,2,1,Married,12,285.6902765,0,13942.51383\n197903,49,28,3,1,Married,10,285.6855511,0,13897.27756\n178304,62,43,2,1,Single,5,285.6495297,0,13909.47649\n104094,55,38,2,1,Single,15,285.5379888,0,13836.89944\n184086,43,23,3,0,Single,12,285.5213968,0,13712.06984\n198838,67,50,3,1,Married,8,285.4702579,0,13759.5129\n121273,64,44,3,1,Married,2,285.3769127,0,13898.84564\n128094,30,13,2,0,Married,11,285.3522292,0,13836.61146\n129355,28,11,1,1,Married,8,285.3417405,0,13984.08703\n164784,28,11,2,1,Married,9,285.2523626,0,13672.61813\n179038,30,10,3,0,Married,13,285.2478079,0,13751.3904\n183666,63,42,3,0,Married,12,285.2398444,0,13735.99222\n178873,60,40,4,0,Married,8,285.1611597,0,13684.05799\n100979,50,29,3,0,Married,15,285.1148334,0,13895.74167\n136520,60,40,1,0,Married,7,285.1038637,0,13805.19319\n186647,41,23,1,1,Married,8,285.0448892,0,13675.24446\n113861,25,6,1,1,Married,12,284.8746199,0,13696.731\n195958,62,45,3,0,Married,9,284.8127977,0,13641.63989\n187100,21,4,3,1,Married,12,284.7555892,0,13902.77946\n182803,17,0,2,1,Married,15,284.7434642,0,13732.17321\n105252,66,46,4,1,Married,9,284.7056429,0,13864.28215\n104751,54,36,2,0,Single,14,284.6947686,0,13986.73843\n160582,56,37,3,0,Married,14,284.6856048,0,13814.28024\n100313,19,1,4,1,Single,12,284.6476935,0,13776.38468\n192867,62,43,1,0,Married,6,284.6270671,0,13785.35336\n139878,65,48,2,0,Married,12,284.5806468,0,13688.03234\n163252,60,39,4,0,Married,6,284.5710122,0,14019.55061\n114356,63,43,2,0,Married,7,284.5705762,0,13808.52881\n187027,70,50,1,0,Single,9,284.5698704,0,13708.49352\n145555,21,4,3,0,Single,11,284.5356716,0,13843.78358\n131253,27,8,4,1,Married,7,284.5326999,0,13735.635\n188171,69,50,4,1,Married,1,284.499931,0,13971.99655\n107201,65,45,2,1,Single,10,284.4893908,0,13938.46954\n134150,35,17,1,1,Married,6,284.4274153,0,13662.37077\n135045,16,0,3,1,Married,8,284.3556184,0,13990.78092\n173005,58,38,1,1,Married,14,284.2330566,0,13732.65283\n195321,68,51,2,0,Married,10,284.1424955,0,13993.12478\n181297,21,4,3,1,Married,14,284.1389726,0,13864.94863\n102783,19,1,3,1,Married,14,284.0776759,0,13784.8838\n196397,41,20,3,1,Married,7,284.0133087,0,13772.66544\n138700,34,15,4,0,Single,14,284.0077688,0,13950.38844\n160873,65,44,1,1,Single,12,283.9687422,0,13608.43711\n191962,41,24,2,1,Single,12,283.9643354,0,13705.21677\n151729,20,1,2,1,Married,12,283.9508851,0,13844.54426\n123535,19,0,4,1,Single,13,283.9207249,0,13873.03625\n126164,17,0,1,1,Married,12,283.9100974,0,13658.50487\n129203,61,44,4,1,Married,6,283.7738187,0,13610.69094\n120187,26,7,1,1,Married,6,283.7506272,0,13825.53136\n193950,68,51,1,0,Married,14,283.7470142,0,13807.35071\n138068,47,28,3,1,Married,13,283.7200076,0,13771.00038\n166545,46,27,1,1,Married,15,283.6638328,0,13616.19164\n151270,54,34,3,1,Single,15,283.6484753,0,13838.42377\n101597,55,35,2,0,Single,11,283.6160347,0,13857.80174\n142676,36,15,2,1,Married,7,283.6065702,0,13979.32851\n127446,25,7,2,1,Married,7,283.5781437,0,13758.90719\n126790,62,41,4,1,Married,1,283.5426922,0,13632.13461\n149035,62,44,3,1,Married,7,283.4877653,0,13863.38827\n175949,68,49,2,0,Single,13,283.4616443,0,13700.08222\n183476,26,9,4,0,Married,13,283.425868,0,13749.2934\n103113,69,49,2,0,Single,8,283.4059068,0,13689.29534\n155465,31,10,1,1,Single,15,283.3677239,0,13955.3862\n104294,21,2,2,0,Married,15,283.3502652,0,13737.51326\n169529,56,37,3,0,Married,13,283.3282852,0,13862.41426\n113117,34,13,4,1,Single,14,283.3004662,0,13794.02331\n181759,19,0,2,1,Single,11,283.2421494,0,13853.10747\n180138,18,1,4,1,Married,15,283.2271113,0,13893.35557\n146690,24,7,3,1,Married,15,283.1980525,0,13592.90263\n113960,69,52,3,0,Single,14,283.1627247,0,13583.13624\n125031,21,3,2,0,Married,15,283.0848197,0,13842.24099\n119020,20,2,2,1,Married,10,283.0697002,0,13910.48501\n177299,62,41,2,1,Married,4,283.0600948,0,13752.00474\n151554,62,44,3,1,Single,8,283.0433401,0,13575.16701\n162559,22,3,1,1,Married,14,283.0236307,0,13742.18154\n143343,41,20,2,0,Married,13,282.9530993,0,13777.65497\n134071,43,23,3,0,Married,13,282.9321492,0,13908.60746\n137466,25,6,1,1,Single,14,282.9126405,0,13759.63203\n134400,63,44,4,1,Single,7,282.8681993,0,13874.40997\n151308,17,0,3,1,Single,11,282.841367,0,13605.06835\n191865,61,42,3,1,Married,7,282.8026485,0,13912.13243\n128684,67,46,2,0,Single,3,282.699963,0,13885.99815\n161320,32,15,3,1,Married,10,282.6876905,0,13837.38453\n161195,21,2,4,1,Married,13,282.6123076,0,13758.61538\n112334,69,51,3,0,Single,10,282.5751978,0,13587.75989\n143837,65,44,3,0,Married,1,282.5451664,0,13565.25832\n160180,39,19,3,1,Married,9,282.542256,0,13836.1128\n158873,53,34,2,1,Married,9,282.4691551,0,13862.45776\n144229,64,43,2,1,Married,1,282.4677621,0,13605.38811\n111093,19,2,1,1,Married,14,282.4138477,0,13885.69239\n198773,18,0,1,0,Married,11,282.3993159,0,13853.9658\n146747,70,51,2,0,Married,10,282.3902591,0,13754.51296\n161019,64,47,3,1,Married,13,282.3852994,0,13906.26497\n120449,66,49,3,0,Married,8,282.3446176,0,13589.23088\n104822,23,5,4,0,Single,15,282.3222806,0,13767.11403\n135261,17,0,1,1,Married,10,282.2230386,0,13693.15193\n121346,18,0,3,0,Married,15,282.1934552,0,13602.67276\n156861,21,2,2,1,Married,4,282.1805458,0,13704.02729\n176444,69,51,4,1,Married,13,282.1491477,0,13545.45739\n152831,54,33,3,1,Married,15,282.1229567,0,13817.14784\n137055,16,0,2,0,Married,12,282.1225824,0,13568.12912\n157805,25,7,2,1,Single,13,282.1028252,0,13632.14126\n160687,27,9,1,1,Married,12,281.9988866,0,13649.94433\n111475,30,11,3,1,Married,10,281.9505713,0,13833.52857\n121591,53,35,4,0,Married,14,281.9285033,0,13659.42517\n145819,46,29,3,1,Single,13,281.9092425,0,13746.46213\n192585,70,50,3,0,Married,11,281.8606979,0,13679.0349\n145578,44,25,1,1,Married,11,281.8028525,0,13536.14263\n194506,21,3,4,0,Married,14,281.7888965,0,13527.44483\n130544,20,1,3,0,Married,13,281.7290259,0,13607.4513\n117176,52,34,2,0,Married,13,281.6759718,0,13708.79859\n171108,63,44,4,0,Married,7,281.6387779,0,13618.9389\n190090,69,49,1,0,Married,11,281.6230067,0,13489.15034\n180914,68,51,2,0,Single,10,281.57493,0,13506.7465\n110464,63,45,4,0,Married,9,281.5223611,0,13601.11806\n124779,67,46,1,1,Single,4,281.4939094,0,13725.69547\n138066,39,19,4,1,Married,6,281.4826615,0,13678.13308\n183958,60,39,4,1,Married,8,281.4717512,0,13738.58756\n171107,32,14,1,1,Single,13,281.440499,0,13596.02495\n136370,28,8,2,1,Married,9,281.4052085,0,13551.26043\n183196,25,8,2,1,Married,11,281.3731044,0,13523.65522\n104022,27,8,3,0,Single,15,281.3557517,0,13580.78759\n109252,22,4,3,1,Single,11,281.2414019,0,13643.0701\n145844,22,4,4,1,Married,14,281.1619271,0,13509.09636\n196348,46,26,4,0,Single,13,281.1185963,0,13673.92982\n180388,21,2,4,0,Married,14,281.1133604,0,13609.66802\n112958,20,2,2,1,Married,12,281.0141047,0,13671.70524\n131608,51,31,3,1,Single,14,280.9900694,0,13662.50347\n150533,67,48,1,1,Single,2,280.9848214,0,13698.24107\n172116,18,0,3,1,Single,12,280.97167,0,13848.5835\n194615,35,14,3,0,Married,15,280.9519112,0,13829.59556\n116608,66,46,3,0,Married,9,280.863753,0,13738.18765\n169288,20,1,3,0,Married,14,280.8443506,0,13589.21753\n169584,35,18,1,0,Married,11,280.7923877,0,13747.61939\n172669,58,37,4,0,Married,11,280.7864789,0,13664.32395\n176789,46,28,3,1,Married,11,280.781273,0,13727.06365\n164070,49,28,2,0,Married,13,280.7433366,0,13545.16683\n114940,61,43,3,1,Single,14,280.6973636,0,13742.86818\n132682,65,45,2,0,Married,1,280.664265,0,13771.21325\n174591,18,0,2,0,Married,14,280.6475126,0,13610.37563\n118997,66,45,2,0,Married,6,280.6386077,0,13485.93039\n122915,66,45,4,1,Single,8,280.6237231,0,13781.18616\n195625,70,52,2,1,Single,5,280.5897624,0,13613.48812\n136546,23,6,2,0,Single,11,280.5738406,0,13445.69203\n136762,65,48,2,1,Married,3,280.5577716,0,13706.88858\n142589,70,51,2,1,Single,5,280.5294298,0,13630.47149\n163511,19,0,4,1,Single,13,280.4403218,0,13706.01609\n123467,68,49,2,1,Single,8,280.1584591,0,13697.92296\n117713,46,26,2,1,Single,12,280.1469219,0,13732.3461\n113975,55,38,2,1,Married,9,280.135132,0,13700.7566\n155474,21,2,3,0,Married,13,280.0843828,0,13576.21914\n173632,16,0,2,1,Married,11,280.0804628,0,13596.02314\n114629,70,53,4,0,Married,6,280.0771197,0,13549.85599\n151573,24,6,3,1,Married,7,279.993291,0,13643.66455\n191368,34,15,1,1,Married,8,279.9932715,0,13734.66358\n163409,25,6,1,1,Married,8,279.9889923,0,13551.44962\n102843,25,8,2,0,Married,15,279.9560585,0,13591.80293\n126956,57,37,3,0,Married,13,279.9207188,0,13484.03594\n179852,66,48,2,1,Married,9,279.9183728,0,13528.91864\n118618,43,22,2,1,Married,6,279.8884006,0,13609.42003\n134977,18,0,1,0,Married,14,279.8615512,0,13613.07756\n191513,41,22,4,1,Married,15,279.8487014,0,13481.43507\n106099,26,6,4,1,Single,14,279.8312708,0,13719.56354\n174733,23,6,1,1,Married,15,279.7679188,0,13758.39594\n118427,62,44,3,1,Single,6,279.7571562,0,13396.85781\n116970,25,8,1,0,Married,12,279.6556808,0,13665.78404\n128959,62,42,2,1,Married,7,279.6486554,0,13630.43277\n110042,25,8,2,1,Married,14,279.5721564,0,13715.60782\n122111,41,20,4,1,Single,11,279.5460828,0,13443.30414\n163639,63,44,2,1,Married,11,279.4194643,0,13750.97322\n139097,19,2,1,1,Single,14,279.4165022,0,13672.82511\n169789,60,43,3,0,Single,8,279.4074456,0,13388.37228\n180143,44,23,2,1,Married,13,279.4071319,0,13695.3566\n198719,68,51,3,0,Married,10,279.3915976,0,13690.57988\n109655,46,29,3,0,Single,15,279.3767853,0,13660.83927\n139484,16,0,1,1,Married,14,279.3074554,0,13522.37277\n172772,63,45,3,0,Married,1,279.3035661,0,13567.17831\n176914,68,50,3,1,Married,4,279.2845698,0,13426.22849\n198271,63,43,3,0,Married,10,279.283507,0,13429.17535\n125550,56,35,3,1,Married,10,279.20235,0,13603.1175\n199026,23,6,4,1,Single,11,279.1963188,0,13554.81594\n124835,67,50,3,0,Married,10,279.1536358,0,13550.68179\n105326,23,6,2,1,Married,9,279.0614405,0,13467.07203\n195852,56,38,4,0,Married,14,279.0504705,0,13561.52353\n186718,35,15,2,0,Single,13,278.9718352,0,13667.59176\n166108,40,20,2,1,Married,12,278.9710642,0,13405.55321\n168710,26,5,1,1,Married,7,278.9689585,0,13599.44793\n137448,20,1,2,0,Married,13,278.9382034,0,13365.91017\n196895,66,46,2,1,Married,7,278.9371994,0,13604.85997\n196666,42,24,3,0,Single,15,278.9160901,0,13389.80451\n157565,21,4,2,1,Single,12,278.9000884,0,13356.00442\n134804,33,14,2,1,Married,14,278.7847833,0,13407.23917\n183150,21,4,2,0,Married,11,278.7830603,0,13631.15302\n187204,48,30,1,0,Single,15,278.7787348,0,13447.93674\n165319,32,15,3,0,Married,11,278.7566055,0,13591.83028\n126663,20,2,4,1,Married,6,278.7454211,0,13502.27106\n126102,16,0,1,0,Married,13,278.6843827,0,13490.21914\n126989,66,46,3,0,Single,14,278.672627,0,13605.63135\n124463,63,46,3,0,Married,6,278.6703519,0,13578.5176\n104777,61,44,2,1,Single,4,278.6510593,0,13365.55297\n177809,22,5,2,1,Married,11,278.5209343,0,13571.04672\n128851,61,43,1,0,Married,6,278.5029941,0,13684.14971\n178277,28,8,3,1,Married,7,278.4953082,0,13360.76541\n168299,18,0,3,0,Married,13,278.4743325,0,13574.71663\n186726,20,2,1,1,Married,13,278.4734322,0,13597.67161\n133989,23,6,3,0,Single,14,278.4217692,0,13517.08846\n132123,46,28,4,0,Single,14,278.3763544,0,13644.81772\n127878,25,8,3,0,Married,12,278.3718827,0,13682.59414\n101880,23,6,2,0,Married,15,278.2510373,0,13318.55187\n181121,62,43,3,0,Single,15,278.1608605,0,13669.04303\n191371,70,52,2,1,Single,12,278.1082209,0,13543.41105\n195314,21,4,4,1,Single,15,278.0756923,0,13491.78462\n110629,23,4,1,1,Married,13,278.0711026,0,13490.55513\n125508,22,3,3,1,Married,12,278.047453,0,13412.37265\n132131,52,32,3,1,Single,15,278.0438613,0,13420.19306\n144279,38,18,3,0,Single,13,278.028599,0,13467.42995\n178228,68,49,3,1,Single,7,277.9042945,0,13593.21473\n121620,25,8,3,0,Married,11,277.8978964,0,13329.89482\n198736,60,42,2,0,Married,12,277.8842178,0,13378.21089\n144993,20,1,3,1,Married,14,277.8466904,0,13496.33452\n148218,18,0,3,1,Married,11,277.8208258,0,13672.04129\n180007,67,48,4,0,Married,6,277.7798696,0,13427.99348\n158716,66,49,2,0,Single,0,277.6940932,0,13465.70466\n113498,31,12,4,0,Married,11,277.5898567,0,13306.49284\n172849,24,5,2,0,Married,12,277.5433395,0,13396.16698\n123887,64,45,3,0,Married,1,277.5412207,0,13628.06104\n169888,67,47,3,1,Single,9,277.4594881,0,13393.97441\n174769,41,21,1,0,Single,15,277.4546101,0,13291.73051\n150141,34,14,2,1,Married,6,277.4110881,0,13365.55441\n165210,67,49,4,1,Married,5,277.3865618,0,13557.32809\n138496,68,49,2,0,Single,5,277.3031401,0,13637.15701\n117473,17,0,1,1,Married,14,277.295218,0,13366.7609\n149385,49,30,3,0,Single,11,277.2942155,0,13646.71078\n115399,33,14,2,1,Married,12,277.1370095,0,13279.85048\n147966,32,14,3,1,Single,14,277.0366197,0,13303.83099\n177308,59,38,1,1,Single,12,277.0128261,0,13337.64131\n189508,68,48,1,0,Married,2,276.9982494,0,13580.91247\n103758,46,27,4,1,Single,15,276.9096614,0,13331.48307\n195718,69,50,3,0,Single,2,276.8951398,0,13464.75699\n139137,68,49,2,0,Single,0,276.8300778,0,13370.50389\n110313,64,45,1,0,Married,10,276.7748308,0,13391.74154\n138261,18,0,4,1,Married,14,276.7711105,0,13283.55553\n177676,51,32,4,0,Married,12,276.6929005,0,13595.64503\n100466,60,39,2,0,Married,2,276.6664704,0,13415.32352\n118478,21,3,4,1,Married,15,276.6608014,0,13233.04007\n163468,19,1,2,1,Married,14,276.603563,0,13393.17815\n180841,19,0,1,1,Married,13,276.5807741,0,13374.03871\n194538,35,16,3,1,Single,15,276.4816299,0,13413.0815\n173354,39,18,1,1,Married,9,276.4724081,0,13521.62041\n129443,66,49,4,1,Single,12,276.4526681,0,13329.63341\n119311,22,3,2,1,Married,15,276.3948562,0,13297.74281\n159816,66,47,2,0,Married,4,276.3640713,0,13605.20357\n183621,68,48,4,0,Single,6,276.3244812,0,13392.22406\n186337,43,22,3,1,Married,14,276.2756458,0,13410.78229\n177520,70,50,2,0,Married,8,276.21984,0,13430.992\n167941,63,45,2,0,Married,8,276.2114965,0,13322.57483\n163961,17,0,2,0,Married,12,276.122448,0,13257.1224\n188607,62,44,1,1,Married,3,276.1089391,0,13507.44696\n146882,61,42,2,0,Married,6,276.0410019,0,13398.0501\n155018,35,15,4,1,Married,12,275.9957072,0,13458.78536\n137606,31,12,3,0,Married,13,275.9303159,0,13518.5158\n100934,23,6,4,1,Single,13,275.9210759,0,13378.0538\n156912,61,40,1,1,Married,8,275.8448446,0,13204.24223\n169592,53,34,4,0,Single,12,275.8334439,0,13310.6722\n135839,25,8,3,1,Married,13,275.7229211,0,13279.14606\n137313,22,5,3,1,Married,15,275.7064957,0,13462.32479\n193197,17,0,1,0,Married,15,275.7037214,0,13403.18607\n148600,64,43,2,1,Married,2,275.6702897,0,13284.51449\n111633,60,39,1,0,Married,11,275.6651835,0,13393.25918\n101493,57,38,2,1,Married,10,275.5481922,0,13425.40961\n137168,39,22,1,0,Single,15,275.4917981,0,13397.58991\n162491,29,8,3,1,Married,7,275.3316651,0,13420.58326\n165026,41,22,1,1,Single,13,275.294995,0,13561.74975\n144955,68,50,3,1,Single,15,275.2849001,0,13361.24501\n139569,41,20,4,1,Single,14,275.2239178,0,13241.19589\n103555,62,45,1,0,Married,0,275.2144091,0,13203.72046\n100406,63,44,2,0,Married,7,275.1142612,0,13156.71306\n149842,17,0,1,0,Married,11,275.0695642,0,13449.47821\n176762,56,39,2,1,Single,15,275.0537291,0,13467.68646\n122042,57,38,3,1,Married,12,275.0114383,0,13173.57192\n127836,19,1,3,0,Single,15,274.9943267,0,13149.71634\n183817,64,46,1,1,Single,8,274.9807802,0,13394.03901\n175335,46,25,1,0,Single,15,274.8857402,0,13517.28701\n107949,61,40,3,0,Single,4,274.8692495,0,13503.46248\n170905,62,41,2,0,Single,5,274.8170949,0,13344.85475\n176967,21,4,4,1,Single,15,274.8065412,0,13333.32706\n120169,68,47,3,0,Married,4,274.7739139,0,13464.6957\n190955,65,47,1,1,Married,10,274.7667043,0,13366.33522\n180566,33,13,3,1,Single,14,274.7218705,0,13207.09353\n124141,61,43,2,1,Married,8,274.702862,0,13185.1431\n143493,57,39,4,1,Married,10,274.6941979,0,13251.7099\n138784,68,50,3,1,Married,0,274.6770646,0,13313.85323\n154885,23,5,4,1,Married,14,274.6576474,0,13381.88237\n160456,65,46,3,0,Married,1,274.6465865,0,13181.32933\n137420,62,42,3,1,Married,7,274.644567,0,13239.22835\n139937,68,51,4,0,Single,14,274.6316652,0,13283.58326\n188513,67,49,4,0,Married,6,274.5961193,0,13240.80597\n143177,50,30,1,1,Married,9,274.564189,0,13197.20945\n196888,16,0,4,1,Married,13,274.4523472,0,13490.61736\n102413,69,51,2,0,Single,3,274.4210471,0,13179.05236\n171513,65,46,2,0,Married,10,274.408471,0,13315.42355\n160746,62,45,2,0,Single,1,274.3831727,0,13271.15863\n124684,55,38,2,0,Single,12,274.3548628,0,13199.74314\n101557,23,4,2,1,Married,13,274.3517512,0,13133.58756\n135696,70,49,1,0,Single,10,274.3217731,0,13416.08866\n196222,50,32,1,0,Single,13,274.3142835,0,13133.71418\n128112,24,5,3,1,Married,14,274.2246925,0,13416.23463\n169046,19,0,3,0,Married,15,274.2005254,0,13302.02627\n128050,60,39,1,0,Single,5,274.194428,0,13123.7214\n161804,63,44,3,0,Married,3,274.189885,0,13223.49425\n122582,66,45,3,0,Married,6,274.1892299,0,13182.4615\n183777,19,1,4,0,Married,11,274.0396233,0,13304.98117\n110896,16,0,2,1,Married,6,274.0220669,0,13376.10335\n132857,66,45,4,1,Married,6,273.9834818,0,13155.17409\n146346,17,0,3,1,Single,14,273.951206,0,13159.5603\n151649,64,46,2,0,Single,2,273.8764314,0,13240.82157\n158347,65,48,2,1,Married,7,273.7696758,0,13155.48379\n117285,19,0,1,0,Married,11,273.7568806,0,13252.84403\n168131,32,13,3,1,Married,12,273.6634165,0,13357.17083\n139225,21,4,4,0,Married,13,273.640839,0,13368.04195\n187168,64,43,2,0,Married,10,273.6257213,0,13401.28607\n192434,65,44,1,0,Single,5,273.523717,0,13131.18585\n176073,61,41,2,1,Single,9,273.5220197,0,13467.10099\n117702,63,43,4,0,Married,10,273.4975821,0,13464.87911\n108926,60,39,2,0,Single,3,273.4860076,0,13176.30038\n123681,22,5,3,1,Single,13,273.4728774,0,13469.64387\n147350,58,40,4,0,Married,12,273.4724339,0,13315.6217\n132533,22,3,4,1,Married,9,273.4440173,0,13106.20087\n137341,19,2,3,1,Married,9,273.4011244,0,13130.05622\n146639,20,3,1,1,Single,13,273.2692258,0,13242.46129\n143776,18,0,1,1,Married,14,273.2499754,0,13130.49877\n148923,63,45,3,0,Single,8,273.2318752,0,13077.59376\n160434,60,39,3,0,Single,4,273.1819312,0,13353.09656\n198709,66,49,1,1,Single,6,273.1567349,0,13299.83675\n125976,24,7,1,0,Married,15,273.1274702,0,13424.37351\n158419,65,44,2,1,Single,14,272.9090816,0,13096.45408\n184811,63,42,2,0,Single,9,272.8974502,0,13062.87251\n175756,67,49,4,0,Single,10,272.8842105,0,13368.21053\n195432,16,0,2,1,Single,11,272.757036,0,13392.8518\n185357,54,36,2,0,Single,12,272.7405242,0,13241.02621\n161813,33,15,3,1,Married,10,272.6997251,0,13187.98626\n185675,23,4,1,1,Married,15,272.6984372,0,13197.92186\n132731,63,43,3,1,Single,11,272.6765058,0,13121.82529\n181837,21,4,1,1,Single,11,272.4924191,0,13345.62096\n143097,65,47,3,0,Single,3,272.4736288,0,13305.68144\n189795,68,51,4,1,Single,10,272.4608285,0,13088.04143\n167417,21,4,2,1,Married,15,272.4149126,0,13183.74563\n106253,30,9,2,1,Single,13,272.3988253,0,13328.94127\n172744,39,22,2,1,Married,6,272.3351511,0,13082.75756\n169944,17,0,4,1,Married,7,272.2854661,0,13067.27331\n125780,48,31,3,0,Married,13,272.2801077,0,13109.00539\n160770,39,22,1,1,Single,14,272.2594633,0,13403.97317\n171325,70,52,3,0,Married,6,272.2354176,0,13158.77088\n111756,30,9,2,0,Single,14,272.1104504,0,13007.52252\n171067,23,5,3,1,Married,15,272.0489279,0,13287.4464\n153044,19,2,3,1,Married,14,272.0471721,0,13177.35861\n153534,16,0,1,1,Single,15,272.0139319,0,13364.6966\n185859,64,45,3,0,Married,6,271.9578787,0,13242.89394\n160694,19,2,1,1,Single,14,271.8929537,0,13064.64769\n141386,20,3,3,1,Married,15,271.8783756,0,13243.91878\n136877,19,2,4,0,Married,13,271.8448659,0,13248.2433\n138491,62,42,2,1,Married,2,271.7521943,0,13334.60972\n104480,70,52,3,1,Married,4,271.7119805,0,13036.59903\n171848,18,0,2,1,Married,12,271.6989729,0,13291.94865\n100535,18,0,1,1,Married,12,271.6985859,0,13380.9293\n143090,21,2,3,0,Married,11,271.6780306,0,13292.90153\n121512,35,14,4,1,Single,13,271.6713621,0,13139.56811\n101080,65,45,4,1,Single,15,271.5216373,0,13375.08187\n169744,25,7,2,1,Married,13,271.4999002,0,13357.99501\n122242,26,9,2,1,Married,13,271.4612047,0,13072.06024\n151662,64,45,3,0,Married,9,271.4513684,0,13195.56842\n141392,70,51,1,1,Single,0,271.4200491,0,13371.00246\n100067,20,1,3,1,Single,12,271.4028246,0,13292.14123\n132107,62,41,4,1,Single,15,271.3816388,0,12975.08194\n101708,31,12,2,1,Single,15,271.3698987,0,13210.49494\n106987,17,0,3,1,Married,11,271.2911711,0,13118.55856\n177343,48,30,3,0,Married,11,271.2293863,0,13229.46932\n175324,23,5,4,0,Married,13,271.1201143,0,13162.00572\n137242,60,40,2,1,Single,12,271.1036119,0,13203.1806\n174908,68,47,3,0,Married,4,271.0117578,0,13237.58789\n130427,69,48,2,1,Single,6,270.9878941,0,13040.39471\n113175,20,2,4,1,Married,14,270.895184,0,12964.7592\n185552,17,0,4,0,Married,13,270.8778565,0,12989.89283\n171066,68,50,3,1,Single,9,270.8397673,0,12975.98837\n136534,16,0,3,0,Married,11,270.8012465,0,13016.06233\n199207,56,39,3,1,Single,14,270.8012223,0,13054.06112\n188702,69,49,2,0,Married,9,270.7597922,0,12979.98961\n147880,63,42,4,0,Married,11,270.7254849,0,12941.27425\n168516,19,2,2,1,Married,10,270.6896074,0,13111.48037\n156108,17,0,1,1,Married,12,270.6146732,0,13150.73366\n111533,20,1,2,1,Married,15,270.5998219,0,12931.9911\n192228,21,3,3,1,Married,15,270.560534,0,13081.0267\n199696,63,43,2,1,Married,6,270.5565864,0,13235.82932\n172040,16,0,2,1,Single,11,270.440841,0,13024.04205\n104424,62,45,3,0,Single,0,270.399887,0,13162.99435\n104536,54,36,3,1,Married,15,270.3934543,0,13189.67272\n158773,33,15,2,0,Single,13,270.3458042,0,13032.29021\n199226,24,5,3,0,Married,14,270.3439618,0,13231.19809\n127032,64,47,1,1,Married,5,270.2730694,0,13058.65347\n176502,70,53,3,1,Married,6,270.2556744,0,12974.78372\n135195,25,7,2,0,Married,15,270.1715725,0,13145.57863\n105707,24,5,2,1,Single,13,270.0766154,0,13274.83077\n122173,37,20,4,1,Married,13,270.0050947,0,13105.25474\n194131,70,52,1,1,Married,8,269.810358,0,12932.5179\n113948,45,27,1,0,Married,14,269.786051,0,13058.30255\n190402,55,36,3,1,Married,14,269.7842954,0,13151.21477\n115433,24,6,4,1,Single,15,269.7292976,0,12955.46488\n159552,62,43,3,0,Single,9,269.5615653,0,12931.07827\n188175,16,0,3,1,Single,13,269.5369389,0,13174.84695\n162148,25,8,2,0,Married,15,269.4863751,0,13047.31876\n126261,68,51,1,1,Married,0,269.4692369,0,13127.46185\n117644,23,5,3,1,Single,11,269.4477175,0,13103.38588\n101576,25,6,3,0,Single,14,269.4409717,0,13044.04859\n173165,61,43,3,1,Married,6,269.4229961,0,13143.14981\n163597,18,1,1,1,Married,12,269.4121493,0,13072.60747\n191765,47,29,3,1,Married,10,269.4113554,0,13022.56777\n135979,62,44,1,1,Single,9,269.3517877,0,13176.58939\n151137,68,50,3,0,Single,5,269.2805318,0,12963.02659\n122797,61,40,2,0,Married,8,269.2613967,0,13060.06984\n156482,18,0,3,0,Married,12,269.239046,0,13198.9523\n109526,22,3,3,0,Married,14,269.235249,0,12995.76245\n178344,17,0,2,1,Single,12,269.2318208,0,13031.59104\n174887,41,22,2,1,Married,15,269.1793261,0,13137.96631\n195493,52,34,3,1,Married,12,269.1665689,0,13060.32845\n163523,66,49,3,0,Single,7,269.1243075,0,13051.21538\n111160,18,1,4,1,Married,8,269.1023927,0,13045.11964\n134746,61,43,4,0,Married,10,269.0218439,0,13084.0922\n109865,70,53,4,0,Single,9,269.0197341,0,12902.98671\n138395,24,5,1,0,Married,13,268.9314723,0,12851.57362\n134554,22,4,3,1,Single,13,268.9297063,0,12936.48532\n198150,27,9,3,1,Single,15,268.8387187,0,13117.93594\n172375,68,47,3,0,Married,9,268.8206765,0,12847.03383\n198313,68,48,4,1,Married,8,268.8178331,0,13203.89166\n198000,59,39,3,0,Single,13,268.7853382,0,13142.26691\n187957,31,10,1,0,Married,14,268.77102,0,13025.551\n158103,69,51,4,0,Married,15,268.6879041,0,12885.39521\n102022,18,1,4,1,Married,15,268.6742597,0,13050.71299\n102921,22,4,1,1,Single,14,268.6718533,0,13053.59267\n174087,35,14,3,1,Single,15,268.6189791,0,13215.94896\n123344,69,52,3,0,Married,12,268.5815213,0,12913.07607\n199427,19,2,2,0,Married,12,268.5775197,0,13065.87599\n100393,61,44,2,1,Single,10,268.5666419,0,12899.3321\n193957,63,45,4,1,Married,7,268.5457094,0,13164.28547\n135252,52,35,1,0,Married,15,268.5415695,0,13099.07848\n154333,31,10,1,1,Married,11,268.5320136,0,13064.60068\n145777,68,47,2,1,Single,13,268.5031131,0,12999.15566\n145757,50,29,3,1,Single,15,268.48299,0,12903.1495\n143428,65,46,3,1,Married,10,268.4666895,0,13200.33448\n139540,18,0,2,0,Married,15,268.461219,0,13066.06095\n165002,67,47,3,1,Single,6,268.4580461,0,13111.90231\n197447,50,30,4,0,Single,14,268.4336686,0,12843.68343\n115707,31,13,2,0,Married,14,268.3762987,0,13168.81494\n187898,39,20,4,0,Married,15,268.351566,0,12833.5783\n181091,27,8,2,1,Single,12,268.3414507,0,13013.07254\n151348,41,20,4,1,Single,11,268.3231338,0,12858.15669\n133794,28,8,3,1,Single,12,268.2158673,0,13203.79337\n106870,63,46,2,1,Married,12,268.2037864,0,12864.18932\n181773,68,51,1,1,Married,12,268.1844154,0,12961.22077\n140774,56,39,4,0,Single,12,268.177543,0,12964.87715\n152203,18,0,3,0,Married,15,268.1598164,0,13180.99082\n133848,48,28,3,0,Married,11,268.1174236,0,13113.87118\n130763,36,16,4,0,Married,14,268.109113,0,13197.45565\n196374,70,50,3,1,Single,12,267.9997867,0,12954.98934\n111388,16,0,4,0,Married,14,267.9816964,0,12877.08482\n120680,68,49,2,0,Single,11,267.943607,0,12824.18035\n178152,63,43,4,1,Single,6,267.8670043,0,13030.35022\n154939,66,47,2,1,Single,11,267.7806547,0,12924.03274\n198810,57,36,3,1,Married,9,267.774759,0,13131.73795\n113183,38,17,2,1,Single,13,267.597903,0,13170.89515\n134103,54,33,2,1,Married,11,267.5866584,0,12813.33292\n129907,26,8,1,0,Single,11,267.485859,0,12794.29295\n113219,65,45,4,1,Married,6,267.3935689,0,13063.67845\n157541,19,1,2,1,Married,12,267.3694438,0,12770.47219\n112376,61,43,3,0,Married,6,267.3346815,0,12864.73408\n141209,26,9,4,1,Single,13,267.2991108,0,13060.95554\n198566,62,42,2,1,Single,6,267.284417,0,13120.22085\n115302,65,47,2,1,Married,7,267.276004,0,12830.8002\n131578,43,23,3,1,Married,13,267.265559,0,12924.27795\n185901,67,50,2,1,Married,3,267.2641669,0,12904.20835\n111659,65,44,2,0,Single,11,267.2544801,0,13098.72401\n119461,39,22,2,0,Married,15,267.2414317,0,13117.07159\n182744,66,45,4,1,Single,7,267.2255956,0,12832.27978\n144413,68,51,2,0,Single,5,267.2097953,0,12972.48977\n107171,25,6,2,1,Married,1,267.20863,0,13026.4315\n129219,63,46,3,1,Single,9,267.059759,0,12910.98795\n128685,34,16,1,1,Married,12,267.0481689,0,12919.40845\n114155,25,6,2,1,Married,11,267.0447435,0,13138.23718\n176575,62,44,2,1,Single,14,266.9167964,0,12957.83982\n166534,38,17,4,1,Married,14,266.8677973,0,13021.38987\n173542,20,3,3,0,Married,11,266.856939,0,12951.84695\n196528,18,1,2,0,Married,12,266.8493854,0,13040.46927\n176055,25,8,3,1,Married,7,266.8474848,0,13129.37424\n179588,20,2,3,1,Married,11,266.7945785,0,13089.72893\n194514,64,47,3,1,Single,14,266.761618,0,13052.0809\n142398,62,45,3,1,Married,5,266.7128611,0,12901.64306\n131913,64,43,3,1,Married,9,266.6488728,0,12862.44364\n190609,24,6,2,0,Married,11,266.6348714,0,12836.74357\n136349,70,53,3,0,Single,6,266.5498728,0,12870.49364\n177515,59,41,3,0,Married,14,266.5304811,0,13016.52406\n110939,38,18,2,0,Single,13,266.5227536,0,12822.13768\n185012,54,33,3,1,Married,12,266.4995397,0,12929.97699\n199634,62,45,1,0,Single,6,266.4859016,0,12859.29508\n102623,21,4,2,0,Married,13,266.2417314,0,12903.08657\n149219,61,41,2,1,Single,12,266.1838957,0,12948.19479\n181590,70,49,4,1,Single,2,266.163006,0,12997.1503\n137758,41,22,4,0,Married,15,266.1462605,0,13063.31303\n180191,20,3,1,1,Single,14,266.0967514,0,12719.83757\n196872,60,43,4,1,Single,5,266.0933319,0,12849.6666\n198523,22,4,1,1,Married,11,266.0329124,0,12743.64562\n136616,17,0,1,1,Married,13,266.0062224,0,12754.31112\n118859,62,45,3,0,Married,9,265.9944631,0,13055.72316\n111219,67,47,2,0,Married,5,265.9392975,0,13011.96488\n168087,17,0,1,0,Married,15,265.8563455,0,12986.81728\n123218,63,42,2,1,Married,10,265.7598887,0,12706.99444\n134279,63,43,2,1,Married,7,265.6889909,0,12687.44955\n159755,23,4,2,1,Married,15,265.6368544,0,12820.84272\n185066,44,26,2,0,Single,13,265.634568,0,12969.7284\n159384,65,44,2,0,Married,6,265.6083469,0,12739.41735\n118526,23,4,2,1,Married,15,265.5556163,0,13011.78082\n179318,23,5,2,1,Married,13,265.3761537,0,12819.80769\n106561,18,0,2,1,Single,15,265.3680179,0,12834.4009\n105391,23,6,1,0,Single,15,265.3413142,0,12702.06571\n121850,43,23,3,1,Married,12,265.3348589,0,12706.74295\n116290,56,36,2,0,Married,12,265.3119797,0,12774.59899\n121215,67,48,3,1,Married,7,265.3039356,0,12803.19678\n146914,20,2,3,1,Single,15,265.2991479,0,12967.9574\n195252,24,5,3,0,Married,12,265.1746075,0,13053.73038\n197502,20,1,2,1,Single,14,265.1163252,0,12921.81626\n108773,38,20,3,1,Married,11,265.0697544,0,12654.48772\n114418,54,33,4,1,Married,14,265.0341188,0,12848.70594\n115199,42,24,3,1,Married,11,265.0117856,0,13042.58928\n113850,32,13,3,0,Single,14,264.9566153,0,12726.83077\n151268,66,47,3,0,Single,2,264.9169424,0,12880.84712\n118167,66,46,3,1,Married,0,264.9156003,0,12964.78002\n140976,52,33,1,0,Married,13,264.9045348,0,12726.22674\n118439,69,52,1,1,Single,9,264.9025025,0,12649.12513\n170772,28,8,2,0,Married,14,264.8209968,0,12827.04984\n102706,17,0,3,1,Married,15,264.8026987,0,12876.13494\n178969,69,51,2,0,Married,5,264.7913926,0,13009.56963\n132104,16,0,1,0,Married,15,264.7891684,0,13026.45842\n143621,32,11,2,1,Single,12,264.7846314,0,12838.23157\n157848,51,31,3,0,Married,11,264.7459169,0,12678.29585\n144286,37,19,2,1,Single,12,264.6726718,0,12802.63359\n107169,25,8,1,1,Married,13,264.656479,0,12694.82395\n168532,60,43,2,1,Married,9,264.655757,0,13019.78785\n167698,55,36,1,0,Single,12,264.6489308,0,12803.44654\n174628,26,9,3,1,Single,15,264.6129075,0,12886.64538\n192237,37,19,2,1,Married,12,264.6079872,0,12856.39936\n195762,61,44,4,0,Married,9,264.600332,0,12717.0166\n103961,54,37,2,1,Married,12,264.5751093,0,12738.75547\n187025,20,2,2,1,Married,13,264.5572383,0,12676.86192\n193474,16,0,2,0,Married,14,264.5211779,0,12778.0589\n109348,19,2,2,1,Married,11,264.520156,0,12957.0078\n159347,16,0,3,0,Married,15,264.5165731,0,12823.82866\n148947,63,44,1,0,Single,13,264.4793918,0,12734.96959\n184695,61,41,3,0,Married,2,264.3370459,0,12824.8523\n132167,70,53,1,0,Single,10,264.2712706,0,12622.56353\n178748,21,4,4,1,Married,12,264.256172,0,12863.8086\n142393,61,43,3,1,Single,8,264.2017463,0,12773.08732\n114662,48,29,3,1,Married,14,264.1574614,0,12806.87307\n145339,18,0,3,0,Married,12,264.1244449,0,12725.22225\n130288,19,1,3,1,Married,14,264.0771175,0,12884.85588\n104090,21,2,3,1,Married,13,264.056303,0,12921.81515\n149881,24,5,3,0,Married,11,263.9893442,0,12985.46721\n157357,23,4,2,1,Married,12,263.9431562,0,12647.15781\n158380,39,19,2,0,Married,11,263.9384452,0,12938.92226\n142107,21,3,3,1,Married,14,263.8780435,0,12747.90218\n101075,64,45,3,1,Married,7,263.8373293,0,12924.86647\n103363,65,47,2,1,Married,9,263.766748,0,12960.3374\n118255,21,4,2,1,Married,13,263.7464543,0,12697.32272\n167123,23,5,2,1,Single,14,263.7207796,0,12697.03898\n165820,32,11,3,0,Single,13,263.7207697,0,12797.03849\n132853,68,49,3,0,Married,6,263.6322305,0,12968.61153\n197533,17,0,4,0,Married,13,263.5806493,0,12694.03247\n154995,42,24,4,0,Single,14,263.4834406,0,12630.17203\n140277,19,1,2,0,Married,12,263.4730577,0,12879.65289\n180149,24,6,4,1,Single,15,263.4604824,0,12914.02412\n165079,38,20,3,1,Married,14,263.4229657,0,12662.14829\n140553,32,15,4,1,Married,14,263.3927649,0,12870.63825\n186308,60,39,3,0,Married,10,263.2503566,0,12780.51783\n107521,27,7,4,0,Single,15,263.1616943,0,12676.08472\n116009,70,49,3,1,Married,6,263.1277167,0,12643.38584\n148156,36,17,3,1,Married,11,263.0936688,0,12941.68344\n109930,55,34,2,1,Married,13,263.0665303,0,12908.32652\n141061,69,49,3,0,Married,9,262.981783,0,12774.08915\n146920,42,25,1,0,Single,14,262.9411765,0,12772.05883\n139723,70,51,2,1,Married,11,262.9003887,0,12742.01944\n177363,21,2,1,1,Married,10,262.8439707,0,12809.19854\n111048,49,30,2,0,Single,11,262.8293092,0,12629.46546\n197636,61,44,3,1,Married,14,262.7851245,0,12691.25623\n164056,42,22,3,0,Married,11,262.6984205,0,12868.92103\n162350,39,19,3,0,Single,13,262.6699139,0,12568.4957\n133918,22,4,2,0,Married,13,262.6320129,0,12652.60065\n176542,28,11,2,1,Single,14,262.5192527,0,12564.96264\n176553,67,49,2,0,Single,14,262.4713199,0,12917.566\n197445,23,5,4,1,Married,15,262.3792553,0,12744.96277\n179609,61,44,3,1,Married,9,262.291223,0,12814.56115\n192598,50,32,3,1,Single,11,262.2647694,0,12723.23847\n137745,21,2,3,0,Married,13,262.2304821,0,12713.52411\n192012,63,44,1,0,Single,0,262.1983443,0,12662.91722\n183715,58,39,4,0,Married,14,262.1914364,0,12732.57182\n142159,22,4,3,1,Single,11,262.1890732,0,12680.45366\n198979,69,51,4,1,Married,7,262.1851171,0,12806.25586\n123019,48,27,3,1,Single,11,262.1489059,0,12727.4453\n118563,16,0,3,1,Single,14,262.0928116,0,12902.64058\n126213,68,50,4,0,Married,6,262.0895176,0,12657.47588\n184669,60,41,2,0,Married,12,262.0311129,0,12830.55565\n117417,55,36,3,1,Single,14,262.0110004,0,12555.55002\n103547,17,0,4,1,Married,15,261.9462079,0,12839.3104\n178226,70,52,3,0,Married,0,261.933688,0,12653.6844\n147580,52,32,2,0,Single,14,261.9109882,0,12547.54941\n126793,21,4,3,1,Single,13,261.7968569,0,12752.84285\n185536,23,6,2,1,Married,12,261.7516407,0,12858.58204\n199047,62,42,4,0,Single,12,261.7209216,0,12516.04608\n178296,61,43,1,0,Single,4,261.6904151,0,12695.52076\n128869,55,37,4,1,Married,13,261.6604347,0,12563.02174\n129009,55,35,2,1,Married,11,261.6586678,0,12633.93339\n148546,62,42,1,1,Single,2,261.6444496,0,12709.22248\n137337,18,0,3,0,Married,13,261.5421899,0,12840.1095\n115407,62,43,2,0,Married,10,261.5115246,0,12537.57623\n185668,63,42,1,1,Married,5,261.5094393,0,12763.47197\n111846,19,2,2,1,Married,10,261.4601079,0,12762.0054\n164351,23,5,3,1,Married,7,261.3574567,0,12811.87284\n178490,27,6,3,1,Single,14,261.3264249,0,12481.32125\n189612,66,47,3,1,Single,10,261.2730634,0,12561.65317\n138646,64,44,2,0,Married,8,261.2635133,0,12628.17567\n156908,62,43,4,0,Married,1,261.1149887,0,12495.74944\n122176,65,44,1,1,Married,7,261.0802834,0,12815.01417\n184105,37,20,1,0,Married,14,261.0602312,0,12593.01156\n115796,53,33,2,1,Married,12,260.947997,0,12827.39985\n175229,33,14,3,1,Single,11,260.9161949,0,12748.80975\n184022,67,48,2,1,Married,4,260.9071055,0,12799.35528\n172044,65,46,3,1,Married,5,260.8163845,0,12649.81923\n115759,23,4,4,1,Single,11,260.7504228,0,12539.52114\n107532,60,41,1,0,Married,6,260.7493276,0,12757.46638\n120176,23,4,3,1,Married,11,260.7172564,0,12789.86282\n190962,25,7,1,0,Married,12,260.7111751,0,12802.55876\n159763,24,5,4,1,Married,12,260.6415955,0,12797.07978\n136502,25,6,2,1,Single,13,260.5924515,0,12511.62258\n183896,18,1,3,1,Single,13,260.524676,0,12549.2338\n122244,32,13,4,1,Married,15,260.3735854,0,12467.67927\n115303,64,46,1,1,Married,11,260.2725911,0,12474.62956\n179672,70,51,3,0,Married,3,260.2474794,0,12619.37397\n172805,17,0,1,0,Married,12,260.2345014,0,12525.72507\n113918,51,32,1,1,Married,15,260.2033477,0,12530.16739\n196919,18,1,2,0,Married,12,260.0719096,0,12551.59548\n133452,22,4,2,1,Married,15,260.0544308,0,12412.72154\n169783,24,5,2,1,Married,12,260.016964,0,12611.8482\n153008,55,37,3,1,Married,14,259.9785414,0,12508.92707\n136960,44,27,1,0,Single,15,259.9723271,0,12761.61636\n111072,53,36,1,0,Single,14,259.8909694,0,12446.54847\n109960,67,50,4,0,Single,15,259.8633131,0,12614.16566\n119986,60,40,2,0,Single,9,259.7822074,0,12783.11037\n189455,24,5,2,1,Single,15,259.7794723,0,12444.97362\n139746,35,17,4,0,Married,11,259.7741753,0,12475.70877\n101138,34,16,2,0,Single,11,259.7734467,0,12416.67234\n134318,26,6,4,0,Married,15,259.7499425,0,12643.49713\n167322,63,44,1,0,Married,8,259.7498031,0,12561.49016\n153192,17,0,2,1,Single,11,259.7446292,0,12622.23146\n165703,70,50,2,0,Single,0,259.7310588,0,12594.55294\n190171,16,0,2,1,Single,12,259.7144582,0,12495.72291\n197817,32,11,1,0,Married,14,259.6195249,0,12438.97625\n138952,66,47,2,0,Single,1,259.4806222,0,12516.03111\n164581,60,40,4,1,Married,12,259.4478149,0,12436.39075\n167802,20,2,2,1,Single,11,259.4363701,0,12474.81851\n167739,22,4,1,0,Married,13,259.4013808,0,12626.06904\n102379,61,40,3,1,Married,6,259.3614327,0,12572.07164\n121935,37,19,2,0,Married,14,259.357527,0,12392.87635\n191172,23,5,2,1,Married,14,259.3449509,0,12428.24755\n140560,66,46,1,1,Married,7,259.3302137,0,12390.51069\n126888,63,43,3,0,Married,2,259.2207378,0,12658.03689\n150201,25,6,3,1,Married,14,259.2170314,0,12408.85157\n101642,58,40,3,1,Single,12,259.0942332,0,12583.71166\n166554,59,40,1,1,Single,11,258.9963475,0,12382.81738\n134074,25,8,2,1,Married,13,258.95265,0,12426.6325\n130412,17,0,2,1,Married,13,258.9133824,0,12472.66912\n150547,21,2,2,0,Single,13,258.9005436,0,12422.02718\n112640,63,42,4,1,Married,10,258.8828219,0,12473.1411\n196294,63,42,2,1,Married,8,258.8729838,0,12464.64919\n155597,68,51,3,1,Married,7,258.834171,0,12468.70855\n181163,37,20,1,1,Married,14,258.8203437,0,12719.01719\n110924,69,50,3,1,Single,9,258.8036676,0,12495.18338\n133731,61,41,3,1,Single,0,258.794741,0,12347.73705\n107287,37,19,2,0,Married,14,258.7126838,0,12526.63419\n198044,18,0,3,1,Single,13,258.6985443,0,12558.92722\n154361,23,6,3,1,Married,12,258.6640919,0,12575.2046\n146550,27,7,3,0,Married,11,258.6368128,0,12725.84064\n167898,50,29,3,1,Married,11,258.6340919,0,12549.7046\n161523,18,0,1,1,Married,13,258.6207438,0,12469.03719\n163030,19,1,3,1,Single,11,258.6034522,0,12711.17261\n168719,20,3,2,0,Married,11,258.5981163,0,12431.90582\n134837,42,23,2,0,Married,11,258.4579809,0,12430.89905\n115732,20,3,3,1,Married,0,258.3707563,0,12709.53782\n119492,16,0,1,1,Married,12,258.3328842,0,12442.64421\n180051,65,46,4,1,Single,4,258.2303987,0,12667.51994\n171604,28,9,2,1,Married,14,258.2119882,0,12682.59941\n140761,33,15,3,0,Single,14,258.1998141,0,12508.99071\n115174,69,48,4,0,Married,4,258.1710632,0,12440.55316\n138945,17,0,1,1,Single,15,257.9940404,0,12435.70202\n100458,22,5,4,1,Single,14,257.9823303,0,12311.11652\n101583,16,0,1,1,Married,10,257.9819928,0,12579.09964\n115853,19,1,4,1,Married,14,257.9728994,0,12439.64497\n141072,19,0,2,0,Married,13,257.8478061,0,12608.39031\n107834,54,33,3,1,Married,15,257.8225661,0,12324.12831\n161586,39,20,2,1,Married,15,257.7862464,0,12625.31232\n134696,41,20,3,0,Married,13,257.7639364,0,12290.19682\n193331,53,34,1,0,Single,14,257.7397425,0,12613.98713\n130823,33,16,4,1,Married,15,257.7345241,0,12427.72621\n134162,19,1,2,1,Married,12,257.6636706,0,12428.18353\n112905,19,2,2,1,Married,14,257.6394232,0,12650.97116\n136173,21,2,3,1,Married,13,257.5727407,0,12513.63704\n115950,60,40,1,1,Single,4,257.538506,0,12513.9253\n145024,22,4,1,1,Married,7,257.4622527,0,12336.11264\n158947,64,46,3,1,Married,3,257.3383251,0,12278.91626\n139786,61,44,4,0,Married,10,257.3348853,0,12278.74427\n106640,62,44,2,1,Married,8,257.3304459,0,12637.5223\n190349,44,27,3,1,Married,11,257.320812,0,12375.0406\n139049,22,5,4,1,Married,6,257.3038848,0,12495.19424\n103924,62,45,2,1,Single,6,257.2819425,0,12651.09713\n196247,20,2,2,1,Single,12,257.2422185,0,12422.11093\n143574,29,9,4,1,Married,13,257.1925233,0,12468.62617\n101116,60,43,1,0,Single,11,257.1813241,0,12513.06621\n170404,67,50,2,1,Single,15,257.157292,0,12301.8646\n170783,34,13,4,1,Single,15,257.1299028,0,12284.49514\n166068,47,26,3,1,Single,11,257.1076017,0,12495.38009\n111662,35,17,2,0,Married,13,257.0650988,0,12356.25494\n196249,51,34,2,0,Single,14,257.0408231,0,12284.04116\n154189,65,44,3,0,Married,15,256.9809464,0,12620.04732\n126006,25,6,2,1,Married,14,256.9070413,0,12634.35207\n148013,25,7,2,0,Married,15,256.904249,0,12335.21245\n121046,62,42,1,1,Married,14,256.9014828,0,12620.07414\n173346,18,1,1,1,Married,15,256.8966878,0,12266.83439\n134914,63,42,3,0,Single,12,256.8653081,0,12246.26541\n173285,33,16,2,1,Married,11,256.8330831,0,12404.65416\n124911,23,5,2,1,Single,11,256.8265508,0,12351.32754\n137312,63,45,4,0,Married,7,256.7659327,0,12360.29664\n107271,16,0,2,1,Married,6,256.7210287,0,12370.05144\n184172,70,53,4,1,Married,10,256.7184947,0,12612.92474\n160682,69,48,3,1,Single,9,256.7040757,0,12304.20379\n151161,32,15,2,1,Single,12,256.6548162,0,12453.74081\n119381,67,47,3,1,Single,1,256.6426024,0,12516.13012\n126690,17,0,3,1,Single,15,256.5646766,0,12628.23383\n150686,60,40,2,1,Married,13,256.5214825,0,12618.07413\n138487,62,41,2,1,Married,13,256.4519265,0,12359.59633\n192823,70,50,1,0,Single,13,256.4290973,0,12617.45487\n111481,19,0,4,1,Married,13,256.4216357,0,12329.08179\n162664,68,49,3,0,Single,11,256.3954396,0,12411.77198\n106931,24,7,2,1,Married,11,256.3592184,0,12401.96092\n186245,17,0,1,0,Married,14,256.3104419,0,12405.5221\n194043,29,8,3,1,Single,14,256.3007473,0,12458.03737\n128032,19,0,2,0,Married,14,256.2949037,0,12297.74519\n116082,67,50,2,1,Married,11,256.2403446,0,12544.01723\n110905,19,0,2,1,Married,15,256.1425496,0,12229.12748\n140922,63,43,2,1,Married,3,256.1034855,0,12346.17428\n185310,62,43,3,0,Married,15,256.071587,0,12308.57935\n103908,61,40,3,1,Married,13,256.0388721,0,12227.94361\n125816,53,35,2,1,Married,15,255.9480053,0,12270.40027\n176272,69,52,2,0,Married,5,255.8123577,0,12542.61789\n166827,27,6,3,1,Married,13,255.7960805,0,12353.80403\n131357,65,44,3,1,Married,2,255.5804782,0,12448.02391\n128137,17,0,2,1,Married,11,255.5687642,0,12561.43821\n137626,23,6,2,1,Married,15,255.527599,0,12206.37995\n163325,61,44,3,0,Single,14,255.4191954,0,12336.95977\n121398,21,3,2,1,Single,12,255.3929748,0,12345.64874\n156406,62,45,3,1,Single,8,255.3001921,0,12394.00961\n147650,24,7,2,1,Married,15,255.2058567,0,12272.29284\n182837,20,1,3,1,Married,15,255.2054927,0,12239.27464\n167861,63,45,1,0,Married,5,255.1907019,0,12442.5351\n121157,23,5,1,1,Single,13,255.1761581,0,12359.80791\n179728,21,3,2,1,Married,15,255.1657713,0,12506.28857\n177948,66,47,2,0,Single,7,255.0436158,0,12333.18079\n191429,18,0,3,1,Married,10,255.0223564,0,12152.11782\n169726,60,40,2,1,Single,0,255.005754,0,12467.2877\n103799,58,39,3,0,Married,12,255.0014446,0,12262.07223\n150919,23,5,4,1,Single,11,254.9418043,0,12180.09022\n151708,36,18,3,0,Single,13,254.8772833,0,12255.86417\n136873,48,29,2,1,Married,15,254.8727625,0,12169.63813\n163067,63,42,3,1,Single,5,254.8645781,0,12400.22891\n105146,55,36,2,0,Married,12,254.8229555,0,12396.14778\n197935,46,27,4,0,Married,12,254.7859673,0,12209.29837\n148138,17,0,1,1,Married,11,254.7692296,0,12526.46148\n140551,69,51,1,1,Single,5,254.7680456,0,12400.40228\n111285,38,21,3,0,Married,13,254.7635616,0,12475.17808\n161766,24,5,2,1,Married,14,254.5990556,0,12375.95278\n149904,22,3,3,0,Married,11,254.5378949,0,12478.89475\n154508,68,48,1,1,Married,8,254.3947227,0,12201.73614\n156589,68,48,4,1,Single,6,254.3091107,0,12474.45554\n119878,43,22,3,0,Married,12,254.2583337,0,12282.91669\n101235,41,23,3,1,Married,15,254.212927,0,12286.64635\n190583,49,28,3,1,Married,12,254.1878307,0,12124.39154\n158233,23,6,3,1,Married,13,254.098712,0,12225.9356\n178351,23,4,1,1,Married,12,254.0958405,0,12171.79203\n117691,19,1,1,0,Married,11,254.0805596,0,12479.02798\n101390,60,39,2,0,Married,6,253.978336,0,12413.9168\n152317,67,46,1,1,Single,10,253.9768052,0,12172.84026\n126766,67,49,3,1,Married,7,253.9659522,0,12296.29761\n171552,25,7,2,1,Married,15,253.9624512,0,12101.12256\n180597,23,6,3,1,Married,15,253.9558468,0,12405.79234\n141018,70,49,2,0,Married,3,253.9444432,0,12456.22216\n199495,68,47,4,1,Married,6,253.7716719,0,12202.5836\n116989,53,34,2,0,Single,12,253.7332963,0,12324.66482\n106326,41,20,4,1,Married,13,253.5777019,0,12121.8851\n179231,60,40,3,0,Married,15,253.5710605,0,12475.55303\n106493,60,41,4,1,Married,12,253.4675589,0,12252.37795\n173295,63,46,1,1,Single,3,253.4663373,0,12327.31687\n143131,43,24,2,1,Married,15,253.4649124,0,12386.24562\n124718,17,0,4,1,Single,14,253.4289936,0,12372.44968\n196980,66,47,3,0,Married,10,253.4225005,0,12289.12503\n141368,62,42,3,1,Married,0,253.4212085,0,12358.06043\n120932,61,42,2,0,Married,5,253.3873605,0,12274.36803\n103819,67,49,2,1,Single,3,253.2816075,0,12444.08038\n110888,67,49,3,1,Single,5,253.2197256,0,12380.98628\n174835,25,8,4,0,Married,12,253.2165616,0,12160.82808\n195194,16,0,2,1,Married,8,253.2009313,0,12062.04657\n157213,50,31,4,1,Married,13,253.1904409,0,12217.52205\n125442,66,49,3,0,Single,13,253.1358009,0,12118.79005\n189113,20,3,3,1,Married,6,253.1215171,0,12193.07586\n170963,61,44,2,0,Single,11,253.083236,0,12133.1618\n162092,30,10,4,0,Married,11,253.0046386,0,12352.23193\n155923,65,48,2,0,Single,12,252.9633203,0,12437.16602\n174515,23,5,4,0,Married,13,252.8911414,0,12430.55707\n183067,66,48,2,1,Married,5,252.7599084,0,12229.99542\n100721,18,1,2,1,Single,13,252.6893344,0,12126.46672\n154895,52,31,4,1,Married,15,252.6448131,0,12129.24066\n185797,57,38,2,1,Married,13,252.6447592,0,12349.23796\n142854,70,49,3,1,Married,10,252.5770707,0,12242.85354\n184646,25,7,4,1,Married,12,252.5429156,0,12312.14578\n132390,22,5,4,1,Married,11,252.520541,0,12151.02705\n121660,61,43,2,1,Married,10,252.4533935,0,12266.66968\n181265,54,34,2,0,Married,11,252.4345156,0,12166.72578\n167556,49,29,3,1,Married,15,252.4183494,0,12120.91747\n153902,21,4,1,0,Married,15,252.3853189,0,12363.26595\n190043,23,5,3,1,Married,7,252.3327088,0,12129.63544\n180898,54,33,2,0,Married,12,252.1813357,0,12017.06679\n165164,19,0,3,1,Married,15,252.1111285,0,12246.55643\n190769,28,8,3,0,Married,13,252.0558287,0,12036.79144\n120064,65,48,3,0,Married,5,252.0520392,0,12119.60196\n153707,44,25,3,0,Married,14,252.0360663,0,12266.80332\n152628,20,1,3,0,Married,11,251.9960488,0,12168.80244\n151220,23,5,1,1,Married,15,251.9734581,0,12384.67291\n186207,36,19,4,0,Single,15,251.9122772,0,12236.61386\n189126,29,12,2,1,Married,11,251.8850248,0,12183.25124\n161476,49,29,3,1,Single,14,251.875814,0,12101.7907\n177319,50,33,4,1,Single,13,251.8672765,0,12359.36383\n130318,69,51,3,1,Single,8,251.7472081,0,12314.36041\n124503,24,6,2,1,Married,15,251.7436492,0,12059.18246\n190502,16,0,2,0,Married,13,251.7212245,0,12220.06123\n145490,67,46,3,0,Single,0,251.5984868,0,12238.92434\n168142,44,23,3,0,Married,15,251.5648271,0,12168.24136\n155539,64,47,3,0,Single,3,251.5561758,0,12086.80879\n131739,17,0,3,1,Married,15,251.4617866,0,11991.08933\n100882,25,7,3,1,Single,15,251.3519536,0,12215.59768\n104434,63,44,3,0,Single,13,251.2715857,0,12320.57929\n195967,18,0,2,1,Married,15,251.2023753,0,12111.11877\n186175,70,53,1,0,Single,15,251.1951899,0,12023.7595\n191769,16,0,3,0,Married,13,251.1469014,0,12005.34507\n127700,67,46,1,0,Single,7,251.1320166,0,12309.60083\n174803,69,52,3,1,Married,0,251.1110221,0,12102.55111\n160751,36,19,3,1,Married,14,251.0929693,0,12302.64847\n150236,23,5,3,0,Married,15,251.0801107,0,11999.00554\n145564,25,6,2,1,Married,14,251.0376094,0,12145.88047\n138557,18,0,2,1,Married,15,250.9612776,0,12129.06388\n122191,55,34,2,1,Single,13,250.9543018,0,12018.71509\n102736,61,42,1,1,Married,9,250.8953606,0,12199.76803\n116611,70,50,3,1,Single,12,250.8693099,0,12301.4655\n192661,25,8,1,1,Married,14,250.8077066,0,12211.38533\n123562,59,38,2,1,Married,14,250.5868113,0,11948.34057\n154317,34,14,2,1,Married,11,250.5664433,0,12044.32217\n194081,16,0,2,1,Married,14,250.5357625,0,12006.78813\n191165,17,0,2,1,Married,11,250.4820965,0,12085.10483\n138205,23,5,4,0,Married,13,250.3969718,0,12183.84859\n148296,69,50,1,1,Married,9,250.2576733,0,12280.88367\n149178,58,37,3,0,Single,11,250.244213,0,11972.21065\n173662,18,0,1,1,Married,15,250.2204478,0,12274.02239\n190389,29,12,1,1,Married,14,250.159522,0,12016.9761\n109686,69,48,3,1,Single,8,250.1486479,0,12265.4324\n168046,29,10,3,1,Married,11,250.1315134,0,12119.57567\n106297,66,47,2,0,Married,12,250.1021146,0,12086.10573\n114314,29,9,2,0,Married,15,250.0997358,0,12036.98679\n128822,21,3,1,0,Married,13,250.099125,0,12017.95625\n137629,20,2,1,0,Married,13,250.0483034,0,12212.41517\n137795,31,14,2,0,Married,13,249.9909233,0,11910.54617\n113169,36,15,1,1,Married,13,249.9467293,0,12058.33647\n131795,36,17,1,1,Married,13,249.8851289,0,12226.25645\n103553,19,0,3,1,Married,14,249.873609,0,12122.68045\n170955,23,5,3,1,Married,14,249.867441,0,12264.37205\n136046,68,51,4,1,Married,10,249.8264059,0,12210.3203\n177609,61,43,3,0,Single,9,249.735638,0,12175.7819\n186335,23,4,3,1,Married,15,249.68224,0,12168.112\n196201,68,51,4,1,Married,8,249.6509133,0,11945.54567\n164806,67,47,2,1,Single,10,249.5596089,0,12170.98045\n115592,20,1,3,1,Married,7,249.5085311,0,11875.42656\n165969,16,0,2,1,Married,12,249.4421413,0,12118.10707\n152108,16,0,2,1,Married,13,249.4198391,0,12157.99196\n106267,51,34,3,0,Married,13,249.4177861,0,12167.88931\n111345,61,44,3,0,Single,14,249.3815901,0,12130.07951\n153109,22,3,1,0,Married,12,249.3555374,0,12009.77687\n130789,60,41,3,1,Single,7,249.3394928,0,12062.97464\n103849,67,49,1,0,Married,3,249.1713156,0,12051.56578\n100565,67,50,1,1,Married,8,249.1075462,0,11865.37731\n135029,61,42,3,0,Married,0,248.8836026,0,12151.18013\n147937,64,47,2,1,Single,7,248.8582016,0,11858.91008\n199225,60,41,3,1,Single,15,248.7837207,0,11887.18604\n141113,55,35,3,1,Single,11,248.7568834,0,12004.84417\n179421,70,53,3,0,Married,10,248.7119133,0,12227.59567\n163270,70,53,3,0,Married,12,248.6125482,0,11837.62741\n140856,19,1,3,1,Single,13,248.6097919,0,12096.4896\n161704,67,46,3,0,Married,9,248.5982222,0,11986.91111\n111174,70,51,1,1,Single,9,248.5762478,0,12136.81239\n145106,30,11,4,1,Married,11,248.5739382,0,12028.69691\n112692,22,4,1,0,Married,15,248.5382876,0,12189.91438\n154945,65,46,4,0,Single,6,248.5372686,0,11965.86343\n163513,66,46,1,1,Married,12,248.5359904,0,11850.79952\n193391,57,40,3,0,Single,12,248.332043,0,12063.60215\n164457,70,49,3,0,Married,8,248.283635,0,12088.18175\n197413,18,0,4,1,Married,7,248.2747272,0,12074.73636\n191785,64,47,1,1,Married,9,248.2674932,0,12025.37466\n183306,58,37,4,0,Married,14,248.2583182,0,12097.91591\n184832,68,51,2,0,Married,13,248.1590625,0,11989.95313\n106311,40,22,3,0,Single,12,248.0955132,0,11937.77566\n161289,19,2,2,1,Married,9,248.0099392,0,11935.49696\n126456,20,2,2,1,Married,12,248.0012105,0,11999.06053\n133167,42,22,3,0,Married,11,247.9977949,0,12091.88975\n196578,68,49,2,1,Single,8,247.9587716,0,12160.93858\n134652,64,46,2,1,Married,10,247.9520139,0,11861.6007\n175026,61,44,3,1,Single,13,247.6704538,0,11783.52269\n124399,22,4,3,1,Married,13,247.666634,0,12105.3317\n112411,43,25,1,1,Married,11,247.6518147,0,11929.59074\n112090,22,4,2,1,Married,14,247.617549,0,11898.87745\n155146,68,51,4,0,Single,15,247.5534711,0,11940.67356\n117228,67,49,2,1,Married,5,247.5417378,0,11896.08689\n184013,19,1,2,1,Married,15,247.4912291,0,11828.56146\n159023,18,0,3,1,Single,15,247.3642922,0,12082.21461\n125378,18,0,3,1,Single,14,247.3580636,0,11936.90318\n181717,67,46,2,1,Single,14,247.3375139,0,11915.8757\n194446,55,34,2,1,Married,15,247.3008553,0,11932.04277\n149148,68,48,2,0,Single,4,247.28952,0,12065.476\n161489,22,5,2,1,Married,14,247.2079108,0,11875.39554\n104025,25,8,2,1,Married,12,247.1359451,0,11900.79726\n121258,16,0,2,1,Married,14,247.1279121,0,11956.39561\n172894,65,46,3,1,Married,14,247.0818669,0,11957.09335\n148119,65,48,2,0,Married,12,247.0451395,0,11923.25698\n194078,62,43,2,0,Married,10,246.9965913,0,11905.82957\n165279,19,1,2,1,Single,11,246.9395067,0,11748.97534\n138903,24,6,2,0,Married,14,246.9232925,0,12022.16463\n156998,56,37,3,0,Single,11,246.873531,0,11750.67655\n151462,66,45,2,0,Married,8,246.8388232,0,11765.94116\n101774,29,10,2,1,Single,13,246.7790164,0,11806.95082\n180480,24,6,1,1,Married,12,246.7782666,0,12010.91333\n110225,68,51,2,0,Single,0,246.7727365,0,11957.63683\n103054,36,16,2,0,Married,14,246.7423689,0,12001.11845\n161068,70,53,2,1,Single,9,246.7127998,0,12047.63999\n110357,17,0,1,1,Married,11,246.7103971,0,11739.51986\n158528,66,47,2,1,Single,1,246.7029727,0,11987.14864\n181437,31,10,1,0,Single,12,246.6193297,0,12121.96649\n130344,65,46,1,1,Married,3,246.6135668,0,12036.67834\n109285,44,25,1,0,Married,14,246.6122491,0,12109.61246\n139131,67,50,3,0,Married,11,246.6048061,0,11951.24031\n174407,20,3,1,0,Single,15,246.5405761,0,12054.02881\n136793,16,0,1,1,Married,14,246.5317349,0,12038.58675\n192662,20,1,4,1,Single,15,246.4211463,0,12045.05732\n112568,60,43,3,0,Single,4,246.4036417,0,11975.18209\n114963,63,45,2,1,Married,7,246.368989,0,11867.44945\n105133,59,40,2,1,Single,12,246.3331978,0,12022.65989\n187273,17,0,4,1,Single,15,246.2737827,0,11809.68914\n120206,33,14,2,0,Single,13,246.2217902,0,12106.08951\n101479,65,48,2,0,Single,11,246.1791269,0,11908.95635\n190266,60,43,4,1,Married,6,246.1529271,0,11964.64636\n132535,58,37,3,0,Married,13,246.0309156,0,12082.54578\n173901,23,4,2,1,Single,13,245.9475767,0,11969.37884\n126905,16,0,3,1,Single,14,245.9460385,0,11973.30193\n147256,19,1,2,1,Married,9,245.8298788,0,11880.49394\n125236,17,0,3,1,Married,12,245.8145155,0,12075.72578\n183025,46,25,1,0,Single,14,245.7979046,0,11818.89523\n194453,50,33,3,0,Single,15,245.7227991,0,11713.13996\n120794,70,50,2,1,Single,2,245.6653967,0,11974.26984\n150779,64,47,1,1,Married,2,245.6001242,0,11804.00621\n197914,64,45,4,1,Married,8,245.557733,0,12016.88665\n128660,16,0,2,1,Married,13,245.5514543,0,11998.57272\n176854,67,48,2,1,Single,2,245.5495025,0,11806.47513\n158747,70,49,3,1,Married,8,245.499107,0,11912.95535\n128390,61,43,1,0,Married,11,245.4681424,0,12015.40712\n120405,22,5,1,0,Married,11,245.4353808,0,12021.76904\n124720,38,18,2,1,Single,15,245.3965721,0,11968.82861\n166571,35,18,2,0,Married,14,245.3859265,0,11675.29633\n129986,25,6,2,0,Married,11,245.3725447,0,11849.62724\n182162,67,49,3,1,Married,7,245.3339849,0,11714.69925\n131255,63,44,2,1,Married,15,245.3088282,0,11922.44141\n190017,36,19,3,0,Single,11,245.3059375,0,12011.29688\n163691,21,4,4,1,Married,12,245.2963139,0,12056.8157\n137250,44,27,3,1,Married,11,245.2365078,0,11773.82539\n118060,53,35,2,0,Single,11,245.1474822,0,11678.37411\n153649,68,49,2,1,Single,8,245.0899707,0,11841.49854\n100976,24,5,4,0,Single,11,244.9397385,0,11907.98693\n171002,22,5,4,1,Single,14,244.891672,0,11644.5836\n184504,18,0,2,0,Married,12,244.8853277,0,11837.26639\n178880,24,5,2,1,Married,14,244.8453631,0,11975.26816\n158437,20,3,4,1,Married,9,244.8310746,0,11917.55373\n132742,19,0,1,1,Single,11,244.7718762,0,12038.59381\n130218,42,25,3,1,Single,13,244.7535805,0,11860.67903\n110194,19,2,2,0,Married,12,244.748224,0,11945.4112\n134286,61,41,2,1,Single,9,244.7033745,0,12035.16873\n101870,64,47,2,0,Married,4,244.6449144,0,11768.24572\n133265,51,33,3,0,Single,15,244.6252522,0,11777.26261\n163146,22,5,3,1,Married,14,244.5733694,0,11932.66847\n102760,68,49,4,0,Married,6,244.5084598,0,11686.42299\n175384,63,46,2,1,Married,10,244.4837712,0,11964.18856\n176820,31,12,2,0,Single,15,244.4753204,0,12001.76602\n107089,64,47,2,1,Single,0,244.4110213,0,11923.55107\n143832,24,6,2,0,Married,14,244.2048053,0,11864.24027\n117811,36,19,1,0,Married,11,244.1962448,0,11902.81224\n143189,64,44,3,1,Married,15,244.1959166,0,11667.79583\n184858,68,50,4,1,Single,3,244.1770033,0,11625.85017\n128107,68,51,3,0,Single,13,244.1757467,0,11719.78734\n174852,63,45,2,0,Married,9,244.1756341,0,11680.78171\n157572,69,50,2,1,Married,6,244.1751053,0,11861.75527\n122628,17,0,3,1,Married,8,244.1398636,0,11835.99318\n195752,67,49,4,0,Married,8,244.1388984,0,11663.94492\n110664,64,44,1,0,Single,10,244.1284514,0,11960.42257\n100302,25,8,2,1,Married,13,244.1245194,0,11865.22597\n161445,66,49,4,0,Single,13,244.1192847,0,11742.96424\n131698,35,17,2,1,Single,14,243.9893016,0,11810.46508\n124198,23,5,2,1,Married,13,243.9805999,0,11996.03\n138394,20,2,1,1,Single,15,243.9793541,0,11936.96771\n136789,25,6,2,1,Married,12,243.9594903,0,11814.97452\n116677,43,23,3,1,Married,15,243.9526945,0,11721.63473\n108504,61,40,2,0,Married,6,243.9249822,0,11847.24911\n184120,48,28,2,1,Married,12,243.8562855,0,11598.81428\n103655,18,1,3,0,Married,15,243.8487109,0,11664.43555\n198920,65,46,4,1,Single,13,243.8226255,0,11827.13128\n123629,44,27,2,0,Single,15,243.782371,0,11907.11855\n111215,63,46,1,0,Married,7,243.7608577,0,11930.04289\n178787,62,41,3,0,Single,7,243.7567173,0,11910.83587\n168426,17,0,2,1,Married,7,243.6923338,0,11663.61669\n153430,38,19,2,1,Single,14,243.6849608,0,11798.24804\n191373,20,1,2,1,Married,15,243.6259786,0,11726.29893\n181332,70,52,4,1,Married,2,243.6051987,0,11705.25994\n166319,66,46,2,1,Married,3,243.5328737,0,11646.64369\n196114,22,5,4,0,Married,15,243.5247613,0,11657.23807\n114449,61,43,3,1,Single,4,243.4541214,0,11928.70607\n185300,19,2,2,0,Married,13,243.4427274,0,11806.13637\n168901,46,27,2,0,Married,11,243.3636502,0,11571.18251\n175330,16,0,2,1,Married,15,243.3503986,0,11729.51993\n177510,65,46,3,1,Single,1,243.3202757,0,11948.01379\n196959,67,48,1,1,Married,8,243.3079366,0,11784.39683\n141588,48,30,2,0,Single,14,243.2859844,0,11943.29922\n181234,41,24,4,0,Married,11,243.2859125,0,11939.29563\n110593,20,2,3,0,Married,12,243.2329213,0,11611.64607\n128252,16,0,2,0,Married,11,243.1939735,0,11803.69868\n187491,21,4,2,0,Married,11,243.1086631,0,11885.43316\n176448,18,0,2,0,Married,14,243.0692844,0,11917.46422\n132688,53,33,1,0,Married,15,242.9716237,0,11591.58119\n129859,20,2,2,0,Married,14,242.9389306,0,11828.94653\n190611,26,9,3,1,Married,15,242.9112158,0,11729.56079\n159545,22,3,2,1,Single,15,242.903909,0,11744.19545\n163905,67,49,1,0,Married,7,242.8887756,0,11889.43878\n121735,63,45,4,1,Single,3,242.881342,0,11617.0671\n135090,19,0,3,1,Married,9,242.878505,0,11876.92525\n192628,16,0,1,1,Single,14,242.7725761,0,11902.62881\n101773,32,14,2,0,Single,14,242.76391,0,11699.1955\n178791,16,0,3,1,Single,15,242.7266242,0,11636.33121\n155636,16,0,3,1,Married,13,242.684074,0,11624.2037\n125838,31,14,1,1,Single,13,242.6541754,0,11712.70877\n133166,63,42,2,0,Single,1,242.6317197,0,11762.58599\n126025,39,20,1,0,Married,15,242.4366585,0,11807.83293\n103790,27,9,3,0,Married,11,242.4077606,0,11791.38803\n129902,19,2,2,1,Married,15,242.3291789,0,11690.45895\n105088,45,24,1,1,Single,11,242.3262443,0,11844.31222\n180267,27,10,1,1,Single,13,242.2763623,0,11514.81812\n187007,63,43,3,0,Married,11,242.2761302,0,11646.80651\n160702,68,48,3,0,Single,13,242.2497696,0,11679.48848\n147188,20,1,2,0,Married,15,242.2109513,0,11776.54757\n180381,63,44,3,0,Single,15,242.1154048,0,11584.77024\n150799,20,1,3,0,Married,15,242.069893,0,11626.49465\n122448,68,51,3,1,Married,1,242.0480509,0,11535.40255\n115669,61,44,2,1,Married,11,241.8722224,0,11567.61112\n194777,66,47,2,0,Married,10,241.7737035,0,11610.68518\n169439,63,42,3,0,Married,5,241.7116028,0,11685.58014\n141643,22,3,2,1,Married,13,241.6787662,0,11676.93831\n178818,38,20,2,1,Married,14,241.6032239,0,11729.1612\n198984,61,44,2,1,Single,7,241.5646908,0,11617.23454\n146387,24,5,3,1,Married,13,241.4800399,0,11661.002\n185211,30,12,1,0,Married,13,241.4614472,0,11629.07236\n116524,22,3,3,1,Married,12,241.4249882,0,11634.24941\n129161,63,42,3,1,Single,2,241.4038842,0,11656.19421\n119194,36,18,1,1,Single,11,241.2948785,0,11743.74393\n161775,63,46,4,0,Married,7,241.281305,0,11619.06525\n121239,67,50,2,0,Married,1,241.2812083,0,11611.06042\n127928,49,28,3,0,Single,12,241.2678611,0,11832.39306\n191430,25,6,2,1,Married,14,241.2589403,0,11498.94702\n139381,22,4,2,0,Married,11,241.235819,0,11556.79095\n141649,64,45,3,0,Married,7,241.2137582,0,11782.68791\n175892,61,43,2,0,Married,8,241.2089516,0,11553.44758\n105871,41,24,3,1,Single,11,241.1847367,0,11491.23684\n106960,53,36,1,0,Married,11,241.126041,0,11595.30205\n151031,35,14,4,1,Married,13,241.0123441,0,11520.61721\n141479,16,0,2,1,Single,15,240.8688041,0,11808.44021\n163089,32,11,3,0,Single,12,240.8530538,0,11606.65269\n141426,64,47,2,0,Married,10,240.6671701,0,11698.35851\n172030,60,42,1,1,Single,8,240.6219433,0,11781.09717\n178636,69,50,2,0,Married,3,240.5232703,0,11666.16352\n181684,60,40,4,1,Married,9,240.4557744,0,11587.78872\n141837,19,2,2,1,Married,14,240.4026167,0,11600.13084\n141363,59,38,4,1,Single,15,240.3603785,0,11787.01893\n148181,23,6,3,1,Married,14,240.3339434,0,11563.69717\n194603,64,47,4,0,Married,9,240.3289721,0,11446.44861\n175931,70,51,4,1,Single,4,240.288773,0,11647.43865\n166450,38,19,2,1,Married,15,240.2642571,0,11749.21286\n169437,70,49,3,1,Married,2,240.2148339,0,11739.7417\n191981,20,2,3,1,Single,11,240.1517478,0,11609.58739\n189749,60,42,2,0,Single,10,240.0642337,0,11529.21169\n147839,69,50,2,1,Married,8,240.0257169,0,11603.28585\n167642,66,45,2,0,Married,6,240.0206554,0,11432.03277\n174940,22,3,1,1,Single,11,240.0029509,0,11468.14755\n161852,21,4,2,1,Single,12,239.9623437,0,11461.11719\n195120,68,47,4,1,Single,12,239.9516656,0,11657.58328\n181122,64,45,3,1,Single,2,239.8447979,0,11681.2399\n178054,42,24,4,0,Single,14,239.8069277,0,11595.34639\n174143,19,0,3,1,Married,7,239.8043929,0,11585.21965\n168401,61,43,1,1,Married,10,239.7626287,0,11787.13144\n137955,63,44,4,0,Single,13,239.7316508,0,11411.58254\n163951,17,0,2,1,Married,14,239.6490383,0,11538.45192\n190312,60,42,3,1,Married,1,239.5380711,0,11557.90356\n153183,65,44,2,0,Single,3,239.5372616,0,11597.86308\n146562,61,40,1,0,Married,10,239.5030073,0,11490.15037\n142257,39,18,2,1,Single,13,239.4395729,0,11436.97865\n156959,69,52,3,0,Married,10,239.4214956,0,11435.07478\n176393,18,1,3,0,Married,15,239.3866933,0,11374.33467\n162163,48,27,3,1,Married,14,239.3755164,0,11700.77582\n112761,69,51,3,0,Single,5,239.28812,0,11605.406\n161436,36,18,3,0,Single,13,239.283694,0,11427.1847\n163233,65,48,1,0,Married,7,239.2136809,0,11614.68405\n127821,66,47,2,0,Married,9,239.1793387,0,11454.96694\n139558,34,17,3,1,Single,13,239.1510886,0,11616.55443\n193692,64,44,4,1,Single,13,239.1381641,0,11507.90821\n184963,63,45,4,1,Married,9,239.1191156,0,11601.95578\n180093,62,44,3,0,Married,2,239.05033,0,11643.5165\n172570,62,41,3,1,Married,7,239.0465548,0,11719.32774\n144121,61,43,2,0,Married,5,239.0118742,0,11562.59371\n179886,64,45,3,0,Married,8,238.9792737,0,11632.96369\n193214,65,47,2,1,Single,7,238.9766815,0,11353.83408\n114711,39,22,4,1,Married,14,238.9538651,0,11390.69326\n194382,31,11,2,0,Married,14,238.8860408,0,11588.30204\n116783,36,15,2,1,Single,13,238.8627913,0,11684.13957\n181797,24,7,2,1,Single,12,238.8361649,0,11417.80825\n162164,25,8,2,0,Married,11,238.828835,0,11386.44175\n131763,16,0,4,1,Married,15,238.747151,0,11573.35755\n175022,47,27,3,0,Single,13,238.7447296,0,11546.23648\n151405,60,41,4,0,Single,10,238.6813378,0,11485.06689\n114098,70,51,3,1,Single,8,238.6651294,0,11394.25647\n176115,62,42,2,0,Married,7,238.649198,0,11655.4599\n190307,52,33,3,0,Single,12,238.6296586,0,11405.48293\n104708,23,4,1,0,Married,14,238.6039585,0,11725.19793\n180693,69,48,2,1,Single,10,238.5960917,0,11697.80459\n154175,21,4,2,1,Married,14,238.4388989,0,11469.94495\n108980,67,47,3,1,Married,6,238.3957425,0,11430.78713\n196887,16,0,1,0,Married,13,238.350289,0,11642.51445\n128942,19,1,1,0,Married,12,238.3462253,0,11415.31127\n116465,19,1,4,1,Married,6,238.344623,0,11617.23115\n157515,21,2,3,1,Married,15,238.3182885,0,11441.91443\n103746,53,36,3,1,Single,15,238.2228723,0,11650.14362\n180649,56,38,3,1,Single,15,238.1997447,0,11610.98724\n107429,22,3,3,1,Married,15,238.1693797,0,11561.46899\n150893,22,3,1,1,Single,14,238.1623425,0,11617.11713\n139032,62,44,3,1,Single,7,238.0341751,0,11334.70876\n135219,25,8,3,1,Married,12,238.0171747,0,11484.85874\n100171,70,53,1,1,Married,14,237.9571792,0,11352.85896\n194944,45,28,1,1,Single,15,237.9548251,0,11472.74126\n146752,70,53,3,1,Single,7,237.8484673,0,11351.42337\n153384,37,19,2,0,Single,13,237.8149266,0,11335.74633\n169457,24,7,3,1,Married,15,237.8048754,0,11547.24377\n111789,41,20,3,1,Married,13,237.7582284,0,11483.91142\n156016,19,0,2,0,Single,12,237.7426178,0,11346.13089\n136060,70,53,3,1,Married,6,237.6977683,0,11518.88842\n142689,43,24,4,0,Married,11,237.6651953,0,11357.25977\n199690,63,43,1,0,Single,6,237.6454656,0,11662.27328\n197153,66,47,4,1,Single,12,237.6114904,0,11656.57452\n129160,43,24,3,1,Single,11,237.6068066,0,11583.34033\n191209,60,43,3,1,Single,9,237.4477042,0,11330.38521\n139499,65,46,4,0,Single,4,237.4197455,0,11336.98728\n165226,22,5,2,1,Married,15,237.2801998,0,11480.00999\n118951,65,46,1,0,Married,1,237.2436329,0,11644.18165\n154024,33,12,2,1,Single,12,237.228971,0,11499.44855\n192278,23,6,1,0,Single,11,237.1568502,0,11321.84251\n187822,48,30,2,1,Married,13,237.1254764,0,11528.27382\n167494,22,4,2,1,Married,10,237.0846862,0,11445.23431\n166349,58,37,3,1,Single,14,237.0564788,0,11552.82394\n189563,20,1,3,1,Married,11,237.0407509,0,11552.03755\n168264,48,30,1,0,Married,12,237.0227564,0,11422.13782\n147625,68,47,1,0,Single,7,237.0065584,0,11302.32792\n147906,44,25,2,0,Married,14,237.0021047,0,11341.10524\n196489,22,4,2,0,Married,13,236.9671667,0,11463.35834\n196516,19,0,2,1,Married,13,236.9340245,0,11310.70123\n155238,60,43,2,1,Single,5,236.8506906,0,11586.53453\n143474,70,53,3,0,Married,9,236.7952419,0,11506.7621\n112009,33,12,2,1,Married,11,236.7238366,0,11319.19183\n143224,61,42,1,1,Married,2,236.7089588,0,11372.44794\n134796,61,41,1,0,Single,15,236.6978813,0,11414.89407\n132916,17,0,2,0,Married,13,236.6929213,0,11439.64607\n134752,54,37,4,1,Single,15,236.6637807,0,11376.18904\n147312,51,34,2,1,Married,14,236.6097765,0,11502.48883\n124158,23,5,1,1,Married,11,236.5676919,0,11368.3846\n142228,16,0,1,1,Married,14,236.5629119,0,11342.1456\n148131,30,9,3,0,Single,11,236.5273888,0,11334.36944\n185159,68,50,1,0,Single,15,236.5013685,0,11446.06843\n187105,41,23,2,1,Married,11,236.3888221,0,11548.44111\n125801,45,26,1,0,Single,12,236.3433438,0,11288.16719\n125908,65,47,1,0,Single,7,236.2200847,0,11540.00424\n183612,66,45,3,0,Single,0,236.1916541,0,11383.58271\n148128,24,7,3,1,Married,15,236.1908828,0,11425.54414\n118998,66,46,2,0,Single,3,236.1840765,0,11239.20383\n174724,65,48,3,1,Single,13,236.0982174,0,11454.91087\n152118,23,6,3,1,Married,13,236.0737906,0,11585.68953\n164407,18,0,2,0,Single,15,236.0549354,0,11351.74677\n183760,63,45,2,0,Single,14,236.0096439,0,11581.4822\n192386,55,35,1,1,Single,12,235.9757695,0,11513.78848\n139548,69,48,1,0,Single,7,235.8586141,0,11421.93071\n144818,23,5,3,0,Married,15,235.7885358,0,11235.42679\n119546,36,18,2,1,Single,15,235.7482469,0,11566.41235\n124482,51,32,1,0,Single,13,235.7394144,0,11415.97072\n133207,21,2,1,1,Single,13,235.7333404,0,11566.66702\n144335,64,44,1,1,Married,2,235.6730053,0,11444.65027\n111841,17,0,3,0,Married,15,235.6602821,0,11216.01411\n172035,70,51,4,0,Married,10,235.6577836,0,11308.88918\n146345,55,38,3,0,Married,11,235.6211136,0,11413.05568\n191625,20,1,1,1,Single,13,235.5727989,0,11249.63995\n144840,22,5,2,1,Married,13,235.5643519,0,11312.2176\n114668,69,52,3,0,Married,0,235.5001354,0,11539.00677\n148855,45,24,4,0,Single,14,235.4909106,0,11317.54553\n146019,67,47,3,0,Married,9,235.4645742,0,11450.22871\n131281,28,7,4,1,Married,11,235.4002033,0,11359.01017\n112764,17,0,3,1,Married,12,235.3708111,0,11237.54056\n126220,19,1,3,1,Single,13,235.3451905,0,11364.25953\n187141,58,38,3,1,Single,14,235.1946644,0,11218.73322\n128057,64,47,4,0,Single,13,235.1618894,0,11311.09447\n176699,68,49,1,1,Married,2,235.1357629,0,11324.78815\n151577,70,53,3,1,Single,2,235.0819294,0,11384.09647\n169347,23,4,1,0,Married,15,235.0776441,0,11313.88221\n186696,63,43,2,1,Married,13,235.020936,0,11523.0468\n176566,62,44,3,1,Married,11,234.9140033,0,11191.70017\n130480,65,48,2,0,Single,15,234.8463574,0,11253.31787\n155277,39,22,3,0,Single,11,234.8444199,0,11433.221\n113034,17,0,1,1,Single,12,234.8382168,0,11458.91084\n123788,69,52,1,1,Single,14,234.7357156,0,11184.78578\n191921,65,45,2,1,Single,14,234.7325107,0,11335.62554\n106026,38,20,3,1,Married,15,234.6940061,0,11413.70031\n173364,70,49,1,1,Married,6,234.6474302,0,11526.37151\n141692,63,45,3,0,Single,4,234.6412859,0,11504.0643\n140497,65,46,2,1,Single,5,234.6325213,0,11182.62607\n106686,68,51,3,0,Married,8,234.4712329,0,11298.56165\n118355,44,26,1,1,Married,15,234.4652252,0,11387.26126\n128930,61,40,3,0,Single,7,234.409396,0,11174.4698\n182372,19,2,4,1,Single,12,234.3263463,0,11241.31732\n175204,19,2,2,1,Married,12,234.2815341,0,11196.07671\n155250,35,16,3,1,Married,15,234.2771423,0,11165.85712\n173882,35,18,1,1,Married,12,234.2473182,0,11492.36591\n137839,65,48,1,0,Single,6,234.2227558,0,11197.13779\n170068,34,16,2,0,Married,11,234.1258388,0,11205.29194\n113819,62,41,2,1,Single,3,234.0169203,0,11472.84602\n170141,62,45,3,0,Married,6,234.0059424,0,11261.29712\n150886,20,2,3,1,Married,14,233.8950101,0,11134.75051\n145089,67,47,3,1,Married,7,233.8176362,0,11482.88181\n139432,68,50,3,0,Single,14,233.8172637,0,11483.86319\n161581,23,6,2,1,Married,12,233.7931559,0,11187.6578\n152369,59,39,3,0,Married,14,233.7902735,0,11397.51368\n127769,62,45,2,0,Married,13,233.7861279,0,11338.3064\n130882,23,6,2,0,Married,14,233.7394111,0,11200.97056\n138992,63,46,2,1,Single,14,233.7057014,0,11232.28507\n119419,24,6,2,1,Married,8,233.6744687,0,11149.72344\n181573,70,50,3,1,Single,9,233.6415368,0,11303.07684\n128589,16,0,2,1,Married,11,233.632557,0,11245.62785\n173187,37,17,4,0,Married,13,233.5515837,0,11234.57919\n142178,22,5,2,1,Single,14,233.5014361,0,11448.07181\n138062,20,1,2,0,Married,15,233.4894332,0,11349.47166\n157328,19,0,4,1,Married,13,233.4680003,0,11419.40002\n188822,36,18,1,0,Married,11,233.4536465,0,11361.68233\n158748,68,50,4,1,Married,1,233.2926778,0,11091.63389\n177263,36,16,2,0,Married,15,233.2750792,0,11073.75396\n104333,18,0,3,1,Married,14,233.2470049,0,11212.35025\n146423,19,0,2,1,Single,15,233.2412985,0,11216.06493\n184637,21,2,1,1,Single,15,233.2261935,0,11303.30968\n133916,45,24,1,1,Married,12,233.2052224,0,11452.26112\n150901,68,48,3,1,Single,9,233.0871264,0,11270.35632\n180651,66,49,2,0,Single,2,233.0582785,0,11186.91393\n101188,19,2,1,1,Married,12,233.0418479,0,11102.0924\n136373,58,38,3,1,Married,13,232.9513357,0,11310.56679\n125222,65,46,1,1,Single,15,232.7596434,0,11260.98217\n175197,70,49,3,0,Single,9,232.7517279,0,11218.5864\n160666,16,0,2,1,Married,13,232.744495,0,11139.22475\n199787,30,11,2,0,Married,15,232.6932893,0,11236.66447\n192042,17,0,2,0,Married,13,232.6281293,0,11080.40647\n113682,45,24,1,1,Married,15,232.5300927,0,11036.50464\n186826,21,3,3,0,Married,12,232.4790034,0,11407.95017\n161393,33,13,3,1,Married,12,232.4521862,0,11171.60931\n137825,26,9,4,1,Single,13,232.2603309,0,11107.01655\n179053,19,1,3,0,Married,13,232.254786,0,11409.7393\n186379,20,3,3,1,Married,14,232.2478375,0,11274.39188\n156162,40,22,2,0,Single,11,232.1852081,0,11302.26041\n140664,22,3,4,0,Married,15,232.1748725,0,11379.74363\n138643,27,9,2,0,Married,11,232.1488457,0,11277.44229\n157355,62,45,3,1,Single,5,232.0335156,0,11137.67578\n105703,50,33,1,1,Married,11,232.0329603,0,11286.64802\n127224,66,49,2,1,Married,5,231.9684781,0,11381.42391\n162389,24,6,3,1,Single,12,231.8729302,0,11171.64651\n141965,58,37,4,1,Single,11,231.7936213,0,11313.68107\n175752,66,49,2,1,Married,10,231.7778468,0,11007.89234\n137174,23,5,3,0,Married,15,231.5194537,0,11002.97269\n143814,69,51,2,1,Single,2,231.4608776,0,11309.04388\n108015,70,51,2,1,Married,12,231.4313883,0,11148.56942\n148471,33,13,4,1,Single,15,231.4087613,0,11232.43807\n136933,61,42,4,1,Married,7,231.1797507,0,11008.98754\n159110,62,42,3,1,Married,6,231.1658511,0,11174.29256\n182367,63,44,3,1,Married,0,231.1091174,0,11085.45587\n164401,25,8,4,0,Married,14,231.0028611,0,11237.14306\n105576,66,46,2,1,Single,11,230.9981212,0,11141.90606\n131521,61,44,2,0,Single,13,230.9838027,0,11266.19014\n160313,59,39,4,0,Married,15,230.9526917,0,10993.63459\n145466,68,47,1,0,Single,4,230.9517193,0,11175.58597\n149337,31,12,3,1,Married,11,230.9309007,0,11267.54504\n117861,60,39,3,0,Married,14,230.930367,0,11052.51835\n177439,54,35,1,0,Married,15,230.9037288,0,11286.18644\n199077,40,20,4,0,Married,14,230.8480023,0,10980.40012\n106899,22,3,2,0,Married,12,230.831422,0,10954.5711\n131981,62,45,2,0,Married,6,230.7990564,0,11322.95282\n153575,49,30,3,0,Married,12,230.7690082,0,11290.45041\n184033,60,40,2,0,Single,7,230.7395709,0,11027.97855\n186176,26,5,1,0,Married,11,230.6920433,0,10949.60217\n182958,65,44,4,0,Single,13,230.6718199,0,11138.591\n164264,47,30,1,0,Single,15,230.6091431,0,11030.45716\n120212,69,49,1,1,Married,10,230.589839,0,11187.49195\n114825,69,48,1,0,Married,12,230.5859594,0,11315.29797\n173397,70,51,3,0,Married,4,230.5496678,0,11028.48339\n170332,25,7,3,1,Married,14,230.4548282,0,11081.74141\n198082,54,33,1,1,Single,14,230.4496289,0,11068.48145\n193389,22,3,2,0,Married,15,230.1703288,0,11037.51644\n110057,19,2,1,0,Married,15,230.1150988,0,11080.75494\n101379,17,0,2,0,Married,15,230.1025724,0,10924.12862\n195897,25,7,3,0,Married,14,229.9060162,0,10998.30081\n174394,66,48,2,1,Married,7,229.8680235,0,11231.40118\n178506,39,20,3,1,Married,11,229.8630713,0,11190.15357\n173396,60,43,4,1,Single,11,229.8413217,0,11187.06609\n177673,69,49,4,1,Single,6,229.7905566,0,11166.52783\n196535,66,47,1,0,Single,8,229.6906948,0,10945.53474\n175701,34,16,1,1,Single,12,229.5774269,0,11124.87135\n152638,63,44,1,1,Married,6,229.5443975,0,11186.21988\n154072,63,42,2,1,Single,14,229.4940513,0,10981.70257\n136603,67,47,1,1,Married,14,229.3791024,0,10978.95512\n148785,60,41,2,1,Married,8,229.341363,0,11030.06815\n108392,25,7,2,1,Married,13,229.2872644,0,11049.36322\n183199,30,9,2,1,Married,15,229.269854,0,11202.4927\n182468,61,44,3,1,Married,8,229.2590542,0,11097.95271\n165744,47,28,4,1,Married,12,229.2315232,0,11066.57616\n100960,16,0,2,1,Single,15,229.1935947,0,10986.67974\n197203,48,29,3,1,Single,15,229.1652738,0,10869.26369\n134743,69,49,2,0,Single,9,229.1536957,0,11088.68479\n101923,60,42,4,1,Married,7,229.1425485,0,10907.12743\n109539,24,5,2,0,Married,12,229.1217479,0,10923.0874\n154597,17,0,2,1,Married,12,229.0497519,0,10958.4876\n197274,22,3,3,0,Married,15,229.0209616,0,11074.04808\n163232,61,41,4,0,Married,6,229.0131944,0,11192.65972\n187323,43,24,2,1,Married,13,228.9655835,0,11227.27918\n148301,54,35,2,0,Married,15,228.9247941,0,10975.23971\n173206,25,8,4,1,Married,11,228.9181882,0,11082.90941\n176317,69,52,1,1,Single,0,228.8331198,0,10924.65599\n181023,67,49,2,0,Single,0,228.811037,0,11159.55185\n173501,18,1,2,0,Married,13,228.8044552,0,11113.22276\n164050,23,5,2,1,Married,12,228.7286679,0,11115.4334\n160589,65,45,2,1,Single,8,228.7047373,0,11015.23687\n124801,18,0,1,1,Married,12,228.6247326,0,10966.23663\n156815,69,51,1,0,Single,4,228.5962068,0,11225.81034\n118029,25,6,2,1,Married,13,228.5841335,0,10894.20668\n113578,66,49,2,0,Married,8,228.5812898,0,10964.06449\n188288,16,0,2,1,Single,13,228.5100351,0,11151.50176\n165780,68,51,3,1,Single,10,228.4789443,0,11071.94722\n191287,67,50,3,0,Single,7,228.4207208,0,11042.03604\n165320,52,31,3,1,Single,12,228.4081003,0,10972.40502\n132808,24,7,3,1,Married,8,228.3200078,0,10875.00039\n137836,68,47,1,1,Single,8,228.2723787,0,10988.61894\n179289,29,8,2,1,Married,15,228.2340701,0,10834.70351\n171233,43,22,1,0,Single,11,228.2297609,0,11071.48805\n187388,58,39,4,1,Married,11,228.2181154,0,10822.90577\n177850,19,0,1,1,Married,15,228.2167881,0,10959.83941\n188932,64,43,3,1,Married,8,228.1734686,0,11032.67343\n190643,64,46,1,1,Married,8,228.1717927,0,10842.58964\n160641,70,52,2,0,Single,15,228.1268705,0,10815.34353\n114085,40,19,3,0,Married,12,228.0744586,0,11149.72293\n135471,56,36,2,1,Married,13,227.967879,0,10930.39395\n185959,25,7,1,0,Married,15,227.9545285,0,11186.72643\n108909,61,41,3,1,Married,7,227.9537517,0,10882.68759\n162831,20,1,2,1,Married,11,227.9283072,0,10852.41536\n125055,61,42,3,1,Single,2,227.7540504,0,10895.70252\n193742,68,50,3,1,Married,0,227.7406491,0,10833.03246\n126647,64,44,4,0,Single,8,227.7282693,0,10844.41347\n104800,22,5,4,1,Married,14,227.7061218,0,10876.30609\n166202,32,13,4,1,Married,13,227.6942386,0,11179.71193\n113124,25,7,4,1,Married,11,227.6795538,0,11039.97769\n120268,60,41,1,1,Married,8,227.6644878,0,10915.22439\n142771,66,48,3,1,Single,5,227.6529975,0,11168.64988\n157115,68,47,2,0,Married,7,227.6438612,0,11096.19306\n157705,62,45,4,0,Single,10,227.5972038,0,10892.86019\n107917,53,36,2,0,Married,12,227.5880033,0,11045.40016\n184834,60,39,1,1,Married,4,227.5730607,0,10778.65304\n116688,22,3,1,1,Single,11,227.5204163,0,11066.02082\n134577,62,41,3,1,Married,11,227.4959551,0,11140.79776\n115133,61,42,2,1,Single,0,227.4842193,0,11102.21097\n158441,16,0,1,1,Single,11,227.3926774,0,11131.63387\n101617,25,6,1,1,Single,14,227.3336458,0,11096.68229\n109088,20,2,3,0,Married,15,227.3249044,0,10931.24522\n187892,67,50,1,1,Married,9,227.2721222,0,10859.60611\n148266,60,42,3,1,Single,10,227.2208439,0,10800.0422\n101732,51,34,3,0,Married,13,227.1612949,0,10835.06475\n129298,38,18,3,1,Married,15,227.0946699,0,10835.7335\n194044,61,44,1,0,Married,6,227.0893362,0,11127.46681\n169518,35,14,2,0,Married,11,227.0030475,0,10954.15238\n163618,17,0,4,1,Married,12,226.9991331,0,11044.95666\n106758,64,43,3,0,Single,7,226.8358175,0,11105.79088\n180655,49,30,2,0,Married,12,226.7195827,0,10856.97914\n106374,20,3,4,1,Single,13,226.6735195,0,10837.67598\n150285,67,47,4,1,Single,6,226.5989951,0,11092.94976\n179749,23,6,3,1,Single,13,226.5918853,0,11089.59427\n183999,21,3,3,1,Married,13,226.5756152,0,10746.78076\n127859,60,43,4,0,Single,12,226.5006233,0,11060.03117\n113624,26,6,3,0,Married,15,226.4835305,0,10905.17653\n149313,67,46,4,0,Married,8,226.4673076,0,10891.36538\n110283,50,31,2,1,Married,12,226.4231869,0,10902.15935\n133527,69,51,3,1,Single,8,226.3846678,0,10976.23339\n143075,57,39,4,1,Married,11,226.3594829,0,10747.97415\n162778,16,0,1,0,Married,14,226.3113181,0,10812.56591\n147285,63,42,3,1,Single,15,226.3008659,0,10920.0433\n170008,48,29,4,0,Single,11,226.2995011,0,10892.97506\n197226,64,43,2,1,Single,7,226.1814157,0,10775.07079\n168168,45,28,4,0,Single,11,226.1603243,0,11025.01622\n131645,70,52,4,0,Single,9,226.1021153,0,11087.10577\n156290,42,25,4,1,Married,11,226.0877025,0,10748.38513\n155661,63,45,2,1,Married,6,226.0781931,0,10799.90966\n156570,65,45,2,0,Married,10,225.9537702,0,10895.68851\n164197,60,40,4,1,Married,4,225.9448107,0,10871.24054\n130135,22,3,4,1,Married,14,225.9193112,0,10890.96556\n116019,23,5,3,1,Married,8,225.9165184,0,10762.82592\n147072,65,46,3,1,Married,7,225.8507068,0,11064.53534\n106028,69,49,4,1,Married,10,225.8124156,0,10897.62078\n125275,61,41,3,1,Married,6,225.8028494,0,10692.14247\n157960,64,47,4,1,Single,7,225.7661269,0,11079.30635\n191440,32,13,3,0,Married,14,225.7347436,0,10989.73718\n105414,60,42,2,0,Married,3,225.6248,0,11068.24\n106607,24,6,3,1,Single,12,225.5672078,0,10840.36039\n105593,33,16,3,0,Married,14,225.5206341,0,10829.03171\n194925,66,48,3,0,Single,3,225.5144867,0,10912.72434\n132030,55,36,2,0,Married,11,225.4633325,0,10984.16663\n138172,69,49,2,1,Married,10,225.4572368,0,10674.86184\n138744,64,45,3,0,Single,9,225.4229908,0,10829.14954\n189771,23,5,1,0,Single,12,225.4107538,0,10752.53769\n114514,25,8,2,0,Married,14,225.4093546,0,10951.46773\n120983,20,2,4,1,Single,11,225.3755843,0,10674.77922\n153673,19,0,2,1,Single,13,225.3479421,0,11045.39711\n154904,28,8,2,0,Married,13,225.344401,0,10893.22005\n197225,56,37,2,0,Married,15,225.1149734,0,10954.74867\n118097,68,47,2,0,Married,14,225.102024,0,10821.1012\n187424,70,50,3,0,Married,14,225.0972474,0,10802.86237\n196942,64,45,2,0,Single,12,225.0781285,0,11018.90643\n113500,70,50,2,1,Married,11,225.0333846,0,10990.66923\n149532,37,20,3,0,Single,12,225.0169511,0,11022.84756\n111336,61,41,2,1,Married,7,225.0140233,0,10941.70117\n117488,63,44,3,0,Single,10,224.9680764,0,10947.40382\n110532,67,48,1,0,Single,9,224.8441035,0,10744.20518\n188144,25,6,1,1,Married,9,224.8163937,0,10874.81969\n128372,40,19,3,0,Married,12,224.7668654,0,11005.34327\n124619,64,43,2,0,Married,6,224.7382043,0,10867.91022\n187257,43,26,3,1,Married,12,224.7339429,0,10839.69715\n126141,43,22,2,0,Single,11,224.6955736,0,11010.77868\n176379,59,38,2,0,Married,15,224.6655498,0,11033.27749\n175033,69,52,3,0,Married,10,224.5342862,0,10650.71431\n126764,18,0,3,0,Married,13,224.4649619,0,10628.2481\n184220,41,23,4,1,Married,14,224.4487186,0,10891.43593\n122587,63,43,3,1,Single,6,224.3856465,0,10874.28233\n149154,67,49,2,1,Single,13,224.2958682,0,10628.79341\n143825,65,45,4,1,Married,5,224.2590729,0,10975.95365\n162989,56,37,4,0,Single,12,224.2033155,0,10787.16578\n160864,39,19,1,1,Married,14,224.1903456,0,10882.51728\n120267,63,44,3,0,Single,15,224.1336125,0,10741.68063\n146129,53,36,3,0,Single,12,224.0934193,0,10810.67097\n132298,62,45,4,0,Married,7,224.0740543,0,10869.70272\n102352,70,51,3,0,Married,7,224.0523894,0,10718.61947\n159840,20,3,2,0,Married,12,224.0309344,0,10870.54672\n169015,67,49,3,1,Single,5,224.0212974,0,10900.06487\n105689,53,35,3,1,Single,15,223.9912977,0,10784.56488\n164763,68,49,2,0,Single,13,223.9625265,0,10686.12633\n191627,66,46,2,1,Single,9,223.9489133,0,10971.44567\n194317,47,27,4,0,Single,11,223.9143018,0,10993.71509\n189288,22,4,3,1,Married,15,223.8614904,0,10756.07452\n136917,16,0,1,1,Married,13,223.813659,0,10619.68295\n160967,45,26,4,0,Married,15,223.8024695,0,10816.12348\n199602,63,45,4,1,Single,9,223.7955877,0,10610.77939\n189085,50,32,3,0,Married,13,223.7777919,0,10608.8896\n176558,63,45,3,1,Married,6,223.7667542,0,10882.33771\n128649,66,47,2,0,Married,7,223.7601178,0,10813.00589\n162166,70,52,4,1,Single,7,223.7433521,0,10770.16761\n119566,58,39,1,1,Single,15,223.6843185,0,10898.21593\n137811,52,32,2,0,Single,14,223.683626,0,10832.1813\n114015,21,4,2,1,Married,10,223.6134939,0,10959.6747\n121318,22,4,4,1,Married,11,223.6058931,0,10690.29466\n135371,66,46,1,0,Married,8,223.5564035,0,10739.82018\n195261,69,49,4,0,Married,8,223.5485958,0,10855.42979\n128557,70,51,3,0,Married,12,223.5059129,0,10719.29565\n190521,21,4,4,1,Married,13,223.4801671,0,10795.00836\n146770,34,16,3,1,Single,12,223.4797734,0,10814.98867\n103657,35,14,3,0,Single,14,223.4491102,0,10901.45551\n167507,42,23,3,0,Single,13,223.4481119,0,10943.4056\n165218,23,6,3,0,Married,14,223.2843328,0,10893.21664\n122039,20,3,3,1,Single,13,223.2821085,0,10650.10543\n103785,62,44,3,1,Single,8,223.2655717,0,10840.27859\n178382,47,28,3,0,Single,14,223.2012912,0,10955.06456\n185884,43,25,4,1,Single,14,223.1991794,0,10762.95897\n138357,27,7,2,0,Married,14,223.1738865,0,10646.69433\n172961,32,11,4,1,Married,14,223.1211246,0,10840.05623\n157563,64,43,4,1,Married,8,223.1133403,0,10811.66702\n187872,58,37,1,1,Married,13,223.0815797,0,10628.07899\n184270,60,40,2,1,Single,9,223.0726304,0,10922.63152\n175234,19,1,3,1,Married,8,223.0245441,0,10838.22721\n145674,26,8,4,0,Single,14,223.0231917,0,10821.15959\n128007,40,20,2,0,Married,13,223.0110434,0,10679.55217\n164306,69,52,1,0,Single,6,222.9949749,0,10594.74875\n135076,55,37,2,1,Married,14,222.9448193,0,10817.24097\n165481,17,0,4,0,Married,13,222.9376199,0,10590.881\n127398,23,5,4,1,Married,14,222.9062998,0,10931.31499\n139116,55,34,3,0,Single,15,222.8133474,0,10769.66737\n183158,34,15,2,1,Married,14,222.8087464,0,10787.43732\n185247,70,50,4,0,Married,3,222.7849424,0,10722.24712\n154877,30,10,4,1,Single,12,222.776852,0,10697.8426\n121437,64,47,2,1,Married,7,222.6386693,0,10819.93347\n124943,46,26,2,1,Married,13,222.6129854,0,10810.64927\n104101,21,3,3,1,Married,13,222.5284037,0,10664.42019\n147708,70,53,3,1,Single,12,222.4809035,0,10630.04518\n171542,67,46,1,1,Married,7,222.4130618,0,10567.65309\n132224,28,8,2,1,Married,13,222.4028062,0,10640.14031\n188944,16,0,3,1,Married,12,222.3914688,0,10596.57344\n142980,23,6,4,0,Married,12,222.3326203,0,10735.63102\n133506,65,47,1,1,Married,2,222.2934213,0,10702.67107\n176701,67,46,2,1,Married,10,222.2513137,0,10615.56569\n184755,68,51,2,1,Single,10,222.221463,0,10696.07315\n135158,45,26,3,0,Single,14,222.1080969,0,10688.40485\n187431,18,0,1,1,Married,11,222.1065657,0,10749.32829\n143648,70,50,4,0,Single,1,222.091632,0,10563.5816\n134288,54,34,4,0,Single,11,221.9293161,0,10509.46581\n155334,51,34,3,0,Single,12,221.8571445,0,10706.85723\n157373,25,6,1,1,Married,15,221.8503711,0,10659.51856\n111655,23,4,1,1,Single,11,221.8433043,0,10548.16522\n119055,61,41,3,1,Married,9,221.7109503,0,10486.54752\n106484,40,22,3,1,Married,14,221.6829192,0,10769.14596\n112035,67,47,2,0,Married,10,221.5763437,0,10504.81719\n152263,22,3,2,1,Married,11,221.563228,0,10491.1614\n184675,67,50,1,0,Married,14,221.5471761,0,10764.35881\n101792,66,47,4,1,Single,14,221.525705,0,10554.28525\n159005,68,51,3,0,Single,9,221.4922591,0,10642.61296\n128115,46,28,2,1,Married,12,221.3601744,0,10795.00872\n146976,62,43,3,1,Married,6,221.3121444,0,10658.60722\n132693,67,47,4,1,Single,11,221.311805,0,10621.59025\n128360,64,44,1,1,Married,9,221.2919568,0,10638.59784\n126737,63,43,3,0,Single,12,221.2165216,0,10685.82608\n145387,61,43,1,1,Single,14,221.2043504,0,10683.21752\n135832,27,10,1,1,Married,13,221.1329875,0,10529.64938\n139591,23,6,3,1,Married,14,221.052516,0,10784.6258\n199612,64,43,2,1,Single,7,220.9166822,0,10470.83411\n134929,17,0,4,1,Married,14,220.9105963,0,10677.52982\n187983,42,24,4,0,Married,13,220.8931975,0,10785.65988\n162140,21,3,3,1,Married,12,220.8900408,0,10568.50204\n102668,68,50,4,1,Married,10,220.8653295,0,10571.26648\n172410,53,33,2,1,Single,12,220.8136093,0,10585.68047\n133148,69,51,1,1,Single,8,220.8134293,0,10839.67147\n131676,18,0,3,0,Married,12,220.7307903,0,10525.53952\n194923,60,40,2,1,Single,10,220.663507,0,10586.17535\n155134,22,3,1,1,Single,11,220.6385329,0,10530.92664\n188172,54,35,1,1,Single,13,220.615156,0,10496.7578\n128853,65,47,2,0,Married,4,220.5537003,0,10673.68502\n183143,67,48,2,1,Married,2,220.5360282,0,10543.80141\n150903,70,52,2,1,Married,9,220.4840155,0,10612.20078\n187071,22,4,1,1,Married,11,220.4425159,0,10554.12579\n126002,23,4,2,1,Married,12,220.3915989,0,10808.57995\n172574,67,46,2,0,Married,10,220.2491894,0,10534.45947\n134669,42,22,3,0,Single,15,220.1787534,0,10534.93767\n199527,53,36,2,1,Married,14,220.1477128,0,10807.38564\n164115,60,39,3,1,Married,12,220.1351237,0,10745.75619\n115248,69,51,1,1,Single,0,220.1163053,0,10671.81527\n133893,64,45,3,1,Single,8,220.0928308,0,10627.64154\n107562,69,49,3,0,Single,11,220.0696315,0,10617.48158\n157310,27,10,2,0,Married,14,219.9800089,0,10702.00045\n174479,69,48,1,1,Single,11,219.9773419,0,10572.8671\n133406,70,53,2,0,Married,5,219.9071599,0,10459.358\n162353,23,5,4,0,Married,12,219.8912405,0,10459.56203\n130621,60,43,3,1,Single,4,219.8676949,0,10701.38475\n186218,21,2,4,1,Single,11,219.7394077,0,10663.97039\n115714,20,2,4,1,Married,11,219.7389538,0,10702.94769\n176668,69,50,3,0,Married,7,219.661608,0,10742.0804\n182750,31,11,2,0,Married,12,219.5942883,0,10616.71442\n155407,64,44,3,0,Single,9,219.549482,0,10710.4741\n127563,61,40,3,1,Married,5,219.3331213,0,10618.65607\n170289,32,12,3,1,Single,12,219.2675302,0,10503.37651\n191567,67,50,1,0,Single,9,219.2630685,0,10568.15343\n131839,25,8,3,0,Married,13,219.2088215,0,10453.44108\n156326,18,0,3,1,Single,13,219.1593044,0,10499.96522\n131341,68,50,3,1,Single,9,219.1549893,0,10636.74947\n119397,67,50,2,0,Married,7,219.1167835,0,10639.83918\n179625,69,51,3,0,Married,1,219.0498408,0,10698.49204\n113036,33,16,2,0,Single,12,219.0374119,0,10520.8706\n111157,29,9,1,1,Married,14,218.9770126,0,10617.85063\n157863,17,0,3,1,Married,11,218.8950009,0,10640.75005\n103807,65,45,1,0,Married,4,218.8769746,0,10438.84873\n195202,32,15,2,0,Married,14,218.8316923,0,10581.58462\n132187,56,38,2,1,Married,12,218.8234575,0,10564.17288\n138756,66,46,2,1,Single,6,218.8142042,0,10701.71021\n116144,60,40,4,0,Single,9,218.8113795,0,10668.56898\n177861,68,47,4,1,Single,6,218.7703356,0,10417.51678\n161204,25,7,3,0,Single,15,218.7631965,0,10413.15983\n181694,25,6,2,0,Married,11,218.7403587,0,10618.01794\n184954,19,2,2,1,Married,13,218.7243513,0,10383.21757\n108631,19,2,3,1,Married,11,218.7236106,0,10658.18053\n112495,68,50,1,0,Single,8,218.6882265,0,10404.41133\n107236,40,23,1,1,Single,11,218.583891,0,10359.19455\n116625,67,47,1,1,Married,4,218.5837842,0,10584.18921\n149064,25,6,4,1,Married,12,218.4827576,0,10478.13788\n102613,24,6,4,0,Married,12,218.4733069,0,10700.66535\n157748,63,45,2,1,Married,11,218.4228579,0,10547.1429\n135049,43,23,1,0,Married,11,218.4008806,0,10358.04403\n121057,68,47,2,1,Single,12,218.3978314,0,10359.89157\n157918,66,49,3,1,Single,12,218.3564541,0,10467.82271\n104669,67,48,3,1,Single,15,218.3320804,0,10697.60402\n158043,16,0,4,1,Single,14,218.3148391,0,10600.74196\n199969,68,48,2,0,Single,13,218.2954901,0,10661.77451\n190594,19,0,4,1,Married,14,218.2815363,0,10690.07682\n174814,68,49,2,1,Single,8,218.2808394,0,10595.04197\n125207,65,48,4,0,Single,4,218.2755328,0,10319.77664\n154559,31,11,4,1,Single,12,218.2384338,0,10549.92169\n147700,62,41,3,0,Single,5,218.2211854,0,10518.05927\n115700,23,5,3,1,Married,11,218.1995744,0,10610.97872\n164082,25,8,2,1,Married,15,218.1612906,0,10703.06453\n113216,70,52,4,1,Married,14,218.1447717,0,10320.23859\n138601,59,39,3,0,Single,15,218.1396062,0,10641.98031\n162290,65,48,2,1,Single,6,218.0505222,0,10423.52611\n155284,65,46,2,1,Married,7,218.0438855,0,10607.19428\n192584,56,37,3,1,Married,12,218.0230937,0,10647.15469\n116233,70,52,3,0,Single,7,218.0029668,0,10502.14834\n123763,67,47,1,0,Single,11,218.0002008,0,10409.01004\n140190,25,8,4,1,Married,11,217.9798893,0,10367.99447\n157539,37,16,1,1,Single,13,217.9595747,0,10315.97874\n190397,66,49,3,0,Single,8,217.9451433,0,10592.25717\n108783,37,16,2,0,Single,15,217.9004107,0,10630.02054\n104998,18,0,3,1,Married,14,217.8683427,0,10638.41714\n115242,68,50,1,0,Single,12,217.8427303,0,10510.13652\n103087,22,5,3,0,Married,12,217.7758088,0,10469.79044\n103927,68,47,4,1,Married,7,217.7755553,0,10483.77777\n191400,61,41,3,0,Married,15,217.741155,0,10370.05775\n189255,18,0,3,0,Married,12,217.6720939,0,10422.6047\n101696,60,43,3,0,Married,13,217.6535603,0,10455.67802\n147949,70,50,3,1,Married,6,217.63747,0,10511.8735\n126702,61,40,3,0,Single,13,217.6126794,0,10380.63397\n101468,46,26,2,1,Married,13,217.5709589,0,10519.54795\n100461,60,39,2,1,Single,4,217.5407121,0,10355.03561\n126857,25,8,3,1,Married,15,217.5385211,0,10570.92606\n135191,63,43,4,1,Single,9,217.5262881,0,10495.31441\n109822,59,42,2,1,Married,11,217.4953575,0,10553.76788\n108116,69,52,2,1,Single,0,217.3825075,0,10447.12538\n101204,22,5,4,1,Single,15,217.2248811,0,10270.24406\n183203,70,52,3,0,Married,8,217.2116428,0,10646.58214\n166343,24,7,3,1,Single,14,217.1717344,0,10493.58672\n163180,64,46,1,0,Single,7,217.1632874,0,10421.16437\n136743,63,43,1,1,Single,4,217.1205792,0,10389.02896\n169826,18,0,2,1,Married,11,217.1088822,0,10357.44411\n139980,40,21,3,0,Married,13,217.088508,0,10454.4254\n144265,59,42,3,0,Married,14,217.0481069,0,10320.40535\n100632,29,10,3,1,Married,12,216.998695,0,10466.93475\n156424,66,46,2,0,Single,3,216.9776741,0,10307.88371\n104349,65,48,2,1,Single,10,216.9588572,0,10565.94286\n123722,41,21,3,1,Single,11,216.9474854,0,10463.37427\n176568,61,44,4,1,Single,7,216.9420344,0,10618.10172\n103561,61,43,4,1,Single,10,216.9190092,0,10499.95046\n111581,18,0,3,1,Single,12,216.9013125,0,10616.06563\n118961,63,46,1,0,Married,12,216.8897072,0,10543.48536\n174357,61,42,2,1,Married,1,216.8792098,0,10363.96049\n106317,20,2,4,1,Married,14,216.8379707,0,10463.89854\n115346,63,46,3,1,Single,7,216.837958,0,10463.8979\n190980,19,0,3,1,Married,11,216.7433355,0,10628.16678\n114053,60,42,3,0,Single,14,216.7305363,0,10409.52682\n102003,62,43,1,1,Married,8,216.683164,0,10534.1582\n130130,18,0,1,0,Married,12,216.6488257,0,10604.44129\n115799,61,42,3,1,Single,11,216.6225747,0,10246.12874\n153886,18,0,4,1,Single,14,216.5953773,0,10530.76887\n198976,70,53,2,1,Single,11,216.5357324,0,10499.78662\n148030,68,51,3,1,Married,14,216.5320216,0,10447.60108\n178221,68,50,3,1,Married,12,216.5277599,0,10302.388\n198115,57,40,3,0,Single,15,216.5253736,0,10619.26868\n143516,38,19,2,0,Single,11,216.4373517,0,10437.86759\n191160,25,8,1,1,Married,12,216.4240688,0,10615.20344\n169564,68,50,3,0,Single,7,216.2477443,0,10498.38722\n197721,23,4,3,1,Married,9,216.229611,0,10211.48055\n184658,70,50,1,1,Single,11,216.1725882,0,10361.62941\n129908,66,46,3,0,Married,10,216.106487,0,10497.32435\n169958,62,43,4,1,Single,13,216.1005343,0,10236.02672\n161541,41,24,1,1,Married,12,216.0974478,0,10231.87239\n115108,47,26,4,1,Married,15,216.0850503,0,10536.25252\n106210,70,52,2,1,Married,5,216.0499587,0,10545.49794\n159012,60,41,2,0,Married,11,216.0407596,0,10343.03798\n177031,68,47,3,0,Married,15,215.998473,0,10525.92365\n106529,55,35,3,1,Married,14,215.938831,0,10197.94155\n154576,34,17,3,1,Married,11,215.9218628,0,10479.09314\n128342,31,12,3,1,Married,13,215.8363076,0,10462.81538\n151605,48,27,2,0,Single,11,215.8307792,0,10565.53896\n180980,23,4,3,1,Married,12,215.7950525,0,10331.75263\n137771,20,3,2,1,Single,15,215.7948489,0,10461.74245\n192942,57,40,2,0,Married,15,215.7074338,0,10567.37169\n147857,19,2,2,0,Married,11,215.5797122,0,10342.98561\n127656,63,44,2,1,Single,0,215.5351089,0,10483.75545\n166435,67,48,3,1,Married,15,215.4968285,0,10345.84143\n135330,24,7,3,1,Married,15,215.4890519,0,10340.4526\n157889,64,43,2,0,Single,9,215.4143946,0,10357.71973\n169128,70,52,2,0,Married,15,215.3720019,0,10314.6001\n119404,70,53,3,0,Single,6,215.3692714,0,10173.46357\n140043,20,2,2,0,Married,11,215.3327311,0,10418.63656\n101829,67,46,2,0,Single,14,215.2318603,0,10447.59302\n184533,17,0,2,1,Married,15,215.2266118,0,10554.33059\n196978,20,3,4,1,Married,11,215.224046,0,10397.2023\n162588,47,28,3,0,Married,12,215.2018204,0,10343.09102\n173101,61,43,2,1,Single,10,215.1046013,0,10193.23007\n195408,51,34,1,0,Single,13,214.9897118,0,10427.48559\n129569,67,47,3,0,Married,2,214.8925247,0,10213.62624\n142016,64,45,3,0,Single,9,214.7957362,0,10456.78681\n190811,69,52,3,1,Single,8,214.7374509,0,10525.87255\n104505,67,50,4,1,Single,9,214.6759856,0,10487.79928\n181662,67,50,2,1,Single,13,214.5655106,0,10334.27553\n140904,63,43,2,1,Married,8,214.5320694,0,10297.60347\n153771,16,0,3,0,Married,13,214.5116593,0,10246.58297\n155472,21,3,3,0,Married,13,214.4838318,0,10129.19159\n145475,63,43,4,1,Single,10,214.4701092,0,10194.50546\n104955,63,44,2,1,Single,3,214.4203495,0,10382.01748\n170899,61,44,2,0,Married,12,214.3767328,0,10450.83664\n189580,66,46,4,0,Single,7,214.342541,0,10288.12705\n181099,68,51,2,1,Married,9,214.3347694,0,10153.73847\n197791,62,44,2,0,Married,9,214.3062182,0,10456.31091\n112904,40,21,4,1,Married,13,214.3016717,0,10471.08359\n163097,69,52,4,1,Single,13,214.2633765,0,10281.16883\n101445,66,45,4,0,Single,3,214.1919435,0,10388.59718\n168844,65,48,3,1,Married,7,214.1776836,0,10361.88418\n182141,29,10,3,1,Married,12,214.1620153,0,10475.10077\n179210,18,0,2,1,Married,14,214.155848,0,10422.7924\n116416,58,38,1,0,Single,15,214.1358992,0,10133.79496\n168558,32,13,1,1,Single,13,214.1314498,0,10485.57249\n128104,43,26,2,0,Married,15,214.126933,0,10377.34665\n192086,63,42,4,0,Married,13,214.0595924,0,10203.97962\n170071,47,28,4,1,Single,12,214.0519855,0,10330.59928\n148243,70,52,4,0,Single,0,213.9903772,0,10326.51886\n166770,22,5,4,0,Married,14,213.8804476,0,10353.02238\n166546,17,0,4,0,Married,13,213.8382363,0,10386.91181\n133292,34,14,3,0,Single,15,213.8203957,0,10478.01979\n196558,65,46,2,0,Married,10,213.8189643,0,10485.94822\n126539,29,9,3,0,Married,14,213.8024002,0,10330.12001\n160917,44,23,3,0,Single,13,213.6662321,0,10153.31161\n157511,21,3,3,1,Married,15,213.634549,0,10422.72745\n154990,34,13,2,0,Married,11,213.6119766,0,10244.59883\n178009,70,53,2,0,Married,8,213.597578,0,10236.8789\n149769,19,0,3,1,Married,15,213.5661255,0,10408.30628\n169357,27,7,2,1,Married,13,213.5544649,0,10405.72325\n140119,64,43,2,0,Married,2,213.5175971,0,10294.87986\n142010,45,28,2,0,Single,15,213.4488362,0,10206.44181\n181230,16,0,2,1,Married,13,213.396302,0,10258.8151\n145426,61,44,3,0,Single,1,213.3879924,0,10338.39962\n161841,69,52,2,0,Married,15,213.322691,0,10235.13455\n128200,64,47,1,0,Married,11,213.2790506,0,10141.95253\n180561,17,0,2,0,Single,14,213.2513253,0,10210.56627\n191584,62,41,3,0,Single,7,213.2164678,0,10358.82339\n130514,38,21,3,1,Married,14,213.2050793,0,10215.25397\n113378,63,43,2,1,Married,7,213.1897429,0,10272.48715\n103844,20,1,4,1,Married,12,213.1578344,0,10359.89172\n110823,48,27,3,1,Married,11,213.1440843,0,10090.20422\n155246,23,5,4,0,Single,12,213.1314131,0,10077.57066\n146342,60,40,3,1,Married,0,213.1146395,0,10083.73198\n122977,66,45,3,1,Married,7,213.0406668,0,10274.03334\n157610,37,18,4,1,Single,13,213.0291996,0,10145.45998\n129066,55,38,2,0,Married,14,212.9556896,0,10250.78448\n166178,37,18,3,1,Married,13,212.7409458,0,10167.04729\n132615,63,44,2,1,Married,5,212.7331935,0,10054.65968\n133473,34,15,3,1,Married,11,212.6998602,0,10163.99301\n106073,63,46,3,0,Married,7,212.6638004,0,10196.19002\n193108,21,4,2,0,Married,12,212.53249,0,10229.6245\n144873,70,52,1,0,Single,12,212.5038196,0,10168.19098\n140269,47,29,3,1,Single,12,212.481478,0,10376.0739\n188655,60,41,3,0,Married,1,212.4583193,0,10212.91597\n144340,70,49,2,0,Married,14,212.3940304,0,10340.70152\n101441,23,4,2,0,Married,12,212.3423871,0,10117.11936\n103717,64,47,1,0,Single,13,212.3076172,0,10153.38086\n116897,65,47,2,1,Single,13,212.2956194,0,10390.78097\n126714,44,25,2,0,Single,13,212.2804581,0,10356.02291\n196637,39,18,2,0,Single,12,212.2239094,0,10341.19547\n184243,57,38,2,0,Single,12,212.2227816,0,10025.13908\n193857,59,42,4,0,Married,15,212.2194107,0,10365.97054\n134269,17,0,3,0,Married,11,212.1423656,0,10365.11828\n109843,21,3,1,0,Married,15,212.0839926,0,10211.19963\n134141,37,18,3,0,Single,12,212.0380752,0,10037.90376\n113730,17,0,2,0,Married,12,211.9856302,0,10295.28151\n172427,60,41,3,0,Married,12,211.7846228,0,10011.23114\n198970,25,6,2,0,Married,11,211.756897,0,10133.84485\n155564,65,44,2,0,Married,6,211.6373126,0,10318.86563\n189896,17,0,4,0,Married,14,211.4928315,0,10041.64158\n112987,45,24,4,1,Single,14,211.3252361,0,10161.26181\n190415,66,48,2,0,Married,7,211.3174496,0,10287.87248\n136795,16,0,4,1,Married,12,211.2528407,0,10181.64204\n127510,23,5,1,1,Married,14,211.2160579,0,10162.8029\n126522,23,6,3,0,Married,12,211.1797329,0,10162.98665\n190491,61,44,4,0,Single,4,211.163816,0,10156.1908\n148984,69,49,3,1,Married,15,211.0603833,0,10316.01917\n193621,65,45,3,0,Married,13,211.0550607,0,10312.75304\n143835,62,45,4,1,Single,12,210.8979095,0,10246.89548\n194688,60,43,4,1,Single,13,210.8383868,0,10188.91934\n121530,64,45,3,1,Married,15,210.829491,0,10210.47455\n158298,60,39,2,0,Single,9,210.8084136,0,10196.42068\n101245,23,4,2,1,Married,11,210.6583084,0,10119.91542\n134355,16,0,4,0,Married,13,210.492293,0,10225.61465\n125118,61,40,4,0,Married,5,210.4615775,0,10079.07888\n132218,38,19,4,0,Single,14,210.3512655,0,9978.563275\n147573,69,51,3,0,Single,7,210.3325329,0,10242.62665\n137463,29,12,3,0,Married,12,210.3156052,0,10014.78026\n138988,41,22,1,1,Married,13,210.312302,0,10023.6151\n156364,58,41,3,1,Single,11,210.2356894,0,9989.78447\n101533,40,21,2,0,Married,15,210.2209069,0,10051.04535\n162004,68,51,3,0,Married,6,210.1623656,0,10051.11828\n173970,49,30,2,0,Married,11,210.0725182,0,10053.62591\n134233,67,50,2,0,Single,9,210.0136448,0,9996.68224\n193277,39,22,4,0,Married,12,209.9582639,0,10228.9132\n180625,53,32,3,1,Married,14,209.7641275,0,10087.20638\n143119,66,46,4,1,Single,14,209.7177944,0,10133.88972\n159076,26,6,2,0,Married,12,209.6780374,0,10279.90187\n177807,67,50,2,0,Single,15,209.6606122,0,10071.03061\n132991,39,19,2,1,Single,12,209.6514751,0,9891.573755\n186922,26,6,2,0,Single,11,209.4683176,0,10073.41588\n148906,22,5,2,1,Single,13,209.4161645,0,10258.80823\n186301,66,48,1,1,Single,7,209.3377482,0,10128.88741\n145558,32,12,1,0,Married,15,209.2601387,0,10007.00694\n115652,51,34,1,1,Married,14,209.161744,0,10202.0872\n183201,46,25,4,0,Married,14,209.1297056,0,10159.48528\n171408,61,42,2,1,Single,13,209.1193573,0,9886.967865\n107896,60,39,3,1,Single,0,209.0993733,0,10185.96867\n165432,67,47,2,0,Married,12,209.0977182,0,10080.88591\n141121,63,46,1,0,Single,13,209.0394555,0,10010.97278\n187051,65,44,4,0,Married,11,209.0290624,0,9980.45312\n160761,53,36,3,1,Single,12,208.9619344,0,9986.09672\n151105,67,48,1,1,Married,7,208.9477017,0,9871.385085\n171613,70,51,3,1,Married,9,208.9475058,0,10003.37529\n126023,48,28,2,1,Single,11,208.9160835,0,10235.80418\n167210,70,50,2,0,Single,15,208.7989506,0,9841.94753\n182825,50,33,2,1,Single,12,208.6607525,0,9930.037625\n102723,67,46,3,0,Single,9,208.6007724,0,10182.03862\n193018,17,0,2,0,Married,12,208.5861351,0,9910.306755\n129451,30,10,2,0,Married,11,208.5272352,0,9923.36176\n120634,25,8,2,1,Single,14,208.4650989,0,10175.25495\n151058,31,11,4,0,Single,13,208.4000403,0,10068.00202\n115530,39,18,3,0,Single,15,208.3850664,0,10080.25332\n169427,52,32,1,0,Married,13,208.3574065,0,9827.870325\n114137,68,48,3,0,Married,15,208.3529943,0,10134.64972\n118242,65,46,4,0,Married,10,208.1482744,0,10012.41372\n139207,64,45,3,0,Single,12,208.090373,0,9920.51865\n142587,21,2,3,1,Single,11,208.0440912,0,10151.20456\n137909,68,48,3,1,Single,4,208.0358699,0,10082.7935\n144698,67,48,2,0,Married,9,208.0302662,0,10093.51331\n129733,69,51,4,1,Married,8,207.9999461,0,9910.997305\n129823,60,41,3,0,Married,15,207.9826322,0,9847.13161\n153045,36,18,2,1,Single,14,207.8974194,0,9794.87097\n135042,56,37,1,1,Single,12,207.8627204,0,9989.13602\n171056,45,24,2,1,Married,13,207.803368,0,9998.1684\n167211,49,31,2,1,Single,15,207.7780178,0,9933.90089\n188827,48,28,4,1,Single,11,207.7646245,0,9836.231225\n183397,67,49,2,1,Married,8,207.7536776,0,10169.68388\n138104,60,39,3,0,Married,0,207.6820103,0,9914.100515\n112370,22,5,3,1,Single,14,207.658368,0,9872.9184\n154532,23,5,3,1,Married,14,207.6335866,0,9848.67933\n185233,67,46,1,1,Married,6,207.5631629,0,9908.158145\n119843,25,8,2,1,Married,12,207.5516057,0,9856.580285\n110355,67,50,3,0,Married,11,207.5480439,0,10020.4022\n124315,43,24,3,1,Married,13,207.5409761,0,9943.048805\n143557,21,4,3,1,Single,14,207.5386901,0,9880.934505\n114872,70,53,2,0,Married,7,207.503429,0,9986.17145\n151482,19,1,3,0,Married,11,207.4152565,0,10028.76283\n167737,40,19,1,0,Married,15,207.3153847,0,9817.769235\n148268,19,2,2,1,Single,11,207.188738,0,9826.4369\n161025,38,17,2,1,Married,14,207.145874,0,9773.2937\n130452,22,3,3,0,Married,15,207.1125283,0,9757.626415\n166876,21,2,1,0,Married,12,207.011744,0,10147.5872\n164160,51,32,2,1,Single,15,206.9912785,0,9896.563925\n131704,22,3,2,1,Married,14,206.9257402,0,10142.28701\n178660,16,0,4,1,Married,15,206.8778665,0,10040.89333\n197733,42,22,1,1,Married,15,206.8762564,0,10059.81282\n103527,61,43,2,0,Single,10,206.8668828,0,10129.34414\n120347,62,44,4,1,Married,13,206.7535913,0,9861.679565\n128634,64,46,1,0,Married,8,206.7379713,0,10091.89857\n117839,26,9,2,1,Married,15,206.6178357,0,9902.891785\n152617,21,4,2,1,Married,11,206.5551444,0,10101.75722\n151353,70,49,2,0,Married,11,206.4216834,0,9786.08417\n117745,17,0,2,1,Married,12,206.3304124,0,9850.52062\n118601,23,4,3,1,Married,12,206.3104833,0,10102.52417\n151447,35,15,3,0,Married,15,206.3023011,0,10042.11506\n169513,60,39,3,1,Single,13,206.2727154,0,10046.63577\n199529,26,5,3,0,Single,13,206.2133582,0,9799.66791\n164727,66,49,2,0,Single,3,206.2083952,0,10061.41976\n121839,51,30,2,0,Single,13,206.0172124,0,9776.86062\n163467,65,46,3,1,Single,13,206.0140439,0,9771.702195\n164823,17,0,2,0,Married,11,205.9999473,0,9804.997365\n119901,46,27,1,1,Married,12,205.9106439,0,9882.532195\n176406,56,38,1,1,Single,12,205.9058749,0,9730.293745\n184612,70,52,3,1,Single,4,205.8680136,0,9923.40068\n154712,18,0,1,1,Married,12,205.8550367,0,9911.751835\n110805,31,12,1,1,Single,14,205.8293962,0,9948.46981\n185882,58,39,3,1,Single,14,205.7879229,0,10088.39615\n112180,66,49,3,1,Single,15,205.7500451,0,10083.50226\n151216,37,17,3,0,Single,11,205.7312224,0,9936.56112\n193305,25,8,4,1,Married,12,205.7232741,0,9834.163705\n160240,25,7,4,1,Married,11,205.7178632,0,9717.89316\n198045,22,5,2,1,Married,14,205.7011349,0,9827.056745\n157423,38,21,1,1,Married,12,205.6650527,0,9920.252635\n177671,17,0,2,1,Married,12,205.6203524,0,9819.01762\n162642,22,5,4,1,Married,14,205.5534585,0,9905.672925\n167551,54,36,2,1,Single,14,205.5315763,0,9725.578815\n153220,60,40,1,0,Married,13,205.4665587,0,9875.327935\n154017,61,40,3,0,Married,15,205.4507752,0,9831.53876\n173603,36,17,2,0,Married,12,205.3902518,0,9960.51259\n155818,63,46,1,1,Married,3,205.3675635,0,9798.378175\n163886,28,9,4,1,Married,11,205.3600391,0,9935.001955\n180877,60,43,2,0,Married,3,205.2984168,0,9862.92084\n161516,67,46,3,1,Married,12,205.2756075,0,10027.78038\n129112,60,40,4,1,Single,15,205.2117461,0,9779.587305\n183339,64,43,2,1,Married,3,205.2059665,0,10045.29833\n199302,67,48,2,0,Single,9,205.1513061,0,9949.565305\n118353,20,3,1,1,Married,15,204.9823703,0,9702.118515\n138552,25,6,3,1,Married,13,204.95908,0,9935.954\n157762,16,0,2,0,Married,13,204.9570603,0,10041.85302\n100488,21,3,3,1,Married,9,204.8841599,0,9814.207995\n137187,25,7,2,1,Married,12,204.869826,0,9733.4913\n113460,69,51,1,0,Married,8,204.8013733,0,9850.068665\n132770,68,51,4,1,Single,7,204.7419495,0,10030.09748\n159464,68,48,3,1,Married,8,204.7237459,0,9725.187295\n153270,30,12,1,0,Married,14,204.6908311,0,9946.541555\n188520,70,50,3,0,Married,13,204.6746092,0,9811.73046\n116367,26,7,3,0,Married,12,204.6722834,0,9880.61417\n138131,70,49,4,0,Married,0,204.6221308,0,9674.10654\n123026,68,47,1,0,Single,6,204.5985099,0,9880.925495\n176376,36,19,4,0,Single,12,204.462118,0,9726.1059\n161900,69,52,3,1,Single,10,204.455335,0,9644.76675\n126181,54,35,4,1,Married,14,204.4297688,0,9979.48844\n113283,20,2,1,1,Married,11,204.4293461,0,9942.467305\n131238,62,44,3,0,Single,10,204.401161,0,9807.05805\n110907,45,24,1,0,Single,15,204.3980122,0,9764.90061\n110130,66,47,2,1,Married,10,204.3919559,0,9983.597795\n139812,24,5,2,1,Married,14,204.3831549,0,9956.157745\n178573,64,43,2,1,Single,3,204.2793002,0,9774.96501\n182659,68,47,4,0,Married,5,204.2376448,0,9972.88224\n168708,16,0,3,1,Single,15,204.2349164,0,9735.74582\n154009,41,22,2,0,Married,11,204.2078793,0,9725.393965\n173239,64,47,4,1,Married,12,204.1908375,0,9828.541875\n132729,16,0,2,1,Married,13,204.122513,0,9967.12565\n118549,60,42,2,0,Single,7,203.9939529,0,9813.697645\n116566,67,46,2,0,Single,12,203.9843161,0,9897.215805\n144431,53,33,1,0,Married,15,203.9808667,0,9856.043335\n157380,25,7,4,1,Married,15,203.7830455,0,9913.152275\n197138,49,31,3,0,Married,14,203.7829559,0,9962.147795\n184701,33,12,2,1,Married,14,203.7546187,0,9697.730935\n183593,21,3,3,1,Married,12,203.6651875,0,9946.259375\n189163,67,47,3,1,Married,8,203.6594537,0,9774.972685\n129202,67,50,3,1,Married,2,203.6277956,0,9785.38978\n119555,24,7,4,1,Married,13,203.6245304,0,9744.22652\n171582,70,53,4,1,Married,15,203.5985154,0,9697.92577\n153157,27,10,4,1,Married,11,203.481941,0,9848.09705\n176005,51,31,1,0,Single,13,203.4147846,0,9681.73923\n143116,40,22,3,1,Married,15,203.3406576,0,9941.03288\n161923,57,40,2,0,Married,15,203.3260782,0,9705.30391\n169722,45,24,3,0,Married,13,203.321005,0,9924.05025\n150024,24,5,1,0,Married,11,203.3195946,0,9852.97973\n170014,61,42,1,1,Single,6,203.2806148,0,9610.03074\n157481,67,48,2,0,Married,12,203.244197,0,9774.20985\n102029,18,0,4,1,Married,15,203.1868301,0,9904.341505\n106521,30,12,4,0,Single,11,203.0570334,0,9909.85167\n185602,64,46,4,0,Single,10,203.0555186,0,9565.77593\n136484,66,45,2,1,Married,8,203.0543293,0,9656.716465\n129730,60,39,1,0,Married,1,203.0429805,0,9860.149025\n197045,19,1,3,1,Married,13,203.0366103,0,9826.830515\n140991,66,48,2,0,Single,10,203.034266,0,9910.7133\n175923,64,44,4,1,Single,7,202.9383455,0,9853.917275\n187634,61,40,4,1,Married,13,202.9013457,0,9830.067285\n154004,33,15,3,0,Married,12,202.8790638,0,9860.95319\n174937,63,46,2,1,Single,11,202.8723728,0,9864.61864\n124138,24,7,4,0,Married,13,202.8604871,0,9770.024355\n164211,24,6,4,0,Married,13,202.7844046,0,9813.22023\n132357,65,45,3,0,Single,9,202.7412844,0,9675.06422\n153722,25,6,2,1,Married,14,202.6975915,0,9724.879575\n157205,42,23,3,1,Married,15,202.6206629,0,9678.033145\n148430,55,38,4,0,Single,15,202.5981806,0,9836.90903\n121593,64,45,1,1,Married,5,202.4989254,0,9728.94627\n197499,66,45,3,1,Married,4,202.4608462,0,9790.04231\n128663,46,26,1,1,Married,12,202.4587138,0,9607.93569\n167712,63,42,3,1,Single,7,202.4144153,0,9581.720765\n180916,63,46,2,1,Married,15,202.3255141,0,9707.275705\n145289,17,0,2,0,Married,12,202.2061182,0,9515.30591\n133226,17,0,3,0,Married,14,202.1994123,0,9774.970615\n156581,64,44,3,1,Married,11,202.1669107,0,9876.345535\n190534,21,2,4,1,Married,15,202.1352795,0,9675.763975\n189409,65,46,3,1,Married,15,202.1033221,0,9854.166105\n163121,28,7,2,1,Single,15,202.0789605,0,9830.948025\n154695,69,50,2,1,Married,8,202.0447247,0,9565.236235\n127647,68,49,3,0,Married,10,202.0056174,0,9886.28087\n178057,52,34,2,0,Married,11,201.989253,0,9548.46265\n106650,30,11,3,0,Single,11,201.945056,0,9808.2528\n101826,34,15,1,0,Married,14,201.8179313,0,9659.896565\n127948,51,32,3,0,Married,12,201.7821036,0,9788.10518\n103314,54,36,3,1,Married,13,201.7431279,0,9714.156395\n115402,68,49,2,1,Married,13,201.7299071,0,9572.495355\n189192,46,29,1,0,Married,12,201.6782523,0,9575.912615\n182086,48,30,4,1,Married,15,201.6354415,0,9825.772075\n173451,41,22,3,0,Married,13,201.6146841,0,9788.734205\n175052,65,44,1,0,Married,0,201.5901302,0,9764.50651\n160087,34,17,2,0,Single,13,201.5720329,0,9711.601645\n150245,68,48,3,1,Married,14,201.5175786,0,9589.87893\n114832,61,42,4,1,Single,6,201.5009295,0,9769.046475\n103992,63,46,3,1,Single,14,201.4868881,0,9728.344405\n128601,26,6,2,0,Married,11,201.4621834,0,9537.10917\n147340,60,40,2,0,Married,13,201.4466206,0,9844.33103\n157431,68,49,2,1,Married,12,201.4450651,0,9787.253255\n160728,69,48,3,1,Single,7,201.3937545,0,9683.687725\n116505,61,44,2,1,Married,9,201.3620415,0,9725.102075\n192726,66,45,4,0,Single,0,201.3255307,0,9754.276535\n131124,68,48,2,0,Married,10,201.3112188,0,9842.56094\n139396,31,11,2,1,Single,15,201.3067102,0,9859.33551\n115989,21,3,3,1,Single,12,201.296301,0,9580.81505\n124277,23,6,4,1,Married,12,201.2941482,0,9750.70741\n128867,16,0,1,0,Married,12,201.2702657,0,9653.513285\n163061,62,42,2,0,Married,0,201.0919381,0,9520.596905\n179564,26,6,3,0,Married,11,201.0865597,0,9757.327985\n151051,44,27,2,0,Married,14,201.0248122,0,9611.24061\n130351,45,28,2,1,Single,14,201.0110126,0,9574.55063\n171857,38,17,4,0,Married,13,200.981019,0,9653.05095\n119284,27,7,3,1,Married,12,200.850912,0,9489.5456\n112976,30,9,1,0,Single,13,200.7912195,0,9477.560975\n182618,66,45,2,0,Single,12,200.7840003,0,9723.200015\n125611,67,48,2,1,Married,14,200.7611311,0,9813.056555\n170383,65,46,2,1,Married,6,200.7420917,0,9728.104585\n157014,63,45,3,1,Married,0,200.6700106,0,9461.50053\n123100,66,45,3,1,Married,10,200.6430165,0,9744.150825\n178601,70,52,2,1,Married,13,200.6360384,0,9520.80192\n141507,62,43,2,1,Married,3,200.5838031,0,9812.190155\n156478,29,10,3,0,Married,12,200.5554985,0,9672.774925\n114288,33,15,2,0,Married,11,200.5543237,0,9497.716185\n122190,63,46,2,0,Married,5,200.5488273,0,9668.441365\n110481,61,41,3,1,Married,12,200.5344542,0,9775.72271\n199870,26,5,2,1,Married,13,200.4837906,0,9662.18953\n185813,65,44,4,1,Single,3,200.4547619,0,9531.738095\n131687,69,50,3,0,Married,6,200.4243184,0,9670.21592\n163331,42,21,4,0,Married,13,200.3970746,0,9698.85373\n136159,51,33,3,0,Single,14,200.3709466,0,9558.54733\n190820,43,22,3,1,Married,13,200.3668453,0,9580.342265\n162946,16,0,4,0,Married,15,200.3590918,0,9650.95459\n102107,67,49,2,0,Married,2,200.3566489,0,9692.832445\n157657,22,5,4,0,Married,11,200.3516389,0,9787.581945\n162020,63,43,3,0,Single,14,200.3485313,0,9518.426565\n156187,17,0,1,1,Married,11,200.343212,0,9756.1606\n106658,43,22,3,1,Single,12,200.3362514,0,9708.81257\n182648,20,1,2,1,Married,13,200.3239374,0,9578.19687\n194434,17,0,2,1,Married,11,200.3149848,0,9727.74924\n197221,25,8,2,1,Married,12,200.3094091,0,9787.470455\n144639,49,32,4,1,Single,11,200.1818598,0,9687.09299\n170564,33,16,4,0,Married,11,200.1626019,0,9494.130095\n169240,68,49,3,1,Single,15,200.1491698,0,9806.45849\n120939,36,17,4,1,Married,15,200.0321643,0,9684.608215\n138224,18,0,2,0,Married,11,200.0279099,0,9537.395495\n146753,24,7,2,1,Married,15,200.0278653,0,9437.393265\n182730,69,52,2,0,Married,10,199.9784707,0,9680.923535\n169685,65,45,3,0,Single,6,199.8544749,0,9524.723745\n143988,66,46,4,1,Married,3,199.8308238,0,9791.54119\n127614,22,4,2,1,Married,11,199.7041546,0,9779.20773\n130907,60,39,3,1,Married,9,199.6621481,0,9638.107405\n170518,48,28,2,0,Single,12,199.5676093,0,9711.380465\n165885,19,2,1,1,Married,11,199.5115967,0,9690.579835\n134039,23,4,4,1,Married,13,199.4459329,0,9736.296645\n126442,60,43,4,1,Single,4,199.4456744,0,9650.28372\n195249,62,42,2,0,Single,10,199.4172719,0,9413.863595\n111459,41,21,2,1,Married,11,199.3998978,0,9447.99489\n116528,26,6,2,0,Married,11,199.3608972,0,9424.04486\n190229,60,41,2,0,Married,8,199.3588822,0,9553.94411\n165302,18,0,3,1,Married,11,199.3309369,0,9539.546845\n142959,25,7,3,0,Married,11,199.3297949,0,9606.489745\n183615,68,47,4,0,Single,7,199.271303,0,9390.56515\n194751,69,52,3,1,Single,11,199.2010198,0,9414.05099\n135957,67,47,3,1,Married,8,199.2006577,0,9693.032885\n105222,40,20,4,1,Married,12,199.1938556,0,9480.69278\n162071,45,28,1,1,Married,15,199.0159526,0,9647.79763\n190784,67,48,2,1,Married,5,199.0056741,0,9657.283705\n191922,68,50,2,0,Single,11,198.9769978,0,9446.84989\n171043,61,42,2,1,Married,7,198.9666197,0,9372.330985\n139550,67,46,3,1,Single,11,198.965951,0,9707.29755\n154762,64,45,2,0,Married,8,198.961681,0,9509.08405\n136868,19,0,4,1,Married,12,198.9362937,0,9543.814685\n131883,68,51,3,0,Single,6,198.9315602,0,9477.57801\n109484,66,49,3,1,Single,0,198.9229634,0,9710.14817\n140170,54,34,1,0,Married,13,198.8889181,0,9713.445905\n162707,60,43,4,1,Single,12,198.8351675,0,9663.758375\n116512,21,4,1,1,Married,11,198.8228959,0,9374.144795\n122223,66,47,3,0,Married,3,198.8215393,0,9518.076965\n175472,47,26,2,1,Married,11,198.7961508,0,9494.80754\n131048,67,48,3,0,Single,10,198.7510108,0,9665.55054\n178696,67,50,3,0,Single,12,198.6812895,0,9544.064475\n175886,61,43,2,1,Married,8,198.6730444,0,9400.65222\n145325,61,40,2,0,Single,11,198.66967,0,9557.4835\n141929,62,45,1,0,Single,8,198.6312063,0,9345.560315\n115238,60,40,4,0,Single,5,198.4374006,0,9536.87003\n172117,53,36,3,0,Married,11,198.4209969,0,9580.049845\n143791,62,44,3,0,Married,11,198.4064948,0,9670.32474\n138789,66,49,3,1,Single,0,198.3613213,0,9612.066065\n121570,66,46,2,1,Single,4,198.3467374,0,9623.33687\n162913,17,0,2,1,Married,14,198.3257158,0,9448.28579\n176728,22,4,3,1,Single,13,198.2290041,0,9711.450205\n124452,19,2,2,1,Married,11,198.1871239,0,9326.356195\n174165,70,53,3,0,Married,13,198.1738019,0,9525.690095\n104141,18,0,3,1,Married,15,198.0938563,0,9458.692815\n198006,23,4,1,1,Married,13,198.011932,0,9395.5966\n169238,59,42,2,0,Married,11,197.9518324,0,9508.59162\n173780,24,6,1,1,Married,15,197.9280454,0,9316.40227\n187351,22,4,1,0,Married,14,197.9124841,0,9363.624205\n144068,18,0,3,1,Married,13,197.8385671,0,9635.928355\n149333,47,27,2,1,Single,11,197.830166,0,9304.5083\n182512,21,2,4,1,Married,11,197.7820461,0,9639.102305\n102115,42,23,3,1,Single,15,197.7668115,0,9505.340575\n108870,40,23,1,1,Married,11,197.7098654,0,9560.49327\n119884,70,49,2,0,Married,6,197.6420414,0,9526.10207\n198829,61,41,2,1,Married,9,197.565334,0,9637.2667\n137975,17,0,4,0,Married,12,197.5427324,0,9551.13662\n141789,16,0,4,0,Married,14,197.4519735,0,9425.598675\n148111,61,44,2,0,Married,1,197.3907116,0,9424.53558\n137134,25,7,2,1,Single,12,197.3667253,0,9282.336265\n133868,25,6,4,0,Married,13,197.3016678,0,9472.08339\n174859,69,49,1,1,Married,6,197.272486,0,9563.6243\n131916,21,4,2,1,Married,12,197.2533859,0,9655.669295\n129321,23,5,3,1,Married,11,197.2419763,0,9363.098815\n172953,51,33,4,1,Married,11,197.1474312,0,9530.37156\n105730,69,48,2,0,Single,9,197.0761174,0,9653.80587\n100273,68,50,3,1,Single,10,197.0621961,0,9466.109805\n143243,29,9,3,1,Married,11,197.0374183,0,9508.870915\n197699,16,0,3,1,Single,12,196.9921736,0,9556.60868\n145854,60,43,2,1,Single,3,196.9877085,0,9496.385425\n108576,28,10,4,1,Single,11,196.9823997,0,9448.119985\n140955,70,53,3,0,Single,2,196.9077858,0,9284.38929\n124003,20,1,2,1,Married,11,196.8994328,0,9621.97164\n186431,25,8,3,1,Married,15,196.8324535,0,9512.622675\n176937,70,49,2,0,Married,10,196.8275736,0,9371.37868\n160066,67,48,2,0,Single,14,196.8111414,0,9434.55707\n108545,53,36,2,0,Married,12,196.7456397,0,9545.281985\n121248,21,4,4,1,Married,11,196.7071274,0,9364.35637\n147496,21,2,4,1,Married,13,196.6010967,0,9239.054835\n149954,63,45,4,0,Single,14,196.5375197,0,9290.875985\n178574,34,14,1,1,Married,12,196.5073839,0,9580.369195\n181725,66,48,3,1,Single,10,196.504217,0,9232.21085\n139864,68,50,4,1,Single,12,196.4903654,0,9253.51827\n182806,69,51,3,0,Single,15,196.4844495,0,9565.222475\n197913,61,41,3,0,Single,0,196.4831555,0,9264.157775\n131127,21,4,3,1,Married,13,196.4292229,0,9504.461145\n101263,18,0,4,1,Single,12,196.4010079,0,9372.050395\n179907,67,48,2,1,Married,7,196.3942623,0,9250.713115\n174318,24,6,3,0,Married,14,196.3814834,0,9463.07417\n144488,64,46,2,1,Single,2,196.2813898,0,9601.06949\n100786,51,31,4,0,Married,12,196.2019206,0,9591.09603\n141788,50,32,4,1,Married,15,196.1852896,0,9518.26448\n171811,37,16,3,1,Single,12,196.1466007,0,9317.330035\n185950,16,0,3,1,Married,12,196.0456832,0,9274.28416\n173982,64,44,4,1,Married,8,196.0038918,0,9322.19459\n180774,55,36,4,0,Single,12,195.891037,0,9389.55185\n108019,67,50,3,0,Single,7,195.7851854,0,9407.25927\n125369,27,10,3,1,Married,13,195.7336016,0,9228.68008\n197780,21,3,2,1,Married,13,195.7200478,0,9352.00239\n158137,41,22,3,0,Single,12,195.7114003,0,9402.570015\n101944,60,43,3,1,Married,6,195.6874702,0,9575.37351\n109590,22,4,2,1,Single,13,195.6228447,0,9415.142235\n154436,66,49,2,1,Single,3,195.606033,0,9544.30165\n178047,60,39,3,0,Single,7,195.5953374,0,9465.76687\n119783,69,48,3,1,Single,4,195.5864596,0,9525.32298\n163300,22,3,1,1,Married,14,195.4700505,0,9431.502525\n132737,70,50,3,0,Married,7,195.396991,0,9510.84955\n121769,68,50,3,1,Single,7,195.2557508,0,9448.78754\n103512,38,18,3,1,Single,11,195.2299201,0,9453.496005\n118175,33,14,2,1,Married,14,195.2139227,0,9533.696135\n145981,70,50,1,0,Single,9,195.1892798,0,9259.46399\n102717,39,19,3,1,Married,11,195.163177,0,9495.15885\n131728,65,44,2,1,Single,9,195.119902,0,9466.9951\n187663,43,24,3,0,Married,12,195.034878,0,9344.7439\n193059,16,0,1,0,Married,14,194.9484606,0,9396.42303\n179760,68,47,3,0,Married,5,194.9321915,0,9190.609575\n132732,20,3,4,0,Married,13,194.8801724,0,9282.00862\n172842,22,5,2,1,Married,13,194.8327799,0,9386.638995\n105519,17,0,3,1,Married,14,194.7675783,0,9494.378915\n184302,31,11,1,1,Single,12,194.7580825,0,9233.904125\n148478,69,52,3,1,Married,11,194.7009916,0,9337.04958\n187274,53,33,2,1,Single,12,194.5732908,0,9422.66454\n175337,67,47,4,1,Married,4,194.5460108,0,9482.30054\n177349,70,49,1,1,Single,5,194.4767543,0,9262.837715\n151129,22,4,2,1,Single,11,194.43627,0,9495.8135\n170774,66,48,3,1,Married,5,194.4116368,0,9154.58184\n170667,18,0,3,1,Single,15,194.3768974,0,9349.84487\n170285,63,45,1,0,Married,8,194.3165171,0,9413.825855\n196038,42,24,3,0,Single,14,194.2705281,0,9265.526405\n147202,18,0,1,1,Married,12,194.2417501,0,9403.087505\n146265,64,45,2,1,Single,13,194.23594,0,9199.797\n189710,66,48,3,0,Married,6,194.2179076,0,9454.89538\n138583,62,41,2,1,Single,0,194.188239,0,9200.41195\n117028,22,3,2,0,Married,11,194.0060904,0,9102.30452\n163827,64,45,3,1,Married,13,193.9876647,0,9429.383235\n138933,39,18,2,0,Married,13,193.9831886,0,9194.15943\n198152,64,47,3,1,Single,6,193.9608062,0,9187.04031\n186894,42,23,1,0,Single,13,193.9378831,0,9444.894155\n184259,65,46,1,0,Married,15,193.9029859,0,9325.149295\n157414,70,50,4,1,Married,15,193.7024626,0,9330.12313\n180498,68,48,3,1,Single,4,193.6240177,0,9416.200885\n117731,65,44,2,0,Married,12,193.5785778,0,9475.92889\n191285,17,0,2,1,Married,14,193.4207041,0,9384.035205\n120085,65,46,3,1,Married,9,193.3740988,0,9448.70494\n169682,26,9,2,1,Married,12,193.3558559,0,9251.792795\n148873,61,40,4,0,Married,9,193.2447361,0,9188.236805\n135923,16,0,4,1,Married,11,193.096925,0,9198.84625\n140942,21,4,2,1,Married,15,193.0892992,0,9442.46496\n180357,47,28,2,1,Married,12,193.0646773,0,9157.233865\n127339,17,0,2,0,Married,14,193.0424347,0,9222.121735\n128605,56,36,1,0,Single,14,193.0311549,0,9413.557745\n178993,61,42,3,1,Single,9,193.0191417,0,9080.957085\n139624,66,49,3,0,Single,1,192.9668642,0,9186.34321\n120884,16,0,3,0,Married,13,192.9318487,0,9194.592435\n113441,39,22,2,0,Married,11,192.9130479,0,9236.652395\n118192,66,48,4,1,Married,14,192.8927852,0,9265.63926\n100684,67,46,2,0,Single,9,192.8122051,0,9127.610255\n143528,60,41,4,0,Married,8,192.6938225,0,9082.691125\n177032,56,36,1,0,Married,12,192.6799087,0,9151.995435\n111626,66,49,2,1,Married,1,192.6596779,0,9233.983895\n190813,63,42,1,1,Single,13,192.5154704,0,9395.77352\n111304,59,38,4,1,Single,11,192.4641554,0,9257.20777\n164802,53,32,2,0,Single,12,192.3880419,0,9067.402095\n141700,33,13,3,1,Married,11,192.2837667,0,9312.188335\n111486,63,42,2,0,Single,14,192.1257344,0,9375.28672\n104548,23,5,3,1,Married,11,192.0896525,0,9201.482625\n134473,25,7,3,1,Married,14,191.9748218,0,9346.74109\n115117,21,4,2,1,Married,14,191.8774987,0,9123.874935\n101692,67,49,3,0,Married,12,191.85171,0,9212.5855\n133183,64,47,1,0,Married,13,191.8395784,0,9317.97892\n136631,20,2,1,1,Married,12,191.8077892,0,9032.38946\n125091,22,3,1,1,Single,12,191.7527247,0,9322.636235\n127123,55,37,1,0,Single,15,191.6213777,0,8992.068885\n161814,62,43,2,1,Married,11,191.6071081,0,8994.355405\n119868,19,0,2,0,Married,14,191.6044838,0,9201.22419\n106621,16,0,3,1,Married,13,191.5885188,0,9160.42594\n189566,69,48,2,0,Single,3,191.5872763,0,9204.363815\n165262,49,32,2,1,Single,15,191.5855411,0,9099.277055\n184163,66,45,4,0,Married,12,191.5424114,0,9088.12057\n192797,58,38,1,1,Married,15,191.5121155,0,9218.605775\n101760,20,1,1,1,Married,14,191.4841522,0,9200.20761\n147647,50,31,2,1,Married,11,191.4664255,0,9029.321275\n193510,60,42,4,1,Single,8,191.4111201,0,9350.556005\n163589,50,31,2,0,Married,11,191.4045912,0,9166.22956\n165452,70,51,1,1,Married,6,191.2679535,0,9059.397675\n108879,62,43,3,0,Married,14,191.2545419,0,9092.727095\n113081,65,46,2,1,Married,10,191.1909845,0,9212.549225\n101552,20,3,3,0,Married,12,191.1613149,0,9186.065745\n160734,22,3,2,1,Married,13,191.0921132,0,9204.60566\n121698,37,19,4,0,Single,11,191.0554736,0,8990.77368\n191744,55,38,1,1,Single,14,191.0414252,0,9073.07126\n175829,69,51,4,1,Married,10,191.019486,0,9094.9743\n100568,67,48,1,0,Single,8,190.9903208,0,9165.51604\n196346,32,13,4,1,Single,15,190.9859457,0,9229.297285\n126710,70,52,4,0,Single,10,190.9236188,0,9341.18094\n119755,18,1,2,1,Married,13,190.9235119,0,9206.175595\n151402,16,0,3,0,Married,15,190.9106916,0,9213.53458\n116088,69,52,3,1,Single,8,190.859132,0,9043.9566\n103536,68,47,3,1,Married,3,190.8082711,0,9138.413555\n123402,22,3,2,1,Married,12,190.7129418,0,9005.64709\n159085,18,0,3,1,Married,13,190.6478915,0,9255.394575\n144054,22,4,1,1,Married,12,190.6377645,0,9313.888225\n104795,60,40,2,1,Married,13,190.6288924,0,9114.44462\n175847,70,51,2,0,Married,13,190.5913758,0,9028.56879\n153110,45,25,3,0,Single,13,190.5893545,0,9267.467725\n163469,64,43,2,0,Married,7,190.5879139,0,8959.395695\n123074,61,43,4,0,Married,0,190.573219,0,9183.66095\n164642,67,48,3,1,Married,7,190.5703824,0,9254.51912\n173257,58,38,2,0,Married,12,190.4604887,0,9034.024435\n108517,43,25,3,0,Married,14,190.4142592,0,9126.71296\n187524,64,47,3,0,Married,14,190.3031871,0,9207.159355\n144062,24,6,4,1,Married,14,190.2866048,0,9274.33024\n109581,28,8,3,1,Single,13,190.2790812,0,9157.95406\n169495,33,12,2,1,Single,12,190.2790068,0,9260.95034\n155382,58,41,3,0,Single,11,190.219481,0,9011.97405\n194310,66,45,2,0,Married,14,190.206769,0,9000.33845\n158427,46,27,2,0,Married,15,190.1974887,0,9292.874435\n177928,70,52,1,0,Married,8,190.0997216,0,9107.98608\n164167,67,50,2,0,Single,9,190.0838598,0,8928.19299\n119429,17,0,3,1,Married,13,190.0467519,0,9248.337595\n109402,41,21,2,0,Married,12,190.0323311,0,9154.616555\n124892,69,51,2,0,Single,2,189.9808457,0,8950.042285\n198533,61,42,2,0,Married,15,189.9784108,0,9224.92054\n155793,65,46,2,1,Single,10,189.9302495,0,8907.512475\n183782,38,18,2,1,Single,15,189.9247473,0,9099.237365\n135590,69,51,3,0,Single,8,189.9229735,0,9282.148675\n119398,28,9,2,0,Married,14,189.8927235,0,8919.636175\n134774,18,1,2,1,Married,12,189.8845127,0,9166.225635\n138565,19,2,2,1,Married,12,189.8172756,0,8923.86378\n171534,65,47,2,1,Married,8,189.801804,0,9239.0902\n158468,69,48,3,1,Married,7,189.7614984,0,9027.07492\n174797,47,26,4,0,Single,12,189.7427782,0,9078.13891\n134044,40,22,2,0,Married,13,189.723144,0,8983.1572\n132002,18,0,2,1,Married,15,189.7144759,0,8949.723795\n133285,24,6,3,1,Married,13,189.6737391,0,9205.686955\n132699,24,5,2,0,Married,11,189.6282815,0,9193.414075\n164771,68,48,2,0,Married,1,189.547546,0,9215.3773\n196740,50,33,3,1,Married,12,189.5383761,0,9002.918805\n193699,16,0,3,1,Married,15,189.5305195,0,9148.525975\n163997,57,39,2,0,Single,13,189.3379564,0,8874.89782\n124490,53,35,3,1,Married,12,189.2697406,0,9238.48703\n157466,24,7,3,1,Married,13,189.2308268,0,9205.54134\n141229,22,3,3,1,Married,15,189.1872376,0,9046.36188\n132905,65,46,2,0,Married,2,189.1190181,0,9080.950905\n174839,36,15,2,0,Married,15,189.1055937,0,9106.279685\n109891,51,30,3,0,Married,15,189.0375584,0,9112.87792\n102360,62,43,2,1,Married,10,189.0358649,0,9236.793245\n114307,69,49,2,1,Married,4,189.0142535,0,9234.712675\n126972,63,44,4,1,Married,14,189.0105214,0,8916.52607\n145098,62,44,3,1,Single,15,188.9333637,0,9014.668185\n117920,57,36,2,1,Single,13,188.9153008,0,9045.76504\n113980,63,44,2,1,Single,2,188.8993416,0,9243.96708\n139734,22,5,3,0,Married,11,188.8720271,0,8878.601355\n190604,68,48,1,1,Married,13,188.8429635,0,9210.148175\n158802,44,24,1,1,Married,14,188.8398295,0,9017.991475\n106476,67,48,3,1,Married,6,188.8283073,0,8955.415365\n174043,61,43,3,0,Married,9,188.826862,0,8863.3431\n159443,55,36,1,1,Married,14,188.8132454,0,8998.66227\n159605,64,43,4,0,Married,7,188.6404174,0,9187.02087\n196332,70,52,2,1,Single,13,188.6227569,0,9193.137845\n146401,51,34,2,0,Single,13,188.5864851,0,8888.324255\n178173,64,47,3,1,Single,7,188.5203952,0,9094.01976\n119233,56,37,4,1,Married,13,188.5005846,0,9171.02923\n146315,66,45,2,1,Married,11,188.4897452,0,9176.48726\n183878,50,29,2,0,Single,15,188.4853743,0,8934.268715\n144754,24,6,1,0,Married,11,188.4712932,0,9083.56466\n130161,64,47,2,1,Single,15,188.4492252,0,9184.46126\n172798,70,51,3,1,Single,8,188.4347158,0,9045.73579\n117343,19,2,3,1,Single,13,188.395066,0,8832.7533\n165375,69,52,1,0,Single,10,188.3179804,0,8844.89902\n118031,16,0,2,1,Married,13,188.3173905,0,9068.869525\n107183,70,49,4,0,Married,12,188.3055871,0,8895.279355\n188061,67,48,2,0,Single,9,188.2925171,0,8832.625855\n180779,61,43,2,0,Single,7,188.2874401,0,8989.372005\n128296,18,0,4,1,Single,12,188.2252113,0,8841.260565\n170704,53,36,2,0,Married,14,188.2102556,0,9016.51278\n147514,66,46,3,1,Single,0,188.2023997,0,8833.119985\n157566,60,39,1,1,Single,10,188.1206318,0,8992.03159\n174965,33,12,4,1,Single,15,188.0890053,0,9030.450265\n191616,21,2,2,1,Married,14,188.0668631,0,8878.343155\n153364,16,0,3,0,Married,12,188.002546,0,9036.1273\n129663,23,6,1,1,Married,15,187.9585249,0,9007.926245\n142037,60,41,2,1,Married,8,187.9258968,0,8992.29484\n188674,26,6,3,1,Single,12,187.9088322,0,8890.44161\n188210,45,24,4,1,Single,15,187.8711782,0,8996.55891\n187042,61,41,4,0,Married,1,187.6599767,0,9135.998835\n154896,70,50,3,1,Single,7,187.6522711,0,9013.613555\n157990,63,46,3,0,Married,7,187.5932977,0,9015.664885\n146056,43,22,3,0,Married,15,187.5931491,0,8848.657455\n139882,18,0,1,1,Married,14,187.5632107,0,9017.160535\n156002,46,28,3,1,Married,11,187.5576346,0,9130.88173\n177506,61,42,2,1,Single,0,187.5365645,0,9086.828225\n195135,60,39,1,0,Single,12,187.5124909,0,9151.624545\n112670,64,46,4,0,Married,7,187.4899744,0,9076.49872\n123908,65,44,3,0,Single,6,187.4859603,0,8931.298015\n118830,44,26,2,1,Single,11,187.420251,0,8844.01255\n178312,49,29,2,1,Single,14,187.4045156,0,8955.22578\n153814,70,49,3,0,Married,14,187.4043212,0,8844.21606\n105247,69,52,3,1,Married,13,187.4042145,0,8863.210725\n164071,58,37,1,0,Married,14,187.4003044,0,8804.01522\n110500,64,46,2,1,Married,13,187.3943975,0,9139.719875\n138680,25,8,4,1,Married,14,187.3454786,0,8841.27393\n132591,19,1,3,1,Single,13,187.3398595,0,9003.992975\n153210,19,2,4,1,Married,15,187.3141493,0,8795.707465\n187343,32,15,2,0,Married,13,187.264194,0,8936.2097\n142699,61,44,4,1,Married,9,187.2638955,0,8886.194775\n111897,17,0,3,0,Married,14,187.2352379,0,8936.761895\n102197,63,44,2,0,Single,13,187.1632252,0,8935.16126\n144638,66,47,3,0,Married,9,187.0689746,0,9034.44873\n130933,67,48,4,0,Married,7,186.993559,0,8893.67795\n162881,56,37,4,0,Married,12,186.985364,0,9123.2682\n115843,70,50,2,0,Married,15,186.9770685,0,9100.853425\n107660,67,50,1,1,Single,0,186.951001,0,8868.55005\n133281,62,45,4,1,Single,4,186.8713381,0,8894.566905\n103307,61,44,3,1,Married,2,186.8503764,0,8804.51882\n145489,62,42,3,1,Married,13,186.7640373,0,8788.201865\n165274,66,48,4,0,Single,4,186.667605,0,8743.38025\n113494,62,42,1,1,Single,9,186.6485083,0,9106.425415\n183026,70,52,4,1,Married,9,186.5952713,0,9125.763565\n191210,23,5,2,0,Married,15,186.5815867,0,8912.079335\n166472,67,47,1,1,Married,2,186.5116392,0,9086.58196\n112707,19,0,2,1,Single,14,186.4975253,0,8945.876265\n131000,23,6,4,1,Married,14,186.3419152,0,8913.09576\n117972,69,49,2,0,Married,7,186.3223813,0,8922.119065\n170916,62,45,3,1,Single,1,186.2644712,0,8968.22356\n141243,31,12,4,1,Married,14,186.2549065,0,8766.745325\n189968,66,49,1,0,Single,5,186.2522302,0,8971.61151\n130063,64,45,3,0,Married,7,186.2505434,0,8853.52717\n120594,38,19,3,0,Single,14,186.1176727,0,8745.883635\n164168,23,4,3,1,Single,15,186.0547352,0,8785.73676\n179266,69,51,3,0,Married,13,185.944511,0,9070.22555\n165561,18,0,3,1,Married,14,185.8723642,0,8904.61821\n115949,61,41,3,1,Single,4,185.8614547,0,8740.072735\n128471,62,44,2,1,Married,13,185.8265531,0,8860.327655\n134161,65,44,2,1,Married,15,185.8110244,0,8903.55122\n131283,18,0,3,0,Married,15,185.7922048,0,8861.61024\n192053,49,30,2,1,Single,15,185.759338,0,8984.9669\n142617,66,45,3,1,Married,8,185.723881,0,8999.19405\n170646,41,21,1,1,Married,15,185.6857913,0,8997.289565\n191944,21,3,3,1,Married,14,185.6792609,0,8700.963045\n147491,29,11,4,1,Married,14,185.6099405,0,8984.497025\n171573,62,45,4,0,Married,10,185.4994852,0,8782.97426\n122107,59,40,2,1,Single,15,185.4654981,0,9060.274905\n101039,22,5,2,0,Married,13,185.4327464,0,8845.63732\n176278,34,16,3,1,Married,13,185.3279228,0,8848.39614\n129345,67,48,3,0,Married,9,185.2837875,0,8978.189375\n148944,39,22,2,0,Single,14,185.2458451,0,8773.292255\n170073,61,43,3,1,Single,4,185.2092509,0,8788.462545\n104444,47,27,1,1,Married,13,185.1463607,0,8946.318035\n134638,62,43,2,1,Married,1,185.1302781,0,8784.513905\n159752,67,50,2,0,Married,3,185.123639,0,8664.18195\n107444,60,39,2,0,Married,6,185.0751905,0,8901.759525\n128479,46,25,3,0,Single,11,185.0649027,0,8924.245135\n146169,67,49,2,0,Single,10,185.0628056,0,8979.14028\n100935,63,43,3,0,Single,15,185.0165371,0,8779.826855\n129110,62,43,3,0,Single,8,184.9488906,0,8691.44453\n103695,63,42,2,1,Single,11,184.9471341,0,8670.356705\n118231,68,48,3,0,Married,10,184.9239301,0,9040.196505\n139883,61,42,4,0,Single,10,184.9221483,0,8908.107415\n197283,66,48,3,0,Single,7,184.8876343,0,9004.381715\n143885,49,31,3,1,Single,12,184.8374195,0,8850.870975\n134476,23,4,2,1,Married,12,184.7622965,0,8709.114825\n122535,68,47,3,1,Married,4,184.7557897,0,8693.789485\n122442,19,1,2,1,Married,13,184.7519116,0,8763.59558\n156093,64,45,1,0,Married,14,184.7351579,0,8677.757895\n134724,62,43,3,1,Single,10,184.7169557,0,8909.847785\n138318,45,24,4,1,Single,11,184.693913,0,8959.69565\n113553,61,42,3,1,Single,12,184.6753971,0,8886.769855\n116241,70,52,3,1,Married,10,184.5360255,0,8860.801275\n159440,67,50,3,1,Single,1,184.5250104,0,8956.25052\n132046,70,50,3,1,Married,9,184.4794014,0,8911.97007\n164558,70,52,1,0,Married,7,184.317085,0,8891.85425\n159342,64,43,3,1,Married,8,184.2904129,0,8837.520645\n182604,18,0,2,1,Married,15,184.2619675,0,8995.098375\n138990,67,49,3,0,Married,13,184.1834476,0,8837.17238\n159694,61,40,3,0,Married,6,184.1530611,0,8843.653055\n148634,64,47,2,0,Married,9,184.1390066,0,8975.95033\n155351,61,41,2,0,Single,11,184.1277861,0,8753.389305\n181130,48,29,3,1,Married,12,184.127155,0,8830.35775\n146192,68,47,2,1,Married,6,184.1173057,0,8929.865285\n181147,20,3,4,1,Married,14,184.0785484,0,8708.92742\n119747,28,7,2,0,Single,11,183.9996698,0,8786.98349\n132026,61,41,2,0,Married,8,183.9851,0,8815.255\n162797,19,1,2,1,Married,14,183.9637908,0,8749.18954\n170823,62,44,2,1,Single,8,183.9014883,0,8801.074415\n161333,29,11,3,1,Married,15,183.8856392,0,8654.28196\n119447,65,44,1,0,Single,8,183.8554703,0,8714.773515\n111725,18,0,4,0,Married,12,183.7710119,0,8870.550595\n125451,25,8,3,0,Married,12,183.728461,0,8749.42305\n172126,28,9,1,0,Married,15,183.7265292,0,8725.32646\n129276,21,3,4,1,Married,13,183.7059455,0,8663.297275\n190853,25,6,1,1,Married,13,183.6143818,0,8658.71909\n138616,51,33,4,1,Married,15,183.6142249,0,8688.711245\n137238,60,41,3,1,Married,6,183.5908763,0,8829.543815\n145997,31,13,3,0,Married,11,183.5154636,0,8635.77318\n173567,18,0,3,0,Married,12,183.4939003,0,8896.695015\n125996,33,15,1,1,Married,15,183.4843533,0,8689.217665\n146327,69,48,3,1,Single,10,183.3820513,0,8858.102565\n117289,25,8,3,1,Married,12,183.3627786,0,8627.13893\n108011,67,49,2,1,Married,8,183.2906958,0,8777.53479\n109726,63,46,3,1,Single,11,183.2439798,0,8835.19899\n115581,18,0,1,1,Married,13,183.2307779,0,8845.538895\n103938,18,0,4,0,Married,11,183.2227266,0,8749.13633\n141646,68,48,3,1,Single,6,183.1017348,0,8893.08674\n135312,70,49,3,0,Single,14,183.0651561,0,8628.257805\n187253,21,2,1,1,Married,13,183.0600479,0,8938.002395\n102110,62,44,4,0,Single,15,183.0472726,0,8830.36363\n178046,67,50,2,0,Single,12,183.0391836,0,8796.95918\n161733,58,39,2,0,Single,13,182.9690787,0,8927.453935\n124116,65,45,4,0,Single,11,182.8816086,0,8565.08043\n148672,61,44,2,0,Single,9,182.8579739,0,8808.898695\n136641,20,3,3,1,Married,11,182.8532659,0,8905.663295\n180762,27,6,4,0,Married,11,182.8311436,0,8779.55718\n190391,53,35,2,0,Single,12,182.8276235,0,8780.381175\n103030,24,6,1,1,Married,12,182.8039805,0,8902.199025\n182981,68,50,4,0,Single,7,182.6635195,0,8666.175975\n107621,59,42,3,1,Married,15,182.6407921,0,8763.039605\n187738,42,21,1,0,Married,11,182.4160381,0,8887.801905\n196691,64,44,3,1,Married,13,182.3851447,0,8818.257235\n125475,53,32,4,1,Married,11,182.3320966,0,8566.60483\n147211,48,28,4,1,Married,13,182.3174409,0,8709.872045\n164014,38,20,2,1,Married,14,182.2446094,0,8593.23047\n115043,67,50,4,0,Married,0,182.2331212,0,8704.65606\n185977,69,51,3,1,Single,3,182.1959742,0,8665.79871\n134228,66,47,2,0,Married,14,182.1895769,0,8726.478845\n123108,37,19,4,0,Married,11,182.180109,0,8593.00545\n196097,64,43,2,0,Single,15,182.1604591,0,8509.022955\n148352,33,16,2,1,Single,12,182.0703443,0,8671.517215\n159633,60,40,3,0,Married,3,182.021842,0,8842.0921\n183366,52,33,1,1,Married,11,181.8773342,0,8507.86671\n193481,68,49,3,0,Single,7,181.7617628,0,8806.08814\n190939,18,1,1,1,Married,14,181.7398301,0,8650.991505\n180588,69,49,2,1,Single,0,181.6719283,0,8759.596415\n142328,23,4,4,0,Married,11,181.6667072,0,8819.33536\n172038,35,14,4,1,Married,14,181.6477538,0,8566.38769\n127183,67,48,3,1,Married,2,181.6005884,0,8493.02942\n119752,68,47,3,1,Single,10,181.5693741,0,8720.468705\n173587,23,4,3,0,Married,12,181.556529,0,8524.82645\n100073,51,30,4,0,Married,12,181.5434522,0,8575.17261\n104695,57,38,4,0,Single,12,181.4862783,0,8633.313915\n113798,63,45,2,0,Married,0,181.4507002,0,8672.53501\n181071,16,0,4,1,Single,14,181.4341208,0,8492.70604\n108745,70,49,2,1,Married,15,181.4338152,0,8521.69076\n155731,69,52,3,0,Single,15,181.3884095,0,8667.420475\n106539,31,10,3,0,Married,13,181.3666447,0,8705.332235\n198110,69,49,2,0,Married,15,181.2225401,0,8571.127005\n168954,24,7,3,1,Married,13,181.1828705,0,8661.143525\n170144,20,1,2,1,Married,11,181.1133935,0,8675.669675\n142508,62,41,2,1,Married,9,181.0643817,0,8520.219085\n119614,62,44,2,1,Single,10,180.8560733,0,8745.803665\n104766,16,0,2,1,Married,11,180.7581336,0,8793.90668\n165470,32,12,2,0,Married,14,180.7486757,0,8532.433785\n135635,70,52,2,0,Married,11,180.7449177,0,8626.245885\n101748,62,45,2,0,Married,9,180.6887643,0,8675.438215\n101937,40,19,1,1,Single,13,180.5799829,0,8786.999145\n107464,31,12,2,1,Married,14,180.4446841,0,8756.234205\n173551,70,53,1,1,Married,11,180.3930764,0,8423.65382\n183076,62,42,2,0,Single,15,180.3825103,0,8749.125515\n177790,66,46,2,0,Married,10,180.3764038,0,8782.82019\n150326,63,45,4,0,Married,10,180.3697143,0,8758.485715\n155233,36,19,3,0,Single,11,180.3267926,0,8425.33963\n112858,16,0,2,1,Married,11,180.3062395,0,8510.311975\n169171,25,7,1,0,Married,14,180.2839243,0,8506.196215\n152003,56,37,2,0,Married,13,180.2726827,0,8785.634135\n193413,20,2,3,1,Single,14,180.211505,0,8680.57525\n125542,24,7,4,1,Married,15,180.1656024,0,8499.28012\n166908,39,19,4,1,Married,14,180.1626966,0,8501.13483\n146463,30,10,3,0,Single,12,180.1317101,0,8737.585505\n163356,63,45,2,1,Married,12,180.0690882,0,8444.45441\n149654,21,4,1,1,Married,11,180.0366847,0,8685.834235\n112045,30,12,2,0,Married,11,180.0011804,0,8541.05902\n125497,70,52,3,1,Married,11,179.9030368,0,8551.15184\n190275,61,44,3,0,Single,3,179.8658272,0,8500.29136\n187857,70,49,1,0,Single,3,179.8226162,0,8690.13081\n117338,68,47,4,1,Married,10,179.8111318,0,8788.55659\n183039,61,41,2,1,Married,4,179.7751336,0,8694.75668\n169793,18,0,2,1,Married,12,179.7314757,0,8429.573785\n104256,64,43,3,0,Married,8,179.7186989,0,8532.934945\n180044,25,7,4,1,Married,13,179.6525692,0,8527.62846\n103401,29,9,3,1,Single,12,179.6450386,0,8442.25193\n147462,67,48,2,0,Single,7,179.5737097,0,8460.685485\n177175,45,28,3,0,Single,14,179.5417467,0,8565.087335\n145644,53,36,2,0,Single,15,179.5108887,0,8557.544435\n175460,41,22,4,1,Married,13,179.50327,0,8644.1635\n178552,69,50,4,0,Single,14,179.4037183,0,8506.185915\n191023,31,13,3,1,Married,15,179.3982397,0,8372.911985\n112351,20,2,2,1,Single,13,179.3510569,0,8739.552845\n163841,69,52,2,1,Married,4,179.2152555,0,8378.762775\n114224,33,14,3,0,Married,12,179.1852303,0,8501.261515\n131422,49,32,4,0,Married,12,179.1359401,0,8392.797005\n106485,64,45,2,0,Single,7,179.1332926,0,8586.66463\n170878,20,1,1,1,Married,12,179.1249378,0,8707.24689\n148374,28,10,1,1,Married,15,179.0536986,0,8448.68493\n198754,61,43,1,0,Married,1,178.9945855,0,8383.729275\n194309,49,29,2,0,Married,12,178.9903899,0,8536.519495\n144854,46,25,4,0,Single,14,178.9685786,0,8684.42893\n133804,70,53,4,1,Married,11,178.943418,0,8531.1709\n189068,69,52,3,0,Single,11,178.9169669,0,8632.848345\n134521,69,48,2,0,Single,9,178.9047923,0,8673.239615\n149404,40,22,2,0,Single,13,178.8576714,0,8342.88357\n190427,46,25,4,1,Single,13,178.8078918,0,8575.39459\n195142,62,41,2,1,Single,1,178.7833904,0,8459.16952\n134240,63,46,3,1,Married,6,178.7704606,0,8485.52303\n143550,61,43,2,0,Single,0,178.7270613,0,8662.353065\n104617,67,50,4,1,Married,8,178.7059053,0,8562.295265\n175351,24,5,3,1,Married,13,178.7058171,0,8457.290855\n103876,61,42,3,0,Married,12,178.6744353,0,8482.721765\n195418,19,1,2,0,Married,11,178.5321406,0,8390.60703\n109548,41,23,2,0,Married,11,178.4860222,0,8539.30111\n120642,68,47,4,1,Single,9,178.4785918,0,8551.92959\n176774,69,48,3,1,Married,7,178.4709609,0,8552.548045\n181720,32,12,1,0,Married,15,178.4623223,0,8334.116115\n178471,62,44,2,0,Single,8,178.3216863,0,8520.084315\n176918,20,1,3,0,Married,11,178.2315351,0,8336.576755\n117770,20,2,1,1,Married,12,178.225309,0,8420.26545\n145636,18,1,2,1,Married,14,178.1916351,0,8520.581755\n145616,27,10,1,0,Single,15,178.1544544,0,8535.72272\n136575,61,41,3,1,Married,3,178.1435445,0,8674.177225\n145560,60,41,1,1,Single,4,178.1144493,0,8435.722465\n115335,26,6,2,1,Married,15,178.0410823,0,8305.054115\n108401,65,48,3,0,Single,8,178.0224268,0,8504.12134\n129821,46,26,1,1,Single,13,178.0214425,0,8426.072125\n187138,22,5,3,0,Married,14,177.998881,0,8468.94405\n100467,68,51,2,1,Single,8,177.9324401,0,8496.622005\n157713,49,28,3,0,Single,13,177.9025636,0,8438.12818\n196112,47,26,4,1,Single,12,177.900394,0,8665.0197\n151780,61,41,2,1,Single,6,177.7606627,0,8370.033135\n163114,63,42,3,0,Married,13,177.7387364,0,8436.93682\n118544,35,16,3,1,Married,11,177.7262584,0,8685.31292\n109757,22,3,4,1,Married,15,177.6937993,0,8516.689965\n195169,64,47,3,1,Married,12,177.6837241,0,8641.186205\n158741,63,46,4,1,Single,6,177.6663666,0,8554.31833\n119361,16,0,3,1,Married,11,177.5911607,0,8319.558035\n116963,29,8,2,0,Married,13,177.4783708,0,8470.91854\n162599,19,1,4,1,Married,14,177.4671892,0,8593.35946\n171030,65,44,1,1,Married,0,177.3780284,0,8393.90142\n119248,22,3,1,1,Married,15,177.3409102,0,8607.04551\n133882,18,0,2,1,Married,14,177.2984363,0,8584.921815\n167117,62,44,2,1,Single,8,177.0450044,0,8594.25022\n192380,23,6,4,1,Married,15,176.9938099,0,8567.690495\n152244,42,24,2,1,Single,13,176.9334845,0,8483.674225\n168465,25,8,1,1,Married,12,176.8627735,0,8616.138675\n127482,61,41,2,0,Single,11,176.6996786,0,8320.98393\n149164,64,44,2,1,Married,9,176.5731112,0,8297.65556\n160307,25,8,3,1,Married,15,176.5082561,0,8385.412805\n126247,64,46,3,0,Married,6,176.4612428,0,8276.06214\n139239,18,1,3,1,Married,12,176.4416255,0,8274.081275\n150652,18,1,3,1,Married,14,176.3763535,0,8523.817675\n158840,67,48,2,0,Married,10,176.3653434,0,8326.26717\n178339,62,45,3,0,Married,8,176.3626753,0,8509.133765\n183801,61,44,2,0,Single,15,176.3209485,0,8458.047425\n195208,69,48,4,1,Married,15,176.2240353,0,8416.201765\n194702,57,36,2,1,Married,15,176.084205,0,8458.21025\n132193,46,25,3,0,Single,11,176.0687113,0,8235.435565\n178927,62,43,2,0,Single,12,176.0482467,0,8484.412335\n131605,16,0,3,0,Married,11,176.0440184,0,8592.20092\n159056,67,50,3,1,Married,2,175.9963419,0,8412.817095\n122932,64,43,2,1,Married,6,175.9839897,0,8299.199485\n178247,61,43,2,0,Single,14,175.983661,0,8210.18305\n128269,54,36,2,0,Married,15,175.9420376,0,8309.10188\n191825,66,49,2,0,Married,13,175.8884718,0,8390.42359\n108833,29,12,4,1,Married,11,175.8800322,0,8233.00161\n188945,59,38,1,0,Single,15,175.7300736,0,8191.50368\n133092,70,51,1,1,Married,7,175.6159266,0,8477.79633\n114444,47,28,1,1,Married,14,175.4767629,0,8335.838145\n115960,69,49,2,1,Single,3,175.4666117,0,8372.330585\n157800,19,0,3,0,Married,14,175.4484132,0,8196.42066\n158932,31,13,2,1,Single,15,175.4208202,0,8214.04101\n159462,61,40,4,1,Married,11,175.3317811,0,8402.589055\n144857,61,44,3,0,Married,7,175.2508525,0,8279.542625\n195412,53,34,1,1,Married,12,175.2074655,0,8452.373275\n133704,21,4,3,0,Married,12,175.1169078,0,8540.84539\n198506,64,47,3,1,Married,12,175.0617314,0,8334.08657\n184448,43,26,3,1,Married,12,175.0369842,0,8527.84921\n139509,65,47,4,0,Married,1,175.020681,0,8223.03405\n171625,67,49,2,1,Married,14,174.9818267,0,8498.091335\n182942,19,2,2,1,Married,13,174.9719929,0,8418.599645\n183300,65,48,1,1,Married,13,174.8904686,0,8365.52343\n155153,28,8,3,1,Married,13,174.7393553,0,8300.967765\n136995,65,45,2,1,Married,12,174.7134006,0,8434.67003\n176415,68,49,2,1,Married,2,174.6473393,0,8312.366965\n181893,59,42,2,1,Single,13,174.6297877,0,8436.489385\n192011,28,9,3,0,Married,13,174.6051107,0,8466.255535\n119306,66,49,2,0,Single,15,174.5920961,0,8276.604805\n196428,66,45,3,1,Married,15,174.5840572,0,8141.20286\n103121,70,49,2,0,Single,7,174.5438715,0,8162.193575\n170695,63,43,1,0,Married,9,174.5264045,0,8251.320225\n161149,70,50,3,0,Single,13,174.5025078,0,8398.12539\n151160,68,49,2,1,Married,12,174.5014413,0,8458.072065\n167264,66,45,3,0,Single,8,174.4246564,0,8206.23282\n170723,49,31,3,1,Single,11,174.3786518,0,8238.93259\n140959,68,51,4,1,Married,7,174.324187,0,8474.20935\n176831,56,35,1,0,Single,15,174.2884032,0,8376.42016\n195559,18,0,2,1,Married,13,173.9147251,0,8144.736255\n177077,20,2,4,1,Married,13,173.9120919,0,8225.604595\n150766,57,38,2,1,Single,11,173.9017406,0,8143.08703\n115225,60,41,3,0,Single,10,173.8695025,0,8309.475125\n127578,50,32,2,0,Married,14,173.8582284,0,8200.91142\n115905,66,49,2,0,Single,8,173.812546,0,8461.6273\n162697,24,6,2,1,Married,13,173.7551777,0,8341.758885\n109702,68,51,3,1,Married,10,173.74814,0,8295.407\n194539,64,44,2,1,Single,10,173.7304605,0,8229.523025\n147120,64,44,4,0,Married,9,173.7223101,0,8147.115505\n120277,16,0,3,0,Married,12,173.687322,0,8140.3661\n163503,37,20,1,1,Married,13,173.5817214,0,8316.08607\n108367,54,33,3,0,Single,14,173.5506182,0,8146.53091\n156399,52,33,3,0,Single,15,173.524937,0,8473.24685\n136589,62,44,2,1,Single,11,173.4938588,0,8449.69294\n149600,61,44,4,0,Single,11,173.467736,0,8134.3868\n106922,61,40,3,1,Married,11,173.464338,0,8375.2169\n109139,65,44,2,1,Married,7,173.4234717,0,8361.173585\n159368,61,40,1,0,Single,9,173.3577025,0,8352.885125\n153842,65,44,3,1,Married,9,173.3381687,0,8394.908435\n142612,67,46,1,0,Single,2,173.2162688,0,8356.81344\n180435,17,0,4,1,Married,13,173.1951407,0,8231.757035\n159782,23,4,2,1,Married,15,172.9344638,0,8211.72319\n119625,70,53,3,1,Married,5,172.8805566,0,8061.02783\n110636,69,50,1,0,Single,6,172.8367227,0,8170.836135\n142590,68,50,3,1,Married,3,172.8255983,0,8426.279915\n153316,60,42,3,1,Married,12,172.6227493,0,8340.137465\n145550,70,52,3,1,Married,7,172.5748663,0,8144.743315\n109455,60,39,3,1,Married,5,172.5732732,0,8245.66366\n177062,64,45,1,0,Single,11,172.5265908,0,8405.32954\n126901,29,9,1,0,Married,15,172.5128998,0,8059.64499\n166825,36,16,3,1,Married,11,172.393355,0,8067.66775\n156845,63,45,2,0,Single,13,172.3885303,0,8336.426515\n190311,69,49,2,1,Single,6,172.3857854,0,8175.28927\n127474,61,40,2,1,Married,1,172.3572205,0,8151.861025\n182152,52,32,3,1,Married,15,172.3553627,0,8202.768135\n163746,43,23,3,0,Married,12,172.3442437,0,8143.212185\n165297,57,40,2,0,Single,14,172.2985294,0,8405.92647\n152304,68,48,2,0,Married,10,172.1532142,0,8288.66071\n173842,66,45,2,1,Married,5,172.1497979,0,8157.489895\n151805,62,44,4,1,Married,10,172.0243517,0,8017.217585\n187115,60,39,3,0,Married,12,172.0000079,0,8178.000395\n117475,67,49,2,0,Single,11,171.9947345,0,8246.736725\n181640,65,44,1,1,Married,0,171.9613068,0,8076.06534\n161595,66,48,1,0,Married,12,171.9308623,0,8260.543115\n174987,63,46,3,0,Single,12,171.8706404,0,8245.53202\n196176,64,47,3,0,Married,13,171.7661605,0,8215.308025\n122746,45,26,1,1,Married,13,171.7162674,0,8237.81337\n161014,58,41,4,0,Married,15,171.6787836,0,8246.93918\n172179,40,20,2,1,Married,14,171.6549449,0,8229.747245\n172018,70,51,3,0,Married,1,171.6544023,0,7992.720115\n198435,69,52,4,1,Single,5,171.6213587,0,8291.067935\n193055,52,35,2,0,Single,12,171.6202607,0,7998.013035\n107832,67,48,3,1,Married,6,171.4715648,0,8090.57824\n161519,16,0,1,1,Single,12,171.3772645,0,8183.863225\n161439,67,50,2,1,Married,4,171.2743369,0,8279.716845\n182077,26,6,2,0,Single,12,171.2690932,0,8259.45466\n110665,55,37,3,0,Married,15,171.2677656,0,7996.38828\n174525,69,52,4,1,Married,1,171.1947519,0,8123.737595\n155733,69,49,2,1,Married,5,171.1449136,0,8236.24568\n109762,25,6,1,1,Married,15,171.1116362,0,8241.58181\n112065,24,6,2,1,Married,14,171.0885338,0,8279.42669\n190382,65,45,1,0,Single,6,171.0101059,0,8064.505295\n162461,66,45,3,1,Single,15,170.9627664,0,8277.13832\n111249,22,4,1,1,Single,13,170.9577581,0,8188.887905\n152550,67,50,1,0,Married,11,170.9532587,0,7953.662935\n152612,66,47,3,1,Married,2,170.914122,0,8002.7061\n117470,70,49,2,1,Married,4,170.8973382,0,8314.86691\n115060,29,12,4,0,Single,14,170.865496,0,8139.2748\n111541,21,3,1,0,Married,13,170.8080013,0,8193.400065\n184656,63,46,3,1,Married,15,170.6766187,0,8213.830935\n101550,63,46,2,0,Single,11,170.6091203,0,8033.456015\n187619,63,45,2,1,Single,6,170.6039685,0,8180.198425\n139096,21,4,2,1,Married,12,170.4990327,0,8320.951635\n174333,69,49,2,0,Married,14,170.4699518,0,8004.49759\n198245,59,42,4,0,Married,15,170.3543122,0,7992.71561\n192464,66,47,3,0,Married,10,170.2748571,0,8107.742855\n147601,67,47,3,1,Single,14,170.1828617,0,7918.143085\n183887,64,46,2,0,Married,5,170.0679613,0,8276.398065\n153809,66,48,2,0,Married,7,170.0476499,0,8083.382495\n162229,69,52,4,1,Single,6,170.0445035,0,8001.225175\n139409,34,16,3,0,Single,14,170.003943,0,7965.19715\n115945,40,19,2,1,Married,11,169.938668,0,7909.9334\n170018,41,22,2,1,Single,14,169.9292973,0,8196.464865\n194420,44,27,2,1,Married,13,169.9274906,0,8011.37453\n111978,68,47,4,1,Married,2,169.9171958,0,8082.85979\n196800,16,0,1,0,Married,15,169.8362524,0,8270.81262\n127009,60,39,2,1,Single,1,169.8270482,0,8111.35241\n103922,41,21,1,0,Single,15,169.8042272,0,7914.21136\n111270,16,0,2,1,Married,12,169.6975258,0,8141.87629\n153115,56,37,4,1,Married,15,169.6628918,0,8025.14459\n142200,67,47,3,0,Married,2,169.6031021,0,8221.155105\n163998,39,19,3,1,Married,11,169.5110743,0,8077.553715\n153977,21,2,2,1,Married,13,169.5087022,0,8113.43511\n196401,69,49,1,0,Married,0,169.4418198,0,7913.09099\n104183,33,16,1,0,Single,13,169.3764585,0,8005.822925\n127716,21,2,2,0,Married,14,169.3389215,0,7910.946075\n147554,18,0,2,1,Married,12,169.3143782,0,8159.71891\n125272,17,0,1,1,Married,14,169.3142533,0,8243.712665\n105150,61,42,3,1,Married,8,169.295741,0,8255.78705\n127574,22,3,4,0,Married,14,169.2278782,0,8180.39391\n189808,62,44,1,0,Single,15,169.0810077,0,7948.050385\n117158,66,46,1,0,Single,13,169.0761351,0,8156.806755\n178218,39,21,3,1,Married,15,168.9975863,0,8196.879315\n102871,61,42,4,1,Married,7,168.9545003,0,8219.725015\n106838,67,49,1,1,Married,15,168.9340361,0,8067.701805\n144417,62,45,3,1,Single,15,168.9206484,0,8111.03242\n171632,70,51,2,0,Single,7,168.8770652,0,8124.85326\n134101,25,8,3,1,Married,12,168.8458668,0,8097.29334\n148338,43,25,4,1,Single,14,168.7608246,0,7998.04123\n194966,50,30,3,0,Single,11,168.7528974,0,8184.64487\n188771,64,46,2,0,Married,3,168.7403015,0,7899.015075\n116743,43,24,3,0,Married,14,168.7395333,0,7844.976665\n173600,70,50,2,1,Single,12,168.6027365,0,8041.136825\n104383,24,5,3,1,Married,14,168.5848448,0,8095.24224\n146163,23,4,3,1,Married,11,168.507276,0,7956.3638\n117941,45,27,2,0,Single,14,168.4427657,0,8073.138285\n110113,45,26,4,1,Single,13,168.3363531,0,8159.817655\n142812,23,5,2,1,Married,11,168.1472026,0,8193.36013\n188043,68,47,2,1,Married,0,168.1164938,0,7821.82469\n156509,64,46,2,0,Married,12,168.0988202,0,7836.94101\n173994,22,5,2,1,Married,13,168.0063452,0,8164.31726\n157712,17,0,1,0,Married,11,167.9065824,0,8143.32912\n192742,35,17,1,0,Married,12,167.8386883,0,8058.934415\n155813,65,45,2,1,Married,7,167.7461765,0,7885.308825\n178345,63,42,2,0,Married,6,167.7456981,0,8173.284905\n102581,50,32,3,0,Single,14,167.7291226,0,7788.45613\n121909,66,47,2,1,Married,6,167.7100834,0,7852.50417\n149172,65,46,3,1,Single,14,167.6625428,0,7817.12714\n181062,17,0,4,1,Married,11,167.6206261,0,7815.031305\n173200,35,18,2,0,Single,15,167.6070249,0,8104.351245\n107066,49,30,3,1,Single,13,167.5979735,0,7929.898675\n104652,55,38,2,1,Single,14,167.5431667,0,8048.158335\n127828,62,45,1,1,Married,4,167.5425068,0,8016.12534\n153469,61,40,2,1,Married,11,167.4991317,0,7918.956585\n186321,36,18,2,0,Married,12,167.4946277,0,7949.731385\n163062,48,29,2,0,Married,12,167.3977343,0,7775.886715\n174210,62,44,4,1,Single,1,167.3491294,0,7960.45647\n148886,67,47,1,1,Married,12,167.3008064,0,8070.04032\n147243,63,43,3,0,Married,5,167.2330632,0,7906.65316\n108673,62,45,1,0,Married,9,167.2173799,0,7995.868995\n102493,65,48,1,1,Married,1,167.1251828,0,8016.25914\n122529,65,44,3,1,Married,9,167.106551,0,8024.32755\n106595,68,50,2,1,Married,10,167.0878234,0,7963.39117\n105601,67,50,1,0,Single,10,167.0764612,0,7874.82306\n115497,69,48,4,0,Married,11,167.0242795,0,8075.213975\n152397,39,18,2,1,Single,13,166.9814039,0,7786.070195\n189812,64,43,3,1,Married,6,166.9618452,0,8095.09226\n118599,61,40,2,1,Married,11,166.9536987,0,7895.684935\n195891,23,6,2,1,Single,12,166.9503447,0,7885.517235\n132734,42,25,3,0,Single,15,166.9423064,0,8046.11532\n100281,20,3,1,0,Married,11,166.9365543,0,7930.827715\n168274,37,18,1,0,Single,12,166.9310997,0,7837.554985\n134189,24,7,1,1,Married,12,166.8433273,0,7927.166365\n110684,26,9,3,1,Single,12,166.793992,0,7950.6996\n182034,19,2,3,1,Married,11,166.7179783,0,7834.898915\n163002,41,21,2,0,Single,11,166.691093,0,7758.55465\n187328,53,35,1,1,Single,13,166.6465849,0,7754.329245\n174204,67,46,4,1,Single,3,166.635409,0,8120.77045\n112397,63,45,3,1,Married,8,166.6288805,0,8069.444025\n134274,29,8,2,1,Married,14,166.6050216,0,7947.25108\n194673,70,49,4,0,Single,12,166.5562894,0,7815.81447\n151108,20,2,1,1,Married,11,166.5373225,0,7985.866125\n133003,62,44,4,0,Single,13,166.501947,0,7770.09735\n184684,45,25,3,1,Married,12,166.4743347,0,7934.716735\n112823,31,11,4,0,Married,12,166.4256636,0,8053.28318\n172056,33,16,3,1,Married,15,166.4127054,0,7983.63527\n171373,67,50,1,0,Married,10,166.3963464,0,7803.81732\n193445,47,27,3,0,Married,14,166.3781897,0,8016.909485\n168060,25,6,2,1,Married,13,166.2965953,0,7725.829765\n122621,68,50,2,1,Married,12,166.2718467,0,7722.592335\n109898,16,0,3,1,Married,11,166.2082968,0,7973.41484\n170345,41,22,4,1,Single,12,166.1953416,0,7847.76708\n136140,16,0,3,1,Married,11,166.1846197,0,7886.230985\n142345,22,3,2,1,Married,15,166.1769046,0,8028.84523\n193822,53,33,3,1,Married,14,166.1118462,0,7940.59231\n175839,55,36,3,0,Single,11,166.1093996,0,7818.46998\n171410,17,0,2,1,Married,13,166.057532,0,7938.8766\n104692,42,24,4,1,Married,12,165.9889146,0,7934.44573\n124781,60,39,1,1,Single,10,165.9840948,0,7940.20474\n175670,51,33,2,1,Single,14,165.9665931,0,7723.329655\n124286,25,6,3,1,Married,13,165.8405147,0,7943.025735\n158126,37,18,3,1,Single,14,165.7243499,0,7832.217495\n186511,22,5,3,0,Married,14,165.7234505,0,7971.172525\n135624,47,27,3,0,Married,11,165.7161892,0,8057.80946\n163581,69,51,4,0,Single,9,165.7078246,0,7754.39123\n105910,43,23,4,1,Single,15,165.679963,0,7929.99815\n179121,67,48,1,1,Married,14,165.6764223,0,7854.821115\n160393,62,43,3,1,Single,5,165.6664059,0,7733.320295\n189401,61,43,2,1,Single,12,165.6591786,0,7736.95893\n188208,21,2,2,1,Married,15,165.62476,0,7985.238\n130239,49,32,4,0,Married,11,165.6106937,0,7894.534685\n180015,49,31,1,0,Single,12,165.6043454,0,7958.21727\n106044,28,8,4,0,Single,11,165.5921294,0,7789.60647\n136102,38,18,3,0,Married,11,165.5710814,0,7946.55407\n177585,18,1,1,1,Married,12,165.552408,0,8037.6204\n142074,19,1,3,1,Single,12,165.5362405,0,7773.812025\n113880,43,24,3,1,Married,14,165.5358994,0,7863.79497\n186539,23,6,2,1,Married,14,165.4759298,0,8001.79649\n167773,68,47,3,0,Single,7,165.4354244,0,7929.77122\n113506,61,44,2,1,Married,5,165.3805781,0,7698.028905\n194881,17,0,4,1,Married,15,165.3738542,0,7713.69271\n124306,63,44,3,1,Married,10,165.2589847,0,7729.949235\n187965,49,30,3,1,Single,14,165.2570255,0,7821.851275\n183153,62,44,4,0,Single,2,165.1976018,0,7877.88009\n136908,57,39,3,0,Married,12,165.1423676,0,7932.11838\n173656,60,39,2,1,Single,14,165.0434016,0,7873.17008\n113400,64,43,1,0,Single,0,164.9924266,0,7941.62133\n163352,65,44,2,0,Single,2,164.9241647,0,8023.208235\n153400,69,52,4,1,Single,8,164.9225449,0,7769.127245\n135472,41,22,2,1,Married,15,164.8625721,0,7662.128605\n160639,68,50,2,0,Married,11,164.8240283,0,7799.201415\n199766,19,2,2,1,Married,11,164.8102033,0,7824.510165\n136723,69,50,3,1,Single,1,164.7885931,0,7759.429655\n168535,50,32,2,1,Married,11,164.7838719,0,7722.193595\n195834,66,45,4,0,Married,9,164.7761872,0,7773.80936\n157379,51,31,4,0,Single,13,164.7506686,0,7723.53343\n101747,27,10,3,1,Single,13,164.717481,0,7820.87405\n124281,46,28,2,1,Single,12,164.6822683,0,7732.113415\n187457,26,9,3,0,Married,12,164.6698998,0,7703.49499\n116936,24,6,3,1,Single,13,164.5682304,0,7772.41152\n193993,18,0,2,0,Married,15,164.5270118,0,8009.35059\n116031,24,5,3,1,Married,11,164.4273179,0,7633.365895\n123461,20,2,1,1,Married,12,164.3226813,0,8007.134065\n194448,60,43,4,1,Married,10,164.2509223,0,7929.546115\n180713,70,51,3,1,Single,10,164.1767484,0,7960.83742\n136674,41,22,2,1,Married,14,164.1363746,0,7951.81873\n143596,68,51,1,0,Married,3,164.0909204,0,7691.54602\n139692,68,49,3,1,Married,9,163.9798518,0,7716.99259\n197334,51,34,3,1,Married,11,163.9311136,0,7639.55568\n133359,54,33,2,0,Single,12,163.907041,0,7907.35205\n133010,43,22,3,0,Married,11,163.9059855,0,7721.299275\n163137,67,48,3,0,Married,10,163.8980662,0,7690.90331\n121740,60,43,3,0,Married,0,163.8877602,0,7881.38801\n160369,67,48,4,0,Married,9,163.8689268,0,7760.44634\n187338,19,1,3,0,Married,12,163.8360284,0,7749.80142\n102772,23,6,4,1,Married,14,163.7824754,0,7876.12377\n179195,18,0,3,1,Married,12,163.7183516,0,7591.91758\n150600,61,43,2,0,Single,12,163.6062869,0,7756.314345\n106322,17,0,2,1,Married,14,163.4888262,0,7888.44131\n195165,67,47,4,1,Single,7,163.1749036,0,7834.74518\n117465,56,36,3,1,Single,14,163.0612602,0,7863.06301\n119505,69,49,3,1,Married,11,162.9468688,0,7926.34344\n192817,44,27,3,1,Single,11,162.9210878,0,7793.05439\n151779,70,52,3,0,Single,12,162.8552724,0,7836.76362\n169103,69,49,4,1,Married,7,162.843711,0,7772.18555\n109752,45,27,2,1,Married,11,162.6375943,0,7679.879715\n146052,68,49,3,1,Single,9,162.6360826,0,7770.80413\n181811,60,41,2,0,Single,7,162.5575411,0,7673.877055\n175469,70,52,1,0,Married,15,162.5111147,0,7910.555735\n164569,37,18,3,1,Single,15,162.4271008,0,7792.35504\n163118,48,30,2,1,Married,13,162.4063403,0,7743.317015\n188455,54,34,3,1,Single,14,162.3829021,0,7723.145105\n138553,60,42,1,0,Married,13,162.3570971,0,7588.854855\n107405,35,15,1,0,Single,14,162.1912987,0,7893.564935\n187079,23,4,4,1,Married,15,162.1769788,0,7643.84894\n109954,48,29,2,0,Married,14,162.1766053,0,7616.830265\n132545,67,46,1,1,Married,5,162.0948232,0,7798.74116\n140592,21,4,3,0,Married,13,162.0895478,0,7575.47739\n103809,61,41,3,1,Married,10,162.04085,0,7525.0425\n124865,67,48,1,1,Married,0,162.021823,0,7748.09115\n129655,69,50,1,0,Single,15,161.9476996,0,7527.38498\n179665,63,44,3,0,Married,7,161.9351747,0,7781.758735\n111646,60,42,1,0,Married,13,161.9238698,0,7685.19349\n174642,60,42,3,1,Married,1,161.7843451,0,7886.217255\n168648,42,21,2,1,Single,11,161.718722,0,7549.9361\n198392,67,47,4,1,Married,11,161.6531642,0,7595.65821\n120056,61,40,4,1,Married,13,161.6488695,0,7793.443475\n197835,69,48,2,1,Married,2,161.6239766,0,7671.19883\n170242,41,23,3,0,Married,11,161.5843569,0,7744.217845\n173214,20,1,1,1,Married,13,161.5795211,0,7831.976055\n102591,69,51,2,0,Married,11,161.4997804,0,7852.98902\n115899,69,51,1,0,Single,12,161.4972836,0,7850.86418\n148492,16,0,3,0,Married,11,161.229285,0,7668.46425\n132989,18,0,2,1,Married,12,161.2216649,0,7580.083245\n176775,27,8,1,1,Married,15,161.1440266,0,7503.20133\n140483,62,42,2,1,Single,12,161.0879016,0,7680.39508\n144420,43,26,2,1,Single,15,161.068729,0,7664.43645\n138566,67,49,1,0,Single,13,161.0657087,0,7495.285435\n185770,53,32,2,0,Married,11,161.021328,0,7512.0664\n176917,49,29,2,1,Married,15,161.0206519,0,7582.032595\n166214,65,47,4,0,Single,2,161.0202216,0,7836.01108\n162185,64,44,3,1,Married,9,160.9837229,0,7836.186145\n110177,23,5,3,1,Married,11,160.8956892,0,7695.78446\n127004,64,45,2,1,Single,10,160.6994065,0,7530.970325\n161005,61,43,2,0,Single,7,160.655388,0,7504.7694\n155004,65,46,2,1,Married,0,160.5910585,0,7675.552925\n113260,67,50,2,0,Married,1,160.5817889,0,7453.089445\n198197,25,6,4,1,Married,15,160.5397728,0,7478.98864\n174916,69,50,2,1,Single,6,160.4764846,0,7681.82423\n116978,64,44,4,0,Married,3,160.4725396,0,7636.62698\n153590,63,46,2,1,Single,8,160.393469,0,7707.67345\n136609,63,42,1,1,Married,7,160.2682984,0,7483.41492\n114029,68,51,2,0,Married,15,160.226286,0,7565.3143\n194432,56,38,3,0,Married,12,160.0331819,0,7663.659095\n117382,47,27,2,1,Married,11,159.9424657,0,7742.123285\n177864,16,0,2,0,Married,11,159.8900168,0,7791.50084\n148403,70,49,3,1,Married,11,159.7829766,0,7527.14883\n148442,46,26,3,0,Married,13,159.7145116,0,7405.72558\n171130,69,49,2,1,Single,15,159.6179518,0,7530.89759\n107640,55,36,4,1,Married,14,159.5991093,0,7657.955465\n121722,70,49,3,1,Single,8,159.5136816,0,7460.68408\n170934,40,22,1,0,Married,13,159.4855752,0,7418.27876\n107023,61,41,1,0,Married,9,159.4451332,0,7713.25666\n123001,16,0,3,1,Married,13,159.4404343,0,7529.021715\n184138,48,29,2,1,Single,11,159.3969869,0,7644.849345\n160508,28,11,1,1,Married,14,159.3673423,0,7393.367115\n197104,56,39,3,1,Married,14,159.3118041,0,7552.590205\n114170,18,0,1,1,Married,12,159.3039355,0,7668.196775\n171559,68,50,1,0,Married,0,159.269216,0,7646.4608\n125396,68,48,1,0,Single,10,159.1670464,0,7584.35232\n170150,53,33,1,0,Married,11,159.1531007,0,7417.655035\n155572,65,44,3,0,Single,12,159.0713667,0,7476.568335\n169561,18,0,4,1,Married,12,158.9961809,0,7374.809045\n122380,61,43,3,1,Married,2,158.9756726,0,7510.78363\n182797,66,47,2,0,Married,15,158.9727499,0,7400.637495\n130070,18,1,4,1,Married,14,158.9726986,0,7370.63493\n154849,70,53,1,1,Single,9,158.83813,0,7475.9065\n155676,66,46,3,1,Single,6,158.835418,0,7520.7709\n174985,69,48,3,0,Single,7,158.6079375,0,7614.396875\n131588,61,43,2,1,Married,6,158.5958011,0,7520.790055\n178435,35,17,3,1,Married,13,158.5750712,0,7573.75356\n187544,46,26,2,0,Married,14,158.5565828,0,7723.82914\n118015,65,47,3,0,Single,10,158.4413402,0,7670.06701\n141262,18,0,2,1,Married,14,158.3725387,0,7613.626935\n122584,59,38,1,1,Married,15,158.3517998,0,7483.58999\n102619,69,48,2,1,Single,12,158.2984678,0,7666.92339\n150080,63,42,3,1,Single,0,158.1009031,0,7365.045155\n152876,18,0,3,1,Married,14,158.0440421,0,7354.202105\n139879,22,5,1,0,Married,14,157.9683334,0,7397.41667\n132978,60,40,3,0,Single,11,157.9560016,0,7534.80008\n165149,70,53,2,0,Married,8,157.8844554,0,7458.22277\n137161,61,44,3,0,Married,9,157.8614352,0,7369.07176\n100526,69,48,4,1,Married,9,157.8040424,0,7662.20212\n125441,66,48,2,0,Single,7,157.7540557,0,7459.702785\n194939,70,50,2,1,Single,7,157.7522036,0,7586.61018\n148945,64,43,2,0,Single,13,157.6960164,0,7644.80082\n145208,64,46,3,0,Single,9,157.4779452,0,7633.89726\n163645,30,13,3,1,Married,11,157.4228629,0,7398.143145\n188199,60,41,4,1,Single,6,157.3960444,0,7318.80222\n189185,23,6,3,1,Married,15,157.395913,0,7607.79565\n155201,67,48,2,0,Single,10,157.375481,0,7570.77405\n121376,70,52,1,0,Married,15,157.3571743,0,7527.858715\n156780,67,49,1,1,Single,14,157.3049769,0,7589.248845\n174611,64,43,2,0,Single,14,157.256396,0,7304.8198\n198468,32,15,3,0,Married,13,157.1967172,0,7418.83586\n199708,61,40,3,1,Single,8,157.1944975,0,7592.724875\n132412,65,48,3,0,Single,3,157.119858,0,7397.9929\n161838,49,28,1,0,Married,15,157.064147,0,7536.20735\n126128,66,48,2,1,Married,13,157.0376403,0,7554.882015\n124776,46,28,3,0,Single,11,157.0372217,0,7380.861085\n100462,60,39,3,0,Single,14,157.0194721,0,7251.973605\n125744,28,9,2,1,Single,12,156.9877317,0,7300.386585\n152105,63,45,3,1,Married,6,156.9771093,0,7471.855465\n171431,39,20,2,1,Married,12,156.9707169,0,7577.535845\n148214,70,51,3,1,Married,7,156.8181207,0,7327.906035\n186340,63,42,3,0,Single,8,156.799638,0,7422.9819\n154125,59,42,2,1,Married,12,156.7735468,0,7603.67734\n165214,70,53,1,0,Married,0,156.7486616,0,7271.43308\n106829,54,35,3,1,Single,14,156.7126802,0,7342.63401\n112321,69,51,2,1,Married,4,156.703677,0,7289.18385\n135522,62,43,1,1,Single,9,156.6812854,0,7329.06427\n142500,20,3,1,1,Single,14,156.6693023,0,7475.465115\n198926,20,1,3,1,Married,14,156.6293566,0,7232.46783\n191302,68,48,2,0,Single,9,156.5890344,0,7443.45172\n180041,68,51,3,0,Married,1,156.5496923,0,7464.484615\n177486,29,10,2,0,Married,12,156.545439,0,7411.27195\n165076,68,49,3,1,Single,13,156.5367839,0,7623.839195\n116469,61,41,2,1,Married,7,156.5355798,0,7444.77899\n185584,39,21,1,1,Married,13,156.5256279,0,7370.281395\n161753,17,0,3,1,Married,12,156.3794748,0,7481.97374\n119548,63,46,3,0,Single,4,156.3483341,0,7591.416705\n111879,41,24,2,0,Married,11,156.1142015,0,7533.710075\n101976,70,53,1,1,Single,10,156.106545,0,7225.32725\n146898,62,42,3,0,Single,8,156.103407,0,7254.17035\n187832,65,44,2,1,Married,11,156.075533,0,7270.77665\n159482,57,39,3,1,Married,14,156.0504168,0,7541.52084\n137119,21,3,3,0,Married,15,156.0473375,0,7283.366875\n109661,46,27,3,0,Married,14,156.0179153,0,7501.895765\n112786,47,30,1,0,Single,15,155.9301797,0,7420.508985\n126485,65,45,1,1,Married,6,155.8039596,0,7402.19798\n112285,54,36,2,1,Married,12,155.7420185,0,7250.100925\n128861,68,49,1,1,Married,9,155.7208447,0,7585.042235\n193125,63,46,4,1,Single,12,155.7034451,0,7496.172255\n109204,20,2,3,1,Married,11,155.7032293,0,7370.161465\n191823,44,25,3,1,Married,14,155.6652916,0,7465.26458\n148935,65,47,3,0,Single,6,155.5908324,0,7549.54162\n199250,69,51,3,0,Single,13,155.5790104,0,7314.95052\n133864,24,6,2,0,Married,15,155.5642248,0,7216.21124\n137592,66,47,1,1,Single,12,155.5223241,0,7302.116205\n102090,66,49,2,1,Single,6,155.4935516,0,7252.67758\n110303,23,6,3,1,Married,14,155.4213447,0,7524.067235\n169329,68,51,3,0,Single,3,155.4189571,0,7474.947855\n189286,18,0,2,1,Married,13,155.371894,0,7523.5947\n145072,30,9,2,1,Married,11,155.2510028,0,7522.55014\n194050,60,40,2,1,Married,13,155.1971736,0,7510.85868\n125738,64,47,2,0,Married,1,155.1176981,0,7476.884905\n152542,68,50,2,0,Married,12,154.8628105,0,7247.140525\n135972,21,4,3,1,Married,14,154.8437996,0,7538.18998\n117381,70,53,2,1,Married,12,154.7307542,0,7360.53771\n177053,70,53,4,0,Single,5,154.5567899,0,7286.839495\n140940,18,0,3,1,Married,15,154.4916574,0,7432.58287\n194922,68,51,3,1,Married,6,154.4412287,0,7287.061435\n168400,44,27,2,0,Married,11,154.3197842,0,7233.98921\n189683,62,43,2,0,Married,0,154.3180839,0,7125.904195\n101316,17,0,2,1,Married,15,154.2538357,0,7252.691785\n190055,16,0,3,1,Married,11,154.2308605,0,7333.543025\n108988,19,0,3,1,Married,13,154.199217,0,7447.96085\n132573,68,50,4,0,Single,8,154.1986373,0,7151.931865\n143462,28,8,1,0,Married,14,154.1620196,0,7470.10098\n104303,70,52,2,0,Married,7,154.1280993,0,7455.404965\n141310,34,16,3,1,Married,14,153.9872357,0,7331.361785\n183081,68,50,1,1,Married,6,153.9380961,0,7399.904805\n177535,22,5,2,1,Married,12,153.8491847,0,7151.459235\n198193,63,44,1,1,Single,0,153.8388035,0,7115.940175\n194121,66,45,2,1,Married,8,153.7567657,0,7348.838285\n150790,70,50,1,0,Married,5,153.752197,0,7088.60985\n198363,21,3,2,1,Married,14,153.7438074,0,7340.19037\n196394,27,7,2,1,Married,14,153.7026157,0,7451.130785\n157760,26,9,2,0,Married,15,153.6713242,0,7339.56621\n145440,39,22,1,0,Married,14,153.61885,0,7153.9425\n141582,62,41,3,0,Single,12,153.5149829,0,7285.749145\n119952,41,20,3,1,Married,13,153.4046273,0,7377.231365\n182664,61,42,3,1,Single,7,153.2983699,0,7178.918495\n158152,63,43,1,0,Married,13,153.2763754,0,7211.81877\n134444,67,49,4,0,Married,14,153.2302241,0,7414.511205\n192469,64,44,4,1,Married,2,153.1984942,0,7379.92471\n107757,69,48,2,1,Married,12,153.1905641,0,7104.528205\n108903,70,51,2,0,Married,2,153.182301,0,7201.11505\n131121,70,53,1,1,Single,14,153.1615919,0,7431.079595\n181341,64,46,2,1,Married,9,153.1456789,0,7246.283945\n106999,65,45,1,1,Married,4,153.1137653,0,7336.688265\n191288,68,48,3,0,Single,7,153.110499,0,7111.52495\n186249,66,47,2,0,Single,14,153.0128534,0,7156.64267\n185663,42,23,4,1,Single,13,152.877192,0,7153.8596\n159832,57,40,3,1,Married,12,152.8377247,0,7285.886235\n189047,68,51,1,0,Married,2,152.8376727,0,7246.883635\n153308,66,47,2,0,Married,4,152.8192257,0,7052.961285\n101331,37,16,3,1,Married,12,152.6981784,0,7160.90892\n109540,32,11,3,0,Married,11,152.6720974,0,7308.60487\n173154,69,52,1,0,Single,8,152.6024359,0,7360.121795\n182783,66,45,4,1,Single,11,152.4554771,0,7319.773855\n113320,66,46,2,1,Single,4,152.4283968,0,7413.41984\n161908,22,5,4,0,Married,13,152.4110337,0,7313.551685\n115139,69,52,1,0,Single,9,152.3553777,0,7042.768885\n180770,25,8,3,0,Married,13,152.287053,0,7114.35265\n106246,69,48,1,0,Single,13,152.279689,0,7380.98445\n140706,66,49,1,0,Married,9,152.1838825,0,7243.194125\n133592,64,45,4,1,Married,7,152.1742871,0,7264.714355\n107827,64,46,3,0,Married,13,152.1678633,0,7115.393165\n134137,61,40,2,1,Single,5,152.1417303,0,7280.086515\n159297,60,40,1,0,Married,13,152.0660029,0,7401.300145\n164279,57,37,2,0,Single,12,152.0623207,0,7008.116035\n142662,69,48,2,0,Married,7,152.0255922,0,7029.27961\n125382,64,46,2,1,Married,14,152.0243906,0,7080.21953\n107057,60,42,4,1,Single,5,151.9905867,0,7011.529335\n103619,69,50,4,1,Single,1,151.9653298,0,7087.26649\n174138,59,41,3,0,Married,15,151.8233735,0,7151.168675\n182169,63,45,3,1,Single,15,151.7419694,0,7279.09847\n174933,66,48,3,1,Married,7,151.6493063,0,6989.465315\n195442,42,23,4,1,Married,11,151.6055874,0,7028.27937\n187652,29,8,3,1,Single,15,151.5827388,0,7022.13694\n104122,25,8,3,0,Married,13,151.5064785,0,7312.323925\n106384,37,16,3,1,Married,15,151.5045773,0,7012.228865\n126985,69,52,1,1,Married,3,151.4867587,0,7232.337935\n158709,56,37,3,0,Married,14,151.4752513,0,7268.762565\n155723,51,31,2,1,Married,14,151.429648,0,7046.4824\n187281,21,2,3,1,Married,14,151.4183442,0,7343.91721\n138897,67,50,3,0,Married,15,151.3785083,0,7053.925415\n113399,60,42,1,0,Married,15,151.2964501,0,7216.822505\n178596,65,44,1,1,Married,8,151.2848344,0,7229.24172\n168283,70,52,2,0,Married,10,151.2817751,0,7241.088755\n141654,34,13,1,1,Married,12,151.2591629,0,7143.958145\n111950,65,47,2,1,Married,0,151.1795166,0,7049.97583\n198489,24,6,3,1,Married,14,151.1045358,0,7234.22679\n144963,60,39,3,0,Married,10,151.0873866,0,6955.36933\n170930,60,42,2,1,Married,10,150.919353,0,7169.96765\n134417,68,50,2,0,Married,3,150.864178,0,7023.2089\n113022,64,47,3,0,Single,8,150.8323571,0,7270.617855\n124160,64,47,3,0,Single,11,150.7902049,0,6994.510245\n158610,69,49,1,1,Married,2,150.6347241,0,6948.736205\n182302,25,8,3,1,Married,14,150.6143353,0,7236.716765\n109212,61,42,4,1,Single,15,150.5963101,0,7151.815505\n173817,41,20,1,1,Single,15,150.5320913,0,7299.604565\n156219,66,49,1,1,Married,7,150.5195128,0,7033.97564\n187364,46,25,1,1,Married,14,150.4301012,0,7318.50506\n109512,41,21,2,1,Single,13,150.4155806,0,6929.77903\n152241,62,43,2,1,Married,12,150.3941983,0,7022.709915\n104175,25,8,4,1,Single,12,150.236184,0,7184.8092\n179962,41,20,2,1,Married,15,150.1916779,0,7125.583895\n171301,38,17,1,1,Married,12,150.128075,0,7171.40375\n169461,60,43,4,1,Married,13,150.1278153,0,7050.390765\n162415,24,5,3,0,Married,11,150.1191536,0,7218.95768\n147475,70,50,2,0,Married,12,150.0765561,0,7211.827805\n140900,66,47,3,1,Single,14,149.8102519,0,7218.512595\n116693,65,47,2,1,Single,13,149.7798516,0,6921.99258\n163281,68,51,4,1,Married,11,149.7620467,0,7224.102335\n134579,63,44,3,0,Single,12,149.5495403,0,7022.477015\n161587,62,43,3,0,Married,2,149.4906992,0,6941.53496\n112844,20,1,3,1,Married,14,149.4781184,0,7262.90592\n140873,66,48,3,1,Married,15,149.4642395,0,7046.211975\n147174,70,50,4,1,Married,11,149.458186,0,7083.9093\n153807,61,44,3,1,Single,2,149.4016837,0,7176.084185\n123293,26,6,2,0,Single,14,149.3552416,0,7165.76208\n173974,29,10,2,1,Single,14,149.3536713,0,7153.683565\n167986,61,42,4,1,Married,6,149.3488157,0,7083.440785\n142807,67,46,4,0,Married,9,149.3350989,0,7203.754945\n128113,65,48,3,0,Married,8,149.3061342,0,7077.30671\n102583,64,45,2,1,Single,11,149.2965848,0,7241.82924\n112865,67,50,3,1,Married,6,149.2953303,0,6892.766515\n119335,53,33,4,1,Single,12,149.2581064,0,6926.90532\n111821,64,47,2,1,Married,15,149.1605053,0,7117.025265\n146928,22,3,4,1,Single,11,149.0841365,0,7190.206825\n178085,69,52,2,0,Married,10,149.0625524,0,6989.12762\n166914,65,47,2,1,Married,12,149.0452224,0,6868.26112\n141586,66,46,3,1,Married,3,149.0157461,0,7200.787305\n119092,44,27,4,0,Single,15,148.9778909,0,7135.894545\n145661,23,6,2,1,Married,14,148.9163613,0,6916.818065\n110178,65,48,2,1,Married,7,148.8802465,0,6866.012325\n110870,64,45,2,0,Married,3,148.8621741,0,7049.108705\n112944,61,40,3,1,Married,9,148.8466413,0,7119.332065\n150675,61,40,3,0,Married,8,148.7518968,0,7181.59484\n104745,68,49,3,0,Single,13,148.6890676,0,7036.45338\n136088,63,44,1,1,Married,3,148.6616823,0,7023.084115\n148646,24,7,3,1,Married,12,148.6353199,0,6973.765995\n110397,70,49,1,1,Married,8,148.5966606,0,6868.83303\n100120,60,42,1,0,Single,6,148.5037972,0,6984.18986\n160302,67,48,4,0,Married,7,148.4713062,0,7064.56531\n123416,27,8,2,1,Married,11,148.4701987,0,7132.509935\n172451,51,33,2,1,Married,13,148.3842226,0,7194.21113\n123355,20,2,1,1,Married,11,148.3836732,0,7206.18366\n108070,17,0,3,1,Married,13,148.3585953,0,6885.929765\n124926,57,36,1,0,Married,14,148.2439976,0,6947.19988\n158997,67,46,2,1,Married,11,148.2309601,0,7096.548005\n103598,60,39,1,1,Married,12,148.1725629,0,7115.628145\n137090,64,44,2,0,Single,15,148.1388958,0,7150.94479\n129376,60,40,3,1,Single,8,148.1313416,0,7182.56708\n159549,65,48,2,0,Single,6,148.1187688,0,6957.93844\n194834,66,48,2,1,Married,11,148.0625438,0,6910.12719\n181938,68,47,2,0,Married,3,148.0620456,0,7120.10228\n170477,19,0,2,1,Married,12,148.0307842,0,7191.53921\n145421,50,31,2,1,Single,12,147.9677119,0,6869.385595\n121934,62,45,3,1,Married,8,147.9488486,0,7032.44243\n110970,60,40,3,1,Single,9,147.9479363,0,6941.396815\n191566,69,52,2,0,Married,2,147.9464928,0,7057.32464\n153190,63,46,4,1,Single,15,147.9328623,0,7159.643115\n147197,24,7,4,1,Married,12,147.8908582,0,7090.54291\n114500,65,46,2,0,Single,4,147.8135462,0,6856.67731\n168785,26,9,1,1,Single,11,147.7489935,0,6917.449675\n165410,65,48,1,1,Married,14,147.7444072,0,6821.22036\n103549,16,0,3,1,Married,12,147.6278424,0,6974.39212\n153614,68,48,3,1,Married,4,147.4983244,0,7131.91622\n162314,70,49,3,0,Married,11,147.4118266,0,6937.59133\n182446,52,32,1,0,Married,11,147.3761696,0,7035.80848\n128507,34,16,1,0,Married,11,147.2741212,0,6826.70606\n112726,27,7,4,1,Single,14,147.2167572,0,6958.83786\n152041,20,3,3,1,Single,12,147.2161158,0,6807.80579\n180972,65,46,1,1,Single,5,147.1745904,0,7086.72952\n126138,27,6,2,0,Married,15,147.0449807,0,7138.249035\n126613,61,41,2,0,Married,11,147.0430071,0,6955.150355\n200406,57,39,3,0,Married,14,146.9718123,0,7012.590615\n154899,66,46,2,1,Single,6,146.9028197,0,6910.140985\n136058,63,44,4,0,Single,2,146.8841318,0,7115.20659\n182567,47,30,1,0,Married,15,146.8817882,0,6925.08941\n180608,53,36,3,1,Married,14,146.8312685,0,6787.563425\n147161,66,49,4,0,Single,10,146.8139734,0,7095.69867\n163095,60,43,4,1,Married,6,146.7581689,0,6906.908445\n121123,66,48,3,0,Married,6,146.6469132,0,7027.34566\n195187,65,45,3,1,Single,10,146.6299982,0,7063.49991\n178290,66,49,1,0,Single,8,146.5715444,0,6743.57722\n154547,67,46,2,1,Single,4,146.3974235,0,6982.871175\n129524,68,47,2,1,Single,3,146.3867019,0,6823.335095\n126854,25,7,4,1,Married,12,146.3538524,0,6739.69262\n182908,18,0,2,1,Married,13,146.1876575,0,7076.382875\n120483,38,17,3,0,Married,12,146.1065191,0,6839.325955\n123153,61,40,1,1,Married,1,146.0636593,0,6741.182965\n190213,55,35,4,1,Married,11,146.0157924,0,7056.78962\n129599,69,48,3,1,Married,10,145.9433377,0,7070.166885\n167074,43,24,3,1,Single,12,145.9076735,0,6853.383675\n106334,35,16,2,1,Single,14,145.8210783,0,6813.053915\n132681,62,41,3,0,Single,11,145.8210181,0,7081.050905\n159058,70,52,3,1,Married,7,145.6857607,0,7082.288035\n108753,36,16,1,1,Married,12,145.6264409,0,6806.322045\n104224,18,0,3,1,Married,15,145.4764498,0,6932.82249\n136991,17,0,3,0,Married,12,145.3968279,0,6990.841395\n170267,68,47,3,1,Single,6,145.3831304,0,6869.15652\n176791,20,3,2,1,Married,14,145.380928,0,6877.0464\n160041,17,0,3,1,Married,15,145.3607974,0,6803.03987\n184449,62,41,3,0,Single,13,145.2461263,0,6755.306315\n149269,25,6,3,1,Married,14,145.2109314,0,6771.54657\n191504,39,18,3,1,Married,12,145.1942492,0,6729.71246\n178830,63,44,3,1,Single,12,145.1676168,0,7006.38084\n102484,62,41,4,0,Single,11,145.1097016,0,6754.48508\n119148,65,45,2,0,Married,14,145.1000834,0,6672.00417\n135391,18,0,3,1,Married,12,145.0555247,0,6897.776235\n110371,22,4,3,0,Married,11,145.0265333,0,6886.326665\n134510,61,40,4,1,Married,6,145.003963,0,6806.19815\n116432,36,17,3,1,Married,15,144.8309384,0,6660.54692\n183178,29,11,3,0,Single,14,144.8065927,0,6807.329635\n122400,26,5,1,1,Married,13,144.6856321,0,6867.281605\n109210,37,16,2,0,Married,13,144.6547725,0,6787.738625\n163775,68,50,2,1,Married,7,144.6236185,0,6643.180925\n165713,68,49,3,1,Single,10,144.562484,0,6680.1242\n126348,49,32,2,1,Married,14,144.5372572,0,6734.86286\n195517,69,51,1,1,Married,6,144.507744,0,7009.3872\n134422,70,51,2,1,Married,12,144.5022633,0,6831.113165\n151757,60,40,2,0,Married,0,144.495086,0,6924.7543\n173811,39,20,1,0,Married,15,144.4791732,0,6766.95866\n180777,29,10,3,1,Married,11,144.4621015,0,6764.105075\n159292,18,0,3,1,Married,12,144.454508,0,6857.7254\n112220,19,2,1,1,Married,11,144.4525566,0,6941.62783\n137257,66,46,2,1,Single,6,144.4047705,0,6642.238525\n190671,27,8,2,1,Single,12,144.3156563,0,6716.782815\n149113,18,0,2,1,Married,15,144.2135895,0,6740.679475\n142870,36,16,4,1,Single,14,144.1626216,0,6697.13108\n196789,70,51,1,1,Married,12,144.1533344,0,6781.66672\n120287,62,42,4,1,Married,9,144.1073474,0,6717.36737\n137505,65,48,4,0,Single,9,144.0930224,0,6816.65112\n133635,38,18,2,1,Married,13,144.07381,0,6776.6905\n167479,62,44,2,1,Single,15,143.9763704,0,6931.81852\n181189,69,51,2,0,Married,9,143.9417697,0,6762.088485\n133968,22,5,3,1,Married,14,143.8981868,0,6833.90934\n111443,18,0,2,1,Married,15,143.8874645,0,6768.373225\n162790,62,44,3,1,Single,0,143.7954583,0,6922.772915\n153623,47,29,2,1,Married,13,143.7807696,0,6610.03848\n151572,62,41,3,0,Married,8,143.6831371,0,6714.156855\n192953,66,45,4,1,Single,9,143.6529969,0,6711.649845\n149463,69,50,2,0,Married,7,143.6121635,0,6672.608175\n111386,20,1,2,1,Married,11,143.6071362,0,6588.35681\n123117,50,30,3,0,Married,11,143.5985795,0,6917.928975\n157530,62,44,2,0,Single,7,143.5748367,0,6851.741835\n123583,68,51,2,0,Married,6,143.5538933,0,6848.694665\n180397,66,48,1,0,Married,7,143.4630701,0,6848.153505\n188409,37,20,1,0,Single,15,143.4336905,0,6948.684525\n123164,22,3,2,1,Married,12,143.4011115,0,6924.055575\n157558,29,8,3,1,Married,12,143.3820035,0,6944.100175\n157692,39,22,4,1,Married,11,143.2968421,0,6830.842105\n155587,19,2,2,1,Married,14,143.2132108,0,6825.66054\n152422,35,18,2,0,Married,14,143.0972717,0,6866.863585\n131523,60,42,4,1,Married,3,143.0938411,0,6898.692055\n192687,16,0,2,1,Married,12,143.0806463,0,6744.032315\n139086,24,5,3,1,Married,11,143.0501778,0,6849.50889\n109770,28,8,3,1,Married,12,143.0281647,0,6553.408235\n192232,68,51,3,1,Single,10,142.9617837,0,6666.089185\n157804,64,43,3,0,Single,12,142.9188975,0,6663.944875\n171367,24,6,3,1,Married,11,142.8980831,0,6722.904155\n146069,45,27,1,1,Married,12,142.8929684,0,6549.64842\n123384,65,47,3,0,Married,6,142.8313511,0,6824.567555\n109411,46,26,3,0,Married,14,142.801774,0,6707.0887\n168812,60,40,2,1,Married,9,142.7739137,0,6876.695685\n186723,57,38,1,1,Married,15,142.7516263,0,6818.581315\n117277,62,43,2,1,Married,10,142.728526,0,6673.4263\n160472,18,0,2,1,Married,13,142.6550883,0,6926.754415\n181619,25,7,2,1,Married,14,142.6101548,0,6758.50774\n186813,70,50,3,0,Married,10,142.6065572,0,6818.32786\n131461,65,46,4,1,Married,0,142.5694647,0,6920.473235\n196406,50,33,1,0,Married,11,142.5319857,0,6535.599285\n151866,46,25,3,0,Married,11,142.5220495,0,6816.102475\n125294,63,46,1,1,Single,1,142.4987316,0,6605.93658\n124321,62,42,3,1,Married,12,142.4978964,0,6553.89482\n176605,70,51,2,1,Married,10,142.4823203,0,6807.116015\n143752,66,48,4,0,Single,0,142.4643846,0,6837.21923\n104918,68,49,3,1,Single,11,142.4226098,0,6913.13049\n143068,61,41,3,1,Single,11,142.4210784,0,6805.05392\n197757,18,0,2,1,Married,11,142.3504531,0,6747.522655\n143466,61,43,3,0,Married,11,142.3332636,0,6769.66318\n177443,35,16,1,1,Married,15,142.2592456,0,6730.96228\n160040,47,26,1,0,Married,12,142.2443975,0,6731.219875\n129017,57,37,2,1,Married,15,142.2245697,0,6579.228485\n149873,62,42,2,0,Married,13,142.1607176,0,6565.03588\n174974,64,47,2,1,Married,4,141.9896861,0,6638.484305\n104843,31,13,2,0,Married,15,141.979313,0,6810.96565\n137580,17,0,2,1,Married,12,141.9759881,0,6858.799405\n123642,62,41,3,1,Single,0,141.9405766,0,6634.02883\n177272,56,37,3,0,Married,11,141.8416532,0,6739.08266\n179694,69,50,2,1,Married,3,141.8314831,0,6577.574155\n138153,60,43,2,1,Married,9,141.8266068,0,6516.33034\n193872,65,47,2,0,Married,9,141.6715803,0,6712.579015\n196152,43,23,2,1,Single,15,141.6558586,0,6562.79293\n138061,47,26,3,1,Married,12,141.5556972,0,6649.78486\n157229,64,47,4,1,Married,8,141.4576799,0,6631.883995\n118245,67,46,2,1,Married,15,141.440566,0,6517.0283\n132775,64,44,3,0,Married,2,141.4184224,0,6683.92112\n124334,63,43,3,0,Single,7,141.3235751,0,6807.178755\n196573,20,3,2,1,Married,11,141.3183162,0,6635.91581\n101926,62,43,2,1,Married,8,141.3103188,0,6714.51594\n168787,64,44,3,1,Married,15,141.2192271,0,6690.961355\n134402,68,51,2,0,Single,6,141.1640445,0,6514.202225\n153617,62,42,3,0,Married,13,141.1557738,0,6786.78869\n161155,46,29,2,1,Married,11,141.1461203,0,6792.306015\n148202,67,49,3,1,Married,15,141.0942506,0,6646.71253\n182636,63,43,4,1,Single,9,141.050927,0,6747.54635\n186451,61,40,2,1,Single,6,141.0243744,0,6459.21872\n118745,60,42,3,1,Single,7,141.0194911,0,6812.974555\n176953,56,35,2,0,Married,14,140.9900455,0,6649.502275\n148922,62,44,4,1,Married,8,140.9300026,0,6492.50013\n146082,69,48,4,1,Single,12,140.8914396,0,6724.57198\n155619,69,49,2,1,Married,7,140.8741022,0,6738.70511\n166436,54,35,3,0,Married,13,140.869927,0,6568.49635\n100018,29,10,1,0,Married,12,140.7946555,0,6705.732775\n161242,35,15,2,1,Married,11,140.7193293,0,6585.966465\n159099,70,52,2,0,Married,5,140.6283107,0,6711.415535\n122620,68,51,2,1,Single,2,140.6200114,0,6744.00057\n190001,63,45,3,0,Single,12,140.4538959,0,6541.694795\n178923,24,6,4,1,Married,13,140.4341427,0,6758.707135\n174179,25,8,3,1,Married,11,140.2345358,0,6622.72679\n183180,70,52,1,1,Single,7,140.2225367,0,6713.126835\n107472,67,49,2,0,Married,13,140.175988,0,6500.7994\n111554,45,25,3,1,Married,12,140.1734136,0,6521.67068\n121938,63,44,3,0,Married,7,140.1000249,0,6452.001245\n191912,39,22,3,0,Married,12,140.0731916,0,6797.65958\n133591,66,48,2,0,Married,1,140.0369644,0,6429.84822\n165964,66,46,2,0,Single,5,140.0041326,0,6584.20663\n165843,62,41,2,1,Single,8,139.9632449,0,6560.162245\n123051,67,49,4,0,Single,11,139.8835245,0,6470.176225\n163390,62,42,1,1,Married,13,139.802146,0,6559.1073\n176995,53,36,1,0,Married,12,139.7488865,0,6546.444325\n146794,69,49,2,0,Married,13,139.7236893,0,6660.184465\n118965,28,7,3,0,Married,12,139.7018171,0,6687.090855\n165307,61,40,3,0,Married,6,139.6749114,0,6678.74557\n126915,40,21,3,0,Married,11,139.6440318,0,6540.20159\n185641,32,12,4,1,Married,13,139.6396144,0,6655.98072\n173228,62,42,2,0,Married,10,139.5981739,0,6529.908695\n164238,35,16,3,0,Married,12,139.4895176,0,6537.47588\n134497,37,17,3,0,Married,15,139.4538857,0,6659.694285\n110849,64,46,2,1,Married,6,139.3373677,0,6372.868385\n200361,21,3,3,1,Married,11,139.1990263,0,6507.951315\n130363,63,42,4,0,Married,10,139.1636665,0,6635.183325\n185096,61,44,4,1,Single,1,139.1003056,0,6488.01528\n187489,69,49,2,0,Single,0,139.045733,0,6654.28665\n170647,30,12,3,0,Single,13,139.0305861,0,6523.529305\n175539,63,46,3,1,Married,12,138.959039,0,6698.95195\n105949,67,50,3,1,Single,14,138.9115574,0,6424.57787\n123886,62,41,4,0,Married,2,138.9066927,0,6608.334635\n142235,66,46,2,1,Married,10,138.8448941,0,6516.244705\n198791,67,48,2,0,Single,5,138.816053,0,6666.80265\n130006,62,42,3,1,Married,14,138.6775023,0,6619.875115\n109874,62,45,3,0,Married,11,138.6518271,0,6619.591355\n117551,58,37,3,0,Married,11,138.6467824,0,6633.33912\n185775,60,41,3,0,Married,1,138.5991513,0,6340.957565\n122238,59,42,4,0,Married,15,138.4931379,0,6363.656895\n113174,61,41,3,1,Married,3,138.4420834,0,6515.10417\n179575,63,45,1,0,Married,12,138.3989724,0,6332.94862\n113891,21,2,3,1,Married,11,138.3497971,0,6522.489855\n100002,53,32,3,1,Single,13,138.2480792,0,6525.40396\n131006,58,40,3,0,Married,15,138.2319906,0,6328.59953\n199525,23,6,1,1,Married,12,138.2124308,0,6700.62154\n106735,20,3,4,1,Married,13,138.1916119,0,6634.580595\n150761,64,46,4,1,Married,5,138.0756959,0,6510.784795\n163463,61,43,4,1,Married,0,138.0732638,0,6356.66319\n122754,34,14,1,0,Married,12,138.0157484,0,6379.78742\n181412,67,48,4,1,Single,14,138.0062197,0,6312.310985\n146672,19,1,2,1,Married,13,137.9957896,0,6314.78948\n160816,32,15,3,0,Married,13,137.9323284,0,6675.61642\n136625,65,46,3,1,Single,15,137.9224414,0,6323.12207\n124235,32,12,4,1,Married,15,137.7164092,0,6527.82046\n105813,67,48,3,0,Married,7,137.6797819,0,6596.989095\n115473,24,5,3,0,Married,14,137.6009186,0,6403.04593\n184818,40,21,4,0,Married,11,137.5946148,0,6615.73074\n122013,41,23,1,1,Single,15,137.5378909,0,6536.894545\n174841,19,1,2,1,Married,15,137.4678068,0,6301.39034\n165118,70,50,4,1,Single,2,137.4275111,0,6488.375555\n109129,31,10,2,1,Single,14,137.3592826,0,6523.96413\n172286,70,52,4,1,Married,7,137.3271349,0,6383.356745\n141887,65,48,2,0,Married,12,137.2012265,0,6340.061325\n173041,19,2,3,1,Married,11,137.17354,0,6525.677\n191657,50,32,2,1,Married,15,137.1415013,0,6284.075065\n194687,65,48,4,0,Single,8,137.106691,0,6506.33455\n108440,23,5,1,0,Married,14,137.0620299,0,6648.101495\n159388,67,49,2,0,Married,2,137.0327478,0,6316.63739\n137063,52,32,2,1,Single,12,137.021926,0,6550.0963\n100144,55,37,3,1,Married,14,137.0154322,0,6277.77161\n166476,68,50,4,0,Married,15,136.9524561,0,6345.622805\n166509,67,48,3,1,Married,14,136.9499219,0,6639.496095\n128222,66,45,3,0,Single,8,136.8656011,0,6398.280055\n165809,60,40,3,1,Married,4,136.8294005,0,6284.470025\n116097,63,46,1,0,Married,6,136.7283326,0,6572.41663\n181851,67,48,1,1,Single,4,136.6655437,0,6440.277185\n130909,65,46,2,1,Single,14,136.6277081,0,6420.385405\n155173,61,42,4,0,Married,8,136.6014107,0,6391.070535\n151172,69,52,2,0,Single,7,136.5511694,0,6566.55847\n142551,69,50,4,0,Married,7,136.5326408,0,6316.63204\n100128,41,20,1,1,Married,14,136.4526367,0,6491.631835\n120046,70,52,2,1,Married,9,136.3567233,0,6378.836165\n119818,69,49,4,1,Single,12,136.3448607,0,6437.243035\n186487,67,47,4,1,Married,13,136.2829968,0,6393.14984\n185601,62,43,2,0,Single,7,136.2646741,0,6270.233705\n176572,61,44,2,0,Single,7,136.1851032,0,6332.25516\n134905,66,45,3,0,Single,8,136.1581248,0,6266.90624\n120710,61,40,3,0,Married,9,136.0925644,0,6254.62822\n142094,70,51,3,0,Single,13,136.073391,0,6491.66955\n136279,67,46,4,1,Single,12,136.0358513,0,6407.792565\n187246,69,51,2,1,Married,13,135.9787403,0,6211.937015\n179453,68,47,4,1,Single,11,135.9413147,0,6483.065735\n119200,68,51,2,1,Single,11,135.8270226,0,6381.35113\n161076,58,37,2,1,Married,11,135.8263113,0,6584.315565\n147166,39,22,2,1,Married,14,135.8245917,0,6512.229585\n148079,66,45,2,1,Single,15,135.7872789,0,6560.363945\n193963,30,13,4,1,Married,14,135.7651654,0,6487.25827\n103713,61,42,2,0,Single,8,135.7425789,0,6498.128945\n114902,32,13,3,0,Married,12,135.4948734,0,6295.74367\n169764,60,41,3,0,Single,9,135.4107831,0,6208.539155\n104541,67,49,3,1,Married,6,135.3421608,0,6249.10804\n124139,25,8,2,1,Married,12,135.2900127,0,6452.500635\n184589,69,52,1,0,Married,13,135.2854399,0,6342.271995\n154096,62,43,1,1,Married,13,135.2248829,0,6413.244145\n194786,69,51,2,1,Married,7,135.1706324,0,6253.53162\n166007,16,0,4,1,Married,13,135.1657465,0,6519.287325\n133235,50,31,3,0,Married,11,135.1341608,0,6256.70804\n127962,65,47,3,1,Single,12,135.1319574,0,6271.59787\n122755,57,38,1,0,Married,11,135.0784212,0,6285.92106\n117534,70,53,4,0,Married,11,135.0308263,0,6187.541315\n174786,60,40,3,0,Married,14,134.9997371,0,6389.986855\n150937,67,48,3,0,Married,6,134.6595529,0,6322.977645\n134779,70,50,4,1,Single,9,134.5427115,0,6438.135575\n153428,22,3,1,1,Married,11,134.5213178,0,6406.06589\n162258,64,47,3,0,Single,7,134.4716114,0,6253.58057\n159409,63,45,4,0,Single,13,134.4660797,0,6225.303985\n167085,68,48,4,0,Single,15,134.349538,0,6229.4769\n122909,65,44,2,0,Married,9,134.3009248,0,6152.04624\n161894,45,27,2,1,Married,11,134.267431,0,6132.37155\n168963,18,1,2,1,Married,14,133.9794244,0,6496.97122\n184418,69,49,2,1,Single,1,133.9532326,0,6248.66163\n113770,68,47,4,1,Single,2,133.9413601,0,6343.068005\n116255,68,50,2,0,Single,15,133.9050267,0,6308.251335\n137192,22,5,2,1,Married,11,133.8298896,0,6209.49448\n193972,56,36,2,1,Single,14,133.8254731,0,6325.273655\n128099,63,43,2,0,Married,11,133.8021934,0,6455.10967\n199871,64,45,3,1,Single,10,133.7874856,0,6401.37428\n104985,55,35,3,1,Married,13,133.7700683,0,6419.503415\n117452,51,30,3,0,Married,11,133.7543921,0,6126.719605\n173500,64,43,3,1,Married,2,133.7400289,0,6281.001445\n140426,66,45,3,0,Married,15,133.7394263,0,6279.971315\n116313,64,46,2,1,Single,13,133.7223801,0,6242.119005\n106721,62,45,2,0,Married,10,133.6531928,0,6128.65964\n109697,51,31,2,1,Married,13,133.6082773,0,6084.413865\n182711,68,48,3,1,Married,2,133.5505717,0,6173.528585\n197576,65,47,1,0,Single,9,133.5021321,0,6438.106605\n142607,69,49,2,0,Married,14,133.4962693,0,6345.813465\n139647,64,46,1,1,Single,15,133.2849044,0,6314.24522\n175676,60,39,1,1,Single,9,133.2658411,0,6126.292055\n142531,67,47,2,1,Married,11,133.2366318,0,6097.83159\n164317,63,45,3,1,Single,13,133.2352704,0,6219.76352\n182514,64,47,4,1,Single,14,133.2210934,0,6242.05467\n113436,63,46,3,1,Married,10,133.1117657,0,6379.588285\n177610,68,50,3,1,Single,7,133.0597403,0,6165.987015\n169364,69,51,3,0,Single,14,133.0207591,0,6434.037955\n194192,68,50,4,1,Single,12,133.0078784,0,6218.39392\n169735,69,49,3,0,Married,8,132.9184966,0,6183.92483\n180738,35,17,2,1,Single,12,132.9111475,0,6168.557375\n153922,57,38,3,1,Married,12,132.8949097,0,6294.745485\n168038,70,50,3,0,Married,0,132.866686,0,6408.3343\n128917,34,14,3,1,Married,11,132.8603678,0,6051.01839\n102924,67,47,2,1,Married,12,132.6892237,0,6196.461185\n165555,63,46,4,0,Married,9,132.6763307,0,6272.816535\n113303,67,50,2,1,Married,8,132.6744557,0,6194.722785\n164835,54,33,3,1,Married,11,132.5911568,0,6217.55784\n141317,70,53,3,0,Married,8,132.5846809,0,6221.234045\n124794,61,43,3,1,Married,5,132.3852185,0,6039.260925\n144030,17,0,2,1,Married,15,132.3798915,0,6342.994575\n114617,63,44,2,0,Married,12,132.314349,0,6255.71745\n107202,24,5,3,1,Married,13,132.3113654,0,6246.56827\n128995,16,0,4,1,Married,12,132.2946781,0,6336.733905\n142363,61,43,3,1,Married,9,132.2840456,0,6121.20228\n162473,54,37,1,1,Married,12,132.1925258,0,6186.62629\n126625,65,48,4,0,Single,11,132.1886584,0,6218.43292\n130749,69,51,4,0,Married,6,132.1610204,0,6381.05102\n138231,60,42,2,1,Married,8,131.9921409,0,6143.607045\n162260,16,0,3,1,Married,12,131.9561189,0,6368.805945\n163100,63,45,4,1,Single,10,131.9174632,0,6225.87316\n152333,61,44,3,1,Married,14,131.7915451,0,6039.577255\n198809,69,51,3,0,Married,10,131.7787154,0,6139.93577\n168337,60,43,2,0,Married,11,131.7476077,0,6120.380385\n108420,69,50,4,1,Married,7,131.6252861,0,6345.264305\n174585,33,12,1,1,Married,14,131.6187607,0,6103.938035\n168184,63,44,3,1,Single,9,131.59412,0,5997.706\n193825,63,46,4,0,Married,0,131.5856775,0,6195.283875\n192463,65,46,3,1,Single,14,131.5417155,0,6187.085775\n105422,68,48,4,0,Married,6,131.529243,0,6117.46215\n149780,60,40,3,1,Married,4,131.413806,0,6118.6903\n135206,63,44,3,1,Single,9,131.3811093,0,6099.055465\n131113,63,44,3,1,Married,12,131.3389266,0,6081.94633\n157533,68,47,4,1,Single,13,131.3252517,0,6118.262585\n144719,35,16,3,1,Married,13,131.2594534,0,6246.97267\n155835,68,47,3,1,Single,14,131.2580644,0,6023.90322\n124575,59,38,4,0,Married,12,131.2553139,0,6092.765695\n162527,68,49,3,0,Single,14,131.2298579,0,6304.492895\n172378,28,10,3,1,Single,13,131.1657194,0,6045.28597\n171270,61,40,3,1,Married,8,131.1560146,0,6310.80073\n118484,69,52,3,0,Single,15,131.122573,0,6201.12865\n133791,29,9,2,1,Married,11,131.1032891,0,6148.164455\n142969,64,45,3,0,Married,12,131.1006337,0,6284.031685\n138510,63,44,1,0,Single,3,131.0544977,0,6194.724885\n102315,57,40,1,1,Married,11,131.0465368,0,6181.32684\n147909,60,43,2,0,Single,11,131.0345546,0,6061.72773\n198379,61,40,4,0,Married,1,131.0132821,0,6229.664105\n137468,54,34,2,1,Married,13,131.0101625,0,6100.508125\n117445,61,41,1,1,Married,15,130.9914388,0,6106.57194\n119033,63,45,4,0,Married,15,130.9616216,0,5960.08108\n105944,42,22,4,1,Married,12,130.8957786,0,6032.78893\n197842,31,10,2,1,Single,11,130.8381263,0,6280.906315\n108477,61,41,3,1,Single,13,130.7757095,0,6085.785475\n109523,62,41,2,0,Married,8,130.7330803,0,6157.654015\n101097,69,52,2,1,Married,10,130.7103753,0,6127.518765\n155937,67,47,4,1,Married,9,130.6352895,0,6111.764475\n163750,60,42,3,1,Single,9,130.5836234,0,6209.18117\n142416,67,50,1,1,Single,1,130.5786189,0,6022.930945\n167148,63,45,2,0,Married,10,130.4559443,0,6021.797215\n107428,63,46,4,0,Single,13,130.2295943,0,5978.479715\n128224,67,47,3,0,Single,12,130.1853094,0,6247.26547\n196751,37,20,1,1,Married,13,130.1393257,0,6241.966285\n163134,68,49,2,1,Single,12,130.1047303,0,6096.236515\n173661,70,53,3,0,Married,0,130.0411373,0,6175.056865\n174729,60,40,3,0,Single,12,129.991384,0,6289.5692\n159533,63,42,3,1,Single,10,129.9683434,0,5974.41717\n171672,67,47,3,0,Married,15,129.8813839,0,5943.069195\n101061,28,7,2,0,Married,12,129.8275184,0,5938.37592\n179908,63,42,1,1,Married,7,129.7356,0,6033.78\n158708,24,6,3,1,Married,15,129.674854,0,6149.7427\n126896,63,42,2,1,Single,12,129.5973895,0,6057.869475\n118878,24,5,1,1,Married,12,129.5130703,0,6244.653515\n174189,28,10,3,1,Married,12,129.3972831,0,5969.864155\n161826,62,44,2,0,Married,6,129.3390843,0,6057.954215\n186780,68,48,2,1,Married,7,129.1156282,0,6088.78141\n103739,49,32,3,0,Married,15,129.0761649,0,6195.808245\n196147,64,45,4,0,Single,2,128.9756382,0,5906.78191\n174309,26,5,1,1,Married,11,128.8692649,0,6196.463245\n179116,68,49,3,1,Married,5,128.7233246,0,5921.16623\n116809,25,7,2,0,Married,14,128.6345383,0,5834.726915\n194376,66,49,3,1,Single,11,128.4923283,0,5900.616415\n194418,65,46,1,1,Married,11,128.3992407,0,5863.962035\n106329,20,2,1,1,Married,14,128.3109544,0,6089.54772\n106563,45,25,2,1,Married,14,128.3079288,0,6133.39644\n197929,24,6,3,1,Married,14,128.2515031,0,6087.575155\n114520,69,52,3,1,Married,3,128.1344342,0,5884.72171\n116440,64,45,2,0,Married,6,127.9956872,0,6030.78436\n178340,61,42,3,0,Married,13,127.970664,0,5925.5332\n155038,49,31,1,1,Married,14,127.9555841,0,6126.779205\n107254,65,46,2,0,Married,7,127.9495891,0,6088.479455\n165636,66,47,2,1,Single,8,127.9202249,0,6087.011245\n136133,63,46,2,1,Married,12,127.8806096,0,5827.03048\n147782,67,47,2,1,Married,1,127.7929693,0,5856.648465\n112132,62,41,3,1,Married,8,127.7680796,0,6174.40398\n151281,60,39,1,0,Single,7,127.5767945,0,5813.839725\n116996,62,41,1,1,Married,5,127.554803,0,6106.74015\n177763,70,51,4,0,Married,6,127.4992474,0,6092.96237\n155399,66,47,3,1,Single,14,127.4626527,0,5780.132635\n126567,62,42,1,1,Single,9,127.4005318,0,6038.02659\n146457,62,44,2,0,Married,14,127.3617176,0,6080.08588\n190750,66,47,3,1,Single,14,127.2663704,0,5929.31852\n105750,67,48,2,1,Married,14,127.1520722,0,5863.60361\n193379,70,53,3,1,Married,8,127.1246684,0,5773.23342\n175408,22,3,2,1,Married,15,127.0787797,0,6094.938985\n166018,32,14,3,1,Married,11,127.0057963,0,5842.289815\n135024,64,44,1,0,Single,9,126.9300629,0,5776.503145\n160677,60,43,2,1,Single,3,126.8795277,0,5948.976385\n186681,66,46,2,1,Single,6,126.8610648,0,6016.05324\n120571,61,44,3,0,Single,9,126.798749,0,6039.93745\n155724,23,6,1,1,Married,14,126.7299892,0,6098.49946\n114767,64,45,2,1,Married,13,126.7240801,0,6118.204005\n177742,46,25,2,0,Married,11,126.4813408,0,5987.06704\n150149,53,36,2,1,Married,11,126.4379963,0,5827.899815\n156296,66,48,2,0,Single,3,126.4213959,0,5763.069795\n133205,57,37,3,0,Married,12,126.3580808,0,5861.90404\n156788,65,47,3,1,Married,12,126.3508732,0,6070.54366\n107793,66,45,2,0,Married,12,126.3438308,0,6033.19154\n156119,33,16,3,1,Married,12,126.1962248,0,5859.81124\n138141,31,10,3,0,Married,13,126.1228945,0,5999.144725\n177909,60,40,4,1,Single,8,126.1104143,0,5890.520715\n158346,35,18,1,1,Married,11,126.0782536,0,5755.91268\n155322,69,48,3,0,Single,14,126.0497697,0,5777.488485\n192294,69,50,4,1,Single,14,125.9758822,0,6088.79411\n142091,26,8,1,1,Married,12,125.8771424,0,5843.85712\n159604,69,51,1,1,Married,7,125.8350503,0,5716.752515\n143790,64,43,2,0,Married,13,125.7636573,0,5964.182865\n165957,69,52,1,1,Single,12,125.7505907,0,5745.529535\n155797,70,52,3,0,Married,12,125.7227704,0,5962.13852\n155521,67,46,2,0,Married,11,125.7203764,0,5952.01882\n191011,25,7,2,1,Married,14,125.7112886,0,5903.56443\n146095,17,0,3,1,Married,13,125.6791073,0,6060.955365\n191995,67,50,2,1,Married,15,125.642613,0,5837.13065\n118755,68,51,3,1,Single,13,125.6315413,0,5870.577065\n199431,70,53,2,1,Single,14,125.4541326,0,5998.70663\n179660,61,43,1,1,Single,6,125.3379165,0,5900.895825\n104154,64,44,3,0,Married,12,125.2914201,0,5963.571005\n195802,62,45,3,1,Single,10,125.1904244,0,5743.52122\n193738,65,45,2,1,Single,13,125.0625266,0,5660.12633\n131246,63,44,3,0,Married,15,125.061786,0,5788.0893\n123147,19,1,2,1,Married,15,125.004172,0,6027.2086\n102067,65,47,2,0,Married,9,124.9805569,0,6012.027845\n117725,68,47,2,1,Married,8,124.974486,0,5923.7243\n128873,62,44,3,0,Single,4,124.9309238,0,5763.54619\n170464,61,42,3,1,Married,3,124.9121363,0,5766.606815\n195819,67,46,2,0,Single,7,124.8982061,0,5768.910305\n105002,30,9,2,1,Single,15,124.8572518,0,5911.86259\n110682,55,36,2,1,Married,11,124.8469363,0,5740.346815\n167755,52,32,4,0,Married,14,124.7120567,0,6022.602835\n113311,66,47,4,1,Married,14,124.7072547,0,5661.362735\n152196,49,30,3,1,Married,11,124.661857,0,5702.09285\n172347,48,30,1,1,Married,11,124.6556691,0,5994.783455\n185332,60,40,3,1,Single,2,124.6380942,0,5643.90471\n164157,38,19,2,1,Married,12,124.6151363,0,5800.756815\n150922,61,44,4,1,Married,11,124.5851478,0,5869.25739\n173294,24,7,3,1,Married,13,124.5419691,0,5732.098455\n155642,62,45,1,0,Married,14,124.498519,0,5907.92595\n180306,20,3,2,1,Married,12,124.4894147,0,5721.470735\n138991,67,49,3,0,Married,4,124.4855245,0,5861.276225\n104906,66,46,3,1,Single,13,124.4374167,0,5879.870835\n161611,63,42,2,1,Single,2,124.4352468,0,5725.76234\n171517,47,28,1,0,Married,15,124.3721637,0,5857.608185\n120364,26,6,2,1,Married,11,124.348319,0,5965.41595\n150329,65,47,1,1,Married,3,124.2207011,0,5965.035055\n108414,70,53,4,0,Married,10,124.1461659,0,5800.308295\n134782,68,49,3,1,Single,14,124.1052986,0,6004.26493\n184541,61,40,3,1,Single,7,124.0761077,0,5660.805385\n110482,63,42,3,1,Married,14,124.0040164,0,5943.20082\n153843,25,8,2,0,Married,12,123.9473868,0,5851.36934\n105550,62,43,2,1,Married,13,123.8890856,0,5867.45428\n165710,38,17,2,0,Married,12,123.8587097,0,5937.935485\n189806,70,51,4,1,Married,12,123.7757506,0,5898.78753\n158451,69,51,4,1,Married,7,123.7747029,0,5713.735145\n175567,64,47,3,1,Married,11,123.7441614,0,5828.20807\n180865,65,48,2,1,Married,13,123.6650647,0,5730.253235\n186772,69,48,3,1,Married,14,123.6269694,0,5760.34847\n143079,62,44,3,0,Single,13,123.6078003,0,5892.390015\n191094,17,0,3,1,Married,12,123.5602154,0,5672.01077\n121838,63,45,2,0,Single,8,123.4575718,0,5707.87859\n162575,49,29,3,0,Married,11,123.3073712,0,5654.36856\n181890,68,50,4,1,Married,12,123.2312106,0,5568.56053\n122889,66,45,1,1,Single,9,123.1604341,0,5797.021705\n116000,69,48,3,1,Married,15,123.1253857,0,5649.269285\n158175,61,44,2,1,Married,12,123.1137754,0,5602.68877\n108914,65,47,2,1,Married,15,123.027051,0,5623.35255\n170444,60,43,2,0,Married,0,122.9757014,0,5935.78507\n134959,45,26,3,0,Married,12,122.9422353,0,5714.111765\n105602,70,50,4,1,Married,7,122.9259417,0,5748.297085\n197918,61,41,1,1,Married,7,122.8351382,0,5867.75691\n104135,68,48,1,1,Married,9,122.8164329,0,5794.821645\n141114,70,51,2,0,Married,6,122.6835746,0,5748.17873\n150724,70,53,3,0,Married,8,122.6117209,0,5812.586045\n165496,62,45,3,1,Single,1,122.5853378,0,5641.26689\n113888,60,43,3,1,Married,1,122.5669928,0,5570.34964\n151963,63,44,2,0,Single,10,122.490126,0,5906.5063\n197435,24,5,3,1,Married,13,122.4885697,0,5623.428485\n180670,64,45,3,1,Single,15,122.4597923,0,5564.989615\n146711,62,45,3,0,Married,9,122.4011314,0,5724.05657\n175391,65,47,1,1,Single,12,122.3285063,0,5751.425315\n143608,66,49,3,1,Single,12,122.2920575,0,5814.602875\n103762,69,52,1,1,Married,12,122.2172897,0,5826.864485\n183354,62,43,3,0,Single,10,122.1896664,0,5894.48332\n183488,66,48,3,1,Married,4,122.0725058,0,5505.62529\n195893,68,50,2,1,Single,15,122.0274734,0,5502.37367\n166997,69,50,2,1,Single,10,121.9628389,0,5831.141945\n130134,18,0,4,1,Married,11,121.9517404,0,5799.58702\n134712,66,45,4,1,Married,8,121.7890849,0,5542.454245\n160928,19,1,3,1,Married,14,121.7661902,0,5836.30951\n101053,65,47,4,1,Married,9,121.5888263,0,5794.441315\n134307,65,45,3,0,Single,11,121.3480129,0,5860.400645\n179188,66,46,3,0,Married,6,121.3427129,0,5813.135645\n190896,23,4,2,1,Married,11,121.329313,0,5710.46565\n157740,60,42,1,1,Married,6,121.2896348,0,5625.48174\n179440,63,46,3,0,Married,9,121.2747787,0,5491.738935\n117215,63,43,2,1,Single,9,121.2528717,0,5512.643585\n138437,60,40,3,1,Single,15,121.2487356,0,5554.43678\n197874,39,18,3,1,Married,11,121.1913592,0,5848.56796\n194661,60,42,2,1,Married,11,121.1234462,0,5808.17231\n150132,19,1,2,1,Married,13,121.0765599,0,5576.827995\n177592,60,41,3,1,Married,14,121.0487195,0,5830.435975\n114199,70,50,3,0,Single,5,120.8152534,0,5577.76267\n148222,66,46,2,0,Single,10,120.7814513,0,5680.072565\n199650,64,46,1,0,Married,14,120.6584421,0,5708.922105\n116190,61,43,2,1,Single,13,120.6554309,0,5459.771545\n171448,62,42,3,1,Married,11,120.5440216,0,5622.20108\n140079,70,50,2,1,Single,6,120.4937763,0,5618.688815\n109058,66,47,2,1,Married,10,120.3573508,0,5469.86754\n133995,68,50,4,1,Single,7,120.26212,0,5501.106\n108946,70,49,3,0,Married,8,120.1212386,0,5628.06193\n132798,65,45,3,1,Married,0,120.0809037,0,5417.045185\n127480,64,46,2,0,Single,10,120.0611593,0,5567.057965\n131774,70,52,1,1,Married,10,120.0064291,0,5690.321455\n173634,62,45,3,0,Single,10,119.9968984,0,5614.84492\n129830,60,42,3,1,Married,6,119.9810556,0,5413.05278\n137785,33,12,1,1,Married,14,119.974399,0,5575.71995\n158970,67,50,3,1,Married,0,119.8880277,0,5677.401385\n164421,60,40,4,1,Single,9,119.7794738,0,5573.97369\n171608,68,47,1,1,Married,7,119.7330443,0,5534.652215\n170979,69,48,1,1,Married,6,119.6500676,0,5741.50338\n165660,64,45,2,0,Single,9,119.6491594,0,5413.45797\n188299,64,45,2,1,Married,11,119.5106847,0,5497.534235\n198415,65,48,1,0,Married,9,119.5070203,0,5637.351015\n195087,62,43,1,1,Single,11,119.3495526,0,5663.47763\n173210,20,1,2,1,Married,13,119.3118833,0,5441.594165\n188084,60,43,3,0,Married,9,119.1686919,0,5360.434595\n189099,63,45,3,1,Married,1,118.8829905,0,5717.149525\n104634,24,5,3,1,Married,13,118.8773941,0,5410.869705\n161409,68,50,3,1,Single,15,118.8089687,0,5508.448435\n172065,70,50,4,1,Single,9,118.8025487,0,5612.127435\n199563,65,47,2,1,Married,6,118.7481336,0,5611.40668\n174375,68,48,1,0,Single,6,118.5946454,0,5499.73227\n191941,69,51,2,0,Married,11,118.5701308,0,5585.50654\n146283,70,53,3,1,Married,14,118.567419,0,5576.37095\n178298,60,43,2,1,Single,7,118.4561493,0,5351.807465\n125647,63,43,1,0,Married,3,118.1535711,0,5356.678555\n155070,22,5,1,1,Married,15,117.9850178,0,5399.25089\n100782,69,52,3,0,Single,13,117.9697311,0,5468.486555\n128204,60,42,2,0,Married,6,117.8851828,0,5397.25914\n191072,61,41,3,1,Single,7,117.8687744,0,5470.43872\n171938,61,44,2,0,Single,12,117.5956818,0,5564.78409\n118753,58,41,1,1,Married,12,117.5921567,0,5508.607835\n191795,69,52,2,1,Married,6,117.5886176,0,5489.43088\n122734,67,49,1,1,Married,6,117.5752762,0,5599.76381\n189685,63,43,2,1,Married,8,117.5557386,0,5340.78693\n137418,44,26,2,0,Married,15,117.5247355,0,5592.236775\n105776,63,43,2,1,Married,7,117.5225301,0,5349.126505\n172754,60,39,1,0,Married,14,117.4743023,0,5579.715115\n144978,69,52,2,0,Married,14,117.261906,0,5414.0953\n181167,64,45,1,1,Married,8,117.1345839,0,5644.729195\n111755,69,52,1,0,Single,15,117.101737,0,5391.08685\n147728,21,4,1,1,Married,14,117.0669225,0,5466.346125\n154198,45,28,1,1,Married,11,117.0495979,0,5317.479895\n114239,18,1,2,1,Married,14,117.0109629,0,5346.548145\n140999,67,47,3,1,Married,9,116.9269408,0,5413.34704\n162111,65,44,2,0,Single,2,116.8334234,0,5536.67117\n151703,70,52,2,1,Married,10,116.8177979,0,5607.889895\n180126,60,43,1,0,Single,3,116.7747096,0,5304.73548\n175206,69,48,2,1,Married,3,116.7150602,0,5264.75301\n168622,70,52,1,0,Married,3,116.6878815,0,5261.394075\n119985,52,31,4,1,Married,12,116.6594267,0,5353.971335\n153371,66,47,3,0,Married,6,116.6539191,0,5523.695955\n104344,60,39,3,0,Married,6,116.601308,0,5397.0654\n132861,62,41,3,0,Single,9,116.5855886,0,5537.27943\n107329,68,51,1,1,Married,13,116.4833382,0,5309.16691\n104155,60,41,3,0,Married,10,116.4824895,0,5375.124475\n106807,62,43,2,0,Married,9,116.4320793,0,5603.603965\n165550,20,1,2,1,Married,15,116.4235412,0,5502.17706\n104616,39,20,2,1,Married,12,116.3270713,0,5219.353565\n132753,63,42,3,0,Married,3,116.2992582,0,5593.96291\n171425,65,47,3,1,Married,9,116.146832,0,5532.3416\n138478,62,41,4,1,Single,2,116.0909781,0,5415.548905\n170182,62,45,2,1,Married,2,116.0408986,0,5286.04493\n180525,70,51,2,0,Single,7,115.9904612,0,5484.52306\n179669,60,43,2,0,Married,1,115.9275168,0,5479.37584\n197144,64,43,3,1,Married,8,115.8444065,0,5330.220325\n127095,61,44,4,0,Single,6,115.7827809,0,5443.139045\n185785,68,47,2,0,Married,15,115.7755309,0,5545.776545\n153419,63,43,1,1,Married,7,115.7704846,0,5588.52423\n187890,60,39,2,0,Married,14,115.7533082,0,5561.66541\n161210,63,44,2,0,Single,3,115.7203446,0,5566.01723\n176097,21,2,2,0,Married,14,115.6370943,0,5306.854715\n176193,68,48,3,0,Single,7,115.4222858,0,5534.11429\n170000,67,50,2,0,Married,9,115.3953038,0,5485.76519\n143585,17,0,2,1,Married,11,115.307812,0,5313.3906\n149292,69,51,2,0,Single,10,115.2835278,0,5314.17639\n127752,70,53,2,0,Single,6,115.2820125,0,5369.100625\n116156,67,47,3,1,Single,13,115.2146289,0,5259.731445\n161806,47,30,4,1,Married,11,115.1744809,0,5382.724045\n166050,64,44,1,1,Single,10,115.134362,0,5418.7181\n184381,25,6,2,1,Married,15,115.0959938,0,5370.79969\n115594,61,42,2,0,Single,8,114.9834089,0,5540.170445\n123547,62,41,1,1,Married,15,114.9781753,0,5313.908765\n137173,69,51,2,1,Married,10,114.976627,0,5420.83135\n146542,68,50,4,1,Married,12,114.7936974,0,5303.68487\n181593,64,44,1,1,Married,13,114.6509439,0,5270.547195\n141988,67,49,3,1,Single,6,114.6169331,0,5241.846655\n113806,64,47,2,1,Married,10,114.6104389,0,5513.521945\n114393,28,7,3,1,Married,14,114.51536,0,5358.768\n146456,62,41,2,0,Single,11,114.4745013,0,5318.725065\n181746,66,47,3,1,Single,13,114.4684687,0,5423.423435\n169087,67,50,4,0,Married,2,114.3150713,0,5317.753565\n125551,61,44,4,1,Married,10,114.302366,0,5323.1183\n179563,62,43,2,0,Single,6,114.2518974,0,5245.59487\n184072,67,49,2,0,Married,10,114.2397792,0,5143.98896\n131592,65,48,3,0,Single,4,114.0439503,0,5420.197515\n146355,16,0,2,1,Married,15,113.9895561,0,5205.477805\n149094,65,44,1,1,Single,11,113.9687533,0,5401.437665\n191318,60,41,4,0,Single,4,113.9536153,0,5202.680765\n165395,21,2,3,1,Married,13,113.9149167,0,5304.745835\n123715,61,44,4,0,Married,6,113.8351057,0,5248.755285\n125856,68,49,2,0,Married,12,113.8333117,0,5323.665585\n104422,62,41,4,0,Married,13,113.7164516,0,5224.82258\n175648,70,53,3,1,Married,12,113.592863,0,5293.64315\n105900,68,50,3,1,Married,15,113.2388088,0,5371.94044\n108645,69,49,3,1,Married,10,113.149769,0,5302.48845\n180986,61,42,4,1,Single,4,113.0978462,0,5203.89231\n174721,65,45,4,1,Single,9,113.0630237,0,5313.151185\n143002,65,46,4,1,Married,7,113.0374475,0,5434.872375\n112036,67,47,3,0,Single,15,112.8720049,0,5082.600245\n111647,70,49,4,0,Married,15,112.7982958,0,5164.91479\n126646,70,50,2,0,Single,3,112.6224518,0,5396.12259\n178149,63,42,3,1,Single,15,112.5311831,0,5386.559155\n169134,68,50,1,1,Married,0,112.2473507,0,5169.367535\n143703,69,49,3,1,Single,15,112.1463546,0,5350.31773\n124043,65,44,3,0,Married,10,112.1420079,0,5180.100395\n141421,46,26,4,1,Married,14,111.9844381,0,5243.221905\n174683,70,49,4,1,Single,7,111.9323709,0,5182.618545\n194536,55,38,1,1,Married,11,111.8798755,0,5295.993775\n118821,62,45,3,1,Single,11,111.8627906,0,5093.13953\n117868,67,50,1,0,Single,10,111.8604052,0,5309.02026\n197655,63,45,2,0,Married,6,111.811625,0,5236.58125\n195404,64,47,3,1,Single,10,111.7814172,0,5122.07086\n191742,67,50,3,1,Married,9,111.7540737,0,5157.703685\n174558,60,43,4,0,Married,1,111.6783922,0,5329.91961\n138073,25,8,2,1,Married,11,111.6679556,0,5230.39778\n107070,61,40,3,1,Single,10,111.6639866,0,5055.19933\n176986,65,47,2,0,Single,1,111.6358758,0,5110.79379\n189016,63,44,3,0,Married,5,111.6084269,0,5173.421345\n132897,62,43,4,0,Single,12,111.4821338,0,5262.10669\n183467,34,13,2,1,Married,14,111.3992454,0,5359.96227\n156747,65,44,4,0,Single,9,111.3796145,0,5196.980725\n197495,67,48,3,0,Married,15,111.3633951,0,5072.169755\n148191,65,48,2,1,Single,1,111.3605932,0,4975.02966\n136050,19,2,4,1,Married,14,111.3219211,0,5146.096055\n193687,62,41,3,1,Single,11,111.3062088,0,5154.31044\n198383,68,49,2,1,Single,13,111.2906968,0,5031.53484\n178591,18,0,2,1,Married,13,111.2766466,0,5270.83233\n164972,64,43,3,1,Single,14,111.2749942,0,5233.74971\n129252,66,45,2,1,Single,14,111.1771647,0,4966.858235\n110850,69,49,3,1,Single,10,111.0382749,0,5137.913745\n104316,68,49,3,0,Married,0,110.9761282,0,5197.80641\n128382,60,42,3,0,Single,2,110.973556,0,5123.6778\n123041,63,44,2,0,Married,15,110.9730614,0,5313.65307\n156889,61,40,3,0,Married,13,110.9295321,0,5210.476605\n117071,67,48,3,1,Single,9,110.8961699,0,5295.808495\n111976,70,53,2,1,Single,0,110.8403382,0,5019.01691\n159506,68,47,4,0,Married,6,110.7679747,0,5295.398735\n198258,70,52,4,1,Married,9,110.764513,0,5086.22565\n125963,63,43,4,1,Married,10,110.7175798,0,5056.87899\n139753,64,47,1,1,Single,11,110.6599318,0,5017.99659\n106217,62,44,3,1,Single,7,110.6444159,0,5069.220795\n164639,60,43,3,1,Married,6,110.6284884,0,5096.42442\n145064,69,48,3,1,Single,12,110.6013722,0,4940.06861\n196704,69,51,2,0,Married,12,110.534983,0,5133.74915\n159455,65,45,4,1,Single,15,110.4387776,0,5093.93888\n190765,69,52,2,1,Married,1,110.4164762,0,5228.82381\n180338,67,50,2,1,Married,4,110.4044529,0,5250.222645\n114563,63,44,4,0,Single,10,110.3872037,0,4955.360185\n110157,60,43,1,1,Married,6,110.3300495,0,5143.502475\n109794,19,0,3,1,Married,15,110.3025653,0,5166.128265\n191725,64,47,4,1,Single,13,110.2807929,0,5257.039645\n104097,63,46,2,0,Married,6,110.2380746,0,5260.90373\n136954,65,45,2,1,Married,13,110.1622715,0,4972.113575\n123062,64,47,3,0,Single,6,109.9055963,0,5189.279815\n131622,61,43,2,1,Married,1,109.5964897,0,4902.824485\n157904,60,39,2,1,Married,7,109.5108721,0,5106.543605\n165523,65,46,4,1,Single,10,109.4723436,0,5088.61718\n119964,65,46,2,1,Single,5,109.4517523,0,5127.587615\n136921,70,52,1,1,Married,12,109.4343565,0,4878.717825\n102159,62,42,3,0,Single,8,109.4005125,0,5228.025625\n173094,63,44,3,0,Single,9,109.3752692,0,5024.76346\n171925,63,43,4,0,Married,14,109.36009,0,5096.0045\n131641,64,43,2,0,Married,6,109.3481556,0,5032.40778\n185542,69,51,2,1,Married,3,109.3434997,0,5245.174985\n175563,67,50,3,0,Single,9,109.2622168,0,5115.11084\n128544,60,43,2,1,Married,13,109.2549184,0,5217.74592\n126631,65,47,2,0,Single,7,109.1803572,0,5081.01786\n153715,70,51,3,0,Married,4,109.1431196,0,5092.15598\n196177,65,46,1,1,Married,3,109.1127004,0,5223.63502\n139099,70,49,2,1,Single,13,109.0816165,0,4894.080825\n116579,64,47,1,0,Married,5,109.0263426,0,5133.31713\n149001,64,46,3,1,Married,13,108.8598924,0,5041.99462\n102417,25,7,3,1,Married,12,108.7571924,0,4970.85962\n162907,69,49,2,0,Single,13,108.4678671,0,4885.393355\n124790,63,43,2,1,Single,15,108.4033084,0,4880.16542\n163604,68,49,2,0,Married,8,108.3790936,0,4888.95468\n128784,24,6,2,1,Married,12,108.3392623,0,5048.963115\n181101,68,47,3,1,Single,0,108.1947388,0,4859.73694\n139778,62,43,3,1,Married,12,108.1910087,0,4931.550435\n134063,61,41,4,0,Single,0,108.1750946,0,4866.75473\n151297,61,44,4,0,Married,12,108.0378484,0,4912.89242\n153258,67,48,4,1,Single,12,107.9707894,0,5174.53947\n147389,67,49,1,1,Married,12,107.968914,0,5109.4457\n189048,69,50,2,0,Single,13,107.9637789,0,5174.188945\n139897,65,48,2,0,Married,6,107.9557709,0,4888.788545\n183570,22,4,1,1,Married,15,107.8775639,0,5072.878195\n175164,60,43,3,1,Single,7,107.8733525,0,5179.667625\n177017,62,41,3,1,Married,11,107.8515639,0,4827.578195\n151563,60,43,4,0,Single,11,107.8114166,0,5048.57083\n116919,69,49,4,1,Married,7,107.7943184,0,5165.71592\n106666,68,47,2,0,Single,5,107.7776996,0,4998.88498\n125020,60,41,2,1,Single,1,107.6786636,0,5049.93318\n179940,63,42,2,0,Married,6,107.6584512,0,5090.92256\n116588,20,2,2,1,Married,13,107.500208,0,5052.0104\n172921,29,10,1,1,Married,11,107.4184383,0,5120.921915\n196534,69,52,2,1,Married,4,107.3456572,0,5156.28286\n119817,65,46,3,1,Single,3,107.2822308,0,5082.11154\n177755,64,44,3,1,Married,2,107.2227766,0,5074.13883\n110662,63,46,2,1,Married,14,107.1457327,0,4994.286635\n162791,19,1,3,1,Married,13,107.0444896,0,5071.22448\n188100,67,46,2,0,Married,2,106.9917583,0,4983.587915\n113853,67,46,2,1,Married,13,106.9446878,0,5090.23439\n110816,64,45,2,1,Married,2,106.9182747,0,5064.913735\n152767,64,45,3,0,Married,6,106.8391346,0,5003.95673\n153275,67,50,2,1,Married,11,106.756089,0,4745.80445\n149691,68,51,4,1,Married,12,106.744504,0,5011.2252\n118032,64,44,3,0,Single,9,106.4613638,0,4904.06819\n155078,61,43,3,1,Married,8,106.4436373,0,4830.181865\n195830,62,41,2,0,Married,14,106.3385177,0,5043.925885\n188497,63,45,3,0,Married,15,106.3036463,0,4989.182315\n141294,67,46,1,0,Married,4,106.2451549,0,4884.257745\n176467,63,44,2,0,Single,12,106.1697165,0,5096.485825\n177736,66,46,2,1,Single,0,106.127757,0,4739.38785\n197625,69,48,3,0,Single,2,106.0529282,0,5053.64641\n116297,61,42,2,1,Married,10,105.9940436,0,4929.70218\n188885,62,42,2,0,Single,15,105.953581,0,5064.67905\n109520,68,48,1,1,Single,4,105.8026452,0,5035.13226\n178919,68,47,2,1,Married,15,105.7788908,0,4785.94454\n147736,62,45,2,0,Married,10,105.6599415,0,4943.997075\n171629,16,0,3,1,Married,11,105.5919565,0,5076.597825\n184217,62,43,1,0,Married,15,105.5850539,0,5008.252695\n179705,17,0,2,1,Married,14,105.5195049,0,4836.975245\n161679,64,45,1,0,Single,0,105.4432074,0,4725.16037\n116585,21,2,1,1,Married,11,105.2846005,0,5022.230025\n139159,67,46,3,0,Married,12,105.2584553,0,5012.922765\n170491,22,4,1,1,Married,11,105.2078607,0,4845.393035\n149551,62,41,2,1,Married,13,105.1956516,0,4761.78258\n126867,67,50,4,0,Married,3,105.1953427,0,4745.767135\n191575,65,47,3,0,Married,11,105.0798029,0,4660.990145\n110506,63,45,3,1,Married,9,104.7313367,0,4995.566835\n160216,70,50,2,0,Single,11,104.418739,0,4996.93695\n127414,67,47,4,1,Single,5,104.3685546,0,4988.42773\n184680,65,45,2,0,Single,15,104.3684605,0,4944.423025\n133818,64,47,1,1,Married,4,104.2926078,0,4991.63039\n121390,66,46,3,1,Married,9,104.2912674,0,4700.56337\n152238,24,7,3,1,Married,13,104.2250878,0,4815.25439\n154285,61,42,2,0,Single,7,104.1123598,0,4992.61799\n132511,62,45,2,0,Single,13,104.0642762,0,4961.21381\n108895,68,49,3,0,Single,14,104.0232158,0,4976.16079\n172082,62,44,3,1,Single,6,103.9977882,0,4631.88941\n195698,64,47,2,0,Married,10,103.9783831,0,4828.919155\n185083,61,41,3,0,Married,12,103.8177576,0,4979.88788\n178706,65,44,4,0,Married,13,103.759105,0,4636.95525\n141030,63,43,2,0,Single,11,103.607787,0,4929.38935\n146908,68,49,2,1,Single,0,103.5921231,0,4968.606155\n128749,16,0,3,1,Married,13,103.5901994,0,4667.50997\n149592,64,44,3,0,Married,5,103.5818012,0,4929.09006\n165882,69,48,3,1,Married,5,103.5107252,0,4643.53626\n145434,65,46,2,0,Single,12,103.3600888,0,4904.00444\n136940,60,41,1,0,Married,15,103.2561255,0,4841.806275\n194509,64,44,2,0,Married,9,103.2539105,0,4795.695525\n154012,70,53,1,1,Single,8,103.2503942,0,4599.51971\n168668,70,50,4,1,Single,14,103.23583,0,4923.7915\n151048,61,44,2,0,Single,11,103.2168077,0,4847.840385\n183778,67,46,2,1,Single,13,103.1529002,0,4948.64501\n182144,66,47,3,1,Married,7,103.0465771,0,4747.328855\n153165,61,44,1,0,Single,11,103.0438125,0,4676.190625\n144515,67,50,2,0,Married,7,103.0383765,0,4707.918825\n126404,68,47,2,0,Married,11,102.9205046,0,4881.02523\n124897,68,48,4,0,Married,9,102.8559119,0,4932.795595\n157377,62,42,4,1,Married,14,102.7519463,0,4643.597315\n184008,68,49,4,1,Married,11,102.7367941,0,4671.839705\n131410,62,41,2,1,Single,13,102.6944895,0,4826.724475\n152227,69,51,1,1,Single,12,102.5836151,0,4554.180755\n140007,61,44,3,1,Single,8,102.5411698,0,4832.05849\n115919,25,7,3,1,Married,13,102.3794161,0,4548.970805\n156828,24,5,1,1,Married,12,102.363644,0,4824.1822\n149779,60,39,3,0,Single,6,102.3460608,0,4866.30304\n130384,61,42,1,1,Married,13,102.3352065,0,4686.760325\n144398,60,39,2,1,Single,15,102.1883069,0,4698.415345\n126084,60,43,3,0,Married,10,102.1563167,0,4900.815835\n164359,69,50,3,1,Married,7,102.1084932,0,4649.42466\n164997,63,46,2,1,Married,5,102.0848918,0,4783.24459\n128550,67,50,1,0,Single,6,102.0728666,0,4842.64333\n190230,61,43,3,1,Single,15,102.0576231,0,4528.881155\n156610,63,42,2,0,Single,7,102.0442511,0,4835.212555\n150881,66,45,2,1,Single,8,101.9951459,0,4613.757295\n145953,68,48,3,1,Married,6,101.9932844,0,4867.66422\n110789,67,46,3,1,Single,13,101.8740628,0,4816.70314\n135944,66,46,3,0,Married,0,101.8565895,0,4773.829475\n171182,67,46,2,0,Single,4,101.7339118,0,4790.69559\n196657,66,45,1,0,Single,1,101.6847135,0,4604.235675\n171031,68,51,3,0,Single,10,101.6246447,0,4670.232235\n177434,63,45,1,0,Married,12,101.5269237,0,4533.346185\n153473,63,43,3,1,Single,10,101.5033229,0,4836.166145\n167394,70,50,3,1,Married,3,101.285223,0,4488.26115\n187212,67,46,4,1,Single,14,101.2815455,0,4540.077275\n110958,70,49,4,1,Single,15,101.2669787,0,4713.348935\n158910,68,47,1,1,Single,15,101.120868,0,4781.0434\n147781,23,6,2,1,Married,15,101.0070034,0,4517.35017\n186771,68,51,3,1,Single,10,100.9401647,0,4717.008235\n177389,63,42,2,0,Single,7,100.8304387,0,4643.521935\n184942,67,48,3,1,Single,12,100.5125468,0,4585.62734\n181946,70,49,3,1,Single,9,100.4205333,0,4789.026665\n149607,60,42,3,1,Single,10,100.3853562,0,4463.26781\n116854,68,48,4,0,Married,8,100.228615,0,4460.43075\n129391,51,31,2,1,Married,12,100.2061501,0,4766.307505\n141866,60,40,2,0,Married,2,100.1397243,0,4672.986215\n196610,62,41,2,0,Single,13,99.98969209,0,4561.484605\n110501,70,50,2,0,Married,12,99.98183865,0,4474.091933\n193446,70,51,4,1,Married,12,99.97282631,0,4744.641316\n105121,63,43,2,1,Married,11,99.96692428,0,4459.346214\n135686,70,49,1,1,Married,14,99.83090559,0,4557.54528\n189981,23,6,3,1,Married,12,99.81541486,0,4562.770743\n145181,63,46,1,0,Married,10,99.59866543,0,4771.933272\n176019,64,45,2,1,Married,4,99.51138783,0,4595.569392\n148963,64,45,4,1,Married,13,99.41027091,0,4434.513546\n129024,67,49,3,0,Single,11,99.39182437,0,4529.591219\n187736,70,53,1,0,Single,11,99.29601274,0,4624.800637\n182401,70,49,3,0,Single,9,99.22745932,0,4757.372966\n173663,67,46,3,1,Single,8,99.15114648,0,4357.557324\n149451,66,45,3,1,Single,10,98.98947239,0,4463.47362\n158679,63,43,3,0,Married,7,98.92988566,0,4485.494283\n134650,67,49,3,1,Married,1,98.87404547,0,4364.702274\n109423,64,46,2,0,Single,8,98.82646966,0,4548.323483\n169141,67,50,2,0,Married,7,98.78782384,0,4680.391192\n146420,64,44,3,0,Single,12,98.58804303,0,4580.402152\n150426,63,42,2,1,Married,8,98.55136527,0,4411.568264\n146994,67,46,1,1,Single,12,98.54406755,0,4420.203378\n190808,70,53,2,0,Married,10,98.40162872,0,4408.081436\n147015,63,43,3,0,Married,10,98.36057673,0,4684.028837\n182191,70,53,3,0,Single,5,98.35032902,0,4429.516451\n146724,62,43,2,1,Married,8,98.25327356,0,4507.663678\n163832,70,53,1,1,Married,13,98.22413289,0,4452.206645\n145906,70,49,3,1,Married,0,98.13246618,0,4345.623309\n176137,66,46,2,1,Single,7,98.13020042,0,4651.510021\n113860,67,47,1,0,Single,10,98.11645572,0,4577.822786\n131927,69,48,1,1,Married,0,98.07355841,0,4459.677921\n169338,66,48,2,1,Married,15,98.03562763,0,4683.781382\n193982,17,0,2,1,Married,15,98.00493411,0,4446.246706\n186797,70,50,1,0,Single,1,97.8872245,0,4603.361225\n170777,60,42,1,0,Single,0,97.82479138,0,4292.239569\n125885,68,51,4,1,Married,6,97.82043236,0,4687.021618\n163695,60,43,4,0,Single,7,97.80988885,0,4367.494443\n156696,70,53,1,0,Married,8,97.80945261,0,4635.472631\n194164,68,48,2,1,Married,6,97.79617698,0,4504.808849\n195711,65,45,2,0,Married,7,97.54387268,0,4585.193634\n186402,70,52,1,0,Married,15,97.49609506,0,4546.804753\n120578,68,51,3,0,Single,9,97.47832546,0,4670.916273\n100660,63,46,3,0,Married,14,97.4740293,0,4391.701465\n182472,67,49,3,0,Single,13,97.36663877,0,4575.331939\n150270,16,0,2,1,Married,15,97.35629417,0,4532.814709\n101678,68,49,2,0,Married,8,97.17633569,0,4332.816785\n133715,69,49,2,1,Single,8,97.16610705,0,4458.305353\n107666,62,45,1,1,Married,6,97.0052298,0,4648.26149\n131438,60,43,4,0,Married,9,96.96107447,0,4302.053724\n153727,69,49,3,0,Married,8,96.94203234,0,4638.101617\n103987,68,50,1,0,Married,7,96.75876211,0,4379.938106\n174510,61,40,2,0,Single,6,96.69153093,0,4402.576547\n180795,61,42,3,1,Married,1,96.66775288,0,4270.387644\n174549,69,50,1,1,Single,7,96.57177764,0,4429.588882\n113899,66,47,3,1,Married,4,96.51896574,0,4227.948287\n160360,68,49,2,1,Single,12,96.42275023,0,4587.137512\n175891,68,47,2,1,Married,7,96.39583307,0,4526.791654\n124942,65,48,1,1,Married,10,96.36032976,0,4371.016488\n110744,69,51,2,1,Married,7,96.32803426,0,4243.401713\n160866,19,1,3,1,Married,14,96.32654401,0,4408.327201\n154515,67,48,3,1,Married,7,96.31551372,0,4285.775686\n188200,64,46,3,1,Married,7,96.20616382,0,4295.308191\n126559,64,43,2,0,Single,2,96.06678677,0,4243.339339\n100459,63,45,3,0,Single,10,95.78214073,0,4487.107037\n118053,63,43,4,0,Married,9,95.74811004,0,4211.405502\n169127,64,46,3,0,Single,9,95.65189276,0,4407.594638\n161043,63,42,1,0,Single,13,95.64112278,0,4372.056139\n193859,68,48,2,1,Single,5,95.47112785,0,4473.556393\n189378,61,44,2,1,Married,4,95.41219208,0,4188.609604\n124754,69,50,1,0,Married,14,95.24364537,0,4431.182269\n139395,70,53,2,1,Single,6,95.23895266,0,4223.947633\n139640,66,46,2,0,Married,4,94.93996425,0,4279.998213\n184537,66,45,2,1,Married,6,94.8925662,0,4506.62831\n162681,25,6,3,1,Married,13,94.57991524,0,4518.995762\n138412,67,50,2,0,Married,13,94.42754236,0,4189.377118\n160000,64,43,2,0,Single,6,94.35787495,0,4336.893748\n193533,61,40,2,0,Married,10,94.32144577,0,4194.072289\n127333,63,45,1,0,Married,7,94.16102473,0,4401.051237\n187870,65,46,2,0,Single,4,94.13262662,0,4311.631331\n169076,63,42,1,0,Married,5,94.0483353,0,4200.416765\n127217,66,45,2,0,Married,15,93.98528936,0,4216.264468\n168894,68,50,2,0,Married,6,93.95976672,0,4138.988336\n138509,69,52,2,0,Single,7,93.95705645,0,4325.852823\n172799,63,46,2,0,Married,13,93.90273659,0,4224.13683\n142739,21,2,2,1,Married,15,93.8245816,0,4482.22908\n176798,70,52,3,1,Single,15,93.7226595,0,4168.132975\n109023,67,46,3,0,Single,6,93.64557058,0,4457.278529\n184410,66,45,2,1,Single,13,93.63595187,0,4092.797594\n180278,70,52,2,1,Married,1,93.57814333,0,4447.907167\n178997,68,50,3,0,Single,1,93.56574673,0,4335.287337\n101158,70,52,2,1,Single,3,93.54095493,0,4215.047747\n191041,67,50,1,0,Married,7,93.51562689,0,4116.781345\n172191,70,52,2,0,Married,11,93.4308714,0,4250.54357\n162595,20,2,3,1,Married,14,93.08350236,0,4188.175118\n188140,68,48,2,1,Single,14,93.06167252,0,4420.083626\n153214,63,43,3,1,Single,7,92.98932417,0,4312.466209\n165243,61,40,1,1,Married,15,92.94404984,0,4121.202492\n135081,62,45,2,1,Single,8,92.90626476,0,4202.313238\n181131,61,40,4,1,Single,1,92.87239312,0,4292.619656\n128464,62,42,1,1,Single,10,92.7900941,0,4194.504705\n113221,61,40,2,1,Married,4,92.74831323,0,4186.415662\n193723,64,45,4,0,Single,7,92.6382434,0,4215.91217\n104092,69,48,2,1,Married,8,92.497508,0,4235.8754\n104816,66,46,3,0,Single,0,92.48026347,0,4125.013174\n161878,61,41,3,0,Married,5,92.47682971,0,4050.841486\n188085,69,49,2,0,Married,15,92.40841781,0,4390.420891\n136479,64,43,2,0,Single,7,92.36532405,0,4168.266203\n144460,63,45,3,1,Single,7,92.17754646,0,4402.877323\n162398,69,51,4,0,Single,9,91.45075257,0,3981.537629\n192993,66,49,4,0,Married,6,91.43353114,0,4297.676557\n139695,62,41,1,0,Single,7,91.22254626,0,4029.127313\n118893,68,51,2,0,Single,3,91.19959014,0,4085.979507\n191518,67,47,3,1,Married,9,90.96473248,0,4110.236624\n139304,61,40,4,0,Single,5,90.9177374,0,3965.88687\n107413,69,48,4,1,Single,10,90.61895816,0,4162.947908\n155220,66,45,2,1,Married,14,90.59799692,0,4045.899846\n191987,62,44,2,0,Married,7,90.58388932,0,4199.194466\n158296,60,41,3,0,Married,9,90.3835635,0,4226.178175\n116492,60,42,2,1,Married,12,90.34735176,0,4065.367588\n128860,62,42,4,1,Single,8,90.32206754,0,4223.103377\n178033,65,46,3,1,Single,4,90.2890851,0,4282.454255\n157345,65,47,4,1,Married,4,90.23492874,0,4092.746437\n169574,68,47,4,1,Married,10,90.18956327,0,4088.478164\n153980,70,52,2,0,Married,15,90.11877425,0,4164.938713\n149742,65,48,4,1,Married,2,90.11203101,0,4202.601551\n144545,61,44,1,0,Married,10,90.01448314,0,3925.724157\n155679,66,47,2,0,Single,0,89.95110132,0,4237.555066\n165940,69,51,2,1,Married,13,89.86262965,0,3920.131483\n107719,63,46,2,0,Married,9,89.85484669,0,4135.742335\n124048,65,44,1,1,Single,6,89.85327017,0,4076.663509\n166195,60,42,1,0,Single,10,89.69023459,0,3894.51173\n104567,64,43,3,1,Married,6,89.6601082,0,4026.00541\n193900,60,39,4,0,Married,7,89.57334772,0,4263.667386\n166666,65,48,2,0,Single,3,89.56574204,0,3901.287102\n130906,63,43,2,1,Married,3,89.54089032,0,4185.044516\n111368,64,45,2,1,Single,5,89.47124329,0,4129.562165\n136071,61,40,1,0,Married,13,89.43264583,0,4113.632292\n114920,62,42,3,1,Married,13,89.30299607,0,4168.149804\n135934,61,43,1,0,Single,5,89.22649655,0,4083.324828\n113156,62,42,2,1,Married,9,89.11187614,0,4114.593807\n192657,65,46,4,0,Single,6,89.07133084,0,4182.566542\n110911,68,47,4,0,Single,7,88.93732999,0,4203.8665\n152042,70,51,2,1,Married,4,88.89470277,0,4088.735139\n143537,61,42,1,1,Married,14,88.79955695,0,4167.977848\n171139,62,43,3,1,Single,14,88.62556914,0,4117.278457\n112627,64,45,4,0,Single,7,88.61306666,0,3940.653333\n111264,64,43,1,0,Married,4,88.54856139,0,4050.42807\n197258,67,50,2,1,Single,13,88.36932498,0,4193.466249\n142908,68,49,3,0,Married,3,88.22713977,0,3956.356989\n107709,69,52,1,0,Married,6,88.21345418,0,3950.672709\n192779,60,40,4,1,Married,9,88.15150547,0,4056.575274\n114225,70,52,2,1,Single,12,88.01923871,0,3936.961936\n193119,67,46,2,0,Single,5,88.0141516,0,4145.70758\n123127,62,43,1,1,Married,11,87.76790241,0,3846.395121\n179475,62,43,4,1,Single,7,87.75144136,0,4094.572068\n119216,66,49,2,0,Married,12,87.4444744,0,4011.22372\n115648,64,47,1,1,Married,1,87.44162875,0,3863.081438\n141609,67,46,1,1,Married,4,87.42638215,0,3944.319108\n117139,67,47,3,1,Married,3,87.39936221,0,4044.968111\n114988,66,49,2,1,Single,15,87.34698788,0,4016.349394\n111483,63,43,3,1,Married,11,87.29106215,0,4020.553108\n109334,66,48,2,1,Married,11,87.21915326,0,3774.957663\n167780,62,41,4,0,Married,12,87.16046168,0,4075.023084\n157072,63,44,4,1,Married,1,87.14737152,0,4059.368576\n123996,70,51,3,1,Single,12,87.09666633,0,3955.833317\n146664,64,44,2,0,Married,2,87.04182469,0,4138.091235\n169983,69,50,4,1,Single,5,86.98662692,0,3879.331346\n114196,69,50,3,0,Married,12,86.94278092,0,4057.139046\n165353,69,52,4,0,Married,14,86.88355432,0,3875.177716\n193810,61,42,3,1,Married,9,86.88180627,0,4140.090314\n135461,65,47,4,1,Single,11,86.84018565,0,3922.009283\n148473,61,41,3,1,Married,13,86.80751319,0,4076.37566\n181084,61,42,2,0,Single,0,86.80603022,0,4054.301511\n128457,66,46,3,0,Single,11,86.74451832,0,3865.225916\n174821,67,47,1,0,Married,5,86.7404906,0,3937.02453\n199294,68,49,2,1,Married,13,86.71810146,0,3945.905073\n152631,68,47,3,1,Single,0,86.65837786,0,4077.918893\n123332,67,48,3,0,Married,11,86.59232756,0,3861.616378\n134670,64,45,4,1,Married,9,86.49540823,0,3985.770412\n109392,68,50,2,0,Single,10,86.40253407,0,3946.126704\n171217,67,47,3,0,Married,12,86.3034625,0,3963.173125\n139113,67,50,3,0,Married,13,86.18806373,0,3994.403187\n110686,62,42,3,1,Married,13,86.11922175,0,3885.961088\n194809,60,43,3,0,Single,2,86.02090789,0,4017.045395\n191144,16,0,3,1,Married,11,85.8812279,0,3841.061395\n161728,65,47,3,1,Married,14,85.80766767,0,3735.383384\n137052,68,48,2,1,Married,14,85.80168233,0,3812.084117\n116131,69,49,2,0,Married,3,85.7968658,0,3933.84329\n172672,68,51,2,1,Single,7,85.73583983,0,3896.791992\n193520,70,50,2,0,Single,6,85.61036078,0,4051.518039\n189507,66,48,1,1,Single,6,85.54529253,0,3809.264627\n156939,61,41,2,1,Single,12,85.48223065,0,3851.111533\n147445,61,40,3,1,Married,13,85.44059006,0,4041.029503\n198681,64,46,2,1,Single,10,85.41548414,0,3908.774207\n133096,61,40,2,0,Single,0,85.40313363,0,3854.156682\n106181,64,43,4,1,Married,14,85.27095726,0,3936.547863\n102109,67,48,4,1,Single,12,85.12375805,0,4032.187903\n100366,69,48,2,0,Married,8,85.12163185,0,3845.081593\n129735,65,47,4,0,Married,12,84.94993314,0,3792.496657\n133990,70,50,1,1,Married,12,84.88651418,0,3788.325709\n170381,65,47,2,1,Married,1,84.86618483,0,3866.309242\n118460,66,48,4,0,Married,1,84.7957942,0,3882.78971\n130589,62,44,1,1,Single,2,84.75366158,0,3649.683079\n187678,63,42,2,0,Single,2,84.57802091,0,3871.901046\n134836,68,47,2,1,Married,11,84.54595559,0,3885.29778\n187201,61,41,2,0,Married,8,84.50606754,0,3629.303377\n150203,69,48,3,1,Married,15,84.41786459,0,3640.89323\n170282,68,51,3,0,Single,8,84.41738979,0,3631.86949\n191325,67,50,2,1,Married,15,84.28116303,0,3921.058152\n176392,67,50,3,1,Married,0,84.05013733,0,3951.506867\n119896,66,46,3,1,Married,12,84.04800903,0,3707.400452\n130840,65,45,2,1,Single,6,84.04450836,0,3848.225418\n151843,68,50,2,0,Single,9,83.85243398,0,3709.621699\n105359,61,42,4,0,Single,13,83.7684502,0,3593.42251\n195497,66,45,4,1,Single,9,83.72879492,0,3679.439746\n144356,69,51,1,1,Single,4,83.64543896,0,3951.271948\n160155,64,44,3,1,Married,14,83.60785695,0,3856.392848\n141347,61,44,2,0,Single,13,83.60707739,0,3667.35387\n142309,68,48,3,1,Single,6,83.52161667,0,3872.080834\n194142,70,53,3,1,Single,4,83.38302321,0,3792.151161\n141567,69,49,1,0,Married,9,83.36179822,0,3954.089911\n142376,69,49,4,1,Single,4,83.35991013,0,3677.995507\n160652,70,53,1,0,Married,9,83.09996407,0,3582.998204\n171140,67,46,3,0,Single,13,82.80010174,0,3591.005087\n181651,63,42,2,0,Married,1,82.69181053,0,3656.590527\n110623,66,47,3,1,Single,3,82.63991354,0,3561.995677\n170556,64,45,2,1,Single,1,82.57091774,0,3707.545887\n130758,66,47,2,0,Married,1,82.53339398,0,3720.669699\n136475,62,45,3,1,Single,6,82.45299676,0,3547.649838\n172308,68,49,1,1,Single,13,82.41769821,0,3643.884911\n144048,70,49,1,1,Married,5,82.40758602,0,3607.379301\n150292,65,47,3,0,Married,3,82.26778019,0,3704.38901\n168016,65,48,3,1,Single,12,82.21585193,0,3862.792597\n126035,68,49,3,1,Single,8,82.14186529,0,3745.093265\n161046,63,44,2,0,Married,7,82.07796289,0,3886.898145\n133986,66,46,3,0,Married,7,82.05360918,0,3875.680459\n179135,67,50,2,0,Single,2,81.92175004,0,3712.087502\n177381,60,43,3,1,Married,14,81.87050588,0,3751.525294\n167884,70,51,3,1,Single,13,81.85843404,0,3551.921702\n131340,63,43,3,1,Married,11,81.7970462,0,3650.85231\n148972,66,47,2,1,Married,7,81.75370667,0,3829.685334\n141976,67,49,4,0,Single,0,81.71550137,0,3527.775069\n120744,70,50,1,1,Married,5,81.55524621,0,3702.762311\n161974,70,52,3,1,Married,14,81.52838355,0,3594.419178\n116349,61,43,3,1,Single,7,81.44744839,0,3490.37242\n115007,66,47,3,0,Single,3,81.4208887,0,3680.044435\n109788,70,51,3,1,Married,8,81.34570916,0,3766.285458\n134049,69,51,2,0,Single,5,81.31264814,0,3697.632407\n140050,63,43,4,1,Married,12,81.22172776,0,3583.086388\n110246,60,43,2,1,Married,9,81.06218672,0,3599.109336\n119204,63,44,3,0,Married,9,80.99372304,0,3833.686152\n137261,67,48,3,0,Single,1,80.8727244,0,3808.63622\n124058,62,45,2,1,Married,7,80.70727476,0,3566.363738\n156544,62,42,2,1,Single,8,80.70565909,0,3800.282955\n102755,68,51,3,1,Married,15,80.59141132,0,3574.570566\n159850,65,48,3,0,Single,11,80.56296598,0,3815.148299\n190934,62,41,2,0,Married,14,80.35244519,0,3599.62226\n151333,64,43,1,0,Married,8,80.34581115,0,3691.290558\n161345,62,44,4,1,Single,8,80.29749149,0,3793.874575\n183413,61,40,4,1,Married,12,80.2596089,0,3639.980445\n121073,70,49,2,0,Married,7,80.24221083,0,3484.110542\n192027,66,45,2,0,Single,8,80.11881426,0,3727.940713\n100927,68,50,3,0,Single,9,80.09244893,0,3768.622447\n148016,68,50,2,1,Married,12,80.03609073,0,3546.804537\n184686,63,43,3,0,Single,9,79.93536521,0,3555.768261\n112676,63,45,4,0,Married,11,79.87546674,0,3764.773337\n128103,66,49,3,0,Married,12,79.82822354,0,3564.411177\n189682,67,49,2,1,Single,13,79.72859359,0,3493.42968\n112620,64,45,2,0,Married,14,79.71157625,0,3615.578813\n101395,63,43,3,0,Single,12,79.57325501,0,3732.662751\n120396,65,47,2,1,Married,12,79.38704147,0,3650.352074\n117630,67,50,3,0,Single,4,79.30935632,0,3565.467816\n154249,70,53,3,0,Married,9,79.30528645,0,3417.264323\n188524,67,50,3,0,Single,6,79.30068479,0,3693.03424\n103047,67,47,2,1,Married,9,79.29913049,0,3681.956525\n174658,69,49,3,1,Single,15,79.29765712,0,3733.882856\n113818,67,47,4,0,Single,7,79.27369925,0,3626.684963\n192799,64,44,1,0,Married,0,79.02764007,0,3729.382004\n107371,66,46,2,1,Single,12,78.95193381,0,3573.596691\n111536,67,49,2,0,Married,11,78.8749044,0,3635.74522\n111213,69,52,4,1,Single,15,78.78334759,0,3510.16738\n199665,66,49,1,1,Single,9,78.76982783,0,3594.491392\n169500,61,43,2,0,Married,14,78.58096229,0,3638.048115\n145869,64,45,2,1,Married,10,78.39925553,0,3488.962777\n188352,67,50,1,0,Single,6,78.38607932,0,3321.303966\n131572,68,48,2,1,Married,4,78.37664648,0,3713.832324\n190332,60,41,2,1,Married,13,78.35393177,0,3599.696589\n168140,67,48,2,1,Single,6,78.31542177,0,3383.771089\n163130,60,42,3,0,Married,14,78.04625637,0,3693.312819\n109749,66,47,3,1,Married,6,78.03983088,0,3514.991544\n120448,69,52,2,1,Married,7,78.00479255,0,3679.239628\n115121,70,49,4,1,Married,1,77.99627349,0,3669.813675\n174548,60,39,3,1,Married,14,77.93429821,0,3341.714911\n163370,66,47,3,0,Married,0,77.83166638,0,3466.583319\n128560,69,50,4,0,Married,12,77.80208494,0,3324.104247\n193960,67,46,4,1,Single,11,77.75878053,0,3297.939027\n125980,65,44,4,1,Married,9,77.58551518,0,3428.275759\n114670,61,40,2,1,Single,9,77.56680706,0,3672.340353\n185414,64,46,3,0,Single,14,77.55603969,0,3483.801985\n155999,60,43,2,0,Married,2,77.42575389,0,3567.287695\n187987,68,50,2,0,Single,6,77.22867015,0,3603.433508\n106840,66,45,2,1,Single,1,77.19025279,0,3557.51264\n176039,65,46,4,1,Single,12,77.12915746,0,3361.457873\n167689,67,49,4,0,Married,10,77.10876788,0,3313.438394\n179730,68,48,2,1,Single,0,77.05782973,0,3626.891487\n196457,70,53,4,1,Married,5,76.89883449,0,3262.941725\n186192,62,44,3,1,Single,3,76.88300323,0,3383.150162\n179298,69,48,2,0,Married,5,76.74981055,0,3300.490528\n163334,67,48,3,1,Single,11,76.70863982,0,3475.431991\n121672,65,48,2,0,Married,4,76.61165366,0,3306.582683\n134273,67,48,3,0,Single,10,76.52820295,0,3467.410148\n100754,64,47,2,0,Married,14,76.32943276,0,3309.471638\n149664,63,46,4,1,Married,15,76.30185594,0,3430.092797\n198167,67,46,1,0,Married,11,75.84119327,0,3276.059664\n169496,63,46,1,0,Married,2,75.81174432,0,3574.587216\n194250,61,42,4,1,Married,3,75.79561871,0,3312.780936\n142918,61,41,2,0,Married,5,75.7535758,0,3496.67879\n171960,63,43,4,0,Married,9,75.74933284,0,3527.466642\n127599,68,50,1,0,Married,6,75.70366892,0,3448.183446\n141782,68,49,1,1,Single,6,75.69629415,0,3484.814708\n159432,67,47,2,1,Married,6,75.60464898,0,3188.232449\n171691,62,43,2,1,Single,10,75.38855302,0,3300.427651\n195756,63,43,3,1,Married,8,75.20657542,0,3441.328771\n107141,61,40,3,1,Single,11,75.17008379,0,3450.50419\n187708,68,47,4,1,Single,11,75.14913897,0,3554.456949\n119400,70,50,3,1,Single,5,75.14196651,0,3176.098326\n199598,60,39,3,0,Single,14,75.09425088,0,3223.712544\n191176,63,44,2,0,Married,8,75.03338088,0,3323.669044\n140462,61,41,3,0,Married,13,75.00200112,0,3410.100056\n181569,63,45,2,0,Married,13,74.96116919,0,3460.05846\n145978,64,44,4,1,Married,13,74.89958368,0,3369.979184\n175822,67,48,3,1,Single,10,74.85602416,0,3231.801208\n106544,65,46,4,0,Single,2,74.7869296,0,3184.34648\n124052,69,50,3,1,Married,13,74.67838008,0,3141.919004\n196681,65,46,1,1,Single,10,74.64014392,0,3346.007196\n100648,63,42,4,0,Single,13,74.5877807,0,3368.389035\n160188,69,48,3,1,Single,9,74.58336239,0,3499.16812\n180933,63,44,3,0,Married,0,74.48355032,0,3522.177516\n104190,60,43,2,1,Single,5,74.39969556,0,3251.984778\n193426,64,44,2,0,Married,12,74.34267212,0,3445.133606\n167663,70,53,2,1,Single,6,74.26939143,0,3405.469572\n116786,60,42,2,1,Married,6,74.08359006,0,3161.179503\n106648,60,42,4,0,Married,14,74.03704187,0,3413.852094\n177832,61,44,3,1,Married,12,73.97578093,0,3151.789047\n147922,68,51,2,1,Single,15,73.92056559,0,3280.02828\n168534,65,46,3,1,Married,12,73.8504676,0,3360.52338\n121180,67,48,2,0,Married,12,73.79096185,0,3142.548093\n135198,63,43,3,0,Single,3,73.77395873,0,3216.697937\n151406,66,47,1,0,Single,3,73.7182254,0,3195.91127\n128305,65,47,4,0,Married,0,73.66004219,0,3216.00211\n163098,63,45,3,0,Married,15,73.64012637,0,3084.006319\n153716,62,41,2,1,Single,10,73.50576102,0,3436.288051\n199252,62,45,3,0,Single,5,73.44526457,0,3096.263229\n151614,64,46,3,0,Married,3,73.38371864,0,3140.185932\n190520,61,41,3,1,Single,3,73.35201539,0,3341.60077\n187718,63,42,2,1,Married,4,73.30751023,0,3210.375512\n151742,61,43,3,0,Single,6,73.30396425,0,3088.198213\n142835,69,50,3,0,Married,11,73.20865642,0,3363.432821\n191821,68,51,3,1,Married,10,73.17241539,0,3315.62077\n117609,68,48,3,0,Single,2,73.11737859,0,3269.86893\n116319,65,48,3,0,Single,5,73.06398872,0,3161.199436\n160191,65,44,1,1,Single,11,72.71566223,0,3284.783112\n127131,70,50,3,1,Single,9,72.66213331,0,3095.106666\n192549,61,42,4,1,Married,4,72.46236019,0,3037.11801\n100902,66,47,1,0,Married,10,72.31203068,0,3293.601534\n128450,68,48,3,0,Married,1,72.21033584,0,3199.516792\n176688,62,42,2,0,Married,7,71.88830779,0,3153.41539\n105295,68,50,4,0,Married,13,71.86205108,0,3094.102554\n128414,69,48,3,1,Married,6,71.80712459,0,3078.35623\n120429,66,49,1,1,Single,0,71.76435204,0,3353.217602\n164764,67,46,2,1,Married,6,71.75674071,0,3087.837036\n106324,69,52,3,0,Married,5,71.75612159,0,3283.80608\n133569,69,50,4,0,Married,1,71.72666395,0,3209.333198\n186300,65,47,3,1,Single,3,71.59925919,0,3263.96296\n125455,62,45,4,1,Single,5,71.59746867,0,3352.873434\n160562,61,43,2,1,Single,13,71.53949632,0,3272.974816\n159500,60,39,2,1,Single,9,71.51716314,0,3254.858157\n110213,68,50,3,1,Single,6,71.49379042,0,3074.689521\n138740,70,53,2,0,Single,15,71.48274719,0,3170.13736\n155258,68,48,1,1,Married,11,71.4770177,0,3128.850885\n197772,61,40,2,1,Single,0,71.45820678,0,3031.910339\n198587,64,43,2,1,Married,3,71.34780307,0,3234.390154\n121933,62,42,3,1,Single,10,71.28362096,0,3363.181048\n166539,70,52,2,0,Single,15,71.14057848,0,3317.028924\n107985,66,48,2,0,Single,1,71.04885364,0,3129.442682\n120341,65,44,2,1,Single,13,71.02245508,0,3140.122754\n138378,70,53,2,1,Married,15,70.90479127,0,2956.239564\n164184,67,50,3,0,Married,6,70.819124,0,3247.9562\n157122,66,48,4,0,Married,6,70.78413813,0,3173.206907\n137269,66,48,2,1,Married,7,70.7391574,0,3162.95787\n187812,70,51,2,1,Single,8,70.5900955,0,3033.504775\n130079,70,50,3,1,Single,6,70.44161206,0,3055.080603\n192105,69,50,2,0,Married,11,70.4133616,0,3187.66808\n118672,69,48,4,1,Single,5,70.36978543,0,3297.489272\n121846,68,51,1,1,Married,13,70.30320012,0,3134.160006\n149617,61,42,2,1,Married,6,70.23548331,0,3233.774166\n103770,68,47,3,0,Married,11,70.1697747,0,3307.488735\n143156,60,42,3,1,Single,7,70.05365,0,3276.6825\n155009,63,42,2,0,Single,10,70.02348197,0,3261.174099\n130710,67,47,3,0,Married,11,70.00565285,0,2915.282643\n164299,61,44,2,1,Single,6,70.00176368,0,3228.088184\n137434,69,48,1,0,Single,5,69.89304091,0,2902.652046\n110617,68,51,2,0,Single,13,69.87144406,0,3211.572203\n118556,62,42,4,1,Married,14,69.6618494,0,3142.09247\n122696,70,53,3,1,Single,3,69.57448047,0,2967.724024\n158147,69,48,3,0,Married,1,69.48465591,0,2981.232796\n191679,67,50,2,0,Single,9,69.48225196,0,2920.112598\n115217,70,50,2,1,Single,13,69.30784152,0,3050.392076\n184810,64,47,1,0,Single,1,69.22817764,0,2931.408882\n163488,64,44,2,0,Married,0,69.10484034,0,3001.242017\n148468,69,49,3,1,Single,5,68.99066105,0,3132.533053\n177816,63,44,4,0,Married,14,68.98139104,0,2943.069552\n123902,61,43,4,0,Married,6,68.89962569,0,2977.981285\n199093,60,41,2,0,Married,2,68.87011296,0,2928.505648\n146654,67,50,3,1,Single,7,68.86972204,0,3130.486102\n127053,63,45,3,0,Married,8,68.79881215,0,3146.940608\n180077,63,43,1,0,Single,8,68.70466955,0,3127.233478\n104358,65,46,4,0,Married,3,68.65999655,0,3043.999828\n193955,69,51,2,1,Married,9,68.62782962,0,3068.391481\n148638,65,46,2,1,Married,4,68.62394417,0,2874.197209\n111066,67,46,4,0,Single,15,68.45241461,0,2908.620731\n126236,64,44,3,0,Single,13,68.35890723,0,3053.945362\n164213,61,40,1,0,Single,5,68.35529078,0,2821.764539\n122192,69,48,4,1,Married,8,68.28344329,0,3131.172165\n145004,60,43,1,0,Single,15,68.2247322,0,3077.23661\n133177,66,46,4,0,Single,8,68.2039752,0,3033.19876\n144903,70,49,2,1,Married,5,68.16716855,0,2906.358428\n185136,67,50,4,0,Single,12,68.15882577,0,3148.941289\n111341,63,46,2,0,Married,11,68.13913798,0,3196.956899\n149278,68,49,3,1,Single,14,67.88157272,0,2933.078636\n100163,65,48,2,1,Married,12,67.60303585,0,2787.151793\n195786,65,48,1,0,Single,7,67.57033377,0,3016.516689\n113866,69,51,2,0,Married,14,67.45867564,0,2978.933782\n197322,60,41,2,0,Single,11,67.44728236,0,3148.364118\n187608,64,44,1,0,Married,15,67.44164804,0,3079.082402\n135853,65,44,4,1,Married,3,67.40853771,0,3151.426886\n140830,70,53,2,1,Married,7,67.1244936,0,3040.22468\n114149,68,51,2,0,Married,7,67.09859785,0,2808.929893\n169432,64,44,1,0,Married,6,67.08095282,0,3120.047641\n197211,69,50,1,0,Single,12,66.92891767,0,2995.445884\n179974,63,42,3,0,Single,7,66.89798915,0,3012.899458\n105976,64,44,3,1,Married,14,66.89404456,0,3068.702228\n133817,69,50,2,1,Married,15,66.89326391,0,2955.663196\n110298,63,44,3,1,Single,3,66.80455665,0,2899.227833\n143980,67,46,2,1,Single,14,66.7865407,0,2881.327035\n162289,64,45,3,1,Married,12,66.60388249,0,2795.194125\n156633,67,48,4,1,Married,6,66.53102098,0,2921.551049\n169308,70,52,4,0,Married,1,66.52075201,0,2820.037601\n120763,63,43,3,1,Married,11,66.48518655,0,3003.259328\n166296,61,44,4,1,Married,10,66.45717733,0,2953.858867\n163773,70,53,3,0,Married,15,66.37737968,0,2850.868984\n164942,63,43,2,1,Married,12,66.30321234,0,2912.160617\n157046,70,51,2,1,Married,7,66.24725714,0,2769.362857\n138241,63,46,2,0,Married,11,66.23357793,0,3077.678897\n199154,65,45,2,1,Married,8,66.10940926,0,2938.470463\n123016,65,44,2,1,Single,5,66.10569402,0,2751.284701\n155631,69,48,3,1,Married,3,65.82613541,0,2958.306771\n189632,64,47,1,0,Single,14,65.68992357,0,2945.496179\n124299,62,42,2,0,Single,8,65.68530084,0,2764.265042\n131225,65,46,1,0,Married,6,65.67824927,0,2896.912464\n199994,70,50,3,0,Married,8,65.66019059,0,2717.00953\n180139,66,47,4,0,Single,7,65.60367336,0,2839.183668\n197831,66,48,4,1,Married,5,65.57457103,0,2690.728552\n142541,62,45,3,0,Married,13,65.53624329,0,2857.812165\n136504,60,39,1,0,Married,2,65.4394601,0,2942.973005\n130817,68,48,4,0,Married,4,65.36114567,0,2848.057284\n192038,65,45,3,0,Single,11,65.3378324,0,2697.89162\n163029,69,50,2,0,Married,12,65.30246234,0,2965.123117\n165865,66,48,2,0,Married,9,65.19885583,0,3040.942792\n185510,64,44,1,1,Married,14,65.10147637,0,2688.073819\n144658,69,52,2,1,Married,6,65.05792786,0,2726.896393\n197424,70,50,3,0,Single,13,64.74551084,0,2886.275542\n125251,69,49,3,1,Married,3,64.74424852,0,2916.212426\n114490,69,48,2,0,Married,2,64.72794527,0,3029.397264\n111826,60,39,2,0,Single,1,64.58064165,0,2978.032083\n160739,67,49,2,1,Married,7,64.56557844,0,2726.278922\n101482,70,52,4,1,Single,8,64.50148927,0,2810.074464\n119169,60,40,3,1,Single,4,64.49974894,0,2752.987447\n105151,70,49,2,0,Married,9,64.38351041,0,2955.175521\n160973,64,47,2,1,Married,1,64.2533819,0,2634.669095\n108325,67,50,3,1,Single,0,64.23003046,0,2709.501523\n144791,67,49,3,0,Married,11,64.04509929,0,2904.254965\n118465,60,39,2,1,Married,0,63.94251473,0,2920.125737\n162974,63,45,3,0,Married,11,63.91577391,0,2657.788696\n135099,60,43,1,0,Married,11,63.83158513,0,2793.579257\n181589,70,49,2,0,Single,0,63.81299519,0,2948.64976\n177563,64,43,3,1,Married,3,63.76613471,0,2723.306736\n125006,68,50,3,0,Married,11,63.75359974,0,2925.679987\n144036,63,45,3,0,Married,2,63.73827093,0,2663.913547\n169935,69,49,3,0,Single,14,63.63810066,0,2733.905033\n194704,70,49,3,1,Single,7,63.63507594,0,2686.753797\n158663,63,44,2,0,Single,9,63.61993252,0,2867.996626\n115206,62,42,3,1,Married,13,63.52049237,0,2818.024619\n105781,65,47,4,1,Single,11,63.51792436,0,2746.896218\n103114,70,53,2,0,Married,15,63.50212418,0,2727.106209\n169129,70,53,3,0,Single,1,63.48688115,0,2620.344058\n132073,64,47,4,0,Single,9,63.36369571,0,2918.184786\n110395,70,53,1,1,Married,8,63.34173415,0,2636.086708\n115370,60,41,3,0,Married,11,63.32357383,0,2693.178692\n130633,60,42,3,1,Single,14,63.23567462,0,2876.783731\n137617,61,41,2,0,Married,5,63.21606807,0,2695.803404\n191471,61,40,3,1,Married,5,63.17475212,0,2681.737606\n141191,69,51,2,1,Married,13,63.16716826,0,2883.358413\n186616,64,43,3,0,Single,11,63.08515924,0,2680.257962\n109600,69,51,3,1,Married,9,62.97042879,0,2636.52144\n152990,62,43,1,1,Married,13,62.94165861,0,2823.082931\n100607,62,43,2,1,Married,8,62.914229,0,2556.71145\n169243,63,43,3,1,Married,14,62.87299302,0,2607.649651\n190579,66,48,1,0,Married,12,62.86831105,0,2884.415553\n164151,68,51,3,0,Married,7,62.85517407,0,2810.758704\n170524,63,44,3,0,Married,10,62.69957597,0,2771.978799\n135756,70,51,2,0,Married,2,62.6291482,0,2789.45741\n132128,63,44,3,0,Single,15,62.61394395,0,2547.697198\n147644,61,43,2,1,Single,3,62.52807026,0,2924.403513\n128193,60,43,2,1,Single,15,62.45029758,0,2789.514879\n109419,70,50,3,1,Single,14,62.40754107,0,2732.377054\n134513,64,45,4,1,Married,13,62.38825937,0,2719.412969\n164859,68,51,4,1,Married,1,62.36394588,0,2756.197294\n146910,67,50,2,1,Single,5,62.35947994,0,2899.973997\n148048,61,41,3,0,Married,15,62.31399058,0,2892.699529\n166179,64,45,2,1,Married,7,62.21862184,0,2597.931092\n140859,61,43,3,0,Single,15,62.21317163,0,2587.658582\n105327,70,49,2,1,Married,14,62.20126455,0,2856.063228\n134587,69,52,2,0,Married,13,62.13109269,0,2666.554635\n130097,63,43,3,0,Single,11,61.95281383,0,2503.640692\n191198,60,40,3,1,Single,3,61.86675766,0,2727.337883\n198857,60,41,3,0,Married,10,61.70621631,0,2633.310816\n145700,70,53,3,1,Single,9,61.69388936,0,2859.694468\n178397,68,49,2,0,Single,8,61.63125261,0,2557.562631\n148262,61,44,2,0,Single,0,61.60442174,0,2750.221087\n176431,64,44,2,1,Single,3,61.42014606,0,2489.007303\n179714,70,50,2,0,Single,7,61.39327511,0,2709.663756\n178671,69,48,3,0,Married,6,61.37332962,0,2836.666481\n192747,64,44,1,1,Married,5,61.3380877,0,2739.904385\n100713,63,45,3,1,Married,1,61.27975132,0,2564.987566\n162323,69,50,2,0,Married,3,61.21346914,0,2548.673457\n113558,62,41,3,1,Single,10,61.17420029,0,2842.710015\n152338,64,45,2,0,Married,15,61.02612944,0,2846.306472\n157408,63,45,1,1,Married,3,60.82862632,0,2539.431316\n134040,63,45,2,1,Single,2,60.64871447,0,2777.435724\n104841,64,44,2,0,Married,4,60.55368144,0,2486.684072\n112447,63,42,2,0,Single,10,60.43868173,0,2681.934087\n127499,69,52,3,0,Single,2,60.43479361,0,2524.739681\n169581,68,48,2,1,Married,15,60.43473783,0,2603.736892\n190541,62,42,2,0,Married,12,60.42093258,0,2724.046629\n183655,64,46,1,1,Married,1,60.38892485,0,2591.446243\n195940,66,47,4,1,Married,13,60.36703842,0,2757.351921\n180458,66,45,3,0,Married,9,60.30212247,0,2425.106124\n193315,61,43,4,1,Married,12,60.11621977,0,2731.810989\n107895,70,51,2,1,Married,1,59.95154582,0,2423.577291\n138045,66,45,4,0,Single,2,59.74835755,0,2721.417878\n153788,61,41,1,0,Single,7,59.64387603,0,2595.193802\n105710,64,47,3,0,Married,10,59.63046307,0,2621.523154\n120726,60,43,2,0,Single,8,59.48996052,0,2418.498026\n143871,60,43,1,0,Single,2,59.46503446,0,2484.251723\n106944,67,48,4,0,Married,8,59.42698007,0,2718.349004\n110153,68,51,3,1,Married,2,59.35788412,0,2628.894206\n104707,66,48,2,1,Single,3,59.24822739,0,2652.41137\n131753,60,42,4,0,Married,1,59.24040111,0,2703.020056\n160895,61,44,2,0,Married,4,59.08454763,0,2460.227382\n161703,66,48,3,0,Married,13,59.06325549,0,2520.162775\n148533,68,51,1,0,Married,15,59.01522562,0,2490.761281\n160827,70,53,1,1,Married,8,59.00575916,0,2585.287958\n102954,63,44,3,0,Married,7,58.88540421,0,2724.270211\n167759,61,43,2,0,Single,3,58.82742683,0,2351.371342\n144815,66,46,2,1,Single,14,58.82300028,0,2360.150014\n140111,63,43,3,1,Married,11,58.74829096,0,2632.414548\n158718,67,50,3,0,Married,6,58.74199899,0,2340.09995\n150150,69,48,3,1,Single,13,58.73057119,0,2519.52856\n177461,64,43,4,1,Married,14,58.70979023,0,2502.489512\n167049,63,46,2,0,Married,14,58.6555409,0,2529.777045\n138354,61,44,2,1,Married,4,58.570891,0,2478.54455\n182956,62,43,1,1,Married,6,58.49819066,0,2466.909533\n189499,61,43,4,1,Single,11,58.42642905,0,2535.321453\n198231,63,43,2,0,Married,11,58.4202252,0,2387.01126\n144351,67,46,3,1,Married,9,58.33931515,0,2496.965758\n133610,69,48,3,0,Single,9,58.17556382,0,2332.778191\n193638,63,44,2,0,Married,14,58.11371062,0,2307.685531\n170301,66,45,4,0,Married,12,57.98909532,0,2508.454766\n196280,70,49,1,1,Single,13,57.96961012,0,2379.480506\n113734,65,47,2,0,Single,5,57.96417136,0,2646.208568\n187210,70,49,2,1,Single,3,57.94600572,0,2620.300286\n122823,70,49,2,0,Single,2,57.82588923,0,2682.294462\n143203,70,50,3,0,Single,14,57.68439327,0,2361.219664\n100764,66,49,2,1,Married,15,57.5769928,0,2515.84964\n177621,62,42,1,0,Single,1,57.5163719,0,2572.818595\n122934,62,44,1,0,Married,15,57.34341336,0,2404.170668\n143523,68,50,2,1,Single,14,57.32931261,0,2605.465631\n142322,63,42,1,0,Married,11,57.22333519,0,2386.16676\n158782,60,40,2,1,Married,1,57.19166287,0,2350.583144\n161095,68,51,2,0,Married,5,57.11119396,0,2291.559698\n161909,62,42,4,1,Single,4,57.02113835,0,2466.056918\n143839,60,40,2,0,Single,1,57.02071414,0,2624.035707\n168127,69,52,2,1,Married,12,56.98060462,0,2498.030231\n119670,60,39,3,0,Married,1,56.94671486,0,2555.335743\n135694,67,48,2,1,Married,15,56.76405585,0,2434.202793\n146798,66,48,2,1,Married,4,56.64461393,0,2399.230697\n111391,69,48,2,1,Single,10,56.59454021,0,2598.727011\n167021,63,44,1,0,Married,14,56.572462,0,2330.6231\n128850,62,41,1,0,Married,5,56.4476765,0,2223.383825\n139933,70,51,1,0,Married,3,56.35271511,0,2401.635756\n152054,61,43,2,1,Single,6,56.28654154,0,2567.327077\n117036,62,42,3,1,Single,10,56.28531315,0,2538.265658\n106285,62,43,1,0,Single,6,56.27149032,0,2247.574516\n146673,68,48,1,1,Married,13,56.19741974,0,2255.870987\n159468,64,46,3,0,Single,1,56.18666438,0,2250.333219\n164170,67,47,3,0,Married,15,56.16608104,0,2245.304052\n162354,60,41,1,1,Single,7,56.0717095,0,2328.585475\n155801,63,45,3,0,Single,4,55.93426459,0,2237.71323\n161762,65,45,2,0,Married,2,55.87164916,0,2518.582458\n182238,61,44,3,0,Single,9,55.86250372,0,2505.125186\n167135,64,45,3,0,Single,6,55.67112525,0,2264.556263\n126277,67,47,4,0,Married,14,55.63394174,0,2326.697087\n146295,68,47,2,1,Single,1,55.59478521,0,2241.739261\n137338,70,51,3,0,Married,10,55.57032626,0,2260.516313\n168017,68,51,4,0,Single,12,55.36811306,0,2197.405653\n146367,69,50,4,1,Single,1,55.20901516,0,2352.450758\n144355,66,46,2,1,Married,9,55.19671094,0,2371.835547\n130138,62,42,2,0,Single,15,55.15740612,0,2427.870306\n101028,61,40,4,1,Married,14,55.15691526,0,2222.845763\n144165,64,46,2,1,Married,6,55.11078038,0,2413.539019\n164101,64,47,2,1,Single,7,55.04694292,0,2406.347146\n178465,68,51,2,1,Married,15,55.03064026,0,2467.532013\n190791,61,44,2,1,Single,12,55.0100417,0,2535.502085\n122591,62,44,1,1,Single,5,54.94484401,0,2416.242201\n125021,61,41,2,1,Married,5,54.84257125,0,2374.128563\n101495,65,44,4,1,Single,2,54.82996678,0,2508.498339\n146185,68,48,1,1,Single,5,54.77794095,0,2188.897048\n148231,60,39,2,0,Married,5,54.76870422,0,2146.435211\n173563,70,49,3,0,Married,11,54.74148115,0,2424.074058\n183569,69,51,2,1,Single,11,54.54551587,0,2396.275794\n184160,63,46,4,1,Married,9,54.38544048,0,2314.272024\n140556,63,46,4,1,Single,13,54.31716805,0,2438.858403\n184298,68,48,4,0,Married,12,54.27492999,0,2118.7465\n113033,69,48,2,0,Married,12,54.20270961,0,2416.135481\n169991,60,39,3,0,Married,1,54.10787889,0,2464.393945\n140672,67,50,3,0,Single,7,54.08364527,0,2436.182264\n186152,66,46,4,0,Married,6,53.98897059,0,2243.44853\n102576,67,49,2,0,Single,9,53.98103733,0,2380.051867\n134080,61,44,3,1,Single,6,53.97127125,0,2316.563563\n197629,69,51,3,0,Married,6,53.92353365,0,2136.176683\n180488,68,47,3,0,Married,10,53.891042,0,2371.5521\n121348,60,43,1,1,Single,7,53.84875132,0,2429.437566\n196620,69,49,4,1,Single,10,53.80049352,0,2229.024676\n187052,61,44,2,0,Married,10,53.73608936,0,2268.804468\n122945,65,44,3,1,Married,6,53.71707444,0,2331.853722\n114241,61,44,2,0,Married,6,53.6449905,0,2265.249525\n127147,60,41,1,0,Single,6,53.64272115,0,2242.136058\n129132,66,47,2,0,Single,9,53.59342313,0,2284.671157\n195078,65,45,1,0,Married,9,53.55900503,0,2117.950252\n156919,68,48,3,0,Married,4,53.49667949,0,2102.833975\n127304,62,42,4,0,Single,14,53.1797087,0,2280.985435\n153155,60,40,4,1,Married,4,53.14456848,0,2262.228424\n183670,64,47,1,1,Married,6,53.08119666,0,2367.059833\n118324,66,48,1,0,Married,6,52.94552614,0,2181.276307\n163420,70,50,2,1,Married,7,52.89583767,0,2313.791884\n157241,63,45,3,0,Married,2,52.84267257,0,2252.133629\n137085,64,43,3,0,Single,8,52.81746327,0,2192.873164\n197284,69,48,3,0,Single,10,52.80196713,0,2157.098357\n132624,60,41,2,0,Single,13,52.79849916,0,2193.924958\n105446,62,42,2,1,Married,1,52.64347366,0,2308.173683\n199698,64,43,1,1,Married,10,52.51168524,0,2395.584262\n186755,67,47,2,0,Single,15,52.49891636,0,2033.945818\n147473,64,43,1,1,Single,3,52.41921348,0,2181.960674\n174427,69,48,3,1,Single,0,52.31631384,0,2332.815692\n148054,65,46,3,0,Single,14,52.25793464,0,2347.896732\n195099,67,48,2,1,Single,9,52.23572795,0,2332.786398\n110367,63,44,3,1,Single,10,52.01138969,0,2210.569485\n114164,60,40,3,1,Single,5,51.84614092,0,2325.307046\n195513,63,44,2,1,Single,12,51.84031551,0,2290.015776\n128659,64,46,3,0,Single,6,51.63782265,0,2379.891133\n117553,63,43,2,1,Married,6,51.5165782,0,2050.82891\n193617,62,42,1,0,Single,9,51.49368116,0,2337.684058\n158396,61,42,3,0,Single,13,51.46807921,0,2002.403961\n187862,65,48,2,0,Single,1,51.42612216,0,2170.306108\n165309,70,49,2,0,Married,11,51.35095439,0,2114.54772\n170210,60,41,3,0,Single,11,51.29370383,0,2060.685192\n191030,63,43,3,1,Single,2,51.27537893,0,2003.768947\n161734,67,46,3,1,Married,6,51.18918863,0,2174.459432\n186767,61,44,2,1,Single,6,51.12059722,0,2279.029861\n199711,63,44,3,0,Single,7,50.93564413,0,2183.782207\n118883,60,39,3,0,Married,8,50.92346602,0,1953.173301\n194621,69,48,4,1,Single,8,50.86308542,0,2118.154271\n140270,66,49,1,1,Single,5,50.71157626,0,2138.578813\n115058,61,40,4,1,Married,9,50.68790597,0,2291.395299\n171269,70,53,2,0,Married,10,50.65198172,0,2136.599086\n118145,70,50,4,0,Single,9,50.63007245,0,2091.503623\n114742,69,51,3,0,Single,13,50.62058206,0,2330.029103\n155361,66,48,3,0,Single,9,50.6039284,0,2181.19642\n115472,68,47,3,1,Married,3,50.60141328,0,2174.070664\n118090,63,42,3,0,Single,9,50.5939685,0,2066.698425\n196693,64,43,2,0,Single,15,50.57339885,0,2250.669943\n147750,64,46,1,1,Married,6,50.40620435,0,2087.310218\n168780,70,52,4,1,Married,10,50.34487695,0,2128.243848\n191721,66,48,2,1,Single,13,50.2255099,0,1938.275495\n105905,68,49,3,0,Married,15,50.16883194,0,1943.441597\n171675,68,47,3,0,Single,13,50.13035055,0,2003.517528\n192507,66,45,2,0,Single,7,50.05503781,0,2183.751891\n170833,65,45,2,0,Married,7,49.96863373,0,1947.431687\n107448,67,48,3,1,Married,8,49.94751491,0,2270.375746\n144156,67,48,3,1,Single,13,49.77959251,0,2281.979626\n162564,69,50,4,1,Single,15,49.66980511,0,2247.490256\n136904,65,48,3,1,Married,14,49.61236035,0,2261.618018\n116260,65,47,1,1,Married,1,49.57582016,0,1954.791008\n155541,70,49,2,0,Single,13,49.57439811,0,2039.719906\n196539,65,44,3,0,Single,15,49.5115222,0,1882.57611\n198778,64,47,3,0,Single,4,49.49748794,0,2198.874397\n102364,64,45,4,1,Married,1,49.48939436,0,1919.469718\n179112,60,41,3,1,Married,7,49.43295102,0,2068.647551\n144506,63,43,3,0,Married,13,49.41264077,0,1927.632039\n171611,67,46,2,1,Single,11,49.41061922,0,2005.530961\n171597,66,45,1,0,Married,9,49.35555095,0,1946.777548\n123294,69,52,2,0,Single,6,49.25237813,0,2054.618907\n151440,63,46,4,1,Single,8,49.08683959,0,2211.34198\n185203,68,47,4,1,Single,2,49.06175555,0,1871.087778\n171271,60,40,2,0,Married,0,48.97986108,0,2071.993054\n100866,63,42,2,0,Married,10,48.95881303,0,2118.940652\n150235,66,48,3,0,Single,10,48.87574286,0,1935.787143\n163542,65,44,3,1,Married,9,48.75805138,0,1892.902569\n131444,70,49,2,0,Married,7,48.7363347,0,2137.816735\n146590,65,48,4,1,Married,2,48.71728852,0,1891.864426\n188506,63,42,2,1,Married,15,48.66470637,0,2203.235319\n152749,64,47,2,1,Single,10,48.64040499,0,1848.02025\n147067,65,47,2,0,Single,9,48.42269189,0,2177.134595\n128734,66,46,4,1,Single,10,48.41311884,0,1930.655942\n131290,61,40,4,0,Single,4,48.38472563,0,2036.236282\n195016,66,48,3,0,Single,11,48.3477202,0,2109.38601\n193036,61,43,3,1,Married,11,48.30701346,0,1898.350673\n168637,66,46,3,1,Married,8,48.2986823,0,2107.934115\n116273,64,47,2,0,Single,11,48.20137783,0,1920.068892\n131942,60,42,2,0,Single,9,48.19660683,0,2096.830342\n161597,68,51,1,0,Single,6,48.10845199,0,2091.4226\n124693,70,52,4,0,Married,8,48.1058628,0,2088.29314\n149465,60,39,3,1,Single,9,48.08326373,0,2141.163187\n168459,60,40,3,1,Married,5,48.07241107,0,2130.620554\n127729,64,47,1,0,Married,11,48.01445411,0,2167.722706\n113141,67,48,2,1,Single,0,47.99292722,0,1834.646361\n176565,65,46,2,0,Single,11,47.95475079,0,1975.73754\n101936,62,43,1,1,Married,8,47.90406835,0,2164.203418\n190485,68,50,4,1,Married,4,47.900954,0,2101.0477\n190465,65,44,4,1,Married,14,47.86517271,0,1959.258636\n181896,65,47,2,0,Married,9,47.80091757,0,1951.045879\n100873,63,46,3,1,Single,13,47.77115772,0,2181.557886\n136786,70,52,4,1,Single,0,47.67779644,0,2035.889822\n148589,65,44,4,1,Single,4,47.60971016,0,2048.485508\n166782,60,41,3,0,Single,14,47.37650767,0,1777.825384\n189009,65,47,1,1,Single,6,47.27603743,0,2148.801872\n106461,61,43,1,1,Married,13,47.21609296,0,2015.804648\n145603,62,45,4,0,Single,9,47.17617264,0,1950.808632\n104522,69,49,1,0,Single,9,47.16275981,0,1887.137991\n137175,61,43,2,1,Single,9,47.1281509,0,1985.407545\n109329,67,46,2,1,Married,14,47.05114417,0,2093.557209\n164088,68,51,4,0,Single,7,47.04962724,0,1893.481362\n105678,70,49,3,1,Married,11,46.85303594,0,2086.651797\n161677,68,51,1,0,Single,7,46.76212563,0,1920.106282\n156707,67,47,2,0,Single,6,46.74914103,0,1967.457052\n116168,65,45,3,1,Married,1,46.67613691,0,2019.806846\n131350,65,46,4,0,Single,8,46.63437622,0,1833.718811\n157066,62,44,4,1,Single,14,46.58064516,0,2127.032258\n177277,64,44,2,1,Single,6,46.53849164,0,1927.924582\n112113,66,47,2,1,Single,11,46.50032692,0,1975.016346\n196064,66,45,2,1,Single,7,46.47222042,0,2036.611021\n184130,68,51,4,0,Married,4,46.46185302,0,2033.092651\n193904,60,41,1,0,Married,12,46.42128042,0,1785.064021\n198046,60,42,1,1,Single,12,46.01271223,0,1967.635612\n136606,68,50,2,0,Single,6,45.97570117,0,1766.785059\n114614,64,47,2,1,Married,8,45.8953299,0,1924.766495\n133862,61,41,1,1,Single,8,45.83002147,0,1925.501074\n196432,62,41,2,1,Single,11,45.68742013,0,1699.371007\n132745,62,41,3,1,Married,3,45.67505452,0,2062.752726\n133877,64,45,2,0,Single,9,45.5612943,0,1884.064715\n105890,68,50,2,0,Single,10,45.39186178,0,2030.593089\n152911,60,41,4,0,Single,9,45.34530098,0,1698.265049\n176463,69,51,1,1,Single,8,45.27574714,0,1732.787357\n178209,68,47,3,1,Married,4,45.2596903,0,1936.984515\n134527,65,45,3,0,Single,10,45.1664325,0,1878.321625\n194840,64,47,3,1,Single,8,45.15699617,0,1838.849809\n115708,68,50,2,1,Single,15,45.13376464,0,1905.688232\n146625,66,47,2,0,Single,8,45.03924025,0,1791.962013\n100775,60,40,3,1,Married,5,45.03801412,0,2001.900706\n153914,66,47,2,0,Single,10,45.008951,0,1789.44755\n155152,63,46,2,0,Single,13,44.94946668,0,1971.473334\n176401,66,49,4,1,Single,2,44.949165,0,1683.45825\n161573,68,47,1,0,Single,15,44.93262419,0,2042.63121\n174935,66,46,3,0,Single,9,44.88695383,0,1978.347692\n169304,66,46,2,1,Single,10,44.8549712,0,1787.74856\n118109,69,49,1,1,Married,14,44.85076261,0,1679.538131\n145231,69,50,2,1,Married,11,44.81424683,0,1995.712342\n196710,60,39,1,1,Single,6,44.73706716,0,1728.853358\n176168,70,49,2,0,Single,12,44.64055267,0,1784.027634\n140519,65,45,4,0,Single,10,44.61956432,0,1894.978216\n199820,69,48,3,1,Single,7,44.52318265,0,1810.159133\n186725,64,45,3,0,Single,1,44.46068165,0,1791.034083\n124322,63,44,2,1,Married,7,44.41811572,0,1761.905786\n104852,68,49,3,1,Single,12,44.414606,0,1989.7303\n153355,69,49,3,0,Single,8,44.39369101,0,1795.684551\n108887,68,51,2,0,Single,8,44.3808888,0,1979.04444\n150019,64,47,3,1,Single,10,44.37077418,0,2010.538709\n129370,61,42,3,1,Single,11,44.34005249,0,1805.002625\n160768,64,46,3,0,Married,8,44.2977249,0,1619.886245\n115463,69,48,4,1,Married,1,44.2414336,0,1703.07168\n172844,70,52,2,0,Single,12,44.22971624,0,1662.485812\n118009,64,47,2,1,Single,9,44.15410129,0,1992.705065\n139453,66,47,3,0,Married,6,44.06714137,0,1640.357069\n173582,69,51,2,1,Married,13,43.91984176,0,1694.992088\n180934,65,47,2,0,Single,10,43.90765359,0,1815.38268\n137314,65,44,1,0,Single,15,43.80902637,0,1729.451319\n118463,60,41,3,0,Single,7,43.7963994,0,1662.81997\n154316,62,43,4,1,Single,2,43.7908123,0,1679.540615\n137415,61,42,3,0,Single,7,43.74710322,0,1764.355161\n142238,60,39,4,0,Single,9,43.74282845,0,1686.141423\n126303,69,50,2,0,Single,1,43.69150749,0,1780.575375\n183757,70,53,4,1,Single,12,43.4980825,0,1722.904125\n194240,60,39,3,1,Married,14,43.48535521,0,1706.267761\n175284,68,49,2,1,Single,1,43.40882502,0,1949.441251\n157158,64,45,4,0,Married,10,43.37500608,0,1780.750304\n122926,61,40,4,1,Single,12,43.25624195,0,1908.812098\n183237,61,43,4,0,Married,1,43.22105671,0,1698.052836\n192544,63,46,2,1,Married,6,43.1104786,0,1763.52393\n142555,64,45,4,0,Single,10,43.04892652,0,1739.446326\n156225,60,43,4,0,Single,13,43.03610311,0,1740.805156\n151850,68,50,2,0,Single,9,42.99304197,0,1642.652099\n195879,64,47,1,1,Single,9,42.93175722,0,1596.587861\n130896,68,51,2,1,Single,9,42.80763176,0,1689.381588\n140673,69,51,3,0,Single,10,42.80605723,0,1621.302862\n177497,61,42,3,1,Married,5,42.79730356,0,1891.865178\n115723,67,50,3,0,Single,7,42.57792575,0,1602.896288\n144626,62,43,4,1,Single,12,42.54588433,0,1578.294217\n163959,66,49,2,0,Married,10,42.54560578,0,1858.280289\n147230,60,42,3,0,Single,9,42.37361933,0,1774.680967\n126382,69,52,2,1,Single,8,42.35932941,0,1891.966471\n149131,67,49,3,1,Single,9,42.04285729,0,1624.142865\n160081,70,51,3,0,Married,12,41.98062044,0,1594.031022\n152726,63,46,4,0,Single,14,41.95861116,0,1565.930558\n161558,66,48,3,0,Married,1,41.87528879,0,1710.76444\n121194,66,47,4,1,Single,12,41.84070321,0,1646.035161\n124808,67,49,2,0,Married,9,41.79881552,0,1592.940776\n112037,67,46,3,1,Single,3,41.70424779,0,1863.21239\n122822,65,46,1,0,Married,9,41.68772747,0,1487.386374\n156024,67,50,4,1,Single,12,41.58975302,0,1716.487651\n118246,65,47,1,1,Single,8,41.57656736,0,1640.828368\n150504,61,41,3,0,Single,12,41.55716101,0,1785.858051\n151474,66,46,1,1,Single,15,41.48382202,0,1727.191101\n196750,65,46,4,1,Single,11,41.47417968,0,1542.708984\n145853,60,42,1,0,Married,4,41.44745886,0,1845.372943\n137612,65,46,3,1,Single,13,41.38235702,0,1567.117851\n126809,67,47,3,1,Married,15,41.21404742,0,1758.702371\n183997,67,50,4,1,Single,10,41.08219521,0,1699.109761\n122809,69,51,2,1,Married,12,41.06894118,0,1846.447059\n103769,66,46,2,0,Single,7,40.97559422,0,1748.779711\n198482,60,43,2,0,Single,12,40.97117488,0,1681.558744\n108791,62,44,4,0,Married,6,40.96079291,0,1604.039646\n106615,67,47,2,1,Single,8,40.95171614,0,1561.585807\n172758,63,43,1,1,Married,13,40.93404521,0,1602.702261\n190537,65,45,2,1,Married,15,40.86242464,0,1591.121232\n199869,68,47,3,0,Single,6,40.80796448,0,1563.398224\n124726,70,49,4,0,Married,10,40.76967803,0,1720.483902\n118239,66,48,3,1,Single,6,40.73239872,0,1537.619936\n120501,66,48,3,1,Married,10,40.62843822,0,1460.421911\n148925,66,45,1,0,Married,7,40.62820414,0,1508.410207\n177908,67,49,4,1,Single,11,40.59822642,0,1586.911321\n157794,60,42,2,1,Single,8,40.57228227,0,1573.614114\n134515,63,43,4,1,Single,10,40.5347543,0,1601.737715\n192393,62,43,2,1,Single,11,40.44687431,0,1447.343716\n165558,64,45,2,0,Single,9,40.36425713,0,1464.212857\n194352,67,47,2,1,Single,8,40.3245526,0,1711.22763\n120823,60,40,2,0,Single,11,40.235724,0,1441.7862\n196608,66,48,3,0,Married,10,40.1800701,0,1488.003505\n176766,69,52,4,0,Married,8,40.09194914,0,1627.597457\n117356,70,49,1,1,Married,7,40.08895078,0,1669.447539\n187609,62,44,1,1,Married,4,39.95489011,0,1668.744506\n133290,63,46,2,1,Married,7,39.9461635,0,1504.308175\n164436,64,47,4,0,Married,6,39.93803147,0,1589.901574\n122519,70,49,3,1,Married,8,39.90354642,0,1713.177321\n113549,70,52,1,1,Married,13,39.86574338,0,1792.287169\n157837,63,45,2,0,Married,10,39.85376081,0,1630.688041\n174207,69,48,2,1,Married,14,39.79180144,0,1587.590072\n155919,66,45,4,1,Married,5,39.76589882,0,1642.294941\n144967,69,51,4,0,Married,6,39.74574801,0,1587.287401\n140659,62,41,1,1,Single,9,39.72257576,0,1555.128788\n168889,60,43,2,1,Single,13,39.69274047,0,1430.637024\n150589,69,51,3,0,Married,7,39.62666635,0,1698.333318\n130258,62,44,2,0,Married,10,39.60422258,0,1422.211129\n188425,62,42,1,1,Married,8,39.59872587,0,1624.936294\n140562,70,49,2,1,Married,5,39.57800425,0,1768.900213\n153696,63,44,4,0,Married,9,39.55406514,0,1420.703257\n113920,62,45,3,0,Married,4,39.5477811,0,1476.389055\n112456,60,40,3,0,Married,15,39.53856189,0,1512.928095\n138263,61,40,4,0,Married,15,39.41953671,0,1485.976836\n132804,62,45,1,0,Single,10,39.37000756,0,1466.500378\n185252,62,44,3,0,Married,10,39.31633036,0,1599.816518\n193489,62,45,2,0,Single,9,39.28796041,0,1688.398021\n126637,60,39,2,1,Single,7,39.218085,0,1496.90425\n195900,68,48,1,0,Married,2,39.12363354,0,1647.181677\n186893,63,42,4,1,Married,2,39.08095218,0,1623.047609\n153042,64,46,3,0,Married,6,39.07322505,0,1571.661253\n194863,61,40,1,0,Married,10,39.06923279,0,1530.46164\n137342,66,48,4,0,Single,11,38.99781338,0,1638.890669\n122916,60,41,2,1,Single,14,38.87372586,0,1371.686293\n171118,69,52,2,0,Single,12,38.86346018,0,1424.173009\n169295,66,49,4,1,Single,9,38.81184155,0,1408.592078\n133072,69,52,4,0,Married,8,38.7881759,0,1611.408795\n138215,62,42,2,1,Single,13,38.75278934,0,1351.639467\n120178,61,42,4,1,Single,12,38.69770761,0,1450.885381\n165986,64,45,3,0,Married,7,38.69400015,0,1383.700008\n135457,62,41,1,1,Married,6,38.63812819,0,1616.90641\n150233,61,43,2,1,Married,10,38.61900356,0,1550.950178\n163758,64,45,4,1,Married,15,38.5875034,0,1535.37517\n125823,70,50,3,1,Single,7,38.57444556,0,1709.722278\n184311,66,49,1,1,Single,11,38.46999158,0,1660.499579\n175045,65,48,1,0,Married,11,38.46779326,0,1554.389663\n161689,64,44,4,0,Single,14,38.39959509,0,1372.979755\n154569,60,41,2,1,Single,7,38.35399028,0,1664.699514\n150685,68,48,2,1,Single,7,38.31712094,0,1325.856047\n101140,64,44,2,1,Single,6,38.29982989,0,1403.991495\n153901,64,47,2,0,Single,15,38.10976558,0,1539.488279\n137244,66,48,4,1,Single,12,38.02391143,0,1641.195572\n181598,70,50,1,0,Married,15,38.00033731,0,1648.016866\n197601,61,43,2,0,Married,11,37.96323838,0,1434.161919\n115567,60,43,3,0,Married,13,37.90618249,0,1491.309125\n101981,62,44,2,0,Married,8,37.8728243,0,1567.641215\n122045,67,47,2,0,Married,6,37.85146275,0,1341.573138\n142829,63,46,3,1,Single,7,37.79738698,0,1545.869349\n129224,67,48,3,1,Married,5,37.76313019,0,1655.15651\n118908,70,51,2,1,Single,9,37.74299444,0,1485.149722\n133077,62,44,4,0,Single,10,37.67848475,0,1317.924238\n171247,61,43,1,0,Married,7,37.58319669,0,1666.159835\n133469,67,50,3,0,Married,1,37.55027467,0,1336.513734\n146703,69,51,2,1,Single,7,37.54144893,0,1657.072447\n109372,67,50,4,1,Single,8,37.5169384,0,1573.84692\n162011,67,48,2,0,Married,13,37.44283273,0,1391.141637\n131083,69,50,3,0,Married,6,37.17637449,0,1305.818725\n141639,66,45,1,1,Single,11,37.17363736,0,1570.681868\n104871,66,49,2,0,Single,14,37.11762558,0,1473.881279\n162049,70,53,4,0,Single,11,37.08766322,0,1382.383161\n155650,70,49,1,0,Married,6,37.08168294,0,1356.084147\n166634,64,47,3,0,Married,8,37.0624991,0,1511.124955\n199783,62,44,2,0,Married,14,37.02154202,0,1465.077101\n152364,64,47,3,0,Single,13,36.87360679,0,1421.68034\n190457,64,45,3,1,Single,14,36.85347931,0,1570.673966\n111528,63,42,1,1,Single,10,36.83406316,0,1316.703158\n137044,66,49,3,0,Married,10,36.77684301,0,1353.842151\n108405,70,50,1,1,Single,14,36.73113056,0,1628.556528\n181894,69,50,2,1,Single,7,36.64815659,0,1465.40783\n157704,66,45,1,0,Married,9,36.63901131,0,1423.950566\n112937,64,47,3,1,Single,11,36.6118076,0,1589.59038\n104824,60,42,1,1,Single,12,36.43842415,0,1551.921208\n171275,70,53,2,1,Married,4,36.43109953,0,1479.554977\n122816,61,44,2,1,Married,14,36.41245947,0,1239.622974\n100642,63,44,4,0,Single,13,36.41125918,0,1606.562959\n166606,60,41,3,0,Married,10,36.38635058,0,1574.317529\n187556,67,47,1,1,Single,0,36.34557314,0,1429.278657\n121304,68,47,4,0,Married,9,36.23159236,0,1602.579618\n103257,68,51,4,1,Married,8,36.17224095,0,1254.612048\n115318,60,40,2,1,Single,7,36.16669187,0,1357.334594\n130180,65,48,3,1,Married,10,36.120624,0,1442.0312\n186226,64,43,3,0,Married,9,36.06111866,0,1429.055933\n104017,65,44,2,0,Married,6,35.99278088,0,1369.639044\n150632,63,46,1,0,Married,6,35.92061417,0,1300.030709\n111451,65,44,2,1,Single,7,35.9044908,0,1454.22454\n147429,63,45,2,1,Single,15,35.87820782,0,1334.910391\n191683,60,39,4,0,Married,10,35.85821979,0,1434.91099\n109416,61,43,3,0,Married,6,35.84183739,0,1204.09187\n187326,60,43,3,0,Married,7,35.84046263,0,1275.023132\n159996,60,40,2,1,Married,6,35.8098218,0,1551.49109\n188615,67,47,2,0,Married,6,35.7787252,0,1296.93626\n162151,68,50,4,1,Married,10,35.77154323,0,1279.577162\n101429,61,41,1,0,Single,11,35.74744486,0,1362.372243\n191667,65,45,3,1,Married,8,35.65714861,0,1275.857431\n133376,70,52,2,0,Married,12,35.64217047,0,1502.108524\n191541,64,43,3,0,Married,9,35.62884287,0,1546.442144\n177686,67,49,2,0,Single,14,35.55235284,0,1488.617642\n122609,64,45,1,1,Married,4,35.48597259,0,1363.29863\n110675,69,49,3,1,Married,12,35.481002,0,1478.0501\n153172,66,47,1,0,Married,9,35.42012774,0,1349.006387\n146057,69,48,2,0,Married,6,35.389962,0,1195.4981\n148841,64,44,2,1,Married,6,35.28752129,0,1209.376065\n154638,64,47,3,0,Married,6,35.28494815,0,1428.247408\n160671,64,46,4,1,Single,9,35.28147697,0,1312.073849\n149037,67,50,3,1,Married,6,35.27560523,0,1555.780262\n197071,62,44,4,1,Married,10,35.2579192,0,1244.89596\n165088,66,45,3,0,Married,7,35.00934622,0,1179.467311\n148560,66,49,1,1,Married,11,34.99649779,0,1364.82489\n168776,64,44,2,1,Single,11,34.92223883,0,1151.111942\n199905,65,44,3,0,Married,8,34.81620613,0,1355.810307\n168872,60,43,2,1,Married,13,34.80998953,0,1404.499477\n155079,69,50,3,0,Married,12,34.78307855,0,1369.153928\n146073,64,44,2,1,Married,15,34.68738518,0,1304.369259\n126554,63,46,1,1,Married,6,34.62634009,0,1395.317005\n114456,64,43,2,1,Married,6,34.56669768,0,1245.334884\n150354,66,49,2,1,Married,6,34.55899311,0,1138.949656\n145976,68,47,4,1,Single,14,34.55719926,0,1482.859963\n112727,66,47,4,1,Married,13,34.54813739,0,1397.40687\n163858,70,49,3,1,Married,15,34.5158146,0,1364.79073\n131643,63,44,2,0,Married,6,34.46317069,0,1482.158535\n157732,66,46,2,0,Single,12,34.41105044,0,1234.552522\n109065,67,48,2,0,Married,7,34.37740054,0,1201.870027\n191167,61,40,2,0,Married,14,34.36715649,0,1150.357825\n118829,63,46,2,1,Married,9,34.36052183,0,1324.026092\n151022,70,51,1,1,Married,10,34.33625065,0,1361.812533\n126801,62,41,2,0,Married,13,34.18558791,0,1114.279396\n160828,69,49,2,1,Married,6,34.17131932,0,1311.565966\n198649,65,45,2,1,Married,9,34.16824007,0,1465.412004\n110207,63,42,1,1,Single,9,34.08131785,0,1387.065893\n188953,68,50,2,1,Married,1,34.07808788,0,1198.904394\n150093,65,48,1,0,Married,10,34.05129958,0,1185.564979\n119255,60,41,3,1,Single,13,33.96946247,0,1275.473124\n188354,70,50,2,1,Married,6,33.92207189,0,1215.103595\n175072,70,53,3,0,Married,7,33.87448799,0,1297.7244\n140708,61,41,2,0,Married,7,33.80039913,0,1313.019957\n166918,60,41,3,0,Married,12,33.7281892,0,1164.40946\n153112,69,51,3,0,Married,9,33.68713333,0,1420.356667\n183084,65,47,4,0,Married,9,33.56408507,0,1176.204254\n194451,62,41,3,1,Married,10,33.47560981,0,1379.780491\n186589,69,48,3,0,Married,10,33.46665127,0,1283.332564\n161522,61,40,2,0,Married,13,33.45428635,0,1392.714318\n123795,60,41,1,0,Married,14,33.45093763,0,1408.546882\n185682,68,49,1,1,Married,7,33.34637246,0,1293.318623\n129589,68,47,3,1,Married,5,33.2907655,0,1232.538275\n152883,60,40,4,1,Married,8,33.23964927,0,1260.982464\n138693,66,48,2,0,Married,10,33.19193201,0,1338.596601\n118945,62,43,4,1,Married,0,33.1677643,0,1151.388215\n174231,67,48,1,1,Married,5,33.15465722,0,1240.732861\n162909,68,50,4,1,Married,9,33.10928734,0,1347.464367\n180425,61,42,1,0,Single,12,33.06159082,0,1296.079541\n107267,65,45,4,1,Married,7,33.04468917,0,1216.234459\n182266,62,42,3,1,Married,4,33.00902796,0,1165.451398\n134528,70,51,1,1,Married,11,32.84523715,0,1177.261858\n125914,68,48,2,0,Married,13,32.80356503,0,1429.178252\n154060,64,43,1,1,Married,14,32.72555801,0,1228.277901\n157085,61,40,4,1,Married,12,32.66640367,0,1414.320184\n113800,69,51,2,1,Single,11,32.66384596,0,1360.192298\n109042,66,46,4,1,Married,6,32.65583535,0,1120.791768\n188066,65,46,4,1,Married,9,32.58045006,0,1074.022503\n165251,60,43,1,0,Married,13,32.51585346,0,1195.792673\n116914,69,48,4,1,Single,14,32.46081575,0,1104.040788\n161975,60,41,4,1,Single,13,32.4096623,0,1028.483115\n180363,70,51,2,1,Married,12,32.376349,0,1341.81745\n101827,60,39,2,1,Married,10,32.3286988,0,1042.43494\n132129,66,45,1,1,Married,14,32.26493105,0,1148.246553\n172145,61,44,2,0,Married,15,32.13850999,0,1117.9255\n161533,62,45,3,1,Single,11,32.05340234,0,1388.670117\n146369,61,42,1,1,Married,9,31.96379288,0,1243.189644\n180437,70,52,3,0,Married,12,31.95768318,0,1379.884159\n163289,65,46,2,1,Married,7,31.93228812,0,1221.614406\n176466,68,49,4,1,Married,11,31.91330615,0,1071.665308\n162106,65,47,2,0,Married,9,31.86598582,0,1186.299291\n185807,62,44,1,0,Married,14,31.83119043,0,1060.559522\n171352,63,42,3,1,Married,8,31.78189277,0,1334.094639\n112718,67,49,1,0,Married,13,31.77827713,0,1331.913857\n163760,61,43,3,1,Married,9,31.70000207,0,1286.000104\n154855,66,49,3,1,Married,13,31.59694774,0,993.847387\n116077,70,51,4,1,Single,11,31.52674399,0,1116.3372\n105925,69,49,4,1,Married,6,31.48278112,0,1127.139056\n198492,67,49,4,1,Married,10,31.29387843,0,1054.693922\n117881,60,40,1,1,Married,8,31.26927851,0,1198.463926\n101083,66,48,3,1,Married,6,31.26546602,0,1206.273301\n147408,60,41,4,1,Married,11,31.24657297,0,1191.328649\n129952,68,51,2,0,Married,12,31.12221416,0,1319.110708\n160033,63,45,3,0,Married,15,31.09246645,0,1241.623323\n198225,68,50,2,0,Married,12,31.07107483,0,1109.553742\n103308,68,50,3,1,Married,6,31.03608979,0,994.8044895\n172647,62,45,1,1,Married,10,30.93276225,0,964.6381125\n120525,69,48,1,1,Single,14,30.84570505,0,1042.285253\n112610,64,44,2,0,Married,13,30.7732617,0,945.663085\n192201,66,46,3,0,Married,15,30.71424992,0,1004.712496\n174068,63,44,2,0,Married,12,30.56477072,0,1028.238536\n178108,67,47,2,1,Married,6,30.4361475,0,1063.807375\n102573,61,42,3,0,Single,14,30.31565715,0,1231.782858\n192305,67,50,3,1,Married,10,30.22512832,0,1285.256416\n140321,65,46,2,0,Single,11,30.18541363,0,1214.270682\n178693,62,43,2,1,Married,15,30.17443424,0,1076.721712\n101209,69,52,3,0,Married,15,30.01958668,0,1195.979334\n161857,67,48,2,1,Married,7,29.94505399,0,1081.2527\n183986,68,49,3,0,Single,12,29.91010708,0,1176.505354\n125904,68,47,1,0,Married,11,29.78216176,0,1181.108088\n179533,61,41,3,0,Married,13,29.64896138,0,1050.448069\n120340,66,47,2,1,Married,9,29.48296987,0,977.1484935\n116022,70,52,3,1,Married,8,29.44775017,0,904.3875085\n121288,61,44,2,0,Single,13,29.1256495,0,979.282475\n150869,62,43,4,1,Married,10,29.1124105,0,1205.620525\n135433,61,44,3,1,Married,15,29.05900014,0,902.950007\n106644,60,43,3,1,Single,14,29.03167189,0,946.5835945\n119438,63,45,3,0,Married,14,29.01610379,0,977.8051895\n161544,65,46,3,1,Married,7,28.86286952,0,907.143476\n113449,70,50,2,1,Single,15,28.82071718,0,868.035859\n185353,69,51,2,1,Single,14,28.69860484,0,1071.930242\n103771,70,52,1,1,Married,12,28.52569444,0,999.284722\n185826,67,48,4,1,Single,13,28.51860588,0,925.930294\n160947,65,46,2,0,Married,13,28.27238083,0,1124.619042\n188632,60,39,2,1,Married,14,28.26507244,0,1077.253622\n158184,66,46,2,1,Married,11,28.05839824,0,1077.919912\n184866,69,52,3,1,Married,13,28.03244612,0,972.622306\n190381,60,39,2,0,Single,13,28.01903018,0,825.951509\n186134,64,47,2,0,Married,15,27.9400468,0,1023.00234\n139843,63,44,2,1,Married,13,27.93650816,0,984.825408\n130198,60,40,3,0,Single,15,27.84268659,0,881.1343295\n143427,64,46,2,1,Married,13,27.82519769,0,798.2598845\n180081,68,51,3,1,Married,12,27.64545617,0,788.2728085\n134902,65,48,3,0,Single,12,27.6123893,0,802.619465\n129973,64,44,3,0,Married,15,27.43700116,0,860.850058\n117684,67,48,2,0,Married,11,27.29098745,0,1130.549373\n155389,67,50,3,1,Single,15,27.26619006,0,964.309503\n122762,62,45,1,1,Married,14,27.13439185,0,1117.719593\n166268,64,45,2,1,Married,15,26.84437074,0,905.218537\n125788,69,48,4,1,Single,13,26.80173728,0,940.086864\n197637,70,49,4,0,Single,14,26.67952108,0,950.976054\n177251,61,40,3,0,Single,12,26.54142028,0,1068.071014\n118323,66,47,1,0,Married,12,26.44116868,0,968.058434\n193973,60,43,3,1,Married,15,26.29789532,0,943.894766\n172278,70,51,3,1,Single,12,25.91082086,0,825.541043\n146306,65,48,1,0,Single,15,25.88680711,0,855.3403555\n105434,61,42,2,0,Single,14,25.78176113,0,948.0880565\n122166,66,49,2,0,Single,15,25.64671403,0,1034.335702\n145034,60,43,3,0,Married,12,25.42452485,0,1017.226243\n122017,62,43,1,1,Single,12,25.39965998,0,913.982999\n100604,70,50,3,1,Married,14,25.37564972,0,698.782486\n135837,65,45,2,1,Single,13,25.2281853,0,971.409265\n181524,62,41,2,1,Single,13,24.67729816,0,817.864908\n130444,62,44,4,1,Married,14,24.66727168,0,935.363584\n114977,60,42,2,1,Married,13,24.50552264,0,646.276132\n196093,68,47,2,1,Single,13,24.4505445,0,830.527225\n150300,62,43,1,1,Single,11,24.39857653,0,918.9288265\n194753,64,43,3,1,Married,13,24.31025624,0,626.512812\n128215,66,49,1,1,Married,11,24.28756977,0,921.3784885\n123394,61,42,2,1,Single,14,24.21601283,0,698.8006415\n115046,60,43,2,0,Single,11,24.14757387,0,882.3786935\n146446,70,51,4,1,Single,12,24.13380504,0,935.690252\n193979,63,43,3,0,Married,15,24.11464048,0,842.732024\n178098,61,41,3,1,Married,13,23.91195282,0,810.597641\n184697,61,41,1,1,Married,11,23.87112656,0,630.556328\n151459,60,43,4,0,Married,15,23.82191985,0,685.0959925\n118320,67,48,2,1,Single,14,23.17656269,0,702.8281345\n142532,70,49,2,0,Married,11,23.15897362,0,643.948681\n126180,64,43,3,1,Married,15,22.66043856,0,567.021928\n143880,63,42,2,1,Married,13,22.47577446,0,733.788723\n157943,67,46,2,0,Married,12,22.4023338,0,520.11669\n139329,67,47,2,0,Married,14,22.38307969,0,780.1539845\n100531,64,45,4,0,Married,11,22.17549513,0,601.7747565\n167485,64,44,4,1,Married,13,22.14732403,0,840.3662015\n183198,61,41,3,1,Married,11,22.1340816,0,820.70408\n143349,61,41,2,1,Married,14,21.89517467,0,495.7587335\n189979,62,43,2,0,Single,12,21.7772422,0,705.86211\n114287,60,43,3,1,Married,13,21.6923393,0,824.616965\n126746,69,49,3,0,Married,12,21.44102999,0,715.0514995\n186299,65,47,4,0,Married,11,21.31311833,0,785.6559165\n135153,64,45,3,0,Single,14,21.13027274,0,656.513637\n165032,67,50,2,1,Single,15,21.04379396,0,608.189698\n171070,62,45,3,1,Married,15,20.85022796,0,540.511398\n129262,63,46,4,1,Single,15,20.71180013,0,735.5900065\n198335,65,47,1,1,Married,15,20.18344798,0,590.172399\n154574,70,52,2,1,Single,14,20.06833059,0,628.4165295\n163778,60,41,3,1,Married,13,19.90344326,0,792.172163\n138334,60,39,4,0,Married,15,19.85951362,0,415.975681\n127778,62,43,2,0,Single,12,19.84465222,0,566.232611\n144950,64,45,1,0,Married,14,19.82277821,0,609.1389105\n122946,60,41,3,1,Married,13,19.7637924,0,447.18962\n132767,65,45,4,1,Married,15,19.74528225,0,649.2641125\n133630,69,49,2,1,Single,11,19.72645556,0,745.322778\n182394,64,47,4,0,Single,15,19.35304501,0,727.6522505\n140518,60,43,2,1,Single,15,19.3055825,0,727.279125\n164586,69,52,2,1,Single,15,19.1986151,0,744.930755\n131247,61,41,3,1,Married,13,18.95850938,0,511.925469\n110052,60,42,4,1,Married,14,18.92269289,0,629.1346445\n103005,60,41,2,1,Married,12,18.87214094,0,701.607047\n167576,70,52,3,1,Single,11,18.79262699,0,359.6313495\n173913,68,49,2,1,Single,12,18.79131042,0,402.565521\n105197,70,53,2,0,Single,15,18.61774917,0,640.8874585\n139104,63,43,2,1,Single,11,18.39388561,0,615.6942805\n182308,66,45,3,1,Single,12,18.26548029,0,398.2740145\n107394,64,45,4,1,Married,11,18.10363382,0,540.181691\n115338,60,43,2,0,Single,14,18.07329299,0,428.6646495\n153800,63,45,3,1,Single,15,17.63866433,0,613.9332165\n159207,62,43,2,1,Married,12,17.62387915,0,431.1939575\n177941,65,44,3,0,Married,13,17.59097452,0,473.548726\n186860,67,46,3,1,Married,14,17.41813348,0,577.906674\n158651,61,41,3,1,Married,13,17.28856652,0,295.428326\n169190,65,47,1,1,Single,11,17.14401051,0,280.2005255\n137145,65,47,3,0,Married,13,16.7670884,0,332.35442\n147362,61,43,1,1,Married,14,16.73906244,0,558.953122\n169100,64,47,2,1,Married,11,16.61784175,0,458.8920875\n171816,70,50,2,0,Single,13,16.55863596,0,524.931798\n195434,67,48,1,1,Single,11,16.53853704,0,367.926852\n138348,67,47,2,1,Single,14,16.47542036,0,357.771018\n107252,68,49,3,1,Single,13,16.47449989,0,297.7249945\n104103,68,48,3,0,Married,14,16.09391973,0,530.6959865\n101640,70,50,2,1,Married,11,15.95334825,0,357.6674125\n185269,67,50,3,0,Single,11,15.87568511,0,345.7842555\n154107,69,52,4,0,Married,13,15.78501164,0,404.250582\n182583,66,45,1,1,Married,12,15.76108943,0,354.0544715\n122855,66,45,1,1,Married,12,15.5892684,0,360.46342\n117196,70,52,2,1,Single,13,15.35319666,0,460.659833\n148660,66,46,3,1,Married,11,15.24467979,0,298.2339895\n177232,67,49,4,1,Married,12,14.86060602,0,179.030301\n183044,63,45,2,1,Married,12,13.90110971,0,126.0554855\n168101,65,45,4,1,Married,15,13.29062799,0,261.5313995\n168897,67,49,4,0,Married,12,12.6140927,0,316.704635\n144717,64,46,3,1,Married,11,12.53452056,0,365.726028\n" }, { "path": "63A-FA/Packages Pre-requisites_v03.R", "content": "install.packages(\"car\")\nlibrary(\"car\")\n\ninstall.packages(\"glmnet\")\nlibrary(\"glmnet\")\n\ninstall.packages(\"caTools\")\nlibrary(caTools)\ninstall.packages(\"dplyr\")\nlibrary(dplyr)\n\n\n\n\ninstall.packages(\"rpart\")\nlibrary(rpart)\n\ninstall.packages(\"CHAID\", repos=\"http://R-Forge.R-project.org\")\nlibrary(CHAID)\n\n\ninstall.packages(\"rpart.plot\")\nlibrary(rpart.plot)\n\ninstall.packages(\"forecast\")\nlibrary(forecast)\n\ninstall.packages(\"party\")\nlibrary(party)\n\ninstall.packages(\"partykit\")\nlibrary(partykit)\n\ninstall.packages(\"arules\")\nlibrary(arules)\n\ninstall.packages(\"amap\")\nlibrary(amap)\n\ninstall.packages(\"rpart\")\nlibrary(rpart)\n\ninstall.packages(\"https://cran.r-project.org/bin/windows/contrib/3.3/RGtk2_2.20.31.zip\", repos=NULL)\n#or\ninstall.packages(\"RGtk2\")\n\n#Install GTK+\nlibrary(RGtk2)\n\ninstall.packages(\"rattle\")\nlibrary(rattle)\n" }, { "path": "63A-FA/aapl.csv", "content": "Date,Open,High,Low,Close,Volume,Adj Close\n2008-10-14,116.26,116.40,103.14,104.08,70749800,104.08\n2008-10-13,104.55,110.53,101.02,110.26,54967000,110.26\n2008-10-10,85.70,100.00,85.00,96.80,79260700,96.80\n2008-10-09,93.35,95.80,86.60,88.74,57763700,88.74\n2008-10-08,85.91,96.33,85.68,89.79,78847900,89.79\n2008-10-07,100.48,101.50,88.95,89.16,67099000,89.16\n2008-10-06,91.96,98.78,87.54,98.14,75264900,98.14\n2008-10-03,104.00,106.50,94.65,97.07,81942800,97.07\n2008-10-02,108.01,108.79,100.00,100.10,57477300,100.10\n2008-10-01,111.92,112.36,107.39,109.12,46303000,109.12\n2008-09-30,108.25,115.00,106.30,113.66,58095800,113.66\n2008-09-29,119.62,119.68,100.59,105.26,93581400,105.26\n2008-09-26,124.91,129.80,123.00,128.24,40208700,128.24\n2008-09-25,129.80,134.79,128.52,131.93,35865600,131.93\n2008-09-24,127.27,130.95,125.15,128.71,37393400,128.71\n2008-09-23,131.85,135.80,126.66,126.84,45727300,126.84\n2008-09-22,139.94,140.25,130.66,131.05,30577300,131.05\n2008-09-19,142.60,144.20,136.31,140.91,51102700,140.91\n2008-09-18,130.57,135.43,120.68,134.09,59819300,134.09\n2008-09-17,138.49,138.51,127.83,127.83,42847200,127.83\n2008-09-16,133.86,142.50,132.15,139.88,42804800,139.88\n2008-09-15,142.03,147.69,140.36,140.36,32852600,140.36\n2008-09-12,150.91,150.91,146.50,148.94,28322400,148.94\n2008-09-11,148.18,152.99,146.00,152.65,34666800,152.65\n2008-09-10,152.32,154.99,148.80,151.61,34755100,151.61\n2008-09-09,156.86,159.96,149.79,151.68,44442500,151.68\n2008-09-08,164.57,164.89,151.46,157.92,37356400,157.92\n2008-09-05,158.59,162.40,157.65,160.18,28083800,160.18\n2008-09-04,165.86,167.91,160.81,161.22,26549500,161.22\n2008-09-03,166.84,168.68,164.00,166.96,26244100,166.96\n2008-09-02,172.40,173.50,165.00,166.19,27884400,166.19\n2008-08-29,172.96,173.50,169.04,169.53,21403200,169.53\n2008-08-28,175.28,176.25,172.75,173.74,15394500,173.74\n2008-08-27,173.31,175.76,172.19,174.67,17045900,174.67\n2008-08-26,172.76,174.88,172.61,173.64,15912500,173.64\n2008-08-25,176.15,176.23,171.66,172.55,17300900,172.55\n2008-08-22,175.82,177.50,175.57,176.79,15700400,176.79\n2008-08-21,174.47,175.45,171.89,174.29,19276600,174.29\n2008-08-20,174.77,176.94,173.61,175.84,18105400,175.84\n2008-08-19,174.54,177.07,171.81,173.53,21997000,173.53\n2008-08-18,175.57,177.81,173.82,175.39,19691200,175.39\n2008-08-15,179.04,179.75,175.05,175.74,25294700,175.74\n2008-08-14,178.33,180.45,177.84,179.32,25393200,179.32\n2008-08-13,177.98,180.00,175.90,179.30,30083800,179.30\n2008-08-12,173.52,179.29,173.51,176.73,29867100,176.73\n2008-08-11,170.07,176.50,169.67,173.56,31821100,173.56\n2008-08-08,163.86,169.65,163.75,169.55,25499900,169.55\n2008-08-07,162.71,166.15,161.50,163.57,24013300,163.57\n2008-08-06,159.97,167.40,158.00,164.19,28264600,164.19\n2008-08-05,155.42,160.80,154.82,160.64,24584700,160.64\n2008-08-04,156.60,157.90,152.91,153.23,21161700,153.23\n2008-08-01,159.90,159.99,155.75,156.66,19451400,156.66\n2008-07-31,157.54,162.20,156.98,158.95,22767800,158.95\n2008-07-30,157.78,160.49,156.08,159.88,25899400,159.88\n2008-07-29,155.41,159.45,153.65,157.08,24431100,157.08\n2008-07-28,162.34,162.47,154.02,154.40,27882600,154.40\n2008-07-25,160.40,163.00,158.65,162.12,22629900,162.12\n2008-07-24,164.32,165.26,158.45,159.03,29986400,159.03\n2008-07-23,164.99,168.37,161.56,166.26,37920300,166.26\n2008-07-22,149.00,162.76,146.53,162.02,67128300,162.02\n2008-07-21,166.90,167.50,161.12,166.29,48588200,166.29\n2008-07-18,168.52,169.65,165.00,165.15,31014800,165.15\n2008-07-17,174.10,174.98,171.39,171.81,27054500,171.81\n2008-07-16,170.20,172.93,168.60,172.81,26706800,172.81\n2008-07-15,172.48,173.74,166.39,169.64,37144400,169.64\n2008-07-14,179.24,179.30,173.08,173.88,31644800,173.88\n2008-07-11,175.47,177.11,171.00,172.58,33214700,172.58\n2008-07-10,174.92,177.34,171.37,176.63,30024600,176.63\n2008-07-09,180.20,180.91,174.14,174.25,31992000,174.25\n2008-07-08,175.40,179.70,172.74,179.55,31726800,179.55\n2008-07-07,173.16,177.13,171.90,175.16,29299700,175.16\n2008-07-03,169.59,172.17,165.75,170.12,18691500,170.12\n2008-07-02,175.20,177.45,168.18,168.18,29911400,168.18\n2008-07-01,164.23,174.72,164.00,174.68,39688600,174.68\n2008-06-30,170.19,172.00,166.62,167.44,24435600,167.44\n2008-06-27,166.51,170.57,164.15,170.09,37223200,170.09\n2008-06-26,174.07,174.84,168.01,168.26,31057500,168.26\n2008-06-25,174.61,178.83,173.88,177.39,23016100,177.39\n2008-06-24,172.37,175.78,171.63,173.25,22212400,173.25\n2008-06-23,174.74,175.88,171.56,173.16,23063600,173.16\n2008-06-20,179.35,181.00,175.00,175.27,31727400,175.27\n2008-06-19,178.55,182.34,176.80,180.90,28283900,180.90\n2008-06-18,181.12,182.20,177.35,178.75,28981000,178.75\n2008-06-17,178.10,181.99,177.41,181.43,32130600,181.43\n2008-06-16,171.30,177.90,169.07,176.84,37561800,176.84\n2008-06-13,171.64,174.16,165.31,172.37,48069900,172.37\n2008-06-12,181.49,182.60,171.20,173.26,46726200,173.26\n2008-06-11,184.34,186.00,179.59,180.81,34341100,180.81\n2008-06-10,180.51,186.78,179.02,185.64,40728600,185.64\n2008-06-09,184.79,184.94,175.75,181.61,67442600,181.61\n2008-06-06,188.00,189.95,185.55,185.64,34438700,185.64\n2008-06-05,186.34,189.84,185.70,189.43,26980200,189.43\n2008-06-04,184.02,187.09,183.23,185.19,25963700,185.19\n2008-06-03,186.86,188.20,182.34,185.37,26804300,185.37\n2008-06-02,188.60,189.65,184.53,186.10,24280000,186.10\n2008-05-30,187.45,189.54,187.38,188.75,21792300,188.75\n2008-05-29,186.76,188.20,185.50,186.69,23113800,186.69\n2008-05-28,187.41,187.95,183.72,187.01,26570700,187.01\n2008-05-27,182.75,186.43,181.84,186.43,28210900,186.43\n2008-05-23,180.77,181.99,177.80,181.17,32389900,181.17\n2008-05-22,179.26,181.33,172.00,177.05,43097700,177.05\n2008-05-21,185.67,187.95,176.25,178.19,41344900,178.19\n2008-05-20,181.82,186.16,180.12,185.90,34637500,185.90\n2008-05-19,187.86,188.69,181.30,183.60,33779300,183.60\n2008-05-16,190.11,190.30,187.00,187.62,27348900,187.62\n2008-05-15,186.81,189.90,184.20,189.73,31186000,189.73\n2008-05-14,191.23,192.24,185.57,186.26,32743700,186.26\n2008-05-13,188.61,191.45,187.86,189.96,29401300,189.96\n2008-05-12,185.21,188.87,182.85,188.16,29234400,188.16\n2008-05-09,183.16,184.25,181.37,183.45,24038300,183.45\n2008-05-08,183.77,186.50,183.07,185.06,32110200,185.06\n2008-05-07,186.05,188.20,180.54,182.59,41326200,182.59\n2008-05-06,184.66,187.12,182.18,186.66,32816800,186.66\n2008-05-05,181.92,185.31,181.05,184.73,30519900,184.73\n2008-05-02,180.19,181.92,178.55,180.94,35931500,180.94\n2008-05-01,174.96,180.00,174.86,180.00,32270600,180.00\n2008-04-30,176.19,180.00,172.92,173.95,40697300,173.95\n2008-04-29,171.11,175.66,170.25,175.05,32981300,175.05\n2008-04-28,169.75,173.75,169.13,172.24,28114800,172.24\n2008-04-25,170.70,171.10,166.42,169.73,35445500,169.73\n2008-04-24,165.34,169.98,159.19,168.94,60573800,168.94\n2008-04-23,164.05,164.84,161.08,162.89,53721100,162.89\n2008-04-22,167.40,168.00,158.09,160.20,51413300,160.20\n2008-04-21,162.21,168.50,161.76,168.16,37112600,168.16\n2008-04-18,159.12,162.26,158.38,161.04,36670200,161.04\n2008-04-17,154.17,156.00,153.35,154.49,25152400,154.49\n2008-04-16,151.72,154.10,150.62,153.70,28420500,153.70\n2008-04-15,149.40,149.72,145.72,148.38,24929900,148.38\n2008-04-14,146.77,149.25,144.54,147.78,30181700,147.78\n2008-04-11,152.72,153.30,146.40,147.14,43217000,147.14\n2008-04-10,151.13,155.42,150.60,154.55,34134400,154.55\n2008-04-09,153.31,153.89,150.46,151.44,31192800,151.44\n2008-04-08,153.55,156.45,152.32,152.84,36224800,152.84\n2008-04-07,156.13,159.69,155.11,155.89,41368800,155.89\n2008-04-04,152.19,154.71,150.75,153.08,30514900,153.08\n2008-04-03,147.06,153.63,147.00,151.61,37556000,151.61\n2008-04-02,148.78,151.20,145.85,147.49,37320300,147.49\n2008-04-01,146.30,149.66,143.61,149.53,36877400,149.53\n2008-03-31,143.27,145.71,142.52,143.50,27430900,143.50\n2008-03-28,141.80,144.65,141.60,143.01,25521800,143.01\n2008-03-27,144.95,145.31,139.99,140.25,35708200,140.25\n2008-03-26,140.87,145.74,140.64,145.06,42217300,145.06\n2008-03-25,139.96,143.10,137.33,140.98,37585400,140.98\n2008-03-24,134.01,140.85,133.64,139.53,38104300,139.53\n2008-03-20,131.12,133.29,129.18,133.27,32456700,133.27\n2008-03-19,133.12,134.29,129.67,129.67,36090600,129.67\n2008-03-18,129.18,133.00,128.67,132.82,43040000,132.82\n2008-03-17,122.55,128.59,122.55,126.73,38307100,126.73\n2008-03-14,129.88,130.30,124.20,126.61,41308600,126.61\n2008-03-13,124.10,129.50,123.00,127.94,45075100,127.94\n2008-03-12,127.04,128.68,125.17,126.03,37843900,126.03\n2008-03-11,124.10,127.48,122.00,127.35,41569400,127.35\n2008-03-10,121.98,123.46,119.37,119.69,35699600,119.69\n2008-03-07,120.41,122.98,119.05,122.25,43945100,122.25\n2008-03-06,124.61,127.50,120.81,120.93,52632100,120.93\n2008-03-05,123.58,125.14,122.25,124.49,43637000,124.49\n2008-03-04,121.99,124.88,120.40,124.62,63763700,124.62\n2008-03-03,124.44,125.98,118.00,121.73,56894400,121.73\n2008-02-29,129.29,130.21,124.80,125.02,44838600,125.02\n2008-02-28,127.20,132.20,125.77,129.91,57794800,129.91\n2008-02-27,118.23,123.05,118.09,122.96,52683500,122.96\n2008-02-26,117.64,121.09,115.44,119.15,53746000,119.15\n2008-02-25,118.59,120.17,116.66,119.74,44884800,119.74\n2008-02-22,122.48,122.51,115.87,119.46,54638500,119.46\n2008-02-21,126.05,126.47,120.86,121.54,33504100,121.54\n2008-02-20,122.20,124.60,121.68,123.82,34551400,123.82\n2008-02-19,125.99,126.75,121.44,122.18,35894500,122.18\n2008-02-15,126.27,127.08,124.06,124.63,32189300,124.63\n2008-02-14,129.40,130.80,127.01,127.46,34074900,127.46\n2008-02-13,126.68,129.78,125.63,129.40,34590500,129.40\n2008-02-12,130.70,131.00,123.62,124.86,43785000,124.86\n2008-02-11,128.01,129.98,127.20,129.45,42908300,129.45\n2008-02-08,122.08,125.70,121.60,125.48,48427600,125.48\n2008-02-07,119.97,124.78,117.27,121.24,74404700,121.24\n2008-02-06,130.83,131.92,121.77,122.00,56188300,122.00\n2008-02-05,130.43,134.00,128.90,129.36,40751500,129.36\n2008-02-04,134.21,135.90,131.42,131.65,32115500,131.65\n2008-02-01,136.24,136.59,132.18,133.75,36098000,133.75\n2008-01-31,129.45,136.65,129.40,135.36,48059800,135.36\n2008-01-30,131.37,135.45,130.00,132.18,44394700,132.18\n2008-01-29,131.15,132.79,129.05,131.54,39285100,131.54\n2008-01-28,128.16,133.20,126.45,130.01,52673000,130.01\n2008-01-25,138.99,139.09,129.61,130.01,55526400,130.01\n2008-01-24,139.99,140.70,132.01,135.60,71638100,135.60\n2008-01-23,136.19,140.00,126.14,139.07,120463200,139.07\n2008-01-22,148.06,159.98,146.00,155.64,86955500,155.64\n2008-01-18,161.71,165.75,159.61,161.36,61583700,161.36\n2008-01-17,161.51,165.36,158.42,160.89,62780700,160.89\n2008-01-16,165.23,169.01,156.70,159.64,79065900,159.64\n2008-01-15,177.72,179.22,164.66,169.04,83688500,169.04\n2008-01-14,177.52,179.42,175.17,178.78,39301800,178.78\n2008-01-11,176.00,177.85,170.00,172.69,44010200,172.69\n2008-01-10,177.58,181.00,175.41,178.02,52963400,178.02\n2008-01-09,171.30,179.50,168.30,179.40,64781500,179.40\n2008-01-08,180.14,182.46,170.80,171.25,54422000,171.25\n2008-01-07,181.25,183.60,170.23,177.64,74006900,177.64\n2008-01-04,191.45,193.00,178.89,180.05,51994000,180.05\n2008-01-03,195.41,197.39,192.69,194.93,30073800,194.93\n2008-01-02,199.27,200.26,192.55,194.84,38542100,194.84\n2007-12-31,199.50,200.50,197.75,198.08,19261900,198.08\n2007-12-28,200.59,201.56,196.88,199.83,24987400,199.83\n2007-12-27,198.95,202.96,197.80,198.57,28411700,198.57\n2007-12-26,199.01,200.96,196.82,198.95,25133300,198.95\n2007-12-24,195.03,199.33,194.79,198.80,17150100,198.80\n2007-12-21,190.12,193.91,189.89,193.91,35498600,193.91\n2007-12-20,185.43,187.83,183.33,187.21,27644900,187.21\n2007-12-19,182.98,184.64,180.90,183.12,29552800,183.12\n2007-12-18,186.52,187.33,178.60,182.98,43664400,182.98\n2007-12-17,190.72,192.65,182.98,184.40,36596200,184.40\n2007-12-14,190.37,193.20,189.54,190.39,24082600,190.39\n2007-12-13,190.19,192.12,187.82,191.83,30879200,191.83\n2007-12-12,193.44,194.48,185.76,190.86,43773600,190.86\n2007-12-11,194.75,196.83,187.39,188.54,39675900,188.54\n2007-12-10,193.59,195.66,192.69,194.21,25799200,194.21\n2007-12-07,190.54,194.99,188.04,194.30,38073800,194.30\n2007-12-06,186.19,190.10,186.12,189.95,32136100,189.95\n2007-12-05,182.89,186.00,182.41,185.50,31871500,185.50\n2007-12-04,177.15,180.90,176.99,179.81,27635700,179.81\n2007-12-03,181.86,184.14,177.70,178.86,34338200,178.86\n2007-11-30,187.34,187.70,179.70,182.22,42421500,182.22\n2007-11-29,179.43,185.17,179.15,184.29,37533100,184.29\n2007-11-28,176.82,180.60,175.35,180.22,41104000,180.22\n2007-11-27,175.22,175.79,170.01,174.81,47036800,174.81\n2007-11-26,173.59,177.27,172.35,172.54,46634100,172.54\n2007-11-23,172.00,172.05,169.75,171.54,16634200,171.54\n2007-11-21,165.84,172.35,164.67,168.46,43493200,168.46\n2007-11-20,165.67,171.79,163.53,168.85,55130100,168.85\n2007-11-19,166.10,168.20,162.10,163.95,41196800,163.95\n2007-11-16,165.30,167.02,159.33,166.39,49391300,166.39\n2007-11-15,166.39,169.59,160.30,164.30,53095600,164.30\n2007-11-14,177.16,177.57,163.74,166.11,51695400,166.11\n2007-11-13,160.85,170.98,153.76,169.96,62034100,169.96\n2007-11-12,165.28,167.70,150.63,153.76,63057700,153.76\n2007-11-09,171.15,175.12,165.21,165.37,54458700,165.37\n2007-11-08,186.67,186.90,167.77,175.47,67458500,175.47\n2007-11-07,190.61,192.68,186.13,186.30,35473400,186.30\n2007-11-06,187.05,192.00,185.27,191.79,34097400,191.79\n2007-11-05,185.29,188.96,184.24,186.18,28720600,186.18\n2007-11-02,189.21,189.44,183.49,187.87,35789800,187.87\n2007-11-01,188.60,190.10,180.00,187.44,28751300,187.44\n2007-10-31,187.63,190.12,184.95,189.95,29761100,189.95\n2007-10-30,186.18,189.37,184.73,187.00,33550500,187.00\n2007-10-29,185.45,186.59,184.70,185.09,19305500,185.09\n2007-10-26,185.29,185.37,182.88,184.70,25219800,184.70\n2007-10-25,184.87,185.90,181.66,182.78,34771500,182.78\n2007-10-24,185.81,187.21,179.24,185.93,46017200,185.93\n2007-10-23,188.56,188.60,182.76,186.16,64113000,186.16\n2007-10-22,170.35,174.90,169.96,174.36,58910700,174.36\n2007-10-19,174.24,174.63,170.00,170.42,46135000,170.42\n2007-10-18,171.50,174.19,171.05,173.50,29417000,173.50\n2007-10-17,172.69,173.04,169.18,172.75,40271900,172.75\n2007-10-16,165.54,170.18,165.15,169.58,38136800,169.58\n2007-10-15,167.98,169.57,163.50,166.98,38497500,166.98\n2007-10-12,163.01,167.28,161.80,167.25,35292000,167.25\n2007-10-11,169.49,171.88,153.21,162.23,58714000,162.23\n2007-10-10,167.55,167.88,165.60,166.79,23842500,166.79\n2007-10-09,170.20,171.11,166.68,167.86,39438800,167.86\n2007-10-08,163.49,167.91,162.97,167.91,29854600,167.91\n2007-10-05,158.37,161.58,157.70,161.45,33695400,161.45\n2007-10-04,158.00,158.08,153.50,156.24,23462800,156.24\n2007-10-03,157.78,159.18,157.01,157.92,24732800,157.92\n2007-10-02,156.55,158.59,155.89,158.45,28288200,158.45\n2007-10-01,154.63,157.41,152.93,156.34,29895300,156.34\n2007-09-28,153.44,154.60,152.75,153.47,21967900,153.47\n2007-09-27,153.77,154.52,152.32,154.50,23507100,154.50\n2007-09-26,154.47,155.00,151.25,152.77,34831000,152.77\n2007-09-25,146.84,153.22,146.82,153.18,42591100,153.18\n2007-09-24,146.73,149.85,146.65,148.28,37577200,148.28\n2007-09-21,141.14,144.65,140.31,144.15,40674300,144.15\n2007-09-20,140.15,141.79,139.32,140.31,24708600,140.31\n2007-09-19,143.02,143.16,139.40,140.77,36674300,140.77\n2007-09-18,139.06,142.85,137.83,140.92,38003200,140.92\n2007-09-17,138.99,140.59,137.60,138.41,28334700,138.41\n2007-09-14,136.57,138.98,136.20,138.81,21690000,138.81\n2007-09-13,138.83,139.00,136.65,137.20,23434400,137.20\n2007-09-12,135.99,139.40,135.75,136.85,36527500,136.85\n2007-09-11,137.90,138.30,133.75,135.49,34710200,135.49\n2007-09-10,136.99,138.04,133.95,136.71,53137100,136.71\n2007-09-07,132.01,132.30,130.00,131.77,51092000,131.77\n2007-09-06,135.56,137.57,132.71,135.01,67902200,135.01\n2007-09-05,144.97,145.84,136.10,136.76,83150800,136.76\n2007-09-04,139.94,145.73,139.84,144.16,47030100,144.16\n2007-08-31,139.49,139.65,137.41,138.48,31317400,138.48\n2007-08-30,132.67,138.25,132.30,136.25,51270800,136.25\n2007-08-29,129.88,134.18,129.54,134.08,41673600,134.08\n2007-08-28,130.99,132.41,126.63,126.82,42120200,126.82\n2007-08-27,133.39,134.66,132.10,132.25,25265700,132.25\n2007-08-24,130.53,135.37,129.81,135.30,32565500,135.30\n2007-08-23,133.09,133.34,129.76,131.07,30958500,131.07\n2007-08-22,131.22,132.75,130.33,132.51,37920200,132.51\n2007-08-21,122.21,128.96,121.00,127.57,46537400,127.57\n2007-08-20,123.96,124.50,120.50,122.22,28689900,122.22\n2007-08-17,122.01,123.50,119.82,122.06,42680800,122.06\n2007-08-16,117.01,118.50,111.62,117.05,66667500,117.05\n2007-08-15,122.74,124.86,119.65,119.90,35459000,119.90\n2007-08-14,128.29,128.30,123.71,124.03,26393100,124.03\n2007-08-13,128.32,129.35,126.50,127.79,26889700,127.79\n2007-08-10,123.12,127.75,120.30,125.00,50383900,125.00\n2007-08-09,131.11,133.00,125.09,126.39,40192700,126.39\n2007-08-08,136.76,136.86,132.00,134.01,28860600,134.01\n2007-08-07,134.94,137.24,132.63,135.03,33926300,135.03\n2007-08-06,132.90,135.27,128.30,135.25,33041800,135.25\n2007-08-03,135.26,135.95,131.50,131.85,24256700,131.85\n2007-08-02,136.65,136.96,134.15,136.49,30451600,136.49\n2007-08-01,133.64,135.38,127.77,135.00,62505600,135.00\n2007-07-31,142.97,143.48,131.52,131.76,62942600,131.76\n2007-07-30,144.33,145.45,139.57,141.43,39535300,141.43\n2007-07-27,146.19,148.92,143.78,143.85,41467800,143.85\n2007-07-26,145.91,148.50,136.96,146.00,78093900,146.00\n2007-07-25,137.35,138.36,135.00,137.26,53435100,137.26\n2007-07-24,138.88,141.00,134.15,134.89,64117600,134.89\n2007-07-23,143.31,145.22,140.93,143.70,37017500,143.70\n2007-07-20,141.65,144.18,140.00,143.75,41706200,143.75\n2007-07-19,140.30,140.81,139.65,140.00,26174700,140.00\n2007-07-18,138.19,138.44,136.04,138.12,27030600,138.12\n2007-07-17,138.30,139.60,137.50,138.91,25355700,138.91\n2007-07-16,138.39,139.98,137.50,138.10,33432600,138.10\n2007-07-13,135.03,137.85,134.52,137.73,32414500,137.73\n2007-07-12,133.85,134.24,132.39,134.07,25164600,134.07\n2007-07-11,132.07,133.70,131.31,132.39,29349000,132.39\n2007-07-10,128.88,134.50,128.81,132.35,44821700,132.35\n2007-07-09,132.38,132.90,129.18,130.33,35565000,130.33\n2007-07-06,133.13,133.34,130.40,132.30,31239100,132.30\n2007-07-05,128.80,132.97,128.69,132.75,51894700,132.75\n2007-07-03,122.00,127.40,121.50,127.17,41517200,127.17\n2007-07-02,121.05,122.09,119.30,121.26,35530800,121.26\n2007-06-29,121.97,124.00,121.09,122.04,40637200,122.04\n2007-06-28,122.36,122.49,120.00,120.56,29933700,120.56\n2007-06-27,120.61,122.04,119.26,121.89,34810600,121.89\n2007-06-26,123.98,124.00,118.72,119.65,48035900,119.65\n2007-06-25,124.19,125.09,121.06,122.34,34478700,122.34\n2007-06-22,123.85,124.45,122.38,123.00,22567000,123.00\n2007-06-21,121.70,124.29,120.72,123.90,30965900,123.90\n2007-06-20,123.87,124.66,121.50,121.55,32054000,121.55\n2007-06-19,124.69,125.01,122.91,123.66,33679500,123.66\n2007-06-18,123.28,125.18,122.54,125.09,32521600,125.09\n2007-06-15,120.62,120.67,119.86,120.50,28972100,120.50\n2007-06-14,117.20,119.45,116.42,118.75,34759500,118.75\n2007-06-13,121.15,121.19,115.40,117.50,61476900,117.50\n2007-06-12,119.35,121.71,118.31,120.38,50948800,120.38\n2007-06-11,126.00,126.15,119.54,120.19,66937800,120.19\n2007-06-08,125.82,125.83,122.29,124.49,44345800,124.49\n2007-06-07,124.99,127.61,123.19,124.07,68395700,124.07\n2007-06-06,122.30,124.05,121.95,123.64,39722900,123.64\n2007-06-05,121.41,122.69,120.50,122.67,32885200,122.67\n2007-06-04,118.63,121.73,117.90,121.33,31666900,121.33\n2007-06-01,121.10,121.19,118.29,118.40,31616500,118.40\n2007-05-31,120.07,122.17,119.54,121.19,46323800,121.19\n2007-05-30,114.30,118.88,113.53,118.77,52801600,118.77\n2007-05-29,114.45,114.86,112.69,114.35,23060500,114.35\n2007-05-25,112.00,113.78,111.50,113.62,22605700,113.62\n2007-05-24,112.81,114.46,110.37,110.69,31691500,110.69\n2007-05-23,114.02,115.00,112.59,112.89,32549100,112.89\n2007-05-22,112.49,113.75,112.01,113.54,20443200,113.54\n2007-05-21,110.31,112.45,110.05,111.98,22853300,111.98\n2007-05-18,110.23,110.64,109.77,110.02,22190900,110.02\n2007-05-17,107.15,109.87,107.15,109.44,26260400,109.44\n2007-05-16,108.53,108.83,103.42,107.34,40241700,107.34\n2007-05-15,109.57,110.20,106.48,107.52,34089800,107.52\n2007-05-14,109.62,110.00,108.25,109.36,23283800,109.36\n2007-05-11,107.74,109.13,106.78,108.74,23346300,108.74\n2007-05-10,106.63,108.84,105.92,107.34,42759200,107.34\n2007-05-09,104.91,106.96,104.89,106.88,25634200,106.88\n2007-05-08,103.47,105.15,103.42,105.06,27999900,105.06\n2007-05-07,101.08,104.35,101.01,103.92,30769900,103.92\n2007-05-04,100.80,101.60,100.50,100.81,13642400,100.81\n2007-05-03,100.73,101.45,100.01,100.40,20574200,100.40\n2007-05-02,99.65,100.54,99.47,100.39,18040900,100.39\n2007-05-01,99.59,100.35,98.55,99.47,19018700,99.47\n2007-04-30,100.09,101.00,99.67,99.80,22018200,99.80\n2007-04-27,98.18,99.95,97.69,99.92,24978700,99.92\n2007-04-26,101.58,102.50,98.30,98.84,62063500,98.84\n2007-04-25,94.23,95.40,93.80,95.35,42398000,95.35\n2007-04-24,93.96,96.39,91.30,93.24,37687600,93.24\n2007-04-23,91.59,93.80,91.42,93.51,27867500,93.51\n2007-04-20,90.89,91.18,90.55,90.97,18670700,90.97\n2007-04-19,90.19,91.25,89.83,90.27,15211200,90.27\n2007-04-18,90.16,90.85,89.60,90.40,16573000,90.40\n2007-04-17,92.00,92.30,89.70,90.35,26854300,90.35\n2007-04-16,90.57,91.50,90.25,91.43,21751200,91.43\n2007-04-13,90.90,91.40,90.06,90.24,25712200,90.24\n2007-04-12,92.04,92.31,90.72,92.19,23452700,92.19\n2007-04-11,93.90,93.95,92.33,92.59,19607800,92.59\n2007-04-10,93.67,94.26,93.41,94.25,12588100,94.25\n2007-04-09,95.21,95.30,93.04,93.65,14762200,93.65\n2007-04-05,94.12,94.68,93.52,94.68,12697000,94.68\n2007-04-04,94.94,95.14,94.13,94.27,17028000,94.27\n2007-04-03,94.14,95.23,93.76,94.50,20854800,94.50\n2007-04-02,94.14,94.25,93.02,93.65,17928300,93.65\n2007-03-30,94.28,94.68,92.75,92.91,21448500,92.91\n2007-03-29,94.19,94.19,92.23,93.75,25918700,93.75\n2007-03-28,94.88,95.40,93.15,93.24,33654900,93.24\n2007-03-27,95.71,96.83,95.00,95.46,33287600,95.46\n2007-03-26,93.99,95.90,93.30,95.85,30892400,95.85\n2007-03-23,93.35,94.07,93.30,93.52,16103000,93.52\n2007-03-22,93.73,94.36,93.00,93.96,20053300,93.96\n2007-03-21,91.99,94.00,91.65,93.87,24532000,93.87\n2007-03-20,91.35,91.84,91.06,91.48,17461300,91.48\n2007-03-19,90.24,91.55,89.59,91.13,25462900,91.13\n2007-03-16,89.54,89.99,89.32,89.59,20418000,89.59\n2007-03-15,89.96,90.36,89.31,89.57,19982100,89.57\n2007-03-14,88.60,90.00,87.92,90.00,28449500,90.00\n2007-03-13,89.41,90.60,88.40,88.40,30996100,88.40\n2007-03-12,88.07,89.99,87.99,89.87,26050300,89.87\n2007-03-09,88.80,88.85,87.40,87.97,16137000,87.97\n2007-03-08,88.59,88.72,87.46,88.00,18250400,88.00\n2007-03-07,88.05,88.97,87.45,87.72,22367300,87.72\n2007-03-06,87.80,88.31,87.40,88.19,25828100,88.19\n2007-03-05,85.89,88.65,85.76,86.32,29960700,86.32\n2007-03-02,86.77,87.54,85.21,85.41,30714300,85.41\n2007-03-01,84.03,88.31,83.75,87.06,50554600,87.06\n2007-02-28,83.00,85.60,83.00,84.61,32838400,84.61\n2007-02-27,86.30,87.08,83.41,83.93,40921900,83.93\n2007-02-26,89.84,90.00,87.61,88.51,21994600,88.51\n2007-02-23,89.16,90.34,88.85,89.07,18496200,89.07\n2007-02-22,90.80,90.81,88.53,89.51,29936600,89.51\n2007-02-21,85.98,89.49,85.96,89.20,41261200,89.20\n2007-02-20,84.65,86.16,84.16,85.90,22060800,85.90\n2007-02-16,85.25,85.41,84.66,84.83,14281000,84.83\n2007-02-15,85.44,85.62,84.78,85.21,12987900,85.21\n2007-02-14,84.63,85.64,84.57,85.30,18142200,85.30\n2007-02-13,85.16,85.29,84.30,84.70,20749500,84.70\n2007-02-12,84.43,85.18,83.63,84.88,25859700,84.88\n2007-02-09,85.88,86.20,83.21,83.27,30733600,83.27\n2007-02-08,85.43,86.51,85.41,86.18,24251100,86.18\n2007-02-07,84.48,86.38,83.55,86.15,38100900,86.15\n2007-02-06,84.45,84.47,82.86,84.15,30871200,84.15\n2007-02-05,84.30,85.23,83.94,83.94,20673300,83.94\n2007-02-02,84.12,85.25,83.70,84.75,22197500,84.75\n2007-02-01,86.23,86.27,84.74,84.74,23726500,84.74\n2007-01-31,84.86,86.00,84.35,85.73,30573900,85.73\n2007-01-30,86.43,86.49,85.25,85.55,20641800,85.55\n2007-01-29,86.30,86.65,85.53,85.94,32202300,85.94\n2007-01-26,87.11,87.37,84.99,85.38,35245500,85.38\n2007-01-25,87.11,88.50,86.03,86.25,32356200,86.25\n2007-01-24,86.68,87.15,86.08,86.70,33136200,86.70\n2007-01-23,85.73,87.51,85.51,85.70,43122300,85.70\n2007-01-22,89.14,89.16,85.65,86.79,51929500,86.79\n2007-01-19,88.63,89.65,88.12,88.50,48731200,88.50\n2007-01-18,92.10,92.11,89.05,89.07,84450200,89.07\n2007-01-17,97.56,97.60,94.82,94.95,58795000,94.95\n2007-01-16,95.68,97.25,95.45,97.10,44431300,97.10\n2007-01-12,94.59,95.06,93.23,94.62,46881800,94.62\n2007-01-11,95.94,96.78,95.10,95.80,51437600,95.80\n2007-01-10,94.75,97.80,93.45,97.00,105460000,97.00\n2007-01-09,86.45,92.98,85.15,92.57,119617800,92.57\n2007-01-08,85.96,86.53,85.28,85.47,28468100,85.47\n2007-01-05,85.77,86.20,84.40,85.05,29812200,85.05\n2007-01-04,84.05,85.95,83.82,85.66,30259300,85.66\n2007-01-03,86.29,86.58,81.90,83.80,44225700,83.80\n2006-12-29,83.95,85.40,83.36,84.84,38443900,84.84\n2006-12-28,80.22,81.25,79.65,80.87,39995600,80.87\n2006-12-27,78.15,82.00,76.77,81.52,69134100,81.52\n2006-12-26,82.15,82.57,80.89,81.51,17524600,81.51\n2006-12-22,83.46,84.04,81.60,82.20,21903700,82.20\n2006-12-21,84.70,85.48,82.20,82.90,32271400,82.90\n2006-12-20,86.47,86.67,84.74,84.76,20274700,84.76\n2006-12-19,84.73,86.68,83.62,86.31,32550200,86.31\n2006-12-18,87.63,88.00,84.59,85.47,25770600,85.47\n2006-12-15,89.02,89.22,87.33,87.72,26426400,87.72\n2006-12-14,89.05,90.00,88.26,88.55,29726100,88.55\n2006-12-13,87.95,89.07,87.15,89.05,30609000,89.05\n2006-12-12,88.61,88.84,85.53,86.14,36665000,86.14\n2006-12-11,88.90,89.30,88.05,88.75,17849300,88.75\n2006-12-08,87.23,89.39,87.00,88.26,28009900,88.26\n2006-12-07,90.03,90.50,86.90,87.04,35886700,87.04\n2006-12-06,90.64,91.39,89.67,89.83,22792300,89.83\n2006-12-05,91.65,92.33,90.87,91.27,23672800,91.27\n2006-12-04,91.88,92.05,90.50,91.12,25340600,91.12\n2006-12-01,91.80,92.33,90.10,91.32,28395700,91.32\n2006-11-30,92.21,92.68,91.06,91.66,31088800,91.66\n2006-11-29,93.00,93.15,90.25,91.80,41324400,91.80\n2006-11-28,90.36,91.97,89.91,91.81,37006200,91.81\n2006-11-27,92.51,93.16,89.50,89.54,38387000,89.54\n2006-11-24,89.53,93.08,89.50,91.63,18524200,91.63\n2006-11-22,88.99,90.75,87.85,90.31,23997900,90.31\n2006-11-21,87.42,88.60,87.11,88.60,22238100,88.60\n2006-11-20,85.40,87.00,85.20,86.47,20385500,86.47\n2006-11-17,85.14,85.94,85.00,85.85,16658000,85.85\n2006-11-16,84.87,86.30,84.62,85.61,24783600,85.61\n2006-11-15,85.05,85.90,84.00,84.05,23404400,84.05\n2006-11-14,84.80,85.00,83.90,85.00,21034100,85.00\n2006-11-13,83.22,84.45,82.64,84.35,16095500,84.35\n2006-11-10,83.55,83.60,82.50,83.12,13352300,83.12\n2006-11-09,82.90,84.69,82.12,83.34,32966200,83.34\n2006-11-08,80.02,82.69,79.89,82.45,24675600,82.45\n2006-11-07,80.45,81.00,80.13,80.51,18783300,80.51\n2006-11-06,78.95,80.06,78.43,79.71,15520600,79.71\n2006-11-03,79.36,79.53,77.79,78.29,15424600,78.29\n2006-11-02,78.92,79.32,78.50,78.98,16624400,78.98\n2006-11-01,81.10,81.38,78.36,79.16,21828300,79.16\n2006-10-31,81.45,81.68,80.23,81.08,17909800,81.08\n2006-10-30,79.99,80.90,79.50,80.42,17854200,80.42\n2006-10-27,81.75,82.45,80.01,80.41,21248800,80.41\n2006-10-26,81.90,82.60,81.13,82.19,15455600,82.19\n2006-10-25,81.35,82.00,81.01,81.68,17329100,81.68\n2006-10-24,81.21,81.68,80.20,81.05,16543300,81.05\n2006-10-23,79.99,81.90,79.75,81.46,29732400,81.46\n2006-10-20,78.97,79.99,78.67,79.95,22836200,79.95\n2006-10-19,79.26,79.95,78.16,78.99,54034900,78.99\n2006-10-18,74.75,75.37,73.91,74.53,40496700,74.53\n2006-10-17,75.04,75.27,74.04,74.29,17175900,74.29\n2006-10-16,75.19,75.88,74.79,75.40,18167600,75.40\n2006-10-13,75.63,76.88,74.74,75.02,24435600,75.02\n2006-10-12,73.61,75.39,73.60,75.26,21173400,75.26\n2006-10-11,73.42,73.98,72.60,73.23,20423400,73.23\n2006-10-10,74.54,74.58,73.08,73.81,18985300,73.81\n2006-10-09,73.80,75.08,73.53,74.63,15650800,74.63\n2006-10-06,74.42,75.04,73.81,74.22,16677100,74.22\n2006-10-05,74.53,76.16,74.13,74.83,24424400,74.83\n2006-10-04,74.10,75.46,73.16,75.38,29610100,75.38\n2006-10-03,74.45,74.95,73.19,74.08,28239600,74.08\n2006-10-02,75.10,75.87,74.30,74.86,25451400,74.86\n2006-09-29,77.11,77.52,76.68,76.98,14493300,76.98\n2006-09-28,77.02,77.48,75.95,77.01,25843200,77.01\n2006-09-27,77.17,77.47,75.82,76.41,28941900,76.41\n2006-09-26,76.18,77.78,76.10,77.61,39391000,77.61\n2006-09-25,73.81,75.86,73.72,75.75,30678300,75.75\n2006-09-22,74.30,74.34,72.58,73.00,23754000,73.00\n2006-09-21,75.25,76.06,74.02,74.65,28361600,74.65\n2006-09-20,74.38,75.68,74.22,75.26,29385400,75.26\n2006-09-19,74.10,74.36,72.80,73.77,25358900,73.77\n2006-09-18,73.80,74.86,73.30,73.89,25188500,73.89\n2006-09-15,74.60,74.98,73.29,74.10,35066200,74.10\n2006-09-14,73.72,74.67,73.46,74.17,28633200,74.17\n2006-09-13,72.85,74.32,72.30,74.20,40933500,74.20\n2006-09-12,72.81,73.45,71.45,72.63,60167400,72.63\n2006-09-11,72.43,73.73,71.42,72.50,33897300,72.50\n2006-09-08,73.37,73.57,71.91,72.52,31997200,72.52\n2006-09-07,70.60,73.48,70.25,72.80,45284200,72.80\n2006-09-06,71.08,71.69,69.70,70.03,34789400,70.03\n2006-09-05,68.97,71.50,68.55,71.48,36159200,71.48\n2006-09-01,68.48,68.65,67.82,68.38,14589100,68.38\n2006-08-31,67.28,68.30,66.66,67.85,20524900,67.85\n2006-08-30,67.34,67.82,66.68,66.96,24290800,66.96\n2006-08-29,66.99,67.26,65.12,66.48,33833300,66.48\n2006-08-28,68.50,68.61,66.68,66.98,26362900,66.98\n2006-08-25,67.34,69.05,67.31,68.75,19427100,68.75\n2006-08-24,67.89,68.19,66.27,67.81,23399700,67.81\n2006-08-23,68.00,68.65,66.94,67.31,19152100,67.31\n2006-08-22,66.68,68.32,66.50,67.62,20606000,67.62\n2006-08-21,67.30,67.31,66.15,66.56,18793800,66.56\n2006-08-18,67.71,68.40,67.26,67.91,19155500,67.91\n2006-08-17,68.00,68.66,67.18,67.59,20755300,67.59\n2006-08-16,67.10,68.07,66.33,67.98,27903000,67.98\n2006-08-15,65.34,66.50,64.80,66.45,30762600,66.45\n2006-08-14,64.05,65.22,63.60,63.94,25629300,63.94\n2006-08-11,63.23,64.13,62.58,63.65,27768900,63.65\n2006-08-10,63.25,64.81,62.70,64.07,24920000,64.07\n2006-08-09,65.43,65.60,63.40,63.59,34137100,63.59\n2006-08-08,67.09,67.11,64.51,64.78,35638000,64.78\n2006-08-07,67.72,69.60,66.31,67.21,44482600,67.21\n2006-08-04,67.05,68.61,64.96,68.30,66173800,68.30\n2006-08-03,67.91,70.00,67.81,69.59,30037300,69.59\n2006-08-02,67.65,68.68,67.51,68.16,19670300,68.16\n2006-08-01,67.22,67.93,65.94,67.18,25420200,67.18\n2006-07-31,66.83,68.63,66.28,67.96,31887200,67.96\n2006-07-28,63.94,65.68,63.50,65.59,24696700,65.59\n2006-07-27,64.50,65.02,62.86,63.40,26251600,63.40\n2006-07-26,62.00,64.64,61.68,63.87,32086700,63.87\n2006-07-25,61.78,62.09,60.78,61.93,21038200,61.93\n2006-07-24,61.26,62.10,60.43,61.42,25816300,61.42\n2006-07-21,59.82,61.15,59.64,60.72,31853300,60.72\n2006-07-20,60.96,61.59,59.72,60.50,70433800,60.50\n2006-07-19,52.96,55.08,52.36,54.10,49669400,54.10\n2006-07-18,53.16,53.85,51.85,52.90,35730300,52.90\n2006-07-17,51.73,53.11,51.65,52.37,36590800,52.37\n2006-07-14,52.50,52.89,50.16,50.67,35465600,50.67\n2006-07-13,52.03,54.12,51.41,52.25,44639500,52.25\n2006-07-12,55.17,55.24,52.92,52.96,33118900,52.96\n2006-07-11,55.11,55.99,54.53,55.65,29465100,55.65\n2006-07-10,55.70,56.49,54.50,55.00,18905200,55.00\n2006-07-07,55.48,56.55,54.67,55.40,28548600,55.40\n2006-07-06,57.09,57.40,55.61,55.77,22614600,55.77\n2006-07-05,57.15,57.60,56.56,57.00,18508600,57.00\n2006-07-03,57.52,58.18,57.34,57.95,6956100,57.95\n2006-06-30,57.59,57.75,56.50,57.27,26417700,57.27\n2006-06-29,56.76,59.09,56.39,58.97,31192800,58.97\n2006-06-28,57.29,57.30,55.41,56.02,30382300,56.02\n2006-06-27,59.09,59.22,57.40,57.43,19664700,57.43\n2006-06-26,59.17,59.20,58.37,58.99,16662000,58.99\n2006-06-23,59.72,60.17,58.73,58.83,23578700,58.83\n2006-06-22,58.20,59.75,58.07,59.58,34486900,59.58\n2006-06-21,57.74,58.71,57.30,57.86,30832000,57.86\n2006-06-20,57.61,58.35,57.29,57.47,24034800,57.47\n2006-06-19,57.83,58.18,57.00,57.20,25163400,57.20\n2006-06-16,58.96,59.19,57.52,57.56,29932200,57.56\n2006-06-15,57.30,59.74,56.75,59.38,42513700,59.38\n2006-06-14,58.28,58.78,56.69,57.61,31362000,57.61\n2006-06-13,57.61,59.10,57.36,58.33,38594400,58.33\n2006-06-12,59.40,59.73,56.96,57.00,25635200,57.00\n2006-06-09,61.18,61.56,59.10,59.24,27708500,59.24\n2006-06-08,58.44,60.93,57.15,60.76,49910100,60.76\n2006-06-07,60.10,60.40,58.35,58.56,26803800,58.56\n2006-06-06,60.22,60.63,58.91,59.72,25929900,59.72\n2006-06-05,61.15,61.15,59.97,60.00,21635200,60.00\n2006-06-02,62.99,63.10,60.88,61.66,24492400,61.66\n2006-06-01,59.85,62.28,59.52,62.17,33661000,62.17\n2006-05-31,61.76,61.79,58.69,59.77,45749200,59.77\n2006-05-30,63.29,63.30,61.22,61.22,20121500,61.22\n2006-05-26,64.31,64.56,63.14,63.55,15462500,63.55\n2006-05-25,64.26,64.45,63.29,64.33,16549000,64.33\n2006-05-24,62.99,63.65,61.56,63.34,32715400,63.34\n2006-05-23,64.86,65.19,63.00,63.15,24800500,63.15\n2006-05-22,63.87,63.99,62.77,63.38,25677700,63.38\n2006-05-19,63.26,64.88,62.82,64.51,35209500,64.51\n2006-05-18,65.68,66.26,63.12,63.18,23515800,63.18\n2006-05-17,64.71,65.70,64.07,65.26,26935500,65.26\n2006-05-16,68.10,68.25,64.75,64.98,33455000,64.98\n2006-05-15,67.37,68.38,67.12,67.79,18899200,67.79\n2006-05-12,67.85,68.69,66.86,67.70,22920500,67.70\n2006-05-11,70.79,70.84,67.55,68.15,29024600,68.15\n2006-05-10,71.29,71.33,69.61,70.60,16424600,70.60\n2006-05-09,71.82,72.56,70.62,71.03,18988100,71.03\n2006-05-08,72.99,73.80,71.72,71.89,21244700,71.89\n2006-05-05,71.86,72.25,71.15,71.89,20139700,71.89\n2006-05-04,71.22,72.89,70.46,71.13,30729300,71.13\n2006-05-03,71.83,71.95,70.18,71.14,24535400,71.14\n2006-05-02,70.15,71.98,70.11,71.62,27559400,71.62\n2006-05-01,70.77,71.54,69.16,69.60,26799300,69.60\n2006-04-28,69.38,71.30,69.20,70.39,27144200,70.39\n2006-04-27,67.73,69.86,67.35,69.36,30212400,69.36\n2006-04-26,66.65,68.28,66.40,68.15,25388800,68.15\n2006-04-25,65.96,66.59,65.56,66.17,18895100,66.17\n2006-04-24,66.85,66.92,65.50,65.75,25251000,65.75\n2006-04-21,68.19,68.64,66.47,67.04,28178100,67.04\n2006-04-20,69.51,70.00,66.20,67.63,59535100,67.63\n2006-04-19,66.82,67.00,65.47,65.65,38786900,65.65\n2006-04-18,65.04,66.47,64.79,66.22,28387300,66.22\n2006-04-17,66.51,66.84,64.35,64.81,25783500,64.81\n2006-04-13,66.34,67.44,65.81,66.47,26238500,66.47\n2006-04-12,68.01,68.17,66.30,66.71,26424800,66.71\n2006-04-11,68.99,69.30,67.07,67.99,33547000,67.99\n2006-04-10,70.29,70.93,68.45,68.67,32268400,68.67\n2006-04-07,70.93,71.21,68.47,69.79,55187100,69.79\n2006-04-06,68.30,72.05,68.20,71.24,95134600,71.24\n2006-04-05,64.71,67.21,64.15,67.21,79764600,67.21\n2006-04-04,62.10,62.22,61.05,61.17,33283000,61.17\n2006-04-03,63.67,64.12,62.61,62.65,29135400,62.65\n2006-03-31,63.25,63.61,62.24,62.72,29119900,62.72\n2006-03-30,62.82,63.30,61.53,62.75,49666100,62.75\n2006-03-29,59.13,62.52,57.67,62.33,83815500,62.33\n2006-03-28,59.63,60.14,58.25,58.71,48940100,58.71\n2006-03-27,60.35,61.38,59.40,59.51,39574000,59.51\n2006-03-24,60.25,60.94,59.03,59.96,38285000,59.96\n2006-03-23,61.82,61.90,59.61,60.16,50993800,60.16\n2006-03-22,62.16,63.25,61.27,61.67,48067700,61.67\n2006-03-21,64.29,64.34,61.39,61.81,47991700,61.81\n2006-03-20,65.22,65.46,63.87,63.99,21622900,63.99\n2006-03-17,64.75,65.54,64.11,64.66,29001500,64.66\n2006-03-16,66.85,66.90,64.30,64.31,26772800,64.31\n2006-03-15,67.71,68.04,65.52,66.23,31857000,66.23\n2006-03-14,65.77,67.32,65.50,67.32,22929300,67.32\n2006-03-13,65.05,66.28,64.79,65.68,30756700,65.68\n2006-03-10,64.05,64.49,62.45,63.19,37255100,63.19\n2006-03-09,65.98,66.47,63.81,63.93,28546600,63.93\n2006-03-08,66.29,67.20,65.35,65.66,23330400,65.66\n2006-03-07,65.76,66.90,65.08,66.31,31174200,66.31\n2006-03-06,67.69,67.72,64.94,65.48,32595200,65.48\n2006-03-03,69.40,69.91,67.53,67.72,26345300,67.72\n2006-03-02,68.99,69.99,68.67,69.61,22331200,69.61\n2006-03-01,68.84,69.49,68.02,69.10,27279200,69.10\n2006-02-28,71.58,72.40,68.10,68.49,45249300,68.49\n2006-02-27,71.99,72.12,70.65,70.99,28258600,70.99\n2006-02-24,72.14,72.89,71.20,71.46,19098000,71.46\n2006-02-23,71.79,73.00,71.43,71.75,30604200,71.75\n2006-02-22,69.00,71.67,68.00,71.32,34937100,71.32\n2006-02-21,70.59,70.80,68.68,69.08,27843100,69.08\n2006-02-17,70.30,70.89,69.61,70.29,20571400,70.29\n2006-02-16,69.91,71.01,69.48,70.57,33863400,70.57\n2006-02-15,67.16,69.62,66.75,69.22,41420400,69.22\n2006-02-14,65.10,68.10,65.00,67.64,41462100,67.64\n2006-02-13,66.63,66.75,64.64,64.71,31553500,64.71\n2006-02-10,65.18,67.67,62.90,67.31,62874200,67.31\n2006-02-09,69.10,69.23,64.53,64.95,41063000,64.95\n2006-02-08,68.49,69.08,66.00,68.81,34039800,68.81\n2006-02-07,68.27,69.48,66.68,67.60,49601100,67.60\n2006-02-06,72.02,72.51,66.74,67.30,58991700,67.30\n2006-02-03,72.24,72.79,71.04,71.85,24718700,71.85\n2006-02-02,75.10,75.36,72.05,72.10,25261500,72.10\n2006-02-01,74.95,76.46,74.64,75.42,18613800,75.42\n2006-01-31,75.50,76.34,73.75,75.51,32626500,75.51\n2006-01-30,71.17,76.60,70.87,75.00,49942900,75.00\n2006-01-27,72.95,73.60,71.10,72.03,34066600,72.03\n2006-01-26,74.53,75.43,71.93,72.33,42192400,72.33\n2006-01-25,77.39,77.50,73.25,74.20,45563800,74.20\n2006-01-24,78.76,79.42,75.77,76.04,40794800,76.04\n2006-01-23,76.10,79.56,76.00,77.67,37847500,77.67\n2006-01-20,79.28,80.04,75.83,76.09,40527100,76.09\n2006-01-19,81.25,81.66,78.74,79.04,60566000,79.04\n2006-01-18,83.08,84.05,81.85,82.49,42879900,82.49\n2006-01-17,85.70,86.38,83.87,84.71,29843700,84.71\n2006-01-13,84.99,86.01,84.60,85.59,27725200,85.59\n2006-01-12,84.97,86.40,83.62,84.29,45743200,84.29\n2006-01-11,83.84,84.80,82.59,83.90,53349800,83.90\n2006-01-10,76.25,81.89,75.83,80.86,81423900,80.86\n2006-01-09,76.73,77.20,75.74,76.05,24108600,76.05\n2006-01-06,75.25,76.70,74.55,76.30,25159200,76.30\n2006-01-05,74.83,74.90,73.75,74.38,16050800,74.38\n2006-01-04,75.13,75.98,74.50,74.97,22128700,74.97\n2006-01-03,72.38,74.75,72.25,74.75,28829800,74.75\n2005-12-30,70.91,72.43,70.34,71.89,22295100,71.89\n2005-12-29,73.78,73.82,71.42,71.45,17500900,71.45\n2005-12-28,74.47,74.76,73.32,73.57,14218400,73.57\n2005-12-27,74.00,75.18,73.95,74.23,21092500,74.23\n2005-12-23,74.17,74.26,73.30,73.35,8209200,73.35\n2005-12-22,73.91,74.49,73.60,74.02,13236100,74.02\n2005-12-21,72.60,73.61,72.54,73.50,16990600,73.50\n2005-12-20,71.63,72.38,71.12,72.11,17111000,72.11\n2005-12-19,71.11,72.60,71.04,71.38,18903400,71.38\n2005-12-16,72.14,72.30,71.06,71.11,23970400,71.11\n2005-12-15,72.68,72.86,71.35,72.18,20041500,72.18\n2005-12-14,72.53,73.30,70.27,72.01,51811300,72.01\n2005-12-13,74.85,75.46,74.21,74.98,17636300,74.98\n2005-12-12,74.87,75.35,74.56,74.91,18749800,74.91\n2005-12-09,74.21,74.59,73.35,74.33,19835800,74.33\n2005-12-08,73.20,74.17,72.60,74.08,28231500,74.08\n2005-12-07,74.23,74.46,73.12,73.95,24266600,73.95\n2005-12-06,73.93,74.83,73.35,74.05,30608200,74.05\n2005-12-05,71.95,72.53,71.49,71.82,20845400,71.82\n2005-12-02,72.27,72.74,70.70,72.63,31991500,72.63\n2005-12-01,68.95,71.73,68.81,71.60,29031900,71.60\n2005-11-30,68.43,68.85,67.52,67.82,21274100,67.82\n2005-11-29,69.99,70.30,67.35,68.10,31836900,68.10\n2005-11-28,70.72,71.07,69.07,69.66,36375700,69.66\n2005-11-25,67.66,69.54,67.50,69.34,14107600,69.34\n2005-11-23,66.88,67.98,66.69,67.11,17351900,67.11\n2005-11-22,64.84,66.76,64.52,66.52,19295800,66.52\n2005-11-21,64.82,65.19,63.72,64.96,18275400,64.96\n2005-11-18,65.31,65.43,64.37,64.56,18748700,64.56\n2005-11-17,65.59,65.88,64.25,64.52,24150200,64.52\n2005-11-16,63.15,65.06,63.09,64.95,28018400,64.95\n2005-11-15,61.60,63.08,61.46,62.28,19172900,62.28\n2005-11-14,61.54,61.98,60.91,61.45,13211900,61.45\n2005-11-11,61.54,62.11,61.34,61.54,15194600,61.54\n2005-11-10,60.64,61.20,59.01,61.18,23762300,61.18\n2005-11-09,60.00,61.21,60.00,60.11,19747500,60.11\n2005-11-08,59.95,60.38,59.10,59.90,16920200,59.90\n2005-11-07,60.85,61.67,60.14,60.23,22815400,60.23\n2005-11-04,60.35,61.24,59.62,61.15,31358400,61.15\n2005-11-03,60.26,62.32,60.07,61.85,31585100,61.85\n2005-11-02,57.72,60.00,57.60,59.95,30609300,59.95\n2005-11-01,57.24,58.14,56.87,57.50,26774500,57.50\n2005-10-31,55.20,57.98,54.75,57.59,33601600,57.59\n2005-10-28,56.04,56.43,54.17,54.47,27492400,54.47\n2005-10-27,56.99,57.01,55.41,55.41,14697900,55.41\n2005-10-26,56.28,57.56,55.92,57.03,22556900,57.03\n2005-10-25,56.40,56.85,55.69,56.10,16611700,56.10\n2005-10-24,55.25,56.79,55.09,56.79,21776900,56.79\n2005-10-21,56.84,56.98,55.36,55.66,28454500,55.66\n2005-10-20,54.47,56.50,54.35,56.14,48491500,56.14\n2005-10-19,52.07,54.96,51.21,54.94,36024400,54.94\n2005-10-18,53.25,53.95,52.20,52.21,21771000,52.21\n2005-10-17,53.98,54.23,52.68,53.44,22029800,53.44\n2005-10-14,54.03,54.35,52.79,54.00,36984000,54.00\n2005-10-13,49.44,53.95,49.27,53.74,66627700,53.74\n2005-10-12,48.65,50.30,47.87,49.25,96338800,49.25\n2005-10-11,51.23,51.87,50.40,51.59,43781600,51.59\n2005-10-10,51.76,51.91,50.28,50.37,18125200,50.37\n2005-10-07,51.72,51.93,50.55,51.30,24210100,51.30\n2005-10-06,53.20,53.49,50.87,51.70,27054900,51.70\n2005-10-05,54.33,54.36,52.75,52.78,21813200,52.78\n2005-10-04,54.95,55.35,53.64,53.75,19266400,53.75\n2005-10-03,54.16,54.54,53.68,54.44,18126900,54.44\n2005-09-30,52.33,53.65,51.88,53.61,18986900,53.61\n2005-09-29,51.23,52.59,50.81,52.34,22744500,52.34\n2005-09-28,53.07,53.11,50.59,51.08,40198000,51.08\n2005-09-27,53.92,54.24,53.43,53.44,12203700,53.44\n2005-09-26,54.03,54.56,53.32,53.84,19520100,53.84\n2005-09-23,52.10,53.50,51.84,53.20,19944900,53.20\n2005-09-22,51.88,52.47,51.32,51.90,16561700,51.90\n2005-09-21,52.96,53.05,51.86,52.11,15526700,52.11\n2005-09-20,52.99,53.81,52.92,53.19,29279600,53.19\n2005-09-19,51.05,52.89,51.05,52.64,27990400,52.64\n2005-09-16,50.23,51.21,49.95,51.21,21107300,51.21\n2005-09-15,50.00,50.18,49.33,49.87,14827000,49.87\n2005-09-14,51.06,51.19,49.46,49.61,16943800,49.61\n2005-09-13,51.02,51.29,50.32,50.82,17603000,50.82\n2005-09-12,51.10,51.63,50.58,51.40,16171300,51.40\n2005-09-09,50.07,51.35,49.79,51.31,21987200,51.31\n2005-09-08,49.35,50.12,49.14,49.78,25094300,49.78\n2005-09-07,49.05,49.40,47.92,48.68,34395500,48.68\n2005-09-06,46.70,48.88,46.55,48.80,29236400,48.80\n2005-09-02,46.30,46.80,46.12,46.22,7942100,46.22\n2005-09-01,47.00,47.17,46.09,46.26,12727400,46.26\n2005-08-31,46.86,47.03,46.27,46.89,14391300,46.89\n2005-08-30,45.99,46.79,45.92,46.57,18527200,46.57\n2005-08-29,45.27,46.03,45.26,45.84,9153400,45.84\n2005-08-26,46.12,46.34,45.36,45.74,9323500,45.74\n2005-08-25,46.12,46.49,45.81,46.06,9866200,46.06\n2005-08-24,45.60,47.12,45.59,45.77,20431100,45.77\n2005-08-23,45.85,46.10,45.32,45.74,10557300,45.74\n2005-08-22,46.15,46.75,45.26,45.87,13847600,45.87\n2005-08-19,46.28,46.70,45.77,45.83,13448900,45.83\n2005-08-18,46.91,47.00,45.75,46.30,15805700,46.30\n2005-08-17,46.40,47.44,46.37,47.15,17847300,47.15\n2005-08-16,47.39,47.50,46.21,46.25,19200800,46.25\n2005-08-15,46.48,48.33,46.45,47.68,38811700,47.68\n2005-08-12,43.46,46.22,43.36,46.10,32715600,46.10\n2005-08-11,43.39,44.12,43.25,44.00,9713700,44.00\n2005-08-10,44.00,44.39,43.31,43.38,12890900,43.38\n2005-08-09,42.93,43.89,42.91,43.82,13601400,43.82\n2005-08-08,43.00,43.25,42.61,42.65,6299400,42.65\n2005-08-05,42.49,43.36,42.02,42.99,8640400,42.99\n2005-08-04,42.89,43.00,42.29,42.71,9618000,42.71\n2005-08-03,43.19,43.31,42.77,43.22,9225800,43.22\n2005-08-02,42.89,43.50,42.61,43.19,10602700,43.19\n2005-08-01,42.57,43.08,42.08,42.75,11223200,42.75\n2005-07-29,43.56,44.38,42.26,42.65,20074300,42.65\n2005-07-28,43.85,44.00,43.30,43.80,8975400,43.80\n2005-07-27,43.83,44.07,42.67,43.99,10133900,43.99\n2005-07-26,44.01,44.11,43.36,43.63,9592600,43.63\n2005-07-25,43.99,44.28,43.73,43.81,10522400,43.81\n2005-07-22,43.44,44.00,43.39,44.00,10753800,44.00\n2005-07-21,43.70,44.04,42.90,43.29,14438000,43.29\n2005-07-20,42.86,43.80,42.65,43.63,16192700,43.63\n2005-07-19,41.52,43.23,41.07,43.19,23966500,43.19\n2005-07-18,41.41,42.10,41.37,41.49,20939200,41.49\n2005-07-15,40.97,41.57,40.46,41.55,24560100,41.55\n2005-07-14,40.79,42.01,40.23,40.75,74859300,40.75\n2005-07-13,38.29,38.50,37.90,38.35,24458400,38.35\n2005-07-12,38.23,38.40,37.91,38.24,13822800,38.24\n2005-07-11,38.37,38.65,37.78,38.10,13885300,38.10\n2005-07-08,37.87,38.28,37.47,38.25,10383400,38.25\n2005-07-07,36.81,37.76,36.80,37.63,13704400,37.63\n2005-07-06,37.71,38.16,37.20,37.39,14093800,37.39\n2005-07-05,36.55,38.15,36.50,37.98,16223900,37.98\n2005-07-01,36.83,36.97,36.29,36.50,8928600,36.50\n2005-06-30,36.61,37.16,36.31,36.81,14942500,36.81\n2005-06-29,37.23,37.29,36.12,36.37,16012800,36.37\n2005-06-28,37.49,37.59,37.17,37.31,12510700,37.31\n2005-06-27,36.84,38.10,36.68,37.10,21434700,37.10\n2005-06-24,39.09,39.12,37.68,37.76,14668200,37.76\n2005-06-23,38.83,39.78,38.65,38.89,24080500,38.89\n2005-06-22,38.26,38.60,38.14,38.55,15175900,38.55\n2005-06-21,37.72,38.19,37.38,37.86,13233100,37.86\n2005-06-20,37.85,38.09,37.45,37.61,11561300,37.61\n2005-06-17,38.47,38.54,37.83,38.31,21290200,38.31\n2005-06-16,37.19,38.08,36.82,37.98,19559800,37.98\n2005-06-15,36.87,37.30,36.30,37.13,20119400,37.13\n2005-06-14,35.92,36.15,35.75,36.00,12423100,36.00\n2005-06-13,35.89,36.61,35.82,35.90,15563300,35.90\n2005-06-10,37.40,37.40,35.52,35.81,24247600,35.81\n2005-06-09,37.00,37.94,36.82,37.65,13937700,37.65\n2005-06-08,36.63,37.25,36.57,36.92,14428800,36.92\n2005-06-07,37.60,37.73,36.45,36.54,26616600,36.54\n2005-06-06,38.33,38.63,37.56,37.92,28998800,37.92\n2005-06-03,38.16,38.58,37.77,38.24,34173900,38.24\n2005-06-02,40.05,40.32,39.60,40.04,13356200,40.04\n2005-06-01,39.89,40.76,39.86,40.30,16207600,40.30\n2005-05-31,40.66,40.74,39.58,39.76,14435900,39.76\n2005-05-27,40.64,40.79,40.01,40.56,11286000,40.56\n2005-05-26,39.94,40.94,39.94,40.74,18768600,40.74\n2005-05-25,39.50,39.95,39.32,39.78,14143100,39.78\n2005-05-24,39.45,39.99,39.03,39.70,21195000,39.70\n2005-05-23,37.85,39.90,37.85,39.76,37234800,39.76\n2005-05-20,37.25,37.65,37.19,37.55,16166100,37.55\n2005-05-19,35.78,37.68,35.78,37.55,28327200,37.55\n2005-05-18,35.45,37.56,34.99,35.84,22740100,35.84\n2005-05-17,35.14,35.46,34.54,35.36,21012300,35.36\n2005-05-16,34.56,35.70,34.53,35.55,16939100,35.55\n2005-05-13,34.20,35.23,34.07,34.77,25096900,34.77\n2005-05-12,35.42,35.59,34.00,34.13,34651500,34.13\n2005-05-11,35.20,35.67,33.11,35.61,72927900,35.61\n2005-05-10,36.75,37.25,36.33,36.42,15723700,36.42\n2005-05-09,37.28,37.45,36.75,36.97,12703400,36.97\n2005-05-06,36.89,37.33,36.79,37.24,11651700,37.24\n2005-05-05,37.25,37.27,36.47,36.68,13834500,36.68\n2005-05-04,36.11,37.20,36.10,37.15,16006300,37.15\n2005-05-03,36.40,36.74,36.03,36.21,17740700,36.21\n2005-05-02,36.21,36.65,36.02,36.43,16640000,36.43\n2005-04-29,36.15,36.23,35.22,36.06,23986800,36.06\n2005-04-28,36.29,36.34,35.24,35.54,20539500,35.54\n2005-04-27,35.89,36.36,35.51,35.95,21924600,35.95\n2005-04-26,36.78,37.51,36.12,36.19,28946700,36.19\n2005-04-25,36.49,37.02,36.11,36.98,26659300,36.98\n2005-04-22,36.84,37.00,34.90,35.50,29968900,35.50\n2005-04-21,36.40,37.21,35.90,37.18,27128300,37.18\n2005-04-20,37.66,37.74,35.44,35.51,33754700,35.51\n2005-04-19,36.60,37.44,35.87,37.09,38630100,37.09\n2005-04-18,35.00,36.30,34.00,35.62,47399200,35.62\n2005-04-15,36.62,37.25,35.28,35.35,61717400,35.35\n2005-04-14,38.81,39.56,36.84,37.26,98328300,37.26\n2005-04-13,42.95,42.99,40.39,41.04,48998100,41.04\n2005-04-12,42.49,43.19,42.01,42.66,35037900,42.66\n2005-04-11,44.15,44.25,41.91,41.92,29345100,41.92\n2005-04-08,43.70,44.45,43.54,43.74,23212500,43.74\n2005-04-07,42.33,43.75,42.25,43.56,18106700,43.56\n2005-04-06,42.40,42.81,42.15,42.33,14815200,42.33\n2005-04-05,41.22,42.24,41.09,41.89,19865700,41.89\n2005-04-04,40.99,41.31,40.16,41.09,20714800,41.09\n2005-04-01,42.09,42.18,40.57,40.89,22903000,40.89\n2005-03-31,42.45,42.52,41.59,41.67,22719100,41.67\n2005-03-30,42.07,42.80,41.82,42.80,14105700,42.80\n2005-03-29,42.56,42.83,41.50,41.75,16477000,41.75\n2005-03-28,42.75,42.96,42.47,42.53,9836100,42.53\n2005-03-24,42.91,43.00,42.50,42.50,12596600,42.50\n2005-03-23,42.45,43.40,42.02,42.55,21779400,42.55\n2005-03-22,43.71,43.96,42.68,42.83,19693400,42.83\n2005-03-21,43.29,43.97,42.86,43.70,19326000,43.70\n2005-03-18,43.33,43.44,42.50,42.96,33576800,42.96\n2005-03-17,41.53,42.88,41.32,42.25,28640000,42.25\n2005-03-16,41.21,42.31,40.78,41.18,24921900,41.18\n2005-03-15,40.64,41.14,40.25,40.96,18164600,40.96\n2005-03-14,40.52,40.79,39.52,40.32,21620900,40.32\n2005-03-11,40.21,40.59,39.80,40.27,22601100,40.27\n2005-03-10,39.53,40.26,39.10,39.83,27753900,39.83\n2005-03-09,39.64,40.28,38.83,39.35,47230900,39.35\n2005-03-08,41.90,42.16,40.10,40.53,36480400,40.53\n2005-03-07,42.80,43.25,42.35,42.75,16094000,42.75\n2005-03-04,42.76,43.01,41.85,42.81,27022100,42.81\n2005-03-03,44.37,44.41,41.22,41.79,50416200,41.79\n2005-03-02,44.25,44.89,44.08,44.12,16362900,44.12\n2005-03-01,44.99,45.11,44.16,44.50,16721000,44.50\n2005-02-28,44.68,45.14,43.96,44.86,23271800,44.86\n2005-02-25,89.62,89.91,88.19,88.99,32696800,44.49\n2005-02-24,88.48,89.31,87.73,88.93,54251000,44.47\n2005-02-23,86.72,88.45,85.55,88.23,48042200,44.12\n2005-02-22,86.30,88.30,85.29,85.29,43546200,42.65\n2005-02-18,87.74,87.86,86.25,86.81,41544800,43.40\n2005-02-17,90.65,90.88,87.45,87.81,54231200,43.90\n2005-02-16,88.15,90.20,87.35,90.13,58544400,45.06\n2005-02-15,86.66,89.08,86.00,88.41,82579200,44.21\n2005-02-14,82.73,84.79,82.05,84.63,45409400,42.31\n2005-02-11,79.86,81.76,78.94,81.21,42894800,40.60\n2005-02-10,78.72,79.28,76.66,78.36,39036400,39.18\n2005-02-09,81.04,81.99,78.10,78.74,42552000,39.37\n2005-02-08,79.07,81.38,78.79,80.90,31786400,40.45\n2005-02-07,78.93,79.35,77.50,78.94,18730600,39.47\n2005-02-04,77.87,78.93,77.53,78.84,20127000,39.42\n2005-02-03,79.10,79.43,77.33,77.81,26130400,38.90\n2005-02-02,77.95,79.91,77.69,79.63,36430800,39.81\n2005-02-01,77.05,77.77,76.58,77.53,24228400,38.76\n2005-01-31,74.58,77.89,74.51,76.90,60039200,38.45\n2005-01-28,72.62,73.98,72.44,73.98,28629000,36.99\n2005-01-27,72.16,72.92,71.55,72.64,17722400,36.32\n2005-01-26,72.66,72.75,71.22,72.25,26410600,36.12\n2005-01-25,71.37,72.84,70.94,72.05,34615400,36.03\n2005-01-24,70.98,71.78,70.55,70.76,30058200,35.38\n2005-01-21,71.31,71.60,70.00,70.49,32547600,35.24\n2005-01-20,69.65,71.27,69.47,70.46,32675800,35.23\n2005-01-19,70.49,71.46,69.75,69.88,26853400,34.94\n2005-01-18,69.85,70.70,67.75,70.65,35945000,35.33\n2005-01-14,70.25,71.72,69.19,70.20,63240800,35.10\n2005-01-13,73.71,74.42,69.73,69.80,113025600,34.90\n2005-01-12,65.45,65.90,63.30,65.46,68560800,32.73\n2005-01-11,68.25,69.15,64.14,64.56,93272400,32.28\n2005-01-10,69.83,70.70,67.88,68.96,61618200,34.48\n2005-01-07,65.00,69.63,64.75,69.25,79551800,34.62\n2005-01-06,64.67,64.91,63.33,64.55,25198400,32.28\n2005-01-05,64.46,65.25,64.05,64.50,24301200,32.25\n2005-01-04,63.79,65.47,62.97,63.94,39171800,31.97\n2005-01-03,64.78,65.11,62.60,63.29,24714000,31.65\n2004-12-31,64.89,65.00,64.03,64.40,9949600,32.20\n2004-12-30,64.81,65.03,64.22,64.80,12333600,32.40\n2004-12-29,63.81,64.98,63.57,64.44,16055800,32.22\n2004-12-28,63.30,64.25,62.05,64.18,21848400,32.09\n2004-12-27,64.80,65.15,62.88,63.16,19981800,31.58\n2004-12-23,63.75,64.25,63.60,64.01,8783200,32.01\n2004-12-22,63.66,64.36,63.40,63.75,20208200,31.88\n2004-12-21,63.56,63.77,61.60,63.69,38014800,31.84\n2004-12-20,65.47,66.00,61.76,62.72,41718800,31.36\n2004-12-17,66.84,67.04,64.90,64.99,27982000,32.49\n2004-12-16,66.15,67.50,66.05,66.60,40218400,33.30\n2004-12-15,65.24,65.46,64.66,65.26,14227200,32.63\n2004-12-14,65.40,65.88,65.02,65.29,14847200,32.65\n2004-12-13,65.62,65.90,64.60,64.91,14108600,32.46\n2004-12-10,65.03,66.05,64.70,65.15,27706200,32.58\n2004-12-09,62.81,64.40,62.07,63.99,26482200,32.00\n2004-12-08,63.08,64.43,62.05,63.28,24710800,31.64\n2004-12-07,65.93,66.73,62.56,62.89,37746400,31.44\n2004-12-06,64.25,66.24,62.95,65.78,44568600,32.89\n2004-12-03,64.53,65.00,61.75,62.68,44244600,31.34\n2004-12-02,66.13,66.90,64.66,65.21,35265800,32.60\n2004-12-01,67.79,67.95,66.27,67.79,28591200,33.90\n2004-11-30,68.79,68.79,67.05,67.05,36732800,33.53\n2004-11-29,68.95,69.57,67.41,68.44,61175600,34.22\n2004-11-26,65.35,65.76,64.34,64.55,19648000,32.28\n2004-11-24,61.69,65.20,61.55,64.05,49671000,32.03\n2004-11-23,62.30,62.45,61.05,61.27,32551800,30.64\n2004-11-22,61.80,64.00,57.90,61.35,91721800,30.67\n2004-11-19,55.49,56.91,54.50,55.17,27331400,27.58\n2004-11-18,54.30,55.45,54.29,55.39,16398200,27.69\n2004-11-17,55.19,55.45,54.22,54.90,14205400,27.45\n2004-11-16,55.16,55.20,54.48,54.94,10539400,27.47\n2004-11-15,55.20,55.46,54.34,55.24,13430200,27.62\n2004-11-12,55.01,55.69,54.84,55.50,14132200,27.75\n2004-11-11,54.95,55.43,54.23,55.30,14546400,27.65\n2004-11-10,53.95,55.39,53.91,54.75,18167000,27.38\n2004-11-09,54.23,54.55,53.38,54.05,16991600,27.02\n2004-11-08,54.27,55.45,53.86,54.38,18818600,27.19\n2004-11-05,54.86,55.00,52.04,54.72,43037400,27.36\n2004-11-04,55.03,55.55,54.37,54.45,33165200,27.23\n2004-11-03,54.37,56.11,53.99,55.31,43006200,27.66\n2004-11-02,52.40,54.08,52.40,53.50,26071000,26.75\n2004-11-01,52.50,53.26,52.04,52.45,21501800,26.23\n2004-10-29,51.84,53.20,51.80,52.40,28936400,26.20\n2004-10-28,49.98,52.22,49.50,52.19,30866600,26.09\n2004-10-27,48.51,50.62,48.17,50.30,42624800,25.15\n2004-10-26,47.45,48.05,46.97,47.97,21227200,23.99\n2004-10-25,47.20,47.84,47.07,47.55,14023000,23.77\n2004-10-22,47.54,47.67,47.02,47.41,17252400,23.70\n2004-10-21,47.48,48.13,47.36,47.94,25875200,23.97\n2004-10-20,47.18,47.60,46.65,47.47,21611000,23.74\n2004-10-19,48.10,48.35,47.31,47.42,28642600,23.71\n2004-10-18,44.70,47.75,44.70,47.75,42884000,23.88\n2004-10-15,44.88,45.61,44.19,45.50,36826000,22.75\n2004-10-14,43.19,45.75,42.55,44.98,98872400,22.49\n2004-10-13,38.87,39.76,38.74,39.75,41536000,19.88\n2004-10-12,38.50,38.58,37.65,38.29,16435400,19.15\n2004-10-11,38.80,39.06,38.20,38.59,11566800,19.30\n2004-10-08,39.56,39.77,38.84,39.06,12829600,19.53\n2004-10-07,40.54,40.93,39.46,39.62,15219600,19.81\n2004-10-06,39.50,40.76,39.47,40.64,15939400,20.32\n2004-10-05,38.56,39.67,38.40,39.37,14505800,19.68\n2004-10-04,39.18,39.18,38.75,38.79,20503000,19.40\n2004-10-01,39.12,39.19,38.58,38.67,16621600,19.33\n2004-09-30,39.00,39.27,38.45,38.75,15179000,19.38\n2004-09-29,37.93,38.86,37.82,38.68,9768200,19.34\n2004-09-28,37.46,38.29,37.45,38.04,12613800,19.02\n2004-09-27,36.95,37.98,36.83,37.53,14197000,18.76\n2004-09-24,37.45,38.00,37.15,37.29,13196000,18.65\n2004-09-23,37.04,37.50,36.93,37.27,14193000,18.64\n2004-09-22,38.10,38.14,36.81,36.92,14346000,18.46\n2004-09-21,37.75,38.87,37.46,38.01,13809000,19.00\n2004-09-20,36.88,37.98,36.87,37.71,8750000,18.85\n2004-09-17,36.55,37.38,36.40,37.14,17939600,18.57\n2004-09-16,35.20,36.76,35.08,36.35,17925600,18.17\n2004-09-15,35.36,35.48,34.80,35.20,8309600,17.60\n2004-09-14,35.24,35.55,34.78,35.49,9100800,17.75\n2004-09-13,35.88,36.07,35.32,35.59,10070600,17.80\n2004-09-10,35.66,36.23,35.46,35.87,11714800,17.93\n2004-09-09,36.10,36.30,35.28,35.70,16476400,17.85\n2004-09-08,35.70,36.57,35.68,36.35,12268800,18.17\n2004-09-07,35.40,36.19,35.23,35.76,10784200,17.88\n2004-09-03,35.01,35.92,35.01,35.23,10481000,17.61\n2004-09-02,35.50,35.81,34.83,35.66,14511600,17.83\n2004-09-01,34.30,35.99,34.19,35.86,18418800,17.93\n2004-08-31,34.07,34.95,34.00,34.49,13448600,17.25\n2004-08-30,34.00,34.72,33.96,34.12,7790800,17.06\n2004-08-27,34.68,34.76,34.00,34.35,13886200,17.17\n2004-08-26,33.04,35.18,32.74,34.66,34137800,17.33\n2004-08-25,31.87,33.15,31.73,33.05,18057800,16.52\n2004-08-24,31.26,31.95,31.19,31.95,13362000,15.98\n2004-08-23,30.86,31.27,30.60,31.08,9095000,15.54\n2004-08-20,30.71,30.99,30.49,30.80,11313600,15.40\n2004-08-19,31.51,31.86,30.36,30.71,13890000,15.35\n2004-08-18,30.51,31.85,30.49,31.74,13023400,15.87\n2004-08-17,30.58,31.13,30.35,30.87,11536400,15.44\n2004-08-16,31.00,31.72,30.64,30.78,15559800,15.39\n2004-08-13,30.60,31.28,30.40,30.84,11716000,15.42\n2004-08-12,30.45,30.85,30.28,30.37,8078600,15.19\n2004-08-11,31.10,31.13,30.26,31.01,11514000,15.51\n2004-08-10,30.39,31.54,30.35,31.52,12537000,15.76\n2004-08-09,29.85,30.45,29.81,30.30,10387400,15.15\n2004-08-06,30.90,31.10,29.70,29.78,17581800,14.89\n2004-08-05,31.81,32.30,31.25,31.39,8732200,15.69\n2004-08-04,31.19,32.12,31.17,31.79,9874600,15.90\n2004-08-03,31.45,31.72,31.15,31.29,7558200,15.65\n2004-08-02,31.18,32.20,31.13,31.58,13039000,15.79\n2004-07-30,32.65,33.00,32.00,32.34,8679400,16.17\n2004-07-29,32.47,32.82,32.13,32.64,7934200,16.32\n2004-07-28,32.31,32.41,31.16,32.27,10180400,16.14\n2004-07-27,31.80,32.75,31.57,32.43,15178800,16.22\n2004-07-26,30.85,31.45,30.78,31.26,14069000,15.63\n2004-07-23,31.53,31.75,30.48,30.70,9770400,15.35\n2004-07-22,31.25,31.73,31.06,31.68,11932800,15.84\n2004-07-21,32.42,32.71,31.34,31.62,10759200,15.81\n2004-07-20,31.95,32.20,31.55,32.20,11562400,16.10\n2004-07-19,32.01,32.22,31.66,31.97,19041800,15.98\n2004-07-16,32.80,32.92,32.12,32.20,17442200,16.10\n2004-07-15,32.66,33.63,32.11,32.93,63133000,16.47\n2004-07-14,28.86,29.97,28.74,29.58,29850000,14.79\n2004-07-13,29.25,29.60,29.02,29.22,11292000,14.61\n2004-07-12,30.02,30.04,28.93,29.14,18272200,14.57\n2004-07-09,30.27,30.50,30.03,30.03,7459400,15.02\n2004-07-08,30.13,30.68,29.95,30.14,8335000,15.07\n2004-07-07,30.85,31.36,30.13,30.39,14214000,15.19\n2004-07-06,31.27,31.42,30.80,30.95,12463600,15.48\n2004-07-02,30.48,31.18,29.73,31.08,32524400,15.54\n2004-07-01,32.10,32.48,31.90,32.30,12212200,16.15\n2004-06-30,32.56,32.97,31.89,32.54,13323000,16.27\n2004-06-29,32.07,32.99,31.41,32.50,21091200,16.25\n2004-06-28,34.18,34.19,32.21,32.49,18610600,16.25\n2004-06-25,33.07,33.70,33.00,33.70,11551000,16.85\n2004-06-24,33.51,33.70,32.98,33.18,9018400,16.59\n2004-06-23,33.00,33.83,32.89,33.70,13959600,16.85\n2004-06-22,32.30,33.09,32.29,33.00,12875400,16.50\n2004-06-21,33.12,33.50,32.12,32.33,13936200,16.17\n2004-06-18,32.66,33.41,32.43,32.91,14509000,16.45\n2004-06-17,32.56,33.13,32.21,32.81,19690000,16.41\n2004-06-16,30.66,33.32,30.53,32.74,32487200,16.37\n2004-06-15,30.54,31.14,30.26,30.69,15879800,15.35\n2004-06-14,30.65,30.68,29.50,30.12,8713800,15.06\n2004-06-10,30.20,30.97,30.20,30.74,9199200,15.37\n2004-06-09,30.09,30.71,30.00,30.20,12471600,15.10\n2004-06-08,29.99,30.44,29.83,30.35,14843600,15.18\n2004-06-07,29.04,29.98,28.81,29.81,10567000,14.90\n2004-06-04,28.56,29.25,28.51,28.78,14254000,14.39\n2004-06-03,28.72,28.99,28.29,28.40,8961800,14.20\n2004-06-02,28.03,29.17,27.80,28.92,11382600,14.46\n2004-06-01,27.79,28.20,27.61,28.06,6504800,14.03\n2004-05-28,28.08,28.27,27.80,28.06,5204200,14.03\n2004-05-27,28.46,28.60,27.82,28.17,8427600,14.09\n2004-05-26,28.33,28.78,28.00,28.51,11506000,14.26\n2004-05-25,27.50,28.51,27.29,28.41,11427800,14.20\n2004-05-24,27.29,27.90,27.11,27.34,8414400,13.67\n2004-05-21,26.90,27.20,26.73,27.11,6424800,13.56\n2004-05-20,26.63,27.00,26.47,26.71,7010600,13.35\n2004-05-19,27.40,27.50,26.42,26.47,13414000,13.23\n2004-05-18,26.97,27.29,26.80,27.06,7359400,13.53\n2004-05-17,26.70,27.06,26.36,26.64,10730200,13.32\n2004-05-14,27.25,27.32,26.45,27.06,9207200,13.53\n2004-05-13,27.10,27.72,26.90,27.19,8209000,13.60\n2004-05-12,26.79,27.34,26.24,27.30,8765000,13.65\n2004-05-11,26.40,27.19,26.40,27.14,10899000,13.57\n2004-05-10,26.27,26.60,25.94,26.28,8927800,13.14\n2004-05-07,26.55,27.57,26.55,26.67,14965600,13.34\n2004-05-06,26.40,26.75,25.90,26.58,9412800,13.29\n2004-05-05,26.20,26.75,25.96,26.65,8503800,13.32\n2004-05-04,25.97,26.55,25.50,26.14,9999400,13.07\n2004-05-03,26.00,26.33,25.74,26.07,10629800,13.03\n2004-04-30,26.71,26.96,25.49,25.78,16660800,12.89\n2004-04-29,26.45,27.00,25.98,26.77,16456800,13.39\n2004-04-28,26.82,27.01,26.34,26.45,8256000,13.23\n2004-04-27,27.24,27.44,26.69,26.94,10138000,13.47\n2004-04-26,27.58,27.64,27.00,27.13,8254600,13.56\n2004-04-23,27.70,28.00,27.05,27.70,11279600,13.85\n2004-04-22,27.56,28.18,27.11,27.78,12306600,13.89\n2004-04-21,27.60,28.12,27.37,27.73,11638400,13.86\n2004-04-20,28.21,28.41,27.56,27.73,12661400,13.86\n2004-04-19,28.12,28.75,27.83,28.35,25441200,14.18\n2004-04-16,29.15,29.31,28.50,29.18,14390400,14.59\n2004-04-15,28.82,29.58,28.16,29.30,62908800,14.65\n2004-04-14,26.74,27.07,26.31,26.64,22847600,13.32\n2004-04-13,27.98,28.03,26.84,26.93,15585600,13.47\n2004-04-12,27.50,28.10,27.49,28.04,8233600,14.02\n2004-04-08,27.88,28.00,27.20,27.53,8604200,13.77\n2004-04-07,27.61,27.70,26.92,27.31,9111400,13.65\n2004-04-06,27.71,28.15,27.43,27.83,9214000,13.91\n2004-04-05,27.48,28.37,27.44,28.32,13774000,14.16\n2004-04-02,27.75,27.93,27.23,27.50,9802800,13.75\n2004-04-01,26.89,27.27,26.62,27.11,11369000,13.56\n2004-03-31,27.92,27.98,26.95,27.04,13956200,13.52\n2004-03-30,27.74,27.95,27.34,27.92,12845600,13.96\n2004-03-29,27.37,27.99,27.20,27.91,12526000,13.95\n2004-03-26,27.00,27.36,26.91,27.04,14996200,13.52\n2004-03-25,26.14,26.91,25.89,26.87,20230200,13.44\n2004-03-24,25.27,25.75,25.27,25.50,15293400,12.75\n2004-03-23,25.88,26.00,25.22,25.29,13768400,12.65\n2004-03-22,25.37,26.17,25.25,25.86,14965400,12.93\n2004-03-19,25.56,26.94,25.54,25.86,14592000,12.93\n2004-03-18,25.94,26.06,25.59,25.67,11467200,12.84\n2004-03-17,25.96,26.38,25.78,26.19,14694000,13.10\n2004-03-16,26.55,26.61,25.39,25.82,21622600,12.91\n2004-03-15,27.03,27.35,26.26,26.45,17204200,13.23\n2004-03-12,27.32,27.78,27.17,27.56,11758000,13.78\n2004-03-11,27.27,28.04,27.09,27.15,21280400,13.57\n2004-03-10,27.04,28.14,26.94,27.68,35963000,13.84\n2004-03-09,25.90,27.23,25.75,27.10,22084400,13.55\n2004-03-08,26.62,26.79,25.80,26.00,18674000,13.00\n2004-03-05,24.95,27.49,24.90,26.74,55021400,13.37\n2004-03-04,23.93,25.22,23.91,25.16,23579400,12.58\n2004-03-03,23.60,24.19,23.60,23.92,8040400,11.96\n2004-03-02,24.00,24.10,23.77,23.81,9167400,11.90\n2004-03-01,24.10,24.30,23.87,24.02,11488600,12.01\n2004-02-27,22.96,24.02,22.95,23.92,16744200,11.96\n2004-02-26,22.88,23.18,22.80,23.04,7086000,11.52\n2004-02-25,22.28,22.90,22.21,22.81,9867000,11.40\n2004-02-24,22.14,22.74,22.00,22.36,9252000,11.18\n2004-02-23,22.34,22.46,21.89,22.19,6931400,11.10\n2004-02-20,22.50,22.51,22.21,22.40,9914400,11.20\n2004-02-19,23.33,23.64,22.41,22.47,11538600,11.23\n2004-02-18,23.18,23.44,23.05,23.26,5058400,11.63\n2004-02-17,23.10,23.49,23.10,23.16,6105600,11.58\n2004-02-13,23.85,24.10,22.83,23.00,11285000,11.50\n2004-02-12,23.61,23.99,23.60,23.73,6571000,11.86\n2004-02-11,23.09,23.87,23.05,23.80,12448000,11.90\n2004-02-10,22.62,23.12,22.44,22.98,9119400,11.49\n2004-02-09,22.62,22.86,22.50,22.67,6723600,11.34\n2004-02-06,22.45,22.89,22.40,22.71,6905000,11.35\n2004-02-05,21.82,22.91,21.81,22.42,12601600,11.21\n2004-02-04,22.00,22.09,21.70,21.79,10912600,10.90\n2004-02-03,22.30,22.40,22.00,22.26,6457600,11.13\n2004-02-02,22.46,22.81,22.08,22.32,10265400,11.16\n2004-01-30,22.65,22.87,22.42,22.56,6617800,11.28\n2004-01-29,22.63,22.80,22.19,22.68,7596400,11.34\n2004-01-28,22.84,23.38,22.41,22.52,9835800,11.26\n2004-01-27,23.04,23.25,22.80,23.07,10966800,11.53\n2004-01-26,22.46,23.06,22.43,23.01,9688200,11.51\n2004-01-23,22.42,22.74,22.25,22.56,8113200,11.28\n2004-01-22,22.56,22.83,22.18,22.18,7321600,11.09\n2004-01-21,22.70,22.97,22.43,22.61,8095000,11.31\n2004-01-20,22.67,22.80,22.25,22.73,11283800,11.36\n2004-01-16,22.89,23.04,22.61,22.72,13315000,11.36\n2004-01-15,22.91,23.40,22.50,22.85,36364600,11.43\n2004-01-14,24.40,24.54,23.78,24.20,22144400,12.10\n2004-01-13,24.70,24.84,23.86,24.12,24250600,12.06\n2004-01-12,23.25,24.00,23.10,23.73,17412400,11.86\n2004-01-09,23.23,24.13,22.79,23.00,15266400,11.50\n2004-01-08,22.84,23.73,22.65,23.36,16439400,11.68\n2004-01-07,22.10,22.83,21.93,22.59,20959800,11.30\n2004-01-06,22.25,22.42,21.71,22.09,18191000,11.05\n2004-01-05,21.42,22.39,21.42,22.17,14107800,11.09\n2004-01-02,21.55,21.75,21.18,21.28,5165800,10.64\n2003-12-31,21.35,21.53,21.18,21.37,6230400,10.69\n2003-12-30,21.18,21.50,21.15,21.28,7316200,10.64\n2003-12-29,20.91,21.16,20.86,21.15,8337800,10.57\n2003-12-26,20.35,20.91,20.34,20.78,3703400,10.39\n2003-12-24,19.72,20.59,19.65,20.41,6338400,10.20\n2003-12-23,19.92,19.95,19.60,19.81,11017800,9.90\n2003-12-22,19.65,19.89,19.25,19.85,13466600,9.93\n2003-12-19,20.19,20.42,19.62,19.70,16198600,9.85\n2003-12-18,19.90,20.18,19.90,20.04,11818400,10.02\n2003-12-17,20.08,20.13,19.79,19.88,9795000,9.94\n2003-12-16,20.19,20.49,20.01,20.12,13355600,10.06\n2003-12-15,21.49,21.49,20.07,20.17,13889600,10.09\n2003-12-12,21.32,21.32,20.70,20.89,6881200,10.44\n2003-12-11,20.25,21.34,20.21,21.21,6540600,10.60\n2003-12-10,20.45,20.61,19.96,20.38,9690600,10.19\n2003-12-09,21.17,21.25,20.40,20.45,4826600,10.23\n2003-12-08,20.78,21.08,20.41,21.05,5294200,10.52\n2003-12-05,20.90,21.15,20.73,20.85,6649200,10.43\n2003-12-04,20.94,21.17,20.77,21.15,6355000,10.57\n2003-12-03,21.54,21.84,20.96,21.03,6832000,10.52\n2003-12-02,21.60,21.90,21.41,21.54,7332000,10.77\n2003-12-01,21.04,21.85,21.00,21.71,12912000,10.85\n2003-11-28,20.78,21.07,20.52,20.91,2717800,10.45\n2003-11-26,20.89,21.15,20.25,20.72,8754600,10.36\n2003-11-25,21.23,21.25,20.61,20.68,9594800,10.34\n2003-11-24,20.50,21.27,20.45,21.15,13636600,10.57\n2003-11-21,20.34,20.58,19.85,20.28,8637000,10.14\n2003-11-20,20.10,21.08,20.10,20.38,8556800,10.19\n2003-11-19,20.56,20.65,20.26,20.42,12306600,10.21\n2003-11-18,21.21,21.34,20.35,20.41,9542600,10.20\n2003-11-17,21.35,21.37,20.95,21.13,8152000,10.56\n2003-11-14,22.48,22.61,21.28,21.46,8466000,10.73\n2003-11-13,22.07,22.56,21.92,22.42,7599000,11.21\n2003-11-12,21.48,22.72,21.48,22.33,10714400,11.16\n2003-11-11,21.90,22.02,21.48,21.54,7681200,10.77\n2003-11-10,22.45,22.65,21.84,21.90,8367000,10.95\n2003-11-07,23.19,23.24,22.45,22.50,7505200,11.25\n2003-11-06,22.91,23.15,22.65,23.12,14181200,11.56\n2003-11-05,22.82,23.13,22.47,23.03,11516800,11.52\n2003-11-04,23.07,23.10,22.59,22.91,8901200,11.45\n2003-11-03,22.83,23.30,22.78,23.15,10815800,11.57\n2003-10-31,23.30,23.35,22.78,22.89,7791200,11.44\n2003-10-30,23.99,24.00,22.87,23.09,9305600,11.55\n2003-10-29,23.51,23.90,23.34,23.69,9538600,11.85\n2003-10-28,22.56,23.77,22.40,23.72,8989800,11.86\n2003-10-27,22.75,22.89,22.49,22.60,5786200,11.30\n2003-10-24,22.56,22.85,22.23,22.60,7852000,11.30\n2003-10-23,22.73,23.15,22.59,22.99,5900400,11.49\n2003-10-22,22.94,23.20,22.68,22.76,5771400,11.38\n2003-10-21,23.31,23.40,22.75,23.18,6302200,11.59\n2003-10-20,22.60,23.34,22.38,23.22,9969000,11.61\n2003-10-17,23.38,23.49,22.43,22.75,12850400,11.38\n2003-10-16,23.80,23.84,22.41,23.25,34845800,11.62\n2003-10-15,24.85,25.01,24.58,24.82,21789400,12.41\n2003-10-14,24.32,24.74,24.19,24.55,9836400,12.27\n2003-10-13,23.73,24.41,23.72,24.35,9995200,12.18\n2003-10-10,23.50,23.81,23.37,23.68,6244200,11.84\n2003-10-09,23.30,23.67,22.79,23.45,12419600,11.73\n2003-10-08,23.25,23.54,22.73,23.06,15309600,11.53\n2003-10-07,22.05,23.41,21.91,23.22,14934800,11.61\n2003-10-06,21.67,22.33,21.58,22.29,9583200,11.15\n2003-10-03,20.99,21.86,20.88,21.69,10700000,10.85\n2003-10-02,20.80,20.80,20.28,20.57,7287800,10.28\n2003-10-01,20.71,21.10,20.19,20.79,8432600,10.40\n2003-09-30,21.09,21.22,20.44,20.72,10193800,10.36\n2003-09-29,21.49,21.67,20.65,21.30,13060800,10.65\n2003-09-26,20.30,21.70,20.15,20.69,12401800,10.35\n2003-09-25,21.34,21.37,20.25,20.43,20513600,10.22\n2003-09-24,22.21,22.31,21.08,21.32,10760200,10.66\n2003-09-23,22.02,22.46,21.88,22.43,4730400,11.22\n2003-09-22,22.18,22.50,21.92,22.08,6422200,11.04\n2003-09-19,22.88,23.05,22.43,22.58,7355600,11.29\n2003-09-18,22.10,22.99,21.95,22.88,9032400,11.44\n2003-09-17,22.37,22.38,21.85,22.12,10335600,11.06\n2003-09-16,22.21,22.69,22.20,22.36,9607400,11.18\n2003-09-15,22.81,22.90,22.12,22.21,8101600,11.10\n2003-09-12,22.51,23.14,22.31,23.10,6428200,11.55\n2003-09-11,22.25,22.79,22.10,22.56,7631600,11.28\n2003-09-10,22.25,22.61,22.11,22.18,8031800,11.09\n2003-09-09,22.53,22.67,22.12,22.37,6441800,11.19\n2003-09-08,22.48,22.79,22.47,22.74,5973000,11.37\n2003-09-05,22.73,23.15,22.41,22.50,8576200,11.25\n2003-09-04,23.16,23.25,22.77,22.83,7135000,11.41\n2003-09-03,22.80,23.32,22.76,22.95,9601000,11.48\n2003-09-02,22.66,22.90,22.40,22.85,8647600,11.43\n2003-08-29,22.20,22.85,22.05,22.61,9398400,11.31\n2003-08-28,21.33,22.22,21.33,22.19,11415200,11.10\n2003-08-27,20.91,21.48,20.66,21.48,8060800,10.74\n2003-08-26,20.75,21.07,20.35,21.05,5891400,10.52\n2003-08-25,20.78,20.91,20.49,20.86,4920800,10.43\n2003-08-22,21.81,22.00,20.64,20.88,8938000,10.44\n2003-08-21,21.03,21.71,20.95,21.68,9118800,10.84\n2003-08-20,20.18,21.27,20.14,21.01,9757600,10.51\n2003-08-19,20.37,20.45,20.00,20.32,4774600,10.16\n2003-08-18,19.86,20.41,19.72,20.34,6884800,10.17\n2003-08-15,20.02,20.07,19.66,19.71,4495200,9.85\n2003-08-14,20.21,20.33,19.94,19.97,6885000,9.98\n2003-08-13,19.86,20.34,19.58,20.18,10146400,10.09\n2003-08-12,19.76,19.80,19.46,19.70,5872800,9.85\n2003-08-11,19.82,19.93,19.51,19.66,4901000,9.83\n2003-08-08,20.11,20.13,19.60,19.64,4916400,9.82\n2003-08-07,19.73,20.09,19.42,19.93,6227800,9.97\n2003-08-06,20.06,20.17,19.50,19.63,8766600,9.81\n2003-08-05,21.35,21.40,20.10,20.38,8908600,10.19\n2003-08-04,20.53,21.50,20.28,21.21,8218400,10.60\n2003-08-01,21.00,21.27,20.64,20.73,5343000,10.36\n2003-07-31,20.74,21.35,20.57,21.08,10766600,10.54\n2003-07-30,20.77,20.90,20.17,20.28,6199800,10.14\n2003-07-29,20.99,21.08,20.52,20.72,7040000,10.36\n2003-07-28,21.50,21.50,20.86,20.99,6084200,10.49\n2003-07-25,20.41,21.57,20.40,21.54,7738800,10.77\n2003-07-24,21.04,21.50,20.38,20.51,8187000,10.26\n2003-07-23,20.95,20.96,20.46,20.79,5108400,10.40\n2003-07-22,20.87,20.96,20.50,20.80,7086600,10.40\n2003-07-21,20.69,20.80,20.30,20.61,6564600,10.31\n2003-07-18,20.90,21.18,20.40,20.86,10672800,10.43\n2003-07-17,20.19,20.95,20.13,20.90,26829000,10.45\n2003-07-16,19.97,20.00,19.38,19.87,8961800,9.94\n2003-07-15,20.02,20.24,19.43,19.61,7380200,9.81\n2003-07-14,20.01,20.40,19.87,19.90,6728800,9.95\n2003-07-11,19.66,20.00,19.53,19.85,4887800,9.93\n2003-07-10,19.88,19.94,19.37,19.58,6104800,9.79\n2003-07-09,20.21,20.45,19.89,19.89,7630200,9.94\n2003-07-08,19.52,20.50,19.49,20.40,9169200,10.20\n2003-07-07,19.27,20.18,19.13,19.87,10224000,9.94\n2003-07-03,19.00,19.55,18.98,19.13,4920400,9.56\n2003-07-02,19.03,19.40,19.02,19.27,11617800,9.64\n2003-07-01,18.87,19.18,18.51,19.09,6464000,9.55\n2003-06-30,18.68,19.21,18.59,19.06,7934000,9.53\n2003-06-27,19.30,19.31,18.48,18.73,13064000,9.36\n2003-06-26,18.70,19.32,18.70,19.29,5775200,9.65\n2003-06-25,18.86,19.40,18.71,19.09,11779000,9.55\n2003-06-24,19.47,19.67,18.72,18.78,18370800,9.39\n2003-06-23,19.30,19.69,18.75,19.06,10977200,9.53\n2003-06-20,19.35,19.58,18.90,19.20,12733800,9.60\n2003-06-19,19.36,19.61,18.77,19.14,13626000,9.57\n2003-06-18,18.45,19.48,18.31,19.12,16249400,9.56\n2003-06-17,18.41,18.50,17.99,18.19,6338000,9.10\n2003-06-16,17.60,18.27,17.45,18.27,8518800,9.14\n2003-06-13,17.75,17.95,17.13,17.42,6830200,8.71\n2003-06-12,17.55,17.88,17.45,17.77,9021000,8.89\n2003-06-11,17.15,17.51,16.81,17.45,8039800,8.73\n2003-06-10,16.89,17.29,16.75,17.18,6308800,8.59\n2003-06-09,16.94,17.04,16.63,16.79,9284000,8.40\n2003-06-06,17.74,18.04,17.14,17.15,8621000,8.57\n2003-06-05,17.45,17.74,17.33,17.64,7339200,8.82\n2003-06-04,17.30,17.79,17.14,17.60,9685800,8.80\n2003-06-03,17.44,17.67,17.02,17.31,12887800,8.65\n2003-06-02,18.10,18.29,17.27,17.45,14949600,8.73\n2003-05-30,18.12,18.18,17.53,17.95,13669600,8.98\n2003-05-29,18.29,18.50,17.90,18.10,11920200,9.05\n2003-05-28,18.50,18.66,18.15,18.28,12131400,9.14\n2003-05-27,17.96,18.90,17.91,18.88,10361800,9.44\n2003-05-23,18.21,18.46,17.96,18.32,7382800,9.16\n2003-05-22,17.89,18.40,17.74,18.24,6373600,9.12\n2003-05-21,17.79,18.09,17.67,17.85,10893200,8.93\n2003-05-20,18.10,18.16,17.60,17.79,14865000,8.90\n2003-05-19,18.53,18.65,18.06,18.10,15924600,9.05\n2003-05-16,18.59,19.01,18.28,18.80,12201000,9.40\n2003-05-15,18.60,18.85,18.47,18.73,10178400,9.36\n2003-05-14,18.83,18.84,18.43,18.55,12696000,9.27\n2003-05-13,18.43,18.97,17.95,18.67,15957000,9.34\n2003-05-12,18.15,18.74,18.13,18.56,14977600,9.28\n2003-05-09,18.33,18.40,17.88,18.30,21013800,9.15\n2003-05-08,17.70,18.07,17.29,18.00,24562000,9.00\n2003-05-07,17.33,18.24,17.11,17.65,37656400,8.82\n2003-05-06,16.12,17.90,16.10,17.50,54089000,8.75\n2003-05-05,14.77,16.88,14.75,16.09,55561000,8.05\n2003-05-02,14.46,14.59,14.34,14.45,11470800,7.22\n2003-05-01,14.25,14.39,14.00,14.36,12241400,7.18\n2003-04-30,13.93,14.35,13.85,14.22,16363400,7.11\n2003-04-29,13.98,14.16,13.58,14.06,16365600,7.03\n2003-04-28,13.48,13.96,13.43,13.86,22742800,6.93\n2003-04-25,13.46,13.58,13.23,13.35,7332800,6.68\n2003-04-24,13.52,13.61,13.00,13.44,11611000,6.72\n2003-04-23,13.53,13.63,13.36,13.58,7488600,6.79\n2003-04-22,13.18,13.62,13.09,13.51,10734600,6.76\n2003-04-21,13.13,13.19,12.98,13.14,5440000,6.57\n2003-04-17,13.20,13.25,12.72,13.12,22009200,6.56\n2003-04-16,12.99,13.67,12.92,13.24,36292000,6.62\n2003-04-15,13.59,13.60,13.30,13.39,10856000,6.70\n2003-04-14,13.71,13.75,13.50,13.58,17962800,6.79\n2003-04-11,14.05,14.44,12.93,13.20,49739600,6.60\n2003-04-10,14.20,14.39,14.20,14.37,3825000,7.18\n2003-04-09,14.52,14.62,14.14,14.19,5240200,7.09\n2003-04-08,14.51,14.65,14.36,14.45,4604800,7.22\n2003-04-07,14.85,14.95,14.41,14.49,7030800,7.24\n2003-04-04,14.52,14.67,14.39,14.41,5215000,7.20\n2003-04-03,14.56,14.70,14.35,14.46,5204000,7.23\n2003-04-02,14.36,14.69,14.27,14.60,6120400,7.30\n2003-04-01,14.20,14.31,14.07,14.16,5512200,7.08\n2003-03-31,14.33,14.53,14.04,14.14,9166400,7.07\n2003-03-28,14.40,14.62,14.37,14.57,5189400,7.28\n2003-03-27,14.32,14.70,14.32,14.49,4371200,7.24\n2003-03-26,14.55,14.56,14.30,14.41,6369400,7.20\n2003-03-25,14.41,14.83,14.37,14.55,5989200,7.28\n2003-03-24,14.67,14.80,14.35,14.37,5753600,7.18\n2003-03-21,15.09,15.15,14.82,15.00,10641000,7.50\n2003-03-20,14.93,14.99,14.60,14.91,5827800,7.45\n2003-03-19,15.07,15.15,14.79,14.95,5047000,7.47\n2003-03-18,15.00,15.09,14.82,15.00,8213600,7.50\n2003-03-17,14.89,15.07,14.71,15.01,14282600,7.51\n2003-03-14,14.68,15.01,14.64,14.78,5467800,7.39\n2003-03-13,14.47,14.80,14.17,14.72,11980200,7.36\n2003-03-12,14.17,14.39,14.06,14.22,7948600,7.11\n2003-03-11,14.36,14.49,14.12,14.23,5756800,7.11\n2003-03-10,14.51,14.67,14.30,14.37,4806200,7.18\n2003-03-07,14.47,14.71,14.31,14.53,7178000,7.26\n2003-03-06,14.58,14.60,14.40,14.56,3566400,7.28\n2003-03-05,14.61,14.80,14.52,14.62,4524400,7.31\n2003-03-04,14.74,14.81,14.44,14.56,4514800,7.28\n2003-03-03,15.01,15.16,14.55,14.65,7277200,7.32\n2003-02-28,14.86,15.09,14.77,15.01,6967800,7.51\n2003-02-27,14.57,15.00,14.51,14.86,5512200,7.43\n2003-02-26,14.99,15.02,14.48,14.50,7753400,7.25\n2003-02-25,14.68,15.08,14.58,15.02,6737200,7.51\n2003-02-24,14.86,15.03,13.80,14.74,6437600,7.37\n2003-02-21,14.82,15.06,14.65,15.00,5623000,7.50\n2003-02-20,14.85,14.96,14.71,14.77,8012600,7.39\n2003-02-19,15.07,15.15,14.68,14.85,8584600,7.43\n2003-02-18,14.75,15.30,14.72,15.27,10389200,7.64\n2003-02-14,14.61,14.72,14.35,14.67,8689200,7.34\n2003-02-13,14.41,14.64,14.24,14.54,7446200,7.27\n2003-02-12,14.27,14.60,14.27,14.39,8167400,7.20\n2003-02-11,14.50,14.63,14.20,14.35,5885000,7.18\n2003-02-10,14.26,14.57,14.06,14.35,5996000,7.18\n2003-02-07,14.55,14.60,14.07,14.15,9632200,7.07\n2003-02-06,14.36,14.59,14.22,14.43,6398200,7.22\n2003-02-05,14.71,14.93,14.44,14.45,7914800,7.22\n2003-02-04,14.45,14.65,14.31,14.60,11336200,7.30\n2003-02-03,14.41,14.91,14.35,14.66,9456600,7.33\n2003-01-31,14.19,14.55,14.05,14.36,12186600,7.18\n2003-01-30,14.98,15.07,14.29,14.32,14537800,7.16\n2003-01-29,14.55,15.10,14.30,14.93,13323000,7.47\n2003-01-28,14.24,14.69,14.16,14.58,10223400,7.29\n2003-01-27,13.68,14.50,13.65,14.13,13978800,7.07\n2003-01-24,14.24,14.24,13.56,13.80,10909600,6.90\n2003-01-23,14.05,14.36,13.95,14.17,8152000,7.09\n2003-01-22,13.98,14.15,13.80,13.88,7683600,6.94\n2003-01-21,14.21,14.41,14.00,14.02,9052000,7.01\n2003-01-17,14.56,14.56,14.08,14.10,9527200,7.05\n2003-01-16,14.21,14.76,14.21,14.62,19966800,7.31\n2003-01-15,14.59,14.70,14.26,14.43,13254600,7.22\n2003-01-14,14.69,14.82,14.49,14.61,6673600,7.30\n2003-01-13,14.90,14.90,14.36,14.63,6390800,7.32\n2003-01-10,14.58,14.82,14.49,14.72,6253600,7.36\n2003-01-09,14.62,14.92,14.50,14.68,7687600,7.34\n2003-01-08,14.58,14.71,14.44,14.55,8201600,7.28\n2003-01-07,14.79,15.00,14.47,14.85,12226600,7.43\n2003-01-06,15.03,15.38,14.88,14.90,13947600,7.45\n2003-01-03,14.80,14.93,14.59,14.90,5266200,7.45\n2003-01-02,14.36,14.92,14.35,14.80,6479600,7.40\n2002-12-31,14.00,14.36,13.95,14.33,7168800,7.16\n2002-12-30,14.08,14.15,13.84,14.07,5537200,7.03\n2002-12-27,14.31,14.38,14.01,14.06,2858400,7.03\n2002-12-26,14.42,14.81,14.28,14.40,3050800,7.20\n2002-12-24,14.44,14.47,14.30,14.36,1405000,7.18\n2002-12-23,14.16,14.55,14.12,14.49,4493800,7.24\n2002-12-20,14.29,14.56,13.78,14.14,11360600,7.07\n2002-12-19,14.53,14.92,14.10,14.20,12411400,7.10\n2002-12-18,14.80,14.86,14.50,14.57,5382200,7.28\n2002-12-17,14.85,15.19,14.66,15.08,7952200,7.54\n2002-12-16,14.81,15.10,14.61,14.85,8986600,7.43\n2002-12-13,15.14,15.15,14.65,14.79,5885000,7.39\n2002-12-12,15.51,15.55,15.01,15.19,5333600,7.59\n2002-12-11,15.30,15.49,15.08,15.49,9053600,7.74\n2002-12-10,14.75,15.45,14.73,15.28,11021800,7.64\n2002-12-09,14.94,14.95,14.67,14.75,8431600,7.38\n2002-12-06,14.65,15.19,14.52,14.95,8762800,7.47\n2002-12-05,15.03,15.08,14.53,14.63,8692800,7.32\n2002-12-04,15.18,15.19,14.50,14.97,11634200,7.49\n2002-12-03,15.20,15.34,15.10,15.16,8138200,7.58\n2002-12-02,15.90,16.10,15.01,15.18,14240800,7.59\n2002-11-29,15.79,15.88,15.41,15.50,5122600,7.75\n2002-11-27,15.60,15.86,15.45,15.72,10242800,7.86\n2002-11-26,15.85,15.90,15.27,15.41,8580800,7.70\n2002-11-25,16.03,16.14,15.71,15.97,7122400,7.99\n2002-11-22,16.09,16.30,15.90,16.01,8137800,8.01\n2002-11-21,15.90,16.44,15.75,16.35,14945800,8.18\n2002-11-20,15.30,15.70,15.25,15.53,7455000,7.76\n2002-11-19,15.55,15.75,15.01,15.27,7534000,7.64\n2002-11-18,16.19,16.20,15.52,15.65,5877800,7.82\n2002-11-15,16.23,16.24,15.76,15.95,5749800,7.97\n2002-11-14,15.90,16.41,15.78,16.30,5061200,8.15\n2002-11-13,15.50,16.07,15.28,15.59,8276400,7.80\n2002-11-12,15.32,16.04,15.28,15.64,7992600,7.82\n2002-11-11,15.74,15.89,15.12,15.16,5463400,7.58\n2002-11-08,16.01,16.20,15.52,15.84,6788000,7.92\n2002-11-07,16.94,17.10,15.81,16.00,12006400,8.00\n2002-11-06,17.08,17.32,16.70,17.22,7728200,8.61\n2002-11-05,16.75,16.96,16.35,16.90,7524800,8.45\n2002-11-04,16.50,17.38,16.35,16.89,13457800,8.44\n2002-11-01,15.94,16.50,15.89,16.36,6779600,8.18\n2002-10-31,15.99,16.44,15.92,16.07,10565600,8.03\n2002-10-30,15.49,16.37,15.48,15.98,9667000,7.99\n2002-10-29,15.57,15.88,14.96,15.44,9256400,7.72\n2002-10-28,15.55,15.95,15.25,15.61,12475000,7.80\n2002-10-25,14.69,15.45,14.59,15.42,9966800,7.71\n2002-10-24,15.02,15.21,14.55,14.69,6241000,7.34\n2002-10-23,14.63,14.98,14.50,14.88,7465600,7.44\n2002-10-22,14.47,14.88,14.26,14.70,7791000,7.35\n2002-10-21,14.26,14.63,14.00,14.56,8518600,7.28\n2002-10-18,14.00,14.35,13.93,14.34,10296400,7.17\n2002-10-17,14.21,14.38,13.98,14.11,16760600,7.05\n2002-10-16,14.86,15.13,13.90,14.56,10986600,7.28\n2002-10-15,15.22,15.25,14.78,15.16,14482800,7.58\n2002-10-14,14.55,14.98,14.44,14.77,6943000,7.39\n2002-10-11,14.25,14.78,14.10,14.51,10524200,7.26\n2002-10-10,13.63,14.22,13.58,14.11,11484800,7.05\n2002-10-09,13.54,13.85,13.41,13.59,12738800,6.80\n2002-10-08,13.90,13.96,13.36,13.68,16201600,6.84\n2002-10-07,13.97,14.21,13.76,13.77,8739200,6.89\n2002-10-04,14.36,14.40,13.99,14.03,6815200,7.01\n2002-10-03,14.18,14.60,14.06,14.30,7782000,7.15\n2002-10-02,14.33,14.63,14.10,14.17,8191000,7.09\n2002-10-01,14.59,14.60,14.00,14.51,12229400,7.26\n2002-09-30,14.40,14.57,14.14,14.50,8489200,7.25\n2002-09-27,14.49,14.85,14.48,14.72,7362600,7.36\n2002-09-26,15.10,15.19,14.55,14.70,7451600,7.35\n2002-09-25,14.69,15.17,14.65,14.93,9095800,7.47\n2002-09-24,14.40,14.82,14.40,14.64,8952200,7.32\n2002-09-23,14.76,14.96,14.45,14.85,9418200,7.43\n2002-09-20,14.62,14.94,14.52,14.87,12599600,7.43\n2002-09-19,14.75,14.80,14.48,14.58,7355200,7.29\n2002-09-18,14.69,15.09,14.52,15.02,11737200,7.51\n2002-09-17,14.57,15.03,14.57,14.80,15285600,7.40\n2002-09-16,14.14,14.61,14.12,14.50,10237200,7.25\n2002-09-13,14.13,14.34,14.05,14.17,10105400,7.09\n2002-09-12,14.20,14.51,14.12,14.14,9636800,7.07\n2002-09-11,14.34,14.60,14.15,14.29,7229000,7.14\n2002-09-10,14.41,14.49,14.12,14.33,8909600,7.16\n2002-09-09,14.28,14.53,14.15,14.37,5651600,7.18\n2002-09-06,14.51,14.65,14.23,14.38,6485400,7.19\n2002-09-05,14.22,14.36,14.05,14.18,8077800,7.09\n2002-09-04,14.20,14.78,14.17,14.48,15023600,7.24\n2002-09-03,14.49,14.55,14.05,14.05,9890600,7.03\n2002-08-30,14.73,15.14,14.58,14.75,6911400,7.38\n2002-08-29,14.65,15.08,14.51,14.70,5863200,7.35\n2002-08-28,14.80,15.12,14.65,14.70,8856200,7.35\n2002-08-27,15.71,15.74,14.71,14.85,9365400,7.43\n2002-08-26,15.95,15.95,15.16,15.53,6784600,7.76\n2002-08-23,15.90,15.93,15.45,15.73,5830200,7.86\n2002-08-22,16.20,16.25,15.66,15.97,9225400,7.99\n2002-08-21,16.01,16.24,15.45,16.12,7229600,8.06\n2002-08-20,15.97,16.09,15.53,15.91,6665200,7.95\n2002-08-19,15.78,16.25,15.72,15.98,7734200,7.99\n2002-08-16,15.45,16.10,15.28,15.81,8758000,7.91\n2002-08-15,15.25,15.75,15.01,15.61,11502800,7.80\n2002-08-14,14.67,15.35,14.54,15.17,14253000,7.59\n2002-08-13,14.90,15.21,14.55,14.59,9638200,7.30\n2002-08-12,14.90,15.02,14.69,14.99,6420200,7.49\n2002-08-09,15.25,15.25,14.75,15.00,7347000,7.50\n2002-08-08,14.77,15.38,14.77,15.30,8119600,7.65\n2002-08-07,15.09,15.36,14.35,15.03,11909800,7.51\n2002-08-06,14.21,15.23,14.08,14.74,9716200,7.37\n2002-08-05,14.51,14.70,13.97,13.99,7286600,6.99\n2002-08-02,14.74,15.00,14.25,14.45,6395000,7.22\n2002-08-01,15.11,15.42,14.73,14.80,8177000,7.40\n2002-07-31,15.40,15.42,14.90,15.26,11096400,7.63\n2002-07-30,14.85,15.51,14.56,15.43,12672800,7.72\n2002-07-29,14.48,15.10,14.37,15.02,9820000,7.51\n2002-07-26,14.46,14.53,13.80,14.34,7418000,7.17\n2002-07-25,14.93,14.95,14.01,14.36,17119800,7.18\n2002-07-24,14.33,15.22,14.25,15.20,14521200,7.60\n2002-07-23,14.90,15.13,14.44,14.47,14281800,7.24\n2002-07-22,14.75,15.19,14.61,14.92,15389200,7.46\n2002-07-19,14.70,15.17,14.53,14.96,13757400,7.48\n2002-07-18,15.50,15.56,14.75,14.99,19980800,7.49\n2002-07-17,16.13,16.20,15.19,15.63,43410200,7.82\n2002-07-16,18.15,18.57,17.61,17.86,15956000,8.93\n2002-07-15,17.43,18.60,16.81,18.23,10571200,9.11\n2002-07-12,18.55,18.79,17.26,17.51,15839000,8.76\n2002-07-11,17.26,18.35,16.97,18.30,13345600,9.15\n2002-07-10,17.71,18.17,17.25,17.32,7388600,8.66\n2002-07-09,18.09,18.29,17.46,17.53,8098200,8.77\n2002-07-08,18.52,18.61,17.68,18.01,7543000,9.01\n2002-07-05,17.71,18.75,17.71,18.74,5773200,9.37\n2002-07-03,16.81,17.68,16.75,17.55,7108200,8.77\n2002-07-02,17.03,17.16,16.83,16.94,10899600,8.47\n2002-07-01,17.71,17.88,17.05,17.06,7953200,8.53\n2002-06-28,17.10,17.82,17.00,17.72,9637800,8.86\n2002-06-27,16.79,17.27,16.42,17.06,8987800,8.53\n2002-06-26,16.80,17.29,15.98,16.55,19962600,8.27\n2002-06-25,17.40,17.68,16.86,17.14,10757200,8.57\n2002-06-24,16.77,17.73,16.70,17.27,15426200,8.64\n2002-06-21,16.97,17.49,16.79,16.85,15899200,8.43\n2002-06-20,17.17,17.60,16.85,17.11,14165600,8.56\n2002-06-19,17.37,17.60,16.88,17.12,61052400,8.56\n2002-06-18,20.42,20.59,19.98,20.15,12620000,10.07\n2002-06-17,20.24,20.63,19.85,20.54,11593200,10.27\n2002-06-14,19.24,20.36,18.11,20.10,15175000,10.05\n2002-06-13,20.02,20.05,19.38,19.54,12574400,9.77\n2002-06-12,20.41,20.75,19.94,20.09,18882800,10.05\n2002-06-11,21.64,21.70,20.41,20.46,12482000,10.23\n2002-06-10,21.48,21.84,21.34,21.48,9913400,10.74\n2002-06-07,21.76,21.94,20.93,21.40,21870600,10.70\n2002-06-06,22.96,23.23,22.04,22.16,9285600,11.08\n2002-06-05,22.83,22.83,22.35,22.72,9895800,11.36\n2002-06-04,22.88,23.04,22.18,22.78,12422200,11.39\n2002-06-03,23.39,23.45,22.58,22.91,8396800,11.45\n2002-05-31,24.09,24.25,23.28,23.30,13053400,11.65\n2002-05-30,23.77,24.38,23.51,24.20,7013400,12.10\n2002-05-29,23.92,24.44,23.45,23.98,7921200,11.99\n2002-05-28,23.69,24.20,23.43,23.98,5347000,11.99\n2002-05-24,24.99,24.99,23.96,24.15,5934800,12.07\n2002-05-23,24.45,25.24,24.07,25.18,13192800,12.59\n2002-05-22,23.37,24.37,23.32,24.32,10388400,12.16\n2002-05-21,24.83,25.00,23.40,23.46,10035400,11.73\n2002-05-20,24.57,24.93,24.53,24.74,9639800,12.37\n2002-05-17,25.49,25.78,24.61,25.01,8446200,12.51\n2002-05-16,25.06,25.45,24.75,25.21,8109000,12.60\n2002-05-15,25.37,25.98,24.84,25.28,11993800,12.64\n2002-05-14,24.45,25.68,24.22,25.61,18803800,12.81\n2002-05-13,23.52,24.09,22.94,23.94,9486000,11.97\n2002-05-10,24.29,24.29,22.98,23.32,8407000,11.66\n2002-05-09,24.25,24.35,23.80,24.19,8022000,12.10\n2002-05-08,23.20,24.52,23.04,24.37,15595800,12.19\n2002-05-07,22.94,22.95,22.14,22.47,8669600,11.23\n2002-05-06,23.35,23.50,22.46,22.65,8916600,11.32\n2002-05-03,23.57,24.02,23.43,23.51,8242200,11.76\n2002-05-02,23.81,24.34,23.60,23.69,8548000,11.85\n2002-05-01,24.29,24.29,23.36,23.98,7668000,11.99\n2002-04-30,23.89,24.38,23.75,24.27,10034400,12.14\n2002-04-29,23.16,24.06,23.09,23.96,9724600,11.98\n2002-04-26,24.28,24.37,23.00,23.01,10892200,11.51\n2002-04-25,23.56,24.34,23.55,24.12,6935800,12.06\n2002-04-24,24.30,24.50,23.68,23.77,5016000,11.89\n2002-04-23,24.54,24.78,24.09,24.25,8338200,12.12\n2002-04-22,24.84,24.93,24.23,24.53,9622400,12.27\n2002-04-19,25.49,25.49,24.93,24.98,13407400,12.49\n2002-04-18,25.50,25.52,24.88,25.41,14346800,12.70\n2002-04-17,25.93,26.17,25.38,26.11,14151800,13.06\n2002-04-16,25.15,25.99,25.12,25.74,21949200,12.87\n2002-04-15,25.06,25.15,24.80,25.00,10691800,12.50\n2002-04-12,25.01,25.17,24.57,25.06,11437200,12.53\n2002-04-11,25.03,25.20,24.75,24.86,14544800,12.43\n2002-04-10,24.21,24.95,24.01,24.66,8035000,12.33\n2002-04-09,24.59,25.00,24.01,24.10,6840400,12.05\n2002-04-08,24.16,24.68,23.78,24.56,9339800,12.28\n2002-04-05,24.95,25.19,24.10,24.74,9941000,12.37\n2002-04-04,23.67,25.05,23.67,24.90,12089200,12.45\n2002-04-03,24.05,24.49,23.60,23.75,7661800,11.88\n2002-04-02,24.00,24.30,23.87,24.07,7278400,12.03\n2002-04-01,23.38,24.70,23.28,24.46,7108800,12.23\n2002-03-28,23.70,23.88,23.46,23.67,3873400,11.84\n2002-03-27,23.35,23.72,23.26,23.47,4560800,11.73\n2002-03-26,23.20,23.64,23.00,23.46,9208600,11.73\n2002-03-25,24.07,24.09,23.24,23.35,9386800,11.68\n2002-03-22,24.22,24.56,23.87,24.09,7221200,12.05\n2002-03-21,23.86,24.30,23.26,24.27,22012600,12.14\n2002-03-20,24.66,25.14,24.50,24.92,10511400,12.46\n2002-03-19,24.69,25.30,24.30,24.85,8655200,12.43\n2002-03-18,24.95,25.05,24.32,24.74,10877000,12.37\n2002-03-15,24.46,24.96,24.25,24.95,8603600,12.48\n2002-03-14,24.30,24.60,23.87,24.43,7760600,12.22\n2002-03-13,24.37,24.85,24.15,24.49,7170200,12.24\n2002-03-12,24.51,24.74,24.10,24.72,9073400,12.36\n2002-03-11,24.60,25.14,24.10,25.06,9385200,12.53\n2002-03-08,24.74,25.09,24.30,24.66,9634800,12.33\n2002-03-07,24.06,24.53,23.61,24.38,9223200,12.19\n2002-03-06,23.48,24.34,22.93,24.07,8078800,12.03\n2002-03-05,24.15,24.43,23.40,23.53,9810800,11.77\n2002-03-04,23.26,24.58,22.76,24.29,12437800,12.15\n2002-03-01,21.93,23.50,21.82,23.45,12464000,11.73\n2002-02-28,22.15,22.59,21.35,21.70,16319200,10.85\n2002-02-27,23.94,24.25,20.94,21.96,36791400,10.98\n2002-02-26,23.91,24.37,23.25,23.67,9290400,11.84\n2002-02-25,22.85,24.72,22.36,23.81,15244600,11.90\n2002-02-22,21.66,22.95,21.50,22.74,14517000,11.37\n2002-02-21,22.92,23.00,21.45,21.50,15955400,10.75\n2002-02-20,22.77,23.20,22.35,23.13,10194400,11.56\n2002-02-19,23.76,23.87,22.48,22.62,13937800,11.31\n2002-02-15,24.53,24.98,23.85,23.90,9292400,11.95\n2002-02-14,25.05,25.23,24.38,24.60,9291800,12.30\n2002-02-13,24.73,25.24,24.65,25.01,11174000,12.51\n2002-02-12,24.66,25.04,24.45,24.71,8010000,12.35\n2002-02-11,23.93,25.00,23.74,24.98,14235800,12.49\n2002-02-08,24.40,24.64,23.37,24.03,12690400,12.02\n2002-02-07,24.65,25.29,24.08,24.30,12422600,12.15\n2002-02-06,25.60,25.98,24.15,24.67,21342000,12.34\n2002-02-05,25.09,25.98,25.08,25.45,16317400,12.73\n2002-02-04,24.32,25.52,24.20,25.35,18656200,12.68\n2002-02-01,24.34,24.96,24.34,24.41,14225200,12.20\n2002-01-31,24.16,24.73,24.11,24.72,16730200,12.36\n2002-01-30,23.07,24.14,22.94,24.09,16842000,12.05\n2002-01-29,23.22,23.54,22.85,23.07,8583000,11.53\n2002-01-28,23.40,23.55,22.72,23.27,6658800,11.64\n2002-01-25,22.89,23.42,22.66,23.25,6639800,11.62\n2002-01-24,22.91,23.51,22.90,23.21,12285800,11.60\n2002-01-23,21.80,23.04,21.59,23.02,15831400,11.51\n2002-01-22,22.27,22.37,21.82,21.82,11689800,10.91\n2002-01-18,22.00,22.60,21.96,22.17,12100400,11.09\n2002-01-17,21.96,22.74,21.87,22.48,23592000,11.24\n2002-01-16,21.41,21.41,20.50,20.78,20246200,10.39\n2002-01-15,21.32,21.76,21.21,21.70,10368600,10.85\n2002-01-14,21.01,21.40,20.90,21.15,14857000,10.57\n2002-01-11,21.39,21.84,20.60,21.05,12457200,10.52\n2002-01-10,21.22,21.46,20.25,21.23,16169200,10.61\n2002-01-09,22.80,22.93,21.28,21.65,11708400,10.82\n2002-01-08,22.75,23.05,22.46,22.61,16072800,11.31\n2002-01-07,23.72,24.00,22.75,22.90,15878000,11.45\n2002-01-04,23.34,23.95,22.99,23.69,14642000,11.85\n2002-01-03,23.00,23.75,22.77,23.58,21857400,11.79\n2002-01-02,22.05,23.30,21.96,23.30,18910600,11.65\n2001-12-31,22.51,22.66,21.83,21.90,4920800,10.95\n2001-12-28,21.97,23.00,21.96,22.43,10683000,11.22\n2001-12-27,21.58,22.25,21.58,22.07,6839600,11.03\n2001-12-26,21.35,22.30,21.14,21.49,5228600,10.74\n2001-12-24,20.90,21.45,20.90,21.36,1808200,10.68\n2001-12-21,21.01,21.54,20.80,21.00,9154800,10.50\n2001-12-20,21.40,21.47,20.62,20.67,7888000,10.34\n2001-12-19,20.58,21.68,20.47,21.62,10355600,10.81\n2001-12-18,20.89,21.33,20.22,21.01,8401400,10.51\n2001-12-17,20.40,21.00,20.19,20.62,6204000,10.31\n2001-12-14,20.73,20.83,20.09,20.39,6781600,10.19\n2001-12-13,21.49,21.55,20.50,21.00,7065800,10.50\n2001-12-12,21.87,21.92,21.25,21.49,6873600,10.74\n2001-12-11,22.67,22.85,21.65,21.78,7338400,10.89\n2001-12-10,22.29,22.99,22.23,22.54,6071800,11.27\n2001-12-07,22.46,22.71,22.00,22.54,7268400,11.27\n2001-12-06,23.48,23.50,22.14,22.78,12104800,11.39\n2001-12-05,22.36,24.03,22.17,23.76,20306400,11.88\n2001-12-04,21.05,22.56,20.72,22.40,13586400,11.20\n2001-12-03,21.06,21.28,20.60,21.05,6470200,10.52\n2001-11-30,20.47,21.44,20.25,21.30,10854000,10.65\n2001-11-29,20.60,20.70,20.19,20.42,7241600,10.21\n2001-11-28,20.85,21.21,20.41,20.53,8950400,10.27\n2001-11-27,21.20,21.52,20.50,21.00,9591200,10.50\n2001-11-26,19.94,21.55,19.88,21.37,16453200,10.69\n2001-11-23,19.71,19.95,19.57,19.84,2143000,9.92\n2001-11-21,19.61,19.80,19.26,19.68,7199400,9.84\n2001-11-20,19.82,20.20,19.50,19.53,9878000,9.77\n2001-11-19,19.00,20.05,18.96,20.00,11878200,10.00\n2001-11-16,19.27,19.29,18.40,18.97,8238000,9.48\n2001-11-15,19.45,19.90,19.23,19.45,7608200,9.73\n2001-11-14,19.59,19.90,19.15,19.61,7898200,9.81\n2001-11-13,19.08,19.39,18.71,19.37,8024000,9.69\n2001-11-12,18.66,19.17,17.96,18.75,7196400,9.38\n2001-11-09,18.60,19.25,18.55,18.71,4796200,9.35\n2001-11-08,19.63,19.89,18.57,18.71,12219400,9.35\n2001-11-07,19.46,20.13,19.33,19.59,13678200,9.80\n2001-11-06,18.96,19.62,18.53,19.57,11286400,9.78\n2001-11-05,18.84,19.25,18.61,19.07,8421200,9.53\n2001-11-02,18.52,18.86,18.16,18.57,7043000,9.28\n2001-11-01,17.65,18.78,17.25,18.59,11178400,9.30\n2001-10-31,17.73,18.40,17.44,17.56,9776800,8.78\n2001-10-30,17.38,18.00,17.06,17.60,9884400,8.80\n2001-10-29,18.57,18.67,17.60,17.63,8542200,8.81\n2001-10-26,18.86,19.25,18.62,18.67,9963000,9.34\n2001-10-25,18.44,19.25,18.16,19.19,9105400,9.60\n2001-10-24,18.06,19.09,17.75,18.95,13372400,9.48\n2001-10-23,19.12,19.42,17.87,18.14,24463600,9.07\n2001-10-22,18.21,19.07,18.09,19.02,13997800,9.51\n2001-10-19,17.94,18.40,17.88,18.30,5956800,9.15\n2001-10-18,17.29,18.23,17.29,18.00,21877600,9.00\n2001-10-17,18.34,18.41,16.96,16.99,10197800,8.49\n2001-10-16,18.09,18.20,17.77,18.01,7248200,9.01\n2001-10-15,17.95,18.38,17.95,17.99,11384000,8.99\n2001-10-12,17.31,18.08,16.86,18.01,10279000,9.01\n2001-10-11,16.92,17.74,16.85,17.74,11934400,8.87\n2001-10-10,16.10,16.85,15.95,16.82,10991400,8.41\n2001-10-09,16.05,16.20,15.63,16.00,6215200,8.00\n2001-10-08,15.57,16.35,15.50,16.20,7428000,8.10\n2001-10-05,15.40,16.15,14.99,16.14,12238800,8.07\n2001-10-04,15.35,16.25,14.99,15.88,14325800,7.94\n2001-10-03,14.95,15.36,14.83,14.98,24394400,7.49\n2001-10-02,15.43,15.83,14.88,15.05,8424400,7.53\n2001-10-01,15.49,15.99,15.23,15.54,7436000,7.77\n2001-09-28,15.71,15.91,15.39,15.51,13039600,7.76\n2001-09-27,15.25,15.75,15.20,15.51,11508600,7.76\n2001-09-26,15.81,15.89,14.93,15.15,17635600,7.57\n2001-09-25,16.14,16.22,15.35,15.54,13371600,7.77\n2001-09-24,16.11,16.84,15.95,16.45,10519200,8.23\n2001-09-21,14.80,16.25,14.68,15.73,20375600,7.86\n2001-09-20,16.29,16.95,15.50,15.68,14684800,7.84\n2001-09-19,16.50,17.10,15.60,17.02,13332800,8.51\n2001-09-18,16.90,17.72,16.17,16.28,11682200,8.14\n2001-09-17,16.00,17.07,15.73,16.99,16357400,8.49\n2001-09-10,17.00,17.50,16.92,17.37,11030200,8.69\n2001-09-07,17.50,18.10,17.20,17.28,8636800,8.64\n2001-09-06,18.40,18.93,17.65,17.72,10084600,8.86\n2001-09-05,18.24,18.95,18.12,18.55,12859200,9.27\n2001-09-04,18.50,19.08,18.18,18.25,12436200,9.12\n2001-08-31,17.73,18.60,17.65,18.55,7746600,9.27\n2001-08-30,17.74,18.18,17.28,17.83,13167600,8.91\n2001-08-29,18.44,18.83,17.83,17.83,8570400,8.91\n2001-08-28,18.90,19.14,18.40,18.40,6133400,9.20\n2001-08-27,18.60,19.30,18.16,18.92,6273000,9.46\n2001-08-24,18.00,18.62,17.65,18.57,10369000,9.28\n2001-08-23,18.20,18.34,17.58,17.81,7752800,8.90\n2001-08-22,17.94,18.25,17.61,18.21,6213400,9.10\n2001-08-21,18.14,18.14,17.70,17.92,6632200,8.96\n2001-08-20,18.14,18.23,17.81,18.12,9010800,9.06\n2001-08-17,18.00,18.45,17.99,18.07,7443800,9.03\n2001-08-16,18.27,18.75,17.97,18.65,10289000,9.32\n2001-08-15,18.76,18.94,18.20,18.44,10331400,9.22\n2001-08-14,19.20,19.36,18.67,18.73,8176800,9.36\n2001-08-13,19.10,19.33,18.76,19.09,5285600,9.55\n2001-08-10,19.04,19.32,18.59,19.02,6677200,9.51\n2001-08-09,18.96,19.15,18.72,19.05,7166600,9.52\n2001-08-08,19.26,19.70,18.54,18.90,9863200,9.45\n2001-08-07,19.33,19.67,18.98,19.25,6019600,9.62\n2001-08-06,19.04,19.66,19.00,19.13,3559000,9.56\n2001-08-03,19.89,19.90,19.00,19.50,6644800,9.75\n2001-08-02,19.65,19.87,19.26,19.82,9003200,9.91\n2001-08-01,19.01,19.78,18.95,19.06,10862000,9.53\n2001-07-31,19.27,19.42,18.51,18.79,8393800,9.40\n2001-07-30,19.12,19.36,18.51,18.93,8691400,9.47\n2001-07-27,18.75,19.25,18.50,18.96,11933400,9.48\n2001-07-26,18.48,18.80,17.85,18.59,13183600,9.30\n2001-07-25,19.12,19.30,17.97,18.47,15852800,9.23\n2001-07-24,19.39,19.92,18.73,19.09,12442000,9.55\n2001-07-23,20.09,20.50,19.51,19.54,8620000,9.77\n2001-07-20,19.70,20.06,19.49,19.98,15878000,9.99\n2001-07-19,21.23,21.42,19.75,19.96,30755000,9.98\n2001-07-18,21.78,22.78,20.42,20.79,40607600,10.40\n2001-07-17,23.98,25.22,23.01,25.10,23136800,12.55\n2001-07-16,24.88,25.10,23.91,23.96,9952400,11.98\n2001-07-13,24.13,25.01,23.84,24.85,16240800,12.43\n2001-07-12,23.30,24.81,23.30,24.36,21957200,12.18\n2001-07-11,21.03,22.55,21.00,22.54,16803800,11.27\n2001-07-10,22.95,23.07,20.84,21.14,14116800,10.57\n2001-07-09,22.09,23.00,21.68,22.70,12052400,11.35\n2001-07-06,22.76,22.96,21.72,22.03,10818600,11.02\n2001-07-05,23.60,23.77,23.01,23.19,5439000,11.60\n2001-07-03,23.51,24.18,23.50,23.84,4019400,11.92\n2001-07-02,23.64,24.23,23.14,23.90,8216000,11.95\n2001-06-29,23.66,25.10,23.20,23.25,18406800,11.62\n2001-06-28,23.05,23.91,22.94,23.54,12443200,11.77\n2001-06-27,23.83,24.00,22.50,23.34,13361800,11.67\n2001-06-26,23.34,23.77,23.01,23.75,9742200,11.88\n2001-06-25,22.50,24.00,22.45,23.99,15698200,11.99\n2001-06-22,22.48,23.00,21.76,22.26,10215200,11.13\n2001-06-21,21.55,23.00,21.10,22.49,12190400,11.24\n2001-06-20,20.00,21.85,19.98,21.67,15415000,10.84\n2001-06-19,20.85,21.40,20.01,20.19,11467400,10.10\n2001-06-18,20.41,20.85,20.00,20.33,12354000,10.16\n2001-06-15,20.10,20.75,19.35,20.44,16236600,10.22\n2001-06-14,20.04,20.45,19.77,19.88,10619600,9.94\n2001-06-13,21.42,21.73,20.06,20.47,18267400,10.23\n2001-06-12,19.77,20.69,19.76,20.31,10849800,10.15\n2001-06-11,21.05,21.07,19.95,20.04,10500000,10.02\n2001-06-08,21.65,21.65,20.71,21.32,12236600,10.66\n2001-06-07,20.71,21.70,20.45,21.66,11613600,10.83\n2001-06-06,20.93,20.93,20.33,20.73,7970600,10.36\n2001-06-05,20.80,21.10,20.35,20.94,16849800,10.47\n2001-06-04,21.08,21.11,20.46,20.66,10068600,10.33\n2001-06-01,20.13,21.09,19.98,20.89,16288400,10.44\n2001-05-31,19.80,20.24,19.49,19.95,15817600,9.98\n2001-05-30,20.76,20.76,19.30,19.78,27752800,9.89\n2001-05-29,22.32,22.50,20.81,21.47,18428200,10.73\n2001-05-25,23.20,23.29,22.50,22.76,5669400,11.38\n2001-05-24,23.29,23.30,22.62,23.20,9705600,11.60\n2001-05-23,23.75,23.75,22.86,23.23,10037200,11.61\n2001-05-22,24.00,24.13,23.40,23.50,14747000,11.75\n2001-05-21,23.63,23.91,23.05,23.56,16464200,11.78\n2001-05-18,23.36,23.64,23.12,23.53,5680400,11.77\n2001-05-17,24.23,24.33,23.25,23.55,11861400,11.77\n2001-05-16,23.26,24.50,22.85,24.10,11511800,12.05\n2001-05-15,23.37,25.50,23.04,23.18,8465200,11.59\n2001-05-14,22.89,23.68,22.75,23.29,11043600,11.65\n2001-05-11,23.01,23.49,22.76,22.85,7251600,11.43\n2001-05-10,24.21,24.50,22.95,23.00,10320600,11.50\n2001-05-09,24.14,24.55,23.67,23.98,11603200,11.99\n2001-05-08,25.35,25.45,23.95,24.57,11265600,12.28\n2001-05-07,25.62,25.76,24.84,24.96,9876800,12.48\n2001-05-04,24.24,25.85,23.96,25.75,10037600,12.88\n2001-05-03,25.97,26.25,24.73,24.96,10769400,12.48\n2001-05-02,26.34,26.70,25.76,26.59,13161600,13.30\n2001-05-01,25.41,26.50,25.20,25.93,15259000,12.97\n2001-04-30,26.70,27.12,24.87,25.49,17670600,12.74\n2001-04-27,25.20,26.29,24.75,26.20,16179000,13.10\n2001-04-26,25.17,26.10,24.68,24.69,28560600,12.35\n2001-04-25,24.21,24.86,23.57,24.72,11813600,12.36\n2001-04-24,24.33,24.75,23.51,24.03,13469200,12.02\n2001-04-23,24.34,25.00,24.00,24.25,19340200,12.12\n2001-04-20,24.93,25.63,24.60,25.04,24764400,12.52\n2001-04-19,25.55,25.75,23.60,25.72,66916800,12.86\n2001-04-18,21.57,24.08,21.08,22.79,39315800,11.40\n2001-04-17,21.20,21.21,19.60,20.40,24471400,10.20\n2001-04-16,22.09,22.40,20.86,21.44,10186600,10.72\n2001-04-12,21.42,23.02,21.15,22.42,10676200,11.21\n2001-04-11,22.98,23.00,21.28,21.80,11932000,10.90\n2001-04-10,20.90,22.70,20.78,22.04,16334800,11.02\n2001-04-09,20.69,21.34,20.06,20.54,9520800,10.27\n2001-04-06,20.80,21.04,19.90,20.59,11603200,10.30\n2001-04-05,20.60,22.50,20.00,20.87,15955800,10.44\n2001-04-04,19.76,20.25,18.75,19.50,24481600,9.75\n2001-04-03,21.36,21.40,20.13,20.24,13167400,10.12\n2001-04-02,22.09,22.66,21.40,21.59,12175400,10.80\n2001-03-30,22.55,22.72,21.34,22.07,14298200,11.03\n2001-03-29,21.77,23.45,21.50,22.53,21895200,11.27\n2001-03-28,22.08,22.50,21.50,22.17,20880800,11.09\n2001-03-27,21.94,23.05,21.90,22.87,19422200,11.44\n2001-03-26,23.13,23.75,21.13,21.78,26230400,10.89\n2001-03-23,22.06,23.56,22.00,23.00,33749400,11.50\n2001-03-22,20.37,21.75,20.19,21.62,25839000,10.81\n2001-03-21,19.78,20.87,19.37,20.12,13265400,10.06\n2001-03-20,20.72,20.94,19.69,19.69,17833800,9.85\n2001-03-19,19.75,20.62,19.50,20.56,12722800,10.28\n2001-03-16,19.00,20.31,18.87,19.62,16806600,9.81\n2001-03-15,20.87,21.37,19.69,19.69,18906600,9.85\n2001-03-14,18.50,20.50,18.44,20.44,17065400,10.22\n2001-03-13,18.87,19.56,18.19,19.56,15840600,9.78\n2001-03-12,19.69,19.87,18.12,18.62,13967800,9.31\n2001-03-09,20.62,20.69,20.00,20.25,10685400,10.12\n2001-03-08,20.69,21.12,20.44,20.81,7325600,10.40\n2001-03-07,21.31,21.62,20.75,21.25,14985600,10.62\n2001-03-06,20.72,22.06,20.69,21.50,26144600,10.75\n2001-03-05,19.37,20.50,19.25,20.37,11587600,10.19\n2001-03-02,18.31,20.44,18.25,19.25,14511200,9.62\n2001-03-01,17.81,18.75,17.19,18.75,11803400,9.38\n2001-02-28,19.37,19.44,18.12,18.25,18157600,9.12\n2001-02-27,19.28,19.44,18.69,19.37,12451000,9.69\n2001-02-26,19.06,19.69,18.56,19.50,7380000,9.75\n2001-02-23,18.62,18.87,18.25,18.81,10503800,9.40\n2001-02-22,19.06,19.37,18.00,18.81,15431200,9.40\n2001-02-21,18.25,19.94,18.25,18.87,13947800,9.44\n2001-02-20,19.19,19.44,18.19,18.31,11249600,9.15\n2001-02-16,19.00,19.50,18.75,19.00,9428400,9.50\n2001-02-15,19.69,20.56,19.69,20.06,11123200,10.03\n2001-02-14,19.19,19.62,18.50,19.50,11040000,9.75\n2001-02-13,19.94,20.44,19.00,19.12,8470600,9.56\n2001-02-12,19.06,20.00,18.81,19.69,9795600,9.85\n2001-02-09,20.50,20.81,18.69,19.12,21082600,9.56\n2001-02-08,20.56,21.06,20.19,20.75,21585000,10.38\n2001-02-07,20.66,20.87,19.81,20.75,14071600,10.38\n2001-02-06,20.16,21.39,20.00,21.12,16528400,10.56\n2001-02-05,20.50,20.56,19.75,20.19,10228800,10.10\n2001-02-02,21.12,21.94,20.50,20.62,15263400,10.31\n2001-02-01,20.69,21.50,20.50,21.12,13205400,10.56\n2001-01-31,21.50,22.50,21.44,21.62,26106000,10.81\n2001-01-30,21.56,22.00,20.87,21.75,24734600,10.88\n2001-01-29,19.56,21.75,19.56,21.69,30562800,10.85\n2001-01-26,19.50,19.81,19.06,19.56,17245600,9.78\n2001-01-25,20.56,20.56,19.75,19.94,17495000,9.97\n2001-01-24,20.62,20.69,19.56,20.50,25616200,10.25\n2001-01-23,19.31,20.94,19.06,20.50,31418400,10.25\n2001-01-22,19.06,19.62,18.44,19.25,18551600,9.62\n2001-01-19,19.44,19.56,18.69,19.50,27748200,9.75\n2001-01-18,17.81,18.75,17.62,18.69,43822800,9.35\n2001-01-17,17.56,17.56,16.50,16.81,30037600,8.40\n2001-01-16,17.44,18.25,17.00,17.12,10940000,8.56\n2001-01-12,17.87,18.00,17.06,17.19,15121000,8.60\n2001-01-11,16.25,18.50,16.25,18.00,28707600,9.00\n2001-01-10,16.69,17.00,16.06,16.56,20743400,8.28\n2001-01-09,16.81,17.64,16.56,17.19,21040600,8.60\n2001-01-08,16.94,16.98,15.94,16.56,13350000,8.28\n2001-01-05,16.94,17.37,16.06,16.37,14731000,8.19\n2001-01-04,18.14,18.50,16.81,17.06,26411000,8.53\n2001-01-03,14.50,16.69,14.44,16.37,29181800,8.19\n2001-01-02,14.88,15.25,14.56,14.88,16161800,7.44\n2000-12-29,14.69,15.00,14.50,14.88,22518800,7.44\n2000-12-28,14.38,14.94,14.31,14.81,10910000,7.41\n2000-12-27,14.34,14.81,14.19,14.81,11626000,7.41\n2000-12-26,14.88,15.00,14.25,14.69,7745400,7.34\n2000-12-22,14.13,15.00,14.13,15.00,11369600,7.50\n2000-12-21,14.25,15.00,13.88,14.06,13102600,7.03\n2000-12-20,13.78,14.63,13.63,14.38,20196200,7.19\n2000-12-19,14.38,15.25,14.00,14.00,13367200,7.00\n2000-12-18,14.56,14.63,13.94,14.25,11645000,7.12\n2000-12-15,14.56,14.69,14.00,14.06,18363800,7.03\n2000-12-14,15.03,15.25,14.44,14.44,9406600,7.22\n2000-12-13,15.56,15.56,14.88,15.00,12327200,7.50\n2000-12-12,15.25,16.00,15.00,15.38,13803400,7.69\n2000-12-11,15.19,15.38,14.88,15.19,11884000,7.59\n2000-12-08,14.81,15.31,14.44,15.06,15568200,7.53\n2000-12-07,14.44,14.88,14.00,14.31,14606600,7.16\n2000-12-06,14.63,15.00,14.00,14.31,49092400,7.16\n2000-12-05,16.94,17.44,16.37,17.00,21932200,8.50\n2000-12-04,17.19,17.19,16.44,16.69,13273400,8.35\n2000-12-01,17.00,17.50,16.81,17.06,13783800,8.53\n2000-11-30,16.69,17.00,16.12,16.50,28922200,8.25\n2000-11-29,18.09,18.31,17.25,17.56,17586200,8.78\n2000-11-28,18.69,19.00,17.94,18.03,9618200,9.02\n2000-11-27,19.87,19.94,18.50,18.69,9244000,9.35\n2000-11-24,18.86,19.50,18.81,19.31,5751800,9.65\n2000-11-22,18.81,19.12,18.37,18.50,10029600,9.25\n2000-11-21,19.19,19.50,18.75,18.81,10786200,9.40\n2000-11-20,18.59,19.50,18.25,18.94,14581600,9.47\n2000-11-17,19.19,19.25,18.25,18.50,15943400,9.25\n2000-11-16,19.50,19.81,18.87,19.00,8554000,9.50\n2000-11-15,20.03,20.19,19.25,19.87,10086600,9.94\n2000-11-14,19.94,20.50,19.56,20.25,14611200,10.12\n2000-11-13,18.75,20.00,18.25,19.37,15423200,9.69\n2000-11-10,19.36,19.87,19.06,19.06,15080600,9.53\n2000-11-09,19.87,20.50,19.06,20.19,17035400,10.10\n2000-11-08,21.37,21.44,19.81,20.06,15082800,10.03\n2000-11-07,21.50,21.81,20.81,21.31,10786800,10.65\n2000-11-06,22.44,22.62,20.87,21.44,14060000,10.72\n2000-11-03,23.00,23.00,21.94,22.25,18423400,11.12\n2000-11-02,21.12,22.44,21.06,22.31,21105400,11.15\n2000-11-01,19.44,20.87,19.44,20.50,20553800,10.25\n2000-10-31,19.75,20.25,19.25,19.56,31649000,9.78\n2000-10-30,19.12,19.94,18.75,19.31,22832800,9.65\n2000-10-27,18.87,19.19,17.87,18.56,26594600,9.28\n2000-10-26,18.81,18.87,17.50,18.50,25780600,9.25\n2000-10-25,19.06,19.19,18.44,18.50,23720600,9.25\n2000-10-24,20.69,20.87,18.81,18.87,28736200,9.44\n2000-10-23,20.27,20.56,19.44,20.37,19694000,10.19\n2000-10-20,19.06,20.37,18.94,19.50,28270400,9.75\n2000-10-19,19.16,19.81,18.31,18.94,53818200,9.47\n2000-10-18,19.44,21.06,18.75,20.12,29803800,10.06\n2000-10-17,21.69,21.94,19.69,20.12,21495600,10.06\n2000-10-16,22.31,23.25,21.37,21.50,29298800,10.75\n2000-10-13,20.25,22.12,20.00,22.06,44564000,11.03\n2000-10-12,20.31,20.81,19.50,20.00,42548200,10.00\n2000-10-11,20.12,21.00,19.12,19.62,42801200,9.81\n2000-10-10,21.62,22.44,20.50,20.87,24683400,10.44\n2000-10-09,22.62,22.87,21.12,21.75,21342600,10.88\n2000-10-06,22.69,22.94,21.00,22.19,21881000,11.10\n2000-10-05,23.50,24.50,22.00,22.06,31189400,11.03\n2000-10-04,22.37,23.75,21.87,23.62,52368200,11.81\n2000-10-03,24.94,25.00,22.19,22.31,72795600,11.15\n2000-10-02,26.69,26.75,23.50,24.25,86610600,12.12\n2000-09-29,28.19,29.00,25.37,25.75,265069000,12.88\n2000-09-28,49.31,53.81,48.13,53.50,34988200,26.75\n2000-09-27,51.75,52.75,48.25,48.94,14370000,24.47\n2000-09-26,53.31,54.75,51.38,51.44,10396600,25.72\n2000-09-25,52.75,55.50,52.06,53.50,15564000,26.75\n2000-09-22,50.31,52.44,50.00,52.19,25961200,26.09\n2000-09-21,58.50,59.63,55.25,56.69,18238400,28.34\n2000-09-20,59.41,61.44,58.56,61.05,8121600,30.52\n2000-09-19,59.75,60.50,58.56,59.94,9706200,29.97\n2000-09-18,55.25,60.75,55.06,60.66,15163200,30.33\n2000-09-15,57.75,58.19,54.25,55.23,14095400,27.61\n2000-09-14,58.56,59.63,56.81,56.86,15241800,28.43\n2000-09-13,56.75,59.50,56.75,58.00,10932600,29.00\n2000-09-12,57.34,60.06,57.00,57.75,6722200,28.88\n2000-09-11,58.69,60.38,58.13,58.44,6699000,29.22\n2000-09-08,61.63,61.63,58.50,58.88,6984400,29.44\n2000-09-07,59.13,62.56,58.25,62.00,7770400,31.00\n2000-09-06,61.38,62.38,57.75,58.44,12700400,29.22\n2000-09-05,62.66,64.12,62.25,62.44,10669000,31.22\n2000-09-01,61.31,63.63,61.13,63.44,9181800,31.72\n2000-08-31,58.97,61.50,58.94,60.94,14988800,30.47\n2000-08-30,59.00,60.00,58.70,59.50,10199600,29.75\n2000-08-29,57.88,59.44,57.69,59.19,9546200,29.59\n2000-08-28,57.25,59.00,57.06,58.06,12822600,29.03\n2000-08-25,56.50,57.50,56.38,56.81,11947800,28.41\n2000-08-24,54.67,56.63,53.38,56.11,11109400,28.06\n2000-08-23,51.47,54.75,51.06,54.31,8470400,27.16\n2000-08-22,50.63,52.81,50.38,51.69,9889000,25.84\n2000-08-21,50.25,51.56,49.63,50.50,4803800,25.25\n2000-08-18,51.38,51.81,49.88,50.00,6798800,25.00\n2000-08-17,48.38,52.44,48.31,51.44,9683400,25.72\n2000-08-16,46.88,49.00,46.81,48.50,5137600,24.25\n2000-08-15,47.25,47.94,46.50,46.69,4089000,23.34\n2000-08-14,47.59,47.69,46.31,47.06,5603400,23.53\n2000-08-11,46.84,48.00,45.56,47.69,8503200,23.84\n2000-08-10,48.00,48.44,47.38,47.56,8995400,23.78\n2000-08-09,48.13,48.44,47.25,47.50,13569000,23.75\n2000-08-08,47.94,48.00,46.31,46.75,6315400,23.38\n2000-08-07,47.88,49.06,47.19,47.94,6697200,23.97\n2000-08-04,49.47,51.25,46.31,47.38,9406800,23.69\n2000-08-03,45.56,48.06,44.25,48.00,12150000,24.00\n2000-08-02,49.00,49.94,47.19,47.25,5808800,23.62\n2000-08-01,50.31,51.16,49.25,49.31,4904600,24.66\n2000-07-31,49.16,51.63,48.75,50.81,5550000,25.41\n2000-07-28,52.28,52.50,46.88,48.31,8505400,24.16\n2000-07-27,50.00,53.25,49.88,52.00,10543800,26.00\n2000-07-26,49.84,51.25,49.25,50.06,7526200,25.03\n2000-07-25,50.31,50.63,49.06,50.06,7567200,25.03\n2000-07-24,52.56,52.88,47.50,48.69,14720600,24.34\n2000-07-21,54.36,55.63,52.94,53.56,7013200,26.78\n2000-07-20,55.00,57.06,54.13,55.13,16631800,27.57\n2000-07-19,55.19,56.81,51.75,52.69,16359600,26.34\n2000-07-18,58.50,58.88,56.88,57.25,11378200,28.62\n2000-07-17,58.25,58.81,57.13,58.31,9289000,29.16\n2000-07-14,57.13,59.00,56.88,57.69,6804400,28.84\n2000-07-13,58.50,60.63,54.75,56.50,15925600,28.25\n2000-07-12,58.13,58.94,56.38,58.88,8057600,29.44\n2000-07-11,57.00,59.25,55.44,56.94,12783200,28.47\n2000-07-10,54.09,58.25,53.75,57.13,14211000,28.57\n2000-07-07,52.59,54.81,52.13,54.44,9422600,27.22\n2000-07-06,52.50,52.94,49.63,51.81,11063800,25.91\n2000-07-05,53.25,55.19,50.75,51.63,9478800,25.82\n2000-07-03,52.13,54.31,52.13,53.31,2535000,26.66\n2000-06-30,52.81,54.94,51.69,52.38,11550000,26.19\n2000-06-29,53.06,53.94,51.06,51.25,7281200,25.62\n2000-06-28,53.31,55.38,51.50,54.44,10235000,27.22\n2000-06-27,53.78,55.50,51.63,51.75,7270600,25.88\n2000-06-26,52.50,54.75,52.13,54.13,6631000,27.07\n2000-06-23,53.78,54.63,50.81,51.69,7320400,25.84\n2000-06-22,55.75,57.63,53.56,53.75,16706200,26.88\n2000-06-21,50.50,56.94,50.31,55.63,17500000,27.82\n2000-06-20,98.50,103.94,98.37,101.25,17922000,25.31\n2000-06-19,90.56,97.87,89.81,96.62,14089200,24.16\n2000-06-16,93.50,93.75,89.06,91.19,10842400,22.80\n2000-06-15,91.25,93.37,89.00,92.37,8898800,23.09\n2000-06-14,94.69,96.25,90.12,90.44,9925200,22.61\n2000-06-13,91.19,94.69,88.19,94.50,12570000,23.62\n2000-06-12,96.37,96.44,90.87,91.19,10374400,22.80\n2000-06-09,96.75,97.94,94.37,95.75,9020000,23.94\n2000-06-08,97.62,98.50,93.12,94.81,8540800,23.70\n2000-06-07,93.62,97.00,91.62,96.56,12056800,24.14\n2000-06-06,91.97,96.75,90.31,92.87,18771200,23.22\n2000-06-05,93.31,95.25,89.69,91.31,11582000,22.83\n2000-06-02,93.75,99.75,89.00,92.56,28336400,23.14\n2000-06-01,81.75,89.56,80.37,89.12,32280000,22.28\n2000-05-31,86.87,91.25,83.81,84.00,15483600,21.00\n2000-05-30,87.62,88.12,81.75,87.56,25481200,21.89\n2000-05-26,88.00,89.87,85.25,86.37,6486400,21.59\n2000-05-25,88.50,92.66,86.00,87.27,14530800,21.82\n2000-05-24,86.19,89.75,83.00,87.69,24248000,21.92\n2000-05-23,90.50,93.37,85.62,85.81,18488000,21.45\n2000-05-22,93.75,93.75,86.00,89.94,26995200,22.49\n2000-05-19,99.25,99.25,93.37,94.00,26459200,23.50\n2000-05-18,103.00,104.94,100.62,100.75,13365600,25.19\n2000-05-17,103.62,103.69,100.37,101.37,14227600,25.34\n2000-05-16,104.52,109.06,102.75,105.69,15736400,26.42\n2000-05-15,108.06,108.06,100.12,101.00,24252000,25.25\n2000-05-12,106.00,110.50,104.77,107.62,10962000,26.91\n2000-05-11,101.37,104.25,99.00,102.81,17852400,25.70\n2000-05-10,104.06,105.00,98.75,99.31,19127600,24.83\n2000-05-09,110.31,111.25,104.87,105.44,11685600,26.36\n2000-05-08,112.09,113.69,110.00,110.12,6605600,27.53\n2000-05-05,110.81,114.75,110.72,113.12,10160000,28.28\n2000-05-04,115.12,115.25,110.56,110.69,14284400,27.67\n2000-05-03,118.94,121.25,111.62,115.06,17500000,28.76\n2000-05-02,123.25,126.25,117.50,117.87,8446400,29.47\n2000-05-01,124.87,125.12,121.87,124.31,8100000,31.08\n2000-04-28,127.12,127.50,121.31,124.06,8932400,31.01\n2000-04-27,117.19,127.00,116.58,126.75,11678000,31.69\n2000-04-26,126.62,128.00,120.00,121.31,13117600,30.33\n2000-04-25,122.12,128.75,122.06,128.31,14002400,32.08\n2000-04-24,115.00,120.50,114.75,120.50,15845600,30.12\n2000-04-20,123.69,124.75,117.06,118.87,25806800,29.72\n2000-04-19,126.19,130.25,119.75,121.12,18586400,30.28\n2000-04-18,123.50,126.87,119.37,126.87,13962400,31.72\n2000-04-17,109.50,123.94,109.06,123.87,14642400,30.97\n2000-04-14,109.31,118.00,109.00,111.87,23845600,27.97\n2000-04-13,111.50,120.00,108.50,113.81,18923600,28.45\n2000-04-12,119.00,119.00,104.87,109.25,33618800,27.31\n2000-04-11,123.50,124.87,118.06,119.44,19368000,29.86\n2000-04-10,131.69,132.75,124.75,125.00,7592400,31.25\n2000-04-07,127.25,131.88,125.50,131.75,8668800,32.94\n2000-04-06,130.63,134.50,123.25,125.19,9290800,31.30\n2000-04-05,126.47,132.88,124.00,130.38,16359200,32.60\n2000-04-04,132.63,133.00,116.75,127.31,23596400,31.83\n2000-04-03,135.50,139.50,129.44,133.31,11742400,33.33\n2000-03-31,127.44,137.25,126.00,135.81,14457600,33.95\n2000-03-30,133.56,137.69,125.44,125.75,14800000,31.44\n2000-03-29,139.38,139.44,133.83,135.94,8568800,33.99\n2000-03-28,137.25,142.00,137.13,139.13,7253600,34.78\n2000-03-27,137.63,144.75,136.88,139.56,9976800,34.89\n2000-03-24,142.44,143.94,135.50,138.69,15962000,34.67\n2000-03-23,142.00,150.38,140.00,141.31,20098000,35.33\n2000-03-22,132.78,144.38,131.56,144.19,20288800,36.05\n2000-03-21,122.56,136.75,121.62,134.94,18729200,33.74\n2000-03-20,123.50,126.25,122.37,123.00,7316400,30.75\n2000-03-17,120.12,125.00,119.62,125.00,10902400,31.25\n2000-03-16,117.31,122.00,114.50,121.56,13516800,30.39\n2000-03-15,115.62,120.25,114.12,116.25,15845200,29.06\n2000-03-14,121.22,124.25,114.00,114.25,15321200,28.56\n2000-03-13,122.12,126.50,119.50,121.31,10864400,30.33\n2000-03-10,121.69,127.94,121.00,125.75,8900800,31.44\n2000-03-09,120.87,125.00,118.25,122.25,9884400,30.56\n2000-03-08,122.87,123.94,118.56,122.00,9690800,30.50\n2000-03-07,126.44,127.44,121.12,122.87,9767600,30.72\n2000-03-06,126.00,129.13,125.00,125.69,7520000,31.42\n2000-03-03,124.87,128.23,120.00,128.00,11565200,32.00\n2000-03-02,127.00,127.94,120.69,122.00,11136800,30.50\n2000-03-01,118.56,132.06,118.50,130.31,38478000,32.58\n2000-02-29,113.56,117.25,112.56,114.62,13186800,28.66\n2000-02-28,110.12,115.00,108.37,113.25,11729200,28.31\n2000-02-25,114.81,117.00,110.12,110.37,8908000,27.59\n2000-02-24,117.31,119.12,111.75,115.20,13446400,28.80\n2000-02-23,113.23,119.00,111.00,116.25,16905600,29.06\n2000-02-22,110.12,116.94,106.69,113.81,15083200,28.45\n2000-02-18,114.62,115.37,110.87,111.25,8346800,27.81\n2000-02-17,115.19,115.50,113.12,114.87,10350000,28.72\n2000-02-16,117.75,118.12,112.12,114.12,13525200,28.53\n2000-02-15,115.25,119.94,115.19,119.00,17363600,29.75\n2000-02-14,109.31,115.87,108.62,115.81,13130000,28.95\n2000-02-11,113.62,114.12,108.25,108.75,7592000,27.19\n2000-02-10,112.87,113.87,110.00,113.50,10832400,28.38\n2000-02-09,114.12,117.12,112.44,112.62,10698000,28.16\n2000-02-08,114.00,116.12,111.25,114.87,14613600,28.72\n2000-02-07,108.00,114.25,105.94,114.06,15770800,28.51\n2000-02-04,103.94,110.00,103.62,108.00,15206800,27.00\n2000-02-03,100.31,104.25,100.25,103.31,16977600,25.83\n2000-02-02,100.75,102.12,97.00,98.81,16588800,24.70\n2000-02-01,104.00,105.00,100.00,100.25,11380000,25.06\n2000-01-31,101.00,103.87,94.50,103.75,25071200,25.94\n2000-01-28,108.19,110.87,100.62,101.62,15142000,25.41\n2000-01-27,108.81,113.00,107.00,110.00,12163600,27.50\n2000-01-26,110.00,114.19,109.75,110.19,13131200,27.55\n2000-01-25,105.00,113.12,102.37,112.25,17775200,28.06\n2000-01-24,108.44,112.75,105.12,106.25,15760000,26.56\n2000-01-21,114.25,114.25,110.19,111.31,17729200,27.83\n2000-01-20,115.50,121.50,113.50,113.50,65418800,28.38\n2000-01-19,105.62,108.75,103.37,106.56,21358000,26.64\n2000-01-18,101.00,106.00,100.44,103.94,16421200,25.99\n2000-01-14,100.00,102.25,99.37,100.44,13954400,25.11\n2000-01-13,94.48,98.75,92.50,96.75,36882400,24.19\n2000-01-12,95.00,95.50,86.50,87.19,34870800,21.80\n2000-01-11,95.94,99.37,90.50,92.75,15775200,23.19\n2000-01-10,102.00,102.25,94.75,97.75,18059200,24.44\n2000-01-07,96.50,101.00,95.50,99.50,16463200,24.88\n2000-01-06,106.12,107.00,95.00,95.00,27443200,23.75\n2000-01-05,103.75,110.56,103.00,104.00,27818000,26.00\n2000-01-04,108.25,110.62,101.19,102.50,18310000,25.62\n2000-01-03,104.87,112.50,101.69,111.94,19144400,27.99\n1999-12-31,100.94,102.87,99.50,102.81,5856400,25.70\n1999-12-30,102.19,104.12,99.62,100.31,7419200,25.08\n1999-12-29,96.81,102.19,95.50,100.69,10161200,25.17\n1999-12-28,99.12,99.62,95.00,98.19,8843200,24.55\n1999-12-27,104.37,104.44,99.25,99.31,6022000,24.83\n1999-12-23,101.81,104.25,101.06,103.50,8218800,25.88\n1999-12-22,102.87,104.56,98.75,99.94,11682000,24.99\n1999-12-21,98.19,103.06,97.94,102.50,11000000,25.62\n1999-12-20,99.56,99.62,96.62,98.00,10155200,24.50\n1999-12-17,100.87,102.00,98.50,100.00,17700800,25.00\n1999-12-16,98.00,98.37,94.00,98.31,16568000,24.58\n1999-12-15,93.25,97.25,91.06,97.00,22254400,24.25\n1999-12-14,98.37,99.75,94.75,94.87,15570800,23.72\n1999-12-13,102.39,102.50,98.94,99.00,18931200,24.75\n1999-12-10,105.31,109.25,99.00,103.00,22786800,25.75\n1999-12-09,111.00,111.00,100.87,105.25,30555600,26.31\n1999-12-08,116.25,117.87,109.50,110.06,14730800,27.51\n1999-12-07,116.56,118.00,114.00,117.81,15901200,29.45\n1999-12-06,114.56,117.31,111.44,116.00,16688000,29.00\n1999-12-03,112.19,115.56,111.87,115.00,23151200,28.75\n1999-12-02,103.12,110.62,101.75,110.19,20275600,27.55\n1999-12-01,101.00,104.50,100.06,103.06,22098000,25.76\n1999-11-30,98.12,103.75,97.37,97.87,30132400,24.47\n1999-11-29,94.25,99.75,93.25,94.56,16586800,23.64\n1999-11-26,94.75,95.50,94.12,95.06,4737600,23.76\n1999-11-24,93.00,95.00,91.69,94.69,7683600,23.67\n1999-11-23,91.75,95.25,88.50,92.81,19406400,23.20\n1999-11-22,91.75,91.75,89.25,90.62,7242400,22.66\n1999-11-19,89.50,92.87,88.06,92.44,11162000,23.11\n1999-11-18,91.06,91.12,88.44,89.62,13043600,22.41\n1999-11-17,90.69,94.75,90.00,90.25,13032000,22.56\n1999-11-16,90.00,91.75,88.50,91.19,8370000,22.80\n1999-11-15,89.62,92.87,88.50,89.44,9283600,22.36\n1999-11-12,91.94,92.00,87.37,90.62,9970000,22.66\n1999-11-11,91.59,92.62,89.87,92.25,9660000,23.06\n1999-11-10,88.25,93.25,88.12,91.44,20661200,22.86\n1999-11-09,94.37,94.50,88.00,89.62,28910000,22.41\n1999-11-08,87.75,97.73,86.75,96.37,33962400,24.09\n1999-11-05,84.62,88.37,84.00,88.31,14889200,22.08\n1999-11-04,82.06,85.37,80.62,83.62,13549200,20.91\n1999-11-03,81.62,83.25,81.00,81.50,11736800,20.38\n1999-11-02,78.00,81.69,77.31,80.25,14268800,20.06\n1999-11-01,80.00,80.69,77.37,77.62,9965600,19.41\n1999-10-29,78.81,81.06,78.81,80.12,18680800,20.03\n1999-10-28,77.06,79.00,76.06,77.87,18005200,19.47\n1999-10-27,74.37,76.62,73.44,76.37,15837600,19.09\n1999-10-26,74.94,75.50,73.31,75.06,12924400,18.76\n1999-10-25,74.25,76.12,73.75,74.50,11677600,18.62\n1999-10-22,77.12,77.25,73.37,73.94,14995200,18.49\n1999-10-21,72.56,77.06,72.37,76.12,28347600,19.03\n1999-10-20,70.00,75.25,70.00,75.12,38633600,18.78\n1999-10-19,71.62,75.00,68.44,68.50,36521200,17.12\n1999-10-18,73.87,74.25,71.12,73.25,27733600,18.31\n1999-10-15,71.12,75.81,70.19,74.56,41910000,18.64\n1999-10-14,69.25,73.31,69.00,73.19,67822400,18.30\n1999-10-13,66.62,69.50,63.75,64.03,22752000,16.01\n1999-10-12,67.87,69.62,67.00,67.69,20142000,16.92\n1999-10-11,66.00,68.25,66.00,66.69,9418000,16.67\n1999-10-08,66.19,66.31,63.50,65.56,13689200,16.39\n1999-10-07,68.44,68.62,64.87,66.37,21660800,16.59\n1999-10-06,69.37,69.62,67.00,67.19,28726400,16.80\n1999-10-05,65.62,68.12,64.75,67.94,29100800,16.99\n1999-10-04,62.38,64.87,62.38,64.56,16408800,16.14\n1999-10-01,62.13,62.44,59.50,61.72,21977600,15.43\n1999-09-30,59.56,64.19,59.25,63.31,32449200,15.83\n1999-09-29,60.25,61.25,58.00,59.06,23493600,14.77\n1999-09-28,61.50,62.00,57.44,59.63,50542400,14.91\n1999-09-27,66.37,66.75,61.19,61.31,33877600,15.33\n1999-09-24,63.38,67.02,63.00,64.94,42148800,16.24\n1999-09-23,71.12,71.25,63.00,63.31,40853200,15.83\n1999-09-22,69.75,71.62,69.02,70.31,40132000,17.58\n1999-09-21,73.19,73.25,69.00,69.25,119931200,17.31\n1999-09-20,77.00,80.12,76.87,79.06,16326400,19.76\n1999-09-17,77.31,77.75,76.25,76.94,9915600,19.24\n1999-09-16,76.06,78.06,73.87,76.81,15793600,19.20\n1999-09-15,78.87,79.12,75.25,75.37,12843200,18.84\n1999-09-14,74.72,78.50,74.69,77.81,13883200,19.45\n1999-09-13,77.06,77.06,74.81,75.00,9000000,18.75\n1999-09-10,76.00,77.69,74.69,77.44,16398000,19.36\n1999-09-09,75.50,75.94,73.87,75.56,19093600,18.89\n1999-09-08,76.19,77.69,74.50,74.50,27233600,18.62\n1999-09-07,73.75,77.94,73.50,76.37,35177600,19.09\n1999-09-03,71.94,75.25,70.50,73.50,58403600,18.38\n1999-09-02,67.62,71.44,66.87,70.56,31975200,17.64\n1999-09-01,67.00,68.81,66.00,68.62,28168000,17.16\n1999-08-31,62.59,65.87,62.06,65.25,22675200,16.31\n1999-08-30,65.00,65.00,62.00,62.06,12033200,15.52\n1999-08-27,62.75,65.00,62.69,64.75,15980000,16.19\n1999-08-26,61.13,63.13,61.13,62.13,14449200,15.53\n1999-08-25,60.69,61.50,60.13,61.38,10553600,15.35\n1999-08-24,60.38,60.75,59.94,60.38,17948000,15.10\n1999-08-23,59.38,61.38,59.31,60.75,12709200,15.19\n1999-08-20,59.25,59.38,58.19,59.19,11730800,14.80\n1999-08-19,59.81,60.50,58.56,58.75,19645600,14.69\n1999-08-18,60.06,62.00,59.63,60.13,16743200,15.03\n1999-08-17,60.31,60.38,58.94,60.31,11474400,15.08\n1999-08-16,59.81,60.69,59.50,60.50,9896400,15.12\n1999-08-13,60.63,62.00,59.88,60.06,10668800,15.02\n1999-08-12,59.06,61.38,58.63,60.00,23806400,15.00\n1999-08-11,56.00,59.75,55.94,59.69,30374400,14.92\n1999-08-10,54.00,56.00,53.63,55.38,14879200,13.85\n1999-08-09,54.34,55.19,54.25,54.44,8338000,13.61\n1999-08-06,54.06,55.31,53.50,54.13,15575600,13.53\n1999-08-05,53.50,54.88,52.13,54.75,11541200,13.69\n1999-08-04,55.19,55.88,53.25,53.81,13279200,13.45\n1999-08-03,56.75,57.44,53.63,55.25,13176800,13.81\n1999-08-02,55.63,58.00,55.50,55.75,12958000,13.94\n1999-07-30,54.50,56.13,54.50,55.69,13685600,13.92\n1999-07-29,53.38,55.25,53.13,53.88,9860000,13.47\n1999-07-28,53.88,55.38,53.00,54.38,11762000,13.60\n1999-07-27,52.63,53.94,52.50,53.69,14150800,13.42\n1999-07-26,52.88,53.00,50.88,50.94,12555200,12.73\n1999-07-23,52.81,53.75,52.69,53.31,8192000,13.33\n1999-07-22,53.63,53.88,51.13,52.38,14529200,13.10\n1999-07-21,54.06,55.44,52.88,54.06,25653600,13.52\n1999-07-20,54.56,55.50,52.75,52.88,15804400,13.22\n1999-07-19,53.94,55.81,52.31,54.44,20050000,13.61\n1999-07-16,53.63,54.50,53.00,53.06,14705600,13.27\n1999-07-15,55.88,55.94,51.31,53.25,60433600,13.31\n1999-07-14,54.50,56.63,54.50,55.94,22320000,13.98\n1999-07-13,53.50,54.19,52.88,53.69,10136800,13.42\n1999-07-12,55.50,55.63,54.19,54.50,10862000,13.62\n1999-07-09,54.50,55.63,53.00,55.63,21750000,13.91\n1999-07-08,51.13,55.06,50.88,54.50,58058000,13.62\n1999-07-07,47.38,50.75,47.00,49.88,39264400,12.47\n1999-07-06,45.94,47.63,45.81,47.38,16212000,11.85\n1999-07-02,45.53,46.88,45.19,46.31,4426800,11.58\n1999-07-01,46.31,46.56,45.25,45.31,5334400,11.33\n1999-06-30,45.69,46.94,44.94,46.31,12270800,11.58\n1999-06-29,42.72,45.56,42.63,45.38,13599200,11.35\n1999-06-28,42.44,42.94,42.38,42.56,9938800,10.64\n1999-06-25,42.50,42.69,42.06,42.19,10518800,10.55\n1999-06-24,43.63,43.63,42.25,42.31,15498000,10.58\n1999-06-23,45.06,45.09,43.56,43.69,18994400,10.92\n1999-06-22,46.31,46.94,45.38,45.38,5415600,11.35\n1999-06-21,47.00,47.25,46.00,46.50,4842000,11.62\n1999-06-18,45.38,47.25,45.19,47.13,7448000,11.78\n1999-06-17,47.63,48.00,45.75,46.38,8022400,11.60\n1999-06-16,46.38,48.06,46.38,47.94,8056800,11.98\n1999-06-15,45.19,46.75,45.13,46.06,4666400,11.52\n1999-06-14,46.50,46.63,45.13,45.44,5615600,11.36\n1999-06-11,48.13,48.50,46.25,46.44,6613600,11.61\n1999-06-10,47.88,48.25,47.31,48.13,11325200,12.03\n1999-06-09,47.44,48.50,47.44,48.44,12655200,12.11\n1999-06-08,48.75,48.81,47.56,47.69,11203200,11.92\n1999-06-07,48.13,49.00,47.50,48.94,14949200,12.23\n1999-06-04,47.63,48.19,47.25,48.13,13171200,12.03\n1999-06-03,46.88,48.00,46.81,47.44,17450800,11.86\n1999-06-02,44.50,47.94,44.00,46.56,18614400,11.64\n1999-06-01,45.00,45.31,44.38,44.81,16479200,11.20\n1999-05-28,43.31,44.31,43.13,44.06,7196400,11.02\n1999-05-27,43.19,43.75,42.69,43.50,12042400,10.88\n1999-05-26,41.75,44.38,41.25,44.06,15642000,11.02\n1999-05-25,41.56,42.44,40.94,41.50,13095200,10.38\n1999-05-24,43.63,44.31,41.88,41.94,9340800,10.48\n1999-05-21,43.00,44.31,42.56,43.94,16555200,10.98\n1999-05-20,45.44,45.75,42.50,42.50,14940000,10.62\n1999-05-19,45.50,45.75,43.50,45.19,10660000,11.30\n1999-05-18,44.81,46.00,44.38,45.25,14954400,11.31\n1999-05-17,43.75,44.69,43.00,44.38,7531200,11.10\n1999-05-14,45.13,45.81,44.38,44.38,8102000,11.10\n1999-05-13,46.44,46.81,45.50,46.19,10573600,11.55\n1999-05-12,44.88,46.50,44.13,46.50,14129200,11.62\n1999-05-11,44.88,46.19,43.56,44.75,16388800,11.19\n1999-05-10,46.75,46.94,44.63,45.25,14055600,11.31\n1999-05-07,44.63,45.88,42.75,45.88,15528800,11.47\n1999-05-06,46.56,46.88,44.00,44.50,15486400,11.12\n1999-05-05,46.31,47.00,44.63,47.00,20694400,11.75\n1999-05-04,48.25,48.63,46.19,46.50,28980000,11.62\n1999-05-03,46.06,50.00,45.75,49.56,52535600,12.39\n1999-04-30,44.00,47.13,44.00,46.00,52596400,11.50\n1999-04-29,43.25,44.38,41.78,43.00,28206400,10.75\n1999-04-28,44.63,45.69,43.63,44.06,34122000,11.02\n1999-04-27,43.00,45.81,43.00,45.75,75225200,11.44\n1999-04-26,39.50,41.25,39.25,40.94,33152000,10.23\n1999-04-23,36.25,39.44,36.25,39.19,37402400,9.80\n1999-04-22,35.06,36.63,35.06,36.38,26454400,9.10\n1999-04-21,34.00,34.38,33.50,34.38,12566800,8.60\n1999-04-20,33.88,34.75,33.50,34.06,18725600,8.52\n1999-04-19,35.69,36.00,33.50,33.88,32923200,8.47\n1999-04-16,35.88,36.06,35.25,35.44,17945600,8.86\n1999-04-15,35.38,36.19,34.31,35.75,61960000,8.94\n1999-04-14,35.25,37.06,35.00,35.53,24323600,8.88\n1999-04-13,36.31,36.81,34.50,34.63,14732400,8.66\n1999-04-12,35.00,36.88,34.88,36.25,14145600,9.06\n1999-04-09,36.25,37.25,35.94,36.75,9608000,9.19\n1999-04-08,36.88,37.06,36.00,36.88,10600800,9.22\n1999-04-07,38.06,38.25,36.38,37.13,14723200,9.28\n1999-04-06,36.81,38.31,36.81,38.00,22455200,9.50\n1999-04-05,36.00,37.88,36.00,37.06,16474400,9.27\n1999-04-01,36.06,36.69,35.75,36.06,9381200,9.02\n1999-03-31,36.38,37.13,35.88,35.94,15086400,8.98\n1999-03-30,35.00,36.38,35.00,35.88,19806800,8.97\n1999-03-29,33.50,35.44,33.44,35.38,20337600,8.85\n1999-03-26,33.75,33.81,33.00,33.25,9080000,8.31\n1999-03-25,34.38,34.88,33.38,33.81,14286800,8.45\n1999-03-24,33.25,33.75,32.50,33.69,14297600,8.42\n1999-03-23,34.44,34.44,32.75,33.00,14842000,8.25\n1999-03-22,34.00,35.19,32.94,35.06,21200800,8.77\n1999-03-19,35.94,36.00,32.88,33.50,19161200,8.38\n1999-03-18,34.38,35.63,34.25,35.50,8126800,8.88\n1999-03-17,35.94,36.06,33.94,34.06,13084400,8.52\n1999-03-16,35.00,35.56,34.94,35.50,14302000,8.88\n1999-03-15,33.31,35.00,33.25,34.06,12586800,8.52\n1999-03-12,32.31,33.50,32.31,33.19,9700000,8.30\n1999-03-11,32.25,33.88,32.00,32.19,16936800,8.05\n1999-03-10,34.19,34.19,32.44,32.56,19526800,8.14\n1999-03-09,34.31,34.38,33.50,34.13,11427600,8.53\n1999-03-08,33.25,34.69,33.19,34.38,19682000,8.60\n1999-03-05,34.31,34.31,32.38,33.19,16735600,8.30\n1999-03-04,34.50,34.50,32.38,33.44,13137600,8.36\n1999-03-03,34.75,35.13,33.50,34.19,10497600,8.55\n1999-03-02,34.13,35.31,33.75,34.63,24414400,8.66\n1999-03-01,34.81,34.81,33.63,33.75,17435200,8.44\n1999-02-26,36.50,37.00,34.50,34.81,23847600,8.70\n1999-02-25,37.31,37.69,36.50,36.94,9455600,9.23\n1999-02-24,38.81,39.00,37.38,37.44,7620000,9.36\n1999-02-23,38.56,39.56,37.94,38.44,11521200,9.61\n1999-02-22,37.38,38.88,37.25,38.44,10682000,9.61\n1999-02-19,36.25,37.69,36.19,37.19,12938800,9.30\n1999-02-18,37.56,37.88,35.56,36.00,17876400,9.00\n1999-02-17,38.13,38.69,36.94,37.00,10581200,9.25\n1999-02-16,38.88,38.88,37.88,38.31,10723600,9.58\n1999-02-12,39.13,39.13,37.00,37.69,15339200,9.42\n1999-02-11,38.75,39.75,38.56,39.63,20200000,9.91\n1999-02-10,36.88,38.69,36.00,38.31,20135200,9.58\n1999-02-09,37.94,39.06,37.06,37.19,25042000,9.30\n1999-02-08,36.69,37.94,36.25,37.75,16723600,9.44\n1999-02-05,38.25,38.38,35.50,36.31,27778000,9.08\n1999-02-04,40.19,40.25,37.75,37.88,16565600,9.47\n1999-02-03,39.00,40.56,38.75,40.19,12108000,10.05\n1999-02-02,40.38,40.75,39.00,39.19,10975600,9.80\n1999-02-01,41.69,41.94,40.31,40.94,9962000,10.23\n1999-01-29,41.19,41.56,40.00,41.19,8684400,10.30\n1999-01-28,40.88,41.25,40.31,40.88,12015600,10.22\n1999-01-27,41.00,41.38,39.94,40.13,13053200,10.03\n1999-01-26,39.94,40.88,39.63,40.50,20002400,10.12\n1999-01-25,39.25,39.56,38.81,39.38,13763200,9.85\n1999-01-22,37.69,39.50,37.06,38.75,12365200,9.69\n1999-01-21,40.44,40.56,37.50,38.81,21449200,9.70\n1999-01-20,41.06,42.00,40.50,40.56,27806800,10.14\n1999-01-19,41.94,42.31,40.38,40.88,19116400,10.22\n1999-01-15,41.81,42.13,40.00,41.31,35933600,10.33\n1999-01-14,45.50,46.00,41.06,41.38,61570000,10.35\n1999-01-13,42.88,47.31,42.25,46.50,37434400,11.62\n1999-01-12,46.31,46.63,44.13,46.13,29330000,11.53\n1999-01-11,45.75,46.06,44.88,45.88,20054400,11.47\n1999-01-08,46.56,46.88,44.00,45.00,24246400,11.25\n1999-01-07,42.25,45.06,42.13,45.00,51056800,11.25\n1999-01-06,44.13,44.13,41.00,41.75,48165200,10.44\n1999-01-05,41.94,43.94,41.50,43.31,50362000,10.83\n1999-01-04,42.13,42.25,40.00,41.25,34049200,10.31\n1998-12-31,40.50,41.38,39.50,40.94,9716400,10.23\n1998-12-30,40.13,41.13,40.00,40.06,8498000,10.02\n1998-12-29,41.13,41.50,40.25,40.81,13853200,10.20\n1998-12-28,39.00,41.13,39.00,40.88,25917600,10.22\n1998-12-24,39.88,40.00,39.19,39.25,7155200,9.81\n1998-12-23,38.63,40.50,38.38,39.81,44124400,9.95\n1998-12-22,36.38,38.13,36.00,38.00,41111200,9.50\n1998-12-21,35.38,35.63,34.25,35.06,12769200,8.77\n1998-12-18,33.38,35.38,33.25,35.19,28283200,8.80\n1998-12-17,32.94,33.75,32.75,33.44,11812000,8.36\n1998-12-16,33.75,34.19,32.63,32.81,13375200,8.20\n1998-12-15,32.75,33.63,32.75,33.56,9462000,8.39\n1998-12-14,32.88,33.31,32.25,32.50,17925200,8.12\n1998-12-11,32.25,34.00,32.00,33.75,24644400,8.44\n1998-12-10,32.69,32.94,31.87,32.00,13980800,8.00\n1998-12-09,32.69,32.88,31.62,32.00,21184400,8.00\n1998-12-08,33.94,33.94,32.00,32.06,24295200,8.02\n1998-12-07,33.38,33.75,32.75,33.75,20255600,8.44\n1998-12-04,34.31,34.44,32.00,32.75,25765200,8.19\n1998-12-03,36.31,36.50,33.63,33.69,22380800,8.42\n1998-12-02,34.13,36.88,33.50,36.00,34382400,9.00\n1998-12-01,32.00,34.81,31.62,34.13,30941200,8.53\n1998-11-30,34.56,34.81,31.75,31.94,20060800,7.99\n1998-11-27,35.06,35.13,34.75,35.06,5483600,8.77\n1998-11-25,35.88,36.06,34.94,35.13,10855600,8.78\n1998-11-24,36.13,36.75,35.75,35.94,11430800,8.98\n1998-11-23,35.56,36.81,35.19,36.25,20642000,9.06\n1998-11-20,36.44,36.75,34.75,35.31,14268000,8.83\n1998-11-19,35.50,37.19,35.44,35.75,12385200,8.94\n1998-11-18,35.19,36.00,34.88,35.44,11781200,8.86\n1998-11-17,35.75,35.81,34.75,34.81,7529200,8.70\n1998-11-16,35.94,36.75,35.44,36.00,13740800,9.00\n1998-11-13,34.94,36.06,34.69,35.69,28301200,8.92\n1998-11-12,33.13,34.44,32.88,34.00,21261200,8.50\n1998-11-11,35.75,35.81,32.75,33.56,33895200,8.39\n1998-11-10,36.19,36.25,35.00,35.13,31576800,8.78\n1998-11-09,37.69,38.13,35.50,36.63,23622000,9.16\n1998-11-06,37.88,38.25,37.25,38.06,28496800,9.52\n1998-11-05,38.38,39.38,38.06,38.19,21684400,9.55\n1998-11-04,38.56,39.13,38.13,38.69,22438000,9.67\n1998-11-03,37.38,38.25,37.31,37.81,13247600,9.45\n1998-11-02,37.50,37.75,37.25,37.63,9076400,9.41\n1998-10-30,36.81,37.50,36.25,37.13,11358000,9.28\n1998-10-29,36.44,37.44,35.81,36.44,12321200,9.11\n1998-10-28,35.25,37.00,35.13,36.81,13006400,9.20\n1998-10-27,38.00,38.94,35.06,35.25,19233200,8.81\n1998-10-26,36.06,37.75,35.50,37.44,17013600,9.36\n1998-10-23,36.75,36.88,35.13,35.50,12732400,8.88\n1998-10-22,36.88,37.63,36.25,36.75,11343200,9.19\n1998-10-21,36.75,37.44,35.75,37.13,15390000,9.28\n1998-10-20,37.94,38.19,36.00,36.06,13649200,9.02\n1998-10-19,36.69,38.06,35.88,37.50,17010000,9.38\n1998-10-16,37.13,38.06,36.50,36.69,21998000,9.17\n1998-10-15,36.25,37.25,35.50,36.63,30037600,9.16\n1998-10-14,39.75,41.31,36.81,37.38,81445600,9.35\n1998-10-13,38.06,39.19,36.00,38.75,33646400,9.69\n1998-10-12,37.50,38.44,36.56,37.44,22250000,9.36\n1998-10-09,31.75,35.25,30.75,35.13,23880000,8.78\n1998-10-08,31.00,31.19,28.50,30.81,24623200,7.70\n1998-10-07,32.38,33.31,31.87,31.94,16920000,7.99\n1998-10-06,33.69,34.31,32.50,32.56,14281200,8.14\n1998-10-05,34.00,34.56,31.50,32.19,19726800,8.05\n1998-10-02,35.50,36.25,34.13,35.06,16998800,8.77\n1998-10-01,36.75,38.00,35.38,35.69,13234400,8.92\n1998-09-30,38.75,39.25,38.00,38.13,5976800,9.53\n1998-09-29,39.06,40.00,38.13,39.50,10907600,9.88\n1998-09-28,39.75,40.19,38.00,39.06,14501200,9.77\n1998-09-25,38.19,39.19,37.63,38.75,8172000,9.69\n1998-09-24,37.88,39.56,37.75,38.50,17246800,9.62\n1998-09-23,37.25,38.38,36.56,38.31,10284400,9.58\n1998-09-22,37.13,37.63,36.38,37.00,9218800,9.25\n1998-09-21,35.69,36.94,35.31,36.94,10570800,9.23\n1998-09-18,36.06,36.75,35.56,36.75,10904400,9.19\n1998-09-17,36.06,37.13,35.88,36.00,9627600,9.00\n1998-09-16,38.63,38.75,37.00,37.31,9248800,9.33\n1998-09-15,36.75,38.56,36.50,38.19,15492000,9.55\n1998-09-14,38.25,38.81,37.13,37.19,8837600,9.30\n1998-09-11,38.50,39.63,36.88,37.63,12593600,9.41\n1998-09-10,36.25,38.25,35.75,38.13,18826800,9.53\n1998-09-09,38.06,38.13,37.00,37.38,12683200,9.35\n1998-09-08,38.00,38.25,36.75,38.25,14400000,9.56\n1998-09-04,35.50,36.44,33.75,35.13,13493200,8.78\n1998-09-03,35.00,35.13,34.00,34.63,14653200,8.66\n1998-09-02,35.50,37.38,35.25,35.56,30122400,8.89\n1998-09-01,31.37,35.38,30.62,34.13,31060000,8.53\n1998-08-31,34.75,34.88,31.00,31.19,31012400,7.80\n1998-08-28,37.13,38.50,34.13,34.19,33303200,8.55\n1998-08-27,39.25,39.25,35.63,37.50,39813600,9.38\n1998-08-26,39.88,41.13,39.50,40.38,14538000,10.10\n1998-08-25,42.38,42.38,40.31,40.81,17709200,10.20\n1998-08-24,43.44,43.50,40.13,41.19,21810000,10.30\n1998-08-21,40.00,43.56,39.00,43.00,29054400,10.75\n1998-08-20,41.00,41.13,40.25,40.63,14018000,10.16\n1998-08-19,43.50,43.75,41.00,41.00,17377600,10.25\n1998-08-18,42.44,43.38,42.25,42.56,21642000,10.64\n1998-08-17,41.00,42.81,39.88,41.94,33248800,10.48\n1998-08-14,40.69,40.75,39.50,40.50,16110000,10.12\n1998-08-13,39.94,40.75,39.38,39.44,13976800,9.86\n1998-08-12,39.75,40.94,39.48,40.06,24654400,10.02\n1998-08-11,37.75,41.00,37.38,39.00,62860000,9.75\n1998-08-10,36.31,38.06,36.25,37.94,17455600,9.48\n1998-08-07,37.19,37.38,36.00,36.50,10645600,9.12\n1998-08-06,35.06,36.88,34.88,36.88,15678800,9.22\n1998-08-05,33.75,36.00,33.50,36.00,16226800,9.00\n1998-08-04,35.50,36.00,34.00,34.19,10506800,8.55\n1998-08-03,34.25,35.56,33.25,35.13,10786800,8.78\n1998-07-31,36.63,36.75,34.50,34.63,6550800,8.66\n1998-07-30,35.81,36.75,35.50,36.50,12950000,9.12\n1998-07-29,33.75,35.88,33.69,35.13,16006800,8.78\n1998-07-28,34.06,34.63,33.00,33.63,8054400,8.41\n1998-07-27,34.25,34.88,33.25,34.44,7657600,8.61\n1998-07-24,35.38,35.50,33.81,34.69,9693600,8.67\n1998-07-23,34.81,35.63,34.75,34.94,9040800,8.73\n1998-07-22,34.94,35.63,34.25,35.00,10040800,8.75\n1998-07-21,36.13,37.00,35.56,35.63,11772400,8.91\n1998-07-20,36.56,36.63,35.50,36.25,13727600,9.06\n1998-07-17,37.25,37.25,36.19,36.88,22486400,9.22\n1998-07-16,37.88,38.13,35.75,37.50,91497600,9.38\n1998-07-15,33.69,34.69,33.50,34.44,21253600,8.61\n1998-07-14,33.94,34.00,33.13,33.44,19607600,8.36\n1998-07-13,31.94,34.13,31.87,33.94,25566400,8.48\n1998-07-10,32.19,32.63,31.75,32.06,10806800,8.02\n1998-07-09,32.94,33.63,31.44,31.69,20256400,7.92\n1998-07-08,30.75,32.94,30.69,32.56,33334400,8.14\n1998-07-07,30.37,30.87,30.00,30.50,8637600,7.62\n1998-07-06,29.50,30.37,29.12,30.37,9697600,7.59\n1998-07-02,29.69,30.06,29.00,29.00,10650800,7.25\n1998-07-01,28.87,30.00,28.50,29.94,11228800,7.49\n1998-06-30,28.62,28.81,28.12,28.69,4681200,7.17\n1998-06-29,28.25,28.81,28.06,28.69,5943600,7.17\n1998-06-26,28.50,28.62,27.75,28.19,3973200,7.05\n1998-06-25,28.56,28.81,28.31,28.56,6856400,7.14\n1998-06-24,27.75,28.62,27.31,28.25,9788800,7.06\n1998-06-23,27.44,28.12,27.25,27.81,8258800,6.95\n1998-06-22,27.00,27.56,26.75,27.37,4809200,6.84\n1998-06-19,27.37,27.44,26.75,27.06,4931200,6.76\n1998-06-18,27.75,28.06,27.19,27.31,4288800,6.83\n1998-06-17,28.00,28.56,27.94,28.12,6687600,7.03\n1998-06-16,27.69,28.12,27.31,28.00,4649200,7.00\n1998-06-15,27.25,28.25,27.25,27.50,4881200,6.88\n1998-06-12,27.62,28.25,27.37,28.12,8014400,7.03\n1998-06-11,28.19,28.62,27.81,27.81,6451200,6.95\n1998-06-10,28.00,29.00,27.62,28.06,8202000,7.01\n1998-06-09,27.37,28.50,27.37,28.25,9852400,7.06\n1998-06-08,27.00,27.69,26.81,27.25,4523600,6.81\n1998-06-05,26.87,27.25,26.37,26.87,4406800,6.72\n1998-06-04,26.62,26.87,25.81,26.81,5585600,6.70\n1998-06-03,27.12,27.25,26.19,26.31,5196800,6.58\n1998-06-02,26.44,27.31,26.00,26.87,6405600,6.72\n1998-06-01,26.50,27.62,25.62,26.25,11427600,6.56\n1998-05-29,27.50,27.56,26.44,26.62,7751200,6.66\n1998-05-28,26.75,27.87,26.75,27.44,10672000,6.86\n1998-05-27,25.69,26.81,25.62,26.75,13233200,6.69\n1998-05-26,28.06,28.25,26.62,26.69,11143200,6.67\n1998-05-22,28.75,28.75,27.31,27.87,9522000,6.97\n1998-05-21,29.56,29.69,28.62,28.87,4700000,7.22\n1998-05-20,29.62,29.87,28.75,29.56,6810000,7.39\n1998-05-19,28.94,29.44,28.81,29.37,7815200,7.34\n1998-05-18,29.37,29.56,28.37,28.50,8310800,7.12\n1998-05-15,30.06,30.37,29.25,29.56,9743600,7.39\n1998-05-14,30.37,30.44,29.75,30.06,5815600,7.51\n1998-05-13,30.06,30.81,29.62,30.44,11245600,7.61\n1998-05-12,30.56,30.75,29.94,30.12,9212000,7.53\n1998-05-11,30.87,31.62,30.75,30.94,23768000,7.74\n1998-05-08,30.06,30.50,29.94,30.44,9690000,7.61\n1998-05-07,30.56,30.62,29.87,30.19,19761200,7.55\n1998-05-06,29.87,30.44,29.25,30.31,32056400,7.58\n1998-05-05,29.25,29.87,29.12,29.69,14982400,7.42\n1998-05-04,28.87,29.50,28.87,29.06,20419200,7.26\n1998-05-01,27.50,28.25,26.87,28.00,6582000,7.00\n1998-04-30,27.37,27.62,27.06,27.37,6442000,6.84\n1998-04-29,26.94,27.44,26.75,27.00,6774400,6.75\n1998-04-28,27.87,28.00,26.25,26.94,8487600,6.74\n1998-04-27,26.75,27.75,26.75,27.75,14655600,6.94\n1998-04-24,27.75,28.25,27.50,27.94,7708000,6.99\n1998-04-23,27.44,29.00,27.19,27.69,16983200,6.92\n1998-04-22,28.75,29.00,27.50,27.50,10186400,6.88\n1998-04-21,29.06,29.12,28.50,29.00,12446400,7.25\n1998-04-20,27.62,29.50,27.56,29.00,18498800,7.25\n1998-04-17,28.56,28.62,27.69,27.94,21165200,6.99\n1998-04-16,29.25,29.62,28.19,28.62,65642000,7.16\n1998-04-15,27.19,27.50,26.62,27.44,19928800,6.86\n1998-04-14,26.37,27.25,26.37,26.94,11725200,6.74\n1998-04-13,25.62,26.69,25.00,26.44,10305600,6.61\n1998-04-09,25.06,25.87,25.00,25.62,6083600,6.41\n1998-04-08,25.25,25.37,24.69,25.00,8044400,6.25\n1998-04-07,25.81,26.00,24.87,25.50,10461200,6.38\n1998-04-06,27.00,27.00,26.19,26.25,12422000,6.56\n1998-04-03,27.12,27.25,26.81,27.06,7259200,6.76\n1998-04-02,27.31,27.44,26.94,27.31,6950800,6.83\n1998-04-01,27.44,27.81,27.06,27.50,6693600,6.88\n1998-03-31,27.44,27.81,27.25,27.50,9538800,6.88\n1998-03-30,26.75,27.50,26.75,27.44,8972400,6.86\n1998-03-27,26.62,27.31,26.37,26.94,9133200,6.74\n1998-03-26,26.75,27.00,26.44,26.56,7253600,6.64\n1998-03-25,27.62,27.75,26.37,27.16,13854400,6.79\n1998-03-24,26.37,28.00,26.25,28.00,24152000,7.00\n1998-03-23,25.94,26.25,24.62,26.12,14818800,6.53\n1998-03-20,26.69,26.87,26.00,26.37,7704400,6.59\n1998-03-19,26.87,26.94,26.56,26.75,5736800,6.69\n1998-03-18,26.00,26.94,26.00,26.94,9900000,6.74\n1998-03-17,26.50,26.69,25.87,26.34,14658800,6.59\n1998-03-16,27.12,27.25,26.19,26.69,14375600,6.67\n1998-03-13,27.25,27.25,26.25,27.12,20231200,6.78\n1998-03-12,26.12,27.00,25.56,27.00,26598000,6.75\n1998-03-11,25.12,26.19,24.56,26.12,43374400,6.53\n1998-03-10,23.00,24.50,22.94,24.06,25472400,6.01\n1998-03-09,23.75,24.31,22.50,22.75,20540800,5.69\n1998-03-06,23.87,24.50,23.37,24.44,23803600,6.11\n1998-03-05,23.25,24.25,23.12,24.06,24129200,6.01\n1998-03-04,22.87,24.75,22.87,24.44,29212400,6.11\n1998-03-03,21.87,23.19,21.62,23.12,11937600,5.78\n1998-03-02,23.56,23.56,22.25,22.75,14313600,5.69\n1998-02-27,23.31,23.87,22.56,23.62,18578000,5.91\n1998-02-26,22.31,23.56,21.87,23.50,21263200,5.88\n1998-02-25,21.31,22.75,20.94,22.31,25459200,5.58\n1998-02-24,21.31,21.37,20.75,21.31,16322000,5.33\n1998-02-23,20.12,21.62,20.00,21.25,17060800,5.31\n1998-02-20,20.50,20.56,19.81,20.00,11634400,5.00\n1998-02-19,20.87,20.94,20.00,20.44,14292400,5.11\n1998-02-18,19.56,20.75,19.56,20.56,17677600,5.14\n1998-02-17,19.50,19.75,19.50,19.62,6530800,4.91\n1998-02-13,19.19,19.87,19.00,19.50,7444400,4.88\n1998-02-12,19.12,19.44,19.06,19.37,7297600,4.84\n1998-02-11,19.50,19.50,18.87,19.00,7582000,4.75\n1998-02-10,19.12,19.56,19.06,19.44,15090000,4.86\n1998-02-09,18.37,19.50,18.37,19.19,17682000,4.80\n1998-02-06,18.37,18.69,18.25,18.50,7241200,4.62\n1998-02-05,18.25,18.50,18.00,18.31,8526400,4.58\n1998-02-04,18.06,18.50,18.00,18.25,6100000,4.56\n1998-02-03,17.69,18.62,17.69,18.31,14390000,4.58\n1998-02-02,18.50,18.50,17.37,17.69,22752400,4.42\n1998-01-30,18.31,18.87,18.25,18.31,5802400,4.58\n1998-01-29,18.94,19.12,18.50,18.50,7571200,4.62\n1998-01-28,19.19,19.37,18.62,19.19,5418000,4.80\n1998-01-27,19.19,19.69,19.00,19.12,4013200,4.78\n1998-01-26,19.44,19.56,18.81,19.44,5246800,4.86\n1998-01-23,19.37,19.69,19.25,19.50,8331200,4.88\n1998-01-22,18.69,19.75,18.62,19.25,11785200,4.81\n1998-01-21,18.75,19.06,18.56,18.91,6812000,4.73\n1998-01-20,19.06,19.31,18.62,19.06,8642400,4.76\n1998-01-16,19.44,19.44,18.69,18.81,8820000,4.70\n1998-01-15,19.19,19.75,18.62,19.19,19982000,4.80\n1998-01-14,19.87,19.94,19.25,19.75,21048000,4.94\n1998-01-13,18.62,19.62,18.50,19.50,22758800,4.88\n1998-01-12,17.44,18.62,17.12,18.25,18444400,4.56\n1998-01-09,18.12,19.37,17.50,18.19,31675200,4.55\n1998-01-08,17.44,18.62,16.94,18.19,27645600,4.55\n1998-01-07,18.81,19.00,17.31,17.50,37201200,4.38\n1998-01-06,15.94,20.00,14.75,18.94,64737600,4.74\n1998-01-05,16.50,16.56,15.19,15.88,23282000,3.97\n1998-01-02,13.63,16.25,13.50,16.25,25650800,4.06\n1997-12-31,13.13,13.63,12.94,13.13,14531200,3.28\n1997-12-30,13.00,13.44,12.75,13.19,12250800,3.30\n1997-12-29,13.31,13.44,12.88,13.13,9944400,3.28\n1997-12-26,13.06,13.38,13.00,13.31,3860000,3.33\n1997-12-24,13.00,13.25,13.00,13.13,3502000,3.28\n1997-12-23,13.13,13.31,12.94,12.94,16402000,3.23\n1997-12-22,13.88,14.00,13.19,13.31,5704400,3.33\n1997-12-19,13.56,13.88,13.25,13.69,6812000,3.42\n1997-12-18,14.00,14.00,13.75,13.81,7225200,3.45\n1997-12-17,14.31,14.56,13.94,13.94,9494400,3.48\n1997-12-16,14.00,14.38,14.00,14.31,6646400,3.58\n1997-12-15,14.13,14.25,13.75,13.94,5927600,3.48\n1997-12-12,14.75,14.88,14.00,14.13,5742400,3.53\n1997-12-11,14.44,14.56,13.88,14.56,9185600,3.64\n1997-12-10,15.06,15.06,14.50,14.75,6960000,3.69\n1997-12-09,15.50,15.69,15.00,15.25,8680800,3.81\n1997-12-08,15.56,15.75,15.38,15.56,4776800,3.89\n1997-12-05,15.56,16.00,15.56,15.81,7926400,3.95\n1997-12-04,16.00,16.00,15.63,15.63,7135600,3.91\n1997-12-03,16.06,16.12,15.69,15.75,12258800,3.94\n1997-12-02,17.37,17.50,15.88,15.88,14178800,3.97\n1997-12-01,17.69,17.94,17.25,17.75,3135600,4.44\n1997-11-28,17.62,17.87,17.44,17.75,1495600,4.44\n1997-11-26,17.37,17.69,17.25,17.50,2178800,4.38\n1997-11-25,17.69,17.87,16.87,17.37,7346400,4.34\n1997-11-24,17.56,18.00,17.50,17.62,5630800,4.41\n1997-11-21,18.62,18.69,18.00,18.19,3498800,4.55\n1997-11-20,18.19,18.62,18.12,18.50,4587600,4.62\n1997-11-19,17.87,18.31,17.87,18.25,2843600,4.56\n1997-11-18,18.50,18.50,18.06,18.06,5258000,4.51\n1997-11-17,18.87,18.94,18.33,18.50,7323600,4.62\n1997-11-14,18.25,18.50,18.00,18.44,4835600,4.61\n1997-11-13,18.00,18.06,17.50,18.00,9218000,4.50\n1997-11-12,18.06,18.50,17.56,17.62,7448000,4.41\n1997-11-11,19.00,19.00,18.12,18.37,11893600,4.59\n1997-11-10,21.00,21.50,18.50,18.69,49946800,4.67\n1997-11-07,18.87,20.00,18.75,19.75,28423200,4.94\n1997-11-06,18.87,19.50,18.87,19.00,22060800,4.75\n1997-11-05,18.25,18.62,18.06,18.37,13840000,4.59\n1997-11-04,17.75,18.12,17.50,17.94,6033200,4.49\n1997-11-03,17.56,17.75,17.06,17.37,4512000,4.34\n1997-10-31,17.37,17.37,16.62,17.03,9549200,4.26\n1997-10-30,17.06,17.56,16.50,16.50,6764400,4.12\n1997-10-29,18.44,18.50,17.25,17.50,6355200,4.38\n1997-10-28,16.00,18.50,15.88,18.12,12273200,4.53\n1997-10-27,16.75,18.12,16.75,16.75,11764400,4.19\n1997-10-24,18.12,18.37,16.50,16.56,13880000,4.14\n1997-10-23,18.00,18.19,17.75,17.75,6688000,4.44\n1997-10-22,19.06,19.25,18.50,18.56,5421200,4.64\n1997-10-21,18.87,19.31,18.69,19.06,16982000,4.76\n1997-10-20,20.12,20.19,18.62,18.69,14724400,4.67\n1997-10-17,21.12,21.12,19.87,20.12,15682000,5.03\n1997-10-16,21.12,22.06,20.87,21.50,26422000,5.38\n1997-10-15,22.12,24.75,22.12,23.81,28982000,5.95\n1997-10-14,22.69,22.75,22.19,22.69,5923200,5.67\n1997-10-13,22.75,22.87,22.19,22.69,5679200,5.67\n1997-10-10,21.50,22.75,21.50,22.69,9666800,5.67\n1997-10-09,21.25,22.50,21.19,21.75,6696400,5.44\n1997-10-08,21.75,21.81,21.31,21.50,3891200,5.38\n1997-10-07,21.87,22.00,21.81,21.81,3916400,5.45\n1997-10-06,22.19,22.25,21.69,21.94,3338800,5.49\n1997-10-03,22.00,22.25,21.69,22.12,5813200,5.53\n1997-10-02,21.44,22.00,21.37,21.94,4856400,5.49\n1997-10-01,21.69,21.75,21.37,21.53,4670800,5.38\n1997-09-30,22.00,22.31,21.69,21.69,5032000,5.42\n1997-09-29,21.69,22.25,21.56,22.06,5980000,5.51\n1997-09-26,21.50,21.94,21.12,21.31,7440000,5.33\n1997-09-25,21.31,21.75,21.00,21.12,7988000,5.28\n1997-09-24,21.69,21.75,21.37,21.50,7957600,5.38\n1997-09-23,22.25,22.25,21.69,21.75,7163200,5.44\n1997-09-22,22.12,23.06,22.00,22.81,7176400,5.70\n1997-09-19,22.19,22.19,21.75,21.94,3407600,5.49\n1997-09-18,21.50,22.50,21.50,22.31,6042400,5.58\n1997-09-17,22.00,22.00,21.69,21.81,3109200,5.45\n1997-09-16,22.06,22.14,21.75,21.94,4812400,5.49\n1997-09-15,21.87,22.12,21.50,21.50,3473200,5.38\n1997-09-12,22.19,22.25,21.44,22.06,4071200,5.51\n1997-09-11,22.87,23.00,22.06,22.37,7504400,5.59\n1997-09-10,21.75,23.12,21.69,22.94,9803600,5.74\n1997-09-09,21.31,21.87,21.25,21.81,5702000,5.45\n1997-09-08,22.25,22.25,21.44,21.50,6264400,5.38\n1997-09-05,22.62,22.87,22.00,22.19,4883600,5.55\n1997-09-04,22.56,22.87,22.25,22.50,4385600,5.62\n1997-09-03,22.37,23.25,22.31,22.50,10163200,5.62\n1997-09-02,22.00,22.56,21.94,22.37,6646800,5.59\n1997-08-29,21.81,22.00,21.50,21.75,3937600,5.44\n1997-08-28,22.12,22.50,22.00,22.00,3426400,5.50\n1997-08-27,22.37,22.75,21.87,22.69,6813200,5.67\n1997-08-26,22.62,23.00,22.12,22.25,8100800,5.56\n1997-08-25,23.62,23.69,22.94,23.06,4968800,5.76\n1997-08-22,23.44,24.00,23.37,23.62,8135200,5.91\n1997-08-21,24.50,24.69,23.87,24.00,9271200,6.00\n1997-08-20,24.44,25.12,24.19,24.62,11595200,6.16\n1997-08-19,23.69,24.50,23.31,24.44,10331200,6.11\n1997-08-18,23.31,23.75,22.75,23.62,7791200,5.91\n1997-08-15,23.12,23.44,22.81,23.25,9320000,5.81\n1997-08-14,23.62,24.25,22.69,23.00,15536400,5.75\n1997-08-13,22.25,23.87,20.44,23.62,42923600,5.91\n1997-08-12,24.06,24.25,21.87,22.06,37444400,5.51\n1997-08-11,26.31,26.44,23.50,24.56,55411200,6.14\n1997-08-08,27.81,28.37,26.12,26.81,64809200,6.70\n1997-08-07,28.75,29.56,28.37,29.19,134124400,7.30\n1997-08-06,25.25,27.75,25.00,26.31,149671200,6.58\n1997-08-05,19.94,20.00,19.48,19.75,8840800,4.94\n1997-08-04,19.19,19.81,19.19,19.75,21851200,4.94\n1997-08-01,17.62,19.19,17.56,19.19,17217600,4.80\n1997-07-31,17.37,17.75,17.25,17.50,9434400,4.38\n1997-07-30,16.94,17.69,16.75,17.37,13372400,4.34\n1997-07-29,16.44,16.62,16.37,16.50,2558000,4.12\n1997-07-28,16.44,16.50,16.25,16.44,3962000,4.11\n1997-07-25,15.88,16.56,15.75,16.25,7798000,4.06\n1997-07-24,16.12,16.12,15.63,15.81,4772000,3.95\n1997-07-23,16.75,16.87,16.00,16.12,5049200,4.03\n1997-07-22,16.37,16.69,16.31,16.56,8274400,4.14\n1997-07-21,17.56,17.69,16.00,16.16,12695600,4.04\n1997-07-18,17.87,17.94,17.06,17.34,11353600,4.34\n1997-07-17,17.00,18.12,16.44,17.50,26659200,4.38\n1997-07-16,15.81,16.50,15.63,16.44,15947600,4.11\n1997-07-15,15.75,16.00,15.63,15.94,14953200,3.98\n1997-07-14,15.25,15.63,14.88,15.63,14700800,3.91\n1997-07-11,13.38,15.50,13.31,15.19,26252400,3.80\n1997-07-10,12.88,13.38,12.75,13.25,17606400,3.31\n1997-07-09,13.81,13.88,13.63,13.69,5090000,3.42\n1997-07-08,13.88,14.00,13.69,13.75,3427600,3.44\n1997-07-07,13.94,14.25,13.75,13.81,6860000,3.45\n1997-07-03,13.13,13.88,13.00,13.69,6688000,3.42\n1997-07-02,13.25,13.38,13.00,13.06,8931200,3.27\n1997-07-01,13.94,14.00,13.13,13.19,16104400,3.30\n1997-06-30,14.75,14.75,14.00,14.25,6132400,3.56\n1997-06-27,14.69,14.81,14.63,14.69,5642000,3.67\n1997-06-26,15.13,15.13,14.63,14.69,13643600,3.67\n1997-06-25,15.31,15.38,15.00,15.13,7102000,3.78\n1997-06-24,15.44,15.56,15.25,15.31,3974800,3.83\n1997-06-23,15.50,15.63,15.38,15.38,3574800,3.85\n1997-06-20,15.69,15.75,15.50,15.56,3943600,3.89\n1997-06-19,16.00,16.00,15.69,15.75,4323600,3.94\n1997-06-18,16.12,16.25,15.75,15.94,3936400,3.98\n1997-06-17,15.56,16.50,15.50,16.34,5080800,4.09\n1997-06-16,15.88,15.88,15.38,15.50,4800800,3.88\n1997-06-13,16.06,16.12,15.75,15.81,4737600,3.95\n1997-06-12,16.37,16.37,16.00,16.06,2816400,4.01\n1997-06-11,16.31,16.44,16.25,16.31,3766800,4.08\n1997-06-10,16.75,16.75,16.06,16.25,4969200,4.06\n1997-06-09,16.69,16.94,16.62,16.62,2689200,4.16\n1997-06-06,16.62,16.75,16.50,16.75,1893200,4.19\n1997-06-05,16.62,17.12,16.56,16.69,2323200,4.17\n1997-06-04,16.62,16.75,16.50,16.62,2889200,4.16\n1997-06-03,16.75,16.94,16.62,16.69,2335600,4.17\n1997-06-02,17.00,17.00,16.75,16.94,1488000,4.24\n1997-05-30,16.50,17.00,16.37,16.62,6340800,4.16\n1997-05-29,17.12,17.12,16.62,16.62,3976800,4.16\n1997-05-28,17.37,17.50,17.00,17.00,3130000,4.25\n1997-05-27,16.75,17.37,16.75,17.25,2938000,4.31\n1997-05-23,16.62,17.00,16.62,16.87,2413200,4.22\n1997-05-22,16.75,16.87,16.50,16.62,2753600,4.16\n1997-05-21,17.12,17.12,16.50,16.87,4369200,4.22\n1997-05-20,17.00,17.44,16.75,17.25,3046400,4.31\n1997-05-19,17.50,17.62,17.00,17.00,1881200,4.25\n1997-05-16,17.50,17.62,17.25,17.25,3338800,4.31\n1997-05-15,17.75,18.00,17.50,17.75,3544800,4.44\n1997-05-14,17.87,18.00,17.50,17.69,4846800,4.42\n1997-05-13,17.50,17.87,17.00,17.56,7056800,4.39\n1997-05-12,17.25,17.62,17.00,17.56,5898800,4.39\n1997-05-09,17.00,17.50,17.00,17.06,6732000,4.26\n1997-05-08,16.62,17.12,16.50,17.00,2963200,4.25\n1997-05-07,16.87,17.00,16.37,16.50,4101200,4.12\n1997-05-06,17.00,17.12,16.75,16.87,2974800,4.22\n1997-05-05,17.00,17.12,16.75,17.00,3538800,4.25\n1997-05-02,17.00,17.12,16.75,17.00,3643600,4.25\n1997-05-01,16.87,17.12,16.75,17.00,2596800,4.25\n1997-04-30,17.00,17.25,16.75,17.00,9202000,4.25\n1997-04-29,18.00,18.00,17.50,17.69,1853200,4.42\n1997-04-28,17.75,17.87,17.50,17.62,1687600,4.41\n1997-04-25,17.62,17.87,17.37,17.50,3121200,4.38\n1997-04-24,18.50,18.50,17.75,17.87,2696800,4.47\n1997-04-23,18.37,18.50,18.12,18.12,1960800,4.53\n1997-04-22,18.12,18.50,17.87,18.50,3392000,4.62\n1997-04-21,18.62,18.62,18.00,18.00,3197600,4.50\n1997-04-18,19.12,19.12,18.37,18.37,5058000,4.59\n1997-04-17,18.25,19.12,18.12,19.00,7859200,4.75\n1997-04-16,18.62,19.00,18.37,18.56,3101200,4.64\n1997-04-15,19.12,19.25,18.12,18.44,4869200,4.61\n1997-04-14,18.37,18.87,18.00,18.75,4020000,4.69\n1997-04-11,18.87,18.87,18.12,18.25,2842400,4.56\n1997-04-10,19.00,19.12,18.50,18.87,4188000,4.72\n1997-04-09,19.25,19.25,18.87,19.00,8766400,4.75\n1997-04-08,19.62,19.62,18.62,19.12,6923600,4.78\n1997-04-07,19.75,19.87,19.25,19.50,9136800,4.88\n1997-04-04,19.12,19.62,19.00,19.25,16980800,4.81\n1997-04-03,18.50,19.12,18.25,18.87,19603200,4.72\n1997-04-02,17.87,18.06,17.62,18.00,7957600,4.50\n1997-04-01,17.62,17.81,17.37,17.50,7881200,4.38\n1997-03-31,18.62,19.37,17.25,18.25,34658000,4.56\n1997-03-27,17.50,19.25,17.25,18.62,40695200,4.66\n1997-03-26,16.37,16.87,16.25,16.75,3824400,4.19\n1997-03-25,16.62,16.62,16.08,16.50,4031200,4.12\n1997-03-24,16.50,16.62,16.25,16.50,2556800,4.12\n1997-03-21,17.50,17.50,16.37,16.62,4892400,4.16\n1997-03-20,16.00,17.50,15.88,17.25,11324400,4.31\n1997-03-19,16.37,16.37,15.88,16.12,7457600,4.03\n1997-03-18,16.37,16.50,16.12,16.25,4548800,4.06\n1997-03-17,16.25,16.50,16.00,16.50,6886400,4.12\n1997-03-14,16.37,16.75,16.25,16.56,8245600,4.14\n1997-03-13,16.37,16.37,16.12,16.37,3772000,4.09\n1997-03-12,16.25,16.75,16.12,16.25,2544400,4.06\n1997-03-11,16.62,16.62,16.00,16.37,3539200,4.09\n1997-03-10,16.62,16.75,16.44,16.62,3554800,4.16\n1997-03-07,16.75,16.75,16.37,16.50,2523200,4.12\n1997-03-06,17.00,17.00,16.50,16.62,4172000,4.16\n1997-03-05,16.62,17.00,16.50,17.00,3453600,4.25\n1997-03-04,16.25,16.50,16.00,16.50,3688800,4.12\n1997-03-03,16.50,16.50,16.00,16.12,4670000,4.03\n1997-02-28,16.87,16.87,16.25,16.25,4371200,4.06\n1997-02-27,17.00,17.12,16.75,17.00,3700000,4.25\n1997-02-26,17.00,17.12,16.75,17.12,3687600,4.28\n1997-02-25,17.00,17.37,16.87,16.87,4938000,4.22\n1997-02-24,16.25,16.87,16.25,16.62,4222000,4.16\n1997-02-21,16.87,17.00,16.00,16.37,7549200,4.09\n1997-02-20,17.62,17.62,17.00,17.00,4474800,4.25\n1997-02-19,17.87,17.87,17.12,17.62,8627600,4.41\n1997-02-18,16.62,17.87,16.25,17.87,13171200,4.47\n1997-02-14,16.25,16.37,16.00,16.31,8492000,4.08\n1997-02-13,15.75,16.12,15.50,16.12,7013200,4.03\n1997-02-12,15.75,15.88,15.50,15.75,6303600,3.94\n1997-02-11,15.88,16.00,15.50,15.69,5004400,3.92\n1997-02-10,16.12,16.12,15.63,15.63,6633600,3.91\n1997-02-07,16.50,16.50,15.75,15.81,8403600,3.95\n1997-02-06,15.25,16.12,15.25,16.00,14283600,4.00\n1997-02-05,15.25,15.63,15.25,15.25,14093600,3.81\n1997-02-04,16.25,16.37,15.13,15.38,25458000,3.85\n1997-02-03,16.87,17.00,16.25,16.31,13162000,4.08\n1997-01-31,16.62,16.62,16.50,16.62,7135200,4.16\n1997-01-30,16.75,16.75,16.50,16.75,5018800,4.19\n1997-01-29,16.62,16.75,16.50,16.62,5428000,4.16\n1997-01-28,17.00,17.00,16.50,16.62,7520000,4.16\n1997-01-27,17.12,17.25,16.62,16.62,7646800,4.16\n1997-01-24,17.25,17.25,16.87,16.87,6726800,4.22\n1997-01-23,17.25,17.37,17.12,17.25,6175200,4.31\n1997-01-22,17.37,17.50,17.00,17.19,7356800,4.30\n1997-01-21,17.00,17.25,16.87,17.25,10179200,4.31\n1997-01-20,16.87,17.12,16.75,16.94,10423600,4.24\n1997-01-17,16.75,17.12,16.62,16.75,11619200,4.19\n1997-01-16,17.12,17.12,16.62,16.75,23983600,4.19\n1997-01-15,18.00,18.00,17.12,17.25,15483200,4.31\n1997-01-14,18.37,18.37,17.75,17.87,9143200,4.47\n1997-01-13,18.50,18.50,18.12,18.12,10942000,4.53\n1997-01-10,17.62,18.25,17.62,18.25,12651200,4.56\n1997-01-09,17.75,17.87,17.50,17.75,15970000,4.44\n1997-01-08,18.25,18.37,17.37,17.62,39296400,4.41\n1997-01-07,18.12,18.25,17.50,17.50,34896400,4.38\n1997-01-06,17.62,18.34,17.25,17.87,67246400,4.47\n1997-01-03,21.12,22.25,21.00,21.75,4295600,5.44\n1997-01-02,21.12,21.25,20.75,21.00,5128800,5.25\n1996-12-31,21.37,21.50,20.75,20.87,13719200,5.22\n1996-12-30,23.12,23.25,21.75,21.75,9366800,5.44\n1996-12-27,22.87,23.75,22.87,23.12,4900000,5.78\n1996-12-26,23.25,23.25,22.87,23.00,3049200,5.75\n1996-12-24,23.25,23.37,22.87,23.12,2067600,5.78\n1996-12-23,24.00,24.25,23.25,23.25,11883600,5.81\n1996-12-20,22.50,23.62,21.37,23.50,19535600,5.88\n1996-12-19,23.00,23.25,22.25,22.25,4893600,5.56\n1996-12-18,22.75,23.12,22.62,23.12,7326400,5.78\n1996-12-17,22.37,22.50,22.25,22.50,5625200,5.62\n1996-12-16,23.50,23.50,22.50,22.62,5335600,5.66\n1996-12-13,23.75,23.87,23.25,23.25,3194400,5.81\n1996-12-12,24.12,24.25,23.87,23.87,3122400,5.97\n1996-12-11,23.75,24.25,23.75,24.00,5853600,6.00\n1996-12-10,24.87,25.00,24.25,24.50,6593600,6.12\n1996-12-09,25.25,25.37,24.81,25.00,5680800,6.25\n1996-12-06,24.37,25.37,24.00,25.12,8210800,6.28\n1996-12-05,25.00,25.25,25.00,25.00,5096800,6.25\n1996-12-04,25.12,25.37,24.87,25.00,6823600,6.25\n1996-12-03,25.25,25.50,25.00,25.12,9840800,6.28\n1996-12-02,24.12,25.12,23.87,25.12,6254400,6.28\n1996-11-29,24.50,24.62,24.00,24.12,1527600,6.03\n1996-11-27,24.12,24.62,24.12,24.50,3191200,6.12\n1996-11-26,24.87,25.00,24.00,24.25,4054800,6.06\n1996-11-25,25.37,25.50,25.00,25.00,2830800,6.25\n1996-11-22,24.50,25.25,24.50,25.25,3732400,6.31\n1996-11-21,24.87,25.00,24.37,24.50,2522400,6.12\n1996-11-20,24.87,25.37,24.87,25.00,3683200,6.25\n1996-11-19,24.87,25.12,24.62,24.87,4446400,6.22\n1996-11-18,25.00,25.12,24.50,24.75,5468800,6.19\n1996-11-15,25.87,26.00,25.00,25.00,4684400,6.25\n1996-11-14,25.50,25.75,25.37,25.62,1740800,6.41\n1996-11-13,25.37,25.87,25.00,25.56,3000800,6.39\n1996-11-12,26.12,26.25,25.12,25.25,5120000,6.31\n1996-11-11,26.37,26.37,25.87,26.00,3318800,6.50\n1996-11-08,25.87,26.25,25.75,26.25,6750800,6.56\n1996-11-07,25.37,26.00,25.25,25.87,5548800,6.47\n1996-11-06,25.62,25.75,24.87,25.50,6462000,6.38\n1996-11-05,24.50,25.87,24.50,25.50,13517600,6.38\n1996-11-04,24.37,24.50,23.75,24.37,3270800,6.09\n1996-11-01,23.37,24.25,23.12,24.25,7563200,6.06\n1996-10-31,23.25,23.37,22.25,23.00,6945600,5.75\n1996-10-30,23.50,24.00,22.87,22.87,9192000,5.72\n1996-10-29,24.62,24.75,23.12,23.25,7135200,5.81\n1996-10-28,25.12,25.12,24.50,24.50,4288800,6.12\n1996-10-25,24.87,25.00,24.50,24.50,2775600,6.12\n1996-10-24,25.00,25.00,24.50,24.75,3020800,6.19\n1996-10-23,24.75,25.25,24.37,24.75,5736800,6.19\n1996-10-22,25.62,25.62,24.25,24.87,7651200,6.22\n1996-10-21,26.50,26.62,25.50,25.62,6712000,6.41\n1996-10-18,26.50,26.62,26.00,26.56,13681200,6.64\n1996-10-17,27.50,27.75,26.37,26.37,36679200,6.59\n1996-10-16,25.25,26.12,24.62,25.75,11975200,6.44\n1996-10-15,25.75,25.87,25.00,25.25,12970000,6.31\n1996-10-14,24.50,25.37,24.25,25.25,9649200,6.31\n1996-10-11,24.37,24.62,24.00,24.25,4327600,6.06\n1996-10-10,23.87,24.50,23.75,24.19,9883200,6.05\n1996-10-09,23.37,23.62,22.87,23.00,3044800,5.75\n1996-10-08,23.50,24.25,23.25,23.25,6802000,5.81\n1996-10-07,23.00,23.37,22.87,23.12,3428800,5.78\n1996-10-04,22.87,23.12,22.12,22.81,4770000,5.70\n1996-10-03,23.62,23.75,22.37,22.37,8140000,5.59\n1996-10-02,23.62,24.62,23.12,23.62,9890000,5.91\n1996-10-01,22.00,24.75,22.00,24.62,19269200,6.16\n1996-09-30,22.12,22.37,22.12,22.19,3058000,5.55\n1996-09-27,22.25,22.37,22.12,22.31,2932000,5.58\n1996-09-26,22.37,22.50,22.25,22.37,3693600,5.59\n1996-09-25,22.50,22.62,22.00,22.37,3902400,5.59\n1996-09-24,22.37,22.87,22.37,22.50,5143600,5.62\n1996-09-23,22.87,22.87,22.37,22.37,1653600,5.59\n1996-09-20,23.37,23.50,22.75,22.87,5330800,5.72\n1996-09-19,23.62,23.62,23.37,23.37,4282000,5.84\n1996-09-18,23.00,24.12,22.87,23.50,12631200,5.88\n1996-09-17,22.87,23.12,22.50,23.00,7487600,5.75\n1996-09-16,21.50,23.00,21.37,22.37,8747600,5.59\n1996-09-13,20.37,21.25,20.37,21.00,5967600,5.25\n1996-09-12,21.00,21.12,20.25,20.37,9340000,5.09\n1996-09-11,21.50,21.75,21.00,21.12,5266800,5.28\n1996-09-10,22.12,22.12,21.50,21.50,5562000,5.38\n1996-09-09,22.62,22.75,21.87,22.00,5302400,5.50\n1996-09-06,23.12,23.25,22.62,23.00,8602000,5.75\n1996-09-05,23.50,23.75,22.87,22.87,9999200,5.72\n1996-09-04,23.87,24.62,23.87,24.12,3636400,6.03\n1996-09-03,24.12,24.37,23.87,24.12,2461200,6.03\n1996-08-30,24.75,24.75,24.25,24.25,3784800,6.06\n1996-08-29,24.87,24.87,24.37,24.50,3829200,6.12\n1996-08-28,24.87,25.00,24.50,24.87,5844400,6.22\n1996-08-27,24.12,25.00,24.00,24.86,10339200,6.22\n1996-08-26,23.87,24.12,23.50,24.12,3204400,6.03\n1996-08-23,23.00,24.00,23.00,23.87,7281200,5.97\n1996-08-22,23.00,23.25,22.87,23.25,3138000,5.81\n1996-08-21,23.50,23.62,22.87,23.00,4052400,5.75\n1996-08-20,23.87,23.87,23.37,23.50,7564400,5.88\n1996-08-19,22.37,23.62,22.37,23.62,8084400,5.91\n1996-08-16,22.62,22.62,22.12,22.50,5075600,5.62\n1996-08-15,22.62,22.75,22.25,22.25,3845600,5.56\n1996-08-14,22.62,23.00,22.62,22.75,2570000,5.69\n1996-08-13,22.87,23.12,22.37,22.50,3706400,5.62\n1996-08-12,23.37,23.62,22.37,23.00,5408000,5.75\n1996-08-09,22.25,23.37,22.12,23.12,8243600,5.78\n1996-08-08,22.37,22.37,21.87,22.12,3640000,5.53\n1996-08-07,21.75,22.62,21.62,22.37,8892400,5.59\n1996-08-06,21.00,21.50,20.75,21.50,3354800,5.38\n1996-08-05,21.62,21.87,20.87,21.00,3612000,5.25\n1996-08-02,21.62,22.00,21.25,21.62,4574800,5.41\n1996-08-01,22.00,22.00,21.12,21.25,3942400,5.31\n1996-07-31,21.25,22.00,21.25,22.00,3332400,5.50\n1996-07-30,22.62,22.75,21.25,21.37,6766800,5.34\n1996-07-29,22.00,22.50,21.75,22.25,7005600,5.56\n1996-07-26,21.50,22.00,21.12,22.00,4426800,5.50\n1996-07-25,21.12,21.37,20.75,21.00,4090800,5.25\n1996-07-24,20.00,21.00,19.87,20.81,9448800,5.20\n1996-07-23,20.50,20.62,20.25,20.50,4651200,5.12\n1996-07-22,20.87,20.87,20.00,20.25,5456400,5.06\n1996-07-19,20.87,21.00,20.75,20.75,9510000,5.19\n1996-07-18,21.50,21.75,20.36,20.87,32058800,5.22\n1996-07-17,17.37,17.50,16.62,16.87,8355600,4.22\n1996-07-16,17.37,17.37,16.00,16.87,10334400,4.22\n1996-07-15,18.12,18.12,17.12,17.19,4779200,4.30\n1996-07-12,18.37,18.37,17.25,18.06,9610800,4.51\n1996-07-11,18.75,18.87,17.37,17.87,10420000,4.47\n1996-07-10,19.12,19.50,18.75,18.75,6055200,4.69\n1996-07-09,19.50,19.62,19.00,19.00,6723600,4.75\n1996-07-08,19.62,19.87,19.00,19.12,6762000,4.78\n1996-07-05,19.37,19.75,19.25,19.50,3808800,4.88\n1996-07-03,20.37,20.37,19.37,19.37,10323200,4.84\n1996-07-02,21.37,21.50,21.00,21.00,3189200,5.25\n1996-07-01,21.12,21.50,21.00,21.50,4732400,5.38\n1996-06-28,20.87,21.00,20.62,21.00,4138000,5.25\n1996-06-27,20.00,21.00,19.75,20.62,8202400,5.16\n1996-06-26,20.62,20.75,19.62,19.87,14440800,4.97\n1996-06-25,22.12,22.25,20.37,20.62,8831200,5.16\n1996-06-24,22.62,22.62,22.12,22.25,4398000,5.56\n1996-06-21,22.87,22.87,22.37,22.62,5792000,5.66\n1996-06-20,23.37,23.37,22.50,22.75,5260800,5.69\n1996-06-19,23.12,23.37,22.62,23.12,4803600,5.78\n1996-06-18,23.62,23.75,22.62,22.75,7979200,5.69\n1996-06-17,24.12,24.12,23.62,23.62,4052000,5.91\n1996-06-14,24.75,24.75,23.87,23.94,5186800,5.99\n1996-06-13,24.37,24.92,24.00,24.62,6856800,6.16\n1996-06-12,24.50,24.50,24.00,24.25,5440000,6.06\n1996-06-11,24.25,24.25,24.00,24.00,5481200,6.00\n1996-06-10,24.37,24.50,24.00,24.12,3820800,6.03\n1996-06-07,24.00,24.37,23.50,24.37,9565200,6.09\n1996-06-06,25.00,25.25,24.12,24.25,12938800,6.06\n1996-06-05,25.37,25.50,24.25,25.12,18228000,6.28\n1996-06-04,24.00,24.37,23.87,24.19,27235600,6.05\n1996-06-03,25.87,26.00,24.75,24.75,4481200,6.19\n1996-05-31,25.62,26.62,25.50,26.12,5813600,6.53\n1996-05-30,24.87,25.75,24.75,25.50,3703600,6.38\n1996-05-29,26.25,26.25,24.75,24.87,7840000,6.22\n1996-05-28,26.75,27.25,26.37,26.37,3658800,6.59\n1996-05-24,26.25,26.87,26.12,26.75,4046800,6.69\n1996-05-23,26.12,26.62,25.75,26.25,4447600,6.56\n1996-05-22,27.37,27.37,25.75,26.06,7215600,6.51\n1996-05-21,28.00,28.12,27.12,27.12,4088000,6.78\n1996-05-20,27.87,28.12,27.62,27.94,3028800,6.99\n1996-05-17,28.37,28.37,27.50,27.62,4405600,6.91\n1996-05-16,28.25,28.62,27.87,28.37,4648800,7.09\n1996-05-15,27.87,28.87,27.75,28.50,10442400,7.12\n1996-05-14,27.75,28.00,27.50,27.50,7068000,6.88\n1996-05-13,27.12,27.62,26.62,27.06,6701200,6.76\n1996-05-10,26.25,27.37,26.00,27.25,3966400,6.81\n1996-05-09,26.37,26.50,25.75,26.12,3515600,6.53\n1996-05-08,27.25,27.25,25.62,26.75,6688800,6.69\n1996-05-07,26.37,27.37,26.25,26.87,12641200,6.72\n1996-05-06,24.87,25.87,24.75,25.62,10349200,6.41\n1996-05-03,24.12,24.12,23.50,23.87,3892400,5.97\n1996-05-02,24.50,24.50,23.50,23.75,6728000,5.94\n1996-05-01,24.37,24.75,24.12,24.37,4039200,6.09\n1996-04-30,24.87,24.87,24.12,24.37,4881200,6.09\n1996-04-29,25.00,25.00,24.50,24.75,4324800,6.19\n1996-04-26,25.00,25.12,24.62,24.75,6759200,6.19\n1996-04-25,24.37,24.87,24.12,24.87,6245200,6.22\n1996-04-24,24.62,24.75,24.19,24.25,4596800,6.06\n1996-04-23,25.12,25.25,24.62,24.75,6086400,6.19\n1996-04-22,25.25,25.50,24.87,25.12,3973200,6.28\n1996-04-19,24.62,25.12,24.62,25.06,3655600,6.26\n1996-04-18,25.37,25.39,24.25,24.75,7780800,6.19\n1996-04-17,25.87,26.00,25.12,25.25,3056400,6.31\n1996-04-16,25.87,26.00,25.62,25.87,3634400,6.47\n1996-04-15,25.50,25.75,25.00,25.75,5515600,6.44\n1996-04-12,25.87,25.87,25.37,25.50,2924400,6.38\n1996-04-11,26.12,26.25,25.50,25.75,3526400,6.44\n1996-04-10,26.12,26.50,25.87,26.00,6242400,6.50\n1996-04-09,24.87,26.50,24.37,26.00,8415600,6.50\n1996-04-08,23.87,24.50,23.75,24.37,6046400,6.09\n1996-04-04,24.62,24.62,24.00,24.12,3092000,6.03\n1996-04-03,25.12,25.12,24.33,24.56,2591200,6.14\n1996-04-02,25.62,25.62,24.87,25.00,3635600,6.25\n1996-04-01,25.12,25.87,24.52,25.50,5680000,6.38\n1996-03-29,24.25,24.75,23.75,24.56,5962400,6.14\n1996-03-28,24.75,25.62,24.12,24.19,10572000,6.05\n1996-03-27,23.25,25.25,23.00,25.25,15338800,6.31\n1996-03-26,24.00,24.50,23.62,23.87,5755600,5.97\n1996-03-25,25.50,25.75,24.00,24.00,5887600,6.00\n1996-03-22,25.25,25.37,24.87,25.37,3842400,6.34\n1996-03-21,25.50,25.50,25.00,25.12,3932400,6.28\n1996-03-20,25.75,25.75,25.12,25.25,4154800,6.31\n1996-03-19,26.37,26.50,25.62,25.75,4442400,6.44\n1996-03-18,25.94,26.12,25.75,26.12,3907600,6.53\n1996-03-15,26.00,26.00,25.50,25.87,3632400,6.47\n1996-03-14,25.87,25.87,25.50,25.62,3342400,6.41\n1996-03-13,25.87,26.12,25.62,25.75,3560000,6.44\n1996-03-12,26.00,26.37,25.62,25.81,3453200,6.45\n1996-03-11,26.25,26.37,25.75,25.87,4544800,6.47\n1996-03-08,25.75,26.25,25.00,26.00,5322400,6.50\n1996-03-07,26.25,26.37,25.37,25.81,9292400,6.45\n1996-03-06,26.75,26.87,26.12,26.19,3547600,6.55\n1996-03-05,26.50,26.75,26.25,26.62,4246800,6.66\n1996-03-04,27.25,27.37,26.25,26.25,6708800,6.56\n1996-03-01,27.62,27.62,26.62,26.87,8263200,6.72\n1996-02-29,27.50,27.75,27.25,27.50,4049200,6.88\n1996-02-28,28.87,28.87,27.62,27.75,6728800,6.94\n1996-02-27,29.87,29.87,28.50,28.62,5331200,7.16\n1996-02-26,30.00,30.12,29.50,29.50,4238000,7.38\n1996-02-23,29.87,30.25,29.62,29.87,6205200,7.47\n1996-02-22,30.00,30.12,29.62,29.87,6588000,7.47\n1996-02-21,29.37,29.75,29.12,29.62,7924400,7.41\n1996-02-20,28.00,29.50,28.00,29.00,13473200,7.25\n1996-02-16,28.12,28.37,27.50,27.50,5602400,6.88\n1996-02-15,27.62,28.12,27.37,28.00,4360000,7.00\n1996-02-14,28.25,28.25,27.44,27.62,5843600,6.91\n1996-02-13,28.00,28.87,27.87,28.12,8161200,7.03\n1996-02-12,28.12,28.50,28.00,28.37,6948800,7.09\n1996-02-09,27.87,28.50,27.62,27.75,7360800,6.94\n1996-02-08,27.50,28.12,27.50,27.87,9420800,6.97\n1996-02-07,29.75,29.75,27.75,28.25,12885200,7.06\n1996-02-06,29.25,30.00,29.25,29.62,8101200,7.41\n1996-02-05,29.69,29.75,29.00,29.25,11396400,7.31\n1996-02-02,28.87,29.62,28.75,29.25,19865600,7.31\n1996-02-01,27.50,28.37,27.50,28.37,11902400,7.09\n1996-01-31,27.75,28.00,27.37,27.62,11736800,6.91\n1996-01-30,27.00,28.12,26.86,27.31,22246800,6.83\n1996-01-29,29.00,29.75,28.75,29.12,11900000,7.28\n1996-01-26,30.37,31.25,28.62,30.62,26297600,7.66\n1996-01-25,31.75,32.00,30.12,30.25,15911200,7.56\n1996-01-24,32.13,32.25,31.75,32.25,23438800,8.06\n1996-01-23,33.75,34.00,31.00,31.62,35305200,7.91\n1996-01-22,29.75,31.00,29.25,30.50,17852400,7.62\n1996-01-19,31.00,31.75,29.37,29.87,29623600,7.47\n1996-01-18,32.88,33.38,30.37,31.94,24955200,7.99\n1996-01-17,34.38,34.38,33.75,34.00,8445200,8.50\n1996-01-16,34.38,34.75,33.63,34.56,12606400,8.64\n1996-01-15,33.75,34.50,33.38,34.13,12971200,8.53\n1996-01-12,34.75,34.75,33.25,33.88,14370000,8.47\n1996-01-11,32.63,35.00,32.38,35.00,27041200,8.75\n1996-01-10,32.50,34.75,32.25,34.25,13057600,8.56\n1996-01-09,34.63,34.63,32.75,32.75,8978800,8.19\n1996-01-08,34.50,35.50,34.00,34.63,4341200,8.66\n1996-01-05,31.62,34.25,31.37,34.25,15929200,8.56\n1996-01-04,32.38,32.38,31.37,31.56,10721200,7.89\n1996-01-03,32.00,32.88,31.87,32.13,15368800,8.03\n1996-01-02,32.25,32.25,31.75,32.13,4983200,8.03\n1995-12-29,32.00,32.38,31.62,31.87,10874400,7.97\n1995-12-28,32.13,32.75,31.87,32.00,8933200,8.00\n1995-12-27,32.13,33.38,31.87,32.38,9609200,8.10\n1995-12-26,32.50,32.50,31.75,32.06,4994800,8.02\n1995-12-22,32.63,32.88,32.13,32.25,8392400,8.06\n1995-12-21,32.75,32.75,31.62,32.50,11893200,8.12\n1995-12-20,33.50,33.63,32.50,32.63,13074400,8.16\n1995-12-19,32.75,33.25,32.25,32.75,15403600,8.19\n1995-12-18,35.13,35.25,31.87,32.25,23807600,8.06\n1995-12-15,35.50,36.63,34.38,35.25,25960000,8.81\n1995-12-14,38.88,39.38,38.00,38.25,11928000,9.56\n1995-12-13,38.25,39.00,36.75,38.38,24472400,9.60\n1995-12-12,38.63,38.63,38.00,38.00,6353200,9.50\n1995-12-11,39.50,39.63,38.38,38.63,4003200,9.66\n1995-12-08,38.75,39.38,37.88,39.38,5053200,9.85\n1995-12-07,38.75,38.75,37.88,38.56,5084800,9.64\n1995-12-06,39.75,39.88,38.38,38.75,7195200,9.69\n1995-12-05,38.50,39.88,38.25,39.50,13000000,9.88\n1995-12-04,40.13,40.13,39.00,39.50,17171200,9.88\n1995-12-01,38.00,38.25,37.13,37.63,7300800,9.41\n1995-11-30,38.88,39.00,38.00,38.13,6247600,9.53\n1995-11-29,40.13,40.13,39.00,39.25,3782000,9.81\n1995-11-28,39.38,40.13,39.25,40.00,6305200,10.00\n1995-11-27,40.63,40.63,39.38,39.38,4148800,9.85\n1995-11-24,38.88,40.38,38.75,40.19,3930800,10.05\n1995-11-22,38.63,39.25,38.50,38.63,3533600,9.66\n1995-11-21,38.75,38.75,37.88,38.63,6845200,9.66\n1995-11-20,40.25,40.25,38.50,38.63,5314400,9.63\n1995-11-17,40.00,40.38,39.75,40.13,4607600,10.00\n1995-11-16,40.88,41.50,39.50,39.94,8102000,9.95\n1995-11-15,42.00,42.00,40.13,41.00,8874400,10.22\n1995-11-14,41.00,42.50,41.00,41.50,14560000,10.34\n1995-11-13,40.25,41.25,40.00,40.88,11343200,10.19\n1995-11-10,39.38,40.25,38.88,39.75,7973200,9.91\n1995-11-09,39.75,40.00,38.88,39.38,9295200,9.81\n1995-11-08,39.75,41.00,38.75,38.88,12823600,9.69\n1995-11-07,37.75,40.50,37.50,39.63,26310800,9.88\n1995-11-06,36.50,38.75,36.38,38.13,11143200,9.50\n1995-11-03,36.75,36.88,35.88,36.50,6413200,9.10\n1995-11-02,36.88,36.88,36.25,36.63,5464400,9.13\n1995-11-01,36.63,37.13,35.50,36.63,6913200,9.13\n1995-10-31,35.25,36.63,35.13,36.31,10334400,9.05\n1995-10-30,34.88,35.25,34.63,35.25,6291200,8.79\n1995-10-27,34.88,34.88,34.13,34.75,5523200,8.66\n1995-10-26,34.88,35.00,34.50,34.88,4503600,8.69\n1995-10-25,35.25,35.38,34.75,34.75,4761200,8.66\n1995-10-24,35.50,35.50,34.88,35.13,7638800,8.76\n1995-10-23,35.13,35.13,34.75,35.13,7078000,8.76\n1995-10-20,35.25,35.25,34.63,35.13,13818800,8.76\n1995-10-19,35.88,36.13,34.75,34.75,33761200,8.66\n1995-10-18,37.00,39.56,36.75,37.38,18311200,9.32\n1995-10-17,36.50,36.88,35.88,36.63,6390000,9.13\n1995-10-16,36.25,37.00,35.88,36.13,6515200,9.00\n1995-10-13,35.75,36.88,35.50,36.00,8422000,8.97\n1995-10-12,35.00,35.38,34.75,35.31,5803200,8.80\n1995-10-11,35.25,35.63,34.13,34.88,11893200,8.69\n1995-10-10,34.38,35.00,33.63,34.69,14303600,8.65\n1995-10-09,35.38,35.75,34.38,34.81,13320800,8.68\n1995-10-06,36.75,37.00,35.63,35.69,11058000,8.89\n1995-10-05,36.25,36.63,35.88,36.50,8737600,9.10\n1995-10-04,36.63,37.00,36.00,36.38,9532000,9.07\n1995-10-03,38.13,38.50,37.13,37.63,10368000,9.38\n1995-10-02,37.75,38.50,37.50,37.63,14000000,9.38\n1995-09-29,38.00,38.25,36.88,37.25,10123200,9.28\n1995-09-28,36.50,37.88,36.50,37.75,11843600,9.41\n1995-09-27,37.50,37.50,34.75,36.25,16135600,9.03\n1995-09-26,37.75,37.88,37.13,37.38,8961200,9.32\n1995-09-25,38.25,38.27,37.38,37.52,11267600,9.35\n1995-09-22,36.88,37.25,36.38,37.06,14258000,9.24\n1995-09-21,36.50,37.50,36.38,37.00,12407600,9.22\n1995-09-20,37.25,37.38,36.50,36.63,11500800,9.13\n1995-09-19,36.75,37.13,36.13,36.75,17512400,9.16\n1995-09-18,36.38,36.81,35.88,36.69,22216400,9.14\n1995-09-15,37.38,39.88,35.50,35.88,43286800,8.94\n1995-09-14,41.38,41.63,39.75,40.00,19675600,9.97\n1995-09-13,42.88,43.38,42.00,42.38,11530800,10.56\n1995-09-12,44.50,44.88,42.63,42.94,11658800,10.70\n1995-09-11,44.88,45.50,44.25,44.25,6160800,11.03\n1995-09-08,44.75,44.88,44.50,44.75,6243200,11.15\n1995-09-07,44.00,45.31,43.75,44.75,9373600,11.15\n1995-09-06,43.88,44.17,43.50,43.75,7175600,10.90\n1995-09-05,43.50,43.50,42.75,43.50,6443200,10.84\n1995-09-01,43.00,43.50,42.88,42.94,3532400,10.70\n1995-08-31,43.38,43.50,43.00,43.00,3148000,10.72\n1995-08-30,43.25,43.75,43.13,43.38,5482000,10.81\n1995-08-29,43.00,43.25,42.50,43.13,11325600,10.75\n1995-08-28,44.88,45.00,43.00,43.00,8680000,10.72\n1995-08-25,45.88,45.88,44.63,44.75,4819200,11.15\n1995-08-24,45.63,46.25,45.50,45.75,10285200,11.40\n1995-08-23,44.88,45.88,44.63,45.50,9078000,11.34\n1995-08-22,44.38,45.13,44.13,44.75,7769200,11.15\n1995-08-21,44.88,45.38,44.13,44.13,9721200,11.00\n1995-08-18,44.88,45.13,43.75,44.88,8620000,11.19\n1995-08-17,44.63,45.50,44.13,44.63,8827600,11.12\n1995-08-16,44.00,44.50,43.63,44.50,10457600,11.09\n1995-08-15,43.88,44.13,43.13,44.06,11370800,10.95\n1995-08-14,43.00,43.75,42.88,43.38,5989200,10.78\n1995-08-11,42.88,43.13,41.88,43.06,7407600,10.70\n1995-08-10,43.13,43.25,42.63,42.75,5868000,10.63\n1995-08-09,42.63,43.75,42.50,43.13,13190000,10.72\n1995-08-08,43.63,43.75,42.38,42.50,8388800,10.56\n1995-08-07,44.13,44.63,43.13,43.38,6920000,10.78\n1995-08-04,45.00,45.13,43.75,44.25,6884400,11.00\n1995-08-03,44.13,45.63,43.88,45.00,7640800,11.18\n1995-08-02,43.88,45.00,43.75,44.38,9840800,11.03\n1995-08-01,44.88,44.88,43.50,43.50,7540000,10.81\n1995-07-31,45.50,45.63,44.75,45.00,5673600,11.18\n1995-07-28,46.75,47.25,45.00,45.50,9341200,11.31\n1995-07-27,45.50,47.50,45.50,46.81,11621200,11.63\n1995-07-26,46.25,46.25,45.38,45.38,6125200,11.28\n1995-07-25,46.00,46.38,45.63,45.75,9418000,11.37\n1995-07-24,44.00,45.50,43.75,45.38,7679200,11.28\n1995-07-21,43.00,44.88,43.00,43.75,27082400,10.87\n1995-07-20,46.00,47.38,45.00,47.06,11848800,11.70\n1995-07-19,47.00,48.00,45.00,45.50,18613200,11.31\n1995-07-18,49.00,49.56,47.75,48.13,9102000,11.96\n1995-07-17,48.88,49.75,48.63,49.00,8098000,12.18\n1995-07-14,47.38,49.00,47.00,48.75,9929200,12.12\n1995-07-13,47.38,48.75,47.13,47.63,12596400,11.84\n1995-07-12,47.25,48.00,46.13,47.00,10145200,11.68\n1995-07-11,47.75,48.63,47.06,47.13,7683200,11.71\n1995-07-10,48.63,49.88,48.13,48.63,10640800,12.09\n1995-07-07,46.88,49.25,46.75,48.63,13840000,12.09\n1995-07-06,46.50,47.00,45.75,47.00,6583200,11.68\n1995-07-05,46.88,47.88,46.50,46.50,6325600,11.56\n1995-07-03,46.50,47.13,46.25,46.94,1410800,11.67\n1995-06-30,47.25,47.88,46.13,46.44,5927600,11.54\n1995-06-29,46.38,48.13,46.00,47.25,8320000,11.74\n1995-06-28,46.00,47.50,45.38,46.63,9531200,11.59\n1995-06-27,47.38,48.25,46.38,46.38,7772400,11.53\n1995-06-26,48.25,48.50,47.63,48.13,5465600,11.96\n1995-06-23,48.75,49.00,47.75,48.75,8286800,12.12\n1995-06-22,49.00,49.63,48.63,49.13,16928800,12.21\n1995-06-21,47.63,50.13,46.75,49.38,22378800,12.27\n1995-06-20,46.00,47.75,46.00,47.38,26385200,11.78\n1995-06-19,43.88,45.25,43.50,44.38,16774400,11.03\n1995-06-16,43.88,44.00,43.50,43.88,3200800,10.91\n1995-06-15,43.63,43.75,43.38,43.63,3331200,10.84\n1995-06-14,43.88,43.88,43.38,43.63,4224800,10.84\n1995-06-13,44.50,44.63,43.88,44.00,4508000,10.94\n1995-06-12,44.00,44.50,43.88,44.17,7584400,10.98\n1995-06-09,43.63,43.75,43.13,43.50,6679200,10.81\n1995-06-08,43.38,43.38,42.13,42.94,4874400,10.67\n1995-06-07,44.13,44.13,43.13,43.13,4451200,10.72\n1995-06-06,43.63,44.38,43.50,44.00,11270800,10.94\n1995-06-05,42.38,43.50,42.13,43.50,9103200,10.81\n1995-06-02,41.88,42.38,41.50,42.13,3783200,10.47\n1995-06-01,41.88,42.50,41.75,42.19,6685200,10.49\n1995-05-31,42.13,42.13,41.00,41.56,5707600,10.33\n1995-05-30,42.63,42.88,41.50,42.00,7021200,10.44\n1995-05-26,43.00,43.13,42.25,42.69,4097600,10.61\n1995-05-25,43.25,44.00,43.00,43.38,6536800,10.75\n1995-05-24,43.75,44.25,42.88,43.50,9459200,10.78\n1995-05-23,44.13,44.38,43.50,43.88,9881200,10.88\n1995-05-22,42.50,44.13,42.25,44.13,13282400,10.94\n1995-05-19,42.88,43.75,42.63,42.75,11522000,10.60\n1995-05-18,44.13,44.13,43.25,43.38,13287600,10.75\n1995-05-17,43.75,44.38,43.50,44.00,9419200,10.91\n1995-05-16,43.13,44.38,42.50,43.75,11895600,10.84\n1995-05-15,43.13,43.75,42.50,43.63,14053200,10.81\n1995-05-12,40.88,43.69,40.50,43.63,23153200,10.81\n1995-05-11,41.63,41.63,40.38,41.00,18712400,10.16\n1995-05-10,41.50,41.88,40.75,41.44,9837600,10.27\n1995-05-09,40.63,41.38,40.00,41.25,11540800,10.22\n1995-05-08,39.88,41.00,39.75,40.50,13832000,10.04\n1995-05-05,38.75,39.13,38.13,38.88,7445200,9.64\n1995-05-04,38.25,39.88,38.00,38.50,10846800,9.54\n1995-05-03,38.25,38.63,38.00,38.13,6043600,9.45\n1995-05-02,38.25,38.38,37.50,38.13,4289200,9.45\n1995-05-01,38.25,38.75,38.00,38.25,6375600,9.48\n1995-04-28,38.00,38.38,37.50,38.25,6984400,9.48\n1995-04-27,38.50,38.50,37.75,37.88,5014800,9.39\n1995-04-26,37.63,38.75,37.38,38.25,8246800,9.48\n1995-04-25,39.13,39.38,37.25,37.75,9780000,9.36\n1995-04-24,39.00,39.63,38.50,39.00,9724400,9.67\n1995-04-21,37.25,39.50,37.13,39.13,23812400,9.70\n1995-04-20,37.13,38.50,36.63,37.63,11772400,9.33\n1995-04-19,37.50,37.50,35.63,36.38,9990800,9.02\n1995-04-18,38.50,38.63,37.50,37.50,8263200,9.29\n1995-04-17,38.13,39.38,37.88,38.38,7467600,9.51\n1995-04-13,39.25,39.25,37.88,38.25,6242400,9.48\n1995-04-12,38.25,39.63,37.38,39.00,16973200,9.67\n1995-04-11,36.75,37.88,36.63,37.75,7673200,9.36\n1995-04-10,36.88,37.00,36.13,36.63,4211200,9.08\n1995-04-07,37.00,37.13,36.25,36.75,10562400,9.11\n1995-04-06,37.25,38.00,35.53,36.75,25823600,9.11\n1995-04-05,34.13,34.75,33.75,34.75,9470000,8.61\n1995-04-04,35.75,35.88,33.63,33.88,15300000,8.40\n1995-04-03,35.50,35.75,35.13,35.50,5528000,8.80\n1995-03-31,35.13,35.63,34.75,35.25,6558000,8.74\n1995-03-30,34.63,35.50,34.50,35.38,9767600,8.77\n1995-03-29,34.00,34.88,33.88,34.38,17760000,8.52\n1995-03-28,36.25,36.34,34.13,34.38,24655600,8.52\n1995-03-27,37.63,37.63,36.63,37.19,5111200,9.22\n1995-03-24,37.38,37.88,37.25,37.75,4584400,9.36\n1995-03-23,37.88,38.00,36.98,37.13,6094400,9.20\n1995-03-22,36.25,39.50,36.25,38.06,17130800,9.43\n1995-03-21,35.50,36.75,35.25,36.25,10920800,8.98\n1995-03-20,35.13,35.63,35.00,35.25,6793600,8.74\n1995-03-17,35.50,35.50,34.88,35.13,7713600,8.71\n1995-03-16,35.25,36.00,35.00,35.25,11330000,8.74\n1995-03-15,35.50,36.25,34.88,35.00,26120800,8.67\n1995-03-14,38.25,38.25,34.50,35.00,26015200,8.67\n1995-03-13,39.63,39.63,38.00,38.13,11653200,9.45\n1995-03-10,39.63,40.38,39.38,39.50,4923200,9.79\n1995-03-09,39.88,40.38,39.38,39.75,7038000,9.85\n1995-03-08,38.75,40.13,37.75,39.56,13048800,9.81\n1995-03-07,39.88,39.88,38.25,38.31,5399200,9.50\n1995-03-06,39.75,40.00,39.50,39.75,4751200,9.85\n1995-03-03,39.75,40.69,39.50,40.25,5209200,9.98\n1995-03-02,40.13,40.75,39.75,40.00,9619200,9.91\n1995-03-01,39.75,40.13,39.42,40.00,8025200,9.91\n1995-02-28,38.50,39.88,38.00,39.50,7965200,9.79\n1995-02-27,38.25,39.00,38.11,38.25,9600800,9.48\n1995-02-24,40.13,40.38,38.50,39.00,20334400,9.67\n1995-02-23,41.13,41.88,40.00,40.19,11262000,9.96\n1995-02-22,40.63,41.00,40.13,40.81,10501200,10.12\n1995-02-21,42.63,42.75,40.88,41.00,10776800,10.16\n1995-02-17,42.88,43.00,42.50,42.50,4366400,10.53\n1995-02-16,43.13,43.25,42.63,43.19,7821200,10.70\n1995-02-15,43.25,43.50,42.50,42.56,6604400,10.55\n1995-02-14,43.75,44.13,42.63,42.94,5934400,10.64\n1995-02-13,43.50,44.50,43.25,43.75,10120800,10.84\n1995-02-10,43.63,44.19,43.38,43.75,12542400,10.81\n1995-02-09,42.13,43.88,42.13,43.63,16988800,10.78\n1995-02-08,41.00,42.38,40.88,42.31,14403600,10.46\n1995-02-07,40.38,41.00,40.00,40.81,7200000,10.09\n1995-02-06,40.75,40.75,39.50,40.50,8702000,10.01\n1995-02-03,42.00,42.13,40.38,40.50,11400800,10.01\n1995-02-02,40.13,41.88,40.13,41.63,7288000,10.29\n1995-02-01,40.75,40.75,39.88,40.13,5665200,9.92\n1995-01-31,40.50,40.88,40.00,40.38,7621200,9.98\n1995-01-30,40.13,40.50,39.88,40.13,8255200,9.92\n1995-01-27,39.88,40.38,39.00,39.88,10676400,9.86\n1995-01-26,40.88,41.50,39.25,39.50,8822000,9.76\n1995-01-25,39.50,42.00,39.50,40.98,18482000,10.13\n1995-01-24,42.25,42.38,41.38,41.63,7805600,10.29\n1995-01-23,41.88,42.63,41.00,42.25,14252400,10.44\n1995-01-20,47.00,47.00,42.50,42.63,35731200,10.54\n1995-01-19,45.50,46.00,45.00,45.88,11238800,11.34\n1995-01-18,45.00,45.63,44.75,45.63,4581200,11.28\n1995-01-17,44.50,45.50,44.13,45.00,11806400,11.12\n1995-01-16,44.88,45.25,44.25,44.50,6765600,11.00\n1995-01-13,46.13,46.13,44.38,44.88,12565600,11.09\n1995-01-12,46.13,46.38,44.75,45.38,19721200,11.22\n1995-01-11,43.75,48.06,42.69,46.75,31212400,11.56\n1995-01-10,41.25,44.00,41.25,43.69,21977600,10.80\n1995-01-09,41.63,41.88,41.00,41.20,9805200,10.18\n1995-01-06,41.63,43.13,41.13,42.00,38456800,10.38\n1995-01-05,39.25,39.38,38.75,38.88,2646800,9.61\n1995-01-04,38.63,39.63,38.63,39.38,5682400,9.73\n1995-01-03,38.88,38.88,37.88,38.38,3726400,9.49\n1994-12-30,39.38,39.88,38.75,39.00,2616400,9.64\n1994-12-29,39.25,39.88,39.13,39.50,4341200,9.76\n1994-12-28,39.13,39.25,38.25,39.13,3198000,9.67\n1994-12-27,39.25,39.75,38.88,39.13,2928800,9.67\n1994-12-23,38.50,39.38,38.50,38.88,3372000,9.61\n1994-12-22,38.50,38.88,38.25,38.63,4771200,9.55\n1994-12-21,37.88,38.50,37.50,38.38,5635600,9.49\n1994-12-20,39.13,39.25,38.38,38.50,6263600,9.52\n1994-12-19,37.25,39.38,37.25,39.13,11890000,9.67\n1994-12-16,37.25,37.75,36.75,37.25,6432400,9.21\n1994-12-15,38.00,38.38,36.88,37.13,8133200,9.18\n1994-12-14,36.50,38.13,36.50,37.88,11123600,9.36\n1994-12-13,36.63,36.94,36.25,36.38,4266800,8.99\n1994-12-12,36.38,36.75,35.50,36.50,8004400,9.02\n1994-12-09,35.88,36.38,34.75,36.25,9329200,8.96\n1994-12-08,36.88,37.00,35.75,35.88,6081200,8.87\n1994-12-07,37.50,37.81,36.06,36.63,4916800,9.05\n1994-12-06,37.00,38.38,36.88,37.56,8516400,9.28\n1994-12-05,36.50,37.38,36.13,37.19,6460000,9.19\n1994-12-02,36.50,36.75,35.63,36.56,6170000,9.04\n1994-12-01,37.00,37.63,36.00,36.19,11051200,8.95\n1994-11-30,38.38,39.38,37.00,37.25,11157600,9.21\n1994-11-29,38.00,38.50,37.75,38.25,5163200,9.45\n1994-11-28,37.63,38.25,37.31,37.81,4971200,9.35\n1994-11-25,36.88,37.75,36.75,37.75,3012400,9.33\n1994-11-23,37.00,37.88,36.38,36.88,11723200,9.12\n1994-11-22,37.75,39.13,37.25,37.38,8018800,9.24\n1994-11-21,40.00,40.25,38.00,38.13,7255600,9.42\n1994-11-18,40.00,40.50,39.63,40.00,5257600,9.89\n1994-11-17,40.88,41.00,39.88,40.00,5380000,9.86\n1994-11-16,40.75,41.56,40.63,40.94,6700000,10.09\n1994-11-15,42.50,43.00,41.25,41.38,6001200,10.20\n1994-11-14,41.25,42.75,41.25,42.50,5002000,10.47\n1994-11-11,41.25,41.50,41.00,41.13,2237600,10.14\n1994-11-10,41.75,41.88,41.00,41.31,5476800,10.18\n1994-11-09,42.75,43.00,41.00,41.63,14530000,10.26\n1994-11-08,40.63,42.63,40.25,42.25,12476400,10.41\n1994-11-07,40.38,41.25,40.13,40.75,4058000,10.04\n1994-11-04,41.50,41.63,40.00,40.38,6869200,9.95\n1994-11-03,41.75,42.00,41.00,41.50,3962400,10.23\n1994-11-02,43.13,43.25,41.38,41.38,7819200,10.20\n1994-11-01,42.88,43.48,42.38,43.13,7805600,10.63\n1994-10-31,42.00,43.38,41.50,43.19,12728000,10.64\n1994-10-28,42.38,42.88,41.75,42.13,9762400,10.38\n1994-10-27,43.25,43.75,42.50,42.75,5700800,10.54\n1994-10-26,42.63,43.27,42.63,43.25,7043200,10.66\n1994-10-25,41.63,42.63,41.50,42.63,10771200,10.51\n1994-10-24,42.75,43.13,41.88,42.25,7316800,10.41\n1994-10-21,40.75,42.75,40.75,42.63,11528000,10.51\n1994-10-20,41.25,41.81,40.50,41.00,7808000,10.10\n1994-10-19,41.00,42.13,41.00,41.25,12549200,10.17\n1994-10-18,40.63,41.63,40.50,41.25,16749200,10.17\n1994-10-17,40.88,41.50,38.88,39.75,10866400,9.80\n1994-10-14,41.50,42.00,40.88,41.13,6292000,10.14\n1994-10-13,42.63,42.88,40.63,41.13,18761200,10.14\n1994-10-12,39.63,42.63,39.13,42.13,21340000,10.38\n1994-10-11,41.38,41.88,39.38,39.63,30083600,9.77\n1994-10-10,37.13,39.63,37.00,38.88,18700800,9.58\n1994-10-07,36.13,37.06,35.50,37.00,13022000,9.12\n1994-10-06,37.38,37.48,36.00,36.25,18828800,8.93\n1994-10-05,33.63,38.13,33.38,37.88,25366800,9.33\n1994-10-04,33.25,34.00,33.00,33.75,5822000,8.32\n1994-10-03,33.63,33.75,32.50,33.13,4644400,8.16\n1994-09-30,34.13,34.50,33.63,33.69,2561200,8.30\n1994-09-29,33.75,34.38,33.38,34.13,3921200,8.41\n1994-09-28,34.00,34.38,33.63,33.88,2914800,8.35\n1994-09-27,33.75,34.13,33.38,33.88,3904800,8.35\n1994-09-26,33.88,34.50,33.63,33.94,5072400,8.36\n1994-09-23,33.88,34.50,33.88,33.94,4760000,8.36\n1994-09-22,34.25,34.25,33.63,33.88,5235600,8.35\n1994-09-21,34.50,34.63,33.75,34.13,8402400,8.41\n1994-09-20,35.13,35.38,34.38,34.56,7047600,8.52\n1994-09-19,36.38,36.75,35.50,35.50,6242000,8.75\n1994-09-16,35.88,37.25,35.50,36.38,13008000,8.97\n1994-09-15,35.13,36.13,35.13,36.00,9253200,8.87\n1994-09-14,35.63,35.75,35.00,35.13,3549200,8.66\n1994-09-13,35.75,36.25,35.63,35.81,3723600,8.82\n1994-09-12,35.63,35.75,35.38,35.75,3252400,8.81\n1994-09-09,35.75,36.00,35.38,35.75,5624400,8.81\n1994-09-08,36.00,36.25,35.63,36.13,5691200,8.90\n1994-09-07,35.63,36.63,35.38,36.13,7283200,8.90\n1994-09-06,35.25,35.63,35.00,35.56,3279200,8.76\n1994-09-02,35.25,35.50,35.00,35.38,3628000,8.72\n1994-09-01,35.38,35.75,34.63,35.00,7305200,8.63\n1994-08-31,36.00,37.38,35.75,36.19,12568800,8.92\n1994-08-30,35.25,36.38,35.13,36.25,6515600,8.93\n1994-08-29,35.75,36.13,35.25,35.38,5450800,8.72\n1994-08-26,35.25,36.13,35.25,35.75,7300000,8.81\n1994-08-25,34.25,36.38,34.25,35.06,10688800,8.64\n1994-08-24,34.75,35.00,34.38,34.88,6132400,8.60\n1994-08-23,34.88,35.88,34.75,35.00,7669200,8.63\n1994-08-22,34.75,35.00,34.63,34.88,5445600,8.60\n1994-08-19,34.75,35.00,34.25,34.88,4674800,8.60\n1994-08-18,34.75,35.25,34.50,34.63,7370000,8.53\n1994-08-17,34.88,35.38,34.63,35.00,10232400,8.63\n1994-08-16,34.38,34.75,34.00,34.75,5563200,8.56\n1994-08-15,34.75,35.00,34.25,34.63,4293200,8.53\n1994-08-12,34.38,35.13,33.88,34.75,6425200,8.53\n1994-08-11,34.25,35.13,33.88,34.31,10649200,8.43\n1994-08-10,33.63,34.88,33.25,34.63,9065200,8.50\n1994-08-09,33.50,33.88,33.13,33.63,2811200,8.26\n1994-08-08,33.13,34.00,33.00,33.75,5048800,8.29\n1994-08-05,32.88,33.38,32.88,33.25,3123200,8.17\n1994-08-04,33.13,33.75,33.13,33.25,6620000,8.17\n1994-08-03,32.75,33.25,32.13,33.13,8113600,8.14\n1994-08-02,33.50,33.63,32.38,32.56,9642400,8.00\n1994-08-01,33.63,33.75,32.75,33.38,8204400,8.20\n1994-07-29,31.87,34.00,31.87,33.69,19853600,8.27\n1994-07-28,31.00,32.13,30.87,31.87,8762000,7.83\n1994-07-27,31.25,31.37,30.62,31.06,4788000,7.63\n1994-07-26,31.75,32.00,31.12,31.37,6756400,7.70\n1994-07-25,31.12,31.87,30.75,31.69,15103200,7.78\n1994-07-22,31.62,31.97,30.00,31.00,28098800,7.61\n1994-07-21,26.62,28.50,26.50,28.00,10348800,6.88\n1994-07-20,27.37,27.62,26.37,26.62,7765200,6.54\n1994-07-19,28.62,28.75,27.37,27.69,4176400,6.80\n1994-07-18,28.12,29.00,28.00,28.37,2734800,6.97\n1994-07-15,28.23,28.62,27.50,28.25,3409200,6.94\n1994-07-14,29.62,29.75,28.25,28.62,6459200,7.03\n1994-07-13,28.50,30.25,28.50,29.69,16081200,7.29\n1994-07-12,27.00,28.44,26.37,28.37,8662000,6.97\n1994-07-11,27.12,27.37,26.62,27.00,3801200,6.63\n1994-07-08,26.50,27.62,26.50,27.06,7457600,6.65\n1994-07-07,25.87,27.00,25.50,26.81,6097600,6.58\n1994-07-06,26.25,26.50,26.00,26.12,3499200,6.41\n1994-07-05,25.62,26.75,25.62,26.50,3080800,6.51\n1994-07-01,26.37,26.50,25.37,25.75,6404400,6.32\n1994-06-30,26.25,26.87,26.25,26.50,3652000,6.51\n1994-06-29,26.75,27.12,25.87,26.12,4842400,6.41\n1994-06-28,26.25,27.12,25.62,26.75,6235200,6.57\n1994-06-27,25.25,26.25,24.62,26.25,9153200,6.45\n1994-06-24,25.12,26.12,24.75,25.61,10470000,6.29\n1994-06-23,26.25,26.25,24.87,25.12,7283200,6.17\n1994-06-22,26.25,26.75,26.00,26.25,4081200,6.45\n1994-06-21,26.87,27.25,25.75,26.00,8693200,6.39\n1994-06-20,26.25,27.25,26.00,27.12,7150000,6.66\n1994-06-17,26.00,26.75,25.87,26.50,8027600,6.51\n1994-06-16,27.75,27.75,26.12,26.37,7812400,6.48\n1994-06-15,27.00,28.00,26.87,27.81,5704400,6.83\n1994-06-14,27.25,27.37,26.62,27.06,5531200,6.65\n1994-06-13,26.37,27.19,26.37,27.00,3339200,6.63\n1994-06-10,27.12,27.37,26.37,26.50,5107600,6.51\n1994-06-09,25.62,27.00,25.50,27.00,10485200,6.63\n1994-06-08,27.50,27.62,26.00,26.12,9809200,6.41\n1994-06-07,27.25,27.75,27.25,27.50,5013600,6.75\n1994-06-06,27.50,27.75,27.00,27.37,4513200,6.72\n1994-06-03,27.12,28.00,26.75,27.62,12649200,6.78\n1994-06-02,28.37,28.50,27.12,27.37,13762400,6.72\n1994-06-01,28.50,28.62,27.87,28.25,13786800,6.94\n1994-05-31,29.50,29.50,28.50,29.25,9211200,7.18\n1994-05-27,30.25,30.75,29.50,29.94,3882400,7.35\n1994-05-26,31.50,31.50,30.25,30.50,2613200,7.46\n1994-05-25,30.25,31.75,30.00,31.25,4873200,7.64\n1994-05-24,31.00,31.25,30.25,30.75,4536400,7.52\n1994-05-23,31.00,31.25,30.00,30.50,4286400,7.46\n1994-05-20,31.75,32.25,31.00,31.06,3519200,7.60\n1994-05-19,30.75,32.50,30.50,32.13,9776800,7.86\n1994-05-18,29.75,30.75,29.25,30.62,4436800,7.49\n1994-05-17,29.75,29.75,28.75,29.37,6450800,7.18\n1994-05-16,30.00,30.50,29.50,29.50,4854800,7.22\n1994-05-13,29.75,30.50,29.25,30.00,3323200,7.34\n1994-05-12,30.50,30.75,29.50,29.69,3839200,7.26\n1994-05-11,31.00,31.50,29.75,30.25,5218000,7.40\n1994-05-10,31.75,32.00,31.00,31.00,5246800,7.58\n1994-05-09,32.25,32.50,30.75,31.25,5026400,7.64\n1994-05-06,32.25,32.75,31.25,32.31,6721200,7.90\n1994-05-05,33.25,33.75,32.25,32.88,10307600,8.04\n1994-05-04,31.00,33.25,30.50,33.00,13008800,8.07\n1994-05-03,31.00,31.25,29.50,30.25,4761200,7.40\n1994-05-02,30.00,31.25,30.00,31.00,4401200,7.58\n1994-04-29,30.00,30.50,29.75,30.00,3399200,7.34\n1994-04-28,31.00,31.25,29.75,30.25,3604400,7.40\n1994-04-26,31.50,31.50,31.00,31.25,5879200,7.64\n1994-04-25,29.75,31.00,29.50,31.00,12846800,7.58\n1994-04-22,31.25,32.00,28.50,29.75,24923600,7.28\n1994-04-21,28.50,30.50,27.00,29.62,14674400,7.25\n1994-04-20,29.25,30.00,28.00,28.25,10080800,6.91\n1994-04-19,29.75,30.00,28.50,29.00,5947600,7.09\n1994-04-18,30.50,30.50,29.25,29.62,8238800,7.25\n1994-04-15,31.25,31.50,30.00,30.25,6730800,7.40\n1994-04-14,30.50,31.75,30.00,31.50,7933200,7.71\n1994-04-13,32.25,32.50,31.25,31.75,8330000,7.77\n1994-04-12,33.38,33.38,31.75,32.00,4890800,7.83\n1994-04-11,33.50,33.50,32.50,33.50,3823600,8.19\n1994-04-08,33.75,34.00,33.25,33.50,6336400,8.19\n1994-04-07,33.50,33.75,32.75,33.38,2764800,8.17\n1994-04-06,34.00,34.00,32.75,33.50,4616400,8.19\n1994-04-05,33.75,34.25,33.50,33.50,3505600,8.19\n1994-04-04,32.25,33.25,31.75,33.25,6016800,8.13\n1994-03-31,32.50,33.50,31.50,33.25,7481200,8.13\n1994-03-30,32.50,33.25,31.75,32.50,6079200,7.95\n1994-03-29,33.25,33.75,32.25,32.75,7640000,8.01\n1994-03-28,33.00,34.00,32.75,33.25,10098800,8.13\n1994-03-25,34.75,34.75,32.75,32.75,12291200,8.01\n1994-03-24,35.13,35.25,34.00,34.63,6738800,8.47\n1994-03-23,35.25,35.50,34.25,35.13,7749200,8.59\n1994-03-22,35.25,35.50,34.50,35.00,8690800,8.56\n1994-03-21,36.38,36.50,35.25,35.50,8806400,8.68\n1994-03-18,36.75,36.75,35.75,36.38,8004400,8.90\n1994-03-17,36.75,37.00,36.25,36.50,5590800,8.93\n1994-03-16,37.50,37.75,36.50,36.75,5265200,8.99\n1994-03-15,38.25,38.25,37.25,37.63,7319200,9.20\n1994-03-14,38.50,38.50,37.75,38.13,15783600,9.33\n1994-03-11,37.00,37.75,36.75,37.25,5791200,9.11\n1994-03-10,37.25,37.63,36.75,37.25,5142400,9.11\n1994-03-09,36.63,37.50,36.00,37.50,8896800,9.17\n1994-03-08,38.00,38.00,36.75,37.00,6647600,9.05\n1994-03-07,37.00,38.13,36.75,37.88,11088800,9.27\n1994-03-04,36.00,37.50,35.75,36.75,8113600,8.99\n1994-03-03,35.75,36.25,35.50,35.75,6737600,8.75\n1994-03-02,35.25,36.25,34.75,35.63,10519200,8.72\n1994-03-01,36.75,36.75,35.75,36.25,7570800,8.87\n1994-02-28,36.25,37.00,36.00,36.50,4434800,8.93\n1994-02-25,37.00,37.25,35.50,36.00,8468000,8.81\n1994-02-24,37.00,37.25,36.25,36.63,7081200,8.96\n1994-02-23,37.25,38.25,37.00,37.25,9318800,9.11\n1994-02-22,36.25,37.50,35.75,37.25,7676400,9.11\n1994-02-18,36.50,37.00,36.25,36.25,5326400,8.87\n1994-02-17,37.25,37.88,36.25,37.00,5197600,9.05\n1994-02-16,37.50,37.50,36.75,36.75,4379200,8.99\n1994-02-15,36.75,37.50,36.25,37.13,4654400,9.08\n1994-02-14,37.00,38.00,36.75,37.00,8775200,9.05\n1994-02-11,36.25,37.50,36.25,37.00,5880800,9.05\n1994-02-10,36.25,37.50,36.00,36.50,10802000,8.93\n1994-02-09,35.75,36.50,35.25,36.25,6699200,8.87\n1994-02-08,36.00,36.50,35.25,35.75,10210800,8.75\n1994-02-07,33.50,37.13,33.50,36.50,25925200,8.93\n1994-02-04,33.50,35.00,33.25,33.50,12645200,8.17\n1994-02-03,33.00,33.63,32.50,33.50,4933200,8.17\n1994-02-02,33.25,33.25,32.50,33.00,5247600,8.04\n1994-02-01,33.00,33.50,32.25,33.25,5618000,8.10\n1994-01-31,33.50,33.75,32.75,32.75,8532400,7.98\n1994-01-28,34.25,34.75,33.75,34.00,4891200,8.29\n1994-01-27,33.50,34.25,33.00,34.13,4724800,8.32\n1994-01-26,33.75,34.00,33.25,33.50,5922400,8.17\n1994-01-25,34.75,35.00,33.25,33.88,15818800,8.26\n1994-01-24,33.25,35.25,33.25,35.00,24742000,8.53\n1994-01-21,33.25,33.50,32.25,33.38,35007600,8.14\n1994-01-20,29.50,30.75,29.50,29.87,9582400,7.28\n1994-01-19,29.25,29.75,28.75,29.25,10066400,7.13\n1994-01-18,30.25,30.25,29.00,29.37,12978000,7.16\n1994-01-17,31.00,31.50,30.00,30.37,5206400,7.40\n1994-01-14,30.75,31.75,30.50,31.00,7673200,7.56\n1994-01-13,30.00,30.75,29.75,30.62,19000000,7.46\n1994-01-12,32.25,32.25,30.50,30.50,15684400,7.43\n1994-01-11,33.50,33.75,31.75,31.87,12700000,7.77\n1994-01-10,33.00,33.88,32.75,33.63,7222000,8.20\n1994-01-07,32.00,33.25,31.25,33.13,10688800,8.08\n1994-01-06,33.75,34.00,32.50,32.75,13095200,7.98\n1994-01-05,31.75,33.88,31.75,33.75,21874400,8.23\n1994-01-04,30.25,31.50,30.00,31.50,10198800,7.68\n1994-01-03,29.50,30.00,29.00,29.87,6485200,7.28\n1993-12-31,29.75,30.25,29.25,29.25,5765200,7.13\n1993-12-30,28.50,30.25,28.50,29.75,11253200,7.25\n1993-12-29,29.25,29.25,28.50,28.50,3853200,6.95\n1993-12-28,28.75,29.50,28.50,29.12,5705600,7.10\n1993-12-27,27.75,28.75,27.25,28.50,5730000,6.95\n1993-12-23,27.25,27.25,26.50,27.25,8120000,6.64\n1993-12-22,27.25,28.50,27.00,28.00,6498800,6.82\n1993-12-21,28.50,28.75,27.25,27.50,8973600,6.70\n1993-12-20,29.25,29.75,28.25,28.50,6768800,6.95\n1993-12-17,29.50,29.75,29.12,29.50,5197600,7.19\n1993-12-16,29.50,29.75,29.00,29.37,4532000,7.16\n1993-12-15,29.00,29.75,29.00,29.75,4438000,7.25\n1993-12-14,29.25,29.75,29.00,29.12,10492400,7.10\n1993-12-13,28.25,29.50,27.75,29.50,8729200,7.19\n1993-12-10,30.25,30.50,27.75,28.25,17781200,6.89\n1993-12-09,31.75,32.00,29.75,30.00,6531200,7.31\n1993-12-08,32.00,32.25,31.50,31.87,1422000,7.77\n1993-12-07,32.00,32.25,31.50,32.25,2280800,7.86\n1993-12-06,31.50,32.50,31.25,32.25,5610000,7.86\n1993-12-03,31.75,32.00,31.00,31.50,4314800,7.68\n1993-12-02,31.75,32.00,31.00,31.75,3614400,7.74\n1993-12-01,32.00,32.25,31.25,31.50,3978800,7.68\n1993-11-30,31.75,32.63,31.50,31.50,4036800,7.68\n1993-11-29,32.25,32.50,31.50,31.75,3462000,7.74\n1993-11-26,32.75,33.00,32.25,32.63,1569200,7.95\n1993-11-24,32.75,33.50,32.63,33.00,3246800,8.04\n1993-11-23,32.50,33.00,31.25,33.00,6653600,8.04\n1993-11-22,32.75,33.00,32.25,32.50,5389200,7.92\n1993-11-19,33.00,33.50,32.50,33.00,4409200,8.04\n1993-11-18,33.50,33.75,33.00,33.50,4089200,8.14\n1993-11-17,34.00,35.00,32.75,33.50,10812400,8.14\n1993-11-16,32.00,34.25,31.75,34.00,10838000,8.26\n1993-11-15,31.50,32.75,31.50,32.00,5616800,7.77\n1993-11-12,31.50,32.00,30.50,31.75,5136800,7.71\n1993-11-11,30.75,32.00,30.50,31.37,5090800,7.62\n1993-11-10,30.25,30.75,30.00,30.75,2765600,7.47\n1993-11-09,31.00,31.25,29.75,30.12,6136400,7.32\n1993-11-08,32.00,32.13,30.50,30.75,5966400,7.47\n1993-11-05,31.87,32.25,30.75,31.87,13513200,7.74\n1993-11-04,31.50,32.25,30.75,32.25,6632000,7.83\n1993-11-03,33.00,33.00,31.00,31.62,6320000,7.68\n1993-11-02,31.25,33.00,31.00,32.75,8013600,7.95\n1993-11-01,30.75,31.50,30.25,31.50,3798800,7.65\n1993-10-29,31.00,31.75,30.50,30.75,4892400,7.47\n1993-10-28,31.75,32.25,31.00,31.00,8736800,7.53\n1993-10-27,30.00,32.25,29.75,31.75,16415200,7.71\n1993-10-26,29.75,30.00,29.00,29.75,7960000,7.23\n1993-10-25,30.25,30.50,29.62,30.00,7840800,7.29\n1993-10-22,30.50,31.50,29.75,30.25,14160000,7.35\n1993-10-21,27.50,31.25,27.25,30.25,22417600,7.35\n1993-10-20,28.00,28.25,27.25,27.75,4956400,6.74\n1993-10-19,28.25,28.50,27.25,27.75,7643200,6.74\n1993-10-18,28.00,28.75,27.75,28.37,11900000,6.89\n1993-10-15,27.75,28.50,26.75,28.25,34136400,6.86\n1993-10-14,24.00,24.50,23.50,23.75,5749200,5.77\n1993-10-13,24.25,24.25,23.50,24.00,6322400,5.83\n1993-10-12,24.00,25.00,23.75,24.00,10952400,5.83\n1993-10-11,22.75,24.00,22.75,23.75,5775200,5.77\n1993-10-08,23.25,23.25,22.25,22.62,4989200,5.49\n1993-10-07,23.50,23.75,22.75,23.00,4828000,5.59\n1993-10-06,23.75,24.00,23.37,23.62,6271200,5.74\n1993-10-05,23.00,24.00,23.00,23.50,6306400,5.71\n1993-10-04,22.62,23.00,22.00,22.75,6891200,5.53\n1993-10-01,22.75,23.00,22.50,22.75,12022000,5.53\n1993-09-30,24.00,24.00,23.00,23.37,9828000,5.68\n1993-09-29,24.25,24.87,23.75,23.87,8463600,5.80\n1993-09-28,24.75,25.00,24.25,24.75,3386400,6.01\n1993-09-27,25.00,25.25,24.25,24.75,4043200,6.01\n1993-09-24,25.00,25.25,24.50,25.00,2743200,6.07\n1993-09-23,25.50,25.50,24.50,24.75,4697600,6.01\n1993-09-22,24.25,25.50,24.25,25.50,3960800,6.19\n1993-09-21,24.75,25.25,23.87,24.50,5250000,5.95\n1993-09-20,25.25,25.50,24.75,24.87,3968800,6.04\n1993-09-17,24.37,25.50,24.25,25.25,6157600,6.13\n1993-09-16,24.25,25.00,24.25,24.75,3086800,6.01\n1993-09-15,24.50,25.00,23.50,24.50,9206800,5.95\n1993-09-14,24.25,25.00,24.00,24.25,9880000,5.89\n1993-09-13,26.25,26.50,24.75,25.25,9143600,6.13\n1993-09-10,26.25,26.25,25.37,26.25,4804800,6.38\n1993-09-09,26.75,27.00,26.00,26.00,5352000,6.31\n1993-09-08,26.25,27.00,26.00,26.75,8102000,6.50\n1993-09-07,26.00,27.00,25.75,26.25,5130000,6.38\n1993-09-03,26.00,26.00,25.25,25.75,5830000,6.25\n1993-09-02,26.00,26.25,25.25,25.75,10081200,6.25\n1993-09-01,26.50,26.75,25.75,26.12,8065200,6.34\n1993-08-31,26.50,26.75,26.00,26.50,4570800,6.44\n1993-08-30,26.50,26.50,25.87,26.00,9785600,6.31\n1993-08-27,27.00,27.00,26.25,26.50,6676400,6.44\n1993-08-26,27.25,27.25,26.50,26.87,6296800,6.53\n1993-08-25,28.00,28.25,26.75,27.25,5209200,6.62\n1993-08-24,28.25,28.75,27.75,28.00,3625600,6.80\n1993-08-23,28.00,28.75,27.50,28.37,3265600,6.89\n1993-08-20,27.75,28.00,27.00,28.00,3574400,6.80\n1993-08-19,28.75,28.75,27.50,27.50,5452000,6.68\n1993-08-18,29.00,29.75,28.25,28.50,6751200,6.92\n1993-08-17,27.75,28.50,27.25,28.37,3876800,6.89\n1993-08-16,27.50,28.00,27.25,27.50,3669200,6.68\n1993-08-13,26.50,27.75,26.25,27.37,4978800,6.62\n1993-08-12,27.50,27.75,26.00,26.50,12098800,6.41\n1993-08-11,28.50,28.50,27.00,27.50,5965200,6.65\n1993-08-10,29.50,29.75,28.25,28.50,5465600,6.89\n1993-08-09,29.25,30.25,29.00,29.75,5767600,7.19\n1993-08-06,29.25,30.25,29.25,29.25,4506800,7.07\n1993-08-05,30.75,30.75,29.00,29.50,7498800,7.13\n1993-08-04,29.25,30.50,29.00,30.25,8700000,7.31\n1993-08-03,29.00,29.25,28.75,29.00,6315600,7.01\n1993-08-02,28.25,29.25,28.00,28.50,7728000,6.89\n1993-07-30,27.50,28.25,27.00,27.75,7669200,6.71\n1993-07-29,27.00,27.50,26.75,27.25,4343200,6.59\n1993-07-28,26.25,27.00,26.25,26.87,3300000,6.50\n1993-07-27,26.75,27.50,26.25,26.50,7100800,6.41\n1993-07-26,26.75,27.50,26.00,26.87,5468000,6.50\n1993-07-23,27.00,27.50,26.00,26.25,8365600,6.35\n1993-07-22,26.00,27.00,25.75,26.50,7554400,6.41\n1993-07-21,26.00,26.75,25.50,26.25,16283600,6.35\n1993-07-20,26.25,27.75,25.75,26.87,19017600,6.50\n1993-07-19,28.00,28.75,25.50,25.62,28813200,6.20\n1993-07-16,28.50,29.62,26.50,27.50,75744400,6.65\n1993-07-15,37.25,37.75,35.25,35.75,12091200,8.64\n1993-07-14,36.75,37.50,35.75,37.25,8816800,9.01\n1993-07-13,38.75,38.75,37.00,37.25,5650800,9.01\n1993-07-12,36.75,38.13,36.25,38.00,6215600,9.19\n1993-07-09,37.00,37.25,36.50,36.75,5604400,8.89\n1993-07-08,36.50,37.50,36.25,36.50,4964800,8.83\n1993-07-07,37.50,37.88,36.25,36.50,8124400,8.83\n1993-07-06,38.25,39.00,37.50,37.75,5558800,9.13\n1993-07-02,38.25,38.75,37.75,38.50,6846400,9.31\n1993-07-01,39.00,39.75,38.00,38.00,7809200,9.19\n1993-06-30,38.75,39.75,38.50,39.50,7170000,9.55\n1993-06-29,40.25,40.25,38.50,39.00,10526400,9.43\n1993-06-28,40.50,40.50,38.75,40.13,12645600,9.70\n1993-06-25,40.38,40.75,39.50,40.00,9198000,9.67\n1993-06-24,40.50,41.75,40.00,41.75,7980000,10.10\n1993-06-23,41.75,41.75,40.00,40.50,6462400,9.79\n1993-06-22,40.88,42.00,39.75,41.38,12021200,10.01\n1993-06-21,40.50,40.50,39.50,39.63,9776800,9.58\n1993-06-18,41.63,42.13,39.75,41.00,11138800,9.91\n1993-06-17,42.50,42.50,40.50,41.25,14635600,9.97\n1993-06-16,42.25,43.25,41.50,42.25,12615600,10.22\n1993-06-15,45.25,45.25,41.88,42.00,16018000,10.16\n1993-06-14,44.00,44.75,43.50,44.63,8927600,10.79\n1993-06-11,45.00,45.25,43.38,43.75,8662400,10.58\n1993-06-10,43.50,44.75,42.75,44.50,19783600,10.76\n1993-06-09,45.00,45.63,44.00,44.25,42090000,10.70\n1993-06-08,48.75,50.00,48.00,49.50,22194400,11.97\n1993-06-07,54.50,54.75,50.38,50.75,17239200,12.27\n1993-06-04,55.75,56.25,54.50,54.88,7649200,13.27\n1993-06-03,57.00,57.25,56.00,56.38,5603200,13.63\n1993-06-02,56.75,58.25,56.00,57.00,7160000,13.78\n1993-06-01,56.50,57.75,56.50,57.00,4837600,13.78\n1993-05-28,57.00,57.50,56.25,56.63,6575200,13.69\n1993-05-27,57.75,58.50,57.25,57.50,7049200,13.87\n1993-05-26,56.00,57.75,55.38,57.75,4353600,13.94\n1993-05-25,56.75,57.50,55.75,56.38,6462400,13.60\n1993-05-24,56.75,58.75,56.75,57.63,5373200,13.91\n1993-05-21,58.75,59.13,56.75,57.50,5300000,13.87\n1993-05-20,57.25,59.00,57.25,58.75,10385200,14.18\n1993-05-19,54.75,57.50,54.50,57.25,6176400,13.81\n1993-05-18,55.50,56.25,55.00,55.50,5860000,13.39\n1993-05-17,55.50,56.00,55.00,55.75,2491200,13.45\n1993-05-14,55.25,56.00,55.00,55.50,4212000,13.39\n1993-05-13,53.50,55.75,53.50,55.50,12940800,13.39\n1993-05-12,54.25,54.75,53.00,53.25,3779200,12.85\n1993-05-11,55.00,55.25,54.00,54.50,5665600,13.15\n1993-05-10,55.00,55.88,55.00,55.00,4929200,13.27\n1993-05-07,53.50,54.75,53.50,54.75,2927600,13.21\n1993-05-06,54.50,54.75,53.50,53.75,2536800,12.97\n1993-05-05,53.00,55.50,53.00,54.50,9059200,13.15\n1993-05-04,52.25,54.25,52.00,53.38,6112400,12.88\n1993-05-03,51.25,52.00,51.00,51.88,2332400,12.52\n1993-04-30,50.75,52.50,50.75,51.25,4730000,12.37\n1993-04-29,51.50,51.75,50.13,50.75,2958000,12.25\n1993-04-28,49.75,52.00,49.75,51.38,5846800,12.40\n1993-04-27,48.75,50.25,48.75,50.25,4648800,12.13\n1993-04-26,49.25,49.75,48.50,49.00,3689200,11.82\n1993-04-23,49.75,50.25,48.75,49.25,4808000,11.88\n1993-04-22,49.25,50.50,49.00,50.00,5648800,12.06\n1993-04-21,50.25,50.75,49.25,49.63,7337600,11.98\n1993-04-20,48.75,50.25,48.25,50.00,8580800,12.06\n1993-04-19,48.50,49.50,48.25,48.50,8148000,11.70\n1993-04-16,48.25,48.75,47.38,48.13,24533200,11.61\n1993-04-15,48.25,48.25,46.75,47.25,7816800,11.40\n1993-04-14,48.25,48.75,47.63,48.75,6092400,11.76\n1993-04-13,50.50,51.25,48.25,48.50,5893600,11.70\n1993-04-12,49.50,51.00,49.50,50.00,3324800,12.06\n1993-04-08,50.00,50.50,49.00,49.75,5857600,12.00\n1993-04-07,49.00,50.75,48.50,50.50,5825200,12.19\n1993-04-06,50.00,50.25,48.75,48.75,6020800,11.76\n1993-04-05,50.00,50.50,49.50,50.00,5332000,12.06\n1993-04-02,50.50,51.25,49.50,50.13,9077600,12.10\n1993-04-01,51.25,52.00,51.00,51.75,3878000,12.49\n1993-03-31,52.50,52.75,51.25,51.50,7968800,12.43\n1993-03-30,51.13,52.25,50.25,52.25,9447600,12.61\n1993-03-29,52.25,52.50,50.75,51.00,9362000,12.31\n1993-03-26,54.75,54.75,52.50,53.25,5431200,12.85\n1993-03-25,53.75,54.75,53.50,54.75,6125200,13.21\n1993-03-24,52.75,54.25,52.50,53.75,5126400,12.97\n1993-03-23,53.25,54.00,52.63,52.75,3674400,12.73\n1993-03-22,53.50,53.88,52.75,53.25,5911200,12.85\n1993-03-19,55.00,55.25,53.50,53.75,5516800,12.97\n1993-03-18,55.00,55.63,54.50,54.50,3810800,13.15\n1993-03-17,56.50,57.00,55.00,55.13,6301200,13.30\n1993-03-16,57.25,57.75,56.50,56.50,3626800,13.63\n1993-03-15,56.00,57.25,55.38,57.00,4868800,13.75\n1993-03-12,56.75,56.75,55.50,56.25,4527600,13.57\n1993-03-11,57.00,57.25,56.25,56.88,5167600,13.73\n1993-03-10,56.75,57.25,56.00,56.75,4738800,13.69\n1993-03-09,56.50,57.50,56.50,56.75,5535200,13.69\n1993-03-08,55.00,56.75,55.00,56.50,6322400,13.63\n1993-03-05,54.75,55.75,54.75,55.00,4001200,13.27\n1993-03-04,54.50,55.25,53.50,55.00,6730000,13.27\n1993-03-03,54.00,55.00,53.25,54.63,7261200,13.18\n1993-03-02,53.00,54.50,53.00,54.25,5294400,13.09\n1993-03-01,53.00,53.50,52.75,53.25,4272400,12.85\n1993-02-26,54.25,54.25,52.25,53.00,10538000,12.79\n1993-02-25,53.25,54.75,53.25,54.75,5979200,13.21\n1993-02-24,52.13,53.88,52.13,53.63,10253600,12.94\n1993-02-23,55.00,55.25,54.00,54.25,6937600,13.09\n1993-02-22,55.00,56.00,54.75,55.13,3531200,13.30\n1993-02-19,55.25,55.50,54.75,55.00,6366800,13.27\n1993-02-18,55.00,55.25,53.50,55.00,10006800,13.27\n1993-02-17,53.25,54.00,52.00,53.88,8932400,13.00\n1993-02-16,53.50,53.50,51.50,53.00,14563200,12.79\n1993-02-12,55.00,55.50,53.75,53.88,9855600,13.00\n1993-02-11,55.75,56.25,55.00,55.13,6015200,13.27\n1993-02-10,57.00,57.25,55.00,55.75,9593600,13.42\n1993-02-09,57.00,57.38,56.50,56.88,8525600,13.70\n1993-02-08,57.00,57.50,55.50,56.50,10060000,13.60\n1993-02-05,59.25,59.50,56.25,57.25,13134400,13.78\n1993-02-04,60.00,60.25,59.00,59.50,7453200,14.33\n1993-02-03,61.00,61.00,58.50,60.00,9455200,14.45\n1993-02-02,60.75,61.50,60.25,60.25,6530000,14.51\n1993-02-01,59.25,61.25,59.25,61.25,8608800,14.75\n1993-01-29,60.25,61.25,59.00,59.50,9516800,14.33\n1993-01-28,60.00,60.25,59.25,59.88,6580000,14.42\n1993-01-27,61.00,61.75,58.75,60.25,8101200,14.51\n1993-01-26,60.50,62.00,60.50,60.75,10201200,14.63\n1993-01-25,59.25,60.50,59.25,60.00,7237600,14.45\n1993-01-22,60.25,60.25,59.00,59.50,5252400,14.33\n1993-01-21,59.75,60.25,58.75,60.00,6601200,14.45\n1993-01-20,59.75,60.25,59.50,60.00,5685600,14.45\n1993-01-19,59.75,60.50,59.25,59.75,9802400,14.39\n1993-01-18,59.50,60.00,58.00,59.50,11935600,14.33\n1993-01-15,61.00,62.25,60.00,60.25,32257600,14.51\n1993-01-14,64.00,65.25,63.75,65.00,13145200,15.65\n1993-01-13,61.50,64.00,61.25,63.50,7135600,15.29\n1993-01-12,62.75,63.75,61.50,61.50,12364400,14.81\n1993-01-11,62.00,64.37,61.75,64.12,9785200,15.44\n1993-01-08,60.75,63.00,59.75,62.25,11474400,14.99\n1993-01-07,61.75,62.50,60.63,61.00,9741200,14.69\n1993-01-06,60.75,62.00,60.50,61.75,10055600,14.87\n1993-01-05,58.00,59.25,57.25,59.25,6658800,14.27\n1993-01-04,59.50,60.00,57.75,58.25,4618800,14.03\n1992-12-31,58.75,60.00,58.75,59.75,3302000,14.39\n1992-12-30,59.75,59.75,58.75,58.75,3610800,14.15\n1992-12-29,59.50,60.75,59.50,59.63,4171200,14.36\n1992-12-28,59.25,59.75,59.25,59.50,2536400,14.33\n1992-12-24,60.00,60.00,59.00,59.00,1642400,14.21\n1992-12-23,60.25,60.50,59.25,59.75,4018800,14.39\n1992-12-22,59.75,61.25,59.75,60.63,10009200,14.60\n1992-12-21,58.25,60.00,58.00,59.63,9159200,14.36\n1992-12-18,57.50,59.25,57.25,58.25,8414400,14.03\n1992-12-17,55.25,57.50,55.25,56.88,8370800,13.70\n1992-12-16,56.25,57.00,54.50,55.00,8085200,13.24\n1992-12-15,56.75,57.00,55.50,56.38,6541200,13.57\n1992-12-14,57.50,57.75,56.75,57.25,3962000,13.78\n1992-12-11,57.25,58.25,57.25,57.50,4299200,13.84\n1992-12-10,57.25,57.63,56.50,57.25,5010800,13.78\n1992-12-09,57.75,58.00,57.25,57.63,5700800,13.88\n1992-12-08,57.75,58.75,57.75,58.13,7035600,14.00\n1992-12-07,56.75,57.75,56.75,57.75,5168000,13.90\n1992-12-04,57.25,57.50,56.50,56.88,3432400,13.70\n1992-12-03,56.50,57.63,56.13,57.50,6710800,13.84\n1992-12-02,58.25,58.50,57.00,57.25,3498800,13.78\n1992-12-01,57.25,59.00,56.75,58.25,4652400,14.03\n1992-11-30,56.25,57.50,55.63,57.50,5739200,13.84\n1992-11-27,56.50,57.25,56.25,56.50,1688800,13.57\n1992-11-25,57.00,57.25,56.00,56.50,4208000,13.57\n1992-11-24,57.00,57.50,56.50,57.50,5601200,13.82\n1992-11-23,56.50,57.00,56.25,56.75,5462400,13.63\n1992-11-20,58.50,58.75,57.00,57.50,5572000,13.82\n1992-11-19,57.75,59.50,57.75,58.25,8608000,14.00\n1992-11-18,56.00,58.25,55.50,57.75,10889200,13.88\n1992-11-17,57.25,57.50,54.88,55.25,6045200,13.27\n1992-11-16,56.25,57.75,56.00,57.38,2419200,13.79\n1992-11-13,57.00,57.25,56.00,56.25,3042000,13.51\n1992-11-12,57.00,57.50,56.38,56.88,3844400,13.67\n1992-11-11,56.50,58.25,56.25,56.75,5023600,13.63\n1992-11-10,55.00,56.50,54.75,56.25,4368000,13.51\n1992-11-09,56.00,56.00,54.75,55.25,4052000,13.27\n1992-11-06,54.75,56.50,54.75,55.75,9443200,13.39\n1992-11-05,52.50,55.00,52.50,55.00,10647600,13.21\n1992-11-04,52.00,52.75,52.00,52.50,5086800,12.61\n1992-11-03,52.50,52.50,51.50,52.00,4042000,12.49\n1992-11-02,52.50,52.75,51.75,52.25,6094400,12.55\n1992-10-30,53.50,53.50,52.00,52.50,4657600,12.61\n1992-10-29,52.25,54.00,51.50,53.25,7661200,12.79\n1992-10-28,51.25,52.75,50.75,52.25,7033200,12.55\n1992-10-27,51.50,52.50,51.00,51.50,7575600,12.37\n1992-10-26,48.75,51.50,48.50,51.50,8972000,12.37\n1992-10-23,49.25,49.50,48.25,48.75,3279200,11.71\n1992-10-22,48.50,49.25,48.25,48.75,3026400,11.71\n1992-10-21,49.25,49.50,48.00,48.50,4080800,11.65\n1992-10-20,49.00,50.00,48.50,49.13,10269200,11.80\n1992-10-19,49.00,49.25,48.50,49.00,7002400,11.77\n1992-10-16,46.75,49.50,46.50,49.00,16142000,11.77\n1992-10-15,45.75,46.00,45.25,45.50,2701200,10.93\n1992-10-14,45.25,46.25,45.00,46.00,3429200,11.05\n1992-10-13,44.75,46.00,44.00,45.38,5265600,10.90\n1992-10-12,43.25,44.25,43.25,44.00,2580000,10.57\n1992-10-09,43.50,44.00,43.00,43.38,2108000,10.42\n1992-10-08,44.00,44.25,43.00,43.50,4543200,10.45\n1992-10-07,45.00,45.25,43.50,43.75,4050800,10.51\n1992-10-06,43.75,45.00,42.75,44.75,4058000,10.75\n1992-10-05,43.25,43.75,41.50,43.50,9475600,10.45\n1992-10-02,44.50,44.75,43.00,43.75,4063600,10.51\n1992-10-01,44.75,45.13,44.25,44.25,4396400,10.63\n1992-09-30,45.00,45.50,44.50,45.13,3580800,10.84\n1992-09-29,44.50,45.50,44.00,44.88,5626400,10.78\n1992-09-28,45.00,45.00,43.75,44.75,5351200,10.75\n1992-09-25,46.25,46.50,45.25,45.50,4926400,10.93\n1992-09-24,47.25,47.75,46.25,46.25,4492000,11.11\n1992-09-23,46.00,47.50,45.50,47.50,4443200,11.41\n1992-09-22,46.75,46.75,45.25,45.75,3996800,10.99\n1992-09-21,46.75,47.75,46.25,46.50,3204400,11.17\n1992-09-18,45.75,46.88,45.25,46.50,4133600,11.17\n1992-09-17,47.25,47.25,45.38,46.00,6180000,11.05\n1992-09-16,47.75,48.25,46.50,47.00,6395600,11.29\n1992-09-15,49.25,49.25,47.75,48.25,7806800,11.59\n1992-09-14,49.00,50.00,48.50,49.50,7682400,11.89\n1992-09-11,49.00,49.25,47.50,47.63,6438000,11.44\n1992-09-10,48.00,49.50,47.50,49.25,8165600,11.83\n1992-09-09,48.00,49.25,47.75,49.00,5622400,11.77\n1992-09-08,46.75,48.00,46.50,47.75,2511200,11.47\n1992-09-04,48.25,48.25,46.75,47.25,2268800,11.35\n1992-09-03,49.00,49.25,47.75,47.75,7570000,11.47\n1992-09-02,46.50,48.75,46.50,48.50,6794400,11.65\n1992-09-01,46.25,46.50,45.75,46.50,2172000,11.17\n1992-08-31,45.00,46.25,44.75,46.00,4328800,11.05\n1992-08-28,44.25,45.25,44.00,45.00,2202400,10.81\n1992-08-27,44.75,45.13,44.25,44.50,2974800,10.69\n1992-08-26,44.25,44.50,43.25,44.25,4325600,10.63\n1992-08-25,43.25,44.50,43.25,44.38,4731200,10.66\n1992-08-24,44.25,44.75,43.25,43.25,5454400,10.39\n1992-08-21,44.75,45.25,44.00,44.63,3926400,10.72\n1992-08-20,44.75,45.00,44.25,44.75,3894800,10.75\n1992-08-19,44.63,45.25,44.50,44.50,6096800,10.69\n1992-08-18,44.50,45.25,44.50,44.75,4017600,10.75\n1992-08-17,44.25,44.75,43.75,44.75,4617600,10.75\n1992-08-14,45.00,45.25,44.50,44.75,4872400,10.72\n1992-08-13,44.50,45.50,44.25,44.75,6122000,10.72\n1992-08-12,43.75,44.25,43.25,44.13,4343600,10.57\n1992-08-11,44.50,44.50,43.00,43.50,4339200,10.42\n1992-08-10,43.25,44.50,43.00,44.13,3280800,10.57\n1992-08-07,42.00,43.75,41.50,43.38,7842400,10.39\n1992-08-06,44.25,44.50,42.75,44.00,9220800,10.54\n1992-08-05,45.50,45.50,44.50,44.75,4981200,10.72\n1992-08-04,45.00,45.75,44.75,45.50,4295600,10.90\n1992-08-03,46.75,47.25,45.50,45.75,2452400,10.96\n1992-07-31,47.25,47.50,46.75,46.75,3262000,11.20\n1992-07-30,47.25,47.50,46.75,47.25,4927600,11.32\n1992-07-29,46.63,47.75,46.50,47.25,8976400,11.32\n1992-07-28,45.50,46.50,45.25,46.50,4813600,11.14\n1992-07-27,45.75,46.50,45.25,45.25,88800,10.84\n1992-07-24,44.50,46.25,44.00,45.88,4832000,10.99\n1992-07-23,44.50,44.75,43.75,44.75,6128800,10.72\n1992-07-22,45.25,45.50,44.00,44.25,5798800,10.60\n1992-07-21,45.50,46.25,45.00,45.75,4730800,10.96\n1992-07-20,44.75,45.25,44.00,44.75,6873600,10.72\n1992-07-17,45.00,46.00,44.63,45.00,15135600,10.78\n1992-07-16,47.75,49.00,47.25,48.75,5011200,11.68\n1992-07-15,47.50,49.00,47.25,48.00,6248000,11.50\n1992-07-14,47.00,48.00,47.00,47.50,4510800,11.38\n1992-07-13,45.75,47.13,45.25,47.00,4486800,11.26\n1992-07-10,46.00,46.25,44.88,45.75,5144400,10.96\n1992-07-09,46.00,46.50,45.75,45.88,5922000,10.99\n1992-07-08,44.00,45.75,44.00,45.75,7020000,10.96\n1992-07-07,46.25,46.25,43.50,44.25,7416400,10.60\n1992-07-06,46.50,46.75,45.50,46.25,4378000,11.08\n1992-07-02,49.00,49.00,45.75,46.25,9169200,11.08\n1992-07-01,48.00,49.50,47.75,49.00,5129200,11.74\n1992-06-30,46.75,48.25,46.50,48.00,6919200,11.50\n1992-06-29,45.75,47.13,45.25,46.75,6735200,11.20\n1992-06-26,45.75,46.00,44.50,45.25,3953600,10.84\n1992-06-25,46.50,46.50,45.25,45.63,5745200,10.93\n1992-06-24,45.50,46.00,45.25,46.00,7548000,11.02\n1992-06-23,45.00,45.50,44.50,45.25,11130800,10.84\n1992-06-22,44.00,44.75,42.75,44.25,13930000,10.60\n1992-06-19,46.00,46.00,43.75,44.75,15280000,10.72\n1992-06-18,47.50,49.00,44.75,45.25,15495600,10.84\n1992-06-17,49.00,49.25,47.00,47.50,10880800,11.38\n1992-06-16,51.75,52.00,48.75,49.25,13053200,11.80\n1992-06-15,54.00,54.00,52.50,52.63,6777600,12.61\n1992-06-12,54.50,55.00,54.25,54.63,3450800,13.09\n1992-06-11,53.75,54.25,53.50,53.88,5028800,12.91\n1992-06-10,54.00,54.75,53.50,53.75,4522400,12.88\n1992-06-09,54.25,54.25,53.50,54.00,3626800,12.94\n1992-06-08,55.00,55.00,54.00,54.25,3730000,13.00\n1992-06-05,54.75,55.25,54.25,54.88,4040800,13.15\n1992-06-04,54.25,54.75,53.50,54.50,6453200,13.06\n1992-06-03,56.50,56.50,54.00,54.13,10743200,12.97\n1992-06-02,57.50,57.50,56.25,56.50,5560000,13.54\n1992-06-01,57.25,59.50,56.00,57.50,8869200,13.78\n1992-05-29,59.75,60.63,59.50,59.75,6369200,14.29\n1992-05-28,60.00,60.25,59.00,59.50,4558000,14.23\n1992-05-27,59.25,60.25,59.00,60.25,5516400,14.41\n1992-05-26,59.50,59.75,58.75,59.25,3423200,14.17\n1992-05-22,59.00,59.75,59.00,59.50,1670800,14.23\n1992-05-21,60.25,60.25,58.75,59.13,4938800,14.14\n1992-05-20,59.75,60.25,59.25,60.00,6200800,14.35\n1992-05-19,60.75,60.75,59.00,59.38,4715600,14.20\n1992-05-18,61.50,61.50,60.00,60.38,4616400,14.44\n1992-05-15,61.00,61.25,60.50,60.63,4339200,14.50\n1992-05-14,62.75,63.00,60.25,61.38,5606800,14.68\n1992-05-13,62.50,63.25,62.25,62.75,3482000,15.01\n1992-05-12,62.25,63.00,61.75,62.25,2769200,14.89\n1992-05-11,62.00,62.75,61.50,62.25,3250000,14.89\n1992-05-08,61.50,62.88,61.00,62.00,7105600,14.83\n1992-05-07,61.50,62.25,60.50,60.75,6175600,14.53\n1992-05-06,60.75,62.13,60.50,61.75,6377600,14.77\n1992-05-05,60.50,60.63,59.50,60.50,6449200,14.47\n1992-05-04,59.50,61.25,59.25,60.50,4402000,14.47\n1992-05-01,60.00,60.75,58.25,59.25,4821200,14.17\n1992-04-30,57.25,60.25,56.50,60.13,9303600,14.38\n1992-04-29,54.25,57.00,54.25,57.00,7116800,13.63\n1992-04-28,55.25,55.75,53.00,54.25,6229200,12.97\n1992-04-27,56.00,56.25,55.00,55.75,5014800,13.33\n1992-04-24,57.00,58.25,56.00,56.50,3526800,13.51\n1992-04-23,57.50,58.25,56.00,57.00,6534400,13.63\n1992-04-22,56.25,58.00,56.25,57.63,6129200,13.78\n1992-04-21,57.00,57.25,56.00,56.25,6442400,13.45\n1992-04-20,59.00,59.00,56.00,56.75,7380800,13.57\n1992-04-16,60.25,60.75,58.50,59.00,9260800,14.11\n1992-04-15,58.00,60.88,57.50,60.50,7764400,14.47\n1992-04-14,57.75,59.25,57.25,58.75,5178000,14.05\n1992-04-13,55.50,56.75,55.25,56.50,4402000,13.51\n1992-04-10,57.25,57.50,55.00,55.50,9803600,13.27\n1992-04-09,56.00,58.25,55.25,57.25,6874400,13.69\n1992-04-08,57.00,57.00,54.75,55.88,13123600,13.36\n1992-04-07,61.00,61.25,57.25,57.25,8234400,13.69\n1992-04-06,59.00,61.00,59.00,60.75,3643600,14.53\n1992-04-03,58.75,59.25,58.50,59.00,4181200,14.11\n1992-04-02,59.00,59.50,58.38,58.75,4798800,14.05\n1992-04-01,57.25,59.25,57.25,59.00,5714400,14.11\n1992-03-31,58.25,59.75,58.00,58.25,7613200,13.93\n1992-03-30,61.25,61.25,57.75,58.13,12124400,13.90\n1992-03-27,63.88,64.00,60.50,61.00,9452000,14.59\n1992-03-26,64.75,65.25,63.75,64.00,4412400,15.30\n1992-03-25,65.00,65.00,64.25,64.50,4353200,15.42\n1992-03-24,63.50,65.00,63.25,65.00,7501200,15.54\n1992-03-23,63.00,63.75,63.00,63.00,1804400,15.07\n1992-03-20,63.00,63.25,63.00,63.25,1942400,15.13\n1992-03-19,63.75,63.75,62.75,63.00,4251200,15.07\n1992-03-18,63.25,64.00,63.00,63.75,2902000,15.25\n1992-03-17,63.50,63.75,62.75,62.88,3061200,15.04\n1992-03-16,62.75,63.50,61.75,63.38,2016400,15.16\n1992-03-13,63.25,63.75,62.00,63.13,2843600,15.10\n1992-03-12,63.25,63.75,61.50,62.75,5472400,15.01\n1992-03-11,63.75,64.25,63.00,63.25,4714400,15.13\n1992-03-10,64.00,64.75,63.75,63.75,4394400,15.25\n1992-03-09,63.75,64.25,63.50,63.75,3896800,15.25\n1992-03-06,63.50,64.00,63.00,64.00,4816400,15.30\n1992-03-05,64.50,65.50,63.00,63.50,8462400,15.19\n1992-03-04,66.25,66.75,64.75,65.00,4120800,15.54\n1992-03-03,67.75,68.00,66.25,66.37,3560000,15.87\n1992-03-02,67.75,68.50,67.25,67.25,3203200,16.08\n1992-02-28,68.50,69.00,67.00,67.50,3244400,16.14\n1992-02-27,70.00,70.00,68.00,68.50,4364800,16.38\n1992-02-26,68.25,70.00,68.25,69.87,8193600,16.71\n1992-02-25,66.25,68.50,65.25,68.50,8134400,16.38\n1992-02-24,66.25,66.50,65.75,66.12,6122400,15.81\n1992-02-21,64.75,65.50,64.50,65.00,5421200,15.54\n1992-02-20,62.50,64.75,62.25,64.62,4692400,15.45\n1992-02-19,62.75,63.00,61.75,62.00,3426400,14.83\n1992-02-18,64.25,64.50,62.75,62.75,2442000,15.01\n1992-02-14,63.75,64.25,63.25,64.12,2610800,15.33\n1992-02-13,65.25,65.25,63.75,64.25,2734400,15.34\n1992-02-12,63.75,65.50,63.00,65.25,4931200,15.57\n1992-02-11,63.00,63.75,62.25,62.88,4378800,15.01\n1992-02-10,64.00,64.25,63.00,63.13,3091200,15.07\n1992-02-07,64.25,64.75,62.75,64.00,5285600,15.28\n1992-02-06,65.75,66.00,64.00,64.12,3330000,15.30\n1992-02-05,66.25,66.75,65.12,66.12,5772400,15.78\n1992-02-04,65.75,66.25,65.00,65.75,6896400,15.69\n1992-02-03,64.75,66.25,64.50,65.75,5652000,15.69\n1992-01-31,64.00,65.25,63.50,64.75,5164400,15.46\n1992-01-30,63.50,63.75,62.75,63.75,3128800,15.22\n1992-01-29,64.75,65.75,63.25,63.25,5164400,15.10\n1992-01-28,64.75,65.37,63.00,65.25,6206800,15.57\n1992-01-27,64.75,65.25,64.25,64.50,2992000,15.40\n1992-01-24,64.50,65.75,64.00,64.62,6356400,15.42\n1992-01-23,64.25,64.75,63.00,64.50,4953200,15.40\n1992-01-22,61.50,63.75,61.25,63.50,6560000,15.16\n1992-01-21,64.25,64.25,61.00,61.13,6938000,14.59\n1992-01-20,64.50,65.25,64.00,64.00,7492400,15.28\n1992-01-17,67.75,69.00,64.75,64.75,30308800,15.46\n1992-01-16,63.75,64.25,62.50,62.75,10485200,14.98\n1992-01-15,64.50,65.00,63.00,63.50,11652400,15.16\n1992-01-14,62.25,64.75,62.25,64.50,9789200,15.40\n1992-01-13,62.25,62.75,61.50,62.00,3858800,14.80\n1992-01-10,61.50,62.50,61.00,62.25,7012400,14.86\n1992-01-09,60.50,62.25,60.25,62.25,7450800,14.86\n1992-01-08,58.50,61.25,58.50,60.50,8330800,14.44\n1992-01-07,57.50,59.50,57.50,59.13,5059200,14.11\n1992-01-06,58.75,59.00,57.75,58.00,4080000,13.84\n1992-01-03,60.00,60.25,58.25,59.00,6814400,14.08\n1992-01-02,55.75,59.75,55.50,59.50,8357600,14.20\n1991-12-31,57.38,58.00,56.00,56.38,4802000,13.46\n1991-12-30,55.00,57.25,55.00,56.75,6580800,13.55\n1991-12-27,54.75,55.75,54.50,55.00,6008000,13.13\n1991-12-26,52.75,55.00,52.25,54.88,4805600,13.10\n1991-12-24,52.00,53.75,51.75,52.25,6742400,12.47\n1991-12-23,50.50,51.75,50.00,51.50,3686800,12.29\n1991-12-20,51.25,51.50,50.25,50.25,4588000,11.99\n1991-12-19,51.25,51.75,50.75,50.75,4140800,12.11\n1991-12-18,50.25,52.00,50.00,51.75,6678000,12.35\n1991-12-17,50.50,51.00,50.25,50.50,3502400,12.05\n1991-12-16,50.38,50.75,50.00,50.50,2777600,12.05\n1991-12-13,49.75,50.75,49.75,50.38,3418000,12.03\n1991-12-12,49.38,49.75,49.00,49.38,3297600,11.79\n1991-12-11,49.25,49.75,48.50,49.00,3031200,11.70\n1991-12-10,49.00,49.50,48.50,49.13,4390000,11.73\n1991-12-09,49.00,50.00,48.75,49.13,3502000,11.73\n1991-12-06,49.50,49.75,48.50,48.75,7055200,11.64\n1991-12-05,50.50,51.00,49.25,50.00,3555600,11.93\n1991-12-04,50.75,50.75,50.00,50.50,2897600,12.05\n1991-12-03,52.00,52.00,50.25,50.50,3692400,12.05\n1991-12-02,50.75,52.00,50.00,51.75,4250000,12.35\n1991-11-29,50.50,51.50,50.50,50.75,1227600,12.11\n1991-11-27,51.25,51.50,50.50,51.00,2268800,12.17\n1991-11-26,51.50,52.00,50.00,51.50,4982000,12.29\n1991-11-25,51.00,52.25,51.00,51.25,2802000,12.23\n1991-11-22,51.00,51.75,50.25,51.25,3502400,12.23\n1991-11-21,50.50,51.75,50.50,51.00,3823200,12.17\n1991-11-20,51.25,52.00,50.25,50.50,6005600,12.05\n1991-11-19,51.75,51.75,49.75,51.25,10216400,12.23\n1991-11-18,50.00,52.50,50.00,52.13,8530000,12.44\n1991-11-15,54.50,54.75,49.75,50.00,9186400,11.91\n1991-11-14,54.25,55.25,54.00,54.75,6733600,13.04\n1991-11-13,54.00,54.50,53.50,54.13,6640000,12.89\n1991-11-12,54.25,54.75,53.75,54.50,5972000,12.98\n1991-11-11,53.50,54.50,53.25,53.75,5896800,12.80\n1991-11-08,51.25,53.75,51.00,53.25,13435200,12.68\n1991-11-07,48.50,50.50,48.25,49.75,10618800,11.85\n1991-11-06,49.00,49.25,47.50,48.00,8466400,11.43\n1991-11-05,49.75,50.50,48.75,48.75,7711200,11.61\n1991-11-04,50.75,50.75,48.50,49.75,6983200,11.85\n1991-11-01,51.25,52.00,50.50,51.00,7203600,12.14\n1991-10-31,50.75,51.75,50.00,51.50,8300800,12.26\n1991-10-30,52.00,52.75,49.50,49.75,5302400,11.85\n1991-10-29,51.50,52.00,50.75,51.75,3624400,12.32\n1991-10-28,51.50,51.75,50.75,51.50,2792400,12.26\n1991-10-25,51.75,52.25,50.75,51.25,3832000,12.20\n1991-10-24,53.00,53.25,51.50,52.13,6372400,12.41\n1991-10-23,55.00,55.25,52.75,53.13,6046400,12.65\n1991-10-22,55.50,56.25,54.50,54.50,7456400,12.98\n1991-10-21,55.25,55.88,54.25,54.75,4172000,13.04\n1991-10-18,55.13,55.50,54.50,55.00,15964400,13.10\n1991-10-17,53.00,53.25,51.50,52.38,5423200,12.47\n1991-10-16,52.50,54.00,52.25,53.50,7182000,12.74\n1991-10-15,50.50,52.50,50.00,52.50,10300800,12.50\n1991-10-14,49.00,50.25,48.75,49.88,4015600,11.88\n1991-10-11,48.13,48.88,46.50,48.50,4292000,11.55\n1991-10-10,48.75,49.00,46.75,47.75,5623200,11.37\n1991-10-09,48.25,48.75,47.75,48.00,4752400,11.43\n1991-10-08,48.13,48.50,46.50,48.25,6170000,11.49\n1991-10-07,48.00,48.75,47.50,48.13,2328000,11.46\n1991-10-04,48.00,48.75,47.50,48.25,2854400,11.49\n1991-10-03,50.00,50.00,47.50,47.75,6478000,11.37\n1991-10-02,51.75,51.75,49.50,49.75,643600,11.85\n1991-10-01,49.25,51.25,49.00,50.75,4698800,12.08\n1991-09-30,49.25,49.75,49.00,49.50,2266800,11.79\n1991-09-27,50.00,50.75,48.75,49.00,2245200,11.67\n1991-09-26,50.25,50.25,49.00,50.00,2556800,11.91\n1991-09-25,50.25,50.50,49.25,50.50,1959200,12.02\n1991-09-24,49.50,50.38,48.25,50.25,3805600,11.97\n1991-09-23,50.00,50.75,49.25,49.50,3136800,11.79\n1991-09-20,49.75,51.00,49.50,50.63,6742000,12.06\n1991-09-19,50.25,50.50,49.50,49.75,6374400,11.85\n1991-09-18,48.75,50.50,48.50,50.13,4342000,11.94\n1991-09-17,47.00,49.00,46.75,49.00,4856400,11.67\n1991-09-16,49.25,49.25,46.50,47.25,7365600,11.25\n1991-09-13,50.00,50.25,48.50,48.63,5974400,11.58\n1991-09-12,51.25,51.25,49.75,50.63,4267600,12.06\n1991-09-11,50.75,51.00,49.50,50.50,6378000,12.02\n1991-09-10,52.75,53.38,49.75,50.13,6535600,11.94\n1991-09-09,51.75,53.50,51.50,53.25,4538000,12.68\n1991-09-06,51.00,51.75,50.50,51.50,2848800,12.26\n1991-09-05,51.50,51.75,50.75,51.00,2793600,12.14\n1991-09-04,52.75,52.75,51.38,51.50,4299200,12.26\n1991-09-03,52.75,53.25,52.00,52.50,2443200,12.50\n1991-08-30,53.00,53.25,52.25,53.00,2363200,12.62\n1991-08-29,53.25,53.88,52.50,53.00,4053200,12.62\n1991-08-28,54.00,54.25,53.13,53.25,3843600,12.68\n1991-08-27,53.00,54.00,52.75,54.00,3597600,12.86\n1991-08-26,53.00,53.50,52.50,53.00,3644400,12.62\n1991-08-23,54.00,55.50,52.75,53.00,8601200,12.62\n1991-08-22,54.00,54.75,53.75,54.25,5936400,12.92\n1991-08-21,52.50,54.13,52.00,53.75,7987600,12.80\n1991-08-20,51.50,51.75,50.50,51.00,7123600,12.14\n1991-08-19,49.50,51.63,48.50,50.50,11538000,12.02\n1991-08-16,52.75,54.25,52.25,53.25,5689200,12.65\n1991-08-15,55.00,55.00,53.00,53.25,5219200,12.65\n1991-08-14,54.75,55.00,53.88,54.88,7173200,13.04\n1991-08-13,52.00,54.00,52.00,53.50,10255200,12.71\n1991-08-12,50.75,52.25,50.50,51.75,5096400,12.29\n1991-08-09,50.50,51.00,49.75,50.75,5533600,12.06\n1991-08-08,50.75,51.75,50.00,50.50,6769200,12.00\n1991-08-07,49.50,51.00,49.38,50.38,7578800,11.97\n1991-08-06,48.75,50.25,47.75,49.50,7890800,11.76\n1991-08-05,49.75,49.75,48.25,48.50,3620800,11.52\n1991-08-02,49.75,50.25,49.00,50.00,9767600,11.88\n1991-08-01,46.00,49.25,45.75,49.13,16023600,11.67\n1991-07-31,46.50,46.88,45.00,46.25,3689200,10.99\n1991-07-30,45.50,46.75,45.50,46.50,3281200,11.05\n1991-07-29,45.25,45.50,44.50,45.50,1916800,10.81\n1991-07-26,45.75,45.75,44.75,44.88,2657600,10.66\n1991-07-25,45.25,45.75,45.00,45.25,2366800,10.75\n1991-07-24,45.25,45.75,44.50,45.00,4703200,10.69\n1991-07-23,46.25,46.50,44.50,45.00,4770000,10.69\n1991-07-22,45.75,46.25,45.50,46.00,3882000,10.93\n1991-07-19,45.25,46.25,45.00,46.00,4601200,10.93\n1991-07-18,44.00,45.13,43.00,44.88,14240000,10.66\n1991-07-17,43.50,44.50,42.25,42.50,7474400,10.10\n1991-07-16,45.50,45.75,43.50,43.75,7966400,10.39\n1991-07-15,46.75,46.75,45.50,45.50,4932400,10.81\n1991-07-12,47.25,47.25,46.25,46.75,4753200,11.11\n1991-07-11,47.00,47.25,46.00,46.75,5217600,11.11\n1991-07-10,47.50,48.25,46.75,47.25,5610000,11.23\n1991-07-09,47.25,48.25,46.50,46.88,8091200,11.14\n1991-07-08,45.25,47.25,45.00,46.75,10971200,11.11\n1991-07-05,43.00,46.00,42.75,45.63,11842000,10.84\n1991-07-03,42.25,43.50,41.75,43.13,11087600,10.25\n1991-07-02,42.25,42.75,41.75,42.25,4296800,10.04\n1991-07-01,42.25,43.00,41.75,42.50,6979200,10.10\n1991-06-28,42.25,42.50,40.25,41.50,8102400,9.86\n1991-06-27,42.50,42.75,41.75,42.50,5400000,10.10\n1991-06-26,42.75,43.50,42.25,43.00,8958000,10.22\n1991-06-25,42.00,43.00,41.75,42.38,8151200,10.07\n1991-06-24,41.75,42.25,41.25,41.75,7443600,9.92\n1991-06-21,42.00,42.50,41.75,42.00,7378800,9.98\n1991-06-20,41.25,42.00,40.75,42.00,5158000,9.98\n1991-06-19,41.75,42.25,41.25,41.75,6408000,9.92\n1991-06-18,42.25,43.25,41.50,42.13,8749200,10.01\n1991-06-17,41.00,42.25,41.00,42.00,5966800,9.98\n1991-06-14,42.75,42.75,40.75,41.13,8049200,9.77\n1991-06-13,42.50,43.00,41.75,42.13,7565200,10.01\n1991-06-12,44.00,44.75,41.25,42.38,15580000,10.07\n1991-06-11,45.00,45.50,44.25,44.63,6742400,10.60\n1991-06-10,46.00,47.13,45.75,46.00,5991200,10.93\n1991-06-07,46.25,47.00,45.63,46.13,5463600,10.96\n1991-06-06,48.25,48.25,46.50,46.63,6028000,11.08\n1991-06-05,49.25,49.25,47.75,48.00,4760800,11.40\n1991-06-04,49.50,49.50,48.50,49.13,6593600,11.67\n1991-06-03,47.00,49.50,46.75,49.25,7870800,11.70\n1991-05-31,47.50,47.75,46.25,47.00,7792400,11.17\n1991-05-30,47.00,47.75,46.50,47.63,5663600,11.32\n1991-05-29,46.25,47.75,45.88,47.00,13733600,11.17\n1991-05-28,46.00,46.25,45.25,46.00,6124400,10.93\n1991-05-24,45.50,46.00,45.00,45.88,3484800,10.90\n1991-05-23,46.50,46.75,44.75,45.13,7458800,10.72\n1991-05-22,45.75,46.50,45.50,46.25,8137600,10.99\n1991-05-21,45.25,46.50,44.75,45.25,12500000,10.75\n1991-05-20,47.25,47.50,44.00,44.25,9365200,10.51\n1991-05-17,48.75,48.75,46.50,47.00,16836800,11.14\n1991-05-16,51.00,51.25,48.50,49.00,13652000,11.61\n1991-05-15,51.50,52.00,49.00,50.50,18530800,11.97\n1991-05-14,52.75,53.75,52.50,53.50,7763600,12.68\n1991-05-13,52.25,53.50,51.50,52.75,8763600,12.50\n1991-05-10,51.50,53.25,50.75,51.25,8652000,12.14\n1991-05-09,50.00,51.50,49.75,50.75,8523200,12.03\n1991-05-08,50.75,50.75,49.25,49.75,6332400,11.79\n1991-05-07,51.00,51.25,50.50,50.63,9671200,12.00\n1991-05-06,48.50,50.50,48.25,50.25,7596400,11.91\n1991-05-03,49.00,49.50,48.25,49.00,8717600,11.61\n1991-05-02,47.75,49.75,47.50,49.00,28973600,11.61\n1991-05-01,48.00,49.00,47.00,47.25,66732000,11.20\n1991-04-30,57.75,58.25,54.50,55.00,25413600,13.03\n1991-04-29,58.50,60.25,58.25,58.25,7395200,13.80\n1991-04-26,58.50,59.00,57.75,58.63,4481200,13.89\n1991-04-25,59.75,59.75,58.50,58.50,11276800,13.86\n1991-04-24,61.75,62.00,60.50,60.75,3769200,14.40\n1991-04-23,62.25,63.00,60.25,61.50,8494400,14.57\n1991-04-22,59.50,62.00,58.75,61.50,9190000,14.57\n1991-04-19,61.00,61.50,59.50,59.63,10272400,14.13\n1991-04-18,62.75,63.00,60.75,61.00,8853600,14.46\n1991-04-17,65.00,65.00,62.00,63.25,11533600,14.99\n1991-04-16,63.25,64.50,62.50,64.25,22176800,15.23\n1991-04-15,61.75,64.50,60.00,62.25,60732400,14.75\n1991-04-12,71.50,73.25,69.75,71.75,13140000,17.00\n1991-04-11,67.75,71.37,67.50,71.00,12710800,16.83\n1991-04-10,68.50,69.25,66.75,66.87,7733600,15.85\n1991-04-09,69.75,70.00,68.25,68.75,4280800,16.29\n1991-04-08,69.25,70.00,68.75,70.00,2604400,16.59\n1991-04-05,71.75,71.75,68.75,69.37,5567600,16.44\n1991-04-04,70.00,72.00,69.50,71.50,6024400,16.94\n1991-04-03,72.50,72.75,70.00,70.00,8585200,16.59\n1991-04-02,69.00,72.75,68.50,72.75,10473600,17.24\n1991-04-01,68.00,69.50,67.50,68.50,4218000,16.23\n1991-03-28,69.25,70.00,67.75,68.00,2816800,16.11\n1991-03-27,70.00,70.25,68.50,69.25,6812400,16.41\n1991-03-26,64.75,70.25,64.75,70.00,11935200,16.59\n1991-03-25,63.50,65.00,63.25,64.50,4858800,15.28\n1991-03-22,64.00,64.75,62.25,63.25,12096400,14.99\n1991-03-21,68.25,68.75,63.75,64.75,10600000,15.34\n1991-03-20,69.25,69.50,66.87,67.75,12939200,16.06\n1991-03-19,66.50,70.25,65.75,69.50,15100000,16.47\n1991-03-18,65.75,68.25,65.75,67.75,7645200,16.06\n1991-03-15,65.75,66.50,65.25,66.25,7335600,15.70\n1991-03-14,66.75,67.50,64.50,65.25,8126400,15.46\n1991-03-13,62.75,66.50,62.75,66.25,6253200,15.70\n1991-03-12,63.00,63.75,62.50,62.88,8360000,14.90\n1991-03-11,64.50,64.75,62.25,63.50,6276400,15.05\n1991-03-08,67.75,68.25,65.00,65.00,11522400,15.40\n1991-03-07,63.50,67.50,63.25,67.25,11497600,15.94\n1991-03-06,64.00,65.62,62.88,63.00,18731200,14.93\n1991-03-05,59.00,63.25,59.00,63.13,15769200,14.96\n1991-03-04,58.00,58.75,57.00,58.38,3175600,13.83\n1991-03-01,57.00,59.00,57.00,57.75,4518800,13.69\n1991-02-28,58.25,58.50,56.25,57.25,8120000,13.57\n1991-02-27,58.25,58.50,57.50,58.25,6243200,13.80\n1991-02-26,57.50,58.75,56.50,58.25,8934400,13.80\n1991-02-25,60.25,60.50,57.50,58.00,12848800,13.74\n1991-02-22,59.00,61.75,58.50,59.75,8320800,14.16\n1991-02-21,61.25,62.25,58.75,59.00,6826400,13.98\n1991-02-20,59.50,61.75,59.25,61.00,7646800,14.46\n1991-02-19,57.50,60.25,57.38,60.00,8080800,14.22\n1991-02-15,57.25,58.50,57.25,57.63,13067600,13.66\n1991-02-14,60.00,60.00,56.75,57.13,13493200,13.51\n1991-02-13,60.00,60.25,58.00,60.00,9130800,14.19\n1991-02-12,61.00,61.25,59.38,60.00,8042000,14.19\n1991-02-11,60.00,61.50,59.75,61.38,11546400,14.51\n1991-02-08,57.50,60.25,57.50,59.88,11220000,14.16\n1991-02-07,57.00,58.75,55.75,57.75,18587600,13.66\n1991-02-06,57.75,58.25,56.50,56.88,7965200,13.45\n1991-02-05,55.25,58.00,54.75,57.75,12740000,13.66\n1991-02-04,55.75,56.00,55.00,55.25,9569200,13.07\n1991-02-01,55.50,57.88,55.50,55.75,15897600,13.18\n1991-01-31,55.50,56.00,54.75,55.50,8677600,13.12\n1991-01-30,53.25,55.75,53.25,55.50,12043200,13.12\n1991-01-29,54.25,54.50,52.25,53.75,7708800,12.71\n1991-01-28,53.25,55.25,53.25,54.50,9771200,12.89\n1991-01-25,52.00,53.63,52.00,53.50,8012000,12.65\n1991-01-24,51.50,52.75,51.50,52.13,8374400,12.33\n1991-01-23,51.25,52.25,51.00,51.75,8725600,12.24\n1991-01-22,51.00,52.50,50.50,51.25,15296400,12.12\n1991-01-21,49.75,51.50,49.75,50.75,11595200,12.00\n1991-01-18,48.75,50.75,48.50,50.25,33691200,11.88\n1991-01-17,52.50,52.75,49.00,51.25,21137600,12.12\n1991-01-16,47.00,50.00,46.75,49.75,13968800,11.76\n1991-01-15,46.50,46.75,46.00,46.75,6870000,11.06\n1991-01-14,46.00,46.75,46.00,46.25,7535600,10.94\n1991-01-11,47.00,47.25,46.00,47.00,11003200,11.11\n1991-01-10,45.75,47.25,45.75,47.13,15562400,11.15\n1991-01-09,44.25,46.00,43.75,45.25,16692400,10.70\n1991-01-08,43.75,43.88,42.50,43.25,7816400,10.23\n1991-01-07,43.00,45.25,43.00,43.25,11111200,10.23\n1991-01-04,43.00,44.25,43.00,43.25,5062400,10.23\n1991-01-03,43.50,44.25,43.00,43.00,5365600,10.17\n1991-01-02,42.75,44.00,42.00,43.50,5543600,10.29\n1990-12-31,43.00,43.25,42.75,43.00,1593200,10.17\n1990-12-28,43.25,43.50,42.75,43.00,2285200,10.17\n1990-12-27,43.25,44.00,43.25,43.50,3492000,10.29\n1990-12-26,44.00,44.25,43.00,43.75,3682000,10.35\n1990-12-24,44.75,45.00,44.00,44.00,2106800,10.40\n1990-12-21,44.25,45.25,43.50,45.00,12363200,10.64\n1990-12-20,41.25,44.50,41.25,44.00,14326400,10.40\n1990-12-19,42.50,42.50,41.13,41.88,5036800,9.90\n1990-12-18,41.00,42.50,40.75,42.25,7899200,9.99\n1990-12-17,39.00,40.50,39.00,40.13,4683600,9.49\n1990-12-14,40.25,40.50,39.50,39.88,3126400,9.43\n1990-12-13,39.50,41.00,39.50,40.75,5752000,9.64\n1990-12-12,39.75,40.00,39.00,39.63,8664400,9.37\n1990-12-11,41.25,41.50,40.00,40.00,12438000,9.46\n1990-12-10,42.25,42.50,41.50,41.75,8966400,9.87\n1990-12-07,41.00,42.75,41.00,42.50,11781200,10.05\n1990-12-06,41.25,41.75,40.50,41.25,19013600,9.75\n1990-12-05,38.50,40.25,37.88,40.13,7822000,9.49\n1990-12-04,37.50,38.75,37.50,38.50,5453200,9.10\n1990-12-03,37.25,38.25,37.00,38.13,5922400,9.02\n1990-11-30,36.25,37.25,36.25,36.75,4350800,8.69\n1990-11-29,37.00,37.00,36.25,36.75,4528000,8.69\n1990-11-28,37.75,38.50,36.75,36.75,6250800,8.69\n1990-11-27,37.00,38.25,36.75,37.50,5899200,8.87\n1990-11-26,36.00,37.00,36.00,36.75,2925600,8.69\n1990-11-23,36.25,37.00,36.00,36.38,1911200,8.60\n1990-11-21,35.25,36.25,34.75,36.13,4400800,8.54\n1990-11-20,36.50,36.75,35.25,35.50,5490800,8.39\n1990-11-19,35.50,36.38,35.25,36.38,8017600,8.60\n1990-11-16,35.75,36.00,34.75,35.13,6545200,8.31\n1990-11-15,36.75,37.00,35.50,36.00,5787600,8.48\n1990-11-14,35.75,37.25,35.75,37.00,6819200,8.72\n1990-11-13,36.25,36.50,35.75,36.00,5086400,8.48\n1990-11-12,35.50,36.75,35.25,36.25,5192000,8.54\n1990-11-09,35.00,35.75,34.50,35.50,7102000,8.37\n1990-11-08,33.00,35.00,33.00,34.50,7136400,8.13\n1990-11-07,33.50,33.75,32.63,33.25,7254400,7.84\n1990-11-06,33.50,34.50,33.25,33.50,6620800,7.90\n1990-11-05,32.25,33.50,32.00,33.25,6604400,7.84\n1990-11-02,30.50,32.38,30.50,31.75,5323200,7.48\n1990-11-01,30.50,31.00,29.75,30.50,3258800,7.19\n1990-10-31,30.50,31.87,30.25,30.75,5331200,7.25\n1990-10-30,29.75,30.75,28.87,30.37,3513600,7.16\n1990-10-29,30.25,30.50,29.75,29.87,4415600,7.04\n1990-10-26,29.75,31.25,29.75,30.00,4811200,7.07\n1990-10-25,30.25,31.25,29.62,30.00,5481200,7.07\n1990-10-24,30.75,31.00,30.00,30.50,5079200,7.19\n1990-10-23,31.00,31.50,30.25,31.00,5969200,7.31\n1990-10-22,31.50,31.50,30.50,31.12,9041200,7.33\n1990-10-19,31.25,31.75,30.25,31.37,33363200,7.39\n1990-10-18,26.50,28.75,26.50,28.50,11255600,6.72\n1990-10-17,25.25,26.50,25.00,26.50,11059200,6.25\n1990-10-16,27.50,27.50,24.25,25.00,10913200,5.89\n1990-10-15,28.50,28.75,26.62,27.75,7190000,6.54\n1990-10-12,28.25,28.50,27.00,28.25,8169200,6.66\n1990-10-11,26.75,27.87,25.50,27.75,7376800,6.54\n1990-10-10,27.25,28.00,26.00,26.50,5283600,6.25\n1990-10-09,28.50,29.00,27.75,28.00,4321200,6.60\n1990-10-08,28.75,29.25,28.25,29.12,2218800,6.86\n1990-10-05,27.00,28.75,27.00,28.00,3572000,6.60\n1990-10-04,26.75,28.00,26.25,28.00,7638800,6.60\n1990-10-03,29.75,29.75,26.75,27.00,9591200,6.36\n1990-10-02,31.00,32.00,29.50,29.62,9699200,6.98\n1990-10-01,29.50,31.00,29.25,30.50,5581200,7.19\n1990-09-28,28.50,29.00,27.25,29.00,6291200,6.83\n1990-09-27,30.00,30.50,28.00,28.25,5085600,6.66\n1990-09-26,30.00,30.50,29.75,29.75,3363200,7.01\n1990-09-25,30.50,30.75,29.25,30.00,5642000,7.07\n1990-09-24,31.50,31.50,29.75,30.25,4961200,7.13\n1990-09-21,32.00,32.50,31.00,31.50,5503600,7.42\n1990-09-20,32.25,32.25,31.25,31.62,3607600,7.45\n1990-09-19,33.25,33.75,32.00,32.50,6536800,7.66\n1990-09-18,33.75,33.75,33.00,33.38,4456400,7.87\n1990-09-17,34.00,35.25,33.50,33.75,2782000,7.95\n1990-09-14,33.50,34.25,33.25,34.00,4084400,8.01\n1990-09-13,34.50,34.75,33.00,33.75,3492400,7.95\n1990-09-12,34.50,34.50,33.50,34.00,3600800,8.01\n1990-09-11,36.00,36.13,33.75,34.00,6370800,8.01\n1990-09-10,37.00,37.00,35.75,35.75,2732400,8.43\n1990-09-07,35.50,36.75,35.13,36.38,2098800,8.57\n1990-09-06,35.50,36.00,35.25,35.75,3134800,8.43\n1990-09-05,37.25,37.25,35.75,36.00,2292000,8.48\n1990-09-04,36.50,37.50,36.50,37.00,2974800,8.72\n1990-08-31,36.00,37.25,36.00,37.00,3570000,8.72\n1990-08-30,37.25,37.50,36.00,36.25,4388800,8.54\n1990-08-29,38.00,38.13,36.75,37.25,5407600,8.78\n1990-08-28,37.50,38.38,37.25,38.13,2877600,8.99\n1990-08-27,36.75,38.00,36.25,37.75,4214800,8.90\n1990-08-24,35.25,36.00,34.75,35.50,2634400,8.37\n1990-08-23,34.25,35.00,33.50,34.50,5138800,8.13\n1990-08-22,37.00,37.00,34.88,35.13,4395600,8.28\n1990-08-21,35.75,36.75,35.25,36.25,5769200,8.54\n1990-08-20,36.50,37.50,36.25,36.75,2681200,8.66\n1990-08-17,38.50,38.50,35.75,36.50,8806400,8.58\n1990-08-16,39.00,39.63,38.50,38.50,4438800,9.05\n1990-08-15,40.00,40.25,39.25,39.25,3292000,9.22\n1990-08-14,40.00,40.00,39.25,39.75,3520800,9.34\n1990-08-13,38.00,40.00,37.88,39.88,5584400,9.37\n1990-08-10,38.75,39.25,38.25,38.75,3683600,9.11\n1990-08-09,40.25,40.50,39.25,39.50,3443600,9.28\n1990-08-08,39.50,40.75,39.50,40.13,3674400,9.43\n1990-08-07,40.25,40.63,38.75,39.50,7096400,9.28\n1990-08-06,39.00,40.50,38.50,39.50,6425600,9.28\n1990-08-03,43.50,43.75,39.75,41.25,9609200,9.69\n1990-08-02,41.25,43.75,41.25,43.50,7973600,10.22\n1990-08-01,42.00,42.75,41.50,42.38,3350800,9.96\n1990-07-31,42.50,42.75,41.50,42.00,3444800,9.87\n1990-07-30,40.75,42.50,40.75,42.38,3058800,9.96\n1990-07-27,41.25,41.75,40.50,41.38,2240000,9.72\n1990-07-26,42.25,42.50,41.00,41.38,2885600,9.72\n1990-07-25,42.00,43.25,41.75,42.25,3762400,9.93\n1990-07-24,42.00,42.25,41.00,42.13,6928800,9.90\n1990-07-23,41.00,41.75,40.00,41.50,9655200,9.75\n1990-07-20,42.00,42.50,40.75,41.00,6858000,9.63\n1990-07-19,40.75,42.50,40.00,41.75,20932400,9.81\n1990-07-18,44.50,45.00,43.00,44.63,10309200,10.49\n1990-07-17,45.75,46.00,44.00,44.25,4892000,10.40\n1990-07-16,46.75,47.13,45.25,45.63,6428000,10.72\n1990-07-13,47.50,47.75,46.75,46.75,8254400,10.99\n1990-07-12,46.75,47.50,46.50,47.38,6537600,11.13\n1990-07-11,46.75,47.00,45.75,47.00,8808800,11.04\n1990-07-10,47.00,47.50,46.75,47.00,12923600,11.04\n1990-07-09,45.00,47.00,44.75,46.63,11281200,10.96\n1990-07-06,43.50,45.00,43.25,44.75,7481200,10.52\n1990-07-05,43.75,44.25,43.25,43.50,3859200,10.22\n1990-07-03,43.88,44.50,43.75,44.00,3572400,10.34\n1990-07-02,44.50,44.50,43.75,44.00,4856400,10.34\n1990-06-29,43.00,44.88,42.75,44.75,11622000,10.52\n1990-06-28,42.75,43.25,41.75,43.00,8930000,10.10\n1990-06-27,40.75,42.00,40.25,41.50,3490800,9.75\n1990-06-26,41.75,42.00,40.38,40.63,4558800,9.55\n1990-06-25,41.50,41.75,40.25,41.25,4378000,9.69\n1990-06-22,42.00,42.63,41.25,41.50,10154400,9.75\n1990-06-21,40.00,42.00,40.00,41.88,7455600,9.84\n1990-06-20,39.88,40.25,39.75,40.00,5530000,9.40\n1990-06-19,39.00,39.75,38.38,39.63,5623600,9.31\n1990-06-18,39.25,39.50,39.00,39.25,3988800,9.22\n1990-06-15,39.75,40.00,39.13,39.50,5163600,9.28\n1990-06-14,40.00,40.25,39.25,39.75,5018000,9.34\n1990-06-13,40.38,40.75,39.75,39.75,4963600,9.34\n1990-06-12,39.13,40.50,38.75,40.50,5902000,9.52\n1990-06-11,37.75,39.00,37.75,39.00,5661200,9.16\n1990-06-08,38.50,38.50,37.50,38.25,11926800,8.99\n1990-06-07,39.50,39.75,38.50,39.00,6668800,9.16\n1990-06-06,39.00,39.50,38.75,39.50,7563600,9.28\n1990-06-05,41.00,41.00,39.00,39.50,10702000,9.28\n1990-06-04,40.75,41.00,39.75,40.75,6412400,9.58\n1990-06-01,41.38,42.00,40.75,40.75,5624400,9.58\n1990-05-31,41.50,41.50,41.00,41.25,3682400,9.69\n1990-05-30,41.63,41.75,41.25,41.38,9890000,9.72\n1990-05-29,40.00,41.25,39.25,41.00,8689200,9.63\n1990-05-25,39.50,40.75,39.00,40.00,11562400,9.40\n1990-05-24,42.25,42.25,41.50,42.00,5296400,9.87\n1990-05-23,41.25,42.50,41.25,42.00,7417600,9.87\n1990-05-22,40.13,41.50,40.00,41.38,10772000,9.72\n1990-05-21,39.50,40.00,38.75,39.50,9382400,9.28\n1990-05-18,41.25,41.50,39.50,39.75,9248000,9.31\n1990-05-17,41.75,42.25,41.00,41.50,5488000,9.72\n1990-05-16,41.75,41.75,41.00,41.63,3139200,9.76\n1990-05-15,41.38,42.00,41.00,41.75,5343600,9.78\n1990-05-14,42.75,42.75,41.25,41.75,8088000,9.78\n1990-05-11,41.38,42.75,40.75,42.63,7691200,9.99\n1990-05-10,41.75,41.75,40.50,41.38,6413600,9.70\n1990-05-09,41.63,42.00,41.25,41.88,3491200,9.81\n1990-05-08,41.00,42.00,41.00,41.75,4025600,9.78\n1990-05-07,39.75,41.75,39.75,41.50,4866400,9.72\n1990-05-04,40.00,40.75,39.25,40.00,6063200,9.37\n1990-05-03,39.75,40.25,39.75,40.00,5950800,9.37\n1990-05-02,39.75,40.00,39.25,39.75,4857600,9.31\n1990-05-01,39.75,40.00,39.38,39.63,5845200,9.29\n1990-04-30,39.25,39.75,39.00,39.38,4888800,9.23\n1990-04-27,39.00,39.50,38.75,39.13,4178800,9.17\n1990-04-26,39.00,39.50,38.13,38.88,5098000,9.11\n1990-04-25,38.75,39.00,38.25,38.75,4743200,9.08\n1990-04-24,40.00,40.50,38.50,38.75,10852000,9.08\n1990-04-23,40.25,40.50,39.50,39.75,4597600,9.31\n1990-04-20,40.88,41.50,39.75,40.25,11573600,9.43\n1990-04-19,41.75,43.13,40.00,40.25,17215600,9.43\n1990-04-18,43.25,43.75,42.50,43.25,6925200,10.13\n1990-04-17,43.25,43.50,42.75,43.25,4683600,10.13\n1990-04-16,43.50,44.25,43.25,43.75,8116400,10.25\n1990-04-12,43.00,44.00,42.50,43.25,7566800,10.13\n1990-04-11,41.50,43.00,41.50,42.50,7620000,9.96\n1990-04-10,41.25,42.00,41.00,41.25,4695600,9.67\n1990-04-09,39.75,41.50,39.50,41.13,3771200,9.64\n1990-04-06,40.25,41.25,39.75,39.88,4235600,9.35\n1990-04-05,41.00,41.25,40.00,40.25,3877600,9.43\n1990-04-04,41.50,42.00,40.75,41.25,5363200,9.67\n1990-04-03,40.50,41.75,40.50,41.75,5006400,9.78\n1990-04-02,40.00,40.63,39.50,40.25,5332000,9.43\n1990-03-30,40.00,41.00,40.00,40.25,7986400,9.43\n1990-03-29,41.00,41.50,40.75,41.13,3472000,9.64\n1990-03-28,42.00,42.13,41.00,41.25,3696800,9.67\n1990-03-27,42.00,42.25,41.25,42.00,3033600,9.84\n1990-03-26,42.50,43.38,42.00,42.25,4581200,9.90\n1990-03-23,41.25,43.00,41.00,42.25,8155200,9.90\n1990-03-22,41.75,42.25,40.75,40.75,8292400,9.55\n1990-03-21,41.25,42.25,41.25,41.63,5463200,9.76\n1990-03-20,42.25,43.00,40.75,41.38,13984400,9.70\n1990-03-19,40.50,42.50,40.00,42.38,15433200,9.93\n1990-03-16,40.00,40.75,39.13,40.25,23042400,9.43\n1990-03-15,36.50,38.00,36.50,36.75,4302000,8.61\n1990-03-14,36.75,37.25,36.50,37.00,3654800,8.67\n1990-03-13,36.50,37.25,36.25,36.88,5321200,8.64\n1990-03-12,37.25,37.50,36.25,36.63,5864400,8.58\n1990-03-09,36.75,37.50,36.25,36.88,8248800,8.64\n1990-03-08,35.75,37.00,35.00,36.75,8013600,8.61\n1990-03-07,35.00,36.00,35.00,35.38,7301200,8.29\n1990-03-06,35.00,35.25,34.50,35.25,5578800,8.26\n1990-03-05,33.50,34.75,33.50,34.50,6537600,8.08\n1990-03-02,33.50,34.75,33.25,33.75,3761200,7.91\n1990-03-01,33.50,34.75,33.25,34.25,7283200,8.03\n1990-02-28,33.50,34.00,33.25,34.00,3918800,7.97\n1990-02-27,34.00,34.25,33.50,33.50,2642000,7.85\n1990-02-26,33.00,34.25,33.00,34.00,2844800,7.97\n1990-02-23,32.75,33.50,32.75,33.25,5375600,7.79\n1990-02-22,34.00,34.50,33.00,33.00,6976800,7.73\n1990-02-21,32.75,34.25,32.50,34.00,6283600,7.97\n1990-02-20,33.50,33.75,33.00,33.50,4402400,7.85\n1990-02-16,34.25,34.50,33.75,33.75,4556400,7.91\n1990-02-15,33.75,34.25,33.50,34.25,3509200,8.00\n1990-02-14,34.50,34.75,33.75,34.25,3448000,8.00\n1990-02-13,34.00,35.00,33.75,34.50,3653600,8.06\n1990-02-12,34.25,34.50,33.75,34.00,2695600,7.94\n1990-02-09,33.50,34.50,33.25,34.25,6004400,8.00\n1990-02-08,33.25,33.50,32.25,33.00,6680000,7.71\n1990-02-07,33.00,34.00,32.50,33.25,11180800,7.77\n1990-02-06,34.75,35.00,34.00,34.75,2640000,8.12\n1990-02-05,34.25,35.25,34.00,35.00,3653200,8.18\n1990-02-02,33.25,34.75,33.25,34.25,4248800,8.00\n1990-02-01,34.50,34.63,33.50,33.63,4193200,7.86\n1990-01-31,34.50,34.75,33.00,34.00,5152400,7.94\n1990-01-30,33.25,34.50,33.00,34.00,4180800,7.94\n1990-01-29,33.00,33.50,32.13,33.25,4284800,7.77\n1990-01-26,34.00,34.00,32.25,32.75,6492000,7.65\n1990-01-25,34.25,34.75,34.00,34.13,3996800,7.97\n1990-01-24,32.50,34.25,32.25,34.00,6077600,7.94\n1990-01-23,33.75,34.25,33.00,33.75,5048800,7.88\n1990-01-22,34.00,34.50,33.25,33.25,5200800,7.77\n1990-01-19,33.75,34.50,33.50,34.25,9485600,8.00\n1990-01-18,33.00,33.50,32.25,32.38,9760800,7.56\n1990-01-17,34.75,34.75,33.00,33.25,7050000,7.77\n1990-01-16,33.50,35.00,32.75,34.88,7658000,8.15\n1990-01-15,34.50,35.75,34.25,34.25,5785600,8.00\n1990-01-12,34.25,34.75,33.75,34.50,6150000,8.06\n1990-01-11,36.25,36.25,34.50,34.50,7547600,8.06\n1990-01-10,37.63,37.63,35.75,36.00,7140000,8.41\n1990-01-09,38.00,38.00,37.00,37.63,3096800,8.79\n1990-01-08,37.50,38.00,37.00,38.00,3643200,8.88\n1990-01-05,37.75,38.25,37.00,37.75,4406400,8.82\n1990-01-04,38.25,38.75,37.25,37.63,7928800,8.79\n1990-01-03,38.00,38.00,37.50,37.50,7444400,8.76\n1990-01-02,35.25,37.50,35.00,37.25,6555600,8.70\n1989-12-29,34.75,35.75,34.38,35.25,5445200,8.23\n1989-12-28,35.00,35.25,34.25,34.63,5403200,8.09\n1989-12-27,35.50,35.75,35.00,35.13,9189200,8.21\n1989-12-26,36.75,36.75,35.25,35.50,4849200,8.29\n1989-12-22,36.25,37.25,36.00,36.50,6610800,8.53\n1989-12-21,35.75,36.25,35.50,36.25,10889200,8.47\n1989-12-20,35.75,36.25,35.25,35.75,6377600,8.35\n1989-12-19,34.50,35.50,34.50,35.00,8977600,8.18\n1989-12-18,33.75,35.00,33.75,34.75,10978000,8.12\n1989-12-15,34.75,35.00,32.50,33.75,18520800,7.88\n1989-12-14,35.75,36.13,34.50,34.88,10886400,8.15\n1989-12-13,36.00,36.50,35.50,36.00,13920000,8.41\n1989-12-12,39.25,39.50,35.00,36.00,36634400,8.41\n1989-12-11,41.00,41.50,38.38,39.25,23223200,9.17\n1989-12-08,42.50,43.00,41.25,41.75,9032400,9.75\n1989-12-07,42.25,43.25,42.00,42.75,6378800,9.99\n1989-12-06,45.00,45.25,41.00,42.75,11965600,9.99\n1989-12-05,45.25,45.75,44.50,45.00,4364800,10.51\n1989-12-04,43.75,45.50,43.75,45.25,3498000,10.57\n1989-12-01,44.50,45.00,43.63,44.00,5235200,10.28\n1989-11-30,43.75,44.50,43.50,44.25,2280800,10.34\n1989-11-29,43.50,44.25,42.50,44.00,5475200,10.28\n1989-11-28,43.75,44.25,42.75,44.13,4854400,10.31\n1989-11-27,44.75,45.25,43.75,44.00,3774800,10.28\n1989-11-24,44.75,45.00,44.75,44.75,1014400,10.45\n1989-11-22,45.50,45.75,44.50,44.75,3508000,10.45\n1989-11-21,45.25,46.50,45.25,45.25,5013600,10.57\n1989-11-20,45.00,45.50,44.50,45.25,3870800,10.57\n1989-11-17,44.50,45.25,44.50,44.75,3164400,10.45\n1989-11-16,44.50,44.75,43.75,44.75,3453600,10.43\n1989-11-15,45.00,45.25,44.00,44.25,3499200,10.31\n1989-11-14,46.50,46.75,44.50,44.75,3021200,10.43\n1989-11-13,46.50,47.25,46.50,46.50,2445600,10.83\n1989-11-10,45.75,47.00,45.75,46.75,2336800,10.89\n1989-11-09,45.00,46.00,44.50,46.00,3166400,10.72\n1989-11-08,44.25,45.25,44.25,45.00,5102000,10.49\n1989-11-07,43.25,44.50,43.25,44.00,5406800,10.25\n1989-11-06,43.50,44.00,43.00,43.25,4416400,10.08\n1989-11-03,44.00,44.50,43.25,43.25,6258800,10.08\n1989-11-02,45.00,45.00,43.00,44.00,16170800,10.25\n1989-11-01,46.25,46.75,45.75,46.13,2199200,10.75\n1989-10-31,45.75,46.50,45.50,46.50,3288800,10.83\n1989-10-30,45.50,46.00,45.00,45.75,3121200,10.66\n1989-10-27,45.25,45.75,44.50,45.25,4634400,10.54\n1989-10-26,45.50,46.50,45.00,45.25,6048000,10.54\n1989-10-25,47.75,47.75,46.25,46.50,4263600,10.83\n1989-10-24,46.25,48.50,45.25,47.63,7735600,11.10\n1989-10-23,48.00,48.25,46.25,46.75,4375600,10.89\n1989-10-20,47.75,49.25,47.50,48.00,9350800,11.18\n1989-10-19,48.25,49.50,48.25,48.75,4016800,11.36\n1989-10-18,46.50,48.25,46.00,48.25,5157600,11.24\n1989-10-17,46.00,48.75,45.00,47.25,8935600,11.01\n1989-10-16,44.75,46.75,42.50,46.75,15184400,10.89\n1989-10-13,48.75,49.50,45.00,45.75,7195600,10.66\n1989-10-12,49.00,49.25,48.50,48.75,2969200,11.36\n1989-10-11,48.75,49.25,48.00,48.88,5608800,11.39\n1989-10-10,49.75,50.38,48.50,49.50,10262400,11.53\n1989-10-09,48.00,49.75,47.50,49.50,6997600,11.53\n1989-10-06,46.25,48.25,46.00,48.13,12939200,11.21\n1989-10-05,44.50,46.50,44.25,45.50,8760000,10.60\n1989-10-04,43.75,44.63,43.50,44.25,5687600,10.31\n1989-10-03,44.25,44.50,43.13,43.63,6094400,10.17\n1989-10-02,44.50,44.75,43.75,44.38,4922400,10.34\n1989-09-29,45.25,45.50,44.50,44.50,2500800,10.37\n1989-09-28,45.00,45.75,45.00,45.50,2856800,10.60\n1989-09-27,44.25,45.13,44.00,44.75,3229200,10.43\n1989-09-26,45.00,45.50,44.75,45.25,2762400,10.54\n1989-09-25,44.75,45.75,44.75,45.25,4875600,10.54\n1989-09-22,44.75,45.25,44.25,44.88,2605600,10.46\n1989-09-21,45.00,46.00,44.25,44.75,7186800,10.43\n1989-09-20,44.00,45.00,43.75,44.63,4230800,10.40\n1989-09-19,44.25,44.50,43.00,43.25,2888800,10.08\n1989-09-18,44.50,45.00,44.00,44.00,2264400,10.25\n1989-09-15,45.00,45.25,44.25,45.00,4470800,10.49\n1989-09-14,45.00,45.25,44.50,44.75,4693600,10.43\n1989-09-13,46.25,46.63,45.00,45.00,4616400,10.49\n1989-09-12,45.50,46.75,45.00,46.00,3710800,10.72\n1989-09-11,44.75,46.00,44.50,45.75,3522000,10.66\n1989-09-08,44.75,45.25,44.50,45.00,2013200,10.49\n1989-09-07,44.75,45.50,44.75,44.75,4083200,10.43\n1989-09-06,44.75,44.88,44.00,44.75,3108800,10.43\n1989-09-05,44.50,45.38,44.50,44.75,4112400,10.43\n1989-09-01,44.50,44.75,44.25,44.63,2651200,10.40\n1989-08-31,44.50,45.00,44.25,44.50,2016400,10.37\n1989-08-30,44.00,44.75,44.00,44.50,4161200,10.37\n1989-08-29,44.75,45.00,43.75,44.13,6339200,10.28\n1989-08-28,44.50,45.00,44.00,44.75,2936800,10.43\n1989-08-25,44.00,45.00,44.00,44.75,5766400,10.43\n1989-08-24,43.75,44.50,43.50,44.13,5829200,10.28\n1989-08-23,43.00,44.25,42.50,43.75,6202400,10.19\n1989-08-22,42.00,43.00,42.00,42.88,4013200,9.99\n1989-08-21,42.25,43.25,42.00,42.25,4923600,9.84\n1989-08-18,41.75,42.50,41.50,42.25,3003600,9.82\n1989-08-17,40.25,41.25,40.00,41.00,5495600,9.53\n1989-08-16,41.50,41.75,40.00,40.38,4318800,9.39\n1989-08-15,40.75,41.50,40.75,41.38,5852000,9.62\n1989-08-14,41.50,42.00,40.50,40.75,3690800,9.47\n1989-08-11,44.00,44.00,41.25,41.88,8226800,9.74\n1989-08-10,44.00,44.00,42.75,43.25,5442400,10.05\n1989-08-09,44.00,45.75,43.88,44.00,6975600,10.23\n1989-08-08,43.50,44.75,43.50,44.13,7366400,10.26\n1989-08-07,43.00,44.00,42.63,43.75,6012000,10.17\n1989-08-04,41.25,42.75,41.13,42.75,6564400,9.94\n1989-08-03,40.50,41.50,40.50,41.25,6185600,9.59\n1989-08-02,39.75,40.50,39.50,40.50,3633600,9.41\n1989-08-01,39.75,40.25,39.25,39.88,4996800,9.27\n1989-07-31,39.25,40.00,39.00,39.75,4014800,9.24\n1989-07-28,39.25,39.75,39.00,39.38,4274400,9.15\n1989-07-27,38.25,39.50,38.00,39.25,6193200,9.12\n1989-07-26,38.25,38.50,37.75,38.25,8363600,8.89\n1989-07-25,39.25,39.75,38.00,38.75,7502400,9.01\n1989-07-24,39.75,39.75,39.25,39.25,4154800,9.12\n1989-07-21,39.75,40.00,39.00,40.00,4993600,9.30\n1989-07-20,40.75,41.25,39.75,40.00,8448800,9.30\n1989-07-19,39.50,40.75,39.00,40.50,8543200,9.41\n1989-07-18,40.75,40.75,38.75,39.25,17050800,9.12\n1989-07-17,40.75,41.25,39.75,40.75,4694400,9.47\n1989-07-14,40.75,41.00,39.75,40.75,9206800,9.47\n1989-07-13,40.00,41.00,39.50,40.63,8057600,9.44\n1989-07-12,39.75,40.25,39.50,40.00,4452000,9.30\n1989-07-11,40.75,41.00,39.75,39.75,8729200,9.24\n1989-07-10,41.00,41.25,40.00,40.50,7294400,9.41\n1989-07-07,41.25,42.00,40.50,41.25,3806400,9.59\n1989-07-06,40.75,41.75,40.25,41.25,6218000,9.59\n1989-07-05,40.50,40.75,40.00,40.50,4264400,9.41\n1989-07-03,41.75,41.75,40.75,40.75,1730800,9.47\n1989-06-30,40.50,41.75,39.50,41.25,5885600,9.59\n1989-06-29,41.00,41.25,40.00,40.63,8351200,9.44\n1989-06-28,42.25,42.25,41.00,41.75,9190800,9.70\n1989-06-27,43.75,44.25,42.50,42.63,3788000,9.91\n1989-06-26,44.00,44.00,43.25,43.50,6568800,10.11\n1989-06-23,43.25,44.25,43.25,43.88,4438800,10.20\n1989-06-22,42.50,43.75,42.00,43.25,4911200,10.05\n1989-06-21,43.00,43.50,42.25,42.50,4659200,9.88\n1989-06-20,44.00,44.00,42.25,43.00,4807600,10.00\n1989-06-19,44.50,44.75,43.50,44.00,6551200,10.23\n1989-06-16,44.75,45.50,43.50,44.50,19378000,10.34\n1989-06-15,49.50,49.75,47.50,47.50,5766800,11.04\n1989-06-14,49.00,50.25,48.25,49.63,8983600,11.54\n1989-06-13,47.50,48.75,47.00,48.50,8254400,11.27\n1989-06-12,46.75,47.75,46.25,47.50,2892400,11.04\n1989-06-09,47.25,47.75,46.50,47.00,3378800,10.93\n1989-06-08,48.50,49.00,47.25,47.63,6378800,11.07\n1989-06-07,46.75,48.50,46.75,48.25,6293200,11.22\n1989-06-06,46.75,47.00,46.25,46.75,5189200,10.87\n1989-06-05,48.75,49.00,46.50,47.00,4451200,10.93\n1989-06-02,48.50,49.50,48.50,49.00,4448800,11.39\n1989-06-01,47.75,49.25,47.50,48.75,6416800,11.33\n1989-05-31,47.50,48.13,47.00,47.75,4134400,11.10\n1989-05-30,48.25,49.00,47.38,47.50,4018000,11.04\n1989-05-26,48.25,49.00,48.00,48.50,4028800,11.27\n1989-05-25,47.25,49.00,47.25,48.25,8309200,11.22\n1989-05-24,45.25,47.75,45.25,47.75,10645200,11.10\n1989-05-23,46.00,46.00,45.25,45.50,4803600,10.58\n1989-05-22,45.75,46.25,45.25,46.00,6800000,10.69\n1989-05-19,44.75,46.25,44.75,45.75,11820800,10.61\n1989-05-18,45.25,45.50,44.75,44.75,7558800,10.38\n1989-05-17,45.25,45.50,45.00,45.25,8892400,10.50\n1989-05-16,46.00,46.25,45.00,45.38,8170800,10.53\n1989-05-15,44.75,46.25,44.75,46.00,11372400,10.67\n1989-05-12,44.50,45.00,44.00,45.00,16685600,10.44\n1989-05-11,43.25,44.25,43.00,43.88,10763600,10.18\n1989-05-10,43.00,43.50,42.50,43.25,8380000,10.03\n1989-05-09,42.00,43.00,42.00,42.50,12398800,9.86\n1989-05-08,41.50,42.25,41.50,42.25,7373600,9.80\n1989-05-05,42.50,42.75,41.50,41.50,16464400,9.63\n1989-05-04,40.25,41.25,40.00,41.00,6762000,9.51\n1989-05-03,39.75,40.75,39.75,40.25,7896800,9.34\n1989-05-02,39.00,40.25,39.00,39.88,7719200,9.25\n1989-05-01,38.50,39.25,38.50,39.00,2881200,9.05\n1989-04-28,39.25,39.50,38.50,39.00,3725600,9.05\n1989-04-27,39.50,40.00,39.00,39.38,4988000,9.13\n1989-04-26,40.00,40.25,39.13,39.75,6652000,9.22\n1989-04-25,40.00,40.50,39.75,40.00,4165600,9.28\n1989-04-24,40.00,40.25,39.50,40.13,3977600,9.31\n1989-04-21,40.50,40.88,39.75,40.13,4132000,9.31\n1989-04-20,40.75,41.50,40.25,40.75,6434400,9.45\n1989-04-19,40.00,41.63,39.75,40.88,15215600,9.48\n1989-04-18,39.50,40.50,39.25,40.13,20055200,9.31\n1989-04-17,38.50,39.25,38.00,39.25,5008000,9.10\n1989-04-14,39.00,39.25,38.25,38.75,4408800,8.99\n1989-04-13,38.75,39.50,38.25,38.50,6493200,8.93\n1989-04-12,38.25,39.25,37.88,38.50,13862000,8.93\n1989-04-11,37.50,38.00,37.00,37.75,5252400,8.76\n1989-04-10,37.25,38.00,36.75,37.00,4854400,8.58\n1989-04-07,36.00,37.50,36.00,37.38,12699200,8.67\n1989-04-06,34.75,36.13,34.50,36.00,5598800,8.35\n1989-04-05,34.50,35.25,34.25,35.00,4303200,8.12\n1989-04-04,34.50,34.88,33.88,34.50,4140800,8.00\n1989-04-03,35.50,36.25,34.75,35.00,5949200,8.12\n1989-03-31,35.00,35.75,34.75,35.63,6630800,8.26\n1989-03-30,34.25,35.00,34.00,34.75,3780800,8.06\n1989-03-29,34.00,34.50,34.00,34.25,2666800,7.94\n1989-03-28,34.00,34.50,34.00,34.00,5047600,7.89\n1989-03-27,34.25,34.50,33.50,33.75,5425600,7.83\n1989-03-23,34.00,34.50,33.75,34.38,4250800,7.97\n1989-03-22,34.25,34.75,33.75,33.88,5180800,7.86\n1989-03-21,35.50,35.50,34.75,34.88,4588800,8.09\n1989-03-20,35.00,35.25,34.50,34.88,6480800,8.09\n1989-03-17,34.50,35.75,34.00,34.88,8485200,8.09\n1989-03-16,35.00,35.50,34.50,35.25,6880000,8.18\n1989-03-15,35.25,35.50,34.75,35.00,3225600,8.12\n1989-03-14,35.00,35.50,34.88,35.25,5796800,8.18\n1989-03-13,35.00,35.50,34.75,35.00,4683600,8.12\n1989-03-10,34.50,35.00,34.25,35.00,3684400,8.12\n1989-03-09,35.25,35.75,34.50,34.50,4768800,8.00\n1989-03-08,35.63,36.25,35.25,35.25,7727600,8.18\n1989-03-07,35.50,36.00,35.00,35.75,9327600,8.29\n1989-03-06,35.00,35.88,34.50,35.50,6028800,8.23\n1989-03-03,35.25,35.25,34.00,34.75,13854400,8.06\n1989-03-02,35.75,36.25,34.75,35.00,13440800,8.12\n1989-03-01,36.25,36.50,35.50,36.00,6096400,8.35\n1989-02-28,36.50,36.75,36.00,36.25,6290000,8.41\n1989-02-27,36.00,36.50,35.75,36.50,4151200,8.47\n1989-02-24,37.00,37.00,36.00,36.00,5452000,8.35\n1989-02-23,36.50,37.00,36.25,36.75,3409200,8.52\n1989-02-22,37.25,37.50,36.50,36.75,8529200,8.52\n1989-02-21,36.88,37.75,36.75,37.50,6808800,8.70\n1989-02-17,36.25,37.00,36.25,36.75,4180800,8.52\n1989-02-16,36.25,37.25,36.00,36.38,9138800,8.41\n1989-02-15,35.75,36.25,35.50,36.25,11812400,8.38\n1989-02-14,36.88,37.00,35.25,35.75,31843200,8.27\n1989-02-13,36.75,37.25,36.75,37.00,8422000,8.56\n1989-02-10,38.25,38.25,37.00,37.25,12441200,8.62\n1989-02-09,38.25,39.00,38.00,38.25,5756400,8.85\n1989-02-08,39.00,39.50,38.00,38.25,5612000,8.85\n1989-02-07,38.25,39.25,38.25,39.00,5908800,9.02\n1989-02-06,39.50,39.50,38.25,38.50,4174400,8.91\n1989-02-03,40.00,40.25,39.00,39.25,6406400,9.08\n1989-02-02,39.50,40.25,39.25,39.75,16927600,9.19\n1989-02-01,37.75,39.63,37.38,39.25,17420000,9.08\n1989-01-31,37.25,37.75,36.75,37.75,16442000,8.73\n1989-01-30,37.63,38.00,37.25,37.38,20961200,8.65\n1989-01-27,38.25,39.25,36.25,37.63,75976400,8.70\n1989-01-26,40.75,42.13,40.63,41.75,10203600,9.66\n1989-01-25,41.75,42.00,41.00,41.50,3963200,9.60\n1989-01-24,41.00,41.75,40.75,41.63,7983200,9.63\n1989-01-23,40.75,41.25,40.75,41.00,6452000,9.48\n1989-01-20,40.50,41.50,40.25,41.00,6207600,9.48\n1989-01-19,40.50,41.00,40.00,40.50,9155200,9.37\n1989-01-18,40.75,41.13,39.50,39.75,17440800,9.19\n1989-01-17,43.25,43.50,40.00,40.38,27033600,9.34\n1989-01-16,43.25,44.00,43.00,43.75,6033200,10.12\n1989-01-13,42.75,43.50,42.38,43.25,6928000,10.00\n1989-01-12,42.25,43.00,42.00,42.75,5373200,9.89\n1989-01-11,42.25,42.50,41.25,42.13,5585200,9.74\n1989-01-10,42.50,42.88,41.50,42.63,3695600,9.86\n1989-01-09,43.00,43.13,42.25,43.00,2850800,9.95\n1989-01-06,42.25,43.50,42.25,42.63,7103600,9.86\n1989-01-05,42.00,43.25,41.25,42.25,10985200,9.77\n1989-01-04,40.75,42.13,40.50,42.00,8575200,9.71\n1989-01-03,40.25,40.50,40.00,40.38,3578800,9.34\n1988-12-30,40.50,41.25,40.25,40.25,2938800,9.31\n1988-12-29,40.25,40.75,40.25,40.50,4212000,9.37\n1988-12-28,40.50,40.75,39.75,40.25,1841200,9.31\n1988-12-27,41.00,41.50,40.50,40.50,2155200,9.37\n1988-12-23,41.00,41.38,41.00,41.13,1475600,9.51\n1988-12-22,41.75,42.00,40.75,41.00,3802000,9.48\n1988-12-21,41.00,42.00,41.00,41.75,8642400,9.66\n1988-12-20,41.00,41.50,40.63,41.00,9810800,9.48\n1988-12-19,40.25,41.00,40.00,40.75,8373600,9.43\n1988-12-16,39.50,40.50,39.25,40.13,6572000,9.28\n1988-12-15,40.00,40.50,39.25,39.50,4032000,9.14\n1988-12-14,38.50,40.00,38.50,39.75,6916800,9.19\n1988-12-13,38.50,38.75,38.25,38.75,4386400,8.96\n1988-12-12,39.25,39.50,38.50,38.50,4215600,8.91\n1988-12-09,39.25,39.50,38.75,39.13,1608800,9.05\n1988-12-08,39.25,39.25,38.75,39.13,2125600,9.05\n1988-12-07,39.00,39.50,38.75,39.38,3518800,9.11\n1988-12-06,39.25,39.75,39.00,39.50,3763200,9.14\n1988-12-05,39.50,40.00,38.75,39.50,5534400,9.14\n1988-12-02,38.25,39.88,38.00,39.25,11940000,9.08\n1988-12-01,37.75,39.00,37.50,38.75,7586800,8.96\n1988-11-30,36.75,38.00,36.75,37.63,6013600,8.70\n1988-11-29,36.50,36.75,36.00,36.75,3326400,8.50\n1988-11-28,36.50,36.75,36.00,36.50,4986800,8.44\n1988-11-25,36.25,36.75,36.00,36.50,1727600,8.44\n1988-11-23,35.75,37.00,35.50,36.88,6733200,8.53\n1988-11-22,36.50,36.88,36.00,36.13,5299200,8.36\n1988-11-21,37.50,37.75,36.25,36.63,7928000,8.47\n1988-11-18,38.50,38.50,38.00,38.00,2066400,8.77\n1988-11-17,38.00,38.50,38.00,38.25,2841200,8.82\n1988-11-16,39.00,39.25,37.75,38.00,5280000,8.77\n1988-11-15,39.00,39.25,38.75,39.00,2866800,9.00\n1988-11-14,38.75,39.00,38.25,38.88,3046400,8.97\n1988-11-11,39.00,39.63,38.50,38.50,3882400,8.88\n1988-11-10,39.50,39.75,39.00,39.50,3573200,9.11\n1988-11-09,38.25,39.38,38.00,39.25,7206800,9.05\n1988-11-08,37.50,38.75,37.38,38.50,5540800,8.88\n1988-11-07,37.25,37.75,37.00,37.50,6093600,8.65\n1988-11-04,36.75,38.00,36.75,37.75,5500000,8.71\n1988-11-03,37.25,37.50,36.75,37.13,8670000,8.57\n1988-11-02,38.25,38.25,36.75,37.25,7451200,8.59\n1988-11-01,38.50,38.75,37.75,38.00,5138800,8.77\n1988-10-31,38.75,38.75,37.50,38.63,8695200,8.91\n1988-10-28,39.00,39.50,38.50,38.50,3026800,8.88\n1988-10-27,38.75,39.25,38.25,39.00,5138000,9.00\n1988-10-26,40.00,40.00,38.50,39.25,6751200,9.05\n1988-10-25,40.25,40.25,39.75,39.88,3043600,9.20\n1988-10-24,41.25,41.25,39.63,40.00,4842400,9.23\n1988-10-21,41.25,41.75,40.75,41.00,4422400,9.46\n1988-10-20,40.00,41.63,40.00,41.50,6215200,9.57\n1988-10-19,39.75,40.75,39.50,40.00,9918000,9.23\n1988-10-18,39.00,39.50,38.25,39.38,5100000,9.08\n1988-10-17,38.50,39.00,38.25,38.50,3360800,8.88\n1988-10-14,39.50,39.50,38.13,38.75,5625200,8.94\n1988-10-13,38.50,39.75,38.50,39.00,5892400,9.00\n1988-10-12,38.50,39.00,38.00,38.75,4763600,8.94\n1988-10-11,38.25,39.50,38.25,39.00,6964400,9.00\n1988-10-10,39.50,39.75,37.50,38.50,11880000,8.88\n1988-10-07,39.00,39.75,38.38,39.75,16355200,9.17\n1988-10-06,40.50,40.88,39.25,39.75,6009200,9.17\n1988-10-05,41.25,41.75,40.50,40.88,4400000,9.43\n1988-10-04,42.25,42.75,41.13,41.50,1847600,9.57\n1988-10-03,43.00,43.25,42.00,42.50,3243200,9.80\n1988-09-30,44.00,44.00,43.25,43.25,3338800,9.98\n1988-09-29,43.75,44.25,43.50,44.00,3804400,10.15\n1988-09-28,43.50,44.13,43.25,43.50,3038800,10.04\n1988-09-27,42.50,43.50,42.50,43.38,5832400,10.01\n1988-09-26,43.75,44.00,42.50,42.75,3124400,9.86\n1988-09-23,43.50,44.25,43.50,43.75,3638000,10.09\n1988-09-22,43.00,44.00,42.75,44.00,5203600,10.15\n1988-09-21,41.75,43.00,41.50,42.75,3274800,9.86\n1988-09-20,41.75,42.25,41.38,41.50,3682400,9.57\n1988-09-19,42.00,42.25,41.25,41.75,3296400,9.63\n1988-09-16,41.50,42.75,41.38,42.25,4431200,9.75\n1988-09-15,42.00,42.75,41.50,41.63,5920000,9.60\n1988-09-14,41.75,42.38,41.50,42.00,8520800,9.69\n1988-09-13,40.25,41.25,40.00,41.00,4293600,9.46\n1988-09-12,41.00,41.75,40.13,41.00,5290800,9.46\n1988-09-09,38.75,41.00,37.75,40.50,8393200,9.34\n1988-09-08,38.25,39.50,37.75,38.75,7403200,8.94\n1988-09-07,39.00,39.50,37.75,38.25,6417600,8.82\n1988-09-06,40.00,40.00,38.75,38.88,5125200,8.97\n1988-09-02,39.50,40.00,39.00,39.75,6661200,9.17\n1988-09-01,39.75,39.75,38.50,38.88,8818800,8.97\n1988-08-31,41.00,41.13,39.50,39.88,8493600,9.20\n1988-08-30,40.75,41.00,40.00,40.88,1809200,9.43\n1988-08-29,40.75,41.00,40.50,40.88,2046400,9.43\n1988-08-26,40.00,40.75,40.00,40.25,1453200,9.29\n1988-08-25,40.25,40.50,39.25,40.13,4560000,9.26\n1988-08-24,39.75,40.75,39.50,40.75,4482000,9.40\n1988-08-23,39.75,40.25,39.25,39.50,5843200,9.11\n1988-08-22,40.25,40.75,39.50,39.75,6100000,9.17\n1988-08-19,42.50,42.75,40.50,40.75,8120000,9.40\n1988-08-18,42.00,43.00,41.75,42.50,2648000,9.80\n1988-08-17,42.50,42.75,41.75,42.00,4252400,9.69\n1988-08-16,41.00,43.25,40.75,42.50,4397600,9.80\n1988-08-15,42.25,42.25,40.50,41.25,5971200,9.52\n1988-08-12,43.00,43.00,42.25,42.50,2771200,9.79\n1988-08-11,42.25,43.25,42.00,43.25,3803200,9.96\n1988-08-10,43.75,43.75,41.75,41.88,5300800,9.64\n1988-08-09,44.00,44.25,43.00,43.50,6090800,10.02\n1988-08-08,44.50,44.75,44.00,44.00,1085600,10.13\n1988-08-05,44.50,45.00,44.25,44.25,1881200,10.19\n1988-08-04,44.75,45.25,44.50,44.63,2473200,10.28\n1988-08-03,44.75,44.75,44.00,44.75,3980800,10.30\n1988-08-02,45.00,45.50,44.50,44.63,4338000,10.28\n1988-08-01,44.50,45.75,44.25,45.00,3085600,10.36\n1988-07-29,43.25,44.50,43.00,44.38,5697600,10.22\n1988-07-28,42.50,43.00,42.25,42.63,3326800,9.82\n1988-07-27,42.75,43.25,42.50,42.75,4162400,9.84\n1988-07-26,42.75,43.25,42.25,42.75,3640800,9.84\n1988-07-25,42.75,43.25,42.25,42.75,3794400,9.84\n1988-07-22,43.00,43.25,42.50,42.50,3724800,9.79\n1988-07-21,43.75,44.00,42.75,43.00,5323600,9.90\n1988-07-20,44.75,45.00,44.00,44.25,4293600,10.19\n1988-07-19,45.00,45.50,43.88,44.75,4372400,10.30\n1988-07-18,45.38,46.00,45.25,45.50,4061200,10.48\n1988-07-15,45.00,45.50,44.75,45.00,2968000,10.36\n1988-07-14,44.75,45.25,44.50,45.00,2245200,10.36\n1988-07-13,44.75,45.00,44.25,44.75,4132000,10.30\n1988-07-12,45.00,45.25,44.50,44.75,3605600,10.30\n1988-07-11,45.50,45.50,44.88,45.13,2646400,10.39\n1988-07-08,45.50,46.00,45.00,45.25,3766400,10.42\n1988-07-07,46.50,46.50,45.25,45.88,3778000,10.56\n1988-07-06,47.13,47.50,46.13,46.50,5608800,10.71\n1988-07-05,46.50,47.25,46.13,47.25,3736400,10.88\n1988-07-01,46.50,46.88,46.25,46.50,3385600,10.71\n1988-06-30,46.25,46.75,46.00,46.25,4104800,10.65\n1988-06-29,46.00,46.75,45.75,46.38,5125200,10.68\n1988-06-28,44.75,46.25,44.50,46.25,5809200,10.65\n1988-06-27,44.50,45.38,44.50,44.50,3001200,10.25\n1988-06-24,45.00,45.50,44.50,45.00,2684400,10.36\n1988-06-23,45.75,45.75,45.00,45.00,2566400,10.36\n1988-06-22,45.50,45.88,45.00,45.63,6998000,10.51\n1988-06-21,44.00,45.00,43.88,44.88,4422000,10.33\n1988-06-20,44.38,44.75,44.00,44.13,2811200,10.16\n1988-06-17,44.75,44.75,44.25,44.75,3410800,10.30\n1988-06-16,45.00,45.25,44.25,44.50,3854400,10.25\n1988-06-15,45.25,45.75,45.00,45.75,4360000,10.53\n1988-06-14,45.25,46.00,45.00,45.25,10445600,10.42\n1988-06-13,45.00,45.25,44.25,45.00,5320000,10.36\n1988-06-10,43.50,44.75,43.00,44.50,6320000,10.25\n1988-06-09,45.00,45.25,43.25,43.50,9640000,10.02\n1988-06-08,44.25,45.50,44.00,45.00,9240000,10.36\n1988-06-07,43.75,45.25,43.50,44.00,11120000,10.13\n1988-06-06,42.75,44.00,42.75,44.00,5880000,10.13\n1988-06-03,41.75,43.25,41.75,43.00,6280000,9.90\n1988-06-02,42.00,42.50,41.50,41.75,4760000,9.61\n1988-06-01,41.50,42.50,41.25,42.50,8200000,9.79\n1988-05-31,40.00,41.50,39.75,41.50,4400000,9.56\n1988-05-27,39.25,40.00,39.00,39.75,3020000,9.15\n1988-05-26,38.50,39.50,38.50,39.38,3076800,9.07\n1988-05-25,39.00,39.75,38.50,38.50,4840000,8.87\n1988-05-24,38.00,39.00,37.75,38.88,5080000,8.95\n1988-05-23,38.50,38.88,37.38,38.00,6560000,8.75\n1988-05-20,39.25,39.50,38.75,38.75,2941200,8.92\n1988-05-19,39.50,39.75,38.50,39.00,8920000,8.98\n1988-05-18,40.50,40.75,39.50,39.75,6240000,9.15\n1988-05-17,41.50,42.00,40.25,40.50,6920000,9.33\n1988-05-16,40.50,41.38,40.00,41.25,2686800,9.50\n1988-05-13,40.25,40.50,40.00,40.50,2566800,9.31\n1988-05-12,39.50,40.25,39.50,39.75,2965600,9.13\n1988-05-11,40.25,40.75,39.50,39.50,6240000,9.08\n1988-05-10,40.50,41.00,40.25,40.88,3439200,9.39\n1988-05-09,41.25,41.25,40.50,40.75,2732000,9.36\n1988-05-06,41.63,41.75,41.25,41.25,3835600,9.48\n1988-05-05,42.00,42.25,41.50,41.75,2536800,9.59\n1988-05-04,41.88,43.13,41.75,42.00,8000000,9.65\n1988-05-03,41.00,42.25,40.75,41.75,4440000,9.59\n1988-05-02,40.75,41.25,40.50,41.00,2944400,9.42\n1988-04-29,41.25,41.50,40.50,41.00,3222000,9.42\n1988-04-28,41.75,42.00,41.25,41.38,3553600,9.51\n1988-04-27,41.75,42.00,41.50,41.75,4520000,9.59\n1988-04-26,41.00,41.75,40.75,41.50,6280000,9.54\n1988-04-25,40.25,41.00,40.00,40.88,5360000,9.39\n1988-04-22,39.75,40.25,39.50,40.13,3846800,9.22\n1988-04-21,40.38,40.50,39.00,39.50,6360000,9.08\n1988-04-20,40.25,40.50,39.25,39.75,7680000,9.13\n1988-04-19,40.13,41.50,40.13,40.25,7596400,9.25\n1988-04-18,39.75,40.75,39.25,40.00,6080000,9.19\n1988-04-15,39.75,40.00,38.50,39.50,8320000,9.08\n1988-04-14,40.50,41.50,39.00,39.50,6720000,9.08\n1988-04-13,41.75,42.00,41.00,41.25,5120000,9.48\n1988-04-12,41.75,42.25,41.25,41.75,6200000,9.59\n1988-04-11,41.75,42.00,41.00,41.50,5320000,9.54\n1988-04-08,40.75,41.75,39.75,41.00,7240000,9.42\n1988-04-07,41.75,42.38,40.75,40.75,5840000,9.36\n1988-04-06,39.50,41.75,39.00,41.75,6800000,9.59\n1988-04-05,39.25,39.50,38.50,39.25,5280000,9.02\n1988-04-04,39.75,40.50,38.50,38.75,6480000,8.91\n1988-03-31,39.75,40.50,39.25,40.00,7760000,9.19\n1988-03-30,40.75,41.25,38.75,39.50,13280000,9.08\n1988-03-29,41.50,42.00,40.63,41.00,7640000,9.42\n1988-03-28,40.00,41.75,39.50,41.50,6160000,9.54\n1988-03-25,40.75,41.25,40.00,40.13,4680000,9.22\n1988-03-24,41.75,42.50,40.00,40.88,11440000,9.39\n1988-03-23,44.00,44.00,41.88,42.50,7480000,9.77\n1988-03-22,44.00,44.50,43.25,44.00,4265600,10.11\n1988-03-21,44.38,44.63,43.00,43.88,8120000,10.08\n1988-03-18,45.00,45.50,44.25,44.75,9720000,10.28\n1988-03-17,46.25,46.50,44.75,45.00,9320000,10.34\n1988-03-16,44.88,46.38,44.50,46.13,4240000,10.60\n1988-03-15,46.00,46.25,44.75,45.00,6480000,10.34\n1988-03-14,45.75,46.50,45.50,46.25,3518000,10.63\n1988-03-11,45.50,45.75,44.50,45.75,5640000,10.51\n1988-03-10,47.00,47.25,45.25,45.25,6320000,10.40\n1988-03-09,46.25,47.25,46.25,46.75,4800000,10.74\n1988-03-08,46.75,47.00,46.00,46.25,5160000,10.63\n1988-03-07,46.75,47.75,46.50,46.88,7400000,10.77\n1988-03-04,46.00,47.00,45.50,46.88,7480000,10.77\n1988-03-03,44.50,47.00,44.50,46.50,16920000,10.69\n1988-03-02,43.75,45.00,43.50,44.75,10440000,10.28\n1988-03-01,43.25,43.50,42.50,43.25,6120000,9.94\n1988-02-29,41.75,43.25,41.50,43.00,4000000,9.88\n1988-02-26,42.00,42.25,41.25,41.75,2952400,9.59\n1988-02-25,42.00,43.00,41.75,41.75,6400000,9.59\n1988-02-24,42.75,43.00,42.00,42.25,5200000,9.71\n1988-02-23,43.25,43.75,42.25,42.75,7880000,9.82\n1988-02-22,41.50,43.63,41.50,43.25,7160000,9.94\n1988-02-19,41.75,42.00,41.50,41.75,3242400,9.59\n1988-02-18,41.63,42.75,41.50,41.75,5120000,9.59\n1988-02-17,41.25,42.50,41.25,41.88,9160000,9.62\n1988-02-16,41.00,41.25,40.00,41.25,5520000,9.48\n1988-02-12,40.63,41.50,40.50,41.00,4920000,9.42\n1988-02-11,41.00,41.25,40.25,40.63,5280000,9.32\n1988-02-10,39.75,41.50,39.75,41.00,8160000,9.40\n1988-02-09,39.00,39.88,38.75,39.75,4160000,9.12\n1988-02-08,38.50,39.25,37.75,38.75,7280000,8.89\n1988-02-05,40.00,40.38,38.50,38.63,4720000,8.86\n1988-02-04,39.50,40.13,39.00,39.75,7120000,9.12\n1988-02-03,41.00,41.25,39.25,39.50,8080000,9.06\n1988-02-02,41.50,41.88,40.50,41.25,6840000,9.46\n1988-02-01,41.75,42.50,41.38,41.75,7120000,9.58\n1988-01-29,41.50,41.75,40.25,41.50,9480000,9.52\n1988-01-28,40.00,41.50,39.75,41.25,8320000,9.46\n1988-01-27,40.25,40.50,38.75,39.75,9240000,9.12\n1988-01-26,40.75,41.00,39.25,39.75,5120000,9.12\n1988-01-25,39.50,41.50,39.50,40.88,7160000,9.38\n1988-01-22,40.50,40.75,38.25,39.25,15920000,9.00\n1988-01-21,40.50,40.75,39.38,40.13,17640000,9.20\n1988-01-20,43.00,43.00,38.25,39.75,24320000,9.12\n1988-01-19,42.25,43.25,41.38,42.75,9800000,9.80\n1988-01-18,43.00,43.00,42.00,42.75,4480000,9.80\n1988-01-15,43.50,45.00,42.50,42.88,12280000,9.83\n1988-01-14,42.75,42.88,42.00,42.25,4720000,9.69\n1988-01-13,42.00,43.25,41.13,42.25,7560000,9.69\n1988-01-12,43.00,43.50,39.75,42.00,14320000,9.63\n1988-01-11,40.00,42.75,39.75,42.50,14440000,9.75\n1988-01-08,44.50,45.25,39.50,40.00,17360000,9.17\n1988-01-07,43.50,44.75,42.50,44.50,7600000,10.21\n1988-01-06,45.00,45.00,43.75,43.75,9600000,10.03\n1988-01-05,46.00,46.25,44.25,44.63,11040000,10.24\n1988-01-04,42.75,44.75,42.25,44.75,11800000,10.26\n1987-12-31,42.50,43.00,41.88,42.00,4200000,9.63\n1987-12-30,42.50,43.75,42.50,43.38,5560000,9.95\n1987-12-29,40.50,42.25,40.25,42.13,4240000,9.66\n1987-12-28,42.25,42.50,39.50,40.25,8200000,9.23\n1987-12-24,42.00,43.00,41.75,42.63,2508000,9.78\n1987-12-23,41.75,42.75,41.25,42.25,6120000,9.69\n1987-12-22,41.75,41.75,40.50,41.50,4600000,9.52\n1987-12-21,40.50,41.75,40.25,41.75,6720000,9.58\n1987-12-18,39.50,41.25,39.25,40.50,10800000,9.29\n1987-12-17,40.50,40.75,39.25,39.25,11640000,9.00\n1987-12-16,37.75,39.75,37.25,39.25,11800000,9.00\n1987-12-15,37.75,38.25,37.00,37.50,10680000,8.60\n1987-12-14,34.50,37.50,34.25,37.25,12200000,8.54\n1987-12-11,34.75,34.75,33.50,34.00,4360000,7.80\n1987-12-10,33.75,36.00,33.25,34.75,9880000,7.97\n1987-12-09,34.50,36.25,33.88,35.00,6400000,8.03\n1987-12-08,33.50,34.88,33.25,34.50,9080000,7.91\n1987-12-07,31.00,33.25,31.00,33.00,7280000,7.57\n1987-12-04,30.25,31.25,29.75,30.75,8720000,7.05\n1987-12-03,33.00,33.38,29.75,30.50,11400000,7.00\n1987-12-02,33.25,33.50,32.50,32.50,5080000,7.45\n1987-12-01,33.50,34.00,32.75,33.25,6480000,7.63\n1987-11-30,33.75,34.50,30.50,33.00,14880000,7.57\n1987-11-27,36.25,36.50,34.75,35.00,2526800,8.03\n1987-11-25,37.00,37.00,36.00,36.50,3311200,8.37\n1987-11-24,36.75,37.75,36.13,37.00,7040000,8.49\n1987-11-23,35.50,36.25,34.75,36.25,3500000,8.31\n1987-11-20,34.00,36.00,33.25,35.50,8960000,8.14\n1987-11-19,36.50,36.50,34.00,34.50,6520000,7.91\n1987-11-18,35.75,36.50,34.50,36.25,9480000,8.31\n1987-11-17,36.75,37.00,35.00,35.00,9600000,8.03\n1987-11-16,37.75,38.50,36.50,36.75,6600000,8.41\n1987-11-13,39.25,39.50,37.00,37.25,5520000,8.52\n1987-11-12,38.50,40.00,38.38,38.75,8800000,8.87\n1987-11-11,37.25,38.25,36.75,37.25,6640000,8.52\n1987-11-10,36.50,37.50,36.00,36.25,8280000,8.30\n1987-11-09,37.00,37.50,36.25,37.25,7520000,8.52\n1987-11-06,38.25,39.50,37.00,37.75,6680000,8.64\n1987-11-05,36.25,38.75,36.25,38.00,9120000,8.70\n1987-11-04,35.50,37.25,34.75,36.00,8360000,8.24\n1987-11-03,38.00,38.50,34.25,36.25,11200000,8.30\n1987-11-02,38.75,39.50,37.50,38.75,6720000,8.87\n1987-10-30,40.00,43.00,38.50,38.63,15040000,8.84\n1987-10-29,34.25,40.00,32.25,39.50,11840000,9.04\n1987-10-28,30.75,33.75,29.25,33.50,14960000,7.67\n1987-10-27,29.50,32.25,29.00,30.25,16280000,6.92\n1987-10-26,34.50,35.00,27.62,28.00,11200000,6.41\n1987-10-23,35.75,36.50,34.25,35.50,7080000,8.12\n1987-10-22,39.25,40.50,36.00,36.75,13760000,8.41\n1987-10-21,38.50,42.00,38.00,40.50,19080000,9.27\n1987-10-20,38.50,42.00,32.63,34.50,20320000,7.90\n1987-10-19,48.25,48.25,35.50,36.50,17000000,8.35\n1987-10-16,52.25,53.00,47.50,48.25,15000000,11.04\n1987-10-15,53.25,54.50,51.75,52.00,12440000,11.90\n1987-10-14,53.75,54.00,52.00,53.25,9240000,12.19\n1987-10-13,54.50,54.75,53.25,54.50,5800000,12.47\n1987-10-12,54.25,54.38,51.75,53.25,7120000,12.19\n1987-10-09,54.25,55.50,54.00,54.13,5200000,12.39\n1987-10-08,55.50,56.00,53.25,54.25,5880000,12.42\n1987-10-07,55.50,55.75,54.25,55.50,8000000,12.70\n1987-10-06,59.50,59.50,55.50,55.75,7200000,12.76\n1987-10-05,58.50,59.75,57.75,59.25,4800000,13.56\n1987-10-02,58.25,58.75,57.50,58.50,3450000,13.39\n1987-10-01,56.75,58.75,56.50,58.25,4160000,13.33\n1987-09-30,54.25,57.00,54.25,56.50,4360000,12.93\n1987-09-29,56.00,56.00,54.25,54.50,6120000,12.47\n1987-09-28,57.50,58.75,55.50,55.75,7280000,12.76\n1987-09-25,56.75,58.00,56.50,57.50,3806800,13.16\n1987-09-24,55.25,57.88,55.25,56.50,6520000,12.93\n1987-09-23,54.13,56.00,53.75,55.25,9098800,12.64\n1987-09-22,50.50,54.25,50.25,54.13,5480000,12.39\n1987-09-21,51.75,52.75,50.25,50.25,4600000,11.50\n1987-09-18,52.00,52.25,51.38,51.75,2555600,11.84\n1987-09-17,52.00,52.25,51.00,52.00,2400000,11.90\n1987-09-16,51.75,52.63,51.25,51.75,6000000,11.84\n1987-09-15,53.00,53.00,51.50,51.75,3744800,11.84\n1987-09-14,54.75,55.25,52.75,53.00,2928000,12.13\n1987-09-11,54.00,55.50,52.75,54.50,4440000,12.47\n1987-09-10,53.25,54.50,53.13,53.75,5000000,12.30\n1987-09-09,50.25,53.00,49.50,52.75,5640000,12.07\n1987-09-08,50.25,50.50,48.50,49.88,6280000,11.42\n1987-09-04,51.25,51.75,50.00,50.50,3891200,11.56\n1987-09-03,52.50,52.75,50.25,51.25,6600000,11.73\n1987-09-02,52.00,53.25,50.75,52.00,8200000,11.90\n1987-09-01,54.75,55.25,52.50,52.50,4960000,12.01\n1987-08-31,52.25,54.25,51.75,54.00,5360000,12.36\n1987-08-28,52.00,52.50,51.50,52.00,3434400,11.90\n1987-08-27,52.25,52.75,51.50,52.00,4440000,11.90\n1987-08-26,53.00,53.50,52.00,52.00,7000000,11.90\n1987-08-25,52.75,53.25,52.00,52.00,4880000,11.90\n1987-08-24,53.00,53.50,52.25,52.25,4320000,11.96\n1987-08-21,51.75,53.75,51.50,53.00,5000000,12.13\n1987-08-20,50.25,52.50,49.75,51.75,6280000,11.84\n1987-08-19,49.50,50.00,49.00,50.00,2404400,11.44\n1987-08-18,49.25,49.50,48.25,48.75,8480000,11.16\n1987-08-17,49.50,50.00,48.75,49.50,5200000,11.33\n1987-08-14,48.50,50.00,48.00,49.00,3758800,11.21\n1987-08-13,48.75,50.25,48.50,49.00,7000000,11.21\n1987-08-12,49.50,49.75,48.25,48.75,5760000,11.16\n1987-08-11,49.50,50.25,48.75,49.50,9680000,11.33\n1987-08-10,48.25,48.25,45.75,48.25,2800000,11.04\n1987-08-07,46.25,47.25,46.00,46.50,5440000,10.63\n1987-08-06,43.25,46.75,42.75,46.25,9000000,10.57\n1987-08-05,42.25,43.50,42.00,43.25,4640000,9.89\n1987-08-04,40.50,42.25,40.00,42.25,4320000,9.66\n1987-08-03,41.00,41.50,40.25,40.25,2275600,9.20\n1987-07-31,41.25,42.00,41.25,41.25,2613600,9.43\n1987-07-30,41.00,41.50,40.75,41.50,3727600,9.49\n1987-07-29,42.00,42.00,40.50,41.00,3534800,9.37\n1987-07-28,42.50,42.75,41.75,41.88,2660800,9.57\n1987-07-27,42.50,43.00,42.00,42.25,2035600,9.66\n1987-07-24,41.50,42.75,41.50,42.50,4200000,9.71\n1987-07-23,43.00,43.50,40.50,41.75,2685600,9.54\n1987-07-22,41.50,42.75,41.25,42.50,2185200,9.71\n1987-07-21,42.00,42.50,41.25,41.38,3966400,9.46\n1987-07-20,43.00,43.25,41.50,41.75,4440000,9.54\n1987-07-17,44.25,44.75,42.75,43.25,3300000,9.89\n1987-07-16,44.00,44.00,43.25,44.00,3388000,10.06\n1987-07-15,43.00,44.75,42.25,44.00,9680000,10.06\n1987-07-14,41.00,43.00,41.00,43.00,9200000,9.83\n1987-07-13,39.00,40.75,38.75,40.50,9120000,9.26\n1987-07-10,38.00,39.25,37.75,38.00,5600000,8.69\n1987-07-09,37.25,38.75,37.25,37.75,8560000,8.63\n1987-07-08,39.25,39.25,36.50,37.25,12200000,8.51\n1987-07-07,40.50,41.00,38.75,39.25,7280000,8.97\n1987-07-06,40.75,41.75,40.50,40.75,3060800,9.31\n1987-07-02,40.00,41.00,39.75,40.63,2931200,9.29\n1987-07-01,40.75,40.75,39.75,40.00,3402000,9.14\n1987-06-30,40.50,41.00,39.75,40.50,5160000,9.26\n1987-06-29,40.50,40.75,40.00,40.75,3628000,9.31\n1987-06-26,40.75,41.50,40.00,40.50,4560000,9.26\n1987-06-25,42.00,42.50,40.50,40.50,4320000,9.26\n1987-06-24,41.50,43.25,40.50,42.00,4240000,9.60\n1987-06-23,42.00,42.13,40.75,41.25,2892000,9.43\n1987-06-22,41.25,42.25,40.88,42.00,6040000,9.60\n1987-06-19,41.50,41.75,40.38,41.00,4480000,9.37\n1987-06-18,40.25,41.75,39.50,41.50,8200000,9.49\n1987-06-17,41.50,42.50,40.00,40.50,10640000,9.26\n1987-06-16,41.50,41.75,38.00,41.50,12240000,9.49\n1987-06-15,79.00,79.50,77.50,78.50,9280000,8.97\n1987-06-12,79.00,79.75,78.75,79.00,3653600,9.03\n1987-06-11,78.50,80.00,78.00,79.00,4521600,9.03\n1987-06-10,78.75,80.25,78.00,78.50,5235200,8.97\n1987-06-09,77.50,79.50,77.50,78.50,4570400,8.97\n1987-06-08,77.75,78.00,76.75,77.75,7213600,8.89\n1987-06-05,78.75,78.75,77.75,77.75,4696000,8.89\n1987-06-04,78.00,78.75,77.00,78.50,5511200,8.97\n1987-06-03,77.25,79.50,77.25,77.75,6140000,8.89\n1987-06-02,77.50,78.00,77.00,77.25,4927200,8.83\n1987-06-01,79.50,79.50,77.50,77.75,2984000,8.89\n1987-05-29,80.25,80.50,79.00,79.00,3322400,9.03\n1987-05-28,79.50,80.25,78.50,80.00,5424000,9.14\n1987-05-27,78.00,80.25,77.50,79.50,6484000,9.09\n1987-05-26,74.50,78.00,74.00,78.00,5481600,8.91\n1987-05-22,75.00,75.50,73.75,74.12,3484000,8.47\n1987-05-21,74.75,75.75,74.50,74.50,6233600,8.51\n1987-05-20,73.00,75.00,72.50,74.50,10320000,8.51\n1987-05-19,75.75,75.75,72.62,73.25,8560000,8.37\n1987-05-18,78.25,78.50,75.50,75.75,8640000,8.66\n1987-05-15,79.25,79.25,78.00,78.25,5220000,8.94\n1987-05-14,78.25,79.50,78.25,79.25,5316000,9.06\n1987-05-13,75.75,78.62,75.50,78.50,11120000,8.97\n1987-05-12,76.00,76.50,75.00,75.50,9280000,8.63\n1987-05-11,77.00,79.50,76.75,77.00,7048800,8.80\n1987-05-08,80.50,81.00,79.00,79.00,6618400,9.01\n1987-05-07,79.75,81.00,79.75,80.25,6488800,9.16\n1987-05-06,80.50,82.25,79.25,80.00,10240000,9.13\n1987-05-05,80.00,80.75,78.00,80.25,8240000,9.16\n1987-05-04,79.50,80.25,79.00,79.75,5095200,9.10\n1987-05-01,79.50,80.00,78.75,80.00,4751200,9.13\n1987-04-30,78.00,80.00,77.75,79.25,9040000,9.04\n1987-04-29,77.25,79.75,77.00,77.75,10400000,8.87\n1987-04-28,75.75,77.87,75.50,77.00,11600000,8.79\n1987-04-27,74.25,75.25,73.25,75.00,13680000,8.56\n1987-04-24,75.75,76.50,74.50,74.75,9120000,8.53\n1987-04-23,74.25,77.25,74.25,76.00,10880000,8.67\n1987-04-22,76.62,77.00,74.00,74.25,14400000,8.47\n1987-04-21,70.25,75.00,69.50,74.75,15440000,8.53\n1987-04-20,71.50,72.75,70.75,71.12,5353600,8.12\n1987-04-16,71.25,73.25,71.00,71.50,12400000,8.16\n1987-04-15,69.50,71.00,68.75,71.00,12480000,8.10\n1987-04-14,66.75,69.75,66.50,68.00,14560000,7.76\n1987-04-13,70.00,70.25,67.50,67.50,5101600,7.70\n1987-04-10,71.25,71.50,69.75,70.25,7791200,8.02\n1987-04-09,68.75,71.50,67.75,71.00,8480000,8.10\n1987-04-08,67.75,70.25,67.50,69.00,8240000,7.87\n1987-04-07,69.75,70.25,67.75,67.75,9280000,7.73\n1987-04-06,71.50,72.75,69.25,70.00,10320000,7.99\n1987-04-03,71.50,71.87,70.25,71.75,19280000,8.19\n1987-04-02,68.25,71.75,67.00,71.75,27760000,8.19\n1987-04-01,63.00,67.00,62.38,66.75,7792800,7.62\n1987-03-31,62.25,64.75,62.25,64.50,9760000,7.36\n1987-03-30,63.50,64.25,62.25,62.50,9280000,7.13\n1987-03-27,67.25,67.50,64.75,65.00,4817600,7.42\n1987-03-26,66.75,67.75,66.50,67.25,5146400,7.67\n1987-03-25,66.50,67.00,65.25,66.75,9760000,7.62\n1987-03-24,67.75,68.50,66.25,66.25,9600000,7.56\n1987-03-23,68.00,68.25,66.25,67.50,8800000,7.70\n1987-03-20,68.25,69.75,68.25,68.25,12400000,7.79\n1987-03-19,65.75,68.50,65.50,68.37,7396000,7.80\n1987-03-18,67.25,67.50,64.75,66.00,10800000,7.53\n1987-03-17,65.50,68.00,65.00,67.00,8720000,7.65\n1987-03-16,63.50,65.25,62.50,65.25,8800000,7.45\n1987-03-13,65.25,66.00,63.50,63.50,7067200,7.25\n1987-03-12,66.00,66.25,63.63,65.25,10800000,7.45\n1987-03-11,67.25,68.00,66.25,66.25,7826400,7.56\n1987-03-10,64.50,66.87,64.50,66.75,8720000,7.62\n1987-03-09,66.50,66.75,64.50,64.62,9120000,7.37\n1987-03-06,67.25,68.37,66.75,67.25,6332800,7.67\n1987-03-05,67.50,69.00,67.25,68.50,12080000,7.82\n1987-03-04,65.75,68.25,65.37,67.62,16000000,7.72\n1987-03-03,67.50,68.12,64.75,65.00,15600000,7.42\n1987-03-02,70.25,70.50,67.00,67.50,14160000,7.70\n1987-02-27,69.12,71.00,67.75,70.00,14480000,7.99\n1987-02-26,69.50,71.37,68.00,69.12,17840000,7.89\n1987-02-25,65.50,69.50,64.62,69.12,16240000,7.89\n1987-02-24,63.25,66.00,63.13,65.50,12720000,7.47\n1987-02-23,60.88,64.25,59.63,63.13,12560000,7.20\n1987-02-20,62.38,62.50,60.63,61.25,6813600,6.99\n1987-02-19,63.50,63.50,61.75,62.38,11200000,7.12\n1987-02-18,66.62,67.37,63.38,63.50,16800000,7.25\n1987-02-17,62.13,66.50,61.88,66.37,14640000,7.57\n1987-02-13,58.63,62.50,58.00,62.13,18240000,7.09\n1987-02-12,57.00,59.88,57.00,58.63,25360000,6.69\n1987-02-11,53.00,56.75,52.75,56.50,12240000,6.45\n1987-02-10,52.50,52.75,51.63,52.75,5977600,6.02\n1987-02-09,52.88,53.38,52.25,52.63,5611200,6.01\n1987-02-06,54.00,54.00,52.88,54.00,10480000,6.16\n1987-02-05,55.00,55.13,53.13,53.88,12160000,6.15\n1987-02-04,55.50,55.50,54.38,55.00,7791200,6.28\n1987-02-03,56.00,56.13,54.75,55.50,6412800,6.33\n1987-02-02,55.50,56.00,54.25,55.88,8800000,6.38\n1987-01-30,54.00,55.88,52.63,55.50,14640000,6.33\n1987-01-29,55.88,57.25,53.38,54.13,19920000,6.18\n1987-01-28,53.00,55.75,52.13,55.38,14800000,6.32\n1987-01-27,50.00,53.13,49.88,52.75,13520000,6.02\n1987-01-26,50.00,50.50,49.50,49.75,12560000,5.68\n1987-01-23,52.50,53.00,50.25,50.25,16400000,5.73\n1987-01-22,48.88,52.63,48.50,52.50,16880000,5.99\n1987-01-21,50.88,51.13,49.00,49.00,19040000,5.59\n1987-01-20,55.00,55.75,51.50,51.63,27680000,5.89\n1987-01-19,48.75,53.13,47.88,53.13,12960000,6.06\n1987-01-16,50.00,50.00,47.75,48.75,14560000,5.56\n1987-01-15,48.25,51.38,48.00,49.88,19520000,5.69\n1987-01-14,44.63,48.25,44.50,48.13,18000000,5.49\n1987-01-13,45.13,45.38,44.63,44.63,7584800,5.09\n1987-01-12,45.50,45.75,44.75,45.50,8320000,5.19\n1987-01-09,44.75,45.75,44.38,45.38,8560000,5.18\n1987-01-08,44.75,45.13,44.50,44.75,10400000,5.11\n1987-01-07,43.88,44.88,43.63,44.75,15520000,5.11\n1987-01-06,43.13,44.00,42.63,43.75,11600000,4.99\n1987-01-05,41.25,43.25,41.00,43.00,8560000,4.91\n1987-01-02,40.38,41.13,40.13,40.88,4360000,4.66\n1986-12-31,41.00,41.38,40.38,40.50,4742400,4.62\n1986-12-30,40.50,41.50,40.38,41.00,5297600,4.68\n1986-12-29,41.00,41.13,40.25,40.50,4224800,4.62\n1986-12-26,41.88,41.88,41.00,41.00,3215200,4.68\n1986-12-24,42.00,42.13,41.63,41.88,3453600,4.78\n1986-12-23,42.25,42.38,41.88,42.13,8720000,4.81\n1986-12-22,42.00,42.50,41.75,42.13,5887200,4.81\n1986-12-19,41.38,42.50,41.38,42.13,7149600,4.81\n1986-12-18,41.13,41.88,40.75,41.38,6258400,4.72\n1986-12-17,42.38,42.50,40.88,41.25,5417600,4.71\n1986-12-16,41.63,42.50,41.63,42.50,5464000,4.85\n1986-12-15,41.00,41.75,40.38,41.75,7481600,4.76\n1986-12-12,42.88,43.00,41.25,41.25,6451200,4.71\n1986-12-11,43.63,43.88,42.63,42.88,8080000,4.89\n1986-12-10,42.38,43.75,42.00,43.50,8720000,4.96\n1986-12-09,42.38,42.63,41.13,42.38,10800000,4.84\n1986-12-08,43.63,43.88,42.38,42.50,12400000,4.85\n1986-12-05,42.63,43.75,42.50,43.75,9360000,4.99\n1986-12-04,42.63,42.75,42.00,42.50,9600000,4.85\n1986-12-03,41.63,43.00,41.50,42.75,12000000,4.88\n1986-12-02,40.50,41.75,40.00,41.50,13200000,4.74\n1986-12-01,40.00,40.13,39.13,40.13,12400000,4.58\n1986-11-28,40.50,40.63,39.63,40.00,7897600,4.56\n1986-11-26,40.13,41.25,40.00,40.50,18080000,4.62\n1986-11-25,38.00,40.38,38.00,40.25,30320000,4.59\n1986-11-24,36.25,38.13,36.00,38.00,13440000,4.34\n1986-11-21,35.25,36.25,35.13,36.00,10240000,4.11\n1986-11-20,34.88,35.38,34.88,35.25,10560000,4.02\n1986-11-19,35.13,35.25,34.50,35.00,10800000,3.99\n1986-11-18,36.38,36.75,35.13,35.38,6115200,4.04\n1986-11-17,35.25,37.00,35.00,36.38,5071200,4.15\n1986-11-14,35.50,35.50,34.88,35.25,4840000,4.02\n1986-11-13,36.50,36.50,35.50,35.50,4928800,4.05\n1986-11-12,35.75,36.63,35.63,36.63,4700000,4.18\n1986-11-11,35.50,35.75,35.25,35.50,1809600,4.05\n1986-11-10,35.88,35.88,35.13,35.38,3793600,4.04\n1986-11-07,36.00,36.13,34.88,35.75,5153600,4.08\n1986-11-06,36.63,36.88,35.75,36.13,11840000,4.12\n1986-11-05,35.75,37.13,35.50,37.00,22320000,4.22\n1986-11-04,34.88,35.88,33.88,35.75,8800000,4.08\n1986-11-03,34.75,35.13,34.63,35.00,5457600,3.99\n1986-10-31,34.25,34.88,34.25,34.63,4338400,3.95\n1986-10-30,33.50,34.75,33.38,34.25,10480000,3.91\n1986-10-29,33.50,33.50,33.13,33.38,3057600,3.81\n1986-10-28,34.00,34.13,33.00,33.38,5102400,3.81\n1986-10-27,33.50,34.00,33.25,34.00,5422400,3.88\n1986-10-24,33.13,33.25,32.75,33.00,2718400,3.77\n1986-10-23,32.50,33.13,32.50,33.13,4441600,3.78\n1986-10-22,32.75,32.88,32.25,32.50,3382400,3.71\n1986-10-21,33.00,33.00,32.63,32.75,4096000,3.74\n1986-10-20,33.50,33.63,32.88,32.88,5344000,3.75\n1986-10-17,33.75,34.00,33.38,33.63,5460000,3.84\n1986-10-16,33.38,33.88,33.25,33.63,4876000,3.84\n1986-10-15,33.50,33.50,32.75,33.38,7367200,3.81\n1986-10-14,34.63,35.25,33.75,34.00,7164000,3.88\n1986-10-13,33.13,34.63,33.00,34.63,3582400,3.95\n1986-10-10,32.88,33.38,32.38,33.25,2096000,3.79\n1986-10-09,32.75,33.25,32.63,33.00,2820000,3.77\n1986-10-08,32.88,33.00,32.25,32.75,4021600,3.74\n1986-10-07,34.00,34.13,32.88,33.00,4577600,3.77\n1986-10-06,33.75,34.25,33.63,34.13,3384000,3.89\n1986-10-03,34.38,34.75,33.38,33.75,4997600,3.85\n1986-10-02,33.75,34.38,33.50,34.13,3401600,3.89\n1986-10-01,33.38,34.50,33.38,34.13,4988800,3.89\n1986-09-30,32.88,33.88,32.63,33.50,6488800,3.82\n1986-09-29,33.63,33.88,31.62,32.50,7475200,3.71\n1986-09-26,34.13,34.38,33.88,34.25,2512800,3.91\n1986-09-25,35.13,35.25,33.63,34.50,6744800,3.94\n1986-09-24,36.13,36.38,34.00,35.13,6360000,4.01\n1986-09-23,35.25,36.25,35.13,36.13,12080000,4.12\n1986-09-22,33.50,35.38,33.50,35.25,8560000,4.02\n1986-09-19,33.75,33.88,33.25,33.63,4601600,3.84\n1986-09-18,34.25,34.50,33.75,34.00,3546400,3.88\n1986-09-17,34.88,35.00,34.25,34.25,4181600,3.91\n1986-09-16,33.13,35.13,32.50,34.88,8800000,3.98\n1986-09-15,32.25,33.13,32.00,33.13,7973600,3.78\n1986-09-12,32.50,32.75,31.75,31.75,8160000,3.62\n1986-09-11,34.63,34.75,32.50,32.63,4842400,3.72\n1986-09-10,35.63,35.88,34.75,35.00,2737600,3.99\n1986-09-09,34.63,36.00,34.63,35.75,5398400,4.08\n1986-09-08,35.00,35.00,33.63,34.75,4522400,3.97\n1986-09-05,35.63,35.88,35.00,35.13,3561600,4.01\n1986-09-04,35.00,35.50,34.75,35.50,7133600,4.05\n1986-09-03,34.75,34.88,34.13,34.75,4216000,3.97\n1986-09-02,37.13,37.13,34.75,34.75,8320000,3.97\n1986-08-29,37.63,38.00,36.88,37.00,4846400,4.22\n1986-08-28,37.00,38.00,36.88,37.75,7849600,4.31\n1986-08-27,36.63,37.00,36.25,37.00,5280000,4.22\n1986-08-26,36.38,36.88,36.38,36.63,4713600,4.18\n1986-08-25,36.50,36.88,36.38,36.38,4533600,4.15\n1986-08-22,35.88,36.63,35.88,36.25,4162400,4.14\n1986-08-21,36.13,36.38,35.75,35.75,6992800,4.08\n1986-08-20,35.25,36.50,35.25,36.25,6140000,4.14\n1986-08-19,35.13,35.50,34.63,35.38,4944000,4.04\n1986-08-18,35.75,35.88,35.00,35.38,5297600,4.04\n1986-08-15,36.13,36.50,35.63,35.75,4910400,4.08\n1986-08-14,36.00,37.00,36.00,36.00,8240000,4.11\n1986-08-13,34.25,36.25,34.25,36.00,16240000,4.11\n1986-08-12,33.38,34.38,33.38,34.25,8720000,3.91\n1986-08-11,31.87,33.50,31.75,33.50,6591200,3.82\n1986-08-08,31.87,32.38,31.62,31.62,3941600,3.61\n1986-08-07,31.12,32.63,31.12,31.75,6211200,3.62\n1986-08-06,32.13,32.13,31.00,31.12,6644800,3.55\n1986-08-05,31.62,32.38,31.50,32.13,4238400,3.67\n1986-08-04,31.37,31.50,30.62,31.50,4653600,3.59\n1986-08-01,31.12,31.75,31.12,31.37,5360000,3.58\n1986-07-31,30.50,31.50,30.50,31.25,10080000,3.57\n1986-07-30,31.25,31.50,30.00,30.50,9120000,3.48\n1986-07-29,32.25,32.25,30.75,31.25,21280000,3.57\n1986-07-28,33.88,34.00,32.25,32.38,8800000,3.69\n1986-07-25,33.13,34.00,33.00,34.00,7769600,3.88\n1986-07-24,34.25,34.38,33.00,33.13,5187200,3.78\n1986-07-23,34.63,34.63,34.13,34.13,6416000,3.89\n1986-07-22,33.50,34.63,33.25,34.63,8560000,3.95\n1986-07-21,33.00,33.75,32.75,33.50,8160000,3.82\n1986-07-18,32.25,32.50,31.25,31.75,11040000,3.62\n1986-07-17,33.50,33.75,32.13,32.25,8960000,3.68\n1986-07-16,35.50,35.63,32.75,33.50,19280000,3.82\n1986-07-15,35.00,35.00,34.25,34.88,10640000,3.98\n1986-07-14,37.13,37.38,36.25,36.25,8480000,4.14\n1986-07-11,35.38,37.75,35.25,37.13,8000000,4.24\n1986-07-10,34.75,35.38,34.63,35.38,7453600,4.04\n1986-07-09,34.25,34.75,34.00,34.63,13040000,3.95\n1986-07-08,35.25,35.25,34.13,34.25,9782400,3.91\n1986-07-07,37.63,37.75,35.38,35.63,6501600,4.07\n1986-07-03,36.13,37.75,35.63,37.63,6509600,4.29\n1986-07-02,35.38,36.25,35.38,36.13,5202400,4.12\n1986-07-01,35.88,36.13,34.75,35.38,3140000,4.04\n1986-06-30,35.88,36.25,35.75,35.88,2553600,4.09\n1986-06-27,36.25,36.75,35.50,35.88,1811200,4.09\n1986-06-26,35.88,36.38,35.50,36.25,4184800,4.14\n1986-06-25,35.00,36.00,35.00,35.88,4755200,4.09\n1986-06-24,34.75,35.13,34.38,34.88,5088800,3.98\n1986-06-23,36.00,36.25,34.63,34.75,4196000,3.97\n1986-06-20,35.00,36.13,35.00,36.00,5761600,4.11\n1986-06-19,34.25,35.75,33.88,35.00,12347200,3.99\n1986-06-18,34.25,34.75,32.50,34.25,15381600,3.91\n1986-06-17,35.88,36.00,34.00,34.25,7936000,3.91\n1986-06-16,36.38,36.88,35.63,35.88,6222400,4.09\n1986-06-13,36.00,36.38,35.25,36.38,5144800,4.15\n1986-06-12,36.13,36.38,36.00,36.00,4638400,4.11\n1986-06-11,36.00,36.25,35.50,36.13,6692800,4.12\n1986-06-10,36.00,36.00,35.13,36.00,8827200,4.11\n1986-06-09,37.75,37.88,35.88,36.00,8835200,4.11\n1986-06-06,38.88,38.88,37.50,37.75,6342400,4.31\n1986-06-05,38.75,39.13,38.50,38.88,5282400,4.44\n1986-06-04,37.88,38.88,37.75,38.75,10747200,4.42\n1986-06-03,37.13,38.13,37.13,37.88,11661600,4.32\n1986-06-02,37.00,37.38,36.75,37.13,7158400,4.24\n1986-05-30,37.00,37.25,36.50,37.00,4591200,4.22\n1986-05-29,37.25,37.25,36.50,37.00,3635200,4.22\n1986-05-28,36.88,37.50,36.75,37.25,7418400,4.25\n1986-05-27,37.00,37.00,36.38,36.88,3058400,4.21\n1986-05-23,36.75,37.13,36.38,37.00,5013600,4.22\n1986-05-22,37.00,37.50,35.75,36.75,7895200,4.19\n1986-05-21,35.38,37.25,35.00,37.00,12418400,4.22\n1986-05-20,35.63,35.63,34.25,35.38,8811200,4.04\n1986-05-19,36.00,36.50,35.50,35.63,7506400,4.07\n1986-05-16,36.00,36.25,35.13,36.00,11424800,4.11\n1986-05-15,36.88,37.00,35.63,36.00,7964000,4.11\n1986-05-14,36.00,37.38,36.00,36.88,17277600,4.21\n1986-05-13,36.38,36.50,35.25,36.00,16876000,4.11\n1986-05-12,33.38,36.63,33.25,36.38,14335200,4.15\n1986-05-09,33.00,33.63,32.75,33.38,7961600,3.81\n1986-05-08,31.50,33.13,31.50,33.00,8342400,3.77\n1986-05-07,32.63,32.88,31.25,31.50,7133600,3.59\n1986-05-06,32.25,33.25,32.25,32.63,7829600,3.72\n1986-05-05,30.50,32.50,30.50,32.13,5364000,3.67\n1986-05-02,30.25,31.00,30.12,30.50,3377600,3.48\n1986-05-01,30.25,30.25,29.75,30.25,9218400,3.45\n1986-04-30,31.25,31.62,30.25,30.25,4944000,3.45\n1986-04-29,32.00,32.25,26.87,31.25,4750400,3.57\n1986-04-28,32.25,32.75,31.75,32.00,5241600,3.65\n1986-04-25,31.37,32.63,31.37,32.25,9348800,3.68\n1986-04-24,29.62,31.50,29.50,31.37,16398400,3.58\n1986-04-23,29.87,30.37,29.37,29.62,9371200,3.38\n1986-04-22,30.37,31.25,29.62,29.87,11726400,3.41\n1986-04-21,29.87,30.75,29.87,30.37,9775200,3.47\n1986-04-18,29.00,29.87,28.75,29.75,8871200,3.39\n1986-04-17,28.25,29.12,28.00,29.00,9672800,3.31\n1986-04-16,27.37,28.50,27.37,28.25,7535200,3.22\n1986-04-15,26.87,27.50,26.87,27.37,4722400,3.12\n1986-04-14,27.00,27.25,26.75,26.87,3076000,3.07\n1986-04-11,27.25,27.50,27.00,27.00,2737600,3.08\n1986-04-10,27.12,27.37,26.87,27.25,3932800,3.11\n1986-04-09,27.62,27.75,26.87,27.12,4851200,3.09\n1986-04-08,27.25,27.75,27.25,27.62,6912800,3.15\n1986-04-07,26.75,27.50,26.25,27.25,4318400,3.11\n1986-04-04,27.00,27.00,26.62,26.75,4508800,3.05\n1986-04-03,27.25,27.62,26.87,27.00,7548800,3.08\n1986-04-02,27.25,27.37,26.25,27.25,11627200,3.11\n1986-04-01,28.25,28.25,27.00,27.25,7973600,3.11\n1986-03-31,28.25,28.50,28.00,28.25,6744800,3.22\n1986-03-27,28.25,29.00,28.25,28.25,7856000,3.22\n1986-03-26,27.87,28.75,27.87,28.25,7941600,3.22\n1986-03-25,26.75,27.87,26.75,27.87,10060000,3.18\n1986-03-24,27.62,27.62,26.37,26.75,10528800,3.05\n1986-03-21,28.25,28.75,27.50,27.62,9309600,3.15\n1986-03-20,28.00,29.62,28.00,28.25,32318400,3.22\n1986-03-19,26.87,27.25,26.37,26.50,6816000,3.02\n1986-03-18,26.00,27.25,25.87,26.87,8920000,3.07\n1986-03-17,26.00,26.00,25.37,26.00,4240000,2.97\n1986-03-14,24.75,26.25,24.75,26.12,13781600,2.98\n1986-03-13,24.75,25.00,24.37,24.75,4176000,2.82\n1986-03-12,24.87,25.12,24.75,24.75,3071200,2.82\n1986-03-11,24.62,24.87,24.50,24.87,3681600,2.84\n1986-03-10,24.75,24.87,24.62,24.62,2727200,2.81\n1986-03-07,25.37,25.37,24.75,24.75,3477600,2.82\n1986-03-06,25.25,25.75,25.12,25.37,3630400,2.89\n1986-03-05,24.62,25.50,24.25,25.25,6324000,2.88\n1986-03-04,24.62,25.00,24.50,24.62,3217600,2.81\n1986-03-03,25.00,25.12,24.50,24.62,3912800,2.81\n1986-02-28,25.62,25.87,24.87,25.00,4507200,2.85\n1986-02-27,26.00,26.12,25.50,25.62,3873600,2.92\n1986-02-26,26.37,26.75,26.00,26.00,5907200,2.97\n1986-02-25,25.75,26.37,25.12,26.37,8041600,3.01\n1986-02-24,25.25,25.75,25.00,25.75,8840000,2.94\n1986-02-21,25.12,25.75,25.12,25.25,6771200,2.88\n1986-02-20,25.00,25.37,24.87,25.12,4951200,2.87\n1986-02-19,23.87,25.50,23.87,25.00,12871200,2.85\n1986-02-18,23.75,24.00,23.25,23.87,5295200,2.72\n1986-02-14,23.87,24.12,23.75,23.75,4928800,2.71\n1986-02-13,24.00,24.00,23.75,23.87,3944000,2.72\n1986-02-12,23.87,24.00,23.75,24.00,4770400,2.74\n1986-02-11,23.87,24.00,23.50,23.87,5504000,2.72\n1986-02-10,24.00,24.50,23.75,23.87,4036000,2.72\n1986-02-07,24.12,24.12,23.50,24.00,4656000,2.74\n1986-02-06,23.75,24.25,23.62,24.12,4835200,2.75\n1986-02-05,23.75,23.87,23.50,23.75,7042400,2.71\n1986-02-04,23.87,24.37,23.75,23.75,9298400,2.71\n1986-02-03,23.12,24.00,22.87,23.87,12512800,2.72\n1986-01-31,23.00,23.25,22.87,23.12,5317600,2.64\n1986-01-30,23.50,23.50,22.87,23.00,8493600,2.62\n1986-01-29,22.25,24.37,22.00,23.62,21064800,2.70\n1986-01-28,22.12,22.37,22.00,22.25,7949600,2.54\n1986-01-27,22.62,22.75,22.00,22.12,13955200,2.52\n1986-01-24,23.00,23.37,22.62,22.62,4044000,2.58\n1986-01-23,23.37,23.50,22.75,23.00,5624000,2.62\n1986-01-22,24.00,24.12,22.37,23.37,5144800,2.67\n1986-01-21,23.87,24.12,23.75,24.00,5464800,2.74\n1986-01-20,24.00,24.00,23.37,23.87,4590400,2.72\n1986-01-17,24.50,24.75,23.87,24.00,12344000,2.74\n1986-01-16,23.87,24.75,23.87,24.50,19132800,2.80\n1986-01-15,23.25,24.00,23.12,23.87,15126400,2.72\n1986-01-14,23.00,23.75,22.50,23.25,9772800,2.65\n1986-01-13,22.75,23.12,22.50,23.00,7701600,2.62\n1986-01-10,22.62,23.12,22.62,22.75,5491200,2.60\n1986-01-09,22.87,23.00,21.87,22.62,16002400,2.58\n1986-01-08,23.00,23.50,22.75,22.87,21711200,2.61\n1986-01-07,22.25,23.00,22.12,23.00,16807200,2.62\n1986-01-06,22.37,22.37,21.87,22.25,6636000,2.54\n1986-01-03,22.25,22.37,22.12,22.37,8653600,2.55\n1986-01-02,22.00,22.25,21.75,22.25,4212800,2.54\n1985-12-31,22.25,22.37,22.00,22.00,3158400,2.51\n1985-12-30,22.37,22.62,22.12,22.25,3848800,2.54\n1985-12-27,21.75,22.62,21.75,22.37,4427200,2.55\n1985-12-26,21.75,22.00,21.62,21.75,1658400,2.48\n1985-12-24,21.87,22.00,21.62,21.75,2344800,2.48\n1985-12-23,22.37,22.50,21.62,21.87,5157600,2.50\n1985-12-20,22.50,22.75,22.25,22.37,7402400,2.55\n1985-12-19,22.25,22.75,22.12,22.50,9673600,2.57\n1985-12-18,21.37,22.87,21.37,22.25,20033600,2.54\n1985-12-17,20.87,21.00,20.37,20.62,3926400,2.35\n1985-12-16,20.00,21.25,20.00,20.87,10362400,2.38\n1985-12-13,20.00,20.25,19.75,20.00,8975200,2.28\n1985-12-12,19.87,20.25,19.87,20.00,4515200,2.28\n1985-12-11,19.50,20.12,19.50,19.75,8489600,2.25\n1985-12-10,19.37,19.62,19.25,19.50,7206400,2.23\n1985-12-09,19.75,20.00,19.25,19.37,5015200,2.21\n1985-12-06,20.12,20.12,19.62,19.75,2347200,2.25\n1985-12-05,20.50,20.75,20.00,20.12,4508800,2.30\n1985-12-04,20.12,20.62,20.12,20.50,5928800,2.34\n1985-12-03,20.25,20.37,20.00,20.12,5548800,2.30\n1985-12-02,20.12,20.25,20.00,20.25,3611200,2.31\n1985-11-29,20.00,20.12,19.87,20.12,3546400,2.30\n1985-11-27,19.37,20.12,19.25,20.00,6873600,2.28\n1985-11-26,19.12,19.50,19.00,19.37,5892800,2.21\n1985-11-25,19.00,19.25,19.00,19.12,3488800,2.18\n1985-11-22,19.00,19.25,18.87,19.00,4620000,2.17\n1985-11-21,19.00,19.25,19.00,19.00,3720000,2.17\n1985-11-20,19.25,19.37,19.00,19.00,3548800,2.17\n1985-11-19,19.87,20.00,19.25,19.25,3373600,2.20\n1985-11-18,19.87,20.00,19.87,19.87,2342400,2.27\n1985-11-15,20.00,20.25,19.87,19.87,2932800,2.27\n1985-11-14,20.00,20.12,20.00,20.00,4995200,2.28\n1985-11-13,19.87,19.87,19.37,19.37,3642400,2.21\n1985-11-12,20.00,20.25,19.87,19.87,6224800,2.27\n1985-11-11,20.50,20.75,20.00,20.00,6421600,2.28\n1985-11-08,20.50,20.75,20.50,20.50,10517600,2.34\n1985-11-07,19.62,19.87,19.62,19.62,11352800,2.24\n1985-11-06,19.25,19.37,19.25,19.25,7181600,2.20\n1985-11-05,18.75,19.12,18.62,18.62,3841600,2.12\n1985-11-04,18.75,19.12,18.75,18.75,5584800,2.14\n1985-11-01,18.62,19.00,18.62,18.62,3320000,2.12\n1985-10-31,19.00,19.25,18.62,18.62,5548800,2.12\n1985-10-30,19.00,19.00,19.00,19.00,8098400,2.17\n1985-10-29,18.00,18.00,17.87,17.87,4693600,2.04\n1985-10-28,18.00,18.12,18.00,18.00,2148800,2.05\n1985-10-25,18.37,18.37,18.00,18.00,2271200,2.05\n1985-10-24,18.37,18.87,18.37,18.37,9768800,2.10\n1985-10-23,18.00,18.50,18.00,18.00,5309600,2.05\n1985-10-22,18.00,18.25,18.00,18.00,15186400,2.05\n1985-10-21,17.75,17.75,17.25,17.25,4248800,1.97\n1985-10-18,18.25,18.37,17.75,17.75,8268800,2.03\n1985-10-17,18.25,19.12,18.25,18.25,12455200,2.08\n1985-10-16,18.00,18.12,18.00,18.00,10336000,2.05\n1985-10-15,17.00,17.12,17.00,17.00,10504800,1.94\n1985-10-14,16.62,16.62,16.62,16.62,5555200,1.90\n1985-10-11,16.00,16.25,16.00,16.00,4261600,1.83\n1985-10-10,15.88,16.00,15.88,15.88,9386400,1.81\n1985-10-09,15.13,15.25,15.00,15.00,3001600,1.71\n1985-10-08,15.13,15.13,15.13,15.13,3144000,1.73\n1985-10-07,15.00,15.25,15.00,15.00,3284800,1.71\n1985-10-04,15.50,15.50,15.00,15.00,2484800,1.71\n1985-10-03,15.63,15.63,15.50,15.50,1784800,1.77\n1985-10-02,15.75,15.88,15.63,15.63,795200,1.78\n1985-10-01,15.75,15.88,15.75,15.75,3175200,1.80\n1985-09-30,15.88,16.00,15.75,15.75,1324800,1.80\n1985-09-27,15.88,16.00,15.88,15.88,250400,1.81\n1985-09-26,15.88,16.00,15.88,15.88,1949600,1.81\n1985-09-25,16.50,16.50,15.88,15.88,3761600,1.81\n1985-09-24,16.87,17.25,16.50,16.50,3161600,1.88\n1985-09-23,16.87,17.12,16.87,16.87,4277600,1.92\n1985-09-20,17.00,17.12,16.75,16.75,4846400,1.91\n1985-09-19,17.00,17.00,17.00,17.00,6662400,1.94\n1985-09-18,16.25,16.25,16.25,16.25,4316000,1.85\n1985-09-17,15.25,15.25,15.25,15.25,6564000,1.74\n1985-09-16,15.75,15.75,15.25,15.25,1344000,1.74\n1985-09-13,16.12,16.12,15.75,15.75,2541600,1.80\n1985-09-12,16.12,16.12,16.12,16.12,3998400,1.84\n1985-09-11,15.50,15.63,15.50,15.50,3150400,1.77\n1985-09-10,15.38,15.63,15.38,15.38,4364800,1.75\n1985-09-09,15.25,15.38,15.25,15.25,4728800,1.74\n1985-09-06,15.00,15.00,15.00,15.00,3333600,1.71\n1985-09-05,14.88,15.00,14.88,14.88,1201600,1.70\n1985-09-04,14.88,15.13,14.88,14.88,1708800,1.70\n1985-09-03,15.00,15.00,14.75,14.75,1369600,1.68\n1985-08-30,15.00,15.00,15.00,15.00,1537600,1.71\n1985-08-29,15.25,15.25,14.88,14.88,2006400,1.70\n1985-08-28,15.25,15.38,15.25,15.25,1475200,1.74\n1985-08-27,15.25,15.25,15.25,15.25,1540000,1.74\n1985-08-26,15.13,15.13,15.13,15.13,1315200,1.73\n1985-08-23,14.88,15.00,14.75,14.75,1601600,1.68\n1985-08-22,15.25,15.25,14.88,14.88,4406400,1.70\n1985-08-21,15.25,15.25,15.25,15.25,2767200,1.74\n1985-08-20,15.25,15.25,15.25,15.25,2431200,1.74\n1985-08-19,15.00,15.25,15.00,15.00,1726400,1.71\n1985-08-16,14.63,14.88,14.63,14.63,3008800,1.67\n1985-08-15,14.63,14.75,14.50,14.50,3800000,1.65\n1985-08-14,15.25,15.25,14.63,14.63,10372800,1.67\n1985-08-13,15.25,15.50,15.25,15.25,1555200,1.74\n1985-08-12,15.25,15.25,15.00,15.00,1988800,1.71\n1985-08-09,15.25,15.25,15.25,15.25,2186400,1.74\n1985-08-08,15.13,15.25,15.13,15.13,5321600,1.73\n1985-08-07,15.25,16.00,14.88,14.88,5452800,1.70\n1985-08-06,15.38,15.75,15.25,15.25,2260000,1.74\n1985-08-05,15.75,15.88,15.38,15.38,3307200,1.75\n1985-08-02,15.88,15.88,15.75,15.75,3501600,1.80\n1985-08-01,15.88,16.12,15.88,15.88,1842400,1.81\n1985-07-31,16.25,16.37,15.88,15.88,2917600,1.81\n1985-07-30,16.25,16.37,16.25,16.25,3237600,1.85\n1985-07-29,16.62,16.62,16.00,16.00,2808800,1.83\n1985-07-26,16.62,16.75,16.62,16.62,4673600,1.90\n1985-07-25,16.62,16.75,16.62,16.62,11282400,1.90\n1985-07-24,16.50,16.75,16.25,16.25,6040000,1.85\n1985-07-23,16.87,17.12,16.50,16.50,6038400,1.88\n1985-07-22,17.37,17.37,16.87,16.87,6906400,1.92\n1985-07-19,17.37,17.37,17.37,17.37,4117600,1.98\n1985-07-18,17.62,17.62,17.25,17.25,6437600,1.97\n1985-07-17,17.62,17.87,17.62,17.62,4255200,2.01\n1985-07-16,17.75,17.87,17.50,17.50,5120000,2.00\n1985-07-15,17.87,18.25,17.75,17.75,2804800,2.03\n1985-07-12,18.00,18.00,17.87,17.87,1680000,2.04\n1985-07-11,18.00,18.12,18.00,18.00,2361600,2.05\n1985-07-10,18.00,18.00,18.00,18.00,3802400,2.05\n1985-07-09,17.62,17.75,17.62,17.62,5284000,2.01\n1985-07-08,17.62,17.75,17.62,17.62,3301600,2.01\n1985-07-05,17.62,17.75,17.62,17.62,1321600,2.01\n1985-07-03,17.50,17.50,17.50,17.50,2472800,2.00\n1985-07-02,18.12,18.25,17.25,17.25,2807200,1.97\n1985-07-01,18.12,18.25,18.12,18.12,3702400,2.07\n1985-06-28,18.37,18.50,18.00,18.00,4875200,2.05\n1985-06-27,18.37,18.50,18.37,18.37,6915200,2.10\n1985-06-26,18.12,18.12,18.12,18.12,4722400,2.07\n1985-06-25,17.50,17.87,17.50,17.50,10506400,2.00\n1985-06-24,17.25,17.50,17.25,17.25,7387200,1.97\n1985-06-21,16.12,16.50,16.12,16.12,5941600,1.84\n1985-06-20,15.75,15.75,15.75,15.75,6822400,1.80\n1985-06-19,15.63,15.88,15.63,15.63,6177600,1.78\n1985-06-18,15.25,15.50,15.25,15.25,9489600,1.74\n1985-06-17,14.88,15.00,14.88,14.88,8464000,1.70\n1985-06-14,14.88,15.75,14.75,14.75,20226400,1.68\n1985-06-13,15.75,15.88,14.88,14.88,13573600,1.70\n1985-06-12,16.12,16.25,15.75,15.75,8888800,1.80\n1985-06-11,16.12,16.50,16.12,16.12,10751200,1.84\n1985-06-10,16.37,16.50,16.12,16.12,11296000,1.84\n1985-06-07,17.00,17.00,16.37,16.37,16980000,1.87\n1985-06-06,17.00,17.00,17.00,17.00,9688800,1.94\n1985-06-05,17.25,17.75,16.87,16.87,10267200,1.92\n1985-06-04,17.25,17.37,17.25,17.25,14373600,1.97\n1985-06-03,17.00,17.00,16.00,16.00,20578400,1.83\n1985-05-31,17.62,18.00,17.37,17.37,13235200,1.98\n1985-05-30,17.62,17.87,17.62,17.62,11273600,2.01\n1985-05-29,17.12,17.25,17.12,17.12,8808800,1.95\n1985-05-28,17.87,17.87,16.87,16.87,18253600,1.92\n1985-05-24,19.75,19.75,18.12,18.12,21060000,2.07\n1985-05-23,20.50,20.50,19.75,19.75,8576000,2.25\n1985-05-22,20.75,20.87,20.62,20.62,4342400,2.35\n1985-05-21,21.25,21.25,20.75,20.75,5452800,2.37\n1985-05-20,21.75,22.25,21.37,21.37,7044000,2.44\n1985-05-17,21.37,22.12,21.25,21.75,7592800,2.48\n1985-05-16,21.37,22.00,21.37,21.37,8275200,2.44\n1985-05-15,20.00,20.37,20.00,20.00,4668800,2.28\n1985-05-14,20.00,20.12,19.75,19.75,4364000,2.25\n1985-05-13,20.25,20.37,20.00,20.00,3157600,2.28\n1985-05-10,20.00,20.50,20.00,20.25,4893600,2.31\n1985-05-09,20.00,20.12,20.00,20.00,4571200,2.28\n1985-05-08,19.87,19.87,19.87,19.87,5177600,2.27\n1985-05-07,20.00,20.00,20.00,20.00,3844800,2.28\n1985-05-06,20.00,20.25,19.75,19.75,2007200,2.25\n1985-05-03,19.25,20.12,19.25,20.00,5673600,2.28\n1985-05-02,20.62,20.62,19.25,19.25,11787200,2.20\n1985-05-01,21.25,21.37,20.87,20.87,2075200,2.38\n1985-04-30,21.25,21.37,21.25,21.25,3396000,2.42\n1985-04-29,21.87,22.00,21.12,21.12,2256000,2.41\n1985-04-26,22.00,22.62,21.87,21.87,4295200,2.50\n1985-04-25,22.00,22.12,22.00,22.00,3135200,2.51\n1985-04-24,22.12,22.50,22.00,22.00,2830400,2.51\n1985-04-23,22.12,22.25,22.12,22.12,4261600,2.52\n1985-04-22,22.50,22.50,21.62,21.62,3700000,2.47\n1985-04-19,22.87,22.87,22.37,22.50,3468800,2.57\n1985-04-18,22.87,23.00,22.87,22.87,7246400,2.61\n1985-04-17,22.62,22.87,22.62,22.62,4402400,2.58\n1985-04-16,21.62,21.75,21.62,21.62,2424800,2.47\n1985-04-15,21.37,21.62,21.37,21.37,2168800,2.44\n1985-04-12,21.37,21.37,20.75,20.87,2607200,2.38\n1985-04-11,21.37,22.00,21.37,21.37,5260000,2.44\n1985-04-10,21.00,21.25,21.00,21.00,8117600,2.40\n1985-04-09,19.62,19.75,19.62,19.62,9461600,2.24\n1985-04-08,20.87,21.00,19.62,19.62,7129600,2.24\n1985-04-04,21.00,21.12,20.62,20.87,5792800,2.38\n1985-04-03,21.00,21.12,21.00,21.00,8681600,2.40\n1985-04-02,21.62,21.75,21.00,21.00,8146400,2.40\n1985-04-01,22.12,22.62,21.62,21.62,4115200,2.47\n1985-03-29,21.87,22.25,21.87,22.12,3155200,2.52\n1985-03-28,21.87,22.25,21.87,21.87,4667200,2.50\n1985-03-27,22.50,22.75,21.87,21.87,4008800,2.50\n1985-03-26,22.50,22.50,22.50,22.50,4346400,2.57\n1985-03-25,22.25,22.25,21.62,21.62,3931200,2.47\n1985-03-22,22.62,23.00,22.25,22.25,2910400,2.54\n1985-03-21,22.62,23.00,22.62,22.62,5826400,2.58\n1985-03-20,22.25,22.62,22.25,22.25,14498400,2.54\n1985-03-19,22.87,23.12,22.00,22.00,6147200,2.51\n1985-03-18,22.87,23.12,22.87,22.87,4487200,2.61\n1985-03-15,21.75,23.12,21.62,22.62,6524000,2.58\n1985-03-14,21.75,21.87,21.75,21.75,8667200,2.48\n1985-03-13,23.00,23.00,21.75,21.75,8973600,2.48\n1985-03-12,23.00,23.25,23.00,23.00,7880000,2.62\n1985-03-11,22.25,22.37,22.25,22.25,10244800,2.54\n1985-03-08,22.12,22.12,20.75,21.50,16931200,2.45\n1985-03-07,24.62,24.75,22.12,22.12,26244000,2.52\n1985-03-06,25.87,25.87,24.62,24.62,6933600,2.81\n1985-03-05,25.87,25.87,25.87,25.87,4687200,2.95\n1985-03-04,25.25,26.00,25.25,25.25,5484000,2.88\n1985-03-01,24.75,24.87,24.00,24.87,8857600,2.84\n1985-02-28,25.12,25.12,24.75,24.75,11415200,2.82\n1985-02-27,26.75,26.75,25.12,25.12,14421600,2.87\n1985-02-26,27.25,27.37,26.75,26.75,6764800,3.05\n1985-02-25,27.62,27.75,27.25,27.25,3564000,3.11\n1985-02-22,26.87,27.87,26.87,27.62,8096000,3.15\n1985-02-21,26.87,27.00,26.87,26.87,11035200,3.07\n1985-02-20,27.62,27.75,26.37,26.37,7864800,3.01\n1985-02-19,27.87,27.87,27.62,27.62,5391200,3.15\n1985-02-15,27.62,28.12,27.37,28.00,6224000,3.19\n1985-02-14,28.37,28.62,27.62,27.62,15268800,3.15\n1985-02-13,29.75,29.75,28.37,28.37,18835200,3.24\n1985-02-12,30.50,30.62,29.75,29.75,8095200,3.39\n1985-02-11,30.50,30.75,30.50,30.50,12431200,3.48\n1985-02-08,29.87,30.00,29.50,29.87,4757600,3.41\n1985-02-07,30.00,30.37,29.87,29.87,8793600,3.41\n1985-02-06,30.00,30.00,30.00,30.00,6980000,3.42\n1985-02-05,29.50,30.00,29.50,29.50,6824800,3.37\n1985-02-04,29.25,29.37,29.25,29.25,7801600,3.34\n1985-02-01,29.00,29.12,28.37,28.62,4941600,3.27\n1985-01-31,29.87,30.00,29.00,29.00,9880000,3.31\n1985-01-30,29.87,30.50,29.87,29.87,17624800,3.41\n1985-01-29,30.25,30.50,29.87,29.87,8029600,3.41\n1985-01-28,30.25,30.62,30.25,30.25,14721600,3.45\n1985-01-25,29.00,29.62,28.37,29.62,11381600,3.38\n1985-01-24,29.62,29.62,29.00,29.00,14192800,3.31\n1985-01-23,30.12,30.25,29.62,29.62,15384000,3.38\n1985-01-22,30.12,30.25,30.12,30.12,15202400,3.44\n1985-01-21,29.25,29.50,29.25,29.25,11635200,3.34\n1985-01-18,28.12,29.25,28.00,28.62,12615200,3.27\n1985-01-17,30.25,30.75,28.12,28.12,19573600,3.21\n1985-01-16,30.25,30.75,30.25,30.25,6816000,3.45\n1985-01-15,30.62,31.12,30.00,30.00,9476000,3.42\n1985-01-14,30.62,30.87,30.62,30.62,9691200,3.49\n1985-01-11,30.00,30.25,29.50,29.75,7347200,3.39\n1985-01-10,30.00,30.12,30.00,30.00,9926400,3.42\n1985-01-09,28.75,29.12,28.75,28.75,5973600,3.28\n1985-01-08,28.25,28.50,28.00,28.00,5040000,3.19\n1985-01-07,28.37,28.50,28.25,28.25,6117600,3.22\n1985-01-04,28.37,28.50,28.00,28.37,4915200,3.24\n1985-01-03,28.37,29.12,28.37,28.37,5967200,3.24\n1985-01-02,29.12,29.12,27.87,27.87,6272800,3.18\n1984-12-31,29.12,29.25,29.12,29.12,7453600,3.32\n1984-12-28,27.75,28.87,27.62,28.75,5941600,3.28\n1984-12-27,27.75,27.87,27.75,27.75,3531200,3.17\n1984-12-26,27.62,27.87,27.62,27.62,2444000,3.15\n1984-12-24,27.50,27.62,27.50,27.50,2418400,3.14\n1984-12-21,27.37,27.50,26.75,27.00,4438400,3.08\n1984-12-20,27.50,28.00,27.37,27.37,5013600,3.12\n1984-12-19,28.62,28.75,27.50,27.50,11372800,3.14\n1984-12-18,28.62,28.75,28.62,28.62,12164800,3.27\n1984-12-17,27.00,27.25,27.00,27.00,4513600,3.08\n1984-12-14,25.75,26.62,25.75,26.37,3475200,3.01\n1984-12-13,25.75,26.25,25.75,25.75,2424800,2.94\n1984-12-12,26.37,26.37,25.50,25.50,3937600,2.91\n1984-12-11,26.75,27.12,26.37,26.37,4432800,3.01\n1984-12-10,27.25,27.25,26.75,26.75,4016000,3.05\n1984-12-07,27.37,28.37,27.12,27.25,17696000,3.11\n1984-12-06,27.37,27.50,27.37,27.37,11360000,3.12\n1984-12-05,26.12,26.12,26.12,26.12,9406400,2.98\n1984-12-04,24.87,25.37,24.87,24.87,4332800,2.84\n1984-12-03,24.75,24.87,24.37,24.37,3533600,2.78\n1984-11-30,25.37,25.62,24.62,24.75,3906400,2.82\n1984-11-29,25.87,25.87,25.37,25.37,6248800,2.89\n1984-11-28,25.87,26.50,25.87,25.87,14673600,2.95\n1984-11-27,24.62,24.87,24.62,24.62,4590400,2.81\n1984-11-26,24.00,24.00,24.00,24.00,3636000,2.74\n1984-11-23,23.37,24.12,23.37,23.75,4904800,2.71\n1984-11-21,23.12,23.25,23.12,23.12,6418400,2.64\n1984-11-20,22.62,22.75,22.62,22.62,9424800,2.58\n1984-11-19,23.25,23.37,21.87,21.87,8321600,2.50\n1984-11-16,23.75,24.12,23.12,23.25,5920000,2.65\n1984-11-15,23.75,24.00,23.75,23.75,3833600,2.71\n1984-11-14,23.75,24.00,23.75,23.75,3752800,2.71\n1984-11-13,24.12,24.62,23.50,23.50,4548800,2.68\n1984-11-12,24.12,24.25,24.12,24.12,4070400,2.75\n1984-11-09,24.75,24.87,23.00,23.25,10518400,2.65\n1984-11-08,25.75,25.75,24.75,24.75,3162400,2.82\n1984-11-07,26.25,26.37,25.75,25.75,8286400,2.94\n1984-11-06,26.25,26.37,26.25,26.25,8073600,3.00\n1984-11-05,24.87,25.37,24.75,24.75,3764800,2.82\n1984-11-02,25.00,25.12,24.75,24.87,1004800,2.84\n1984-11-01,25.00,25.25,25.00,25.00,1680000,2.85\n1984-10-31,25.00,25.25,24.87,24.87,2191200,2.84\n1984-10-30,25.00,25.25,25.00,25.00,2677600,2.85\n1984-10-29,24.75,24.87,24.75,24.75,1836000,2.82\n1984-10-26,25.25,25.25,24.50,24.62,4113600,2.81\n1984-10-25,26.25,26.25,25.25,25.25,5676000,2.88\n1984-10-24,26.25,26.50,26.25,26.25,5989600,3.00\n1984-10-23,26.00,26.25,26.00,26.00,6668800,2.97\n1984-10-22,25.62,26.00,25.37,25.37,4108800,2.89\n1984-10-19,25.62,27.37,25.50,25.62,11673600,2.92\n1984-10-18,25.62,25.75,25.62,25.62,8842400,2.92\n1984-10-17,24.87,25.00,24.87,24.87,5636000,2.84\n1984-10-16,24.00,24.12,23.87,23.87,4246400,2.72\n1984-10-15,24.00,24.25,24.00,24.00,8715200,2.74\n1984-10-12,23.75,23.87,22.50,22.75,9522400,2.60\n1984-10-11,23.87,24.50,23.75,23.75,6553600,2.71\n1984-10-10,24.62,24.62,23.87,23.87,13070400,2.72\n1984-10-09,24.87,25.00,24.62,24.62,4515200,2.81\n1984-10-08,24.87,25.00,24.87,24.87,1721600,2.84\n1984-10-05,25.37,25.37,24.75,24.87,3510400,2.84\n1984-10-04,25.37,25.62,25.37,25.37,4482400,2.89\n1984-10-03,25.12,25.50,25.12,25.12,4335200,2.87\n1984-10-02,24.75,25.62,24.75,24.75,4258400,2.82\n1984-10-01,25.00,25.00,24.50,24.50,3521600,2.80\n1984-09-28,25.75,25.75,24.62,25.12,8344800,2.87\n1984-09-27,25.75,25.87,25.75,25.75,3796000,2.94\n1984-09-26,26.12,27.25,25.75,25.75,3987200,2.94\n1984-09-25,26.50,26.50,26.12,26.12,5977600,2.98\n1984-09-24,26.87,27.00,26.62,26.62,2833600,3.04\n1984-09-21,27.12,27.87,26.50,26.87,3591200,3.07\n1984-09-20,27.12,27.37,27.12,27.12,2387200,3.09\n1984-09-19,27.62,27.87,27.00,27.00,3816000,3.08\n1984-09-18,28.62,28.87,27.62,27.62,3495200,3.15\n1984-09-17,28.62,29.00,28.62,28.62,6886400,3.27\n1984-09-14,27.62,28.50,27.62,27.87,8826400,3.18\n1984-09-13,27.50,27.62,27.50,27.50,7429600,3.14\n1984-09-12,26.87,27.00,26.12,26.12,4773600,2.98\n1984-09-11,26.62,27.37,26.62,26.87,5444000,3.07\n1984-09-10,26.50,26.62,25.87,26.37,2346400,3.01\n1984-09-07,26.50,26.87,26.25,26.50,2981600,3.02\n" }, { "path": "63A-FA/fa-iitg-dataanalysis.R", "content": "# Finance Stock Analysis\n#Stock Download\n\nlibrary(Quandl)\n#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW\n\nQuandl.api_key(\"4D8hkYAV4WEkcTmD9LMW\")\n\n## Download the data Set\nICICI = Quandl(\"NSE/ICICIBANK\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nICICI\nHDFC = Quandl(\"NSE/HDFCBANK\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nHDFC\nPNB= Quandl(\"NSE/PNB\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nSBI=Quandl(\"NSE/SBIN\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\n\n## Add another (\"Stock\") column in Datasets using cbind command\nhead(ICICI)\n## Paste the stock name in stock column\n\nICICI$Stock = \"ICICI\"\nPNB$Stock = \"PNB\"\nSBI$Stock = \"SBI\"\nSBI\n\nallstocks = rbind(ICICI, PNB, SBI)\nstr(allstocks)\nallstocks$Stock = factor(allstocks$Stock)\nstr(allstocks)\nnames(allstocks)\nnames(allstocks)[c(7,8)] = c('Qty','Turnover')\nnames(allstocks)\n\n\n#Using Aggregations\nnames(allstocks)\ndim(allstocks)\naggregate(allstocks$Close, by=list(allstocks$Stock), mean)\naggregate(cbind(Close, Open) ~ Stock , data= allstocks, mean)\n#https://www.statmethods.net/input/dates.html\naggregate(allstocks['Close'], by=list(allstocks$Date), mean)\naggregate(allstocks['Close'], by=list(format(allstocks$Date,\"%d\")), mean)\naggregate(allstocks['Close'], by=list(format(allstocks$Date,\"%b\")), mean)\naggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,\"%m\")), mean)\naggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,\"%Y\")), mean)\n\n#Library to store data in xlsx files\nSys.setenv(JAVA_HOME=\"C:\\\\Program Files\\\\Java\\\\jre1.8.0_191\")\nlibrary(xlsx) #needs rJava\n\n#using Dplyr Package to do Data Manipulation\nlibrary(dplyr)\nnames(allstocks)\n#select Columns\n(df1 <- allstocks %>% select(Stock, Open, Close))\ndf1 = as.data.frame(df1)\nwrite.xlsx(df1, './data/iitgfa.xlsx', sheetName = \"IITG12\", append = T)\n\n\n#Groupby\nallstocks %>% group_by(Stock) #nothing summarised\n\n#summarise\nallstocks %>% summarise(mean(Open), max(High))\n\nallstocks %>% group_by(Stock) %>% summarise_all(mean)\n#store this data into DF\n(df2 <- allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean) )\nwrite.csv(df2, './data/iitgfa.csv')\n\n#http://www.sthda.com/english/wiki/writing-data-from-r-to-excel-files-xls-xlsx#using-xlsx-package\n\n\n#write.xlsx(USArrests, file = \"myworkbook.xlsx\", #check with this file sheetName = \"USA-ARRESTS\", append = FALSE)\ndf2 = as.data.frame(df2)\nwrite.xlsx(df2, './data/iitgfa.xlsx', sheetName = \"IITG2\", append = T)\n\n\noptions(dplyr.print_max = 1e9) #print all rows\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)\n\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))\n\n#sample using dplyr\nallstocks %>% sample_n(10)\n\nallstocks %>% sample_frac(.05)\nallstocks %>% group_by(Stock) %>% sample_n(2)\n\nallstocks %>% group_by(Stock) %>% tally(Qty) %>% top_n(1)\nallstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)\n\n\n# Linear Modeling\nnames(ICICI)\ndf= cbind(ICICI[2],SBI[2] )\nnames(df) = c('icici','sbi')\nhead(df)\nplot(df)\nfit = lm(icici ~ sbi, data=df)\nsummary(fit)\nnew1 = data.frame(sbi=200)\n(p1=predict(fit,new=new1, interval='confidence' ))\ncbind(new1, p1)\nplot(x=df$sbi, y=residuals(fit)) #Linearity\nqqnorm(residuals(fit))\nqqline(residuals(fit))\n\nlibrary(car)\n#Multiple LM\ndf1 = cbind(ICICI[2],SBI[2],PNB[2] )\nnames(df1) = c('icici','sbi','pnb')\nhead(df1)\n#No Plots\npairs(df1)\nfit1 = lm(icici ~ sbi + pnb, data=df1)\nsummary(fit1)\nsummary(fit1)$r.squared\nsummary(fit1)$adj.r.squared\nnew2= data.frame(sbi=c(200,300), pnb=c(250,350))\np2=predict(fit1, new=new2, interval='confidence')\ncbind(new2, p2)\n\ncrPlots(fit1)\nvif(fit1)# variance inflation factors \nsqrt(vif(fit1)) > 2 # problem? Use only 1 variable\ndurbinWatsonTest(fit1)\ncar::outlierTest(fit1) # Bonferonni p-value for most extreme obs\ncar::qqPlot(fit1, main=\"QQ Plot\")\nlibrary(gvlma)\ngvmodel <- gvlma(fit1) \nsummary(gvmodel)\n\nav.Plots(fit1)\n?car::av.Plots\ncar::influencePlot(fit1,\tid.method=\"identify\", main=\"Influence Plot\", sub=\"Circle size is proportial to Cook's Distance\" )\ncar::ncvTest(fit1)\ncar::spreadLevelPlot(fit1)\n" }, { "path": "63A-FA/finTS1.R", "content": "#create TS Data\n#https://www.r-bloggers.com/r-financial-time-series-tips-everyone-should-know-about/\n#\nlibrary(xts)\nmyTs <- xts(floor(runif(400,40,100)),order.by=seq(Sys.Date()-399,Sys.Date(),\"days\"))\nhead(myTs)\nsummary(myTs)\nstr(myTs)\n\n#head & tail together\nht <- function(d, m=5, n=m){\n # print the head and tail together\n list(HEAD = head(d,m), TAIL = tail(d,n))\n}\n\n\n#Joining 2 TS\nmyOtherTs <- xts(floor(runif(200, 60,100)),order.by=seq(Sys.Date()-199,Sys.Date(),\"days\"))\nmyOtherTs\nht(myOtherTs)\nhead\n\n# dates intersection\ntsInter <- merge.xts(myTs,myOtherTs,join=\"inner\")\nht(tsInter)\ndim(tsInter)\n\n# dates union and blanks filled with NAs\ntsUnion <- merge.xts(myTs,myOtherTs,join=\"outer\")\ndim(tsUnion)\ntsUnion\n\n\nmonthlyRtn = apply.monthly(tsInter[,2], sum)\nht(monthlyRtn)\nyearlyRtn = apply.yearly(tsInter[,2], sum)\nyearlyRtn\n\n\n#End Points\n(newTs.m = endpoints(tsInter, on='months'))\ntsInter[1:30]\n(newTs.y = endpoints(tsInter, on='years'))\n#days, weeks, months, quarters, years\nht(tsInter)\n?endpoints\n\n\n#Missing Values\nn = 100; k = 5; N = k *n\nprices = cumsum(floor(runif(N, 5,10)))\nht(prices)\ntheSample = sample(c(1:length(prices)), 10)\nprices[theSample] = NA\ncumsum(prices) # NA after first NA value\nprices2 = na.locf(prices)\ncumsum(prices2)\ndf1=(cbind(prices, prices2))\ndf1[!complete.cases(df1),]\ndf1\n\nlibrary(PerformanceAnalytics)\n# Graphs\nprices3 = xts(prices2, order.by = seq(Sys.Date()- (length(prices)-1), Sys.Date(),'days'))\ncharts.PerformanceSummary(Return.calculate(prices3, method='discrete'))\n?charts." }, { "path": "63A-FA/findata1.R", "content": "#Finance\n#https://msperlin.github.io/pafdR/Financial-data.html#data-from-financial-markets\n\n# set project cashflow\nCF <- c(-8000, 2500, 3000, 3000, 5000)\nnames(CF) <- paste('Year', 0:(length(CF)-1))\n\n# print result\nprint(CF)\n\n#first element is negative. This means that, at time zero, cash will be spent. Now, we can use package FinCalc (Fan 2016) to evaluate the project using NPV and IRR.\n\n\nlibrary(FinCal)\n\n# set discount rate\nr <- 0.1\n\n# calculate npv\nmy.NPV <- npv(r = r, cf = CF)\nmy.NPV\n\n# calculate irr\nmy.IRR <- irr(cf = CF)\nmy.IRR\n\n# calculate payback\nmy.payback <- min(which(cumsum(CF) > 0)) - 1\nmy.payback\n\n# print results\ncat('NPV = ', my.NPV )\n## NPV = 2421.078\ncat('IRR = ', my.IRR)\n## IRR = 0.2183874\ncat('Payback = ', my.payback, 'years')\n## Payback = 3 years\n\n#The initial investment is paid back in 3 years. From the financial side, this fictional project creates value and should be accepted.\n\n\n\n\n#2----\n\n#set seed for reproducibility\nset.seed(10)\n\n# set number of time periods\nnT <- 5\n\n# simulate prices\nP.1 <- 10*(cumprod(1+rnorm(nT,mean = 0, sd = 0.1)))\nP.2 <- 20*(cumprod(1+rnorm(nT,mean = 0, sd = 0.1)))\nP.3 <- 30*(cumprod(1+rnorm(nT,mean = 0, sd = 0.1)))\nP.1; P.2; P.3\n# gather info in df\nmy.df <- data.frame(ref.date = Sys.Date()+1:nT, prices = c(P.1,P.2,P.3),\nticker = paste('Stock ',c(rep('A',nT),rep('B',nT),rep('C',nT))))\n\n# print result \nprint(my.df)\n\n# set price matrix\nmy.price.mat <- matrix(my.df$prices, nrow = nT)\nmy.price.mat\n\n# set row and col names\ncolnames(my.price.mat) <- unique(my.df$ticker)\nrownames(my.price.mat) <- as.character(unique(my.df$ref.date))\n\n# print result\nprint(my.price.mat)\n\n9.12/10.08-1; 8.94/9.12 -1\n# apply return formula for each column\nmy.ret.mat <- my.price.mat[2: nrow(my.price.mat) , ]/\n my.price.mat[1:(nrow(my.price.mat)-1), ] -1 \nmy.ret.mat\n\n\n# set weight of portfolio (evenly weighted)\nw <- matrix(rep(1/3,3), nrow = 3)\nw\n# calculate return of portfolio over time\nret.port <- my.ret.mat %*% w\nret.port\n# print result\nprint(ret.port)\n\n#Vector ret.port show the return an investor would receive if the total value of the portfolio is evenly divided between the stocks." }, { "path": "63A-FA/finstmts1.R", "content": "#Financial Statements\n#https://github.com/bergant/finstr\nlibrary(finstr)\n\nlibrary(devtools)\ninstall_github(\"bergant/finstr\")\n\nlibrary(XBRL)\n# parse XBRL (Apple 10-K report)\nxbrl_url2014 <- \"https://www.sec.gov/Archives/edgar/data/320193/000119312514383437/aapl-20140927.xml\"\nxbrl_url2013 <- \"https://www.sec.gov/Archives/edgar/data/320193/000119312513416534/aapl-20130928.xml\"\nold_o <- options(stringsAsFactors = FALSE)\nxbrl_data_aapl2014 <- xbrlDoAll(xbrl_url2014)\nxbrl_data_aapl2013 <- xbrlDoAll(xbrl_url2013)\noptions(old_o)\n\n\nlibrary(finstr)\n\nst2013 <- xbrl_get_statements(xbrl_data_aapl2013)\nst2014 <- xbrl_get_statements(xbrl_data_aapl2014)\nst2014\n#> Financial statements repository\n#> From To Rows Columns\n#> StatementOfIncome 2012-09-29 2014-09-27 3 15\n#> StatementOfOtherComprehensiveIncome 2012-09-29 2014-09-27 3 14\n#> StatementOfFinancialPositionClassified 2013-09-28 2014-09-27 2 33\n#> StatementOfCashFlowsIndirect 2012-09-29 2014-09-27 3 33\n#\nbalance_sheet2013 <- st2013$StatementOfFinancialPositionClassified\nbalance_sheet2014 <- st2014$StatementOfFinancialPositionClassified\nincome2013 <- st2013$StatementOfIncome\nincome2014 <- st2014$StatementOfIncome\nbalance_sheet2014\n\ncheck <- check_statement(balance_sheet2014)\ncheck\n#> Number of errors: 0 \n#> Number of elements in errors: 0\n\ncheck_statement(\n within(balance_sheet2014, InventoryNet <- InventoryNet * 2)\n)\n\n\ncheck <- check_statement(income2014, element_id = \"OperatingIncomeLoss\")\ncheck\n#> Number of errors: 0 \n#> Number of elements in errors: 0\ncheck$expression[1]\n#> [1] \"+ GrossProfit - OperatingExpenses\"\ncheck$calculated / 10^6\n#> [1] 55241 48999 52503\nbalance_sheet <- merge( balance_sheet2013, balance_sheet2014 )\nbalance_sheet\n# merge all statements\nst_all <- merge( st2013, st2014 )\n# check if balance sheets are merged:\nbalance_sheet <- st_all$StatementOfFinancialPositionClassified\nbalance_sheet$endDate\n#> [1] \"2012-09-29\" \"2013-09-28\" \"2014-09-27\"\nmerge.statement(\n st_all$StatementOfFinancialPositionClassified, \n st_all$StatementOfIncome )\n\n\nlibrary(dplyr)\n\nbalance_sheet %>% transmute(\n date = endDate, \n CurrentRatio = AssetsCurrent / LiabilitiesCurrent\n)\nbalance_sheet %>% calculate( digits = 2,\n \n Current_Ratio = AssetsCurrent / LiabilitiesCurrent,\n \n Quick_Ratio = \n ( CashAndCashEquivalentsAtCarryingValue + \n AvailableForSaleSecuritiesCurrent +\n AccountsReceivableNetCurrent\n ) / LiabilitiesCurrent\n \n)\n\nmerge(balance_sheet, st_all$StatementOfIncome ) %>% calculate( digits = 2,\n \n .AccountReceivableLast = lag(AccountsReceivableNetCurrent),\n .AccountReceivableAvg = (.AccountReceivableLast + AccountsReceivableNetCurrent)/2,\n \n DaysSalesOutstanding = .AccountReceivableAvg / SalesRevenueNet * 365 \n)\n\n" }, { "path": "63A-FA/finstmts2.R", "content": "#Computational Finance\n#http://computationalfinance.lsi.upc.edu/?page_id=104\n\n##Technical analysis with quantmod\nlibrary(quantmod)\n##Chart a stock with MA(50), Bollinger, Volume and RSI\ngetSymbols(\"AAPL\")\nchartSeries(AAPL, subset='2008-06::2009-04',theme=chartTheme('white',up.col='green',dn.col='red'),TA=c(addBBands(n=20,sd=2,),addSMA(n=50,col=\"blue\"),addSMA(n=10,col=\"black\"),addRSI(n=14)))\n#up.col, dn.col= color of up (down) candle,\n#\nlibrary(quantmod)\n##getFinancials retrives financials from src (default google)\n##returns six individual matrices organized in a list of class ‘financials’:\n# IS a list containing (Q)uarterly and (A)nnual Income Statements\n# BS a list containing (Q)uarterly and (A)nnual Balance Sheets\n# CF a list containing (Q)uarterly and (A)nnual Cash Flow Statements\ngetFinancials('AAPL',src=\"yahoo\") #returns AAPL.f to \"env\"\nviewFin(AAPL.f, \"CF\", \"A\") #view Annual Cash Flows \n#above code NW\nviewFin(AAPL.f, \"BS\", \"A\", \"2010/2011\")\n\napplBS = viewFin(AAPL.f, \"BS\",\"A\")\ncolnames(applBS) ##give four columns labeled by the date (yyyy-mm-dd) the statement was issued\nrownames(applBS) ##give 42 rows labelled with financial indicators\n\napplBS[\"Total Current Assets\",\"2012-09-29\"] ##returns the value of Total Current Assets in that year\n\n##Compute some liquidity measures:\nCurrentRat=applBS[\"Total Current Assets\",\"2012-09-29\"]/applBS[\"Total Current Liabilities\",\"2012-09-29\"]\nCurrentRat" }, { "path": "63A-FA/finstmts3.R", "content": "# Financial Statements\n\n\nlibrary(quantmod)\nchooseCRANmirror(0)\ngetFin(\"LNKD\")\ngetFin(\"SBIN.NS\")\n\nhead(viewFin(LNKD.f, type= 'BS', period = 'Q' ))\nfin <- c('GOOGL','AAPL','AMZN','EBAY','CRM','FB','LNKD','ALBIY')\n\ngetFin_HANDLED <- function (symb) {\n return(tryCatch(getFin(symb, auto.assign = \"FALSE\"), error=function(e) NULL)) \n}\n\nfin.f <- lapply(fin, getFin_HANDLED) \nhead(fin.f)\n\n\nmyData <- new.env()\nclass(myData)\nls(myData)\nNVS <- getFinancials('NVS', env = myData, src = \"yahoo\", auto.assign=TRUE)\ngetFinancials('APPL', env = .GlobalEnv, src = \"yahoo\", \n auto.assign = TRUE)\n\n\nviewFinancials(x, type=c('BS','IS','CF'), period=c('A','Q'),\n subset = NULL)\n\nJAVA <- getFinancials('JAVA')\nAAPL <- getFin('AAPL')\n\nJAVA$IS$Q # Quarterly Income Statement\nAAPL$CF$A # Annual Cash Flows\n\nstr(AAPL)" }, { "path": "63A-FA/gtrends1.R", "content": "# Google Trends\n# https://datascienceplus.com/analyzing-google-trends-data-in-r/\n# \nlibrary(gtrendsR)\nlibrary(reshape2)\n\ngoogle.trends = gtrends(c(\"pnb\"), gprop = \"web\", time = \"all\")[[1]]\ngoogle.trends\ngoogle.trends = dcast(google.trends, date ~ keyword + geo, value.var = \"hits\")\nrownames(google.trends) = google.trends$date\ngoogle.trends$date = NULL\n\nSys.setenv(TZ = \"GMT\")\n\ngoogle.trends = gtrends(c(\"skiing\"), geo = c(\"CA\", \"NZ\"), gprop = \"web\", time = \"2010-06-30 2017-06-30\")[[1]]\ngoogle.trends\n\nlibrary(gtrendsR)\ngeo.codes = sort(unique(countries[substr(countries$sub_code, 1, 2) == \"IT\", ]$sub_code))\ngeo.codes\n" }, { "path": "63A-FA/intrino1.R", "content": "\nlibrary(quantmod)\ngetFinancials('GE')\nviewFinancials(GE.f)\nTo get only the income statement, reported anually, as a data frame use this:\n \nviewFinancials(GE.f, \"IS\", \"A\")\n\n\n#Create variables for your usename and password, get those at intrinio.com/login\nusername <- \"62125b9aa13fe147cd0f3477be69d602\"\npassword <- \"b00a3fc5c70b0a5c20a0049b298adfc9\"\n\n#Making an api call for roic. This puts together the different parts of the API call\n\nbase <- \"https://api.intrinio.com/\"\nendpoint <- \"financials/\"\ntype <- \"standardized\"\nstock <- \"YUM\"\nstatement <- \"income_statement\"\nfiscal_period <- \"Q2\"\nfiscal_year <- \"2015\"\n\n#Pasting them together to make the API call\ncall1 <- paste(base,endpoint,type,\"?\",\"identifier\",\"=\", stock, \"&\",\"statement\",\"=\",statement,\"&\",\"fiscal_period\",\n \"=\", fiscal_period, \"&\", \"fiscal_year\", \"=\", fiscal_year, sep=\"\")\n\n# call1 Looks like this \"https://api.intrinio.com/financials/standardized?identifier=YUM&statement=income_statement&fiscal_period=Q2&fiscal_year=2015\"\n\n#Now we use the API call to request the data from Intrinio's database\n\nYUM_Income <- GET(call1, authenticate(username,password, type = \"basic\"))\n\n#That gives us the ROIC value, but it isn't in a good format so we parse it\n\ntest1 <- unlist(content(YUM_Income, \"text\"))\n\n#Convert from JSON to flattened list\n\nparsed_statement <- fromJSON(test1)\n\n#Then make your data frame:\n\ndf1 <- data.frame(parsed_statement)\n\n" }, { "path": "63A-FA/intrino2.R", "content": "\n\n#rm(list=ls(())\n#install.packages(\"httr\")\nrequire(\"httr\")\n\n\n\n\n\n\nusername <- \"62125b9aa13fe147cd0f3477be69d602\"\npassword <- \"b00a3fc5c70b0a5c20a0049b298adfc9\"\nbase <- \"https://api.intrinio.com/\"\nendpoint <- \"data_point\"\nstock <- \"AAPL\"\nitem1 <- \"close_price\"\nitem2 <- \"pricetoearnings\"\ncall1 <- paste(base,endpoint,\"?\",\"ticker\",\"=\", stock, \"&\",\"item\",\"=\",item1, sep=\"\")\ncall2 <- paste(base,endpoint,\"?\",\"ticker\",\"=\", stock, \"&\",\"item\",\"=\",item2, sep=\"\")\n\n\napple_price <- get(call1, authenticate(username,password, type = \"basic\"))\napple_pricetoearnings <- get(call2, authenticate(username,password, type = \"basic\"))\n\ntest1 <- unlist(content(apple_price,\"parsed\"))\ntest2 <- unlist(content(apple_pricetoearnings,\"parsed\"))\n\ndf <- data.frame(test1,test2)\n\n\n\n\n\n\n#1----2\nprices <- function(ticker){\n price_base <- \"https://api.intrinio.com/prices?identifier=\" \n uusername <- \"62125b9aa13fe147cd0f3477be69d602\"\n password <- \"b00a3fc5c70b0a5c20a0049b298adfc9\"\n \n price <- paste(price_base,ticker,sep=\"\") \n tp <- GET(price, authenticate(username, password, type = \"basic\")) \n z <- unlist(content(tp,\"parsed\"))\n \n n=length(z)\n b=as.data.frame(matrix(z[1:(n-5)],(n-5)/13, byrow = T))\n names(b)=names(z)[1:13]\n \n return(b)\n}\n\n#If you call the function, creating an object, and using your own API keys for the \"username\" and \"password\" objects within the function, you will have your data frame:\n \nt <- prices(\"AAPL\")\nt\n#The really nice part about this function is that you can now create data frames quickly by swapping out AAPL for any tickers you are interested in analyzing. Graphing prices becomes very easy, but my goal is quant modeling.\n\n#For that, I need financial metrics that I expect to be correlated with price. This second function returns daily historical data over the period of your choice for the metric of your choice.\n\nhistory <- function(ticker, item, start_date, end_date){\n history_base <- \"https://api.intrinio.com/historical_data?ticker=\" \n username <- \"a543b029ec930ab0c7add95bfa1ea3ac\" \n password <- \"991d8ca925d74ecfbe7f78b4784d88b0\"\n historical <- paste(history_base, ticker, \"&item=\", item, \"&start_date=\", start_date, \"&end_date=\", end_date, sep=\"\") \n tp <- GET(historical, authenticate(username, password, type = \"basic\")) \n z <- unlist(content(tp,\"parsed\"))\n n=length(z)\n b=as.data.frame(matrix(z[1:(n-5)],(n-5)/2, byrow = T))\n names(b)=names(z)[1:2]\n return(b)\n}\n\n#This time, the function needs a couple of inputs because we are looking at a certain period for a specific financial metric. You could, for example, call the function with these inputs to get the daily market cap for AAPL ranging from October 1, 2016 back to January 1, 2010.\n\napp_hist <- history(\"AAPL\",\"marketcap\", \"2010-01-01\",\"2016-10-01\")\n" }, { "path": "63A-FA/lag1.R", "content": "#Lag\n\nx <- sample(c(1:9), 10, replace = T)\ny <- lag(x, 1)\n(ds <- cbind(x, y))\nds\n\ny <- lag(x, 2)\n(ds <- cbind(x, y))\n\n\nrequire(zoo)\nset.seed(123)\nx <- zoo(sample(c(1:9), 10, replace = T))\ny <- lag(x, -1, na.pad = TRUE) #shift down\ncbind(x, y)\n\ny <- lag(x, 1, na.pad = TRUE) #from bottom\ncbind(x, y)\n\n\n#diff\n\nx\ndiff(x)\nlag(x, k=-1)\ndiff(x) / lag(x, k=-1)\nlog(x)\ndiff(log(x)) #x2-x1\n\n" }, { "path": "63A-FA/logistic_regression.R", "content": "# Logistic Regression\n# Import the dataset\n\nsetwd(\"E:\\\\HHges - Mkt Anyts\\\\BITS Pilani Bussiness Anallytics\\\\04.Logistic Regression\")\n\ndataset = read.csv('Customer Churn.csv')\nsummary(dataset)\n# Split the dataset into the Training set and Test set\n\n#install.packages('caTools')\nlibrary(caTools)\n\n\nset.seed(2000)\n#Split data from vector Y into two sets in predefined ratio \n#while preserving relative ratios of different labels in Y. \n#Used to split the data used during classification into train and test subsets\nsplit = sample.split(dataset$Churn, SplitRatio = 0.75)\ntail(dataset)\ntraining_set = subset(dataset, split == TRUE)\ntest_set = subset(dataset, split == FALSE)\n\n\n\n\n\nnames(training_set)\n\n# Fitting Logistic Regression to the Training set\nclassifier = glm(formula = Churn ~\n as.factor(Gender)+\n Age+\n EstimatedSalary,\n family = binomial,\n data = training_set)\n\nsummary(classifier)\n\n# Fitting Logistic Regression to the Training set - Gender is being insignificant is dropped here\nclassifier = glm(formula = Churn ~ Age+EstimatedSalary,\n family = binomial,\n data = training_set)\n\nsummary(classifier)$coefficient\n\n# Predicting the Test set results \n# type=reponse is used to give predicted probibilites\nprob_pred = predict(classifier, type = 'response', \n newdata = test_set)\n\ndf_prob_pred = as.data.frame(prob_pred)\nhead(df_prob_pred)\n\ny_pred = ifelse(prob_pred > 0.5, 1, 0)\ny_pred\n\n# Making the Confusion Matrix\ncm = table(test_set[,5], y_pred)\ncm\n\nforecast::accuracy(test_set[,5], y_pred)\nforecast::con" }, { "path": "63A-FA/qf1.R", "content": "# Quantitative Finance in R\n#Chap-1\n#https://github.com/matplotlib/sample_data/blob/master/aapl.csv\naapl = read.zoo('aapl.csv', sep=',', header=T, format=\"%Y-%m-%d\")\n\nplot(aapl, main='Apple Closing Prices on NASDAQ', ylab='Price USD', xlab='Date')\n\nht <- function(d, m=5, n=m){\n list(HEAD = head(d,m), TAIL = tail(d,n))\n}\n#First & Last Part of TS\nht(aapl)\n#all time high on open\naapl[which.max(aapl$Open)]\n\n#simple returns\nx = c(1,5,3,7)\ndiff(x)\nlag(x,1)\nlag(x, k=-1)\nret_simple = diff(aapl)/ lag(aapl, k=-1) * 100\nret_simple\n#compound returns\nret_cont = diff(log(aapl)) * 100\nret_cont\n\n#summary stats about simple returns\nhead(ret_simple)\nsummary(coredata(ret_simple)) # biggest loss -51\n?coredata\n\nhist(ret_simple, breaks=100, main='Histogram of Simple Returns', xlab='%')\ntail(aapl)\naapl_2007 = window(aapl, start = '2007-01-01', end='2007-12-31')\nhead(aapl_2007)\naapl_2007[which.max(aapl_2007$Close)]\n\nquantile(ret_simple, probs=0.01)\n" }, { "path": "63A-FA/sentianalysistrading1.R", "content": "# Sentimental Analysis in Trading\n# https://www.quantinsti.com/blog/sentiment-analysis-in-trading/\n# \n# " }, { "path": "63A-FA/shares1.R", "content": "#https://ntguardian.wordpress.com/2017/03/27/introduction-stock-market-data-r-1/\n#Stock Analysis \n\n# Get quantmod\nif (!require(\"quantmod\")) {\n install.packages(\"quantmod\")\n library(quantmod)\n}\n\nstart <- as.Date(\"2017-01-01\")\nend <- as.Date(\"2018-04-01\")\n\n# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the\n# quantmod function getSymbols, and pass a string as a first argument to\n# identify the desired ticker symbol, pass 'yahoo' to src for Yahoo!\n# Finance, and from and to specify date ranges\n\n# The default behavior for getSymbols is to load data directly into the\n# global environment, with the object being named after the loaded ticker\n# symbol. This feature may become deprecated in the future, but we exploit\n# it now.\n\ngetSymbols(\"AAPL\", src = \"yahoo\", from = start, to = end)\n\n# What is AAPL?\nclass(AAPL)\nhead(AAPL)\n\nplot(AAPL[, \"AAPL.Close\"], main = \"AAPL\")\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\")\n\n\n# Let's get data for Microsoft (MSFT) and Google (GOOG) (actually, Google is\n# held by a holding company called Alphabet, Inc., which is the company\n# traded on the exchange and uses the ticker symbol GOOG).\ngetSymbols(c(\"MSFT\", \"GOOG\"), src = \"yahoo\", from = start, to = end)\n\n# Create an xts object (xts is loaded with quantmod) that contains closing\n# prices for AAPL, MSFT, and GOOG\nstocks = as.xts(data.frame(AAPL = AAPL[, \"AAPL.Close\"], MSFT = MSFT[, \"MSFT.Close\"], GOOG = GOOG[, \"GOOG.Close\"]))\nhead(stocks)\n\n\n# Create a plot showing all series as lines; must use as.zoo to use the zoo\n# method for plot, which allows for multiple series to be plotted on same\n# plot\nplot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Price\")\nlegend(\"right\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\nplot(as.zoo(stocks[, c(\"AAPL.Close\", \"MSFT.Close\")]), screens = 1, lty = 1:2, xlab = \"Date\", ylab = \"Price\")\npar(new = TRUE)\nplot(as.zoo(stocks[, \"GOOG.Close\"]), screens = 1, lty = 3, xaxt = \"n\", yaxt = \"n\", xlab = \"\", ylab = \"\")\naxis(4)\nmtext(\"Price\", side = 4, line = 3)\nlegend(\"topleft\", c(\"AAPL (left)\", \"MSFT (left)\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\n\n# Get pipe operator!\nif (!require(\"magrittr\")) {\n install.packages(\"magrittr\")\n library(magrittr)\n}\nstock_return = apply(stocks, 1, function(x) {x / stocks[1,]}) %>% \n t %>% as.xts\n\nhead(stock_return)\n\nplot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Return\")\nlegend(\"topleft\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5)\n\nstock_change = stocks %>% log %>% diff\nhead(stock_change)\n\nplot(as.zoo(stock_change), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Log Difference\")\nlegend(\"topleft\", c(\"AAPL\", \"MSFT\", \"GOOG\"), lty = 1:3, cex = 0.5) \n\n\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\")\naddSMA(n = 20)\n\n\n\nstart = as.Date(\"2010-01-01\")\ngetSymbols(c(\"AAPL\", \"MSFT\", \"GOOG\"), src = \"yahoo\", from = start, to = end)\n# The subset argument allows specifying the date range to view in the chart.\n# This uses xts style subsetting. Here, I'm using the idiom\n# 'YYYY-MM-DD/YYYY-MM-DD', where the date on the left-hand side of the / is\n# the start date, and the date on the right-hand side is the end date. If\n# either is left blank, either the earliest date or latest date in the\n# series is used (as appropriate). This method can be used for any xts\n# object, say, AAPL\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\", subset = \"2016-01-04/\")\naddSMA(n = 20)\n\ncandleChart(AAPL, up.col = \"black\", dn.col = \"red\", theme = \"white\", subset = \"2016-01-04/\")\naddSMA(n = c(20, 50, 200))\n" }, { "path": "63A-FA/shares2.R", "content": "# Indian Stocks\n#stocks2\n\n# Get quantmod\nif (!require(\"quantmod\")) {\n install.packages(\"quantmod\")\n library(quantmod)\n}\n\nstart <- as.Date(\"2017-01-01\")\nend <- as.Date(\"2018-04-01\")\ngetSymbols(\"SBIN.NS\", src = \"yahoo\", from = start, to = end)\n# What is SBI?\nclass(SBIN.NS)\nhead(SBIN.NS)\n\nplot(SBIN.NS[, \"SBIN.NS.Close\"], main = \"SBIN.NS\")\ncandleChart(SBIN.NS, up.col = \"black\", dn.col = \"red\", theme = \"white\")\n\n\n#ICICIBANK.NS\n#TATAMOTORS.NS\ngetSymbols(c(\"ICICIBANK.NS\", \"TATAMOTORS.NS\"), src = \"yahoo\", from = start, to = end)\n\nstocks = as.xts(data.frame(SBIN = SBIN.NS[, \"SBIN.NS.Close\"]))\nstocks\nstocks = as.xts(data.frame(SBIN = SBIN.NS[, \"SBIN.NS.Close\"], ICICI = ICICIBANK.NS[, \"ICICIBANK.NS.Close\"], TATAMOTORS = TATAMOTORS.NS[, \"TATAMOTORS.NS.Close\"]))\nhead(stocks)\n\nplot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = \"Date\", ylab = \"Price\")\nlegend(\"right\", c(\"SBIN\", \"ICICI\", \"TATATMOTORS\"), lty = 1:3, cex = 0.5)\n\n" }, { "path": "63A-FA/stock3.R", "content": "# Getting stock Details\n\nlibrary(quantmod)\n\nmdate = '2017-01-01'\namzPrices.y = getSymbols('AMZN', from=mdate, auto.assign = 0)\namzPrices.y\n\namzRets = ROC(amzPrices.y, type='discrete')\namzRets\n\namzRets2 = periodReturn(amzPrices.y, period='monthly', type='log', subset='2018')\namzRets2\namzPrices.g = getSymbols.google('AMZN', from=mdate, auto.assign = 0)\namzPrices.g\n\n#----\ngetSymbols('SBIN.NS', src='yahoo', from=mdate)\nSBIN.NS\ngetSymbols('SBIN', src='google', from=mdate)\nSBIN.NS\n\nx <- getSymbols('^GSPC', auto.assign = F)\nx\nfrom <- c(\"USD\", \"USD\", \"EUR\", \"GBP\", \"EUR\", \"EUR\")\nto <- c(\"CAD\", \"JPY\", \"USD\", \"CAD\", \"JPY\", \"AUD\")\ndf <- getQuote(paste0(from, to, \"=X\"))\ndf\n\ngetSymbols(Symbols = \"LIQUIDBEES\", src = \"google\") \ngetSymbols.google(Symbols = \"LIQUIDBEES\")\n\n#-----\nfrom <- \"2016-04-01\"\nto <- Sys.Date()\nperiod <- \"daily\"\nenvir <- globalenv()\ncrumb <- \"yourCrumb\"\nDLdir <- \"~/shares/\"\nassetList <- c(\"AAPL\", \"ADBE\", \"AMAT\")\nlapply(assetList, getSymbols.yahoo, from, to, envir = globalenv(), crumb = crumb, DLdir)\n" }, { "path": "63A-FA/stockanalysis1.R", "content": "# Finance Stock Analysis\n# https://www.analyticsvidhya.com/blog/2017/09/comparative-stock-analysis/\n#Install Packages \n#pckgs<-c(\"Quandl\",\"Sweep\",\"tidyverse\",\"tidyquant\",\"ggplot\",\"forcats\",\"stringr\")\n#install.packages(pckgs,dependencies = TRUE)\n\nlibrary(Quandl)\nlibrary(tidyverse)\nlibrary(ggplot2)\nlibrary(tidyquant)\nlibrary(timetk)\nlibrary(forcats)\nlibrary(stringr)\nlibrary(gganimate) #install as mentioned below\n#devtools::install_github(\"dgrtwo/gganimate\")\nlibrary(plyr)\nlibrary(stringr)\nlibrary(gridExtra)\n\n#Quandl(Code=“NSE/—”,collapse=“—”,start_date=“—-”,type=“…”)\n#ICICI BOB CANARA AXIS SBI PNB price band of Rs 200 to Rs 500. \n#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW\n\nQuandl.api_key(\"4D8hkYAV4WEkcTmD9LMW\")\n\n## Download the data Set\nICICI = Quandl(\"NSE/ICICIBANK\",collapse=\"daily\",start_date=\"2016-09-01\",type=\"raw\")\nPNB= Quandl(\"NSE/PNB\",collapse=\"daily\",start_date=\"2016-09-01\",type=\"raw\")\nAxis=Quandl(\"NSE/AXISBANK\",collapse=\"daily\",start_date=\"2016-09-01\",type=\"raw\")\nCanara=Quandl(\"NSE/CANBK\",collapse=\"daily\",start_date=\"2016-09-01\",type=\"raw\")\nBOB=Quandl(\"NSE/BANKBARODA\",collapse=\"daily\",start_date=\"2016-09-01\",type=\"raw\")\nSBI=Quandl(\"NSE/SBIN\",collapse=\"daily\",start_date=\"2016-09-01\",type=\"raw\")\n\n## Add another (\"Stock\") column in Datasets using cbind command\nhead(ICICI)\nICICI<-cbind(ICICI,Stock=\"\")\nPNB<-cbind(PNB,Stock=\"\")\nAxis<-cbind(Axis,Stock=\"\")\nSBI<-cbind(SBI,Stock=\"\")\nCanara<-cbind(Canara,Stock=\"\")\nBOB<-cbind(BOB,Stock=\"\")\n\n## Paste the stock name in stock column\n\nICICI$Stock<-paste(ICICI$Stock,\"ICICI\",sep=\"\")\nPNB$Stock<-paste(PNB$Stock,\"PNB\",sep=\"\")\nAxis$Stock<-paste(Axis$Stock,\"Axis\",sep=\"\")\nSBI$Stock<-paste(SBI$Stock,\"SBI\",sep=\"\")\nCanara$Stock<-paste(Canara$Stock,\"Canara\",sep=\"\")\nBOB$Stock<-paste(BOB$Stock,\"BOB\",sep=\"\")\n\n## Consolidate under one dataset\nMaster_Data<-rbind(ICICI,PNB,Axis,SBI,Canara,BOB)\n\n## Visualisation in ggplot2 (\"Comparative Visulisation of Close Price listed on NSE\")\n\n## Convert the dates into character in order to split the coloumn into \"Y\" \"m\" \"dd\"\" columns\nMaster_Data$Date<-as.character(Master_Data$Date)\n\n## Split the date and create a list for the same\n\nlist<-strsplit(Master_Data$Date,\"-\")\n\n## Convert the list into dataframe\nlibrary(plyr)\nMaster_Date1<-ldply(list)\ncolnames(Master_Date1)<-c(\"Year\",\"Month\",\"Day\")\n\n## Column bind with the main dataframe\nMaster_Data<-cbind(Master_Data,Master_Date1)\nnames(Master_Data)\n\n## Change the scale for Traded Quantity\n\nMaster_Data$`Total Trade Quantity`<-Master_Data$`Total Trade Quantity`/100000\n\n## Convert the Date to as.Date()\n\nMaster_Data$Date<-as.Date(Master_Data$Date)\n\n## Visualisation with Bubble Plot\nP<- ggplot(Master_Data,aes(factor(Stock),Close,color=Stock,frame=Month)) +\n geom_jitter(aes(size = Close, colour=Stock, alpha=.02)) +\n ylim(0,1000)+\n labs(title = \"Bank Stock Monthly Prices\", x = \"Banks\", y= \"Close Price\") +\n theme(panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"grey61\", size = 0.5, linetype = \"dotted\"),\n panel.grid.minor = element_blank(),\n axis.line=element_line(colour=\"black\"),\n plot.title = element_text(hjust = 0.5,size=18,colour=\"indianred4\"))+\n theme(legend.position=\"none\")\n\nP1<-gganimate(P,'Price_Range.gif',ani.width=600,ani.height=400,interval=1)\n\n## Group By Stock\n\nMaster_Data<-Master_Data%>%\n tibble::as.tibble()%>%\n group_by(Stock)\n\n## Visualisation for Daily Stock Prices\n\nMaster_Data %>% ggplot(aes(x = Date, y = Close, color = Stock)) + geom_point() + labs(title = \"Daily Close Price\", x = \"Month\",y=\"Close Price\") + facet_wrap(~ Stock, ncol = 3, scale = \"free_y\") + scale_fill_tq(fill=\"green4\",theme=\"light\") + theme_tq() + theme(panel.border = element_blank(), panel.grid.major = element_line(colour = \"grey61\", size = 0.5, linetype = \"dotted\"), panel.grid.minor = element_blank(), axis.line=element_line(colour=\"black\"), plot.title = element_text(hjust = 0.5,size=18,colour=\"indianred4\"))+ theme(legend.position=\"none\")\n\n\n## Traded Quantity vs Price\nnames(Master_Data)\n\nz<-Master_Data %>% ggplot(aes(x = `Total Trade Quantity`, y = Close, color = Stock, frame=Month)) + geom_smooth(method='loess') + xlim(0,400)+ labs(title = \"Monthly Traded Quantity vs Price\", x = \"Traded Quantity (Lacs)\",y=\"Close Price\") + facet_wrap(~ Stock, ncol = 3, scale = \"free_y\") + scale_fill_tq(fill=\"green4\",theme=\"light\") + theme_tq() + theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), plot.title = element_text(hjust = 0.5,size=18,colour=\"indianred4\"), axis.line = element_line(colour = \"black\"))+ theme(legend.position=\"none\")\n\n#Error\nz1<-gganimate(z,'Quantity_Price.gif',ani.width=600,ani.height=400,interval=0.7)\n\n## Deviation from High & Low Price\nMaster_Data_High<-Master_Data%>%mutate(Dev_High=High-Open)\nMaster_Data_Low<-Master_Data%>%mutate(Dev_Low=Open-Low)\n\n## Computation of weekly average for high Price\n\nMaster_Data_High_Week <- Master_Data_High %>%\n tq_transmute(\n select = Dev_High,\n mutate_fun = apply.weekly, \n FUN = mean,\n na.rm = TRUE,\n col_rename = \"Dev_High_Mean\"\n )\n\n## Computation weekly average for Low Price\n\nMaster_Data_Low_Week<-Master_Data_Low%>%\n tq_transmute(\n select = Dev_Low,\n mutate_fun = apply.weekly,\n FUN = mean,\n na.rm = TRUE,\n col_rename = \"Dev_Low_Mean\"\n )\n\n## Visualisation of density distribution of High Price\n\nHigh<-Master_Data_High_Week%>%ggplot(aes(x=Dev_High_Mean,color=Stock))+\n geom_dotplot(binwidth=0.50,aes(fill=Stock))+\n xlim(0,10)+\n scale_fill_manual(values=c(\"#999999\", \"#E69F00\",\"#CC9933\",\"#99FF00\",\"#CC3399\",\"#FF9933\"))+\n labs(title=\"Distribution of High Price Deviation from Open Price\",x=\"Weekly Mean Deviation\")+\n facet_wrap(~Stock,ncol=3,scale=\"free_y\")+\n scale_color_tq(values=c(\"#999999\"))+\n theme_tq()+\n theme(panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"grey61\", size = 0.5, linetype = \"dotted\"),\n panel.grid.minor = element_blank(),\n axis.line=element_line(colour=\"black\"),\n plot.title = element_text(hjust = 0.5,size=16,colour=\"indianred4\"))+\n theme(legend.position=\"none\")\n\n## Visualisation of density distribution of Low Price\nLow<-Master_Data_Low_Week%>%ggplot(aes(x=Dev_Low_Mean,color=Stock))+\n geom_dotplot(binwidth=0.50,aes(fill=Stock))+\n xlim(0,10)+\n scale_fill_manual(values=c(\"#999999\", \"#E69F00\",\"#CC9933\",\"#99FF00\",\"#CC3399\",\"#FF9933\"))+\n labs(title=\"Distribution of Weekly Low Price Deviation from Open Price\",x=\"Weekly Mean Deviation\")+\n facet_wrap(~Stock,ncol=3,scale=\"free_y\")+\n scale_color_tq(values=c(\"#999999\"))+\n theme_tq()+\n theme(panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"grey61\", size = 0.5, linetype = \"dotted\"),\n panel.grid.minor = element_blank(),\n axis.line=element_line(colour=\"black\"),\n plot.title = element_text(hjust = 0.5,size=16,colour=\"indianred4\"))+\n theme(legend.position=\"none\")\n## Arrange\ngrid.arrange(High,Low,ncol = 2, nrow = 1)\n\n\nk <- 1:180\ncol_names <- paste0(\"lag_\", k)\n\n## Only Select Columns \"Date\" and \"Close\" from hte master data frame.\n\nMaster_Data_lags<-Master_Data%>%\n tibble::as_tibble() %>%\n group_by(Stock)\n\nMaster_Data_lags<-Master_Data_lags%>%select(Date,Close)\n# Apply lag.xts function using tq_mutate\n\nMaster_Data_lags<-Master_Data_lags%>%\n tq_mutate(\n select = Close,\n mutate_fun = lag.xts,\n k=1:180,\n col_rename=col_names\n )\n\n# Calculate the autocorrelations and 95% cutoffs\n\nMaster_Data_AutoCorrelations<-Master_Data_lags %>%\n gather(key = \"lag\", value = \"lag_value\", -c(Stock,Date, Close)) %>%\n mutate(lag = str_sub(lag, start = 5) %>% as.numeric) %>%\n group_by(Stock, lag) %>%\n summarize(\n cor = cor(x = Close, y = lag_value, use = \"pairwise.complete.obs\"),\n cutoff_upper = 2/(n())^0.5,\n cutoff_lower = -2/(n())^0.5\n )\n\n## Visualisation of Autocorrelation: ACF Plot\n\nMaster_Data_AutoCorrelations %>%\n ggplot(aes(x = lag, y = cor, color = Stock, group = Stock)) +\n \n # Add horizontal line a y=0\n geom_hline(yintercept = 0) +\n \n # Plot autocorrelations\n geom_point(size = 2) +\n geom_segment(aes(xend = lag, yend = 0), size = 1) +\n \n # Add cutoffs\n geom_line(aes(y = cutoff_upper), color = \"blue\", linetype = 2) +\n geom_line(aes(y = cutoff_lower), color = \"blue\", linetype = 2) +\n \n # Add facets\n facet_wrap(~ Stock, ncol = 3) +\n \n # Aesthetics\n expand_limits(y = c(-1, 1)) +\n scale_color_tq() +\n theme_tq() +\n labs(\n title = paste0(\"Tidyverse ACF Plot: Lags \", rlang::expr_text(k)),\n x = \"Lags\"\n ) +\n theme(\n legend.position = \"none\",\n axis.text.x = element_text(angle = 45, hjust = 1),\n panel.grid.major = element_line(colour = \"grey61\", size = 0.5, linetype = \"dotted\"),\n plot.title = element_text(hjust = 0.5,size=18,colour=\"indianred4\")\n )\n" }, { "path": "63A-FA/stocks5.R", "content": "# Financial Statements\n#Not working through Google Financee\n\nlibrary(quantmod)\ngetFin(\"LNKD\")\nhead(viewFin(LNKD.f, type= 'BS', period = 'Q' ))\nfin <- c('GOOGL','AAPL','AMZN','EBAY','CRM','FB','LNKD','ALBIY')\n" }, { "path": "63A-FA/stocksanalysis3.R", "content": "# Finance Stock Analysis\n\n#Install Packages \n#pckgs<-c(\"Quandl\",\"Sweep\",\"tidyverse\")\n#install.packages(pckgs,dependencies = TRUE)\n\nlibrary(Quandl)\n# library(tidyverse)\n# library(ggplot2)\n# library(stringr)\n# library(plyr)\n# library(stringr)\n# library(gridExtra)\n\n#Quandl(Code=“NSE/—”,collapse=“—”,start_date=“—-”,type=“…”)\n#ICICI SBI PNB price band of Rs 200 to Rs 500. \n#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW\n\nQuandl.api_key(\"4D8hkYAV4WEkcTmD9LMW\")\n\n## Download the data Set\nICICI = Quandl(\"NSE/ICICIBANK\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nPNB= Quandl(\"NSE/PNB\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\nSBI=Quandl(\"NSE/SBIN\",collapse=\"daily\",start_date=\"2017-09-01\",type=\"raw\")\n\n## Add another (\"Stock\") column in Datasets using cbind command\nhead(ICICI)\n## Paste the stock name in stock column\n\nICICI$Stock = \"ICICI\"\nPNB$Stock = \"PNB\"\nSBI$Stock = \"SBI\"\nSBI\n\nallstocks = rbind(ICICI, PNB, SBI)\nstr(allstocks)\nallstocks$Stock = factor(allstocks$Stock)\nstr(allstocks)\nnames(allstocks)\nnames(allstocks)[c(7,8)] = c('Qty','Turnover')\nnames(allstocks)\n\n\n#Using Aggregations\nnames(allstocks)\naggregate(allstocks$Close, by=list(allstocks$Stock), mean)\naggregate(cbind(Close, Open) ~ Stock , data= allstocks, mean)\n#https://www.statmethods.net/input/dates.html\naggregate(allstocks['Close'], by=list(allstocks$Date), mean)\naggregate(allstocks['Close'], by=list(format(allstocks$Date,\"%d\")), mean)\naggregate(allstocks['Close'], by=list(format(allstocks$Date,\"%b\")), mean)\naggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,\"%m\")), mean)\naggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,\"%Y\")), mean)\n\n#using Dplyr Package to do Data Manipulation\nlibrary(dplyr)\nnames(allstocks)\n#select Columns\nallstocks %>% select(Stock, Open, Close)\n\n#Groupby\nallstocks %>% group_by(Stock) #nothing summarised\n\n#summarise\nallstocks %>% summarise(mean(Open), max(High))\n\nallstocks %>% group_by(Stock) %>% summarise_all(mean)\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)\noptions(dplyr.print_max = 1e9)\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)\n\nallstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))\n\n#sample using dplyr\nallstocks %>% sample_n(10)\nallstocks %>% sample_frac(.05)\nallstocks %>% group_by(Stock) %>% sample_n(2)\n\nallstocks %>% group_by(Stock) %>% tally(Qty) %>% top_n(1)\nallstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)\n\n\n# Linear Modeling\nnames(ICICI)\ndf= cbind(ICICI[2],SBI[2] )\nnames(df) = c('icici','sbi')\nhead(df)\nplot(df)\nfit = lm(icici ~ sbi, data=df)\nsummary(fit)\nnew1 = data.frame(sbi=200)\n(p1=predict(fit,new=new1, interval='confidence' ))\ncbind(new1, p1)\nplot(x=df$sbi, y=residuals(fit)) #Linearity\nqqnorm(residuals(fit))\nqqline(residuals(fit))\n\nlibrary(car)\n#Multiple LM\ndf1 = cbind(ICICI[2],SBI[2],PNB[2] )\nnames(df1) = c('icici','sbi','pnb')\nhead(df1)\n#No Plots\npairs(df1)\nfit1 = lm(icici ~ sbi + pnb, data=df1)\nsummary(fit1)\nsummary(fit1)$r.squared\nsummary(fit1)$adj.r.squared\nnew2= data.frame(sbi=c(200,300), pnb=c(250,350))\np2=predict(fit1, new=new2, interval='confidence')\ncbind(new2, p2)\n\ncrPlots(fit1)\nvif(fit1)# variance inflation factors \nsqrt(vif(fit1)) > 2 # problem? Use only 1 variable\ndurbinWatsonTest(fit1)\ncar::outlierTest(fit1) # Bonferonni p-value for most extreme obs\ncar::qqPlot(fit1, main=\"QQ Plot\")\nlibrary(gvlma)\ngvmodel <- gvlma(fit1) \nsummary(gvmodel)\n\nav.Plots(fit1)\n?car::av.Plots\ncar::influencePlot(fit1,\tid.method=\"identify\", main=\"Influence Plot\", sub=\"Circle size is proportial to Cook's Distance\" )\ncar::ncvTest(fit1)\ncar::spreadLevelPlot(fit1)\n" }, { "path": "63A-FA/stocksanalysis4.R", "content": "# Stock Prices Analysis\n#https://www.r-bloggers.com/stock-analysis-using-r/\nlibrary('quantmod')\n\n\ngetSymbols('AAPL')\nchartSeries(AAPL, subset='last 3 months')\naddBBands()\n\n#getSymbols function is used to retrieve stock data. Data can originate in a number of locations. In the example above, we are obtaining a single stock, Apple. If you wanted to download several different stock quotes, you can do so in a single command.\n\ngetSymbols(c('ORCL','IBM'))\n\n#Once you have retrieved stock data, you can focus on subsets of dates quickly.\nAAPL['2010-06-01::2010-06-26']\n\n#also merge data to view comparisons.\n\nhead(as.xts(merge(ORCL,IBM)))\n\n#The chartSeries command creates the plot pictured above. It captures a large amount of information, the date, open and close price, and volume of trading for each day. Finally, the addBBands() call adds Bollinger Bands to the chart. Informally, this amounts to a line indicating moving average and two lines a standard deviation above and below this moving average. For the uninitiated, technical indicators (and overlays) can be broken up into four categories – Trend, Volatility, Momentum, and Volume. \n" }, { "path": "63A-FA/volatity1.R", "content": "#Asset Volatity\n\n#https://rviews.rstudio.com/2017/07/12/introduction-to-volatility/\n\nlibrary(quantmod)\n#A vector of symbol for our ETFs.\nsymbols <- sort(c(\"SPY\",\"VGT\",\"EFA\",\"DBC\",\"AGG\"))\n\n# Pipe them to getSymbols, extract the closing prices, and merge to one xts object. \n# Take a look at result before moving on to calculate the returns.\n# Notice that we are only grabbing prices from 2013 to present, but that is \n# only to keep the loading time shorter for the post. \nprices <- \n getSymbols(symbols, src = 'yahoo', from = \"2016-01-01\", \n auto.assign = TRUE, warnings = FALSE) \nhead(SPY)\nhead(VGT)\n\nlibrary(ti)\n\n# We are going to make heavy use of the tidyquant package to get monthly returns.\nportfolio_component_monthly_returns_xts <- \n prices %>% \n # Convert to tibble so can stay in the tidyquant/verse.\n as_tibble(preserve_row_names = TRUE) %>%\n # Add a date column. \n mutate(date = ymd(row.names)) %>% \n # Remove the row.names column; it's not needed anymore.\n select(-row.names) %>% \n # I like to have the date column as the first column. \n select(date, everything()) %>% \n # We need to gather into long format in order to use tq_transmute().\n gather(asset, return, -date) %>%\n group_by(asset) %>% \n # Use the function from tidyquant; note how easily we could change to\n # a different time period like weekly or yearly.\n tq_transmute(mutate_fun = periodReturn, period = \"monthly\") %>%\n # Put the results back to wide format.\n spread(asset, monthly.returns) %>% \n # Convert back to an xts, so we can use the cov() and StdDev() functions.\n as_xts(date_col = date)\n\nhead(portfolio_component_monthly_returns_xts)\n" }, { "path": "70a-report/report1.Rmd", "content": "---\ntitle: \"iitgn\"\nauthor: \"dhiraj\"\ndate: \"April 1, 2018\"\noutput: pdf_document\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE)\n```\n\n## R Markdown\n\nThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see .\n\nWhen you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:\n\n```{r cars}\nsummary(cars)\n```\n\n## Including Plots\n\nYou can also embed plots, for example:\n\n```{r pressure, echo=FALSE}\nplot(pressure)\n```\n\nNote that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.\n" }, { "path": "70a-report/report1.tex", "content": "\\documentclass[]{article}\n\\usepackage{lmodern}\n\\usepackage{amssymb,amsmath}\n\\usepackage{ifxetex,ifluatex}\n\\usepackage{fixltx2e} % provides \\textsubscript\n\\ifnum 0\\ifxetex 1\\fi\\ifluatex 1\\fi=0 % if pdftex\n \\usepackage[T1]{fontenc}\n \\usepackage[utf8]{inputenc}\n\\else % if luatex or xelatex\n \\ifxetex\n \\usepackage{mathspec}\n \\else\n \\usepackage{fontspec}\n \\fi\n \\defaultfontfeatures{Ligatures=TeX,Scale=MatchLowercase}\n\\fi\n% use upquote if available, for straight quotes in verbatim environments\n\\IfFileExists{upquote.sty}{\\usepackage{upquote}}{}\n% use microtype if available\n\\IfFileExists{microtype.sty}{%\n\\usepackage{microtype}\n\\UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts\n}{}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{hyperref}\n\\hypersetup{unicode=true,\n pdftitle={iitgn},\n pdfauthor={dhiraj},\n pdfborder={0 0 0},\n breaklinks=true}\n\\urlstyle{same} % don't use monospace font for urls\n\\usepackage{color}\n\\usepackage{fancyvrb}\n\\newcommand{\\VerbBar}{|}\n\\newcommand{\\VERB}{\\Verb[commandchars=\\\\\\{\\}]}\n\\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\\\\{\\}}\n% Add ',fontsize=\\small' for more characters per line\n\\usepackage{framed}\n\\definecolor{shadecolor}{RGB}{248,248,248}\n\\newenvironment{Shaded}{\\begin{snugshade}}{\\end{snugshade}}\n\\newcommand{\\KeywordTok}[1]{\\textcolor[rgb]{0.13,0.29,0.53}{\\textbf{#1}}}\n\\newcommand{\\DataTypeTok}[1]{\\textcolor[rgb]{0.13,0.29,0.53}{#1}}\n\\newcommand{\\DecValTok}[1]{\\textcolor[rgb]{0.00,0.00,0.81}{#1}}\n\\newcommand{\\BaseNTok}[1]{\\textcolor[rgb]{0.00,0.00,0.81}{#1}}\n\\newcommand{\\FloatTok}[1]{\\textcolor[rgb]{0.00,0.00,0.81}{#1}}\n\\newcommand{\\ConstantTok}[1]{\\textcolor[rgb]{0.00,0.00,0.00}{#1}}\n\\newcommand{\\CharTok}[1]{\\textcolor[rgb]{0.31,0.60,0.02}{#1}}\n\\newcommand{\\SpecialCharTok}[1]{\\textcolor[rgb]{0.00,0.00,0.00}{#1}}\n\\newcommand{\\StringTok}[1]{\\textcolor[rgb]{0.31,0.60,0.02}{#1}}\n\\newcommand{\\VerbatimStringTok}[1]{\\textcolor[rgb]{0.31,0.60,0.02}{#1}}\n\\newcommand{\\SpecialStringTok}[1]{\\textcolor[rgb]{0.31,0.60,0.02}{#1}}\n\\newcommand{\\ImportTok}[1]{#1}\n\\newcommand{\\CommentTok}[1]{\\textcolor[rgb]{0.56,0.35,0.01}{\\textit{#1}}}\n\\newcommand{\\DocumentationTok}[1]{\\textcolor[rgb]{0.56,0.35,0.01}{\\textbf{\\textit{#1}}}}\n\\newcommand{\\AnnotationTok}[1]{\\textcolor[rgb]{0.56,0.35,0.01}{\\textbf{\\textit{#1}}}}\n\\newcommand{\\CommentVarTok}[1]{\\textcolor[rgb]{0.56,0.35,0.01}{\\textbf{\\textit{#1}}}}\n\\newcommand{\\OtherTok}[1]{\\textcolor[rgb]{0.56,0.35,0.01}{#1}}\n\\newcommand{\\FunctionTok}[1]{\\textcolor[rgb]{0.00,0.00,0.00}{#1}}\n\\newcommand{\\VariableTok}[1]{\\textcolor[rgb]{0.00,0.00,0.00}{#1}}\n\\newcommand{\\ControlFlowTok}[1]{\\textcolor[rgb]{0.13,0.29,0.53}{\\textbf{#1}}}\n\\newcommand{\\OperatorTok}[1]{\\textcolor[rgb]{0.81,0.36,0.00}{\\textbf{#1}}}\n\\newcommand{\\BuiltInTok}[1]{#1}\n\\newcommand{\\ExtensionTok}[1]{#1}\n\\newcommand{\\PreprocessorTok}[1]{\\textcolor[rgb]{0.56,0.35,0.01}{\\textit{#1}}}\n\\newcommand{\\AttributeTok}[1]{\\textcolor[rgb]{0.77,0.63,0.00}{#1}}\n\\newcommand{\\RegionMarkerTok}[1]{#1}\n\\newcommand{\\InformationTok}[1]{\\textcolor[rgb]{0.56,0.35,0.01}{\\textbf{\\textit{#1}}}}\n\\newcommand{\\WarningTok}[1]{\\textcolor[rgb]{0.56,0.35,0.01}{\\textbf{\\textit{#1}}}}\n\\newcommand{\\AlertTok}[1]{\\textcolor[rgb]{0.94,0.16,0.16}{#1}}\n\\newcommand{\\ErrorTok}[1]{\\textcolor[rgb]{0.64,0.00,0.00}{\\textbf{#1}}}\n\\newcommand{\\NormalTok}[1]{#1}\n\\usepackage{graphicx,grffile}\n\\makeatletter\n\\def\\maxwidth{\\ifdim\\Gin@nat@width>\\linewidth\\linewidth\\else\\Gin@nat@width\\fi}\n\\def\\maxheight{\\ifdim\\Gin@nat@height>\\textheight\\textheight\\else\\Gin@nat@height\\fi}\n\\makeatother\n% Scale images if necessary, so that they will not overflow the page\n% margins by default, and it is still possible to overwrite the defaults\n% using explicit options in \\includegraphics[width, height, ...]{}\n\\setkeys{Gin}{width=\\maxwidth,height=\\maxheight,keepaspectratio}\n\\IfFileExists{parskip.sty}{%\n\\usepackage{parskip}\n}{% else\n\\setlength{\\parindent}{0pt}\n\\setlength{\\parskip}{6pt plus 2pt minus 1pt}\n}\n\\setlength{\\emergencystretch}{3em} % prevent overfull lines\n\\providecommand{\\tightlist}{%\n \\setlength{\\itemsep}{0pt}\\setlength{\\parskip}{0pt}}\n\\setcounter{secnumdepth}{0}\n% Redefines (sub)paragraphs to behave more like sections\n\\ifx\\paragraph\\undefined\\else\n\\let\\oldparagraph\\paragraph\n\\renewcommand{\\paragraph}[1]{\\oldparagraph{#1}\\mbox{}}\n\\fi\n\\ifx\\subparagraph\\undefined\\else\n\\let\\oldsubparagraph\\subparagraph\n\\renewcommand{\\subparagraph}[1]{\\oldsubparagraph{#1}\\mbox{}}\n\\fi\n\n%%% Use protect on footnotes to avoid problems with footnotes in titles\n\\let\\rmarkdownfootnote\\footnote%\n\\def\\footnote{\\protect\\rmarkdownfootnote}\n\n%%% Change title format to be more compact\n\\usepackage{titling}\n\n% Create subtitle command for use in maketitle\n\\newcommand{\\subtitle}[1]{\n \\posttitle{\n \\begin{center}\\large#1\\end{center}\n }\n}\n\n\\setlength{\\droptitle}{-2em}\n \\title{iitgn}\n \\pretitle{\\vspace{\\droptitle}\\centering\\huge}\n \\posttitle{\\par}\n \\author{dhiraj}\n \\preauthor{\\centering\\large\\emph}\n \\postauthor{\\par}\n \\predate{\\centering\\large\\emph}\n \\postdate{\\par}\n \\date{April 1, 2018}\n\n\n\\begin{document}\n\\maketitle\n\n\\subsection{R Markdown}\\label{r-markdown}\n\nThis is an R Markdown document. Markdown is a simple formatting syntax\nfor authoring HTML, PDF, and MS Word documents. For more details on\nusing R Markdown see \\url{http://rmarkdown.rstudio.com}.\n\nWhen you click the \\textbf{Knit} button a document will be generated\nthat includes both content as well as the output of any embedded R code\nchunks within the document. You can embed an R code chunk like this:\n\n\\begin{Shaded}\n\\begin{Highlighting}[]\n\\KeywordTok{summary}\\NormalTok{(cars)}\n\\end{Highlighting}\n\\end{Shaded}\n\n\\begin{verbatim}\n## speed dist \n## Min. : 4.0 Min. : 2.00 \n## 1st Qu.:12.0 1st Qu.: 26.00 \n## Median :15.0 Median : 36.00 \n## Mean :15.4 Mean : 42.98 \n## 3rd Qu.:19.0 3rd Qu.: 56.00 \n## Max. :25.0 Max. :120.00\n\\end{verbatim}\n\n\\subsection{Including Plots}\\label{including-plots}\n\nYou can also embed plots, for example:\n\n\\includegraphics{report1_files/figure-latex/pressure-1.pdf}\n\nNote that the \\texttt{echo\\ =\\ FALSE} parameter was added to the code\nchunk to prevent printing of the R code that generated the plot.\n\n\n\\end{document}\n" }, { "path": "70d-myTest/lm-sim-test1.R", "content": "#LM Simulation\n\nlibrary(car)\nlibrary(PerformanceAnalytics)\nlibrary(corrgram)\nlibrary(corrplot)\n\n#Data\nset.seed(1234); x1 <- rnorm(100, 70, 10) + rnorm(100)\nset.seed(1234); x2 <- runif(100, 60, 90)\nset.seed(1234); x3 <- .75 * x1 + rnorm(10,7,2)\nset.seed(1234); x4 = factor(sample(c('M','F'), size=100, replace=T, prob=c(.7,.3)))\nset.seed(1234); x5 <- runif(100, 30,50) - rnorm(20,5,2)\n\nset.seed(1234); error <- rnorm(100,15,3)\nalias(fit)\ny = 5 + 1.2 * x1 + 0.005 * x2 - x3 - .6 * x5 + error\ndf = data.frame(y, x1, x2, x3, x4, x5)\nhead(df)\nstr(df)\n\n#LM\nfit = lm(y ~ x1 + x2 + x3 + x4 + x5, data= df)\nalias(fit) #any built in correlation\nx6 = .1*x1\nalias(lm(y ~ x1 + x2 + x6)) #x6 related to x1\n\nattributes(fit)\nattributes(summary(fit))\nsummary(fit)\nsummary(fit)$r.squared\nsummary(fit)$adj.r.squared\nsummary(fit)$sigma # SEE\n\n#Model worthiness\nanova(fit)\n\n#Interpret\nnames(fit)\nfit$residuals #resid(fit)\nfitted(fit) # predict(fit, df)\n\n#assumptions\n#A1:Linearity IV-DV----\ncar::crPlots(lm(formula, data=df))\ncorrgram::corrgram(df)\ncorfit = cor(df[,-5])\ncorrplot::corrplot(cor(df[,-5]), method=\"pie\")\ncorrplot::corrplot(cor(df[,-5]), method=\"number\")\nPerformanceAnalytics::chart.Correlation(df[,-5], histogram=TRUE, pch=19)\n\n#A : Normality of Residues---\ncar::qqPlot(fit)\n\n#A2:Autocollinearity\ncar::durbinWatsonTest(fit)\n#if pvalues < 0.05, autocorr exists\n\n#A4:Homoscedascity of Residuals\ncar::ncvTest(fit)\nplot(fit, which=1)\n\n\n#MultiCollinearity----\ncar::vif(fit) # variance inflation factors \nsqrt(vif(fit)) > 2 # problem?\n#remove variable one by one start from highest vif value\n\n\n# Assessing Outliers\ncar::outlierTest(fit) # Bonferonni p-value for most extreme obs\ncar::qqPlot(fit, main=\"QQ Plot\") #qq plot for studentized resid car::leveragePlots(fit) # leverage plots\n\n#Influencing Values\nplot(fit,which=4)\ncar::influencePlot(fit)\navPlots(fit)\n\n\n\n#Model2\ndf\nsummary(fit)\nfit2 = lm(y ~ x1 + x3 + x5, data= df)\nsummary(fit2)\n\n#Model Compare\nanova(fit2,fit) #do not reject Ho ie. Model1 is better\nAIC(fit2, fit) # whichever lower AIC is better\n\n" }, { "path": "70e-phd/attendance2.R", "content": "attendance1 = read.csv('./attendance/attendance1.csv')\nattendance1\nstr(attendance1)\n\nattendance2 = read.csv('./attendance/attendance2.csv')\nhead(names(attendance2))\nstr(attendance2)\nattendance2a = attendance2[,-1]\nstr(attendance2a)\nattendance2a$Student = as.character(attendance2a$Student)\nstr(attendance2a)\n\n#head(names(mutate(attendance2, RollNo=NULL))) \n\nlibrary(tidyr)\ntidyr_attend = gather(attendance2a, Item, Score, 2:41)\nstr(tidyr_attend)\n\n" }, { "path": "70e-phd/grades.R", "content": "library(readr)\nmaths = read_csv('./grades/grades.csv')\nmaths\nstr(maths)\nnames(maths)\nhead(maths)\ndim(maths)\nlibrary(knitr)\nkable(head(maths[,5:9]))\n\n\n#subsetting & Cleaning\ndim(maths) # 100 rows, 13 colns\n#general info like comments can be dropped\n#dplyr - select, contains\nlibrary(dplyr)\nnames(maths)\nmaths2 = select(maths, `student`, contains('Internal ')\n , contains('Viva'), contains('Assignment'), contains('Attendance')\n , contains('Final'), contains('Assessment')\n , contains('Grade'), - contains('Comment'))\nmaths2\nnames(maths2)\nhead(names(maths2))\n\n# removing long names from anems\n# stringr - str_sub, str_trim\n\nlibrary(stringr) \ndim(maths2)\nnames(maths2)[1:8]\nstr_sub(names(maths2)[2:3], start=1, end=-8) # only 2 & 3 have long names\nnames(maths2)[2:3] = str_sub(names(maths2)[2:3], start=1, end=-8)\nnames(maths2)\n\nnames(maths2) = str_trim(names(maths2)) # remove blank spaces\nnames(maths2)\ndim(maths2)\n\n# multiple colns - collapse - key value pairs\nlibrary(tidyr)\ntidy_maths = gather(maths2, item,score,2:6)\nhead(tidy_maths)\ndim(tidy_maths)\n\nkable(tidy_maths)\n\n#add another column based on certain values\ntidy_maths2 = mutate(tidy_maths, type = ifelse(grepl('Internal', item), 'Internals',\n ifelse(grepl('Viva', item), 'Viva',\n ifelse(grepl('Attendance', item),'Attendance',\n ifelse(grepl('Assignment', item),'Assignment','Other' )))),\n result=ifelse(`Calculated Grade` > 60,'Passed', 'Failed'))\n\nhead(tidy_maths2)\ntable(tidy_maths2$type)\n\nstr(tidy_maths2)\n\nlibrary(magrittr)\nstr(tidy_maths2)\nnames(maths)\nmaths3 = maths[c('student', 'course', 'batch', 'gender')]\nrich_maths = left_join(tidy_maths2, maths3, by='student')\nrich_maths\n\nrich_maths %>% filter(score > 0, type=='Viva') %>% group_by(student) %>%\n summarize(Viva_Mean = round(mean(score),2)) %>% arrange(desc(Viva_Mean)) %>% head(n=10) %>% kable() \n\nrich_maths %>% filter(score > 0, type=='Internals') %>% group_by(item) %>%\n summarize(Mean = mean(score), Median=median(score), Std_dev = sd(score)) %>% kable() \n\ntable(rich_maths$type)\nrich_maths %>% filter(score > 0, type=='Attendance' | type== 'Viva') %>% \n group_by(gender, type) %>% summarize(Mean_Score= round(mean(score),2))\n\ncor(rich_maths$score, rich_maths$Final)\n\n# Correlation\nrich_maths %>% group_by(item) %>% summarize(corr_to_final = round(cor(score,Final),2)) %>%\n arrange(corr_to_final) %>% head() %>% kable()\nx1 = rich_maths %>% group_by(item)\nnames(x1)\n\nlibrary(ggplot2)\nggplot(rich_maths, aes(Final)) + geom_histogram(binwidth=5, fill='lightgreen', \n color='black') + ggtitle('Histogram') + theme_minimal()\n\nggplot(rich_maths, aes(`Calculated Grade`)) + geom_histogram(binwidth=5, fill='lightgreen', \n color='black') + ggtitle('Histogram') + theme_minimal()\n\nggplot(rich_maths, aes(`score`)) + geom_histogram(binwidth=2, fill='lightgreen', \n color='black') + ggtitle('Histogram') + facet_wrap(~item) +\n theme_minimal()\n " }, { "path": "75a-sports/cricket1.R", "content": "# Sports Analytics\n#https://gigadom.wordpress.com/2016/05/14/re-introducing-cricketr-an-r-package-to-analyze-performances-of-cricketers/\n#http://www.espncricinfo.com/india/content/player/35320.html\n\n\n#install.packages('cricketr') \n\nlibrary(cricketr)\n\n# Retrieve the file path of a data file installed with cricketr\npathToFile ,\"Sachin Tendulkar\")\n\n# sachin - 35320\n\n#stores players data in csv file\ntendulkar <- getPlayerData(35320,dir=\"./sports\",file=\"tendulkar.csv\",type=\"batting\",homeOrAway=c(1,2), result=c(1,2,4))\ntendulkar <- getPlayerData(35320,dir=\"./sports\",file=\"tendulkar.csv\",type=\"batting\",homeOrAway=c(1,2), result=c(1,2,4))\nSachin Tendulkar \n#Jacques Kallis http://www.espncricinfo.com/southafrica/content/player/45789.html\nRicky Ponting \nKumara Sangakarra\n\n\n\n?getPlayerData\ntendulkar\n?tendulkar\n\npar(mfrow=c(1,3))\npar(mar=c(4,4,2,2))\nbatsmanRunsFreqPerf(tendulkar,\"Sachin Tendulkar\")\n\nbatsmanRunsFreqPerf(\"./sports/tendulkar.csv\",\"Sachin Tendulkar\")\nbatsmanMeanStrikeRate(\"./sports/tendulkar.csv\",\"Sachin Tendulkar\")\nbatsmanRunsRanges(\"./sports/tendulkar.csv\",\"Sachin Tendulkar\")\n\n\npar(mfrow=c(1,3))\npar(mar=c(4,4,2,2))\nbatsman4s(\"./sports/tendulkar.csv\",\"Tendulkar\")\nbatsman6s(\"./sports/tendulkar.csv\",\"Tendulkar\")\nbatsmanDismissals(\"./sports/tendulkar.csv\",\"Tendulkar\")\n\nbattingPerf3d(\"./sports/tendulkar.csv\",\"Sachin Tendulkar\")\nbatsmanAvgRunsGround(\"./sports/tendulkar.csv\",\"Sachin Tendulkar\")\nbatsmanAvgRunsOpposition(\"./sports/tendulkar.csv\",\"Tendulkar\")\nbatsmanRunsLikelihood(\"./sports/tendulkar.csv\",\"Sachin Tendulkar\")\nbatsmanPerfBoxHist(\"./sports/tendulkar.csv\",\"Sachin Tendulkar\")\n\n\nbatsmanPerfBoxHist(\"./sports/kallis.csv\",\"Jacques Kallis\")" }, { "path": "75a-sports/cricket2-york.R", "content": "# Cricket Analysis\n#https://gigadom.wordpress.com/2016/04/02/introducing-cricket-package-yorkr-part-1-beaten-by-sheer-pace/\n\ninstall.packages('yorkr')\nlibrary(yorkr)\n\nconvertYaml2RDataframe(\"225171.yaml\",\"./source\",\"./sports\") \n" }, { "path": "76a-Misc/dhmethods.R", "content": "#Various Functions on Data\n#Handle objects ----\nx = 1:5\n\n#Functions/ Methods applicable to Objects\nmethods(class=class(x))\n#helps you know which function is applicable for which type of object\nmethods(class=class(mtcars))\n\n#seq\n1:5\nrev(x)\nmatch(5, x)\n\n\n#all combinations---\n(df=expand.grid(x=c('A','B','C','D'), y=1:3))\n\nchoose(6,4)\n\nweighted.mean(c(1,2), c(3,4))\n(1*3 + 2*4)/(3+4)\n\ndf2 = data.frame(x=c(1,2), y=c(3,4), z=c(5,6))\nstars(df2[,1:2])\nstars(df2[,1:3])\n\nfilled.contour((df2))\n?image\nimage(df2)\n\ndf2 = mtcars[1:5,1:8]\n" }, { "path": "76a-Misc/funcpgm1.R", "content": "# Functional Programming\n#http://adv-r.had.co.nz/Functional-programming.html\n\n?Map\nMap(sum, 1:5, 1:5, 1:5)\nMap(sum, 1:5, 1:5, 1:5, simplify2array=F)\nMap(sum, 1:5, 11:15, 21:25, simplify=T)\n\nReduce(f, x, init, right = FALSE, accumulate = FALSE)\nFilter(f, x)\nFind(f, x, right = FALSE, nomatch = NULL)\nMap(f, ...)\nNegate(f)\nPosition(f, x, right = FALSE, nomatch = NA_integer_)\n\nx = 1:12\nPosition(f=Find, x)\n\n\n#http://www.rmanchester.org/presentations/2013/05/ManchesterR_-_FP_in_R_-_David_Springate_-_20130502.pdf\n\n'+'(1,2)\n':'(1,10)\n" }, { "path": "76a-Misc/h2o.R", "content": "#Package H20\n#https://cran.r-project.org/web/packages/h2o/h2o.pdf\n\nlibrary(h2o)\nh2o.init()\niris.hex <- as.h2o(iris)\ndim(iris.hex" }, { "path": "76a-Misc/skimr-package.R", "content": "#Data Understanding\n#https://cran.r-project.org/web/packages/skimr/vignettes/Using_skimr.html\n#https://cran.r-project.org/web/packages/skimr/skimr.pdf\n\nlibrary(skimr)\n#skimr, a package for quickly skimming data by data type\n\nskim(iris)\nskim(iris) %>% dplyr::filter(stat == \"mean\")\nskim(mtcars) %>% dplyr::filter(stat == \"mean\")\n\nmtcars %>% dplyr::group_by(gear) %>% skim()\n\nskim(iris, Sepal.Length, Species)\n\nskim(datasets::lynx)\n\niris_setosa <- iris %>% skim_tee(iris) %>% dplyr::filter(Species == \"setosa\")\n\niris %>% skim_to_list()\n\nn_missing(mtcars)\n" }, { "path": "78b-json/21b-json-format.R", "content": "\n#https://data.metinyazici.org/2017/10/working-with-web-data-in-r.html\n\nlibrary(jsonlite)\n\nchocolate_brands <- c('\n[ { \"name\" : \"Dove Bar\",\"founded\" : 1956},\n { \"name\" : \"Toblerone\", \"founded\" : 1908}\n]')\nchocolate_brands\n\nchoco_json <- fromJSON(chocolate_brands, simplifyVector = F) # parse that with fromJSON()\nprint(choco_json)\n\nlibrary(dplyr)\nname <- choco_json %>% bind_rows %>% select(name, founded)\nprint(name)\n\n\n" }, { "path": "78b-json/23b-xml-import.R", "content": "\n\nlibrary(xml2)\nurl = \"https://en.wikipedia.org/wiki/List_of_chocolate_bar_brands\" #Repeat again here\nchoco_xml <- read_xml(url)\nprint(choco_xml)\n\nwrite_xml(choco_xml,'chocolates.xml',options=\"as_xml\")\nwrite_html(choco_xml,file='chocolates.html',options=\"as_html\")\n\nr <- xml_find_all(choco_xml, \"//table[@class='wikitable sortable']\")\nhead(r)\na <- xml_children(r)\nhead(a)\nx <- xml_text(a)\nhead(x)\n\n\nz01 <- strsplit(x, '\\n', fixed = T)\n#set column names by the first element of list `z01[[1]]`\nz01\ndf <- setNames(data.frame(matrix(unlist(z01), nrow = 285, byrow = T)), z01[[1]])\n#not need the first row that became the column names\ndf <- df[-1,]\n#not need \"Image\" column so remove\ndf$Image <- NULL" }, { "path": "78b-json/25a-httr1.R", "content": "#httr package to import data from webpages\n\n#Load the httr package\nlibrary(\"httr\")\nget_http <- GET(\"http://httpbin.org/get\")\nstr(get_http, list.len = 3)\n\n\narg_pars <- list(country = \"UK\", manufacturer = \"Mars\")\narg_resp <- GET(\"https://httpbin.org/get\", query = arg_pars)\nprint(arg_resp)\n#Status: 200, means successful request\n\n#POST request\npost_http <- POST(url=\"http://httpbin.org/post\", body=\"This message is from strboul\")\nprint(post_http)\n\n\n#You are expected to be reasonable and respect the API service1, for sure. APIs have access tokens to control the misuse of their services, which usually limit the number of requests coming by unique users or sessions making the servers very busy or locked. Giving an example, Twitter controls the number of requests to the server by rate-limiting. That makes available GET requests in two ways: “15 calls every 15 minutes, and 180 calls every 15 minutes.” To correspond this request time, you can use Sys.sleep() function in a for loop in order to send expressions in a specified time interval.\n" }, { "path": "78b-json/25c-httr2.R", "content": "#httr2\n\nlibrary(httr)\npage_source <- function(url_name, parse_format){\n url <- modify_url(\n \"https://en.wikipedia.org/w/api.php?\",\n query = list(\n action = \"query\",\n titles = url_name,\n prop = \"revisions\",\n rvprop = \"content\",\n format = parse_format)\n )\n response <- GET(url)\n if(http_error(response)){\n stop(\"The request failed\")}\n else {\n result <- content(response)\n return(result)\n }\n}\n\n\na <- page_source(\"List_of_chocolate_bar_brands\", \"json\")\nstr(a)\n\na01 <- page_source(\"List_of_chocolate_bar_brands\", \"jsonfm\")\nprint(a01)\n" }, { "path": "78b-json/json-1.R", "content": "\n\ninstall.packages(\"rjson\")\ninstall.packages(\"RJSONIO\")\ninstall.packages(\"(RCurl\")\n\n\n{ \n \"ID\":[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\" ],\n \"Name\":[\"Rick\",\"Dan\",\"Michelle\",\"Ryan\",\"Gary\",\"Nina\",\"Simon\",\"Guru\" ],\n \"Salary\":[\"623.3\",\"515.2\",\"611\",\"729\",\"843.25\",\"578\",\"632.8\",\"722.5\" ],\n \"StartDate\":[ \"1/1/2012\",\"9/23/2013\",\"11/15/2014\",\"5/11/2014\",\"3/27/2015\",\"5/21/2013\", \"7/30/2013\",\"6/17/2014\"],\n \"Dept\":[ \"IT\",\"Operations\",\"IT\",\"HR\",\"Finance\",\"IT\",\"Operations\",\"Finance\"]\n}\n\n# Load the package required to read JSON files.\nlibrary(\"rjson\")\n\n# Give the input file name to the function.\nresult <- fromJSON(file = \"input.json\")\n\n\nlibrary(RJSONIO)\nlibrary(RCurl)\njson_file = getURL(\"https://raw.githubusercontent.com/isrini/SI_IS607/master/books.json\")\njson_file2 = RJSONIO::fromJSON(json_file)\nhead(json_file2)\n# Print the result.\nprint(json_file2)\ndf <- as.data.frame(json_file2)\ndf\nstr(df)\n\n#--------------------------\n\nToken <- \"245432532532\"\nsource <- \"http://.......\"\nheader_type <- \"applcation/json\"\nfull_token <- paste0(\"Bearer \", Token)\nresponse <- GET(n_source, add_headers(Authorization = full_token, Accept = h_type), timeout(120), verbose())\ntext_json <- content(response, type = 'text', encoding = \"UTF-8\")\njfile <- fromJSON(text_json)\ndf <- as.data.frame(jfile)\n\n\ndata(Adult)\nAdult\n## select first 10 transactions\nAdult[1:10]\n## select first 10 items for first 100 transactions\nAdult[1:100, 1:10]\n## select the first 100 transactions for the items containing\n## \"income\" or \"age=Young\" in their labels\nAdult[1:100, c(\"income=small\", \"income=large\" ,\"age=Young\")]\n\n\nlibrary(\"rjson\")\n\n{\n \"TemperatureRegionID\" : 419,\n \"StartDate\" : \"07/23/2018 01:55:45 PM\",\n \"StopDate\" : \"07/23/2018 09:55:45 PM\",\n \"Interval\" : \"Minute\",\n}\n\n\n#http://adminapi.fat.test.fleetmode.io/temperature-regions/get-history/\n\nlibrary(jsonlite)\nlibrary(httr)\n\nreq <- POST(\"http://api.scb.se/OV0104/v1/doris/sv/ssd/START/PR/PR0101/PR0101A/KPIFastM2\", body = '{ \"query\": [], \"response\": { \"format\": \"json\" } }')\nhttp://adminapi.fat.test.fleetmode.io/temperature-regions/get-history/\n req <- POST(\"http://adminapi.fat.test.fleetmode.io/temperature-regions/get-history/\", body = '{ \"query\": [], \"response\": { \"format\": \"json\" } }')\n\nreq \n\nstop_for_status(req)\njson <- content(req, \"text\")\n\n\n# JSON starts with an invalid character:\nvalidate(json)\njson <- substring(json, 2)\nvalidate(json)\n\n# Now we can parse\nobject <- jsonlite::fromJSON(json)\nprint(objects)\n\n\nlibrary(jsonlite)\n\nlibrary(httr)\n\npc_json <- list{(\n 'TemperatureRegionID' : 419,\n 'StartDate' : '07/23/2018 01:55:45 PM',\n 'StopDate' : '07/23/2018 09:55:45 PM',\n 'Interval' : 'Minute',\n)}\n\n\nres <- POST(\"https://api.postcodes.io/postcodes\"\n , body = pc_json\n , encode = \"json\")\nappData <- content(res)\n\nlibrary(jsonlite)\n\n# A JSON array of primitives\njson <- '[\"Mario\", \"Peach\", null, \"Bowser\"]'\njson\n\n# Simplifies into an atomic vector\nfromJSON(json)\n\n# No simplification:\nfromJSON(json, simplifyVector = FALSE)\n\n# to Df\n# No simplification:\njson <-\n '[\n{\"Name\" : \"Mario\", \"Age\" : 32, \"Occupation\" : \"Plumber\"}, \n{\"Name\" : \"Peach\", \"Age\" : 21, \"Occupation\" : \"Princess\"},\n{},\n{\"Name\" : \"Bowser\", \"Occupation\" : \"Koopa\"}\n]'\nmydf <- fromJSON(json, simplifyDataFrame = TRUE)\nmydf\nstr(mydf)\n\n#----\n\njson <-\n '[\n{\"Name\" : \"Mario\", \"Age\" : 32, \"Occupation\" : \"Plumber\"}, \n{\"Name\" : \"Peach\", \"Age\" : 21, \"Occupation\" : \"Princess\"},\n{},\n{\"Name\" : \"Bowser\", \"Occupation\" : \"Koopa\"}\n]'\n\n{\n \"TemperatureRegionID\" : 419,\n \"StartDate\" : \"07/23/2018 01:55:45 PM\",\n \"StopDate\" : \"07/23/2018 09:55:45 PM\",\n \"Interval\" : \"Minute\",\n}\n" }, { "path": "78b-json/json2.R", "content": "# read json file\n\nlibrary(jsonlite)\nzips <- stream_in(url(\"http://media.mongodb.org/zips.json\"))\n#If the server uses https, you can use the curl package:\nstr(zips)\n \nlibrary(jsonlite)\nlibrary(curl)\nzips <- stream_in(curl(\"http://media.mongodb.org/zips.json\"))\n\nlibrary(dplyr)\nmyJSON <- . %>% file(open=\"r\") %>% stream_in()\nmyJSON('test.json')\n\ndf <- filename %>% file %>% stream_in\n\ndatasets <- lapply(res.clean[1:2, ], jsonlite::fromJSON)\n\n#If each element is a data frame you can subsequently use\nalldata <- jsonlite:: rbind_pages(datasets)\n\n\n\n\nrequire(jsonlite)\nid = \"dup1966\"\nurl = paste0(\"https://gdata.youtube.com/feeds/api/videos/\", id, \"?v=2&alt=json\")\nraw.data <- readLines(url, warn = \"F\")\nrd <- fromJSON(raw.data)\nterm <- rd$entry$category$term[2]\nlabel <- rd$entry$category$label[2]\ntitle <- rd$entry$title\nauthor <- rd$entry$author[1]\nduration <- rd$entry$`media$group`$`media$content`$duration[1]" }, { "path": "80a-artwork/AuctionsData - artwork.csv", "content": "recid,auccur,aucdate,aucid,idafn,auctitle,auchouse,aucloc,lotno,awid,awtitle,awcat,arname,lotesthigh,lotestlow,saleprice,lotstatus\n2547,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,1,6998013,Untitled,PAINTINGS,Christopher Wool,\"600,000\",\"400,000\",\"729,000\",Sold\n2548,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,10,6998014,Angel Of The North,SCULPTURE,Antony Gormley,\"2,000,000\",\"1,500,000\",\"2,860,600\",Sold\n2549,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,11,6998015,Some People (Welcome Series),PAINTINGS,Hurvin Anderson,\"800,000\",\"600,000\",\"1,029,000\",Sold\n2550,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,12,6998016,The Architect's Home In The Ravine,PAINTINGS,Peter Doig,\"18,000,000\",\"14,000,000\",\"14,376,400\",Sold\n2551,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,13,6998017,A Swan Comforting A Snake,PAINTINGS,Cecily Brown,\"1,500,000\",\"1,000,000\",\"1,209,000\",Sold\n2552,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,14,6998018,Eifrau Die Man Nicht Schubladieren Kann (Egg Lady Who Can’t Be Piegeonholed),PAINTINGS,Martin Kippenberger,\"3,000,000\",\"2,000,000\",\"2,169,000\",Sold\n2553,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,15,6998019,Memory Ware Flat #29,PAINTINGS,Mike Kelley,\"2,000,000\",\"1,500,000\",\"1,689,000\",Sold\n2554,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,16,6998020,Yellow Cell With Triple Conduit,PAINTINGS,Peter Halley,\"350,000\",\"250,000\",\"513,000\",Sold\n2555,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,17,6998021,Untitled,SCULPTURE,Richard Prince,\"1,500,000\",\"1,000,000\",\"2,049,000\",Sold\n2556,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,18,6998022,Nov. 8,PAINTINGS,On Kawara,\"800,000\",\"600,000\",\"789,000\",Sold\n2557,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,19,6998023,Untitled,PAINTINGS,Rudolf Stingel,\"6,000,000\",\"4,000,000\",\"4,667,000\",Sold\n2558,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,2,6998024,Untitled,PAINTINGS,Laura Owens,\"350,000\",\"250,000\",\"435,000\",Sold\n2559,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,20,6998025,Amanti (Lovers),PAINTINGS,Michelangelo Pistoletto,\"3,000,000\",\"2,300,000\",\"2,634,800\",Sold\n2560,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,21,6998026,Tenda Blu (Blue Curtain),PAINTINGS,Michelangelo Pistoletto,\"600,000\",\"400,000\",\"621,000\",Sold\n2561,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,22,6998027,Figura Femminile Con Fiori (Female Figure With Flowers),SCULPTURE,Lucio Fontana,\"2,000,000\",\"1,500,000\",\"1,809,000\",Sold\n2562,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,23,6998028,\"Oiks, Tarts, Wierdoes And Contemporary Art\",SCULPTURE,Grayson Perry,\"120,000\",\"80,000\",\"175,000\",Sold\n2563,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,24,6998029,Border Country,PAINTINGS,Peter Doig,\"700,000\",\"500,000\",\"609,000\",Sold\n2564,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,25,6998030,In Central Park,PAINTINGS,Howard Hodgkin,\"800,000\",\"600,000\",,Unsold\n2565,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,26,6998031,\"Different Kinds Of Water Pouring Into A Swimming Pool, Santa Monica\",PAINTINGS,David Hockney,\"8,000,000\",\"6,000,000\",,Unsold\n2566,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,27,6998032,\"Concetto Spaziale, Attese\",PAINTINGS,Lucio Fontana,\"3,000,000\",\"2,000,000\",\"5,005,700\",Sold\n2567,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,28,6998033,Gelbgrun (Yellow-green),PAINTINGS,Gerhard Richter,\"10,000,000\",\"7,000,000\",\"10,876,500\",Sold\n2568,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,29,6998034,Untitled (19.3.86),PAINTINGS,Gerhard Richter,\"900,000\",\"700,000\",\"849,000\",Sold\n2569,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,3,6998035,Untitled,PAINTINGS,Albert Oehlen,\"1,500,000\",\"1,000,000\",\"1,929,000\",Sold\n2570,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,30,6998036,Kleine Geister (Little Ghosts),SCULPTURE,Thomas Schutte,\"450,000\",\"350,000\",\"405,000\",Sold\n2571,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,31,6998037,Alphabet (U),SCULPTURE,Danh Vo,\"200,000\",\"150,000\",,Unsold\n2572,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,32,6998038,After Micro W #2,PAINTINGS,Sam Gilliam,\"200,000\",\"150,000\",\"200,000\",Sold\n2573,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,33,6998039,Untitled,SCULPTURE,Francesco Lo Savio,\"350,000\",\"250,000\",\"309,000\",Sold\n2574,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,34,6998040,Edera (Ivy),SCULPTURE,Luciano Fabro,\"700,000\",\"500,000\",\"609,000\",Sold\n2575,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,35,6998041,Sandinsel (Sand Island),SCULPTURE,Gunther Uecker,\"800,000\",\"600,000\",\"753,000\",Sold\n2576,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,36,6998042,Untitled (Relief No. 348),SCULPTURE,Sergio de Camargo,\"900,000\",\"700,000\",,Unsold\n2577,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,37,6998043,Infinity Nets (Oq4),PAINTINGS,Yayoi Kusama,\"1,200,000\",\"800,000\",\"1,269,000\",Sold\n2578,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,38,6998044,Untitled,PAINTINGS,Rudolf Stingel,\"2,000,000\",\"1,500,000\",\"1,869,000\",Sold\n2579,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,39,6998045,Caribou Skull,SCULPTURE,Sherrie Levine,\"600,000\",\"400,000\",\"489,000\",Sold\n2580,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,4,6998046,Untitled,PAINTINGS,Christopher Wool,\"6,200,000\",\"4,200,000\",\"10,424,900\",Sold\n2581,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,40,6998047,The Housewife And The Grocer,PAINTINGS,Richard Prince,\"1,800,000\",\"1,200,000\",\"1,269,000\",Sold\n2582,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,41,6998048,O.t. (Death’s Heads),SCULPTURE,Rosemarie Trockel,\"600,000\",\"400,000\",\"465,000\",Sold\n2583,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,42,6998049,Down Under,SCULPTURE,Jason Rhoades,\"550,000\",\"350,000\",\"345,000\",Sold\n2584,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,43,6998050,Untitled,PAINTINGS,Albert Oehlen,\"450,000\",\"350,000\",\"429,000\",Sold\n2585,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,44,6998051,Different From Case To Case,PAINTINGS,Sigmar Polke,\"600,000\",\"400,000\",\"753,000\",Sold\n2586,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,45,6998052,Greifbar 40,PHOTOGRAPHS,Wolfgang Tillmans,\"300,000\",\"200,000\",\"297,000\",Sold\n2587,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,46,6998053,Untitled (Cowboy),PHOTOGRAPHS,Richard Prince,\"900,000\",\"700,000\",\"1,029,000\",Sold\n2588,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,47,6998054,Mount Something Or Other,PAINTINGS,Ed Ruscha,\"1,200,000\",\"800,000\",,Unsold\n2589,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,48,6998055,Hammer And Sickle,PAINTINGS,Andy Warhol,\"3,500,000\",\"2,500,000\",\"3,538,000\",Sold\n2590,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,49,6998056,Two Guys Twice,PAINTINGS,Richard Prince,\"800,000\",\"600,000\",\"693,000\",Sold\n2591,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,5,6998057,Untitled (Figure Jmb #1),PAINTINGS,Jean-Michel Basquiat,\"2,800,000\",\"2,400,000\",\"2,634,800\",Sold\n2592,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,50,6998058,African Elephant,PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",\"1,569,000\",Sold\n2593,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,51,6998059,Ero,PAINTINGS,Jean-Michel Basquiat,\"2,000,000\",\"1,500,000\",\"2,409,000\",Sold\n2594,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,52,6998060,Pink Circus: Embrace Peace And Darkness Within Thy Heart,PAINTINGS,Takashi Murakami,\"800,000\",\"600,000\",\"849,000\",Sold\n2595,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,53,6998061,Marilyn Monroe (Marilyn),PRINTS,Andy Warhol,\"1,500,000\",\"1,000,000\",\"2,349,000\",Sold\n2596,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,54,6998062,Bacchus At The Seaside,PAINTINGS,Banksy,\"350,000\",\"250,000\",\"669,000\",Sold\n2597,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,55,6998063,\"Sometimes Life Is Really, Really, Really Dark\",PAINTINGS,Damien Hirst,\"500,000\",\"300,000\",\"309,000\",Sold\n2598,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,56,6998064,Study To Homage To The Square: Evident,PAINTINGS,Josef Albers,\"650,000\",\"450,000\",\"429,000\",Sold\n2599,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,57,6998065,\"Untitled (July 23, 1988)\",TEXTILES,Alighiero e Boetti,\"350,000\",\"250,000\",\"381,000\",Sold\n2600,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,58,6998066,Incontri E Scontri (Meetings And Collisions),WORKS ON PAPER,Alighiero e Boetti,\"300,000\",\"200,000\",\"345,000\",Sold\n2601,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,59,6998067,\"Concetto Spaziale, Attese\",PAINTINGS,Lucio Fontana,\"1,200,000\",\"800,000\",\"1,329,000\",Sold\n2602,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,6,6998068,Wind,PAINTINGS,Gerhard Richter,\"3,500,000\",\"2,500,000\",\"3,086,400\",Sold\n2603,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,60,6998069,Three White Dots On Orange Stack,SCULPTURE,Alexander Calder,\"650,000\",\"450,000\",,Unsold\n2604,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,61,6998070,Untitled Film Still #57,PHOTOGRAPHS,Cindy Sherman,\"300,000\",\"200,000\",\"225,000\",Sold\n2605,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,7,6998071,Autounfall (Car Crash) Or The Three Sisters,PAINTINGS,Sigmar Polke,\"2,000,000\",\"1,500,000\",\"2,169,000\",Sold\n2606,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,8,6998072,1025 Farben,PAINTINGS,Gerhard Richter,\"7,000,000\",\"5,000,000\",\"7,376,600\",Sold\n2607,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,9,6998073,Loving You,PAINTINGS,Damien Hirst,\"1,200,000\",\"800,000\",\"993,000\",Sold\n2608,GBP,3/8/2018,23,20th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,1,6998817,Bright Moments: For R.R. Kirk,PAINTINGS,Jack Whitten,\"300,000\",\"200,000\",\"333,000\",Sold\n2609,GBP,3/8/2018,23,21st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,2,6998818,Politics,PAINTINGS,Lynette Yiadom Boakye,\"90,000\",\"70,000\",\"273,000\",Sold\n2610,GBP,3/8/2018,23,22nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,3,6998819,Greifbar 27,PHOTOGRAPHS,Wolfgang Tillmans,\"300,000\",\"200,000\",\"465,000\",Sold\n2611,GBP,3/8/2018,23,23rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,4,6998820,The Man,PHOTOGRAPHS,Gilbert and George,\"500,000\",\"300,000\",\"321,000\",Sold\n2612,GBP,3/8/2018,23,24th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,5,6998821,T-riffic,PAINTINGS,Allen Jones,\"350,000\",\"250,000\",\"705,000\",Sold\n2613,GBP,3/8/2018,23,25th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,6,6998822,Family Group,SCULPTURE,Henry Moore,\"350,000\",\"250,000\",\"93,000\",Sold\n2614,GBP,3/8/2018,23,26th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,7,6998823,Piccolo Cavaliere,SCULPTURE,Marino Marini,\"600,000\",\"400,000\",\"429,000\",Sold\n2615,GBP,3/8/2018,23,27th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,8,6998824,Two Three-Quarter Figures on Base,SCULPTURE,Henry Moore,\"600,000\",\"400,000\",\"729,000\",Sold\n2616,GBP,3/8/2018,23,28th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,9,6998825,Nu allongé I (Aurore),SCULPTURE,Henri Matisse,\"7,000,000\",\"5,000,000\",\"14,859,000\",Sold\n2617,GBP,3/8/2018,23,29th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,10,6998826,La Dormeuse,PAINTINGS,Pablo Picasso,\"18,000,000\",\"12,000,000\",\"41,859,000\",Sold\n2618,GBP,3/8/2018,23,30th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,11,6998827,Small Naked Portrait,PAINTINGS,Lucian Freud,\"600,000\",\"400,000\",\"789,000\",Sold\n2619,GBP,3/8/2018,23,31st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,12,6998828,Untitled,WORKS ON PAPER,Cy Twombly,\"700,000\",\"500,000\",\"609,000\",Sold\n2620,GBP,3/8/2018,23,32nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,13,6998829,The Pilgrim,PAINTINGS,Marlene Dumas,\"2,000,000\",\"1,500,000\",\"1,749,000\",Sold\n2621,GBP,3/8/2018,23,33rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,14,6998830,Helter Skelter I,PAINTINGS,Mark Bradford,\"8,000,000\",\"6,000,000\",\"8,671,500\",Sold\n2622,GBP,3/8/2018,23,34th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,15,6998831,Singing in the Rain,PAINTINGS,Luc Tuymans,\"700,000\",\"500,000\",\"729,000\",Sold\n2623,GBP,3/8/2018,23,35th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,16,6998832,Untitled,PAINTINGS,Albert Oehlen,\"350,000\",\"250,000\",\"345,000\",Sold\n2624,GBP,3/8/2018,23,36th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,17,6998833,P.D. Idol,PAINTINGS,Georg Baselitz,\"2,000,000\",\"1,500,000\",\"1,929,000\",Sold\n2625,GBP,3/8/2018,23,37th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,18,6998834,Die Meistersinger,PAINTINGS,Anselm Kiefer,\"2,000,000\",\"1,500,000\",\"1,809,000\",Sold\n2626,GBP,3/8/2018,23,38th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,19,6998835,Untitled,PAINTINGS,Sigmar Polke,\"1,200,000\",\"800,000\",\"969,000\",Sold\n2627,GBP,3/8/2018,23,39th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,20,6998836,Omegasachen-35,PAINTINGS,Albert Oehlen,\"600,000\",\"400,000\",\"465,000\",Sold\n2628,GBP,3/8/2018,23,40th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,21,6998837,Untitled,SCULPTURE,Rudolf Stingel,\"6,000,000\",\"4,000,000\",\"5,746,500\",Sold\n2629,GBP,3/8/2018,23,41st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,22,6998838,Monochrome rose sans titre (MP 27),WORKS ON PAPER,Yves Klein,\"1,200,000\",\"800,000\",\"1,029,000\",Sold\n2630,GBP,3/8/2018,23,42nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,23,6998839,Bridlington Violets,PAINTINGS,David Hockney,\"700,000\",\"500,000\",\"609,000\",Sold\n2631,GBP,3/8/2018,23,43rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,24,6998840,Woman & Man,PAINTINGS,George Condo,\"800,000\",\"600,000\",\"1,329,000\",Sold\n2632,GBP,3/8/2018,23,44th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,25,6998841,Untitled #220,PHOTOGRAPHS,Cindy Sherman,\"200,000\",\"150,000\",\"175,000\",Sold\n2633,GBP,3/8/2018,23,45th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,26,6998842,Untitled,PAINTINGS,Jean-Michel Basquiat,\"1,500,000\",\"1,000,000\",\"1,089,000\",Sold\n2634,GBP,3/8/2018,23,46th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,27,6998843,Profil Genre Aztèque,PAINTINGS,Jean Dubuffet,\"1,800,000\",\"1,200,000\",\"1,449,000\",Sold\n2635,GBP,3/8/2018,23,47th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,28,6998844,Colombe blanche,PAINTINGS,Max Ernst,\"500,000\",\"350,000\",\"393,000\",Sold\n2636,GBP,3/8/2018,23,48th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,29,6998845,Le Surréalisme et la peinture,WORKS ON PAPER,Max Ernst,\"450,000\",\"350,000\",\"429,000\",Sold\n2637,GBP,3/8/2018,23,49th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,30,6998846,Ohne Titel,WORKS ON PAPER,Wassily Kandinsky,\"550,000\",\"350,000\",,Unsold\n2638,GBP,3/8/2018,23,50th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,31,6998847,Feuille de vigne femelle,SCULPTURE,Marcel Duchamp,\"400,000\",\"300,000\",\"501,000\",Sold\n2639,GBP,3/8/2018,23,51st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,32,6998848,Diamond Dust Shoes (Pink),PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",,Unsold\n2640,GBP,3/8/2018,23,52nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,33,6998849,Diamond Dust Shoes (Green),PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",,Unsold\n2641,GBP,3/8/2018,23,53rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,34,6998850,\"Beautiful, Half a Red Headless Centipede (Painting)\",PAINTINGS,Damien Hirst,\"350,000\",\"250,000\",\"273,000\",Sold\n2642,GBP,3/8/2018,23,54th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,35,6998851,Grey Red,PAINTINGS,Sean Scully,\"800,000\",\"600,000\",\"1,149,000\",Sold\n2643,GBP,3/8/2018,23,55th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,36,6998852,\"Peinture 130 x 162 cm, 12 mai 1965\",PAINTINGS,Pierre Soulages,\"1,200,000\",\"800,000\",\"933,000\",Sold\n2644,GBP,3/8/2018,23,56th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,37,6998853,Cuadro 128,PAINTINGS,Manolo Millares,\"150,000\",\"100,000\",\"206,250\",Sold\n2645,GBP,3/8/2018,23,57th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,38,6998854,Tôle,WORKS ON PAPER,Raymond Hains,\"200,000\",\"150,000\",,Unsold\n2646,GBP,3/8/2018,23,58th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,39,6998855,Opalka 1965/1 – ∞ Detail 5210331 - 5226270,PAINTINGS,Roman Opalka,\"600,000\",\"400,000\",,Unsold\n2647,GBP,3/8/2018,23,59th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,40,6998856,Superficie bianca,PAINTINGS,Enrico Castellani,\"350,000\",\"250,000\",\"267,000\",Sold\n2648,GBP,3/8/2018,23,60th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,41,6998857,\"Concetto spaziale, Attese\",PAINTINGS,Lucio Fontana,\"850,000\",\"650,000\",\"669,000\",Sold\n2649,GBP,3/8/2018,23,61st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,42,6998858,Métal sur le Losange,SCULPTURE,Jesus Rafael Soto,\"250,000\",\"180,000\",\"321,000\",Sold\n2650,GBP,3/8/2018,23,62nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,43,6998859,Reihung,WORKS ON PAPER,Gunther Uecker,\"350,000\",\"250,000\",\"249,000\",Sold\n2651,GBP,3/8/2018,23,63rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,44,6998860,R72-73-M-14,PAINTINGS,Jan Schoonhoven,\"350,000\",\"250,000\",\"441,000\",Sold\n2652,GBP,3/8/2018,23,64th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,45,6998861,Concetto spaziale,PAINTINGS,Lucio Fontana,\"700,000\",\"500,000\",,Unsold\n2653,GBP,3/8/2018,23,65th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,46,6998862,Homage to the Square,PAINTINGS,Josef Albers,\"500,000\",\"300,000\",\"381,000\",Sold\n2654,GBP,3/8/2018,23,66th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,47,6998863,Found,PAINTINGS,Adrian Ghenie,\"500,000\",\"300,000\",\"249,000\",Sold\n2655,GBP,3/8/2018,23,67th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,48,6998864,Without You,SCULPTURE,Damien Hirst,\"700,000\",\"500,000\",\"585,000\",Sold\n2656,GBP,3/8/2018,23,68th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,49,6998865,Apparition,PAINTINGS,Damien Hirst,\"700,000\",\"500,000\",\"609,000\",Sold\n2657,GBP,3/8/2018,23,69th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,50,6998866,Skulldiver II,PAINTINGS,Cecily Brown,\"600,000\",\"400,000\",\"513,000\",Sold" }, { "path": "80a-artwork/AuctionsData - set1.csv", "content": "RecID,AuctionCurrency,AuctionDate,AuctionID,Indicator-Day-Afternoon,Auction Title,Auction House,AuctionLocation,lot LotNo,artwork ID,Artwork Title,ArtworkCategory,Artist_Full_Name,lot_EstimateHigh,lot_EstimateLow,Sale Price,Lot Status\n288822,,,,,,,,0,,,,,191810000,137080000,207017950,\nnumeric,character,character,numeric,character,character,character,character,numeric,numeric,character,factor,character,numeric,numeric,numeric,factor\nrecid,auccur,aucdate,aucid,idafn,auctitle,auchouse,aucloc,lotno,awid,awtitle,awcat,arname,lotesthigh,lotestlow,saleprice,lotstatus\n2547,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,1,6998013,Untitled,PAINTINGS,Christopher Wool,\"600,000\",\"400,000\",\"729,000\",Sold\n2548,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,10,6998014,Angel Of The North,SCULPTURE,Antony Gormley,\"2,000,000\",\"1,500,000\",\"2,860,600\",Sold\n2549,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,11,6998015,Some People (Welcome Series),PAINTINGS,Hurvin Anderson,\"800,000\",\"600,000\",\"1,029,000\",Sold\n2550,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,12,6998016,The Architect's Home In The Ravine,PAINTINGS,Peter Doig,\"18,000,000\",\"14,000,000\",\"14,376,400\",Sold\n2551,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,13,6998017,A Swan Comforting A Snake,PAINTINGS,Cecily Brown,\"1,500,000\",\"1,000,000\",\"1,209,000\",Sold\n2552,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,14,6998018,Eifrau Die Man Nicht Schubladieren Kann (Egg Lady Who Can’t Be Piegeonholed),PAINTINGS,Martin Kippenberger,\"3,000,000\",\"2,000,000\",\"2,169,000\",Sold\n2553,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,15,6998019,Memory Ware Flat #29,PAINTINGS,Mike Kelley,\"2,000,000\",\"1,500,000\",\"1,689,000\",Sold\n2554,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,16,6998020,Yellow Cell With Triple Conduit,PAINTINGS,Peter Halley,\"350,000\",\"250,000\",\"513,000\",Sold\n2555,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,17,6998021,Untitled,SCULPTURE,Richard Prince,\"1,500,000\",\"1,000,000\",\"2,049,000\",Sold\n2556,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,18,6998022,Nov. 8,PAINTINGS,On Kawara,\"800,000\",\"600,000\",\"789,000\",Sold\n2557,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,19,6998023,Untitled,PAINTINGS,Rudolf Stingel,\"6,000,000\",\"4,000,000\",\"4,667,000\",Sold\n2558,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,2,6998024,Untitled,PAINTINGS,Laura Owens,\"350,000\",\"250,000\",\"435,000\",Sold\n2559,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,20,6998025,Amanti (Lovers),PAINTINGS,Michelangelo Pistoletto,\"3,000,000\",\"2,300,000\",\"2,634,800\",Sold\n2560,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,21,6998026,Tenda Blu (Blue Curtain),PAINTINGS,Michelangelo Pistoletto,\"600,000\",\"400,000\",\"621,000\",Sold\n2561,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,22,6998027,Figura Femminile Con Fiori (Female Figure With Flowers),SCULPTURE,Lucio Fontana,\"2,000,000\",\"1,500,000\",\"1,809,000\",Sold\n2562,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,23,6998028,\"Oiks, Tarts, Wierdoes And Contemporary Art\",SCULPTURE,Grayson Perry,\"120,000\",\"80,000\",\"175,000\",Sold\n2563,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,24,6998029,Border Country,PAINTINGS,Peter Doig,\"700,000\",\"500,000\",\"609,000\",Sold\n2564,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,25,6998030,In Central Park,PAINTINGS,Howard Hodgkin,\"800,000\",\"600,000\",,Unsold\n2565,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,26,6998031,\"Different Kinds Of Water Pouring Into A Swimming Pool, Santa Monica\",PAINTINGS,David Hockney,\"8,000,000\",\"6,000,000\",,Unsold\n2566,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,27,6998032,\"Concetto Spaziale, Attese\",PAINTINGS,Lucio Fontana,\"3,000,000\",\"2,000,000\",\"5,005,700\",Sold\n2567,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,28,6998033,Gelbgrun (Yellow-green),PAINTINGS,Gerhard Richter,\"10,000,000\",\"7,000,000\",\"10,876,500\",Sold\n2568,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,29,6998034,Untitled (19.3.86),PAINTINGS,Gerhard Richter,\"900,000\",\"700,000\",\"849,000\",Sold\n2569,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,3,6998035,Untitled,PAINTINGS,Albert Oehlen,\"1,500,000\",\"1,000,000\",\"1,929,000\",Sold\n2570,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,30,6998036,Kleine Geister (Little Ghosts),SCULPTURE,Thomas Schutte,\"450,000\",\"350,000\",\"405,000\",Sold\n2571,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,31,6998037,Alphabet (U),SCULPTURE,Danh Vo,\"200,000\",\"150,000\",,Unsold\n2572,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,32,6998038,After Micro W #2,PAINTINGS,Sam Gilliam,\"200,000\",\"150,000\",\"200,000\",Sold\n2573,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,33,6998039,Untitled,SCULPTURE,Francesco Lo Savio,\"350,000\",\"250,000\",\"309,000\",Sold\n2574,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,34,6998040,Edera (Ivy),SCULPTURE,Luciano Fabro,\"700,000\",\"500,000\",\"609,000\",Sold\n2575,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,35,6998041,Sandinsel (Sand Island),SCULPTURE,Gunther Uecker,\"800,000\",\"600,000\",\"753,000\",Sold\n2576,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,36,6998042,Untitled (Relief No. 348),SCULPTURE,Sergio de Camargo,\"900,000\",\"700,000\",,Unsold\n2577,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,37,6998043,Infinity Nets (Oq4),PAINTINGS,Yayoi Kusama,\"1,200,000\",\"800,000\",\"1,269,000\",Sold\n2578,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,38,6998044,Untitled,PAINTINGS,Rudolf Stingel,\"2,000,000\",\"1,500,000\",\"1,869,000\",Sold\n2579,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,39,6998045,Caribou Skull,SCULPTURE,Sherrie Levine,\"600,000\",\"400,000\",\"489,000\",Sold\n2580,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,4,6998046,Untitled,PAINTINGS,Christopher Wool,\"6,200,000\",\"4,200,000\",\"10,424,900\",Sold\n2581,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,40,6998047,The Housewife And The Grocer,PAINTINGS,Richard Prince,\"1,800,000\",\"1,200,000\",\"1,269,000\",Sold\n2582,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,41,6998048,O.t. (Death’s Heads),SCULPTURE,Rosemarie Trockel,\"600,000\",\"400,000\",\"465,000\",Sold\n2583,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,42,6998049,Down Under,SCULPTURE,Jason Rhoades,\"550,000\",\"350,000\",\"345,000\",Sold\n2584,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,43,6998050,Untitled,PAINTINGS,Albert Oehlen,\"450,000\",\"350,000\",\"429,000\",Sold\n2585,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,44,6998051,Different From Case To Case,PAINTINGS,Sigmar Polke,\"600,000\",\"400,000\",\"753,000\",Sold\n2586,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,45,6998052,Greifbar 40,PHOTOGRAPHS,Wolfgang Tillmans,\"300,000\",\"200,000\",\"297,000\",Sold\n2587,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,46,6998053,Untitled (Cowboy),PHOTOGRAPHS,Richard Prince,\"900,000\",\"700,000\",\"1,029,000\",Sold\n2588,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,47,6998054,Mount Something Or Other,PAINTINGS,Ed Ruscha,\"1,200,000\",\"800,000\",,Unsold\n2589,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,48,6998055,Hammer And Sickle,PAINTINGS,Andy Warhol,\"3,500,000\",\"2,500,000\",\"3,538,000\",Sold\n2590,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,49,6998056,Two Guys Twice,PAINTINGS,Richard Prince,\"800,000\",\"600,000\",\"693,000\",Sold\n2591,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,5,6998057,Untitled (Figure Jmb #1),PAINTINGS,Jean-Michel Basquiat,\"2,800,000\",\"2,400,000\",\"2,634,800\",Sold\n2592,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,50,6998058,African Elephant,PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",\"1,569,000\",Sold\n2593,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,51,6998059,Ero,PAINTINGS,Jean-Michel Basquiat,\"2,000,000\",\"1,500,000\",\"2,409,000\",Sold\n2594,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,52,6998060,Pink Circus: Embrace Peace And Darkness Within Thy Heart,PAINTINGS,Takashi Murakami,\"800,000\",\"600,000\",\"849,000\",Sold\n2595,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,53,6998061,Marilyn Monroe (Marilyn),PRINTS,Andy Warhol,\"1,500,000\",\"1,000,000\",\"2,349,000\",Sold\n2596,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,54,6998062,Bacchus At The Seaside,PAINTINGS,Banksy,\"350,000\",\"250,000\",\"669,000\",Sold\n2597,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,55,6998063,\"Sometimes Life Is Really, Really, Really Dark\",PAINTINGS,Damien Hirst,\"500,000\",\"300,000\",\"309,000\",Sold\n2598,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,56,6998064,Study To Homage To The Square: Evident,PAINTINGS,Josef Albers,\"650,000\",\"450,000\",\"429,000\",Sold\n2599,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,57,6998065,\"Untitled (July 23, 1988)\",TEXTILES,Alighiero e Boetti,\"350,000\",\"250,000\",\"381,000\",Sold\n2600,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,58,6998066,Incontri E Scontri (Meetings And Collisions),WORKS ON PAPER,Alighiero e Boetti,\"300,000\",\"200,000\",\"345,000\",Sold\n2601,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,59,6998067,\"Concetto Spaziale, Attese\",PAINTINGS,Lucio Fontana,\"1,200,000\",\"800,000\",\"1,329,000\",Sold\n2602,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,6,6998068,Wind,PAINTINGS,Gerhard Richter,\"3,500,000\",\"2,500,000\",\"3,086,400\",Sold\n2603,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,60,6998069,Three White Dots On Orange Stack,SCULPTURE,Alexander Calder,\"650,000\",\"450,000\",,Unsold\n2604,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,61,6998070,Untitled Film Still #57,PHOTOGRAPHS,Cindy Sherman,\"300,000\",\"200,000\",\"225,000\",Sold\n2605,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,7,6998071,Autounfall (Car Crash) Or The Three Sisters,PAINTINGS,Sigmar Polke,\"2,000,000\",\"1,500,000\",\"2,169,000\",Sold\n2606,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,8,6998072,1025 Farben,PAINTINGS,Gerhard Richter,\"7,000,000\",\"5,000,000\",\"7,376,600\",Sold\n2607,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,9,6998073,Loving You,PAINTINGS,Damien Hirst,\"1,200,000\",\"800,000\",\"993,000\",Sold\n2608,GBP,3/8/2018,23,20th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,1,6998817,Bright Moments: For R.R. Kirk,PAINTINGS,Jack Whitten,\"300,000\",\"200,000\",\"333,000\",Sold\n2609,GBP,3/8/2018,23,21st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,2,6998818,Politics,PAINTINGS,Lynette Yiadom Boakye,\"90,000\",\"70,000\",\"273,000\",Sold\n2610,GBP,3/8/2018,23,22nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,3,6998819,Greifbar 27,PHOTOGRAPHS,Wolfgang Tillmans,\"300,000\",\"200,000\",\"465,000\",Sold\n2611,GBP,3/8/2018,23,23rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,4,6998820,The Man,PHOTOGRAPHS,Gilbert and George,\"500,000\",\"300,000\",\"321,000\",Sold\n2612,GBP,3/8/2018,23,24th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,5,6998821,T-riffic,PAINTINGS,Allen Jones,\"350,000\",\"250,000\",\"705,000\",Sold\n2613,GBP,3/8/2018,23,25th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,6,6998822,Family Group,SCULPTURE,Henry Moore,\"350,000\",\"250,000\",\"93,000\",Sold\n2614,GBP,3/8/2018,23,26th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,7,6998823,Piccolo Cavaliere,SCULPTURE,Marino Marini,\"600,000\",\"400,000\",\"429,000\",Sold\n2615,GBP,3/8/2018,23,27th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,8,6998824,Two Three-Quarter Figures on Base,SCULPTURE,Henry Moore,\"600,000\",\"400,000\",\"729,000\",Sold\n2616,GBP,3/8/2018,23,28th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,9,6998825,Nu allongé I (Aurore),SCULPTURE,Henri Matisse,\"7,000,000\",\"5,000,000\",\"14,859,000\",Sold\n2617,GBP,3/8/2018,23,29th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,10,6998826,La Dormeuse,PAINTINGS,Pablo Picasso,\"18,000,000\",\"12,000,000\",\"41,859,000\",Sold\n2618,GBP,3/8/2018,23,30th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,11,6998827,Small Naked Portrait,PAINTINGS,Lucian Freud,\"600,000\",\"400,000\",\"789,000\",Sold\n2619,GBP,3/8/2018,23,31st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,12,6998828,Untitled,WORKS ON PAPER,Cy Twombly,\"700,000\",\"500,000\",\"609,000\",Sold\n2620,GBP,3/8/2018,23,32nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,13,6998829,The Pilgrim,PAINTINGS,Marlene Dumas,\"2,000,000\",\"1,500,000\",\"1,749,000\",Sold\n2621,GBP,3/8/2018,23,33rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,14,6998830,Helter Skelter I,PAINTINGS,Mark Bradford,\"8,000,000\",\"6,000,000\",\"8,671,500\",Sold\n2622,GBP,3/8/2018,23,34th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,15,6998831,Singing in the Rain,PAINTINGS,Luc Tuymans,\"700,000\",\"500,000\",\"729,000\",Sold\n2623,GBP,3/8/2018,23,35th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,16,6998832,Untitled,PAINTINGS,Albert Oehlen,\"350,000\",\"250,000\",\"345,000\",Sold\n2624,GBP,3/8/2018,23,36th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,17,6998833,P.D. Idol,PAINTINGS,Georg Baselitz,\"2,000,000\",\"1,500,000\",\"1,929,000\",Sold\n2625,GBP,3/8/2018,23,37th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,18,6998834,Die Meistersinger,PAINTINGS,Anselm Kiefer,\"2,000,000\",\"1,500,000\",\"1,809,000\",Sold\n2626,GBP,3/8/2018,23,38th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,19,6998835,Untitled,PAINTINGS,Sigmar Polke,\"1,200,000\",\"800,000\",\"969,000\",Sold\n2627,GBP,3/8/2018,23,39th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,20,6998836,Omegasachen-35,PAINTINGS,Albert Oehlen,\"600,000\",\"400,000\",\"465,000\",Sold\n2628,GBP,3/8/2018,23,40th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,21,6998837,Untitled,SCULPTURE,Rudolf Stingel,\"6,000,000\",\"4,000,000\",\"5,746,500\",Sold\n2629,GBP,3/8/2018,23,41st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,22,6998838,Monochrome rose sans titre (MP 27),WORKS ON PAPER,Yves Klein,\"1,200,000\",\"800,000\",\"1,029,000\",Sold\n2630,GBP,3/8/2018,23,42nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,23,6998839,Bridlington Violets,PAINTINGS,David Hockney,\"700,000\",\"500,000\",\"609,000\",Sold\n2631,GBP,3/8/2018,23,43rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,24,6998840,Woman & Man,PAINTINGS,George Condo,\"800,000\",\"600,000\",\"1,329,000\",Sold\n2632,GBP,3/8/2018,23,44th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,25,6998841,Untitled #220,PHOTOGRAPHS,Cindy Sherman,\"200,000\",\"150,000\",\"175,000\",Sold\n2633,GBP,3/8/2018,23,45th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,26,6998842,Untitled,PAINTINGS,Jean-Michel Basquiat,\"1,500,000\",\"1,000,000\",\"1,089,000\",Sold\n2634,GBP,3/8/2018,23,46th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,27,6998843,Profil Genre Aztèque,PAINTINGS,Jean Dubuffet,\"1,800,000\",\"1,200,000\",\"1,449,000\",Sold\n2635,GBP,3/8/2018,23,47th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,28,6998844,Colombe blanche,PAINTINGS,Max Ernst,\"500,000\",\"350,000\",\"393,000\",Sold\n2636,GBP,3/8/2018,23,48th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,29,6998845,Le Surréalisme et la peinture,WORKS ON PAPER,Max Ernst,\"450,000\",\"350,000\",\"429,000\",Sold\n2637,GBP,3/8/2018,23,49th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,30,6998846,Ohne Titel,WORKS ON PAPER,Wassily Kandinsky,\"550,000\",\"350,000\",,Unsold\n2638,GBP,3/8/2018,23,50th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,31,6998847,Feuille de vigne femelle,SCULPTURE,Marcel Duchamp,\"400,000\",\"300,000\",\"501,000\",Sold\n2639,GBP,3/8/2018,23,51st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,32,6998848,Diamond Dust Shoes (Pink),PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",,Unsold\n2640,GBP,3/8/2018,23,52nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,33,6998849,Diamond Dust Shoes (Green),PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",,Unsold\n2641,GBP,3/8/2018,23,53rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,34,6998850,\"Beautiful, Half a Red Headless Centipede (Painting)\",PAINTINGS,Damien Hirst,\"350,000\",\"250,000\",\"273,000\",Sold\n2642,GBP,3/8/2018,23,54th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,35,6998851,Grey Red,PAINTINGS,Sean Scully,\"800,000\",\"600,000\",\"1,149,000\",Sold\n2643,GBP,3/8/2018,23,55th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,36,6998852,\"Peinture 130 x 162 cm, 12 mai 1965\",PAINTINGS,Pierre Soulages,\"1,200,000\",\"800,000\",\"933,000\",Sold\n2644,GBP,3/8/2018,23,56th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,37,6998853,Cuadro 128,PAINTINGS,Manolo Millares,\"150,000\",\"100,000\",\"206,250\",Sold\n2645,GBP,3/8/2018,23,57th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,38,6998854,Tôle,WORKS ON PAPER,Raymond Hains,\"200,000\",\"150,000\",,Unsold\n2646,GBP,3/8/2018,23,58th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,39,6998855,Opalka 1965/1 – ∞ Detail 5210331 - 5226270,PAINTINGS,Roman Opalka,\"600,000\",\"400,000\",,Unsold\n2647,GBP,3/8/2018,23,59th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,40,6998856,Superficie bianca,PAINTINGS,Enrico Castellani,\"350,000\",\"250,000\",\"267,000\",Sold\n2648,GBP,3/8/2018,23,60th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,41,6998857,\"Concetto spaziale, Attese\",PAINTINGS,Lucio Fontana,\"850,000\",\"650,000\",\"669,000\",Sold\n2649,GBP,3/8/2018,23,61st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,42,6998858,Métal sur le Losange,SCULPTURE,Jesus Rafael Soto,\"250,000\",\"180,000\",\"321,000\",Sold\n2650,GBP,3/8/2018,23,62nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,43,6998859,Reihung,WORKS ON PAPER,Gunther Uecker,\"350,000\",\"250,000\",\"249,000\",Sold\n2651,GBP,3/8/2018,23,63rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,44,6998860,R72-73-M-14,PAINTINGS,Jan Schoonhoven,\"350,000\",\"250,000\",\"441,000\",Sold\n2652,GBP,3/8/2018,23,64th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,45,6998861,Concetto spaziale,PAINTINGS,Lucio Fontana,\"700,000\",\"500,000\",,Unsold\n2653,GBP,3/8/2018,23,65th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,46,6998862,Homage to the Square,PAINTINGS,Josef Albers,\"500,000\",\"300,000\",\"381,000\",Sold\n2654,GBP,3/8/2018,23,66th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,47,6998863,Found,PAINTINGS,Adrian Ghenie,\"500,000\",\"300,000\",\"249,000\",Sold\n2655,GBP,3/8/2018,23,67th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,48,6998864,Without You,SCULPTURE,Damien Hirst,\"700,000\",\"500,000\",\"585,000\",Sold\n2656,GBP,3/8/2018,23,68th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,49,6998865,Apparition,PAINTINGS,Damien Hirst,\"700,000\",\"500,000\",\"609,000\",Sold\n2657,GBP,3/8/2018,23,69th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,50,6998866,Skulldiver II,PAINTINGS,Cecily Brown,\"600,000\",\"400,000\",\"513,000\",Sold" }, { "path": "80a-artwork/artwork-cls1.R", "content": "#Artwork - Classification\n# Build Decision Tree for predicting and Classification how sale happened\n\n\nauctiondata2\nnames(auctiondata2)\nkeepcols = c('idafn', 'auchouse', 'aucloc', 'awcat', 'lotesthigh', 'lotestlow', 'saleprice','lotstatus') \ndf_cls = auctiondata2[keepcols]\nsummary(df_cls)\n\nlibrary(rpart)\nlibrary(rpart.plot)\n#model1 Anova\nclstree1 = rpart(saleprice ~ awcat + lotesthigh + lotestlow + auchouse, minbucket=5, method='anova', data=df_cls)\n#plot(clstree1)\nclstree1\nrpart.plot(clstree1)\nprp(clstree1, type=4, ni=T, fallen.leaves = T, left=T, xflip=F, varlen = 0, faclen=0)\n\n\n#model2 Anova\nclstree2 = rpart(lotstatus ~ awcat + lotesthigh , method='class', data=df_cls, control=rpart(control(cp=.2)))\n#plot(clstree)\nclstree2\nrpart.plot(clstree2)\nprp(clstree2, type=1, ni=T, fallen.leaves = T, left=T, xflip=F, varlen = 0, faclen=0)\nprintcp(clstree2)\n" }, { "path": "80a-artwork/artwork-descp.R", "content": "#Artwork 2 : Descriptive Analysis\n\nlibrary(dplyr)\nlibrary(data.table)\noptions(dplyr.width = Inf)\n\n#Numeric Data\nnumdata = auctiondata2[sapply(auctiondata2,is.numeric)]\nnumdata\n(summarise_all(numdata, funs(n(),mean,median, max, min)))\n\n?transpose\n#Factor Data\nfacdata = auctiondata2[sapply(auctiondata2,is.factor)]\nfacdata\nsummarise_all(auctiondata2[names(facdata)], funs(nlevels(.), nmiss=sum(is.na(.))))\n\n\n#Arrange Data\narrange(auctiondata2, lotstatus, desc(saleprice), awcat) %>% select(lotstatus, saleprice, awcat)\nauctiondata2 %>% select(lotstatus, saleprice, awcat) %>% arrange(lotstatus, desc(saleprice), awcat)\n\n\n\n#Select Random Rows\n(dt = sample_n(select(auctiondata2, awtitle, awcat),10))\n(dt = auctiondata2 %>% select(awtitle, awcat) %>% sample_n(10))\n\n#Group By\ngroup_by(auctiondata2, awcat)\nauctiondata2 %>% group_by(awcat) \n\n#Summaries by Groupby\n(t = summarise_at(group_by(auctiondata2, awcat), vars(lotesthigh, lotestlow, saleprice), funs(n(), max, min, mean(., na.rm = TRUE))))\n\n#The above code can also be written like\n(t = na.omit(auctiondata2) %>% group_by(awcat) %>%\n summarise_at(vars(lotesthigh, lotestlow, saleprice), funs(n(), mean(., na.rm = TRUE))))\nwrite.csv(t, 'output2.csv')\n\n#Compute within group : Top2 rows from each gp\n(t = na.omit(auctiondata2) %>% select(awcat, auchouse, arname, saleprice, lotesthigh, lotestlow) %>% filter(awcat %in% c(\"PAINTINGS\", \"PHOTOGRAPHS\")) %>% group_by(awcat, auchouse) %>% do(head( . , 2)))\nwrite.csv(t, 'output3.csv')\n\n# 3rd & 4th Max Value of each cat\n(t = auctiondata2 %>% select(awcat, auchouse, saleprice) %>%\nfilter(awcat %in% c(\"PAINTINGS\", \"PHOTOGRAPHS\")) %>%\n group_by(awcat) %>%\n do(arrange(.,desc(saleprice))) %>% slice(3:4))\n\n\n# Rank for variables\n\nmutate_at(auctiondata2, vars(lotesthigh:saleprice), funs(Rank=min_rank(.))) %>% select(recid, lotesthigh_Rank, lotestlow_Rank, saleprice, saleprice_Rank ) %>% arrange(desc(saleprice))\n#min_rank() assigns 1 to the smallest value and high number to the largest value\n\nmutate_at(auctiondata2, vars(lotesthigh:saleprice), funs(Rank=min_rank(desc(.)))) %>% select(recid, lotesthigh_Rank, lotestlow_Rank, saleprice, saleprice_Rank ) %>% arrange(desc(saleprice))\n\n#Category which generated max saleprice and its artist name\nauctiondata2 %>% group_by(awcat) %>% filter(min_rank(desc(saleprice)) == 1) %>% select(recid, awcat, arname, saleprice)\n\n\n\n#Select Varaibles\nselect(auctiondata2, starts_with(\"l\"))\nselect(auctiondata2, -starts_with(\"a\"), -starts_with(\"l\"))\nnames(auctiondata2)\nselect(auctiondata2, contains(\"lot\"))\n\nselect(auctiondata2, saleprice, everything()) %>% select(saleprice, awcat)\n\nfilter(auctiondata2, awcat == \"PAINTINGS\") %>% select(saleprice, awcat)\nfilter(auctiondata2, !awcat == \"PAINTINGS\") %>% select(saleprice, awcat)\nfilter(auctiondata2, awcat %in% c(\"PAINTINGS\", \"PHOTOGRAPHS\") & saleprice >= 10000000 ) %>% select(saleprice, awcat, awtitle)\nfilter(auctiondata2, awcat %in% c(\"PAINTINGS\", \"PHOTOGRAPHS\") |saleprice >= 10000000 ) %>% select(saleprice, awcat, awtitle)\n\nfilter(auctiondata2, grepl(\"Art\", auctitle)) %>% select(awcat, auctitle)\n\n\nauctiondata2 %>% if_else(saleprice < lotesthigh, \"SP > Est High\", \"SP < Est High\", missing = \"missing value\")\n\n#if else\ndf <- c(-10,2, NA)\nif_else(df < 0, \"negative\", \"positive\", missing = \"missing value\")\n\n\nauctiondata2 %>% group_by(auchouse) %>%\n summarise(Pecentile_25=quantile(saleprice, probs=0.25, na.rm = TRUE),\n Pecentile_50=quantile(saleprice, probs=0.5, na.rm = TRUE),\n Pecentile_75=quantile(saleprice, probs=0.75, na.rm = TRUE),\n Pecentile_99=quantile(saleprice, probs=0.99, na.rm = TRUE))\n\nauctiondata2 %>% filter(!is.na(saleprice)) %>% summarise(Pecentile_25=quantile(saleprice, probs=0.25))\n\n\n\n#Max in Lot High, Low Low, SP\nauctiondata2 %>%\n rowwise() %>% mutate(Max= max(lotesthigh:lotestlow)) %>%\n select(aucid, awcat, lotesthigh:lotestlow,Max)\n\n" }, { "path": "80a-artwork/artwork-eda1.R", "content": "# Artwork EDA1\noptions(scipen = 999999999)\nhead(auctiondata2)\n\n#Check the data\nhead(auctiondata2)\n\n#Descriptive Analysis\n\n#Missing values\nsum(is.na(auctiondata2))\ncolSums(is.na(auctiondata2))\nrowSums(is.na(auctiondata2))\nauctiondata2[complete.cases(auctiondata2),][c('recid','awid','lotstatus')]\n\n\n#Set Numeric Columns\nnumcols = c('lotesthigh','lotestlow', 'saleprice') \n\n#Describe the data set\nlibrary(Hmisc)\nHmisc::describe(auctiondata2)\n\nHmisc::describe(auctiondata2[,c(numcols)])\n\nlibrary(pastecs)\npastecs::stat.desc(auctiondata2) \n\nlibrary(psych) #describe by group\npsych::describe.by(auctiondata2, auctiondata2$auchouse)\n\nnames(auctiondata2)\nboxplot(auctiondata2[,c('saleprice')])\n#outliers / Extreme values\nboxplot(auctiondata2[,numcols], col=1:3)\nylim= range(auctiondata2$saleprice, na.rm=T)\nboxplot(auctiondata2[,numcols], col=8:5, ylim=c(90000,4000000))\nabline(h=median(auctiondata2$lotesthigh))\n\n\n# Table\n# Count Artwork Cat vs Auction House\n(t1= table( auctiondata2$awcat, auctiondata2$auchouse))\n(mt1= addmargins(t1,c(1,2), FUN= list(sum)))\nprop.table(table( auctiondata2$awcat, auctiondata2$auchouse))\n\n#Table Summary/ Count - Status - Category - Auction House\ntable( auctiondata2$awcat, auctiondata2$auchouse, auctiondata2$lotstatus )\n\n\n\n#Artists\nlength(unique(auctiondata2$arname))\n" }, { "path": "80a-artwork/artwork-eda2.R", "content": "# Artwork - EDA2\n\n#Aggregations\nnames(auctiondata2)\nauctiondata2[c('recid','lotno')]\nlength(unique(auctiondata2$lotno))\naggregate( recid ~ lotno, data=auctiondata2, FUN=length)\n\n#One lot could have more than 1 recid\n\nnames(auctiondata2)\n#Average Sale on each day\n(ag1a = aggregate( saleprice ~ aucdate, data= auctiondata2, FUN=sum))\n(ag1b = aggregate( saleprice ~ aucdate, data = auctiondata2, FUN = function(x) c(mean = mean(x), (sum= sum(x)), (max=max(x)), (min=min(x)), (numbers = length(x)) ) ))\n \n#Artwork Category Total Sales in 2 days\n\n(ag2a = aggregate(saleprice ~ awcat , data=auctiondata2, FUN=sum))\nag2a = ag2a[ order(ag2a$saleprice),]\n(sp = round(ag2a$saleprice/ sum(ag2a$saleprice)*100,1))\npie(ag2a$saleprice, labels=paste0(ag2a$awcat,\"\\n\", sp, \" %\"), radius=1.1) \n\n\n(ag2b = aggregate(saleprice ~ awcat + auchouse, data=auctiondata2, FUN=sum))\n(ag2b = ag2b[ order(ag2a$saleprice),])\n\n\nnames(ag2b)\n\n\n(g1 = ggplot(ag2b, aes(x=1,y=saleprice,fill=awcat)) + \n geom_bar(stat=\"identity\",width=2) + coord_polar(theta='y')+\n theme(axis.ticks=element_blank(), axis.title=element_blank(),axis.text.y = element_blank(),axis.text.x = element_blank(), panel.grid = element_blank())) + facet_wrap(~ auchouse) \n\n\n\n#Compare via Barplots\n(ag3 = aggregate(saleprice ~ awcat + auchouse, data=auctiondata2, FUN=sum))\n\n# Stacked barplot with multiple groups\nggplot(data=ag3, aes(x=awcat, fill=auchouse, y=saleprice)) +\n geom_bar(stat=\"identity\")\n# Use position=position_dodge()\nggplot(data=ag3, aes(x=awcat, fill=auchouse, y=saleprice)) +\n geom_bar(stat=\"identity\", position=position_dodge())\n\n\nggplot(data=ag3, aes(x=awcat, fill=auchouse, y=saleprice)) +\n geom_bar(stat=\"identity\", position=position_dodge()) + coord_flip()\n\n#Bubble Chart\nrange(auctiondata2$lotesthigh)\n#ggplot(data=na.omit(auctiondata2), aes(x=lotestlow, y=lotesthigh)) + geom_point(aes(size=saleprice)) +\n scale_size_continuous(range=c(80000,20000000)) +\n theme(legend.position = \"none\")\n\nna.omit(auctiondata2) %>% ggplot(aes(x=lotestlow, y=lotesthigh, size=saleprice, colour = awcat, shape = auchouse)) + geom_point(alpha = 0.7, position = position_jitter()) \n\ng1a = stat_smooth(method = \"lm\")\n\n\n\n#Box Plot\nggplot(subset(na.omit(auctiondata2), lotstatus=='Sold'), aes(x = awcat, y = lotestlow , group=auchouse)) + geom_boxplot()\n\nggplot(subset(auctiondata2, !is.na(saleprice)), aes(x = awcat, y = saleprice)) + geom_boxplot()\n\nggplot(subset(auctiondata2, !is.na(saleprice)), aes(x = awcat, y = saleprice)) + geom_point(alpha=0.3, color=\"tomato\", position = \"jitter\") + geom_boxplot(alpha=0) + coord_flip()\n\n\nggplot(subset(auctiondata2, !is.na(saleprice)) , aes(x=auchouse, y=saleprice)) + geom_point(alpha=0.3, color=\"tomato\", position = \"jitter\") + geom_boxplot(alpha=0) + coord_flip() + facet_wrap( ~ awcat, scales = \"free\")\n\n# expand_limits(pred=range_act, value=range_act) \nscales = \"free_x\"\n\n\n\nnames(auctiondata2)\n\n\n#Artists whose art fetch more value : mean\nlibrary(dplyr) \nauctiondata2 %>%\n group_by(arname) %>%\n summarize(mean_sale = mean(saleprice, na.rm = TRUE),\n max_price = max(saleprice, na.rm = TRUE), \n nos= length(awid)) %>%\n arrange(desc(mean_sale), desc(nos), arname)\n\n\n#Difference with Max and Min Lot estimate of SalePrice\nauctiondata2 %>%\n filter(lotstatus == 'Sold') %>% \n select(recid, awid, awcat, lotesthigh, lotestlow, saleprice, lotstatus ) \ndiffwithSP = auctiondata2[auctiondata2$lotstatus =='Sold', c('awid', 'awcat', 'lotesthigh', 'lotestlow', 'saleprice', 'lotstatus')]\ndiffwithSP$diffmax = diffwithSP$saleprice - diffwithSP$lotesthigh \ndiffwithSP$diffmin = diffwithSP$saleprice - diffwithSP$lotestlow #should b +ve\nhead(diffwithSP)\ndiffwithSP\n\n#Items sold below LowEst\ndiffwithSP[ diffwithSP$diffmin < 0,][c(1,3:9)]\ndiffwithSP %>% select(c(1,3:9)) %>% filter(diffmin < 0) %>% arrange(diffmin)\n\n#Items sold above High\ndiffwithSP[ diffwithSP$diffmax > 0,][c(1,3:9)]\ndiffwithSP %>% select(c(1,3:9)) %>% filter(diffmax > 0) %>% arrange(desc(diffmax))\n\n" }, { "path": "80a-artwork/artwork-rought.R", "content": "# rough work -\n\ndocurl = \"https://docs.google.com/spreadsheets/d/\"\nsheeturl = paste0(\"1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k\",\"/edit#gid=\")\nsheetname = \"560796239\"\nfullurl = paste0(docurl, sheeturl, sheetname)\nfullurl\ndf = as.data.frame(gsheet::gsheet2tbl(fullurl, skip=2))\nsummary(df)\nstr(df)\n\n\ncolcs1 = c('numeric','character', NA,NA,'character', 'character',\t'character',\t'character'\t,'numeric',\t'numeric',\t'character','factor',\t'character',\t'numeric','numeric'\t,'numeric',\t'factor')\nlength(colcs1)\nauctiondata = read.csv('./Data/AuctionsData - set1.csv', skip=2)\ndim(auctiondata)\nstr(auctiondata)\n\n\n#(sp = round(ag2b$saleprice/ sum(ag2b$saleprice)*100,1))\nnames(ag2b)\ngb1 = ggplot(data = ag2b, aes(x = \"\", y = saleprice, fill = auchouse ))\ngb2 = geom_bar(stat = \"identity\", position = position_fill()) \ngb3 = geom_text(aes(label = awcat), position = position_fill(vjust = 0.5))\n\ngb4 = coord_polar(theta = \"y\")\n\ngb5 = facet_wrap(~ auchouse)\ngb6 = theme(axis.title.x = element_blank(), axis.title.y = element_blank())\ngb7 = theme(legend.position='bottom')\ngb8 = guides(fill=guide_legend(nrow=2,byrow=TRUE))\ngb1+gb2+gb3 + gb4 + gb5 + gb6 + gb7 + gb8 \n+ gb4 + ga5" }, { "path": "80a-artwork/artwork1.R", "content": "# Auction Data Analysis\n# Data Set\n\ndocurl = \"https://docs.google.com/spreadsheets/d/\"\nsheeturl = paste0(\"1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k\",\"/edit#gid=\")\nsheetname = \"560796239\"\nfullurl = paste0(docurl, sheeturl, sheetname)\nfullurl\ndf = as.data.frame(gsheet::gsheet2tbl(fullurl, skip=2))\nsummary(df)\nstr(df)\n\n\n\n\nauctiondata = read.csv('data/AuctionsData - set1.csv', skip=1)\ncolclass = c('character'. )\n#auctiondata = read.csv(file.choose())\nstr(auctiondata)\nnames(auctiondata)\nauctiondata$lotesthigh = as.numeric(auctiondata$lotesthigh)\n" }, { "path": "80a-artwork/artwork2.R", "content": "# Auction Data Analysis\n# Data Set\n\nauctiondata = read.csv('./Data/AuctionsData - set1.csv', skip=3, stringsAsFactors = F, na.strings=c(\"\"))\nnames(auctiondata)\ndim(auctiondata)\n\n#auctiondata = read.csv(file.choose())\nstr(auctiondata)\nsummary(auctiondata)\ncolSums(is.na(auctiondata))\n\nauctiondata$recid = as.numeric(auctiondata$recid)\nsummary(auctiondata$recid)\nauctiondata$aucdate = as.Date(auctiondata$aucdate, format=\"%m/%d/%Y\") \nsummary(auctiondata$aucdate)\n\nauctiondata$auchouse = as.factor(auctiondata$auchouse)\nauctiondata$auchouse\n\nauctiondata$aucloc = as.factor(auctiondata$aucloc)\nauctiondata$aucloc\nauctiondata$lotno = as.numeric(auctiondata$lotno)\nauctiondata$lotno\n\nauctiondata$awcat = as.factor(auctiondata$awcat)\nauctiondata$awcat\nauctiondata$lotesthigh = as.double(auctiondata$lotesthigh)\nsummary(auctiondata$lotesthigh)\nsummary(x)\nas.numeric(x)\nas.character(auctiondata$lotesthigh)\n\nauctiondata$lotestlow = as.numeric(auctiondata$lotestlow)\n\nauctiondata$saleprice = as.numeric(auctiondata$saleprice)\nauctiondata$lotstatus = as.factor(auctiondata$lotstatus)\n\nsummary(auctiondata)\n\n" }, { "path": "80a-artwork/artwork4.R", "content": "# Artwork 4\n\nlibrary(readr)\nAuctionsData_set1 <- read_csv(\"Data/AuctionsData - set1.csv\", skip = 3)\n#View(AuctionsData_set1)\n\nsummary(AuctionsData_set1)\nauctiondata2 = as.data.frame(AuctionsData_set1)\nstr(auctiondata2)\n\nnames(auctiondata2)\nsummary(auctiondata2)\nnumcols = c('lotesthigh','lotestlow', 'saleprice') \nauctiondata2$auchouse = as.factor(auctiondata2$auchouse)\nauctiondata2$awcat = as.factor(auctiondata2$awcat)\nauctiondata2$lotstatus= as.factor(auctiondata2$lotstatus)\n\nauctiondata2$recid = as.character(auctiondata2$recid)\nauctiondata2$awid = as.character(auctiondata2$awid)\nauctiondata2$lotno = as.character(auctiondata2$lotno)\n\nauctiondata2$aucur = as.factor(auctiondata2$auccur)\nauctiondata2$aucid = as.factor(auctiondata2$aucid)\n\n\nsummary(auctiondata2)\n\n\n\n" }, { "path": "80a-artwork/awdata1.R", "content": "# Artwork Data \nlibrary(readr)\nAuctionsData_set1 <- read_csv(\"Data/AuctionsData - set1.csv\", skip = 3)\n#View(AuctionsData_set1)\n\nsummary(AuctionsData_set1)\nauctiondata2 = as.data.frame(AuctionsData_set1)\nstr(auctiondata2)\n\nnames(auctiondata2)\nsummary(auctiondata2)\nauctiondata2$recid = as.character(auctiondata2$recid)\nauctiondata2$auccur = as.factor(auctiondata2$auccur)\nauctiondata2$aucdate= as.Date(auctiondata2$aucdate,'%m/%d/%Y')\nauctiondata2$aucid = as.factor(auctiondata2$aucid)\nauctiondata2$auchouse = as.factor(auctiondata2$auchouse)\nauctiondata2$awid = as.numeric(auctiondata2$awid)\nauctiondata2$awcat = as.factor(auctiondata2$awcat)\nauctiondata2$aucloc = as.factor(auctiondata2$aucloc)\n\nauctiondata2$aucid= as.character(auctiondata2$aucid)\nauctiondata2$awid = as.character(auctiondata2$awid)\nauctiondata2$lotno = as.character(auctiondata2$lotno)\nauctiondata2$lotstatus= as.factor(auctiondata2$lotstatus)\n\nsummary(auctiondata2)\n\n\n#Structure Columnswise\n\nfor (i in 1:length(names(auctiondata2)))\n print(summary(auctiondata2[i]))\n\nsaveRDS('auctiondata2','./data/artwork.rds' )\n\n" }, { "path": "80a-artwork/density.R", "content": "\ndensity_standard_norm <- function(x)\n{\n 1/sqrt(2*pi)*exp(-0.5*x^2)\n}\n\ndnorm(1, mean = 0, sd = 1)\n#[1] 0.2419707\ndensity_standard_norm(1)\n#[1] 0.2419707\n\ndnorm(2, mean = 0, sd = 1)\n\n#[1] 0.05399097\n\ndensity_standard_norm(2) \n#[1] 0.0539909\n\n\ndnorm(0) #0.3989423\ndnorm(0)*sqrt(2*pi) #[1] 1\ndnorm(0,mean=4) #[1] 0.0001338302\ndnorm(0,mean=4,sd=10) # 0.03682701\nv <- c(0,1,2) \ndnorm(v) #0.39894228 0.24197072 0.05399097\nx <- seq(-20,20,by=.1)\ny <- dnorm(x)\nplot(x,y)\ny <- dnorm(x,mean=2.5,sd=0.1)\nplot(x,y)\n#----------\n#\n\nset.seed(3000)\nxseq<-seq(-4,4,.01)\ndensities<-dnorm(xseq, 0,1)\ncumulative<-pnorm(xseq, 0, 1)\nrandomdeviates<-rnorm(1000,0,1)\n\npar(mfrow=c(1,3), mar=c(3,4,4,2))\n\nplot(xseq, densities, col=\"darkgreen\",xlab=\"\", ylab=\"Density\", type=\"l\",lwd=2, cex=2, main=\"PDF of Standard Normal\", cex.axis=.8)\n\nplot(xseq, cumulative, col=\"darkorange\", xlab=\"\", ylab=\"Cumulative Probability\",type=\"l\",lwd=2, cex=2, main=\"CDF of Standard Normal\", cex.axis=.8)\n\nhist(randomdeviates, main=\"Random draws from Std Normal\", cex.axis=.8, xlim=c(-4,4))\n\n# Histogram\nxseq<-seq(-4,4,.01)\ny<-2*xseq + rnorm(length(xseq),0,5.5)\nrange(y)\npar(mfrow=c(1,1))\nhist(y, prob=TRUE, ylim=c(0,.06), breaks=20)\ncurve(dnorm(x, mean(y), sd(y)), add=TRUE, col=\"darkblue\", lwd=2)\n\ny1 = c(2,3,4,4,5)\nhist(y1, prob=T)\n" }, { "path": "80a-artwork/file2.R", "content": "par(mfrow=c(2, 2))\npar(mar=c(7, 0, 3, 1))\npar(mex=0.7)\ndotchart(t(VADeaths[1:3,]), xlim = c(0,40), cex=0.6,\n labels=c(\"RM\", \"RF\", \"UM\", \"UF\"))\n\npar(mex=1)\npar(mar=c(5, 3, 2, 1))\nsunflowerplot(x=sort(round(rnorm(100))), y= round(rnorm(100),0),\n xlab=\"\", ylab=\"\", xlim=c(-3, 3), ylim=c(-3, 3), cex=0.5,\n size=1/12, seg.lwd=1, seg.col=\"grey\",\n axes=FALSE)\naxis(1, at=seq(-3, 3, 3))\naxis(2, at=seq(-3, 3, 3))\n\n\nhc <- hclust(dist(USArrests), \"ave\")\ndend1 <- as.dendrogram(hc)\ndend2 <- cut(dend1, h=70)\npar(cex=0.7)\npar(mar=c(1, 0, 2, 5.5))\n# dend2$lower is *NOT* a dendrogram, but a list of .. :\nplot(dend2$lower[[3]], \n horiz = TRUE, type = \"tr\", axes=FALSE, cex=0.8)\npar(mar=c(6, 0, 2, 0))\n# \"inner\" and \"leaf\" edges in different type & color :\nplot(dend2$lower[[2]], \n edgePar = list(col=c(\"black\", \"grey\")), edge.root=TRUE, \n axes=FALSE, cex=0.8)\n\nlibrary(lattice)\ntrellis.par.set(theme = canonical.theme(\"postscript\", col=FALSE))\ntrellis.par.set(list(dot.symbol=list(pch=4)))\nprint(\n xyplot(lat ~ long, data=quakes, pch=\"*\")\n \n)\ntplot <- xyplot(lat ~ long, data=quakes, pch=\".\")\n\n\n\ntrellis.par.set(theme = canonical.theme(\"postscript\", col=FALSE))\ntrellis.par.set(list(dot.symbol=list(pch=1)))\nprint(\n update(tplot, \n main=\"Earthquakes in the Pacific Ocean\\n(since 1964)\")\n \n)\n\ntrellis.par.set(theme = canonical.theme(\"postscript\", col=FALSE))\ntrellis.par.set(list(fontsize=list(text=9, points=8)))\nshow.settings()\nlibrary(grid)\npushViewport(plotViewport(c(5, 4, 2, 2)))\npushViewport(dataViewport(pressure$temperature,\n pressure$pressure,\n name=\"plotRegion\"))\npushViewport(plotViewport(c(3,34,2,3)))\n?pushViewport\n# push the same viewport several times\ngrid.newpage()\nvp <- viewport(width=0.5, height=0.5)\npushViewport(vp)\ngrid.rect(gp=gpar(col=\"blue\"))\ngrid.text(\"Quarter of the device\",\n y=unit(1, \"npc\") - unit(1, \"lines\"), gp=gpar(col=\"blue\"))\npushViewport(vp)\ngrid.rect(gp=gpar(col=\"red\"))\ngrid.text(\"Quarter of the parent viewport\",\n y=unit(1, \"npc\") - unit(1, \"lines\"), gp=gpar(col=\"red\"))\npopViewport(2)\n# push several viewports then navigate amongst them\ngrid.newpage()\ngrid.rect(gp=gpar(col=\"grey\"))\n" }, { "path": "80c-studqueries/Sapient_Big Data.R", "content": "library(dplyr)\nlibrary(TTR)\nlibrary(tidyr)\nlibrary(stringr)\ntrain = read.csv('./data/Analytics_Vidhya/household.csv', header = T, stringsAsFactors = F)\ntest1 = read.csv('./data/Analytics_Vidhya/alert_type_1.csv', header = T, stringsAsFactors = F)\ntest2 = read.csv('./data/Analytics_Vidhya/alert_type_2.csv', header = T, stringsAsFactors = F)\nstr(train)\nstr(test1)\nstr(test2)\nhead(train)\nhead(test1)\nhead(test2)\nsummary(train)\nrange(train$timestamp)\nnames(train)\nnrow(train)\n#check for duplicates, select only non-duplicates\ntrain[duplicated(train[c(1,2,3)]),]\nsubset(train, duplicated(subset(train, select=c(1:3))))\n#No duplicates proceed with data\nanyNA(train)\ndf1 = train\ntest1 = separate(data = test1, col = \"id\", into = c(\"hid\",\"hhid\",\"date\",\"hr\"),sep = \"_\")\nstr(test1)\nView(test1)\ntest2 = separate(data = test2, col = \"id\", into = c(\"hid\",\"hhid\",\"date\",\"hr\"),sep = \"_\")\nView(test2)\nstr(test2)\nas.data.frame(colSums(is.na(test1)))\nas.data.frame(colSums(is.na(test2)))\ntest1$date = as.Date(test1$date, \"%d-%m-%Y\"); test1$date\nstr(test1)\nView(test1)\ntest2$date = as.Date(test2$date, \"%d-%m-%Y\"); test2$date\nstr(test2)\ntest1$date = format(as.Date(test1$date),\"%d-%m-%Y\"); test1$date\ntest2$date = format(as.Date(test2$date),\"%d-%m-%Y\"); test2$date\n\n" }, { "path": "80c-studqueries/achal1.R", "content": "# Extract Strings in R : Achal\n#https://cran.r-project.org/web/packages/stringr/vignettes/stringr.html\n#https://github.com/siskavera/r4ds_exercises/blob/master/strings.R\n\ndata = read.csv(file.choose())\ndata\nstr(data)\n\n#Output\nUser\tGroup\t\t\t\nA\tCat_Svc_Users\t\t\t\nA\tCat_UXSu_Users\t\t\t\nB\tCat_27_Users\t\t\t\nB\tCat_29_Users\t\t\t\nB\tCat_58_Replay\t\t\t\nB\tCat_58_Users\t\t\t\nB\tCat_UXS_Users\t\t\t\n\nA\tCat_Svc_Users\tCat_UXSu_Users\t\t\nB\tCat_27_Users\tCat_29_Users\tCat_58_Replay\tCat_UXS_Users\n\n#----\nUser\tGroup\nA\tCat_Svc_Users\nA\tCat_UXSu_Users\nB\tCat_27_Users\nB\tCat_29_Users\nB\tCat_58_Replay\nB\tCat_58_Users\nB\tCat_UXS_Users\n\nlibrary(stringr)\n\n#Extract Character Strings\nmatchstr1 = \"Cat\"\nmatchstr2 = \"Cat[^a-zA-Z0-9]\"\n\nstr_detect()\nstr_subset()\n#(similar to grep() with value = TRUE)`.\n\n# Which strings contain phone numbers?\nstr_detect(data$Group, matchstr2)\nstr_subset(data$Group, matchstr2)\nstr_count(data$Group, matchstr2)\n(loc <- str_locate(data$Group, matchstr2))\n(locall <- str_locate_all(data$Group, matchstr2))\ndata\n\n\nstr_extract(data$Group, matchstr2)\nstr_extract_all(data$Group, matchstr2)\nstr_extract_all(data$Group, matchstr2, simplify = TRUE)\n\nword(data$Group,1)\n\n\n\n\n#grep\n# some text\ntext=data$Group\n# pattern\npat = \"Cat\"\n# default usage\ngrep(pat, text)\nregexec(pat,text)\nstrsplit(text, ' ')\nstrsplit('Dhiraj Upadhyaya', ' ')\ntext\nstr_extract_all(text, 'Cat_[A-Za-z0-9]{1}')\nwords = c('Cat_27_Users', 'Cat_29_Users')\ngrep('Cat', words)\ngrepl('Cat', words)\ngrepl(\"\\\\% group_by(User) %>% paste(Group)\n\n\n#2nd part\nsmatch= \"\\\\bCat_[a-zA-Z0-9_]+\\\\b\"\n\ndata$Group\nstr_split(data$Group, boundary(\"word\"))\nstr_split(data$Group, \"CN\")\nx1= str_extract_all(data$Group, boundary(\"word\"))\nclass(x1)\ndf2= data.frame(unlist(x1))\nclass(df2)\n\n# to list\nx1= str_extract_all(data$Group, boundary(\"word\"))\nx1\n\n#List to DF\nlibrary(qdapTools)\nlist2df(x1)\n" }, { "path": "80c-studqueries/achal2.csv", "content": "\"V1\",\"V2\"\n\"A\",\"Cat_Svc_Users\"\n\"A\",\"Cat_UXSu_Users\"\n\"B\",\"Cat_27_Users\"\n\"B\",\"Cat_29_Users\"\n\"B\",\"Cat_58_Replay\"\n\"B\",\"Cat_58_Users\"\n\"B\",\"Cat_UXS_Users\"\n\"C\",\"Cat_27_Users\"\n\"C\",\"Cat_29_Users\"\n\"C\",\"Cat_58_Replay\"\n\"C\",\"Cat_58_Users\"\n\"C\",\"Cat_UXS_Users\"\n" }, { "path": "80c-studqueries/deepak.R", "content": "#####################################\n#####Author Deepak Sharma###########\n#####Date 15 Sep 2018###############\n##### The File inlcude \n#######Data Processing (Outlier and Missing Values analysis)#####\n#######Build Decison Tree, Random Forest, Logistic Regression, K-Nearest Neighbour and KMeans Clustering\n\n#Clearing the memory\nrm(list=ls(all=T))\n\n#Setting the working directory\nsetwd(\"E:/CodeSample/MarketingCampaign\")\n\n#Load Libraries\nx = c(\"ggplot2\", \"corrgram\", \"DMwR\", \"caret\", \"randomForest\", \"unbalanced\", \"C50\", \"dummies\", \"e1071\", \"Information\",\n \"MASS\", \"rpart\", \"gbm\", \"ROSE\", 'sampling', 'DataCombine', 'inTrees')\n\n#install.packages(x)\nlapply(x, require, character.only = TRUE)\nrm(x)\n\n## Read the data\nmarketing_train = read.csv(\"marketing.csv\", header = T, na.strings = c(\" \", \"\", \"NA\"))\n\n###########################################Explore the data##########################################\nstr(marketing_train)\n\n## Univariate Analysis and Variable Consolidation\nmarketing_train$schooling[marketing_train$schooling %in% \"illiterate\"] = \"unknown\"\nmarketing_train$schooling[marketing_train$schooling %in% c(\"basic.4y\",\"basic.6y\",\"basic.9y\",\"high.school\",\"professional.course\")] = \"high.school\"\nmarketing_train$default[marketing_train$default %in% \"yes\"] = \"unknown\"\nmarketing_train$default = as.factor(as.character(marketing_train$default))\nmarketing_train$marital[marketing_train$marital %in% \"unknown\"] = \"married\"\nmarketing_train$marital = as.factor(as.character(marketing_train$marital))\nmarketing_train$month[marketing_train$month %in% c(\"sep\",\"oct\",\"mar\",\"dec\")] = \"dec\"\nmarketing_train$month[marketing_train$month %in% c(\"aug\",\"jul\",\"jun\",\"may\",\"nov\")] = \"jun\"\nmarketing_train$month = as.factor(as.character(marketing_train$month))\nmarketing_train$loan[marketing_train$loan %in% \"unknown\"] = \"no\"\nmarketing_train$loan = as.factor(as.character(marketing_train$loan))\nmarketing_train$schooling = as.factor(as.character(marketing_train$schooling))\nmarketing_train$profession[marketing_train$profession %in% c(\"management\",\"unknown\",\"unemployed\",\"admin.\")] = \"admin.\"\nmarketing_train$profession[marketing_train$profession %in% c(\"blue-collar\",\"housemaid\",\"services\",\"self-employed\",\"entrepreneur\",\"technician\")] = \"blue-collar\"\nmarketing_train$profession = as.factor(as.character(marketing_train$profession))\n\n##################################Missing Values Analysis###############################################\nmissing_val = data.frame(apply(marketing_train,2,function(x){sum(is.na(x))}))\nmissing_val$Columns = row.names(missing_val)\nnames(missing_val)[1] = \"Missing_percentage\"\nmissing_val$Missing_percentage = (missing_val$Missing_percentage/nrow(marketing_train)) * 100\nmissing_val = missing_val[order(-missing_val$Missing_percentage),]\nrow.names(missing_val) = NULL\nmissing_val = missing_val[,c(2,1)]\n\n#Writing the csv file at local machine\nwrite.csv(missing_val, \"Missing_perc.csv\", row.names = F)\n\n# ggplot(data = missing_val[1:3,], aes(x=reorder(Columns, -Missing_percentage),y = Missing_percentage))+\n# geom_bar(stat = \"identity\",fill = \"grey\")+xlab(\"Parameter\")+\n# ggtitle(\"Missing data percentage (Train)\") + theme_bw()\n\n#Mean Method\n#marketing_train$custAge[is.na(marketing_train$custAge)] = mean(marketing_train$custAge, na.rm = T)\n\n#Median Method\n#marketing_train$custAge[is.na(marketing_train$custAge)] = median(marketing_train$custAge, na.rm = T)\n\n# Using kNN to impute missing Values \nmarketing_train = knnImputation(marketing_train, k = 3)\n#sum(is.na(marketing_train))\n\n#write.csv(marketing_train, 'marketing_train_missing.csv', row.names = F)\n\n##Data Manupulation; convert string categories into factor numeric\nfor(i in 1:ncol(marketing_train)){\n \n if(class(marketing_train[,i]) == 'factor'){\n \n marketing_train[,i] = factor(marketing_train[,i], labels=(1:length(levels(factor(marketing_train[,i])))))\n \n }\n}\n\n\n############################################Outlier Analysis#############################################\n# ## BoxPlots - Distribution and Outlier Check\nnumeric_index = sapply(marketing_train,is.numeric) #selecting only numeric\n\nnumeric_data = marketing_train[,numeric_index]\n\ncnames = colnames(numeric_data)\n# \n# for (i in 1:length(cnames))\n# {\n# assign(paste0(\"gn\",i), ggplot(aes_string(y = (cnames[i]), x = \"responded\"), data = subset(marketing_train))+ \n# stat_boxplot(geom = \"errorbar\", width = 0.5) +\n# geom_boxplot(outlier.colour=\"red\", fill = \"grey\" ,outlier.shape=18,\n# outlier.size=1, notch=FALSE) +\n# theme(legend.position=\"bottom\")+\n# labs(y=cnames[i],x=\"responded\")+\n# ggtitle(paste(\"Box plot of responded for\",cnames[i])))\n# }\n# \n# ## Plotting plots together\n# gridExtra::grid.arrange(gn1,gn5,gn2,ncol=3)\n# gridExtra::grid.arrange(gn6,gn7,ncol=2)\n# gridExtra::grid.arrange(gn8,gn9,ncol=2)\n# \n# # #Remove outliers using boxplot method\n# df = marketing_train\n# marketing_train = df\n# \n# val = marketing_train$previous[marketing_train$previous %in% boxplot.stats(marketing_train$previous)$out]\n# \n# marketing_train = marketing_train[which(!marketing_train$previous %in% val),]\n# \n# # #loop to remove from all variables\n# for(i in cnames){\n# print(i)\n# val = marketing_train[,i][marketing_train[,i] %in% boxplot.stats(marketing_train[,i])$out]\n# #print(length(val))\n# marketing_train = marketing_train[which(!marketing_train[,i] %in% val),]\n# }\n# \n# #Replace all outliers with NA and impute\n# #create NA on \"custAge\n# for(i in cnames){\n# val = marketing_train[,i][marketing_train[,i] %in% boxplot.stats(marketing_train[,i])$out]\n# #print(length(val))\n# marketing_train[,i][marketing_train[,i] %in% val] = NA\n# }\n# \n# marketing_train = knnImputation(marketing_train, k = 3)\n\n##################################Feature Selection################################################\n## Correlation Plot \ncorrgram(marketing_train[,numeric_index], order = F,\n upper.panel=panel.pie, text.panel=panel.txt, main = \"Correlation Plot\")\n\n## Chi-squared Test of Independence\nfactor_index = sapply(marketing_train,is.factor)\nfactor_data = marketing_train[,factor_index]\n\nfor (i in 1:10)\n{\n print(names(factor_data)[i])\n print(chisq.test(table(factor_data$responded,factor_data[,i])))\n}\n\n## Dimension Reduction\nmarketing_train = subset(marketing_train, \n select = -c(pdays,emp.var.rate,day_of_week, loan, housing))\n\n##################################Feature Scaling################################################\n#Normality check\nqqnorm(marketing_train$custAge)\nhist(marketing_train$campaign)\n\n#Normalisation - Bringing the scale between 0 and 1\ncnames = c(\"custAge\",\"campaign\",\"previous\",\"cons.price.idx\",\"cons.conf.idx\",\"euribor3m\",\"nr.employed\",\n \"pmonths\",\"pastEmail\")\n\nfor(i in cnames){\n print(i)\n marketing_train[,i] = (marketing_train[,i] - min(marketing_train[,i]))/\n (max(marketing_train[,i] - min(marketing_train[,i])))\n}\n\n# #Standardisation\n# for(i in cnames){\n# print(i)\n# marketing_train[,i] = (marketing_train[,i] - mean(marketing_train[,i]))/\n# sd(marketing_train[,i])\n# }\n\n#############################################Sampling#############################################\n# ##Simple Random Sampling\n# data_sample = marketing_train[sample(nrow(marketing_train), 4000, replace = F), ]\n# \n# ##Stratified Sampling\n# stratas = strata(marketing_train, c(\"profession\"), size = c(100, 199, 10, 5), method = \"srswor\")\n# stratified_data = getdata(marketing_train, stratas)\n# \n# ##Systematic sampling\n# #Function to generate Kth index\n# sys.sample = function(N,n){\n# k = ceiling(N/n)\n# r = sample(1:k, 1)\n# sys.samp = seq(r, r + k*(n-1), k)\n# }\n# \n# lis = sys.sample(7414, 400) #select the repective rows\n# \n# #Create index variable in the data\n# marketing_train$index = 1:7414\n# \n# #Extract subset from whole data\n# systematic_data = marketing_train[which(marketing_train$index %in% lis),]\n\n###################################Model Development#######################################\n#Clean the environment\nrmExcept(\"marketing_train\")\n\n#Divide data into train and test using stratified sampling method\nset.seed(1234)\ntrain.index = createDataPartition(marketing_train$responded, p = .80, list = FALSE)\ntrain = marketing_train[ train.index,]\ntest = marketing_train[-train.index,]\n\n##Decision tree for classification\n#Develop Model on training data\nC50_model = C5.0(responded ~., train, trials = 100, rules = TRUE)\n\n#Summary of DT model\nsummary(C50_model)\n\n#write rules into disk\nwrite(capture.output(summary(C50_model)), \"c50Rules.txt\")\n\n#Lets predict for test cases\nC50_Predictions = predict(C50_model, test[,-17], type = \"class\")\n\n##Evaluate the performance of classification model\nConfMatrix_C50 = table(test$responded, C50_Predictions)\nconfusionMatrix(ConfMatrix_C50)\n\n#False Negative rate\nFNR = FN/FN+TP \n\n\n\n###Random Forest\nRF_model = randomForest(responded ~ ., train, importance = TRUE, ntree = 500)\n\n#Extract rules fromn random forest\n#transform rf object to an inTrees' format\n# treeList = RF2List(RF_model) \n# \n# #Extract rules\n# exec = extractRules(treeList, train[,-17]) # R-executable conditions\n# \n# #Visualize some rules\n# exec[1:2,]\n# \n# #Make rules more readable:\n# readableRules = presentRules(exec, colnames(train))\n# readableRules[1:2,]\n# \n# #Get rule metrics\n# ruleMetric = getRuleMetric(exec, train[,-17], train$responded) # get rule metrics\n# \n# #evaulate few rules\n# ruleMetric[1:2,]\n\n#Presdict test data using random forest model\nRF_Predictions = predict(RF_model, test[,-17])\n\n##Evaluate the performance of classification model\nConfMatrix_RF = table(test$responded, RF_Predictions)\nconfusionMatrix(ConfMatrix_RF)\n\n#False Negative rate\nFNR = FN/FN+TP \n\n\n#Logistic Regression\nlogit_model = glm(responded ~ ., data = train, family = \"binomial\")\n\n#summary of the model\nsummary(logit_model)\n\n#predict using logistic regression\nlogit_Predictions = predict(logit_model, newdata = test, type = \"response\")\n\n#convert prob\nlogit_Predictions = ifelse(logit_Predictions > 0.5, 1, 0)\n\n\n##Evaluate the performance of classification model\nConfMatrix_RF = table(test$responded, logit_Predictions)\n\n#False Negative rate\nFNR = FN/FN+TP \n\n\n##KNN Implementation\nlibrary(class)\n\n#Predict test data\nKNN_Predictions = knn(train[, 1:16], test[, 1:16], train$responded, k = 7)\n\n#Confusion matrix\nConf_matrix = table(KNN_Predictions, test$responded)\n\n#Accuracy\nsum(diag(Conf_matrix))/nrow(test)\n\n#False Negative rate\nFNR = FN/FN+TP \n\n#naive Bayes\nlibrary(e1071)\n\n#Develop model\nNB_model = naiveBayes(responded ~ ., data = train)\n\n#predict on test cases #raw\nNB_Predictions = predict(NB_model, test[,1:16], type = 'class')\n\n#Look at confusion matrix\nConf_matrix = table(observed = test[,17], predicted = NB_Predictions)\nconfusionMatrix(Conf_matrix)\n\n\n#statical way\nmean(NB_Predictions == test$responded)\n\n#Kmeans implementation\nirisCluster <- kmeans(train[,1:16], 2, nstart = 20)\n\ntable(irisCluster$cluster, train$responded)\n\n\n" }, { "path": "80c-studqueries/hitesh-dec18.R", "content": "#Hitesh - Dec 18\n\ndata = read.csv(file.choose())\nhead(data)\nsummary(data)\nhead(data,100)\n# Data is samples at 1 minute interval and for 2 months\n# Here, below are the definition of each data point:\n# voltage > 0 means good power,\n# Voltage == 0 means Power failure but UPS giving backup\n# Voltage data missing means UPS is fully discharged \nlibrary(dplyr)\nnames(data)\ndata2<- data %>% mutate(site1 = ifelse(is.na(Site.1), 'UPS-Discharged',(ifelse(Site.1 > 0, 'Good', (ifelse(Site.1 == 0, 'UPS', ''))))))\n\ndata3 <- data2 %>% mutate(site2 = ifelse(is.na(Site.2), 'UPS-Discharged',(ifelse(Site.2 > 0, 'Good', (ifelse(Site.2 == 0, 'UPS', ''))))))\n\nhead(data3)\n\n# Problem Statement:\n# Summarise the instances of Power Failure on the following grounds:\n#count of Power Failure and when UPS-Discharged\ndata3 %>% filter(site1=='UPS' | site1=='UPS-Discharged' ) %>% tally\ndata3 %>% filter(site2=='UPS' | site2=='UPS-Discharged') %>% tally\n\n\n# Number & Duration of Power Failures.\n#Power failure is when voltage shifts from > 0 to 0 and remains in 0 till voltage comes ie > 0\nlag(data3$Site.1)\n\n# Number of times & Duration for which UPS did not provide any backup.\n\n# Number of times & Duration for which UPS provided backup.\n\n# Any Trends/Patterns in the Power Failure.\n\n\n# Compare the UPS performance at 2 sites\n\n\n# Also compare the two sites on the basis of above analysis.\n\n\n\n# Try to summarise your observations using some self-explanatory plots." }, { "path": "80c-studqueries/hitesh1.R", "content": "#hitesh TS\nlibrary(ts)\nlibrary(forecast)\ntsdata = read.csv(file.choose())\nstr(tsdata)\ntsdata$date = as.Date(tsdata$date)\ntsdata$views = as.integer(tsdata$views)\nstr(tsdata)\nsummary(tsdata)\ndim(tsdata)\naggregate(tsdata$chid, by=list(tsdata$chid), FUN=NROW)\n\nlibrary(dplyr)\n\ntable(tsdata$chid)\n# for each Channel Predict Views for next 3 months\n\nchn1 = tsdata$chid[1]\n#subset data\nnames(tsdata)\n\ntsdata.subset <- subset(tsdata, chid == chn1, select = c(views, subscriber, videoscount, date))\ntsdata.subset\n\nnames(tsdata.subset)\nlibrary(xts)\nsubscriber = xts(tsdata.subset[,2], order.by=tsdata.subset$date)\nstr(subscriber)\nhead(subscriber)\nplot(subscriber)\n\n#Manual Method NOT Working : use autoarima\nlibrary(tseries)\nadf.test(subscriber)\nndiffs(subscriber)\ndsub = diff(subscriber)\ndsub = na.omit(dsub)\nplot(dsub)\nadf.test(dsub)\n?adf.test\nacf(dsub)\nPacf(dsub)\nfit1a = arima(subscriber, order= c(0,1,1))\nfit1a\nforecast(fit1a, 90)\n\n\nlibrary(forecast)\nfit1b = auto.arima(subscriber)\nfit1b\nforecast(fit1b, 90)\naccuracy(fit1b)\n\n\n\n# Views\nhead(tsdata.subset)\nlibrary(xts)\nviews = xts(tsdata.subset[,1], order.by=tsdata.subset$date)\nhead(views)\nfit2b = auto.arima(views)\nfit2b\nforecast(fit2b, 90)\naccuracy(fit2b)\n\n## Videos Cpimt\nhead(tsdata.subset)\n\nvideocount = xts(tsdata.subset[,3], order.by=tsdata.subset$date)\nhead(videocount)\nfit3b = auto.arima(videocount)\nfit3b\nforecast(fit3b, 90)\naccuracy(fit3b)\n" }, { "path": "80c-studqueries/hitesh2.R", "content": "# Ad Views - Hitesh - May 18\n\ndfTrain = read.csv(file.choose())\ndfTest = read.csv(file.choose())\n\nnames(dfTrain)\nnames(dfTest)\n\n#predict adview \nsummary(dfTrain)\nunique(dfTrain$adview)\ntable(dfTrain$adview)\n\ntable(dfTrain$duration)\n#Extract Time duration in seconds\ns1 = c(\"PT1H57M3S\", \"PT1H3M46S\",\"PT1H33S\",\"PT1H2M\", \"PT18S\",\"PT18M9S\", \"PT1H39M22S\")\n\nlibrary(tidyverse)\nlibrary(stringi)\nout <- map2_df(stri_extract_all_regex(s1, \"\\\\d+\"), \n stri_extract_all_regex(s1, \"[HMS]\"), ~ .x %>%\n as.integer %>%\n as.list %>% \n set_names(.y) ) \nout\n\nout2 <- map2_df(stri_extract_all_regex(dfTrain$duration, \"\\\\d+\"), \n stri_extract_all_regex(dfTrain$duration, \"[HMS]\"), ~ .x %>%\n as.integer %>%\n as.list %>% \n set_names(.y) ) \nout2\nout2[is.na(out2)] <- 0\ndfTrain1 = cbind(dfTrain, out2)\nnames(dfTrain1)\ndfTrain1[,c('duration','H','M','S') ]\ndfTrain1$totalTime = dfTrain1$H*60*60 + dfTrain1$M*60 + dfTrain1$S\ndfTrain1[,c('duration','H','M','S', 'totalTime') ]\n\nnames(dfTrain1)\nhead(dfTrain1)\nsummary(dfTrain1)\napply(dfTrain,2, class)\n# convert numeric columns \ndfTrain1$adview = as.numeric(dfTrain1$adview)\ndfTrain1$views = as.numeric(dfTrain1$views)\ndfTrain1$dislikes = as.numeric(dfTrain1$dislikes)\n\ndfTrain1$likes = as.numeric(dfTrain1$likes)\ndfTrain1$comment = as.numeric(dfTrain1$comment)\ndfTrain1$published = as.Date(dfTrain1$published)\ndfTrain1$dayspub = as.numeric(Sys.Date() - dfTrain1$published)\nhead(dfTrain1$dayspub)\n\nhead(dfTrain1$published)\nhead(dfTrain1)\nsummary(dfTrain1)\nnames(dfTrain1)\nplot(y=dfTrain1$adview, x= dfTrain1[,3])\n#pairs(c('adview','views','likes','dislikes','comment','totalTime','dayspub'), data=dfTrain1)\n#car::scatterplot(c('adview','views','likes','dislikes','comment','totalTime','dayspub'), data=dfTrain1)\n#pairs(~adview + views + likes + dislikes + comment + totalTime + dayspub,data=dfTrain1) \n\nlibrary(rpart)\nfit1 = rpart(adview ~ views + likes + dislikes + comment + category + totalTime + dayspub, data= dfTrain1, method='anova')\nfit1\nlibrary(rpart.plot)\n\nrpart.plot(fit1)\nsummary(dfTrain1$adview)\nboxplot(dfTrain1$adview)\nboxplot(dfTrain1$dayspub)\n\n\n#ctree\nlibrary(party)\nfit2 = ctree(adview ~ views + likes + dislikes + comment + category + totalTime + dayspub, data= dfTrain1)\nfit2\n\nprint(fit2)\nplot(fit2)\n" }, { "path": "80c-studqueries/hitesh3.R", "content": "#hitesh - 11 Jun 18\n\ndf1 = read.csv(file.choose())\ndf2 = read.csv(file.choose())\n#Test and filter outliers.\n#Understand price fluctuations accounting the seasonal effect\n#Detect seasonality type (multiplicative or additive) for each cluster of APMC and commodities\n#De-seasonalise prices for each commodity and APMC according to the detected seasonality type\n#Compare prices in APMC/Mandi with MSP(Minimum Support Price)- raw and deseasonalised\n#Flag set of APMC/mandis and commodities with highest price fluctuation across different commodities in each relevant season, and year.\n#Data: https://drive.google.com/drive/u/0/folders/0B-zoMsiXW40gZlNtNnlINEszRTg\n# Variable description:\n# msprice- Minimum Support Price\n# arrivals_in_qtl- Quantity arrival in market (in quintal)\n# min_price- Minimum price charged per quintal\n# max_price- Maximum price charged per quintal\n# modal_price- Mode (Average) price charged per quintal\n\n# Submissions:Final cleaned file(s). \n# if the files are shared using GitHub with well versioned log)\n# Documentation around the methodology, analysis, and final results that you want to share with the\n# Government of Maharashtra. Do use graphs and charts to substantiate your analysis\n\n\n#Outliers\n#(https://stackoverflow.com/questions/16089572/how-to-detect-outliers-in-the-columns-of-a-dataframe-in-r)\n \n#(https://eurekastatistics.com/using-mahalanobis-distance-to-find-outliers/)\n#(http://www.questionflow.org/2017/12/26/combined-outlier-detection-with-dplyr-and-ruler/)\n#(http://rischanlab.github.io/Outlier.html#Outlier_Detection_in_R)\n#(https://www.math.csi.cuny.edu/Statistics/R/simpleR/printable/simpleR.pdf)\n#(https://cran.r-project.org/web/packages/mvoutlier/mvoutlier.pdf)\n\n#Modify this code\nquantiles<-tapply(var1,names,quantile)\nminq <- sapply(names, function(x) quantiles[[x]][\"25%\"])\nmaxq <- sapply(names, function(x) quantiles[[x]][\"75%\"])\nvar1[var1maxq] <- NA\n#----\n\n\nstr(df1)\nhead(df1)\noutlier_values <- boxplot.stats(df1$msprice)$out \noutlier_values\nboxplot(df1$msprice)\n#no outliers\n\n# for df2 - you will have split DF based in factor columns and then find outliers\n# see links above\n\n\n\n\n#Seasonality\nnames(df2)\n#Detect seasonality type (multiplicative or additive) for each cluster of APMC and commodities\nsummary(df2)\ndf2$APMC = factor(df2$APMC)\ndf2$Commodity = factor(df2$Commodity)\nsummary(df2)\nlength(unique(df2$APMC))\nlength(unique(df2$Commodity))\nhead(df2)\n\ndim(df2)\n#Create a Time Series Format data from Year and Month\n\nsplt.by <- c('APMC','Commodity')\nX1= split(df2, df2[,splt.by] )\nX1[[1]]\nlapply(X1, length)\n\n\n\nX2 = split(df2, df2[,c('Commodity')])\nX2[[1]]\nlapply(X2, length)\n\nX3 = split(df2, df2[,c('APMC')])\nX3[[1]]\nhead(lapply(X3, length))\n\n\n#(https://stackoverflow.com/questions/29046311/how-to-convert-data-frame-into-time-series-in-r)\n#Convert to time series\nX2[[1]]\n\n\nhead(df2, n=20)\nnames(df2)\ndf2a = df2[,c('date','Commodity','modal_price')]\nhead(df2a,n=20)\ndf2a[order(df2a$Commodity),][1:25,]\nclass(df2a$date) #character\n\nlibrary(xts)\nstocks <- xts(df[,-1], order.by=as.Date(df[,1], \"%m/%d/%Y\"))\nas.Date('2015-04-01',format='%Y-%m-%d')\n\n#(https://www.displayr.com/r-date-conversion/)\nlibrary(lubridate)\nparse_date_time(\"July/1998\", orders = c(\"bdy\", \"bY\"))\nparse_date_time('2015-04',orders=c('Ym'))\n\n#(https://www.earthdatascience.org/courses/earth-analytics/time-series-data/summarize-time-series-by-month-in-r/)\n\nlibrary(zoo)\n\ndf2a$ym <- as.yearmon(df2a$date)\nhead(df2a$ym) # look at it\nnames(df2a)\nplot(modal_price ~ ym, df2a) # plot it\n\n#or you could create a zoo object:\n \ndfz <- zoo(df2a$modal_price, df2a$ym) #error since index values are not unique\n#head(z) # look at it\n#plot(z) # plot it\n\n# convert your data to xts object\nlibrary(xts)\ndata.xts <- xts(df2a$modal_price,\n as.POSIXct(df2a$ym))\nlibrary(rts)\n# aggregate by ..\nep <- endpoints(data.xts,'years') # extract the end index on each year period\nep\nndvi.y <- period.apply(data.xts,ep,mean) # apply the mean function on each year\nndvi.y\n\nepm <- endpoints(data.xts,'month') # month period\nepm\nndvi.m <- period.apply(data.xts,epm,mean) # apply the mean function on each month\nndvi.m\ndataTS = ndvi.m\n\nlibrary(ts)\n#now use this for TS Analysis\nstl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time\nstl(dataTS, s.window = 'periodic') #less period\ndataTS\nplot(dataTS)\n#(https://stackoverflow.com/questions/12330581/too-few-periods-for-decompose) read this\n#data less for extraction of seasonal componenet\nmonthplot(dataTS)\n?stl\nstl(dataTS, s.window = 4) #less period\n" }, { "path": "80c-studqueries/hitesh4.R", "content": "#Hitesh Time Series Jul 18\n#Hitesh 06 Jul 18\n\ndata = read.csv(file.choose())\nstr(data)\nsummary(data)\nhead(data)\nrange(data$timestamp)\nnames(data)\nnrow(data)\n#check for duplicates, select only non-duplicates\ndata[duplicated(data[c(1,2,3)]),]\nsubset(data, duplicated(subset(data, select=c(1:3)))) #another method\n#No duplicates proceed with data\n\n#Missing Values\nanyNA(data)\n\n#make a copy\ndf= data\n#str from time eg\nhelp(strftime)\nt = Sys.time()\ncat(t,\"\\n\") #1390506463\nstrftime(t,\"%Y-%m-%d %H:%M:%S\")\n(timeStamp <- strftime(t,\"%Y-%m-%d %H:%M\"))\ntypeof(timeStamp)\n\n# Time from Timestamp\nas.POSIXct(1377986400,origin=\"1970-01-01\")\ndf$timestamp2 = as.POSIXct(df$timestamp ,origin=\"1970-01-01\")\n\nlibrary(lubridate)\nlibrary(xts)\nnames(df)\ndf.xts = xts(df[1:4], order.by=df$timestamp2)\nhead(df.xts)\n\n#subset(ts, ts$household_id==1) #working\napply.daily(df.xts$value,mean) #daily mean across all household & houseid\n\n#use dplyr\nlibrary(dplyr)\nhour <- as.POSIXlt(as.POSIXct(df$timestamp ,origin=\"1970-01-01\"))$hour\nhead(hour,n=100)\nhead(df);names(df)\n\n\nhourdf <- df %>%\n group_by(hour=floor_date(timestamp2, \"1 hour\"), house_id, household_id) %>%\n summarize(sumV=sum(value), meanV = mean(value), sdV = sd(value))\n\nprint(tbl_df(hourdf), n=30)\nsummary(df)\nhourdf %>% arrange(hour, house_id, household_id) %>% select(hour, house_id, household_id, sumV,meanV, sdV) %>% filter(house_id==0) %>% print(n=100)\n\n\n# Need more ways of grouping and rollmean, rollsd \n\nends <- endpoints(df.xts,'hours',2) \nperiod.apply(df.xts,ends ,sum)\n\n\nlibrary(TTR)\nTTR::rollsd\n" }, { "path": "80c-studqueries/hiteshJul18.R", "content": "#Hitesh 06 Jul 18\n\ndata = read.csv(file.choose())\nstr(data)\nsummary(data)\nhead(data)\nrange(data$timestamp)\nnames(data)\nnrow(data)\n#check for duplicates, select only non-duplicates\ndata[duplicated(data[c(1,2,3)]),]\ndata2= subset(data, !duplicated(subset(data, select=c(1:3))))\nnrow(data2)\n#No duplicates proceed with data\naggregate(data$value, by=list(data$house_id), FUN=mean)\naggregate(data$value, by=list(data$house_id, data$household_id), FUN=mean)\n# this is what we dont need\n\n#make a copy\ndf= data2\n#str from time eg\nhelp(strftime)\nt = Sys.time()\ncat(t,\"\\n\") #1390506463\nstrftime(t,\"%Y-%m-%d %H:%M:%S\")\n(timeStamp <- strftime(t,\"%Y-%m-%d %H:%M\"))\ntypeof(timeStamp)\n\n# Time from Timstamp\nas.POSIXct(1377986400,origin=\"1970-01-01\")\ndf$timestamp2 = as.POSIXct(df$timestamp ,origin=\"1970-01-01\")\nhead(df)\n\nlibrary(lubridate)\nlibrary(xts)\nnames(df)\ndf.xts = xts(df[1:4], order.by=df$timestamp2)\nhead(df.xts)\n#subset(ts, ts$household_id==1) #working\nstr(ts)\naggregate(ts$value, by=list(ts$house_id,ts$household_id), FUN=mean)\napply.daily(ts,mean) #daily mean across all household & houseid\n?apply.daily\n\nlibrary(highfrequency)\naggregatets(ts, FUN=\"previoustick\", on=\"minutes\", k=1, weights=NULL,\n dropna=FALSE)\n\nnames(ts)\naggregate(ts, by=list(ts$house_id), FUN = sum, sd,\n regular = NULL, frequency = NULL, coredata = TRUE)\n\n\n\n# Cumulative average for last 60 min for each household and house id.\n# then compare\n" }, { "path": "80c-studqueries/lalit1.R", "content": "# Lalit\n# \n\n#Missing Values\nitem = c('item1', 'item2', 'item1', 'item1', 'item2')\nrate = c(59, 60, NA, NA, NA)\nclass = c('fmcg','type2', 'type2', 'type1', NA)\ndf = data.frame(item,rate,class)\ndf\n\nsum(is.na(df$rate))\n\naggregate(.~ item, df, FUN=NROW, na.rm=T, na.action=NULL)\n\naggregate(. ~ item, df, function(x) sum(x, na.rm=NULL), na.action = na.pass)\n\ncomplete.cases(df)\n\nsapply(df, function(x) sum(is.na(x)))\n\nlibrary(dplyr)\ndf %>% group_by(item) %>% \n summarise_each(funs(sum(., na.rm = NULL)))\n\nlibrary(tidyverse)\nmap(df, ~sum(is.na(.)))\n\n#dplyr -----\nlibrary(dplyr)\ndf %>%\n select(everything()) %>% # replace to your needs\n summarise_all(funs(sum(is.na(.))))\n\napply(df, MARGIN = 1, function(x) sum(is.na(x)))\napply(df, MARGIN = 2, function(x) sum(is.na(x)))\n\ndf %>% rowwise %>% summarise(NA_per_row = sum(is.na(.)))\n\n#zoo ------\nlibrary(zoo)\nna.aggregate(df, by=1,FUN=mean,na.rm = FALSE,maxgap = Inf)\ndf\n\n(df2=na.aggregate(df[,1:2], by=list(item), FUN=first))\n(df2=na.aggregate(df[,c('item','rate')], by=list(item), FUN=first))\n\ndf2\n(df3=na.aggregate(df, by=list(item), FUN=first))\nstr(df3)\ndf3$rate = as.numeric(df3$rate)\nstr(df3)\nna.aggregate(df, item)\n\n#dplyr ------ for filter\nlibrary(dplyr)\ndf %>% tally()\ndf %>% count(item)\ndf %>% add_tally()\ndf %>% count(item, sort = TRUE)\ndf\ndf %>% add_count(item) %>% filter(n >= 3)\ndf %>% add_tally(class) %>% filter(n >= 1)\n\n\n#dplyr & zoo -----\nlibrary(\"dplyr\")\nlibrary(\"zoo\")\n\ndf %>% group_by(item) %>% transmute(date=na.locf(rate, na.rm=FALSE))\ndf %>% group_by(item) %>% transmute(date=na.locf(class, na.rm=FALSE))\n\n#data- table -----\nrequire(data.table)\n(dfT= setDT(df)) ## change it to data.table in place\nsetkey(dfT, item) ## needed for fast join\ndfT.woNA <- dfT[!is.na(rate)] ## version without the NA rows\ndfT.woNA\n# rolling self-join will return what you want\ndfT.woNA[dfT$item, roll=TRUE] ## will match previous if not found\n\n#Plyr ---------------\n\nimpute <- function(x, fun) {\n missing <- is.na(x)\n replace(x, missing, fun(x[!missing]))\n}\nlibrary(plyr)\nplyr::ddply(df, ~ item, transform, rate = impute(rate, first))\nplyr::ddply(df, ~ item, transform, class = impute(class, first))\n\n\n# aggreage ----\ncount_zero<-function(x){\n sum(x==60,na.rm=TRUE)\n}\naggregate(df[,-1],list(df$item),count_zero)\n\ndf\ncount_NA<-function(x){\n sum(is.na(x),na.rm=F)\n}\naggregate(df[,-1],list(df$item),count_NA)\ndf\n\n\ndf %>% group_by(item) %>% summarise(rateNA=sum(is.na(rate)), classNA=sum(is.na(class)))\n#%>% arrange(desc(rateNA))\n\n\nlibrary(magrittr)\naggregate(df,list(df$item), . %>% is.NA %>% sum(na.rm=F))\n\n\n\n#filter - dplyr\nfilter(df, item == \"item1\")\nselect(df, item, class)\ndf %>% filter(item == \"item1\" | \"item2\") %>% select(item, rate)\n\ndf %>% mutate(rate = first(rate)) %>% filter(is.na(class)) %>% head\ndetach(\"package:plyr\", unload=TRUE)\nlibrary(dplyr)\ndf %>% group_by(rate) %>% summarize(n=n()) \n\naggregate(df$rate, by=list(item, rate), FUN=length, na.action='keep')\n\n\nlibrary(visdat)\nvis_miss(df)\n\nlibrary(Amelia)\nmissmap(df)\n\nrow.has.na <- apply(df, 1, function(x){any(is.na(x))})\nrow.has.na\n\ndf\ndf %>% mutate(condition = complete.cases(select(., item:class)))\ndf %>% mutate(condition = complete.cases(select(., class)))\n\n\n\n\n\n\n\ncat = c('small', 'medium',NA, NA)\ntype = c('Grocery', NA,'SM1','SM1')\ntier = c('T1','T2',NA,'T2')\ndf5 = data.frame(cat, type, tier)\ndf5\nis.na(df5)\nvis_miss(df5)\n\n\ndf\ndf$gender = c(rep('NA',5))\ndf\ndf[item=='item1' & class=='type1',]\ndf[item=='item1' & class=='type1',]\ndf$gender[ item=='item1' | class=='type1'] = 'Male'\ndf\ndf %>% filter(item=='item1')\ndf[1:2,3:4]\ndf\n" }, { "path": "80c-studqueries/meena1", "content": "Please find your assignment. Submission Deadline : ASAP \n\nAssignment:\n\nHope you are aware of Chit Funds available in market, where a group of people pool in some money and the person who win the lottery every month takes the lump some that particular month and pay back monthly a pre-fixed amount .\n\nSo for the complex cash flows & returns, build a R program to get the IRR(Internal Rate of Return) for the investment.\n\nSample chit to work on :\n\nDuration : 40 Months\n\nInstalment : 5000/- Per month till he lift the chit from group. After he lift - he start paying 7000/-\n\nCase 1: Customer lift the chit in 4th Month : He paid 3X5000 + 37X7000 = 274000/- But Customer offered an yield of 203000/-\n\nCase 2: Customer lift the chit in the 40th Month : He paid 39X5000 + 1X7000 = 202000/- Customer offered an yield of 239000/-\n\nAttaching the Chit Fund details for reference- Reach me @ 8800446604 for any queries\n" }, { "path": "80c-studqueries/meena2.R", "content": "#Meena IRR\n#https://www.rdocumentation.org/packages/FinancialMath/versions/0.1.1/topics/IRR\nlibrary(FinancialMath)\n\n#IRR(cf0,cf,times,plot=FALSE)\n\nIRR(cf0=100,cf=c(1,1,30,40,50,1),times=c(1,1,3,4,5,6))\nIRR(cf0=100,cf=c(1,1,30,40,50,1),times=c(1,1,3,4,5,6), plot=T)\n\n#Case - DU\nlibrary(FinCal)\nirr(cf=c(-5, 1.6, 2.4, 2.8))\n\n\n\n\n\n\n#Case 1: Customer lift the chit in 4th Month : He paid 3X5000 + 37X7000 = 274000/- But Customer offered an yield of 203000/-\n#Method1\ncf1=c(rep(5000,3),rep(7000,37))\nlength(cf1)\ntimes1=c(1:3,4:40)\nlength(times1)\ncf01=203000\n#IRR(cf0=203000,cf=c(rep(5000,3),rep(7000,37)), times=c(1:3,4:40))\nIRR(cf0=cf01,cf=cf1, times=times1)\n\n#Method2\ncf1=c(5000, 7000)\nlength(cf1)\ntimes1=c(3,37)\nlength(times1)\ncf01=203000\nIRR(cf0=cf01,cf=cf1, times=times1)\n\n \n#Case 2: Customer lift the chit in the 40th Month : He paid 39X5000 + 1X7000 = 202000/- Customer offered an yield of 239000/-\n\n#Method1\ncf2=c(rep(5000,39),rep(7000,1))\nlength(cf2)\ntimes2=c(1:39,40)\nlength(times2)\ncf02=239000\nIRR(cf0=cf02,cf=cf2, times=times2,plot=T)\n\n\n#Method2\ncf2=c(5000, 7000)\nlength(cf2)\ntimes2=c(39,1)\nlength(times2)\ncf02=239000\nIRR(cf0=cf02,cf=cf2, times=times2)\n\n\n\n?IRR\n#https://cran.r-project.org/web/packages/FinCal/FinCal.pdf\n#https://cran.r-project.org/web/packages/irr/irr.pdf\n#https://stackoverflow.com/questions/11660187/any-r-package-available-to-calculate-irr-from-uneven-payments-on-specific-dates\n#\n#Meena2\nlibrary(financial)\n?discount.rate\nFinCal::discount.rate(n, pv, fv, pmt, type = 0)\n\n?irr\nFinCal::irr(cf=c(-5, 1.6, 2.4, 2.8))\n?irr2\nFinCal::irr2(cf=c(-5, 1.6, 2.4, 2.8))\nFinCal::irr2(cf=c(-200, 50, 60, -70, 30, 20))\n\n?IRR\nFinancialMath::IRR(cf0=1,cf=c(1,2,1),times=c(1,3,4))\nFinancialMath::IRR(cf0=100,cf=c(1,1,30,40,50,1),times=c(1,1,3,4,5,6))\nFinancialMath::IRR(cf0=203000,cf=c(5000, 7000),times=c(3,37))\n\n\nFinCal::irr2(cf=c(-203000, rep(5000,3), rep(7000,37))) #high processing time\n\n#Case1:203000, 5000 x 3, 7000 x 37\ns1= sum(rep(5000,3), rep(7000,37))\ns1-203000\nrep(5000,3)\nIRR.Project1 <- IRR(-203000, c(5000,5000,5000,7000,7000,7000,7000,7000,7000,7000,7000,7000,7000,7000,7000), times=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15))\nIRR.Project1\n?IRR\n\n\n#Case2: 239000, 5000 x 29, 7000 x 1\ns2= sum(rep(5000,39), rep(7000,1))\ns2-239000\n\n\n#Case\n\nIRR.Project1 <- IRR(-1000, c(1250, 10, 10, 20, 20), c(1:5))\nIRR.Project2 <- IRR(-1000, c(-10, 0, 10, 20, 2000), c(1:5))" }, { "path": "80c-studqueries/meena3.R", "content": "#Meena2\n#https://www.free-online-calculator-use.com/xirr-calculator.html\n\nlibrary(financial)\n?discount.rate\nFinCal::discount.rate(n, pv, fv, pmt, type = 0)\n\n?irr\nFinCal::irr(cf=c(-5, 1.6, 2.4, 2.8))\n?irr2\nFinCal::irr2(cf=c(-5, 1.6, 2.4, 2.8))\nFinCal::irr2(cf=c(-200, 50, 60, -70, 30, 20))\n\n?IRR\nFinancialMath::IRR(cf0=1,cf=c(1,2,1),times=c(1,3,4))\nFinancialMath::IRR(cf0=100,cf=c(1,1,30,40,50,1),times=c(1,1,3,4,5,6))\nFinancialMath::IRR(cf0=203000,cf=c(5000, 7000),times=c(3,37))\n\n\nFinCal::irr2(cf=c(-203000, rep(5000,3), rep(7000,37))) #high processing time\nFinCal::irr2(cf=c(-100, rep(5,3), rep(7,37))) #this worked #0.059908\n\n\n#Case1:203000, 5000 x 3, 7000 x 37\ns1= sum(rep(5000,3), rep(7000,37))\ns1-203000\nrep(5000,3)\n(IRR.Project1 <- IRR(-1000, c(1250, 10, 10, 20, 20), c(1:5)))\n(IRR.Project1 <- IRR(-203000, c(rep(5000,3), rep(7000,37)), c(1:40)))\n\n\n#Case2: 239000, 5000 x 29, 7000 x 1\ns2= sum(rep(5000,39), rep(7000,1))\ns2-239000\n\n\n#Case\n\nIRR.Project1 <- IRR(-1000, c(1250, 10, 10, 20, 20), c(1:5))\nIRR.Project2 <- IRR(-1000, c(-10, 0, 10, 20, 2000), c(1:5))" }, { "path": "80c-studqueries/sidana2.R", "content": "#sidhana\n\nbank = read.csv(file.choose())\nhead(bank)\nbank$y = factor(bank$y)\nlibrary(rpart)\nlibrary(rpart.plot)\n\nfit = rpart(y ~. , data= bank)\nfit\nlibrary(rattle)\nfancyRpartPlot(fit)\nrpart.plot::prp(fit, extra=104, type=4, fallen.leaves = T)\n" }, { "path": "Data/AuctionsData - set1.csv", "content": "RecID,AuctionCurrency,AuctionDate,AuctionID,Indicator-Day-Afternoon,Auction Title,Auction House,AuctionLocation,lot LotNo,artwork ID,Artwork Title,ArtworkCategory,Artist_Full_Name,lot_EstimateHigh,lot_EstimateLow,Sale Price,Lot Status\n288822,,,,,,,,3166,,,,,191810000,137080000,207017950,\nnumeric,character,character,numeric,character,character,character,character,numeric,numeric,character,factor,character,numeric,numeric,numeric,factor\nrecid,auccur,aucdate,aucid,idafn,auctitle,auchouse,aucloc,lotno,awid,awtitle,awcat,arname,lotesthigh,lotestlow,saleprice,lotstatus\n2547,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,1.00,6998013,Untitled,PAINTINGS,Christopher Wool,\"600,000\",\"400,000\",\"729,000\",Sold\n2548,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,10.00,6998014,Angel Of The North,SCULPTURE,Antony Gormley,\"2,000,000\",\"1,500,000\",\"2,860,600\",Sold\n2549,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,11.00,6998015,Some People (Welcome Series),PAINTINGS,Hurvin Anderson,\"800,000\",\"600,000\",\"1,029,000\",Sold\n2550,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,12.00,6998016,The Architect's Home In The Ravine,PAINTINGS,Peter Doig,\"18,000,000\",\"14,000,000\",\"14,376,400\",Sold\n2551,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,13.00,6998017,A Swan Comforting A Snake,PAINTINGS,Cecily Brown,\"1,500,000\",\"1,000,000\",\"1,209,000\",Sold\n2552,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,14.00,6998018,Eifrau Die Man Nicht Schubladieren Kann (Egg Lady Who Can’t Be Piegeonholed),PAINTINGS,Martin Kippenberger,\"3,000,000\",\"2,000,000\",\"2,169,000\",Sold\n2553,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,15.00,6998019,Memory Ware Flat #29,PAINTINGS,Mike Kelley,\"2,000,000\",\"1,500,000\",\"1,689,000\",Sold\n2554,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,16.00,6998020,Yellow Cell With Triple Conduit,PAINTINGS,Peter Halley,\"350,000\",\"250,000\",\"513,000\",Sold\n2555,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,17.00,6998021,Untitled,SCULPTURE,Richard Prince,\"1,500,000\",\"1,000,000\",\"2,049,000\",Sold\n2556,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,18.00,6998022,Nov. 8,PAINTINGS,On Kawara,\"800,000\",\"600,000\",\"789,000\",Sold\n2557,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,19.00,6998023,Untitled,PAINTINGS,Rudolf Stingel,\"6,000,000\",\"4,000,000\",\"4,667,000\",Sold\n2558,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,2.00,6998024,Untitled,PAINTINGS,Laura Owens,\"350,000\",\"250,000\",\"435,000\",Sold\n2559,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,20.00,6998025,Amanti (Lovers),PAINTINGS,Michelangelo Pistoletto,\"3,000,000\",\"2,300,000\",\"2,634,800\",Sold\n2560,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,21.00,6998026,Tenda Blu (Blue Curtain),PAINTINGS,Michelangelo Pistoletto,\"600,000\",\"400,000\",\"621,000\",Sold\n2561,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,22.00,6998027,Figura Femminile Con Fiori (Female Figure With Flowers),SCULPTURE,Lucio Fontana,\"2,000,000\",\"1,500,000\",\"1,809,000\",Sold\n2562,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,23.00,6998028,\"Oiks, Tarts, Wierdoes And Contemporary Art\",SCULPTURE,Grayson Perry,\"120,000\",\"80,000\",\"175,000\",Sold\n2563,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,24.00,6998029,Border Country,PAINTINGS,Peter Doig,\"700,000\",\"500,000\",\"609,000\",Sold\n2564,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,25.00,6998030,In Central Park,PAINTINGS,Howard Hodgkin,\"800,000\",\"600,000\",,Unsold\n2565,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,26.00,6998031,\"Different Kinds Of Water Pouring Into A Swimming Pool, Santa Monica\",PAINTINGS,David Hockney,\"8,000,000\",\"6,000,000\",,Unsold\n2566,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,27.00,6998032,\"Concetto Spaziale, Attese\",PAINTINGS,Lucio Fontana,\"3,000,000\",\"2,000,000\",\"5,005,700\",Sold\n2567,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,28.00,6998033,Gelbgrun (Yellow-green),PAINTINGS,Gerhard Richter,\"10,000,000\",\"7,000,000\",\"10,876,500\",Sold\n2568,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,29.00,6998034,Untitled (19.3.86),PAINTINGS,Gerhard Richter,\"900,000\",\"700,000\",\"849,000\",Sold\n2569,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,3.00,6998035,Untitled,PAINTINGS,Albert Oehlen,\"1,500,000\",\"1,000,000\",\"1,929,000\",Sold\n2570,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,30.00,6998036,Kleine Geister (Little Ghosts),SCULPTURE,Thomas Schutte,\"450,000\",\"350,000\",\"405,000\",Sold\n2571,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,31.00,6998037,Alphabet (U),SCULPTURE,Danh Vo,\"200,000\",\"150,000\",,Unsold\n2572,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,32.00,6998038,After Micro W #2,PAINTINGS,Sam Gilliam,\"200,000\",\"150,000\",\"200,000\",Sold\n2573,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,33.00,6998039,Untitled,SCULPTURE,Francesco Lo Savio,\"350,000\",\"250,000\",\"309,000\",Sold\n2574,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,34.00,6998040,Edera (Ivy),SCULPTURE,Luciano Fabro,\"700,000\",\"500,000\",\"609,000\",Sold\n2575,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,35.00,6998041,Sandinsel (Sand Island),SCULPTURE,Gunther Uecker,\"800,000\",\"600,000\",\"753,000\",Sold\n2576,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,36.00,6998042,Untitled (Relief No. 348),SCULPTURE,Sergio de Camargo,\"900,000\",\"700,000\",,Unsold\n2577,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,37.00,6998043,Infinity Nets (Oq4),PAINTINGS,Yayoi Kusama,\"1,200,000\",\"800,000\",\"1,269,000\",Sold\n2578,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,38.00,6998044,Untitled,PAINTINGS,Rudolf Stingel,\"2,000,000\",\"1,500,000\",\"1,869,000\",Sold\n2579,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,39.00,6998045,Caribou Skull,SCULPTURE,Sherrie Levine,\"600,000\",\"400,000\",\"489,000\",Sold\n2580,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,4.00,6998046,Untitled,PAINTINGS,Christopher Wool,\"6,200,000\",\"4,200,000\",\"10,424,900\",Sold\n2581,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,40.00,6998047,The Housewife And The Grocer,PAINTINGS,Richard Prince,\"1,800,000\",\"1,200,000\",\"1,269,000\",Sold\n2582,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,41.00,6998048,O.t. (Death’s Heads),SCULPTURE,Rosemarie Trockel,\"600,000\",\"400,000\",\"465,000\",Sold\n2583,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,42.00,6998049,Down Under,SCULPTURE,Jason Rhoades,\"550,000\",\"350,000\",\"345,000\",Sold\n2584,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,43.00,6998050,Untitled,PAINTINGS,Albert Oehlen,\"450,000\",\"350,000\",\"429,000\",Sold\n2585,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,44.00,6998051,Different From Case To Case,PAINTINGS,Sigmar Polke,\"600,000\",\"400,000\",\"753,000\",Sold\n2586,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,45.00,6998052,Greifbar 40,PHOTOGRAPHS,Wolfgang Tillmans,\"300,000\",\"200,000\",\"297,000\",Sold\n2587,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,46.00,6998053,Untitled (Cowboy),PHOTOGRAPHS,Richard Prince,\"900,000\",\"700,000\",\"1,029,000\",Sold\n2588,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,47.00,6998054,Mount Something Or Other,PAINTINGS,Ed Ruscha,\"1,200,000\",\"800,000\",,Unsold\n2589,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,48.00,6998055,Hammer And Sickle,PAINTINGS,Andy Warhol,\"3,500,000\",\"2,500,000\",\"3,538,000\",Sold\n2590,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,49.00,6998056,Two Guys Twice,PAINTINGS,Richard Prince,\"800,000\",\"600,000\",\"693,000\",Sold\n2591,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,5.00,6998057,Untitled (Figure Jmb #1),PAINTINGS,Jean-Michel Basquiat,\"2,800,000\",\"2,400,000\",\"2,634,800\",Sold\n2592,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,50.00,6998058,African Elephant,PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",\"1,569,000\",Sold\n2593,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,51.00,6998059,Ero,PAINTINGS,Jean-Michel Basquiat,\"2,000,000\",\"1,500,000\",\"2,409,000\",Sold\n2594,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,52.00,6998060,Pink Circus: Embrace Peace And Darkness Within Thy Heart,PAINTINGS,Takashi Murakami,\"800,000\",\"600,000\",\"849,000\",Sold\n2595,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,53.00,6998061,Marilyn Monroe (Marilyn),PRINTS,Andy Warhol,\"1,500,000\",\"1,000,000\",\"2,349,000\",Sold\n2596,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,54.00,6998062,Bacchus At The Seaside,PAINTINGS,Banksy,\"350,000\",\"250,000\",\"669,000\",Sold\n2597,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,55.00,6998063,\"Sometimes Life Is Really, Really, Really Dark\",PAINTINGS,Damien Hirst,\"500,000\",\"300,000\",\"309,000\",Sold\n2598,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,56.00,6998064,Study To Homage To The Square: Evident,PAINTINGS,Josef Albers,\"650,000\",\"450,000\",\"429,000\",Sold\n2599,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,57.00,6998065,\"Untitled (July 23, 1988)\",TEXTILES,Alighiero e Boetti,\"350,000\",\"250,000\",\"381,000\",Sold\n2600,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,58.00,6998066,Incontri E Scontri (Meetings And Collisions),WORKS ON PAPER,Alighiero e Boetti,\"300,000\",\"200,000\",\"345,000\",Sold\n2601,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,59.00,6998067,\"Concetto Spaziale, Attese\",PAINTINGS,Lucio Fontana,\"1,200,000\",\"800,000\",\"1,329,000\",Sold\n2602,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,6.00,6998068,Wind,PAINTINGS,Gerhard Richter,\"3,500,000\",\"2,500,000\",\"3,086,400\",Sold\n2603,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,60.00,6998069,Three White Dots On Orange Stack,SCULPTURE,Alexander Calder,\"650,000\",\"450,000\",,Unsold\n2604,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,61.00,6998070,Untitled Film Still #57,PHOTOGRAPHS,Cindy Sherman,\"300,000\",\"200,000\",\"225,000\",Sold\n2605,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,7.00,6998071,Autounfall (Car Crash) Or The Three Sisters,PAINTINGS,Sigmar Polke,\"2,000,000\",\"1,500,000\",\"2,169,000\",Sold\n2606,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,8.00,6998072,1025 Farben,PAINTINGS,Gerhard Richter,\"7,000,000\",\"5,000,000\",\"7,376,600\",Sold\n2607,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,9.00,6998073,Loving You,PAINTINGS,Damien Hirst,\"1,200,000\",\"800,000\",\"993,000\",Sold\n2608,GBP,3/8/2018,23,20th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,1.00,6998817,Bright Moments: For R.R. Kirk,PAINTINGS,Jack Whitten,\"300,000\",\"200,000\",\"333,000\",Sold\n2609,GBP,3/8/2018,23,21st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,2.00,6998818,Politics,PAINTINGS,Lynette Yiadom Boakye,\"90,000\",\"70,000\",\"273,000\",Sold\n2610,GBP,3/8/2018,23,22nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,3.00,6998819,Greifbar 27,PHOTOGRAPHS,Wolfgang Tillmans,\"300,000\",\"200,000\",\"465,000\",Sold\n2611,GBP,3/8/2018,23,23rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,4.00,6998820,The Man,PHOTOGRAPHS,Gilbert and George,\"500,000\",\"300,000\",\"321,000\",Sold\n2612,GBP,3/8/2018,23,24th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,5.00,6998821,T-riffic,PAINTINGS,Allen Jones,\"350,000\",\"250,000\",\"705,000\",Sold\n2613,GBP,3/8/2018,23,25th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,6.00,6998822,Family Group,SCULPTURE,Henry Moore,\"350,000\",\"250,000\",\"93,000\",Sold\n2614,GBP,3/8/2018,23,26th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,7.00,6998823,Piccolo Cavaliere,SCULPTURE,Marino Marini,\"600,000\",\"400,000\",\"429,000\",Sold\n2615,GBP,3/8/2018,23,27th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,8.00,6998824,Two Three-Quarter Figures on Base,SCULPTURE,Henry Moore,\"600,000\",\"400,000\",\"729,000\",Sold\n2616,GBP,3/8/2018,23,28th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,9.00,6998825,Nu allongé I (Aurore),SCULPTURE,Henri Matisse,\"7,000,000\",\"5,000,000\",\"14,859,000\",Sold\n2617,GBP,3/8/2018,23,29th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,10.00,6998826,La Dormeuse,PAINTINGS,Pablo Picasso,\"18,000,000\",\"12,000,000\",\"41,859,000\",Sold\n2618,GBP,3/8/2018,23,30th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,11.00,6998827,Small Naked Portrait,PAINTINGS,Lucian Freud,\"600,000\",\"400,000\",\"789,000\",Sold\n2619,GBP,3/8/2018,23,31st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,12.00,6998828,Untitled,WORKS ON PAPER,Cy Twombly,\"700,000\",\"500,000\",\"609,000\",Sold\n2620,GBP,3/8/2018,23,32nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,13.00,6998829,The Pilgrim,PAINTINGS,Marlene Dumas,\"2,000,000\",\"1,500,000\",\"1,749,000\",Sold\n2621,GBP,3/8/2018,23,33rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,14.00,6998830,Helter Skelter I,PAINTINGS,Mark Bradford,\"8,000,000\",\"6,000,000\",\"8,671,500\",Sold\n2622,GBP,3/8/2018,23,34th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,15.00,6998831,Singing in the Rain,PAINTINGS,Luc Tuymans,\"700,000\",\"500,000\",\"729,000\",Sold\n2623,GBP,3/8/2018,23,35th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,16.00,6998832,Untitled,PAINTINGS,Albert Oehlen,\"350,000\",\"250,000\",\"345,000\",Sold\n2624,GBP,3/8/2018,23,36th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,17.00,6998833,P.D. Idol,PAINTINGS,Georg Baselitz,\"2,000,000\",\"1,500,000\",\"1,929,000\",Sold\n2625,GBP,3/8/2018,23,37th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,18.00,6998834,Die Meistersinger,PAINTINGS,Anselm Kiefer,\"2,000,000\",\"1,500,000\",\"1,809,000\",Sold\n2626,GBP,3/8/2018,23,38th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,19.00,6998835,Untitled,PAINTINGS,Sigmar Polke,\"1,200,000\",\"800,000\",\"969,000\",Sold\n2627,GBP,3/8/2018,23,39th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,20.00,6998836,Omegasachen-35,PAINTINGS,Albert Oehlen,\"600,000\",\"400,000\",\"465,000\",Sold\n2628,GBP,3/8/2018,23,40th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,21.00,6998837,Untitled,SCULPTURE,Rudolf Stingel,\"6,000,000\",\"4,000,000\",\"5,746,500\",Sold\n2629,GBP,3/8/2018,23,41st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,22.00,6998838,Monochrome rose sans titre (MP 27),WORKS ON PAPER,Yves Klein,\"1,200,000\",\"800,000\",\"1,029,000\",Sold\n2630,GBP,3/8/2018,23,42nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,23.00,6998839,Bridlington Violets,PAINTINGS,David Hockney,\"700,000\",\"500,000\",\"609,000\",Sold\n2631,GBP,3/8/2018,23,43rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,24.00,6998840,Woman & Man,PAINTINGS,George Condo,\"800,000\",\"600,000\",\"1,329,000\",Sold\n2632,GBP,3/8/2018,23,44th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,25.00,6998841,Untitled #220,PHOTOGRAPHS,Cindy Sherman,\"200,000\",\"150,000\",\"175,000\",Sold\n2633,GBP,3/8/2018,23,45th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,26.00,6998842,Untitled,PAINTINGS,Jean-Michel Basquiat,\"1,500,000\",\"1,000,000\",\"1,089,000\",Sold\n2634,GBP,3/8/2018,23,46th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,27.00,6998843,Profil Genre Aztèque,PAINTINGS,Jean Dubuffet,\"1,800,000\",\"1,200,000\",\"1,449,000\",Sold\n2635,GBP,3/8/2018,23,47th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,28.00,6998844,Colombe blanche,PAINTINGS,Max Ernst,\"500,000\",\"350,000\",\"393,000\",Sold\n2636,GBP,3/8/2018,23,48th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,29.00,6998845,Le Surréalisme et la peinture,WORKS ON PAPER,Max Ernst,\"450,000\",\"350,000\",\"429,000\",Sold\n2637,GBP,3/8/2018,23,49th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,30.00,6998846,Ohne Titel,WORKS ON PAPER,Wassily Kandinsky,\"550,000\",\"350,000\",,Unsold\n2638,GBP,3/8/2018,23,50th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,31.00,6998847,Feuille de vigne femelle,SCULPTURE,Marcel Duchamp,\"400,000\",\"300,000\",\"501,000\",Sold\n2639,GBP,3/8/2018,23,51st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,32.00,6998848,Diamond Dust Shoes (Pink),PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",,Unsold\n2640,GBP,3/8/2018,23,52nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,33.00,6998849,Diamond Dust Shoes (Green),PAINTINGS,Andy Warhol,\"1,800,000\",\"1,400,000\",,Unsold\n2641,GBP,3/8/2018,23,53rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,34.00,6998850,\"Beautiful, Half a Red Headless Centipede (Painting)\",PAINTINGS,Damien Hirst,\"350,000\",\"250,000\",\"273,000\",Sold\n2642,GBP,3/8/2018,23,54th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,35.00,6998851,Grey Red,PAINTINGS,Sean Scully,\"800,000\",\"600,000\",\"1,149,000\",Sold\n2643,GBP,3/8/2018,23,55th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,36.00,6998852,\"Peinture 130 x 162 cm, 12 mai 1965\",PAINTINGS,Pierre Soulages,\"1,200,000\",\"800,000\",\"933,000\",Sold\n2644,GBP,3/8/2018,23,56th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,37.00,6998853,Cuadro 128,PAINTINGS,Manolo Millares,\"150,000\",\"100,000\",\"206,250\",Sold\n2645,GBP,3/8/2018,23,57th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,38.00,6998854,Tôle,WORKS ON PAPER,Raymond Hains,\"200,000\",\"150,000\",,Unsold\n2646,GBP,3/8/2018,23,58th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,39.00,6998855,Opalka 1965/1 – ∞ Detail 5210331 - 5226270,PAINTINGS,Roman Opalka,\"600,000\",\"400,000\",,Unsold\n2647,GBP,3/8/2018,23,59th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,40.00,6998856,Superficie bianca,PAINTINGS,Enrico Castellani,\"350,000\",\"250,000\",\"267,000\",Sold\n2648,GBP,3/8/2018,23,60th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,41.00,6998857,\"Concetto spaziale, Attese\",PAINTINGS,Lucio Fontana,\"850,000\",\"650,000\",\"669,000\",Sold\n2649,GBP,3/8/2018,23,61st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,42.00,6998858,Métal sur le Losange,SCULPTURE,Jesus Rafael Soto,\"250,000\",\"180,000\",\"321,000\",Sold\n2650,GBP,3/8/2018,23,62nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,43.00,6998859,Reihung,WORKS ON PAPER,Gunther Uecker,\"350,000\",\"250,000\",\"249,000\",Sold\n2651,GBP,3/8/2018,23,63rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,44.00,6998860,R72-73-M-14,PAINTINGS,Jan Schoonhoven,\"350,000\",\"250,000\",\"441,000\",Sold\n2652,GBP,3/8/2018,23,64th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,45.00,6998861,Concetto spaziale,PAINTINGS,Lucio Fontana,\"700,000\",\"500,000\",,Unsold\n2653,GBP,3/8/2018,23,65th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,46.00,6998862,Homage to the Square,PAINTINGS,Josef Albers,\"500,000\",\"300,000\",\"381,000\",Sold\n2654,GBP,3/8/2018,23,66th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,47.00,6998863,Found,PAINTINGS,Adrian Ghenie,\"500,000\",\"300,000\",\"249,000\",Sold\n2655,GBP,3/8/2018,23,67th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,48.00,6998864,Without You,SCULPTURE,Damien Hirst,\"700,000\",\"500,000\",\"585,000\",Sold\n2656,GBP,3/8/2018,23,68th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,49.00,6998865,Apparition,PAINTINGS,Damien Hirst,\"700,000\",\"500,000\",\"609,000\",Sold\n2657,GBP,3/8/2018,23,69th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,50.00,6998866,Skulldiver II,PAINTINGS,Cecily Brown,\"600,000\",\"400,000\",\"513,000\",Sold" }, { "path": "Data/Churn.csv", "content": "customerID,gender,SeniorCitizen,Partner,Dependents,tenure,PhoneService,MultipleLines,InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges,Churn\n7590-VHVEG,Female,0,Yes,No,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.85,29.85,No\n5575-GNVDE,Male,0,No,No,34,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,56.95,1889.5,No\n3668-QPYBK,Male,0,No,No,2,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,53.85,108.15,Yes\n7795-CFOCW,Male,0,No,No,45,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),42.3,1840.75,No\n9237-HQITU,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.7,151.65,Yes\n9305-CDSKC,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.65,820.5,Yes\n1452-KIOVK,Male,0,No,Yes,22,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.1,1949.4,No\n6713-OKOMC,Female,0,No,No,10,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.75,301.9,No\n7892-POOKP,Female,0,Yes,No,28,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.8,3046.05,Yes\n6388-TABGU,Male,0,No,Yes,62,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Bank transfer (automatic),56.15,3487.95,No\n9763-GRSKD,Male,0,Yes,Yes,13,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.95,587.45,No\n7469-LKBCI,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),18.95,326.8,No\n8091-TTVAX,Male,0,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),100.35,5681.1,No\n0280-XJGEX,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.7,5036.3,Yes\n5129-JLPIS,Male,0,No,No,25,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,2686.05,No\n3655-SNQYZ,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),113.25,7895.15,No\n8191-XWSZG,Female,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,1022.95,No\n9959-WOFKT,Male,0,No,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),106.7,7382.25,No\n4190-MFLUW,Female,0,Yes,Yes,10,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),55.2,528.35,Yes\n4183-MYFRB,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,1862.9,No\n8779-QRDMV,Male,1,No,No,1,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,39.65,39.65,Yes\n1680-VDCWW,Male,0,Yes,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,202.25,No\n1066-JKSGK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,Yes\n3638-WEABW,Female,0,Yes,No,58,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),59.9,3505.1,No\n6322-HRPFA,Male,0,Yes,Yes,49,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),59.6,2970.3,No\n6865-JZNKO,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.3,1530.6,No\n6467-CHFZW,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.35,4749.15,Yes\n8665-UTDHZ,Male,0,Yes,Yes,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.2,30.2,Yes\n5248-YGIJN,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.25,6369.45,No\n8773-HHUOZ,Female,0,No,Yes,17,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,64.7,1093.1,Yes\n3841-NFECX,Female,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),96.35,6766.95,No\n4929-XIHVW,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.5,181.65,No\n6827-IEAUQ,Female,0,Yes,Yes,27,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Mailed check,66.15,1874.45,No\n7310-EGVHZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.2,20.2,No\n3413-BMNZE,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.25,45.25,No\n6234-RAAPL,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),99.9,7251.7,No\n6047-YHPVI,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,316.9,Yes\n6572-ADKRS,Female,0,No,No,46,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),74.8,3548.3,No\n5380-WJKOV,Male,0,No,No,34,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,3549.25,Yes\n8168-UQWWF,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.85,1105.4,Yes\n8865-TNMNX,Male,0,Yes,Yes,10,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Mailed check,49.55,475.7,No\n9489-DEDVP,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),69.2,4872.35,No\n9867-JCZSP,Female,0,Yes,Yes,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.75,418.25,No\n4671-VJLCL,Female,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),79.85,4861.45,No\n4080-IIARD,Female,0,Yes,No,13,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,76.2,981.45,No\n3714-NTNFO,Female,0,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.5,3906.7,No\n5948-UJZLF,Male,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.25,97,No\n7760-OYPDY,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.65,144.15,Yes\n7639-LIAYI,Male,0,No,No,52,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.75,4217.8,No\n2954-PIBKO,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),64.15,4254.1,No\n8012-SOUDQ,Female,1,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.25,3838.75,No\n9420-LOJKX,Female,0,No,No,15,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.1,1426.4,Yes\n6575-SUVOI,Female,1,Yes,No,25,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),69.5,1752.65,No\n7495-OOKFY,Female,1,Yes,No,8,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.65,633.3,Yes\n4667-QONEA,Female,1,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),74.85,4456.35,No\n1658-BYGOY,Male,1,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,1752.55,Yes\n8769-KKTPH,Female,0,Yes,Yes,63,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),99.65,6311.2,No\n5067-XJQFU,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,108.45,7076.35,No\n3957-SQXML,Female,0,Yes,Yes,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.95,894.3,No\n5954-BDFSG,Female,0,No,No,72,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),107.5,7853.7,No\n0434-CSFON,Female,0,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,4707.1,No\n1215-FIGMP,Male,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.9,5450.7,No\n0526-SXDJP,Male,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),42.1,2962,No\n0557-ASKVU,Female,0,Yes,Yes,18,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),54.4,957.1,No\n5698-BQJOH,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.4,857.25,Yes\n5122-CYFXA,Female,0,No,No,3,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,75.3,244.1,No\n8627-ZYGSZ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Electronic check,78.9,3650.35,No\n3410-YOQBQ,Female,0,No,No,31,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,79.2,2497.2,No\n3170-NMYVV,Female,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,930.9,No\n7410-OIEDU,Male,0,No,No,10,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.85,887.35,No\n2273-QCKXA,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,49.05,49.05,No\n0731-EBJQB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.4,1090.65,No\n1891-QRQSA,Male,1,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.6,7099,No\n8028-PNXHQ,Male,0,Yes,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.25,1424.6,No\n5630-AHZIL,Female,0,No,Yes,3,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.5,177.4,No\n2673-CXQEU,Female,1,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,110.5,6139.5,No\n6416-JNVRK,Female,0,No,No,46,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Credit card (automatic),55.65,2688.85,No\n5590-ZSKRV,Female,0,Yes,Yes,8,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.65,482.25,No\n0191-ZHSKZ,Male,1,No,No,30,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.75,2111.3,No\n3887-PBQAO,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.9,1216.6,No\n5919-TMRGD,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.35,79.35,Yes\n8108-UXRQN,Female,0,Yes,Yes,11,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,50.55,565.35,No\n9191-MYQKX,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.15,496.9,Yes\n9919-YLNNG,Female,0,No,No,42,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.8,4327.5,No\n0318-ZOPWS,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.15,973.35,No\n4445-ZJNMU,Male,0,No,No,9,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.3,918.75,No\n4808-YNLEU,Female,0,Yes,No,35,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Bank transfer (automatic),62.15,2215.45,No\n1862-QRWPE,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1057,No\n2796-NNUFI,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,927.1,No\n3016-KSVCP,Male,0,Yes,No,29,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,33.75,1009.25,No\n4767-HZZHQ,Male,0,Yes,Yes,30,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),82.05,2570.2,No\n2424-WVHPL,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,74.7,74.7,No\n7233-PAHHL,Male,0,Yes,Yes,66,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84,5714.25,No\n6067-NGCEU,Female,0,No,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),111.05,7107,No\n9848-JQJTX,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),100.9,7459.05,No\n8637-XJIVR,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,927.35,Yes\n9803-FTJCG,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),66.85,4748.7,No\n0278-YXOOG,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,21.05,113.85,Yes\n3212-KXOCR,Male,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21,1107.2,No\n4598-XLKNJ,Female,1,Yes,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,2514.5,Yes\n6380-ARCEH,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No\n3679-XASPY,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.45,19.45,No\n7123-WQUHX,Male,0,No,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),95,3605.6,No\n5386-THSLQ,Female,1,Yes,No,66,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),45.55,3027.25,No\n3192-NQECA,Male,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110,7611.85,Yes\n6180-YBIQI,Male,0,No,No,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.3,100.2,No\n6728-DKUCO,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,104.15,7303.05,No\n9750-BOOHV,Female,0,No,No,32,No,No phone service,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,30.15,927.65,No\n8597-CWYHH,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Mailed check,94.35,3921.3,No\n2848-YXSMW,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1363.25,No\n0486-HECZI,Male,0,Yes,No,55,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,96.75,5238.9,Yes\n4549-ZDQYY,Female,0,No,No,52,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),57.95,3042.25,No\n5712-AHQNN,Female,0,No,No,43,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.65,3954.1,No\n4846-WHAFZ,Female,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,2868.15,Yes\n5256-SKJGO,Female,0,Yes,Yes,64,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,54.6,3423.5,No\n3071-VBYPO,Male,0,Yes,Yes,3,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,89.85,248.4,No\n9560-BBZXK,Female,0,No,No,36,No,No phone service,DSL,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),31.05,1126.35,No\n5299-RULOA,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,1064.65,Yes\n8402-OOOHJ,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.65,835.15,No\n9445-ZUEQE,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),85.2,2151.6,No\n1091-SOZGA,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),99.8,5515.45,No\n2928-HLDBA,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,112.75,No\n0404-SWRVG,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,229.55,Yes\n6497-TILVL,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.7,350.35,No\n7219-TLZHO,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,62.9,No\n4622-YNKIJ,Male,0,No,No,33,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Two year,Yes,Electronic check,88.95,3027.65,No\n4412-YLTKF,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.05,2135.5,Yes\n6734-PSBAW,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),23.55,1723.95,No\n3930-ZGWVE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,No\n2639-UGMAZ,Male,1,No,No,71,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,56.45,3985.35,No\n2876-GZYZC,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.95,1215.65,No\n6207-WIOLX,Female,0,Yes,Yes,25,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),58.6,1502.65,Yes\n8587-XYZSF,Male,0,No,No,67,Yes,No,DSL,No,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),50.55,3260.1,No\n3091-FYHKI,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.45,35.45,Yes\n2372-HWUHI,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.35,81.25,Yes\n7799-LGRDP,Female,0,No,No,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.7,1188.2,No\n7850-VWJUU,Female,0,No,No,23,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75,1778.5,No\n3774-VBNXY,Female,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,1277.75,No\n6217-KDYWC,Male,0,No,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.6,1170.55,No\n0390-DCFDQ,Female,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.45,70.45,Yes\n3146-MSEGF,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.05,6425.65,No\n4080-OGPJL,Female,0,No,No,8,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,71.15,563.65,Yes\n1095-WGNGG,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),101.05,5971.25,No\n2636-SJDOU,Male,0,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),84.3,5289.05,No\n1131-QQZEB,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.95,1756.2,No\n5716-EZXZN,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),99.05,6416.7,No\n6837-BJYDQ,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,61.35,No\n2135-RXIHG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.65,45.65,Yes\n6440-DKQGE,Male,0,No,Yes,30,Yes,No,DSL,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),64.5,1929.95,No\n3466-BYAVD,Male,0,Yes,Yes,15,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,69.5,1071.4,No\n3780-YVMFA,Female,0,Yes,Yes,8,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,68.55,564.35,No\n3874-EQOEP,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,95,655.5,Yes\n1679-JRFBR,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),108.15,7930.55,No\n9073-ZZIAY,Male,0,Yes,Yes,62,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,86.1,5215.25,No\n3077-RSNTJ,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,113.5,No\n6551-GNYDG,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),80.9,1152.8,No\n9167-APMXZ,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),84.15,1821.95,No\n2749-CTKAJ,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,419.9,No\n6371-NZYEG,Male,0,Yes,Yes,16,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Two year,No,Mailed check,64.25,1024,No\n7554-NEWDD,Male,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.7,251.6,No\n8992-VONJD,Female,0,No,Yes,13,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,56,764.55,No\n0867-MKZVY,Female,0,Yes,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,82.4,1592.35,Yes\n4482-EWFMI,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,135.2,No\n4648-YPBTM,Male,0,No,No,53,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),73.9,3958.25,No\n2907-ILJBN,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,233.9,No\n6345-FZOQH,Male,0,Yes,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,1363.45,No\n3376-BMGFE,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.9,273,Yes\n5997-OPVFA,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.05,6254.45,No\n3445-HXXGF,Male,1,Yes,No,58,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,45.3,2651.2,Yes\n1159-WFSGR,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.4,321.4,No\n7654-YWJUF,Male,0,Yes,No,43,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),84.25,3539.25,No\n1875-QIVME,Female,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,242.8,Yes\n6727-IOTLZ,Male,0,Yes,No,14,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81.95,1181.75,No\n0691-JVSYA,Female,0,Yes,No,53,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94.85,5000.2,Yes\n5918-VUKWP,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.55,654.55,No\n1744-JHKYS,Female,0,Yes,No,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,24.7,780.2,No\n2656-FMOKZ,Female,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.45,1145.7,Yes\n2070-FNEXE,Female,1,No,No,7,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),76.45,503.6,Yes\n5947-SGKCL,Female,0,Yes,Yes,15,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,1559.25,No\n3712-PKXZA,Male,0,Yes,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,1252,No\n6317-YPKDH,Female,0,No,No,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.95,29.95,Yes\n6582-OIVSP,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.3,45.3,No\n9367-WXLCH,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),84.5,662.65,Yes\n5524-KHNJP,Male,0,Yes,Yes,33,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),74.75,2453.3,No\n1918-ZBFQJ,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.25,1111.65,Yes\n1024-GUALD,Female,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.8,24.8,Yes\n4827-USJHP,Male,0,No,No,20,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,51.8,1023.85,No\n8167-GJLRN,Male,0,No,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,30.4,82.15,No\n0956-SYCWG,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.65,244.8,No\n8017-UVSZU,Female,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),56.6,2379.1,No\n7100-FQPRV,Male,0,Yes,Yes,43,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),71.9,3173.35,No\n2472-OVKUP,Male,0,Yes,No,6,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,91,531,Yes\n2984-RGEYA,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1375.4,No\n9680-NIAUV,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),109.7,8129.3,No\n2146-EGVDT,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.3,1192.7,No\n2604-IJPDU,Female,0,Yes,No,20,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,96.55,1901.65,No\n9178-JHUVJ,Male,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.1,587.4,No\n6168-YBYNP,Male,0,No,No,59,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.35,6519.75,No\n7255-SSFBC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.25,8041.65,No\n3645-DEYGF,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,20.75,No\n9323-HGFWY,Female,0,Yes,No,27,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),101.9,2681.15,No\n8544-GOQSH,Female,0,No,No,14,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),80.05,1112.3,No\n3363-DTIVD,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Electronic check,105.55,7405.5,No\n7018-WBJNK,Male,0,No,Yes,13,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),78.3,1033.95,No\n9142-KZXOP,Male,0,No,No,44,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),68.85,2958.95,No\n7674-YTAFD,Female,0,No,No,33,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),79.95,2684.85,No\n6348-SNFUS,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),55.45,4179.2,No\n1285-OKIPP,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,79.9,79.9,Yes\n7825-ECJRF,Female,0,No,No,19,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.6,1934.45,Yes\n1347-KTTTA,Male,0,Yes,No,64,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),102.45,6654.1,No\n7841-TZDMQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),46,84.5,Yes\n4195-NZGTA,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,25.25,No\n7157-SMCFK,Male,0,No,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,1124.2,No\n4709-LKHYG,Female,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20,540.05,No\n2504-DSHIH,Male,1,Yes,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.8,1975.85,No\n0699-NDKJM,Female,0,Yes,No,57,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),58.75,3437.45,No\n9286-BHDQG,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),45.25,3139.8,No\n0230-WEQUW,Male,0,Yes,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),56.6,3789.2,No\n2040-LDIWQ,Male,0,Yes,Yes,65,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.2,5324.5,No\n6496-JDSSB,Female,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),80,624.6,No\n9408-SSNVZ,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,268.35,Yes\n4443-EMBNA,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1836.9,No\n6469-MRVET,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.2,20.2,No\n0742-MOABM,Male,0,Yes,No,4,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50.05,179.35,Yes\n5961-VUSRV,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.35,219.35,No\n6778-JFCMK,Male,0,No,No,24,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Mailed check,50.6,1288.75,No\n6341-JVQGF,Female,0,Yes,Yes,31,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,81.15,2545.75,No\n2232-DMLXU,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.2,55.2,Yes\n4811-JBUVU,Male,0,No,No,30,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.9,2723.15,No\n0945-TSONX,Female,0,Yes,Yes,47,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.3,4107.25,No\n2651-ZCBXV,Male,0,No,No,54,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108,5760.65,No\n3316-UWXUY,Male,0,No,No,50,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),93.5,4747.5,No\n8937-RDTHP,Male,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,84.6,84.6,Yes\n7083-MIOPC,Female,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1566.9,No\n1984-GPTEH,Female,0,No,No,29,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25.15,702,No\n1251-KRREG,Male,0,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,54.4,114.1,Yes\n0621-JFHOL,Female,0,No,No,10,No,No phone service,DSL,No,No,No,Yes,No,No,Two year,Yes,Mailed check,29.6,299.05,No\n9903-LYSAB,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.15,1305.95,No\n0094-OIFMO,Female,1,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95,1120.3,Yes\n9227-UAQFT,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,284.35,No\n7301-ABVAD,Female,0,No,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.6,6350.5,No\n6614-FHDBO,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),109.2,7878.3,No\n7576-ASEJU,Female,0,Yes,Yes,41,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),74.7,3187.65,No\n9058-HRZSV,Female,1,Yes,No,65,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,94.4,6126.15,No\n4522-AKYLR,Female,1,No,No,13,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,54.8,731.3,No\n0221-WMXNQ,Male,1,No,No,4,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.35,273.4,No\n0303-UNCIP,Male,0,No,No,41,Yes,Yes,DSL,No,No,Yes,No,No,Yes,One year,No,Mailed check,65,2531.8,No\n9947-OTFQU,Male,1,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,1074.3,Yes\n0322-YINQP,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.55,48.55,Yes\n0959-WHOKV,Male,0,No,No,42,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,99,4298.45,No\n4075-JFPGR,Female,0,Yes,No,51,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,93.5,4619.55,No\n4629-NRXKX,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,147.15,Yes\n9514-JDSKI,Male,1,Yes,No,1,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,40.2,40.2,Yes\n3282-ZISZV,Male,0,No,Yes,32,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),83.7,2633.3,No\n3675-YDUPJ,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.85,193.05,No\n4111-BNXIF,Female,0,Yes,Yes,67,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,59.55,4103.9,No\n7017-VFHAY,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.1,7008.15,No\n6655-LHBYW,Male,0,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),114.35,5791.1,No\n4959-JOSRX,Female,0,Yes,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.6,80.55,Yes\n5046-NUHWD,Female,1,Yes,No,29,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,45,1228.65,No\n7273-TEFQD,Male,1,No,No,3,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,41.15,132.2,Yes\n3606-TWKGI,Male,1,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.9,1364.3,Yes\n7529-ZDFXI,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.85,4925.35,No\n7605-BDWDC,Female,0,No,No,31,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),49.85,1520.1,No\n1950-KSVVJ,Female,0,Yes,No,45,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,113.3,5032.25,No\n0123-CRBRT,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,88.1,5526.75,No\n6292-TOSSS,Male,0,No,No,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.9,1195.25,No\n3197-ARFOY,Female,1,No,No,19,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,105,2007.25,No\n6323-AYBRX,Male,0,No,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,1099.6,Yes\n7014-ZZXAW,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.25,1732.95,No\n4385-GZQXV,Female,1,No,No,16,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,1511.2,Yes\n7633-MVPUY,Male,0,Yes,No,57,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Electronic check,59.75,3450.15,No\n6366-ZGQGL,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),24.8,24.8,Yes\n4716-HHKQH,Male,1,Yes,No,20,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,107.05,2172.05,No\n5940-AHUHD,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.6,70.6,Yes\n6432-TWQLB,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.4,401.1,Yes\n4484-GLZOU,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.05,5624.85,Yes\n3179-GBRWV,Male,1,Yes,No,21,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.95,1339.8,No\n8645-KWHJO,Male,0,No,No,14,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,55,771.95,No\n4130-MZLCC,Female,0,No,No,5,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.55,244.75,No\n0314-TKOSI,Female,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,55.15,322.9,No\n8229-MYEJZ,Female,0,No,No,10,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,51.2,498.25,No\n2080-SRCDE,Female,0,No,Yes,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.4,25.4,No\n9577-WJVCQ,Female,0,No,No,68,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,54.45,3687.75,No\n9512-UIBFX,Male,0,Yes,Yes,18,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,95.15,1779.95,Yes\n6202-DYYFX,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,One year,Yes,Credit card (automatic),76,1783.6,No\n3808-HFKDE,Female,0,No,No,20,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.35,927.15,No\n5583-SXDAG,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70,70,Yes\n3488-PGMQJ,Male,1,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,606.55,Yes\n3580-REOAC,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.85,435.4,Yes\n7534-BFESC,Male,1,No,No,24,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.1,1712.7,Yes\n3727-OWVYD,Male,0,No,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Mailed check,61.2,2021.2,No\n2294-SALNE,Male,0,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,No,Mailed check,86.8,1940.8,No\n4847-TAJYI,Female,1,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.35,567.8,No\n1563-IWQEX,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,220.35,No\n8203-XJZRC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,20.25,No\n6556-DBKZF,Female,0,Yes,Yes,71,Yes,No,Fiber optic,No,No,Yes,No,No,No,Two year,No,Electronic check,76.05,5436.45,No\n6851-WEFYX,Male,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,3437.5,No\n2985-JUUBZ,Male,0,Yes,Yes,40,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.55,3015.75,No\n6390-DSAZX,Female,0,No,Yes,1,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.6,73.6,Yes\n0895-LMRSF,Male,0,No,No,23,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),64.9,1509.8,No\n8098-LLAZX,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,396.1,Yes\n8266-VBFQL,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.4,356.65,No\n8181-YHCMF,Female,0,Yes,Yes,68,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),60.3,4109,No\n2240-HSJQD,Male,0,No,Yes,38,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,81.85,3141.7,No\n1248-DYXUB,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.8,1229.1,No\n0265-EDXBD,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,2303.35,Yes\n4115-BNPJY,Male,0,Yes,Yes,29,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,75.55,2054.4,No\n3167-SNQPL,Male,1,Yes,Yes,38,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,3741.85,No\n4091-TVOCN,Male,0,No,Yes,48,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),78.75,3682.45,No\n1098-TDVUQ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.25,19.25,No\n7277-OZCGZ,Female,0,No,No,22,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.05,1886.25,No\n1557-EMYVT,Female,0,No,No,43,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.05,4895.1,No\n2799-ARNLO,Female,1,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,341.6,No\n7563-BIUPC,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.6,415.55,Yes\n5027-YOCXN,Male,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),110.05,5686.4,No\n3973-SKMLN,Male,0,No,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.9,1355.1,No\n2321-OMBXY,Female,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),80.3,3058.65,Yes\n2840-XANRC,Male,1,Yes,No,24,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.15,2231.05,Yes\n6745-JEFZB,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),91.5,3236.35,No\n5020-ZSTTY,Female,1,No,No,54,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),82.45,4350.1,Yes\n9880-TDQAC,Female,0,Yes,Yes,72,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,60,4264,No\n8705-WZCYL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.8,44.8,No\n7102-JJVTX,Female,0,Yes,Yes,9,Yes,No,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,48.6,422.3,No\n8626-PTQGE,Male,0,No,No,69,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.05,4176.7,No\n4983-CLMLV,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.7,5138.1,No\n5701-YVSVF,Female,1,Yes,No,11,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,82.9,880.05,No\n5804-LEPIM,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,139.05,Yes\n5697-GOMBF,Female,1,Yes,Yes,28,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,35.9,973.65,No\n2739-CACDQ,Female,1,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),82.65,1470.05,No\n9385-EHGDO,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,739.35,No\n9498-FIMXL,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.2,161.95,No\n2379-GYFLQ,Male,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),94.9,4422.95,No\n0122-OAHPZ,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.85,511.25,Yes\n2868-SNELZ,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,80.6,155.8,Yes\n4322-RCYMT,Male,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.8,5293.95,Yes\n6680-NENYN,Female,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,104.6,4759.85,Yes\n2088-IEBAU,Female,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,88.15,6148.45,No\n7982-VCELR,Female,0,No,No,36,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.8,3565.65,No\n1343-EHPYB,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.4,6603,Yes\n6035-BXTTY,Female,1,No,No,32,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.65,1830.1,No\n6885-PKOAM,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),85.75,6223.8,No\n7520-HQWJU,Female,0,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),67.45,4508.65,No\n9639-BUJXT,Male,0,No,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.5,1328.15,No\n5924-SNGKP,Female,0,No,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.25,865,No\n0021-IKXGC,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,72.1,72.1,No\n2034-GDRCN,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.4,168.2,Yes\n8966-SNIZF,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.45,1303.5,No\n6243-OZGFH,Female,0,No,No,23,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.95,996.85,No\n4654-DLAMQ,Female,1,Yes,No,64,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),97,6430.9,No\n0513-RBGPE,Male,0,Yes,Yes,37,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),62.8,2278.75,No\n5160-UXJED,Male,0,No,Yes,17,Yes,No,DSL,No,No,No,No,No,No,One year,No,Mailed check,44.6,681.4,No\n4115-NZRKS,Female,1,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.15,574.35,No\n0219-YTZUE,Male,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,371.9,Yes\n0623-IIHUG,Female,1,No,No,21,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,41.9,840.1,Yes\n4572-DVCGN,Female,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),80.25,846,Yes\n3351-NGXYI,Female,1,No,No,16,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,54.1,889,No\n8984-EYLLL,Male,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,105.25,6823.4,No\n9057-MSWCO,Male,1,Yes,No,27,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),30.75,805.1,Yes\n9833-TGFHX,Male,0,Yes,Yes,42,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Electronic check,97.1,4016.75,No\n9294-TDIPC,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,83.75,No\n5229-DTFYB,Female,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),98.8,3959.15,No\n0104-PPXDV,Male,0,Yes,No,58,Yes,No,DSL,No,No,Yes,No,No,No,One year,No,Credit card (automatic),50.3,2878.55,No\n5176-LMJXE,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.55,945.7,No\n3583-KRKMD,Male,0,No,No,18,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),75.9,1373.05,No\n1010-DIAUQ,Male,0,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),96.5,492.55,Yes\n9069-LGEUL,Male,0,Yes,No,23,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),59.95,1406,No\n7302-ZHMHP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.15,19.15,No\n9571-EDEBV,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),98.65,6962.85,No\n3520-FJGCV,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),112.6,8126.65,No\n6563-VRERX,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,690.25,No\n0259-GBZSH,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.65,181.5,Yes\n6122-EFVKN,Male,0,No,Yes,24,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,35.75,830.8,No\n2805-EDJPQ,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),99.75,5608.4,No\n6862-CQUMB,Male,0,No,No,37,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),96.1,3646.8,No\n7156-MXBJE,Female,0,No,No,43,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),85.1,3662.25,No\n6158-HDPXZ,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.35,25.35,No\n9601-BRXPO,Female,0,Yes,No,25,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.95,2566.5,Yes\n2863-IMQDR,Female,0,No,No,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,89.65,5308.7,No\n5686-CMAWK,Male,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,86.75,1410.25,No\n5651-CRHKQ,Female,0,Yes,No,41,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),86.2,3339.05,No\n6905-NIQIN,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.65,50.65,Yes\n8204-YJCLA,Male,1,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),64.8,4732.35,No\n5167-ZFFMM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.85,90.85,Yes\n6583-SZVGP,Male,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.1,5067.45,No\n4895-TMWIR,Male,1,Yes,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,214.75,Yes\n0533-BNWKF,Female,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.45,4874.7,Yes\n1708-PBBOA,Female,0,No,No,42,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,54.75,2348.45,No\n8782-LKFPK,Male,0,No,No,44,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,90.4,4063,No\n5522-JBWMO,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No\n3597-MVHJT,Female,0,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.6,2595.25,No\n9774-NRNAU,Male,1,Yes,No,27,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),84.8,2309.55,No\n0224-RLWWD,Female,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.3,89.3,No\n9967-ATRFS,Female,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,367.55,No\n3951-NJCVI,Female,1,Yes,No,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.05,3944.5,No\n2977-CEBSX,Female,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.05,5965.95,No\n0177-PXBAT,Male,1,Yes,No,33,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.9,3694.7,No\n6599-CEBNN,Female,0,No,No,34,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.95,2524.45,Yes\n2519-ERQOJ,Male,1,No,No,33,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,54.6,1803.7,No\n5876-QMYLD,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,415.1,No\n2277-AXSDC,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.75,624.15,No\n9442-JTWDL,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,237.7,No\n0979-PHULV,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.45,7007.6,Yes\n3067-SVMTC,Female,0,Yes,No,68,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),55.9,3848.8,No\n5495-GPSRW,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,419.4,No\n7606-BPHHN,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1468.75,No\n4742-DRORA,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),95.4,5812,No\n0111-KLBQG,Male,1,Yes,Yes,32,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.95,2861.45,No\n4800-VHZKI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,19.9,Yes\n7989-CHGTL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,19.6,Yes\n0334-GDDSO,Male,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.35,233.7,Yes\n4163-NCJAK,Female,0,Yes,No,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.45,1066.15,No\n5233-AOZUF,Female,0,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,2149.05,No\n5973-EJGDP,Male,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,87.35,4473,No\n1996-DBMUS,Female,1,Yes,No,48,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.65,3545.05,No\n7916-VCCPB,Female,0,Yes,Yes,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),73.25,1195.75,No\n4686-GEFRM,Male,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),98.7,6858.9,No\n5249-QYHEX,Female,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1024.7,No\n0578-SKVMF,Female,0,Yes,Yes,22,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,83.3,1845.9,Yes\n5564-NEMQO,Female,1,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.3,75.3,Yes\n2233-FAGXV,Female,0,Yes,Yes,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.3,132.25,No\n5605-IYGFG,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.85,515.45,No\n7663-ZTEGJ,Male,0,No,Yes,29,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),100.55,2830.45,No\n3935-TBRZZ,Male,0,Yes,Yes,44,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.7,1110.5,No\n8111-BKVDS,Female,0,No,No,10,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),40.7,449.3,No\n2055-SIFSS,Female,1,Yes,No,55,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),51.65,2838.55,No\n2806-MLNTI,Male,1,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.1,5376.4,No\n8734-DKSTZ,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.95,858.6,No\n4360-PNRQB,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.6,1395.05,No\n6152-ONASV,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),58.25,3975.7,No\n9063-ZGTUY,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,1182.55,Yes\n7781-HVGMK,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.2,4784.45,No\n2181-UAESM,Male,0,No,No,2,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Electronic check,53.45,119.5,No\n2957-LOLHO,Male,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.4,518.9,Yes\n6048-NJXHX,Male,0,Yes,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,19.75,899.45,No\n2320-SLKMB,Female,0,No,No,26,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),44.45,1183.8,No\n4980-URKXC,Male,0,Yes,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.85,720.05,No\n4376-KFVRS,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.05,8468.2,No\n5886-VLQVU,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.85,3161.2,No\n3577-AMVUX,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,55.05,55.05,No\n0771-WLCLA,Female,0,Yes,Yes,16,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,112.95,1882.55,No\n5628-RKIFK,Female,1,No,No,49,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),101.55,5070.4,No\n0206-TBWLC,Female,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,114.65,6049.5,No\n2937-FTHUR,Female,0,No,Yes,18,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,64.8,1166.7,No\n1910-FMXJM,Female,0,Yes,No,36,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.4,2937.65,No\n7752-XUSCI,Female,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,6396.45,Yes\n4110-PFEUZ,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.55,69.55,Yes\n0732-OCQOC,Female,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.05,1270.25,No\n5168-MSWXT,Male,0,Yes,Yes,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,759.55,No\n1090-ESELR,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),105.5,7611.55,No\n8592-PLTMQ,Female,0,No,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.7,1642.75,No\n5760-WRAHC,Female,1,No,No,22,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,69.75,1545.4,No\n8847-GEOOQ,Male,0,Yes,No,60,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),60.2,3582.4,No\n0256-LTHVJ,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.05,2227.1,Yes\n4785-FCIFB,Female,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.4,1417.9,No\n8313-NDOIA,Female,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.15,2494.65,No\n5149-CUZUJ,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),92.9,2768.35,No\n0942-KOWSM,Female,0,Yes,Yes,30,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.8,2369.3,No\n4237-CLSMM,Male,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,38,No\n1452-VOQCH,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,75.1,No\n4719-UMSIY,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,100.9,No\n6614-VBEGU,Female,0,Yes,No,24,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),69.45,1614.05,No\n0880-TKATG,Male,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,385.9,Yes\n3811-VBYBZ,Male,0,No,No,7,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,673.25,Yes\n1480-BKXGA,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,8404.9,No\n2996-XAUVF,Male,0,No,No,70,No,No phone service,DSL,No,No,No,Yes,No,Yes,Two year,Yes,Mailed check,40.05,2799.75,No\n9076-AXYIK,Male,1,Yes,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,102.1,6538.45,No\n5968-XQIVE,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,89.7,6588.95,No\n8896-RAZCR,Female,0,No,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,868.1,No\n4640-UHDOS,Female,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,55.95,734.35,Yes\n4933-IKULF,Female,1,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,330.6,No\n3583-EKAPL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55,55,Yes\n1304-BCCFO,Male,0,Yes,No,9,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,70.05,564.4,No\n4104-PVRPS,Male,0,Yes,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,53.6,1315.35,No\n9399-APLBT,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes\n2359-KMGLI,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,1861.5,Yes\n3780-DDGSE,Male,1,Yes,Yes,35,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,76.05,2747.2,No\n4431-EDMIQ,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,75.7,554.05,No\n0306-JAELE,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96.1,453.4,Yes\n6227-HWPWX,Female,0,No,Yes,15,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69,994.8,Yes\n0486-LGCCH,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,225.75,No\n0447-BEMNG,Female,0,Yes,No,48,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),45.3,2145,Yes\n4612-SSVHJ,Female,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.45,1671.6,No\n5168-MQQCA,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.5,8003.8,No\n5949-XIKAE,Female,0,Yes,Yes,8,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.55,680.05,Yes\n7971-HLVXI,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Two year,Yes,Credit card (automatic),84.5,6130.85,No\n9094-AZPHK,Female,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.15,1415,No\n3649-JPUGY,Male,0,No,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),88.6,6201.95,No\n4472-LVYGI,Female,0,Yes,Yes,0,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),52.55, ,No\n8372-JUXUI,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,74.35,Yes\n3552-CTCYF,Male,0,Yes,Yes,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.8,6597.25,No\n6778-YSNIH,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,59,114.15,No\n0388-EOPEX,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,139.4,Yes\n5756-OZRIO,Male,1,Yes,No,61,Yes,Yes,DSL,No,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),64.05,3902.6,No\n6579-JPICP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,No\n8205-OTCHB,Male,0,No,No,22,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),43.75,903.6,Yes\n4134-BSXLX,Male,0,Yes,No,28,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,60.9,1785.65,No\n0505-SPOOW,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,1397.65,No\n6235-VDHOM,Female,1,No,No,5,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,28.45,131.05,Yes\n7783-YKGDV,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),99.7,1238.45,Yes\n4374-YMUSQ,Male,0,No,No,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),116.25,3899.05,No\n4513-CXYIX,Female,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,Yes,Credit card (automatic),80.7,5676,No\n3957-HHLMR,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,No,No,No,Yes,One year,No,Bank transfer (automatic),65.2,4543.15,No\n7803-XOCCZ,Female,0,Yes,Yes,52,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),84.05,4326.8,No\n5736-YEJAX,Male,0,No,Yes,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),79.45,5502.55,No\n5609-CEBID,Female,1,No,No,20,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.1,1782.4,Yes\n8981-FJGLA,Male,0,No,Yes,11,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,78,851.8,No\n7218-HKQFK,Male,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,167.5,Yes\n4636-QRJKY,Female,0,Yes,Yes,6,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.5,502.85,Yes\n1135-LMECX,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,19.85,No\n4332-MUOEZ,Male,1,Yes,Yes,20,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),94.3,1818.3,No\n8535-SFUTN,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.45,6300.15,No\n5956-VKDTT,Female,1,Yes,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,334.8,Yes\n8677-HDZEE,Female,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.45,5916.95,No\n2475-MROZF,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),95,2852.4,No\n9412-GHEEC,Male,0,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),104.8,4131.95,Yes\n3482-ABPKK,Female,0,No,No,28,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54.3,1546.3,No\n6705-LXORM,Female,1,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.05,302.6,No\n0257-ZESQC,Female,1,Yes,No,27,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.2,1929.35,Yes\n7531-GQHME,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,265.45,No\n5174-ITUMV,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.4,6989.45,No\n4109-CYRBD,Male,1,Yes,No,29,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.6,1442,No\n0913-XWSCN,Male,0,Yes,Yes,55,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),85.5,4713.4,No\n6825-UYPFK,Female,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.6,1758.6,Yes\n8397-MVTAZ,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3480,Yes\n0750-EBAIU,Male,0,No,No,52,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Electronic check,91.25,4738.3,No\n8606-CIQUL,Male,1,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.75,8399.15,No\n3571-DPYUH,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),94.7,5430.35,No\n7601-GNDYK,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,686.95,No\n0356-OBMAC,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),99.9,5706.3,No\n8067-NIOYM,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),21.1,490.65,No\n1403-GYAFU,Male,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1360.25,No\n4234-XTNEA,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,174.45,No\n1297-VQDRP,Male,1,Yes,Yes,68,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),107.15,7379.8,No\n9282-IZGQK,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,Yes\n5348-CAGXB,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.55,1021.75,No\n0621-HJWXJ,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),81.55,5029.05,No\n5844-QVTAT,Female,0,Yes,Yes,33,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,58.45,1955.4,No\n8905-IAZPF,Female,0,Yes,No,69,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.65,6744.2,No\n5394-MEITZ,Female,0,Yes,Yes,60,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.6,4946.7,No\n6859-QNXIQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.1,8248.5,No\n2782-LFZVW,Female,0,No,No,11,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,58.95,601.6,No\n2866-IKBTM,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No\n1342-JPNKI,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),86.05,834.1,Yes\n2817-NTQDO,Male,0,No,No,13,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),45.55,597,Yes\n7129-AZJDE,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Bank transfer (automatic),78.95,2647.2,No\n6986-IJDHX,Male,0,Yes,Yes,39,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,86.3,3266,Yes\n2560-PPCHE,Female,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.05,6744.25,No\n4676-MQUEA,Male,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),101.9,5265.5,No\n8138-EALND,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,311.6,No\n3580-HYCSP,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.3,7966.9,No\n1352-HNSAW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.6,8220.4,No\n2075-PUEPR,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1153.25,No\n1982-FEBTD,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.6,514.75,No\n5301-GAUUY,Male,0,No,No,32,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.35,2596.15,Yes\n5791-KAJFD,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),68.75,3808,No\n2654-VBVPB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.9,19.9,No\n1154-HYWWO,Male,0,No,No,38,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Mailed check,70.6,2708.2,No\n2501-XWWTZ,Male,0,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.2,760.05,No\n3716-UVSPD,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.3,49.3,No\n6815-ABQFQ,Male,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,107.25,6033.3,No\n7343-EOBEU,Male,0,Yes,No,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,23.6,89.05,No\n3701-SFMUH,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),69.7,516.15,No\n6103-LIANB,Male,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),99.5,5861.75,No\n7319-VENRZ,Male,0,No,No,7,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),64.3,445.95,No\n5846-NEQVZ,Male,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),70.85,4973.4,No\n6967-QIQRV,Male,0,Yes,Yes,15,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,No,Electronic check,101.9,1667.25,No\n5781-RFZRP,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),73.5,5357.75,No\n0939-YAPAF,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,3527.6,Yes\n0308-IVGOK,Female,0,No,No,11,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),40.4,422.6,No\n7293-LSCDV,Female,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.25,1103.25,No\n7025-WCBNE,Male,1,No,No,47,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),59.6,2754,No\n5756-JYOJT,Female,0,No,No,11,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),64.9,697.25,No\n4710-FDUIZ,Male,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),100.3,5614.45,Yes\n6030-REHUX,Female,1,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.85,3204.4,No\n9548-LIGTA,Male,0,Yes,No,61,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,81.05,4747.65,No\n5150-LJNSR,Male,0,Yes,Yes,31,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Bank transfer (automatic),98.05,3082.1,No\n8270-RKSAP,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.5,597.9,No\n6522-YRBXD,Male,1,Yes,No,35,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,94.55,3365.4,No\n2640-LYMOV,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,38.8,No\n1218-VKFPE,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,233.55,Yes\n3627-FHKBK,Female,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,75.3,75.3,Yes\n2865-TCHJW,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,346.2,Yes\n1423-BMPBQ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19,19,No\n2393-DIVAI,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20,61.7,No\n5192-EBGOV,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.7,85.7,Yes\n4568-KNYWR,Male,0,No,No,52,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),63.25,3342.45,No\n8752-IMQOS,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,85.1,No\n0742-LAFQK,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,99.15,7422.1,No\n0795-LAFGP,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.4,6668.05,No\n0619-OLYUR,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.9,8071.05,No\n5512-IDZEI,Male,0,Yes,Yes,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.9,1174.8,No\n0459-SPZHJ,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),83.5,5435,No\n0215-BQKGS,Male,0,No,No,30,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.3,2438.6,No\n9244-ZVAPM,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.6,45.6,No\n0719-SYFRB,Female,0,No,No,12,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,61.65,713.75,Yes\n8208-EUMTE,Male,0,No,No,16,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,54.85,916.15,No\n5172-MIGPM,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,65.55,237.2,No\n1710-RCXUS,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),90.35,4614.55,No\n0374-FIUCA,Male,0,Yes,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.4,1414.45,No\n5839-SUYVZ,Male,0,No,No,16,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),74.55,1170.5,No\n5173-ZXXXL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,47.7,No\n1096-ADRUX,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),74.25,4859.25,No\n2001-MCUUW,Male,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,108.65,4903.2,No\n2731-GJRDG,Female,0,No,No,32,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.55,3608,No\n4723-BEGSG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.65,6094.25,No\n6516-NKQBO,Male,0,Yes,Yes,38,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),81,3084.9,No\n8672-OAUPW,Male,0,No,Yes,51,Yes,No,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),47.85,2356.75,No\n8207-DMRVL,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.55,8306.05,No\n3419-SNJJD,Female,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),105.25,6786.4,Yes\n6543-CPZMK,Male,0,Yes,Yes,9,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.95,248.95,Yes\n4765-OXPPD,Female,0,Yes,Yes,9,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Mailed check,65,663.05,Yes\n2804-ETQDK,Male,0,No,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1357.1,No\n6689-VRRTK,Female,1,No,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.8,4860.35,No\n7138-GIRSH,Male,0,No,No,50,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),69.5,3418.2,No\n9396-ZSFLL,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,48.85,631.4,No\n6464-KEXXH,Male,0,No,No,8,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.25,186.3,No\n7134-MJPDY,Female,1,No,No,66,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),102.85,6976.75,No\n5240-CAOYT,Female,0,No,No,57,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),87.55,4884.85,No\n4059-IIEBK,Female,0,No,No,7,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),78.55,522.95,No\n4881-JVQOD,Male,1,Yes,Yes,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),34.55,362.6,No\n0516-UXRMT,Female,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,92.05,5755.8,No\n4851-BQDNX,Male,0,Yes,Yes,40,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,85.05,3355.65,No\n5148-HKFIR,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,406.95,No\n1009-IRMNA,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20,137.6,Yes\n3003-CMDUU,Female,0,Yes,No,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.15,2395.7,No\n5016-IBERQ,Male,0,Yes,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,84.25,1968.1,No\n6797-UCJHZ,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),104.6,6819.45,No\n2469-DTSGX,Female,1,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,111.65,7943.45,No\n4554-YGZIH,Male,1,Yes,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),90.05,4547.25,Yes\n5099-BAILX,Male,1,Yes,Yes,43,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),110.75,4687.9,Yes\n9931-KGHOA,Female,0,Yes,No,46,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),55,2473.95,No\n1775-KWJKQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.85,6562.9,No\n7665-VIGUD,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,176.3,No\n9411-TPQQV,Female,0,No,No,40,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),54.55,2236.2,No\n7207-RMRDB,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,6985.65,Yes\n7954-MLBUN,Male,0,No,No,31,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),99.45,3109.9,No\n2077-DDHJK,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),70.9,4911.35,No\n4913-EHYUI,Male,1,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.55,5794.65,Yes\n0195-IESCP,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.25,855.3,Yes\n9574-BOSMD,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.4,1620.2,No\n4580-TMHJU,Female,0,Yes,Yes,43,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,56.15,2499.3,Yes\n0970-ETWGE,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.55,89.55,Yes\n4908-XAXAY,Female,1,No,No,49,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),89.85,4287.2,No\n8404-VLQFB,Female,0,Yes,Yes,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.25,394.85,No\n1626-ERCMM,Male,1,Yes,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.55,1899.65,Yes\n0887-HJGAR,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.7,45.7,Yes\n2391-IPLOP,Male,0,Yes,Yes,50,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,69.65,3442.15,No\n5644-PDMZC,Female,1,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.5,161.5,Yes\n3509-GWQGF,Male,1,No,No,24,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70,1732.6,No\n9576-ANLXO,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.55,222.3,Yes\n2024-BASKD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,74.6,74.6,Yes\n5845-BZZIB,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,655.3,No\n1140-UKVZG,Female,0,No,No,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.8,475.25,No\n4160-AMJTL,Female,1,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.65,164.3,Yes\n5183-SNMJQ,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.1,865.1,No\n8100-PNJMH,Male,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.85,6132.7,No\n7838-LAZFO,Male,0,Yes,No,45,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),78.8,3597.5,No\n4464-JCOLN,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.85,35.9,Yes\n2085-JVGAD,Male,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.35,697.65,No\n5650-VDUDS,Female,0,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.25,96.05,Yes\n8095-WANWK,Female,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.25,428.7,No\n3030-ZKIWL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No\n9565-FLVCG,Male,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Two year,Yes,Mailed check,69.55,4459.15,No\n8755-OGKNA,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.5,1167.6,No\n2800-VEQXM,Female,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,74.75,238.1,No\n7538-GWHML,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,145.15,Yes\n5533-RJFTJ,Male,0,No,No,49,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.2,1453.1,No\n3859-CVCET,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,191.05,Yes\n0214-JHPFW,Female,0,Yes,No,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),57.8,4039.3,No\n5642-MHDQT,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,1039.45,No\n3088-FVYWK,Male,0,Yes,Yes,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.55,1336.1,No\n3276-HDUEG,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.05,75.05,Yes\n9092-GDZKO,Male,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),24.85,493.4,No\n0823-HSCDJ,Male,1,No,No,52,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.15,2550.9,Yes\n3729-OWRVL,Male,1,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.35,7246.15,No\n2324-AALNO,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.55,1203.95,No\n0822-GAVAP,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,34.7,62.25,Yes\n5760-IFJOZ,Male,0,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,107.95,313.6,No\n2826-UWHIS,Male,0,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),81.4,3775.85,No\n1448-PWKYE,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80,80,Yes\n7501-IWUNG,Female,0,Yes,Yes,61,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),73.8,4616.05,No\n4957-TREIR,Male,0,No,No,3,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),64.4,195.65,No\n7251-LJBQN,Female,1,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),103.75,4188.4,No\n8040-MNRTF,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,71.1,71.1,No\n1536-HBSWP,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.9,49.9,No\n5313-FPXWG,Male,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.6,1266.4,No\n0067-DKWBL,Male,1,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.25,91.1,Yes\n0946-FKYTX,Male,0,No,No,52,No,No phone service,DSL,No,Yes,No,No,No,No,One year,No,Mailed check,30.1,1623.4,No\n5076-YVXCM,Male,0,No,No,51,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),83.4,4149.45,No\n8262-COGGB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,20.45,No\n6663-JOCQO,Male,0,Yes,Yes,31,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.25,2344.5,No\n9620-QJREV,Male,0,No,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.55,1013.05,No\n2276-YDAVZ,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.1,270.7,Yes\n2682-KEVRP,Female,1,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.05,417,No\n2480-JZOSN,Female,0,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.65,20.65,No\n0078-XZMHT,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.15,6316.2,No\n5896-NPFWW,Male,0,Yes,Yes,3,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,50.15,168.15,Yes\n9978-HYCIN,Male,1,Yes,Yes,47,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),84.95,4018.05,No\n8338-QIUNR,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),66.5,4811.6,No\n1525-LNLOJ,Male,0,Yes,Yes,66,Yes,Yes,DSL,No,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),63.3,4189.7,No\n9450-TRJUU,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,83.15,2848.45,No\n1766-GKNMI,Male,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,2516.2,No\n6942-LBFDP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,33.6,No\n1456-TWCGB,Male,0,No,No,4,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.25,208.45,No\n7133-VBDCG,Female,0,No,No,25,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.85,2015.35,Yes\n7596-ZYWBB,Female,0,No,No,65,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,59.6,3739.8,No\n8329-UTMVM,Male,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.65,2964,No\n3014-WJKSM,Male,0,Yes,No,29,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,2263.4,No\n3347-YJZZE,Male,0,Yes,Yes,29,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),80.1,2211.8,No\n1029-QFBEN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,Yes\n7929-DMBCV,Female,0,Yes,No,20,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,81,1683.7,No\n9661-JALZV,Female,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.7,1519,No\n5433-KYGHE,Female,0,No,Yes,14,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,86,1164.05,No\n4312-KFRXN,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1710.9,No\n5575-TPIZQ,Male,0,No,No,46,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.15,4245.55,No\n0114-IGABW,Female,0,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),58.25,4145.9,No\n9944-AEXBM,Male,0,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.65,2664.3,No\n1853-ARAAQ,Female,0,No,No,26,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.35,1277.5,No\n6952-OMNWB,Male,1,Yes,No,68,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.35,5589.3,No\n4697-LUPSU,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.2,34.75,No\n8434-VGEQQ,Male,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1305.95,No\n4952-YSOGZ,Female,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.95,381.3,Yes\n1589-AGTLK,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.35,141.5,Yes\n5244-IRFIH,Male,1,Yes,No,33,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.5,3105.55,Yes\n6549-YMFAW,Male,1,Yes,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,21.25,204.55,No\n4950-HKQTE,Female,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.25,605.9,No\n6786-OBWQR,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.85,356.1,No\n2684-EIWEO,Female,1,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Credit card (automatic),91.7,2758.15,Yes\n2753-JMMCV,Male,0,No,No,65,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),74.2,4805.65,No\n6439-GTPCA,Female,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,87.25,3941.7,Yes\n6621-YOBKI,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,92.75,No\n1216-JWVUX,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,1901.05,No\n7564-GHCVB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),79.05,5730.7,No\n1173-NOEYG,Female,0,Yes,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),90.15,2423.4,No\n7595-EHCDL,Male,0,Yes,Yes,32,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),50.6,1653.45,No\n6647-ZEDXT,Female,0,No,No,30,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),110.45,3327.05,No\n2521-NPUZR,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),101,7085.5,No\n1307-TVUFB,Male,1,No,No,42,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.35,3344.1,No\n7503-MIOGA,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.85,6697.35,No\n4381-MHQDC,Female,0,No,No,47,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Mailed check,65,2879.9,No\n6923-JHPMP,Female,0,No,No,2,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.45,137.1,No\n5138-WVKYJ,Male,0,No,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.55,1008.55,Yes\n4018-PPNDW,Female,0,Yes,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.1,1551.6,No\n1635-FJFCC,Female,0,No,No,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.05,202.15,No\n2499-AJYUA,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,7882.25,No\n6919-ELBGL,Male,1,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.95,8196.4,No\n3966-HRMZA,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,75.05,202.9,No\n6425-JWTDV,Male,0,Yes,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,855.1,No\n8405-IGQFX,Female,0,No,No,63,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),90.05,5817,No\n8224-IVVPA,Female,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,56.7,1652.95,No\n9477-LGWQI,Male,0,Yes,Yes,70,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),80.15,5600.15,No\n1410-RSCMR,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),71.35,515.75,No\n3115-CZMZD,Male,0,No,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25, ,No\n0139-IVFJG,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,90.35,190.5,No\n6683-VLCTZ,Male,1,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.55,1842.8,Yes\n5730-DBDSI,Male,0,No,No,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1253.8,No\n0030-FNXPP,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,57.2,No\n2189-WWOEW,Female,0,No,Yes,15,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.9,1269.55,Yes\n5684-FJVYR,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),90.35,6563.4,No\n4013-GUXND,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,20.8,No\n1894-IGFSG,Female,0,No,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.25,1907.85,Yes\n7379-POKDZ,Male,0,Yes,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,208.85,Yes\n1266-NZYUI,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),66.85,4758.8,No\n7969-FFOWG,Male,0,Yes,Yes,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,1292.6,No\n4718-DHSMV,Female,0,No,No,11,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,35.8,363.15,No\n5175-WLYXL,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,78.85,1600.25,No\n7817-OMJNA,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.4,275.7,No\n8728-SKJLR,Male,0,No,No,41,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,74.25,3089.1,No\n3137-NYQQI,Male,0,Yes,No,17,Yes,No,DSL,Yes,Yes,No,No,No,Yes,One year,No,Mailed check,64.8,1175.6,No\n7706-DZNKK,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,237.3,No\n0236-HFWSV,Male,0,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.35,1444.65,Yes\n3900-AQPHZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,Yes\n5842-POCOP,Female,0,Yes,No,5,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,88.9,454.15,Yes\n2037-XJFUP,Male,0,Yes,No,33,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,95.8,3036.75,Yes\n8823-RLPWL,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),110.65,8065.65,No\n9505-SQFSW,Female,0,Yes,Yes,3,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,40.3,92.5,No\n7314-OXENN,Male,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82,184.65,Yes\n3758-CKOQL,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),107,6152.3,No\n0322-CHQRU,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.35,89.5,Yes\n5676-CFLYY,Male,0,Yes,Yes,71,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),73.35,5154.5,No\n7521-AFHAB,Female,0,Yes,Yes,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.8,220.45,No\n0285-INHLN,Male,0,Yes,Yes,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),54.75,1510.3,No\n4678-DVQEO,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,52.2,52.2,Yes\n5125-CNDSP,Male,0,No,Yes,63,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),40.6,2588.95,No\n0691-IFBQW,Female,1,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,110,4874.8,Yes\n4992-LTJNE,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),55.3,3983.6,No\n2202-OUTMO,Female,0,Yes,No,34,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),60.85,2003.6,No\n0810-BDHAW,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,Yes,No,No,Yes,Yes,One year,No,Electronic check,78.4,1832.4,No\n0229-LFJAF,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.65,4908.25,No\n7131-ZQZNK,Female,0,Yes,Yes,60,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),59.85,3590.2,No\n3442-ZHHCC,Male,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),76.9,5023,No\n5726-CVNYA,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,146.6,No\n9871-ELEYA,Female,0,No,Yes,34,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),67.65,2339.3,No\n4257-GAESD,Female,0,No,No,6,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),45,298.7,No\n5173-WXOQV,Male,0,Yes,No,2,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,64.2,143.65,No\n2040-OBMLJ,Male,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,No,Credit card (automatic),81.7,2548.65,No\n6286-ZHAOK,Female,0,Yes,Yes,20,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.55,507.4,No\n3807-XHCJH,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,20,No\n3009-JWMPU,Male,0,No,No,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,96.75,6125.4,Yes\n5671-RQRLP,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,No,No,No,No,No,Two year,Yes,Credit card (automatic),75.65,5411.4,No\n1450-GALXR,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,1058.25,Yes\n8859-AXJZP,Male,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,23.8,903.8,No\n3174-AKMAS,Female,0,Yes,No,46,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),64.2,3009.5,No\n3138-BKYAV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.35,489.45,Yes\n9926-PJHDQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),76.8,5468.45,No\n7382-DFJTU,Male,0,No,No,18,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),55.2,1058.1,No\n2798-NYLMZ,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.55,7616,No\n4289-DTDKW,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.3,4113.1,Yes\n1820-TQVEV,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,69.55,Yes\n2239-JALAW,Male,0,No,No,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.25,6017.65,Yes\n4853-RULSV,Male,0,No,No,70,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104,7250.15,Yes\n8098-TDCBU,Female,0,Yes,No,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,25.25,1108.2,No\n3551-GAEGL,Male,0,Yes,Yes,34,No,No phone service,DSL,Yes,No,No,No,No,No,One year,No,Bank transfer (automatic),30.4,938.65,No\n4785-NKHCX,Male,1,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.05,94.15,No\n3196-NVXLZ,Female,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.6,2088.05,No\n6275-YDUVO,Female,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,86.2,178.7,Yes\n0036-IHMOT,Female,0,Yes,Yes,55,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.7,5656.75,No\n0115-TFERT,Male,0,Yes,No,21,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.2,2317.1,Yes\n4178-EGMON,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),88,5986.45,No\n4220-TINQT,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,6751.35,No\n5318-YKDPV,Male,0,Yes,Yes,43,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.15,3566.6,No\n7975-TZMLR,Male,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.1,4889.3,No\n0295-QVKPB,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),63.95,318.1,No\n4335-BSMJS,Female,0,No,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.8,1563.95,No\n2311-QYMUQ,Female,0,Yes,Yes,16,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.45,1430.25,Yes\n3643-AHCFP,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.6,644.35,Yes\n9146-JRIOX,Female,0,Yes,Yes,14,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.55,372.45,No\n3104-OWCGK,Male,0,Yes,Yes,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,No,Electronic check,90.95,5453.4,Yes\n5337-IIWKZ,Male,0,Yes,Yes,34,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,44.85,1442.6,No\n9101-BWFSS,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Electronic check,108.55,5610.7,Yes\n9650-VBUOG,Male,0,Yes,Yes,38,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,963.95,No\n3487-EARAT,Female,0,Yes,Yes,70,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),74.1,5222.3,No\n2672-TGEFF,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.8,3340.55,No\n9231-ZJYAM,Female,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.85,292.8,Yes\n4250-WAROZ,Male,1,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.25,5774.55,No\n8184-WMOFI,Male,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),71.4,4487.3,No\n6982-SSHFK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.4,44.4,Yes\n6092-QZVPP,Male,0,No,No,36,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),79.2,2854.95,No\n4625-LAMOB,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.4,905.55,No\n0727-BMPLR,Female,1,No,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100,5509.3,Yes\n0392-BZIUW,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),105,7589.8,No\n1038-ZAGBI,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,229.6,Yes\n6549-NNDYT,Female,0,No,No,13,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.85,394.1,No\n3027-ZTDHO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.9,89.9,Yes\n0422-OHQHQ,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.55,295.95,No\n6916-HIJSE,Female,0,No,No,65,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.85,5459.2,No\n2316-ESMLS,Female,0,Yes,Yes,12,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),33.15,444.75,No\n9778-OGKQZ,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92,6782.15,No\n7408-OFWXJ,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.8,6510.45,No\n6007-TCTST,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.8,8476.5,No\n2252-NKNSI,Male,0,No,Yes,52,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,85.15,4461.85,No\n8713-IGZSO,Male,0,No,No,2,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.85,62,No\n7905-TVXTA,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,64.35,352.65,No\n7695-PKLCZ,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1424.9,No\n2382-BCKQJ,Female,0,No,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100.15,6413.65,Yes\n8374-UULRV,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.05,6309.65,No\n2207-NHRJK,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.8,50.8,Yes\n3224-DFQNQ,Female,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,No,Electronic check,89,5898.6,No\n5275-PMFUT,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.8,4719.75,No\n4795-UXVCJ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,457.3,No\n9777-IQHWP,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),93.4,5822.3,No\n0947-MUGVO,Male,1,Yes,No,20,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.65,1463.5,Yes\n9944-HKVVB,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,307.4,Yes\n4124-MMETB,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.65,2104.55,Yes\n3671-SHRSP,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,80.6,319.15,Yes\n0979-MOZQI,Male,0,Yes,No,62,No,No phone service,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),39,2337.45,No\n2732-ISEZX,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,104.3,No\n3313-QKNKB,Male,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,One year,No,Electronic check,85.55,5084.65,Yes\n0323-XWWTN,Male,0,No,Yes,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26.4,121.25,No\n1937-OTUKY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),98.2,7015.9,No\n1573-LGXBA,Male,0,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),97.55,5598,No\n1764-VUUMT,Male,0,No,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,1269.1,No\n5073-WXOYN,Female,0,No,No,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.8,3027.4,Yes\n4713-ZBURT,Male,0,No,Yes,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),99.7,4634.35,No\n3050-GBUSH,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),34.8,113.95,No\n0207-MDKNV,Female,0,No,No,15,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,105.1,1582.75,Yes\n7876-AEHIG,Female,0,No,Yes,51,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),60.15,3077,No\n7945-HLKEA,Female,0,No,No,60,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,64.75,4039.5,No\n9342-VNIMQ,Male,0,No,No,33,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.65,1665.2,No\n9851-KIELU,Male,0,No,No,10,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.1,1043.3,Yes\n3523-BRGUW,Male,1,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,504.2,No\n3908-BLSYF,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,83.9,497.55,Yes\n3199-NPKCN,Female,0,Yes,No,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.25,7511.65,No\n5170-PTRKA,Female,0,Yes,Yes,49,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Credit card (automatic),35.8,1782,No\n4661-NJEUX,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,20.05,No\n2123-AGEEN,Female,1,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.35,609.65,No\n1258-YMZNM,Female,1,No,No,27,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),110.5,2857.6,No\n0048-LUMLS,Male,0,Yes,Yes,37,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Credit card (automatic),91.2,3247.55,No\n7549-MYGPK,Female,0,Yes,Yes,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,100.55,6215.35,Yes\n5898-IGSLP,Male,0,Yes,Yes,31,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,89.3,2823,No\n3804-RVTGV,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.85,5017.9,Yes\n6259-WJQLC,Male,1,No,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),81.1,2619.25,No\n9227-LUNBG,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.6,24.6,Yes\n7997-EASSD,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),81.2,4965.1,No\n0730-KOAVE,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.3,2679.7,No\n8975-SKGRX,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.1,8310.55,No\n0678-RLHVP,Female,0,No,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,105.55,5682.25,No\n4315-MURBD,Female,0,No,No,12,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.9,1120.95,Yes\n2267-FPIMA,Male,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.4,4914.9,No\n1051-GEJLJ,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,27.55,No\n9734-YWGEX,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),98.3,923.5,Yes\n2719-BDAQO,Male,0,No,No,17,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,93.85,1625.65,Yes\n5285-MVEHD,Female,0,Yes,No,56,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.6,6068.65,No\n0379-DJQHR,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),81.35,5398.6,No\n0781-LKXBR,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,918.6,Yes\n5543-QDCRY,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,56.4,234.85,No\n0297-RBCSG,Male,0,No,No,19,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),65.35,1231.85,No\n4694-PHWFW,Female,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,170.9,No\n0835-JKADZ,Female,0,No,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.25,7984.15,No\n1907-UBQFC,Male,1,No,No,10,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,72.85,688.65,Yes\n7508-SMHXL,Female,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Credit card (automatic),89,1288.3,No\n3865-YIOTT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.1,7848.5,No\n5993-BQHEA,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,267,No\n6024-RUGGH,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.2,1798.9,No\n6513-EECDB,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.55,73.55,Yes\n3956-CJUST,Female,1,No,No,23,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.4,1643.55,No\n4079-WWQQQ,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.55,4807.45,No\n6103-BOCOU,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.7,2193,No\n5149-TGWDZ,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,104.55,2239.4,No\n7471-WNSUF,Male,0,Yes,No,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.15,1505.9,No\n8942-DBMHZ,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,255.35,No\n4301-VVZKA,Male,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,1189.4,No\n9199-PWQVC,Female,0,Yes,No,63,Yes,Yes,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),79.7,4786.15,No\n4824-GUCBY,Female,1,No,No,22,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.7,1820.9,No\n5393-HJZSM,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),76.3,2404.15,No\n7074-IEVOJ,Female,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.4,205.05,Yes\n9625-QSTYE,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.15,952.3,Yes\n0862-PRCBS,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),103.75,7039.45,No\n8812-ZRHFP,Female,0,Yes,Yes,30,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,One year,No,Electronic check,86.45,2538.05,No\n5146-CBVOE,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.1,1212.85,No\n0454-OKRCT,Male,0,No,No,33,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),80.6,2651.1,No\n5787-KXGIY,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.3,1304.8,No\n4750-ZRXIU,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,360.1,Yes\n4198-VFOEA,Female,0,No,No,12,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,33.6,435.45,No\n6630-UJZMY,Female,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.25,308.05,No\n5709-LVOEQ,Female,0,Yes,Yes,0,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Mailed check,80.85, ,No\n6400-BWQKW,Female,0,No,No,6,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.05,434.5,Yes\n2692-AQCPF,Female,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),108.05,7118.9,No\n0347-UBKUZ,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,320.45,No\n0835-DUUIQ,Female,0,No,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),21.05,531.55,No\n0811-GSDTP,Female,0,No,Yes,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.15,382.2,No\n7567-ECMCM,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),79.85,2001,No\n6115-ZTBFQ,Female,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),65.5,4919.7,No\n6353-BRMMA,Female,0,Yes,Yes,54,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.1,5645.8,No\n6680-WKXRZ,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.4,215.8,Yes\n6231-WFGFH,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,77.6,No\n9904-EHEVJ,Female,1,Yes,Yes,32,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,91.35,2896.55,No\n7028-DVOIQ,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.05,3395.8,Yes\n6169-PPETC,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,759.35,No\n4208-UFFGW,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.95,85.15,Yes\n8584-KMVXD,Female,0,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,75.6,535.55,No\n8467-WYNSR,Male,0,No,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,55.1,1253.15,No\n0851-DFJKB,Female,0,No,No,15,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,58.95,955.15,No\n5382-SOYZL,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,2162.6,No\n9448-REEVD,Male,0,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),44.7,44.7,Yes\n3261-CQXOL,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,1813.35,No\n8388-FYNPZ,Male,0,No,No,4,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Electronic check,56.75,245.15,No\n4002-BQWPQ,Male,0,No,No,25,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),81.75,2028.8,No\n5651-YLPRD,Female,0,Yes,Yes,32,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.1,2723.75,No\n2826-DXLQO,Male,1,Yes,No,7,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.8,220.45,No\n4378-BZYFP,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.5,365.8,No\n9489-UTFKA,Male,1,Yes,No,8,Yes,No,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),60.9,551.95,No\n4849-PYRLQ,Female,1,No,No,56,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),73.25,4054.2,No\n9117-SHLZX,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.7,45.7,Yes\n9889-TMAHG,Male,1,No,No,8,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),100.3,832.35,Yes\n4541-RMRLG,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.25,112.3,Yes\n7764-BDPEE,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,60.65,No\n3429-IFLEM,Female,0,No,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),77.35,5550.1,No\n3158-MOERK,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96,174.8,Yes\n7294-TMAOP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.55,90.55,Yes\n5002-GCQFH,Male,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.85,4733.1,No\n0556-FJEGU,Male,0,No,No,58,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),70.1,4048.95,No\n8919-FYFQZ,Male,1,Yes,No,44,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.35,1359.7,Yes\n0604-THJFP,Female,0,Yes,Yes,59,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),75.95,4542.35,No\n2834-JRTUA,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.05,7532.15,Yes\n5875-YPQFJ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,Yes\n5879-SESNB,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,888.65,No\n6646-QVXLR,Male,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.75,6383.35,Yes\n6461-PPAXN,Female,0,Yes,Yes,35,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),54.95,1916,No\n3318-ISQFQ,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.5,413,No\n1106-HRLKZ,Male,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,808.95,No\n2483-XSSMZ,Female,0,No,No,39,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,47.85,1886.4,No\n8603-IJWDN,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.6,86.6,Yes\n8165-ZJRNM,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.75,1679.25,No\n9369-XFEHK,Female,1,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.6,2656.5,Yes\n2604-XVDAM,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),43.8,540.95,No\n3717-OFRTN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.75,19.75,No\n9046-JBFWA,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.15,537.35,No\n3280-NMUVX,Male,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,678.8,No\n1206-EHBDD,Female,0,Yes,No,56,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Two year,No,Bank transfer (automatic),80.3,4513.65,No\n8361-LBRDI,Female,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.35,1423.85,No\n4883-KCPZJ,Female,0,Yes,Yes,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,555.4,No\n9108-EQPNQ,Female,0,Yes,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26.1,225.55,No\n7277-KAMWT,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,268.45,No\n3842-IYKUE,Female,0,No,No,35,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),85.3,2917.5,Yes\n6641-XRPSU,Female,0,No,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70,2416.1,Yes\n1374-DMZUI,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.3,424.45,Yes\n2545-LXYVJ,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.7,1492.1,No\n3234-VKACU,Male,0,No,No,2,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,70.3,132.4,No\n8357-EQXFO,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.35,660.9,Yes\n1989-PRJHP,Male,1,Yes,No,27,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,1893.95,Yes\n8120-JDCAM,Male,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.55,284.9,No\n8917-FAEMR,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,784.25,No\n7047-YXDMZ,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,417.7,No\n2858-EIMXH,Female,1,Yes,No,53,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.85,5016.25,No\n9524-EGPJC,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.1,1612.75,Yes\n6993-OHLXR,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.95,119.75,Yes\n8818-XYFCQ,Male,0,Yes,Yes,32,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.55,3204.65,Yes\n6419-ZTTLE,Male,1,Yes,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.75,485.2,No\n0929-HYQEW,Male,0,No,No,3,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,160.85,No\n6614-YOLAC,Female,0,Yes,Yes,71,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Mailed check,58.65,4145.25,No\n7426-RHZGU,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.9,827.45,No\n4065-JJAVA,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,49.5,49.5,No\n4695-VADHF,Male,0,Yes,Yes,18,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,57.45,990.85,Yes\n3863-IUBJR,Male,0,Yes,Yes,12,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Credit card (automatic),53.65,696.35,Yes\n7649-SIJJF,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,80.1,5585.4,No\n9361-YNQWJ,Female,0,No,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.4,1601.2,No\n3748-FVMZZ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,40.05,162.45,No\n9391-TTOYH,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,470.2,No\n1452-XRSJV,Female,0,Yes,Yes,39,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.05,2066,No\n3422-WJOYD,Male,0,Yes,No,28,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54.35,1426.45,No\n8242-SOQUO,Female,1,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.7,392.5,No\n7460-ITWWP,Female,1,Yes,No,45,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,86.1,3861.45,No\n7147-AYBAA,Male,0,No,No,37,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.35,2552.9,No\n7868-TMWMZ,Female,1,Yes,No,60,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110,6668.35,No\n4822-RVYBB,Male,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,819.4,Yes\n6732-FZUGP,Female,0,No,No,47,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Credit card (automatic),94.9,4615.25,No\n8436-BJUMM,Male,0,Yes,Yes,26,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.75,2070.6,Yes\n4184-TJFAN,Female,1,Yes,Yes,3,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,88.3,273.75,Yes\n8329-GWVPJ,Female,0,Yes,Yes,50,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.75,3557.7,No\n1352-VHKAJ,Male,0,Yes,Yes,27,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),71.6,1957.1,No\n4145-UQXUQ,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.1,729.95,Yes\n2632-TACXW,Female,0,Yes,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.65,1416.75,No\n8146-QQKZH,Female,0,Yes,No,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),81.85,5924.4,No\n1767-CJKBA,Male,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,1697.7,No\n6445-TNRXS,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.7,7849.85,No\n4581-LNWUM,Female,0,No,No,13,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,49.15,649.4,No\n4869-EPIUS,Male,0,Yes,No,56,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Electronic check,80.9,4557.5,No\n9948-YPTDG,Male,0,Yes,No,38,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.45,3013.05,Yes\n1236-WFCDV,Male,1,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),90.45,1266.1,Yes\n1915-OAKWD,Female,0,No,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.3,360.35,No\n7296-PIXQY,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.2,1046.5,No\n4883-QICIH,Male,0,Yes,Yes,32,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.75,2347.9,Yes\n3354-OADJP,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.25,447.75,No\n3524-WQDSG,Female,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.3,4209.95,No\n0810-DHDBD,Female,0,No,No,52,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Credit card (automatic),74,3877.65,No\n4026-SKKHW,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.25,152.3,No\n2829-HYVZP,Male,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.8,572.2,No\n8329-IBCTI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,Yes\n1271-SJBGZ,Male,1,No,No,12,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,43.65,526.95,Yes\n3845-JHAMY,Female,0,Yes,Yes,16,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),35.5,552.7,No\n7013-PSXHK,Female,0,No,No,40,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,80.75,3208.65,No\n5669-SRAIP,Female,0,No,No,5,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,39.5,210.75,Yes\n5981-ITEMU,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.1,3706.95,Yes\n3486-NPGST,Female,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,620.75,No\n6941-PMGEP,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80,412.5,Yes\n1624-WOIWJ,Female,0,No,No,10,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.7,832.05,Yes\n2074-GKOWZ,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),89.55,185.55,Yes\n0376-YMCJC,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,90.6,1943.2,Yes\n6100-FJZDG,Male,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.05,505.9,No\n4829-ZLJTK,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,112.4,8046.85,No\n1730-VFMWO,Female,0,Yes,No,34,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),50.2,1815.3,No\n7143-BQIBA,Male,0,No,No,10,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),62.25,612.95,No\n3800-LYTRK,Female,0,No,No,14,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,55.7,795.15,No\n0634-SZPQA,Female,0,No,No,23,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.05,2169.8,Yes\n9646-NMHXE,Male,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,973.1,No\n7030-NJVDP,Male,0,Yes,No,24,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.25,2210.2,No\n5536-RTPWK,Male,0,Yes,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.05,4853.75,Yes\n8883-GRDWQ,Male,1,No,No,20,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54,1055.9,No\n6166-ILMNY,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.75,144.55,Yes\n3097-NNSPB,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.05,91.1,Yes\n7771-ZONAT,Male,0,No,No,22,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),56.75,1304.85,No\n0655-RBDUG,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.05,713,Yes\n2111-DWYHN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,21.1,21.1,No\n4194-WHFCB,Female,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),96.65,5580.8,No\n4121-AGSIN,Female,0,Yes,Yes,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),24.5,1497.9,No\n4361-BKAXE,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),114.5,4527.45,Yes\n9845-PEEKO,Female,1,No,No,59,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Two year,Yes,Credit card (automatic),79.2,4590.35,No\n0455-XFASS,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,200.2,No\n0301-KOBTQ,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,614.45,No\n1751-NCDLI,Male,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.85,4564.9,No\n4367-NUYAO,Male,0,Yes,Yes,0,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75, ,No\n9878-TNQGW,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.95,171.15,Yes\n9170-ARBTB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,1012.4,No\n4441-NIHPT,Female,1,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.3,940.35,Yes\n8999-BOHSE,Female,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),89.7,1047.7,Yes\n7241-AJHFS,Male,0,No,No,32,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),87.65,2766.4,No\n7029-RPUAV,Male,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.45,1622.45,Yes\n4546-FOKWR,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.75,1129.35,No\n9036-CSKBW,Female,0,No,No,51,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),107.45,5680.9,No\n5832-TRLPB,Male,0,No,No,29,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),75.35,2243.9,No\n8590-YFFQO,Male,0,Yes,No,70,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),64.95,4523.25,No\n8659-IOOPU,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Electronic check,100.45,7159.7,No\n1338-CECEE,Male,0,Yes,Yes,41,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),68.5,2839.95,No\n7439-DKZTW,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.55,80.55,No\n4646-QZXTF,Female,0,Yes,No,7,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81.25,580.1,No\n4607-CHPCA,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,90.4,2178.6,Yes\n9742-XOKTS,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,No,Electronic check,89.55,6038.55,No\n6921-OZMFH,Male,0,Yes,Yes,5,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.7,259.4,No\n9578-FOMUK,Male,0,No,Yes,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,24.8,324.15,No\n4712-UYOOI,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,417.65,No\n8824-RWFXJ,Male,0,Yes,Yes,3,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,56.15,168.15,Yes\n7722-CVFXN,Male,0,Yes,Yes,54,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,105.2,5637.85,No\n8717-VCTXJ,Male,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.55,839.4,No\n7363-QTBIW,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.75,769.1,No\n4159-NAAIX,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),97.45,6253,No\n0971-QIFJK,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.25,1641.8,No\n9397-TZSHA,Female,0,No,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.6,1678.05,No\n3391-JSQEW,Male,0,Yes,No,40,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,2058.5,No\n0343-QLUZP,Male,0,No,No,60,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),39.6,2424.5,No\n9763-PDTKK,Female,0,No,No,4,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),94.4,387.2,Yes\n2176-LVPNX,Female,1,No,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,89.85,6293.45,No\n7627-JKIAZ,Female,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),78.95,2839.65,Yes\n3312-UUMZW,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.85,3145.9,No\n1271-UODNO,Male,0,No,No,39,Yes,Yes,DSL,No,No,No,Yes,No,No,Two year,Yes,Credit card (automatic),53.85,2200.7,No\n8461-EFQYM,Female,0,No,No,38,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,24.25,914.4,No\n6900-RBKER,Male,0,No,No,52,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),89.45,4577.75,No\n6891-JPYFF,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.25,4997.5,No\n1459-QNFQT,Male,0,Yes,Yes,70,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.5,4144.8,No\n1047-NNCBF,Male,0,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.55,1493.55,No\n3696-XRIEN,Female,0,No,No,50,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82.5,4179.1,No\n4081-DYXAV,Female,0,No,No,19,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),44.85,893.55,Yes\n0074-HDKDG,Male,0,Yes,Yes,25,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),61.6,1611,No\n8791-GFXLZ,Male,0,No,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.05,593.05,No\n8111-SLLHI,Male,1,Yes,No,39,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.65,4284.8,Yes\n0927-LCSMG,Male,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,74.65,544.55,Yes\n9330-DHBFL,Female,0,Yes,Yes,23,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Mailed check,66.25,1533.8,No\n0098-BOWSO,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.4,529.8,No\n3452-ABWRL,Male,1,No,No,47,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),86.05,3865.6,No\n5859-HZYLF,Male,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.15,515.75,No\n8257-RZAHR,Female,0,Yes,No,14,Yes,Yes,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),64.7,941,Yes\n5293-WXJAK,Female,1,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.05,1133.65,Yes\n3156-QLHBO,Male,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,48.35,No\n2208-NQBCT,Female,0,Yes,Yes,26,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.95,2070.05,No\n1779-PWPMG,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.65,8333.95,No\n6621-NRZAK,Female,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,1209.25,No\n0831-JNISG,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.8,1396.25,No\n0774-IFUVM,Male,0,Yes,Yes,11,Yes,Yes,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),65.15,723.35,No\n3082-WQRVY,Male,1,Yes,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,228.65,No\n9553-DLCLU,Female,0,No,Yes,13,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.95,1161.75,No\n1641-BYBTK,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,98.35,No\n2460-NGXBJ,Male,1,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.2,775.3,No\n2446-ZKVAF,Male,0,Yes,No,18,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),56.8,1074.65,No\n0841-NULXI,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.55,35.55,Yes\n3522-CDKHF,Female,0,Yes,No,32,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.5,2324.7,No\n1430-SFQSA,Male,0,No,No,29,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,35.6,1072.6,No\n0411-EZJZE,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,60.25,170.5,No\n7851-WZEKY,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.15,196.9,Yes\n8844-TONUD,Male,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,96.65,1162.85,Yes\n8807-ARQET,Female,0,No,No,41,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,40.35,1677.85,No\n8992-CEUEN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,18.85,18.85,No\n4320-QMLLA,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,54.85,370.4,No\n8777-PVYGU,Female,0,Yes,No,52,Yes,No,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Mailed check,64.3,3410.6,No\n8292-ITGYJ,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.65,1138.8,No\n6870-ZWMNX,Male,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),76.1,5264.25,No\n0621-CXBKL,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.7,1005.7,No\n5268-DSMNQ,Female,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),97.95,5936.55,No\n5334-JLAXU,Female,0,Yes,No,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.1,5475.9,No\n4086-YQSNZ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.4,224.05,Yes\n6242-MBHPK,Female,1,Yes,No,23,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,2326.05,No\n5868-CZJDR,Male,0,No,Yes,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,31.35,31.35,Yes\n9359-UGBTK,Female,0,No,No,67,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),72.35,4991.5,No\n0135-NMXAP,Female,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.75,1052.4,Yes\n4782-OSFXZ,Female,1,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),82.7,5831.2,No\n6479-OAUSD,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,510.8,No\n7129-ACFOG,Female,0,No,No,5,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,53.8,283.95,No\n4189-NAKJS,Male,0,No,No,26,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),51.55,1295.4,No\n5562-BETPV,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,19.65,No\n1282-IHQAC,Male,1,No,No,70,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),44.05,3011.65,No\n9127-FHJBZ,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114,8093.15,No\n6270-OMFIW,Male,0,Yes,No,60,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,94.4,5610.25,Yes\n1641-RQDAY,Female,1,Yes,Yes,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.4,3217.65,No\n0107-WESLM,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.85,19.85,Yes\n6994-ORCWG,Female,0,No,No,14,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Mailed check,54.25,773.2,No\n1346-UFHAX,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80,1029.35,No\n3992-YWPKO,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.9,669.45,Yes\n2933-XEUJM,Female,0,No,No,46,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Mailed check,79.2,3593.8,No\n0125-LZQXK,Male,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,1553.95,Yes\n5461-QKNTN,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,94.3,3953.15,No\n4835-YSJMR,Male,0,No,No,39,Yes,No,DSL,No,No,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),49.8,1971.15,No\n8399-YNDCH,Male,1,No,No,21,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),60.05,1236.15,Yes\n3164-YAXFY,Male,0,No,No,57,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,53.75,3196,No\n0887-WBJVH,Female,0,Yes,No,53,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,93.45,4872.2,No\n4660-IRIBM,Male,0,Yes,Yes,18,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,87.9,1500.5,No\n5673-FSSMF,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,60.15,60.15,Yes\n7670-ZBPOQ,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),61.05,3478.75,No\n8089-UZWLX,Female,1,No,No,71,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),104.05,7413.55,No\n0080-OROZO,Female,0,No,No,35,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,99.25,3532,No\n3916-NRPAP,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),85.7,256.75,No\n6807-SIWJI,Male,0,No,No,38,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,104.85,3887.25,No\n8221-HVAYI,Male,0,Yes,Yes,35,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),69.15,2490.15,No\n1579-KLYDT,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,90.45,593.45,Yes\n5232-NXPAY,Female,0,No,No,47,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,74.45,3510.3,No\n8967-SZQAS,Female,0,No,No,14,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.45,765.45,No\n4468-KAZHE,Female,1,Yes,No,20,Yes,No,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,60,1259.35,No\n0455-ENTCR,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,85.25,5538.35,No\n8944-AILEF,Male,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.45,340.85,No\n5542-NKVRU,Female,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.75,844.45,No\n7126-RBHSD,Female,0,Yes,No,17,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,78.9,1348.95,No\n5370-IIVVL,Male,0,No,No,37,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.5,3778,No\n6789-HJBWG,Female,0,No,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,611.65,No\n3927-NLNRY,Male,0,Yes,No,53,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94.25,4867.95,Yes\n9087-EYCPR,Female,0,No,No,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1505.05,No\n6791-YBNAK,Male,0,Yes,Yes,18,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25.55,467.85,No\n6358-LYNGM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,74.9,Yes\n6077-BDPXA,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,194.2,No\n0013-MHZWF,Female,0,No,Yes,9,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),69.4,571.45,No\n5494-HECPR,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.25,80.25,Yes\n8268-YDIXR,Male,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,93.15,5253.95,No\n9824-BEMCV,Male,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69,1149.65,Yes\n1373-ORVIZ,Female,0,Yes,Yes,11,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,66.35,740.8,Yes\n4291-SHSBH,Male,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,521.35,No\n6980-IMXXE,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1412.65,No\n9866-QEVEE,Male,0,No,No,19,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),86,1532.45,Yes\n9897-KXHCM,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.3,250.05,Yes\n0040-HALCW,Male,0,Yes,Yes,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.4,1090.6,No\n0784-GTUUK,Male,0,Yes,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),23.75,1446.8,No\n7979-CORPM,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.55,2282.55,Yes\n2294-DMMUS,Female,0,Yes,Yes,62,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),70.45,4300.45,No\n0872-JCPIB,Male,0,No,No,17,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),65.75,1111.2,No\n3055-MJDSB,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.6,190.1,No\n9091-WTUUY,Male,0,Yes,Yes,64,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,69.25,4447.75,No\n1618-CFHME,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.9,143.35,Yes\n3165-HDOEW,Male,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,Yes\n6581-NQCBA,Female,0,Yes,No,16,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,49.95,810.2,Yes\n7115-IRDHS,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.65,1830.05,No\n8496-DMZUK,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),90.4,2820.65,No\n2040-VZIKE,Female,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,100.85,4847.35,No\n9068-VPWQQ,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),75.35,4729.3,No\n0178-SZBHO,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,87.2,4017.45,No\n0384-RVBPI,Male,0,No,No,20,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),64.4,1398.6,No\n1689-MRZQR,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.3,2564.3,Yes\n1299-AURJA,Female,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.7,1685.9,No\n4525-VZCZG,Male,0,No,Yes,54,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,105.85,5826.65,No\n1543-LLLFT,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Mailed check,98.3,6066.55,No\n5835-BEQEU,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.95,228.4,Yes\n2788-CJQAQ,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,270.2,No\n5565-FILXA,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.15,1529.2,Yes\n0319-QZTCO,Female,0,Yes,Yes,3,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),58.7,168.6,No\n2120-SMPEX,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.15,536.35,No\n0096-FCPUF,Male,0,No,No,30,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,64.5,1888.45,No\n0668-OGMHD,Female,0,Yes,No,21,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,28.5,629.35,No\n5552-ZNFSJ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.3,45.3,Yes\n2223-KAGMX,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,289.3,Yes\n6507-ZJSUR,Male,1,Yes,No,23,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.45,2117.25,No\n9408-HRXRK,Female,0,Yes,Yes,45,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.15,4730.9,No\n5593-SUAOO,Female,0,Yes,Yes,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),83.15,2033.05,No\n7321-PKUYW,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,90.15,987.95,Yes\n2833-SLKDQ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.05,45.05,Yes\n6766-HFKLA,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),103.2,5744.35,No\n7595-EUIVN,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,75.8,75.8,Yes\n7617-EYGLW,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.45,19.45,No\n2026-TGDHM,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.3,523.15,Yes\n9220-ZNKJI,Female,1,Yes,No,55,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),88.8,4805.3,No\n4030-VPZBD,Female,0,No,No,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,30.9,59.05,Yes\n2226-ICFDO,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.9,6110.75,No\n0723-VSOBE,Female,1,No,No,45,No,No phone service,DSL,No,No,No,No,No,Yes,One year,No,Electronic check,34.2,1596.6,No\n5529-GIBVH,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,1046.2,No\n4187-CINZD,Female,1,No,No,46,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),95.25,4424.2,Yes\n9992-UJOEL,Male,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.3,92.75,No\n4741-WWJQZ,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.15,194.55,No\n6625-UTXEW,Female,0,Yes,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,51.25,612.1,No\n6818-WOBHJ,Female,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.6,6127.6,Yes\n6244-BESBM,Male,0,Yes,Yes,69,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),95.2,6671.7,No\n1004-NOZNR,Male,1,No,Yes,56,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Credit card (automatic),94.8,5264.3,No\n1251-STYSZ,Female,1,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,303.7,No\n2612-PHGOX,Male,0,Yes,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),76.1,4818.8,No\n2408-TZMJL,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,110.15,6448.05,Yes\n8480-PPONV,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),115.55,7159.05,No\n8780-IHCRN,Male,0,Yes,Yes,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.65,1574.5,No\n4598-ZADCK,Female,0,No,No,53,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,53.6,2879.2,No\n1257-SXUXQ,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,86.05,No\n9681-KYGYB,Male,1,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,88.2,4159.45,No\n7182-OVLBJ,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),101.15,6638.35,No\n5095-ETBRJ,Female,0,Yes,No,55,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,56.8,3112.05,No\n7005-CYUIL,Female,1,Yes,No,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Electronic check,99.4,7168.25,No\n4821-WQOYN,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.1,1326.25,No\n4730-AWNAU,Male,0,Yes,Yes,36,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),60.7,2234.55,No\n3452-FLHYD,Male,0,Yes,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.95,495.15,No\n2388-LAESQ,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.85,8317.95,No\n9531-NSBMR,Female,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.25,679.8,No\n6260-ONULR,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,62.8,62.8,No\n4389-UEFCZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,105.5,7544,No\n8711-LOBKY,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,1188.25,No\n9134-CEQMF,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,676.7,Yes\n8985-OOPOS,Female,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),74.1,74.1,No\n8800-ZKRFW,Female,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.5,3242.5,No\n2616-FLVQC,Male,0,No,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,1240.15,No\n9968-FFVVH,Male,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Bank transfer (automatic),68.8,4111.35,No\n3108-PCCGG,Male,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),84.45,5899.85,No\n7993-PYKOF,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75,632.95,Yes\n8390-FESFV,Female,0,No,No,62,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),84.5,5193.2,No\n3022-BEXHZ,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.2,7530.8,No\n5027-XWQHA,Male,0,No,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.75,270.95,Yes\n6248-TKCQV,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.6,5460.2,No\n6729-GDNGC,Female,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),80.7,1614.2,No\n6198-ZFIOJ,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.6,402.5,No\n5989-OMNJE,Female,0,Yes,Yes,24,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,57.6,1367.75,No\n4566-QVRRW,Female,0,Yes,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.05,483.7,Yes\n1291-CUOCY,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.6,7962.2,No\n9795-SHUHB,Female,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),58.2,3810.8,No\n3230-IUALN,Female,0,Yes,Yes,45,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,81,3533.6,No\n0042-RLHYP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1396.9,No\n8519-IMDHU,Male,1,Yes,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.6,1345.55,Yes\n4945-RVMTE,Female,0,No,No,28,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.55,1646.45,No\n0201-OAMXR,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),115.55,8127.6,Yes\n1866-NXPSP,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Mailed check,75.55,2680.15,No\n3372-KWFBM,Male,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.6,1281,Yes\n7831-QGOXH,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.2,1553.9,Yes\n6393-WRYZE,Female,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.65,3207.55,Yes\n3941-XTSKM,Male,0,Yes,Yes,42,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),45.1,2049.05,No\n1661-CZBAU,Male,0,No,No,48,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),70.95,3629.2,No\n6599-RCLCJ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.55,5124.55,Yes\n9831-BPFRI,Female,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,89.55,3474.45,Yes\n5158-RIVOP,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.9,202.3,No\n9788-YTFGE,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,147.5,No\n9277-JOOMO,Female,0,No,No,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.6,86.35,No\n1907-YLNYW,Male,0,No,No,8,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,66.7,579,No\n1725-MIMXW,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.45,19.45,Yes\n8947-YRTDV,Male,0,Yes,Yes,32,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Mailed check,94.8,3131.55,No\n3161-ONRWK,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),65.85,3928.3,No\n0114-RSRRW,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.95,187.75,No\n4565-NLZBV,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.65,1710.15,No\n0031-PVLZI,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,76.35,Yes\n7206-GZCDC,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,69.25,Yes\n6682-VCIXC,Female,0,Yes,Yes,43,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),51.25,2151.6,No\n4791-QRGMF,Male,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),99.5,5961.1,Yes\n6475-VHUIZ,Female,0,Yes,No,23,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,54.25,1221.55,No\n3910-MRQOY,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.4,1496.45,No\n0661-WCQNQ,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Credit card (automatic),56.25,1292.2,No\n7537-RBWEA,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.15,25.15,No\n4656-CAURT,Male,0,No,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.95,1713.1,No\n0121-SNYRK,Male,0,No,No,50,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,35.4,1748.9,No\n1768-ZAIFU,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.2,25.2,Yes\n4671-LXRDQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45,96.45,Yes\n3733-LSYCE,Female,0,Yes,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.35,1114.55,No\n6265-FRMTQ,Male,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,609.1,No\n1934-SJVJK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,Yes\n3838-OZURD,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105,7133.25,Yes\n1371-DWPAZ,Female,0,Yes,Yes,0,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),56.05, ,No\n9269-CQOOL,Male,0,No,Yes,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),54.7,169.45,Yes\n2017-CCBLH,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,141.6,No\n7690-KPNCU,Male,0,Yes,Yes,64,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),73.05,4688.65,No\n0536-BGFMZ,Female,0,Yes,No,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,563.05,No\n2293-IJWPS,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.75,5985,No\n2845-HSJCY,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,87.25,1258.6,Yes\n5469-NUJUR,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,373.5,No\n1184-PJVDB,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,857.2,Yes\n2625-TRCZQ,Female,0,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.65,2553.35,No\n4102-HLENU,Female,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Mailed check,65.65,4322.85,No\n7266-GSSJX,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.45,250.8,No\n7722-VJRQD,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),60.95,4549.05,No\n7073-QETQY,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.35,1359.5,No\n9415-DPEWS,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,88.35,1639.3,Yes\n5624-RYAMH,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.5,178.85,No\n0196-JTUQI,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.2,633.85,No\n7130-YXBRO,Male,0,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),111.45,5315.1,No\n9272-LSVYH,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.15,735.5,No\n7943-RQCHR,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,889.9,Yes\n3793-MMFUH,Female,1,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.05,1290,Yes\n3249-ZPQRG,Male,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,78.45,330.05,Yes\n2568-BRGYX,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,237.95,Yes\n3084-DOWLE,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92,6474.4,No\n1084-MNSMJ,Female,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),85.5,4421.95,No\n7721-JXEAW,Male,0,Yes,No,59,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),41.05,2452.7,Yes\n7249-WBIYX,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.6,813.85,Yes\n4238-HFHSN,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82.15,4904.85,No\n6250-CGGUN,Male,0,No,No,54,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,84.4,4484.05,No\n5478-JJVZK,Female,0,No,No,33,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,60.9,2033.85,No\n7596-IIWYC,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.25,538.2,No\n6567-HOOPW,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.2,79.2,Yes\n9793-WECQC,Male,0,No,No,23,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Mailed check,95.3,2192.9,No\n4291-HPAXL,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,19.85,No\n8999-YPYBV,Male,0,Yes,Yes,45,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84.35,3858.05,No\n1839-FBNFR,Female,0,Yes,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.85,854.8,No\n3164-AALRN,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,70,347.4,Yes\n3071-MVJCD,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),82.3,5815.15,No\n1697-BCSHV,Female,0,Yes,Yes,58,Yes,Yes,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),66.8,3970.4,No\n0562-KBDVM,Female,0,No,No,70,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),44.6,3058.15,No\n1131-SUEKT,Male,0,Yes,Yes,61,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),98.45,6145.2,No\n3717-OEAUQ,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.7,129.2,No\n4538-WNTMJ,Female,0,Yes,Yes,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.95,1165.9,No\n3334-CTHOL,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.95,49.95,Yes\n4704-ERYFC,Female,0,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,1554,Yes\n9432-RUVSL,Female,0,No,No,48,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),102.5,4904.25,No\n8060-HIWJJ,Male,0,No,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.55,5632.55,No\n7684-XSZIY,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,1643.25,No\n9089-UOWJG,Female,0,Yes,Yes,12,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),58.35,740.55,No\n8621-MNIHH,Female,1,Yes,No,34,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.25,3217.55,Yes\n8039-ACLPL,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Credit card (automatic),68.75,4888.2,No\n9885-AIBVB,Male,0,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.8,2440.25,No\n1934-MKPXS,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,620.55,No\n2592-YKDIF,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,20.35,No\n2272-JKMSI,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.8,6840.95,No\n0471-LVHGK,Male,0,Yes,No,41,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,73,3001.2,Yes\n9518-RWHZL,Female,0,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.05,6254.2,Yes\n8714-CTZJW,Female,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),82.85,319.6,No\n3569-EDBPQ,Female,0,No,No,24,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,84.35,1938.05,No\n3131-NWVFJ,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,294.5,No\n7521-YXVZY,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,58.3,No\n5419-CONWX,Female,1,No,No,4,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.8,442.85,Yes\n6240-EURKS,Female,0,No,Yes,18,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35,553,Yes\n2373-NTKOD,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,66.25,546.45,No\n1970-KKFWL,Female,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.3,797.1,No\n6960-HVYXR,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,76,76,Yes\n9337-SRRNI,Male,0,No,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1673.8,No\n0895-UADGO,Male,0,No,Yes,8,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Two year,Yes,Mailed check,44.55,343.45,No\n5678-VFNEQ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.1,7412.25,No\n5977-CKHON,Female,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),92.55,4039,No\n7024-OHCCK,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,170.85,Yes\n2692-BUCFV,Male,1,No,No,29,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.45,2948.6,No\n7861-UVUFT,Female,0,Yes,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.3,1308.4,Yes\n1830-GGFNM,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),94.55,6078.75,No\n5302-BDJNT,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,95.5,3418.2,No\n5223-UZAVK,Male,0,No,No,64,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.3,6603.8,No\n4859-ZSRDZ,Female,0,Yes,Yes,58,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),55.5,3166.9,No\n5651-WYIPH,Female,1,No,No,18,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.85,865.75,No\n9350-VLHMB,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.55,6373.1,No\n3498-LZGQZ,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.15,1177.05,No\n8785-CJSHH,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,5985.75,No\n5357-TZHPP,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.4,732.5,Yes\n3870-SPZSI,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.05,7869.05,No\n0680-DFNNY,Male,0,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.1,1504.05,Yes\n7560-QRBXH,Female,0,No,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,936.7,No\n7077-XJMET,Male,0,Yes,No,12,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.15,741.4,No\n8752-GHJFU,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,92,6585.2,No\n6896-SRVYQ,Male,1,No,No,44,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,73.85,3122.4,No\n7767-UXAGJ,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.45,50.45,Yes\n4652-ODEVH,Male,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.45,1088.25,No\n6510-UPNKS,Female,0,No,No,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.8,615.35,No\n6718-BDGHG,Female,0,Yes,No,43,Yes,Yes,DSL,No,No,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),64.85,2908.2,No\n9046-DQMTP,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.75,739.9,No\n6439-LAJXL,Male,0,Yes,No,9,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,68.95,593.85,No\n1571-SAVHK,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.95,1132.75,Yes\n9052-VKDUW,Female,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.4,7227.45,No\n9546-CQJSU,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.4,193.6,Yes\n1666-JZPZT,Male,0,No,No,27,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,49,1291.35,No\n5777-KJIRB,Female,0,No,No,40,Yes,No,DSL,No,No,Yes,No,No,No,One year,Yes,Mailed check,50.25,2023.55,No\n0506-LVNGN,Female,1,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.55,349.65,Yes\n7677-SJJJK,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,153.95,No\n2480-EJWYP,Female,1,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),97.8,5458.8,No\n3253-HKOKL,Female,0,Yes,Yes,52,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,100.3,5244.45,No\n7055-HNEOJ,Male,0,Yes,No,3,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,55.8,154.55,No\n5514-YQENT,Male,0,No,Yes,41,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.15,4507.15,No\n3211-AAPKX,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,98.55,2031.95,No\n8445-DNBAE,Male,0,No,Yes,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.05,50.05,No\n2951-QOQTK,Male,0,No,Yes,4,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),80.8,332.45,Yes\n2958-NHPPS,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.85,473.9,No\n6806-YDEUL,Female,1,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,106.8,No\n1735-XMJVH,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.35,152.6,No\n6890-PFRQX,Male,0,No,No,18,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,69.5,1199.4,No\n0222-CNVPT,Male,1,No,No,52,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),48.8,2555.05,No\n5899-OUVKV,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.5,2979.2,No\n8681-ICONS,Male,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.65,654.85,No\n1621-YNCJH,Female,0,Yes,No,36,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),106.05,3834.4,No\n3473-XIIIT,Female,0,Yes,No,16,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100,1534.75,Yes\n6362-QHAFM,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,108.3,4586.15,No\n7893-IXHRQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.55,20.55,Yes\n3070-BDOQC,Female,0,No,No,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,99.65,5941.05,No\n2952-QAYZF,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.3,424.15,Yes\n6234-PFPXL,Male,0,Yes,No,22,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,No,Credit card (automatic),95.9,2234.95,No\n9824-QCJPK,Male,0,Yes,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,666.75,No\n4763-PGDPO,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,281,Yes\n4283-IVYCI,Male,0,No,No,9,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,64.95,547.8,No\n1866-OBPNR,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.6,74.6,No\n8205-MQUGY,Male,0,Yes,Yes,12,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.2,571.15,No\n8970-ANWXO,Female,0,No,No,23,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,73.75,1756.6,No\n9480-BQJEI,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Bank transfer (automatic),92.3,5731.45,No\n5394-SVGJV,Male,0,No,No,37,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),98.8,3475.55,Yes\n6979-TNDEU,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,156.85,No\n9777-WJJPR,Male,0,Yes,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Credit card (automatic),88.65,2683.2,No\n9283-LZQOH,Male,0,Yes,Yes,13,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.4,896.75,Yes\n7079-QRCBC,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.75,2407.3,Yes\n9495-SKLKD,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.95,4456.65,No\n6048-UWKAL,Female,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),105.4,6998.95,No\n5067-DGXLL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,36.8,No\n5469-CTCWN,Male,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,106,6547.7,Yes\n9851-QXEEQ,Male,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.7,4346.4,Yes\n6281-FKEWS,Female,0,No,No,44,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,49.05,2265,No\n8898-KASCD,Male,0,No,No,39,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,No,Mailed check,35.55,1309.15,No\n9242-TKFSV,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.1,4754.3,No\n9290-SHCMB,Female,1,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.85,1235.55,No\n0743-HNPFG,Female,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Credit card (automatic),69.75,3562.5,No\n2277-BKJKN,Female,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,99.2,7213.75,No\n9809-IMGCQ,Male,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.7,2082.95,Yes\n5208-HFSBT,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55.05,102.75,Yes\n5035-PGZXH,Female,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.8,5914.4,No\n8695-WDYEA,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,51.25,51.25,No\n6543-JXSOO,Female,0,Yes,Yes,23,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,57.75,1282.85,No\n8016-ZMGMO,Female,1,Yes,No,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),70.85,4738.85,No\n8605-ITULD,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No\n3254-YRILK,Male,1,No,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.2,1775.8,Yes\n6416-YJTTB,Male,0,No,No,11,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.5,795.65,No\n2667-WYLWJ,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.75,145,Yes\n4472-VESGY,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),98.15,4993.4,No\n3195-TQDZX,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.25,61.45,No\n3128-YOVTD,Female,0,Yes,Yes,51,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.15,4018.55,No\n0529-ONKER,Male,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.65,1146.65,Yes\n1728-CXQBE,Male,1,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.25,6081.4,No\n7041-TXQJH,Female,0,No,No,37,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.2,1478.85,No\n5014-GSOUQ,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,243.65,No\n5724-BIDBU,Male,0,Yes,No,49,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,55.35,2633.95,No\n0481-SUMCB,Female,1,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),102.15,4735.35,No\n1769-GRUIK,Female,0,No,No,18,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.1,1247.75,No\n5240-IJOQT,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes\n8819-WFGGJ,Male,0,Yes,No,68,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),54.1,3794.5,No\n7427-AUFPY,Male,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,1008.7,No\n2811-POVEX,Female,1,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),88.45,2130.55,No\n1092-GANHU,Male,0,No,No,17,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,76.65,1313.55,Yes\n7898-PDWQE,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),80.4,5727.15,No\n9972-EWRJS,Female,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.25,1372.9,No\n9314-IJWSQ,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.8,1203.9,No\n0661-XEYAN,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.8,25.8,Yes\n5799-JRCZO,Female,0,No,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1215.1,No\n1921-KYSAY,Female,0,No,No,41,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Electronic check,68.6,2877.05,No\n6198-RTPMF,Female,0,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,92.6,1579.7,No\n2924-KHUVI,Male,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,100.55,5514.95,No\n1925-GMVBW,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.55,96.1,No\n7881-EVUAD,Female,0,No,No,2,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,42.6,72.4,Yes\n6184-DYUOB,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,55.25,No\n9207-ZPANB,Male,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,67.45,2443.3,No\n5766-XQXMQ,Female,0,No,No,29,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),68.85,1970.5,Yes\n9327-QSDED,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.55,335.4,No\n1656-DRSMG,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.85,7002.95,No\n3012-VFFMN,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,158.95,No\n2984-AFWNC,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.4,293.15,No\n0640-YJTPY,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21,1493.75,No\n8096-LOIST,Female,0,No,No,19,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,56.2,1093.4,No\n9764-REAFF,Female,0,Yes,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),18.4,1057.85,No\n3703-VAVCL,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),90,190.05,Yes\n7107-UBYKY,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.75,882.55,No\n4881-GQJTW,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.6,300.4,No\n8519-QJGJD,Female,0,No,No,14,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.35,1025.95,Yes\n7876-DNYAP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,1436.95,No\n7905-NJMXS,Male,1,Yes,No,7,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,64.2,475,No\n2882-WDTBA,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.75,5388.15,No\n2091-GPPIQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,78.95,5730.15,No\n6326-MTTXK,Male,0,No,No,8,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,100.85,819.55,Yes\n5071-FBJFS,Female,0,Yes,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,50.3,217.1,No\n2796-UUZZO,Male,0,Yes,Yes,63,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.3,4896.35,No\n2429-AYKKO,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1434.1,No\n9798-OPFEM,Female,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,21.1,937.1,No\n0330-IVZHA,Female,0,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.95,330.15,Yes\n3794-NFNCH,Male,0,Yes,No,30,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50,1474.9,No\n5193-QLVZB,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.75,6536.5,No\n7114-AEOZE,Female,0,No,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1128.1,No\n2886-KEFUM,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.5,6873.75,Yes\n5522-NYKPB,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,85.9,2199.05,No\n4237-RLAQD,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,Yes\n9957-YODKZ,Male,1,Yes,No,6,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.8,457.1,No\n6518-KZXCB,Male,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,566.5,No\n2245-ADZFJ,Female,0,Yes,Yes,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.55,2471.6,No\n7776-QGYJC,Female,0,Yes,Yes,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),81.5,3107.3,No\n9313-QOLTZ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,518.3,No\n9651-GTSAQ,Female,0,Yes,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.1,5769.75,Yes\n3186-BAXNB,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Electronic check,91.7,91.7,Yes\n4672-FOTSD,Male,0,No,No,12,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,67.25,832.3,No\n0637-YLETY,Female,0,No,No,16,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),95.6,1555.65,Yes\n9818-XQCUV,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,45.3,No\n7338-ERIVA,Male,0,No,Yes,39,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),45.05,1790.6,No\n1157-BQCUW,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,74.95,Yes\n8259-NFJTV,Female,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,34.65,246.6,No\n3223-DWFIO,Male,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.35,261.65,No\n2660-EMUBI,Male,1,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.35,898.35,Yes\n6968-GMKPR,Female,0,No,No,55,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.55,4509.5,No\n4751-ERMAN,Male,0,Yes,Yes,72,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),75.4,5480.25,No\n1436-ZMJAN,Female,0,Yes,No,10,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),67.8,653.15,No\n3292-PBZEJ,Male,1,No,No,11,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.4,1183.05,No\n0799-DDIHE,Female,0,Yes,Yes,15,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46.3,639.45,No\n3070-FNFZQ,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,478.75,No\n2812-SFXMJ,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No\n7675-OZCZG,Female,1,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45,127.1,Yes\n5014-WUQMG,Male,0,Yes,Yes,47,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,96.1,4391.45,No\n5312-TSZVC,Female,0,No,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,270.6,No\n2003-CKLOR,Male,0,No,No,66,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.5,6710.5,Yes\n0993-OSGPT,Female,1,Yes,No,68,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),60.65,3975.9,No\n9254-RBFON,Female,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),98.6,1704.95,Yes\n1205-WNWPJ,Female,0,No,No,7,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,59.5,415.95,Yes\n9391-EOYLI,Male,1,Yes,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.45,950.2,Yes\n7108-DGVUU,Female,1,Yes,No,21,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),71.7,1497.05,No\n2782-JEEBU,Male,0,No,No,21,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,36,780.15,No\n5127-BZENZ,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,No,Bank transfer (automatic),65.2,3512.15,No\n2720-FVBQP,Female,0,Yes,Yes,6,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.95,273.25,No\n9906-NHHVC,Female,1,No,No,65,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),53.5,3517.9,No\n4522-XRWWI,Male,0,Yes,No,42,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Credit card (automatic),80.45,3375.9,No\n3766-EJLFL,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.05,7508.55,No\n5939-SXWHM,Male,0,Yes,Yes,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.3,1245.05,No\n8152-UOBNY,Female,1,No,No,50,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,106.8,5347.95,No\n7351-KYHQH,Female,1,No,No,7,Yes,No,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,64.95,493.65,No\n7643-RCHXS,Female,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.35,1263.85,No\n8246-SHFGA,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.1,385.55,No\n8387-MOJJT,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),77.95,3384,Yes\n0620-XEFWH,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.85,84.2,No\n6485-QXWWE,Female,0,No,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26,1638.7,No\n2761-OCIAX,Male,1,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,165.4,Yes\n7321-VGNKU,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,120.25,No\n5327-CNLUQ,Male,0,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),96.9,4473.45,No\n7552-KEYGT,Male,0,Yes,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,520.55,No\n5816-JMLGY,Female,0,Yes,Yes,70,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.4,5717.85,No\n3068-OMWZA,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.8,88.8,Yes\n2927-QRRQV,Male,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,94.65,4312.5,No\n6032-KRXXO,Male,0,No,No,30,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.25,2755.35,Yes\n7459-RRWQZ,Female,0,No,No,15,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.65,994.55,Yes\n6265-SXWBU,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),95.75,6511.25,No\n7941-RCJOW,Male,0,No,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.55,1218.65,No\n6374-NTQLP,Male,1,Yes,Yes,72,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.1,7447.7,No\n4154-AQUGT,Male,1,Yes,No,13,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.05,1169.35,Yes\n2387-KDZQY,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.1,279.5,No\n3584-WKTTW,Male,0,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.55,5720.35,No\n3399-BMLVW,Male,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),60.5,3121.45,No\n1971-DTCZB,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),90.95,6468.6,No\n3092-IGHWF,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,87.4,5918.8,Yes\n3374-PZLXD,Male,0,No,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,675.6,No\n3813-DHBBB,Male,0,Yes,No,67,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),50.95,3521.7,No\n2812-REYAT,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.05,923.1,No\n6518-PPLMZ,Male,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.4,1110.35,No\n4939-KYYPY,Male,0,No,No,27,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,59.45,1611.65,No\n8017-LXHFA,Female,1,No,No,23,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,2293.6,Yes\n5930-GBIWP,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),81.5,5553.25,No\n6022-KOUQO,Female,0,Yes,Yes,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),29.05,44.75,No\n6352-TWCAU,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.45,3029.1,Yes\n2361-UPSND,Female,0,Yes,No,46,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,No,Mailed check,70.6,3231.05,No\n6035-RIIOM,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),97.2,5129.45,No\n2929-QNSRW,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,98.25,5508.35,Yes\n1262-OPMFY,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.75,655.9,Yes\n9504-DSHWM,Male,0,No,No,20,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),59.2,1191.2,No\n5035-BVCXS,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,75.9,866.4,No\n6267-DCFFZ,Female,1,Yes,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.05,2627.2,No\n3533-UVMOM,Male,0,Yes,No,68,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),70.95,4741.45,No\n2439-LYPMQ,Male,1,Yes,No,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.6,4009.2,No\n4248-QPAVC,Female,1,Yes,No,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.35,1463.45,Yes\n1899-VXWXM,Male,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),106.1,5082.8,Yes\n1478-VPOAD,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,43.8,43.8,No\n9995-HOTOH,Male,0,Yes,Yes,63,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,59,3707.6,No\n2988-PLAHS,Female,0,No,No,3,Yes,No,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,69.95,220.45,No\n1371-OJCEK,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.35,1133.7,No\n4999-IEZLT,Male,0,No,No,66,No,No phone service,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),29.45,1983.15,No\n8883-ANODQ,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),84.4,5746.75,No\n4690-LLKUA,Male,1,No,No,17,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.05,770.6,Yes\n2351-RRBUE,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,134.05,No\n5980-BDHPY,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),87.1,6230.1,No\n1498-DQNRX,Female,0,Yes,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,573.05,No\n9469-WEJBT,Male,0,Yes,No,37,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.35,3419.3,No\n3331-HQDTW,Female,0,No,No,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,109.8,3587.25,Yes\n9490-DFPMD,Female,1,No,No,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.65,3541.35,Yes\n2581-VKIRT,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,65.5,3801.3,No\n5442-XSDCW,Male,0,Yes,Yes,11,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),79.5,868.5,Yes\n7426-WEIJX,Male,1,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.95,4859.1,No\n2851-MMUTZ,Female,0,No,No,27,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,56.15,1439.35,No\n3049-NDXFL,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,85.8,85.8,Yes\n8580-AECUZ,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.1,79.1,Yes\n3307-TLCUD,Male,0,Yes,No,17,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,34.4,592.75,No\n6625-FLENO,Male,0,Yes,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1185.95,No\n2967-MXRAV,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,18.8,No\n7963-GQRMY,Female,0,Yes,Yes,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.3,134.5,Yes\n8189-HBVRW,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,90.8,4921.2,No\n4163-KIUHY,Male,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.6,901.25,No\n1228-FZFRV,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),105.95,5341.8,Yes\n3500-NSDOA,Male,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Credit card (automatic),70.8,4859.95,No\n1171-TYKUR,Male,0,Yes,No,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25.4,1139.2,No\n3761-FLYZI,Female,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.8,7082.45,No\n2058-DCJBE,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.75,324.6,Yes\n5364-XYIRR,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,4812.75,Yes\n4829-AUOAX,Female,0,No,No,46,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),96.05,4399.5,Yes\n1219-NNDDO,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.85,663.55,No\n8388-DMKAE,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25,174.65,No\n4403-BWPAY,Male,0,No,No,14,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),24.8,321.7,No\n9659-QEQSY,Female,0,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,115.65,5125.5,No\n5405-ZMYXQ,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.6,548.9,No\n5047-LHVLY,Male,1,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50.15,50.15,Yes\n1442-OKRJE,Male,0,Yes,Yes,66,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.15,7031.3,No\n4737-AQCPU,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),72.1,5016.65,No\n9158-VCTQB,Female,0,Yes,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),113.6,4594.95,Yes\n2808-CHTDM,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,611.45,No\n6311-UEUME,Female,0,No,No,29,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.9,2384.15,Yes\n0793-TWELN,Female,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.15,319.85,No\n3283-WCWXT,Male,0,Yes,Yes,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,153.3,No\n1060-ENTOF,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),105.4,7035.6,No\n0999-QXNSA,Male,1,No,Yes,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.75,344.2,No\n5451-MHQOF,Male,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.45,1431.65,No\n4836-WNFNO,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25,1849.2,No\n9225-BZLNZ,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,85.25,6083.1,No\n0354-VXMJC,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,426.65,No\n4422-QVIJA,Female,0,No,Yes,35,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.15,1655.35,No\n9365-SRSZE,Male,1,Yes,No,27,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.55,1943.9,No\n6839-ITVZJ,Female,0,Yes,Yes,26,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,60.05,1616.15,Yes\n8332-OSJDW,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,26.4,314.95,No\n4735-BJKOU,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,804.85,No\n0274-JKUJR,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,58.85,465.7,No\n5740-YHGTW,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),97.55,6669.05,No\n8917-SZTTJ,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,1161.75,No\n1696-MZVAU,Male,0,Yes,Yes,39,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),25.25,947.75,No\n7359-WWYJV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.45,8375.05,No\n0375-HVGXO,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,34.7,34.7,Yes\n4906-ZHGPK,Male,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,70.7,3770,No\n8593-WHYHV,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.3,264.8,Yes\n3795-GWTRD,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),75.55,4707.85,No\n1298-PHBTI,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Two year,Yes,Electronic check,84.8,6152.4,No\n6223-DHJGV,Female,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.65,958.1,No\n6961-MJKBO,Male,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.45,943,No\n6097-EQISJ,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),102.45,6615.15,Yes\n4423-YLHDV,Female,0,Yes,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.4,2200.25,Yes\n8158-WPEZG,Male,0,No,No,11,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,35.65,425.1,No\n0107-YHINA,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.75,99.75,Yes\n4918-FYJNT,Female,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,90.45,5044.8,No\n0727-BNRLG,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),97.65,6743.55,No\n4854-CIDCF,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,73.85,196.4,No\n8640-SDGKB,Male,0,No,No,4,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.4,299.7,Yes\n3280-MRDOF,Male,1,No,No,30,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),69.1,2093.9,No\n6435-SRWBJ,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,82.75,417.75,No\n9964-WBQDJ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.4,1725.4,No\n6303-KFWSL,Female,0,No,No,29,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Electronic check,55.25,1620.2,No\n1702-CCFNJ,Male,0,Yes,No,52,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.35,3169.55,No\n8932-CZHRQ,Male,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),76.75,5233.25,No\n0386-CWRGM,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,967.85,No\n5515-RUGKN,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),54.75,438.05,No\n0404-AHASP,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1421.9,No\n7279-NMVJC,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,323.15,No\n2081-VEYEH,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.95,318.6,No\n6407-UTSLV,Female,1,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),83.8,163.7,No\n4116-TZAQJ,Female,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.25,639.65,Yes\n9060-HJJRW,Female,0,No,No,51,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),56.4,2928.5,No\n2587-YNLES,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,100.35,No\n7398-SKNQZ,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.9,273.2,No\n5935-FCCNB,Female,1,No,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,1608.15,No\n1958-RNRKS,Male,0,Yes,No,30,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,49.9,1441.95,No\n5136-RGMZO,Male,0,No,No,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,71.05,2168.15,No\n8345-MVDYC,Female,0,No,No,45,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),81.65,3618.7,No\n8226-BXGES,Male,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),89.45,5692.65,No\n3877-JRJIP,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.85,59.85,Yes\n8375-DKEBR,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes\n9705-IOVQQ,Male,1,Yes,Yes,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,99,5969.3,No\n1015-OWJKI,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,19.05,No\n7511-YMXVQ,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.4,418.8,Yes\n2040-XBAVJ,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.45,8100.55,No\n7551-JOHTI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,19.5,Yes\n8887-IPQNC,Female,0,Yes,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.25,313.45,No\n8646-JCOMS,Female,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),90.55,6130.95,No\n9804-ICWBG,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,Yes\n1222-KJNZD,Male,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.4,745.3,No\n0106-GHRQR,Male,0,No,No,16,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),71.4,1212.1,No\n5318-IXUZF,Female,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87.15,183.75,Yes\n3768-VHXQO,Male,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.85,1583.5,No\n8952-WCVCD,Female,0,Yes,No,41,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.45,4162.05,No\n2418-TPEUN,Female,0,Yes,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1119.9,No\n3963-RYFNS,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.45,8013.55,No\n3198-VELRD,Female,0,Yes,Yes,3,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),84.75,264.85,Yes\n8540-ZQGEA,Female,0,Yes,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1102.4,No\n1320-REHCS,Male,1,No,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.75,5832,No\n4137-JOPHL,Female,0,No,No,50,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),89.7,4304.5,No\n9436-ZBZCT,Male,0,No,No,14,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,89.95,1178.4,Yes\n7801-CEDNV,Male,0,Yes,No,27,Yes,No,DSL,Yes,No,No,No,No,No,One year,No,Credit card (automatic),48.7,1421.75,No\n2057-BOYKM,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),96.6,6827.5,No\n3658-QQJYD,Male,0,No,No,62,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),74.3,4698.05,No\n1803-BGNBD,Female,0,No,No,12,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.3,654.5,No\n0134-XWXCE,Female,1,No,No,44,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),74.85,3268.05,No\n6950-TWMYB,Male,0,Yes,Yes,54,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),79.95,4362.05,No\n5848-FHRFC,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,1386.9,No\n2243-FNMMI,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,415.4,No\n2511-MORQY,Male,0,Yes,Yes,50,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,No,Bank transfer (automatic),54.9,2614.1,No\n5356-KZCKT,Male,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.45,1513.6,No\n9470-XCCEM,Male,0,Yes,Yes,35,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.65,3161.6,No\n6519-CFDBX,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.4,80.95,No\n3902-MIVLE,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,75.7,4676.7,No\n0409-WTMPL,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),110.65,6526.65,No\n8763-KIAFH,Female,0,Yes,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.55,583.3,No\n3669-WHAFY,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.15,8078.1,No\n3055-VTCGS,Female,0,No,No,63,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),58.55,3503.5,No\n3144-KMTWZ,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.25,6669.45,No\n7279-BUYWN,Female,1,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,113.2,4689.5,Yes\n7156-MHUGY,Male,1,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.5,1201.15,Yes\n7198-GLXTC,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79,143.65,Yes\n2007-QVGAW,Female,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.35,1292.65,No\n5207-PLSTK,Male,0,Yes,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.75,48.75,No\n2307-FYNNL,Male,1,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.05,7108.2,No\n5605-XNWEN,Male,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25,1802.55,No\n2155-AMQRX,Female,0,No,No,28,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),54.9,1505.15,No\n6181-AXXYF,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,1859.1,No\n5091-HFAZW,Female,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.15,168.5,No\n0516-VRYBW,Female,0,No,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.15,390.85,Yes\n2519-LBNQL,Male,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.35,6339.45,No\n8623-ULFNQ,Female,1,No,No,26,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,66.05,1652.4,No\n8380-PEFPE,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,71.65,Yes\n5687-DKDTV,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,77.5,Yes\n1568-LJSZU,Male,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92.2,6392.85,No\n7530-HDYDS,Female,0,No,No,38,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),84.25,3264.5,Yes\n7789-HKSBS,Female,1,Yes,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,4599.15,No\n7416-CKTEP,Female,0,Yes,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.6,1134.25,No\n2586-CWXVV,Male,0,Yes,No,54,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.4,1621.35,No\n3096-IZETN,Female,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),78.1,947.3,Yes\n2348-KCJLT,Female,0,Yes,No,44,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,61.5,2722.2,No\n8401-EMUWF,Male,0,Yes,Yes,42,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.4,3058.3,No\n4193-IBKSW,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1769.6,No\n5377-NDTOU,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,91.05,6293.75,No\n5922-ABDVO,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,89.65,1761.05,Yes\n2474-LCNUE,Female,0,Yes,No,23,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),73.65,1642.75,No\n0839-QNXME,Female,0,No,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,578.5,No\n3506-OVLKD,Male,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),26.2,954.9,No\n9172-ANCRX,Female,0,No,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,973.25,Yes\n6650-VJONK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),43.85,43.85,No\n2178-PMGCJ,Male,0,No,No,22,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.7,1490.4,No\n7492-TAFJD,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Mailed check,38.55,280,No\n2773-MADBQ,Female,0,No,No,36,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,53.1,1901.25,No\n6016-LVTJQ,Female,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,716.4,No\n7860-KSUGX,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),64.45,4720,No\n8966-KZXXA,Male,0,Yes,Yes,36,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,930.95,No\n6910-HADCM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.35,76.35,Yes\n4816-LXZYW,Female,0,No,No,23,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,1676.95,Yes\n9606-PBKBQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,2642.05,Yes\n5149-QYTTU,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),95.15,6770.85,No\n2070-XYMFH,Female,1,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.35,1835.3,No\n2085-BOJKI,Male,0,Yes,No,17,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),96.65,1588.25,No\n0817-HSUSE,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,75.5,75.5,No\n5442-PPTJY,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,258.35,No\n1927-QEWMY,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1502.25,No\n1663-MHLHE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,19.2,No\n5663-QBGIS,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),98.35,6929.4,No\n4450-MDZFX,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.35,4453.3,No\n6701-DHKWQ,Female,0,Yes,Yes,61,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),51.35,3244.4,No\n7554-AKDQF,Female,0,Yes,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,323.45,No\n3536-IQCTX,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.3,2661.1,Yes\n4911-BANWH,Female,0,No,Yes,31,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),86.55,2697.4,Yes\n8496-EJAUI,Male,0,No,No,19,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.85,1424.5,Yes\n0794-YVSGE,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,1401.15,No\n5423-BHIXO,Female,0,No,No,32,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,54.2,1739.6,No\n6908-VVYHM,Male,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.65,5931,No\n2959-EEXWB,Female,0,Yes,Yes,45,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),50.9,2333.85,No\n1839-UMACK,Male,0,No,No,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.05,949.85,No\n3030-YDNRM,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,572.45,No\n7321-KKSDU,Male,0,No,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,696.8,No\n3402-XRIUO,Female,1,Yes,No,22,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,63.55,1381.8,No\n3132-TVFDZ,Male,1,Yes,No,57,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.85,2572.95,Yes\n8286-AFUYI,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,47.95,47.95,No\n8080-DDEMJ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.1,45.1,Yes\n8356-WUAOJ,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45,45,No\n6365-MTGZX,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96,2122.45,Yes\n1349-WXNGG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.05,20.05,Yes\n8058-DMYRU,Male,1,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,4931.8,No\n9350-ZXYJC,Female,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,116.95,No\n6990-YNRIO,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),108.65,6937.95,Yes\n8958-JPTRR,Female,0,Yes,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.3,1261.7,No\n6959-GQEGV,Male,0,No,No,45,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,75.95,3273.8,No\n3173-WSSUE,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1415.85,No\n1265-HVPZB,Male,0,Yes,No,59,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),66.4,3958.2,No\n4115-UMJFQ,Male,0,No,No,69,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),35.75,2492.25,No\n7369-TRPFD,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,279.2,No\n1098-KFQEC,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1083,No\n7190-XHTWJ,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,755.5,No\n0621-TWIEM,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.55,402.6,Yes\n3537-RYBHH,Female,1,Yes,No,47,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),67.45,3252,No\n2485-ITVKB,Female,0,Yes,No,2,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.1,68.75,Yes\n3669-OYSJI,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),46.2,46.2,Yes\n1612-EOHDH,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.15,45.15,Yes\n6702-OHFWR,Male,1,No,No,1,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,43.3,43.3,Yes\n5296-BFCYD,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.1,936.85,No\n4510-PYUSH,Female,1,No,No,38,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),57.15,2250.65,Yes\n9359-JANWS,Female,0,Yes,No,65,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),58.9,3857.1,No\n7517-SAWMO,Female,0,Yes,No,19,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.2,1441.1,Yes\n4143-HHPMK,Male,0,No,No,52,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.35,4338.6,Yes\n3279-DYZQM,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.45,1378.45,No\n7054-LGEQW,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.95,45.95,Yes\n0523-VNGTF,Female,1,No,No,52,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,50.5,2566.3,No\n9575-IWCAZ,Male,0,Yes,No,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.1,171,No\n7105-MXJLL,Female,1,Yes,No,26,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Mailed check,60.7,1597.4,No\n7064-FRRSW,Male,1,No,No,48,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,99,4744.35,No\n7940-UQQUG,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.4,6721.6,No\n0923-PNFUB,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,83.75,247.25,Yes\n3961-SXAXY,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.05,44.05,No\n7010-BRBUU,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.1,1734.65,No\n3566-HJGPK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.55,45.55,No\n3062-ICYZQ,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.8,4539.35,No\n9938-PRCVK,Female,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,804.25,No\n0973-KYVNF,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),70.65,5011.15,No\n5129-HHMZC,Female,0,Yes,No,43,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,3574.5,No\n9637-CDTKZ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.1,8086.4,No\n3946-JEWRQ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.2,4563,No\n7873-CVMAW,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.55,6362.35,No\n0463-WZZKO,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.75,67.1,No\n3494-JCHRQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.05,70.05,Yes\n6474-FVJLC,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86,165.45,Yes\n4524-QCSSM,Male,0,No,No,26,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),44.65,1156.55,No\n5832-EXGTT,Male,0,Yes,Yes,29,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,60.2,1834.15,No\n8840-DQLGN,Female,1,Yes,No,35,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),100.5,3653.35,Yes\n2039-JONDJ,Male,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Bank transfer (automatic),55.45,1477.65,No\n7217-JYHOQ,Male,0,Yes,Yes,24,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),70.3,1706.45,No\n6695-FRVEC,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),60.4,3953.7,No\n4547-LYTDD,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,72.65,1194.3,Yes\n9894-QMIMJ,Female,0,No,No,23,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),55.8,1327.85,No\n8069-YQQAJ,Male,0,No,No,14,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,31.1,419.7,No\n6770-XUAGN,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21,21,Yes\n4193-ORFCL,Female,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.1,45.1,Yes\n1636-NTNCO,Male,1,No,No,4,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.95,207.35,No\n3466-WAESX,Male,0,No,Yes,16,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,69.1,1083.7,No\n9281-PKKZE,Female,0,Yes,No,46,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,43.95,2007.85,No\n3638-VBZTA,Male,0,No,Yes,68,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.5,5882.75,No\n7459-IMVYU,Male,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,69.95,2657.55,No\n7776-QWNFX,Male,1,Yes,No,30,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.4,1527.5,Yes\n6689-TCZHQ,Female,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.95,378.4,Yes\n8563-OYMQY,Male,0,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.95,1612.2,No\n0754-EEBDC,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,76.65,No\n5777-ZPQNC,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.15,260.7,No\n1951-IEYXM,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.6,6441.85,No\n3318-NMQXL,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92,266.8,No\n3143-ILDAL,Male,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,94.45,5124.6,Yes\n1022-RKXDR,Female,0,No,No,41,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.85,962.25,No\n2361-FJWNO,Male,0,No,No,40,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Credit card (automatic),36,1382.9,No\n2272-UOINI,Female,0,No,No,7,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,78.5,571.05,No\n8232-UTFOZ,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,1399.35,No\n3750-YHRYO,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,150,No\n6637-KYRCV,Female,0,Yes,Yes,5,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.5,167.2,No\n5668-MEISB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),106.1,7657.4,No\n0129-QMPDR,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.5,865.05,No\n7188-CBBBA,Female,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,95.5,6153.85,No\n5356-CSVSQ,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,64.6,174.2,No\n3221-CJMSG,Male,0,No,No,24,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),51.1,1269.6,No\n4720-VSTSI,Female,0,No,No,44,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,3862.55,Yes\n3219-JQRSL,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.1,6352.4,No\n2801-NISEI,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.95,1348.5,No\n3946-MHCZW,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,50.9,50.9,Yes\n4623-ZKHLY,Male,0,Yes,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.45,471.55,No\n6732-VAILE,Male,0,Yes,Yes,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.95,5931.75,No\n8201-AAXCB,Male,0,Yes,Yes,25,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,60.35,1404.65,No\n7696-CFTAT,Male,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,726.1,No\n1845-CSBRZ,Female,1,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),85.35,1961.6,No\n2123-VSCOT,Female,0,Yes,Yes,59,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),72.1,4194.85,No\n6651-AZVTJ,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4872.45,Yes\n4566-GOLUK,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.35,5118.95,Yes\n2484-DGXPZ,Female,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,658.95,Yes\n2018-QKYGT,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.05,81.05,No\n2792-VPPET,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.5,76.95,No\n7409-JURKQ,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),111.8,5809.75,No\n3247-MHJKM,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No\n1964-SVLEA,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.7,415.9,No\n4587-NUKOX,Female,0,No,No,3,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.1,246.5,Yes\n7297-DVYGA,Female,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,996.95,No\n2239-CGBUZ,Female,0,Yes,No,51,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.5,3145.15,No\n0854-UYHZD,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,265.3,No\n7243-LCGGZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,No\n8267-KFGYD,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,21.05,21.05,No\n4890-VMUAV,Male,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,71.5,4576.3,No\n9261-WDCAF,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,54.65,189.1,No\n3764-MNMOI,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.2,908.15,No\n7442-YGZFK,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.8,49.8,No\n0420-BWTPW,Male,0,No,Yes,8,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.5,215.2,Yes\n8229-BUJHX,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,1500.95,No\n7449-HVPIV,Male,0,Yes,Yes,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.4,5099.15,No\n5504-WSIUR,Female,0,No,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),90.25,6385.95,No\n8183-ONMXC,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.75,159.45,Yes\n8466-PZBLH,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),104.6,6885.75,No\n9614-RMGHA,Male,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),91.85,5940.85,Yes\n8735-IJJEG,Male,0,Yes,No,14,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),50.2,668.85,No\n0564-MUUQK,Female,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.5,1916.2,No\n5054-IEXZT,Male,0,No,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,75.35,Yes\n5834-ASPWA,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.45,75.45,Yes\n0701-RFGFI,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.4,4613.95,No\n0019-EFAEP,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),101.3,7261.25,No\n5619-PTMIK,Female,0,Yes,No,46,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,53.1,2459.8,No\n3737-XBQDD,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.85,2048.8,No\n5882-CMAZQ,Female,0,Yes,Yes,5,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,34.25,163.55,No\n5846-QFDFI,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),88.6,2888.7,No\n4445-KWOKW,Female,0,No,No,42,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),60.15,2421.6,No\n3511-APPBJ,Male,0,No,No,23,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),99.95,2292.75,No\n7967-HYCDE,Male,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.7,553.4,No\n2430-RRYUW,Male,0,No,No,66,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Mailed check,54.8,3465.7,No\n3948-XHGNA,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.55,1210.4,Yes\n3723-BFBGR,Male,1,No,No,24,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),54.8,1291.3,No\n0565-IYCGT,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),78.6,5356.45,Yes\n5447-WZAFP,Female,0,No,No,53,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,100.3,5200.8,No\n5110-CHOPY,Female,0,No,No,60,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Electronic check,53.6,3237.05,No\n5445-UTODQ,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81.1,576.65,Yes\n4425-OWHWB,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.35,433.75,No\n7892-QVYKW,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.6,1868.4,No\n9675-ICXCT,Male,0,Yes,Yes,72,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,80.8,5728.55,No\n1024-VRZHF,Male,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,825.7,Yes\n4703-MQYKT,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,390.4,No\n9497-QCMMS,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.55,93.55,Yes\n5692-ICXLW,Male,1,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.7,2845.15,No\n0602-DDUML,Female,0,No,No,57,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,69.75,3894.4,No\n2208-MPXIO,Female,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20,886.4,No\n1960-UYCNN,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.25,1021.55,No\n0348-SDKOL,Female,0,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.1,5885.4,Yes\n3190-FZATL,Male,0,No,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,268.4,No\n7336-RLLRH,Male,0,Yes,No,27,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Mailed check,80.85,2204.35,No\n4373-MAVJG,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),90.9,1259,Yes\n8901-HJXTF,Female,0,Yes,Yes,12,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.2,309.1,Yes\n7710-JSYOA,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),93.3,6398.05,No\n5419-KLXBN,Female,0,Yes,Yes,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.15,2257.75,Yes\n3424-NMNBO,Male,1,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.85,6287.25,Yes\n9885-MFVSU,Female,0,Yes,Yes,35,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),46.35,1662.05,No\n4514-GFCFI,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.75,1350.15,Yes\n0607-MVMGC,Male,0,Yes,Yes,45,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78.75,3600.65,No\n3365-SAIGS,Female,0,No,No,17,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.55,1329.15,No\n9828-AOQLM,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.7,45.7,Yes\n8022-BECSI,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,422.5,No\n8000-REIQB,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.95,69.95,Yes\n9993-LHIEB,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,67.85,4627.65,No\n0266-CLZKZ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.65,6717.9,No\n7615-ESMYF,Female,0,Yes,No,2,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,44.6,97.1,Yes\n3858-VOBET,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,1710.45,Yes\n7020-OZKXZ,Female,1,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),75.5,637.4,No\n3977-QCRSL,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,117.95,No\n0017-DINOC,Male,0,No,No,54,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Credit card (automatic),45.2,2460.55,No\n1447-PJGGA,Female,0,No,No,57,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,95.25,5464.65,Yes\n8565-CLBZW,Male,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,89.85,2165.05,Yes\n9139-WQQDY,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Mailed check,100.45,4941.8,Yes\n0224-HJAPT,Male,0,No,No,5,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,47.15,223.15,Yes\n8086-OVPWV,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.2,181.1,Yes\n9430-FRQOC,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,87.1,341.45,Yes\n7639-OPLNG,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),79.25,5731.85,No\n3074-GQWYX,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.9,357.75,Yes\n1492-QGCLU,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,85.7,4616.1,No\n6845-RGTYS,Female,0,Yes,No,47,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,98.75,4533.7,Yes\n7328-OWMOM,Female,0,No,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,589.25,No\n4418-LZMSV,Male,0,Yes,Yes,13,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.8,750.1,No\n5155-AZQPB,Female,0,Yes,Yes,28,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,49.9,1410.25,No\n8861-HGGKB,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.45,830.85,Yes\n1087-GRUYI,Male,0,Yes,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,743.5,No\n7065-YUNRY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.3,45.3,Yes\n7694-VLBWQ,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,104.1,7040.85,Yes\n2546-KZAAT,Male,0,Yes,No,52,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,75.4,3865.45,No\n0181-RITDD,Male,0,Yes,Yes,62,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,108.15,6825.65,No\n5989-PGKJB,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.25,1340.1,No\n4795-KTRTH,Female,1,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81,371.65,Yes\n8272-ONJLV,Male,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,95.7,1184,No\n1488-PBLJN,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.85,8477.7,No\n0308-GIQJT,Male,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),105.75,7382.85,No\n3778-FOAQW,Female,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,456.85,No\n4452-ROHMO,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,331.6,No\n6481-OGDOO,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),90.6,6056.15,Yes\n3090-LETTY,Male,0,No,Yes,2,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),60.95,134.6,No\n5349-AZPEW,Female,0,Yes,Yes,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.05,125.5,No\n3753-TSEMP,Female,0,Yes,No,15,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,88.15,1390.6,Yes\n8305-VHZBZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes\n9720-JJJOR,Male,0,Yes,Yes,41,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),60.3,2511.3,No\n8100-HZZLJ,Female,0,No,Yes,43,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,63.95,2737.05,No\n8775-ERLNB,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.3,74.3,No\n8309-IEYJD,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.6,70.6,No\n9172-JITSM,Female,0,Yes,Yes,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.8,2361.8,Yes\n6298-QDFNH,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.35,1730.35,Yes\n7398-HPYZQ,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),90.55,6404,No\n3546-GHEAE,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.45,165.35,No\n7361-YPXFS,Female,1,No,No,28,Yes,Yes,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),64.45,1867.6,No\n6557-BZXLQ,Male,1,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,1043.3,No\n2550-QHZGP,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,128.6,No\n7519-JTWQH,Female,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.5,7455.45,No\n2538-OIMXF,Female,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.7,24.7,No\n8543-MSDMF,Male,0,No,No,3,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,77.4,206.15,No\n9961-JBNMK,Male,1,No,No,21,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),96.8,2030.3,Yes\n1170-SASML,Female,0,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.4,5869.4,No\n4872-JCVCA,Female,0,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),47.6,3377.8,No\n5346-BZCHP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,1346.2,No\n2038-LLMLM,Female,0,No,No,48,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.85,4946.05,No\n6173-ITPWD,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),83.35,4065,Yes\n9734-UYXQI,Female,0,No,No,2,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,106.55,Yes\n1216-BGTSP,Male,0,No,No,45,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.45,4964.7,No\n4138-NAXED,Male,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),81,4085.75,No\n2189-UXTKY,Female,0,Yes,No,22,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,79.2,1742.75,Yes\n0744-BIKKF,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.65,6224.8,No\n7483-IQWIB,Male,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,92.95,3415.25,No\n5248-KWLAR,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.35,6325.25,No\n4958-GZWIY,Male,0,Yes,Yes,7,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,48.7,340.25,Yes\n7996-MHXLW,Female,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.15,1683.6,No\n7833-PKIHD,Male,0,Yes,Yes,51,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),76.4,3966.3,No\n7061-OVMIM,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.55,608.5,No\n5153-RTHKF,Female,0,No,No,34,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,85.35,2896.6,No\n1852-QSWCD,Male,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1514.85,No\n4832-VRBMR,Male,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.15,6792.45,No\n9079-LWTFD,Male,0,No,No,47,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,100.75,4669.2,No\n6356-ELRKD,Female,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,95.6,Yes\n8624-GIOUT,Female,0,No,No,49,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),59.75,2934.3,Yes\n3392-EHMNK,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),94.1,6302.8,No\n5986-WWXDV,Male,0,No,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.35,779.2,No\n3061-BCKYI,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,283.75,No\n6179-GJPSO,Female,1,No,No,43,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.15,4600.7,Yes\n7901-TBKJX,Male,1,No,No,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.05,5594,No\n7228-PAQPD,Female,0,No,No,14,Yes,No,DSL,No,No,No,Yes,No,Yes,One year,No,Credit card (automatic),59.1,772.85,No\n3177-LASXD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,71.35,71.35,Yes\n7746-QYVCO,Male,0,Yes,Yes,16,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Mailed check,55.85,857.8,No\n5804-HYIEZ,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.05,7554.05,No\n9919-FZDED,Male,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),84.1,5981.65,No\n5934-TSSAU,Female,0,Yes,Yes,23,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,1702.9,No\n3486-KHMLI,Male,0,No,Yes,21,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.7,467.15,No\n4897-QSUYC,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.15,20.15,Yes\n1084-UQCHV,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.75,69.75,Yes\n8290-YWKHZ,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.2,2931,Yes\n2955-BJZHG,Male,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,80.85,1400.85,Yes\n3806-DXQOM,Female,0,No,No,4,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,33.65,137.85,Yes\n6784-XYJAE,Female,0,No,No,36,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,No,Electronic check,55.8,1941.5,No\n3933-DQPWX,Female,0,No,No,50,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),39.7,1932.75,No\n6661-EIPZC,Female,0,Yes,Yes,48,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),29.5,1423.05,No\n8957-THMOA,Female,0,No,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.15,970.85,No\n2251-PYLPB,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),79.55,5810.9,No\n5555-RNPGT,Male,0,No,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),24.8,223.9,No\n1057-FOGLZ,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,391.7,No\n9300-RENDD,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,79.95,Yes\n0761-AETCS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.3,19.3,Yes\n8087-LGYHQ,Male,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.05,811.65,No\n4137-BTIKL,Male,0,No,No,2,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,90.75,174.75,No\n2190-BCXEC,Female,0,Yes,No,40,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),78.85,3126.85,No\n6227-FBDXH,Male,0,Yes,No,69,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),99.5,6841.45,No\n2153-MREFK,Female,0,Yes,No,37,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Electronic check,99.2,3754.6,Yes\n2911-WDXMV,Male,0,No,Yes,18,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.55,1406.65,No\n7206-PQBBZ,Male,1,Yes,No,11,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,834.7,No\n3106-ULWFW,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.2,627.4,Yes\n0925-VYDLG,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,242,Yes\n4547-FZJWE,Male,0,Yes,Yes,55,Yes,No,DSL,Yes,No,No,No,No,Yes,One year,No,Credit card (automatic),59.45,3157,No\n7422-WNBTY,Male,0,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.35,3092,No\n0842-IWYCP,Female,0,No,No,46,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.95,2168.9,No\n3521-HTQTV,Male,0,No,No,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.1,980.35,No\n3744-ZBHON,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.2,65.95,No\n3373-DIUUN,Male,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21.25,711.9,No\n8383-SGHJU,Female,0,No,No,33,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Electronic check,59.4,1952.8,No\n7607-QKKTJ,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),95,4368.85,No\n7707-PYBBH,Male,0,No,No,40,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,61.9,2647.1,No\n8984-HPEMB,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,118.65,8477.6,No\n4349-GFQHK,Male,0,No,No,1,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,54.35,54.35,Yes\n4139-DETXS,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),64.45,4528,No\n9779-DPNEJ,Female,0,Yes,Yes,22,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.15,1790.65,No\n9805-FILKB,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,845.6,No\n5793-YOLJN,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21,1210.3,No\n0673-IGUQO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,20.45,No\n4123-FCVCB,Female,0,No,No,12,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,75.85,854.45,No\n8819-IMISP,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.45,2429.1,No\n7802-EFKNY,Male,0,Yes,No,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.95,100.4,Yes\n8311-UEUAB,Female,0,Yes,Yes,67,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,Two year,Yes,Electronic check,75.5,5229.45,No\n5858-EAFCZ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.45,44.45,No\n8035-BUYVG,Male,0,Yes,No,40,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,42.35,1716.45,Yes\n1163-ONYEY,Male,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),74.55,3023.55,No\n9787-XVQIU,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.3,75.3,Yes\n8945-MUQUF,Male,0,No,No,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Electronic check,94.8,4837.6,Yes\n5656-MJEFC,Male,0,Yes,Yes,42,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),48.15,2032.3,No\n6082-OQFBA,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,436.9,No\n8051-HJRLT,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.55,70.55,Yes\n8974-OVACP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,No\n4010-YLMVT,Female,0,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),106.6,5893.95,No\n1379-FRVEB,Male,0,No,Yes,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91,1430.05,No\n8612-GXIDD,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.4,313,No\n6288-CHQJB,Female,0,Yes,Yes,54,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),69.95,3871.85,No\n8160-HOWOX,Female,0,No,No,7,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,66.85,458.1,No\n3023-GFLBR,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),86.15,2745.7,Yes\n6648-INWPS,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.15,341.35,No\n4223-BKEOR,Female,0,No,Yes,21,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Mailed check,64.85,1336.8,No\n4079-VTGLK,Male,1,Yes,No,30,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,74.85,2181.75,No\n1763-WQFUK,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.5,147.75,No\n1391-UBDAR,Male,0,No,No,11,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,72.9,818.45,No\n8894-JVDCV,Female,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),115.05,7133.45,No\n2023-VQFDL,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19,348.8,No\n1345-GKDZZ,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,128.6,No\n2014-MKGMH,Female,0,No,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.1,4674.4,No\n5628-FCGYG,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.1,1737.45,No\n2560-WBWXF,Male,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.15,1498.85,No\n0248-IPDFW,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.1,50.1,No\n7978-DKUQH,Female,0,Yes,No,25,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.6,1797.75,No\n4335-UPJSI,Female,0,No,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,498.1,No\n0524-IAVZO,Female,0,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,2624.25,Yes\n2737-YNGYW,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.55,184.1,Yes\n1784-EZDKJ,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.8,5498.8,No\n9297-FVVDH,Female,0,Yes,Yes,57,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.5,4845.4,No\n8007-YYPWD,Female,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.05,369.1,No\n7101-HRBLJ,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),83.7,6096.9,No\n5159-YFPKQ,Female,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,75.8,160.75,Yes\n6635-CPNUN,Male,0,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Credit card (automatic),96.6,2684.35,No\n4021-RQSNY,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,3004.15,Yes\n5453-YBTWV,Male,0,Yes,Yes,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),101.1,6994.8,No\n5039-LZRQT,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.2,273.25,No\n2931-VUVJN,Female,1,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,94.05,5483.9,No\n9061-TIHDA,Male,1,Yes,No,13,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.25,1233.65,Yes\n8699-ASUFO,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.4,527.9,Yes\n6418-PIQSP,Female,0,Yes,No,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81,4985.9,No\n8220-OCUFY,Female,0,No,No,21,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Electronic check,60.25,1258.35,No\n3995-WFCSM,Female,0,No,No,2,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,60.85,111.4,No\n1895-QTKDO,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),43.95,43.95,No\n2038-OEQZH,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,86.05,308.1,No\n1178-PZGAB,Female,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.25,383.65,No\n7927-AUXBZ,Female,0,No,No,30,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,85.15,2555.9,Yes\n2626-VEEWG,Male,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.4,1284.2,No\n2878-RMWXY,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),102.65,7550.3,No\n1657-DYMBM,Male,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1110.05,No\n7311-MQJCH,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.55,99.6,No\n7375-WMVMT,Male,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),95.5,6707.15,No\n1136-XGEQU,Female,0,Yes,Yes,50,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),84.15,4164.4,No\n2530-FMFXO,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,103.2,5873.75,No\n6844-DZKRF,Male,0,No,No,2,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),50.2,109.25,No\n4695-WJZUE,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.55,179.25,Yes\n7619-ODSGN,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.75,1338.15,Yes\n5970-GHJAW,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,862.4,No\n8879-XUAHX,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,116.25,8564.75,No\n3689-MOZGR,Female,0,No,No,29,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,31.2,926.2,No\n4195-PNGZS,Male,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1718.2,No\n5003-XZWWO,Male,0,Yes,No,71,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.2,5956.85,No\n3988-RQIXO,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.3,91.3,Yes\n7622-FWGEW,Male,1,Yes,No,56,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.65,4824.45,No\n6922-NCEDI,Male,0,No,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,21.2,1238.65,No\n2514-GINMM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.5,79.5,Yes\n9891-NQDBD,Female,0,Yes,No,28,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.55,672.2,No\n6131-IUNXN,Female,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,382.2,No\n8548-AWOFC,Male,0,Yes,No,66,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,63.85,4264.6,No\n9798-DRYDS,Female,0,Yes,Yes,17,Yes,Yes,DSL,No,No,Yes,Yes,No,No,One year,Yes,Mailed check,61.95,1070.7,No\n8532-UEFWH,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1345.85,No\n6296-DDOOR,Female,0,No,No,19,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,No,Electronic check,58.2,1045.25,No\n7951-VRDVK,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),85.85,3003.55,No\n5931-FLJJF,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.1,467.55,Yes\n4815-YOSUK,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),104.9,7537.5,No\n2659-VXMWZ,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,111.3,7482.1,Yes\n0380-NEAVX,Male,1,No,No,34,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.85,3343.15,No\n3207-OYBWH,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.25,5427.05,Yes\n4285-GYRQC,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,86.25,587.1,Yes\n7216-EWTRS,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,100.8,Yes\n4365-MSDYN,Male,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.55,161.15,No\n7036-TYDEC,Female,0,No,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,104,7028.5,No\n5802-ADBRC,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,104.4,5232.9,No\n8076-FEZKJ,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,225.85,No\n5197-YPYBZ,Female,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,274.7,No\n8337-MSSXB,Female,0,No,No,14,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.3,1180.95,Yes\n4312-GVYNH,Female,0,Yes,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),49.85,3370.2,No\n8495-LJDFO,Female,1,No,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.95,7111.3,No\n0839-JTCUD,Female,0,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.9,5958.85,No\n5494-WOZRZ,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82,5999.85,No\n1302-UHBDD,Male,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.95,1648.45,No\n2234-EOFPT,Male,0,Yes,No,72,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.35,5753.25,No\n8619-IJNDK,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),64.05,4492.9,No\n8378-LKJAF,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.15,3956.7,No\n8182-BJDSI,Female,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.95,2625.55,Yes\n2528-HFYZX,Male,1,Yes,No,17,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,76.45,1233.4,Yes\n1153-GNOLC,Male,0,No,No,33,No,No phone service,DSL,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,39.1,1309,No\n3298-QEICA,Female,0,Yes,Yes,23,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,34.6,813.45,No\n0788-DXBFY,Male,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.55,1108.8,No\n3597-YASZG,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.45,7349.35,No\n3496-LFSZU,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.5,294.2,No\n5242-UOWHD,Male,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,929.2,No\n2482-CZGBB,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,740,Yes\n6479-SZPLM,Male,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,754.5,No\n8097-VBQTZ,Male,0,No,No,54,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,69.9,3883.3,No\n4500-HKANN,Male,0,Yes,Yes,23,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,No,Mailed check,59.7,1414.2,No\n9917-KWRBE,Female,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),78.35,3211.2,No\n3420-ZDBMA,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.45,371.6,No\n2212-LYASK,Male,0,Yes,Yes,27,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),45.85,1246.4,No\n1393-IMKZG,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.85,95.85,No\n8069-RHUXK,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Credit card (automatic),35.7,2545.7,No\n3398-GCPMU,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),89.55,6448.85,No\n2908-WGAXL,Female,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1468.9,No\n3378-AJRAO,Male,0,Yes,Yes,44,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.85,1013.6,No\n1013-QCWAM,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,6690.75,No\n0866-QLSIR,Female,0,No,No,34,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,64.4,2088.75,Yes\n6050-FFXES,Female,0,Yes,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),105.35,7240.65,No\n7181-BQYBV,Female,0,Yes,Yes,1,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.45,102.45,Yes\n0362-RAOQO,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,830.25,No\n9554-DFKIC,Male,0,Yes,Yes,30,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.45,1588.7,No\n5527-ACHSO,Female,0,No,No,11,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,70.5,829.3,No\n0829-DDVLK,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,302.45,No\n1399-UBQIU,Male,0,No,No,11,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,69.35,712.25,No\n1813-JLKWR,Female,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,1336.65,No\n0336-PIKEI,Male,1,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),74.4,5360.75,No\n7322-OCWHC,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),93.05,6735.05,No\n9537-VHDTA,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,51.2,51.2,No\n4957-TIALW,Female,0,No,Yes,15,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Credit card (automatic),65.6,1010,No\n2054-PJOCK,Female,0,No,No,60,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),80.55,4847.05,No\n9150-HEPMB,Male,0,No,No,56,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,52.7,3019.7,No\n9030-QGZNL,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,161.65,No\n6204-IEUXJ,Female,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),80.1,217.55,Yes\n3126-WQMGH,Female,0,Yes,No,49,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),52.15,2583.75,No\n4529-CKBCL,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.2,146.05,Yes\n2506-CLAKW,Female,0,No,No,6,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),98.15,567.45,Yes\n7176-WRTNX,Male,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.95,7711.25,No\n1583-IHQZE,Male,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,112.95,1384.75,Yes\n5732-IKGQH,Male,1,Yes,No,52,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.45,5481.25,No\n9239-GZHZE,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.65,8124.2,No\n7205-BAIAD,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.6,827.3,No\n0151-ONTOV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.9,70.9,Yes\n4140-MUHUG,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,86.85,220.95,Yes\n0093-EXYQL,Female,1,No,No,40,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.55,3673.6,No\n8064-RAVOH,Male,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.85,49.85,No\n0219-QAERP,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,576.65,No\n0320-JDNQG,Male,0,Yes,Yes,23,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.85,2331.3,Yes\n7180-PISOG,Male,0,Yes,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,74.5,Yes\n9168-INPSZ,Female,1,Yes,No,44,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,104.15,4495.65,No\n3571-RFHAR,Male,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.15,6941.2,Yes\n0015-UOCOJ,Female,1,No,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,48.2,340.35,No\n5334-AFQJB,Male,1,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.1,1789.9,No\n2754-SDJRD,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.15,908.55,No\n9578-VRMNM,Female,0,No,No,16,Yes,Yes,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),65.2,1043.35,Yes\n1587-FKLZB,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.5,6822.15,Yes\n6140-QNRQQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.55,71.55,Yes\n8963-JLGJT,Male,0,No,Yes,3,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,55.9,157.55,No\n4307-KTUMW,Male,0,Yes,No,53,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,93.9,5029.2,Yes\n1465-LNTLJ,Male,1,Yes,No,8,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),64.4,581.7,No\n5440-FLBQG,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.4,7318.2,Yes\n2135-DQWAQ,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,85.3,420.45,No\n4056-QHXHZ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,107.45,7576.7,No\n7470-MCQTK,Female,0,Yes,No,13,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),48.75,633.4,Yes\n7488-MXJIV,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.65,321.65,Yes\n7401-JIXNM,Female,0,Yes,Yes,54,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),91.3,4965,No\n9339-FIIJL,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.95,6151.9,No\n2027-FECZV,Male,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.7,1253.9,Yes\n2672-DZUOY,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.15,25.15,Yes\n4706-DGAHW,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.2,45.2,No\n3870-MQAMG,Female,0,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,110.35,5893.15,Yes\n6670-MFRPK,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Two year,Yes,Credit card (automatic),79.2,5420.65,No\n6177-PEVRA,Female,0,No,No,48,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,No,Credit card (automatic),55.5,2627.35,No\n4800-CZMPC,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),103.25,5037.55,Yes\n4813-HQMGZ,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.25,743.75,No\n7579-KKLOE,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,91.25,6589.6,No\n7377-DMMRI,Male,0,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,47.8,92.45,Yes\n8402-EIVQS,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.9,6733.15,No\n4947-DSMXK,Male,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.7,3410,No\n7245-NIIWQ,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,199.85,No\n0002-ORFBO,Female,0,Yes,Yes,9,Yes,No,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Mailed check,65.6,593.3,No\n3324-OIRTO,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.65,7288.4,No\n5414-OFQCB,Male,0,No,No,57,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),90.45,5229.8,No\n4967-WPNCF,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.7,4464.8,No\n8552-OBVRU,Female,1,Yes,Yes,48,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.5,5068.05,No\n8499-BRXTD,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,401.85,No\n9154-QDGTH,Male,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.3,4451.85,No\n8197-BFWVU,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),93.25,6688.95,No\n2577-GVSIL,Male,0,Yes,Yes,35,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),73.45,2661.1,No\n9367-OIUXP,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,73.05,No\n6770-UAYGJ,Female,0,Yes,Yes,49,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1211.65,No\n6463-HHXJR,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Bank transfer (automatic),100.5,7030.65,No\n7928-VJYAB,Male,0,Yes,Yes,11,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.6,1020.2,No\n1187-WILMM,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),89.4,5597.65,No\n9776-OJUZI,Female,1,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),95.45,6223.3,No\n1306-RPWXZ,Female,0,No,Yes,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.45,1024.65,No\n8949-JTMAY,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.6,2933.2,Yes\n2774-LVQUS,Female,1,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,83.05,1258.3,Yes\n3097-PYWXL,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.95,82.9,No\n2266-SJNAT,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),109.15,7789.6,No\n0869-PAPRP,Female,1,Yes,No,26,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.7,2067,No\n4238-JSSWH,Female,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),102.05,3452.55,No\n2972-YDYUW,Female,0,No,No,57,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,One year,No,Electronic check,94.7,5468.95,No\n1104-FEJAM,Male,0,Yes,Yes,28,Yes,Yes,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,64.4,1802.15,No\n2809-ILCYT,Female,0,Yes,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,26.8,733.55,No\n5499-ECUTN,Female,0,Yes,No,47,Yes,No,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),66.05,3021.45,No\n4981-FLTMF,Female,0,Yes,Yes,57,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),65.2,3687.85,No\n9121-PHQSR,Male,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.05,1391.15,No\n3113-IWHLC,Male,0,No,No,5,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.8,274.35,No\n3211-ILJTT,Male,0,Yes,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.4,1214.05,Yes\n4612-THJBS,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.75,5510.65,Yes\n4277-BWBML,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1322.85,No\n4094-NSEDU,Female,1,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,1973.75,Yes\n0234-TEVTT,Female,0,Yes,Yes,48,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,No,Credit card (automatic),45,2196.3,No\n4304-TSPVK,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.9,7843.55,No\n1552-AAGRX,Female,0,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),106.4,3211.9,No\n2637-FKFSY,Female,0,Yes,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,46.1,130.15,No\n9796-MVYXX,Female,1,No,No,14,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,39.7,692.35,No\n7874-ECPQJ,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.05,85.5,No\n0020-INWCK,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),95.75,6849.4,No\n7089-RKVSZ,Male,0,Yes,Yes,8,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.4,203.95,No\n2683-JXWQQ,Male,0,Yes,Yes,61,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),33.6,2117.2,No\n9548-ZMVTX,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),90.45,6565.85,No\n8739-XNIKG,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84,424.75,No\n9755-JHNMN,Female,0,No,No,49,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),67.4,3306.85,No\n3981-QSVQI,Male,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.7,168.9,No\n2789-HQBOU,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.35,253.8,No\n9424-CMPOG,Male,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,197.4,No\n5067-WJEUN,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),54.2,3838.2,No\n3450-WXOAT,Male,0,No,No,46,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.2,2065.15,No\n9251-WNSOD,Female,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Mailed check,75.1,5064.45,No\n6974-DAFLI,Female,0,Yes,No,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.7,1140.05,No\n2616-UUTFK,Male,0,Yes,No,33,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),72.75,2447.45,No\n7064-JHXCE,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1263.9,No\n5103-MHMHY,Female,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.95,45.95,Yes\n7989-AWGEH,Male,0,Yes,Yes,49,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.2,1838.15,No\n4373-VVHQL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.75,44.75,No\n4559-UWIHT,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,82.65,1185,No\n7268-IGMFD,Male,1,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),93.9,1743.9,No\n1846-XWOQN,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,70.15,Yes\n0235-KGSLC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.55,85.55,Yes\n6650-BWFRT,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),117.15,8529.5,No\n9570-KYEUA,Male,0,No,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),99.25,6549.45,No\n6993-YGFJV,Male,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.55,7806.5,No\n2712-SYWAY,Female,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.7,25.7,No\n0730-BGQGF,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.3,6287.3,No\n5498-IBWPI,Female,0,Yes,Yes,66,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),49.4,3251.85,No\n9101-NTIXF,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,50.6,No\n0013-SMEOE,Female,1,Yes,No,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.7,7904.25,No\n9314-QDMDW,Male,0,No,No,11,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,61.25,729.95,No\n9308-ANMVE,Male,0,No,Yes,47,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,55.3,2654.05,No\n2884-GBPFB,Female,0,Yes,No,35,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,2416.55,Yes\n3757-NJYBX,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),106.35,3520.75,Yes\n3413-DHLPB,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.75,5969.95,No\n7649-PHJVR,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,226.8,No\n6114-TCFID,Female,0,No,No,29,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),39.5,1082.75,No\n3787-TRIAL,Male,0,Yes,Yes,21,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,26.05,565.75,No\n2573-GYRUU,Male,1,Yes,No,48,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),91.05,4370.75,No\n5156-UMKOW,Female,0,No,Yes,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.65,90.05,No\n1247-QBVSH,Female,0,Yes,Yes,43,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.2,2169.4,No\n6734-GMPVK,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,105.3,550.6,No\n9822-OAOVB,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.45,55.45,No\n6161-ERDGD,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,85.45,6300.85,No\n1226-JZNKR,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.8,160.05,No\n7318-EIVKO,Male,0,No,No,8,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.25,436.6,No\n3771-PZOBW,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.7,1781.35,No\n5136-KCKGI,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,103.7,3467,Yes\n8231-BSWXX,Male,0,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Credit card (automatic),79.05,5552.5,No\n6486-LHTMA,Female,1,Yes,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,90.7,2835.5,No\n4083-BFNYK,Female,1,Yes,No,38,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Credit card (automatic),95,3591.25,No\n3722-WPXTK,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.35,88.35,Yes\n7389-KBFIT,Female,0,Yes,Yes,2,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,30.25,63.75,No\n7176-WIONM,Female,0,Yes,No,12,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),49.85,617.15,No\n5141-ZUVBH,Female,0,No,Yes,9,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),93,870.25,No\n1089-HDMKP,Male,1,No,Yes,11,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.55,601.25,No\n7623-HKYRK,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,111.65,No\n0310-SUCIN,Female,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),84.8,6046.1,No\n5197-PYEPU,Female,0,Yes,Yes,42,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),94.45,3923.8,No\n2929-ERCFZ,Female,0,Yes,Yes,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,777.3,Yes\n7548-SEPYI,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.25,512.45,Yes\n8835-VSDSE,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.7,141.45,Yes\n6619-RPLQZ,Female,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,892.15,No\n3275-RHRNE,Male,0,Yes,Yes,28,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60,1682.05,No\n3503-TYDAY,Female,0,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,80.45,3398.9,No\n6901-GOGZG,Male,0,No,Yes,60,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.95,4984.85,No\n1623-NLDOT,Female,0,Yes,No,42,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Mailed check,33.55,1445.3,Yes\n7021-XSNYE,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),49.65,305.55,No\n9621-OUPYD,Female,0,Yes,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,507.9,No\n9898-KZQDZ,Female,1,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.55,3640.45,Yes\n8982-NHAVY,Male,0,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.5,2673.45,No\n4307-KWMXE,Male,0,No,No,10,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,35.75,389.8,No\n0141-YEAYS,Female,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),86.45,2401.05,No\n2450-ZKEED,Female,0,No,No,11,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),53.8,651.55,No\n3694-DELSO,Male,0,Yes,Yes,4,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),38.55,156.1,No\n3893-JRNFS,Male,0,No,No,68,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),39.9,2796.35,No\n9603-OAIHC,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.05,70.05,No\n1133-KXCGE,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,407.05,No\n5236-PERKL,Female,0,No,No,57,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.95,6465,Yes\n0142-GVYSN,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.3,511.25,No\n1049-FYSYG,Female,0,Yes,No,17,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),35.65,646.05,No\n7854-FOKSF,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,35.9,35.9,Yes\n2519-FAKOD,Male,0,No,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.25,3777.15,Yes\n8406-LNMHF,Male,1,Yes,No,59,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),82.95,4903.15,No\n1821-BUCWY,Male,0,No,No,30,Yes,No,DSL,Yes,No,Yes,No,No,No,Two year,Yes,Mailed check,55.65,1653.85,No\n8263-JQAIK,Male,1,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.45,47.5,Yes\n0023-UYUPN,Female,1,Yes,No,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.2,1306.3,No\n8314-DPQHL,Male,0,No,No,9,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Mailed check,50.8,463.6,No\n1465-WCZVT,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,60.65,No\n9481-SFCQY,Female,0,No,Yes,14,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),59.8,824.85,No\n6360-SVNWV,Female,1,No,No,31,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.55,2094.65,No\n0567-GGCAC,Female,0,No,No,7,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,61.4,438.9,No\n7089-IVVAZ,Female,0,No,No,8,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.35,847.3,Yes\n8884-MRNSU,Male,0,Yes,Yes,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,329.75,No\n2171-UDMFD,Male,0,Yes,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.45,674.55,No\n9050-IKDZA,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,81.5,162.55,No\n2205-YMZZJ,Male,1,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.8,546.95,Yes\n9802-CAQUT,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),109.55,7887.25,No\n1254-IZEYF,Female,1,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.95,3186.65,Yes\n0187-WZNAB,Female,0,Yes,Yes,27,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,74.4,1972.35,No\n9492-TOKRI,Female,0,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90,1527.35,Yes\n1475-VWVDO,Male,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.9,490.55,No\n9221-OTIVJ,Female,1,No,No,14,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.85,1531.4,Yes\n1357-MVDOZ,Male,0,Yes,Yes,11,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.65,683.25,No\n7602-MVRMB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.45,8058.85,No\n3325-FUYCG,Male,0,Yes,Yes,28,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Electronic check,106.1,2847.4,Yes\n5908-QMGOE,Male,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.2,1133.9,Yes\n1197-BVMVG,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.45,294.45,No\n5406-KGRMX,Female,0,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.55,1719.15,No\n8481-YYXWG,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.35,461.7,Yes\n5968-HYJRZ,Male,0,Yes,Yes,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1160.45,No\n5198-EFNBM,Male,1,Yes,No,57,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,90.65,5199.8,No\n7516-GMHUV,Male,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),105.05,5163.3,No\n7140-ADSMJ,Male,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,162.3,No\n2230-XTUWL,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,883.35,No\n7706-YLMQA,Female,0,No,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.7,1341.5,No\n2585-KTFRE,Male,0,No,Yes,1,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),70.45,70.45,No\n7994-UYIVZ,Male,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.65,659.45,No\n2609-IAICY,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,77.15,77.15,Yes\n1740-CSDJP,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),35.25,35.25,Yes\n7717-BICXI,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1205.05,No\n6559-RAKOZ,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,97.95,4917.9,No\n2636-OHFMN,Male,0,Yes,No,4,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.55,201,Yes\n4716-MRVEN,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,599.3,No\n2143-LJULT,Female,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.25,1733.15,No\n1323-OOEPC,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.4,5149.5,Yes\n3200-MNQTF,Male,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),70.9,4677.1,No\n6164-HXUGH,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,119.3,No\n5630-IXDXV,Female,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,4849.1,No\n0320-DWVTU,Female,0,No,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Mailed check,99.5,5424.25,No\n9135-MGVPY,Male,0,Yes,No,69,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84.7,5878.9,No\n1212-GLHMD,Male,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,86.05,244.85,No\n7878-JGDKK,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.55,220.75,No\n1088-AUUZZ,Male,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),75.85,4261.2,No\n0397-GZBBC,Male,1,Yes,No,59,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.85,5574.75,Yes\n6614-YWYSC,Male,1,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1501.75,No\n9588-OZDMQ,Female,0,Yes,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45,89.75,No\n7641-EUYET,Male,1,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,100.7,4541.2,Yes\n6476-EPYZR,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,255.5,No\n9921-ZVRHG,Female,0,No,No,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.45,1072,Yes\n1174-FGIFN,Female,0,Yes,Yes,28,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Electronic check,90.45,2509.25,No\n6620-HVDUJ,Male,0,No,No,24,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),60.45,1440.75,No\n4701-MLJPN,Male,0,No,No,31,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,55.25,1715.65,Yes\n1032-MAELW,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,78.45,5333.35,No\n7641-TQFHN,Male,0,No,Yes,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,No,Mailed check,100.55,3895.35,No\n1552-TKMXS,Female,0,Yes,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,869.9,No\n9206-GVPEQ,Male,0,Yes,No,13,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,54.45,706.85,Yes\n8622-ZLFKO,Female,0,Yes,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.75,512.25,No\n1596-BBVTG,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.35,2636.05,Yes\n6188-UXBBR,Female,0,Yes,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.25,814.75,No\n2333-KWEWW,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,388.6,No\n5702-SKUOB,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,93.45,No\n1134-YWTYF,Male,0,Yes,No,27,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,53.8,1389.85,No\n6061-GWWAV,Male,0,No,Yes,41,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,70.2,2894.55,No\n0679-TDGAK,Male,0,Yes,Yes,50,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Electronic check,75.5,4025.6,No\n6585-WCEWR,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.35,1354.4,No\n9067-YGSCA,Female,0,No,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),26.05,1856.4,No\n9067-SQTNS,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.6,926,No\n8433-WXGNA,Male,0,No,No,2,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.7,189.2,Yes\n1776-SPBWV,Female,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.1,682.1,No\n8735-SDUFN,Female,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,1778.7,No\n9668-PUGNU,Male,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,24.5,1816.2,No\n9405-GPBBG,Female,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.5,7069.25,No\n1926-QUZNN,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.25,1841.2,No\n3707-GNWHM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.25,74.25,Yes\n7016-NVRIC,Male,1,Yes,No,29,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,One year,No,Bank transfer (automatic),90.1,2656.7,No\n5829-NVSQN,Female,0,Yes,No,23,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),68.75,1689.45,No\n9565-AXSMR,Male,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.2,1054.75,No\n8922-LIEGH,Female,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.7,2187.55,Yes\n8869-LIHMK,Female,0,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.1,7334.05,No\n8245-UMPYT,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.4,1581.2,Yes\n5186-SAMNZ,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.5,69.5,Yes\n0447-RXSGD,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.65,2404.85,No\n4927-WWOOZ,Male,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.45,171.45,No\n5788-YPOEG,Female,0,Yes,Yes,34,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,84.75,2839.45,No\n5373-SFODM,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.25,3132.75,Yes\n0661-KBKPA,Male,0,Yes,Yes,53,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,78.75,3942.45,No\n9081-WWXKP,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,873.4,No\n0784-ZQJZX,Male,0,No,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1529.65,No\n3133-PZNSR,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),97.75,6991.6,No\n5766-ZJYBB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,19.4,Yes\n8931-GJJIQ,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.3,803.3,Yes\n4277-PVRAN,Female,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),80.1,679.3,Yes\n0022-TCJCI,Male,1,No,No,45,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Credit card (automatic),62.7,2791.5,Yes\n0722-SVSFK,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.4,715,No\n3612-YVGSJ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.45,1681.6,No\n9825-YCXWZ,Female,1,No,No,41,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),101.1,4016.2,No\n5397-NSKQG,Male,0,Yes,Yes,67,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Two year,No,Credit card (automatic),50.9,3281.65,No\n8565-HBFNN,Male,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),107.2,7317.1,No\n2000-DHJUY,Female,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),92.2,6474.45,No\n0203-HHYIJ,Male,0,No,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.3,676.35,Yes\n8670-ERCJH,Male,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),113.4,8164.1,No\n7758-UJWYS,Male,0,Yes,Yes,34,No,No phone service,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Electronic check,40.55,1325.85,No\n2050-ONYDQ,Female,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26,1654.85,No\n7055-JCGNI,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.95,7795.95,No\n0739-UUAJR,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),53.8,3952.45,No\n4826-DXMUP,Male,0,No,Yes,35,Yes,No,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,72.1,2495.15,No\n0952-KMEEH,Male,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,98.15,1230.25,Yes\n7285-KLOTR,Female,0,Yes,No,12,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,78.85,876.75,No\n0654-PQKDW,Female,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),70.75,4263.45,No\n7175-NTIXE,Female,0,No,No,25,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),76.15,1992.95,No\n7963-SHNDT,Female,0,No,No,52,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,39.1,1982.1,No\n9796-BPKIW,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,562.7,No\n0188-GWFLE,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,33.7,No\n8129-GMVGI,Female,0,Yes,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,1090.1,No\n2882-DDZPG,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.45,227.45,No\n3547-LQRIK,Female,0,Yes,No,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,26.9,1250.85,No\n9137-NOQKA,Male,1,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.2,37.2,No\n5843-TTHGI,Female,0,No,No,18,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50,892.7,No\n1849-RJYIG,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,60,487.75,No\n8868-GAGIO,Male,0,Yes,No,45,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,84.55,3713.95,No\n4061-UKJWL,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.45,141.7,No\n5380-XPJNZ,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,678.2,No\n8263-QMNTJ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,115.55,8425.3,No\n8178-EYZUO,Male,0,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.7,4154.8,Yes\n3230-JCNZS,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),99,7061.65,No\n0277-ORXQS,Male,0,Yes,Yes,66,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),50.55,3364.55,No\n5130-IEKQT,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,105.95,2655.25,Yes\n3230-WYKIR,Male,0,No,No,18,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82,1425.45,Yes\n7996-BPXHY,Female,0,Yes,Yes,13,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25,332.5,No\n3227-WLKLI,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),91.55,5963.95,No\n0407-BDJKB,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.75,5742.9,Yes\n2266-FUBDZ,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,278.85,No\n8237-ULIXL,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.85,1871.85,No\n9600-UDOPK,Male,0,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.05,2866.45,Yes\n8216-AZUUZ,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,100.4,4303.65,No\n9153-BTBVV,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1753,No\n4074-SJFFA,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,54.75,54.75,Yes\n3269-ATYWD,Male,1,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.65,3759.05,Yes\n5186-PEIZU,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.25,617.65,No\n1074-AMIOH,Female,0,Yes,Yes,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.25,5935.1,No\n4910-GMJOT,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,94.6,Yes\n0354-WYROK,Female,1,Yes,Yes,31,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.9,2911.3,Yes\n4361-FEBGN,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.15,982.95,No\n7748-UMTRK,Female,1,No,Yes,30,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.3,2974.5,No\n0380-ZCSBI,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20,198.7,No\n7145-FEJWU,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,1275.65,No\n6463-MVYRY,Female,1,No,No,57,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),69.85,4003,No\n3969-JQABI,Female,0,Yes,No,58,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),65.25,3791.6,No\n9624-EGDEQ,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.8,813.3,No\n1051-EQPZR,Female,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,780.25,No\n5849-ASHZJ,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,552.9,No\n5780-INQIK,Female,0,No,No,8,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,49.4,408.25,No\n7576-OYWBN,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,231.8,Yes\n4526-ZJJTM,Female,1,Yes,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),88.4,2191.15,No\n8384-FZBJK,Female,0,Yes,Yes,57,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,5611.7,No\n3750-RNQKR,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,246.25,No\n0962-CQPWQ,Female,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,1296.15,No\n3096-YXENJ,Female,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.65,7082.85,No\n1265-BCFEO,Female,0,Yes,No,71,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.45,5662.25,No\n5837-LXSDN,Female,0,Yes,Yes,21,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),58.85,1215.45,No\n5945-AZYHT,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,109.6,7854.15,No\n8325-QRPZR,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,75.15,525,No\n6384-VMJHP,Female,0,No,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),73,5265.2,No\n2262-SLNVK,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.1,70.1,No\n7730-CLDSV,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),98.65,7129.45,No\n1135-HIORI,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.45,7266.95,No\n0164-APGRB,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114.9,8496.7,No\n6481-ESCNL,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),100.55,2878.75,No\n1790-NESIO,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.4,261.3,No\n1550-EENBN,Female,0,No,No,31,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,104.35,3205.6,No\n5539-TMZLF,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.75,69.75,Yes\n4323-SADQS,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,34.5,279.25,Yes\n4446-BZKHU,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.55,6281.45,Yes\n5202-IVJNU,Female,0,No,Yes,39,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),30.1,1131.3,Yes\n5976-JCJRH,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,738.2,Yes\n8198-RKSZG,Female,0,Yes,Yes,14,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),80.45,1137.05,No\n0137-OCGAB,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.2,80.2,Yes\n3351-NQLDI,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),94.35,6341.45,Yes\n9297-EONCV,Female,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),91.35,6697.2,No\n7593-JNWRU,Male,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.6,260.8,Yes\n4588-YBNIB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,19.6,Yes\n1069-QJOEE,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.9,505.45,No\n3336-JORSO,Female,1,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.45,3655.45,Yes\n7799-DSEWS,Male,0,No,No,18,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,68.35,1299.8,No\n8766-PAFNE,Male,0,Yes,No,71,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.1,5564.85,No\n5315-CKEQK,Male,1,Yes,Yes,28,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Electronic check,51,1381.8,No\n3130-ICDUP,Female,0,No,Yes,2,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),80.55,188.1,No\n0820-FNRNX,Male,0,Yes,Yes,17,Yes,Yes,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,66.7,1077.05,No\n0880-FVFWF,Male,0,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,86.4,4922.4,No\n4611-ANLQC,Female,0,Yes,No,60,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,50.05,2911.5,No\n4213-HKBJO,Female,0,No,No,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.7,826.1,No\n2792-LSHWX,Female,0,No,No,1,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,83.4,83.4,No\n5028-GZLDO,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.7,140.7,Yes\n0014-BMAQU,Male,0,Yes,No,63,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Two year,Yes,Credit card (automatic),84.65,5377.8,No\n6861-XWTWQ,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.25,665.45,Yes\n9018-PCIOK,Female,0,No,No,55,Yes,No,DSL,No,Yes,Yes,No,No,Yes,Two year,Yes,Mailed check,64.75,3617.1,No\n4837-QUSFT,Female,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),100.15,6643.5,No\n6877-TJMBR,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,84.8,84.8,Yes\n9953-ZMKSM,Male,0,No,No,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1559.3,No\n0907-HQNTS,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113,7987.6,No\n7665-NKLAV,Female,0,Yes,Yes,36,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),40.65,1547.35,No\n6769-DCQLI,Male,0,No,No,52,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105,5426.85,Yes\n2433-KMEAS,Male,0,No,No,22,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,54.45,1127.35,Yes\n4391-LNRXK,Male,0,No,No,22,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),94.95,2142.8,No\n8250-ZNGGW,Female,1,No,No,5,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),59.9,287.85,No\n2195-ZRVAX,Female,0,Yes,No,47,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85.3,4045.65,Yes\n3550-SAHFP,Female,0,No,No,33,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.35,2757.85,Yes\n2011-TRQYE,Male,0,No,No,18,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),33.5,600,Yes\n8562-GHPPI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.8,19.8,No\n5893-PYOLZ,Male,0,No,No,56,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),81.8,4534.45,No\n4986-MXSFP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20,40.9,No\n6131-FOYAS,Male,0,No,No,35,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,59.6,2094.9,No\n3027-YNWZU,Female,0,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25,1584.8,No\n5609-IMCGG,Female,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.35,1302.65,No\n4873-ILOLJ,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.35,2238.5,Yes\n4727-MCYZG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,55.55,55.55,No\n9481-WHGWY,Female,0,Yes,Yes,70,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),75.35,5437.75,No\n2725-KXXWT,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.75,90.75,Yes\n9565-DJPIB,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Mailed check,89.6,365.65,Yes\n4328-VUFWD,Female,0,No,No,39,Yes,No,DSL,No,No,No,Yes,No,Yes,One year,No,Electronic check,59.3,2209.15,No\n3301-LSLWQ,Female,0,No,No,29,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,66.1,1912.15,No\n7473-ZBDSN,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.8,255.55,No\n3166-PNEOF,Female,0,No,No,61,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,5175.3,No\n5639-NTUPK,Male,0,No,Yes,13,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),52.1,670.65,No\n8780-YRMTT,Female,0,No,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Mailed check,47.4,3177.25,No\n8348-HFYIV,Male,0,No,No,2,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.25,90.35,Yes\n5140-FOMCQ,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.15,6557.75,No\n6242-SGYTS,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),94.95,5791.85,No\n8166-ORCHU,Male,1,Yes,No,33,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,Yes,Electronic check,93.55,3055.5,No\n8414-OOEEL,Male,0,No,Yes,66,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.5,5196.1,No\n8454-AATJP,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.05,8405,No\n6849-WLEYG,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,Yes\n4659-NZRUF,Female,0,No,No,19,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,95.15,1789.25,Yes\n4531-AUZNK,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,One year,Yes,Mailed check,95.15,5000.05,No\n4191-XOVOM,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,105.4,6713.2,No\n2150-OEGBV,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.1,562.6,No\n8429-XIBUM,Male,0,No,No,22,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.35,2317.1,Yes\n1855-CFULU,Female,1,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,91.45,No\n4878-BUNFV,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.7,828.85,No\n1872-EBWSC,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,617.35,No\n2608-BHKFN,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70.05,266.9,Yes\n7026-YMSBE,Male,0,No,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,625.05,No\n7341-LXCAF,Male,0,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.65,301.4,Yes\n6997-UVGOX,Male,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.45,6029.9,No\n9674-EHPPG,Male,0,Yes,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),40.4,1842.7,No\n9462-MJUAW,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.4,206.6,Yes\n8128-YVJRG,Female,0,No,No,7,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,79.65,604.7,Yes\n5440-VHLUL,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,7386.05,No\n5781-BKHOP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Bank transfer (automatic),100.65,7334.05,No\n4283-FUTGF,Male,1,No,No,19,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.85,1471.75,Yes\n5213-TWWJU,Male,0,No,No,28,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,No,Electronic check,91,2626.15,No\n1569-TTNYJ,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.75,412.1,Yes\n8628-MFKAX,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.75,8277.05,No\n2397-BRLOM,Male,1,Yes,Yes,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80.45,583.45,Yes\n7629-WIXZF,Female,0,No,No,7,Yes,No,DSL,Yes,No,No,No,No,Yes,One year,Yes,Electronic check,59.1,369.25,No\n5445-GLVOT,Female,0,No,No,22,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.8,1049.05,No\n3976-HXHCE,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,1414.8,No\n2466-NEJOJ,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.65,169.75,No\n0254-KCJGT,Male,0,Yes,No,52,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),81.4,4354.45,No\n5472-CVMDX,Female,0,No,No,68,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,38.9,2719.2,No\n6461-SZMCV,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),87.95,6365.35,No\n8150-QUDFX,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,51.6,No\n9508-ILZDG,Female,1,No,No,34,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,96.35,3190.25,No\n2346-DJQTB,Female,0,No,No,35,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.15,812.5,No\n1697-LYYYX,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.1,1143.8,No\n1942-OQFRW,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No\n4749-VFKVB,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.1,50.1,Yes\n9640-ZSLDC,Female,0,Yes,Yes,53,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),60.6,3297,No\n4231-LZUYM,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.65,1887,No\n7598-UAASY,Male,0,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.4,151.8,Yes\n7938-OUHIO,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,293.65,Yes\n8510-AWCXC,Female,1,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,1308.1,Yes\n6128-AQBMT,Male,1,Yes,No,41,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,53.95,2215.4,No\n3594-BDSOA,Female,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.4,482.8,No\n0431-APWVY,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),90.1,2598.95,Yes\n5133-VRSAB,Male,0,No,No,8,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.35,216.45,No\n5996-DAOQL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,20.45,No\n6838-YAUVY,Female,0,No,No,54,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),95.1,5064.85,No\n0484-JPBRU,Male,0,No,No,41,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.25,996.45,No\n7883-ROJOC,Female,0,Yes,No,19,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.9,839.65,No\n0244-LGNFY,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92.65,6733,No\n7274-CGTOD,Male,0,No,No,62,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),43.7,2618.3,No\n4295-YURET,Female,1,Yes,Yes,56,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),72.6,4084.35,No\n3426-NIYYL,Male,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,51.55,765.5,Yes\n8225-BTJAU,Male,1,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.25,793.55,Yes\n4635-EJYPD,Male,0,Yes,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.95,613.95,No\n1866-ZSLJM,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.5,402.85,No\n4636-TVXVG,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.95,1244.8,No\n4236-UJPWO,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.5,46.4,No\n9392-XBGTD,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,581.85,No\n3387-VATUS,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.85,462.8,Yes\n6402-SSEJG,Female,0,No,No,25,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,61.05,1540.2,No\n1143-NMNQJ,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.7,169.8,Yes\n1169-SAOCL,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),106.65,5168.1,No\n1110-KYLGQ,Female,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),108.25,6780.1,No\n9929-PLVPA,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.4,94.5,No\n3518-PZXZQ,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,55.3,55.3,No\n2371-JUNGC,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,208,No\n7693-QPEFS,Male,1,No,No,52,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.95,3829.75,No\n0924-BJCRC,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.45,5294.6,No\n5074-FBGHB,Male,0,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.65,6889.8,No\n2351-BKRZW,Female,0,Yes,Yes,43,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),75.2,3254.35,No\n4455-BFSPD,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),101.15,6383.9,No\n0415-MOSGF,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.4,44.4,Yes\n1724-BQUHA,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,477.7,Yes\n3948-KXDUF,Male,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),68.75,4447.55,No\n2323-ARSVR,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.05,7321.05,No\n0815-MFZGM,Female,0,Yes,No,42,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),99,4135,No\n4826-XTSOH,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.05,86.05,Yes\n5480-XTFFL,Female,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21,697.7,No\n8295-FHIVV,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,168.65,No\n2495-INZWQ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.55,174.3,Yes\n9086-YJYXS,Male,0,Yes,Yes,34,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),77.2,2753.8,No\n1179-INLAT,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.45,69.25,No\n7909-FIOIY,Female,0,Yes,Yes,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.85,434.8,No\n4139-SUGLD,Male,0,Yes,Yes,31,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),35.4,1077.5,Yes\n6857-VWJDT,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,95.65,95.65,Yes\n6351-SCJKT,Male,0,No,No,3,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,41.35,107.25,No\n5468-BPMMO,Male,0,Yes,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.6,851.2,No\n5624-BQSSA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.95,20.95,Yes\n0197-PNKNK,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,No,Bank transfer (automatic),84.45,5848.6,No\n2439-QKJUL,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,109.8,No\n1194-SPVSP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.65,19.65,No\n9534-NSXEM,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.65,595.5,No\n2408-WITXK,Female,1,No,No,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,34.7,329.8,Yes\n8929-KSWIH,Male,0,No,No,25,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),99.3,2513.5,No\n2250-IVBWA,Male,0,Yes,Yes,64,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.05,5135.35,No\n1810-MVMAI,Male,0,Yes,Yes,30,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,67.6,2000.2,No\n9506-UXUSK,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,931.75,No\n1229-RCALF,Female,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,115,7396.15,No\n3572-UOLYZ,Female,0,No,Yes,46,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,3958.85,No\n1429-UYJSV,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,260.9,No\n5577-OTWWW,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.75,297.3,Yes\n6100-QQHEB,Male,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.55,1515.1,Yes\n6366-XIVKZ,Female,0,Yes,Yes,13,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,63.15,816.8,No\n3470-OBUET,Female,0,Yes,Yes,67,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),74,4868.4,No\n2770-NSVDG,Male,0,Yes,No,24,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,29.1,688,No\n9375-MHRRS,Male,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50.05,288.35,No\n2634-HCZGT,Male,1,Yes,No,53,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Electronic check,60.05,3229.65,Yes\n7503-QQRVF,Male,1,Yes,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.3,1178.25,Yes\n4860-YZGZM,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,185.4,No\n6599-GZWCM,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Mailed check,74.65,966.25,No\n2691-NZETQ,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.35,758.6,Yes\n4404-HIBDJ,Female,0,No,No,25,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Mailed check,74.3,1863.8,Yes\n5533-NHFRF,Male,1,No,No,7,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,44.4,265.8,No\n7037-MTYVW,Male,0,Yes,Yes,38,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.4,3297,No\n4760-THGOT,Female,0,Yes,No,43,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,94.1,4107.3,No\n7295-JOMMD,Female,0,No,Yes,4,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.1,396.3,Yes\n4016-BJKTZ,Female,0,No,No,25,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,108.9,2809.05,No\n6584-VQMYT,Male,0,No,Yes,27,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,56.2,1567.55,No\n9838-BFCQT,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.1,1851.45,No\n2790-XUYMV,Male,0,No,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),85.45,6028.95,No\n0581-MDMPW,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),88.95,2072.75,No\n5013-SBUIH,Female,0,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,109.65,5551.15,Yes\n1023-BQXZE,Male,0,No,No,57,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.35,4317.35,No\n9163-GHAYE,Female,0,No,No,15,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,48.85,736.8,No\n3904-UKFRE,Male,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.1,336.15,No\n5353-WILCI,Female,0,No,No,28,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,56.05,1522.65,No\n0709-TVGUR,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,622.9,Yes\n0058-EVZWM,Female,0,Yes,No,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.8,4959.6,No\n6023-YEBUP,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.95,329.95,Yes\n7209-JCUDS,Male,0,No,No,10,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.9,1048.85,Yes\n7009-PCARS,Male,0,No,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.1,1001.5,No\n0519-DRGTI,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,442.6,No\n1017-FBQMM,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106.05,6703.5,No\n3001-CBHLQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.9,3351.55,Yes\n1985-MBRYP,Female,0,No,No,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.65,779.25,No\n6158-DWPZT,Male,0,Yes,No,9,No,No phone service,DSL,No,No,No,No,No,No,One year,No,Bank transfer (automatic),24.1,259.8,Yes\n9372-TXXPS,Female,0,Yes,No,60,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),59.85,3483.45,No\n3259-QMXUN,Male,0,Yes,No,58,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,86.1,4890.5,No\n1015-JPFYW,Male,0,No,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,136.75,No\n6645-MXQJT,Male,0,Yes,Yes,2,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,97.1,184.15,No\n4360-QRAVE,Male,1,No,No,37,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,36.65,1315,No\n8433-WPJTV,Female,1,Yes,Yes,65,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.9,6767.1,No\n5804-JMYIO,Female,0,Yes,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,757.95,No\n3763-GCZHZ,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,104.05,6890,Yes\n6484-LATFU,Male,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1657.4,No\n7599-NTMDP,Female,0,Yes,Yes,62,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),48.7,3008.55,No\n0772-GYEQQ,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,88.35,262.05,Yes\n0536-ACXIP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,109.55,8165.1,No\n0936-NQLJU,Female,0,Yes,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.65,875.55,No\n4831-EOBFE,Male,0,Yes,Yes,29,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),94.65,2649.15,Yes\n1575-KRZZE,Female,0,No,No,4,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.2,220.65,No\n3251-YMVWZ,Male,0,No,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.05,1301.9,No\n4102-OQUPX,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,74.4,Yes\n9170-GYZJC,Female,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),79.9,3326.2,Yes\n4884-LEVMQ,Male,0,Yes,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.45,790,No\n8857-CUPFQ,Male,0,Yes,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,1237.65,No\n7610-TVOPG,Male,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,26.35,378.6,No\n3638-DIMPH,Female,0,Yes,No,13,Yes,No,DSL,No,No,No,No,No,No,One year,No,Electronic check,43.8,592.65,No\n1845-ZLLIG,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,50.15,No\n8559-WNQZS,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,20.45,No\n3999-QGRJH,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,560.85,Yes\n1699-UOTXU,Male,0,No,No,60,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,61.4,3638.25,No\n2454-RPBRZ,Female,1,Yes,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,98.1,1060.2,Yes\n0635-WKOLD,Male,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),70.75,2921.75,No\n5993-JSUWV,Female,0,No,No,66,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),61.15,4017.45,No\n4518-FZBSX,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,854.9,No\n5387-ASZNZ,Female,1,No,No,66,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),63.85,4174.35,No\n6988-CJEYV,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.7,4920.55,No\n2002-MZHWP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,Yes\n3097-FQTVJ,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,810.3,No\n5465-BUBFA,Female,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,772.4,No\n7299-GNVPL,Female,0,Yes,Yes,23,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,84.4,1936.85,No\n9743-DQKQW,Male,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.1,79.8,No\n1215-VFYVK,Female,0,No,No,4,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,No,Mailed check,48.25,202.25,No\n9371-BITHB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,1070.5,No\n2265-CYWIV,Female,1,Yes,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.6,347.65,Yes\n9093-FPDLG,Female,0,No,No,11,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,94.2,999.9,No\n0541-FITGH,Female,0,Yes,No,2,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,62.15,113.1,No\n6985-HAYWX,Female,0,Yes,No,26,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),79.3,2015.8,No\n7508-KBIMB,Male,0,Yes,Yes,24,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),56.25,1454.25,No\n6838-HVLXG,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,246.7,No\n2277-DJJDL,Male,1,Yes,No,60,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99,6017.9,No\n1897-OKVMW,Female,0,Yes,Yes,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.6,5817.45,No\n5485-ITNPC,Male,0,Yes,No,66,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.9,5595.3,No\n0233-FTHAV,Female,0,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),79.2,4765,No\n4644-PIZRT,Male,0,Yes,Yes,17,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),70.35,1201.65,No\n8922-NPKBJ,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,867.3,No\n2740-TVLFN,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.15,50.15,No\n7771-CFQRQ,Female,0,Yes,Yes,47,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),63.8,3007.25,No\n9512-PHSMG,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,252.75,No\n6963-EZQEE,Male,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.55,6306.5,No\n2452-KDRRH,Male,1,No,No,67,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.4,6841.05,No\n2004-OCQXK,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.95,81.95,Yes\n5027-QPKTE,Male,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,451.1,Yes\n4146-SVFUD,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.6,44.6,No\n1564-HJUVY,Male,0,No,No,4,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),63.75,226.2,No\n3617-XLSGQ,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),109.25,7082.5,No\n7517-LDMPS,Female,0,No,No,12,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,1017.35,No\n7244-KXYZN,Female,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.45,527.35,No\n5226-NOZFC,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.75,2146.5,No\n2672-HUYVI,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.1,455.3,Yes\n1400-WIVLL,Male,0,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,107.95,5969.85,No\n7508-MYBOG,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.1,1235.55,Yes\n8148-NLEGT,Female,0,Yes,Yes,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,22.95,1014.25,No\n0148-DCDOS,Male,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.7,2362.1,Yes\n8347-GDTMP,Female,0,Yes,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,1225.65,No\n9992-RRAMN,Male,0,Yes,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.1,1873.7,Yes\n6746-WAUWT,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.7,386.5,No\n0310-MVLET,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.15,6010.05,Yes\n0428-IKYCP,Male,0,Yes,No,22,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87,1850.65,No\n4550-VBOFE,Male,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.95,7101.5,Yes\n2187-PKZAY,Male,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),79.95,1043.4,No\n4003-OCTMP,Female,0,Yes,No,31,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,64,1910.75,No\n6652-YFFJO,Female,0,No,No,11,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,64.9,716.1,No\n7225-IILWY,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1686.15,No\n5248-RPYWW,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),90.15,6716.45,No\n7923-IYJWY,Male,1,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.1,7839.85,No\n3170-GWYKC,Female,0,No,No,60,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.95,6236.75,No\n5696-QURRL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.05,45.05,Yes\n7409-KIUTL,Female,1,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71,71,Yes\n9830-ECLEN,Male,0,Yes,Yes,58,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,50,2919.85,No\n1732-VHUBQ,Female,1,Yes,Yes,47,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.55,3309.25,Yes\n0495-RVCBF,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.7,79.7,Yes\n2668-TZSPS,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,20.45,No\n7854-EDSSA,Male,0,No,No,22,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,59,1254.7,Yes\n8869-TORSS,Female,0,No,No,48,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),60.35,2896.4,Yes\n7446-SFAOA,Female,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,717.5,No\n9522-ZSINC,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.95,253.8,No\n8234-GSZYK,Male,0,No,No,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.45,1110.05,No\n6505-OZNPG,Female,0,No,No,6,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,63.4,348.8,No\n6164-HAQTX,Male,0,No,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),53.95,3888.65,No\n0679-IDSTG,Female,1,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,69.25,Yes\n7765-LWVVH,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Electronic check,95.1,6843.15,No\n1658-TJVOA,Female,1,No,No,6,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.1,450.9,No\n0953-LGOVU,Male,0,Yes,Yes,12,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,35.5,432.25,No\n2115-BFTIW,Male,0,No,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.95,1767.35,Yes\n4692-NNQRU,Female,0,Yes,No,21,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,79.2,1742.45,No\n7742-MYPGI,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,48.8,297.35,No\n0611-DFXKO,Male,0,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89,1820.45,Yes\n7780-OTDSO,Male,0,Yes,No,18,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.4,1742.95,Yes\n9619-GSATL,Female,0,No,No,43,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,No,Electronic check,55.45,2444.25,No\n5622-UEJFI,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.4,949.8,No\n9747-DDZOS,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),73.5,73.5,Yes\n0674-DGMAQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.5,2970.8,No\n6203-HBZPA,Male,0,No,No,52,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),63.9,3334.95,No\n0484-FFVBJ,Male,0,No,No,32,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),64.85,2010.95,No\n0301-FIDRB,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.8,4684.3,No\n4139-JPIAM,Male,0,No,No,51,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),44.45,2181.55,No\n2181-TIDSV,Male,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.95,1303.25,No\n2761-XECQW,Male,1,Yes,No,8,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,43.35,371.4,No\n1936-CZAKF,Male,0,Yes,No,49,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,No,Credit card (automatic),49.65,2409.9,No\n9453-PATOS,Female,0,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,85.1,6155.4,No\n0564-JJHGS,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.5,829.1,Yes\n3348-CFRNX,Female,0,Yes,No,28,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,92.35,2602.9,Yes\n4402-FTBXC,Male,0,No,No,54,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,89.8,4667,No\n1428-GTBJJ,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,824.75,Yes\n2931-XIQBR,Female,0,Yes,Yes,50,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,103.05,5153.5,No\n0106-UGRDO,Female,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,116,8182.85,No\n5542-TBBWB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,No\n1930-QPBVZ,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),95.1,6683.4,No\n2606-PKWJB,Male,0,No,Yes,40,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,40.25,1564.05,No\n5322-ZSMZY,Male,0,Yes,Yes,31,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,755.6,No\n8059-UDZFY,Female,1,No,Yes,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,3465.05,No\n7740-BTPUX,Male,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.6,6292.7,No\n5220-AGAAX,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24,1664.3,No\n0208-BPQEJ,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,198.1,No\n9435-JMLSX,Male,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.1,6045.9,No\n3352-ALMCK,Male,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,102.65,4108.15,No\n5312-IRCFR,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,92.85,5980.75,No\n5294-IMHHT,Male,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,One year,No,Bank transfer (automatic),97.75,5043.2,No\n8802-UNOJF,Male,1,No,No,12,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,83.8,1029.75,Yes\n6313-GIDIT,Male,1,No,No,53,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,54.45,2854.55,Yes\n6176-YJWAS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),97.95,7114.25,No\n5310-NOOVA,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.95,907.05,No\n4526-EXKKN,Male,0,No,No,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.6,973.95,No\n5311-IHLEI,Male,0,No,No,12,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),50.95,605.75,No\n3987-KQDDU,Male,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.6,661.55,No\n3404-JNXAX,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.75,4116.9,No\n5131-PONJI,Male,0,Yes,Yes,49,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.4,4494.65,No\n2845-AFFTX,Male,1,Yes,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4259.3,Yes\n3489-VSFRD,Female,0,No,No,56,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),60.25,3282.75,No\n3345-JHUEO,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.2,55.7,No\n5815-HGGHV,Male,0,Yes,No,20,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Mailed check,64.15,1274.45,No\n5260-UMPWX,Female,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.25,493.95,No\n2249-YPRNG,Female,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.85,2239.65,Yes\n7410-YTJIK,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75.45,480.75,Yes\n4626-GYCZP,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,635.6,Yes\n5649-RXQTV,Male,0,No,No,51,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99,5038.15,No\n2674-MIAHT,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.3,324.2,No\n2576-HXMPA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.55,19.55,No\n7587-AOVVU,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,No,Electronic check,100.75,2793.55,No\n5590-BYNII,Male,0,No,No,22,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.75,2095,Yes\n6898-MDLZW,Male,0,No,No,12,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,53.75,648.65,No\n3237-AJGEH,Female,0,Yes,Yes,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,31,95.05,Yes\n4707-YNOQA,Female,0,Yes,Yes,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.6,917.15,No\n9357-UJRUN,Male,0,Yes,No,24,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Electronic check,58.35,1346.9,No\n1591-NFNLQ,Male,0,No,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),80,4242.35,Yes\n8648-PFRMP,Female,1,No,No,14,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,46.35,672.7,No\n7663-CUXZB,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,113.75,6561.25,No\n0258-NOKBL,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,90.4,268.45,No\n1163-VIPRI,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,109.3,7337.55,No\n2815-CPTUL,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.25,331.9,Yes\n9348-ROUAI,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.3,5194.05,No\n9443-JUBUO,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.25,4478.85,No\n0596-BQCEQ,Female,0,Yes,Yes,62,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.15,6283.3,Yes\n1481-ZUWZA,Male,0,No,No,28,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.5,2659.4,Yes\n3043-TYBNO,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,60.65,196.9,No\n4830-FAXFM,Male,0,No,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),24.1,439.2,No\n5906-BFOZT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,19.5,No\n2960-NKRSO,Male,0,No,No,24,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,85.95,2107.15,No\n8996-ZROXE,Male,1,No,No,57,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,Yes,Electronic check,53.5,3035.8,No\n5598-IKHQQ,Female,0,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.45,1866.45,No\n0397-ZXWQF,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,1430.95,No\n5266-PFRQK,Male,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.85,1071.6,No\n4674-HGNUA,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.9,6457.15,No\n6765-MBQNU,Female,0,Yes,No,26,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,26,684.05,No\n9786-IJYDL,Female,0,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,113.2,3914.05,No\n1303-SRDOK,Female,0,Yes,Yes,55,Yes,No,Fiber optic,No,No,No,No,No,No,Two year,Yes,Credit card (automatic),69.05,3842.6,No\n3769-MHZNV,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,670.35,No\n6295-OSINB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,109.65,7880.25,No\n3308-MHOOC,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.2,19.2,No\n2309-OSFEU,Male,0,No,No,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,33.9,298.45,Yes\n2207-QPJED,Female,1,Yes,No,37,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,90,3371.75,No\n4177-JPDFU,Male,0,No,No,12,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,34,442.45,No\n2239-CFOUJ,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.4,20.4,No\n8046-DNVTL,Male,0,Yes,No,62,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),38.6,2345.55,No\n2209-XADXF,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),25.25,25.25,No\n6620-JDYNW,Female,0,No,No,18,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,60.6,1156.35,No\n1891-FZYSA,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.95,6143.15,Yes\n4770-UEZOX,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,144.8,No\n1038-RQOST,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,414.95,No\n7613-LLQFO,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.45,1059.55,Yes\n4568-TTZRT,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,181.8,No\n9513-DXHDA,Male,0,No,No,27,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,81.7,2212.55,No\n2640-PMGFL,Male,0,No,Yes,27,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,79.5,2180.55,Yes\n3801-HMYNL,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,89.15,89.15,Yes\n0516-QREYC,Female,1,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,459.95,No\n9685-WKZGT,Male,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,1036.75,Yes\n6022-UGGSO,Female,1,No,No,32,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,2276.95,Yes\n8084-OIVBS,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,211.95,No\n8896-BQTTI,Male,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25,25,No\n3865-QBWSJ,Male,1,No,Yes,38,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,80.45,3162.65,No\n3352-RICWQ,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,210.65,No\n2160-GPFXD,Male,0,Yes,Yes,54,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),65.65,3566.7,No\n2065-MMKGR,Female,0,No,No,29,Yes,Yes,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),71,2080.1,No\n5857-TYBCJ,Male,1,Yes,No,44,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,4040.2,No\n1498-NHTLT,Male,0,Yes,Yes,59,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,86.75,5186,No\n4484-CGXFK,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.3,196.15,Yes\n1402-PTHGN,Female,0,Yes,Yes,18,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,61.5,1087.45,No\n4176-RELJR,Male,1,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,1672.15,No\n6214-EDAKZ,Female,0,Yes,Yes,22,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,55.15,1206.05,Yes\n5199-FPUSP,Male,0,No,Yes,33,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),34.05,1113.95,No\n6377-KSLXC,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,107.05,No\n1796-JANOW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,38.15,No\n0238-WHBIQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.7,6339.3,No\n8735-NBLWT,Male,0,No,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,184.1,No\n6651-RLGGM,Male,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.3,1688.9,No\n6127-ISGTU,Female,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.95,1378.25,Yes\n1614-JBEBI,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.7,137.6,No\n8740-XLHDR,Male,0,No,No,5,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,43.25,219,Yes\n1208-DNHLN,Male,0,Yes,Yes,23,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),48.35,1067.15,Yes\n1761-AEZZR,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.55,79.55,Yes\n3923-CSIHK,Female,1,Yes,No,50,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.05,3444.85,Yes\n5696-EXCYS,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,369.05,No\n5795-KTGUD,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.8,7553.6,No\n7120-RFMVS,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.5,84.5,Yes\n7924-GJZFI,Female,1,Yes,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.3,1813.1,No\n4702-HDRKD,Male,0,No,No,67,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),49.35,3321.35,No\n8512-WIWYV,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.35,707.5,No\n5897-ZYEKH,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,105.6,7112.15,No\n5456-ITGIC,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.45,4641.1,No\n7317-GGVPB,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.6,7690.9,Yes\n1406-PUQVY,Male,0,No,Yes,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.9,49.9,No\n1322-AGOQM,Male,0,No,No,46,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),30.3,1380.1,Yes\n3677-IYRBF,Female,1,No,No,2,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,30.4,78.65,Yes\n5692-FPTAH,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.4,45.4,Yes\n9114-AAFQH,Female,0,Yes,No,48,Yes,No,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,65.65,3094.65,No\n8715-KKTFG,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.3,6518.35,No\n1550-LOAHA,Female,0,Yes,No,32,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),84.15,2585.95,Yes\n1728-BQDMA,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.45,82.7,No\n0268-QKIWO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,58.85,Yes\n0876-WDUUZ,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),85.4,425.9,Yes\n5117-ZSMHQ,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),89.9,6342.7,No\n5151-HQRDG,Male,0,Yes,No,37,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,55.05,2030.75,No\n0960-HUWBM,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.1,6700.05,No\n6465-GSRCL,Female,0,No,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),106.6,7244.7,No\n0617-FHSGK,Male,0,No,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.2,3678.3,Yes\n0263-FJTQO,Male,0,Yes,Yes,50,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.5,3486.65,No\n7319-ZNRTR,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.6,411.15,No\n2858-MOFSQ,Female,0,No,Yes,17,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,Yes,Mailed check,55.85,937.5,Yes\n7503-EPSZW,Female,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.05,1559.15,No\n7089-XXAYG,Male,0,Yes,No,25,No,No phone service,DSL,No,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),38.1,970.4,No\n8118-LSUEL,Male,1,No,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,2483.5,Yes\n8070-AAWZP,Male,1,Yes,No,24,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,34.25,828.2,No\n1666-JXLKU,Female,0,No,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3810.55,No\n7855-DIWPO,Female,0,No,No,21,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.65,1493.2,No\n5133-POWUA,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.8,45.8,No\n5652-MSDEY,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.75,777.3,No\n7005-CCBKV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.4,556.35,Yes\n1810-BOHSY,Male,0,Yes,No,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),96.4,4911.05,No\n1784-BXEFA,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,187.35,No\n7351-MHQVU,Female,0,No,No,6,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),50.95,307.6,No\n9224-VTYID,Male,0,Yes,Yes,47,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.5,4318.35,No\n9500-IWPXQ,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,79.4,4820.55,No\n5762-TJXGK,Female,0,No,No,52,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),58.75,3038.55,No\n4504-YOULA,Female,0,Yes,Yes,35,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),59.45,2136.9,No\n5569-IDSEY,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.7,7472.15,No\n4250-FDVOU,Female,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,56.25,389.1,Yes\n7284-ZZLOH,Male,0,Yes,No,45,Yes,No,DSL,Yes,No,Yes,No,No,No,Two year,No,Credit card (automatic),53.3,2296.25,No\n5277-ZLOOR,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.55,187.45,Yes\n2141-RRYGO,Female,0,No,No,4,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),68.65,261.25,Yes\n1777-JYQPJ,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.3,38.45,No\n6376-GAHQE,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,77.85,299.2,Yes\n3401-URHDA,Male,0,No,No,51,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),59.9,3043.6,No\n1599-EAHXY,Male,0,Yes,Yes,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),23.95,1506.4,No\n8631-XVRZL,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.15,163.7,No\n5052-PNLOS,Male,0,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,323.25,Yes\n3853-LYGAM,Male,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.65,1640,No\n6979-ZNSFF,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,87.05,762.1,Yes\n5751-USDBL,Male,0,Yes,Yes,46,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81,3846.35,No\n5680-LQOGP,Female,0,No,No,68,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82.45,5646.6,No\n2386-LAHRK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,53.5,53.5,Yes\n8189-DUKMV,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,79.05,No\n6032-IGALN,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.1,25.1,Yes\n9931-DCEZH,Male,0,No,Yes,28,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.4,1516.6,No\n1898-JSNDC,Female,0,No,No,39,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),58.6,2224.5,No\n0315-LVCRK,Male,0,No,No,11,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,84.8,888.75,No\n3911-RSNHI,Female,0,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),61.4,4310.35,No\n2410-CIYFZ,Male,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,42.9,No\n4248-HCETZ,Male,1,Yes,No,30,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.65,2365.15,Yes\n5505-OVWQW,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,353.65,No\n7271-AJDTL,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),94.45,5073.1,No\n1867-TJHTS,Female,0,No,No,58,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.8,4526.85,No\n0516-WJVXC,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.2,308.25,Yes\n9174-FKWZE,Female,1,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.45,19.45,Yes\n6860-YRJZP,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.05,678.45,No\n4429-WYGFR,Male,0,No,No,26,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),49.15,1237.3,No\n2817-LVCPP,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1023.95,No\n5038-ETMLM,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.65,8182.75,No\n5056-FIMPT,Female,0,No,Yes,43,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106,4532.3,No\n4521-WFJAI,Male,0,No,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.95,1444.05,No\n6569-KTMDU,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.1,19.1,No\n8809-RIHDD,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,103.4,7372.65,Yes\n8809-XKHMD,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),100.55,7325.1,No\n0396-YCHWO,Male,0,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.4,3474.2,No\n0867-LDTTC,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),75.15,392.65,No\n4822-NGOCH,Female,0,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.45,1058.6,Yes\n9391-DXGGG,Female,1,No,No,44,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),89.15,3990.75,No\n9844-FELAJ,Female,1,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,107.9,7475.85,No\n2122-SZZZD,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.5,835.5,No\n5307-DZCVC,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.95,2628.6,Yes\n3148-AOIQT,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1718.35,No\n8679-JOEVF,Female,1,No,No,16,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,59.4,1023.9,Yes\n8395-ETZKQ,Male,1,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.5,1193.55,No\n6692-YQHXC,Male,0,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,1205.5,No\n3889-VWBID,Male,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),82.85,5776.45,No\n5222-JCXZT,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,78.9,No\n9754-CLVZW,Female,0,Yes,Yes,26,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),38.85,1025.15,No\n1432-FPAXX,Female,0,No,No,29,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.6,856.35,Yes\n2739-CCZMB,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,122,No\n7080-TNUWP,Male,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),95,6602.9,No\n0496-AHOOK,Male,0,Yes,No,24,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),74.4,1712.9,No\n8336-TAVKX,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),78.45,5682.25,No\n2468-SJFLM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,74.3,74.3,No\n3181-VTHOE,Male,0,Yes,No,70,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),51.05,3635.15,No\n7168-HDQHG,Female,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.2,702.9,No\n4803-AXVYP,Female,1,No,No,38,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.55,3734.25,Yes\n3884-HCSWG,Female,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,1144.5,Yes\n4567-AKPIA,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.1,4454.25,No\n5077-DXTCG,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.3,45.3,Yes\n1142-WACZW,Male,0,No,No,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),29.85,75.6,Yes\n7901-HXJVA,Male,0,No,No,14,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.45,1117.55,No\n5649-ANRML,Male,1,No,No,2,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95.1,180.25,Yes\n4892-VLANZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.8,19.8,Yes\n5924-IFQTT,Male,0,Yes,Yes,13,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,72.8,930.05,No\n7968-QUXNS,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.95,110.15,No\n2919-HBCJO,Female,0,No,No,4,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),76.65,333.6,Yes\n4236-XPXAV,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.15,465.05,Yes\n8903-WMRNW,Female,0,Yes,No,15,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.75,1669.4,No\n2452-SNHFZ,Female,0,No,No,47,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.45,3545.1,No\n3629-WEAAM,Female,0,No,No,8,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,No,Mailed check,64.1,504.05,No\n6029-CSMJE,Male,0,No,No,17,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.65,440.2,No\n7993-NQLJE,Male,0,Yes,Yes,15,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,75.1,1151.55,No\n9909-DFRJA,Female,0,No,No,26,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),95.85,2475.35,No\n9099-FTUHS,Female,0,No,No,23,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.4,1249.25,No\n0581-BXBUB,Female,1,No,No,4,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,72.75,317.75,No\n4962-CHQPW,Male,0,No,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.85,535.05,No\n9467-ROOLM,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,461.3,No\n3030-YZADT,Male,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.95,431,Yes\n7410-KTVFV,Male,0,Yes,No,18,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.55,878.35,Yes\n5150-ITWWB,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.85,335.75,No\n2253-KPMNB,Female,0,Yes,Yes,69,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),46.25,3121.4,No\n1345-ZUKID,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.35,324.8,No\n6429-SHBCB,Male,0,No,No,19,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,69.6,1394.55,No\n9281-OFDMF,Male,1,No,No,39,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,90.7,3413.25,No\n2603-HVKCG,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,3143.65,No\n1834-WULEG,Male,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,439.75,No\n9097-ZUBYC,Male,0,Yes,No,14,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),48.8,664.4,No\n5148-ORICT,Female,0,Yes,No,64,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,74.35,4759.55,No\n4893-GYUJU,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.35,1033,No\n4578-PHJYZ,Male,0,Yes,Yes,52,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,68.75,3482.85,No\n7272-QDCKA,Male,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,100.2,2688.45,No\n8908-SLFCJ,Female,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.85,435.25,No\n8393-DLHGA,Male,0,No,Yes,25,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.9,2448.75,Yes\n9766-HGEDE,Female,0,Yes,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.35,307,No\n6968-MHOMU,Male,0,Yes,No,58,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),45,2689.35,No\n7395-IGJOS,Male,1,Yes,No,17,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),81.5,1329.2,Yes\n5044-XDPYX,Female,0,Yes,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.5,1281.25,No\n1814-WFGVS,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,48.9,3527,No\n2834-SPCJV,Male,0,Yes,No,52,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.1,4348.65,Yes\n3721-WKIIL,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.6,561.15,No\n6734-CKRSM,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,63.6,No\n1265-ZFOSD,Female,0,Yes,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,81.3,5129.3,No\n6568-POCUI,Female,0,Yes,No,45,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),95.2,4285.8,No\n7890-VYYWG,Male,1,Yes,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,36.45,93.7,Yes\n5197-LQXXH,Female,0,Yes,No,71,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.3,5894.5,No\n9907-SWKKF,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.05,25.05,Yes\n3457-PQBYH,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1160.75,No\n7682-AZNDK,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.85,3091.75,No\n0587-DMGBH,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.85,365.55,Yes\n5384-ZTTWP,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.8,272.95,No\n3745-HRPHI,Male,0,Yes,Yes,66,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),54.65,3632,No\n7636-OWBPG,Male,1,No,No,12,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),29.35,381.2,No\n1231-YNDEK,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.15,1035.5,No\n1407-DIGZV,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.1,52,No\n6397-JNZZG,Female,1,Yes,No,43,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),55.55,2342.2,Yes\n0570-BFQHT,Female,0,No,No,9,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,80.55,653.9,No\n4393-OBCRR,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,71.2,No\n3523-QRQLL,Female,0,Yes,Yes,22,Yes,No,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),69.5,1498.2,Yes\n8564-LDKFL,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106,4178.65,No\n3696-DFHHB,Female,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.5,1821.8,No\n0895-DQHEW,Male,0,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.3,5278.15,Yes\n4717-GHADL,Female,0,No,No,50,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,79.6,4024.2,No\n5501-TVMGM,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.25,55.25,No\n5879-HMFFH,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),88.05,6520.8,No\n6772-WFQRD,Male,0,No,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.4,854.9,No\n3810-DVDQQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),117.6,8308.9,No\n6972-SNKKW,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,109.2,No\n3694-GLTJM,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.65,92.05,No\n8550-XSXUQ,Male,0,Yes,No,48,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),70.55,3420.5,No\n8149-RSOUN,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,93.85,Yes\n9055-MOJJJ,Female,0,Yes,Yes,64,Yes,No,DSL,Yes,No,No,Yes,Yes,No,One year,No,Mailed check,65.8,4068,No\n4359-INNWN,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,337.9,No\n0585-EGDDA,Male,0,Yes,No,40,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,80,3168.75,No\n4032-RMHCI,Female,0,Yes,Yes,41,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),35.4,1412.4,No\n0549-CYCQN,Male,1,No,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),79.6,3974.7,No\n3481-JHUZH,Male,0,Yes,No,41,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,3439,No\n7250-EQKIY,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,50.45,50.45,Yes\n3594-IVHJZ,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,42.45,No\n6869-FGJJC,Male,0,No,No,68,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Credit card (automatic),79.6,5461.45,No\n3896-ZVNET,Female,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.7,571.75,No\n8205-VSLRB,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),77.3,5498.2,No\n5960-MVTUK,Male,0,No,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.75,96.85,No\n6817-WTYHE,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.9,111.05,No\n3082-VQXNH,Male,0,Yes,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),29.8,94.4,No\n4013-UBXWQ,Female,0,No,No,7,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.65,521.1,Yes\n4931-TRZWN,Female,0,No,No,13,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,71.95,923.85,No\n0750-EKNGL,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,141.1,No\n7669-LCRSD,Male,0,Yes,Yes,12,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,56.3,628.65,No\n3567-PQTSO,Male,0,Yes,Yes,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.25,5576.3,No\n5519-NPHVG,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,1046.1,Yes\n8043-PNYSD,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,1245.6,No\n9938-EKRGF,Female,0,No,No,15,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Mailed check,84.45,1287.85,No\n2703-AMTUL,Male,0,Yes,Yes,36,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,53.65,1939.35,No\n0928-JMXNP,Male,1,Yes,No,4,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.9,118.25,No\n8173-RXAYP,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,452.55,No\n4825-XJGDM,Female,0,No,No,61,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Credit card (automatic),43.7,2696.55,No\n5402-HTOTQ,Male,0,No,No,16,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.3,875.35,No\n6734-JDTTV,Male,0,Yes,Yes,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.85,1267.05,No\n7850-THJMU,Female,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,494.9,No\n3890-RTCMS,Male,0,No,No,16,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.45,799,No\n8849-GYOKR,Female,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),106.55,5763.3,Yes\n3148-BLQJT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,20.1,Yes\n3717-FDJFU,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,106.9,No\n3665-JATSN,Female,0,No,No,19,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.7,710.05,No\n7966-YOTQW,Male,0,No,No,10,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),54.5,568.2,No\n0461-CVKMU,Female,0,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,83.8,1900.25,Yes\n8806-EAGWC,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.15,159.15,Yes\n8853-TZDGH,Female,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.6,8012.75,No\n7779-LGOVN,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.65,856.65,Yes\n4324-BZCKL,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.55,551.3,No\n6924-TDGMT,Male,0,Yes,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.55,184.95,No\n9710-ZUSHQ,Female,1,No,No,37,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),106.75,4056.75,Yes\n1536-YHDOE,Male,0,Yes,Yes,17,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,62.1,1096.65,No\n8123-QBNAZ,Female,0,Yes,Yes,36,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,3684.95,No\n3629-ZNKXA,Male,1,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.8,1752.45,No\n4827-LTQRJ,Female,1,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110.6,7210.85,No\n7711-YIJWC,Male,0,Yes,Yes,61,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),84.9,5264.5,No\n5482-VXSXJ,Male,0,No,No,22,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.2,2157.3,No\n7365-BVCJH,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.4,24.4,No\n9620-ENEJV,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.55,433.95,No\n4183-WCSEP,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,78.45,2435.15,Yes\n2378-YIZKA,Female,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85,5607.75,No\n5498-TXHLF,Female,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,87.45,2874.15,Yes\n0689-DSXGL,Female,0,Yes,Yes,52,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),85.8,4433.3,No\n6818-DJXAA,Female,0,No,Yes,10,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,91.1,964.35,No\n9722-UJOJR,Male,0,Yes,Yes,29,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.75,1974.8,Yes\n0464-WJTKO,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1460.85,No\n8902-ZEOVF,Male,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,951.55,No\n6778-EICRF,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,74.8,1821.2,No\n2662-NNTDK,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.8,1600.95,No\n4132-KALRO,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,100.85,399.25,No\n3902-FOIGH,Male,1,Yes,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,1218.55,Yes\n6772-KSATR,Male,0,No,No,1,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,81.7,81.7,Yes\n7112-OPOTK,Male,0,No,No,33,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.25,2171.15,Yes\n2874-YXVVA,Female,0,No,No,34,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.1,3634.8,No\n1245-HARPS,Female,0,Yes,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.4,292.4,No\n4210-QFJMF,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.15,317.25,Yes\n4323-ELYYB,Male,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,218.55,No\n4293-ETKAP,Female,0,Yes,Yes,65,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.4,5071.9,No\n6064-PUPMC,Male,0,Yes,Yes,23,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),57.2,1423.35,No\n6504-VBLFL,Male,0,Yes,No,55,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,58.6,3068.6,No\n6322-PJJDJ,Male,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Electronic check,94.8,4690.65,No\n0330-BGYZE,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),102.5,6157.6,No\n1085-LDWAM,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.35,1442.65,No\n7586-ZATGZ,Male,0,No,No,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.9,3369.05,No\n7197-VOJMM,Male,0,Yes,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),69.2,4671.65,No\n3318-OSATS,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,3474.05,Yes\n5828-AVIPD,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.95,1875.55,Yes\n1843-TLSGD,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,272.35,No\n9626-VFRGG,Female,0,No,Yes,41,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),88.5,3645.05,No\n7075-BNDVQ,Female,0,No,No,4,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,35,135.75,No\n9143-CANJF,Female,0,Yes,Yes,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,55.15,1319.85,No\n7284-BUYEC,Female,0,No,No,5,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.95,229.4,No\n2041-JIJCI,Female,0,No,No,5,Yes,Yes,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,64,370.25,No\n1086-LXKFY,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.1,69.1,Yes\n4900-MSOMT,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),80.2,5714.2,No\n2229-VWQJH,Female,0,No,No,24,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),49.3,1233.25,No\n9194-GFVOI,Female,0,Yes,No,42,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.35,3571.6,No\n1336-EZFZY,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,83.3,No\n4282-MSACW,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),117.2,8035.95,No\n1403-LKLIK,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,579.4,No\n2636-ALXXZ,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes\n7774-OJSXI,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.45,3066.45,Yes\n7786-WBJYI,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),77.95,305.55,Yes\n0136-IFMYD,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.95,7634.25,No\n5144-TVGLP,Male,1,No,No,38,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.75,3653,No\n9643-AVVWI,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,80,241.3,No\n0253-ZTEOB,Female,0,Yes,Yes,48,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,79.65,3870.3,No\n2706-QZIHY,Female,0,Yes,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.2,387.9,No\n6061-PQHMK,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,527.5,No\n9885-CSMWE,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.45,78.45,Yes\n6137-MFAJN,Female,0,No,No,48,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,44.8,2104.55,No\n9122-UMROB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,20.3,No\n4232-JGKIY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.2,19.2,No\n2402-TAIRZ,Female,0,No,No,37,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,80.05,3019.1,No\n9659-ZTWSM,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),107.35,7051.95,No\n9139-TWBAS,Female,0,Yes,No,26,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),47.85,1190.5,No\n6685-GBWJZ,Male,0,Yes,No,63,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),70.8,4448.8,No\n5016-ETTFF,Male,0,No,No,10,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,29.5,255.25,Yes\n3866-MDTUB,Female,0,No,No,2,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,70.75,146.9,Yes\n2195-VVRJF,Male,1,Yes,No,18,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),59.1,1011.05,No\n3913-RDSJZ,Female,0,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.55,1714.95,No\n8058-JMEQO,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.45,762.5,Yes\n4203-QGNZA,Female,0,No,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,535.35,No\n5043-TRZWM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.55,75.55,No\n0697-ZMSWS,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.65,338.9,Yes\n7657-DYEPJ,Male,1,No,No,38,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.15,2497.35,Yes\n9494-BDNNC,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Electronic check,95.3,6273.4,No\n1640-PLFMP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,70.25,No\n5366-OBVMR,Female,0,Yes,No,18,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.3,908.75,No\n8276-MQBYC,Male,1,No,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.8,4913.3,Yes\n7644-OMVMY,Male,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85, ,No\n7593-XFKDI,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46.3,46.3,Yes\n4573-JKNAE,Male,0,No,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,212.3,No\n0337-CNPZE,Female,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.3,4443.45,Yes\n9817-APLHW,Male,0,No,No,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25,316.2,No\n8380-MQINP,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.3,1079.05,No\n0840-DFEZH,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,564.65,No\n1513-XNPPH,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.4,1095.65,Yes\n8690-ZVLCL,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88,6161.9,No\n6015-VVHHE,Female,1,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),83.15,446.05,Yes\n1125-SNVCK,Female,1,No,No,49,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.8,2106.05,No\n0384-LPITE,Male,0,No,No,40,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),62.05,2511.55,No\n4616-EWBNJ,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.1,318.6,No\n6347-DCUIK,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.15,811.8,Yes\n1335-HQMKX,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.35,7323.15,No\n2545-EBUPK,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.05,186.05,No\n6923-AQONU,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.9,454,No\n2172-EJXVF,Female,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.9,7521.95,No\n0897-FEGMU,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,1056.95,Yes\n7663-RGWBC,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.15,44.15,Yes\n1120-BMWUB,Female,0,No,No,16,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,53.9,834.15,Yes\n9124-LHCJQ,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.45,85.45,Yes\n4536-PLEQY,Male,0,Yes,No,12,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.05,999.8,No\n7029-IJEJK,Female,0,No,No,54,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,One year,No,Bank transfer (automatic),44.1,2369.7,No\n8871-JLMHM,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),90.2,6297.65,No\n2235-ZGKPT,Female,0,Yes,Yes,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.85,239.55,Yes\n3891-PUQOD,Female,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,59.2,59.2,Yes\n5447-VYTKW,Male,0,No,No,27,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,53.45,1461.45,No\n3623-FQBOX,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,416.4,No\n0689-NKYLF,Male,0,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,83.2,1060.6,Yes\n8659-HDIYE,Female,1,No,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),74.65,4869.35,No\n3658-KIBGF,Female,0,No,No,1,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,54.9,54.9,Yes\n3474-BAFSJ,Male,0,Yes,No,57,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),57.5,3265.95,No\n5519-YLDGW,Female,0,Yes,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.9,2254.2,Yes\n3865-ZFZIB,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,358.15,No\n1855-AGAWH,Male,0,Yes,No,31,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,93.8,2939.8,No\n7109-CQYUZ,Male,0,No,No,52,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,89.25,4652.4,No\n1370-GGAWX,Female,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,94.15,4408.45,No\n4680-KUTAJ,Female,1,No,No,11,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,55.6,580.8,No\n5307-UVGNB,Female,0,Yes,Yes,53,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),48.7,2495.2,No\n4946-EDSEW,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.25,180.3,Yes\n2883-ILGWO,Male,1,No,No,57,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.9,5913.95,No\n2516-VQRRV,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.45,158.4,Yes\n7580-UGXNC,Female,1,No,No,2,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.85,104.2,Yes\n3642-BYHDO,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1389.35,No\n9629-NHXFW,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,19.4,No\n2696-RZVZW,Male,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,1629.2,No\n5766-FTRTS,Male,0,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,84.45,6033.1,No\n0396-HUJBP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,44.4,No\n5178-LMXOP,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,95.1,Yes\n8879-ZKJOF,Female,0,No,No,41,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),79.85,3320.75,No\n6285-FTQBF,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.55,1867.7,No\n8185-UPYBR,Male,0,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,438,Yes\n4585-HETAI,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.75,325.45,Yes\n7526-BEZQB,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.05,1148.1,Yes\n1474-JUWSM,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,68.4,3972.25,No\n3530-CRZSB,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,155.9,No\n8498-XXGWA,Female,0,Yes,No,65,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.15,3673.15,No\n9617-INGJY,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.6,70.6,No\n0621-TSSMU,Male,0,Yes,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.95,1126.75,No\n7234-KMNRQ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,73.45,No\n7636-PEPNS,Female,0,Yes,Yes,58,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,44.1,2413.05,No\n4683-WYDOU,Male,0,Yes,No,62,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.6,6912.7,No\n9052-DHNKM,Male,0,No,No,26,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,No,Electronic check,61.55,1581.95,No\n6794-HKIAJ,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),90.7,5586.45,No\n5578-NKCXI,Female,0,Yes,Yes,58,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),99.25,5846.65,No\n1642-HMARX,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,91.7,6424.7,No\n3096-WPXBT,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),100.7,6018.65,No\n8434-PNQZX,Female,0,No,No,42,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),78.45,3373.4,No\n5950-AAAGJ,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),84.3,1537.9,No\n4299-OPXEJ,Female,0,No,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.55,1080.55,No\n4951-UKAAQ,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,88.95,355.2,Yes\n9618-LFJRU,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.45,82.85,No\n6693-DJWTY,Female,0,No,Yes,35,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Credit card (automatic),55.6,2016.45,No\n0744-GKNGE,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),86.8,5327.25,No\n6447-EGDIV,Female,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.95,683.25,No\n1167-OYZJF,Female,1,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,1263.05,No\n2108-YKQTY,Female,0,No,No,4,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),50.7,151.3,Yes\n4806-DXQCE,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.65,7714.65,No\n4918-QLLIW,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),53.4,188.7,Yes\n7056-IMHCC,Male,1,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.9,5549.4,Yes\n4854-SSLTN,Male,0,Yes,Yes,2,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,59.5,130.5,No\n5294-DMSFH,Female,0,Yes,Yes,29,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),87.8,2621.75,No\n4837-PZTIC,Female,0,No,No,47,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,41.9,1875.25,No\n1625-JAIIY,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,Yes,Electronic check,83,5685.8,Yes\n0603-OLQDC,Male,0,No,Yes,12,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,69.85,837.5,No\n3272-VUHPV,Female,0,Yes,Yes,8,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),56.3,401.5,No\n4176-FXYBO,Male,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,109.55,6118.95,No\n1063-DHQJF,Male,0,Yes,Yes,69,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,92.15,6480.9,No\n8663-UPDGF,Female,0,No,No,26,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.5,1800.05,No\n6719-FGEDO,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),97,7104.2,No\n1837-YQUCE,Female,0,No,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),58.35,4214.25,No\n2947-DOMLJ,Male,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.6,50.6,Yes\n4112-LUEIZ,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,863.1,Yes\n0369-ZGOVK,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.4,1992.2,No\n4510-HIMLV,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,69.8,Yes\n9919-KNPOO,Female,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.3,1948.35,No\n8749-JMNKX,Male,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.8,4750.95,Yes\n7872-BAAZR,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.55,1007.9,No\n9391-LMANN,Male,0,No,Yes,53,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,95.95,5036.9,No\n2430-USGXP,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.05,2391.8,Yes\n8174-TBVCF,Female,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),94.8,6859.05,No\n1698-XFZCI,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.75,6521.9,No\n1877-HKBQX,Female,0,No,No,11,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,54.6,617.85,No\n4450-DLLMH,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),71.3,157.75,No\n0428-AXXLJ,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.5,516.3,No\n2746-DIJLO,Female,0,No,No,41,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Credit card (automatic),56.3,2364,No\n5955-ERIHD,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,1687.95,Yes\n0917-EZOLA,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.15,7689.95,Yes\n3508-VLHCZ,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.55,6239.05,No\n4086-ATNFV,Female,0,Yes,Yes,34,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,60.8,2042.05,No\n0468-YRPXN,Male,0,No,No,29,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.8,2807.1,No\n5996-NRVXR,Male,1,Yes,No,40,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),98.15,4116.8,No\n3739-YBWAB,Male,0,Yes,No,36,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.35,1317.95,No\n7047-FWEYA,Female,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,103.15,4594.65,No\n2000-MPKCA,Female,0,No,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.75,6332.75,No\n9762-YAQAA,Male,0,No,No,39,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,No,Credit card (automatic),81.4,3213.75,No\n5949-EBSQK,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),61.45,229.55,Yes\n5473-KHBPS,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),95.7,4976.15,No\n0100-DUVFC,Male,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.8,7308.95,No\n5397-TUPSH,Male,1,Yes,No,65,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.95,4555.2,No\n8950-MTZNV,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.95,44.95,No\n0326-VDYXE,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.65,6982.5,No\n6773-LQTVT,Female,1,Yes,Yes,29,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,35.65,1025.15,No\n7274-RTAPZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.55,90.55,Yes\n0436-TWFFZ,Female,0,No,No,67,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,85.25,5714.2,No\n8566-YPRGL,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,19.5,No\n0311-UNPFF,Female,0,No,No,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),88.8,2274.35,Yes\n6609-MXJHJ,Female,0,Yes,Yes,30,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,25.1,789.55,No\n2669-OIDSD,Female,0,Yes,No,48,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.05,4834,No\n8400-WZICQ,Female,0,Yes,Yes,55,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.7,3131.8,No\n3834-XUIFC,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.2,602.55,Yes\n9576-SYUHJ,Male,0,No,No,37,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.15,3369.25,No\n0410-IPFTY,Female,0,Yes,No,31,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),83.85,2674.15,No\n2831-EBWRN,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.9,199.75,No\n9430-NKQLY,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.1,1790.8,No\n8414-MYSHR,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.4,449.75,Yes\n0247-SLUJI,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,19.7,No\n9402-ORRAH,Female,1,No,No,15,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.5,1400.3,No\n6483-OATDN,Male,0,Yes,Yes,8,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),51.3,411.6,No\n1293-HHSHJ,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,21.1,741,No\n4840-ORQXB,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,104.75,5841.35,No\n7599-FKVXZ,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),106.15,4512.7,Yes\n3982-DQLUS,Male,1,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,85.75,5688.45,No\n8610-ZIKJJ,Female,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,31.9,No\n3756-VNWDH,Male,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,100.75,6674.65,No\n7801-KICAO,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.15,1345.75,No\n7673-LPRNY,Female,0,No,No,23,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,78.55,1843.05,No\n8229-TNIQA,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.3,196.95,Yes\n6060-QBMGV,Male,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1433.8,No\n7339-POGZN,Female,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,50.7,214.55,No\n2828-SLQPF,Male,0,No,No,19,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45,865.85,No\n4465-VDKIQ,Female,0,No,No,18,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,77.8,1358.6,No\n2921-XWDJH,Female,1,Yes,No,38,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.45,3147.15,No\n6221-AVQYL,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.25,131.05,Yes\n2558-BUOZZ,Male,0,No,No,47,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),94.8,4535.85,No\n9257-AZMTZ,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1078.75,No\n0003-MKNFE,Male,0,No,No,9,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,59.9,542.4,No\n0975-UYDTX,Female,0,Yes,No,26,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),90.1,2312.55,No\n7743-EXURX,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,51.05,415.05,Yes\n4682-BLBUC,Male,0,Yes,No,44,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,70.95,3250.45,No\n0975-VOOVL,Female,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.2,98.5,No\n5968-VXZLG,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),46.6,87.9,No\n5569-KGJHX,Female,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.35,754.65,Yes\n4988-IQIGL,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,75.35,Yes\n5201-FRKKS,Male,0,No,No,25,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.3,1952.25,No\n9799-CAYJJ,Female,1,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.3,153.8,No\n7730-IUTDZ,Male,0,No,No,43,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.2,3198.6,Yes\n0426-TIRNE,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,Yes\n5443-SCMKX,Female,0,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,94.3,5610.15,No\n8295-KMENE,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Mailed check,76.45,4519.5,No\n6738-ISCBM,Male,0,No,No,44,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,54,2440.25,No\n9821-BESNZ,Male,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,104.25,6860.6,No\n3678-MNGZX,Male,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1377.7,No\n1335-NTIUC,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.95,190.25,No\n2916-BQZLN,Male,0,No,No,19,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.75,1651.95,No\n4558-CGYCZ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,78.3,No\n2368-GAKKQ,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.65,7939.25,No\n8710-YGLWG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.9,44.9,No\n3199-XGZCY,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.25,576.7,No\n3785-KTYSH,Male,0,No,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.6,1279,No\n6814-ZPWFQ,Male,1,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25,1260.7,No\n4063-EIKNQ,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,267.35,No\n6993-YCOBK,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.6,6586.85,No\n5206-HPJKM,Male,0,No,No,17,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55.5,934.15,No\n7587-RZNME,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,43.3,123.65,Yes\n0748-RDGGM,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.5,7534.65,Yes\n8393-JMVMB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,19.45,No\n5019-GQVCR,Male,1,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,3645.6,No\n6036-TTFYU,Female,0,Yes,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,314.45,No\n2550-AEVRU,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Electronic check,53.45,3053,No\n0969-RGKCU,Male,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,677.05,No\n0378-CJKPV,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),112.1,7965.95,No\n4706-AXVKM,Female,1,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.8,906.85,Yes\n5889-JTMUL,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.05,4888.7,Yes\n9026-RNUJS,Male,1,No,No,5,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,50.35,237.25,Yes\n3746-EUBYR,Male,0,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,74.6,Yes\n2332-EFBJY,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.7,342.4,Yes\n2880-FPNAE,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.2,140.1,No\n1703-MGIAB,Female,0,No,No,17,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,69,1108,No\n4311-QTTAI,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.35,295.55,No\n9474-PHLYD,Female,0,No,No,15,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,59.45,892.65,Yes\n4318-RAJVY,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.8,198.25,No\n2165-VOEGB,Female,0,No,Yes,46,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,4822.85,Yes\n3612-YUNGG,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.2,6741.15,No\n2254-DLXRI,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,79.15,79.15,No\n5062-CJJKH,Male,0,Yes,Yes,25,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,No,Mailed check,53.65,1355.45,No\n9028-LIHRP,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),100.2,7209,No\n2219-MVUSO,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.15,438.4,Yes\n1053-MXTTK,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.65,7726.35,No\n1530-ZTDOZ,Female,0,Yes,No,49,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),40.65,2070.75,No\n1301-LOPVR,Male,0,Yes,Yes,29,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),55.35,1636.95,No\n0853-TWRVK,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),105.6,7581.5,No\n8914-RBTSB,Male,0,Yes,No,31,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,93.8,3019.5,Yes\n6212-ATMLK,Female,0,No,No,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.7,4729.75,No\n8200-LGKSR,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,83.2,6126.1,No\n1568-BEKZM,Male,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.05,6333.4,No\n0670-ANMUU,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,No,Credit card (automatic),97.65,6687.85,No\n6897-RWMUB,Male,0,Yes,Yes,61,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),68.05,4158.25,No\n0963-ZBDRN,Male,0,No,No,32,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96.2,3183.4,Yes\n7594-RQHXR,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.6,79.6,Yes\n6999-CHVCF,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Bank transfer (automatic),102.1,7149.35,No\n6134-KWTBV,Male,0,No,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.4,1429.65,No\n1077-HUUJM,Female,0,No,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.05,472.65,No\n6331-EWIEB,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.25,1734.5,Yes\n0895-LNKRC,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,113.5,No\n2045-BMBTJ,Female,1,No,No,33,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),59.45,1884.65,No\n8417-GSODA,Male,0,Yes,Yes,28,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),92.2,2568.15,No\n5171-EPLKN,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,470,No\n4452-QIIEB,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.9,278.4,No\n5998-VVEJY,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.5,2088.8,Yes\n1624-NALOJ,Male,1,No,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.8,502.6,Yes\n6741-EGCBI,Male,1,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.5,2595.85,No\n7820-ZYGNY,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,No,Credit card (automatic),90.45,5825.5,No\n4317-VTEOA,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,50.75,50.75,Yes\n9677-AVKED,Female,0,No,Yes,53,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,84.6,4449.75,No\n1670-SVOWZ,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.65,1208.35,Yes\n2227-JRSJX,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.15,1956.4,No\n9847-HNVGP,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,310.6,No\n6696-YDAYZ,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,290.55,No\n8980-WQFWL,Female,0,No,No,35,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,62.1,2096.1,No\n1730-ZMAME,Female,1,No,No,32,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.5,2665,No\n6506-EYCNH,Female,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.55,543.8,No\n4634-JLRJT,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.35,20.35,No\n9501-UKKNL,Male,0,No,No,59,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,51.7,3005.8,No\n2560-QTSBS,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),23.3,1623.15,No\n7541-YLXCL,Male,0,No,No,36,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,65.4,2498.4,Yes\n4795-WRNVT,Female,0,No,No,40,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,No,Mailed check,65.1,2586,No\n6080-TCMYC,Male,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.2,3292.3,No\n0260-ZDLGK,Female,0,No,Yes,9,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,72.9,651.4,Yes\n7860-UXCRM,Male,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),74.5,4674.55,No\n6357-JJPQT,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.5,232.35,No\n2292-XQWSV,Male,0,Yes,Yes,40,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,60.3,2448.5,No\n9552-TGUZV,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,75,658.1,No\n5913-INRQV,Male,1,Yes,No,34,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,One year,No,Mailed check,90.15,3128.8,No\n8722-NGNBH,Male,0,No,No,5,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,40,223.45,Yes\n6210-KBBPI,Male,1,No,No,9,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.45,919.4,Yes\n9643-YBLUR,Male,0,Yes,No,9,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),69.05,653.95,No\n8734-FNWVH,Male,0,Yes,Yes,31,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),59.7,1825.5,No\n9447-YPTBX,Female,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,943.1,No\n0133-BMFZO,Female,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,86.25,181.65,Yes\n6128-CZOMY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.65,45.65,Yes\n9540-JYROE,Male,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.1,551.35,Yes\n9026-LHEVG,Female,0,No,No,9,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,40.75,359.4,No\n2260-USTRB,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.2,115.95,Yes\n3656-TKRVZ,Female,0,No,No,3,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.35,165.2,Yes\n7011-CVEUC,Male,0,Yes,No,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.7,2338.35,No\n6185-TASNN,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46.3,46.3,No\n7925-PNRGI,Female,0,Yes,Yes,45,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,No,Mailed check,81.3,3541.1,No\n8623-TMRBY,Male,1,Yes,Yes,51,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.2,4146.05,Yes\n1552-CZCLL,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20,1087.25,No\n3038-PQIUY,Female,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),66.15,2522.4,No\n1501-SGHBW,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.85,81,Yes\n9313-CDOGY,Male,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.6,717.3,No\n8148-BPLZQ,Male,0,No,No,34,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.8,1734.2,No\n2623-DRYAM,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.75,7069.3,No\n9987-LUTYD,Female,0,No,No,13,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,55.15,742.9,No\n3208-YPIOE,Male,0,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,3017.65,Yes\n4488-KQFDT,Female,0,No,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.95,6423,No\n2612-RANWT,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.15,1164.3,Yes\n5693-PIPCS,Male,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),99.65,4220.35,No\n0491-KAPQG,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.7,1558.7,No\n1925-LFCZZ,Male,1,No,No,55,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),50.05,2743.45,No\n8039-EQPIM,Male,0,Yes,No,69,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),60.25,4055.5,No\n6900-PXRMS,Male,1,Yes,Yes,26,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,105.75,2710.25,Yes\n8707-RMEZH,Female,1,Yes,No,69,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Credit card (automatic),87.3,6055.55,No\n3346-BRMIS,Female,1,Yes,No,18,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.35,810.7,Yes\n3209-ZPKFI,Male,0,Yes,Yes,47,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.25,2538.2,No\n6479-VDGRK,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.3,6129.2,No\n9373-WSLOY,Male,1,Yes,No,33,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,50,1750.85,No\n2933-FILNV,Female,0,Yes,Yes,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.4,36.55,Yes\n3569-JFODW,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.95,6652.45,No\n9819-FBNSV,Male,1,Yes,No,37,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.25,2575.45,No\n9544-PYPSJ,Female,1,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),96.1,6019.35,No\n1591-XWLGB,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1379.6,No\n0396-UKGAI,Male,0,No,Yes,23,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),55.3,1284.2,No\n3243-ZHOHY,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,296.15,No\n5186-EJEGL,Male,0,No,No,9,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,69.5,653.25,No\n7527-QNRUS,Male,0,Yes,Yes,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,25.15,412.6,No\n9986-BONCE,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,85.5,Yes\n2722-JMONI,Female,1,Yes,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.55,49.55,Yes\n2203-GHNWN,Female,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.65,1928.7,No\n3878-AVSOQ,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.25,71.25,No\n3258-SYSWS,Male,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),113.8,7845.8,No\n5296-PSYVW,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.55,1750.7,No\n6319-QSUSR,Female,0,No,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,216.2,No\n4083-EUGRJ,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,178.5,Yes\n7579-OOPEC,Female,1,Yes,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),50.15,115.1,Yes\n0512-FLFDW,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.5,6029,No\n1771-OADNZ,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.9,2745.2,Yes\n0487-CRLZF,Female,0,No,No,49,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.45,3721.9,No\n2107-FBPTK,Female,1,No,No,30,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.1,3121.1,No\n9451-WLYRI,Female,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.05,990.45,No\n8219-VYBVI,Male,0,No,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1004.35,No\n1991-VOPLL,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,157.65,No\n9738-QLWTP,Male,0,No,No,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,81.9,3219.75,No\n1016-DJTSV,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,572.85,No\n9419-IPPBE,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,One year,Yes,Electronic check,90.15,4554.85,No\n0174-QRVVY,Male,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.35,1847.55,No\n5699-BNCAS,Male,1,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.65,1766.75,No\n2900-PHPLN,Female,1,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.55,1462.05,No\n0612-RTZZA,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,25.25,Yes\n8106-GWQOK,Male,0,Yes,No,38,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60,2193.2,No\n8751-EDEKA,Female,0,Yes,No,28,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),89.9,2433.5,No\n6878-GGDWG,Female,0,Yes,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,641.15,No\n4039-PIMHX,Male,1,Yes,No,49,Yes,No,DSL,No,Yes,No,No,No,No,Two year,No,Mailed check,49.8,2398.4,No\n8450-LUGUK,Female,0,Yes,Yes,37,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.1,861.85,No\n3604-WLABM,Female,0,No,No,10,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,54.25,583,No\n0795-GMVQO,Male,0,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),109.9,7332.4,No\n2259-OUUSZ,Male,0,No,No,7,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),35.5,249.55,No\n1142-IHLOO,Female,0,No,No,51,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Credit card (automatic),87.55,4475.9,No\n6410-LEFEN,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.15,416.45,Yes\n4633-MKHYU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,88.4,788.6,No\n6257-RJOHI,Male,0,No,No,4,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.8,202.3,No\n1545-JFUML,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Electronic check,99,6994.6,No\n3194-ORPIK,Female,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),84.4,4116.15,Yes\n7826-VVKWT,Female,1,Yes,Yes,24,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,96.55,2263.45,No\n1353-GHZOS,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Bank transfer (automatic),59.75,1374.35,No\n2587-EKXTS,Male,0,No,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,111.5,4915.15,No\n5442-BXVND,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),24.25,838.5,No\n8816-VXNZD,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,75.1,Yes\n9378-FXTIZ,Female,0,Yes,No,54,Yes,Yes,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),70.15,3715.65,Yes\n1723-HKXJQ,Male,0,No,No,42,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.75,4273.45,Yes\n7825-GKXMW,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.8,45.8,Yes\n9753-OYLBX,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,No\n2364-UFROM,Male,0,No,No,30,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Electronic check,70.4,2044.75,No\n6656-JWRQX,Female,0,No,No,1,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,30.55,30.55,No\n6473-ULUHT,Male,0,Yes,Yes,16,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,84.9,1398.25,No\n1240-KNSEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.1,20.1,Yes\n0836-SEYLU,Male,0,Yes,No,9,No,No phone service,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Mailed check,40.65,328.95,Yes\n4433-JCGCG,Male,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101,4680.05,Yes\n3716-BDVDB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,69.1,Yes\n7688-AWMDX,Male,0,Yes,No,71,Yes,No,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),54.5,3778.2,No\n2842-BCQGE,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.35,3161.4,No\n7244-QWYHG,Male,0,Yes,No,50,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),44.45,2188.45,No\n5899-MQZZL,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75,999.45,Yes\n6849-OYAMU,Male,0,Yes,Yes,19,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),100,1888.65,Yes\n5312-UXESG,Female,0,No,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),98.05,3990.6,No\n9488-HGMJH,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.15,71.15,Yes\n6038-GCYEC,Female,0,No,No,24,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),54.15,1240.25,Yes\n2150-WLKUW,Female,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),63.9,2635,No\n7159-FVYPK,Female,0,Yes,Yes,3,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.15,235,No\n7032-LMBHI,Female,0,No,No,37,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,No,Bank transfer (automatic),64.65,2347.85,No\n1150-WFARN,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.75,7156.2,Yes\n6088-BXMRG,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.85,3089.6,No\n3144-AUDBS,Female,0,Yes,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.15,270.8,Yes\n4821-SJHJV,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,89.6,2901.8,No\n7346-MEDWM,Female,0,No,No,59,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,83.25,4949.1,No\n1137-DGOWI,Female,0,Yes,No,30,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),70.25,2198.9,No\n5616-PRTNT,Male,0,No,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.4,374.5,Yes\n3812-LRZIR,Female,0,Yes,Yes,27,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.5,761.95,No\n4818-QIUFN,Female,1,No,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,1520.9,Yes\n9483-GCPWE,Male,0,No,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,190.25,No\n4227-OJHAL,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),73,5163,No\n9220-CXRSC,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Two year,No,Credit card (automatic),61.4,4059.85,No\n4993-JCRGJ,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,84.3,2281.6,No\n9537-JALFH,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1356.7,No\n6698-OXETB,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.4,231.45,No\n2103-ZRXFN,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.75,50.75,No\n3724-BSCVH,Male,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,242.4,No\n4877-TSOFF,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.75,4264.25,No\n9209-NWPGU,Male,0,No,No,44,Yes,No,DSL,Yes,No,No,Yes,Yes,No,One year,No,Electronic check,65.4,2774.55,No\n6961-VCPMC,Male,1,Yes,No,46,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.4,3605.2,Yes\n7306-YDSOI,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.75,4069.9,No\n5288-AHOUP,Male,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.5,874.2,No\n6688-UZPWD,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,102,1145.35,Yes\n0655-YDGFJ,Male,0,No,No,29,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),48.95,1323.7,No\n8468-EHYJA,Female,0,Yes,No,57,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.65,5497.05,No\n6823-SIDFQ,Male,0,No,No,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),18.25,534.7,No\n1097-FSPVW,Female,0,No,No,42,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),54.55,2455.05,No\n2839-RFSQE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,38.7,Yes\n8328-SKJNO,Male,0,No,Yes,23,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),40.65,947.4,No\n7010-ZMVBF,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,357,No\n5201-CBWYG,Male,0,Yes,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.8,1476.25,No\n0968-GSIKN,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.8,70.8,Yes\n1965-DDBWU,Male,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.05,1448.6,Yes\n9057-SIHCH,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.6,291.9,Yes\n6352-GIGGQ,Male,0,No,No,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),88.8,5903.15,No\n3635-QQRQD,Male,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.05,1201.65,No\n6771-XWBDM,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,5921.35,Yes\n2897-DOVND,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,146.65,No\n3082-YVEKW,Female,0,Yes,Yes,23,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),77.15,1759.4,No\n0191-EQUUH,Female,0,No,Yes,25,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),35.05,844.45,No\n7134-HBPBS,Female,1,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.1,7774.05,No\n3489-HHPFY,Female,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.05,134.05,No\n2926-JEJJC,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,140.95,No\n8601-QACRS,Female,0,No,No,5,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.6,249.95,Yes\n4950-BDEUX,Male,0,No,No,35,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,49.2,1701.65,No\n5789-LDFXO,Male,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.6,592.65,No\n0508-OOLTO,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,135.75,No\n2984-TBYKU,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),104.9,7732.65,No\n9822-WMWVG,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,106.5,4282.4,No\n2391-SOORI,Male,0,No,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.35,219.65,Yes\n9040-KZVWO,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,No,Bank transfer (automatic),75.5,2018.1,No\n2645-QTLMB,Male,0,No,No,7,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,669,Yes\n4806-HIPDW,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.95,68.95,Yes\n1548-ARAGG,Female,0,Yes,Yes,4,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,58.5,224.85,No\n6339-RZCBJ,Male,0,No,No,48,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),78.9,3771.5,No\n4424-TKOPW,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,196.75,Yes\n4415-WNGVR,Female,1,Yes,No,12,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,79.2,943.85,No\n2522-AHJXR,Male,0,Yes,No,60,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.45,6572.85,No\n0412-UCCNP,Male,0,No,No,55,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Electronic check,59.2,3175.85,No\n1816-FLZDK,Male,0,No,No,1,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,29.15,29.15,No\n7096-UCLNH,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No\n8443-ZRDBZ,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,318.9,Yes\n9136-ALYBR,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.45,24.45,Yes\n4615-PIVVU,Female,0,No,No,42,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),66.5,2762.75,No\n8885-QSQBX,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),49.55,49.55,No\n7479-NITWS,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.35,631.85,Yes\n8617-ENBDS,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),73.6,232.5,No\n9385-NXKDA,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),82.65,5919.35,No\n0430-IHCDJ,Male,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49,749.25,No\n2959-MJHIC,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.35,307.4,Yes\n6674-KVJHG,Female,0,No,No,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.2,245.15,No\n1814-DKOLC,Female,0,No,No,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.45,134.75,No\n4201-JMNGR,Female,1,No,No,1,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,55.8,55.8,Yes\n9351-HXDMR,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.9,8240.85,No\n3537-HPKQT,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),77.75,4266.4,No\n2129-ALKBS,Female,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,26.2,1077.5,No\n5821-MMEIL,Female,0,Yes,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1115.6,No\n5960-WPXQM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.05,79.05,Yes\n0684-AOSIH,Male,0,Yes,No,1,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95,95,Yes\n8260-NGFNY,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.2,25.2,Yes\n1309-XGFSN,Male,1,Yes,Yes,52,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.85,4079.55,No\n1866-RZZQS,Male,1,No,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.4,4187.75,Yes\n6968-URWQU,Male,0,Yes,No,43,Yes,No,DSL,No,No,No,No,Yes,No,One year,Yes,Mailed check,56.35,2391.15,No\n4742-TXUEX,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,890.5,No\n9631-XEYKE,Male,0,No,No,3,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.4,137.25,No\n8631-NBHFZ,Male,1,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),79.4,5154.6,Yes\n1335-MXCSE,Male,0,Yes,Yes,55,Yes,No,DSL,No,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),55.25,3119.9,No\n8873-TMKGR,Male,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.1,529.5,No\n8800-JOOCF,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.05,966.55,No\n1469-LBJQJ,Female,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.2,6936.85,No\n4958-XCBDQ,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,3496.3,Yes\n8337-UPOAQ,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.8,914.3,Yes\n0064-YIJGF,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.75,1929,No\n2386-OWURY,Female,0,No,No,58,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.3,5817.7,No\n1725-IQNIY,Male,0,Yes,No,54,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),109.75,6110.2,Yes\n0310-VQXAM,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,178.8,No\n6598-RFFVI,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,28.3,Yes\n3453-RTHJQ,Male,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,435,No\n6712-OAWRH,Female,1,No,No,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.25,2351.8,Yes\n6278-FEPBZ,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,186.15,No\n4280-DLSHD,Male,0,Yes,No,8,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,54.75,445.85,No\n1508-DFXCU,Male,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81.45,912,No\n8614-VGMMV,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.1,679.55,Yes\n7868-BGSZA,Male,0,Yes,No,43,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.2,3581.6,No\n2800-QQUSO,Male,0,No,No,42,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.3,4222.95,No\n2930-UOTMB,Female,0,No,No,31,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),65.25,1994.3,Yes\n7973-DZRKH,Female,0,No,Yes,66,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),90.95,5930.05,No\n5914-DVBWJ,Female,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.45,1505.85,Yes\n7698-YFGEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,20,No\n2533-QVMSK,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Electronic check,94.1,5638.3,Yes\n9773-PEQBZ,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,79.85,797.25,No\n8644-XLFBW,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,71.65,Yes\n0650-BWOZN,Female,1,No,No,18,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.55,1359.45,No\n8625-AZYZY,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.65,2542.45,Yes\n7785-RDVIG,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,54.7,No\n9602-WCXPI,Male,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.15,989.05,No\n2903-YYTBW,Male,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.55,44.55,No\n3620-MWJNE,Male,0,No,No,2,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,54.45,87.3,No\n8573-JGCZW,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.65,351.55,No\n6837-HAEVO,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,105,7297.75,No\n6701-YVNQG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.7,6301.7,No\n9306-CPCBC,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.25,210.3,Yes\n4304-XUMGI,Male,1,Yes,Yes,50,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Bank transfer (automatic),75.15,3822.45,No\n9504-YAZWB,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25,1048.45,No\n8819-ZBYNA,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.1,6393.65,No\n6174-NRBTZ,Male,0,No,No,46,No,No phone service,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),30.75,1489.3,No\n9481-IEBZY,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),112.9,8061.5,No\n1833-VGRUM,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.2,74.2,Yes\n6137-NICCO,Female,0,Yes,Yes,6,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,94.05,518.75,No\n9065-ZCPQX,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),78.85,5763.15,No\n6402-EJMWF,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,55.3,238.5,No\n3620-EHIMZ,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,1031.7,No\n3213-VVOLG,Male,0,Yes,Yes,0,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.35, ,No\n6870-ECSHE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,34.8,No\n8747-UDCOI,Female,0,Yes,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1319.95,No\n8374-XGEJJ,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101,4388.4,Yes\n2656-TABEH,Male,0,Yes,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,420.2,Yes\n6946-LMSQS,Male,1,Yes,No,25,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,One year,Yes,Electronic check,89.05,2177.45,Yes\n4626-OZDTJ,Female,0,Yes,No,51,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),78.65,3950.85,No\n9169-BSVIN,Male,0,No,No,12,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),74.75,827.05,No\n2325-ZUSFD,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),70.1,3913.3,Yes\n8194-PEEBY,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,533.5,No\n6872-HXFNF,Female,0,Yes,No,64,Yes,Yes,DSL,No,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),58.35,3756.45,No\n3932-CMDTD,Female,0,No,No,4,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,105.65,443.9,Yes\n7714-YXSMB,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.5,2599.95,No\n8387-UGUSU,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,284.3,No\n1080-BWSYE,Male,1,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.65,1740.8,No\n4039-HEUNW,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.5,3436.1,Yes\n1194-HVAIF,Female,0,Yes,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),95,2462.55,No\n0812-WUPTB,Male,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.85,70.85,Yes\n3594-UVONA,Female,0,No,No,35,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),85.95,3110.1,Yes\n0004-TLHLJ,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.9,280.85,Yes\n1767-TGTKO,Female,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.45,411.75,No\n8439-LTUGF,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20,198.6,No\n8805-JNRAZ,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.2,103.7,No\n5089-IFSDP,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.45,6144.55,Yes\n6418-HNFED,Male,0,Yes,No,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.25,4089.45,No\n0206-OYVOC,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.25,864.2,No\n7291-CDTMJ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.65,19.65,No\n7128-GGCNO,Male,0,No,No,46,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),72.8,3249.4,No\n0237-YFUTL,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.65,5405.8,No\n7691-KGKGP,Male,0,Yes,Yes,53,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,No,Credit card (automatic),65,3363.8,No\n6710-HSJRD,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,114.1,7132.15,No\n3675-EQOZA,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.65,93.55,No\n0840-DCNZE,Male,0,No,No,47,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),86.95,4138.9,No\n1732-FEKLD,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),94.75,5121.75,No\n9862-KJTYK,Male,0,No,Yes,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.35,566.1,No\n0479-HMSWA,Female,0,No,Yes,26,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,105.45,2715.3,No\n5854-KSRBJ,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1782.05,No\n0365-BZUWY,Male,0,Yes,No,17,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.55,1742.5,No\n4817-VYYWS,Female,0,No,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,2983.8,Yes\n5701-SVCWR,Female,0,No,Yes,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24,24,No\n3129-AAQOU,Female,0,Yes,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.6,485.9,No\n4922-CVPDX,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),73.5,1905.7,No\n1396-QWFBJ,Female,0,Yes,Yes,21,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.05,1565.7,Yes\n1357-BIJKI,Male,0,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,98.25,4858.7,No\n1099-BTKWT,Female,0,Yes,No,68,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,54.4,3723.65,No\n5299-SJCZT,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.55,298.35,Yes\n2018-PZKMU,Male,0,Yes,Yes,9,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.1,970.45,No\n7340-KEFQE,Female,0,Yes,Yes,51,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),34.2,1782,No\n4570-QHXHL,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,43.75,405.7,No\n2403-BCASL,Male,1,Yes,Yes,41,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.95,4534.9,Yes\n7392-YYPYJ,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.65,2415.95,No\n2898-LSJGD,Female,0,Yes,Yes,21,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,55.95,1157.05,Yes\n2223-GDSHL,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,8297.5,No\n7359-PTSXY,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.75,45.75,Yes\n9470-RTWDV,Male,0,Yes,Yes,26,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82,2083.1,No\n2176-OSJUV,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),65.15,4681.75,No\n9348-YVOMK,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.8,176.2,No\n8104-OSKWT,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.8,1001.2,No\n4521-YEEHE,Female,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,88.85,1594.75,No\n4090-KPJIP,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,212.4,No\n3786-WOVKF,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.85,7677.4,No\n4503-BDXBD,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.95,815.5,Yes\n6086-ESGRL,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),80.15,80.15,Yes\n2592-HODOV,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,259.65,No\n3886-CERTZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.25,8109.8,Yes\n7995-ZHHNZ,Male,0,Yes,No,42,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,No,Credit card (automatic),56.1,2386.85,No\n3824-RHKVR,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,340.35,No\n5816-SCGFC,Female,1,No,No,7,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,51.3,419.35,No\n5989-AXPUC,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,118.6,7990.05,No\n4979-HPRFL,Male,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.15,1402.25,No\n8590-OHDIW,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,749.35,No\n8015-IHCGW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,115.5,8425.15,No\n2332-TODQS,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),25.05,1171.5,No\n9278-VZKCD,Female,1,Yes,No,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,109.1,5647.95,No\n8008-OTEZX,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,708.8,No\n2773-OVBPK,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.3,7567.2,No\n5999-LCXAO,Female,0,No,No,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.9,29.9,No\n5206-XZZQI,Male,0,No,No,53,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,80.6,4348.1,No\n4803-LBYPN,Male,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,635.9,No\n5759-RCVCB,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),35.2,108.95,Yes\n6372-RFVNS,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.8,78.8,Yes\n2139-FQHLM,Male,0,No,No,19,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Mailed check,89.95,1682.4,No\n8261-GWDBQ,Female,1,Yes,No,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,6925.9,No\n1093-YSWCA,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,223.15,No\n8938-UMKPI,Female,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,5127.95,Yes\n0991-BRRFB,Male,0,No,No,18,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.4,874.8,Yes\n3882-IYOIJ,Female,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.25,6758.45,No\n8749-TZYEC,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.8,1874.3,No\n1755-FZQEC,Male,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,791.15,No\n3115-JPJDD,Female,0,Yes,No,59,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),81.25,4639.45,No\n5600-KTXFM,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.95,143.9,No\n3871-IKPYH,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.1,69.1,Yes\n6319-IEJWJ,Male,0,Yes,Yes,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.2,1776.55,Yes\n9025-AOMKI,Female,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.55,536.4,Yes\n6339-TBELP,Male,0,No,No,71,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),86.4,6172,No\n7964-YESJC,Female,0,Yes,No,24,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,66.3,1559.45,No\n6173-GOLSU,Male,1,Yes,No,67,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.65,6079,No\n5275-SQEIZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,80.85,80.85,Yes\n7716-YTYHG,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Mailed check,82.05,4029.95,Yes\n9938-ZREHM,Female,0,Yes,No,37,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,No,Mailed check,72.1,2658.4,No\n7950-XWOVN,Male,0,No,No,11,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,34.7,383.55,No\n4390-KYULV,Male,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.55,51.15,Yes\n4647-MUZON,Female,0,Yes,No,18,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.95,1745.5,No\n9702-AIUJO,Male,0,Yes,Yes,50,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),44.8,2230.85,No\n8849-PRIQJ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.4,7281.6,No\n8851-RAGOV,Female,0,Yes,No,25,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,71.05,1837.7,No\n1304-NECVQ,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.55,149.55,Yes\n5396-IZEPB,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,180.7,No\n1729-VLAZJ,Female,0,No,Yes,10,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,40.25,411.45,No\n8285-ABVLB,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1328.35,No\n0701-TJSEF,Male,0,No,No,9,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),68.25,576.95,No\n5712-VBOXD,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.15,68.45,No\n6629-LADHQ,Female,0,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.95,123.05,No\n8945-GRKHX,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.65,78.65,Yes\n1559-DTODC,Male,0,No,No,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.15,468.35,No\n4797-MIWUM,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.25,174.7,No\n6959-UWKHF,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,42.9,42.9,Yes\n8720-RQSBJ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No\n4537-CIBHB,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,172.35,No\n3815-SLMEF,Female,0,No,No,3,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,34.25,139.35,Yes\n5154-VEKBL,Female,0,No,No,9,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Mailed check,58.5,539.85,Yes\n4324-AHJKS,Female,0,No,No,5,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),55.8,300.4,No\n4355-CVPVS,Female,0,Yes,Yes,56,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),88.9,4968,No\n4495-LHSSK,Female,0,No,Yes,18,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,57.65,992.7,No\n5655-JSMZM,Male,1,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.2,4718.25,Yes\n5915-ANOEI,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,79.15,5536.5,No\n9861-PDSZP,Female,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.05,7806.6,No\n4505-EXZHB,Female,1,No,No,6,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,74.4,434.1,Yes\n7225-CBZPL,Male,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.8,1563.9,No\n6704-UTUKK,Male,0,Yes,No,29,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.9,1332.4,No\n4587-VVTOX,Female,0,Yes,No,6,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,545.2,Yes\n2019-HDCZY,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,102.6,6296.75,No\n4652-NNHNY,Male,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),73.85,1284.2,Yes\n8788-DOXSU,Male,0,No,No,59,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),61.35,3645.5,No\n7404-JLKQG,Female,0,No,No,3,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,57.55,161.45,No\n7421-ZLUPA,Female,0,No,No,8,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,29.25,226.95,No\n8972-HJWNV,Female,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.55,646.85,Yes\n3274-NSDWE,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,1441.65,No\n2632-IVXVF,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),111.75,7511.3,No\n3692-JHONH,Female,1,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.5,5621.85,No\n8915-NNTRC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),107.7,7919.8,No\n5914-GXMDA,Female,0,Yes,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,593.2,No\n7463-IFMQU,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,1423.65,No\n2920-RNCEZ,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,69.95,No\n2541-YGPKE,Male,0,Yes,Yes,42,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,No,Credit card (automatic),63.7,2763.35,No\n8515-OCTJS,Female,0,No,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,692.1,Yes\n5382-TEMLV,Male,0,No,No,45,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.9,2298.55,No\n3441-CGZJH,Female,0,Yes,Yes,43,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60.4,2640.55,No\n9592-ERDKV,Male,0,Yes,No,37,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Mailed check,79.25,2911.8,No\n2860-RANUS,Female,1,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.8,1727.5,Yes\n5261-QSHQM,Female,0,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.45,86.6,Yes\n4778-IZARL,Male,0,Yes,No,63,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.1,6705.7,No\n0432-CAJZV,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,90.7,237.65,No\n7008-LZVOZ,Male,0,Yes,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1672.35,No\n8868-WOZGU,Male,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.7,2979.5,Yes\n1200-TUZHR,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.2,695.75,No\n9365-CSLBQ,Male,0,No,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.35,1654.6,No\n1334-FJSVR,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.25,24.25,Yes\n5884-FBCTL,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.1,1857.85,No\n7130-CTCUS,Male,1,Yes,No,16,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.55,825.1,No\n6242-FEGFD,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,96.6,6424.25,No\n7625-XCQRH,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,837.95,Yes\n6194-HBGQN,Male,0,No,No,51,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),81.15,4126.2,No\n7634-WSWDB,Female,0,No,Yes,8,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,38.5,330.8,No\n6986-IXNDM,Male,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.9,1337.45,No\n1731-TVIUK,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,93.5,362.2,Yes\n2987-BJXIK,Female,0,No,No,70,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Mailed check,84.7,5991.05,No\n9769-TSBZE,Female,0,No,Yes,70,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Electronic check,66,4891.5,No\n0406-BPDVR,Female,1,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),101.5,5373.1,Yes\n0618-XWMSS,Male,0,No,Yes,28,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,2068.55,Yes\n1395-WSWXR,Male,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,487.05,No\n6023-GSSXW,Female,0,Yes,No,69,Yes,No,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Credit card (automatic),61.45,4131.2,No\n6752-APNJL,Male,0,Yes,Yes,42,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.5,2301.15,No\n5276-KQWHG,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,131.65,Yes\n0420-HLGXF,Female,1,No,No,39,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.75,4036,No\n7446-KQISO,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.75,4900.65,No\n9823-EALYC,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),80.85,5727.45,No\n0582-AVCLN,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.3,743.05,No\n5803-NQJZO,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),67.8,4804.65,No\n2565-JSLRY,Male,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.05,24.05,Yes\n2607-DHDAK,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1414.65,No\n1073-XXCZD,Male,0,Yes,No,55,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.7,1443.65,No\n0743-HRVFF,Female,0,Yes,Yes,51,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,56.15,2898.95,No\n4006-HKYHO,Male,0,No,No,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.7,5309.5,No\n3727-JEZTU,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,No\n8143-ETQTI,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.65,451.55,No\n6689-KXGBO,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.55,50.55,Yes\n9667-TKTVZ,Female,0,No,No,2,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,54.35,117.05,No\n3657-COGMW,Female,1,No,No,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),108.1,5839.3,No\n8570-KLJYJ,Female,0,No,No,36,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,54.45,1893.5,No\n7754-IXRMC,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.35,45.35,No\n9473-CBZOP,Female,0,No,No,28,Yes,Yes,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,59,1654.45,No\n2969-WGHQO,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Electronic check,69.45,477.05,No\n6615-NGGZJ,Male,0,No,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.55,1415.55,Yes\n4334-HOWRP,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.95,4546,No\n4255-DDUOU,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,20.5,Yes\n5863-OOKCL,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.85,163.2,No\n1686-STUHN,Male,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,849.9,No\n1329-VHWNP,Female,0,No,No,7,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),25.05,189.95,No\n2984-MIIZL,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.8,321.9,Yes\n0266-GMEAO,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.3,8058.55,No\n5590-YRFJT,Female,0,Yes,No,20,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.45,482.8,Yes\n5574-NXZIU,Male,0,No,No,63,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.2,7049.75,No\n0019-GFNTW,Female,0,No,No,56,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),45.05,2560.1,No\n4256-ZWTZI,Female,0,No,Yes,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,51,286.8,No\n8309-PPCED,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.45,7982.5,No\n4098-NAUKP,Male,1,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.65,5683.6,No\n5196-WPYOW,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,60.05,3994.05,No\n4608-LCIMN,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.65,369.15,No\n1485-YDHMM,Male,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.25,4631.7,No\n4054-CUMIA,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,401.95,No\n0603-TPMIB,Female,0,Yes,Yes,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.45,1534.05,No\n2525-GVKQU,Female,0,No,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,1093,No\n8161-QYMTT,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.1,701.3,No\n9581-GVBXT,Male,0,Yes,Yes,59,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,34.8,1980.3,No\n5862-BRIXZ,Male,0,No,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.75,2893.4,No\n5404-GGUKR,Male,0,No,No,5,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,51.35,262.3,No\n3308-JSGML,Male,1,Yes,No,59,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.05,3886.85,No\n2126-GSEGL,Female,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.8,5917.55,No\n3677-TNKIO,Female,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),71,914,Yes\n0440-QEXBZ,Female,0,No,No,44,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,2139.1,No\n2434-EEVDB,Female,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),94.6,5948.7,No\n6762-QVYJO,Female,0,Yes,Yes,58,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,59.75,3624.35,No\n6199-IWKGC,Female,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Electronic check,100.25,4753.85,No\n2675-DHUTR,Male,1,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.9,5780.7,No\n8152-VETUR,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Credit card (automatic),97.7,6869.7,No\n9667-EQRXU,Female,1,No,No,30,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,40.3,1172.95,Yes\n7446-YPODE,Male,1,No,No,11,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),60.25,662.95,No\n9522-BNTHX,Female,1,No,No,34,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,56.25,1765.95,No\n4676-WLUHT,Male,0,No,No,54,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Two year,No,Bank transfer (automatic),46.2,2431.95,No\n3329-WDIOK,Female,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.6,155.35,Yes\n7980-MHFLQ,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.9,1859.2,No\n2873-ZLIWT,Female,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,84.85,3303.05,Yes\n3415-TAILE,Female,0,Yes,Yes,2,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,65.7,134.35,Yes\n0757-WCUUZ,Male,0,Yes,Yes,54,Yes,Yes,DSL,No,No,Yes,No,No,Yes,Two year,No,Credit card (automatic),63.35,3409.1,No\n1629-DQQVB,Female,0,No,No,14,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.1,709.5,No\n1915-IOFGU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.5,70.5,Yes\n3045-XETSH,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.85,953.45,Yes\n0374-AACSZ,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,50.15,50.15,No\n7239-HZZCX,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,Yes\n4872-VXRIL,Male,0,No,No,56,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),64.65,3665.55,No\n9140-CZQZZ,Female,0,Yes,No,68,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.6,5515.8,No\n3423-HHXAO,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.5,272,No\n9938-TKDGL,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,99.55,6668,No\n0531-ZZJWQ,Male,1,Yes,No,55,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74,4052.4,No\n6537-QLGEX,Female,0,No,No,16,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,38.9,664.4,No\n2688-BHGOG,Male,1,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.55,718.55,No\n7683-CBDKJ,Male,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,65.45,937.6,Yes\n0946-CLJTI,Male,1,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,5812.6,Yes\n3160-TYXLT,Male,0,No,No,53,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),46.3,2546.85,No\n1325-USMEC,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),99.35,6944.5,No\n0916-QOFDP,Female,1,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.8,1346.3,No\n0628-CNQRM,Male,0,Yes,Yes,22,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),67.5,1544.05,Yes\n5606-AMZBO,Female,0,Yes,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),78.15,765.15,No\n6199-IPCAO,Female,0,Yes,Yes,29,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.1,692.55,No\n7665-TOALD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes\n0112-QWPNC,Male,0,Yes,No,49,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,84.35,4059.35,Yes\n0324-BRPCJ,Female,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,6851.65,Yes\n2777-PHDEI,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.05,78.05,Yes\n5640-CAXOA,Female,0,No,No,30,No,No phone service,DSL,No,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),40.35,1187.05,No\n2235-EZAIK,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),79.2,5401.9,No\n3847-BAERP,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.9,247,No\n1196-AMORA,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,73.6,520,Yes\n4282-YMKNA,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,706.6,Yes\n1453-RZFON,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,49.9,49.9,No\n8263-OKETD,Female,0,No,No,20,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),68.9,1370.35,No\n0670-KDOMA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No\n2476-YGEFM,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),76,2215.25,No\n8687-BAFGU,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,74,74,No\n0641-EVBOJ,Male,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,82.3,214.4,No\n0829-XXPLX,Female,0,No,No,20,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.4,1871.15,No\n9974-JFBHQ,Male,0,No,Yes,64,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.15,6171.2,No\n5356-RHIPP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes\n6624-JDRDS,Female,0,No,No,6,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.45,161.45,No\n0608-JDVEC,Male,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.8,1013.2,No\n9780-FKVVF,Male,0,No,No,6,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),59.15,336.7,No\n1919-RTPQD,Male,0,Yes,Yes,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.75,333.65,No\n5214-NLTIT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),90.8,6511.8,No\n3345-PBBFH,Male,0,Yes,No,8,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.55,393.45,No\n5055-BRMNE,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),106.7,7009.5,No\n9189-JWSHV,Female,1,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.55,2264.05,Yes\n2190-PHBHR,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Two year,Yes,Credit card (automatic),94.45,6921.7,No\n2650-GYRYL,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.45,600.25,No\n0746-JTRFU,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.05,56.35,Yes\n5208-FVQKB,Male,0,Yes,No,70,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,67.95,4664.15,No\n7184-LRUUR,Female,0,No,No,22,Yes,No,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),65.25,1441.8,No\n6627-CFOSN,Female,0,No,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),99.45,5623.7,No\n3982-JGSFD,Male,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.35,695.85,No\n1574-DYCWE,Female,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1028.75,No\n7247-XOZPB,Male,0,Yes,No,53,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),77.4,4155.95,No\n2466-FCCPT,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.7,395.6,No\n6211-WWLTF,Male,0,Yes,No,63,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),99.7,6330.4,No\n4826-TZEVA,Female,0,No,No,40,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.8,2971.7,No\n6016-NXBNJ,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.15,638,No\n9138-EFSMO,Male,0,Yes,Yes,26,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,No,Bank transfer (automatic),78.95,2034.25,No\n0576-WNXXC,Male,1,Yes,No,27,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.55,2510.2,Yes\n4632-XJMEX,Male,0,No,Yes,53,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),62.85,3419.5,No\n8910-LEDAG,Male,1,Yes,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),71.55,2427.35,No\n3452-GWUIN,Female,1,Yes,No,19,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.95,1760.25,No\n2716-GFZOR,Male,0,Yes,No,43,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),86.1,3551.65,No\n3724-UCSHY,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,122.9,No\n8626-XHBIE,Male,0,No,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1424.2,No\n4628-CTTLA,Male,0,No,No,57,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,39.3,2111.45,Yes\n3192-LNKRK,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,84.05,2909.95,No\n4439-YRNVD,Female,0,No,No,10,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,36.25,374,No\n2876-VBBBL,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,Yes\n6834-NXDCA,Female,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,23.9,300.8,Yes\n2208-UGTGR,Male,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.6,5581.05,No\n3005-TYFRD,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),103.65,5676.65,No\n8670-MEFCP,Female,0,Yes,Yes,36,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92.9,3379.25,No\n3079-BCHLN,Male,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,942.95,No\n7777-UNYHB,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.1,232.4,No\n5597-GLBUC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,85.45,85.45,Yes\n7278-CKDNC,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.5,2088.45,No\n2748-MYRVK,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),99.9,6137,Yes\n8450-UYIBU,Female,1,No,No,35,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.85,1434.6,No\n2969-VAPYH,Female,0,No,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),60.5,3870,No\n4822-YCXMX,Male,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.8,2043.45,Yes\n7181-OQCUT,Male,0,No,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,103.85,2215,No\n8474-UMLNT,Female,0,No,No,13,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),67.8,842.25,No\n9821-POOTN,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.2,2576.2,Yes\n3836-FZSDJ,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.85,1901,No\n9142-XMYJH,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.35,601.6,No\n6559-ILWKJ,Male,0,Yes,No,71,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,No,Electronic check,49.35,3515.25,Yes\n0187-QSXOE,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89,605.45,No\n7733-UDMTP,Female,1,No,No,57,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,55,3094.05,No\n2649-HWLYB,Male,0,Yes,No,65,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),76.15,4929.55,No\n5214-CHIWJ,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,595.05,No\n4229-CZMLL,Male,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,469.8,Yes\n6904-JLBGY,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),117.35,8436.25,No\n2465-BLLEU,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.75,19.75,No\n1685-VAYJF,Male,0,No,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),45.2,492,No\n8874-EJNSR,Male,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.2,987.95,No\n6917-YACBP,Female,1,No,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,89.75,5496.9,No\n5049-MUBWG,Male,0,No,No,26,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),75,1908.35,No\n4223-WOZCM,Male,1,No,No,2,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,49.95,107.1,No\n0769-MURVM,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),65.7,4575.35,No\n9253-VIFJQ,Male,0,Yes,No,65,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,One year,No,Credit card (automatic),67.05,4309.55,No\n7030-FZTFM,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.9,7922.75,No\n9741-YLNTD,Male,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),87.95,522.35,No\n5917-RYRMG,Male,1,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,587.7,No\n5120-ZBLAI,Female,0,Yes,No,50,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),75.7,3876.2,No\n1194-BHJYC,Male,0,Yes,No,61,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,62.15,3778.85,No\n3663-MITLP,Female,0,No,No,15,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,1457.25,Yes\n0906-QVPMS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),115.15,8349.45,No\n9025-ZRPVR,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.95,185.6,Yes\n1905-OEILC,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,19.5,No\n7858-GTZSP,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.55,1066.9,No\n3285-UCQVC,Female,0,No,No,37,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),28.6,973.55,Yes\n6248-BSHKG,Male,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.4,1226.45,No\n4685-TFLLS,Male,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,342.3,No\n3470-BTGQO,Male,0,No,Yes,21,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,985.05,No\n1209-VFFOC,Male,0,Yes,Yes,68,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,56.4,3948.45,No\n8224-DWCKX,Male,1,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.3,828.05,No\n5482-PLVPE,Female,1,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.35,41.85,Yes\n4553-DVPZG,Female,0,Yes,No,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,6164.7,No\n4902-OHLSK,Female,1,No,No,29,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,98.65,2862.75,Yes\n6917-IAYHD,Male,0,No,Yes,1,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,33.6,33.6,No\n0495-ZBNGW,Male,1,Yes,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.9,343.95,Yes\n8620-RJPZN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.7,20.7,No\n7572-KPVKK,Male,0,No,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,104.05,6590.5,No\n2642-MAWLJ,Female,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.25,717.95,No\n2357-COQEK,Female,1,No,No,28,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.3,2890.65,Yes\n7103-ZGVNT,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),73.7,4885.85,No\n3737-GCSPV,Female,0,Yes,No,11,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.2,1222.05,Yes\n2027-WKXMW,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,108.75,6871.7,No\n7137-NAXML,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.15,405.6,No\n2428-ZMCTB,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.75,208.25,No\n2961-VNFKL,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.95,1801.9,No\n1768-HNVGJ,Female,1,No,No,45,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,70.05,3062.45,No\n6963-KQYQB,Female,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.05,1764.75,No\n5934-RMPOV,Female,0,No,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.75,1816.75,No\n1207-BLKSA,Female,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.05,1255.1,No\n4088-YLDSU,Male,0,Yes,No,55,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.15,5743.05,Yes\n8316-BBQAY,Female,0,No,No,65,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),59.95,3921.1,No\n1166-PQLGG,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,1463.45,No\n3146-JTQHR,Male,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.6,189.45,No\n4291-YZODP,Female,0,No,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,96.8,No\n4395-PZMSN,Male,1,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.55,408.5,No\n6427-FEFIG,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),78.6,1846.65,No\n0017-IUDMW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.8,8456.75,No\n8706-HRADD,Male,0,No,No,21,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,43.55,1011.5,No\n9955-QOPOY,Male,0,Yes,No,69,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60.8,4263.4,No\n4385-ZKVNW,Male,0,Yes,Yes,44,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),54.9,2549.1,No\n5446-DKWYW,Female,1,Yes,Yes,61,Yes,Yes,DSL,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,65.2,3965.05,No\n2034-CGRHZ,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.95,2496.7,Yes\n5797-APWZC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.6,90.6,Yes\n3683-QKIUE,Female,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),50.8,288.05,Yes\n4228-ZGYUW,Male,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,368.1,Yes\n9031-ZVQPT,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.2,7840.6,No\n2990-IAJSV,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92,6632.75,No\n8107-KNCIM,Male,1,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,1013.35,No\n9027-YFHQJ,Male,0,No,No,7,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.05,152.95,No\n5176-LDKUH,Female,0,No,No,48,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,No,Electronic check,75.15,3772.65,No\n4644-OBGFZ,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,1026.35,No\n7926-IJOOU,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.3,19.3,No\n6480-YAGIY,Male,0,No,No,45,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),112.2,5031.85,No\n4029-HPFVY,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.3,220.4,No\n7602-DBTOU,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,1416.5,No\n5345-BMKWB,Male,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,158.35,No\n3519-ZKXGG,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.85,256.6,Yes\n5457-COLHT,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.65,5542.55,No\n6416-TVAIH,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.5,68.5,Yes\n5451-YHYPW,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.75,8443.7,No\n5108-ADXWO,Male,0,No,No,11,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.5,791.75,Yes\n7998-WNZEM,Male,0,No,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),80.6,5708.2,No\n3066-RRJIO,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.95,69.95,Yes\n4664-NJCMS,Female,0,Yes,No,33,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.55,2016.3,No\n0307-BCOPK,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.05,326.65,No\n7629-WFGLW,Female,1,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,95.65,5471.75,No\n2542-HYGIQ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.95,19.95,No\n6715-OFDBP,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,70.05,346.4,Yes\n5016-LIPDW,Male,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,1061.6,No\n6481-LXPWL,Male,0,Yes,Yes,56,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),36.1,1971.5,No\n1567-DSCIC,Male,0,No,No,8,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,94,773.65,Yes\n2150-UWTFY,Female,0,Yes,Yes,22,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,61.15,1422.05,Yes\n6124-ACRHJ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,No\n9362-MWODR,Female,0,No,Yes,40,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),64.1,2460.35,No\n9975-GPKZU,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.75,856.5,No\n9625-RZFUK,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,1275.85,No\n5153-LXKDT,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,110.2,7467.5,No\n5161-UBZXI,Male,0,Yes,Yes,69,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),106.35,7261.75,No\n1020-JPQOW,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,90.55,5116.6,No\n7919-ZODZZ,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Mailed check,65.9,660.05,No\n0565-JUPYD,Male,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,No,Credit card (automatic),104.5,6590.8,No\n6867-ACCZI,Female,0,Yes,Yes,24,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,52.5,1208.15,No\n5939-XAIXZ,Female,0,No,No,19,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,56.1,1033.9,No\n9054-FOWNV,Male,0,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.75,1885.15,No\n2683-BPJSO,Male,0,Yes,No,29,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,84.45,2467.1,Yes\n2157-MXBJS,Male,0,Yes,No,13,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,75.3,989.45,Yes\n8207-VVMYB,Female,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26,2006.95,No\n9732-EQMWY,Female,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.4,5025,No\n9360-AHGNL,Female,1,Yes,No,43,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,109.55,4830.25,Yes\n1087-UDSIH,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,59.75,Yes\n5269-NRGDP,Male,0,Yes,Yes,42,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,73.15,3088.25,No\n6195-MELTI,Male,0,No,No,57,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Mailed check,54.65,3134.7,No\n5485-WUYWF,Male,1,No,No,2,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),66.4,94.55,Yes\n2121-JAFOM,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.55,8312.4,No\n8818-DOPVL,Female,1,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.45,4863.85,No\n2632-UCGVD,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.05,6871.9,Yes\n5445-PZWGX,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,No,Electronic check,102,6529.25,Yes\n1227-UDMZR,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),91.15,6637.9,No\n5198-HQAEN,Male,0,Yes,Yes,35,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,89.7,3165.6,No\n9170-CCKOU,Male,0,Yes,No,17,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Credit card (automatic),90.2,1454.15,Yes\n2167-FQSTQ,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,92.4,6786.1,No\n2819-GWENI,Female,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,543,No\n1043-YCUTE,Male,0,Yes,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.15,1327.15,Yes\n3814-MLAXC,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),79.85,2404.15,Yes\n3572-UUHRS,Male,0,No,No,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),18.85,867.3,No\n2692-PFYTJ,Female,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25.75,25.75,No\n1226-UDFZR,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.6,114.7,Yes\n5955-EPOAZ,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.95,109.5,No\n6821-JPCDC,Female,0,Yes,No,48,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.05,4692.95,No\n8815-LMFLX,Male,0,Yes,Yes,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.4,546.85,No\n9135-HSWOC,Male,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.7,1274.05,No\n8582-KRHPJ,Male,0,No,No,50,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,35,1782.4,No\n5811-IWXYM,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),101.25,5301.1,No\n7044-YAACC,Male,1,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,280.35,No\n8189-XRIKE,Female,1,No,No,32,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.95,2897.95,No\n1506-YJTYT,Male,0,Yes,Yes,45,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),73.85,3371,No\n4123-DVHPH,Male,0,Yes,No,9,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,88.05,801.3,No\n6425-YQLLO,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.95,6975.25,Yes\n5442-UTCVD,Male,0,No,Yes,3,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,91.85,257.05,Yes\n2228-BZDEE,Female,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,1079.45,No\n7734-DBOAI,Female,0,Yes,Yes,1,No,No phone service,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,40.1,40.1,Yes\n2789-CZANW,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),110.3,6997.3,No\n2091-RFFBA,Female,1,No,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.9,2217.15,Yes\n2114-MGINA,Female,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89.8,1129.1,Yes\n7596-LDUXP,Female,0,No,No,12,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),85.15,979.05,No\n3740-RLMVT,Male,1,Yes,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.95,4119.4,No\n4489-SNOJF,Female,0,Yes,Yes,35,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,72.25,2568.55,Yes\n2192-OZITF,Female,0,No,No,45,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Two year,No,Mailed check,73.55,3349.1,No\n5651-CPDND,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46,492.1,No\n2186-QZEYA,Female,1,No,No,29,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),58.55,1718.95,No\n1131-ALZWV,Female,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.6,605.25,No\n7729-XBTWX,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.75,1344.5,No\n9134-WYRVP,Male,0,No,No,51,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,One year,No,Mailed check,86.35,4267.15,No\n3284-SVCRO,Female,0,Yes,No,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.5,1121.05,No\n9732-OUYRN,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19,918.7,No\n0559-CKHUS,Female,0,Yes,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.55,521.8,No\n2931-SVLTV,Male,0,Yes,Yes,40,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),110.1,4469.1,No\n6899-PPEEA,Female,1,No,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.55,3580.3,Yes\n7504-UWHNB,Male,0,No,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.75,1729.35,No\n2582-FFFZR,Female,0,No,No,22,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.6,1073.3,No\n8908-NMQTX,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.6,4566.5,No\n2187-LZGPL,Female,0,No,No,7,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.1,293.3,Yes\n1431-AIDJQ,Male,0,Yes,Yes,33,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),82.1,2603.1,No\n6288-LBEAR,Female,0,No,No,23,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,79.1,1783.75,No\n4597-NUCQV,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,2440.15,Yes\n9019-QVLZD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79.55,79.55,Yes\n8413-YNHNV,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,90.65,6322.1,No\n5808-TOTXO,Female,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,57.4,No\n4369-NYSCF,Male,0,No,No,56,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),75.75,4284.65,No\n6333-YDVLT,Male,0,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110,7138.65,No\n5324-KTGCG,Male,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.85,1539.75,No\n5599-HVLTW,Female,1,No,No,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.35,1058.1,No\n7394-LWLYN,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,123.8,No\n3707-LRWZD,Female,0,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,No,Electronic check,84.05,2781.85,Yes\n6873-UDNLD,Male,0,No,No,40,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,67.45,2731,No\n2700-LUEVA,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.75,20.75,No\n1455-ESIQH,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.1,89.1,Yes\n0958-YHXGP,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.9,497.3,No\n1101-SSWAG,Female,0,Yes,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.1,711.15,No\n2979-SXESE,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,94.4,1607.2,Yes\n4013-TLDHQ,Male,0,No,No,19,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.25,1490.95,Yes\n5743-KHMNA,Male,0,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.55,1898.1,No\n4194-FJARJ,Female,0,Yes,Yes,54,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),60,3273.95,No\n5325-UWTWJ,Male,0,Yes,No,31,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),90.55,2929.75,No\n3969-GYXEL,Female,0,No,No,11,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,76.4,838.7,No\n9208-OLGAQ,Female,1,No,No,18,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,84.95,1443.65,No\n3244-CQPHU,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.1,7746.7,No\n2674-MLXMN,Female,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.65,6951.15,No\n9708-HPXWZ,Male,1,No,No,5,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Credit card (automatic),45.4,214.75,No\n6992-TKNYO,Male,0,Yes,No,38,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),69,2669.45,No\n4468-YDOVK,Male,0,No,Yes,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),48.65,235.2,No\n3754-DXMRT,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.15,92.65,Yes\n5792-JALQC,Female,1,No,No,52,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Two year,No,Bank transfer (automatic),59.85,3103.25,No\n7130-VTEWQ,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.75,606.25,Yes\n2200-DSAAL,Female,0,No,No,68,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,80.65,5330.2,No\n4302-ZYFEL,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,1403.1,No\n9351-LZYGF,Female,0,Yes,No,42,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),66.4,2727.8,No\n4366-CTOUZ,Female,0,No,No,50,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,100.2,5038.45,No\n6121-VZNQB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.1,19.1,Yes\n7903-CMPEY,Male,1,Yes,No,1,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.3,80.3,Yes\n1518-OMDIK,Male,0,Yes,No,33,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.55,1462.6,No\n6671-NGWON,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,150.6,No\n0595-ITUDF,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,91.8,5960.5,No\n6996-KNSML,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.9,74.9,Yes\n1955-IBMMB,Male,0,No,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.2,1192.3,No\n0096-BXERS,Female,0,Yes,No,6,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,50.35,314.55,No\n3806-YAZOV,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.8,56,No\n7998-ZLXWN,Female,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.45,330.8,No\n6253-WRFHY,Male,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,64.75,877.35,No\n9133-AYJZG,Female,0,No,No,23,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.7,2249.1,No\n2675-OTVVJ,Male,1,Yes,No,31,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,89.45,2807.65,No\n8680-CGLTP,Male,0,No,No,29,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,58.75,1696.2,No\n8430-TWCBX,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.7,1032.05,No\n5018-GWURO,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.6,4902.8,No\n6741-QRLUP,Female,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),80.3,4995.35,No\n4737-HOBAX,Male,0,Yes,No,63,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),79.8,5034.05,No\n0537-QYZZN,Male,1,Yes,Yes,24,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.85,1857.75,No\n3340-QBBFM,Male,1,Yes,No,36,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),54.1,1992.85,No\n1704-NRWYE,Female,1,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.85,751.65,Yes\n2592-SEIFQ,Male,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.75,66.95,Yes\n8200-KLNYW,Female,0,Yes,No,21,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,80.9,1714.95,No\n3372-CDXFJ,Male,0,Yes,Yes,13,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.5,343.6,No\n4781-ZXYGU,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.15,20.15,No\n7632-YUTXB,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,520.1,No\n2718-YSKCS,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.6,1387.45,No\n9896-UYMIE,Male,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),114.3,7383.7,No\n0853-NWIFK,Female,0,No,No,45,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,100.3,4483.95,No\n8212-CRQXP,Female,0,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80,1706.45,No\n6980-CDGFC,Female,0,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.85,1327.4,No\n7691-XVTZH,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),89.95,5974.3,No\n2520-SGTTA,Female,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20, ,No\n7481-ATQQS,Female,1,No,No,49,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.85,4515.85,Yes\n2277-VWCNI,Female,1,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,48.75,179.85,No\n1088-CNNKB,Male,0,Yes,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),80,5040.2,No\n8642-GVWRF,Female,0,Yes,No,2,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),79.7,165,Yes\n1930-BZLHI,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.35,422.7,No\n3400-ESFUW,Male,0,Yes,Yes,55,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),57.55,3046.4,Yes\n5868-YTYKS,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No\n3525-DVKFN,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,358.05,No\n1482-OXZSY,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.4,2936.25,No\n0377-JBKKT,Male,0,Yes,Yes,22,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,57.95,1271.8,No\n5778-BVOFB,Female,0,No,No,9,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.5,530.05,No\n3373-YZZYM,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,19.2,No\n2057-ZBLPD,Female,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.5,1808.7,Yes\n0228-MAUWC,Male,0,No,No,19,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,59.55,1144.6,No\n5502-RLUYV,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.95,7446.9,Yes\n0023-HGHWL,Male,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.1,25.1,Yes\n7663-YJHSN,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),103.95,7556.9,No\n4915-BFSXL,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Two year,No,Credit card (automatic),68.95,4858.7,No\n3086-RUCRN,Female,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),103.1,6595,No\n5215-LNLDJ,Female,0,Yes,Yes,7,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.7,149.05,No\n7576-JMYWV,Female,1,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.2,4972.1,No\n9039-RBEEE,Male,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),48.95,1880.85,Yes\n0257-KXZGU,Female,0,Yes,Yes,32,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),62.45,2045.55,No\n1307-ATKGB,Male,0,No,No,24,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.55,2187.15,No\n8417-FMLZI,Male,0,Yes,Yes,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),83.55,477.55,Yes\n3714-XPXBW,Female,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),78.9,2976.95,No\n1850-AKQEP,Male,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,178.7,No\n2824-DXNKN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),71.45,5025.85,No\n5227-JSCFE,Male,1,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),46.35,3353.4,No\n0848-ZGQIJ,Female,0,Yes,No,16,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.65,1461.15,No\n3621-CHYVB,Female,0,Yes,No,57,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),49.9,2782.4,No\n8042-RNLKO,Male,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.45,1699.15,No\n1792-UXAFY,Female,1,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.15,1496.9,Yes\n5372-FBKBN,Female,0,No,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,452.2,No\n0420-TXVSG,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),66.1,4428.45,No\n6217-TOWGS,Female,0,Yes,No,17,Yes,Yes,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),75.4,1322.55,No\n0515-YPMCW,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.45,70.45,Yes\n0378-XSZPU,Male,0,Yes,No,58,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Credit card (automatic),60.3,3563.8,Yes\n1045-LTCYT,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.05,139.75,No\n1544-JJMYL,Male,0,No,No,27,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Credit card (automatic),69.35,1927.3,No\n4636-JGAAI,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,88.85,3000.25,No\n6874-SGLHU,Male,0,No,No,30,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),97,3021.3,No\n5951-AOFIH,Male,0,No,No,33,Yes,No,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),66.4,2245.4,No\n4729-XKASR,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.75,24.75,Yes\n3059-NGMXB,Male,0,Yes,Yes,14,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,69.2,944.65,No\n8652-YHIYU,Female,0,No,Yes,16,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),79.5,1264.2,No\n5278-PNYOX,Female,0,No,No,49,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),100.65,4917.75,No\n5449-FIBXJ,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.3,2012.7,Yes\n3486-HOOGQ,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),79.7,5743.3,No\n5061-PBXFW,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.4,1864.65,No\n8630-FJLIB,Female,0,No,No,18,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,1221.65,No\n3891-NLXJB,Male,0,No,No,37,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,40.55,1390.85,No\n4749-OJKQU,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.65,302.35,No\n0577-WHMEV,Female,0,Yes,No,16,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,90.7,1374.9,No\n5453-AXEPF,Male,0,Yes,No,17,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.5,1336.9,No\n3639-XJHKQ,Female,0,No,Yes,19,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,60.6,1297.8,No\n3716-LRGXK,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),101.15,6067.4,No\n2263-SFSQZ,Male,0,Yes,Yes,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.95,1222.25,No\n9950-MTGYX,Male,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.3,487.95,No\n3397-AVTKU,Male,0,No,No,43,Yes,No,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Electronic check,60,2548.55,No\n4825-FUREZ,Male,0,Yes,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.25,835.5,No\n9837-BMCLM,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.5,242.05,Yes\n2672-OJQZP,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.75,44.75,No\n0137-UDEUO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,63.75,Yes\n3134-DSHVC,Female,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Credit card (automatic),98,6218.45,No\n6161-UUUTA,Male,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.9,260.9,Yes\n7821-DPRQE,Male,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.7,7320.9,No\n6543-XRMYR,Female,1,No,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.7,2967.35,Yes\n2001-EWBQU,Female,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,104.7,6333.8,No\n4925-LMHOK,Male,0,No,No,15,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),58.6,939.7,Yes\n1608-GMEWB,Male,1,No,No,45,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.9,4200.25,No\n6695-AMZUF,Female,0,Yes,No,70,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,5950.2,No\n1455-UGQVH,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,1037.75,Yes\n8410-BGQXN,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,93.4,No\n8280-MQRQN,Female,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50.45,50.45,Yes\n1619-YWUBB,Female,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1614.9,No\n7611-YKYTC,Male,0,Yes,Yes,22,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75,1573.95,No\n2037-SGXHH,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,94.65,3624.3,Yes\n3178-FESZO,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.25,100.25,Yes\n0365-GXEZS,Male,0,Yes,No,18,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,78.2,1468.75,No\n0082-OQIQY,Male,0,No,No,29,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,2607.6,No\n6087-MVHJH,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.45,1422.1,Yes\n4603-JANFB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,69.85,Yes\n3018-TFTSU,Male,0,No,No,12,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),81.7,858.6,Yes\n3606-SBKRY,Male,0,No,No,31,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,50.05,1523.4,No\n4806-KEXQR,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.9,324.3,Yes\n0667-NSRGI,Female,0,Yes,No,48,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Mailed check,69.55,3435.6,No\n7083-YNSKY,Female,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),25.4,399.6,Yes\n6893-ODYYE,Male,0,No,No,50,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),90.1,4549.45,No\n8242-JSVBO,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.65,322.5,No\n2479-BRAMR,Male,1,Yes,No,41,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),83.75,3273.55,Yes\n9541-ZPSEA,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Two year,No,Credit card (automatic),80.35,5375.15,No\n2665-NPTGL,Female,1,Yes,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.1,2510.7,No\n1139-WUOAH,Male,0,No,No,57,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),53.35,3090.05,No\n4693-VWVBO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.55,61.05,No\n7434-SHXLS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,Yes\n6937-GCDGQ,Male,0,Yes,Yes,19,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),48.95,955.6,No\n0988-JRWWP,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.2,140.4,No\n5075-JSDKI,Female,0,No,No,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.45,1493.1,No\n7908-QCBCA,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.4,69.4,Yes\n8644-XYTSV,Male,0,Yes,No,42,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),40.15,1626.05,No\n6711-FLDFB,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,541.15,Yes\n3873-WOSBC,Male,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1784.9,No\n7465-ZZRVX,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,70.35,No\n7975-JMZNT,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),91.7,6075.9,No\n7251-XFOIL,Female,0,No,No,61,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,89.2,5500.6,No\n4116-IQRFR,Male,0,Yes,Yes,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.1,73.1,No\n3714-JTVOV,Female,1,Yes,No,42,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.15,3229.4,Yes\n8259-DZLIZ,Female,0,Yes,Yes,64,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),53.85,3399.85,No\n0442-ZXKVS,Female,1,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),115.6,6431.05,No\n7853-WNZSY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.75,19.75,No\n3120-FAZKD,Male,0,Yes,Yes,54,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.05,1230.9,No\n8606-OEGQZ,Female,0,No,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.3,454.65,No\n0225-ZORZP,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,84.3,235.05,No\n4702-IOQDC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,70.1,Yes\n9489-JMTTN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.75,6595.9,No\n0575-CUQOV,Male,1,Yes,No,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.95,5867,No\n0967-BMLBD,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20,196.35,No\n6178-KFNHS,Female,0,No,Yes,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.3,909.25,Yes\n2830-LEWOA,Male,0,Yes,Yes,61,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),103.9,6449.15,No\n5006-MXVRN,Female,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.7,762.45,No\n1264-BYWMS,Male,0,No,No,55,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.8,5283.95,Yes\n9658-WYUFB,Female,0,No,No,17,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.4,1617.5,Yes\n8327-WKMIE,Male,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.15,785.75,No\n6917-FIJHC,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26,1776,No\n5329-KRDTM,Male,1,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),77.35,5396.25,No\n7797-EJMDP,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),66.05,574.5,No\n3530-VWVGU,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,400.3,No\n2013-SGDXK,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,84.3,84.3,Yes\n6368-NWMCE,Female,0,No,No,38,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),68.15,2656.3,No\n3633-CDBUW,Male,0,No,Yes,17,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,80.85,1445.95,No\n0707-HOVVN,Female,1,No,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.5,5212.65,No\n8580-QVLOC,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),92.45,6440.25,Yes\n3956-MGXOG,Female,0,No,No,28,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.6,2244.95,No\n9274-UARKJ,Female,0,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),83.2,1130,No\n4077-HWUYD,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),87.55,6463.15,No\n2012-NWRPA,Female,1,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.55,1131.2,Yes\n8808-ELEHO,Male,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.25,585.95,Yes\n0103-CSITQ,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.4,6252.7,No\n3506-LCJDC,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.95,19.95,Yes\n8671-KKKOS,Female,0,Yes,No,46,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,45.55,2062.15,No\n7305-ZWMAJ,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.7,587.1,No\n9518-XXBXE,Male,1,Yes,No,10,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.3,720.45,No\n3934-HXCFZ,Male,0,Yes,No,23,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,99.25,2186.4,Yes\n6578-KRMAW,Male,0,No,No,32,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),93.4,2979.3,No\n4860-IJUDE,Male,0,No,No,13,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.75,956.65,No\n7666-WKRON,Female,0,No,No,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Two year,Yes,Electronic check,80.45,3201.55,Yes\n9688-YGXVR,Female,0,No,No,44,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),88.15,3973.2,No\n0423-UDIJQ,Male,1,No,No,9,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,49.2,447.9,No\n1945-XISKS,Female,0,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,1335.2,No\n4910-AQFFX,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.35,661.25,Yes\n2154-KVJFF,Female,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.75,1111.85,Yes\n5360-LJCNJ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.15,7555,No\n2607-FBDFF,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),49,49,No\n9647-ERGBE,Female,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3046.15,Yes\n3428-XZMAZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,69.35,Yes\n1100-DDVRV,Male,0,Yes,No,17,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,49.8,836.35,No\n0378-TOVMS,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,85.8,272.2,Yes\n4355-HBJHH,Male,0,Yes,Yes,67,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,79.7,5293.4,Yes\n6728-WYQBC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,20.95,No\n9058-CBREO,Female,1,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.55,50.55,Yes\n9029-FEGVJ,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,79.3,2570,No\n7216-KAOID,Male,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.5,798.2,No\n0318-QUUOB,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.55,80.55,Yes\n6145-NNPNO,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.15,44.15,No\n5520-FVEWJ,Female,0,Yes,Yes,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.5,916.9,Yes\n5339-TJFEK,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,6487.2,No\n5572-ZDXHY,Female,0,No,No,22,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,84.3,1855.65,Yes\n2074-GUHPQ,Female,0,No,Yes,17,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),92.7,1556.85,No\n4625-XMOYM,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26.25,1988.05,No\n5827-MWCZK,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.95,5432.2,No\n1385-TQOZW,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,147.55,No\n5914-XRFQB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.8,8424.9,No\n4329-YPDDQ,Male,0,No,No,20,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.2,2203.7,No\n4804-NCPET,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,387.4,No\n3750-CKVKH,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,67.75,116.65,Yes\n5944-UGLLK,Male,0,No,No,53,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),54.9,3045.75,No\n8063-GBATB,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.25,2287.25,Yes\n7787-BNTZM,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,130.5,No\n2252-ISRNH,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,90.35,767.9,No\n9415-TPKRV,Female,0,Yes,Yes,8,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.75,446.8,No\n5322-TEUJK,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.6,8100.25,No\n4547-KQRTM,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.05,830.7,Yes\n4877-EVATK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,20,Yes\n1866-DIOQZ,Female,0,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),66.8,4689.15,No\n8375-KVTHK,Male,0,Yes,No,68,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.3,6754.35,No\n2697-NQBPF,Male,0,No,No,34,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.35,3540.65,No\n3709-OIJEA,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,85.2,2184.6,No\n7639-SUPCW,Female,0,No,No,22,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.8,1054.6,Yes\n1386-ZIKUV,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.95,130.55,No\n0599-XNYDO,Female,0,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.8,1540.35,No\n6377-WHAOX,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.15,6411.25,No\n6855-VLGOS,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.55,1432.55,No\n8999-XXGNS,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.75,7629.85,No\n8746-OQQRW,Male,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.25,101.9,No\n3181-MIZBN,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,313.4,No\n0471-ARVMX,Female,1,Yes,No,62,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.85,6312.9,No\n7766-CLTIC,Female,0,No,No,10,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,60.95,629.55,No\n5650-YLIBA,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.15,2640.55,No\n0825-CPPQH,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.1,1372.45,No\n9084-OAYKL,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.8,1212.25,No\n6122-LJADA,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.15,6237.05,No\n2400-XIWIO,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.1,6310.9,No\n7524-VRLPL,Male,1,No,No,69,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),74.1,5031,No\n1069-XAIEM,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.05,85.05,Yes\n7569-NMZYQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),118.75,8672.45,No\n5201-USSQZ,Female,0,Yes,No,26,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),85.9,2196.45,No\n2105-PHWON,Female,0,Yes,No,33,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95,3008.15,No\n1494-EJZDW,Female,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,220.8,No\n4884-TVUQF,Female,1,No,No,57,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),101.3,5779.6,No\n8003-EWNDZ,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21.2,222.65,No\n1897-RCFUM,Female,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.2,914.6,No\n9256-JTBNZ,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.3,246.3,No\n4658-HCOHW,Female,0,Yes,Yes,21,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,102.8,2110.15,Yes\n2211-RMNHO,Female,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.3,5560,No\n2432-TFSMK,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),89.6,1633,No\n3440-JPSCL,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.95,547.65,Yes\n4929-ROART,Male,0,No,No,18,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,56.25,969.85,No\n1834-ABKHQ,Female,0,Yes,Yes,52,Yes,No,DSL,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),50.95,2610.65,No\n1741-WTPON,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,115.85,6567.9,No\n3932-IJWDZ,Male,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.65,4747.85,No\n1240-HCBOH,Female,0,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26.1,1759.55,No\n3594-KADLU,Male,0,Yes,No,3,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,35.1,101.1,No\n8065-BVEPF,Male,1,No,No,65,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.1,6496.15,No\n3796-ENZGF,Male,0,Yes,No,63,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Mailed check,67.25,4234.15,No\n1734-ZMNTZ,Female,0,Yes,Yes,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,300.7,No\n2853-CWQFQ,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,59.55,59.55,No\n0813-TAXXS,Male,0,No,No,55,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),77.8,4323.35,No\n2519-TWKFS,Male,0,Yes,Yes,25,Yes,Yes,DSL,No,No,No,Yes,No,No,One year,Yes,Mailed check,55.1,1466.1,No\n2889-FPWRM,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),117.8,8684.8,Yes\n0626-QXNGV,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.15,1776.45,No\n6723-CEGQI,Female,0,No,Yes,65,No,No phone service,DSL,No,Yes,No,Yes,Yes,No,Two year,No,Mailed check,45.25,2933.95,No\n6987-XQSJT,Female,1,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.5,4370.25,Yes\n4732-RRJZC,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,144.35,No\n9499-XPZXM,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),64.75,4804.75,No\n4338-EYCER,Male,0,Yes,No,21,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),54.6,1125.2,No\n3007-FDPEA,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.7,39.85,No\n6350-XFYGW,Male,1,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,422.4,No\n4290-BSXUX,Male,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),79.65,251.75,Yes\n8513-OLYGY,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.8,8332.15,No\n0311-QYWSS,Female,0,No,No,6,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.45,314.6,No\n8705-DWKTI,Male,0,No,No,52,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),83.8,4331.4,No\n8755-IWJHN,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.35,6382,No\n0325-XBFAC,Male,0,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,740.3,Yes\n4480-QQRHC,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.05,600.15,No\n0929-PECLO,Female,1,No,No,63,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.6,5538.8,No\n3680-CTHUH,Male,0,No,No,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),116.6,7049.5,No\n1202-KKGFU,Female,0,Yes,No,12,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.2,690.5,No\n6112-KTHFQ,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,279.3,No\n1852-XEMDW,Male,0,No,No,22,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,65.05,1427.55,No\n2462-XIIJB,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,92.5,452.7,Yes\n4760-XOHVN,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,19.45,Yes\n1820-DJFPH,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.05,1709.15,No\n9426-SXNHE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),18.75,53.15,No\n9068-FHQHD,Female,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,777.35,No\n1269-FOYWN,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20,860.85,No\n9470-YFUYI,Male,1,Yes,No,71,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),71,5012.1,No\n7817-BOQPW,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.55,166.3,Yes\n7402-EYFXX,Male,1,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.6,2404.1,No\n1853-UDXBW,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,70,Yes\n9895-VFOXH,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.4,24.4,No\n5458-CQJTA,Male,0,Yes,Yes,65,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.8,4820.15,Yes\n1230-QAJDW,Male,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,65.25,209.9,No\n5701-ZIKJE,Male,0,No,No,13,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.55,610.75,No\n5219-YIPTK,Female,0,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,3409.6,Yes\n5032-MIYKT,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.7,70.7,No\n7396-VJUZB,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.25,155.35,No\n9717-QEBGU,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,144,No\n5172-RKOCB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.95,7875,No\n6509-TSGWN,Female,0,Yes,Yes,37,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,26.45,911.6,No\n3540-RZJYU,Female,0,No,No,15,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,86.2,1270.2,Yes\n3178-CIFOT,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.65,478.1,No\n2091-MJTFX,Female,0,Yes,Yes,30,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),51.2,1561.5,Yes\n4530-NDRKU,Female,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.05,761.85,No\n5985-TBABQ,Female,0,No,No,32,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,74.75,2282.95,No\n9800-ONTFE,Female,0,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.8,1615.1,No\n6743-HHQPF,Male,0,Yes,No,42,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.1,1097.15,No\n8455-HIRAQ,Female,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.45,369.3,No\n6616-AALSR,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),104.3,6725.3,No\n0883-EIBTI,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,31.55,Yes\n9415-ZNBSX,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89,6293.2,No\n4018-KJYUY,Male,0,No,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.15,432.5,No\n0722-TROQR,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,321.75,Yes\n7571-YXDAD,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),74.9,147.15,Yes\n3884-UEBXB,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),36.15,2434.45,No\n8780-RSYYU,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.2,532.1,No\n5537-UXXVS,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.25,375.25,No\n1791-PQHBB,Female,0,No,Yes,2,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),61.2,125.95,No\n8701-DGLVH,Male,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,1042.65,No\n8741-LQOBK,Female,0,Yes,Yes,46,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),35.05,1620.25,No\n3393-FMZPV,Female,0,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,2387.75,Yes\n8082-GHXOP,Male,0,No,Yes,13,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44,659.35,No\n6402-ZFPPI,Female,1,No,No,25,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,102.8,2660.2,Yes\n0980-FEXWF,Male,0,Yes,Yes,26,Yes,No,DSL,No,No,Yes,No,No,No,One year,No,Mailed check,50.35,1285.8,No\n7486-KSRVI,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100,4211.55,Yes\n3011-WQKSZ,Male,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20,377.55,No\n8443-WVPSS,Male,0,Yes,No,10,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.85,990.9,Yes\n3006-XIMLN,Female,0,No,Yes,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.2,193.8,Yes\n8218-FFJDS,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.4,6058.95,No\n1986-PHGZF,Male,1,No,No,18,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),58.4,964.9,No\n9965-YOKZB,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.85,790.15,Yes\n0504-HHAPI,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),88.3,2467.75,Yes\n9360-OMDZZ,Male,0,No,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.1,2322.85,No\n6305-YLBMM,Male,0,No,No,69,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.05,7262,Yes\n0345-XMMUG,Female,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.9,4854.3,No\n8024-XNAFQ,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),107.4,7748.75,No\n3647-GMGDH,Male,0,Yes,No,22,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.7,1914.9,Yes\n2988-GBIVW,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.85,6470.1,No\n2832-SCUCO,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.9,57.4,No\n7036-ZZKBD,Male,0,Yes,No,31,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),66.4,2019.8,No\n3791-LGQCY,Female,1,Yes,No,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.65,5688.05,Yes\n0265-PSUAE,Female,0,Yes,Yes,16,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),100.7,1522.7,No\n0463-TXOAK,Male,0,No,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1334.5,No\n0594-UFTUL,Male,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.85,252,No\n9509-MPYOD,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.75,700.45,No\n9128-CPXKI,Female,0,No,No,59,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,95.8,5655.45,No\n0129-KPTWJ,Male,0,Yes,No,72,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,6747.35,No\n8166-ZZTFS,Female,1,Yes,No,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.55,5265.1,Yes\n5855-EIBDE,Female,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),106.65,5174.35,No\n5365-LLFYV,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,105.6,No\n5956-YHHRX,Male,1,No,No,21,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.35,2271.85,No\n6008-NAIXK,Male,1,No,No,54,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,55.45,2966.95,No\n2956-GGUCQ,Male,1,Yes,No,24,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.85,1772.25,Yes\n1928-BXYIV,Male,0,No,No,1,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,61.15,61.15,No\n5760-FXFVO,Male,0,No,No,6,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,494.95,No\n6595-COKXZ,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.45,44.45,Yes\n0961-ZWLVI,Male,0,No,Yes,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.2,5290.45,No\n5181-OABFK,Female,0,Yes,Yes,56,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),61.3,3346.8,No\n4007-NHVHI,Female,1,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,96.85,5219.65,No\n7816-VGHTO,Female,0,Yes,Yes,6,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Mailed check,40.55,217.5,No\n5871-DGTXZ,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,607.7,No\n7550-WIQVA,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.25,5431.4,No\n7544-ZVIKX,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.05,6004.85,No\n0016-QLJIS,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.45,5957.9,No\n7508-DQAKK,Female,0,No,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),86.4,5442.05,No\n5176-OLSKT,Female,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),66.9,4370.25,No\n9356-AXGMP,Male,0,Yes,No,38,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.7,4428.6,No\n3556-BVQGL,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20,416.45,No\n0362-ZBZWJ,Male,0,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,3067.2,Yes\n8979-CAMGB,Male,1,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,102.1,6688.1,No\n4211-MMAZN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,20.25,Yes\n7581-EBBOU,Female,0,No,No,60,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.15,4224.7,No\n4274-OWWYO,Male,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,74.35,Yes\n5073-RZGBK,Female,0,Yes,Yes,50,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),80.05,4042.2,No\n1541-ETJZO,Male,0,No,No,1,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,62.05,62.05,Yes\n2192-CKRLV,Female,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,49.2,3580.95,No\n3154-HMWUU,Male,0,Yes,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.5,1198.8,No\n6119-SPUDB,Male,0,No,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,38.25,1755.35,No\n6522-OIQSX,Female,0,Yes,Yes,69,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),54.95,3772.5,No\n7813-ZGGAW,Male,1,No,No,31,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),96.6,2877.95,No\n8748-HFWBO,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,357.7,No\n1052-QJIBV,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1397.3,No\n1319-YLZJG,Male,0,Yes,No,12,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.6,959.9,No\n4106-HADHQ,Male,0,Yes,Yes,39,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),80,3182.95,Yes\n0723-FDLAY,Male,0,No,No,44,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),85.25,3704.15,No\n3050-RLLXC,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),81.25,4620.4,No\n9298-WGMRW,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.5,8312.75,No\n2369-UAPKZ,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,104.1,541.9,Yes\n3088-LHEFH,Female,0,No,No,11,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79,929.3,No\n2778-OCLGR,Male,1,Yes,No,24,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),39.1,971.3,Yes\n6583-KQJLK,Female,1,Yes,No,15,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,1285.05,No\n8544-JNBOX,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.8,1521.2,No\n6681-ZSEXG,Male,0,Yes,No,56,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),59.5,3389.25,No\n6139-ZZRBQ,Male,1,No,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,1198.05,No\n6116-RFVHN,Female,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.45,3414.65,No\n0637-KVDLV,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,162.45,Yes\n8884-FEEWR,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.6,754,No\n3125-RAHBV,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.3,467.15,No\n6633-MPWBS,Male,0,No,No,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.2,216.9,Yes\n0373-AIVNJ,Male,0,No,No,9,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,One year,No,Mailed check,39.55,373,No\n8785-EPNCG,Male,0,No,No,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),23.15,245.2,Yes\n1682-VCOIO,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,481.1,No\n5729-KLZAR,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.85,302.75,Yes\n2072-ZVJJX,Male,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.25,1728.2,No\n8849-AYPTR,Male,0,Yes,No,33,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,91.25,2964.05,No\n9518-IMLHK,Male,0,No,No,31,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),72.45,2156.25,No\n1582-RAFML,Male,0,No,No,1,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,60.1,60.1,Yes\n3646-ITDGM,Female,0,No,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1051.9,No\n8740-CRYFY,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.95,78.95,Yes\n3569-VLDHH,Male,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,75.1,5013,No\n8224-KDLKN,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1738.9,No\n7594-LZNWR,Male,1,No,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),69.15,2275.1,No\n4001-TSBTV,Female,0,Yes,Yes,58,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.55,5511.65,No\n2962-XPMCQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.15,98.5,Yes\n8727-XDPUD,Male,0,No,No,37,No,No phone service,DSL,No,No,No,No,Yes,No,Two year,No,Credit card (automatic),35.8,1316.9,No\n1043-UXOVO,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.15,7993.3,No\n1064-FBXNK,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.85,19.85,No\n3996-ZNWYK,Male,1,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,1388.45,No\n2878-DHMIN,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.9,666,No\n7762-ONLJY,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,94.45,No\n1678-FYZOW,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.4,244.65,Yes\n5795-BKOYE,Female,0,Yes,Yes,69,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,No,Bank transfer (automatic),59.1,4134.7,No\n8627-EHGIP,Male,0,No,No,44,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,53.95,2375.4,Yes\n7402-PWYJJ,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Electronic check,91.15,4862.5,No\n5480-TBGPH,Female,0,Yes,No,24,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.3,2431.35,Yes\n0018-NYROU,Female,0,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.95,351.5,No\n7501-VTYLJ,Female,0,No,Yes,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,51.55,106.2,No\n7608-RGIRO,Male,0,No,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.4,1413,No\n0480-BIXDE,Female,0,Yes,No,19,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.8,1743.05,No\n5895-QSXOD,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.05,657.5,No\n9814-AOUDH,Male,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1050.5,No\n6175-IRFIT,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,78.75,426.35,No\n8760-ZRHKE,Female,1,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,One year,No,Electronic check,69.2,4982.5,No\n1622-HSHSF,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,19.55,No\n8854-CCVSQ,Male,0,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.65,1451.9,Yes\n6749-UTDVX,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,103.65,7634.8,No\n5542-DHSXL,Female,0,Yes,No,4,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.7,235.05,No\n9102-OXKFY,Male,0,No,No,59,Yes,Yes,DSL,No,No,Yes,No,No,No,Two year,No,Credit card (automatic),54.15,3116.15,No\n0511-JTEOY,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,71.1,71.1,Yes\n5216-WASFJ,Female,1,Yes,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.85,2633.4,No\n7808-DVWEP,Male,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20,49.65,No\n7067-KSAZT,Female,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.25,6979.8,Yes\n2284-VFLKH,Male,0,Yes,No,49,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),99.25,4920.8,No\n0366-NQSHS,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,46.35,No\n6532-YLWSI,Female,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.8,1021.8,Yes\n3807-BPOMJ,Female,0,Yes,No,55,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,94.75,5276.1,No\n3892-NXAZG,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.05,8289.2,No\n4948-WBBKL,Female,1,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,2659.45,No\n4182-BGSIQ,Female,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.8,196.75,No\n9300-AGZNL,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,94,Yes\n0988-AADSA,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),80.85,5824.75,No\n9972-NKTFD,Female,0,No,No,28,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.65,1517.5,No\n3717-LNXKW,Male,0,Yes,No,38,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),91.7,3479.05,No\n5734-EJKXG,Female,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,118.6,7365.7,No\n2207-RYYRL,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1331.05,No\n6729-FZWSY,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.45,1195.95,No\n9695-IDRZR,Female,0,No,Yes,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.15,3946.9,No\n8144-DGHXP,Female,0,No,No,54,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,80.6,4299.95,No\n7814-LEEVE,Female,0,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.3,20.3,No\n1112-CUNAO,Female,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.85,1424.95,Yes\n5175-AOBHI,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,46,193.6,Yes\n5174-RNGBH,Female,0,No,No,9,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,66.25,620.55,Yes\n8631-WUXGY,Female,0,No,Yes,46,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4391.25,No\n7270-BDIOA,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,90,1993.8,Yes\n9565-JSNFM,Male,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.45,2597.6,Yes\n5906-DVAPM,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),75,4213.9,No\n6654-QGBZZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,No\n8348-JLBUG,Male,1,Yes,No,64,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),80.3,5017.7,No\n0607-DAAHE,Male,0,No,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1052.35,Yes\n5641-DMBFJ,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.3,4916.4,No\n9200-NLNPD,Male,0,Yes,No,56,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),54.05,2959.8,No\n2664-XJZNO,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.9,7559.55,No\n9732-KPKBW,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,53.95,53.95,Yes\n3339-EAQNV,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),97.25,7133.1,No\n0921-OHLVP,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,83.05,1799.3,No\n3389-YGYAI,Female,1,No,No,8,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,829.55,Yes\n9560-ARGQJ,Female,0,No,Yes,16,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81,1312.15,Yes\n8350-NYMVI,Female,0,No,No,39,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),41.1,1597.05,No\n1600-DILPE,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45,524.35,No\n0536-ESJEP,Male,0,Yes,No,54,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.55,4191.45,No\n4654-GGUII,Female,0,No,No,18,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,40.2,711.95,No\n6478-HRRCZ,Male,0,Yes,No,32,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,No,Mailed check,70.5,2201.75,No\n8510-BBWMU,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.75,806.95,No\n6857-TKDJV,Male,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.65,1620.45,No\n2360-RDGRO,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.25,6812.95,No\n0584-BJQGZ,Female,0,No,No,25,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),78.35,1837.9,No\n5134-IKDAY,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,69.8,Yes\n1360-XFJMR,Female,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),109.7,7344.45,No\n3070-DVEYC,Male,1,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,73.75,545.15,Yes\n5730-RIITO,Female,1,No,No,43,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),33.45,1500.25,No\n9058-MJLZC,Female,0,No,No,24,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,2283.15,No\n5707-ORNDZ,Male,1,No,No,9,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.55,494.05,Yes\n0902-XKXPN,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1376.5,No\n5177-RVZNU,Female,0,No,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.3,755.4,No\n0056-EPFBG,Male,0,Yes,Yes,20,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),39.4,825.4,No\n8993-IZEUX,Male,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.15,488.65,No\n8696-JKZNU,Female,1,No,No,37,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.25,2841.55,Yes\n5380-AFSSK,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.9,486.85,Yes\n8190-ZTQFB,Male,0,No,No,41,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,51.35,2075.1,No\n0931-MHTEM,Female,0,No,No,54,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.05,5299.65,No\n2055-PDADH,Female,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.4,204.7,Yes\n7515-LODFU,Male,1,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1356.3,No\n0440-UEDAI,Female,0,No,No,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),94.45,5042.75,No\n1337-BOZWO,Male,0,Yes,Yes,18,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),46.4,812.4,No\n0868-VJRDR,Male,0,Yes,No,64,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.05,6605.55,No\n8714-EUHJO,Female,0,Yes,Yes,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,91.15,2995.45,Yes\n6344-SFJVH,Female,0,No,No,20,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),24.9,505.95,No\n3950-VPYJB,Male,0,Yes,Yes,57,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,59.6,3509.4,No\n8041-TMEID,Male,1,Yes,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),108.5,6991.9,No\n7321-ZNSLA,Male,0,Yes,Yes,13,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,40.55,590.35,No\n6941-KXRRV,Female,1,Yes,No,48,Yes,No,DSL,No,Yes,No,No,No,Yes,One year,Yes,Bank transfer (automatic),58.95,2789.7,No\n3721-CNEYS,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.95,137.95,Yes\n8727-JQFHV,Male,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1123.15,No\n9475-NNDGC,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),113.15,7953.25,No\n1355-KUSBG,Female,0,Yes,Yes,7,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,48.8,349.8,No\n3688-FTHLT,Female,0,No,No,16,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),63.05,1067.05,No\n0899-LIIBW,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.85,3527.3,No\n2568-OIADY,Female,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,3762,Yes\n1384-RCUXW,Male,0,No,No,16,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.55,1248.9,No\n5825-XJOCM,Male,0,No,Yes,48,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),64.4,3035.35,No\n6848-YLDFR,Male,0,Yes,Yes,58,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),75.2,4300.8,No\n8125-QPFJD,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.9,6065.3,No\n2320-YKQBO,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,144.95,No\n1193-RTSLK,Female,0,No,No,38,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),83.9,3233.6,Yes\n2302-ANTDP,Female,1,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,117.45,5438.9,Yes\n5923-GXUOC,Male,0,No,No,10,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,1081.45,Yes\n4973-RLZVI,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Credit card (automatic),74.65,2308.6,No\n7869-ZYDST,Male,0,Yes,No,31,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),59.05,1882.8,No\n4501-UYKBC,Female,1,No,No,46,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),69.1,3255.35,No\n1215-EXRMO,Male,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1067.65,No\n2305-MRGLV,Male,0,Yes,No,28,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),76.55,2065.4,No\n8404-VIOMB,Female,0,No,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),62.5,4136.4,No\n5233-GEEAX,Male,1,No,No,8,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.4,221.9,Yes\n2359-KLTEK,Female,0,Yes,Yes,41,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.9,3848,No\n5304-EFJLP,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.65,8022.85,No\n2673-ZALNP,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,173.15,No\n9184-GALIL,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,781.25,No\n4393-RYCRE,Male,0,No,No,44,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.05,4510.8,No\n9746-MDMBK,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),113.45,5317.8,No\n3162-ZJZFU,Male,0,Yes,Yes,53,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,92.55,4779.45,No\n8404-GFGCZ,Male,0,Yes,No,4,Yes,Yes,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,65.6,250.1,No\n8875-AKBYH,Male,1,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.35,1745.2,No\n4432-ADRLB,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.65,74.9,Yes\n0533-UCAAU,Male,1,Yes,No,57,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),71.1,4140.1,No\n1394-SUIUH,Female,1,Yes,No,44,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.15,3670.5,No\n3521-MNKLV,Male,0,No,No,24,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.7,1167.8,No\n2533-TIBIX,Male,0,Yes,Yes,15,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.2,469.65,No\n8993-PHFWD,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,58.9,Yes\n1565-RHDJD,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),84.05,333.55,Yes\n7137-RYLPP,Male,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.7,3171.15,Yes\n3765-JXVKY,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes\n5092-STPKP,Female,0,No,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),56.35,1381.2,No\n7330-WZLNC,Female,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.8,455.5,Yes\n0114-PEGZZ,Female,0,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,107.55,3645.5,No\n3359-DSRKA,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,1158.85,No\n8639-NHQEI,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Bank transfer (automatic),95.9,6954.15,No\n7161-DFHUF,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.85,1672.1,No\n3957-LXOLK,Female,1,No,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.15,3152.5,Yes\n3720-DBRWL,Male,0,Yes,No,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.85,4307.1,No\n6635-MYYYZ,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),85.35,2530.4,Yes\n8565-WUXZU,Male,1,Yes,No,72,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),84.8,6141.65,No\n5281-BUZGT,Male,1,No,No,36,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.85,3186.7,Yes\n4994-OBRSZ,Male,0,No,Yes,14,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),76.1,1054.8,No\n0562-FGDCR,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Mailed check,74.55,5430.65,No\n2436-QBZFP,Female,0,Yes,Yes,22,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,39.2,849.9,No\n3420-YJLQT,Female,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,79.55,151.75,No\n6040-CGACY,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,299.4,No\n6582-PLFUU,Male,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,1086.75,No\n8242-PDSGJ,Male,0,Yes,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),39.15,2692.75,No\n0264-CNITK,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1389.6,No\n0089-IIQKO,Female,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.95,3767.4,No\n7839-NUIAA,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,59.8,3641.5,No\n6075-QMNRR,Female,0,No,No,52,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.75,2535.55,No\n5378-IKEEG,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,35.75,35.75,Yes\n5966-EMAZU,Male,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,108.5,6880.85,No\n0440-EKDCF,Male,0,Yes,No,62,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),60.15,3753.2,No\n4774-HHGGS,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.05,637.55,No\n2718-GAXQD,Female,1,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46,181.6,Yes\n2055-BFOCC,Male,1,Yes,No,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),84,5329.55,No\n8180-AKMJV,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.55,44.55,No\n4298-OYIFC,Male,0,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.45,1539.8,No\n5566-SOEZD,Male,0,Yes,Yes,27,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),80.65,2209.75,No\n9842-EFSYY,Female,0,No,Yes,4,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,57.2,223.75,No\n2272-WUSPA,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,110.75,7751.7,No\n4584-LBNMK,Male,1,Yes,No,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.7,1174.35,No\n3898-GUYTS,Female,1,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,97.05,4385.05,No\n0930-EHUZA,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Mailed check,76.35,2606.35,No\n6413-XKKPU,Male,0,Yes,Yes,17,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.4,1539.45,Yes\n9975-SKRNR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.9,18.9,No\n3703-KBKZP,Male,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.45,1261.35,No\n1449-XQEMT,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.8,58.15,Yes\n2626-URJFX,Male,0,Yes,Yes,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.9,225.6,Yes\n4973-MGTON,Female,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),84.4,5969.3,No\n3682-YEUWS,Male,0,Yes,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.4,253.9,No\n1223-UNPKS,Male,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,400,No\n2612-RRIDN,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81,340.85,Yes\n4735-ASGMA,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.35,2515.3,Yes\n3446-QDSZF,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),55.5,227.35,No\n3669-LVWZB,Male,0,No,No,5,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,51,305.95,Yes\n6892-EZDTG,Female,0,Yes,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.65,365.4,Yes\n5117-IFGPS,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.3,2357.75,No\n7379-FNIUJ,Male,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,100.2,198.5,No\n1627-AFWVJ,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,554.25,No\n9725-SCPZG,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.85,90.85,Yes\n5884-GCYMI,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.4,69.4,Yes\n3217-FZDMN,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.45,742.95,Yes\n4486-EFAEB,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.4,251.65,No\n0060-FUALY,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.75,5597.65,No\n7853-GVUDZ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.15,20.15,Yes\n7480-QNVZJ,Male,1,No,No,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.7,4816.7,Yes\n6954-OOYZZ,Male,0,Yes,No,18,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.35,768.05,No\n7088-FBAWU,Female,0,Yes,Yes,17,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Mailed check,74.55,1215.8,No\n8313-AFGBW,Male,0,Yes,No,47,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,73.6,3522.65,No\n3943-KDREE,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.95,1834.95,Yes\n9239-ZBZZV,Female,0,No,No,6,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,47.95,305.1,Yes\n3097-IDVPU,Male,0,Yes,Yes,19,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),50.1,910.45,No\n4398-HSCJH,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,63.6,155.65,Yes\n2197-OMWGI,Female,1,Yes,Yes,68,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,53,3656.25,No\n2303-PJYHN,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.85,52,No\n9795-VOWON,Male,0,No,No,7,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),24.35,150.85,No\n1237-WIYYZ,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,389.25,No\n1987-AUELQ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,1873.7,No\n7852-LECYP,Male,1,Yes,No,13,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),93.8,1261,No\n4430-UZIPO,Male,0,No,No,3,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,36.85,108.7,Yes\n4822-LPTYJ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),103.75,7346.2,No\n8165-CBKXO,Male,0,Yes,Yes,66,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),56.75,3708.4,No\n6527-PZFPV,Male,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.8,469.65,No\n4855-SNKMY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.1,44.1,Yes\n9593-CVZKR,Female,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.45,1385.85,No\n8595-SIZNC,Female,1,Yes,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.6,548.8,No\n8008-HAWED,Male,0,No,No,14,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.75,712.75,Yes\n7124-UGSUR,Female,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.4,6405,Yes\n1862-SKORY,Female,1,Yes,No,40,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,39.3,1637.4,Yes\n5236-XMZJY,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),59.65,2536.55,No\n4827-DPADN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),83.3,6042.7,No\n2694-CIUMO,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),79.55,958.25,No\n5846-ABOBJ,Male,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1730.65,No\n1439-LCGVL,Female,0,Yes,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.2,459.6,No\n0909-SDHNU,Female,0,No,Yes,7,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.8,201.95,No\n4647-XXZAM,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.5,285.2,No\n1502-XFCVR,Female,0,No,No,58,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.45,6145.85,Yes\n7740-KKCXF,Male,0,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),30.05,1529.45,No\n5360-XGYAZ,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.65,4664.5,No\n9692-TUSXH,Female,0,No,No,18,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.05,1740.7,Yes\n7912-SYRQT,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.1,552.95,Yes\n3557-HTYWR,Female,0,No,No,47,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,74.05,3496.3,No\n4816-JBHOV,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.7,93.7,Yes\n8920-NAVAY,Male,1,No,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),110.75,7053.35,No\n1699-TLDLZ,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,301.55,No\n5600-PDUJF,Male,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),49.5,312.7,No\n8292-TYSPY,Male,0,No,No,19,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55,1046.5,Yes\n0567-XRHCU,Female,0,Yes,Yes,69,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),43.95,2960.1,No\n1867-BDVFH,Male,0,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,834.2,Yes\n2067-QYTCF,Female,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.15,6953.4,No\n2359-QWQUL,Female,0,Yes,No,39,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.7,4134.85,Yes\n9103-TCIHJ,Female,0,No,No,15,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,55.7,899.8,Yes\n7407-SUJIZ,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,541.5,No\n9150-KPBJQ,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,116.85,No\n0052-DCKON,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),115.8,7942.15,No\n3654-ARMGP,Female,0,No,No,61,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.65,5321.25,No\n9699-UBQFS,Female,1,Yes,No,43,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.5,4156.8,No\n9367-TCUYN,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,223.6,No\n1261-FWTTE,Male,1,No,No,23,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),34.65,768.45,No\n3528-HFRIQ,Male,1,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),52.3,3765.05,No\n0708-SJDIS,Female,0,No,No,34,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Mailed check,65,2157.5,No\n4140-WJAWW,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.85,108.05,Yes\n2073-QBVBI,Female,0,Yes,No,41,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Mailed check,35.45,1391.65,No\n6928-ONTRW,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1379.8,No\n3320-VEOYC,Male,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,1273.3,No\n5231-FIQPA,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,810.45,No\n6617-WLBQC,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,One year,No,Credit card (automatic),81.85,1810.85,No\n2599-CIPQE,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.3,7782.85,No\n6653-CBBOM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes\n8774-GSBUN,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1797.1,No\n7326-RIGQZ,Male,0,Yes,Yes,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,377.85,No\n1401-FTHFQ,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20,445.3,No\n3247-ZVOUO,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.55,851.75,Yes\n0254-FNMCI,Female,0,No,No,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.9,7624.2,No\n1848-LBZHY,Female,0,Yes,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.3,355.1,No\n6101-IMRMM,Male,0,No,Yes,6,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,94.5,575.45,Yes\n8118-TJAFG,Male,0,Yes,Yes,9,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.5,906.85,No\n5429-LWCMV,Female,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.15,1057.55,No\n7298-IZWLY,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,19.4,No\n9758-MFWGD,Female,1,Yes,Yes,48,No,No phone service,DSL,No,Yes,No,No,No,No,One year,No,Bank transfer (automatic),29.9,1388.75,No\n3955-JBZZM,Male,0,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.8,1641.3,No\n1268-ASBGA,Female,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.35,1375.15,Yes\n8943-URTMR,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,79.65,152.7,Yes\n4815-TUMEQ,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.3,185.2,No\n4713-LZDRV,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.6,195.05,Yes\n9909-IDLEK,Male,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,96.8,1826.7,No\n4092-OFQZS,Male,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,417.5,No\n1561-BWHIN,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,344.5,No\n4325-NFSKC,Male,1,No,No,19,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.6,1660,Yes\n9927-DSWDF,Male,0,Yes,No,22,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.6,2180.55,No\n9500-LTVBP,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),80.05,2835.9,No\n7252-NTGSS,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.15,45.15,No\n8149-AIQCG,Male,0,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.15,2730.85,No\n1360-JYXKQ,Female,1,Yes,No,54,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,99.1,5437.1,No\n2955-PSXOE,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.2,20.2,Yes\n7762-URZQH,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),106.05,6981.35,Yes\n0899-WZRSD,Male,0,No,No,56,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,105.35,5794.45,No\n3255-GRXMG,Male,0,No,Yes,18,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.65,747.2,No\n4828-FAZPK,Female,0,Yes,Yes,16,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),79.95,1267.95,No\n6094-ZIVKX,Female,0,No,No,68,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),54.45,3674.95,No\n6925-BAYGL,Female,1,Yes,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,1275.6,No\n3262-EIDHV,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.7,5893.9,No\n7354-OIJLX,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.85,724.65,No\n1376-HHBDV,Female,0,No,No,30,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.8,1536.75,No\n6907-NZZIJ,Female,0,No,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.15,3615.6,Yes\n6133-OZILE,Female,0,No,No,18,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,35.2,607.3,No\n4135-FRWKJ,Female,1,Yes,Yes,55,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Electronic check,76.25,4154.55,No\n2911-UREFD,Female,0,Yes,No,39,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,55.9,2184.35,Yes\n3744-ZRRDZ,Male,0,No,No,21,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82.35,1852.85,Yes\n5673-TIYIB,Male,0,No,No,2,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),40.4,77.15,Yes\n6551-ZCOTS,Male,1,No,No,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.9,847.8,No\n7191-ADRGF,Male,0,Yes,No,44,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),54.3,2390.45,No\n5018-LXQQG,Female,0,Yes,Yes,30,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),66.3,1923.5,No\n6892-BOGQE,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.9,1493.2,No\n1602-IJQQE,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.35,338.1,Yes\n4628-WQCQQ,Male,0,No,Yes,35,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,85.15,3030.6,Yes\n1746-TGTWV,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),75.35,75.35,No\n5995-SNNEW,Male,1,Yes,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.45,2184.85,No\n8050-WYBND,Female,0,No,Yes,22,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),49.45,1031.4,No\n2821-WARNZ,Female,0,No,Yes,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.45,921.3,No\n4879-GZLFH,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.15,3875.4,No\n1644-IRKSF,Female,0,Yes,Yes,33,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,93.8,3124.5,Yes\n8314-HTWVE,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,144.15,No\n9402-ROUMJ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),100.25,6689,No\n2507-QZPQS,Male,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,No,Electronic check,95.7,1451.1,No\n7159-NOKYQ,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,93.15,6368.2,No\n5707-ZMDJP,Male,0,Yes,Yes,53,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,69.7,3729.6,No\n8779-YIQQA,Male,0,Yes,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,350.1,No\n7136-IHZJA,Female,0,Yes,Yes,40,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,71.35,2847.2,No\n8966-OIQHG,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.75,452.35,No\n7074-STDCN,Male,0,No,No,39,No,No phone service,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,40.6,1494.5,No\n3705-PSNGL,Male,0,No,No,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.4,930.45,Yes\n8739-QOTTN,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,41.85,No\n1399-OUPJN,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1272.05,No\n0277-BKSQP,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.4,475.1,No\n6502-HCJTI,Male,1,Yes,No,7,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.7,673.1,Yes\n2606-RMDHZ,Male,0,No,No,6,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),30.5,208.7,Yes\n5774-XZTQC,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,150.75,No\n2676-SSLTO,Male,0,No,No,49,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),66.15,3199,No\n6266-QHOJZ,Female,0,No,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,89.85,5844.65,No\n7269-JISCY,Female,0,No,No,55,Yes,No,DSL,No,No,No,No,No,No,One year,No,Bank transfer (automatic),45.05,2462.6,No\n0363-SVHYR,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.85,6263.8,No\n8547-NSBBO,Male,0,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,96.75,3403.4,No\n8258-GSTJK,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,77,237.75,Yes\n6861-OKBCE,Female,0,No,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.1,221.35,Yes\n9940-RHLFB,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.3,75.3,Yes\n6591-QGOYB,Male,0,No,No,17,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),106.65,1672.1,No\n9070-BCKQP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.15,7881.2,No\n6421-SZVEM,Female,0,Yes,Yes,28,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,One year,Yes,Bank transfer (automatic),82.85,2320.8,No\n1328-EUZHC,Female,0,Yes,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,370.5,No\n2995-UPRYS,Female,1,Yes,No,40,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),99.2,4062.2,Yes\n8878-RYUKI,Female,0,No,No,52,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,59.45,3043.7,No\n9633-DENPU,Female,0,Yes,No,47,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),58.6,2723.4,No\n7811-JIVPF,Female,0,No,No,23,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),49.7,1081.25,No\n7113-HIPFI,Male,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,65.85,4097.05,No\n8541-QVFKM,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.5,632.2,No\n6686-YPGHK,Male,1,No,No,47,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,85.5,4042.3,Yes\n1383-EZRWL,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,164.85,Yes\n9258-CNWAC,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),113.65,8166.8,No\n5371-VYLSX,Female,1,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,83.4,4113.7,No\n6374-AFWOX,Male,0,Yes,No,46,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,65.65,3047.15,No\n4759-TRPLW,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,70.4,Yes\n5317-FLPJF,Female,0,No,No,66,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),61.35,4193.4,No\n7621-VPNET,Female,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),85.9,3729.75,No\n6034-YMTOB,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,75.65,399.45,No\n8563-IIOXK,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,49.75,331.3,Yes\n4903-CNOZC,Male,0,No,No,29,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),70.9,1964.6,No\n4353-HYOJD,Female,0,Yes,Yes,27,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.85,1336.15,No\n8020-BWHYL,Female,1,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,1147.45,Yes\n7267-FRMJW,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,486.05,No\n2982-VPSGI,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,1078.9,Yes\n4188-FRABG,Male,0,Yes,No,57,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,103.05,5925.75,No\n8199-ZLLSA,Male,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),118.35,7804.15,Yes\n4128-ETESU,Female,1,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.7,4747.2,No\n0620-DLSLK,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,81.9,1028.9,No\n4625-EWPTF,Male,0,No,No,8,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,30.45,226.45,Yes\n5980-NOPLP,Female,0,Yes,No,44,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.1,4364.1,Yes\n3850-OKINF,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,Yes,Electronic check,66.2,4692.55,No\n6892-XPFPU,Male,1,Yes,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.25,2433.9,Yes\n8010-EZLOU,Male,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.2,1217.25,Yes\n1156-ZFYDO,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,19.75,No\n3295-YVUSR,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,72.6,154.3,No\n8016-NCFVO,Male,1,No,No,55,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,116.5,6382.55,No\n4119-ZYPZY,Male,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,106.8,7623.2,No\n5549-ZGHFB,Male,0,Yes,Yes,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.95,1261.45,No\n7577-SWIFR,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.25,89.25,No\n0303-WMMRN,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.25,86.05,No\n6408-OTUBZ,Female,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),104.55,6779.05,No\n5204-HMGYF,Female,0,Yes,Yes,49,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,87.2,4345,No\n1078-TDCRN,Female,1,Yes,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.75,82.85,No\n3727-OVPRY,Male,0,Yes,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.7,1714.55,No\n3797-FKOGQ,Male,0,No,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.2,893.2,No\n7622-NXQZR,Male,0,No,No,28,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),30.1,810.85,No\n6196-HBOBZ,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Electronic check,99.35,6347.55,No\n3970-XGJDU,Female,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.2,1123.65,No\n7017-VFULY,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.1,43.15,No\n5562-YJQGT,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,35.1,No\n8807-OPMBM,Female,0,Yes,Yes,55,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.65,1388,No\n5439-WIKXB,Male,1,Yes,No,41,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.55,3851.45,No\n9874-QLCLH,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,1743.5,Yes\n8294-UIMBA,Female,0,No,No,30,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),94.4,2638.1,No\n8109-YUOHE,Male,0,No,No,17,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,56.1,946.95,No\n5840-NVDCG,Female,0,Yes,Yes,16,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),68.25,1114.85,No\n8092-NLTGF,Male,0,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1777.6,No\n5928-QLDHB,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.25,684.85,No\n9840-EFJQB,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.35,74.35,No\n0696-UKTOX,Male,0,No,Yes,23,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,54.15,1312.45,No\n4801-KFYKL,Male,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.45,159.2,No\n3472-OAOOR,Male,0,Yes,Yes,19,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,34.95,610.2,No\n6135-OZQVA,Female,0,No,No,7,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,53.65,404.35,No\n8062-YBDOE,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes\n2252-JHJGE,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,104,6363.45,No\n4188-PCPIG,Female,0,Yes,No,57,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.35,4124.65,No\n6000-APYLU,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.8,713.1,Yes\n7105-BENQF,Male,0,No,Yes,15,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Mailed check,64.85,950.75,No\n7721-DVEKZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No\n3566-VVORZ,Female,0,Yes,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.9,505.95,No\n9507-HSMMZ,Male,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,1149.65,No\n2480-SQIOB,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.8,169.65,No\n0947-IDHRQ,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.3,526.7,Yes\n7813-TKCVO,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,393.15,No\n0128-MKWSG,Female,0,No,Yes,26,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,45.8,1147,No\n3672-YITQD,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.1,3021.6,Yes\n4350-ZTLPI,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.95,5718.2,No\n9048-JVYVF,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,69.35,191.35,Yes\n0361-HJRDX,Female,0,No,No,68,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),64.35,4539.6,No\n5727-MYATE,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.8,6397.6,No\n3823-KYNQY,Male,0,No,No,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.95,280.4,No\n8988-ECPJR,Female,1,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,79.6,2718.3,Yes\n7570-WELNY,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),84.7,5711.05,No\n3021-VLNRJ,Female,0,No,Yes,50,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.8,3478.15,No\n3776-EKTKM,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,36.45,36.45,Yes\n2080-CAZNM,Female,1,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,4133.95,No\n1028-FFNJK,Male,1,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.5,2917.65,No\n6907-FLBER,Male,0,No,No,1,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.3,54.3,No\n3001-UNBTL,Male,1,Yes,Yes,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.95,2964.8,No\n5982-PSMKW,Female,0,Yes,Yes,23,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),91.1,2198.3,No\n3507-GASNP,Male,0,No,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1189.9,No\n7096-ZNBZI,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26.45,1914.5,No\n1902-XBTFB,Male,0,No,Yes,22,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.4,2001.5,Yes\n1676-MQAOA,Male,0,No,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.1,5336.35,No\n0786-IVLAW,Female,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),108.1,7238.6,No\n7566-DSRLQ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.15,7998.8,No\n0643-OKLRP,Female,1,Yes,No,47,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,80.35,3825.85,Yes\n7245-JMTTQ,Female,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.5,5703.25,No\n6050-IJRHS,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.5,7397,No\n6202-JVYEU,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.9,164.6,No\n8591-TKMZH,Male,0,Yes,Yes,59,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,111.1,6555.2,No\n0734-OXWBT,Male,0,No,Yes,3,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,70.7,225.65,No\n4282-ACRXS,Male,1,Yes,No,38,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.85,955.75,No\n0365-TRTPY,Female,0,No,No,37,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),91.2,3382.3,No\n7349-ALMUX,Male,0,No,No,37,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),65.6,2313.8,No\n9381-NDKME,Female,1,Yes,No,24,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.65,933.3,Yes\n6502-KUGLL,Female,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),59.45,780.85,No\n1841-YSJGV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.95,7852.4,No\n2794-XIMMO,Male,0,Yes,No,53,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,60.45,3184.25,Yes\n8382-SHQEH,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,764.95,Yes\n3511-BFTJW,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),38.5,2763,No\n7668-XCFYV,Female,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.55,1614.7,No\n2983-ZANRP,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),73.55,145.4,Yes\n7845-URHJN,Female,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,156.25,Yes\n3034-ZBEQN,Female,0,Yes,No,48,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,No,Mailed check,34.7,1604.5,Yes\n5018-HEKFO,Female,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.5,270.15,No\n2923-ARZLG,Male,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.7, ,No\n3976-NLDEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,20.6,No\n2282-YGNOR,Female,0,No,No,29,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),58,1734.5,No\n5336-UFNZP,Female,1,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.45,7047.5,No\n6854-EXGSF,Female,0,No,No,8,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),65.5,573.15,No\n1241-EZFMJ,Male,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,1538.6,No\n9233-PSYHO,Female,1,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.15,4459.8,No\n5376-PCKNB,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.45,7459,No\n8044-BGWPI,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21.15,306.05,No\n4060-LDNLU,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,96.2,639.7,No\n3589-PPVKW,Male,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.4,348.15,No\n8327-LZKAS,Female,1,Yes,No,43,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,107.55,4533.9,Yes\n5887-IKKYO,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),94.35,5563.65,No\n1075-BGWOH,Male,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.75,1587.55,Yes\n1755-RMCXH,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,40.25,No\n0302-JOIVN,Female,0,Yes,No,8,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,842.9,Yes\n3858-XHYJO,Female,0,Yes,No,40,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),105.75,4228.55,No\n4299-SIMNS,Male,0,No,No,9,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,81.15,784.45,No\n7025-IWFHT,Male,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,89.55,3729.75,No\n6261-LHRTG,Female,0,No,No,26,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),54.75,1406.9,No\n7841-FCRQD,Female,0,Yes,No,33,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),53.75,1857.3,No\n2056-EVGZL,Male,0,Yes,Yes,68,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),105.75,7322.5,No\n8777-MBMTS,Female,1,Yes,No,65,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),105.85,6725.5,No\n7753-USQYQ,Male,0,No,No,55,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Electronic check,64.2,3627.3,No\n5366-IJEQJ,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.7,1761.45,Yes\n7661-CPURM,Male,0,No,No,19,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),87.7,1725.95,No\n6233-HXJMX,Female,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),89.3,4192.15,No\n5902-WBLSE,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.15,1411.2,No\n1981-INRFU,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.75,164.5,Yes\n4971-PUYQO,Female,0,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,94.55,2724.6,Yes\n3239-TPHPZ,Female,0,Yes,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,20.05,264.55,No\n5115-GZDEL,Male,0,No,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),67.2,4671.7,No\n3338-CVVEH,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.55,1173.55,No\n8485-GJCDN,Female,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.05,318.5,Yes\n2615-YVMYX,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,107.5,7713.55,No\n2851-STERV,Male,1,No,No,35,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,73,2471.25,No\n4393-GEADV,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.75,7842.3,No\n8486-AYEQH,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,2227.8,No\n1527-SXDPN,Male,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),96.25,4990.25,Yes\n6029-WTIPC,Male,1,No,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.1,3744.05,Yes\n8634-CILSZ,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.7,7220.35,Yes\n5419-JKZNQ,Male,1,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,77.9,2351.45,No\n2495-KZNFB,Female,0,No,No,33,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.65,2989.6,No\n1409-PHXTF,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.45,6077.75,No\n4560-WQAQW,Female,0,No,No,59,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,No,Bank transfer (automatic),68.7,4070.95,No\n9591-YVTEB,Male,1,No,No,55,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,44.85,2479.05,No\n9309-BZGNT,Male,1,Yes,No,69,No,No phone service,DSL,No,No,Yes,No,No,No,One year,Yes,Credit card (automatic),29.8,2134.3,No\n4274-DRSQT,Female,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),88.9,6000.1,No\n2027-DNKIV,Male,0,Yes,Yes,37,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Mailed check,58.75,2203.1,No\n8075-GXIUB,Male,1,Yes,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.85,183.15,No\n2885-HIJDH,Male,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.9,6194.1,No\n5424-RLQLC,Male,0,No,No,10,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,59.65,638.95,No\n6682-QJDGB,Male,0,No,Yes,40,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,55.25,2139.2,Yes\n6507-DTJZV,Male,0,No,Yes,13,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),66.4,831.75,No\n8780-IXSTS,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.1,521.3,Yes\n7673-BQGKU,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,1337.5,No\n6723-WSNTY,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),108.1,7181.95,No\n9530-EHPOH,Male,0,No,No,11,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,53.75,608,Yes\n2725-IWWBA,Male,0,Yes,Yes,46,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Mailed check,56.9,2560.1,No\n0345-HKJVM,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.3,577.6,Yes\n1061-PNTHC,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,109.6,5953,No\n0394-YONDK,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,1790.15,No\n6574-MCOEH,Female,0,Yes,Yes,33,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,79.15,2531.4,No\n7399-QHBJS,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),66.75,4760.3,No\n3049-SOLAY,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.2,292.85,Yes\n0997-YTLNY,Female,0,No,Yes,19,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,48.8,953.65,No\n3317-HRTNN,Female,1,No,No,5,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,45.7,198,Yes\n9479-HYNYL,Female,0,Yes,No,71,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),80.7,5705.05,No\n3235-ETOOB,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,609.9,Yes\n9708-KFDBY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.55,20.55,No\n8058-INTPH,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.65,79.65,Yes\n3642-GKTCT,Female,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,115.1,6993.65,No\n0774-RMNUW,Female,0,Yes,Yes,71,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.7,4122.65,No\n1334-PDUKM,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Credit card (automatic),86.45,5762.95,No\n0756-MPZRL,Male,0,No,No,46,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),33.7,1537.85,No\n2242-MFOTG,Male,0,No,No,33,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,One year,No,Bank transfer (automatic),80.1,2603.3,No\n2927-CVULT,Female,0,Yes,Yes,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),104.05,5566.4,No\n2144-BFDSO,Female,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),108.75,5431.9,No\n8745-PVESG,Female,0,No,No,57,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),41.1,2258.25,No\n7647-GYYKX,Female,0,Yes,Yes,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.35,1092.35,No\n5647-FXOTP,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,6401.25,No\n5569-OUICF,Female,1,Yes,No,28,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.3,2812.2,Yes\n8821-XNHVZ,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.05,80.05,Yes\n2082-OJVTK,Male,0,Yes,Yes,29,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.2,2698.35,Yes\n9700-ISPUP,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,65.5,616.9,No\n9839-ETQOE,Male,0,No,Yes,43,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,40.45,1912.85,No\n6078-VESFR,Male,1,Yes,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.45,849.1,No\n9027-TMATR,Female,0,Yes,No,43,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,78.8,3460.3,No\n5940-NFXKV,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.65,1465.75,Yes\n1941-HOSAM,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.1,90.1,No\n9110-HSGTV,Female,0,No,No,69,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82.45,5555.3,No\n0704-VCUMB,Female,0,Yes,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1278.8,No\n6171-ZTVYB,Male,0,Yes,No,43,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),66.25,2907.35,No\n8053-WWDRO,Female,0,Yes,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,146.3,Yes\n9564-KCLHR,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,51.25,51.25,Yes\n1935-IMVBB,Male,0,Yes,No,56,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,89.7,4952.95,No\n2535-PBCGC,Female,0,Yes,No,70,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),64.55,4504.9,No\n2082-CEFLT,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.6,45.6,Yes\n1470-PSXNM,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,93.65,4520.15,No\n1213-NGCUN,Female,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),49.65,267.35,Yes\n2498-XLDZR,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Mailed check,73.6,2316.85,No\n9866-OCCKE,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,109.75,8075.35,No\n5338-YHWYT,Male,0,No,Yes,37,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),61.45,2302.35,No\n7718-UPSKJ,Female,0,Yes,No,69,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106.4,7251.9,No\n8731-WBBMB,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81.9,2078.55,No\n1448-CYWKC,Female,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.2,6225.4,No\n7901-IIDQV,Male,0,No,No,24,Yes,Yes,DSL,No,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.6,1242.25,No\n2690-DVRVK,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.55,99.45,No\n5688-KZTSN,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20,288.05,Yes\n1270-XKUCC,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,599.25,No\n0334-ZFJSR,Female,0,Yes,No,55,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),66.05,3462.1,No\n2894-QOJRX,Female,0,Yes,No,25,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),34,853,Yes\n5747-PMBSQ,Male,1,Yes,No,10,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,92.5,934.1,Yes\n5583-EJXRD,Male,0,Yes,Yes,44,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),54.05,2375.2,No\n4565-EVZMJ,Female,0,No,No,47,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,58.9,2813.05,No\n3143-JQEGI,Female,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,88.35,1222.8,Yes\n6386-SZZKH,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.95,5293.2,Yes\n3327-YBAKM,Female,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,96.9,6314.35,No\n9441-QHEVC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.1,19.1,No\n6705-LNMDD,Male,0,No,No,20,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50,1003.05,No\n2580-ASVVY,Female,0,Yes,No,37,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Electronic check,45.4,1593.1,No\n3370-GQEAL,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,85.45,2509.95,No\n5032-USPKF,Female,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),84.1,3187.65,No\n2982-IHMFT,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.45,74.45,Yes\n3521-SYVOR,Female,0,No,No,37,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,64.75,2345.2,Yes\n4254-QPEDE,Female,0,Yes,No,52,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),66.25,3330.1,No\n6283-GITPX,Male,0,No,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),76.9,5522.7,No\n9526-JAWYF,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,89.8,2335.3,Yes\n0771-CHWSK,Male,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),74.6,4798.4,No\n9788-HNGUT,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.95,8594.4,No\n9495-REDIY,Male,0,No,Yes,25,No,No phone service,DSL,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),40.65,970.55,No\n5375-XLDOF,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.35,7665.8,No\n1172-VIYBP,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Two year,No,Bank transfer (automatic),69.7,3686.05,No\n5649-VUKMC,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Mailed check,95.5,1115.15,Yes\n6559-PDZLR,Male,0,No,No,26,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.65,2537,No\n8992-OBVDG,Male,0,No,No,21,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,61.65,1393.6,No\n4273-MBHYA,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.35,89.35,No\n4724-WXVWF,Male,0,No,No,48,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,95.4,4445.3,No\n5701-GUXDC,Female,0,Yes,No,26,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),35.4,978.6,No\n1460-UZPRJ,Male,0,Yes,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1258.15,No\n6082-GLJIX,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,331.35,No\n5143-EGQFK,Female,1,No,No,10,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.65,291.4,Yes\n4919-IKATY,Male,0,Yes,Yes,5,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.5,453.75,Yes\n2495-TTHBQ,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.4,84.75,No\n0485-ZBSLN,Male,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,1715.1,No\n3810-PJUHR,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.35,1715.15,No\n9050-QLROH,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.7,1597.25,Yes\n0847-HGRML,Male,0,No,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20,1250.1,No\n8232-CTLKO,Female,0,Yes,Yes,66,Yes,No,DSL,Yes,No,No,No,Yes,No,Two year,Yes,Electronic check,59.75,3996.8,No\n9227-YBAXE,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),82.5,5215.1,No\n6168-WFVVF,Female,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,235.5,Yes\n9860-LISIZ,Female,0,No,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,673.2,No\n0112-QAWRZ,Male,0,Yes,Yes,16,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.8,1442.2,No\n0877-SDMBN,Female,0,No,No,54,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.95,5639.05,Yes\n2786-GCDPI,Female,1,No,No,50,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.95,5222.35,No\n4043-MKDTV,Male,0,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,105.25,7291.75,No\n8065-YKXKD,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,799.65,Yes\n9637-EIHEQ,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,50.8,50.8,Yes\n9229-RQABD,Male,0,No,No,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,23.75,424.5,No\n8313-KTIHG,Male,0,No,No,4,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,61.3,249.4,No\n5320-BRKGK,Female,0,Yes,Yes,58,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,75.8,4415.75,No\n6284-KMNUF,Female,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,98,5270.6,No\n9689-PTNPG,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.25,144.55,Yes\n9465-RWMXL,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.9,2447.95,Yes\n6229-UOLQL,Male,0,Yes,Yes,56,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Mailed check,52,2884.9,No\n2362-IBOOY,Male,0,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.75,3050.15,Yes\n3137-LUPIX,Female,0,No,No,4,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,64.4,253,No\n0843-WTBXE,Male,0,No,No,53,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),85.45,4517.25,Yes\n6143-JQKEA,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.8,436.2,No\n8676-OOQEJ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,30.5,118.4,No\n5515-IDEJJ,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,Yes\n9701-CDXHR,Female,0,Yes,No,51,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.15,3649.6,No\n7450-NWRTR,Male,1,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.45,1200.15,Yes\n8124-NZVGJ,Female,0,No,No,6,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,49.25,255.6,No\n2162-FRZAA,Male,0,Yes,Yes,63,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),39.35,2395.05,No\n5376-DEQCP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.6,70.6,Yes\n5118-MUEYH,Female,0,Yes,No,48,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.1,5083.55,No\n9095-HFAFX,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),81,389.6,Yes\n5627-TVBPP,Female,0,No,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.1,644.5,No\n2379-ENZGV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,523.5,Yes\n3936-QQFLL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,39.3,No\n2589-AYCRP,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.75,989.05,No\n4067-HLYQI,Female,0,No,No,33,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.4,2406.1,No\n5124-EOGYE,Male,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,638.55,No\n5057-RKGLH,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,191.1,No\n0292-WEGCH,Female,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,86.2,4524.05,No\n8910-ICHIU,Female,0,No,No,46,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),95.65,4664.2,No\n4097-YODCF,Male,0,No,Yes,34,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,103.8,3470.8,No\n9715-WZCLW,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Electronic check,97.2,6910.3,No\n9786-YWNHU,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,63.55,4014.2,No\n6407-GSJNL,Female,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.95,1288,No\n6005-OBZPH,Female,1,No,No,26,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.15,2277.65,Yes\n4049-ZPALD,Female,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),99,6375.8,No\n7932-WPTDS,Female,1,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.8,24.8,Yes\n3733-UOCWF,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),85.55,5251.75,No\n6833-JMZYP,Female,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94,1505.45,No\n2722-VOJQL,Male,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.65,6903.1,Yes\n1310-QRITU,Female,0,No,No,18,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.3,913.3,No\n8961-QDZZJ,Female,0,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,95,5535.8,No\n9715-SBVSU,Male,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Two year,Yes,Bank transfer (automatic),61.4,815.55,No\n8490-BXHEO,Male,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.55,1411.65,No\n7173-TETGO,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),78.5,5602.25,No\n7929-SKFGK,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.3,8244.3,No\n2300-RQGOI,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,741.5,No\n3563-SVYLG,Male,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),62.65,4375.8,No\n5228-EXCET,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.85,1008.7,Yes\n6435-VWCCY,Male,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),92.7,5968.4,No\n4998-IKFSE,Female,0,No,No,30,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.45,3096.9,No\n8630-QSGXK,Male,0,Yes,No,51,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),75.2,3901.25,No\n2455-USLMV,Female,0,No,No,31,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.75,2613.4,No\n7394-FKDNK,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.45,853.1,Yes\n9488-FVZCC,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),79.5,5661.7,No\n6331-LWDTQ,Male,0,No,No,10,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),72.15,794.25,Yes\n5995-WWKKG,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.8,695.05,No\n1597-FZREH,Female,0,No,No,2,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,76.4,160.8,Yes\n7879-CGSFV,Male,0,No,No,55,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,100.9,5552.05,No\n9921-EZKBY,Male,0,No,Yes,33,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.3,3275.15,No\n7432-FFVAR,Female,0,Yes,Yes,46,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),90.95,4236.6,No\n7246-ZGQDF,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.5,54.5,No\n7000-WCEVQ,Female,1,No,No,20,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,61.6,1174.35,Yes\n0727-IWKVK,Male,0,Yes,No,9,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.9,741.7,Yes\n5959-BELXA,Male,1,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),96.15,3019.25,Yes\n4456-RHSNB,Female,0,Yes,Yes,19,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.6,962.9,No\n3512-IZIKN,Female,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),65.3,4759.75,Yes\n2481-SBOYW,Female,0,No,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25,1498.35,No\n7109-MFBYV,Male,0,No,No,26,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.45,1233.15,No\n1833-TCXKK,Male,0,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.75,4882.8,No\n1832-PEUTS,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),89.1,5411.65,No\n6141-OOXUQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,Yes\n8660-BUETV,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.75,148.05,No\n0580-PIQHM,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),101.6,3930.55,No\n5696-CEIQJ,Male,0,Yes,Yes,67,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),103.15,6895.5,No\n7503-ZGUZJ,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.65,84.65,Yes\n1696-HXOWK,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Mailed check,95.65,6856.95,No\n3026-ATZYV,Female,0,Yes,Yes,37,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),75.1,2658.8,No\n4012-YCFAI,Male,0,Yes,No,60,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,61.35,3766.2,No\n2506-TNFCO,Female,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,69.55,Yes\n9970-QBCDA,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,129.55,No\n8718-PTMEZ,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,31.05,347.25,Yes\n9496-IVVRP,Female,0,Yes,Yes,11,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),51,581.7,No\n2207-OBZNX,Male,0,No,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,51,354.05,Yes\n2657-VPXTA,Female,0,Yes,Yes,10,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.85,929.45,No\n6551-VLJMV,Male,0,Yes,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,679,No\n1221-GHZEP,Female,0,No,No,62,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Two year,Yes,Mailed check,65.1,3846.75,No\n9289-LBQVU,Male,0,Yes,No,64,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,70.15,4480.7,No\n6142-VSJQO,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.35,44.35,Yes\n3458-IDMFK,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.75,499.4,No\n6933-FHBZC,Female,0,No,No,26,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,Yes,Mailed check,56.05,1553.2,No\n0221-NAUXK,Male,0,No,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.95,219.5,No\n9770-KXGQU,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,98.6,5311.85,No\n6437-UKHMV,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.7,586.05,Yes\n6538-POCHL,Male,0,No,No,33,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),79,2576.8,No\n6726-NNFWD,Female,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,No,Credit card (automatic),89.45,6435.25,No\n3002-WQZWT,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.2,1993.25,No\n4277-UDIEF,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81,1923.85,No\n1208-NBVFH,Male,0,Yes,Yes,20,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.6,939.8,No\n4912-PIGUY,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.6,84.6,No\n1114-CENIM,Male,0,No,Yes,54,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55,3092.65,Yes\n6060-DRTNL,Female,1,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,84.85,415.55,Yes\n2725-TTRIQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.2,5986.55,No\n3374-TTZTK,Male,0,Yes,No,52,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,106.3,5487,No\n8990-ZXLSU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.05,651.5,No\n2275-RBYQS,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.4,45.4,No\n8473-VUVJN,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.65,73.65,Yes\n6289-CPNLD,Male,0,Yes,Yes,33,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,73.9,2405.05,Yes\n5536-SLHPM,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,77.75,4458.15,Yes\n4419-UJMUS,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,99.35,6856.45,No\n7794-JASDG,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.75,50.75,No\n7609-YBPXG,Male,0,No,No,54,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),87.1,4735.2,No\n5519-TEEUH,Male,0,No,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.15,682.15,No\n7856-GANIL,Male,1,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),98.7,4525.8,No\n0804-XBFBV,Female,0,No,Yes,11,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.2,321.05,No\n2676-OXPPQ,Male,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.7,335.65,No\n3703-TTEPD,Male,0,No,No,21,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),65.35,1424.4,No\n2799-TSLAG,Female,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.3,1748.55,No\n0196-VULGZ,Female,1,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.35,474.9,Yes\n8837-VVWLQ,Female,0,No,No,8,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.95,668.4,Yes\n2696-NARTR,Male,0,No,No,11,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,73.85,926.25,Yes\n2208-NKVVH,Male,0,Yes,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.25,1077.95,No\n7614-QVWQL,Male,0,Yes,Yes,49,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),51.8,2541.25,Yes\n1976-CFOCS,Female,1,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46,46,Yes\n4631-OACRM,Male,1,No,No,15,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.4,1156.1,Yes\n7139-JZFVG,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),60.5,3694.45,No\n4987-GQWPO,Male,0,No,No,17,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25.1,382.8,No\n3755-JBMNH,Male,1,Yes,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),71.8,1167.8,Yes\n1757-TCATG,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.05,746.75,No\n6345-ULYRW,Male,1,Yes,No,44,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,No,Mailed check,88.4,3912.9,Yes\n4776-XSKYQ,Female,0,Yes,Yes,12,No,No phone service,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),30.25,368.85,No\n8048-DSDFQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No\n5753-QQWPW,Female,0,No,No,28,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,59.9,1654.7,No\n6010-DDPPW,Male,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.15,1940.85,No\n1809-DMJHQ,Female,0,No,Yes,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46,221.7,Yes\n6693-FRIRW,Male,0,No,No,18,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),101.3,1794.65,No\n6586-MYGKD,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),76.95,5289.8,No\n3173-NVMPX,Female,0,Yes,Yes,9,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,55.3,501.2,No\n0248-PGHBZ,Female,1,No,No,67,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),92.45,6140.85,No\n0623-GDISB,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),48.45,48.45,No\n2892-GESUL,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,309.25,No\n6923-EFPNL,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.75,201.1,Yes\n7472-EQOAV,Male,1,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),86.7,6179.35,No\n9574-RKJIF,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.4,2838.7,Yes\n2043-WVTQJ,Male,0,Yes,No,1,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,55.7,55.7,No\n8034-RYTVV,Female,0,No,No,55,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),84.25,4589.85,No\n3863-QSTYI,Male,0,No,No,59,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,64.65,3735.45,No\n2619-WFQWU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,70.15,Yes\n5044-LRQAQ,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.2,477.55,No\n1716-LSAMB,Male,0,Yes,Yes,45,Yes,No,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),54.65,2553.7,No\n1333-PBMXB,Female,0,Yes,Yes,54,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.75,1342.15,No\n6546-OPBBH,Male,0,Yes,Yes,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,23.95,1216.35,No\n5985-BEHZK,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),105,7578.05,No\n9127-QRZMH,Male,0,Yes,No,44,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),59.85,2603.95,No\n5919-VCZYM,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,42.7,No\n9644-KVCNC,Female,0,No,No,66,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),92.15,6056.9,No\n2137-DQMEV,Male,0,Yes,Yes,68,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,No,Mailed check,44.8,2983.65,No\n8174-LNWMW,Female,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.9,689.35,No\n9279-CJEOJ,Female,1,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.4,2025.1,No\n7964-VEXDG,Male,0,No,Yes,21,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,80.35,1747.2,No\n2404-JIBFC,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.1,4657.95,No\n0778-NELLA,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),34.7,296.1,Yes\n8029-XYPWT,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.05,8016.6,No\n4614-NUVZD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.1,81.1,Yes\n4632-PAOYU,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,433.5,No\n3803-KMQFW,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.55,20.55,Yes\n6804-GDMOI,Female,0,No,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,106.6,6428.4,Yes\n2990-OGYTD,Female,0,Yes,No,67,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.15,5883.85,No\n6646-JPPHA,Female,1,No,No,14,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,78.85,1043.8,No\n9572-MTILT,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,106.75,6252.9,Yes\n7658-UYUQS,Male,1,Yes,No,21,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),86.55,1857.25,No\n7880-XSOJX,Male,0,No,No,4,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,42.4,146.4,No\n9611-CTWIH,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.45,240.45,Yes\n4589-IUAJB,Male,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.25,1724.15,No\n0329-GTIAJ,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,97.9,315.3,Yes\n8746-BFOAJ,Male,1,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,429.55,No\n8457-XIGKN,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.6,356.15,No\n6072-NUQCB,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.25,488.25,No\n6629-CZTTH,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,55.7,55.7,Yes\n8838-GPHZP,Female,0,No,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.6,1298.7,No\n8750-QWZAJ,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,1378.75,No\n5364-EVNIB,Male,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.8,973.45,Yes\n6918-UMQCG,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,80.2,384.25,No\n0675-NCDYU,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.4,8543.25,No\n6339-DKLMK,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,31.65,389.95,No\n1346-PJWTK,Male,0,Yes,No,61,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),94.15,5731.85,No\n5088-QZLRL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.65,20.65,No\n8023-QHAIO,Female,1,Yes,No,56,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),76.85,4275.75,No\n4397-FRLTA,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,84.5,No\n3057-VJJQE,Male,0,No,No,35,Yes,Yes,DSL,Yes,No,No,No,No,No,Two year,No,Mailed check,55.25,1924.1,No\n5087-SUURX,Female,0,Yes,No,18,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.05,669.85,Yes\n2302-OUZXB,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),82.15,5784.3,No\n8133-ANHHJ,Female,1,No,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),103,5166.2,No\n2270-CHBFN,Female,0,Yes,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),95.1,4060.55,No\n0013-EXCHZ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,83.9,267.4,Yes\n5982-FPVQN,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.15,3532.85,No\n9107-UKCKY,Male,0,Yes,No,61,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,79.8,4914.8,No\n4654-ULTTN,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),74.8,5315.8,No\n5550-VFRLC,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,69.85,Yes\n5546-BYZSM,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.45,775.6,No\n1492-KGETH,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),78.35,5445.95,No\n4929-BSTRX,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,53.55,53.55,Yes\n4163-HFTUK,Male,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.1,1007.8,No\n8215-NGSPE,Female,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,833.55,No\n2225-ZRGSG,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),93.9,6579.05,Yes\n8859-YSTWS,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.95,1004.5,No\n5271-YNWVR,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.15,7856,Yes\n7601-DHFWZ,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24,1183.05,No\n2657-ALMWY,Female,1,Yes,No,26,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,84.95,2169.75,Yes\n6330-JKLPC,Male,0,Yes,No,11,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.5,896.9,Yes\n4667-OHGKG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,19.3,Yes\n1998-VHJHK,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.15,501.35,No\n1707-HABPF,Female,1,No,No,46,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),91.3,4126.35,No\n1660-HSOOQ,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.65,49.65,Yes\n7253-UVNDW,Female,0,No,No,46,Yes,No,DSL,No,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),54.35,2460.15,Yes\n0487-VVUVK,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.15,477.6,No\n7228-OMTPN,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.45,370.65,Yes\n5063-IUOKK,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,265.75,No\n4508-OEBEY,Male,0,Yes,No,31,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),75.5,2424.45,No\n0027-KWYKW,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.75,1849.95,No\n2308-STERM,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,61.05,No\n5982-XMDEX,Female,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.5,1698.55,No\n6284-AHOOQ,Male,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.5,1910.6,Yes\n6051-PTVNS,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.15,998.1,No\n2344-JMOGN,Male,0,Yes,No,9,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,94.85,890.6,Yes\n3799-ISUZQ,Male,0,Yes,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.95,529.5,Yes\n2877-VDUER,Female,0,Yes,Yes,35,No,No phone service,DSL,No,No,No,Yes,Yes,No,One year,No,Mailed check,40.9,1383.6,No\n9152-AMKAK,Male,0,No,No,6,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,80.25,493.4,No\n1794-HBQTJ,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.6,48.6,Yes\n9432-VOFYX,Male,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.8,1207,No\n8049-WJCLQ,Male,0,Yes,Yes,10,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,60.2,563.5,No\n6586-PSJOX,Male,0,No,Yes,15,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),55.2,864.55,No\n2460-FPSYH,Female,1,No,No,40,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,55.8,2109.35,Yes\n9705-ZJBCG,Female,0,Yes,Yes,13,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.15,701.05,No\n4818-DRBQT,Male,0,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.15,2265.25,Yes\n1320-HTRDR,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,220.6,Yes\n6847-KJLTS,Female,1,Yes,No,58,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.4,5749.8,No\n9670-BPNXF,Female,0,No,No,45,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),62.55,2796.45,No\n3913-FCUUW,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),70.45,5165.7,No\n3301-VKTGC,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),85.5,5696.6,No\n1493-AMTIE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.2,20.2,Yes\n9555-SAHUZ,Female,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.5,2076.05,No\n0816-TSPHQ,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,44.2,No\n9932-WBWIK,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,215.25,No\n4619-EVPHY,Female,1,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),91,1859.5,No\n5286-YHCVC,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.8,7470.1,No\n6424-ELEYH,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.75,229.5,Yes\n4391-RESHN,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,104.05,2470.1,Yes\n9115-YQHGA,Male,0,No,No,40,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,51.1,2092.9,No\n4462-CYWMH,Male,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.8,5629.55,No\n8963-MQVYN,Female,0,No,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.55,469.85,No\n2458-EOMRE,Female,0,No,No,11,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),64.05,733.95,No\n9334-GWGOW,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,485.25,No\n6821-BUXUX,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,96.65,1244.5,Yes\n5028-HTLJB,Male,1,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,Yes\n9801-GDWGV,Female,0,No,No,39,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.45,3994.45,Yes\n6542-LWGXJ,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25,78.25,No\n5567-GZKQY,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1131.5,No\n1222-LRYKO,Male,0,No,Yes,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,26.35,184.05,No\n2320-JRSDE,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.9,19.9,Yes\n2087-QAREY,Female,0,Yes,No,22,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,54.7,1178.75,No\n0601-WZHJF,Male,0,Yes,No,14,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,46.35,667.7,Yes\n4423-JWZJN,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),90.25,5629.15,No\n5143-WMWOG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.95,19.95,Yes\n6490-FGZAT,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,109.3,No\n5393-RXQSZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,79.6,79.6,Yes\n7452-FOLON,Male,0,No,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.45,958.45,No\n2320-TZRRH,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,403.15,No\n0231-LXVAP,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,75.9,75.9,Yes\n9444-JTXHZ,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.2,76.2,Yes\n4942-VZZOM,Male,0,Yes,No,64,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),66.15,4392.5,No\n5510-BOIUJ,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,19.25,Yes\n7502-BNYGS,Female,0,Yes,No,46,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),69.1,3168,No\n4291-HYEBC,Female,1,Yes,Yes,28,No,No phone service,DSL,No,Yes,No,No,No,Yes,One year,Yes,Electronic check,39.1,1096.6,No\n6147-CBCRA,Female,0,Yes,No,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,669.45,No\n6047-SUHPR,Male,0,Yes,Yes,39,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Electronic check,59.8,2343.85,No\n4471-KXAUH,Female,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,84.3,3588.4,Yes\n9752-ZNQUT,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,48.6,48.6,No\n7638-QVMVY,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79,522.95,Yes\n1576-PFZIW,Male,1,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),105.35,7511.9,No\n5666-MBJPT,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.1,1725,No\n7312-XSBAT,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.75,49.75,No\n3096-GKWEB,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,1691.9,No\n2371-JQHZZ,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93,2248.05,No\n0674-GCDXG,Male,0,No,No,63,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),71.9,4479.2,No\n1121-QSIVB,Female,0,No,Yes,44,Yes,Yes,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,77.55,3471.1,No\n4396-KLSEH,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.85,63,No\n3244-DCJWY,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,70.25,Yes\n2824-MYYBN,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.25,3314.15,No\n0875-CABNR,Female,1,No,No,10,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.6,865.55,Yes\n6345-HOVES,Male,0,No,No,34,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.05,852.7,No\n8318-LCNBW,Male,0,Yes,No,35,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),53.15,1930.9,No\n6469-QJKZW,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.15,91.4,No\n0147-ESWWR,Female,1,Yes,No,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,3949.15,No\n1217-VASWC,Male,1,Yes,No,43,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.55,4304,No\n7812-FZHPE,Female,0,Yes,Yes,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.1,409.9,Yes\n6370-ZVHDV,Female,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.3,1554.9,No\n5915-DGNVC,Female,0,Yes,No,49,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,71.8,3472.05,No\n6260-XLACS,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,117.8,No\n3566-CAAYU,Female,0,Yes,Yes,64,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,Two year,No,Electronic check,49.85,3210.35,No\n4983-CCWMC,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.6,207.4,No\n9103-CXVOK,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,19.75,No\n2896-TBNBE,Male,0,Yes,No,40,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,80.8,3132.75,No\n4797-AXPXK,Female,0,No,Yes,1,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,60,60,Yes\n5229-PRWKT,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.55,649.65,Yes\n6982-UQZLY,Female,1,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.85,20.85,Yes\n2522-WLNSF,Female,1,Yes,No,34,Yes,No,DSL,No,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),64.2,2106.3,No\n3841-CONLJ,Female,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,35,35,No\n5057-LCOUI,Female,0,No,No,39,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.75,2011.4,Yes\n0193-ESZXP,Female,1,Yes,No,58,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.5,6205.5,Yes\n4475-NVTLU,Male,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.2,903.7,No\n4369-HTUIF,Male,1,No,No,6,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.15,503.6,Yes\n9386-LDCZR,Male,0,No,No,43,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),90.65,3882.3,No\n9585-KKMFD,Male,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20,879.8,No\n5399-ZIMKF,Male,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.65,383.65,No\n0336-KXKFK,Male,0,No,No,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),61.2,4390.25,No\n5619-XZZKR,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,68.2,No\n3948-FVVRP,Male,0,Yes,Yes,9,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,54.8,452.8,No\n5327-XOKKY,Male,1,Yes,No,72,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),73.45,5329,No\n0983-TATYJ,Female,0,Yes,No,33,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Mailed check,51.45,1758.9,No\n1813-JYWTO,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),80.45,5737.6,No\n0156-FVPTA,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.2,1152.7,Yes\n1984-FCOWB,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.5,7674.55,Yes\n0654-HMSHN,Male,0,Yes,Yes,21,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,104.4,2157.95,Yes\n6719-OXYBR,Male,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,85.3,1219.85,No\n3312-ZWLGF,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,79.3,2414.55,No\n6476-YHMGA,Female,0,Yes,Yes,15,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),76.5,1155.6,No\n5287-QWLKY,Male,1,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.1,7548.1,Yes\n0795-XCCTE,Male,1,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.4,1809.35,No\n0168-XZKBB,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.85,1564.4,No\n3785-NRHYR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No\n5196-SGOAK,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.7,75.7,Yes\n4537-DKTAL,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.55,84.4,No\n7072-MBHEV,Female,1,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),78.1,864.85,No\n5602-BVFMK,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,228.75,No\n6297-NOOPG,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.5,7752.05,No\n1891-UAWWU,Female,1,Yes,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,90.8,1951,Yes\n8204-TIFGJ,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.3,470.6,No\n8050-DVOJX,Male,1,No,No,49,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),81.35,4060.9,No\n6108-OQZDQ,Female,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.95,384.5,Yes\n0363-QJVFX,Male,0,No,No,32,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.15,3432.9,Yes\n8903-XEBGX,Male,0,No,Yes,2,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.3,108.65,No\n8169-SAEJD,Male,1,Yes,No,69,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,Two year,No,Credit card (automatic),56.55,3952.65,No\n9840-DVNDC,Male,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80.5,463.05,Yes\n1089-XZWHH,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,494.05,No\n2325-WINES,Female,0,No,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.05,3416.85,No\n6064-ZATLR,Female,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),52.85,1498.65,No\n3096-JRDSO,Female,1,Yes,No,27,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.3,2867.75,Yes\n7824-PANSQ,Male,0,No,No,58,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),80.65,4807.35,No\n8042-JVNFH,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.35,71.35,Yes\n9648-BCHKM,Female,0,Yes,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.65,471.35,No\n2888-ADFAO,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),21.3,1041.8,No\n8707-HOEDG,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.2,7689.8,No\n3374-LXDEV,Female,0,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,89.4,1132.35,Yes\n4072-IPYLT,Female,0,Yes,Yes,36,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.05,1815,No\n7167-PCEYD,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.8,1311.3,No\n4936-YPJNK,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,199.45,No\n1580-BMCMR,Male,1,No,No,19,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,87.3,1637.3,No\n4817-KEQSP,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1326.35,No\n6408-WHTEF,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,89.4,6376.55,No\n9251-AWQGT,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,935.9,No\n8749-CLJXC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No\n7989-VCQOH,Male,0,Yes,Yes,18,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,83.25,1611.15,No\n5049-GLYVG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,20.6,Yes\n4199-QHJNM,Male,1,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),102.9,6989.7,No\n8882-TLVRW,Male,0,Yes,Yes,69,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,39.1,2779.5,No\n5883-GTGVD,Male,0,No,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.95,1931.75,Yes\n5135-GRQJV,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,114.5,8331.95,No\n2384-OVPSA,Female,1,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,735.9,No\n1371-WEPDS,Male,1,Yes,No,40,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Electronic check,55.8,2283.3,No\n3580-GICBM,Female,0,Yes,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.2,1445.2,No\n4817-QRJSX,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),81,818.05,Yes\n9730-DRTMJ,Male,0,Yes,No,32,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,One year,Yes,Credit card (automatic),72.8,2333.05,No\n4686-UXDML,Female,0,No,No,21,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.85,1992.55,No\n3349-ANQNH,Female,1,No,No,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.5,5890,No\n2398-YPMUR,Female,1,Yes,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,916.75,Yes\n0932-YIXYU,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1029.8,No\n7480-SPLEF,Male,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26,1796.55,No\n7636-XUHWW,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.9,33.7,No\n1431-CYWMH,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.05,454.05,No\n5937-EORGB,Male,1,Yes,No,15,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,96.5,1392.25,No\n6349-JDHQP,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1049.6,No\n5539-HIVAK,Female,1,Yes,No,28,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.7,734.6,No\n5847-MXBEO,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,475.1,No\n9985-MWVIX,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,70.15,Yes\n4583-PARNH,Male,1,Yes,No,16,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,91.55,1540.05,No\n1116-FRYVH,Female,0,Yes,Yes,48,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,39.4,1978.65,No\n1421-HCERK,Male,1,Yes,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),105.7,3181.8,No\n9633-XQABV,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,229.7,No\n0716-BQNDX,Male,1,No,No,57,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,Two year,No,Electronic check,93.75,5625.55,No\n1265-XTECC,Female,1,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),96.55,6581.9,Yes\n1183-CANVH,Female,0,Yes,No,23,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),60,1347.15,No\n1972-XMUWV,Female,0,Yes,No,65,Yes,No,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),59.8,3808.2,No\n8679-LZBMD,Male,0,Yes,No,44,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),90.65,3974.15,No\n3407-JMJQQ,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109,7661.8,No\n6252-DFGTK,Female,0,Yes,No,37,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),68.1,2479.25,No\n1539-LNKHM,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,266.6,No\n8645-KOMJQ,Male,0,Yes,Yes,69,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),81.95,5601.4,No\n0289-IVARM,Female,0,No,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,60.55,1982.6,No\n9761-XUJWD,Male,0,No,No,5,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),65.6,339.9,No\n4057-FKCZK,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Two year,No,Bank transfer (automatic),82.5,4828.05,No\n4291-TPNFG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),82.3,5980.55,No\n6087-YPWHO,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,68.15,4808.7,No\n3090-QFUVD,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.3,20.3,No\n2237-ZFSMY,Female,0,No,No,39,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,95.55,3692.85,Yes\n1722-LDZJS,Male,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.2,1068.15,No\n1818-ESQMW,Female,0,No,No,27,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.2,2383.6,No\n4871-JTKJF,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes\n1415-YFWLT,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.3,89.3,Yes\n7401-RUBNK,Female,0,Yes,No,18,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,74.8,1438.05,No\n0506-YLVKJ,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,917.45,No\n8661-BOYNW,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),84.4,6096.45,No\n6711-VTNRE,Female,0,No,No,36,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,87.55,3078.1,Yes\n4815-GBTCD,Female,0,Yes,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.15,99.95,No\n2805-AUFQN,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,475.2,No\n0980-PVMRC,Female,0,Yes,Yes,40,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.85,2036.55,No\n7233-DRTRF,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.4,6444.05,No\n3500-RMZLT,Female,1,No,No,15,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,96.3,1426.75,Yes\n3873-NFTGI,Male,0,No,No,14,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55.5,767.55,No\n1162-ECVII,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.75,7932.5,No\n7048-GXDAY,Male,0,No,No,39,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,4040.65,No\n8571-ZCMCX,Female,0,Yes,Yes,47,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),60,2768.65,No\n1169-WCVAK,Male,0,Yes,No,19,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),88.8,1672.35,No\n4548-SDBKE,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.2,474.8,Yes\n8066-POXGX,Female,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.1,446.1,Yes\n7129-CAKJW,Female,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),80.05,1345.65,No\n8695-ARGXZ,Male,1,Yes,No,34,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.55,2425.4,No\n3570-YUEKJ,Female,0,No,No,42,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.55,2077.95,No\n4143-OOBWZ,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.3,416.3,Yes\n1555-HAPSU,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),23.9,1663.5,No\n1697-NVVGY,Male,1,Yes,No,19,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),66.4,1286.05,No\n7854-EKTJL,Female,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.6,35.85,Yes\n8464-EETCQ,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),18.8,1094.35,No\n2419-FSORS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.4,7719.5,No\n8132-YPVBX,Female,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.95,514.6,No\n9102-IAYHT,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.45,1451.6,Yes\n6754-LZUKA,Male,0,Yes,No,61,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),80.9,4932.5,No\n3422-LYEPQ,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71,71,Yes\n8099-MZPUJ,Male,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),111.8,5443.65,No\n0787-LHDYT,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.6,330.25,No\n2642-DTVCO,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.05,746.5,Yes\n0374-IOEGQ,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.6,122.7,No\n4361-JEIVL,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.4,44.4,Yes\n3197-NNYNB,Male,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),105.1,6631.85,No\n3396-DKDEL,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.15,8250,No\n1752-OZXFY,Male,0,Yes,No,60,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Mailed check,59.8,3561.15,No\n9066-QRSDU,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,26.3,1763.55,No\n9112-WSNPU,Female,1,No,No,35,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.55,2419,No\n9867-NNXLC,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,470.2,No\n5321-NTRKC,Male,0,Yes,Yes,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),79.85,5234.95,No\n1363-TXLSL,Male,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes\n9507-EXLTT,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),79.35,79.35,Yes\n3161-GETRM,Male,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.05,3097,No\n0402-OAMEN,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1709.1,No\n6933-VLYFX,Male,0,Yes,Yes,31,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,59.95,1848.8,No\n6754-WKSHP,Male,0,No,Yes,30,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.35,723.3,No\n9846-GKXAS,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),90.8,809.75,Yes\n6017-PPLPX,Male,0,Yes,Yes,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.45,1470.95,Yes\n4976-LNFVV,Male,1,Yes,No,19,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),34.3,577.15,No\n5055-MGMGF,Female,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.05,6914.95,No\n4641-FROLU,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.3,602.9,No\n2862-JVEOY,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.15,124.4,No\n2969-QWUBZ,Female,0,No,No,2,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,51.4,96.8,No\n9822-BIIGN,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,71.85,3827.9,No\n2077-MPJQO,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,533.05,No\n1534-OULXE,Female,0,Yes,Yes,61,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),49.7,2961.4,No\n7136-RVDTZ,Male,1,No,No,70,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,45.25,3264.45,Yes\n2971-SGAFL,Female,0,No,No,13,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,78.75,995.35,No\n1480-IVEVR,Male,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),81.6,2815.25,No\n2905-KFQUV,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70.4,154.8,No\n4581-SSPWD,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.8,246.3,Yes\n3370-HXOPH,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,76.1,257.6,No\n9391-YZEJW,Female,0,No,No,62,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94,5757.2,No\n9958-MEKUC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),103.95,7517.7,No\n0281-CNTZX,Male,0,Yes,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.95,1234.8,No\n6927-WTFIV,Male,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),71.3,1389.2,Yes\n4118-CEVPF,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),110.8,3836.3,No\n3398-ZOUAA,Male,1,Yes,No,21,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,1474.75,Yes\n9114-VEPUF,Male,0,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Electronic check,96.1,6001.45,No\n7876-BEUTG,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.8,720.1,No\n2338-BQEZT,Female,0,No,No,55,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),50.55,2832.75,No\n9873-MNDKV,Female,0,No,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.65,472.25,No\n0378-NHQXU,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,88.25,1460.65,Yes\n8241-JUIQO,Female,0,No,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.45,1336.35,No\n2194-IIQOF,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.3,6388.65,No\n4512-ZUIYL,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70,153.05,Yes\n9631-RXVJM,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,677.9,No\n9584-EXCDZ,Female,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.5,1165.6,No\n7969-AULMZ,Female,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.35,2119.5,Yes\n5093-FEGLU,Female,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.65,921.55,No\n2621-UDNLU,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,72,No\n7526-IVLYU,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,68.35,No\n2428-HYUNX,Male,1,Yes,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.35,847.25,No\n9214-EKVXR,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44,44,No\n3410-MHHUM,Female,0,Yes,Yes,44,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Electronic check,94.4,4295.35,No\n0568-ONFPC,Male,0,Yes,Yes,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.9,135,Yes\n0733-VUNUW,Male,0,No,No,24,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.65,1400.55,Yes\n4550-EVXNY,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes\n2122-YWVYA,Female,0,No,No,18,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,1380.4,No\n2968-SSGAA,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,1060.2,Yes\n2296-DKZFP,Female,0,Yes,No,65,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),71,4386.2,No\n4844-JJWUY,Female,1,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86,86,Yes\n6777-TGHTM,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),106.95,5785.5,Yes\n8909-BOLNL,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,21.2,52.05,No\n6603-YRDCJ,Male,0,Yes,No,33,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,61.05,2018.4,No\n3538-WZPHD,Male,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.6,79.45,Yes\n3229-USWAR,Female,0,No,No,34,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),79.95,2727.3,No\n2138-VFAPZ,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,263.65,No\n6131-JLWZM,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,275.4,No\n4785-QRJHC,Male,1,Yes,No,46,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),59.9,2816.65,Yes\n0269-XFESX,Male,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.35,538.5,No\n1709-EJDOX,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,948.9,No\n4316-XCSLJ,Male,0,No,Yes,17,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.3,846.8,No\n8610-WFCJF,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,4783.5,Yes\n9693-XMUOB,Male,1,Yes,No,59,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.25,2997.45,No\n0383-CLDDA,Female,0,No,No,69,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.35,5897.4,No\n4905-JEFDW,Male,0,No,No,11,No,No phone service,DSL,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,41.6,470.6,Yes\n2709-UQGNP,Male,0,No,No,10,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,51.65,524.5,No\n3549-ZTMNH,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24,269.65,No\n7881-INRLC,Male,0,No,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.85,4740,Yes\n8033-ATFAS,Female,0,Yes,Yes,39,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,59.85,2341.5,No\n1635-NZATJ,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.45,1789.65,No\n0562-HKHML,Male,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),23.9,1626.4,No\n8277-RVRSV,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.15,800.3,No\n9943-VSZUV,Male,1,No,No,67,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.7,5060.85,No\n0117-LFRMW,Male,0,Yes,Yes,37,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),40.2,1448.8,Yes\n6172-FECYY,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.5,4254.85,Yes\n7054-ENNGU,Female,0,Yes,No,9,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.85,466.6,No\n1455-HFBXA,Male,0,Yes,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),91.6,4627.8,No\n9402-CXWPL,Female,0,No,No,70,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,98.9,6838.6,No\n1628-BIZYP,Male,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,No\n1965-AKTSX,Female,1,No,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,1101.85,Yes\n0082-LDZUE,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.3,44.3,No\n4786-UKSNZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,Yes\n5343-SGUBI,Female,0,No,No,52,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Mailed check,80.2,4297.6,No\n0856-NAOES,Male,0,No,No,6,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,60.9,414.1,No\n1976-AZZPJ,Male,0,Yes,No,7,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,34.2,256.6,No\n7248-VZQLC,Female,1,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.2,3969.35,Yes\n6156-UZDLF,Female,0,No,No,26,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),87.15,2274.1,No\n3099-OONVS,Male,0,Yes,Yes,25,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.3,1296.8,No\n9500-WBGRP,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.1,1268.85,No\n5183-KLYEM,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),112.75,8192.6,No\n5144-PQCDZ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,59.25,No\n8514-VZHEB,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.5,1147.85,No\n6625-IUTTT,Male,0,No,No,67,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),65.55,4361.55,No\n9355-NPPFS,Female,1,No,No,26,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.8,2006.1,No\n0068-FIGTF,Female,0,No,No,27,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,78.2,2078.95,No\n5965-GGPRW,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),105.25,7609.75,No\n8035-PWSEV,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.25,487.05,No\n2236-HILPA,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.65,1218.45,No\n1840-BIUOG,Male,0,No,No,20,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,68.7,1416.2,No\n1356-MKYSK,Male,0,No,No,6,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),78.65,483.3,No\n6080-LNESI,Male,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.75,1234.6,No\n1830-IPXVJ,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.75,1311.6,No\n5828-DWPIL,Male,1,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,89.1,5618.3,No\n8398-TBIYD,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),84.7,6185.15,No\n0011-IGKFF,Male,1,Yes,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98,1237.85,Yes\n4192-GORJT,Male,0,Yes,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,498.1,Yes\n6496-SLWHQ,Male,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105,294.45,Yes\n9912-GVSEQ,Female,1,Yes,No,26,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.85,2381.55,Yes\n2931-FSOHN,Male,1,No,No,13,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.9,788.35,No\n9418-RUKPH,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,756.4,No\n5383-MMTWC,Female,1,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84,613.4,Yes\n9971-ZWPBF,Male,1,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,108.9,3625.2,No\n4712-AUQZO,Male,0,No,No,18,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,33.6,550.35,No\n9711-FJTBX,Male,0,Yes,Yes,56,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Mailed check,85.85,4793.8,No\n2908-ZTPNF,Female,0,No,No,36,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),34.85,1267.2,No\n7240-ETPTR,Female,0,Yes,No,9,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.75,442.2,Yes\n2202-CUYXZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,84.85,Yes\n5995-OIGLP,Male,0,No,No,12,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,56.65,654.85,Yes\n8421-WZOOW,Female,1,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),95.3,5567.45,No\n6261-RCVNS,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),73.9,3160.55,Yes\n7951-QKZPL,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.5,740.3,Yes\n1414-YADCW,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),84.6,5706.2,No\n8756-RDDLT,Female,0,No,No,68,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,No,Electronic check,44.95,3085.35,No\n2862-PFNIK,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.7,24.7,No\n5816-QVHRX,Female,0,No,No,37,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),100.3,3541.4,No\n4430-YHXGG,Female,0,No,Yes,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25.45,84.2,No\n6888-SBYAI,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.7,50.7,No\n7395-XWZOY,Male,0,No,No,20,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),55,1165.55,No\n7169-YWAMK,Male,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),68.4,4855.35,No\n3640-PHQXK,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.9,2806.9,Yes\n6877-LGWXO,Male,1,Yes,No,18,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.55,1422.65,Yes\n6859-RKMZJ,Male,0,Yes,No,11,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),55.05,608.15,No\n2729-VNVAP,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,641.35,No\n2957-JIRMN,Female,1,No,No,62,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,No,Electronic check,84.45,4959.15,No\n2049-BAFNW,Female,0,No,No,1,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,35.9,35.9,No\n3842-QTGDL,Male,0,Yes,No,16,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),80.75,1321.3,No\n7163-OCEQI,Male,0,Yes,Yes,22,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,78.65,1663.75,No\n9782-LGXMC,Female,0,Yes,Yes,49,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.75,3024.15,No\n5975-BAICR,Male,0,Yes,Yes,36,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Credit card (automatic),63.7,2188.5,No\n8019-ENHXU,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,99.45,4138.05,Yes\n5167-GBFRE,Male,1,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.2,102.5,Yes\n9033-EOXWV,Female,0,No,No,12,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,No,Mailed check,74.05,872.65,Yes\n4673-KKSLS,Female,0,No,No,31,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,87.6,2724.25,No\n4853-OITSN,Male,0,Yes,No,5,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.15,413.25,No\n9800-OUIGR,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,1374.2,No\n1324-NLTJE,Female,1,No,No,15,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),55,757.1,Yes\n2324-EFHVG,Male,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.4,6692.65,No\n7094-MSZAO,Male,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.05,218.5,No\n1522-VVDMG,Male,0,Yes,Yes,7,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,89.75,608.8,Yes\n6907-CQGPN,Male,0,No,No,29,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,34.3,1004.75,No\n0780-XNZFN,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1125.6,No\n0239-OXEXL,Female,0,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,84.25,3847.6,No\n2675-IJRGJ,Male,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.65,978,No\n7049-GKVZY,Female,0,No,No,17,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),79.85,1387.35,No\n8577-QSOCG,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,746.05,No\n3721-CNZHX,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,304.6,No\n0212-ISBBF,Female,0,No,No,22,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.35,1098.85,No\n9185-TQCVP,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.15,1139.2,Yes\n3585-YNADK,Female,0,Yes,No,57,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),74.6,4368.95,No\n5271-DBYSJ,Male,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),79.15,827.7,No\n1099-GODLO,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,20.35,No\n6828-HMKWP,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21.05,262.05,No\n5567-WSELE,Male,1,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,279.55,Yes\n1472-TNCWL,Male,0,No,Yes,36,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,3512.5,No\n8063-RJYNF,Male,0,No,No,16,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,1483.25,Yes\n0687-ZVTHB,Male,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),72.45,4653.85,Yes\n2080-GKCWQ,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.95,151.75,No\n9661-ACXBS,Female,0,No,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.2,4400.75,Yes\n2193-SFWQW,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),111.95,8033.1,No\n5656-JAMLX,Male,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1253.65,No\n3462-BJQQA,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.75,552.65,No\n0442-TDYUO,Male,0,Yes,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.05,1036,No\n6733-LRIZX,Male,0,No,No,35,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.95,4025.5,No\n9503-XJUME,Male,0,No,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.65,928.4,No\n4367-NHWMM,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.9,24.9,No\n3727-RJMEO,Male,0,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,82.85,460.25,Yes\n3779-OSWCF,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),93.2,6506.15,No\n6736-DHUQI,Female,0,Yes,Yes,67,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),84.8,5598.3,No\n3915-ODIYG,Male,1,No,No,60,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,One year,Yes,Electronic check,71.75,4374.55,No\n1360-RCYRT,Male,0,Yes,Yes,23,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),30.35,678.75,No\n2724-FJDYW,Male,0,No,Yes,39,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),54.85,2191.7,No\n4451-RWASJ,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.5,239.75,No\n6646-VRFOL,Male,1,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.85,5485.5,Yes\n3460-TJBWI,Male,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.2,609.05,No\n5917-HBSDW,Female,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.35,683.75,No\n5685-IIXLY,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.6,404.2,Yes\n5671-UUNXD,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.65,5189.75,No\n0956-ACVZC,Female,0,No,No,54,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),94.1,5060.9,No\n2325-NBPZG,Female,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,233.65,No\n4250-ZBWLV,Male,0,No,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,108.45,7176.55,Yes\n4482-FTFFX,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,56.15,291.45,No\n8859-DZTGQ,Male,0,No,No,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,689.75,No\n0440-MOGPM,Female,0,No,No,41,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.55,3263.9,No\n0020-JDNXP,Female,0,Yes,Yes,34,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,61.25,1993.2,No\n3752-CQSJI,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,254.5,No\n5025-GOOKI,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),18.9,347.65,No\n4698-KVLLG,Female,1,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,967.9,No\n5095-AESKG,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),91.5,242.95,Yes\n2887-JPYLU,Female,0,No,Yes,41,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.2,1841.9,No\n4770-QAZXN,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.45,232.1,No\n4896-CPRPF,Male,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,809.25,No\n1871-MOWRM,Male,0,Yes,No,12,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),80.85,866.45,Yes\n9714-EDSUC,Male,0,No,No,4,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,94.9,360.55,No\n2027-OAQQC,Female,0,No,No,43,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),49.05,2076.2,Yes\n0282-NVSJS,Female,1,Yes,Yes,12,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,29.3,355.9,No\n9090-SGQXL,Male,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,7299.65,Yes\n6595-YGXIT,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.95,2291.2,Yes\n7353-YOWFP,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,129.15,No\n9835-ZIITK,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.85,7491.75,Yes\n8008-ESFLK,Female,0,Yes,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,110.5,5835.5,No\n7537-CBQUZ,Male,1,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.4,7031.45,No\n1555-DJEQW,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.2,7723.9,Yes\n5649-TJHOV,Male,1,Yes,No,27,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),36.5,1032,Yes\n0519-XUZJU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.75,70.75,Yes\n3363-EWLGO,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,109.6,No\n4750-UKWJK,Female,1,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,727.8,No\n6338-AVWCY,Male,0,No,No,3,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,40.15,130.75,Yes\n1689-YQBYY,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.6,893,No\n4487-ZYJZK,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,763.1,No\n2959-FENLU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.3,781.4,No\n0708-LGSMF,Male,0,Yes,No,13,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,65.85,902.25,No\n9253-QXKBE,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,2653.65,Yes\n7634-HLQJR,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1016.7,No\n0487-RPVUM,Male,0,Yes,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),99.4,5943.65,No\n4079-ULGFR,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,275.7,No\n2516-XSJKX,Female,0,Yes,Yes,41,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,78.45,3126.45,No\n0057-QBUQH,Female,0,No,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,25.1,1070.15,No\n9445-SZLCH,Female,0,Yes,Yes,36,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),97.35,3457.9,Yes\n6599-SFQVE,Female,0,No,No,6,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55,340.4,No\n8331-ZXFOE,Female,0,No,No,58,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),71.1,4299.2,No\n4003-FUSHP,Male,0,No,No,19,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),61.55,1093.2,No\n0356-ERHVT,Male,0,Yes,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.9,521.9,No\n7325-ENZFI,Female,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),40.3,1630.4,No\n4884-ZTHVF,Female,1,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,87.1,713.6,No\n3920-HIHMQ,Female,0,No,Yes,26,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.5,1265.65,No\n3055-OYMSE,Female,1,No,No,53,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.8,4003.85,No\n0613-WUXUM,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.2,1401.4,No\n7568-PODML,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,45.3,45.3,Yes\n4458-KVRBJ,Male,0,No,No,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1510.5,No\n5349-IECLD,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,94.95,178.1,Yes\n1397-XKKWR,Male,0,No,No,7,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.3,264.8,No\n3945-GFWQL,Female,0,No,Yes,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.55,480.6,Yes\n8097-OMULG,Male,0,Yes,Yes,59,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),76.75,4541.9,No\n6013-BHCAW,Male,0,Yes,Yes,61,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),81,4976.15,No\n0401-WDBXM,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.55,7542.25,No\n3387-PLKUI,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.8,251.25,No\n6096-EGVTU,Female,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.9,1595.5,No\n3797-VTIDR,Male,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,23.45,23.45,Yes\n5081-NWSUP,Female,0,No,No,10,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,No,Mailed check,64.9,685.55,No\n2580-ATZSQ,Female,0,Yes,Yes,65,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),61.35,3874.1,No\n8085-MSNLK,Female,0,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,113.95,6891.4,No\n9691-HKOVS,Female,0,Yes,No,55,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,90.15,4916.95,No\n9881-VCZEP,Female,0,Yes,No,25,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.1,1373,No\n9526-BIHHD,Male,0,No,No,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.7,29.7,Yes\n8757-TFHHJ,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.8,49.8,No\n4523-WXCEF,Female,0,Yes,No,59,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.1,6039.9,Yes\n1468-DEFNC,Male,1,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.4,1548.65,No\n5119-KEPFY,Male,0,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95,3440.25,No\n8364-TRMMK,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.65,151.3,Yes\n0916-KNFAJ,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,No,Yes,No,No,Yes,Two year,Yes,Mailed check,69.9,4226.7,No\n8319-QBEHW,Male,0,No,Yes,26,No,No phone service,DSL,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),39.95,1023.75,No\n3063-QFSZL,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.4,55.4,Yes\n8775-LHDJH,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.6,90.6,Yes\n9625-QNLUX,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),103.25,7074.4,No\n3097-NQYSN,Male,1,Yes,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,86.85,156.35,Yes\n4024-CSNBY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),94.25,6849.75,No\n7110-BDTWG,Female,0,Yes,No,71,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Two year,Yes,Electronic check,47.05,3263.6,No\n7493-GVFIO,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1252.85,No\n0690-SRQID,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,67.55,No\n9605-WGJVW,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,70.2,No\n9114-DPSIA,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),81,5750,No\n4700-UBQMV,Male,0,Yes,Yes,21,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),75.9,1549.75,No\n7711-GQBZC,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.7,1810.55,No\n3565-UNOCC,Female,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.05,2952.85,Yes\n4627-MIHJH,Female,1,No,No,69,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.25,7467.55,No\n9795-NREXC,Female,0,Yes,No,64,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85,5484.4,No\n8573-CGOCC,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.75,294.9,No\n7356-AYNJP,Female,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,23.9,97.5,No\n9249-FXSCK,Female,0,No,No,52,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),111.25,5916.45,Yes\n5493-SDRDQ,Male,0,No,No,2,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.1,113.35,Yes\n6286-SUUWT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,19.95,No\n4819-HJPIW,Male,0,No,No,18,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.15,476.8,No\n8654-DHAOW,Female,0,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.15,101.65,No\n5438-QMDDL,Female,0,Yes,No,19,Yes,No,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,59.8,1130.85,No\n9812-GHVRI,Female,0,No,No,40,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),83.85,3532.25,No\n5553-AOINX,Female,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.9,6891.45,No\n1228-ZLNBX,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75.3,1570.7,No\n4226-KKDON,Male,0,No,No,8,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,66.65,520.95,No\n3831-YCPUO,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.5,7854.9,No\n7445-WMRBW,Female,0,No,No,48,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),73.85,3581.4,No\n3308-DGHKL,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.3,1447.9,No\n9924-JPRMC,Male,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,118.2,8547.15,No\n0080-EMYVY,Female,0,No,No,14,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Credit card (automatic),51.45,727.85,No\n6218-KNUBD,Male,0,No,No,6,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,59.45,357.6,No\n7337-CINUD,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.5,159.35,No\n7609-NRNCA,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,280.85,No\n0623-EJQEG,Male,0,No,No,65,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,No,Electronic check,93.55,6069.25,No\n7153-CHRBV,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Mailed check,59.3,3274.35,No\n0871-URUWO,Male,0,Yes,No,13,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.25,1359,Yes\n9190-MFJLN,Male,1,No,No,19,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.9,1777.9,Yes\n6198-PNNSZ,Female,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),109.8,6109.65,No\n3317-VLGQT,Female,0,Yes,No,14,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.1,1122.4,No\n4132-POCZS,Male,0,Yes,No,52,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.9,2020.9,No\n5614-DNZCE,Female,0,No,No,58,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),64.9,3795.45,No\n1095-JUDTC,Female,1,No,No,47,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,95.05,4504.55,Yes\n3896-RCYYE,Female,0,No,No,67,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),53.4,3579.15,No\n9638-JIQYA,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),24.9,49.7,No\n3258-SANFR,Male,1,No,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),44.7,276.5,No\n3726-TBHQT,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114,8175.9,No\n3190-ITQXP,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.25,890.35,No\n0870-VEMYL,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),53.85,259.8,Yes\n4833-QTJNO,Male,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),83.85,5588.8,No\n3039-MJSLN,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.2,50.6,No\n0178-CIIKR,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,58,No\n5385-SUIRI,Male,1,Yes,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,5568.35,Yes\n3982-XWFZQ,Female,0,Yes,No,42,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.25,2203.65,Yes\n5774-QPLTF,Male,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.35,938.95,No\n2322-VCZHZ,Male,1,Yes,No,23,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90,2024.1,No\n5010-IPEAQ,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),54.2,3623.95,No\n4009-ALQFH,Female,0,No,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,2369.05,Yes\n6383-ZTSIW,Female,1,Yes,No,39,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Mailed check,99.1,3877.95,No\n8990-YOZLV,Female,0,No,No,69,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,66.9,4577.9,No\n3069-SSVSN,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,One year,No,Mailed check,25.85,25.85,No\n5222-IMUKT,Male,0,No,No,32,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,91.05,2871.5,No\n3627-FCRDW,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,71,672.55,Yes\n7562-GSUHK,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),93.2,1573.7,Yes\n6685-XSHHU,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.95,1270.55,No\n8901-UPRHR,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.2,7711.45,No\n4903-UYAVB,Male,0,Yes,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,126.05,Yes\n0118-JPNOY,Female,1,No,No,26,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.8,2193.65,No\n6776-TLWOI,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,64.55,Yes\n3845-FXCYS,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.65,31.2,No\n5857-XRECV,Female,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,38.25,No\n0725-CXOTM,Female,0,No,No,36,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),89.65,3348.1,No\n4343-EJVQB,Male,0,No,No,7,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,74.35,533.6,No\n9000-PLFUZ,Female,1,Yes,No,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,Yes,Credit card (automatic),49.45,2907.55,No\n3427-GGZZI,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.1,1620.8,No\n7779-ORAEL,Male,1,Yes,No,45,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),75.15,3480.35,No\n0644-OQMDK,Male,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.65,293.85,No\n4077-CROMM,Female,0,Yes,Yes,31,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,3243.45,Yes\n3154-CFSZG,Male,0,Yes,Yes,47,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,90.05,4137.2,No\n2868-MZAGQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.25,79.25,Yes\n4847-QNOKA,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.9,44.9,Yes\n2220-IAHLS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.4,19.4,No\n1658-XUHBX,Female,1,Yes,Yes,59,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),88.75,5348.65,No\n6379-RXJRQ,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,659.65,Yes\n2378-HTWFW,Male,1,No,No,35,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),91,3180.5,No\n8650-RHRKE,Male,0,No,No,4,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.65,118.5,Yes\n4377-VDHYI,Male,0,Yes,Yes,32,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Electronic check,90.8,3023.85,No\n0475-RIJEP,Male,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,77.85,3365.85,Yes\n1260-TTRXI,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,54.3,195.3,Yes\n3719-TDVQB,Female,1,Yes,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,18.95,1031.1,No\n6328-ZPBGN,Female,1,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.15,997.65,Yes\n0201-MIBOL,Female,1,No,No,66,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),102.4,6471.85,No\n4937-QPZPO,Male,0,Yes,Yes,61,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.9,6241.35,No\n2925-VDZHY,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.7,6501.35,No\n6981-TDRFT,Male,0,Yes,Yes,44,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,54.3,2317.1,No\n3413-CSSTH,Male,0,No,No,41,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.7,2237.55,No\n8033-VCZGH,Male,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.95,5231.3,No\n4789-KWMXN,Male,0,Yes,No,47,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110.85,5275.8,Yes\n0224-NIJLP,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,165.5,No\n7542-CYDDM,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,358.5,No\n4718-WXBGI,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),91.95,6614.9,No\n2867-UIMSS,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.5,80.5,Yes\n8495-PRWFH,Female,1,No,No,42,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,55.65,2421.75,No\n0439-IFYUN,Female,1,No,No,18,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,1294.6,No\n5716-LIBJC,Female,0,No,Yes,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.15,1299.1,No\n2868-LLSKM,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),83.65,5733.4,No\n8111-RKSPX,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.2,305.55,Yes\n2988-QRAJY,Male,0,No,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),110.05,7430.75,No\n1585-MQSSU,Male,0,No,No,17,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,51.5,900.5,Yes\n0071-NDAFP,Male,0,Yes,Yes,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.5,630.6,No\n2856-NNASM,Male,1,No,No,43,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89.55,3856.75,Yes\n7328-ZJAJO,Female,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1222.65,No\n6458-CYIDZ,Female,1,No,No,5,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,80.7,374.8,No\n8559-CIZFV,Male,0,Yes,Yes,21,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Mailed check,77.5,1625,Yes\n1090-PYKCI,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),105.1,7234.8,No\n3058-WQDRE,Male,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.15,331.85,No\n7547-EKNFS,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),95.25,3959.35,Yes\n2279-AXJJK,Male,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95.65,5088.4,No\n6769-DYBQN,Male,1,No,No,46,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,85,3969.4,Yes\n0909-SELIE,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),80.8,4860.85,No\n3417-TSCIC,Male,0,No,No,29,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,24.85,788.05,No\n1042-HFUCW,Female,0,No,Yes,25,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.75,1266.35,No\n4439-JMPMT,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,85.75,470.95,Yes\n6537-OTKMY,Male,0,No,No,15,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,50.75,688.2,No\n8999-EXMNO,Female,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,387.7,No\n6725-TPKJO,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,845.25,No\n2446-BEGGB,Female,1,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.25,560.6,Yes\n7162-WPHPM,Male,0,Yes,Yes,58,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),71.6,4230.25,No\n1599-MMYRQ,Male,0,Yes,Yes,62,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),81.45,4983.05,No\n8821-KVZKQ,Female,0,Yes,Yes,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),58.4,4113.15,No\n1496-GGSUK,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.7,25.7,Yes\n6434-TTGJP,Male,0,Yes,Yes,10,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),53.7,521,No\n0042-JVWOJ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.6,471.85,No\n0130-SXOUN,Male,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),89.4,5976.9,No\n2575-GFSOE,Female,0,Yes,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69,506.9,Yes\n2330-PQGDQ,Male,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),84.2,4299.75,No\n1452-UZOSF,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),106.1,7548.6,No\n2097-YVPKN,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1654.75,No\n2842-JTCCU,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),46.05,80.35,Yes\n8597-CTXVJ,Male,0,No,Yes,70,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),64.95,4551.5,No\n6631-HMANX,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.45,6227.5,No\n9962-BFPDU,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No\n3296-SILRA,Female,1,Yes,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,76.4,76.4,Yes\n9681-OXGVC,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,514,Yes\n6394-HHHZM,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,57.5,No\n5995-LFTLE,Male,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1474.35,No\n5180-UCIIQ,Male,1,Yes,Yes,22,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,40.05,880.2,Yes\n1932-UEDCX,Male,1,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.6,3270.25,No\n7153-OANIO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.95,69.95,Yes\n5188-HGMLP,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74,3919.15,No\n0665-XHDJU,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Electronic check,99.4,7285.7,No\n6521-YYTYI,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.3,93.3,Yes\n8878-HMWBV,Male,0,No,No,3,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.15,169.05,Yes\n8265-HKSOW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),107.45,7658.3,No\n3544-FBCAS,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),83.6,5959.3,No\n5331-RGMTT,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.05,5295.7,No\n4759-PXTAN,Female,0,Yes,No,59,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,80.1,4693.2,No\n7054-DMVAS,Male,0,No,No,54,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Bank transfer (automatic),65.3,3512.9,No\n3428-MMGUB,Male,0,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Electronic check,89.55,5231.2,No\n6549-BTYPG,Female,0,Yes,No,60,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),60.8,3603.45,No\n7823-JSOAG,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.5,217.45,No\n3705-RHRFR,Female,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),99.15,6875.35,No\n9374-YOLBJ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.25,19.25,No\n9074-KGVOX,Male,0,Yes,Yes,50,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),39.45,2021.35,No\n6128-DAFVY,Female,0,No,No,56,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),44.85,2564.95,No\n9933-QRGTX,Female,0,Yes,No,60,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,97.2,5611.75,No\n4476-OSWTN,Male,1,Yes,No,69,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,110.55,7610.1,No\n3751-KTZEL,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,35.05,35.05,Yes\n1977-STDKI,Female,1,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73,73,Yes\n5066-GFJMM,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,45.75,No\n2661-GKBTK,Male,0,Yes,Yes,60,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),76.95,4543.95,No\n0458-HEUZG,Female,0,No,No,13,No,No phone service,DSL,No,No,Yes,Yes,No,No,Two year,No,Mailed check,35.4,450.4,No\n7268-WNTCP,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.45,1297.35,No\n0824-VWDPO,Female,0,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),96.75,4442.75,No\n8409-WQJUX,Female,0,No,No,25,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,54.2,1423.15,No\n2578-JQPHZ,Male,0,No,No,44,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),100.1,4378.35,No\n3278-FSIXX,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.25,74.2,No\n5813-UECBU,Male,1,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),83.85,2716.3,Yes\n0328-GRPMV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,70.1,Yes\n9746-UGFAC,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.85,450.65,No\n2946-KIQSP,Female,0,No,No,35,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,33.45,1175.85,No\n0625-AFOHS,Female,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,558.8,No\n6726-WEXXK,Male,1,Yes,No,27,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Electronic check,85.9,2220.1,No\n4636-OLWOE,Male,0,No,Yes,54,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,61,3283.05,No\n4342-HENTK,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.65,142.35,Yes\n4685-ERGHK,Male,0,No,No,57,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,Yes,Electronic check,86.9,4939.25,No\n0885-HMGPY,Male,0,No,No,62,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.4,4237.5,No\n5003-OKNNK,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.35,335.95,No\n4195-SMMNX,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,33.2,Yes\n7208-PSIHR,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.3,7188.5,No\n4012-ZTHBR,Female,0,Yes,Yes,21,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.95,926.25,No\n0489-WMEMG,Female,0,No,Yes,23,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Electronic check,49.45,1119.35,No\n7435-ZNUYY,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,116.6,No\n1354-YZFNB,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,68.8,No\n6956-SMUCM,Female,0,No,No,3,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99,287.4,Yes\n2985-FMWYF,Female,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.5,2341.55,No\n2812-ENYMO,Male,0,No,No,26,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Credit card (automatic),54.55,1362.85,No\n2717-HVIZY,Female,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,163.6,No\n3223-WZWJM,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.95,2254.2,Yes\n3422-GALYP,Male,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.45,145.15,No\n9053-JZFKV,Male,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),116.2,7752.3,Yes\n2530-ENDWQ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),93.7,6585.35,Yes\n2890-WFBHU,Female,0,No,No,59,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),79.85,4786.1,No\n0327-WFZSY,Male,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,100,3835.55,No\n7977-HXJKU,Male,0,No,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,397,No\n6615-ZGEDR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,19.7,Yes\n7033-CLAMM,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1027.25,No\n4023-RTIQM,Female,1,Yes,No,31,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),50.4,1580.1,No\n0864-FVJNJ,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,113.35,7222.75,No\n7356-IWLFW,Male,0,Yes,Yes,46,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80,3769.7,No\n9541-PWTWO,Female,0,No,No,52,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.95,4233.95,No\n3967-VQOGC,Female,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.9,1680.25,No\n7872-RDDLZ,Female,1,No,No,67,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.9,3725.5,No\n9250-WYPLL,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.55,413.65,Yes\n6308-CQRBU,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,109.25,7707.7,No\n2754-XBHTB,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),77.65,714.15,Yes\n1597-LHYNC,Female,1,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,95,2497.2,Yes\n0186-CAERR,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),116.3,8309.55,No\n3043-SUDUA,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.9,601.55,No\n5442-BHQNG,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.35,139.25,No\n2169-RRLFW,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1888.25,No\n5872-OEQNH,Female,0,No,No,60,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Electronic check,44.45,2773.9,No\n3162-KKZXO,Female,1,No,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100.15,5409.75,No\n7055-VKGDA,Male,0,No,No,54,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.4,5643.4,Yes\n0754-UKWQP,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.85,197.7,Yes\n8873-GLDMH,Female,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.85,401.3,No\n5696-JVVQY,Female,0,Yes,Yes,48,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),70.1,3238.4,No\n8946-BFWSG,Male,0,Yes,Yes,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1573.05,No\n7493-TPUWZ,Male,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,79.15,Yes\n0547-HURJB,Male,0,No,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.05,235.8,No\n8904-OPDCK,Male,1,Yes,No,54,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),24.95,1364.75,No\n8845-LWKGE,Female,0,Yes,Yes,30,Yes,No,DSL,No,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,64.5,1985.15,No\n1577-HKTFG,Female,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.65,655.85,Yes\n8752-STIVR,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79,303.15,Yes\n0691-NIKRI,Female,0,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.95,4335.2,No\n5022-JNQEQ,Female,0,Yes,Yes,9,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,75.85,647.5,No\n4558-FANTW,Female,0,Yes,Yes,17,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,91.85,1574.45,Yes\n3249-VHRIP,Female,0,No,No,62,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),43.6,2748.7,No\n3736-BLEPA,Male,0,No,No,28,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),91.25,2483.65,No\n4701-LKOZD,Female,0,No,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.75,6367.2,No\n6603-QWSPR,Female,0,No,No,46,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.4,4904.2,No\n9921-QFQUL,Female,0,Yes,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,90.15,2044.95,No\n1929-ZCBHE,Male,0,Yes,Yes,47,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,40.3,1794.8,No\n4378-MYPGO,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.25,7173.15,No\n8651-ENBZX,Female,1,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,106,6441.4,Yes\n4129-LYCOI,Female,0,No,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,104,7039.05,No\n7798-JVXYM,Female,0,No,No,14,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,69.65,921.4,No\n8647-SDTWQ,Male,0,Yes,Yes,57,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.3,4018.35,No\n2696-ECXKC,Female,0,Yes,Yes,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,100.9,5448.6,No\n2081-KJSQF,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No\n7623-TRNQN,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.9,49.9,Yes\n3323-CPBWR,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.9,2085.45,No\n9330-IJWIO,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.35,1358.85,Yes\n3384-CTMSF,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.1,5135.15,No\n0902-RFHOF,Male,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,730.1,No\n9286-DOJGF,Female,1,Yes,No,38,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.95,2869.85,Yes\n6048-QBXKL,Female,1,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),56.55,118.25,No\n9661-MHUMO,Male,1,Yes,Yes,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),49.25,49.25,Yes\n7718-RXDGG,Male,0,Yes,No,15,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),68.6,1108.6,No\n1025-FALIX,Female,0,No,No,26,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.05,1815.65,No\n3451-VAWLI,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,730.4,No\n3407-QGWLG,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,75.45,No\n1842-EZJMK,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.7,5071.05,Yes\n2347-WKKAE,Male,0,Yes,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.4,4014.6,No\n8735-DCXNF,Male,0,Yes,No,10,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),54.95,568.85,No\n3214-IYUUQ,Female,0,Yes,No,61,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,No,Bank transfer (automatic),93.7,5860.7,No\n1936-UAFEH,Female,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.25,7279.35,No\n3587-PMCOY,Male,0,No,No,10,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,98.9,1064.95,No\n8079-XRJRS,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Electronic check,89.75,5769.6,Yes\n1027-LKKQQ,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.45,5886.85,No\n7148-XZPHA,Male,0,Yes,Yes,55,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,79.4,4238.45,No\n8073-IJDCM,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,20.3,Yes\n5309-TAIKL,Female,0,No,No,7,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),62.8,418.3,No\n6856-RAURS,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.9,136.05,No\n8778-LMWTJ,Female,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,708.2,No\n9717-IOAAF,Male,0,No,Yes,27,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.85,788.55,No\n8884-ADFVN,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.95,700.85,Yes\n2845-KDHVX,Female,0,Yes,No,64,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,68.3,4378.8,No\n0848-SOMKO,Male,0,No,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),48.4,3442.8,No\n2720-WGKHP,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,181.7,Yes\n6734-FQAJX,Male,1,No,No,67,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,105.05,7171.7,No\n2869-ADAWR,Female,0,No,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.3,4016.85,Yes\n1535-VTJOQ,Female,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,553,No\n6368-TZZDT,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,96.85,No\n0943-ZQPXH,Male,0,Yes,Yes,44,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),92.95,4122.9,No\n1293-BSEUN,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.7,1482.3,No\n9894-EZEWG,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.3,74.3,Yes\n1302-TPUBN,Male,0,No,No,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1240.8,No\n7851-FLGGQ,Male,0,No,No,1,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,44.65,44.65,Yes\n0637-UBJRP,Male,0,Yes,Yes,13,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,84.05,1095.3,Yes\n0940-OUQEC,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.7,788.8,Yes\n2378-VTKDH,Male,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.35,6578.55,No\n0927-CNGRH,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No\n8608-OZTLB,Male,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Electronic check,74.05,2802.3,No\n2335-GSODA,Male,0,No,Yes,23,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,40.1,857.75,No\n1952-DVVSW,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,184.4,No\n0702-PGIBZ,Male,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.7,364.55,Yes\n6656-GULJQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.55,6093.3,No\n9853-JFZDU,Female,0,Yes,No,35,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,56.85,1861.1,No\n1963-VAUKV,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,Yes\n3777-XROBG,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,1079.65,No\n6994-FGRHH,Male,0,Yes,Yes,70,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,106.15,7475.1,No\n4000-VGMQP,Male,0,Yes,Yes,38,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78.95,2862.55,No\n3999-WRNGR,Female,0,Yes,Yes,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.75,3069.45,No\n3466-RITXD,Male,0,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),92.4,2349.8,No\n2680-XKKNJ,Female,0,No,No,8,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),58.2,469.25,No\n3058-HJCUY,Male,0,Yes,Yes,41,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,102.6,4213.35,Yes\n2974-GGUXS,Female,1,Yes,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),91.95,3301.05,No\n7994-XIRTR,Male,1,No,No,54,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),65.25,3529.95,No\n3259-FDWOY,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),106,7723.7,Yes\n2101-RANCD,Female,0,No,No,55,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.1,4144.9,No\n7921-BEPCI,Female,0,No,No,72,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),59.75,4265,No\n0807-ZABDG,Female,0,No,Yes,3,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.1,154.65,Yes\n8510-TMWYB,Female,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.8,3246.45,No\n3258-ZKPAI,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.6,8337.45,No\n1428-IEDPR,Male,0,No,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.3,5731.4,No\n5298-GSTLM,Female,1,No,No,60,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),101.4,6176.6,No\n4450-YOOHP,Female,0,No,No,39,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.65,1905.4,No\n6728-CZFEI,Female,0,No,No,15,Yes,No,DSL,No,No,No,No,Yes,No,One year,No,Mailed check,56.15,931.9,No\n5748-RNCJT,Male,0,No,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),106.5,7348.8,Yes\n3653-NCRDJ,Male,0,Yes,Yes,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.2,776.25,No\n6121-TNHBO,Female,1,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),83,5243.05,No\n5119-NZPTV,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.1,141.65,No\n6519-ZHPXP,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.3,7679.65,No\n7739-LAXOG,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),91.05,2954.5,Yes\n3472-QPRCH,Male,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,1006.9,No\n8224-UAXBZ,Female,0,Yes,No,58,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,Yes,Electronic check,45.35,2540.1,No\n9610-WCESF,Male,0,No,No,67,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Electronic check,43.9,3097.2,No\n9776-CLUJA,Female,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,77.5,3807.35,Yes\n2486-WYVVE,Male,0,Yes,No,31,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,79.3,2484,No\n3865-ZYKAD,Male,1,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),84.9,5785.65,No\n1422-DGUBX,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Electronic check,79.25,2619.15,No\n2999-AANRQ,Female,0,No,No,21,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),71.05,1524.85,No\n8668-KNZTI,Male,0,No,No,52,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Electronic check,53.75,2790.65,No\n0480-KYJVA,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.25,1784.5,No\n6034-ZRYCV,Female,0,Yes,No,72,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,54.2,3937.45,Yes\n9746-YKGXB,Male,0,Yes,Yes,52,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,No,Bank transfer (automatic),44.25,2276.1,No\n3926-YZVVX,Female,0,No,No,41,Yes,No,DSL,No,No,No,Yes,No,No,One year,No,Bank transfer (automatic),50.05,2029.05,No\n6000-UKLWI,Male,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,802.35,No\n2079-FBMZK,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.25,418.4,Yes\n6332-FBZRI,Male,0,Yes,Yes,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),69.35,4653.25,No\n2096-XOTMO,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,275.9,No\n3266-FTKHB,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.15,343.45,No\n8221-EQDGL,Male,0,Yes,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,61,2130.45,No\n2346-LOCWC,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1191.4,No\n6608-QQLVK,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,50.5,50.5,Yes\n0298-XACET,Male,0,Yes,Yes,52,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,50.2,2554,No\n7560-QJAVJ,Female,0,No,No,70,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),79.6,5589.45,No\n2995-YWTCD,Female,0,Yes,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.9,467.7,No\n3551-HUAZH,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,74.4,Yes\n9137-UIYPG,Female,0,Yes,Yes,35,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.9,3756.45,No\n2809-ZMYOQ,Female,0,No,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,3334.9,No\n5084-OOVCJ,Female,0,Yes,Yes,17,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55.35,920.5,No\n0254-WWRKD,Female,0,Yes,Yes,67,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Credit card (automatic),50.55,3431.75,No\n7727-SHVZV,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,150.35,No\n6302-JGYRJ,Male,0,No,Yes,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Mailed check,79.45,2587.7,Yes\n5339-PXDVH,Male,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,90.65,367.95,No\n2205-LPVGL,Male,1,Yes,Yes,58,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),89.85,5125.75,No\n8782-NUUOL,Male,0,No,No,60,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,79,4801.1,No\n3685-YLCMQ,Male,0,No,No,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.65,6219.6,Yes\n7601-WFVZV,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No\n4609-KNNWG,Female,0,Yes,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,550.1,No\n4868-AADLV,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,116.25,7862.25,No\n9529-OFXHY,Male,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87.75,1242.2,No\n8634-MPHTR,Male,1,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,4871.05,Yes\n2669-QVCRG,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),81.3,3190.65,No\n2478-EEWWM,Male,0,Yes,No,59,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.3,2666.75,No\n9174-IHETN,Female,0,No,Yes,50,Yes,No,Fiber optic,No,No,No,No,No,No,Two year,No,Credit card (automatic),70.35,3533.6,No\n2233-TXSIU,Male,0,Yes,Yes,17,No,No phone service,DSL,Yes,No,Yes,No,Yes,No,One year,No,Credit card (automatic),44.45,792.15,No\n9644-UMGQA,Male,0,Yes,Yes,6,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.15,295.65,No\n3256-EZDBI,Male,1,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),29.45,1459.35,No\n8761-NSOBC,Male,0,No,No,44,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,100.55,4398.15,Yes\n0419-YAAPX,Male,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.3,4297.95,No\n8413-VONUO,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.65,167.3,Yes\n0872-CASZJ,Male,0,Yes,No,59,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Mailed check,69.1,4096.9,No\n4726-DLWQN,Male,1,No,No,50,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),70.35,3454.6,No\n8164-OCKUJ,Female,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,1286,No\n3094-JOJAI,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,74.15,1387,No\n9494-MRNYX,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.05,786.3,No\n2599-CZABP,Male,0,Yes,No,14,Yes,No,DSL,No,No,No,No,No,No,One year,No,Electronic check,44.6,641.25,No\n7945-PRBVF,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21.45,705.45,No\n4544-RXFMG,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,43.45,345.5,No\n0859-YGKFW,Male,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.05,345.9,No\n1150-FTQGN,Female,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),94.15,5811.8,No\n9223-UCPVT,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,94.4,94.4,Yes\n7196-LIWRH,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,124.45,No\n5448-VWNAM,Female,0,No,Yes,19,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,75.9,1375.6,No\n1177-XZBJL,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),64.15,3491.55,No\n3518-FSTWG,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.55,7920.7,No\n9330-VOFSZ,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,6640.7,No\n4351-QLCSU,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),55,55,Yes\n0939-EREMR,Female,0,No,Yes,13,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,53.45,718.1,No\n9389-ACWBI,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),69.95,320.4,No\n5419-JPRRN,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.45,101.45,Yes\n3644-QXEHN,Male,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,97,1334.45,No\n2911-IJORQ,Male,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),90.6,3358.65,No\n5921-NGYRH,Male,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.55,4764,No\n2100-BDNSN,Female,0,Yes,No,5,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),67.95,350.3,Yes\n5998-DZLYR,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),94.35,5703,No\n0488-GSLFR,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.5,69.5,Yes\n9318-NKNFC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.85,18.85,Yes\n3985-HOYPM,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,525.55,No\n9728-FTTVZ,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.2,69.2,Yes\n9548-LERKT,Male,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,483.15,No\n6576-FBXOJ,Male,0,Yes,No,17,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,54.6,934.8,No\n4310-KEDTB,Female,0,No,Yes,26,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,29.8,786.5,No\n7254-IQWOZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes\n2474-BRUCM,Male,1,Yes,No,40,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.85,4086.3,Yes\n4062-HBMOS,Male,0,No,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.05,5364.8,No\n0742-NXBGR,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,82.3,82.3,Yes\n2676-ISHSF,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,20.3,No\n9236-NDUCW,Female,0,No,No,21,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,35.1,770.4,No\n4753-PADAS,Female,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.7,6816.95,No\n6103-QCKFX,Female,0,Yes,Yes,44,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),56.25,2419.55,No\n7781-EWARA,Male,0,Yes,Yes,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,60.35,4138.7,No\n6110-OHIHY,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79.25,267.6,Yes\n7018-FPXHH,Male,0,Yes,Yes,56,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.8,3457.45,No\n5889-LFOLL,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,1115.2,Yes\n5708-EVONK,Female,0,Yes,Yes,58,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.4,5435.6,Yes\n8708-XPXHZ,Female,0,Yes,Yes,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,4186.3,Yes\n0616-ATFGB,Male,1,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.05,25.05,Yes\n0572-ZJKLT,Female,0,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,99.65,4630.2,No\n5135-RDDQL,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,No,No,No,No,No,Two year,Yes,Bank transfer (automatic),50.65,3221.25,No\n1353-LJWEM,Male,0,No,No,11,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,60.9,688.5,No\n1794-SWWKL,Male,0,Yes,Yes,15,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),59.65,867.1,No\n6166-YIPFO,Male,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,64.7,4746.05,No\n7016-BPGEU,Female,0,No,No,29,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.1,712.85,Yes\n6876-ADESB,Male,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,48.95,48.95,Yes\n9332-GYWLO,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,54.85,355.1,No\n1963-SVUCV,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.3,45.3,Yes\n4184-VODJZ,Male,0,Yes,Yes,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,Yes,Electronic check,91.35,5764.7,No\n0220-EBGCE,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85.85,167.3,Yes\n1092-WPIVQ,Female,0,Yes,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,428.45,No\n7233-IOQNP,Female,0,Yes,No,43,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),34,1505.35,No\n2834-JKOOW,Female,0,No,No,15,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Mailed check,45.9,693.45,No\n2754-VDLTR,Male,0,No,Yes,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.2,930.4,Yes\n2400-FEQME,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,1177.95,No\n3190-XFANI,Male,0,No,Yes,49,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),100.6,5069.65,Yes\n7551-DACSP,Male,0,Yes,Yes,6,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.3,324.25,Yes\n4957-SREEC,Male,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.35,1458.1,No\n7235-NXZCP,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.85,156.4,Yes\n0230-UBYPQ,Male,1,Yes,No,63,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),36.1,2298.9,No\n4923-ADWXJ,Female,0,No,No,25,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),65.8,1679.65,No\n1763-KUAAW,Female,1,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.35,369.6,No\n1104-TNLZA,Male,1,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.8,2998,No\n5195-KPUNQ,Female,1,No,No,53,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,96.75,5206.55,No\n0520-FDVVT,Male,0,No,No,35,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),102.35,3626.1,Yes\n3439-GVUSX,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.4,24.4,No\n1444-VVSGW,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),115.65,7968.85,Yes\n5712-PTIWW,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,79.85,152.45,Yes\n5949-HGVJL,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,73.05,1959.5,No\n5203-XEHAX,Female,0,No,No,34,Yes,No,DSL,Yes,Yes,No,No,No,Yes,One year,No,Electronic check,64.35,2053.05,No\n1635-HDGFT,Female,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,398.55,No\n3315-TOTBP,Male,0,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,76,1130.85,Yes\n8050-XGRVL,Female,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),54.75,3425.35,No\n6661-HBGWL,Female,1,No,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.75,4323.45,Yes\n1518-VOWAV,Female,0,No,No,9,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.65,703.55,Yes\n9828-QHFBK,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.15,1275.7,No\n3908-MKIMJ,Male,1,Yes,No,68,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Electronic check,41.95,2965.75,No\n5012-YSPJJ,Male,0,Yes,Yes,31,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,54.35,1647,No\n5909-ECHUI,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,56.25,56.25,Yes\n1309-BXVOQ,Male,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.1,2249.95,Yes\n6728-VOIFY,Female,0,Yes,No,63,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,96,6109.75,No\n9317-WZPGV,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),79.75,159.4,Yes\n4891-NLUBA,Female,0,Yes,Yes,61,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),61.45,3751.15,No\n2856-HYAPG,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.65,68.65,Yes\n8969-PRHFK,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,411.25,No\n0661-KQHNK,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19,105.5,No\n8709-KRDVL,Female,0,No,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100,3320.6,No\n4488-PSYCG,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.25,327.45,Yes\n1427-VERSM,Female,0,Yes,No,56,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,5669.5,No\n9801-NOSHQ,Male,0,No,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,465.45,No\n6339-YPSAH,Male,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.8,704.3,No\n3621-CEOVK,Female,1,Yes,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,1369.8,Yes\n6906-ANDWJ,Male,0,Yes,Yes,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,1107.25,Yes\n2159-TURXX,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,95.55,No\n1548-FEHVL,Male,0,Yes,No,61,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.2,6375.2,No\n3795-CAWEX,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.55,8152.3,No\n6215-NQCPY,Male,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.7,1566.75,No\n5647-URDKA,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,130.25,No\n2229-DPMBI,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.5,162.15,No\n9626-WEQRM,Female,0,No,No,4,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,29.15,110.05,No\n2188-SXWVT,Female,0,No,No,34,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,55,1885.15,No\n6258-PVZWJ,Male,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.8,6302.85,No\n1094-BKOSX,Female,0,Yes,Yes,45,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),51,2264.5,No\n6969-MVBAI,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,90.1,816.8,No\n2978-XXSOG,Female,0,No,No,22,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,59.05,1253.5,No\n2342-CKIAO,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,41.2,No\n6953-PBDIN,Male,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),72.95,5265.55,No\n3898-BSJYF,Female,0,No,Yes,10,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),73.55,693.3,No\n3938-YFPXD,Male,0,No,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.3,5997.1,No\n7696-AMHOD,Female,0,Yes,Yes,49,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78,3824.2,No\n2453-SAFNS,Female,1,No,No,54,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,No,Mailed check,72.1,3886.05,No\n9558-IHEZX,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),106.75,7283.25,No\n9617-UDPEU,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.25,412.55,No\n6340-DACFT,Female,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1070.25,No\n4955-VCWBI,Female,0,Yes,Yes,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,817.95,No\n6813-GZQCG,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.65,1171.3,No\n7426-GSWPO,Male,1,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.5,6548.65,No\n3389-KTRXV,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.85,625.65,No\n3118-UHVVQ,Female,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.8,1911.5,No\n1031-IIDEO,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.85,70.85,Yes\n5832-XKAES,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.8,134.7,Yes\n3205-MXZRA,Male,0,No,No,26,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,No,Credit card (automatic),59.45,1507,No\n4701-AHWMW,Male,0,Yes,No,55,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),54.55,2978.3,Yes\n8317-BVKSO,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.05,299.3,No\n5702-KVQRD,Male,0,Yes,No,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,82.55,5832.65,No\n0083-PIVIK,Male,0,No,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Electronic check,81.25,5567.55,No\n9210-IAHGH,Female,0,No,No,7,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),70.75,450.8,Yes\n6169-PGNCD,Female,0,No,No,57,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),74.3,4166.35,No\n0023-XUOPT,Female,0,Yes,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,94.1,1215.6,Yes\n4592-IWTJI,Female,0,Yes,Yes,3,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),29.7,91.7,Yes\n4909-JOUPP,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.7,7898.45,No\n4657-FWVFY,Female,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),96.35,3915.4,No\n2257-BOVXD,Male,0,Yes,No,14,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),66.6,979.5,No\n7729-JTEEC,Male,0,Yes,Yes,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.5,90.05,No\n7632-MNYOY,Male,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),110.9,7432.05,Yes\n6518-LGAOV,Female,0,Yes,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105,4026.4,Yes\n7242-QZLXF,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.3,25.3,Yes\n4576-CSAJH,Male,0,No,No,22,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),55.15,1193.05,Yes\n1000-AJSLD,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.1,20.1,Yes\n9696-RMYBA,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.1,398.55,No\n8690-UPCZI,Male,0,Yes,Yes,29,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),69.05,1958.45,No\n1062-LHZOD,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.9,69.9,Yes\n9770-LXDBK,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,63.15,No\n4086-WITJG,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1301.1,No\n8344-WFMFH,Male,0,No,Yes,9,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,50.1,484.05,No\n9600-NAXZN,Male,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,4528,Yes\n8822-KNBHV,Female,0,No,No,48,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),83.45,3887.85,No\n8404-FYDIB,Male,0,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),86.65,2208.75,No\n2791-SFVEW,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,238.15,No\n6457-USBER,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.8,80.8,Yes\n6762-NSODU,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,958.15,No\n4662-EKDPQ,Male,0,No,No,2,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),62.05,118.3,Yes\n9248-OJYKK,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.45,76.45,Yes\n9888-ZCUMM,Male,0,Yes,Yes,64,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.05,3845.45,No\n3398-FSHON,Female,1,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.3,1094.5,Yes\n3319-DWOEP,Male,1,Yes,No,6,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.75,573.75,Yes\n1074-WVEVG,Female,0,Yes,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,1267,No\n9979-RGMZT,Female,0,No,No,7,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,94.05,633.45,No\n6437-UDQJM,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),84.1,6129.65,No\n1226-IENZN,Male,1,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.75,1218.25,No\n2108-GLPQB,Male,0,Yes,No,25,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),55.55,1405.3,No\n9259-PACGQ,Female,0,Yes,No,34,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,62.65,2274.9,Yes\n4415-IJZTP,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,74.5,Yes\n1465-VINDH,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.1,1068.85,Yes\n5474-LAMUQ,Male,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,533.9,No\n3468-DRVQJ,Female,0,Yes,Yes,10,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,One year,No,Electronic check,70.3,676.15,No\n1122-YJBCS,Male,0,Yes,No,69,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),53.65,3804.4,No\n4990-ALDGW,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1118.8,No\n4816-OKWNX,Male,0,Yes,Yes,50,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.4,5236.4,No\n3315-IKYZQ,Male,0,Yes,Yes,28,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Mailed check,50.8,1386.8,No\n2810-FTLEM,Female,0,No,No,16,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.15,762.25,Yes\n4747-LCAQL,Male,0,No,No,25,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,79,1902,No\n3411-WLRSQ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,239.05,No\n0898-XCGTF,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),96.5,5673.7,No\n8006-PYCSW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,39.8,No\n8249-THVEC,Male,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,997.75,No\n8722-PRFDV,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),77.55,5574.35,No\n2164-SOQXL,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,406.05,No\n4020-KIUDI,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.85,138.85,No\n5850-BDWCY,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,123.65,No\n2038-YSEZE,Female,0,No,No,29,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,67.45,1801.1,No\n0827-ITJPH,Male,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),18.55,689,No\n8799-OXZMD,Female,0,No,No,28,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,29.75,790.7,No\n1925-TIBLE,Female,0,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.5,582.5,Yes\n1705-GUHPV,Female,0,No,No,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.2,1618.2,No\n4779-ZGICK,Male,0,Yes,Yes,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.55,1173.35,No\n0048-PIHNL,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.45,900.9,No\n0818-OCPZO,Male,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.45,2122.05,Yes\n3967-KXAPS,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),92.3,6719.9,No\n1447-GIQMR,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.15,69.15,Yes\n3704-IEAXF,Female,0,Yes,Yes,72,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),53.65,3784,No\n6810-VCAEX,Female,0,No,No,47,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),39.65,1798.65,No\n0674-EYYZV,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.65,54.65,No\n4480-MBMLB,Female,0,No,No,36,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.8,3886.45,No\n1395-OFUWC,Male,0,Yes,Yes,43,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),29.3,1224.05,No\n0822-QGCXA,Female,1,Yes,No,27,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,83.85,2310.2,No\n0872-NXJYS,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.55,723.4,Yes\n2832-KJCRD,Female,0,No,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.65,3988.5,No\n3588-WSTTJ,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.05,3554.6,No\n4075-WKNIU,Female,0,Yes,Yes,0,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,73.35, ,No\n5090-EMGTC,Female,0,Yes,No,59,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,100.05,6034.85,No\n2346-CZYIL,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.35,531.6,No\n3798-EPWRR,Female,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,43.95,85.1,No\n6258-NGCNG,Male,0,No,No,7,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,23.5,173,No\n4186-ZBUEW,Female,0,No,No,36,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,70.7,2511.95,No\n5274-XHAKY,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),94.3,3893.6,No\n1442-BQPVU,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,29.15,357.15,No\n3978-YNKDD,Male,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.85,467.5,No\n4940-KHCWD,Female,0,Yes,No,60,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),37.7,2288.7,No\n9412-ARGBX,Female,0,No,Yes,48,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Mailed check,95.5,4627.85,Yes\n1389-CXMLU,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,91.05,289.1,Yes\n9589-ABEPT,Male,0,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,92.45,6460.55,No\n9388-ZEYVT,Male,0,No,No,43,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,One year,No,Electronic check,44.15,1931.3,No\n0305-SQECB,Female,0,No,Yes,11,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,36.05,402.6,No\n7605-SNLQG,Female,0,Yes,No,45,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),50.25,2221.55,No\n4670-TABXH,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.75,7758.9,No\n5561-NWEVX,Female,1,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,79.2,172.85,Yes\n0064-SUDOG,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,224.5,No\n8561-NMTBD,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),112.35,7388.45,No\n5161-XEUVX,Male,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,No,Mailed check,94.3,3460.95,No\n8292-FRFZQ,Female,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),41.15,1700.9,No\n8591-NXRCV,Female,0,No,No,41,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),74.65,3090.65,No\n7895-VONWT,Female,0,No,No,25,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.25,1293.8,No\n4526-RMTLL,Male,0,Yes,Yes,8,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),76.15,645.8,No\n6253-GNHWH,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,71.1,5224.95,No\n4566-NECEV,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,96.55,500.1,No\n8080-POTJR,Female,0,No,No,30,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),79.3,2427.1,No\n6906-MPARY,Male,0,No,No,40,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,One year,Yes,Credit card (automatic),89.6,3488.15,No\n3662-FXJFO,Female,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.5,1035.7,No\n8107-RZLNV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),106.3,7565.35,No\n0516-OOHAR,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),100.35,2799,No\n2027-CWDNU,Male,0,Yes,Yes,18,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,85.6,1601.5,No\n2737-WFVYW,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.25,85.5,Yes\n9912-OMZDS,Female,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.15,6256.2,No\n3733-ZEECP,Male,0,Yes,Yes,22,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,51.1,1232.9,No\n7878-RTCZG,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,No\n2452-MRMZF,Female,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.7,1937.4,No\n2272-QAGFO,Female,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.3,1096.25,Yes\n7103-IPXPJ,Male,0,Yes,No,50,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Electronic check,99.4,5059.75,No\n4342-HFXWS,Female,0,Yes,Yes,48,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),69.7,3023.65,No\n3545-CNWRG,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.35,4889.2,No\n1785-BPHTP,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,85.45,2289.9,No\n4989-LIXVT,Male,1,No,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.9,6503.2,No\n7315-WYOAW,Male,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,100.75,1313.25,No\n1173-XZPYF,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,990.3,No\n9850-OWRHQ,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.1,228,Yes\n3768-NLUBH,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.6,5746.15,Yes\n8676-TRMJS,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75,209.1,Yes\n2509-TFPJU,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.75,1864.2,No\n9530-GRMJG,Male,0,Yes,Yes,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.1,5979.7,No\n6898-RBTLU,Female,0,Yes,Yes,49,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),79.3,3902.45,No\n5481-NTDOH,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),107.05,7142.5,No\n2068-WWXQZ,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,902,No\n7359-SSBJK,Female,1,No,No,64,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.2,4481,Yes\n2061-VVFST,Female,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,805.2,No\n0685-MLYYM,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.75,154.85,Yes\n0749-IRGQE,Female,1,Yes,No,13,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,45.3,528.45,No\n2380-DAMQP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,115.15,8349.7,No\n2256-YLYLP,Male,0,Yes,Yes,68,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),72.95,4953.25,No\n1272-ILHFG,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,332.65,No\n0164-XAIRP,Female,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.55,470.2,No\n5666-CYCYZ,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.55,2259.35,No\n4011-ARPHK,Male,0,No,No,27,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.35,1411.35,No\n8182-PNAGI,Male,0,No,No,12,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),50.25,593.75,Yes\n6365-HITVU,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),87.25,6328.7,No\n7558-IMLMT,Male,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.8,1411.9,No\n9488-FYQAU,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.25,6841.4,No\n3590-TCXTB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,20.35,No\n6994-KERXL,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.9,238.5,No\n7957-RYHQD,Female,1,No,No,40,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),79.2,3233.85,Yes\n2180-DXNEG,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96,1062.1,Yes\n1960-UOTYM,Male,0,Yes,Yes,52,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,79.2,4016.3,No\n2740-JFBOK,Male,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,24,226.55,No\n6500-JVEGC,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),101.35,7110.75,No\n5515-AKOAJ,Female,0,No,No,54,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.1,5440.9,Yes\n8976-OQHGT,Female,0,Yes,Yes,4,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,56.5,235.1,Yes\n5245-VDBUR,Female,0,Yes,No,52,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.45,1958.95,No\n6230-BSUXY,Female,1,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,Yes\n8469-SNFFH,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),79.4,5528.9,No\n2144-ESWKO,Male,0,No,No,43,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),35.2,1463.7,No\n1241-FPMOF,Male,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,1025.05,No\n9928-BZVLZ,Female,0,No,No,12,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,49.85,552.1,No\n4114-QMKVN,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),68.75,3815.4,No\n2775-SEFEE,Male,0,No,Yes,0,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),61.9, ,No\n9003-CPATH,Male,0,No,No,42,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,79.9,3313.4,No\n1754-GKYPY,Male,1,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),89.75,1938.9,No\n5294-CDGWY,Male,0,Yes,Yes,51,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,59.3,3014.65,Yes\n7956-XQWGU,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.4,460.25,No\n1591-MQJTP,Male,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),93.65,4839.15,No\n5295-PCJOO,Male,0,No,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,184.4,Yes\n2369-FEVNO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,19.9,No\n9863-JZAIC,Male,0,No,No,35,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55,2010.55,Yes\n7471-MQPOS,Male,1,Yes,Yes,71,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),72.9,5139.65,No\n7660-HDPJV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.2,69.2,Yes\n5115-SQAAU,Female,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.6,1673.4,No\n9845-QOMAD,Male,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.75,309.35,No\n7672-VFMXZ,Male,0,Yes,No,57,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,55.7,3171.6,No\n9739-JLPQJ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),117.5,8670.1,No\n9916-AYHTC,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,916,No\n3855-ONCAR,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,78.9,299.75,No\n1488-SYSFC,Male,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.65,702.05,No\n7931-PXHFC,Male,0,No,No,38,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Mailed check,62.3,2354.8,Yes\n3990-QYKBE,Male,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.5,3473.4,Yes\n0970-QXPXW,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No\n3259-KNMRR,Male,1,No,No,57,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.75,4438.2,No\n6120-RJKLU,Female,1,Yes,No,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.95,4819.75,No\n9572-WUKSB,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.9,92.25,No\n5893-KCLGT,Female,0,No,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.75,1567,No\n0076-LVEPS,Male,0,No,Yes,29,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,45,1242.45,No\n3640-JQGJG,Male,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.8,559.2,No\n4919-MOAVT,Male,0,No,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,220.1,Yes\n1596-OQSPS,Female,0,No,No,11,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,51.1,531.15,No\n9867-XOBQA,Female,0,No,Yes,21,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,53.15,1183.2,No\n9546-KDTRB,Female,0,No,No,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),24.7,465.85,No\n3090-HAWSU,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.6,6876.05,Yes\n9820-RMCQV,Female,0,No,No,11,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),48.55,501,Yes\n3174-RKMOW,Male,0,Yes,Yes,35,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.95,3782.4,No\n1760-CAZHT,Male,0,No,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,460.2,No\n7839-QRKXN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes\n8441-SHIPE,Female,0,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.6,1790.35,No\n5204-QZXPU,Male,0,No,No,19,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.65,733.35,Yes\n4597-ELFTS,Male,0,No,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.9,1334,Yes\n1320-GVNHT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),108.4,7767.25,No\n1047-RNXZV,Male,0,No,No,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,876.15,No\n3541-ZNUHK,Female,0,Yes,Yes,55,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,85.1,4600.95,No\n8450-JOVAH,Male,0,Yes,Yes,2,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,56.7,113.55,Yes\n8725-JEDFD,Male,0,No,No,27,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.05,1793.25,No\n7562-UXTPG,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,886.7,No\n8071-SBTRN,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Mailed check,111.15,7737.55,No\n1113-IUJYX,Female,0,Yes,No,14,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Mailed check,105.95,1348.9,Yes\n6668-CNMFP,Female,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),89.35,1686.85,No\n5626-MGTUK,Female,0,No,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.1,1879.25,No\n5681-LLOEI,Male,0,Yes,Yes,43,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),91.25,4013.8,No\n8212-DJRCH,Male,0,Yes,Yes,5,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,90.35,434.5,No\n4323-OHFOW,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.55,7195.35,No\n4933-BSAIP,Female,0,Yes,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.1,780.1,No\n2030-BTZRO,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.4,107.6,No\n1116-DXXDF,Male,0,No,No,39,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,100.45,3801.7,No\n9274-CNFMO,Male,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.95,308.7,Yes\n7758-XKCBS,Male,0,No,No,15,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.7,438.25,Yes\n8992-JQYUN,Male,0,Yes,No,1,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.35,50.35,Yes\n6563-VNPMN,Female,1,No,No,45,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),85.7,3778.1,No\n0617-AQNWT,Female,0,Yes,No,64,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Electronic check,47.85,3147.5,Yes\n2267-WTPYD,Female,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),94,5438.95,No\n0270-THENM,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.85,5102.35,No\n2446-PLQVO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes\n4707-MAXGU,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.85,1872.2,No\n2710-WYVXG,Female,0,No,No,3,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,71.1,213.35,No\n3005-NFMTA,Male,1,No,No,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.8,5617.75,Yes\n6300-BWMJX,Female,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,93.35,5386.5,No\n8784-CGILN,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.85,1776.95,Yes\n5389-FFVKB,Male,1,Yes,No,32,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),80.3,2483.05,Yes\n7009-LGECI,Female,0,No,No,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.55,235.65,No\n8444-WRIDW,Female,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.45,5224.35,Yes\n5022-KVDQT,Male,0,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.3,2272.8,No\n7976-CICYS,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.7,83.75,No\n1036-GUDCL,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),79.05,4663.4,No\n2005-DWQZJ,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.05,201.7,No\n8148-WOCMK,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,125,No\n7815-PDTHL,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,684.4,No\n9451-LPGOO,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.8,620.35,Yes\n9173-IVZVP,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.9,1146.05,No\n9129-UXERG,Female,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),103.6,1806.35,No\n3635-JBPSG,Female,0,No,No,15,No,No phone service,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Mailed check,38.8,603,No\n7964-ZRKKG,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),88.4,5798.3,No\n5868-YWPDW,Male,1,Yes,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.2,519.15,Yes\n6229-LSCKB,Male,1,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.7,497.6,No\n2025-JKFWI,Male,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99,1301.7,Yes\n4078-SAYYN,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.75,1129.75,Yes\n1724-IQWNM,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,19.3,No\n8217-QYOHV,Male,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.75,266.95,No\n5546-QUERU,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.95,257,No\n9940-HPQPG,Female,0,Yes,No,9,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),91.75,865.8,Yes\n6897-UUBNU,Male,0,No,No,29,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,89.65,2623.65,No\n6127-IYJOZ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,No\n6618-RYATB,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.55,79.55,Yes\n8930-XOTDP,Female,0,Yes,Yes,18,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.95,1082.8,No\n5916-QEWPT,Female,0,Yes,No,2,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),69,147.8,No\n5060-TQUQN,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),83.55,2570.2,No\n0531-XBKMM,Male,0,No,Yes,66,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),65.7,4378.9,No\n8465-SBRXP,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),94.9,3616.25,No\n2408-PSJVE,Male,0,Yes,Yes,44,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,61.9,2924.05,No\n9079-YEXQJ,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,111.1,6014.85,Yes\n9700-ZCLOT,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,32.7,No\n8738-JOKAR,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,67.7,2882.25,No\n4534-WGCIR,Female,0,Yes,Yes,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,1509.9,No\n1930-WNXSB,Male,0,Yes,Yes,58,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.85,5305.05,No\n2685-SREOM,Female,0,Yes,Yes,25,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.1,2368.4,Yes\n3508-CFVZL,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,111.3,7985.9,No\n0402-CQAJN,Female,0,No,No,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.9,3545.35,Yes\n6692-UDPJC,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,91.65,1301,Yes\n1273-MTETI,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.85,372.45,Yes\n0657-DOGUM,Female,0,Yes,No,48,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.6,2985.25,No\n5480-HPRRX,Female,1,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.3,77.75,Yes\n8792-AOROI,Female,0,Yes,No,8,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Two year,No,Mailed check,65.5,564.35,No\n0295-PPHDO,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,95.45,Yes\n5146-YYFRZ,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1311.75,No\n1195-OIYEJ,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.1,1135.7,Yes\n5906-CVLHP,Female,0,Yes,Yes,45,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.15,2319.8,Yes\n3452-SRFEG,Male,0,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,3720.35,No\n4070-OKWVH,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),94.6,5025.8,No\n5297-MDOIR,Female,0,Yes,No,63,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),81.15,5224.5,No\n6369-MCAKO,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.05,6185.8,No\n4926-UMJZD,Female,0,Yes,No,31,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.2,1498.55,No\n6848-HJTXY,Female,0,Yes,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,1208.6,No\n6341-AEVKX,Female,0,Yes,No,62,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.3,6613.65,No\n4501-EQDRN,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.7,69.7,Yes\n2990-HWIML,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,573.3,Yes\n0682-USIXD,Female,0,Yes,No,21,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,86.05,1818.9,No\n6976-BWGLQ,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.2,1787.35,No\n7078-NVFAM,Female,0,Yes,Yes,32,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,35.15,1051.05,No\n8065-QBYTO,Female,1,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),99.65,7181.25,No\n2133-TSRRM,Female,0,No,Yes,34,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,3688.6,No\n7384-GHBPI,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.15,99.75,Yes\n7603-USHJS,Male,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.75,871.4,Yes\n7234-FECYN,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.35,780.5,Yes\n2511-ALLCS,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,821.6,No\n3191-CSNMG,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.7,239.45,No\n7952-OBOYL,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,89.85,244.45,No\n7470-DYNOE,Male,0,No,No,53,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,Yes,Electronic check,61.1,3357.9,No\n7853-OETYL,Female,0,Yes,No,4,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,29.05,129.6,No\n1545-ACTAS,Female,0,Yes,Yes,48,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),99.7,4977.2,No\n5876-HZVZM,Female,0,Yes,Yes,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),55.9,365.35,Yes\n1400-MMYXY,Male,1,Yes,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,334.65,Yes\n5126-RCXYW,Male,0,Yes,Yes,54,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),46,2424.05,No\n3085-QUOZK,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.95,43.95,Yes\n2363-BJLSL,Male,0,No,No,62,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),80.4,4981.15,No\n1956-YIFGE,Male,0,Yes,Yes,22,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,100.05,2090.25,No\n3926-CUQZX,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.1,45.1,Yes\n7921-LMDFQ,Male,1,No,No,51,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94,4905.75,No\n2925-MXLSX,Female,0,No,No,30,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),68.95,2038.7,No\n5820-PTRYM,Female,1,Yes,No,56,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),68.45,4014,No\n9609-BENEA,Male,0,Yes,No,35,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,69,2441.7,No\n6211-WHMYA,Female,1,No,No,64,No,No phone service,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,43.85,2751,No\n4459-BBGHE,Male,0,No,Yes,30,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.5,1307.8,No\n9945-PSVIP,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,18.7,383.65,No\n1041-RXHRA,Female,0,No,No,41,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.25,2868.05,Yes\n1750-CSKKM,Male,0,No,Yes,9,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,55.35,449.75,Yes\n9108-EJFJP,Female,0,Yes,No,1,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,53.55,53.55,No\n0530-IJVDB,Male,0,No,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114.6,7882.5,No\n0508-SQWPL,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.1,1087.7,No\n2215-ZAFGX,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.5,791.7,No\n8213-TAZPM,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.75,7493.05,No\n7142-HVGBG,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103,4414.3,Yes\n1304-SEGFY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,97.85,6841.3,No\n7242-EDTYC,Male,0,No,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,819.95,No\n2904-GGUAZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),84.05,6052.25,No\n8267-ZNYVZ,Female,0,Yes,No,33,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,103.75,3361.05,Yes\n5136-GFPMB,Male,0,No,No,54,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),89.4,4869.5,No\n2595-KIWPV,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,509.3,No\n5243-SAOTC,Male,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.85,4308.25,No\n9398-MMQTO,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.45,221.1,No\n7619-PLRLP,Female,0,Yes,No,53,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),74.1,3833.95,No\n6457-GIRWB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,69.35,Yes\n6508-NJYRO,Male,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,294.95,No\n1450-SKCVI,Female,0,No,No,56,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.85,4092.85,Yes\n4710-NKCAW,Male,0,Yes,Yes,5,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),64.4,316.9,No\n5306-BVTKJ,Male,0,Yes,Yes,48,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),55.8,2651.2,No\n0357-NVCRI,Female,0,No,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,471.7,No\n5570-PTWEH,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.15,216.75,Yes\n2371-KFUOG,Male,0,No,No,58,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),99.15,5720.95,No\n0463-ZSDNT,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),56.75,503.25,No\n6502-MJQAE,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes\n6257-DTAYD,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.15,7365.3,No\n4616-ULAOA,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,7245.9,No\n7693-LCKZL,Male,0,Yes,Yes,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.15,385,Yes\n7746-AWNQW,Female,0,No,No,28,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,35.75,961.4,No\n5996-EBTKM,Female,0,Yes,Yes,67,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),69.9,4615.9,No\n2758-RNWXS,Male,0,No,No,35,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,89.2,3251.3,No\n2314-TNDJQ,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),55.65,3880.05,No\n2405-LBMUW,Female,0,Yes,Yes,61,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),50.7,3088.75,No\n3454-JFUBC,Male,1,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20,1396,No\n0032-PGELS,Female,0,Yes,Yes,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),30.5,30.5,Yes\n9039-ZVJDC,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.1,53.05,No\n6797-LNAQX,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),98.3,6859.5,Yes\n9013-AQORL,Female,0,No,Yes,48,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),45.55,2108.35,No\n2898-MRKPI,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),101.05,6770.5,No\n2750-BJLSB,Female,0,No,No,47,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,103.7,4730.6,No\n3648-GZPHF,Male,0,Yes,Yes,32,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,Yes,Mailed check,36.25,1151.05,No\n2075-RMJIK,Female,0,Yes,Yes,5,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.4,232.55,No\n9588-YRFHY,Male,0,No,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.9,1022.6,No\n6394-MFYNG,Female,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.4,5121.3,Yes\n1564-NTYXF,Female,1,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,82,1127.2,Yes\n9364-YKUVW,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.8,309.4,No\n5392-AKEMH,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.05,523.1,No\n2451-YMUXS,Male,1,No,No,67,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),64.55,4250.1,No\n3914-FDRHP,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,86.25,770.5,No\n3078-ZKNTS,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.75,246.7,No\n1024-KPRBB,Female,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Mailed check,89.1,3342,No\n4690-PKDQG,Female,1,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.55,3930.6,No\n8155-IBNHG,Female,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.4,1747.85,Yes\n0886-QGENL,Female,1,Yes,No,27,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,2754.45,Yes\n9547-ITEFG,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.6,897.75,No\n1264-FUHCX,Female,0,Yes,No,49,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),56.3,2780.6,No\n7789-CRUVC,Female,1,Yes,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),94.2,5895.45,No\n6598-KELSS,Male,0,No,Yes,50,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),43.05,2208.05,No\n8739-WWKDU,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.5,2196.15,Yes\n8685-WHQPW,Female,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.4,1692.6,Yes\n4745-LSPLO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,Yes\n8083-YTZES,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,265.35,Yes\n7240-FQLHE,Female,1,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),99.75,1836.25,Yes\n6664-FPDAC,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.95,6418.9,Yes\n9972-VAFJJ,Female,1,Yes,No,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,94,4871.45,No\n0422-UXFAP,Female,0,Yes,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,98.85,4947.55,No\n1904-WAJAA,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,64.35,1558.65,No\n5130-YPIRV,Female,0,Yes,No,62,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),72,4284.2,No\n2843-CQMEG,Male,0,No,No,24,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.7,1218.25,No\n6439-PKTRR,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Electronic check,80.7,5617.95,No\n5351-QESIO,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.2,24.2,No\n0786-VSSUD,Male,1,No,No,16,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,39,679.85,Yes\n5568-DMXZS,Female,0,No,No,8,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,65.45,554.45,No\n8468-FZTOE,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Electronic check,74.35,5237.4,No\n6633-SYEUS,Female,0,No,No,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),83.2,2032.3,No\n6447-GORXK,Male,0,No,Yes,31,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25,789.2,No\n6967-PEJLL,Male,0,Yes,Yes,37,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Electronic check,40.2,1525.35,No\n3976-BWUCK,Female,0,Yes,No,30,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),94.1,2804.45,Yes\n5981-ZVXOT,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.35,3726.15,No\n1684-FLBGS,Female,0,Yes,Yes,23,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),69.5,1652.1,No\n1389-WNUIB,Female,0,Yes,Yes,20,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),76,1588.75,No\n0376-OIWME,Male,0,Yes,No,36,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.6,3366.05,No\n3585-ISXZP,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),95.65,778.1,Yes\n0218-QNVAS,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),100.55,7113.75,No\n6583-QGCSI,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.05,4367.35,Yes\n0804-YGEQV,Female,0,Yes,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.45,993.15,No\n7164-BPTUT,Male,0,No,Yes,57,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,89.55,5012.35,No\n4174-LPGTI,Female,0,Yes,Yes,41,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),66.5,2728.6,Yes\n2523-EWWZL,Female,0,Yes,No,27,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,76.1,2093.4,No\n0928-XUTSN,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.5,1011.8,No\n2108-XWMPY,Male,0,No,No,3,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,35.45,106.85,Yes\n0052-YNYOT,Female,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.55,1343.4,No\n6304-IJFSQ,Male,0,No,No,3,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.9,130.1,Yes\n9586-JGQKH,Female,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.4,6794.75,No\n4501-VCPFK,Male,0,No,No,26,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,35.75,1022.5,No\n6075-SLNIL,Male,0,No,No,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),95.1,3691.2,No\n9347-AERRL,Male,0,Yes,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,486.2,No\n0093-XWZFY,Male,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,4036.85,Yes\n2274-XUATA,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.1,4685.55,No\n1980-KXVPM,Female,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.05,256.25,Yes\n7703-ZEKEF,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81,1917.1,Yes\n0723-DRCLG,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.45,74.45,Yes\n5482-NUPNA,Female,0,No,No,4,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,60.4,272.15,Yes\n6691-CCIHA,Female,0,Yes,No,62,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,84.95,5150.55,No\n1685-BQULA,Female,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.4,3756.4,No\n9053-EJUNL,Male,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,3645.75,No\n0666-UXTJO,Male,1,Yes,No,34,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.2,2874.45,No\n1471-GIQKQ,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,49.95,49.95,No\n4807-IZYOZ,Female,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1020.75,No\n1122-JWTJW,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.65,70.65,Yes\n9710-NJERN,Female,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,826,No\n9837-FWLCH,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.2,239,No\n1699-HPSBG,Male,0,No,No,12,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,Yes,Electronic check,59.8,727.8,Yes\n7203-OYKCT,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.95,7544.3,No\n1035-IPQPU,Female,1,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.5,6479.4,No\n7398-LXGYX,Male,0,Yes,No,44,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.8,3626.35,No\n2823-LKABH,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.05,1679.4,No\n8775-CEBBJ,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.2,403.35,Yes\n0550-DCXLH,Male,0,No,No,13,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,73.35,931.55,No\n9281-CEDRU,Female,0,Yes,No,68,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),64.1,4326.25,No\n2235-DWLJU,Female,1,No,No,6,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.4,263.05,No\n0871-OPBXW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,39.25,No\n3605-JISKB,Male,1,Yes,No,55,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,No,Credit card (automatic),60,3316.1,No\n6894-LFHLY,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.75,75.75,Yes\n9767-FFLEM,Male,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.5,2625.25,No\n0639-TSIQW,Female,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),102.95,6886.25,Yes\n8456-QDAVC,Male,0,No,No,19,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),78.7,1495.1,No\n7750-EYXWZ,Female,0,No,No,12,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,60.65,743.3,No\n2569-WGERO,Female,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),21.15,1419.4,No\n6840-RESVB,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,84.8,1990.5,No\n2234-XADUH,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),103.2,7362.9,No\n4801-JZAZL,Female,0,Yes,Yes,11,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,29.6,346.45,No\n8361-LTMKD,Male,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.4,306.6,Yes\n3186-AJIEK,Male,0,No,No,66,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.65,6844.5,No\n" }, { "path": "Data/Computers.csv", "content": 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}, { "path": "Data/MMM_raw_data_v02.csv", "content": "Weekend,Unit_Sold,Print_Spend,Radio_Spend,TV_Spend,Promotion,Promotion_Dummy\n9,2023,49099,858,51.14285714,Yes,1\n227,780,48603,807,11.02857143,No,0\n134,705,47249,785,11.48571429,Yes,1\n60,678,46654,718,11.8,Yes,1\n231,666,43688,822,10.71428571,No,0\n48,715,45042,742,16.51428571,No,0\n181,707,44454,737,9.31428571,Yes,1\n103,710,46747,621,5.17142857,Yes,1\n91,655,44564,694,5.08571429,Yes,1\n44,719,47989,565,6.08571429,No,0\n203,670,38676,885,5.77142857,No,0\n195,622,39428,851,15.74285714,No,0\n152,709,41488,767,2.71428571,No,0\n79,675,46475,564,1.71428571,Yes,1\n124,664,45140,591,11.48571429,Yes,1\n158,680,37673,860,-0.71428571,Yes,1\n21,598,48885,352,-0.14285714,No,0\n8,628,39889,703,2.8,Yes,1\n64,596,47563,370,5.6,No,0\n224,606,39307,696,15.57142857,No,0\n133,659,36706,795,2.54285714,Yes,1\n54,628,40313,640,-0.51428571,No,0\n105,577,35763,811,1.37142857,Yes,1\n192,599,48681,272,9.22857143,Yes,1\n81,569,48598,272,15.2,Yes,1\n61,639,47936,295,9.14285714,No,0\n109,616,48387,267,13.11428571,No,0\n156,623,32886,886,-1.91428571,No,0\n31,588,42767,491,2.08571429,Yes,1\n198,633,40425,575,-3.94285714,No,0\n173,575,42320,492,6,Yes,1\n213,604,35300,774,2,Yes,1\n7,605,46044,347,-3,No,0\n25,599,41781,509,12.94285714,No,0\n157,559,35072,777,1.94285714,No,0\n162,581,38183,651,8.91428571,Yes,1\n93,568,46091,330,-0.11428571,No,0\n127,622,44157,391,9.8,No,0\n132,636,43885,396,-0.25714286,No,0\n238,581,32303,860,1.74285714,No,0\n104,612,45498,325,14.71428571,No,0\n219,581,44401,366,6.68571429,Yes,1\n116,569,48536,203,13.68571429,No,0\n138,625,43825,381,8.62857143,Yes,1\n11,577,36701,648,-2.51428571,Yes,1\n26,583,46352,259,4.42857143,Yes,1\n6,612,41655,430,8.34285714,Yes,1\n101,602,39645,507,10.31428571,Yes,1\n233,538,48860,129,13.25714286,No,0\n207,533,45029,261,3.11428571,Yes,1\n225,540,39700,464,-2.94285714,Yes,1\n140,518,33928,686,12,No,0\n59,544,29155,885,9,No,0\n56,541,30733,810,11.94285714,No,0\n65,603,35927,598,-1.08571429,Yes,1\n45,571,37643,523,6.88571429,No,0\n218,517,35421,592,-5.28571429,No,0\n146,568,42870,285,-3.31428571,No,0\n229,542,36628,525,11.57142857,Yes,1\n50,527,30595,761,13.57142857,No,0\n117,587,31080,735,2.51428571,No,0\n100,579,35422,548,-0.57142857,No,0\n71,526,35900,522,7.37142857,No,0\n24,493,43475,209,-0.71428571,Yes,1\n72,564,36167,493,-0.74285714,Yes,1\n14,542,44938,138,3.22857143,No,0\n210,540,29065,778,-5.77142857,No,0\n135,571,38215,397,12.14285714,Yes,1\n206,525,29086,752,-0.94285714,No,0\n220,506,26428,846,4,Yes,1\n75,528,37654,394,2.97142857,No,0\n52,568,34735,507,8.91428571,Yes,1\n29,496,41342,233,1.85714286,No,0\n99,517,29403,704,0.8,Yes,1\n143,503,35773,438,6.68571429,Yes,1\n150,543,31603,598,2.65714286,No,0\n239,468,32552,549,-0.4,No,0\n188,557,34891,437,10.45714286,Yes,1\n107,470,28612,688,12.45714286,Yes,1\n228,548,35204,426,-2.54285714,Yes,1\n112,517,40053,226,-5.54285714,No,0\n46,545,38363,290,-4.57142857,No,0\n168,464,36569,354,-2.65714286,No,0\n205,478,38018,291,-0.68571429,No,0\n4,504,37363,316,7.31428571,Yes,1\n190,477,35886,376,0.28571429,No,0\n214,469,36659,323,-6.82857143,No,0\n10,462,34102,420,8.14285714,No,0\n141,547,30680,561,2.11428571,No,0\n58,550,35321,364,-4.91428571,Yes,1\n145,527,29229,598,7,Yes,1\n226,516,31098,513,8.94285714,No,0\n98,459,25574,720,-6.17142857,No,0\n232,541,36129,295,11.8,Yes,1\n5,495,36037,301,7.8,No,0\n194,495,38077,208,-7.25714286,Yes,1\n167,513,40035,128,-7.25714286,Yes,1\n42,530,36831,253,-0.28571429,No,0\n1,473,29747,535,-4.31428571,Yes,1\n200,481,40057,119,10.68571429,Yes,1\n94,492,40217,111,-1.34285714,No,0\n125,530,39217,149,-0.37142857,No,0\n97,490,22655,811,8.6,Yes,1\n95,439,38326,177,1.6,Yes,1\n240,515,20208,891,4.51428571,Yes,1\n85,437,33618,345,2.45714286,Yes,1\n113,472,23172,754,5.4,Yes,1\n137,522,27701,568,-5.62857143,Yes,1\n170,457,38502,139,6.37142857,No,0\n180,427,27972,554,0.34285714,No,0\n178,465,33244,338,8.28571429,Yes,1\n171,506,36928,182,0.22857143,No,0\n202,429,34629,271,-0.77142857,Yes,1\n119,490,19600,874,-7.77142857,Yes,1\n215,460,31167,397,2.11428571,No,0\n39,505,30831,406,5.08571429,Yes,1\n221,428,19763,848,0.08571429,No,0\n209,418,21791,748,2.94285714,Yes,1\n36,448,17983,897,-3.08571429,No,0\n86,416,24036,648,2.85714286,No,0\n111,452,35774,158,1.74285714,No,0\n142,475,19563,802,-6.28571429,Yes,1\n73,406,34579,199,-1.31428571,No,0\n41,405,20212,762,-3.4,Yes,1\n237,476,16963,883,6.51428571,No,0\n67,471,20017,754,-3.51428571,No,0\n123,471,30847,307,5.37142857,Yes,1\n129,427,27574,429,3.31428571,No,0\n128,440,27371,429,-6.77142857,Yes,1\n38,391,17847,811,-3.77142857,Yes,1\n208,477,21534,655,0.2,No,0\n199,400,24161,546,9.2,Yes,1\n32,437,29001,346,9.14285714,Yes,1\n43,388,27420,410,5.14285714,No,0\n182,400,28827,333,-2,No,0\n217,430,31412,220,-1.08571429,Yes,1\n155,372,19550,682,-0.17142857,Yes,1\n230,399,16554,789,6.74285714,No,0\n40,382,25718,413,-9.34285714,Yes,1\n80,408,32630,130,4.6,Yes,1\n191,394,28159,294,3.51428571,Yes,1\n33,389,22739,503,4.45714286,Yes,1\n16,431,23252,483,-4.54285714,Yes,1\n185,403,13751,852,3.37142857,No,0\n12,361,17644,694,-3.62857143,Yes,1\n187,459,14664,812,6.37142857,No,0\n177,406,22467,489,-7.71428571,No,0\n189,434,28995,226,-6.71428571,No,0\n118,416,24508,400,-0.74285714,Yes,1\n62,390,30100,177,9.25714286,No,0\n84,351,19123,532,-1.371428571,No,0\n184,351,21729,416,-9.457142857,Yes,1\n204,336,18566,535,-4.485714286,Yes,1\n175,338,16173,630,1.514285714,No,0\n19,392,17494,557,3.342857143,No,0\n149,422,11329,801,-10.65714286,Yes,1\n83,408,25527,226,-6.685714286,No,0\n186,368,11355,795,-8.714285714,No,0\n51,344,20593,423,-4.714285714,No,0\n122,349,15159,640,-6.714285714,Yes,1\n183,372,27148,153,-2.771428571,No,0\n161,325,26340,190,-9.771428571,Yes,1\n159,380,26834,156,-5.885714286,No,0\n20,335,21378,360,-2.942857143,No,0\n30,346,19448,436,3.057142857,Yes,1\n179,357,9975,812,8.028571429,Yes,1\n110,365,18372,479,-3.971428571,No,0\n78,400,23630,248,-10.08571429,Yes,1\n131,351,24608,195,5.8,Yes,1\n148,304,19638,390,-7.228571429,No,0\n55,384,8741,819,-2.257142857,No,0\n89,313,18188,439,-11.28571429,Yes,1\n37,397,26306,113,1.714285714,Yes,1\n216,374,24374,186,7.685714286,Yes,1\n165,354,15995,523,-5.342857143,No,0\n130,352,7067,863,-5.457142857,No,0\n35,306,21016,296,-11.51428571,Yes,1\n68,372,6282,859,-4.714285714,No,0\n174,288,19697,295,-3.885714286,Yes,1\n164,286,14883,463,1.942857143,Yes,1\n90,353,22662,134,-12.17142857,No,0\n17,289,21040,193,-5.228571429,Yes,1\n87,362,8378,696,-0.285714286,Yes,1\n108,317,10784,595,-2.314285714,No,0\n234,348,18322,282,-12.37142857,No,0\n212,305,17999,281,-11.45714286,Yes,1\n47,260,17017,313,-11.51428571,Yes,1\n115,347,17485,280,-9.657142857,Yes,1\n13,341,10265,564,6.342857143,Yes,1\n22,275,18558,218,-2.742857143,No,0\n102,260,20873,130,3.257142857,Yes,1\n15,276,12626,452,-11.8,Yes,1\n88,312,16413,291,-9.857142857,Yes,1\n196,292,3059,821,-10.88571429,No,0\n144,307,1991,854,0.057142857,Yes,1\n96,269,12647,428,-7.942857143,No,0\n114,254,18206,175,-13.17142857,No,0\n57,303,2907,772,-4.257142857,No,0\n201,302,14226,309,-1.342857143,Yes,1\n82,271,9129,499,-12.45714286,No,0\n53,226,11308,409,4.514285714,No,0\n3,293,16124,209,-8.514285714,Yes,1\n28,221,8230,502,-9.685714286,No,0\n153,306,5076,623,-7.714285714,Yes,1\n34,221,9146,452,-6.771428571,No,0\n106,302,14965,205,-4.885714286,No,0\n121,235,2776,691,1.085714286,Yes,1\n2,243,16324,144,0.085714286,No,0\n147,264,12106,304,-8.971428571,Yes,1\n163,239,4129,621,1,No,0\n27,209,14777,179,-14.11428571,No,0\n236,273,12584,270,-2.114285714,Yes,1\n151,229,2739,650,-1.2,No,0\n18,288,9811,361,-5.257142857,Yes,1\n23,274,1183,707,0.742857143,Yes,1\n49,216,12910,203,-14.48571429,No,0\n172,236,6727,451,-5.514285714,Yes,1\n211,222,4350,543,3.485714286,Yes,1\n222,269,14536,124,-7.6,Yes,1\n197,214,4379,521,-8.657142857,No,0\n74,240,12467,195,-13.65714286,Yes,1\n77,263,1092,650,-13.65714286,No,0\n66,245,11981,200,-2.771428571,No,0\n69,267,2707,573,-4.771428571,No,0\n160,256,2013,565,-7.028571429,No,0\n235,205,12177,136,-7.171428571,No,0\n70,183,4119,435,1.685714286,Yes,1\n136,260,1740,534,-1.314285714,No,0\n92,192,9015,220,-5.457142857,No,0\n120,188,1071,480,-10.88571429,Yes,1\n193,148,7384,195,-4.142857143,Yes,1\n139,212,6850,181,-1.371428571,No,0\n176,184,7207,165,-8.371428571,Yes,1\n126,198,7333,152,-10.48571429,Yes,1\n76,155,3133,261,-10.85714286,Yes,1\n223,126,2367,222,-7.371428571,Yes,1\n166,128,4406,129,-0.457142857,Yes,1\n154,94,3566,129,-6.657142857,No,0\n63,172,1923,189,-11.71428571,Yes,1\n169,84,2918,120,-9.914285714,Yes,1\n" }, { "path": "Data/NPS Data Food Order v01.csv", "content": "Cust_Id,NPS_Score,NPS_Category,Timely_delivery,No_of_calls_in_an_Order,Loyalty_Years,C_Marital_Status,Tier_City,Mode_of_Payment,No_of_Items_in_Order,Gender,Order_Mode,Score_on_Food_Quality,Score_on_Order_Logistics,Menu_Presentation_Score,Score_on_Recommendations,Score_on_Delivery_Person_Friendliness\n1,1,Detractor,N,4,5,Married,Tier1,Card,7,M,call,2,5,4,8,5\n2,7,Passives,N,4,6,Not Married,Tier2,e-Wallet,5,F,m-comm,8,10,6,4,3\n3,0,Detractor,N,4,7,Married,Tier3,Cash,1,M,call,4,2,7,8,2\n4,0,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,1,F,call,9,3,4,10,5\n5,4,Detractor,N,4,10,Married,Tier2,Card,6,M,call,7,1,6,7,4\n6,10,Promoter,N,4,7,Married,Tier1,e-Wallet,8,F,m-comm,10,6,4,2,10\n7,0,Detractor,N,4,3,Married,Tier1,Cash,7,M,call,9,4,3,3,1\n8,1,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,4,F,call,9,1,5,10,2\n9,6,Detractor,N,4,7,Married,Tier3,Card,4,M,call,5,5,10,1,1\n10,9,Promoter,N,4,5,Married,Tier3,e-Wallet,10,F,m-comm,7,10,7,7,8\n11,4,Detractor,N,4,9,Married,Tier2,Cash,4,M,call,10,1,1,1,6\n12,8,Passives,Y,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,6,2,1,7,3\n13,2,Detractor,N,4,4,Married,Tier1,Card,3,M,call,9,3,6,6,3\n14,7,Passives,N,4,9,Not Married,Tier2,e-Wallet,1,F,m-comm,9,3,9,8,7\n15,6,Detractor,N,4,3,Married,Tier3,Cash,7,M,call,7,8,5,2,9\n16,6,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,3,F,call,10,5,8,5,4\n17,6,Detractor,N,4,3,Married,Tier2,Card,5,M,call,10,2,6,10,4\n18,2,Detractor,N,4,5,Not Married,Tier3,e-Wallet,4,F,call,4,4,10,4,8\n19,4,Detractor,N,4,4,Married,Tier1,Cash,4,M,call,10,2,7,6,4\n20,3,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,5,F,call,10,9,8,8,3\n21,8,Passives,Y,4,8,Married,Tier3,Card,4,M,call,4,7,6,6,6\n22,0,Detractor,Y,4,10,Not Married,Tier1,e-Wallet,4,F,e-comm,10,9,5,1,6\n23,5,Detractor,Y,4,2,Married,Tier2,Cash,1,M,e-comm,10,1,3,9,4\n24,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,10,F,e-comm,10,9,5,4,8\n25,0,Detractor,Y,4,1,Married,Tier1,Card,2,M,e-comm,9,10,5,3,3\n26,1,Detractor,Y,4,3,Not Married,Tier2,e-Wallet,6,F,e-comm,9,7,5,4,4\n27,1,Detractor,N,4,10,Married,Tier3,Cash,4,M,call,5,6,2,1,2\n28,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,10,F,m-comm,10,5,10,5,10\n29,10,Promoter,Y,4,4,Married,Tier1,Card,9,M,m-comm,9,2,7,5,9\n30,6,Detractor,N,4,1,Not Married,Tier3,e-Wallet,6,F,call,8,4,7,1,4\n31,4,Detractor,Y,4,2,Married,Tier1,Cash,2,M,e-comm,10,8,4,2,5\n32,7,Passives,N,4,6,Not Married,Tier2,Ziggy Credit,4,F,m-comm,9,7,7,8,10\n33,3,Detractor,N,4,4,Married,Tier3,Card,3,M,call,7,2,4,2,4\n34,7,Passives,N,4,4,Not Married,Tier1,e-Wallet,3,F,e-comm,10,6,7,10,10\n35,8,Passives,Y,4,1,Married,Tier2,Cash,4,M,m-comm,10,9,9,4,2\n36,4,Detractor,N,4,10,Not Married,Tier3,Ziggy Credit,4,F,call,4,6,2,3,1\n37,3,Detractor,Y,4,3,Married,Tier1,Card,5,M,e-comm,10,3,8,1,3\n38,6,Detractor,Y,4,9,Not Married,Tier2,e-Wallet,1,F,e-comm,9,5,3,7,4\n39,7,Passives,N,4,1,Married,Tier3,Cash,5,M,call,5,9,5,7,8\n40,5,Detractor,Y,4,8,Not Married,Tier1,Ziggy Credit,3,F,call,10,8,10,5,4\n41,7,Passives,Y,4,7,Married,Tier2,Card,4,M,m-comm,10,6,3,4,5\n42,10,Promoter,N,4,7,Married,Tier1,e-Wallet,5,F,m-comm,9,3,3,1,10\n43,8,Passives,Y,4,1,Married,Tier1,Cash,3,M,e-comm,9,3,1,7,6\n44,0,Detractor,Y,4,8,Not Married,Tier2,Ziggy Credit,2,F,call,10,2,6,10,4\n45,7,Passives,N,4,9,Married,Tier3,Card,4,M,call,6,9,6,3,1\n46,6,Detractor,Y,4,5,Not Married,Tier1,e-Wallet,1,F,call,10,3,8,3,3\n47,7,Passives,Y,4,3,Married,Tier2,Cash,7,M,m-comm,9,5,1,8,3\n48,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,7,9,4,2,1\n49,1,Detractor,N,4,8,Married,Tier1,Card,1,M,call,10,7,9,3,2\n50,8,Passives,N,4,3,Not Married,Tier2,e-Wallet,4,F,m-comm,9,10,4,5,7\n51,7,Passives,N,4,3,Married,Tier3,Cash,1,M,call,4,1,2,8,7\n52,0,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,4,F,call,9,6,4,8,5\n53,1,Detractor,N,4,5,Married,Tier2,Card,1,M,call,9,1,3,4,4\n54,1,Detractor,Y,4,5,Not Married,Tier3,e-Wallet,6,F,call,6,9,1,7,9\n55,6,Detractor,N,4,2,Married,Tier1,Cash,6,M,call,9,1,1,5,5\n56,10,Promoter,N,4,6,Not Married,Tier1,Ziggy Credit,9,F,m-comm,10,8,2,5,9\n57,1,Detractor,N,4,8,Married,Tier3,Card,4,M,call,8,7,7,6,7\n58,5,Detractor,Y,4,3,Not Married,Tier1,e-Wallet,1,F,m-comm,10,9,9,7,6\n59,10,Promoter,Y,4,3,Married,Tier2,Cash,8,M,e-comm,9,4,6,1,10\n60,7,Passives,Y,4,10,Not Married,Tier3,Ziggy Credit,6,F,call,8,6,6,8,1\n61,1,Detractor,Y,4,8,Married,Tier1,Card,1,M,m-comm,9,4,3,10,2\n62,8,Passives,N,4,4,Not Married,Tier2,e-Wallet,3,F,m-comm,10,2,2,4,1\n63,7,Passives,N,4,2,Married,Tier3,Cash,7,M,call,5,8,2,3,2\n64,5,Detractor,Y,4,2,Not Married,Tier1,Ziggy Credit,5,F,m-comm,10,4,6,3,2\n65,5,Detractor,Y,4,4,Married,Tier2,Card,1,M,m-comm,9,3,1,9,5\n66,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,1,F,call,7,7,7,2,1\n67,0,Detractor,Y,4,2,Married,Tier1,Cash,1,M,m-comm,9,3,8,10,4\n68,4,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,3,F,m-comm,9,4,6,6,1\n69,10,Promoter,Y,4,2,Married,Tier1,Card,10,M,m-comm,10,10,3,6,10\n70,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,1,5,5,7\n71,9,Promoter,Y,4,7,Married,Tier1,Cash,5,M,m-comm,10,8,1,7,7\n72,1,Detractor,N,4,10,Not Married,Tier3,Ziggy Credit,7,F,call,6,1,7,9,4\n73,1,Detractor,Y,4,1,Married,Tier2,Card,6,M,m-comm,10,6,2,5,7\n74,6,Detractor,Y,4,5,Not Married,Tier2,e-Wallet,1,F,m-comm,9,6,3,4,1\n75,10,Promoter,Y,4,4,Married,Tier1,Cash,9,M,m-comm,10,6,6,2,8\n76,2,Detractor,Y,4,4,Not Married,Tier1,Ziggy Credit,4,F,m-comm,9,5,1,6,5\n77,0,Detractor,Y,4,9,Married,Tier2,Card,7,M,m-comm,9,6,4,7,1\n78,8,Passives,Y,4,2,Not Married,Tier3,e-Wallet,1,F,call,5,6,6,5,6\n79,4,Detractor,Y,4,5,Married,Tier1,Cash,2,M,m-comm,10,9,9,4,4\n80,5,Detractor,Y,4,8,Not Married,Tier2,Ziggy Credit,7,F,m-comm,9,2,6,3,1\n81,8,Passives,Y,4,7,Married,Tier3,Card,6,M,call,4,3,9,1,8\n82,3,Detractor,Y,4,9,Not Married,Tier1,e-Wallet,7,F,m-comm,9,8,3,6,6\n83,2,Detractor,Y,4,5,Married,Tier2,Cash,1,M,m-comm,10,4,5,2,3\n84,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,2,F,call,5,8,5,1,1\n85,2,Detractor,Y,4,9,Married,Tier1,Card,3,M,m-comm,9,4,8,2,1\n86,1,Detractor,Y,4,3,Not Married,Tier2,e-Wallet,6,F,m-comm,10,8,10,10,1\n87,9,Promoter,N,4,8,Married,Tier3,Cash,2,M,call,7,7,4,10,7\n88,1,Detractor,N,4,6,Married,Tier1,Ziggy Credit,10,F,e-comm,9,9,2,8,10\n89,7,Passives,Y,4,6,Married,Tier2,Card,5,M,m-comm,9,2,9,3,5\n90,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,3,F,call,6,5,2,4,2\n91,6,Detractor,Y,4,3,Married,Tier1,Cash,3,M,m-comm,9,1,3,3,6\n92,5,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,7,F,m-comm,10,1,9,3,3\n93,7,Passives,N,4,5,Married,Tier3,Card,4,M,call,4,1,4,8,1\n94,3,Detractor,Y,4,9,Not Married,Tier1,e-Wallet,1,F,m-comm,10,6,4,6,1\n95,1,Detractor,Y,4,4,Married,Tier2,Cash,7,M,m-comm,9,7,6,2,6\n96,7,Passives,Y,4,4,Not Married,Tier3,Ziggy Credit,1,F,call,7,1,6,2,6\n97,10,Promoter,Y,4,3,Married,Tier2,Card,10,M,m-comm,9,10,2,2,9\n98,2,Detractor,Y,4,8,Not Married,Tier2,e-Wallet,1,F,m-comm,10,8,6,10,6\n99,7,Passives,N,4,5,Married,Tier3,Cash,2,M,call,4,6,4,4,1\n100,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,10,4,6,10,10\n101,5,Detractor,Y,4,2,Married,Tier2,Card,3,M,m-comm,10,6,3,5,5\n102,1,Detractor,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,4,10,5,4,4\n103,4,Detractor,Y,4,4,Married,Tier1,Cash,3,M,m-comm,10,7,6,3,1\n104,1,Detractor,Y,4,7,Not Married,Tier2,Ziggy Credit,5,F,m-comm,9,9,5,8,3\n105,7,Passives,N,4,8,Married,Tier3,Card,1,M,call,8,5,3,10,3\n106,9,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,9,1,9,5,7\n107,6,Detractor,Y,4,1,Married,Tier2,Cash,1,M,m-comm,10,3,10,4,3\n108,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,7,F,m-comm,10,8,10,9,9\n109,8,Passives,Y,4,8,Married,Tier1,Card,1,M,e-comm,10,9,4,3,3\n110,6,Detractor,Y,4,8,Not Married,Tier2,e-Wallet,7,F,m-comm,9,3,7,1,2\n111,10,Promoter,Y,4,7,Married,Tier1,Cash,6,M,e-comm,10,4,5,7,9\n112,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,10,4,1,10,10\n113,0,Detractor,Y,4,10,Married,Tier2,Card,2,M,m-comm,9,4,10,10,6\n114,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,4,F,call,5,8,8,1,7\n115,4,Detractor,Y,4,8,Married,Tier1,Cash,4,M,m-comm,10,2,7,7,5\n116,2,Detractor,Y,4,9,Not Married,Tier2,Ziggy Credit,3,F,m-comm,9,1,4,8,1\n117,7,Passives,N,4,2,Married,Tier3,Card,4,M,call,7,8,3,3,10\n118,10,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,8,8,2,9\n119,9,Promoter,Y,4,5,Married,Tier1,Cash,8,M,m-comm,10,7,10,3,10\n120,7,Passives,Y,4,6,Not Married,Tier3,Ziggy Credit,2,F,call,7,9,5,5,1\n121,8,Passives,Y,4,7,Married,Tier1,Card,5,M,e-comm,10,8,6,5,9\n122,10,Promoter,N,4,6,Married,Tier1,e-Wallet,5,F,m-comm,9,9,7,7,9\n123,10,Promoter,Y,4,9,Married,Tier1,Cash,6,M,m-comm,9,2,8,10,10\n124,4,Detractor,Y,4,8,Not Married,Tier1,Ziggy Credit,6,F,m-comm,10,6,10,3,2\n125,0,Detractor,Y,4,7,Married,Tier2,Card,6,M,m-comm,9,1,1,3,6\n126,1,Detractor,N,4,1,Not Married,Tier3,e-Wallet,7,F,call,4,8,9,2,3\n127,0,Detractor,Y,4,9,Married,Tier1,Cash,3,M,m-comm,10,6,6,8,5\n128,9,Promoter,N,4,7,Not Married,Tier1,Ziggy Credit,6,F,e-comm,9,7,3,6,9\n129,10,Promoter,Y,4,6,Married,Tier1,Card,7,M,m-comm,9,8,5,2,7\n130,10,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,9,7,6,7,9\n131,0,Detractor,Y,4,7,Married,Tier2,Cash,5,M,m-comm,9,2,10,7,6\n132,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,4,F,call,4,8,5,1,9\n133,1,Detractor,Y,4,4,Married,Tier1,Card,3,M,m-comm,9,4,1,9,4\n134,5,Detractor,Y,4,10,Not Married,Tier2,e-Wallet,6,F,m-comm,10,9,3,9,2\n135,8,Passives,Y,4,4,Married,Tier3,Cash,2,M,call,5,8,5,3,1\n136,7,Passives,N,4,10,Not Married,Tier1,Ziggy Credit,6,F,e-comm,9,3,1,10,10\n137,2,Detractor,Y,4,4,Married,Tier2,Card,7,M,m-comm,9,7,3,6,2\n138,9,Promoter,N,4,7,Married,Tier1,e-Wallet,9,F,m-comm,10,10,8,5,10\n139,9,Promoter,Y,4,4,Married,Tier1,Cash,10,M,m-comm,10,9,9,10,10\n140,6,Detractor,Y,4,1,Not Married,Tier2,Ziggy Credit,2,F,m-comm,9,3,1,8,2\n141,10,Promoter,Y,4,1,Married,Tier1,Card,8,M,e-comm,10,6,5,5,7\n142,7,Passives,N,4,7,Not Married,Tier1,e-Wallet,2,F,e-comm,9,9,2,10,3\n143,4,Detractor,Y,4,10,Married,Tier2,Cash,4,M,m-comm,9,3,8,5,2\n144,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,7,F,call,8,9,4,1,9\n145,0,Detractor,Y,4,2,Married,Tier1,Card,1,M,m-comm,9,5,9,5,5\n146,3,Detractor,Y,4,4,Not Married,Tier2,e-Wallet,2,F,m-comm,9,10,4,3,6\n147,10,Promoter,Y,4,7,Married,Tier1,Cash,6,M,m-comm,10,2,2,6,10\n148,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,9,9,9,1,8\n149,2,Detractor,Y,4,3,Married,Tier2,Card,1,M,m-comm,10,5,7,10,3\n150,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,6,F,call,6,6,9,9,1\n151,2,Detractor,Y,4,6,Married,Tier1,Cash,5,M,m-comm,9,4,4,2,6\n152,1,Detractor,Y,4,4,Not Married,Tier2,Ziggy Credit,7,F,m-comm,10,3,10,1,4\n153,10,Promoter,Y,4,8,Married,Tier1,Card,9,M,m-comm,9,6,2,5,7\n154,5,Detractor,Y,4,10,Not Married,Tier1,e-Wallet,5,F,m-comm,9,7,5,10,5\n155,6,Detractor,Y,4,2,Married,Tier2,Cash,5,M,m-comm,9,2,10,9,1\n156,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,1,F,call,5,2,7,3,4\n157,10,Promoter,Y,4,8,Married,Tier1,Card,5,M,e-comm,9,10,6,7,10\n158,3,Detractor,Y,4,2,Not Married,Tier2,e-Wallet,4,F,m-comm,9,5,8,3,1\n159,7,Passives,Y,4,10,Married,Tier3,Cash,6,M,call,4,8,2,7,5\n160,2,Detractor,Y,4,8,Not Married,Tier1,Ziggy Credit,1,F,m-comm,10,2,4,9,4\n161,2,Detractor,Y,4,10,Married,Tier2,Card,7,M,m-comm,10,8,7,3,4\n162,7,Passives,Y,4,8,Not Married,Tier3,e-Wallet,6,F,call,5,3,8,10,2\n163,1,Detractor,Y,4,8,Married,Tier1,Cash,5,M,m-comm,10,9,6,8,5\n164,6,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,2,F,m-comm,9,1,6,5,2\n165,7,Passives,N,4,10,Married,Tier3,Card,4,M,call,7,5,4,6,5\n166,5,Detractor,Y,4,1,Not Married,Tier1,e-Wallet,5,F,m-comm,9,9,3,3,5\n167,2,Detractor,Y,4,7,Married,Tier2,Cash,7,M,m-comm,10,1,7,2,6\n168,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,6,F,call,4,3,3,2,4\n169,0,Detractor,Y,4,4,Married,Tier1,Card,5,M,m-comm,9,7,1,10,2\n170,6,Detractor,Y,4,8,Not Married,Tier2,e-Wallet,7,F,m-comm,10,10,4,7,3\n171,10,Promoter,Y,4,3,Married,Tier1,Cash,5,M,m-comm,10,5,8,7,7\n172,7,Passives,N,4,2,Not Married,Tier1,Ziggy Credit,4,F,e-comm,10,2,6,7,10\n173,0,Detractor,Y,4,1,Married,Tier2,Card,2,M,m-comm,10,4,1,2,4\n174,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,6,6,8,9,4\n175,6,Detractor,Y,4,6,Married,Tier1,Cash,1,M,m-comm,10,3,8,10,2\n176,5,Detractor,Y,4,2,Not Married,Tier2,Ziggy Credit,2,F,m-comm,9,3,2,4,6\n177,7,Passives,N,4,10,Married,Tier3,Card,6,M,call,6,4,3,2,8\n178,1,Detractor,Y,4,4,Not Married,Tier1,e-Wallet,1,F,m-comm,10,3,1,3,4\n179,9,Promoter,Y,4,2,Married,Tier1,Cash,5,M,m-comm,10,9,8,2,8\n180,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,3,F,call,8,8,2,4,2\n181,1,Detractor,Y,4,4,Married,Tier1,Card,3,M,m-comm,10,6,9,6,4\n182,2,Detractor,Y,4,2,Not Married,Tier2,e-Wallet,1,F,m-comm,10,10,1,4,4\n183,7,Passives,N,4,8,Married,Tier3,Cash,3,M,call,8,4,6,1,7\n184,8,Passives,N,4,8,Not Married,Tier1,Ziggy Credit,1,F,e-comm,10,4,9,5,2\n185,7,Passives,Y,4,5,Married,Tier2,Card,5,M,m-comm,10,9,6,2,7\n186,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,4,1,4,6,8\n187,2,Detractor,Y,4,5,Married,Tier1,Cash,3,M,m-comm,10,2,7,8,1\n188,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,9,F,m-comm,9,8,9,9,9\n189,7,Passives,N,4,7,Married,Tier3,Card,3,M,call,4,6,2,8,1\n190,1,Detractor,Y,4,3,Not Married,Tier1,e-Wallet,2,F,m-comm,10,8,9,3,5\n191,2,Detractor,Y,4,1,Married,Tier2,Cash,5,M,m-comm,10,4,1,9,2\n192,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,10,10,9,3,10\n193,1,Detractor,Y,4,5,Married,Tier1,Card,7,M,m-comm,10,5,7,3,3\n194,7,Passives,N,4,6,Not Married,Tier2,e-Wallet,6,F,m-comm,10,10,9,2,3\n195,9,Promoter,N,4,3,Married,Tier3,Cash,7,M,call,4,8,3,3,5\n196,5,Detractor,Y,4,1,Not Married,Tier1,Ziggy Credit,4,F,m-comm,10,9,10,2,3\n197,9,Promoter,Y,4,6,Married,Tier1,Card,10,M,call,10,9,5,7,9\n198,8,Passives,Y,4,2,Not Married,Tier3,e-Wallet,2,F,call,7,9,6,3,9\n199,4,Detractor,Y,4,8,Married,Tier1,Cash,1,M,m-comm,9,5,5,4,6\n200,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,10,2,9,2,8\n201,7,Passives,N,4,6,Married,Tier3,Card,2,M,call,7,5,2,7,5\n202,0,Detractor,Y,4,3,Not Married,Tier1,e-Wallet,6,F,m-comm,10,7,9,10,3\n203,5,Detractor,Y,4,10,Married,Tier2,Cash,3,M,m-comm,9,9,1,7,1\n204,8,Passives,Y,4,7,Not Married,Tier3,Ziggy Credit,6,F,call,8,5,7,1,8\n205,5,Detractor,Y,4,1,Married,Tier1,Card,4,M,m-comm,9,5,3,1,2\n206,4,Detractor,Y,4,9,Not Married,Tier2,e-Wallet,3,F,m-comm,10,1,1,7,3\n207,7,Passives,N,4,6,Married,Tier3,Cash,4,M,call,4,5,8,10,6\n208,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,7,F,m-comm,10,2,5,4,9\n209,9,Promoter,Y,4,3,Married,Tier1,Card,8,M,m-comm,10,5,5,7,7\n210,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,3,F,call,7,8,10,3,3\n211,3,Detractor,Y,4,1,Married,Tier1,Cash,1,M,m-comm,10,9,5,5,5\n212,5,Detractor,Y,4,8,Not Married,Tier2,Ziggy Credit,1,F,m-comm,9,4,8,2,1\n213,7,Passives,N,4,6,Married,Tier3,Card,1,M,call,5,9,4,3,7\n214,9,Promoter,N,4,7,Married,Tier1,e-Wallet,10,F,m-comm,9,5,10,3,9\n215,3,Detractor,Y,4,3,Married,Tier2,Cash,3,M,m-comm,9,4,10,1,2\n216,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,3,F,call,8,5,9,7,8\n217,3,Detractor,Y,4,1,Married,Tier1,Card,6,M,m-comm,10,3,8,1,1\n218,10,Promoter,Y,4,5,Married,Tier1,e-Wallet,9,F,call,9,1,7,10,7\n219,8,Passives,Y,4,5,Married,Tier3,Cash,2,M,call,6,6,4,4,6\n220,5,Detractor,Y,4,6,Not Married,Tier1,Ziggy Credit,6,F,m-comm,10,8,4,3,6\n221,4,Detractor,Y,4,10,Married,Tier2,Card,4,M,m-comm,9,8,10,2,5\n222,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,1,F,call,6,1,3,10,3\n223,1,Detractor,Y,4,8,Married,Tier1,Cash,1,M,m-comm,9,7,8,5,2\n224,5,Detractor,Y,4,1,Not Married,Tier2,Ziggy Credit,5,F,m-comm,10,7,5,2,5\n225,7,Passives,N,4,7,Married,Tier3,Card,1,M,call,4,6,4,2,2\n226,4,Detractor,Y,4,7,Not Married,Tier1,e-Wallet,5,F,m-comm,9,2,6,4,4\n227,1,Detractor,Y,4,4,Married,Tier2,Cash,1,M,m-comm,10,7,5,7,3\n228,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,4,F,call,5,7,3,3,9\n229,7,Passives,Y,4,7,Married,Tier1,Card,4,M,e-comm,9,5,4,8,10\n230,5,Detractor,Y,4,3,Not Married,Tier2,e-Wallet,1,F,m-comm,10,2,4,9,4\n231,7,Passives,Y,4,2,Married,Tier3,Cash,1,M,call,6,7,8,2,4\n232,2,Detractor,Y,4,2,Not Married,Tier1,Ziggy Credit,2,F,m-comm,10,1,6,5,5\n233,8,Passives,Y,4,9,Married,Tier2,Card,7,M,m-comm,10,2,2,5,2\n234,9,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,9,10,5,8,7\n235,10,Promoter,Y,4,4,Married,Tier3,Cash,6,M,m-comm,8,5,5,3,9\n236,4,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,4,F,m-comm,10,7,4,7,2\n237,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,5,2,2,10,2\n238,1,Detractor,Y,4,1,Not Married,Tier1,e-Wallet,7,F,m-comm,9,5,9,4,5\n239,9,Promoter,Y,4,7,Married,Tier1,Cash,10,M,m-comm,9,8,1,5,8\n240,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,7,F,call,4,4,2,6,9\n241,10,Promoter,Y,4,9,Married,Tier1,Card,6,M,call,10,2,4,4,10\n242,6,Detractor,Y,4,7,Not Married,Tier2,e-Wallet,1,F,m-comm,10,1,4,5,3\n243,10,Promoter,Y,4,2,Married,Tier1,Cash,8,M,m-comm,10,3,6,2,9\n244,1,Detractor,Y,4,9,Not Married,Tier1,Ziggy Credit,6,F,m-comm,10,7,10,3,5\n245,7,Passives,Y,4,3,Married,Tier2,Card,1,M,m-comm,9,3,2,10,10\n246,8,Passives,Y,4,8,Not Married,Tier3,e-Wallet,3,F,call,5,5,6,5,2\n247,8,Passives,Y,4,2,Married,Tier1,Cash,6,M,e-comm,10,2,1,8,4\n248,8,Passives,N,4,7,Not Married,Tier2,Ziggy Credit,7,F,m-comm,10,2,9,3,10\n249,7,Passives,N,4,4,Married,Tier3,Card,2,M,call,5,7,5,4,6\n250,8,Passives,N,4,9,Not Married,Tier1,e-Wallet,3,F,e-comm,10,9,9,5,8\n251,3,Detractor,Y,4,3,Married,Tier2,Cash,3,M,m-comm,10,2,5,5,2\n252,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,2,F,call,6,9,4,6,5\n253,10,Promoter,Y,4,3,Married,Tier1,Card,5,M,m-comm,9,9,2,10,8\n254,4,Detractor,Y,4,10,Not Married,Tier2,e-Wallet,5,F,m-comm,9,6,2,10,4\n255,9,Promoter,Y,4,9,Married,Tier1,Cash,10,M,m-comm,9,3,6,10,9\n256,7,Passives,N,4,3,Not Married,Tier1,Ziggy Credit,7,F,e-comm,9,7,4,1,6\n257,8,Passives,Y,4,4,Married,Tier2,Card,4,M,m-comm,9,10,1,4,6\n258,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,6,F,call,7,7,1,10,3\n259,3,Detractor,Y,4,3,Married,Tier1,Cash,7,M,call,10,4,8,2,3\n260,4,Detractor,Y,4,7,Not Married,Tier2,Ziggy Credit,3,F,call,10,8,5,8,6\n261,7,Passives,N,4,6,Married,Tier3,Card,7,M,call,8,4,6,2,7\n262,6,Detractor,Y,4,9,Not Married,Tier1,e-Wallet,3,F,call,10,9,5,6,4\n263,2,Detractor,Y,4,5,Married,Tier2,Cash,4,M,call,9,6,9,5,1\n264,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,5,F,call,8,10,6,7,4\n265,8,Passives,Y,4,7,Married,Tier1,Card,7,M,e-comm,10,4,9,1,5\n266,10,Promoter,Y,4,7,Married,Tier1,e-Wallet,7,F,call,9,9,9,1,10\n267,10,Promoter,Y,4,6,Married,Tier1,Cash,6,M,m-comm,10,9,3,5,9\n268,7,Passives,N,4,4,Not Married,Tier1,Ziggy Credit,6,F,e-comm,10,3,3,6,2\n269,9,Promoter,Y,4,3,Married,Tier1,Card,7,M,m-comm,10,1,10,6,7\n270,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,3,F,call,4,4,3,1,4\n271,6,Detractor,Y,4,9,Married,Tier1,Cash,2,M,call,9,6,3,6,1\n272,8,Passives,N,4,9,Not Married,Tier2,Ziggy Credit,6,F,m-comm,10,7,1,1,1\n273,7,Passives,N,4,3,Married,Tier3,Card,2,M,call,6,5,4,3,9\n274,6,Detractor,Y,4,2,Not Married,Tier1,e-Wallet,5,F,call,9,9,8,7,5\n275,8,Passives,Y,4,3,Married,Tier2,Cash,1,M,m-comm,10,3,10,2,3\n276,7,Passives,Y,4,6,Not Married,Tier3,Ziggy Credit,3,F,call,8,3,2,6,3\n277,3,Detractor,N,4,7,Married,Tier1,Card,7,M,call,9,1,10,5,1\n278,7,Passives,N,4,8,Not Married,Tier2,e-Wallet,4,F,m-comm,9,8,2,7,2\n279,7,Passives,N,4,3,Married,Tier3,Cash,4,M,call,8,9,6,4,4\n280,5,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,3,F,call,9,7,8,2,1\n281,1,Detractor,N,4,8,Married,Tier2,Card,5,M,call,10,4,3,1,6\n282,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,4,F,call,4,4,6,6,7\n283,7,Passives,Y,4,2,Married,Tier1,Cash,7,M,e-comm,10,7,3,4,5\n284,0,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,2,F,call,10,7,6,10,3\n285,8,Passives,Y,4,2,Married,Tier3,Card,7,M,call,7,4,7,10,4\n286,5,Detractor,N,4,4,Not Married,Tier1,e-Wallet,2,F,call,10,10,9,3,2\n287,6,Detractor,N,4,3,Married,Tier2,Cash,1,M,call,9,4,4,8,3\n288,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,7,2,10,6,4\n289,0,Detractor,N,4,4,Married,Tier1,Card,5,M,call,9,1,7,5,4\n290,8,Passives,N,4,1,Not Married,Tier2,e-Wallet,6,F,m-comm,9,2,2,10,1\n291,9,Promoter,Y,4,4,Married,Tier3,Cash,5,M,call,7,10,3,2,9\n292,5,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,5,F,call,9,3,10,8,3\n293,2,Detractor,N,4,1,Married,Tier2,Card,6,M,call,9,7,7,3,1\n294,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,5,7,5,8,10\n295,5,Detractor,N,4,10,Married,Tier1,Cash,4,M,call,10,10,5,7,2\n296,0,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,3,F,call,9,9,2,8,4\n297,7,Passives,Y,4,1,Married,Tier3,Card,2,M,call,7,1,8,3,6\n298,0,Detractor,N,4,6,Not Married,Tier1,e-Wallet,1,F,call,6,5,10,2,3\n299,8,Passives,Y,4,9,Married,Tier2,Cash,1,M,m-comm,9,4,9,5,2\n300,8,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,1,F,call,8,8,6,4,8\n301,4,Detractor,N,4,5,Married,Tier1,Card,1,M,call,10,8,9,6,6\n302,1,Detractor,N,4,10,Not Married,Tier2,e-Wallet,5,F,call,7,9,4,6,5\n303,7,Passives,N,4,5,Married,Tier3,Cash,1,M,call,8,1,8,8,10\n304,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,3,F,call,6,6,7,10,3\n305,8,Passives,Y,4,9,Married,Tier2,Card,6,M,m-comm,8,8,5,3,1\n306,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,3,F,call,5,7,5,9,6\n307,0,Detractor,N,4,7,Married,Tier1,Cash,7,M,call,4,10,10,7,6\n308,6,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,1,F,call,9,10,4,4,4\n309,7,Passives,N,4,6,Married,Tier3,Card,6,M,call,5,3,8,9,10\n310,3,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,8,2,3,10,3\n311,0,Detractor,N,4,9,Married,Tier2,Cash,3,M,call,2,10,8,8,3\n312,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,2,F,call,5,1,3,4,8\n313,7,Passives,Y,4,6,Married,Tier1,Card,3,M,e-comm,5,2,10,7,3\n314,1,Detractor,N,4,2,Not Married,Tier2,e-Wallet,3,F,call,9,5,6,9,3\n315,7,Passives,Y,4,6,Married,Tier3,Cash,1,M,call,4,9,4,8,7\n316,2,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,4,F,call,8,5,3,1,2\n317,7,Passives,Y,4,3,Married,Tier2,Card,2,M,m-comm,8,4,9,7,3\n318,10,Promoter,Y,4,5,Married,Tier1,e-Wallet,5,F,call,3,1,2,6,7\n319,7,Passives,Y,4,9,Married,Tier1,Cash,6,M,e-comm,10,1,5,9,1\n320,3,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,1,F,call,9,6,10,6,6\n321,8,Passives,Y,4,4,Married,Tier3,Card,3,M,call,8,5,9,2,4\n322,10,Promoter,N,4,5,Married,Tier1,e-Wallet,6,F,m-comm,4,3,3,5,8\n323,0,Detractor,N,4,8,Married,Tier2,Cash,7,M,call,6,5,2,7,2\n324,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,1,F,call,4,3,3,8,3\n325,9,Promoter,Y,4,5,Married,Tier1,Card,10,M,m-comm,2,9,7,4,10\n326,10,Promoter,N,4,7,Married,Tier1,e-Wallet,9,F,m-comm,3,4,10,5,9\n327,7,Passives,N,4,6,Married,Tier3,Cash,7,M,call,7,2,5,1,4\n328,5,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,4,F,call,3,3,4,1,5\n329,8,Passives,Y,4,2,Married,Tier2,Card,1,M,m-comm,9,5,7,6,10\n330,8,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,8,7,6,5,5\n331,5,Detractor,Y,4,8,Married,Tier1,Cash,2,M,call,10,2,3,4,1\n332,4,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,6,F,call,4,1,2,6,4\n333,7,Passives,N,4,4,Married,Tier3,Card,4,M,call,5,10,5,5,10\n334,0,Detractor,N,4,3,Not Married,Tier1,e-Wallet,1,F,call,5,9,2,4,2\n335,0,Detractor,N,4,5,Married,Tier2,Cash,2,M,call,3,9,4,6,2\n336,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,6,F,call,7,4,10,5,1\n337,9,Promoter,Y,4,4,Married,Tier1,Card,5,M,m-comm,6,1,10,8,10\n338,5,Detractor,N,4,9,Not Married,Tier2,e-Wallet,5,F,call,2,10,5,10,1\n339,9,Promoter,N,4,10,Married,Tier3,Cash,7,M,call,6,10,3,9,5\n340,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,2,F,call,7,2,5,5,6\n341,6,Detractor,N,4,1,Married,Tier2,Card,5,M,call,9,7,9,10,2\n342,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,6,F,call,6,4,8,5,7\n343,10,Promoter,Y,4,10,Married,Tier1,Cash,8,M,m-comm,1,10,9,3,8\n344,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,8,F,m-comm,6,9,2,9,10\n345,10,Promoter,Y,4,5,Married,Tier1,Card,9,M,m-comm,5,7,6,3,8\n346,7,Passives,N,4,9,Not Married,Tier1,e-Wallet,2,F,e-comm,9,9,6,8,2\n347,1,Detractor,N,4,4,Married,Tier2,Cash,4,M,call,10,3,10,9,6\n348,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,3,F,call,6,8,2,5,8\n349,2,Detractor,N,4,3,Married,Tier1,Card,2,M,call,4,1,5,9,5\n350,4,Detractor,N,4,8,Not Married,Tier2,e-Wallet,3,F,call,1,9,10,3,2\n351,10,Promoter,Y,4,9,Married,Tier1,Cash,7,M,m-comm,5,10,7,6,10\n352,6,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,5,F,call,9,8,7,3,5\n353,2,Detractor,N,4,6,Married,Tier2,Card,5,M,call,9,9,10,3,4\n354,10,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,8,10,8,6,7\n355,6,Detractor,N,4,5,Married,Tier1,Cash,2,M,call,7,5,10,5,3\n356,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,10,9,3,6,7\n357,7,Passives,N,4,5,Married,Tier3,Card,6,M,call,5,1,9,1,6\n358,4,Detractor,N,4,6,Not Married,Tier1,e-Wallet,4,F,call,1,6,9,3,5\n359,2,Detractor,N,4,9,Married,Tier2,Cash,2,M,call,4,5,7,1,3\n360,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,4,F,call,7,1,7,5,8\n361,0,Detractor,N,4,10,Married,Tier1,Card,2,M,call,8,5,5,1,4\n362,6,Detractor,N,4,10,Not Married,Tier2,e-Wallet,1,F,call,9,10,5,5,4\n363,9,Promoter,N,4,3,Married,Tier3,Cash,6,M,call,7,4,8,8,5\n364,4,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,2,F,call,3,7,4,2,1\n365,10,Promoter,Y,4,2,Married,Tier1,Card,8,M,m-comm,8,2,2,5,10\n366,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,4,F,call,5,1,6,2,6\n367,8,Passives,Y,4,3,Married,Tier1,Cash,7,M,e-comm,7,4,1,7,3\n368,8,Passives,N,4,2,Not Married,Tier2,Ziggy Credit,7,F,m-comm,1,9,5,7,6\n369,10,Promoter,Y,4,1,Married,Tier1,Card,9,M,m-comm,2,7,8,3,7\n370,2,Detractor,N,4,8,Not Married,Tier1,e-Wallet,1,F,call,2,8,4,9,3\n371,3,Detractor,N,4,7,Married,Tier2,Cash,2,M,call,4,5,1,1,6\n372,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,2,F,call,5,3,3,9,4\n373,7,Passives,Y,4,4,Married,Tier1,Card,5,M,e-comm,3,3,2,6,1\n374,4,Detractor,N,4,7,Not Married,Tier2,e-Wallet,4,F,call,8,9,4,4,5\n375,9,Promoter,N,4,2,Married,Tier3,Cash,2,M,call,7,2,9,10,8\n376,1,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,6,F,call,3,1,5,9,3\n377,6,Detractor,N,4,9,Married,Tier2,Card,2,M,call,2,8,9,3,2\n378,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,5,F,call,4,9,2,9,8\n379,1,Detractor,N,4,7,Married,Tier1,Cash,1,M,call,4,9,10,8,6\n380,5,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,6,F,call,3,10,8,3,2\n381,9,Promoter,Y,4,3,Married,Tier1,Card,8,M,m-comm,9,9,1,7,10\n382,9,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,10,1,6,6,10\n383,10,Promoter,Y,4,10,Married,Tier1,Cash,8,M,m-comm,9,8,8,1,8\n384,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,1,F,call,5,8,2,4,7\n385,1,Detractor,N,4,8,Married,Tier1,Card,3,M,call,1,10,7,6,6\n386,5,Detractor,N,4,4,Not Married,Tier2,e-Wallet,2,F,call,9,3,10,3,1\n387,9,Promoter,N,4,5,Married,Tier3,Cash,1,M,call,8,9,8,1,6\n388,8,Passives,N,4,10,Not Married,Tier1,Ziggy Credit,7,F,e-comm,4,9,9,6,5\n389,1,Detractor,N,4,7,Married,Tier2,Card,6,M,call,3,9,8,6,1\n390,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,2,F,call,8,3,7,3,6\n391,0,Detractor,N,4,4,Married,Tier1,Cash,5,M,call,3,1,5,7,6\n392,8,Passives,N,4,4,Not Married,Tier2,Ziggy Credit,7,F,m-comm,6,7,2,3,2\n393,7,Passives,N,4,10,Married,Tier3,Card,7,M,call,5,6,8,5,7\n394,2,Detractor,N,4,2,Not Married,Tier1,e-Wallet,5,F,call,3,3,7,3,5\n395,1,Detractor,N,4,8,Married,Tier2,Cash,4,M,call,6,1,5,8,2\n396,8,Passives,Y,4,10,Not Married,Tier3,Ziggy Credit,2,F,call,6,9,6,3,2\n397,6,Detractor,N,4,7,Married,Tier1,Card,4,M,call,8,4,6,7,2\n398,4,Detractor,N,4,5,Not Married,Tier2,e-Wallet,3,F,call,6,3,6,8,5\n399,9,Promoter,Y,4,10,Married,Tier3,Cash,6,M,call,5,10,4,9,1\n400,8,Passives,N,4,5,Not Married,Tier1,Ziggy Credit,4,F,e-comm,9,8,4,10,4\n401,7,Passives,Y,4,3,Married,Tier2,Card,1,M,m-comm,4,1,6,9,8\n402,7,Passives,Y,4,8,Not Married,Tier3,e-Wallet,6,F,call,8,8,5,7,8\n403,0,Detractor,N,4,4,Married,Tier1,Cash,1,M,call,2,1,8,8,3\n404,1,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,7,F,call,6,10,3,7,4\n405,9,Promoter,Y,4,8,Married,Tier1,Card,9,M,m-comm,10,7,5,7,10\n406,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,7,F,call,5,4,5,5,4\n407,1,Detractor,N,4,4,Married,Tier2,Cash,4,M,call,6,9,4,1,6\n408,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,7,10,8,9,6\n409,6,Detractor,N,4,8,Married,Tier1,Card,1,M,call,6,6,3,1,4\n410,4,Detractor,N,4,1,Not Married,Tier2,e-Wallet,3,F,call,8,4,2,7,3\n411,9,Promoter,N,4,10,Married,Tier3,Cash,7,M,call,5,4,3,1,6\n412,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,6,3,1,3,10\n413,7,Passives,Y,4,9,Married,Tier2,Card,7,M,m-comm,10,5,1,9,6\n414,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,7,4,7,2,7\n415,6,Detractor,N,4,7,Married,Tier1,Cash,5,M,call,2,8,6,3,4\n416,6,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,call,5,5,3,9,3\n417,10,Promoter,Y,4,8,Married,Tier1,Card,8,M,m-comm,1,1,1,9,9\n418,1,Detractor,N,4,4,Not Married,Tier1,e-Wallet,1,F,call,5,4,5,3,1\n419,4,Detractor,N,4,1,Married,Tier2,Cash,7,M,call,10,9,4,2,4\n420,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,6,F,call,5,9,2,7,4\n421,9,Promoter,Y,4,2,Married,Tier1,Card,6,M,m-comm,5,4,9,8,7\n422,3,Detractor,N,4,8,Not Married,Tier2,e-Wallet,6,F,call,4,7,1,3,1\n423,9,Promoter,N,4,9,Married,Tier3,Cash,6,M,call,8,9,8,6,1\n424,4,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,7,F,call,1,5,7,6,1\n425,9,Promoter,Y,4,10,Married,Tier1,Card,9,M,m-comm,4,2,5,4,8\n426,10,Promoter,N,4,6,Married,Tier1,e-Wallet,6,F,m-comm,7,7,6,9,7\n427,2,Detractor,N,4,3,Married,Tier1,Cash,2,M,call,3,6,9,6,4\n428,1,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,5,F,call,7,6,2,1,5\n429,9,Promoter,Y,4,10,Married,Tier1,Card,5,M,m-comm,5,10,6,6,7\n430,7,Passives,N,4,7,Not Married,Tier1,e-Wallet,7,F,e-comm,7,9,2,3,7\n431,6,Detractor,N,4,5,Married,Tier2,Cash,2,M,call,6,7,2,1,4\n432,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,6,F,call,5,7,8,6,1\n433,3,Detractor,N,4,6,Married,Tier1,Card,5,M,call,1,4,10,10,5\n434,7,Passives,N,4,4,Not Married,Tier2,e-Wallet,5,F,m-comm,8,2,9,5,1\n435,9,Promoter,N,4,4,Married,Tier3,Cash,3,M,call,5,1,9,3,5\n436,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,6,5,4,6,9\n437,2,Detractor,N,4,10,Married,Tier2,Card,7,M,call,2,9,4,5,4\n438,8,Passives,Y,4,7,Not Married,Tier3,e-Wallet,4,F,call,6,2,9,10,9\n439,3,Detractor,N,4,10,Married,Tier1,Cash,2,M,call,2,4,4,9,4\n440,2,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,1,F,call,5,2,7,4,2\n441,7,Passives,N,4,8,Married,Tier3,Card,3,M,call,7,9,3,2,10\n442,7,Passives,N,4,10,Not Married,Tier1,e-Wallet,3,F,e-comm,6,3,3,4,2\n443,0,Detractor,N,4,8,Married,Tier2,Cash,6,M,call,6,3,6,4,4\n444,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,5,10,7,4,7\n445,8,Passives,Y,4,9,Married,Tier1,Card,2,M,e-comm,10,2,6,3,3\n446,6,Detractor,N,4,10,Not Married,Tier2,e-Wallet,7,F,call,3,2,1,6,6\n447,9,Promoter,N,4,5,Married,Tier3,Cash,7,M,call,5,6,6,2,7\n448,1,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,2,F,call,2,3,9,7,6\n449,3,Detractor,N,4,3,Married,Tier2,Card,6,M,call,10,4,9,9,3\n450,9,Promoter,N,4,5,Married,Tier1,e-Wallet,7,F,m-comm,9,10,9,6,7\n451,8,Passives,Y,4,1,Married,Tier1,Cash,3,M,e-comm,9,6,2,1,9\n452,7,Passives,N,4,4,Not Married,Tier2,Ziggy Credit,3,F,m-comm,2,2,1,9,10\n453,8,Passives,Y,4,9,Married,Tier3,Card,7,M,call,4,9,3,2,2\n454,0,Detractor,N,4,9,Not Married,Tier1,e-Wallet,2,F,call,5,8,4,1,6\n455,9,Promoter,Y,4,9,Married,Tier1,Cash,7,M,m-comm,9,4,3,6,7\n456,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,6,F,call,7,9,8,2,1\n457,4,Detractor,N,4,8,Married,Tier1,Card,3,M,call,3,9,7,10,4\n458,10,Promoter,N,4,5,Married,Tier1,e-Wallet,9,F,m-comm,3,9,8,9,7\n459,10,Promoter,Y,4,5,Married,Tier1,Cash,5,M,m-comm,7,7,5,8,8\n460,3,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,1,F,call,4,8,10,9,3\n461,0,Detractor,N,4,2,Married,Tier2,Card,3,M,call,6,6,7,10,3\n462,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,4,F,call,5,10,1,1,5\n463,4,Detractor,N,4,7,Married,Tier1,Cash,4,M,call,7,5,7,3,2\n464,6,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,3,F,call,5,4,3,10,6\n465,7,Passives,N,4,7,Married,Tier3,Card,6,M,call,7,7,10,5,6\n466,2,Detractor,N,4,10,Not Married,Tier1,e-Wallet,4,F,call,1,5,4,9,6\n467,4,Detractor,N,4,2,Married,Tier2,Cash,5,M,call,7,6,7,2,4\n468,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,6,F,m-comm,8,6,3,10,7\n469,5,Detractor,N,4,9,Married,Tier1,Card,2,M,call,7,8,9,4,2\n470,9,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,1,5,5,9,8\n471,9,Promoter,Y,4,2,Married,Tier3,Cash,5,M,call,4,7,5,1,6\n472,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,5,9,8,7,7\n473,5,Detractor,N,4,7,Married,Tier2,Card,4,M,call,2,7,4,1,6\n474,8,Passives,Y,4,3,Not Married,Tier3,e-Wallet,1,F,call,4,10,6,3,7\n475,5,Detractor,N,4,5,Married,Tier1,Cash,2,M,call,7,9,2,1,4\n476,3,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,6,F,call,7,6,10,4,6\n477,8,Passives,Y,4,1,Married,Tier3,Card,2,M,call,4,9,6,10,7\n478,4,Detractor,N,4,2,Not Married,Tier1,e-Wallet,5,F,call,7,8,1,2,3\n479,7,Passives,Y,4,2,Married,Tier2,Cash,7,M,m-comm,5,9,7,10,3\n480,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,2,F,call,7,2,7,3,2\n481,2,Detractor,N,4,1,Married,Tier1,Card,6,M,call,9,10,5,9,1\n482,5,Detractor,N,4,1,Not Married,Tier2,e-Wallet,3,F,call,8,8,3,6,6\n483,7,Passives,N,4,1,Married,Tier3,Cash,4,M,call,8,9,10,4,5\n484,1,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,1,F,call,10,8,9,4,4\n485,8,Passives,Y,4,6,Married,Tier2,Card,1,M,m-comm,10,3,2,1,7\n486,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,1,F,call,7,3,5,8,7\n487,6,Detractor,N,4,5,Married,Tier1,Cash,1,M,call,2,8,2,3,4\n488,1,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,4,F,call,7,1,5,2,2\n489,8,Passives,Y,4,9,Married,Tier3,Card,5,M,call,7,10,7,5,1\n490,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,6,F,call,2,6,10,5,1\n491,5,Detractor,N,4,2,Married,Tier2,Cash,2,M,call,8,9,2,5,6\n492,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,1,F,call,5,5,6,3,9\n493,1,Detractor,N,4,9,Married,Tier1,Card,2,M,call,2,10,10,1,4\n494,4,Detractor,N,4,2,Not Married,Tier2,e-Wallet,2,F,call,4,2,10,7,1\n495,7,Passives,N,4,7,Married,Tier3,Cash,5,M,call,5,9,10,6,1\n496,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,7,F,m-comm,9,1,7,10,8\n497,2,Detractor,N,4,3,Married,Tier2,Card,3,M,call,8,2,3,8,2\n498,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,2,F,call,7,1,9,10,3\n499,4,Detractor,N,4,8,Married,Tier1,Cash,1,M,call,4,5,4,1,5\n500,2,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,4,F,call,6,5,3,1,6\n501,7,Passives,N,4,8,Married,Tier3,Card,2,M,call,7,6,2,2,3\n502,5,Detractor,N,4,6,Not Married,Tier1,e-Wallet,6,F,call,5,1,10,10,4\n503,7,Passives,Y,4,8,Married,Tier2,Cash,7,M,m-comm,8,9,7,6,2\n504,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,1,F,call,6,9,4,10,9\n505,2,Detractor,N,4,3,Married,Tier1,Card,2,M,call,7,5,9,1,3\n506,3,Detractor,N,4,3,Not Married,Tier2,e-Wallet,7,F,call,5,1,8,8,2\n507,9,Promoter,Y,4,10,Married,Tier1,Cash,6,M,m-comm,3,3,2,4,7\n508,7,Passives,N,4,7,Not Married,Tier1,Ziggy Credit,3,F,e-comm,1,9,1,2,2\n509,6,Detractor,N,4,10,Married,Tier2,Card,7,M,call,6,1,4,5,1\n510,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,7,F,call,8,10,1,4,4\n511,4,Detractor,N,4,4,Married,Tier1,Cash,3,M,call,4,1,3,7,5\n512,7,Passives,N,4,6,Not Married,Tier2,Ziggy Credit,6,F,m-comm,3,7,5,3,5\n513,7,Passives,N,4,8,Married,Tier3,Card,1,M,call,5,8,5,3,1\n514,2,Detractor,N,4,5,Not Married,Tier1,e-Wallet,7,F,call,5,9,9,7,3\n515,7,Passives,Y,4,6,Married,Tier2,Cash,6,M,m-comm,9,8,6,3,4\n516,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,8,10,8,2,6\n517,2,Detractor,N,4,3,Married,Tier1,Card,1,M,call,3,9,5,6,4\n518,8,Passives,N,4,7,Not Married,Tier2,e-Wallet,5,F,m-comm,10,5,5,8,8\n519,7,Passives,N,4,6,Married,Tier3,Cash,3,M,call,8,1,5,6,10\n520,5,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,5,F,call,9,9,5,1,5\n521,2,Detractor,N,4,10,Married,Tier2,Card,2,M,call,6,5,8,1,6\n522,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,4,3,6,10,4\n523,8,Passives,Y,4,7,Married,Tier1,Cash,5,M,e-comm,2,4,3,10,8\n524,5,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,7,F,call,9,4,3,4,3\n525,7,Passives,N,4,4,Married,Tier3,Card,3,M,call,4,2,10,2,8\n526,9,Promoter,N,4,6,Married,Tier1,e-Wallet,5,F,m-comm,9,10,6,7,8\n527,9,Promoter,Y,4,10,Married,Tier1,Cash,6,M,m-comm,8,9,3,4,9\n528,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,7,6,3,8,10\n529,4,Detractor,N,4,3,Married,Tier1,Card,7,M,call,3,6,3,7,6\n530,4,Detractor,N,4,3,Not Married,Tier2,e-Wallet,1,F,call,7,8,2,9,5\n531,7,Passives,N,4,1,Married,Tier3,Cash,5,M,call,4,3,4,10,7\n532,7,Passives,N,4,10,Not Married,Tier1,Ziggy Credit,6,F,e-comm,7,3,8,2,6\n533,4,Detractor,N,4,2,Married,Tier2,Card,2,M,call,4,2,2,1,3\n534,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,5,F,call,8,7,9,3,3\n535,8,Passives,Y,4,5,Married,Tier1,Cash,5,M,e-comm,4,9,1,2,2\n536,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,1,F,m-comm,9,2,7,4,5\n537,7,Passives,N,4,5,Married,Tier3,Card,2,M,call,4,4,7,5,5\n538,3,Detractor,N,4,9,Not Married,Tier1,e-Wallet,5,F,call,9,4,10,3,1\n539,8,Passives,Y,4,10,Married,Tier2,Cash,5,M,m-comm,2,3,9,1,1\n540,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,5,F,call,7,8,5,1,9\n541,0,Detractor,N,4,5,Married,Tier1,Card,4,M,call,5,6,8,10,2\n542,2,Detractor,N,4,9,Not Married,Tier2,e-Wallet,5,F,call,2,6,8,4,1\n543,7,Passives,N,4,7,Married,Tier3,Cash,1,M,call,7,8,6,6,10\n544,2,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,1,F,call,6,6,1,2,4\n545,3,Detractor,N,4,5,Married,Tier2,Card,3,M,call,2,4,5,1,2\n546,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,3,F,call,7,1,6,9,4\n547,6,Detractor,Y,4,1,Married,Tier1,Cash,6,M,call,8,8,9,5,6\n548,10,Promoter,Y,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,3,10,2,10,10\n549,10,Promoter,N,4,8,Married,Tier1,Card,7,M,m-comm,4,10,4,5,8\n550,8,Passives,N,4,4,Not Married,Tier1,e-Wallet,4,F,e-comm,7,2,8,9,6\n551,7,Passives,Y,4,5,Married,Tier2,Cash,2,M,m-comm,4,8,6,9,7\n552,7,Passives,Y,4,2,Not Married,Tier3,Ziggy Credit,4,F,call,4,6,4,9,10\n553,3,Detractor,N,4,7,Married,Tier1,Card,3,M,call,5,10,8,8,1\n554,10,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,1,1,10,9,10\n555,8,Passives,Y,4,2,Married,Tier3,Cash,1,M,call,4,7,7,6,5\n556,6,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,4,F,call,3,8,9,10,4\n557,4,Detractor,N,4,6,Married,Tier2,Card,5,M,call,3,9,7,2,3\n558,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,7,F,call,8,10,9,8,10\n559,4,Detractor,N,4,6,Married,Tier1,Cash,3,M,call,2,2,9,9,6\n560,2,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,6,F,call,4,2,3,10,2\n561,7,Passives,N,4,2,Married,Tier3,Card,2,M,call,6,6,3,7,4\n562,3,Detractor,N,4,5,Not Married,Tier1,e-Wallet,4,F,call,7,10,2,9,5\n563,0,Detractor,N,4,1,Married,Tier2,Cash,1,M,call,1,6,2,7,1\n564,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,5,F,call,5,3,10,9,7\n565,9,Promoter,Y,4,4,Married,Tier1,Card,6,M,m-comm,6,6,10,1,10\n566,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,7,10,3,10\n567,7,Passives,N,4,3,Married,Tier3,Cash,6,M,call,8,9,5,9,9\n568,3,Detractor,N,4,3,Not Married,Tier1,Ziggy Credit,1,F,call,5,10,5,1,6\n569,2,Detractor,N,4,5,Married,Tier2,Card,2,M,call,7,8,1,1,6\n570,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,6,2,3,6,10\n571,3,Detractor,N,4,1,Married,Tier1,Cash,3,M,call,10,8,1,6,1\n572,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,3,9,3,6,10\n573,7,Passives,Y,4,10,Married,Tier3,Card,2,M,call,4,9,7,9,5\n574,3,Detractor,N,4,4,Not Married,Tier1,e-Wallet,3,F,call,2,9,3,7,4\n575,9,Promoter,Y,4,7,Married,Tier1,Cash,7,M,m-comm,1,8,4,1,7\n576,8,Passives,Y,4,3,Not Married,Tier3,Ziggy Credit,7,F,call,4,4,10,8,3\n577,5,Detractor,N,4,4,Married,Tier1,Card,6,M,call,7,3,1,10,6\n578,10,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,5,5,9,7,9\n579,7,Passives,N,4,10,Married,Tier3,Cash,3,M,call,4,4,10,9,6\n580,8,Passives,N,4,10,Not Married,Tier1,Ziggy Credit,6,F,e-comm,4,3,4,1,3\n581,0,Detractor,N,4,1,Married,Tier2,Card,2,M,call,9,4,3,2,4\n582,10,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,9,10,2,2,10\n583,1,Detractor,N,4,8,Married,Tier1,Cash,4,M,call,1,10,8,7,5\n584,0,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,2,F,call,1,9,3,3,6\n585,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,4,9,9,4,2\n586,5,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,7,1,1,9,1\n587,6,Detractor,N,4,2,Married,Tier2,Cash,2,M,call,10,8,4,8,3\n588,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,3,F,call,6,3,1,9,1\n589,6,Detractor,N,4,8,Married,Tier1,Card,1,M,call,8,10,3,9,1\n590,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,9,5,9,3,7\n591,9,Promoter,Y,4,2,Married,Tier1,Cash,10,M,m-comm,9,10,3,6,10\n592,2,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,1,F,call,5,7,2,9,1\n593,2,Detractor,N,4,9,Married,Tier2,Card,4,M,call,2,5,8,7,5\n594,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,6,F,call,6,1,5,10,9\n595,6,Detractor,N,4,5,Married,Tier1,Cash,7,M,call,8,2,6,2,1\n596,5,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,1,F,call,8,1,9,3,2\n597,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,7,9,4,9,3\n598,5,Detractor,N,4,4,Not Married,Tier1,e-Wallet,3,F,call,7,9,6,2,4\n599,6,Detractor,N,4,9,Married,Tier2,Cash,3,M,call,6,6,9,10,1\n600,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,8,F,m-comm,3,10,6,4,8\n601,6,Detractor,N,4,5,Married,Tier1,Card,5,M,call,2,9,4,10,5\n602,1,Detractor,N,4,5,Not Married,Tier2,e-Wallet,1,F,call,6,10,8,1,1\n603,7,Passives,N,4,1,Married,Tier3,Cash,7,M,call,5,10,9,2,8\n604,7,Passives,N,4,3,Not Married,Tier1,Ziggy Credit,2,F,e-comm,5,5,10,4,4\n605,2,Detractor,N,4,3,Married,Tier2,Card,5,M,call,6,3,8,9,5\n606,8,Passives,N,4,5,Not Married,Tier3,e-Wallet,2,F,call,5,6,2,5,1\n607,5,Detractor,N,4,8,Married,Tier1,Cash,4,M,call,7,1,3,6,2\n608,2,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,4,F,call,3,6,1,6,1\n609,7,Passives,N,4,7,Married,Tier3,Card,1,M,call,8,7,4,9,9\n610,0,Detractor,N,4,8,Not Married,Tier1,e-Wallet,2,F,call,7,1,10,4,6\n611,8,Passives,Y,4,1,Married,Tier2,Cash,5,M,m-comm,5,7,6,2,2\n612,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,2,F,call,6,7,1,9,2\n613,4,Detractor,N,4,5,Married,Tier1,Card,5,M,call,10,10,6,1,3\n614,8,Passives,N,4,3,Not Married,Tier2,e-Wallet,3,F,m-comm,10,4,9,6,5\n615,7,Passives,N,4,6,Married,Tier3,Cash,1,M,call,4,3,1,5,4\n616,6,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,6,F,call,3,4,9,3,6\n617,4,Detractor,N,4,9,Married,Tier2,Card,6,M,call,10,6,3,8,6\n618,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,6,F,call,7,5,1,9,6\n619,2,Detractor,N,4,2,Married,Tier1,Cash,7,M,call,9,6,6,4,2\n620,5,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,2,F,call,5,9,8,3,1\n621,7,Passives,Y,4,7,Married,Tier3,Card,1,M,call,5,8,7,10,3\n622,5,Detractor,N,4,4,Not Married,Tier1,e-Wallet,4,F,call,6,6,3,2,5\n623,4,Detractor,N,4,3,Married,Tier2,Cash,3,M,call,10,9,9,2,2\n624,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,4,F,call,7,5,4,7,1\n625,10,Promoter,Y,4,3,Married,Tier1,Card,9,M,m-comm,3,9,3,1,7\n626,9,Promoter,N,4,6,Married,Tier1,e-Wallet,8,F,m-comm,6,7,9,3,9\n627,7,Passives,Y,4,4,Married,Tier3,Cash,6,M,call,4,6,4,6,10\n628,6,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,8,3,10,5,2\n629,4,Detractor,N,4,7,Married,Tier2,Card,7,M,call,2,7,1,5,6\n630,10,Promoter,N,4,6,Married,Tier1,e-Wallet,6,F,m-comm,1,10,3,10,10\n631,2,Detractor,N,4,4,Married,Tier1,Cash,5,M,call,7,3,3,8,2\n632,2,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,5,F,call,8,4,1,8,4\n633,7,Passives,N,4,9,Married,Tier3,Card,3,M,call,8,2,6,8,3\n634,8,Passives,N,4,5,Not Married,Tier1,e-Wallet,4,F,e-comm,6,2,4,3,6\n635,3,Detractor,N,4,6,Married,Tier2,Cash,5,M,call,5,1,7,4,2\n636,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,4,F,call,5,9,4,8,5\n637,6,Detractor,N,4,1,Married,Tier1,Card,1,M,call,8,7,1,2,6\n638,3,Detractor,N,4,4,Not Married,Tier2,e-Wallet,3,F,call,4,5,2,7,5\n639,9,Promoter,Y,4,5,Married,Tier3,Cash,1,M,call,8,5,6,2,6\n640,8,Passives,N,4,5,Not Married,Tier1,Ziggy Credit,4,F,e-comm,10,5,9,1,10\n641,3,Detractor,N,4,6,Married,Tier2,Card,6,M,call,5,4,8,6,1\n642,8,Passives,N,4,1,Not Married,Tier3,e-Wallet,2,F,call,7,10,2,4,6\n643,8,Passives,Y,4,3,Married,Tier1,Cash,6,M,e-comm,10,2,7,1,3\n644,4,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,5,F,call,6,10,6,4,2\n645,7,Passives,N,4,8,Married,Tier3,Card,4,M,call,6,2,2,6,4\n646,1,Detractor,N,4,6,Not Married,Tier1,e-Wallet,2,F,call,4,3,8,10,6\n647,1,Detractor,N,4,2,Married,Tier2,Cash,3,M,call,6,7,9,7,1\n648,8,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,4,F,call,8,6,6,3,8\n649,3,Detractor,N,4,8,Married,Tier1,Card,1,M,call,9,3,6,1,3\n650,2,Detractor,N,4,4,Not Married,Tier2,e-Wallet,1,F,call,10,9,9,1,1\n651,9,Promoter,N,4,5,Married,Tier3,Cash,3,M,call,6,2,3,2,8\n652,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,2,F,call,1,2,1,7,3\n653,0,Detractor,N,4,10,Married,Tier2,Card,5,M,call,10,9,8,7,6\n654,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,1,F,call,7,9,3,8,3\n655,6,Detractor,N,4,6,Married,Tier1,Cash,5,M,call,1,2,6,10,3\n656,1,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,1,F,call,8,7,10,7,6\n657,7,Passives,N,4,7,Married,Tier3,Card,1,M,call,7,3,3,6,7\n658,7,Passives,N,4,9,Not Married,Tier1,e-Wallet,2,F,e-comm,9,2,5,2,2\n659,10,Promoter,Y,4,2,Married,Tier1,Cash,7,M,m-comm,5,5,9,9,7\n660,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,7,F,call,8,1,4,7,10\n661,0,Detractor,N,4,7,Married,Tier1,Card,5,M,call,8,5,1,1,5\n662,4,Detractor,N,4,3,Not Married,Tier2,e-Wallet,1,F,call,2,9,3,2,4\n663,9,Promoter,N,4,3,Married,Tier3,Cash,1,M,call,8,5,8,6,6\n664,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,7,F,m-comm,5,8,2,6,7\n665,7,Passives,Y,4,5,Married,Tier2,Card,2,M,m-comm,6,3,7,3,7\n666,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,6,F,call,8,4,3,1,7\n667,6,Detractor,N,4,10,Married,Tier1,Cash,1,M,call,5,5,10,7,2\n668,6,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,1,F,call,2,8,10,3,4\n669,7,Passives,N,4,4,Married,Tier3,Card,7,M,call,7,2,1,1,10\n670,6,Detractor,N,4,5,Not Married,Tier1,e-Wallet,1,F,call,8,2,3,10,2\n671,5,Detractor,N,4,10,Married,Tier2,Cash,3,M,call,10,6,2,4,6\n672,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,6,F,call,7,9,8,8,5\n673,7,Passives,Y,4,10,Married,Tier1,Card,3,M,e-comm,7,10,5,9,6\n674,7,Passives,N,4,5,Not Married,Tier2,e-Wallet,6,F,m-comm,3,1,4,8,7\n675,9,Promoter,N,4,9,Married,Tier3,Cash,4,M,call,7,1,4,8,4\n676,3,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,1,F,call,7,3,2,7,3\n677,10,Promoter,Y,4,1,Married,Tier1,Card,7,M,m-comm,1,1,5,7,8\n678,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,5,6,3,9,3\n679,8,Passives,Y,4,1,Married,Tier1,Cash,3,M,e-comm,3,8,2,2,2\n680,6,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,6,F,call,7,6,1,7,6\n681,7,Passives,N,4,9,Married,Tier3,Card,4,M,call,8,2,6,5,1\n682,9,Promoter,N,4,5,Married,Tier1,e-Wallet,9,F,m-comm,1,8,3,1,8\n683,10,Promoter,Y,4,7,Married,Tier1,Cash,5,M,m-comm,9,10,1,5,10\n684,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,4,F,call,5,7,6,2,7\n685,9,Promoter,Y,4,10,Married,Tier1,Card,6,M,m-comm,4,5,6,9,10\n686,10,Promoter,N,4,5,Married,Tier1,e-Wallet,9,F,m-comm,2,6,6,5,9\n687,9,Promoter,N,4,10,Married,Tier3,Cash,1,M,call,7,3,8,8,2\n688,0,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,6,F,call,4,3,2,9,2\n689,10,Promoter,Y,4,8,Married,Tier1,Card,8,M,m-comm,10,4,5,3,10\n690,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,1,F,call,5,8,7,3,6\n691,2,Detractor,N,4,8,Married,Tier1,Cash,7,M,call,4,6,5,7,4\n692,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,7,F,m-comm,4,1,5,1,10\n693,7,Passives,N,4,4,Married,Tier3,Card,7,M,call,4,10,5,3,6\n694,0,Detractor,N,4,1,Not Married,Tier1,e-Wallet,7,F,call,6,7,7,10,1\n695,5,Detractor,N,4,3,Married,Tier2,Cash,4,M,call,4,6,4,4,2\n696,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,5,F,call,7,9,5,9,2\n697,10,Promoter,Y,4,4,Married,Tier1,Card,6,M,m-comm,5,7,7,2,9\n698,3,Detractor,N,4,6,Not Married,Tier2,e-Wallet,4,F,call,2,9,4,1,4\n699,9,Promoter,N,4,8,Married,Tier3,Cash,7,M,call,8,9,1,7,2\n700,0,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,3,F,call,5,9,4,4,6\n701,4,Detractor,N,4,2,Married,Tier2,Card,3,M,call,2,8,6,5,4\n702,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,3,F,call,5,9,2,3,1\n703,5,Detractor,N,4,7,Married,Tier1,Cash,5,M,call,10,10,8,9,1\n704,3,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,7,F,call,3,10,4,7,2\n705,8,Passives,Y,4,6,Married,Tier3,Card,1,M,call,6,3,4,4,5\n706,0,Detractor,N,4,7,Not Married,Tier1,e-Wallet,7,F,call,5,5,4,7,2\n707,2,Detractor,N,4,6,Married,Tier2,Cash,7,M,call,4,10,2,1,2\n708,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,1,F,call,8,5,8,6,10\n709,6,Detractor,N,4,6,Married,Tier1,Card,2,M,call,5,5,6,7,6\n710,3,Detractor,N,4,1,Not Married,Tier2,e-Wallet,4,F,call,9,6,5,10,6\n711,9,Promoter,N,4,6,Married,Tier3,Cash,5,M,call,4,6,4,7,5\n712,1,Detractor,N,4,3,Not Married,Tier1,Ziggy Credit,7,F,call,4,9,6,9,4\n713,0,Detractor,N,4,8,Married,Tier2,Card,5,M,call,10,1,9,1,4\n714,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,5,F,call,6,4,1,6,6\n715,1,Detractor,N,4,7,Married,Tier1,Cash,6,M,call,7,10,7,9,1\n716,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,9,3,8,1,10\n717,7,Passives,Y,4,7,Married,Tier3,Card,4,M,call,5,4,7,8,7\n718,9,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,3,3,5,10,9\n719,3,Detractor,N,4,2,Married,Tier2,Cash,3,M,call,9,1,7,8,4\n720,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,2,F,call,8,5,8,5,5\n721,7,Passives,Y,4,8,Married,Tier1,Card,2,M,e-comm,7,3,2,4,7\n722,10,Promoter,N,4,7,Married,Tier1,e-Wallet,10,F,m-comm,6,6,3,8,10\n723,9,Promoter,N,4,9,Married,Tier3,Cash,1,M,call,7,8,2,5,7\n724,5,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,2,F,call,7,4,9,8,5\n725,10,Promoter,Y,4,5,Married,Tier1,Card,8,M,m-comm,1,7,1,9,10\n726,10,Promoter,N,4,6,Married,Tier1,e-Wallet,7,F,m-comm,3,9,9,10,9\n727,4,Detractor,N,4,4,Married,Tier1,Cash,7,M,call,1,6,3,8,2\n728,8,Passives,N,4,3,Not Married,Tier2,Ziggy Credit,7,F,m-comm,1,6,8,8,6\n729,7,Passives,N,4,9,Married,Tier3,Card,2,M,call,8,3,2,3,2\n730,1,Detractor,N,4,6,Not Married,Tier1,e-Wallet,7,F,call,8,9,2,5,2\n731,3,Detractor,N,4,1,Married,Tier2,Cash,3,M,call,8,4,1,2,1\n732,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,2,F,call,8,6,2,6,10\n733,3,Detractor,N,4,2,Married,Tier1,Card,6,M,call,7,8,5,4,4\n734,9,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,10,1,9,3,8\n735,9,Promoter,N,4,7,Married,Tier3,Cash,4,M,call,5,7,8,4,8\n736,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,8,F,m-comm,8,4,9,9,9\n737,8,Passives,Y,4,10,Married,Tier2,Card,4,M,m-comm,2,4,2,4,9\n738,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,7,2,9,1,9\n739,8,Passives,Y,4,4,Married,Tier1,Cash,1,M,e-comm,4,3,7,3,4\n740,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,6,F,m-comm,1,5,2,7,7\n741,7,Passives,N,4,6,Married,Tier3,Card,1,M,call,8,5,6,8,2\n742,9,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,4,7,5,4,9\n743,0,Detractor,N,4,5,Married,Tier2,Cash,1,M,call,2,8,4,3,6\n744,8,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,4,F,call,6,6,3,2,2\n745,9,Promoter,Y,4,3,Married,Tier1,Card,10,M,m-comm,8,8,1,3,8\n746,1,Detractor,N,4,5,Not Married,Tier2,e-Wallet,7,F,call,9,9,2,3,3\n747,9,Promoter,N,4,4,Married,Tier3,Cash,4,M,call,8,9,5,8,6\n748,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,4,9,1,1,8\n749,1,Detractor,N,4,8,Married,Tier2,Card,7,M,call,4,10,4,3,5\n750,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,5,F,call,8,10,8,8,9\n751,7,Passives,Y,4,5,Married,Tier1,Cash,3,M,e-comm,10,5,7,3,9\n752,6,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,7,F,call,3,5,7,4,1\n753,7,Passives,N,4,5,Married,Tier3,Card,6,M,call,5,7,3,6,6\n754,2,Detractor,N,4,4,Not Married,Tier1,e-Wallet,7,F,call,2,9,7,2,4\n755,5,Detractor,N,4,4,Married,Tier2,Cash,2,M,call,3,6,4,7,6\n756,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,4,F,call,8,8,9,10,10\n757,6,Detractor,N,4,9,Married,Tier1,Card,4,M,call,5,6,6,6,6\n758,8,Passives,N,4,2,Not Married,Tier2,e-Wallet,6,F,m-comm,10,9,3,9,8\n759,9,Promoter,Y,4,5,Married,Tier3,Cash,1,M,call,8,4,10,8,3\n760,3,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,2,F,call,10,9,8,2,2\n761,6,Detractor,N,4,6,Married,Tier2,Card,1,M,call,2,1,4,7,4\n762,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,4,F,call,7,8,5,4,6\n763,10,Promoter,Y,4,8,Married,Tier2,Cash,9,M,m-comm,5,6,10,2,7\n764,2,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,5,F,call,2,4,7,3,5\n765,7,Passives,Y,4,10,Married,Tier3,Card,7,M,call,7,1,2,5,5\n766,4,Detractor,N,4,8,Not Married,Tier1,e-Wallet,6,F,call,3,4,7,6,3\n767,0,Detractor,N,4,9,Married,Tier2,Cash,1,M,call,7,9,10,5,4\n768,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,7,4,8,7,1\n769,4,Detractor,N,4,1,Married,Tier1,Card,7,M,call,8,7,10,5,1\n770,5,Detractor,N,4,2,Not Married,Tier2,e-Wallet,6,F,call,9,9,4,1,6\n771,9,Promoter,N,4,9,Married,Tier3,Cash,5,M,call,6,3,6,2,8\n772,1,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,2,F,call,5,1,4,10,3\n773,9,Promoter,Y,4,1,Married,Tier1,Card,5,M,m-comm,5,8,9,8,7\n774,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,6,F,call,5,7,8,10,5\n775,6,Detractor,N,4,7,Married,Tier1,Cash,6,M,call,1,8,5,8,2\n776,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,7,F,m-comm,6,3,10,4,9\n777,9,Promoter,Y,4,2,Married,Tier1,Card,8,M,m-comm,8,5,10,10,7\n778,3,Detractor,N,4,7,Not Married,Tier1,e-Wallet,2,F,call,1,3,6,10,4\n779,9,Promoter,Y,4,1,Married,Tier1,Cash,9,M,m-comm,8,8,10,6,7\n780,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,4,F,call,8,1,8,9,7\n781,5,Detractor,N,4,8,Married,Tier1,Card,4,M,call,1,8,7,3,3\n782,0,Detractor,N,4,1,Not Married,Tier2,e-Wallet,2,F,call,2,4,1,10,3\n783,9,Promoter,N,4,5,Married,Tier3,Cash,7,M,call,4,8,10,3,9\n784,2,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,1,F,call,2,1,9,8,3\n785,4,Detractor,N,4,8,Married,Tier2,Card,4,M,call,7,9,3,4,3\n786,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,6,F,call,6,10,1,8,3\n787,3,Detractor,N,4,9,Married,Tier1,Cash,2,M,call,3,2,5,7,3\n788,0,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,7,F,call,3,8,5,2,5\n789,7,Passives,N,4,2,Married,Tier3,Card,7,M,call,6,8,4,9,5\n790,6,Detractor,N,4,6,Not Married,Tier1,e-Wallet,2,F,call,1,6,10,1,5\n791,7,Passives,Y,4,6,Married,Tier2,Cash,5,M,m-comm,10,9,9,2,5\n792,9,Promoter,N,4,6,Married,Tier2,Ziggy Credit,7,F,m-comm,2,3,7,10,9\n793,8,Passives,Y,4,1,Married,Tier1,Card,1,M,e-comm,8,5,8,1,3\n794,1,Detractor,N,4,6,Not Married,Tier2,e-Wallet,1,F,call,2,3,10,3,5\n795,9,Promoter,Y,4,2,Married,Tier3,Cash,6,M,call,8,4,8,6,3\n796,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,7,F,m-comm,3,4,8,9,9\n797,10,Promoter,Y,4,10,Married,Tier1,Card,5,M,m-comm,10,1,3,9,7\n798,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,2,F,call,5,4,4,2,5\n799,5,Detractor,N,4,3,Married,Tier1,Cash,1,M,call,9,7,3,1,1\n800,6,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,1,F,call,1,10,1,1,5\n801,7,Passives,N,4,10,Married,Tier3,Card,3,M,call,7,8,8,3,7\n802,6,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,7,10,5,8,3\n803,8,Passives,Y,4,2,Married,Tier2,Cash,1,M,m-comm,7,4,8,3,8\n804,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,6,F,call,4,4,9,6,6\n805,10,Promoter,Y,4,7,Married,Tier1,Card,6,M,m-comm,2,10,10,3,7\n806,0,Detractor,N,4,5,Not Married,Tier2,e-Wallet,7,F,call,8,9,10,9,4\n807,9,Promoter,N,4,4,Married,Tier3,Cash,3,M,call,4,2,2,8,8\n808,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,7,F,call,5,8,2,7,6\n809,1,Detractor,N,4,2,Married,Tier2,Card,6,M,call,8,2,3,7,6\n810,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,2,F,call,4,4,4,10,7\n811,8,Passives,Y,4,10,Married,Tier1,Cash,3,M,e-comm,10,1,8,7,10\n812,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,8,3,8,8,8\n813,10,Promoter,Y,4,10,Married,Tier1,Card,10,M,m-comm,5,6,10,2,9\n814,7,Passives,N,4,4,Not Married,Tier1,e-Wallet,6,F,e-comm,7,3,6,4,2\n815,2,Detractor,N,4,7,Married,Tier2,Cash,5,M,call,6,5,2,4,6\n816,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,3,F,call,4,10,8,3,5\n817,5,Detractor,N,4,6,Married,Tier1,Card,4,M,call,9,8,10,3,1\n818,1,Detractor,N,4,9,Not Married,Tier2,e-Wallet,4,F,call,9,4,9,9,5\n819,9,Promoter,N,4,6,Married,Tier3,Cash,1,M,call,5,1,4,6,6\n820,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,6,2,2,10,10\n821,6,Detractor,N,4,6,Married,Tier2,Card,6,M,call,5,3,6,8,6\n822,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,7,F,call,4,8,8,3,5\n823,5,Detractor,N,4,2,Married,Tier1,Cash,5,M,call,5,1,3,5,2\n824,4,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,3,F,call,10,9,2,10,5\n825,7,Passives,N,4,10,Married,Tier3,Card,5,M,call,6,1,5,5,10\n826,10,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,7,4,6,2,8\n827,2,Detractor,N,4,6,Married,Tier2,Cash,2,M,call,8,9,6,10,6\n828,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,7,1,4,7,10\n829,7,Passives,Y,4,3,Married,Tier1,Card,7,M,e-comm,1,4,2,4,8\n830,10,Promoter,N,4,5,Married,Tier1,e-Wallet,7,F,m-comm,7,6,5,3,9\n831,9,Promoter,N,4,6,Married,Tier3,Cash,3,M,call,4,3,1,8,3\n832,3,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,1,F,call,1,6,2,4,2\n833,1,Detractor,N,4,9,Married,Tier2,Card,7,M,call,7,7,1,5,6\n834,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,4,F,call,4,6,10,8,1\n835,1,Detractor,N,4,4,Married,Tier1,Cash,2,M,call,2,5,5,8,3\n836,0,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,4,F,call,4,4,3,4,3\n837,7,Passives,N,4,8,Married,Tier3,Card,6,M,call,6,2,9,7,10\n838,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,6,F,call,10,9,9,9,3\n839,7,Passives,Y,4,5,Married,Tier2,Cash,1,M,m-comm,2,3,4,2,4\n840,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,2,F,call,8,9,3,8,1\n841,7,Passives,Y,4,4,Married,Tier1,Card,2,M,e-comm,6,1,9,2,6\n842,3,Detractor,N,4,10,Not Married,Tier2,e-Wallet,1,F,call,9,5,7,10,1\n843,10,Promoter,Y,4,5,Married,Tier1,Cash,9,M,m-comm,7,8,9,3,8\n844,4,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,1,F,call,1,6,3,7,6\n845,9,Promoter,Y,4,2,Married,Tier1,Card,8,M,m-comm,2,1,9,8,9\n846,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,4,F,call,8,6,3,1,10\n847,4,Detractor,N,4,10,Married,Tier1,Cash,5,M,call,5,6,7,9,3\n848,8,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,6,F,m-comm,6,4,9,4,6\n849,7,Passives,N,4,2,Married,Tier3,Card,3,M,call,5,6,10,6,1\n850,2,Detractor,N,4,7,Not Married,Tier1,e-Wallet,1,F,call,8,5,7,6,6\n851,5,Detractor,N,4,7,Married,Tier2,Cash,6,M,call,10,7,8,9,2\n852,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,2,F,call,4,10,8,5,10\n853,3,Detractor,N,4,8,Married,Tier1,Card,1,M,call,5,6,2,9,4\n854,5,Detractor,N,4,9,Not Married,Tier2,e-Wallet,3,F,call,6,5,8,4,5\n855,10,Promoter,Y,4,6,Married,Tier1,Cash,10,M,m-comm,8,3,5,7,7\n856,6,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,1,F,call,4,10,7,2,2\n857,3,Detractor,N,4,9,Married,Tier2,Card,5,M,call,1,4,1,3,5\n858,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,6,F,call,5,9,9,2,9\n859,5,Detractor,N,4,7,Married,Tier1,Cash,7,M,call,4,10,5,1,1\n860,8,Passives,N,4,4,Not Married,Tier2,Ziggy Credit,6,F,m-comm,7,7,5,8,8\n861,7,Passives,N,4,1,Married,Tier3,Card,2,M,call,5,6,1,1,6\n862,3,Detractor,N,4,4,Not Married,Tier1,e-Wallet,3,F,call,6,4,10,3,4\n863,7,Passives,Y,4,5,Married,Tier2,Cash,1,M,m-comm,2,4,7,6,3\n864,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,7,F,call,8,5,3,2,3\n865,5,Detractor,N,4,10,Married,Tier1,Card,6,M,call,5,2,2,7,3\n866,0,Detractor,N,4,10,Not Married,Tier2,e-Wallet,3,F,call,7,6,7,4,5\n867,9,Promoter,Y,4,10,Married,Tier3,Cash,6,M,call,5,3,9,1,9\n868,3,Detractor,N,4,3,Not Married,Tier1,Ziggy Credit,4,F,call,2,5,5,10,6\n869,8,Passives,Y,4,1,Married,Tier2,Card,5,M,m-comm,2,5,8,3,6\n870,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,7,F,call,8,1,1,1,6\n871,1,Detractor,N,4,3,Married,Tier1,Cash,7,M,call,5,3,2,3,6\n872,5,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,5,F,call,8,3,3,9,5\n873,7,Passives,N,4,4,Married,Tier3,Card,4,M,call,5,5,9,10,6\n874,7,Passives,N,4,2,Not Married,Tier1,e-Wallet,1,F,e-comm,1,10,3,9,2\n875,0,Detractor,N,4,2,Married,Tier2,Cash,3,M,call,5,5,2,1,1\n876,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,10,F,m-comm,10,3,4,2,7\n877,6,Detractor,N,4,10,Married,Tier1,Card,2,M,call,8,2,4,7,2\n878,0,Detractor,N,4,8,Not Married,Tier2,e-Wallet,2,F,call,6,4,5,2,6\n879,9,Promoter,N,4,4,Married,Tier3,Cash,1,M,call,5,9,10,5,8\n880,7,Passives,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,e-comm,8,5,8,10,3\n881,5,Detractor,N,4,1,Married,Tier2,Card,4,M,call,1,9,7,3,3\n882,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,7,F,call,4,3,3,9,2\n883,6,Detractor,N,4,7,Married,Tier1,Cash,6,M,call,4,2,4,5,3\n884,7,Passives,N,4,10,Not Married,Tier2,Ziggy Credit,3,F,m-comm,2,4,1,5,10\n885,7,Passives,N,4,6,Married,Tier3,Card,3,M,call,8,1,3,10,3\n886,1,Detractor,N,4,6,Not Married,Tier1,e-Wallet,3,F,call,10,4,2,3,2\n887,7,Passives,Y,4,2,Married,Tier2,Cash,3,M,m-comm,9,1,4,1,3\n888,8,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,8,4,8,5,9\n889,6,Detractor,N,4,6,Married,Tier1,Card,1,M,call,1,6,1,5,4\n890,3,Detractor,N,4,2,Not Married,Tier2,e-Wallet,2,F,call,6,10,4,8,4\n891,9,Promoter,N,4,5,Married,Tier3,Cash,1,M,call,5,8,1,4,1\n892,6,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,6,F,call,6,3,9,7,4\n893,3,Detractor,N,4,7,Married,Tier2,Card,1,M,call,10,8,4,5,4\n894,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,2,F,call,5,3,1,2,2\n895,8,Passives,Y,4,10,Married,Tier1,Cash,7,M,e-comm,8,6,10,10,9\n896,1,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,4,F,call,2,10,9,1,6\n897,8,Passives,Y,4,6,Married,Tier3,Card,1,M,call,8,1,8,4,1\n898,8,Passives,N,4,5,Not Married,Tier1,e-Wallet,7,F,e-comm,10,4,2,3,5\n899,0,Detractor,N,4,1,Married,Tier2,Cash,1,M,call,4,7,9,7,6\n900,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,2,F,call,4,7,8,2,6\n901,7,Passives,Y,4,6,Married,Tier1,Card,1,M,e-comm,7,9,5,4,1\n902,3,Detractor,N,4,9,Not Married,Tier2,e-Wallet,2,F,call,1,4,2,1,6\n903,9,Promoter,N,4,7,Married,Tier3,Cash,4,M,call,4,1,1,4,2\n904,4,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,call,9,10,2,7,6\n905,0,Detractor,N,4,3,Married,Tier2,Card,4,M,call,10,1,6,7,6\n906,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,2,F,call,8,7,3,3,9\n907,8,Passives,Y,4,6,Married,Tier1,Cash,4,M,e-comm,7,6,3,1,6\n908,0,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,3,F,call,1,10,7,1,1\n909,7,Passives,N,4,8,Married,Tier3,Card,5,M,call,8,5,2,8,3\n910,7,Passives,N,4,1,Not Married,Tier1,e-Wallet,6,F,e-comm,2,10,2,5,8\n911,3,Detractor,N,4,10,Married,Tier2,Cash,1,M,call,1,10,8,6,6\n912,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,7,F,call,7,7,8,10,5\n913,6,Detractor,N,4,7,Married,Tier1,Card,3,M,call,6,9,1,1,4\n914,5,Detractor,N,4,10,Not Married,Tier2,e-Wallet,5,F,call,3,10,8,4,2\n915,9,Promoter,Y,4,9,Married,Tier3,Cash,2,M,call,4,4,1,2,7\n916,3,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,5,F,call,5,7,2,4,3\n917,5,Detractor,N,4,4,Married,Tier2,Card,5,M,call,6,8,5,7,3\n918,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,1,F,call,6,6,5,7,4\n919,10,Promoter,Y,4,3,Married,Tier1,Cash,6,M,m-comm,9,8,1,7,9\n920,7,Passives,N,4,7,Not Married,Tier2,Ziggy Credit,5,F,m-comm,7,5,10,9,1\n921,7,Passives,N,4,6,Married,Tier3,Card,2,M,call,5,9,2,4,10\n922,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,5,F,call,8,8,1,9,2\n923,6,Detractor,N,4,7,Married,Tier2,Cash,4,M,call,3,7,6,4,6\n924,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,7,F,call,7,3,10,1,3\n925,2,Detractor,N,4,1,Married,Tier1,Card,2,M,call,9,3,3,4,2\n926,1,Detractor,N,4,3,Not Married,Tier2,e-Wallet,3,F,call,3,8,1,3,1\n927,9,Promoter,N,4,9,Married,Tier3,Cash,7,M,call,8,8,5,10,3\n928,1,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,2,F,call,5,4,4,4,5\n929,10,Promoter,Y,4,7,Married,Tier1,Card,5,M,m-comm,10,4,5,5,10\n930,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,7,F,call,6,7,6,5,10\n931,7,Passives,Y,4,10,Married,Tier1,Cash,2,M,e-comm,10,1,6,10,7\n932,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,9,F,m-comm,2,5,4,8,7\n933,7,Passives,N,4,7,Married,Tier3,Card,4,M,call,7,6,7,4,9\n934,1,Detractor,N,4,5,Not Married,Tier1,e-Wallet,5,F,call,9,4,9,1,1\n935,6,Detractor,N,4,5,Married,Tier2,Cash,4,M,call,1,2,9,7,3\n936,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,6,F,call,4,2,6,1,7\n937,7,Passives,Y,4,5,Married,Tier1,Card,4,M,e-comm,2,9,1,7,2\n938,5,Detractor,N,4,9,Not Married,Tier2,e-Wallet,7,F,call,8,10,7,1,1\n939,9,Promoter,N,4,9,Married,Tier3,Cash,1,M,call,6,4,5,8,8\n940,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,4,4,10,8,10\n941,2,Detractor,N,4,7,Married,Tier2,Card,7,M,call,9,8,10,1,5\n942,9,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,10,10,5,3,9\n943,5,Detractor,N,4,4,Married,Tier1,Cash,5,M,call,9,5,5,7,4\n944,2,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,call,1,7,9,7,3\n945,8,Passives,Y,4,9,Married,Tier3,Card,7,M,call,5,8,8,8,10\n946,7,Passives,N,4,3,Not Married,Tier1,e-Wallet,2,F,e-comm,8,2,8,1,4\n947,7,Passives,Y,4,7,Married,Tier2,Cash,5,M,m-comm,2,8,10,2,4\n948,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,7,F,call,5,4,9,2,10\n949,6,Detractor,N,4,8,Married,Tier1,Card,6,M,call,3,10,6,7,3\n950,2,Detractor,N,4,6,Not Married,Tier2,e-Wallet,2,F,call,1,6,9,8,3\n951,9,Promoter,Y,4,9,Married,Tier1,Cash,10,M,m-comm,5,9,3,8,9\n952,0,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,4,F,call,2,9,8,5,6\n953,4,Detractor,N,4,9,Married,Tier2,Card,2,M,call,5,10,8,2,3\n954,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,2,F,call,4,2,8,10,8\n955,9,Promoter,Y,4,1,Married,Tier1,Cash,5,M,m-comm,8,1,3,5,8\n956,2,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,7,F,call,4,5,4,9,2\n957,10,Promoter,Y,4,9,Married,Tier1,Card,9,M,m-comm,5,9,3,6,8\n958,8,Passives,N,4,6,Not Married,Tier1,e-Wallet,5,F,e-comm,8,4,9,9,8\n959,10,Promoter,Y,4,1,Married,Tier1,Cash,8,M,m-comm,7,1,7,10,8\n960,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,10,4,5,3,7\n961,8,Passives,Y,4,7,Married,Tier1,Card,6,M,e-comm,2,2,3,3,8\n962,10,Promoter,N,4,6,Married,Tier1,e-Wallet,7,F,m-comm,3,9,5,6,8\n963,9,Promoter,N,4,5,Married,Tier3,Cash,3,M,call,5,1,2,7,6\n964,6,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,1,F,call,1,3,7,5,6\n965,3,Detractor,N,4,3,Married,Tier2,Card,2,M,call,9,6,5,6,1\n966,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,1,F,call,7,4,9,10,2\n967,3,Detractor,N,4,7,Married,Tier1,Cash,1,M,call,5,8,3,6,6\n968,6,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,5,F,call,4,10,10,2,4\n969,7,Passives,N,4,8,Married,Tier3,Card,2,M,call,8,6,3,10,8\n970,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,2,F,call,9,3,10,7,2\n971,2,Detractor,N,4,7,Married,Tier2,Cash,3,M,call,7,9,1,7,6\n972,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,8,F,m-comm,8,10,6,1,7\n973,9,Promoter,Y,4,1,Married,Tier1,Card,8,M,m-comm,1,2,3,6,9\n974,5,Detractor,N,4,3,Not Married,Tier2,e-Wallet,7,F,call,2,9,9,8,5\n975,9,Promoter,N,4,2,Married,Tier3,Cash,3,M,call,8,6,6,7,4\n976,5,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,5,F,call,1,1,2,5,5\n977,5,Detractor,N,4,9,Married,Tier2,Card,7,M,call,2,2,3,5,4\n978,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,8,1,9,3,3\n979,10,Promoter,Y,4,6,Married,Tier1,Cash,10,M,m-comm,2,10,5,8,8\n980,0,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,7,F,call,4,2,9,4,2\n981,7,Passives,N,4,7,Married,Tier3,Card,7,M,call,6,6,7,1,7\n982,5,Detractor,N,4,5,Not Married,Tier1,e-Wallet,7,F,call,7,9,9,9,5\n983,4,Detractor,N,4,2,Married,Tier2,Cash,7,M,call,5,2,8,8,6\n984,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,7,8,9,10,9\n985,10,Promoter,Y,4,2,Married,Tier1,Card,10,M,m-comm,2,9,5,3,8\n986,10,Promoter,N,4,5,Married,Tier1,e-Wallet,6,F,m-comm,8,7,9,7,10\n987,9,Promoter,N,4,10,Married,Tier3,Cash,1,M,call,8,6,1,9,1\n988,7,Passives,N,4,5,Not Married,Tier1,Ziggy Credit,6,F,e-comm,2,7,2,7,9\n989,5,Detractor,N,4,2,Married,Tier2,Card,3,M,call,7,5,10,9,6\n990,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,2,F,call,6,4,1,7,9\n991,0,Detractor,N,4,6,Married,Tier1,Cash,4,M,call,2,2,8,9,1\n992,8,Passives,N,4,3,Not Married,Tier2,Ziggy Credit,3,F,m-comm,1,4,4,6,6\n993,7,Passives,Y,4,1,Married,Tier3,Card,4,M,call,7,10,1,7,4\n994,10,Promoter,N,4,7,Married,Tier1,e-Wallet,10,F,m-comm,7,6,1,6,8\n995,2,Detractor,N,4,10,Married,Tier2,Cash,4,M,call,6,1,7,10,5\n996,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,2,F,call,7,1,5,9,9\n997,9,Promoter,Y,4,6,Married,Tier1,Card,6,M,m-comm,4,6,8,2,9\n998,10,Promoter,N,4,5,Married,Tier1,e-Wallet,7,F,m-comm,6,3,6,9,7\n999,9,Promoter,N,4,1,Married,Tier3,Cash,7,M,call,6,8,4,10,3\n1000,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,8,10,9,5,10\n1001,1,Detractor,N,4,8,Married,Tier2,Card,7,M,call,3,8,3,2,6\n1002,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,3,F,call,7,9,1,10,4\n1003,6,Detractor,N,4,8,Married,Tier1,Cash,2,M,call,8,3,1,6,4\n1004,3,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,1,F,call,5,7,6,2,1\n1005,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,4,1,7,8,6\n1006,3,Detractor,N,4,8,Not Married,Tier1,e-Wallet,5,F,call,1,5,2,9,2\n1007,4,Detractor,N,4,9,Married,Tier2,Cash,5,M,call,10,9,10,9,4\n1008,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,1,F,call,8,1,9,10,6\n1009,1,Detractor,N,4,8,Married,Tier1,Card,5,M,call,2,4,3,1,3\n1010,5,Detractor,N,4,4,Not Married,Tier2,e-Wallet,6,F,call,1,9,7,7,3\n1011,7,Passives,N,4,5,Married,Tier3,Cash,3,M,call,5,10,7,8,5\n1012,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,5,F,m-comm,2,4,10,2,8\n1013,3,Detractor,N,4,6,Married,Tier2,Card,3,M,call,8,9,8,1,6\n1014,9,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,1,2,9,6,8\n1015,10,Promoter,Y,4,9,Married,Tier1,Cash,6,M,m-comm,9,9,9,8,7\n1016,2,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,1,F,call,3,7,1,2,5\n1017,7,Passives,N,4,9,Married,Tier3,Card,7,M,call,8,5,2,3,5\n1018,7,Passives,N,4,7,Not Married,Tier1,e-Wallet,6,F,e-comm,10,2,3,2,8\n1019,10,Promoter,Y,4,6,Married,Tier1,Cash,10,M,m-comm,3,5,4,10,8\n1020,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,7,F,call,6,2,7,5,5\n1021,2,Detractor,N,4,6,Married,Tier1,Card,3,M,call,3,10,1,1,2\n1022,4,Detractor,N,4,5,Not Married,Tier2,e-Wallet,5,F,call,8,5,2,10,6\n1023,7,Passives,Y,4,7,Married,Tier3,Cash,7,M,call,7,7,5,8,6\n1024,2,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,6,F,call,3,6,3,1,1\n1025,1,Detractor,N,4,4,Married,Tier2,Card,3,M,call,7,6,2,2,2\n1026,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,4,F,call,8,1,3,8,6\n1027,8,Passives,Y,4,9,Married,Tier1,Cash,5,M,e-comm,7,10,9,4,4\n1028,5,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,3,F,call,10,2,7,10,6\n1029,7,Passives,N,4,1,Married,Tier3,Card,7,M,call,7,8,5,7,10\n1030,4,Detractor,N,4,5,Not Married,Tier1,e-Wallet,1,F,call,6,3,8,8,6\n1031,1,Detractor,N,4,3,Married,Tier2,Cash,5,M,call,1,4,8,3,4\n1032,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,2,F,call,7,7,9,10,7\n1033,5,Detractor,N,4,9,Married,Tier1,Card,1,M,call,5,2,9,10,4\n1034,7,Passives,N,4,3,Not Married,Tier2,e-Wallet,6,F,m-comm,9,7,8,5,3\n1035,7,Passives,Y,4,8,Married,Tier3,Cash,5,M,call,6,10,6,4,2\n1036,1,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,4,F,call,5,9,6,10,3\n1037,3,Detractor,N,4,2,Married,Tier2,Card,4,M,call,1,1,5,6,2\n1038,9,Promoter,N,4,6,Married,Tier1,e-Wallet,8,F,m-comm,7,5,5,4,10\n1039,2,Detractor,N,4,8,Married,Tier1,Cash,6,M,call,8,4,2,6,1\n1040,4,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,5,F,call,7,9,1,4,4\n1041,8,Passives,Y,4,8,Married,Tier3,Card,3,M,call,4,8,8,4,9\n1042,6,Detractor,N,4,4,Not Married,Tier1,e-Wallet,4,F,call,5,8,7,8,2\n1043,0,Detractor,N,4,5,Married,Tier2,Cash,1,M,call,6,10,10,5,2\n1044,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,7,7,1,5,9\n1045,8,Passives,Y,4,2,Married,Tier1,Card,3,M,e-comm,9,4,1,2,4\n1046,9,Promoter,N,4,5,Married,Tier1,e-Wallet,8,F,m-comm,3,8,6,2,10\n1047,7,Passives,N,4,7,Married,Tier3,Cash,6,M,call,4,2,8,4,3\n1048,3,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,7,F,call,3,9,6,7,3\n1049,4,Detractor,N,4,3,Married,Tier2,Card,1,M,call,4,6,7,6,3\n1050,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,8,10,10,10,10\n1051,4,Detractor,N,4,9,Married,Tier1,Cash,5,M,call,5,4,10,8,4\n1052,1,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,5,F,call,3,7,1,10,6\n1053,7,Passives,N,4,1,Married,Tier3,Card,6,M,call,5,5,10,2,6\n1054,6,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,1,5,7,1,1\n1055,1,Detractor,N,4,4,Married,Tier2,Cash,4,M,call,5,4,4,4,1\n1056,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,6,F,call,7,1,4,3,5\n1057,8,Passives,Y,4,1,Married,Tier1,Card,3,M,e-comm,1,3,8,4,10\n1058,9,Promoter,N,4,6,Married,Tier1,e-Wallet,5,F,m-comm,8,9,9,4,10\n1059,7,Passives,N,4,9,Married,Tier3,Cash,5,M,call,7,7,3,10,7\n1060,0,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,4,F,call,6,3,3,1,4\n1061,6,Detractor,N,4,8,Married,Tier2,Card,2,M,call,1,10,6,8,3\n1062,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,4,F,call,4,9,5,4,7\n1063,8,Passives,Y,4,10,Married,Tier1,Cash,1,M,e-comm,9,10,5,7,9\n1064,3,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,4,F,call,8,8,6,8,2\n1065,9,Promoter,Y,4,10,Married,Tier1,Card,7,M,m-comm,5,5,5,1,10\n1066,4,Detractor,N,4,6,Not Married,Tier1,e-Wallet,1,F,call,3,7,5,5,4\n1067,6,Detractor,N,4,8,Married,Tier2,Cash,1,M,call,6,6,3,4,6\n1068,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,5,F,m-comm,9,5,7,4,9\n1069,8,Passives,Y,4,8,Married,Tier1,Card,7,M,e-comm,2,9,7,1,10\n1070,0,Detractor,N,4,10,Not Married,Tier2,e-Wallet,4,F,call,5,2,10,9,4\n1071,10,Promoter,Y,4,3,Married,Tier1,Cash,7,M,m-comm,1,4,10,10,7\n1072,5,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,5,F,call,9,3,3,10,5\n1073,10,Promoter,Y,4,9,Married,Tier1,Card,7,M,m-comm,3,3,3,9,8\n1074,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,5,F,call,6,5,8,4,4\n1075,6,Detractor,N,4,2,Married,Tier1,Cash,6,M,call,4,2,10,10,4\n1076,4,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,1,F,call,1,9,2,3,2\n1077,7,Passives,N,4,1,Married,Tier3,Card,5,M,call,5,9,1,8,8\n1078,1,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,9,8,8,10,4\n1079,3,Detractor,N,4,7,Married,Tier2,Cash,2,M,call,4,1,7,7,2\n1080,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,1,1,1,7,7\n1081,6,Detractor,N,4,6,Married,Tier1,Card,6,M,call,6,4,5,7,6\n1082,9,Promoter,N,4,5,Married,Tier1,e-Wallet,9,F,m-comm,7,10,6,5,10\n1083,10,Promoter,Y,4,7,Married,Tier1,Cash,7,M,m-comm,9,7,3,9,8\n1084,1,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,5,5,1,7,5\n1085,0,Detractor,N,4,2,Married,Tier2,Card,5,M,call,7,2,1,10,6\n1086,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,2,F,call,7,9,10,6,8\n1087,9,Promoter,Y,4,9,Married,Tier1,Cash,9,M,m-comm,9,9,4,3,8\n1088,5,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,5,F,call,2,9,1,8,4\n1089,7,Passives,N,4,4,Married,Tier3,Card,3,M,call,4,6,5,6,1\n1090,4,Detractor,N,4,8,Not Married,Tier1,e-Wallet,7,F,call,8,1,2,5,2\n1091,9,Promoter,Y,4,9,Married,Tier1,Cash,7,M,m-comm,8,3,2,7,8\n1092,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,3,F,call,5,10,6,10,8\n1093,0,Detractor,N,4,3,Married,Tier1,Card,4,M,call,8,1,6,5,4\n1094,3,Detractor,N,4,10,Not Married,Tier2,e-Wallet,6,F,call,1,7,5,9,1\n1095,7,Passives,N,4,5,Married,Tier3,Cash,6,M,call,4,3,2,6,9\n1096,10,Promoter,N,4,7,Married,Tier2,Ziggy Credit,5,F,m-comm,6,5,8,2,7\n1097,4,Detractor,N,4,10,Married,Tier2,Card,7,M,call,8,2,1,6,3\n1098,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,7,F,call,8,6,2,10,9\n1099,6,Detractor,N,4,9,Married,Tier1,Cash,7,M,call,7,2,4,4,4\n1100,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,10,F,m-comm,8,3,8,8,9\n1101,7,Passives,N,4,1,Married,Tier3,Card,2,M,call,7,5,2,2,5\n1102,8,Passives,N,4,9,Not Married,Tier1,e-Wallet,6,F,e-comm,1,5,3,4,7\n1103,8,Passives,Y,4,5,Married,Tier2,Cash,3,M,m-comm,6,8,6,6,9\n1104,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,6,F,call,8,10,4,6,10\n1105,1,Detractor,N,4,6,Married,Tier1,Card,4,M,call,2,2,4,1,3\n1106,9,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,8,4,7,8,7\n1107,9,Promoter,Y,4,7,Married,Tier1,Cash,10,M,m-comm,1,10,10,5,9\n1108,8,Passives,N,4,2,Not Married,Tier1,Ziggy Credit,1,F,e-comm,1,9,1,2,9\n1109,6,Detractor,N,4,6,Married,Tier2,Card,3,M,call,2,4,2,3,4\n1110,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,5,F,call,7,5,8,1,8\n1111,4,Detractor,N,4,10,Married,Tier1,Cash,1,M,call,10,6,7,6,2\n1112,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,10,F,m-comm,10,6,3,9,10\n1113,7,Passives,N,4,9,Married,Tier3,Card,2,M,call,8,3,2,1,9\n1114,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,5,3,10,8,9\n1115,2,Detractor,N,4,8,Married,Tier2,Cash,1,M,call,2,8,5,6,6\n1116,8,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,1,F,call,6,6,1,7,7\n1117,6,Detractor,N,4,7,Married,Tier1,Card,1,M,call,8,9,6,10,5\n1118,1,Detractor,N,4,4,Not Married,Tier2,e-Wallet,3,F,call,4,8,8,3,3\n1119,7,Passives,N,4,6,Married,Tier3,Cash,3,M,call,4,6,2,3,1\n1120,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,2,F,call,5,7,4,3,2\n1121,0,Detractor,N,4,7,Married,Tier2,Card,2,M,call,8,1,7,6,5\n1122,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,1,F,call,6,9,1,5,4\n1123,8,Passives,Y,4,10,Married,Tier1,Cash,4,M,e-comm,2,5,4,5,3\n1124,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,10,F,m-comm,5,8,5,5,8\n1125,7,Passives,N,4,2,Married,Tier3,Card,7,M,call,8,10,3,8,2\n1126,6,Detractor,N,4,9,Not Married,Tier1,e-Wallet,6,F,call,7,1,9,9,6\n1127,8,Passives,Y,4,3,Married,Tier2,Cash,4,M,m-comm,7,2,10,3,7\n1128,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,4,F,call,8,7,10,7,2\n1129,7,Passives,Y,4,8,Married,Tier1,Card,4,M,e-comm,8,4,6,7,8\n1130,5,Detractor,N,4,8,Not Married,Tier2,e-Wallet,1,F,call,7,6,5,7,4\n1131,7,Passives,N,4,8,Married,Tier3,Cash,6,M,call,4,9,7,7,3\n1132,4,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,3,F,call,10,9,9,3,3\n1133,1,Detractor,N,4,3,Married,Tier2,Card,3,M,call,3,9,2,10,1\n1134,9,Promoter,N,4,7,Married,Tier1,e-Wallet,5,F,m-comm,6,5,8,10,10\n1135,3,Detractor,N,4,4,Married,Tier1,Cash,2,M,call,3,4,5,8,4\n1136,5,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,7,F,call,5,6,8,8,3\n1137,7,Passives,N,4,7,Married,Tier3,Card,1,M,call,8,8,7,4,4\n1138,1,Detractor,N,4,9,Not Married,Tier1,e-Wallet,4,F,call,5,4,1,10,4\n1139,9,Promoter,Y,4,6,Married,Tier1,Cash,9,M,m-comm,1,4,6,6,9\n1140,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,9,F,m-comm,5,2,2,1,7\n1141,3,Detractor,N,4,3,Married,Tier1,Card,5,M,call,3,6,6,1,1\n1142,0,Detractor,N,4,4,Not Married,Tier2,e-Wallet,7,F,call,3,9,5,9,1\n1143,8,Passives,Y,4,8,Married,Tier3,Cash,4,M,call,4,4,10,2,8\n1144,4,Detractor,N,4,3,Not Married,Tier1,Ziggy Credit,6,F,call,8,9,9,6,4\n1145,5,Detractor,N,4,6,Married,Tier2,Card,1,M,call,5,3,9,7,6\n1146,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,2,F,call,7,10,4,1,1\n1147,1,Detractor,N,4,1,Married,Tier1,Cash,2,M,call,1,1,6,10,6\n1148,5,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,3,F,call,10,3,8,5,4\n1149,7,Passives,N,4,7,Married,Tier3,Card,3,M,call,8,2,2,1,8\n1150,1,Detractor,N,4,6,Not Married,Tier1,e-Wallet,6,F,call,8,3,10,3,2\n1151,5,Detractor,N,4,2,Married,Tier2,Cash,6,M,call,2,1,2,1,2\n1152,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,9,F,m-comm,3,7,3,2,9\n1153,10,Promoter,Y,4,9,Married,Tier1,Card,6,M,m-comm,6,8,1,5,10\n1154,1,Detractor,N,4,1,Not Married,Tier2,e-Wallet,7,F,call,9,8,5,10,6\n1155,7,Passives,N,4,4,Married,Tier3,Cash,6,M,call,6,1,9,5,7\n1156,1,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,1,F,call,7,10,3,1,3\n1157,4,Detractor,N,4,7,Married,Tier2,Card,5,M,call,4,5,4,4,5\n1158,10,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,9,3,1,8,8\n1159,3,Detractor,N,4,1,Married,Tier1,Cash,1,M,call,10,7,5,10,4\n1160,5,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,call,5,4,10,1,4\n1161,7,Passives,N,4,3,Married,Tier3,Card,3,M,call,7,8,5,6,9\n1162,2,Detractor,N,4,8,Not Married,Tier1,e-Wallet,1,F,call,10,5,9,2,6\n1163,6,Detractor,N,4,5,Married,Tier2,Cash,1,M,call,8,1,9,7,1\n1164,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,4,F,call,7,2,5,5,2\n1165,8,Passives,Y,4,8,Married,Tier1,Card,5,M,e-comm,6,1,10,9,8\n1166,7,Passives,N,4,1,Not Married,Tier2,e-Wallet,4,F,m-comm,8,3,10,1,4\n1167,7,Passives,N,4,1,Married,Tier3,Cash,3,M,call,7,3,10,2,4\n1168,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,10,F,m-comm,7,8,5,7,9\n1169,9,Promoter,Y,4,3,Married,Tier1,Card,5,M,m-comm,8,9,5,10,10\n1170,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,2,F,call,4,3,10,2,4\n1171,5,Detractor,N,4,7,Married,Tier1,Cash,2,M,call,1,7,7,6,2\n1172,5,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,6,F,call,9,6,2,8,5\n1173,7,Passives,N,4,2,Married,Tier3,Card,1,M,call,4,10,7,2,5\n1174,3,Detractor,N,4,9,Not Married,Tier1,e-Wallet,7,F,call,8,2,10,6,6\n1175,7,Passives,Y,4,7,Married,Tier2,Cash,3,M,m-comm,3,10,7,5,7\n1176,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,5,F,call,7,3,3,3,10\n1177,9,Promoter,Y,4,4,Married,Tier1,Card,10,M,m-comm,6,2,2,3,9\n1178,4,Detractor,N,4,5,Not Married,Tier2,e-Wallet,5,F,call,9,2,1,9,2\n1179,7,Passives,N,4,8,Married,Tier3,Cash,5,M,call,6,7,1,5,1\n1180,0,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,1,F,call,9,10,8,3,2\n1181,4,Detractor,N,4,1,Married,Tier2,Card,6,M,call,7,4,3,10,4\n1182,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,5,F,call,7,10,6,2,3\n1183,1,Detractor,N,4,2,Married,Tier1,Cash,1,M,call,6,5,5,8,5\n1184,7,Passives,N,4,8,Not Married,Tier2,Ziggy Credit,7,F,m-comm,6,7,2,1,7\n1185,9,Promoter,Y,4,1,Married,Tier1,Card,9,M,m-comm,9,7,1,3,8\n1186,5,Detractor,N,4,4,Not Married,Tier1,e-Wallet,6,F,call,2,5,1,10,2\n1187,2,Detractor,N,4,8,Married,Tier2,Cash,3,M,call,1,10,2,6,1\n1188,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,5,F,call,5,9,4,6,3\n1189,2,Detractor,N,4,3,Married,Tier1,Card,4,M,call,9,5,4,6,6\n1190,7,Passives,N,4,6,Not Married,Tier2,e-Wallet,7,F,m-comm,5,2,3,2,4\n1191,7,Passives,N,4,3,Married,Tier3,Cash,6,M,call,8,4,2,5,6\n1192,5,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,call,2,5,9,1,1\n1193,6,Detractor,N,4,1,Married,Tier2,Card,4,M,call,3,4,9,6,6\n1194,9,Promoter,N,4,7,Married,Tier1,e-Wallet,5,F,m-comm,8,4,3,6,7\n1195,5,Detractor,N,4,2,Married,Tier1,Cash,5,M,call,1,10,9,5,6\n1196,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,8,F,m-comm,3,2,3,3,7\n1197,7,Passives,N,4,9,Married,Tier3,Card,5,M,call,4,5,4,1,2\n1198,9,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,3,3,9,9,9\n1199,6,Detractor,N,4,7,Married,Tier2,Cash,6,M,call,8,6,10,10,2\n1200,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,1,F,call,7,7,6,10,8\n1201,7,Passives,Y,4,10,Married,Tier1,Card,3,M,e-comm,4,2,2,1,8\n1202,7,Passives,N,4,10,Not Married,Tier2,e-Wallet,3,F,m-comm,4,2,6,7,6\n1203,7,Passives,N,4,4,Married,Tier3,Cash,2,M,call,6,10,10,6,9\n1204,3,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,1,10,10,10,2\n1205,4,Detractor,N,4,7,Married,Tier2,Card,4,M,call,4,1,9,4,2\n1206,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,2,F,call,4,1,8,4,5\n1207,0,Detractor,N,4,8,Married,Tier1,Cash,5,M,call,7,6,9,4,5\n1208,4,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,7,F,call,6,3,8,4,1\n1209,7,Passives,N,4,10,Married,Tier3,Card,2,M,call,7,3,7,1,9\n1210,10,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,2,10,6,10,10\n1211,4,Detractor,N,4,2,Married,Tier2,Cash,6,M,call,7,6,1,2,3\n1212,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,5,2,8,8,6\n1213,0,Detractor,N,4,5,Married,Tier1,Card,7,M,call,5,8,8,9,5\n1214,2,Detractor,N,4,7,Not Married,Tier2,e-Wallet,6,F,call,2,8,4,6,2\n1215,7,Passives,N,4,1,Married,Tier3,Cash,3,M,call,4,5,4,5,3\n1216,1,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,3,F,call,7,1,9,9,5\n1217,3,Detractor,N,4,10,Married,Tier2,Card,2,M,call,7,8,4,7,2\n1218,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,3,F,call,7,4,9,2,10\n1219,8,Passives,Y,4,4,Married,Tier1,Cash,4,M,e-comm,9,8,6,1,1\n1220,5,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,4,F,call,3,10,7,1,2\n1221,10,Promoter,Y,4,2,Married,Tier1,Card,8,M,m-comm,10,1,8,6,9\n1222,8,Passives,N,4,8,Not Married,Tier1,e-Wallet,3,F,e-comm,7,1,5,4,4\n1223,4,Detractor,N,4,9,Married,Tier2,Cash,1,M,call,2,6,7,6,5\n1224,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,2,F,call,4,5,2,6,5\n1225,4,Detractor,N,4,10,Married,Tier1,Card,5,M,call,3,5,2,6,5\n1226,0,Detractor,N,4,9,Not Married,Tier2,e-Wallet,5,F,call,10,7,1,6,5\n1227,7,Passives,N,4,6,Married,Tier3,Cash,6,M,call,6,1,2,9,10\n1228,6,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,6,F,call,9,1,8,1,5\n1229,0,Detractor,N,4,8,Married,Tier2,Card,2,M,call,4,1,7,3,6\n1230,8,Passives,N,4,8,Not Married,Tier3,e-Wallet,3,F,call,6,5,1,7,10\n1231,7,Passives,Y,4,2,Married,Tier1,Cash,5,M,e-comm,10,3,5,8,6\n1232,5,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,call,6,6,4,7,3\n1233,8,Passives,Y,4,1,Married,Tier3,Card,3,M,call,6,7,8,3,2\n1234,2,Detractor,N,4,2,Not Married,Tier1,e-Wallet,4,F,call,10,3,7,7,6\n1235,2,Detractor,N,4,2,Married,Tier2,Cash,6,M,call,1,8,7,5,4\n1236,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,6,F,call,8,2,4,4,3\n1237,9,Promoter,Y,4,4,Married,Tier1,Card,10,M,m-comm,10,4,5,4,9\n1238,6,Detractor,N,4,9,Not Married,Tier2,e-Wallet,1,F,call,5,7,10,9,2\n1239,8,Passives,Y,4,7,Married,Tier3,Cash,1,M,call,6,2,4,6,9\n1240,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,7,F,m-comm,6,10,10,5,9\n1241,6,Detractor,N,4,10,Married,Tier2,Card,1,M,call,10,1,5,6,1\n1242,8,Passives,N,4,5,Not Married,Tier3,e-Wallet,7,F,call,5,5,6,3,8\n1243,6,Detractor,N,4,6,Married,Tier1,Cash,4,M,call,10,2,5,2,6\n1244,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,2,4,9,3,8\n1245,7,Passives,N,4,7,Married,Tier3,Card,6,M,call,6,2,10,7,4\n1246,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,7,8,8,7\n1247,8,Passives,Y,4,9,Married,Tier2,Cash,6,M,m-comm,8,3,5,5,1\n1248,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,7,2,3,8,10\n1249,1,Detractor,N,4,6,Married,Tier1,Card,4,M,call,8,3,10,5,5\n1250,5,Detractor,N,4,5,Not Married,Tier2,e-Wallet,4,F,call,5,3,7,5,1\n1251,7,Passives,N,4,1,Married,Tier3,Cash,6,M,call,7,7,2,2,6\n1252,8,Passives,N,4,4,Not Married,Tier1,Ziggy Credit,6,F,e-comm,9,7,2,7,1\n1253,0,Detractor,N,4,6,Married,Tier2,Card,1,M,call,3,9,4,6,2\n1254,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,1,F,call,8,5,1,7,10\n1255,6,Detractor,N,4,8,Married,Tier1,Cash,3,M,call,10,8,5,8,2\n1256,5,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,1,F,call,10,4,1,4,1\n1257,10,Promoter,Y,4,7,Married,Tier1,Card,8,M,m-comm,10,8,8,1,8\n1258,10,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,9,7,4,4,9\n1259,6,Detractor,N,4,6,Married,Tier2,Cash,4,M,call,5,5,5,2,3\n1260,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,2,F,call,8,10,2,9,2\n1261,5,Detractor,N,4,5,Married,Tier1,Card,5,M,call,4,3,2,3,5\n1262,8,Passives,N,4,4,Not Married,Tier2,e-Wallet,6,F,m-comm,9,8,4,3,9\n1263,7,Passives,N,4,2,Married,Tier3,Cash,6,M,call,5,9,7,2,10\n1264,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,7,F,m-comm,8,1,2,4,10\n1265,9,Promoter,Y,4,3,Married,Tier1,Card,9,M,m-comm,10,1,3,1,7\n1266,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,3,F,call,7,10,7,8,2\n1267,5,Detractor,N,4,9,Married,Tier1,Cash,3,M,call,9,7,2,8,4\n1268,1,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,4,F,call,3,1,3,7,2\n1269,7,Passives,Y,4,4,Married,Tier3,Card,5,M,call,4,1,8,8,10\n1270,7,Passives,N,4,8,Not Married,Tier1,e-Wallet,2,F,e-comm,3,3,4,8,4\n1271,1,Detractor,N,4,1,Married,Tier2,Cash,2,M,call,3,4,7,3,1\n1272,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,4,F,call,7,9,2,7,6\n1273,5,Detractor,N,4,4,Married,Tier1,Card,4,M,call,4,4,7,8,1\n1274,6,Detractor,N,4,4,Not Married,Tier2,e-Wallet,6,F,call,8,1,3,6,1\n1275,7,Passives,N,4,3,Married,Tier3,Cash,7,M,call,6,10,8,8,1\n1276,1,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,3,F,call,8,1,4,2,5\n1277,2,Detractor,N,4,7,Married,Tier2,Card,2,M,call,8,3,5,3,5\n1278,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,7,F,call,7,6,3,5,3\n1279,3,Detractor,N,4,8,Married,Tier1,Cash,2,M,call,1,4,2,1,1\n1280,3,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,1,F,call,4,3,5,8,2\n1281,9,Promoter,Y,4,2,Married,Tier1,Card,9,M,m-comm,10,4,7,4,8\n1282,2,Detractor,N,4,1,Not Married,Tier1,e-Wallet,3,F,call,9,8,8,3,4\n1283,6,Detractor,N,4,3,Married,Tier2,Cash,3,M,call,9,8,5,4,6\n1284,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,5,F,call,4,10,2,1,8\n1285,3,Detractor,N,4,10,Married,Tier1,Card,4,M,call,8,5,9,5,6\n1286,9,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,1,10,3,8,9\n1287,7,Passives,Y,4,5,Married,Tier3,Cash,2,M,call,5,9,2,3,5\n1288,3,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,3,F,call,5,10,9,3,4\n1289,7,Passives,Y,4,7,Married,Tier2,Card,3,M,m-comm,5,3,4,8,3\n1290,10,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,6,4,8,7,8\n1291,7,Passives,Y,4,7,Married,Tier1,Cash,6,M,e-comm,9,1,2,5,10\n1292,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,5,F,m-comm,7,2,10,2,10\n1293,7,Passives,N,4,1,Married,Tier3,Card,7,M,call,4,6,2,8,4\n1294,2,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,1,8,10,1,1\n1295,4,Detractor,N,4,4,Married,Tier2,Cash,2,M,call,9,1,2,6,4\n1296,8,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,4,2,6,7,10\n1297,8,Passives,Y,4,1,Married,Tier1,Card,3,M,e-comm,7,8,3,3,9\n1298,3,Detractor,N,4,9,Not Married,Tier2,e-Wallet,3,F,call,1,6,8,9,6\n1299,10,Promoter,Y,4,10,Married,Tier1,Cash,7,M,m-comm,6,6,7,9,10\n1300,8,Passives,N,4,5,Not Married,Tier1,Ziggy Credit,6,F,e-comm,2,9,3,9,4\n1301,4,Detractor,N,4,9,Married,Tier2,Card,5,M,call,3,5,4,8,6\n1302,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,1,F,call,4,7,2,6,8\n1303,1,Detractor,N,4,9,Married,Tier1,Cash,7,M,call,4,1,7,7,3\n1304,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,5,F,m-comm,5,10,7,6,10\n1305,7,Passives,N,4,10,Married,Tier3,Card,1,M,call,6,4,8,8,7\n1306,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,3,F,call,2,6,8,7,2\n1307,1,Detractor,N,4,5,Married,Tier2,Cash,3,M,call,5,3,6,5,5\n1308,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,7,9,10,3,10\n1309,4,Detractor,N,4,3,Married,Tier1,Card,5,M,call,3,5,6,9,3\n1310,4,Detractor,N,4,7,Not Married,Tier2,e-Wallet,7,F,call,5,8,9,5,1\n1311,7,Passives,N,4,5,Married,Tier3,Cash,3,M,call,4,2,7,1,3\n1312,8,Passives,N,4,1,Not Married,Tier1,Ziggy Credit,7,F,e-comm,8,10,4,6,4\n1313,10,Promoter,Y,4,1,Married,Tier1,Card,10,M,m-comm,7,2,6,2,9\n1314,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,4,F,call,4,2,10,8,5\n1315,7,Passives,Y,4,1,Married,Tier1,Cash,1,M,e-comm,4,4,6,9,1\n1316,0,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,7,F,call,8,10,3,1,3\n1317,7,Passives,N,4,4,Married,Tier3,Card,2,M,call,8,7,6,10,5\n1318,2,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,8,4,9,2,1\n1319,4,Detractor,N,4,7,Married,Tier2,Cash,5,M,call,6,4,10,10,3\n1320,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,2,F,call,5,3,5,1,8\n1321,1,Detractor,N,4,1,Married,Tier1,Card,3,M,call,9,4,10,4,2\n1322,7,Passives,N,4,6,Not Married,Tier2,e-Wallet,1,F,m-comm,1,10,2,8,2\n1323,7,Passives,N,4,6,Married,Tier3,Cash,6,M,call,8,3,9,5,9\n1324,5,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,2,F,call,10,9,1,6,3\n1325,9,Promoter,Y,4,7,Married,Tier1,Card,9,M,m-comm,6,10,9,9,10\n1326,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,7,1,6,7,8\n1327,5,Detractor,N,4,5,Married,Tier1,Cash,6,M,call,4,6,8,8,1\n1328,8,Passives,N,4,4,Not Married,Tier2,Ziggy Credit,1,F,m-comm,5,8,4,7,1\n1329,7,Passives,N,4,5,Married,Tier3,Card,2,M,call,7,2,10,8,4\n1330,0,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,1,8,9,3,4\n1331,9,Promoter,Y,4,4,Married,Tier1,Cash,10,M,m-comm,6,9,6,3,9\n1332,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,7,F,call,6,9,3,8,9\n1333,7,Passives,Y,4,2,Married,Tier1,Card,2,M,e-comm,9,9,8,3,2\n1334,6,Detractor,N,4,7,Not Married,Tier2,e-Wallet,4,F,call,7,2,1,5,4\n1335,7,Passives,N,4,4,Married,Tier3,Cash,2,M,call,6,3,5,6,3\n1336,2,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,1,F,call,1,8,2,8,6\n1337,6,Detractor,N,4,10,Married,Tier2,Card,4,M,call,8,5,3,7,6\n1338,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,3,F,call,6,6,5,2,8\n1339,2,Detractor,N,4,9,Married,Tier1,Cash,7,M,call,3,1,3,7,5\n1340,5,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,6,F,call,10,1,4,7,5\n1341,10,Promoter,Y,4,10,Married,Tier1,Card,9,M,m-comm,2,7,1,1,7\n1342,9,Promoter,N,4,6,Married,Tier1,e-Wallet,8,F,m-comm,8,9,7,6,9\n1343,8,Passives,Y,4,10,Married,Tier2,Cash,6,M,m-comm,3,9,3,6,4\n1344,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,3,F,call,7,2,7,2,9\n1345,8,Passives,Y,4,6,Married,Tier1,Card,6,M,e-comm,2,1,4,7,6\n1346,10,Promoter,N,4,6,Married,Tier1,e-Wallet,8,F,m-comm,10,6,5,1,10\n1347,7,Passives,N,4,6,Married,Tier3,Cash,2,M,call,4,10,5,6,3\n1348,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,8,5,3,6,10\n1349,2,Detractor,N,4,5,Married,Tier2,Card,7,M,call,5,3,8,6,5\n1350,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,3,F,call,6,3,7,9,1\n1351,3,Detractor,N,4,2,Married,Tier1,Cash,1,M,call,10,9,1,10,5\n1352,7,Passives,N,4,8,Not Married,Tier2,Ziggy Credit,1,F,m-comm,3,2,2,4,2\n1353,10,Promoter,Y,4,10,Married,Tier1,Card,5,M,m-comm,6,2,3,6,8\n1354,1,Detractor,N,4,2,Not Married,Tier1,e-Wallet,2,F,call,9,6,8,8,6\n1355,9,Promoter,Y,4,7,Married,Tier1,Cash,8,M,m-comm,2,10,3,3,10\n1356,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,10,F,m-comm,1,1,3,5,7\n1357,4,Detractor,N,4,2,Married,Tier1,Card,7,M,call,7,5,7,6,4\n1358,7,Passives,N,4,5,Not Married,Tier2,e-Wallet,2,F,m-comm,7,2,3,8,5\n1359,7,Passives,N,4,5,Married,Tier3,Cash,6,M,call,4,10,2,4,4\n1360,4,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,5,F,call,2,4,1,5,5\n1361,1,Detractor,N,4,9,Married,Tier2,Card,4,M,call,8,8,4,10,6\n1362,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,6,5,9,10,2\n1363,1,Detractor,N,4,5,Married,Tier1,Cash,6,M,call,3,1,5,3,5\n1364,5,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,6,F,call,10,9,6,6,3\n1365,7,Passives,Y,4,3,Married,Tier3,Card,1,M,call,5,10,6,1,10\n1366,6,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,7,3,1,10,4\n1367,10,Promoter,Y,4,8,Married,Tier1,Cash,5,M,m-comm,1,1,9,5,7\n1368,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,4,F,call,5,4,4,10,2\n1369,1,Detractor,N,4,5,Married,Tier1,Card,1,M,call,1,3,6,4,6\n1370,4,Detractor,N,4,9,Not Married,Tier2,e-Wallet,4,F,call,4,5,7,2,1\n1371,7,Passives,N,4,4,Married,Tier3,Cash,4,M,call,4,6,1,4,10\n1372,5,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,6,F,call,2,7,8,5,1\n1373,6,Detractor,N,4,6,Married,Tier2,Card,7,M,call,4,1,4,1,1\n1374,8,Passives,N,4,8,Not Married,Tier3,e-Wallet,2,F,call,5,3,6,5,7\n1375,10,Promoter,Y,4,4,Married,Tier1,Cash,10,M,m-comm,10,8,3,1,9\n1376,7,Passives,N,4,1,Not Married,Tier2,Ziggy Credit,3,F,m-comm,5,10,6,9,3\n1377,7,Passives,N,4,1,Married,Tier3,Card,5,M,call,5,4,5,9,9\n1378,10,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,5,3,7,10\n1379,1,Detractor,N,4,2,Married,Tier2,Cash,5,M,call,9,8,9,8,1\n1380,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,1,F,call,5,8,3,6,6\n1381,0,Detractor,N,4,9,Married,Tier1,Card,7,M,call,1,10,4,3,3\n1382,8,Passives,N,4,1,Not Married,Tier2,e-Wallet,5,F,m-comm,9,10,4,4,7\n1383,7,Passives,N,4,2,Married,Tier3,Cash,2,M,call,8,5,1,7,9\n1384,6,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,1,F,call,6,2,3,6,4\n1385,0,Detractor,N,4,8,Married,Tier2,Card,7,M,call,9,7,1,9,4\n1386,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,6,6,10,2,5\n1387,5,Detractor,N,4,9,Married,Tier1,Cash,3,M,call,3,8,5,6,4\n1388,2,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,7,F,call,8,3,7,1,6\n1389,7,Passives,N,4,10,Married,Tier3,Card,2,M,call,5,1,7,5,10\n1390,10,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,7,9,10,5,9\n1391,5,Detractor,N,4,6,Married,Tier2,Cash,4,M,call,3,9,2,6,1\n1392,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,8,F,m-comm,1,6,3,5,7\n1393,3,Detractor,N,4,4,Married,Tier1,Card,2,M,call,5,4,6,6,1\n1394,1,Detractor,N,4,8,Not Married,Tier2,e-Wallet,2,F,call,1,4,6,6,5\n1395,7,Passives,Y,4,4,Married,Tier3,Cash,5,M,call,5,10,4,6,5\n1396,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,8,F,m-comm,8,8,6,7,8\n1397,4,Detractor,N,4,9,Married,Tier2,Card,1,M,call,10,9,8,3,3\n1398,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,1,F,call,4,3,3,6,2\n1399,2,Detractor,N,4,3,Married,Tier1,Cash,6,M,call,5,3,6,8,2\n1400,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,3,F,m-comm,8,9,10,1,10\n1401,7,Passives,N,4,10,Married,Tier3,Card,4,M,call,7,8,4,3,2\n1402,7,Passives,N,4,3,Not Married,Tier1,e-Wallet,6,F,e-comm,4,7,8,9,1\n1403,1,Detractor,N,4,7,Married,Tier2,Cash,4,M,call,8,7,7,1,4\n1404,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,2,F,call,6,1,8,4,10\n1405,8,Passives,Y,4,5,Married,Tier1,Card,4,M,e-comm,4,6,5,1,9\n1406,1,Detractor,N,4,4,Not Married,Tier2,e-Wallet,2,F,call,5,2,3,5,4\n1407,7,Passives,N,4,8,Married,Tier3,Cash,4,M,call,6,1,5,9,8\n1408,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,7,F,m-comm,8,2,2,4,8\n1409,9,Promoter,Y,4,9,Married,Tier1,Card,7,M,m-comm,3,3,5,6,8\n1410,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,8,5,7,4,8\n1411,8,Passives,Y,4,8,Married,Tier1,Cash,6,M,e-comm,4,1,7,2,7\n1412,8,Passives,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,m-comm,4,4,4,5,10\n1413,10,Promoter,Y,4,7,Married,Tier2,Card,8,M,m-comm,1,7,1,1,10\n1414,5,Detractor,N,4,9,Not Married,Tier1,e-Wallet,1,F,call,9,9,10,10,1\n1415,0,Detractor,N,4,4,Married,Tier2,Cash,3,M,call,4,2,2,4,6\n1416,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,9,7,9,5,10\n1417,3,Detractor,N,4,8,Married,Tier1,Card,1,M,call,4,8,6,10,4\n1418,5,Detractor,N,4,8,Not Married,Tier2,e-Wallet,7,F,call,5,2,7,3,4\n1419,10,Promoter,Y,4,10,Married,Tier1,Cash,10,M,m-comm,4,1,4,2,10\n1420,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,9,F,m-comm,9,7,2,8,8\n1421,6,Detractor,N,4,9,Married,Tier2,Card,6,M,call,3,1,4,4,5\n1422,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,5,3,10,7,10\n1423,7,Passives,Y,4,3,Married,Tier1,Cash,3,M,e-comm,4,4,1,5,5\n1424,8,Passives,N,4,2,Not Married,Tier2,Ziggy Credit,7,F,m-comm,3,4,10,10,5\n1425,7,Passives,N,4,6,Married,Tier3,Card,4,M,call,6,5,4,5,7\n1426,1,Detractor,N,4,5,Not Married,Tier1,e-Wallet,7,F,call,6,1,10,7,6\n1427,2,Detractor,N,4,7,Married,Tier2,Cash,5,M,call,8,9,3,7,4\n1428,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,5,F,call,8,4,6,10,10\n1429,7,Passives,Y,4,8,Married,Tier1,Card,6,M,e-comm,4,6,5,9,5\n1430,6,Detractor,N,4,3,Not Married,Tier2,e-Wallet,7,F,call,5,5,2,2,2\n1431,7,Passives,Y,4,8,Married,Tier3,Cash,7,M,call,6,6,2,1,5\n1432,6,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,1,F,call,9,10,9,3,4\n1433,5,Detractor,N,4,3,Married,Tier2,Card,2,M,call,4,10,10,3,4\n1434,10,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,5,9,3,1,9\n1435,7,Passives,Y,4,10,Married,Tier1,Cash,2,M,e-comm,1,10,10,7,8\n1436,1,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,2,F,call,2,9,8,4,6\n1437,7,Passives,N,4,6,Married,Tier3,Card,6,M,call,4,9,4,9,4\n1438,10,Promoter,N,4,5,Married,Tier2,e-Wallet,6,F,m-comm,4,6,7,1,10\n1439,4,Detractor,N,4,6,Married,Tier2,Cash,5,M,call,8,5,4,3,3\n1440,8,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,4,F,call,7,1,5,2,5\n1441,6,Detractor,N,4,10,Married,Tier1,Card,5,M,call,8,2,4,2,6\n1442,10,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,8,1,6,3,10\n1443,7,Passives,Y,4,3,Married,Tier3,Cash,1,M,call,4,4,8,2,4\n1444,3,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,3,F,call,8,2,9,9,1\n1445,0,Detractor,N,4,6,Married,Tier2,Card,4,M,call,10,2,8,10,6\n1446,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,4,1,7,9,10\n1447,9,Promoter,Y,4,1,Married,Tier1,Cash,7,M,m-comm,5,2,10,8,9\n1448,0,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,4,F,call,1,10,5,5,5\n1449,9,Promoter,Y,4,8,Married,Tier1,Card,5,M,m-comm,8,4,5,5,7\n1450,6,Detractor,N,4,1,Not Married,Tier1,e-Wallet,4,F,call,8,5,4,5,3\n1451,6,Detractor,N,4,9,Married,Tier2,Cash,7,M,call,1,9,4,5,6\n1452,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,2,1,1,9,10\n1453,10,Promoter,Y,4,2,Married,Tier1,Card,5,M,m-comm,6,9,4,9,9\n1454,8,Passives,N,4,4,Not Married,Tier2,e-Wallet,3,F,m-comm,4,7,6,7,7\n1455,7,Passives,Y,4,6,Married,Tier3,Cash,5,M,call,4,5,6,2,1\n1456,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,9,F,m-comm,5,6,5,9,8\n1457,4,Detractor,N,4,5,Married,Tier2,Card,5,M,call,10,5,4,6,5\n1458,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,5,F,call,8,6,5,5,8\n1459,8,Passives,Y,4,5,Married,Tier1,Cash,6,M,e-comm,4,10,2,8,8\n1460,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,6,F,m-comm,8,6,8,4,3\n1461,7,Passives,N,4,4,Married,Tier3,Card,6,M,call,6,4,2,9,4\n1462,7,Passives,N,4,10,Not Married,Tier1,e-Wallet,5,F,e-comm,4,3,9,8,1\n1463,7,Passives,Y,4,8,Married,Tier2,Cash,3,M,m-comm,2,10,7,9,6\n1464,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,5,10,9,9,10\n1465,9,Promoter,Y,4,3,Married,Tier1,Card,6,M,m-comm,5,7,3,9,7\n1466,8,Passives,N,4,1,Not Married,Tier2,e-Wallet,4,F,m-comm,7,5,7,10,8\n1467,7,Passives,N,4,7,Married,Tier3,Cash,5,M,call,7,6,7,4,1\n1468,0,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,1,F,call,7,2,8,9,4\n1469,9,Promoter,Y,4,4,Married,Tier1,Card,9,M,m-comm,10,2,8,1,10\n1470,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,7,10,9,3,5\n1471,9,Promoter,Y,4,9,Married,Tier1,Cash,9,M,m-comm,1,7,4,1,10\n1472,6,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,4,F,call,6,6,9,5,6\n1473,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,5,3,5,7,2\n1474,10,Promoter,N,4,6,Married,Tier1,e-Wallet,6,F,m-comm,4,9,6,2,10\n1475,7,Passives,Y,4,9,Married,Tier2,Cash,4,M,m-comm,1,3,2,4,6\n1476,8,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,5,F,call,6,3,3,6,7\n1477,7,Passives,Y,4,2,Married,Tier1,Card,4,M,e-comm,5,1,4,2,3\n1478,1,Detractor,N,4,3,Not Married,Tier2,e-Wallet,2,F,call,9,6,7,2,4\n1479,8,Passives,Y,4,4,Married,Tier3,Cash,2,M,call,4,4,2,3,5\n1480,3,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,call,8,2,8,7,3\n1481,2,Detractor,N,4,2,Married,Tier2,Card,3,M,call,8,1,10,2,4\n1482,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,1,10,10,5,9\n1483,8,Passives,Y,4,4,Married,Tier1,Cash,7,M,e-comm,10,4,1,5,3\n1484,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,2,4,7,9,9\n1485,7,Passives,N,4,1,Married,Tier3,Card,6,M,call,7,7,5,2,7\n1486,4,Detractor,N,4,5,Not Married,Tier1,e-Wallet,5,F,call,6,3,4,9,6\n1487,1,Detractor,N,4,1,Married,Tier2,Cash,2,M,call,7,2,10,5,4\n1488,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,9,F,m-comm,10,6,2,1,8\n1489,2,Detractor,N,4,2,Married,Tier1,Card,7,M,call,7,10,5,8,2\n1490,6,Detractor,N,4,3,Not Married,Tier2,e-Wallet,1,F,call,6,1,6,4,4\n1491,8,Passives,Y,4,7,Married,Tier3,Cash,3,M,call,8,6,5,2,9\n1492,1,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,call,10,8,10,1,2\n1493,2,Detractor,N,4,2,Married,Tier2,Card,5,M,call,4,1,2,5,5\n1494,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,7,4,3,6,3\n1495,1,Detractor,N,4,5,Married,Tier1,Cash,3,M,call,3,4,10,2,4\n1496,0,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,3,F,call,3,7,6,8,2\n1497,7,Passives,Y,4,1,Married,Tier3,Card,7,M,call,6,5,5,1,1\n1498,7,Passives,N,4,5,Not Married,Tier1,e-Wallet,4,F,e-comm,1,10,6,5,2\n1499,7,Passives,Y,4,10,Married,Tier2,Cash,1,M,m-comm,3,3,6,8,9\n1500,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,5,F,call,5,1,4,4,5\n1501,1,Detractor,N,4,3,Married,Tier1,Card,7,M,call,10,4,4,7,6\n1502,3,Detractor,N,4,1,Not Married,Tier2,e-Wallet,3,F,call,1,1,7,1,5\n1503,10,Promoter,Y,4,2,Married,Tier1,Cash,7,M,m-comm,2,9,5,7,10\n1504,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,3,3,3,8,7\n1505,1,Detractor,N,4,4,Married,Tier2,Card,2,M,call,10,7,6,5,4\n1506,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,3,F,call,6,9,8,4,4\n1507,3,Detractor,N,4,1,Married,Tier1,Cash,6,M,call,6,5,3,1,6\n1508,5,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,1,F,call,5,5,6,4,3\n1509,7,Passives,N,4,7,Married,Tier3,Card,4,M,call,6,6,5,8,8\n1510,5,Detractor,N,4,9,Not Married,Tier1,e-Wallet,1,F,call,1,9,9,5,3\n1511,4,Detractor,N,4,7,Married,Tier2,Cash,5,M,call,9,5,10,1,3\n1512,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,6,F,call,6,8,4,7,6\n1513,3,Detractor,N,4,8,Married,Tier1,Card,5,M,call,3,3,10,2,1\n1514,4,Detractor,N,4,4,Not Married,Tier2,e-Wallet,7,F,call,4,4,9,4,1\n1515,7,Passives,N,4,1,Married,Tier3,Cash,3,M,call,4,9,3,1,4\n1516,0,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,4,F,call,2,1,1,1,3\n1517,3,Detractor,N,4,10,Married,Tier2,Card,6,M,call,4,10,4,1,4\n1518,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,7,F,call,6,6,8,8,9\n1519,6,Detractor,N,4,6,Married,Tier1,Cash,5,M,call,2,10,7,4,6\n1520,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,6,F,m-comm,4,7,2,9,7\n1521,7,Passives,N,4,7,Married,Tier3,Card,6,M,call,6,9,4,1,3\n1522,8,Passives,N,4,3,Not Married,Tier1,e-Wallet,2,F,e-comm,8,8,4,5,3\n1523,6,Detractor,N,4,2,Married,Tier2,Cash,5,M,call,7,10,5,5,5\n1524,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,7,5,9,8,7\n1525,5,Detractor,N,4,2,Married,Tier1,Card,3,M,call,5,3,6,8,1\n1526,2,Detractor,N,4,10,Not Married,Tier2,e-Wallet,6,F,call,3,1,6,1,5\n1527,7,Passives,N,4,2,Married,Tier3,Cash,7,M,call,6,1,8,6,5\n1528,7,Passives,N,4,8,Not Married,Tier1,Ziggy Credit,6,F,e-comm,6,3,2,10,3\n1529,5,Detractor,N,4,1,Married,Tier2,Card,3,M,call,10,7,8,1,3\n1530,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,6,F,call,6,10,2,9,2\n1531,1,Detractor,N,4,2,Married,Tier1,Cash,2,M,call,10,9,7,8,1\n1532,5,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,3,F,call,8,9,4,10,6\n1533,7,Passives,N,4,5,Married,Tier3,Card,2,M,call,6,2,3,1,10\n1534,10,Promoter,N,4,5,Married,Tier1,e-Wallet,6,F,m-comm,1,1,8,4,9\n1535,8,Passives,Y,4,9,Married,Tier2,Cash,5,M,m-comm,7,4,10,6,10\n1536,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,4,F,call,4,1,5,8,10\n1537,7,Passives,Y,4,9,Married,Tier1,Card,2,M,e-comm,8,6,5,4,9\n1538,7,Passives,N,4,3,Not Married,Tier2,e-Wallet,3,F,m-comm,9,10,8,10,2\n1539,9,Promoter,Y,4,2,Married,Tier1,Cash,7,M,m-comm,9,1,3,6,7\n1540,4,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,5,F,call,1,4,3,6,2\n1541,1,Detractor,N,4,6,Married,Tier2,Card,7,M,call,4,2,2,1,3\n1542,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,3,F,call,4,1,4,4,3\n1543,10,Promoter,Y,4,2,Married,Tier1,Cash,8,M,m-comm,6,8,8,9,7\n1544,1,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,2,F,call,10,9,8,1,1\n1545,7,Passives,N,4,8,Married,Tier3,Card,6,M,call,6,4,3,2,3\n1546,6,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,8,7,5,2,3\n1547,10,Promoter,Y,4,8,Married,Tier1,Cash,8,M,m-comm,7,5,4,5,10\n1548,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,3,F,call,7,7,8,7,4\n1549,2,Detractor,N,4,7,Married,Tier1,Card,4,M,call,9,1,9,5,2\n1550,9,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,1,7,3,7,9\n1551,7,Passives,N,4,7,Married,Tier3,Cash,2,M,call,7,2,10,5,9\n1552,1,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,7,F,call,1,4,7,5,5\n1553,0,Detractor,N,4,6,Married,Tier2,Card,2,M,call,9,4,3,1,1\n1554,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,1,F,call,6,9,9,5,4\n1555,4,Detractor,N,4,9,Married,Tier1,Cash,5,M,call,4,10,8,5,3\n1556,8,Passives,N,4,8,Not Married,Tier2,Ziggy Credit,6,F,m-comm,2,5,8,7,7\n1557,10,Promoter,Y,4,9,Married,Tier1,Card,7,M,m-comm,10,9,5,1,7\n1558,8,Passives,N,4,7,Not Married,Tier1,e-Wallet,6,F,e-comm,3,4,5,3,5\n1559,1,Detractor,N,4,4,Married,Tier2,Cash,4,M,call,9,1,1,2,2\n1560,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,4,F,call,6,6,9,6,3\n1561,9,Promoter,Y,4,8,Married,Tier1,Card,10,M,m-comm,4,6,5,5,8\n1562,2,Detractor,N,4,7,Not Married,Tier2,e-Wallet,2,F,call,9,9,6,9,6\n1563,7,Passives,N,4,9,Married,Tier3,Cash,4,M,call,5,6,2,8,4\n1564,7,Passives,N,4,4,Not Married,Tier1,Ziggy Credit,5,F,e-comm,5,3,3,4,10\n1565,7,Passives,Y,4,7,Married,Tier2,Card,6,M,m-comm,9,10,6,8,2\n1566,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,4,F,call,5,5,5,9,9\n1567,4,Detractor,N,4,9,Married,Tier1,Cash,6,M,call,10,5,9,7,1\n1568,1,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,5,F,call,8,9,2,5,5\n1569,7,Passives,N,4,4,Married,Tier3,Card,4,M,call,7,10,7,6,4\n1570,8,Passives,N,4,9,Not Married,Tier1,e-Wallet,1,F,e-comm,7,9,2,8,6\n1571,8,Passives,Y,4,6,Married,Tier2,Cash,7,M,m-comm,7,8,1,8,6\n1572,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,1,F,call,4,10,9,8,7\n1573,1,Detractor,N,4,7,Married,Tier1,Card,4,M,call,2,3,3,9,2\n1574,0,Detractor,N,4,8,Not Married,Tier2,e-Wallet,1,F,call,1,9,10,10,1\n1575,7,Passives,Y,4,2,Married,Tier3,Cash,6,M,call,8,5,5,1,7\n1576,4,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,10,1,1,2,2\n1577,3,Detractor,N,4,9,Married,Tier2,Card,5,M,call,8,6,4,1,5\n1578,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,3,F,call,8,5,7,2,9\n1579,1,Detractor,N,4,9,Married,Tier1,Cash,7,M,call,3,4,3,2,4\n1580,3,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,3,F,call,10,5,9,6,4\n1581,7,Passives,N,4,1,Married,Tier3,Card,7,M,call,8,2,5,10,5\n1582,10,Promoter,N,4,5,Married,Tier1,e-Wallet,7,F,m-comm,7,8,4,1,9\n1583,5,Detractor,N,4,2,Married,Tier2,Cash,3,M,call,8,7,4,8,6\n1584,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,7,F,m-comm,8,2,3,6,7\n1585,8,Passives,Y,4,10,Married,Tier1,Card,5,M,e-comm,8,3,9,5,7\n1586,7,Passives,N,4,10,Not Married,Tier2,e-Wallet,5,F,m-comm,4,5,5,4,7\n1587,7,Passives,N,4,7,Married,Tier3,Cash,2,M,call,4,3,5,1,1\n1588,4,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,3,F,call,10,10,2,4,2\n1589,0,Detractor,N,4,10,Married,Tier2,Card,7,M,call,4,9,1,4,2\n1590,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,5,8,2,5,2\n1591,5,Detractor,N,4,7,Married,Tier1,Cash,7,M,call,3,5,10,2,4\n1592,0,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,3,F,call,1,4,1,1,4\n1593,9,Promoter,Y,4,5,Married,Tier1,Card,6,M,m-comm,8,6,2,1,7\n1594,2,Detractor,N,4,6,Not Married,Tier1,e-Wallet,4,F,call,4,2,9,4,4\n1595,4,Detractor,N,4,1,Married,Tier2,Cash,7,M,call,6,5,7,8,1\n1596,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,4,F,call,7,10,9,9,5\n1597,0,Detractor,N,4,8,Married,Tier1,Card,3,M,call,6,3,1,7,5\n1598,4,Detractor,N,4,1,Not Married,Tier2,e-Wallet,3,F,call,6,4,6,1,6\n1599,7,Passives,N,4,1,Married,Tier3,Cash,2,M,call,6,1,5,3,6\n1600,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,9,F,m-comm,10,4,6,3,7\n1601,0,Detractor,N,4,5,Married,Tier2,Card,5,M,call,8,1,1,5,5\n1602,10,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,7,6,9,5,8\n1603,8,Passives,Y,4,9,Married,Tier1,Cash,6,M,e-comm,3,10,4,1,5\n1604,5,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,5,F,call,2,2,7,10,1\n1605,7,Passives,N,4,10,Married,Tier3,Card,7,M,call,5,10,4,1,3\n1606,5,Detractor,N,4,8,Not Married,Tier1,e-Wallet,1,F,call,7,7,6,5,4\n1607,2,Detractor,N,4,7,Married,Tier2,Cash,6,M,call,2,5,6,6,5\n1608,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,6,F,call,4,7,8,4,3\n1609,5,Detractor,N,4,2,Married,Tier1,Card,5,M,call,3,2,3,2,6\n1610,7,Passives,N,4,5,Not Married,Tier2,e-Wallet,3,F,m-comm,8,3,5,1,9\n1611,7,Passives,Y,4,3,Married,Tier3,Cash,6,M,call,7,5,2,1,5\n1612,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,1,6,5,5,9\n1613,9,Promoter,Y,4,2,Married,Tier1,Card,10,M,m-comm,7,6,2,10,10\n1614,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,5,F,call,8,8,7,4,8\n1615,5,Detractor,N,4,6,Married,Tier1,Cash,3,M,call,5,1,6,2,4\n1616,1,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,2,F,call,8,1,6,10,1\n1617,7,Passives,N,4,8,Married,Tier3,Card,1,M,call,8,8,6,10,7\n1618,3,Detractor,N,4,5,Not Married,Tier1,e-Wallet,1,F,call,7,7,8,10,3\n1619,3,Detractor,N,4,5,Married,Tier2,Cash,2,M,call,4,2,5,9,1\n1620,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,7,F,call,5,8,8,6,2\n1621,2,Detractor,N,4,8,Married,Tier1,Card,4,M,call,1,6,10,4,4\n1622,2,Detractor,N,4,4,Not Married,Tier2,e-Wallet,5,F,call,4,5,9,9,6\n1623,7,Passives,N,4,1,Married,Tier3,Cash,5,M,call,8,7,9,9,7\n1624,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,5,F,m-comm,8,8,5,8,10\n1625,2,Detractor,N,4,4,Married,Tier2,Card,1,M,call,8,4,6,7,6\n1626,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,6,F,call,5,7,5,9,6\n1627,10,Promoter,Y,4,5,Married,Tier1,Cash,9,M,m-comm,9,2,9,1,7\n1628,9,Promoter,N,4,7,Not Married,Tier1,Ziggy Credit,9,F,m-comm,4,3,4,3,10\n1629,7,Passives,N,4,5,Married,Tier3,Card,4,M,call,4,1,5,10,6\n1630,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,9,4,6,2,10\n1631,7,Passives,Y,4,8,Married,Tier2,Cash,6,M,m-comm,3,2,2,1,2\n1632,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,1,F,call,7,3,5,3,8\n1633,9,Promoter,Y,4,2,Married,Tier1,Card,9,M,m-comm,7,4,4,10,8\n1634,6,Detractor,N,4,9,Not Married,Tier2,e-Wallet,1,F,call,3,9,8,9,6\n1635,7,Passives,N,4,9,Married,Tier3,Cash,5,M,call,6,10,2,8,1\n1636,7,Passives,N,4,7,Not Married,Tier1,Ziggy Credit,5,F,e-comm,8,7,1,9,7\n1637,2,Detractor,N,4,2,Married,Tier2,Card,1,M,call,2,8,10,9,3\n1638,7,Passives,Y,4,5,Not Married,Tier3,e-Wallet,4,F,call,5,4,10,4,10\n1639,6,Detractor,N,4,6,Married,Tier1,Cash,6,M,call,6,8,3,2,5\n1640,1,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,3,F,call,8,8,9,8,3\n1641,7,Passives,N,4,6,Married,Tier3,Card,2,M,call,5,10,2,10,5\n1642,2,Detractor,N,4,3,Not Married,Tier1,e-Wallet,4,F,call,3,7,6,3,5\n1643,4,Detractor,N,4,6,Married,Tier2,Cash,6,M,call,9,9,3,2,5\n1644,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,8,9,7,10,10\n1645,0,Detractor,N,4,10,Married,Tier1,Card,2,M,call,8,8,5,6,5\n1646,3,Detractor,N,4,7,Not Married,Tier2,e-Wallet,4,F,call,7,1,2,2,1\n1647,8,Passives,Y,4,1,Married,Tier3,Cash,5,M,call,6,3,1,4,7\n1648,4,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,1,F,call,8,9,4,3,6\n1649,3,Detractor,N,4,7,Married,Tier2,Card,7,M,call,10,10,3,8,4\n1650,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,2,F,call,5,8,3,9,10\n1651,8,Passives,Y,4,5,Married,Tier1,Cash,7,M,e-comm,9,3,9,5,3\n1652,6,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,2,F,call,8,5,9,5,2\n1653,7,Passives,N,4,5,Married,Tier3,Card,3,M,call,7,9,3,5,6\n1654,10,Promoter,N,4,7,Married,Tier1,e-Wallet,5,F,m-comm,10,6,6,1,10\n1655,4,Detractor,N,4,9,Married,Tier2,Cash,1,M,call,2,6,9,8,4\n1656,10,Promoter,N,4,5,Not Married,Tier1,Ziggy Credit,5,F,m-comm,1,8,3,2,8\n1657,5,Detractor,N,4,2,Married,Tier1,Card,5,M,call,6,5,8,3,6\n1658,2,Detractor,N,4,2,Not Married,Tier2,e-Wallet,3,F,call,3,2,9,3,6\n1659,9,Promoter,Y,4,8,Married,Tier1,Cash,5,M,m-comm,10,5,8,3,8\n1660,5,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,3,F,call,4,6,2,2,1\n1661,10,Promoter,Y,4,7,Married,Tier1,Card,5,M,call,6,3,2,4,7\n1662,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,3,F,call,8,9,2,10,2\n1663,7,Passives,Y,4,10,Married,Tier1,Cash,5,M,e-comm,1,1,4,1,10\n1664,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,6,5,10,7,9\n1665,7,Passives,N,4,10,Married,Tier3,Card,3,M,call,5,1,8,3,9\n1666,2,Detractor,N,4,4,Not Married,Tier1,e-Wallet,1,F,call,2,9,1,4,3\n1667,5,Detractor,N,4,5,Married,Tier2,Cash,2,M,call,9,4,10,8,3\n1668,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,6,F,call,6,8,4,9,1\n1669,9,Promoter,Y,4,6,Married,Tier1,Card,6,M,m-comm,3,3,1,4,10\n1670,2,Detractor,N,4,3,Not Married,Tier2,e-Wallet,4,F,call,4,9,5,1,2\n1671,7,Passives,N,4,9,Married,Tier3,Cash,4,M,call,5,9,1,9,3\n1672,0,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,9,1,3,5,4\n1673,1,Detractor,N,4,7,Married,Tier2,Card,6,M,call,8,3,10,6,5\n1674,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,2,F,call,7,8,10,8,2\n1675,10,Promoter,Y,4,7,Married,Tier1,Cash,10,M,m-comm,10,1,8,1,10\n1676,0,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,6,F,call,8,8,2,5,3\n1677,7,Passives,Y,4,1,Married,Tier3,Card,2,M,call,6,9,5,2,7\n1678,0,Detractor,N,4,6,Not Married,Tier1,e-Wallet,6,F,call,4,1,7,9,3\n1679,7,Passives,Y,4,8,Married,Tier2,Cash,3,M,m-comm,7,1,4,10,2\n1680,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,3,F,call,5,8,7,6,2\n1681,3,Detractor,N,4,10,Married,Tier1,Card,2,M,call,10,6,5,3,6\n1682,5,Detractor,N,4,2,Not Married,Tier2,e-Wallet,7,F,call,5,6,10,3,2\n1683,10,Promoter,Y,4,8,Married,Tier1,Cash,5,M,m-comm,10,3,10,5,7\n1684,9,Promoter,Y,4,7,Married,Tier1,Ziggy Credit,8,F,call,3,7,1,1,8\n1685,6,Detractor,N,4,6,Married,Tier2,Card,2,M,call,6,1,7,8,2\n1686,7,Passives,Y,4,9,Not Married,Tier3,e-Wallet,7,F,call,7,8,6,8,4\n1687,1,Detractor,N,4,6,Married,Tier1,Cash,5,M,call,10,6,2,10,5\n1688,3,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,1,F,call,3,2,3,5,5\n1689,7,Passives,N,4,2,Married,Tier3,Card,7,M,call,5,1,10,10,3\n1690,10,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,7,2,1,1,9\n1691,8,Passives,Y,4,6,Married,Tier2,Cash,2,M,m-comm,5,10,9,4,2\n1692,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,3,F,call,7,10,1,4,2\n1693,7,Passives,Y,4,1,Married,Tier1,Card,1,M,e-comm,2,8,7,2,2\n1694,7,Passives,N,4,8,Not Married,Tier2,e-Wallet,5,F,m-comm,9,5,7,1,1\n1695,7,Passives,N,4,10,Married,Tier3,Cash,1,M,call,6,1,6,9,1\n1696,1,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,2,F,call,10,2,5,9,3\n1697,6,Detractor,N,4,2,Married,Tier2,Card,3,M,call,9,10,6,3,2\n1698,8,Passives,Y,4,5,Not Married,Tier3,e-Wallet,1,F,call,5,7,3,6,8\n1699,0,Detractor,N,4,2,Married,Tier1,Cash,1,M,call,10,3,8,2,2\n1700,1,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,7,F,call,2,3,10,5,5\n1701,7,Passives,N,4,3,Married,Tier3,Card,2,M,call,4,2,10,8,8\n1702,3,Detractor,N,4,7,Not Married,Tier1,e-Wallet,7,F,call,7,7,5,7,4\n1703,2,Detractor,N,4,10,Married,Tier2,Cash,5,M,call,1,10,6,2,6\n1704,7,Passives,Y,4,4,Not Married,Tier3,Ziggy Credit,7,F,call,7,10,6,10,6\n1705,5,Detractor,N,4,3,Married,Tier1,Card,3,M,call,6,2,7,8,5\n1706,2,Detractor,N,4,5,Not Married,Tier2,e-Wallet,6,F,call,9,10,7,6,3\n" }, { "path": "Data/Predict Merchant_Sales v01.csv", "content": "Merchant_Id,Annual_Sales,Merchant_Location,Zone,No_of_helpers,Daily_OPEN_hours,Years_Open,Owner's_Age,Owner's_Education,Cashier_Sales_Person_Age,Merchant_Type,Shopping_Complex_Or_Standalone\n247,195,NJ City,E,1,20,18,66,High School,33,Owner,Standalone\n9026,417,NJ City,S,1,10,14,43,Graduation,21,Patnership,Complex\n5245,147.46,Boston,N,2,23,1,46,High School,24,Patnership,Standalone\n6644,500,Boston,W,2,24,17,59,Graduation,22,Franchise,Complex\n4497,111.19,NJ City,N,20,11,4,76,Graduation,22,Patnership,Standalone\n2571,113,NJ City,E,1,24,14,31,High School,22,Owner,Standalone\n6534,447,Boston,S,1,22,12,25,Graduation,31,Patnership,Complex\n7201,393,Boston,N,1,12,16,49,Graduation,21,Patnership,Standalone\n9051,434,NJ City,E,2,13,14,79,Graduation,19,Owner,Standalone\n4157,183,NJ City,N,3,18,6,81,High School,32,Patnership,Standalone\n3746,110,NJ City,S,2,15,21,57,High School,35,Patnership,Complex\n6386,381,Boston,S,3,17,10,50,Graduation,25,Patnership,Complex\n6094,283.85,Boston,S,15,8,2,80,High School,22,Patnership,Complex\n4090,375.26,NJ City,S,5,13,4,60,High School,28,Patnership,Complex\n9536,370,NJ City,W,3,8,18,62,Graduation,23,Franchise,Complex\n7047,414,Boston,E,2,16,7,61,Graduation,27,Owner,Standalone\n3199,244,NJ City,E,2,21,16,46,High School,33,Owner,Standalone\n3548,122,NJ City,W,2,20,6,76,High School,31,Franchise,Complex\n3694,289,NJ City,S,18,21,1,54,High School,31,Patnership,Complex\n6101,467,Boston,N,2,8,23,54,Graduation,28,Patnership,Standalone\n8857,69.28,NJ City,N,3,20,1,45,High School,30,Patnership,Standalone\n7901,379,Boston,N,2,10,19,59,Graduation,24,Patnership,Standalone\n5720,420,Boston,W,2,14,17,79,Graduation,35,Franchise,Complex\n8322,487,NJ City,S,3,20,7,67,Graduation,35,Patnership,Complex\n2789,193,NJ City,N,3,16,6,75,High School,25,Patnership,Standalone\n7970,474,Boston,S,1,20,22,28,Graduation,21,Patnership,Complex\n4478,117.18,NJ City,S,25,21,3,29,No education,19,Patnership,Complex\n2109,70.5,NJ City,N,15,19,4,81,Graduation,33,Patnership,Standalone\n8430,77.67,NJ City,S,17,18,2,32,Graduation,30,Patnership,Complex\n6569,275.81,Boston,N,12,10,2,69,No education,22,Patnership,Standalone\n4965,92.68,Boston,N,8,18,1,47,Graduation,34,Patnership,Standalone\n7817,387,Boston,N,3,8,10,54,Graduation,18,Patnership,Standalone\n1798,299,NJ City,S,2,20,11,56,High School,31,Patnership,Complex\n3433,188,NJ City,N,3,14,24,47,High School,31,Patnership,Standalone\n591,284,NJ City,E,1,18,17,39,High School,22,Owner,Standalone\n1863,238,NJ City,E,2,17,16,36,High School,29,Owner,Standalone\n4537,23.48,NJ City,N,9,13,4,80,High School,22,Patnership,Standalone\n1544,122.44,NJ City,W,12,13,3,57,No education,28,Franchise,Complex\n529,470,NJ City,N,2,20,5,51,Graduation,25,Patnership,Standalone\n8065,438,NJ City,N,1,20,7,31,Graduation,35,Patnership,Standalone\n3427,127.88,NJ City,E,2,24,1,74,High School,26,Owner,Standalone\n4624,366,NJ City,W,1,16,5,27,Graduation,21,Franchise,Complex\n1816,160,NJ City,W,2,24,7,56,High School,20,Franchise,Complex\n4232,462.12,NJ City,W,1,24,14,74,No education,24,Franchise,Complex\n3295,252,NJ City,E,2,20,9,77,High School,30,Owner,Standalone\n6953,176.16,Boston,N,1,8,2,77,No education,29,Patnership,Standalone\n9907,352,NJ City,E,1,24,18,38,Graduation,23,Owner,Standalone\n7869,499,Boston,N,2,18,7,55,Graduation,20,Patnership,Standalone\n3405,84.91,NJ City,N,13,10,4,50,Graduation,20,Patnership,Standalone\n2119,276,NJ City,E,2,23,22,48,High School,20,Owner,Standalone\n4418,220,NJ City,S,2,17,24,26,High School,27,Patnership,Complex\n5843,426,Boston,E,1,19,20,84,Graduation,29,Owner,Standalone\n3947,86.88,NJ City,E,24,9,3,84,No education,28,Owner,Standalone\n4495,434,NJ City,E,3,14,5,39,Graduation,18,Owner,Standalone\n9350,358,NJ City,S,1,21,14,47,Graduation,35,Patnership,Complex\n5950,482,Boston,S,2,18,23,72,Graduation,31,Patnership,Complex\n7564,490,Boston,W,2,13,15,58,Graduation,25,Franchise,Complex\n3541,239,NJ City,N,1,19,9,42,High School,23,Patnership,Standalone\n97,100.27,NJ City,N,18,17,4,55,High School,34,Patnership,Standalone\n3745,272.24,NJ City,N,17,10,2,33,High School,28,Patnership,Standalone\n1415,20.59,NJ City,N,24,19,3,63,No education,19,Owner,Standalone\n2198,237,NJ City,S,2,14,11,42,High School,25,Patnership,Complex\n4256,227.68,NJ City,W,8,9,3,32,No education,35,Franchise,Complex\n3942,150.48,NJ City,S,4,12,1,77,Graduation,34,Patnership,Complex\n7751,215.34,Boston,E,8,19,3,85,No education,31,Owner,Standalone\n172,318,NJ City,W,1,17,7,41,High School,20,Franchise,Complex\n370,249,NJ City,S,2,13,17,38,High School,25,Patnership,Complex\n5095,489,Boston,E,3,12,10,41,Graduation,18,Owner,Standalone\n7605,427,Boston,N,3,22,7,71,Graduation,20,Patnership,Standalone\n2056,204.08,NJ City,W,12,22,4,44,High School,34,Franchise,Complex\n3073,115,NJ City,N,1,8,17,85,High School,28,Patnership,Standalone\n8017,409,Boston,N,3,8,15,66,Graduation,31,Patnership,Standalone\n8240,29.53,NJ City,N,21,16,4,75,No education,22,Franchise,Complex\n1080,131,NJ City,W,3,18,7,27,High School,20,Franchise,Complex\n6470,77.33,Boston,S,14,10,1,33,No education,23,Patnership,Complex\n6181,95.11,Boston,N,9,13,3,59,High School,31,Patnership,Standalone\n2392,281,NJ City,W,1,11,7,79,High School,32,Franchise,Complex\n919,162,NJ City,E,3,24,12,43,High School,33,Owner,Standalone\n9770,240.75,NJ City,S,8,24,3,66,No education,27,Patnership,Complex\n6106,384,Boston,S,2,12,7,63,Graduation,24,Patnership,Complex\n3605,270,NJ City,N,1,10,23,64,High School,26,Patnership,Standalone\n8651,125.31,NJ City,E,15,18,1,83,No education,30,Owner,Standalone\n8125,234.86,NJ City,N,2,12,4,55,High School,22,Patnership,Standalone\n2904,178,NJ City,W,3,24,19,74,High School,35,Franchise,Complex\n9211,481.81,NJ City,E,1,21,4,74,High School,22,Owner,Standalone\n4538,235.56,NJ City,S,3,24,11,35,No education,18,Patnership,Complex\n2401,379,NJ City,N,3,8,5,76,Graduation,26,Patnership,Standalone\n8444,474,NJ City,W,1,22,19,41,Graduation,30,Franchise,Complex\n1376,487,NJ City,W,2,24,5,40,Graduation,33,Franchise,Complex\n6265,385,Boston,N,2,22,8,83,Graduation,34,Patnership,Standalone\n2936,119.05,NJ City,W,20,18,2,72,No education,19,Franchise,Complex\n7411,44.62,Boston,N,15,23,1,51,High School,28,Owner,Standalone\n9900,210.94,NJ City,W,4,19,3,70,Graduation,30,Franchise,Complex\n3413,267,NJ City,N,1,8,25,40,High School,22,Patnership,Standalone\n1329,406.51,NJ City,N,7,8,4,64,Graduation,19,Patnership,Standalone\n9989,71.1,NJ City,N,7,16,1,37,No education,25,Patnership,Standalone\n6303,350,Boston,E,1,11,25,26,Graduation,20,Owner,Standalone\n3453,116,NJ City,N,2,13,18,69,High School,23,Patnership,Standalone\n4374,119,NJ City,S,3,12,15,48,High School,23,Patnership,Complex\n1453,247.19,NJ City,N,4,11,2,33,High School,31,Patnership,Standalone\n8699,455,NJ City,E,2,9,18,46,Graduation,34,Owner,Standalone\n9917,416,NJ City,N,2,22,16,67,Graduation,31,Patnership,Standalone\n1247,269,NJ City,E,2,22,10,36,High School,19,Owner,Standalone\n2689,174,NJ City,N,1,9,21,27,High School,35,Patnership,Standalone\n1086,307,NJ City,S,3,22,21,81,High School,18,Patnership,Complex\n5366,496,Boston,S,2,11,23,30,Graduation,27,Patnership,Complex\n8595,426,NJ City,E,3,17,5,66,Graduation,25,Owner,Standalone\n1602,50.25,NJ City,S,1,21,1,25,Graduation,19,Patnership,Complex\n8115,200.25,NJ City,E,1,9,2,83,Graduation,22,Owner,Standalone\n1338,64.3,NJ City,N,22,14,3,33,Graduation,33,Patnership,Complex\n1242,116,NJ City,S,3,24,11,41,High School,34,Patnership,Complex\n3304,243,NJ City,W,3,13,8,38,High School,28,Franchise,Complex\n2948,265,NJ City,W,2,18,24,67,High School,32,Franchise,Complex\n79,105,Boston,E,2,16,8,85,High School,31,Owner,Standalone\n2367,174,NJ City,E,3,17,22,46,High School,24,Owner,Standalone\n5530,79.05,Boston,S,20,10,1,42,High School,21,Patnership,Complex\n5110,449,Boston,S,2,16,18,79,Graduation,30,Patnership,Complex\n1811,170,NJ City,E,3,21,14,83,High School,28,Owner,Standalone\n8650,400,NJ City,S,3,22,24,80,Graduation,32,Patnership,Complex\n1466,376.99,NJ City,S,2,21,19,42,No education,28,Patnership,Complex\n2157,101,NJ City,N,2,14,13,83,High School,28,Patnership,Standalone\n3146,247,NJ City,S,1,18,7,59,High School,19,Patnership,Complex\n6659,8.39,Boston,N,2,19,2,41,No education,25,Owner,Standalone\n1967,299.59,NJ City,E,16,14,3,28,No education,30,Owner,Standalone\n951,165,NJ City,E,2,18,20,63,High School,24,Owner,Standalone\n7642,408,Boston,S,2,13,11,32,Graduation,21,Patnership,Complex\n7013,496,Boston,N,2,10,22,41,Graduation,35,Patnership,Standalone\n754,179,NJ City,S,2,22,15,53,High School,24,Patnership,Complex\n8290,373,NJ City,S,2,23,24,52,Graduation,30,Patnership,Complex\n4363,233,NJ City,E,3,14,11,62,High School,32,Owner,Standalone\n816,154,NJ City,W,1,18,18,81,High School,30,Franchise,Complex\n1278,343.28,NJ City,S,24,15,3,46,Graduation,22,Patnership,Complex\n4689,27.31,NJ City,N,2,9,1,44,Graduation,26,Patnership,Standalone\n2814,225,NJ City,S,1,16,23,71,High School,34,Patnership,Complex\n3087,54.81,NJ City,E,2,20,4,35,Graduation,22,Owner,Standalone\n5810,481,Boston,S,3,18,19,85,Graduation,31,Patnership,Complex\n9164,386,NJ City,W,3,10,6,80,Graduation,23,Franchise,Complex\n8245,3.34,NJ City,N,9,12,3,56,High School,23,Patnership,Standalone\n5098,43.74,Boston,N,13,24,1,64,High School,35,Patnership,Complex\n1494,89.26,NJ City,S,10,23,2,59,Graduation,29,Patnership,Complex\n6677,363,Boston,N,2,22,20,62,Graduation,27,Patnership,Standalone\n1869,211,NJ City,N,1,18,20,30,High School,21,Patnership,Standalone\n513,240,NJ City,N,1,17,7,39,High School,22,Patnership,Standalone\n1093,316,NJ City,N,1,18,25,36,High School,26,Patnership,Standalone\n6120,479,Boston,W,3,9,7,45,Graduation,30,Franchise,Complex\n1474,143,NJ City,S,1,14,24,33,High School,26,Patnership,Complex\n2836,142.48,NJ City,W,21,13,1,63,High School,28,Franchise,Complex\n4744,177,NJ City,W,1,13,10,67,High School,30,Franchise,Complex\n7054,462,Boston,S,3,19,24,25,Graduation,25,Patnership,Complex\n6203,368,Boston,E,3,10,13,84,Graduation,29,Owner,Standalone\n6977,450,Boston,N,1,19,14,84,Graduation,18,Patnership,Standalone\n1423,300,NJ City,E,1,20,25,52,High School,27,Owner,Standalone\n9604,351,NJ City,W,2,20,18,26,Graduation,29,Franchise,Complex\n8246,11.98,NJ City,N,16,21,1,40,No education,35,Patnership,Complex\n9124,160.23,NJ City,W,8,19,1,47,High School,32,Franchise,Complex\n3631,127.1,NJ City,E,10,21,3,48,High School,31,Owner,Standalone\n4395,117,NJ City,E,2,22,18,40,High School,25,Owner,Standalone\n3839,35.99,NJ City,N,22,12,3,85,No education,24,Owner,Standalone\n5016,411,Boston,W,1,22,6,70,Graduation,34,Franchise,Complex\n7932,170.39,Boston,W,21,8,1,82,Graduation,31,Franchise,Complex\n5386,398.73,Boston,S,3,19,1,47,High School,28,Patnership,Complex\n4031,147,NJ City,E,3,23,20,67,High School,24,Owner,Standalone\n34,53.18,Boston,S,17,8,4,67,High School,24,Patnership,Complex\n253,203,NJ City,N,3,12,20,36,High School,19,Patnership,Standalone\n3057,474,NJ City,N,1,21,5,76,Graduation,34,Patnership,Standalone\n9505,480.59,NJ City,N,23,18,2,37,High School,32,Patnership,Standalone\n9181,435,NJ City,N,3,22,15,81,Graduation,23,Patnership,Standalone\n9126,424,NJ City,S,1,12,13,59,Graduation,19,Patnership,Complex\n7610,472,Boston,S,3,19,6,42,Graduation,34,Patnership,Complex\n1704,161,NJ City,W,1,16,18,68,High School,24,Franchise,Complex\n5949,429,Boston,N,3,21,14,64,Graduation,35,Patnership,Standalone\n8070,354,NJ City,S,3,10,12,72,Graduation,35,Patnership,Complex\n8592,422,NJ City,W,3,13,20,77,Graduation,29,Franchise,Complex\n3524,1.56,NJ City,N,11,16,2,66,No education,34,Franchise,Complex\n6745,461,Boston,N,3,21,8,78,Graduation,31,Patnership,Standalone\n3385,23.42,NJ City,N,9,22,1,41,High School,20,Patnership,Standalone\n1121,124.39,NJ City,N,16,16,4,58,No education,21,Patnership,Standalone\n311,288,NJ City,E,1,13,18,54,High School,21,Owner,Standalone\n2005,403,NJ City,N,3,11,5,59,Graduation,30,Patnership,Standalone\n1825,258,NJ City,N,2,22,10,36,High School,22,Patnership,Standalone\n1322,40,NJ City,N,17,12,4,25,No education,25,Patnership,Complex\n6366,465,Boston,S,1,15,24,50,Graduation,25,Patnership,Complex\n6449,471,Boston,N,1,20,7,29,Graduation,25,Patnership,Standalone\n4797,271,NJ City,N,1,22,16,32,High School,18,Patnership,Standalone\n4289,215,NJ City,N,2,22,14,63,High School,20,Patnership,Standalone\n9370,412,NJ City,S,3,19,23,29,Graduation,20,Patnership,Complex\n7006,497,Boston,S,3,10,24,32,Graduation,22,Patnership,Complex\n1843,106.05,NJ City,E,17,23,1,57,High School,34,Owner,Standalone\n8311,21.83,NJ City,N,22,8,4,77,High School,20,Owner,Standalone\n9633,418,NJ City,N,2,21,23,50,Graduation,22,Patnership,Standalone\n8607,422.53,NJ City,E,13,20,3,54,Graduation,28,Owner,Standalone\n7894,244.72,Boston,S,23,16,3,57,High School,20,Patnership,Complex\n2355,388.74,NJ City,E,1,17,10,47,No education,24,Owner,Standalone\n3378,456.79,NJ City,S,9,22,2,32,Graduation,34,Patnership,Complex\n5561,354,Boston,N,3,22,23,25,Graduation,26,Patnership,Standalone\n9540,472,NJ City,W,2,21,20,35,Graduation,22,Franchise,Complex\n8562,383,NJ City,S,3,8,13,35,Graduation,29,Patnership,Complex\n4734,278,NJ City,S,1,20,20,34,High School,23,Patnership,Complex\n7182,460,Boston,S,1,12,25,37,Graduation,26,Patnership,Complex\n1847,123,NJ City,E,2,16,11,57,High School,35,Owner,Standalone\n2812,149,NJ City,W,1,16,13,50,High School,26,Franchise,Complex\n5250,387,Boston,S,1,10,16,40,Graduation,28,Patnership,Complex\n1351,303,NJ City,E,1,13,10,65,High School,18,Owner,Standalone\n1206,286,NJ City,S,1,18,20,52,High School,22,Patnership,Complex\n6133,485,Boston,N,1,9,18,70,Graduation,34,Patnership,Standalone\n9113,498,NJ City,N,1,24,14,55,Graduation,34,Patnership,Standalone\n8762,382,NJ City,S,2,11,13,58,Graduation,30,Patnership,Complex\n7443,419,Boston,E,3,15,22,72,Graduation,30,Owner,Standalone\n4244,121.07,NJ City,W,24,15,4,50,No education,18,Franchise,Complex\n6699,358,Boston,E,2,12,25,68,Graduation,21,Owner,Standalone\n3756,40.89,NJ City,N,15,22,3,60,Graduation,32,Franchise,Complex\n4030,385.24,NJ City,S,3,17,3,64,High School,25,Patnership,Complex\n2687,196,NJ City,E,1,16,13,74,High School,20,Owner,Standalone\n7206,64.95,Boston,N,25,18,1,72,Graduation,19,Patnership,Complex\n4853,126,NJ City,N,2,19,13,66,High School,18,Patnership,Standalone\n6325,368,Boston,N,3,20,17,71,Graduation,22,Patnership,Standalone\n4028,121,NJ City,W,3,11,21,51,High School,31,Franchise,Complex\n4771,224,NJ City,E,1,8,23,45,High School,32,Owner,Standalone\n8479,23.19,NJ City,N,8,16,2,53,High School,23,Owner,Standalone\n2560,64.33,NJ City,N,6,19,2,40,High School,19,Franchise,Complex\n1835,168,NJ City,E,2,9,25,71,High School,23,Owner,Standalone\n1459,99.7,NJ City,E,19,9,2,28,High School,29,Owner,Standalone\n8023,402,Boston,E,3,24,20,73,Graduation,35,Owner,Standalone\n7352,416,Boston,W,3,13,24,39,Graduation,25,Franchise,Complex\n5889,44.94,Boston,N,25,16,4,45,Graduation,23,Patnership,Standalone\n9791,44.32,NJ City,N,5,19,3,50,No education,22,Owner,Standalone\n5630,35.77,Boston,N,15,8,3,58,No education,35,Patnership,Complex\n3596,19.55,NJ City,N,12,13,4,43,No education,27,Franchise,Complex\n7798,467,Boston,S,2,11,6,56,Graduation,25,Patnership,Complex\n3187,257.52,NJ City,E,3,21,20,59,No education,20,Owner,Standalone\n1314,175,NJ City,S,2,24,18,80,High School,25,Patnership,Complex\n6226,369,Boston,S,3,24,5,45,Graduation,28,Patnership,Complex\n7132,92.11,Boston,W,8,24,2,73,High School,18,Franchise,Complex\n9353,484,NJ City,N,3,9,14,40,Graduation,29,Patnership,Standalone\n867,94.19,NJ City,E,4,20,4,30,Graduation,21,Owner,Standalone\n9832,366,NJ City,W,3,22,17,29,Graduation,26,Franchise,Complex\n3755,156,NJ City,E,1,19,11,30,High School,32,Owner,Standalone\n8397,374.58,NJ City,N,6,15,1,44,Graduation,24,Patnership,Standalone\n5183,493,Boston,E,3,16,12,78,Graduation,18,Owner,Standalone\n5753,62.7,Boston,N,1,14,2,37,No education,21,Patnership,Standalone\n4557,74.82,NJ City,N,23,12,1,81,Graduation,28,Patnership,Standalone\n9662,462,NJ City,S,3,23,10,35,Graduation,26,Patnership,Complex\n9396,426,NJ City,W,2,14,13,54,Graduation,31,Franchise,Complex\n8588,448,NJ City,W,2,21,12,49,Graduation,26,Franchise,Complex\n1105,6.19,NJ City,N,13,15,4,58,High School,29,Patnership,Standalone\n1588,162,NJ City,W,2,10,14,48,High School,19,Franchise,Complex\n2664,482.34,NJ City,W,24,17,3,53,Graduation,19,Franchise,Complex\n1943,191,NJ City,E,3,11,17,78,High School,23,Owner,Standalone\n661,8.61,NJ City,N,17,17,2,53,High School,29,Patnership,Standalone\n4125,75.13,NJ City,N,20,12,2,48,Graduation,19,Patnership,Standalone\n9718,13.05,NJ City,N,14,23,1,80,High School,34,Patnership,Complex\n4885,125.37,NJ City,N,11,14,2,80,High School,22,Patnership,Standalone\n7256,208,Boston,W,15,17,2,76,No education,33,Franchise,Complex\n6695,453,Boston,E,2,15,5,72,Graduation,22,Owner,Standalone\n5767,476,Boston,E,2,16,18,37,Graduation,28,Owner,Standalone\n3791,268.34,NJ City,E,24,14,3,33,No education,30,Owner,Standalone\n2027,103,NJ City,E,1,16,20,53,High School,35,Owner,Standalone\n2621,155,NJ City,N,2,22,19,75,High School,29,Patnership,Standalone\n8568,326.99,NJ City,W,2,23,3,33,Graduation,21,Franchise,Complex\n5825,26.62,Boston,N,19,19,2,65,No education,26,Patnership,Standalone\n3962,241,NJ City,S,1,9,6,82,High School,19,Patnership,Complex\n2303,101,NJ City,E,2,13,22,79,High School,35,Owner,Standalone\n8751,394,NJ City,E,1,10,7,38,Graduation,25,Owner,Standalone\n5013,43.38,Boston,N,8,15,3,56,Graduation,19,Patnership,Standalone\n3648,114.86,NJ City,W,8,23,4,74,Graduation,23,Franchise,Complex\n6012,79.04,Boston,W,1,18,4,74,Graduation,35,Franchise,Complex\n7319,363,Boston,E,3,12,15,30,Graduation,30,Owner,Standalone\n8954,397,NJ City,S,3,8,11,57,Graduation,34,Patnership,Complex\n9229,499.39,NJ City,N,23,12,2,71,High School,33,Patnership,Standalone\n3314,5.56,NJ City,N,20,19,3,37,No education,31,Patnership,Complex\n3576,167,NJ City,W,2,17,17,70,High School,27,Franchise,Complex\n6864,472,Boston,W,1,21,13,60,Graduation,27,Franchise,Complex\n6558,462,Boston,S,3,14,16,36,Graduation,33,Patnership,Complex\n6996,380,Boston,W,1,16,5,49,Graduation,22,Franchise,Complex\n8038,361,Boston,S,1,24,14,50,Graduation,21,Patnership,Complex\n3936,101.02,NJ City,W,7,9,1,85,Graduation,23,Franchise,Complex\n8960,314.37,NJ City,W,13,10,3,61,No education,23,Franchise,Complex\n2777,202,NJ City,N,1,18,14,35,High School,34,Patnership,Standalone\n6479,482,Boston,E,1,11,22,31,Graduation,33,Owner,Standalone\n5051,4.4,Boston,N,19,8,1,40,No education,35,Owner,Standalone\n4532,66.67,NJ City,N,15,16,4,30,No education,21,Franchise,Complex\n8443,393,NJ City,E,1,13,23,83,Graduation,32,Owner,Standalone\n9943,495,NJ City,E,3,18,24,48,Graduation,34,Owner,Standalone\n6503,462,Boston,E,2,19,22,61,Graduation,31,Owner,Standalone\n4732,280,NJ City,W,1,19,9,52,High School,20,Franchise,Complex\n8350,446,NJ City,S,3,11,23,25,Graduation,22,Patnership,Complex\n1097,4.31,NJ City,N,3,12,2,44,No education,28,Patnership,Standalone\n6005,361,Boston,N,3,9,19,50,Graduation,19,Patnership,Standalone\n2442,45.04,NJ City,N,8,20,2,64,Graduation,25,Patnership,Complex\n4585,443.5,NJ City,N,23,11,2,44,High School,25,Patnership,Standalone\n7878,398,Boston,S,3,11,8,54,Graduation,24,Patnership,Complex\n5060,457,Boston,W,2,19,6,57,Graduation,33,Franchise,Complex\n2195,276,NJ City,E,2,15,12,64,High School,27,Owner,Standalone\n2333,160.43,NJ City,N,9,10,1,60,No education,33,Patnership,Standalone\n6214,409,Boston,S,2,12,11,66,Graduation,34,Patnership,Complex\n6085,480,Boston,N,3,12,20,42,Graduation,29,Patnership,Standalone\n4380,111.61,NJ City,W,10,15,1,38,Graduation,30,Franchise,Complex\n2201,407,NJ City,N,3,15,5,41,Graduation,28,Patnership,Standalone\n4384,211,NJ City,W,3,21,6,34,High School,31,Franchise,Complex\n7338,454,Boston,S,3,8,22,59,Graduation,19,Patnership,Complex\n6387,453,Boston,E,3,18,15,51,Graduation,31,Owner,Standalone\n5281,481,Boston,N,3,8,5,59,Graduation,26,Patnership,Standalone\n8100,366,NJ City,W,3,14,6,80,Graduation,30,Franchise,Complex\n6733,103.2,Boston,N,14,9,4,64,High School,26,Patnership,Standalone\n2885,116.99,NJ City,N,10,24,3,26,No education,22,Patnership,Standalone\n2393,189.2,NJ City,N,11,22,4,79,No education,32,Patnership,Standalone\n2774,389.24,NJ City,S,18,23,2,34,No education,26,Patnership,Complex\n1574,165,NJ City,S,3,11,19,73,High School,27,Patnership,Complex\n74,159,Boston,S,3,16,15,71,High School,23,Patnership,Complex\n2356,196,NJ City,W,3,17,11,42,High School,31,Franchise,Complex\n3702,314,NJ City,S,2,19,21,63,High School,21,Patnership,Complex\n7543,456.35,Boston,E,10,21,4,63,High School,30,Owner,Standalone\n2441,113,NJ City,N,1,10,19,47,High School,25,Patnership,Standalone\n773,270,NJ City,N,1,12,16,78,High School,22,Patnership,Standalone\n3259,93.28,NJ City,E,8,9,4,56,High School,29,Owner,Standalone\n8267,433,NJ City,E,2,10,10,56,Graduation,20,Owner,Standalone\n6031,27.79,Boston,N,25,8,2,30,High School,35,Owner,Standalone\n3981,216,NJ City,N,2,22,17,68,High School,31,Patnership,Standalone\n9001,462,NJ City,N,2,8,24,49,Graduation,24,Patnership,Standalone\n3697,191,NJ City,N,2,18,23,47,High School,31,Patnership,Standalone\n6917,449,Boston,N,3,14,23,41,Graduation,32,Patnership,Standalone\n6393,25.18,Boston,N,1,15,1,75,Graduation,19,Patnership,Standalone\n3127,197,NJ City,E,3,17,16,78,High School,26,Owner,Standalone\n9935,460,NJ City,E,3,18,14,65,Graduation,34,Owner,Standalone\n2141,312,NJ City,N,3,21,16,60,High School,25,Patnership,Standalone\n6466,443,Boston,S,2,20,10,32,Graduation,29,Patnership,Complex\n372,61.02,NJ City,N,11,16,4,42,Graduation,30,Franchise,Complex\n2062,350,NJ City,S,2,22,5,26,Graduation,19,Patnership,Complex\n6954,492.44,Boston,S,5,8,4,59,Graduation,32,Patnership,Complex\n4385,233,NJ City,N,3,14,14,44,High School,35,Patnership,Standalone\n4861,306.66,NJ City,N,4,16,1,39,High School,28,Patnership,Standalone\n7019,474,Boston,E,3,16,5,59,Graduation,20,Owner,Standalone\n1284,26.35,NJ City,N,25,13,4,48,Graduation,28,Franchise,Complex\n4814,441.56,NJ City,S,22,19,2,52,No education,27,Patnership,Complex\n2944,59.01,NJ City,W,16,15,4,34,High School,27,Franchise,Complex\n8449,432,NJ City,N,3,13,15,39,Graduation,21,Patnership,Standalone\n4995,470.17,Boston,E,11,20,4,83,Graduation,23,Owner,Standalone\n3220,14.47,NJ City,N,9,23,2,41,High School,21,Franchise,Complex\n1737,106.81,NJ City,N,9,23,2,74,Graduation,32,Patnership,Standalone\n4645,22.47,NJ City,N,14,16,4,26,High School,19,Patnership,Standalone\n4389,351.14,NJ City,N,25,13,3,77,Graduation,22,Patnership,Standalone\n645,191,NJ City,N,2,15,13,27,High School,20,Patnership,Standalone\n5161,88.6,Boston,N,9,10,1,56,High School,34,Patnership,Standalone\n1721,237,NJ City,N,2,21,11,58,High School,31,Patnership,Standalone\n2845,303.96,NJ City,N,16,10,3,81,High School,30,Patnership,Standalone\n462,113.62,NJ City,S,8,11,1,58,Graduation,27,Patnership,Complex\n3482,42.57,NJ City,N,23,8,1,52,No education,24,Patnership,Complex\n3829,110,NJ City,N,3,22,11,33,High School,31,Patnership,Standalone\n9610,372,NJ City,S,3,13,24,67,Graduation,35,Patnership,Complex\n4132,96.29,NJ City,W,3,15,4,32,High School,26,Franchise,Complex\n8847,453.6,NJ City,E,5,12,2,35,Graduation,35,Owner,Standalone\n6252,54.52,Boston,W,18,15,2,43,Graduation,23,Franchise,Complex\n1851,94.59,NJ City,E,18,14,4,42,Graduation,33,Owner,Standalone\n7625,455,Boston,N,1,21,23,59,Graduation,32,Patnership,Standalone\n8901,438,NJ City,N,3,10,24,76,Graduation,29,Patnership,Standalone\n5222,365,Boston,S,1,11,15,79,Graduation,19,Patnership,Complex\n3847,251,NJ City,E,2,17,8,68,High School,24,Owner,Standalone\n1372,43.02,NJ City,N,15,19,4,73,High School,32,Franchise,Complex\n8594,442,NJ City,S,1,12,21,45,Graduation,28,Patnership,Complex\n9103,500,NJ City,E,2,23,21,29,Graduation,21,Owner,Standalone\n4534,131,NJ City,S,2,21,15,80,High School,34,Patnership,Complex\n5321,4.57,Boston,N,6,11,3,29,No education,24,Patnership,Standalone\n7946,22.61,Boston,N,8,14,1,67,No education,19,Patnership,Complex\n801,74.1,NJ City,N,4,8,4,53,Graduation,33,Patnership,Standalone\n3873,314,NJ City,N,1,19,10,85,High School,34,Patnership,Standalone\n9563,91.21,NJ City,E,13,17,1,59,No education,24,Owner,Standalone\n3632,273,NJ City,W,1,19,17,31,High School,18,Franchise,Complex\n6462,82.57,Boston,S,5,17,1,47,Graduation,31,Patnership,Complex\n6904,403,Boston,W,3,18,20,26,Graduation,23,Franchise,Complex\n9781,489,NJ City,N,3,15,10,28,Graduation,29,Patnership,Standalone\n1181,228,NJ City,N,1,15,6,25,High School,34,Patnership,Standalone\n5601,166.93,Boston,N,20,8,4,64,Graduation,22,Patnership,Standalone\n7164,69.08,Boston,N,17,24,4,30,Graduation,21,Franchise,Complex\n6456,440.99,Boston,W,1,17,4,75,Graduation,22,Franchise,Complex\n9067,475,NJ City,E,2,21,17,44,Graduation,32,Owner,Standalone\n797,278,NJ City,N,2,14,22,40,High School,33,Patnership,Standalone\n90,121,Boston,S,1,21,20,73,High School,35,Patnership,Complex\n7532,97.32,Boston,W,14,11,3,27,No education,18,Franchise,Complex\n7734,426,Boston,S,3,20,7,43,Graduation,35,Patnership,Complex\n681,132,NJ City,N,2,15,25,55,High School,27,Patnership,Standalone\n7599,402,Boston,E,3,10,23,73,Graduation,35,Owner,Standalone\n8577,432,NJ City,N,3,22,21,58,Graduation,25,Patnership,Standalone\n4621,243,NJ City,N,1,10,7,67,High School,20,Patnership,Standalone\n3442,274,NJ City,S,3,13,16,67,High School,18,Patnership,Complex\n1399,155,NJ City,E,1,16,12,49,High School,20,Owner,Standalone\n6249,486,Boston,N,3,8,6,39,Graduation,22,Patnership,Standalone\n6147,371,Boston,E,2,20,13,61,Graduation,34,Owner,Standalone\n4985,35.66,Boston,N,13,19,1,72,No education,26,Patnership,Standalone\n4205,179,NJ City,N,2,10,6,73,High School,22,Patnership,Standalone\n6477,97.37,Boston,N,23,10,2,37,Graduation,18,Patnership,Standalone\n6591,398,Boston,E,1,18,9,49,Graduation,29,Owner,Standalone\n3739,270,NJ City,E,3,11,8,64,High School,24,Owner,Standalone\n8108,386,NJ City,W,2,13,15,56,Graduation,30,Franchise,Complex\n4055,248,NJ City,E,1,10,20,76,High School,33,Owner,Standalone\n4870,179,NJ City,S,1,21,21,50,High School,35,Patnership,Complex\n9495,453,NJ City,E,3,24,24,32,Graduation,31,Owner,Standalone\n5385,82.59,Boston,N,10,8,1,34,Graduation,25,Patnership,Standalone\n2468,107.79,NJ City,W,7,12,1,28,No education,33,Franchise,Complex\n8501,376,NJ City,N,2,21,24,51,Graduation,24,Patnership,Standalone\n9975,427,NJ City,E,3,17,23,38,Graduation,23,Owner,Standalone\n9762,108.97,NJ City,S,4,15,2,29,Graduation,28,Patnership,Complex\n5955,388,Boston,E,3,19,21,75,Graduation,27,Owner,Standalone\n9543,40.93,NJ City,N,9,22,1,29,Graduation,26,Owner,Standalone\n4391,255,NJ City,E,2,11,13,50,High School,28,Owner,Standalone\n5773,375,Boston,N,2,23,16,60,Graduation,30,Patnership,Standalone\n971,394.51,NJ City,E,20,10,4,50,No education,28,Owner,Standalone\n4175,196,NJ City,E,3,22,14,31,High School,29,Owner,Standalone\n7423,35.09,Boston,N,23,15,1,34,High School,29,Owner,Standalone\n1485,102,NJ City,N,1,8,20,28,High School,33,Patnership,Standalone\n6353,88.83,Boston,N,20,15,3,29,No education,20,Patnership,Standalone\n5820,382,Boston,W,3,23,11,63,Graduation,25,Franchise,Complex\n3665,96.32,NJ City,N,8,8,2,33,No education,33,Patnership,Standalone\n8551,31.49,NJ City,N,15,10,2,34,High School,31,Owner,Standalone\n1540,127,NJ City,W,3,11,24,77,High School,24,Franchise,Complex\n5999,485,Boston,E,3,8,11,32,Graduation,19,Owner,Standalone\n9487,477,NJ City,E,1,8,16,59,Graduation,23,Owner,Standalone\n8011,7.77,Boston,N,13,9,2,33,High School,24,Owner,Standalone\n1667,49.17,NJ City,E,12,8,1,25,No education,19,Owner,Standalone\n2633,220.3,NJ City,N,9,10,1,49,No education,25,Patnership,Standalone\n1636,47.13,NJ City,N,24,22,1,60,High School,26,Franchise,Complex\n4168,7.2,NJ City,N,3,11,3,41,High School,24,Franchise,Complex\n2130,208,NJ City,S,1,11,8,76,High School,24,Patnership,Complex\n162,225,NJ City,S,2,10,24,26,High School,32,Patnership,Complex\n8949,452,NJ City,N,1,11,19,43,Graduation,29,Patnership,Standalone\n2318,248,NJ City,S,1,9,9,58,High School,28,Patnership,Complex\n2849,76.33,NJ City,N,7,21,4,57,No education,33,Patnership,Standalone\n4895,285,NJ City,E,3,22,20,67,High School,28,Owner,Standalone\n845,264,NJ City,N,1,17,14,54,High School,23,Patnership,Standalone\n3792,441,NJ City,W,2,11,5,40,Graduation,25,Franchise,Complex\n7122,73.1,Boston,N,15,12,1,49,Graduation,22,Patnership,Complex\n9692,7.69,NJ City,N,6,16,1,46,No education,19,Franchise,Complex\n3634,126,NJ City,S,2,11,14,40,High School,35,Patnership,Complex\n4558,66.91,NJ City,N,9,10,3,50,High School,19,Patnership,Complex\n5212,482,Boston,W,3,21,11,55,Graduation,31,Franchise,Complex\n7152,460,Boston,W,3,15,11,58,Graduation,27,Franchise,Complex\n1518,270,NJ City,S,2,13,21,76,High School,26,Patnership,Complex\n9498,411.76,NJ City,S,2,12,3,81,Graduation,19,Patnership,Complex\n2160,246,NJ City,W,3,8,13,46,High School,19,Franchise,Complex\n5046,431,Boston,S,2,19,11,48,Graduation,25,Patnership,Complex\n3462,279,NJ City,S,1,15,14,34,High School,33,Patnership,Complex\n432,66.76,NJ City,N,13,14,1,48,Graduation,34,Franchise,Complex\n4077,216,NJ City,N,3,24,16,66,High School,33,Patnership,Standalone\n6556,372,Boston,W,2,17,17,37,Graduation,29,Franchise,Complex\n3000,312.68,NJ City,W,9,15,2,85,Graduation,21,Franchise,Complex\n6342,481,Boston,S,2,8,21,32,Graduation,20,Patnership,Complex\n3589,104,NJ City,N,1,23,12,67,High School,24,Patnership,Standalone\n2894,27.47,NJ City,N,9,18,4,43,No education,28,Patnership,Complex\n8373,440,NJ City,N,2,17,19,52,Graduation,31,Patnership,Standalone\n7771,365,Boston,E,2,13,24,73,Graduation,34,Owner,Standalone\n5633,437,Boston,N,2,13,14,59,Graduation,34,Patnership,Standalone\n8357,420,NJ City,N,2,21,25,44,Graduation,19,Patnership,Standalone\n8198,362,NJ City,S,2,21,16,61,Graduation,20,Patnership,Complex\n4388,297,NJ City,W,2,19,24,30,High School,26,Franchise,Complex\n2286,295,NJ City,S,2,14,6,43,High School,18,Patnership,Complex\n2934,121,NJ City,S,3,22,21,82,High School,27,Patnership,Complex\n7841,249.56,Boston,N,18,15,2,82,No education,34,Patnership,Standalone\n1037,150,NJ City,N,2,12,6,68,High School,29,Patnership,Standalone\n5966,412,Boston,S,1,13,19,82,Graduation,29,Patnership,Complex\n9504,118.38,NJ City,W,15,19,4,73,Graduation,34,Franchise,Complex\n8711,497,NJ City,E,2,12,19,41,Graduation,30,Owner,Standalone\n1652,194.09,NJ City,W,9,19,2,66,No education,29,Franchise,Complex\n9358,27.58,NJ City,N,13,18,2,47,High School,26,Patnership,Complex\n6383,63.95,Boston,N,4,12,3,30,No education,23,Owner,Standalone\n255,143,NJ City,E,3,21,17,45,High School,18,Owner,Standalone\n2428,290,NJ City,W,3,17,10,57,High School,30,Franchise,Complex\n9267,13.75,NJ City,N,11,9,4,32,Graduation,23,Owner,Standalone\n2371,317.82,NJ City,E,8,18,4,82,High School,34,Owner,Standalone\n6935,436,Boston,E,2,11,7,73,Graduation,19,Owner,Standalone\n5514,383.16,Boston,S,17,12,2,58,Graduation,23,Patnership,Complex\n1337,266,NJ City,N,3,18,21,51,High School,18,Patnership,Standalone\n9163,497,NJ City,E,2,18,14,85,Graduation,21,Owner,Standalone\n6500,498,Boston,W,2,17,25,40,Graduation,26,Franchise,Complex\n7975,498,Boston,E,3,18,25,35,Graduation,20,Owner,Standalone\n789,284,NJ City,N,3,11,16,28,High School,30,Patnership,Standalone\n5234,469.41,Boston,S,21,15,3,62,No education,28,Patnership,Complex\n6000,14.87,Boston,N,21,13,3,64,Graduation,20,Franchise,Complex\n7037,371,Boston,N,2,17,23,33,Graduation,24,Patnership,Standalone\n235,111,NJ City,E,3,14,21,82,High School,18,Owner,Standalone\n1750,20.82,NJ City,N,15,20,2,67,High School,21,Patnership,Complex\n2339,220,NJ City,E,3,21,17,72,High School,29,Owner,Standalone\n9216,352,NJ City,W,2,13,5,70,Graduation,22,Franchise,Complex\n7688,183.73,Boston,W,17,8,2,38,No education,34,Franchise,Complex\n3527,115.48,NJ City,E,19,12,1,73,No education,34,Owner,Standalone\n2366,82.4,NJ City,S,16,22,4,83,No education,19,Patnership,Complex\n4692,186,NJ City,W,3,20,23,33,High School,19,Franchise,Complex\n1461,240,NJ City,N,3,21,20,68,High School,26,Patnership,Standalone\n1902,111,NJ City,S,1,13,17,40,High School,21,Patnership,Complex\n6849,71.01,Boston,N,20,17,2,73,Graduation,28,Patnership,Standalone\n7262,290.46,Boston,S,7,16,2,52,No education,34,Patnership,Complex\n6982,493,Boston,S,2,23,12,37,Graduation,24,Patnership,Complex\n6379,53.2,Boston,E,12,11,3,41,High School,34,Owner,Standalone\n7700,470,Boston,W,1,13,14,27,Graduation,33,Franchise,Complex\n8363,404,NJ City,E,2,16,15,82,Graduation,28,Owner,Standalone\n631,305.67,NJ City,E,22,24,2,63,High School,29,Owner,Standalone\n2409,134,NJ City,N,1,8,20,78,High School,25,Patnership,Standalone\n7670,73.96,Boston,N,9,13,3,57,No education,25,Patnership,Complex\n8008,424,Boston,W,2,11,11,39,Graduation,34,Franchise,Complex\n8925,376,NJ City,N,1,13,9,59,Graduation,27,Patnership,Standalone\n4390,65.09,NJ City,N,9,11,3,79,High School,21,Patnership,Complex\n2891,247,NJ City,E,3,16,21,79,High School,28,Owner,Standalone\n4124,209,NJ City,W,1,13,8,43,High School,23,Franchise,Complex\n66,197,Boston,S,2,22,17,84,High School,23,Patnership,Complex\n282,168,NJ City,S,2,23,14,63,High School,28,Patnership,Complex\n6362,58.35,Boston,S,22,19,4,45,No education,35,Patnership,Complex\n2033,122.31,NJ City,N,19,18,4,67,No education,21,Patnership,Standalone\n65,268,Boston,N,2,12,11,50,High School,18,Patnership,Standalone\n7768,469,Boston,W,1,16,10,60,Graduation,31,Franchise,Complex\n3003,303,NJ City,E,3,15,10,83,High School,31,Owner,Standalone\n2723,262.36,NJ City,E,11,14,4,33,No education,27,Owner,Standalone\n4580,213,NJ City,W,2,9,25,85,High School,34,Franchise,Complex\n8821,422.86,NJ City,N,7,9,1,68,High School,32,Patnership,Standalone\n9558,451,NJ City,S,2,10,14,50,Graduation,18,Patnership,Complex\n7000,388,Boston,W,3,15,17,32,Graduation,33,Franchise,Complex\n7099,350,Boston,E,3,10,23,65,Graduation,33,Owner,Standalone\n1176,401.7,NJ City,W,24,12,2,34,Graduation,20,Franchise,Complex\n5658,460,Boston,S,2,11,19,36,Graduation,24,Patnership,Complex\n980,74.93,NJ City,W,17,15,4,46,No education,33,Franchise,Complex\n7602,388,Boston,S,3,17,6,44,Graduation,22,Patnership,Complex\n1870,232,NJ City,S,1,10,14,72,High School,23,Patnership,Complex\n9534,164.4,NJ City,S,4,24,4,42,Graduation,34,Patnership,Complex\n5058,473.81,Boston,S,22,23,1,76,Graduation,29,Patnership,Complex\n3471,108.01,NJ City,E,10,12,2,78,Graduation,25,Owner,Standalone\n1180,108,NJ City,W,3,8,8,63,High School,30,Franchise,Complex\n2486,100,NJ City,S,1,23,21,36,High School,21,Patnership,Complex\n1416,71.27,NJ City,N,7,20,3,46,Graduation,29,Franchise,Complex\n2927,355.38,NJ City,E,25,15,3,44,No education,28,Owner,Standalone\n8630,478,NJ City,S,3,15,10,60,Graduation,30,Patnership,Complex\n355,99.03,NJ City,E,1,14,4,26,High School,23,Owner,Standalone\n1678,196,NJ City,S,3,8,16,43,High School,32,Patnership,Complex\n173,58.86,NJ City,N,19,8,1,62,No education,33,Patnership,Standalone\n2623,267,NJ City,E,3,19,23,46,High School,35,Owner,Standalone\n2838,100.98,NJ City,S,22,19,3,74,Graduation,21,Patnership,Complex\n8945,15.49,NJ City,N,11,11,3,37,No education,30,Patnership,Standalone\n375,114,NJ City,E,2,11,7,63,High School,25,Owner,Standalone\n1762,149,NJ City,S,3,14,12,49,High School,26,Patnership,Complex\n5558,47.43,Boston,S,12,18,3,35,No education,25,Patnership,Complex\n7306,358,Boston,S,3,19,24,31,Graduation,33,Patnership,Complex\n8432,486.15,NJ City,W,25,14,4,70,No education,32,Franchise,Complex\n6145,447,Boston,N,3,20,11,52,Graduation,28,Patnership,Standalone\n7739,478,Boston,E,3,9,9,39,Graduation,25,Owner,Standalone\n3047,150,NJ City,E,2,19,7,84,High School,22,Owner,Standalone\n9973,448,NJ City,N,2,22,17,26,Graduation,26,Patnership,Standalone\n6491,383,Boston,E,2,17,7,47,Graduation,32,Owner,Standalone\n4802,147,NJ City,S,3,15,19,54,High School,20,Patnership,Complex\n9923,364,NJ City,E,3,14,19,25,Graduation,21,Owner,Standalone\n1356,460.64,NJ City,W,10,16,1,68,Graduation,30,Franchise,Complex\n3196,52.45,NJ City,W,4,24,1,40,High School,34,Franchise,Complex\n3135,121,NJ City,E,3,13,15,72,High School,22,Owner,Standalone\n7016,363.69,Boston,W,11,12,1,52,No education,19,Franchise,Complex\n3840,193,NJ City,W,1,22,25,41,High School,32,Franchise,Complex\n8006,350,Boston,S,2,22,8,80,Graduation,18,Patnership,Complex\n5896,415,Boston,W,1,9,14,85,Graduation,33,Franchise,Complex\n3251,30.96,NJ City,N,15,24,2,40,No education,31,Owner,Standalone\n9148,382,NJ City,W,1,17,5,69,Graduation,22,Franchise,Complex\n9376,446,NJ City,W,1,24,21,40,Graduation,34,Franchise,Complex\n6744,29.82,Boston,N,25,22,1,83,Graduation,28,Franchise,Complex\n6055,114.99,Boston,E,15,23,4,78,High School,34,Owner,Standalone\n8211,493,NJ City,E,1,21,5,34,Graduation,26,Owner,Standalone\n7801,436,Boston,N,3,24,24,37,Graduation,25,Patnership,Standalone\n4567,263,NJ City,E,1,18,18,37,High School,22,Owner,Standalone\n261,211,NJ City,N,3,18,8,76,High School,30,Patnership,Standalone\n441,303.7,NJ City,N,25,15,4,67,Graduation,27,Patnership,Standalone\n2539,108,NJ City,E,3,14,18,84,High School,23,Owner,Standalone\n3417,177,NJ City,N,1,11,20,43,High School,20,Patnership,Standalone\n6735,0.88,Boston,N,12,8,1,67,Graduation,20,Owner,Standalone\n7112,10.83,Boston,N,11,18,4,34,No education,22,Franchise,Complex\n5502,350,Boston,S,3,19,18,28,Graduation,23,Patnership,Complex\n8238,127.15,NJ City,S,9,12,2,79,Graduation,25,Patnership,Complex\n7631,104.8,Boston,E,15,17,3,75,No education,34,Owner,Standalone\n4515,120.95,NJ City,E,8,8,1,81,Graduation,32,Owner,Standalone\n7875,82.28,Boston,E,13,18,3,55,Graduation,23,Owner,Standalone\n9908,370,NJ City,W,2,10,20,43,Graduation,35,Franchise,Complex\n9057,78.95,NJ City,N,18,10,3,34,Graduation,31,Patnership,Standalone\n332,163,NJ City,W,1,14,21,61,High School,22,Franchise,Complex\n1214,138,NJ City,S,3,18,24,84,High School,34,Patnership,Complex\n4874,23.54,NJ City,N,2,21,1,41,No education,28,Patnership,Complex\n8533,443,NJ City,N,3,12,16,34,Graduation,22,Patnership,Standalone\n6162,372,Boston,S,3,18,21,40,Graduation,20,Patnership,Complex\n2786,91.49,NJ City,S,3,8,3,69,No education,31,Patnership,Complex\n1899,112,NJ City,E,1,11,13,70,High School,25,Owner,Standalone\n4920,81,NJ City,W,9,16,2,38,Graduation,23,Franchise,Complex\n4683,287,NJ City,E,2,20,16,35,High School,22,Owner,Standalone\n6842,497,Boston,S,2,10,20,53,Graduation,24,Patnership,Complex\n2738,297.1,NJ City,S,17,15,4,41,No education,20,Patnership,Complex\n3198,108,NJ City,S,3,23,15,37,High School,28,Patnership,Complex\n1388,398,NJ City,W,2,21,5,36,Graduation,24,Franchise,Complex\n7139,483,Boston,E,1,8,20,36,Graduation,20,Owner,Standalone\n1015,52.52,NJ City,E,24,13,2,68,High School,33,Owner,Standalone\n5995,60,Boston,E,17,23,2,76,High School,18,Owner,Standalone\n5374,453,Boston,S,2,24,13,51,Graduation,23,Patnership,Complex\n5723,411,Boston,E,2,21,16,78,Graduation,19,Owner,Standalone\n6765,363,Boston,N,2,14,21,60,Graduation,26,Patnership,Standalone\n5442,36.1,Boston,N,8,10,2,77,Graduation,31,Patnership,Complex\n4784,291,NJ City,W,1,9,15,46,High School,26,Franchise,Complex\n6691,192.87,Boston,E,6,12,2,70,High School,21,Owner,Standalone\n7974,405,Boston,S,2,10,6,76,Graduation,30,Patnership,Complex\n9304,13.99,NJ City,N,17,8,4,81,High School,28,Franchise,Complex\n7535,481,Boston,E,1,13,7,82,Graduation,27,Owner,Standalone\n4319,310,NJ City,E,2,22,25,35,High School,22,Owner,Standalone\n5127,43.41,Boston,N,6,17,2,78,Graduation,27,Owner,Standalone\n9957,389,NJ City,N,3,21,13,57,Graduation,19,Patnership,Standalone\n7226,75.22,Boston,S,19,11,4,68,No education,20,Patnership,Complex\n238,250,NJ City,S,1,9,14,79,High School,35,Patnership,Complex\n4043,333.93,NJ City,E,4,24,1,69,No education,27,Owner,Standalone\n2477,117,NJ City,N,3,15,10,52,High School,18,Patnership,Standalone\n3610,26.45,NJ City,N,10,11,4,29,High School,27,Patnership,Complex\n9207,125.65,NJ City,E,3,17,4,52,Graduation,35,Owner,Standalone\n3312,14.33,NJ City,N,15,18,4,78,Graduation,34,Franchise,Complex\n6062,481,Boston,S,3,24,5,52,Graduation,23,Patnership,Complex\n9134,427.38,NJ City,S,17,19,1,66,No education,35,Patnership,Complex\n3104,148,NJ City,W,3,21,19,38,High School,26,Franchise,Complex\n2418,306.33,NJ City,S,14,18,3,43,Graduation,20,Patnership,Complex\n5195,499,Boston,E,2,17,13,30,Graduation,28,Owner,Standalone\n1012,250,NJ City,W,2,11,15,48,High School,18,Franchise,Complex\n6535,11.64,Boston,N,4,17,2,42,High School,18,Owner,Standalone\n6684,320.56,Boston,W,6,11,2,57,Graduation,20,Franchise,Complex\n1617,221,NJ City,N,1,10,9,66,High School,20,Patnership,Standalone\n9581,388,NJ City,N,3,8,22,49,Graduation,34,Patnership,Standalone\n1339,276,NJ City,E,3,14,19,71,High School,35,Owner,Standalone\n1644,240,NJ City,W,2,19,18,57,High School,33,Franchise,Complex\n5376,85.98,Boston,W,25,8,2,27,Graduation,31,Franchise,Complex\n5076,413,Boston,W,2,23,23,31,Graduation,29,Franchise,Complex\n28,171,Boston,W,1,23,14,55,High School,24,Franchise,Complex\n2666,205,NJ City,S,3,16,13,63,High School,30,Patnership,Complex\n1343,162,NJ City,E,3,8,13,30,High School,24,Owner,Standalone\n2357,271,NJ City,N,3,22,23,44,High School,32,Patnership,Standalone\n7611,131.11,Boston,E,21,16,3,57,Graduation,30,Owner,Standalone\n4320,246,NJ City,W,2,17,14,26,High School,20,Franchise,Complex\n1925,5.64,NJ City,N,10,20,3,31,No education,24,Patnership,Standalone\n2913,258,NJ City,N,1,21,21,69,High School,34,Patnership,Standalone\n7898,354,Boston,S,2,17,13,57,Graduation,25,Patnership,Complex\n8490,30.16,NJ City,N,14,17,3,72,Graduation,26,Patnership,Complex\n7843,431,Boston,E,3,21,25,83,Graduation,22,Owner,Standalone\n8853,362,NJ City,N,1,10,23,76,Graduation,19,Patnership,Standalone\n2880,319,NJ City,W,1,9,11,26,High School,23,Franchise,Complex\n6653,65.96,Boston,N,22,18,2,62,No education,24,Patnership,Standalone\n5684,390,Boston,W,2,10,11,43,Graduation,31,Franchise,Complex\n1691,271,NJ City,E,2,14,13,28,High School,20,Owner,Standalone\n3164,283,NJ City,W,2,8,20,72,High School,34,Franchise,Complex\n3275,372.91,NJ City,E,24,22,2,67,No education,30,Owner,Standalone\n7056,74.04,Boston,W,15,15,4,78,Graduation,29,Franchise,Complex\n8064,414,NJ City,W,3,23,14,32,Graduation,26,Franchise,Complex\n2489,464,NJ City,N,1,20,5,34,Graduation,30,Patnership,Standalone\n4404,413,NJ City,W,3,11,5,48,Graduation,26,Franchise,Complex\n3062,67.07,NJ City,N,8,12,4,58,No education,24,Patnership,Complex\n4147,311,NJ City,E,1,13,12,45,High School,26,Owner,Standalone\n9166,408,NJ City,S,3,24,16,51,Graduation,29,Patnership,Complex\n2288,183,NJ City,W,2,23,12,68,High School,32,Franchise,Complex\n1156,129,NJ City,W,3,20,22,42,High School,20,Franchise,Complex\n9529,491,NJ City,N,2,15,10,74,Graduation,26,Patnership,Standalone\n2084,123.74,NJ City,W,9,8,2,77,No education,29,Franchise,Complex\n2321,203,NJ City,N,2,24,6,59,High School,25,Patnership,Standalone\n8541,14.22,NJ City,N,14,18,3,43,Graduation,33,Patnership,Standalone\n9648,428,NJ City,W,1,19,12,60,Graduation,23,Franchise,Complex\n9329,361,NJ City,N,3,19,5,33,Graduation,22,Patnership,Standalone\n7735,491,Boston,E,3,13,24,28,Graduation,18,Owner,Standalone\n4842,73.72,NJ City,N,13,12,2,59,Graduation,30,Patnership,Complex\n8537,382,NJ City,N,1,13,22,85,Graduation,26,Patnership,Standalone\n3534,254,NJ City,S,2,8,13,80,High School,32,Patnership,Complex\n7446,108.53,Boston,S,10,14,3,79,Graduation,18,Patnership,Complex\n8570,399.46,NJ City,S,5,11,1,25,No education,28,Patnership,Complex\n7177,101.23,Boston,N,23,16,2,61,High School,30,Patnership,Standalone\n4984,144,Boston,W,3,10,9,75,High School,26,Franchise,Complex\n8892,482,NJ City,W,2,10,20,25,Graduation,33,Franchise,Complex\n1654,312,NJ City,S,1,17,16,75,High School,23,Patnership,Complex\n3946,124.19,NJ City,S,3,21,3,46,High School,31,Patnership,Complex\n3285,117.46,NJ City,N,13,21,4,51,Graduation,20,Patnership,Standalone\n9228,445,NJ City,W,1,20,8,33,Graduation,30,Franchise,Complex\n3766,13.17,NJ City,N,10,20,2,51,High School,34,Patnership,Complex\n5185,273.55,Boston,N,5,19,3,77,High School,29,Patnership,Standalone\n7308,11.83,Boston,N,20,24,4,40,Graduation,33,Franchise,Complex\n7617,482,Boston,N,1,21,21,84,Graduation,25,Patnership,Standalone\n4880,124,NJ City,W,1,24,19,54,High School,27,Franchise,Complex\n9305,169.03,NJ City,N,7,16,2,81,No education,26,Patnership,Standalone\n7664,417,Boston,W,3,10,18,45,Graduation,19,Franchise,Complex\n5980,394.28,Boston,W,11,10,2,79,High School,20,Franchise,Complex\n5524,116.09,Boston,W,2,11,1,45,High School,33,Franchise,Complex\n3184,106.11,NJ City,W,8,9,2,70,High School,35,Franchise,Complex\n2163,282.34,NJ City,E,16,22,3,85,Graduation,20,Owner,Standalone\n1287,1.38,NJ City,N,15,24,2,56,Graduation,34,Owner,Standalone\n9117,37.27,NJ City,N,14,12,3,71,Graduation,20,Patnership,Standalone\n8780,213.13,NJ City,W,3,18,3,39,No education,32,Franchise,Complex\n9422,441,NJ City,S,1,15,16,72,Graduation,33,Patnership,Complex\n3051,191,NJ City,E,3,15,23,42,High School,18,Owner,Standalone\n9035,89.12,NJ City,E,3,18,2,75,No education,30,Owner,Standalone\n948,75.05,NJ City,W,8,12,2,46,Graduation,29,Franchise,Complex\n7669,468,Boston,N,3,23,24,26,Graduation,30,Patnership,Standalone\n3944,204,NJ City,W,1,8,12,79,High School,32,Franchise,Complex\n8930,415,NJ City,S,3,18,18,36,Graduation,29,Patnership,Complex\n9708,396,NJ City,W,3,9,9,55,Graduation,26,Franchise,Complex\n7304,30.36,Boston,N,2,16,2,64,No education,22,Franchise,Complex\n1295,173,NJ City,E,1,18,15,39,High School,34,Owner,Standalone\n3784,294,NJ City,W,3,22,15,35,High School,31,Franchise,Complex\n436,90.41,NJ City,W,3,12,4,59,High School,32,Franchise,Complex\n1553,193,NJ City,N,3,10,16,48,High School,24,Patnership,Standalone\n5697,95.3,Boston,N,19,15,1,81,Graduation,26,Patnership,Standalone\n5587,403,Boston,E,3,21,9,75,Graduation,28,Owner,Standalone\n2749,184,NJ City,N,1,14,9,47,High School,32,Patnership,Standalone\n7281,99.19,Boston,N,14,11,3,68,Graduation,33,Patnership,Standalone\n5644,89.49,Boston,W,5,8,2,26,High School,19,Franchise,Complex\n2261,51.58,NJ City,N,17,24,2,54,No education,19,Patnership,Standalone\n4432,127,NJ City,W,1,16,24,85,High School,21,Franchise,Complex\n9286,430,NJ City,S,3,10,16,35,Graduation,34,Patnership,Complex\n422,140,NJ City,S,2,10,20,32,High School,22,Patnership,Complex\n4923,145,NJ City,E,2,13,14,82,High School,19,Owner,Standalone\n6858,429,Boston,S,3,18,14,59,Graduation,20,Patnership,Complex\n8257,460,NJ City,N,2,16,19,79,Graduation,22,Patnership,Standalone\n5975,481,Boston,E,3,24,19,74,Graduation,29,Owner,Standalone\n5709,490.07,Boston,N,4,17,2,29,Graduation,31,Patnership,Standalone\n8514,367,NJ City,S,3,20,22,34,Graduation,35,Patnership,Complex\n1738,292,NJ City,S,1,19,25,49,High School,20,Patnership,Complex\n570,108,NJ City,S,3,12,10,64,High School,22,Patnership,Complex\n5296,457,Boston,W,1,15,5,66,Graduation,27,Franchise,Complex\n9121,387,NJ City,N,2,10,21,26,Graduation,22,Patnership,Standalone\n4934,147,NJ City,S,2,18,22,84,High School,21,Patnership,Complex\n7662,409,Boston,S,3,19,10,41,Graduation,22,Patnership,Complex\n3014,407.91,NJ City,S,14,16,1,31,No education,27,Patnership,Complex\n409,239,NJ City,N,2,18,9,39,High School,31,Patnership,Standalone\n1919,198,NJ City,E,1,14,11,63,High School,20,Owner,Standalone\n9072,112.83,NJ City,W,25,17,1,63,Graduation,23,Franchise,Complex\n4162,104,NJ City,S,1,21,10,55,High School,25,Patnership,Complex\n4414,230,NJ City,S,1,12,22,71,High School,34,Patnership,Complex\n891,419,NJ City,E,2,9,5,82,Graduation,25,Owner,Standalone\n2331,34.07,NJ City,N,17,16,2,58,Graduation,31,Owner,Standalone\n5300,481,Boston,W,1,14,13,58,Graduation,20,Franchise,Complex\n8938,500,NJ City,S,3,22,20,25,Graduation,18,Patnership,Complex\n9008,428,NJ City,W,1,22,11,45,Graduation,20,Franchise,Complex\n6115,453,Boston,E,1,9,23,58,Graduation,26,Owner,Standalone\n1562,289,NJ City,S,2,11,24,69,High School,32,Patnership,Complex\n9263,156.9,NJ City,E,23,9,4,77,No education,29,Owner,Standalone\n8997,50.8,NJ City,N,9,12,3,79,Graduation,30,Patnership,Standalone\n9739,452,NJ City,E,1,22,22,71,Graduation,19,Owner,Standalone\n8233,432,NJ City,N,1,21,15,75,Graduation,18,Patnership,Standalone\n931,195,NJ City,E,3,8,9,50,High School,24,Owner,Standalone\n9335,353,NJ City,E,2,20,15,76,Graduation,25,Owner,Standalone\n453,300,NJ City,N,2,14,14,53,High School,28,Patnership,Standalone\n8468,27.86,NJ City,N,10,19,2,66,No education,25,Franchise,Complex\n8976,486,NJ City,W,2,21,17,64,Graduation,34,Franchise,Complex\n7119,286,Boston,E,1,8,2,46,Graduation,20,Owner,Standalone\n5579,430.29,Boston,E,1,15,2,52,No education,21,Owner,Standalone\n8845,90.15,NJ City,N,6,11,1,84,High School,34,Patnership,Standalone\n7860,418,Boston,W,3,14,13,34,Graduation,24,Franchise,Complex\n6681,48.42,Boston,N,10,20,2,55,Graduation,24,Patnership,Standalone\n3863,230,NJ City,E,3,12,7,65,High School,29,Owner,Standalone\n2382,461.99,NJ City,S,2,21,4,29,Graduation,23,Patnership,Complex\n4457,255,NJ City,N,3,12,24,74,High School,26,Patnership,Standalone\n8675,450,NJ City,E,2,20,8,38,Graduation,18,Owner,Standalone\n1254,131,NJ City,S,3,9,13,50,High School,30,Patnership,Complex\n3269,98.19,NJ City,N,17,12,4,64,No education,26,Patnership,Standalone\n2150,1.27,NJ City,N,17,11,2,33,No education,22,Patnership,Complex\n5348,497,Boston,W,2,22,9,44,Graduation,20,Franchise,Complex\n8139,394,NJ City,E,3,17,5,73,Graduation,23,Owner,Standalone\n6452,88.36,Boston,W,11,10,4,67,No education,27,Franchise,Complex\n2522,287,NJ City,S,3,14,23,85,High School,25,Patnership,Complex\n8331,360,NJ City,E,1,24,11,60,Graduation,26,Owner,Standalone\n9859,410.17,NJ City,E,5,10,4,59,High School,35,Owner,Standalone\n1868,120.44,NJ City,W,12,8,1,42,No education,26,Franchise,Complex\n5692,493,Boston,W,1,21,15,52,Graduation,26,Franchise,Complex\n7933,6.83,Boston,N,6,22,2,54,High School,20,Patnership,Standalone\n3511,233,NJ City,E,3,18,23,82,High School,30,Owner,Standalone\n6933,472,Boston,N,2,15,19,38,Graduation,31,Patnership,Standalone\n2003,112.48,NJ City,E,7,21,2,63,No education,34,Owner,Standalone\n3924,288,NJ City,W,3,9,20,81,High School,34,Franchise,Complex\n5717,453.53,Boston,N,12,16,3,48,No education,29,Patnership,Standalone\n2305,251,NJ City,N,1,12,15,27,High School,21,Patnership,Standalone\n4878,296,NJ City,S,1,21,13,76,High School,21,Patnership,Complex\n8841,381,NJ City,N,1,14,13,29,Graduation,32,Patnership,Standalone\n4091,179,NJ City,E,1,24,7,72,High School,23,Owner,Standalone\n9970,111.07,NJ City,S,15,10,4,46,High School,24,Patnership,Complex\n2332,303,NJ City,W,1,20,22,81,High School,32,Franchise,Complex\n5329,369,Boston,N,3,21,24,37,Graduation,18,Patnership,Standalone\n4944,86.42,Boston,W,25,12,2,56,Graduation,21,Franchise,Complex\n2436,101,NJ City,W,3,17,19,78,High School,18,Franchise,Complex\n9754,454,NJ City,S,3,11,20,85,Graduation,19,Patnership,Complex\n1572,349.01,NJ City,W,4,21,2,62,Graduation,33,Franchise,Complex\n3406,234,NJ City,S,1,15,18,70,High School,34,Patnership,Complex\n7043,399,Boston,E,2,23,18,35,Graduation,18,Owner,Standalone\n6899,389,Boston,E,3,10,13,55,Graduation,24,Owner,Standalone\n6532,427.05,Boston,W,10,16,1,29,High School,31,Franchise,Complex\n9860,47.89,NJ City,W,21,13,3,59,No education,32,Franchise,Complex\n2886,204,NJ City,S,2,20,17,35,High School,33,Patnership,Complex\n8029,487,Boston,N,1,23,9,78,Graduation,28,Patnership,Standalone\n3327,73.8,NJ City,N,1,13,3,75,Graduation,33,Owner,Standalone\n6986,463,Boston,S,2,23,12,57,Graduation,19,Patnership,Complex\n3991,196,NJ City,E,3,21,22,32,High School,22,Owner,Standalone\n152,127.29,NJ City,W,12,15,2,32,No education,22,Franchise,Complex\n9458,355,NJ City,S,1,23,24,75,Graduation,24,Patnership,Complex\n103,138,NJ City,E,1,24,8,39,High School,32,Owner,Standalone\n1720,129,NJ City,W,1,11,25,74,High School,32,Franchise,Complex\n6720,381,Boston,W,3,17,22,73,Graduation,26,Franchise,Complex\n2583,132.85,NJ City,E,17,15,1,77,Graduation,22,Owner,Standalone\n955,132,NJ City,E,2,22,7,33,High School,23,Owner,Standalone\n9888,16.46,NJ City,N,25,8,3,77,Graduation,24,Franchise,Complex\n8280,388,NJ City,W,3,22,16,65,Graduation,35,Franchise,Complex\n9690,371,NJ City,S,1,17,15,38,Graduation,26,Patnership,Complex\n1522,495.07,NJ City,S,15,11,3,66,High School,27,Patnership,Complex\n4111,273,NJ City,E,2,15,22,60,High School,29,Owner,Standalone\n4811,207,NJ City,E,2,15,25,36,High School,32,Owner,Standalone\n6257,463,Boston,N,1,15,9,69,Graduation,30,Patnership,Standalone\n53,111,NY City,N,3,9,6,31,High School,23,Patnership,Standalone\n2750,98.87,NJ City,S,21,24,4,26,No education,23,Patnership,Complex\n8069,98.56,NJ City,N,19,11,1,79,No education,24,Patnership,Standalone\n8550,398,NJ City,S,3,10,17,39,Graduation,32,Patnership,Complex\n5592,406,Boston,W,2,18,19,61,Graduation,22,Franchise,Complex\n6054,94.07,Boston,S,22,23,3,37,Graduation,27,Patnership,Complex\n9479,14.1,NJ City,N,20,9,1,34,No education,20,Owner,Standalone\n4937,279,Boston,N,1,23,10,50,High School,24,Patnership,Standalone\n7927,489.69,Boston,E,13,24,1,47,High School,31,Owner,Standalone\n2420,37.81,NJ City,N,16,8,4,57,No education,34,Franchise,Complex\n5837,414,Boston,N,1,20,21,39,Graduation,20,Patnership,Standalone\n8463,376,NJ City,E,3,10,17,66,Graduation,35,Owner,Standalone\n4791,61.61,NJ City,N,11,13,1,45,Graduation,29,Owner,Standalone\n9462,451,NJ City,S,2,19,21,78,Graduation,31,Patnership,Complex\n4589,301,NJ City,N,2,16,21,84,High School,18,Patnership,Standalone\n2472,303,NJ City,W,3,8,25,44,High School,24,Franchise,Complex\n9193,33.73,NJ City,N,7,18,2,59,High School,30,Patnership,Standalone\n1913,46.17,NJ City,N,25,23,2,36,No education,32,Patnership,Standalone\n8153,398,NJ City,N,2,14,21,34,Graduation,32,Patnership,Standalone\n7321,394,Boston,N,2,22,23,51,Graduation,21,Patnership,Standalone\n7014,412,Boston,S,3,13,15,71,Graduation,32,Patnership,Complex\n8058,26.52,NJ City,N,12,20,1,84,Graduation,21,Patnership,Complex\n7481,396.9,Boston,N,4,11,1,48,No education,32,Patnership,Standalone\n3567,166,NJ City,E,2,18,21,78,High School,22,Owner,Standalone\n2911,302,NJ City,E,1,11,11,73,High School,27,Owner,Standalone\n7033,439,Boston,N,3,12,24,61,Graduation,22,Patnership,Standalone\n970,40.56,NJ City,N,18,13,1,36,High School,20,Patnership,Complex\n9960,76.95,NJ City,W,19,12,1,31,Graduation,26,Franchise,Complex\n853,177,NJ City,N,1,9,24,67,High School,34,Patnership,Standalone\n8236,414,NJ City,W,2,12,12,63,Graduation,34,Franchise,Complex\n4364,148,NJ City,W,3,23,9,70,High School,20,Franchise,Complex\n1641,21.5,NJ City,N,14,8,3,81,Graduation,22,Patnership,Standalone\n5495,112.96,Boston,E,7,15,3,54,No education,20,Owner,Standalone\n8039,472,Boston,E,3,15,18,57,Graduation,33,Owner,Standalone\n3856,261,NJ City,W,3,22,20,26,High School,26,Franchise,Complex\n1830,264.49,NJ City,S,11,10,4,40,Graduation,24,Patnership,Complex\n5740,488,Boston,W,1,8,15,59,Graduation,19,Franchise,Complex\n5793,414,Boston,N,1,17,14,54,Graduation,25,Patnership,Standalone\n1350,128,NJ City,S,1,9,9,38,High School,20,Patnership,Complex\n4850,136,NJ City,S,1,14,20,71,High School,21,Patnership,Complex\n8283,407,NJ City,E,1,22,24,35,Graduation,21,Owner,Standalone\n7548,497.46,Boston,W,3,20,4,79,Graduation,21,Franchise,Complex\n3600,280,NJ City,W,2,14,25,28,High School,23,Franchise,Complex\n6168,338.3,Boston,W,16,10,2,71,Graduation,33,Franchise,Complex\n5860,89.47,Boston,W,15,8,4,39,High School,31,Franchise,Complex\n8027,200.7,Boston,E,22,15,2,74,No education,33,Owner,Standalone\n3614,230,NJ City,S,2,19,15,85,High School,24,Patnership,Complex\n4341,165,NJ City,N,1,17,22,36,High School,29,Patnership,Standalone\n6827,34.4,Boston,N,10,12,4,49,No education,28,Owner,Standalone\n4540,197,NJ City,W,3,17,24,54,High School,29,Franchise,Complex\n1529,135,NJ City,N,1,9,22,57,High School,24,Patnership,Standalone\n3049,158,NJ City,N,3,19,15,72,High School,34,Patnership,Standalone\n8401,489,NJ City,N,2,18,6,67,Graduation,19,Patnership,Standalone\n4705,213,NJ City,N,1,8,22,80,High School,19,Patnership,Standalone\n7234,433,Boston,S,2,20,24,27,Graduation,18,Patnership,Complex\n2232,143,NJ City,W,3,14,20,51,High School,27,Franchise,Complex\n963,93.7,NJ City,E,20,13,2,69,Graduation,25,Owner,Standalone\n2980,228,NJ City,W,1,19,20,35,High School,29,Franchise,Complex\n9658,230.37,NJ City,S,2,22,4,44,High School,25,Patnership,Complex\n3007,52.42,NJ City,E,24,19,3,53,High School,19,Owner,Standalone\n7409,63.06,Boston,N,21,11,1,26,No education,21,Patnership,Standalone\n2657,238,NJ City,N,2,13,16,60,High School,21,Patnership,Standalone\n1771,268,NJ City,E,2,19,25,33,High School,31,Owner,Standalone\n22,270,NJ City,S,3,20,8,80,High School,23,Patnership,Complex\n3476,261,NJ City,W,1,24,7,67,High School,20,Franchise,Complex\n5857,499,Boston,N,2,22,14,30,Graduation,19,Patnership,Standalone\n9858,443,NJ City,S,2,15,8,79,Graduation,34,Patnership,Complex\n2607,115,NJ City,E,3,13,16,81,High School,24,Owner,Standalone\n9342,89.99,NJ City,S,22,11,4,69,Graduation,26,Patnership,Complex\n2965,348.12,NJ City,N,25,8,2,46,High School,28,Patnership,Standalone\n5756,275.21,Boston,W,1,8,4,25,No education,19,Franchise,Complex\n3653,145,NJ City,N,1,8,20,29,High School,27,Patnership,Standalone\n4358,311,NJ City,S,3,13,22,57,High School,34,Patnership,Complex\n487,136.23,NJ City,E,20,18,3,55,High School,20,Owner,Standalone\n5140,107.15,Boston,W,3,11,4,42,High School,29,Franchise,Complex\n9120,436,NJ City,W,3,20,24,46,Graduation,22,Franchise,Complex\n1505,182,NJ City,N,1,20,13,46,High School,28,Patnership,Standalone\n8048,353,NJ City,W,1,12,12,42,Graduation,34,Franchise,Complex\n4743,486,NJ City,E,1,12,5,70,Graduation,34,Owner,Standalone\n511,49.06,NJ City,E,24,15,3,44,High School,30,Owner,Standalone\n6028,228.43,Boston,W,21,20,2,80,High School,31,Franchise,Complex\n1164,36.09,NJ City,N,2,8,4,31,Graduation,29,Franchise,Complex\n9966,387,NJ City,S,3,15,23,54,Graduation,23,Patnership,Complex\n4650,326,NJ City,S,14,17,4,74,Graduation,30,Patnership,Complex\n5473,319.69,Boston,N,24,13,4,52,High School,18,Patnership,Standalone\n9010,123.47,NJ City,S,1,9,1,81,High School,18,Patnership,Complex\n4590,82.45,NJ City,S,25,23,2,69,Graduation,22,Patnership,Complex\n6388,113.32,Boston,W,9,17,2,56,High School,35,Franchise,Complex\n9674,25.33,NJ City,N,7,20,4,83,No education,29,Patnership,Complex\n6987,401,Boston,E,3,17,13,41,Graduation,34,Owner,Standalone\n1190,275,NJ City,S,3,8,16,70,High School,34,Patnership,Complex\n8488,488,NJ City,W,1,10,22,33,Graduation,21,Franchise,Complex\n3721,277,NJ City,N,3,12,9,48,High School,29,Patnership,Standalone\n4271,235,NJ City,E,3,13,17,33,High School,32,Owner,Standalone\n2328,6.62,NJ City,N,9,13,3,53,Graduation,20,Franchise,Complex\n7213,84.56,Boston,N,18,15,3,73,High School,33,Patnership,Standalone\n6658,500,Boston,S,3,22,18,82,Graduation,18,Patnership,Complex\n2511,311,NJ City,E,1,15,19,25,High School,29,Owner,Standalone\n1298,263,NJ City,S,1,17,11,67,High School,34,Patnership,Complex\n1056,250,NJ City,W,2,16,9,56,High School,34,Franchise,Complex\n189,105,NJ City,N,3,11,6,34,High School,19,Patnership,Standalone\n6349,373,Boston,N,2,18,22,35,Graduation,24,Patnership,Standalone\n7317,421,Boston,N,1,18,14,49,Graduation,22,Patnership,Standalone\n1052,252,NJ City,W,2,16,6,32,High School,35,Franchise,Complex\n623,156,NJ City,E,1,18,22,44,High School,18,Owner,Standalone\n1898,132,NJ City,S,3,20,19,84,High School,31,Patnership,Complex\n7501,423,Boston,N,3,10,15,75,Graduation,34,Patnership,Standalone\n3085,50.4,NJ City,N,12,24,1,36,High School,30,Patnership,Standalone\n8109,364,NJ City,N,2,23,25,38,Graduation,35,Patnership,Standalone\n7783,81.95,Boston,E,24,21,1,85,High School,32,Owner,Standalone\n5993,93.95,Boston,N,15,17,3,77,No education,35,Patnership,Standalone\n233,182,NJ City,N,3,16,10,48,High School,35,Patnership,Standalone\n757,389.55,NJ City,N,1,16,3,77,High School,28,Patnership,Standalone\n9465,497.92,NJ City,N,5,15,1,83,Graduation,35,Patnership,Standalone\n1165,303,NJ City,N,2,19,19,51,High School,25,Patnership,Standalone\n2580,2.99,NJ City,N,3,10,2,69,Graduation,26,Franchise,Complex\n7639,92.77,Boston,E,22,16,1,52,High School,18,Owner,Standalone\n4105,139,NJ City,N,3,15,15,26,High School,31,Patnership,Standalone\n8893,363,NJ City,N,2,11,15,59,Graduation,30,Patnership,Standalone\n7457,435,Boston,N,2,24,6,62,Graduation,31,Patnership,Standalone\n8415,497,NJ City,E,3,8,21,32,Graduation,18,Owner,Standalone\n4974,142.32,Boston,S,1,13,1,53,Graduation,19,Patnership,Complex\n6205,444,Boston,N,3,11,6,31,Graduation,20,Patnership,Standalone\n3690,0.52,NJ City,N,14,11,4,83,Graduation,19,Patnership,Complex\n2848,154,NJ City,W,1,23,19,28,High School,33,Franchise,Complex\n3355,295,NJ City,E,3,16,9,79,High School,27,Owner,Standalone\n7920,84.93,Boston,W,16,21,4,35,Graduation,32,Franchise,Complex\n8748,383,NJ City,W,1,16,5,37,Graduation,23,Franchise,Complex\n3310,316,NJ City,S,3,9,12,33,High School,35,Patnership,Complex\n5257,496,Boston,N,3,15,24,43,Graduation,21,Patnership,Standalone\n6800,383.18,Boston,W,13,8,3,53,No education,19,Franchise,Complex\n9556,462,NJ City,W,3,22,20,57,Graduation,31,Franchise,Complex\n1896,164,NJ City,W,1,17,24,43,High School,30,Franchise,Complex\n7327,113.94,Boston,E,18,13,3,26,High School,18,Owner,Standalone\n88,169,Boston,W,3,23,15,38,High School,33,Franchise,Complex\n2637,14.07,NJ City,N,22,10,2,49,Graduation,32,Patnership,Standalone\n3263,274,NJ City,E,2,8,6,30,High School,23,Owner,Standalone\n4948,78.43,Boston,W,19,20,4,79,High School,28,Franchise,Complex\n6928,499,Boston,W,1,8,24,75,Graduation,18,Franchise,Complex\n7836,453,Boston,W,1,8,5,25,Graduation,27,Franchise,Complex\n7935,429,Boston,E,3,16,20,48,Graduation,20,Owner,Standalone\n2778,17.7,NJ City,N,7,9,1,66,Graduation,19,Patnership,Complex\n9875,103.76,NJ City,E,9,10,1,74,No education,20,Owner,Standalone\n4398,59.73,NJ City,S,9,13,3,43,Graduation,20,Patnership,Complex\n6036,3.84,Boston,N,21,23,4,30,Graduation,23,Franchise,Complex\n9645,11.1,NJ City,N,13,16,1,38,Graduation,31,Patnership,Standalone\n7415,489,Boston,E,3,22,8,50,Graduation,21,Owner,Standalone\n4832,213,NJ City,W,2,12,7,39,High School,20,Franchise,Complex\n843,247,NJ City,E,2,18,13,42,High School,29,Owner,Standalone\n6664,360,Boston,W,1,18,20,56,Graduation,20,Franchise,Complex\n8566,377.11,NJ City,S,10,8,2,27,High School,33,Patnership,Complex\n6422,370,Boston,S,3,17,24,58,Graduation,31,Patnership,Complex\n86,205,Boston,S,2,10,19,79,High School,33,Patnership,Complex\n6217,461,Boston,N,3,20,14,59,Graduation,28,Patnership,Standalone\n7528,479,Boston,W,3,8,23,46,Graduation,33,Franchise,Complex\n8719,494,NJ City,E,1,22,6,77,Graduation,33,Owner,Standalone\n8469,226.39,NJ City,N,8,13,1,78,Graduation,23,Patnership,Standalone\n1438,6.67,NJ City,N,2,16,3,45,High School,24,Patnership,Complex\n8646,464,NJ City,S,2,16,17,44,Graduation,20,Patnership,Complex\n4570,187,NJ City,S,1,17,8,40,High School,35,Patnership,Complex\n2922,150,NJ City,S,2,9,12,78,High School,34,Patnership,Complex\n444,262,NJ City,W,2,14,25,82,High School,24,Franchise,Complex\n9015,265.59,NJ City,E,17,18,3,84,Graduation,34,Owner,Standalone\n5494,476,Boston,S,2,23,17,51,Graduation,35,Patnership,Complex\n2858,186,NJ City,S,1,19,10,56,High School,29,Patnership,Complex\n6165,22.51,Boston,N,21,22,4,42,Graduation,27,Patnership,Standalone\n5671,321.86,Boston,E,9,17,4,28,High School,33,Owner,Standalone\n7976,88.06,Boston,W,19,13,1,33,No education,32,Franchise,Complex\n5739,350,Boston,E,3,10,16,66,Graduation,19,Owner,Standalone\n2655,292,NJ City,E,3,15,12,45,High School,25,Owner,Standalone\n5307,22.88,Boston,N,25,10,3,48,Graduation,18,Owner,Standalone\n214,423,NJ City,S,3,22,5,25,Graduation,31,Patnership,Complex\n67,132,NJ City,E,1,20,19,63,High School,29,Owner,Standalone\n8647,38.94,NJ City,N,13,18,3,67,High School,21,Owner,Standalone\n8786,321.08,NJ City,S,21,8,2,81,No education,20,Patnership,Complex\n8890,418,NJ City,S,3,19,16,42,Graduation,27,Patnership,Complex\n7570,477,Boston,S,1,18,25,28,Graduation,32,Patnership,Complex\n3038,318,NJ City,S,2,21,23,31,High School,24,Patnership,Complex\n4085,112,NJ City,N,2,8,12,29,High School,26,Patnership,Standalone\n5984,82.51,Boston,W,22,11,3,42,No education,24,Franchise,Complex\n8624,485,NJ City,W,2,9,12,26,Graduation,32,Franchise,Complex\n5092,43.21,Boston,N,22,13,3,25,High School,25,Franchise,Complex\n3613,494.83,NJ City,N,4,11,2,41,High School,20,Patnership,Standalone\n2970,219,NJ City,S,3,22,17,37,High School,27,Patnership,Complex\n1648,7.18,NJ City,N,1,18,3,43,High School,35,Franchise,Complex\n158,188,NJ City,S,3,24,17,26,High School,35,Patnership,Complex\n4236,216,NJ City,W,2,21,9,74,High School,21,Franchise,Complex\n2656,80.92,NJ City,W,21,12,2,59,High School,32,Franchise,Complex\n8975,9.66,NJ City,N,6,19,1,62,No education,32,Owner,Standalone\n8119,472,NJ City,E,1,20,5,54,Graduation,20,Owner,Standalone\n457,10.78,NJ City,N,4,18,2,40,High School,26,Patnership,Standalone\n1090,173,NJ City,S,3,24,6,75,High School,26,Patnership,Complex\n5370,458,Boston,S,2,24,10,27,Graduation,33,Patnership,Complex\n5631,476,Boston,E,3,18,23,63,Graduation,28,Owner,Standalone\n4872,194,NJ City,W,2,10,22,82,High School,34,Franchise,Complex\n7793,16.82,Boston,N,24,17,3,28,No education,30,Patnership,Standalone\n4130,166,NJ City,S,1,9,8,73,High School,35,Patnership,Complex\n3158,293,NJ City,S,1,19,23,43,High School,35,Patnership,Complex\n3531,274,NJ City,E,3,15,7,81,High School,25,Owner,Standalone\n3794,408.87,NJ City,S,1,10,1,66,No education,20,Patnership,Complex\n8137,423,NJ City,N,1,13,19,59,Graduation,29,Patnership,Standalone\n4835,338.28,NJ City,E,15,22,4,51,No education,30,Owner,Standalone\n1137,103,NJ City,N,2,22,9,57,High School,23,Patnership,Standalone\n85,157,Boston,N,2,18,19,56,High School,22,Patnership,Standalone\n5785,23.05,Boston,N,4,18,3,29,High School,23,Patnership,Standalone\n3738,403.17,NJ City,S,5,9,4,63,Graduation,27,Patnership,Complex\n3129,27.59,NJ City,N,12,23,4,75,Graduation,31,Patnership,Standalone\n2304,269,NJ City,W,3,21,7,55,High School,28,Franchise,Complex\n7953,289.83,Boston,N,1,17,3,81,Graduation,26,Patnership,Standalone\n676,151,NJ City,W,2,14,17,67,High School,22,Franchise,Complex\n5426,433,Boston,S,3,23,8,39,Graduation,27,Patnership,Complex\n4011,106.89,NJ City,E,17,15,1,49,Graduation,18,Owner,Standalone\n8338,396,NJ City,S,2,12,7,66,Graduation,31,Patnership,Complex\n5849,154.13,Boston,N,9,8,4,33,No education,35,Patnership,Standalone\n3896,107,NJ City,W,3,19,17,66,High School,29,Franchise,Complex\n549,7.83,NJ City,N,22,9,2,61,Graduation,26,Patnership,Standalone\n3545,28.33,NJ City,N,10,19,1,34,No education,28,Patnership,Standalone\n9349,40.49,NJ City,N,5,22,4,47,High School,18,Patnership,Standalone\n1629,84.46,NJ City,N,8,20,3,75,Graduation,21,Patnership,Standalone\n7826,365,Boston,S,3,18,12,56,Graduation,34,Patnership,Complex\n3751,497,NJ City,E,3,18,5,47,Graduation,19,Owner,Standalone\n3163,130.21,NJ City,E,7,19,2,55,High School,25,Owner,Standalone\n4927,190,NJ City,E,3,22,12,66,High School,21,Owner,Standalone\n3683,130,NJ City,E,3,11,6,25,High School,35,Owner,Standalone\n1125,117.97,NJ City,N,2,17,3,28,Graduation,34,Patnership,Standalone\n4587,169,NJ City,E,3,20,21,65,High School,30,Owner,Standalone\n4740,281,NJ City,W,3,13,13,67,High School,23,Franchise,Complex\n4518,325.85,NJ City,S,2,8,3,80,Graduation,33,Patnership,Complex\n5382,374,Boston,S,3,8,14,28,Graduation,27,Patnership,Complex\n2578,293,NJ City,S,3,18,17,67,High School,20,Patnership,Complex\n9175,107.75,NJ City,E,17,24,3,56,High School,23,Owner,Standalone\n9340,125.61,NJ City,W,19,21,3,28,High School,31,Franchise,Complex\n4774,278,NJ City,S,1,9,19,77,High School,28,Patnership,Complex\n9627,264.21,NJ City,E,2,12,3,42,Graduation,24,Owner,Standalone\n9579,15.56,NJ City,N,16,17,1,39,Graduation,26,Owner,Standalone\n5569,498.03,Boston,N,23,12,1,59,High School,25,Patnership,Standalone\n6510,85.6,Boston,S,9,13,2,44,Graduation,34,Patnership,Complex\n1460,16.45,NJ City,N,11,17,4,41,No education,20,Franchise,Complex\n9186,62.28,NJ City,N,7,9,3,60,Graduation,22,Patnership,Complex\n9291,421,NJ City,E,2,12,22,27,Graduation,28,Owner,Standalone\n4044,5.62,NJ City,N,4,9,1,84,Graduation,21,Franchise,Complex\n1017,137,NJ City,N,3,8,14,63,High School,23,Patnership,Standalone\n285,123,NJ City,N,2,10,22,50,High School,19,Patnership,Standalone\n7263,103.68,Boston,E,4,15,1,36,Graduation,24,Owner,Standalone\n3165,450,NJ City,N,2,23,5,61,Graduation,20,Patnership,Standalone\n2807,146,NJ City,E,1,24,21,74,High School,18,Owner,Standalone\n7106,43.31,Boston,N,17,22,4,63,No education,19,Patnership,Complex\n7485,81.21,Boston,N,6,12,2,44,Graduation,18,Patnership,Standalone\n6670,360,Boston,S,2,23,10,68,Graduation,23,Patnership,Complex\n9547,476,NJ City,E,3,11,25,63,Graduation,27,Owner,Standalone\n9823,479,NJ City,E,2,12,7,40,Graduation,29,Owner,Standalone\n6632,115.53,Boston,W,21,24,2,30,No education,29,Franchise,Complex\n5598,384,Boston,S,1,21,9,84,Graduation,28,Patnership,Complex\n6483,42.54,Boston,N,9,17,3,55,Graduation,31,Owner,Standalone\n3677,233,NJ City,N,2,23,18,43,High School,19,Patnership,Standalone\n2187,222,NJ City,E,3,17,19,51,High School,19,Owner,Standalone\n5227,482,Boston,E,3,17,18,26,Graduation,29,Owner,Standalone\n2897,1.1,NJ City,W,18,15,3,49,No education,33,Patnership,Standalone\n4067,62.16,NJ City,N,21,16,3,39,No education,33,Owner,Standalone\n359,85.73,NJ City,E,6,15,2,66,No education,33,Owner,Standalone\n8126,415,NJ City,S,1,16,21,27,Graduation,28,Patnership,Complex\n7558,476,Boston,S,3,18,13,43,Graduation,19,Patnership,Complex\n6918,488,Boston,S,3,12,17,25,Graduation,32,Patnership,Complex\n3257,190,NJ City,N,3,24,13,49,High School,21,Patnership,Standalone\n3021,434.46,NJ City,N,16,17,4,25,Graduation,29,Patnership,Standalone\n8288,377,NJ City,W,1,9,9,60,Graduation,19,Franchise,Complex\n6017,357,Boston,N,2,23,6,85,Graduation,19,Patnership,Standalone\n4191,395,NJ City,E,2,9,5,61,Graduation,21,Owner,Standalone\n2209,244,NJ City,N,2,23,23,82,High School,28,Patnership,Standalone\n7143,355.1,Boston,E,10,15,4,81,Graduation,31,Owner,Standalone\n20,159.61,NY City,W,12,22,1,84,No education,34,Franchise,Complex\n1779,165,NJ City,E,2,10,10,32,High School,35,Owner,Standalone\n1914,114,NJ City,S,2,20,20,58,High School,27,Patnership,Complex\n9309,385.03,NJ City,N,19,23,3,79,Graduation,35,Patnership,Standalone\n8557,452,NJ City,N,2,14,12,34,Graduation,31,Patnership,Standalone\n364,39.48,NJ City,N,5,15,2,79,High School,34,Franchise,Complex\n7404,364,Boston,W,2,16,5,68,Graduation,25,Franchise,Complex\n2573,78.8,NJ City,N,24,23,2,37,No education,29,Patnership,Standalone\n6126,352,Boston,S,2,20,15,84,Graduation,34,Patnership,Complex\n8078,262.61,NJ City,S,1,13,3,46,No education,34,Patnership,Complex\n8072,429,NJ City,W,3,24,7,54,Graduation,24,Franchise,Complex\n7028,472,Boston,W,3,18,8,41,Graduation,29,Franchise,Complex\n9436,428.44,NJ City,W,15,12,2,44,High School,31,Franchise,Complex\n3748,34.58,NJ City,N,12,19,4,68,High School,27,Franchise,Complex\n9834,429,NJ City,S,2,21,25,63,Graduation,26,Patnership,Complex\n3885,100,NJ City,N,3,13,14,41,High School,21,Patnership,Standalone\n1978,146,NJ City,S,1,8,17,65,High School,23,Patnership,Complex\n4618,53.57,NJ City,S,22,19,4,40,High School,21,Patnership,Complex\n5396,383,Boston,W,3,20,17,34,Graduation,30,Franchise,Complex\n7484,355,Boston,W,1,21,9,36,Graduation,20,Franchise,Complex\n1768,30.57,NJ City,N,22,14,2,53,High School,26,Franchise,Complex\n2406,258,NJ City,S,2,15,24,25,High School,24,Patnership,Complex\n5808,399,Boston,W,2,11,21,44,Graduation,22,Franchise,Complex\n7513,357,Boston,N,2,24,17,59,Graduation,28,Patnership,Standalone\n4506,278.48,NJ City,S,17,18,2,58,Graduation,19,Patnership,Complex\n8559,372,NJ City,E,1,24,20,80,Graduation,21,Owner,Standalone\n8793,440,NJ City,N,2,12,25,77,Graduation,27,Patnership,Standalone\n3194,8.55,NJ City,N,4,23,1,33,No education,24,Patnership,Complex\n9170,440.21,NJ City,S,1,9,2,49,No education,28,Patnership,Complex\n8187,443,NJ City,E,3,15,12,44,Graduation,19,Owner,Standalone\n7960,102.2,Boston,W,6,11,3,79,High School,20,Franchise,Complex\n5042,357,Boston,S,1,13,18,39,Graduation,21,Patnership,Complex\n8294,458,NJ City,S,3,9,14,60,Graduation,31,Patnership,Complex\n5562,351,Boston,S,2,15,23,52,Graduation,21,Patnership,Complex\n1209,10.92,NJ City,N,12,20,2,78,Graduation,29,Patnership,Standalone\n8750,489,NJ City,S,2,19,22,38,Graduation,28,Patnership,Complex\n5650,449,Boston,S,1,21,14,31,Graduation,18,Patnership,Complex\n2124,208,NJ City,W,1,24,16,71,High School,20,Franchise,Complex\n8591,13.37,NJ City,N,12,20,2,37,No education,18,Owner,Standalone\n6288,430,Boston,W,3,17,9,39,Graduation,27,Franchise,Complex\n205,115,NJ City,N,3,20,7,40,High School,31,Patnership,Standalone\n7346,368,Boston,S,2,12,6,66,Graduation,24,Patnership,Complex\n5686,392,Boston,S,1,18,24,54,Graduation,21,Patnership,Complex\n4198,2.71,NJ City,S,8,12,1,53,High School,27,Patnership,Complex\n5331,117.9,Boston,E,11,13,3,80,Graduation,23,Owner,Standalone\n1405,5.33,NJ City,N,11,20,3,25,High School,26,Patnership,Standalone\n2413,143,NJ City,N,2,8,11,42,High School,25,Patnership,Standalone\n8700,46.69,NJ City,N,9,19,1,26,Graduation,18,Franchise,Complex\n3968,288,NJ City,W,1,22,11,30,High School,29,Franchise,Complex\n976,120,NJ City,W,2,19,9,51,High School,30,Franchise,Complex\n6815,87.33,Boston,E,12,15,4,85,No education,24,Owner,Standalone\n6332,384,Boston,W,1,15,17,42,Graduation,19,Franchise,Complex\n5634,481,Boston,S,2,24,6,53,Graduation,26,Patnership,Complex\n2536,113.42,NJ City,W,1,9,2,60,High School,32,Franchise,Complex\n5654,368,Boston,S,2,9,7,73,Graduation,24,Patnership,Complex\n7517,436,Boston,N,1,9,22,41,Graduation,25,Patnership,Standalone\n7872,490,Boston,W,1,18,14,54,Graduation,26,Franchise,Complex\n2492,244,NJ City,W,3,18,15,83,High School,29,Franchise,Complex\n9355,383,NJ City,E,2,9,6,48,Graduation,35,Owner,Standalone\n9967,377,NJ City,E,3,9,21,75,Graduation,25,Owner,Standalone\n4177,42.12,NJ City,N,25,24,4,51,High School,23,Patnership,Standalone\n3580,156,NJ City,W,2,11,11,27,High School,28,Franchise,Complex\n687,137,NJ City,E,1,22,14,77,High School,26,Owner,Standalone\n2823,238,NJ City,E,1,21,25,40,High School,21,Owner,Standalone\n358,112,NJ City,S,3,9,11,52,High School,18,Patnership,Complex\n4761,21.52,NJ City,N,19,17,1,46,Graduation,25,Patnership,Standalone\n3825,173,NJ City,N,2,15,24,72,High School,25,Patnership,Standalone\n6737,474.49,Boston,N,16,24,1,84,No education,27,Patnership,Standalone\n6417,359,Boston,N,3,18,25,29,Graduation,31,Patnership,Standalone\n4483,292,NJ City,E,3,16,12,83,High School,23,Owner,Standalone\n4909,158,NJ City,N,1,19,23,77,High School,26,Patnership,Standalone\n128,354.2,NJ City,W,24,21,2,74,No education,20,Franchise,Complex\n5334,440,Boston,S,1,19,7,51,Graduation,27,Patnership,Complex\n7667,354,Boston,E,3,21,18,67,Graduation,22,Owner,Standalone\n7746,406,Boston,S,2,17,13,47,Graduation,23,Patnership,Complex\n7866,465,Boston,S,2,12,11,67,Graduation,22,Patnership,Complex\n6542,71.68,Boston,N,19,15,3,39,No education,21,Patnership,Complex\n4602,43.09,NJ City,N,13,22,1,73,Graduation,19,Patnership,Complex\n2185,75.91,NJ City,N,25,11,2,51,High School,26,Patnership,Standalone\n190,262,NJ City,S,1,16,13,27,High School,24,Patnership,Complex\n8733,90.31,NJ City,N,2,16,4,54,Graduation,20,Patnership,Standalone\n818,285.31,NJ City,S,23,15,2,58,No education,35,Patnership,Complex\n1585,154,NJ City,N,3,12,7,34,High School,28,Patnership,Standalone\n3288,106.63,NJ City,W,22,14,1,42,Graduation,20,Franchise,Complex\n1104,107,NJ City,W,1,12,20,45,High School,18,Franchise,Complex\n9666,122.48,NJ City,S,25,20,2,62,Graduation,33,Patnership,Complex\n4751,267,NJ City,E,2,23,19,40,High School,18,Owner,Standalone\n6463,402,Boston,E,2,19,21,44,Graduation,20,Owner,Standalone\n2302,295,NJ City,S,3,15,11,59,High School,22,Patnership,Complex\n5865,435,Boston,N,2,15,13,74,Graduation,34,Patnership,Standalone\n8262,413,NJ City,S,3,19,16,83,Graduation,23,Patnership,Complex\n1134,449.74,NJ City,S,16,15,2,47,Graduation,21,Patnership,Complex\n4667,115,NJ City,E,3,18,20,25,High School,32,Owner,Standalone\n384,50.19,NJ City,W,12,15,3,54,Graduation,23,Franchise,Complex\n8098,437,NJ City,S,2,22,23,38,Graduation,35,Patnership,Complex\n9497,57.78,NJ City,N,18,22,4,51,No education,28,Patnership,Standalone\n4604,36.75,NJ City,N,6,12,1,76,No education,27,Franchise,Complex\n762,295.1,NJ City,S,15,21,1,26,Graduation,30,Patnership,Complex\n1893,176,NJ City,N,1,12,7,27,High School,23,Patnership,Standalone\n2244,197,NJ City,W,2,22,20,59,High School,21,Franchise,Complex\n3916,53.69,NJ City,W,6,18,2,65,High School,18,Franchise,Complex\n9730,495.28,NJ City,S,22,23,4,35,High School,29,Patnership,Complex\n3077,195,NJ City,N,1,21,12,61,High School,24,Patnership,Standalone\n4159,166,NJ City,E,2,18,12,35,High School,22,Owner,Standalone\n2202,258,NJ City,S,1,17,23,55,High School,34,Patnership,Complex\n8504,19.8,NJ City,N,4,23,3,60,No education,19,Franchise,Complex\n8349,421,NJ City,N,3,15,19,63,Graduation,22,Patnership,Standalone\n3551,310,NJ City,E,1,22,13,59,High School,23,Owner,Standalone\n804,199,NJ City,W,3,21,6,49,High School,21,Franchise,Complex\n930,214,NJ City,S,1,8,21,45,High School,32,Patnership,Complex\n5310,406,Boston,S,3,10,23,38,Graduation,19,Patnership,Complex\n9078,380,NJ City,S,2,20,8,51,Graduation,28,Patnership,Complex\n5270,435,Boston,S,1,21,21,41,Graduation,26,Patnership,Complex\n6813,84.8,Boston,N,14,13,1,62,Graduation,31,Patnership,Standalone\n4403,276,NJ City,E,3,17,20,26,High School,26,Owner,Standalone\n6118,405,Boston,S,3,10,14,29,Graduation,22,Patnership,Complex\n9840,463,NJ City,W,1,20,18,28,Graduation,23,Franchise,Complex\n900,38.64,NJ City,N,7,24,3,26,Graduation,25,Franchise,Complex\n8895,429,NJ City,E,1,22,9,60,Graduation,32,Owner,Standalone\n2419,488.54,NJ City,E,21,23,4,66,High School,26,Owner,Standalone\n8275,475,NJ City,E,3,18,14,35,Graduation,21,Owner,Standalone\n2547,290,NJ City,E,2,22,18,49,High School,21,Owner,Standalone\n274,124.82,NJ City,S,21,20,3,34,High School,20,Patnership,Complex\n99,115,NJ City,E,1,8,14,28,High School,30,Owner,Standalone\n3105,112,NJ City,N,2,14,15,80,High School,26,Patnership,Standalone\n9857,365,NJ City,N,3,23,23,63,Graduation,32,Patnership,Standalone\n1094,20.49,NJ City,N,16,17,3,36,No education,34,Patnership,Complex\n7686,406,Boston,S,1,24,10,39,Graduation,34,Patnership,Complex\n9693,122.08,NJ City,N,25,9,3,47,Graduation,35,Patnership,Standalone\n5174,70.54,Boston,N,19,11,3,57,No education,30,Patnership,Complex\n8509,473,NJ City,N,1,23,6,67,Graduation,19,Patnership,Standalone\n6469,27.71,Boston,N,2,21,1,78,High School,25,Patnership,Standalone\n5657,89.54,Boston,N,18,10,1,62,No education,32,Patnership,Standalone\n345,280,NJ City,N,3,20,12,45,High School,31,Patnership,Standalone\n4012,241,NJ City,W,3,24,20,73,High School,34,Franchise,Complex\n6783,367,Boston,E,2,8,5,67,Graduation,21,Owner,Standalone\n5362,471,Boston,S,2,10,15,81,Graduation,31,Patnership,Complex\n336,223,NJ City,W,3,13,10,76,High School,26,Franchise,Complex\n8747,496,NJ City,E,1,21,9,85,Graduation,30,Owner,Standalone\n6706,434,Boston,S,2,15,24,72,Graduation,21,Patnership,Complex\n7721,396,Boston,N,2,22,17,28,Graduation,19,Patnership,Standalone\n2989,89.42,NJ City,N,10,11,1,52,High School,25,Patnership,Standalone\n3945,304,NJ City,N,1,19,11,65,High School,23,Patnership,Standalone\n7534,494,Boston,S,1,15,9,49,Graduation,19,Patnership,Complex\n7612,405,Boston,W,3,13,5,56,Graduation,32,Franchise,Complex\n9562,387,NJ City,S,3,16,16,67,Graduation,30,Patnership,Complex\n629,64.99,NJ City,N,5,22,1,70,No education,23,Patnership,Standalone\n5729,447,Boston,N,2,17,7,36,Graduation,21,Patnership,Standalone\n5745,379,Boston,N,2,22,21,60,Graduation,31,Patnership,Standalone\n5672,477,Boston,W,2,9,16,50,Graduation,22,Franchise,Complex\n7589,369,Boston,N,2,23,11,38,Graduation,25,Patnership,Standalone\n7211,492,Boston,E,2,23,14,74,Graduation,28,Owner,Standalone\n7698,499,Boston,S,1,13,12,49,Graduation,32,Patnership,Complex\n5237,451,Boston,N,3,16,14,78,Graduation,35,Patnership,Standalone\n9449,62.58,NJ City,N,7,20,3,32,No education,22,Patnership,Standalone\n9994,461,NJ City,S,2,18,25,73,Graduation,30,Patnership,Complex\n9,166,Maine,N,3,21,24,73,High School,27,Patnership,Standalone\n4468,90.77,NJ City,W,4,12,2,60,High School,21,Franchise,Complex\n392,220,NJ City,W,1,24,22,33,High School,18,Franchise,Complex\n8304,184.78,NJ City,W,6,24,4,42,Graduation,25,Franchise,Complex\n8815,57.86,NJ City,E,1,19,2,48,High School,32,Owner,Standalone\n7813,253.67,Boston,N,22,20,3,76,High School,32,Patnership,Standalone\n9238,354,NJ City,S,3,23,23,39,Graduation,26,Patnership,Complex\n7919,56.64,Boston,E,4,19,3,49,No education,33,Owner,Standalone\n6423,402,Boston,E,3,24,10,27,Graduation,35,Owner,Standalone\n7907,123.36,Boston,E,9,8,4,53,No education,18,Owner,Standalone\n9721,354,NJ City,N,3,9,20,35,Graduation,27,Patnership,Standalone\n7095,440,Boston,E,3,15,21,66,Graduation,27,Owner,Standalone\n4255,0.22,NJ City,N,20,13,3,46,High School,29,Owner,Standalone\n188,426,NJ City,W,1,12,5,52,Graduation,27,Franchise,Complex\n1866,451.84,NJ City,S,2,8,1,25,Graduation,24,Patnership,Complex\n1162,19.46,NJ City,N,14,10,2,53,High School,35,Patnership,Complex\n8832,78.53,NJ City,W,20,24,2,65,Graduation,26,Franchise,Complex\n4623,210,NJ City,E,3,9,21,62,High School,22,Owner,Standalone\n5198,380,Boston,S,3,23,19,38,Graduation,28,Patnership,Complex\n4638,47.28,NJ City,N,18,8,1,44,Graduation,28,Patnership,Complex\n4591,213,NJ City,E,2,23,24,28,High School,33,Owner,Standalone\n4601,289,NJ City,N,2,19,6,59,High School,18,Patnership,Standalone\n3388,3.47,NJ City,N,21,13,4,36,High School,23,Franchise,Complex\n9919,374,NJ City,E,3,10,6,32,Graduation,32,Owner,Standalone\n704,284,NJ City,W,2,24,18,34,High School,35,Franchise,Complex\n2376,145,NJ City,W,1,21,12,52,High School,26,Franchise,Complex\n5271,362,Boston,E,2,14,15,44,Graduation,19,Owner,Standalone\n8961,440,NJ City,N,3,9,18,81,Graduation,19,Patnership,Standalone\n4129,156,NJ City,N,2,11,23,67,High School,22,Patnership,Standalone\n7840,104.82,Boston,W,9,23,2,72,High School,33,Franchise,Complex\n2017,260,NJ City,N,3,17,6,79,High School,26,Patnership,Standalone\n3446,237,NJ City,S,2,8,7,39,High School,28,Patnership,Complex\n4227,281,NJ City,E,2,11,15,72,High School,24,Owner,Standalone\n9699,359,NJ City,E,3,17,11,30,Graduation,20,Owner,Standalone\n9930,447,NJ City,S,1,19,20,28,Graduation,31,Patnership,Complex\n2928,120,NJ City,W,3,10,19,66,High School,28,Franchise,Complex\n124,45.1,NJ City,N,24,16,3,45,High School,27,Franchise,Complex\n27,249,Boston,E,2,10,10,84,High School,24,Owner,Standalone\n8978,361,NJ City,S,2,10,12,46,Graduation,24,Patnership,Complex\n3902,283.52,NJ City,S,5,19,2,26,No education,21,Patnership,Complex\n7997,395,Boston,N,2,12,13,60,Graduation,22,Patnership,Standalone\n6605,428.53,Boston,N,13,16,4,34,No education,33,Patnership,Standalone\n9926,432,NJ City,S,3,20,8,30,Graduation,18,Patnership,Complex\n4951,13.74,Boston,N,3,16,4,66,High School,34,Owner,Standalone\n8584,52.95,NJ City,W,25,8,3,83,High School,22,Franchise,Complex\n6768,390,Boston,W,3,19,18,80,Graduation,30,Franchise,Complex\n4214,117,NJ City,S,1,17,19,72,High School,28,Patnership,Complex\n7375,426,Boston,E,1,18,7,69,Graduation,22,Owner,Standalone\n8281,66.26,NJ City,N,14,9,3,70,High School,32,Patnership,Standalone\n3478,271,NJ City,S,3,22,9,67,High School,35,Patnership,Complex\n2728,61.25,NJ City,N,15,21,2,72,High School,24,Franchise,Complex\n2854,304,NJ City,S,2,8,15,76,High School,24,Patnership,Complex\n5694,365,Boston,S,3,15,6,37,Graduation,22,Patnership,Complex\n563,168,NJ City,E,2,21,23,68,High School,28,Owner,Standalone\n559,127,NJ City,E,1,19,21,50,High School,29,Owner,Standalone\n360,44.03,NJ City,N,23,19,3,72,Graduation,29,Franchise,Complex\n9501,478,NJ City,N,1,11,21,64,Graduation,22,Patnership,Standalone\n3470,269,NJ City,S,3,8,25,79,High School,22,Patnership,Complex\n1797,427,NJ City,N,2,18,5,53,Graduation,28,Patnership,Standalone\n2180,61.3,NJ City,N,25,12,4,72,No education,35,Franchise,Complex\n653,137,NJ City,N,2,9,13,48,High School,25,Patnership,Standalone\n6908,440.55,Boston,W,18,22,1,65,No education,21,Franchise,Complex\n1071,391.36,NJ City,E,8,14,3,37,Graduation,26,Owner,Standalone\n2190,24.33,NJ City,N,6,22,4,75,Graduation,30,Patnership,Complex\n6034,362,Boston,S,3,19,24,25,Graduation,25,Patnership,Complex\n6674,410,Boston,S,2,12,23,46,Graduation,31,Patnership,Complex\n9022,378,NJ City,S,2,14,14,48,Graduation,22,Patnership,Complex\n7386,410,Boston,S,1,23,8,84,Graduation,23,Patnership,Complex\n7088,469,Boston,W,1,17,13,37,Graduation,30,Franchise,Complex\n312,243,NJ City,W,2,16,13,59,High School,22,Franchise,Complex\n3416,252,NJ City,W,2,8,17,43,High School,28,Franchise,Complex\n3368,276,NJ City,W,1,21,8,80,High School,22,Franchise,Complex\n6627,498,Boston,E,1,10,8,76,Graduation,35,Owner,Standalone\n805,132,NJ City,N,1,16,23,58,High School,20,Patnership,Standalone\n8356,492,NJ City,W,3,15,20,43,Graduation,21,Franchise,Complex\n8694,298.9,NJ City,S,6,13,3,28,Graduation,21,Patnership,Complex\n1958,68.72,NJ City,N,2,17,3,31,No education,35,Patnership,Complex\n198,126,NJ City,S,1,11,25,31,High School,19,Patnership,Complex\n7678,7.57,Boston,N,9,12,3,72,High School,22,Patnership,Complex\n7050,371,Boston,S,1,13,13,67,Graduation,35,Patnership,Complex\n3530,124.12,NJ City,S,11,24,4,69,No education,22,Patnership,Complex\n2993,162,NJ City,N,3,16,11,28,High School,20,Patnership,Standalone\n9689,389,NJ City,N,3,8,24,55,Graduation,30,Patnership,Standalone\n6633,416,Boston,N,3,24,17,74,Graduation,30,Patnership,Standalone\n831,229,NJ City,E,3,8,14,61,High School,22,Owner,Standalone\n9778,493,NJ City,S,3,10,11,81,Graduation,30,Patnership,Complex\n9615,367,NJ City,E,1,13,23,62,Graduation,27,Owner,Standalone\n3823,356.7,NJ City,E,1,8,3,35,High School,29,Owner,Standalone\n8759,118.95,NJ City,E,11,21,2,72,No education,19,Owner,Standalone\n6431,463.72,Boston,E,15,8,1,37,No education,19,Owner,Standalone\n7378,407,Boston,S,2,9,7,49,Graduation,27,Patnership,Complex\n1942,145,NJ City,S,3,13,8,52,High School,24,Patnership,Complex\n8805,395,NJ City,N,3,21,13,84,Graduation,20,Patnership,Standalone\n3678,150,NJ City,S,1,17,6,46,High School,23,Patnership,Complex\n195,488.82,NJ City,E,13,20,3,80,Graduation,20,Owner,Standalone\n950,184,NJ City,S,1,10,14,65,High School,27,Patnership,Complex\n1504,305,NJ City,W,1,13,22,37,High School,24,Franchise,Complex\n7893,387,Boston,N,1,15,18,29,Graduation,27,Patnership,Standalone\n7964,80.63,Boston,W,12,11,3,71,No education,33,Franchise,Complex\n9099,350,NJ City,E,3,15,20,51,Graduation,33,Owner,Standalone\n2991,291,NJ City,E,2,15,19,58,High School,21,Owner,Standalone\n2808,134.03,NJ City,W,17,16,3,46,Graduation,25,Franchise,Complex\n7067,450,Boston,E,1,13,6,72,Graduation,31,Owner,Standalone\n8448,469,NJ City,W,2,8,8,77,Graduation,21,Franchise,Complex\n1427,146,NJ City,E,2,10,8,29,High School,28,Owner,Standalone\n272,152,NJ City,W,1,19,17,51,High School,19,Franchise,Complex\n5509,356,Boston,N,1,17,8,48,Graduation,21,Patnership,Standalone\n527,403,NJ City,E,2,21,5,41,Graduation,22,Owner,Standalone\n4336,268.01,NJ City,W,16,15,1,63,High School,29,Franchise,Complex\n911,288,NJ City,E,1,13,15,85,High School,21,Owner,Standalone\n1981,222,NJ City,N,1,11,22,64,High School,18,Patnership,Standalone\n2476,279,NJ City,W,2,16,13,37,High School,27,Franchise,Complex\n2565,157,NJ City,N,1,11,24,82,High School,32,Patnership,Standalone\n5238,366,Boston,S,1,11,14,56,Graduation,34,Patnership,Complex\n2653,197,NJ City,N,2,14,20,70,High School,35,Patnership,Standalone\n6841,98.8,Boston,N,13,20,1,54,High School,20,Patnership,Standalone\n2844,234,NJ City,W,3,17,7,67,High School,26,Franchise,Complex\n9681,376.28,NJ City,N,18,11,1,66,Graduation,25,Patnership,Standalone\n1203,120,NJ City,E,3,17,17,77,High School,23,Owner,Standalone\n774,24.25,NJ City,N,10,11,1,33,Graduation,25,Patnership,Complex\n1523,266,NJ City,E,2,8,25,58,High School,35,Owner,Standalone\n8409,455,NJ City,N,3,18,12,39,Graduation,21,Patnership,Standalone\n1342,297,NJ City,S,1,17,7,62,High School,30,Patnership,Complex\n1087,219.05,NJ City,E,4,24,2,84,High School,32,Owner,Standalone\n8279,430,NJ City,E,3,14,16,35,Graduation,28,Owner,Standalone\n8434,18.34,NJ City,N,2,18,4,58,High School,18,Patnership,Complex\n181,240,NJ City,N,2,12,24,59,High School,22,Patnership,Standalone\n9219,59.06,NJ City,E,15,18,4,50,Graduation,31,Owner,Standalone\n5867,397,Boston,E,1,24,6,49,Graduation,23,Owner,Standalone\n8718,459,NJ City,S,2,23,22,34,Graduation,29,Patnership,Complex\n367,196,NJ City,E,3,20,17,77,High School,25,Owner,Standalone\n3282,197,NJ City,S,1,12,9,55,High School,22,Patnership,Complex\n7162,351,Boston,S,3,18,14,66,Graduation,32,Patnership,Complex\n2915,105,NJ City,E,1,21,21,74,High School,23,Owner,Standalone\n1259,371.92,NJ City,E,13,14,2,57,No education,23,Owner,Standalone\n1029,241,NJ City,N,1,15,8,83,High School,23,Patnership,Standalone\n517,22.49,NJ City,N,3,15,3,77,High School,32,Patnership,Standalone\n3149,266,NJ City,N,1,22,16,29,High School,29,Patnership,Standalone\n5777,453,Boston,N,2,12,18,49,Graduation,20,Patnership,Standalone\n2816,411.98,NJ City,W,25,20,3,48,No education,35,Franchise,Complex\n2070,218,NJ City,S,1,16,13,35,High School,22,Patnership,Complex\n8439,371,NJ City,E,2,8,5,33,Graduation,27,Owner,Standalone\n1323,231,NJ City,E,2,22,17,60,High School,27,Owner,Standalone\n6812,375.11,Boston,W,4,10,2,47,No education,23,Franchise,Complex\n6026,75.49,Boston,S,8,23,3,49,No education,26,Patnership,Complex\n1803,69.48,NJ City,N,5,16,1,66,Graduation,25,Owner,Standalone\n6502,18.17,Boston,N,9,19,3,48,High School,26,Patnership,Complex\n8134,479,NJ City,S,1,14,13,84,Graduation,21,Patnership,Complex\n1514,113.68,NJ City,S,21,16,3,26,No education,29,Patnership,Complex\n4169,121,NJ City,N,1,18,25,41,High School,19,Patnership,Standalone\n1637,57.38,NJ City,N,1,20,1,39,No education,20,Patnership,Standalone\n2308,13.2,NJ City,N,10,16,3,30,High School,29,Franchise,Complex\n8525,83.85,NJ City,N,22,24,3,68,No education,30,Patnership,Standalone\n9801,404,NJ City,N,1,16,22,66,Graduation,26,Patnership,Standalone\n1827,389,NJ City,E,2,19,5,58,Graduation,22,Owner,Standalone\n5157,357,Boston,N,3,24,15,25,Graduation,18,Patnership,Standalone\n1586,238,NJ City,S,1,22,6,67,High School,30,Patnership,Complex\n6202,374,Boston,S,2,14,7,51,Graduation,30,Patnership,Complex\n2649,155,NJ City,N,2,13,25,32,High School,35,Patnership,Standalone\n8842,496,NJ City,S,2,15,9,56,Graduation,18,Patnership,Complex\n4434,306,NJ City,S,3,24,17,50,High School,21,Patnership,Complex\n7938,422,Boston,S,2,16,12,36,Graduation,35,Patnership,Complex\n8324,72.96,NJ City,N,18,16,1,64,No education,22,Franchise,Complex\n7573,430,Boston,N,2,15,6,60,Graduation,25,Patnership,Standalone\n5704,46.23,Boston,N,2,21,2,81,High School,33,Franchise,Complex\n4109,114,NJ City,N,2,16,17,72,High School,33,Patnership,Standalone\n5647,113.12,Boston,E,11,10,3,42,High School,31,Owner,Standalone\n437,3.16,NJ City,N,21,9,3,33,No education,22,Patnership,Standalone\n7283,465,Boston,E,2,20,13,33,Graduation,24,Owner,Standalone\n8450,474,NJ City,S,3,9,8,64,Graduation,31,Patnership,Complex\n5,185,NY City,N,2,9,25,29,High School,32,Franchise,Complex\n8498,437,NJ City,S,1,8,15,35,Graduation,24,Patnership,Complex\n8882,396,NJ City,S,1,8,7,32,Graduation,29,Patnership,Complex\n2519,282,NJ City,E,1,18,18,38,High School,30,Owner,Standalone\n7723,465,Boston,E,1,20,18,53,Graduation,18,Owner,Standalone\n1853,45.66,NJ City,N,15,24,2,49,No education,30,Patnership,Standalone\n1364,113.4,NJ City,W,17,10,3,60,No education,29,Franchise,Complex\n3938,82.32,NJ City,S,22,23,3,66,No education,19,Patnership,Complex\n4484,113.87,NJ City,W,3,18,4,32,No education,25,Franchise,Complex\n3088,291,NJ City,W,3,9,11,79,High School,34,Franchise,Complex\n1619,241,NJ City,E,2,12,7,40,High School,18,Owner,Standalone\n8888,370,NJ City,W,1,16,19,51,Graduation,22,Franchise,Complex\n3851,93.4,NJ City,E,10,16,2,38,No education,33,Owner,Standalone\n916,122.13,NJ City,W,11,11,4,55,High School,23,Franchise,Complex\n9119,396.79,NJ City,E,7,18,4,47,No education,24,Owner,Standalone\n5080,450,Boston,W,3,10,11,47,Graduation,18,Franchise,Complex\n8913,348.33,NJ City,N,12,19,4,29,Graduation,31,Patnership,Standalone\n3930,125,NJ City,S,3,19,11,34,High School,18,Patnership,Complex\n1345,131,NJ City,N,3,23,20,73,High School,22,Patnership,Standalone\n6529,493,Boston,N,2,17,10,64,Graduation,29,Patnership,Standalone\n8517,398,NJ City,N,3,8,11,60,Graduation,33,Patnership,Standalone\n2234,53.13,NJ City,S,23,15,4,82,No education,21,Patnership,Complex\n9262,91.8,NJ City,S,3,8,3,69,High School,33,Patnership,Complex\n2103,117.26,NJ City,E,6,10,1,35,Graduation,27,Owner,Standalone\n2627,180,NJ City,E,2,21,13,66,High School,30,Owner,Standalone\n8306,443,NJ City,S,1,15,24,83,Graduation,29,Patnership,Complex\n690,172,NJ City,S,1,19,19,61,High School,20,Patnership,Complex\n9639,405,NJ City,E,1,13,24,83,Graduation,31,Owner,Standalone\n6384,473,Boston,W,2,12,6,59,Graduation,25,Franchise,Complex\n7511,1.61,Boston,N,1,14,1,26,No education,23,Owner,Standalone\n9941,351,NJ City,N,2,22,8,73,Graduation,22,Patnership,Standalone\n8303,360,NJ City,E,3,24,18,73,Graduation,25,Owner,Standalone\n1261,6.98,NJ City,N,19,14,1,52,High School,19,Patnership,Standalone\n9598,362,NJ City,S,1,15,17,36,Graduation,26,Patnership,Complex\n2340,47.35,NJ City,W,22,11,1,66,Graduation,25,Franchise,Complex\n1188,130,NJ City,W,2,15,23,84,High School,29,Franchise,Complex\n5738,476,Boston,S,3,10,9,70,Graduation,22,Patnership,Complex\n7908,432,Boston,W,3,17,7,45,Graduation,22,Franchise,Complex\n3435,78.43,NJ City,E,18,21,1,43,Graduation,27,Owner,Standalone\n7161,457.67,Boston,N,18,19,2,62,Graduation,18,Patnership,Standalone\n2259,88.45,NJ City,E,13,16,4,67,Graduation,24,Owner,Standalone\n6135,5.47,Boston,N,17,11,3,35,Graduation,21,Owner,Standalone\n6895,352,Boston,E,3,10,11,81,Graduation,34,Owner,Standalone\n193,62.07,NJ City,N,1,24,1,60,High School,29,Patnership,Standalone\n196,275,NJ City,W,1,8,21,45,High School,20,Franchise,Complex\n5546,430,Boston,S,1,22,18,61,Graduation,33,Patnership,Complex\n5208,432,Boston,W,3,19,6,66,Graduation,19,Franchise,Complex\n6601,7.71,Boston,N,13,10,4,61,High School,23,Patnership,Standalone\n3811,12.44,NJ City,N,13,23,2,41,High School,19,Owner,Standalone\n719,28.8,NJ City,N,3,13,1,76,No education,19,Owner,Standalone\n5952,264,Boston,W,5,20,4,40,Graduation,22,Franchise,Complex\n2960,111,NJ City,W,2,8,23,67,High School,27,Franchise,Complex\n4854,151.76,NJ City,S,14,18,2,69,Graduation,32,Patnership,Complex\n3320,184,NJ City,W,2,8,23,36,High School,25,Franchise,Complex\n5595,475,Boston,E,2,14,17,66,Graduation,24,Owner,Standalone\n1685,272.67,NJ City,N,13,24,1,31,No education,18,Patnership,Standalone\n3730,230.25,NJ City,S,18,15,4,80,High School,24,Patnership,Complex\n5552,10.62,Boston,N,25,8,4,57,No education,27,Franchise,Complex\n5253,353,Boston,N,1,15,13,73,Graduation,35,Patnership,Standalone\n8686,382,NJ City,S,2,17,17,54,Graduation,35,Patnership,Complex\n3252,258,NJ City,W,2,15,8,38,High School,20,Franchise,Complex\n3526,112.73,NJ City,S,2,21,4,72,High School,33,Patnership,Complex\n4677,109,NJ City,N,3,17,25,68,High School,35,Patnership,Standalone\n686,218,NJ City,S,1,20,6,38,High School,28,Patnership,Complex\n1707,29.98,NJ City,N,13,21,3,37,Graduation,31,Owner,Standalone\n7572,105.9,Boston,W,12,12,1,26,Graduation,31,Franchise,Complex\n1900,51.8,NJ City,W,5,8,4,81,High School,25,Franchise,Complex\n7601,410,Boston,N,1,20,16,64,Graduation,21,Patnership,Standalone\n1952,434.62,NJ City,W,20,17,3,79,No education,21,Franchise,Complex\n2495,154,NJ City,E,3,13,24,55,High School,23,Owner,Standalone\n8335,389,NJ City,E,2,13,9,55,Graduation,21,Owner,Standalone\n3848,174,NJ City,W,1,21,20,34,High School,32,Franchise,Complex\n5419,318.49,Boston,E,4,24,3,84,High School,31,Owner,Standalone\n3140,16,NJ City,N,20,24,2,55,No education,24,Franchise,Complex\n7023,132.94,Boston,E,10,22,4,83,Graduation,27,Owner,Standalone\n6211,0.75,Boston,N,3,14,3,59,High School,32,Owner,Standalone\n6347,408.52,Boston,E,12,18,2,75,No education,31,Owner,Standalone\n2389,62.49,NJ City,N,24,14,3,82,High School,34,Patnership,Standalone\n2175,16.71,NJ City,N,16,19,2,67,Graduation,24,Owner,Standalone\n2324,28.17,NJ City,N,7,10,1,31,No education,21,Franchise,Complex\n4641,244,NJ City,N,2,20,8,76,High School,27,Patnership,Standalone\n7910,291.45,Boston,S,14,9,4,40,No education,31,Patnership,Complex\n3358,150,NJ City,S,2,20,24,69,High School,18,Patnership,Complex\n6894,79.93,Boston,S,7,18,4,70,Graduation,23,Patnership,Complex\n5920,435,Boston,W,2,24,15,54,Graduation,28,Franchise,Complex\n4310,38.86,NJ City,N,11,23,2,51,No education,31,Patnership,Complex\n5197,52.48,Boston,N,12,18,3,34,High School,18,Patnership,Standalone\n5945,6.69,Boston,N,19,23,1,43,No education,30,Patnership,Standalone\n9230,475,NJ City,S,3,10,5,28,Graduation,33,Patnership,Complex\n7049,413,Boston,N,1,18,14,41,Graduation,29,Patnership,Standalone\n9845,372,NJ City,N,3,16,25,54,Graduation,24,Patnership,Standalone\n8506,450,NJ City,S,1,12,20,38,Graduation,23,Patnership,Complex\n8452,367,NJ City,W,1,10,25,81,Graduation,29,Franchise,Complex\n40,166,NY City,W,3,24,14,27,High School,34,Franchise,Complex\n6170,46.46,Boston,N,10,10,3,78,No education,34,Patnership,Complex\n9883,438,NJ City,E,1,15,9,68,Graduation,18,Owner,Standalone\n7879,398,Boston,E,3,18,20,57,Graduation,34,Owner,Standalone\n2137,138,NJ City,N,1,14,6,40,High School,29,Patnership,Standalone\n357,135.2,NJ City,N,3,14,4,40,Graduation,34,Patnership,Standalone\n262,243.02,NJ City,S,2,24,4,49,High School,35,Patnership,Complex\n7999,46.88,Boston,N,18,10,4,78,High School,34,Owner,Standalone\n2708,272,NJ City,W,2,19,13,38,High School,34,Franchise,Complex\n7081,464,Boston,N,3,24,13,66,Graduation,30,Patnership,Standalone\n2236,63.52,NJ City,N,1,13,2,30,High School,35,Franchise,Complex\n8184,495,NJ City,W,3,12,13,60,Graduation,18,Franchise,Complex\n2346,185,NJ City,S,1,12,19,38,High School,20,Patnership,Complex\n9851,390,NJ City,E,3,23,20,71,Graduation,27,Owner,Standalone\n4569,104,NJ City,N,1,12,20,77,High School,20,Patnership,Standalone\n6555,108.25,Boston,E,21,13,1,59,Graduation,30,Owner,Standalone\n6276,466,Boston,W,1,20,25,61,Graduation,20,Franchise,Complex\n8103,455,NJ City,E,2,19,23,26,Graduation,18,Owner,Standalone\n5935,498,Boston,E,2,9,9,46,Graduation,26,Owner,Standalone\n6742,34.75,Boston,N,12,10,3,42,High School,32,Patnership,Complex\n8737,493,NJ City,N,2,14,20,35,Graduation,18,Patnership,Standalone\n3857,20.14,NJ City,N,16,12,2,55,No education,20,Patnership,Standalone\n3321,36.67,NJ City,N,11,17,4,55,Graduation,27,Patnership,Standalone\n8271,382,NJ City,E,2,20,11,67,Graduation,20,Owner,Standalone\n9464,434.77,NJ City,W,2,21,4,43,No education,32,Franchise,Complex\n6374,424,Boston,S,2,17,20,61,Graduation,27,Patnership,Complex\n9496,461,NJ City,W,3,14,18,77,Graduation,30,Franchise,Complex\n5934,408,Boston,S,1,16,21,85,Graduation,27,Patnership,Complex\n3359,100.33,NJ City,E,25,18,4,32,No education,24,Owner,Standalone\n1931,298,NJ City,E,1,17,12,26,High School,33,Owner,Standalone\n140,226,NJ City,W,1,20,17,43,High School,29,Franchise,Complex\n5279,99.62,Boston,E,18,9,2,75,No education,31,Owner,Standalone\n873,101,NJ City,N,1,22,16,51,High School,20,Patnership,Standalone\n6281,469,Boston,N,1,20,15,41,Graduation,33,Patnership,Standalone\n5247,419,Boston,E,3,12,12,52,Graduation,26,Owner,Standalone\n8201,48.51,NJ City,N,14,18,1,30,No education,33,Patnership,Standalone\n2001,227,NJ City,N,3,19,19,68,High School,20,Patnership,Standalone\n6776,439,Boston,W,2,14,8,47,Graduation,30,Franchise,Complex\n1915,290.21,NJ City,E,4,17,4,82,High School,23,Owner,Standalone\n2981,17.39,NJ City,N,24,19,2,25,No education,21,Patnership,Standalone\n6671,79.59,Boston,E,14,24,1,82,No education,19,Owner,Standalone\n2635,200,NJ City,E,1,9,9,55,High School,33,Owner,Standalone\n9484,346.37,NJ City,W,22,23,2,34,High School,29,Franchise,Complex\n3844,280,NJ City,W,3,16,17,57,High School,32,Franchise,Complex\n2240,258,NJ City,W,3,9,20,76,High School,23,Franchise,Complex\n1554,10.64,NJ City,N,6,21,1,54,Graduation,22,Patnership,Complex\n3975,57.81,NJ City,E,7,23,2,44,Graduation,34,Owner,Standalone\n1632,22.45,NJ City,N,2,15,3,54,Graduation,34,Franchise,Complex\n2528,54.07,NJ City,W,16,13,2,39,No education,18,Franchise,Complex\n1686,481.11,NJ City,S,12,12,4,56,Graduation,25,Patnership,Complex\n8141,0.37,NJ City,N,6,16,1,63,No education,21,Patnership,Standalone\n1968,491.43,NJ City,W,16,17,1,76,Graduation,27,Franchise,Complex\n5728,469,Boston,W,3,8,12,39,Graduation,32,Franchise,Complex\n8739,37.19,NJ City,N,15,14,3,52,Graduation,29,Owner,Standalone\n6122,100.32,Boston,S,23,13,2,40,No education,30,Patnership,Complex\n2493,121.74,NJ City,N,20,12,4,52,Graduation,35,Patnership,Standalone\n7444,42.32,Boston,N,16,14,3,33,High School,19,Franchise,Complex\n9273,379,NJ City,N,3,20,20,69,Graduation,21,Patnership,Standalone\n7508,34.78,Boston,N,15,23,1,85,No education,32,Franchise,Complex\n3374,183,NJ City,S,2,17,15,41,High School,19,Patnership,Complex\n6520,446,Boston,W,2,8,13,81,Graduation,35,Franchise,Complex\n9728,431,NJ City,W,1,19,5,33,Graduation,35,Franchise,Complex\n876,190,NJ City,W,2,13,11,76,High School,35,Franchise,Complex\n5802,58.87,Boston,S,10,10,2,62,Graduation,19,Patnership,Complex\n8583,100.13,NJ City,E,13,15,4,77,Graduation,32,Owner,Standalone\n1526,287.5,NJ City,S,11,22,2,75,No education,26,Patnership,Complex\n6656,434,Boston,W,3,22,20,56,Graduation,34,Franchise,Complex\n7273,474,Boston,N,3,15,5,47,Graduation,26,Patnership,Standalone\n1039,133.3,NJ City,E,22,23,3,82,High School,25,Owner,Standalone\n8844,64.87,NJ City,N,5,13,4,75,Graduation,28,Franchise,Complex\n9312,397,NJ City,W,1,24,14,31,Graduation,29,Franchise,Complex\n1335,228,NJ City,E,1,21,24,28,High School,33,Owner,Standalone\n3380,275,NJ City,W,3,10,25,79,High School,31,Franchise,Complex\n7574,356,Boston,S,1,19,20,76,Graduation,27,Patnership,Complex\n1770,91.46,NJ City,S,4,12,4,31,Graduation,22,Patnership,Complex\n4068,223,NJ City,W,3,16,24,52,High School,21,Franchise,Complex\n6622,121.96,Boston,S,4,24,3,83,High School,27,Patnership,Complex\n3556,159,NJ City,W,2,11,13,85,High School,21,Franchise,Complex\n8408,457,NJ City,W,1,24,25,56,Graduation,34,Franchise,Complex\n6835,98.95,Boston,E,10,18,4,73,High School,26,Owner,Standalone\n8007,495,Boston,E,2,17,17,69,Graduation,29,Owner,Standalone\n4818,244,NJ City,S,2,11,8,52,High School,20,Patnership,Complex\n7323,462,Boston,E,2,24,14,35,Graduation,24,Owner,Standalone\n9122,115.65,NJ City,S,13,15,4,42,No education,35,Patnership,Complex\n1533,149,NJ City,N,1,21,12,59,High School,24,Patnership,Standalone\n7330,104.79,Boston,S,12,16,3,80,High School,26,Patnership,Complex\n8819,386,NJ City,E,3,19,6,66,Graduation,18,Owner,Standalone\n4963,227,Boston,E,3,11,7,76,High School,27,Owner,Standalone\n972,177,NJ City,W,2,18,18,80,High School,18,Franchise,Complex\n590,218,NJ City,S,1,12,11,53,High School,35,Patnership,Complex\n2075,311,NJ City,E,3,10,21,68,High School,28,Owner,Standalone\n5371,117.52,Boston,E,25,12,1,38,High School,22,Owner,Standalone\n8146,437,NJ City,S,2,16,23,76,Graduation,25,Patnership,Complex\n3283,177,NJ City,E,2,20,13,85,High School,23,Owner,Standalone\n9401,416,NJ City,N,3,13,6,30,Graduation,31,Patnership,Standalone\n2188,387,NJ City,W,3,16,5,59,Graduation,32,Franchise,Complex\n1550,162,NJ City,S,3,22,14,58,High School,35,Patnership,Complex\n1565,109,NJ City,N,3,17,20,58,High School,24,Patnership,Standalone\n2698,261,NJ City,S,2,22,20,78,High School,26,Patnership,Complex\n6238,398,Boston,S,3,23,16,53,Graduation,32,Patnership,Complex\n4686,460.94,NJ City,S,21,20,3,74,Graduation,25,Patnership,Complex\n8012,469,Boston,W,3,12,18,40,Graduation,27,Franchise,Complex\n3177,359.02,NJ City,N,7,23,3,33,Graduation,32,Patnership,Standalone\n872,265,NJ City,W,1,24,20,42,High School,26,Franchise,Complex\n640,405,NJ City,W,3,22,5,62,Graduation,24,Franchise,Complex\n3203,126.13,NJ City,E,6,24,2,82,No education,24,Owner,Standalone\n9345,375,NJ City,N,3,17,17,67,Graduation,25,Patnership,Standalone\n9231,98.4,NJ City,E,13,13,2,54,Graduation,26,Owner,Standalone\n1075,151,NJ City,E,3,8,7,25,High School,24,Owner,Standalone\n1435,303,NJ City,E,2,17,24,80,High School,28,Owner,Standalone\n9466,460,NJ City,S,3,22,25,80,Graduation,32,Patnership,Complex\n7684,412,Boston,W,3,12,12,78,Graduation,33,Franchise,Complex\n2579,264,NJ City,E,1,23,14,50,High School,22,Owner,Standalone\n423,49.69,NJ City,E,23,11,3,81,Graduation,32,Owner,Standalone\n6901,40.2,Boston,N,1,8,4,27,High School,30,Patnership,Standalone\n8435,52.7,NJ City,E,7,8,2,59,No education,27,Owner,Standalone\n7524,36.25,Boston,N,1,16,1,28,Graduation,18,Franchise,Complex\n2548,173.81,NJ City,W,2,8,4,36,High School,32,Franchise,Complex\n7414,95.27,Boston,S,15,11,2,42,High School,25,Patnership,Complex\n9242,482,NJ City,S,1,24,14,58,Graduation,23,Patnership,Complex\n5236,364,Boston,W,1,18,23,63,Graduation,21,Franchise,Complex\n715,224,NJ City,E,3,9,14,69,High School,32,Owner,Standalone\n8336,490,NJ City,W,3,12,13,37,Graduation,29,Franchise,Complex\n434,317,NJ City,S,2,15,18,81,High School,30,Patnership,Complex\n9599,415.3,NJ City,E,1,15,4,65,No education,21,Owner,Standalone\n4492,463.83,NJ City,W,9,10,3,58,High School,29,Franchise,Complex\n5718,494,Boston,S,2,18,11,39,Graduation,21,Patnership,Complex\n2452,481,NJ City,W,2,9,5,68,Graduation,22,Franchise,Complex\n6857,490,Boston,N,1,19,18,75,Graduation,25,Patnership,Standalone\n4069,478,NJ City,N,3,8,5,63,Graduation,33,Patnership,Standalone\n1498,75.68,NJ City,S,6,10,3,33,High School,27,Patnership,Complex\n9252,38.5,NJ City,N,1,15,1,46,Graduation,21,Franchise,Complex\n6955,39.7,Boston,N,12,12,1,27,High School,32,Owner,Standalone\n4803,83.08,NJ City,E,1,18,2,31,Graduation,22,Owner,Standalone\n9139,391,NJ City,E,1,16,24,47,Graduation,25,Owner,Standalone\n1420,112,NJ City,W,3,18,10,25,High School,27,Franchise,Complex\n6957,436,Boston,N,2,19,19,49,Graduation,24,Patnership,Standalone\n8151,422,NJ City,E,3,24,6,36,Graduation,23,Owner,Standalone\n1373,197,NJ City,N,1,11,22,52,High School,18,Patnership,Standalone\n5368,495,Boston,W,2,20,23,60,Graduation,27,Franchise,Complex\n3659,4.45,NJ City,N,9,11,1,40,No education,18,Owner,Standalone\n5152,297.03,Boston,W,20,10,4,51,High School,26,Franchise,Complex\n1821,174,NJ City,N,3,14,10,36,High School,21,Patnership,Standalone\n3461,93.09,NJ City,N,6,11,3,79,No education,29,Patnership,Standalone\n1223,90.38,NJ City,E,25,8,4,64,No education,18,Owner,Standalone\n6734,109.65,Boston,S,13,21,4,28,No education,28,Patnership,Complex\n7473,424,Boston,N,3,16,12,42,Graduation,27,Patnership,Standalone\n9918,495,NJ City,S,2,18,19,64,Graduation,29,Patnership,Complex\n5024,367.56,Boston,W,7,22,1,31,No education,31,Franchise,Complex\n7884,430,Boston,W,3,16,9,62,Graduation,30,Franchise,Complex\n6646,463,Boston,S,1,12,16,32,Graduation,29,Patnership,Complex\n1582,114,NJ City,S,3,16,9,74,High School,34,Patnership,Complex\n7107,357,Boston,E,2,17,8,85,Graduation,22,Owner,Standalone\n2569,293,NJ City,N,3,21,16,62,High School,24,Patnership,Standalone\n6424,497,Boston,W,2,9,5,33,Graduation,19,Franchise,Complex\n9043,93.36,NJ City,E,21,16,4,56,High School,27,Owner,Standalone\n4706,153,NJ City,S,3,24,10,30,High School,25,Patnership,Complex\n4691,205,NJ City,E,2,15,13,26,High School,22,Owner,Standalone\n7464,105.22,Boston,W,15,15,2,81,Graduation,33,Franchise,Complex\n3999,188,NJ City,E,1,19,19,59,High School,33,Owner,Standalone\n5541,371,Boston,N,1,11,16,67,Graduation,34,Patnership,Standalone\n7222,384,Boston,S,3,10,19,53,Graduation,20,Patnership,Complex\n5500,394,Boston,W,1,23,15,71,Graduation,35,Franchise,Complex\n4943,272,Boston,E,2,12,15,47,High School,18,Owner,Standalone\n2465,303,NJ City,N,3,10,21,61,High School,33,Patnership,Standalone\n1211,196.93,NJ City,E,17,18,3,53,No education,28,Owner,Standalone\n2805,228,NJ City,N,1,20,11,54,High School,22,Patnership,Standalone\n5852,390,Boston,W,2,15,23,41,Graduation,23,Franchise,Complex\n8438,22.16,NJ City,N,14,13,3,74,No education,27,Patnership,Complex\n6481,38.38,Boston,N,6,13,2,48,High School,23,Patnership,Standalone\n7897,420,Boston,N,3,17,20,57,Graduation,23,Patnership,Standalone\n4790,311,NJ City,S,1,14,15,47,High School,29,Patnership,Complex\n379,201,NJ City,E,3,23,17,52,High School,21,Owner,Standalone\n5850,406,Boston,S,3,19,20,33,Graduation,34,Patnership,Complex\n605,312,NJ City,N,2,18,16,49,High School,35,Patnership,Standalone\n7455,46.34,Boston,N,14,16,1,37,Graduation,35,Owner,Standalone\n7402,476,Boston,S,2,8,21,27,Graduation,29,Patnership,Complex\n5044,100.77,Boston,W,18,23,3,28,High School,20,Franchise,Complex\n2327,6.22,NJ City,N,8,15,2,27,No education,22,Owner,Standalone\n9750,429,NJ City,S,2,13,17,34,Graduation,21,Patnership,Complex\n6182,96.03,Boston,S,18,23,4,51,No education,34,Patnership,Complex\n5369,358,Boston,N,3,9,21,70,Graduation,26,Patnership,Standalone\n6186,398,Boston,S,2,8,18,79,Graduation,33,Patnership,Complex\n4481,222,NJ City,N,2,24,7,74,High School,20,Patnership,Standalone\n905,148,NJ City,N,2,16,14,75,High School,31,Patnership,Standalone\n1788,169,NJ City,W,1,21,7,62,High School,23,Franchise,Complex\n6253,406,Boston,N,2,10,25,46,Graduation,27,Patnership,Standalone\n3867,111.31,NJ City,E,25,9,3,26,Graduation,27,Owner,Standalone\n7740,385,Boston,W,1,14,20,59,Graduation,34,Franchise,Complex\n1091,235,NJ City,E,2,22,22,84,High School,34,Owner,Standalone\n8812,487,NJ City,W,2,9,22,41,Graduation,34,Franchise,Complex\n5170,417,Boston,S,2,18,16,67,Graduation,35,Patnership,Complex\n5242,448,Boston,S,1,12,17,49,Graduation,33,Patnership,Complex\n3652,319,NJ City,W,1,16,19,36,High School,34,Franchise,Complex\n8629,87.03,NJ City,N,16,12,4,74,High School,23,Patnership,Standalone\n1576,172,NJ City,W,1,8,6,76,High School,32,Franchise,Complex\n2212,124,NJ City,W,1,8,11,46,High School,34,Franchise,Complex\n3655,24.71,NJ City,N,15,17,3,37,High School,21,Owner,Standalone\n4235,218,NJ City,E,3,18,6,25,High School,24,Owner,Standalone\n1432,88.58,NJ City,W,17,24,4,51,High School,31,Franchise,Complex\n9079,282.86,NJ City,E,10,19,2,44,High School,18,Owner,Standalone\n875,74.62,NJ City,E,6,17,3,45,No education,24,Owner,Standalone\n2533,158,NJ City,N,3,24,14,69,High School,27,Patnership,Standalone\n7994,407,Boston,S,1,8,11,43,Graduation,19,Patnership,Complex\n7797,462,Boston,N,1,12,11,52,Graduation,26,Patnership,Standalone\n5318,498,Boston,S,1,12,5,74,Graduation,25,Patnership,Complex\n7778,419,Boston,S,2,9,25,73,Graduation,34,Patnership,Complex\n3173,256,NJ City,N,3,24,19,74,High School,19,Patnership,Standalone\n9794,376,NJ City,S,2,21,17,25,Graduation,28,Patnership,Complex\n6696,341.44,Boston,W,20,12,1,80,Graduation,31,Franchise,Complex\n2727,100.26,NJ City,E,25,10,1,75,Graduation,34,Owner,Standalone\n565,273,NJ City,N,3,8,12,62,High School,21,Patnership,Standalone\n6229,91.88,Boston,N,3,18,4,52,High School,32,Patnership,Standalone\n6199,379,Boston,E,1,19,25,35,Graduation,21,Owner,Standalone\n8637,351,NJ City,N,1,19,9,29,Graduation,22,Patnership,Standalone\n7228,367,Boston,W,2,8,25,71,Graduation,26,Franchise,Complex\n6814,16.69,Boston,N,9,13,2,43,High School,18,Patnership,Complex\n1642,102,NJ City,S,1,23,23,84,High School,27,Patnership,Complex\n8919,448,NJ City,E,2,22,22,51,Graduation,19,Owner,Standalone\n8254,24.33,NJ City,N,10,17,1,53,High School,35,Patnership,Complex\n1888,100,NJ City,W,3,18,24,35,High School,31,Franchise,Complex\n4971,114,Boston,E,3,11,17,34,High School,26,Owner,Standalone\n1846,196,NJ City,S,3,24,9,73,High School,25,Patnership,Complex\n4685,208,NJ City,N,1,12,13,67,High School,20,Patnership,Standalone\n8016,403,Boston,W,2,17,17,73,Graduation,28,Franchise,Complex\n8344,380,NJ City,W,3,20,14,45,Graduation,28,Franchise,Complex\n8206,469,NJ City,S,1,14,10,59,Graduation,18,Patnership,Complex\n4161,305,NJ City,N,1,19,14,80,High School,24,Patnership,Standalone\n7507,454,Boston,E,3,11,15,59,Graduation,25,Owner,Standalone\n1712,186,NJ City,W,2,15,8,29,High School,32,Franchise,Complex\n7918,396,Boston,S,3,19,10,45,Graduation,33,Patnership,Complex\n5422,464.11,Boston,S,17,20,1,47,High School,24,Patnership,Complex\n5670,43.42,Boston,N,2,10,2,32,Graduation,27,Patnership,Complex\n1793,209,NJ City,N,2,15,12,57,High School,29,Patnership,Standalone\n5809,106.98,Boston,N,11,11,4,66,High School,27,Patnership,Standalone\n7009,387,Boston,N,2,10,25,50,Graduation,27,Patnership,Standalone\n1669,160,NJ City,N,3,18,23,85,High School,33,Patnership,Standalone\n7972,392,Boston,W,1,23,15,74,Graduation,33,Franchise,Complex\n8544,405,NJ City,W,3,9,14,46,Graduation,23,Franchise,Complex\n1468,82.89,NJ City,W,14,12,4,70,High School,18,Franchise,Complex\n5082,36.95,Boston,N,5,23,1,82,Graduation,29,Patnership,Complex\n9278,108.64,NJ City,S,18,22,4,41,No education,34,Patnership,Complex\n2917,207,NJ City,N,1,19,9,38,High School,31,Patnership,Standalone\n3900,139,NJ City,W,1,16,11,79,High School,28,Franchise,Complex\n3278,177,NJ City,S,1,14,10,52,High School,29,Patnership,Complex\n9802,465,NJ City,S,1,21,15,70,Graduation,19,Patnership,Complex\n7401,53.9,Boston,N,24,24,1,45,Graduation,22,Patnership,Standalone\n7225,458,Boston,N,3,9,9,26,Graduation,26,Patnership,Standalone\n3101,467.06,NJ City,N,2,9,3,54,No education,32,Patnership,Standalone\n5642,412,Boston,S,1,16,6,82,Graduation,33,Patnership,Complex\n1939,465,NJ City,E,1,9,5,73,Graduation,35,Owner,Standalone\n9500,471,NJ City,W,1,11,7,68,Graduation,24,Franchise,Complex\n7157,40.54,Boston,N,15,21,1,71,No education,24,Patnership,Standalone\n2055,311,NJ City,E,1,14,23,56,High School,33,Owner,Standalone\n2010,195,NJ City,S,2,17,20,36,High School,34,Patnership,Complex\n4767,112.24,NJ City,E,5,19,3,65,Graduation,23,Owner,Standalone\n6179,364,Boston,E,2,17,16,48,Graduation,25,Owner,Standalone\n6563,485,Boston,E,1,19,21,77,Graduation,21,Owner,Standalone\n7716,476,Boston,W,1,8,19,70,Graduation,31,Franchise,Complex\n9901,102.97,NJ City,N,9,12,4,61,High School,21,Patnership,Standalone\n3810,268,NJ City,S,1,16,24,26,High School,33,Patnership,Complex\n5699,474.81,Boston,E,3,22,5,44,No education,27,Owner,Standalone\n4490,164,NJ City,S,1,13,8,63,High School,32,Patnership,Complex\n6979,75.43,Boston,E,24,11,1,85,High School,19,Owner,Standalone\n6263,404,Boston,E,2,15,6,29,Graduation,23,Owner,Standalone\n9594,19.32,NJ City,N,18,13,3,53,Graduation,34,Patnership,Complex\n8936,384,NJ City,W,2,17,24,49,Graduation,31,Franchise,Complex\n8874,119.71,NJ City,S,3,17,3,82,Graduation,20,Patnership,Complex\n5925,394,Boston,N,2,24,19,53,Graduation,28,Patnership,Standalone\n751,154,NJ City,E,1,20,9,37,High School,21,Owner,Standalone\n7100,72.24,Boston,N,20,10,2,70,No education,23,Franchise,Complex\n9006,92.9,NJ City,S,1,8,3,79,Graduation,24,Patnership,Complex\n3982,149,NJ City,S,3,23,19,57,High School,26,Patnership,Complex\n7066,435,Boston,S,3,10,10,58,Graduation,24,Patnership,Complex\n3034,94.84,NJ City,S,20,24,4,34,High School,18,Patnership,Complex\n8696,352,NJ City,W,3,16,24,51,Graduation,19,Franchise,Complex\n7074,1,Boston,N,5,21,1,48,Graduation,33,Patnership,Complex\n504,418.57,NJ City,W,25,19,4,68,Graduation,26,Franchise,Complex\n3953,38.08,NJ City,N,20,18,4,41,No education,19,Patnership,Standalone\n1375,274,NJ City,E,2,16,13,40,High School,27,Owner,Standalone\n3874,116.45,NJ City,S,19,18,2,70,High School,35,Patnership,Complex\n4516,57.96,NJ City,W,1,21,3,55,High School,26,Franchise,Complex\n7609,435,Boston,N,1,21,24,65,Graduation,30,Patnership,Standalone\n2422,76.96,NJ City,S,14,24,4,59,High School,25,Patnership,Complex\n6976,325.61,Boston,W,5,8,4,44,High School,35,Franchise,Complex\n2831,221,NJ City,E,1,11,19,45,High School,30,Owner,Standalone\n5637,424,Boston,N,3,18,22,51,Graduation,34,Patnership,Standalone\n5050,65.17,Boston,N,14,14,4,32,High School,18,Patnership,Complex\n8773,377,NJ City,N,3,15,20,56,Graduation,25,Patnership,Standalone\n7580,87.85,Boston,W,18,24,3,73,No education,24,Franchise,Complex\n817,309,NJ City,N,3,22,25,62,High School,35,Patnership,Standalone\n7895,116.92,Boston,E,7,15,2,48,No education,24,Owner,Standalone\n8165,450.37,NJ City,N,4,14,3,71,No education,19,Patnership,Standalone\n2364,260,NJ City,W,3,14,21,63,High School,29,Franchise,Complex\n4298,5.72,NJ City,N,3,20,3,70,No education,33,Patnership,Complex\n6784,320.35,Boston,W,3,17,4,80,High School,33,Franchise,Complex\n7663,42.91,Boston,N,10,8,1,26,High School,23,Owner,Standalone\n8922,455,NJ City,S,2,18,16,76,Graduation,27,Patnership,Complex\n7138,436,Boston,S,3,23,14,48,Graduation,26,Patnership,Complex\n4950,108.86,Boston,S,5,10,2,51,Graduation,31,Patnership,Complex\n5743,406,Boston,E,3,21,6,31,Graduation,22,Owner,Standalone\n239,363.95,NJ City,E,21,10,3,83,No education,18,Owner,Standalone\n2222,176,NJ City,S,1,17,7,58,High School,22,Patnership,Complex\n8663,494,NJ City,E,2,11,9,84,Graduation,19,Owner,Standalone\n1292,104,NJ City,W,3,9,15,81,High School,19,Franchise,Complex\n5094,379,Boston,S,1,9,17,72,Graduation,19,Patnership,Complex\n3089,174,NJ City,N,1,12,8,78,High School,24,Patnership,Standalone\n5880,399,Boston,W,3,22,13,78,Graduation,21,Franchise,Complex\n3835,52.31,NJ City,E,4,8,3,66,High School,25,Owner,Standalone\n7911,490,Boston,E,3,9,14,29,Graduation,33,Owner,Standalone\n8628,412,NJ City,W,1,22,5,84,Graduation,22,Franchise,Complex\n1531,376.27,NJ City,E,21,23,1,84,High School,21,Owner,Standalone\n1777,162.51,NJ City,N,9,21,3,81,High School,24,Patnership,Standalone\n1800,271,NJ City,W,2,21,11,72,High School,25,Franchise,Complex\n9920,491.72,NJ City,W,7,23,1,30,No education,20,Franchise,Complex\n2488,49.2,NJ City,W,17,12,4,44,High School,29,Franchise,Complex\n3757,271,NJ City,N,1,22,8,32,High School,23,Patnership,Standalone\n1073,12.17,NJ City,N,20,16,3,85,No education,30,Patnership,Standalone\n4936,224,Boston,W,3,12,24,67,High School,28,Franchise,Complex\n7322,353.24,Boston,S,12,24,2,56,No education,20,Patnership,Complex\n7888,51.73,Boston,W,2,15,3,33,High School,26,Franchise,Complex\n286,290,NJ City,S,2,8,23,36,High School,23,Patnership,Complex\n7982,104.02,Boston,S,18,19,2,27,No education,18,Patnership,Complex\n8094,392,NJ City,S,1,15,18,33,Graduation,19,Patnership,Complex\n8617,190.61,NJ City,N,1,13,5,84,High School,22,Patnership,Standalone\n6584,341.13,Boston,W,12,14,4,49,No education,32,Franchise,Complex\n545,218,NJ City,N,2,18,6,40,High School,31,Patnership,Standalone\n9538,434,NJ City,S,1,8,20,70,Graduation,23,Patnership,Complex\n8178,57.68,NJ City,S,14,8,4,55,Graduation,18,Patnership,Complex\n8917,466,NJ City,N,2,24,23,71,Graduation,31,Patnership,Standalone\n1050,270,NJ City,S,2,18,13,51,High School,27,Patnership,Complex\n9045,396,NJ City,N,3,22,10,43,Graduation,20,Patnership,Standalone\n4681,231,NJ City,N,1,15,6,54,High School,26,Patnership,Standalone\n8640,476,NJ City,W,2,22,9,27,Graduation,29,Franchise,Complex\n9317,394,NJ City,N,3,19,18,84,Graduation,25,Patnership,Standalone\n2963,214,NJ City,E,3,9,20,74,High School,24,Owner,Standalone\n9951,414,NJ City,E,1,14,25,51,Graduation,31,Owner,Standalone\n2227,234.07,NJ City,E,20,14,1,68,High School,18,Owner,Standalone\n5138,475,Boston,S,3,24,11,46,Graduation,28,Patnership,Complex\n5285,57.88,Boston,N,15,16,2,49,No education,33,Patnership,Standalone\n4049,282,NJ City,N,1,16,10,70,High School,30,Patnership,Standalone\n4290,254,NJ City,S,3,20,7,84,High School,34,Patnership,Complex\n2712,118,NJ City,W,1,19,22,64,High School,25,Franchise,Complex\n7800,408,Boston,W,1,12,6,63,Graduation,32,Franchise,Complex\n8775,475,NJ City,E,2,12,5,47,Graduation,33,Owner,Standalone\n2097,182,NJ City,N,1,22,19,43,High School,27,Patnership,Standalone\n8599,48.16,NJ City,E,9,13,4,77,High School,34,Owner,Standalone\n6988,93.91,Boston,W,23,13,4,65,High School,26,Franchise,Complex\n8510,419,NJ City,S,1,17,14,50,Graduation,24,Patnership,Complex\n4554,268,NJ City,S,1,23,18,41,High School,32,Patnership,Complex\n6959,17.48,Boston,N,3,18,1,40,No education,23,Owner,Standalone\n439,146,NJ City,E,3,15,9,26,High School,28,Owner,Standalone\n2971,65.7,NJ City,N,20,23,1,35,High School,34,Owner,Standalone\n4123,155,NJ City,E,3,21,17,30,High School,29,Owner,Standalone\n4680,194,NJ City,W,2,20,15,55,High School,25,Franchise,Complex\n9000,445,NJ City,W,2,12,13,39,Graduation,32,Franchise,Complex\n3699,82.58,NJ City,E,9,16,3,59,Graduation,33,Owner,Standalone\n968,292,NJ City,W,1,20,9,62,High School,27,Franchise,Complex\n8091,438,NJ City,E,3,10,25,57,Graduation,26,Owner,Standalone\n779,104.21,NJ City,E,1,16,4,76,No education,33,Owner,Standalone\n6880,495,Boston,W,3,11,25,71,Graduation,32,Franchise,Complex\n7459,424,Boston,E,3,20,17,29,Graduation,24,Owner,Standalone\n8585,33.96,NJ City,N,17,20,1,83,No education,25,Patnership,Standalone\n4501,244,NJ City,N,3,10,11,26,High School,35,Patnership,Standalone\n6150,290.69,Boston,S,7,20,1,47,Graduation,33,Patnership,Complex\n9058,89.22,NJ City,S,1,11,4,33,High School,21,Patnership,Complex\n8502,42.57,NJ City,N,24,24,4,79,Graduation,34,Patnership,Complex\n57,109,Maine,N,2,22,23,73,High School,35,Patnership,Standalone\n8182,353,NJ City,S,3,13,13,82,Graduation,33,Patnership,Complex\n7367,390,Boston,E,3,19,23,41,Graduation,29,Owner,Standalone\n5651,455,Boston,E,1,22,9,68,Graduation,18,Owner,Standalone\n7959,370,Boston,E,1,14,22,32,Graduation,23,Owner,Standalone\n8542,239.72,NJ City,S,1,24,1,28,High School,29,Patnership,Complex\n7545,303.55,Boston,N,6,15,2,71,Graduation,19,Patnership,Standalone\n117,307,NJ City,N,3,17,23,35,High School,27,Patnership,Standalone\n1959,192,NJ City,E,2,14,6,59,High School,32,Owner,Standalone\n206,257.67,NJ City,S,10,20,1,43,No education,27,Patnership,Complex\n6195,459,Boston,E,3,21,8,45,Graduation,20,Owner,Standalone\n5560,87.27,Boston,W,3,18,1,41,High School,22,Franchise,Complex\n2,11.64,NY City,N,20,22,4,55,No education,23,Franchise,Standalone\n4264,292,NJ City,W,1,17,6,68,High School,28,Franchise,Complex\n4577,77.41,NJ City,N,2,14,3,59,No education,32,Patnership,Standalone\n9757,280.91,NJ City,N,18,17,1,47,High School,35,Patnership,Standalone\n7341,431,Boston,N,3,11,12,78,Graduation,32,Patnership,Standalone\n7389,268.24,Boston,N,8,24,4,78,Graduation,31,Patnership,Standalone\n3786,62.25,NJ City,N,23,11,2,27,Graduation,30,Patnership,Complex\n5015,377,Boston,E,2,24,20,41,Graduation,28,Owner,Standalone\n5551,483,Boston,E,1,24,20,27,Graduation,23,Owner,Standalone\n4925,122,NJ City,N,1,24,15,72,High School,27,Patnership,Standalone\n546,266,NJ City,S,2,24,16,53,High School,19,Patnership,Complex\n2721,421.57,NJ City,N,2,16,3,62,Graduation,18,Patnership,Standalone\n3004,209,NJ City,W,3,17,13,38,High School,25,Franchise,Complex\n1219,274,NJ City,E,2,23,14,71,High School,25,Owner,Standalone\n9421,487,NJ City,N,2,18,20,56,Graduation,20,Patnership,Standalone\n112,308,NJ City,W,3,24,16,68,High School,22,Franchise,Complex\n9655,477,NJ City,E,3,10,13,65,Graduation,19,Owner,Standalone\n6676,99.7,Boston,W,16,24,1,84,High School,33,Franchise,Complex\n7288,381,Boston,W,1,15,13,58,Graduation,34,Franchise,Complex\n9187,490,NJ City,E,1,15,22,26,Graduation,27,Owner,Standalone\n1484,112,NJ City,W,3,15,10,30,High School,28,Franchise,Complex\n6087,398,Boston,E,1,21,23,39,Graduation,20,Owner,Standalone\n5244,7.42,Boston,N,20,24,3,53,Graduation,24,Franchise,Complex\n781,121,NJ City,N,1,17,21,29,High School,19,Patnership,Standalone\n4489,155,NJ City,N,3,23,9,26,High School,29,Patnership,Standalone\n2581,299,NJ City,N,2,15,23,42,High School,34,Patnership,Standalone\n378,224,NJ City,S,2,19,23,51,High School,32,Patnership,Complex\n6557,468,Boston,N,2,17,9,74,Graduation,20,Patnership,Standalone\n2953,262.1,NJ City,N,5,18,3,81,High School,21,Patnership,Standalone\n8277,68.82,NJ City,N,3,9,4,39,Graduation,33,Patnership,Standalone\n5145,390,Boston,N,3,21,7,59,Graduation,30,Patnership,Standalone\n6925,0.7,Boston,N,1,19,1,47,High School,32,Patnership,Standalone\n3886,310,NJ City,S,1,10,16,58,High School,20,Patnership,Complex\n4464,77.47,NJ City,W,8,22,4,32,Graduation,20,Franchise,Complex\n2869,155.34,NJ City,N,2,14,5,55,High School,35,Patnership,Standalone\n6176,413.81,Boston,W,3,19,5,25,No education,35,Franchise,Complex\n1780,159,NJ City,W,3,11,18,40,High School,24,Franchise,Complex\n1347,316,NJ City,E,3,11,11,44,High School,35,Owner,Standalone\n3123,308,NJ City,E,1,17,20,80,High School,19,Owner,Standalone\n7398,384,Boston,S,3,14,20,36,Graduation,22,Patnership,Complex\n5735,497.42,Boston,E,3,20,5,40,No education,22,Owner,Standalone\n8169,459,NJ City,N,3,14,17,66,Graduation,32,Patnership,Standalone\n584,242,NJ City,W,3,13,24,59,High School,35,Franchise,Complex\n7955,81.16,Boston,E,20,8,4,85,No education,25,Owner,Standalone\n8484,355.31,NJ City,W,24,16,3,59,Graduation,25,Franchise,Complex\n9034,78.34,NJ City,S,13,23,3,57,High School,24,Patnership,Complex\n494,262,NJ City,S,3,14,18,35,High School,34,Patnership,Complex\n3044,216,NJ City,W,2,15,22,51,High School,28,Franchise,Complex\n7488,415,Boston,W,2,20,24,44,Graduation,32,Franchise,Complex\n2598,211,NJ City,S,1,16,15,84,High School,24,Patnership,Complex\n5102,15.47,Boston,N,4,24,3,43,No education,18,Patnership,Complex\n3716,383.32,NJ City,W,4,15,2,69,No education,19,Franchise,Complex\n8622,387,NJ City,S,2,15,10,73,Graduation,21,Patnership,Complex\n2470,148,NJ City,S,2,22,13,29,High School,31,Patnership,Complex\n5861,468,Boston,N,3,24,20,32,Graduation,24,Patnership,Standalone\n6428,498,Boston,W,3,22,15,51,Graduation,29,Franchise,Complex\n8454,403,NJ City,S,2,8,23,73,Graduation,28,Patnership,Complex\n6172,425.25,Boston,W,18,11,2,58,High School,22,Franchise,Complex\n2278,132,NJ City,S,1,20,6,64,High School,23,Patnership,Complex\n4260,313,NJ City,W,3,20,25,58,High School,29,Franchise,Complex\n4356,217,NJ City,W,2,24,22,44,High School,30,Franchise,Complex\n3564,115,NJ City,W,3,20,9,44,High School,30,Franchise,Complex\n5913,491,Boston,N,1,14,9,77,Graduation,20,Patnership,Standalone\n8174,424,NJ City,S,3,20,9,69,Graduation,23,Patnership,Complex\n5071,156.45,Boston,E,21,23,2,52,High School,18,Owner,Standalone\n363,184,NJ City,E,1,12,22,84,High School,27,Owner,Standalone\n113,21.74,NJ City,N,6,21,2,55,No education,35,Patnership,Standalone\n7129,368,Boston,N,1,18,22,30,Graduation,23,Patnership,Standalone\n7977,48.86,Boston,N,24,21,4,82,Graduation,29,Patnership,Standalone\n9510,496,NJ City,S,1,19,13,72,Graduation,34,Patnership,Complex\n310,57.75,NJ City,S,17,22,4,37,High School,33,Patnership,Complex\n9374,439,NJ City,S,3,8,22,30,Graduation,32,Patnership,Complex\n713,150,NJ City,N,1,10,11,66,High School,26,Patnership,Standalone\n6097,339.96,Boston,N,7,9,2,39,High School,21,Patnership,Standalone\n8273,431,NJ City,N,3,14,12,60,Graduation,34,Patnership,Standalone\n650,258.45,NJ City,S,9,22,2,78,No education,34,Patnership,Complex\n4548,126.81,NJ City,W,22,8,4,79,Graduation,19,Franchise,Complex\n5786,495,Boston,S,1,20,19,71,Graduation,19,Patnership,Complex\n95,45.45,NJ City,N,12,12,1,48,No education,25,Owner,Standalone\n896,97.24,NJ City,W,12,15,1,54,No education,18,Franchise,Complex\n6103,32.7,Boston,N,21,18,2,50,High School,19,Owner,Standalone\n889,63.97,NJ City,N,1,18,2,57,High School,20,Patnership,Standalone\n2688,317,NJ City,W,1,18,18,85,High School,22,Franchise,Complex\n7905,13.47,Boston,N,21,18,3,53,Graduation,23,Patnership,Standalone\n7557,469,Boston,N,3,20,25,52,Graduation,27,Patnership,Standalone\n414,50.18,NJ City,S,1,17,4,48,Graduation,26,Patnership,Complex\n1864,228,NJ City,W,2,19,25,47,High School,23,Franchise,Complex\n9320,355,NJ City,W,1,15,9,74,Graduation,33,Franchise,Complex\n9654,443,NJ City,S,2,17,20,77,Graduation,25,Patnership,Complex\n184,304,NJ City,W,3,13,22,53,High School,32,Franchise,Complex\n8897,484,NJ City,N,2,15,23,56,Graduation,22,Patnership,Standalone\n6278,500,Boston,S,2,22,15,37,Graduation,21,Patnership,Complex\n4408,312,NJ City,W,1,24,19,57,High School,30,Franchise,Complex\n464,55.19,NJ City,W,17,12,4,34,No education,32,Franchise,Complex\n551,242,NJ City,E,2,19,6,75,High School,20,Owner,Standalone\n6279,473,Boston,E,2,13,14,29,Graduation,23,Owner,Standalone\n2134,245.84,NJ City,S,19,22,4,64,High School,25,Patnership,Complex\n8112,473,NJ City,W,1,20,14,76,Graduation,24,Franchise,Complex\n4186,240,NJ City,S,3,12,17,30,High School,22,Patnership,Complex\n5202,393,Boston,S,3,19,19,54,Graduation,23,Patnership,Complex\n6074,386,Boston,S,3,11,20,59,Graduation,32,Patnership,Complex\n1132,197,NJ City,W,3,18,22,46,High School,34,Franchise,Complex\n6878,101.66,Boston,S,17,22,2,74,No education,25,Patnership,Complex\n9014,454,NJ City,S,2,16,22,82,Graduation,21,Patnership,Complex\n5953,21.69,Boston,N,10,9,2,41,High School,23,Patnership,Standalone\n4669,467.97,NJ City,N,11,12,3,56,High School,29,Patnership,Standalone\n411,191,NJ City,E,2,22,11,32,High School,32,Owner,Standalone\n6701,440,Boston,N,3,21,22,42,Graduation,32,Patnership,Standalone\n1034,108.97,NJ City,S,21,23,1,59,No education,19,Patnership,Complex\n3039,45.28,NJ City,N,10,15,3,55,Graduation,34,Owner,Standalone\n3828,253,NJ City,W,1,17,19,75,High School,35,Franchise,Complex\n8692,401,NJ City,W,2,11,6,78,Graduation,18,Franchise,Complex\n6991,452,Boston,E,2,16,8,30,Graduation,21,Owner,Standalone\n2506,74.43,NJ City,S,21,24,3,37,High School,34,Patnership,Complex\n5590,415,Boston,S,3,21,11,47,Graduation,22,Patnership,Complex\n6997,420,Boston,N,2,17,10,33,Graduation,21,Patnership,Standalone\n465,123.63,NJ City,N,7,12,3,53,Graduation,34,Patnership,Standalone\n683,235,NJ City,E,1,8,7,34,High School,24,Owner,Standalone\n3779,52.32,NJ City,E,3,24,4,43,No education,29,Owner,Standalone\n2809,96.8,NJ City,N,9,9,4,27,High School,29,Patnership,Standalone\n9603,93.14,NJ City,E,20,16,4,28,Graduation,23,Owner,Standalone\n473,63.76,NJ City,N,3,9,3,48,No education,33,Patnership,Standalone\n5986,416,Boston,S,3,8,21,71,Graduation,30,Patnership,Complex\n3418,276.79,NJ City,S,22,10,4,69,High School,30,Patnership,Complex\n1511,244,NJ City,E,1,9,23,84,High School,34,Owner,Standalone\n520,344.13,NJ City,W,8,14,3,80,High School,33,Franchise,Complex\n6024,456,Boston,W,3,16,6,56,Graduation,22,Franchise,Complex\n1573,183,NJ City,N,1,15,15,57,High School,32,Patnership,Standalone\n2731,483.17,NJ City,E,15,23,4,69,High School,34,Owner,Standalone\n5014,374,Boston,S,1,22,6,51,Graduation,19,Patnership,Complex\n6467,392,Boston,E,2,8,18,72,Graduation,21,Owner,Standalone\n9933,62.88,NJ City,N,9,22,1,56,Graduation,33,Patnership,Standalone\n1724,484.46,NJ City,W,2,11,5,27,No education,30,Franchise,Complex\n2799,247,NJ City,E,2,12,17,36,High School,29,Owner,Standalone\n9431,372,NJ City,E,2,14,15,52,Graduation,22,Owner,Standalone\n9905,116.82,NJ City,N,21,13,1,44,No education,18,Patnership,Standalone\n9876,484,NJ City,W,2,23,25,78,Graduation,35,Franchise,Complex\n2865,132,NJ City,N,3,15,14,30,High School,35,Patnership,Standalone\n8180,378,NJ City,W,1,20,14,26,Graduation,32,Franchise,Complex\n9277,414,NJ City,N,1,11,16,43,Graduation,35,Patnership,Standalone\n7069,33.82,Boston,N,7,21,2,36,High School,34,Patnership,Standalone\n4394,159,NJ City,S,2,22,15,37,High School,26,Patnership,Complex\n8756,421,NJ City,W,3,14,25,83,Graduation,35,Franchise,Complex\n6740,343.36,Boston,W,16,10,2,53,No education,18,Franchise,Complex\n6799,411,Boston,E,2,22,10,83,Graduation,26,Owner,Standalone\n320,193.56,NJ City,W,3,11,5,63,No education,28,Franchise,Complex\n9425,380,NJ City,N,2,19,17,58,Graduation,18,Patnership,Standalone\n6828,10.24,Boston,N,8,11,1,29,Graduation,32,Franchise,Complex\n4756,283,NJ City,W,1,23,12,33,High School,33,Franchise,Complex\n6310,181.5,Boston,S,4,23,4,78,High School,18,Patnership,Complex\n3658,310,NJ City,S,1,23,17,53,High School,32,Patnership,Complex\n1646,220,NJ City,S,1,20,16,67,High School,20,Patnership,Complex\n2343,165,NJ City,E,1,17,21,70,High School,24,Owner,Standalone\n9735,439,NJ City,E,1,16,17,43,Graduation,30,Owner,Standalone\n4423,54.87,NJ City,E,25,9,4,75,High School,27,Owner,Standalone\n8222,461,NJ City,S,2,19,21,82,Graduation,23,Patnership,Complex\n2238,363,NJ City,S,2,23,5,57,Graduation,29,Patnership,Complex\n8107,46.53,NJ City,N,5,20,1,46,High School,19,Owner,Standalone\n4528,231,NJ City,W,3,17,6,58,High School,20,Franchise,Complex\n7445,360,Boston,N,1,9,16,57,Graduation,26,Patnership,Standalone\n3888,48.2,NJ City,W,24,21,3,76,Graduation,20,Franchise,Complex\n6823,96.84,Boston,E,24,14,3,71,High School,21,Owner,Standalone\n3870,136,NJ City,S,1,15,15,46,High School,26,Patnership,Complex\n614,169,NJ City,S,2,18,12,66,High School,21,Patnership,Complex\n9990,382,NJ City,S,3,11,18,81,Graduation,18,Patnership,Complex\n3401,211,NJ City,N,2,14,16,36,High School,28,Patnership,Standalone\n5314,72.18,Boston,N,6,10,4,43,High School,27,Patnership,Complex\n4048,235,NJ City,W,3,14,24,59,High School,30,Franchise,Complex\n836,143,NJ City,W,2,13,20,36,High School,19,Franchise,Complex\n4999,404.87,Boston,E,5,20,4,52,High School,29,Owner,Standalone\n8864,125,NJ City,W,24,10,4,39,No education,19,Franchise,Complex\n11,32.81,NY City,N,18,22,1,38,No education,31,Owner,Standalone\n8766,494,NJ City,S,14,12,18,61,Graduation,33,Patnership,Complex\n4912,6.59,NJ City,N,6,23,1,80,High School,20,Franchise,Complex\n4980,131.41,Boston,W,19,20,15,63,High School,30,Franchise,Complex\n3603,59.41,NJ City,E,12,23,2,42,Graduation,33,Owner,Standalone\n5498,470,Boston,S,13,10,17,41,Graduation,32,Patnership,Complex\n8033,435,Boston,N,18,16,18,76,Graduation,27,Patnership,Standalone\n6873,356.99,Boston,N,25,10,3,71,Graduation,28,Patnership,Standalone\n8297,25.53,NJ City,N,3,23,3,65,No education,30,Patnership,Standalone\n302,237,NJ City,S,1,15,15,51,High School,30,Patnership,Complex\n7931,460,Boston,E,2,10,15,70,Graduation,29,Owner,Standalone\n348,480.25,NJ City,W,19,9,7,65,High School,23,Franchise,Complex\n9509,109.17,NJ City,N,13,21,1,28,No education,18,Patnership,Standalone\n2199,388.95,NJ City,E,15,17,7,61,High School,25,Owner,Standalone\n7031,390,Boston,E,4,19,21,33,Graduation,27,Owner,Standalone\n8645,465,NJ City,N,8,18,11,79,Graduation,30,Patnership,Standalone\n5505,126.75,Boston,N,8,16,1,46,Graduation,28,Patnership,Standalone\n5655,480,Boston,E,15,18,17,74,Graduation,34,Owner,Standalone\n2435,254.45,NJ City,E,11,23,3,78,No education,21,Owner,Standalone\n3375,140,NJ City,E,2,8,14,47,High School,21,Owner,Standalone\n6673,443,Boston,N,7,24,15,29,Graduation,35,Patnership,Standalone\n8239,407,NJ City,E,1,14,19,50,Graduation,21,Owner,Standalone\n6426,376,Boston,S,21,23,12,39,Graduation,26,Patnership,Complex\n4279,215.8,NJ City,E,4,16,1,82,High School,27,Owner,Standalone\n2910,341.76,NJ City,S,24,11,21,63,High School,20,Patnership,Complex\n554,315.23,NJ City,S,24,11,4,34,No education,21,Patnership,Complex\n4939,127.52,Boston,E,23,11,1,73,High School,25,Owner,Standalone\n9337,498,NJ City,N,18,8,23,35,Graduation,23,Patnership,Standalone\n572,124.05,NJ City,W,8,8,3,76,No education,34,Franchise,Complex\n6751,499,Boston,E,12,23,23,26,Graduation,32,Owner,Standalone\n9871,440,NJ City,E,15,14,19,36,Graduation,35,Owner,Standalone\n1703,239.26,NJ City,E,19,23,13,78,High School,29,Owner,Standalone\n7279,361,Boston,E,24,18,25,55,Graduation,35,Owner,Standalone\n697,60.82,NJ City,N,4,19,3,47,High School,24,Patnership,Standalone\n9235,112.11,NJ City,E,21,17,4,68,High School,22,Owner,Standalone\n3695,169.53,NJ City,E,21,18,5,36,No education,21,Owner,Standalone\n558,306.52,NJ City,S,22,15,4,69,Graduation,22,Patnership,Complex\n2464,335.09,NJ City,W,17,16,2,44,High School,27,Franchise,Complex\n1140,434.72,NJ City,W,9,16,8,58,High School,34,Franchise,Complex\n3624,373.77,NJ City,W,19,11,18,26,High School,23,Franchise,Complex\n9208,463,NJ City,W,19,24,18,77,Graduation,33,Franchise,Complex\n6724,360,Boston,W,9,16,25,72,Graduation,24,Franchise,Complex\n5073,441.9,Boston,N,18,23,3,55,Graduation,35,Patnership,Standalone\n3985,253.01,NJ City,N,6,18,24,34,High School,33,Patnership,Standalone\n9098,6.54,NJ City,N,9,16,2,66,No education,30,Patnership,Complex\n1499,128.87,NJ City,E,23,23,13,31,High School,21,Owner,Standalone\n1832,453.27,NJ City,W,19,11,8,43,High School,31,Franchise,Complex\n4764,169.83,NJ City,W,20,18,18,70,High School,25,Franchise,Complex\n9566,399,NJ City,S,9,21,20,61,Graduation,29,Patnership,Complex\n8348,495,NJ City,W,5,14,23,73,Graduation,30,Franchise,Complex\n5760,457,Boston,W,14,22,8,37,Graduation,24,Franchise,Complex\n154,415.51,NJ City,S,10,23,8,64,High School,20,Patnership,Complex\n5846,102.99,Boston,S,3,22,2,56,No education,31,Patnership,Complex\n1802,295.83,NJ City,S,6,22,4,77,No education,21,Patnership,Complex\n3328,295,NJ City,W,22,11,15,77,High School,21,Franchise,Complex\n3238,127.41,NJ City,S,8,19,3,59,High School,31,Patnership,Complex\n9435,385.01,NJ City,E,20,20,1,43,Graduation,18,Owner,Standalone\n628,332.95,NJ City,W,9,19,3,59,High School,18,Franchise,Complex\n2732,231,NJ City,W,1,14,11,59,High School,26,Franchise,Complex\n6613,491,Boston,N,21,16,7,63,Graduation,32,Patnership,Standalone\n2345,481.21,NJ City,N,15,11,21,73,High School,26,Patnership,Standalone\n4101,187.24,NJ City,N,5,13,23,64,High School,33,Patnership,Standalone\n1635,188,NJ City,E,2,17,17,25,High School,20,Owner,Standalone\n6137,387,Boston,N,17,22,22,56,Graduation,29,Patnership,Standalone\n2995,31.53,NJ City,N,22,18,1,75,High School,29,Owner,Standalone\n6667,376,Boston,E,8,15,11,68,Graduation,25,Owner,Standalone\n1407,10.46,NJ City,N,23,8,2,70,Graduation,32,Owner,Standalone\n325,366.65,NJ City,N,4,15,20,85,High School,29,Patnership,Standalone\n1216,441.75,NJ City,W,25,9,22,33,High School,21,Franchise,Complex\n5822,431,Boston,S,25,21,24,76,Graduation,29,Patnership,Complex\n5903,118.36,Boston,E,7,13,4,59,No education,18,Owner,Standalone\n9737,296.53,NJ City,N,16,10,1,44,No education,19,Patnership,Standalone\n7465,406,Boston,N,25,23,11,85,Graduation,20,Patnership,Standalone\n6285,357,Boston,N,9,22,20,58,Graduation,23,Patnership,Standalone\n1230,278,NJ City,S,2,15,13,67,High School,34,Patnership,Complex\n92,462.54,NJ City,W,16,12,25,81,High School,26,Franchise,Complex\n2572,383.74,NJ City,W,22,18,1,31,High School,18,Franchise,Complex\n6587,104.1,Boston,E,7,11,4,57,No education,18,Owner,Standalone\n4633,219.37,NJ City,N,14,9,16,31,High School,29,Patnership,Standalone\n1352,413.07,NJ City,W,12,18,24,52,High School,35,Franchise,Complex\n8384,471,NJ City,W,24,16,24,37,Graduation,22,Franchise,Complex\n4606,447.53,NJ City,S,24,13,4,85,High School,35,Patnership,Complex\n46,266.2,Boston,S,19,21,9,36,High School,26,Patnership,Complex\n8050,374,NJ City,S,12,22,21,44,Graduation,33,Patnership,Complex\n1385,58.04,NJ City,N,4,15,2,47,No education,32,Patnership,Standalone\n2110,410.91,NJ City,S,15,8,8,75,High School,29,Patnership,Complex\n323,204,NJ City,E,2,20,22,79,High School,27,Owner,Standalone\n3361,453.7,NJ City,N,18,13,17,27,High School,26,Patnership,Standalone\n6717,181.77,Boston,N,15,16,3,41,Graduation,24,Patnership,Standalone\n7092,359.94,Boston,W,14,19,3,31,Graduation,35,Franchise,Complex\n1949,218.78,NJ City,N,19,17,14,64,High School,32,Patnership,Standalone\n6948,370,Boston,W,5,8,13,78,Graduation,30,Franchise,Complex\n258,235.6,NJ City,S,18,22,14,40,High School,25,Patnership,Complex\n2125,114.94,NJ City,N,21,11,2,69,High School,35,Patnership,Standalone\n9102,350,NJ City,S,24,15,19,36,Graduation,26,Patnership,Complex\n7867,373,Boston,E,19,10,14,71,Graduation,31,Owner,Standalone\n8720,449,NJ City,W,21,8,25,56,Graduation,19,Franchise,Complex\n6262,438,Boston,S,21,17,18,29,Graduation,29,Patnership,Complex\n2901,408.31,NJ City,N,6,20,2,44,Graduation,32,Patnership,Standalone\n6967,378,Boston,E,12,14,25,25,Graduation,21,Owner,Standalone\n1736,317.93,NJ City,W,19,18,13,50,High School,31,Franchise,Complex\n3086,94.06,NJ City,S,3,16,3,82,No education,34,Patnership,Complex\n9841,59.6,NJ City,N,22,14,3,63,High School,20,Patnership,Standalone\n1220,84.32,NJ City,W,19,13,3,85,No education,22,Franchise,Complex\n4420,133.74,NJ City,W,20,17,3,46,High School,23,Franchise,Complex\n8106,43.09,NJ City,N,25,11,3,64,Graduation,24,Patnership,Complex\n6287,26.89,Boston,N,6,8,4,68,No education,31,Owner,Standalone\n6738,278.34,Boston,S,24,13,3,75,Graduation,31,Patnership,Complex\n9817,430,NJ City,N,12,14,11,30,Graduation,28,Patnership,Standalone\n5037,358,Boston,N,2,17,9,33,Graduation,28,Patnership,Standalone\n2410,63.54,NJ City,N,18,14,4,77,High School,19,Patnership,Complex\n8351,408,NJ City,E,14,17,15,80,Graduation,32,Owner,Standalone\n3366,79.68,NJ City,S,14,8,3,36,Graduation,22,Patnership,Complex\n7497,496,Boston,N,12,20,22,72,Graduation,23,Patnership,Standalone\n7432,67.96,Boston,N,10,18,1,61,High School,29,Franchise,Complex\n24,101.09,Maine,W,2,12,1,59,Graduation,26,Franchise,Complex\n8287,354,NJ City,E,17,14,20,30,Graduation,25,Owner,Standalone\n5320,414.52,Boston,W,2,17,1,51,High School,24,Franchise,Complex\n581,105.61,NJ City,N,7,18,2,55,No education,27,Patnership,Standalone\n2138,304.3,NJ City,S,15,18,12,40,High School,27,Patnership,Complex\n883,168.97,NJ City,E,10,21,24,49,High School,26,Owner,Standalone\n2354,351.61,NJ City,S,13,14,25,38,High School,31,Patnership,Complex\n3940,132.23,NJ City,W,16,10,18,70,High School,31,Franchise,Complex\n6875,408,Boston,E,4,24,9,51,Graduation,26,Owner,Standalone\n2228,130.29,NJ City,W,25,24,25,30,High School,26,Franchise,Complex\n5971,75.62,Boston,E,22,11,3,45,High School,28,Owner,Standalone\n8197,372.78,NJ City,N,6,8,5,61,High School,18,Patnership,Standalone\n1676,329.22,NJ City,W,10,17,22,29,High School,32,Franchise,Complex\n7145,490,Boston,N,10,9,10,44,Graduation,27,Patnership,Standalone\n9545,471,NJ City,N,7,8,19,30,Graduation,21,Patnership,Standalone\n4777,208.95,NJ City,N,18,17,16,71,High School,26,Patnership,Standalone\n4158,482.05,NJ City,S,21,22,25,46,High School,35,Patnership,Complex\n3439,100.34,NJ City,E,11,20,4,80,High School,22,Owner,Standalone\n6560,487,Boston,W,9,16,24,40,Graduation,30,Franchise,Complex\n5956,497,Boston,W,15,13,23,30,Graduation,20,Franchise,Complex\n9075,458,NJ City,E,2,18,25,81,Graduation,30,Owner,Standalone\n8445,357,NJ City,N,11,23,10,53,Graduation,32,Patnership,Standalone\n3024,410,NJ City,W,8,18,5,33,Graduation,35,Franchise,Complex\n9180,396,NJ City,W,14,17,17,49,Graduation,21,Franchise,Complex\n6050,86.78,Boston,S,3,14,1,46,No education,20,Patnership,Complex\n4615,148.33,NJ City,E,2,8,2,37,High School,20,Owner,Standalone\n5554,94.26,Boston,S,17,13,3,31,High School,28,Patnership,Complex\n6640,496,Boston,W,17,13,25,77,Graduation,29,Franchise,Complex\n4267,309.49,NJ City,E,4,19,10,32,High School,19,Owner,Standalone\n1218,300.03,NJ City,S,8,16,10,36,High School,32,Patnership,Complex\n9318,368,NJ City,S,9,21,17,43,Graduation,34,Patnership,Complex\n308,351.62,NJ City,W,5,19,22,77,High School,19,Franchise,Complex\n5231,361,Boston,E,4,9,24,45,Graduation,23,Owner,Standalone\n6618,479,Boston,S,21,24,15,75,Graduation,24,Patnership,Complex\n3258,325.46,NJ City,S,6,9,12,67,High School,22,Patnership,Complex\n5392,464,Boston,W,23,14,20,28,Graduation,31,Franchise,Complex\n9874,325.77,NJ City,S,20,20,1,26,High School,34,Patnership,Complex\n4074,311.56,NJ City,S,1,11,3,28,Graduation,29,Patnership,Complex\n9569,495,NJ City,N,5,18,15,67,Graduation,35,Patnership,Standalone\n4688,69.43,NJ City,N,25,15,1,36,No education,30,Franchise,Complex\n7738,381,Boston,S,1,21,24,67,Graduation,34,Patnership,Complex\n9997,379,NJ City,N,11,14,9,74,Graduation,28,Patnership,Standalone\n4855,97.9,NJ City,E,25,12,3,47,High School,27,Owner,Standalone\n5612,90.01,Boston,W,24,20,3,36,No education,30,Franchise,Complex\n2544,27.21,NJ City,N,25,9,2,75,Graduation,29,Franchise,Complex\n1501,381.03,NJ City,N,15,17,19,65,High School,33,Patnership,Standalone\n8777,177.88,NJ City,N,21,8,4,37,No education,24,Patnership,Standalone\n9844,119.08,NJ City,W,20,15,2,55,High School,34,Franchise,Complex\n8953,494,NJ City,N,5,18,25,48,Graduation,23,Patnership,Standalone\n5325,377,Boston,N,24,19,18,35,Graduation,19,Patnership,Standalone\n7643,461.38,Boston,E,16,20,4,43,No education,34,Owner,Standalone\n635,133.26,NJ City,E,4,17,25,38,High School,35,Owner,Standalone\n288,471.69,NJ City,W,25,14,23,75,High School,31,Franchise,Complex\n7345,39.02,Boston,N,19,15,1,42,High School,34,Patnership,Standalone\n7450,2.39,Boston,S,12,13,3,34,High School,26,Patnership,Complex\n660,85.24,NJ City,W,2,23,3,47,Graduation,23,Franchise,Complex\n8249,469,NJ City,N,17,13,7,27,Graduation,24,Patnership,Standalone\n6790,66.91,Boston,N,24,21,4,40,High School,23,Patnership,Complex\n3303,48.83,NJ City,E,1,21,2,43,Graduation,30,Owner,Standalone\n6410,490,Boston,S,19,23,21,48,Graduation,34,Patnership,Complex\n2481,402.04,NJ City,N,24,11,2,81,Graduation,18,Patnership,Standalone\n1848,408.87,NJ City,W,13,10,1,67,Graduation,29,Franchise,Complex\n6981,380,Boston,N,22,8,12,79,Graduation,28,Patnership,Standalone\n1710,432.69,NJ City,S,5,10,11,37,High School,27,Patnership,Complex\n8935,358,NJ City,E,10,18,16,57,Graduation,34,Owner,Standalone\n4625,435.63,NJ City,N,18,10,13,73,High School,25,Patnership,Standalone\n679,350.16,NJ City,E,22,23,4,36,High School,29,Owner,Standalone\n5544,376,Boston,W,13,24,5,28,Graduation,30,Franchise,Complex\n5747,426,Boston,E,17,17,25,51,Graduation,29,Owner,Standalone\n1778,442.65,NJ City,S,6,16,22,35,High School,22,Patnership,Complex\n2462,317.54,NJ City,S,20,13,17,59,High School,23,Patnership,Complex\n7732,94.49,Boston,W,4,18,2,58,High School,21,Franchise,Complex\n1366,216.51,NJ City,S,12,11,24,66,High School,29,Patnership,Complex\n4596,35.36,NJ City,N,18,24,3,48,Graduation,34,Franchise,Complex\n7478,483,Boston,S,8,18,6,32,Graduation,33,Patnership,Complex\n48,403,Boston,W,17,12,5,84,Graduation,24,Franchise,Complex\n4337,100.91,NJ City,N,7,11,1,78,No education,32,Patnership,Standalone\n3200,129.15,NJ City,W,23,9,19,66,High School,21,Franchise,Complex\n1130,145,NJ City,S,2,15,16,68,High School,30,Patnership,Complex\n7470,417,Boston,S,5,14,16,75,Graduation,21,Patnership,Complex\n3644,116.59,NJ City,W,23,21,3,44,No education,24,Franchise,Complex\n6893,429,Boston,N,24,19,15,59,Graduation,26,Patnership,Standalone\n8110,454,NJ City,S,7,10,14,44,Graduation,20,Patnership,Complex\n9094,175.93,NJ City,S,23,17,5,25,High School,25,Patnership,Complex\n6763,465,Boston,E,1,22,21,85,Graduation,33,Owner,Standalone\n9025,356,NJ City,N,24,9,15,64,Graduation,18,Patnership,Standalone\n996,490.82,NJ City,W,4,13,22,41,High School,22,Franchise,Complex\n5034,390,Boston,S,19,16,15,54,Graduation,24,Patnership,Complex\n589,489.68,NJ City,N,19,20,13,31,High School,31,Patnership,Standalone\n31,58.94,Boston,E,5,12,2,29,High School,18,Owner,Standalone\n3492,52.43,NJ City,W,23,23,1,30,Graduation,33,Franchise,Complex\n7588,430,Boston,W,11,24,18,74,Graduation,19,Franchise,Complex\n6562,66.57,Boston,N,18,24,2,50,High School,25,Patnership,Complex\n7369,448,Boston,N,13,23,23,37,Graduation,31,Patnership,Standalone\n1671,216.4,NJ City,E,9,15,14,72,High School,35,Owner,Standalone\n9112,90.6,NJ City,W,3,21,4,47,High School,27,Franchise,Complex\n9736,96.43,NJ City,W,2,14,2,63,High School,25,Franchise,Complex\n6427,418,Boston,E,11,17,9,45,Graduation,19,Owner,Standalone\n9609,500,NJ City,N,1,11,5,75,Graduation,25,Patnership,Standalone\n1202,212.07,NJ City,S,18,19,2,29,No education,27,Patnership,Complex\n5856,418,Boston,W,23,10,12,38,Graduation,24,Franchise,Complex\n3781,448.77,NJ City,N,19,18,15,76,High School,32,Patnership,Standalone\n5565,434.29,Boston,N,19,8,4,52,Graduation,21,Patnership,Standalone\n648,158.22,NJ City,W,14,21,17,68,High School,21,Franchise,Complex\n5492,474,Boston,W,17,18,20,53,Graduation,26,Franchise,Complex\n6213,409,Boston,N,18,9,13,78,Graduation,20,Patnership,Standalone\n5306,415,Boston,S,12,18,13,84,Graduation,25,Patnership,Complex\n4118,196.86,NJ City,S,20,13,7,51,High School,30,Patnership,Complex\n4399,25.98,NJ City,N,13,14,3,50,High School,29,Owner,Standalone\n2315,11.63,NJ City,N,16,11,1,59,No education,27,Owner,Standalone\n3141,360.36,NJ City,N,16,19,19,30,High School,28,Patnership,Standalone\n9341,34.57,NJ City,N,10,8,3,36,No education,18,Patnership,Standalone\n8530,316.66,NJ City,S,24,19,4,75,High School,23,Patnership,Complex\n7645,413,Boston,N,20,23,19,71,Graduation,29,Patnership,Standalone\n7900,350.67,Boston,W,3,17,1,60,High School,22,Franchise,Complex\n2375,111.79,NJ City,E,8,18,2,82,No education,19,Owner,Standalone\n8573,436.01,NJ City,N,8,20,4,83,No education,31,Patnership,Standalone\n5451,425,Boston,E,9,8,13,27,Graduation,33,Owner,Standalone\n166,244,NJ City,S,3,18,19,79,High School,35,Patnership,Complex\n1236,187.36,NJ City,W,17,13,24,56,High School,25,Franchise,Complex\n7376,486,Boston,W,4,15,20,26,Graduation,35,Franchise,Complex\n637,13.23,NJ City,N,16,15,2,35,High School,34,Patnership,Standalone\n887,264.51,NJ City,E,12,18,15,82,High School,34,Owner,Standalone\n1950,295.62,NJ City,S,5,10,14,38,High School,24,Patnership,Complex\n8247,356,NJ City,E,7,24,16,57,Graduation,19,Owner,Standalone\n6811,11.25,Boston,N,4,20,4,53,High School,26,Owner,Standalone\n6183,63.51,Boston,N,7,21,4,31,Graduation,20,Owner,Standalone\n5906,498,Boston,S,9,21,12,53,Graduation,31,Patnership,Complex\n3808,102.13,NJ City,W,9,15,2,47,High School,21,Franchise,Complex\n1354,321.13,NJ City,S,12,23,9,82,High School,34,Patnership,Complex\n8608,359,NJ City,W,12,10,24,69,Graduation,35,Franchise,Complex\n5877,25.2,Boston,N,11,18,3,75,Graduation,18,Patnership,Standalone\n9410,410,NJ City,S,20,15,18,52,Graduation,35,Patnership,Complex\n3969,117.52,NJ City,N,12,21,4,76,Graduation,24,Patnership,Standalone\n9004,397,NJ City,W,23,19,20,50,Graduation,33,Franchise,Complex\n4676,360.69,NJ City,W,20,11,16,65,High School,26,Franchise,Complex\n6964,359.99,Boston,W,13,15,3,35,High School,21,Franchise,Complex\n616,188.19,NJ City,W,6,13,22,42,High School,22,Franchise,Complex\n1706,490.79,NJ City,S,22,22,4,31,No education,29,Patnership,Complex\n5345,335.07,Boston,N,1,16,4,52,No education,19,Patnership,Standalone\n5388,95.6,Boston,W,13,22,2,64,Graduation,25,Franchise,Complex\n7027,41.06,Boston,N,1,19,4,41,High School,28,Owner,Standalone\n5787,119.27,Boston,E,18,17,4,47,Graduation,21,Owner,Standalone\n2978,137.92,NJ City,S,10,16,13,28,High School,21,Patnership,Complex\n6264,16.49,Boston,N,4,8,4,43,Graduation,30,Franchise,Complex\n4047,220.66,NJ City,E,15,18,14,85,High School,32,Owner,Standalone\n9007,271.52,NJ City,E,21,14,1,61,High School,18,Owner,Standalone\n4440,207,NJ City,W,3,20,20,70,High School,28,Franchise,Complex\n4658,218.83,NJ City,S,5,8,5,70,No education,32,Patnership,Complex\n2263,123.71,NJ City,E,25,20,4,74,High School,18,Owner,Standalone\n5287,65.14,Boston,N,3,11,1,85,High School,33,Owner,Standalone\n2912,168.73,NJ City,W,16,14,18,51,High School,18,Franchise,Complex\n1118,182.35,NJ City,S,10,11,17,32,High School,20,Patnership,Complex\n5608,467,Boston,W,18,21,23,74,Graduation,31,Franchise,Complex\n745,307.13,NJ City,N,18,18,9,80,High School,20,Patnership,Standalone\n9825,18.69,NJ City,N,7,16,1,61,Graduation,25,Patnership,Standalone\n9399,499,NJ City,E,18,16,16,62,Graduation,29,Owner,Standalone\n1307,139.32,NJ City,E,25,14,10,35,High School,34,Owner,Standalone\n5169,49.1,Boston,N,24,21,2,55,Graduation,31,Patnership,Standalone\n6190,396,Boston,S,13,11,21,29,Graduation,19,Patnership,Complex\n17,168,Boston,N,2,8,19,52,High School,28,Patnership,Standalone\n7253,484,Boston,N,19,23,8,67,Graduation,29,Patnership,Standalone\n4284,168.14,NJ City,W,9,16,25,26,High School,31,Franchise,Complex\n2864,359.71,NJ City,W,8,17,3,79,No education,23,Franchise,Complex\n435,154.37,NJ City,E,13,21,20,26,High School,19,Owner,Standalone\n3477,96.29,NJ City,N,25,24,4,61,Graduation,34,Patnership,Standalone\n3322,230,NJ City,S,2,21,22,42,High School,35,Patnership,Complex\n2861,347.57,NJ City,N,22,15,17,66,High School,29,Patnership,Standalone\n8984,378,NJ City,W,13,23,9,39,Graduation,30,Franchise,Complex\n3428,469.03,NJ City,W,13,20,15,61,High School,35,Franchise,Complex\n2611,274.84,NJ City,E,8,24,16,56,High School,29,Owner,Standalone\n5870,39.55,Boston,N,14,13,4,46,No education,35,Patnership,Complex\n9751,190.39,NJ City,E,2,11,1,75,High School,31,Owner,Standalone\n3594,48.88,NJ City,S,23,14,3,68,Graduation,25,Patnership,Complex\n1920,250.34,NJ City,W,13,11,11,48,High School,19,Franchise,Complex\n2785,280.62,NJ City,N,14,19,21,42,High School,30,Patnership,Standalone\n9423,493,NJ City,E,4,21,13,62,Graduation,21,Owner,Standalone\n5022,437,Boston,S,6,14,12,57,Graduation,18,Patnership,Complex\n3782,368.74,NJ City,S,15,16,14,29,High School,28,Patnership,Complex\n6533,350,Boston,N,4,12,11,76,Graduation,21,Patnership,Standalone\n5119,422,Boston,E,10,18,11,52,Graduation,29,Owner,Standalone\n1516,224.16,NJ City,W,6,22,23,56,High School,20,Franchise,Complex\n3740,414.25,NJ City,W,8,8,5,38,No education,30,Franchise,Complex\n476,219.27,NJ City,W,18,20,18,42,High School,32,Franchise,Complex\n8687,491,NJ City,E,10,18,10,84,Graduation,18,Owner,Standalone\n4257,294,NJ City,N,3,19,8,80,High School,28,Patnership,Standalone\n727,110.63,NJ City,E,17,10,2,38,High School,20,Owner,Standalone\n5074,477,Boston,S,11,10,25,78,Graduation,33,Patnership,Complex\n8970,397,NJ City,S,17,20,24,37,Graduation,22,Patnership,Complex\n7637,386,Boston,N,18,9,8,76,Graduation,28,Patnership,Standalone\n1806,389.28,NJ City,S,24,19,21,81,High School,25,Patnership,Complex\n2608,246.92,NJ City,W,20,12,1,35,High School,20,Franchise,Complex\n5594,400,Boston,S,14,22,18,85,Graduation,35,Patnership,Complex\n7958,426,Boston,S,13,24,8,31,Graduation,23,Patnership,Complex\n215,273.56,NJ City,E,20,16,16,51,High School,35,Owner,Standalone\n4154,318.04,NJ City,S,20,9,6,63,High School,19,Patnership,Complex\n6961,378,Boston,N,14,10,20,59,Graduation,24,Patnership,Standalone\n8309,363,NJ City,N,11,9,19,31,Graduation,28,Patnership,Standalone\n6084,452,Boston,W,4,11,17,52,Graduation,27,Franchise,Complex\n4082,93.21,NJ City,S,19,20,4,47,No education,21,Patnership,Complex\n7585,461,Boston,N,4,24,14,34,Graduation,30,Patnership,Standalone\n8158,455,NJ City,S,23,21,19,44,Graduation,22,Patnership,Complex\n1628,457.02,NJ City,W,16,10,8,57,High School,29,Franchise,Complex\n8508,95.16,NJ City,W,18,20,2,80,Graduation,31,Franchise,Complex\n6581,475,Boston,N,21,23,7,67,Graduation,22,Patnership,Standalone\n1035,347.53,NJ City,E,13,12,9,65,High School,22,Owner,Standalone\n8656,472.01,NJ City,W,2,10,3,46,High School,24,Franchise,Complex\n5462,460,Boston,S,4,8,6,42,Graduation,26,Patnership,Complex\n3965,343.72,NJ City,N,22,21,9,76,High School,26,Patnership,Standalone\n6816,411,Boston,W,25,14,18,58,Graduation,20,Franchise,Complex\n1283,227.64,NJ City,E,24,23,20,36,High School,35,Owner,Standalone\n2773,141.13,NJ City,N,4,9,4,68,High School,22,Patnership,Standalone\n5387,354,Boston,E,7,18,6,45,Graduation,24,Owner,Standalone\n488,377.9,NJ City,W,19,12,25,26,High School,18,Franchise,Complex\n8854,266.68,NJ City,S,8,21,2,34,High School,27,Patnership,Complex\n4240,113,NJ City,W,2,19,23,44,High School,28,Franchise,Complex\n4992,425.23,Boston,W,8,20,7,75,High School,21,Franchise,Complex\n9747,33.71,NJ City,N,20,15,1,49,Graduation,25,Owner,Standalone\n2671,493.63,NJ City,E,25,11,16,63,High School,33,Owner,Standalone\n9755,52.53,NJ City,E,9,9,1,56,No education,18,Owner,Standalone\n8657,447,NJ City,N,18,18,18,75,Graduation,20,Patnership,Standalone\n5453,61.38,Boston,N,18,11,1,27,No education,24,Patnership,Standalone\n8200,413,NJ City,W,19,8,17,60,Graduation,25,Franchise,Complex\n5713,392,Boston,N,12,12,6,32,Graduation,35,Patnership,Standalone\n8513,448,NJ City,N,15,11,18,72,Graduation,26,Patnership,Standalone\n5944,215.58,Boston,W,24,8,3,46,High School,23,Franchise,Complex\n6119,372,Boston,E,17,22,14,81,Graduation,27,Owner,Standalone\n5425,368,Boston,N,14,17,12,41,Graduation,25,Patnership,Standalone\n5888,454,Boston,W,24,14,21,39,Graduation,33,Franchise,Complex\n2181,293,NJ City,N,1,14,11,55,High School,26,Patnership,Standalone\n3043,328.08,NJ City,E,23,14,2,43,High School,21,Owner,Standalone\n9756,67.87,NJ City,N,20,9,2,42,Graduation,20,Franchise,Complex\n6617,370,Boston,N,17,12,20,44,Graduation,26,Patnership,Standalone\n145,398.42,NJ City,N,22,15,4,63,High School,22,Patnership,Standalone\n556,491.49,NJ City,W,13,15,25,53,High School,35,Franchise,Complex\n4171,483.38,NJ City,E,14,16,11,73,High School,33,Owner,Standalone\n3029,207.18,NJ City,N,7,17,13,63,High School,22,Patnership,Standalone\n6793,271.19,Boston,N,14,13,4,62,High School,24,Patnership,Standalone\n9572,350,NJ City,W,5,18,20,25,Graduation,18,Franchise,Complex\n9477,305.77,NJ City,N,2,20,4,62,Graduation,32,Patnership,Standalone\n8172,376,NJ City,W,18,15,8,55,Graduation,30,Franchise,Complex\n2460,206,NJ City,W,2,8,8,84,High School,30,Franchise,Complex\n6803,283.67,Boston,E,6,23,3,46,No education,23,Owner,Standalone\n1905,409.53,NJ City,N,11,23,3,83,Graduation,25,Patnership,Standalone\n6041,395.46,Boston,N,4,12,4,43,No education,34,Patnership,Standalone\n3591,315.97,NJ City,E,19,17,6,50,High School,34,Owner,Standalone\n123,146.13,NJ City,E,23,19,6,40,High School,32,Owner,Standalone\n6184,338.83,Boston,W,19,14,1,65,High School,32,Franchise,Complex\n854,308.19,NJ City,S,10,11,6,63,High School,31,Patnership,Complex\n8244,62.13,NJ City,N,11,23,2,57,Graduation,28,Franchise,Complex\n4020,208,NJ City,W,2,13,9,73,High School,27,Franchise,Complex\n4465,156.13,NJ City,N,17,11,8,25,High School,27,Patnership,Standalone\n8301,406,NJ City,N,4,23,25,47,Graduation,22,Patnership,Standalone\n6277,494,Boston,N,11,9,12,48,Graduation,28,Patnership,Standalone\n3308,24.23,NJ City,N,13,10,2,73,No education,27,Franchise,Complex\n9080,20.39,NJ City,N,23,24,4,45,No education,34,Franchise,Complex\n8994,156.72,NJ City,S,20,18,2,83,Graduation,21,Patnership,Complex\n3708,459.95,NJ City,W,8,21,16,63,High School,22,Franchise,Complex\n4956,391.21,Boston,W,7,18,16,79,High School,23,Franchise,Complex\n8036,18.03,Boston,N,18,16,2,56,No education,32,Franchise,Complex\n8836,479,NJ City,W,23,24,22,29,Graduation,25,Franchise,Complex\n8483,370,NJ City,E,6,13,9,36,Graduation,23,Owner,Standalone\n3877,136,NJ City,N,18,18,10,52,High School,19,Patnership,Standalone\n4063,108.84,NJ City,E,4,16,2,71,High School,31,Owner,Standalone\n5355,417,Boston,E,14,17,23,46,Graduation,25,Owner,Standalone\n7188,371,Boston,W,18,14,25,65,Graduation,28,Franchise,Complex\n4006,238.4,NJ City,S,17,20,20,46,High School,33,Patnership,Complex\n7266,366,Boston,S,24,12,17,65,Graduation,26,Patnership,Complex\n4268,203.42,NJ City,W,5,21,7,70,High School,26,Franchise,Complex\n9528,158.6,NJ City,W,18,12,1,59,Graduation,31,Franchise,Complex\n5118,494,Boston,S,22,18,24,69,Graduation,22,Patnership,Complex\n7328,10.41,Boston,N,8,17,4,58,No education,29,Franchise,Complex\n420,321.2,NJ City,W,20,9,7,65,High School,20,Franchise,Complex\n4402,51.01,NJ City,S,21,19,4,76,High School,32,Patnership,Complex\n2873,432.61,NJ City,N,23,22,13,42,High School,22,Patnership,Standalone\n9659,388,NJ City,E,17,20,23,57,Graduation,27,Owner,Standalone\n8292,427,NJ City,W,16,24,24,53,Graduation,19,Franchise,Complex\n9217,9.59,NJ City,N,14,8,1,60,High School,25,Patnership,Standalone\n1702,188.98,NJ City,S,13,10,17,52,High School,30,Patnership,Complex\n142,149.03,NJ City,S,7,21,10,77,High School,25,Patnership,Complex\n4509,18.93,NJ City,N,3,13,2,41,Graduation,18,Patnership,Standalone\n7759,460,Boston,E,25,9,7,36,Graduation,20,Owner,Standalone\n8274,86.64,NJ City,S,11,23,3,80,Graduation,24,Patnership,Complex\n6320,70.52,Boston,N,6,21,2,38,No education,32,Franchise,Complex\n4720,174.44,NJ City,W,19,15,16,38,High School,22,Franchise,Complex\n844,206.46,NJ City,W,22,20,3,85,High School,33,Franchise,Complex\n512,207.26,NJ City,W,7,23,18,78,High School,33,Franchise,Complex\n2029,68.29,NJ City,N,8,19,2,46,High School,33,Patnership,Standalone\n5788,358,Boston,W,18,10,10,79,Graduation,32,Franchise,Complex\n5430,94.78,Boston,S,22,18,1,26,Graduation,32,Patnership,Complex\n8044,42.41,NJ City,N,10,20,4,42,High School,19,Franchise,Complex\n4822,1.41,NJ City,N,10,10,3,31,High School,22,Patnership,Complex\n2659,171,NJ City,E,2,9,13,29,High School,25,Owner,Standalone\n2711,296.31,NJ City,E,8,16,8,65,High School,35,Owner,Standalone\n5008,459,Boston,W,24,13,11,47,Graduation,24,Franchise,Complex\n2205,176.7,NJ City,N,6,14,1,81,Graduation,19,Patnership,Standalone\n1033,356.66,NJ City,N,7,23,22,31,High School,28,Patnership,Standalone\n7045,474.63,Boston,N,23,21,1,78,High School,32,Patnership,Standalone\n3517,488.37,NJ City,N,9,19,15,29,High School,23,Patnership,Standalone\n8688,112.47,NJ City,W,16,22,1,79,Graduation,32,Franchise,Complex\n186,215.17,NJ City,S,19,21,25,83,High School,20,Patnership,Complex\n9602,449,NJ City,S,7,12,23,49,Graduation,30,Patnership,Complex\n9531,385,NJ City,E,23,13,20,43,Graduation,26,Owner,Standalone\n5593,472,Boston,N,13,11,22,39,Graduation,19,Patnership,Standalone\n9088,258.55,NJ City,W,13,20,3,68,High School,21,Franchise,Complex\n7383,95.75,Boston,E,13,9,1,31,Graduation,31,Owner,Standalone\n5927,449,Boston,E,22,10,8,32,Graduation,24,Owner,Standalone\n9457,453,NJ City,N,19,19,16,43,Graduation,32,Patnership,Standalone\n5926,497,Boston,S,21,14,22,44,Graduation,27,Patnership,Complex\n8369,399,NJ City,N,13,8,19,43,Graduation,23,Patnership,Standalone\n39,234,NJ City,E,3,10,17,29,High School,33,Owner,Standalone\n3219,231,NJ City,E,1,19,7,25,High School,23,Owner,Standalone\n1729,20.55,NJ City,N,23,8,4,75,High School,29,Patnership,Standalone\n3323,439.18,NJ City,E,5,10,24,80,High School,23,Owner,Standalone\n9414,480.62,NJ City,S,1,23,4,79,Graduation,28,Patnership,Complex\n571,141.07,NJ City,E,11,8,10,58,High School,23,Owner,Standalone\n9046,106.75,NJ City,S,25,16,3,61,High School,34,Patnership,Complex\n8068,36.59,NJ City,N,2,11,2,52,High School,21,Franchise,Complex\n2093,383.54,NJ City,N,18,23,3,59,No education,31,Patnership,Standalone\n8241,126.08,NJ City,N,6,10,4,67,Graduation,32,Patnership,Standalone\n7051,124.05,Boston,E,8,14,4,50,High School,25,Owner,Standalone\n7364,443,Boston,W,19,10,8,68,Graduation,21,Franchise,Complex\n5589,18.39,Boston,N,14,24,4,65,Graduation,33,Patnership,Standalone\n7818,420,Boston,S,4,11,22,55,Graduation,34,Patnership,Complex\n2531,450.67,NJ City,E,22,10,14,63,High School,28,Owner,Standalone\n3480,375.15,NJ City,W,16,18,11,26,High School,25,Franchise,Complex\n1222,254,NJ City,S,2,22,7,66,High School,20,Patnership,Complex\n2159,160.88,NJ City,E,16,21,14,84,High School,19,Owner,Standalone\n8431,445,NJ City,E,19,16,15,83,Graduation,34,Owner,Standalone\n1726,96.97,NJ City,S,20,20,1,55,High School,20,Patnership,Complex\n6848,207.55,Boston,W,23,15,13,46,Graduation,27,Franchise,Complex\n419,351.89,NJ City,E,15,21,18,26,High School,34,Owner,Standalone\n7392,208.83,Boston,W,24,9,13,37,Graduation,27,Franchise,Complex\n3761,371.15,NJ City,N,19,24,24,44,High School,21,Patnership,Standalone\n7576,285.66,Boston,W,9,22,19,62,Graduation,27,Franchise,Complex\n2116,98.42,NJ City,W,16,8,3,37,High School,34,Franchise,Complex\n7176,71.52,Boston,N,7,12,2,60,Graduation,27,Franchise,Complex\n2566,240,NJ City,S,1,16,7,77,High School,20,Patnership,Complex\n8959,122.13,NJ City,E,9,18,2,73,High School,25,Owner,Standalone\n1627,196,NJ City,E,1,21,22,81,High School,27,Owner,Standalone\n646,464.58,NJ City,S,12,19,1,58,High School,20,Patnership,Complex\n4565,406.35,NJ City,N,8,11,4,73,No education,35,Patnership,Standalone\n9090,332.75,NJ City,S,25,24,17,68,Graduation,32,Patnership,Complex\n6792,194.79,Boston,W,20,13,13,80,Graduation,32,Franchise,Complex\n1098,495.57,NJ City,S,25,8,13,80,High School,29,Patnership,Complex\n3131,392.09,NJ City,E,18,19,17,33,High School,35,Owner,Standalone\n6642,406.89,Boston,S,15,24,24,79,Graduation,31,Patnership,Complex\n2023,11.37,NJ City,N,10,16,4,70,High School,25,Owner,Standalone\n8385,325.5,NJ City,N,10,11,21,34,Graduation,30,Patnership,Standalone\n6580,333.56,Boston,W,10,11,3,38,High School,33,Franchise,Complex\n9407,279.25,NJ City,E,11,16,5,72,No education,25,Owner,Standalone\n5967,454.97,Boston,E,12,19,21,83,Graduation,20,Owner,Standalone\n1590,25.79,NJ City,N,25,11,4,78,Graduation,33,Patnership,Complex\n598,143.5,NJ City,S,20,23,12,32,High School,23,Patnership,Complex\n9839,174.64,NJ City,E,15,22,8,27,Graduation,19,Owner,Standalone\n3827,468.27,NJ City,E,16,12,12,26,High School,19,Owner,Standalone\n8778,225.9,NJ City,S,6,18,7,85,Graduation,28,Patnership,Complex\n9307,244.8,NJ City,E,7,17,13,81,Graduation,29,Owner,Standalone\n1112,370.26,NJ City,W,1,11,3,42,No education,26,Franchise,Complex\n2758,43.35,NJ City,N,15,20,3,79,High School,35,Patnership,Complex\n9882,73.35,NJ City,N,15,24,4,58,Graduation,23,Patnership,Complex\n2650,51.43,NJ City,S,23,24,3,85,High School,19,Patnership,Complex\n9247,15.98,NJ City,N,10,10,3,44,High School,21,Owner,Standalone\n3561,310.23,NJ City,N,13,22,8,85,High School,26,Patnership,Standalone\n7114,24.42,Boston,N,25,16,3,38,High School,25,Patnership,Complex\n8399,444.96,NJ City,E,19,8,13,78,Graduation,31,Owner,Standalone\n7626,186.25,Boston,S,13,18,13,43,Graduation,28,Patnership,Complex\n5356,396.32,Boston,W,18,20,21,72,Graduation,27,Franchise,Complex\n3445,197.66,NJ City,N,24,13,22,45,High School,19,Patnership,Standalone\n6528,460.2,Boston,W,8,21,16,78,Graduation,19,Franchise,Complex\n4172,115.67,NJ City,W,10,17,2,62,No education,28,Franchise,Complex\n9255,178.46,NJ City,E,17,16,23,36,Graduation,27,Owner,Standalone\n7943,54.63,Boston,E,4,10,1,31,No education,31,Owner,Standalone\n2165,40.62,NJ City,N,19,19,2,64,No education,19,Patnership,Standalone\n1257,58.11,NJ City,N,23,10,1,31,Graduation,20,Patnership,Standalone\n1670,70.01,NJ City,N,21,10,4,61,No education,20,Patnership,Complex\n9587,449,NJ City,E,4,10,12,63,Graduation,28,Owner,Standalone\n6363,14.71,Boston,N,6,18,2,31,Graduation,25,Owner,Standalone\n8529,461.58,NJ City,N,9,15,21,61,Graduation,35,Patnership,Standalone\n8941,75.83,NJ City,N,19,14,1,41,High School,31,Patnership,Standalone\n7242,452.6,Boston,S,10,11,8,51,Graduation,34,Patnership,Complex\n3,150.95,Maine,E,23,11,17,76,High School,34,Patnership,Complex\n1625,416.59,NJ City,N,6,16,24,84,High School,29,Patnership,Standalone\n8931,220.43,NJ City,E,16,9,17,41,Graduation,26,Owner,Standalone\n1916,310.17,NJ City,W,15,17,23,41,High School,24,Franchise,Complex\n9964,475.54,NJ City,W,12,22,18,25,Graduation,19,Franchise,Complex\n969,249.67,NJ City,N,13,16,18,42,High School,23,Patnership,Standalone\n664,171.82,NJ City,W,15,20,17,70,High School,25,Franchise,Complex\n2974,138.97,NJ City,S,19,19,18,83,High School,28,Patnership,Complex\n8731,303.95,NJ City,E,19,21,11,73,Graduation,25,Owner,Standalone\n5463,340.64,Boston,E,22,9,25,35,Graduation,30,Owner,Standalone\n1003,367.88,NJ City,E,22,21,4,43,High School,26,Owner,Standalone\n9049,141.56,NJ City,N,22,19,14,81,Graduation,30,Patnership,Standalone\n9352,459.89,NJ City,W,3,15,12,40,Graduation,33,Franchise,Complex\n120,20.24,NJ City,N,1,19,4,55,Graduation,33,Franchise,Complex\n8856,348.7,NJ City,W,6,14,2,79,Graduation,20,Franchise,Complex\n8554,72.91,NJ City,N,12,16,1,67,High School,28,Patnership,Complex\n9483,390.29,NJ City,E,12,15,13,81,Graduation,23,Owner,Standalone\n4807,450.25,NJ City,E,19,18,16,43,High School,29,Owner,Standalone\n8752,432.54,NJ City,W,25,22,23,71,Graduation,34,Franchise,Complex\n2057,230,NJ City,N,3,14,15,38,High School,33,Patnership,Standalone\n5121,441.33,Boston,N,1,23,4,69,Graduation,21,Patnership,Standalone\n167,1.37,NJ City,N,8,15,2,85,No education,23,Owner,Standalone\n6059,167.3,Boston,E,19,9,9,42,Graduation,28,Owner,Standalone\n8728,492.68,NJ City,W,12,11,18,58,Graduation,31,Franchise,Complex\n4783,212.61,NJ City,E,22,9,2,85,High School,24,Owner,Standalone\n4738,297.67,NJ City,S,10,9,13,36,High School,27,Patnership,Complex\n3525,19.95,NJ City,N,8,11,1,42,Graduation,20,Patnership,Standalone\n7235,258.67,Boston,E,10,19,25,59,Graduation,33,Owner,Standalone\n691,56.89,NJ City,E,12,9,4,78,High School,29,Owner,Standalone\n8131,418.69,NJ City,E,18,19,10,33,Graduation,23,Owner,Standalone\n2325,101.43,NJ City,N,20,16,3,51,Graduation,30,Patnership,Standalone\n4133,308.69,NJ City,N,22,10,4,46,No education,35,Patnership,Standalone\n3546,218.6,NJ City,S,4,15,13,76,High School,28,Patnership,Complex\n5609,102.73,Boston,N,11,11,1,32,No education,30,Patnership,Standalone\n2523,299.59,NJ City,E,13,21,6,29,High School,28,Owner,Standalone\n6634,328.09,Boston,S,16,15,14,54,Graduation,32,Patnership,Complex\n2638,477.64,NJ City,S,23,24,25,73,High School,26,Patnership,Complex\n2484,429.39,NJ City,W,18,15,23,47,High School,28,Franchise,Complex\n9669,353.34,NJ City,N,9,20,22,66,Graduation,34,Patnership,Standalone\n5117,393.34,Boston,N,11,10,13,64,Graduation,26,Patnership,Standalone\n7583,332.92,Boston,E,20,21,8,79,Graduation,18,Owner,Standalone\n5508,202.62,Boston,W,23,15,25,46,Graduation,23,Franchise,Complex\n8163,3,NJ City,N,4,13,1,29,Graduation,23,Owner,Standalone\n4306,23.59,NJ City,N,4,18,1,31,High School,32,Patnership,Complex\n8339,494.07,NJ City,E,9,12,20,32,Graduation,29,Owner,Standalone\n4167,34.57,NJ City,N,15,19,1,83,Graduation,24,Owner,Standalone\n9765,411.81,NJ City,N,15,24,6,26,Graduation,35,Patnership,Standalone\n6271,389.37,Boston,E,25,9,13,35,Graduation,24,Owner,Standalone\n4248,310.06,NJ City,W,11,23,12,51,High School,27,Franchise,Complex\n5390,480.78,Boston,S,13,21,22,37,Graduation,31,Patnership,Complex\n3193,466.5,NJ City,N,24,21,16,31,High School,32,Patnership,Standalone\n3643,353.84,NJ City,E,24,15,11,50,High School,34,Owner,Standalone\n7786,82.39,Boston,S,15,22,4,73,High School,27,Patnership,Complex\n4307,202,NJ City,E,2,19,20,64,High School,23,Owner,Standalone\n8148,418.62,NJ City,W,12,24,9,65,Graduation,31,Franchise,Complex\n2162,311.94,NJ City,S,10,20,11,68,High School,21,Patnership,Complex\n9398,140.04,NJ City,S,3,20,10,29,Graduation,28,Patnership,Complex\n5900,252.59,Boston,W,5,13,20,38,Graduation,34,Franchise,Complex\n1412,3.32,NJ City,N,1,19,2,84,No education,20,Franchise,Complex\n4674,6.38,NJ City,N,4,12,4,79,Graduation,25,Patnership,Complex\n2083,197.71,NJ City,E,16,24,21,76,High School,33,Owner,Standalone\n9280,385.8,NJ City,W,17,9,18,70,Graduation,19,Franchise,Complex\n8318,102.86,NJ City,S,14,8,1,75,No education,32,Patnership,Complex\n9271,152.67,NJ City,E,8,18,20,49,Graduation,34,Owner,Standalone\n105,172.22,NJ City,N,16,16,1,83,Graduation,20,Patnership,Standalone\n2933,435.35,NJ City,N,11,14,8,63,High School,29,Patnership,Standalone\n8199,154.47,NJ City,E,18,8,20,65,Graduation,21,Owner,Standalone\n5981,194.93,Boston,N,25,23,13,26,Graduation,32,Patnership,Standalone\n994,343.29,NJ City,S,13,22,18,30,High School,28,Patnership,Complex\n7475,209.13,Boston,E,20,11,21,73,Graduation,28,Owner,Standalone\n2115,53.3,NJ City,E,21,21,2,68,Graduation,24,Owner,Standalone\n8995,11.91,NJ City,N,23,22,4,56,High School,18,Owner,Standalone\n6870,14.64,Boston,N,7,18,4,68,Graduation,26,Patnership,Complex\n271,275.4,NJ City,E,20,13,2,74,High School,26,Owner,Standalone\n7981,281.48,Boston,N,5,18,18,77,Graduation,21,Patnership,Standalone\n7765,153.52,Boston,N,7,21,11,47,Graduation,26,Patnership,Standalone\n6501,71.44,Boston,N,18,24,1,64,Graduation,27,Patnership,Standalone\n2699,60.47,NJ City,N,1,16,1,76,No education,34,Owner,Standalone\n2992,238.66,NJ City,W,25,19,13,27,High School,34,Franchise,Complex\n7644,171.2,Boston,W,17,23,2,62,Graduation,26,Franchise,Complex\n1393,224,NJ City,N,2,18,18,49,High School,18,Patnership,Standalone\n8794,367.79,NJ City,S,19,11,11,57,Graduation,19,Patnership,Complex\n1823,457.16,NJ City,E,12,21,10,37,High School,21,Owner,Standalone\n4027,416.06,NJ City,E,5,16,18,29,High School,29,Owner,Standalone\n3434,396.56,NJ City,S,13,10,13,81,High School,34,Patnership,Complex\n7988,444.47,Boston,W,14,21,11,30,Graduation,21,Franchise,Complex\n5526,490.84,Boston,S,3,24,3,48,Graduation,31,Patnership,Complex\n9922,164.11,NJ City,S,17,22,6,25,Graduation,22,Patnership,Complex\n1095,150.43,NJ City,E,5,20,19,66,High School,26,Owner,Standalone\n6822,198.98,Boston,S,9,22,17,44,Graduation,22,Patnership,Complex\n5112,221.8,Boston,W,21,16,16,78,Graduation,32,Franchise,Complex\n381,135.02,NJ City,N,1,21,3,39,Graduation,31,Patnership,Standalone\n9617,299.26,NJ City,N,16,22,21,73,Graduation,32,Patnership,Standalone\n4058,467.01,NJ City,S,24,12,22,25,High School,26,Patnership,Complex\n4431,367.38,NJ City,E,23,18,17,83,High School,18,Owner,Standalone\n9984,419.13,NJ City,W,18,24,19,61,Graduation,21,Franchise,Complex\n3169,190,NJ City,N,1,17,18,40,High School,28,Patnership,Standalone\n7909,184.38,Boston,N,7,16,11,59,Graduation,21,Patnership,Standalone\n6614,141.13,Boston,S,5,8,18,62,Graduation,23,Patnership,Complex\n2034,197.58,NJ City,S,9,24,20,27,High School,32,Patnership,Complex\n4815,54.6,NJ City,E,15,19,3,62,Graduation,27,Owner,Standalone\n696,106.9,NJ City,W,24,19,2,64,Graduation,20,Franchise,Complex\n9962,267.36,NJ City,S,15,21,2,62,No education,20,Patnership,Complex\n9565,414.84,NJ City,N,2,12,6,62,Graduation,32,Patnership,Standalone\n6930,62.24,Boston,N,24,19,4,43,Graduation,32,Patnership,Complex\n499,433.43,NJ City,E,19,13,8,77,High School,26,Owner,Standalone\n2966,392.92,NJ City,S,6,16,19,46,High School,24,Patnership,Complex\n3764,245.42,NJ City,W,7,16,16,77,High School,21,Franchise,Complex\n9441,132.65,NJ City,N,17,24,12,43,Graduation,33,Patnership,Standalone\n1542,482.68,NJ City,S,13,15,2,76,Graduation,35,Patnership,Complex\n8947,478.26,NJ City,E,25,15,11,69,Graduation,22,Owner,Standalone\n1320,98.61,NJ City,W,10,11,1,30,Graduation,18,Franchise,Complex\n5667,429.89,Boston,E,2,14,10,39,Graduation,20,Owner,Standalone\n1053,145,NJ City,N,1,18,16,63,High School,27,Patnership,Standalone\n3264,304.88,NJ City,W,14,8,1,49,Graduation,18,Franchise,Complex\n5791,337.42,Boston,E,5,20,1,45,High School,27,Owner,Standalone\n2275,186.13,NJ City,E,23,14,21,47,High School,29,Owner,Standalone\n8870,328.48,NJ City,S,19,14,12,30,Graduation,26,Patnership,Complex\n6663,170.51,Boston,E,16,16,13,76,Graduation,32,Owner,Standalone\n8996,478.59,NJ City,W,6,19,22,81,Graduation,22,Franchise,Complex\n2326,54.52,NJ City,S,9,20,2,43,High School,22,Patnership,Complex\n9157,41.57,NJ City,N,13,9,3,42,High School,20,Patnership,Standalone\n6604,357.89,Boston,W,16,21,16,79,Graduation,35,Franchise,Complex\n997,211.13,NJ City,N,17,20,24,57,High School,21,Patnership,Standalone\n4859,280.27,NJ City,E,19,11,13,52,High School,26,Owner,Standalone\n6923,141.6,Boston,E,23,16,14,31,Graduation,33,Owner,Standalone\n4994,205.41,Boston,S,22,8,14,54,High School,24,Patnership,Complex\n5456,92.45,Boston,W,3,20,2,33,No education,22,Franchise,Complex\n9856,63.45,NJ City,N,22,17,3,69,High School,30,Franchise,Complex\n8082,347.71,NJ City,S,3,10,3,74,Graduation,31,Patnership,Complex\n1205,342.58,NJ City,N,23,23,6,38,High School,26,Patnership,Standalone\n8173,178.85,NJ City,N,1,17,14,76,Graduation,34,Patnership,Standalone\n7556,269.57,Boston,W,7,16,22,33,Graduation,35,Franchise,Complex\n3871,437.86,NJ City,E,25,17,3,27,High School,22,Owner,Standalone\n9224,113.08,NJ City,W,22,21,1,60,No education,28,Franchise,Complex\n6189,327.25,Boston,N,1,22,25,51,Graduation,27,Patnership,Standalone\n299,108.42,NJ City,E,24,14,2,37,No education,22,Owner,Standalone\n6125,83.67,Boston,N,15,14,4,66,No education,31,Patnership,Standalone\n933,399.27,NJ City,N,13,21,1,39,Graduation,18,Patnership,Standalone\n397,222.39,NJ City,N,17,23,18,58,High School,26,Patnership,Standalone\n2946,164.62,NJ City,S,16,20,4,74,Graduation,28,Patnership,Complex\n4846,84.43,NJ City,S,9,11,1,71,High School,32,Patnership,Complex\n3468,214.29,NJ City,W,7,19,19,48,High School,28,Franchise,Complex\n7377,177.43,Boston,N,19,15,6,61,Graduation,25,Patnership,Standalone\n8633,417.91,NJ City,N,24,9,23,60,Graduation,28,Patnership,Standalone\n6007,156.99,Boston,E,14,12,3,79,High School,28,Owner,Standalone\n3573,103,NJ City,N,1,19,24,41,High School,28,Patnership,Standalone\n5626,412.55,Boston,S,8,21,22,39,Graduation,24,Patnership,Complex\n9544,85.35,NJ City,W,14,9,2,63,High School,35,Franchise,Complex\n799,4.24,NJ City,N,2,11,4,65,High School,35,Owner,Standalone\n1146,102.03,NJ City,S,6,22,2,65,Graduation,34,Patnership,Complex\n4793,221.99,NJ City,N,25,8,23,32,High School,31,Patnership,Standalone\n2937,417.51,NJ City,N,6,23,6,47,High School,18,Patnership,Standalone\n156,288,NJ City,W,2,8,21,38,High School,32,Franchise,Complex\n7238,178.87,Boston,S,22,24,14,42,Graduation,29,Patnership,Complex\n3806,46.77,NJ City,N,4,23,4,35,No education,27,Patnership,Complex\n9253,176.35,NJ City,N,16,22,24,50,Graduation,27,Patnership,Standalone\n5207,223.63,Boston,E,1,12,22,73,Graduation,35,Owner,Standalone\n8904,387.32,NJ City,W,25,10,8,44,Graduation,29,Franchise,Complex\n2846,160.57,NJ City,S,25,15,13,60,High School,21,Patnership,Complex\n5855,9.23,Boston,N,2,12,2,80,No education,26,Owner,Standalone\n8293,44.17,NJ City,N,9,22,1,84,High School,29,Patnership,Standalone\n1948,177.94,NJ City,W,24,23,19,58,High School,19,Franchise,Complex\n6200,471.73,Boston,W,8,8,6,56,Graduation,34,Franchise,Complex\n7102,479.81,Boston,S,2,11,25,44,Graduation,26,Patnership,Complex\n9560,413.33,NJ City,W,22,24,23,77,Graduation,33,Franchise,Complex\n289,23.31,NJ City,N,22,8,2,28,High School,35,Patnership,Standalone\n3340,51.34,NJ City,W,18,23,4,60,High School,22,Franchise,Complex\n3696,183.34,NJ City,W,24,15,1,30,Graduation,22,Franchise,Complex\n842,204.94,NJ City,S,16,21,21,54,High School,22,Patnership,Complex\n543,364.26,NJ City,E,6,12,1,71,Graduation,29,Owner,Standalone\n6995,6.32,Boston,N,19,23,1,74,No education,30,Owner,Standalone\n1674,482.07,NJ City,S,19,13,23,27,High School,35,Patnership,Complex\n8237,284.63,NJ City,N,15,19,18,49,Graduation,24,Patnership,Standalone\n3016,217.14,NJ City,W,24,10,10,41,High School,29,Franchise,Complex\n907,499.06,NJ City,E,25,23,11,35,High School,18,Owner,Standalone\n6215,110.88,Boston,E,11,9,2,38,No education,19,Owner,Standalone\n7767,454.88,Boston,E,20,24,13,27,Graduation,35,Owner,Standalone\n9294,428.76,NJ City,S,20,23,5,47,Graduation,20,Patnership,Complex\n9201,17.53,NJ City,N,9,12,2,29,Graduation,26,Patnership,Standalone\n4294,344.87,NJ City,S,24,17,23,47,High School,26,Patnership,Complex\n4170,32.08,NJ City,N,5,24,3,71,Graduation,27,Patnership,Complex\n3838,369.37,NJ City,S,23,20,25,85,High School,29,Patnership,Complex\n9204,476.74,NJ City,W,19,17,6,85,Graduation,18,Franchise,Complex\n767,25.36,NJ City,N,6,18,4,59,No education,20,Owner,Standalone\n988,348.11,NJ City,W,5,10,16,44,High School,25,Franchise,Complex\n2919,445.08,NJ City,E,9,22,9,69,High School,26,Owner,Standalone\n8666,10.01,NJ City,N,8,20,1,57,No education,34,Patnership,Complex\n956,475.18,NJ City,W,19,8,18,33,High School,22,Franchise,Complex\n4349,466.63,NJ City,N,13,8,19,34,High School,30,Patnership,Standalone\n4736,216.67,NJ City,W,12,16,25,30,High School,31,Franchise,Complex\n6128,414.55,Boston,W,11,17,21,52,Graduation,33,Franchise,Complex\n3280,320.58,NJ City,W,16,16,13,47,High School,19,Franchise,Complex\n8801,490.97,NJ City,N,20,18,20,39,Graduation,22,Patnership,Standalone\n9031,468.22,NJ City,E,2,15,17,63,Graduation,27,Owner,Standalone\n6754,237.13,Boston,S,1,16,10,84,Graduation,31,Patnership,Complex\n906,321.16,NJ City,S,6,8,1,27,Graduation,28,Patnership,Complex\n8863,49.55,NJ City,E,20,21,1,69,High School,27,Owner,Standalone\n8194,73.98,NJ City,S,13,18,3,58,High School,31,Patnership,Complex\n3963,283.42,NJ City,E,12,11,22,30,High School,22,Owner,Standalone\n4745,134.34,NJ City,N,16,17,19,28,High School,32,Patnership,Standalone\n9824,209.64,NJ City,W,7,20,7,27,Graduation,34,Franchise,Complex\n5571,337.47,Boston,E,13,21,25,39,Graduation,31,Owner,Standalone\n6588,201.39,Boston,W,6,23,22,39,Graduation,24,Franchise,Complex\n9551,25.53,NJ City,N,13,17,1,79,No education,34,Owner,Standalone\n7381,331.25,Boston,N,19,17,1,32,High School,31,Patnership,Standalone\n5338,242.94,Boston,S,9,21,4,38,High School,22,Patnership,Complex\n3241,211.02,NJ City,N,25,13,21,49,High School,35,Patnership,Standalone\n3931,222.72,NJ City,E,19,24,8,35,High School,26,Owner,Standalone\n7649,289.34,Boston,N,3,10,7,66,Graduation,28,Patnership,Standalone\n3300,295.34,NJ City,W,20,13,23,60,High School,23,Franchise,Complex\n4657,339.19,NJ City,N,25,16,3,44,High School,34,Patnership,Standalone\n7353,31.08,Boston,N,10,22,4,79,Graduation,31,Patnership,Standalone\n393,119.07,NJ City,N,15,18,3,42,Graduation,25,Patnership,Standalone\n3092,331.85,NJ City,W,7,17,19,47,High School,23,Franchise,Complex\n5606,223.63,Boston,S,11,19,19,70,Graduation,35,Patnership,Complex\n5413,439.44,Boston,N,7,13,8,78,Graduation,23,Patnership,Standalone\n8040,439.01,Boston,W,18,12,15,72,Graduation,28,Franchise,Complex\n3490,169.48,NJ City,S,17,15,20,41,High School,32,Patnership,Complex\n7456,239.19,Boston,W,11,15,16,49,Graduation,31,Franchise,Complex\n2146,474.25,NJ City,S,22,9,17,79,High School,23,Patnership,Complex\n380,66.62,NJ City,N,18,23,3,72,No education,32,Franchise,Complex\n7714,391.7,Boston,S,7,22,6,83,Graduation,20,Patnership,Complex\n6006,319.38,Boston,S,21,23,17,60,Graduation,31,Patnership,Complex\n1894,454.29,NJ City,S,6,14,19,65,High School,24,Patnership,Complex\n3059,274.33,NJ City,E,21,13,5,78,No education,20,Owner,Standalone\n636,253.35,NJ City,W,14,14,15,36,High School,24,Franchise,Complex\n810,17.95,NJ City,N,20,19,4,60,Graduation,28,Patnership,Complex\n710,147.12,NJ City,S,24,22,14,45,High School,30,Patnership,Complex\n4228,342.74,NJ City,W,10,11,6,64,High School,33,Franchise,Complex\n5951,355.14,Boston,E,23,23,18,59,Graduation,31,Owner,Standalone\n4527,42.02,NJ City,N,24,19,4,50,Graduation,28,Owner,Standalone\n8549,103.18,NJ City,N,2,10,2,54,No education,35,Patnership,Standalone\n9723,410.19,NJ City,E,12,18,7,79,Graduation,27,Owner,Standalone\n7165,400.32,Boston,N,18,12,25,78,Graduation,24,Patnership,Standalone\n877,304.76,NJ City,N,10,13,25,50,High School,30,Patnership,Standalone\n1906,369.64,NJ City,S,12,8,21,32,High School,27,Patnership,Complex\n7331,317.66,Boston,E,19,8,13,27,Graduation,26,Owner,Standalone\n4801,117.09,NJ City,N,3,23,3,59,High School,30,Patnership,Standalone\n3821,421.89,NJ City,N,16,10,2,66,No education,33,Patnership,Standalone\n6707,321.71,Boston,E,12,15,21,81,Graduation,19,Owner,Standalone\n5265,182.72,Boston,N,8,11,6,44,Graduation,21,Patnership,Standalone\n9033,278.59,NJ City,N,25,14,11,25,Graduation,29,Patnership,Standalone\n2210,419.17,NJ City,S,21,23,3,69,No education,20,Patnership,Complex\n2782,156.08,NJ City,S,11,9,21,47,High School,19,Patnership,Complex\n6547,139.93,Boston,E,16,23,2,85,High School,31,Owner,Standalone\n5563,462.38,Boston,E,24,14,3,38,High School,21,Owner,Standalone\n6161,151.44,Boston,N,16,22,9,52,Graduation,24,Patnership,Standalone\n2685,287.67,NJ City,N,8,10,19,45,High School,31,Patnership,Standalone\n9665,306.73,NJ City,N,5,13,14,84,Graduation,30,Patnership,Standalone\n599,48.54,NJ City,E,6,24,1,82,No education,27,Owner,Standalone\n2512,171.67,NJ City,W,20,24,13,53,High School,18,Franchise,Complex\n2204,321.12,NJ City,W,13,19,24,32,High School,24,Franchise,Complex\n538,488.76,NJ City,S,4,23,20,36,High School,21,Patnership,Complex\n8535,15.43,NJ City,N,6,17,2,71,Graduation,29,Owner,Standalone\n8395,412.16,NJ City,E,9,16,15,35,Graduation,33,Owner,Standalone\n2126,81.96,NJ City,S,3,24,1,77,No education,29,Patnership,Complex\n2431,341.47,NJ City,E,10,8,15,65,High School,30,Owner,Standalone\n9911,314.37,NJ City,E,7,9,11,49,Graduation,22,Owner,Standalone\n9237,237.67,NJ City,N,7,23,6,54,Graduation,22,Patnership,Standalone\n4223,347.33,NJ City,E,9,20,3,42,No education,30,Owner,Standalone\n1421,102.34,NJ City,N,2,14,4,77,No education,28,Patnership,Standalone\n3347,19.41,NJ City,N,12,19,3,72,No education,35,Owner,Standalone\n4838,127,NJ City,S,1,11,23,31,High School,30,Patnership,Complex\n5939,447.95,Boston,E,2,13,22,55,Graduation,34,Owner,Standalone\n9702,152.98,NJ City,S,17,19,7,74,Graduation,31,Patnership,Complex\n6149,248.08,Boston,N,23,18,23,35,Graduation,32,Patnership,Standalone\n815,460.59,NJ City,E,11,17,5,30,No education,22,Owner,Standalone\n1714,71.92,NJ City,N,8,15,1,35,High School,29,Patnership,Complex\n6879,419.98,Boston,E,14,8,12,53,Graduation,33,Owner,Standalone\n6025,470.01,Boston,N,11,16,19,56,Graduation,25,Patnership,Standalone\n6457,47.21,Boston,N,25,9,2,75,High School,30,Patnership,Standalone\n4200,300.63,NJ City,W,4,12,15,61,High School,32,Franchise,Complex\n5381,154.67,Boston,N,10,21,7,59,Graduation,29,Patnership,Standalone\n9233,287.59,NJ City,N,6,11,21,26,Graduation,29,Patnership,Standalone\n1598,10.83,NJ City,N,3,24,1,51,No education,33,Patnership,Complex\n2317,220.95,NJ City,N,17,10,15,41,High School,19,Patnership,Standalone\n800,226.39,NJ City,W,16,23,16,71,High School,34,Franchise,Complex\n7709,291.84,Boston,N,21,20,23,47,Graduation,29,Patnership,Standalone\n5898,298.66,Boston,S,15,12,15,53,Graduation,30,Patnership,Complex\n5340,370.57,Boston,W,25,9,3,52,Graduation,34,Franchise,Complex\n5103,413.8,Boston,E,15,18,10,42,Graduation,19,Owner,Standalone\n4856,390.65,NJ City,W,7,9,24,47,High School,18,Franchise,Complex\n5215,426.86,Boston,E,21,24,22,69,Graduation,35,Owner,Standalone\n3520,290.15,NJ City,W,20,8,16,84,High School,24,Franchise,Complex\n4184,379.2,NJ City,W,6,19,25,62,High School,26,Franchise,Complex\n7441,321.45,Boston,N,23,9,24,39,Graduation,28,Patnership,Standalone\n1849,417.73,NJ City,N,5,24,22,59,High School,18,Patnership,Standalone\n7224,46.98,Boston,N,2,24,3,37,Graduation,25,Franchise,Complex\n2914,99.84,NJ City,S,20,19,2,44,High School,18,Patnership,Complex\n8785,370.61,NJ City,N,19,16,21,55,Graduation,20,Patnership,Standalone\n4836,473.48,NJ City,W,24,20,14,82,High School,30,Franchise,Complex\n3116,281.7,NJ City,W,8,16,23,68,High School,35,Franchise,Complex\n9482,316.22,NJ City,S,7,12,2,26,No education,18,Patnership,Complex\n9326,217.37,NJ City,S,11,12,7,38,Graduation,31,Patnership,Complex\n9209,328.44,NJ City,N,15,23,4,61,No education,30,Patnership,Standalone\n1782,359.41,NJ City,S,1,9,2,70,Graduation,26,Patnership,Complex\n8226,446,NJ City,S,14,23,17,28,Graduation,20,Patnership,Complex\n7094,449.8,Boston,S,3,15,3,78,No education,30,Patnership,Complex\n752,183.43,NJ City,W,3,9,5,68,No education,31,Franchise,Complex\n1615,266.65,NJ City,E,21,12,14,26,High School,20,Owner,Standalone\n1154,265.85,NJ City,S,2,18,5,70,No education,25,Patnership,Complex\n3012,396.81,NJ City,W,18,18,23,67,High School,33,Franchise,Complex\n9393,298.15,NJ City,N,10,16,2,25,Graduation,29,Patnership,Standalone\n2025,248.03,NJ City,N,6,23,5,31,Graduation,27,Patnership,Standalone\n721,55.76,NJ City,N,18,13,4,66,High School,32,Patnership,Standalone\n7472,345.3,Boston,W,6,9,11,42,Graduation,24,Franchise,Complex\n9488,104.29,NJ City,W,24,21,3,79,No education,19,Franchise,Complex\n1634,148.97,NJ City,S,19,8,24,58,High School,31,Patnership,Complex\n3860,256.76,NJ City,W,14,15,19,55,High School,22,Franchise,Complex\n3054,73.55,NJ City,N,2,23,1,54,Graduation,20,Patnership,Complex\n1517,231.02,NJ City,N,2,17,2,29,No education,19,Patnership,Standalone\n1450,457.02,NJ City,S,16,10,2,78,High School,31,Patnership,Complex\n4608,100,NJ City,W,3,9,9,81,High School,33,Franchise,Complex\n6033,368.37,Boston,N,15,18,19,67,Graduation,20,Patnership,Standalone\n3536,474.7,NJ City,W,6,15,23,58,High School,18,Franchise,Complex\n1239,101.68,NJ City,E,17,24,4,49,Graduation,28,Owner,Standalone\n608,130.25,NJ City,W,19,8,5,45,No education,28,Franchise,Complex\n6941,454.36,Boston,N,2,12,17,67,Graduation,35,Patnership,Standalone\n3569,313.35,NJ City,N,8,19,1,81,No education,27,Patnership,Standalone\n1756,392.65,NJ City,W,25,15,12,84,High School,26,Franchise,Complex\n7733,198.78,Boston,N,24,24,18,32,Graduation,27,Patnership,Standalone\n5722,306.42,Boston,S,11,12,16,38,Graduation,26,Patnership,Complex\n5055,419.25,Boston,E,24,8,8,67,Graduation,25,Owner,Standalone\n2606,301,NJ City,S,19,11,4,80,No education,20,Patnership,Complex\n3772,343.62,NJ City,W,20,8,6,58,High School,34,Franchise,Complex\n2996,188.76,NJ City,W,23,24,22,77,High School,26,Franchise,Complex\n1321,263.17,NJ City,N,24,14,23,55,High School,33,Patnership,Standalone\n3128,202.79,NJ City,W,23,20,24,36,High School,18,Franchise,Complex\n4270,326.48,NJ City,S,10,15,21,49,High School,31,Patnership,Complex\n2909,366.44,NJ City,N,21,19,5,50,No education,27,Patnership,Standalone\n864,347.39,NJ City,W,13,15,21,46,High School,25,Franchise,Complex\n4371,441.01,NJ City,E,6,19,7,33,High School,23,Owner,Standalone\n8851,119.79,NJ City,E,24,11,1,85,High School,23,Owner,Standalone\n3207,29.28,NJ City,N,22,12,2,36,Graduation,34,Owner,Standalone\n6919,130.17,Boston,E,20,20,8,75,Graduation,30,Owner,Standalone\n1022,97.88,NJ City,S,17,13,3,57,No education,24,Patnership,Complex\n5976,176.9,Boston,W,16,17,18,60,Graduation,20,Franchise,Complex\n6789,240.99,Boston,N,23,12,7,72,Graduation,35,Patnership,Standalone\n5858,362.73,Boston,S,18,16,13,40,Graduation,19,Patnership,Complex\n1178,308.93,NJ City,S,24,12,18,80,High School,33,Patnership,Complex\n9298,28.91,NJ City,N,16,20,4,79,High School,32,Patnership,Complex\n2599,218.02,NJ City,E,23,9,24,32,High School,23,Owner,Standalone\n7533,183.11,Boston,N,20,21,6,50,Graduation,21,Patnership,Standalone\n540,207.22,NJ City,W,19,11,15,32,High School,21,Franchise,Complex\n4968,370,Boston,W,13,15,10,27,High School,19,Franchise,Complex\n3686,279.07,NJ City,S,17,20,8,37,High School,23,Patnership,Complex\n8701,314.85,NJ City,N,12,12,21,84,Graduation,27,Patnership,Standalone\n9652,287.6,NJ City,W,20,12,4,38,High School,19,Franchise,Complex\n3151,2.93,NJ City,E,15,13,1,54,High School,29,Owner,Standalone\n8005,123.5,Boston,N,13,12,2,81,High School,23,Patnership,Standalone\n6661,30.11,Boston,N,11,8,2,42,High School,30,Patnership,Standalone\n471,409.74,NJ City,E,18,21,7,40,High School,33,Owner,Standalone\n3139,82.35,NJ City,E,6,19,3,58,High School,35,Owner,Standalone\n8104,308.03,NJ City,W,3,16,15,32,Graduation,25,Franchise,Complex\n9475,29.8,NJ City,N,1,13,3,52,High School,27,Owner,Standalone\n5391,333.15,Boston,E,3,10,8,28,Graduation,27,Owner,Standalone\n7980,6.13,Boston,N,6,14,1,51,Graduation,28,Franchise,Complex\n6761,119.05,Boston,N,19,19,3,84,No education,31,Patnership,Standalone\n139,293.78,NJ City,E,12,18,16,45,High School,35,Owner,Standalone\n9865,63.69,NJ City,N,2,22,4,80,High School,29,Patnership,Standalone\n4189,47.2,NJ City,N,12,17,1,59,High School,30,Patnership,Standalone\n1921,32.27,NJ City,N,7,11,1,84,High School,24,Patnership,Standalone\n9667,244.41,NJ City,E,12,21,22,85,Graduation,19,Owner,Standalone\n7065,68.16,Boston,N,2,21,2,70,Graduation,18,Patnership,Standalone\n8526,484.02,NJ City,S,17,8,14,62,Graduation,22,Patnership,Complex\n7215,459.25,Boston,E,7,12,21,40,Graduation,31,Owner,Standalone\n7682,120.04,Boston,S,19,14,1,52,No education,29,Patnership,Complex\n1212,305.7,NJ City,W,20,15,25,48,High School,22,Franchise,Complex\n8111,220.82,NJ City,E,24,20,14,29,Graduation,32,Owner,Standalone\n3156,230,NJ City,W,1,15,20,48,High School,19,Franchise,Complex\n7764,495.89,Boston,W,11,20,11,42,Graduation,21,Franchise,Complex\n2015,428.63,NJ City,E,8,9,11,49,High School,31,Owner,Standalone\n8610,329.26,NJ City,S,16,20,14,32,Graduation,35,Patnership,Complex\n1515,110.57,NJ City,E,21,23,2,77,Graduation,26,Owner,Standalone\n8875,326,NJ City,E,15,23,18,41,Graduation,21,Owner,Standalone\n9085,452.96,NJ City,N,9,9,4,36,High School,19,Patnership,Standalone\n8062,433.12,NJ City,S,13,11,8,32,Graduation,28,Patnership,Complex\n5135,407.62,Boston,E,22,18,14,76,Graduation,22,Owner,Standalone\n4152,146.28,NJ City,W,15,13,25,41,High School,23,Franchise,Complex\n2705,322.27,NJ City,N,20,10,16,57,High School,21,Patnership,Standalone\n3316,182,NJ City,W,1,9,6,36,High School,35,Franchise,Complex\n7339,380.54,Boston,E,5,10,2,76,High School,23,Owner,Standalone\n813,65.69,NJ City,N,17,11,4,49,Graduation,25,Patnership,Standalone\n1723,246.59,NJ City,E,10,23,15,27,High School,29,Owner,Standalone\n6772,260.01,Boston,W,4,8,20,28,Graduation,18,Franchise,Complex\n7789,72.31,Boston,N,11,19,1,61,High School,33,Patnership,Standalone\n2988,283.49,NJ City,W,4,23,7,26,High School,20,Franchise,Complex\n539,374.05,NJ City,E,6,10,11,30,High School,31,Owner,Standalone\n8674,291.55,NJ City,S,24,9,16,49,Graduation,31,Patnership,Complex\n1313,195.35,NJ City,N,17,10,5,71,No education,34,Patnership,Standalone\n2114,39.61,NJ City,N,7,8,1,53,No education,24,Patnership,Complex\n2007,4.5,NJ City,N,17,20,3,26,Graduation,31,Owner,Standalone\n9454,309.72,NJ City,S,21,8,17,33,Graduation,18,Patnership,Complex\n9866,26.99,NJ City,N,12,15,3,71,No education,20,Patnership,Complex\n2073,446.72,NJ City,N,21,13,3,26,Graduation,24,Patnership,Standalone\n7876,446.66,Boston,W,23,19,14,65,Graduation,35,Franchise,Complex\n9623,334.87,NJ City,E,17,10,13,37,Graduation,23,Owner,Standalone\n587,54.76,NJ City,E,16,10,3,37,No education,24,Owner,Standalone\n1944,330.16,NJ City,W,11,9,15,58,High School,35,Franchise,Complex\n1783,419.73,NJ City,E,11,15,2,67,High School,31,Owner,Standalone\n3488,238.87,NJ City,W,18,13,6,78,High School,23,Franchise,Complex\n3201,470.95,NJ City,N,4,21,10,42,High School,31,Patnership,Standalone\n8738,7.19,NJ City,N,12,9,3,66,No education,26,Patnership,Complex\n4303,477.58,NJ City,E,15,18,1,61,High School,19,Owner,Standalone\n9456,396.39,NJ City,W,21,16,7,46,Graduation,33,Franchise,Complex\n9418,463.02,NJ City,S,12,16,6,85,Graduation,22,Patnership,Complex\n5213,100.03,Boston,N,22,8,2,83,No education,21,Patnership,Standalone\n4215,64.89,NJ City,N,11,16,3,72,Graduation,19,Owner,Standalone\n9898,131.6,NJ City,S,15,19,13,35,Graduation,31,Patnership,Complex\n5283,105.16,Boston,E,13,13,1,38,Graduation,19,Owner,Standalone\n1708,169,NJ City,W,1,23,18,37,High School,33,Franchise,Complex\n1262,211.28,NJ City,S,9,11,8,59,High School,25,Patnership,Complex\n6515,247.71,Boston,E,22,8,18,43,Graduation,26,Owner,Standalone\n1363,40.79,NJ City,N,21,12,3,80,High School,18,Owner,Standalone\n4155,311,NJ City,E,2,10,12,59,High School,35,Owner,Standalone\n5983,178.83,Boston,E,25,9,14,51,Graduation,28,Owner,Standalone\n6608,258.85,Boston,W,1,23,9,57,Graduation,32,Franchise,Complex\n6247,103.42,Boston,E,23,11,4,84,High School,24,Owner,Standalone\n1044,336.33,NJ City,W,12,9,23,56,High School,27,Franchise,Complex\n823,3.53,NJ City,N,21,21,1,74,High School,30,Owner,Standalone\n6030,73.09,Boston,N,10,22,1,51,Graduation,35,Patnership,Complex\n8722,302.51,NJ City,S,7,12,8,52,Graduation,25,Patnership,Complex\n8993,167,NJ City,N,14,14,3,69,No education,23,Patnership,Standalone\n9174,196.15,NJ City,S,2,8,3,42,Graduation,25,Patnership,Complex\n3122,497.64,NJ City,S,22,21,7,73,High School,20,Patnership,Complex\n9597,337.12,NJ City,N,13,21,10,49,Graduation,28,Patnership,Standalone\n1813,278,NJ City,N,2,17,20,46,High School,33,Patnership,Standalone\n8202,224.29,NJ City,S,6,12,20,28,Graduation,18,Patnership,Complex\n3498,167.99,NJ City,S,23,15,2,77,Graduation,24,Patnership,Complex\n5864,310.7,Boston,W,12,24,16,30,Graduation,27,Franchise,Complex\n5596,200.83,Boston,W,9,21,24,52,Graduation,20,Franchise,Complex\n8121,101.23,NJ City,N,12,20,2,73,Graduation,29,Patnership,Standalone\n2716,246.7,NJ City,W,24,19,3,83,High School,23,Franchise,Complex\n8638,108.47,NJ City,S,1,15,4,84,High School,33,Patnership,Complex\n5450,260.36,Boston,S,8,13,9,82,Graduation,28,Patnership,Complex\n7506,152.72,Boston,S,13,10,9,70,Graduation,27,Patnership,Complex\n309,283.52,NJ City,N,14,20,3,83,Graduation,32,Patnership,Standalone\n4212,112.23,NJ City,W,6,16,4,83,Graduation,24,Franchise,Complex\n5133,287.37,Boston,N,3,15,4,38,Graduation,20,Patnership,Standalone\n8658,277.92,NJ City,S,16,8,12,28,Graduation,22,Patnership,Complex\n3349,114.4,NJ City,N,18,8,3,37,High School,29,Patnership,Standalone\n993,1.72,NJ City,N,9,13,4,52,Graduation,24,Patnership,Standalone\n2667,250.98,NJ City,E,5,16,19,41,High School,33,Owner,Standalone\n1520,364.28,NJ City,W,24,17,9,40,High School,31,Franchise,Complex\n1997,55.65,NJ City,N,18,24,1,58,No education,19,Patnership,Standalone\n521,127.93,NJ City,N,12,18,11,55,High School,30,Patnership,Standalone\n9877,226.3,NJ City,N,17,8,10,34,Graduation,32,Patnership,Standalone\n45,402.83,Boston,N,21,15,21,75,High School,30,Patnership,Standalone\n8648,198.21,NJ City,W,20,24,24,77,Graduation,18,Franchise,Complex\n9039,131.71,NJ City,E,7,19,18,74,Graduation,35,Owner,Standalone\n3555,153.38,NJ City,E,5,12,2,48,Graduation,32,Owner,Standalone\n4072,480.11,NJ City,W,12,19,9,72,High School,19,Franchise,Complex\n5087,477.96,Boston,E,11,23,21,61,Graduation,31,Owner,Standalone\n102,478.99,NJ City,S,7,10,24,44,High School,28,Patnership,Complex\n5891,157.7,Boston,E,10,16,25,38,Graduation,34,Owner,Standalone\n3423,98.7,NJ City,E,20,9,2,34,Graduation,24,Owner,Standalone\n8586,306.44,NJ City,S,2,13,13,69,Graduation,34,Patnership,Complex\n151,43.87,NJ City,N,23,22,3,66,High School,20,Owner,Standalone\n9884,9.66,NJ City,N,1,24,4,25,No education,18,Franchise,Complex\n3119,187.68,NJ City,E,23,19,15,38,High School,22,Owner,Standalone\n5768,85.89,Boston,W,20,10,3,67,No education,34,Franchise,Complex\n3309,74.32,NJ City,N,23,23,1,60,Graduation,35,Patnership,Standalone\n5330,449.8,Boston,S,22,24,15,35,Graduation,23,Patnership,Complex\n8729,490.09,NJ City,N,1,9,23,77,Graduation,29,Patnership,Standalone\n5795,107.41,Boston,E,12,22,2,34,No education,25,Owner,Standalone\n9250,457.86,NJ City,S,19,10,16,58,Graduation,28,Patnership,Complex\n9784,174.08,NJ City,W,4,10,21,66,Graduation,33,Franchise,Complex\n4610,137,NJ City,S,3,12,23,27,High School,21,Patnership,Complex\n9248,318.13,NJ City,W,24,17,16,62,Graduation,19,Franchise,Complex\n4000,127.02,NJ City,W,3,14,1,70,High School,21,Franchise,Complex\n4014,184.54,NJ City,S,21,17,21,27,High School,28,Patnership,Complex\n524,378.17,NJ City,W,13,15,24,40,High School,20,Franchise,Complex\n3084,84.86,NJ City,W,3,15,1,25,Graduation,33,Franchise,Complex\n7638,100,Boston,S,24,24,4,29,Graduation,29,Patnership,Complex\n6107,333.22,Boston,E,14,17,18,77,Graduation,26,Owner,Standalone\n4018,334.46,NJ City,S,20,21,2,71,High School,32,Patnership,Complex\n9985,202.74,NJ City,N,9,9,24,72,Graduation,27,Patnership,Standalone\n953,315.62,NJ City,N,21,19,16,73,High School,20,Patnership,Standalone\n4451,40.55,NJ City,N,9,21,4,37,No education,28,Owner,Standalone\n6389,51.26,Boston,N,11,9,1,40,No education,32,Patnership,Standalone\n4940,408.06,Boston,W,4,14,9,40,High School,26,Franchise,Complex\n6295,467.18,Boston,E,12,18,3,30,High School,25,Owner,Standalone\n2920,414.54,NJ City,W,20,11,5,44,High School,32,Franchise,Complex\n833,285.75,NJ City,N,12,23,11,40,High School,28,Patnership,Standalone\n4612,118.29,NJ City,W,9,10,4,31,High School,34,Franchise,Complex\n7870,430.93,Boston,S,1,10,8,49,Graduation,35,Patnership,Complex\n2856,140.83,NJ City,W,20,9,12,83,High School,20,Franchise,Complex\n6222,8.95,Boston,N,17,23,2,72,Graduation,29,Patnership,Complex\n1577,29.01,NJ City,N,23,11,3,63,No education,28,Patnership,Standalone\n9616,235.05,NJ City,W,11,24,22,51,Graduation,29,Franchise,Complex\n6926,199.82,Boston,S,17,22,21,49,Graduation,32,Patnership,Complex\n1715,169.94,NJ City,E,19,14,2,40,No education,32,Owner,Standalone\n2260,472.92,NJ City,W,20,24,23,77,High School,24,Franchise,Complex\n6989,358.53,Boston,N,12,23,7,83,Graduation,25,Patnership,Standalone\n4401,100,NJ City,N,1,18,18,60,High School,31,Patnership,Standalone\n470,472.36,NJ City,S,24,9,15,28,High School,18,Patnership,Complex\n7197,494.02,Boston,N,20,18,2,49,Graduation,25,Patnership,Standalone\n3270,165.57,NJ City,S,18,24,22,26,High School,31,Patnership,Complex\n4762,375.32,NJ City,S,25,24,11,29,High School,32,Patnership,Complex\n81,356.36,Boston,N,23,12,5,35,Graduation,29,Patnership,Standalone\n5027,297.32,Boston,E,1,12,25,65,Graduation,31,Owner,Standalone\n1934,296.35,NJ City,S,23,15,1,50,No education,22,Patnership,Complex\n5624,34.39,Boston,N,20,14,2,65,No education,31,Franchise,Complex\n2943,155,NJ City,E,1,21,17,69,High School,29,Owner,Standalone\n3221,152.57,NJ City,N,25,18,15,35,High School,33,Patnership,Standalone\n4176,121.77,NJ City,W,16,24,1,38,Graduation,32,Franchise,Complex\n1567,469.5,NJ City,E,6,10,23,79,High School,32,Owner,Standalone\n7776,196.94,Boston,W,21,10,3,32,Graduation,32,Franchise,Complex\n8981,56.54,NJ City,N,14,24,4,57,No education,23,Patnership,Standalone\n1719,111.58,NJ City,E,13,18,1,54,Graduation,23,Owner,Standalone\n449,369.04,NJ City,N,17,11,18,59,High School,18,Patnership,Standalone\n1659,296.01,NJ City,E,21,21,19,42,High School,27,Owner,Standalone\n3284,80.36,NJ City,W,1,18,2,68,No education,23,Franchise,Complex\n5332,318,Boston,W,14,23,23,82,Graduation,19,Franchise,Complex\n7868,5.64,Boston,N,12,16,1,74,No education,21,Franchise,Complex\n8746,450.48,NJ City,S,15,14,12,60,Graduation,20,Patnership,Complex\n5830,385.57,Boston,S,24,13,22,58,Graduation,21,Patnership,Complex\n523,461.93,NJ City,E,6,20,23,31,High School,18,Owner,Standalone\n4387,68.47,NJ City,N,22,10,4,43,High School,19,Owner,Standalone\n6001,373.78,Boston,N,21,19,7,29,Graduation,21,Patnership,Standalone\n4779,379.83,NJ City,E,25,22,11,85,High School,19,Owner,Standalone\n1903,56.97,NJ City,E,8,17,4,77,High School,27,Owner,Standalone\n2249,81.14,NJ City,N,3,17,2,74,No education,22,Patnership,Standalone\n4446,484.27,NJ City,S,15,20,20,71,High School,26,Patnership,Complex\n7108,456.09,Boston,W,21,9,6,37,Graduation,22,Franchise,Complex\n4491,41.45,NJ City,N,16,16,1,50,Graduation,23,Owner,Standalone\n2434,132.95,NJ City,S,17,15,5,80,High School,22,Patnership,Complex\n642,291,NJ City,S,2,16,20,78,High School,21,Patnership,Complex\n2458,59.17,NJ City,S,19,18,4,25,High School,21,Patnership,Complex\n8282,363.46,NJ City,S,14,13,12,53,Graduation,19,Patnership,Complex\n5427,396.65,Boston,E,23,24,17,36,Graduation,18,Owner,Standalone\n9979,324.52,NJ City,E,11,16,25,74,Graduation,29,Owner,Standalone\n7737,295.78,Boston,N,16,15,23,66,Graduation,30,Patnership,Standalone\n1502,451.77,NJ City,S,12,19,23,27,High School,18,Patnership,Complex\n5957,262.64,Boston,N,23,14,12,44,Graduation,25,Patnership,Standalone\n9630,190.46,NJ City,S,17,14,14,45,Graduation,19,Patnership,Complex\n9192,457.16,NJ City,W,20,8,15,31,Graduation,19,Franchise,Complex\n5083,332.55,Boston,E,2,16,7,37,Graduation,29,Owner,Standalone\n6338,474.01,Boston,S,9,23,20,78,Graduation,20,Patnership,Complex\n7584,57.68,Boston,W,24,19,2,82,Graduation,28,Franchise,Complex\n7412,250.81,Boston,W,23,24,23,34,Graduation,33,Franchise,Complex\n2740,11.06,NJ City,N,22,20,4,40,High School,18,Franchise,Complex\n3578,155.1,NJ City,S,21,24,4,44,No education,28,Patnership,Complex\n6951,230.24,Boston,E,16,8,5,80,Graduation,35,Owner,Standalone\n6304,381.41,Boston,W,23,12,16,78,Graduation,32,Franchise,Complex\n9836,327.13,NJ City,W,1,18,17,68,Graduation,20,Franchise,Complex\n3976,377.08,NJ City,W,11,13,10,84,High School,35,Franchise,Complex\n9789,29.82,NJ City,N,1,19,2,32,Graduation,28,Patnership,Standalone\n2139,439.44,NJ City,E,13,15,15,35,High School,18,Owner,Standalone\n2588,233.21,NJ City,W,17,9,16,66,High School,19,Franchise,Complex\n2636,264.27,NJ City,W,11,23,23,41,High School,22,Franchise,Complex\n1910,184.33,NJ City,S,11,12,11,82,High School,28,Patnership,Complex\n4016,417.43,NJ City,W,21,19,19,45,High School,18,Franchise,Complex\n857,226.38,NJ City,N,16,13,13,85,High School,29,Patnership,Standalone\n6435,195.49,Boston,E,7,8,19,55,Graduation,30,Owner,Standalone\n6647,391.35,Boston,E,20,16,7,71,Graduation,33,Owner,Standalone\n9740,288.32,NJ City,W,25,15,12,65,Graduation,18,Franchise,Complex\n5349,374.51,Boston,N,17,9,16,57,Graduation,31,Patnership,Standalone\n9993,287.2,NJ City,N,1,8,8,25,Graduation,24,Patnership,Standalone\n3121,11.59,NJ City,N,12,16,1,49,High School,21,Patnership,Standalone\n613,494.51,NJ City,N,4,12,18,51,High School,24,Patnership,Standalone\n600,420.46,NJ City,W,15,10,19,29,High School,34,Franchise,Complex\n3192,33.68,NJ City,N,8,18,2,37,Graduation,26,Franchise,Complex\n1833,445.21,NJ City,N,25,23,25,55,High School,26,Patnership,Standalone\n8088,358.21,NJ City,W,7,8,7,85,Graduation,29,Franchise,Complex\n1221,438.1,NJ City,N,21,20,20,41,High School,18,Patnership,Standalone\n7115,415.8,Boston,E,23,23,15,73,Graduation,31,Owner,Standalone\n3563,254.69,NJ City,E,10,14,24,55,High School,26,Owner,Standalone\n2781,424.45,NJ City,N,4,19,16,61,High School,18,Patnership,Standalone\n2000,453.15,NJ City,W,11,21,25,59,High School,34,Franchise,Complex\n1316,72.31,NJ City,N,10,20,3,46,No education,18,Franchise,Complex\n7086,152.76,Boston,S,8,17,13,44,Graduation,30,Patnership,Complex\n8076,168.06,NJ City,W,23,19,1,52,Graduation,25,Franchise,Complex\n4787,110.09,NJ City,E,3,24,2,68,No education,35,Owner,Standalone\n4666,335.39,NJ City,S,2,24,4,67,High School,23,Patnership,Complex\n4242,203.23,NJ City,S,14,13,15,41,High School,22,Patnership,Complex\n1268,344.87,NJ City,W,13,10,1,57,No education,27,Franchise,Complex\n6490,106.49,Boston,S,24,17,2,51,High School,19,Patnership,Complex\n7616,265.33,Boston,W,10,17,21,83,Graduation,18,Franchise,Complex\n9459,465.23,NJ City,E,19,20,12,52,Graduation,34,Owner,Standalone\n2206,150.45,NJ City,S,16,22,9,31,High School,33,Patnership,Complex\n3892,89.44,NJ City,W,24,19,3,58,High School,29,Franchise,Complex\n3125,107,NJ City,N,2,16,19,68,High School,23,Patnership,Standalone\n8555,154.75,NJ City,E,7,10,10,73,Graduation,25,Owner,Standalone\n4530,456.5,NJ City,S,20,23,8,74,High School,18,Patnership,Complex\n7780,404.55,Boston,W,10,8,22,60,Graduation,18,Franchise,Complex\n2068,94.88,NJ City,W,21,8,1,46,High School,22,Franchise,Complex\n8235,465.97,NJ City,E,4,23,23,54,Graduation,29,Owner,Standalone\n2494,491.05,NJ City,S,25,13,1,49,High School,32,Patnership,Complex\n3025,121.41,NJ City,N,4,9,4,42,High School,27,Patnership,Standalone\n5201,321.81,Boston,N,16,20,21,54,Graduation,25,Patnership,Standalone\n7565,482,Boston,N,12,20,1,61,No education,20,Patnership,Standalone\n6861,180.93,Boston,N,19,18,6,59,Graduation,18,Patnership,Standalone\n8942,469.33,NJ City,S,25,22,16,49,Graduation,32,Patnership,Complex\n2695,355.07,NJ City,E,16,12,7,66,High School,29,Owner,Standalone\n821,410.18,NJ City,N,8,15,16,62,High School,26,Patnership,Standalone\n6371,203.67,Boston,E,12,16,5,43,No education,27,Owner,Standalone\n5940,101.53,Boston,W,16,11,1,43,Graduation,34,Franchise,Complex\n3222,81.09,NJ City,S,8,17,2,28,Graduation,34,Patnership,Complex\n2014,55.51,NJ City,S,14,20,4,80,High School,30,Patnership,Complex\n5586,22.26,Boston,N,18,17,4,46,Graduation,30,Patnership,Complex\n9019,348.23,NJ City,E,24,10,22,67,Graduation,24,Owner,Standalone\n2863,378.58,NJ City,E,22,14,20,68,High School,20,Owner,Standalone\n622,98.16,NJ City,S,2,20,1,69,High School,35,Patnership,Complex\n6177,114.07,Boston,N,4,12,2,66,Graduation,25,Patnership,Standalone\n9076,482.17,NJ City,W,21,11,21,69,Graduation,25,Franchise,Complex\n8266,305.32,NJ City,S,10,16,15,72,Graduation,20,Patnership,Complex\n9064,404.8,NJ City,W,3,20,25,41,Graduation,28,Franchise,Complex\n4199,75.75,NJ City,E,22,22,4,38,No education,25,Owner,Standalone\n1083,154.2,NJ City,E,21,17,7,51,High School,26,Owner,Standalone\n4588,352.86,NJ City,W,17,20,15,56,High School,34,Franchise,Complex\n1767,391.67,NJ City,E,23,13,15,51,High School,35,Owner,Standalone\n8223,261.41,NJ City,E,22,15,17,44,Graduation,21,Owner,Standalone\n6044,213,Boston,W,25,18,1,50,No education,22,Franchise,Complex\n8024,174.75,Boston,W,16,8,18,74,Graduation,19,Franchise,Complex\n6499,335.88,Boston,E,2,9,13,25,Graduation,26,Owner,Standalone\n5770,309.2,Boston,S,11,22,10,81,Graduation,25,Patnership,Complex\n7087,80.46,Boston,E,21,21,1,63,High School,25,Owner,Standalone\n9703,120.32,NJ City,E,3,10,2,40,High School,29,Owner,Standalone\n8891,496.08,NJ City,E,16,14,15,82,Graduation,34,Owner,Standalone\n1690,347.48,NJ City,S,23,14,21,59,High School,26,Patnership,Complex\n7476,128.29,Boston,W,17,11,7,34,Graduation,18,Franchise,Complex\n183,233.96,NJ City,E,22,18,17,39,High School,23,Owner,Standalone\n106,319.15,NJ City,S,13,17,2,25,High School,35,Patnership,Complex\n1308,67.73,NJ City,N,9,14,3,70,Graduation,22,Franchise,Complex\n9595,8.22,NJ City,N,6,18,2,67,High School,20,Owner,Standalone\n3543,114.66,NJ City,E,16,10,2,50,Graduation,23,Owner,Standalone\n5210,53.05,Boston,S,13,10,1,48,No education,31,Patnership,Complex\n4153,115.31,NJ City,N,9,10,3,41,High School,32,Patnership,Standalone\n1228,189,NJ City,W,2,13,22,43,High School,30,Franchise,Complex\n2761,490.65,NJ City,N,19,20,5,43,High School,34,Patnership,Standalone\n8313,172.88,NJ City,N,22,14,1,71,Graduation,27,Patnership,Standalone\n4933,181.4,NJ City,N,21,17,22,44,High School,31,Patnership,Standalone\n9129,286.33,NJ City,N,23,22,20,41,Graduation,23,Patnership,Standalone\n6482,20.4,Boston,N,17,20,1,46,No education,21,Patnership,Complex\n5409,176.8,Boston,N,10,10,4,79,Graduation,21,Patnership,Standalone\n4851,10.21,NJ City,N,19,10,4,34,Graduation,29,Owner,Standalone\n110,17.25,NJ City,N,1,9,1,60,No education,26,Patnership,Complex\n1804,257.93,NJ City,W,15,13,22,25,High School,23,Franchise,Complex\n4350,208.97,NJ City,S,15,20,18,69,High School,22,Patnership,Complex\n4541,257,NJ City,N,1,15,18,61,High School,27,Patnership,Standalone\n5537,256.87,Boston,N,4,22,19,78,Graduation,28,Patnership,Standalone\n880,126.86,NJ City,W,11,14,4,78,High School,23,Franchise,Complex\n5377,289.83,Boston,N,18,20,24,37,Graduation,20,Patnership,Standalone\n8156,167.94,NJ City,W,25,15,24,54,Graduation,20,Franchise,Complex\n3208,122.89,NJ City,W,24,12,1,39,High School,28,Franchise,Complex\n9588,265.66,NJ City,W,21,17,5,85,Graduation,21,Franchise,Complex\n9771,271.4,NJ City,E,7,10,4,80,Graduation,18,Owner,Standalone\n4224,164.39,NJ City,W,23,17,17,30,High School,28,Franchise,Complex\n4250,222.35,NJ City,S,13,15,15,28,High School,24,Patnership,Complex\n426,389.1,NJ City,S,10,18,16,32,High School,28,Patnership,Complex\n6585,451.05,Boston,N,15,8,22,33,Graduation,21,Patnership,Standalone\n7806,363.17,Boston,S,23,8,23,79,Graduation,22,Patnership,Complex\n8774,169.04,NJ City,S,17,8,12,35,Graduation,20,Patnership,Complex\n1640,79.06,NJ City,W,9,16,1,26,No education,28,Franchise,Complex\n7772,82.86,Boston,W,5,20,2,78,No education,31,Franchise,Complex\n1946,136.37,NJ City,S,6,18,2,81,No education,33,Patnership,Complex\n9891,378.76,NJ City,E,8,20,2,25,Graduation,19,Owner,Standalone\n9683,16.49,NJ City,N,7,22,2,83,No education,31,Owner,Standalone\n3432,272.73,NJ City,W,22,17,21,38,High School,22,Franchise,Complex\n5467,484.81,Boston,E,20,24,19,36,Graduation,35,Owner,Standalone\n9974,120.86,NJ City,S,15,8,1,25,No education,32,Patnership,Complex\n826,97.56,NJ City,S,6,17,2,54,High School,25,Patnership,Complex\n33,122.01,Boston,N,3,12,4,53,Graduation,25,Patnership,Standalone\n6713,445.32,Boston,N,2,15,12,82,Graduation,27,Patnership,Standalone\n91,87.62,Boston,E,17,18,2,63,High School,20,Owner,Standalone\n4424,295.17,NJ City,W,6,20,11,77,High School,28,Franchise,Complex\n6480,191.7,Boston,W,17,10,21,28,Graduation,29,Franchise,Complex\n2745,154,NJ City,N,1,20,18,79,High School,26,Patnership,Standalone\n4806,117.6,NJ City,S,18,9,3,26,Graduation,28,Patnership,Complex\n281,126.13,NJ City,N,3,18,4,54,No education,29,Patnership,Standalone\n6380,260.23,Boston,W,2,20,15,71,Graduation,27,Franchise,Complex\n4888,50.64,NJ City,W,19,19,3,35,High School,25,Franchise,Complex\n3816,301.75,NJ City,W,13,15,19,38,High School,32,Franchise,Complex\n8371,250.55,NJ City,E,21,16,13,64,Graduation,24,Owner,Standalone\n3341,304.33,NJ City,N,11,8,9,60,High School,27,Patnership,Standalone\n4986,227.78,Boston,S,20,8,12,61,High School,27,Patnership,Complex\n1266,28.23,NJ City,N,1,16,3,81,Graduation,22,Patnership,Complex\n8669,132.38,NJ City,N,25,11,25,52,Graduation,31,Patnership,Standalone\n865,470.7,NJ City,N,2,10,4,74,High School,26,Patnership,Standalone\n279,82.64,NJ City,E,13,22,1,52,Graduation,18,Owner,Standalone\n2784,137.36,NJ City,W,3,12,1,27,Graduation,27,Franchise,Complex\n6139,176.84,Boston,E,3,14,16,80,Graduation,31,Owner,Standalone\n7536,124.95,Boston,W,6,20,2,67,Graduation,19,Franchise,Complex\n3728,216,NJ City,W,3,22,8,62,High School,18,Franchise,Complex\n2929,46.69,NJ City,N,9,20,3,47,High School,20,Patnership,Standalone\n1319,339.57,NJ City,E,24,11,13,74,High School,30,Owner,Standalone\n8899,387.9,NJ City,E,3,24,7,63,Graduation,25,Owner,Standalone\n3744,367.73,NJ City,W,5,15,1,75,Graduation,23,Franchise,Complex\n7940,276.85,Boston,W,16,11,9,46,Graduation,27,Franchise,Complex\n1449,406.06,NJ City,N,20,23,19,51,High School,24,Patnership,Standalone\n7362,77.88,Boston,S,24,9,4,48,Graduation,35,Patnership,Complex\n9264,73.04,NJ City,N,4,21,2,85,Graduation,19,Franchise,Complex\n6224,406.57,Boston,W,19,15,14,75,Graduation,21,Franchise,Complex\n1396,60.21,NJ City,W,19,15,4,43,High School,29,Franchise,Complex\n643,141.9,NJ City,E,8,14,5,51,High School,28,Owner,Standalone\n1386,134.64,NJ City,S,5,14,20,67,High School,21,Patnership,Complex\n5814,207.95,Boston,S,17,8,6,40,Graduation,26,Patnership,Complex\n6256,87.63,Boston,W,12,18,2,47,High School,32,Franchise,Complex\n1932,0.74,NJ City,N,3,18,3,38,Graduation,22,Franchise,Complex\n3890,460.34,NJ City,S,13,13,10,37,High School,35,Patnership,Complex\n1016,67.84,NJ City,N,13,10,4,72,No education,29,Franchise,Complex\n6839,146.69,Boston,E,15,18,24,53,Graduation,19,Owner,Standalone\n1985,355.85,NJ City,N,14,20,2,52,No education,28,Patnership,Standalone\n8707,137.53,NJ City,E,22,15,2,42,High School,28,Owner,Standalone\n6741,169.07,Boston,N,8,10,24,67,Graduation,35,Patnership,Standalone\n270,305.74,NJ City,S,15,12,19,58,High School,18,Patnership,Complex\n6909,424.88,Boston,N,8,21,22,73,Graduation,24,Patnership,Standalone\n445,94.98,NJ City,N,10,9,2,46,High School,18,Patnership,Standalone\n1000,48.86,NJ City,W,22,8,4,63,High School,32,Franchise,Complex\n3134,78.67,NJ City,S,16,17,4,48,No education,18,Patnership,Complex\n7418,392.43,Boston,S,25,20,20,72,Graduation,21,Patnership,Complex\n9198,143.43,NJ City,S,18,10,9,31,Graduation,22,Patnership,Complex\n9212,22,NJ City,N,24,21,4,54,No education,24,Franchise,Complex\n2082,140.95,NJ City,S,21,12,16,31,High School,20,Patnership,Complex\n5048,32.09,Boston,N,20,11,3,71,No education,21,Franchise,Complex\n7311,118.45,Boston,E,3,23,2,34,Graduation,34,Owner,Standalone\n4931,170.24,NJ City,E,10,20,13,53,High School,21,Owner,Standalone\n5226,390.78,Boston,S,23,19,4,74,Graduation,30,Patnership,Complex\n3076,105.37,NJ City,W,23,16,2,55,High School,34,Franchise,Complex\n9928,225.17,NJ City,W,2,10,17,40,Graduation,28,Franchise,Complex\n4866,62.56,NJ City,N,11,12,3,55,Graduation,30,Patnership,Complex\n4145,179.1,NJ City,N,14,21,11,27,High School,24,Patnership,Standalone\n615,176.19,NJ City,E,7,22,21,77,High School,29,Owner,Standalone\n4477,494.94,NJ City,N,5,12,10,65,High School,34,Patnership,Standalone\n4144,402.19,NJ City,W,5,22,23,34,High School,32,Franchise,Complex\n9940,63.39,NJ City,N,2,9,2,77,High School,20,Franchise,Complex\n5828,465.71,Boston,W,19,15,3,30,No education,25,Franchise,Complex\n4021,140.05,NJ City,N,12,22,22,75,High School,35,Patnership,Standalone\n2916,266.29,NJ City,W,20,21,6,51,High School,18,Franchise,Complex\n1023,294.59,NJ City,E,19,12,11,40,High School,33,Owner,Standalone\n878,320.14,NJ City,S,8,14,2,35,No education,20,Patnership,Complex\n8896,78.39,NJ City,W,8,9,4,43,High School,33,Franchise,Complex\n7430,96.33,Boston,S,7,20,3,83,No education,19,Patnership,Complex\n4568,39.65,NJ City,N,25,17,1,36,No education,30,Franchise,Complex\n4142,151,NJ City,S,2,8,20,38,High School,19,Patnership,Complex\n9893,437.36,NJ City,N,7,13,3,70,No education,24,Patnership,Standalone\n1886,57.94,NJ City,S,25,22,4,51,No education,22,Patnership,Complex\n6154,461.29,Boston,S,4,11,1,31,High School,22,Patnership,Complex\n5540,116.18,Boston,W,14,16,2,73,No education,26,Franchise,Complex\n3552,83.23,NJ City,W,21,11,2,55,Graduation,27,Franchise,Complex\n3022,368.32,NJ City,S,8,18,20,85,High School,28,Patnership,Complex\n5375,44.04,Boston,N,11,19,3,77,No education,32,Owner,Standalone\n2337,187.76,NJ City,N,15,24,8,64,High School,31,Patnership,Standalone\n8407,388.98,NJ City,E,19,16,3,55,High School,23,Owner,Standalone\n1658,496.6,NJ City,S,6,20,24,65,High School,35,Patnership,Complex\n4597,412.77,NJ City,N,20,23,17,43,High School,21,Patnership,Standalone\n7542,280.36,Boston,S,13,16,3,64,Graduation,34,Patnership,Complex\n8472,404.96,NJ City,W,19,20,10,35,Graduation,21,Franchise,Complex\n7057,73.53,Boston,N,17,13,2,47,High School,33,Patnership,Standalone\n9297,190.12,NJ City,N,24,11,18,33,Graduation,31,Patnership,Standalone\n5380,249.98,Boston,W,23,24,14,47,Graduation,32,Franchise,Complex\n9600,181.2,NJ City,W,23,14,24,48,Graduation,31,Franchise,Complex\n467,46.16,NJ City,N,25,21,2,38,No education,23,Owner,Standalone\n506,266,NJ City,S,3,14,18,45,High School,18,Patnership,Complex\n1820,113.24,NJ City,W,8,22,2,32,No education,28,Franchise,Complex\n9021,404.23,NJ City,N,16,11,14,68,Graduation,26,Patnership,Standalone\n3994,255.94,NJ City,S,14,16,6,85,High School,30,Patnership,Complex\n1374,210.6,NJ City,S,15,24,8,45,High School,34,Patnership,Complex\n7874,471.73,Boston,S,15,13,3,57,No education,18,Patnership,Complex\n9110,362.15,NJ City,S,10,12,2,26,No education,18,Patnership,Complex\n2316,289.59,NJ City,W,9,24,23,35,High School,34,Franchise,Complex\n2710,164.66,NJ City,S,12,17,6,76,High School,18,Patnership,Complex\n5260,79.62,Boston,W,19,23,1,34,High School,32,Franchise,Complex\n7861,249.34,Boston,N,24,24,19,26,Graduation,26,Patnership,Standalone\n8332,106.68,NJ City,W,23,19,1,79,High School,29,Franchise,Complex\n2016,41.65,NJ City,N,20,18,1,64,Graduation,27,Franchise,Complex\n7097,48.29,Boston,N,2,14,3,60,No education,25,Patnership,Standalone\n4572,237.89,NJ City,W,2,8,4,70,Graduation,22,Franchise,Complex\n3496,343.24,NJ City,W,5,24,6,33,High School,23,Franchise,Complex\n4476,90.41,NJ City,W,25,12,4,33,Graduation,34,Franchise,Complex\n8560,16.95,NJ City,N,11,24,2,70,High School,27,Franchise,Complex\n8765,360.61,NJ City,N,2,9,12,30,Graduation,21,Patnership,Standalone\n2388,471.84,NJ City,W,17,24,13,57,High School,34,Franchise,Complex\n6293,198.09,Boston,N,25,12,21,74,Graduation,31,Patnership,Standalone\n4034,52,NJ City,S,10,18,3,36,No education,19,Patnership,Complex\n1998,71.3,NJ City,N,14,21,4,78,Graduation,33,Patnership,Complex\n2241,430.29,NJ City,N,15,13,19,35,High School,21,Patnership,Standalone\n1560,217.62,NJ City,W,14,18,8,36,High School,35,Franchise,Complex\n2755,88.27,NJ City,E,5,23,4,57,High School,18,Owner,Standalone\n5669,468.41,Boston,N,18,15,8,54,Graduation,31,Patnership,Standalone\n798,47.03,NJ City,N,23,22,1,73,Graduation,27,Patnership,Complex\n6544,95.72,Boston,W,25,9,2,81,High School,31,Franchise,Complex\n8440,170.17,NJ City,W,23,9,1,77,High School,19,Franchise,Complex\n1055,273.51,NJ City,E,5,8,1,77,No education,19,Owner,Standalone\n604,20.31,NJ City,N,5,11,1,59,High School,20,Franchise,Complex\n9392,489.06,NJ City,W,17,17,14,52,Graduation,32,Franchise,Complex\n5428,42.13,Boston,N,3,22,1,46,High School,19,Franchise,Complex\n9853,6.71,NJ City,N,19,8,2,35,High School,22,Patnership,Standalone\n2752,69.08,NJ City,N,1,17,1,84,High School,28,Franchise,Complex\n2860,478.6,NJ City,W,12,21,21,42,High School,28,Franchise,Complex\n979,33.4,NJ City,N,8,22,4,62,High School,30,Owner,Standalone\n3700,71.44,NJ City,N,4,20,2,40,High School,29,Franchise,Complex\n1358,167.38,NJ City,S,6,23,3,40,No education,33,Patnership,Complex\n6232,203.58,Boston,W,19,19,3,51,High School,32,Franchise,Complex\n6914,107.97,Boston,S,7,21,2,74,No education,19,Patnership,Complex\n2294,441.33,NJ City,S,8,16,17,64,High School,24,Patnership,Complex\n4932,267.17,NJ City,W,10,8,4,81,Graduation,24,Franchise,Complex\n14,180.44,Boston,S,20,17,23,50,High School,19,Patnership,Complex\n3933,216,NJ City,N,2,10,17,25,High School,34,Patnership,Standalone\n677,382.63,NJ City,N,25,21,1,78,No education,30,Patnership,Standalone\n9978,441.83,NJ City,S,12,17,10,68,Graduation,28,Patnership,Complex\n1845,354.35,NJ City,N,9,8,22,61,High School,30,Patnership,Standalone\n3959,466.86,NJ City,E,12,15,14,37,High School,25,Owner,Standalone\n9783,261.58,NJ City,E,24,10,11,57,Graduation,35,Owner,Standalone\n9220,58.54,NJ City,W,19,12,4,69,High School,18,Franchise,Complex\n4609,109.97,NJ City,N,7,16,3,58,High School,22,Patnership,Standalone\n4964,99.19,Boston,W,10,24,3,40,No education,22,Franchise,Complex\n3639,373.44,NJ City,E,10,9,3,52,Graduation,34,Owner,Standalone\n7391,176.71,Boston,E,12,22,7,63,Graduation,23,Owner,Standalone\n531,4.02,NJ City,N,4,21,2,38,Graduation,32,Owner,Standalone\n2104,132.43,NJ City,W,12,12,15,80,High School,33,Franchise,Complex\n2108,93.88,NJ City,W,22,11,2,73,No education,23,Franchise,Complex\n8791,305.52,NJ City,E,13,23,10,84,Graduation,31,Owner,Standalone\n9826,160.17,NJ City,S,4,16,10,55,Graduation,19,Patnership,Complex\n8477,88.3,NJ City,N,21,23,4,46,No education,26,Patnership,Standalone\n4520,346.52,NJ City,W,11,8,11,56,High School,24,Franchise,Complex\n5268,475.89,Boston,W,23,14,14,71,Graduation,21,Franchise,Complex\n438,189.77,NJ City,S,9,9,5,48,Graduation,29,Patnership,Complex\n3769,297.31,NJ City,N,24,20,10,76,High School,19,Patnership,Standalone\n9688,48.13,NJ City,W,25,15,2,71,High School,26,Franchise,Complex\n8122,307.7,NJ City,S,22,23,7,39,Graduation,23,Patnership,Complex\n8460,267.18,NJ City,W,14,17,5,82,Graduation,28,Franchise,Complex\n2982,163.99,NJ City,S,8,20,5,40,Graduation,26,Patnership,Complex\n1286,233,NJ City,S,2,17,20,33,High School,23,Patnership,Complex\n6447,383.85,Boston,E,25,13,8,57,Graduation,29,Owner,Standalone\n4869,270,NJ City,N,3,12,15,78,High School,27,Patnership,Standalone\n2652,126.57,NJ City,W,5,10,4,66,Graduation,25,Franchise,Complex\n4517,222.58,NJ City,N,18,13,12,73,High School,27,Patnership,Standalone\n7555,330.66,Boston,E,19,20,10,80,Graduation,28,Owner,Standalone\n8581,487.07,NJ City,N,15,18,12,75,Graduation,25,Patnership,Standalone\n2233,311.02,NJ City,N,19,17,13,54,High School,29,Patnership,Standalone\n3372,464.08,NJ City,W,21,15,10,70,High School,24,Franchise,Complex\n9050,212.14,NJ City,S,20,14,10,26,Graduation,34,Patnership,Complex\n9822,195.57,NJ City,S,14,24,22,49,Graduation,28,Patnership,Complex\n6808,390.44,Boston,W,4,12,4,82,High School,35,Franchise,Complex\n3002,158.02,NJ City,S,10,8,21,72,High School,29,Patnership,Complex\n3237,493.77,NJ City,N,20,22,8,25,High School,19,Patnership,Standalone\n8989,217.68,NJ City,N,8,24,17,44,Graduation,18,Patnership,Standalone\n7489,451.75,Boston,N,9,23,14,44,Graduation,18,Patnership,Standalone\n9450,405.08,NJ City,S,11,12,16,83,Graduation,24,Patnership,Complex\n8955,260.58,NJ City,E,8,12,17,35,Graduation,30,Owner,Standalone\n7373,17.65,Boston,N,13,9,4,67,No education,31,Patnership,Standalone\n9419,242.41,NJ City,E,10,8,4,31,No education,19,Owner,Standalone\n6960,90.8,Boston,W,20,8,2,77,Graduation,35,Franchise,Complex\n6246,78.02,Boston,S,20,17,3,54,Graduation,30,Patnership,Complex\n7454,483.77,Boston,S,14,22,12,69,Graduation,24,Patnership,Complex\n1442,234.17,NJ City,S,18,19,4,41,No education,27,Patnership,Complex\n2078,6.76,NJ City,N,1,24,4,36,No education,27,Patnership,Complex\n6344,250.31,Boston,W,2,23,22,75,Graduation,22,Franchise,Complex\n101,99.27,NJ City,N,8,10,3,61,No education,22,Patnership,Standalone\n6690,253.14,Boston,S,16,15,25,32,Graduation,35,Patnership,Complex\n9130,244.31,NJ City,S,4,18,2,29,High School,24,Patnership,Complex\n9814,40.1,NJ City,N,2,19,3,61,High School,23,Patnership,Complex\n6038,349.36,Boston,S,9,8,5,67,No education,34,Patnership,Complex\n3881,314.82,NJ City,N,21,19,22,48,High School,28,Patnership,Standalone\n2277,123.21,NJ City,N,13,18,1,78,Graduation,23,Patnership,Standalone\n4634,198.25,NJ City,S,10,19,22,74,High School,33,Patnership,Complex\n633,304.99,NJ City,N,22,12,13,82,High School,26,Patnership,Standalone\n252,84.13,NJ City,W,1,16,1,53,Graduation,25,Franchise,Complex\n2553,473.26,NJ City,N,14,13,7,35,High School,19,Patnership,Standalone\n8680,27.23,NJ City,N,18,12,4,26,High School,35,Franchise,Complex\n1677,239.95,NJ City,N,6,18,4,46,Graduation,34,Patnership,Standalone\n6846,95.49,Boston,S,20,22,3,74,Graduation,18,Patnership,Complex\n5144,58.27,Boston,W,14,11,3,81,No education,18,Franchise,Complex\n8523,90.03,NJ City,E,18,16,4,49,Graduation,22,Owner,Standalone\n7703,441.66,Boston,E,19,8,14,35,Graduation,24,Owner,Standalone\n6459,180.78,Boston,E,14,14,2,38,Graduation,28,Owner,Standalone\n7995,207.95,Boston,E,4,21,5,67,Graduation,34,Owner,Standalone\n9969,370.38,NJ City,N,17,16,11,62,Graduation,23,Patnership,Standalone\n6090,426.61,Boston,S,7,17,16,55,Graduation,28,Patnership,Complex\n1999,440.87,NJ City,E,19,8,22,40,High School,26,Owner,Standalone\n2129,317.46,NJ City,N,20,24,16,30,High School,18,Patnership,Standalone\n3256,411.9,NJ City,W,25,10,25,59,High School,33,Franchise,Complex\n7842,36.8,Boston,N,25,11,4,69,Graduation,29,Patnership,Complex\n7195,220.23,Boston,E,3,8,15,57,Graduation,28,Owner,Standalone\n9679,115.57,NJ City,E,15,16,3,38,High School,19,Owner,Standalone\n793,48.38,NJ City,N,13,10,2,67,High School,18,Patnership,Standalone\n7248,166.3,Boston,W,23,8,4,43,Graduation,25,Franchise,Complex\n1810,497.39,NJ City,S,5,16,17,80,High School,19,Patnership,Complex\n8166,454.56,NJ City,S,3,22,11,61,Graduation,30,Patnership,Complex\n720,477.63,NJ City,W,19,16,3,81,Graduation,22,Franchise,Complex\n5615,15.05,Boston,N,11,21,2,50,No education,25,Owner,Standalone\n5361,421.83,Boston,N,19,10,7,47,Graduation,20,Patnership,Standalone\n3616,146.81,NJ City,W,21,24,18,44,High School,25,Franchise,Complex\n2117,75.44,NJ City,N,1,8,3,85,No education,24,Patnership,Standalone\n8677,140.58,NJ City,N,14,13,5,61,High School,25,Patnership,Standalone\n5643,204.92,Boston,E,11,22,8,40,Graduation,30,Owner,Standalone\n6567,92.29,Boston,E,1,21,2,52,Graduation,28,Owner,Standalone\n912,472.2,NJ City,W,11,17,23,61,High School,22,Franchise,Complex\n7427,131.75,Boston,E,3,17,16,40,Graduation,30,Owner,Standalone\n8798,457.12,NJ City,S,15,14,3,53,No education,33,Patnership,Complex\n1293,271,NJ City,N,3,15,19,49,High School,33,Patnership,Standalone\n1907,330.22,NJ City,E,6,16,24,39,High School,34,Owner,Standalone\n3599,106.85,NJ City,E,5,24,3,30,No education,26,Owner,Standalone\n4089,299.5,NJ City,N,8,8,25,27,High School,25,Patnership,Standalone\n9038,26.03,NJ City,N,21,23,2,58,No education,23,Patnership,Complex\n5528,9.44,Boston,N,18,23,1,73,No education,29,Franchise,Complex\n2064,263.05,NJ City,W,15,22,17,60,High School,29,Franchise,Complex\n7805,50.58,Boston,N,22,16,4,46,No education,18,Patnership,Standalone\n8956,324.46,NJ City,W,11,8,16,25,Graduation,28,Franchise,Complex\n5099,439.9,Boston,E,6,9,1,80,No education,27,Owner,Standalone\n5142,345.36,Boston,S,25,24,17,61,Graduation,25,Patnership,Complex\n7784,260.89,Boston,W,14,9,12,43,Graduation,31,Franchise,Complex\n1546,20.77,NJ City,N,9,12,4,71,High School,19,Patnership,Complex\n9726,294.27,NJ City,S,24,8,23,48,Graduation,23,Patnership,Complex\n8128,286.97,NJ City,W,4,14,13,45,Graduation,25,Franchise,Complex\n2099,489.39,NJ City,E,16,21,22,77,High School,33,Owner,Standalone\n1537,391.47,NJ City,N,18,21,3,62,High School,27,Patnership,Standalone\n8609,396.13,NJ City,N,5,22,14,65,Graduation,19,Patnership,Standalone\n4187,144.73,NJ City,E,12,15,16,47,High School,35,Owner,Standalone\n3911,221.28,NJ City,E,8,20,3,61,No education,21,Owner,Standalone\n2810,213.31,NJ City,S,17,11,25,58,High School,35,Patnership,Complex\n3765,9.85,NJ City,N,3,10,2,60,Graduation,25,Patnership,Standalone\n1483,178.28,NJ City,E,13,11,21,48,High School,23,Owner,Standalone\n1991,117,NJ City,E,3,20,19,33,High School,27,Owner,Standalone\n6236,477.53,Boston,W,14,10,1,35,No education,23,Franchise,Complex\n4843,172.17,NJ City,E,5,12,16,67,High School,22,Owner,Standalone\n3799,157.24,NJ City,E,25,24,3,40,High School,31,Owner,Standalone\n1976,105,NJ City,W,2,15,17,45,High School,32,Franchise,Complex\n7782,10.1,Boston,N,8,8,3,68,Graduation,20,Patnership,Complex\n5567,231.78,Boston,E,15,11,18,84,Graduation,22,Owner,Standalone\n2678,173.88,NJ City,S,19,23,8,67,High School,32,Patnership,Complex\n474,279.25,NJ City,S,14,10,20,36,High School,35,Patnership,Complex\n3688,311.59,NJ City,W,12,21,16,76,High School,19,Franchise,Complex\n4143,78.81,NJ City,E,8,16,2,75,Graduation,27,Owner,Standalone\n7719,338.79,Boston,E,18,23,12,35,Graduation,25,Owner,Standalone\n6645,248.02,Boston,N,19,8,2,80,Graduation,33,Patnership,Standalone\n4512,283,NJ City,W,2,20,15,53,High School,33,Franchise,Complex\n8020,23.67,Boston,N,15,12,4,40,High School,31,Franchise,Complex\n5678,346.99,Boston,S,24,16,10,62,Graduation,25,Patnership,Complex\n4369,354.7,NJ City,N,15,11,22,56,High School,27,Patnership,Standalone\n7343,317.24,Boston,E,15,23,15,85,Graduation,27,Owner,Standalone\n4576,478.75,NJ City,W,6,18,2,69,High School,30,Franchise,Complex\n9793,111.71,NJ City,N,1,19,1,49,High School,22,Patnership,Standalone\n2962,214.44,NJ City,S,9,17,4,44,High School,27,Patnership,Complex\n6346,176.44,Boston,S,18,18,18,53,Graduation,25,Patnership,Complex\n6506,218.67,Boston,S,13,11,11,50,Graduation,25,Patnership,Complex\n1409,309.6,NJ City,N,6,20,5,56,No education,23,Patnership,Standalone\n734,31.56,NJ City,N,6,13,4,45,No education,20,Patnership,Complex\n84,176.3,Boston,W,23,20,3,37,Graduation,25,Franchise,Complex\n7890,455.26,Boston,S,25,21,21,28,Graduation,30,Patnership,Complex\n2038,481.57,NJ City,S,17,16,18,28,High School,22,Patnership,Complex\n9426,446.09,NJ City,S,23,13,8,75,Graduation,34,Patnership,Complex\n5018,478.2,Boston,S,20,8,13,76,Graduation,23,Patnership,Complex\n2069,124.27,NJ City,N,2,19,2,74,No education,24,Patnership,Standalone\n5662,383.67,Boston,S,18,19,5,73,High School,24,Patnership,Complex\n5457,78.56,Boston,N,8,14,4,58,Graduation,27,Patnership,Standalone\n3798,462.9,NJ City,S,17,9,21,36,High School,28,Patnership,Complex\n2949,291.47,NJ City,N,14,8,24,65,High School,21,Patnership,Standalone\n1072,62.33,NJ City,N,1,24,2,77,High School,26,Franchise,Complex\n1123,17.79,NJ City,N,7,19,3,50,High School,24,Owner,Standalone\n1656,403.97,NJ City,W,4,20,2,80,Graduation,21,Franchise,Complex\n220,223.92,NJ City,W,4,14,9,47,High School,28,Franchise,Complex\n3907,169.2,NJ City,E,4,17,16,41,High School,23,Owner,Standalone\n5784,67.54,Boston,N,23,20,2,46,Graduation,20,Franchise,Complex\n4901,442.36,NJ City,N,23,14,16,55,High School,29,Patnership,Standalone\n7525,343.04,Boston,N,4,18,13,56,Graduation,27,Patnership,Standalone\n2290,224.57,NJ City,S,4,8,17,44,High School,24,Patnership,Complex\n2348,259,NJ City,W,1,15,23,75,High School,33,Franchise,Complex\n9151,281.24,NJ City,E,13,13,9,69,Graduation,18,Owner,Standalone\n3950,79.19,NJ City,S,19,13,3,37,No education,35,Patnership,Complex\n9873,149.12,NJ City,N,22,10,12,83,Graduation,34,Patnership,Standalone\n3144,389.9,NJ City,W,10,17,5,44,Graduation,28,Franchise,Complex\n4555,292.08,NJ City,E,9,8,18,81,High School,20,Owner,Standalone\n4718,453.22,NJ City,S,11,13,9,37,High School,31,Patnership,Complex\n8612,188.31,NJ City,W,4,13,22,66,Graduation,27,Franchise,Complex\n5171,25.28,Boston,N,14,18,3,34,No education,23,Owner,Standalone\n7356,52.27,Boston,W,15,13,4,48,Graduation,29,Franchise,Complex\n8494,262.32,NJ City,S,14,8,9,61,Graduation,30,Patnership,Complex\n9448,416.55,NJ City,W,17,17,9,35,Graduation,22,Franchise,Complex\n9769,435.06,NJ City,N,20,11,14,38,Graduation,29,Patnership,Standalone\n76,71.73,Boston,N,18,18,3,53,High School,24,Franchise,Complex\n30,78.5,Boston,S,15,19,3,55,Graduation,33,Patnership,Complex\n8189,111.41,NJ City,N,24,14,4,77,No education,23,Patnership,Standalone\n9218,489.42,NJ City,S,13,22,22,64,Graduation,33,Patnership,Complex\n7496,94.06,Boston,W,20,13,1,68,No education,35,Franchise,Complex\n490,26.23,NJ City,N,9,20,3,39,High School,31,Patnership,Complex\n6969,319.56,Boston,N,4,15,20,48,Graduation,20,Patnership,Standalone\n2787,216.56,NJ City,E,14,9,23,74,High School,30,Owner,Standalone\n3707,233.89,NJ City,E,14,17,1,66,No education,21,Owner,Standalone\n7292,402.75,Boston,W,13,11,6,60,Graduation,29,Franchise,Complex\n2776,123.92,NJ City,W,8,10,3,72,High School,25,Franchise,Complex\n4953,239.62,Boston,N,25,18,7,60,High School,24,Patnership,Standalone\n2881,338.38,NJ City,N,7,9,25,64,High School,31,Patnership,Standalone\n3743,275.4,NJ City,E,19,14,21,79,High School,22,Owner,Standalone\n2835,109.97,NJ City,E,1,9,3,76,Graduation,30,Owner,Standalone\n9672,407.59,NJ City,W,21,15,7,58,Graduation,32,Franchise,Complex\n9284,357.91,NJ City,W,3,22,18,47,Graduation,22,Franchise,Complex\n3319,323.55,NJ City,E,9,22,1,26,High School,27,Owner,Standalone\n3880,164.12,NJ City,W,22,17,10,53,High School,22,Franchise,Complex\n3497,320.94,NJ City,N,18,10,4,66,No education,30,Patnership,Standalone\n7665,211.66,Boston,N,7,15,13,63,Graduation,27,Patnership,Standalone\n5656,498.43,Boston,W,8,13,8,52,Graduation,30,Franchise,Complex\n1074,253.17,NJ City,S,11,20,9,27,High School,21,Patnership,Complex\n9503,443.62,NJ City,E,7,20,18,44,Graduation,28,Owner,Standalone\n1592,488.82,NJ City,W,23,24,7,84,High School,27,Franchise,Complex\n3906,337.42,NJ City,S,19,23,9,47,High School,29,Patnership,Complex\n5081,470.57,Boston,N,24,14,17,53,Graduation,35,Patnership,Standalone\n7422,65.74,Boston,N,13,12,3,65,Graduation,27,Patnership,Complex\n9541,90.65,NJ City,N,22,21,4,35,High School,24,Patnership,Standalone\n9097,424.22,NJ City,N,9,11,19,56,Graduation,29,Patnership,Standalone\n3522,197.38,NJ City,S,18,14,25,54,High School,18,Patnership,Complex\n1253,413.18,NJ City,N,16,14,17,66,High School,34,Patnership,Standalone\n9546,275.66,NJ City,S,21,8,12,77,Graduation,22,Patnership,Complex\n3460,90.37,NJ City,W,20,23,2,60,High School,29,Franchise,Complex\n6992,10.85,Boston,N,24,15,1,43,No education,35,Franchise,Complex\n4062,35.5,NJ City,N,10,19,4,67,Graduation,31,Patnership,Complex\n6862,283.15,Boston,S,22,10,14,45,Graduation,25,Patnership,Complex\n3307,329.09,NJ City,E,12,13,11,38,High School,34,Owner,Standalone\n8814,30.36,NJ City,N,14,9,1,64,Graduation,24,Patnership,Complex\n6377,261.24,Boston,N,21,11,14,80,Graduation,24,Patnership,Standalone\n3430,181.05,NJ City,S,25,16,24,60,High School,18,Patnership,Complex\n4141,268.63,NJ City,N,15,23,17,76,High School,27,Patnership,Standalone\n334,43.16,NJ City,N,9,12,3,61,High School,22,Patnership,Complex\n1929,34.31,NJ City,N,16,22,2,60,Graduation,24,Patnership,Standalone\n8712,125.4,NJ City,W,7,14,3,51,Graduation,27,Franchise,Complex\n6820,40.12,Boston,N,21,23,2,32,High School,34,Franchise,Complex\n5515,287.24,Boston,E,19,14,8,53,Graduation,24,Owner,Standalone\n8314,242.61,NJ City,S,15,21,18,39,Graduation,35,Patnership,Complex\n2733,24.65,NJ City,N,23,15,3,65,Graduation,27,Patnership,Standalone\n4088,358.62,NJ City,W,20,22,8,54,High School,24,Franchise,Complex\n19,107.16,NJ City,E,14,17,4,48,High School,19,Owner,Standalone\n2986,393.23,NJ City,S,8,20,18,63,High School,32,Patnership,Complex\n2832,195.75,NJ City,W,1,17,4,41,Graduation,33,Franchise,Complex\n3922,418.5,NJ City,S,25,21,23,52,High School,18,Patnership,Complex\n4335,149,NJ City,E,3,22,12,25,High School,20,Owner,Standalone\n4875,112.28,NJ City,E,13,10,3,39,Graduation,33,Owner,Standalone\n9620,359.08,NJ City,W,16,19,10,26,Graduation,28,Franchise,Complex\n5902,466.4,Boston,S,20,12,15,85,Graduation,25,Patnership,Complex\n5812,449.86,Boston,W,17,14,20,58,Graduation,18,Franchise,Complex\n4647,142.06,NJ City,E,4,19,19,66,High School,34,Owner,Standalone\n5991,479.84,Boston,E,3,24,3,47,Graduation,26,Owner,Standalone\n6419,431.44,Boston,E,24,8,15,54,Graduation,35,Owner,Standalone\n6759,499.65,Boston,E,4,13,14,28,Graduation,34,Owner,Standalone\n3160,208,NJ City,W,2,18,17,67,High School,33,Franchise,Complex\n4293,135,NJ City,N,1,17,16,27,High School,26,Patnership,Standalone\n6270,478.44,Boston,S,15,23,22,52,Graduation,34,Patnership,Complex\n6448,377.47,Boston,W,4,10,15,85,Graduation,31,Franchise,Complex\n3584,259.39,NJ City,W,11,16,2,41,No education,31,Franchise,Complex\n6704,485.78,Boston,W,24,9,1,76,No education,25,Franchise,Complex\n3419,324.93,NJ City,E,20,18,16,73,High School,32,Owner,Standalone\n2081,316.47,NJ City,N,20,10,8,57,High School,27,Patnership,Standalone\n1173,23.66,NJ City,N,21,19,2,55,Graduation,25,Patnership,Standalone\n8346,359.85,NJ City,S,3,10,11,29,Graduation,22,Patnership,Complex\n1066,295.2,NJ City,S,8,21,12,32,High School,19,Patnership,Complex\n3977,234.6,NJ City,N,13,8,5,63,No education,23,Patnership,Standalone\n8312,168.26,NJ City,W,15,14,17,58,Graduation,18,Franchise,Complex\n6494,393.23,Boston,S,19,17,4,43,No education,20,Patnership,Complex\n671,121.85,NJ City,E,19,18,4,30,No education,28,Owner,Standalone\n3544,192.77,NJ City,W,12,23,2,38,High School,32,Franchise,Complex\n2610,309.41,NJ City,S,19,22,19,31,High School,21,Patnership,Complex\n9524,398.94,NJ City,W,13,8,11,76,Graduation,19,Franchise,Complex\n8054,121.51,NJ City,S,9,21,1,38,No education,31,Patnership,Complex\n7137,47.39,Boston,N,2,18,2,29,Graduation,22,Patnership,Standalone\n3864,462.39,NJ City,W,10,9,21,82,High School,19,Franchise,Complex\n6847,175.94,Boston,E,1,14,15,54,Graduation,31,Owner,Standalone\n2297,298.52,NJ City,N,13,8,25,55,High School,29,Patnership,Standalone\n9371,363.73,NJ City,E,17,23,18,32,Graduation,33,Owner,Standalone\n508,319.9,NJ City,W,4,12,24,40,High School,32,Franchise,Complex\n1871,297.59,NJ City,E,16,19,10,38,High School,26,Owner,Standalone\n1157,443.85,NJ City,N,8,19,23,26,High School,23,Patnership,Standalone\n9068,355.42,NJ City,W,6,21,17,60,Graduation,24,Franchise,Complex\n5511,218.39,Boston,E,2,12,14,81,Graduation,27,Owner,Standalone\n4556,13.47,NJ City,N,12,17,4,76,No education,28,Franchise,Complex\n3607,107.59,NJ City,E,18,20,4,77,High School,24,Owner,Standalone\n2647,411.9,NJ City,E,4,9,22,31,High School,35,Owner,Standalone\n5116,348.58,Boston,W,9,9,25,35,Graduation,18,Franchise,Complex\n8181,192.75,NJ City,N,11,18,10,30,Graduation,30,Patnership,Standalone\n2938,259.64,NJ City,S,2,13,3,61,High School,30,Patnership,Complex\n9017,415.52,NJ City,N,4,23,16,43,Graduation,22,Patnership,Standalone\n927,470.83,NJ City,E,5,9,18,45,High School,19,Owner,Standalone\n8987,168.63,NJ City,E,8,13,5,57,No education,21,Owner,Standalone\n8183,276.58,NJ City,E,17,23,1,66,No education,22,Owner,Standalone\n8779,276.1,NJ City,E,25,13,24,33,Graduation,18,Owner,Standalone\n2306,91.54,NJ City,S,24,17,1,83,No education,28,Patnership,Complex\n8518,469.62,NJ City,S,15,21,3,81,High School,28,Patnership,Complex\n2798,180.18,NJ City,S,8,10,22,26,High School,27,Patnership,Complex\n1457,19.87,NJ City,N,21,18,1,81,No education,19,Patnership,Standalone\n4013,60.9,NJ City,N,22,23,2,41,No education,21,Patnership,Standalone\n4794,148.52,NJ City,S,13,19,18,51,High School,25,Patnership,Complex\n2843,484.48,NJ City,E,23,17,21,50,High School,34,Owner,Standalone\n8754,272.05,NJ City,S,19,14,16,47,Graduation,20,Patnership,Complex\n8252,297.61,NJ City,W,16,15,8,58,Graduation,22,Franchise,Complex\n4847,89.62,NJ City,E,8,20,1,77,No education,28,Owner,Standalone\n4629,33.01,NJ City,N,16,21,2,44,Graduation,34,Patnership,Standalone\n9016,479.15,NJ City,W,8,8,12,85,Graduation,22,Franchise,Complex\n5801,230.32,Boston,N,23,9,17,61,Graduation,35,Patnership,Standalone\n8534,263.17,NJ City,S,6,23,21,52,Graduation,26,Patnership,Complex\n4757,102.27,NJ City,N,11,13,3,55,No education,18,Patnership,Standalone\n8671,480.25,NJ City,E,5,19,17,51,Graduation,18,Owner,Standalone\n2759,285.83,NJ City,E,11,22,19,47,High School,23,Owner,Standalone\n9748,393.26,NJ City,W,19,9,3,82,High School,25,Franchise,Complex\n4668,180.13,NJ City,W,7,10,24,57,High School,22,Franchise,Complex\n7815,156.19,Boston,E,2,17,21,47,Graduation,35,Owner,Standalone\n2802,204.46,NJ City,S,16,12,25,57,High School,30,Patnership,Complex\n4593,367.72,NJ City,N,13,21,20,54,High School,35,Patnership,Standalone\n1742,151.5,NJ City,S,23,19,16,76,High School,31,Patnership,Complex\n2035,374.41,NJ City,E,18,19,10,40,High School,22,Owner,Standalone\n9104,421.72,NJ City,W,25,18,19,32,Graduation,34,Franchise,Complex\n718,332.41,NJ City,S,19,12,23,51,High School,26,Patnership,Complex\n8459,137.94,NJ City,E,5,20,20,35,Graduation,23,Owner,Standalone\n4460,467.39,NJ City,W,4,11,15,63,High School,26,Franchise,Complex\n1390,228.29,NJ City,S,12,9,13,30,High School,23,Patnership,Complex\n3443,177.39,NJ City,E,13,15,14,28,High School,27,Owner,Standalone\n6978,351.13,Boston,S,9,20,17,41,Graduation,32,Patnership,Complex\n2797,449.24,NJ City,N,18,14,17,31,High School,27,Patnership,Standalone\n5072,188.07,Boston,W,24,16,19,54,Graduation,28,Franchise,Complex\n8659,349.68,NJ City,E,11,23,8,54,Graduation,29,Owner,Standalone\n2404,63.7,NJ City,N,7,12,1,59,High School,28,Franchise,Complex\n3205,69.39,NJ City,N,17,17,1,83,High School,29,Patnership,Standalone\n2221,128.53,NJ City,N,6,16,17,76,High School,33,Patnership,Standalone\n8631,393.14,NJ City,E,9,14,18,52,Graduation,21,Owner,Standalone\n1264,104.89,NJ City,W,13,19,1,69,High School,27,Franchise,Complex\n1096,223.37,NJ City,W,9,23,12,63,High School,28,Franchise,Complex\n6078,70.55,Boston,N,7,12,2,27,Graduation,24,Patnership,Complex\n9861,311.11,NJ City,N,25,15,7,36,Graduation,31,Patnership,Standalone\n6056,102.16,Boston,W,18,13,4,33,No education,26,Franchise,Complex\n7128,481.29,Boston,W,12,10,19,64,Graduation,27,Franchise,Complex\n3997,240.18,NJ City,N,23,8,17,77,High School,35,Patnership,Standalone\n7250,456.82,Boston,S,3,16,1,55,No education,30,Patnership,Complex\n2338,122.7,NJ City,S,24,13,3,78,High School,26,Patnership,Complex\n9988,223.33,NJ City,W,15,11,9,77,Graduation,33,Franchise,Complex\n8392,0.22,NJ City,N,12,22,2,72,High School,21,Franchise,Complex\n2255,418.17,NJ City,E,21,14,22,54,High School,21,Owner,Standalone\n232,257.53,NJ City,W,2,22,2,27,High School,25,Franchise,Complex\n5750,16.33,Boston,N,18,11,3,78,No education,26,Patnership,Complex\n8916,141.7,NJ City,W,13,23,8,72,Graduation,22,Franchise,Complex\n2164,172.47,NJ City,W,25,13,7,32,High School,28,Franchise,Complex\n2871,88.03,NJ City,E,6,16,4,80,Graduation,32,Owner,Standalone\n1539,233,NJ City,E,1,17,6,50,High School,33,Owner,Standalone\n8353,332.03,NJ City,N,3,23,1,29,High School,24,Patnership,Standalone\n8497,200.28,NJ City,N,1,10,20,80,Graduation,31,Patnership,Standalone\n3080,19.17,NJ City,N,22,10,4,53,No education,27,Franchise,Complex\n1862,137.64,NJ City,S,13,24,9,71,High School,33,Patnership,Complex\n4116,96.11,NJ City,W,7,18,3,44,Graduation,19,Franchise,Complex\n7117,318.42,Boston,N,7,10,15,70,Graduation,20,Patnership,Standalone\n3305,108.29,NJ City,N,12,22,2,38,No education,26,Patnership,Standalone\n1403,113.41,NJ City,E,21,19,4,65,No education,30,Owner,Standalone\n2726,260.7,NJ City,S,9,8,11,35,High School,18,Patnership,Complex\n6206,188.54,Boston,S,1,11,20,29,Graduation,27,Patnership,Complex\n6194,117.66,Boston,S,17,15,4,37,No education,22,Patnership,Complex\n9646,481.79,NJ City,S,1,8,9,69,Graduation,20,Patnership,Complex\n5516,217.36,Boston,W,1,13,25,55,Graduation,30,Franchise,Complex\n55,284.64,NJ City,E,11,22,3,60,High School,25,Owner,Standalone\n1951,313,NJ City,E,13,15,16,34,High School,23,Owner,Standalone\n4586,427.5,NJ City,S,18,21,25,71,High School,34,Patnership,Complex\n264,23.93,NJ City,N,16,24,2,33,Graduation,31,Franchise,Complex\n7802,376.63,Boston,S,4,13,12,52,Graduation,26,Patnership,Complex\n4441,248.58,NJ City,N,22,23,21,64,High School,24,Patnership,Standalone\n7916,466.41,Boston,W,9,23,23,63,Graduation,23,Franchise,Complex\n6781,406.9,Boston,N,2,16,3,38,High School,26,Patnership,Standalone\n1928,460.98,NJ City,W,10,22,18,52,High School,25,Franchise,Complex\n3776,56.54,NJ City,W,4,17,3,47,No education,18,Franchise,Complex\n7924,492.52,Boston,W,1,19,7,75,Graduation,34,Franchise,Complex\n8427,237.4,NJ City,E,10,11,25,74,Graduation,32,Owner,Standalone\n6049,99.12,Boston,N,12,13,2,27,High School,28,Patnership,Standalone\n1607,488.52,NJ City,E,19,22,21,54,High School,28,Owner,Standalone\n6504,90.77,Boston,W,14,24,1,44,Graduation,25,Franchise,Complex\n3804,26.75,NJ City,N,7,18,2,67,Graduation,18,Franchise,Complex\n5101,3.8,Boston,N,9,15,1,79,High School,32,Patnership,Standalone\n8950,147.73,NJ City,S,20,19,16,73,Graduation,31,Patnership,Complex\n9416,404.78,NJ City,W,21,18,23,78,Graduation,24,Franchise,Complex\n3360,108.79,NJ City,W,25,8,4,26,Graduation,28,Franchise,Complex\n6629,179.91,Boston,N,14,8,21,32,Graduation,33,Patnership,Standalone\n3081,239.75,NJ City,N,11,12,3,62,Graduation,34,Patnership,Standalone\n9626,106.83,NJ City,S,10,9,4,44,No education,20,Patnership,Complex\n2008,320.6,NJ City,W,13,15,25,73,High School,27,Franchise,Complex\n6331,75.68,Boston,E,17,24,3,50,High School,25,Owner,Standalone\n5725,289.07,Boston,N,13,20,9,39,Graduation,21,Patnership,Standalone\n3396,195.7,NJ City,W,11,23,13,39,High School,33,Franchise,Complex\n1935,242.86,NJ City,E,21,8,24,54,High School,22,Owner,Standalone\n77,316,Boston,N,1,8,15,83,High School,29,Patnership,Standalone\n4470,39.01,NJ City,N,17,20,4,60,Graduation,33,Patnership,Complex\n7121,50.47,Boston,N,2,22,3,67,No education,29,Patnership,Standalone\n7856,82.52,Boston,W,1,12,4,53,No education,20,Franchise,Complex\n4913,180.2,NJ City,N,8,15,2,69,No education,19,Patnership,Standalone\n5566,259.77,Boston,S,10,18,15,40,Graduation,25,Patnership,Complex\n3392,403.36,NJ City,W,20,19,11,61,High School,25,Franchise,Complex\n58,155.3,Boston,S,12,24,18,33,High School,31,Patnership,Complex\n2271,469.66,NJ City,E,18,9,10,69,High School,29,Owner,Standalone\n216,305.51,NJ City,W,7,11,5,31,Graduation,28,Franchise,Complex\n7581,51.18,Boston,N,25,20,3,37,Graduation,26,Patnership,Standalone\n2225,436.47,NJ City,N,25,23,25,39,High School,20,Patnership,Standalone\n6185,112.96,Boston,N,14,19,2,33,No education,18,Patnership,Standalone\n69,400.58,Maine,N,21,8,11,66,High School,31,Patnership,Standalone\n1775,53.45,NJ City,E,5,14,2,75,No education,33,Owner,Standalone\n522,218,NJ City,S,2,14,25,82,High School,28,Patnership,Complex\n8580,63.3,NJ City,N,22,9,1,43,Graduation,26,Franchise,Complex\n922,144.62,NJ City,S,12,24,4,40,High School,21,Patnership,Complex\n1030,9.78,NJ City,N,12,21,4,27,High School,18,Patnership,Complex\n3069,472.87,NJ City,N,21,24,7,65,High School,32,Patnership,Standalone\n3974,127.17,NJ City,S,15,10,2,31,No education,18,Patnership,Complex\n8866,84.84,NJ City,S,11,15,3,75,High School,29,Patnership,Complex\n1814,9.36,NJ City,N,10,24,1,44,No education,33,Patnership,Complex\n706,126.46,NJ City,S,2,11,3,57,High School,24,Patnership,Complex\n5466,159.47,Boston,S,24,11,9,76,Graduation,34,Patnership,Complex\n4217,221.54,NJ City,N,14,15,5,67,No education,31,Patnership,Standalone\n9041,179.97,NJ City,N,8,18,9,82,Graduation,31,Patnership,Standalone\n119,102.64,NJ City,E,12,13,2,47,No education,34,Owner,Standalone\n7827,313.82,Boston,E,19,11,8,82,Graduation,18,Owner,Standalone\n4017,253.55,NJ City,N,6,15,14,35,High School,23,Patnership,Standalone\n5150,461.66,Boston,S,12,12,25,38,Graduation,22,Patnership,Complex\n738,77.62,NJ City,S,23,8,3,73,Graduation,19,Patnership,Complex\n4603,201.22,NJ City,E,10,16,18,40,High School,25,Owner,Standalone\n2503,360.74,NJ City,E,19,8,25,43,High School,35,Owner,Standalone\n4494,388.76,NJ City,S,22,8,14,76,High School,21,Patnership,Complex\n1443,484.02,NJ City,E,22,23,18,76,High School,28,Owner,Standalone\n3513,471.63,NJ City,N,24,13,25,61,High School,21,Patnership,Standalone\n8567,191.16,NJ City,E,14,11,18,74,Graduation,24,Owner,Standalone\n8380,141.46,NJ City,W,15,15,17,26,Graduation,21,Franchise,Complex\n9946,108.21,NJ City,S,3,20,1,44,High School,30,Patnership,Complex\n223,264.33,NJ City,E,16,17,4,42,High School,26,Owner,Standalone\n6367,51.88,Boston,E,1,17,3,38,High School,22,Owner,Standalone\n6075,304.79,Boston,E,13,21,5,58,Graduation,23,Owner,Standalone\n7329,138.27,Boston,N,2,20,5,75,Graduation,22,Patnership,Standalone\n7136,338.23,Boston,W,24,18,10,55,Graduation,20,Franchise,Complex\n1400,185.83,NJ City,W,16,13,13,54,High School,26,Franchise,Complex\n7039,395.75,Boston,E,5,17,17,34,Graduation,22,Owner,Standalone\n4346,239,NJ City,S,2,22,18,40,High School,33,Patnership,Complex\n5052,320.35,Boston,W,13,17,22,51,Graduation,23,Franchise,Complex\n9522,90.75,NJ City,S,22,16,3,79,Graduation,28,Patnership,Complex\n4652,264,NJ City,W,1,11,18,26,High School,28,Franchise,Complex\n5904,434.52,Boston,W,19,22,25,46,Graduation,33,Franchise,Complex\n3352,102,NJ City,W,3,13,20,69,High School,19,Franchise,Complex\n8877,146.96,NJ City,N,2,8,7,83,Graduation,29,Patnership,Standalone\n8398,207.01,NJ City,S,5,11,17,25,Graduation,28,Patnership,Complex\n2397,39.5,NJ City,N,19,16,4,55,Graduation,29,Patnership,Standalone\n5379,417.83,Boston,E,23,24,7,77,Graduation,30,Owner,Standalone\n178,381.88,NJ City,S,7,21,23,77,High School,26,Patnership,Complex\n1325,412.02,NJ City,N,25,23,11,75,High School,30,Patnership,Standalone\n5618,287.98,Boston,S,2,16,17,82,Graduation,29,Patnership,Complex\n8698,101.08,NJ City,S,15,22,4,71,High School,25,Patnership,Complex\n4792,354.85,NJ City,W,24,17,5,45,High School,32,Franchise,Complex\n7990,106.47,Boston,S,13,16,2,75,High School,31,Patnership,Complex\n2509,200.76,NJ City,N,13,24,18,55,High School,22,Patnership,Standalone\n1189,152.84,NJ City,N,9,19,5,50,High School,27,Patnership,Standalone\n848,160,NJ City,W,1,15,7,73,High School,31,Franchise,Complex\n2076,350.74,NJ City,W,13,21,18,63,High School,31,Franchise,Complex\n5219,432.34,Boston,E,6,15,11,52,Graduation,25,Owner,Standalone\n9642,92.93,NJ City,S,19,16,4,36,Graduation,28,Patnership,Complex\n5759,99.53,Boston,E,22,20,1,81,No education,30,Owner,Standalone\n6721,451.02,Boston,N,15,17,20,25,Graduation,31,Patnership,Standalone\n7654,416.13,Boston,S,25,12,14,69,Graduation,32,Patnership,Complex\n1462,320.66,NJ City,S,7,16,25,37,High School,25,Patnership,Complex\n4632,256,NJ City,W,2,15,24,82,High School,34,Franchise,Complex\n1429,185.16,NJ City,N,16,14,8,29,High School,20,Patnership,Standalone\n389,76.91,NJ City,N,10,12,4,69,No education,22,Patnership,Standalone\n8943,486.51,NJ City,E,7,16,19,79,Graduation,30,Owner,Standalone\n3197,372.62,NJ City,N,12,16,14,38,High School,29,Patnership,Standalone\n8761,83.34,NJ City,N,20,21,2,36,High School,28,Patnership,Standalone\n1716,149.32,NJ City,W,15,17,19,58,High School,33,Franchise,Complex\n1241,126.36,NJ City,N,22,19,4,58,No education,24,Patnership,Standalone\n902,337.39,NJ City,S,16,9,20,42,High School,30,Patnership,Complex\n240,23.38,NJ City,N,6,19,1,48,Graduation,35,Franchise,Complex\n9123,451.18,NJ City,E,10,8,20,81,Graduation,29,Owner,Standalone\n698,191.15,NJ City,S,23,17,2,35,No education,26,Patnership,Complex\n4413,84.59,NJ City,N,5,15,2,41,Graduation,25,Patnership,Standalone\n2876,91.18,NJ City,W,4,12,1,79,No education,35,Franchise,Complex\n941,164.31,NJ City,N,16,10,13,68,High School,26,Patnership,Standalone\n5090,131.94,Boston,S,17,16,16,37,Graduation,28,Patnership,Complex\n1114,106.53,NJ City,S,24,18,3,71,High School,28,Patnership,Complex\n4071,258.54,NJ City,E,4,15,13,62,High School,32,Owner,Standalone\n1882,158.2,NJ City,S,19,23,16,35,High School,20,Patnership,Complex\n9346,107.77,NJ City,S,20,19,4,53,High School,18,Patnership,Complex\n9065,286.38,NJ City,N,5,13,24,41,Graduation,27,Patnership,Standalone\n5581,380,Boston,N,19,22,19,30,Graduation,30,Patnership,Standalone\n4301,315.32,NJ City,N,4,10,13,38,High School,35,Patnership,Standalone\n2921,355.12,NJ City,N,24,23,6,28,High School,34,Patnership,Standalone\n5841,84.54,Boston,N,21,11,4,51,Graduation,32,Patnership,Standalone\n9944,249.28,NJ City,W,12,10,24,40,Graduation,20,Franchise,Complex\n2387,103,NJ City,E,1,19,8,80,High School,28,Owner,Standalone\n4080,428.55,NJ City,W,25,12,14,31,High School,26,Franchise,Complex\n1841,397.93,NJ City,N,22,14,14,74,High School,21,Patnership,Standalone\n8473,195.3,NJ City,N,17,19,12,26,Graduation,22,Patnership,Standalone\n866,389.2,NJ City,S,17,18,24,68,High School,26,Patnership,Complex\n7987,299.52,Boston,E,15,13,8,47,Graduation,20,Owner,Standalone\n3437,481.88,NJ City,N,10,15,25,42,High School,30,Patnership,Standalone\n8907,474.77,NJ City,E,16,15,9,85,Graduation,23,Owner,Standalone\n2148,289.09,NJ City,W,5,16,7,73,High School,31,Franchise,Complex\n7503,238.06,Boston,E,21,11,4,29,Graduation,35,Owner,Standalone\n9981,56.37,NJ City,N,10,23,1,63,Graduation,24,Patnership,Standalone\n7406,79.79,Boston,S,10,14,1,82,No education,27,Patnership,Complex\n8130,495.19,NJ City,S,16,19,9,57,Graduation,27,Patnership,Complex\n4998,183.31,Boston,S,17,21,8,25,High School,29,Patnership,Complex\n2307,72.52,NJ City,N,11,23,4,50,Graduation,25,Owner,Standalone\n4979,306.17,Boston,E,22,8,23,43,High School,18,Owner,Standalone\n1428,273.89,NJ City,W,5,20,14,44,High School,25,Franchise,Complex\n408,274.87,NJ City,W,13,12,6,38,High School,21,Franchise,Complex\n6309,249.81,Boston,N,9,13,9,45,Graduation,22,Patnership,Standalone\n2524,140.63,NJ City,W,22,9,25,38,High School,22,Franchise,Complex\n460,74.94,NJ City,W,19,15,4,81,High School,28,Franchise,Complex\n5872,62.89,Boston,N,25,24,4,32,High School,27,Franchise,Complex\n3110,493.22,NJ City,S,4,23,4,44,No education,26,Patnership,Complex\n6834,499.81,Boston,S,10,13,1,32,Graduation,32,Patnership,Complex\n8149,168.94,NJ City,N,14,8,22,33,Graduation,22,Patnership,Standalone\n1285,216.73,NJ City,N,16,8,17,62,High School,34,Patnership,Standalone\n5744,1.5,Boston,N,21,12,4,79,No education,19,Franchise,Complex\n6413,175.52,Boston,N,24,21,20,71,Graduation,30,Patnership,Standalone\n35,107.46,Boston,E,3,14,2,43,No education,26,Owner,Standalone\n1681,395.62,NJ City,N,6,20,20,77,High School,33,Patnership,Standalone\n9265,481.09,NJ City,N,25,23,24,27,Graduation,25,Patnership,Standalone\n51,46.95,Boston,N,22,23,2,48,Graduation,25,Owner,Standalone\n9221,290.73,NJ City,N,3,24,22,83,Graduation,30,Patnership,Standalone\n9710,487.34,NJ City,S,15,11,14,56,Graduation,18,Patnership,Complex\n5273,390.75,Boston,N,1,23,13,75,Graduation,31,Patnership,Standalone\n9107,331.01,NJ City,E,19,9,15,57,Graduation,35,Owner,Standalone\n8493,478.79,NJ City,N,20,21,6,58,Graduation,18,Patnership,Standalone\n4334,278,NJ City,S,3,16,14,76,High School,18,Patnership,Complex\n230,340.7,NJ City,S,16,12,5,33,No education,35,Patnership,Complex\n7171,59.9,Boston,E,9,8,2,37,High School,18,Owner,Standalone\n2679,433.45,NJ City,E,13,18,13,61,High School,25,Owner,Standalone\n6523,156.82,Boston,E,25,13,17,74,Graduation,31,Owner,Standalone\n6973,224.42,Boston,N,10,14,5,72,High School,29,Patnership,Standalone\n7661,411.72,Boston,N,19,22,17,60,Graduation,28,Patnership,Standalone\n9056,112.94,NJ City,W,8,16,2,84,No education,31,Franchise,Complex\n6818,492.97,Boston,S,8,16,15,38,Graduation,25,Patnership,Complex\n4991,410.05,Boston,E,12,23,14,79,High School,23,Owner,Standalone\n6775,261.47,Boston,E,3,20,24,47,Graduation,31,Owner,Standalone\n5153,76.68,Boston,N,12,20,3,50,No education,27,Patnership,Standalone\n1213,152.17,NJ City,N,23,17,10,25,High School,23,Patnership,Standalone\n8758,324.92,NJ City,S,16,15,1,72,High School,21,Patnership,Complex\n8641,168.07,NJ City,N,4,22,9,75,Graduation,29,Patnership,Standalone\n4614,87.44,NJ City,S,5,20,2,43,Graduation,29,Patnership,Complex\n9906,50.24,NJ City,S,19,13,4,53,Graduation,18,Patnership,Complex\n6454,112.81,Boston,S,21,20,4,81,High School,26,Patnership,Complex\n6098,276.51,Boston,S,11,19,7,77,Graduation,19,Patnership,Complex\n4571,106.61,NJ City,E,22,23,3,25,No education,24,Owner,Standalone\n8145,48.68,NJ City,N,23,23,3,31,Graduation,32,Patnership,Standalone\n2412,300.28,NJ City,W,22,21,12,73,High School,20,Franchise,Complex\n2701,219.7,NJ City,N,5,10,5,51,High School,18,Patnership,Standalone\n2651,173.6,NJ City,E,24,16,23,32,High School,27,Owner,Standalone\n7567,393.01,Boston,E,20,13,24,68,Graduation,23,Owner,Standalone\n3249,417.17,NJ City,N,17,17,6,49,High School,27,Patnership,Standalone\n9359,450.94,NJ City,E,10,22,5,43,No education,20,Owner,Standalone\n9695,200.4,NJ City,E,9,22,12,76,Graduation,32,Owner,Standalone\n8940,16.76,NJ City,N,20,19,2,66,Graduation,21,Franchise,Complex\n4229,106,NJ City,N,1,9,19,35,High School,32,Patnership,Standalone\n1789,70.54,NJ City,N,9,21,1,28,High School,23,Patnership,Standalone\n3459,11.71,NJ City,N,14,11,1,54,Graduation,28,Owner,Standalone\n2361,148.94,NJ City,N,22,12,22,83,High School,19,Patnership,Standalone\n5085,357.49,Boston,N,10,10,1,28,Graduation,35,Patnership,Standalone\n3268,298.64,NJ City,W,22,19,17,42,High School,23,Franchise,Complex\n3407,233.44,NJ City,E,6,24,24,38,High School,30,Owner,Standalone\n9700,214.42,NJ City,W,19,10,9,55,Graduation,34,Franchise,Complex\n8215,4.88,NJ City,N,2,17,4,61,High School,33,Owner,Standalone\n7147,137.66,Boston,E,20,12,7,28,Graduation,34,Owner,Standalone\n5575,270.4,Boston,E,10,8,6,58,Graduation,18,Owner,Standalone\n8639,283.66,NJ City,E,16,21,17,28,Graduation,26,Owner,Standalone\n6163,345.1,Boston,E,4,21,1,30,High School,24,Owner,Standalone\n6723,104.78,Boston,E,14,13,2,64,Graduation,24,Owner,Standalone\n3869,115.51,NJ City,N,24,11,3,84,No education,33,Patnership,Standalone\n9800,173.39,NJ City,W,2,11,3,25,No education,35,Franchise,Complex\n7657,176.81,Boston,N,19,8,20,65,Graduation,28,Patnership,Standalone\n1147,360.81,NJ City,E,5,20,11,76,High School,27,Owner,Standalone\n7677,392.53,Boston,N,12,24,4,25,Graduation,30,Patnership,Standalone\n2039,213,NJ City,E,2,15,6,79,High School,29,Owner,Standalone\n1700,25.37,NJ City,N,11,17,1,83,No education,30,Franchise,Complex\n4665,370.8,NJ City,N,19,21,8,71,High School,26,Patnership,Standalone\n3957,185.86,NJ City,N,19,13,22,33,High School,28,Patnership,Standalone\n4699,121.08,NJ City,E,20,17,4,49,High School,32,Owner,Standalone\n6376,377.43,Boston,W,23,12,18,63,Graduation,32,Franchise,Complex\n9141,52.74,NJ City,N,8,20,4,66,Graduation,26,Patnership,Standalone\n9096,224.74,NJ City,W,18,17,9,84,Graduation,28,Franchise,Complex\n7883,115.38,Boston,E,7,16,1,59,No education,20,Owner,Standalone\n1500,364.86,NJ City,W,6,23,12,26,High School,30,Franchise,Complex\n9387,193.32,NJ City,E,4,9,22,56,Graduation,26,Owner,Standalone\n7159,267.81,Boston,E,11,19,11,78,Graduation,26,Owner,Standalone\n777,416.1,NJ City,N,16,21,17,40,High School,26,Patnership,Standalone\n6209,28.67,Boston,N,10,14,2,54,No education,19,Patnership,Standalone\n2646,385.21,NJ City,S,13,8,13,41,High School,27,Patnership,Complex\n7571,73.31,Boston,N,1,11,3,29,No education,30,Owner,Standalone\n5322,108.42,Boston,S,9,22,2,62,Graduation,32,Patnership,Complex\n6250,209.16,Boston,S,1,17,9,59,Graduation,30,Patnership,Complex\n4663,205,NJ City,E,1,8,6,74,High School,20,Owner,Standalone\n8932,31.23,NJ City,N,15,14,4,83,High School,22,Franchise,Complex\n1169,131,NJ City,N,1,15,19,39,High School,30,Patnership,Standalone\n3485,430.37,NJ City,N,11,16,3,40,No education,22,Patnership,Standalone\n3159,458.63,NJ City,E,6,20,19,59,High School,24,Owner,Standalone\n8276,178.63,NJ City,W,1,12,1,63,No education,30,Franchise,Complex\n6378,217.39,Boston,S,18,13,9,56,Graduation,28,Patnership,Complex\n6966,157.81,Boston,S,8,10,3,44,Graduation,18,Patnership,Complex\n1158,278.34,NJ City,S,13,9,5,52,Graduation,28,Patnership,Complex\n3245,67.12,NJ City,N,11,18,4,65,No education,24,Patnership,Standalone\n2092,213.43,NJ City,W,6,19,15,66,High School,18,Franchise,Complex\n5333,314.27,Boston,N,22,24,15,34,Graduation,24,Patnership,Standalone\n1909,41.39,NJ City,N,2,20,4,51,High School,19,Patnership,Standalone\n3124,82.41,NJ City,W,21,10,1,55,High School,32,Franchise,Complex\n386,241,NJ City,S,3,22,11,35,High School,26,Patnership,Complex\n5931,488.81,Boston,E,25,14,7,58,Graduation,19,Owner,Standalone\n1809,444.36,NJ City,N,4,10,17,39,High School,25,Patnership,Standalone\n4817,77.73,NJ City,N,7,22,2,75,No education,19,Patnership,Standalone\n8295,171.59,NJ City,E,22,19,5,54,Graduation,21,Owner,Standalone\n23,302.16,NY City,E,3,8,1,57,No education,28,Owner,Standalone\n7646,335.76,Boston,S,8,14,10,51,Graduation,23,Patnership,Complex\n7477,328.14,Boston,N,11,19,14,45,Graduation,25,Patnership,Standalone\n4865,396.75,NJ City,N,18,22,20,45,High School,19,Patnership,Standalone\n3618,2.15,NJ City,S,4,16,3,43,Graduation,34,Patnership,Complex\n1486,90.53,NJ City,S,10,24,4,73,High School,30,Patnership,Complex\n6927,217.89,Boston,E,3,22,23,85,Graduation,29,Owner,Standalone\n4719,53.85,NJ City,E,9,12,2,51,Graduation,35,Owner,Standalone\n2900,414.38,NJ City,W,9,17,2,82,No education,27,Franchise,Complex\n7325,286.85,Boston,N,9,12,17,76,Graduation,25,Patnership,Standalone\n2179,125.62,NJ City,E,12,10,1,81,High School,23,Owner,Standalone\n7693,322.52,Boston,N,3,17,3,29,High School,27,Patnership,Standalone\n722,118.24,NJ City,S,17,24,2,74,No education,28,Patnership,Complex\n4902,125,NJ City,S,3,15,13,46,High School,33,Patnership,Complex\n9077,83.7,NJ City,N,8,19,3,39,No education,28,Patnership,Standalone\n3474,126.48,NJ City,S,21,13,2,27,Graduation,29,Patnership,Complex\n8009,482.54,Boston,N,13,23,21,32,Graduation,31,Patnership,Standalone\n2216,157.55,NJ City,W,24,21,2,68,No education,19,Franchise,Complex\n3118,249.13,NJ City,S,23,18,10,46,High School,28,Patnership,Complex\n6404,2.93,Boston,W,1,19,3,39,No education,32,Franchise,Complex\n8939,203.32,NJ City,E,9,13,21,35,Graduation,18,Owner,Standalone\n1566,318,NJ City,S,1,19,12,79,High School,28,Patnership,Complex\n9570,427.49,NJ City,S,10,11,14,69,Graduation,34,Patnership,Complex\n6686,56.84,Boston,S,14,17,4,36,No education,22,Patnership,Complex\n8887,89.91,NJ City,E,8,19,2,38,High School,34,Owner,Standalone\n1569,332.99,NJ City,N,21,15,3,82,Graduation,25,Patnership,Standalone\n3472,7.65,NJ City,N,24,10,1,51,High School,28,Franchise,Complex\n7284,339.8,Boston,W,23,22,9,41,Graduation,20,Franchise,Complex\n3535,318.34,NJ City,E,16,13,18,64,High School,25,Owner,Standalone\n6753,130.89,Boston,N,6,12,16,65,Graduation,20,Patnership,Standalone\n8660,450.36,NJ City,W,11,12,17,70,Graduation,33,Franchise,Complex\n701,325.83,NJ City,N,22,17,19,25,High School,25,Patnership,Standalone\n8830,66.54,NJ City,N,19,9,2,40,High School,30,Patnership,Complex\n1982,323.27,NJ City,S,11,22,10,81,High School,19,Patnership,Complex\n4945,25.16,Boston,N,13,14,2,69,High School,21,Patnership,Standalone\n6067,316.67,Boston,E,24,23,7,42,Graduation,35,Owner,Standalone\n4266,53.38,NJ City,S,16,17,3,55,Graduation,24,Patnership,Complex\n2857,379.5,NJ City,N,8,22,10,63,High School,21,Patnership,Standalone\n9571,155.62,NJ City,E,17,21,19,73,Graduation,19,Owner,Standalone\n9048,164.95,NJ City,W,8,24,4,82,Graduation,18,Franchise,Complex\n742,8.59,NJ City,N,11,16,1,37,High School,33,Patnership,Complex\n5964,198.33,Boston,W,11,9,10,41,Graduation,21,Franchise,Complex\n4828,27.64,NJ City,N,21,18,1,80,High School,28,Franchise,Complex\n3858,258,NJ City,S,13,23,1,55,Graduation,32,Patnership,Complex\n5228,444.26,Boston,W,22,23,11,61,Graduation,25,Franchise,Complex\n918,421.33,NJ City,S,12,19,12,80,High School,25,Patnership,Complex\n3130,246.01,NJ City,S,18,21,4,33,High School,26,Patnership,Complex\n2178,280.92,NJ City,S,25,17,25,58,High School,26,Patnership,Complex\n3608,166.15,NJ City,W,3,20,1,41,No education,23,Franchise,Complex\n2018,209.28,NJ City,S,9,18,13,34,High School,31,Patnership,Complex\n2408,99.59,NJ City,W,5,22,3,54,No education,35,Franchise,Complex\n2086,453.56,NJ City,S,13,24,16,28,High School,31,Patnership,Complex\n4046,262,NJ City,S,2,15,21,41,High School,27,Patnership,Complex\n5435,107.31,Boston,E,6,16,4,48,No education,22,Owner,Standalone\n8764,121.84,NJ City,W,12,15,2,71,High School,32,Franchise,Complex\n7387,166.13,Boston,E,16,16,19,51,Graduation,18,Owner,Standalone\n1897,80.32,NJ City,N,7,24,1,30,High School,35,Patnership,Standalone\n4065,273.32,NJ City,N,4,19,19,33,High School,23,Patnership,Standalone\n8285,304.41,NJ City,N,9,9,7,26,Graduation,31,Patnership,Standalone\n2196,279.05,NJ City,W,10,19,3,41,Graduation,33,Franchise,Complex\n3729,465.5,NJ City,N,20,19,21,54,High School,33,Patnership,Standalone\n1006,430.87,NJ City,S,14,15,14,43,High School,30,Patnership,Complex\n6408,367.14,Boston,W,24,20,22,53,Graduation,24,Franchise,Complex\n3872,210.22,NJ City,W,24,8,4,51,No education,28,Franchise,Complex\n2140,376.84,NJ City,W,20,23,15,57,High School,18,Franchise,Complex\n306,143.04,NJ City,S,23,8,16,60,High School,31,Patnership,Complex\n3598,239.36,NJ City,S,14,9,18,57,High School,29,Patnership,Complex\n3226,233,NJ City,S,2,14,6,56,High School,20,Patnership,Complex\n1019,37.27,NJ City,N,10,19,1,55,No education,32,Owner,Standalone\n8138,136.99,NJ City,S,1,20,11,48,Graduation,35,Patnership,Complex\n9961,3.44,NJ City,N,14,16,1,60,High School,29,Patnership,Standalone\n2796,346.39,NJ City,W,13,23,17,36,High School,18,Franchise,Complex\n6610,198.7,Boston,S,19,8,24,46,Graduation,30,Patnership,Complex\n3216,294.87,NJ City,W,4,18,6,41,High School,19,Franchise,Complex\n874,481.44,NJ City,S,7,10,7,75,High School,28,Patnership,Complex\n8868,231.87,NJ City,W,10,13,24,50,Graduation,24,Franchise,Complex\n7811,400.85,Boston,E,11,8,9,52,Graduation,19,Owner,Standalone\n9872,469.37,NJ City,W,4,19,2,54,No education,22,Franchise,Complex\n2702,90.37,NJ City,S,2,23,2,70,No education,32,Patnership,Complex\n4712,348.07,NJ City,W,20,18,5,29,No education,21,Franchise,Complex\n926,390.71,NJ City,S,22,18,12,37,High School,28,Patnership,Complex\n7814,403.78,Boston,S,4,19,7,81,Graduation,21,Patnership,Complex\n3692,225.83,NJ City,W,19,18,14,79,High School,18,Franchise,Complex\n9696,157.26,NJ City,W,18,24,14,53,Graduation,33,Franchise,Complex\n8831,187.27,NJ City,E,3,16,12,48,Graduation,26,Owner,Standalone\n4935,110.52,NJ City,E,21,15,4,34,Graduation,21,Owner,Standalone\n6974,387.08,Boston,S,22,22,2,33,No education,29,Patnership,Complex\n2591,161,NJ City,E,3,18,22,44,High School,18,Owner,Standalone\n840,451.2,NJ City,W,8,8,2,28,Graduation,28,Franchise,Complex\n5009,451.35,Boston,N,22,19,14,82,Graduation,19,Patnership,Standalone\n6819,206.62,Boston,E,5,15,1,42,Graduation,35,Owner,Standalone\n9239,4.33,NJ City,N,22,18,3,42,No education,25,Owner,Standalone\n5063,45.98,Boston,N,15,13,2,70,No education,30,Owner,Standalone\n3785,58.1,NJ City,N,5,19,4,38,No education,31,Patnership,Standalone\n9555,14.64,NJ City,N,15,18,1,84,Graduation,23,Owner,Standalone\n4898,356.12,NJ City,S,20,8,15,74,High School,31,Patnership,Complex\n8985,15.23,NJ City,N,7,20,1,83,Graduation,24,Patnership,Standalone\n2066,16.96,NJ City,N,20,20,4,68,No education,34,Patnership,Complex\n133,235,NJ City,N,1,21,25,69,High School,30,Patnership,Standalone\n9818,55.75,NJ City,S,17,14,3,56,No education,23,Patnership,Complex\n3491,480.29,NJ City,E,15,10,1,46,No education,23,Owner,Standalone\n3154,127.9,NJ City,S,17,22,2,34,High School,21,Patnership,Complex\n1299,173.68,NJ City,E,18,19,24,56,High School,18,Owner,Standalone\n3620,100.38,NJ City,W,19,21,4,56,No education,27,Franchise,Complex\n2194,89.67,NJ City,S,6,15,4,26,High School,22,Patnership,Complex\n2604,344.83,NJ City,W,4,22,5,60,Graduation,32,Franchise,Complex\n2853,131.92,NJ City,N,11,15,3,71,Graduation,34,Patnership,Standalone\n6361,366.5,Boston,N,1,13,21,79,Graduation,21,Patnership,Standalone\n1772,221.45,NJ City,W,13,9,23,26,High School,22,Franchise,Complex\n2230,138.95,NJ City,S,1,10,3,75,High School,22,Patnership,Complex\n135,103.04,NJ City,E,10,22,1,41,Graduation,33,Owner,Standalone\n1860,69.5,NJ City,N,17,19,3,63,Graduation,30,Franchise,Complex\n3597,228.48,NJ City,N,5,16,7,65,High School,27,Patnership,Standalone\n6710,376.24,Boston,S,18,14,17,58,Graduation,27,Patnership,Complex\n3628,446.69,NJ City,W,14,11,16,56,High School,35,Franchise,Complex\n1092,460.38,NJ City,W,12,23,10,62,High School,23,Franchise,Complex\n8781,360.66,NJ City,N,24,21,18,62,Graduation,31,Patnership,Standalone\n2215,246.29,NJ City,E,11,10,13,60,High School,28,Owner,Standalone\n6290,186.37,Boston,S,24,10,10,36,Graduation,19,Patnership,Complex\n5061,329.57,Boston,N,23,19,9,26,Graduation,34,Patnership,Standalone\n5114,302.57,Boston,S,4,13,17,80,Graduation,29,Patnership,Complex\n3717,6.62,NJ City,N,2,13,3,68,Graduation,27,Patnership,Standalone\n4181,286,NJ City,N,2,23,21,51,High School,23,Patnership,Standalone\n932,439.06,NJ City,W,4,20,15,35,High School,32,Franchise,Complex\n9724,232.01,NJ City,W,2,20,25,45,Graduation,27,Franchise,Complex\n9879,175.64,NJ City,E,17,10,18,63,Graduation,20,Owner,Standalone\n8606,172.32,NJ City,S,20,14,4,25,No education,32,Patnership,Complex\n1237,416.73,NJ City,N,5,24,24,65,High School,28,Patnership,Standalone\n9887,140.48,NJ City,E,9,16,10,43,Graduation,18,Owner,Standalone\n2089,377.17,NJ City,N,24,11,20,25,High School,25,Patnership,Standalone\n382,77.84,NJ City,S,13,20,3,29,High School,26,Patnership,Complex\n1482,178.11,NJ City,S,20,21,6,26,High School,27,Patnership,Complex\n6780,241.33,Boston,W,9,15,11,77,Graduation,33,Franchise,Complex\n1818,51.95,NJ City,S,24,17,2,67,Graduation,22,Patnership,Complex\n260,245.26,NJ City,W,25,19,14,75,High School,24,Franchise,Complex\n8171,445.64,NJ City,E,18,20,13,37,Graduation,23,Owner,Standalone\n6395,258.32,Boston,E,23,24,22,51,Graduation,30,Owner,Standalone\n9092,205.51,NJ City,W,20,17,16,74,Graduation,25,Franchise,Complex\n4311,442.73,NJ City,E,13,21,22,76,High School,24,Owner,Standalone\n10000,159,NJ City,W,16,24,12,59,Graduation,35,Franchise,Complex\n8299,330.28,NJ City,E,11,21,5,68,High School,33,Owner,Standalone\n2136,307.01,NJ City,W,16,14,18,68,High School,22,Franchise,Complex\n4075,315,NJ City,E,1,24,12,49,High School,33,Owner,Standalone\n3846,263.78,NJ City,S,4,22,22,81,High School,32,Patnership,Complex\n2446,166.09,NJ City,S,12,20,20,63,High School,29,Patnership,Complex\n7243,299.7,Boston,E,6,21,15,67,Graduation,22,Owner,Standalone\n7316,207.18,Boston,W,3,22,3,29,No education,18,Franchise,Complex\n8615,472.84,NJ City,E,17,20,19,46,Graduation,18,Owner,Standalone\n6420,83.73,Boston,W,22,11,2,53,Graduation,18,Franchise,Complex\n1699,103.95,NJ City,E,6,18,2,85,High School,30,Owner,Standalone\n7101,250.54,Boston,N,17,23,6,26,Graduation,34,Patnership,Standalone\n9927,436.79,NJ City,E,13,15,4,27,Graduation,31,Owner,Standalone\n2174,468.13,NJ City,S,11,9,10,33,High School,27,Patnership,Complex\n2640,429.66,NJ City,W,13,18,12,57,High School,33,Franchise,Complex\n8442,389.1,NJ City,S,12,13,17,71,Graduation,31,Patnership,Complex\n9179,297.11,NJ City,E,20,24,7,36,Graduation,25,Owner,Standalone\n4225,499.4,NJ City,N,14,8,24,68,High School,28,Patnership,Standalone\n7091,438.74,Boston,E,14,17,17,78,Graduation,25,Owner,Standalone\n293,20.49,NJ City,N,24,11,1,54,No education,34,Patnership,Standalone\n5679,230.27,Boston,E,20,8,14,28,Graduation,23,Owner,Standalone\n7881,187.2,Boston,N,24,11,24,80,Graduation,32,Patnership,Standalone\n9983,302.9,NJ City,E,15,24,16,47,Graduation,22,Owner,Standalone\n7676,317.61,Boston,W,3,15,20,48,Graduation,30,Franchise,Complex\n9744,7.73,NJ City,N,7,22,2,28,Graduation,28,Franchise,Complex\n4037,480.56,NJ City,N,10,17,22,54,High School,20,Patnership,Standalone\n5661,356.8,Boston,N,6,19,17,65,Graduation,19,Patnership,Standalone\n4958,229.5,Boston,S,17,11,15,33,High School,26,Patnership,Complex\n822,322.28,NJ City,S,17,22,19,49,High School,24,Patnership,Complex\n6313,226.89,Boston,N,6,17,4,77,High School,26,Patnership,Standalone\n4709,151.29,NJ City,N,12,8,20,41,High School,25,Patnership,Standalone\n2549,238.41,NJ City,N,21,15,4,80,No education,25,Patnership,Standalone\n3464,86.31,NJ City,W,8,10,3,30,No education,22,Franchise,Complex\n3020,169.5,NJ City,W,12,16,12,49,High School,28,Franchise,Complex\n3010,63.17,NJ City,N,9,8,4,67,High School,22,Patnership,Complex\n9532,58.82,NJ City,W,9,8,2,69,High School,25,Franchise,Complex\n1758,403.92,NJ City,S,20,14,24,50,High School,22,Patnership,Complex\n9924,470.37,NJ City,W,3,20,21,58,Graduation,29,Franchise,Complex\n6434,319.36,Boston,S,11,9,7,28,Graduation,26,Patnership,Complex\n2822,272.68,NJ City,S,20,21,4,66,No education,19,Patnership,Complex\n350,232.05,NJ City,S,6,14,22,75,High School,24,Patnership,Complex\n1579,240.23,NJ City,E,18,21,19,84,High School,35,Owner,Standalone\n5404,363.74,Boston,W,12,22,15,54,Graduation,34,Franchise,Complex\n5838,217.17,Boston,S,24,9,6,85,Graduation,28,Patnership,Complex\n3061,47.13,NJ City,N,11,20,1,37,High School,21,Patnership,Standalone\n4531,6.65,NJ City,N,2,9,1,25,High School,20,Owner,Standalone\n4226,347.13,NJ City,S,19,12,14,78,High School,25,Patnership,Complex\n54,212.17,Maine,S,4,22,22,60,High School,26,Patnership,Complex\n5932,309.73,Boston,W,17,14,12,45,Graduation,25,Franchise,Complex\n8402,143.25,NJ City,S,7,24,1,39,No education,19,Patnership,Complex\n6460,352.3,Boston,W,9,11,24,58,Graduation,23,Franchise,Complex\n2662,161.64,NJ City,S,9,22,3,27,High School,22,Patnership,Complex\n9514,248.08,NJ City,S,23,16,21,69,Graduation,26,Patnership,Complex\n6767,343.25,Boston,E,11,18,11,82,Graduation,33,Owner,Standalone\n3447,174.36,NJ City,E,12,17,25,49,High School,21,Owner,Standalone\n5840,108.91,Boston,W,6,16,1,34,No education,23,Franchise,Complex\n127,274.88,NJ City,E,8,12,1,84,High School,24,Owner,Standalone\n6860,345.22,Boston,W,7,16,23,54,Graduation,19,Franchise,Complex\n685,455.08,NJ City,N,13,20,5,26,High School,23,Patnership,Standalone\n1698,6.88,NJ City,N,5,24,3,27,Graduation,35,Patnership,Complex\n7270,370.86,Boston,S,8,15,17,80,Graduation,19,Patnership,Complex\n6898,234.26,Boston,S,1,20,16,62,Graduation,32,Patnership,Complex\n5448,335.39,Boston,W,20,21,13,31,Graduation,32,Franchise,Complex\n1171,424.19,NJ City,E,9,18,9,35,High School,29,Owner,Standalone\n9270,378.94,NJ City,S,13,18,24,63,Graduation,29,Patnership,Complex\n475,309.95,NJ City,E,14,22,22,53,High School,29,Owner,Standalone\n3951,5.11,NJ City,N,19,16,2,32,Graduation,26,Owner,Standalone\n5299,426.81,Boston,E,11,22,11,46,Graduation,22,Owner,Standalone\n2840,278.56,NJ City,W,13,23,6,61,High School,32,Franchise,Complex\n6939,478.98,Boston,E,12,8,5,71,Graduation,32,Owner,Standalone\n8400,332.37,NJ City,W,25,14,2,28,Graduation,26,Franchise,Complex\n7073,314.69,Boston,N,16,19,23,37,Graduation,28,Patnership,Standalone\n126,425.63,NJ City,S,20,15,11,60,High School,20,Patnership,Complex\n7044,34.32,Boston,N,16,17,2,59,Graduation,26,Franchise,Complex\n1493,225.12,NJ City,N,5,24,22,76,High School,23,Patnership,Standalone\n2736,442.98,NJ City,W,8,14,18,50,High School,30,Franchise,Complex\n9256,257.29,NJ City,W,10,22,24,76,Graduation,25,Franchise,Complex\n1175,274.33,NJ City,E,16,20,20,31,High School,33,Owner,Standalone\n8802,270.1,NJ City,S,18,10,11,30,Graduation,27,Patnership,Complex\n2094,303.65,NJ City,S,8,14,4,44,Graduation,22,Patnership,Complex\n9439,66.98,NJ City,N,15,17,1,66,High School,32,Owner,Standalone\n4447,363.01,NJ City,E,24,15,22,52,High School,30,Owner,Standalone\n5475,448.69,Boston,E,14,8,19,42,Graduation,34,Owner,Standalone\n2118,331.99,NJ City,S,20,24,10,26,High School,31,Patnership,Complex\n4749,226.38,NJ City,N,6,12,2,28,Graduation,21,Patnership,Standalone\n7914,154.57,Boston,S,16,15,13,54,Graduation,26,Patnership,Complex\n5969,45.37,Boston,N,17,14,2,73,No education,27,Patnership,Standalone\n791,108.19,NJ City,E,23,16,1,68,No education,35,Owner,Standalone\n2247,380.66,NJ City,E,6,20,14,78,High School,34,Owner,Standalone\n1179,330.88,NJ City,E,19,14,10,63,High School,25,Owner,Standalone\n8084,301.79,NJ City,W,10,8,13,80,Graduation,33,Franchise,Complex\n7436,432.01,Boston,W,8,22,21,37,Graduation,35,Franchise,Complex\n7471,378.1,Boston,E,5,9,16,42,Graduation,26,Owner,Standalone\n2956,50.55,NJ City,W,22,17,2,78,High School,25,Franchise,Complex\n3397,48.14,NJ City,N,20,19,1,34,High School,26,Patnership,Standalone\n3606,262.07,NJ City,S,22,10,4,27,Graduation,35,Patnership,Complex\n6489,164.55,Boston,N,17,8,12,41,Graduation,35,Patnership,Standalone\n8467,26.19,NJ City,N,12,14,3,40,High School,35,Owner,Standalone\n2872,469.51,NJ City,W,6,17,10,84,High School,20,Franchise,Complex\n7058,420.72,Boston,S,5,15,19,43,Graduation,19,Patnership,Complex\n9215,192.44,NJ City,E,13,11,10,85,Graduation,25,Owner,Standalone\n7144,37.45,Boston,N,6,22,2,56,High School,34,Franchise,Complex\n243,52.42,NJ City,E,20,15,2,67,Graduation,19,Owner,Standalone\n6316,53.08,Boston,W,20,11,4,30,High School,27,Franchise,Complex\n3891,11.45,NJ City,N,20,10,4,54,Graduation,30,Owner,Standalone\n6402,488.55,Boston,S,3,16,20,77,Graduation,31,Patnership,Complex\n5602,352.84,Boston,S,9,14,14,25,Graduation,26,Patnership,Complex\n7030,165.02,Boston,S,16,18,11,50,Graduation,26,Patnership,Complex\n2074,109.8,NJ City,S,19,10,2,54,High School,26,Patnership,Complex\n7083,195.18,Boston,E,14,22,6,57,Graduation,29,Owner,Standalone\n1296,333.67,NJ City,W,8,23,6,84,High School,24,Franchise,Complex\n9323,269.11,NJ City,E,16,23,9,53,Graduation,30,Owner,Standalone\n2253,105.05,NJ City,N,2,9,1,44,Graduation,21,Patnership,Standalone\n6319,258.56,Boston,E,14,14,15,70,Graduation,18,Owner,Standalone\n1875,128.16,NJ City,E,22,18,9,26,High School,29,Owner,Standalone\n3206,470.11,NJ City,S,11,16,1,81,No education,22,Patnership,Complex\n9843,296.74,NJ City,E,10,13,3,51,Graduation,33,Owner,Standalone\n2874,257.6,NJ City,S,12,23,6,26,High School,31,Patnership,Complex\n6233,341.52,Boston,N,18,24,1,39,No education,21,Patnership,Standalone\n4877,433.94,NJ City,N,8,22,13,49,High School,34,Patnership,Standalone\n1580,83.39,NJ City,W,19,18,4,34,No education,25,Franchise,Complex\n9319,245.03,NJ City,E,1,11,25,56,Graduation,22,Owner,Standalone\n6392,268.94,Boston,W,14,11,10,47,Graduation,21,Franchise,Complex\n1689,458.87,NJ City,N,17,15,4,25,Graduation,32,Patnership,Standalone\n5424,237.07,Boston,W,11,22,24,72,Graduation,35,Franchise,Complex\n1697,123.46,NJ City,N,10,10,3,36,No education,32,Patnership,Standalone\n6152,127.88,Boston,W,10,13,2,37,No education,18,Franchise,Complex\n7125,158.59,Boston,N,23,20,24,80,Graduation,35,Patnership,Standalone\n8315,78.16,NJ City,E,24,12,4,76,No education,21,Owner,Standalone\n2642,439.3,NJ City,S,22,13,4,60,No education,33,Patnership,Complex\n1182,293.57,NJ City,S,10,18,7,48,High School,34,Patnership,Complex\n9691,476.08,NJ City,E,11,15,10,26,Graduation,30,Owner,Standalone\n574,75.3,NJ City,S,10,19,3,41,High School,18,Patnership,Complex\n4286,158.12,NJ City,S,7,21,7,56,High School,33,Patnership,Complex\n2801,322.86,NJ City,N,23,9,7,60,High School,34,Patnership,Standalone\n1472,259.17,NJ City,W,13,19,21,38,High School,24,Franchise,Complex\n5702,48.63,Boston,S,1,19,4,71,No education,35,Patnership,Complex\n9111,103.4,NJ City,E,4,15,3,82,Graduation,29,Owner,Standalone\n6464,403.26,Boston,W,11,9,22,52,Graduation,30,Franchise,Complex\n8543,40.84,NJ City,N,15,14,2,35,No education,23,Owner,Standalone\n4770,100.28,NJ City,S,21,20,3,60,Graduation,26,Patnership,Complex\n6140,108.26,Boston,W,23,11,1,28,No education,26,Franchise,Complex\n3913,270.08,NJ City,N,13,11,11,35,High School,33,Patnership,Standalone\n632,101,NJ City,W,1,13,14,85,High School,22,Franchise,Complex\n9758,451.61,NJ City,S,12,9,12,49,Graduation,28,Patnership,Complex\n8456,194.22,NJ City,W,9,13,14,39,Graduation,31,Franchise,Complex\n8080,300.54,NJ City,W,20,17,17,68,Graduation,30,Franchise,Complex\n5481,368.46,Boston,N,3,20,21,29,Graduation,25,Patnership,Standalone\n567,333.33,NJ City,E,13,22,11,39,High School,34,Owner,Standalone\n1741,71.08,NJ City,N,23,22,4,55,High School,21,Patnership,Standalone\n7413,494.21,Boston,N,23,19,15,81,Graduation,20,Patnership,Standalone\n2246,10.63,NJ City,N,22,24,2,59,No education,26,Patnership,Complex\n2142,467.12,NJ City,S,14,20,11,63,High School,26,Patnership,Complex\n3836,246.95,NJ City,W,13,9,22,43,High School,30,Franchise,Complex\n7290,436.51,Boston,S,21,13,18,35,Graduation,29,Patnership,Complex\n4045,134.49,NJ City,N,16,23,1,55,High School,30,Patnership,Standalone\n7172,16.87,Boston,N,16,19,1,38,No education,18,Franchise,Complex\n3602,54.31,NJ City,S,19,22,1,29,No education,27,Patnership,Complex\n4899,295,NJ City,E,2,20,7,26,High School,33,Owner,Standalone\n3190,145.13,NJ City,S,20,11,14,32,High School,30,Patnership,Complex\n8316,433.84,NJ City,W,18,16,18,73,Graduation,34,Franchise,Complex\n8097,450.19,NJ City,N,13,18,6,71,Graduation,21,Patnership,Standalone\n424,326.9,NJ City,W,20,14,16,25,High School,21,Franchise,Complex\n2818,321.62,NJ City,S,11,15,25,42,High School,30,Patnership,Complex\n3217,79.94,NJ City,N,11,11,4,32,High School,28,Patnership,Standalone\n9516,82.24,NJ City,W,7,11,3,82,Graduation,27,Franchise,Complex\n630,358.5,NJ City,S,15,18,15,82,High School,27,Patnership,Complex\n3989,284.15,NJ City,N,12,8,20,73,High School,19,Patnership,Standalone\n3142,388.98,NJ City,S,4,12,14,70,High School,21,Patnership,Complex\n2629,465.23,NJ City,N,7,11,23,61,High School,20,Patnership,Standalone\n8229,465.99,NJ City,N,14,15,3,77,Graduation,26,Patnership,Standalone\n6651,145.57,Boston,E,15,11,23,70,Graduation,34,Owner,Standalone\n6300,224.98,Boston,W,10,13,21,73,Graduation,34,Franchise,Complex\n9146,390.98,NJ City,S,2,14,7,34,Graduation,33,Patnership,Complex\n7494,222.36,Boston,S,11,9,11,77,Graduation,22,Patnership,Complex\n3654,318.45,NJ City,S,21,23,2,43,Graduation,28,Patnership,Complex\n8347,341.21,NJ City,E,3,15,12,60,Graduation,18,Owner,Standalone\n659,293.83,NJ City,E,24,15,10,69,High School,21,Owner,Standalone\n7007,364,Boston,E,16,22,5,45,No education,29,Owner,Standalone\n9063,227.18,NJ City,E,11,12,3,80,Graduation,35,Owner,Standalone\n6134,118.22,Boston,S,19,23,3,30,No education,26,Patnership,Complex\n915,127.45,NJ City,E,20,13,1,46,Graduation,32,Owner,Standalone\n2677,164.09,NJ City,N,9,16,6,50,High School,29,Patnership,Standalone\n2540,362.99,NJ City,W,12,18,13,45,High School,29,Franchise,Complex\n9024,291.94,NJ City,W,24,9,20,58,Graduation,25,Franchise,Complex\n6980,397,Boston,W,9,18,20,76,Graduation,33,Franchise,Complex\n1327,269.81,NJ City,E,7,23,22,60,High School,32,Owner,Standalone\n4106,35.51,NJ City,N,11,24,3,47,No education,34,Patnership,Complex\n7889,188.15,Boston,N,17,24,9,62,Graduation,22,Patnership,Standalone\n7349,420.18,Boston,N,17,9,12,65,Graduation,27,Patnership,Standalone\n2628,92.87,NJ City,W,14,22,2,52,Graduation,22,Franchise,Complex\n1879,378.21,NJ City,E,10,22,5,58,High School,25,Owner,Standalone\n4084,185.24,NJ City,W,21,20,25,51,High School,22,Franchise,Complex\n9332,229.29,NJ City,W,2,17,5,29,No education,20,Franchise,Complex\n1041,315.39,NJ City,N,17,8,6,81,High School,32,Patnership,Standalone\n4054,307.76,NJ City,S,2,11,4,33,High School,25,Patnership,Complex\n3519,61.97,NJ City,N,1,10,3,56,Graduation,34,Owner,Standalone\n9720,5.83,NJ City,N,2,8,3,54,Graduation,33,Franchise,Complex\n1608,223,NJ City,W,2,9,7,48,High School,27,Franchise,Complex\n2497,128.81,NJ City,N,24,14,18,57,High School,22,Patnership,Standalone\n8852,219.03,NJ City,W,14,14,7,50,Graduation,18,Franchise,Complex\n6029,298.41,Boston,N,15,12,1,41,No education,32,Patnership,Standalone\n3071,267.8,NJ City,E,15,22,22,40,High School,31,Owner,Standalone\n5818,181.28,Boston,S,3,15,7,65,Graduation,33,Patnership,Complex\n5988,257.65,Boston,W,1,16,8,75,Graduation,20,Franchise,Complex\n402,329.46,NJ City,S,21,8,20,74,High School,20,Patnership,Complex\n7089,0.26,Boston,N,7,21,4,57,Graduation,23,Patnership,Standalone\n3611,478.64,NJ City,E,17,23,24,38,High School,19,Owner,Standalone\n6596,171.56,Boston,W,24,17,22,59,Graduation,27,Franchise,Complex\n712,349.37,NJ City,W,18,17,6,49,High School,31,Franchise,Complex\n237,256.26,NJ City,N,9,18,6,51,High School,31,Patnership,Standalone\n9254,495.62,NJ City,S,9,21,19,47,Graduation,25,Patnership,Complex\n3875,258.13,NJ City,E,11,15,14,65,High School,27,Owner,Standalone\n8218,150.05,NJ City,S,4,12,23,63,Graduation,33,Patnership,Complex\n1271,232.62,NJ City,E,6,14,6,72,High School,25,Owner,Standalone\n3410,22.52,NJ City,N,5,11,3,69,No education,21,Patnership,Complex\n7526,102.5,Boston,S,24,24,1,32,No education,22,Patnership,Complex\n9395,242.99,NJ City,E,25,18,21,41,Graduation,33,Owner,Standalone\n2717,307.12,NJ City,N,21,11,19,41,High School,34,Patnership,Standalone\n9471,336.95,NJ City,E,12,14,6,32,Graduation,26,Owner,Standalone\n9362,210.92,NJ City,S,11,22,9,25,Graduation,31,Patnership,Complex\n3583,95.82,NJ City,E,12,14,4,67,High School,24,Owner,Standalone\n160,220.01,NJ City,W,6,24,10,74,High School,19,Franchise,Complex\n2707,161.55,NJ City,E,12,14,18,71,High School,23,Owner,Standalone\n515,420.27,NJ City,E,21,8,9,65,High School,19,Owner,Standalone\n692,418.91,NJ City,W,4,24,23,80,High School,25,Franchise,Complex\n4727,294.46,NJ City,E,6,24,10,45,High School,21,Owner,Standalone\n9519,1.34,NJ City,N,3,16,2,38,Graduation,19,Owner,Standalone\n8210,390.66,NJ City,S,17,13,10,79,Graduation,28,Patnership,Complex\n4955,41.8,Boston,N,6,24,1,35,No education,23,Owner,Standalone\n4066,229.84,NJ City,S,25,17,6,34,High School,21,Patnership,Complex\n7608,199.47,Boston,W,12,20,24,43,Graduation,20,Franchise,Complex\n9986,165.63,NJ City,S,1,24,4,38,No education,23,Patnership,Complex\n6890,153.32,Boston,S,12,9,1,62,No education,27,Patnership,Complex\n3068,44.03,NJ City,N,24,9,1,54,No education,28,Franchise,Complex\n7835,25.53,Boston,N,5,15,2,44,No education,23,Owner,Standalone\n7551,400.49,Boston,E,19,24,4,35,Graduation,23,Owner,Standalone\n5125,129.95,Boston,N,19,23,1,66,High School,33,Patnership,Standalone\n8196,308.66,NJ City,W,24,16,7,35,Graduation,18,Franchise,Complex\n1277,162,NJ City,N,13,23,20,25,High School,22,Patnership,Standalone\n7679,395.84,Boston,E,24,11,10,82,Graduation,23,Owner,Standalone\n7495,51.28,Boston,E,8,10,4,27,High School,19,Owner,Standalone\n3802,237,NJ City,S,2,15,19,58,High School,27,Patnership,Complex\n9290,339.46,NJ City,S,21,23,3,55,No education,34,Patnership,Complex\n461,60.74,NJ City,N,13,12,3,39,No education,21,Patnership,Standalone\n7029,393.1,Boston,N,24,18,3,47,Graduation,29,Patnership,Standalone\n116,339.76,NJ City,W,23,13,2,37,No education,24,Franchise,Complex\n7839,132.09,Boston,E,19,12,20,55,Graduation,19,Owner,Standalone\n4903,79.65,NJ City,E,18,15,2,41,High School,32,Owner,Standalone\n3009,424.1,NJ City,N,5,22,13,61,High School,28,Patnership,Standalone\n6451,242.73,Boston,E,4,13,25,33,Graduation,22,Owner,Standalone\n1930,5.8,NJ City,N,8,24,2,62,High School,29,Patnership,Complex\n1984,56.5,NJ City,W,18,16,1,65,High School,29,Franchise,Complex\n5604,478.2,Boston,W,8,18,8,55,Graduation,25,Franchise,Complex\n8965,387.49,NJ City,N,12,22,11,33,Graduation,33,Patnership,Standalone\n9299,442.48,NJ City,E,8,21,17,49,Graduation,28,Owner,Standalone\n2514,181.04,NJ City,S,25,24,6,76,High School,24,Patnership,Complex\n8898,223.64,NJ City,S,25,19,9,75,Graduation,35,Patnership,Complex\n8413,477,NJ City,N,23,8,1,74,High School,28,Patnership,Standalone\n1368,35.27,NJ City,N,20,11,4,44,Graduation,27,Franchise,Complex\n227,499.36,NJ City,E,19,20,12,29,High School,30,Owner,Standalone\n49,9.25,Boston,N,3,9,2,33,High School,25,Patnership,Standalone\n5455,387.07,Boston,E,18,8,15,57,Graduation,23,Owner,Standalone\n5123,110.17,Boston,E,14,16,3,81,No education,23,Owner,Standalone\n4452,25.56,NJ City,N,25,18,1,39,Graduation,31,Franchise,Complex\n3345,419.07,NJ City,N,15,18,20,62,High School,32,Patnership,Standalone\n6787,199.39,Boston,E,20,18,22,80,Graduation,29,Owner,Standalone\n2293,134.4,NJ City,N,9,9,2,34,High School,23,Patnership,Standalone\n5899,463.08,Boston,E,9,11,6,40,Graduation,31,Owner,Standalone\n493,387.41,NJ City,N,6,23,22,83,High School,19,Patnership,Standalone\n3899,150.65,NJ City,E,4,13,16,73,High School,32,Owner,Standalone\n6711,142.66,Boston,E,21,17,23,48,Graduation,34,Owner,Standalone\n8902,313.79,NJ City,S,17,23,1,45,High School,24,Patnership,Complex\n8393,379.4,NJ City,N,20,11,22,84,Graduation,19,Patnership,Standalone\n1290,191.04,NJ City,S,1,18,2,57,Graduation,32,Patnership,Complex\n7269,410.4,Boston,N,16,8,13,67,Graduation,25,Patnership,Standalone\n9032,471.14,NJ City,W,2,18,17,65,Graduation,26,Franchise,Complex\n9322,382.32,NJ City,S,22,19,3,66,High School,31,Patnership,Complex\n6465,56,Boston,N,13,18,3,65,Graduation,32,Patnership,Standalone\n204,49.59,NJ City,W,24,8,2,29,Graduation,26,Franchise,Complex\n560,14.73,NJ City,N,16,12,4,74,No education,33,Franchise,Complex\n2383,343.43,NJ City,E,13,22,10,37,High School,24,Owner,Standalone\n8538,36.66,NJ City,N,16,22,1,80,Graduation,34,Patnership,Complex\n9992,394.74,NJ City,W,24,8,4,30,No education,33,Franchise,Complex\n7690,316.51,Boston,S,15,19,4,35,High School,26,Patnership,Complex\n7706,331.84,Boston,S,9,17,17,55,Graduation,21,Patnership,Complex\n2186,449.37,NJ City,S,8,15,16,51,High School,30,Patnership,Complex\n4798,133.81,NJ City,S,15,21,20,50,High School,21,Patnership,Complex\n7773,435.02,Boston,N,11,18,2,44,Graduation,22,Patnership,Standalone\n9489,459.71,NJ City,N,10,12,25,58,Graduation,31,Patnership,Standalone\n5936,336.83,Boston,W,7,22,2,76,No education,22,Franchise,Complex\n2584,433.78,NJ City,W,23,13,5,63,High School,32,Franchise,Complex\n4315,229.4,NJ City,E,15,11,12,76,High School,21,Owner,Standalone\n2009,105.26,NJ City,N,16,23,1,70,No education,32,Patnership,Standalone\n1148,251.5,NJ City,W,5,16,25,48,High School,34,Franchise,Complex\n8846,481.03,NJ City,S,13,15,9,41,Graduation,26,Patnership,Complex\n7727,417.43,Boston,E,14,18,25,67,Graduation,32,Owner,Standalone\n1955,114,NJ City,E,3,9,14,77,High School,35,Owner,Standalone\n4372,34.89,NJ City,N,24,22,3,40,High School,20,Franchise,Complex\n3394,338.94,NJ City,S,6,12,19,70,High School,18,Patnership,Complex\n3710,300.41,NJ City,S,4,8,18,56,High School,24,Patnership,Complex\n8634,16.9,NJ City,N,25,20,1,27,Graduation,27,Patnership,Complex\n7655,71.95,Boston,N,2,16,1,29,No education,28,Owner,Standalone\n7500,317.89,Boston,W,10,20,15,49,Graduation,21,Franchise,Complex\n5477,252.24,Boston,N,16,12,19,27,Graduation,26,Patnership,Standalone\n829,266.93,NJ City,N,5,18,15,40,High School,33,Patnership,Standalone\n8087,2.94,NJ City,E,23,12,3,74,No education,33,Owner,Standalone\n7758,370.98,Boston,S,10,23,18,70,Graduation,25,Patnership,Complex\n3260,193.05,NJ City,W,13,20,13,77,High School,28,Franchise,Complex\n2170,423.64,NJ City,S,17,9,1,79,High School,19,Patnership,Complex\n1199,475.06,NJ City,E,8,22,25,57,High School,21,Owner,Standalone\n6837,266.2,Boston,N,17,19,23,64,Graduation,27,Patnership,Standalone\n1159,187.76,NJ City,E,10,24,6,36,High School,29,Owner,Standalone\n1265,399.48,NJ City,N,7,24,20,54,High School,26,Patnership,Standalone\n6180,150.9,Boston,W,25,24,18,74,Graduation,23,Franchise,Complex\n338,5.02,NJ City,N,22,24,4,51,No education,25,Patnership,Complex\n6450,7.76,Boston,N,20,8,1,31,Graduation,35,Patnership,Complex\n8116,178.72,NJ City,W,4,12,4,52,High School,18,Franchise,Complex\n9656,340.56,NJ City,W,15,15,19,61,Graduation,33,Franchise,Complex\n8071,414.13,NJ City,E,10,11,4,32,High School,26,Owner,Standalone\n5168,281.71,Boston,W,20,17,11,34,Graduation,33,Franchise,Complex\n7554,28.09,Boston,N,5,9,1,74,Graduation,35,Patnership,Complex\n7293,183.98,Boston,N,23,14,3,61,Graduation,20,Patnership,Standalone\n256,431.55,NJ City,W,8,23,8,60,High School,20,Franchise,Complex\n416,86.35,NJ City,W,19,8,1,75,No education,19,Franchise,Complex\n2529,391.83,NJ City,N,13,24,24,50,High School,35,Patnership,Standalone\n4302,281.38,NJ City,S,10,23,8,60,High School,24,Patnership,Complex\n4957,215.15,Boston,N,11,23,8,29,High School,19,Patnership,Standalone\n3095,78.17,NJ City,E,7,12,4,46,No education,25,Owner,Standalone\n221,387.03,NJ City,N,21,16,1,44,No education,22,Patnership,Standalone\n7653,59.89,Boston,N,15,9,2,29,Graduation,30,Patnership,Standalone\n3878,14.23,NJ City,N,21,8,2,83,No education,31,Patnership,Complex\n5461,229.25,Boston,N,1,18,3,68,High School,24,Patnership,Standalone\n3604,69.51,NJ City,N,10,19,4,80,High School,24,Franchise,Complex\n8755,180.84,NJ City,E,11,13,16,30,Graduation,26,Owner,Standalone\n8161,358.08,NJ City,N,9,19,3,67,High School,33,Patnership,Standalone\n2827,319.82,NJ City,E,19,16,4,76,High School,30,Owner,Standalone\n3015,360.46,NJ City,E,17,20,1,33,Graduation,19,Owner,Standalone\n7061,314.98,Boston,N,21,21,11,72,Graduation,24,Patnership,Standalone\n674,167.04,NJ City,S,20,16,4,55,No education,20,Patnership,Complex\n4730,34.19,NJ City,N,21,23,4,67,No education,23,Patnership,Complex\n3630,59.83,NJ City,S,13,18,4,40,Graduation,34,Patnership,Complex\n6485,297.45,Boston,N,16,10,14,45,Graduation,28,Patnership,Standalone\n3100,8.79,NJ City,N,10,11,2,69,High School,23,Franchise,Complex\n3451,308.52,NJ City,E,15,15,5,52,High School,21,Owner,Standalone\n146,50.69,NJ City,S,18,10,4,75,No education,25,Patnership,Complex\n6791,331.31,Boston,E,16,11,21,81,Graduation,19,Owner,Standalone\n6142,66.34,Boston,N,25,24,1,50,High School,27,Patnership,Complex\n4919,464.61,NJ City,E,25,21,21,28,High School,18,Owner,Standalone\n8032,219.31,Boston,W,12,12,1,48,High School,31,Franchise,Complex\n6292,497.54,Boston,W,14,11,24,30,Graduation,18,Franchise,Complex\n3126,185.4,NJ City,S,13,14,9,50,High School,33,Patnership,Complex\n1446,463.19,NJ City,S,20,9,20,64,High School,28,Patnership,Complex\n1115,354.42,NJ City,E,5,15,25,59,High School,29,Owner,Standalone\n385,418.97,NJ City,N,14,18,25,36,High School,24,Patnership,Standalone\n4338,33.23,NJ City,N,18,21,4,71,Graduation,24,Patnership,Complex\n9012,160.51,NJ City,W,3,18,19,78,Graduation,33,Franchise,Complex\n6806,112.96,Boston,S,17,13,4,75,No education,32,Patnership,Complex\n8437,86.48,NJ City,N,24,22,1,34,High School,24,Patnership,Standalone\n7915,399.61,Boston,E,2,11,12,57,Graduation,31,Owner,Standalone\n4086,302.97,NJ City,S,10,18,18,61,High School,22,Patnership,Complex\n6294,164.52,Boston,S,24,9,10,80,Graduation,28,Patnership,Complex\n36,407.36,NJ City,W,9,20,5,43,Graduation,33,Franchise,Complex\n8859,193.63,NJ City,E,23,13,23,26,Graduation,26,Owner,Standalone\n4496,253.14,NJ City,W,8,21,2,49,No education,33,Franchise,Complex\n4628,489.4,NJ City,W,16,15,16,52,High School,21,Franchise,Complex\n6370,2.99,Boston,N,24,8,4,76,High School,32,Patnership,Complex\n6586,379.37,Boston,S,24,10,1,40,High School,30,Patnership,Complex\n5483,399.16,Boston,E,23,19,11,39,Graduation,31,Owner,Standalone\n9678,111.04,NJ City,S,2,8,4,82,Graduation,28,Patnership,Complex\n7276,64.97,Boston,N,21,23,2,29,High School,22,Franchise,Complex\n7227,11.27,Boston,N,13,8,1,57,Graduation,21,Owner,Standalone\n7179,141.66,Boston,E,20,13,2,77,Graduation,21,Owner,Standalone\n1776,162.82,NJ City,W,13,14,23,38,High School,35,Franchise,Complex\n1153,493.86,NJ City,N,20,18,16,27,High School,21,Patnership,Standalone\n6260,462.72,Boston,W,4,13,25,36,Graduation,20,Franchise,Complex\n9232,446.88,NJ City,W,21,18,4,51,High School,25,Franchise,Complex\n6068,230.24,Boston,W,23,18,9,60,Graduation,26,Franchise,Complex\n5998,394.52,Boston,S,25,21,10,79,Graduation,32,Patnership,Complex\n5930,24.14,Boston,N,17,21,1,73,No education,22,Patnership,Complex\n7388,17.18,Boston,N,9,14,4,44,No education,34,Franchise,Complex\n7371,248.91,Boston,E,10,19,5,85,Graduation,20,Owner,Standalone\n3147,450.88,NJ City,E,24,15,25,40,High School,24,Owner,Standalone\n3854,422.95,NJ City,S,18,12,5,34,No education,29,Patnership,Complex\n9863,120.76,NJ City,E,13,13,2,27,No education,34,Owner,Standalone\n6830,199.9,Boston,S,5,13,4,42,No education,29,Patnership,Complex\n3932,455.38,NJ City,W,7,14,19,45,High School,19,Franchise,Complex\n1336,205.97,NJ City,W,14,10,9,85,High School,30,Franchise,Complex\n8326,244.62,NJ City,S,12,17,18,68,Graduation,35,Patnership,Complex\n736,209.68,NJ City,W,19,18,9,41,High School,27,Franchise,Complex\n2135,195.77,NJ City,E,25,12,1,60,No education,18,Owner,Standalone\n13,73.48,Boston,N,25,15,2,70,High School,26,Patnership,Standalone\n7170,459.81,Boston,S,2,19,18,31,Graduation,22,Patnership,Complex\n2690,448.31,NJ City,S,17,10,25,76,High School,29,Patnership,Complex\n3469,493.75,NJ City,N,25,14,12,67,High School,31,Patnership,Standalone\n920,460.9,NJ City,W,8,11,3,32,No education,22,Franchise,Complex\n9333,365.9,NJ City,N,18,14,13,29,Graduation,26,Patnership,Standalone\n6715,105.75,Boston,E,4,20,1,30,High School,31,Owner,Standalone\n5979,28.32,Boston,N,8,14,2,85,Graduation,20,Owner,Standalone\n5039,238.38,Boston,E,12,18,25,54,Graduation,30,Owner,Standalone\n4333,69.18,NJ City,N,13,11,3,58,High School,20,Patnership,Standalone\n8865,299.85,NJ City,N,16,11,5,48,Graduation,20,Patnership,Standalone\n7351,204.31,Boston,E,25,22,11,80,Graduation,26,Owner,Standalone\n3883,33.55,NJ City,N,23,24,1,38,High School,33,Owner,Standalone\n7514,403.09,Boston,S,10,8,14,36,Graduation,28,Patnership,Complex\n4640,159.25,NJ City,W,16,15,23,48,High School,28,Franchise,Complex\n8937,175.18,NJ City,N,5,24,22,80,Graduation,22,Patnership,Standalone\n2672,362.69,NJ City,W,4,17,22,35,High School,19,Franchise,Complex\n9827,473.36,NJ City,E,8,9,20,84,Graduation,34,Owner,Standalone\n3030,157,NJ City,S,1,24,23,37,High School,20,Patnership,Complex\n8516,77.68,NJ City,W,12,18,3,71,No education,19,Franchise,Complex\n7729,92.63,Boston,N,7,16,1,79,High School,35,Patnership,Standalone\n1201,293.82,NJ City,N,14,10,11,76,High School,23,Patnership,Standalone\n4524,53.69,NJ City,W,2,20,1,51,Graduation,28,Franchise,Complex\n8973,218.68,NJ City,N,1,22,16,35,Graduation,26,Patnership,Standalone\n4339,319.45,NJ City,E,6,22,15,52,High School,18,Owner,Standalone\n3529,122.61,NJ City,N,2,15,1,67,High School,30,Patnership,Standalone\n9759,67.36,NJ City,N,24,24,3,40,Graduation,30,Owner,Standalone\n8234,186.59,NJ City,S,24,18,4,31,No education,18,Patnership,Complex\n8457,492.96,NJ City,N,2,24,19,53,Graduation,18,Patnership,Standalone\n147,156.92,NJ City,E,21,14,4,65,Graduation,18,Owner,Standalone\n3463,474.63,NJ City,E,9,9,2,47,High School,34,Owner,Standalone\n5229,441.54,Boston,N,21,12,14,32,Graduation,29,Patnership,Standalone\n5885,387.98,Boston,N,5,14,21,53,Graduation,24,Patnership,Standalone\n5164,260.41,Boston,W,2,20,24,83,Graduation,31,Franchise,Complex\n2368,194.69,NJ City,W,10,8,3,61,High School,31,Franchise,Complex\n2779,297.08,NJ City,E,7,24,21,70,High School,26,Owner,Standalone\n9149,55.78,NJ City,N,16,24,1,52,No education,34,Patnership,Standalone\n6061,292.12,Boston,N,19,18,21,56,Graduation,27,Patnership,Standalone\n1141,261.82,NJ City,N,16,22,9,81,High School,19,Patnership,Standalone\n3376,173.76,NJ City,W,4,23,4,31,High School,24,Franchise,Complex\n6648,467.49,Boston,W,3,22,15,43,Graduation,31,Franchise,Complex\n917,323.66,NJ City,N,25,23,10,39,High School,21,Patnership,Standalone\n2403,213,NJ City,E,1,16,18,61,High School,30,Owner,Standalone\n9437,307.66,NJ City,N,2,15,21,36,Graduation,18,Patnership,Standalone\n9158,69.6,NJ City,N,23,8,1,69,No education,23,Patnership,Complex\n4622,96.62,NJ City,S,16,8,2,50,No education,27,Patnership,Complex\n2313,497.94,NJ City,N,22,8,20,33,High School,27,Patnership,Standalone\n9849,451.12,NJ City,N,9,8,3,36,Graduation,25,Patnership,Standalone\n3425,163.86,NJ City,N,5,13,17,53,High School,33,Patnership,Standalone\n5089,146.13,Boston,N,25,10,8,28,Graduation,20,Patnership,Standalone\n7848,235.42,Boston,W,16,20,10,26,Graduation,29,Franchise,Complex\n1289,306.32,NJ City,N,13,17,11,45,High School,20,Patnership,Standalone\n6576,360.53,Boston,W,20,22,13,30,Graduation,23,Franchise,Complex\n7816,21.74,Boston,N,1,12,3,65,High School,28,Franchise,Complex\n9138,353.37,NJ City,S,3,24,23,43,Graduation,22,Patnership,Complex\n5235,307.75,Boston,E,6,9,9,44,Graduation,29,Owner,Standalone\n6836,444.5,Boston,W,12,11,1,45,No education,21,Franchise,Complex\n4626,378.73,NJ City,S,7,15,15,71,High School,32,Patnership,Complex\n3232,7.47,NJ City,N,7,23,1,41,High School,34,Franchise,Complex\n2811,414.32,NJ City,E,5,21,20,26,High School,27,Owner,Standalone\n4486,395.12,NJ City,S,13,11,9,66,High School,21,Patnership,Complex\n7937,321.46,Boston,N,21,15,21,77,Graduation,34,Patnership,Standalone\n8948,389.03,NJ City,W,16,8,15,45,Graduation,22,Franchise,Complex\n2550,225.85,NJ City,S,18,11,19,40,High School,21,Patnership,Complex\n5625,50.18,Boston,N,18,15,3,49,Graduation,30,Patnership,Standalone\n7142,104.42,Boston,S,15,23,4,51,No education,20,Patnership,Complex\n8632,285.45,NJ City,W,17,14,10,51,Graduation,19,Franchise,Complex\n6341,186.69,Boston,N,23,16,22,71,Graduation,31,Patnership,Standalone\n1079,343.36,NJ City,E,24,12,10,67,High School,28,Owner,Standalone\n6207,30.21,Boston,N,13,8,3,26,Graduation,32,Owner,Standalone\n8872,423.91,NJ City,W,3,17,11,35,Graduation,35,Franchise,Complex\n7594,416.37,Boston,S,22,23,12,34,Graduation,27,Patnership,Complex\n6844,176.38,Boston,W,20,17,4,36,High School,31,Franchise,Complex\n295,414.48,NJ City,E,7,19,6,52,High School,21,Owner,Standalone\n5358,189.26,Boston,S,20,21,21,33,Graduation,21,Patnership,Complex\n9054,79.93,NJ City,S,2,15,2,65,Graduation,32,Patnership,Complex\n1965,15,NJ City,N,2,18,2,69,Graduation,26,Patnership,Standalone\n7512,378.18,Boston,W,17,11,14,82,Graduation,18,Franchise,Complex\n7426,129.36,Boston,S,18,21,16,32,Graduation,32,Patnership,Complex\n9463,212.85,NJ City,E,25,16,9,64,Graduation,29,Owner,Standalone\n3262,295.21,NJ City,S,25,16,8,56,High School,35,Patnership,Complex\n5771,207.07,Boston,E,15,9,12,80,Graduation,23,Owner,Standalone\n8059,217.16,NJ City,E,16,20,19,28,Graduation,18,Owner,Standalone\n3760,186,NJ City,W,3,19,23,82,High School,28,Franchise,Complex\n8569,221.97,NJ City,N,17,18,24,48,Graduation,33,Patnership,Standalone\n6769,430.3,Boston,N,18,21,4,66,High School,32,Patnership,Standalone\n6509,437.98,Boston,N,23,11,24,32,Graduation,18,Patnership,Standalone\n482,208.04,NJ City,S,21,19,18,68,High School,19,Patnership,Complex\n3850,117.73,NJ City,S,21,12,4,72,High School,30,Patnership,Complex\n2372,7.2,NJ City,N,25,16,2,31,No education,33,Franchise,Complex\n8461,369.09,NJ City,N,23,8,10,67,Graduation,20,Patnership,Standalone\n6123,271.3,Boston,E,15,11,9,69,Graduation,29,Owner,Standalone\n1111,39.89,NJ City,N,16,14,3,65,High School,23,Owner,Standalone\n8342,410.8,NJ City,S,11,11,5,76,No education,29,Patnership,Complex\n9133,191.35,NJ City,N,13,22,25,47,Graduation,25,Patnership,Standalone\n3574,440.57,NJ City,S,12,22,6,48,High School,23,Patnership,Complex\n3365,108,NJ City,N,2,19,12,49,High School,22,Patnership,Standalone\n1936,43.34,NJ City,N,6,22,3,48,High School,26,Franchise,Complex\n8455,389.68,NJ City,E,11,22,18,77,Graduation,30,Owner,Standalone\n882,210.5,NJ City,S,4,11,12,65,High School,31,Patnership,Complex\n2467,24.55,NJ City,N,2,14,1,85,High School,31,Owner,Standalone\n5272,2.2,Boston,W,11,22,2,31,High School,31,Franchise,Complex\n547,333.19,NJ City,E,14,9,19,66,High School,34,Owner,Standalone\n5732,114.1,Boston,W,13,19,2,75,No education,34,Franchise,Complex\n4400,187.06,NJ City,W,4,19,2,52,No education,27,Franchise,Complex\n2577,320.78,NJ City,N,4,17,14,84,High School,26,Patnership,Standalone\n1645,8.04,NJ City,N,21,13,3,59,High School,32,Patnership,Standalone\n7124,245.26,Boston,W,1,17,19,83,Graduation,29,Franchise,Complex\n3379,175.67,NJ City,E,9,22,13,26,High School,35,Owner,Standalone\n228,142.79,NJ City,W,17,23,7,72,High School,35,Franchise,Complex\n3277,117.04,NJ City,N,20,9,2,25,High School,18,Patnership,Standalone\n3635,160.91,NJ City,E,17,21,23,55,High School,34,Owner,Standalone\n5258,128.81,Boston,S,22,19,9,31,Graduation,21,Patnership,Complex\n7951,101.35,Boston,E,6,24,2,66,High School,26,Owner,Standalone\n72,203.31,Maine,W,18,16,22,65,High School,20,Franchise,Complex\n5275,93.27,Boston,E,18,21,4,42,High School,20,Owner,Standalone\n2682,191.31,NJ City,S,16,17,2,69,Graduation,27,Patnership,Complex\n7452,97.05,Boston,W,22,9,1,82,Graduation,21,Franchise,Complex\n9772,121.16,NJ City,W,14,22,3,70,High School,35,Franchise,Complex\n4083,101.84,NJ City,E,20,12,4,83,Graduation,23,Owner,Standalone\n2841,257.78,NJ City,N,22,9,15,72,High School,21,Patnership,Standalone\n6242,10.12,Boston,N,10,20,4,57,No education,31,Patnership,Complex\n4993,146.59,Boston,N,21,16,13,75,High School,22,Patnership,Standalone\n3961,465.25,NJ City,N,18,12,7,58,High School,29,Patnership,Standalone\n7193,278.37,Boston,N,25,22,6,32,Graduation,27,Patnership,Standalone\n8354,382.14,NJ City,S,11,14,24,68,Graduation,21,Patnership,Complex\n8589,126.78,NJ City,N,25,10,3,31,Graduation,31,Patnership,Standalone\n6468,77.25,Boston,W,16,19,3,56,Graduation,23,Franchise,Complex\n4179,110.97,NJ City,E,23,20,3,46,Graduation,28,Owner,Standalone\n155,300.76,NJ City,E,13,13,3,80,No education,18,Owner,Standalone\n7361,155.59,Boston,N,23,16,19,71,Graduation,26,Patnership,Standalone\n856,155.87,NJ City,W,25,24,23,62,High School,33,Franchise,Complex\n9136,428.11,NJ City,W,7,12,7,52,Graduation,22,Franchise,Complex\n870,100.88,NJ City,S,13,22,4,85,Graduation,19,Patnership,Complex\n5523,287.76,Boston,E,22,15,14,30,Graduation,33,Owner,Standalone\n9325,86.14,NJ City,N,1,12,4,64,High School,18,Patnership,Standalone\n9472,471.02,NJ City,W,9,11,11,47,Graduation,35,Franchise,Complex\n2737,311.59,NJ City,N,17,11,17,37,High School,29,Patnership,Standalone\n6355,415.36,Boston,E,17,8,17,73,Graduation,23,Owner,Standalone\n2143,91.16,NJ City,E,14,16,3,67,High School,29,Owner,Standalone\n2601,326.98,NJ City,N,22,22,18,37,High School,34,Patnership,Standalone\n904,8.18,NJ City,N,11,23,2,82,High School,22,Franchise,Complex\n4355,483.67,NJ City,E,21,9,24,73,High School,25,Owner,Standalone\n3996,379.36,NJ City,W,10,23,7,73,High School,30,Franchise,Complex\n7196,179.97,Boston,W,20,18,10,50,Graduation,23,Franchise,Complex\n6239,134.53,Boston,E,4,9,22,83,Graduation,31,Owner,Standalone\n8265,390.05,NJ City,N,5,14,14,79,Graduation,26,Patnership,Standalone\n1108,467.62,NJ City,W,4,21,14,42,High School,24,Franchise,Complex\n6345,8.62,Boston,N,22,9,4,45,Graduation,20,Patnership,Standalone\n5700,11.38,Boston,N,16,8,4,40,Graduation,28,Franchise,Complex\n5470,363.32,Boston,S,6,24,18,58,Graduation,19,Patnership,Complex\n9295,18.28,NJ City,N,25,13,3,75,High School,20,Owner,Standalone\n9625,402.76,NJ City,N,18,17,15,85,Graduation,26,Patnership,Standalone\n2958,405.8,NJ City,S,12,19,22,73,High School,34,Patnership,Complex\n4704,369.07,NJ City,W,17,20,12,83,High School,30,Franchise,Complex\n6002,268.71,Boston,S,20,8,20,45,Graduation,19,Patnership,Complex\n7084,430.61,Boston,W,3,21,19,32,Graduation,31,Franchise,Complex\n6929,140.54,Boston,N,5,18,6,79,Graduation,19,Patnership,Standalone\n5504,315.31,Boston,W,24,24,21,65,Graduation,18,Franchise,Complex\n8620,128.57,NJ City,W,10,8,11,65,Graduation,27,Franchise,Complex\n9564,374.29,NJ City,W,7,15,24,50,Graduation,26,Franchise,Complex\n627,298.77,NJ City,E,6,11,18,62,High School,35,Owner,Standalone\n1831,469.72,NJ City,E,25,23,4,83,High School,28,Owner,Standalone\n9518,181.31,NJ City,S,25,15,4,71,No education,35,Patnership,Complex\n8298,410.26,NJ City,S,14,24,8,41,Graduation,35,Patnership,Complex\n4900,124.1,NJ City,W,16,14,2,51,High School,34,Franchise,Complex\n5407,138.23,Boston,E,18,18,24,62,Graduation,33,Owner,Standalone\n3588,121.75,NJ City,W,23,9,3,79,Graduation,28,Franchise,Complex\n1061,4.22,NJ City,N,14,20,4,66,No education,19,Patnership,Standalone\n892,377.64,NJ City,W,23,24,1,46,High School,22,Franchise,Complex\n4052,466.95,NJ City,W,7,8,20,28,High School,29,Franchise,Complex\n8030,35.58,Boston,N,7,21,2,36,No education,22,Patnership,Complex\n824,400.23,NJ City,W,21,24,14,51,High School,24,Franchise,Complex\n6687,241.83,Boston,E,10,20,17,56,Graduation,18,Owner,Standalone\n6643,390.33,Boston,E,25,22,17,71,Graduation,28,Owner,Standalone\n2748,92.2,NJ City,W,1,16,4,44,Graduation,32,Franchise,Complex\n1276,255.07,NJ City,W,21,20,3,29,High School,28,Franchise,Complex\n7032,485.4,Boston,W,17,19,9,27,Graduation,22,Franchise,Complex\n9086,375.14,NJ City,S,8,9,2,78,No education,30,Patnership,Complex\n2463,133.93,NJ City,E,16,16,10,80,High School,33,Owner,Standalone\n5783,205.7,Boston,E,25,14,7,26,Graduation,34,Owner,Standalone\n4430,481.45,NJ City,S,24,11,17,67,High School,31,Patnership,Complex\n9583,273.33,NJ City,E,12,10,3,54,High School,33,Owner,Standalone\n5282,391.21,Boston,S,18,16,8,59,Graduation,26,Patnership,Complex\n5487,482.74,Boston,E,9,24,8,59,Graduation,31,Owner,Standalone\n4584,101.38,NJ City,W,20,17,2,53,Graduation,23,Franchise,Complex\n7180,291.53,Boston,W,7,23,17,81,Graduation,30,Franchise,Complex\n225,293.52,NJ City,N,25,23,7,59,High School,20,Patnership,Standalone\n812,153,NJ City,W,1,16,7,26,High School,35,Franchise,Complex\n885,111.82,NJ City,N,7,12,2,49,Graduation,19,Patnership,Standalone\n7302,151.07,Boston,S,1,17,22,47,Graduation,23,Patnership,Complex\n9909,154.66,NJ City,N,13,8,19,58,Graduation,28,Patnership,Standalone\n5482,495.53,Boston,S,8,16,19,85,Graduation,32,Patnership,Complex\n8101,277.25,NJ City,N,20,15,25,32,Graduation,26,Patnership,Standalone\n9954,46.06,NJ City,N,15,18,3,28,Graduation,19,Patnership,Complex\n5730,313.7,Boston,S,9,10,17,80,Graduation,25,Patnership,Complex\n981,327.42,NJ City,N,20,23,7,50,High School,33,Patnership,Standalone\n8,2.68,NY City,W,17,11,2,76,No education,31,Franchise,Complex\n9842,408.85,NJ City,S,7,19,14,83,Graduation,31,Patnership,Complex\n7127,164.24,Boston,E,1,8,14,36,Graduation,19,Owner,Standalone\n3734,372.39,NJ City,S,22,16,3,82,No education,23,Patnership,Complex\n7831,14.29,Boston,N,18,16,1,84,High School,24,Owner,Standalone\n4263,17.48,NJ City,N,8,16,3,36,Graduation,33,Owner,Standalone\n7287,107.51,Boston,E,8,15,4,49,Graduation,30,Owner,Standalone\n2237,105.82,NJ City,N,22,8,2,45,No education,19,Patnership,Standalone\n7447,219.86,Boston,E,3,24,12,65,Graduation,25,Owner,Standalone\n6636,483.47,Boston,W,25,10,17,41,Graduation,23,Franchise,Complex\n4760,47.86,NJ City,W,22,11,4,70,No education,33,Franchise,Complex\n9921,235.93,NJ City,N,3,20,15,70,Graduation,30,Patnership,Standalone\n2101,466.64,NJ City,N,24,23,21,37,High School,24,Patnership,Standalone\n9446,482.6,NJ City,S,21,15,12,76,Graduation,30,Patnership,Complex\n6020,242.03,Boston,W,3,17,15,58,Graduation,27,Franchise,Complex\n3005,95.8,NJ City,N,12,10,1,50,No education,28,Patnership,Standalone\n3409,209.45,NJ City,N,18,10,15,35,High School,26,Patnership,Standalone\n2791,347.62,NJ City,E,19,20,4,25,High School,18,Owner,Standalone\n7174,73.53,Boston,N,1,19,4,41,High School,27,Patnership,Complex\n8308,358.17,NJ City,W,21,10,11,67,Graduation,31,Franchise,Complex\n6689,395.76,Boston,N,23,10,6,37,Graduation,20,Patnership,Standalone\n8952,229.12,NJ City,W,9,9,25,79,Graduation,28,Franchise,Complex\n3023,70.84,NJ City,N,23,21,3,27,No education,19,Owner,Standalone\n975,483.82,NJ City,E,10,10,25,64,High School,35,Owner,Standalone\n361,214.32,NJ City,N,12,24,20,73,High School,22,Patnership,Standalone\n1603,334.84,NJ City,E,24,10,20,52,High School,34,Owner,Standalone\n5167,393.37,Boston,E,1,10,20,29,Graduation,35,Owner,Standalone\n3980,336.12,NJ City,W,4,21,9,41,High School,32,Franchise,Complex\n1250,315.7,NJ City,S,10,18,23,35,High School,35,Patnership,Complex\n4967,356.71,Boston,E,4,14,4,75,No education,24,Owner,Standalone\n5716,320.2,Boston,W,12,22,4,25,High School,25,Franchise,Complex\n1317,82.75,NJ City,N,23,18,3,41,Graduation,26,Patnership,Standalone\n5126,261.19,Boston,S,7,12,2,33,No education,25,Patnership,Complex\n8579,346.77,NJ City,E,8,24,8,34,Graduation,27,Owner,Standalone\n8251,161.1,NJ City,E,2,12,4,53,High School,34,Owner,Standalone\n8053,446.98,NJ City,N,21,10,21,32,Graduation,29,Patnership,Standalone\n1109,134.47,NJ City,N,20,18,19,25,High School,34,Patnership,Standalone\n8367,68.9,NJ City,N,17,15,1,45,Graduation,25,Owner,Standalone\n5088,334.8,Boston,W,20,22,13,45,Graduation,30,Franchise,Complex\n9731,274.8,NJ City,E,15,13,2,39,No education,32,Owner,Standalone\n2087,258.26,NJ City,E,9,21,14,82,High School,32,Owner,Standalone\n3143,11.71,NJ City,N,20,14,3,84,No education,24,Owner,Standalone\n89,139.1,Boston,N,19,16,17,74,High School,34,Patnership,Standalone\n5685,307.75,Boston,N,2,24,8,59,Graduation,24,Patnership,Standalone\n4772,291,NJ City,W,3,11,12,76,High School,31,Franchise,Complex\n1769,372.41,NJ City,N,25,12,21,72,High School,24,Patnership,Standalone\n2214,202.27,NJ City,S,17,16,8,66,High School,22,Patnership,Complex\n8771,31.19,NJ City,N,1,22,2,60,No education,30,Owner,Standalone\n5446,186.3,Boston,S,13,14,11,51,Graduation,24,Patnership,Complex\n7846,423.45,Boston,S,11,14,9,70,Graduation,21,Patnership,Complex\n9470,161.54,NJ City,S,7,10,24,30,Graduation,30,Patnership,Complex\n6746,186.23,Boston,S,17,15,24,77,Graduation,20,Patnership,Complex\n8203,151.92,NJ City,E,23,14,3,59,High School,35,Owner,Standalone\n8837,63.44,NJ City,N,11,17,4,36,No education,24,Patnership,Standalone\n400,464.37,NJ City,W,24,17,1,45,High School,25,Franchise,Complex\n3837,59.17,NJ City,N,16,13,1,65,Graduation,23,Patnership,Standalone\n3737,239.1,NJ City,N,8,16,14,34,High School,31,Patnership,Standalone\n5506,305.14,Boston,S,23,12,3,55,High School,22,Patnership,Complex\n768,163.18,NJ City,W,22,21,7,39,High School,25,Franchise,Complex\n2373,471.64,NJ City,N,15,16,23,59,High School,28,Patnership,Standalone\n4573,429.57,NJ City,N,6,8,15,68,High School,19,Patnership,Standalone\n8602,44.92,NJ City,N,24,13,2,44,High School,18,Patnership,Complex\n5230,428.95,Boston,S,18,8,12,60,Graduation,30,Patnership,Complex\n3441,223.27,NJ City,N,9,13,11,25,High School,32,Patnership,Standalone\n6042,160.86,Boston,S,14,15,4,38,Graduation,19,Patnership,Complex\n4812,198,NJ City,W,2,11,18,32,High School,22,Franchise,Complex\n8753,149.11,NJ City,N,7,18,25,28,Graduation,30,Patnership,Standalone\n3841,10.52,NJ City,N,19,24,4,54,High School,30,Patnership,Standalone\n7833,37.34,Boston,N,24,9,2,29,Graduation,19,Patnership,Standalone\n3813,188.15,NJ City,N,23,19,6,33,High School,28,Patnership,Standalone\n5354,257.37,Boston,S,3,15,8,76,Graduation,24,Patnership,Complex\n7912,205.46,Boston,W,22,14,20,39,Graduation,30,Franchise,Complex\n5309,82.07,Boston,N,1,11,1,74,No education,29,Patnership,Standalone\n9259,199.43,NJ City,E,23,9,9,53,Graduation,21,Owner,Standalone\n667,0.12,NJ City,N,22,12,1,77,High School,31,Owner,Standalone\n8157,194.35,NJ City,N,25,18,10,85,Graduation,28,Patnership,Standalone\n6757,488,Boston,N,11,18,6,82,Graduation,33,Patnership,Standalone\n6498,309.64,Boston,S,12,11,5,29,Graduation,20,Patnership,Complex\n2520,51.55,NJ City,W,6,15,2,59,Graduation,18,Franchise,Complex\n8988,68.17,NJ City,N,12,11,4,76,Graduation,29,Franchise,Complex\n2287,380.16,NJ City,E,5,22,23,43,High School,34,Owner,Standalone\n8736,149.62,NJ City,W,17,23,3,49,Graduation,26,Franchise,Complex\n6692,487.24,Boston,W,17,21,19,49,Graduation,18,Franchise,Complex\n8790,296.69,NJ City,S,11,9,21,70,Graduation,28,Patnership,Complex\n5859,361.87,Boston,E,24,15,18,40,Graduation,22,Owner,Standalone\n8587,320.08,NJ City,E,14,13,2,83,High School,18,Owner,Standalone\n1397,190,NJ City,N,1,8,17,37,High School,32,Patnership,Standalone\n2959,116.12,NJ City,E,2,22,2,47,High School,32,Owner,Standalone\n3558,55.88,NJ City,S,21,16,2,71,Graduation,26,Patnership,Complex\n5105,266.49,Boston,N,1,12,24,57,Graduation,35,Patnership,Standalone\n6824,14.98,Boston,N,11,18,4,40,No education,26,Franchise,Complex\n6113,183.37,Boston,N,11,21,12,67,Graduation,25,Patnership,Standalone\n1138,476.8,NJ City,S,15,10,6,65,High School,32,Patnership,Complex\n578,400.4,NJ City,S,15,11,25,67,High School,25,Patnership,Complex\n8333,152.2,NJ City,N,18,18,10,30,Graduation,31,Patnership,Standalone\n7487,9.43,Boston,N,11,8,4,61,No education,25,Owner,Standalone\n2219,126.87,NJ City,E,11,16,4,66,No education,34,Owner,Standalone\n9455,346.86,NJ City,E,18,11,3,48,No education,31,Owner,Standalone\n8770,369.28,NJ City,S,15,8,18,74,Graduation,18,Patnership,Complex\n3685,278.5,NJ City,N,20,8,8,63,High School,28,Patnership,Standalone\n7425,179.24,Boston,N,7,21,20,73,Graduation,27,Patnership,Standalone\n4051,126,NJ City,E,2,11,8,34,High School,19,Owner,Standalone\n7490,209.25,Boston,S,5,15,21,72,Graduation,30,Patnership,Complex\n5868,418.61,Boston,W,16,19,2,48,Graduation,34,Franchise,Complex\n8396,491.61,NJ City,W,2,18,10,62,Graduation,28,Franchise,Complex\n6702,366.82,Boston,S,10,18,24,65,Graduation,20,Patnership,Complex\n7416,165.92,Boston,W,16,14,21,30,Graduation,28,Franchise,Complex\n8717,200.45,NJ City,N,25,20,11,85,Graduation,28,Patnership,Standalone\n8386,163.98,NJ City,S,8,13,20,50,Graduation,33,Patnership,Complex\n3132,13.72,NJ City,N,19,8,2,55,Graduation,21,Franchise,Complex\n1584,341.93,NJ City,W,17,15,19,77,High School,25,Franchise,Complex\n6971,436.62,Boston,E,2,24,3,39,No education,18,Owner,Standalone\n9725,433.3,NJ City,N,20,16,13,27,Graduation,28,Patnership,Standalone\n8886,396.52,NJ City,S,1,24,11,33,Graduation,27,Patnership,Complex\n2931,464.57,NJ City,E,18,19,7,59,High School,24,Owner,Standalone\n8142,192.1,NJ City,S,22,8,13,33,Graduation,19,Patnership,Complex\n3289,172.01,NJ City,N,1,11,2,38,High School,24,Patnership,Standalone\n7021,195.13,Boston,N,4,17,19,46,Graduation,24,Patnership,Standalone\n203,487.82,NJ City,E,24,14,19,56,High School,29,Owner,Standalone\n899,309.79,NJ City,E,15,17,10,50,High School,30,Owner,Standalone\n5742,285.48,Boston,S,10,10,12,56,Graduation,20,Patnership,Complex\n1380,128,NJ City,W,3,15,19,56,High School,23,Franchise,Complex\n3956,328.87,NJ City,W,14,14,1,38,No education,24,Franchise,Complex\n705,420.55,NJ City,N,23,19,1,60,Graduation,22,Patnership,Standalone\n1945,434.32,NJ City,N,10,9,13,62,High School,25,Patnership,Standalone\n1131,319.76,NJ City,E,25,22,12,56,High School,21,Owner,Standalone\n8002,111.94,Boston,S,21,11,3,53,High School,26,Patnership,Complex\n8129,114.59,NJ City,N,11,18,2,55,No education,23,Patnership,Standalone\n5959,132.22,Boston,E,6,10,16,82,Graduation,29,Owner,Standalone\n4386,230.56,NJ City,S,12,15,19,28,High School,26,Patnership,Complex\n5824,160.19,Boston,W,6,19,1,69,High School,20,Franchise,Complex\n7819,142.21,Boston,E,22,15,23,31,Graduation,33,Owner,Standalone\n1731,74.68,NJ City,E,18,15,4,32,Graduation,21,Owner,Standalone\n6680,496.49,Boston,W,9,19,21,37,Graduation,31,Franchise,Complex\n5680,296.49,Boston,W,8,10,22,56,Graduation,23,Franchise,Complex\n5572,125.39,Boston,W,2,9,3,51,High School,34,Franchise,Complex\n2714,169.21,NJ City,S,21,20,5,60,No education,27,Patnership,Complex\n6091,62.92,Boston,N,22,14,4,65,High School,21,Owner,Standalone\n50,485.15,Boston,S,14,21,3,26,No education,30,Patnership,Complex\n9892,157.26,NJ City,W,3,21,7,39,Graduation,29,Franchise,Complex\n7903,139.97,Boston,E,5,17,15,32,Graduation,19,Owner,Standalone\n9912,303.57,NJ City,W,19,19,11,44,Graduation,32,Franchise,Complex\n9820,138.73,NJ City,W,2,23,4,76,High School,21,Franchise,Complex\n7744,289.97,Boston,W,5,20,2,64,High School,26,Franchise,Complex\n4475,195,NJ City,E,2,13,15,76,High School,31,Owner,Standalone\n2362,205,NJ City,S,1,19,19,77,High School,32,Patnership,Complex\n9339,379.16,NJ City,E,11,18,9,33,Graduation,24,Owner,Standalone\n2426,177.56,NJ City,S,12,18,6,44,High School,33,Patnership,Complex\n3272,462.16,NJ City,W,18,14,7,73,High School,28,Franchise,Complex\n830,187.76,NJ City,S,7,18,18,64,High School,19,Patnership,Complex\n4087,114.28,NJ City,E,25,11,4,85,High School,29,Owner,Standalone\n6237,112.49,Boston,N,13,12,1,46,Graduation,23,Patnership,Standalone\n5297,20.72,Boston,N,3,15,1,34,No education,24,Patnership,Standalone\n9415,250.51,NJ City,E,10,8,19,52,Graduation,33,Owner,Standalone\n9727,41.59,NJ City,N,12,24,4,62,High School,21,Owner,Standalone\n869,91.64,NJ City,N,17,8,2,33,No education,30,Patnership,Standalone\n4281,216.44,NJ City,N,23,23,13,77,High School,33,Patnership,Standalone\n7598,475.77,Boston,S,23,19,22,68,Graduation,20,Patnership,Complex\n6825,106.79,Boston,N,3,15,2,81,Graduation,24,Patnership,Standalone\n3093,302.96,NJ City,N,12,8,18,44,High School,31,Patnership,Standalone\n6088,177.75,Boston,W,14,9,12,63,Graduation,34,Franchise,Complex\n111,137.49,NJ City,E,13,13,1,71,Graduation,32,Owner,Standalone\n2438,118.44,NJ City,S,14,8,1,80,No education,26,Patnership,Complex\n6043,484.66,Boston,E,25,19,4,83,High School,24,Owner,Standalone\n7720,346.22,Boston,W,18,21,25,25,Graduation,18,Franchise,Complex\n3680,184.23,NJ City,W,17,21,10,75,High School,34,Franchise,Complex\n8152,14.63,NJ City,N,4,22,3,42,High School,32,Franchise,Complex\n3503,461.85,NJ City,E,14,22,21,29,High School,26,Owner,Standalone\n7344,45.1,Boston,N,17,10,4,54,Graduation,23,Franchise,Complex\n4166,409.26,NJ City,S,11,14,24,39,High School,33,Patnership,Complex\n8042,274.83,Boston,S,18,19,19,73,Graduation,23,Patnership,Complex\n7504,148.43,Boston,W,12,22,10,76,Graduation,28,Franchise,Complex\n668,306.34,NJ City,W,16,11,11,79,High School,30,Franchise,Complex\n1807,430.72,NJ City,E,20,15,25,40,High School,34,Owner,Standalone\n1792,47.02,NJ City,N,4,18,1,42,High School,23,Franchise,Complex\n1786,314.04,NJ City,S,11,19,25,69,High School,23,Patnership,Complex\n9810,258.69,NJ City,S,24,20,14,70,Graduation,34,Patnership,Complex\n5878,252.44,Boston,S,10,10,21,37,Graduation,21,Patnership,Complex\n8193,140.03,NJ City,N,1,13,14,46,Graduation,35,Patnership,Standalone\n8908,334.13,NJ City,W,21,21,4,28,High School,18,Franchise,Complex\n2893,483.73,NJ City,N,11,17,25,57,High School,19,Patnership,Standalone\n4988,451.13,Boston,W,5,14,25,49,High School,28,Franchise,Complex\n8855,306.54,NJ City,E,23,15,18,76,Graduation,30,Owner,Standalone\n8924,260.15,NJ City,W,11,22,2,80,No education,20,Franchise,Complex\n825,384.36,NJ City,N,20,15,25,30,High School,35,Patnership,Standalone\n6743,174.44,Boston,E,18,16,25,44,Graduation,21,Owner,Standalone\n716,213.27,NJ City,W,16,23,2,50,No education,35,Franchise,Complex\n2323,316.68,NJ City,E,9,9,14,67,High School,33,Owner,Standalone\n8678,264.24,NJ City,S,22,16,7,83,Graduation,30,Patnership,Complex\n3861,230.68,NJ City,N,22,23,15,47,High School,28,Patnership,Standalone\n3919,2.01,NJ City,E,15,23,4,43,High School,23,Owner,Standalone\n1161,428.88,NJ City,N,8,19,8,74,High School,18,Patnership,Standalone\n2045,115.55,NJ City,N,24,15,3,77,No education,33,Patnership,Standalone\n9244,317.24,NJ City,W,2,15,21,25,Graduation,28,Franchise,Complex\n8649,19.11,NJ City,N,5,13,4,34,Graduation,21,Patnership,Standalone\n296,349.53,NJ City,W,7,15,5,72,No education,19,Franchise,Complex\n6487,140.59,Boston,E,6,14,24,27,Graduation,20,Owner,Standalone\n959,168.48,NJ City,E,15,13,22,77,High School,25,Owner,Standalone\n6524,410.51,Boston,W,10,21,13,73,Graduation,27,Franchise,Complex\n1808,46.03,NJ City,N,24,22,4,48,No education,33,Franchise,Complex\n4507,104,NJ City,E,2,11,15,64,High School,24,Owner,Standalone\n6531,281.32,Boston,E,19,9,14,54,Graduation,21,Owner,Standalone\n8099,192.31,NJ City,E,22,16,11,62,Graduation,26,Owner,Standalone\n7178,141.49,Boston,S,1,13,20,43,Graduation,19,Patnership,Complex\n9753,442.38,NJ City,N,11,20,23,54,Graduation,33,Patnership,Standalone\n2320,80.92,NJ City,W,19,17,4,81,High School,22,Franchise,Complex\n2888,132,NJ City,W,3,21,10,60,High School,32,Franchise,Complex\n7672,37.83,Boston,N,6,17,2,44,High School,26,Franchise,Complex\n4285,472.12,NJ City,N,21,21,19,66,High School,26,Patnership,Standalone\n7011,355.62,Boston,E,20,12,4,33,Graduation,35,Owner,Standalone\n7961,319.14,Boston,N,4,11,19,48,Graduation,31,Patnership,Standalone\n9963,190.08,NJ City,E,14,9,24,77,Graduation,19,Owner,Standalone\n8944,2.72,NJ City,W,22,22,4,41,High School,19,Franchise,Complex\n8858,84.32,NJ City,S,19,17,2,66,No education,34,Patnership,Complex\n4466,145.6,NJ City,S,2,18,3,63,No education,31,Patnership,Complex\n7527,423.89,Boston,E,24,24,19,66,Graduation,25,Owner,Standalone\n7618,145.03,Boston,S,1,10,1,72,High School,19,Patnership,Complex\n481,426.56,NJ City,N,14,17,18,84,High School,26,Patnership,Standalone\n5075,320.89,Boston,E,16,22,19,28,Graduation,32,Owner,Standalone\n1666,245.25,NJ City,S,10,18,3,61,High School,28,Patnership,Complex\n7701,214.97,Boston,N,17,22,13,50,Graduation,19,Patnership,Standalone\n1215,255,NJ City,E,3,19,7,28,High School,31,Owner,Standalone\n1536,212.57,NJ City,W,16,24,12,45,High School,24,Franchise,Complex\n6616,333.81,Boston,W,20,12,18,68,Graduation,25,Franchise,Complex\n8920,139.59,NJ City,W,17,16,5,81,High School,19,Franchise,Complex\n4321,196.77,NJ City,N,22,10,7,54,High School,24,Patnership,Standalone\n5108,72.75,Boston,N,13,9,2,66,No education,26,Franchise,Complex\n5701,318.12,Boston,N,8,24,10,68,Graduation,32,Patnership,Standalone\n44,427.42,Boston,W,25,23,18,53,High School,29,Franchise,Complex\n9632,11.14,NJ City,N,10,8,1,77,No education,33,Franchise,Complex\n3231,443.56,NJ City,E,12,13,13,25,High School,25,Owner,Standalone\n3403,183.38,NJ City,E,23,20,22,78,High School,18,Owner,Standalone\n8272,232.89,NJ City,W,25,10,23,27,Graduation,32,Franchise,Complex\n518,355.98,NJ City,S,22,10,22,29,High School,28,Patnership,Complex\n5317,254.13,Boston,N,13,20,14,27,Graduation,35,Patnership,Standalone\n8259,256.35,NJ City,E,5,11,2,48,Graduation,26,Owner,Standalone\n4197,145.65,NJ City,N,4,11,10,61,High School,22,Patnership,Standalone\n3117,143.46,NJ City,N,18,19,8,82,High School,22,Patnership,Standalone\n4429,39.07,NJ City,N,18,9,1,80,High School,31,Patnership,Standalone\n6897,374.35,Boston,N,18,15,24,41,Graduation,30,Patnership,Standalone\n8248,45.84,NJ City,N,19,9,2,77,High School,18,Franchise,Complex\n4600,493.01,NJ City,W,15,18,5,25,High School,18,Franchise,Complex\n4262,290.93,NJ City,S,24,21,25,41,High School,24,Patnership,Complex\n7929,309.99,Boston,N,12,16,14,69,Graduation,19,Patnership,Standalone\n1311,93.61,NJ City,E,24,19,4,39,Graduation,24,Owner,Standalone\n2022,260.41,NJ City,S,24,14,20,53,High School,31,Patnership,Complex\n756,397.24,NJ City,W,5,16,17,56,High School,35,Franchise,Complex\n9428,435.18,NJ City,W,24,9,2,67,No education,26,Franchise,Complex\n7491,108.15,Boston,E,8,14,3,41,Graduation,25,Owner,Standalone\n4327,94.86,NJ City,E,20,13,4,35,High School,34,Owner,Standalone\n7085,23.05,Boston,N,22,21,2,57,No education,24,Patnership,Standalone\n6010,104.46,Boston,S,14,18,3,70,High School,19,Patnership,Complex\n7244,408.48,Boston,W,8,16,13,82,Graduation,23,Franchise,Complex\n8769,489.76,NJ City,N,15,13,19,77,Graduation,28,Patnership,Standalone\n1774,423.41,NJ City,S,5,21,15,31,High School,25,Patnership,Complex\n2720,358.39,NJ City,W,12,15,18,76,High School,33,Franchise,Complex\n7462,50.26,Boston,S,6,12,4,34,High School,21,Patnership,Complex\n6668,248.99,Boston,W,1,8,24,71,Graduation,27,Franchise,Complex\n1379,128.75,NJ City,E,22,10,10,81,High School,31,Owner,Standalone\n5520,75.76,Boston,W,5,11,4,73,Graduation,27,Franchise,Complex\n7372,73,Boston,N,21,21,2,74,High School,34,Franchise,Complex\n3444,290,NJ City,W,3,12,13,42,High School,31,Franchise,Complex\n5711,358.61,Boston,E,22,11,11,76,Graduation,24,Owner,Standalone\n7236,326.08,Boston,W,22,13,9,74,Graduation,31,Franchise,Complex\n56,6.86,NY City,N,3,23,3,74,No education,19,Franchise,Complex\n9288,458.08,NJ City,W,16,23,24,49,Graduation,20,Franchise,Complex\n5721,336.95,Boston,N,25,18,10,43,Graduation,23,Patnership,Standalone\n6178,270.67,Boston,S,25,10,4,52,High School,28,Patnership,Complex\n387,6.44,NJ City,N,18,12,2,74,Graduation,32,Owner,Standalone\n7695,423.15,Boston,E,1,19,7,28,Graduation,27,Owner,Standalone\n6624,249.44,Boston,W,9,23,12,27,Graduation,25,Franchise,Complex\n3053,10.83,NJ City,N,8,22,4,85,No education,28,Patnership,Standalone\n3204,105.72,NJ City,W,16,24,1,31,Graduation,18,Franchise,Complex\n4188,235.48,NJ City,W,11,18,25,29,High School,20,Franchise,Complex\n9682,98.56,NJ City,S,3,13,3,53,High School,20,Patnership,Complex\n5973,145.85,Boston,N,21,8,18,66,Graduation,34,Patnership,Standalone\n6060,455.26,Boston,W,5,12,19,81,Graduation,24,Franchise,Complex\n7296,43.62,Boston,N,13,24,1,80,Graduation,32,Franchise,Complex\n7079,404.85,Boston,E,14,8,2,30,No education,30,Owner,Standalone\n8496,246.53,NJ City,W,14,21,19,27,Graduation,34,Franchise,Complex\n6518,47.67,Boston,S,2,9,3,39,No education,35,Patnership,Complex\n8154,211.08,NJ City,S,4,11,8,40,Graduation,35,Patnership,Complex\n6782,195.07,Boston,S,4,16,2,51,No education,18,Patnership,Complex\n9797,494.44,NJ City,N,23,22,4,48,No education,31,Patnership,Standalone\n9767,190.1,NJ City,E,14,8,16,75,Graduation,22,Owner,Standalone\n7838,276.29,Boston,S,6,14,19,51,Graduation,19,Patnership,Complex\n6164,179.08,Boston,W,9,13,2,43,No education,20,Franchise,Complex\n9161,63.12,NJ City,N,10,23,2,69,No education,33,Patnership,Standalone\n9373,382.85,NJ City,N,11,16,3,41,High School,34,Patnership,Standalone\n536,483.22,NJ City,W,25,23,8,37,High School,26,Franchise,Complex\n7298,478.34,Boston,S,23,8,5,27,No education,25,Patnership,Complex\n2482,440.74,NJ City,S,21,23,24,76,High School,30,Patnership,Complex\n9949,16.72,NJ City,N,21,23,2,58,High School,31,Patnership,Standalone\n4435,395.97,NJ City,E,25,15,8,79,High School,29,Owner,Standalone\n9413,333.69,NJ City,N,22,14,9,78,Graduation,24,Patnership,Standalone\n5614,369.89,Boston,S,11,9,6,36,Graduation,27,Patnership,Complex\n3670,90.73,NJ City,S,25,12,1,43,High School,33,Patnership,Complex\n301,25.01,NJ City,N,2,9,1,69,High School,19,Patnership,Standalone\n5134,96.7,Boston,S,16,17,4,63,High School,29,Patnership,Complex\n9093,497.19,NJ City,N,22,18,22,29,Graduation,24,Patnership,Standalone\n194,87.41,NJ City,S,22,19,1,73,No education,35,Patnership,Complex\n3651,163.15,NJ City,E,25,10,16,70,High School,23,Owner,Standalone\n4362,462.38,NJ City,S,25,12,5,85,Graduation,22,Patnership,Complex\n8132,351.73,NJ City,W,3,14,19,60,Graduation,35,Franchise,Complex\n2756,121.09,NJ City,W,7,14,1,44,No education,18,Franchise,Complex\n1361,192.43,NJ City,N,6,18,3,65,No education,21,Patnership,Standalone\n2730,263.22,NJ City,S,7,15,16,84,High School,32,Patnership,Complex\n9512,153.07,NJ City,W,21,17,13,66,Graduation,30,Franchise,Complex\n8022,473.53,Boston,S,24,11,18,77,Graduation,23,Patnership,Complex\n5100,231.24,Boston,W,17,12,19,49,Graduation,29,Franchise,Complex\n7996,168.69,Boston,W,22,9,4,49,High School,24,Franchise,Complex\n4769,119.35,NJ City,N,7,24,2,60,No education,29,Patnership,Standalone\n5813,119.99,Boston,N,20,19,4,70,No education,34,Patnership,Standalone\n1649,468.52,NJ City,N,18,18,17,29,High School,20,Patnership,Standalone\n6942,467.93,Boston,S,4,16,11,64,Graduation,34,Patnership,Complex\n305,50.11,NJ City,N,23,18,2,75,No education,34,Patnership,Standalone\n6856,401.9,Boston,W,15,10,1,68,High School,35,Franchise,Complex\n4754,268,NJ City,S,2,11,16,39,High School,29,Patnership,Complex\n489,32.77,NJ City,N,25,14,1,50,Graduation,23,Patnership,Standalone\n7906,173.99,Boston,S,11,11,10,63,Graduation,29,Patnership,Complex\n6104,251.91,Boston,W,22,14,10,70,Graduation,33,Franchise,Complex\n978,30.42,NJ City,N,14,9,3,47,Graduation,19,Patnership,Complex\n6874,314.35,Boston,S,14,12,23,66,Graduation,26,Patnership,Complex\n3302,51.28,NJ City,S,16,10,1,74,No education,27,Patnership,Complex\n9996,283.37,NJ City,W,11,8,4,70,Graduation,29,Franchise,Complex\n4448,261.08,NJ City,W,25,19,11,36,High School,22,Franchise,Complex\n8527,361.45,NJ City,E,3,10,20,43,Graduation,19,Owner,Standalone\n6998,312.85,Boston,S,18,17,8,35,Graduation,18,Patnership,Complex\n861,125.8,NJ City,N,23,9,2,56,Graduation,33,Patnership,Standalone\n2658,162.38,NJ City,S,25,18,17,51,High School,26,Patnership,Complex\n9338,70.02,NJ City,N,18,19,1,52,No education,22,Patnership,Complex\n4002,110,NJ City,S,3,22,9,61,High School,30,Patnership,Complex\n6641,472.49,Boston,N,15,21,6,44,Graduation,25,Patnership,Standalone\n7857,351.56,Boston,N,2,13,6,36,Graduation,18,Patnership,Standalone\n3986,90,NJ City,S,18,13,1,37,No education,27,Patnership,Complex\n989,280.97,NJ City,N,5,11,15,29,High School,24,Patnership,Standalone\n7437,252.99,Boston,N,11,11,13,66,Graduation,32,Patnership,Standalone\n4112,134.1,NJ City,W,9,11,14,26,High School,35,Franchise,Complex\n552,461.11,NJ City,W,14,21,22,30,High School,31,Franchise,Complex\n3663,358.85,NJ City,E,23,21,5,40,Graduation,25,Owner,Standalone\n1492,306,NJ City,W,2,15,15,36,High School,20,Franchise,Complex\n3458,256.03,NJ City,S,24,8,11,74,High School,27,Patnership,Complex\n1859,290.81,NJ City,E,14,15,21,65,High School,21,Owner,Standalone\n5132,283.83,Boston,W,11,18,25,26,Graduation,32,Franchise,Complex\n6360,177.49,Boston,W,21,17,10,41,Graduation,22,Franchise,Complex\n2352,453.64,NJ City,W,13,23,1,36,Graduation,23,Franchise,Complex\n5617,181.62,Boston,N,5,17,11,66,Graduation,28,Patnership,Standalone\n1512,157,NJ City,W,3,20,20,59,High School,30,Franchise,Complex\n1424,360.73,NJ City,W,25,21,5,33,No education,34,Franchise,Complex\n6559,129.15,Boston,E,12,15,18,38,Graduation,20,Owner,Standalone\n641,251.45,NJ City,N,24,17,3,35,No education,23,Patnership,Standalone\n618,227.62,NJ City,S,24,14,11,54,High School,19,Patnership,Complex\n6486,408.39,Boston,S,6,13,11,74,Graduation,20,Patnership,Complex\n5556,175.4,Boston,W,6,23,1,59,Graduation,35,Franchise,Complex\n8604,22.22,NJ City,N,1,10,3,84,Graduation,18,Franchise,Complex\n7692,228.02,Boston,W,6,24,4,73,Graduation,25,Franchise,Complex\n1444,152.98,NJ City,W,24,8,16,60,High School,19,Franchise,Complex\n6458,155.29,Boston,S,25,9,11,63,Graduation,35,Patnership,Complex\n236,2.12,NJ City,W,20,14,2,59,No education,29,Franchise,Complex\n7257,320.77,Boston,N,23,16,15,59,Graduation,34,Patnership,Standalone\n8982,245.72,NJ City,S,8,11,19,53,Graduation,21,Patnership,Complex\n7336,35.19,Boston,N,12,21,4,71,High School,35,Franchise,Complex\n1746,249.13,NJ City,S,9,13,19,41,High School,28,Patnership,Complex\n569,181.29,NJ City,N,12,21,13,28,High School,33,Patnership,Standalone\n1594,446.11,NJ City,S,2,15,4,50,High School,30,Patnership,Complex\n5454,44.44,Boston,N,14,9,1,36,Graduation,18,Patnership,Complex\n8883,415.49,NJ City,E,24,23,16,76,Graduation,33,Owner,Standalone\n3815,297.18,NJ City,E,9,15,22,55,High School,30,Owner,Standalone\n9722,101.53,NJ City,S,12,10,1,63,No education,33,Patnership,Complex\n7173,229.68,Boston,N,18,13,10,55,Graduation,28,Patnership,Standalone\n25,332.8,Boston,N,18,11,18,75,High School,33,Patnership,Standalone\n6505,115.36,Boston,N,1,19,3,68,High School,27,Patnership,Standalone\n5636,298.8,Boston,W,5,24,6,82,Graduation,34,Franchise,Complex\n3954,186.52,NJ City,S,15,11,18,39,High School,27,Patnership,Complex\n4253,302,NJ City,N,3,14,13,67,High School,21,Patnership,Standalone\n5800,328.63,Boston,W,12,19,7,58,Graduation,28,Franchise,Complex\n6859,477.52,Boston,E,18,13,23,63,Graduation,30,Owner,Standalone\n8458,171.03,NJ City,S,4,11,17,33,Graduation,28,Patnership,Complex\n2558,79.93,NJ City,S,1,22,4,72,No education,27,Patnership,Complex\n2686,474.3,NJ City,S,14,9,20,42,High School,26,Patnership,Complex\n4213,291.18,NJ City,N,7,22,21,68,High School,30,Patnership,Standalone\n6174,287.69,Boston,S,15,8,13,79,Graduation,28,Patnership,Complex\n5559,256.9,Boston,E,25,19,22,55,Graduation,23,Owner,Standalone\n2264,58.41,NJ City,W,8,23,3,58,No education,32,Franchise,Complex\n5853,119.25,Boston,N,19,21,4,56,Graduation,32,Patnership,Standalone\n7395,58.45,Boston,E,16,16,3,55,Graduation,32,Owner,Standalone\n573,98.05,NJ City,N,4,10,3,47,Graduation,22,Patnership,Standalone\n2751,276.58,NJ City,E,24,18,13,57,High School,28,Owner,Standalone\n986,92.09,NJ City,S,1,18,1,33,No education,22,Patnership,Complex\n4073,474.92,NJ City,N,15,8,13,35,High School,28,Patnership,Standalone\n1062,196,NJ City,S,1,18,23,26,High School,21,Patnership,Complex\n4360,272.98,NJ City,W,11,15,25,81,High School,29,Franchise,Complex\n5914,19.98,Boston,N,10,14,1,30,High School,18,Patnership,Complex\n526,92.82,NJ City,S,15,19,4,75,High School,29,Patnership,Complex\n1812,357.48,NJ City,W,14,21,12,41,High School,26,Franchise,Complex\n5137,44.49,Boston,N,23,23,2,75,High School,25,Patnership,Standalone\n1198,104,NJ City,S,1,9,12,44,High School,24,Patnership,Complex\n1760,302.06,NJ City,W,7,21,24,42,High School,33,Franchise,Complex\n8268,341.25,NJ City,W,4,8,2,65,Graduation,19,Franchise,Complex\n5122,55.81,Boston,S,3,19,1,69,High School,27,Patnership,Complex\n936,2.09,NJ City,W,13,17,3,80,Graduation,29,Franchise,Complex\n9059,188.26,NJ City,E,9,17,16,78,Graduation,24,Owner,Standalone\n9622,118.79,NJ City,S,17,23,1,49,High School,32,Patnership,Complex\n6289,446.69,Boston,N,18,13,20,36,Graduation,26,Patnership,Standalone\n2925,223.29,NJ City,N,18,21,17,28,High School,35,Patnership,Standalone\n5350,257.14,Boston,S,11,17,2,41,High School,30,Patnership,Complex\n8135,342.63,NJ City,E,17,22,16,37,Graduation,19,Owner,Standalone\n1957,442.33,NJ City,N,17,19,23,76,High School,18,Patnership,Standalone\n5548,88.3,Boston,W,18,18,4,68,High School,29,Franchise,Complex\n4038,379.81,NJ City,S,15,11,22,62,High School,20,Patnership,Complex\n5512,170.67,Boston,W,24,23,3,85,High School,23,Franchise,Complex\n7804,121.31,Boston,W,21,12,2,70,High School,30,Franchise,Complex\n1246,409.05,NJ City,S,23,22,13,58,High School,34,Patnership,Complex\n2824,41.56,NJ City,N,3,18,1,35,High School,25,Franchise,Complex\n2310,481.8,NJ City,S,5,23,16,85,High School,20,Patnership,Complex\n6058,227.66,Boston,S,12,16,2,70,High School,24,Patnership,Complex\n3036,401.65,NJ City,W,15,20,25,65,High School,31,Franchise,Complex\n903,134.49,NJ City,E,7,12,12,33,High School,34,Owner,Standalone\n3083,50.25,NJ City,E,1,17,3,54,No education,25,Owner,Standalone\n8388,7.22,NJ City,N,9,17,2,36,Graduation,25,Franchise,Complex\n1791,233.44,NJ City,E,14,22,20,55,High School,31,Owner,Standalone\n246,165.52,NJ City,S,25,17,18,61,High School,32,Patnership,Complex\n2562,21.57,NJ City,N,24,21,3,45,Graduation,29,Patnership,Complex\n3749,482.62,NJ City,N,6,14,19,85,High School,30,Patnership,Standalone\n2350,142.02,NJ City,S,12,17,5,77,High School,31,Patnership,Complex\n9671,409.42,NJ City,E,10,21,6,44,Graduation,22,Owner,Standalone\n7705,283.28,Boston,N,20,13,10,33,Graduation,28,Patnership,Standalone\n9013,43.15,NJ City,N,4,19,1,34,High School,19,Patnership,Standalone\n2596,451.9,NJ City,W,25,17,18,25,High School,19,Franchise,Complex\n4093,464.59,NJ City,N,18,18,6,79,High School,29,Patnership,Standalone\n6130,224.98,Boston,S,23,23,21,27,Graduation,22,Patnership,Complex\n5480,152.77,Boston,W,25,16,18,74,Graduation,23,Franchise,Complex\n9714,303.31,NJ City,S,8,14,6,27,Graduation,33,Patnership,Complex\n3210,71.39,NJ City,N,14,13,2,51,Graduation,29,Patnership,Complex\n9596,386.69,NJ City,W,14,9,24,82,Graduation,24,Franchise,Complex\n6947,290.9,Boston,E,9,12,2,52,No education,24,Owner,Standalone\n8216,46.33,NJ City,N,24,10,2,42,No education,30,Franchise,Complex\n3001,411.79,NJ City,N,9,18,6,57,High School,22,Patnership,Standalone\n7828,105.77,Boston,W,11,19,3,37,High School,19,Franchise,Complex\n2766,201.37,NJ City,S,16,24,12,65,High School,29,Patnership,Complex\n8066,448.53,NJ City,S,7,9,9,30,Graduation,23,Patnership,Complex\n7774,281.3,Boston,S,3,18,13,67,Graduation,27,Patnership,Complex\n2850,242,NJ City,S,3,16,18,58,High School,30,Patnership,Complex\n8026,350.37,Boston,S,2,22,1,45,High School,23,Patnership,Complex\n6739,120.48,Boston,E,17,19,3,85,High School,34,Owner,Standalone\n5922,176.61,Boston,S,20,24,5,69,Graduation,19,Patnership,Complex\n138,227.21,NJ City,S,22,18,24,27,High School,28,Patnership,Complex\n863,75.4,NJ City,E,1,15,1,61,No education,30,Owner,Standalone\n7212,236.72,Boston,W,5,14,10,27,Graduation,19,Franchise,Complex\n3937,75.64,NJ City,N,5,12,3,31,High School,25,Patnership,Standalone\n7191,271.73,Boston,E,3,21,1,78,Graduation,24,Owner,Standalone\n3521,78.57,NJ City,N,7,10,4,35,No education,18,Patnership,Standalone\n1665,9.49,NJ City,N,14,11,3,84,Graduation,23,Patnership,Standalone\n3801,190.16,NJ City,N,8,8,13,41,High School,33,Patnership,Standalone\n9114,51.17,NJ City,S,9,17,2,35,Graduation,25,Patnership,Complex\n1043,30.09,NJ City,N,12,21,2,66,No education,22,Owner,Standalone\n9095,39.95,NJ City,N,4,18,3,69,No education,27,Owner,Standalone\n5886,107.11,Boston,S,5,11,4,60,Graduation,34,Patnership,Complex\n8910,11.73,NJ City,N,7,20,2,70,Graduation,26,Patnership,Complex\n5360,341.95,Boston,W,13,14,3,70,No education,25,Franchise,Complex\n4406,147.92,NJ City,S,17,8,2,59,No education,18,Patnership,Complex\n2203,90.08,NJ City,E,1,13,2,43,High School,26,Owner,Standalone\n6100,148.44,Boston,W,7,10,22,43,Graduation,28,Franchise,Complex\n5901,269.07,Boston,N,16,11,2,35,Graduation,21,Patnership,Standalone\n7385,237.61,Boston,N,23,13,24,52,Graduation,26,Patnership,Standalone\n6013,13.21,Boston,N,12,18,4,62,High School,31,Patnership,Standalone\n1252,338.79,NJ City,W,18,20,20,26,High School,19,Franchise,Complex\n6910,236.96,Boston,S,13,11,21,50,Graduation,33,Patnership,Complex\n3091,369.61,NJ City,E,24,21,16,75,High School,29,Owner,Standalone\n1887,489.08,NJ City,E,9,9,9,30,High School,27,Owner,Standalone\n6662,87.28,Boston,S,20,22,3,64,No education,31,Patnership,Complex\n8414,85.27,NJ City,S,20,13,1,26,No education,23,Patnership,Complex\n923,19.46,NJ City,N,22,22,3,30,No education,19,Owner,Standalone\n9575,97.86,NJ City,E,15,22,3,73,No education,21,Owner,Standalone\n5963,301.68,Boston,E,17,21,5,41,No education,24,Owner,Standalone\n9037,425.28,NJ City,N,7,20,25,74,Graduation,33,Patnership,Standalone\n846,303.53,NJ City,S,6,18,18,84,High School,25,Patnership,Complex\n5104,112.01,Boston,W,16,22,2,82,High School,25,Franchise,Complex\n6593,302.44,Boston,N,11,8,4,80,No education,29,Patnership,Standalone\n7591,240.53,Boston,E,13,12,23,41,Graduation,34,Owner,Standalone\n3812,182.33,NJ City,W,6,21,9,45,High School,28,Franchise,Complex\n7017,68.24,Boston,N,21,24,4,80,Graduation,19,Patnership,Standalone\n3917,365.72,NJ City,N,11,17,10,64,High School,26,Patnership,Standalone\n5778,254.3,Boston,S,9,22,21,40,Graduation,30,Patnership,Complex\n7075,232.33,Boston,E,6,9,6,54,Graduation,30,Owner,Standalone\n1759,87.73,NJ City,E,6,12,3,75,High School,28,Owner,Standalone\n2051,198.49,NJ City,E,15,21,23,64,High School,35,Owner,Standalone\n4910,91.91,NJ City,S,8,12,1,31,No education,34,Patnership,Complex\n4343,126.03,NJ City,E,3,19,4,71,No education,29,Owner,Standalone\n7111,56.96,Boston,E,8,20,3,53,High School,21,Owner,Standalone\n1028,258.07,NJ City,W,20,19,8,57,High School,22,Franchise,Complex\n3915,357.89,NJ City,E,20,24,22,71,High School,18,Owner,Standalone\n7310,163.08,Boston,S,20,12,15,55,Graduation,31,Patnership,Complex\n3904,36.79,NJ City,N,15,23,4,46,High School,30,Franchise,Complex\n8593,260.45,NJ City,N,10,14,3,41,High School,30,Patnership,Standalone\n6798,35.78,Boston,N,9,16,1,76,Graduation,22,Patnership,Complex\n8614,48,NJ City,S,25,17,3,65,High School,33,Patnership,Complex\n15,52.26,Boston,E,11,10,3,83,Graduation,19,Owner,Standalone\n3325,45.78,NJ City,N,8,21,1,81,High School,23,Patnership,Standalone\n9913,461.86,NJ City,N,25,22,8,61,Graduation,25,Patnership,Standalone\n315,230,NJ City,E,3,8,19,30,High School,30,Owner,Standalone\n6160,145.8,Boston,W,23,8,10,54,Graduation,32,Franchise,Complex\n354,259.66,NJ City,S,8,8,1,32,Graduation,27,Patnership,Complex\n2242,198.63,NJ City,S,7,8,20,78,High School,23,Patnership,Complex\n2987,387.73,NJ City,E,12,8,19,51,High School,31,Owner,Standalone\n1527,437.2,NJ City,E,21,8,17,49,High School,24,Owner,Standalone\n6495,233.81,Boston,E,14,17,3,60,Graduation,33,Owner,Standalone\n5780,269.52,Boston,W,7,9,5,73,No education,33,Franchise,Complex\n1988,442.49,NJ City,W,10,9,12,72,High School,27,Franchise,Complex\n1341,149.65,NJ City,N,6,14,18,46,High School,18,Patnership,Standalone\n1583,444.51,NJ City,E,14,19,20,80,High School,32,Owner,Standalone\n291,314.07,NJ City,E,19,8,1,75,Graduation,31,Owner,Standalone\n897,90.21,NJ City,N,16,17,1,59,Graduation,27,Patnership,Standalone\n8359,38.63,NJ City,N,3,22,3,32,High School,26,Owner,Standalone\n1966,134.46,NJ City,S,18,24,4,61,High School,24,Patnership,Complex\n4525,53.55,NJ City,N,11,18,4,40,High School,34,Patnership,Standalone\n1226,382.84,NJ City,S,17,17,2,74,No education,30,Patnership,Complex\n8684,407.41,NJ City,W,16,19,19,76,Graduation,21,Franchise,Complex\n7358,51.89,Boston,S,15,18,4,73,No education,22,Patnership,Complex\n862,233.29,NJ City,S,21,16,3,73,High School,22,Patnership,Complex\n8176,242.52,NJ City,W,14,24,25,39,Graduation,22,Franchise,Complex\n5806,196.54,Boston,S,20,12,14,63,Graduation,28,Patnership,Complex\n4544,23.17,NJ City,N,8,11,2,78,No education,33,Franchise,Complex\n412,352.07,NJ City,W,12,14,3,38,High School,29,Franchise,Complex\n6527,8.05,Boston,N,12,14,2,47,No education,30,Owner,Standalone\n3384,39.89,NJ City,N,1,13,4,71,Graduation,27,Franchise,Complex\n7366,155.59,Boston,S,14,18,15,27,Graduation,18,Patnership,Complex\n2258,77.29,NJ City,S,13,20,4,80,No education,28,Patnership,Complex\n7757,12.19,Boston,N,4,11,4,72,No education,19,Patnership,Standalone\n3351,249.55,NJ City,E,14,11,8,37,High School,19,Owner,Standalone\n6666,150.07,Boston,S,1,22,22,67,Graduation,25,Patnership,Complex\n5364,225.31,Boston,W,14,11,11,41,Graduation,32,Franchise,Complex\n2127,99.3,NJ City,E,6,21,3,78,Graduation,32,Owner,Standalone\n2417,471.28,NJ City,N,8,11,6,79,High School,18,Patnership,Standalone\n8090,189.99,NJ City,S,1,13,12,48,Graduation,22,Patnership,Complex\n9904,449.54,NJ City,W,24,19,23,26,Graduation,19,Franchise,Complex\n168,235.37,NJ City,W,9,17,23,75,High School,26,Franchise,Complex\n7005,495.37,Boston,N,6,16,11,79,Graduation,32,Patnership,Standalone\n298,368.61,NJ City,S,22,17,18,70,High School,26,Patnership,Complex\n7509,149.87,Boston,N,5,21,21,52,Graduation,31,Patnership,Standalone\n8511,253.38,NJ City,E,10,8,11,65,Graduation,25,Owner,Standalone\n1911,273.17,NJ City,E,3,8,5,53,Graduation,25,Owner,Standalone\n61,437.29,Boston,N,21,20,5,58,High School,34,Patnership,Standalone\n6907,352.42,Boston,E,16,16,1,56,High School,19,Owner,Standalone\n8880,215.39,NJ City,W,1,10,8,51,Graduation,28,Franchise,Complex\n2564,497.75,NJ City,W,23,21,12,82,High School,33,Franchise,Complex\n7342,18.67,Boston,N,18,12,1,56,High School,32,Patnership,Complex\n4564,112.98,NJ City,W,13,11,4,32,High School,29,Franchise,Complex\n1458,53.14,NJ City,S,21,22,4,62,Graduation,19,Patnership,Complex\n2771,468.89,NJ City,E,14,12,21,39,High School,33,Owner,Standalone\n2314,463.1,NJ City,S,17,19,5,65,High School,18,Patnership,Complex\n555,197.87,NJ City,E,10,9,14,63,High School,35,Owner,Standalone\n8803,401.43,NJ City,E,10,12,17,41,Graduation,21,Owner,Standalone\n5176,445.21,Boston,W,18,12,6,43,Graduation,32,Franchise,Complex\n3506,453.03,NJ City,S,25,19,16,65,High School,23,Patnership,Complex\n9372,315.93,NJ City,W,24,23,7,27,Graduation,22,Franchise,Complex\n4960,413.09,Boston,W,8,19,3,33,High School,30,Franchise,Complex\n7335,281.15,Boston,E,21,20,14,64,Graduation,22,Owner,Standalone\n7754,304.52,Boston,S,2,16,5,51,No education,18,Patnership,Complex\n5313,202.39,Boston,N,11,21,11,75,Graduation,22,Patnership,Standalone\n1855,6.4,NJ City,N,14,13,1,54,High School,24,Owner,Standalone\n9734,126.83,NJ City,S,16,14,3,33,No education,18,Patnership,Complex\n2243,203.57,NJ City,E,7,12,2,60,No education,18,Owner,Standalone\n8878,265.99,NJ City,S,16,14,25,50,Graduation,19,Patnership,Complex\n6750,386.42,Boston,S,14,20,10,72,Graduation,24,Patnership,Complex\n63,168.98,Boston,E,13,24,8,78,High School,19,Owner,Standalone\n726,382.83,NJ City,S,10,12,15,60,High School,18,Patnership,Complex\n7796,425.31,Boston,W,6,8,7,50,Graduation,26,Franchise,Complex\n1571,481.59,NJ City,E,14,9,5,29,No education,27,Owner,Standalone\n8037,304.45,Boston,N,22,21,10,66,Graduation,26,Patnership,Standalone\n7026,322.95,Boston,S,7,20,6,68,Graduation,35,Patnership,Complex\n7630,362.1,Boston,S,24,17,9,67,Graduation,30,Patnership,Complex\n2030,246.34,NJ City,S,19,20,16,29,High School,34,Patnership,Complex\n788,391.94,NJ City,W,3,11,5,63,No education,26,Franchise,Complex\n3763,313.03,NJ City,E,14,22,21,30,High School,27,Owner,Standalone\n5146,106.29,Boston,S,10,21,4,33,High School,34,Patnership,Complex\n1470,17.15,NJ City,N,4,9,2,50,Graduation,19,Patnership,Complex\n4032,204,NJ City,W,3,24,6,81,High School,31,Franchise,Complex\n7864,480.47,Boston,W,11,10,19,30,Graduation,18,Franchise,Complex\n1722,162.59,NJ City,S,24,14,11,71,High School,21,Patnership,Complex\n2554,469.11,NJ City,S,23,18,4,65,High School,20,Patnership,Complex\n276,189.09,NJ City,W,3,24,4,34,Graduation,22,Franchise,Complex\n2349,338.28,NJ City,N,13,16,18,83,High School,20,Patnership,Standalone\n4989,211.96,Boston,N,21,11,4,81,Graduation,35,Patnership,Standalone\n5965,396.53,Boston,N,1,17,10,70,Graduation,18,Patnership,Standalone\n5398,56.17,Boston,S,11,14,1,38,High School,19,Patnership,Complex\n5871,407.6,Boston,E,12,12,24,66,Graduation,33,Owner,Standalone\n2692,167.32,NJ City,W,20,11,24,52,High School,32,Franchise,Complex\n7479,345.59,Boston,E,23,24,24,32,Graduation,27,Owner,Standalone\n9934,399.84,NJ City,S,24,15,2,49,High School,28,Patnership,Complex\n8421,245.15,NJ City,N,7,24,6,47,Graduation,27,Patnership,Standalone\n6730,389.98,Boston,S,18,15,9,84,Graduation,22,Patnership,Complex\n992,354.49,NJ City,W,23,9,6,48,High School,20,Franchise,Complex\n2429,215.17,NJ City,N,9,18,20,31,High School,23,Patnership,Standalone\n9053,273.02,NJ City,N,18,23,20,72,Graduation,22,Patnership,Standalone\n6571,278.2,Boston,E,2,23,8,57,Graduation,32,Owner,Standalone\n3455,58.24,NJ City,E,2,15,4,52,No education,31,Owner,Standalone\n2541,6.67,NJ City,N,9,20,3,66,Graduation,23,Patnership,Standalone\n6990,80.34,Boston,S,17,13,1,28,Graduation,34,Patnership,Complex\n6729,123.72,Boston,N,19,16,3,40,Graduation,22,Patnership,Standalone\n5917,49.26,Boston,N,3,11,2,42,High School,30,Patnership,Standalone\n9251,429.53,NJ City,E,16,12,20,44,Graduation,32,Owner,Standalone\n1692,229.96,NJ City,W,7,11,8,41,High School,30,Franchise,Complex\n3178,383.21,NJ City,S,17,20,1,27,High School,26,Patnership,Complex\n6845,412.11,Boston,N,24,8,24,78,Graduation,28,Patnership,Standalone\n6155,388.37,Boston,E,6,9,19,68,Graduation,28,Owner,Standalone\n6368,133.37,Boston,W,1,19,24,25,Graduation,30,Franchise,Complex\n7845,322.88,Boston,N,7,22,4,84,Graduation,19,Patnership,Standalone\n8601,100.78,NJ City,N,19,14,2,26,Graduation,18,Patnership,Standalone\n4273,350.77,NJ City,N,19,13,20,60,High School,33,Patnership,Standalone\n7632,494.21,Boston,W,1,8,20,74,Graduation,35,Franchise,Complex\n9293,296.04,NJ City,N,19,17,2,83,No education,29,Patnership,Standalone\n7134,296.06,Boston,S,7,13,17,53,Graduation,29,Patnership,Complex\n6655,275.09,Boston,E,1,18,20,71,Graduation,19,Owner,Standalone\n4122,434.87,NJ City,S,16,8,14,74,High School,25,Patnership,Complex\n3595,212.86,NJ City,E,11,21,6,51,High School,31,Owner,Standalone\n3512,305.22,NJ City,W,20,21,21,47,High School,23,Franchise,Complex\n1082,431.94,NJ City,S,24,15,15,43,High School,21,Patnership,Complex\n5703,408.95,Boston,E,7,19,17,43,Graduation,24,Owner,Standalone\n2994,458.12,NJ City,S,5,13,21,37,High School,25,Patnership,Complex\n9411,16.13,NJ City,N,21,21,2,53,Graduation,33,Owner,Standalone\n6944,388.78,Boston,W,18,17,14,39,Graduation,27,Franchise,Complex\n1065,438.49,NJ City,N,11,22,12,64,High School,26,Patnership,Standalone\n5162,364.55,Boston,S,20,18,25,33,Graduation,34,Patnership,Complex\n9060,321.77,NJ City,W,2,13,9,63,Graduation,18,Franchise,Complex\n2552,230.17,NJ City,W,12,13,19,79,High School,28,Franchise,Complex\n1145,112.33,NJ City,N,7,12,4,78,No education,21,Patnership,Standalone\n4209,250.64,NJ City,N,10,8,2,53,Graduation,21,Patnership,Standalone\n349,53.01,NJ City,N,16,14,4,60,High School,35,Patnership,Standalone\n4780,431.56,NJ City,W,19,8,4,47,High School,26,Franchise,Complex\n3691,442.21,NJ City,E,23,16,21,33,High School,29,Owner,Standalone\n2768,83.81,NJ City,W,14,17,4,26,No education,31,Franchise,Complex\n6138,186.51,Boston,S,14,24,13,49,Graduation,30,Patnership,Complex\n4917,211.96,NJ City,N,13,19,11,80,High School,31,Patnership,Standalone\n5811,14.69,Boston,N,19,22,1,76,Graduation,24,Owner,Standalone\n9314,193.69,NJ City,S,9,10,5,83,No education,31,Patnership,Complex\n8520,349.85,NJ City,W,3,19,12,32,Graduation,34,Franchise,Complex\n5109,263.55,Boston,N,16,16,24,35,Graduation,27,Patnership,Standalone\n6261,351.67,Boston,N,10,13,2,81,Graduation,33,Patnership,Standalone\n7681,49.01,Boston,N,25,14,4,27,High School,30,Patnership,Standalone\n1297,481.73,NJ City,N,6,20,5,61,High School,32,Patnership,Standalone\n8073,69.82,NJ City,N,2,23,3,74,Graduation,24,Patnership,Standalone\n2020,227,NJ City,W,2,20,23,82,High School,28,Franchise,Complex\n4239,412.1,NJ City,E,16,18,25,65,High School,35,Owner,Standalone\n3337,287.84,NJ City,N,12,13,18,81,High School,34,Patnership,Standalone\n7105,69.48,Boston,N,13,8,2,43,High School,35,Patnership,Standalone\n6697,62.5,Boston,N,2,9,3,47,High School,31,Patnership,Standalone\n9902,78.74,NJ City,S,12,17,1,34,No education,29,Patnership,Complex\n3494,102.57,NJ City,S,14,11,4,57,No education,35,Patnership,Complex\n6144,489.94,Boston,W,11,16,25,50,Graduation,27,Franchise,Complex\n62,291.91,Boston,S,16,15,23,47,High School,29,Patnership,Complex\n966,452.72,NJ City,S,5,8,2,66,Graduation,22,Patnership,Complex\n8547,252.73,NJ City,E,23,11,19,42,Graduation,29,Owner,Standalone\n9205,231.15,NJ City,N,4,16,15,51,Graduation,20,Patnership,Standalone\n9589,227.81,NJ City,N,17,20,15,60,Graduation,25,Patnership,Standalone\n8227,310.67,NJ City,E,13,13,14,51,Graduation,26,Owner,Standalone\n9914,136.42,NJ City,S,21,16,25,81,Graduation,33,Patnership,Complex\n6282,83.92,Boston,S,2,17,4,73,Graduation,18,Patnership,Complex\n4982,257.65,Boston,S,24,16,18,57,High School,22,Patnership,Complex\n2396,137.79,NJ City,W,6,19,25,57,High School,27,Franchise,Complex\n4259,295.27,NJ City,E,8,21,11,50,High School,24,Owner,Standalone\n2193,131.09,NJ City,N,13,21,2,38,Graduation,22,Patnership,Standalone\n3987,488.54,NJ City,E,21,18,19,43,High School,34,Owner,Standalone\n9408,234.65,NJ City,W,12,15,17,60,Graduation,31,Franchise,Complex\n4412,385.45,NJ City,W,19,22,3,57,No education,20,Franchise,Complex\n2248,22.27,NJ City,N,25,11,1,82,High School,22,Franchise,Complex\n5584,310.51,Boston,W,9,16,21,28,Graduation,23,Franchise,Complex\n2803,224.97,NJ City,E,17,18,25,48,High School,32,Owner,Standalone\n492,257.71,NJ City,W,13,16,13,66,High School,24,Franchise,Complex\n2377,449.43,NJ City,N,12,24,3,84,High School,27,Patnership,Standalone\n6356,164.56,Boston,W,21,9,21,35,Graduation,30,Franchise,Complex\n7063,182.52,Boston,E,22,8,23,46,Graduation,23,Owner,Standalone\n1346,222,NJ City,S,2,21,16,25,High School,31,Patnership,Complex\n5992,488.12,Boston,W,23,15,20,56,Graduation,28,Franchise,Complex\n2713,82.28,NJ City,N,11,18,3,35,High School,19,Patnership,Standalone\n4115,433.76,NJ City,E,13,9,17,62,High School,26,Owner,Standalone\n949,70.92,NJ City,N,11,16,3,53,High School,23,Patnership,Standalone\n4789,58.31,NJ City,N,7,10,4,52,High School,31,Patnership,Standalone\n2457,132,NJ City,N,2,16,19,43,High School,25,Patnership,Standalone\n8499,414.85,NJ City,E,16,23,11,53,Graduation,23,Owner,Standalone\n8972,126.09,NJ City,W,22,13,2,75,No education,27,Franchise,Complex\n4078,48.64,NJ City,S,7,14,4,70,High School,25,Patnership,Complex\n2575,498.53,NJ City,E,18,17,15,56,High School,32,Owner,Standalone\n3560,151.56,NJ City,W,13,14,20,28,High School,18,Franchise,Complex\n3585,403.61,NJ City,N,21,13,6,82,High School,31,Patnership,Standalone\n5269,419.69,Boston,N,5,24,18,34,Graduation,18,Patnership,Standalone\n6649,413.99,Boston,N,12,17,11,37,Graduation,22,Patnership,Standalone\n10,142.72,NJ City,S,24,15,25,31,High School,22,Patnership,Complex\n2047,2.73,NJ City,E,9,15,1,62,High School,29,Owner,Standalone\n8816,28.38,NJ City,N,1,11,4,59,No education,24,Franchise,Complex\n9830,365.77,NJ City,S,13,20,17,69,Graduation,30,Patnership,Complex\n7820,456.83,Boston,W,6,10,20,43,Graduation,33,Franchise,Complex\n5691,499.75,Boston,E,16,20,7,39,Graduation,33,Owner,Standalone\n4864,188.72,NJ City,W,7,15,7,26,High School,28,Franchise,Complex\n5557,318.07,Boston,N,16,19,15,48,Graduation,28,Patnership,Standalone\n9760,137.54,NJ City,W,2,17,25,63,Graduation,21,Franchise,Complex\n5288,241.54,Boston,W,25,21,8,74,Graduation,29,Franchise,Complex\n2668,304.28,NJ City,W,6,19,25,57,High School,29,Franchise,Complex\n9154,150.57,NJ City,S,2,18,7,46,Graduation,20,Patnership,Complex\n8426,73.82,NJ City,N,21,8,4,29,No education,19,Patnership,Complex\n2011,239.86,NJ City,E,4,23,1,53,High School,33,Owner,Standalone\n5875,372.76,Boston,E,13,12,15,71,Graduation,29,Owner,Standalone\n682,299.64,NJ City,S,16,12,5,81,High School,33,Patnership,Complex\n6921,168.61,Boston,N,13,9,18,36,Graduation,29,Patnership,Standalone\n6492,160.06,Boston,W,24,19,19,76,Graduation,31,Franchise,Complex\n4886,75.7,NJ City,S,21,9,2,69,No education,22,Patnership,Complex\n9269,166.3,NJ City,N,8,13,2,79,No education,22,Patnership,Standalone\n7620,286.89,Boston,W,3,11,23,30,Graduation,34,Franchise,Complex\n2400,368.36,NJ City,W,16,17,1,35,Graduation,35,Franchise,Complex\n9611,470.31,NJ City,E,10,10,7,73,Graduation,21,Owner,Standalone\n9438,214.04,NJ City,S,22,16,20,83,Graduation,28,Patnership,Complex\n8305,308.02,NJ City,N,24,16,14,72,Graduation,26,Patnership,Standalone\n275,162.77,NJ City,E,5,13,25,41,High School,21,Owner,Standalone\n8045,81.45,NJ City,N,9,9,3,61,No education,30,Patnership,Standalone\n1624,411.69,NJ City,W,7,21,20,46,High School,23,Franchise,Complex\n7619,443,Boston,E,11,20,16,66,Graduation,22,Owner,Standalone\n1447,168.64,NJ City,E,25,19,1,53,High School,19,Owner,Standalone\n5266,376.56,Boston,S,10,21,16,49,Graduation,21,Patnership,Complex\n2111,269,NJ City,E,2,13,15,76,High School,20,Owner,Standalone\n3905,189.53,NJ City,N,19,17,25,80,High School,31,Patnership,Standalone\n3704,125.04,NJ City,W,2,14,1,65,No education,26,Franchise,Complex\n3523,30.33,NJ City,N,23,24,3,70,High School,24,Owner,Standalone\n6758,436.98,Boston,S,15,8,4,40,No education,25,Patnership,Complex\n8334,463.92,NJ City,S,14,18,10,50,Graduation,20,Patnership,Complex\n2516,476.91,NJ City,W,13,13,22,62,High School,19,Franchise,Complex\n9653,143.45,NJ City,N,9,19,13,77,Graduation,31,Patnership,Standalone\n4863,449.67,NJ City,E,8,8,10,68,High School,31,Owner,Standalone\n4026,44.76,NJ City,N,17,10,2,71,Graduation,25,Patnership,Complex\n3586,495.79,NJ City,S,22,14,14,49,High School,30,Patnership,Complex\n2826,61.08,NJ City,N,11,8,2,44,Graduation,33,Patnership,Complex\n2451,155.23,NJ City,E,10,20,21,50,High School,31,Owner,Standalone\n6284,99.88,Boston,W,13,18,4,62,No education,28,Franchise,Complex\n5933,70.94,Boston,N,8,20,4,33,No education,24,Patnership,Standalone\n5259,310.52,Boston,E,13,23,1,53,Graduation,18,Owner,Standalone\n850,73.33,NJ City,N,24,13,3,70,High School,28,Patnership,Complex\n4498,305.51,NJ City,S,18,12,13,78,High School,26,Patnership,Complex\n5616,281.08,Boston,W,22,17,15,53,Graduation,22,Franchise,Complex\n6,418.18,Maine,S,9,16,6,49,High School,18,Patnership,Complex\n6151,308.25,Boston,E,10,21,20,76,Graduation,18,Owner,Standalone\n4100,368.71,NJ City,W,5,24,3,82,No education,30,Franchise,Complex\n5664,54.89,Boston,W,10,14,1,56,Graduation,35,Franchise,Complex\n7568,339.02,Boston,W,10,24,16,44,Graduation,24,Franchise,Complex\n7460,175.84,Boston,W,3,11,25,25,Graduation,26,Franchise,Complex\n7251,22.22,Boston,N,20,21,3,42,Graduation,20,Owner,Standalone\n5408,488.91,Boston,W,10,19,15,49,Graduation,21,Franchise,Complex\n8085,146.29,NJ City,N,23,12,23,36,Graduation,20,Patnership,Standalone\n5255,169.76,Boston,E,15,15,11,32,Graduation,33,Owner,Standalone\n938,154.98,NJ City,S,24,22,22,69,High School,22,Patnership,Complex\n5136,10.85,Boston,N,3,9,2,68,Graduation,31,Franchise,Complex\n9385,114.73,NJ City,N,11,16,3,44,High School,18,Patnership,Standalone\n3797,189,NJ City,N,2,18,8,70,High School,28,Patnership,Standalone\n5490,325.8,Boston,S,4,20,18,73,Graduation,18,Patnership,Complex\n93,114.35,NJ City,N,22,15,3,78,Graduation,20,Patnership,Standalone\n5154,264.46,Boston,S,3,18,12,29,Graduation,24,Patnership,Complex\n3436,190.75,NJ City,W,4,24,11,80,High School,27,Franchise,Complex\n990,182.98,NJ City,S,10,20,20,59,High School,18,Patnership,Complex\n3626,212.7,NJ City,S,23,17,7,30,High School,32,Patnership,Complex\n5583,215.24,Boston,E,4,20,1,53,Graduation,33,Owner,Standalone\n2399,79.92,NJ City,E,8,18,2,25,No education,20,Owner,Standalone\n4723,276.17,NJ City,E,18,11,18,50,High School,33,Owner,Standalone\n9612,296.32,NJ City,W,7,21,13,38,Graduation,21,Franchise,Complex\n4287,73.74,NJ City,N,9,20,1,39,Graduation,26,Owner,Standalone\n3373,109.07,NJ City,N,4,13,3,80,High School,20,Patnership,Standalone\n2739,321.02,NJ City,E,21,14,25,25,High School,25,Owner,Standalone\n2498,305.01,NJ City,S,7,8,7,73,High School,23,Patnership,Complex\n910,251,NJ City,S,2,11,17,83,High School,22,Patnership,Complex\n5264,407.19,Boston,W,18,13,24,53,Graduation,26,Franchise,Complex\n1126,305.04,NJ City,S,5,17,14,71,High School,18,Patnership,Complex\n8818,120.67,NJ City,S,23,18,1,81,High School,29,Patnership,Complex\n4607,364.59,NJ City,E,25,13,7,73,High School,31,Owner,Standalone\n5507,476.68,Boston,E,23,8,16,69,Graduation,21,Owner,Standalone\n2590,62.04,NJ City,N,22,9,4,83,High School,28,Patnership,Complex\n3901,115.46,NJ City,N,22,11,3,55,High School,32,Patnership,Standalone\n5443,181.1,Boston,E,12,11,14,26,Graduation,32,Owner,Standalone\n4411,319.06,NJ City,E,10,21,9,76,High School,31,Owner,Standalone\n8839,415.16,NJ City,E,18,21,14,51,Graduation,19,Owner,Standalone\n2597,381.4,NJ City,N,6,19,25,71,High School,22,Patnership,Standalone\n6111,307.18,Boston,E,9,15,9,79,Graduation,27,Owner,Standalone\n7606,1.7,Boston,N,5,14,2,40,High School,33,Patnership,Complex\n4354,121.98,NJ City,S,7,14,2,47,High School,31,Patnership,Complex\n4976,251.73,Boston,W,13,20,7,62,High School,33,Franchise,Complex\n7666,14.62,Boston,N,24,22,3,58,High School,34,Patnership,Complex\n273,477.72,NJ City,N,5,14,2,52,Graduation,31,Patnership,Standalone\n3973,340.69,NJ City,N,9,8,10,31,High School,32,Patnership,Standalone\n534,335.13,NJ City,S,13,21,24,49,High School,34,Patnership,Complex\n9867,341.86,NJ City,E,20,11,15,79,Graduation,35,Owner,Standalone\n7711,8.97,Boston,N,18,13,2,74,High School,24,Owner,Standalone\n2616,87.73,NJ City,W,13,13,1,27,Graduation,33,Franchise,Complex\n3412,282.13,NJ City,W,14,10,19,25,High School,32,Franchise,Complex\n9116,265.76,NJ City,W,11,19,10,40,Graduation,21,Franchise,Complex\n1819,216.88,NJ City,E,7,16,12,52,High School,28,Owner,Standalone\n9391,108.57,NJ City,E,14,19,3,58,High School,25,Owner,Standalone\n8095,16.53,NJ City,N,12,22,1,69,High School,21,Owner,Standalone\n1001,434.03,NJ City,N,14,19,25,81,High School,18,Patnership,Standalone\n3656,240.89,NJ City,W,14,11,22,67,High School,30,Franchise,Complex\n5181,210.75,Boston,N,8,18,12,36,Graduation,32,Patnership,Standalone\n759,214.5,NJ City,E,19,18,17,59,High School,33,Owner,Standalone\n9971,375.27,NJ City,E,19,17,10,62,Graduation,18,Owner,Standalone\n132,275.59,NJ City,W,14,19,4,30,Graduation,32,Franchise,Complex\n1924,8.58,NJ City,N,7,21,1,81,High School,33,Franchise,Complex\n4858,330.01,NJ City,S,13,16,9,26,High School,33,Patnership,Complex\n9234,395.92,NJ City,S,5,23,8,43,Graduation,27,Patnership,Complex\n4883,394.98,NJ City,E,21,12,9,42,High School,35,Owner,Standalone\n4292,292,NJ City,W,2,19,18,65,High School,19,Franchise,Complex\n1163,286.52,NJ City,E,18,20,1,79,No education,21,Owner,Standalone\n8270,368.23,NJ City,S,12,15,16,38,Graduation,21,Patnership,Complex\n6299,31.52,Boston,N,1,20,4,77,No education,26,Owner,Standalone\n7354,7.84,Boston,N,16,21,2,43,High School,29,Patnership,Complex\n6568,122.34,Boston,W,8,23,1,65,High School,30,Franchise,Complex\n5668,75.07,Boston,W,14,24,4,70,High School,26,Franchise,Complex\n1876,220.37,NJ City,W,24,17,11,28,High School,26,Franchise,Complex\n2725,425.76,NJ City,N,14,8,11,33,High School,35,Patnership,Standalone\n2133,142.09,NJ City,N,4,11,24,70,High School,30,Patnership,Standalone\n4821,344.38,NJ City,N,5,15,15,41,High School,23,Patnership,Standalone\n1933,99.93,NJ City,N,5,18,1,33,High School,28,Patnership,Standalone\n8783,326.35,NJ City,E,24,8,25,43,Graduation,21,Owner,Standalone\n2877,444.74,NJ City,N,4,9,19,64,High School,32,Patnership,Standalone\n1467,339.93,NJ City,E,21,23,20,59,High School,30,Owner,Standalone\n5047,172.61,Boston,E,20,11,15,79,Graduation,20,Owner,Standalone\n6298,376.35,Boston,S,10,15,11,42,Graduation,23,Patnership,Complex\n8411,82.43,NJ City,E,11,12,3,45,No education,25,Owner,Standalone\n8787,298.9,NJ City,E,20,17,16,45,Graduation,31,Owner,Standalone\n8302,360.01,NJ City,S,5,12,25,78,Graduation,31,Patnership,Complex\n9663,176.19,NJ City,E,6,9,7,58,Graduation,35,Owner,Standalone\n8102,132.86,NJ City,S,6,22,22,83,Graduation,33,Patnership,Complex\n9106,441.81,NJ City,S,13,13,3,38,High School,21,Patnership,Complex\n7024,33.36,Boston,N,2,19,1,32,High School,29,Franchise,Complex\n4163,195.09,NJ City,E,24,16,8,45,High School,34,Owner,Standalone\n2440,245.42,NJ City,W,8,17,16,73,High School,23,Franchise,Complex\n9315,47.56,NJ City,E,12,10,4,76,Graduation,28,Owner,Standalone\n4834,258.3,NJ City,S,5,13,23,63,High School,35,Patnership,Complex\n1255,466.34,NJ City,E,6,11,23,74,High School,34,Owner,Standalone\n322,432.33,NJ City,S,24,11,4,53,High School,32,Patnership,Complex\n717,482.63,NJ City,N,23,12,14,43,High School,28,Patnership,Standalone\n8362,167.14,NJ City,S,7,18,2,30,High School,24,Patnership,Complex\n3040,152.3,NJ City,W,16,15,17,28,High School,34,Franchise,Complex\n639,73.63,NJ City,N,4,8,2,34,Graduation,20,Owner,Standalone\n784,383.07,NJ City,W,14,14,22,57,High School,30,Franchise,Complex\n1149,448.05,NJ City,N,6,15,19,28,High School,29,Patnership,Standalone\n5727,340.73,Boston,E,15,11,12,84,Graduation,24,Owner,Standalone\n7621,30.67,Boston,N,2,20,4,43,High School,32,Patnership,Standalone\n5158,223.32,Boston,S,15,16,1,49,High School,33,Patnership,Complex\n5472,430.28,Boston,W,16,22,2,71,Graduation,31,Franchise,Complex\n619,240.47,NJ City,E,1,22,2,60,High School,34,Owner,Standalone\n4906,299.06,NJ City,S,4,23,12,63,High School,28,Patnership,Complex\n4221,140.94,NJ City,N,19,21,7,65,High School,34,Patnership,Standalone\n6934,426.4,Boston,S,6,21,8,36,Graduation,24,Patnership,Complex\n7403,31.92,Boston,N,8,22,3,72,No education,24,Owner,Standalone\n9807,328.65,NJ City,E,7,19,1,58,Graduation,22,Owner,Standalone\n4682,270.39,NJ City,S,19,23,17,69,High School,25,Patnership,Complex\n9200,135.65,NJ City,W,14,19,21,37,Graduation,33,Franchise,Complex\n5690,379.5,Boston,S,13,20,22,68,Graduation,30,Patnership,Complex\n9197,245.79,NJ City,N,15,16,13,32,Graduation,25,Patnership,Standalone\n5564,451.98,Boston,W,4,11,20,65,Graduation,19,Franchise,Complex\n9716,47.32,NJ City,W,21,23,4,59,No education,21,Franchise,Complex\n2765,182.66,NJ City,N,23,18,20,85,High School,35,Patnership,Standalone\n2012,108.64,NJ City,W,15,15,1,46,No education,23,Franchise,Complex\n2594,244.21,NJ City,S,23,13,10,26,High School,18,Patnership,Complex\n3297,387.09,NJ City,N,20,19,3,66,Graduation,35,Patnership,Standalone\n5232,439.48,Boston,W,18,17,21,70,Graduation,24,Franchise,Complex\n4893,6.05,NJ City,N,1,18,1,30,Graduation,21,Patnership,Standalone\n5389,233.03,Boston,N,4,20,17,79,Graduation,29,Patnership,Standalone\n3048,341.63,NJ City,W,25,9,12,74,High School,28,Franchise,Complex\n6826,25.34,Boston,N,5,14,4,81,High School,25,Patnership,Complex\n2239,401.84,NJ City,E,23,16,14,67,High School,25,Owner,Standalone\n5041,362.58,Boston,N,1,13,4,56,High School,26,Patnership,Standalone\n6807,371.67,Boston,E,15,20,4,56,Graduation,25,Owner,Standalone\n8114,82.43,NJ City,S,8,10,2,46,No education,21,Patnership,Complex\n3172,432.46,NJ City,W,3,22,3,56,High School,29,Franchise,Complex\n9167,454.5,NJ City,E,25,15,19,46,Graduation,27,Owner,Standalone\n2715,36.01,NJ City,N,4,18,4,52,Graduation,32,Owner,Standalone\n3852,333.16,NJ City,W,17,24,8,46,High School,35,Franchise,Complex\n2794,320.31,NJ City,S,15,11,24,59,High School,19,Patnership,Complex\n1730,192.17,NJ City,S,17,24,25,42,High School,30,Patnership,Complex\n3978,94.17,NJ City,S,17,9,4,83,Graduation,29,Patnership,Complex\n4514,252.59,NJ City,S,18,16,2,84,No education,24,Patnership,Complex\n8188,136.77,NJ City,W,8,17,11,81,Graduation,34,Franchise,Complex\n4437,5.51,NJ City,N,6,22,3,34,Graduation,32,Patnership,Standalone\n3921,345.14,NJ City,N,25,24,24,78,High School,23,Patnership,Standalone\n443,346.23,NJ City,E,7,16,24,27,High School,34,Owner,Standalone\n3318,186.73,NJ City,S,17,19,19,32,High School,34,Patnership,Complex\n1273,317.92,NJ City,N,14,14,8,30,High School,31,Patnership,Standalone\n5465,17.42,Boston,N,11,13,4,40,No education,32,Patnership,Standalone\n2724,206.86,NJ City,W,16,15,11,83,High School,29,Franchise,Complex\n5741,422.79,Boston,N,8,19,8,48,Graduation,20,Patnership,Standalone\n3261,156,NJ City,N,3,14,20,75,High School,34,Patnership,Standalone\n9144,385.68,NJ City,W,4,9,10,37,Graduation,29,Franchise,Complex\n130,135.99,NJ City,S,3,24,3,31,High School,28,Patnership,Complex\n6938,255.47,Boston,S,16,18,23,46,Graduation,34,Patnership,Complex\n4962,485.51,Boston,S,19,16,14,75,High School,24,Patnership,Complex\n6348,11.42,Boston,N,1,23,3,45,Graduation,19,Franchise,Complex\n6797,271.73,Boston,N,10,19,6,56,Graduation,27,Patnership,Standalone\n3718,161.55,NJ City,S,15,21,2,83,High School,21,Patnership,Complex\n4245,270.07,NJ City,N,22,17,14,75,High School,23,Patnership,Standalone\n5706,67.11,Boston,N,1,9,4,30,Graduation,19,Patnership,Complex\n1040,141.46,NJ City,W,20,12,9,48,High School,29,Franchise,Complex\n4308,148.73,NJ City,W,13,8,25,80,High School,26,Franchise,Complex\n7516,208.72,Boston,W,22,9,25,73,Graduation,20,Franchise,Complex\n9833,489.17,NJ City,N,9,16,18,79,Graduation,32,Patnership,Standalone\n1081,176.96,NJ City,N,19,21,5,35,High School,19,Patnership,Standalone\n6070,155.07,Boston,S,4,20,18,25,Graduation,28,Patnership,Complex\n7010,153.34,Boston,S,12,17,9,25,Graduation,29,Patnership,Complex\n2734,52.83,NJ City,S,7,18,1,32,High School,25,Patnership,Complex\n9982,444.61,NJ City,S,25,8,1,46,High School,32,Patnership,Complex\n5028,350.42,Boston,W,1,10,9,68,Graduation,29,Franchise,Complex\n3568,489.44,NJ City,W,14,24,20,29,High School,33,Franchise,Complex\n4409,36.41,NJ City,N,24,19,4,70,No education,25,Patnership,Standalone\n1509,304.84,NJ City,N,14,16,5,67,Graduation,23,Patnership,Standalone\n655,353.57,NJ City,E,8,22,4,48,High School,18,Owner,Standalone\n5120,332.52,Boston,W,23,8,17,76,Graduation,33,Franchise,Complex\n1059,188.69,NJ City,E,2,9,3,50,Graduation,21,Owner,Standalone\n5239,69.77,Boston,N,23,11,4,61,High School,23,Owner,Standalone\n1117,57.84,NJ City,N,10,17,2,37,High School,24,Patnership,Standalone\n3152,150.8,NJ City,W,24,14,23,72,High School,19,Franchise,Complex\n9199,198.82,NJ City,E,9,18,14,48,Graduation,34,Owner,Standalone\n2067,30.6,NJ City,N,3,18,4,79,Graduation,20,Owner,Standalone\n2072,494.76,NJ City,W,8,11,8,28,High School,34,Franchise,Complex\n1234,27.31,NJ City,N,3,23,2,31,High School,28,Patnership,Complex\n2834,106.98,NJ City,S,13,17,3,43,No education,24,Patnership,Complex\n5884,385.25,Boston,W,21,10,13,80,Graduation,24,Franchise,Complex\n937,182.28,NJ City,N,20,9,2,58,High School,23,Patnership,Standalone\n6327,135.15,Boston,E,3,9,8,79,Graduation,23,Owner,Standalone\n3967,241.42,NJ City,E,18,19,12,37,High School,19,Owner,Standalone\n5622,464.91,Boston,S,22,13,3,46,Graduation,30,Patnership,Complex\n8563,90.98,NJ City,E,20,22,1,79,High School,30,Owner,Standalone\n1954,155.72,NJ City,S,15,15,14,42,High School,20,Patnership,Complex\n8204,222.04,NJ City,W,16,20,21,54,Graduation,25,Franchise,Complex\n2167,164,NJ City,E,3,12,9,38,High School,21,Owner,Standalone\n4543,84.28,NJ City,E,16,14,2,38,High School,35,Owner,Standalone\n4687,406.1,NJ City,E,15,24,9,49,High School,26,Owner,Standalone\n9150,194.15,NJ City,S,19,10,4,44,Graduation,20,Patnership,Complex\n597,482.37,NJ City,N,25,11,8,61,High School,22,Patnership,Standalone\n928,135.92,NJ City,W,16,17,2,73,High School,33,Franchise,Complex\n9474,346.19,NJ City,S,6,17,15,79,Graduation,27,Patnership,Complex\n8662,322.78,NJ City,S,13,15,9,38,Graduation,28,Patnership,Complex\n9101,396.88,NJ City,N,15,22,8,67,Graduation,32,Patnership,Standalone\n3032,113.77,NJ City,W,3,22,1,68,No education,21,Franchise,Complex\n2645,288.28,NJ City,N,10,15,21,55,High School,33,Patnership,Standalone\n765,346.95,NJ City,N,25,18,13,81,High School,30,Patnership,Standalone\n1510,122.17,NJ City,S,7,16,3,26,High School,23,Patnership,Complex\n8418,304.24,NJ City,S,22,11,21,84,Graduation,22,Patnership,Complex\n3703,120.97,NJ City,E,6,13,4,26,High School,31,Owner,Standalone\n37,162,NY City,N,1,22,11,27,High School,27,Patnership,Standalone\n2485,422.42,NJ City,N,8,19,6,40,High School,22,Patnership,Standalone\n5641,92.68,Boston,N,5,13,1,56,High School,28,Patnership,Standalone\n2563,381.69,NJ City,E,21,20,11,46,High School,28,Owner,Standalone\n4092,497.56,NJ City,W,18,21,13,78,High School,34,Franchise,Complex\n1369,88.3,NJ City,N,21,8,4,54,High School,28,Patnership,Standalone\n7110,11.07,Boston,N,15,19,4,80,Graduation,35,Patnership,Complex\n8828,486.54,NJ City,W,15,24,4,76,No education,35,Franchise,Complex\n9236,375.36,NJ City,W,12,12,14,81,Graduation,33,Franchise,Complex\n4750,349.76,NJ City,S,18,9,25,62,High School,18,Patnership,Complex\n4007,95.75,NJ City,E,18,16,3,28,No education,29,Owner,Standalone\n4419,39.78,NJ City,N,9,9,2,43,Graduation,34,Owner,Standalone\n2251,446.57,NJ City,E,8,21,16,65,High School,31,Owner,Standalone\n1799,408.33,NJ City,E,9,18,2,54,No education,23,Owner,Standalone\n4295,225.85,NJ City,E,17,21,18,64,High School,19,Owner,Standalone\n5093,182.44,Boston,N,16,9,12,77,Graduation,24,Patnership,Standalone\n6228,123.16,Boston,W,20,19,1,58,Graduation,29,Franchise,Complex\n5513,473.96,Boston,N,8,22,3,67,No education,27,Patnership,Standalone\n3910,438.19,NJ City,S,10,23,18,72,High School,22,Patnership,Complex\n4907,70.04,NJ City,N,21,18,4,42,No education,30,Owner,Standalone\n8809,391.28,NJ City,N,14,18,15,83,Graduation,30,Patnership,Standalone\n4459,319.82,NJ City,E,18,14,3,80,High School,22,Owner,Standalone\n7040,266.58,Boston,W,9,9,12,68,Graduation,19,Franchise,Complex\n9804,118.96,NJ City,W,9,10,1,35,Graduation,34,Franchise,Complex\n180,284.28,NJ City,W,5,16,18,34,High School,31,Franchise,Complex\n9763,385.92,NJ City,E,2,21,24,62,Graduation,18,Owner,Standalone\n9697,120.39,NJ City,N,12,22,1,26,High School,32,Patnership,Standalone\n1451,116.47,NJ City,E,24,12,2,32,No education,29,Owner,Standalone\n4472,18.22,NJ City,N,15,10,2,28,No education,20,Franchise,Complex\n5043,111.31,Boston,E,3,19,4,60,Graduation,35,Owner,Standalone\n5764,41.14,Boston,N,7,20,4,59,High School,32,Franchise,Complex\n2224,124.24,NJ City,W,1,23,1,66,High School,24,Franchise,Complex\n4342,271.71,NJ City,S,5,12,1,52,High School,35,Patnership,Complex\n3060,497.51,NJ City,W,15,8,16,48,High School,32,Franchise,Complex\n6444,321.62,Boston,W,11,15,9,45,Graduation,18,Franchise,Complex\n8485,438.75,NJ City,N,15,10,15,57,Graduation,33,Patnership,Standalone\n1160,191.08,NJ City,W,25,17,20,47,High School,24,Franchise,Complex\n7468,497.84,Boston,W,11,22,23,68,Graduation,24,Franchise,Complex\n3162,434.33,NJ City,S,5,17,24,32,High School,33,Patnership,Complex\n8123,239.27,NJ City,E,5,17,14,29,Graduation,27,Owner,Standalone\n9379,197.76,NJ City,E,22,20,14,81,Graduation,27,Owner,Standalone\n2788,390.49,NJ City,W,10,23,15,84,High School,28,Franchise,Complex\n2147,155,NJ City,E,3,20,12,29,High School,32,Owner,Standalone\n4019,225.59,NJ City,E,20,10,3,58,No education,23,Owner,Standalone\n944,472.37,NJ City,W,21,15,6,32,High School,30,Franchise,Complex\n1990,477.53,NJ City,S,5,13,10,43,High School,32,Patnership,Complex\n3674,138.98,NJ City,S,6,21,5,35,No education,20,Patnership,Complex\n2028,128.13,NJ City,W,16,8,16,75,High School,30,Franchise,Complex\n7651,464.18,Boston,E,14,12,18,60,Graduation,30,Owner,Standalone\n4351,201.95,NJ City,E,21,15,1,80,High School,20,Owner,Standalone\n3479,37.45,NJ City,N,21,15,3,47,No education,23,Owner,Standalone\n7650,63.7,Boston,N,14,21,4,83,Graduation,25,Patnership,Complex\n6727,141.32,Boston,E,10,11,17,77,Graduation,23,Owner,Standalone\n3183,121.4,NJ City,E,14,19,4,46,Graduation,31,Owner,Standalone\n7207,105.49,Boston,E,21,24,3,52,High School,23,Owner,Standalone\n9850,371.21,NJ City,S,14,14,9,84,Graduation,34,Patnership,Complex\n2665,97.22,NJ City,N,3,11,4,69,High School,35,Patnership,Standalone\n4502,27.12,NJ City,N,12,17,1,48,No education,28,Patnership,Complex\n468,230.17,NJ City,W,10,11,8,38,High School,23,Franchise,Complex\n6931,46.25,Boston,N,22,14,3,70,High School,28,Owner,Standalone\n541,448.27,NJ City,N,23,9,12,52,High School,18,Patnership,Standalone\n7275,46.62,Boston,N,2,19,2,82,Graduation,27,Owner,Standalone\n371,15.85,NJ City,N,2,22,1,66,No education,28,Owner,Standalone\n7363,279.79,Boston,E,4,20,9,56,Graduation,30,Owner,Standalone\n4679,359.99,NJ City,E,23,11,6,79,High School,33,Owner,Standalone\n208,414.39,NJ City,W,13,8,15,65,High School,23,Franchise,Complex\n1992,278,NJ City,W,3,8,10,56,High School,21,Franchise,Complex\n2967,352.75,NJ City,E,11,23,9,42,High School,18,Owner,Standalone\n9388,370.58,NJ City,W,1,22,3,33,High School,23,Franchise,Complex\n3590,152.77,NJ City,S,8,18,8,52,High School,29,Patnership,Complex\n1559,60.42,NJ City,N,19,23,3,50,No education,23,Owner,Standalone\n319,262.09,NJ City,E,13,22,11,72,High School,25,Owner,Standalone\n4450,134.34,NJ City,S,14,10,21,58,High School,32,Patnership,Complex\n7018,94.48,Boston,S,14,8,1,31,High School,34,Patnership,Complex\n6116,195.84,Boston,W,5,12,2,72,No education,34,Franchise,Complex\n2644,400.81,NJ City,W,11,10,23,59,High School,26,Franchise,Complex\n5124,474.01,Boston,W,14,23,21,52,Graduation,28,Franchise,Complex\n2218,36.02,NJ City,N,8,24,3,33,High School,34,Patnership,Complex\n1021,237.62,NJ City,N,8,20,3,83,High School,25,Patnership,Standalone\n6838,358.22,Boston,S,6,23,19,65,Graduation,31,Patnership,Complex\n6965,492.5,Boston,N,21,18,14,76,Graduation,34,Patnership,Standalone\n4282,91.05,NJ City,S,6,22,3,33,High School,30,Patnership,Complex\n1764,168.53,NJ City,W,23,16,17,67,High School,29,Franchise,Complex\n8772,295.55,NJ City,W,1,13,7,31,Graduation,33,Franchise,Complex\n3414,404.76,NJ City,S,7,17,1,35,Graduation,25,Patnership,Complex\n4582,471.35,NJ City,S,12,21,20,31,High School,22,Patnership,Complex\n5762,175.67,Boston,S,23,23,3,72,No education,25,Patnership,Complex\n2291,324.87,NJ City,E,23,24,23,34,High School,22,Owner,Standalone\n9852,274.84,NJ City,W,24,14,6,30,Graduation,20,Franchise,Complex\n7318,352.93,Boston,S,16,10,6,73,Graduation,28,Patnership,Complex\n4283,151.31,NJ City,E,10,22,25,39,High School,33,Owner,Standalone\n3876,324.53,NJ City,W,13,17,6,38,High School,25,Franchise,Complex\n4655,356.2,NJ City,E,4,19,10,36,High School,26,Owner,Standalone\n5224,301.78,Boston,W,2,14,5,35,High School,28,Franchise,Complex\n4535,375.21,NJ City,E,15,23,20,59,High School,26,Owner,Standalone\n9972,466.19,NJ City,W,11,21,4,38,Graduation,23,Franchise,Complex\n1371,241.1,NJ City,E,24,18,7,38,High School,34,Owner,Standalone\n5017,435.87,Boston,N,14,17,3,60,High School,31,Patnership,Standalone\n7973,283.15,Boston,N,10,16,22,63,Graduation,24,Patnership,Standalone\n8446,88.21,NJ City,S,23,11,1,76,High School,33,Patnership,Complex\n2173,306.99,NJ City,N,15,21,20,62,High School,24,Patnership,Standalone\n335,140.19,NJ City,E,12,8,2,83,No education,20,Owner,Standalone\n6166,162.83,Boston,S,4,14,11,82,Graduation,21,Patnership,Complex\n2806,413,NJ City,S,16,21,19,81,High School,25,Patnership,Complex\n3145,213.45,NJ City,N,24,10,6,45,High School,30,Patnership,Standalone\n672,283.15,NJ City,W,17,22,9,77,High School,32,Franchise,Complex\n2208,432.8,NJ City,W,20,13,24,84,High School,23,Franchise,Complex\n1795,240.45,NJ City,E,24,18,17,54,High School,34,Owner,Standalone\n1167,303,NJ City,E,3,14,6,26,High School,24,Owner,Standalone\n3633,215.28,NJ City,N,12,24,23,25,High School,28,Patnership,Standalone\n4539,241.19,NJ City,E,7,13,24,77,High School,19,Owner,Standalone\n6539,133.42,Boston,E,1,17,4,41,No education,29,Owner,Standalone\n2043,37.84,NJ City,N,2,9,3,38,Graduation,30,Owner,Standalone\n8826,489.83,NJ City,S,19,12,21,54,Graduation,19,Patnership,Complex\n6508,51.7,Boston,W,24,20,1,58,High School,21,Franchise,Complex\n2507,482.68,NJ City,E,25,21,21,28,High School,35,Owner,Standalone\n3052,108.92,NJ City,W,6,10,4,84,High School,25,Franchise,Complex\n5436,76.06,Boston,W,23,13,3,82,Graduation,31,Franchise,Complex\n894,446.73,NJ City,S,25,24,10,77,High School,21,Patnership,Complex\n5652,400.73,Boston,W,19,20,22,41,Graduation,30,Franchise,Complex\n3803,57.27,NJ City,E,20,11,2,69,No education,18,Owner,Standalone\n6358,410.66,Boston,S,19,11,25,25,Graduation,20,Patnership,Complex\n8963,10.21,NJ City,N,10,15,4,33,No education,24,Owner,Standalone\n2525,172.44,NJ City,N,22,24,2,27,No education,21,Patnership,Standalone\n943,54.97,NJ City,E,9,20,3,43,High School,32,Owner,Standalone\n7968,88.91,Boston,W,24,23,2,80,Graduation,35,Franchise,Complex\n1183,348.92,NJ City,E,25,16,11,30,High School,26,Owner,Standalone\n6359,263.44,Boston,E,2,18,5,36,No education,27,Owner,Standalone\n8545,447.28,NJ City,N,19,22,17,40,Graduation,28,Patnership,Standalone\n52,103.36,NJ City,W,7,22,2,59,High School,23,Franchise,Complex\n7822,447.18,Boston,S,24,20,7,84,Graduation,32,Patnership,Complex\n8797,116.54,NJ City,N,17,10,3,29,High School,31,Patnership,Standalone\n2450,339.62,NJ City,S,9,19,4,81,No education,31,Patnership,Complex\n1051,312.07,NJ City,E,15,17,20,57,High School,29,Owner,Standalone\n390,427.59,NJ City,S,16,18,3,63,Graduation,27,Patnership,Complex\n1923,426.66,NJ City,E,23,16,4,56,Graduation,30,Owner,Standalone\n7486,117.75,Boston,S,8,17,4,54,High School,25,Patnership,Complex\n7561,283.61,Boston,N,17,12,7,38,Graduation,21,Patnership,Standalone\n501,201.71,NJ City,N,21,23,6,27,High School,25,Patnership,Standalone\n2945,391.9,NJ City,N,14,8,18,59,High School,34,Patnership,Standalone\n2852,84.3,NJ City,W,14,11,1,60,No education,32,Franchise,Complex\n7149,71.23,Boston,N,5,8,1,52,Graduation,29,Patnership,Standalone\n129,328.94,NJ City,N,15,15,10,84,High School,29,Patnership,Standalone\n4892,435.01,NJ City,W,20,13,9,82,High School,35,Franchise,Complex\n9526,56.16,NJ City,S,8,22,2,34,High School,30,Patnership,Complex\n4361,114.74,NJ City,N,9,21,4,68,No education,24,Patnership,Standalone\n1605,11.85,NJ City,N,7,8,2,42,Graduation,30,Patnership,Standalone\n8253,18.44,NJ City,N,11,23,2,44,Graduation,29,Patnership,Standalone\n3215,12.98,NJ City,N,18,22,4,29,No education,30,Owner,Standalone\n7433,411.39,Boston,N,11,14,17,70,Graduation,29,Patnership,Standalone\n8792,353.72,NJ City,W,18,13,16,59,Graduation,29,Franchise,Complex\n1334,481.22,NJ City,S,5,23,23,85,High School,24,Patnership,Complex\n7658,310.09,Boston,S,9,10,14,45,Graduation,24,Patnership,Complex\n9382,25.91,NJ City,N,25,18,2,38,High School,25,Patnership,Complex\n3889,226.67,NJ City,N,19,21,24,34,High School,27,Patnership,Standalone\n1328,106.89,NJ City,W,3,20,1,50,No education,31,Franchise,Complex\n1244,473.76,NJ City,W,20,14,17,35,High School,19,Franchise,Complex\n3918,215.39,NJ City,S,11,12,17,81,High School,30,Patnership,Complex\n9044,468.74,NJ City,W,17,11,2,30,No education,34,Franchise,Complex\n5199,422.52,Boston,E,14,18,10,25,Graduation,32,Owner,Standalone\n7668,215.22,Boston,W,5,22,13,39,Graduation,34,Franchise,Complex\n8725,354.12,NJ City,N,14,15,16,80,Graduation,18,Patnership,Standalone\n9173,298.05,NJ City,N,7,23,5,44,No education,25,Patnership,Standalone\n6046,164.56,Boston,S,1,16,1,36,High School,29,Patnership,Complex\n9261,163,NJ City,N,15,17,23,28,Graduation,28,Patnership,Standalone\n1815,195.21,NJ City,E,22,21,1,64,Graduation,32,Owner,Standalone\n3636,412.84,NJ City,W,24,22,7,32,High School,24,Franchise,Complex\n1953,324.44,NJ City,N,8,18,3,49,Graduation,25,Patnership,Standalone\n755,5.82,NJ City,N,4,15,4,56,No education,31,Owner,Standalone\n3726,384.97,NJ City,S,22,11,23,50,High School,20,Patnership,Complex\n606,30.58,NJ City,N,2,19,4,36,Graduation,24,Patnership,Complex\n6537,195.13,Boston,N,8,13,7,60,Graduation,35,Patnership,Standalone\n1587,83.41,NJ City,E,10,9,2,31,Graduation,21,Owner,Standalone\n7696,476.87,Boston,W,21,17,2,40,High School,35,Franchise,Complex\n1850,18.78,NJ City,N,12,19,2,79,No education,28,Patnership,Complex\n5674,445.96,Boston,S,2,10,11,59,Graduation,18,Patnership,Complex\n1763,395.76,NJ City,E,21,24,25,51,High School,32,Owner,Standalone\n8124,88.72,NJ City,W,1,23,2,66,Graduation,33,Franchise,Complex\n4426,226.15,NJ City,S,17,11,8,84,High School,34,Patnership,Complex\n1683,142.1,NJ City,E,23,14,7,80,High School,22,Owner,Standalone\n8224,52.63,NJ City,W,15,23,2,78,High School,33,Franchise,Complex\n6438,244.62,Boston,S,1,15,21,45,Graduation,26,Patnership,Complex\n8423,111.09,NJ City,E,21,18,2,45,No education,18,Owner,Standalone\n9143,290.45,NJ City,E,10,19,20,60,Graduation,21,Owner,Standalone\n9585,482.53,NJ City,N,19,21,5,32,Graduation,29,Patnership,Standalone\n7439,323.75,Boston,E,25,16,20,84,Graduation,35,Owner,Standalone\n7777,314.46,Boston,N,2,15,8,52,Graduation,31,Patnership,Standalone\n6553,395.2,Boston,N,9,22,9,82,Graduation,33,Patnership,Standalone\n5280,75.29,Boston,W,19,23,4,71,Graduation,33,Franchise,Complex\n8118,433.08,NJ City,S,15,10,14,34,Graduation,31,Patnership,Complex\n8873,293.08,NJ City,N,23,15,9,30,Graduation,35,Patnership,Standalone\n4839,414.94,NJ City,E,7,8,19,83,High School,32,Owner,Standalone\n3031,277.91,NJ City,E,16,16,23,81,High School,27,Owner,Standalone\n3111,443.88,NJ City,E,24,15,8,58,High School,32,Owner,Standalone\n8300,343.76,NJ City,W,14,8,21,43,Graduation,23,Franchise,Complex\n7340,275.42,Boston,W,8,20,18,72,Graduation,19,Franchise,Complex\n5766,330.34,Boston,S,4,22,24,65,Graduation,34,Patnership,Complex\n4330,435.19,NJ City,S,7,8,20,84,High School,22,Patnership,Complex\n2250,384.3,NJ City,S,23,20,4,67,Graduation,29,Patnership,Complex\n6418,448.41,Boston,S,23,8,20,57,Graduation,26,Patnership,Complex\n9864,90.91,NJ City,W,1,21,4,52,Graduation,23,Franchise,Complex\n377,115.04,NJ City,N,14,9,4,53,No education,24,Patnership,Standalone\n4249,151.4,NJ City,N,8,16,22,38,High School,26,Patnership,Standalone\n6398,29.22,Boston,N,22,24,4,75,No education,21,Patnership,Complex\n6352,243.3,Boston,W,9,15,6,77,Graduation,31,Franchise,Complex\n2425,108.83,NJ City,N,11,20,2,77,High School,26,Patnership,Standalone\n421,216.69,NJ City,N,14,17,6,36,High School,30,Patnership,Standalone\n6766,236.3,Boston,S,21,12,21,41,Graduation,20,Patnership,Complex\n2955,241,NJ City,E,2,14,12,74,High School,23,Owner,Standalone\n3510,206,NJ City,S,2,17,20,38,High School,29,Patnership,Complex\n1168,232.12,NJ City,W,21,22,25,27,High School,33,Franchise,Complex\n1144,309.86,NJ City,W,19,11,18,49,High School,23,Franchise,Complex\n5790,460.98,Boston,S,14,16,16,70,Graduation,24,Patnership,Complex\n4598,413.75,NJ City,S,17,10,20,56,High School,22,Patnership,Complex\n3108,47.75,NJ City,W,11,13,3,58,Graduation,32,Franchise,Complex\n1620,57.99,NJ City,W,14,9,3,84,Graduation,22,Franchise,Complex\n9896,427.9,NJ City,W,15,12,3,42,No education,20,Franchise,Complex\n4375,153.74,NJ City,E,19,12,21,68,High School,24,Owner,Standalone\n1994,293,NJ City,S,2,22,20,78,High School,22,Patnership,Complex\n2285,82.22,NJ City,N,4,17,3,26,No education,25,Patnership,Standalone\n1107,200.2,NJ City,E,4,14,17,29,High School,20,Owner,Standalone\n786,267.4,NJ City,S,6,14,10,25,High School,32,Patnership,Complex\n6484,3.34,Boston,N,13,24,2,72,High School,27,Franchise,Complex\n2042,139.48,NJ City,S,23,14,25,26,High School,18,Patnership,Complex\n8840,452.19,NJ City,W,6,21,6,60,Graduation,26,Franchise,Complex\n7299,9.69,Boston,N,15,9,4,76,Graduation,29,Owner,Standalone\n5315,198.11,Boston,E,17,20,15,60,Graduation,29,Owner,Standalone\n6108,301.64,Boston,W,6,12,1,28,Graduation,19,Franchise,Complex\n8914,87.31,NJ City,S,10,11,2,62,High School,29,Patnership,Complex\n8382,143.67,NJ City,S,14,14,5,72,Graduation,23,Patnership,Complex\n744,267.66,NJ City,W,24,16,2,84,Graduation,18,Franchise,Complex\n8979,137.95,NJ City,E,16,23,24,58,Graduation,21,Owner,Standalone\n8964,204.81,NJ City,W,8,20,2,81,Graduation,26,Franchise,Complex\n1479,271.89,NJ City,E,19,20,21,66,High School,18,Owner,Standalone\n4871,138.71,NJ City,E,13,10,23,76,High School,25,Owner,Standalone\n5254,360.24,Boston,S,11,19,10,79,Graduation,27,Patnership,Complex\n8850,423.06,NJ City,S,16,10,6,69,Graduation,29,Patnership,Complex\n6602,349.26,Boston,S,16,24,6,79,Graduation,35,Patnership,Complex\n3078,256.5,NJ City,S,25,18,5,78,Graduation,30,Patnership,Complex\n4254,29.91,NJ City,N,22,12,2,53,Graduation,21,Patnership,Complex\n5021,36.54,Boston,N,12,23,2,69,No education,21,Patnership,Standalone\n7652,1.3,Boston,N,18,23,1,62,No education,34,Franchise,Complex\n2021,37.86,NJ City,N,8,13,4,65,No education,20,Patnership,Standalone\n9592,341.14,NJ City,W,23,12,24,78,Graduation,21,Franchise,Complex\n3235,173.55,NJ City,E,10,13,3,68,High School,31,Owner,Standalone\n6956,394.09,Boston,W,16,24,1,70,No education,18,Franchise,Complex\n159,122.92,NJ City,E,25,11,1,78,Graduation,31,Owner,Standalone\n3018,397.59,NJ City,S,8,19,18,34,High School,28,Patnership,Complex\n8749,41.2,NJ City,N,24,12,2,31,High School,23,Patnership,Standalone\n9283,214.53,NJ City,E,5,9,11,65,Graduation,32,Owner,Standalone\n1937,275.21,NJ City,N,14,18,19,81,High School,18,Patnership,Standalone\n5536,19.81,Boston,N,4,16,1,75,High School,33,Franchise,Complex\n2741,300.46,NJ City,N,13,20,16,46,High School,22,Patnership,Standalone\n507,59.33,NJ City,E,6,20,4,79,Graduation,33,Owner,Standalone\n1591,153.63,NJ City,E,10,11,2,45,High School,23,Owner,Standalone\n1185,193.27,NJ City,N,21,14,3,33,Graduation,31,Patnership,Standalone\n1784,438.7,NJ City,W,10,17,18,30,High School,35,Franchise,Complex\n3336,348.05,NJ City,W,24,15,14,76,High School,32,Franchise,Complex\n2866,258.12,NJ City,S,8,8,5,25,High School,34,Patnership,Complex\n8881,215.35,NJ City,N,20,21,17,47,Graduation,26,Patnership,Standalone\n4748,86.92,NJ City,W,9,20,3,85,No education,23,Franchise,Complex\n5312,483.97,Boston,W,10,23,1,78,No education,27,Franchise,Complex\n7770,390.76,Boston,S,1,14,10,50,Graduation,35,Patnership,Complex\n5970,263.27,Boston,S,13,11,3,83,Graduation,18,Patnership,Complex\n3949,229.41,NJ City,N,23,18,7,41,High School,26,Patnership,Standalone\n2624,311.17,NJ City,W,6,16,9,26,High School,24,Franchise,Complex\n5056,463.72,Boston,W,14,24,23,42,Graduation,26,Franchise,Complex\n9513,446.18,NJ City,N,13,12,3,63,Graduation,30,Patnership,Standalone\n6512,216.58,Boston,W,21,15,19,61,Graduation,23,Franchise,Complex\n2449,394.5,NJ City,N,5,21,4,71,High School,29,Patnership,Standalone\n4844,444.99,NJ City,W,10,10,5,41,No education,21,Franchise,Complex\n7156,154.46,Boston,W,8,17,21,72,Graduation,34,Franchise,Complex\n114,190.29,NJ City,S,25,24,4,41,Graduation,35,Patnership,Complex\n2924,397.52,NJ City,W,19,11,8,70,High School,34,Franchise,Complex\n2942,413.97,NJ City,S,3,16,1,80,No education,30,Patnership,Complex\n5714,374.74,Boston,S,16,12,13,71,Graduation,32,Patnership,Complex\n7209,204.47,Boston,N,23,13,17,48,Graduation,25,Patnership,Standalone\n4561,291.73,NJ City,N,8,19,8,27,High School,21,Patnership,Standalone\n6099,3.56,Boston,N,5,19,3,46,Graduation,35,Owner,Standalone\n6167,355.17,Boston,E,4,8,21,64,Graduation,21,Owner,Standalone\n4840,175.79,NJ City,W,13,19,22,61,High School,19,Franchise,Complex\n8323,269.07,NJ City,E,4,19,22,44,Graduation,32,Owner,Standalone\n472,348.36,NJ City,W,7,13,1,75,High School,27,Franchise,Complex\n3064,280.27,NJ City,W,12,21,9,62,High School,24,Franchise,Complex\n2556,243.61,NJ City,W,22,20,2,28,Graduation,21,Franchise,Complex\n6946,358.26,Boston,S,18,9,9,38,Graduation,35,Patnership,Complex\n3657,53.86,NJ City,N,14,23,3,79,Graduation,25,Patnership,Standalone\n4702,465.29,NJ City,S,14,11,3,85,High School,30,Patnership,Complex\n6076,294.75,Boston,W,25,10,24,78,Graduation,34,Franchise,Complex\n3547,461.2,NJ City,E,16,9,16,59,High School,25,Owner,Standalone\n3346,266.29,NJ City,S,10,18,4,48,High School,30,Patnership,Complex\n2878,186.48,NJ City,S,13,16,3,37,High School,19,Patnership,Complex\n5444,48.31,Boston,W,21,8,2,44,No education,25,Franchise,Complex\n9936,455.42,NJ City,W,6,20,4,25,Graduation,23,Franchise,Complex\n5030,86.48,Boston,S,12,16,4,80,No education,32,Patnership,Complex\n5178,374.8,Boston,S,17,14,11,58,Graduation,35,Patnership,Complex\n9738,294.02,NJ City,S,12,20,20,79,Graduation,24,Patnership,Complex\n485,167.37,NJ City,N,14,20,19,27,High School,27,Patnership,Standalone\n3592,185.16,NJ City,W,19,10,7,77,High School,35,Franchise,Complex\n3539,153.08,NJ City,E,17,17,13,73,High School,32,Owner,Standalone\n2024,407.66,NJ City,W,21,21,6,70,High School,22,Franchise,Complex\n8167,217.58,NJ City,E,5,15,25,59,Graduation,19,Owner,Standalone\n592,108.01,NJ City,W,19,17,4,66,High School,33,Franchise,Complex\n3712,228.24,NJ City,W,11,19,1,40,High School,23,Franchise,Complex\n9713,109.77,NJ City,N,6,9,1,33,No education,20,Patnership,Standalone\n6755,18.82,Boston,N,8,9,3,76,No education,22,Owner,Standalone\n8159,410.92,NJ City,E,7,18,2,49,No education,18,Owner,Standalone\n4983,429.12,Boston,E,6,24,11,40,High School,23,Owner,Standalone\n772,210.35,NJ City,W,7,17,20,72,High School,21,Franchise,Complex\n2046,178,NJ City,S,3,12,12,52,High School,21,Patnership,Complex\n1027,323.03,NJ City,E,7,19,10,43,High School,26,Owner,Standalone\n403,474.13,NJ City,E,17,18,1,85,High School,20,Owner,Standalone\n940,396.2,NJ City,W,16,16,8,64,High School,31,Franchise,Complex\n6843,173,Boston,E,5,21,2,52,Graduation,21,Owner,Standalone\n5294,38.59,Boston,N,10,21,3,46,No education,23,Patnership,Complex\n5151,17.12,Boston,N,2,8,2,67,Graduation,25,Owner,Standalone\n2171,269,NJ City,E,3,21,6,48,High School,31,Owner,Standalone\n1431,228.82,NJ City,E,6,11,10,81,High School,32,Owner,Standalone\n6330,80.08,Boston,S,2,15,1,26,Graduation,35,Patnership,Complex\n8860,268.25,NJ City,W,14,24,1,52,High School,20,Franchise,Complex\n5574,128.99,Boston,S,14,8,15,45,Graduation,26,Patnership,Complex\n9732,418.38,NJ City,W,17,18,17,76,Graduation,32,Franchise,Complex\n5724,239.79,Boston,W,2,14,11,75,Graduation,19,Franchise,Complex\n952,250,NJ City,W,2,22,22,64,High School,24,Franchise,Complex\n7231,159.69,Boston,E,5,14,22,53,Graduation,32,Owner,Standalone\n480,377.89,NJ City,W,19,21,19,72,High School,23,Franchise,Complex\n391,406.72,NJ City,E,8,23,16,47,High School,30,Owner,Standalone\n9214,208.43,NJ City,S,16,8,18,31,Graduation,34,Patnership,Complex\n1085,333.93,NJ City,N,2,11,1,28,No education,23,Patnership,Standalone\n7060,200.79,Boston,W,2,21,18,78,Graduation,32,Franchise,Complex\n7428,169.07,Boston,W,9,19,4,36,Graduation,32,Franchise,Complex\n4546,414.03,NJ City,S,5,22,2,59,High School,22,Patnership,Complex\n2890,328.79,NJ City,S,24,17,15,41,High School,27,Patnership,Complex\n9677,394.58,NJ City,N,23,10,18,84,Graduation,23,Patnership,Standalone\n2036,475.89,NJ City,W,22,18,21,77,High School,23,Franchise,Complex\n2379,485.84,NJ City,E,18,21,6,58,High School,30,Owner,Standalone\n1535,284.27,NJ City,E,11,20,19,70,High School,33,Owner,Standalone\n5916,234.53,Boston,W,15,17,22,27,Graduation,19,Franchise,Complex\n3673,64.77,NJ City,N,22,20,4,52,High School,21,Patnership,Standalone\n5529,261.5,Boston,N,10,19,12,72,Graduation,24,Patnership,Standalone\n5897,65.84,Boston,N,15,22,4,58,No education,22,Patnership,Standalone\n2615,67.18,NJ City,N,9,22,1,62,No education,18,Owner,Standalone\n8231,315.11,NJ City,E,20,17,25,57,Graduation,21,Owner,Standalone\n4029,117.81,NJ City,N,14,12,1,71,Graduation,27,Patnership,Standalone\n3398,290.27,NJ City,S,11,8,9,39,High School,20,Patnership,Complex\n8556,232.28,NJ City,W,25,16,24,77,Graduation,35,Franchise,Complex\n6156,142.22,Boston,W,24,9,15,46,Graduation,33,Franchise,Complex\n9773,355.09,NJ City,N,22,24,11,65,Graduation,32,Patnership,Standalone\n8539,151.56,NJ City,E,4,22,22,68,Graduation,28,Owner,Standalone\n5393,296.12,Boston,N,14,22,1,75,No education,18,Patnership,Standalone\n7852,403.41,Boston,W,21,9,1,58,High School,35,Franchise,Complex\n4328,251,NJ City,W,3,15,6,49,High School,22,Franchise,Complex\n8618,450.43,NJ City,S,7,24,6,82,Graduation,31,Patnership,Complex\n3505,80.76,NJ City,N,10,12,4,65,High School,31,Patnership,Standalone\n8849,305.15,NJ City,N,22,16,3,47,No education,22,Patnership,Standalone\n8667,456.62,NJ City,E,12,18,11,33,Graduation,31,Owner,Standalone\n7699,52.41,Boston,E,3,8,2,78,High School,22,Owner,Standalone\n3741,52.08,NJ City,N,8,23,4,31,Graduation,25,Patnership,Standalone\n1367,24.45,NJ City,N,23,13,2,39,No education,31,Owner,Standalone\n5067,250.04,Boston,E,3,17,2,60,Graduation,35,Owner,Standalone\n3771,28.17,NJ City,N,1,8,1,60,Graduation,26,Owner,Standalone\n4265,217.24,NJ City,N,25,16,11,42,High School,24,Patnership,Standalone\n8476,95.26,NJ City,W,8,21,1,43,High School,23,Franchise,Complex\n2002,180.97,NJ City,S,16,24,22,31,High School,19,Patnership,Complex\n8278,456.08,NJ City,S,7,24,25,74,Graduation,26,Patnership,Complex\n2542,468.64,NJ City,S,25,9,1,82,High School,20,Patnership,Complex\n265,273,NJ City,N,3,22,24,39,High School,20,Patnership,Standalone\n8923,301.71,NJ City,E,17,16,18,75,Graduation,31,Owner,Standalone\n7985,188.24,Boston,N,12,20,4,40,No education,20,Patnership,Standalone\n2973,231.6,NJ City,N,16,19,8,54,High School,35,Patnership,Standalone\n4876,296.47,NJ City,W,22,11,3,84,High School,33,Franchise,Complex\n1507,311.62,NJ City,E,12,12,7,81,High School,30,Owner,Standalone\n4322,190.51,NJ City,S,25,9,20,79,High School,18,Patnership,Complex\n4788,64.68,NJ City,N,11,16,1,79,Graduation,30,Franchise,Complex\n4726,121.7,NJ City,S,20,14,4,29,High School,18,Patnership,Complex\n8086,259.75,NJ City,S,1,13,15,44,Graduation,22,Patnership,Complex\n8697,165.4,NJ City,N,10,12,14,62,Graduation,25,Patnership,Standalone\n9517,18.31,NJ City,N,10,9,1,82,High School,19,Patnership,Standalone\n7858,340.59,Boston,S,14,15,11,36,Graduation,22,Patnership,Complex\n2508,340.74,NJ City,W,4,22,1,33,Graduation,30,Franchise,Complex\n3115,449.85,NJ City,E,7,15,20,48,High School,33,Owner,Standalone\n6698,488.61,Boston,S,6,23,6,66,Graduation,23,Patnership,Complex\n537,285.97,NJ City,N,14,15,4,58,Graduation,28,Patnership,Standalone\n6158,262.24,Boston,S,18,15,6,74,Graduation,25,Patnership,Complex\n5372,437.82,Boston,W,19,10,4,84,No education,21,Franchise,Complex\n2903,414.52,NJ City,E,13,18,5,66,No education,23,Owner,Standalone\n4095,10.57,NJ City,N,16,8,4,63,Graduation,25,Owner,Standalone\n2052,91.25,NJ City,W,14,22,2,31,Graduation,34,Franchise,Complex\n2703,230,NJ City,E,3,13,15,81,High School,32,Owner,Standalone\n2780,122,NJ City,W,1,17,16,81,High School,24,Franchise,Complex\n1600,282.7,NJ City,W,6,14,11,29,High School,25,Franchise,Complex\n2744,317.11,NJ City,W,14,19,20,42,High School,28,Franchise,Complex\n176,134.83,NJ City,W,19,19,1,78,No education,32,Franchise,Complex\n8998,304.53,NJ City,S,12,19,9,79,Graduation,21,Patnership,Complex\n6719,193.7,Boston,E,18,19,3,43,No education,21,Owner,Standalone\n4768,197.28,NJ City,W,18,12,21,55,High School,25,Franchise,Complex\n9745,150.65,NJ City,N,12,18,11,42,Graduation,20,Patnership,Standalone\n2095,283.72,NJ City,E,5,23,4,84,High School,26,Owner,Standalone\n1877,14.69,NJ City,N,4,24,2,65,No education,29,Patnership,Standalone\n1679,303.67,NJ City,E,13,14,11,55,High School,20,Owner,Standalone\n5206,443.87,Boston,S,6,13,15,38,Graduation,35,Patnership,Complex\n1281,202.65,NJ City,N,6,22,19,68,High School,35,Patnership,Standalone\n4891,63.46,NJ City,N,6,20,1,83,High School,26,Owner,Standalone\n7278,379.02,Boston,S,18,14,21,79,Graduation,28,Patnership,Complex\n9686,140.91,NJ City,S,9,9,11,44,Graduation,35,Patnership,Complex\n6678,333.34,Boston,S,6,11,5,32,Graduation,22,Patnership,Complex\n5545,159.23,Boston,N,3,18,13,64,Graduation,20,Patnership,Standalone\n5547,448.04,Boston,E,10,18,14,32,Graduation,34,Owner,Standalone\n9087,235.12,NJ City,E,21,16,20,51,Graduation,20,Owner,Standalone\n5894,92.38,Boston,S,10,21,1,41,No education,30,Patnership,Complex\n3313,11.48,NJ City,N,9,12,3,26,High School,23,Patnership,Standalone\n2815,213,NJ City,E,1,21,13,68,High School,33,Owner,Standalone\n207,105.2,NJ City,E,1,17,4,61,Graduation,29,Owner,Standalone\n3714,247,NJ City,S,3,16,19,47,High School,23,Patnership,Complex\n5096,256.28,Boston,W,25,18,25,58,Graduation,24,Franchise,Complex\n2386,280,NJ City,S,16,11,16,48,High School,29,Patnership,Complex\n1532,412.56,NJ City,W,24,12,23,47,High School,19,Franchise,Complex\n700,293.84,NJ City,W,16,12,6,38,High School,32,Franchise,Complex\n4102,432.68,NJ City,S,16,18,20,30,High School,23,Patnership,Complex\n7563,52.65,Boston,E,14,9,2,39,Graduation,35,Owner,Standalone\n7549,119.08,Boston,N,8,11,3,76,High School,26,Patnership,Standalone\n4445,408,NJ City,N,22,14,13,36,High School,28,Patnership,Standalone\n1622,453.45,NJ City,S,11,22,10,83,High School,18,Patnership,Complex\n2475,114.74,NJ City,E,15,20,2,57,Graduation,25,Owner,Standalone\n8900,416.79,NJ City,W,12,8,15,76,Graduation,32,Franchise,Complex\n6442,388.39,Boston,S,4,9,10,83,Graduation,32,Patnership,Complex\n5997,413.39,Boston,N,11,21,21,41,Graduation,22,Patnership,Standalone\n9492,64.24,NJ City,N,22,16,3,78,Graduation,32,Franchise,Complex\n9440,319.54,NJ City,W,25,16,16,27,Graduation,30,Franchise,Complex\n4733,243.45,NJ City,N,21,11,23,55,High School,28,Patnership,Standalone\n3928,464.88,NJ City,W,10,20,8,29,High School,34,Franchise,Complex\n2019,341.69,NJ City,E,16,11,20,84,High School,31,Owner,Standalone\n3783,76.28,NJ City,E,13,16,3,43,Graduation,28,Owner,Standalone\n8358,20.55,NJ City,N,15,20,4,68,Graduation,24,Patnership,Complex\n662,263,NJ City,S,3,12,10,37,High School,21,Patnership,Complex\n8673,241.3,NJ City,N,10,24,6,56,Graduation,33,Patnership,Standalone\n5510,168.31,Boston,S,11,20,8,65,Graduation,19,Patnership,Complex\n3960,18.65,NJ City,N,4,9,4,60,Graduation,26,Franchise,Complex\n5395,307.36,Boston,E,24,12,21,73,Graduation,20,Owner,Standalone\n9525,427.86,NJ City,N,18,8,15,50,Graduation,28,Patnership,Standalone\n3227,395.32,NJ City,E,16,24,17,67,High School,31,Owner,Standalone\n3668,103.42,NJ City,W,20,10,1,80,No education,23,Franchise,Complex\n9537,496.68,NJ City,N,16,8,2,44,Graduation,32,Patnership,Standalone\n8031,473.93,Boston,E,2,24,23,83,Graduation,28,Owner,Standalone\n6958,212.97,Boston,S,3,11,24,57,Graduation,19,Patnership,Complex\n1631,201.15,NJ City,E,23,11,23,35,High School,23,Owner,Standalone\n7312,10.68,Boston,N,12,8,3,53,High School,34,Franchise,Complex\n7760,348.7,Boston,W,9,10,1,72,No education,29,Franchise,Complex\n2500,184.85,NJ City,W,6,11,3,64,High School,32,Franchise,Complex\n8429,324.79,NJ City,N,8,19,13,30,Graduation,34,Patnership,Standalone\n3112,431.08,NJ City,W,18,13,4,52,High School,35,Franchise,Complex\n4094,149.52,NJ City,S,12,12,11,63,High School,35,Patnership,Complex\n3326,148.97,NJ City,S,13,15,18,38,High School,28,Patnership,Complex\n5682,239.7,Boston,S,16,13,13,69,Graduation,30,Patnership,Complex\n5978,288.03,Boston,S,2,19,16,48,Graduation,33,Patnership,Complex\n5817,73.56,Boston,N,11,17,1,85,Graduation,20,Patnership,Standalone\n5610,345.41,Boston,S,22,21,17,85,Graduation,23,Patnership,Complex\n6251,145.63,Boston,E,20,18,7,85,Graduation,33,Owner,Standalone\n6255,177.24,Boston,E,19,18,12,39,Graduation,30,Owner,Standalone\n3671,89.76,NJ City,E,2,15,4,52,No education,19,Owner,Standalone\n1530,497.63,NJ City,S,10,9,13,82,High School,20,Patnership,Complex\n6051,126.87,Boston,E,10,21,2,36,Graduation,23,Owner,Standalone\n2168,483.01,NJ City,W,17,23,4,41,No education,31,Franchise,Complex\n1248,33.29,NJ City,N,12,10,2,29,Graduation,23,Franchise,Complex\n5458,446.01,Boston,S,24,11,18,78,Graduation,29,Patnership,Complex\n6326,496.44,Boston,S,5,16,8,34,Graduation,33,Patnership,Complex\n4131,313.4,NJ City,E,24,15,8,29,High School,20,Owner,Standalone\n6619,24.75,Boston,N,2,17,4,80,High School,34,Owner,Standalone\n388,174,NJ City,W,1,15,10,33,High School,30,Franchise,Complex\n5534,15.59,Boston,N,22,17,4,74,No education,21,Patnership,Complex\n625,224.91,NJ City,N,15,22,16,47,High School,20,Patnership,Standalone\n8092,417.14,NJ City,W,9,10,3,35,High School,24,Franchise,Complex\n2586,366.17,NJ City,S,24,12,25,64,High School,21,Patnership,Complex\n5035,251.61,Boston,E,21,17,8,35,Graduation,25,Owner,Standalone\n9089,350.56,NJ City,N,4,17,21,39,Graduation,22,Patnership,Standalone\n5491,355.02,Boston,E,15,15,22,25,Graduation,23,Owner,Standalone\n8242,100.92,NJ City,S,7,22,2,85,High School,34,Patnership,Complex\n2879,9.04,NJ City,N,21,10,1,66,No education,32,Owner,Standalone\n6324,216.9,Boston,W,22,21,6,85,Graduation,34,Franchise,Complex\n879,353.24,NJ City,E,1,23,2,45,Graduation,20,Owner,Standalone\n5698,9.29,Boston,N,17,18,4,84,High School,27,Patnership,Complex\n8225,22.53,NJ City,N,7,23,1,46,No education,23,Patnership,Standalone\n1150,477.06,NJ City,S,14,17,5,53,High School,35,Patnership,Complex\n4904,445.16,NJ City,W,10,21,16,64,High School,20,Franchise,Complex\n9591,195.78,NJ City,E,16,14,14,45,Graduation,24,Owner,Standalone\n3153,259.83,NJ City,N,12,11,13,67,High School,32,Patnership,Standalone\n5518,399.96,Boston,S,8,18,10,54,Graduation,31,Patnership,Complex\n491,84.38,NJ City,E,7,19,1,40,No education,29,Owner,Standalone\n2402,55.65,NJ City,S,1,11,3,63,No education,28,Patnership,Complex\n4776,263.81,NJ City,W,7,9,9,73,High School,31,Franchise,Complex\n7691,344.67,Boston,E,25,15,1,62,No education,25,Owner,Standalone\n6196,219.27,Boston,W,14,23,25,55,Graduation,29,Franchise,Complex\n7219,233.82,Boston,E,15,21,19,71,Graduation,22,Owner,Standalone\n3236,427.74,NJ City,W,18,19,10,25,High School,27,Franchise,Complex\n6334,304.52,Boston,S,23,22,21,49,Graduation,28,Patnership,Complex\n9792,106.6,NJ City,W,16,21,3,28,Graduation,35,Franchise,Complex\n5937,40.43,Boston,N,14,9,4,59,Graduation,28,Patnership,Standalone\n7133,292.18,Boston,N,15,19,5,27,No education,23,Patnership,Standalone\n3046,270.69,NJ City,S,19,19,18,39,High School,27,Patnership,Complex\n7189,252.48,Boston,N,5,16,14,26,Graduation,30,Patnership,Standalone\n3909,320.58,NJ City,N,22,17,6,59,High School,35,Patnership,Standalone\n6339,176.01,Boston,E,6,19,1,43,Graduation,34,Owner,Standalone\n7451,443.72,Boston,E,19,18,3,72,No education,28,Owner,Standalone\n3826,290.04,NJ City,S,15,12,13,33,High School,25,Patnership,Complex\n7220,380.05,Boston,W,24,24,2,30,No education,18,Franchise,Complex\n6902,34.74,Boston,N,24,19,2,62,No education,29,Patnership,Complex\n1891,353.03,NJ City,E,10,18,18,68,High School,28,Owner,Standalone\n8360,319.34,NJ City,W,12,15,12,85,Graduation,23,Franchise,Complex\n8776,470.74,NJ City,W,7,14,4,68,High School,32,Franchise,Complex\n2918,29.23,NJ City,N,2,24,1,41,No education,20,Patnership,Complex\n3731,64.28,NJ City,N,25,24,3,39,No education,22,Owner,Standalone\n2272,32.68,NJ City,N,20,14,1,32,High School,18,Franchise,Complex\n7286,237.56,Boston,S,25,15,6,70,Graduation,34,Patnership,Complex\n1796,412.02,NJ City,W,24,17,22,47,High School,21,Franchise,Complex\n182,165.68,NJ City,S,18,24,3,34,No education,19,Patnership,Complex\n7397,384.13,Boston,N,14,23,20,67,Graduation,35,Patnership,Standalone\n442,167.38,NJ City,S,23,22,14,73,High School,25,Patnership,Complex\n7795,233.45,Boston,E,24,20,13,57,Graduation,24,Owner,Standalone\n5972,491.57,Boston,W,22,14,2,81,No education,33,Franchise,Complex\n7722,387.69,Boston,S,11,8,10,32,Graduation,26,Patnership,Complex\n1020,451.39,NJ City,W,20,24,25,74,High School,35,Franchise,Complex\n2282,209,NJ City,S,1,16,7,71,High School,32,Patnership,Complex\n1892,30.61,NJ City,N,9,13,2,34,No education,29,Franchise,Complex\n7552,225.83,Boston,W,25,21,20,63,Graduation,19,Franchise,Complex\n9210,58.17,NJ City,S,8,15,4,43,Graduation,25,Patnership,Complex\n4713,415.38,NJ City,N,18,15,2,76,Graduation,35,Patnership,Standalone\n9052,215.45,NJ City,W,3,10,9,63,Graduation,20,Franchise,Complex\n1895,356.13,NJ City,E,25,20,14,54,High School,23,Owner,Standalone\n2704,140.43,NJ City,W,11,8,1,37,High School,32,Franchise,Complex\n9327,499.27,NJ City,E,17,15,14,57,Graduation,29,Owner,Standalone\n1204,490.1,NJ City,W,5,19,1,75,High School,31,Franchise,Complex\n7277,116.72,Boston,N,23,19,4,34,No education,25,Patnership,Standalone\n3344,144.41,NJ City,W,12,13,12,39,High School,35,Franchise,Complex\n8600,460.34,NJ City,W,2,22,14,37,Graduation,24,Franchise,Complex\n3170,267.1,NJ City,S,4,12,14,76,High School,24,Patnership,Complex\n7671,319.67,Boston,E,13,17,2,68,Graduation,21,Owner,Standalone\n5646,197.39,Boston,S,23,11,13,65,Graduation,27,Patnership,Complex\n8695,417.74,NJ City,E,7,13,4,51,High School,18,Owner,Standalone\n4619,484.96,NJ City,E,12,24,16,77,High School,31,Owner,Standalone\n8018,351.23,Boston,S,10,19,5,77,No education,26,Patnership,Complex\n2619,249.18,NJ City,E,14,14,6,31,High School,35,Owner,Standalone\n4471,460.16,NJ City,E,16,16,14,62,High School,33,Owner,Standalone\n2898,150.34,NJ City,S,6,10,23,36,High School,26,Patnership,Complex\n9848,369.46,NJ City,W,10,18,2,84,No education,19,Franchise,Complex\n7104,392.6,Boston,W,20,9,7,66,Graduation,24,Franchise,Complex\n4759,385.65,NJ City,E,15,22,25,51,High School,21,Owner,Standalone\n7607,92.83,Boston,E,6,15,2,70,No education,21,Owner,Standalone\n7569,433.72,Boston,N,5,20,21,43,Graduation,21,Patnership,Standalone\n7969,150.81,Boston,N,22,22,10,79,Graduation,22,Patnership,Standalone\n7123,122.02,Boston,E,15,8,2,34,High School,34,Owner,Standalone\n1170,48.42,NJ City,S,20,24,4,34,Graduation,25,Patnership,Complex\n2790,260.16,NJ City,S,22,23,21,34,High School,23,Patnership,Complex\n1753,47.1,NJ City,N,18,20,3,48,High School,26,Patnership,Standalone\n7498,264.32,Boston,S,2,18,14,64,Graduation,32,Patnership,Complex\n2884,426.09,NJ City,W,23,22,19,84,High School,32,Franchise,Complex\n2385,479.43,NJ City,N,7,21,6,83,High School,29,Patnership,Standalone\n9894,245.46,NJ City,S,22,20,19,55,Graduation,35,Patnership,Complex\n2631,480.04,NJ City,E,24,24,15,53,High School,22,Owner,Standalone\n1471,419.31,NJ City,E,21,24,8,75,High School,34,Owner,Standalone\n383,48.59,NJ City,E,25,14,3,74,No education,23,Owner,Standalone\n6092,221.93,Boston,W,22,13,5,85,No education,19,Franchise,Complex\n4064,145.16,NJ City,W,10,24,9,66,High School,23,Franchise,Complex\n2617,208,NJ City,N,1,10,15,58,High School,34,Patnership,Standalone\n440,157,NJ City,W,3,15,7,57,High School,35,Franchise,Complex\n9694,234.63,NJ City,S,24,22,6,32,Graduation,20,Patnership,Complex\n9854,205,NJ City,S,16,19,6,65,Graduation,35,Patnership,Complex\n1761,51.71,NJ City,N,9,20,4,48,Graduation,30,Patnership,Standalone\n5278,188.03,Boston,S,23,19,2,54,High School,25,Patnership,Complex\n9451,224.51,NJ City,E,11,24,18,41,Graduation,27,Owner,Standalone\n8492,296.54,NJ City,W,8,13,3,45,No education,20,Franchise,Complex\n131,356.33,NJ City,E,8,21,11,56,High School,18,Owner,Standalone\n1657,246.56,NJ City,N,20,22,23,38,High School,31,Patnership,Standalone\n2319,196.39,NJ City,E,25,10,9,28,High School,29,Owner,Standalone\n500,212.94,NJ City,W,11,8,17,68,High School,20,Franchise,Complex\n7853,471.38,Boston,N,13,24,7,83,Graduation,26,Patnership,Standalone\n4579,224.27,NJ City,E,24,13,1,83,High School,27,Owner,Standalone\n1251,183.65,NJ City,E,24,10,11,31,High School,25,Owner,Standalone\n3538,383.2,NJ City,S,24,23,12,78,High School,34,Patnership,Complex\n6234,41.96,Boston,N,23,8,2,51,Graduation,25,Patnership,Complex\n3465,435.71,NJ City,N,15,16,6,80,High School,32,Patnership,Standalone\n9707,288.62,NJ City,E,3,22,24,77,Graduation,27,Owner,Standalone\n7917,134.43,Boston,N,24,12,17,48,Graduation,20,Patnership,Standalone\n1664,173.57,NJ City,W,14,8,5,43,No education,34,Franchise,Complex\n7502,476.83,Boston,S,6,9,21,79,Graduation,35,Patnership,Complex\n7223,256.01,Boston,E,2,22,18,58,Graduation,25,Owner,Standalone\n6660,8.21,Boston,N,22,24,4,79,Graduation,34,Franchise,Complex\n4799,17.88,NJ City,N,22,23,4,46,No education,28,Owner,Standalone\n5412,270.99,Boston,W,15,17,12,36,Graduation,32,Franchise,Complex\n174,61.53,NJ City,N,23,9,2,83,Graduation,18,Patnership,Complex\n834,194,NJ City,S,2,13,17,52,High School,24,Patnership,Complex\n6785,238.23,Boston,N,21,15,12,70,Graduation,18,Patnership,Standalone\n3623,477.22,NJ City,E,5,9,25,36,High School,25,Owner,Standalone\n6171,495.46,Boston,E,12,23,11,71,Graduation,19,Owner,Standalone\n5066,141.81,Boston,S,22,19,2,76,No education,33,Patnership,Complex\n2298,499.52,NJ City,S,10,15,13,80,High School,30,Patnership,Complex\n3649,147.6,NJ City,N,13,22,12,78,High School,27,Patnership,Standalone\n852,137,NJ City,W,3,14,6,25,High School,19,Franchise,Complex\n2555,198.21,NJ City,E,24,20,4,43,No education,24,Owner,Standalone\n6611,189.69,Boston,E,3,13,4,81,No education,35,Owner,Standalone\n8503,175.54,NJ City,E,14,9,18,50,Graduation,19,Owner,Standalone\n5873,466.94,Boston,N,6,23,17,76,Graduation,28,Patnership,Standalone\n4208,134.86,NJ City,W,19,11,1,28,No education,19,Franchise,Complex\n760,163.78,NJ City,W,25,10,3,63,High School,25,Franchise,Complex\n1437,413.77,NJ City,N,25,11,8,36,High School,34,Patnership,Standalone\n1395,330.61,NJ City,E,8,15,3,56,Graduation,29,Owner,Standalone\n6760,203.91,Boston,W,17,19,11,85,Graduation,34,Franchise,Complex\n8361,119.51,NJ City,N,3,17,4,76,Graduation,28,Patnership,Standalone\n5960,422.8,Boston,W,12,21,24,52,Graduation,21,Franchise,Complex\n2295,246,NJ City,E,1,15,23,45,High School,21,Owner,Standalone\n8929,120.05,NJ City,N,4,8,4,45,High School,24,Patnership,Standalone\n1013,87.17,NJ City,N,6,15,2,67,No education,24,Patnership,Standalone\n9469,211.8,NJ City,N,23,12,4,73,High School,19,Patnership,Standalone\n9461,305.26,NJ City,N,2,10,24,67,Graduation,25,Patnership,Standalone\n1387,133.21,NJ City,E,21,23,3,36,High School,21,Owner,Standalone\n9227,202.1,NJ City,E,8,8,5,31,No education,20,Owner,Standalone\n6022,170.64,Boston,S,24,16,24,75,Graduation,33,Patnership,Complex\n4947,467.35,Boston,E,19,15,18,25,High School,20,Owner,Standalone\n2040,327.6,NJ City,W,14,11,4,81,Graduation,29,Franchise,Complex\n934,292.86,NJ City,S,7,13,10,26,High School,24,Patnership,Complex\n9354,135.77,NJ City,S,8,17,4,39,Graduation,30,Patnership,Complex\n4848,316.02,NJ City,W,21,24,5,34,Graduation,25,Franchise,Complex\n4521,415.87,NJ City,N,25,19,9,85,High School,21,Patnership,Standalone\n5794,382.98,Boston,S,20,12,1,34,High School,19,Patnership,Complex\n6867,455.27,Boston,E,15,17,13,56,Graduation,26,Owner,Standalone\n4025,134,NJ City,N,1,21,12,30,High School,24,Patnership,Standalone\n3393,475.85,NJ City,N,4,21,21,59,High School,29,Patnership,Standalone\n4104,159.14,NJ City,W,25,10,14,38,High School,20,Franchise,Complex\n7118,255.03,Boston,S,16,14,12,80,Graduation,32,Patnership,Complex\n7109,53.26,Boston,N,17,12,2,63,No education,22,Patnership,Standalone\n6385,242.53,Boston,N,10,19,24,38,Graduation,27,Patnership,Standalone\n2979,87.3,NJ City,E,21,22,3,57,Graduation,33,Owner,Standalone\n1693,9.07,NJ City,N,5,14,1,64,High School,33,Patnership,Standalone\n1873,56.74,NJ City,N,1,10,4,33,High School,21,Patnership,Standalone\n6297,460.85,Boston,N,16,12,4,66,Graduation,26,Patnership,Standalone\n7991,57.71,Boston,E,6,18,1,67,No education,18,Owner,Standalone\n4140,205.35,NJ City,W,19,13,7,45,High School,25,Franchise,Complex\n5367,384.03,Boston,E,18,13,2,70,Graduation,34,Owner,Standalone\n2997,126.26,NJ City,N,2,17,4,71,Graduation,32,Patnership,Standalone\n8185,190.99,NJ City,N,24,12,20,69,Graduation,25,Patnership,Standalone\n7420,204.31,Boston,W,18,21,24,46,Graduation,31,Franchise,Complex\n9434,23.66,NJ City,N,25,21,3,53,No education,20,Patnership,Complex\n9942,278.11,NJ City,S,13,22,5,31,Graduation,20,Patnership,Complex\n9768,212.98,NJ City,W,2,10,8,35,Graduation,29,Franchise,Complex\n9301,188.49,NJ City,N,22,21,9,66,Graduation,26,Patnership,Standalone\n7,324.73,NJ City,E,5,21,24,44,High School,32,Owner,Standalone\n562,391.57,NJ City,S,17,10,8,33,High School,20,Patnership,Complex\n7382,299.85,Boston,S,9,18,16,84,Graduation,24,Patnership,Complex\n2612,471.35,NJ City,W,10,10,13,54,High School,27,Franchise,Complex\n2746,400.66,NJ City,S,22,12,18,26,High School,33,Patnership,Complex\n8951,429.12,NJ City,E,24,15,5,79,No education,26,Owner,Standalone\n248,144.01,NJ City,W,19,18,13,46,High School,22,Franchise,Complex\n4646,223.04,NJ City,S,1,11,2,33,No education,18,Patnership,Complex\n1106,150.47,NJ City,S,22,12,9,33,High School,19,Patnership,Complex\n5292,197.62,Boston,W,24,23,14,31,Graduation,32,Franchise,Complex\n2614,348.01,NJ City,S,3,22,1,64,High School,33,Patnership,Complex\n8804,182.15,NJ City,W,17,9,1,38,No education,30,Franchise,Complex\n2543,340.28,NJ City,E,12,22,19,64,High School,32,Owner,Standalone\n1070,453.51,NJ City,S,9,12,16,27,High School,35,Patnership,Complex\n7072,299.02,Boston,W,16,12,5,43,High School,32,Franchise,Complex\n4673,405.79,NJ City,N,19,11,2,70,No education,29,Patnership,Standalone\n6606,479.04,Boston,S,3,23,23,69,Graduation,30,Patnership,Complex\n4594,310.32,NJ City,S,16,18,18,42,High School,27,Patnership,Complex\n5961,297.97,Boston,N,2,10,6,73,Graduation,23,Patnership,Standalone\n4613,177.46,NJ City,N,14,13,2,56,No education,28,Patnership,Standalone\n9890,277.06,NJ City,S,20,12,9,76,Graduation,20,Patnership,Complex\n1256,228.76,NJ City,W,7,19,25,58,High School,35,Franchise,Complex\n8616,68.11,NJ City,N,20,13,2,60,Graduation,25,Franchise,Complex\n2706,7.6,NJ City,N,25,15,4,38,Graduation,30,Patnership,Complex\n2926,261.82,NJ City,S,9,12,6,41,High School,32,Patnership,Complex\n7950,416.05,Boston,S,10,21,5,56,Graduation,34,Patnership,Complex\n8760,349.14,NJ City,W,20,22,5,71,Graduation,23,Franchise,Complex\n3243,492.65,NJ City,E,5,21,17,58,High School,25,Owner,Standalone\n3075,439.36,NJ City,E,16,22,1,72,Graduation,27,Owner,Standalone\n2335,207.52,NJ City,E,11,15,22,57,High School,19,Owner,Standalone\n1838,162.15,NJ City,S,14,8,21,30,High School,25,Patnership,Complex\n9405,87.26,NJ City,N,1,19,3,40,Graduation,30,Patnership,Standalone\n1068,431.37,NJ City,W,13,9,11,72,High School,33,Franchise,Complex\n5660,82.31,Boston,W,16,19,1,68,No education,25,Franchise,Complex\n7925,116.76,Boston,N,14,20,1,80,No education,31,Patnership,Standalone\n6082,152,Boston,S,18,19,24,79,Graduation,34,Patnership,Complex\n9980,312.24,NJ City,W,5,16,20,34,Graduation,26,Franchise,Complex\n3137,228.52,NJ City,N,23,8,25,72,High School,20,Patnership,Standalone\n8980,246.25,NJ City,W,10,24,12,80,Graduation,27,Franchise,Complex\n4693,311.81,NJ City,N,7,12,16,76,High School,31,Patnership,Standalone\n838,246.24,NJ City,S,16,24,17,29,High School,31,Patnership,Complex\n177,395.62,NJ City,N,17,9,16,63,High School,22,Patnership,Standalone\n8822,377.71,NJ City,S,5,23,3,29,No education,33,Patnership,Complex\n217,24.91,NJ City,N,22,24,1,54,High School,24,Patnership,Standalone\n7707,235.25,Boston,E,11,18,10,52,Graduation,35,Owner,Standalone\n1744,422.18,NJ City,W,21,14,13,44,High School,31,Franchise,Complex\n2421,433.07,NJ City,N,14,20,11,49,High School,19,Patnership,Standalone\n5765,332.31,Boston,N,11,8,9,43,Graduation,33,Patnership,Standalone\n6786,470.66,Boston,S,18,9,7,79,Graduation,31,Patnership,Complex\n4583,211.32,NJ City,E,24,10,1,29,No education,20,Owner,Standalone\n6390,257.33,Boston,S,7,8,24,74,Graduation,25,Patnership,Complex\n3429,94.48,NJ City,N,23,24,1,61,Graduation,19,Patnership,Standalone\n3679,305.23,NJ City,E,14,17,20,76,High School,26,Owner,Standalone\n945,8.12,NJ City,N,16,24,2,45,Graduation,33,Patnership,Standalone\n7899,459.52,Boston,E,4,14,7,79,Graduation,29,Owner,Standalone\n3842,473.27,NJ City,S,16,12,3,61,No education,26,Patnership,Complex\n6999,219.87,Boston,E,11,20,1,27,Graduation,33,Owner,Standalone\n7301,150.74,Boston,N,22,16,6,72,Graduation,31,Patnership,Standalone\n7604,317.9,Boston,W,10,18,6,45,Graduation,20,Franchise,Complex\n6023,327.53,Boston,E,21,14,13,48,Graduation,32,Owner,Standalone\n2120,116,NJ City,W,1,19,9,63,High School,28,Franchise,Complex\n7483,245.61,Boston,E,16,20,13,79,Graduation,20,Owner,Standalone\n4700,26.74,NJ City,N,21,14,3,53,No education,30,Franchise,Complex\n4458,315.69,NJ City,S,18,23,4,31,Graduation,18,Patnership,Complex\n7660,272.81,Boston,W,19,19,25,40,Graduation,29,Franchise,Complex\n5599,269.6,Boston,E,19,23,18,76,Graduation,32,Owner,Standalone\n185,471.54,NJ City,N,10,15,8,39,High School,26,Patnership,Standalone\n3722,378.64,NJ City,S,15,15,14,68,High School,24,Patnership,Complex\n3107,384.41,NJ City,E,3,12,3,38,No education,33,Owner,Standalone\n6080,321.5,Boston,W,8,15,12,78,Graduation,34,Franchise,Complex\n4134,55.55,NJ City,S,14,16,1,25,Graduation,35,Patnership,Complex\n1310,170.23,NJ City,S,8,8,8,70,High School,21,Patnership,Complex\n2559,126,NJ City,E,1,16,18,83,High School,18,Owner,Standalone\n2363,32.34,NJ City,N,18,24,3,43,No education,31,Owner,Standalone\n5359,450.14,Boston,E,14,18,5,70,High School,23,Owner,Standalone\n8083,171.44,NJ City,E,13,24,2,85,High School,28,Owner,Standalone\n8093,38.27,NJ City,N,25,11,4,31,No education,35,Patnership,Standalone\n7522,6.19,Boston,N,6,14,4,47,High School,35,Patnership,Complex\n1570,335.03,NJ City,S,6,18,4,53,High School,18,Patnership,Complex\n3339,121.07,NJ City,E,9,18,1,30,Graduation,25,Owner,Standalone\n8745,273.29,NJ City,N,8,18,7,45,Graduation,25,Patnership,Standalone\n2365,470.22,NJ City,N,14,11,23,67,High School,19,Patnership,Standalone\n795,385.53,NJ City,E,25,24,8,36,High School,24,Owner,Standalone\n6626,465.67,Boston,S,19,16,4,59,No education,29,Patnership,Complex\n3364,120.24,NJ City,W,10,21,1,59,High School,32,Franchise,Complex\n2527,192.73,NJ City,E,12,14,23,55,High School,24,Owner,Standalone\n8789,124.99,NJ City,N,19,17,3,77,No education,33,Patnership,Standalone\n9777,26.92,NJ City,N,11,23,4,82,Graduation,32,Patnership,Standalone\n5336,82.91,Boston,W,8,20,3,47,No education,29,Franchise,Complex\n7337,135.23,Boston,N,17,23,15,26,Graduation,35,Patnership,Standalone\n2968,450.97,NJ City,W,19,21,14,55,High School,21,Franchise,Complex\n1972,382.58,NJ City,W,4,16,22,50,High School,28,Franchise,Complex\n3230,264.73,NJ City,S,14,19,13,54,High School,34,Patnership,Complex\n3179,235.71,NJ City,E,15,22,13,38,High School,28,Owner,Standalone\n5573,250.05,Boston,N,3,14,7,37,Graduation,19,Patnership,Standalone\n5632,145.37,Boston,W,18,22,25,31,Graduation,32,Franchise,Complex\n6439,39.52,Boston,N,22,13,1,28,High School,24,Owner,Standalone\n7680,455.16,Boston,W,23,10,2,28,Graduation,23,Franchise,Complex\n2480,196.85,NJ City,W,10,16,12,59,High School,34,Franchise,Complex\n5414,332.02,Boston,S,11,20,8,30,Graduation,23,Patnership,Complex\n5204,444.67,Boston,W,10,23,6,68,Graduation,35,Franchise,Complex\n9715,89.38,NJ City,E,15,16,4,60,High School,22,Owner,Standalone\n3106,263.67,NJ City,S,19,15,10,55,High School,34,Patnership,Complex\n1036,317.26,NJ City,W,19,17,22,44,High School,32,Franchise,Complex\n985,168,NJ City,N,3,15,20,25,High School,27,Patnership,Standalone\n6235,295.33,Boston,E,18,13,16,72,Graduation,20,Owner,Standalone\n808,394.77,NJ City,W,21,23,20,41,High School,27,Franchise,Complex\n6373,288.29,Boston,N,19,23,6,48,Graduation,18,Patnership,Standalone\n3698,162.16,NJ City,S,24,16,21,81,High School,22,Patnership,Complex\n7155,447.02,Boston,E,21,20,20,69,Graduation,32,Owner,Standalone\n4918,167.39,NJ City,S,23,21,9,52,High School,28,Patnership,Complex\n7544,273.78,Boston,W,22,19,9,58,Graduation,19,Franchise,Complex\n8521,336.72,NJ City,N,2,21,13,32,Graduation,27,Patnership,Standalone\n5640,223.8,Boston,W,13,24,7,35,Graduation,18,Franchise,Complex\n1970,295.56,NJ City,S,14,16,17,44,High School,18,Patnership,Complex\n5688,172.77,Boston,W,10,22,18,26,Graduation,23,Franchise,Complex\n5493,155.23,Boston,N,19,24,3,36,Graduation,20,Patnership,Standalone\n675,8.61,NJ City,N,15,21,2,28,Graduation,22,Owner,Standalone\n6525,205.63,Boston,N,17,23,20,39,Graduation,25,Patnership,Standalone\n4393,422.42,NJ City,N,6,10,9,82,High School,18,Patnership,Standalone\n3224,62.24,NJ City,N,19,9,1,34,No education,34,Franchise,Complex\n3276,129,NJ City,W,3,10,12,44,High School,35,Franchise,Complex\n2274,121,NJ City,S,2,19,4,74,Graduation,35,Patnership,Complex\n9020,220.08,NJ City,W,23,18,3,76,No education,32,Franchise,Complex\n5111,171.56,Boston,E,19,19,21,60,Graduation,26,Owner,Standalone\n6621,28.98,Boston,N,7,14,2,47,Graduation,30,Patnership,Standalone\n3754,318.53,NJ City,S,10,19,25,47,High School,33,Patnership,Complex\n7380,298.56,Boston,W,12,12,11,50,Graduation,23,Franchise,Complex\n6204,86.47,Boston,W,2,18,4,67,Graduation,19,Franchise,Complex\n8428,334.49,NJ City,W,25,14,7,73,Graduation,27,Franchise,Complex\n2065,82.27,NJ City,N,2,22,4,50,High School,33,Patnership,Standalone\n7198,387.82,Boston,S,2,17,4,65,High School,26,Patnership,Complex\n5187,260.88,Boston,E,5,10,15,27,Graduation,19,Owner,Standalone\n9953,12.87,NJ City,N,24,23,1,25,No education,24,Patnership,Standalone\n9109,368.94,NJ City,N,2,16,1,74,High School,20,Patnership,Standalone\n2416,113.72,NJ City,W,14,17,3,84,High School,28,Franchise,Complex\n458,130.83,NJ City,S,17,23,10,33,High School,32,Patnership,Complex\n6267,195.85,Boston,E,1,22,23,55,Graduation,20,Owner,Standalone\n5343,302.37,Boston,E,18,14,7,38,Graduation,25,Owner,Standalone\n6305,470.08,Boston,N,10,24,24,71,Graduation,22,Patnership,Standalone\n5429,329.02,Boston,N,3,20,16,71,Graduation,22,Patnership,Standalone\n9742,290.71,NJ City,S,23,21,22,56,Graduation,24,Patnership,Complex\n7264,335.41,Boston,W,9,15,17,78,Graduation,30,Franchise,Complex\n8378,360.59,NJ City,S,11,19,17,53,Graduation,27,Patnership,Complex\n6886,48.04,Boston,S,4,19,3,52,High School,23,Patnership,Complex\n4128,268.73,NJ City,W,8,10,14,78,High School,33,Franchise,Complex\n5411,481.2,Boston,E,6,15,14,66,Graduation,23,Owner,Standalone\n2053,320.84,NJ City,N,24,20,1,36,High School,30,Patnership,Standalone\n6962,497.81,Boston,S,24,12,15,59,Graduation,21,Patnership,Complex\n1745,144.68,NJ City,N,17,20,22,63,High School,33,Patnership,Standalone\n1503,287.28,NJ City,E,5,11,24,25,High School,19,Owner,Standalone\n3807,66.38,NJ City,N,5,18,2,75,Graduation,18,Owner,Standalone\n4510,352.07,NJ City,S,17,23,22,32,High School,23,Patnership,Complex\n5943,208.4,Boston,E,13,15,1,57,Graduation,31,Owner,Standalone\n9091,353.11,NJ City,E,15,15,17,71,Graduation,31,Owner,Standalone\n6323,219.35,Boston,E,1,23,12,37,Graduation,27,Owner,Standalone\n7863,60.36,Boston,E,10,23,2,79,Graduation,28,Owner,Standalone\n5577,55.13,Boston,N,19,17,3,55,Graduation,25,Patnership,Standalone\n1519,484.26,NJ City,E,21,14,12,67,High School,25,Owner,Standalone\n7647,146.64,Boston,E,18,13,25,65,Graduation,20,Owner,Standalone\n6169,494.76,Boston,N,11,11,19,42,Graduation,33,Patnership,Standalone\n2453,311,NJ City,N,1,18,25,25,High School,24,Patnership,Standalone\n5881,335.31,Boston,N,5,24,13,46,Graduation,33,Patnership,Standalone\n5503,302.76,Boston,E,4,10,14,40,Graduation,21,Owner,Standalone\n7956,275.1,Boston,W,2,17,10,84,Graduation,31,Franchise,Complex\n7467,443.89,Boston,E,18,19,10,65,Graduation,27,Owner,Standalone\n1612,193.04,NJ City,W,4,14,24,31,High School,34,Franchise,Complex\n157,100,NJ City,N,3,24,19,82,High School,34,Patnership,Standalone\n4868,272.26,NJ City,W,12,17,11,28,High School,25,Franchise,Complex\n9938,374.4,NJ City,S,11,22,16,77,Graduation,24,Patnership,Complex\n7560,95.95,Boston,W,4,11,1,42,Graduation,23,Franchise,Complex\n6254,473.13,Boston,S,17,21,23,77,Graduation,34,Patnership,Complex\n778,40.95,NJ City,N,5,11,4,76,High School,30,Patnership,Complex\n1610,54.15,NJ City,S,16,19,1,40,No education,35,Patnership,Complex\n9266,278.85,NJ City,S,17,9,5,26,No education,35,Patnership,Complex\n783,14.62,NJ City,N,5,16,2,72,Graduation,22,Owner,Standalone\n70,452,NJ City,S,15,12,23,54,High School,27,Patnership,Complex\n4867,174.58,NJ City,E,11,19,23,26,High School,24,Owner,Standalone\n5862,15.76,Boston,N,9,14,3,35,Graduation,27,Patnership,Complex\n6032,202.57,Boston,W,13,15,13,34,Graduation,29,Franchise,Complex\n1383,85.99,NJ City,E,1,18,1,66,Graduation,25,Owner,Standalone\n8120,141.17,NJ City,W,19,15,7,38,Graduation,22,Franchise,Complex\n2284,241.93,NJ City,W,17,22,1,31,High School,26,Franchise,Complex\n2983,486.81,NJ City,E,5,9,5,64,High School,18,Owner,Standalone\n1002,102.06,NJ City,S,9,20,2,48,Graduation,23,Patnership,Complex\n1225,392.04,NJ City,N,19,10,18,83,High School,31,Patnership,Standalone\n6425,255.67,Boston,N,3,11,10,61,Graduation,18,Patnership,Standalone\n8727,449.93,NJ City,E,20,22,15,46,Graduation,21,Owner,Standalone\n1880,486.35,NJ City,W,21,20,10,56,High School,34,Franchise,Complex\n4278,147.91,NJ City,S,25,9,10,42,High School,27,Patnership,Complex\n9073,14.86,NJ City,N,5,10,3,61,High School,29,Patnership,Standalone\n4008,103.77,NJ City,W,22,23,4,30,Graduation,19,Franchise,Complex\n187,329.82,NJ City,E,22,22,7,62,High School,35,Owner,Standalone\n1638,497.45,NJ City,S,13,15,8,29,High School,34,Patnership,Complex\n4211,429.32,NJ City,E,25,21,16,72,High School,32,Owner,Standalone\n1382,60.88,NJ City,N,17,18,4,34,No education,24,Patnership,Complex\n3011,268.27,NJ City,E,11,22,5,80,No education,20,Owner,Standalone\n6538,220.01,Boston,S,18,18,1,63,High School,29,Patnership,Complex\n7546,445.34,Boston,S,23,17,12,54,Graduation,23,Patnership,Complex\n9226,197.42,NJ City,S,3,15,5,76,High School,30,Patnership,Complex\n2570,406.68,NJ City,S,14,18,23,44,High School,18,Patnership,Complex\n6638,248.04,Boston,S,12,20,8,57,Graduation,18,Patnership,Complex\n5002,447.1,Boston,S,5,17,9,85,Graduation,25,Patnership,Complex\n1473,424.97,NJ City,N,13,16,23,77,High School,35,Patnership,Standalone\n3725,224,NJ City,N,2,11,21,58,High School,31,Patnership,Standalone\n9225,161.92,NJ City,N,10,18,22,73,Graduation,20,Patnership,Standalone\n2006,330.08,NJ City,S,10,15,15,74,High School,32,Patnership,Complex\n3920,293.39,NJ City,W,10,23,23,66,High School,27,Franchise,Complex\n7799,333.5,Boston,E,22,23,1,51,No education,35,Owner,Standalone\n7103,342.62,Boston,E,9,12,23,36,Graduation,27,Owner,Standalone\n3862,475.27,NJ City,S,19,24,19,29,High School,24,Patnership,Complex\n6003,198.84,Boston,E,14,13,8,77,Graduation,25,Owner,Standalone\n3389,271.67,NJ City,N,5,12,25,31,High School,29,Patnership,Standalone\n1696,20.28,NJ City,N,9,17,4,76,High School,25,Franchise,Complex\n8683,199.14,NJ City,E,15,23,9,73,Graduation,25,Owner,Standalone\n4113,342.03,NJ City,N,13,24,13,81,High School,20,Patnership,Standalone\n8635,275.51,NJ City,E,12,24,17,45,Graduation,32,Owner,Standalone\n8000,475.13,Boston,W,1,21,17,44,Graduation,32,Franchise,Complex\n2013,75.42,NJ City,N,13,24,2,26,Graduation,20,Patnership,Standalone\n2279,341.53,NJ City,E,2,19,2,39,No education,33,Owner,Standalone\n617,174.33,NJ City,N,22,23,21,63,High School,31,Patnership,Standalone\n1639,245.62,NJ City,E,21,23,1,82,High School,18,Owner,Standalone\n8179,211.11,NJ City,E,2,15,12,33,Graduation,33,Owner,Standalone\n502,11.2,NJ City,N,24,18,2,77,High School,30,Patnership,Complex\n6700,314.06,Boston,W,10,22,6,35,Graduation,24,Franchise,Complex\n6273,210.03,Boston,N,25,10,19,53,Graduation,20,Patnership,Standalone\n5400,291.64,Boston,W,1,24,6,67,Graduation,34,Franchise,Complex\n6983,213.56,Boston,E,6,17,21,27,Graduation,31,Owner,Standalone\n231,145.08,NJ City,E,15,14,11,35,High School,30,Owner,Standalone\n2889,291.03,NJ City,N,9,21,9,40,High School,29,Patnership,Standalone\n7808,478.2,Boston,W,14,8,11,63,Graduation,21,Franchise,Complex\n9628,70.96,NJ City,N,3,12,1,58,High School,31,Franchise,Complex\n702,66.69,NJ City,N,22,15,1,78,Graduation,30,Patnership,Complex\n924,98.33,NJ City,W,24,15,2,48,Graduation,21,Franchise,Complex\n4117,29.47,NJ City,N,16,15,3,67,High School,28,Patnership,Standalone\n2128,366.04,NJ City,W,21,12,13,55,High School,31,Franchise,Complex\n6726,327.59,Boston,S,13,15,25,83,Graduation,33,Patnership,Complex\n1963,473.56,NJ City,E,7,18,20,56,High School,29,Owner,Standalone\n4039,167.29,NJ City,E,4,11,17,76,High School,24,Owner,Standalone\n8195,264.87,NJ City,E,10,11,20,63,Graduation,33,Owner,Standalone\n2444,301.5,NJ City,W,7,24,4,35,No education,32,Franchise,Complex\n6883,211.42,Boston,E,10,15,8,82,Graduation,30,Owner,Standalone\n3487,272.79,NJ City,E,19,18,1,81,High School,33,Owner,Standalone\n8653,7.94,NJ City,N,18,14,1,47,High School,35,Patnership,Standalone\n4678,402.68,NJ City,S,3,22,5,56,High School,28,Patnership,Complex\n431,412.49,NJ City,E,4,15,17,40,High School,25,Owner,Standalone\n5006,44.29,Boston,N,25,16,3,83,No education,18,Patnership,Complex\n4996,138.13,Boston,W,15,14,7,48,High School,32,Franchise,Complex\n9785,85.45,NJ City,N,9,20,1,79,No education,21,Patnership,Standalone\n3042,24.94,NJ City,N,15,13,4,80,Graduation,30,Patnership,Complex\n100,282.19,NJ City,W,24,15,15,55,High School,21,Franchise,Complex\n290,476.65,NJ City,S,16,17,25,48,High School,34,Patnership,Complex\n4325,335.74,NJ City,N,5,19,16,71,High School,29,Patnership,Standalone\n6809,147.9,Boston,N,18,13,18,81,Graduation,33,Patnership,Standalone\n5580,19.25,Boston,N,8,19,2,78,Graduation,35,Franchise,Complex\n5977,58.85,Boston,N,19,8,2,46,High School,25,Patnership,Standalone\n8524,211.04,NJ City,W,1,15,17,71,Graduation,18,Franchise,Complex\n2821,238.79,NJ City,N,14,14,15,48,High School,22,Patnership,Standalone\n1136,8.24,NJ City,N,6,22,2,68,No education,34,Franchise,Complex\n7204,378.53,Boston,W,17,9,14,59,Graduation,27,Franchise,Complex\n4057,497.18,NJ City,N,13,17,20,38,High School,22,Patnership,Standalone\n1191,299.67,NJ City,E,12,20,4,62,Graduation,27,Owner,Standalone\n9272,461.96,NJ City,W,24,22,9,33,Graduation,27,Franchise,Complex\n7992,38.81,Boston,N,2,10,2,77,Graduation,21,Franchise,Complex\n8977,94.38,NJ City,N,1,24,3,74,High School,30,Patnership,Standalone\n6018,285.86,Boston,S,17,11,9,57,Graduation,18,Patnership,Complex\n6975,373.82,Boston,E,19,20,7,71,Graduation,34,Owner,Standalone\n2059,262,NJ City,E,2,18,7,45,High School,21,Owner,Standalone\n5915,193.32,Boston,E,23,15,13,62,Graduation,18,Owner,Standalone\n7529,103,Boston,N,24,20,1,75,No education,19,Patnership,Standalone\n3244,230.41,NJ City,W,16,13,14,60,High School,24,Franchise,Complex\n9947,102.06,NJ City,E,12,24,3,39,No education,22,Owner,Standalone\n1867,76.65,NJ City,E,3,17,3,33,High School,18,Owner,Standalone\n5243,365.45,Boston,E,3,19,4,69,No education,35,Owner,Standalone\n2895,365.13,NJ City,E,20,8,25,66,High School,30,Owner,Standalone\n8337,355.1,NJ City,N,12,16,16,46,Graduation,30,Patnership,Standalone\n6637,366.93,Boston,N,24,24,11,34,Graduation,26,Patnership,Standalone\n5007,335.27,Boston,E,2,20,4,81,Graduation,23,Owner,Standalone\n3817,77.66,NJ City,N,15,19,3,61,High School,26,Patnership,Standalone\n3161,26.31,NJ City,N,15,24,3,34,No education,23,Patnership,Standalone\n612,407.79,NJ City,W,23,11,2,59,Graduation,29,Franchise,Complex\n5923,73.51,Boston,N,7,11,2,82,High School,28,Owner,Standalone\n7775,410.04,Boston,E,16,20,24,73,Graduation,20,Owner,Standalone\n1476,435.05,NJ City,W,19,15,1,56,Graduation,24,Franchise,Complex\n5468,249.54,Boston,W,15,21,21,28,Graduation,28,Franchise,Complex\n9948,98.85,NJ City,W,22,22,2,38,Graduation,25,Franchise,Complex\n3211,345.05,NJ City,E,17,11,14,34,High School,29,Owner,Standalone\n3995,334.06,NJ City,E,22,13,9,48,High School,30,Owner,Standalone\n557,308.66,NJ City,N,8,15,18,43,High School,32,Patnership,Standalone\n2183,392.52,NJ City,E,7,21,24,41,High School,29,Owner,Standalone\n1304,217.92,NJ City,W,15,13,1,78,No education,30,Franchise,Complex\n4382,58.98,NJ City,S,22,11,1,53,No education,29,Patnership,Complex\n7303,68.54,Boston,N,5,10,1,38,High School,34,Owner,Standalone\n6865,39.31,Boston,N,22,22,1,64,High School,21,Patnership,Standalone\n1129,182.1,NJ City,N,8,18,2,37,High School,26,Patnership,Standalone\n8710,47.73,NJ City,S,2,19,3,77,High School,18,Patnership,Complex\n2177,148.94,NJ City,N,1,8,5,46,No education,26,Patnership,Standalone\n9292,87.24,NJ City,W,17,13,3,46,High School,29,Franchise,Complex\n9925,18.23,NJ City,N,22,14,3,72,High School,25,Patnership,Standalone\n6774,335.73,Boston,S,21,19,14,30,Graduation,18,Patnership,Complex\n5488,306.79,Boston,W,7,21,1,27,High School,32,Franchise,Complex\n9159,412.82,NJ City,E,24,12,6,64,Graduation,23,Owner,Standalone\n9631,473.88,NJ City,E,22,20,16,83,Graduation,31,Owner,Standalone\n1353,249,NJ City,N,2,18,23,56,High School,24,Patnership,Standalone\n9460,269.16,NJ City,W,8,9,2,67,High School,29,Franchise,Complex\n3109,391.28,NJ City,N,8,22,17,75,High School,25,Patnership,Standalone\n1564,116,NJ City,W,2,9,25,81,High School,34,Franchise,Complex\n1973,178.97,NJ City,N,11,19,21,80,High School,22,Patnership,Standalone\n5627,120.24,Boston,E,7,17,2,26,No education,25,Owner,Standalone\n7148,17.37,Boston,N,14,17,2,27,No education,20,Franchise,Complex\n3195,449.18,NJ City,E,15,8,22,46,High School,34,Owner,Standalone\n6105,459.56,Boston,N,1,22,15,76,Graduation,27,Patnership,Standalone\n1133,243.74,NJ City,N,15,22,16,73,High School,27,Patnership,Standalone\n3612,60.37,NJ City,N,22,14,4,70,Graduation,35,Franchise,Complex\n9752,168.58,NJ City,W,1,15,14,57,Graduation,21,Franchise,Complex\n278,424.15,NJ City,S,23,24,8,83,High School,23,Patnership,Complex\n5192,275.66,Boston,W,9,23,6,46,Graduation,32,Franchise,Complex\n7715,52.69,Boston,E,2,23,3,45,No education,35,Owner,Standalone\n82,155.86,Boston,S,16,24,20,27,High School,32,Patnership,Complex\n3311,305.58,NJ City,E,14,13,20,69,High School,24,Owner,Standalone\n6336,467.85,Boston,W,6,8,8,56,Graduation,28,Franchise,Complex\n5059,168.71,Boston,E,7,19,8,69,Graduation,32,Owner,Standalone\n6065,318.06,Boston,N,22,20,18,75,Graduation,29,Patnership,Standalone\n6396,379.65,Boston,W,4,21,19,34,Graduation,21,Franchise,Complex\n7160,51.91,Boston,W,13,16,1,48,No education,18,Franchise,Complex\n427,295.75,NJ City,E,12,22,9,33,High School,24,Owner,Standalone\n3941,421.07,NJ City,N,7,18,2,62,No education,23,Patnership,Standalone\n163,449.89,NJ City,E,1,20,1,34,High School,20,Owner,Standalone\n7752,375.68,Boston,W,4,16,12,28,Graduation,26,Franchise,Complex\n4340,24.74,NJ City,N,18,12,3,53,No education,19,Franchise,Complex\n6275,3.4,Boston,N,15,18,2,61,No education,23,Owner,Standalone\n313,99.91,NJ City,N,22,22,4,48,High School,33,Patnership,Standalone\n9404,297.41,NJ City,W,18,10,24,42,Graduation,35,Franchise,Complex\n1049,426.83,NJ City,N,8,13,3,69,No education,21,Patnership,Standalone\n1960,1.41,NJ City,W,4,19,1,71,High School,27,Franchise,Complex\n3082,16.16,NJ City,N,9,22,1,77,High School,27,Patnership,Complex\n7350,337.99,Boston,S,3,8,1,47,Graduation,24,Patnership,Complex\n1194,234.8,NJ City,S,11,8,3,37,Graduation,23,Patnership,Complex\n3681,8.99,NJ City,N,9,13,1,50,Graduation,23,Patnership,Standalone\n6906,344.43,Boston,S,12,18,19,69,Graduation,32,Patnership,Complex\n6952,94.37,Boston,W,7,19,2,34,High School,26,Franchise,Complex\n6329,71.51,Boston,N,1,8,2,52,No education,25,Patnership,Standalone\n4716,70.79,NJ City,N,8,14,1,25,Graduation,32,Franchise,Complex\n8690,464.65,NJ City,S,11,8,8,30,Graduation,35,Patnership,Complex\n3750,9.58,NJ City,N,9,15,4,79,Graduation,28,Patnership,Complex\n3175,179.47,NJ City,E,16,12,6,34,High School,33,Owner,Standalone\n267,98.98,NJ City,E,14,13,1,68,Graduation,25,Owner,Standalone\n8730,327.43,NJ City,S,14,19,24,72,Graduation,31,Patnership,Complex\n6493,339.73,Boston,N,11,19,22,26,Graduation,18,Patnership,Standalone\n8990,307.61,NJ City,S,9,11,18,40,Graduation,26,Patnership,Complex\n5708,116.85,Boston,W,20,24,2,79,No education,34,Franchise,Complex\n2252,179.37,NJ City,W,6,12,15,68,High School,22,Franchise,Complex\n5734,176.18,Boston,S,7,12,19,84,Graduation,32,Patnership,Complex\n2334,102.23,NJ City,S,5,24,2,45,Graduation,29,Patnership,Complex\n7823,439.38,Boston,E,25,14,15,65,Graduation,32,Owner,Standalone\n5433,421.26,Boston,N,10,10,18,39,Graduation,31,Patnership,Standalone\n9300,158.99,NJ City,W,8,10,3,26,Graduation,19,Franchise,Complex\n5128,359.36,Boston,W,11,19,11,78,Graduation,24,Franchise,Complex\n7745,313,Boston,N,24,8,24,36,Graduation,35,Patnership,Standalone\n6889,442.12,Boston,N,3,22,9,71,Graduation,34,Patnership,Standalone\n5648,41.72,Boston,N,9,24,3,39,No education,29,Franchise,Complex\n7952,498.37,Boston,W,13,20,5,84,No education,19,Franchise,Complex\n7025,129.46,Boston,N,16,16,17,47,Graduation,34,Patnership,Standalone\n5188,456.46,Boston,W,22,20,11,64,Graduation,22,Franchise,Complex\n477,116.76,NJ City,N,3,13,4,43,Graduation,31,Patnership,Standalone\n8150,17.81,NJ City,N,24,9,1,56,No education,26,Patnership,Complex\n2060,433.09,NJ City,W,12,15,18,46,High School,22,Franchise,Complex\n8835,332.21,NJ City,E,17,22,4,54,Graduation,35,Owner,Standalone\n2833,444.41,NJ City,N,23,18,17,64,High School,35,Patnership,Standalone\n3509,493.89,NJ City,N,5,8,5,52,No education,25,Patnership,Standalone\n4949,433.86,Boston,N,23,19,11,27,High School,35,Patnership,Standalone\n8212,402.02,NJ City,W,20,10,6,44,Graduation,21,Franchise,Complex\n6315,123.79,Boston,E,4,24,4,27,Graduation,28,Owner,Standalone\n6157,167.49,Boston,N,20,22,8,49,Graduation,33,Patnership,Standalone\n7166,302.66,Boston,S,25,13,2,51,No education,18,Patnership,Complex\n6124,78.29,Boston,W,4,9,1,43,High School,22,Franchise,Complex\n3335,389.75,NJ City,E,19,14,15,54,High School,35,Owner,Standalone\n8034,6.24,Boston,N,4,14,2,63,Graduation,28,Patnership,Complex\n1754,25.1,NJ City,N,24,23,1,54,No education,28,Patnership,Complex\n5497,348.48,Boston,N,18,20,2,41,High School,22,Patnership,Standalone\n3168,361.19,NJ City,W,9,11,2,41,Graduation,20,Franchise,Complex\n2696,212.96,NJ City,W,22,23,25,74,High School,23,Franchise,Complex\n2161,388.54,NJ City,N,21,16,19,31,High School,24,Patnership,Standalone\n5553,94.95,Boston,N,8,16,4,44,Graduation,21,Patnership,Standalone\n9369,218.34,NJ City,N,16,20,20,48,Graduation,19,Patnership,Standalone\n656,235.52,NJ City,W,9,22,18,51,High School,25,Franchise,Complex\n7466,186.79,Boston,S,10,15,12,40,Graduation,27,Patnership,Complex\n7683,129.42,Boston,E,11,8,19,36,Graduation,32,Owner,Standalone\n5910,478.41,Boston,S,7,22,10,42,Graduation,35,Patnership,Complex\n5947,203.56,Boston,E,7,24,25,77,Graduation,29,Owner,Standalone\n4938,433.51,Boston,S,16,18,17,67,High School,18,Patnership,Complex\n7850,165.93,Boston,S,15,24,23,72,Graduation,19,Patnership,Complex\n5346,404.46,Boston,S,12,9,13,47,Graduation,23,Patnership,Complex\n4127,142.57,NJ City,E,8,21,9,82,High School,23,Owner,Standalone\n7158,207.53,Boston,S,16,13,24,71,Graduation,22,Patnership,Complex\n9952,475.18,NJ City,W,5,22,12,83,Graduation,19,Franchise,Complex\n2473,376.01,NJ City,N,7,21,12,77,High School,28,Patnership,Standalone\n9223,44.79,NJ City,N,7,21,1,83,High School,26,Owner,Standalone\n5471,235.03,Boston,E,13,20,20,45,Graduation,19,Owner,Standalone\n7896,260.37,Boston,W,16,15,10,52,Graduation,23,Franchise,Complex\n611,327.35,NJ City,E,6,9,1,70,No education,22,Owner,Standalone\n4135,278.55,NJ City,E,24,22,9,50,High School,33,Owner,Standalone\n5342,338.81,Boston,S,12,9,1,36,No education,32,Patnership,Complex\n3984,308.72,NJ City,W,21,22,2,76,Graduation,24,Franchise,Complex\n5196,61.76,Boston,N,15,14,3,45,Graduation,21,Franchise,Complex\n8969,85.95,NJ City,N,2,13,1,74,No education,21,Patnership,Standalone\n259,428.94,NJ City,E,16,24,14,55,High School,25,Owner,Standalone\n2545,11.36,NJ City,N,22,10,4,41,High School,22,Patnership,Standalone\n6552,27.06,Boston,N,8,8,3,50,Graduation,26,Franchise,Complex\n7730,218.42,Boston,S,24,23,24,85,Graduation,18,Patnership,Complex\n3793,136.5,NJ City,N,24,20,20,61,High School,27,Patnership,Standalone\n6136,85.23,Boston,W,11,16,3,69,High School,20,Franchise,Complex\n5578,269.78,Boston,S,19,22,9,42,Graduation,32,Patnership,Complex\n4070,435.84,NJ City,S,1,10,3,48,No education,26,Patnership,Complex\n638,82.2,NJ City,S,15,8,1,70,No education,25,Patnership,Complex\n8676,106.28,NJ City,W,17,8,3,51,Graduation,25,Franchise,Complex\n9202,168.94,NJ City,S,18,16,15,78,Graduation,28,Patnership,Complex\n7233,407.41,Boston,N,23,9,19,76,Graduation,18,Patnership,Standalone\n3515,320.01,NJ City,E,15,8,13,47,High School,20,Owner,Standalone\n2311,290.83,NJ City,E,12,23,24,29,High School,30,Owner,Standalone\n6543,130.56,Boston,E,10,8,10,68,Graduation,32,Owner,Standalone\n73,394.3,Boston,N,7,18,5,63,High School,31,Patnership,Standalone\n5347,122.88,Boston,E,25,11,3,81,High School,26,Owner,Standalone\n2634,254.03,NJ City,S,6,9,1,67,Graduation,29,Patnership,Complex\n9183,85.31,NJ City,E,21,15,4,51,Graduation,35,Owner,Standalone\n2423,158.77,NJ City,E,11,19,13,70,High School,25,Owner,Standalone\n8049,102.16,NJ City,N,18,17,4,78,Graduation,28,Patnership,Standalone\n4553,190.65,NJ City,N,10,8,15,62,High School,31,Patnership,Standalone\n8561,122.48,NJ City,N,4,8,2,82,No education,26,Patnership,Standalone\n9846,100.39,NJ City,S,10,9,2,35,Graduation,32,Patnership,Complex\n748,62.77,NJ City,N,2,13,1,39,High School,35,Franchise,Complex\n454,159.46,NJ City,S,19,16,12,50,High School,34,Patnership,Complex\n8685,492.41,NJ City,N,19,13,9,70,Graduation,26,Patnership,Standalone\n2969,389.41,NJ City,N,11,14,2,68,No education,19,Patnership,Standalone\n4379,21.6,NJ City,N,10,12,1,43,No education,33,Owner,Standalone\n5439,332.74,Boston,E,21,18,14,75,Graduation,26,Owner,Standalone\n8704,319.67,NJ City,W,14,14,9,38,Graduation,30,Franchise,Complex\n4160,435.81,NJ City,W,9,8,14,31,High School,35,Franchise,Complex\n2648,101.95,NJ City,W,19,9,2,80,No education,32,Franchise,Complex\n6937,473.66,Boston,N,21,19,5,44,High School,23,Patnership,Standalone\n7326,335.83,Boston,S,3,21,18,25,Graduation,32,Patnership,Complex\n3466,495.64,NJ City,S,15,8,7,51,High School,32,Patnership,Complex\n2411,279.71,NJ City,E,7,13,23,74,High School,22,Owner,Standalone\n5418,440.04,Boston,S,11,18,22,51,Graduation,24,Patnership,Complex\n3571,265.78,NJ City,E,25,18,12,38,High School,33,Owner,Standalone\n6519,99.8,Boston,E,11,17,1,80,Graduation,30,Owner,Standalone\n5189,369.03,Boston,N,9,23,14,34,Graduation,33,Patnership,Standalone\n9809,340.31,NJ City,N,12,23,10,40,Graduation,30,Patnership,Standalone\n8703,123.95,NJ City,E,14,24,2,71,Graduation,28,Owner,Standalone\n7407,137.1,Boston,E,14,9,6,71,Graduation,30,Owner,Standalone\n8164,189.56,NJ City,W,10,14,17,46,Graduation,28,Franchise,Complex\n7587,101.59,Boston,E,6,21,3,31,Graduation,32,Owner,Standalone\n6436,390.33,Boston,W,19,19,19,30,Graduation,23,Franchise,Complex\n1613,310.7,NJ City,N,7,19,4,67,No education,24,Patnership,Standalone\n5305,4.6,Boston,N,16,15,3,40,High School,32,Patnership,Standalone\n7463,402.64,Boston,E,9,17,9,47,Graduation,21,Owner,Standalone\n1826,454.1,NJ City,S,6,24,23,69,High School,19,Patnership,Complex\n7593,339.59,Boston,N,5,8,4,71,Graduation,24,Patnership,Standalone\n6650,181.96,Boston,S,20,18,1,80,No education,34,Patnership,Complex\n3367,249.91,NJ City,E,9,22,20,53,High School,32,Owner,Standalone\n287,463.54,NJ City,E,19,17,2,83,No education,22,Owner,Standalone\n1752,44.35,NJ City,N,6,16,2,53,Graduation,28,Franchise,Complex\n3191,135.15,NJ City,E,8,12,15,37,High School,20,Owner,Standalone\n1142,448.02,NJ City,S,23,8,5,71,No education,24,Patnership,Complex\n9476,217.52,NJ City,W,20,21,22,54,Graduation,29,Franchise,Complex\n6682,345.91,Boston,S,21,13,17,40,Graduation,35,Patnership,Complex\n3334,484.74,NJ City,S,25,18,24,31,High School,30,Patnership,Complex\n7200,457.66,Boston,W,10,24,12,79,Graduation,35,Franchise,Complex\n9780,122.13,NJ City,W,10,16,2,44,Graduation,30,Franchise,Complex\n1357,208.79,NJ City,N,6,23,4,64,High School,24,Patnership,Standalone\n7865,302.3,Boston,N,24,17,16,51,Graduation,28,Patnership,Standalone\n6675,400.47,Boston,E,11,24,21,83,Graduation,23,Owner,Standalone\n1207,270.12,NJ City,E,8,23,25,63,High School,18,Owner,Standalone\n118,168.85,NJ City,S,16,12,19,64,High School,34,Patnership,Complex\n4694,235.2,NJ City,S,15,14,20,26,High School,24,Patnership,Complex\n9303,279.93,NJ City,E,20,22,14,57,Graduation,28,Owner,Standalone\n6570,342.19,Boston,S,24,12,20,49,Graduation,30,Patnership,Complex\n64,455.93,Boston,W,23,9,17,33,High School,32,Franchise,Complex\n1705,231.72,NJ City,N,10,16,8,54,High School,35,Patnership,Standalone\n1618,468.26,NJ City,S,25,22,17,66,High School,21,Patnership,Complex\n2526,259.23,NJ City,S,13,12,21,30,High School,23,Patnership,Complex\n7390,371.2,Boston,S,8,19,8,66,Graduation,27,Patnership,Complex\n6887,343.86,Boston,E,25,14,16,84,Graduation,26,Owner,Standalone\n1128,316.82,NJ City,W,10,22,13,26,High School,24,Franchise,Complex\n8966,408.6,NJ City,S,3,20,18,36,Graduation,19,Patnership,Complex\n7945,392.19,Boston,N,4,21,13,54,Graduation,27,Patnership,Standalone\n1217,242.23,NJ City,N,15,22,17,45,High School,29,Patnership,Standalone\n5434,406.72,Boston,S,6,14,1,33,High School,25,Patnership,Complex\n8269,408.34,NJ City,N,19,17,8,63,Graduation,28,Patnership,Standalone\n1064,265.09,NJ City,W,4,16,13,29,High School,23,Franchise,Complex\n787,418.57,NJ City,E,18,11,8,73,High School,22,Owner,Standalone\n6173,424.77,Boston,N,14,24,14,59,Graduation,35,Patnership,Standalone\n837,464.02,NJ City,N,11,12,17,42,High School,33,Patnership,Standalone\n8258,116.31,NJ City,S,5,22,2,34,No education,23,Patnership,Complex\n4299,90.51,NJ City,E,9,24,3,61,Graduation,25,Owner,Standalone\n9855,251.71,NJ City,E,23,20,13,35,Graduation,20,Owner,Standalone\n7873,94.99,Boston,N,13,22,1,58,High School,24,Patnership,Standalone\n1489,124.34,NJ City,N,5,10,2,30,High School,25,Patnership,Standalone\n368,383.01,NJ City,W,15,16,8,26,High School,32,Franchise,Complex\n3008,56.69,NJ City,W,4,17,2,38,No education,27,Franchise,Complex\n4845,346.18,NJ City,N,20,8,25,39,High School,24,Patnership,Standalone\n3214,425.96,NJ City,S,4,23,21,64,High School,25,Patnership,Complex\n3058,184,NJ City,S,1,19,9,73,High School,28,Patnership,Complex\n7163,78.13,Boston,E,25,14,1,34,No education,32,Owner,Standalone\n2309,85.26,NJ City,N,12,21,3,55,No education,30,Patnership,Standalone\n3113,310.37,NJ City,N,24,13,25,70,High School,20,Patnership,Standalone\n9363,214.8,NJ City,E,4,20,11,32,Graduation,32,Owner,Standalone\n535,35.77,NJ City,N,11,16,1,25,High School,33,Owner,Standalone\n6833,180.41,Boston,N,13,22,1,83,No education,25,Patnership,Standalone\n6911,210.01,Boston,E,6,20,22,62,Graduation,27,Owner,Standalone\n1993,116.24,NJ City,N,16,19,4,71,High School,35,Patnership,Standalone\n6312,140.16,Boston,W,20,12,10,69,Graduation,27,Franchise,Complex\n9240,461.56,NJ City,W,13,13,5,70,Graduation,25,Franchise,Complex\n9313,32.56,NJ City,N,8,19,2,52,High School,33,Patnership,Standalone\n4041,436.72,NJ City,N,10,24,18,64,High School,22,Patnership,Standalone\n2301,242.25,NJ City,N,6,19,11,40,High School,31,Patnership,Standalone\n331,123.5,NJ City,E,13,21,3,58,High School,20,Owner,Standalone\n8507,350.48,NJ City,E,6,19,22,33,Graduation,32,Owner,Standalone\n2384,192.66,NJ City,W,10,12,10,72,High School,33,Franchise,Complex\n9507,144.96,NJ City,E,7,22,13,36,Graduation,26,Owner,Standalone\n8486,363.08,NJ City,S,14,13,11,46,Graduation,19,Patnership,Complex\n7168,238.78,Boston,W,4,23,15,70,Graduation,21,Franchise,Complex\n199,326.07,NJ City,E,1,19,2,75,High School,32,Owner,Standalone\n2722,488.48,NJ City,S,17,14,16,38,High School,20,Patnership,Complex\n607,5.12,NJ City,N,2,24,1,53,High School,33,Owner,Standalone\n9862,42.88,NJ City,N,4,12,2,50,High School,27,Patnership,Complex\n1463,482.67,NJ City,E,18,12,4,80,No education,27,Owner,Standalone\n38,41.18,Maine,N,2,23,2,33,No education,30,Patnership,Complex\n1616,231.16,NJ City,W,14,16,10,73,High School,21,Franchise,Complex\n7449,319.74,Boston,N,23,8,14,41,Graduation,25,Patnership,Standalone\n26,28.93,Boston,N,16,16,3,31,No education,35,Patnership,Complex\n1668,85.54,NJ City,W,14,12,1,55,Graduation,33,Franchise,Complex\n484,393.99,NJ City,W,8,19,18,26,High School,27,Franchise,Complex\n1672,19.75,NJ City,N,6,12,1,54,High School,28,Franchise,Complex\n9249,439.34,NJ City,N,8,23,5,54,Graduation,20,Patnership,Standalone\n5677,418.19,Boston,N,20,18,19,78,Graduation,18,Patnership,Standalone\n3966,498.24,NJ City,S,16,11,7,80,High School,22,Patnership,Complex\n3925,34.22,NJ City,N,25,17,3,30,High School,19,Patnership,Standalone\n2795,15.71,NJ City,N,14,22,2,64,No education,32,Owner,Standalone\n429,202.91,NJ City,N,14,8,16,48,High School,18,Patnership,Standalone\n9380,393.42,NJ City,W,21,9,18,80,Graduation,28,Franchise,Complex\n7575,264.98,Boston,E,6,14,9,34,Graduation,27,Owner,Standalone\n6350,92.92,Boston,S,5,15,3,77,No education,26,Patnership,Complex\n5779,103.49,Boston,E,5,21,3,60,High School,20,Owner,Standalone\n4305,401.51,NJ City,N,13,20,10,32,High School,25,Patnership,Standalone\n5831,60.66,Boston,N,7,20,1,69,No education,18,Owner,Standalone\n3448,139.61,NJ City,W,4,23,19,49,High School,29,Franchise,Complex\n9155,387.69,NJ City,E,24,23,7,83,Graduation,35,Owner,Standalone\n3399,207.83,NJ City,E,22,9,24,51,High School,28,Owner,Standalone\n7941,197.66,Boston,N,24,17,24,48,Graduation,34,Patnership,Standalone\n7748,211.89,Boston,W,4,8,21,60,Graduation,20,Franchise,Complex\n9675,47.93,NJ City,E,6,16,3,78,Graduation,25,Owner,Standalone\n7154,41.85,Boston,N,14,17,4,28,No education,23,Patnership,Complex\n9798,88.18,NJ City,S,22,20,3,56,Graduation,24,Patnership,Complex\n849,289.84,NJ City,N,20,9,18,54,High School,25,Patnership,Standalone\n2510,111.95,NJ City,S,23,9,4,82,No education,19,Patnership,Complex\n316,398.81,NJ City,W,21,15,16,42,High School,26,Franchise,Complex\n1398,332.92,NJ City,S,21,17,8,45,High School,29,Patnership,Complex\n2954,327.44,NJ City,S,19,11,6,31,High School,22,Patnership,Complex\n8991,489.46,NJ City,E,15,23,22,35,Graduation,35,Owner,Standalone\n6590,378.61,Boston,S,18,21,22,73,Graduation,24,Patnership,Complex\n300,368.91,NJ City,W,12,19,14,73,High School,20,Franchise,Complex\n8228,241.98,NJ City,W,18,18,19,47,Graduation,22,Franchise,Complex\n8377,268.62,NJ City,N,19,11,17,55,Graduation,35,Patnership,Standalone\n7153,228.1,Boston,N,17,14,20,27,Graduation,33,Patnership,Standalone\n6970,456.08,Boston,S,8,23,17,37,Graduation,21,Patnership,Complex\n2091,297.95,NJ City,E,9,8,17,38,High School,33,Owner,Standalone\n4461,417.56,NJ City,N,14,22,23,57,High School,35,Patnership,Standalone\n5172,383.91,Boston,W,25,19,11,80,Graduation,23,Franchise,Complex\n2964,221.03,NJ City,W,23,23,1,34,Graduation,25,Franchise,Complex\n6146,245.91,Boston,S,25,15,5,66,No education,25,Patnership,Complex\n1078,197.83,NJ City,S,25,10,22,68,High School,29,Patnership,Complex\n9521,340.39,NJ City,N,20,11,14,28,Graduation,35,Patnership,Standalone\n1961,100.35,NJ City,N,13,15,2,55,No education,31,Patnership,Standalone\n2592,469.22,NJ City,W,15,16,2,68,Graduation,30,Franchise,Complex\n4804,157.92,NJ City,W,6,9,14,31,High School,25,Franchise,Complex\n3055,482.01,NJ City,E,16,22,20,83,High School,34,Owner,Standalone\n4376,242.4,NJ City,W,11,20,6,83,High School,28,Franchise,Complex\n763,366.57,NJ City,E,22,22,13,67,High School,33,Owner,Standalone\n3660,359.77,NJ City,W,18,11,17,84,High School,22,Franchise,Complex\n6497,106.48,Boston,N,14,11,2,58,No education,20,Patnership,Standalone\n9082,457.31,NJ City,S,7,21,13,44,Graduation,27,Patnership,Complex\n6198,468.91,Boston,S,16,18,23,29,Graduation,26,Patnership,Complex\n8464,232.81,NJ City,W,21,22,5,26,High School,23,Franchise,Complex\n2445,236.4,NJ City,N,5,20,14,37,High School,31,Patnership,Standalone\n4529,232.06,NJ City,N,11,8,24,77,High School,32,Patnership,Standalone\n6693,50.9,Boston,N,13,13,3,76,Graduation,33,Patnership,Standalone\n7629,44.75,Boston,N,1,15,2,44,Graduation,32,Patnership,Standalone\n4819,364.1,NJ City,E,11,19,4,31,High School,29,Owner,Standalone\n3301,103.23,NJ City,N,4,13,1,72,High School,32,Patnership,Standalone\n5499,113.95,Boston,E,2,11,4,39,Graduation,19,Owner,Standalone\n4439,128.56,NJ City,E,16,9,3,47,No education,26,Owner,Standalone\n8480,497.71,NJ City,W,13,14,18,48,Graduation,29,Franchise,Complex\n5989,178.26,Boston,N,14,8,18,28,Graduation,32,Patnership,Standalone\n3250,99.82,NJ City,S,4,8,1,74,High School,20,Patnership,Complex\n7408,422.99,Boston,W,21,20,14,72,Graduation,25,Franchise,Complex\n3701,431.85,NJ City,N,22,22,22,55,High School,31,Patnership,Standalone\n5004,330.58,Boston,W,24,11,24,55,Graduation,30,Franchise,Complex\n7267,222.35,Boston,E,15,24,23,80,Graduation,18,Owner,Standalone\n6077,486.98,Boston,N,19,11,9,85,Graduation,26,Patnership,Standalone\n9635,154.1,NJ City,E,6,10,8,73,Graduation,26,Owner,Standalone\n5319,413.56,Boston,E,15,16,9,82,Graduation,30,Owner,Standalone\n4915,1.05,NJ City,N,8,11,4,57,High School,31,Owner,Standalone\n6892,2.88,Boston,W,24,12,3,40,High School,24,Franchise,Complex\n3903,7.47,NJ City,N,14,21,1,69,Graduation,28,Owner,Standalone\n9576,148.26,NJ City,W,3,13,6,45,Graduation,21,Franchise,Complex\n6536,420.15,Boston,W,24,11,13,50,Graduation,34,Franchise,Complex\n4473,14.36,NJ City,N,18,16,3,65,Graduation,32,Patnership,Standalone\n6545,328.55,Boston,N,8,10,23,26,Graduation,33,Patnership,Standalone\n7249,325.89,Boston,N,19,19,13,80,Graduation,25,Patnership,Standalone\n5815,98.61,Boston,E,25,19,3,70,High School,20,Owner,Standalone\n5295,59.43,Boston,E,4,21,3,62,Graduation,20,Owner,Standalone\n8613,130.53,NJ City,N,24,12,12,78,Graduation,30,Patnership,Standalone\n6876,233.89,Boston,W,5,23,5,38,Graduation,33,Franchise,Complex\n5445,425.28,Boston,N,13,16,13,51,Graduation,20,Patnership,Standalone\n6382,372.68,Boston,S,13,14,7,62,Graduation,29,Patnership,Complex\n3735,424.7,NJ City,E,21,13,4,30,Graduation,30,Owner,Standalone\n4009,468.64,NJ City,N,12,20,16,31,High School,25,Patnership,Standalone\n5311,323.89,Boston,E,25,24,20,53,Graduation,30,Owner,Standalone\n4241,161.25,NJ City,N,10,10,7,62,High School,24,Patnership,Standalone\n8605,327.66,NJ City,N,10,19,25,34,Graduation,34,Patnership,Standalone\n5823,488.58,Boston,E,12,8,16,71,Graduation,28,Owner,Standalone\n4499,106.15,NJ City,E,14,24,1,38,No education,20,Owner,Standalone\n9508,30.33,NJ City,N,10,15,2,42,High School,24,Franchise,Complex\n3342,257.32,NJ City,S,21,20,19,61,High School,27,Patnership,Complex\n4060,449.48,NJ City,W,16,18,5,40,High School,31,Franchise,Complex\n376,88.29,NJ City,W,6,24,4,77,High School,34,Franchise,Complex\n8422,186.99,NJ City,S,3,18,3,54,High School,31,Patnership,Complex\n8491,461.97,NJ City,E,9,17,22,71,Graduation,20,Owner,Standalone\n245,65.11,NJ City,N,22,15,1,82,No education,25,Patnership,Standalone\n4500,305.4,NJ City,W,19,17,5,59,Graduation,27,Franchise,Complex\n4192,408.05,NJ City,W,4,10,23,79,High School,20,Franchise,Complex\n4831,154.77,NJ City,E,19,9,7,77,High School,25,Owner,Standalone\n9009,197.9,NJ City,N,17,16,21,72,Graduation,27,Patnership,Standalone\n7559,160.54,Boston,E,7,9,3,54,No education,34,Owner,Standalone\n1294,115.72,NJ City,S,4,10,3,62,High School,28,Patnership,Complex\n6795,176.96,Boston,E,16,9,7,81,Graduation,25,Owner,Standalone\n4563,59.91,NJ City,E,17,15,1,53,Graduation,31,Owner,Standalone\n1464,120.75,NJ City,W,12,21,2,43,Graduation,26,Franchise,Complex\n197,118.58,NJ City,N,3,20,1,46,No education,35,Patnership,Standalone\n8796,227.5,NJ City,W,4,10,21,40,Graduation,22,Franchise,Complex\n4185,337.75,NJ City,N,16,18,19,61,High School,25,Patnership,Standalone\n2567,333,NJ City,E,2,17,4,51,No education,26,Owner,Standalone\n5695,34.62,Boston,N,25,15,3,75,High School,25,Owner,Standalone\n9573,129.65,NJ City,N,23,16,20,68,Graduation,22,Patnership,Standalone\n8160,49.31,NJ City,W,11,16,2,62,Graduation,29,Franchise,Complex\n3369,192.02,NJ City,N,20,16,16,82,High School,32,Patnership,Standalone\n4690,499.85,NJ City,S,1,17,5,78,High School,35,Patnership,Complex\n6550,326.28,Boston,S,19,13,25,84,Graduation,32,Patnership,Complex\n3475,48.38,NJ City,E,17,23,2,50,High School,21,Owner,Standalone\n9108,208.22,NJ City,W,21,24,22,64,Graduation,27,Franchise,Complex\n7309,408.08,Boston,N,15,15,1,68,High School,21,Patnership,Standalone\n7347,209.2,Boston,E,7,9,18,80,Graduation,19,Owner,Standalone\n3948,93.45,NJ City,W,23,16,1,53,Graduation,24,Franchise,Complex\n9676,495.54,NJ City,W,7,18,6,58,Graduation,26,Franchise,Complex\n7230,401.46,Boston,S,15,11,5,71,Graduation,27,Patnership,Complex\n8319,240.39,NJ City,E,7,24,25,63,Graduation,32,Owner,Standalone\n4637,477.95,NJ City,N,1,13,5,55,No education,20,Patnership,Standalone\n3752,304.41,NJ City,W,19,11,18,36,High School,30,Franchise,Complex\n1481,191.98,NJ City,N,21,24,12,36,High School,33,Patnership,Standalone\n7566,411.02,Boston,S,4,18,22,36,Graduation,23,Patnership,Complex\n5323,343.86,Boston,E,22,17,1,43,High School,28,Owner,Standalone\n6394,307.96,Boston,S,16,9,10,69,Graduation,27,Patnership,Complex\n2131,222.8,NJ City,E,10,17,19,41,High School,34,Owner,Standalone\n1355,374.28,NJ City,E,18,12,5,57,No education,19,Owner,Standalone\n9127,349.85,NJ City,E,3,23,9,42,Graduation,25,Owner,Standalone\n3079,464.59,NJ City,E,9,20,2,79,High School,26,Owner,Standalone\n5357,479.89,Boston,N,5,10,6,32,Graduation,35,Patnership,Standalone\n5113,316.63,Boston,N,3,23,19,41,Graduation,24,Patnership,Standalone\n5328,10.92,Boston,N,2,18,2,42,Graduation,26,Franchise,Complex\n6446,441.55,Boston,S,15,21,1,53,No education,23,Patnership,Complex\n8788,171.97,NJ City,W,22,11,12,76,Graduation,31,Franchise,Complex\n6573,347.09,Boston,N,21,13,22,57,Graduation,28,Patnership,Standalone\n4857,429.32,NJ City,N,7,23,3,60,Graduation,24,Patnership,Standalone\n673,473.91,NJ City,N,4,10,8,67,High School,22,Patnership,Standalone\n4463,402.01,NJ City,E,16,13,22,42,High School,20,Owner,Standalone\n4433,292.43,NJ City,N,8,10,16,73,High School,31,Patnership,Standalone\n4741,57.79,NJ City,N,24,22,2,52,High School,25,Patnership,Standalone\n8603,64.15,NJ City,N,24,12,3,56,No education,31,Owner,Standalone\n6478,413.52,Boston,S,13,16,3,55,High School,24,Patnership,Complex\n9649,222.61,NJ City,N,16,8,7,48,Graduation,24,Patnership,Standalone\n3006,118.48,NJ City,S,20,15,1,54,Graduation,35,Patnership,Complex\n6071,143.24,Boston,E,14,17,25,85,Graduation,23,Owner,Standalone\n3298,27.67,NJ City,N,4,21,3,77,High School,20,Patnership,Complex\n5045,460.36,Boston,N,21,12,2,53,No education,23,Patnership,Standalone\n8946,191.66,NJ City,S,25,19,7,47,Graduation,28,Patnership,Complex\n961,56.05,NJ City,N,7,9,1,43,High School,19,Patnership,Standalone\n369,218.92,NJ City,N,7,14,23,32,High School,27,Patnership,Standalone\n5033,491.83,Boston,N,3,24,2,40,No education,33,Patnership,Standalone\n6008,463.83,Boston,W,14,24,15,29,Graduation,33,Franchise,Complex\n8296,191.87,NJ City,W,23,19,18,82,Graduation,26,Franchise,Complex\n5774,407.27,Boston,S,24,8,17,35,Graduation,29,Patnership,Complex\n294,283.91,NJ City,S,20,17,4,73,Graduation,18,Patnership,Complex\n7794,438.54,Boston,S,19,12,3,73,Graduation,21,Patnership,Complex\n4879,109.86,NJ City,E,21,20,4,77,High School,19,Owner,Standalone\n7832,170.04,Boston,W,14,11,4,62,No education,26,Franchise,Complex\n5423,281.73,Boston,E,12,12,8,51,Graduation,31,Owner,Standalone\n8726,444.57,NJ City,S,21,18,7,61,Graduation,32,Patnership,Complex\n5892,458.09,Boston,W,11,17,6,51,Graduation,35,Franchise,Complex\n9176,379.03,NJ City,W,6,11,4,46,No education,24,Franchise,Complex\n6924,393.71,Boston,W,8,10,21,75,Graduation,24,Franchise,Complex\n6985,278.17,Boston,N,18,14,24,26,Graduation,21,Patnership,Standalone\n1922,38.97,NJ City,N,21,15,3,69,No education,32,Patnership,Complex\n680,433.81,NJ City,W,14,15,9,50,High School,21,Franchise,Complex\n9011,96.59,NJ City,E,8,15,2,38,No education,34,Owner,Standalone\n7169,36.61,Boston,N,16,15,1,45,No education,26,Patnership,Standalone\n9638,314.18,NJ City,S,21,14,17,31,Graduation,20,Patnership,Complex\n8824,55.54,NJ City,W,12,15,2,82,High School,29,Franchise,Complex\n4190,365.26,NJ City,S,7,21,24,33,High School,32,Patnership,Complex\n3353,151.81,NJ City,N,19,8,16,72,High School,26,Patnership,Standalone\n5012,417.91,Boston,W,3,11,4,78,No education,31,Franchise,Complex\n2281,264.21,NJ City,N,19,21,8,83,High School,22,Patnership,Standalone\n5070,344.97,Boston,S,13,12,6,44,Graduation,33,Patnership,Complex\n4438,223.69,NJ City,S,21,9,22,30,High School,23,Patnership,Complex\n9705,79.41,NJ City,N,14,12,4,26,Graduation,32,Patnership,Standalone\n6333,414.79,Boston,N,16,19,8,30,Graduation,21,Patnership,Standalone\n6117,480.19,Boston,N,21,13,17,64,Graduation,34,Patnership,Standalone\n9042,27.63,NJ City,N,12,20,2,68,Graduation,30,Patnership,Complex\n6603,317.03,Boston,E,25,17,10,68,Graduation,18,Owner,Standalone\n4454,317.64,NJ City,S,5,20,8,30,High School,29,Patnership,Complex\n3537,287.57,NJ City,N,14,22,2,41,Graduation,35,Patnership,Standalone\n2106,81.32,NJ City,S,12,15,1,85,Graduation,26,Patnership,Complex\n3788,23.45,NJ City,N,13,9,3,77,No education,35,Franchise,Complex\n3666,158.97,NJ City,S,13,13,8,63,High School,33,Patnership,Complex\n2182,193.21,NJ City,S,25,9,21,63,High School,28,Patnership,Complex\n7076,475.06,Boston,W,4,22,3,72,No education,32,Franchise,Complex\n4233,380.44,NJ City,N,5,9,6,52,High School,31,Patnership,Standalone\n7379,327.83,Boston,E,8,8,4,65,No education,28,Owner,Standalone\n8768,212.83,NJ City,W,11,9,9,34,Graduation,19,Franchise,Complex\n4533,168.6,NJ City,N,10,16,9,82,High School,25,Patnership,Standalone\n1495,382.95,NJ City,E,8,8,17,68,High School,22,Owner,Standalone\n8713,106,NJ City,N,8,8,3,71,High School,22,Patnership,Standalone\n6400,44.49,Boston,N,20,23,1,55,High School,27,Franchise,Complex\n3348,71.07,NJ City,N,9,9,3,54,Graduation,35,Franchise,Complex\n3970,410.46,NJ City,S,13,11,14,36,High School,18,Patnership,Complex\n9367,190.81,NJ City,E,11,22,7,56,Graduation,20,Owner,Standalone\n7272,265.82,Boston,W,8,21,10,83,Graduation,30,Franchise,Complex\n5942,340.16,Boston,S,11,22,4,50,No education,30,Patnership,Complex\n5755,495.67,Boston,E,23,21,3,44,High School,34,Owner,Standalone\n344,12.66,NJ City,N,10,21,4,55,No education,28,Franchise,Complex\n1232,440.17,NJ City,W,19,22,11,43,High School,35,Franchise,Complex\n6210,439.39,Boston,S,1,15,16,53,Graduation,18,Patnership,Complex\n8665,44.88,NJ City,N,3,21,3,59,High School,34,Patnership,Standalone\n1024,204.34,NJ City,W,4,12,1,65,High School,35,Franchise,Complex\n4482,178.08,NJ City,S,14,13,14,74,High School,28,Patnership,Complex\n9222,356.56,NJ City,S,13,8,19,76,Graduation,23,Patnership,Complex\n7417,333.17,Boston,N,4,16,2,54,High School,23,Patnership,Standalone\n3912,104.07,NJ City,W,5,19,2,39,Graduation,28,Franchise,Complex\n107,412.4,NJ City,E,21,13,16,69,High School,23,Owner,Standalone\n6230,220.53,Boston,S,14,12,2,50,No education,32,Patnership,Complex\n2344,119.79,NJ City,W,1,9,2,52,High School,34,Franchise,Complex\n7830,18.1,Boston,N,20,10,3,58,Graduation,34,Patnership,Complex\n9828,284.45,NJ City,W,7,20,20,69,Graduation,31,Franchise,Complex\n8489,347.45,NJ City,N,14,11,12,81,Graduation,23,Patnership,Standalone\n4627,17.76,NJ City,N,5,13,4,27,High School,26,Owner,Standalone\n3637,388.69,NJ City,N,10,12,14,73,High School,34,Patnership,Standalone\n6896,8.46,Boston,N,18,23,2,55,No education,22,Franchise,Complex\n4275,241.65,NJ City,E,24,13,2,53,Graduation,32,Owner,Standalone\n2684,326.85,NJ City,W,18,14,10,58,High School,31,Franchise,Complex\n2535,243.87,NJ City,E,8,21,12,59,High School,32,Owner,Standalone\n585,391.41,NJ City,N,21,8,4,49,Graduation,33,Patnership,Standalone\n731,132.94,NJ City,E,8,18,20,54,High School,32,Owner,Standalone\n9361,115.47,NJ City,N,19,16,4,76,High School,18,Patnership,Standalone\n4833,385.7,NJ City,N,6,18,1,84,Graduation,19,Patnership,Standalone\n6197,201.31,Boston,N,18,15,5,45,No education,18,Patnership,Standalone\n3723,15.59,NJ City,N,16,17,2,64,Graduation,34,Owner,Standalone\n2474,295.67,NJ City,S,6,17,21,84,High School,34,Patnership,Complex\n3090,24.9,NJ City,N,15,15,1,64,Graduation,35,Patnership,Complex\n5715,487.35,Boston,E,8,24,4,72,Graduation,23,Owner,Standalone\n5726,446.35,Boston,S,12,16,8,43,Graduation,25,Patnership,Complex\n7140,196.65,Boston,W,5,22,1,29,Graduation,33,Franchise,Complex\n5084,288.84,Boston,W,16,12,12,36,Graduation,18,Franchise,Complex\n582,238.42,NJ City,S,23,14,13,78,High School,33,Patnership,Complex\n1597,159.37,NJ City,N,13,15,15,25,High School,23,Patnership,Standalone\n9813,380.51,NJ City,N,20,17,3,29,Graduation,33,Patnership,Standalone\n6872,335.99,Boston,W,9,8,9,35,Graduation,26,Franchise,Complex\n5924,324.02,Boston,W,5,9,12,81,Graduation,18,Franchise,Complex\n5962,416.49,Boston,S,1,14,8,48,Graduation,29,Patnership,Complex\n881,452.94,NJ City,N,24,11,7,65,High School,35,Patnership,Standalone\n7384,230.16,Boston,W,13,9,14,59,Graduation,25,Franchise,Complex\n2975,131.73,NJ City,E,13,23,8,48,High School,31,Owner,Standalone\n1728,210.52,NJ City,W,15,8,23,39,High School,35,Franchise,Complex\n1839,229.47,NJ City,E,23,18,24,26,High School,29,Owner,Standalone\n3601,495.9,NJ City,N,10,14,17,33,High School,31,Patnership,Standalone\n3645,466.46,NJ City,N,10,24,4,48,Graduation,26,Patnership,Standalone\n8056,132.3,NJ City,W,22,18,15,75,Graduation,30,Franchise,Complex\n4314,380.86,NJ City,S,18,16,17,52,High School,29,Patnership,Complex\n1465,73.49,NJ City,N,13,18,3,43,High School,22,Patnership,Standalone\n8320,271.03,NJ City,W,6,22,13,68,Graduation,22,Franchise,Complex\n609,317.77,NJ City,N,24,17,1,27,Graduation,28,Patnership,Standalone\n292,119.04,NJ City,W,4,20,1,40,High School,29,Franchise,Complex\n9206,385.15,NJ City,S,9,12,13,34,Graduation,27,Patnership,Complex\n9018,293.18,NJ City,S,23,11,17,53,Graduation,27,Patnership,Complex\n5365,490.57,Boston,N,16,12,23,70,Graduation,25,Patnership,Standalone\n5399,453.94,Boston,E,14,19,3,73,No education,32,Owner,Standalone\n5246,43.38,Boston,N,15,14,2,38,No education,24,Patnership,Complex\n303,2.32,NJ City,E,20,12,4,54,Graduation,18,Owner,Standalone\n5479,309.04,Boston,E,11,19,20,50,Graduation,19,Owner,Standalone\n3440,144.41,NJ City,W,14,13,2,72,No education,33,Franchise,Complex\n1291,426.73,NJ City,E,15,22,23,28,High School,33,Owner,Standalone\n7003,403.44,Boston,E,21,11,22,40,Graduation,24,Owner,Standalone\n6565,94.51,Boston,N,15,16,3,29,High School,27,Patnership,Standalone\n8470,335.84,NJ City,S,10,20,21,35,Graduation,19,Patnership,Complex\n6854,409.68,Boston,S,6,23,13,73,Graduation,32,Patnership,Complex\n3279,354.6,NJ City,E,23,23,7,45,High School,21,Owner,Standalone\n3778,109,NJ City,S,1,20,9,63,High School,18,Patnership,Complex\n7790,468.49,Boston,S,5,16,10,59,Graduation,29,Patnership,Complex\n8741,472.8,NJ City,N,15,24,2,78,No education,34,Patnership,Standalone\n1200,348.72,NJ City,W,17,24,16,45,High School,27,Franchise,Complex\n7590,402.56,Boston,S,12,18,14,62,Graduation,30,Patnership,Complex\n5905,107.34,Boston,N,18,22,2,55,High School,21,Patnership,Standalone\n1983,443.45,NJ City,E,20,21,10,49,High School,25,Owner,Standalone\n4654,262,NJ City,S,3,17,12,40,High School,32,Patnership,Complex\n7187,109.99,Boston,E,3,13,3,65,No education,27,Owner,Standalone\n5341,137.39,Boston,N,10,16,24,32,Graduation,35,Patnership,Standalone\n6564,104.75,Boston,W,14,12,2,37,Graduation,35,Franchise,Complex\n9433,281.48,NJ City,N,9,8,25,58,Graduation,19,Patnership,Standalone\n3174,310,NJ City,S,1,16,10,80,High School,19,Patnership,Complex\n5193,457.02,Boston,N,16,8,10,71,Graduation,33,Patnership,Standalone\n4887,492.48,NJ City,E,9,17,16,79,High School,25,Owner,Standalone\n6877,176.47,Boston,N,7,22,19,41,Graduation,30,Patnership,Standalone\n3287,369.87,NJ City,E,13,14,16,30,High School,23,Owner,Standalone\n6652,37.03,Boston,N,6,21,1,75,High School,18,Franchise,Complex\n7396,242.59,Boston,W,4,14,7,62,Graduation,18,Franchise,Complex\n2166,20.78,NJ City,N,6,21,2,34,Graduation,25,Patnership,Complex\n2329,172.34,NJ City,N,7,22,13,31,High School,28,Patnership,Standalone\n5821,204.98,Boston,N,24,13,4,61,High School,21,Patnership,Standalone\n9835,107.08,NJ City,E,4,11,1,58,High School,28,Owner,Standalone\n2370,317.21,NJ City,S,16,13,9,36,High School,30,Patnership,Complex\n7001,99.21,Boston,N,17,21,3,66,No education,22,Patnership,Standalone\n895,84.54,NJ City,E,15,16,3,51,High School,18,Owner,Standalone\n4862,160.45,NJ City,S,13,9,11,76,High School,26,Patnership,Complex\n5308,341.25,Boston,W,8,20,5,66,High School,21,Franchise,Complex\n9796,14.84,NJ City,N,23,10,3,51,High School,21,Franchise,Complex\n2098,485.46,NJ City,S,17,9,23,45,High School,22,Patnership,Complex\n2585,14.5,NJ City,N,1,9,3,52,No education,33,Patnership,Standalone\n9808,274.6,NJ City,W,8,20,10,54,Graduation,25,Franchise,Complex\n2443,303,NJ City,E,1,12,6,56,High School,25,Owner,Standalone\n1755,198.05,NJ City,E,10,17,8,81,High School,28,Owner,Standalone\n498,134.84,NJ City,S,16,18,13,54,High School,28,Patnership,Complex\n3955,230.87,NJ City,E,7,11,9,32,High School,24,Owner,Standalone\n1392,296.67,NJ City,W,1,23,4,59,Graduation,35,Franchise,Complex\n7623,121.32,Boston,E,7,13,4,69,Graduation,30,Owner,Standalone\n5351,144.71,Boston,E,9,18,4,50,No education,24,Owner,Standalone\n9790,301.05,NJ City,S,13,14,21,78,Graduation,24,Patnership,Complex\n7297,389.24,Boston,N,12,15,12,36,Graduation,23,Patnership,Standalone\n1872,246.83,NJ City,W,17,21,4,42,Graduation,29,Franchise,Complex\n7541,285.49,Boston,N,24,24,6,27,Graduation,29,Patnership,Standalone\n5611,235.02,Boston,E,3,20,20,33,Graduation,28,Owner,Standalone\n8811,113.22,NJ City,E,19,14,3,60,Graduation,22,Owner,Standalone\n2226,72.33,NJ City,N,9,23,4,35,Graduation,32,Patnership,Complex\n5736,490.23,Boston,W,21,22,3,32,Graduation,26,Franchise,Complex\n4551,372.08,NJ City,E,12,16,20,64,High School,24,Owner,Standalone\n847,131.81,NJ City,E,15,21,13,59,High School,26,Owner,Standalone\n6665,395.66,Boston,N,21,24,25,34,Graduation,31,Patnership,Standalone\n8175,228.72,NJ City,E,6,20,12,68,Graduation,26,Owner,Standalone\n2700,335.3,NJ City,W,21,15,14,43,High School,26,Franchise,Complex\n201,438.49,NJ City,N,23,16,25,36,High School,29,Patnership,Standalone\n5053,393.29,Boston,N,14,24,10,34,Graduation,31,Patnership,Standalone\n5851,78.32,Boston,E,2,13,3,31,High School,26,Owner,Standalone\n4234,201.37,NJ City,S,25,17,9,33,High School,31,Patnership,Complex\n5597,477.47,Boston,N,23,20,1,27,No education,35,Patnership,Standalone\n1912,59.77,NJ City,W,22,8,4,72,High School,23,Franchise,Complex\n9409,99.07,NJ City,N,2,18,3,43,High School,25,Patnership,Standalone\n898,436.15,NJ City,S,20,11,14,77,High School,25,Patnership,Complex\n2414,417.55,NJ City,S,8,11,2,38,No education,30,Patnership,Complex\n6430,413.12,Boston,S,13,22,16,79,Graduation,27,Patnership,Complex\n4324,261.67,NJ City,W,8,17,16,43,High School,28,Franchise,Complex\n3833,333.97,NJ City,N,7,18,4,61,No education,31,Patnership,Standalone\n8885,387.92,NJ City,N,8,24,25,42,Graduation,27,Patnership,Standalone\n143,225.18,NJ City,E,24,10,23,25,High School,35,Owner,Standalone\n1962,103.41,NJ City,S,19,12,4,51,Graduation,29,Patnership,Complex\n860,43.38,NJ City,N,21,23,2,26,No education,21,Franchise,Complex\n9195,79.3,NJ City,E,8,12,1,36,Graduation,28,Owner,Standalone\n5522,52.96,Boston,S,5,18,1,80,No education,28,Patnership,Complex\n5223,9.96,Boston,N,22,8,2,76,Graduation,23,Owner,Standalone\n8912,430.94,NJ City,W,4,15,14,46,Graduation,32,Franchise,Complex\n7135,484.44,Boston,E,22,12,20,63,Graduation,32,Owner,Standalone\n9582,234.79,NJ City,S,12,13,7,56,Graduation,18,Patnership,Complex\n8926,438.23,NJ City,S,17,24,6,45,Graduation,25,Patnership,Complex\n3370,449.01,NJ City,S,13,13,17,26,High School,23,Patnership,Complex\n6779,398.6,Boston,E,20,10,4,55,No education,27,Owner,Standalone\n8626,241.88,NJ City,S,20,14,17,52,Graduation,31,Patnership,Complex\n7954,298.58,Boston,S,14,11,11,62,Graduation,24,Patnership,Complex\n3037,154.42,NJ City,N,18,17,16,60,High School,21,Patnership,Standalone\n8364,376.46,NJ City,W,9,10,13,60,Graduation,22,Franchise,Complex\n9344,389.31,NJ City,W,20,8,12,38,Graduation,23,Franchise,Complex\n2643,396.57,NJ City,E,5,18,5,34,Graduation,35,Owner,Standalone\n2256,225.47,NJ City,W,16,8,21,79,High School,22,Franchise,Complex\n6705,337.09,Boston,N,5,10,8,62,Graduation,30,Patnership,Standalone\n1272,278.64,NJ City,W,11,8,22,85,High School,34,Franchise,Complex\n2381,425.35,NJ City,N,25,12,3,59,No education,29,Patnership,Standalone\n7442,471.97,Boston,S,5,17,16,28,Graduation,20,Patnership,Complex\n7877,212,Boston,N,17,10,14,85,Graduation,32,Patnership,Standalone\n8047,450.08,NJ City,E,20,20,25,45,Graduation,29,Owner,Standalone\n6474,379.73,Boston,S,25,13,16,31,Graduation,27,Patnership,Complex\n356,10.01,NJ City,N,11,19,4,54,No education,18,Franchise,Complex\n4916,49.49,NJ City,W,5,12,2,60,No education,22,Franchise,Complex\n3167,15.77,NJ City,N,6,11,2,81,No education,32,Owner,Standalone\n579,292.4,NJ City,E,12,17,22,57,High School,30,Owner,Standalone\n9910,482.88,NJ City,S,16,12,5,40,High School,30,Patnership,Complex\n8261,117.51,NJ City,N,7,10,3,54,No education,21,Patnership,Standalone\n5746,298.24,Boston,S,4,14,1,78,High School,21,Patnership,Complex\n8536,66.02,NJ City,N,9,18,3,76,High School,20,Franchise,Complex\n6912,22.72,Boston,N,19,12,3,26,Graduation,29,Franchise,Complex\n9816,56.04,NJ City,W,10,17,4,35,Graduation,32,Franchise,Complex\n5751,101.17,Boston,E,21,19,3,56,Graduation,34,Owner,Standalone\n8644,160.29,NJ City,W,9,16,4,44,High School,19,Franchise,Complex\n9071,117.15,NJ City,E,12,19,4,56,No education,33,Owner,Standalone\n327,162.09,NJ City,E,4,12,11,69,High School,31,Owner,Standalone\n7210,19.91,Boston,N,18,11,3,29,High School,32,Patnership,Complex\n3242,173.66,NJ City,S,15,15,10,48,High School,33,Patnership,Complex\n1333,163,NJ City,N,3,16,7,45,High School,28,Patnership,Standalone\n1384,259.72,NJ City,W,9,16,5,38,High School,19,Franchise,Complex\n2862,60.73,NJ City,N,10,20,4,25,Graduation,33,Patnership,Complex\n3742,242.72,NJ City,S,20,13,12,81,High School,33,Patnership,Complex\n3266,118.23,NJ City,S,4,8,1,25,No education,33,Patnership,Complex\n9432,5.02,NJ City,N,14,17,2,74,Graduation,27,Franchise,Complex\n4766,291.33,NJ City,S,13,12,10,57,High School,28,Patnership,Complex\n2155,253.77,NJ City,E,10,20,6,63,High School,27,Owner,Standalone\n9607,450.41,NJ City,E,6,14,1,70,High School,28,Owner,Standalone\n3324,37.21,NJ City,N,21,18,3,35,Graduation,29,Franchise,Complex\n7077,250.83,Boston,N,23,9,5,54,Graduation,22,Patnership,Standalone\n8693,57.21,NJ City,N,14,8,4,43,No education,34,Patnership,Standalone\n9493,209.46,NJ City,N,5,19,4,36,High School,19,Patnership,Standalone\n3553,56.93,NJ City,N,19,8,1,83,High School,19,Patnership,Standalone\n859,321.84,NJ City,E,16,9,18,54,High School,33,Owner,Standalone\n6322,222.87,Boston,S,12,19,17,81,Graduation,27,Patnership,Complex\n5525,282.35,Boston,N,24,10,23,57,Graduation,33,Patnership,Standalone\n2189,262.7,NJ City,N,25,16,4,49,No education,29,Patnership,Standalone\n8144,12.48,NJ City,N,6,16,3,37,No education,24,Franchise,Complex\n3865,464.38,NJ City,N,10,23,13,55,High School,33,Patnership,Standalone\n6421,445.77,Boston,N,6,12,11,26,Graduation,30,Patnership,Standalone\n2145,399.78,NJ City,N,11,22,10,40,High School,29,Patnership,Standalone\n7769,151.24,Boston,N,3,8,7,45,Graduation,26,Patnership,Standalone\n8879,450.22,NJ City,E,8,11,17,40,Graduation,24,Owner,Standalone\n4522,365.62,NJ City,S,25,15,17,56,High School,32,Patnership,Complex\n6905,269.32,Boston,N,17,17,5,44,No education,32,Patnership,Standalone\n6433,290.98,Boston,N,6,12,19,66,Graduation,34,Patnership,Standalone\n1568,206,NJ City,W,1,16,22,75,High School,23,Franchise,Complex\n9190,34.22,NJ City,N,18,20,4,52,High School,28,Patnership,Complex\n9172,480.36,NJ City,W,25,13,12,75,Graduation,24,Franchise,Complex\n3332,269.02,NJ City,W,8,18,15,31,High School,25,Franchise,Complex\n1127,428.31,NJ City,E,24,21,13,51,High School,22,Owner,Standalone\n1904,440.63,NJ City,W,23,9,20,73,High School,27,Franchise,Complex\n4523,133.66,NJ City,E,25,9,19,45,High School,18,Owner,Standalone\n6597,435.54,Boston,N,2,24,20,29,Graduation,19,Patnership,Standalone\n5186,248.44,Boston,S,21,21,23,48,Graduation,29,Patnership,Complex\n3641,458.36,NJ City,N,7,21,13,50,High School,29,Patnership,Standalone\n9182,471.71,NJ City,S,4,16,6,69,Graduation,30,Patnership,Complex\n6159,303.63,Boston,E,23,15,19,41,Graduation,26,Owner,Standalone\n1727,92.02,NJ City,E,9,12,3,41,No education,22,Owner,Standalone\n268,261.42,NJ City,W,17,11,25,33,High School,19,Franchise,Complex\n2551,85.06,NJ City,E,3,15,1,56,High School,27,Owner,Standalone\n7034,358.7,Boston,S,6,11,14,76,Graduation,26,Patnership,Complex\n2432,349.36,NJ City,W,7,9,25,58,High School,27,Franchise,Complex\n2269,145.22,NJ City,N,16,24,16,66,High School,31,Patnership,Standalone\n7365,90.22,Boston,N,22,8,1,32,Graduation,26,Patnership,Standalone\n5301,105.76,Boston,N,1,11,2,81,Graduation,30,Patnership,Standalone\n6188,186.48,Boston,W,8,22,12,35,Graduation,19,Franchise,Complex\n1302,235,NJ City,S,1,11,23,38,High School,27,Patnership,Complex\n1578,402.71,NJ City,S,18,24,11,85,High School,32,Patnership,Complex\n8574,337.81,NJ City,S,17,8,2,66,Graduation,34,Patnership,Complex\n7936,450.57,Boston,W,21,9,4,65,High School,23,Franchise,Complex\n4924,234.67,NJ City,W,7,9,11,85,High School,31,Franchise,Complex\n5143,67.89,Boston,N,13,15,3,76,High School,31,Owner,Standalone\n3240,173.11,NJ City,W,18,21,22,70,High School,29,Franchise,Complex\n169,231.65,NJ City,N,12,10,8,51,High School,31,Patnership,Standalone\n5761,427.8,Boston,N,20,8,1,51,High School,30,Patnership,Standalone\n3415,399.54,NJ City,E,15,10,6,62,High School,31,Owner,Standalone\n7702,47.76,Boston,S,3,16,4,31,High School,29,Patnership,Complex\n6335,144.87,Boston,E,6,20,4,28,No education,21,Owner,Standalone\n4365,26.95,NJ City,N,23,17,2,34,Graduation,24,Patnership,Standalone\n9147,410.44,NJ City,E,11,10,1,82,Graduation,22,Owner,Standalone\n6369,467.17,Boston,N,15,13,4,27,Graduation,24,Patnership,Standalone\n5240,447.06,Boston,W,15,14,12,53,Graduation,27,Franchise,Complex\n343,313,NJ City,E,2,18,6,41,High School,30,Owner,Standalone\n7807,435.99,Boston,E,5,22,1,53,High School,23,Owner,Standalone\n3554,211.92,NJ City,S,8,12,25,27,High School,30,Patnership,Complex\n9987,495.22,NJ City,E,7,13,17,52,Graduation,25,Owner,Standalone\n1031,131.51,NJ City,E,14,10,19,41,High School,27,Owner,Standalone\n5719,212.94,Boston,E,4,19,17,31,Graduation,18,Owner,Standalone\n4550,477.76,NJ City,S,4,10,13,38,High School,21,Patnership,Complex\n2691,236.17,NJ City,E,25,22,6,53,High School,26,Owner,Standalone\n6127,175.48,Boston,E,6,17,13,58,Graduation,29,Owner,Standalone\n3830,74.28,NJ City,S,1,18,2,60,No education,33,Patnership,Complex\n8170,71.05,NJ City,N,13,10,1,65,High School,35,Patnership,Complex\n2096,194.97,NJ City,W,4,15,18,27,High School,21,Franchise,Complex\n8578,463.72,NJ City,S,7,12,17,52,Graduation,30,Patnership,Complex\n9241,51.23,NJ City,N,9,15,3,36,High School,31,Patnership,Standalone\n4479,64.88,NJ City,N,21,17,1,70,Graduation,26,Owner,Standalone\n5010,262.59,Boston,S,11,10,9,44,Graduation,28,Patnership,Complex\n4505,241.26,NJ City,N,22,10,22,33,High School,35,Patnership,Standalone\n466,378.14,NJ City,S,14,15,10,66,High School,20,Patnership,Complex\n7547,18.03,Boston,N,16,22,4,67,No education,34,Owner,Standalone\n1733,392.43,NJ City,N,11,16,23,65,High School,33,Patnership,Standalone\n1436,198.97,NJ City,W,7,19,13,30,High School,20,Franchise,Complex\n8060,173.62,NJ City,W,5,19,7,39,Graduation,18,Franchise,Complex\n1661,405.77,NJ City,N,18,19,3,59,No education,34,Patnership,Standalone\n7922,161.76,Boston,S,15,18,7,85,Graduation,24,Patnership,Complex\n4036,463.32,NJ City,W,19,18,16,67,High School,27,Franchise,Complex\n5406,73.97,Boston,S,17,11,2,37,Graduation,32,Patnership,Complex\n5911,248.59,Boston,E,21,8,13,59,Graduation,31,Owner,Standalone\n210,205,NJ City,S,3,10,9,82,High School,28,Patnership,Complex\n6725,251.54,Boston,N,19,9,16,62,Graduation,20,Patnership,Standalone\n6994,282.8,Boston,S,21,23,17,47,Graduation,20,Patnership,Complex\n509,429.34,NJ City,N,13,22,6,73,High School,32,Patnership,Standalone\n5476,288.56,Boston,W,15,8,20,63,Graduation,24,Franchise,Complex\n330,44.9,NJ City,N,6,8,2,56,Graduation,35,Patnership,Complex\n8096,444.62,NJ City,W,6,18,16,84,Graduation,25,Franchise,Complex\n9929,373.2,NJ City,N,4,11,9,70,Graduation,27,Patnership,Standalone\n6223,139.83,Boston,E,22,19,1,35,High School,28,Owner,Standalone\n5639,468.8,Boston,E,17,10,18,66,Graduation,20,Owner,Standalone\n4296,350.21,NJ City,W,13,24,8,48,High School,33,Franchise,Complex\n6016,430.39,Boston,W,3,10,11,76,Graduation,22,Franchise,Complex\n6021,37.56,Boston,N,4,21,4,29,Graduation,30,Patnership,Standalone\n8352,384.72,NJ City,W,1,20,21,58,Graduation,30,Franchise,Complex\n1865,185.72,NJ City,N,10,18,18,75,High School,22,Patnership,Standalone\n3056,59.51,NJ City,W,6,16,4,40,No education,31,Franchise,Complex\n3041,250.17,NJ City,N,9,17,22,53,High School,20,Patnership,Standalone\n4258,429.76,NJ City,S,9,16,14,33,High School,30,Patnership,Complex\n4908,53.11,NJ City,W,7,13,3,31,Graduation,34,Franchise,Complex\n5410,229.54,Boston,S,10,10,13,46,Graduation,19,Patnership,Complex\n9916,179.84,NJ City,W,14,17,1,26,High School,24,Franchise,Complex\n3855,437.3,NJ City,E,10,8,24,73,High School,22,Owner,Standalone\n5605,312.11,Boston,N,14,14,14,69,Graduation,28,Patnership,Standalone\n7595,418.75,Boston,E,7,23,12,62,Graduation,28,Owner,Standalone\n83,305.49,Boston,E,11,20,22,76,High School,29,Owner,Standalone\n6066,211.73,Boston,S,13,21,24,47,Graduation,24,Patnership,Complex\n4156,2.63,NJ City,W,21,18,4,60,High School,35,Franchise,Complex\n1687,376.18,NJ City,E,2,21,1,43,High School,35,Owner,Standalone\n469,237.41,NJ City,N,8,18,2,36,High School,20,Patnership,Standalone\n496,5.99,NJ City,N,10,8,1,70,High School,33,Franchise,Complex\n6015,232.42,Boston,E,12,9,22,61,Graduation,31,Owner,Standalone\n1139,49.43,NJ City,E,21,9,4,60,No education,30,Owner,Standalone\n8117,43.44,NJ City,N,7,18,4,44,No education,29,Patnership,Standalone\n2061,267.47,NJ City,N,13,22,10,72,High School,18,Patnership,Standalone\n2754,80.87,NJ City,S,17,9,2,55,Graduation,24,Patnership,Complex\n4724,112.5,NJ City,W,2,10,2,40,No education,35,Franchise,Complex\n9743,386.94,NJ City,E,2,10,17,51,Graduation,24,Owner,Standalone\n7203,338.91,Boston,E,19,24,8,36,Graduation,18,Owner,Standalone\n211,430.58,NJ City,E,20,8,10,43,High School,35,Owner,Standalone\n7405,175.26,Boston,N,15,15,8,31,Graduation,23,Patnership,Standalone\n2951,28.15,NJ City,N,13,12,2,73,No education,21,Owner,Standalone\n9279,172.41,NJ City,E,23,16,8,73,Graduation,29,Owner,Standalone\n9527,243.22,NJ City,E,7,15,18,45,Graduation,21,Owner,Standalone\n5538,418.25,Boston,S,7,19,20,44,Graduation,26,Patnership,Complex\n4575,295.28,NJ City,E,7,19,19,29,High School,35,Owner,Standalone\n7126,454.26,Boston,S,11,13,24,64,Graduation,24,Patnership,Complex\n8861,330.34,NJ City,N,11,13,9,26,Graduation,31,Patnership,Standalone\n8974,229.07,NJ City,S,13,14,25,73,Graduation,27,Patnership,Complex\n1315,375.14,NJ City,E,25,13,10,71,High School,33,Owner,Standalone\n9749,244.71,NJ City,N,13,20,25,30,Graduation,29,Patnership,Standalone\n1411,27.3,NJ City,N,8,10,2,73,High School,35,Owner,Standalone\n6551,401.71,Boston,E,20,22,15,72,Graduation,33,Owner,Standalone\n7499,218.26,Boston,E,20,22,22,37,Graduation,32,Owner,Standalone\n6227,338.36,Boston,E,6,16,19,65,Graduation,20,Owner,Standalone\n9027,474.61,NJ City,E,23,20,4,42,Graduation,20,Owner,Standalone\n5469,134.54,Boston,N,22,22,21,71,Graduation,31,Patnership,Standalone\n2461,440,NJ City,N,7,23,14,49,High School,35,Patnership,Standalone\n7435,282.4,Boston,E,5,24,23,62,Graduation,19,Owner,Standalone\n365,399.81,NJ City,N,15,13,9,42,High School,31,Patnership,Standalone\n9805,180.81,NJ City,N,24,23,15,78,Graduation,25,Patnership,Standalone\n2079,108.23,NJ City,E,8,11,3,84,Graduation,34,Owner,Standalone\n8317,474.31,NJ City,N,7,10,17,37,Graduation,32,Patnership,Standalone\n3381,382.33,NJ City,N,6,13,17,27,High School,28,Patnership,Standalone\n753,7.52,NJ City,N,25,11,4,84,Graduation,35,Patnership,Standalone\n8715,480.64,NJ City,E,6,12,7,38,Graduation,26,Owner,Standalone\n9055,53.14,NJ City,E,7,24,4,82,High School,31,Owner,Standalone\n6240,442.94,Boston,W,12,24,4,38,Graduation,19,Franchise,Complex\n2398,141.05,NJ City,S,24,10,20,81,High School,28,Patnership,Complex\n7540,87.83,Boston,W,6,19,1,63,High School,34,Franchise,Complex\n6193,225.56,Boston,N,22,14,25,69,Graduation,34,Patnership,Standalone\n8014,309.37,Boston,S,20,21,3,50,High School,35,Patnership,Complex\n7205,130.61,Boston,N,24,24,17,75,Graduation,29,Patnership,Standalone\n9897,164.87,NJ City,N,19,23,18,35,Graduation,27,Patnership,Standalone\n6243,477.22,Boston,E,5,14,11,43,Graduation,35,Owner,Standalone\n8825,217.82,NJ City,N,13,24,17,65,Graduation,29,Patnership,Standalone\n802,449.62,NJ City,S,25,8,18,77,High School,23,Patnership,Complex\n2632,213.14,NJ City,W,9,17,21,85,High School,30,Franchise,Complex\n9803,214.32,NJ City,E,10,8,1,84,No education,30,Owner,Standalone\n9779,164.35,NJ City,E,1,9,9,44,Graduation,28,Owner,Standalone\n1227,471.59,NJ City,E,21,9,3,82,Graduation,28,Owner,Standalone\n5954,299,Boston,S,5,21,18,68,Graduation,20,Patnership,Complex\n59,128.43,Boston,E,25,23,21,60,High School,34,Owner,Standalone\n2220,319.1,NJ City,W,1,24,5,54,Graduation,20,Franchise,Complex\n2875,371.24,NJ City,E,6,22,6,55,High School,31,Owner,Standalone\n5874,245.67,Boston,S,15,13,23,78,Graduation,28,Patnership,Complex\n352,130.07,NJ City,W,16,19,10,67,High School,21,Franchise,Complex\n9494,107.66,NJ City,S,20,17,2,78,No education,31,Patnership,Complex\n7741,475.46,Boston,N,14,9,24,55,Graduation,18,Patnership,Standalone\n8799,338.1,NJ City,E,24,10,16,77,Graduation,27,Owner,Standalone\n347,466.67,NJ City,E,11,21,1,62,No education,32,Owner,Standalone\n8668,359.4,NJ City,W,5,23,23,26,Graduation,25,Franchise,Complex\n5274,205.33,Boston,S,22,13,15,52,Graduation,30,Patnership,Complex\n5985,390.55,Boston,N,8,10,24,69,Graduation,25,Patnership,Standalone\n5649,410.21,Boston,N,5,9,14,27,Graduation,27,Patnership,Standalone\n406,281.21,NJ City,S,9,11,4,68,High School,30,Patnership,Complex\n9336,492.91,NJ City,W,21,17,19,50,Graduation,23,Franchise,Complex\n2829,265.91,NJ City,N,16,20,10,79,High School,30,Patnership,Standalone\n946,40.01,NJ City,N,19,17,4,43,High School,28,Patnership,Complex\n8576,492.11,NJ City,W,15,18,1,40,No education,24,Franchise,Complex\n2694,215.76,NJ City,S,23,19,22,59,High School,33,Patnership,Complex\n1402,35.74,NJ City,N,16,22,2,30,High School,24,Patnership,Complex\n2360,234.84,NJ City,W,1,17,4,78,No education,28,Franchise,Complex\n6728,346.21,Boston,W,3,13,17,62,Graduation,31,Franchise,Complex\n2867,75.73,NJ City,E,6,8,2,68,No education,30,Owner,Standalone\n6455,123.05,Boston,E,24,16,2,43,No education,29,Owner,Standalone\n4392,252,NJ City,W,3,8,25,76,High School,32,Franchise,Complex\n1193,302,NJ City,N,1,17,25,40,High School,29,Patnership,Standalone\n3202,140.76,NJ City,S,13,19,25,49,High School,29,Patnership,Complex\n7295,54.82,Boston,E,17,19,4,83,No education,24,Owner,Standalone\n3853,173,NJ City,N,1,8,16,67,High School,28,Patnership,Standalone\n1874,458.34,NJ City,S,11,15,18,38,High School,26,Patnership,Complex\n9606,155.57,NJ City,S,2,21,25,29,Graduation,28,Patnership,Complex\n42,10.15,Boston,N,6,20,1,65,Graduation,19,Patnership,Complex\n5836,184.64,Boston,W,4,24,24,61,Graduation,25,Franchise,Complex\n4035,460.41,NJ City,E,24,9,12,26,High School,23,Owner,Standalone\n1549,211.57,NJ City,N,8,16,25,72,High School,21,Patnership,Standalone\n7393,383.75,Boston,N,25,18,18,26,Graduation,24,Patnership,Standalone\n1306,245.24,NJ City,S,19,22,24,75,High School,25,Patnership,Complex\n9142,417.24,NJ City,S,3,11,12,37,Graduation,23,Patnership,Complex\n9868,467.67,NJ City,W,13,18,6,30,Graduation,19,Franchise,Complex\n561,355.27,NJ City,N,4,20,10,53,High School,22,Patnership,Standalone\n4729,449.87,NJ City,N,17,13,15,39,High School,32,Patnership,Standalone\n2156,120.22,NJ City,W,15,9,3,82,No education,19,Franchise,Complex\n7113,490.4,Boston,N,18,19,8,72,Graduation,19,Patnership,Standalone\n4467,5.12,NJ City,N,10,24,3,59,Graduation,28,Owner,Standalone\n6522,212.15,Boston,S,14,15,7,41,Graduation,26,Patnership,Complex\n9386,172.4,NJ City,S,16,9,12,39,Graduation,32,Patnership,Complex\n6308,384.19,Boston,W,20,9,1,30,No education,24,Franchise,Complex\n1980,44.17,NJ City,N,8,16,3,30,Graduation,29,Franchise,Complex\n1695,100.77,NJ City,E,8,24,3,78,Graduation,26,Owner,Standalone\n3382,303.5,NJ City,S,20,12,12,72,High School,25,Patnership,Complex\n6657,123.22,Boston,N,6,22,2,27,Graduation,27,Patnership,Standalone\n7080,220.25,Boston,W,24,17,5,62,Graduation,22,Franchise,Complex\n6884,165.29,Boston,W,23,19,7,79,Graduation,23,Franchise,Complex\n9258,409.38,NJ City,S,9,23,20,70,Graduation,29,Patnership,Complex\n2407,90.88,NJ City,E,24,18,3,68,High School,28,Owner,Standalone\n6511,453.34,Boston,E,24,9,22,78,Graduation,23,Owner,Standalone\n6079,146.92,Boston,E,13,9,14,46,Graduation,18,Owner,Standalone\n1528,242.05,NJ City,W,5,15,14,62,High School,24,Franchise,Complex\n2763,468.1,NJ City,E,10,11,20,26,High School,30,Owner,Standalone\n1680,170.03,NJ City,W,8,17,15,69,High School,32,Franchise,Complex\n9774,330.63,NJ City,S,24,13,3,39,Graduation,19,Patnership,Complex\n5883,238.34,Boston,E,20,19,20,56,Graduation,21,Owner,Standalone\n8067,338.01,NJ City,E,12,16,16,64,Graduation,24,Owner,Standalone\n8500,374.49,NJ City,W,4,8,14,73,Graduation,30,Franchise,Complex\n3067,299.99,NJ City,E,23,8,23,60,High School,32,Owner,Standalone\n3533,48.27,NJ City,N,20,21,3,33,No education,29,Patnership,Standalone\n1524,34.7,NJ City,N,22,8,2,28,Graduation,18,Franchise,Complex\n5757,288.22,Boston,N,25,11,16,63,Graduation,21,Patnership,Standalone\n2347,400.3,NJ City,E,18,17,13,25,High School,30,Owner,Standalone\n9955,210.8,NJ City,E,20,10,18,29,Graduation,26,Owner,Standalone\n3072,208.62,NJ City,W,11,19,1,73,Graduation,27,Franchise,Complex\n6286,242.9,Boston,S,9,9,21,70,Graduation,26,Patnership,Complex\n9634,163.21,NJ City,S,14,23,23,85,Graduation,34,Patnership,Complex\n7939,206.76,Boston,E,12,16,14,58,Graduation,28,Owner,Standalone\n3402,385.11,NJ City,S,20,18,15,25,High School,18,Patnership,Complex\n229,223.24,NJ City,N,11,10,8,39,High School,33,Patnership,Standalone\n8813,178.43,NJ City,N,23,22,14,63,Graduation,29,Patnership,Standalone\n1418,470.57,NJ City,S,17,21,18,64,High School,27,Patnership,Complex\n9684,499.93,NJ City,W,11,13,24,70,Graduation,21,Franchise,Complex\n41,140,Maine,N,2,22,23,44,High School,30,Patnership,Standalone\n796,76.79,NJ City,W,7,20,3,34,High School,32,Franchise,Complex\n9276,415.96,NJ City,W,3,20,2,43,Graduation,31,Franchise,Complex\n4661,439.45,NJ City,N,7,18,6,79,High School,18,Patnership,Standalone\n2292,158.1,NJ City,W,18,24,19,36,High School,21,Franchise,Complex\n3831,173.63,NJ City,E,11,22,11,36,High School,22,Owner,Standalone\n7469,473.74,Boston,N,24,9,17,46,Graduation,29,Patnership,Standalone\n9641,193.24,NJ City,N,7,20,10,33,Graduation,21,Patnership,Standalone\n5036,221.16,Boston,W,8,23,2,29,No education,32,Franchise,Complex\n7062,457.84,Boston,S,19,19,17,30,Graduation,32,Patnership,Complex\n8564,126.42,NJ City,W,11,18,4,78,No education,35,Franchise,Complex\n5752,72.94,Boston,N,13,21,4,81,High School,26,Franchise,Complex\n6453,103.84,Boston,N,24,11,2,77,Graduation,30,Patnership,Standalone\n1842,454.4,NJ City,S,13,21,24,29,High School,19,Patnership,Complex\n6296,368.35,Boston,W,12,10,21,59,Graduation,34,Franchise,Complex\n4701,167.48,NJ City,N,24,13,1,33,Graduation,33,Patnership,Standalone\n828,151.12,NJ City,W,4,15,18,28,High School,28,Franchise,Complex\n771,372.88,NJ City,E,13,14,18,61,High School,27,Owner,Standalone\n5091,310.97,Boston,E,12,18,4,56,Graduation,20,Owner,Standalone\n5220,246.7,Boston,W,20,24,11,42,Graduation,28,Franchise,Complex\n3120,499.63,NJ City,W,24,13,19,55,High School,27,Franchise,Complex\n9084,243.3,NJ City,W,16,10,14,39,Graduation,25,Franchise,Complex\n5990,399.44,Boston,S,23,15,25,60,Graduation,28,Patnership,Complex\n4703,274.88,NJ City,E,6,20,16,28,High School,18,Owner,Standalone\n4096,253.76,NJ City,W,22,21,6,51,High School,30,Franchise,Complex\n5792,262.12,Boston,W,16,11,21,64,Graduation,24,Franchise,Complex\n1986,141.68,NJ City,S,25,17,11,80,High School,22,Patnership,Complex\n4329,421.83,NJ City,N,18,16,9,37,High School,20,Patnership,Standalone\n2813,40.48,NJ City,N,9,23,3,50,No education,33,Patnership,Standalone\n770,34.94,NJ City,N,25,16,2,76,No education,20,Patnership,Complex\n967,210.66,NJ City,E,19,13,22,78,High School,28,Owner,Standalone\n5539,98.88,Boston,E,12,16,2,62,High School,22,Owner,Standalone\n9403,430.01,NJ City,E,1,13,4,84,High School,33,Owner,Standalone\n9741,70.83,NJ City,N,12,10,4,35,Graduation,30,Patnership,Standalone\n2192,223.89,NJ City,W,21,23,16,51,High School,33,Franchise,Complex\n4107,127.28,NJ City,E,25,8,3,38,Graduation,28,Owner,Standalone\n3689,372.84,NJ City,N,5,19,20,46,High School,19,Patnership,Standalone\n6546,217.69,Boston,S,6,24,23,28,Graduation,22,Patnership,Complex\n1975,395.72,NJ City,E,15,22,4,30,High School,29,Owner,Standalone\n7717,158.59,Boston,N,7,9,23,65,Graduation,27,Patnership,Standalone\n3662,259,NJ City,S,1,20,9,48,High School,21,Patnership,Complex\n5683,475.44,Boston,E,3,11,16,83,Graduation,31,Owner,Standalone\n2905,385.83,NJ City,N,12,20,3,80,High School,35,Patnership,Standalone\n5666,316.68,Boston,S,20,17,20,41,Graduation,29,Patnership,Complex\n6321,178.84,Boston,N,12,16,24,35,Graduation,32,Patnership,Standalone\n7410,396.64,Boston,S,22,19,2,71,Graduation,30,Patnership,Complex\n8003,48.29,Boston,E,24,14,4,84,No education,34,Owner,Standalone\n9194,349.61,NJ City,S,4,19,1,85,No education,25,Patnership,Complex\n7779,314.46,Boston,E,8,22,24,30,Graduation,24,Owner,Standalone\n5879,446.87,Boston,E,13,20,17,62,Graduation,30,Owner,Standalone\n161,333.62,NJ City,N,20,21,24,77,High School,32,Patnership,Standalone\n987,37.91,NJ City,N,19,10,3,75,Graduation,22,Owner,Standalone\n7962,142.88,Boston,S,21,24,9,50,Graduation,20,Patnership,Complex\n1941,89.21,NJ City,N,5,21,4,35,Graduation,29,Patnership,Standalone\n7431,13.78,Boston,N,10,13,1,42,Graduation,27,Owner,Standalone\n2026,339.43,NJ City,S,7,8,8,70,High School,28,Patnership,Complex\n6218,377.19,Boston,S,17,20,3,44,No education,25,Patnership,Complex\n1785,304.64,NJ City,N,24,16,6,74,High School,25,Patnership,Standalone\n2793,186.6,NJ City,N,16,9,8,53,High School,29,Patnership,Standalone\n8208,39.45,NJ City,N,22,23,1,73,Graduation,30,Franchise,Complex\n7245,177.05,Boston,N,21,8,16,36,Graduation,28,Patnership,Standalone\n807,41.75,NJ City,N,25,9,3,65,Graduation,32,Owner,Standalone\n2427,210.2,NJ City,E,7,21,6,78,High School,28,Owner,Standalone\n947,190.35,NJ City,E,8,13,2,81,No education,21,Owner,Standalone\n7214,409.19,Boston,S,13,23,18,64,Graduation,23,Patnership,Complex\n1987,408.88,NJ City,E,3,15,4,49,High School,20,Owner,Standalone\n7334,242.54,Boston,S,15,11,11,43,Graduation,26,Patnership,Complex\n4644,225.52,NJ City,W,9,9,16,33,High School,22,Franchise,Complex\n1956,328.39,NJ City,W,19,23,11,48,High School,23,Franchise,Complex\n4001,75.37,NJ City,N,3,22,3,53,No education,26,Patnership,Standalone\n1908,143.15,NJ City,W,25,11,5,71,Graduation,30,Franchise,Complex\n1548,436.37,NJ City,W,12,23,8,34,High School,26,Franchise,Complex\n1120,333.35,NJ City,W,23,19,15,73,High School,21,Franchise,Complex\n5211,407.16,Boston,E,7,17,25,60,Graduation,19,Owner,Standalone\n7579,266.25,Boston,E,8,8,19,53,Graduation,20,Owner,Standalone\n983,446.46,NJ City,E,16,13,11,27,High School,25,Owner,Standalone\n4456,47.54,NJ City,W,16,20,2,72,High School,25,Franchise,Complex\n809,481.65,NJ City,N,4,21,21,67,High School,18,Patnership,Standalone\n4201,66.63,NJ City,N,9,12,4,45,High School,22,Patnership,Standalone\n5378,370.24,Boston,S,25,9,11,51,Graduation,25,Patnership,Complex\n4396,12.17,NJ City,N,16,21,2,32,High School,23,Franchise,Complex\n8394,298.23,NJ City,S,17,17,11,45,Graduation,24,Patnership,Complex\n7070,366.82,Boston,S,8,18,13,25,Graduation,32,Patnership,Complex\n4079,25.04,NJ City,N,11,13,1,84,No education,28,Owner,Standalone\n4763,363.85,NJ City,E,4,23,18,28,High School,26,Owner,Standalone\n1940,354.09,NJ City,W,17,24,18,60,High School,32,Franchise,Complex\n115,463.9,NJ City,E,19,13,2,49,High School,34,Owner,Standalone\n4436,341.6,NJ City,W,18,22,5,52,No education,34,Franchise,Complex\n1861,415.31,NJ City,N,14,20,16,68,High School,21,Patnership,Standalone\n8597,13.82,NJ City,N,10,19,2,42,No education,32,Patnership,Standalone\n4882,242.6,NJ City,S,20,18,23,32,High School,25,Patnership,Complex\n209,54.51,NJ City,N,7,15,3,55,No education,26,Patnership,Standalone\n9520,476.37,NJ City,W,16,8,6,84,Graduation,32,Franchise,Complex\n6993,376.34,Boston,N,6,17,5,75,Graduation,26,Patnership,Standalone\n2254,440.13,NJ City,S,5,22,2,55,High School,28,Patnership,Complex\n8918,105.14,NJ City,S,24,18,1,35,No education,19,Patnership,Complex\n7641,102.77,Boston,N,16,11,2,84,Graduation,25,Patnership,Standalone\n8636,177.16,NJ City,W,14,9,20,62,Graduation,31,Franchise,Complex\n9486,207.51,NJ City,S,22,10,22,67,Graduation,21,Patnership,Complex\n1662,263.6,NJ City,S,12,15,9,81,High School,26,Patnership,Complex\n2172,186.27,NJ City,W,4,15,2,56,Graduation,24,Franchise,Complex\n1947,276.92,NJ City,E,19,17,17,62,High School,24,Owner,Standalone\n5799,103.09,Boston,E,10,16,3,59,Graduation,34,Owner,Standalone\n7766,486.3,Boston,S,9,14,8,38,Graduation,27,Patnership,Complex\n6340,413.5,Boston,W,9,18,1,71,High School,25,Franchise,Complex\n525,167.95,NJ City,N,2,23,1,74,Graduation,20,Patnership,Standalone\n9260,26.28,NJ City,N,20,15,1,64,No education,24,Franchise,Complex\n9467,310.77,NJ City,E,5,18,9,47,Graduation,23,Owner,Standalone\n8495,364.38,NJ City,E,5,24,14,65,Graduation,29,Owner,Standalone\n8389,84.16,NJ City,N,10,12,4,80,High School,19,Patnership,Standalone\n4050,150.99,NJ City,S,21,18,17,73,High School,33,Patnership,Complex\n8365,29.58,NJ City,N,17,9,2,30,High School,32,Patnership,Standalone\n6579,245.36,Boston,E,4,18,24,34,Graduation,26,Owner,Standalone\n1837,348.1,NJ City,N,25,10,1,60,High School,19,Patnership,Standalone\n6615,221.72,Boston,E,2,24,6,29,Graduation,31,Owner,Standalone\n5184,204.64,Boston,W,15,8,22,72,Graduation,21,Franchise,Complex\n448,400.23,NJ City,W,18,13,24,62,High School,31,Franchise,Complex\n2004,209.22,NJ City,W,12,20,17,25,High School,31,Franchise,Complex\n4204,269.42,NJ City,W,10,19,24,67,High School,22,Franchise,Complex\n9390,455.03,NJ City,S,1,17,18,72,Graduation,25,Patnership,Complex\n5832,321.89,Boston,W,13,13,10,32,Graduation,29,Franchise,Complex\n7622,393.88,Boston,S,15,8,15,40,Graduation,31,Patnership,Complex\n398,347.48,NJ City,S,12,18,4,28,No education,27,Patnership,Complex\n9368,59.6,NJ City,W,16,11,3,74,No education,21,Franchise,Complex\n8672,484.54,NJ City,W,2,13,24,77,Graduation,19,Franchise,Complex\n4146,389.16,NJ City,S,11,24,12,50,High School,29,Patnership,Complex\n2063,369.38,NJ City,E,4,9,2,29,No education,27,Owner,Standalone\n8894,272.05,NJ City,S,23,21,12,41,Graduation,34,Patnership,Complex\n5829,192.01,Boston,N,22,9,11,83,Graduation,32,Patnership,Standalone\n75,382.05,Boston,E,24,16,22,45,High School,34,Owner,Standalone\n9523,73.42,NJ City,N,21,9,4,60,High School,22,Owner,Standalone\n1717,271.4,NJ City,N,5,18,16,85,High School,34,Patnership,Standalone\n3882,75.49,NJ City,S,2,9,1,61,Graduation,21,Patnership,Complex\n8462,74.75,NJ City,S,22,11,1,51,No education,26,Patnership,Complex\n5946,337.15,Boston,S,13,17,15,62,Graduation,28,Patnership,Complex\n4040,305,NJ City,W,3,21,18,48,High School,33,Franchise,Complex\n9687,432.29,NJ City,E,10,20,7,32,Graduation,31,Owner,Standalone\n8598,206.11,NJ City,S,20,12,21,46,Graduation,30,Patnership,Complex\n7592,42.02,Boston,N,25,15,4,50,No education,28,Franchise,Complex\n9549,150,NJ City,N,12,11,8,60,Graduation,30,Patnership,Standalone\n7071,246.58,Boston,E,15,13,3,62,Graduation,18,Owner,Standalone\n5441,182.08,Boston,N,21,14,25,29,Graduation,35,Patnership,Standalone\n8205,101.72,NJ City,N,11,20,2,36,Graduation,26,Patnership,Standalone\n6712,407.95,Boston,W,5,12,19,66,Graduation,25,Franchise,Complex\n8481,203.44,NJ City,N,8,18,17,39,Graduation,30,Patnership,Standalone\n5848,424.09,Boston,W,13,24,16,28,Graduation,35,Franchise,Complex\n8884,152.48,NJ City,W,15,9,21,40,Graduation,24,Franchise,Complex\n5996,322.92,Boston,W,14,19,24,83,Graduation,19,Franchise,Complex\n5327,155.12,Boston,E,15,21,25,56,Graduation,29,Owner,Standalone\n9162,214.74,NJ City,S,11,23,15,30,Graduation,22,Patnership,Complex\n4739,435.31,NJ City,E,17,14,22,83,High School,25,Owner,Standalone\n2447,165.59,NJ City,E,24,22,13,39,High School,24,Owner,Standalone\n516,24.75,NJ City,N,18,11,1,31,Graduation,21,Franchise,Complex\n5065,326.77,Boston,N,12,18,15,61,Graduation,22,Patnership,Standalone\n4581,457.87,NJ City,N,6,18,22,78,High School,32,Patnership,Standalone\n9289,472.36,NJ City,N,8,15,25,34,Graduation,34,Patnership,Standalone\n6748,171.63,Boston,W,6,14,24,81,Graduation,35,Franchise,Complex\n7659,496.21,Boston,E,8,10,2,40,Graduation,31,Owner,Standalone\n5062,477.12,Boston,S,12,23,22,39,Graduation,21,Patnership,Complex\n977,374.44,NJ City,N,14,14,11,27,High School,23,Patnership,Standalone\n8307,297.64,NJ City,E,20,9,25,73,Graduation,34,Owner,Standalone\n820,190.53,NJ City,W,20,20,23,78,High School,23,Franchise,Complex\n418,357.1,NJ City,S,8,21,4,83,High School,27,Patnership,Complex\n2265,61.76,NJ City,N,7,16,1,68,Graduation,18,Patnership,Standalone\n9383,105.18,NJ City,E,4,16,3,82,No education,25,Owner,Standalone\n6019,63.1,Boston,N,22,22,3,84,High School,19,Owner,Standalone\n4183,188.3,NJ City,E,17,11,12,79,High School,28,Owner,Standalone\n3033,497.3,NJ City,N,13,22,20,35,High School,26,Patnership,Standalone\n9711,279.3,NJ City,E,7,19,25,30,Graduation,35,Owner,Standalone\n2772,2.56,NJ City,W,14,24,1,49,Graduation,18,Franchise,Complex\n3579,248.64,NJ City,E,14,18,12,54,High School,29,Owner,Standalone\n8213,98.64,NJ City,N,13,10,4,25,No education,24,Patnership,Standalone\n9998,163.82,NJ City,S,5,22,1,31,No education,24,Patnership,Complex\n3225,123.95,NJ City,N,3,15,4,52,Graduation,23,Patnership,Standalone\n4485,368.7,NJ City,N,11,19,24,38,High School,28,Patnership,Standalone\n7315,304.62,Boston,E,21,23,11,31,Graduation,20,Owner,Standalone\n1151,268.11,NJ City,E,7,13,25,35,High School,20,Owner,Standalone\n6747,147.25,Boston,E,19,22,15,25,Graduation,32,Owner,Standalone\n8013,317.35,Boston,N,9,24,25,73,Graduation,21,Patnership,Standalone\n9542,140.29,NJ City,S,6,13,6,53,Graduation,26,Patnership,Complex\n8260,254.77,NJ City,W,1,9,17,77,Graduation,23,Franchise,Complex\n3528,280.88,NJ City,W,14,15,14,32,High School,23,Franchise,Complex\n7880,396.33,Boston,W,5,10,8,61,Graduation,20,Franchise,Complex\n200,251.59,NJ City,W,21,15,12,56,High School,21,Franchise,Complex\n5478,100.76,Boston,S,6,23,3,61,Graduation,23,Patnership,Complex\n9365,325.5,NJ City,N,23,10,6,81,Graduation,21,Patnership,Standalone\n7871,126.17,Boston,E,15,18,4,34,No education,31,Owner,Standalone\n9950,203.66,NJ City,S,23,18,14,58,Graduation,23,Patnership,Complex\n769,326.34,NJ City,N,18,24,23,68,High School,33,Patnership,Standalone\n224,102.43,NJ City,W,16,15,4,75,No education,24,Franchise,Complex\n1235,116,NJ City,E,3,17,14,51,High School,28,Owner,Standalone\n6578,351.54,Boston,S,17,8,25,61,Graduation,18,Patnership,Complex\n5166,400.42,Boston,S,16,19,1,58,Graduation,27,Patnership,Complex\n839,107.32,NJ City,E,20,20,2,36,No education,32,Owner,Standalone\n6600,10.22,Boston,N,4,13,2,62,Graduation,31,Franchise,Complex\n7520,415.03,Boston,W,13,21,24,85,Graduation,20,Franchise,Complex\n8004,416.36,Boston,W,22,11,2,36,Graduation,30,Franchise,Complex\n5252,307.69,Boston,W,14,11,23,72,Graduation,28,Franchise,Complex\n1401,331.56,NJ City,N,10,15,20,67,High School,18,Patnership,Standalone\n3542,277.17,NJ City,S,6,19,17,46,High School,22,Patnership,Complex\n7141,492.73,Boston,N,10,16,5,69,High School,25,Patnership,Standalone\n8376,351.13,NJ City,W,11,11,14,54,Graduation,23,Franchise,Complex\n3228,378.86,NJ City,W,16,9,16,31,High School,31,Franchise,Complex\n6461,407.93,Boston,N,16,21,1,57,No education,22,Patnership,Standalone\n9580,341.32,NJ City,W,13,15,17,81,Graduation,28,Franchise,Complex\n9356,167.29,NJ City,W,21,20,21,26,Graduation,30,Franchise,Complex\n6900,102.51,Boston,W,2,10,1,50,Graduation,24,Franchise,Complex\n3650,11.25,NJ City,N,6,11,4,55,No education,24,Patnership,Complex\n1365,29.61,NJ City,N,23,23,3,41,Graduation,25,Patnership,Standalone\n4114,328.21,NJ City,S,14,19,3,54,High School,23,Patnership,Complex\n6069,167,Boston,N,6,10,11,41,Graduation,21,Patnership,Standalone\n1378,289.26,NJ City,S,7,8,22,79,High School,29,Patnership,Complex\n4194,247.02,NJ City,S,13,19,16,58,High School,29,Patnership,Complex\n121,228.92,NJ City,N,24,12,5,69,High School,29,Patnership,Standalone\n4443,42.3,NJ City,N,20,15,4,78,Graduation,28,Owner,Standalone\n3627,19.5,NJ City,N,7,23,3,36,Graduation,23,Owner,Standalone\n764,468.84,NJ City,W,14,22,23,60,High School,35,Franchise,Complex\n1331,479.08,NJ City,E,4,18,23,57,High School,20,Owner,Standalone\n7254,66.29,Boston,N,25,11,4,79,Graduation,26,Patnership,Complex\n1732,460.93,NJ City,W,16,11,11,57,High School,30,Franchise,Complex\n5613,105.16,Boston,N,18,22,2,66,Graduation,28,Patnership,Standalone\n9023,236.92,NJ City,E,18,17,19,47,Graduation,19,Owner,Standalone\n9881,362.43,NJ City,N,18,16,7,73,Graduation,21,Patnership,Standalone\n413,49.9,NJ City,N,12,21,1,27,No education,23,Patnership,Standalone\n7728,116.87,Boston,W,19,10,1,31,Graduation,28,Franchise,Complex\n3371,206.05,NJ City,E,4,21,3,58,No education,29,Owner,Standalone\n8286,354.82,NJ City,S,21,10,4,44,Graduation,30,Patnership,Complex\n914,64.16,NJ City,N,25,20,4,75,No education,18,Patnership,Complex\n6773,83.16,Boston,N,4,20,3,36,No education,27,Patnership,Standalone\n6328,80.25,Boston,W,3,20,1,31,High School,18,Franchise,Complex\n3572,176.77,NJ City,W,24,18,13,39,High School,23,Franchise,Complex\n6089,58.2,Boston,N,22,9,2,60,No education,34,Patnership,Standalone\n219,284.08,NJ City,E,10,18,18,78,High School,24,Owner,Standalone\n6801,94.73,Boston,N,12,14,4,35,Graduation,25,Patnership,Standalone\n9829,54.19,NJ City,N,3,16,4,32,High School,23,Patnership,Standalone\n5057,222.29,Boston,N,20,9,20,68,Graduation,33,Patnership,Standalone\n3884,338.86,NJ City,W,17,16,6,40,High School,22,Franchise,Complex\n6437,434.98,Boston,N,13,20,3,48,No education,18,Patnership,Standalone\n714,49.82,NJ City,S,4,10,4,45,Graduation,24,Patnership,Complex\n7947,103.38,Boston,E,2,24,4,79,Graduation,19,Owner,Standalone\n610,341.23,NJ City,S,24,20,7,58,High School,33,Patnership,Complex\n4873,25.15,NJ City,N,11,22,3,46,High School,20,Patnership,Standalone\n9311,419.65,NJ City,E,13,16,24,73,Graduation,26,Owner,Standalone\n4508,30.12,NJ City,N,6,19,3,58,No education,31,Franchise,Complex\n2289,334.82,NJ City,N,17,19,22,53,High School,25,Patnership,Standalone\n192,126.66,NJ City,W,4,10,1,72,Graduation,31,Franchise,Complex\n9491,499.17,NJ City,E,25,20,21,66,Graduation,25,Owner,Standalone\n9105,52.96,NJ City,N,16,10,4,64,Graduation,19,Patnership,Standalone\n6731,110.03,Boston,E,2,18,2,50,No education,30,Owner,Standalone\n8721,374.74,NJ City,N,21,22,2,77,Graduation,31,Patnership,Standalone\n8905,308.05,NJ City,N,9,9,21,51,Graduation,22,Patnership,Standalone\n5582,143.96,Boston,S,14,11,21,83,Graduation,20,Patnership,Complex\n7742,146.6,Boston,S,19,11,4,76,No education,18,Patnership,Complex\n9429,362.7,NJ City,N,23,21,18,69,Graduation,27,Patnership,Standalone\n3789,98.91,NJ City,N,20,13,2,74,Graduation,24,Patnership,Standalone\n3421,428.73,NJ City,N,17,17,9,43,High School,35,Patnership,Standalone\n4921,354.15,NJ City,N,5,13,1,25,High School,31,Patnership,Standalone\n2031,368.24,NJ City,E,25,18,17,46,High School,20,Owner,Standalone\n8230,386.39,NJ City,S,22,21,6,65,Graduation,32,Patnership,Complex\n1673,340.95,NJ City,N,17,13,18,40,High School,20,Patnership,Standalone\n1099,394.44,NJ City,E,21,11,20,26,High School,29,Owner,Standalone\n7704,316.16,Boston,W,15,15,10,66,Graduation,21,Franchise,Complex\n1042,246.73,NJ City,S,21,11,9,72,High School,31,Patnership,Complex\n2515,35.35,NJ City,N,21,13,1,37,High School,30,Owner,Standalone\n280,156.27,NJ City,W,22,21,10,33,High School,27,Franchise,Complex\n8155,415.54,NJ City,E,1,19,7,45,Graduation,22,Owner,Standalone\n1751,444.5,NJ City,E,3,10,2,64,No education,21,Owner,Standalone\n5731,461.58,Boston,E,25,9,4,75,High School,21,Owner,Standalone\n6283,460.9,Boston,E,8,15,16,85,Graduation,20,Owner,Standalone\n1541,360.73,NJ City,N,13,19,16,62,High School,28,Patnership,Standalone\n5895,403.73,Boston,E,22,24,1,82,Graduation,28,Owner,Standalone\n7648,246.96,Boston,W,15,9,22,67,Graduation,29,Franchise,Complex\n3780,164.32,NJ City,W,14,14,20,54,High School,32,Franchise,Complex\n7247,22.02,Boston,N,3,11,4,31,No education,33,Owner,Standalone\n8986,139.61,NJ City,S,11,9,7,49,Graduation,26,Patnership,Complex\n8957,288.83,NJ City,N,12,9,23,37,Graduation,19,Patnership,Standalone\n2792,241.1,NJ City,W,15,24,10,79,High School,21,Franchise,Complex\n1604,363.21,NJ City,W,23,22,11,82,High School,20,Franchise,Complex\n3290,41.68,NJ City,N,8,15,3,29,No education,26,Patnership,Complex\n9152,295.92,NJ City,W,25,16,11,27,Graduation,20,Franchise,Complex\n5665,469.5,Boston,N,11,21,4,29,High School,26,Patnership,Standalone\n1878,118.1,NJ City,S,15,22,3,44,Graduation,28,Patnership,Complex\n9384,428.01,NJ City,W,5,9,13,46,Graduation,19,Franchise,Complex\n2681,370.77,NJ City,N,11,13,7,54,High School,19,Patnership,Standalone\n6221,82.78,Boston,N,17,22,4,76,No education,26,Patnership,Standalone\n5402,446.21,Boston,S,10,14,18,42,Graduation,29,Patnership,Complex\n5437,471.8,Boston,N,16,18,14,52,Graduation,31,Patnership,Standalone\n1046,212.54,NJ City,S,12,15,8,32,High School,19,Patnership,Complex\n6598,368.35,Boston,S,6,12,3,79,High School,33,Patnership,Complex\n4276,247.44,NJ City,W,5,15,23,37,High School,31,Franchise,Complex\n4022,184.54,NJ City,S,19,11,18,58,High School,24,Patnership,Complex\n6672,267.27,Boston,W,7,24,18,49,Graduation,33,Franchise,Complex\n1410,296.5,NJ City,S,15,9,23,52,High School,21,Patnership,Complex\n4317,131.32,NJ City,N,22,8,15,52,High School,33,Patnership,Standalone\n553,426.47,NJ City,N,21,19,19,55,High School,22,Patnership,Standalone\n9145,350.18,NJ City,N,9,10,16,51,Graduation,31,Patnership,Standalone\n4345,150.55,NJ City,N,19,8,15,34,High School,34,Patnership,Standalone\n9561,161.03,NJ City,N,10,11,24,51,Graduation,30,Patnership,Standalone\n374,364.63,NJ City,S,11,18,24,31,High School,26,Patnership,Complex\n8370,362.74,NJ City,S,4,14,6,35,Graduation,25,Patnership,Complex\n3493,477.64,NJ City,N,18,13,19,38,High School,30,Patnership,Standalone\n2054,322.32,NJ City,S,18,21,17,58,High School,33,Patnership,Complex\n9420,198.7,NJ City,W,18,12,24,82,Graduation,22,Franchise,Complex\n723,488.11,NJ City,E,8,21,6,29,High School,34,Owner,Standalone\n8565,435.55,NJ City,N,20,19,24,29,Graduation,26,Patnership,Standalone\n2530,9.12,NJ City,N,17,12,1,80,High School,23,Patnership,Complex\n9443,376.47,NJ City,E,22,24,13,68,Graduation,33,Owner,Standalone\n6718,444.29,Boston,S,5,8,3,38,High School,28,Patnership,Complex\n3229,140.98,NJ City,N,11,22,7,80,High School,28,Patnership,Standalone\n2669,98.56,NJ City,N,21,14,3,84,No education,33,Patnership,Standalone\n2415,28.06,NJ City,N,15,9,3,38,Graduation,22,Owner,Standalone\n9837,222.97,NJ City,N,17,19,14,34,Graduation,19,Patnership,Standalone\n1224,311.49,NJ City,W,11,8,24,71,High School,24,Franchise,Complex\n803,124.72,NJ City,E,21,10,4,72,No education,18,Owner,Standalone\n6514,56.76,Boston,S,7,18,1,82,High School,31,Patnership,Complex\n3518,174,NJ City,S,3,20,18,52,High School,25,Patnership,Complex\n7048,213.17,Boston,W,15,13,17,44,Graduation,19,Franchise,Complex\n2121,281.6,NJ City,N,3,11,3,47,Graduation,31,Patnership,Standalone\n3625,423.75,NJ City,N,16,11,24,38,High School,19,Patnership,Standalone\n4206,264.79,NJ City,S,25,12,10,74,High School,33,Patnership,Complex\n7640,277.36,Boston,W,21,9,1,68,No education,25,Franchise,Complex\n1054,382.97,NJ City,S,15,17,24,76,High School,21,Patnership,Complex\n964,341.15,NJ City,W,14,10,20,43,High School,28,Franchise,Complex\n6429,446.79,Boston,N,20,17,4,68,Graduation,34,Patnership,Standalone\n3255,142,NJ City,E,1,17,14,84,High School,29,Owner,Standalone\n342,247.12,NJ City,S,19,15,7,52,High School,29,Patnership,Complex\n8217,448.87,NJ City,N,25,18,5,57,Graduation,25,Patnership,Standalone\n4097,68.96,NJ City,N,4,18,1,53,No education,35,Patnership,Standalone\n1101,37.73,NJ City,N,25,24,4,49,Graduation,29,Patnership,Standalone\n741,393.11,NJ City,N,22,12,7,44,High School,21,Patnership,Standalone\n684,125.36,NJ City,W,20,19,3,31,Graduation,32,Franchise,Complex\n9903,121.69,NJ City,E,11,12,1,45,Graduation,26,Owner,Standalone\n2353,69.92,NJ City,N,10,19,2,25,High School,20,Patnership,Standalone\n5929,273.99,Boston,N,9,12,15,68,Graduation,35,Patnership,Standalone\n304,387.86,NJ City,W,25,8,15,32,High School,34,Franchise,Complex\n5179,13.67,Boston,N,3,10,1,75,High School,26,Owner,Standalone\n5079,30.51,Boston,N,9,10,1,25,Graduation,26,Owner,Standalone\n8515,200.62,NJ City,E,16,20,24,64,Graduation,22,Owner,Standalone\n4415,102.38,NJ City,E,23,9,2,50,No education,23,Owner,Standalone\n8343,28.75,NJ City,N,12,9,1,78,Graduation,31,Owner,Standalone\n4954,37.64,Boston,N,21,22,4,77,High School,33,Patnership,Complex\n5284,8.1,Boston,N,19,10,2,49,High School,24,Franchise,Complex\n4422,409.16,NJ City,S,5,20,16,32,High School,35,Patnership,Complex\n7993,115.81,Boston,N,14,11,4,83,High School,20,Patnership,Standalone\n3473,104.1,NJ City,N,6,13,3,85,No education,34,Patnership,Standalone\n3350,363.4,NJ City,S,11,9,18,71,High School,34,Patnership,Complex\n2483,286.56,NJ City,E,23,21,15,32,High School,23,Owner,Standalone\n6932,207.5,Boston,W,18,20,3,67,No education,30,Franchise,Complex\n318,447.11,NJ City,S,9,13,16,26,High School,34,Patnership,Complex\n4004,163,NJ City,W,3,13,21,83,High School,24,Franchise,Complex\n7131,223.5,Boston,E,3,24,12,67,Graduation,21,Owner,Standalone\n7736,487.17,Boston,W,14,11,5,61,No education,35,Franchise,Complex\n9709,213.02,NJ City,N,2,22,19,58,Graduation,26,Patnership,Standalone\n5543,113.97,Boston,E,18,22,4,28,No education,26,Owner,Standalone\n222,306.29,NJ City,S,13,22,23,43,High School,24,Patnership,Complex\n1545,433.03,NJ City,N,13,19,3,29,Graduation,26,Patnership,Standalone\n2268,306.83,NJ City,W,20,17,19,42,High School,26,Franchise,Complex\n6869,222.34,Boston,N,19,16,8,75,Graduation,31,Patnership,Standalone\n4852,442.69,NJ City,W,13,14,10,57,High School,24,Franchise,Complex\n8321,328.34,NJ City,N,22,13,24,35,Graduation,21,Patnership,Standalone\n568,197.64,NJ City,W,15,12,4,44,High School,31,Franchise,Complex\n6574,375.24,Boston,S,17,20,1,40,High School,27,Patnership,Complex\n1348,398.02,NJ City,W,7,20,10,29,High School,20,Franchise,Complex\n1926,445.4,NJ City,S,12,16,9,82,High School,28,Patnership,Complex\n2391,206.65,NJ City,E,22,24,22,70,High School,27,Owner,Standalone\n2471,316.88,NJ City,E,5,21,3,68,No education,20,Owner,Standalone\n7851,329.06,Boston,E,4,8,19,63,Graduation,32,Owner,Standalone\n6027,257.06,Boston,E,4,24,14,62,Graduation,35,Owner,Standalone\n1766,118.37,NJ City,S,16,11,3,72,No education,35,Patnership,Complex\n2283,341.53,NJ City,E,18,15,17,45,High School,24,Owner,Standalone\n5339,307.69,Boston,E,8,21,24,63,Graduation,25,Owner,Standalone\n5928,392.29,Boston,W,14,17,24,55,Graduation,30,Franchise,Complex\n2296,465.32,NJ City,W,7,20,13,63,High School,28,Franchise,Complex\n1979,244.12,NJ City,E,8,10,23,52,High School,31,Owner,Standalone\n4304,131.89,NJ City,W,12,17,17,81,High School,26,Franchise,Complex\n7192,440.36,Boston,W,17,19,9,29,Graduation,19,Franchise,Complex\n3354,46.67,NJ City,N,14,14,1,58,Graduation,26,Patnership,Complex\n6110,414.4,Boston,S,11,19,2,49,No education,20,Patnership,Complex\n9939,322.31,NJ City,E,14,15,7,57,Graduation,26,Owner,Standalone\n6045,343.69,Boston,N,24,8,20,30,Graduation,19,Patnership,Standalone\n148,275.72,NJ City,W,5,12,14,61,High School,18,Franchise,Complex\n9535,88.6,NJ City,E,24,17,1,32,High School,31,Owner,Standalone\n583,450.83,NJ City,E,21,16,16,50,High School,30,Owner,Standalone\n728,1.9,NJ City,N,25,10,4,71,No education,19,Franchise,Complex\n1901,68.77,NJ City,N,24,14,4,54,No education,31,Patnership,Standalone\n6810,295.86,Boston,S,10,8,7,31,Graduation,25,Patnership,Complex\n5659,140.18,Boston,E,18,12,23,49,Graduation,35,Owner,Standalone\n2231,468.71,NJ City,E,4,17,13,36,High School,34,Owner,Standalone\n1725,144.32,NJ City,N,15,14,8,30,High School,31,Patnership,Standalone\n6984,152.64,Boston,W,23,16,13,64,Graduation,18,Franchise,Complex\n80,136,Boston,W,1,23,20,54,High School,29,Franchise,Complex\n8810,334.82,NJ City,S,22,21,4,42,No education,35,Patnership,Complex\n324,64.28,NJ City,N,4,23,2,36,Graduation,27,Franchise,Complex\n4664,68.67,NJ City,N,17,20,4,39,No education,33,Franchise,Complex\n8417,274.23,NJ City,N,18,9,8,65,Graduation,19,Patnership,Standalone\n2837,29.68,NJ City,N,13,17,2,54,No education,27,Patnership,Standalone\n9468,326.35,NJ City,W,23,21,8,59,Graduation,28,Franchise,Complex\n1025,263.01,NJ City,N,20,8,6,43,High School,24,Patnership,Standalone\n8621,355.91,NJ City,N,21,21,1,75,No education,27,Patnership,Standalone\n4574,188,NJ City,S,3,21,11,70,High School,21,Patnership,Complex\n5261,432.52,Boston,N,18,8,11,30,Graduation,18,Patnership,Standalone\n4216,299.66,NJ City,W,7,19,7,25,High School,29,Franchise,Complex\n8744,122.36,NJ City,W,11,14,2,62,No education,24,Franchise,Complex\n3155,345.18,NJ City,E,12,8,16,28,High School,27,Owner,Standalone\n3550,119.58,NJ City,S,9,13,4,70,High School,27,Patnership,Complex\n12,269,Maine,W,3,23,14,42,High School,34,Franchise,Complex\n9316,101.86,NJ City,W,10,9,4,27,High School,35,Franchise,Complex\n1828,106.05,NJ City,W,1,14,2,26,High School,29,Franchise,Complex\n4747,100.64,NJ City,E,8,20,4,67,High School,29,Owner,Standalone\n5248,485.64,Boston,W,23,9,6,40,Graduation,27,Franchise,Complex\n7821,4.81,Boston,N,3,21,2,64,Graduation,22,Patnership,Standalone\n401,137.46,NJ City,N,13,19,5,80,No education,34,Patnership,Standalone\n7834,306.82,Boston,S,18,21,11,79,Graduation,20,Patnership,Complex\n4808,293.79,NJ City,W,23,21,23,69,High School,21,Franchise,Complex\n4911,214.78,NJ City,E,15,19,21,48,High School,20,Owner,Standalone\n5893,290.47,Boston,N,19,12,12,64,Graduation,34,Patnership,Standalone\n5303,23.19,Boston,N,24,17,2,47,No education,32,Owner,Standalone\n9746,133.98,NJ City,S,23,21,15,70,Graduation,21,Patnership,Complex\n3501,125.16,NJ City,N,6,15,4,38,Graduation,25,Patnership,Standalone\n3188,446.49,NJ City,W,1,22,4,74,No education,29,Franchise,Complex\n3770,408.08,NJ City,S,6,18,6,48,High School,24,Patnership,Complex\n7762,374.9,Boston,S,9,12,17,76,Graduation,30,Patnership,Complex\n4366,269,NJ City,S,2,17,7,33,High School,34,Patnership,Complex\n4010,195.97,NJ City,S,23,20,3,44,No education,35,Patnership,Complex\n4651,270.4,NJ City,E,4,16,14,37,High School,19,Owner,Standalone\n6488,218.04,Boston,W,16,19,23,52,Graduation,25,Franchise,Complex\n6530,439.56,Boston,S,19,8,12,49,Graduation,29,Patnership,Complex\n8558,91.14,NJ City,S,16,12,1,41,No education,35,Patnership,Complex\n4715,378.07,NJ City,E,9,19,21,55,High School,25,Owner,Standalone\n6607,96.86,Boston,E,1,18,3,80,High School,27,Owner,Standalone\n7167,342.09,Boston,E,21,10,8,32,Graduation,23,Owner,Standalone\n1829,82.39,NJ City,N,17,11,2,29,No education,25,Patnership,Standalone\n6850,213.31,Boston,S,21,17,8,59,Graduation,32,Patnership,Complex\n9717,118.83,NJ City,N,9,24,1,60,Graduation,25,Patnership,Standalone\n9156,487.76,NJ City,W,8,13,3,64,Graduation,27,Franchise,Complex\n3809,65.85,NJ City,N,19,19,1,49,No education,19,Patnership,Standalone\n5440,396.43,Boston,W,12,21,22,50,Graduation,19,Franchise,Complex\n1011,459.09,NJ City,E,3,19,1,56,Graduation,26,Owner,Standalone\n7246,186.93,Boston,S,7,24,10,37,Graduation,33,Patnership,Complex\n134,466.99,NJ City,S,24,24,1,72,No education,30,Patnership,Complex\n6913,204.3,Boston,N,4,16,16,81,Graduation,33,Patnership,Standalone\n2184,12.71,NJ City,N,3,10,2,38,Graduation,29,Franchise,Complex\n1623,109.63,NJ City,E,20,20,2,70,Graduation,21,Owner,Standalone\n353,487.12,NJ City,N,20,13,8,74,High School,24,Patnership,Standalone\n8057,298.16,NJ City,N,6,16,16,27,Graduation,19,Patnership,Standalone\n4513,297.22,NJ City,N,18,15,3,63,High School,26,Patnership,Standalone\n326,98.41,NJ City,S,23,11,4,47,No education,26,Patnership,Complex\n9028,221.6,NJ City,W,6,23,10,60,Graduation,21,Franchise,Complex\n4781,204,NJ City,N,1,16,22,71,High School,25,Patnership,Standalone\n5588,403.12,Boston,W,14,24,19,51,Graduation,35,Franchise,Complex\n4178,241,NJ City,S,4,18,24,29,High School,26,Patnership,Complex\n7291,478.77,Boston,E,3,11,15,44,Graduation,21,Owner,Standalone\n9444,389.26,NJ City,W,9,22,25,73,Graduation,30,Franchise,Complex\n2466,183,NJ City,S,1,10,15,40,High School,32,Patnership,Complex\n5531,169.69,Boston,E,9,23,2,72,No education,20,Owner,Standalone\n9347,311.11,NJ City,E,1,17,5,55,No education,30,Owner,Standalone\n8571,82.47,NJ City,E,19,14,3,54,Graduation,23,Owner,Standalone\n7241,422.38,Boston,N,12,8,21,67,Graduation,19,Patnership,Standalone\n2223,316.31,NJ City,E,22,24,25,65,High School,18,Owner,Standalone\n5190,438.69,Boston,S,11,24,4,66,Graduation,23,Patnership,Complex\n1422,471.94,NJ City,S,11,8,20,63,High School,20,Patnership,Complex\n9999,357.37,NJ City,E,5,21,17,41,Graduation,35,Owner,Standalone\n7314,387.14,Boston,S,8,11,7,73,Graduation,33,Patnership,Complex\n888,132.91,NJ City,W,23,10,14,66,High School,26,Franchise,Complex\n8664,243.25,NJ City,W,19,8,17,69,Graduation,23,Franchise,Complex\n973,230.45,NJ City,N,10,21,15,37,High School,30,Patnership,Standalone\n4442,131.86,NJ City,S,11,17,22,75,High School,24,Patnership,Complex\n3843,114.79,NJ City,E,8,11,4,55,Graduation,28,Owner,Standalone\n6472,119.41,Boston,W,16,19,1,72,High School,29,Franchise,Complex\n4714,220.37,NJ City,S,4,24,6,49,High School,28,Patnership,Complex\n6507,274.74,Boston,E,20,8,16,70,Graduation,21,Owner,Standalone\n4238,143.74,NJ City,S,6,9,19,62,High School,34,Patnership,Complex\n3356,291.13,NJ City,W,13,18,1,77,No education,27,Franchise,Complex\n5619,332.53,Boston,E,1,15,4,42,Graduation,34,Owner,Standalone\n2245,253.41,NJ City,N,19,24,11,27,High School,22,Patnership,Standalone\n2595,439.02,NJ City,E,12,22,20,56,High School,29,Owner,Standalone\n1497,288,NJ City,N,3,22,17,43,High School,32,Patnership,Standalone\n658,150.14,NJ City,S,17,11,11,48,High School,20,Patnership,Complex\n8243,2.11,NJ City,E,10,20,2,65,No education,29,Owner,Standalone\n2639,80.76,NJ City,E,5,18,4,37,No education,29,Owner,Standalone\n4098,497.98,NJ City,S,20,11,17,33,High School,28,Patnership,Complex\n3452,20.23,NJ City,N,1,9,4,76,No education,28,Franchise,Complex\n7237,73.09,Boston,N,6,19,1,64,High School,28,Patnership,Standalone\n841,59.49,NJ City,N,13,20,3,53,High School,26,Patnership,Standalone\n5209,115.19,Boston,N,21,11,3,51,High School,33,Patnership,Standalone\n3489,268.06,NJ City,N,20,8,11,77,High School,32,Patnership,Standalone\n8962,392.63,NJ City,S,1,10,14,25,Graduation,31,Patnership,Complex\n9838,439.83,NJ City,S,7,24,17,63,Graduation,24,Patnership,Complex\n1155,268.85,NJ City,E,20,16,17,46,High School,24,Owner,Standalone\n7186,52.2,Boston,S,12,13,3,65,High School,22,Patnership,Complex\n3050,32.16,NJ City,N,13,9,1,62,No education,18,Patnership,Complex\n1330,426.64,NJ City,S,22,19,16,75,High School,22,Patnership,Complex\n3669,300.11,NJ City,N,23,13,1,78,Graduation,33,Patnership,Standalone\n6630,1.53,Boston,N,14,21,2,60,Graduation,27,Patnership,Complex\n6306,59.37,Boston,S,15,9,3,76,Graduation,35,Patnership,Complex\n2899,154.06,NJ City,E,10,19,17,25,High School,32,Owner,Standalone\n913,413.38,NJ City,N,8,11,13,33,High School,35,Patnership,Standalone\n2908,383.94,NJ City,W,15,12,12,59,High School,29,Franchise,Complex\n1089,83.95,NJ City,N,21,20,3,32,Graduation,23,Patnership,Standalone\n6575,288.65,Boston,E,7,14,1,72,No education,29,Owner,Standalone\n170,269.46,NJ City,S,24,14,2,79,No education,35,Patnership,Complex\n2405,61.27,NJ City,N,11,13,3,45,No education,28,Patnership,Standalone\n1749,62.63,NJ City,N,12,17,4,60,Graduation,28,Patnership,Standalone\n5797,446,Boston,N,5,14,21,56,Graduation,27,Patnership,Standalone\n7743,136.15,Boston,E,21,23,9,58,Graduation,18,Owner,Standalone\n3687,420.74,NJ City,E,24,17,7,30,High School,18,Owner,Standalone\n4695,198.81,NJ City,E,12,18,3,48,Graduation,20,Owner,Standalone\n5555,376.19,Boston,E,3,12,1,68,No education,34,Owner,Standalone\n3787,126.31,NJ City,E,12,22,4,80,High School,20,Owner,Standalone\n2882,310.11,NJ City,S,20,13,15,68,High School,34,Patnership,Complex\n7603,357.86,Boston,E,11,15,22,78,Graduation,21,Owner,Standalone\n2454,139.64,NJ City,S,17,14,6,38,High School,22,Patnership,Complex\n3483,63.56,NJ City,N,6,18,1,68,Graduation,32,Owner,Standalone\n9608,321.35,NJ City,W,20,16,18,69,Graduation,22,Franchise,Complex\n5432,225.08,Boston,W,23,16,24,31,Graduation,23,Franchise,Complex\n7294,90.11,Boston,S,17,15,3,43,High School,30,Patnership,Complex\n8724,312.33,NJ City,W,14,22,22,59,Graduation,29,Franchise,Complex\n2972,431.88,NJ City,W,23,11,22,44,High School,22,Franchise,Complex\n9577,276.66,NJ City,N,13,21,12,47,Graduation,26,Patnership,Standalone\n6093,398.85,Boston,N,21,24,1,46,Graduation,35,Patnership,Standalone\n1067,182.89,NJ City,E,12,22,14,67,High School,32,Owner,Standalone\n4922,183.4,NJ City,S,12,23,9,66,High School,32,Patnership,Complex\n3676,261.96,NJ City,W,4,17,23,44,High School,19,Franchise,Complex\n4165,263.26,NJ City,N,20,11,24,37,High School,22,Patnership,Standalone\n1538,118.48,NJ City,S,17,18,3,27,No education,32,Patnership,Complex\n3819,276.83,NJ City,E,14,17,13,77,High School,23,Owner,Standalone\n8512,27.21,NJ City,N,12,10,4,43,High School,32,Franchise,Complex\n9128,390.02,NJ City,W,18,17,20,32,Graduation,23,Franchise,Complex\n1305,35.41,NJ City,N,12,11,2,46,Graduation,33,Patnership,Standalone\n3705,122.96,NJ City,N,10,16,2,44,Graduation,24,Patnership,Standalone\n1119,14.96,NJ City,N,4,10,3,76,Graduation,25,Owner,Standalone\n1184,41.78,NJ City,N,23,15,3,59,No education,19,Franchise,Complex\n8867,103.54,NJ City,E,7,20,2,39,No education,20,Owner,Standalone\n5689,421.79,Boston,N,24,17,3,47,High School,34,Patnership,Standalone\n8643,59.83,NJ City,E,15,18,2,66,Graduation,25,Owner,Standalone\n1647,346.1,NJ City,E,4,11,15,66,High School,28,Owner,Standalone\n3988,287,NJ City,W,2,24,14,81,High School,23,Franchise,Complex\n2961,299,NJ City,N,3,10,7,57,High School,24,Patnership,Standalone\n6083,264.29,Boston,E,12,15,24,42,Graduation,18,Owner,Standalone\n7921,439.33,Boston,N,13,15,13,61,Graduation,20,Patnership,Standalone\n6441,296.97,Boston,N,1,8,11,38,Graduation,31,Patnership,Standalone\n8691,271.48,NJ City,E,10,22,4,50,Graduation,24,Owner,Standalone\n122,37.87,NJ City,N,3,17,1,33,No education,19,Patnership,Complex\n4352,428.08,NJ City,W,20,12,1,80,No education,27,Franchise,Complex\n9047,131.2,NJ City,E,5,16,7,63,Graduation,22,Owner,Standalone\n4493,26.34,NJ City,N,16,9,1,54,No education,34,Patnership,Standalone\n6153,249.7,Boston,N,1,13,6,54,Graduation,21,Patnership,Standalone\n3774,306.64,NJ City,S,3,15,4,27,Graduation,25,Patnership,Complex\n3136,286.48,NJ City,W,23,18,19,68,High School,18,Franchise,Complex\n4800,59.91,NJ City,W,1,10,3,61,Graduation,35,Franchise,Complex\n5416,113.23,Boston,W,19,12,3,68,High School,21,Franchise,Complex\n9847,152.84,NJ City,E,9,19,12,45,Graduation,32,Owner,Standalone\n4981,211.42,Boston,N,16,15,1,57,High School,24,Patnership,Standalone\n3438,167.15,NJ City,S,11,24,5,37,Graduation,25,Patnership,Complex\n4180,183.06,NJ City,W,22,23,5,66,High School,34,Franchise,Complex\n1630,80.1,NJ City,S,22,19,3,44,High School,33,Patnership,Complex\n6802,64.9,Boston,N,24,20,3,42,High School,18,Patnership,Complex\n8582,177.91,NJ City,S,7,16,16,36,Graduation,30,Patnership,Complex\n5994,299.96,Boston,S,8,9,21,75,Graduation,18,Patnership,Complex\n4246,40.11,NJ City,N,25,21,4,37,High School,21,Patnership,Complex\n5623,240.07,Boston,E,9,23,22,50,Graduation,20,Owner,Standalone\n8391,170.25,NJ City,E,10,14,8,42,Graduation,30,Owner,Standalone\n4367,281.53,NJ City,E,8,22,9,34,High School,18,Owner,Standalone\n9657,292.5,NJ City,N,11,16,20,36,Graduation,25,Patnership,Standalone\n7886,404.35,Boston,S,10,21,17,84,Graduation,34,Patnership,Complex\n995,162.43,NJ City,E,9,23,1,78,No education,33,Owner,Standalone\n8416,341.56,NJ City,W,21,24,12,59,Graduation,22,Franchise,Complex\n670,413.36,NJ City,S,25,24,12,42,High School,27,Patnership,Complex\n6554,455.89,Boston,S,2,15,17,49,Graduation,29,Patnership,Complex\n3331,114.24,NJ City,E,10,21,4,55,High School,20,Owner,Standalone\n5484,352.23,Boston,W,3,18,3,55,Graduation,26,Franchise,Complex\n2144,204.99,NJ City,W,22,23,24,48,High School,22,Franchise,Complex\n3570,479.96,NJ City,S,2,22,5,71,Graduation,27,Patnership,Complex\n5696,392.75,Boston,W,10,14,8,69,Graduation,31,Franchise,Complex\n1324,306.2,NJ City,W,11,24,11,52,High School,31,Franchise,Complex\n6517,125.34,Boston,N,7,16,4,26,High School,35,Patnership,Standalone\n575,263,NJ City,E,3,23,20,67,High School,24,Owner,Standalone\n5591,321.27,Boston,E,13,20,10,41,Graduation,21,Owner,Standalone\n5816,425.58,Boston,W,11,20,17,80,Graduation,34,Franchise,Complex\n1790,394.4,NJ City,S,5,15,3,55,No education,33,Patnership,Complex\n7749,186.2,Boston,N,12,13,7,33,Graduation,18,Patnership,Standalone\n4672,133.1,NJ City,W,10,8,16,56,High School,25,Franchise,Complex\n1135,473.6,NJ City,E,11,14,25,60,High School,19,Owner,Standalone\n4975,127.93,Boston,E,20,15,1,26,High School,33,Owner,Standalone\n7012,118.42,Boston,W,24,22,4,42,High School,30,Franchise,Complex\n5781,96.81,Boston,N,10,14,3,48,Graduation,30,Patnership,Standalone\n234,342.2,NJ City,S,9,21,1,27,Graduation,32,Patnership,Complex\n1231,9.31,NJ City,N,20,18,4,40,High School,33,Owner,Standalone\n1844,67.62,NJ City,N,7,17,4,55,No education,26,Franchise,Complex\n8081,362.2,NJ City,N,15,15,22,81,Graduation,23,Patnership,Standalone\n3166,165.51,NJ City,S,18,22,23,35,High School,19,Patnership,Complex\n2902,411.96,NJ City,S,13,16,22,42,High School,24,Patnership,Complex\n2998,84.51,NJ City,S,19,23,3,36,High School,26,Patnership,Complex\n1448,435.58,NJ City,W,15,15,2,65,No education,24,Franchise,Complex\n1480,178.79,NJ City,W,6,10,13,51,High School,35,Franchise,Complex\n9324,388.09,NJ City,W,11,21,6,58,Graduation,22,Franchise,Complex\n5077,220.71,Boston,N,9,23,10,46,Graduation,18,Patnership,Standalone\n1663,46.62,NJ City,N,19,13,1,80,High School,20,Owner,Standalone\n1781,453.31,NJ City,N,13,9,3,74,No education,21,Patnership,Standalone\n7537,151.42,Boston,N,8,22,3,35,High School,34,Patnership,Standalone\n3063,84.04,NJ City,E,5,13,1,31,Graduation,30,Owner,Standalone\n8682,98.82,NJ City,S,24,20,4,64,Graduation,18,Patnership,Complex\n962,43.52,NJ City,N,19,11,3,26,No education,27,Patnership,Complex\n2673,340.96,NJ City,N,13,14,2,40,Graduation,27,Patnership,Standalone\n8374,305.9,NJ City,S,20,19,4,72,High School,23,Patnership,Complex\n4359,233.54,NJ City,E,17,13,12,43,High School,21,Owner,Standalone\n8366,414.84,NJ City,S,25,8,4,37,No education,20,Patnership,Complex\n9651,185.63,NJ City,E,25,18,19,33,Graduation,19,Owner,Standalone\n6274,193.07,Boston,S,3,11,15,32,Graduation,25,Patnership,Complex\n3795,186,NJ City,E,2,13,22,36,High School,34,Owner,Standalone\n6476,409.84,Boston,W,15,11,24,62,Graduation,29,Franchise,Complex\n8052,433.07,NJ City,W,15,16,14,34,Graduation,27,Franchise,Complex\n8475,319.48,NJ City,E,25,12,12,33,Graduation,27,Owner,Standalone\n8372,279.99,NJ City,W,9,18,6,58,Graduation,30,Franchise,Complex\n1,205.93,NJ City,N,16,11,12,75,High School,34,Owner,Complex\n6840,339.05,Boston,W,6,12,18,81,Graduation,32,Franchise,Complex\n1406,352.87,NJ City,S,19,18,8,26,High School,35,Patnership,Complex\n8708,458.34,NJ City,W,22,20,7,35,Graduation,18,Franchise,Complex\n4595,30.32,NJ City,N,8,21,3,53,No education,34,Owner,Standalone\n3979,123.26,NJ City,E,3,15,3,82,High School,21,Owner,Standalone\n3516,436.84,NJ City,W,2,10,3,49,Graduation,26,Franchise,Complex\n2448,51.41,NJ City,W,2,24,4,49,Graduation,28,Franchise,Complex\n430,9.38,NJ City,N,9,10,4,81,High School,26,Patnership,Complex\n6216,120.31,Boston,W,7,11,1,36,Graduation,18,Franchise,Complex\n6141,434.1,Boston,N,11,23,15,37,Graduation,22,Patnership,Standalone\n2977,100.32,NJ City,N,4,21,3,26,High School,19,Patnership,Standalone\n1270,232,NJ City,S,2,9,15,81,High School,28,Patnership,Complex\n7673,244.94,Boston,N,16,15,2,45,No education,27,Patnership,Standalone\n1174,15.83,NJ City,N,20,16,3,65,High School,32,Patnership,Complex\n351,18.98,NJ City,N,15,21,1,62,Graduation,29,Owner,Standalone\n1414,212,NJ City,S,3,8,11,46,High School,34,Patnership,Complex\n5097,95.69,Boston,N,14,17,3,71,Graduation,19,Patnership,Standalone\n5276,458.2,Boston,W,8,11,8,74,Graduation,20,Franchise,Complex\n4469,237.24,NJ City,N,4,10,13,49,High School,35,Patnership,Standalone\n8328,197.24,NJ City,W,25,18,22,72,Graduation,21,Franchise,Complex\n9650,440.28,NJ City,S,22,15,8,51,Graduation,21,Patnership,Complex\n2105,385.16,NJ City,N,9,8,23,81,High School,25,Patnership,Standalone\n9306,33.82,NJ City,N,3,15,4,66,Graduation,24,Patnership,Complex\n1274,342.62,NJ City,S,22,18,3,82,No education,33,Patnership,Complex\n6709,132.07,Boston,N,21,16,10,28,Graduation,19,Patnership,Standalone\n9880,375.07,NJ City,W,14,16,3,76,High School,31,Franchise,Complex\n8806,8.59,NJ City,N,9,20,3,34,High School,18,Patnership,Complex\n4449,147.96,NJ City,N,18,11,13,33,High School,35,Patnership,Standalone\n5827,304.62,Boston,E,7,10,25,67,Graduation,25,Owner,Standalone\n78,285.14,Boston,S,7,11,14,73,High School,29,Patnership,Complex\n958,200,NJ City,S,1,22,10,69,High School,26,Patnership,Complex\n2775,141,NJ City,E,3,11,17,47,High School,29,Owner,Standalone\n6301,240.37,Boston,N,11,16,17,82,Graduation,23,Patnership,Standalone\n3185,37.32,NJ City,N,14,16,4,48,No education,32,Patnership,Standalone\n7208,394.41,Boston,W,2,24,18,44,Graduation,20,Franchise,Complex\n6628,19.37,Boston,N,20,21,2,76,High School,31,Franchise,Complex\n8256,316.81,NJ City,W,24,13,14,58,Graduation,25,Franchise,Complex\n3234,234.43,NJ City,S,4,14,14,72,High School,24,Patnership,Complex\n8575,253.09,NJ City,E,7,21,19,78,Graduation,22,Owner,Standalone\n9397,302.68,NJ City,N,23,10,9,70,Graduation,22,Patnership,Standalone\n7755,301.67,Boston,E,6,18,5,63,Graduation,33,Owner,Standalone\n2176,452.77,NJ City,W,2,24,3,34,High School,33,Franchise,Complex\n1088,205.76,NJ City,W,13,19,9,40,High School,29,Franchise,Complex\n9685,362.5,NJ City,N,17,18,21,29,Graduation,33,Patnership,Standalone\n2088,285.88,NJ City,W,2,21,2,65,Graduation,28,Franchise,Complex\n5486,423.78,Boston,S,2,18,7,28,Graduation,19,Patnership,Complex\n3450,79.46,NJ City,S,18,10,1,59,Graduation,27,Patnership,Complex\n3181,372.89,NJ City,N,14,20,18,47,High School,24,Patnership,Standalone\n7825,462.6,Boston,N,22,18,5,33,High School,25,Patnership,Standalone\n4444,62.66,NJ City,N,11,11,1,25,High School,31,Franchise,Complex\n6943,281.14,Boston,E,16,14,3,29,High School,35,Owner,Standalone\n1881,28.32,NJ City,N,15,14,3,38,Graduation,24,Patnership,Standalone\n4928,339.83,NJ City,W,13,17,5,78,No education,19,Franchise,Complex\n1743,108.94,NJ City,E,10,12,3,39,Graduation,25,Owner,Standalone\n218,332.2,NJ City,S,25,22,14,35,High School,33,Patnership,Complex\n8528,195.98,NJ City,W,4,12,2,53,No education,27,Franchise,Complex\n7064,270.96,Boston,W,10,23,24,48,Graduation,24,Franchise,Complex\n2683,348.2,NJ City,E,7,17,22,52,High School,33,Owner,Standalone\n3070,418.53,NJ City,S,15,20,5,46,High School,19,Patnership,Complex\n7218,259.22,Boston,S,14,22,19,43,Graduation,31,Patnership,Complex\n4737,279.92,NJ City,N,7,18,15,43,High School,31,Patnership,Standalone\n6635,412.53,Boston,E,1,19,25,45,Graduation,33,Owner,Standalone\n8019,306.88,Boston,E,5,21,9,37,Graduation,31,Owner,Standalone\n3709,480.04,NJ City,N,4,20,10,75,High School,30,Patnership,Standalone\n7636,273.83,Boston,W,25,8,1,41,High School,20,Franchise,Complex\n2479,423.28,NJ City,E,24,20,7,74,High School,28,Owner,Standalone\n1787,471,NJ City,E,23,11,25,41,High School,29,Owner,Standalone\n6208,350.9,Boston,W,11,13,17,42,Graduation,18,Franchise,Complex\n3990,315.33,NJ City,S,11,22,7,75,High School,20,Patnership,Complex\n1243,148.27,NJ City,E,18,9,7,64,High School,31,Owner,Standalone\n456,159.75,NJ City,W,21,10,9,71,High School,24,Franchise,Complex\n6950,346.9,Boston,S,16,23,18,52,Graduation,24,Patnership,Complex\n6752,471.68,Boston,W,15,24,16,45,Graduation,28,Franchise,Complex\n1477,43.05,NJ City,N,10,20,1,64,High School,21,Patnership,Standalone\n5863,92.15,Boston,E,8,11,3,57,High School,22,Owner,Standalone\n149,191.77,NJ City,N,8,17,19,62,High School,19,Patnership,Standalone\n4261,26.42,NJ City,N,21,24,3,51,High School,27,Patnership,Standalone\n8425,347.39,NJ City,N,6,22,8,43,Graduation,30,Patnership,Standalone\n1005,292.39,NJ City,N,10,9,18,39,High School,20,Patnership,Standalone\n2923,71.3,NJ City,N,6,12,2,74,High School,28,Owner,Standalone\n8740,230.59,NJ City,W,2,23,5,44,High School,19,Franchise,Complex\n4033,465.97,NJ City,N,7,22,7,72,High School,29,Patnership,Standalone\n9870,325.42,NJ City,S,24,15,10,39,Graduation,28,Patnership,Complex\n7792,135.36,Boston,W,12,11,8,49,Graduation,29,Franchise,Complex\n9378,143.36,NJ City,S,18,24,22,83,Graduation,24,Patnership,Complex\n8471,380.5,NJ City,E,19,18,11,57,Graduation,32,Owner,Standalone\n6009,364.62,Boston,N,23,22,9,70,Graduation,21,Patnership,Standalone\n740,109,NJ City,W,2,11,25,67,High School,27,Franchise,Complex\n9706,57.84,NJ City,S,15,21,3,74,High School,28,Patnership,Complex\n4616,118.6,NJ City,W,23,22,4,73,No education,23,Franchise,Complex\n9590,419.31,NJ City,S,18,24,5,28,No education,27,Patnership,Complex\n7320,41.39,Boston,N,17,9,2,72,Graduation,21,Franchise,Complex\n4059,331.43,NJ City,E,17,18,14,42,High School,26,Owner,Standalone\n6805,196.03,Boston,N,5,17,25,44,Graduation,35,Patnership,Standalone\n7934,397.09,Boston,S,24,23,10,77,Graduation,33,Patnership,Complex\n3426,346.5,NJ City,S,19,17,18,54,High School,33,Patnership,Complex\n7199,393.7,Boston,E,20,18,3,42,No education,25,Owner,Standalone\n9070,116.18,NJ City,S,14,10,2,63,High School,22,Patnership,Complex\n3562,70.05,NJ City,N,2,21,2,67,High School,24,Patnership,Complex\n212,186.64,NJ City,W,17,16,22,70,High School,20,Franchise,Complex\n8623,193.87,NJ City,E,17,24,13,31,Graduation,22,Owner,Standalone\n3593,128.85,NJ City,N,22,18,15,62,High School,26,Patnership,Standalone\n1735,444.99,NJ City,E,12,23,8,66,High School,24,Owner,Standalone\n6225,78.69,Boston,N,15,24,2,60,Graduation,18,Patnership,Standalone\n8838,130.91,NJ City,S,24,13,14,50,Graduation,26,Patnership,Complex\n8642,393.35,NJ City,S,4,9,24,36,Graduation,32,Patnership,Complex\n4696,416.27,NJ City,W,9,20,24,25,High School,35,Franchise,Complex\n4526,280,NJ City,S,2,18,25,74,High School,30,Patnership,Complex\n9511,9.18,NJ City,N,12,13,4,28,High School,20,Owner,Standalone\n5025,33.73,Boston,N,16,9,4,82,Graduation,19,Patnership,Standalone\n6794,210.95,Boston,S,7,21,2,80,No education,18,Patnership,Complex\n8833,191.11,NJ City,N,23,8,16,76,Graduation,19,Patnership,Standalone\n8143,486.9,NJ City,E,19,17,23,75,Graduation,23,Owner,Standalone\n399,476.07,NJ City,E,11,23,15,32,High School,35,Owner,Standalone\n5854,99.58,Boston,S,12,22,3,31,High School,19,Patnership,Complex\n1389,55.17,NJ City,N,22,22,3,66,Graduation,35,Patnership,Standalone\n5147,195.95,Boston,E,19,24,21,76,Graduation,24,Owner,Standalone\n5585,355.04,Boston,N,23,8,15,74,Graduation,28,Patnership,Standalone\n7474,450.61,Boston,S,8,11,23,36,Graduation,26,Patnership,Complex\n9168,48.58,NJ City,W,18,15,4,48,Graduation,32,Franchise,Complex\n7282,56.24,Boston,S,5,18,1,42,High School,20,Patnership,Complex\n5267,474.61,Boston,E,16,24,20,71,Graduation,25,Owner,Standalone\n8889,114.32,NJ City,N,5,8,3,50,Graduation,28,Patnership,Standalone\n999,443.44,NJ City,E,5,10,23,48,High School,28,Owner,Standalone\n4397,364.73,NJ City,N,20,20,16,37,High School,19,Patnership,Standalone\n3386,308.76,NJ City,S,8,20,5,27,No education,31,Patnership,Complex\n1469,90.14,NJ City,N,21,16,1,71,No education,27,Patnership,Standalone\n942,111.12,NJ City,S,4,14,4,33,Graduation,21,Patnership,Complex\n8051,175.31,NJ City,E,23,17,23,60,Graduation,28,Owner,Standalone\n1430,32.98,NJ City,N,19,20,1,73,No education,34,Patnership,Complex\n4884,104.16,NJ City,W,5,10,4,50,Graduation,32,Franchise,Complex\n5570,424.78,Boston,S,25,23,7,77,Graduation,35,Patnership,Complex\n8136,463.39,NJ City,W,18,11,24,37,Graduation,19,Franchise,Complex\n2770,255.39,NJ City,S,19,16,10,66,High School,30,Patnership,Complex\n602,64.87,NJ City,N,20,12,1,41,No education,31,Patnership,Complex\n1857,134.85,NJ City,N,25,24,23,69,High School,25,Patnership,Standalone\n7052,20.71,Boston,N,5,19,3,64,No education,20,Franchise,Complex\n4959,321.14,Boston,E,16,24,18,84,High School,24,Owner,Standalone\n3587,126.73,NJ City,E,21,23,2,58,No education,18,Owner,Standalone\n6936,196.44,Boston,W,15,21,4,67,Graduation,23,Franchise,Complex\n8531,297.99,NJ City,E,1,18,25,42,Graduation,26,Owner,Standalone\n1496,157.94,NJ City,W,2,19,3,35,No education,19,Franchise,Complex\n7761,179.13,Boston,N,3,23,19,75,Graduation,27,Patnership,Standalone\n5233,288.08,Boston,N,11,9,15,34,Graduation,28,Patnership,Standalone\n165,219.52,NJ City,N,8,20,8,32,High School,25,Patnership,Standalone\n855,377.84,NJ City,E,19,21,8,60,High School,26,Owner,Standalone\n269,363.14,NJ City,N,20,10,24,47,High School,27,Patnership,Standalone\n9899,221.1,NJ City,E,9,10,13,54,Graduation,27,Owner,Standalone\n1426,77.32,NJ City,S,22,21,1,79,High School,33,Patnership,Complex\n6375,421.51,Boston,E,12,15,20,36,Graduation,35,Owner,Standalone\n5845,130.7,Boston,N,10,24,15,38,Graduation,21,Patnership,Standalone\n3274,183,NJ City,S,2,18,7,78,High School,22,Patnership,Complex\n3180,367.8,NJ City,W,10,12,12,68,High School,19,Franchise,Complex\n794,368.69,NJ City,S,5,19,1,41,No education,21,Patnership,Complex\n5191,333.39,Boston,E,13,8,23,65,Graduation,21,Owner,Standalone\n3943,383.13,NJ City,E,19,11,20,51,High School,18,Owner,Standalone\n2847,67.13,NJ City,N,13,14,4,71,Graduation,23,Owner,Standalone\n2950,88.28,NJ City,S,1,23,1,42,High School,20,Patnership,Complex\n4108,341.21,NJ City,W,13,12,14,70,High School,33,Franchise,Complex\n4987,11.34,Boston,N,19,24,2,54,High School,34,Owner,Standalone\n8140,440.81,NJ City,W,22,19,8,56,Graduation,27,Franchise,Complex\n5304,442.82,Boston,W,11,19,6,50,Graduation,30,Franchise,Complex\n9453,135.46,NJ City,N,8,8,15,29,Graduation,20,Patnership,Standalone\n782,463.38,NJ City,S,15,22,13,64,High School,18,Patnership,Complex\n9115,175.21,NJ City,E,9,17,18,62,Graduation,19,Owner,Standalone\n8075,143.82,NJ City,E,5,22,2,47,No education,35,Owner,Standalone\n7578,152.63,Boston,S,14,13,12,47,Graduation,33,Patnership,Complex\n7518,387.85,Boston,S,6,18,16,37,Graduation,34,Patnership,Complex\n4357,4.66,NJ City,N,22,15,4,81,High School,33,Patnership,Standalone\n5982,431.23,Boston,S,24,17,18,64,Graduation,31,Patnership,Complex\n3333,8.24,NJ City,N,22,14,4,37,Graduation,35,Patnership,Standalone\n9364,354.85,NJ City,W,20,13,12,84,Graduation,21,Franchise,Complex\n7202,386.98,Boston,S,4,10,10,70,Graduation,34,Patnership,Complex\n4126,437.16,NJ City,S,5,15,23,74,High School,29,Patnership,Complex\n8325,131.64,NJ City,N,12,8,25,38,Graduation,29,Patnership,Standalone\n6272,98.16,Boston,W,11,23,4,46,No education,33,Franchise,Complex\n8255,36.44,NJ City,N,4,20,3,41,No education,19,Owner,Standalone\n5847,302.95,Boston,E,25,15,17,57,Graduation,21,Owner,Standalone\n2906,132.79,NJ City,S,5,9,13,31,High School,32,Patnership,Complex\n730,137.7,NJ City,S,14,21,2,43,High School,20,Patnership,Complex\n175,303.86,NJ City,E,22,12,2,41,High School,26,Owner,Standalone\n2456,238,NJ City,W,1,10,14,54,High School,34,Franchise,Complex\n6716,342.37,Boston,W,21,13,5,64,No education,32,Franchise,Complex\n241,109,NJ City,N,3,20,24,26,High School,30,Patnership,Standalone\n3138,471.9,NJ City,S,8,20,24,70,High School,31,Patnership,Complex\n2984,182.36,NJ City,W,16,14,4,74,No education,25,Franchise,Complex\n5968,458.15,Boston,W,18,15,5,52,High School,18,Franchise,Complex\n9782,304.63,NJ City,S,15,23,20,50,Graduation,26,Patnership,Complex\n2517,302.74,NJ City,N,25,17,25,44,High School,23,Patnership,Standalone\n3929,303.73,NJ City,N,13,14,7,53,High School,33,Patnership,Standalone\n699,232.8,NJ City,E,22,16,14,39,High School,24,Owner,Standalone\n7965,391.69,Boston,N,15,19,19,85,Graduation,24,Patnership,Standalone\n5517,166.29,Boston,N,13,8,15,75,Graduation,23,Patnership,Standalone\n7710,310.5,Boston,S,19,8,6,58,Graduation,27,Patnership,Complex\n2546,498.11,NJ City,S,25,22,8,82,High School,32,Patnership,Complex\n1077,23.24,NJ City,N,5,24,1,73,Graduation,32,Patnership,Standalone\n2032,50.44,NJ City,W,12,16,1,70,High School,29,Franchise,Complex\n8911,65.45,NJ City,N,8,16,4,76,High School,28,Owner,Standalone\n9729,95.05,NJ City,N,10,10,4,48,Graduation,29,Patnership,Standalone\n651,108,NJ City,E,2,23,22,47,High School,29,Owner,Standalone\n1653,84.67,NJ City,N,1,11,3,40,Graduation,34,Patnership,Standalone\n4103,133.88,NJ City,E,12,16,14,82,High School,32,Owner,Standalone\n4,93.99,NJ City,W,19,9,3,42,High School,29,Owner,Standalone\n2593,20.7,NJ City,N,4,15,2,26,High School,20,Patnership,Standalone\n6268,176.2,Boston,W,12,8,20,40,Graduation,33,Franchise,Complex\n7255,379.6,Boston,E,23,15,21,59,Graduation,21,Owner,Standalone\n2887,357.86,NJ City,E,12,13,19,38,High School,23,Owner,Standalone\n2532,466.53,NJ City,W,4,10,10,29,High School,19,Franchise,Complex\n3706,489.87,NJ City,S,14,13,9,84,High School,30,Patnership,Complex\n4889,344.2,NJ City,N,25,14,2,70,No education,26,Patnership,Standalone\n4930,383.09,NJ City,S,16,24,11,30,High School,34,Patnership,Complex\n3182,35.32,NJ City,N,19,12,2,31,No education,19,Patnership,Complex\n2122,85.83,NJ City,S,25,19,1,27,High School,26,Patnership,Complex\n5203,50.6,Boston,E,19,21,3,66,High School,33,Owner,Standalone\n8168,426.91,NJ City,W,23,24,23,53,Graduation,24,Franchise,Complex\n8035,481.89,Boston,E,24,23,14,82,Graduation,34,Owner,Standalone\n5200,402.62,Boston,W,2,21,2,37,High School,33,Franchise,Complex\n8915,454.71,NJ City,E,23,22,1,73,No education,23,Owner,Standalone\n5987,133.74,Boston,E,19,17,13,64,Graduation,34,Owner,Standalone\n4503,22.83,NJ City,N,2,18,2,62,Graduation,25,Owner,Standalone\n2661,145,NJ City,N,1,18,12,47,High School,29,Patnership,Standalone\n4519,330.93,NJ City,E,8,9,6,33,High School,23,Owner,Standalone\n7862,225.62,Boston,S,25,14,9,38,Graduation,26,Patnership,Complex\n2496,247.52,NJ City,W,11,11,4,82,Graduation,22,Franchise,Complex\n3557,494.88,NJ City,N,12,23,6,33,High School,25,Patnership,Standalone\n7785,453.09,Boston,N,8,19,13,50,Graduation,24,Patnership,Standalone\n3329,166.55,NJ City,N,8,16,10,25,High School,19,Patnership,Standalone\n2760,383.2,NJ City,W,19,16,19,71,High School,31,Franchise,Complex\n7718,112.62,Boston,S,22,23,3,29,No education,30,Patnership,Complex\n6583,356.85,Boston,E,4,18,8,80,Graduation,35,Owner,Standalone\n8340,28.12,NJ City,N,1,18,4,46,Graduation,27,Franchise,Complex\n693,78.38,NJ City,N,24,10,3,63,Graduation,28,Patnership,Standalone\n4313,181.57,NJ City,N,7,9,2,54,No education,33,Patnership,Standalone\n8800,123.32,NJ City,W,23,12,4,85,High School,28,Franchise,Complex\n4373,233.03,NJ City,N,12,14,19,85,High School,26,Patnership,Standalone\n2041,135.52,NJ City,N,19,12,11,67,High School,28,Patnership,Standalone\n9302,295.3,NJ City,S,16,11,25,50,Graduation,23,Patnership,Complex\n1260,115.5,NJ City,W,18,15,3,68,Graduation,24,Franchise,Complex\n8848,10.83,NJ City,N,5,20,2,74,High School,18,Franchise,Complex\n125,264,NJ City,N,2,19,15,70,High School,26,Patnership,Standalone\n3897,313.68,NJ City,N,25,21,3,27,Graduation,29,Patnership,Standalone\n2151,434.5,NJ City,E,20,17,12,40,High School,27,Owner,Standalone\n3467,418.49,NJ City,E,17,12,7,83,High School,30,Owner,Standalone\n1854,219.05,NJ City,S,12,16,1,40,Graduation,20,Patnership,Complex\n9165,391.49,NJ City,N,19,18,15,48,Graduation,18,Patnership,Standalone\n3998,166.16,NJ City,S,7,16,8,43,High School,25,Patnership,Complex\n3248,98.78,NJ City,W,21,21,1,58,No education,22,Franchise,Complex\n4309,433.41,NJ City,N,6,23,25,73,High School,29,Patnership,Standalone\n5205,137.98,Boston,N,12,10,4,36,Graduation,20,Patnership,Standalone\n8909,83.69,NJ City,N,9,8,2,31,No education,22,Patnership,Standalone\n689,58.02,NJ City,N,18,8,4,37,No education,22,Patnership,Standalone\n1614,352.6,NJ City,S,23,20,10,68,High School,27,Patnership,Complex\n5175,414.88,Boston,E,25,14,23,28,Graduation,18,Owner,Standalone\n8522,276.49,NJ City,S,11,14,1,73,No education,23,Patnership,Complex\n9787,233.6,NJ City,E,6,16,20,61,Graduation,33,Owner,Standalone\n893,408.8,NJ City,N,25,13,13,62,High School,35,Patnership,Standalone\n5798,37.31,Boston,N,19,24,3,59,No education,28,Patnership,Complex\n5629,270.73,Boston,N,22,13,23,39,Graduation,27,Patnership,Standalone\n3834,416.12,NJ City,S,13,22,19,73,High School,24,Patnership,Complex\n6881,16.84,Boston,N,20,13,1,48,No education,29,Patnership,Standalone\n9915,73.75,NJ City,N,18,18,1,77,Graduation,26,Owner,Standalone\n1995,162.72,NJ City,E,8,18,12,61,High School,23,Owner,Standalone\n5474,66.97,Boston,N,8,23,2,69,No education,29,Patnership,Complex\n8310,132.35,NJ City,S,5,24,18,64,Graduation,21,Patnership,Complex\n9074,456.45,NJ City,S,17,17,16,76,Graduation,33,Patnership,Complex\n5737,45.08,Boston,N,24,14,2,61,High School,32,Patnership,Standalone\n9553,376.48,NJ City,N,5,13,19,28,Graduation,19,Patnership,Standalone\n3621,16.33,NJ City,N,20,11,3,38,Graduation,35,Patnership,Standalone\n2049,212.46,NJ City,N,16,19,3,31,Graduation,25,Patnership,Standalone\n3296,48.27,NJ City,W,10,19,1,34,No education,19,Franchise,Complex\n1552,365.07,NJ City,W,7,10,5,30,High School,32,Franchise,Complex\n4914,144.16,NJ City,S,20,24,16,80,High School,34,Patnership,Complex\n577,144.45,NJ City,N,14,18,13,75,High School,34,Patnership,Standalone\n1506,381.87,NJ City,S,23,16,7,42,High School,22,Patnership,Complex\n1047,245.43,NJ City,E,4,24,11,28,High School,27,Owner,Standalone\n9481,120.22,NJ City,N,17,12,4,41,High School,23,Patnership,Standalone\n6516,62.66,Boston,N,8,20,2,26,Graduation,24,Franchise,Complex\n749,232.1,NJ City,N,17,23,3,35,No education,24,Patnership,Standalone\n2680,458.7,NJ City,W,25,17,8,77,High School,20,Franchise,Complex\n6863,20.47,Boston,N,7,20,4,38,No education,32,Owner,Standalone\n851,480.87,NJ City,E,21,20,14,61,High School,35,Owner,Standalone\n4405,169.41,NJ City,N,2,16,5,26,High School,27,Patnership,Standalone\n1282,479.94,NJ City,S,15,19,1,74,High School,31,Patnership,Complex\n2439,222.64,NJ City,E,6,14,13,79,High School,20,Owner,Standalone\n1547,388.53,NJ City,E,5,15,17,68,High School,29,Owner,Standalone\n3879,75.66,NJ City,E,22,17,4,59,Graduation,18,Owner,Standalone\n410,277.68,NJ City,S,24,12,7,75,High School,28,Patnership,Complex\n7989,139.91,Boston,N,3,24,9,63,Graduation,31,Patnership,Standalone\n6039,113.41,Boston,E,25,14,4,57,Graduation,23,Owner,Standalone\n4237,392.44,NJ City,N,4,19,11,44,High School,30,Patnership,Standalone\n6473,393.1,Boston,N,25,24,8,25,Graduation,25,Patnership,Standalone\n1765,190.81,NJ City,N,6,23,25,71,High School,24,Patnership,Standalone\n5941,391.41,Boston,N,11,19,21,57,Graduation,24,Patnership,Standalone\n9188,488.54,NJ City,W,18,18,21,26,Graduation,35,Franchise,Complex\n663,438.86,NJ City,E,14,23,20,27,High School,33,Owner,Standalone\n909,440.62,NJ City,N,13,22,17,31,High School,33,Patnership,Standalone\n7239,235.33,Boston,E,12,8,1,67,Graduation,25,Owner,Standalone\n1192,277.18,NJ City,W,11,9,4,53,High School,22,Franchise,Complex\n9394,159.01,NJ City,S,8,15,18,42,Graduation,29,Patnership,Complex\n2842,480.04,NJ City,S,16,13,18,33,High School,22,Patnership,Complex\n479,297,NJ City,E,2,18,19,45,High School,28,Owner,Standalone\n3759,433.1,NJ City,E,11,9,25,35,High School,20,Owner,Standalone\n1010,394.77,NJ City,S,8,19,23,70,High School,30,Patnership,Complex\n4269,219.28,NJ City,N,19,18,7,73,High School,33,Patnership,Standalone\n9719,459.99,NJ City,E,8,13,13,69,Graduation,18,Owner,Standalone\n9764,103.49,NJ City,W,17,19,4,35,No education,30,Franchise,Complex\n2071,276.63,NJ City,E,7,11,22,70,High School,22,Owner,Standalone\n9125,470.26,NJ City,N,25,22,3,54,No education,35,Patnership,Standalone\n2828,170.93,NJ City,W,15,11,25,37,High School,34,Franchise,Complex\n5139,237.29,Boston,E,8,11,19,85,Graduation,21,Owner,Standalone\n5353,192.7,Boston,N,21,24,23,25,Graduation,28,Patnership,Standalone\n8390,294.51,NJ City,S,16,21,2,77,No education,28,Patnership,Complex\n6244,377.31,Boston,W,24,18,15,58,Graduation,32,Franchise,Complex\n3343,476.18,NJ City,E,5,15,21,34,High School,25,Owner,Standalone\n5148,385.01,Boston,W,8,17,24,30,Graduation,30,Franchise,Complex\n5218,392.1,Boston,S,7,9,3,32,High School,23,Patnership,Complex\n6040,381.23,Boston,W,15,18,16,25,Graduation,35,Franchise,Complex\n644,311.36,NJ City,W,18,18,10,71,High School,18,Franchise,Complex\n2217,19.5,NJ City,N,16,13,3,26,Graduation,29,Patnership,Standalone\n483,409.13,NJ City,E,24,11,13,44,High School,34,Owner,Standalone\n6762,444.49,Boston,S,9,24,7,51,Graduation,19,Patnership,Complex\n5621,33.28,Boston,N,8,23,4,28,No education,31,Patnership,Standalone\n4969,209.1,Boston,N,9,15,24,46,High School,21,Patnership,Standalone\n6037,416.46,Boston,N,3,17,8,51,Graduation,28,Patnership,Standalone\n4636,376.16,NJ City,W,12,21,11,35,High School,24,Franchise,Complex\n4316,393.35,NJ City,W,4,19,2,42,No education,21,Franchise,Complex\n250,439.52,NJ City,S,24,8,7,45,High School,32,Patnership,Complex\n2505,83.57,NJ City,N,25,16,2,68,Graduation,27,Patnership,Standalone\n2123,80.3,NJ City,E,5,8,4,45,No education,29,Owner,Standalone\n9137,150.06,NJ City,N,20,14,21,78,Graduation,35,Patnership,Standalone\n7597,97.53,Boston,N,7,16,2,54,High School,22,Patnership,Standalone\n9424,455.23,NJ City,W,1,16,12,26,Graduation,34,Franchise,Complex\n9040,340.41,NJ City,W,4,16,2,63,High School,27,Franchise,Complex\n621,335.31,NJ City,N,20,24,8,51,High School,32,Patnership,Standalone\n5289,164.23,Boston,N,24,13,1,65,Graduation,24,Patnership,Standalone\n9036,0.94,NJ City,N,4,20,1,67,Graduation,28,Franchise,Complex\n603,138.14,NJ City,E,14,19,1,27,Graduation,33,Owner,Standalone\n9673,299.94,NJ City,N,10,20,20,50,Graduation,21,Patnership,Standalone\n2341,101.93,NJ City,N,7,14,1,55,High School,30,Patnership,Standalone\n7194,230.22,Boston,S,7,22,24,75,Graduation,35,Patnership,Complex\n6771,250.75,Boston,E,3,14,3,83,Graduation,21,Owner,Standalone\n3431,342.87,NJ City,E,22,20,17,84,High School,33,Owner,Standalone\n3640,470.04,NJ City,W,6,12,22,59,High School,29,Franchise,Complex\n1102,342.69,NJ City,S,18,19,2,43,High School,24,Patnership,Complex\n96,494.33,NJ City,W,7,17,10,41,High School,27,Franchise,Complex\n249,495.2,NJ City,N,8,15,1,62,Graduation,19,Patnership,Standalone\n5003,400.73,Boston,E,7,14,23,58,Graduation,34,Owner,Standalone\n8763,112.04,NJ City,E,3,10,1,77,Graduation,35,Owner,Standalone\n5890,29.12,Boston,N,25,12,3,46,High School,29,Patnership,Complex\n8041,129.77,Boston,N,17,21,23,63,Graduation,27,Patnership,Standalone\n6688,499.76,Boston,W,25,20,20,30,Graduation,26,Franchise,Complex\n5775,256.95,Boston,E,18,10,19,40,Graduation,34,Owner,Standalone\n2213,179.11,NJ City,N,9,23,3,46,No education,33,Patnership,Standalone\n6561,370.42,Boston,N,5,18,7,53,Graduation,19,Patnership,Standalone\n8250,438.22,NJ City,S,12,12,25,51,Graduation,22,Patnership,Complex\n929,207.38,NJ City,N,18,18,5,78,No education,31,Patnership,Standalone\n649,289.02,NJ City,N,11,12,18,75,High School,22,Patnership,Standalone\n6623,365.7,Boston,E,5,8,21,79,Graduation,23,Owner,Standalone\n6916,384.71,Boston,W,25,22,23,25,Graduation,34,Franchise,Complex\n3682,110.36,NJ City,S,1,11,3,55,High School,31,Patnership,Complex\n4742,443.89,NJ City,S,7,9,2,40,No education,35,Patnership,Complex\n6409,62.9,Boston,N,15,15,4,26,High School,18,Patnership,Standalone\n2491,397.9,NJ City,E,4,23,1,43,High School,35,Owner,Standalone\n9761,375.25,NJ City,N,24,18,21,77,Graduation,33,Patnership,Standalone\n1116,381.07,NJ City,W,14,17,4,79,Graduation,20,Franchise,Complex\n761,441.32,NJ City,N,21,24,15,26,High School,20,Patnership,Standalone\n2985,92.83,NJ City,N,4,19,4,70,Graduation,31,Patnership,Standalone\n3383,442.63,NJ City,E,25,17,21,44,High School,32,Owner,Standalone\n7627,294.96,Boston,E,5,13,4,70,High School,32,Owner,Standalone\n8670,408.72,NJ City,S,22,10,10,58,Graduation,32,Patnership,Complex\n2270,493.73,NJ City,S,25,13,24,81,High School,26,Patnership,Complex\n1280,191.44,NJ City,W,7,9,15,70,High School,18,Franchise,Complex\n7844,275.54,Boston,W,8,9,11,84,Graduation,26,Franchise,Complex\n7829,237.5,Boston,N,21,24,8,53,Graduation,32,Patnership,Standalone\n7697,249.45,Boston,N,1,15,10,28,Graduation,24,Patnership,Standalone\n3246,159.98,NJ City,S,8,19,2,41,Graduation,21,Patnership,Complex\n5194,233.56,Boston,S,19,9,8,27,Graduation,32,Patnership,Complex\n5501,239.22,Boston,N,3,20,21,56,Graduation,21,Patnership,Standalone\n5464,198.85,Boston,W,8,20,20,60,Graduation,35,Franchise,Complex\n4081,127.6,NJ City,N,10,21,1,45,High School,22,Patnership,Standalone\n8074,293.58,NJ City,S,23,8,11,36,Graduation,32,Patnership,Complex\n7713,285.88,Boston,N,21,21,20,25,Graduation,28,Patnership,Standalone\n4332,121.69,NJ City,W,16,11,4,76,Graduation,18,Franchise,Complex\n9243,53.99,NJ City,E,19,13,4,83,Graduation,19,Owner,Standalone\n4810,355.17,NJ City,S,19,20,18,26,High School,19,Patnership,Complex\n6853,106.59,Boston,N,7,21,4,53,High School,34,Patnership,Standalone\n3390,119.39,NJ City,S,21,10,3,75,Graduation,26,Patnership,Complex\n7059,100.92,Boston,E,1,21,1,46,Graduation,21,Owner,Standalone\n3753,423.92,NJ City,N,22,22,14,45,High School,31,Patnership,Standalone\n4966,157.92,Boston,S,11,23,23,48,High School,35,Patnership,Complex\n6592,390.68,Boston,W,8,13,18,46,Graduation,33,Franchise,Complex\n9530,124.58,NJ City,S,8,18,1,38,No education,29,Patnership,Complex\n8433,498.36,NJ City,N,3,22,5,35,Graduation,35,Patnership,Standalone\n9485,97.92,NJ City,N,16,15,1,31,No education,35,Patnership,Standalone\n935,306,NJ City,E,1,23,12,62,High School,28,Owner,Standalone\n7055,207.42,Boston,E,20,15,10,29,Graduation,30,Owner,Standalone\n5438,437.04,Boston,S,4,13,24,82,Graduation,32,Patnership,Complex\n5607,137.54,Boston,E,18,8,10,34,Graduation,24,Owner,Standalone\n455,192.53,NJ City,E,14,19,15,47,High School,30,Owner,Standalone\n5710,179.33,Boston,S,14,20,4,36,High School,23,Patnership,Complex\n8742,457.66,NJ City,S,25,12,6,77,Graduation,30,Patnership,Complex\n4617,244.2,NJ City,N,11,14,7,84,High School,22,Patnership,Standalone\n6513,17.63,Boston,N,6,22,4,62,Graduation,18,Patnership,Standalone\n4841,146.44,NJ City,N,18,19,16,66,High School,31,Patnership,Standalone\n4219,308.11,NJ City,E,7,17,6,76,High School,25,Owner,Standalone\n428,320.44,NJ City,W,5,17,6,73,High School,29,Franchise,Complex\n6475,27.68,Boston,N,13,10,1,35,High School,29,Owner,Standalone\n1172,174.45,NJ City,W,14,9,11,41,High School,26,Franchise,Complex\n3926,389.92,NJ City,S,23,12,5,36,No education,33,Patnership,Complex\n5948,234.85,Boston,W,15,8,21,74,Graduation,20,Franchise,Complex\n9473,399.14,NJ City,N,3,12,10,29,Graduation,20,Patnership,Standalone\n9931,382.82,NJ City,E,18,20,17,36,Graduation,26,Owner,Standalone\n2630,66.92,NJ City,N,12,15,2,61,No education,31,Patnership,Complex\n3028,341.58,NJ City,W,11,20,18,63,High School,32,Franchise,Complex\n7539,216.71,Boston,E,8,14,5,61,Graduation,18,Owner,Standalone\n5290,355.96,Boston,S,10,11,15,84,Graduation,22,Patnership,Complex\n6540,184.3,Boston,W,23,20,15,73,Graduation,28,Franchise,Complex\n3066,465.62,NJ City,S,19,18,9,76,High School,31,Patnership,Complex\n746,450.09,NJ City,S,22,19,7,50,High School,30,Patnership,Complex\n982,343.63,NJ City,S,17,14,5,34,High School,21,Patnership,Complex\n4326,399.42,NJ City,S,17,9,16,60,High School,33,Patnership,Complex\n3404,495.42,NJ City,W,6,22,23,44,High School,32,Franchise,Complex\n8419,129.72,NJ City,E,23,8,5,39,High School,30,Owner,Standalone\n7930,316.98,Boston,S,5,17,1,28,High School,20,Patnership,Complex\n3758,283.3,NJ City,S,10,23,25,63,High School,33,Patnership,Complex\n2342,249.86,NJ City,S,21,14,22,28,High School,23,Patnership,Complex\n5384,305.17,Boston,W,2,16,14,70,Graduation,23,Franchise,Complex\n1425,445.05,NJ City,N,10,17,10,42,High School,35,Patnership,Standalone\n4765,227.31,NJ City,N,21,11,9,77,High School,35,Patnership,Standalone\n7221,199.21,Boston,N,20,23,15,57,Graduation,20,Patnership,Standalone\n7184,338.56,Boston,W,13,23,1,63,No education,30,Franchise,Complex\n1822,144.87,NJ City,S,5,18,2,49,High School,28,Patnership,Complex\n3958,128.65,NJ City,S,8,11,6,77,High School,35,Patnership,Complex\n4731,19.55,NJ City,N,2,12,3,38,Graduation,21,Owner,Standalone\n6736,181.23,Boston,W,25,11,16,31,Graduation,27,Franchise,Complex\n7837,243.09,Boston,N,9,12,21,76,Graduation,20,Patnership,Standalone\n8345,426.56,NJ City,N,6,11,14,37,Graduation,26,Patnership,Standalone\n8113,402.37,NJ City,N,16,18,19,26,Graduation,31,Patnership,Standalone\n6625,430.08,Boston,N,13,18,5,62,High School,35,Patnership,Standalone\n2478,342.88,NJ City,S,21,13,17,54,High School,34,Patnership,Complex\n3484,48.38,NJ City,W,23,9,3,33,High School,26,Franchise,Complex\n3281,164,NJ City,N,2,18,12,50,High School,31,Patnership,Standalone\n497,473.25,NJ City,N,6,21,21,41,High School,28,Patnership,Standalone\n415,350.13,NJ City,E,8,19,8,72,High School,32,Owner,Standalone\n5049,292.85,Boston,N,21,20,19,74,Graduation,24,Patnership,Standalone\n7613,439.17,Boston,N,18,12,4,77,No education,29,Patnership,Standalone\n1711,261.15,NJ City,E,17,16,9,73,High School,27,Owner,Standalone\n8436,120.91,NJ City,W,15,21,1,25,Graduation,29,Franchise,Complex\n9296,198.5,NJ City,W,24,12,22,59,Graduation,34,Franchise,Complex\n3713,74.05,NJ City,N,17,21,2,69,No education,18,Patnership,Standalone\n9445,273.38,NJ City,N,1,22,9,54,Graduation,22,Patnership,Standalone\n7042,478.17,Boston,S,5,23,1,52,High School,35,Patnership,Complex\n8714,114.85,NJ City,S,18,11,2,31,No education,32,Patnership,Complex\n8341,0.88,NJ City,N,16,9,2,41,High School,22,Patnership,Standalone\n5645,383.39,Boston,N,2,9,2,81,No education,34,Patnership,Standalone\n5803,7.31,Boston,N,12,8,3,26,High School,26,Owner,Standalone\n9618,162.76,NJ City,S,13,19,24,77,Graduation,25,Patnership,Complex\n9083,76.93,NJ City,E,1,9,2,26,No education,35,Owner,Standalone\n433,348.18,NJ City,N,21,22,13,35,High School,24,Patnership,Standalone\n2990,7.19,NJ City,N,6,21,4,26,No education,22,Patnership,Complex\n1852,29.44,NJ City,N,7,19,4,51,High School,34,Franchise,Complex\n4722,10.03,NJ City,N,13,15,1,58,Graduation,24,Patnership,Complex\n7983,303.35,Boston,E,13,21,18,51,Graduation,33,Owner,Standalone\n9660,85.4,NJ City,W,14,9,4,64,Graduation,29,Franchise,Complex\n9321,253.93,NJ City,N,7,18,20,47,Graduation,34,Patnership,Standalone\n2336,185.15,NJ City,W,6,18,14,38,High School,23,Franchise,Complex\n1288,261,NJ City,W,2,22,21,34,High School,19,Franchise,Complex\n5909,149.63,Boston,N,3,8,22,38,Graduation,30,Patnership,Standalone\n3102,256.55,NJ City,S,12,11,8,44,High School,34,Patnership,Complex\n3330,431.3,NJ City,S,12,11,24,82,High School,30,Patnership,Complex\n5420,193.01,Boston,W,15,24,5,65,No education,22,Franchise,Complex\n8186,488.45,NJ City,S,14,22,22,32,Graduation,25,Patnership,Complex\n2868,435.94,NJ City,W,9,17,19,79,High School,33,Franchise,Complex\n8441,338.49,NJ City,N,21,15,11,76,Graduation,32,Patnership,Standalone\n7515,4.41,Boston,N,14,19,1,59,Graduation,31,Owner,Standalone\n8383,306.68,NJ City,E,9,20,7,59,Graduation,25,Owner,Standalone\n3114,297.11,NJ City,S,4,22,5,30,Graduation,24,Patnership,Complex\n9593,141.12,NJ City,N,9,19,3,64,No education,30,Patnership,Standalone\n307,425.5,NJ City,E,13,24,21,68,High School,35,Owner,Standalone\n7803,33.5,Boston,N,8,10,2,83,Graduation,34,Owner,Standalone\n1152,346.29,NJ City,W,23,13,6,52,High School,34,Franchise,Complex\n1312,207.5,NJ City,W,5,12,23,46,High School,25,Franchise,Complex\n9287,213.14,NJ City,E,9,15,24,64,Graduation,20,Owner,Standalone\n337,388.85,NJ City,N,6,20,20,71,High School,33,Patnership,Standalone\n6722,123.5,Boston,S,6,24,2,37,No education,30,Patnership,Complex\n9554,216.86,NJ City,S,12,17,11,39,Graduation,33,Patnership,Complex\n3887,497.87,NJ City,E,17,21,15,67,High School,20,Owner,Standalone\n2534,206.72,NJ City,S,17,14,23,27,High School,22,Patnership,Complex\n7913,382.19,Boston,N,19,10,18,66,Graduation,31,Patnership,Standalone\n505,19.86,NJ City,N,18,18,2,72,High School,21,Patnership,Standalone\n4099,405.38,NJ City,E,23,16,7,64,High School,22,Owner,Standalone\n9191,200.68,NJ City,E,9,20,20,85,Graduation,34,Owner,Standalone\n4370,454.96,NJ City,S,11,8,11,43,High School,27,Patnership,Complex\n1561,165.57,NJ City,N,7,13,19,50,High School,33,Patnership,Standalone\n2058,15.98,NJ City,N,8,14,1,35,Graduation,26,Patnership,Complex\n766,330.19,NJ City,S,19,24,15,45,High School,22,Patnership,Complex\n1269,407.02,NJ City,N,7,10,8,82,High School,25,Patnership,Standalone\n8732,163.5,NJ City,W,17,24,12,52,Graduation,28,Franchise,Complex\n8654,234.57,NJ City,S,20,24,24,41,Graduation,29,Patnership,Complex\n6219,27.97,Boston,N,23,16,2,62,Graduation,22,Owner,Standalone\n4231,169.88,NJ City,E,4,13,22,27,High School,26,Owner,Standalone\n8162,365.1,NJ City,S,4,14,12,48,Graduation,21,Patnership,Complex\n6851,436.81,Boston,E,10,8,2,77,No education,25,Owner,Standalone\n1300,169.96,NJ City,W,6,9,16,77,High School,29,Franchise,Complex\n7849,336.06,Boston,N,5,14,24,66,Graduation,18,Patnership,Standalone\n8105,27.64,NJ City,N,1,10,1,35,No education,28,Patnership,Standalone\n9348,319.69,NJ City,W,10,11,19,53,Graduation,18,Franchise,Complex\n5705,12.78,Boston,N,7,13,3,25,No education,28,Patnership,Standalone\n7300,214.25,Boston,W,18,8,9,63,Graduation,22,Franchise,Complex\n5958,412.59,Boston,S,12,22,14,48,Graduation,20,Patnership,Complex\n153,364.51,NJ City,N,17,17,25,84,High School,34,Patnership,Standalone\n3672,210.47,NJ City,W,19,10,18,54,High School,30,Franchise,Complex\n3213,349.9,NJ City,N,10,22,19,83,High School,33,Patnership,Standalone\n4427,228.57,NJ City,E,13,10,24,53,High School,33,Owner,Standalone\n3582,137,NJ City,S,3,10,14,48,High School,22,Patnership,Complex\n5748,94.87,Boston,W,3,15,1,38,Graduation,26,Franchise,Complex\n68,259.54,NY City,W,16,22,20,31,High School,18,Franchise,Complex\n4136,119,NJ City,W,3,8,18,29,High School,24,Franchise,Complex\n1249,300.64,NJ City,N,18,14,2,58,High School,20,Patnership,Standalone\n4182,173.87,NJ City,S,25,18,14,41,High School,25,Patnership,Complex\n3820,443.92,NJ City,W,17,17,8,60,High School,26,Franchise,Complex\n4753,493.49,NJ City,N,7,15,10,65,High School,25,Patnership,Standalone\n2154,395.62,NJ City,S,15,23,9,37,High School,35,Patnership,Complex\n6191,239.83,Boston,E,7,17,12,50,Graduation,22,Owner,Standalone\n7550,473.93,Boston,S,16,22,17,71,Graduation,35,Patnership,Complex\n8191,44.3,NJ City,N,15,9,1,64,High School,24,Owner,Standalone\n3581,26.8,NJ City,N,10,15,2,39,No education,28,Patnership,Standalone\n6831,302.68,Boston,E,16,8,21,26,Graduation,23,Owner,Standalone\n6443,342.31,Boston,E,17,21,14,29,Graduation,21,Owner,Standalone\n5373,32.27,Boston,N,12,18,2,79,Graduation,35,Patnership,Standalone\n8025,4.48,Boston,N,11,12,3,60,Graduation,34,Patnership,Standalone\n2568,365.46,NJ City,W,25,12,11,82,High School,21,Franchise,Complex\n2718,439.72,NJ City,S,4,18,22,81,High School,19,Patnership,Complex\n4207,205,NJ City,E,2,13,14,55,High School,25,Owner,Standalone\n1757,442.62,NJ City,N,15,13,21,85,High School,22,Patnership,Standalone\n5086,374.04,Boston,S,14,18,5,80,High School,31,Patnership,Complex\n4042,165.55,NJ City,S,24,23,25,74,High School,32,Patnership,Complex\n4827,215,NJ City,E,3,23,8,39,High School,22,Owner,Standalone\n340,356.78,NJ City,W,12,24,4,83,High School,22,Franchise,Complex\n1556,136.97,NJ City,W,15,13,6,55,High School,22,Franchise,Complex\n6548,11.05,Boston,N,15,13,2,45,No education,33,Franchise,Complex\n6248,133.33,Boston,W,7,15,6,38,Graduation,32,Franchise,Complex\n2941,256.48,NJ City,N,24,16,23,68,High School,23,Patnership,Standalone\n9375,301.74,NJ City,E,1,23,17,69,Graduation,25,Owner,Standalone\n2048,284.43,NJ City,W,1,18,1,44,No education,26,Franchise,Complex\n2100,25.76,NJ City,N,1,15,1,34,Graduation,26,Franchise,Complex\n2753,207.69,NJ City,N,19,18,24,62,High School,33,Patnership,Standalone\n3189,199,NJ City,N,2,9,23,62,High School,21,Patnership,Standalone\n1377,26.86,NJ City,N,10,17,3,68,Graduation,32,Patnership,Standalone\n486,273,NJ City,S,1,10,11,56,High School,20,Patnership,Complex\n5324,426.9,Boston,W,8,13,7,82,Graduation,32,Franchise,Complex\n5165,492.48,Boston,N,17,19,24,34,Graduation,29,Patnership,Standalone\n9257,403.91,NJ City,N,14,21,17,49,Graduation,18,Patnership,Standalone\n3457,227.15,NJ City,N,7,11,17,55,High School,27,Patnership,Standalone\n164,470.5,NJ City,W,7,10,19,84,High School,28,Franchise,Complex\n6415,311.92,Boston,E,13,15,14,42,Graduation,35,Owner,Standalone\n6405,286.25,Boston,N,22,20,7,42,Graduation,25,Patnership,Standalone\n7429,101.8,Boston,N,11,12,3,52,High School,31,Patnership,Standalone\n1824,52.65,NJ City,W,9,20,2,85,Graduation,27,Franchise,Complex\n9131,303.79,NJ City,E,22,20,20,41,Graduation,28,Owner,Standalone\n3894,125.95,NJ City,S,10,13,1,33,Graduation,32,Patnership,Complex\n4381,365.35,NJ City,N,21,24,2,31,High School,19,Patnership,Standalone\n5397,306.05,Boston,N,24,12,18,82,Graduation,21,Patnership,Standalone\n3822,399.7,NJ City,S,23,24,4,37,Graduation,27,Patnership,Complex\n7419,441.57,Boston,E,15,21,5,34,Graduation,25,Owner,Standalone\n4711,174.86,NJ City,E,6,17,2,52,High School,19,Owner,Standalone\n694,205,NJ City,S,3,16,20,27,High School,28,Patnership,Complex\n2262,415.62,NJ City,S,13,20,25,27,High School,29,Patnership,Complex\n7324,470.66,Boston,W,18,10,20,79,Graduation,19,Franchise,Complex\n3094,318.92,NJ City,S,23,19,25,79,High School,27,Patnership,Complex\n9644,314.66,NJ City,W,23,23,17,25,Graduation,28,Franchise,Complex\n4708,445.62,NJ City,W,17,13,21,33,High School,23,Franchise,Complex\n5882,38.95,Boston,N,4,9,2,56,No education,27,Patnership,Complex\n8679,357.9,NJ City,E,1,24,9,67,Graduation,29,Owner,Standalone\n1748,360.21,NJ City,W,20,21,1,61,No education,33,Franchise,Complex\n4825,494.81,NJ City,N,7,12,1,72,High School,20,Patnership,Standalone\n1817,324.79,NJ City,N,25,13,10,39,High School,21,Patnership,Standalone\n6679,426.94,Boston,E,17,19,18,55,Graduation,22,Owner,Standalone\n4488,282.62,NJ City,W,23,14,6,25,High School,32,Franchise,Complex\n6354,255.91,Boston,S,7,20,11,27,Graduation,23,Patnership,Complex\n2158,50.03,NJ City,S,6,22,3,31,High School,33,Patnership,Complex\n6414,391.82,Boston,S,15,15,16,46,Graduation,18,Patnership,Complex\n586,328.22,NJ City,S,11,21,12,71,High School,31,Patnership,Complex\n729,187.96,NJ City,N,16,20,10,57,High School,30,Patnership,Standalone\n9733,283.66,NJ City,N,9,18,9,82,Graduation,18,Patnership,Standalone\n9586,173.83,NJ City,S,20,14,16,32,Graduation,24,Patnership,Complex\n8999,241.42,NJ City,E,10,9,10,75,Graduation,25,Owner,Standalone\n4243,460.97,NJ City,E,6,17,24,57,High School,31,Owner,Standalone\n5805,159.35,Boston,N,8,16,22,59,Graduation,25,Patnership,Standalone\n954,115.64,NJ City,S,18,13,3,55,Graduation,30,Patnership,Complex\n6714,193.48,Boston,S,23,16,15,26,Graduation,31,Patnership,Complex\n242,475.22,NJ City,S,8,10,13,74,High School,30,Patnership,Complex\n8921,370.12,NJ City,N,19,19,8,70,Graduation,26,Patnership,Standalone\n4824,96.43,NJ City,W,22,16,3,67,Graduation,25,Franchise,Complex\n9636,332.37,NJ City,W,21,13,8,76,Graduation,24,Franchise,Complex\n1557,452.53,NJ City,N,3,16,2,72,Graduation,22,Patnership,Standalone\n3299,278,NJ City,E,2,9,10,81,High School,33,Owner,Standalone\n9308,11.86,NJ City,N,19,14,1,40,No education,22,Franchise,Complex\n5177,329.41,Boston,N,22,11,10,71,Graduation,35,Patnership,Standalone\n7694,170.75,Boston,S,16,16,19,63,Graduation,33,Patnership,Complex\n6888,192.15,Boston,W,22,23,5,60,Graduation,29,Franchise,Complex\n5326,293.9,Boston,S,14,13,12,35,Graduation,27,Patnership,Complex\n7967,313.95,Boston,E,24,16,18,37,Graduation,19,Owner,Standalone\n6852,219.15,Boston,W,15,15,4,50,Graduation,33,Franchise,Complex\n87,485.04,Boston,E,9,23,9,49,High School,26,Owner,Standalone\n871,376.68,NJ City,E,7,14,16,76,High School,23,Owner,Standalone\n9177,60.3,NJ City,N,1,14,3,77,Graduation,20,Patnership,Standalone\n5844,310.65,Boston,W,5,12,8,28,Graduation,34,Franchise,Complex\n2330,458.43,NJ City,S,23,11,1,67,No education,29,Patnership,Complex\n550,145.05,NJ City,S,23,10,20,35,High School,33,Patnership,Complex\n9442,264.02,NJ City,S,9,10,20,41,Graduation,28,Patnership,Complex\n1701,106.89,NJ City,N,22,10,4,57,Graduation,32,Patnership,Standalone\n4547,166.98,NJ City,E,21,22,10,49,High School,27,Owner,Standalone\n8043,299.64,Boston,E,3,20,15,34,Graduation,34,Owner,Standalone\n2469,164.81,NJ City,N,8,19,15,47,High School,32,Patnership,Standalone\n1834,369.91,NJ City,S,20,16,23,36,High School,21,Patnership,Complex\n3622,189.66,NJ City,S,20,12,8,71,High School,18,Patnership,Complex\n144,37.4,NJ City,N,20,9,1,53,Graduation,22,Franchise,Complex\n5344,391.07,Boston,W,21,13,14,27,Graduation,34,Franchise,Complex\n1434,243.44,NJ City,S,25,23,19,49,High School,31,Patnership,Complex\n9381,487.77,NJ City,N,16,9,8,57,Graduation,30,Patnership,Standalone\n9704,443.11,NJ City,W,12,22,15,82,Graduation,30,Franchise,Complex\n5807,220.96,Boston,E,9,12,11,45,Graduation,30,Owner,Standalone\n8133,238.33,NJ City,N,8,12,13,42,Graduation,31,Patnership,Standalone\n4752,82.48,NJ City,W,20,23,3,75,Graduation,32,Franchise,Complex\n3927,366.1,NJ City,E,10,18,19,64,High School,23,Owner,Standalone\n1440,159.35,NJ City,W,13,23,19,46,High School,31,Franchise,Complex\n4659,312.24,NJ City,E,21,17,16,37,High School,30,Owner,Standalone\n7305,134.08,Boston,N,3,13,21,67,Graduation,27,Patnership,Standalone\n6708,377.39,Boston,W,23,8,8,82,Graduation,26,Franchise,Complex\n747,335.12,NJ City,E,11,16,20,78,High School,35,Owner,Standalone\n9100,162.33,NJ City,W,1,17,3,27,High School,23,Franchise,Complex\n3773,89.7,NJ City,N,19,13,1,83,No education,23,Patnership,Standalone\n8933,159.26,NJ City,N,9,22,2,34,No education,29,Patnership,Standalone\n5417,92.08,Boston,N,25,24,2,36,No education,23,Patnership,Standalone\n9574,5.52,NJ City,N,3,23,3,82,High School,19,Patnership,Complex\n6014,106.25,Boston,S,13,14,4,76,No education,34,Patnership,Complex\n4148,365.13,NJ City,W,12,21,9,66,High School,32,Franchise,Complex\n5040,120.71,Boston,W,8,21,2,62,Graduation,30,Franchise,Complex\n4174,121.14,NJ City,S,17,17,3,72,High School,31,Patnership,Complex\n2359,60.14,NJ City,E,17,9,2,31,High School,27,Owner,Standalone\n7004,201.6,Boston,W,10,9,17,30,Graduation,24,Franchise,Complex\n1048,376.36,NJ City,W,20,21,8,85,High School,33,Franchise,Complex\n7979,449.93,Boston,E,8,22,9,66,Graduation,28,Owner,Standalone\n921,424.46,NJ City,N,13,21,8,64,High School,27,Patnership,Standalone\n2112,331.2,NJ City,W,14,18,1,48,Graduation,29,Franchise,Complex\n5782,69.07,Boston,N,16,18,3,69,High School,28,Patnership,Complex\n1360,173.44,NJ City,W,15,14,21,66,High School,20,Franchise,Complex\n9680,116.44,NJ City,W,1,22,1,26,No education,33,Franchise,Complex\n2152,357.94,NJ City,W,19,20,16,66,High School,31,Franchise,Complex\n1989,337.41,NJ City,N,20,19,2,49,Graduation,32,Patnership,Standalone\n1258,474.52,NJ City,S,7,18,18,77,High School,32,Patnership,Complex\n1709,317,NJ City,N,1,22,13,84,High School,19,Patnership,Standalone\n3824,154.63,NJ City,W,7,12,18,79,High School,28,Franchise,Complex\n7519,312.11,Boston,E,17,8,9,25,Graduation,21,Owner,Standalone\n2378,484.4,NJ City,S,25,21,11,51,High School,26,Patnership,Complex\n542,175.87,NJ City,S,16,11,15,69,High School,19,Patnership,Complex\n2930,293.38,NJ City,S,9,8,4,81,No education,30,Patnership,Complex\n1195,16.86,NJ City,N,14,14,4,67,High School,28,Owner,Standalone\n8655,371.8,NJ City,E,19,19,13,74,Graduation,28,Owner,Standalone\n8055,193.38,NJ City,E,9,15,12,68,Graduation,24,Owner,Standalone\n4670,300,NJ City,S,1,22,6,61,High School,26,Patnership,Complex\n1009,170,NJ City,N,3,17,19,78,High School,32,Patnership,Standalone\n708,15.69,NJ City,N,22,13,3,26,Graduation,23,Franchise,Complex\n7333,237.38,Boston,N,3,16,12,32,Graduation,20,Patnership,Standalone\n2589,190.36,NJ City,N,6,24,13,47,High School,20,Patnership,Standalone\n6129,445.9,Boston,N,24,9,12,84,Graduation,34,Patnership,Standalone\n6922,326.16,Boston,S,25,15,18,39,Graduation,23,Patnership,Complex\n1267,142.67,NJ City,E,6,16,23,63,High School,25,Owner,Standalone\n9360,126.97,NJ City,W,13,22,1,43,Graduation,29,Franchise,Complex\n4786,90.34,NJ City,S,23,22,4,35,High School,19,Patnership,Complex\n9559,67.88,NJ City,N,12,13,1,27,High School,22,Owner,Standalone\n9584,225.14,NJ City,W,3,18,21,42,Graduation,23,Franchise,Complex\n2102,376.92,NJ City,S,15,16,10,30,High School,19,Patnership,Complex\n8661,104.17,NJ City,N,19,10,3,35,Graduation,33,Patnership,Standalone\n451,265.54,NJ City,E,25,12,22,81,High School,29,Owner,Standalone\n7634,425.55,Boston,S,11,16,14,67,Graduation,34,Patnership,Complex\n1938,300,NJ City,S,3,21,22,36,High School,34,Patnership,Complex\n1433,210.82,NJ City,N,4,12,20,39,High School,19,Patnership,Standalone\n4562,68.37,NJ City,N,6,18,3,57,No education,18,Patnership,Complex\n7260,153.73,Boston,W,9,8,23,66,Graduation,19,Franchise,Complex\n3507,252.55,NJ City,E,21,20,13,38,High School,30,Owner,Standalone\n1740,305.63,NJ City,W,3,10,4,53,Graduation,19,Franchise,Complex\n7078,151.91,Boston,S,12,17,2,47,High School,34,Patnership,Complex\n1032,389.99,NJ City,W,17,10,15,33,High School,31,Franchise,Complex\n1650,197.11,NJ City,S,12,9,18,28,High School,32,Patnership,Complex\n8406,219.5,NJ City,S,6,17,8,72,Graduation,24,Patnership,Complex\n9427,79.63,NJ City,E,8,8,1,47,High School,23,Owner,Standalone\n7348,11.59,Boston,N,5,17,1,39,High School,24,Franchise,Complex\n6968,17.71,Boston,N,18,21,2,25,No education,34,Franchise,Complex\n8820,354.28,NJ City,W,6,12,14,32,Graduation,33,Franchise,Complex\n519,206.82,NJ City,E,11,9,12,26,High School,19,Owner,Standalone\n2587,495.04,NJ City,E,8,24,3,54,High School,35,Owner,Standalone\n1060,114.95,NJ City,W,11,19,2,43,High School,22,Franchise,Complex\n5869,242.89,Boston,N,11,14,1,55,High School,20,Patnership,Standalone\n7628,387.17,Boston,W,5,24,16,74,Graduation,20,Franchise,Complex\n7624,379.57,Boston,W,14,10,24,80,Graduation,23,Franchise,Complex\n3186,269.93,NJ City,S,5,21,14,42,High School,31,Patnership,Complex\n3411,492.01,NJ City,E,9,9,20,84,High School,19,Owner,Standalone\n3720,435.96,NJ City,W,14,22,3,47,Graduation,24,Franchise,Complex\n3362,177.12,NJ City,S,6,23,21,64,High School,34,Patnership,Complex\n8147,256.96,NJ City,E,15,22,13,60,Graduation,24,Owner,Standalone\n8876,89.66,NJ City,W,24,20,3,29,No education,31,Franchise,Complex\n9330,385.98,NJ City,S,21,14,19,85,Graduation,27,Patnership,Complex\n2675,477.55,NJ City,E,5,10,10,48,High School,23,Owner,Standalone\n4758,329.01,NJ City,S,11,23,13,53,High School,27,Patnership,Complex\n1694,274,NJ City,S,3,19,18,75,High School,21,Patnership,Complex\n5918,254.88,Boston,S,19,9,23,76,Graduation,34,Patnership,Complex\n1575,28.05,NJ City,N,14,10,1,31,Graduation,30,Owner,Standalone\n6915,128.7,Boston,E,19,14,9,43,Graduation,19,Owner,Standalone\n4611,320.46,NJ City,E,10,19,23,38,High School,27,Owner,Standalone\n4348,5.4,NJ City,N,22,10,3,42,High School,19,Franchise,Complex\n8387,180.24,NJ City,E,7,8,1,83,No education,20,Owner,Standalone\n5019,115.89,Boston,E,1,11,2,63,Graduation,33,Owner,Standalone\n6175,376.26,Boston,E,20,13,4,49,High School,32,Owner,Standalone\n7265,497.06,Boston,N,4,9,19,44,Graduation,34,Patnership,Standalone\n3800,219.6,NJ City,W,14,11,19,29,High School,19,Franchise,Complex\n8540,472.7,NJ City,W,10,24,2,76,No education,33,Franchise,Complex\n5758,194.88,Boston,S,1,13,4,47,High School,29,Patnership,Complex\n6011,27.29,Boston,N,12,22,1,70,No education,35,Owner,Standalone\n3715,250,NJ City,E,6,21,8,60,High School,23,Owner,Standalone\n2767,432.96,NJ City,E,5,23,15,79,High School,26,Owner,Standalone\n2800,306.37,NJ City,W,9,21,14,69,High School,30,Franchise,Complex\n6595,90.73,Boston,E,8,22,4,33,High School,33,Owner,Standalone\n6749,319.21,Boston,N,22,12,10,36,Graduation,24,Patnership,Standalone\n7957,110.27,Boston,N,14,23,3,52,High School,27,Patnership,Standalone\n7229,107.08,Boston,N,10,8,3,51,No education,28,Patnership,Standalone\n6764,30.93,Boston,N,21,13,4,33,No education,35,Franchise,Complex\n5023,152.91,Boston,E,20,18,8,62,Graduation,29,Owner,Standalone\n3098,434.32,NJ City,S,4,10,4,62,No education,24,Patnership,Complex\n9030,313.89,NJ City,S,11,19,4,36,Graduation,29,Patnership,Complex\n7090,16.68,Boston,N,21,11,3,40,High School,32,Patnership,Complex\n1856,311.95,NJ City,W,21,9,10,69,High School,23,Franchise,Complex\n6307,446.85,Boston,E,15,22,20,30,Graduation,21,Owner,Standalone\n3790,130.83,NJ City,S,18,8,7,76,High School,26,Patnership,Complex\n1113,83.27,NJ City,N,10,15,3,49,Graduation,18,Patnership,Standalone\n4247,480.28,NJ City,E,8,24,19,63,High School,22,Owner,Standalone\n4785,443.21,NJ City,N,6,13,16,75,High School,26,Patnership,Standalone\n8405,162.35,NJ City,N,16,16,11,60,Graduation,25,Patnership,Standalone\n3223,388.49,NJ City,E,11,11,4,53,High School,18,Owner,Standalone\n1057,256.42,NJ City,N,22,14,5,61,High School,32,Patnership,Standalone\n2602,264.27,NJ City,S,19,16,3,65,High School,21,Patnership,Complex\n9328,56.05,NJ City,W,18,10,4,57,High School,23,Franchise,Complex\n6245,44.53,Boston,N,19,14,4,40,No education,28,Patnership,Standalone\n7510,146.46,Boston,S,19,19,6,71,Graduation,20,Patnership,Complex\n7577,372.6,Boston,N,19,11,17,59,Graduation,29,Patnership,Standalone\n4347,231.53,NJ City,E,11,15,18,78,High School,23,Owner,Standalone\n329,248,NJ City,N,3,12,25,40,High School,28,Patnership,Standalone\n5663,1.4,Boston,N,5,24,3,58,No education,27,Owner,Standalone\n5262,372.33,Boston,S,19,8,1,41,Graduation,19,Patnership,Complex\n2050,7.82,NJ City,N,25,9,1,49,High School,18,Patnership,Complex\n750,299.57,NJ City,S,6,14,10,40,High School,30,Patnership,Complex\n4417,344.78,NJ City,N,2,8,2,34,High School,25,Patnership,Standalone\n5804,290.92,Boston,W,6,16,12,32,Graduation,26,Franchise,Complex\n7041,98.07,Boston,N,8,22,2,51,Graduation,33,Patnership,Standalone\n6566,317.86,Boston,S,3,23,18,29,Graduation,30,Patnership,Complex\n886,141.69,NJ City,S,15,24,23,78,High School,30,Patnership,Complex\n3265,235,NJ City,N,2,21,6,63,High School,27,Patnership,Standalone\n5675,149.73,Boston,E,5,15,6,38,Graduation,20,Owner,Standalone\n141,287,NJ City,N,3,9,13,76,High School,28,Patnership,Standalone\n2504,8.41,NJ City,N,7,17,2,83,No education,27,Franchise,Complex\n5401,300.5,Boston,N,1,15,22,49,Graduation,26,Patnership,Standalone\n2883,239.37,NJ City,E,24,13,24,31,High School,24,Owner,Standalone\n109,157.06,NJ City,N,22,22,18,57,High School,30,Patnership,Standalone\n532,462.48,NJ City,W,4,15,22,79,High School,29,Franchise,Complex\n5001,91.78,Boston,N,16,16,4,66,Graduation,27,Patnership,Standalone\n1589,349.38,NJ City,N,19,10,12,49,High School,29,Patnership,Standalone\n2374,15.15,NJ City,N,16,14,4,78,High School,25,Patnership,Complex\n446,183.76,NJ City,S,16,13,5,54,No education,33,Patnership,Complex\n3292,308.17,NJ City,W,10,19,2,38,High School,26,Franchise,Complex\n593,488.2,NJ City,N,13,21,14,33,High School,18,Patnership,Standalone\n3218,44.8,NJ City,N,14,12,4,67,No education,21,Patnership,Complex\n4277,360.19,NJ City,N,23,12,13,66,High School,28,Patnership,Standalone\n594,76.95,NJ City,S,25,21,2,70,Graduation,28,Patnership,Complex\n1456,390.19,NJ City,W,15,9,22,46,High School,25,Franchise,Complex\n2424,101.13,NJ City,W,1,16,3,65,Graduation,35,Franchise,Complex\n4648,89.14,NJ City,W,2,23,4,64,High School,26,Franchise,Complex\n3495,115.93,NJ City,E,13,16,4,61,Graduation,19,Owner,Standalone\n4416,65.1,NJ City,N,12,12,2,78,Graduation,20,Franchise,Complex\n7258,436.68,Boston,S,25,13,24,73,Graduation,32,Patnership,Complex\n5834,307.71,Boston,S,8,10,10,66,Graduation,28,Patnership,Complex\n136,234.76,NJ City,W,16,23,10,52,High School,19,Franchise,Complex\n4121,196.17,NJ City,N,7,20,10,38,High School,25,Patnership,Standalone\n4511,459.12,NJ City,E,12,11,22,80,High School,33,Owner,Standalone\n8412,53.02,NJ City,W,6,24,3,34,Graduation,23,Franchise,Complex\n5754,286.99,Boston,S,7,14,10,60,Graduation,25,Patnership,Complex\n1885,430.96,NJ City,N,7,9,23,65,High School,32,Patnership,Standalone\n9775,495.41,NJ City,E,6,17,25,43,Graduation,34,Owner,Standalone\n5403,403.14,Boston,E,5,15,18,55,Graduation,30,Owner,Standalone\n3895,285.32,NJ City,E,5,14,6,58,High School,18,Owner,Standalone\n1883,135.02,NJ City,E,12,17,19,69,High School,34,Owner,Standalone\n5638,354.78,Boston,S,23,23,9,51,Graduation,35,Patnership,Complex\n9795,79.33,NJ City,E,23,24,3,38,Graduation,22,Owner,Standalone\n5106,42.96,Boston,N,5,21,4,80,Graduation,30,Patnership,Complex\n9878,196.36,NJ City,S,5,17,19,69,Graduation,23,Patnership,Complex\n4331,194.55,NJ City,E,5,19,8,83,High School,27,Owner,Standalone\n6096,164.66,Boston,W,2,18,17,47,Graduation,25,Franchise,Complex\n5833,420.39,Boston,N,2,11,7,83,Graduation,24,Patnership,Standalone\n4202,225.26,NJ City,S,14,9,9,38,High School,19,Patnership,Complex\n8061,386.36,NJ City,N,20,9,21,77,Graduation,24,Patnership,Standalone\n2613,150,NJ City,N,3,24,21,65,High School,22,Patnership,Standalone\n2499,10.75,NJ City,N,17,8,2,74,Graduation,29,Owner,Standalone\n6403,412.73,Boston,E,18,13,14,30,Graduation,35,Owner,Standalone\n7750,117.94,Boston,S,24,14,4,35,High School,27,Patnership,Complex\n8823,237.37,NJ City,E,23,14,24,69,Graduation,25,Owner,Standalone\n8028,252.99,Boston,W,5,13,13,36,Graduation,19,Franchise,Complex\n3027,375.16,NJ City,E,23,9,16,43,High School,31,Owner,Standalone\n3391,111.05,NJ City,E,5,9,4,58,High School,18,Owner,Standalone\n7038,267.11,Boston,S,4,18,6,43,Graduation,31,Patnership,Complex\n9956,253.13,NJ City,W,8,21,19,85,Graduation,21,Franchise,Complex\n1508,203.33,NJ City,W,22,17,14,66,High School,20,Franchise,Complex\n4721,119,NJ City,N,2,13,7,35,High School,21,Patnership,Standalone\n9539,353.93,NJ City,E,16,15,5,43,No education,35,Owner,Standalone\n1445,168,NJ City,N,1,21,10,57,High School,33,Patnership,Standalone\n8505,112.58,NJ City,N,21,23,3,40,Graduation,25,Patnership,Standalone\n3500,402.13,NJ City,W,18,21,15,49,High School,22,Franchise,Complex\n4973,244.02,Boston,N,23,16,14,75,High School,20,Patnership,Standalone\n8590,487.01,NJ City,S,9,11,11,44,Graduation,32,Patnership,Complex\n733,450.79,NJ City,N,5,20,20,41,High School,28,Patnership,Standalone\n150,268.21,NJ City,S,18,11,17,43,High School,19,Patnership,Complex\n2609,172.98,NJ City,N,8,8,20,28,High School,18,Patnership,Standalone\n1747,294.54,NJ City,E,4,16,16,80,High School,33,Owner,Standalone\n1884,150.3,NJ City,W,23,15,11,72,High School,33,Franchise,Complex\n9499,447.41,NJ City,E,9,20,3,26,High School,25,Owner,Standalone\n4300,323.02,NJ City,W,5,10,24,46,High School,21,Franchise,Complex\n8782,448.51,NJ City,S,23,13,11,74,Graduation,34,Patnership,Complex\n4318,10.02,NJ City,N,9,21,4,81,High School,24,Patnership,Complex\n6612,36.76,Boston,N,10,16,2,29,Graduation,19,Franchise,Complex\n4735,362.87,NJ City,E,15,14,1,54,High School,29,Owner,Standalone\n7421,119.12,Boston,N,25,22,2,58,No education,28,Patnership,Standalone\n3577,27.01,NJ City,N,12,10,1,85,High School,19,Patnership,Standalone\n9196,427.81,NJ City,W,13,16,12,72,Graduation,23,Franchise,Complex\n6121,78.01,Boston,N,21,20,4,80,High School,25,Patnership,Standalone\n6399,178.98,Boston,E,16,10,4,50,Graduation,31,Owner,Standalone\n7505,365.35,Boston,N,24,16,25,68,Graduation,35,Patnership,Standalone\n5291,428.93,Boston,E,2,18,18,84,Graduation,20,Owner,Standalone\n9799,276.96,NJ City,E,14,16,1,57,High School,18,Owner,Standalone\n775,63.72,NJ City,N,21,8,3,25,High School,34,Owner,Standalone\n6756,314.98,Boston,W,1,10,18,38,Graduation,20,Franchise,Complex\n8474,40.05,NJ City,N,22,13,3,39,No education,33,Patnership,Complex\n9932,260.53,NJ City,W,16,12,25,37,Graduation,22,Franchise,Complex\n6871,414.39,Boston,E,8,24,8,85,Graduation,25,Owner,Standalone\n7374,53.24,Boston,S,13,9,2,64,Graduation,22,Patnership,Complex\n2719,316.02,NJ City,E,1,8,2,55,High School,26,Owner,Standalone\n8403,224.16,NJ City,E,23,18,22,35,Graduation,35,Owner,Standalone\n9640,102.07,NJ City,W,23,14,4,58,High School,23,Franchise,Complex\n7357,347.9,Boston,N,1,20,9,65,Graduation,32,Patnership,Standalone\n8329,80.09,NJ City,N,10,8,2,78,High School,22,Patnership,Standalone\n9629,494.44,NJ City,N,25,23,5,80,No education,31,Patnership,Standalone\n5452,353.79,Boston,W,5,11,5,45,High School,31,Franchise,Complex\n2312,85.02,NJ City,W,1,16,3,55,No education,24,Franchise,Complex\n1688,1.36,NJ City,N,20,15,3,45,No education,25,Franchise,Complex\n5600,225.58,Boston,W,1,11,11,78,Graduation,27,Franchise,Complex\n3638,186,NJ City,S,2,13,19,52,High School,35,Patnership,Complex\n8519,66.42,NJ City,N,9,20,2,26,No education,20,Owner,Standalone\n8807,108.01,NJ City,E,7,12,1,39,No education,26,Owner,Standalone\n8869,292.7,NJ City,N,13,12,7,68,Graduation,19,Patnership,Standalone\n3306,262.93,NJ City,S,25,22,22,35,High School,32,Patnership,Complex\n564,44.83,NJ City,N,8,22,1,57,Graduation,29,Franchise,Complex\n2907,264.2,NJ City,E,12,12,18,52,High School,34,Owner,Standalone\n9621,341.24,NJ City,N,5,16,16,29,Graduation,20,Patnership,Standalone\n7261,399.33,Boston,N,11,14,2,60,High School,25,Patnership,Standalone\n957,354.03,NJ City,N,23,8,21,61,High School,31,Patnership,Standalone\n1840,182.6,NJ City,W,8,13,22,31,High School,35,Franchise,Complex\n7892,197.82,Boston,W,6,13,11,42,Graduation,34,Franchise,Complex\n4639,460.22,NJ City,E,10,14,16,72,High School,25,Owner,Standalone\n1836,116.36,NJ City,W,10,17,3,62,Graduation,32,Franchise,Complex\n137,17.78,NJ City,N,17,19,3,63,No education,28,Patnership,Standalone\n3642,18.03,NJ City,N,8,12,4,35,Graduation,28,Patnership,Complex\n8834,159.76,NJ City,S,6,8,25,54,Graduation,30,Patnership,Complex\n9003,391.9,NJ City,E,7,10,2,53,Graduation,30,Owner,Standalone\n1858,236.4,NJ City,S,13,14,20,39,High School,23,Patnership,Complex\n5449,285.42,Boston,N,16,21,8,85,Graduation,32,Patnership,Standalone\n9886,66.54,NJ City,N,13,15,2,67,High School,31,Patnership,Complex\n2169,104.06,NJ City,N,3,18,2,79,Graduation,25,Patnership,Standalone\n1581,424.09,NJ City,N,10,11,1,85,Graduation,29,Patnership,Standalone\n1534,98.88,NJ City,S,18,20,3,31,High School,30,Patnership,Complex\n4881,337.02,NJ City,N,9,22,15,53,High School,27,Patnership,Standalone\n739,233.51,NJ City,E,12,21,21,78,High School,27,Owner,Standalone\n396,317.65,NJ City,W,15,18,9,67,High School,32,Franchise,Complex\n6778,221.31,Boston,S,15,10,23,78,Graduation,19,Patnership,Complex\n8089,379.75,NJ City,N,21,9,11,62,Graduation,34,Patnership,Standalone\n3026,221.86,NJ City,S,1,9,1,48,No education,33,Patnership,Complex\n6343,252.79,Boston,E,10,13,24,27,Graduation,22,Owner,Standalone\n8221,362.7,NJ City,N,9,16,13,33,Graduation,32,Patnership,Standalone\n2149,366.27,NJ City,N,21,9,24,82,High School,33,Patnership,Standalone\n4487,208.2,NJ City,E,5,16,10,27,High School,35,Owner,Standalone\n1332,4.87,NJ City,N,5,22,2,47,Graduation,18,Franchise,Complex\n4193,196.93,NJ City,N,20,21,9,71,High School,27,Patnership,Standalone\n620,91.49,NJ City,W,7,22,4,54,No education,19,Franchise,Complex\n18,425.42,Boston,S,24,20,25,64,High School,27,Patnership,Complex\n4970,50.83,Boston,S,3,17,4,32,No education,30,Patnership,Complex\n1245,138.13,NJ City,N,5,18,22,37,High School,30,Patnership,Standalone\n9895,2.12,NJ City,E,4,23,3,41,High School,35,Owner,Standalone\n3805,260.27,NJ City,N,14,22,6,80,High School,20,Patnership,Standalone\n6407,134.51,Boston,E,14,13,9,69,Graduation,26,Owner,Standalone\n1413,47.27,NJ City,N,13,18,3,60,Graduation,32,Patnership,Standalone\n1563,324.2,NJ City,E,13,14,1,65,Graduation,32,Owner,Standalone\n4941,291.44,Boston,N,18,19,25,41,High School,33,Patnership,Standalone\n4643,344.5,NJ City,E,25,15,19,47,High School,20,Owner,Standalone\n1076,394.87,NJ City,W,19,10,17,33,High School,24,Franchise,Complex\n2743,380.95,NJ City,E,14,9,9,39,High School,19,Owner,Standalone\n2820,299.21,NJ City,W,12,13,8,41,High School,32,Franchise,Complex\n758,455.76,NJ City,S,4,13,1,31,No education,28,Patnership,Complex\n6521,352.98,Boston,N,11,21,19,65,Graduation,22,Patnership,Standalone\n2839,59.39,NJ City,E,14,17,4,41,High School,24,Owner,Standalone\n580,498.23,NJ City,W,20,22,15,40,High School,18,Franchise,Complex\n339,156.54,NJ City,E,18,9,20,56,High School,34,Owner,Standalone\n6057,85.83,Boston,N,25,11,2,47,Graduation,20,Patnership,Standalone\n9081,259.7,NJ City,N,22,24,24,45,Graduation,29,Patnership,Standalone\n3395,42.21,NJ City,N,10,18,3,72,No education,30,Owner,Standalone\n4110,351.59,NJ City,S,10,13,23,85,High School,28,Patnership,Complex\n3747,161.25,NJ City,E,14,19,10,71,High School,19,Owner,Standalone\n9140,494.16,NJ City,W,15,15,24,81,Graduation,33,Franchise,Complex\n5653,203.12,Boston,N,16,11,2,46,High School,23,Patnership,Standalone\n7944,316.9,Boston,W,8,22,20,62,Graduation,18,Franchise,Complex\n3422,23.62,NJ City,N,21,18,1,83,No education,35,Patnership,Complex\n9366,157.35,NJ City,S,8,11,20,25,Graduation,32,Patnership,Complex\n3099,17.02,NJ City,N,4,18,3,70,Graduation,20,Owner,Standalone\n3338,327.45,NJ City,S,14,15,25,29,High School,31,Patnership,Complex\n4474,273.05,NJ City,S,21,14,20,49,High School,22,Patnership,Complex\n7904,310.24,Boston,W,13,14,20,34,Graduation,28,Franchise,Complex\n9977,302.97,NJ City,N,21,23,4,85,No education,33,Patnership,Standalone\n1240,39.11,NJ City,N,14,11,3,58,High School,24,Franchise,Complex\n1303,173,NJ City,E,3,16,10,70,High School,19,Owner,Standalone\n3619,73.37,NJ City,N,15,9,3,47,High School,24,Owner,Standalone\n7687,50.19,Boston,E,14,22,2,52,High School,34,Owner,Standalone\n4890,30.03,NJ City,N,20,21,1,78,Graduation,19,Patnership,Complex\n7232,21.54,Boston,N,19,10,4,57,No education,27,Franchise,Complex\n8451,390.09,NJ City,E,24,22,3,43,Graduation,29,Owner,Standalone\n9275,48.62,NJ City,E,19,22,3,38,No education,29,Owner,Standalone\n6095,235.33,Boston,E,23,13,21,71,Graduation,25,Owner,Standalone\n8817,149.92,NJ City,N,20,13,22,55,Graduation,18,Patnership,Standalone\n4542,59.22,NJ City,S,25,14,3,72,Graduation,29,Patnership,Complex\n362,392.8,NJ City,S,5,15,16,75,High School,32,Patnership,Complex\n9400,269.04,NJ City,W,15,19,5,65,High School,27,Franchise,Complex\n1490,44.72,NJ City,N,3,22,4,67,No education,34,Patnership,Complex\n2892,279.83,NJ City,W,12,22,24,43,High School,35,Franchise,Complex\n4164,186.67,NJ City,W,15,24,16,71,High School,27,Franchise,Complex\n7438,393.92,Boston,S,24,17,21,51,Graduation,33,Patnership,Complex\n7948,101.25,Boston,W,9,10,1,82,High School,24,Franchise,Complex\n3133,195.92,NJ City,N,6,14,25,52,High School,28,Patnership,Standalone\n1439,209.14,NJ City,E,25,12,16,56,High School,35,Owner,Standalone\n3357,274.66,NJ City,N,11,13,10,49,High School,33,Patnership,Standalone\n6048,4.35,Boston,N,14,16,1,39,Graduation,31,Franchise,Complex\n4053,496.53,NJ City,N,18,16,16,84,High School,19,Patnership,Standalone\n9062,261.69,NJ City,S,20,23,11,63,Graduation,35,Patnership,Complex\n4024,469.5,NJ City,W,24,17,1,38,High School,33,Franchise,Complex\n7810,192.66,Boston,S,17,19,15,49,Graduation,24,Patnership,Complex\n4592,335.85,NJ City,W,10,14,10,65,High School,27,Franchise,Complex\n9184,370.79,NJ City,W,20,15,10,51,Graduation,29,Franchise,Complex\n7098,122.3,Boston,S,17,18,3,44,Graduation,21,Patnership,Complex\n8063,228.8,NJ City,E,6,15,17,44,Graduation,30,Owner,Standalone\n8291,439.23,NJ City,E,25,24,3,68,No education,32,Owner,Standalone\n1555,470.9,NJ City,E,14,16,25,67,High School,25,Owner,Standalone\n3609,273.32,NJ City,N,14,15,9,75,High School,27,Patnership,Standalone\n4150,28.57,NJ City,N,19,16,3,27,High School,35,Patnership,Complex\n2735,111.14,NJ City,E,6,11,1,37,No education,33,Owner,Standalone\n3486,269.55,NJ City,S,18,24,9,43,High School,29,Patnership,Complex\n8743,352.98,NJ City,E,9,17,22,26,Graduation,21,Owner,Standalone\n3377,452.97,NJ City,N,16,19,19,68,High School,28,Patnership,Standalone\n5733,151.34,Boston,N,24,11,10,45,Graduation,18,Patnership,Standalone\n8706,137.25,NJ City,S,10,19,3,33,Graduation,35,Patnership,Complex\n3646,0.48,NJ City,N,1,12,4,31,High School,18,Patnership,Complex\n2618,369.27,NJ City,S,18,9,21,77,High School,32,Patnership,Complex\n6318,199.12,Boston,S,13,11,25,69,Graduation,33,Patnership,Complex\n417,199.89,NJ City,N,19,19,12,42,High School,30,Patnership,Standalone\n544,50.52,NJ City,W,13,12,2,80,High School,34,Franchise,Complex\n5842,479.73,Boston,S,13,20,13,68,Graduation,35,Patnership,Complex\n9959,1.18,NJ City,N,14,13,2,58,No education,23,Owner,Standalone\n709,230.03,NJ City,N,25,22,13,85,High School,20,Patnership,Standalone\n6241,33.19,Boston,N,9,24,2,34,High School,31,Patnership,Standalone\n8077,182.66,NJ City,N,23,18,6,77,Graduation,23,Patnership,Standalone\n3983,93.63,NJ City,E,17,19,2,47,No education,23,Owner,Standalone\n7855,45.39,Boston,N,10,19,3,31,High School,24,Owner,Standalone\n7633,459.38,Boston,N,3,15,4,31,High School,20,Patnership,Standalone\n8482,72.98,NJ City,N,2,17,3,82,High School,32,Patnership,Complex\n7963,455.62,Boston,E,22,15,3,63,High School,27,Owner,Standalone\n7538,383.51,Boston,S,24,22,6,49,Graduation,23,Patnership,Complex\n9619,301.39,NJ City,E,17,20,9,84,Graduation,23,Owner,Standalone\n8420,487.84,NJ City,W,25,9,25,43,Graduation,30,Franchise,Complex\n1684,470.95,NJ City,W,24,15,20,26,High School,35,Franchise,Complex\n1488,292.57,NJ City,W,15,17,21,36,High School,35,Franchise,Complex\n3868,83.84,NJ City,W,16,24,1,41,High School,25,Franchise,Complex\n6365,245.55,Boston,N,3,17,7,84,Graduation,34,Patnership,Standalone\n2947,262.81,NJ City,E,6,16,23,84,High School,30,Owner,Standalone\n5383,262.09,Boston,E,11,20,4,36,High School,30,Owner,Standalone\n790,148,NJ City,S,2,20,20,25,High School,31,Patnership,Complex\n254,0.67,NJ City,N,11,17,1,32,No education,33,Patnership,Complex\n4698,167.63,NJ City,S,5,22,15,84,High School,20,Patnership,Complex\n4222,101.47,NJ City,S,22,14,1,29,High School,23,Patnership,Complex\n666,243,NJ City,S,1,9,10,33,High School,22,Patnership,Complex\n2044,307,NJ City,W,3,8,18,80,High School,28,Franchise,Complex\n2080,7.8,NJ City,N,8,23,1,61,High School,31,Franchise,Complex\n3273,356.62,NJ City,N,6,15,24,44,High School,25,Patnership,Standalone\n8424,267.68,NJ City,W,2,13,2,47,Graduation,24,Franchise,Complex\n9452,338.72,NJ City,W,7,14,24,30,Graduation,26,Franchise,Complex\n5459,453.82,Boston,E,7,14,18,63,Graduation,27,Owner,Standalone\n2455,222.85,NJ City,E,25,12,14,57,High School,27,Owner,Standalone\n2433,292.41,NJ City,N,6,22,13,63,High School,22,Patnership,Standalone\n7185,355.27,Boston,N,14,18,7,57,Graduation,32,Patnership,Standalone\n2859,26.68,NJ City,N,5,21,4,72,Graduation,24,Owner,Standalone\n2459,102.98,NJ City,E,25,21,4,27,No education,28,Owner,Standalone\n7440,338.88,Boston,W,3,8,9,65,Graduation,25,Franchise,Complex\n7190,154.56,Boston,S,1,24,21,66,Graduation,28,Patnership,Complex\n8379,321.32,NJ City,E,10,16,13,33,Graduation,19,Owner,Standalone\n3777,244,NJ City,N,3,11,11,40,High School,30,Patnership,Standalone\n7332,6.7,Boston,N,5,14,2,46,Graduation,31,Franchise,Complex\n3684,437.2,NJ City,W,8,11,2,78,Graduation,32,Franchise,Complex\n5293,470.02,Boston,N,3,10,4,82,High School,30,Patnership,Standalone\n7175,375.74,Boston,E,21,23,8,33,Graduation,25,Owner,Standalone\n3972,405.72,NJ City,W,17,10,1,42,Graduation,25,Franchise,Complex\n3424,293,NJ City,W,3,19,8,69,High School,27,Franchise,Complex\n7480,275.15,Boston,W,19,16,5,78,High School,33,Franchise,Complex\n8709,379.18,NJ City,N,11,20,14,56,Graduation,31,Patnership,Standalone\n1004,262.09,NJ City,W,23,20,17,82,High School,21,Franchise,Complex\n5054,45.8,Boston,N,4,8,2,31,No education,33,Patnership,Complex\n7685,469.12,Boston,N,5,8,3,70,No education,27,Patnership,Standalone\n6266,421.81,Boston,S,1,13,4,34,No education,28,Patnership,Complex\n6788,72.47,Boston,N,21,20,4,65,No education,29,Franchise,Complex\n1964,280.24,NJ City,W,25,10,19,71,High School,20,Franchise,Complex\n3239,452.65,NJ City,E,9,16,23,26,High School,33,Owner,Standalone\n6109,415.25,Boston,N,22,21,4,51,High School,28,Patnership,Standalone\n108,237.69,NJ City,W,18,20,17,80,High School,32,Franchise,Complex\n9945,165,NJ City,N,13,22,14,51,Graduation,26,Patnership,Standalone\n3045,465.03,NJ City,N,13,20,22,56,High School,24,Patnership,Standalone\n9701,208.05,NJ City,N,7,23,9,81,Graduation,20,Patnership,Standalone\n6364,95.93,Boston,W,19,8,4,84,High School,20,Franchise,Complex\n6683,494.72,Boston,E,24,14,9,79,Graduation,30,Owner,Standalone\n6201,123.97,Boston,N,9,18,2,48,Graduation,35,Patnership,Standalone\n2620,385.01,NJ City,W,10,17,23,58,High School,19,Franchise,Complex\n4297,96.67,NJ City,N,23,22,1,80,High School,18,Patnership,Standalone\n4642,340.09,NJ City,S,6,12,6,43,High School,31,Patnership,Complex\n3566,436.53,NJ City,S,21,22,22,63,High School,30,Patnership,Complex\n8934,1.66,NJ City,N,13,19,1,50,Graduation,21,Patnership,Complex\n5693,179.12,Boston,N,8,14,21,28,Graduation,19,Patnership,Standalone\n1521,167.6,NJ City,N,10,14,6,26,High School,20,Patnership,Standalone\n7971,343.3,Boston,E,3,17,18,45,Graduation,29,Owner,Standalone\n4599,200.92,NJ City,E,9,21,15,34,High School,23,Owner,Standalone\n2390,128.91,NJ City,S,4,19,1,59,No education,26,Patnership,Complex\n8627,269.03,NJ City,E,17,9,24,44,Graduation,24,Owner,Standalone\n7582,326.31,Boston,S,24,8,15,41,Graduation,29,Patnership,Complex\n2622,47.92,NJ City,S,14,10,3,84,Graduation,31,Patnership,Complex\n5866,436.57,Boston,S,22,12,23,37,Graduation,20,Patnership,Complex\n9069,397.03,NJ City,N,17,23,4,64,Graduation,21,Patnership,Standalone\n5673,363.76,Boston,N,16,24,16,38,Graduation,21,Patnership,Standalone\n7763,42.06,Boston,N,16,18,3,78,No education,32,Owner,Standalone\n984,433.51,NJ City,W,22,10,6,29,High School,26,Franchise,Complex\n263,308.71,NJ City,E,3,23,2,69,No education,19,Owner,Standalone\n4138,29.71,NJ City,N,21,21,4,56,High School,35,Patnership,Complex\n5576,54.49,Boston,W,1,23,2,61,No education,19,Franchise,Complex\n4775,476.12,NJ City,E,3,13,5,79,No education,25,Owner,Standalone\n9786,416.32,NJ City,S,2,10,9,84,Graduation,29,Patnership,Complex\n4552,127.22,NJ City,W,20,18,1,68,High School,32,Franchise,Complex\n6891,431.19,Boston,E,25,11,24,83,Graduation,34,Owner,Standalone\n1143,219.11,NJ City,E,18,10,5,60,Graduation,20,Owner,Standalone\n4725,274,NJ City,N,1,10,18,36,High School,35,Patnership,Standalone\n4137,57.72,NJ City,N,8,13,3,57,Graduation,18,Patnership,Standalone\n4455,462.22,NJ City,E,11,10,1,40,Graduation,35,Owner,Standalone\n3736,49.74,NJ City,W,25,8,2,58,High School,18,Franchise,Complex\n743,490.1,NJ City,E,5,14,24,56,High School,32,Owner,Standalone\n8552,131.56,NJ City,W,7,10,21,49,Graduation,20,Franchise,Complex\n3565,154.26,NJ City,N,20,16,16,66,High School,25,Patnership,Standalone\n7008,284.01,Boston,W,10,15,17,26,Graduation,31,Franchise,Complex\n5681,7.45,Boston,N,10,9,4,79,No education,21,Patnership,Standalone\n1279,339.53,NJ City,E,11,21,17,35,High School,31,Owner,Standalone\n4977,221.04,Boston,N,21,14,13,56,High School,21,Patnership,Standalone\n2235,17.12,NJ City,N,4,10,3,61,Graduation,18,Owner,Standalone\n9213,207.61,NJ City,N,22,14,23,53,Graduation,18,Patnership,Standalone\n6397,190.95,Boston,N,21,8,4,38,High School,20,Patnership,Standalone\n647,27.23,NJ City,N,8,17,4,62,No education,26,Owner,Standalone\n9132,55.66,NJ City,W,25,12,1,61,Graduation,24,Franchise,Complex\n5796,123.94,Boston,W,16,16,2,31,Graduation,24,Franchise,Complex\n8355,376.71,NJ City,E,2,17,7,50,Graduation,35,Owner,Standalone\n7394,6.21,Boston,N,20,14,4,35,No education,35,Patnership,Complex\n4620,219.06,NJ City,W,10,21,8,84,High School,31,Franchise,Complex\n3661,145.25,NJ City,N,18,21,5,39,High School,32,Patnership,Standalone\n4280,52.22,NJ City,W,11,15,4,31,No education,31,Franchise,Complex\n2207,385.5,NJ City,E,20,18,9,77,High School,19,Owner,Standalone\n3233,202.06,NJ City,N,11,15,6,50,High School,28,Patnership,Standalone\n321,66.77,NJ City,N,10,13,3,46,Graduation,18,Patnership,Standalone\n2077,398.03,NJ City,N,9,12,11,46,High School,31,Patnership,Standalone\n4816,29.11,NJ City,N,9,22,4,55,High School,26,Franchise,Complex\n735,448.04,NJ City,E,19,21,23,69,High School,24,Owner,Standalone\n1601,449.13,NJ City,N,10,18,21,70,High School,28,Patnership,Standalone\n9811,186.85,NJ City,E,21,21,13,27,Graduation,29,Owner,Standalone\n7725,266.51,Boston,N,9,24,20,56,Graduation,22,Patnership,Standalone\n1069,112.7,NJ City,N,23,20,1,52,High School,27,Patnership,Standalone\n6053,126.71,Boston,N,12,22,2,73,No education,19,Patnership,Standalone\n5628,76.05,Boston,W,13,13,4,82,Graduation,22,Franchise,Complex\n7978,97.78,Boston,S,18,13,4,85,High School,18,Patnership,Complex\n8375,0.93,NJ City,N,25,11,2,62,No education,32,Owner,Standalone\n7986,345.33,Boston,S,7,12,14,58,Graduation,21,Patnership,Complex\n695,181.23,NJ City,E,17,18,10,44,High School,24,Owner,Standalone\n4990,322.77,Boston,S,6,15,4,71,High School,20,Patnership,Complex\n4425,307,NJ City,N,2,17,22,42,High School,35,Patnership,Standalone\n5130,58.05,Boston,S,18,20,1,74,Graduation,24,Patnership,Complex\n366,434.25,NJ City,S,10,23,20,75,High School,20,Patnership,Complex\n478,290.06,NJ City,S,20,15,7,51,High School,34,Patnership,Complex\n9502,236.16,NJ City,S,11,13,5,26,High School,25,Patnership,Complex\n7787,390.22,Boston,E,4,12,24,45,Graduation,32,Owner,Standalone\n6654,187.45,Boston,S,5,19,3,35,Graduation,24,Patnership,Complex\n171,370.97,NJ City,E,25,10,9,49,High School,35,Owner,Standalone\n2395,253.76,NJ City,E,18,13,4,85,High School,20,Owner,Standalone\n1394,35.59,NJ City,N,2,19,3,54,No education,20,Patnership,Complex\n5519,136.16,Boston,E,8,23,11,36,Graduation,31,Owner,Standalone\n8548,400.93,NJ City,W,3,13,4,52,High School,31,Franchise,Complex\n7712,242.2,Boston,W,22,16,16,62,Graduation,29,Franchise,Complex\n9118,195.84,NJ City,S,6,11,22,43,Graduation,22,Patnership,Complex\n7902,93.01,Boston,S,19,24,4,81,Graduation,31,Patnership,Complex\n5789,14.55,Boston,N,16,8,1,40,No education,24,Patnership,Standalone\n7530,196.82,Boston,S,17,24,13,83,Graduation,26,Patnership,Complex\n5912,240.14,Boston,W,17,20,2,30,No education,19,Franchise,Complex\n191,459.08,NJ City,E,6,9,10,84,High School,32,Owner,Standalone\n5149,371.75,Boston,N,3,10,24,69,Graduation,31,Patnership,Standalone\n5159,209.01,Boston,E,16,24,11,53,Graduation,19,Owner,Standalone\n6620,49.34,Boston,W,7,21,4,71,No education,27,Franchise,Complex\n9869,380.5,NJ City,N,21,17,19,34,Graduation,24,Patnership,Standalone\n2191,47.9,NJ City,E,9,18,1,57,High School,34,Owner,Standalone\n9171,208.18,NJ City,E,25,22,1,81,Graduation,29,Owner,Standalone\n1609,44.63,NJ City,N,20,15,1,32,High School,28,Patnership,Standalone\n1525,7.11,NJ City,N,15,19,4,68,High School,29,Patnership,Standalone\n1596,283.6,NJ City,W,17,10,13,57,High School,30,Franchise,Complex\n7966,246.22,Boston,S,3,17,13,55,Graduation,19,Patnership,Complex\n1340,338.15,NJ City,W,12,23,14,77,High School,33,Franchise,Complex\n7885,180.54,Boston,N,6,13,11,31,Graduation,33,Patnership,Standalone\n3664,498.49,NJ City,W,19,23,15,54,High School,20,Franchise,Complex\n1441,235.41,NJ City,N,14,21,6,57,High School,19,Patnership,Standalone\n9968,147.07,NJ City,W,11,13,22,35,Graduation,20,Franchise,Complex\n9506,117.15,NJ City,S,10,19,4,34,No education,20,Patnership,Complex\n4377,169.73,NJ City,N,20,12,14,27,High School,26,Patnership,Standalone\n6882,5.54,Boston,N,9,14,1,74,Graduation,24,Patnership,Complex\n8553,150.3,NJ City,N,25,22,21,82,Graduation,20,Patnership,Standalone\n1058,96.01,NJ City,S,8,14,3,49,No education,24,Patnership,Complex\n2107,322.55,NJ City,E,25,17,8,47,High School,25,Owner,Standalone\n4778,218,NJ City,S,3,17,23,72,High School,32,Patnership,Complex\n8209,388.3,NJ City,N,21,10,15,67,Graduation,21,Patnership,Standalone\n7216,205.42,Boston,W,6,8,8,30,Graduation,18,Franchise,Complex\n7448,466.56,Boston,W,21,21,14,42,Graduation,29,Franchise,Complex\n4120,196,NJ City,W,18,20,13,64,High School,21,Franchise,Complex\n4630,254.82,NJ City,S,25,14,3,28,High School,21,Patnership,Complex\n2952,11.31,NJ City,N,18,17,4,74,Graduation,28,Franchise,Complex\n737,203,NJ City,N,2,21,6,64,High School,22,Patnership,Standalone\n2276,469.93,NJ City,W,13,17,6,73,High School,26,Franchise,Complex\n3103,234,NJ City,E,2,10,19,74,High School,34,Owner,Standalone\n7847,52.1,Boston,E,4,24,4,44,No education,18,Owner,Standalone\n3952,161.07,NJ City,W,23,10,5,66,High School,20,Franchise,Complex\n226,21.35,NJ City,N,14,24,4,26,High School,35,Patnership,Complex\n9430,138.19,NJ City,S,13,11,7,63,Graduation,29,Patnership,Complex\n665,112.47,NJ City,N,5,24,3,85,No education,35,Patnership,Standalone\n3291,84.28,NJ City,E,5,18,2,57,Graduation,23,Owner,Standalone\n8487,354.85,NJ City,E,25,15,16,34,Graduation,34,Owner,Standalone\n9668,295.56,NJ City,W,25,8,19,61,Graduation,32,Franchise,Complex\n43,394.95,Boston,E,12,13,25,81,High School,25,Owner,Standalone\n1309,249.31,NJ City,N,8,11,2,66,High School,23,Patnership,Standalone\n5763,260.5,Boston,E,7,17,5,29,Graduation,33,Owner,Standalone\n8220,401.66,NJ City,W,21,15,16,81,Graduation,23,Franchise,Complex\n9377,495.24,NJ City,N,12,9,18,57,Graduation,29,Patnership,Standalone\n5249,373.95,Boston,N,17,19,20,60,Graduation,24,Patnership,Standalone\n3617,427.12,NJ City,N,19,20,15,45,High School,27,Patnership,Standalone\n6639,347.11,Boston,E,18,23,21,53,Graduation,34,Owner,Standalone\n7523,336.93,Boston,E,7,22,2,59,No education,32,Owner,Standalone\n2037,262,NJ City,N,3,17,7,55,High School,19,Patnership,Standalone\n9647,428.61,NJ City,E,7,19,23,42,Graduation,34,Owner,Standalone\n4671,495.35,NJ City,E,25,16,19,76,High School,27,Owner,Standalone\n827,290.21,NJ City,E,4,22,22,58,High School,20,Owner,Standalone\n6432,199.16,Boston,W,6,8,17,69,Graduation,30,Franchise,Complex\n5405,56.95,Boston,N,18,22,2,83,No education,22,Patnership,Standalone\n7259,248.44,Boston,E,8,22,22,49,Graduation,28,Owner,Standalone\n5277,125.3,Boston,N,9,9,4,26,Graduation,19,Patnership,Standalone\n404,101.62,NJ City,W,20,12,2,80,No education,25,Franchise,Complex\n7562,488.01,Boston,S,5,16,22,35,Graduation,20,Patnership,Complex\n2090,311,NJ City,S,2,18,14,39,High School,18,Patnership,Complex\n8829,236.03,NJ City,N,21,8,17,37,Graduation,22,Patnership,Standalone\n8001,88.33,Boston,N,7,19,2,34,Graduation,35,Patnership,Standalone\n1166,346.97,NJ City,S,16,9,7,43,High School,35,Patnership,Complex\n6572,338.79,Boston,W,22,14,24,60,Graduation,19,Franchise,Complex\n1660,319.67,NJ City,W,12,14,21,38,High School,18,Franchise,Complex\n6081,263.68,Boston,N,25,11,21,80,Graduation,25,Patnership,Standalone\n2626,47.63,NJ City,S,10,24,2,85,High School,23,Patnership,Complex\n7854,228.51,Boston,S,5,16,21,62,Graduation,33,Patnership,Complex\n1008,5.16,NJ City,N,19,15,4,49,Graduation,22,Franchise,Complex\n4462,70.14,NJ City,N,18,22,3,55,High School,31,Patnership,Complex\n5921,342.83,Boston,N,9,8,13,56,Graduation,19,Patnership,Standalone\n60,187.42,Boston,W,8,19,16,53,High School,32,Franchise,Complex\n5026,203.07,Boston,S,25,17,8,49,Graduation,30,Patnership,Complex\n8289,108.47,NJ City,N,8,8,3,53,Graduation,25,Patnership,Standalone\n213,9.15,NJ City,N,9,18,3,75,Graduation,20,Patnership,Standalone\n6541,126.38,Boston,N,20,23,1,43,High School,35,Patnership,Standalone\n1177,142,NJ City,N,3,24,12,50,High School,19,Patnership,Standalone\n9490,147.78,NJ City,S,23,9,16,56,Graduation,28,Patnership,Complex\n5068,354.1,Boston,W,10,10,15,66,Graduation,21,Franchise,Complex\n3675,215.74,NJ City,E,22,9,2,68,Graduation,27,Owner,Standalone\n3647,374.4,NJ City,E,8,19,25,36,High School,32,Owner,Standalone\n9637,206.44,NJ City,N,2,24,18,54,Graduation,33,Patnership,Standalone\n283,241,NJ City,E,1,10,16,27,High School,21,Owner,Standalone\n5676,100.47,Boston,W,9,9,3,83,Graduation,31,Franchise,Complex\n5363,216.11,Boston,E,11,20,16,48,Graduation,21,Owner,Standalone\n7035,225.27,Boston,E,7,22,7,72,Graduation,31,Owner,Standalone\n4559,487.7,NJ City,E,14,19,10,61,High School,33,Owner,Standalone\n4015,70.09,NJ City,N,18,22,3,61,High School,31,Owner,Standalone\n7285,148.53,Boston,N,5,14,21,36,Graduation,22,Patnership,Standalone\n5521,340.24,Boston,N,25,16,18,52,Graduation,27,Patnership,Standalone\n9806,404.17,NJ City,S,9,14,24,44,Graduation,31,Patnership,Complex\n2830,342.93,NJ City,S,16,21,9,55,High School,27,Patnership,Complex\n8808,409.54,NJ City,W,9,21,3,52,Graduation,26,Franchise,Complex\n7307,375.79,Boston,E,11,11,1,49,No education,35,Owner,Standalone\n2582,391.96,NJ City,S,12,22,20,66,High School,26,Patnership,Complex\n4707,179.52,NJ City,E,10,15,25,74,High School,28,Owner,Standalone\n9812,218.84,NJ City,W,10,17,19,46,Graduation,28,Franchise,Complex\n3796,52.58,NJ City,W,14,14,4,31,High School,30,Franchise,Complex\n7689,73.78,Boston,N,3,24,1,77,Graduation,23,Patnership,Standalone\n2280,176.67,NJ City,W,9,9,18,73,High School,18,Franchise,Complex\n2819,132.38,NJ City,E,23,14,23,42,High School,25,Owner,Standalone\n5776,298.21,Boston,W,25,8,15,31,Graduation,28,Franchise,Complex\n5919,101.65,Boston,E,12,21,2,43,Graduation,22,Owner,Standalone\n7355,488.66,Boston,E,8,17,5,31,No education,32,Owner,Standalone\n6577,396.22,Boston,N,8,14,5,83,High School,33,Patnership,Standalone\n776,71.57,NJ City,N,9,12,1,80,No education,31,Franchise,Complex\n1063,265.06,NJ City,E,16,21,11,63,High School,31,Owner,Standalone\n7002,324.85,Boston,S,25,24,15,62,Graduation,23,Patnership,Complex\n884,284.8,NJ City,W,25,9,1,57,No education,32,Franchise,Complex\n1007,367.99,NJ City,E,20,24,1,55,No education,23,Owner,Standalone\n1675,250.84,NJ City,E,5,19,17,53,High School,33,Owner,Standalone\n5155,287.76,Boston,E,5,16,10,61,Graduation,32,Owner,Standalone\n9815,392.18,NJ City,E,20,20,2,85,No education,29,Owner,Standalone\n297,103.27,NJ City,N,18,18,3,83,Graduation,31,Patnership,Standalone\n3508,61.38,NJ City,N,1,24,4,66,High School,24,Franchise,Complex\n1611,105,NJ City,E,3,8,3,72,Graduation,27,Owner,Standalone\n2437,8.34,NJ City,N,16,24,4,65,High School,20,Patnership,Standalone\n5217,154.41,Boston,N,16,19,6,63,Graduation,21,Patnership,Standalone\n8681,401.5,NJ City,N,9,12,1,48,No education,30,Patnership,Standalone\n6694,163.77,Boston,S,10,22,18,58,Graduation,28,Patnership,Complex\n9351,374.26,NJ City,E,13,19,10,50,Graduation,25,Owner,Standalone\n678,167.65,NJ City,S,15,18,21,76,High School,31,Patnership,Complex\n8079,334.87,NJ City,E,1,17,18,62,Graduation,30,Owner,Standalone\n9135,186.65,NJ City,E,17,22,1,40,Graduation,24,Owner,Standalone\n2257,436.17,NJ City,N,7,16,9,77,High School,29,Patnership,Standalone\n510,294.96,NJ City,S,3,23,2,66,Graduation,28,Patnership,Complex\n7068,391.27,Boston,W,2,24,17,62,Graduation,22,Franchise,Complex\n328,167.52,NJ City,W,15,10,19,53,High School,22,Franchise,Complex\n9643,480.76,NJ City,E,8,18,20,84,Graduation,18,Owner,Standalone\n9402,91.23,NJ City,S,5,23,3,85,Graduation,21,Patnership,Complex\n6280,177.23,Boston,W,5,23,19,52,Graduation,33,Franchise,Complex\n1404,355.16,NJ City,W,23,13,10,62,High School,33,Franchise,Complex\n3814,23.49,NJ City,N,2,12,2,85,High School,34,Patnership,Complex\n5550,153.14,Boston,S,13,18,5,54,Graduation,34,Patnership,Complex\n6372,360.38,Boston,W,11,13,19,52,Graduation,34,Franchise,Complex\n528,306.76,NJ City,W,25,19,16,27,High School,28,Franchise,Complex\n4353,335.84,NJ City,N,7,14,4,34,Graduation,23,Patnership,Standalone\n2999,316.53,NJ City,E,15,9,20,30,High School,32,Owner,Standalone\n2132,53.36,NJ City,W,23,14,2,34,No education,33,Franchise,Complex\n9712,295.63,NJ City,W,7,14,12,67,Graduation,28,Franchise,Complex\n7313,446.82,Boston,N,3,12,10,70,Graduation,35,Patnership,Standalone\n2430,333.05,NJ City,S,14,16,11,29,High School,30,Patnership,Complex\n8906,392.2,NJ City,S,22,19,6,41,Graduation,27,Patnership,Complex\n4717,134,NJ City,N,1,21,15,57,High School,26,Patnership,Standalone\n4813,272.23,NJ City,N,8,16,10,83,High School,29,Patnership,Standalone\n6381,193.17,Boston,N,5,18,12,48,Graduation,28,Patnership,Standalone\n2513,81.54,NJ City,N,12,16,4,61,No education,35,Patnership,Standalone\n2574,76.77,NJ City,S,14,19,4,25,Graduation,29,Patnership,Complex\n9568,237.79,NJ City,W,9,23,4,38,High School,31,Franchise,Complex\n1026,462.12,NJ City,S,14,10,13,85,High School,22,Patnership,Complex\n2518,212.94,NJ City,S,18,16,12,33,High School,27,Patnership,Complex\n3732,410.05,NJ City,W,11,23,9,74,High School,28,Franchise,Complex\n7280,387.24,Boston,W,13,19,20,62,Graduation,31,Franchise,Complex\n3939,297.41,NJ City,E,5,10,24,68,High School,21,Owner,Standalone\n2660,105.15,NJ City,W,23,19,2,39,No education,20,Franchise,Complex\n2764,82.04,NJ City,W,24,18,2,42,High School,18,Franchise,Complex\n8466,61.45,NJ City,N,18,22,4,33,Graduation,32,Patnership,Complex\n4076,250.93,NJ City,W,20,11,11,70,High School,25,Franchise,Complex\n1208,448.26,NJ City,W,13,18,15,30,High School,24,Franchise,Complex\n1918,269.7,NJ City,S,17,14,17,26,High School,21,Patnership,Complex\n6496,81.75,Boston,W,12,20,1,36,High School,18,Franchise,Complex\n2729,450.52,NJ City,N,6,10,4,31,No education,28,Patnership,Standalone\n6148,333.75,Boston,W,21,23,25,43,Graduation,20,Franchise,Complex\n9937,33.04,NJ City,N,22,8,1,68,High School,20,Patnership,Standalone\n7923,28.76,Boston,N,3,12,2,79,Graduation,33,Owner,Standalone\n1718,232.64,NJ City,S,20,24,14,44,High School,28,Patnership,Complex\n9245,287.98,NJ City,N,14,16,22,70,Graduation,18,Patnership,Standalone\n3859,142,NJ City,E,3,22,16,54,High School,21,Owner,Standalone\n1773,468.39,NJ City,N,24,20,3,35,Graduation,26,Patnership,Standalone\n8652,322.5,NJ City,W,13,18,22,67,Graduation,34,Franchise,Complex\n9550,246.6,NJ City,S,16,22,5,73,High School,29,Patnership,Complex\n425,200,NJ City,N,1,17,22,53,High School,34,Patnership,Standalone\n8177,315.62,NJ City,N,5,13,2,47,No education,20,Patnership,Standalone\n8784,495.03,NJ City,W,17,18,23,34,Graduation,26,Franchise,Complex\n3096,124.32,NJ City,W,16,15,3,61,Graduation,20,Franchise,Complex\n576,243.98,NJ City,W,25,23,1,84,Graduation,31,Franchise,Complex\n707,106.43,NJ City,E,24,14,3,62,No education,23,Owner,Standalone\n9160,120.09,NJ City,W,23,24,2,51,High School,35,Franchise,Complex\n8827,63.22,NJ City,N,9,21,4,36,High School,24,Owner,Standalone\n2783,65.91,NJ City,N,22,24,3,47,No education,28,Owner,Standalone\n1621,435.72,NJ City,N,19,11,12,79,High School,21,Patnership,Standalone\n5489,74.27,Boston,N,22,23,3,70,No education,29,Patnership,Standalone\n939,240.85,NJ City,E,10,18,7,67,High School,31,Owner,Standalone\n1233,1.78,NJ City,N,6,13,4,43,Graduation,26,Patnership,Standalone\n4578,54.1,NJ City,S,24,22,3,53,Graduation,18,Patnership,Complex\n1513,155.3,NJ City,N,10,16,3,41,High School,20,Patnership,Standalone\n8992,152.82,NJ City,W,14,13,24,55,Graduation,33,Franchise,Complex\n6291,424.23,Boston,E,14,19,8,44,Graduation,25,Owner,Standalone\n5535,436.92,Boston,E,5,11,22,36,Graduation,19,Owner,Standalone\n7926,231,Boston,S,12,10,16,50,Graduation,24,Patnership,Complex\n7151,434.91,Boston,E,22,14,18,55,Graduation,21,Owner,Standalone\n2674,130.94,NJ City,S,10,18,21,54,High School,31,Patnership,Complex\n3148,450.9,NJ City,W,22,23,17,61,High School,33,Franchise,Complex\n1977,257.49,NJ City,N,13,9,13,31,High School,20,Patnership,Standalone\n5078,314.89,Boston,S,12,12,15,33,Graduation,24,Patnership,Complex\n991,336.02,NJ City,E,15,22,5,76,High School,30,Owner,Standalone\n9885,161.06,NJ City,N,10,20,16,64,Graduation,34,Patnership,Standalone\n9965,61.19,NJ City,N,25,11,1,33,No education,28,Patnership,Standalone\n7615,309.51,Boston,E,3,14,15,38,Graduation,23,Owner,Standalone\n5707,448.41,Boston,E,3,10,6,26,Graduation,21,Owner,Standalone\n6311,161.91,Boston,E,25,11,10,78,Graduation,30,Owner,Standalone\n1593,227.26,NJ City,N,13,8,20,66,High School,32,Patnership,Standalone\n1452,161.66,NJ City,W,6,22,9,35,High School,24,Franchise,Complex\n3540,393.68,NJ City,W,12,20,8,56,High School,26,Franchise,Complex\n7859,274.71,Boston,E,3,11,9,39,Graduation,25,Owner,Standalone\n5533,195.44,Boston,N,7,19,15,39,Graduation,26,Patnership,Standalone\n9613,37.68,NJ City,N,12,21,2,57,High School,26,Patnership,Standalone\n4897,184.22,NJ City,N,15,21,6,71,High School,35,Patnership,Standalone\n5163,205.43,Boston,E,9,14,22,82,Graduation,34,Owner,Standalone\n9389,203.45,NJ City,N,12,9,13,63,Graduation,21,Patnership,Standalone\n2825,327.04,NJ City,N,14,8,12,58,High School,27,Patnership,Standalone\n5064,446.45,Boston,W,22,11,6,34,Graduation,34,Franchise,Complex\n6114,160.35,Boston,S,23,17,13,60,Graduation,19,Patnership,Complex\n6220,402.38,Boston,W,2,13,3,48,High School,24,Franchise,Complex\n7596,275.57,Boston,W,25,14,23,81,Graduation,20,Franchise,Complex\n8968,280.82,NJ City,W,7,8,18,33,Graduation,35,Franchise,Complex\n8021,143.51,Boston,N,15,11,5,58,No education,19,Patnership,Standalone\n6631,346.9,Boston,E,3,10,25,68,Graduation,30,Owner,Standalone\n6192,436.19,Boston,W,8,12,18,49,Graduation,33,Franchise,Complex\n6594,161.33,Boston,S,7,20,15,66,Graduation,28,Patnership,Complex\n595,394.15,NJ City,E,21,18,9,71,High School,35,Owner,Standalone\n395,224.57,NJ City,E,25,19,5,38,No education,25,Owner,Standalone\n7274,128.8,Boston,S,17,21,16,40,Graduation,22,Patnership,Complex\n6732,55.33,Boston,W,23,16,2,39,Graduation,24,Franchise,Complex\n9478,324.69,NJ City,S,3,15,17,70,Graduation,28,Patnership,Complex\n6868,125.5,Boston,W,10,17,1,66,High School,35,Franchise,Complex\n4796,18.25,NJ City,N,1,8,1,72,No education,18,Franchise,Complex\n9991,311.91,NJ City,E,10,14,1,44,High School,26,Owner,Standalone\n1643,164.68,NJ City,E,22,13,25,51,High School,30,Owner,Standalone\n4312,110.83,NJ City,W,24,12,2,46,High School,31,Franchise,Complex\n1362,355.18,NJ City,S,16,18,6,53,High School,24,Patnership,Complex\n98,428.65,NJ City,S,24,15,19,35,High School,22,Patnership,Complex\n3504,106.03,NJ City,W,5,23,4,78,Graduation,22,Franchise,Complex\n588,33.88,NJ City,N,2,12,2,27,Graduation,23,Franchise,Complex\n9976,7.06,NJ City,N,22,23,2,51,High School,30,Franchise,Complex\n7022,131.93,Boston,S,7,9,2,63,No education,33,Patnership,Complex\n6102,118.2,Boston,S,18,19,1,39,Graduation,27,Patnership,Complex\n4605,314.34,NJ City,N,8,12,2,70,Graduation,19,Patnership,Standalone\n3971,198.81,NJ City,E,13,10,4,46,No education,19,Owner,Standalone\n8702,241.26,NJ City,S,4,10,1,47,No education,21,Patnership,Complex\n3733,233.88,NJ City,N,12,13,25,75,High School,26,Patnership,Standalone\n4545,264.76,NJ City,N,24,8,25,46,High School,32,Patnership,Standalone\n2487,478.33,NJ City,E,18,16,24,71,High School,31,Owner,Standalone\n394,21.31,NJ City,N,1,11,1,25,High School,35,Patnership,Complex\n6855,5.22,Boston,N,24,16,1,81,Graduation,27,Owner,Standalone\n6052,356.44,Boston,W,16,11,10,61,Graduation,22,Franchise,Complex\n6406,34.7,Boston,N,25,15,2,39,High School,18,Patnership,Complex\n624,373.09,NJ City,W,17,8,22,73,High School,30,Franchise,Complex\n4151,448.33,NJ City,E,19,14,12,70,High School,21,Owner,Standalone\n32,281.83,Boston,W,21,12,23,66,High School,30,Franchise,Complex\n6401,96.07,Boston,N,23,11,3,80,No education,18,Patnership,Standalone\n2939,261.35,NJ City,E,9,10,15,81,High School,31,Owner,Standalone\n9153,420.41,NJ City,N,7,18,19,64,Graduation,26,Patnership,Standalone\n7756,65.02,Boston,N,25,14,3,33,High School,34,Franchise,Complex\n3667,184.73,NJ City,E,10,12,17,52,High School,32,Owner,Standalone\n6777,42.68,Boston,N,11,15,2,50,Graduation,29,Patnership,Standalone\n8232,376.42,NJ City,W,23,20,16,54,Graduation,21,Franchise,Complex\n2817,286.17,NJ City,N,9,15,3,54,Graduation,20,Patnership,Standalone\n9447,46.75,NJ City,N,17,13,3,51,Graduation,25,Owner,Standalone\n3176,499.81,NJ City,W,14,14,22,72,High School,24,Franchise,Complex\n6445,193,Boston,N,22,8,18,33,Graduation,23,Patnership,Standalone\n8795,290.54,NJ City,E,14,21,11,31,Graduation,32,Owner,Standalone\n8381,209.32,NJ City,N,16,12,24,71,Graduation,28,Patnership,Standalone\n832,172.02,NJ City,W,17,12,24,42,High School,23,Franchise,Complex\n8734,122.52,NJ City,S,6,12,4,51,High School,32,Patnership,Complex\n251,416.86,NJ City,E,10,13,20,81,High School,34,Owner,Standalone\n5485,32,Boston,N,20,9,3,40,High School,24,Patnership,Standalone\n9268,105.84,NJ City,W,3,20,2,62,High School,21,Franchise,Complex\n3934,229.28,NJ City,S,13,13,17,64,High School,35,Patnership,Complex\n2576,405.61,NJ City,W,3,12,5,47,No education,35,Franchise,Complex\n21,181.33,Maine,N,9,15,2,76,Graduation,18,Patnership,Standalone\n3294,496.65,NJ City,S,17,24,20,39,High School,32,Patnership,Complex\n2870,339.21,NJ City,S,8,23,9,75,High School,34,Patnership,Complex\n4635,302.72,NJ City,E,17,13,24,85,High School,18,Owner,Standalone\n8843,74.77,NJ City,E,15,9,3,30,No education,24,Owner,Standalone\n7809,492.58,Boston,N,10,10,24,85,Graduation,28,Patnership,Standalone\n9357,26.91,NJ City,N,2,11,2,73,Graduation,21,Patnership,Standalone\n6903,54.62,Boston,E,6,21,1,43,Graduation,34,Owner,Standalone\n8716,121.86,NJ City,W,6,11,1,63,High School,22,Franchise,Complex\n9578,404.81,NJ City,S,8,24,2,82,No education,26,Patnership,Complex\n407,271,NJ City,E,2,15,7,28,High School,31,Owner,Standalone\n7183,13.26,Boston,N,13,9,2,49,High School,24,Owner,Standalone\n5839,175.38,Boston,E,9,24,4,26,High School,35,Owner,Standalone\n9661,378.03,NJ City,N,10,9,18,35,Graduation,30,Patnership,Standalone\n8757,313.08,NJ City,N,10,13,17,29,Graduation,34,Patnership,Standalone\n4061,293.77,NJ City,N,12,16,2,48,No education,24,Patnership,Standalone\n1370,347.05,NJ City,S,4,10,1,54,No education,22,Patnership,Complex\n8010,451.79,Boston,S,24,18,11,73,Graduation,22,Patnership,Complex\n7724,46.34,Boston,N,1,19,1,61,No education,20,Franchise,Complex\n8015,258.87,Boston,E,20,11,18,56,Graduation,24,Owner,Standalone\n4196,85.61,NJ City,W,19,13,3,68,No education,33,Franchise,Complex\n1417,312.76,NJ City,N,7,19,2,73,High School,18,Patnership,Standalone\n5298,257.46,Boston,S,11,16,23,76,Graduation,34,Patnership,Complex\n4023,324.72,NJ City,E,15,18,18,67,High School,33,Owner,Standalone\n8219,465.62,NJ City,E,16,15,7,50,Graduation,27,Owner,Standalone\n7268,57.47,Boston,W,23,21,2,84,No education,31,Franchise,Complex\n5532,116.75,Boston,W,7,13,4,79,Graduation,23,Franchise,Complex\n2358,8.28,NJ City,N,9,8,2,71,Graduation,21,Patnership,Complex\n8264,389.83,NJ City,W,10,23,22,50,Graduation,26,Franchise,Complex\n7791,497.98,Boston,E,16,13,23,47,Graduation,31,Owner,Standalone\n5214,490.41,Boston,S,6,8,24,65,Graduation,24,Patnership,Complex\n652,158.51,NJ City,W,7,10,11,80,High School,23,Franchise,Complex\n5549,40.33,Boston,N,21,11,1,48,No education,31,Patnership,Standalone\n7531,276.64,Boston,E,3,19,23,31,Graduation,35,Owner,Standalone\n6259,106.87,Boston,E,19,23,4,52,High School,33,Owner,Standalone\n3893,82.51,NJ City,N,18,23,4,73,No education,22,Patnership,Standalone\n2940,284.07,NJ City,W,10,23,8,40,High School,19,Franchise,Complex\n6258,222.42,Boston,S,19,23,24,48,Graduation,32,Patnership,Complex\n5069,358.88,Boston,N,5,11,12,35,Graduation,28,Patnership,Standalone\n965,145.23,NJ City,N,9,15,4,28,No education,27,Patnership,Standalone\n2153,389.81,NJ City,N,10,15,13,67,High School,27,Patnership,Standalone\n732,60.48,NJ City,N,17,8,4,55,Graduation,31,Franchise,Complex\n4428,277,NJ City,W,2,12,9,76,High School,20,Franchise,Complex\n6945,473.74,Boston,N,19,19,8,48,Graduation,31,Patnership,Standalone\n7887,117.11,Boston,E,18,13,2,49,Graduation,18,Owner,Standalone\n4005,183.47,NJ City,N,8,20,16,34,High School,25,Patnership,Standalone\n3866,460.27,NJ City,S,21,12,7,51,High School,23,Patnership,Complex\n8767,327.85,NJ City,E,1,23,12,82,Graduation,23,Owner,Standalone\n6963,180.03,Boston,E,8,23,12,65,Graduation,33,Owner,Standalone\n4946,459.13,Boston,S,20,19,14,51,High School,22,Patnership,Complex\n463,286.14,NJ City,E,8,10,14,34,High School,25,Owner,Standalone\n5974,6.81,Boston,N,5,12,4,60,High School,29,Patnership,Complex\n7600,437.13,Boston,W,24,12,16,77,Graduation,19,Franchise,Complex\n7788,199.53,Boston,W,7,11,13,79,Graduation,32,Franchise,Complex\n6589,104.93,Boston,N,5,10,3,35,High School,18,Patnership,Standalone\n1186,348.67,NJ City,S,17,16,5,40,High School,29,Patnership,Complex\n6112,78.97,Boston,W,21,24,3,76,High School,34,Franchise,Complex\n16,240.1,Boston,W,11,24,22,65,High School,31,Franchise,Complex\n8478,62.51,NJ City,N,16,15,4,75,Graduation,31,Patnership,Complex\n341,255.07,NJ City,N,13,17,18,39,High School,31,Patnership,Standalone\n4896,67.66,NJ City,N,15,23,2,36,Graduation,28,Franchise,Complex\n2394,422.26,NJ City,S,10,17,19,38,High School,25,Patnership,Complex\n8410,453.47,NJ City,S,3,13,19,28,Graduation,23,Patnership,Complex\n8572,205.67,NJ City,W,20,20,24,31,Graduation,31,Franchise,Complex\n6866,138.22,Boston,S,25,21,5,35,No education,22,Patnership,Complex\n6269,378.29,Boston,N,25,24,19,39,Graduation,24,Patnership,Standalone\n5687,474.51,Boston,E,21,19,25,65,Graduation,34,Owner,Standalone\n9958,253.33,NJ City,S,20,8,10,41,Graduation,28,Patnership,Complex\n6609,334.21,Boston,N,23,11,17,30,Graduation,22,Patnership,Standalone\n5107,379.66,Boston,E,21,11,5,70,High School,31,Owner,Standalone\n3845,351.87,NJ City,N,15,12,17,35,High School,31,Patnership,Standalone\n8971,273.42,NJ City,E,19,21,7,36,Graduation,30,Owner,Standalone\n3035,394.56,NJ City,E,18,16,1,60,No education,29,Owner,Standalone\n7949,149.86,Boston,N,10,18,9,30,Graduation,28,Patnership,Standalone\n8368,243.35,NJ City,W,13,18,24,76,Graduation,32,Franchise,Complex\n5251,303.34,Boston,E,25,11,6,28,Graduation,29,Owner,Standalone\n4755,346.05,NJ City,E,24,14,4,56,Graduation,23,Owner,Standalone\n7096,459.49,Boston,W,1,14,19,46,Graduation,18,Franchise,Complex\n4675,71.68,NJ City,N,6,18,1,51,High School,25,Owner,Standalone\n3549,175.39,NJ City,N,7,24,19,47,High School,19,Patnership,Standalone\n6004,309.2,Boston,W,14,17,14,84,Graduation,33,Franchise,Complex\n5182,241.53,Boston,S,17,10,9,52,Graduation,19,Patnership,Complex\n2557,114,NJ City,N,2,15,8,75,High School,34,Patnership,Standalone\n9624,220.47,NJ City,W,11,11,13,31,Graduation,20,Franchise,Complex\n5568,226.59,Boston,W,15,19,16,56,Graduation,31,Franchise,Complex\n5032,348.8,Boston,W,19,19,25,64,Graduation,21,Franchise,Complex\n1045,110.61,NJ City,N,13,12,1,26,High School,34,Patnership,Standalone\n3150,251.81,NJ City,S,23,20,7,63,High School,26,Patnership,Complex\n5011,189.16,Boston,E,10,9,1,70,High School,27,Owner,Standalone\n7998,182.41,Boston,S,1,11,5,50,Graduation,24,Patnership,Complex\n2300,304.62,NJ City,W,22,10,20,31,High School,21,Franchise,Complex\n8927,47.28,NJ City,N,4,9,1,69,No education,24,Owner,Standalone\n6526,387.11,Boston,S,9,10,10,32,Graduation,21,Patnership,Complex\n257,439.39,NJ City,N,14,9,6,76,High School,34,Patnership,Standalone\n1974,79.16,NJ City,S,25,21,2,40,Graduation,19,Patnership,Complex\n1805,124,NJ City,N,3,11,10,29,High School,30,Patnership,Standalone\n71,436.56,NY City,E,24,8,11,41,High School,23,Owner,Standalone\n960,153.85,NJ City,W,6,11,17,82,High School,25,Franchise,Complex\n908,315.67,NJ City,W,15,22,8,41,High School,34,Franchise,Complex\n3013,337.2,NJ City,N,11,22,8,35,High School,24,Patnership,Standalone\n3499,420.47,NJ City,E,6,24,11,60,High School,21,Owner,Standalone\n7731,220.06,Boston,E,7,23,16,82,Graduation,25,Owner,Standalone\n8862,170.91,NJ City,S,6,10,22,62,Graduation,33,Patnership,Complex\n4421,258.36,NJ City,N,21,16,21,60,High School,28,Patnership,Standalone\n2851,456.39,NJ City,E,18,16,15,59,High School,20,Owner,Standalone\n3719,166.67,NJ City,E,6,22,11,75,High School,32,Owner,Standalone\n5542,204.5,Boston,S,13,11,16,30,Graduation,30,Patnership,Complex\n868,147,NJ City,W,3,8,6,74,High School,34,Franchise,Complex\n5907,499.17,Boston,E,6,9,15,30,Graduation,33,Owner,Standalone\n1014,152.89,NJ City,S,12,16,2,30,Graduation,27,Patnership,Complex\n6821,164.7,Boston,N,3,21,18,46,Graduation,18,Patnership,Standalone\n1454,183.27,NJ City,S,7,15,25,47,High School,33,Patnership,Complex\n4139,109.24,NJ City,E,12,12,2,71,No education,23,Owner,Standalone\n4368,433.7,NJ City,W,11,13,6,38,High School,28,Franchise,Complex\n835,479.58,NJ City,E,8,8,4,74,High School,26,Owner,Standalone\n5286,335.28,Boston,S,14,24,12,44,Graduation,33,Patnership,Complex\n9515,217.39,NJ City,E,1,22,6,79,Graduation,32,Owner,Standalone\n725,430,NJ City,N,10,16,6,69,High School,31,Patnership,Standalone\n8625,463.43,NJ City,N,15,21,18,68,Graduation,31,Patnership,Standalone\n3775,7.02,NJ City,N,7,20,4,64,High School,24,Owner,Standalone\n9331,163.97,NJ City,E,2,13,5,27,High School,28,Owner,Standalone\n4929,93.78,NJ City,N,14,9,3,27,Graduation,23,Patnership,Standalone\n9819,495.08,NJ City,E,24,17,19,80,Graduation,31,Owner,Standalone\n9548,381.73,NJ City,W,3,8,9,31,Graduation,35,Franchise,Complex\n4849,126.6,NJ City,N,6,22,2,39,High School,32,Patnership,Standalone\n4119,41.52,NJ City,N,11,12,4,83,Graduation,33,Owner,Standalone\n4378,481.39,NJ City,S,4,8,10,40,High School,21,Patnership,Complex\n792,378.48,NJ City,W,21,20,12,82,High School,29,Franchise,Complex\n7781,151.94,Boston,N,2,22,24,84,Graduation,29,Patnership,Standalone\n9601,52.71,NJ City,N,16,12,3,34,High School,19,Patnership,Standalone\n7020,24.42,Boston,N,1,14,4,68,Graduation,25,Franchise,Complex\n3575,33.92,NJ City,N,20,13,4,67,No education,33,Owner,Standalone\n5938,405.96,Boston,S,25,24,6,52,Graduation,20,Patnership,Complex\n4978,235,Boston,S,3,13,16,38,High School,27,Patnership,Complex\n4805,322.09,NJ City,N,15,10,14,60,High School,25,Patnership,Standalone\n4220,122.78,NJ City,W,8,18,1,75,No education,29,Franchise,Complex\n1419,76.86,NJ City,E,9,12,4,45,Graduation,29,Owner,Standalone\n890,102.51,NJ City,S,17,17,2,41,No education,27,Patnership,Complex\n3456,417.1,NJ City,W,20,19,6,62,High School,27,Franchise,Complex\n9189,122.19,NJ City,N,2,12,3,81,Graduation,18,Patnership,Standalone\n4773,321.97,NJ City,N,23,22,5,41,Graduation,30,Patnership,Standalone\n3818,1.69,NJ City,N,6,21,3,40,No education,22,Patnership,Complex\n7146,411.24,Boston,S,11,20,13,85,Graduation,29,Patnership,Complex\n4323,120.2,NJ City,E,9,16,3,50,Graduation,20,Owner,Standalone\n4631,432.7,NJ City,E,16,13,18,49,High School,19,Owner,Standalone\n4823,473.48,NJ City,E,23,20,1,85,No education,21,Owner,Standalone\n314,87.05,NJ City,S,11,22,2,72,No education,33,Patnership,Complex\n8546,365.2,NJ City,S,24,18,4,36,No education,27,Patnership,Complex\n3908,211.56,NJ City,W,5,15,12,81,High School,30,Franchise,Complex\n4251,415.29,NJ City,E,21,11,15,53,High School,23,Owner,Standalone\n9552,149.96,NJ City,W,7,23,15,79,Graduation,30,Franchise,Complex\n7675,127.82,Boston,E,17,22,2,82,High School,19,Owner,Standalone\n1599,318.53,NJ City,E,12,8,10,51,High School,23,Owner,Standalone\n2351,312,NJ City,E,2,22,21,78,High School,27,Owner,Standalone\n4809,290.74,NJ City,N,14,17,6,40,High School,18,Patnership,Standalone\n450,397.16,NJ City,S,15,11,25,64,High School,18,Patnership,Complex\n4274,491.52,NJ City,S,19,12,9,36,High School,21,Patnership,Complex\n3559,395.75,NJ City,E,4,16,3,80,High School,27,Owner,Standalone\n5256,244.04,Boston,W,17,11,12,33,Graduation,27,Franchise,Complex\n9605,61.88,NJ City,N,1,12,3,54,No education,22,Patnership,Standalone\n2299,136,NJ City,E,3,8,24,73,High School,18,Owner,Standalone\n3271,206.63,NJ City,E,21,18,20,72,High School,18,Owner,Standalone\n2976,496.86,NJ City,W,15,22,12,51,High School,33,Franchise,Complex\n4383,178.44,NJ City,E,14,8,9,29,High School,34,Owner,Standalone\n3762,269,NJ City,S,2,23,19,67,High School,23,Patnership,Complex\n1318,11.63,NJ City,N,11,17,2,47,High School,32,Patnership,Complex\n6063,432.39,Boston,E,23,18,7,41,Graduation,34,Owner,Standalone\n4272,403.27,NJ City,W,15,20,16,62,High School,21,Franchise,Complex\n9061,123.21,NJ City,N,3,16,2,36,High School,35,Patnership,Standalone\n9005,299.61,NJ City,N,2,23,22,47,Graduation,25,Patnership,Standalone\n1543,12.15,NJ City,N,9,9,2,77,High School,26,Owner,Standalone\n7130,477.24,Boston,S,13,17,16,28,Graduation,27,Patnership,Complex\n6047,24.52,Boston,N,15,17,4,63,No education,22,Owner,Standalone\n6416,187.33,Boston,W,12,8,19,73,Graduation,31,Franchise,Complex\n819,199.56,NJ City,E,10,24,2,31,Graduation,30,Owner,Standalone\n4997,111,Boston,N,1,17,23,52,High School,34,Patnership,Standalone\n266,347.18,NJ City,S,14,17,3,43,No education,21,Patnership,Complex\n1633,11.78,NJ City,N,13,16,1,66,High School,32,Patnership,Standalone\n3724,202.53,NJ City,W,20,15,1,31,High School,33,Franchise,Complex\n724,201.65,NJ City,W,25,21,14,44,High School,25,Franchise,Complex\n5887,260.81,Boston,E,24,9,13,65,Graduation,34,Owner,Standalone\n9776,426.17,NJ City,W,3,9,21,30,Graduation,33,Franchise,Complex\n5431,469.7,Boston,E,7,21,18,51,Graduation,27,Owner,Standalone\n3615,61.88,NJ City,N,12,17,4,76,Graduation,29,Owner,Standalone\n6086,327.55,Boston,S,13,12,11,52,Graduation,22,Patnership,Complex\n9995,468.77,NJ City,E,1,14,22,82,Graduation,26,Owner,Standalone\n3247,64.71,NJ City,N,21,24,3,81,High School,34,Owner,Standalone\n1349,31.99,NJ City,N,15,8,1,29,No education,30,Patnership,Standalone\n7036,188.41,Boston,W,8,19,7,50,Graduation,21,Franchise,Complex\n7252,151.39,Boston,W,14,15,15,69,Graduation,20,Franchise,Complex\n5769,429.6,Boston,N,15,20,5,83,Graduation,24,Patnership,Standalone\n7614,52.68,Boston,S,14,18,2,33,Graduation,33,Patnership,Complex\n8596,192.54,NJ City,W,3,22,13,57,Graduation,30,Franchise,Complex\n925,237.82,NJ City,N,23,13,10,40,High School,25,Patnership,Standalone\n8327,92.55,NJ City,E,10,23,2,47,No education,34,Owner,Standalone\n5876,19.74,Boston,N,23,19,2,79,No education,25,Franchise,Complex\n3914,499.6,NJ City,S,14,18,8,85,High School,33,Patnership,Complex\n9417,430.68,NJ City,N,24,17,7,25,Graduation,22,Patnership,Standalone\n2229,160.86,NJ City,N,9,17,21,84,High School,21,Patnership,Standalone\n4653,424.5,NJ City,N,25,8,9,85,High School,19,Patnership,Standalone\n346,331.36,NJ City,S,12,14,10,72,High School,34,Patnership,Complex\n3514,298.42,NJ City,S,8,20,16,81,High School,30,Patnership,Complex\n9406,369.02,NJ City,S,5,11,4,51,High School,18,Patnership,Complex\n2762,48.91,NJ City,S,22,10,1,67,No education,31,Patnership,Complex\n2670,275.7,NJ City,S,8,24,4,48,Graduation,32,Patnership,Complex\n4003,176,NJ City,E,3,20,21,66,High School,34,Owner,Standalone\n4656,169.85,NJ City,W,12,14,2,27,Graduation,25,Franchise,Complex\n1739,40.06,NJ City,N,20,19,3,55,No education,26,Owner,Standalone\n1100,343.79,NJ City,W,15,10,7,36,High School,24,Franchise,Complex\n6391,451.38,Boston,E,13,13,25,66,Graduation,26,Owner,Standalone\n5835,287.87,Boston,E,18,15,24,57,Graduation,28,Owner,Standalone\n277,120.5,NJ City,N,10,16,3,27,High School,21,Patnership,Standalone\n4961,485.2,Boston,N,19,16,24,56,High School,19,Patnership,Standalone\n9246,168.01,NJ City,S,7,16,24,45,Graduation,34,Patnership,Complex\n3502,191.74,NJ City,S,15,10,7,44,High School,26,Patnership,Complex\n5216,243.73,Boston,W,25,11,10,64,Graduation,22,Franchise,Complex\n6073,292.77,Boston,N,22,11,5,60,High School,28,Patnership,Standalone\n5415,214.14,Boston,E,5,22,10,58,Graduation,35,Owner,Standalone\n2502,62.66,NJ City,N,6,9,3,51,Graduation,24,Patnership,Complex\n8465,499.86,NJ City,N,19,9,24,64,Graduation,23,Patnership,Standalone\n7928,212.55,Boston,W,1,14,16,68,Graduation,25,Franchise,Complex\n4549,348.94,NJ City,N,18,17,23,80,High School,28,Patnership,Standalone\n7674,80.2,Boston,S,18,24,1,56,Graduation,20,Patnership,Complex\n5241,167.76,Boston,N,7,14,11,40,Graduation,35,Patnership,Standalone\n3363,299.35,NJ City,E,4,17,3,30,Graduation,29,Owner,Standalone\n6972,107.46,Boston,W,8,16,2,46,Graduation,19,Franchise,Complex\n7181,239.96,Boston,N,19,12,18,80,Graduation,19,Patnership,Standalone\n5180,499.33,Boston,W,19,13,25,72,Graduation,32,Franchise,Complex\n669,60.06,NJ City,N,15,19,3,32,Graduation,21,Patnership,Standalone\n5749,42.63,Boston,N,13,21,2,56,High School,35,Patnership,Standalone\n3727,365.06,NJ City,E,7,24,4,50,High School,22,Owner,Standalone\n4697,159.24,NJ City,N,11,21,12,29,High School,25,Patnership,Standalone\n1263,359.67,NJ City,E,18,23,24,59,High School,20,Owner,Standalone\n3254,245.67,NJ City,S,12,18,8,50,High School,26,Patnership,Complex\n9282,330.86,NJ City,S,15,20,3,73,Graduation,31,Patnership,Complex\n1927,418.02,NJ City,E,21,15,17,55,High School,30,Owner,Standalone\n1103,247.67,NJ City,E,20,17,24,58,High School,29,Owner,Standalone\n5031,321.81,Boston,E,15,19,15,62,Graduation,19,Owner,Standalone\n495,218,NJ City,E,3,19,7,40,High School,26,Owner,Standalone\n1196,113.01,NJ City,W,14,19,4,39,No education,32,Franchise,Complex\n3935,498.08,NJ City,E,10,22,16,38,High School,20,Owner,Standalone\n5337,47.47,Boston,N,12,22,1,60,Graduation,31,Patnership,Standalone\n1455,140,NJ City,E,9,23,13,62,High School,35,Owner,Standalone\n7120,162.68,Boston,W,7,14,16,27,Graduation,32,Franchise,Complex\n3267,398.92,NJ City,E,12,14,14,64,High School,35,Owner,Standalone\n7217,295.94,Boston,N,20,17,18,63,Graduation,29,Patnership,Standalone\n6317,304.77,Boston,N,3,20,6,63,Graduation,21,Patnership,Standalone\n5335,415.83,Boston,E,18,18,9,56,Graduation,33,Owner,Standalone\n459,77.82,NJ City,E,22,24,4,34,Graduation,34,Owner,Standalone\n6337,185.03,Boston,N,10,8,23,66,Graduation,26,Patnership,Standalone\n2267,471.18,NJ City,E,19,24,18,73,High School,26,Owner,Standalone\n4894,302.66,NJ City,S,3,11,5,44,High School,24,Patnership,Complex\n3408,332.39,NJ City,W,15,19,8,46,High School,29,Franchise,Complex\n596,38.3,NJ City,N,13,11,2,41,No education,25,Franchise,Complex\n7984,170.92,Boston,W,18,22,19,61,Graduation,27,Franchise,Complex\n1551,233.81,NJ City,E,24,13,14,28,High School,31,Owner,Standalone\n4407,148.72,NJ City,E,16,18,11,69,High School,32,Owner,Standalone\n1238,342.46,NJ City,S,21,22,11,34,High School,32,Patnership,Complex\n5000,338.2,Boston,W,22,18,9,70,Graduation,33,Franchise,Complex\n6796,370.11,Boston,W,9,21,9,35,Graduation,21,Franchise,Complex\n4566,483.5,NJ City,S,12,21,18,41,High School,28,Patnership,Complex\n9281,201.03,NJ City,N,22,14,3,44,No education,18,Patnership,Standalone\n7882,343.77,Boston,S,6,12,5,73,High School,35,Patnership,Complex\n5527,421.19,Boston,E,8,23,23,79,Graduation,19,Owner,Standalone\n2521,404.18,NJ City,N,5,8,24,60,High School,31,Patnership,Standalone\n8903,134.49,NJ City,E,19,9,8,43,Graduation,28,Owner,Standalone\n373,188,NJ City,N,21,16,7,67,High School,33,Patnership,Standalone\n3532,247,NJ City,W,1,23,22,28,High School,27,Franchise,Complex\n7492,124.3,Boston,W,20,12,3,49,High School,34,Franchise,Complex\n5447,173.95,Boston,E,3,19,24,77,Graduation,25,Owner,Standalone\n2697,67.65,NJ City,N,2,18,4,53,Graduation,33,Patnership,Standalone\n2769,197.13,NJ City,N,11,22,13,55,High School,33,Patnership,Standalone\n5421,414.46,Boston,N,17,8,5,31,Graduation,29,Patnership,Standalone\n9533,447.44,NJ City,N,25,10,23,79,Graduation,24,Patnership,Standalone\n6949,199.91,Boston,N,20,15,20,25,Graduation,24,Patnership,Standalone\n7116,298.41,Boston,W,15,16,19,37,Graduation,25,Franchise,Complex\n9567,264.32,NJ City,E,18,24,16,25,Graduation,24,Owner,Standalone\n7461,153.21,Boston,N,23,16,2,56,Graduation,24,Patnership,Standalone\n1475,416.02,NJ City,E,13,23,12,56,High School,23,Owner,Standalone\n3449,185.05,NJ City,N,6,9,24,37,High School,29,Patnership,Standalone\n5141,274.9,Boston,N,20,10,6,61,Graduation,19,Patnership,Standalone\n9412,116.08,NJ City,W,18,23,1,76,High School,18,Franchise,Complex\n2538,94.17,NJ City,S,1,14,2,46,Graduation,22,Patnership,Complex\n3317,271.74,NJ City,N,6,11,8,52,High School,25,Patnership,Standalone\n6231,358.5,Boston,E,11,21,23,51,Graduation,21,Owner,Standalone\n5603,385.18,Boston,E,11,13,24,37,Graduation,29,Owner,Standalone\n6817,92.95,Boston,N,19,21,2,66,High School,22,Patnership,Standalone\n2380,140.11,NJ City,W,14,11,18,74,High School,34,Franchise,Complex\n8611,444.22,NJ City,E,2,13,13,38,Graduation,35,Owner,Standalone\n6703,198.27,Boston,E,12,11,23,68,Graduation,23,Owner,Standalone\n3065,443,NJ City,N,19,22,2,55,No education,27,Patnership,Standalone\n1391,196,NJ City,E,3,23,25,83,High School,21,Owner,Standalone\n3420,248.71,NJ City,W,12,12,9,25,High School,30,Franchise,Complex\n4173,299.33,NJ City,N,15,24,17,65,High School,29,Patnership,Standalone\n1595,91.5,NJ City,E,13,16,1,30,No education,30,Owner,Standalone\n9831,264.67,NJ City,E,7,14,4,79,Graduation,34,Owner,Standalone\n1381,117.74,NJ City,N,15,14,4,67,High School,35,Patnership,Standalone\n806,175.12,NJ City,S,5,14,1,79,No education,18,Patnership,Complex\n7082,307.44,Boston,S,19,17,1,59,No education,28,Patnership,Complex\n2322,96.25,NJ City,S,17,10,1,28,Graduation,28,Patnership,Complex\n333,195.19,NJ City,N,11,20,4,83,Graduation,34,Patnership,Standalone\n9670,230.48,NJ City,S,23,22,15,53,Graduation,28,Patnership,Complex\n2501,278.94,NJ City,N,22,17,17,43,High School,18,Patnership,Standalone\n8735,84.06,NJ City,E,12,11,1,31,No education,29,Owner,Standalone\n2855,450.96,NJ City,E,13,9,12,34,High School,33,Owner,Standalone\n2709,168.77,NJ City,N,14,16,8,81,High School,20,Patnership,Standalone\n8404,186.88,NJ City,W,10,14,15,60,Graduation,22,Franchise,Complex\n2600,434.99,NJ City,W,14,22,5,43,No education,18,Franchise,Complex\n4056,206.03,NJ City,W,7,11,13,59,High School,31,Franchise,Complex\n9178,406.25,NJ City,S,9,8,4,59,High School,32,Patnership,Complex\n514,79.92,NJ City,S,11,23,1,43,High School,35,Patnership,Complex\n4252,397.85,NJ City,W,14,11,15,85,High School,32,Franchise,Complex\n3157,117.53,NJ City,N,16,18,2,79,High School,22,Patnership,Standalone\n1038,255.38,NJ City,S,18,11,2,53,Graduation,19,Patnership,Complex\n2663,314.67,NJ City,E,16,9,15,28,High School,31,Owner,Standalone\n3992,93.3,NJ City,W,25,15,2,55,No education,24,Franchise,Complex\n548,383.75,NJ City,W,6,12,18,36,High School,18,Franchise,Complex\n5225,180.29,Boston,N,18,9,19,84,Graduation,31,Patnership,Standalone\n6314,43.28,Boston,N,1,20,2,61,No education,29,Patnership,Complex\n6549,213.25,Boston,N,4,17,18,84,Graduation,24,Patnership,Standalone\n2757,88.97,NJ City,N,22,22,1,81,Graduation,28,Patnership,Standalone\n2603,320.99,NJ City,E,25,9,20,27,High School,29,Owner,Standalone\n8723,405.84,NJ City,E,21,22,6,53,Graduation,28,Owner,Standalone\n711,87.82,NJ City,E,23,20,4,65,Graduation,29,Owner,Standalone\n5115,441.74,Boston,E,24,14,19,34,Graduation,19,Owner,Standalone\n6804,430.6,Boston,W,16,21,2,29,Graduation,30,Franchise,Complex\n703,194.03,NJ City,E,13,17,12,63,High School,24,Owner,Standalone\n8619,458.58,NJ City,E,12,10,21,84,Graduation,33,Owner,Standalone\n1651,83.3,NJ City,E,22,16,3,80,High School,23,Owner,Standalone\n9889,490.33,NJ City,N,18,22,8,55,Graduation,20,Patnership,Standalone\n9002,192.08,NJ City,S,5,20,15,43,Graduation,31,Patnership,Complex\n2896,345.65,NJ City,W,11,23,7,74,High School,34,Franchise,Complex\n4820,103.65,NJ City,W,24,16,3,71,No education,25,Franchise,Complex\n4860,22.92,NJ City,N,13,19,2,28,Graduation,18,Franchise,Complex\n4972,54.36,Boston,W,14,14,1,29,High School,30,Franchise,Complex\n7635,334.06,Boston,E,8,15,17,61,Graduation,31,Owner,Standalone\n9310,339.53,NJ City,S,9,15,21,36,Graduation,33,Patnership,Complex\n6412,238.57,Boston,W,17,21,17,64,Graduation,26,Franchise,Complex\n6920,491.6,Boston,W,4,23,5,62,No education,26,Franchise,Complex\n4480,266.09,NJ City,W,15,18,18,47,High School,35,Franchise,Complex\n3454,307.48,NJ City,S,19,13,7,52,High School,19,Patnership,Complex\n1487,235,NJ City,E,3,10,21,68,High School,19,Owner,Standalone\n5173,225.59,Boston,N,5,15,3,69,High School,24,Patnership,Standalone\n3017,412.77,NJ City,N,23,19,13,74,High School,23,Patnership,Standalone\n6471,362.9,Boston,E,22,23,1,52,Graduation,20,Owner,Standalone\n1084,22.59,NJ City,N,20,23,1,30,High School,33,Franchise,Complex\n8284,332.07,NJ City,W,3,11,23,77,Graduation,27,Franchise,Complex\n6072,57.9,Boston,W,9,11,4,39,Graduation,31,Franchise,Complex\n6131,416.4,Boston,E,25,12,15,57,Graduation,32,Owner,Standalone\n6357,52.34,Boston,N,23,23,1,31,Graduation,22,Patnership,Standalone\n8532,86.89,NJ City,W,13,14,1,80,Graduation,24,Franchise,Complex\n5038,476.17,Boston,S,22,13,13,69,Graduation,21,Patnership,Complex\n1210,252.06,NJ City,S,6,13,24,62,High School,35,Patnership,Complex\n4662,191.95,NJ City,S,10,15,23,27,High School,26,Patnership,Complex\n566,106.4,NJ City,S,7,13,2,39,No education,21,Patnership,Complex\n9185,248.11,NJ City,N,21,21,5,36,No education,29,Patnership,Standalone\n5029,226.12,Boston,N,4,12,11,75,Graduation,34,Patnership,Standalone\n7289,80.79,Boston,N,16,10,2,55,No education,31,Patnership,Standalone\n6940,119.84,Boston,W,3,8,3,29,High School,20,Franchise,Complex\n4504,177.24,NJ City,W,23,18,12,63,High School,33,Franchise,Complex\n5460,167.43,Boston,W,24,10,24,81,Graduation,20,Franchise,Complex\n447,235.92,NJ City,E,16,17,1,33,Graduation,31,Owner,Standalone\n4195,206,NJ City,E,3,8,24,84,High School,33,Owner,Standalone\n7240,64.89,Boston,N,8,18,4,68,High School,23,Franchise,Complex\n8871,114.09,NJ City,E,23,20,3,85,Graduation,30,Owner,Standalone\n6351,79.36,Boston,E,20,20,3,64,Graduation,23,Owner,Standalone\n3711,349.2,NJ City,E,12,9,23,69,High School,19,Owner,Standalone\n2747,292.92,NJ City,E,13,12,24,26,High School,23,Owner,Standalone\n2957,293.48,NJ City,N,25,17,15,72,High School,27,Patnership,Standalone\n858,229.04,NJ City,S,16,18,10,37,High School,35,Patnership,Complex\n657,71.11,NJ City,N,21,17,4,63,Graduation,22,Patnership,Standalone\n3832,12.93,NJ City,N,1,15,1,71,High School,20,Franchise,Complex\n1917,27.76,NJ City,N,15,12,2,85,Graduation,21,Patnership,Standalone\n6582,46.95,Boston,N,4,23,2,57,Graduation,30,Patnership,Complex\n5316,412.13,Boston,W,8,22,13,34,Graduation,34,Franchise,Complex\n179,90.72,NJ City,E,15,8,3,39,No education,19,Owner,Standalone\n6885,167.22,Boston,N,5,22,12,40,Graduation,23,Patnership,Standalone\n2676,243.35,NJ City,W,16,20,15,38,High School,22,Franchise,Complex\n2935,169.46,NJ City,E,19,8,8,80,High School,32,Owner,Standalone\n7434,82.81,Boston,S,3,23,2,51,Graduation,28,Patnership,Complex\n4410,12.4,NJ City,N,19,9,3,77,Graduation,32,Patnership,Complex\n7046,251.47,Boston,S,16,9,9,51,Graduation,35,Patnership,Complex\n7424,447.19,Boston,W,4,24,19,31,Graduation,28,Franchise,Complex\n4453,379.93,NJ City,N,12,9,2,60,High School,23,Patnership,Standalone\n4218,232.61,NJ City,S,16,14,1,50,Graduation,34,Patnership,Complex\n7726,393.15,Boston,S,9,11,22,58,Graduation,26,Patnership,Complex\n8967,116.28,NJ City,E,9,12,4,36,Graduation,28,Owner,Standalone\n1491,131.14,NJ City,E,5,19,18,59,High School,33,Owner,Standalone\n1969,169.49,NJ City,N,14,10,11,65,High School,35,Patnership,Standalone\n3964,170.23,NJ City,W,17,21,3,82,High School,20,Franchise,Complex\n7586,43.9,Boston,N,12,11,1,80,No education,35,Patnership,Complex\n2561,408.66,NJ City,N,15,22,21,70,High School,19,Patnership,Standalone\n5908,258.37,Boston,W,24,12,15,66,Graduation,28,Franchise,Complex\n3993,266.4,NJ City,N,8,20,2,41,Graduation,20,Patnership,Standalone\n3293,304.58,NJ City,N,16,8,10,75,High School,27,Patnership,Standalone\n4829,185.06,NJ City,N,22,16,21,75,High School,31,Patnership,Standalone\n6669,121.66,Boston,N,18,19,4,83,Graduation,35,Patnership,Standalone\n5160,347.67,Boston,W,1,10,11,29,Graduation,18,Franchise,Complex\n1110,479.73,NJ City,S,17,18,12,28,High School,35,Patnership,Complex\n7656,79.86,Boston,W,4,15,2,81,Graduation,24,Franchise,Complex\n5221,61.35,Boston,N,14,10,1,64,High School,22,Patnership,Standalone\n780,108.7,NJ City,W,18,11,1,48,Graduation,27,Franchise,Complex\n9788,128.06,NJ City,W,24,24,1,79,No education,18,Franchise,Complex\n4837,264.46,NJ City,N,24,22,15,61,High School,29,Patnership,Standalone\n1801,466.04,NJ City,N,16,23,18,45,High School,30,Patnership,Standalone\n7812,26.36,Boston,N,9,18,4,84,Graduation,18,Franchise,Complex\n7015,258.79,Boston,E,22,22,8,57,Graduation,23,Owner,Standalone\n3286,201.03,NJ City,S,3,23,3,54,High School,27,Patnership,Complex\n1408,69.53,NJ City,N,17,14,1,31,High School,29,Franchise,Complex\n7521,147.1,Boston,N,21,23,3,69,Graduation,23,Patnership,Standalone\n654,73.7,NJ City,N,9,17,1,57,Graduation,33,Patnership,Complex\n1229,351.47,NJ City,N,16,8,17,39,High School,18,Patnership,Standalone\n3767,452.07,NJ City,E,13,22,17,62,High School,25,Owner,Standalone\n4795,418.83,NJ City,E,16,18,18,77,High School,30,Owner,Standalone\n3019,88.86,NJ City,E,15,15,3,61,High School,24,Owner,Standalone\n5352,391.36,Boston,W,8,12,15,40,Graduation,25,Franchise,Complex\n1558,417.63,NJ City,S,12,16,9,74,High School,32,Patnership,Complex\n1124,390.37,NJ City,W,21,18,18,76,High School,26,Franchise,Complex\n530,424.11,NJ City,S,24,16,21,37,High School,34,Patnership,Complex\n8127,252.31,NJ City,E,23,14,24,37,Graduation,30,Owner,Standalone\n7053,71.36,Boston,N,9,20,2,65,Graduation,24,Patnership,Standalone\n2804,470.58,NJ City,W,7,20,25,72,High School,30,Franchise,Complex\n94,273.94,NJ City,S,15,20,3,37,High School,31,Patnership,Complex\n5772,96.14,Boston,W,20,14,1,39,Graduation,25,Franchise,Complex\n2932,495.22,NJ City,W,25,15,13,62,High School,30,Franchise,Complex\n1713,451.7,NJ City,N,4,17,22,25,High School,32,Patnership,Standalone\n9029,329.68,NJ City,N,6,21,4,57,No education,29,Patnership,Standalone\n2537,48.61,NJ City,N,2,9,1,46,No education,35,Patnership,Standalone\n8453,20.85,NJ City,N,9,18,2,30,No education,30,Patnership,Standalone\n1326,30.36,NJ City,N,12,18,2,26,Graduation,31,Patnership,Complex\n7891,138.46,Boston,E,19,19,20,53,Graduation,27,Owner,Standalone\n9334,109.28,NJ City,S,24,15,4,60,High School,20,Patnership,Complex\n1996,347.02,NJ City,W,16,9,13,38,High School,31,Franchise,Complex\n3253,110.84,NJ City,N,25,9,4,35,High School,30,Patnership,Standalone\n4203,14.24,NJ City,N,7,20,1,48,Graduation,27,Owner,Standalone\n2641,303.7,NJ City,N,13,16,10,31,High School,27,Patnership,Standalone\n634,275.65,NJ City,S,9,9,15,60,High School,20,Patnership,Complex\n2200,391.11,NJ City,W,8,11,22,59,High School,25,Franchise,Complex\n8705,36.96,NJ City,N,16,9,1,56,No education,29,Patnership,Standalone\n5819,328.44,Boston,E,12,16,3,53,No education,22,Owner,Standalone\n6064,95.96,Boston,W,22,14,4,71,High School,19,Franchise,Complex\n1734,155.46,NJ City,S,25,22,4,39,Graduation,26,Patnership,Complex\n6599,492.82,Boston,E,3,11,11,50,Graduation,30,Owner,Standalone\n1301,417.53,NJ City,N,18,13,23,33,High School,31,Patnership,Standalone\n5496,327.01,Boston,W,6,13,18,52,Graduation,24,Franchise,Complex\n2266,482.22,NJ City,S,19,22,9,46,High School,21,Patnership,Complex\n4830,490.19,NJ City,S,17,13,25,27,High School,33,Patnership,Complex\n7708,197.03,Boston,W,12,9,21,58,Graduation,24,Franchise,Complex\n4560,45.69,NJ City,N,4,16,4,76,Graduation,35,Franchise,Complex\n4782,422.82,NJ City,S,23,14,7,80,High School,20,Patnership,Complex\n8958,295.01,NJ City,S,18,21,4,61,Graduation,21,Patnership,Complex\n3315,185.28,NJ City,E,4,10,22,71,High School,18,Owner,Standalone\n4660,122.11,NJ City,W,7,22,3,68,High School,20,Franchise,Complex\n1889,39.84,NJ City,N,10,12,2,38,No education,26,Patnership,Standalone\n974,311.05,NJ City,S,19,10,16,31,High School,19,Patnership,Complex\n7824,334.66,Boston,W,22,16,20,40,Graduation,33,Franchise,Complex\n4926,219,NJ City,S,3,10,13,53,High School,30,Patnership,Complex\n6685,53.91,Boston,N,13,16,3,75,High School,26,Patnership,Standalone\n4710,292.99,NJ City,S,9,18,3,39,Graduation,27,Patnership,Complex\n9203,447.17,NJ City,E,18,18,4,81,No education,25,Owner,Standalone\n8046,366.52,NJ City,S,6,13,1,46,Graduation,27,Patnership,Complex\n6440,287.86,Boston,W,5,14,23,40,Graduation,27,Franchise,Complex\n244,151.04,NJ City,W,24,22,18,81,High School,26,Franchise,Complex\n317,312.21,NJ City,N,15,17,1,38,No education,31,Patnership,Standalone\n4344,407.67,NJ City,W,10,17,13,59,High School,32,Franchise,Complex\n533,390.97,NJ City,N,10,18,14,34,High School,18,Patnership,Standalone\n3212,336.11,NJ City,W,17,12,20,59,High School,23,Franchise,Complex\n2211,337.32,NJ City,E,1,15,3,42,Graduation,20,Owner,Standalone\n4649,302.92,NJ City,N,13,12,16,63,High School,29,Patnership,Standalone\n9698,15.98,NJ City,N,14,16,4,84,No education,21,Patnership,Complex\n1655,480.3,NJ City,E,18,8,20,49,High School,22,Owner,Standalone\n5394,26.45,Boston,N,20,18,4,52,Graduation,35,Patnership,Complex\n1890,243.07,NJ City,S,21,8,23,71,High School,19,Patnership,Complex\n785,424.61,NJ City,N,10,10,1,62,No education,28,Patnership,Standalone\n452,298.39,NJ City,W,9,13,16,82,High School,18,Franchise,Complex\n9557,311.15,NJ City,N,24,11,25,80,Graduation,34,Patnership,Standalone\n5131,343.3,Boston,E,10,14,24,49,Graduation,21,Owner,Standalone\n6035,499.88,Boston,E,22,11,2,74,No education,27,Owner,Standalone\n4149,43.8,NJ City,N,15,18,1,61,Graduation,30,Patnership,Standalone\n7271,327.63,Boston,E,25,13,21,40,Graduation,22,Owner,Standalone\n5263,102.89,Boston,E,6,16,4,62,High School,35,Owner,Standalone\n4288,201.89,NJ City,W,4,23,21,85,High School,35,Franchise,Complex\n1682,333.76,NJ City,S,23,23,3,30,No education,24,Patnership,Complex\n8928,253.05,NJ City,W,17,8,10,49,Graduation,32,Franchise,Complex\n814,478.98,NJ City,S,13,22,25,46,High School,25,Patnership,Complex\n7360,39.21,Boston,N,15,18,2,28,High School,30,Franchise,Complex\n29,349.03,Boston,N,4,17,20,32,High School,23,Patnership,Standalone\n5712,361.21,Boston,W,17,8,21,42,Graduation,22,Franchise,Complex\n6411,405.54,Boston,E,1,20,19,25,Graduation,28,Owner,Standalone\n3923,190.08,NJ City,E,12,21,21,67,High School,20,Owner,Standalone\n4746,227.79,NJ City,S,8,10,21,78,High School,22,Patnership,Complex\n5156,26.63,Boston,N,7,12,1,41,No education,19,Franchise,Complex\n3481,292.16,NJ City,N,13,21,11,77,High School,21,Patnership,Standalone\n7150,305.97,Boston,S,15,17,22,53,Graduation,26,Patnership,Complex\n601,161.32,NJ City,N,24,10,2,82,High School,19,Patnership,Standalone\n9614,263.38,NJ City,S,6,18,18,38,Graduation,29,Patnership,Complex\n1359,0.3,NJ City,N,16,19,4,73,Graduation,33,Owner,Standalone\n2490,259,NJ City,S,3,22,6,46,High School,18,Patnership,Complex\n8330,48.85,NJ City,S,13,18,1,37,No education,27,Patnership,Complex\n2197,124.59,NJ City,N,22,17,3,77,High School,31,Patnership,Standalone\n104,196.29,NJ City,W,4,24,25,67,High School,21,Franchise,Complex\n7370,383.6,Boston,S,3,12,16,55,Graduation,28,Patnership,Complex\n8447,42.57,NJ City,N,23,15,1,38,No education,34,Owner,Standalone\n1344,495.74,NJ City,W,6,23,19,49,High School,34,Franchise,Complex\n3693,62.24,NJ City,N,8,19,2,81,Graduation,25,Patnership,Standalone\n1018,71.84,NJ City,N,11,16,2,54,High School,33,Patnership,Complex\n2605,13.65,NJ City,N,6,9,4,40,High School,20,Patnership,Standalone\n3849,297.52,NJ City,N,10,17,17,77,High School,28,Patnership,Standalone\n405,467.63,NJ City,N,18,16,15,28,High School,24,Patnership,Standalone\n7553,8.06,Boston,N,12,21,2,81,No education,19,Patnership,Standalone\n7482,13.72,Boston,N,11,21,3,49,Graduation,27,Patnership,Complex\n8207,284.41,NJ City,E,13,15,5,34,No education,27,Owner,Standalone\n3171,351.54,NJ City,E,19,20,21,72,High School,31,Owner,Standalone\n688,402.29,NJ City,W,25,14,8,27,High School,24,Franchise,Complex\n5020,139.89,Boston,W,25,11,9,79,Graduation,32,Franchise,Complex\n4942,289.12,Boston,S,11,11,17,47,High School,18,Patnership,Complex\n2113,463.62,NJ City,N,17,13,13,29,High School,21,Patnership,Standalone\n9169,107.16,NJ City,N,9,15,4,48,High School,23,Patnership,Standalone\n6212,118.9,Boston,W,5,24,1,61,No education,27,Franchise,Complex\n3629,12.77,NJ City,N,20,13,4,31,No education,19,Patnership,Standalone\n4826,358.19,NJ City,S,10,19,15,68,High School,28,Patnership,Complex\n8689,435.57,NJ City,N,24,9,22,80,Graduation,18,Patnership,Standalone\n7493,305.23,Boston,N,14,22,18,67,Graduation,30,Patnership,Standalone\n1971,118,NJ City,E,1,12,17,68,High School,28,Owner,Standalone\n5826,396.97,Boston,S,18,12,14,54,Graduation,25,Patnership,Complex\n9480,414.59,NJ City,W,2,16,7,49,Graduation,20,Franchise,Complex\n5005,204.97,Boston,N,19,8,3,83,High School,32,Patnership,Standalone\n9285,0.86,NJ City,N,14,22,3,30,Graduation,29,Patnership,Standalone\n7747,419.17,Boston,E,10,20,21,54,Graduation,19,Owner,Standalone\n3400,225.99,NJ City,W,21,18,8,28,High School,30,Franchise,Complex\n5129,380.47,Boston,N,14,8,25,42,Graduation,25,Patnership,Standalone\n3387,367.35,NJ City,E,4,21,11,81,High School,24,Owner,Standalone\n998,70.75,NJ City,N,11,9,2,82,No education,32,Patnership,Complex\n7093,194.19,Boston,N,13,14,9,84,Graduation,32,Patnership,Standalone\n7368,136.21,Boston,W,1,16,19,77,Graduation,31,Franchise,Complex\n4230,37.89,NJ City,N,3,8,4,66,Graduation,30,Patnership,Complex\n1626,163.63,NJ City,S,18,22,25,59,High School,33,Patnership,Complex\n1122,314.96,NJ City,S,5,19,14,85,High School,22,Patnership,Complex\n2742,81.05,NJ City,S,8,11,1,61,Graduation,29,Patnership,Complex\n3209,302.48,NJ City,N,23,9,4,66,No education,26,Patnership,Standalone\n1275,298.19,NJ City,E,10,12,6,36,High School,28,Owner,Standalone\n9664,435.61,NJ City,W,2,11,9,63,Graduation,24,Franchise,Complex\n47,94.1,Boston,E,6,16,2,43,No education,31,Owner,Standalone\n202,50.09,NJ City,S,20,24,1,74,High School,32,Patnership,Complex\n9066,143.72,NJ City,S,3,12,25,25,Graduation,31,Patnership,Complex\n6187,263.65,Boston,E,8,21,4,33,High School,33,Owner,Standalone\n4905,325,NJ City,N,21,12,6,49,High School,23,Patnership,Standalone\n4210,316.46,NJ City,S,15,9,13,59,High School,25,Patnership,Complex\n7359,95.93,Boston,E,16,11,3,84,Graduation,26,Owner,Standalone\n8983,402.87,NJ City,E,25,18,11,78,Graduation,18,Owner,Standalone\n7399,257.17,Boston,E,7,20,25,69,Graduation,20,Owner,Standalone\n9821,64.34,NJ City,N,10,18,3,55,No education,35,Patnership,Standalone\n7453,162.93,Boston,N,8,8,7,62,Graduation,26,Patnership,Standalone\n4728,263.83,NJ City,W,13,16,11,42,High School,30,Franchise,Complex\n9274,414.83,NJ City,S,23,14,15,66,Graduation,35,Patnership,Complex\n2369,4.68,NJ City,N,14,22,3,51,No education,18,Patnership,Standalone\n8192,126.48,NJ City,W,16,16,3,36,No education,32,Franchise,Complex\n2625,40.33,NJ City,N,2,18,2,42,Graduation,27,Patnership,Standalone\n6829,477.77,Boston,N,14,22,9,39,Graduation,33,Patnership,Standalone\n8190,199.26,NJ City,S,6,10,13,57,Graduation,33,Patnership,Complex\n7458,410.57,Boston,S,15,11,11,74,Graduation,21,Patnership,Complex\n8263,484.48,NJ City,E,6,16,2,47,High School,19,Owner,Standalone\n6132,276.31,Boston,W,17,24,10,59,Graduation,27,Franchise,Complex\n8214,277.73,NJ City,S,4,11,15,35,Graduation,18,Patnership,Complex\n626,123,NJ City,S,1,23,15,45,High School,27,Patnership,Complex\n2654,472.16,NJ City,S,12,17,16,42,High School,35,Patnership,Complex\n6832,486.14,Boston,W,8,9,10,67,Graduation,33,Franchise,Complex\n1606,388.74,NJ City,S,9,12,13,46,High School,23,Patnership,Complex\n1794,163.27,NJ City,S,13,19,25,25,High School,29,Patnership,Complex\n3097,183.45,NJ City,N,18,23,5,32,High School,35,Patnership,Standalone\n7753,36.91,Boston,N,16,18,2,42,High School,24,Patnership,Standalone\n1197,314.67,NJ City,N,15,14,13,84,High School,23,Patnership,Standalone\n9766,416,NJ City,S,9,23,22,36,Graduation,19,Patnership,Complex\n3898,183.83,NJ City,S,13,22,15,30,High School,22,Patnership,Complex\n4291,23.78,NJ City,N,21,20,2,53,High School,21,Owner,Standalone\n6770,29.81,Boston,N,7,21,4,77,No education,22,Patnership,Complex\n6302,4.99,Boston,N,18,11,1,80,No education,27,Patnership,Complex\n5635,8.26,Boston,N,18,21,2,68,High School,31,Owner,Standalone\n3074,473.93,NJ City,S,11,15,16,57,High School,19,Patnership,Complex\n7400,256.82,Boston,W,2,20,9,62,Graduation,27,Franchise,Complex\n503,31.38,NJ City,N,25,8,4,79,No education,32,Owner,Standalone\n2693,179,NJ City,N,1,13,22,76,High School,20,Patnership,Standalone\n9343,373.47,NJ City,E,2,22,15,58,Graduation,28,Owner,Standalone\n5302,62.78,Boston,N,3,21,2,71,High School,23,Patnership,Complex\n3768,425.43,NJ City,W,23,16,13,31,High School,24,Franchise,Complex\n7942,161.77,Boston,S,13,13,1,56,High School,23,Patnership,Complex\n811,453.14,NJ City,E,23,8,7,38,High School,23,Owner,Standalone\n1187,196.21,NJ City,E,5,22,3,70,No education,22,Owner,Standalone\n901,81.19,NJ City,N,24,17,3,60,High School,27,Patnership,Standalone\n4684,11.69,NJ City,N,1,21,4,78,High School,35,Franchise,Complex\n284,19.65,NJ City,N,9,11,2,68,No education,33,Franchise,Complex\n4536,278,NJ City,W,1,14,20,31,High School,18,Franchise,Complex\n5620,442.51,Boston,W,6,19,5,51,High School,18,Franchise,Complex\n6143,219.34,Boston,E,23,23,2,74,No education,21,Owner,Standalone\n2085,308.46,NJ City,N,25,8,6,81,High School,22,Patnership,Standalone\n1478,125,NJ City,S,2,11,14,26,High School,31,Patnership,Complex\n2273,91.63,NJ City,N,21,23,3,33,No education,18,Patnership,Standalone\n4952,91.91,Boston,W,14,11,2,28,No education,25,Franchise,Complex\n" }, { "path": "Data/Prostate_Cancer.csv", "content": "id,diagnosis_result,radius,texture,perimeter,area,smoothness,compactness,symmetry,fractal_dimension\n1,M,23,12,151,954,0.143,0.278,0.242,0.079\n2,B,9,13,133,1326,0.143,0.079,0.181,0.057\n3,M,21,27,130,1203,0.125,0.16,0.207,0.06\n4,M,14,16,78,386,0.07,0.284,0.26,0.097\n5,M,9,19,135,1297,0.141,0.133,0.181,0.059\n6,B,25,25,83,477,0.128,0.17,0.209,0.076\n7,M,16,26,120,1040,0.095,0.109,0.179,0.057\n8,M,15,18,90,578,0.119,0.165,0.22,0.075\n9,M,19,24,88,520,0.127,0.193,0.235,0.074\n10,M,25,11,84,476,0.119,0.24,0.203,0.082\n11,M,24,21,103,798,0.082,0.067,0.153,0.057\n12,M,17,15,104,781,0.097,0.129,0.184,0.061\n13,B,14,15,132,1123,0.097,0.246,0.24,0.078\n14,M,12,22,104,783,0.084,0.1,0.185,0.053\n15,M,12,13,94,578,0.113,0.229,0.207,0.077\n16,M,22,19,97,659,0.114,0.16,0.23,0.071\n17,M,10,16,95,685,0.099,0.072,0.159,0.059\n18,M,15,14,108,799,0.117,0.202,0.216,0.074\n19,M,20,14,130,1260,0.098,0.103,0.158,0.054\n20,B,17,11,87,566,0.098,0.081,0.189,0.058\n21,B,16,14,86,520,0.108,0.127,0.197,0.068\n22,B,17,24,60,274,0.102,0.065,0.182,0.069\n23,M,20,27,103,704,0.107,0.214,0.252,0.07\n24,M,19,12,137,1404,0.094,0.102,0.177,0.053\n25,M,9,13,110,905,0.112,0.146,0.2,0.063\n26,M,19,27,116,913,0.119,0.228,0.304,0.074\n27,M,10,24,97,645,0.105,0.187,0.225,0.069\n28,M,16,24,122,1094,0.094,0.107,0.17,0.057\n29,M,15,15,102,732,0.108,0.17,0.193,0.065\n30,M,11,16,115,955,0.098,0.116,0.174,0.061\n31,M,11,22,125,1088,0.106,0.189,0.218,0.062\n32,M,23,26,78,441,0.111,0.152,0.23,0.078\n33,M,20,18,113,899,0.12,0.15,0.225,0.064\n34,M,11,21,128,1162,0.094,0.172,0.185,0.063\n35,M,16,23,107,807,0.104,0.156,0.2,0.065\n36,M,10,13,110,870,0.096,0.134,0.19,0.057\n37,M,18,12,94,633,0.098,0.11,0.189,0.061\n38,B,21,11,83,524,0.09,0.038,0.147,0.059\n39,M,11,15,96,699,0.094,0.051,0.157,0.055\n40,M,10,14,88,559,0.102,0.126,0.172,0.064\n41,M,24,16,86,563,0.082,0.06,0.178,0.056\n42,M,19,27,72,371,0.123,0.122,0.19,0.069\n43,M,11,11,128,1104,0.091,0.219,0.231,0.063\n44,M,15,21,87,545,0.104,0.144,0.197,0.068\n45,M,10,15,85,532,0.097,0.105,0.175,0.062\n46,M,18,11,124,1076,0.11,0.169,0.191,0.06\n47,B,22,12,52,202,0.086,0.059,0.177,0.065\n48,M,20,14,86,535,0.116,0.123,0.213,0.068\n49,B,20,21,78,449,0.103,0.091,0.168,0.06\n50,B,25,11,87,561,0.088,0.077,0.181,0.057\n51,B,19,25,75,428,0.086,0.05,0.15,0.059\n52,B,19,22,87,572,0.077,0.061,0.135,0.06\n53,B,25,15,76,438,0.083,0.048,0.187,0.061\n54,M,14,26,120,1033,0.115,0.149,0.209,0.063\n55,M,18,25,97,713,0.091,0.071,0.162,0.057\n56,B,18,13,73,409,0.095,0.055,0.192,0.059\n57,M,10,19,126,1152,0.105,0.127,0.192,0.06\n58,M,17,20,96,657,0.114,0.137,0.203,0.068\n59,B,22,15,83,527,0.081,0.038,0.182,0.055\n60,B,23,26,54,225,0.098,0.053,0.168,0.072\n61,B,15,18,65,312,0.113,0.081,0.274,0.07\n62,B,25,15,55,222,0.124,0.09,0.183,0.068\n63,M,12,22,96,646,0.105,0.201,0.195,0.073\n64,B,24,17,59,261,0.077,0.088,0.234,0.07\n65,M,16,19,83,499,0.112,0.126,0.191,0.066\n66,M,11,21,97,668,0.117,0.148,0.195,0.067\n67,B,12,13,60,269,0.104,0.078,0.172,0.069\n68,B,18,12,72,394,0.081,0.047,0.152,0.057\n69,B,16,17,59,251,0.107,0.141,0.211,0.08\n70,B,17,21,81,503,0.098,0.052,0.159,0.057\n71,M,21,18,124,1130,0.09,0.103,0.158,0.055\n72,B,9,26,59,244,0.098,0.153,0.19,0.09\n73,M,21,12,114,929,0.107,0.183,0.193,0.065\n74,M,22,25,90,584,0.101,0.128,0.166,0.066\n75,B,18,13,79,471,0.092,0.068,0.172,0.059\n76,M,21,18,104,818,0.092,0.084,0.18,0.054\n77,B,10,17,88,559,0.129,0.105,0.24,0.066\n78,M,11,21,120,1006,0.107,0.215,0.215,0.067\n79,M,16,18,144,1245,0.129,0.345,0.291,0.081\n80,B,22,16,83,506,0.099,0.095,0.172,0.06\n81,B,10,18,74,402,0.11,0.094,0.184,0.07\n82,B,17,21,86,520,0.108,0.154,0.194,0.069\n83,M,10,15,172,1878,0.106,0.267,0.183,0.068\n84,M,20,14,129,1132,0.122,0.179,0.163,0.072\n85,B,25,21,77,443,0.097,0.072,0.208,0.06\n86,M,14,13,121,1075,0.099,0.105,0.213,0.06\n87,M,19,26,94,648,0.094,0.099,0.208,0.056\n88,M,19,11,122,1076,0.09,0.121,0.195,0.056\n89,B,11,11,80,466,0.088,0.094,0.193,0.064\n90,B,12,23,96,652,0.113,0.134,0.212,0.063\n91,B,23,27,95,663,0.09,0.086,0.169,0.059\n92,M,10,12,100,728,0.092,0.104,0.172,0.061\n93,B,14,14,85,552,0.074,0.051,0.139,0.053\n94,B,10,17,87,555,0.102,0.082,0.164,0.057\n95,M,22,26,100,706,0.104,0.155,0.186,0.063\n96,M,23,16,132,1264,0.091,0.131,0.21,0.056\n97,B,22,14,78,451,0.105,0.071,0.19,0.066\n98,B,19,27,62,295,0.102,0.053,0.135,0.069\n99,B,21,24,74,413,0.09,0.075,0.162,0.066\n100,M,16,27,94,643,0.098,0.114,0.188,0.064\n" }, { "path": "Data/Rdatasets.R", "content": "# Rdata sets\n\nhttps://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html\n\ndata(CO2)\nhead(CO2)\n" }, { "path": "Data/Sales.csv", "content": "Month-Year,Sales\n1/1/2003,141\n2/1/2003,157\n3/1/2003,185\n4/1/2003,199\n5/1/2003,203\n6/1/2003,189\n7/1/2003,207\n8/1/2003,207\n9/1/2003,171\n10/1/2003,150\n11/1/2003,138\n12/1/2003,165\n1/1/2004,145\n2/1/2004,168\n3/1/2004,197\n4/1/2004,208\n5/1/2004,210\n6/1/2004,209\n7/1/2004,238\n8/1/2004,238\n9/1/2004,199\n10/1/2004,168\n11/1/2004,152\n12/1/2004,196\n1/1/2005,183\n2/1/2005,200\n3/1/2005,249\n4/1/2005,251\n5/1/2005,289\n6/1/2005,249\n7/1/2005,279\n8/1/2005,279\n9/1/2005,232\n10/1/2005,204\n11/1/2005,194\n12/1/2005,232\n1/1/2006,215\n2/1/2006,239\n3/1/2006,270\n4/1/2006,279\n5/1/2006,307\n6/1/2006,305\n7/1/2006,322\n8/1/2006,339\n9/1/2006,263\n10/1/2006,241\n11/1/2006,229\n12/1/2006,272\n1/1/2007,247\n2/1/2007,261\n3/1/2007,330\n4/1/2007,362\n5/1/2007,385\n6/1/2007,340\n7/1/2007,370\n8/1/2007,381\n9/1/2007,299\n10/1/2007,266\n11/1/2007,239\n12/1/2007,281\n1/1/2008,257\n2/1/2008,250\n3/1/2008,329\n4/1/2008,350\n5/1/2008,393\n6/1/2008,370\n7/1/2008,423\n8/1/2008,410\n9/1/2008,326\n10/1/2008,289\n11/1/2008,270\n12/1/2008,321\n1/1/2009,305\n2/1/2009,310\n3/1/2009,374\n4/1/2009,414\n5/1/2009,454\n6/1/2009,441\n7/1/2009,510\n8/1/2009,486\n9/1/2009,393\n10/1/2009,345\n11/1/2009,315\n12/1/2009,389\n1/1/2010,358\n2/1/2010,368\n3/1/2010,444\n4/1/2010,482\n5/1/2010,534\n6/1/2010,524\n7/1/2010,578\n8/1/2010,567\n9/1/2010,447\n10/1/2010,386\n11/1/2010,360\n12/1/2010,428\n1/1/2011,397\n2/1/2011,400\n3/1/2011,498\n4/1/2011,536\n5/1/2011,596\n6/1/2011,591\n7/1/2011,651\n8/1/2011,654\n9/1/2011,509\n10/1/2011,437\n11/1/2011,406\n12/1/2011,470\n1/1/2012,428\n2/1/2012,423\n3/1/2012,507\n4/1/2012,536\n5/1/2012,610\n6/1/2012,609\n7/1/2012,687\n8/1/2012,707\n9/1/2012,509\n10/1/2012,452\n11/1/2012,412\n12/1/2012,472\n1/1/2013,454\n2/1/2013,455\n3/1/2013,568\n4/1/2013,610\n5/1/2013,706\n6/1/2013,661\n7/1/2013,767\n8/1/2013,783\n9/1/2013,583\n10/1/2013,513\n11/1/2013,481\n12/1/2013,567\n1/1/2014,525\n2/1/2014,520\n3/1/2014,587\n4/1/2014,710\n5/1/2014,793\n6/1/2014,749\n7/1/2014,871\n8/1/2014,848\n9/1/2014,640\n10/1/2014,581\n11/1/2014,519\n12/1/2014,605\n" 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Dependents,Urban_residence,Social,Outstation_Travel_last_year\n9400,15,1,1,4,0,1,12,1,6,1,1,1\n8418,15,1.7,1,3,0,1,12,1,4,1,1,4\n10559,15,1.8,1,2,0,1,12,1,3,1,1,4\n9924,15,1.8,1,2,0,1,15,1,6,1,1,6\n9823,15,2.1,1,5,0,1,13,1,3,1,1,1\n8995,15,2.1,1,1,0,1,12,1,5,1,1,3\n10472,15,2.3,1,4,0,1,9,1,7,1,1,7\n2480,15,2.7,1,4,0,1,12,0,5,1,1,2\n6744,15,2.8,1,1,0,1,9,1,1,1,1,3\n9571,15,3.8,1,2,0,1,10,1,2,1,1,4\n1422,15,3.8,1,3,0,1,14,0,8,1,1,0\n7839,15,4,1,4,0,1,9,1,6,1,1,1\n9375,15,4.9,1,2,0,1,14,1,4,1,1,1\n1457,15,4.9,1,2,0,1,12,0,6,1,1,2\n1351,15,5,1,1,0,1,10,0,3,1,1,4\n1454,15,5.1,1,2,0,1,12,0,3,1,1,0\n9624,15,5.5,1,3,0,1,12,1,3,1,1,2\n8314,15,5.6,1,4,0,1,12,1,5,1,1,0\n6074,15,5.9,1,3,0,1,12,1,8,1,1,5\n6750,15,5.9,1,5,0,1,15,1,1,1,1,5\n10808,15,6.2,1,4,0,1,14,1,3,1,1,3\n4600,15,6.3,1,1,0,1,15,0,8,1,1,3\n4599,15,6.4,1,5,0,1,10,0,7,1,1,1\n4252,15,6.5,1,3,0,1,10,0,2,1,1,6\n8096,15,6.5,1,4,0,1,11,1,5,1,1,6\n1834,15,6.6,1,4,0,1,10,0,5,1,1,0\n9393,15,6.8,1,5,0,1,14,1,5,1,1,1\n8120,15,8.2,1,3,0,1,11,1,7,1,1,0\n7414,15,8.7,1,3,0,1,12,1,7,1,1,7\n8894,15,8.7,1,3,0,1,11,1,7,1,1,6\n64,15,8.9,1,2,0,1,15,0,1,1,1,5\n1981,15,9.1,1,2,0,1,11,0,2,1,1,1\n1892,15,9.3,1,2,0,1,12,0,8,1,1,5\n295,15,9.4,1,4,0,1,11,0,4,1,1,1\n1402,15,9.8,1,4,0,1,12,0,3,1,1,1\n490,15,10.1,1,4,0,1,12,0,7,1,1,10\n4071,15,10.3,1,5,0,1,14,0,6,1,1,1\n9132,15,10.7,1,2,0,1,13,1,6,1,1,7\n8642,15,11.5,1,4,0,1,15,1,4,1,1,7\n2113,15,11.6,1,2,0,1,11,0,8,1,1,1\n10273,15,11.9,1,3,0,1,12,1,3,1,1,1\n3134,15,11.9,1,5,0,1,12,0,2,1,1,3\n5000,15,12.2,1,4,0,1,11,0,3,1,1,3\n3961,15,12.2,1,2,0,1,10,0,6,1,1,3\n3621,15,12.6,1,3,0,1,14,0,6,1,1,0\n6450,15,12.7,1,1,0,1,12,1,2,1,1,8\n691,15,12.9,1,4,0,1,11,0,5,1,1,3\n4838,15,12.9,1,5,0,1,12,0,3,1,1,4\n3850,15,13.4,1,3,0,1,14,0,2,1,1,1\n4818,15,14,1,4,0,1,14,0,6,1,1,9\n1272,15,14.5,1,4,0,1,12,0,3,1,1,2\n6530,15,14.6,1,3,0,1,9,1,6,1,1,10\n4512,15,15.1,1,3,0,1,13,0,6,1,1,4\n3595,15,15.1,1,2,0,1,11,0,1,1,1,0\n5026,15,15.9,1,1,0,1,10,0,3,1,1,7\n4539,15,16.1,1,1,0,1,13,0,5,1,1,3\n7422,15,16.2,1,4,0,1,10,1,8,1,1,10\n6960,15,16.4,1,1,0,1,9,1,1,1,1,9\n1677,15,16.7,1,1,0,1,13,0,2,1,1,0\n1411,15,16.8,1,1,0,1,12,0,1,1,1,4\n10816,15,16.8,1,3,0,1,14,1,6,1,1,8\n3090,15,16.9,1,5,0,1,10,0,8,1,1,6\n642,15,17.5,1,3,0,1,10,0,2,1,1,10\n8174,15,17.7,1,5,0,1,9,1,2,1,1,0\n9297,15,18,1,2,0,1,12,1,7,1,1,1\n3548,15,18.3,1,5,0,1,15,0,1,1,1,3\n5848,15,19,1,3,0,1,13,1,4,1,1,1\n3370,15,19.1,1,2,0,1,12,0,1,1,1,7\n7932,15,19.4,1,1,0,1,11,1,3,1,1,8\n9477,15,19.6,1,5,0,1,10,1,2,1,1,0\n2332,15,20,1,5,0,1,13,0,7,1,1,2\n10404,15,21.1,1,3,0,1,13,1,1,1,1,7\n7979,15,21.3,1,5,0,1,9,1,2,1,1,3\n10624,15,21.5,1,4,0,1,9,1,7,1,1,7\n3254,15,22,1,3,0,1,14,0,6,1,1,1\n4632,15,22.1,1,5,0,1,12,0,2,1,1,1\n8384,15,22.5,1,3,0,1,12,1,4,1,1,9\n1340,15,22.9,1,3,0,1,13,0,7,1,1,3\n2684,15,23.4,1,1,0,1,12,0,1,1,1,0\n3111,15,23.6,1,3,0,1,15,0,3,1,1,0\n4425,15,23.8,1,2,0,1,14,0,6,1,1,2\n3044,15,24.1,1,3,0,1,10,0,5,1,1,2\n634,15,24.1,1,4,0,1,9,0,6,1,1,2\n10275,15,24.2,1,4,0,1,13,1,3,1,1,2\n1067,15,24.4,1,5,0,1,15,0,7,1,1,1\n7983,15,24.4,1,3,0,1,10,1,1,1,1,4\n4337,15,24.6,1,1,0,1,12,0,4,1,1,1\n3977,15,25.3,1,1,0,1,12,0,8,1,1,4\n10846,15,26.4,1,4,0,1,13,1,1,1,1,8\n4335,15,26.5,1,2,0,1,10,0,8,1,1,3\n3832,15,26.7,1,3,0,1,13,0,3,1,1,8\n10798,15,26.9,1,5,0,1,13,1,5,1,1,9\n3741,15,27.1,1,3,0,1,12,0,1,1,1,2\n7886,15,27.1,1,5,0,1,9,1,2,1,1,8\n10857,15,27.6,1,2,0,1,12,1,4,1,1,1\n7995,15,27.6,1,1,0,1,12,1,1,1,1,3\n1936,15,28.8,1,2,0,1,9,0,4,1,1,7\n2786,15,28.8,1,5,0,1,9,0,8,1,1,0\n8650,15,29.1,1,1,0,1,10,1,2,1,1,5\n10263,15,29.2,1,3,0,1,12,1,5,1,1,4\n2813,15,29.2,1,5,0,1,12,0,4,1,1,4\n7380,15,29.4,1,5,0,1,12,1,6,1,1,6\n1202,15,29.5,1,4,0,1,15,0,1,1,1,2\n10544,15,29.6,1,5,0,1,11,1,3,1,1,5\n3778,15,29.7,1,5,0,1,12,0,3,1,1,3\n9618,15,29.8,1,3,0,1,14,1,4,1,1,9\n1828,15,29.9,1,2,0,1,9,0,1,1,1,5\n8906,15,29.9,1,5,0,1,13,1,5,1,1,7\n3779,16,0.2,1,1,0,1,12,0,3,1,1,0\n278,16,0.6,1,1,0,1,13,0,3,1,1,4\n3959,16,0.8,1,1,0,1,12,0,8,1,1,3\n2930,16,0.8,1,5,0,1,12,0,5,1,1,1\n7715,16,0.8,1,5,0,1,14,1,3,1,1,0\n3252,16,1,1,4,0,1,15,0,4,1,1,3\n5869,16,1.2,1,2,0,1,9,1,4,1,1,1\n4666,16,1.6,1,3,0,1,12,0,4,1,1,3\n7625,16,1.7,1,3,0,1,15,1,3,1,1,4\n1094,16,1.8,1,5,0,1,15,0,6,1,1,1\n1502,16,1.9,1,4,0,1,10,0,1,1,1,6\n2398,16,1.9,1,5,0,1,10,0,1,1,1,4\n8129,16,2.1,1,2,0,1,9,1,4,1,1,3\n8972,16,2.2,1,2,0,1,15,1,2,1,1,9\n4776,16,2.3,1,5,0,1,11,0,3,1,1,0\n6652,16,2.3,1,4,0,1,13,1,8,1,1,2\n10744,16,2.3,1,3,0,1,12,1,7,1,1,8\n7234,16,2.4,1,4,0,1,12,1,8,1,1,5\n9596,16,2.7,1,1,0,1,14,1,2,1,1,6\n1756,16,2.8,1,5,0,1,12,0,8,1,1,1\n9960,16,2.8,1,4,0,1,12,1,8,1,1,1\n2310,16,3.1,1,3,0,1,9,0,7,1,1,2\n8782,16,3.3,1,4,0,1,12,1,6,1,1,7\n10022,16,3.8,1,1,0,1,15,1,1,1,1,6\n2564,16,4.2,1,3,0,1,9,0,5,1,1,6\n5834,16,4.3,1,5,0,1,10,1,8,1,1,2\n8488,16,4.7,1,2,0,1,10,1,5,1,1,10\n8813,16,5.4,1,1,0,1,9,1,4,1,1,3\n4394,16,6,1,2,0,1,13,0,5,1,1,5\n1966,16,6.3,1,5,0,1,15,0,2,1,1,0\n7026,16,6.6,1,3,0,1,10,1,4,1,1,10\n4362,16,6.7,1,1,0,1,12,0,5,1,1,4\n8297,16,6.9,1,4,0,1,14,1,2,1,1,0\n2521,16,7.1,1,2,0,1,10,0,5,1,1,3\n9002,16,7.3,1,1,0,1,10,1,4,1,1,9\n10485,16,7.4,1,1,0,1,15,1,3,1,1,1\n9948,16,7.4,1,1,0,1,12,1,3,1,1,2\n4992,16,7.5,1,5,0,1,12,0,6,1,1,1\n6664,16,7.8,1,3,0,1,15,1,2,1,1,10\n392,16,7.9,1,4,0,1,12,0,5,1,1,7\n5920,16,8.1,1,5,0,1,12,1,1,1,1,10\n10442,16,8.3,1,2,0,1,15,1,3,1,1,2\n8246,16,8.4,1,3,0,1,12,1,3,1,1,0\n7037,16,8.8,1,5,0,1,9,1,8,1,1,2\n838,16,9.3,1,4,0,1,13,0,3,1,1,4\n8803,16,9.8,1,5,0,1,15,1,8,1,1,4\n4654,16,10,1,3,0,1,13,0,7,1,1,4\n5123,16,10.3,1,1,0,1,12,0,1,1,1,3\n4885,16,10.4,1,1,0,1,11,0,7,1,1,1\n3736,16,10.7,1,1,0,1,14,0,2,1,1,1\n10118,16,11.4,1,3,0,1,13,1,7,1,1,1\n8254,16,11.8,1,4,0,1,12,1,2,1,1,3\n5790,16,11.9,1,5,0,1,11,1,8,1,1,8\n2356,16,12.1,1,5,0,1,12,0,5,1,1,5\n1450,16,12.2,1,2,0,1,14,0,7,1,1,8\n8216,16,12.2,1,5,0,1,10,1,5,1,1,5\n1241,16,12.7,1,4,0,1,11,0,6,1,1,2\n6774,16,13.2,1,2,0,1,13,1,4,1,1,9\n5964,16,13.6,1,5,0,1,12,1,8,1,1,2\n8068,16,13.6,1,2,0,1,15,1,4,1,1,5\n3360,16,14,1,2,0,1,10,0,1,1,1,5\n4379,16,14,1,3,0,1,12,0,5,1,1,1\n6964,16,15.2,1,2,0,1,10,1,6,1,1,0\n8048,16,15.3,1,5,0,1,13,1,5,1,1,6\n10326,16,15.7,1,2,0,1,12,1,6,1,1,1\n3846,16,16,1,3,0,1,15,0,2,1,1,2\n8808,16,16.3,1,5,0,1,12,1,4,1,1,5\n8102,16,16.5,1,3,0,1,14,1,6,1,1,8\n2134,16,17,1,1,0,1,13,0,7,1,1,3\n4963,16,17.2,1,2,0,1,12,0,2,1,1,0\n2196,16,18.2,1,5,0,1,12,0,4,1,1,7\n9381,16,18.2,1,4,0,1,10,1,3,1,1,0\n6577,16,18.9,1,1,0,1,14,1,5,1,1,4\n4916,16,19,1,3,0,1,12,0,6,1,1,8\n9092,16,19.2,1,3,0,1,15,1,8,1,1,9\n5078,16,19.4,1,2,0,1,14,0,3,1,1,1\n8829,16,19.5,1,3,0,1,14,1,6,1,1,3\n3812,16,19.7,1,5,0,1,12,0,1,1,1,0\n2756,16,19.8,1,4,0,1,10,0,1,1,1,2\n5068,16,19.8,1,5,0,1,13,0,1,1,1,1\n6483,16,20.1,1,5,0,1,15,1,4,1,1,4\n2576,16,20.3,1,4,0,1,11,0,2,1,1,2\n1925,16,20.5,1,5,0,1,12,0,3,1,1,2\n1020,16,20.7,1,5,0,1,11,0,8,1,1,3\n7535,16,21.1,1,1,0,1,10,1,7,1,1,2\n2699,16,21.6,1,3,0,1,12,0,3,1,1,1\n8428,16,21.6,1,3,0,1,11,1,1,1,1,9\n10730,16,21.7,1,5,0,1,15,1,8,1,1,2\n9076,16,22.3,1,1,0,1,11,1,6,1,1,7\n6331,16,22.5,1,5,0,1,11,1,8,1,1,2\n5161,16,22.9,1,3,0,1,14,0,4,1,1,4\n7758,16,23.1,1,3,0,1,9,1,8,1,1,9\n1657,16,23.3,1,4,0,1,14,0,6,1,1,1\n8713,16,24.1,1,5,0,1,15,1,4,1,1,4\n2728,16,24.1,1,2,0,1,12,0,4,1,1,7\n3877,16,24.5,1,3,0,1,9,0,4,1,1,3\n394,16,24.5,1,3,0,1,9,0,2,1,1,0\n1484,16,24.7,1,2,0,1,14,0,7,1,1,3\n5917,16,24.9,1,1,0,1,10,1,8,1,1,2\n993,16,25.2,1,2,0,1,12,0,7,1,1,2\n7174,16,25.2,1,3,0,1,9,1,1,1,1,0\n3057,16,25.2,1,2,0,1,9,0,2,1,1,2\n2696,16,25.2,1,3,0,1,12,0,7,1,1,2\n7188,16,25.4,1,5,0,1,15,1,3,1,1,4\n3923,16,25.5,1,4,0,1,14,0,7,1,1,2\n452,16,25.6,1,4,0,1,14,0,7,1,1,9\n6394,16,26,1,2,0,1,13,1,5,1,1,7\n8049,16,26.1,1,1,0,1,13,1,2,1,1,4\n3428,16,26.2,1,1,0,1,12,0,7,1,1,10\n828,16,26.2,1,3,0,1,13,0,7,1,1,1\n4353,16,26.3,1,1,0,1,10,0,2,1,1,4\n5168,16,26.7,1,3,0,1,10,0,4,1,1,1\n972,16,26.9,1,2,0,1,15,0,8,1,1,8\n3120,16,27.4,1,4,0,1,9,0,8,1,1,4\n8470,16,27.4,1,1,0,1,13,1,6,1,1,4\n3522,16,27.5,1,3,0,1,12,0,1,1,1,8\n7789,16,27.7,1,2,0,1,15,1,5,1,1,4\n3584,16,27.8,1,3,0,1,9,0,6,1,1,9\n2000,16,27.9,1,1,0,1,13,0,3,1,1,1\n3065,16,28.1,1,5,0,1,12,0,2,1,1,4\n2898,16,28.5,1,4,0,1,15,0,7,1,1,3\n3602,16,28.6,1,3,0,1,12,0,5,1,1,8\n3996,16,29,1,1,0,1,13,0,6,1,1,8\n7572,16,29.3,1,5,0,1,12,1,1,1,1,6\n3413,17,0.3,1,2,0,1,10,0,2,1,1,2\n6359,17,0.4,1,2,0,1,11,1,1,1,1,0\n7160,17,0.8,1,3,0,1,12,1,1,1,1,8\n7388,17,1.3,1,5,0,1,12,1,2,1,1,9\n4810,17,1.5,1,2,0,1,9,0,5,1,1,8\n7619,17,2.9,1,1,0,1,9,1,2,1,1,4\n8550,17,3.1,1,3,0,1,9,1,6,1,1,10\n9848,17,3.1,1,1,0,1,13,1,2,1,1,3\n10558,17,3.4,1,2,0,1,13,1,2,1,1,4\n3461,17,3.9,1,2,0,1,11,0,5,1,1,0\n7309,17,4,1,4,0,1,9,1,4,1,1,3\n10836,17,4.1,1,4,0,1,12,1,3,1,1,1\n4938,17,4.3,1,2,0,1,10,0,5,1,1,5\n4791,17,5.1,1,2,0,1,11,0,4,1,1,2\n4442,17,5.3,1,5,0,1,12,0,7,1,1,8\n737,17,5.3,1,1,0,1,15,0,6,1,1,1\n9900,17,6,1,3,0,1,11,1,7,1,1,3\n4168,17,6.3,1,2,0,1,12,0,4,1,1,1\n5908,17,7.1,1,3,0,1,12,1,1,1,1,1\n9862,17,7.2,1,2,0,1,10,1,7,1,1,4\n3394,17,7.9,1,2,0,1,11,0,5,1,1,4\n2581,17,8,1,5,0,1,13,0,5,1,1,3\n1220,17,8,1,2,0,1,11,0,6,1,1,2\n4464,17,8,1,4,0,1,15,0,4,1,1,8\n1022,17,8.8,1,2,0,1,13,0,6,1,1,4\n4166,17,9.4,1,2,0,1,10,0,7,1,1,2\n8564,17,9.4,1,1,0,1,12,1,5,1,1,7\n3754,17,9.7,1,1,0,1,14,0,2,1,1,5\n9295,17,9.8,1,2,0,1,12,1,3,1,1,2\n2673,17,10.1,1,1,0,1,12,0,7,1,1,3\n3814,17,10.2,1,1,0,1,13,0,2,1,1,6\n9190,17,10.2,1,1,0,1,12,1,7,1,1,9\n4952,17,10.4,1,2,0,1,10,0,7,1,1,5\n6669,17,10.6,1,5,0,1,12,1,7,1,1,1\n3300,17,10.6,1,1,0,1,10,0,1,1,1,7\n8518,17,10.9,1,4,0,1,12,1,8,1,1,1\n5878,17,10.9,1,3,0,1,11,1,6,1,1,7\n5006,17,11.5,1,5,0,1,14,0,8,1,1,1\n3082,17,11.8,1,3,0,1,12,0,8,1,1,0\n4188,17,12.3,1,1,0,1,12,0,8,1,1,9\n1324,17,12.5,1,1,0,1,12,0,1,1,1,7\n3242,17,12.6,1,5,0,1,14,0,3,1,1,9\n5736,17,12.8,1,4,0,1,14,1,4,1,1,9\n2905,17,12.8,1,2,0,1,14,0,5,1,1,3\n103,17,12.9,1,1,0,1,11,0,2,1,1,0\n2296,17,14.2,1,1,0,1,13,0,2,1,1,1\n4470,17,15.3,1,5,0,1,10,0,6,1,1,2\n536,17,15.9,1,3,0,1,9,0,7,1,1,9\n8790,17,16.1,1,5,0,1,9,1,2,1,1,5\n10637,17,16.1,1,5,0,1,10,1,3,1,1,4\n10055,17,16.2,1,2,0,1,9,1,1,1,1,1\n2698,17,17,1,2,0,1,14,0,7,1,1,3\n1119,17,17.3,1,2,0,1,14,0,7,1,1,2\n4752,17,17.4,1,5,0,1,14,0,4,1,1,6\n9018,17,17.5,1,3,0,1,12,1,2,1,1,8\n10178,17,18.1,1,4,0,1,11,1,5,1,1,5\n128,17,18.4,1,1,0,1,11,0,7,1,1,10\n4767,17,19,1,5,0,1,11,0,6,1,1,4\n8036,17,19.4,1,1,0,1,15,1,1,1,1,0\n10338,17,19.4,1,4,0,1,12,1,4,1,1,8\n10074,17,19.6,1,5,0,1,10,1,5,1,1,2\n6738,17,19.8,1,5,0,1,9,1,1,1,1,10\n4367,17,20.5,1,3,0,1,13,0,2,1,1,4\n6160,17,20.5,1,3,0,1,13,1,7,1,1,7\n6258,17,21.8,1,4,0,1,12,1,2,1,1,3\n5130,17,22.1,1,5,0,1,9,0,7,1,1,0\n2420,17,22.5,1,1,0,1,9,0,7,1,1,5\n8358,17,22.7,1,3,0,1,11,1,1,1,1,1\n5108,17,23,1,5,0,1,12,0,2,1,1,10\n108,17,23.1,1,2,0,1,11,0,1,1,1,1\n2231,17,23.1,1,1,0,1,11,0,3,1,1,2\n6491,17,23.3,1,2,0,1,14,1,6,1,1,0\n3219,17,24.4,1,5,0,1,12,0,2,1,1,0\n650,17,24.5,1,4,0,1,12,0,1,1,1,6\n9237,17,24.9,1,5,0,1,14,1,3,1,1,4\n9612,17,25.1,1,2,0,1,11,1,7,1,1,7\n4295,17,25.4,1,1,0,1,14,0,3,1,1,4\n5231,17,25.5,1,3,0,1,9,0,6,1,1,3\n2111,17,26.2,1,4,0,1,9,0,7,1,1,4\n5571,17,26.5,1,5,0,1,9,1,6,1,1,4\n10443,17,27.3,1,1,0,1,10,1,5,1,1,0\n4035,17,27.4,1,1,0,1,13,0,5,1,1,3\n900,17,27.6,1,2,0,1,14,0,2,1,1,0\n1368,17,27.7,1,5,0,1,9,0,4,1,1,8\n3104,17,27.7,1,4,0,1,9,0,4,1,1,4\n5538,17,27.8,1,3,0,1,11,1,3,1,1,10\n6043,17,28.3,1,5,0,1,9,1,8,1,1,1\n2523,17,28.3,1,4,0,1,13,0,7,1,1,4\n3358,17,28.4,1,4,0,1,9,0,1,1,1,7\n8574,17,28.4,1,5,0,1,9,1,2,1,1,4\n6634,17,28.7,1,2,0,1,9,1,4,1,1,0\n2580,17,29.1,1,1,0,1,10,0,7,1,1,0\n1399,17,29.2,1,2,0,1,9,0,5,1,1,1\n7344,17,29.4,1,5,0,1,12,1,5,1,1,3\n7468,17,29.7,1,4,0,1,11,1,8,1,1,1\n2634,18,0.2,1,1,0,1,12,0,7,1,1,6\n3549,18,1.1,1,2,0,1,15,0,2,1,1,0\n124,18,1.2,1,5,0,1,15,0,6,1,1,8\n5866,18,1.3,1,3,0,1,14,1,7,1,1,8\n8708,18,1.4,1,3,0,1,11,1,3,1,1,8\n9247,18,1.7,1,3,0,1,13,1,7,1,1,4\n7741,18,1.9,1,2,0,1,15,1,2,1,1,0\n9207,18,2.5,1,1,0,1,12,1,2,1,1,1\n6977,18,2.6,1,4,0,1,15,1,8,1,1,1\n9084,18,3.2,1,3,0,1,12,1,5,1,1,0\n10513,18,3.4,1,2,0,1,14,1,8,1,1,2\n7452,18,3.5,1,4,0,1,10,1,7,1,1,4\n8037,18,3.9,1,2,0,1,9,1,3,1,1,3\n3894,18,3.9,1,1,0,1,13,0,3,1,1,0\n1976,18,4,1,4,0,1,12,0,6,1,1,4\n3144,18,4.3,1,1,0,1,14,0,6,1,1,6\n9887,18,4.6,1,4,0,1,9,1,4,1,1,4\n1335,18,4.7,1,4,0,1,13,0,7,1,1,1\n10494,18,4.8,1,3,0,1,11,1,5,1,1,0\n8099,18,4.9,1,1,0,1,10,1,3,1,1,1\n6171,18,5,1,5,0,1,13,1,8,1,1,2\n2808,18,5,1,5,0,1,12,0,6,1,1,2\n3604,18,5.1,1,4,0,1,9,0,8,1,1,2\n5671,18,5.2,1,5,0,1,12,1,5,1,1,0\n8346,18,5.7,1,2,0,1,10,1,6,1,1,5\n6913,18,6.2,1,3,0,1,12,1,1,1,1,2\n8085,18,6.2,1,1,0,1,9,1,4,1,1,0\n1236,18,6.3,1,1,0,1,9,0,7,1,1,0\n4730,18,6.4,1,3,0,1,14,0,6,1,1,2\n8964,18,6.6,1,4,0,1,10,1,7,1,1,9\n1919,18,6.9,1,3,0,1,13,0,4,1,1,4\n7838,18,7.3,1,2,0,1,12,1,6,1,1,1\n8878,18,8,1,4,0,1,9,1,7,1,1,8\n1590,18,8.1,1,4,0,1,12,0,8,1,1,10\n5348,18,8.4,1,3,0,1,14,0,6,1,1,2\n9233,18,8.8,1,3,0,1,12,1,3,1,1,2\n4490,18,9.7,1,2,0,1,10,0,2,1,1,10\n7454,18,10,1,1,0,1,10,1,1,1,1,3\n7763,18,10.2,1,1,0,1,12,1,1,1,1,2\n5140,18,10.3,1,4,0,1,15,0,6,1,1,0\n10477,18,10.5,1,3,0,1,14,1,1,1,1,1\n5686,18,11.2,1,4,0,1,12,1,6,1,1,8\n7946,18,11.2,1,2,0,1,13,1,5,1,1,3\n486,18,11.6,1,5,0,1,12,0,5,1,1,1\n7639,18,11.8,1,3,0,1,11,1,5,1,1,1\n2461,18,12,1,2,0,1,12,0,4,1,1,3\n646,18,12,1,5,0,1,15,0,1,1,1,2\n5216,18,12.2,1,4,0,1,14,0,7,1,1,8\n5874,18,12.4,1,1,0,1,11,1,8,1,1,0\n976,18,12.5,1,3,0,1,10,0,6,1,1,2\n8705,18,12.7,1,2,0,1,11,1,7,1,1,3\n6787,18,12.8,1,1,0,1,9,1,7,1,1,0\n10662,18,12.9,1,3,0,1,11,1,7,1,1,9\n9726,18,13.2,1,5,0,1,15,1,7,1,1,0\n527,18,13.3,1,5,0,1,10,0,4,1,1,1\n8976,18,13.6,1,5,0,1,11,1,3,1,1,1\n872,18,14,1,5,0,1,15,0,8,1,1,2\n6970,18,14.1,1,2,0,1,14,1,6,1,1,2\n2214,18,14.3,1,3,0,1,11,0,8,1,1,6\n2248,18,14.6,1,2,0,1,9,0,6,1,1,2\n8637,18,14.7,1,5,0,1,9,1,4,1,1,3\n4034,18,15.1,1,1,0,1,15,0,6,1,1,0\n6765,18,15.5,1,5,0,1,12,1,6,1,1,4\n864,18,16,1,5,0,1,12,0,3,1,1,8\n2110,18,16.4,1,3,0,1,13,0,3,1,1,1\n9073,18,16.5,1,3,0,1,15,1,3,1,1,2\n8656,18,16.6,1,3,0,1,15,1,2,1,1,8\n8486,18,17,1,2,0,1,15,1,4,1,1,8\n10859,18,17.4,1,5,0,1,11,1,1,1,1,2\n2304,18,17.7,1,4,0,1,10,0,7,1,1,8\n3490,18,17.9,1,1,0,1,14,0,5,1,1,4\n10703,18,18.1,1,5,0,1,10,1,5,1,1,2\n10452,18,18.2,1,3,0,1,12,1,7,1,1,4\n4494,18,18.3,1,3,0,1,9,0,2,1,1,6\n118,18,18.7,1,4,0,1,9,0,8,1,1,6\n977,18,19.5,1,2,0,1,11,0,2,1,1,1\n8882,18,19.5,1,4,0,1,9,1,5,1,1,7\n2940,18,19.6,1,2,0,1,9,0,6,1,1,0\n5624,18,19.6,1,4,0,1,13,1,7,1,1,3\n3492,18,19.7,1,1,0,1,15,0,1,1,1,6\n8280,18,19.7,1,2,0,1,13,1,3,1,1,9\n10194,18,20.3,1,5,0,1,12,1,8,1,1,7\n3938,18,20.7,1,2,0,1,12,0,8,1,1,7\n4332,18,21,1,4,0,1,12,0,1,1,1,6\n8580,18,21.1,1,4,0,1,15,1,2,1,1,2\n5673,18,21.4,1,5,0,1,14,1,5,1,1,1\n9797,18,22.1,1,1,0,1,15,1,2,1,1,3\n9331,18,22.3,1,4,0,1,15,1,6,1,1,0\n5574,18,22.9,1,4,0,1,13,1,5,1,1,5\n2084,18,23.1,1,5,0,1,11,0,1,1,1,10\n436,18,23.4,1,2,0,1,12,0,2,1,1,8\n1133,18,23.6,1,2,0,1,14,0,4,1,1,3\n1371,18,23.6,1,2,0,1,12,0,4,1,1,0\n2732,18,23.9,1,3,0,1,12,0,3,1,1,8\n6434,18,23.9,1,2,0,1,15,1,6,1,1,8\n6528,18,23.9,1,5,0,1,14,1,2,1,1,8\n844,18,24,1,1,0,1,12,0,8,1,1,10\n4032,18,24.2,1,3,0,1,14,0,3,1,1,6\n1712,18,24.2,1,4,0,1,11,0,3,1,1,1\n4899,18,24.8,1,3,0,1,14,0,7,1,1,2\n7456,18,24.9,1,4,0,1,9,1,4,1,1,1\n9060,18,25.2,1,5,0,1,12,1,3,1,1,4\n628,18,25.4,1,2,0,1,13,0,3,1,1,0\n1458,18,25.4,1,1,0,1,13,0,6,1,1,3\n10779,18,25.7,1,5,0,1,14,1,7,1,1,1\n8874,18,25.8,1,2,0,1,13,1,2,1,1,0\n1126,18,26,1,5,0,1,10,0,6,1,1,1\n1134,18,26.1,1,5,0,1,14,0,8,1,1,1\n4737,18,26.1,1,5,0,1,12,0,8,1,1,4\n1380,18,26.7,1,5,0,1,10,0,8,1,1,7\n8930,18,27.3,1,4,0,1,9,1,4,1,1,2\n7927,18,27.3,1,2,0,1,15,1,1,1,1,3\n7080,18,27.7,1,2,0,1,11,1,5,1,1,7\n5665,18,28,1,3,0,1,15,1,8,1,1,2\n7972,18,28.2,1,3,0,1,14,1,7,1,1,5\n9007,18,28.3,1,2,0,1,14,1,4,1,1,1\n1346,18,28.6,1,5,0,1,12,0,7,1,1,0\n8695,18,29.7,1,4,0,1,14,1,6,1,1,2\n9536,18,29.9,1,1,0,1,9,1,7,1,1,1\n3866,19,0.2,1,3,0,1,12,0,1,1,1,0\n9480,19,0.3,1,3,0,1,9,1,5,1,1,7\n4482,19,0.7,1,3,0,1,14,0,7,1,1,4\n2002,19,0.7,1,2,0,1,9,0,8,1,1,8\n1824,19,0.9,1,4,0,1,15,0,5,1,1,7\n878,19,1.1,1,1,0,1,9,0,8,1,1,5\n5258,19,1.5,1,3,0,1,12,0,5,1,1,0\n10689,19,1.6,1,1,0,1,9,1,6,1,1,4\n4718,19,1.9,1,4,0,1,15,0,4,1,1,1\n10623,19,1.9,1,4,0,1,12,1,7,1,1,1\n3498,19,1.9,1,3,0,1,12,0,6,1,1,4\n8609,19,2,1,1,0,1,14,1,7,1,1,0\n7486,19,2.6,1,1,0,1,9,1,3,1,1,4\n2518,19,2.7,1,2,0,1,14,0,4,1,1,10\n438,19,3.4,1,5,0,1,15,0,2,1,1,3\n7351,19,3.7,1,1,0,1,12,1,3,1,1,3\n4658,19,3.7,1,5,0,1,11,0,7,1,1,9\n338,19,3.9,1,2,0,1,11,0,2,1,1,6\n10421,19,4.4,1,5,0,1,12,1,2,1,1,2\n7192,19,5.1,1,5,0,1,10,1,3,1,1,7\n7480,19,5.8,1,4,0,1,13,1,7,1,1,9\n928,19,5.9,1,3,0,1,13,0,1,1,1,7\n7958,19,5.9,1,5,0,1,15,1,5,1,1,0\n9817,19,5.9,1,5,0,1,12,1,1,1,1,4\n5553,19,6.1,1,4,0,1,9,1,4,1,1,0\n5558,19,6.2,1,5,0,1,11,1,1,1,1,9\n7218,19,6.9,1,4,0,1,13,1,4,1,1,10\n9826,19,7.6,1,1,0,1,9,1,2,1,1,2\n3907,19,7.6,1,4,0,1,11,0,3,1,1,2\n4159,19,7.6,1,5,0,1,15,0,4,1,1,2\n3690,19,8,1,1,0,1,9,0,1,1,1,2\n5486,19,8.7,1,5,0,1,13,1,1,1,1,7\n1390,19,8.7,1,5,0,1,13,0,4,1,1,2\n7987,19,8.9,1,4,0,1,15,1,3,1,1,0\n6811,19,8.9,1,5,0,1,9,1,7,1,1,3\n2068,19,9.2,1,4,0,1,12,0,2,1,1,10\n2760,19,9.2,1,1,0,1,13,0,2,1,1,6\n1870,19,9.7,1,2,0,1,10,0,3,1,1,7\n8114,19,9.8,1,3,0,1,14,1,6,1,1,1\n4475,19,10.1,1,5,0,1,12,0,1,1,1,3\n6618,19,10.6,1,4,0,1,10,1,7,1,1,4\n2275,19,10.8,1,2,0,1,10,0,4,1,1,0\n4706,19,11.3,1,5,0,1,12,0,5,1,1,5\n10427,19,11.4,1,4,0,1,12,1,7,1,1,0\n9644,19,11.4,1,3,0,1,15,1,3,1,1,2\n4039,19,11.5,1,4,0,1,11,0,7,1,1,2\n2344,19,11.9,1,3,0,1,11,0,6,1,1,10\n1928,19,12.1,1,3,0,1,10,0,5,1,1,3\n6466,19,12.1,1,5,0,1,12,1,5,1,1,7\n8547,19,12.1,1,1,0,1,10,1,6,1,1,2\n9356,19,12.5,1,3,0,1,9,1,2,1,1,2\n5870,19,12.8,1,2,0,1,15,1,4,1,1,10\n5264,19,12.8,1,5,0,1,11,0,2,1,1,8\n10378,19,12.9,1,3,0,1,11,1,4,1,1,10\n8020,19,13,1,3,0,1,14,1,3,1,1,7\n6876,19,13.2,1,5,0,1,10,1,7,1,1,10\n2776,19,13.6,1,1,0,1,15,0,1,1,1,1\n2583,19,14,1,4,0,1,9,0,7,1,1,0\n6714,19,15.1,1,3,0,1,10,1,7,1,1,3\n7941,19,15.2,1,3,0,1,10,1,5,1,1,3\n8514,19,15.4,1,2,0,1,12,1,1,1,1,10\n9198,19,15.5,1,3,0,1,11,1,1,1,1,2\n4219,19,16.1,1,3,0,1,13,0,4,1,1,3\n1666,19,16.3,1,4,0,1,14,0,1,1,1,4\n4110,19,16.3,1,4,0,1,12,0,7,1,1,2\n1816,19,16.8,1,1,0,1,11,0,6,1,1,7\n9072,19,16.9,1,1,0,1,10,1,1,1,1,7\n9043,19,17,1,4,0,1,13,1,4,1,1,4\n3268,19,17.5,1,4,0,1,14,0,1,1,1,10\n9763,19,17.6,1,4,0,1,12,1,3,1,1,3\n556,19,17.6,1,3,0,1,14,0,4,1,1,4\n9812,19,17.6,1,5,0,1,12,1,3,1,1,6\n6543,19,18.2,1,4,0,1,12,1,8,1,1,1\n4057,19,18.6,1,5,0,1,12,0,8,1,1,3\n8577,19,18.7,1,4,0,1,15,1,2,1,1,1\n575,19,18.7,1,5,0,1,11,0,7,1,1,3\n9066,19,19.1,1,5,0,1,11,1,2,1,1,9\n635,19,19.4,1,1,0,1,12,0,3,1,1,4\n6681,19,19.4,1,3,0,1,12,1,6,1,1,2\n636,19,19.6,1,4,0,1,15,0,5,1,1,0\n3018,19,19.8,1,1,0,1,11,0,7,1,1,8\n2818,19,19.9,1,3,0,1,14,0,4,1,1,7\n6343,19,20,1,4,0,1,11,1,5,1,1,3\n9042,19,20.1,1,4,0,1,10,1,4,1,1,0\n5300,19,20.1,1,1,0,1,12,0,3,1,1,3\n2140,19,20.8,1,4,0,1,14,0,5,1,1,7\n3620,19,20.9,1,2,0,1,15,0,2,1,1,2\n2545,19,20.9,1,1,0,1,13,0,4,1,1,2\n2862,19,21.1,1,5,0,1,13,0,6,1,1,9\n6832,19,21.7,1,4,0,1,12,1,3,1,1,0\n1836,19,22.3,1,3,0,1,12,0,8,1,1,10\n2434,19,22.5,1,1,0,1,13,0,2,1,1,7\n6237,19,22.6,1,4,0,1,10,1,3,1,1,3\n2096,19,22.7,1,4,0,1,10,0,7,1,1,8\n9397,19,23,1,2,0,1,12,1,5,1,1,0\n3478,19,23.2,1,1,0,1,11,0,6,1,1,2\n8160,19,23.2,1,5,0,1,12,1,5,1,1,8\n3407,19,24.2,1,3,0,1,15,0,2,1,1,1\n10231,19,24.2,1,1,0,1,14,1,8,1,1,0\n10286,19,24.2,1,3,0,1,10,1,6,1,1,6\n8732,19,24.7,1,2,0,1,11,1,1,1,1,5\n8673,19,24.8,1,4,0,1,12,1,5,1,1,3\n6976,19,25,1,3,0,1,12,1,8,1,1,10\n6860,19,25.2,1,2,0,1,14,1,8,1,1,5\n7584,19,25.7,1,3,0,1,12,1,2,1,1,1\n412,19,25.7,1,4,0,1,12,0,7,1,1,2\n3529,19,25.9,1,1,0,1,11,0,4,1,1,0\n3889,19,25.9,1,3,0,1,9,0,4,1,1,4\n1291,19,26.2,1,2,0,1,11,0,1,1,1,3\n6812,19,26.3,1,2,0,1,15,1,3,1,1,1\n5388,19,26.5,1,5,0,1,15,0,7,1,1,1\n3611,19,26.7,1,5,0,1,13,0,6,1,1,2\n6802,19,27.3,1,3,0,1,12,1,6,1,1,10\n6170,19,27.3,1,4,0,1,10,1,8,1,1,9\n7328,19,27.5,1,2,0,1,12,1,2,1,1,3\n9436,19,27.6,1,4,0,1,10,1,5,1,1,3\n1964,19,27.9,1,5,0,1,10,0,6,1,1,2\n9370,19,28.2,1,1,0,1,13,1,1,1,1,0\n6795,19,28.9,1,1,0,1,12,1,2,1,1,4\n6826,19,29.2,1,2,0,1,9,1,8,1,1,6\n4568,19,29.5,1,5,0,1,11,0,4,1,1,9\n7412,20,0.1,1,2,0,1,12,1,6,1,1,4\n6886,20,0.4,1,4,0,1,11,1,4,1,1,1\n5838,20,0.4,1,3,0,1,15,1,1,1,1,5\n5191,20,0.4,1,1,0,1,12,0,1,1,1,0\n1746,20,0.4,1,1,0,1,14,0,2,1,1,6\n462,20,1.3,1,2,0,1,9,0,8,1,1,5\n4114,20,1.4,1,4,0,1,14,0,1,1,1,6\n7236,20,1.5,1,1,0,1,11,1,2,1,1,2\n6214,20,2,1,2,0,1,9,1,7,1,1,9\n4686,20,2.4,1,2,0,1,9,0,7,1,1,1\n5007,20,2.6,1,5,0,1,12,0,6,1,1,0\n6220,20,2.7,1,4,0,1,10,1,2,1,1,0\n5580,20,2.8,1,4,0,1,13,1,2,1,1,7\n1580,20,3.5,1,3,0,1,11,0,7,1,1,8\n3644,20,4.2,1,5,0,1,13,0,1,1,1,7\n8965,20,4.2,1,2,0,1,12,1,6,1,1,1\n1406,20,4.5,1,2,0,1,15,0,1,1,1,4\n9254,20,4.6,1,4,0,1,14,1,2,1,1,0\n2046,20,5.1,1,5,0,1,14,0,6,1,1,3\n8192,20,5.4,1,3,0,1,13,1,4,1,1,6\n8142,20,5.6,1,4,0,1,11,1,4,1,1,4\n2069,20,5.7,1,2,0,1,13,0,4,1,1,1\n9156,20,6,1,3,0,1,12,1,5,1,1,0\n494,20,6,1,1,0,1,11,0,8,1,1,3\n10189,20,6,1,3,0,1,11,1,4,1,1,1\n5901,20,6.2,1,4,0,1,14,1,4,1,1,2\n3950,20,6.3,1,5,0,1,10,0,8,1,1,4\n8794,20,6.3,1,1,0,1,10,1,1,1,1,7\n2795,20,6.7,1,3,0,1,12,0,2,1,1,3\n4028,20,6.9,1,4,0,1,11,0,5,1,1,1\n6048,20,7.1,1,4,0,1,14,1,6,1,1,3\n3140,20,7.1,1,3,0,1,11,0,6,1,1,0\n2603,20,7.1,1,5,0,1,11,0,7,1,1,2\n3656,20,7.2,1,3,0,1,11,0,5,1,1,3\n2744,20,7.2,1,2,0,1,15,0,1,1,1,0\n780,20,7.4,1,1,0,1,9,0,4,1,1,0\n10635,20,7.7,1,3,0,1,15,1,8,1,1,2\n6056,20,7.9,1,2,0,1,9,1,6,1,1,4\n9544,20,8,1,5,0,1,14,1,4,1,1,8\n46,20,8,1,4,0,1,11,0,4,1,1,1\n1751,20,8,1,4,0,1,15,0,5,1,1,0\n5456,20,8.4,1,3,0,1,9,1,3,1,1,3\n860,20,8.7,1,3,0,1,11,0,3,1,1,10\n417,20,9,1,4,0,1,10,0,7,1,1,1\n9856,20,9.3,1,4,0,1,14,1,5,1,1,4\n7536,20,9.7,1,4,0,1,15,1,8,1,1,8\n5351,20,10,1,2,0,1,12,0,3,1,1,1\n10698,20,10.2,1,3,0,1,13,1,5,1,1,10\n5692,20,10.2,1,3,0,1,11,1,3,1,1,2\n5112,20,10.9,1,4,0,1,10,0,6,1,1,3\n1108,20,10.9,1,4,0,1,14,0,2,1,1,0\n1461,20,11,1,3,0,1,15,0,5,1,1,2\n2120,20,11.1,1,3,0,1,15,0,4,1,1,1\n7386,20,11.5,1,3,0,1,12,1,7,1,1,6\n2906,20,11.8,1,2,0,1,12,0,2,1,1,9\n6312,20,11.9,1,1,0,1,12,1,6,1,1,5\n920,20,12.1,1,2,0,1,12,0,3,1,1,6\n468,20,12.7,1,2,0,1,11,0,6,1,1,1\n7124,20,12.9,1,1,0,1,12,1,1,1,1,0\n9771,20,13.1,1,5,0,1,9,1,4,1,1,1\n8405,20,13.4,1,5,0,1,10,1,4,1,1,2\n1975,20,13.4,1,4,0,1,13,0,7,1,1,3\n416,20,14,1,2,0,1,15,0,4,1,1,4\n7764,20,14.5,1,5,0,1,14,1,7,1,1,3\n1161,20,14.6,1,2,0,1,15,0,3,1,1,4\n3998,20,14.9,1,4,0,1,9,0,5,1,1,4\n4393,20,15,1,3,0,1,10,0,4,1,1,3\n10153,20,15.2,1,1,0,1,10,1,2,1,1,0\n10370,20,15.3,1,4,0,1,11,1,6,1,1,0\n4065,20,15.4,1,5,0,1,15,0,2,1,1,3\n9105,20,15.6,1,4,0,1,12,1,3,1,1,1\n6349,20,15.8,1,4,0,1,14,1,6,1,1,4\n4136,20,15.8,1,1,0,1,15,0,1,1,1,8\n4826,20,15.9,1,1,0,1,10,0,2,1,1,2\n6907,20,16,1,1,0,1,14,1,8,1,1,2\n8988,20,16.3,1,2,0,1,13,1,2,1,1,9\n5278,20,16.4,1,5,0,1,15,0,1,1,1,3\n8365,20,16.4,1,5,0,1,10,1,1,1,1,2\n7791,20,17,1,2,0,1,14,1,8,1,1,1\n5954,20,17,1,4,0,1,9,1,5,1,1,9\n4298,20,17.5,1,3,0,1,10,0,2,1,1,3\n9894,20,17.6,1,4,0,1,14,1,1,1,1,6\n49,20,18,1,2,0,1,10,0,3,1,1,3\n7006,20,18.1,1,1,0,1,12,1,8,1,1,5\n3308,20,18.7,1,5,0,1,10,0,7,1,1,10\n9606,20,18.8,1,5,0,1,12,1,6,1,1,3\n3166,20,18.9,1,3,0,1,9,0,1,1,1,3\n5209,20,19.2,1,3,0,1,11,0,1,1,1,4\n6636,20,19.2,1,4,0,1,9,1,7,1,1,3\n7620,20,19.3,1,3,0,1,13,1,1,1,1,9\n7183,20,19.6,1,1,0,1,12,1,5,1,1,1\n6965,20,19.6,1,1,0,1,14,1,7,1,1,4\n1917,20,19.8,1,1,0,1,15,0,5,1,1,2\n5226,20,20.1,1,1,0,1,11,0,5,1,1,4\n3784,20,20.2,1,4,0,1,10,0,4,1,1,5\n7420,20,20.6,1,5,0,1,15,1,8,1,1,3\n5428,20,20.6,1,2,0,1,13,1,7,1,1,2\n6404,20,21.3,1,2,0,1,14,1,2,1,1,8\n5262,20,21.5,1,4,0,1,9,0,6,1,1,0\n2051,20,21.8,1,1,0,1,9,0,8,1,1,3\n5041,20,22,1,1,0,1,15,0,8,1,1,4\n7172,20,22.8,1,1,0,1,12,1,4,1,1,10\n3486,20,23.7,1,4,0,1,12,0,5,1,1,2\n6499,20,23.7,1,1,0,1,9,1,7,1,1,3\n2415,20,24.1,1,5,0,1,10,0,1,1,1,0\n10298,20,24.2,1,5,0,1,11,1,1,1,1,10\n8079,20,24.4,1,2,0,1,12,1,4,1,1,4\n7126,20,24.6,1,4,0,1,12,1,5,1,1,9\n601,20,24.7,1,2,0,1,12,0,1,1,1,4\n6628,20,24.8,1,4,0,1,9,1,7,1,1,3\n1101,20,25.1,1,4,0,1,15,0,7,1,1,1\n3367,20,25.7,1,1,0,1,12,0,1,1,1,0\n7474,20,25.7,1,5,0,1,14,1,5,1,1,1\n5426,20,25.8,1,3,0,1,13,1,2,1,1,2\n3576,20,26.2,1,4,0,1,15,0,4,1,1,9\n10414,20,26.3,1,5,0,1,12,1,1,1,1,0\n7649,20,26.5,1,5,0,1,11,1,2,1,1,3\n9374,20,26.7,1,2,0,1,13,1,8,1,1,10\n6544,20,27.2,1,2,0,1,15,1,5,1,1,7\n6656,20,27.5,1,3,0,1,12,1,1,1,1,0\n5516,20,27.5,1,3,0,1,15,1,3,1,1,4\n5554,20,27.6,1,5,0,1,9,1,4,1,1,7\n6658,20,27.6,1,2,0,1,10,1,3,1,1,6\n1578,20,27.8,1,3,0,1,10,0,3,1,1,4\n6956,20,27.9,1,5,0,1,12,1,2,1,1,8\n8668,20,28.6,1,4,0,1,11,1,4,1,1,7\n3724,20,28.7,1,1,0,1,12,0,4,1,1,4\n6057,20,28.7,1,5,0,1,15,1,4,1,1,0\n9011,20,28.8,1,4,0,1,9,1,2,1,1,4\n351,20,28.9,1,1,0,1,11,0,7,1,1,3\n10436,20,29,1,2,0,1,12,1,3,1,1,8\n2094,20,29,1,2,0,1,12,0,8,1,1,7\n9358,20,29.4,1,3,0,1,12,1,5,1,1,1\n6144,20,29.6,1,3,0,1,11,1,3,1,1,8\n5856,21,0.3,1,1,0,1,13,1,1,1,1,5\n8044,21,1.5,1,4,0,1,11,1,8,1,1,0\n716,21,1.5,1,2,0,1,11,0,4,1,1,9\n1378,21,1.7,1,2,0,1,12,0,6,1,1,4\n6234,21,2.7,1,5,0,1,10,1,3,1,1,7\n9758,21,2.8,1,3,0,1,12,1,5,1,1,5\n524,21,2.9,1,3,0,1,15,0,3,1,1,8\n7733,21,3,1,3,0,1,15,1,4,1,1,2\n1010,21,3.8,1,1,0,1,10,0,7,1,1,4\n10820,21,4,1,3,0,1,15,1,1,1,1,8\n3336,21,4.2,1,5,0,1,9,0,4,1,1,2\n3294,21,4.7,1,2,0,1,15,0,6,1,1,3\n4451,21,5.8,1,1,0,1,11,0,6,1,1,2\n8370,21,6,1,5,0,1,15,1,5,1,1,3\n8467,21,6.1,1,5,0,1,10,1,4,1,1,3\n8553,21,6.5,1,4,0,1,10,1,5,1,1,1\n3642,21,6.8,1,1,0,1,9,0,3,1,1,3\n1604,21,6.9,1,5,0,1,15,0,4,1,1,3\n7549,21,7.1,1,4,0,1,11,1,3,1,1,1\n2312,21,7.7,1,1,0,1,10,0,2,1,1,6\n1418,21,8.1,1,5,0,1,9,0,6,1,1,1\n3354,21,8.4,1,1,0,1,14,0,7,1,1,10\n3862,21,8.4,1,5,0,1,9,0,4,1,1,5\n4989,21,8.9,1,1,0,1,10,0,7,1,1,1\n2426,21,8.9,1,5,0,1,13,0,8,1,1,9\n10198,21,9,1,3,0,1,13,1,5,1,1,4\n4581,21,9.5,1,1,0,1,15,0,1,1,1,2\n1877,21,9.7,1,4,0,1,12,0,4,1,1,4\n221,21,9.7,1,5,0,1,11,0,6,1,1,4\n582,21,9.9,1,3,0,1,15,0,7,1,1,5\n10040,21,10,1,4,0,1,13,1,3,1,1,4\n764,21,10.3,1,4,0,1,15,0,8,1,1,5\n3546,21,10.4,1,2,0,1,15,0,5,1,1,7\n4402,21,11.1,1,2,0,1,14,0,1,1,1,7\n2916,21,11.5,1,3,0,1,11,0,8,1,1,8\n63,21,11.7,1,1,0,1,14,0,7,1,1,1\n5636,21,11.8,1,5,0,1,13,1,2,1,1,6\n6453,21,12,1,4,0,1,13,1,4,1,1,3\n1058,21,12,1,5,0,1,12,0,1,1,1,10\n630,21,12.4,1,2,0,1,10,0,8,1,1,5\n3852,21,12.8,1,2,0,1,11,0,8,1,1,7\n10480,21,13.1,1,4,0,1,14,1,5,1,1,0\n6261,21,13.2,1,1,0,1,12,1,6,1,1,1\n3315,21,13.2,1,4,0,1,11,0,4,1,1,3\n903,21,13.2,1,5,0,1,14,0,5,1,1,0\n10848,21,13.2,1,5,0,1,13,1,8,1,1,1\n3234,21,13.2,1,3,0,1,11,0,5,1,1,7\n6019,21,13.4,1,2,0,1,12,1,3,1,1,4\n9852,21,14.1,1,2,0,1,10,1,1,1,1,1\n3859,21,14.1,1,5,0,1,15,0,7,1,1,2\n10529,21,14.2,1,2,0,1,12,1,2,1,1,0\n6244,21,14.3,1,5,0,1,9,1,7,1,1,8\n10464,21,14.6,1,2,0,1,9,1,8,1,1,0\n5025,21,14.9,1,5,0,1,9,0,2,1,1,1\n3960,21,15,1,5,0,1,10,0,8,1,1,1\n5948,21,15,1,1,0,1,13,1,3,1,1,5\n1938,21,15.4,1,4,0,1,12,0,3,1,1,0\n6897,21,16.1,1,5,0,1,9,1,5,1,1,1\n3266,21,16.2,1,5,0,1,13,0,3,1,1,3\n881,21,16.2,1,1,0,1,11,0,4,1,1,3\n3982,21,16.4,1,3,0,1,14,0,5,1,1,3\n9888,21,16.9,1,3,0,1,13,1,1,1,1,4\n9984,21,18,1,3,0,1,14,1,3,1,1,3\n1278,21,18,1,2,0,1,10,0,1,1,1,9\n7493,21,18.1,1,5,0,1,10,1,7,1,1,4\n4721,21,18.6,1,5,0,1,12,0,4,1,1,1\n10428,21,18.8,1,2,0,1,10,1,7,1,1,6\n1556,21,19.8,1,2,0,1,14,0,8,1,1,3\n948,21,20.5,1,2,0,1,12,0,8,1,1,10\n8406,21,20.7,1,2,0,1,12,1,6,1,1,10\n3715,21,20.8,1,1,0,1,14,0,3,1,1,3\n5478,21,21.1,1,5,0,1,12,1,8,1,1,1\n3100,21,21.2,1,1,0,1,12,0,3,1,1,6\n2032,21,21.7,1,2,0,1,15,0,5,1,1,1\n10358,21,22,1,3,0,1,13,1,8,1,1,0\n414,21,22.2,1,5,0,1,12,0,2,1,1,6\n7125,21,22.2,1,5,0,1,12,1,6,1,1,0\n7319,21,22.3,1,3,0,1,10,1,6,1,1,0\n4009,21,22.7,1,4,0,1,9,0,3,1,1,1\n9678,21,22.8,1,5,0,1,15,1,4,1,1,9\n10743,21,23.4,1,3,0,1,14,1,1,1,1,4\n9886,21,23.4,1,2,0,1,14,1,3,1,1,10\n6566,21,23.6,1,5,0,1,9,1,4,1,1,7\n2658,21,23.7,1,3,0,1,13,0,2,1,1,5\n10420,21,24,1,3,0,1,11,1,1,1,1,2\n1546,21,24.1,1,5,0,1,10,0,4,1,1,5\n1525,21,24.2,1,1,0,1,12,0,4,1,1,3\n3264,21,24.5,1,1,0,1,15,0,1,1,1,10\n3845,21,24.8,1,5,0,1,9,0,1,1,1,2\n4462,21,24.9,1,4,0,1,14,0,6,1,1,2\n10226,21,24.9,1,4,0,1,10,1,4,1,1,8\n6760,21,25.7,1,3,0,1,9,1,7,1,1,9\n8400,21,25.8,1,3,0,1,12,1,2,1,1,4\n4164,21,26,1,1,0,1,9,0,1,1,1,6\n10641,21,26.2,1,2,0,1,14,1,6,1,1,1\n8733,21,26.6,1,4,0,1,11,1,7,1,1,3\n6274,21,26.7,1,2,0,1,11,1,8,1,1,2\n6556,21,26.8,1,4,0,1,12,1,6,1,1,2\n8776,21,27,1,1,0,1,14,1,3,1,1,2\n1639,21,27.2,1,2,0,1,14,0,3,1,1,1\n8088,21,27.3,1,2,0,1,12,1,2,1,1,6\n968,21,27.5,1,1,0,1,13,0,7,1,1,9\n3365,21,27.7,1,3,0,1,14,0,5,1,1,4\n9562,21,28.7,1,2,0,1,12,1,5,1,1,4\n7228,21,28.8,1,5,0,1,14,1,1,1,1,3\n2396,21,29.2,1,2,0,1,12,0,1,1,1,4\n9988,21,29.2,1,1,0,1,12,1,6,1,1,5\n4852,21,29.8,1,1,0,1,14,0,5,1,1,9\n1949,21,29.8,1,5,0,1,13,0,3,1,1,2\n2330,21,30,1,3,0,1,9,0,3,1,1,6\n9857,22,0.2,1,5,0,1,15,1,8,1,1,1\n6708,22,0.9,1,5,0,1,10,1,2,1,1,5\n2778,22,1,1,4,0,1,15,0,5,1,1,7\n35,22,1.3,1,1,0,1,12,0,8,1,1,0\n2811,22,1.5,1,2,0,1,12,0,5,1,1,2\n10258,22,1.9,1,2,0,1,14,1,8,1,1,4\n10061,22,2.8,1,3,0,1,12,1,8,1,1,1\n8361,22,3.3,1,4,0,1,14,1,2,1,1,4\n1649,22,3.7,1,5,0,1,12,0,3,1,1,0\n1972,22,3.7,1,2,0,1,12,0,7,1,1,4\n9884,22,3.9,1,5,0,1,11,1,6,1,1,1\n2715,22,4,1,2,0,1,12,0,7,1,1,1\n7312,22,4.4,1,4,0,1,12,1,6,1,1,10\n9635,22,4.7,1,4,0,1,12,1,4,1,1,1\n7634,22,4.7,1,2,0,1,15,1,6,1,1,2\n6722,22,4.7,1,4,0,1,12,1,5,1,1,7\n2021,22,4.9,1,3,0,1,14,0,2,1,1,3\n9031,22,4.9,1,3,0,1,10,1,4,1,1,0\n8235,22,5.1,1,3,0,1,13,1,2,1,1,2\n8340,22,5.4,1,5,0,1,11,1,6,1,1,2\n4649,22,5.4,1,1,0,1,12,0,8,1,1,3\n752,22,5.4,1,2,0,1,15,0,7,1,1,2\n8666,22,5.4,1,5,0,1,14,1,7,1,1,6\n3089,22,6.4,1,5,0,1,13,0,3,1,1,1\n1153,22,7.2,1,4,0,1,12,0,3,1,1,0\n6408,22,7.6,1,3,0,1,9,1,8,1,1,8\n4342,22,7.7,1,2,0,1,12,0,5,1,1,3\n6006,22,8.6,1,1,0,1,15,1,5,1,1,6\n10408,22,8.7,1,2,0,1,11,1,5,1,1,9\n6818,22,8.7,1,5,0,1,9,1,5,1,1,2\n6366,22,8.8,1,2,0,1,12,1,2,1,1,8\n5439,22,8.8,1,1,0,1,10,1,3,1,1,1\n6323,22,9.7,1,3,0,1,12,1,1,1,1,0\n3372,22,10.1,1,5,0,1,12,0,5,1,1,0\n5620,22,10.5,1,5,0,1,12,1,4,1,1,3\n4029,22,10.6,1,5,0,1,14,0,4,1,1,1\n8716,22,10.9,1,4,0,1,12,1,3,1,1,2\n1315,22,10.9,1,2,0,1,14,0,2,1,1,0\n6428,22,10.9,1,1,0,1,12,1,2,1,1,7\n1392,22,11.1,1,2,0,1,15,0,1,1,1,5\n66,22,11.6,1,5,0,1,15,0,6,1,1,9\n2104,22,11.7,1,3,0,1,14,0,4,1,1,1\n3430,22,12,1,4,0,1,12,0,8,1,1,7\n8039,22,12.3,1,5,0,1,12,1,1,1,1,1\n10481,22,12.7,1,5,0,1,10,1,5,1,1,1\n8670,22,13,1,2,0,1,14,1,5,1,1,1\n4312,22,13.7,1,2,0,1,11,0,2,1,1,6\n680,22,13.9,1,3,0,1,11,0,4,1,1,6\n294,22,14.1,1,3,0,1,9,0,2,1,1,0\n4303,22,14.4,1,1,0,1,11,0,4,1,1,4\n6979,22,14.5,1,1,0,1,10,1,3,1,1,0\n2472,22,15.3,1,2,0,1,13,0,4,1,1,5\n1009,22,15.4,1,5,0,1,12,0,5,1,1,0\n9616,22,15.5,1,3,0,1,11,1,8,1,1,7\n7194,22,15.9,1,4,0,1,15,1,3,1,1,6\n5364,22,15.9,1,5,0,1,12,0,7,1,1,2\n7890,22,16.6,1,5,0,1,11,1,6,1,1,10\n8720,22,16.8,1,5,0,1,15,1,1,1,1,1\n9188,22,16.8,1,5,0,1,12,1,2,1,1,9\n4060,22,17.2,1,1,0,1,9,0,3,1,1,4\n3709,22,17.3,1,2,0,1,10,0,6,1,1,0\n6840,22,18,1,5,0,1,12,1,1,1,1,0\n5732,22,18.3,1,3,0,1,10,1,3,1,1,1\n375,22,18.5,1,2,0,1,15,0,1,1,1,2\n6384,22,18.7,1,2,0,1,14,1,6,1,1,9\n4685,22,18.7,1,5,0,1,14,0,3,1,1,4\n7692,22,18.9,1,5,0,1,14,1,1,1,1,6\n5524,22,19.9,1,5,0,1,13,1,5,1,1,3\n8680,22,20.1,1,4,0,1,14,1,3,1,1,10\n5709,22,20.7,1,3,0,1,12,1,4,1,1,1\n5462,22,21.2,1,5,0,1,11,1,4,1,1,1\n5824,22,21.6,1,2,0,1,14,1,8,1,1,7\n6344,22,21.6,1,2,0,1,14,1,2,1,1,5\n2997,22,22.1,1,2,0,1,13,0,7,1,1,0\n2455,22,22.3,1,5,0,1,14,0,7,1,1,0\n4378,22,22.3,1,5,0,1,10,0,5,1,1,10\n2616,22,22.4,1,2,0,1,15,0,3,1,1,4\n5064,22,23.5,1,3,0,1,15,0,4,1,1,5\n8443,22,23.5,1,3,0,1,14,1,5,1,1,0\n4120,22,23.6,1,5,0,1,9,0,3,1,1,10\n1948,22,23.6,1,3,0,1,15,0,1,1,1,7\n5138,22,24,1,4,0,1,15,0,2,1,1,2\n504,22,24.7,1,1,0,1,12,0,6,1,1,5\n9611,22,24.9,1,4,0,1,11,1,1,1,1,1\n7750,22,25,1,1,0,1,10,1,2,1,1,5\n9008,22,25.5,1,5,0,1,9,1,8,1,1,6\n3145,22,25.6,1,5,0,1,12,0,4,1,1,0\n6548,22,26.2,1,3,0,1,15,1,6,1,1,6\n10045,22,26.7,1,4,0,1,10,1,6,1,1,4\n2270,22,27.1,1,1,0,1,11,0,8,1,1,7\n3587,22,27.6,1,5,0,1,15,0,8,1,1,0\n8084,22,27.6,1,4,0,1,12,1,1,1,1,8\n5135,22,28.2,1,5,0,1,11,0,2,1,1,4\n1308,22,28.8,1,4,0,1,10,0,5,1,1,10\n3174,22,29,1,3,0,1,12,0,3,1,1,8\n10815,22,29.5,1,4,0,1,12,1,7,1,1,0\n8402,23,0.3,1,1,0,1,13,1,5,1,1,8\n6239,23,0.5,1,4,0,1,13,1,1,1,1,1\n4947,23,0.8,1,1,0,1,9,0,6,1,1,4\n110,23,1.2,1,1,0,1,10,0,3,1,1,0\n6586,23,1.3,1,5,0,1,14,1,7,1,1,0\n1514,23,2.1,1,4,0,1,13,0,1,1,1,3\n5469,23,2.5,1,5,0,1,15,1,5,1,1,3\n5711,23,2.8,1,2,0,1,15,1,1,1,1,2\n1684,23,3.1,1,1,0,1,9,0,7,1,1,3\n6254,23,3.4,1,1,0,1,14,1,2,1,1,0\n8110,23,3.5,1,2,0,1,12,1,6,1,1,10\n517,23,3.6,1,4,0,1,12,0,7,1,1,2\n1182,23,3.8,1,1,0,1,11,0,2,1,1,2\n10554,23,4,1,5,0,1,15,1,5,1,1,7\n5953,23,4.5,1,2,0,1,12,1,2,1,1,0\n2913,23,4.7,1,5,0,1,13,0,8,1,1,3\n9633,23,5,1,3,0,1,12,1,4,1,1,0\n883,23,5.1,1,4,0,1,15,0,7,1,1,1\n10292,23,5.2,1,5,0,1,15,1,1,1,1,8\n4976,23,5.5,1,2,0,1,12,0,8,1,1,0\n8132,23,5.8,1,5,0,1,12,1,4,1,1,6\n939,23,6,1,1,0,1,13,0,5,1,1,3\n2948,23,6.3,1,1,0,1,14,0,5,1,1,0\n6742,23,6.4,1,3,0,1,14,1,4,1,1,0\n9283,23,6.5,1,3,0,1,10,1,5,1,1,2\n2822,23,6.5,1,3,0,1,10,0,4,1,1,9\n9012,23,6.8,1,2,0,1,12,1,3,1,1,5\n2633,23,7,1,2,0,1,14,0,8,1,1,0\n6351,23,7,1,1,0,1,10,1,2,1,1,2\n3042,23,7.3,1,4,0,1,10,0,6,1,1,7\n5432,23,7.5,1,4,0,1,13,1,6,1,1,4\n2334,23,8.4,1,2,0,1,13,0,4,1,1,8\n1784,23,8.5,1,2,0,1,15,0,3,1,1,2\n380,23,9,1,1,0,1,12,0,4,1,1,6\n3831,23,9.1,1,5,0,1,11,0,3,1,1,3\n2655,23,9.1,1,4,0,1,10,0,2,1,1,4\n8204,23,9.3,1,5,0,1,11,1,2,1,1,7\n1200,23,9.7,1,4,0,1,9,0,6,1,1,8\n3114,23,9.8,1,1,0,1,9,0,7,1,1,4\n3720,23,9.8,1,3,0,1,15,0,7,1,1,6\n5228,23,10,1,3,0,1,13,0,4,1,1,1\n4894,23,10.5,1,4,0,1,10,0,7,1,1,10\n1488,23,10.7,1,4,0,1,9,0,1,1,1,9\n9126,23,11.4,1,1,0,1,14,1,8,1,1,2\n8188,23,11.7,1,2,0,1,12,1,2,1,1,9\n2589,23,11.8,1,2,0,1,14,0,7,1,1,4\n9366,23,11.8,1,2,0,1,12,1,8,1,1,2\n4646,23,11.9,1,5,0,1,11,0,1,1,1,2\n4116,23,12,1,3,0,1,12,0,7,1,1,0\n2984,23,12.5,1,5,0,1,9,0,7,1,1,7\n6054,23,13.2,1,4,0,1,15,1,3,1,1,5\n1967,23,13.3,1,5,0,1,9,0,7,1,1,4\n1791,23,13.6,1,1,0,1,15,0,1,1,1,4\n800,23,13.7,1,1,0,1,15,0,2,1,1,8\n946,23,13.8,1,2,0,1,11,0,6,1,1,4\n8244,23,13.9,1,4,0,1,14,1,7,1,1,6\n5904,23,13.9,1,5,0,1,14,1,5,1,1,3\n2618,23,14.2,1,3,0,1,15,0,4,1,1,9\n612,23,14.3,1,5,0,1,15,0,2,1,1,10\n4106,23,14.4,1,5,0,1,15,0,6,1,1,5\n2719,23,14.6,1,1,0,1,12,0,1,1,1,2\n5600,23,14.8,1,2,0,1,15,1,8,1,1,6\n8797,23,15.1,1,3,0,1,12,1,6,1,1,4\n9832,23,15.1,1,3,0,1,10,1,4,1,1,9\n9062,23,15.9,1,3,0,1,12,1,6,1,1,1\n884,23,15.9,1,4,0,1,11,0,7,1,1,6\n9218,23,16.2,1,3,0,1,12,1,7,1,1,10\n7782,23,17,1,1,0,1,14,1,2,1,1,2\n792,23,17.1,1,4,0,1,14,0,5,1,1,5\n5994,23,17.2,1,2,0,1,10,1,8,1,1,2\n1019,23,17.6,1,1,0,1,10,0,7,1,1,2\n7083,23,17.8,1,5,0,1,14,1,3,1,1,1\n9225,23,17.9,1,4,0,1,9,1,6,1,1,2\n7277,23,18.1,1,2,0,1,11,1,7,1,1,4\n2490,23,18.3,1,4,0,1,15,0,6,1,1,5\n7254,23,18.7,1,3,0,1,14,1,3,1,1,10\n1096,23,18.8,1,5,0,1,13,0,8,1,1,2\n4444,23,19,1,3,0,1,12,0,3,1,1,7\n5446,23,19.1,1,5,0,1,10,1,1,1,1,2\n9291,23,19.1,1,2,0,1,12,1,7,1,1,1\n8456,23,19.2,1,2,0,1,11,1,8,1,1,7\n8097,23,19.3,1,2,0,1,14,1,3,1,1,3\n2646,23,19.4,1,4,0,1,9,0,7,1,1,3\n4970,23,19.6,1,4,0,1,14,0,2,1,1,1\n7988,23,20,1,5,0,1,11,1,3,1,1,10\n579,23,20.5,1,5,0,1,14,0,1,1,1,1\n3350,23,20.6,1,2,0,1,13,0,8,1,1,7\n1252,23,20.7,1,4,0,1,12,0,4,1,1,10\n9284,23,20.7,1,2,0,1,11,1,6,1,1,8\n5380,23,21.5,1,4,0,1,14,0,1,1,1,9\n5652,23,21.6,1,3,0,1,12,1,8,1,1,6\n1296,23,21.8,1,2,0,1,12,0,7,1,1,5\n2348,23,22.5,1,3,0,1,11,0,3,1,1,10\n4582,23,22.8,1,2,0,1,10,0,5,1,1,4\n234,23,22.8,1,3,0,1,15,0,1,1,1,4\n7831,23,24.3,1,2,0,1,10,1,8,1,1,1\n3148,23,24.6,1,1,0,1,12,0,2,1,1,0\n5939,23,24.7,1,5,0,1,15,1,2,1,1,3\n9330,23,24.9,1,3,0,1,12,1,2,1,1,8\n6158,23,25.3,1,1,0,1,14,1,8,1,1,9\n6034,23,25.7,1,5,0,1,13,1,6,1,1,1\n4414,23,26,1,3,0,1,11,0,2,1,1,2\n4671,23,26.1,1,3,0,1,12,0,8,1,1,2\n6108,23,26.2,1,5,0,1,13,1,4,1,1,9\n10196,23,26.4,1,2,0,1,12,1,5,1,1,0\n8892,23,27.1,1,1,0,1,13,1,3,1,1,6\n4524,23,27.2,1,4,0,1,15,0,5,1,1,7\n268,23,27.3,1,4,0,1,15,0,7,1,1,7\n10218,23,27.5,1,2,0,1,12,1,4,1,1,9\n10264,23,27.7,1,2,0,1,10,1,7,1,1,9\n3666,23,27.8,1,4,0,1,9,0,5,1,1,6\n154,23,28.2,1,2,0,1,12,0,4,1,1,8\n9700,23,28.8,1,5,0,1,11,1,5,1,1,8\n4200,23,28.8,1,1,0,1,12,0,2,1,1,6\n8522,23,29.3,1,3,0,1,15,1,8,1,1,0\n7537,23,29.4,1,2,0,1,15,1,5,1,1,2\n2607,23,29.5,1,4,0,1,12,0,2,1,1,1\n7719,23,29.6,1,4,0,1,9,1,7,1,1,1\n1031,23,29.7,1,4,0,1,9,0,5,1,1,4\n4656,24,0.2,1,5,0,1,15,0,1,1,1,9\n531,24,0.4,1,1,0,1,12,0,8,1,1,3\n4189,24,1.3,1,3,0,1,12,0,8,1,1,3\n5466,24,2.2,1,5,0,1,12,1,1,1,1,3\n1699,24,2.8,1,3,0,1,9,0,4,1,1,2\n1199,24,3,1,3,0,1,15,0,8,1,1,3\n4688,24,3.1,1,4,0,1,9,0,5,1,1,8\n9324,24,3.2,1,4,0,1,10,1,6,1,1,1\n6322,24,4,1,4,0,1,14,1,8,1,1,8\n7310,24,4.9,1,1,0,1,14,1,4,1,1,7\n5105,24,5.2,1,4,0,1,11,0,4,1,1,0\n1046,24,5.4,1,3,0,1,11,0,7,1,1,0\n8008,24,5.8,1,1,0,1,15,1,1,1,1,2\n7278,24,6.4,1,2,0,1,11,1,5,1,1,6\n9488,24,7,1,1,0,1,15,1,3,1,1,2\n5766,24,7.5,1,2,0,1,12,1,5,1,1,3\n7826,24,7.9,1,2,0,1,12,1,7,1,1,1\n6106,24,8,1,2,0,1,12,1,1,1,1,1\n7506,24,9.1,1,4,0,1,9,1,3,1,1,5\n1510,24,9.6,1,4,0,1,12,0,6,1,1,2\n6858,24,9.6,1,1,0,1,9,1,1,1,1,1\n5517,24,10,1,3,0,1,11,1,4,1,1,4\n4559,24,10.1,1,2,0,1,12,0,8,1,1,4\n5722,24,10.5,1,5,0,1,12,1,6,1,1,3\n2876,24,10.5,1,5,0,1,12,0,6,1,1,9\n1270,24,10.5,1,4,0,1,12,0,5,1,1,0\n9773,24,10.6,1,2,0,1,9,1,8,1,1,3\n8681,24,11.1,1,4,0,1,13,1,4,1,1,4\n9696,24,11.3,1,1,0,1,11,1,6,1,1,1\n5200,24,12.4,1,4,0,1,12,0,7,1,1,2\n2710,24,12.8,1,3,0,1,12,0,4,1,1,2\n8938,24,12.9,1,4,0,1,13,1,2,1,1,2\n7909,24,13.3,1,2,0,1,11,1,1,1,1,1\n1362,24,13.4,1,4,0,1,15,0,7,1,1,4\n7306,24,13.6,1,1,0,1,10,1,4,1,1,4\n7518,24,14,1,5,0,1,12,1,6,1,1,7\n4888,24,14.3,1,2,0,1,15,0,4,1,1,8\n8040,24,14.9,1,1,0,1,13,1,2,1,1,10\n8311,24,15,1,1,0,0,15,1,1,1,1,5\n8219,24,15,1,2,0,0,15,1,2,1,1,7\n247,24,15.1,1,5,0,0,10,0,1,1,1,5\n8299,24,15.1,1,3,0,0,13,1,1,1,1,6\n919,24,15.1,1,1,0,0,13,0,2,1,1,8\n2185,24,15.1,1,5,0,0,9,0,2,1,1,7\n905,24,15.2,1,4,0,0,11,0,0,1,1,9\n3381,24,15.2,1,2,0,0,14,0,2,1,1,7\n4291,24,15.2,1,5,0,0,15,0,2,1,1,5\n8363,24,15.3,1,4,0,1,13,1,5,1,1,0\n5799,24,15.3,1,4,0,0,11,1,0,1,1,10\n8665,24,15.3,1,2,0,0,9,1,0,1,1,5\n2691,24,15.3,1,4,0,0,9,0,2,1,1,8\n10371,24,15.3,1,3,0,0,13,1,1,1,1,6\n69,24,15.3,1,2,0,0,15,0,2,1,1,9\n2197,24,15.3,1,2,0,0,15,0,0,1,1,5\n1641,24,15.3,1,1,0,0,12,0,0,1,1,6\n989,24,15.3,1,2,0,0,14,0,0,1,1,6\n6581,24,15.3,1,2,0,0,13,1,1,1,1,7\n8436,24,15.4,1,3,0,1,10,1,4,1,1,6\n2251,24,15.4,1,3,0,0,15,0,0,1,1,7\n10677,24,15.4,1,1,0,0,9,1,1,1,1,7\n5997,24,15.5,1,4,0,0,12,1,2,1,1,7\n7007,24,15.5,1,2,0,0,15,1,1,1,1,8\n10215,24,15.6,1,1,0,0,15,1,2,1,1,8\n571,24,15.6,1,5,0,0,13,0,1,1,1,7\n10563,24,15.6,1,1,0,0,13,1,0,1,1,6\n7947,24,15.7,1,4,0,0,14,1,2,1,1,6\n3235,24,15.7,1,5,0,0,14,0,1,1,1,5\n9519,24,15.7,1,2,0,0,9,1,2,1,1,10\n8411,24,15.8,1,5,0,0,9,1,1,1,1,7\n2163,24,15.8,1,4,0,0,14,0,1,1,1,5\n10339,24,15.8,1,1,0,0,15,1,2,1,1,6\n2247,24,15.8,1,2,0,0,9,0,0,1,1,7\n7695,24,15.8,1,5,0,0,9,1,0,1,1,10\n5225,24,15.9,1,5,0,0,12,0,1,1,1,5\n9561,24,15.9,1,1,0,0,14,1,2,1,1,8\n1627,24,15.9,1,5,0,0,10,0,0,1,1,5\n8975,24,15.9,1,2,0,0,9,1,2,1,1,8\n10817,24,15.9,1,4,0,0,9,1,2,1,1,5\n4905,24,16,1,2,0,0,14,0,2,1,1,7\n7133,24,16,1,2,0,0,14,1,2,1,1,7\n8757,24,16,1,2,0,0,15,1,2,1,1,9\n6875,24,16,1,2,0,0,9,1,0,1,1,6\n3969,24,16,1,1,0,0,14,0,1,1,1,5\n2955,24,16.1,1,1,0,0,9,0,0,1,1,6\n157,24,16.1,1,1,0,0,12,0,1,1,1,6\n7345,24,16.1,1,5,0,0,10,1,1,1,1,5\n6097,24,16.1,1,1,0,0,13,1,1,1,1,7\n7783,24,16.1,1,5,0,0,9,1,1,1,1,9\n7333,24,16.1,1,5,0,0,11,1,0,1,1,6\n2423,24,16.2,1,1,0,0,12,0,1,1,1,5\n795,24,16.2,1,4,0,0,11,0,1,1,1,5\n1177,24,16.3,1,2,0,1,14,0,1,1,1,2\n4261,24,16.3,1,3,0,0,13,0,1,1,1,5\n6287,24,16.3,1,1,0,0,10,1,1,1,1,10\n4103,24,16.4,1,5,0,0,12,0,2,1,1,7\n6519,24,16.4,1,2,0,0,10,1,1,1,1,6\n9935,24,16.4,1,2,0,0,15,1,0,1,1,5\n10699,24,16.4,1,1,0,0,9,1,0,1,1,8\n1607,24,16.5,1,2,0,0,9,0,1,1,1,8\n7473,24,16.5,1,5,0,0,10,1,0,1,1,9\n3713,24,16.6,1,3,0,0,12,0,0,1,1,6\n10573,24,16.6,1,1,0,0,9,1,1,1,1,5\n1929,24,16.6,1,4,0,0,12,0,0,1,1,5\n1933,24,16.6,1,5,0,0,14,0,1,1,1,6\n8625,24,16.6,1,1,0,0,14,1,0,1,1,9\n431,24,16.6,1,2,0,0,12,0,1,1,1,9\n8141,24,16.6,1,4,0,0,14,1,1,1,1,9\n9205,24,16.6,1,2,0,0,9,1,0,1,1,6\n5355,24,16.6,1,3,0,0,10,0,0,1,1,8\n3355,24,16.6,1,1,0,0,15,0,0,1,1,6\n1678,24,16.7,1,1,0,1,12,0,3,1,1,10\n2617,24,16.7,1,3,0,0,15,0,1,1,1,9\n9061,24,16.7,1,4,0,0,11,1,1,1,1,10\n1879,24,16.7,1,2,0,0,13,0,2,1,1,6\n6175,24,16.8,1,2,0,0,13,1,2,1,1,5\n8885,24,16.8,1,1,0,0,10,1,1,1,1,9\n1564,24,16.8,1,5,0,1,14,0,4,1,1,0\n5637,24,16.8,1,5,0,0,9,1,1,1,1,9\n9759,24,16.9,1,1,0,0,15,1,2,1,1,8\n8799,24,16.9,1,4,0,1,12,1,2,1,1,3\n8769,24,16.9,1,1,0,0,14,1,2,1,1,6\n1862,24,16.9,1,1,0,1,14,0,1,1,1,7\n3761,24,16.9,1,5,0,0,12,0,0,1,1,8\n10683,24,17,1,3,0,0,14,1,0,1,1,5\n4007,24,17,1,4,0,0,12,0,2,1,1,9\n8171,24,17,1,1,0,1,12,1,7,1,1,1\n6857,24,17,1,2,0,0,11,1,0,1,1,10\n2081,24,17,1,4,0,0,12,0,2,1,1,8\n203,24,17.1,1,1,0,0,10,0,1,1,1,9\n6937,24,17.1,1,1,0,0,10,1,1,1,1,5\n7058,24,17.1,1,5,0,1,12,1,5,1,1,1\n4675,24,17.2,1,4,0,0,12,0,1,1,1,5\n5787,24,17.2,1,4,0,0,13,1,1,1,1,8\n5321,24,17.2,1,2,0,0,12,0,2,1,1,6\n4569,24,17.3,1,5,0,0,12,0,0,1,1,10\n4069,24,17.3,1,3,0,0,13,0,0,1,1,6\n8273,24,17.3,1,3,0,0,10,1,0,1,1,5\n6737,24,17.4,1,3,0,0,15,1,1,1,1,8\n7559,24,17.4,1,5,0,0,14,1,0,1,1,5\n1011,24,17.4,1,4,0,0,12,0,0,1,1,7\n1121,24,17.4,1,1,0,0,10,0,1,1,1,5\n9917,24,17.4,1,1,0,0,11,1,1,1,1,6\n10467,24,17.5,1,3,0,0,14,1,1,1,1,8\n9733,24,17.5,1,4,0,0,12,1,2,1,1,9\n1427,24,17.5,1,2,0,0,12,0,1,1,1,9\n8557,24,17.6,1,5,0,0,10,1,1,1,1,10\n10567,24,17.6,1,4,0,0,13,1,1,1,1,6\n5401,24,17.6,1,4,0,0,12,0,2,1,1,8\n4239,24,17.6,1,2,0,0,10,0,0,1,1,7\n1021,24,17.6,1,1,0,0,14,0,2,1,1,8\n10687,24,17.6,1,3,0,0,11,1,0,1,1,5\n10173,24,17.7,1,3,0,0,13,1,1,1,1,5\n917,24,17.8,1,2,0,0,14,0,2,1,1,8\n7235,24,17.8,1,4,0,0,12,1,0,1,1,10\n10063,24,17.8,1,5,0,0,11,1,1,1,1,8\n6631,24,17.9,1,3,0,0,13,1,1,1,1,5\n8253,24,17.9,1,5,0,0,11,1,1,1,1,10\n999,24,17.9,1,5,0,0,10,0,2,1,1,5\n1935,24,17.9,1,2,0,0,11,0,2,1,1,10\n7759,24,17.9,1,1,0,0,12,1,1,1,1,10\n2917,24,17.9,1,1,0,0,14,0,2,1,1,7\n9145,24,17.9,1,1,0,0,11,1,2,1,1,7\n4595,24,17.9,1,3,0,0,13,0,1,1,1,6\n583,24,17.9,1,2,0,0,12,0,0,1,1,8\n10255,24,17.9,1,5,0,0,12,1,1,1,1,6\n5111,24,17.9,1,3,0,0,11,0,2,1,1,6\n4679,24,18,1,5,0,0,11,0,0,1,1,8\n2857,24,18,1,5,0,0,9,0,2,1,1,7\n10713,24,18,1,5,0,0,11,1,0,1,1,5\n7211,24,18,1,3,0,0,12,1,0,1,1,7\n4479,24,18.1,1,1,0,0,10,0,0,1,1,8\n229,24,18.1,1,3,0,0,15,0,0,1,1,7\n3851,24,18.1,1,2,0,0,12,0,1,1,1,8\n6093,24,18.1,1,3,0,0,12,1,2,1,1,9\n2761,24,18.1,1,5,0,0,15,0,0,1,1,8\n1349,24,18.1,1,1,0,0,9,0,2,1,1,8\n7677,24,18.1,1,2,0,0,9,1,0,1,1,7\n3605,24,18.2,1,2,0,0,10,0,1,1,1,9\n6063,24,18.2,1,2,0,0,15,1,2,1,1,9\n9881,24,18.2,1,3,0,0,11,1,2,1,1,9\n2772,24,18.2,1,4,0,1,10,0,3,1,1,4\n3085,24,18.2,1,3,0,0,13,0,2,1,1,7\n5763,24,18.2,1,3,0,0,14,1,1,1,1,6\n2128,24,18.3,1,5,0,1,10,0,1,1,1,8\n2615,24,18.3,1,5,0,0,10,0,0,1,1,7\n2283,24,18.3,1,4,0,0,11,0,0,1,1,7\n6127,24,18.3,1,4,0,0,12,1,2,1,1,7\n1405,24,18.3,1,2,0,0,12,0,2,1,1,10\n4719,24,18.3,1,1,0,0,9,0,0,1,1,5\n7786,24,18.3,1,3,0,1,11,1,4,1,1,9\n6341,24,18.3,1,1,0,0,12,1,0,1,1,6\n2514,24,18.4,1,4,0,1,13,0,3,1,1,1\n6251,24,18.4,1,2,0,0,14,1,2,1,1,5\n3107,24,18.4,1,4,0,0,13,0,0,1,1,10\n1621,24,18.5,1,1,0,0,9,0,2,1,1,5\n1745,24,18.5,1,1,0,0,12,0,2,1,1,7\n6357,24,18.5,1,2,0,0,14,1,2,1,1,9\n6881,24,18.5,1,1,0,0,11,1,2,1,1,8\n2747,24,18.5,1,2,0,0,12,0,0,1,1,10\n7407,24,18.6,1,3,0,0,12,1,1,1,1,7\n4933,24,18.6,1,3,0,0,12,0,0,1,1,10\n1221,24,18.6,1,3,0,0,12,0,0,1,1,5\n6645,24,18.6,1,2,0,0,11,1,2,1,1,7\n7427,24,18.7,1,3,0,0,9,1,0,1,1,10\n2191,24,18.7,1,4,0,0,13,0,2,1,1,5\n6073,24,18.7,1,4,0,0,12,1,0,1,1,7\n637,24,18.8,1,1,0,0,10,0,1,1,1,9\n7159,24,18.8,1,3,0,0,12,1,2,1,1,9\n1681,24,18.8,1,4,0,0,10,0,0,1,1,9\n3083,24,18.8,1,5,0,0,14,0,0,1,1,7\n5261,24,18.8,1,3,0,0,13,0,2,1,1,9\n9385,24,18.8,1,3,0,0,12,1,2,1,1,6\n1739,24,18.8,1,3,0,0,15,0,1,1,1,8\n8959,24,18.8,1,2,0,0,13,1,2,1,1,10\n4971,24,18.8,1,2,0,0,15,0,2,1,1,5\n10737,24,18.8,1,3,0,0,11,1,2,1,1,5\n10813,24,18.9,1,3,0,0,12,1,1,1,1,6\n1027,24,18.9,1,3,0,0,9,0,1,1,1,9\n3071,24,18.9,1,2,0,0,15,0,2,1,1,6\n10778,24,18.9,1,3,0,1,11,1,3,1,1,1\n2481,24,19,1,2,0,0,14,0,1,1,1,8\n6541,24,19,1,3,0,0,14,1,0,1,1,8\n783,24,19,1,2,0,0,10,0,1,1,1,9\n5633,24,19,1,1,0,0,12,1,1,1,1,9\n10679,24,19.1,1,4,0,0,12,1,2,1,1,5\n4973,24,19.1,1,2,0,0,12,0,1,1,1,8\n1343,24,19.1,1,5,0,0,11,0,1,1,1,7\n1137,24,19.2,1,1,0,0,9,0,0,1,1,5\n10389,24,19.2,1,1,0,0,14,1,2,1,1,7\n5483,24,19.2,1,1,0,0,11,1,0,1,1,7\n1375,24,19.2,1,3,0,0,11,0,1,1,1,10\n2121,24,19.2,1,2,0,0,13,0,2,1,1,5\n7491,24,19.2,1,2,0,0,12,1,1,1,1,10\n1219,24,19.2,1,4,0,0,9,0,0,1,1,9\n6383,24,19.3,1,1,0,0,9,1,0,1,1,9\n205,24,19.3,1,3,0,0,11,0,2,1,1,6\n3550,24,19.3,1,5,0,1,9,0,3,1,1,1\n6887,24,19.3,1,1,0,0,12,1,2,1,1,8\n9989,24,19.3,1,1,0,0,12,1,2,1,1,10\n523,24,19.3,1,5,0,1,14,0,6,1,1,2\n1183,24,19.4,1,1,0,0,15,0,1,1,1,7\n2263,24,19.4,1,2,0,0,12,0,1,1,1,10\n10745,24,19.4,1,5,0,0,12,1,0,1,1,10\n2327,24,19.5,1,1,0,0,14,0,1,1,1,9\n10095,24,19.5,1,1,0,0,9,1,0,1,1,7\n5837,24,19.6,1,1,0,0,12,1,0,1,1,10\n10043,24,19.6,1,3,0,0,15,1,2,1,1,8\n10611,24,19.6,1,2,0,0,13,1,2,1,1,8\n7571,24,19.7,1,2,0,0,14,1,0,1,1,9\n10241,24,19.7,1,4,0,0,9,1,2,1,1,10\n2085,24,19.7,1,4,0,0,9,0,0,1,1,9\n4031,24,19.7,1,4,0,0,12,0,0,1,1,6\n2447,24,19.8,1,5,0,0,13,0,0,1,1,6\n8289,24,19.8,1,2,0,0,11,1,2,1,1,7\n10553,24,19.8,1,3,0,0,12,1,1,1,1,6\n1029,24,19.8,1,1,0,0,15,0,0,1,1,8\n5909,24,19.9,1,1,0,0,10,1,1,1,1,10\n9549,24,19.9,1,1,0,0,15,1,1,1,1,8\n7221,24,19.9,1,5,0,0,13,1,0,1,1,9\n10607,24,20,1,5,0,0,12,1,2,1,1,5\n1173,24,20,1,1,0,0,14,0,1,1,1,8\n7269,24,20,1,2,0,0,11,1,0,1,1,9\n6741,24,20,1,5,0,1,12,1,3,1,1,4\n6585,24,20,1,2,0,0,10,1,2,1,1,10\n243,24,20.1,1,1,0,0,11,0,2,1,1,10\n8067,24,20.1,1,5,0,0,12,1,0,1,1,7\n1617,24,20.2,1,5,0,0,10,0,1,1,1,9\n6273,24,20.2,1,4,0,0,9,1,2,1,1,7\n1593,24,20.2,1,1,0,0,12,0,1,1,1,9\n7215,24,20.2,1,1,0,0,12,1,1,1,1,10\n8531,24,20.2,1,2,0,0,12,1,1,1,1,5\n10765,24,20.3,1,1,0,0,12,1,2,1,1,10\n911,24,20.3,1,1,0,0,15,0,2,1,1,6\n1409,24,20.3,1,2,0,0,14,0,2,1,1,9\n9827,24,20.4,1,4,0,0,10,1,2,1,1,6\n8387,24,20.4,1,1,0,0,14,1,1,1,1,9\n9365,24,20.4,1,5,0,0,15,1,1,1,1,7\n6935,24,20.5,1,3,0,0,15,1,0,1,1,5\n3095,24,20.5,1,1,0,0,10,0,0,1,1,8\n2625,24,20.5,1,3,0,0,9,0,1,1,1,5\n9545,24,20.5,1,1,0,0,10,1,1,1,1,5\n4525,24,20.5,1,1,0,0,10,0,1,1,1,8\n4847,24,20.5,1,2,0,0,12,0,2,1,1,7\n6807,24,20.5,1,2,0,0,13,1,2,1,1,6\n7603,24,20.6,1,4,0,0,9,1,1,1,1,10\n1169,24,20.6,1,4,0,0,13,0,0,1,1,8\n10233,24,20.6,1,1,0,0,12,1,2,1,1,6\n9113,24,20.6,1,1,0,0,13,1,1,1,1,7\n7661,24,20.7,1,5,0,0,14,1,0,1,1,5\n3455,24,20.7,1,2,0,0,10,0,1,1,1,8\n8879,24,20.7,1,5,0,0,13,1,2,1,1,8\n9599,24,20.8,1,5,0,0,12,1,2,1,1,10\n1851,24,20.8,1,5,0,0,11,0,0,1,1,8\n1671,24,20.9,1,5,0,0,10,0,0,1,1,7\n1053,24,20.9,1,3,0,0,13,0,0,1,1,5\n4771,24,20.9,1,4,0,0,9,0,1,1,1,6\n10811,24,21,1,5,0,0,14,1,2,1,1,5\n6143,24,21,1,4,0,0,14,1,1,1,1,7\n8517,24,21,1,3,0,0,9,1,1,1,1,6\n10818,24,21,1,3,0,1,14,1,7,1,1,2\n2311,24,21,1,3,0,0,11,0,1,1,1,5\n5727,24,21.1,1,1,0,0,9,1,0,1,1,6\n9021,24,21.1,1,5,0,0,9,1,0,1,1,7\n3356,24,21.2,1,2,0,1,11,0,5,1,1,3\n935,24,21.2,1,5,0,0,12,0,0,1,1,8\n9507,24,21.2,1,3,0,0,14,1,1,1,1,7\n5277,24,21.2,1,3,0,0,11,0,1,1,1,8\n5681,24,21.2,1,5,0,0,9,1,1,1,1,6\n6233,24,21.2,1,5,0,0,14,1,1,1,1,6\n4123,24,21.2,1,2,0,0,11,0,0,1,1,9\n389,24,21.2,1,5,0,0,14,0,2,1,1,10\n6574,24,21.3,1,1,0,1,12,1,4,1,1,0\n8131,24,21.3,1,2,0,0,13,1,0,1,1,5\n5503,24,21.3,1,1,0,0,15,1,1,1,1,5\n8491,24,21.3,1,1,0,0,12,1,1,1,1,10\n4879,24,21.4,1,3,0,0,12,0,1,1,1,7\n7609,24,21.4,1,1,0,0,12,1,1,1,1,7\n10782,24,21.5,1,3,0,1,14,1,7,1,1,8\n907,24,21.5,1,1,0,0,11,0,0,1,1,8\n695,24,21.5,1,2,0,0,13,0,0,1,1,7\n9035,24,21.6,1,4,0,0,10,1,1,1,1,6\n5807,24,21.6,1,4,0,0,9,1,0,1,1,10\n125,24,21.6,1,4,0,0,11,0,2,1,1,7\n4563,24,21.7,1,2,0,0,14,0,1,1,1,10\n8935,24,21.7,1,3,0,0,12,1,2,1,1,5\n10175,24,21.7,1,4,0,0,12,1,1,1,1,10\n8307,24,21.7,1,3,0,0,12,1,1,1,1,8\n655,24,21.7,1,4,0,0,15,0,2,1,1,8\n4555,24,21.7,1,1,0,0,14,0,2,1,1,7\n9261,24,21.7,1,1,0,0,14,1,1,1,1,8\n2435,24,21.7,1,2,0,0,12,0,2,1,1,8\n7547,24,21.7,1,4,0,0,13,1,1,1,1,5\n3093,24,21.8,1,5,0,0,11,0,2,1,1,9\n9037,24,21.8,1,1,0,0,15,1,1,1,1,10\n2169,24,21.8,1,3,0,0,9,0,2,1,1,9\n735,24,21.9,1,2,0,0,15,0,1,1,1,8\n7021,24,21.9,1,4,0,0,12,1,2,1,1,6\n3873,24,21.9,1,4,0,1,13,0,4,1,1,2\n171,24,21.9,1,4,0,0,15,0,0,1,1,5\n5393,24,21.9,1,3,0,0,12,0,2,1,1,8\n10139,24,22,1,2,0,0,10,1,2,1,1,9\n7343,24,22,1,3,0,0,12,1,1,1,1,10\n3781,24,22,1,2,0,0,12,0,1,1,1,6\n1227,24,22,1,4,0,0,15,0,2,1,1,9\n10065,24,22,1,1,0,0,13,1,1,1,1,6\n10223,24,22,1,5,0,0,13,1,1,1,1,7\n4583,24,22,1,2,0,0,10,0,0,1,1,10\n8761,24,22,1,4,0,0,9,1,2,1,1,8\n5743,24,22,1,3,0,0,13,1,1,1,1,9\n47,24,22.1,1,1,0,0,14,0,0,1,1,8\n3035,24,22.1,1,2,0,0,12,0,1,1,1,5\n3171,24,22.1,1,3,0,0,15,0,2,1,1,6\n3759,24,22.2,1,1,0,0,15,0,2,1,1,9\n5341,24,22.2,1,2,0,0,10,0,2,1,1,9\n1545,24,22.2,1,3,0,0,15,0,2,1,1,7\n4149,24,22.2,1,2,0,0,15,0,2,1,1,10\n5197,24,22.2,1,4,0,1,10,0,8,1,1,1\n8285,24,22.4,1,4,0,0,13,1,1,1,1,7\n5694,24,22.4,1,5,0,1,9,1,7,1,1,7\n5151,24,22.4,1,1,0,0,12,0,2,1,1,7\n4061,24,22.4,1,5,0,0,14,0,0,1,1,6\n1939,24,22.4,1,3,0,0,15,0,2,1,1,8\n1585,24,22.5,1,4,0,0,11,0,1,1,1,9\n3575,24,22.5,1,4,0,0,12,0,1,1,1,5\n8551,24,22.5,1,3,0,0,12,1,1,1,1,10\n4689,24,22.5,1,2,0,1,10,0,6,1,1,4\n4109,24,22.6,1,2,0,0,9,0,1,1,1,7\n3038,24,22.6,1,2,0,1,11,0,7,1,1,0\n2653,24,22.6,1,2,0,0,11,0,0,1,1,5\n8181,24,22.6,1,4,0,0,12,1,1,1,1,7\n4183,24,22.7,1,2,0,0,14,0,2,1,1,6\n5623,24,22.7,1,1,0,0,14,1,2,1,1,8\n67,24,22.8,1,1,0,0,13,0,2,1,1,6\n3309,24,22.8,1,1,0,0,12,0,2,1,1,5\n8969,24,22.8,1,4,0,0,9,1,1,1,1,10\n5033,24,22.8,1,1,0,0,12,0,0,1,1,6\n7727,24,22.8,1,4,0,0,12,1,2,1,1,7\n777,24,22.9,1,3,0,0,9,0,1,1,1,10\n1570,24,22.9,1,2,0,1,9,0,3,1,1,4\n8563,24,22.9,1,5,0,0,11,1,2,1,1,5\n5509,24,22.9,1,2,0,0,10,1,0,1,1,8\n2909,24,23,1,1,0,0,11,0,0,1,1,7\n2249,24,23,1,1,0,0,9,0,1,1,1,6\n4297,24,23.1,1,3,0,0,14,0,0,1,1,9\n10741,24,23.1,1,4,0,0,14,1,1,1,1,5\n4345,24,23.1,1,3,0,0,10,0,2,1,1,10\n9661,24,23.1,1,5,0,0,13,1,1,1,1,7\n1691,24,23.2,1,1,0,0,12,0,0,1,1,7\n7951,24,23.2,1,1,0,0,9,1,1,1,1,10\n8851,24,23.3,1,1,0,0,15,1,1,1,1,8\n8997,24,23.3,1,3,0,0,12,1,0,1,1,8\n4256,24,23.3,1,5,0,1,12,0,8,1,1,9\n9321,24,23.3,1,1,0,0,12,1,0,1,1,8\n9893,24,23.3,1,4,0,0,11,1,1,1,1,9\n4041,24,23.3,1,4,0,0,9,0,0,1,1,10\n6117,24,23.3,1,4,0,0,10,1,1,1,1,9\n5323,24,23.3,1,1,0,0,15,0,0,1,1,5\n7683,24,23.3,1,2,0,0,12,1,2,1,1,10\n6291,24,23.3,1,1,0,0,12,1,2,1,1,8\n1049,24,23.4,1,2,0,0,13,0,2,1,1,10\n3241,24,23.4,1,1,0,0,11,0,1,1,1,9\n5903,24,23.4,1,4,0,0,13,1,2,1,1,9\n1687,24,23.4,1,5,0,0,9,0,1,1,1,10\n3819,24,23.4,1,2,0,0,10,0,1,1,1,10\n7841,24,23.5,1,3,0,0,10,1,1,1,1,10\n887,24,23.5,1,1,0,0,11,0,2,1,1,5\n10855,24,23.5,1,1,0,0,9,1,2,1,1,10\n659,24,23.5,1,3,0,0,13,0,1,1,1,10\n10027,24,23.5,1,2,0,0,14,1,2,1,1,6\n7223,24,23.5,1,2,0,0,15,1,0,1,1,10\n10127,24,23.6,1,4,0,0,9,1,1,1,1,10\n3989,24,23.6,1,1,0,0,15,0,2,1,1,6\n4125,24,23.6,1,1,0,0,10,0,2,1,1,5\n4575,24,23.6,1,3,0,0,13,0,0,1,1,10\n10015,24,23.7,1,2,0,0,15,1,0,1,1,10\n5113,24,23.7,1,2,0,0,14,0,0,1,1,7\n5911,24,23.7,1,5,0,1,15,1,7,1,1,3\n4627,24,23.7,1,1,0,0,11,0,0,1,1,8\n10117,24,23.7,1,2,0,0,12,1,0,1,1,7\n647,24,23.7,1,4,0,0,14,0,1,1,1,6\n2915,24,23.7,1,1,0,0,15,0,2,1,1,10\n4603,24,23.7,1,1,0,0,12,0,2,1,1,8\n2609,24,23.7,1,1,0,0,15,0,2,1,1,6\n7573,24,23.8,1,3,0,0,15,1,0,1,1,7\n7113,24,23.8,1,4,0,0,10,1,2,1,1,8\n10773,24,23.8,1,3,0,1,12,1,6,1,1,1\n4637,24,23.8,1,5,0,0,12,0,0,1,1,6\n6565,24,23.9,1,3,0,0,12,1,1,1,1,5\n10235,24,23.9,1,1,0,0,12,1,2,1,1,6\n623,24,23.9,1,5,0,0,12,0,1,1,1,9\n3369,24,23.9,1,5,0,0,12,0,2,1,1,5\n3479,24,24,1,2,0,0,9,0,2,1,1,10\n7255,24,24,1,1,0,0,12,1,1,1,1,6\n424,24,24,1,1,0,1,9,0,4,1,1,3\n9591,24,24,1,1,0,0,10,1,2,1,1,6\n5211,24,24,1,1,0,0,10,0,2,1,1,6\n473,24,24.1,1,3,0,0,12,0,0,1,1,8\n6257,24,24.1,1,3,0,0,15,1,0,1,1,10\n10537,24,24.1,1,2,0,0,9,1,2,1,1,7\n1961,24,24.1,1,4,0,0,15,0,0,1,1,8\n4249,24,24.2,1,1,0,0,9,0,2,1,1,8\n9597,24,24.2,1,5,0,0,12,1,0,1,1,9\n1901,24,24.2,1,2,0,0,13,0,2,1,1,7\n6721,24,24.2,1,4,0,0,11,1,0,1,1,6\n8301,24,24.2,1,2,0,0,9,1,2,1,1,10\n5363,24,24.3,1,3,0,0,9,0,0,1,1,6\n6709,24,24.3,1,1,0,0,11,1,0,1,1,10\n10561,24,24.3,1,5,0,0,11,1,2,1,1,10\n1047,24,24.3,1,3,0,0,13,0,2,1,1,9\n1793,24,24.3,1,1,0,0,10,0,1,1,1,9\n5251,24,24.3,1,2,0,0,9,0,2,1,1,9\n333,24,24.3,1,4,0,0,13,0,2,1,1,6\n6623,24,24.3,1,3,0,0,13,1,2,1,1,5\n2005,24,24.4,1,3,0,0,12,0,2,1,1,5\n729,24,24.4,1,5,0,0,9,0,2,1,1,9\n2213,24,24.4,1,4,0,0,12,0,1,1,1,8\n10329,24,24.5,1,2,0,0,13,1,0,1,1,5\n5049,24,24.5,1,3,0,0,9,0,1,1,1,7\n425,24,24.5,1,5,0,0,13,0,1,1,1,7\n2861,24,24.6,1,1,0,0,12,0,2,1,1,6\n9559,24,24.6,1,3,0,0,13,1,1,1,1,8\n5983,24,24.6,1,3,0,0,10,1,0,1,1,10\n10769,24,24.6,1,4,0,0,12,1,1,1,1,10\n1309,24,24.6,1,4,0,0,9,0,2,1,1,8\n1087,24,24.6,1,5,0,0,10,0,2,1,1,7\n3151,24,24.7,1,2,0,0,14,0,0,1,1,5\n5309,24,24.7,1,4,0,0,9,0,2,1,1,5\n10644,24,24.7,1,3,0,1,13,1,2,1,1,4\n9627,24,24.7,1,2,0,0,10,1,2,1,1,7\n3747,24,24.7,1,2,0,0,15,0,1,1,1,6\n8323,24,24.8,1,4,0,0,11,1,2,1,1,7\n5067,24,24.8,1,1,0,0,15,0,2,1,1,9\n9315,24,24.9,1,1,0,0,9,1,2,1,1,10\n7489,24,24.9,1,1,0,0,9,1,2,1,1,6\n8333,24,25,1,1,0,0,13,1,1,1,1,8\n193,24,25,1,3,0,0,9,0,2,1,1,8\n2750,24,25,1,4,0,1,10,0,7,1,1,4\n327,24,25.1,1,2,0,0,12,0,2,1,1,7\n1495,24,25.1,1,5,0,0,12,0,1,1,1,7\n4395,24,25.1,1,3,0,1,15,0,3,1,1,0\n7629,24,25.1,1,3,0,0,15,1,2,1,1,6\n8005,24,25.1,1,1,0,0,9,1,1,1,1,8\n6345,24,25.2,1,3,0,0,12,1,1,1,1,10\n3973,24,25.2,1,3,0,0,10,0,1,1,1,5\n6621,24,25.2,1,3,0,0,12,1,2,1,1,8\n269,24,25.3,1,3,0,0,15,0,0,1,1,8\n6219,24,25.3,1,3,0,0,14,1,1,1,1,5\n9017,24,25.3,1,3,0,0,14,1,1,1,1,7\n10405,24,25.3,1,4,0,0,12,1,2,1,1,9\n9573,24,25.3,1,1,0,0,9,1,1,1,1,6\n387,24,25.3,1,5,0,1,12,0,4,1,1,2\n8445,24,25.4,1,5,0,0,13,1,0,1,1,8\n489,24,25.4,1,3,0,0,13,0,0,1,1,10\n4517,24,25.5,1,5,0,0,15,0,1,1,1,9\n8485,24,25.5,1,4,0,0,9,1,1,1,1,9\n5581,24,25.5,1,5,0,0,12,1,2,1,1,10\n8359,24,25.5,1,1,0,0,15,1,2,1,1,6\n9029,24,25.5,1,3,0,0,14,1,2,1,1,8\n10087,24,25.6,1,2,0,0,10,1,0,1,1,7\n6461,24,25.6,1,3,0,0,12,1,0,1,1,6\n1,24,25.6,1,3,0,0,14,0,0,1,1,7\n2805,24,25.6,1,1,0,0,11,0,2,1,1,7\n9271,24,25.7,1,2,0,0,15,1,0,1,1,6\n10229,24,25.7,1,5,0,0,13,1,1,1,1,7\n3403,24,25.7,1,3,0,0,11,0,0,1,1,10\n8723,24,25.7,1,2,0,0,10,1,1,1,1,10\n7997,24,25.7,1,1,0,0,13,1,2,1,1,6\n1873,24,25.7,1,5,0,0,9,0,0,1,1,10\n6069,24,25.7,1,5,0,0,9,1,1,1,1,9\n1987,24,25.8,1,2,0,0,14,0,2,1,1,6\n1043,24,25.8,1,3,0,0,15,0,0,1,1,10\n9949,24,25.8,1,1,0,0,10,1,2,1,1,6\n8946,24,25.8,1,1,0,1,14,1,8,1,1,6\n2963,24,25.9,1,2,0,0,15,0,0,1,1,9\n10785,24,25.9,1,5,0,0,15,1,1,1,1,9\n7419,24,25.9,1,3,0,0,12,1,1,1,1,5\n599,24,25.9,1,2,0,0,13,0,0,1,1,5\n2591,24,25.9,1,1,0,0,11,0,1,1,1,8\n9435,24,26,1,5,0,0,10,1,1,1,1,5\n10323,24,26,1,3,0,0,9,1,2,1,1,7\n10169,24,26,1,1,0,0,11,1,0,1,1,8\n7873,24,26,1,3,0,0,15,1,0,1,1,6\n3993,24,26,1,2,0,0,9,0,1,1,1,9\n7013,24,26,1,4,0,0,9,1,0,1,1,5\n2357,24,26,1,4,0,0,11,0,1,1,1,7\n2661,24,26,1,2,0,0,11,0,1,1,1,9\n839,24,26,1,5,0,0,12,0,0,1,1,6\n4911,24,26,1,4,0,0,14,0,2,1,1,10\n3027,24,26.1,1,5,0,0,14,0,1,1,1,8\n9240,24,26.1,1,2,0,1,12,1,7,1,1,5\n4201,24,26.1,1,1,0,0,11,0,2,1,1,7\n6691,24,26.1,1,3,0,0,14,1,1,1,1,6\n10111,24,26.1,1,3,0,0,11,1,0,1,1,10\n6545,24,26.2,1,3,0,0,14,1,0,1,1,6\n383,24,26.2,1,3,0,0,9,0,0,1,1,7\n7581,24,26.3,1,5,0,0,12,1,0,1,1,10\n2369,24,26.3,1,5,0,0,12,0,0,1,1,6\n8447,24,26.3,1,2,0,0,15,1,1,1,1,7\n2029,24,26.3,1,4,0,0,12,0,0,1,1,7\n9445,24,26.3,1,5,0,0,12,1,2,1,1,8\n2293,24,26.3,1,1,0,0,14,0,1,1,1,8\n8091,24,26.3,1,1,0,0,14,1,0,1,1,7\n660,24,26.3,1,4,0,1,12,0,3,1,1,8\n9489,24,26.3,1,1,0,0,12,1,2,1,1,6\n7233,24,26.4,1,5,0,0,11,1,0,1,1,7\n10245,24,26.4,1,2,0,0,12,1,0,1,1,7\n3127,24,26.5,1,1,0,0,13,0,2,1,1,8\n705,24,26.5,1,2,0,0,14,0,2,1,1,5\n7009,24,26.5,1,5,0,0,11,1,0,1,1,6\n4713,24,26.5,1,5,0,0,12,0,0,1,1,8\n9329,24,26.6,1,2,0,0,12,1,1,1,1,5\n6571,24,26.6,1,5,0,0,10,1,1,1,1,8\n7717,24,26.6,1,2,0,0,14,1,2,1,1,10\n5679,24,26.6,1,3,0,0,9,1,2,1,1,9\n8783,24,26.6,1,1,0,0,13,1,1,1,1,8\n6205,24,26.6,1,2,0,0,10,1,2,1,1,10\n4547,24,26.7,1,3,0,0,11,0,1,1,1,9\n6133,24,26.7,1,5,0,0,12,1,2,1,1,8\n2161,24,26.7,1,1,0,0,12,0,1,1,1,10\n5851,24,26.8,1,5,0,0,10,1,2,1,1,9\n10425,24,26.8,1,1,0,0,13,1,1,1,1,8\n9803,24,26.8,1,4,0,0,10,1,1,1,1,8\n1299,24,26.8,1,5,0,0,9,0,1,1,1,6\n10281,24,26.8,1,1,0,0,12,1,1,1,1,8\n1125,24,26.9,1,4,0,0,13,0,2,1,1,6\n7299,24,26.9,1,5,0,0,12,1,0,1,1,7\n4127,24,26.9,1,2,0,0,15,0,2,1,1,8\n3267,24,26.9,1,2,0,0,11,0,1,1,1,10\n2925,24,27,1,5,0,0,14,0,0,1,1,6\n7617,24,27,1,3,0,0,12,1,1,1,1,5\n4317,24,27,1,3,0,0,12,0,2,1,1,6\n4907,24,27.1,1,2,0,0,9,0,0,1,1,5\n8073,24,27.1,1,2,0,0,11,1,2,1,1,8\n5923,24,27.2,1,3,0,0,11,1,2,1,1,9\n5601,24,27.2,1,5,0,0,10,1,0,1,1,7\n1437,24,27.3,1,2,0,1,14,0,8,1,1,4\n10300,24,27.4,1,5,0,1,11,1,4,1,1,8\n10181,24,27.4,1,5,0,0,12,1,2,1,1,6\n7887,24,27.5,1,1,0,0,9,1,2,1,1,5\n1971,24,27.5,1,2,0,0,11,0,1,1,1,5\n7029,24,27.5,1,4,0,0,9,1,0,1,1,7\n10455,24,27.5,1,4,0,0,12,1,0,1,1,5\n6471,24,27.6,1,5,0,0,9,1,1,1,1,10\n7281,24,27.6,1,4,0,0,9,1,1,1,1,9\n4413,24,27.6,1,2,0,0,13,0,1,1,1,5\n7443,24,27.6,1,5,0,0,15,1,2,1,1,5\n4895,24,27.6,1,2,0,0,10,0,0,1,1,10\n6485,24,27.7,1,1,0,0,12,1,1,1,1,6\n3443,24,27.7,1,1,0,0,12,0,0,1,1,6\n1697,24,27.7,1,5,0,0,12,0,1,1,1,10\n2443,24,27.8,1,5,0,0,9,0,1,1,1,9\n9149,24,27.8,1,1,0,0,10,1,0,1,1,10\n3415,24,27.8,1,4,0,0,13,0,0,1,1,9\n1486,24,27.8,1,3,0,1,12,0,2,1,1,1\n10759,24,27.8,1,2,0,0,15,1,1,1,1,7\n1819,24,27.8,1,4,0,0,11,0,1,1,1,5\n6281,24,27.9,1,1,0,0,13,1,2,1,1,8\n3997,24,27.9,1,1,0,0,15,0,0,1,1,8\n6353,24,27.9,1,4,0,0,11,1,1,1,1,9\n7151,24,27.9,1,1,0,0,12,1,1,1,1,8\n4185,24,27.9,1,2,0,0,15,0,0,1,1,6\n71,24,28,1,2,0,0,12,0,0,1,1,10\n3211,24,28,1,2,0,0,15,0,2,1,1,10\n9807,24,28,1,1,0,0,12,1,0,1,1,9\n4619,24,28.1,1,2,0,0,11,0,2,1,1,6\n3402,24,28.1,1,3,0,1,9,0,4,1,1,2\n10353,24,28.2,1,5,0,0,10,1,0,1,1,6\n5533,24,28.3,1,1,0,0,13,1,2,1,1,6\n8410,24,28.3,1,3,0,1,13,1,3,1,1,5\n2215,24,28.3,1,5,0,0,12,0,1,1,1,10\n4043,24,28.3,1,5,0,0,9,0,0,1,1,10\n6037,24,28.3,1,2,0,0,15,1,2,1,1,7\n9055,24,28.4,1,5,0,0,10,1,1,1,1,9\n4277,24,28.4,1,5,0,0,12,0,0,1,1,10\n8905,24,28.4,1,5,0,0,10,1,2,1,1,5\n9013,24,28.5,1,2,0,0,12,1,0,1,1,6\n475,24,28.5,1,3,0,0,15,0,0,1,1,9\n5001,24,28.5,1,5,0,1,13,0,8,1,1,0\n9587,24,28.6,1,5,0,0,12,1,0,1,1,9\n9791,24,28.6,1,1,0,0,10,1,0,1,1,9\n7056,24,28.6,1,2,0,1,10,1,5,1,1,7\n5303,24,28.6,1,4,0,0,12,0,2,1,1,7\n2711,24,28.6,1,1,0,0,12,0,0,1,1,7\n587,24,28.6,1,1,0,0,13,0,2,1,1,10\n287,24,28.6,1,2,0,0,10,0,2,1,1,5\n5631,24,28.7,1,2,0,0,9,1,0,1,1,5\n3839,24,28.7,1,3,0,0,14,0,0,1,1,5\n8315,24,28.7,1,1,0,0,9,1,1,1,1,7\n8525,24,28.8,1,4,0,0,10,1,2,1,1,9\n711,24,28.8,1,5,0,0,15,0,1,1,1,10\n1831,24,28.8,1,5,0,0,13,0,0,1,1,10\n8111,24,28.8,1,2,0,0,9,1,2,1,1,6\n10617,24,28.8,1,5,0,0,10,1,1,1,1,6\n4523,24,28.8,1,3,0,1,11,0,5,1,1,0\n6425,24,28.8,1,5,0,0,12,1,1,1,1,9\n7967,24,28.9,1,3,0,0,9,1,0,1,1,6\n9249,24,28.9,1,2,0,0,13,1,0,1,1,5\n1181,24,28.9,1,2,0,0,10,0,0,1,1,7\n2701,24,28.9,1,2,0,0,11,0,0,1,1,9\n5173,24,28.9,1,5,0,0,12,0,1,1,1,5\n7483,24,28.9,1,1,0,0,9,1,1,1,1,7\n5825,24,28.9,1,2,0,0,9,1,0,1,1,10\n455,24,29,1,2,0,0,9,0,2,1,1,8\n1569,24,29,1,2,0,0,13,0,0,1,1,9\n5259,24,29,1,3,0,0,10,0,1,1,1,6\n5253,24,29,1,1,0,0,9,0,1,1,1,8\n3737,24,29.1,1,1,0,0,13,0,1,1,1,6\n603,24,29.1,1,4,0,0,15,0,2,1,1,5\n4869,24,29.2,1,2,0,0,11,0,2,1,1,6\n951,24,29.2,1,4,0,0,14,0,0,1,1,9\n1179,24,29.3,1,2,0,0,13,0,2,1,1,9\n5399,24,29.3,1,5,0,0,13,0,1,1,1,8\n1823,24,29.3,1,4,0,0,10,0,0,1,1,8\n81,24,29.3,1,2,0,0,12,0,1,1,1,10\n8899,24,29.3,1,2,0,0,12,1,1,1,1,7\n2529,24,29.4,1,5,0,0,10,0,1,1,1,5\n4129,24,29.5,1,4,0,0,11,0,2,1,1,7\n3225,24,29.5,1,2,0,0,10,0,1,1,1,6\n4795,24,29.5,1,2,0,0,11,0,1,1,1,10\n6761,24,29.5,1,2,0,0,14,1,2,1,1,10\n8917,24,29.6,1,4,0,0,12,1,2,1,1,9\n195,24,29.6,1,3,0,0,9,0,1,1,1,9\n8151,24,29.7,1,4,0,0,9,1,1,1,1,7\n7917,24,29.7,1,2,0,0,13,1,2,1,1,7\n10489,24,29.7,1,2,0,0,14,1,1,1,1,6\n9279,24,29.7,1,1,0,0,12,1,2,1,1,7\n6985,24,29.7,1,5,0,0,15,1,1,1,1,7\n2067,24,29.7,1,1,0,0,10,0,0,1,1,9\n8847,24,29.7,1,1,0,0,10,1,2,1,1,8\n8727,24,29.8,1,2,0,0,14,1,1,1,1,6\n355,24,29.8,1,4,0,0,10,0,1,1,1,8\n1821,24,29.8,1,3,0,0,12,0,0,1,1,7\n1365,24,29.8,1,5,0,0,15,0,1,1,1,10\n2973,24,29.8,1,2,0,0,12,0,0,1,1,9\n423,24,29.8,1,5,0,0,13,0,1,1,1,6\n6901,24,29.8,1,3,0,0,13,1,2,1,1,7\n6767,24,29.8,1,1,0,0,14,1,0,1,1,5\n8077,24,29.9,1,2,0,0,11,1,1,1,1,8\n3019,24,29.9,1,1,0,1,15,0,2,1,1,0\n2217,24,30,1,4,0,0,15,0,0,1,1,7\n3283,25,0.1,1,3,0,1,9,0,3,1,1,1\n7138,25,0.5,1,5,0,1,12,1,7,1,1,4\n9158,25,0.7,1,1,0,1,10,1,4,1,1,7\n1940,25,0.8,1,5,0,1,15,0,1,1,1,7\n7296,25,1,1,1,0,1,12,1,7,1,1,3\n5206,25,1.3,1,1,0,1,15,0,5,1,1,5\n6082,25,2.1,1,4,0,1,12,1,1,1,1,0\n7892,25,2.3,1,2,0,1,10,1,7,1,1,4\n2800,25,2.5,1,1,0,1,12,0,6,1,1,3\n3742,25,2.7,1,4,0,1,10,0,6,1,1,10\n446,25,2.8,1,1,0,1,9,0,5,1,1,8\n4270,25,3.9,1,5,0,1,13,0,4,1,1,1\n8932,25,4.4,1,3,0,1,12,1,6,1,1,7\n574,25,4.5,1,4,0,1,15,0,7,1,1,1\n6324,25,4.7,1,5,0,1,14,1,4,1,1,5\n8607,25,4.8,1,4,0,1,14,1,3,1,1,2\n10282,25,5.3,1,5,0,1,9,1,8,1,1,10\n8217,25,5.5,1,3,0,1,10,1,6,1,1,2\n9910,25,5.8,1,2,0,1,15,1,1,1,1,8\n7548,25,6.2,1,5,0,1,9,1,6,1,1,10\n2188,25,6.5,1,2,0,1,10,0,4,1,1,2\n3440,25,6.5,1,1,0,1,12,0,3,1,1,6\n6114,25,6.6,1,5,0,1,10,1,3,1,1,5\n5338,25,6.7,1,2,0,1,12,0,5,1,1,1\n5828,25,7,1,4,0,1,12,1,7,1,1,8\n5528,25,7.4,1,4,0,1,11,1,2,1,1,2\n4786,25,7.5,1,3,0,1,15,0,6,1,1,7\n3192,25,8.1,1,1,0,1,14,0,2,1,1,2\n5090,25,8.6,1,3,0,1,9,0,3,1,1,1\n7734,25,8.7,1,3,0,1,9,1,6,1,1,7\n7674,25,9.2,1,4,0,1,13,1,2,1,1,0\n722,25,9.2,1,2,0,1,11,0,2,1,1,8\n7934,25,9.4,1,4,0,1,11,1,3,1,1,9\n8742,25,10,1,2,0,1,13,1,4,1,1,3\n10790,25,10.1,1,1,0,1,12,1,5,1,1,2\n1500,25,10.4,1,1,0,1,15,0,1,1,1,2\n9034,25,10.8,1,1,0,1,9,1,4,1,1,6\n8771,25,10.9,1,4,0,1,15,1,4,1,1,0\n7991,25,11.1,1,5,0,1,12,1,6,1,1,3\n9275,25,11.1,1,5,0,1,13,1,4,1,1,2\n967,25,11.2,1,5,0,1,12,0,7,1,1,4\n9005,25,11.2,1,5,0,1,12,1,6,1,1,1\n8740,25,11.6,1,1,0,1,14,1,2,1,1,3\n2355,25,12.9,1,2,0,1,14,0,4,1,1,3\n4913,25,13.1,1,1,0,1,14,0,7,1,1,1\n4392,25,13.2,1,5,0,1,14,0,6,1,1,1\n2726,25,13.4,1,5,0,1,15,0,4,1,1,2\n1006,25,13.5,1,5,0,1,14,0,6,1,1,4\n1223,25,13.6,1,1,0,1,11,0,4,1,1,2\n6161,25,13.7,1,4,0,1,13,1,7,1,1,2\n7688,25,14,1,4,0,1,13,1,1,1,1,6\n8144,25,14.3,1,2,0,1,12,1,5,1,1,7\n10294,25,14.4,1,4,0,1,10,1,2,1,1,6\n4002,25,15,1,5,0,1,12,0,6,1,1,3\n9401,25,15,1,2,0,0,13,1,1,1,1,10\n9123,25,15,1,3,0,0,13,1,2,1,1,5\n4051,25,15,1,1,0,0,12,0,2,1,1,6\n707,25,15,1,3,0,0,10,0,0,1,1,10\n7499,25,15,1,4,0,0,15,1,2,1,1,8\n5269,25,15.1,1,4,0,0,14,0,1,1,1,9\n5925,25,15.1,1,5,0,0,10,1,0,1,1,5\n2833,25,15.1,1,3,0,0,10,0,0,1,1,8\n113,25,15.1,1,3,0,0,15,0,1,1,1,6\n9255,25,15.2,1,1,0,0,10,1,2,1,1,7\n7173,25,15.2,1,2,0,0,10,1,1,1,1,9\n10265,25,15.2,1,4,0,0,14,1,0,1,1,10\n8999,25,15.2,1,3,0,0,15,1,1,1,1,7\n6387,25,15.2,1,5,0,0,13,1,2,1,1,6\n5588,25,15.2,1,5,0,1,12,1,5,1,1,8\n9001,25,15.3,1,1,0,0,15,1,0,1,1,5\n7469,25,15.3,1,5,0,0,12,1,2,1,1,6\n10377,25,15.3,1,1,0,0,15,1,1,1,1,7\n2329,25,15.3,1,4,0,0,9,0,1,1,1,6\n7833,25,15.3,1,3,0,0,12,1,1,1,1,9\n10667,25,15.3,1,2,0,1,11,1,7,1,1,1\n2167,25,15.3,1,2,0,0,15,0,1,1,1,6\n10441,25,15.3,1,2,0,0,15,1,1,1,1,10\n4247,25,15.3,1,3,0,0,9,0,1,1,1,6\n7593,25,15.4,1,1,0,0,10,1,2,1,1,7\n2035,25,15.4,1,1,0,0,9,0,0,1,1,8\n3807,25,15.5,1,4,0,0,14,0,2,1,1,6\n7705,25,15.5,1,1,0,0,12,1,2,1,1,7\n5699,25,15.5,1,5,0,0,14,1,0,1,1,8\n8348,25,15.5,1,4,0,1,15,1,6,1,1,8\n6087,25,15.5,1,4,0,0,12,1,2,1,1,6\n19,25,15.5,1,1,0,0,12,0,1,1,1,9\n2353,25,15.6,1,2,0,0,12,0,2,1,1,8\n7977,25,15.6,1,2,0,0,12,1,0,1,1,9\n6536,25,15.7,1,2,0,1,13,1,8,1,1,3\n8197,25,15.7,1,5,0,0,12,1,1,1,1,8\n2391,25,15.7,1,1,0,0,15,0,2,1,1,6\n4267,25,15.7,1,3,0,0,14,0,0,1,1,8\n10819,25,15.8,1,4,0,0,12,1,0,1,1,9\n5659,25,15.8,1,5,0,0,13,1,2,1,1,7\n10333,25,15.8,1,1,0,0,12,1,2,1,1,10\n5339,25,15.9,1,3,0,1,13,0,6,1,1,2\n3833,25,15.9,1,1,0,0,12,0,0,1,1,6\n2407,25,15.9,1,1,0,0,10,0,2,1,1,5\n8541,25,15.9,1,4,0,0,12,1,1,1,1,10\n10538,25,15.9,1,5,0,1,15,1,5,1,1,2\n5157,25,15.9,1,2,0,1,10,0,8,1,1,0\n101,25,16,1,4,0,0,13,0,2,1,1,6\n1615,25,16.1,1,5,0,0,12,0,2,1,1,6\n10729,25,16.1,1,1,0,0,12,1,0,1,1,6\n9441,25,16.1,1,2,0,0,9,1,2,1,1,9\n9253,25,16.2,1,5,0,0,13,1,1,1,1,8\n9667,25,16.2,1,5,0,0,10,1,1,1,1,6\n10033,25,16.2,1,4,0,0,14,1,2,1,1,7\n1431,25,16.2,1,5,0,0,13,0,2,1,1,6\n3311,25,16.2,1,5,0,0,11,0,2,1,1,10\n4543,25,16.2,1,5,0,0,9,0,1,1,1,8\n1729,25,16.2,1,1,0,0,15,0,2,1,1,10\n5459,25,16.2,1,1,0,0,12,1,0,1,1,9\n8497,25,16.3,1,1,0,0,14,1,1,1,1,5\n2473,25,16.3,1,5,0,0,14,0,1,1,1,10\n3942,25,16.3,1,5,0,1,13,0,4,1,1,1\n2503,25,16.4,1,3,0,0,12,0,2,1,1,8\n2741,25,16.4,1,1,0,0,11,0,2,1,1,8\n4797,25,16.4,1,2,0,0,12,0,0,1,1,7\n1911,25,16.4,1,1,0,0,12,0,1,1,1,9\n3327,25,16.4,1,5,0,0,13,0,0,1,1,8\n4111,25,16.4,1,4,0,0,14,0,1,1,1,9\n3499,25,16.5,1,5,0,0,9,0,0,1,1,9\n5663,25,16.5,1,2,0,0,11,1,0,1,1,8\n10757,25,16.5,1,2,0,0,15,1,0,1,1,9\n10461,25,16.5,1,3,0,0,9,1,1,1,1,7\n1329,25,16.5,1,5,0,0,15,0,1,1,1,8\n3943,25,16.6,1,4,0,0,11,0,1,1,1,10\n10491,25,16.6,1,4,0,0,15,1,1,1,1,5\n6045,25,16.6,1,3,0,0,15,1,1,1,1,6\n4167,25,16.7,1,4,0,0,12,0,0,1,1,10\n8135,25,16.7,1,5,0,0,9,1,0,1,1,10\n7587,25,16.7,1,4,0,0,10,1,2,1,1,7\n8653,25,16.7,1,4,0,0,13,1,0,1,1,10\n4841,25,16.8,1,4,0,0,13,0,0,1,1,10\n9975,25,16.8,1,1,0,0,14,1,2,1,1,7\n4819,25,16.8,1,5,0,0,10,0,0,1,1,10\n8179,25,16.8,1,2,0,0,10,1,2,1,1,7\n7453,25,16.9,1,5,0,0,14,1,1,1,1,8\n3963,25,17,1,1,0,0,14,0,1,1,1,10\n3545,25,17,1,4,0,0,11,0,2,1,1,9\n909,25,17,1,2,0,0,14,0,1,1,1,5\n6591,25,17,1,1,0,0,11,1,0,1,1,9\n9150,25,17,1,5,0,1,13,1,4,1,1,7\n3445,25,17.1,1,5,0,0,13,0,1,1,1,9\n1419,25,17.1,1,4,0,0,15,0,1,1,1,10\n3585,25,17.1,1,4,0,0,11,0,2,1,1,7\n5819,25,17.1,1,5,0,0,12,1,2,1,1,5\n133,25,17.1,1,1,0,0,11,0,2,1,1,9\n8529,25,17.2,1,1,0,0,14,1,0,1,1,5\n2853,25,17.2,1,3,0,0,12,0,0,1,1,6\n10509,25,17.2,1,2,0,0,10,1,1,1,1,9\n1115,25,17.3,1,3,0,0,13,0,1,1,1,9\n1447,25,17.3,1,2,0,0,9,0,0,1,1,8\n3184,25,17.3,1,4,0,1,12,0,4,1,1,1\n10835,25,17.3,1,3,0,0,13,1,1,1,1,6\n4119,25,17.3,1,3,0,0,13,0,0,1,1,10\n7653,25,17.4,1,5,0,0,9,1,2,1,1,7\n5,25,17.4,1,2,0,0,13,0,1,1,1,6\n1807,25,17.4,1,1,0,0,13,0,2,1,1,7\n403,25,17.4,1,2,0,0,9,0,1,1,1,5\n5057,25,17.5,1,3,0,0,15,0,0,1,1,5\n10823,25,17.5,1,5,0,0,12,1,0,1,1,8\n10069,25,17.5,1,1,0,0,9,1,1,1,1,9\n1907,25,17.5,1,1,0,0,12,0,0,1,1,8\n6415,25,17.5,1,5,0,0,12,1,1,1,1,10\n9647,25,17.6,1,2,0,0,12,1,2,1,1,7\n8419,25,17.6,1,5,0,0,15,1,0,1,1,9\n1703,25,17.6,1,3,0,0,12,0,1,1,1,7\n8639,25,17.6,1,3,0,0,10,1,2,1,1,9\n9323,25,17.6,1,2,0,0,13,1,0,1,1,7\n7329,25,17.7,1,4,0,0,11,1,1,1,1,5\n461,25,17.7,1,5,0,0,14,0,0,1,1,10\n6215,25,17.7,1,2,0,0,9,1,1,1,1,6\n9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5,0,2,1,1,9\n9151,25,20.9,1,3,0,1,14,1,7,1,1,3\n3801,25,21,1,2,0,0,15,0,1,1,1,10\n4377,25,21,1,2,0,0,13,0,0,1,1,8\n1993,25,21,1,1,0,0,14,0,1,1,1,7\n1251,25,21,1,5,0,0,12,0,1,1,1,10\n1625,25,21,1,4,0,0,11,0,2,1,1,5\n273,25,21,1,3,0,0,12,0,2,1,1,10\n4601,25,21.1,1,2,0,0,14,0,0,1,1,5\n6675,25,21.1,1,5,0,0,9,1,2,1,1,5\n2613,25,21.1,1,2,0,0,11,0,0,1,1,7\n239,25,21.1,1,5,0,0,10,0,2,1,1,10\n5407,25,21.2,1,3,0,0,12,0,0,1,1,8\n4177,25,21.2,1,1,0,0,11,0,0,1,1,5\n7311,25,21.2,1,3,0,0,9,1,2,1,1,5\n521,25,21.2,1,5,0,0,15,0,2,1,1,8\n3345,25,21.2,1,4,0,0,9,0,1,1,1,6\n9475,25,21.3,1,5,0,0,11,1,0,1,1,10\n4845,25,21.3,1,3,0,0,15,0,0,1,1,5\n2895,25,21.3,1,1,0,0,12,0,2,1,1,5\n2269,25,21.3,1,5,0,0,9,0,1,1,1,7\n4505,25,21.3,1,3,0,0,13,0,0,1,1,5\n5993,25,21.4,1,5,0,0,12,1,2,1,1,10\n7031,25,21.4,1,2,0,0,11,1,0,1,1,8\n9299,25,21.4,1,4,0,0,12,1,1,1,1,7\n105,25,21.4,1,1,0,0,11,0,1,1,1,6\n161,25,21.4,1,4,0,0,11,0,0,1,1,10\n3561,25,21.4,1,4,0,0,13,0,0,1,1,9\n1988,25,21.4,1,1,0,1,11,0,6,1,1,6\n7205,25,21.4,1,2,0,0,14,1,1,1,1,9\n8543,25,21.4,1,4,0,0,11,1,1,1,1,6\n8271,25,21.5,1,5,0,0,12,1,0,1,1,9\n8051,25,21.5,1,4,0,0,10,1,1,1,1,10\n1647,25,21.6,1,5,0,0,11,0,1,1,1,10\n8056,25,21.7,1,1,0,1,15,1,7,1,1,3\n9137,25,21.7,1,2,0,0,14,1,1,1,1,9\n7385,25,21.7,1,5,0,0,13,1,0,1,1,8\n2469,25,21.7,1,3,0,0,14,0,2,1,1,9\n2749,25,21.7,1,1,0,0,12,0,0,1,1,8\n8741,25,21.8,1,3,0,0,14,1,2,1,1,9\n2291,25,21.8,1,5,0,0,10,0,1,1,1,9\n8207,25,21.8,1,5,0,0,10,1,2,1,1,5\n1035,25,21.8,1,4,0,0,9,0,1,1,1,9\n8559,25,21.8,1,5,0,0,13,1,1,1,1,7\n5449,25,21.8,1,3,0,0,12,1,2,1,1,7\n8991,25,21.9,1,4,0,0,13,1,1,1,1,8\n9639,25,22,1,3,0,0,12,1,2,1,1,9\n6301,25,22,1,4,0,0,9,1,1,1,1,7\n7861,25,22,1,4,0,0,12,1,0,1,1,8\n10619,25,22,1,5,0,0,14,1,0,1,1,5\n4871,25,22,1,3,0,0,11,0,0,1,1,6\n7219,25,22,1,3,0,0,12,1,0,1,1,6\n91,25,22,1,5,0,0,12,0,2,1,1,8\n2687,25,22,1,5,0,0,10,0,2,1,1,6\n2785,25,22,1,3,0,0,10,0,1,1,1,6\n3185,25,22.1,1,3,0,0,12,0,1,1,1,7\n1985,25,22.1,1,3,0,0,12,0,1,1,1,7\n1883,25,22.1,1,4,0,0,12,0,1,1,1,5\n10657,25,22.1,1,3,0,0,13,1,0,1,1,5\n833,25,22.1,1,1,0,0,13,0,1,1,1,8\n9615,25,22.1,1,2,0,0,14,1,0,1,1,8\n539,25,22.1,1,5,0,0,13,0,1,1,1,5\n4139,25,22.2,1,4,0,0,12,0,1,1,1,6\n2511,25,22.2,1,5,0,0,11,0,0,1,1,8\n9305,25,22.2,1,1,0,0,12,1,0,1,1,9\n3783,25,22.3,1,5,0,0,15,0,2,1,1,7\n3735,25,22.3,1,2,0,0,12,0,0,1,1,8\n341,25,22.3,1,5,0,0,11,0,0,1,1,9\n3991,25,22.4,1,1,0,0,12,0,2,1,1,10\n899,25,22.4,1,3,0,0,9,0,2,1,1,9\n10397,25,22.4,1,4,0,0,10,1,2,1,1,7\n6687,25,22.5,1,4,0,0,14,1,2,1,1,7\n10147,25,22.5,1,3,0,0,15,1,0,1,1,7\n10409,25,22.5,1,3,0,0,12,1,0,1,1,10\n9211,25,22.5,1,2,0,0,12,1,0,1,1,8\n7891,25,22.5,1,1,0,0,13,1,0,1,1,5\n2961,25,22.5,1,5,0,0,12,0,2,1,1,7\n6883,25,22.6,1,1,0,0,15,1,2,1,1,10\n6330,25,22.6,1,5,0,1,9,1,6,1,1,7\n3063,25,22.6,1,4,0,0,13,0,0,1,1,6\n4161,25,22.6,1,2,0,0,13,0,2,1,1,8\n1204,25,22.7,1,1,0,1,12,0,1,1,1,3\n6841,25,22.7,1,3,0,0,14,1,2,1,1,5\n6225,25,22.7,1,5,0,0,15,1,2,1,1,5\n4587,25,22.7,1,3,0,0,14,0,0,1,1,6\n1787,25,22.7,1,2,0,0,12,0,1,1,1,8\n3869,25,22.7,1,1,0,0,12,0,1,1,1,9\n5987,25,22.7,1,3,0,0,15,1,1,1,1,9\n7577,25,22.7,1,2,0,0,11,1,0,1,1,5\n5155,25,22.8,1,1,0,0,13,0,2,1,1,5\n6689,25,22.8,1,3,0,0,12,1,2,1,1,7\n559,25,22.8,1,5,0,0,11,0,0,1,1,9\n10705,25,22.9,1,1,0,0,12,1,1,1,1,8\n1603,25,22.9,1,4,0,0,14,0,0,1,1,5\n631,25,22.9,1,1,0,0,11,0,2,1,1,10\n36,25,22.9,1,2,0,1,14,0,5,1,1,1\n2775,25,22.9,1,2,0,0,14,0,0,1,1,8\n5955,25,22.9,1,3,0,0,10,1,1,1,1,8\n5284,25,23,1,5,0,1,10,0,7,1,1,10\n3856,25,23,1,1,0,1,10,0,2,1,1,7\n8462,25,23,1,5,0,1,10,1,6,1,1,1\n6719,25,23,1,1,0,0,10,1,1,1,1,5\n8241,25,23.1,1,2,0,0,11,1,1,1,1,7\n2848,25,23.1,1,2,0,1,9,0,2,1,1,2\n3073,25,23.1,1,4,0,0,12,0,1,1,1,9\n1533,25,23.2,1,5,0,0,13,0,2,1,1,9\n5829,25,23.2,1,3,0,0,13,1,0,1,1,7\n2929,25,23.2,1,5,0,0,14,0,2,1,1,9\n7467,25,23.2,1,4,0,0,14,1,1,1,1,6\n8399,25,23.2,1,4,0,0,12,1,0,1,1,10\n5383,25,23.2,1,3,0,0,10,0,0,1,1,7\n3097,25,23.3,1,5,0,0,12,0,0,1,1,9\n5097,25,23.3,1,5,0,0,10,0,2,1,1,7\n7171,25,23.3,1,5,0,0,15,1,1,1,1,6\n4195,25,23.4,1,3,0,0,11,0,1,1,1,6\n207,25,23.4,1,4,0,0,15,0,2,1,1,5\n5789,25,23.4,1,4,0,0,11,1,1,1,1,9\n6321,25,23.4,1,1,0,0,12,1,2,1,1,7\n1855,25,23.4,1,1,0,0,10,0,1,1,1,6\n7711,25,23.4,1,2,0,0,12,1,2,1,1,9\n931,25,23.4,1,1,0,0,15,0,2,1,1,8\n2849,25,23.4,1,2,0,0,12,0,2,1,1,9\n4290,25,23.5,1,3,0,1,15,0,3,1,1,0\n827,25,23.5,1,3,0,0,14,0,1,1,1,9\n10711,25,23.5,1,5,0,0,14,1,1,1,1,9\n3537,25,23.5,1,2,0,0,15,0,1,1,1,9\n9417,25,23.5,1,5,0,0,14,1,2,1,1,9\n6017,25,23.5,1,5,0,0,9,1,2,1,1,8\n2445,25,23.5,1,5,0,0,11,0,1,1,1,5\n5926,25,23.5,1,2,0,1,11,1,8,1,1,3\n2513,25,23.5,1,1,0,0,12,0,2,1,1,8\n5377,25,23.6,1,1,0,0,9,0,2,1,1,5\n957,25,23.6,1,2,0,0,12,0,1,1,1,9\n6991,25,23.6,1,5,0,1,12,1,6,1,1,2\n7647,25,23.6,1,3,0,0,13,1,0,1,1,10\n1385,25,23.6,1,1,0,0,15,0,2,1,1,5\n3295,25,23.6,1,1,0,0,10,0,2,1,1,7\n8185,25,23.6,1,3,0,0,15,1,1,1,1,7\n9455,25,23.7,1,3,0,0,12,1,2,1,1,10\n259,25,23.7,1,4,0,0,10,0,1,1,1,10\n2974,25,23.7,1,5,0,1,15,0,4,1,1,5\n9747,25,23.8,1,3,0,0,10,1,0,1,1,9\n10781,25,23.8,1,3,0,0,15,1,0,1,1,9\n9333,25,23.8,1,1,0,0,11,1,1,1,1,5\n2759,25,23.8,1,2,0,1,12,0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4,1,1,1,1,5\n4623,25,27.2,1,2,0,0,10,0,0,1,1,10\n45,25,27.2,1,5,0,0,14,0,1,1,1,10\n1023,25,27.2,1,1,0,0,15,0,1,1,1,6\n9243,25,27.2,1,5,0,1,13,1,5,1,1,1\n1899,25,27.3,1,5,0,0,12,0,1,1,1,6\n1459,25,27.3,1,3,0,0,15,0,0,1,1,5\n4245,25,27.3,1,4,0,0,10,0,0,1,1,9\n1721,25,27.3,1,5,0,0,12,0,2,1,1,8\n337,25,27.3,1,3,0,0,9,0,1,1,1,9\n7919,25,27.3,1,1,0,0,14,1,2,1,1,5\n6911,25,27.3,1,5,0,0,13,1,2,1,1,8\n6601,25,27.4,1,3,0,0,10,1,0,1,1,9\n2277,25,27.4,1,1,0,0,9,0,2,1,1,6\n1235,25,27.5,1,4,0,0,11,0,1,1,1,5\n2605,25,27.5,1,1,0,0,10,0,0,1,1,5\n5641,25,27.5,1,1,0,0,9,1,0,1,1,10\n3571,25,27.6,1,3,0,0,13,0,0,1,1,8\n2025,25,27.6,1,2,0,0,9,0,0,1,1,7\n1093,25,27.6,1,1,0,0,10,0,1,1,1,9\n3881,25,27.7,1,1,0,0,14,0,1,1,1,10\n4181,25,27.7,1,4,0,0,11,0,0,1,1,10\n4717,25,27.7,1,3,0,0,11,0,1,1,1,7\n10867,25,27.7,1,4,0,0,10,1,2,1,1,8\n3799,25,27.7,1,1,0,0,9,0,0,1,1,7\n4743,25,27.8,1,2,0,0,10,0,1,1,1,6\n305,25,27.8,1,5,0,0,14,0,0,1,1,7\n8773,25,27.8,1,1,0,0,13,1,1,1,1,10\n8687,25,27.9,1,4,0,0,10,1,2,1,1,7\n3480,25,27.9,1,4,0,1,12,0,2,1,1,1\n2133,25,27.9,1,4,0,0,9,0,2,1,1,9\n437,25,27.9,1,2,0,0,14,0,1,1,1,5\n4647,25,27.9,1,4,0,0,15,0,1,1,1,5\n10655,25,27.9,1,4,0,0,10,1,1,1,1,8\n3173,25,28,1,4,0,0,14,0,1,1,1,8\n3289,25,28,1,4,0,0,15,0,0,1,1,10\n7485,25,28,1,3,0,0,12,1,2,1,1,8\n429,25,28,1,2,0,0,9,0,0,1,1,10\n7429,25,28,1,5,0,0,14,1,1,1,1,5\n3931,25,28.1,1,5,0,0,9,0,0,1,1,9\n4589,25,28.1,1,3,0,0,13,0,2,1,1,8\n8937,25,28.1,1,3,0,0,12,1,0,1,1,5\n4861,25,28.1,1,2,0,0,12,0,1,1,1,9\n10861,25,28.1,1,2,0,0,12,1,1,1,1,7\n4453,25,28.1,1,5,0,1,14,0,1,1,1,2\n6051,25,28.2,1,5,0,0,15,1,2,1,1,5\n169,25,28.2,1,2,0,0,13,0,1,1,1,5\n9553,25,28.2,1,4,0,0,12,1,1,1,1,10\n2333,25,28.2,1,3,0,0,13,0,0,1,1,10\n6559,25,28.2,1,4,0,0,15,1,2,1,1,7\n9085,25,28.2,1,3,0,0,13,1,2,1,1,9\n6033,25,28.2,1,5,0,0,14,1,0,1,1,9\n303,25,28.3,1,4,0,0,11,0,0,1,1,6\n10134,25,28.3,1,4,0,1,9,1,4,1,1,5\n2573,25,28.3,1,2,0,0,12,0,2,1,1,6\n9987,25,28.3,1,4,0,0,11,1,2,1,1,5\n7447,25,28.3,1,2,0,0,12,1,0,1,1,8\n3175,25,28.3,1,2,0,0,14,0,2,1,1,6\n2677,25,28.4,1,4,0,0,9,0,0,1,1,6\n8523,25,28.4,1,1,0,0,12,1,2,1,1,5\n10237,25,28.5,1,3,0,0,14,1,1,1,1,8\n1871,25,28.5,1,3,0,0,12,0,1,1,1,8\n10792,25,28.6,1,5,0,1,14,1,8,1,1,6\n3017,25,28.6,1,1,0,0,11,0,1,1,1,9\n3515,25,28.6,1,2,0,0,15,0,2,1,1,9\n9819,25,28.6,1,1,0,0,15,1,1,1,1,10\n861,25,28.6,1,4,0,0,10,0,0,1,1,9\n8993,25,28.7,1,5,0,0,15,1,0,1,1,7\n4811,25,28.7,1,2,0,0,13,0,0,1,1,5\n5575,25,28.7,1,3,0,0,13,1,2,1,1,6\n3419,25,28.7,1,3,0,0,12,0,1,1,1,9\n2253,25,28.7,1,1,0,0,15,0,0,1,1,6\n1983,25,28.8,1,2,0,0,10,0,1,1,1,8\n5667,25,28.8,1,1,0,0,15,1,2,1,1,9\n3453,25,28.8,1,5,0,0,12,0,0,1,1,9\n10102,25,28.8,1,3,0,1,12,1,7,1,1,1\n2205,25,28.8,1,1,0,0,9,0,2,1,1,6\n7933,25,28.8,1,1,0,0,10,1,2,1,1,5\n4667,25,28.8,1,5,0,0,13,0,0,1,1,5\n10221,25,28.8,1,3,0,0,9,1,1,1,1,7\n5035,25,28.8,1,2,0,0,15,0,1,1,1,6\n725,25,28.9,1,2,0,0,13,0,0,1,1,8\n1377,25,28.9,1,3,0,0,10,0,0,1,1,6\n6701,25,28.9,1,4,0,0,13,1,0,1,1,5\n2563,25,29,1,4,0,0,11,0,0,1,1,8\n5281,25,29,1,3,0,0,9,0,0,1,1,9\n6725,25,29,1,2,0,0,9,1,0,1,1,5\n7389,25,29,1,1,0,0,12,1,1,1,1,7\n5975,25,29,1,3,0,0,12,1,2,1,1,8\n10527,25,29,1,2,0,0,13,1,2,1,1,7\n2411,25,29,1,4,0,0,12,0,0,1,1,9\n10827,25,29.1,1,1,0,0,11,1,0,1,1,5\n4980,25,29.1,1,2,0,1,13,0,6,1,1,0\n6025,25,29.1,1,3,0,0,14,1,2,1,1,6\n9203,25,29.1,1,3,0,0,15,1,1,1,1,5\n4063,25,29.1,1,4,0,0,12,0,0,1,1,7\n5723,25,29.2,1,1,0,0,15,1,2,1,1,8\n10493,25,29.2,1,3,0,0,12,1,2,1,1,7\n10789,25,29.2,1,4,0,0,12,1,0,1,1,6\n1417,25,29.2,1,5,0,0,11,0,0,1,1,6\n873,25,29.2,1,2,0,0,13,0,1,1,1,5\n3269,25,29.2,1,1,0,0,12,0,2,1,1,5\n9023,25,29.2,1,3,0,0,12,1,2,1,1,8\n9175,25,29.3,1,4,0,0,10,1,0,1,1,7\n4309,25,29.3,1,1,0,0,12,0,2,1,1,8\n5611,25,29.3,1,1,0,0,15,1,0,1,1,10\n4625,25,29.3,1,5,0,0,13,0,0,1,1,8\n8831,25,29.4,1,4,0,0,14,1,1,1,1,9\n449,25,29.4,1,5,0,0,9,0,2,1,1,5\n3170,25,29.4,1,5,0,1,9,0,4,1,1,1\n5897,25,29.4,1,2,0,0,13,1,1,1,1,6\n8867,25,29.5,1,4,0,1,15,1,5,1,1,2\n7801,25,29.5,1,1,0,0,14,1,2,1,1,7\n6365,25,29.5,1,3,0,0,9,1,0,1,1,10\n9319,25,29.5,1,5,0,0,15,1,1,1,1,7\n7779,25,29.6,1,4,0,0,12,1,1,1,1,6\n2863,25,29.6,1,4,0,0,12,0,1,1,1,10\n5079,25,29.6,1,2,0,0,10,0,2,1,1,8\n7703,25,29.6,1,3,0,0,12,1,2,1,1,6\n1045,25,29.7,1,1,0,0,14,0,1,1,1,5\n7971,25,29.7,1,2,0,0,14,1,1,1,1,8\n5295,25,29.7,1,3,0,0,14,0,2,1,1,6\n5307,25,29.8,1,1,0,0,10,0,0,1,1,8\n2991,25,29.9,1,1,0,0,9,0,2,1,1,9\n4537,25,29.9,1,2,0,0,10,0,1,1,1,8\n8071,25,29.9,1,5,0,0,9,1,0,1,1,5\n8105,25,29.9,1,2,0,0,12,1,2,1,1,7\n9239,25,29.9,1,2,0,0,12,1,2,1,1,10\n4415,25,29.9,1,5,0,0,9,0,2,1,1,5\n8735,25,30,1,1,0,0,12,1,2,1,1,10\n529,25,30,1,2,0,0,9,0,0,1,1,9\n1163,26,0.6,1,1,0,1,9,0,7,1,1,3\n7665,26,0.6,1,1,0,1,11,1,4,1,1,0\n615,26,0.8,1,3,0,1,15,0,4,1,1,2\n2380,26,1.1,1,2,0,1,10,0,5,1,1,6\n2102,26,1.2,1,3,0,1,15,0,6,1,1,5\n2919,26,1.5,1,4,0,1,9,0,1,1,1,1\n570,26,2,1,3,0,1,10,0,2,1,1,9\n8578,26,2,1,2,0,1,12,1,1,1,1,10\n2260,26,2.3,1,2,0,1,13,0,6,1,1,6\n102,26,2.4,1,4,0,1,13,0,1,1,1,2\n3462,26,3.1,1,3,0,1,12,0,5,1,1,4\n9338,26,3.4,1,2,0,1,13,1,1,1,1,9\n7369,26,3.7,1,2,0,1,10,1,1,1,1,1\n6116,26,4.1,1,5,0,1,15,1,6,1,1,1\n8382,26,4.6,1,1,0,1,12,1,4,1,1,9\n533,26,5.2,1,3,0,1,11,0,4,1,1,4\n4331,26,5.2,1,3,0,1,9,0,1,1,1,1\n4473,26,5.7,1,1,0,1,12,0,2,1,1,4\n4070,26,6.1,1,2,0,1,10,0,4,1,1,8\n8176,26,6.1,1,3,0,1,13,1,3,1,1,5\n5756,26,7.4,1,3,0,1,9,1,2,1,1,10\n1192,26,7.5,1,2,0,1,12,0,5,1,1,2\n5412,26,7.9,1,3,0,1,12,0,2,1,1,3\n5036,26,7.9,1,5,0,1,14,0,5,1,1,7\n826,26,8,1,2,0,1,9,0,3,1,1,6\n3422,26,8.2,1,4,0,1,15,0,5,1,1,1\n2371,26,8.3,1,1,0,1,13,0,6,1,1,3\n9946,26,8.4,1,3,0,1,14,1,6,1,1,6\n10804,26,8.5,1,1,0,1,12,1,6,1,1,2\n2265,26,8.9,1,2,0,1,12,0,4,1,1,3\n6164,26,8.9,1,2,0,1,9,1,1,1,1,2\n8157,26,8.9,1,1,0,1,10,1,6,1,1,3\n596,26,10.1,1,5,0,1,12,0,5,1,1,0\n10824,26,10.4,1,3,0,1,11,1,5,1,1,10\n10070,26,10.6,1,1,0,1,12,1,7,1,1,10\n6611,26,10.6,1,1,0,1,15,1,7,1,1,1\n2476,26,11.3,1,5,0,1,13,0,2,1,1,9\n7638,26,12.4,1,4,0,1,12,1,7,1,1,0\n2560,26,12.8,1,4,0,1,14,0,2,1,1,1\n5583,26,13.4,1,3,0,1,12,1,2,1,1,4\n7195,26,14.1,1,2,0,1,12,1,5,1,1,0\n1090,26,14.2,1,3,0,1,10,0,5,1,1,9\n4066,26,14.7,1,4,0,1,13,0,5,1,1,7\n7199,26,14.8,1,3,0,1,12,1,2,1,1,1\n727,26,15,1,3,0,0,12,0,1,1,1,7\n1577,26,15,1,3,0,0,14,0,1,1,1,5\n1595,26,15,1,4,0,0,10,0,1,1,1,10\n6853,26,15,1,3,0,0,14,1,1,1,1,9\n9423,26,15.2,1,1,0,0,15,1,1,1,1,10\n1159,26,15.2,1,4,0,0,9,0,1,1,1,8\n3139,26,15.2,1,4,0,0,10,0,0,1,1,5\n7019,26,15.2,1,5,0,0,11,1,1,1,1,8\n8567,26,15.2,1,4,0,0,15,1,2,1,1,6\n2489,26,15.2,1,2,0,0,12,0,0,1,1,7\n8509,26,15.3,1,1,0,0,12,1,1,1,1,9\n5491,26,15.3,1,1,0,0,10,1,1,1,1,9\n3123,26,15.3,1,3,0,0,9,0,1,1,1,7\n6183,26,15.3,1,5,0,0,13,1,0,1,1,9\n9101,26,15.3,1,1,0,0,14,1,1,1,1,10\n4101,26,15.3,1,2,0,0,11,0,1,1,1,5\n6773,26,15.4,1,4,0,0,14,1,1,1,1,10\n803,26,15.4,1,1,0,0,14,0,1,1,1,5\n1157,26,15.4,1,5,0,0,12,0,2,1,1,8\n5535,26,15.4,1,5,0,0,14,1,2,1,1,6\n6135,26,15.5,1,5,0,0,11,1,2,1,1,5\n9697,26,15.5,1,3,0,0,11,1,1,1,1,6\n5015,26,15.5,1,4,0,0,10,0,0,1,1,6\n8963,26,15.5,1,1,0,0,12,1,0,1,1,8\n2245,26,15.5,1,3,0,0,11,0,0,1,1,5\n10549,26,15.5,1,2,0,0,14,1,0,1,1,8\n9655,26,15.5,1,3,0,0,13,1,2,1,1,8\n1080,26,15.6,1,1,0,1,12,0,1,1,1,0\n4107,26,15.6,1,1,0,0,12,0,1,1,1,8\n3793,26,15.6,1,4,0,0,10,0,2,1,1,7\n4815,26,15.7,1,5,0,0,12,0,2,1,1,5\n5881,26,15.7,1,3,0,0,12,1,1,1,1,7\n4163,26,15.7,1,5,0,0,10,0,0,1,1,9\n6891,26,15.7,1,3,0,0,13,1,1,1,1,9\n5677,26,15.7,1,1,0,0,15,1,1,1,1,8\n4657,26,15.7,1,3,0,1,15,0,1,1,1,3\n1091,26,15.8,1,2,0,0,13,0,1,1,1,9\n4883,26,15.8,1,5,0,0,14,0,1,1,1,7\n5731,26,15.8,1,2,0,0,14,1,0,1,1,10\n8347,26,15.9,1,2,0,0,15,1,2,1,1,10\n5054,26,15.9,1,4,0,1,13,0,5,1,1,5\n7657,26,15.9,1,2,0,0,12,1,2,1,1,5\n3425,26,15.9,1,1,0,0,13,0,1,1,1,9\n9693,26,15.9,1,1,0,0,11,1,2,1,1,9\n2939,26,16,1,2,0,0,13,0,2,1,1,9\n4215,26,16,1,5,0,0,9,0,0,1,1,6\n7849,26,16,1,1,0,0,13,1,1,1,1,8\n7239,26,16,1,3,0,0,13,1,2,1,1,7\n5985,26,16,1,3,0,0,12,1,0,1,1,5\n9563,26,16,1,4,0,0,12,1,0,1,1,8\n6715,26,16,1,2,0,1,13,1,5,1,1,3\n8651,26,16.1,1,4,0,0,12,1,0,1,1,7\n6253,26,16.1,1,1,0,0,10,1,2,1,1,9\n4862,26,16.1,1,3,0,1,12,0,5,1,1,9\n2933,26,16.1,1,2,0,0,13,0,2,1,1,5\n3613,26,16.1,1,4,0,0,14,0,1,1,1,9\n4500,26,16.2,1,5,0,1,11,0,3,1,1,4\n5557,26,16.2,1,2,0,0,9,1,0,1,1,8\n9141,26,16.2,1,2,0,0,12,1,1,1,1,6\n1535,26,16.3,1,1,0,0,11,0,0,1,1,5\n3947,26,16.3,1,4,0,0,9,0,2,1,1,8\n4695,26,16.3,1,1,0,0,12,0,1,1,1,5\n4557,26,16.3,1,1,0,0,11,0,2,1,1,9\n8537,26,16.3,1,4,0,0,13,1,2,1,1,9\n5989,26,16.3,1,5,0,0,11,1,0,1,1,10\n6933,26,16.4,1,2,0,0,13,1,1,1,1,9\n2831,26,16.4,1,2,0,0,12,0,0,1,1,7\n10187,26,16.4,1,3,0,0,14,1,0,1,1,5\n10083,26,16.4,1,1,0,0,9,1,1,1,1,6\n8909,26,16.4,1,1,0,0,12,1,1,1,1,10\n2139,26,16.5,1,1,0,0,14,0,1,1,1,10\n2593,26,16.5,1,4,0,0,12,0,2,1,1,9\n8499,26,16.5,1,2,0,0,14,1,1,1,1,10\n7737,26,16.5,1,3,0,1,13,1,2,1,1,2\n7819,26,16.5,1,5,0,0,10,1,0,1,1,9\n10359,26,16.5,1,4,0,0,11,1,1,1,1,7\n1659,26,16.6,1,1,0,0,10,0,1,1,1,10\n5593,26,16.6,1,4,0,0,11,1,2,1,1,9\n8979,26,16.7,1,1,0,0,9,1,2,1,1,9\n9811,26,16.7,1,3,0,0,11,1,0,1,1,5\n10501,26,16.8,1,4,0,0,13,1,2,1,1,7\n8549,26,16.8,1,3,0,0,10,1,2,1,1,7\n5915,26,16.9,1,3,0,0,13,1,0,1,1,8\n6535,26,16.9,1,3,0,0,12,1,2,1,1,9\n8392,26,16.9,1,4,0,1,9,1,6,1,1,3\n3,26,17,1,5,0,0,15,0,1,1,1,9\n6095,26,17,1,5,0,0,12,1,0,1,1,7\n7045,26,17.1,1,5,0,0,13,1,1,1,1,9\n85,26,17.1,1,5,0,0,13,0,1,1,1,7\n4049,26,17.1,1,5,0,0,15,0,0,1,1,7\n4001,26,17.1,1,3,0,0,12,0,0,1,1,5\n597,26,17.1,1,5,0,0,12,0,0,1,1,5\n5381,26,17.1,1,3,0,0,15,0,1,1,1,10\n6241,26,17.2,1,1,0,0,15,1,1,1,1,10\n9447,26,17.2,1,4,0,0,12,1,1,1,1,9\n9929,26,17.2,1,1,0,0,15,1,2,1,1,5\n5055,26,17.2,1,2,0,1,13,0,4,1,1,4\n3669,26,17.2,1,4,0,0,11,0,2,1,1,6\n4327,26,17.2,1,3,0,0,9,0,1,1,1,9\n9675,26,17.2,1,3,0,0,13,1,1,1,1,6\n1243,26,17.2,1,2,0,0,12,0,1,1,1,6\n1799,26,17.3,1,1,0,0,14,0,2,1,1,10\n8699,26,17.3,1,3,0,0,10,1,2,1,1,7\n4693,26,17.3,1,4,0,0,13,0,2,1,1,9\n4439,26,17.4,1,3,0,0,13,0,1,1,1,8\n10777,26,17.4,1,3,0,0,13,1,0,1,1,8\n10775,26,17.4,1,2,0,0,11,1,2,1,1,8\n6871,26,17.4,1,1,0,0,9,1,0,1,1,10\n10471,26,17.4,1,1,0,0,14,1,1,1,1,6\n4887,26,17.4,1,5,0,0,9,0,0,1,1,5\n2439,26,17.4,1,1,0,0,15,0,2,1,1,6\n9805,26,17.5,1,3,0,0,14,1,0,1,1,6\n1353,26,17.5,1,4,0,0,12,0,2,1,1,7\n421,26,17.5,1,1,0,0,12,0,0,1,1,8\n7117,26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7\n4849,26,24.6,1,2,0,0,9,0,1,1,1,7\n1445,26,24.6,1,1,0,0,15,0,0,1,1,9\n8231,26,24.7,1,3,0,0,14,1,1,1,1,6\n9449,26,24.7,1,4,0,0,15,1,2,1,1,8\n7645,26,24.7,1,5,0,0,9,1,1,1,1,5\n2243,26,24.8,1,4,0,0,14,0,1,1,1,8\n5448,26,24.8,1,1,0,1,12,1,6,1,1,8\n145,26,24.8,1,5,0,1,12,0,3,1,1,4\n6953,26,24.8,1,4,0,0,10,1,1,1,1,6\n1611,26,24.9,1,4,0,0,10,0,1,1,1,8\n6845,26,24.9,1,5,0,0,9,1,0,1,1,8\n4469,26,24.9,1,4,0,0,10,0,2,1,1,7\n6055,26,24.9,1,3,0,0,12,1,1,1,1,10\n2173,26,24.9,1,5,0,0,15,0,0,1,1,9\n8981,26,24.9,1,3,0,0,11,1,2,1,1,9\n6927,26,24.9,1,2,0,1,10,1,7,1,1,3\n10293,26,25,1,2,0,0,13,1,2,1,1,8\n2768,26,25,1,4,0,1,13,0,4,1,1,2\n7943,26,25,1,2,0,0,12,1,2,1,1,7\n2871,26,25,1,1,0,0,15,0,0,1,1,6\n5721,26,25,1,1,0,0,13,1,1,1,1,7\n8203,26,25,1,5,0,0,11,1,0,1,1,5\n165,26,25.1,1,3,0,0,13,0,2,1,1,6\n5949,26,25.1,1,4,0,0,14,1,1,1,1,10\n7863,26,25.1,1,2,0,0,14,1,2,1,1,6\n7157,26,25.1,1,3,0,0,13,1,2,1,1,6\n6360,26,25.1,1,2,0,1,12,1,2,1,1,9\n4075,26,25.2,1,2,0,0,14,0,1,1,1,6\n6825,26,25.2,1,2,0,0,9,1,0,1,1,10\n3421,26,25.2,1,3,0,0,12,0,2,1,1,5\n10003,26,25.2,1,3,0,0,10,1,1,1,1,10\n4954,26,25.2,1,4,0,1,15,0,8,1,1,4\n5299,26,25.3,1,1,0,0,14,0,2,1,1,10\n5227,26,25.3,1,3,0,0,10,0,1,1,1,9\n2659,26,25.3,1,4,0,0,14,0,0,1,1,8\n10047,26,25.3,1,1,0,0,15,1,2,1,1,5\n7925,26,25.3,1,4,0,0,12,1,2,1,1,7\n9499,26,25.4,1,3,0,0,11,1,1,1,1,10\n55,26,25.4,1,4,0,0,13,0,1,1,1,10\n1747,26,25.4,1,5,0,0,9,0,1,1,1,8\n299,26,25.5,1,3,0,0,10,0,0,1,1,8\n6633,26,25.5,1,1,0,0,11,1,1,1,1,6\n95,26,25.6,1,3,0,1,12,0,2,1,1,0\n151,26,25.6,1,5,0,0,10,0,2,1,1,9\n4549,26,25.6,1,4,0,0,10,0,1,1,1,6\n2707,26,25.6,1,5,0,0,13,0,2,1,1,10\n4463,26,25.7,1,1,0,0,11,0,1,1,1,7\n6759,26,25.7,1,4,0,0,10,1,2,1,1,10\n4983,26,25.7,1,1,0,0,14,0,2,1,1,9\n8261,26,25.8,1,4,0,0,10,1,2,1,1,6\n411,26,25.8,1,3,0,0,9,0,2,1,1,6\n8375,26,25.8,1,5,0,0,15,1,1,1,1,7\n7752,26,25.8,1,1,0,1,9,1,1,1,1,4\n1261,26,25.8,1,3,0,0,12,0,1,1,1,5\n4121,26,25.8,1,4,0,0,14,0,1,1,1,5\n3691,26,25.8,1,3,0,0,12,0,2,1,1,9\n3913,26,25.8,1,3,0,0,12,0,2,1,1,5\n6320,26,25.9,1,5,0,1,12,1,4,1,1,6\n9465,26,25.9,1,5,0,1,15,1,3,1,1,1\n6139,26,25.9,1,5,0,0,12,1,0,1,1,6\n2851,26,25.9,1,4,0,0,9,0,2,1,1,6\n6783,26,25.9,1,5,0,0,13,1,0,1,1,10\n2119,26,26,1,4,0,0,15,0,0,1,1,8\n2323,26,26,1,2,0,0,9,0,2,1,1,9\n4955,26,26,1,3,0,0,13,0,2,1,1,8\n5285,26,26,1,5,0,0,10,0,1,1,1,5\n6679,26,26.1,1,1,0,0,14,1,1,1,1,7\n5481,26,26.1,1,4,0,1,13,1,3,1,1,3\n9453,26,26.1,1,2,0,0,12,1,2,1,1,7\n10151,26,26.2,1,2,0,0,10,1,1,1,1,10\n1099,26,26.2,1,3,0,0,12,0,1,1,1,5\n3811,26,26.2,1,4,0,0,12,0,0,1,1,6\n6869,26,26.2,1,4,0,0,12,1,0,1,1,9\n3975,26,26.2,1,3,0,0,9,0,2,1,1,9\n671,26,26.3,1,1,0,0,15,0,0,1,1,7\n3389,26,26.3,1,3,0,0,11,0,1,1,1,8\n3195,26,26.3,1,1,0,0,15,0,1,1,1,9\n10201,26,26.3,1,5,0,0,10,1,0,1,1,5\n8751,26,26.4,1,2,0,0,9,1,0,1,1,8\n9116,26,26.4,1,5,0,1,12,1,2,1,1,0\n9199,26,26.4,1,3,0,0,14,1,2,1,1,8\n10299,26,26.4,1,4,0,0,12,1,0,1,1,7\n6081,26,26.4,1,3,0,0,10,1,2,1,1,6\n9357,26,26.5,1,1,0,0,9,1,2,1,1,6\n10317,26,26.5,1,4,0,0,11,1,2,1,1,10\n3722,26,26.5,1,2,0,1,13,0,5,1,1,1\n1249,26,26.5,1,2,0,0,11,0,1,1,1,6\n8821,26,26.5,1,2,0,0,11,1,2,1,1,7\n7225,26,26.5,1,1,0,0,9,1,1,1,1,9\n8504,26,26.5,1,3,0,1,13,1,3,1,1,8\n6229,26,26.6,1,3,0,0,10,1,0,1,1,9\n2419,26,26.6,1,4,0,0,10,0,2,1,1,7\n1731,26,26.6,1,3,0,0,10,0,0,1,1,7\n6963,26,26.6,1,4,0,0,12,1,0,1,1,7\n7633,26,26.6,1,1,0,0,13,1,1,1,1,6\n1095,26,26.6,1,1,0,0,11,0,0,1,1,9\n5739,26,26.7,1,4,0,0,9,1,2,1,1,6\n10787,26,26.7,1,2,0,0,14,1,2,1,1,7\n4687,26,26.7,1,3,0,0,13,0,1,1,1,7\n7699,26,26.7,1,1,0,0,13,1,0,1,1,6\n3633,26,26.7,1,1,0,0,12,0,0,1,1,8\n5455,26,26.7,1,3,0,0,11,1,2,1,1,9\n5761,26,26.7,1,3,0,0,10,1,0,1,1,5\n4877,26,26.8,1,5,0,0,15,0,2,1,1,8\n1619,26,26.8,1,4,0,0,12,0,2,1,1,5\n10197,26,26.9,1,1,0,0,11,1,1,1,1,8\n10109,26,26.9,1,4,0,0,14,1,2,1,1,5\n4853,26,26.9,1,5,0,0,12,0,2,1,1,7\n2187,26,26.9,1,4,0,0,12,0,0,1,1,7\n7077,26,26.9,1,2,0,0,12,1,1,1,1,10\n8415,26,27,1,2,0,0,10,1,1,1,1,7\n8335,26,27,1,4,0,0,10,1,0,1,1,8\n1817,26,27,1,4,0,0,12,0,1,1,1,8\n1097,26,27.1,1,3,0,0,13,0,1,1,1,5\n5391,26,27.1,1,4,0,0,10,0,1,1,1,8\n9121,26,27.1,1,2,0,0,11,1,2,1,1,7\n4077,26,27.1,1,1,0,0,9,0,0,1,1,7\n6337,26,27.1,1,2,0,0,13,1,2,1,1,6\n4519,26,27.1,1,4,0,0,15,0,2,1,1,6\n3015,26,27.1,1,2,0,0,12,0,0,1,1,5\n7167,26,27.1,1,5,0,0,10,1,0,1,1,6\n477,26,27.1,1,3,0,0,14,0,1,1,1,5\n3497,26,27.2,1,1,0,0,13,0,2,1,1,7\n10755,26,27.3,1,4,0,0,11,1,2,1,1,6\n4155,26,27.3,1,4,0,0,13,0,1,1,1,5\n8777,26,27.3,1,2,0,0,13,1,2,1,1,6\n251,26,27.4,1,4,0,0,10,0,1,1,1,10\n10327,26,27.4,1,5,0,0,12,1,0,1,1,5\n3363,26,27.4,1,4,0,0,12,0,0,1,1,9\n6635,26,27.4,1,1,0,0,10,1,2,1,1,8\n2267,26,27.4,1,2,0,0,10,0,1,1,1,10\n892,26,27.4,1,2,0,1,12,0,6,1,1,7\n693,26,27.4,1,2,0,0,15,0,1,1,1,8\n649,26,27.4,1,5,0,0,11,0,0,1,1,6\n8689,26,27.4,1,1,0,0,10,1,0,1,1,10\n10675,26,27.4,1,3,0,0,12,1,0,1,1,9\n5649,26,27.4,1,5,0,0,14,1,0,1,1,9\n2491,26,27.5,1,2,0,0,14,0,0,1,1,8\n1429,26,27.5,1,3,0,0,13,0,2,1,1,7\n10717,26,27.5,1,2,0,0,10,1,0,1,1,6\n8585,26,27.5,1,5,0,0,11,1,1,1,1,9\n4605,26,27.5,1,3,0,0,9,0,0,1,1,8\n252,26,27.5,1,2,0,1,11,0,8,1,1,1\n8469,26,27.5,1,5,0,0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,1,3,1,1,6\n4386,27,10.7,1,4,0,1,14,0,8,1,1,0\n6292,27,11,1,1,0,1,11,1,7,1,1,4\n10642,27,11.8,1,2,0,1,9,1,2,1,1,2\n8094,27,11.8,1,4,0,1,10,1,5,1,1,4\n1557,27,11.9,1,4,0,1,10,0,3,1,1,3\n10809,27,12.1,1,3,0,1,10,1,6,1,1,2\n9184,27,12.2,1,2,0,1,10,1,4,1,1,8\n7497,27,12.6,1,4,0,1,12,1,1,1,1,1\n718,27,12.7,1,4,0,1,13,0,8,1,1,8\n9442,27,13.1,1,5,0,1,9,1,6,1,1,4\n5791,27,13.3,1,3,0,1,12,1,8,1,1,3\n3366,27,13.4,1,1,0,1,14,0,1,1,1,7\n10594,27,13.7,1,4,0,1,13,1,8,1,1,2\n5952,27,14.1,1,2,0,1,15,1,8,1,1,9\n1761,27,14.3,1,5,0,1,12,0,8,1,1,2\n9458,27,14.3,1,4,0,1,11,1,4,1,1,9\n564,27,14.6,1,3,0,1,12,0,3,1,1,8\n1168,27,15,1,2,0,1,9,0,3,1,1,2\n8147,27,15,1,4,0,0,12,1,2,1,1,5\n9565,27,15,1,4,0,0,11,1,2,1,1,10\n9245,27,15,1,4,0,0,14,1,2,1,1,7\n8569,27,15,1,1,0,0,13,1,1,1,1,6\n2103,27,15.1,1,3,0,0,15,0,2,1,1,6\n7809,27,15.1,1,1,0,0,10,1,2,1,1,6\n1297,27,15.1,1,3,0,0,11,0,2,1,1,6\n5805,27,15.1,1,2,0,0,10,1,2,1,1,7\n7479,27,15.1,1,2,0,0,12,1,2,1,1,6\n9181,27,15.2,1,4,0,0,9,1,0,1,1,8\n3135,27,15.2,1,5,0,0,13,0,1,1,1,9\n10321,27,15.2,1,1,0,0,13,1,0,1,1,5\n3313,27,15.3,1,2,0,0,12,0,0,1,1,6\n7989,27,15.4,1,3,0,0,12,1,2,1,1,10\n8473,27,15.5,1,3,0,0,12,1,0,1,1,10\n6799,27,15.5,1,1,0,0,13,1,0,1,1,6\n6197,27,15.5,1,4,0,1,15,1,3,1,1,4\n6987,27,15.6,1,1,0,0,14,1,1,1,1,8\n10257,27,15.6,1,2,0,0,9,1,0,1,1,10\n5029,27,15.7,1,1,0,0,15,0,0,1,1,7\n5038,27,15.7,1,4,0,1,9,0,2,1,1,9\n10479,27,15.7,1,4,0,0,11,1,2,1,1,9\n3049,27,15.7,1,4,0,0,11,0,0,1,1,7\n5086,27,15.8,1,1,0,1,14,0,3,1,1,6\n9649,27,15.8,1,2,0,0,11,1,2,1,1,7\n4478,27,15.9,1,2,0,1,12,0,8,1,1,9\n2791,27,15.9,1,1,0,0,14,0,2,1,1,9\n9063,27,15.9,1,1,0,0,14,1,1,1,1,9\n479,27,15.9,1,2,0,0,13,0,1,1,1,9\n2097,27,15.9,1,3,0,0,14,0,1,1,1,8\n8501,27,16,1,1,0,0,12,1,1,1,1,9\n4389,27,16.1,1,5,0,0,15,0,1,1,1,6\n2181,27,16.1,1,3,0,0,12,0,0,1,1,9\n8317,27,16.1,1,1,0,0,13,1,2,1,1,8\n2463,27,16.1,1,5,0,0,14,0,0,1,1,6\n5753,27,16.1,1,4,0,0,12,1,0,1,1,6\n9301,27,16.2,1,4,0,0,10,1,1,1,1,9\n297,27,16.2,1,5,0,0,15,0,1,1,1,9\n2966,27,16.2,1,2,0,1,11,0,5,1,1,0\n669,27,16.3,1,3,0,0,12,0,1,1,1,8\n9292,27,16.3,1,4,0,1,10,1,3,1,1,2\n8691,27,16.3,1,2,0,0,9,1,1,1,1,6\n9849,27,16.3,1,3,0,0,12,1,1,1,1,5\n8765,27,16.4,1,1,0,0,11,1,1,1,1,7\n581,27,16.4,1,4,0,0,12,0,2,1,1,10\n813,27,16.4,1,5,0,0,12,0,2,1,1,7\n4615,27,16.4,1,4,0,0,9,0,1,1,1,6\n3301,27,16.4,1,5,0,0,11,0,0,1,1,6\n10631,27,16.5,1,4,0,0,13,1,1,1,1,10\n2679,27,16.5,1,2,0,0,15,0,2,1,1,5\n5147,27,16.5,1,3,0,0,14,0,1,1,1,9\n5957,27,16.5,1,2,0,1,11,1,5,1,1,3\n1453,27,16.5,1,2,0,0,15,0,2,1,1,9\n1317,27,16.6,1,4,0,0,10,0,1,1,1,9\n4467,27,16.6,1,4,0,0,9,0,2,1,1,5\n9179,27,16.6,1,2,0,0,9,1,2,1,1,10\n7063,27,16.6,1,5,0,0,12,1,0,1,1,7\n3979,27,16.6,1,5,0,0,9,0,0,1,1,7\n2368,27,16.6,1,2,0,1,14,0,4,1,1,10\n5527,27,16.7,1,5,0,0,12,1,0,1,1,10\n4385,27,16.7,1,4,0,0,11,0,0,1,1,6\n249,27,16.7,1,2,0,0,10,0,1,1,1,10\n3883,27,16.7,1,5,0,1,12,0,6,1,1,4\n2383,27,16.7,1,4,0,0,9,0,1,1,1,8\n3434,27,16.7,1,5,0,1,14,0,5,1,1,9\n189,27,16.7,1,4,0,0,12,0,0,1,1,6\n4097,27,16.9,1,4,0,0,10,0,1,1,1,10\n10103,27,16.9,1,3,0,0,12,1,1,1,1,5\n8243,27,16.9,1,4,0,0,11,1,1,1,1,7\n3333,27,16.9,1,2,0,0,12,0,0,1,1,7\n6249,27,16.9,1,3,0,0,9,1,1,1,1,5\n7105,27,16.9,1,2,0,0,13,1,0,1,1,7\n9103,27,16.9,1,4,0,0,10,1,1,1,1,5\n10075,27,16.9,1,5,0,0,11,1,1,1,1,6\n7153,27,16.9,1,1,0,0,15,1,0,1,1,8\n5137,27,17,1,2,0,0,13,0,0,1,1,9\n945,27,17,1,1,0,0,12,0,2,1,1,9\n6705,27,17,1,4,0,0,10,1,0,1,1,8\n1289,27,17.1,1,5,0,0,11,0,1,1,1,10\n10429,27,17.1,1,3,0,0,15,1,2,1,1,9\n2649,27,17.1,1,3,0,0,14,0,2,1,1,5\n9717,27,17.1,1,4,0,0,15,1,1,1,1,7\n2725,27,17.1,1,5,0,0,15,0,2,1,1,6\n3133,27,17.1,1,3,0,0,12,0,1,1,1,8\n3821,27,17.1,1,4,0,0,13,0,2,1,1,6\n3837,27,17.1,1,3,0,0,11,0,1,1,1,5\n600,27,17.2,1,3,0,1,9,0,5,1,1,5\n5591,27,17.2,1,3,0,0,10,1,1,1,1,5\n6505,27,17.2,1,2,0,0,11,1,2,1,1,6\n4401,27,17.2,1,4,0,0,12,0,0,1,1,8\n5395,27,17.2,1,1,0,0,12,0,1,1,1,5\n1709,27,17.2,1,5,0,0,9,0,0,1,1,9\n6893,27,17.3,1,2,0,0,12,1,1,1,1,9\n1231,27,17.3,1,3,0,0,14,0,2,1,1,6\n4227,27,17.3,1,2,0,0,13,0,0,1,1,7\n2255,27,17.3,1,4,0,0,15,0,0,1,1,10\n5485,27,17.5,1,4,0,0,15,1,0,1,1,5\n9276,27,17.5,1,2,0,1,11,1,2,1,1,6\n1129,27,17.5,1,2,0,0,9,0,1,1,1,6\n9529,27,17.5,1,3,0,0,13,1,2,1,1,6\n755,27,17.5,1,2,0,0,12,0,1,1,1,8\n7413,27,17.6,1,2,0,0,12,1,1,1,1,10\n8849,27,17.6,1,3,0,0,13,1,0,1,1,8\n5185,27,17.6,1,2,0,0,12,0,1,1,1,5\n2977,27,17.6,1,4,0,0,12,0,2,1,1,5\n5513,27,17.6,1,1,0,0,13,1,0,1,1,7\n2557,27,17.7,1,2,0,0,9,0,2,1,1,5\n1135,27,17.7,1,4,0,0,10,0,1,1,1,10\n345,27,17.7,1,5,0,0,11,0,0,1,1,9\n7751,27,17.8,1,1,0,1,15,1,3,1,1,0\n3003,27,17.8,1,2,0,0,10,0,2,1,1,6\n7197,27,17.8,1,1,0,0,9,1,0,1,1,9\n3213,27,17.8,1,4,0,0,15,0,2,1,1,7\n3555,27,17.8,1,4,0,0,10,0,2,1,1,6\n9613,27,17.8,1,4,0,0,11,1,2,1,1,8\n9407,27,17.9,1,5,0,0,9,1,0,1,1,7\n7111,27,17.9,1,5,0,0,10,1,1,1,1,5\n8845,27,17.9,1,2,0,0,15,1,1,1,1,10\n6692,27,17.9,1,3,0,1,14,1,8,1,1,8\n10557,27,17.9,1,2,0,0,14,1,1,1,1,8\n10305,27,17.9,1,3,0,0,13,1,1,1,1,5\n6245,27,17.9,1,5,0,0,15,1,2,1,1,5\n6071,27,18,1,5,0,0,11,1,0,1,1,5\n5331,27,18.1,1,1,0,0,14,0,2,1,1,10\n4435,27,18.1,1,4,0,0,11,0,1,1,1,8\n3023,27,18.2,1,4,0,0,12,0,1,1,1,7\n2387,27,18.2,1,3,0,0,12,0,0,1,1,7\n1947,27,18.2,1,3,0,0,9,0,1,1,1,9\n1909,27,18.2,1,2,0,0,9,0,1,1,1,9\n9609,27,18.2,1,5,0,0,10,1,1,1,1,10\n9533,27,18.3,1,1,0,0,12,1,0,1,1,8\n4135,27,18.3,1,2,0,0,12,0,1,1,1,8\n5769,27,18.3,1,5,0,0,11,1,0,1,1,5\n7615,27,18.3,1,1,0,0,10,1,2,1,1,7\n3167,27,18.3,1,4,0,0,12,0,2,1,1,5\n5619,27,18.4,1,3,0,0,10,1,0,1,1,9\n8719,27,18.4,1,5,0,0,10,1,0,1,1,8\n7342,27,18.4,1,4,0,1,15,1,3,1,1,2\n2947,27,18.4,1,1,0,0,10,0,2,1,1,6\n10383,27,18.4,1,3,0,0,12,1,0,1,1,5\n4779,27,18.4,1,1,0,0,10,0,0,1,1,7\n843,27,18.4,1,3,0,1,12,0,6,1,1,4\n7879,27,18.4,1,4,0,0,9,1,2,1,1,6\n4641,27,18.4,1,5,0,0,11,0,2,1,1,6\n3659,27,18.5,1,3,0,0,10,0,0,1,1,8\n1565,27,18.5,1,4,0,0,15,0,1,1,1,9\n1141,27,18.5,1,5,0,0,15,0,1,1,1,5\n4085,27,18.5,1,3,0,0,10,0,1,1,1,5\n8377,27,18.5,1,3,0,0,12,1,0,1,1,6\n2599,27,18.5,1,1,0,0,12,0,2,1,1,10\n7859,27,18.5,1,5,0,0,9,1,1,1,1,6\n2307,27,18.6,1,2,0,0,15,0,2,1,1,8\n8636,27,18.6,1,1,0,1,12,1,2,1,1,10\n6177,27,18.6,1,4,0,0,12,1,1,1,1,7\n5257,27,18.6,1,2,0,0,9,0,2,1,1,5\n7529,27,18.6,1,3,0,0,11,1,1,1,1,6\n3965,27,18.7,1,5,0,0,12,0,0,1,1,10\n4207,27,18.7,1,1,0,1,13,0,3,1,1,4\n8449,27,18.7,1,3,0,0,15,1,0,1,1,7\n1941,27,18.7,1,2,0,1,14,0,6,1,1,1\n4357,27,18.7,1,5,0,0,12,0,2,1,1,10\n419,27,18.7,1,1,0,0,9,0,1,1,1,7\n1547,27,18.7,1,2,0,0,14,0,2,1,1,6\n4639,27,18.7,1,1,0,0,11,0,1,1,1,10\n947,27,18.8,1,3,0,0,12,0,0,1,1,6\n5505,27,18.8,1,5,0,0,14,1,2,1,1,6\n9451,27,18.8,1,4,0,0,14,1,2,1,1,8\n2721,27,18.8,1,2,0,0,11,0,2,1,1,5\n7689,27,18.8,1,2,0,0,12,1,1,1,1,9\n381,27,18.8,1,1,0,0,12,0,1,1,1,6\n6531,27,18.9,1,5,0,0,13,1,2,1,1,10\n3303,27,18.9,1,3,0,0,14,0,0,1,1,5\n831,27,18.9,1,4,0,0,11,0,1,1,1,8\n4003,27,18.9,1,1,0,0,11,0,2,1,1,8\n10793,27,18.9,1,1,0,0,11,1,2,1,1,7\n4308,27,18.9,1,4,0,1,13,0,5,1,1,0\n2105,27,18.9,1,1,0,0,12,0,0,1,1,6\n871,27,18.9,1,1,0,0,12,0,2,1,1,9\n2515,27,19,1,5,0,0,9,0,2,1,1,10\n8119,27,19,1,1,0,0,11,1,1,1,1,5\n6863,27,19,1,5,0,0,15,1,0,1,1,5\n1683,27,19,1,4,0,0,12,0,1,1,1,9\n10391,27,19,1,1,0,0,11,1,2,1,1,7\n1913,27,19,1,3,0,0,11,0,1,1,1,10\n9983,27,19,1,2,0,0,12,1,0,1,1,5\n6639,27,19,1,4,0,0,13,1,1,1,1,5\n8869,27,19.1,1,3,0,0,14,1,0,1,1,6\n7771,27,19.1,1,1,0,0,15,1,1,1,1,6\n2401,27,19.1,1,5,0,0,9,0,1,1,1,7\n3647,27,19.1,1,1,0,0,14,0,2,1,1,5\n9925,27,19.1,1,4,0,0,12,1,1,1,1,7\n9419,27,19.2,1,4,0,0,13,1,2,1,1,8\n6259,27,19.2,1,3,0,0,13,1,2,1,1,10\n2261,27,19.2,1,3,0,0,15,0,1,1,1,9\n8425,27,19.2,1,1,0,0,12,1,2,1,1,5\n7127,27,19.2,1,3,0,0,15,1,1,1,1,6\n6575,27,19.2,1,1,0,0,13,1,1,1,1,5\n10767,27,19.2,1,5,0,0,13,1,0,1,1,5\n3125,27,19.3,1,1,0,0,9,0,2,1,1,8\n6747,27,19.3,1,2,0,0,11,1,1,1,1,8\n8629,27,19.3,1,4,0,0,14,1,1,1,1,10\n4355,27,19.3,1,1,0,0,13,0,2,1,1,8\n1143,27,19.4,1,5,0,0,14,0,2,1,1,6\n8331,27,19.4,1,2,0,0,12,1,2,1,1,6\n9641,27,19.4,1,2,0,0,12,1,0,1,1,10\n6041,27,19.5,1,4,0,0,9,1,1,1,1,8\n7137,27,19.6,1,1,0,0,13,1,0,1,1,7\n5027,27,19.6,1,5,0,0,15,0,0,1,1,7\n2127,27,19.6,1,4,0,0,10,0,0,1,1,8\n4083,27,19.6,1,4,0,0,13,0,0,1,1,8\n1381,27,19.7,1,4,0,0,12,0,1,1,1,7\n8662,27,19.8,1,4,0,1,12,1,5,1,1,7\n5149,27,19.8,1,3,0,0,11,0,0,1,1,8\n7599,27,19.9,1,3,0,0,9,1,1,1,1,9\n7757,27,19.9,1,5,0,0,11,1,1,1,1,8\n10453,27,19.9,1,5,0,0,14,1,1,1,1,5\n3221,27,19.9,1,5,0,0,9,0,1,1,1,7\n1509,27,19.9,1,2,0,0,11,0,1,1,1,9\n5543,27,19.9,1,1,0,0,12,1,1,1,1,6\n4217,27,19.9,1,5,0,0,10,0,2,1,1,8\n5389,27,20,1,5,0,0,9,0,2,1,1,10\n5119,27,20,1,2,0,0,12,0,2,1,1,5\n7601,27,20,1,5,0,0,13,1,1,1,1,9\n9567,27,20,1,2,0,0,12,1,1,1,1,7\n427,27,20,1,4,0,0,11,0,1,1,1,6\n107,27,20,1,5,0,0,15,0,0,1,1,9\n8351,27,20,1,5,0,0,15,1,2,1,1,9\n10093,27,20,1,3,0,0,13,1,0,1,1,10\n8907,27,20.1,1,1,0,0,9,1,1,1,1,8\n7575,27,20.1,1,3,0,0,12,1,0,1,1,6\n10593,27,20.1,1,4,0,1,14,1,6,1,1,4\n2183,27,20.1,1,4,0,0,10,0,0,1,1,7\n4715,27,20.1,1,1,0,0,10,0,1,1,1,8\n2949,27,20.2,1,4,0,1,9,0,1,1,1,0\n353,27,20.2,1,1,0,0,10,0,1,1,1,8\n253,27,20.2,1,3,0,0,12,0,1,1,1,7\n3155,27,20.2,1,4,0,0,11,0,1,1,1,7\n6763,27,20.2,1,4,0,0,12,1,1,1,1,10\n3257,27,20.2,1,5,0,0,12,0,0,1,1,9\n2209,27,20.2,1,5,0,0,13,0,0,1,1,8\n10774,27,20.3,1,2,0,1,15,1,1,1,1,10\n5081,27,20.3,1,3,0,0,13,0,0,1,1,7\n4293,27,20.3,1,3,0,0,9,0,0,1,1,10\n8661,27,20.3,1,3,0,0,15,1,0,1,1,6\n495,27,20.4,1,3,0,0,9,0,1,1,1,10\n4527,27,20.4,1,1,0,0,14,0,0,1,1,7\n8397,27,20.4,1,3,0,0,14,1,2,1,1,7\n5559,27,20.4,1,3,0,0,12,1,0,1,1,8\n9969,27,20.4,1,5,0,0,15,1,0,1,1,8\n9787,27,20.4,1,3,0,0,11,1,1,1,1,8\n6375,27,20.5,1,3,0,0,12,1,2,1,1,9\n7366,27,20.5,1,3,0,1,12,1,5,1,1,6\n593,27,20.5,1,4,0,0,12,0,0,1,1,6\n1001,27,20.5,1,4,0,0,12,0,0,1,1,7\n5367,27,20.5,1,2,0,0,12,0,0,1,1,8\n8973,27,20.5,1,5,0,0,12,1,2,1,1,9\n7213,27,20.6,1,4,0,0,11,1,1,1,1,5\n6729,27,20.6,1,1,0,0,12,1,0,1,1,8\n3579,27,20.6,1,5,0,0,15,0,2,1,1,6\n8035,27,20.6,1,3,0,0,9,1,2,1,1,5\n7509,27,20.7,1,2,0,0,9,1,2,1,1,10\n3617,27,20.7,1,3,0,0,14,0,1,1,1,10\n3079,27,20.7,1,5,0,0,12,0,2,1,1,5\n3047,27,20.7,1,1,0,0,13,0,0,1,1,9\n9469,27,20.8,1,2,0,0,14,1,0,1,1,9\n7055,27,20.8,1,2,0,0,13,1,1,1,1,9\n1491,27,20.8,1,4,0,0,9,0,1,1,1,7\n8811,27,20.8,1,3,0,0,12,1,0,1,1,6\n1695,27,20.9,1,1,0,0,14,0,1,1,1,5\n4269,27,20.9,1,5,0,0,13,0,2,1,1,9\n2165,27,21,1,4,0,0,12,0,0,1,1,9\n9781,27,21,1,4,0,0,12,1,2,1,1,7\n4931,27,21.1,1,1,0,0,10,0,0,1,1,8\n7495,27,21.1,1,1,0,0,15,1,2,1,1,7\n6419,27,21.1,1,3,0,0,10,1,2,1,1,5\n4365,27,21.2,1,2,0,0,13,0,2,1,1,5\n9433,27,21.2,1,2,0,0,12,1,2,1,1,8\n6745,27,21.2,1,2,0,0,13,1,0,1,1,9\n4271,27,21.2,1,4,0,0,9,0,0,1,1,7\n6555,27,21.2,1,2,0,0,12,1,1,1,1,8\n9065,27,21.2,1,4,0,0,15,1,1,1,1,6\n9911,27,21.2,1,3,0,0,9,1,2,1,1,8\n2063,27,21.3,1,3,0,0,15,0,0,1,1,8\n5444,27,21.3,1,4,0,1,10,1,5,1,1,4\n2351,27,21.3,1,1,0,0,13,0,1,1,1,6\n2059,27,21.3,1,2,0,0,9,0,0,1,1,8\n5737,27,21.3,1,4,0,0,9,1,1,1,1,5\n8729,27,21.3,1,4,0,0,10,1,2,1,1,10\n8477,27,21.4,1,2,0,0,10,1,2,1,1,10\n9077,27,21.4,1,3,0,0,12,1,2,1,1,10\n4591,27,21.4,1,5,0,0,12,0,2,1,1,6\n3835,27,21.4,1,4,0,0,11,0,1,1,1,7\n6039,27,21.5,1,3,0,0,15,1,0,1,1,8\n10597,27,21.5,1,4,0,0,11,1,2,1,1,10\n8763,27,21.5,1,3,0,0,14,1,1,1,1,10\n10548,27,21.5,1,1,0,1,9,1,1,1,1,9\n9411,27,21.5,1,3,0,0,12,1,0,1,1,7\n613,27,21.6,1,2,0,0,13,0,0,1,1,6\n3957,27,21.6,1,4,0,0,12,0,1,1,1,8\n673,27,21.6,1,4,0,0,9,0,1,1,1,10\n4213,27,21.7,1,5,0,0,10,0,1,1,1,5\n4495,27,21.7,1,5,0,0,9,0,0,1,1,6\n3553,27,21.7,1,3,0,0,12,0,0,1,1,7\n4937,27,21.7,1,4,0,0,14,0,0,1,1,10\n8325,27,21.8,1,1,0,0,9,1,0,1,1,5\n9761,27,21.8,1,1,0,0,10,1,2,1,1,10\n7907,27,21.8,1,2,0,0,15,1,0,1,1,9\n3245,27,21.8,1,4,0,0,12,0,1,1,1,8\n6207,27,21.9,1,1,0,0,14,1,1,1,1,10\n6467,27,21.9,1,4,0,0,11,1,0,1,1,8\n10279,27,21.9,1,1,0,0,12,1,2,1,1,9\n5781,27,21.9,1,4,0,0,11,1,1,1,1,9\n5467,27,21.9,1,2,0,0,12,1,1,1,1,7\n1575,27,21.9,1,3,0,0,13,0,1,1,1,9\n4253,27,21.9,1,1,0,0,13,0,0,1,1,5\n10723,27,21.9,1,2,0,0,12,1,1,1,1,9\n3503,27,21.9,1,3,0,0,12,0,2,1,1,5\n1499,27,21.9,1,2,0,1,12,0,4,1,1,0\n7623,27,21.9,1,1,0,0,9,1,2,1,1,6\n1041,27,22,1,2,0,0,11,0,0,1,1,7\n10709,27,22,1,3,0,0,12,1,2,1,1,8\n5291,27,22,1,1,0,0,12,0,2,1,1,9\n5487,27,22.1,1,4,0,0,15,1,1,1,1,5\n8679,27,22.1,1,4,0,0,9,1,0,1,1,7\n10295,27,22.1,1,1,0,0,14,1,0,1,1,5\n1813,27,22.1,1,3,0,0,9,0,2,1,1,8\n7827,27,22.1,1,2,0,0,12,1,2,1,1,10\n2837,27,22.1,1,4,0,0,15,0,2,1,1,8\n709,27,22.2,1,3,0,0,12,0,1,1,1,5\n7905,27,22.2,1,4,0,0,11,1,1,1,1,10\n6697,27,22.2,1,2,0,0,12,1,1,1,1,5\n10325,27,22.3,1,2,0,0,13,1,2,1,1,5\n9663,27,22.4,1,5,0,0,12,1,0,1,1,6\n10144,27,22.4,1,4,0,1,12,1,1,1,1,9\n9026,27,22.4,1,4,0,1,14,1,5,1,1,1\n10575,27,22.4,1,5,0,0,9,1,2,1,1,9\n167,27,22.5,1,4,0,0,12,0,1,1,1,7\n3888,27,22.5,1,5,0,1,12,0,5,1,1,0\n6385,27,22.5,1,4,0,0,13,1,0,1,1,8\n7131,27,22.5,1,4,0,0,12,1,1,1,1,9\n2783,27,22.5,1,2,0,0,10,0,1,1,1,10\n1767,27,22.6,1,1,0,0,9,0,2,1,1,8\n9191,27,22.6,1,2,0,0,15,1,0,1,1,5\n2347,27,22.7,1,5,0,0,14,0,1,1,1,8\n6699,27,22.7,1,5,0,0,15,1,0,1,1,5\n4957,27,22.7,1,2,0,0,12,0,1,1,1,10\n4738,27,22.7,1,2,0,1,15,0,3,1,1,10\n6667,27,22.7,1,2,0,0,9,1,1,1,1,5\n1395,27,22.7,1,5,0,0,15,0,0,1,1,10\n5715,27,22.7,1,2,0,0,11,1,1,1,1,9\n8451,27,22.8,1,2,0,0,14,1,2,1,1,8\n5519,27,22.8,1,4,0,0,14,1,2,1,1,10\n8510,27,22.8,1,3,0,1,10,1,8,1,1,4\n1123,27,22.8,1,2,0,0,13,0,1,1,1,9\n4789,27,22.8,1,1,0,0,15,0,0,1,1,10\n8889,27,22.9,1,2,0,0,15,1,0,1,1,7\n741,27,22.9,1,4,0,0,10,0,0,1,1,10\n7993,27,22.9,1,1,0,0,14,1,2,1,1,5\n9665,27,22.9,1,2,0,0,13,1,2,1,1,10\n9585,27,22.9,1,2,0,0,12,1,2,1,1,10\n7339,27,22.9,1,2,0,0,9,1,1,1,1,9\n2381,27,23,1,4,0,0,12,0,1,1,1,9\n4843,27,23,1,3,0,0,13,0,0,1,1,10\n8480,27,23.1,1,4,0,1,9,1,4,1,1,0\n10681,27,23.1,1,4,0,0,14,1,1,1,1,7\n901,27,23.1,1,2,0,0,12,0,1,1,1,6\n10199,27,23.1,1,2,0,0,12,1,2,1,1,8\n3899,27,23.1,1,1,0,0,11,0,2,1,1,7\n5425,27,23.1,1,5,0,0,11,1,1,1,1,8\n7399,27,23.2,1,2,0,0,15,1,2,1,1,7\n1155,27,23.2,1,1,0,0,11,0,0,1,1,5\n5865,27,23.2,1,5,0,0,15,1,2,1,1,6\n6671,27,23.2,1,2,0,0,11,1,1,1,1,8\n2601,27,23.2,1,3,0,0,12,0,1,1,1,6\n2289,27,23.2,1,5,0,0,12,0,1,1,1,5\n1197,27,23.3,1,5,0,0,12,0,2,1,1,9\n10247,27,23.3,1,3,0,0,15,1,0,1,1,10\n9931,27,23.4,1,2,0,0,11,1,2,1,1,10\n5043,27,23.4,1,3,0,0,9,0,1,1,1,5\n501,27,23.4,1,4,0,0,12,0,0,1,1,8\n7899,27,23.4,1,2,0,0,11,1,1,1,1,6\n9971,27,23.5,1,2,0,0,13,1,1,1,1,10\n3615,27,23.5,1,4,0,0,12,0,2,1,1,10\n3695,27,23.5,1,5,0,0,12,0,1,1,1,8\n3364,27,23.6,1,2,0,1,12,0,4,1,1,0\n6141,27,23.6,1,3,0,0,15,1,2,1,1,8\n4053,27,23.6,1,5,0,0,9,0,2,1,1,8\n7313,27,23.6,1,2,0,0,10,1,1,1,1,9\n1869,27,23.6,1,4,0,0,10,0,2,1,1,7\n6311,27,23.6,1,1,0,0,13,1,1,1,1,9\n3865,27,23.6,1,5,0,0,12,0,0,1,1,10\n3165,27,23.6,1,4,0,0,9,0,1,1,1,8\n8017,27,23.7,1,2,0,0,12,1,2,1,1,7\n8749,27,23.7,1,2,0,0,12,1,2,1,1,10\n8167,27,23.7,1,5,0,0,13,1,1,1,1,8\n6507,27,23.7,1,5,0,0,14,1,1,1,1,9\n2341,27,23.7,1,4,0,0,14,0,2,1,1,8\n3061,27,23.8,1,4,0,0,12,0,0,1,1,9\n2829,27,23.8,1,3,0,0,12,0,2,1,1,10\n4323,27,23.8,1,5,0,0,10,0,0,1,1,10\n1225,27,23.9,1,2,0,0,14,0,1,1,1,8\n2855,27,23.9,1,3,0,0,12,0,0,1,1,10\n10639,27,24,1,5,0,0,11,1,2,1,1,7\n10025,27,24,1,2,0,0,12,1,0,1,1,6\n10853,27,24,1,2,0,0,11,1,1,1,1,5\n6865,27,24,1,5,0,0,9,1,1,1,1,9\n1501,27,24.1,1,3,0,0,12,0,2,1,1,10\n2487,27,24.1,1,5,0,0,12,0,1,1,1,9\n3449,27,24.1,1,2,0,0,13,0,0,1,1,9\n4449,27,24.2,1,1,0,0,12,0,1,1,1,5\n10077,27,24.3,1,1,0,0,10,1,0,1,1,10\n2471,27,24.3,1,3,0,0,14,0,2,1,1,8\n8711,27,24.3,1,1,0,0,11,1,2,1,1,7\n6743,27,24.3,1,1,0,0,13,1,0,1,1,9\n9777,27,24.3,1,5,0,0,10,1,2,1,1,6\n4369,27,24.4,1,2,0,0,13,0,0,1,1,10\n1567,27,24.4,1,5,0,0,12,0,2,1,1,9\n471,27,24.4,1,3,0,0,14,0,2,1,1,7\n2547,27,24.4,1,2,0,0,15,0,0,1,1,6\n9821,27,24.4,1,2,0,0,15,1,2,1,1,6\n5803,27,24.6,1,1,0,0,11,1,0,1,1,9\n7143,27,24.6,1,4,0,0,12,1,2,1,1,8\n10183,27,24.6,1,5,0,0,14,1,0,1,1,6\n1591,27,24.6,1,3,0,0,12,0,0,1,1,6\n1815,27,24.6,1,1,0,0,13,0,1,1,1,8\n3921,27,24.7,1,4,0,0,10,0,2,1,1,10\n6839,27,24.7,1,1,0,0,13,1,0,1,1,10\n4341,27,24.7,1,2,0,0,15,0,2,1,1,10\n3581,27,24.7,1,1,0,0,12,0,0,1,1,8\n10603,27,24.7,1,2,0,0,9,1,1,1,1,7\n4093,27,24.8,1,5,0,0,12,0,1,1,1,6\n4381,27,24.8,1,1,0,0,11,0,0,1,1,8\n6152,27,24.8,1,1,0,1,15,1,4,1,1,2\n9965,27,24.8,1,1,0,0,13,1,0,1,1,9\n323,27,24.9,1,3,0,0,13,0,1,1,1,7\n8647,27,24.9,1,2,0,0,15,1,2,1,1,10\n9138,27,24.9,1,5,0,1,9,1,7,1,1,7\n6223,27,24.9,1,4,0,0,11,1,1,1,1,5\n7263,27,24.9,1,1,0,0,11,1,2,1,1,7\n1955,27,24.9,1,3,0,0,14,0,1,1,1,7\n7679,27,25,1,5,0,0,12,1,0,1,1,8\n10659,27,25,1,3,0,0,12,1,1,1,1,10\n4179,27,25.1,1,2,0,0,13,0,0,1,1,6\n9695,27,25.1,1,2,0,0,11,1,0,1,1,5\n5811,27,25.1,1,3,0,0,12,1,0,1,1,10\n6603,27,25.2,1,3,0,0,13,1,1,1,1,8\n2331,27,25.2,1,4,0,0,14,0,0,1,1,10\n1443,27,25.3,1,1,0,0,9,0,2,1,1,9\n6549,27,25.3,1,5,0,0,13,1,0,1,1,5\n6333,27,25.3,1,5,0,0,10,1,1,1,1,7\n491,27,25.3,1,3,0,0,14,0,2,1,1,9\n4489,27,25.3,1,5,0,0,13,0,2,1,1,5\n9287,27,25.3,1,4,0,0,9,1,0,1,1,10\n7521,27,25.4,1,1,0,0,12,1,2,1,1,7\n4279,27,25.4,1,2,0,0,14,0,0,1,1,10\n8667,27,25.4,1,4,0,0,9,1,1,1,1,10\n10081,27,25.4,1,2,0,0,11,1,2,1,1,5\n9645,27,25.4,1,4,0,0,12,1,2,1,1,7\n2935,27,25.5,1,3,0,0,12,0,2,1,1,8\n4299,27,25.5,1,4,0,0,9,0,0,1,1,10\n4889,27,25.5,1,2,0,0,11,0,2,1,1,6\n9257,27,25.5,1,2,0,0,14,1,2,1,1,8\n759,27,25.5,1,2,0,0,11,0,2,1,1,7\n399,27,25.6,1,2,0,0,13,0,0,1,1,10\n5176,27,25.6,1,4,0,1,12,0,4,1,1,4\n5091,27,25.6,1,2,0,0,10,0,1,1,1,6\n10449,27,25.7,1,3,0,0,13,1,1,1,1,9\n1923,27,25.7,1,2,0,0,10,0,1,1,1,6\n4773,27,25.8,1,5,0,0,10,0,1,1,1,8\n2535,27,25.8,1,2,0,0,14,0,0,1,1,8\n617,27,25.8,1,1,0,0,10,0,2,1,1,10\n3655,27,25.8,1,1,0,0,13,0,1,1,1,10\n5965,27,25.8,1,1,0,0,14,1,0,1,1,7\n8767,27,25.9,1,1,0,0,9,1,0,1,1,7\n6313,27,25.9,1,5,0,0,15,1,2,1,1,5\n1811,27,25.9,1,5,0,1,11,0,7,1,1,3\n3705,27,25.9,1,2,0,0,14,0,2,1,1,5\n3277,27,26,1,1,0,0,12,0,2,1,1,9\n8429,27,26,1,4,0,0,12,1,0,1,1,8\n3293,27,26,1,1,0,1,15,0,7,1,1,4\n5031,27,26,1,5,0,0,11,0,1,1,1,8\n5242,27,26,1,1,0,1,14,0,1,1,1,10\n2875,27,26,1,2,0,0,10,0,2,1,1,10\n10459,27,26.1,1,2,0,0,15,1,2,1,1,9\n8781,27,26.1,1,1,0,0,14,1,1,1,1,5\n7589,27,26.1,1,3,0,0,15,1,2,1,1,5\n9595,27,26.1,1,1,0,0,12,1,1,1,1,5\n8123,27,26.1,1,2,0,0,12,1,2,1,1,10\n2145,27,26.1,1,5,0,0,15,0,1,1,1,6\n393,27,26.1,1,5,0,0,14,0,2,1,1,7\n8103,27,26.1,1,1,0,0,12,1,1,1,1,5\n7729,27,26.1,1,4,0,0,11,1,0,1,1,6\n7579,27,26.2,1,1,0,0,13,1,2,1,1,6\n5075,27,26.3,1,1,0,0,13,0,1,1,1,5\n9095,27,26.3,1,5,0,0,15,1,1,1,1,7\n4565,27,26.3,1,3,0,0,12,0,0,1,1,5\n6433,27,26.3,1,3,0,0,9,1,1,1,1,8\n2993,27,26.3,1,1,0,0,12,0,2,1,1,5\n2723,27,26.3,1,4,0,0,11,0,0,1,1,7\n10097,27,26.3,1,4,0,0,11,1,2,1,1,7\n5945,27,26.4,1,5,0,0,10,1,0,1,1,9\n8139,27,26.4,1,4,0,0,12,1,0,1,1,10\n1425,27,26.4,1,2,0,0,14,0,0,1,1,10\n5039,27,26.4,1,3,0,0,13,0,1,1,1,5\n2845,27,26.5,1,1,0,0,15,0,2,1,1,5\n9737,27,26.5,1,2,0,0,12,1,2,1,1,8\n8109,27,26.5,1,4,0,0,9,1,1,1,1,6\n7375,27,26.5,1,5,0,0,10,1,0,1,1,9\n1551,27,26.5,1,2,0,0,14,0,1,1,1,6\n9099,27,26.5,1,5,0,0,12,1,2,1,1,10\n2493,27,26.5,1,4,0,0,14,0,1,1,1,9\n2083,27,26.5,1,5,0,0,12,0,2,1,1,8\n743,27,26.5,1,1,0,0,15,0,1,1,1,7\n9795,27,26.5,1,4,0,0,10,1,1,1,1,7\n187,27,26.6,1,2,0,0,9,0,0,1,1,10\n609,27,26.7,1,1,0,0,12,0,0,1,1,7\n3259,27,26.7,1,5,0,0,13,0,2,1,1,6\n2927,27,26.7,1,3,0,0,10,0,0,1,1,9\n3523,27,26.7,1,5,0,0,11,0,0,1,1,10\n3875,27,26.7,1,2,0,0,13,0,2,1,1,7\n6781,27,26.8,1,4,0,0,10,1,2,1,1,5\n5311,27,26.8,1,1,0,0,12,0,0,1,1,9\n6703,27,26.8,1,3,0,0,12,1,2,1,1,10\n545,27,26.9,1,3,0,0,13,0,2,1,1,9\n5943,27,26.9,1,3,0,0,13,1,2,1,1,6\n2815,27,27,1,5,0,0,9,0,1,1,1,6\n2611,27,27,1,1,0,0,12,0,1,1,1,7\n2159,27,27,1,4,0,0,9,0,2,1,1,9\n4545,27,27,1,5,0,0,11,0,1,1,1,6\n8163,27,27.1,1,5,0,0,9,1,2,1,1,6\n3113,27,27.1,1,5,0,0,15,0,0,1,1,10\n8055,27,27.1,1,4,0,0,12,1,0,1,1,5\n9109,27,27.2,1,5,0,0,11,1,0,1,1,6\n6905,27,27.2,1,2,0,0,12,1,0,1,1,9\n7583,27,27.2,1,3,0,0,10,1,1,1,1,10\n605,27,27.2,1,5,0,0,14,0,1,1,1,6\n3395,27,27.2,1,4,0,0,13,0,1,1,1,10\n6325,27,27.3,1,2,0,0,9,1,0,1,1,8\n5776,27,27.3,1,1,0,1,14,1,3,1,1,1\n6379,27,27.3,1,4,0,0,12,1,0,1,1,8\n2713,27,27.3,1,4,0,0,12,0,1,1,1,9\n4701,27,27.3,1,5,0,0,12,0,2,1,1,9\n2575,27,27.3,1,1,0,0,15,0,2,1,1,10\n7959,27,27.4,1,4,0,0,11,1,2,1,1,9\n9901,27,27.4,1,4,0,0,9,1,1,1,1,8\n4409,27,27.4,1,1,0,0,15,0,0,1,1,6\n4259,27,27.4,1,1,0,0,10,0,2,1,1,7\n3177,27,27.4,1,3,0,0,11,0,2,1,1,8\n9087,27,27.4,1,3,0,0,12,1,2,1,1,5\n9217,27,27.5,1,1,0,0,10,1,2,1,1,10\n8081,27,27.5,1,1,0,0,13,1,2,1,1,10\n5373,27,27.5,1,3,0,0,12,0,2,1,1,7\n6957,27,27.5,1,3,0,0,12,1,0,1,1,6\n507,27,27.5,1,1,0,0,12,0,0,1,1,5\n4251,27,27.6,1,1,0,0,11,0,2,1,1,9\n1965,27,27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1,1,1\n1775,28,17.6,1,4,0,0,10,0,2,1,1,9\n6222,28,17.7,1,1,0,1,9,1,3,1,1,7\n2199,28,17.7,1,2,0,0,13,0,2,1,1,10\n1612,28,17.8,1,2,0,1,12,0,5,1,1,6\n1061,28,17.8,1,5,0,0,10,0,0,1,1,6\n6919,28,17.8,1,4,0,0,12,1,1,1,1,6\n5689,28,17.8,1,4,0,0,12,1,0,1,1,10\n3827,28,17.8,1,3,0,0,12,0,1,1,1,8\n4945,28,17.9,1,3,0,0,14,0,0,1,1,5\n7813,28,17.9,1,2,0,0,10,1,1,1,1,7\n6247,28,17.9,1,1,0,0,15,1,1,1,1,10\n10177,28,17.9,1,2,0,0,15,1,2,1,1,6\n5809,28,17.9,1,3,0,0,15,1,0,1,1,5\n1229,28,17.9,1,2,0,0,9,0,2,1,1,8\n7135,28,18,1,2,0,0,9,1,2,1,1,6\n8575,28,18,1,2,0,0,11,1,2,1,1,9\n1991,28,18,1,4,0,0,13,0,0,1,1,5\n6829,28,18,1,3,0,0,12,1,2,1,1,5\n6169,28,18,1,5,0,0,12,1,0,1,1,7\n6515,28,18.1,1,5,0,0,9,1,2,1,1,10\n9897,28,18.1,1,4,0,0,13,1,1,1,1,7\n8234,28,18.1,1,5,0,1,13,1,4,1,1,4\n7673,28,18.1,1,4,0,0,11,1,1,1,1,6\n4927,28,18.1,1,3,0,0,14,0,0,1,1,9\n302,28,18.1,1,1,0,1,13,0,5,1,1,8\n10031,28,18.1,1,1,0,0,13,1,2,1,1,10\n3825,28,18.1,1,3,0,0,14,0,1,1,1,5\n2669,28,18.2,1,3,0,0,13,0,2,1,1,5\n7769,28,18.2,1,3,0,0,11,1,1,1,1,5\n9837,28,18.3,1,4,0,0,13,1,2,1,1,9\n385,28,18.3,1,2,0,0,11,0,0,1,1,10\n9281,28,18.3,1,1,0,0,10,1,2,1,1,10\n1725,28,18.4,1,1,0,0,10,0,1,1,1,7\n9782,28,18.4,1,1,0,1,14,1,3,1,1,1\n1209,28,18.4,1,1,0,0,10,0,2,1,1,6\n4825,28,18.4,1,2,0,0,14,0,0,1,1,8\n1388,28,18.5,1,3,0,1,11,0,5,1,1,4\n2497,28,18.5,1,4,0,0,15,0,0,1,1,10\n5847,28,18.5,1,5,0,0,11,1,2,1,1,10\n8865,28,18.5,1,3,0,0,11,1,1,1,1,7\n10457,28,18.6,1,2,0,0,13,1,2,1,1,9\n2077,28,18.6,1,4,0,0,10,0,2,1,1,6\n409,28,18.6,1,1,0,0,13,0,1,1,1,6\n767,28,18.6,1,4,0,0,15,0,1,1,1,10\n1397,28,18.6,1,1,0,0,10,0,2,1,1,6\n10419,28,18.6,1,4,0,0,10,1,0,1,1,10\n9941,28,18.7,1,1,0,0,9,1,1,1,1,8\n4501,28,18.7,1,2,0,0,10,0,1,1,1,5\n2377,28,18.7,1,2,0,0,12,0,1,1,1,10\n2675,28,18.7,1,2,0,0,10,0,0,1,1,5\n3739,28,18.8,1,1,0,0,14,0,0,1,1,10\n3505,28,18.9,1,4,0,0,11,0,1,1,1,10\n8367,28,18.9,1,5,0,0,12,1,1,1,1,10\n1643,28,19,1,5,0,0,12,0,1,1,1,10\n6951,28,19,1,3,0,0,10,1,0,1,1,6\n5457,28,19.1,1,3,0,0,9,1,2,1,1,7\n10365,28,19.1,1,5,0,0,11,1,0,1,1,7\n7795,28,19.2,1,2,0,0,12,1,2,1,1,7\n3703,28,19.3,1,4,0,0,11,0,2,1,1,6\n9569,28,19.3,1,5,0,0,13,1,2,1,1,6\n2309,28,19.3,1,3,0,0,10,0,0,1,1,10\n8195,28,19.3,1,5,0,0,11,1,0,1,1,8\n10185,28,19.4,1,2,0,0,12,1,0,1,1,9\n6665,28,19.4,1,4,0,0,11,1,2,1,1,7\n2171,28,19.4,1,3,0,0,11,0,1,1,1,10\n377,28,19.4,1,1,0,0,11,0,2,1,1,7\n57,28,19.4,1,3,0,0,9,0,2,1,1,10\n5661,28,19.4,1,2,0,0,14,1,1,1,1,5\n10707,28,19.5,1,4,0,0,10,1,1,1,1,6\n1285,28,19.5,1,2,0,0,14,0,2,1,1,8\n6403,28,19.5,1,2,0,0,9,1,1,1,1,9\n4949,28,19.5,1,1,0,0,11,0,0,1,1,6\n5445,28,19.6,1,5,0,0,15,1,2,1,1,9\n9539,28,19.6,1,1,0,0,14,1,1,1,1,8\n1217,28,19.6,1,2,0,0,14,0,2,1,1,10\n8697,28,19.6,1,1,0,0,10,1,0,1,1,7\n5599,28,19.6,1,2,0,0,10,1,2,1,1,5\n2195,28,19.7,1,1,0,0,15,0,1,1,1,8\n10805,28,19.7,1,3,0,0,12,1,2,1,1,5\n8925,28,19.7,1,2,0,0,13,1,1,1,1,6\n8533,28,19.7,1,5,0,0,10,1,2,1,1,7\n3143,28,19.8,1,4,0,0,11,0,1,1,1,6\n2011,28,19.8,1,1,0,0,11,0,0,1,1,5\n10363,28,19.8,1,5,0,0,13,1,0,1,1,6\n4923,28,19.9,1,5,0,0,14,0,2,1,1,5\n1213,28,19.9,1,2,0,0,11,0,0,1,1,6\n2883,28,19.9,1,5,0,0,15,0,1,1,1,8\n8881,28,20,1,1,0,0,11,1,1,1,1,7\n1323,28,20,1,2,0,0,15,0,1,1,1,5\n6733,28,20,1,2,0,0,12,1,2,1,1,6\n10601,28,20.1,1,5,0,0,11,1,0,1,1,5\n1393,28,20.1,1,2,0,0,9,0,2,1,1,6\n8571,28,20.1,1,4,0,0,12,1,1,1,1,8\n5021,28,20.1,1,2,0,0,10,0,0,1,1,5\n3181,28,20.1,1,1,0,0,12,0,1,1,1,7\n9997,28,20.2,1,5,0,0,9,1,1,1,1,9\n2417,28,20.3,1,1,0,0,10,0,1,1,1,9\n9657,28,20.3,1,2,0,0,15,1,0,1,1,7\n3937,28,20.3,1,1,0,0,11,0,1,1,1,7\n7337,28,20.4,1,4,0,0,10,1,0,1,1,5\n9221,28,20.4,1,4,0,0,15,1,2,1,1,5\n7533,28,20.4,1,3,0,0,14,1,1,1,1,5\n9727,28,20.4,1,3,0,0,10,1,2,1,1,5\n4935,28,20.5,1,3,0,0,12,0,2,1,1,9\n1103,28,20.5,1,2,0,0,11,0,2,1,1,6\n6053,28,20.5,1,4,0,0,12,1,2,1,1,5\n2397,28,20.6,1,5,0,0,9,0,0,1,1,10\n2271,28,20.6,1,5,0,0,15,0,1,1,1,5\n9173,28,20.7,1,4,0,0,13,1,0,1,1,7\n2027,28,20.7,1,4,0,0,15,0,1,1,1,10\n6989,28,20.7,1,2,0,0,10,1,1,1,1,10\n7807,28,20.8,1,4,0,0,12,1,1,1,1,10\n9223,28,20.8,1,3,0,0,14,1,2,1,1,5\n5639,28,20.8,1,4,0,0,11,1,1,1,1,9\n6329,28,20.8,1,4,0,0,10,1,2,1,1,5\n8747,28,20.8,1,3,0,0,14,1,2,1,1,7\n893,28,20.8,1,4,0,0,10,0,0,1,1,6\n5713,28,20.8,1,1,0,0,12,1,0,1,1,5\n1931,28,20.8,1,3,0,0,14,0,0,1,1,8\n5427,28,20.8,1,1,0,0,14,1,2,1,1,9\n10647,28,20.8,1,2,0,0,9,1,1,1,1,10\n1597,28,21,1,1,0,0,10,0,0,1,1,10\n2835,28,21,1,2,0,0,15,0,2,1,1,9\n1479,28,21,1,1,0,0,12,0,2,1,1,7\n5177,28,21,1,5,0,0,9,0,2,1,1,8\n5011,28,21,1,4,0,0,11,0,0,1,1,8\n4233,28,21.1,1,5,0,0,11,0,1,1,1,5\n8503,28,21.1,1,4,0,0,11,1,0,1,1,5\n6851,28,21.1,1,3,0,0,15,1,2,1,1,5\n3323,28,21.1,1,3,0,0,9,0,1,1,1,5\n5617,28,21.1,1,3,0,0,14,1,2,1,1,5\n8535,28,21.2,1,3,0,0,12,1,0,1,1,7\n9776,28,21.2,1,4,0,1,10,1,3,1,1,7\n6059,28,21.2,1,3,0,0,15,1,2,1,1,9\n5735,28,21.2,1,5,0,0,15,1,1,1,1,7\n7039,28,21.2,1,3,0,0,14,1,2,1,1,7\n1207,28,21.2,1,1,0,0,15,0,2,1,1,10\n5165,28,21.2,1,3,0,0,11,0,0,1,1,5\n723,28,21.2,1,1,0,0,12,0,2,1,1,5\n4799,28,21.3,1,1,0,0,15,0,1,1,1,5\n3765,28,21.3,1,3,0,0,14,0,1,1,1,8\n5587,28,21.3,1,1,0,0,12,1,2,1,1,7\n5871,28,21.4,1,2,0,0,9,1,2,1,1,7\n7743,28,21.4,1,2,0,0,9,1,1,1,1,5\n2587,28,21.4,1,4,0,0,14,0,2,1,1,9\n543,28,21.4,1,2,0,0,13,0,2,1,1,6\n5477,28,21.5,1,1,0,0,10,1,2,1,1,9\n7130,28,21.5,1,1,0,1,15,1,7,1,1,5\n1303,28,21.5,1,1,0,0,15,0,2,1,1,6\n3795,28,21.5,1,5,0,0,11,0,2,1,1,5\n8009,28,21.5,1,3,0,0,11,1,1,1,1,10\n7085,28,21.5,1,1,0,0,12,1,1,1,1,10\n8383,28,21.5,1,1,0,0,9,1,1,1,1,8\n4419,28,21.7,1,1,0,0,12,0,1,1,1,6\n9877,28,21.7,1,4,0,0,15,1,2,1,1,7\n7361,28,21.8,1,1,0,0,12,1,2,1,1,10\n4147,28,21.8,1,3,0,0,15,0,0,1,1,5\n343,28,21.8,1,2,0,0,12,0,1,1,1,5\n8581,28,21.8,1,2,0,0,10,1,1,1,1,6\n6217,28,21.8,1,4,0,0,15,1,1,1,1,7\n230,28,21.8,1,4,0,1,9,0,1,1,1,1\n321,28,21.8,1,3,0,0,15,0,2,1,1,8\n6527,28,21.9,1,5,0,0,15,1,2,1,1,8\n3417,28,22,1,4,0,0,10,0,2,1,1,7\n6647,28,22,1,1,0,0,9,1,2,1,1,7\n6145,28,22.1,1,5,0,0,13,1,0,1,1,6\n6061,28,22.1,1,3,0,0,14,1,2,1,1,5\n10399,28,22.1,1,2,0,0,12,1,0,1,1,8\n5475,28,22.2,1,1,0,0,14,1,2,1,1,9\n10053,28,22.2,1,4,0,0,10,1,1,1,1,8\n5289,28,22.2,1,3,0,0,11,0,0,1,1,8\n5003,28,22.2,1,3,0,0,9,0,1,1,1,10\n3247,28,22.2,1,4,0,0,15,0,2,1,1,8\n37,28,22.3,1,1,0,0,9,0,0,1,1,7\n10585,28,22.3,1,3,0,0,14,1,0,1,1,7\n9959,28,22.4,1,1,0,0,10,1,0,1,1,8\n8875,28,22.4,1,4,0,0,15,1,2,1,1,5\n5551,28,22.4,1,4,0,0,9,1,0,1,1,7\n3981,28,22.4,1,2,0,0,12,0,2,1,1,5\n8211,28,22.5,1,2,0,0,13,1,0,1,1,8\n8161,28,22.5,1,4,0,0,12,1,2,1,1,9\n5919,28,22.5,1,4,0,0,11,1,2,1,1,6\n5333,28,22.5,1,3,0,0,12,0,1,1,1,7\n10417,28,22.5,1,2,0,0,11,1,2,1,1,5\n5059,28,22.6,1,2,0,0,9,0,2,1,1,8\n7445,28,22.6,1,4,0,0,9,1,2,1,1,8\n3915,28,22.7,1,2,0,0,13,0,0,1,1,10\n4373,28,22.7,1,4,0,0,12,0,1,1,1,9\n6213,28,22.8,1,4,0,0,12,1,1,1,1,5\n2303,28,22.8,1,3,0,0,13,0,2,1,1,7\n697,28,22.9,1,1,0,0,12,0,1,1,1,7\n9495,28,22.9,1,5,0,0,12,1,0,1,1,6\n10011,28,22.9,1,4,0,0,9,1,2,1,1,8\n9183,28,22.9,1,1,0,0,12,1,0,1,1,5\n5887,28,22.9,1,2,0,0,9,1,2,1,1,9\n549,28,22.9,1,4,0,0,12,0,2,1,1,6\n8631,28,22.9,1,3,0,0,11,1,0,1,1,9\n10851,28,23,1,3,0,0,12,1,1,1,1,7\n5695,28,23,1,4,0,0,10,1,0,1,1,9\n9325,28,23,1,4,0,0,10,1,1,1,1,10\n9523,28,23,1,5,0,0,12,1,0,1,1,9\n8643,28,23,1,3,0,0,11,1,2,1,1,6\n1471,28,23,1,4,0,0,10,0,1,1,1,10\n7888,28,23,1,5,0,1,12,1,6,1,1,4\n3951,28,23.1,1,5,0,0,15,0,2,1,1,6\n641,28,23.1,1,2,0,0,12,0,2,1,1,8\n1325,28,23.1,1,5,0,1,15,0,3,1,1,4\n10099,28,23.1,1,4,0,0,9,1,0,1,1,6\n7251,28,23.2,1,2,0,1,15,1,1,1,1,1\n10469,28,23.2,1,5,0,0,15,1,1,1,1,10\n8115,28,23.2,1,3,0,0,13,1,0,1,1,8\n5757,28,23.2,1,1,0,0,13,1,0,1,1,7\n8305,28,23.2,1,4,0,0,13,1,0,1,1,10\n829,28,23.3,1,4,0,0,15,0,2,1,1,5\n6035,28,23.3,1,3,0,0,12,1,0,1,1,6\n2013,28,23.3,1,3,0,0,11,0,1,1,1,10\n6551,28,23.3,1,2,0,0,10,1,2,1,1,10\n4953,28,23.3,1,2,0,0,10,0,1,1,1,5\n3891,28,23.4,1,1,0,0,12,0,1,1,1,9\n1025,28,23.5,1,2,0,0,10,0,0,1,1,5\n7893,28,23.5,1,1,0,0,12,1,1,1,1,7\n8759,28,23.5,1,5,0,0,10,1,1,1,1,9\n5063,28,23.6,1,5,0,0,14,0,2,1,1,10\n6455,28,23.6,1,3,0,0,13,1,0,1,1,8\n9343,28,23.6,1,1,0,0,10,1,0,1,1,6\n6269,28,23.6,1,4,0,0,12,1,1,1,1,6\n9185,28,23.7,1,5,0,0,12,1,2,1,1,6\n6941,28,23.7,1,1,0,0,14,1,2,1,1,10\n3429,28,23.7,1,3,0,0,11,0,2,1,1,5\n3091,28,23.7,1,1,0,0,14,0,0,1,1,9\n5101,28,23.7,1,2,0,0,12,0,1,1,1,6\n835,28,23.8,1,2,0,0,12,0,1,1,1,7\n10483,28,23.8,1,3,0,0,11,1,0,1,1,8\n5507,28,23.8,1,5,0,0,9,1,1,1,1,8\n1661,28,23.8,1,3,0,0,15,0,0,1,1,6\n8795,28,23.8,1,2,0,0,12,1,1,1,1,10\n9581,28,23.9,1,2,0,0,12,1,2,1,1,7\n8614,28,23.9,1,2,0,1,10,1,7,1,1,4\n10385,28,23.9,1,5,0,0,12,1,0,1,1,8\n5335,28,24,1,5,0,0,12,0,2,1,1,6\n4757,28,24,1,3,0,0,15,0,1,1,1,7\n7075,28,24,1,1,0,0,14,1,2,1,1,6\n3279,28,24,1,5,0,0,10,0,0,1,1,10\n7081,28,24,1,2,0,0,12,1,1,1,1,6\n4661,28,24,1,4,0,0,12,0,0,1,1,7\n8915,28,24.1,1,5,0,0,14,1,0,1,1,6\n4941,28,24.1,1,3,0,0,14,0,2,1,1,10\n3573,28,24.1,1,3,0,0,15,0,0,1,1,9\n7543,28,24.1,1,2,0,0,11,1,2,1,1,8\n8873,28,24.1,1,5,0,0,15,1,2,1,1,6\n9373,28,24.1,1,2,0,0,12,1,0,1,1,9\n8189,28,24.2,1,1,0,0,11,1,1,1,1,8\n3697,28,24.2,1,5,0,0,9,0,2,1,1,5\n6599,28,24.2,1,4,0,0,11,1,1,1,1,9\n9721,28,24.2,1,1,0,0,9,1,2,1,1,5\n6843,28,24.3,1,4,0,0,12,1,0,1,1,8\n9683,28,24.3,1,2,0,0,10,1,1,1,1,7\n7817,28,24.3,1,2,0,0,10,1,2,1,1,6\n5497,28,24.3,1,2,0,0,11,1,0,1,1,8\n233,28,24.3,1,5,0,0,15,0,1,1,1,10\n9337,28,24.3,1,5,0,0,13,1,0,1,1,5\n809,28,24.4,1,2,0,0,13,0,0,1,1,8\n6085,28,24.4,1,4,0,0,15,1,2,1,1,9\n6441,28,24.5,1,1,0,0,12,1,2,1,1,7\n1783,28,24.5,1,3,0,0,10,0,2,1,1,5\n1345,28,24.5,1,2,0,1,12,0,8,1,1,1\n5139,28,24.5,1,2,0,0,10,0,2,1,1,7\n8768,28,24.5,1,1,0,1,9,1,3,1,1,7\n6185,28,24.5,1,3,0,0,9,1,2,1,1,9\n1515,28,24.6,1,3,0,0,9,0,2,1,1,6\n6805,28,24.6,1,3,0,0,11,1,1,1,1,9\n877,28,24.6,1,2,0,0,12,0,2,1,1,10\n753,28,24.7,1,2,0,0,12,0,0,1,1,6\n7903,28,24.7,1,5,0,0,14,1,1,1,1,9\n6003,28,24.8,1,3,0,0,13,1,2,1,1,7\n5657,28,24.8,1,4,0,0,11,1,2,1,1,10\n8947,28,24.8,1,1,0,0,11,1,1,1,1,5\n9607,28,24.8,1,2,0,0,11,1,0,1,1,7\n4477,28,24.9,1,1,0,0,12,0,0,1,1,8\n9471,28,25,1,1,0,0,10,1,1,1,1,5\n8227,28,25,1,2,0,0,12,1,2,1,1,10\n865,28,25,1,5,0,0,9,0,2,1,1,9\n7761,28,25,1,5,0,0,15,1,1,1,1,9\n2235,28,25.1,1,5,0,0,10,0,0,1,1,5\n9303,28,25.1,1,4,0,0,15,1,0,1,1,5\n1389,28,25.1,1,5,0,0,10,0,0,1,1,8\n5071,28,25.1,1,3,0,0,14,0,2,1,1,9\n8919,28,25.1,1,3,0,0,10,1,2,1,1,6\n4483,28,25.1,1,1,0,0,10,0,2,1,1,7\n997,28,25.2,1,3,0,0,10,0,1,1,1,9\n7805,28,25.2,1,2,0,0,13,1,1,1,1,10\n8519,28,25.2,1,3,0,0,14,1,1,1,1,6\n5891,28,25.2,1,2,0,0,12,1,0,1,1,8\n2885,28,25.2,1,5,0,0,14,0,1,1,1,8\n7181,28,25.3,1,1,0,0,12,1,2,1,1,5\n7637,28,25.3,1,5,0,0,12,1,0,1,1,5\n3521,28,25.4,1,4,0,0,11,0,2,1,1,9\n7921,28,25.4,1,1,0,0,10,1,0,1,1,10\n8957,28,25.5,1,1,0,0,13,1,1,1,1,7\n9241,28,25.5,1,1,0,0,15,1,0,1,1,10\n9783,28,25.5,1,3,0,0,10,1,0,1,1,9\n627,28,25.6,1,3,0,0,10,0,1,1,1,10\n5931,28,25.6,1,5,0,0,12,1,2,1,1,7\n7643,28,25.6,1,2,0,0,11,1,0,1,1,8\n8373,28,25.6,1,5,0,0,10,1,2,1,1,8\n3661,28,25.7,1,3,0,0,12,0,0,1,1,6\n6764,28,25.7,1,3,0,1,14,1,4,1,1,5\n5115,28,25.7,1,4,0,0,10,0,0,1,1,8\n1283,28,25.7,1,2,0,0,14,0,0,1,1,9\n5061,28,25.7,1,5,0,0,11,0,0,1,1,8\n5913,28,25.7,1,5,0,0,12,1,2,1,1,6\n6753,28,25.7,1,3,0,0,10,1,1,1,1,6\n129,28,25.8,1,3,0,0,13,0,1,1,1,9\n245,28,25.8,1,1,0,0,14,0,2,1,1,7\n1963,28,25.8,1,1,0,0,14,0,0,1,1,8\n93,28,25.9,1,3,0,0,14,0,1,1,1,6\n6915,28,25.9,1,2,0,0,15,1,2,1,1,8\n9347,28,25.9,1,2,0,0,10,1,1,1,1,10\n1481,28,25.9,1,1,0,0,12,0,0,1,1,9\n5189,28,25.9,1,5,0,0,10,0,0,1,1,7\n7528,28,25.9,1,5,0,1,11,1,4,1,1,1\n990,28,26,1,3,0,1,11,0,8,1,1,6\n2477,28,26,1,4,0,0,13,0,1,1,1,10\n9067,28,26,1,1,0,0,9,1,0,1,1,8\n7129,28,26.1,1,5,0,0,12,1,0,1,1,6\n7053,28,26.1,1,1,0,0,13,1,2,1,1,9\n6479,28,26.1,1,5,0,0,15,1,1,1,1,7\n6823,28,26.1,1,4,0,0,15,1,2,1,1,8\n6877,28,26.2,1,3,0,0,13,1,0,1,1,7\n2995,28,26.2,1,2,0,0,10,0,2,1,1,10\n2739,28,26.2,1,3,0,0,13,0,2,1,1,10\n8379,28,26.2,1,5,0,0,9,1,2,1,1,9\n6309,28,26.2,1,1,0,0,12,1,0,1,1,8\n3641,28,26.3,1,5,0,0,10,0,1,1,1,8\n2771,28,26.4,1,5,0,0,15,0,0,1,1,8\n2577,28,26.4,1,5,0,0,9,0,1,1,1,5\n2881,28,26.4,1,3,0,0,13,0,0,1,1,7\n9608,28,26.4,1,2,0,1,14,1,3,1,1,1\n3707,28,26.4,1,2,0,0,12,0,0,1,1,10\n2379,28,26.4,1,1,0,0,14,0,0,1,1,8\n5823,28,26.4,1,1,0,1,13,1,7,1,1,4\n9405,28,26.5,1,5,0,0,12,1,2,1,1,8\n7393,28,26.5,1,4,0,0,9,1,1,1,1,8\n8977,28,26.6,1,1,0,0,12,1,2,1,1,8\n5313,28,26.6,1,4,0,0,13,0,2,1,1,9\n2475,28,26.6,1,4,0,0,12,0,1,1,1,10\n8693,28,26.6,1,5,0,0,14,1,0,1,1,6\n5479,28,26.6,1,5,0,0,12,1,2,1,1,10\n4655,28,26.7,1,5,0,0,12,0,0,1,1,10\n6785,28,26.7,1,4,0,0,12,1,2,1,1,10\n10381,28,26.7,1,4,0,0,12,1,2,1,1,7\n2483,28,26.7,1,4,0,0,15,0,0,1,1,6\n9779,28,26.7,1,3,0,0,12,1,1,1,1,10\n1519,28,26.7,1,2,0,0,12,0,2,1,1,8\n2241,28,26.8,1,2,0,0,9,0,2,1,1,10\n10017,28,26.8,1,4,0,0,14,1,1,1,1,9\n1321,28,26.8,1,3,0,0,9,0,2,1,1,10\n293,28,26.8,1,1,0,0,14,0,0,1,1,5\n255,28,26.8,1,5,0,0,14,0,2,1,1,7\n8801,28,26.9,1,3,0,0,11,1,0,1,1,7\n9485,28,26.9,1,3,0,0,13,1,1,1,1,5\n7911,28,27,1,3,0,0,14,1,1,1,1,8\n8463,28,27,1,1,0,0,12,1,2,1,1,5\n6255,28,27,1,3,0,1,12,1,2,1,1,0\n6283,28,27.1,1,2,0,0,14,1,2,1,1,9\n5095,28,27.1,1,1,0,0,12,0,1,1,1,8\n1361,28,27.1,1,5,0,0,12,0,2,1,1,10\n6993,28,27.1,1,3,0,0,11,1,0,1,1,9\n4131,28,27.1,1,3,0,0,15,0,2,1,1,5\n4197,28,27.1,1,5,0,0,15,0,2,1,1,8\n9765,28,27.2,1,2,0,0,13,1,1,1,1,7\n6101,28,27.2,1,3,0,0,12,1,2,1,1,8\n6409,28,27.3,1,2,0,0,11,1,1,1,1,6\n3725,28,27.3,1,2,0,0,12,0,0,1,1,9\n9479,28,27.3,1,5,0,0,12,1,2,1,1,9\n4347,28,27.4,1,1,0,0,11,0,0,1,1,7\n3967,28,27.4,1,1,0,0,11,0,2,1,1,6\n4047,28,27.4,1,1,0,0,10,0,2,1,1,9\n5273,28,27.4,1,3,0,0,12,0,2,1,1,5\n9699,28,27.4,1,3,0,1,10,1,5,1,1,0\n6142,28,27.4,1,1,0,1,9,1,4,1,1,10\n6975,28,27.5,1,2,0,1,15,1,4,1,1,4\n5961,28,27.5,1,5,0,0,12,1,0,1,1,5\n5963,28,27.5,1,5,0,0,12,1,0,1,1,9\n7855,28,27.5,1,5,0,0,12,1,0,1,1,9\n8465,28,27.6,1,4,0,0,9,1,1,1,1,9\n378,28,27.6,1,2,0,1,9,0,3,1,1,7\n3319,28,27.6,1,4,0,0,14,0,0,1,1,10\n2343,28,27.6,1,4,0,1,14,0,4,1,1,4\n2825,28,27.6,1,4,0,0,13,0,2,1,1,9\n5288,28,27.7,1,4,0,1,15,0,8,1,1,7\n721,28,27.7,1,2,0,0,13,0,1,1,1,7\n6837,28,27.7,1,3,0,0,10,1,2,1,1,6\n10105,28,27.8,1,2,0,0,12,1,0,1,1,7\n5361,28,27.8,1,2,0,0,9,0,0,1,1,8\n2901,28,27.8,1,5,0,0,14,0,2,1,1,5\n9503,28,27.8,1,4,0,0,12,1,2,1,1,6\n1589,28,27.8,1,2,0,0,13,0,2,1,1,7\n7461,28,27.8,1,3,0,0,15,1,2,1,1,5\n50,28,27.8,1,1,0,1,10,0,5,1,1,8\n3785,28,27.9,1,4,0,0,10,0,1,1,1,6\n10615,28,27.9,1,2,0,0,9,1,2,1,1,10\n3343,28,27.9,1,5,0,0,9,0,1,1,1,6\n5415,28,27.9,1,3,0,0,12,0,1,1,1,9\n9251,28,27.9,1,4,0,0,12,1,1,1,1,9\n5371,28,27.9,1,2,0,0,12,0,1,1,1,6\n6305,28,28,1,4,0,0,10,1,1,1,1,8\n1175,28,28,1,2,0,0,12,0,0,1,1,7\n2299,28,28,1,1,0,0,15,0,2,1,1,7\n6523,28,28,1,1,0,0,12,1,0,1,1,6\n10640,28,28,1,4,0,1,10,1,6,1,1,6\n633,28,28.1,1,5,0,0,12,0,2,1,1,10\n3433,28,28.1,1,1,0,0,14,0,0,1,1,5\n5237,28,28.1,1,2,0,0,9,0,2,1,1,5\n2053,28,28.2,1,4,0,0,11,0,0,1,1,9\n3191,28,28.2,1,3,0,0,15,0,0,1,1,8\n1837,28,28.2,1,1,0,0,12,0,0,1,1,5\n315,28,28.2,1,2,0,0,12,0,2,1,1,8\n6181,28,28.3,1,2,0,0,13,1,1,1,1,8\n10863,28,28.3,1,5,0,0,13,1,2,1,1,6\n8279,28,28.3,1,4,0,0,13,1,0,1,1,5\n2943,28,28.4,1,2,0,0,11,0,2,1,1,7\n703,28,28.4,1,4,0,0,12,0,1,1,1,10\n9371,28,28.4,1,3,0,0,15,1,2,1,1,6\n2337,28,28.4,1,2,0,0,13,0,2,1,1,10\n10571,28,28.4,1,2,0,0,10,1,2,1,1,10\n3813,28,28.5,1,3,0,0,12,0,0,1,1,8\n4281,28,28.5,1,1,0,0,10,0,1,1,1,6\n8245,28,28.5,1,5,0,0,10,1,2,1,1,7\n4579,28,28.5,1,2,0,0,11,0,2,1,1,5\n7209,28,28.6,1,1,0,0,12,1,0,1,1,10\n7249,28,28.6,1,4,0,0,12,1,0,1,1,5\n9951,28,28.6,1,1,0,0,13,1,0,1,1,5\n7957,28,28.6,1,5,0,0,12,1,1,1,1,5\n987,28,28.7,1,5,0,0,10,0,1,1,1,7\n5053,28,28.7,1,5,0,0,12,0,2,1,1,7\n6157,28,28.8,1,5,0,0,12,1,1,1,1,5\n585,28,28.8,1,3,0,0,12,0,2,1,1,6\n2753,28,28.8,1,5,0,0,12,0,2,1,1,6\n9033,28,28.9,1,3,0,0,14,1,2,1,1,7\n8291,28,28.9,1,5,0,0,12,1,1,1,1,6\n3629,28,28.9,1,3,0,0,12,0,1,1,1,7\n8047,28,28.9,1,3,0,0,12,1,0,1,1,8\n7439,28,28.9,1,2,0,0,9,1,0,1,1,7\n8303,28,28.9,1,4,0,0,11,1,2,1,1,6\n2246,28,29,1,4,0,1,12,0,7,1,1,10\n7325,28,29,1,5,0,0,15,1,2,1,1,8\n1281,28,29,1,1,0,0,14,0,0,1,1,7\n10829,28,29.1,1,4,0,0,12,1,2,1,1,8\n965,28,29.1,1,2,0,0,12,0,1,1,1,7\n6439,28,29.1,1,2,0,0,12,1,0,1,1,8\n7247,28,29.1,1,5,0,0,12,1,1,1,1,7\n354,28,29.2,1,5,0,1,12,0,1,1,1,3\n10119,28,29.2,1,2,0,0,13,1,2,1,1,8\n10341,28,29.2,1,4,0,0,10,1,2,1,1,5\n7937,28,29.2,1,5,0,0,15,1,2,1,1,10\n7567,28,29.3,1,1,0,0,11,1,1,1,1,5\n10447,28,29.3,1,4,0,0,9,1,1,1,1,8\n2971,28,29.4,1,3,0,0,13,0,1,1,1,9\n3607,28,29.4,1,1,0,0,14,0,1,1,1,5\n851,28,29.4,1,5,0,0,12,0,1,1,1,6\n1743,28,29.4,1,2,0,0,13,0,2,1,1,7\n4027,28,29.5,1,2,0,0,12,0,1,1,1,10\n4733,28,29.5,1,2,0,0,11,0,2,1,1,7\n10632,28,29.6,1,3,0,1,11,1,3,1,1,6\n5933,28,29.6,1,3,0,1,14,1,6,1,1,1\n9535,28,29.6,1,3,0,0,11,1,0,1,1,6\n1927,28,29.7,1,1,0,0,11,0,2,1,1,7\n2221,28,29.7,1,1,0,0,9,0,2,1,1,5\n4199,28,29.7,1,1,0,0,14,0,0,1,1,7\n3619,28,29.7,1,5,0,0,15,0,1,1,1,5\n10747,28,29.7,1,3,0,1,15,1,4,1,1,4\n5471,28,29.7,1,3,0,0,12,1,2,1,1,7\n4967,28,29.8,1,3,0,0,14,0,2,1,1,6\n9361,28,29.8,1,2,0,0,10,1,0,1,1,6\n9541,28,29.8,1,4,0,0,14,1,2,1,1,8\n25,28,29.8,1,5,0,0,11,0,2,1,1,8\n5701,28,29.8,1,1,0,0,13,1,2,1,1,6\n7908,28,29.9,1,2,0,1,13,1,1,1,1,0\n4493,28,29.9,1,5,0,0,11,0,1,1,1,5\n17,28,29.9,1,2,0,0,12,0,2,1,1,6\n10783,28,29.9,1,1,0,0,11,1,0,1,1,6\n4015,28,29.9,1,1,0,0,9,0,2,1,1,10\n265,28,29.9,1,5,0,0,12,0,1,1,1,10\n5337,28,29.9,1,2,0,0,13,0,0,1,1,10\n1915,28,30,1,3,0,0,13,0,1,1,1,10\n139,29,0.1,1,5,0,1,12,0,2,1,1,0\n2681,29,0.8,1,4,0,1,12,0,6,1,1,0\n1523,29,0.9,1,2,0,1,11,0,7,1,1,3\n10306,29,1.1,1,4,0,1,12,1,4,1,1,1\n6900,29,1.3,1,5,0,1,12,1,6,1,1,1\n5163,29,2,1,2,0,1,13,0,3,1,1,0\n5556,29,2.3,1,5,0,1,14,1,6,1,1,8\n5326,29,2.3,1,5,0,1,12,0,5,1,1,7\n10148,29,2.9,1,5,0,1,14,1,5,1,1,8\n4254,29,3.2,1,3,0,1,15,0,1,1,1,7\n8942,29,3.2,1,4,0,1,13,1,3,1,1,7\n9829,29,3.3,1,1,0,1,9,1,5,1,1,3\n5476,29,3.7,1,1,0,1,10,1,7,1,1,9\n6084,29,4.2,1,1,0,1,14,1,8,1,1,7\n6216,29,4.4,1,3,0,1,10,1,6,1,1,8\n8626,29,4.5,1,2,0,1,12,1,3,1,1,0\n7049,29,4.6,1,4,0,1,10,1,2,1,1,1\n4078,29,4.6,1,5,0,1,9,0,7,1,1,7\n220,29,5,1,5,0,1,9,0,2,1,1,2\n8593,29,5.4,1,5,0,1,11,1,1,1,1,1\n9057,29,5.5,1,2,0,1,9,1,6,1,1,0\n9166,29,5.6,1,4,0,1,9,1,8,1,1,5\n2359,29,6.4,1,2,0,1,9,0,7,1,1,3\n9071,29,6.5,1,4,0,1,15,1,4,1,1,4\n1970,29,6.6,1,5,0,1,13,0,3,1,1,4\n4361,29,6.7,1,1,0,1,15,0,8,1,1,2\n5710,29,6.8,1,3,0,1,11,1,2,1,1,5\n9296,29,7.6,1,1,0,1,11,1,6,1,1,2\n1462,29,7.9,1,1,0,1,15,0,2,1,1,1\n4676,29,8.3,1,2,0,1,13,0,3,1,1,3\n2727,29,8.3,1,5,0,1,13,0,3,1,1,0\n2340,29,8.8,1,4,0,1,9,0,1,1,1,4\n6187,29,9,1,5,0,1,15,1,4,1,1,0\n5793,29,9.2,1,4,0,1,11,1,3,1,1,0\n9064,29,9.3,1,1,0,1,15,1,5,1,1,7\n3193,29,10,1,1,0,1,13,0,7,1,1,3\n6268,29,10.2,1,5,0,1,9,1,8,1,1,6\n4613,29,10.4,1,5,0,1,12,0,6,1,1,2\n1860,29,10.6,1,5,0,1,13,0,5,1,1,6\n5436,29,10.9,1,3,0,1,10,1,4,1,1,10\n956,29,11,1,5,0,1,12,0,2,1,1,1\n8526,29,11.1,1,4,0,1,9,1,3,1,1,4\n2508,29,11.2,1,2,0,1,13,0,3,1,1,1\n1145,29,11.5,1,5,0,1,11,0,1,1,1,2\n2636,29,12.1,1,3,0,1,11,0,1,1,1,4\n7107,29,12.1,1,1,0,1,11,1,1,1,1,2\n9637,29,12.3,1,5,0,1,10,1,1,1,1,2\n4280,29,12.3,1,4,0,1,10,0,7,1,1,7\n10684,29,12.6,1,2,0,1,14,1,1,1,1,0\n10738,29,12.9,1,4,0,1,10,1,8,1,1,0\n6562,29,13.1,1,5,0,1,13,1,3,1,1,10\n4503,29,13.2,1,2,0,1,10,0,2,1,1,1\n7008,29,13.5,1,3,0,1,10,1,5,1,1,0\n5906,29,13.5,1,3,0,1,10,1,2,1,1,6\n5806,29,13.7,1,2,0,1,9,1,6,1,1,1\n5461,29,13.7,1,4,0,1,14,1,5,1,1,1\n5861,29,13.9,1,3,0,1,13,1,5,1,1,3\n10259,29,13.9,1,5,0,1,12,1,6,1,1,3\n407,29,14.3,1,1,0,1,12,0,5,1,1,1\n10753,29,15.2,1,3,0,1,13,1,6,1,1,1\n6688,29,15.4,1,1,0,1,13,1,5,1,1,1\n674,29,15.4,1,2,0,1,12,0,5,1,1,4\n6695,29,15.6,1,2,0,1,9,1,1,1,1,3\n2090,29,15.6,1,5,0,1,11,0,8,1,1,3\n74,29,16.2,1,1,0,1,13,0,5,1,1,9\n5205,29,16.2,1,3,0,1,9,0,4,1,1,3\n7071,29,16.2,1,3,0,1,11,1,7,1,1,3\n10012,29,16.6,1,3,0,1,11,1,4,1,1,3\n7428,29,16.9,1,5,0,1,9,1,2,1,1,3\n8404,29,17,1,4,0,1,10,1,3,1,1,7\n10219,29,17.3,1,4,0,1,12,1,1,1,1,1\n1798,29,17.4,1,2,0,1,15,0,4,1,1,6\n159,29,17.5,1,1,0,1,9,0,4,1,1,3\n1366,29,17.7,1,4,0,1,10,0,5,1,1,6\n3206,29,17.9,1,4,0,1,12,0,4,1,1,0\n3194,29,17.9,1,2,0,1,10,0,6,1,1,3\n3320,29,18.1,1,4,0,1,11,0,4,1,1,2\n2132,29,18.5,1,4,0,1,10,0,4,1,1,7\n6736,29,18.6,1,5,0,1,12,1,5,1,1,4\n3954,29,18.7,1,5,0,1,11,0,7,1,1,2\n5349,29,18.9,1,2,0,1,15,0,4,1,1,1\n2118,29,18.9,1,1,0,1,10,0,7,1,1,5\n2180,29,19.1,1,4,0,1,15,0,8,1,1,9\n6209,29,19.2,1,3,0,1,13,1,3,1,1,3\n9360,29,20.2,1,3,0,1,15,1,2,1,1,1\n9082,29,20.3,1,1,0,1,12,1,2,1,1,6\n1707,29,21.6,1,1,0,1,9,0,4,1,1,1\n7654,29,21.8,1,4,0,1,11,1,3,1,1,3\n4735,29,21.9,1,3,0,1,14,0,3,1,1,1\n7610,29,22,1,1,0,1,12,1,5,1,1,2\n9768,29,22.4,1,1,0,1,10,1,2,1,1,10\n2233,29,22.9,1,1,0,1,14,0,7,1,1,3\n6460,29,23.3,1,3,0,1,12,1,2,1,1,10\n8354,29,23.6,1,4,0,1,9,1,6,1,1,2\n8472,29,23.7,1,3,0,1,11,1,5,1,1,6\n9834,29,24,1,4,0,1,12,1,2,1,1,7\n10284,29,24,1,3,0,1,12,1,6,1,1,8\n6276,29,24,1,2,0,1,13,1,7,1,1,8\n9383,29,24.1,1,5,0,1,15,1,7,1,1,3\n1396,29,24.5,1,3,0,1,10,0,6,1,1,10\n6793,29,24.7,1,1,0,1,15,1,8,1,1,3\n3909,29,25,1,2,0,1,15,0,4,1,1,3\n6314,29,25.1,1,5,0,1,15,1,1,1,1,7\n1960,29,25.4,1,4,0,1,13,0,3,1,1,1\n9926,29,25.7,1,1,0,1,10,1,3,1,1,3\n9679,29,25.9,1,5,0,1,9,1,8,1,1,3\n8042,29,25.9,1,5,0,1,12,1,5,1,1,8\n9308,29,26.7,1,3,0,1,13,1,4,1,1,3\n10542,29,27,1,2,0,1,12,1,5,1,1,10\n10296,29,27.4,1,3,0,1,11,1,1,1,1,0\n5235,29,27.4,1,3,0,1,11,0,3,1,1,3\n2865,29,27.6,1,2,0,1,12,0,2,1,1,2\n7616,29,28,1,3,0,1,10,1,8,1,1,6\n7078,29,28.7,1,5,0,1,11,1,2,1,1,7\n9532,29,28.8,1,3,0,1,11,1,8,1,1,9\n7574,29,28.9,1,1,0,1,12,1,8,1,1,7\n8750,29,29,1,2,0,1,11,1,4,1,1,3\n7494,29,29,1,2,0,1,10,1,8,1,1,4\n3238,29,29.5,1,5,0,1,13,0,2,1,1,2\n1690,29,29.9,1,2,0,1,11,0,6,1,1,0\n7942,30,0.2,1,4,0,1,8,1,8,1,1,10\n7340,30,0.4,1,2,0,1,6,1,5,1,1,3\n2430,30,0.6,1,1,0,1,5,0,3,1,1,9\n6642,30,0.7,1,1,0,1,7,1,1,1,1,10\n6626,30,1,1,3,0,1,6,1,5,1,1,7\n8164,30,1.1,1,1,0,1,6,1,8,1,1,4\n8064,30,1.6,1,1,0,1,6,1,3,1,1,9\n9430,30,1.8,1,1,0,1,7,1,8,1,1,4\n4182,30,2.4,1,4,0,1,7,0,6,1,1,0\n7870,30,2.4,1,3,0,1,8,1,3,1,1,0\n2988,30,2.4,1,3,0,1,6,0,6,1,1,1\n5698,30,3.4,1,3,0,1,7,1,2,1,1,6\n3412,30,3.5,1,1,0,1,8,0,7,1,1,1\n61,30,3.7,1,2,0,1,7,0,5,1,1,3\n4544,30,3.8,1,4,0,1,8,0,8,1,1,1\n7177,30,3.9,1,5,0,1,8,1,2,1,1,3\n2965,30,4.2,1,3,0,1,5,0,5,1,1,0\n190,30,4.3,1,1,0,1,6,0,4,1,1,2\n3258,30,4.5,1,5,0,1,7,0,6,1,1,6\n5024,30,4.6,1,1,0,1,5,0,3,1,1,1\n9846,30,4.8,1,2,0,1,8,1,2,1,1,7\n542,30,5,1,5,0,1,8,0,4,1,1,2\n3636,30,5.3,1,4,0,1,7,0,7,1,1,6\n2384,30,5.4,1,4,0,1,6,0,1,1,1,9\n1946,30,5.7,1,5,0,1,8,0,1,1,1,7\n9380,30,5.9,1,5,0,1,8,1,1,1,1,2\n10532,30,6,1,4,0,1,7,1,6,1,1,5\n8778,30,6.3,1,2,0,1,5,1,4,1,1,3\n2586,30,6.4,1,5,0,1,7,0,1,1,1,3\n1640,30,6.6,1,2,0,1,6,0,7,1,1,8\n3010,30,6.7,1,2,0,1,5,0,1,1,1,9\n6346,30,7.1,1,2,0,1,5,1,1,1,1,4\n9980,30,7.2,1,2,0,1,7,1,7,1,1,5\n6027,30,7.2,1,1,0,1,7,1,7,1,1,4\n6740,30,7.8,1,3,0,1,6,1,6,1,1,5\n6296,30,7.9,1,5,0,1,7,1,5,1,1,10\n6872,30,8,1,1,0,1,8,1,8,1,1,1\n4440,30,8.3,1,5,0,1,7,0,8,1,1,0\n5674,30,8.9,1,1,0,1,7,1,1,1,1,2\n5402,30,9.1,1,5,0,1,7,0,8,1,1,8\n140,30,9.2,1,5,0,1,7,0,6,1,1,7\n3627,30,9.2,1,4,0,1,5,0,4,1,1,2\n1016,30,9.3,1,5,0,1,7,0,6,1,1,5\n1789,30,9.4,1,1,0,1,5,0,3,1,1,1\n8922,30,10.1,1,2,0,1,5,1,4,1,1,8\n7912,30,10.7,1,5,0,1,6,1,4,1,1,6\n6606,30,10.8,1,3,0,1,7,1,3,1,1,2\n5708,30,11.5,1,3,0,1,5,1,4,1,1,5\n4246,30,11.6,1,5,0,1,7,0,8,1,1,1\n5960,30,12.1,1,2,0,1,6,1,4,1,1,8\n5991,30,12.2,1,5,0,1,6,1,4,1,1,2\n10166,30,12.8,1,3,0,1,8,1,6,1,1,0\n1233,30,12.8,1,4,0,1,6,0,2,1,1,0\n3678,30,13.2,1,3,0,1,7,0,7,1,1,4\n10692,30,13.6,1,2,0,1,8,1,6,1,1,5\n8122,30,13.9,1,4,0,1,8,1,2,1,1,10\n10130,30,14.2,1,3,0,1,5,1,8,1,1,2\n7796,30,14.3,1,4,0,1,8,1,5,1,1,0\n9497,30,14.6,1,1,0,1,7,1,4,1,1,0\n329,30,14.8,1,2,0,1,7,0,6,1,1,2\n3749,30,15.7,1,3,0,1,7,0,4,1,1,3\n10026,30,15.7,1,5,0,1,6,1,5,1,1,1\n1446,30,15.7,1,5,0,1,6,0,2,1,1,10\n698,30,15.8,1,4,0,1,5,0,6,1,1,2\n9746,30,17.1,1,2,0,1,6,1,7,1,1,8\n3928,30,17.3,1,3,0,1,5,0,2,1,1,6\n6396,30,18,1,3,0,1,5,1,4,1,1,8\n2522,30,18,1,3,0,1,7,0,2,1,1,5\n9367,30,18,1,3,0,1,8,1,4,1,1,3\n5199,30,18.2,1,3,0,1,7,0,1,1,1,3\n2718,30,18.2,1,2,0,1,8,0,3,1,1,4\n10248,30,18.4,1,1,0,1,6,1,3,1,1,6\n248,30,18.4,1,4,0,1,5,0,4,1,1,7\n4260,30,18.5,1,1,0,1,5,0,1,1,1,5\n9019,30,18.6,1,1,0,1,5,1,5,1,1,3\n2774,30,18.8,1,4,0,1,7,0,8,1,1,3\n7182,30,18.9,1,3,0,1,8,1,8,1,1,3\n3744,30,19,1,1,0,1,8,0,2,1,1,4\n9512,30,19,1,1,0,1,7,1,2,1,1,3\n9446,30,19.3,1,1,0,1,7,1,5,1,1,4\n9698,30,19.4,1,5,0,1,5,1,2,1,1,5\n2302,30,19.6,1,1,0,1,8,0,8,1,1,6\n6800,30,19.6,1,2,0,1,5,1,1,1,1,3\n8792,30,19.9,1,4,0,1,7,1,7,1,1,10\n7914,30,20.1,1,3,0,1,7,1,2,1,1,5\n8128,30,20.6,1,4,0,1,5,1,2,1,1,10\n9517,30,20.9,1,2,0,1,7,1,2,1,1,3\n4354,30,21,1,3,0,1,7,0,4,1,1,4\n3099,30,21.1,1,5,0,1,6,0,2,1,1,2\n3393,30,21.3,1,5,0,1,7,0,4,1,1,3\n5562,30,21.4,1,1,0,1,8,1,5,1,1,3\n8701,30,21.4,1,1,0,1,5,1,6,1,1,4\n8725,30,22.2,1,4,0,1,5,1,3,1,1,0\n5452,30,22.2,1,5,0,1,8,1,1,1,1,4\n7002,30,23.9,1,4,0,1,8,1,4,1,1,6\n848,30,24,1,5,0,1,8,0,4,1,1,9\n9620,30,24.2,1,4,0,1,8,1,8,1,1,4\n9222,30,24.6,1,3,0,1,8,1,6,1,1,1\n10386,30,24.9,1,2,0,1,7,1,7,1,1,5\n7700,30,25.6,1,3,0,1,5,1,2,1,1,7\n1774,30,25.6,1,3,0,1,7,0,3,1,1,7\n5283,30,25.7,1,1,0,1,5,0,1,1,1,2\n1945,30,26.4,1,5,0,1,5,0,5,1,1,4\n262,30,27.2,1,1,0,1,5,0,3,1,1,3\n4286,30,27.3,1,3,0,1,8,0,2,1,1,9\n9953,30,28.2,1,4,0,1,5,1,6,1,1,4\n8572,30,28.3,1,3,0,1,8,1,2,1,1,9\n1866,30,28.3,1,5,0,1,8,0,5,1,1,7\n3305,30,28.6,1,3,0,1,6,0,2,1,1,1\n9078,30,28.7,1,4,0,1,8,1,1,1,1,7\n6717,30,28.9,1,5,0,1,7,1,3,1,1,1\n1318,30,28.9,1,4,0,1,5,0,7,1,1,10\n782,30,29.2,1,1,0,1,8,0,3,1,1,6\n2484,30,29.2,1,1,0,1,8,0,4,1,1,6\n4382,30,29.2,1,4,0,1,5,0,3,1,1,4\n7288,30,29.6,1,2,0,1,8,1,3,1,1,4\n8837,30,29.7,1,1,0,1,8,1,7,1,1,3\n5876,31,0.6,1,4,0,1,6,1,1,1,1,6\n8065,31,0.7,1,3,0,1,7,1,8,1,1,2\n4562,31,1,1,4,0,1,8,0,5,1,1,6\n9836,31,1.1,1,1,0,1,8,1,1,1,1,10\n9526,31,1.7,1,4,0,1,7,1,2,1,1,0\n5596,31,2.7,1,4,0,1,5,1,8,1,1,7\n3572,31,3.2,1,2,0,1,5,0,2,1,1,5\n5532,31,3.9,1,3,0,1,6,1,1,1,1,7\n209,31,4.4,1,5,0,1,5,0,1,1,1,0\n9490,31,4.7,1,1,0,1,5,1,5,1,1,7\n3976,31,5.1,1,3,0,1,5,0,4,1,1,0\n10278,31,5.2,1,2,0,1,8,1,7,1,1,7\n6710,31,5.3,1,2,0,1,7,1,8,1,1,5\n6140,31,5.3,1,4,0,1,6,1,1,1,1,5\n2743,31,5.4,1,2,0,1,7,0,3,1,1,4\n3730,31,5.5,1,4,0,1,8,0,6,1,1,6\n5166,31,6.2,1,3,0,1,6,0,5,1,1,8\n6336,31,7.4,1,5,0,1,5,1,3,1,1,5\n2770,31,7.4,1,4,0,1,5,0,4,1,1,0\n6594,31,7.6,1,2,0,1,6,1,2,1,1,2\n3240,31,7.9,1,2,0,1,7,0,5,1,1,5\n3886,31,8.1,1,4,0,1,8,0,3,1,1,9\n6610,31,8.9,1,4,0,1,6,1,3,1,1,1\n3244,31,8.9,1,5,0,1,6,0,1,1,1,7\n313,31,9.1,1,3,0,1,7,0,1,1,1,3\n5804,31,9.2,1,4,0,1,8,1,1,1,1,1\n6044,31,9.8,1,1,0,1,8,1,4,1,1,0\n5552,31,9.9,1,2,0,1,5,1,6,1,1,0\n4577,31,9.9,1,4,0,1,8,0,3,1,1,2\n8601,31,10,1,5,0,1,6,1,1,1,1,1\n5234,31,10.8,1,1,0,1,8,0,1,1,1,7\n8568,31,10.9,1,1,0,1,5,1,3,1,1,0\n10373,31,11.4,1,5,0,1,7,1,6,1,1,0\n2868,31,11.5,1,5,0,1,7,0,5,1,1,9\n10658,31,11.5,1,4,0,1,5,1,1,1,1,1\n5646,31,11.9,1,3,0,1,6,1,2,1,1,3\n3829,31,12,1,1,0,1,6,0,6,1,1,4\n1065,31,13,1,5,0,1,6,0,1,1,1,2\n88,31,13.6,1,5,0,1,8,0,7,1,1,0\n1778,31,13.8,1,2,0,1,6,0,8,1,1,0\n1256,31,14,1,4,0,1,6,0,3,1,1,1\n3838,31,14.5,1,1,0,1,5,0,4,1,1,4\n7015,31,14.6,1,5,0,1,6,1,1,1,1,3\n10086,31,14.9,1,3,0,1,6,1,7,1,1,7\n2803,31,15.2,1,4,0,1,5,0,6,1,1,0\n6022,31,15.3,1,3,0,1,6,1,2,1,1,10\n484,31,15.3,1,4,0,1,6,0,5,1,1,3\n10649,31,16.1,1,1,0,1,8,1,6,1,1,0\n4016,31,16.7,1,1,0,1,6,0,1,1,1,5\n2986,31,16.8,1,1,0,1,5,0,6,1,1,7\n9394,31,17.9,1,3,0,1,8,1,3,1,1,9\n8556,31,18.3,1,2,0,1,6,1,1,1,1,7\n7214,31,18.5,1,4,0,1,7,1,5,1,1,0\n2686,31,19.4,1,5,0,1,7,0,2,1,1,5\n5563,31,19.4,1,1,0,1,7,1,6,1,1,4\n1482,31,19.5,1,5,0,1,7,0,1,1,1,0\n3878,31,19.5,1,3,0,1,7,0,5,1,1,3\n7944,31,19.5,1,3,0,1,5,1,8,1,1,3\n7648,31,20.1,1,5,0,1,7,1,2,1,1,3\n4160,31,20.8,1,5,0,1,5,0,1,1,1,3\n1558,31,20.9,1,2,0,1,8,0,3,1,1,1\n6852,31,21.1,1,3,0,1,7,1,3,1,1,6\n8455,31,21.3,1,3,0,1,5,1,6,1,1,2\n3464,31,22.4,1,1,0,1,6,0,4,1,1,9\n10191,31,22.8,1,3,0,1,6,1,3,1,1,3\n3926,31,22.9,1,5,0,1,7,0,3,1,1,6\n7799,31,23,1,5,0,1,7,1,1,1,1,3\n384,31,23.7,1,5,0,1,8,0,7,1,1,4\n6083,31,24.1,1,1,0,1,8,1,7,1,1,2\n2890,31,24.2,1,2,0,1,7,0,8,1,1,5\n8956,31,24.3,1,1,0,1,8,1,1,1,1,8\n7824,31,24.5,1,3,0,1,6,1,3,1,1,4\n10500,31,24.5,1,1,0,1,8,1,3,1,1,7\n10521,31,25.2,1,2,0,1,7,1,6,1,1,3\n9238,31,25.8,1,3,0,1,7,1,1,1,1,3\n5518,31,25.9,1,4,0,1,6,1,8,1,1,4\n8901,31,25.9,1,2,0,1,8,1,2,1,1,2\n4683,31,26,1,2,0,1,6,0,6,1,1,4\n8149,31,26.1,1,1,0,1,7,1,5,1,1,2\n598,31,26.4,1,3,0,1,6,0,1,1,1,5\n5198,31,26.4,1,2,0,1,6,0,6,1,1,4\n3068,31,26.4,1,2,0,1,8,0,3,1,1,5\n2614,31,26.5,1,1,0,1,6,0,4,1,1,8\n6831,31,26.8,1,1,0,1,5,1,8,1,1,3\n9399,31,27.1,1,4,0,1,7,1,1,1,1,0\n442,31,27.9,1,5,0,1,8,0,6,1,1,6\n7262,31,28.3,1,2,0,1,7,1,4,1,1,2\n1493,31,28.8,1,4,0,1,5,0,2,1,1,0\n2942,31,28.8,1,4,0,1,8,0,8,1,1,8\n9650,31,29.2,1,1,0,1,8,1,7,1,1,5\n1822,31,29.3,1,5,0,1,5,0,4,1,1,10\n4296,31,29.3,1,1,0,1,5,0,4,1,1,1\n9163,31,29.6,1,2,0,1,8,1,6,1,1,3\n4660,31,29.7,1,3,0,1,8,0,1,1,1,5\n5872,32,0.6,1,1,0,1,7,1,1,1,1,7\n10133,32,0.9,1,2,0,1,6,1,1,1,1,4\n1838,32,0.9,1,1,0,1,8,0,3,1,1,0\n112,32,1.9,1,5,0,1,5,0,2,1,1,8\n7330,32,2,1,4,0,1,6,1,6,1,1,2\n9228,32,2.6,1,3,0,1,7,1,5,1,1,2\n1456,32,2.8,1,5,0,1,6,0,8,1,1,10\n1668,32,2.8,1,5,0,1,5,0,2,1,1,9\n4208,32,3,1,2,0,1,7,0,3,1,1,1\n9226,32,3,1,3,0,1,7,1,1,1,1,8\n8840,32,3.1,1,2,0,1,8,1,3,1,1,1\n8296,32,3.6,1,3,0,1,7,1,7,1,1,4\n7198,32,3.8,1,4,0,1,5,1,8,1,1,10\n3956,32,3.8,1,2,0,1,6,0,4,1,1,1\n10182,32,4.3,1,2,0,1,6,1,2,1,1,0\n3481,32,4.7,1,4,0,1,6,0,2,1,1,2\n8371,32,5.1,1,4,0,1,7,1,7,1,1,4\n1680,32,5.4,1,4,0,1,7,0,2,1,1,7\n4162,32,5.5,1,3,0,1,7,0,3,1,1,2\n3239,32,5.8,1,1,0,1,6,0,6,1,1,4\n1944,32,5.8,1,1,0,1,5,0,6,1,1,7\n4994,32,6.2,1,3,0,1,6,0,6,1,1,8\n4054,32,6.2,1,3,0,1,6,0,3,1,1,5\n2896,32,6.8,1,4,0,1,5,0,3,1,1,9\n2685,32,7.2,1,5,0,1,6,0,2,1,1,1\n7284,32,7.6,1,3,0,1,8,1,5,1,1,1\n10048,32,7.8,1,5,0,1,5,1,5,1,1,6\n7410,32,8.2,1,2,0,1,5,1,3,1,1,3\n10204,32,8.9,1,1,0,1,7,1,6,1,1,10\n1630,32,9.4,1,4,0,1,6,0,4,1,1,2\n3196,32,10.7,1,2,0,1,5,0,4,1,1,6\n3980,32,11.2,1,5,0,1,6,0,1,1,1,7\n5640,32,11.3,1,1,0,1,7,1,1,1,1,5\n4248,32,11.5,1,5,0,1,8,0,6,1,1,10\n374,32,11.6,1,4,0,1,7,0,1,1,1,8\n6884,32,11.7,1,3,0,1,7,1,6,1,1,4\n8928,32,11.7,1,1,0,1,5,1,3,1,1,0\n5914,32,12.7,1,1,0,1,7,1,1,1,1,1\n3316,32,13.1,1,4,0,1,7,0,6,1,1,6\n3454,32,13.4,1,3,0,1,7,0,8,1,1,9\n9282,32,13.5,1,5,0,1,5,1,5,1,1,10\n3466,32,13.7,1,5,0,1,5,0,6,1,1,7\n9725,32,13.9,1,4,0,1,8,1,4,1,1,3\n8820,32,14,1,2,0,1,7,1,1,1,1,10\n2762,32,14,1,1,0,1,5,0,3,1,1,9\n9874,32,14.5,1,2,0,1,5,1,6,1,1,4\n8648,32,15.1,1,1,0,1,6,1,7,1,1,3\n8321,32,16.2,1,3,0,1,7,1,4,1,1,0\n2714,32,16.2,1,4,0,1,6,0,8,1,1,5\n4628,32,16.8,1,2,0,1,8,0,8,1,1,8\n1795,32,17.1,1,4,0,1,8,0,4,1,1,2\n4760,32,17.1,1,5,0,1,8,0,1,1,1,4\n6969,32,17.2,1,5,0,1,8,1,7,1,1,1\n10724,32,17.3,1,4,0,1,5,1,5,1,1,6\n6173,32,17.5,1,2,0,1,8,1,6,1,1,1\n7558,32,17.6,1,4,0,1,6,1,2,1,1,10\n4112,32,17.8,1,4,0,1,5,0,2,1,1,9\n1734,32,18.1,1,3,0,1,8,0,2,1,1,4\n4305,32,18.1,1,5,0,1,7,0,8,1,1,1\n1635,32,18.2,1,3,0,1,6,0,5,1,1,0\n3096,32,18.4,1,1,0,1,5,0,3,1,1,6\n4301,32,18.4,1,4,0,1,7,0,8,1,1,3\n10140,32,18.4,1,2,0,1,6,1,8,1,1,7\n7406,32,18.4,1,4,0,1,7,1,4,1,1,7\n4837,32,18.7,1,1,0,1,8,0,5,1,1,0\n6200,32,18.8,1,4,0,1,6,1,7,1,1,5\n2281,32,18.9,1,2,0,1,5,0,6,1,1,0\n285,32,19.1,1,1,0,1,7,0,1,1,1,1\n5981,32,19.3,1,1,0,1,6,1,6,1,1,0\n361,32,19.5,1,3,0,1,6,0,5,1,1,2\n6029,32,19.7,1,4,0,1,7,1,5,1,1,2\n563,32,20.2,1,5,0,1,7,0,4,1,1,2\n6486,32,20.8,1,1,0,1,8,1,6,1,1,0\n6620,32,21.1,1,4,0,1,8,1,5,1,1,2\n1720,32,21.5,1,5,0,1,5,0,7,1,1,5\n5221,32,21.9,1,4,0,1,5,0,4,1,1,0\n3205,32,21.9,1,1,0,1,6,0,4,1,1,2\n8494,32,22.1,1,3,0,1,7,1,8,1,1,5\n5275,32,22.8,1,4,0,1,5,0,7,1,1,2\n6355,32,22.8,1,2,0,1,5,1,5,1,1,2\n5482,32,23,1,3,0,1,8,1,6,1,1,0\n6766,32,23.3,1,3,0,1,6,1,5,1,1,1\n619,32,23.4,1,2,0,1,5,0,6,1,1,3\n2370,32,23.4,1,2,0,1,5,0,4,1,1,4\n2632,32,23.5,1,3,0,1,8,0,8,1,1,0\n9028,32,23.6,1,5,0,1,7,1,8,1,1,6\n10614,32,24,1,1,0,1,7,1,2,1,1,10\n9135,32,24.9,1,3,0,1,5,1,3,1,1,4\n2352,32,25,1,5,0,1,8,0,3,1,1,2\n10082,32,25.2,1,1,0,1,6,1,5,1,1,10\n4400,32,25.2,1,5,0,1,6,0,3,1,1,2\n6195,32,25.8,1,3,0,1,6,1,2,1,1,1\n10207,32,25.8,1,3,0,1,6,1,2,1,1,3\n5978,32,26,1,5,0,1,5,1,2,1,1,6\n9934,32,26.2,1,4,0,1,8,1,6,1,1,9\n2922,32,26.4,1,5,0,1,8,0,3,1,1,2\n10700,32,26.4,1,3,0,1,7,1,7,1,1,1\n7754,32,26.5,1,3,0,1,8,1,6,1,1,8\n2101,32,26.6,1,1,0,1,6,0,3,1,1,2\n1372,32,27.3,1,4,0,1,7,0,4,1,1,6\n10669,32,27.3,1,3,0,1,7,1,5,1,1,2\n7426,32,27.5,1,3,0,1,5,1,1,1,1,10\n8026,32,27.5,1,2,0,1,7,1,1,1,1,0\n10388,32,28,1,4,0,1,8,1,8,1,1,2\n7631,32,28.3,1,4,0,1,8,1,4,1,1,2\n5332,32,28.8,1,1,0,1,7,0,3,1,1,2\n9878,32,28.9,1,4,0,1,7,1,1,1,1,8\n7289,32,29,1,2,0,1,8,1,6,1,1,3\n8431,32,29.1,1,3,0,1,5,1,5,1,1,4\n3654,32,29.2,1,2,0,1,8,0,7,1,1,5\n4812,32,29.5,1,1,0,1,6,0,7,1,1,3\n9406,32,29.6,1,1,0,1,6,1,7,1,1,2\n3458,32,29.9,1,5,0,1,7,0,3,1,1,1\n5676,33,0.3,1,1,0,1,5,1,8,1,1,7\n8343,33,0.9,1,5,0,1,6,1,7,1,1,0\n5597,33,1,1,1,0,1,8,1,5,1,1,4\n10432,33,1.1,1,2,0,1,5,1,2,1,1,10\n2724,33,2,1,5,0,1,7,0,1,1,1,0\n10322,33,2.2,1,4,0,1,7,1,5,1,1,4\n6836,33,2.3,1,5,0,1,6,1,1,1,1,5\n7229,33,3,1,5,0,1,5,1,4,1,1,2\n8166,33,3.4,1,2,0,1,7,1,1,1,1,5\n4399,33,3.8,1,2,0,1,6,0,2,1,1,0\n3614,33,4.8,1,4,0,1,5,0,5,1,1,7\n1930,33,5,1,2,0,1,7,0,6,1,1,3\n98,33,5.3,1,1,0,1,6,0,4,1,1,2\n7675,33,5.5,1,5,0,1,8,1,8,1,1,3\n3208,33,5.6,1,4,0,1,7,0,3,1,1,4\n7542,33,5.9,1,2,0,1,6,1,1,1,1,6\n7362,33,6,1,3,0,1,8,1,3,1,1,9\n2388,33,6.1,1,4,0,1,6,0,8,1,1,1\n1932,33,6.6,1,2,0,1,5,0,3,1,1,10\n7984,33,6.6,1,1,0,1,6,1,1,1,1,9\n4918,33,7.3,1,5,0,1,5,0,8,1,1,1\n4262,33,7.5,1,2,0,1,8,0,1,1,1,8\n3077,33,8,1,3,0,1,7,0,7,1,1,1\n4033,33,8.3,1,4,0,1,5,0,8,1,1,4\n3220,33,8.8,1,1,0,1,6,0,1,1,1,4\n1516,33,8.9,1,2,0,1,5,0,6,1,1,2\n9602,33,9.3,1,4,0,1,6,1,6,1,1,5\n1151,33,9.8,1,4,0,1,8,0,1,1,1,3\n499,33,10.3,1,3,0,1,7,0,3,1,1,3\n4670,33,10.4,1,1,0,1,7,0,6,1,1,4\n3574,33,10.6,1,4,0,1,8,0,8,1,1,2\n1758,33,11.5,1,3,0,1,5,0,5,1,1,8\n6115,33,11.5,1,5,0,1,5,1,1,1,1,1\n7408,33,11.5,1,4,0,1,7,1,6,1,1,1\n9506,33,12,1,3,0,1,7,1,4,1,1,3\n6430,33,12,1,5,0,1,5,1,2,1,1,10\n386,33,12,1,3,0,1,8,0,3,1,1,8\n941,33,12.6,1,4,0,1,8,0,6,1,1,0\n4447,33,12.6,1,1,0,1,5,0,7,1,1,3\n9229,33,12.9,1,4,0,1,8,1,3,1,1,1\n3849,33,13,1,5,0,1,5,0,3,1,1,4\n10578,33,13.2,1,4,0,1,7,1,4,1,1,5\n1670,33,14,1,1,0,1,6,0,2,1,1,10\n482,33,14.1,1,3,0,1,8,0,7,1,1,9\n1710,33,14.5,1,1,0,1,8,0,3,1,1,5\n8414,33,14.6,1,5,0,1,7,1,8,1,1,4\n281,33,14.8,1,4,0,1,7,0,8,1,1,1\n8258,33,15,1,5,0,1,7,1,8,1,1,10\n1864,33,15.1,1,3,0,1,6,0,6,1,1,2\n10476,33,15.7,1,4,0,1,7,1,3,1,1,1\n1468,33,15.7,1,1,0,1,8,0,4,1,1,4\n7918,33,16.6,1,3,0,1,8,1,4,1,1,1\n10826,33,16.8,1,1,0,1,5,1,3,1,1,6\n7560,33,17.1,1,2,0,1,8,1,8,1,1,6\n1109,33,17.3,1,4,0,1,8,0,3,1,1,1\n100,33,18.1,1,3,0,1,7,0,6,1,1,1\n2432,33,18.1,1,4,0,1,7,0,5,1,1,2\n6502,33,18.2,1,4,0,1,5,1,3,1,1,4\n4310,33,18.2,1,1,0,1,8,0,5,1,1,0\n10595,33,18.2,1,4,0,1,5,1,4,1,1,0\n4844,33,18.3,1,4,0,1,5,0,6,1,1,5\n9589,33,18.5,1,5,0,1,6,1,4,1,1,3\n2635,33,18.9,1,4,0,1,5,0,4,1,1,4\n2540,33,19.5,1,5,0,1,8,0,8,1,1,2\n347,33,19.5,1,3,0,1,5,0,7,1,1,3\n6080,33,19.7,1,3,0,1,6,1,6,1,1,7\n8137,33,20.3,1,4,0,1,8,1,2,1,1,3\n5100,33,20.7,1,1,0,1,7,0,7,1,1,3\n995,33,20.8,1,4,0,1,6,0,6,1,1,4\n8850,33,20.9,1,2,0,1,8,1,4,1,1,7\n152,33,21,1,5,0,1,8,0,3,1,1,5\n204,33,21.2,1,1,0,1,7,0,6,1,1,10\n10552,33,21.2,1,5,0,1,5,1,7,1,1,8\n82,33,21.3,1,4,0,1,5,0,8,1,1,6\n8823,33,21.6,1,2,0,1,5,1,3,1,1,2\n2631,33,22.3,1,2,0,1,5,0,3,1,1,3\n7756,33,22.5,1,2,0,1,7,1,3,1,1,7\n8858,33,23.7,1,2,0,1,7,1,3,1,1,2\n8448,33,23.8,1,2,0,1,6,1,3,1,1,7\n10036,33,23.8,1,5,0,1,8,1,5,1,1,1\n5762,33,23.8,1,3,0,1,8,1,1,1,1,4\n2814,33,24.3,1,2,0,1,8,0,7,1,1,8\n1337,33,24.4,1,1,0,1,5,0,8,1,1,4\n400,33,24.4,1,2,0,1,6,0,2,1,1,10\n18,33,25,1,4,0,1,5,0,4,1,1,2\n10780,33,25.1,1,2,0,1,7,1,7,1,1,5\n6693,33,25.3,1,1,0,1,5,1,5,1,1,0\n8726,33,26.3,1,2,0,1,5,1,6,1,1,6\n5956,33,26.5,1,4,0,1,5,1,6,1,1,8\n3451,33,26.7,1,1,0,1,5,0,7,1,1,4\n10168,33,27.2,1,2,0,1,8,1,3,1,1,7\n6125,33,27.4,1,2,0,1,7,1,5,1,1,1\n4084,33,27.6,1,1,0,1,5,0,3,1,1,3\n4158,33,27.8,1,3,0,1,8,0,3,1,1,7\n8075,33,28.5,1,4,0,1,6,1,1,1,1,3\n9981,33,28.8,1,2,0,1,5,1,4,1,1,1\n2622,33,29.2,1,4,0,1,8,0,5,1,1,7\n4180,33,29.3,1,1,0,1,6,0,3,1,1,0\n8516,33,29.4,1,3,0,1,6,1,6,1,1,1\n856,33,29.9,1,2,0,1,8,0,2,1,1,6\n9148,34,0.9,1,2,0,1,7,1,1,1,1,3\n7864,34,1,1,4,0,1,5,1,2,1,1,0\n1608,34,1,1,1,0,1,8,0,8,1,1,9\n8134,34,1.4,1,2,0,1,5,1,1,1,1,6\n9212,34,1.4,1,2,0,1,5,1,3,1,1,9\n90,34,1.5,1,2,0,1,5,0,8,1,1,7\n1132,34,2.3,1,2,0,1,7,0,8,1,1,10\n3906,34,2.5,1,1,0,1,5,0,7,1,1,5\n10192,34,2.6,1,3,0,1,5,1,6,1,1,4\n7798,34,2.7,1,1,0,1,7,1,5,1,1,8\n4285,34,2.8,1,4,0,1,8,0,4,1,1,2\n2014,34,3,1,4,0,1,8,0,5,1,1,8\n5098,34,3.7,1,1,0,1,5,0,1,1,1,5\n2639,34,4.3,1,2,0,1,6,0,2,1,1,2\n6822,34,4.4,1,3,0,1,8,1,4,1,1,9\n9460,34,4.5,1,4,0,1,5,1,5,1,1,6\n2436,34,4.7,1,5,0,1,5,0,3,1,1,10\n541,34,4.8,1,3,0,1,5,0,2,1,1,3\n6868,34,5.2,1,3,0,1,5,1,1,1,1,8\n80,34,5.3,1,3,0,1,6,0,3,1,1,10\n2294,34,5.9,1,2,0,1,8,0,1,1,1,8\n4936,34,6.1,1,3,0,1,7,0,7,1,1,2\n1120,34,6.2,1,1,0,1,7,0,6,1,1,1\n7722,34,6.6,1,3,0,1,8,1,6,1,1,3\n7767,34,6.8,1,2,0,1,7,1,1,1,1,0\n3243,34,7.1,1,2,0,1,5,0,7,1,1,4\n5423,34,7.2,1,1,0,1,6,1,3,1,1,3\n2904,34,7.2,1,3,0,1,5,0,4,1,1,8\n9079,34,7.3,1,4,0,1,7,1,7,1,1,2\n7253,34,7.4,1,4,0,1,5,1,8,1,1,0\n1234,34,7.6,1,2,0,1,6,0,1,1,1,7\n10645,34,7.7,1,2,0,1,6,1,7,1,1,2\n10568,34,8.1,1,1,0,1,6,1,3,1,1,10\n1843,34,8.6,1,3,0,1,5,0,8,1,1,0\n9248,34,9,1,5,0,1,8,1,4,1,1,6\n1904,34,9,1,4,0,1,6,0,6,1,1,0\n1146,34,9.1,1,3,0,1,8,0,4,1,1,10\n6398,34,9.2,1,3,0,1,5,1,1,1,1,7\n10862,34,9.2,1,3,0,1,7,1,1,1,1,10\n4550,34,9.3,1,4,0,1,8,0,5,1,1,8\n10315,34,9.6,1,2,0,1,6,1,1,1,1,4\n1886,34,9.9,1,1,0,1,8,0,4,1,1,3\n4851,34,10.1,1,4,0,1,7,0,4,1,1,0\n1301,34,10.9,1,4,0,1,5,0,3,1,1,4\n4423,34,11.3,1,4,0,1,6,0,5,1,1,1\n1277,34,11.4,1,4,0,1,6,0,4,1,1,0\n3008,34,11.5,1,4,0,1,8,0,6,1,1,3\n3974,34,11.9,1,4,0,1,7,0,7,1,1,8\n7082,34,12.2,1,5,0,1,6,1,1,1,1,0\n10446,34,12.2,1,2,0,1,6,1,6,1,1,6\n8021,34,12.3,1,4,0,1,7,1,4,1,1,3\n8728,34,12.3,1,3,0,1,5,1,8,1,1,0\n5685,34,12.5,1,3,0,1,8,1,1,1,1,3\n9443,34,12.8,1,4,0,1,5,1,1,1,1,1\n2464,34,13.1,1,3,0,1,7,0,5,1,1,5\n1194,34,14,1,1,0,1,8,0,1,1,1,4\n10458,34,14.2,1,4,0,1,8,1,1,1,1,0\n10560,34,14.4,1,4,0,1,6,1,2,1,1,4\n7852,34,14.4,1,3,0,1,5,1,2,1,1,3\n6978,34,14.5,1,3,0,1,7,1,5,1,1,5\n7915,34,15.1,1,1,0,1,7,1,6,1,1,0\n215,34,15.6,1,5,0,1,5,0,3,1,1,1\n6474,34,15.7,1,3,0,1,6,1,6,1,1,2\n6104,34,15.8,1,4,0,1,7,1,5,1,1,7\n2086,34,16.4,1,4,0,1,6,0,8,1,1,6\n1744,34,16.5,1,1,0,1,8,0,5,1,1,2\n8583,34,16.8,1,1,0,1,6,1,5,1,1,4\n2062,34,16.9,1,3,0,1,8,0,8,1,1,1\n6298,34,17.1,1,2,0,1,8,1,8,1,1,10\n5434,34,17.4,1,3,0,1,5,1,6,1,1,5\n208,34,17.5,1,2,0,1,8,0,4,1,1,2\n9168,34,17.6,1,4,0,1,5,1,7,1,1,7\n4222,34,17.9,1,2,0,1,6,0,3,1,1,3\n4939,34,18.3,1,3,0,1,6,0,5,1,1,3\n5062,34,18.4,1,2,0,1,6,0,3,1,1,2\n10184,34,18.7,1,3,0,1,5,1,3,1,1,6\n2665,34,18.9,1,4,0,1,6,0,5,1,1,1\n3745,34,19.1,1,3,0,1,8,0,5,1,1,3\n8461,34,19.3,1,3,0,1,5,1,1,1,1,4\n10007,34,19.4,1,2,0,1,7,1,4,1,1,3\n802,34,19.6,1,3,0,1,5,0,5,1,1,2\n10146,34,19.7,1,4,0,1,6,1,8,1,1,4\n9651,34,19.9,1,3,0,1,5,1,5,1,1,3\n10332,34,19.9,1,3,0,1,6,1,7,1,1,5\n5774,34,20.5,1,2,0,1,5,1,6,1,1,4\n1403,34,20.5,1,2,0,1,6,0,5,1,1,1\n7186,34,20.8,1,5,0,1,5,1,6,1,1,1\n9860,34,21.4,1,4,0,1,8,1,4,1,1,1\n5511,34,21.4,1,3,0,1,7,1,7,1,1,0\n1528,34,21.4,1,5,0,1,7,0,6,1,1,3\n10106,34,21.7,1,4,0,1,6,1,6,1,1,5\n2017,34,22,1,1,0,1,5,0,2,1,1,0\n4226,34,22,1,3,0,1,8,0,3,1,1,0\n1332,34,22.1,1,3,0,1,5,0,4,1,1,9\n5246,34,22.3,1,2,0,1,7,0,4,1,1,4\n2794,34,22.4,1,3,0,1,5,0,6,1,1,9\n3037,34,22.4,1,3,0,1,7,0,2,1,1,1\n8492,34,23.3,1,1,0,1,7,1,6,1,1,0\n6227,34,23.5,1,3,0,1,8,1,2,1,1,1\n8877,34,23.5,1,3,0,1,5,1,8,1,1,1\n6350,34,23.8,1,2,0,1,8,1,7,1,1,4\n9260,34,24.1,1,5,0,1,8,1,2,1,1,6\n1336,34,24.9,1,1,0,1,8,0,7,1,1,4\n6147,34,25,1,2,0,1,5,1,7,1,1,4\n7217,34,25,1,5,0,1,8,1,6,1,1,3\n8345,34,25.3,1,2,0,1,6,1,8,1,1,1\n7356,34,25.8,1,2,0,1,5,1,4,1,1,2\n8016,34,26.2,1,5,0,1,5,1,1,1,1,10\n7822,34,26.6,1,2,0,1,5,1,8,1,1,9\n1912,34,27.2,1,5,0,1,8,0,1,1,1,4\n2018,34,27.3,1,3,0,1,8,0,4,1,1,3\n4712,34,27.4,1,3,0,1,8,0,7,1,1,3\n9290,34,27.5,1,1,0,1,6,1,8,1,1,1\n4946,34,29,1,2,0,1,7,0,7,1,1,6\n1357,34,29.2,1,4,0,1,5,0,7,1,1,2\n6683,34,29.5,1,4,0,1,5,1,8,1,1,2\n7524,34,29.6,1,2,0,1,8,1,3,1,1,5\n9348,34,29.7,1,3,0,1,6,1,2,1,1,0\n5512,35,0.4,1,2,0,1,5,1,5,1,1,7\n10005,35,1,1,4,0,1,6,1,6,1,1,0\n7992,35,1.5,1,1,0,1,7,1,2,1,1,6\n5208,35,1.5,1,2,0,1,8,0,5,1,1,3\n8807,35,2.2,1,5,0,1,8,1,6,1,1,4\n8754,35,2.2,1,5,0,1,6,1,2,1,1,8\n7017,35,2.5,1,2,0,1,8,1,2,1,1,0\n364,35,2.6,1,4,0,1,5,0,3,1,1,10\n9928,35,2.7,1,5,0,1,8,1,8,1,1,7\n144,35,2.8,1,2,0,1,5,0,7,1,1,0\n5696,35,2.9,1,5,0,1,7,1,1,1,1,6\n2282,35,2.9,1,5,0,1,5,0,3,1,1,6\n9872,35,3.1,1,1,0,1,7,1,4,1,1,5\n7438,35,3.4,1,2,0,1,8,1,2,1,1,10\n4714,35,3.4,1,2,0,1,7,0,6,1,1,9\n9730,35,3.5,1,4,0,1,5,1,7,1,1,0\n5488,35,3.5,1,2,0,1,8,1,3,1,1,10\n8832,35,3.6,1,3,0,1,6,1,4,1,1,2\n6067,35,3.6,1,5,0,1,6,1,5,1,1,0\n1622,35,3.7,1,1,0,1,5,0,8,1,1,1\n5868,35,3.7,1,2,0,1,7,1,4,1,1,7\n1586,35,3.7,1,3,0,1,8,0,8,1,1,3\n7550,35,3.8,1,5,0,1,6,1,4,1,1,5\n6100,35,4.2,1,2,0,1,5,1,6,1,1,6\n6242,35,4.3,1,2,0,1,6,1,6,1,1,8\n10163,35,4.4,1,5,0,1,5,1,6,1,1,2\n6410,35,4.6,1,4,0,1,5,1,3,1,1,4\n5193,35,4.8,1,3,0,1,5,0,5,1,1,4\n4108,35,4.8,1,1,0,1,8,0,1,1,1,6\n276,35,5.1,1,2,0,1,7,0,8,1,1,0\n5474,35,5.4,1,2,0,1,8,1,6,1,1,6\n8458,35,5.4,1,5,0,1,5,1,3,1,1,3\n2050,35,5.5,1,3,0,1,7,0,2,1,1,8\n7088,35,6.5,1,3,0,1,5,1,6,1,1,8\n6801,35,7.1,1,5,0,1,8,1,1,1,1,3\n6469,35,7.1,1,3,0,1,8,1,6,1,1,1\n223,35,7.1,1,5,0,1,7,0,7,1,1,2\n1645,35,7.8,1,1,0,1,8,0,7,1,1,0\n1768,35,7.9,1,2,0,1,7,0,4,1,1,0\n4430,35,8.6,1,2,0,1,7,0,8,1,1,3\n6153,35,8.8,1,1,0,1,7,1,7,1,1,1\n7118,35,9.4,1,5,0,1,7,1,7,1,1,8\n1044,35,10.2,1,1,0,1,5,0,8,1,1,2\n9564,35,10.3,1,2,0,1,7,1,4,1,1,8\n6590,35,10.5,1,1,0,1,6,1,7,1,1,6\n3867,35,10.5,1,4,0,1,7,0,5,1,1,4\n731,35,10.6,1,1,0,1,5,0,4,1,1,1\n1384,35,10.7,1,3,0,1,6,0,4,1,1,10\n9270,35,10.8,1,5,0,1,7,1,5,1,1,1\n2012,35,10.8,1,1,0,1,8,0,7,1,1,5\n3510,35,11.1,1,2,0,1,5,0,6,1,1,7\n6290,35,11.5,1,2,0,1,6,1,6,1,1,9\n3536,35,11.6,1,3,0,1,5,0,8,1,1,3\n2638,35,11.7,1,4,0,1,7,0,5,1,1,5\n5144,35,4,1,1,0,1,5,0,6,1,1,3\n2444,35,4,1,5,0,1,6,0,3,1,1,6\n4836,35,6,1,5,0,1,8,0,7,1,1,2\n1186,35,2,1,5,0,1,5,0,8,1,1,2\n402,35,5,1,3,0,1,8,0,7,1,1,7\n4010,35,6,1,3,0,1,8,0,6,1,1,2\n4745,35,1,1,2,0,1,6,0,3,1,1,3\n3344,35,6,1,1,0,1,7,0,2,1,1,10\n7227,35,4,1,4,0,1,5,1,3,1,1,1\n1136,35,4,1,2,0,1,5,0,2,1,1,2\n4284,35,6,1,1,0,1,8,0,6,1,1,1\n3992,35,4,1,4,0,1,5,0,1,1,1,7\n3146,35,7,1,5,0,1,8,0,3,1,1,10\n6230,35,2,1,5,0,1,5,1,5,1,1,2\n5849,35,7,1,2,0,1,6,1,8,1,1,1\n5051,35,6,1,2,0,1,6,0,6,1,1,3\n3696,35,3,1,4,0,1,7,0,4,1,1,4\n6193,35,1,1,5,0,1,6,1,6,1,1,1\n3232,35,7,1,3,0,1,7,0,1,1,1,1\n8876,35,6,1,1,0,1,6,1,1,1,1,4\n6690,35,3,1,1,0,1,8,1,5,1,1,9\n10621,35,1,1,2,0,1,6,1,8,1,1,4\n4621,35,3,1,1,0,1,8,0,7,1,1,0\n2236,35,2,1,5,0,1,6,0,3,1,1,5\n459,35,7,1,2,0,1,5,0,8,1,1,4\n3376,35,4,1,3,0,1,8,0,6,1,1,2\n6438,35,2,1,3,0,1,8,1,1,1,1,5\n6192,35,2,1,4,0,1,6,1,5,1,1,6\n3217,35,1,1,3,0,1,7,0,7,1,1,2\n3418,35,7,1,1,0,1,5,0,1,1,1,6\n8508,35,7,1,1,0,1,6,1,4,1,1,3\n3719,35,7,1,4,0,1,6,0,3,1,1,0\n3136,35,2,1,2,0,1,8,0,8,1,1,9\n7882,35,5,1,5,0,1,7,1,7,1,1,7\n3712,35,2,1,1,0,1,7,0,5,1,1,8\n2226,35,2,1,3,0,1,5,0,3,1,1,8\n8092,35,2,1,2,0,1,8,1,8,1,1,3\n7298,35,3,1,5,0,1,6,1,2,1,1,4\n3868,35,6,1,1,0,1,5,0,5,1,1,5\n4756,35,6,1,4,0,1,7,0,2,1,1,3\n10497,35,5,1,3,0,1,5,1,1,1,1,2\n4272,35,5,1,3,0,1,6,0,8,1,1,6\n8599,35,7,1,1,0,1,7,1,6,1,1,2\n7090,35,2,1,3,0,1,6,1,5,1,1,5\n9920,35,1,1,1,0,1,8,1,2,1,1,1\n4203,35,2,1,2,0,1,6,0,8,1,1,2\n5396,35,6,1,3,0,1,5,0,7,1,1,8\n5840,35,6,1,2,0,1,8,1,6,1,1,2\n5286,35,1,1,4,0,1,8,0,5,1,1,1\n7526,35,2,1,3,0,1,5,1,7,1,1,4\n10592,35,7,1,2,0,1,7,1,5,1,1,2\n4986,35,6,1,5,0,1,6,0,4,1,1,3\n8210,35,4,1,3,0,1,7,1,4,1,1,8\n2164,35,2,1,5,0,1,5,0,5,1,1,10\n4094,35,3,1,2,0,1,7,0,3,1,1,0\n918,35,6,1,3,0,1,7,0,5,1,1,9\n8276,35,7,1,4,0,1,7,1,8,1,1,7\n9525,35,7,1,3,0,1,5,1,8,1,1,4\n7842,35,3,1,2,0,1,7,1,5,1,1,4\n8263,35,6,1,2,0,1,6,1,8,1,1,1\n5376,35,4,1,1,0,1,7,0,2,1,1,7\n4266,35,3,1,3,0,1,5,0,5,1,1,8\n1038,35,7,1,4,0,1,7,0,6,1,1,4\n2395,35,5,1,2,0,1,6,0,1,1,1,0\n7931,35,6,1,2,0,1,7,1,3,1,1,0\n1990,35,7,1,5,0,1,8,0,5,1,1,6\n10498,35,1,1,3,0,1,5,1,2,1,1,10\n10157,35,5,1,3,0,1,7,1,7,1,1,0\n2918,35,3,1,3,0,1,5,0,8,1,1,1\n5037,35,2,1,4,0,1,5,0,1,1,1,2\n2125,35,2,1,5,0,1,6,0,8,1,1,2\n7851,35,4,1,5,0,1,5,1,6,1,1,3\n8819,35,2,1,1,0,1,8,1,3,1,1,4\n5040,35,3,1,3,0,1,7,0,7,1,1,2\n788,35,5,1,1,0,1,5,0,6,1,1,2\n8101,35,2,1,2,0,1,8,1,1,1,1,0\n10850,35,3,1,1,0,1,8,1,3,1,1,6\n10239,35,5,1,5,0,1,5,1,3,1,1,1\n4040,36,0.2,1,3,0,1,7,0,2,1,1,0\n1624,36,0.2,1,2,0,1,7,0,5,1,1,9\n7640,36,0.2,1,1,0,1,5,1,8,1,1,3\n4497,36,0.2,1,4,0,1,7,0,7,1,1,0\n8939,36,1.4,1,2,0,1,7,1,6,1,1,3\n7982,36,1.7,1,4,0,1,8,1,5,1,1,5\n8910,36,2,1,3,0,1,8,1,5,1,1,10\n2325,36,2.4,1,5,0,1,7,0,4,1,1,4\n4528,36,3.4,1,5,0,1,8,0,4,1,1,8\n9508,36,3.5,1,1,0,1,6,1,6,1,1,6\n7910,36,3.9,1,5,0,1,7,1,2,1,1,1\n3201,36,4.5,1,5,0,1,5,0,5,1,1,3\n1172,36,4.5,1,4,0,1,7,0,8,1,1,10\n10150,36,4.8,1,2,0,1,8,1,1,1,1,9\n4964,36,5.1,1,1,0,1,7,0,1,1,1,3\n6316,36,6,1,5,0,1,7,1,1,1,1,10\n5542,36,6.5,1,3,0,1,8,1,8,1,1,7\n4710,36,6.9,1,1,0,1,6,0,6,1,1,8\n5719,36,7.1,1,1,0,1,6,1,7,1,1,0\n6111,36,7.1,1,1,0,1,6,1,6,1,1,3\n5306,36,7.3,1,5,0,1,6,0,2,1,1,7\n2324,36,7.3,1,1,0,1,8,0,8,1,1,8\n1530,36,7.4,1,1,0,1,7,0,6,1,1,8\n8606,36,7.4,1,1,0,1,7,1,4,1,1,10\n5900,36,7.5,1,5,0,1,6,1,6,1,1,0\n1521,36,8,1,3,0,1,5,0,2,1,1,4\n7411,36,8.2,1,3,0,1,6,1,3,1,1,1\n3016,36,8.3,1,4,0,1,7,0,1,1,1,2\n9472,36,8.3,1,3,0,1,7,1,1,1,1,5\n2558,36,8.4,1,1,0,1,7,0,4,1,1,9\n10502,36,8.5,1,1,0,1,5,1,7,1,1,0\n8700,36,8.6,1,2,0,1,7,1,2,1,1,4\n7714,36,8.7,1,2,0,1,5,1,4,1,1,3\n7510,36,8.8,1,5,0,1,6,1,2,1,1,2\n8495,36,9.4,1,2,0,1,7,1,6,1,1,1\n5907,36,9.9,1,5,0,1,7,1,4,1,1,3\n5202,36,10.1,1,4,0,1,5,0,4,1,1,2\n10475,36,10.9,1,3,0,1,8,1,3,1,1,4\n4908,36,11,1,5,0,1,6,0,3,1,1,10\n3689,36,11.9,1,5,0,1,5,0,4,1,1,3\n1750,36,7,1,4,0,1,6,0,8,1,1,7\n1689,36,1,1,4,0,1,8,0,3,1,1,3\n8034,36,3,1,4,0,1,7,1,1,1,1,5\n2606,36,5,1,1,0,1,5,0,5,1,1,7\n4872,36,6,1,5,0,1,5,0,7,1,1,10\n2168,36,2,1,4,0,1,6,0,7,1,1,6\n1752,36,1,1,5,0,1,5,0,5,1,1,4\n9102,36,6,1,3,0,1,8,1,3,1,1,0\n441,36,4,1,5,0,1,8,0,1,1,1,1\n4302,36,5,1,1,0,1,6,0,1,1,1,2\n29,36,6,1,4,0,1,6,0,4,1,1,2\n4130,36,3,1,1,0,1,6,0,5,1,1,4\n4429,36,3,1,2,0,1,6,0,7,1,1,1\n5882,36,3,1,1,0,1,8,1,8,1,1,7\n8310,36,1,1,3,0,1,6,1,4,1,1,4\n6533,36,3,1,1,0,1,5,1,2,1,1,0\n9089,36,5,1,5,0,1,7,1,7,1,1,2\n3755,36,1,1,5,0,1,7,0,6,1,1,2\n7671,36,7,1,5,0,1,6,1,1,1,1,2\n5614,36,2,1,3,0,1,8,1,2,1,1,9\n7505,36,5,1,3,0,1,8,1,8,1,1,3\n1531,36,2,1,3,0,1,5,0,3,1,1,1\n3329,36,7,1,2,0,1,8,0,6,1,1,2\n520,36,7,1,4,0,1,8,0,7,1,1,0\n2406,36,2,1,1,0,1,5,0,8,1,1,9\n1274,36,5,1,4,0,1,6,0,1,1,1,9\n8565,36,7,1,2,0,1,7,1,6,1,1,3\n6279,36,6,1,3,0,1,5,1,4,1,1,2\n8835,36,4,1,5,0,1,5,1,1,1,1,1\n6711,36,6,1,2,0,1,7,1,2,1,1,0\n4132,36,5,1,3,0,1,8,0,6,1,1,8\n4755,36,2,1,4,0,1,5,0,4,1,1,1\n3630,36,4,1,5,0,1,6,0,6,1,1,9\n3826,36,5,1,2,0,1,5,0,4,1,1,4\n342,36,3,1,2,0,1,8,0,5,1,1,9\n6563,36,4,1,1,0,1,7,1,4,1,1,3\n1352,36,3,1,2,0,1,7,0,1,1,1,1\n5142,36,7,1,1,0,1,7,0,7,1,1,0\n2872,36,4,1,3,0,1,6,0,2,1,1,7\n9939,36,7,1,3,0,1,6,1,3,1,1,0\n1875,36,2,1,4,0,1,8,0,7,1,1,4\n4968,36,7,1,4,0,1,8,0,1,1,1,0\n4492,36,1,1,4,0,1,8,0,6,1,1,5\n8334,36,3,1,1,0,1,8,1,1,1,1,8\n1623,36,1,1,3,0,1,5,0,4,1,1,2\n9557,36,4,1,2,0,1,6,1,8,1,1,3\n5938,36,6,1,4,0,1,8,1,7,1,1,7\n4762,36,5,1,4,0,1,6,0,1,1,1,7\n5312,36,7,1,2,0,1,5,0,5,1,1,0\n781,36,4,1,1,0,1,8,0,7,1,1,1\n2220,36,6,1,1,0,1,5,0,5,1,1,10\n8352,36,7,1,4,0,1,5,1,2,1,1,4\n3716,36,3,1,1,0,1,8,0,5,1,1,1\n3066,36,2,1,3,0,1,5,0,7,1,1,0\n2305,36,4,1,3,0,1,8,0,1,1,1,3\n7514,36,4,1,1,0,1,7,1,4,1,1,0\n4192,36,3,1,1,0,1,7,0,3,1,1,6\n2367,36,2,1,5,0,1,8,0,7,1,1,2\n578,36,6,1,2,0,1,7,0,8,1,1,8\n817,36,7,1,4,0,1,5,0,6,1,1,1\n7923,36,6,1,1,0,1,7,1,1,1,1,3\n4602,36,3,1,1,0,1,8,0,5,1,1,4\n7628,36,6,1,5,0,1,7,1,3,1,1,10\n10488,36,3,1,5,0,1,6,1,6,1,1,8\n9970,36,3,1,1,0,1,8,1,8,1,1,10\n5802,36,6,1,2,0,1,6,1,2,1,1,8\n10206,36,4,1,4,0,1,7,1,3,1,1,8\n10167,36,5,1,4,0,1,8,1,5,1,1,0\n8786,36,6,1,5,0,1,8,1,2,1,1,1\n3738,36,5,1,3,0,1,7,0,3,1,1,0\n10346,36,7,1,4,0,1,6,1,2,1,1,9\n5023,36,2,1,3,0,1,8,0,2,1,1,3\n10668,36,3,1,5,0,1,6,1,6,1,1,10\n5195,36,3,1,1,0,1,8,0,2,1,1,4\n4046,36,6,1,3,0,1,8,0,6,1,1,6\n9674,36,4,1,3,0,1,8,1,2,1,1,0\n10625,36,6,1,4,0,1,5,1,4,1,1,2\n10123,36,4,1,5,0,1,5,1,3,1,1,2\n5368,36,6,1,4,0,1,6,0,1,1,1,4\n10071,37,0.6,1,1,0,1,8,1,8,1,1,1\n1334,37,0.7,1,3,0,1,5,0,2,1,1,8\n10576,37,1,1,5,0,1,5,1,1,1,1,0\n4037,37,1.3,1,4,0,1,5,0,4,1,1,3\n1513,37,1.4,1,2,0,1,6,0,2,1,1,0\n2830,37,1.9,1,3,0,1,7,0,6,1,1,8\n8061,37,2.4,1,3,0,1,6,1,3,1,1,3\n6716,37,2.9,1,4,0,1,7,1,4,1,1,3\n5748,37,3.3,1,3,0,1,7,1,7,1,1,1\n7442,37,3.5,1,2,0,1,6,1,7,1,1,6\n7590,37,4.1,1,3,0,1,6,1,4,1,1,1\n8862,37,4.2,1,4,0,1,5,1,1,1,1,8\n6032,37,4.8,1,1,0,1,5,1,8,1,1,10\n8214,37,5,1,1,0,1,7,1,8,1,1,2\n7906,37,5.6,1,4,0,1,7,1,2,1,1,8\n9252,37,5.6,1,5,0,1,5,1,1,1,1,8\n8374,37,5.7,1,3,0,1,8,1,6,1,1,10\n4096,37,5.8,1,1,0,1,7,0,2,1,1,5\n2734,37,6.4,1,3,0,1,7,0,8,1,1,10\n9030,37,6.8,1,4,0,1,5,1,7,1,1,8\n10328,37,6.9,1,3,0,1,5,1,5,1,1,7\n8582,37,7,1,1,0,1,5,1,7,1,1,1\n8649,37,7.5,1,5,0,1,5,1,5,1,1,3\n8853,37,7.6,1,1,0,1,8,1,3,1,1,0\n4922,37,8.1,1,5,0,1,7,0,5,1,1,2\n7024,37,8.3,1,1,0,1,7,1,2,1,1,1\n4985,37,9,1,5,0,1,5,0,4,1,1,0\n3298,37,9.1,1,1,0,1,8,0,1,1,1,6\n2588,37,9.2,1,2,0,1,8,0,6,1,1,4\n170,37,9.3,1,2,0,1,8,0,7,1,1,2\n155,37,9.9,1,5,0,1,8,0,8,1,1,0\n7397,37,9.9,1,2,0,1,8,1,1,1,1,2\n7395,37,10,1,5,0,1,6,1,7,1,1,3\n2731,37,10.1,1,1,0,1,6,0,8,1,1,4\n5099,37,10.2,1,1,0,1,8,0,3,1,1,4\n8118,37,10.5,1,3,0,1,5,1,3,1,1,3\n9749,37,10.9,1,5,0,1,6,1,7,1,1,2\n8306,37,11.6,1,4,0,1,8,1,4,1,1,9\n3798,37,11.9,1,2,0,1,8,0,8,1,1,9\n774,37,12,1,3,0,1,5,0,5,1,1,1\n8269,37,3,1,1,0,1,5,1,4,1,1,4\n7955,37,2,1,1,0,1,6,1,5,1,1,2\n232,37,3,1,5,0,1,6,0,2,1,1,7\n2842,37,2,1,4,0,1,6,0,7,1,1,9\n390,37,2,1,2,0,1,6,0,1,1,1,8\n9124,37,3,1,3,0,1,7,1,2,1,1,2\n8598,37,2,1,2,0,1,8,1,6,1,1,8\n10646,37,3,1,1,0,1,6,1,6,1,1,9\n2860,37,7,1,1,0,1,8,0,3,1,1,8\n4909,37,7,1,5,0,1,8,0,3,1,1,1\n9524,37,7,1,1,0,1,7,1,4,1,1,8\n1850,37,2,1,5,0,1,7,0,6,1,1,0\n6102,37,3,1,1,0,1,7,1,2,1,1,8\n8257,37,3,1,2,0,1,8,1,5,1,1,0\n3324,37,5,1,1,0,1,7,0,1,1,1,6\n9090,37,2,1,4,0,1,6,1,7,1,1,8\n8338,37,2,1,5,0,1,8,1,2,1,1,4\n9723,37,4,1,4,0,1,8,1,1,1,1,2\n1451,37,3,1,1,0,1,8,0,6,1,1,4\n9816,37,2,1,1,0,1,8,1,3,1,1,10\n10533,37,1,1,5,0,1,5,1,5,1,1,1\n6506,37,7,1,2,0,1,7,1,5,1,1,0\n717,37,3,1,1,0,1,6,0,2,1,1,2\n464,37,2,1,4,0,1,6,0,4,1,1,9\n8344,37,3,1,3,0,1,5,1,2,1,1,7\n10746,37,1,1,2,0,1,7,1,4,1,1,8\n4154,37,7,1,2,0,1,7,0,5,1,1,3\n622,37,4,1,1,0,1,6,0,8,1,1,1\n2358,37,1,1,4,0,1,7,0,1,1,1,3\n1602,37,5,1,2,0,1,5,0,8,1,1,10\n3547,37,1,1,1,0,1,6,0,8,1,1,1\n3544,37,5,1,4,0,1,6,0,7,1,1,9\n576,37,3,1,5,0,1,5,0,3,1,1,7\n10671,37,6,1,4,0,1,8,1,8,1,1,2\n10313,37,4,1,5,0,1,6,1,2,1,1,4\n8312,37,1,1,3,0,1,8,1,3,1,1,6\n8326,37,5,1,5,0,1,7,1,3,1,1,3\n2527,37,7,1,1,0,1,5,0,2,1,1,4\n1572,37,4,1,2,0,1,6,0,5,1,1,6\n2767,37,1,1,5,0,1,6,0,4,1,1,4\n3609,37,1,1,3,0,1,8,0,4,1,1,4\n3318,37,2,1,3,0,1,5,0,8,1,1,0\n7720,37,6,1,5,0,1,5,1,6,1,1,8\n1077,37,7,1,4,0,1,8,0,6,1,1,3\n10337,37,3,1,3,0,1,5,1,8,1,1,1\n7162,37,7,1,4,0,1,5,1,3,1,1,7\n8270,37,6,1,1,0,1,8,1,8,1,1,1\n6068,37,4,1,5,0,1,6,1,2,1,1,10\n2600,37,7,1,5,0,1,8,0,7,1,1,9\n3925,37,4,1,3,0,1,6,0,1,1,1,0\n7766,37,4,1,5,0,1,6,1,1,1,1,1\n970,37,4,1,1,0,1,6,0,7,1,1,3\n5996,38,0.2,1,1,0,1,8,1,1,1,1,2\n1618,38,0.3,1,5,0,1,6,0,2,1,1,10\n6156,38,0.3,1,2,0,1,6,1,1,1,1,5\n10318,38,0.6,1,1,0,1,7,1,6,1,1,6\n7232,38,1.3,1,5,0,1,5,1,2,1,1,3\n5065,38,1.4,1,4,0,1,5,0,3,1,1,4\n10311,38,1.6,1,2,0,1,5,1,2,1,1,3\n5271,38,1.7,1,4,0,1,6,0,7,1,1,2\n1394,38,2.1,1,1,0,1,8,0,4,1,1,8\n5148,38,2.5,1,3,0,1,8,0,4,1,1,3\n10361,38,2.6,1,1,0,1,5,1,2,1,1,4\n3348,38,2.6,1,1,0,1,5,0,1,1,1,6\n8548,38,2.7,1,1,0,1,7,1,4,1,1,9\n10608,38,2.9,1,2,0,1,7,1,8,1,1,7\n1688,38,3.2,1,3,0,1,5,0,7,1,1,10\n7790,38,3.4,1,5,0,1,6,1,8,1,1,4\n7150,38,3.5,1,2,0,1,6,1,3,1,1,3\n3500,38,3.8,1,5,0,1,6,0,6,1,1,7\n2887,38,3.9,1,5,0,1,6,0,2,1,1,3\n8706,38,4,1,3,0,1,6,1,7,1,1,5\n10039,38,4.1,1,2,0,1,7,1,7,1,1,2\n1465,38,4.1,1,5,0,1,6,0,4,1,1,1\n5655,38,4.1,1,2,0,1,8,1,1,1,1,3\n9213,38,5.3,1,4,0,1,6,1,7,1,1,4\n1986,38,5.5,1,3,0,1,6,0,2,1,1,4\n8446,38,5.6,1,4,0,1,8,1,2,1,1,3\n6918,38,6.1,1,4,0,1,5,1,1,1,1,9\n8692,38,6.2,1,1,0,1,5,1,2,1,1,10\n5935,38,6.8,1,1,0,1,5,1,7,1,1,0\n9839,38,6.9,1,2,0,1,6,1,5,1,1,2\n3593,38,7.3,1,3,0,1,6,0,2,1,1,3\n7023,38,7.4,1,2,0,1,8,1,1,1,1,2\n9598,38,7.4,1,1,0,1,5,1,6,1,1,9\n4117,38,7.4,1,4,0,1,5,0,5,1,1,3\n787,38,7.5,1,2,0,1,6,0,3,1,1,4\n8658,38,7.7,1,1,0,1,8,1,4,1,1,2\n7434,38,7.9,1,2,0,1,8,1,3,1,1,0\n3351,38,8.4,1,4,0,1,7,0,5,1,1,1\n8936,38,9,1,2,0,1,8,1,4,1,1,9\n8490,38,9.3,1,2,0,1,8,1,5,1,1,3\n4893,38,10,1,2,0,1,5,0,5,1,1,2\n4865,38,10.3,1,2,0,1,6,0,4,1,1,3\n10392,38,10.6,1,5,0,1,8,1,7,1,1,6\n10696,38,10.6,1,1,0,1,8,1,4,1,1,6\n8891,38,10.8,1,4,0,1,8,1,8,1,1,3\n7770,38,11,1,1,0,1,6,1,6,1,1,2\n2604,38,11,1,3,0,1,7,0,8,1,1,4\n3248,38,11.4,1,4,0,1,5,0,7,1,1,9\n2894,38,1,1,2,0,1,5,0,6,1,1,8\n10250,38,5,1,4,0,1,7,1,7,1,1,7\n9457,38,3,1,5,0,1,7,1,4,1,1,1\n349,38,6,1,1,0,1,5,0,4,1,1,2\n1878,38,6,1,1,0,1,6,0,6,1,1,9\n2954,38,2,1,2,0,1,5,0,8,1,1,7\n7538,38,5,1,3,0,1,6,1,4,1,1,9\n8542,38,2,1,4,0,1,7,1,6,1,1,2\n56,38,1,1,3,0,1,6,0,2,1,1,7\n1420,38,1,1,5,0,1,5,0,2,1,1,3\n10504,38,5,1,2,0,1,5,1,3,1,1,3\n9119,38,2,1,5,0,1,8,1,4,1,1,0\n560,38,2,1,4,0,1,8,0,4,1,1,2\n2470,38,7,1,1,0,1,8,0,7,1,1,0\n9341,38,7,1,4,0,1,5,1,2,1,1,4\n1906,38,2,1,4,0,1,7,0,8,1,1,3\n5016,38,6,1,1,0,1,5,0,7,1,1,2\n6597,38,1,1,3,0,1,7,1,4,1,1,2\n8369,38,2,1,4,0,1,7,1,5,1,1,3\n8552,38,4,1,2,0,1,6,1,7,1,1,3\n3511,38,1,1,4,0,1,7,0,5,1,1,0\n3362,38,4,1,1,0,1,5,0,1,1,1,6\n4498,38,1,1,1,0,1,5,0,3,1,1,0\n6771,38,2,1,5,0,1,8,1,8,1,1,4\n9919,38,7,1,4,0,1,8,1,1,1,1,4\n3944,38,5,1,5,0,1,5,0,4,1,1,2\n1098,38,1,1,5,0,1,7,0,2,1,1,1\n10260,38,7,1,1,0,1,8,1,7,1,1,5\n5022,38,5,1,4,0,1,5,0,5,1,1,0\n10758,38,3,1,3,0,1,6,1,4,1,1,0\n7929,38,7,1,1,0,1,6,1,5,1,1,3\n3110,38,5,1,1,0,1,7,0,1,1,1,5\n3534,38,3,1,2,0,1,6,0,8,1,1,9\n9504,38,1,1,2,0,1,8,1,3,1,1,7\n9354,38,5,1,3,0,1,7,1,8,1,1,2\n86,38,4,1,5,0,1,7,0,6,1,1,9\n5431,38,3,1,5,0,1,6,1,8,1,1,0\n2424,38,7,1,3,0,1,8,0,4,1,1,6\n4531,38,5,1,4,0,1,5,0,1,1,1,3\n5429,38,2,1,1,0,1,5,1,2,1,1,3\n5102,38,2,1,4,0,1,5,0,3,1,1,6\n4481,38,2,1,1,0,1,6,0,3,1,1,3\n6151,38,3,1,2,0,1,6,1,3,1,1,1\n7261,38,1,1,2,0,1,7,1,7,1,1,3\n283,38,5,1,4,0,1,6,0,6,1,1,0\n3132,38,4,1,3,0,1,5,0,3,1,1,0\n5582,38,3,1,5,0,1,5,1,6,1,1,7\n222,38,5,1,3,0,1,8,0,6,1,1,9\n9386,38,6,1,3,0,1,7,1,4,1,1,6\n8423,38,5,1,1,0,1,7,1,8,1,1,4\n7738,38,4,1,1,0,1,8,1,7,1,1,5\n10246,38,1,1,4,0,1,8,1,6,1,1,7\n5496,38,4,1,5,0,1,6,1,4,1,1,7\n2826,38,2,1,1,0,1,5,0,4,1,1,0\n6443,38,2,1,4,0,1,6,1,5,1,1,4\n1415,38,3,1,2,0,1,7,0,8,1,1,0\n3382,38,2,1,5,0,1,8,0,7,1,1,1\n134,38,1,1,3,0,1,8,0,5,1,1,4\n2394,38,3,1,5,0,1,7,0,5,1,1,0\n7317,38,1,1,3,0,1,7,1,8,1,1,4\n9259,38,6,1,1,0,1,7,1,6,1,1,4\n796,39,0.2,1,3,0,1,7,0,5,1,1,0\n770,39,0.4,1,3,0,1,8,0,6,1,1,8\n4764,39,0.9,1,4,0,1,8,0,8,1,1,9\n3060,39,1.3,1,4,0,1,7,0,3,1,1,0\n4726,39,1.5,1,4,0,1,6,0,5,1,1,0\n433,39,2.1,1,2,0,1,8,0,7,1,1,3\n6553,39,2.4,1,4,0,1,6,1,4,1,1,1\n2158,39,2.5,1,5,0,1,8,0,8,1,1,0\n6447,39,2.5,1,5,0,1,8,1,4,1,1,4\n2700,39,3.2,1,2,0,1,7,0,2,1,1,0\n8240,39,3.5,1,5,0,1,5,1,8,1,1,10\n6030,39,3.7,1,4,0,1,6,1,4,1,1,7\n9418,39,4.1,1,5,0,1,5,1,5,1,1,4\n3036,39,4.7,1,4,0,1,7,0,1,1,1,8\n4513,39,4.7,1,1,0,1,6,0,1,1,1,1\n4978,39,5.3,1,4,0,1,7,0,5,1,1,6\n6588,39,5.5,1,4,0,1,8,1,3,1,1,3\n2376,39,5.6,1,5,0,1,5,0,6,1,1,7\n8187,39,6.1,1,2,0,1,8,1,8,1,1,4\n4408,39,6.5,1,3,0,1,8,0,1,1,1,4\n6134,39,6.6,1,1,0,1,8,1,2,1,1,7\n10208,39,6.7,1,4,0,1,6,1,2,1,1,3\n5276,39,6.9,1,2,0,1,8,0,3,1,1,0\n4465,39,7.2,1,3,0,1,7,0,5,1,1,0\n626,39,7.4,1,1,0,1,6,0,1,1,1,1\n4099,39,7.5,1,4,0,1,8,0,1,1,1,2\n9890,39,7.8,1,3,0,1,5,1,2,1,1,1\n10725,39,8.3,1,5,0,1,6,1,5,1,1,0\n2820,39,8.4,1,2,0,1,6,0,8,1,1,5\n4876,39,8.7,1,1,0,1,8,0,4,1,1,4\n2285,39,9.2,1,1,0,1,6,0,5,1,1,2\n225,39,9.4,1,4,0,1,8,0,4,1,1,0\n1920,39,9.7,1,5,0,1,6,0,5,1,1,2\n10174,39,9.8,1,4,0,1,8,1,8,1,1,5\n3046,39,10,1,4,0,1,6,0,2,1,1,3\n665,39,10.1,1,1,0,1,8,0,4,1,1,4\n6724,39,11.4,1,5,0,1,8,1,8,1,1,0\n9767,39,11.4,1,3,0,1,8,1,5,1,1,4\n8362,39,11.7,1,1,0,1,6,1,2,1,1,3\n8760,39,11.7,1,2,0,1,8,1,2,1,1,8\n2278,39,12,1,4,0,1,6,0,7,1,1,8\n1386,39,12,1,1,0,1,5,0,5,1,1,0\n9216,39,12,1,4,0,1,6,1,6,1,1,3\n6086,39,12,1,3,0,1,7,1,8,1,1,8\n6052,39,6,1,4,0,1,5,1,5,1,1,7\n786,39,2,1,3,0,1,7,0,6,1,1,8\n6938,39,1,1,5,0,1,6,1,5,1,1,4\n4648,39,2,1,1,0,1,5,0,6,1,1,8\n6990,39,2,1,4,0,1,7,1,8,1,1,0\n10158,39,1,1,2,0,1,6,1,3,1,1,3\n7268,39,7,1,4,0,1,8,1,8,1,1,5\n258,39,7,1,3,0,1,6,0,7,1,1,6\n9528,39,6,1,5,0,1,6,1,7,1,1,7\n3041,39,1,1,2,0,1,8,0,6,1,1,4\n7981,39,1,1,1,0,1,6,1,3,1,1,4\n9268,39,5,1,4,0,1,8,1,5,1,1,7\n9558,39,3,1,3,0,1,7,1,3,1,1,1\n10128,39,6,1,2,0,1,8,1,6,1,1,4\n2428,39,7,1,1,0,1,6,0,7,1,1,4\n1287,39,5,1,5,0,1,7,0,6,1,1,2\n7580,39,7,1,1,0,1,5,1,4,1,1,3\n6815,39,7,1,2,0,1,8,1,8,1,1,1\n2100,39,2,1,1,0,1,6,0,4,1,1,3\n5122,39,4,1,2,0,1,7,0,2,1,1,5\n8082,39,3,1,3,0,1,7,1,2,1,1,8\n4901,39,1,1,2,0,1,6,0,1,1,1,3\n10382,39,2,1,5,0,1,8,1,8,1,1,7\n8532,39,7,1,3,0,1,8,1,4,1,1,0\n1265,39,6,1,2,0,1,7,0,6,1,1,2\n5390,39,3,1,1,0,1,7,0,5,1,1,10\n5342,39,7,1,5,0,1,7,0,5,1,1,6\n4388,39,2,1,3,0,1,6,0,1,1,1,9\n138,39,7,1,2,0,1,7,0,5,1,1,6\n8238,39,7,1,5,0,1,8,1,3,1,1,5\n10664,39,2,1,5,0,1,6,1,6,1,1,10\n3330,39,2,1,5,0,1,6,0,4,1,1,4\n3138,39,6,1,4,0,1,6,0,6,1,1,7\n3004,39,4,1,2,0,1,6,0,8,1,1,3\n7845,39,2,1,4,0,1,6,1,1,1,1,4\n9666,39,4,1,3,0,1,7,1,2,1,1,10\n2150,39,6,1,5,0,1,6,0,4,1,1,10\n5850,39,6,1,1,0,1,5,1,1,1,1,9\n6931,39,6,1,2,0,1,7,1,5,1,1,4\n358,39,5,1,4,0,1,7,0,4,1,1,4\n7180,39,6,1,5,0,1,8,1,2,1,1,2\n2286,39,3,1,4,0,1,6,0,3,1,1,4\n6481,39,4,1,4,0,1,8,1,8,1,1,4\n16,39,3,1,2,0,1,5,0,5,1,1,10\n4048,39,5,1,2,0,1,7,0,6,1,1,5\n2144,39,4,1,5,0,1,5,0,7,1,1,9\n6596,39,1,1,5,0,1,7,1,7,1,1,5\n1844,39,5,1,2,0,1,7,0,3,1,1,7\n9104,39,7,1,1,0,1,5,1,1,1,1,6\n8242,39,6,1,2,0,1,8,1,4,1,1,8\n6659,39,6,1,4,0,1,8,1,3,1,1,0\n7477,39,5,1,2,0,1,8,1,1,1,1,1\n5220,39,6,1,4,0,1,8,0,5,1,1,8\n6920,39,7,1,4,0,1,5,1,1,1,1,10\n7646,39,2,1,1,0,1,5,1,1,1,1,0\n2065,39,5,1,4,0,1,7,0,4,1,1,4\n8844,39,1,1,1,0,1,8,1,4,1,1,0\n3994,39,2,1,3,0,1,7,0,8,1,1,10\n7437,39,3,1,4,0,1,5,1,3,1,1,4\n3183,39,3,1,3,0,1,8,0,5,1,1,0\n174,39,2,1,1,0,1,5,0,5,1,1,0\n246,39,6,1,3,0,1,5,0,8,1,1,8\n710,39,1,1,1,0,1,7,0,2,1,1,0\n3124,39,5,1,4,0,1,5,0,8,1,1,9\n8814,39,4,1,4,0,1,6,1,8,1,1,7\n8528,40,0.5,1,5,0,1,5,1,8,1,1,8\n8256,40,0.8,1,4,0,1,8,1,1,1,1,0\n9927,40,1.6,1,3,0,1,6,1,4,1,1,4\n1686,40,1.7,1,5,0,1,5,0,7,1,1,9\n6646,40,1.7,1,2,0,1,6,1,7,1,1,7\n4634,40,1.8,1,4,0,1,7,0,4,1,1,8\n8586,40,2.6,1,4,0,1,5,1,1,1,1,8\n2595,40,2.7,1,2,0,1,7,0,6,1,1,3\n3508,40,4.2,1,1,0,1,6,0,3,1,1,4\n7336,40,4.3,1,4,0,1,5,1,2,1,1,5\n2375,40,4.6,1,5,0,1,6,0,3,1,1,4\n5921,40,5.1,1,2,0,1,5,1,1,1,1,3\n3346,40,5.2,1,2,0,1,6,0,6,1,1,4\n6917,40,5.3,1,5,0,1,8,1,5,1,1,2\n1885,40,5.7,1,5,0,1,6,0,5,1,1,0\n8041,40,5.8,1,1,0,1,5,1,2,1,1,0\n9180,40,6.5,1,1,0,1,6,1,4,1,1,1\n6136,40,6.6,1,5,0,1,7,1,2,1,1,4\n7433,40,6.7,1,5,0,1,7,1,3,1,1,3\n6755,40,6.9,1,2,0,1,5,1,2,1,1,2\n6399,40,7.4,1,2,0,1,7,1,4,1,1,1\n6600,40,7.5,1,1,0,1,5,1,2,1,1,7\n8364,40,7.7,1,3,0,1,8,1,5,1,1,9\n7605,40,8,1,2,0,1,7,1,2,1,1,1\n8045,40,8.1,1,1,0,1,6,1,1,1,1,3\n4578,40,8.2,1,3,0,1,6,0,2,1,1,10\n10355,40,8.6,1,4,0,1,6,1,2,1,1,4\n6522,40,8.9,1,3,0,1,8,1,1,1,1,5\n4240,40,9,1,4,0,1,7,0,7,1,1,2\n2135,40,9.2,1,3,0,1,7,0,8,1,1,2\n5110,40,9.3,1,1,0,1,5,0,3,1,1,2\n715,40,10,1,2,0,1,5,0,8,1,1,1\n2683,40,10.3,1,5,0,1,5,0,4,1,1,4\n7142,40,10.5,1,4,0,1,8,1,4,1,1,10\n1996,40,10.5,1,4,0,1,8,0,1,1,1,6\n5862,40,10.8,1,1,0,1,6,1,4,1,1,2\n7348,40,10.9,1,1,0,1,8,1,5,1,1,10\n7010,40,11.5,1,2,0,1,6,1,3,1,1,4\n4708,40,12,1,1,0,1,6,0,2,1,1,6\n10352,40,7,1,4,0,1,7,1,5,1,1,4\n7465,40,4,1,5,0,1,8,1,7,1,1,3\n6468,40,5,1,3,0,1,6,1,4,1,1,1\n5976,40,4,1,2,0,1,7,1,8,1,1,10\n592,40,1,1,4,0,1,8,0,6,1,1,3\n675,40,5,1,3,0,1,6,0,5,1,1,1\n1037,40,3,1,3,0,1,7,0,2,1,1,3\n1868,40,7,1,2,0,1,6,0,5,1,1,10\n6119,40,3,1,4,0,1,5,1,1,1,1,3\n346,40,3,1,1,0,1,5,0,7,1,1,0\n7676,40,7,1,1,0,1,8,1,6,1,1,0\n4892,40,4,1,1,0,1,6,0,8,1,1,10\n6746,40,3,1,5,0,1,5,1,8,1,1,10\n9789,40,3,1,2,0,1,8,1,3,1,1,4\n5800,40,5,1,1,0,1,6,1,2,1,1,9\n2539,40,5,1,2,0,1,6,0,5,1,1,2\n7202,40,1,1,5,0,1,7,1,6,1,1,5\n930,40,5,1,5,0,1,6,0,1,1,1,2\n9843,40,1,1,2,0,1,6,1,4,1,1,2\n8950,40,6,1,2,0,1,7,1,8,1,1,10\n8555,40,5,1,1,0,1,5,1,8,1,1,4\n7498,40,3,1,4,0,1,6,1,8,1,1,3\n5502,40,5,1,4,0,1,5,1,1,1,1,4\n9719,40,1,1,3,0,1,6,1,8,1,1,4\n760,40,2,1,2,0,1,6,0,6,1,1,9\n638,40,6,1,1,0,1,7,0,4,1,1,1\n1827,40,1,1,5,0,1,7,0,8,1,1,1\n768,40,1,1,3,0,1,5,0,2,1,1,0\n7594,40,7,1,3,0,1,8,1,6,1,1,0\n7016,40,5,1,3,0,1,7,1,8,1,1,7\n7102,40,3,1,2,0,1,6,1,3,1,1,5\n2099,40,1,1,3,0,1,6,0,4,1,1,3\n5814,40,3,1,2,0,1,5,1,6,1,1,6\n9289,40,5,1,1,0,1,7,1,7,1,1,0\n2170,40,2,1,4,0,1,7,0,1,1,1,6\n121,40,4,1,4,0,1,6,0,2,1,1,4\n10422,40,7,1,2,0,1,5,1,4,1,1,7\n5972,40,2,1,5,0,1,8,1,6,1,1,9\n10203,40,5,1,1,0,1,5,1,8,1,1,1\n966,40,5,1,1,0,1,7,0,5,1,1,2\n3158,40,5,1,1,0,1,5,0,4,1,1,1\n10369,40,4,1,1,0,1,7,1,3,1,1,1\n10406,40,6,1,2,0,1,5,1,8,1,1,9\n422,40,3,1,3,0,1,8,0,8,1,1,10\n4874,40,6,1,1,0,1,7,0,1,1,1,9\n2571,40,1,1,5,0,1,7,0,7,1,1,2\n10267,40,4,1,2,0,1,8,1,2,1,1,3\n3054,40,1,1,3,0,1,5,0,1,1,1,2\n1762,40,2,1,5,0,1,7,0,1,1,1,8\n4798,40,5,1,4,0,1,6,0,5,1,1,1\n5973,40,3,1,4,0,1,7,1,6,1,1,4\n720,40,2,1,5,0,1,8,0,8,1,1,0\n9670,40,2,1,2,0,1,7,1,5,1,1,5\n8013,40,5,1,2,0,1,8,1,4,1,1,0\n8914,40,4,1,4,0,1,8,1,3,1,1,2\n1433,40,5,1,1,0,1,5,0,4,1,1,3\n2566,40,6,1,5,0,1,5,0,6,1,1,5\n6778,40,1,1,2,0,1,6,1,1,1,1,9\n7746,40,7,1,1,0,1,7,1,8,1,1,1\n8001,41,0.4,1,4,0,1,7,1,4,1,1,0\n2779,41,1.2,1,2,0,1,6,0,7,1,1,4\n6236,41,1.4,1,2,0,1,8,1,1,1,1,9\n9936,41,1.8,1,2,0,1,7,1,7,1,1,1\n9326,41,2.1,1,2,0,1,8,1,8,1,1,7\n1295,41,2.1,1,1,0,1,5,0,7,1,1,2\n6864,41,2.3,1,2,0,1,8,1,1,1,1,9\n10630,41,2.3,1,3,0,1,6,1,4,1,1,8\n6748,41,2.4,1,2,0,1,7,1,3,1,1,1\n1154,41,2.5,1,5,0,1,8,0,7,1,1,1\n2202,41,2.5,1,4,0,1,6,0,3,1,1,9\n9128,41,2.9,1,2,0,1,7,1,1,1,1,5\n6630,41,3.2,1,3,0,1,8,1,5,1,1,3\n1698,41,3.3,1,5,0,1,6,0,1,1,1,3\n9378,41,3.5,1,3,0,1,7,1,4,1,1,6\n1028,41,3.8,1,2,0,1,5,0,8,1,1,1\n7525,41,3.8,1,3,0,1,8,1,7,1,1,2\n7896,41,4.2,1,1,0,1,5,1,8,1,1,5\n7430,41,4.3,1,5,0,1,8,1,2,1,1,2\n3854,41,4.6,1,5,0,1,7,0,6,1,1,4\n10763,41,5,1,4,0,1,7,1,6,1,1,0\n2650,41,5.2,1,5,0,1,6,0,8,1,1,10\n1413,41,5.5,1,5,0,1,8,0,8,1,1,4\n1076,41,5.6,1,1,0,1,6,0,7,1,1,5\n7300,41,5.7,1,4,0,1,6,1,7,1,1,9\n558,41,5.7,1,1,0,1,5,0,2,1,1,8\n8498,41,6.4,1,2,0,1,7,1,6,1,1,6\n8401,41,6.7,1,1,0,1,7,1,4,1,1,2\n2551,41,6.8,1,5,0,1,8,0,8,1,1,3\n9070,41,6.8,1,5,0,1,8,1,1,1,1,10\n9098,41,7.2,1,1,0,1,6,1,7,1,1,2\n6368,41,7.3,1,4,0,1,6,1,3,1,1,9\n2234,41,7.5,1,4,0,1,5,0,7,1,1,4\n5643,41,7.5,1,3,0,1,5,1,2,1,1,1\n8058,41,7.9,1,3,0,1,8,1,5,1,1,9\n1198,41,7.9,1,2,0,1,8,0,2,1,1,6\n256,41,8.2,1,2,0,1,8,0,1,1,1,8\n5152,41,8.3,1,2,0,1,8,0,5,1,1,0\n7735,41,8.5,1,5,0,1,6,1,4,1,1,0\n10016,41,9.2,1,5,0,1,6,1,2,1,1,1\n10673,41,9.5,1,4,0,1,6,1,3,1,1,0\n6916,41,9.8,1,3,0,1,5,1,6,1,1,10\n6704,41,10,1,3,0,1,6,1,7,1,1,1\n9309,41,10.5,1,5,0,1,6,1,1,1,1,1\n6882,41,10.8,1,5,0,1,5,1,5,1,1,8\n1376,41,10.8,1,4,0,1,8,0,1,1,1,1\n4212,41,11.1,1,4,0,1,5,0,2,1,1,7\n2258,41,11.2,1,3,0,1,6,0,4,1,1,5\n9918,41,11.5,1,5,0,1,7,1,7,1,1,9\n2952,41,11.5,1,2,0,1,6,0,8,1,1,6\n4725,41,11.6,1,5,0,1,5,0,7,1,1,4\n7731,41,11.7,1,2,0,1,8,1,8,1,1,3\n2956,41,11.7,1,3,0,1,6,0,6,1,1,6\n10712,41,11.8,1,2,0,1,5,1,2,1,1,8\n1074,41,12,1,3,0,1,8,0,4,1,1,6\n1271,41,2,1,5,0,1,6,0,5,1,1,2\n8618,41,1,1,4,0,1,6,1,8,1,1,8\n10714,41,7,1,5,0,1,5,1,8,1,1,0\n9621,41,4,1,4,0,1,8,1,7,1,1,4\n7466,41,5,1,3,0,1,5,1,6,1,1,4\n3917,41,6,1,1,0,1,7,0,6,1,1,4\n7072,41,1,1,2,0,1,8,1,3,1,1,8\n8815,41,5,1,4,0,1,6,1,4,1,1,0\n9052,41,4,1,3,0,1,5,1,8,1,1,5\n7803,41,2,1,3,0,1,6,1,8,1,1,3\n6202,41,4,1,3,0,1,7,1,4,1,1,2\n7724,41,2,1,2,0,1,6,1,7,1,1,5\n10110,41,4,1,2,0,1,7,1,1,1,1,4\n3182,41,6,1,1,0,1,7,0,4,1,1,8\n9745,41,3,1,5,0,1,8,1,1,1,1,4\n3911,41,6,1,4,0,1,6,0,5,1,1,3\n7458,41,6,1,4,0,1,8,1,1,1,1,1\n3005,41,1,1,3,0,1,8,0,3,1,1,1\n8688,41,7,1,1,0,1,6,1,5,1,1,6\n8394,41,2,1,4,0,1,7,1,6,1,1,2\n4460,41,4,1,5,0,1,8,0,5,1,1,10\n572,41,1,1,2,0,1,5,0,3,1,1,4\n2678,41,4,1,1,0,1,8,0,4,1,1,3\n2088,41,7,1,5,0,1,8,0,6,1,1,8\n9349,41,4,1,4,0,1,5,1,1,1,1,0\n6091,41,6,1,3,0,1,6,1,1,1,1,4\n7470,41,7,1,2,0,1,7,1,2,1,1,4\n6118,41,7,1,4,0,1,8,1,7,1,1,1\n4340,41,5,1,2,0,1,8,0,7,1,1,10\n2672,41,4,1,3,0,1,6,0,5,1,1,4\n8224,41,6,1,1,0,1,8,1,6,1,1,10\n6529,41,1,1,5,0,1,7,1,4,1,1,1\n10014,41,7,1,1,0,1,8,1,4,1,1,6\n7379,41,3,1,5,0,1,8,1,1,1,1,4\n1350,41,7,1,1,0,1,5,0,6,1,1,3\n9170,41,4,1,5,0,1,5,1,3,1,1,7\n6470,41,2,1,4,0,1,7,1,6,1,1,0\n5610,41,5,1,2,0,1,5,1,7,1,1,2\n7484,41,6,1,2,0,1,6,1,1,1,1,8\n4842,41,2,1,2,0,1,7,0,5,1,1,0\n3456,41,6,1,2,0,1,8,0,4,1,1,10\n6395,41,7,1,1,0,1,6,1,5,1,1,1\n5666,41,4,1,2,0,1,5,1,8,1,1,8\n2338,41,5,1,5,0,1,7,0,2,1,1,10\n3122,41,4,1,2,0,1,5,0,8,1,1,6\n7666,41,1,1,1,0,1,5,1,3,1,1,1\n5141,41,2,1,3,0,1,6,0,2,1,1,4\n1474,41,5,1,2,0,1,5,0,8,1,1,1\n1538,41,4,1,3,0,1,7,0,8,1,1,4\n4050,41,6,1,5,0,1,5,0,2,1,1,0\n8686,41,4,1,5,0,1,8,1,2,1,1,9\n736,41,7,1,5,0,1,8,0,1,1,1,3\n3542,41,6,1,4,0,1,8,0,7,1,1,0\n734,41,5,1,5,0,1,8,0,7,1,1,3\n5360,41,7,1,5,0,1,7,0,7,1,1,3\n6040,41,6,1,3,0,1,6,1,5,1,1,3\n2804,41,4,1,5,0,1,6,0,6,1,1,8\n2951,41,5,1,1,0,1,7,0,5,1,1,3\n7611,41,2,1,1,0,1,7,1,5,1,1,2\n4330,41,6,1,5,0,1,7,0,4,1,1,7\n8366,41,1,1,1,0,1,5,1,5,1,1,8\n1201,41,7,1,4,0,1,5,0,1,1,1,2\n4487,41,6,1,4,0,1,5,0,1,1,1,0\n10517,41,1,1,4,0,1,5,1,6,1,1,0\n6572,41,4,1,5,0,1,6,1,8,1,1,2\n4118,41,3,1,4,0,1,7,0,3,1,1,4\n8454,41,4,1,2,0,1,8,1,1,1,1,1\n5247,41,5,1,4,0,1,5,0,1,1,1,2\n2452,42,0.4,1,2,0,1,8,0,6,1,1,6\n4418,42,0.8,1,5,0,1,8,0,3,1,1,6\n639,42,1,1,4,0,1,6,0,1,1,1,2\n8125,42,1.3,1,1,0,1,5,1,3,1,1,0\n5296,42,1.4,1,3,0,1,5,0,7,1,1,4\n119,42,1.6,1,2,0,1,6,0,1,1,1,0\n3312,42,1.9,1,2,0,1,7,0,3,1,1,7\n9648,42,2,1,3,0,1,6,1,2,1,1,3\n9986,42,2.1,1,1,0,1,5,1,3,1,1,0\n8104,42,2.2,1,5,0,1,6,1,6,1,1,8\n5836,42,2.2,1,2,0,1,8,1,3,1,1,10\n6698,42,2.8,1,3,0,1,7,1,3,1,1,8\n2702,42,2.9,1,2,0,1,8,0,4,1,1,0\n10213,42,2.9,1,4,0,1,7,1,3,1,1,4\n8502,42,3.3,1,1,0,1,6,1,3,1,1,5\n7103,42,3.7,1,2,0,1,7,1,1,1,1,3\n6096,42,3.8,1,4,0,1,5,1,5,1,1,10\n10486,42,4.6,1,4,0,1,7,1,2,1,1,1\n5683,42,4.7,1,2,0,1,8,1,8,1,1,2\n7287,42,4.8,1,2,0,1,7,1,3,1,1,0\n10054,42,5.2,1,2,0,1,5,1,6,1,1,6\n7854,42,5.2,1,3,0,1,5,1,8,1,1,1\n9892,42,5.3,1,4,0,1,6,1,8,1,1,6\n8173,42,5.3,1,5,0,1,5,1,6,1,1,2\n2980,42,5.5,1,5,0,1,8,0,3,1,1,6\n10821,42,5.5,1,1,0,1,6,1,8,1,1,3\n1156,42,5.8,1,2,0,1,5,0,3,1,1,9\n176,42,6.1,1,5,0,1,5,0,2,1,1,2\n5214,42,6.3,1,4,0,1,8,0,4,1,1,8\n2006,42,6.4,1,1,0,1,6,0,3,1,1,7\n685,42,6.6,1,3,0,1,6,0,5,1,1,1\n4062,42,6.9,1,4,0,1,7,0,1,1,1,9\n8444,42,7.5,1,5,0,1,5,1,6,1,1,0\n9415,42,7.9,1,3,0,1,8,1,3,1,1,1\n4722,42,8,1,5,0,1,6,0,6,1,1,8\n594,42,8,1,3,0,1,6,0,1,1,1,4\n1884,42,8.2,1,3,0,1,8,0,7,1,1,2\n3117,42,8.4,1,1,0,1,7,0,2,1,1,0\n1410,42,8.5,1,4,0,1,7,0,4,1,1,4\n2314,42,9.1,1,2,0,1,6,0,6,1,1,1\n6286,42,9.2,1,5,0,1,6,1,1,1,1,0\n5858,42,9.9,1,3,0,1,8,1,8,1,1,5\n9462,42,10.9,1,4,0,1,6,1,8,1,1,7\n6050,42,11,1,1,0,1,6,1,5,1,1,7\n8791,42,11.1,1,2,0,1,8,1,7,1,1,0\n9658,42,11.7,1,4,0,1,8,1,4,1,1,10\n1952,42,11.8,1,2,0,1,6,0,5,1,1,0\n4268,42,3,1,4,0,1,5,0,5,1,1,1\n9638,42,6,1,4,0,1,7,1,3,1,1,0\n7858,42,5,1,2,0,1,5,1,2,1,1,4\n9582,42,1,1,4,0,1,7,1,6,1,1,8\n9642,42,7,1,5,0,1,8,1,7,1,1,1\n3985,42,6,1,1,0,1,7,0,6,1,1,4\n9155,42,1,1,5,0,1,5,1,7,1,1,0\n6772,42,3,1,1,0,1,8,1,6,1,1,2\n4662,42,6,1,1,0,1,7,0,2,1,1,2\n1782,42,6,1,1,0,1,5,0,1,1,1,9\n6784,42,4,1,5,0,1,6,1,3,1,1,2\n7302,42,5,1,3,0,1,8,1,3,1,1,3\n8000,42,7,1,4,0,1,8,1,8,1,1,2\n2298,42,2,1,5,0,1,6,0,6,1,1,6\n9463,42,3,1,4,0,1,5,1,2,1,1,0\n1900,42,2,1,5,0,1,6,0,2,1,1,3\n7435,42,7,1,1,0,1,5,1,8,1,1,3\n6819,42,6,1,5,0,1,8,1,2,1,1,1\n1726,42,3,1,2,0,1,5,0,4,1,1,10\n2460,42,3,1,2,0,1,5,0,7,1,1,5\n4926,42,7,1,1,0,1,7,0,1,1,1,5\n114,42,6,1,1,0,1,6,0,2,1,1,5\n7165,42,7,1,4,0,1,6,1,7,1,1,4\n6676,42,6,1,3,0,1,7,1,4,1,1,5\n7092,42,7,1,2,0,1,8,1,1,1,1,8\n3022,42,2,1,5,0,1,8,0,6,1,1,4\n757,42,1,1,3,0,1,7,0,8,1,1,1\n3704,42,2,1,5,0,1,5,0,5,1,1,9\n6190,42,6,1,3,0,1,5,1,3,1,1,0\n4823,42,5,1,4,0,1,7,0,3,1,1,1\n2570,42,1,1,3,0,1,8,0,4,1,1,0\n9143,42,6,1,4,0,1,5,1,3,1,1,4\n3711,42,1,1,2,0,1,5,0,7,1,1,2\n9122,42,2,1,2,0,1,8,1,3,1,1,8\n9160,42,6,1,2,0,1,7,1,5,1,1,9\n2553,42,1,1,1,0,1,8,0,1,1,1,4\n2060,42,3,1,4,0,1,7,0,5,1,1,7\n7811,42,7,1,1,0,1,7,1,5,1,1,3\n2544,42,6,1,5,0,1,5,0,2,1,1,3\n9998,42,7,1,2,0,1,8,1,1,1,1,7\n3118,42,1,1,2,0,1,7,0,3,1,1,10\n3887,42,5,1,1,0,1,6,0,2,1,1,2\n8507,42,2,1,1,0,1,8,1,2,1,1,3\n3282,42,1,1,1,0,1,7,0,3,1,1,8\n10091,42,5,1,3,0,1,7,1,2,1,1,2\n2914,42,1,1,1,0,1,7,0,8,1,1,6\n9841,42,1,1,2,0,1,8,1,2,1,1,1\n5936,42,4,1,3,0,1,6,1,1,1,1,7\n8360,42,1,1,3,0,1,8,1,5,1,1,2\n5629,42,7,1,5,0,1,5,1,6,1,1,1\n6168,42,2,1,3,0,1,7,1,1,1,1,9\n9593,42,3,1,1,0,1,5,1,3,1,1,0\n5233,42,2,1,2,0,1,8,0,2,1,1,1\n2306,42,3,1,4,0,1,5,0,7,1,1,3\n1072,42,1,1,3,0,1,5,0,8,1,1,8\n1845,42,1,1,4,0,1,6,0,1,1,1,1\n5514,42,2,1,3,0,1,8,1,4,1,1,2\n1741,42,6,1,2,0,1,7,0,8,1,1,2\n6768,42,7,1,3,0,1,5,1,5,1,1,4\n9753,42,3,1,4,0,1,7,1,3,1,1,0\n9372,42,6,1,2,0,1,5,1,4,1,1,4\n1448,42,5,1,4,0,1,7,0,3,1,1,7\n3663,43,0.6,1,2,0,1,7,0,5,1,1,1\n5365,43,0.7,1,5,0,1,8,0,8,1,1,1\n6789,43,1.7,1,2,0,1,5,1,1,1,1,4\n6758,43,1.7,1,2,0,1,8,1,5,1,1,2\n2967,43,1.8,1,3,0,1,7,0,8,1,1,4\n9412,43,2.1,1,5,0,1,6,1,6,1,1,7\n9402,43,2.2,1,5,0,1,7,1,4,1,1,3\n8025,43,3,1,3,0,1,5,1,5,1,1,4\n7158,43,3.1,1,4,0,1,5,1,8,1,1,9\n4854,43,3.5,1,4,0,1,5,0,8,1,1,3\n3764,43,4.1,1,1,0,1,7,0,5,1,1,2\n9954,43,4.7,1,4,0,1,7,1,7,1,1,0\n4205,43,5.2,1,1,0,1,8,0,6,1,1,1\n9515,43,5.4,1,2,0,1,8,1,6,1,1,0\n798,43,6,1,4,0,1,5,0,6,1,1,0\n10057,43,6.1,1,1,0,1,5,1,1,1,1,3\n7898,43,6.1,1,1,0,1,6,1,1,1,1,3\n8060,43,6.3,1,2,0,1,6,1,4,1,1,1\n8931,43,6.4,1,3,0,1,5,1,8,1,1,2\n5196,43,6.6,1,2,0,1,5,0,2,1,1,4\n6948,43,7,1,4,0,1,6,1,8,1,1,7\n7145,43,7.1,1,3,0,1,8,1,5,1,1,2\n750,43,7.3,1,5,0,1,8,0,5,1,1,7\n9359,43,7.3,1,4,0,1,5,1,3,1,1,4\n8904,43,7.4,1,5,0,1,8,1,8,1,1,10\n7206,43,7.4,1,4,0,1,6,1,1,1,1,5\n10629,43,7.4,1,4,0,1,6,1,6,1,1,2\n2738,43,7.7,1,1,0,1,8,0,6,1,1,1\n5327,43,7.9,1,1,0,1,7,0,2,1,1,4\n9774,43,8,1,2,0,1,7,1,3,1,1,2\n6278,43,8,1,2,0,1,6,1,7,1,1,10\n4398,43,8,1,3,0,1,6,0,8,1,1,7\n10837,43,8.1,1,5,0,1,7,1,5,1,1,4\n2844,43,8.1,1,3,0,1,7,0,8,1,1,1\n1520,43,9.2,1,2,0,1,6,0,3,1,1,5\n10814,43,9.3,1,1,0,1,6,1,1,1,1,1\n7285,43,9.9,1,1,0,1,8,1,6,1,1,2\n96,43,10,1,5,0,1,5,0,5,1,1,10\n4243,43,10.4,1,1,0,1,8,0,1,1,1,3\n6810,43,10.4,1,5,0,1,5,1,8,1,1,10\n4145,43,10.5,1,2,0,1,8,0,5,1,1,2\n8140,43,10.7,1,5,0,1,5,1,1,1,1,7\n6297,43,11.6,1,5,0,1,7,1,7,1,1,4\n1218,43,11.6,1,3,0,1,5,0,1,1,1,4\n10530,43,3,1,5,0,1,6,1,7,1,1,4\n2543,43,3,1,2,0,1,5,0,2,1,1,4\n2374,43,2,1,1,0,1,6,0,7,1,1,0\n8200,43,1,1,2,0,1,8,1,5,1,1,1\n210,43,2,1,3,0,1,7,0,1,1,1,2\n4036,43,3,1,1,0,1,7,0,3,1,1,8\n9202,43,4,1,4,0,1,5,1,5,1,1,8\n3287,43,3,1,1,0,1,5,0,6,1,1,3\n10600,43,1,1,4,0,1,5,1,8,1,1,6\n7280,43,6,1,2,0,1,8,1,6,1,1,5\n591,43,1,1,2,0,1,8,0,4,1,1,4\n7472,43,5,1,1,0,1,5,1,6,1,1,6\n1543,43,6,1,1,0,1,6,0,3,1,1,3\n819,43,2,1,3,0,1,5,0,2,1,1,4\n7684,43,5,1,4,0,1,7,1,3,1,1,6\n236,43,1,1,3,0,1,7,0,7,1,1,4\n3626,43,2,1,1,0,1,7,0,2,1,1,10\n7820,43,3,1,5,0,1,8,1,2,1,1,4\n2740,43,3,1,3,0,1,7,0,7,1,1,5\n7259,43,4,1,2,0,1,8,1,6,1,1,3\n6739,43,7,1,3,0,1,5,1,8,1,1,0\n363,43,2,1,1,0,1,5,0,2,1,1,0\n6550,43,3,1,2,0,1,8,1,8,1,1,9\n9766,43,7,1,1,0,1,6,1,2,1,1,7\n7814,43,1,1,3,0,1,6,1,5,1,1,5\n9364,43,1,1,2,0,1,7,1,3,1,1,0\n5419,43,6,1,3,0,1,8,1,1,1,1,0\n1518,43,2,1,4,0,1,7,0,4,1,1,2\n3999,43,6,1,3,0,1,5,0,7,1,1,3\n8002,43,6,1,4,0,1,7,1,3,1,1,10\n7503,43,3,1,3,0,1,5,1,4,1,1,3\n1554,43,6,1,4,0,1,6,0,3,1,1,8\n5841,43,7,1,4,0,1,8,1,5,1,1,2\n6128,43,1,1,2,0,1,7,1,4,1,1,8\n9513,43,6,1,5,0,1,8,1,5,1,1,4\n2026,43,5,1,1,0,1,6,0,7,1,1,7\n7360,43,3,1,2,0,1,6,1,3,1,1,3\n3400,43,2,1,4,0,1,8,0,1,1,1,4\n9473,43,4,1,2,0,1,7,1,4,1,1,2\n4567,43,7,1,4,0,1,8,0,2,1,1,2\n3670,43,7,1,4,0,1,6,0,3,1,1,3\n9566,43,6,1,5,0,1,6,1,4,1,1,7\n1632,43,2,1,3,0,1,7,0,1,1,1,10\n3901,43,3,1,4,0,1,8,0,5,1,1,4\n1738,43,5,1,4,0,1,8,0,5,1,1,8\n4635,43,2,1,4,0,1,6,0,7,1,1,1\n7276,43,2,1,4,0,1,5,1,8,1,1,9\n3006,43,7,1,4,0,1,5,0,6,1,1,7\n10066,43,3,1,5,0,1,7,1,3,1,1,7\n2498,43,4,1,3,0,1,5,0,6,1,1,7\n6401,43,1,1,4,0,1,5,1,7,1,1,3\n9873,43,6,1,3,0,1,8,1,7,1,1,4\n10240,43,5,1,4,0,1,8,1,6,1,1,1\n6364,43,7,1,5,0,1,5,1,5,1,1,6\n7152,43,7,1,4,0,1,7,1,7,1,1,1\n6780,43,7,1,2,0,1,5,1,4,1,1,9\n2509,43,3,1,2,0,1,6,0,2,1,1,2\n7880,43,5,1,3,0,1,6,1,3,1,1,5\n8043,43,2,1,1,0,1,8,1,8,1,1,1\n9933,43,7,1,4,0,1,8,1,1,1,1,1\n1548,43,4,1,3,0,1,8,0,1,1,1,9\n7363,43,7,1,5,0,1,6,1,8,1,1,2\n2152,43,7,1,3,0,1,7,0,8,1,1,2\n3591,44,0.1,1,3,0,1,6,0,8,1,1,0\n1847,44,0.2,1,1,0,1,6,0,2,1,1,4\n1770,44,0.2,1,3,0,1,5,0,3,1,1,7\n6094,44,0.7,1,3,0,1,8,1,7,1,1,2\n1728,44,2,1,4,0,1,8,0,1,1,1,4\n2076,44,2.2,1,3,0,1,7,0,2,1,1,9\n1052,44,2.3,1,4,0,1,7,0,5,1,1,10\n10528,44,2.4,1,5,0,1,5,1,2,1,1,0\n2345,44,2.5,1,2,0,1,8,0,3,1,1,0\n1048,44,2.6,1,5,0,1,5,0,6,1,1,3\n3431,44,2.6,1,3,0,1,7,0,5,1,1,4\n1609,44,2.8,1,4,0,1,6,0,5,1,1,0\n10546,44,3,1,3,0,1,5,1,7,1,1,9\n7973,44,3.1,1,5,0,1,5,1,3,1,1,0\n7060,44,3.3,1,3,0,1,8,1,2,1,1,7\n1306,44,3.6,1,2,0,1,5,0,6,1,1,0\n7952,44,3.9,1,2,0,1,5,1,7,1,1,1\n8671,44,4.3,1,2,0,1,8,1,4,1,1,0\n2934,44,4.6,1,5,0,1,6,0,3,1,1,8\n1651,44,4.9,1,3,0,1,5,0,6,1,1,3\n10002,44,4.9,1,3,0,1,6,1,8,1,1,2\n6524,44,5,1,4,0,1,7,1,1,1,1,10\n197,44,5.1,1,1,0,1,5,0,3,1,1,0\n4988,44,5.2,1,4,0,1,7,0,2,1,1,5\n7922,44,5.3,1,2,0,1,5,1,6,1,1,7\n10833,44,5.5,1,2,0,1,8,1,1,1,1,3\n2526,44,5.7,1,2,0,1,8,0,5,1,1,7\n5843,44,6.1,1,4,0,1,8,1,8,1,1,3\n8294,44,6.2,1,3,0,1,5,1,7,1,1,2\n950,44,6.2,1,4,0,1,5,0,2,1,1,1\n7134,44,6.8,1,2,0,1,6,1,2,1,1,6\n8220,44,6.9,1,3,0,1,5,1,8,1,1,6\n7282,44,6.9,1,5,0,1,7,1,3,1,1,3\n1856,44,7.4,1,2,0,1,7,0,7,1,1,0\n1280,44,7.4,1,1,0,1,8,0,1,1,1,2\n5890,44,8.1,1,3,0,1,8,1,1,1,1,4\n10101,44,8.3,1,5,0,1,7,1,1,1,1,2\n7871,44,8.8,1,2,0,1,7,1,8,1,1,3\n6480,44,9,1,5,0,1,7,1,7,1,1,10\n8715,44,9.6,1,4,0,1,8,1,2,1,1,1\n9437,44,9.8,1,3,0,1,7,1,5,1,1,3\n4669,44,10.6,1,4,0,1,5,0,8,1,1,3\n10686,44,11.4,1,5,0,1,7,1,5,1,1,6\n10200,44,11.8,1,3,0,1,8,1,2,1,1,1\n2886,44,3,1,1,0,1,8,0,6,1,1,10\n9056,44,5,1,4,0,1,5,1,5,1,1,2\n9660,44,6,1,4,0,1,8,1,6,1,1,0\n3352,44,4,1,1,0,1,7,0,2,1,1,10\n8784,44,2,1,1,0,1,6,1,2,1,1,4\n1740,44,3,1,5,0,1,6,0,2,1,1,6\n182,44,2,1,2,0,1,7,0,7,1,1,4\n9320,44,1,1,5,0,1,6,1,2,1,1,7\n8028,44,3,1,4,0,1,6,1,6,1,1,1\n3506,44,4,1,5,0,1,5,0,5,1,1,3\n9398,44,2,1,1,0,1,5,1,3,1,1,7\n4468,44,6,1,4,0,1,7,0,2,1,1,8\n961,44,6,1,1,0,1,7,0,8,1,1,1\n5308,44,6,1,1,0,1,5,0,6,1,1,4\n1893,44,3,1,2,0,1,8,0,5,1,1,4\n8895,44,5,1,3,0,1,8,1,5,1,1,3\n3818,44,2,1,1,0,1,8,0,8,1,1,3\n4769,44,7,1,1,0,1,8,0,2,1,1,1\n7354,44,7,1,1,0,1,5,1,3,1,1,9\n5270,44,7,1,1,0,1,8,0,8,1,1,0\n1485,44,2,1,1,0,1,8,0,8,1,1,1\n8019,44,4,1,2,0,1,7,1,5,1,1,2\n9280,44,3,1,2,0,1,7,1,6,1,1,6\n3335,44,2,1,1,0,1,8,0,6,1,1,3\n8493,44,5,1,5,0,1,7,1,6,1,1,4\n4370,44,5,1,3,0,1,6,0,4,1,1,0\n6870,44,4,1,5,0,1,8,1,6,1,1,10\n1000,44,5,1,1,0,1,5,0,7,1,1,7\n5104,44,1,1,2,0,1,6,0,1,1,1,6\n6902,44,6,1,5,0,1,6,1,4,1,1,7\n953,44,6,1,1,0,1,6,0,4,1,1,3\n10444,44,6,1,1,0,1,5,1,2,1,1,6\n10261,44,1,1,1,0,1,5,1,6,1,1,1\n5397,44,4,1,5,0,1,6,0,7,1,1,2\n498,44,1,1,2,0,1,8,0,2,1,1,5\n4606,44,6,1,1,0,1,8,0,6,1,1,6\n1804,44,5,1,5,0,1,7,0,4,1,1,0\n10768,44,7,1,5,0,1,7,1,4,1,1,10\n2938,44,5,1,5,0,1,7,0,5,1,1,3\n7070,44,7,1,4,0,1,8,1,6,1,1,6\n7364,44,4,1,1,0,1,6,1,3,1,1,0\n10520,44,5,1,2,0,1,7,1,5,1,1,3\n8212,44,7,1,5,0,1,5,1,4,1,1,3\n7668,44,3,1,4,0,1,5,1,2,1,1,0\n4572,44,2,1,5,0,1,6,0,2,1,1,1\n10092,44,7,1,5,0,1,6,1,6,1,1,8\n9438,44,4,1,5,0,1,8,1,3,1,1,6\n9844,44,4,1,2,0,1,7,1,7,1,1,6\n936,44,1,1,5,0,1,8,0,1,1,1,3\n2538,44,7,1,5,0,1,5,0,6,1,1,5\n8098,44,2,1,5,0,1,7,1,3,1,1,4\n1412,44,4,1,1,0,1,6,0,8,1,1,3\n8108,44,6,1,4,0,1,5,1,8,1,1,6\n955,44,2,1,5,0,1,6,0,6,1,1,2\n10354,44,6,1,4,0,1,5,1,5,1,1,9\n3698,44,1,1,1,0,1,7,0,8,1,1,10\n2079,44,6,1,3,0,1,6,0,6,1,1,3\n9081,44,5,1,4,0,1,5,1,7,1,1,4\n3590,44,7,1,1,0,1,7,0,4,1,1,2\n4098,45,0.5,1,3,0,1,8,0,6,1,1,5\n2016,45,0.6,1,3,0,1,7,0,3,1,1,6\n9486,45,0.8,1,4,0,1,6,1,4,1,1,6\n9038,45,0.9,1,1,0,1,8,1,4,1,1,2\n5172,45,0.9,1,2,0,1,8,0,8,1,1,6\n9450,45,1.1,1,5,0,1,8,1,4,1,1,5\n6914,45,1.2,1,3,0,1,7,1,1,1,1,4\n4928,45,1.8,1,5,0,1,8,0,3,1,1,7\n9300,45,1.8,1,1,0,1,5,1,1,1,1,9\n200,45,2.2,1,2,0,1,5,0,6,1,1,10\n1891,45,2.2,1,5,0,1,8,0,6,1,1,1\n3618,45,2.2,1,5,0,1,6,0,2,1,1,5\n9728,45,2.3,1,2,0,1,8,1,5,1,1,7\n5615,45,3.2,1,5,0,1,5,1,8,1,1,0\n5508,45,3.3,1,5,0,1,7,1,3,1,1,0\n5605,45,3.4,1,1,0,1,6,1,5,1,1,4\n8588,45,3.8,1,3,0,1,7,1,4,1,1,6\n9194,45,3.8,1,1,0,1,5,1,8,1,1,9\n7068,45,4.1,1,4,0,1,5,1,7,1,1,5\n3930,45,4.1,1,4,0,1,5,0,1,1,1,0\n4650,45,4.3,1,2,0,1,7,0,2,1,1,3\n4416,45,4.5,1,3,0,1,8,0,2,1,1,5\n8180,45,4.6,1,5,0,1,6,1,8,1,1,1\n4690,45,4.6,1,1,0,1,5,0,4,1,1,1\n9994,45,5.4,1,2,0,1,6,1,7,1,1,0\n6318,45,5.8,1,1,0,1,7,1,3,1,1,2\n10348,45,5.8,1,1,0,1,5,1,3,1,1,2\n7794,45,5.9,1,2,0,1,6,1,6,1,1,8\n2657,45,5.9,1,2,0,1,7,0,5,1,1,0\n9550,45,6.2,1,1,0,1,6,1,8,1,1,2\n3250,45,7.1,1,1,0,1,5,0,6,1,1,2\n9604,45,7.4,1,1,0,1,6,1,7,1,1,6\n552,45,7.9,1,5,0,1,5,0,2,1,1,4\n2602,45,8.1,1,2,0,1,7,0,7,1,1,0\n7098,45,8.1,1,2,0,1,6,1,3,1,1,2\n4729,45,8.4,1,5,0,1,7,0,3,1,1,2\n876,45,8.5,1,5,0,1,7,0,6,1,1,8\n3624,45,8.7,1,5,0,1,8,0,2,1,1,2\n6718,45,8.8,1,4,0,1,8,1,1,1,1,4\n10653,45,8.9,1,2,0,1,6,1,7,1,1,0\n10398,45,9.1,1,3,0,1,7,1,4,1,1,0\n6498,45,9.4,1,2,0,1,5,1,1,1,1,0\n2095,45,9.5,1,4,0,1,7,0,4,1,1,2\n8595,45,9.6,1,2,0,1,8,1,1,1,1,4\n9492,45,9.7,1,4,0,1,6,1,4,1,1,10\n3752,45,9.9,1,2,0,1,7,0,3,1,1,2\n3439,45,10.7,1,5,0,1,5,0,2,1,1,0\n2433,45,10.8,1,2,0,1,8,0,5,1,1,0\n301,45,11.3,1,3,0,1,8,0,3,1,1,3\n4674,45,11.6,1,1,0,1,8,0,5,1,1,1\n5356,45,11.8,1,3,0,1,8,0,5,1,1,1\n10682,45,7,1,4,0,1,5,1,5,1,1,2\n6058,45,4,1,1,0,1,5,1,6,1,1,8\n5069,45,2,1,4,0,1,6,0,7,1,1,1\n2530,45,7,1,3,0,1,5,0,5,1,1,9\n6638,45,4,1,5,0,1,6,1,8,1,1,8\n7554,45,1,1,2,0,1,5,1,8,1,1,2\n8505,45,3,1,1,0,1,5,1,1,1,1,2\n2737,45,3,1,3,0,1,8,0,1,1,1,1\n6267,45,3,1,5,0,1,8,1,1,1,1,0\n992,45,5,1,2,0,1,8,0,7,1,1,0\n832,45,2,1,2,0,1,8,0,5,1,1,1\n3952,45,3,1,2,0,1,5,0,1,1,1,3\n6472,45,6,1,5,0,1,8,1,2,1,1,4\n4958,45,4,1,5,0,1,7,0,8,1,1,0\n2819,45,5,1,3,0,1,8,0,6,1,1,1\n6496,45,6,1,4,0,1,8,1,4,1,1,2\n3556,45,7,1,3,0,1,8,0,2,1,1,8\n532,45,6,1,3,0,1,7,0,6,1,1,9\n6720,45,2,1,4,0,1,5,1,2,1,1,5\n1344,45,6,1,4,0,1,7,0,7,1,1,3\n4900,45,5,1,1,0,1,6,0,4,1,1,6\n7730,45,2,1,2,0,1,6,1,1,1,1,3\n4530,45,7,1,5,0,1,5,0,1,1,1,2\n9350,45,1,1,4,0,1,6,1,1,1,1,1\n1475,45,6,1,2,0,1,5,0,3,1,1,3\n5353,45,6,1,4,0,1,7,0,1,1,1,0\n4610,45,5,1,3,0,1,6,0,8,1,1,10\n3916,45,2,1,3,0,1,7,0,3,1,1,10\n10445,45,4,1,5,0,1,5,1,7,1,1,0\n6644,45,7,1,1,0,1,6,1,2,1,1,10\n6731,45,5,1,2,0,1,6,1,5,1,1,0\n9792,45,2,1,5,0,1,5,1,5,1,1,9\n6332,45,1,1,3,0,1,8,1,3,1,1,5\n5644,45,7,1,1,0,1,5,1,7,1,1,7\n4020,45,7,1,3,0,1,7,0,5,1,1,5\n10749,45,6,1,2,0,1,6,1,2,1,1,4\n7237,45,5,1,2,0,1,8,1,3,1,1,0\n5750,45,5,1,1,0,1,5,1,1,1,1,0\n6660,45,6,1,2,0,1,7,1,1,1,1,5\n5398,45,6,1,5,0,1,7,0,3,1,1,2\n9520,45,7,1,4,0,1,8,1,7,1,1,5\n3314,45,3,1,3,0,1,6,0,2,1,1,0\n5362,45,5,1,5,0,1,7,0,1,1,1,2\n10100,45,3,1,5,0,1,6,1,8,1,1,6\n6270,45,6,1,2,0,1,7,1,3,1,1,7\n3882,45,3,1,2,0,1,5,0,3,1,1,3\n10224,45,6,1,3,0,1,8,1,1,1,1,0\n6684,45,6,1,4,0,1,6,1,8,1,1,7\n1540,45,7,1,3,0,1,7,0,1,1,1,3\n408,45,3,1,3,0,1,7,0,4,1,1,8\n9096,45,2,1,4,0,1,8,1,3,1,1,10\n9094,45,3,1,3,0,1,8,1,7,1,1,5\n9909,45,6,1,1,0,1,8,1,7,1,1,2\n2066,45,6,1,4,0,1,5,0,7,1,1,2\n1042,45,1,1,3,0,1,7,0,6,1,1,10\n9482,45,4,1,5,0,1,5,1,5,1,1,7\n5867,45,4,1,5,0,1,6,1,4,1,1,3\n3432,45,5,1,3,0,1,6,0,4,1,1,7\n5930,45,3,1,2,0,1,7,1,3,1,1,5\n311,45,4,1,1,0,1,7,0,8,1,1,3\n3463,45,6,1,1,0,1,6,0,4,1,1,3\n2413,45,3,1,2,0,1,6,0,4,1,1,3\n5668,45,6,1,2,0,1,5,1,5,1,1,9\n1685,45,7,1,4,0,1,5,0,2,1,1,1\n1059,45,3,1,1,0,1,6,0,7,1,1,4\n3332,45,6,1,5,0,1,8,0,8,1,1,7\n4052,45,6,1,4,0,1,5,0,1,1,1,6\n1212,45,3,1,3,0,1,7,0,2,1,1,1\n2422,45,2,1,5,0,1,6,0,4,1,1,6\n3442,46,0.2,1,4,0,1,8,0,1,1,1,8\n658,46,0.3,1,5,0,1,5,0,6,1,1,6\n5895,46,0.6,1,5,0,1,5,1,3,1,1,3\n4128,46,0.7,1,5,0,1,5,0,7,1,1,3\n5499,46,0.7,1,3,0,1,5,1,1,1,1,4\n8466,46,0.8,1,5,0,1,5,1,8,1,1,0\n5764,46,1.1,1,1,0,1,5,1,3,1,1,1\n10236,46,1.2,1,3,0,1,6,1,1,1,1,8\n4012,46,1.5,1,3,0,1,8,0,6,1,1,8\n8860,46,1.5,1,2,0,1,7,1,4,1,1,5\n339,46,1.8,1,4,0,1,6,0,4,1,1,2\n2717,46,2.1,1,3,0,1,8,0,4,1,1,0\n6464,46,2.2,1,2,0,1,6,1,8,1,1,9\n10531,46,2.5,1,4,0,1,5,1,1,1,1,0\n3763,46,3.1,1,1,0,1,7,0,4,1,1,4\n8984,46,3.8,1,1,0,1,5,1,3,1,1,5\n10732,46,3.9,1,3,0,1,7,1,8,1,1,5\n9625,46,4.9,1,4,0,1,8,1,8,1,1,0\n5056,46,5.4,1,2,0,1,5,0,2,1,1,7\n10360,46,5.9,1,2,0,1,5,1,1,1,1,6\n1573,46,5.9,1,3,0,1,6,0,7,1,1,3\n10574,46,6,1,1,0,1,8,1,3,1,1,3\n6413,46,6.3,1,3,0,1,8,1,5,1,1,3\n78,46,6.6,1,1,0,1,5,0,6,1,1,3\n2528,46,7.6,1,3,0,1,8,0,2,1,1,5\n10402,46,7.9,1,4,0,1,8,1,1,1,1,10\n4801,46,7.9,1,3,0,1,8,0,2,1,1,1\n1979,46,7.9,1,5,0,1,5,0,5,1,1,0\n6892,46,8.2,1,3,0,1,8,1,8,1,1,0\n5822,46,8.3,1,1,0,1,8,1,4,1,1,1\n1511,46,8.6,1,3,0,1,5,0,8,1,1,4\n2788,46,8.7,1,2,0,1,7,0,5,1,1,1\n10490,46,9.1,1,4,0,1,7,1,5,1,1,10\n1559,46,9.1,1,1,0,1,8,0,4,1,1,2\n2923,46,9.2,1,5,0,1,6,0,8,1,1,3\n9898,46,9.8,1,2,0,1,7,1,2,1,1,7\n3493,46,10,1,5,0,1,5,0,4,1,1,3\n5980,46,10.2,1,1,0,1,8,1,6,1,1,5\n6752,46,10.2,1,2,0,1,7,1,3,1,1,3\n59,46,10.6,1,2,0,1,5,0,3,1,1,4\n6613,46,10.7,1,2,0,1,6,1,5,1,1,3\n1742,46,11.2,1,1,0,1,5,0,7,1,1,10\n5181,46,11.2,1,2,0,1,7,0,7,1,1,0\n2975,46,11.9,1,4,0,1,6,0,3,1,1,3\n4073,46,12,1,4,0,1,6,0,7,1,1,3\n5203,46,3,1,5,0,1,5,0,3,1,1,0\n6769,46,7,1,1,0,1,7,1,2,1,1,2\n2592,46,1,1,1,0,1,6,0,8,1,1,4\n6026,46,5,1,4,0,1,7,1,2,1,1,1\n8573,46,3,1,4,0,1,6,1,1,1,1,4\n7940,46,2,1,4,0,1,8,1,2,1,1,2\n624,46,7,1,5,0,1,6,0,8,1,1,8\n6782,46,1,1,4,0,1,6,1,4,1,1,1\n231,46,7,1,1,0,1,5,0,4,1,1,3\n9594,46,4,1,5,0,1,5,1,7,1,1,10\n3278,46,7,1,3,0,1,7,0,7,1,1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1,1\n8764,47,4,1,4,0,1,5,1,7,1,1,2\n6226,47,6,1,5,0,1,6,1,2,1,1,6\n10350,47,1,1,1,0,1,5,1,5,1,1,9\n7440,47,3,1,3,0,1,6,1,6,1,1,6\n8262,47,2,1,2,0,1,8,1,7,1,1,0\n4300,47,4,1,5,0,1,7,0,6,1,1,3\n8968,47,7,1,3,0,1,5,1,2,1,1,3\n5928,47,5,1,2,0,1,8,1,2,1,1,4\n7630,47,2,1,2,0,1,5,1,2,1,1,10\n450,47,4,1,1,0,1,6,0,3,1,1,4\n404,47,3,1,5,0,1,5,0,1,1,1,5\n3876,47,5,1,4,0,1,7,0,7,1,1,1\n150,47,3,1,5,0,1,7,0,1,1,1,4\n1333,47,7,1,3,0,1,6,0,2,1,1,3\n460,47,4,1,2,0,1,6,0,8,1,1,9\n10058,47,5,1,3,0,1,5,1,6,1,1,5\n2666,47,6,1,2,0,1,5,0,3,1,1,0\n1594,47,2,1,3,0,1,6,0,6,1,1,6\n9293,47,2,1,5,0,1,5,1,3,1,1,4\n1262,47,1,1,1,0,1,6,0,4,1,1,2\n1442,47,6,1,1,0,1,5,0,5,1,1,8\n270,48,0.2,1,3,0,1,7,0,8,1,1,9\n5951,48,0.3,1,1,0,1,8,1,3,1,1,0\n4196,48,0.5,1,3,0,1,8,0,4,1,1,5\n9046,48,0.6,1,1,0,1,7,1,8,1,1,9\n6888,48,1,1,4,0,1,7,1,8,1,1,4\n4624,48,1.3,1,5,0,1,5,0,6,1,1,10\n9851,48,1.7,1,4,0,1,5,1,4,1,1,1\n5392,48,1.8,1,5,0,1,6,0,5,1,1,8\n10543,48,1.9,1,5,0,1,5,1,5,1,1,1\n7231,48,2,1,5,0,1,6,1,7,1,1,0\n3483,48,2.1,1,3,0,1,7,0,4,1,1,4\n7265,48,2.2,1,5,0,1,5,1,3,1,1,4\n148,48,2.2,1,1,0,1,6,0,4,1,1,1\n9172,48,2.6,1,5,0,1,5,1,5,1,1,4\n2453,48,2.8,1,5,0,1,5,0,8,1,1,3\n1282,48,3.6,1,2,0,1,5,0,4,1,1,8\n1676,48,4,1,5,0,1,8,0,6,1,1,3\n7448,48,4.4,1,5,0,1,8,1,8,1,1,2\n1926,48,6,1,1,0,1,5,0,5,1,1,6\n4990,48,6,1,4,0,1,6,0,2,1,1,0\n6196,48,6.6,1,5,0,1,5,1,5,1,1,10\n10202,48,7.1,1,1,0,1,7,1,8,1,1,6\n1036,48,7.6,1,1,0,1,5,0,5,1,1,8\n9688,48,7.7,1,5,0,1,6,1,5,1,1,4\n6754,48,7.8,1,5,0,1,5,1,1,1,1,9\n274,48,7.8,1,5,0,1,5,0,6,1,1,7\n8641,48,7.9,1,3,0,1,6,1,3,1,1,0\n8368,48,8.4,1,3,0,1,5,1,4,1,1,1\n228,48,8.5,1,1,0,1,8,0,8,0,1,2\n1797,48,8.8,1,3,0,1,5,0,3,0,1,3\n10230,48,8.9,1,2,0,1,6,1,5,0,1,2\n8190,48,9.9,1,1,0,1,6,1,3,0,1,3\n9444,48,10,1,2,0,1,6,1,1,0,1,0\n9708,48,10.2,1,3,0,1,8,1,7,0,1,9\n7320,48,10.4,1,1,0,1,8,1,3,0,1,0\n1452,48,10.4,1,5,0,1,8,0,4,0,1,6\n9074,48,10.8,1,3,0,1,6,1,6,0,1,2\n7184,48,11.2,1,4,0,1,7,1,1,0,1,10\n9623,48,11.6,1,1,0,1,6,1,2,0,1,2\n9201,48,3,1,1,0,1,8,1,1,0,1,4\n672,48,2,1,2,0,1,6,0,2,0,1,6\n8949,48,4,1,3,0,1,8,1,4,0,1,1\n4584,48,3,1,1,0,1,8,0,5,0,1,1\n9540,48,4,1,2,0,1,5,1,4,0,1,4\n8903,48,5,1,4,0,1,8,1,8,0,1,4\n2852,48,5,1,3,0,1,7,0,7,0,1,2\n9556,48,7,1,1,0,1,5,1,8,0,1,3\n104,48,6,1,5,0,1,8,0,5,0,1,1\n2957,48,1,1,2,0,1,6,0,2,0,1,3\n9867,48,7,1,3,0,1,6,1,7,0,1,0\n9619,48,6,1,4,0,1,5,1,6,0,1,3\n5494,48,2,1,1,0,1,6,1,4,0,1,10\n9813,48,2,1,2,0,1,6,1,1,0,1,4\n8589,48,1,1,5,0,1,8,1,5,0,1,3\n9974,48,4,1,2,0,1,6,1,6,0,1,7\n3533,48,6,1,1,0,1,7,0,6,0,1,1\n8440,48,4,1,1,0,1,5,1,5,0,1,8\n2902,48,5,1,4,0,1,6,0,5,0,1,2\n9790,48,7,1,3,0,1,5,1,2,0,1,4\n1999,48,7,1,2,0,1,7,0,4,0,1,1\n5826,48,4,1,5,0,1,7,1,4,0,1,10\n1014,48,7,1,5,0,1,5,0,3,0,1,10\n1552,48,5,1,5,0,1,7,0,2,0,1,10\n8471,48,7,1,3,0,1,7,1,7,0,1,4\n2802,48,7,1,1,0,1,6,0,5,0,1,4\n240,48,7,1,4,0,1,6,0,8,0,1,1\n5613,48,3,1,1,0,1,6,1,1,0,1,4\n24,48,7,1,1,0,1,5,0,7,0,1,2\n6376,48,3,1,1,0,1,5,1,3,0,1,7\n5982,48,5,1,2,0,1,7,1,2,0,1,7\n10212,48,3,1,5,0,1,5,1,6,0,1,6\n5182,48,7,1,2,0,1,7,0,1,0,1,3\n1472,48,2,1,2,0,1,7,0,3,0,1,10\n4553,48,3,1,3,0,1,7,0,6,0,1,4\n10262,48,4,1,4,0,1,7,1,4,0,1,5\n10145,48,3,1,5,0,1,5,1,7,0,1,0\n4780,48,2,1,3,0,1,5,0,7,0,1,0\n7884,48,3,1,5,0,1,5,1,4,0,1,7\n6235,48,4,1,2,0,1,7,1,2,0,1,4\n5786,48,5,1,2,0,1,5,1,1,0,1,7\n6580,48,6,1,2,0,1,5,1,5,0,1,4\n4374,48,2,1,1,0,1,6,0,5,0,1,4\n3610,48,3,1,3,0,1,5,0,7,0,1,4\n7444,48,2,1,2,0,1,8,1,6,0,1,7\n9937,48,4,1,3,0,1,6,1,8,0,1,2\n10285,48,6,1,3,0,1,5,1,4,0,1,2\n8158,48,3,1,1,0,1,6,1,1,0,1,3\n5386,48,5,1,4,0,1,8,0,7,0,1,0\n4404,48,4,1,4,0,1,7,0,6,0,1,2\n3802,48,5,1,4,0,1,8,0,1,0,1,2\n6418,48,2,1,3,0,1,7,1,3,0,1,0\n6727,48,4,1,2,0,1,6,1,1,0,1,1\n4784,48,7,1,1,0,1,6,0,6,0,1,8\n7681,48,4,1,4,0,1,8,1,2,0,1,0\n9672,48,2,1,5,0,1,5,1,3,0,1,10\n6552,48,5,1,2,0,1,6,1,7,0,1,0\n6521,48,5,1,4,0,1,7,1,2,0,1,1\n8640,48,5,1,5,0,1,7,1,7,0,1,5\n3396,48,7,1,1,0,1,6,0,6,0,1,8\n6224,48,1,1,4,0,1,6,1,7,0,1,7\n3675,48,5,1,5,0,1,7,0,5,0,1,0\n4644,48,6,1,5,0,1,8,0,3,0,1,5\n9547,48,1,1,4,0,1,7,1,7,0,1,0\n9760,48,6,1,5,0,1,5,1,3,0,1,1\n663,48,2,1,4,0,1,7,0,1,0,1,1\n9186,48,5,1,5,0,1,7,1,3,0,1,5\n9662,48,4,1,2,0,1,6,1,2,0,1,8\n758,48,6,1,2,0,1,5,0,4,0,1,2\n3383,49,0.4,1,5,0,1,8,0,7,0,1,0\n9187,49,0.5,1,2,0,1,8,1,6,0,1,0\n7441,49,0.6,1,4,0,1,6,1,4,0,1,3\n9404,49,0.6,1,2,0,1,6,1,6,0,1,2\n1849,49,0.6,1,3,0,1,8,0,2,0,1,3\n5404,49,1,1,2,0,1,6,0,1,0,1,10\n7030,49,1,1,3,0,1,7,1,3,0,1,7\n3786,49,1.2,1,5,0,1,8,0,6,0,1,10\n7596,49,1.7,1,3,0,1,7,1,6,0,1,5\n9006,49,1.8,1,1,0,1,5,1,6,0,1,4\n9715,49,1.9,1,4,0,1,8,1,3,0,1,1\n7087,49,2.6,1,3,0,1,8,1,8,0,1,3\n84,49,2.6,1,2,0,1,6,0,8,0,1,6\n4359,49,2.9,1,5,0,1,6,0,1,0,1,4\n2519,49,3.2,1,4,0,1,7,0,4,0,1,0\n7978,49,3.3,1,2,0,1,5,1,8,0,1,3\n3684,49,3.6,1,3,0,1,8,0,8,0,1,3\n5256,49,3.6,1,3,0,1,5,0,3,0,1,8\n3202,49,3.8,1,5,0,1,8,0,6,0,1,0\n1111,49,4,1,1,0,1,8,0,6,0,1,3\n6284,49,4.3,1,5,0,1,5,1,8,0,1,6\n537,49,4.3,1,2,0,1,8,0,5,0,1,2\n6442,49,4.9,1,2,0,1,8,1,1,0,1,1\n5418,49,5,1,1,0,1,8,0,5,0,1,9\n8069,49,5.6,1,2,0,1,7,1,8,0,1,0\n465,49,5.6,1,4,0,1,6,0,8,0,1,2\n10335,49,5.6,1,4,0,1,5,1,6,0,1,1\n226,49,5.8,1,1,0,1,5,0,1,0,1,4\n6098,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10162,70,7.2,0,3,0,1,8,1,3,0,1,9\n8534,70,7.6,0,5,0,1,6,1,8,0,1,7\n9876,70,7.9,0,1,0,1,7,1,5,0,1,9\n7924,70,7.9,0,1,0,1,8,1,8,0,1,5\n264,70,8,0,1,0,1,5,0,4,0,1,4\n6940,70,8.2,0,3,0,1,6,1,1,0,1,9\n8846,70,8.4,0,2,0,1,6,1,3,1,1,2\n7062,70,8.4,0,3,0,1,6,1,7,1,1,9\n749,70,8.9,0,4,0,1,7,0,4,1,1,4\n682,70,8.9,0,5,0,1,5,0,2,1,1,0\n7920,70,9,0,4,0,1,5,1,1,1,1,3\n2319,70,9.8,0,5,0,1,5,0,4,1,1,4\n7290,70,10.1,0,4,0,1,5,1,3,1,1,3\n2926,70,10.1,0,1,0,1,6,0,2,1,1,4\n7502,70,10.4,0,5,0,1,7,1,4,1,1,6\n8611,70,10.7,0,2,0,1,8,1,5,1,1,0\n3860,70,10.8,0,3,0,1,8,0,8,1,1,6\n7753,70,11,0,4,0,1,5,1,2,1,1,4\n8540,70,11.1,0,4,0,1,7,1,3,1,1,5\n9922,70,11.4,0,1,0,1,6,1,8,1,1,0\n5107,70,11.5,0,1,0,1,5,0,7,1,1,2\n6732,70,11.6,0,1,0,1,7,1,8,1,1,2\n1826,70,12.1,0,4,0,1,6,0,2,1,1,4\n618,70,12.4,0,4,0,1,8,0,3,1,1,3\n1477,70,12.7,0,1,0,1,8,0,2,1,1,1\n4445,70,13.1,0,4,0,1,7,0,1,1,1,3\n9784,70,13.2,0,3,0,1,8,1,4,1,1,0\n3465,70,13.8,0,5,0,1,5,0,4,1,1,0\n7091,70,13.9,0,2,0,1,5,1,8,1,1,1\n1696,70,14.3,0,1,0,1,8,0,7,1,1,7\n3598,70,14.4,0,2,0,1,8,0,4,1,1,2\n1982,70,15,0,1,0,1,5,0,4,1,1,4\n9956,70,15.6,0,4,0,1,5,1,3,1,1,4\n7358,70,15.7,0,3,0,1,7,1,3,1,1,2\n1950,70,15.8,0,2,0,1,7,0,1,1,1,4\n10674,70,16,0,5,0,1,6,1,5,1,1,0\n7488,70,16.5,0,4,0,1,6,1,6,1,1,8\n10,70,16.6,0,5,0,1,7,0,8,1,1,6\n3898,70,16.8,0,5,0,1,5,0,8,1,1,1\n7036,70,16.8,0,2,0,1,6,1,4,1,1,0\n2360,70,17.1,0,1,0,1,7,0,5,1,1,8\n6146,70,17.6,0,1,0,1,8,1,3,1,1,3\n194,70,17.8,0,3,0,1,7,0,1,1,1,3\n1629,70,18,0,4,0,1,8,0,2,1,1,4\n7998,70,18.1,0,2,0,1,8,1,7,1,1,6\n1127,70,18.3,0,3,0,1,8,0,5,1,1,0\n2408,70,18.9,0,3,0,1,6,0,4,1,1,6\n2656,70,19.3,0,2,0,1,5,0,7,1,1,7\n3578,70,19.7,0,4,0,1,8,0,5,1,1,0\n1026,70,19.9,0,3,0,1,8,0,5,1,1,6\n7696,70,20.2,0,5,0,1,7,1,1,1,1,0\n7755,70,20.4,0,1,0,1,5,1,2,1,1,0\n1060,70,20.8,0,1,0,1,5,0,1,1,1,9\n3782,70,20.8,0,1,0,1,6,0,4,1,1,6\n1300,70,20.9,0,1,0,1,7,0,5,1,1,9\n2346,70,21.1,0,4,0,1,6,0,8,1,1,7\n8990,70,21.3,0,1,0,1,8,1,7,1,1,4\n2087,70,21.5,0,2,0,1,5,0,2,1,1,0\n4320,70,21.6,0,1,0,1,6,0,5,1,1,4\n7144,70,21.7,0,1,0,1,8,1,6,1,1,9\n3130,70,21.8,0,4,0,1,8,0,6,1,1,3\n4334,70,21.9,0,3,0,1,7,0,2,1,1,2\n869,70,22.1,0,3,0,1,8,0,5,1,1,3\n8478,70,22.6,0,4,0,1,7,1,7,1,1,8\n178,70,22.7,0,3,0,1,7,0,6,1,1,10\n7334,70,23.1,0,3,0,1,8,1,6,1,1,5\n2585,70,23.2,0,1,0,1,6,0,8,1,1,4\n1426,70,23.3,0,1,0,1,8,0,8,1,1,9\n7176,70,23.8,0,4,0,1,6,1,6,1,1,6\n1255,70,23.9,0,3,0,1,5,0,1,1,1,3\n9861,70,24.2,0,3,0,1,7,1,5,1,1,3\n2250,70,24.4,0,3,0,1,5,0,5,1,1,8\n10756,70,24.9,0,4,0,1,7,1,3,1,1,10\n2028,70,25.2,0,3,0,1,7,0,5,1,1,4\n5724,70,25.7,0,4,0,1,7,1,6,1,1,2\n565,70,26.5,0,4,0,1,8,0,6,1,1,4\n1715,70,26.7,0,2,0,1,5,0,4,1,1,2\n8685,70,26.9,0,4,0,1,7,1,7,1,1,4\n9269,70,27.3,0,5,0,1,8,1,5,1,1,1\n2517,70,27.4,0,1,0,1,6,0,2,1,1,4\n472,70,27.4,0,1,0,1,5,0,5,1,1,3\n8698,70,27.8,0,1,0,1,8,1,3,1,1,1\n9425,70,28,0,3,0,1,7,1,6,1,1,4\n10310,70,28.2,0,3,0,1,8,1,6,1,1,0\n1579,70,28.3,0,2,0,1,6,0,4,1,1,2\n6228,70,29,0,3,0,1,7,1,7,1,1,2\n2651,70,29.1,0,3,0,1,8,0,2,1,1,0\n6820,70,29.4,0,5,0,1,5,1,2,1,1,7\n7368,70,29.8,0,2,0,1,5,1,5,1,1,5\n8660,70,29.9,0,1,0,1,8,1,4,1,1,5\n362,70,29.9,0,3,0,1,6,0,5,1,1,5\n1702,71,0.2,0,3,0,1,7,0,3,1,1,6\n7109,71,1,0,4,0,1,8,1,3,1,1,4\n7740,71,1,0,4,0,1,8,1,1,1,1,7\n10288,71,1.2,0,1,0,1,8,1,7,1,1,4\n2596,71,1.3,0,1,0,1,8,0,5,1,1,8\n6070,71,1.7,0,1,0,1,8,1,2,1,1,6\n2824,71,1.9,0,5,0,1,8,0,8,1,1,1\n5334,71,2,0,4,0,1,6,0,2,1,1,1\n10680,71,2.1,0,5,0,1,5,1,5,1,1,8\n1066,71,2.3,0,1,0,1,6,0,8,1,1,7\n10628,71,2.5,0,4,0,1,7,1,1,1,1,5\n2403,71,2.6,0,2,0,1,7,0,5,1,1,1\n10822,71,2.9,0,3,0,1,7,1,6,1,1,0\n10113,71,3.1,0,5,0,1,6,1,2,1,1,0\n6974,71,3.2,0,4,0,1,6,1,1,1,1,2\n906,71,3.3,0,5,0,1,5,0,4,1,1,10\n10736,71,3.3,0,4,0,1,6,1,7,1,1,1\n9722,71,3.8,0,5,0,1,8,1,7,1,1,3\n5888,71,3.9,0,5,0,1,6,1,7,1,1,1\n9379,71,4.2,0,5,0,1,5,1,4,1,1,0\n5988,71,4.4,0,4,0,1,8,1,3,1,1,8\n10590,71,4.6,0,2,0,1,8,1,8,1,1,2\n9868,71,4.7,0,5,0,1,7,1,6,1,1,7\n8839,71,5,0,5,0,1,7,1,4,1,1,1\n9694,71,5,0,1,0,1,6,1,5,1,1,6\n500,71,5.7,0,4,0,1,5,0,7,1,1,9\n4858,71,5.7,0,3,0,1,8,0,7,1,1,9\n3772,71,6.7,0,1,0,1,7,0,5,1,1,2\n6734,71,6.8,0,5,0,1,8,1,6,1,1,9\n8511,71,7.1,0,3,0,1,7,1,2,1,1,0\n5604,71,7.3,0,3,0,1,6,1,6,1,1,9\n2764,71,7.6,0,4,0,1,7,0,3,1,1,3\n3518,71,7.7,0,3,0,1,7,0,5,1,1,6\n5672,71,7.7,0,4,0,1,8,1,5,1,1,2\n8481,71,8.3,0,1,0,1,6,1,6,1,1,0\n5929,71,8.4,0,3,0,1,7,1,6,1,1,3\n7244,71,8.6,0,5,0,1,8,1,2,1,1,2\n7515,71,8.9,0,2,0,1,5,1,6,1,1,1\n8664,71,9.2,0,5,0,1,6,1,2,1,1,5\n4638,71,9.4,0,2,0,1,5,0,8,1,1,10\n862,71,10.3,0,4,0,1,7,0,5,1,1,4\n136,71,10.4,0,5,0,1,5,0,7,1,1,0\n4995,71,10.4,0,1,0,1,8,0,5,1,1,4\n4516,71,10.7,0,3,0,1,8,0,7,1,1,10\n1107,71,10.8,0,1,0,1,6,0,5,1,1,4\n5813,71,11.5,0,2,0,1,8,1,3,1,1,0\n3702,71,11.7,0,1,0,1,7,0,8,1,1,5\n5546,71,11.8,0,4,0,1,5,1,1,1,1,4\n9510,71,12.3,0,3,0,1,7,1,3,1,1,5\n8421,71,12.5,0,3,0,1,6,1,1,1,1,4\n7916,71,12.6,0,3,0,1,7,1,7,1,1,8\n4831,71,12.6,0,1,0,1,8,0,7,1,1,2\n8998,71,12.6,0,3,0,1,5,1,7,1,1,5\n9009,71,12.6,0,4,0,1,7,1,2,1,1,0\n7347,71,12.6,0,1,0,1,5,1,3,1,1,1\n7728,71,12.9,0,5,0,1,5,1,7,1,1,6\n846,71,13.1,0,5,0,1,6,0,1,1,1,7\n9140,71,13.5,0,3,0,1,6,1,3,1,1,9\n1024,71,13.6,0,5,0,1,5,0,6,1,1,4\n9692,71,13.6,0,1,0,1,5,1,1,1,1,2\n1692,71,13.6,0,2,0,1,5,0,5,1,1,2\n9125,71,14,0,2,0,1,8,1,6,1,1,1\n6010,71,14.3,0,2,0,1,5,1,4,1,1,7\n1830,71,14.3,0,5,0,1,7,0,7,1,1,5\n9496,71,14.4,0,5,0,1,7,1,4,1,1,2\n3000,71,14.4,0,5,0,1,5,0,8,1,1,2\n960,71,14.5,0,5,0,1,6,0,8,1,1,8\n3640,71,14.6,0,3,0,1,5,0,8,1,1,9\n5567,71,15.6,0,3,0,1,7,1,6,1,1,1\n1245,71,15.7,0,4,0,1,8,0,7,1,1,2\n2751,71,15.7,0,2,0,1,6,0,4,1,1,4\n2912,71,16,0,2,0,1,8,0,3,1,1,9\n3468,71,16.2,0,2,0,1,5,0,5,1,1,1\n830,71,16.3,0,3,0,1,6,0,1,1,1,3\n7500,71,16.4,0,5,0,1,6,1,5,1,1,1\n4021,71,17,0,4,0,1,7,0,6,1,1,1\n9754,71,17.2,0,3,0,1,6,1,2,1,1,2\n5248,71,17.3,0,2,0,1,5,0,3,1,1,5\n7840,71,17.4,0,4,0,1,5,1,1,1,1,4\n10256,71,17.6,0,1,0,1,7,1,8,1,1,5\n6926,71,17.8,0,4,0,1,6,1,5,1,1,7\n9196,71,17.9,0,5,0,1,6,1,7,1,1,6\n4966,71,18,0,4,0,1,6,0,2,1,1,3\n4903,71,18.4,0,2,0,1,7,0,6,1,1,1\n9880,71,18.6,0,3,0,1,8,1,4,1,1,0\n5690,71,18.7,0,4,0,1,6,1,6,1,1,6\n6908,71,19,0,5,0,1,7,1,6,1,1,7\n9313,71,20.3,0,2,0,1,8,1,2,1,1,2\n8521,71,20.4,0,5,0,1,6,1,2,1,1,1\n3539,71,21,0,2,0,1,5,0,4,1,1,1\n9631,71,21.6,0,2,0,1,6,1,3,1,1,1\n4384,71,21.7,0,2,0,1,6,0,2,1,1,10\n7793,71,21.7,0,1,0,1,6,1,6,1,1,0\n7561,71,21.8,0,4,0,1,5,1,6,1,1,2\n4772,71,21.8,0,2,0,1,8,0,5,1,1,0\n4204,71,22.3,0,1,0,1,5,0,5,1,1,5\n7965,71,22.5,0,1,0,1,8,1,1,1,1,1\n10243,71,23.1,0,2,0,1,6,1,7,1,1,1\n5564,71,23.4,0,5,0,1,6,1,8,1,1,10\n5664,71,23.6,0,5,0,1,7,1,7,1,1,5\n8287,71,24.1,0,2,0,1,5,1,7,1,1,2\n6422,71,24.2,0,1,0,1,6,1,8,1,1,5\n5537,71,24.8,0,5,0,1,6,1,3,1,1,4\n8070,71,24.8,0,3,0,1,8,1,4,1,1,10\n9546,71,24.9,0,3,0,1,6,1,3,1,1,7\n5302,71,25,0,3,0,1,5,0,2,1,1,10\n6126,71,25.2,0,2,0,1,8,1,1,1,1,5\n8596,71,25.2,0,2,0,1,5,1,8,1,1,10\n9630,71,25.3,0,3,0,1,6,1,7,1,1,0\n9424,71,25.6,0,5,0,1,8,1,7,1,1,4\n5290,71,25.6,0,1,0,1,6,0,7,1,1,1\n8730,71,25.6,0,1,0,1,6,1,6,1,1,6\n60,71,25.9,0,1,0,1,7,0,3,1,1,3\n2057,71,26,0,2,0,1,5,0,3,1,1,3\n7116,71,26.1,0,3,0,1,7,1,5,1,1,5\n6414,71,26.6,0,5,0,1,8,1,4,1,1,10\n9342,71,26.7,0,2,0,1,7,1,1,1,1,8\n3897,71,26.7,0,5,0,1,5,0,6,1,1,0\n6546,71,27.8,0,1,0,1,6,1,1,1,1,10\n2382,71,28.5,0,5,0,1,5,0,7,1,1,10\n350,71,28.5,0,5,0,1,5,0,8,1,1,4\n9750,71,28.6,0,5,0,1,7,1,1,1,1,2\n551,71,29,0,1,0,1,8,0,6,1,1,0\n52,71,29.1,0,5,0,1,7,0,5,1,1,1\n2114,72,0.5,0,1,0,1,8,0,3,1,1,7\n5974,72,0.6,0,4,0,1,8,1,4,1,1,7\n2712,72,0.6,0,5,0,1,8,0,2,1,1,8\n8883,72,0.8,0,2,0,1,8,1,1,1,1,4\n3164,72,1,0,4,0,1,6,0,2,1,1,2\n6473,72,1.2,0,5,0,1,6,1,1,1,1,3\n9493,72,1.4,0,4,0,1,7,1,1,1,1,2\n5358,72,1.5,0,1,0,1,7,0,7,1,1,9\n5465,72,1.5,0,1,0,1,8,1,3,1,1,4\n2662,72,1.6,0,3,0,1,6,0,2,1,1,1\n6263,72,1.7,0,1,0,1,6,1,8,1,1,2\n1560,72,2.2,0,2,0,1,7,0,2,1,1,1\n186,72,2.8,0,5,0,1,7,0,7,1,1,4\n75,72,3.1,0,2,0,1,8,0,5,1,1,4\n8053,72,3.7,0,1,0,1,7,1,2,1,1,2\n439,72,3.7,0,3,0,1,7,0,3,1,1,0\n1865,72,4.4,0,2,0,1,7,0,4,1,1,2\n4018,72,5.3,0,2,0,1,5,0,1,1,1,9\n1004,72,6.2,0,4,0,1,8,0,8,1,1,9\n9054,72,7,0,3,0,1,5,1,5,1,1,1\n6060,72,7.7,0,3,0,1,6,1,2,1,1,6\n3721,72,8.3,0,5,0,1,7,0,7,1,1,1\n211,72,8.4,0,4,0,1,5,0,8,1,1,1\n9814,72,8.4,0,4,0,1,5,1,5,1,1,3\n8093,72,8.5,0,5,0,1,6,1,1,1,1,2\n5755,72,8.6,0,3,0,1,5,1,7,1,1,0\n7402,72,9.2,0,1,0,1,8,1,1,1,1,2\n1694,72,9.4,0,1,0,1,7,0,2,1,1,10\n8412,72,10,0,5,0,1,5,1,2,1,1,6\n7853,72,10.1,0,2,0,1,6,1,6,1,1,2\n9668,72,10.4,0,5,0,1,5,1,7,1,1,5\n3668,72,10.9,0,5,0,1,8,0,3,1,1,8\n244,72,11.2,0,4,0,1,8,0,4,1,1,7\n9409,72,11.7,0,1,0,1,7,1,8,1,1,2\n3771,72,12,0,2,0,1,8,0,4,1,1,0\n10690,72,12.3,0,3,0,1,8,1,3,1,1,10\n9355,72,12.3,0,1,0,1,7,1,3,1,1,0\n9106,72,12.7,0,1,0,1,6,1,1,1,1,4\n8836,72,13,0,2,0,1,8,1,6,1,1,8\n6248,72,13.1,0,4,0,1,7,1,8,1,1,5\n4739,72,13.2,0,1,0,1,5,0,7,1,1,3\n3758,72,13.3,0,5,0,1,5,0,6,1,1,1\n10079,72,13.7,0,1,0,1,6,1,6,1,1,4\n164,72,14,0,5,0,1,6,0,4,1,1,2\n6264,72,14,0,5,0,1,7,1,8,1,1,3\n9382,72,14.4,0,3,0,1,6,1,8,1,1,1\n8734,72,14.6,0,1,0,1,7,1,4,1,1,2\n8908,72,14.6,0,4,0,1,6,1,8,1,1,5\n7093,72,15.1,0,3,0,1,8,1,3,1,1,2\n9059,72,15.2,0,1,0,1,6,1,6,1,1,0\n4022,72,15.2,0,5,0,1,8,0,2,1,1,9\n7353,72,15.4,0,4,0,1,7,1,5,1,1,2\n688,72,15.4,0,2,0,1,7,0,4,1,1,1\n6513,72,15.4,0,4,0,1,6,1,3,1,1,0\n2,72,15.7,0,2,0,1,5,0,3,1,1,5\n506,72,15.9,0,5,0,1,8,0,7,1,1,10\n8143,72,16.6,0,1,0,1,8,1,6,1,1,0\n7349,72,16.6,0,2,0,1,7,1,2,1,1,4\n8260,72,16.6,0,3,0,1,8,1,2,1,1,3\n1075,72,16.6,0,3,0,1,8,0,3,1,1,4\n904,72,16.6,0,1,0,1,8,0,5,1,1,6\n3806,72,16.9,0,3,0,1,5,0,7,1,1,0\n5280,72,17.4,0,1,0,1,7,0,8,1,1,9\n4216,72,17.9,0,5,0,1,7,0,6,1,1,6\n4620,72,18.3,0,1,0,1,6,0,2,1,1,4\n1158,72,18.4,0,1,0,1,7,0,3,1,1,3\n1587,72,19.3,0,5,0,1,6,0,5,1,1,2\n5343,72,19.4,0,1,0,1,5,0,4,1,1,1\n8106,72,19.6,0,4,0,1,5,1,1,1,1,8\n4552,72,19.6,0,2,0,1,5,0,1,1,1,3\n512,72,19.7,0,4,0,1,6,0,2,1,1,5\n7532,72,19.9,0,3,0,1,6,1,6,1,1,6\n7990,72,20.4,0,3,0,1,6,1,3,1,1,7\n4774,72,20.7,0,1,0,1,6,0,8,1,1,9\n10587,72,20.7,0,4,0,1,5,1,7,1,1,2\n352,72,20.9,0,1,0,1,6,0,2,1,1,0\n73,72,21.5,0,4,0,1,8,0,8,1,1,4\n2162,72,21.5,0,1,0,1,7,0,6,1,1,4\n6024,72,21.8,0,5,0,1,5,1,8,1,1,5\n8672,72,21.9,0,2,0,1,7,1,3,1,1,5\n219,72,21.9,0,5,0,1,8,0,3,1,1,2\n4896,72,22,0,3,0,1,7,0,2,1,1,0\n3058,72,22.2,0,3,0,1,6,0,8,1,1,7\n1266,72,22.3,0,4,0,1,7,0,1,1,1,10\n6232,72,22.5,0,3,0,1,8,1,6,1,1,0\n6786,72,22.8,0,3,0,1,5,1,7,1,1,2\n68,72,22.9,0,4,0,1,5,0,6,1,1,6\n3199,72,23,0,4,0,1,5,0,2,1,1,2\n8848,72,23.2,0,1,0,1,7,1,6,1,1,8\n7462,72,23.4,0,3,0,1,6,1,3,1,1,2\n6958,72,23.7,0,1,0,1,7,1,6,1,1,4\n9346,72,23.9,0,1,0,1,8,1,4,1,1,5\n7642,72,24.2,0,1,0,1,6,1,3,1,1,0\n4013,72,24.7,0,3,0,1,7,0,6,1,1,3\n6567,72,25.8,0,1,0,1,7,1,2,1,1,3\n6980,72,26,0,3,0,1,6,1,3,1,1,0\n7166,72,26.8,0,2,0,1,6,1,4,1,1,3\n3734,72,26.9,0,3,0,1,7,0,3,1,1,5\n1298,72,26.9,0,4,0,1,6,0,5,1,1,3\n6458,72,27.1,0,3,0,1,8,1,6,1,1,0\n3251,72,27.3,0,1,0,1,6,0,6,1,1,2\n9219,72,27.4,0,2,0,1,5,1,8,1,1,0\n7136,72,27.9,0,2,0,1,8,1,6,1,1,10\n162,72,27.9,0,5,0,1,8,0,6,1,1,8\n2224,72,28.2,0,1,0,1,5,0,8,1,1,6\n7108,72,28.6,0,5,0,1,5,1,5,1,1,10\n212,72,29.1,0,5,0,1,8,0,5,1,1,7\n6002,72,29.9,0,1,0,1,5,1,4,1,1,2\n6363,73,0.1,0,4,0,1,8,1,3,1,1,1\n3646,73,0.2,0,5,0,1,8,0,3,1,1,0\n1781,73,0.7,0,1,0,1,8,0,8,1,1,0\n1718,73,0.9,0,3,0,1,7,0,5,1,1,7\n982,73,1,0,2,0,1,8,0,5,1,1,6\n10238,73,1.3,0,5,0,1,5,1,4,1,1,3\n2502,73,1.3,0,3,0,1,8,0,8,1,1,0\n324,73,1.7,0,5,0,1,6,0,8,1,1,10\n7985,73,1.8,0,2,0,1,6,1,6,1,1,0\n6997,73,2.1,0,1,0,1,7,1,6,1,1,1\n5239,73,2.3,0,2,0,1,8,0,8,1,1,0\n289,73,2.7,0,5,0,1,7,0,7,1,1,4\n1914,73,3.5,0,1,0,1,8,0,1,1,1,10\n7122,73,4.1,0,2,0,1,7,1,1,1,1,3\n738,73,4.1,0,4,0,1,6,0,7,1,1,2\n5443,73,4.6,0,4,0,1,5,1,7,1,1,4\n4696,73,4.6,0,3,0,1,6,0,4,1,1,2\n1160,73,4.6,0,4,0,1,5,0,5,1,1,3\n10726,73,5,0,5,0,1,6,1,1,1,1,8\n3026,73,5.3,0,4,0,1,6,0,3,1,1,10\n3971,73,5.8,0,1,0,1,8,0,3,1,1,3\n8924,73,5.9,0,3,0,1,6,1,4,1,1,6\n9514,73,5.9,0,2,0,1,7,1,1,1,1,4\n762,73,6,0,3,0,1,5,0,6,1,1,7\n7163,73,6.1,0,3,0,1,6,1,8,1,1,2\n540,73,6.1,0,3,0,1,8,0,1,1,1,7\n1934,73,6.5,0,5,0,1,8,0,3,1,1,1\n10720,73,6.7,0,3,0,1,8,1,3,1,1,5\n372,73,7,0,5,0,1,7,0,3,1,1,1\n640,73,7,0,3,0,1,8,0,4,1,1,7\n5250,73,7.2,0,4,0,1,6,0,4,1,1,4\n9454,73,7.4,0,4,0,1,7,1,2,1,1,2\n7788,73,8,0,1,0,1,6,1,2,1,1,0\n282,73,8.6,0,4,0,1,5,0,6,1,1,2\n5422,73,9.2,0,1,0,1,8,1,8,1,1,10\n5746,73,9.2,0,2,0,1,5,1,3,1,1,0\n3596,73,9.7,0,1,0,1,7,0,8,1,1,2\n7367,73,10.5,0,3,0,1,5,1,3,1,1,0\n9208,73,10.7,0,1,0,1,8,1,3,1,1,5\n6558,73,11.1,0,3,0,1,5,1,7,1,1,4\n3398,73,11.7,0,1,0,1,7,0,8,1,1,7\n4224,73,11.8,0,5,0,1,7,0,2,1,1,8\n5560,73,12,0,2,0,1,7,1,4,1,1,1\n304,73,12.3,0,1,0,1,6,0,1,1,1,9\n5480,73,12.3,0,5,0,1,5,1,7,1,1,10\n4813,73,12.4,0,4,0,1,6,0,7,1,1,4\n9690,73,12.4,0,3,0,1,5,1,2,1,1,2\n1610,73,12.5,0,4,0,1,7,0,2,1,1,6\n3905,73,12.7,0,4,0,1,5,0,3,1,1,4\n6432,73,13.5,0,3,0,1,6,1,2,1,1,9\n944,73,13.6,0,2,0,1,7,0,6,1,1,0\n2366,73,14.2,0,2,0,1,8,0,8,1,1,4\n1174,73,14.6,0,4,0,1,6,0,4,1,1,7\n6873,73,14.7,0,1,0,1,5,1,3,1,1,4\n10297,73,14.9,0,5,0,1,7,1,2,1,1,4\n5691,73,15,0,1,0,1,6,1,2,1,1,3\n6730,73,15,0,3,0,1,8,1,4,1,1,3\n3441,73,15.2,0,4,0,1,8,0,4,1,1,3\n8513,73,15.4,0,2,0,1,5,1,6,1,1,2\n8074,73,15.7,0,1,0,1,7,1,3,1,1,7\n5153,73,15.8,0,2,0,1,8,0,4,1,1,1\n2458,73,16.4,0,4,0,1,7,0,7,1,1,4\n5576,73,17.5,0,5,0,1,7,1,6,1,1,1\n1404,73,17.7,0,3,0,1,5,0,7,1,1,9\n92,73,17.8,0,3,0,1,7,0,4,1,1,0\n5768,73,17.9,0,4,0,1,7,1,2,1,1,7\n8627,73,18.5,0,4,0,1,7,1,7,1,1,4\n9044,73,18.8,0,5,0,1,5,1,3,1,1,6\n971,73,19.1,0,3,0,1,7,0,8,1,1,1\n3922,73,19.4,0,5,0,1,6,0,1,1,1,5\n2663,73,19.4,0,3,0,1,8,0,5,1,1,1\n6856,73,20.1,0,3,0,1,7,1,7,1,1,2\n4574,73,20.2,0,3,0,1,8,0,3,1,1,0\n3842,73,20.4,0,3,0,1,6,0,5,1,1,9\n2931,73,20.4,0,5,0,1,7,0,3,1,1,2\n216,73,20.4,0,2,0,1,7,0,5,1,1,10\n8872,73,20.6,0,3,0,1,7,1,4,1,1,1\n2141,73,20.7,0,2,0,1,5,0,6,1,1,0\n9955,73,21.4,0,1,0,1,5,1,1,1,1,1\n6405,73,21.5,0,1,0,1,7,1,6,1,1,1\n9314,73,22,0,2,0,1,5,1,8,1,1,1\n610,73,22.2,0,3,0,1,7,0,1,1,1,1\n7294,73,22.2,0,2,0,1,7,1,7,1,1,7\n10064,73,22.2,0,2,0,1,6,1,5,1,1,7\n202,73,22.3,0,3,0,1,8,0,1,1,1,6\n6132,73,22.5,0,3,0,1,8,1,2,1,1,0\n5374,73,22.5,0,5,0,1,7,0,6,1,1,8\n8226,73,22.6,0,3,0,1,7,1,3,1,1,3\n6130,73,22.9,0,4,0,1,7,1,4,1,1,8\n7020,73,22.9,0,3,0,1,6,1,7,1,1,10\n366,73,24.1,0,4,0,1,6,0,8,1,1,6\n26,73,24.3,0,1,0,1,6,0,1,1,1,3\n7511,73,24.3,0,5,0,1,8,1,4,1,1,4\n8635,73,24.4,0,5,0,1,7,1,7,1,1,2\n5592,73,25.2,0,3,0,1,8,1,4,1,1,10\n9391,73,25.2,0,1,0,1,7,1,5,1,1,2\n4403,73,25.2,0,1,0,1,6,0,1,1,1,3\n6952,73,25.7,0,2,0,1,8,1,2,1,1,10\n1588,73,25.8,0,2,0,1,7,0,1,1,1,6\n3560,73,25.9,0,4,0,1,6,0,8,1,1,3\n4740,73,26.1,0,4,0,1,7,0,2,1,1,0\n6936,73,26.1,0,2,0,1,5,1,6,1,1,10\n6150,73,26.7,0,3,0,1,6,1,7,1,1,10\n1142,73,26.8,0,5,0,1,8,0,5,1,1,7\n801,73,27,0,4,0,1,6,0,7,1,1,3\n4471,73,27.1,0,4,0,1,7,0,7,1,1,3\n590,73,27.7,0,2,0,1,7,0,8,1,1,4\n4904,73,27.9,0,5,0,1,6,0,7,1,1,4\n775,73,28,0,1,0,1,6,0,6,1,1,3\n5317,73,28,0,1,0,1,5,0,4,1,1,2\n686,73,28.1,0,4,0,1,8,0,2,1,1,10\n5305,73,28.5,0,4,0,1,6,0,5,1,1,4\n156,73,28.6,0,5,0,1,8,0,7,1,1,4\n10376,73,28.8,0,2,0,1,5,1,1,1,1,8\n8388,73,28.9,0,3,0,1,5,1,5,1,1,5\n6518,73,29,0,3,0,1,7,1,5,1,1,2\n5536,73,29,0,2,0,1,5,1,2,1,1,7\n938,73,29,0,2,0,1,8,0,4,1,1,9\n4962,73,29.2,0,4,0,1,5,0,5,1,1,5\n5944,73,29.4,0,2,0,1,6,1,5,1,1,7\n5120,73,29.5,0,2,0,1,8,0,4,1,1,6\n10506,73,29.6,0,1,0,1,8,1,4,1,1,3\n10460,73,29.8,0,2,0,1,5,1,8,1,1,7\n4504,74,0.2,0,1,0,1,7,0,5,1,1,0\n5304,74,0.2,0,2,0,1,6,0,4,1,1,3\n4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3319,Male,33,43000,0\n15759066,Female,33,60000,0\n15814816,Male,31,66000,0\n15724402,Female,20,82000,0\n15571059,Female,33,41000,0\n15674206,Male,35,72000,0\n15715160,Male,28,32000,0\n15730448,Male,24,84000,0\n15662067,Female,19,26000,0\n15779581,Male,29,43000,0\n15662901,Male,19,70000,0\n15689751,Male,28,89000,0\n15667742,Male,34,43000,0\n15738448,Female,30,79000,0\n15680243,Female,20,36000,0\n15745083,Male,26,80000,0\n15708228,Male,35,22000,0\n15628523,Male,35,39000,0\n15708196,Male,49,74000,0\n15735549,Female,39,134000,1\n15809347,Female,41,71000,0\n15660866,Female,58,101000,1\n15766609,Female,47,47000,0\n15654230,Female,55,130000,1\n15794566,Female,52,114000,0\n15800890,Female,40,142000,1\n15697424,Female,46,22000,0\n15724536,Female,48,96000,1\n15735878,Male,52,150000,1\n15707596,Female,59,42000,0\n15657163,Male,35,58000.0,0\n15622478,Male,47.0,43000,0\n15779529,Female,60,108000,1\n15636023,Male,49,65000,0\n15582066,Male,40,78000,0\n15666675,Female,46,96000,0\n15732987,Male,59,143000,1\n15789432,Female,41,80000,0\n15663161,Male,35,91000,1\n15694879,Male,37,144000,1\n15593715,Male,60,102000,1\n15575002,Female,35,60000,0\n15622171,Male,37,53000,0\n15795224,Female,36,126000,1\n15685346,Male,56,133000,1\n15691808,Female,40,72000,0\n15721007,Female,42,80000,1\n15794253,Female,35,147000,1\n15694453,Male,39,42000,0\n15813113,Male,40,107000,1\n15614187,Male,49,86000,1\n15619407,Female,38,112000,0\n15646227,Male,46,79000,1\n15660541,Male,40,57000,0\n15753874,Female,37,80000,0\n15617877,Female,46,82000,0\n15772073,Female,53,143000,1\n15701537,Male,42,149000,1\n15736228,Male,38,59000,0\n15780572,Female,50,88000,1\n15769596,Female,56,104000,1\n15586996,Female,41,72000,0\n15722061,Female,51,146000,1\n15638003,Female,35,50000,0\n15775590,Female,57,122000,1\n15730688,Male,41,52000,0\n15753102,Female,35,97000,1\n15810075,Female,44,39000,0\n15723373,Male,37,52000,0\n15795298,Female,48,134000,1\n15584320,Female,37,146000,1\n15724161,Female,50,44000,0\n15750056,Female,52,90000,1\n15609637,Female,41,72000,0\n15794493,Male,40,57000,0\n15569641,Female,58,95000,1\n15815236,Female,45,131000,1\n15811177,Female,35,77000,0\n15680587,Male,36,144000,1\n15672821,Female,55,125000,1\n15767681,Female,35,72000,0\n15600379,Male,48,90000,1\n15801336,Female,42,108000,1\n15721592,Male,40,75000,0\n15581282,Male,37,74000,0\n15746203,Female,47,144000,1\n15583137,Male,40,61000,0\n15680752,Female,43,133000,0\n15688172,Female,59,76000,1\n15791373,Male,60,42000,1\n15589449,Male,39,106000,1\n15692819,Female,57,26000,1\n15727467,Male,57,74000,1\n15734312,Male,38,71000,0\n15764604,Male,49,88000,1\n15613014,Female,52,38000,1\n15759684,Female,50,36000,1\n15609669,Female,59,88000,1\n15685536,Male,35,61000,0\n15750447,Male,37,70000,1\n15663249,Female,52,21000,1\n15638646,Male,48,141000,0\n15734161,Female,37,93000,1\n15631070,Female,37,62000,0\n15761950,Female,48,138000,1\n15649668,Male,41,79000,0\n15713912,Female,37,78000,1\n15586757,Male,39,134000,1\n15596522,Male,49,89000,1\n15625395,Male,55,39000,1\n15760570,Male,37,77000,0\n15566689,Female,35,57000,0\n15725794,Female,36,63000,0\n15673539,Male,42,73000,1\n15705298,Female,43,112000,1\n15675791,Male,45,79000,0\n15747043,Male,46,117000,1\n15736397,Female,58,38000,1\n15678201,Male,48,74000,1\n15720745,Female,37,137000,1\n15637593,Male,37,79000,1\n15598070,Female,40,60000,0\n15787550,Male,42,54000,0\n15603942,Female,51,134000,0\n15733973,Female,47,113000,1\n15596761,Male,36,125000,1\n15652400,Female,38,50000,0\n15717893,Female,42,70000,0\n15622585,Male,39,96000,1\n15733964,Female,38,50000,0\n15753861,Female,49,141000,1\n15747097,Female,39,79000,0\n15594762,Female,39,75000,1\n15667417,Female,54,104000,1\n15684861,Male,35,55000,0\n15742204,Male,45,32000,1\n15623502,Male,36,60000,0\n15774872,Female,52,138000,1\n15611191,Female,53,82000,1\n15674331,Male,41,52000,0\n15619465,Female,48,30000,1\n15575247,Female,48,131000,1\n15695679,Female,41,60000,0\n15713463,Male,41,72000,0\n15785170,Female,42,75000,0\n15796351,Male,36,118000,1\n15639576,Female,47,107000,1\n15693264,Male,38,51000,0\n15589715,Female,48,119000,1\n15769902,Male,42,65000,0\n15587177,Male,40,65000,0\n15814553,Male,57,60000,1\n15601550,Female,36,54000,0\n15664907,Male,58,144000,1\n15612465,Male,35,79000,0\n15810800,Female,38,55000,0\n15665760,Male,39,122000,1\n15588080,Female,53,104000,1\n15776844,Male,35,75000,0\n15717560,Female,38,65000,0\n15629739,Female,47,51000,1\n15729908,Male,47,105000,1\n15716781,Female,41,63000,0\n15646936,Male,53,72000,1\n15768151,Female,54,108000,1\n15579212,Male,39,77000,0\n15721835,Male,38,61000,0\n15800515,Female,38,113000,1\n15591279,Male,37,75000,0\n15587419,Female,42,90000,1\n15750335,Female,37,57000,0\n15699619,Male,36,99000,1\n15606472,Male,60,34000,1\n15778368,Male,54,70000,1\n15671387,Female,41,72000,0\n15573926,Male,40,71000,1\n15709183,Male,42,54000,0\n15577514,Male,43,129000,1\n15778830,Female,53,34000,1\n15768072,Female,47,50000,1\n15768293,Female,42,79000,0\n15654456,Male,42,104000,1\n15807525,Female,59,2900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"RollNo,Student,4-Aug-17,5-Aug-17,6-Aug-17,8-Aug-17,8-Aug-17,12-Aug-17,19-Aug-17,20-Aug-17,26-Aug-17,27-Aug-17,30-Aug-17,3-Sep-17,5-Sep-17,6-Sep-17,8-Sep-17,9-Sep-17,10-Sep-17,12-Sep-17,16-Sep-17,17-Sep-17,20-Sep-17,22-Sep-17,8-Oct-17,9-Oct-17,11-Oct-17,14-Oct-17,15-Oct-17,16-Oct-17,27-Oct-17,3-Nov-17,5-Nov-17,7-Nov-17,8-Nov-17,10-Nov-17,11-Nov-17,13-Nov-17,15-Nov-17,17-Nov-17,19-Nov-17,24-Nov-17\n120030102001,S001,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,A,P,A,P,P,A,P,A,P,P,P,P,P,P,P,P,A,A,P,P,P,P,P,P,A\n120030102002,S002,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,A,A,P,P,A,A,P,A,P,P,A,P,P,P,P,A,A,A,P,A,A,P,P,P,A\n120030102003,S003,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P\n120030102004,S004,P,A,P,P,P,P,P,A,A,A,A,P,A,A,A,A,A,P,P,A,A,P,A,P,P,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102007,S005,A,A,A,A,A,A,P,P,P,P,A,P,A,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,A,A,A,P,P,P,P,P,A,P,P,P\n120030102008,S006,A,A,P,P,P,P,P,P,P,P,A,P,A,A,A,A,A,A,P,P,A,P,A,P,P,P,A,A,A,A,A,A,P,P,P,A,A,A,A,A\n120030102009,S007,P,A,P,P,P,P,P,A,A,A,A,A,A,A,A,A,A,A,P,P,A,P,A,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102011,S008,A,p,P,A,A,P,P,P,P,P,p,P,A,A,P,P,P,P,P,A,A,P,A,P,P,A,P,A,A,A,A,A,A,A,A,A,A,P,A,P\n120030102013,S009,A,A,P,A,A,P,A,P,A,P,A,P,P,A,P,A,A,P,P,P,A,P,P,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,P,P\n120030102014,S010,A,A,P,P,P,A,P,A,A,P,A,P,A,A,P,A,P,A,A,A,A,P,A,P,P,A,A,A,A,A,P,A,A,P,P,A,A,P,P,A\n120030102015,S011,A,P,A,P,P,P,A,P,P,P,A,P,P,A,A,A,P,P,P,A,A,P,A,P,A,P,A,P,A,P,P,A,A,P,P,A,P,P,A,A\n120030102016,S012,A,P,A,P,P,P,A,A,A,A,A,P,A,A,A,A,A,A,P,A,A,P,A,A,P,A,P,A,A,A,A,A,A,A,A,A,A,P,A,A\n120030102017,S013,A,A,A,A,A,A,P,P,P,P,p,P,P,A,P,P,P,P,P,P,A,P,P,P,P,P,P,A,A,P,P,P,P,P,P,P,P,P,P,P\n120030102018,S014,A,A,A,P,P,P,A,P,P,P,p,P,A,A,P,P,P,A,P,A,A,P,A,A,P,A,P,A,A,A,P,A,A,P,A,A,P,P,P,P\n120030102019,S015,A,P,P,P,P,P,P,A,P,P,A,P,P,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,P,P,A,P,P,P,P,P,P,P\n120030102020,S016,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,A,A,P,P,P,A,P,P,P,P,P,P,A\n120030102021,S017,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,A,A,A,P,P,A,P,P,P,P\n120030102022,S018,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,P,P,A,A,P,A,P,P,A,P,A,A,A,P,A,A,P,A,A,A,A,A,A\n120030102023,S019,A,A,P,A,A,P,A,A,A,A,A,P,A,A,A,A,A,A,P,A,A,P,A,P,A,A,P,A,A,A,P,A,A,P,A,A,A,P,A,P\n120030102024,S020,P,P,P,P,P,P,P,P,P,P,A,A,A,A,A,A,P,P,P,P,A,P,A,P,P,A,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102025,S021,P,P,P,P,P,P,P,P,P,P,A,P,A,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102026,S022,A,A,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A\n120030102027,S023,A,A,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,A,P,P,A\n120030102028,S024,A,A,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A\n120030102029,S025,A,A,A,A,A,P,P,P,A,P,A,P,A,A,A,A,A,A,P,A,A,P,A,P,A,A,A,A,A,A,A,A,A,P,A,A,A,A,A,A\n120030102030,S026,A,P,A,A,A,P,P,P,P,P,A,A,A,A,P,P,P,P,P,A,A,P,A,P,A,A,P,A,P,A,P,A,A,P,P,A,A,P,A,P\n120030102031,S027,A,A,P,A,A,P,P,A,P,A,A,A,A,A,A,A,A,P,P,P,A,P,A,P,A,P,P,A,P,A,P,P,A,P,P,A,A,P,P,P\n120030102032,S028,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,A,P,A,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A\n120030102033,S029,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102034,S030,P,P,P,P,P,P,P,P,P,P,p,P,A,A,P,P,P,P,P,P,A,A,A,P,P,P,A,P,P,P,A,A,A,P,P,P,P,P,A,P\n120030102035,S031,P,P,P,P,P,P,P,P,P,P,p,P,P,A,P,P,A,A,P,P,A,P,P,P,A,A,P,P,A,P,P,P,P,P,P,P,A,P,P,P\n120030102036,S032,A,A,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,A,A,P,P,P,P,P,P,A\n120030102037,S033,A,A,A,A,A,P,P,A,P,A,A,A,A,A,A,A,A,A,P,A,A,P,A,P,A,A,A,A,A,P,A,A,A,A,A,A,A,P,A,A\n120030102038,S034,P,P,P,P,P,P,P,P,P,P,p,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102039,S035,A,P,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,A,A,P,P,P,P,P,P,A\n120030102040,S036,A,A,A,A,A,A,A,A,P,P,p,P,P,A,P,P,P,P,P,P,A,P,P,P,A,A,P,P,A,P,P,P,P,P,P,P,A,P,P,P\n120030102041,S037,A,A,A,A,A,A,P,P,P,P,A,P,A,A,A,A,A,A,P,P,A,P,A,A,A,P,A,A,A,P,A,P,A,A,P,A,A,P,A,P\n120030102042,S038,P,A,P,P,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102043,S039,P,P,P,A,A,P,P,P,P,P,A,P,A,A,P,P,P,A,A,A,A,P,A,P,P,P,P,P,A,P,P,A,A,P,P,A,A,P,P,A\n120030102044,S040,A,A,A,A,A,P,P,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,P,P,P,A,P,A,P,A,A,P,P,A,A,A,P,A,A\n120030102045,S041,P,P,A,A,A,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102047,S042,A,A,A,A,A,P,P,P,P,P,A,P,A,A,P,P,P,A,P,A,A,A,A,A,A,A,P,A,A,A,A,A,A,P,P,A,A,P,A,A\n120030102048,S043,A,A,A,A,A,P,A,A,P,P,A,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102049,S044,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102050,S045,P,P,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,P,A,A,P,P,P,P,P,P,P\n120030103023,S046,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,P\n620030102001,S047,A,A,A,A,A,A,A,A,A,A,A,A,A,A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}, { "path": "Data/attendance2.csv", "content": "RollNo,Student,D1,D2,D3,D4,D5,D6,D7,D8,D9,D10,D11,D12,D13,D14,D15,D16,D17,D18,D19,D20,D21,D22,D23,D24,D25,D26,D27,D28,D29,D30,D31,D32,D33,D34,D35,D36,D37,D38,D39,D40\n120030102001,S001,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,A,P,A,P,P,A,P,A,P,P,P,P,P,P,P,P,A,A,P,P,P,P,P,P,A\n120030102002,S002,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,A,A,P,P,A,A,P,A,P,P,A,P,P,P,P,A,A,A,P,A,A,P,P,P,A\n120030102003,S003,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P\n120030102004,S004,P,A,P,P,P,P,P,A,A,A,A,P,A,A,A,A,A,P,P,A,A,P,A,P,P,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102007,S005,A,A,A,A,A,A,P,P,P,P,A,P,A,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,A,A,A,P,P,P,P,P,A,P,P,P\n120030102008,S006,A,A,P,P,P,P,P,P,P,P,A,P,A,A,A,A,A,A,P,P,A,P,A,P,P,P,A,A,A,A,A,A,P,P,P,A,A,A,A,A\n120030102009,S007,P,A,P,P,P,P,P,A,A,A,A,A,A,A,A,A,A,A,P,P,A,P,A,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102011,S008,A,p,P,A,A,P,P,P,P,P,p,P,A,A,P,P,P,P,P,A,A,P,A,P,P,A,P,A,A,A,A,A,A,A,A,A,A,P,A,P\n120030102013,S009,A,A,P,A,A,P,A,P,A,P,A,P,P,A,P,A,A,P,P,P,A,P,P,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,P,P\n120030102014,S010,A,A,P,P,P,A,P,A,A,P,A,P,A,A,P,A,P,A,A,A,A,P,A,P,P,A,A,A,A,A,P,A,A,P,P,A,A,P,P,A\n120030102015,S011,A,P,A,P,P,P,A,P,P,P,A,P,P,A,A,A,P,P,P,A,A,P,A,P,A,P,A,P,A,P,P,A,A,P,P,A,P,P,A,A\n120030102016,S012,A,P,A,P,P,P,A,A,A,A,A,P,A,A,A,A,A,A,P,A,A,P,A,A,P,A,P,A,A,A,A,A,A,A,A,A,A,P,A,A\n120030102017,S013,A,A,A,A,A,A,P,P,P,P,p,P,P,A,P,P,P,P,P,P,A,P,P,P,P,P,P,A,A,P,P,P,P,P,P,P,P,P,P,P\n120030102018,S014,A,A,A,P,P,P,A,P,P,P,p,P,A,A,P,P,P,A,P,A,A,P,A,A,P,A,P,A,A,A,P,A,A,P,A,A,P,P,P,P\n120030102019,S015,A,P,P,P,P,P,P,A,P,P,A,P,P,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,P,P,A,P,P,P,P,P,P,P\n120030102020,S016,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,A,A,P,P,P,A,P,P,P,P,P,P,A\n120030102021,S017,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,A,A,A,P,P,A,P,P,P,P\n120030102022,S018,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,P,P,A,A,P,A,P,P,A,P,A,A,A,P,A,A,P,A,A,A,A,A,A\n120030102023,S019,A,A,P,A,A,P,A,A,A,A,A,P,A,A,A,A,A,A,P,A,A,P,A,P,A,A,P,A,A,A,P,A,A,P,A,A,A,P,A,P\n120030102024,S020,P,P,P,P,P,P,P,P,P,P,A,A,A,A,A,A,P,P,P,P,A,P,A,P,P,A,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102025,S021,P,P,P,P,P,P,P,P,P,P,A,P,A,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102026,S022,A,A,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A\n120030102027,S023,A,A,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,A,P,P,A\n120030102028,S024,A,A,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A\n120030102029,S025,A,A,A,A,A,P,P,P,A,P,A,P,A,A,A,A,A,A,P,A,A,P,A,P,A,A,A,A,A,A,A,A,A,P,A,A,A,A,A,A\n120030102030,S026,A,P,A,A,A,P,P,P,P,P,A,A,A,A,P,P,P,P,P,A,A,P,A,P,A,A,P,A,P,A,P,A,A,P,P,A,A,P,A,P\n120030102031,S027,A,A,P,A,A,P,P,A,P,A,A,A,A,A,A,A,A,P,P,P,A,P,A,P,A,P,P,A,P,A,P,P,A,P,P,A,A,P,P,P\n120030102032,S028,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,A,P,A,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A\n120030102033,S029,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102034,S030,P,P,P,P,P,P,P,P,P,P,p,P,A,A,P,P,P,P,P,P,A,A,A,P,P,P,A,P,P,P,A,A,A,P,P,P,P,P,A,P\n120030102035,S031,P,P,P,P,P,P,P,P,P,P,p,P,P,A,P,P,A,A,P,P,A,P,P,P,A,A,P,P,A,P,P,P,P,P,P,P,A,P,P,P\n120030102036,S032,A,A,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,A,A,P,P,P,P,P,P,A\n120030102037,S033,A,A,A,A,A,P,P,A,P,A,A,A,A,A,A,A,A,A,P,A,A,P,A,P,A,A,A,A,A,P,A,A,A,A,A,A,A,P,A,A\n120030102038,S034,P,P,P,P,P,P,P,P,P,P,p,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102039,S035,A,P,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,A,A,P,P,P,P,P,P,A\n120030102040,S036,A,A,A,A,A,A,A,A,P,P,p,P,P,A,P,P,P,P,P,P,A,P,P,P,A,A,P,P,A,P,P,P,P,P,P,P,A,P,P,P\n120030102041,S037,A,A,A,A,A,A,P,P,P,P,A,P,A,A,A,A,A,A,P,P,A,P,A,A,A,P,A,A,A,P,A,P,A,A,P,A,A,P,A,P\n120030102042,S038,P,A,P,P,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102043,S039,P,P,P,A,A,P,P,P,P,P,A,P,A,A,P,P,P,A,A,A,A,P,A,P,P,P,P,P,A,P,P,A,A,P,P,A,A,P,P,A\n120030102044,S040,A,A,A,A,A,P,P,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,P,P,P,A,P,A,P,A,A,P,P,A,A,A,P,A,A\n120030102045,S041,P,P,A,A,A,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n120030102047,S042,A,A,A,A,A,P,P,P,P,P,A,P,A,A,P,P,P,A,P,A,A,A,A,A,A,A,P,A,A,A,A,A,A,P,P,A,A,P,A,A\n120030102048,S043,A,A,A,A,A,P,A,A,P,P,A,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102049,S044,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n120030102050,S045,P,P,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,P,A,A,P,P,P,P,P,P,P\n120030103023,S046,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,P\n620030102001,S047,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n110030102040,S048,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A\n10030102040,S049,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P\n110030102033,S050,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,A,A,A,P,P,A,A,A,A,P,A,A,A,A" }, { "path": "Data/bakery.csv", "content": "daywk,weekday,sdate,cakes,pies,cookies,smoothies,coffee,promotion\nTuesday,2,01-01-13,79,46,518,60,233,none\nWednesday,3,02-01-13,91,50,539,161,427,none\nThursday,4,03-01-13,47,60,222,166,347,none\nFriday,5,04-01-13,89,64,734,153,358,none\nSaturday,6,05-01-13,112,73,764,240,392,none\nSunday,7,06-01-13,89,57,922,259,510,none\nMonday,1,07-01-13,70,50,476,120,334,none\nTuesday,2,08-01-13,70,48,496,222,316,none\nWednesday,3,09-01-13,59,37,587,181,156,none\nThursday,4,10-01-13,71,36,488,178,298,none\nFriday,5,11-01-13,74,50,645,100,490,none\nSaturday,6,12-01-13,119,71,438,162,416,none\nSunday,7,13-01-13,90,51,568,137,434,none\nMonday,1,14-01-13,96,48,585,194,573,promotion\nTuesday,2,15-01-13,62,56,536,112,287,none\nWednesday,3,16-01-13,48,33,336,151,420,none\nThursday,4,17-01-13,58,67,404,166,376,none\nFriday,5,18-01-13,74,74,533,181,456,none\nSaturday,6,19-01-13,94,45,470,208,584,none\nSunday,7,20-01-13,97,54,395,231,411,none\nMonday,1,21-01-13,79,36,431,159,308,none\nTuesday,2,22-01-13,67,39,276,204,382,none\nWednesday,3,23-01-13,81,43,387,184,295,none\nThursday,4,24-01-13,71,42,456,114,365,none\nFriday,5,25-01-13,95,58,647,113,362,none\nSaturday,6,26-01-13,95,16,597,178,425,none\nSunday,7,27-01-13,93,66,470,192,355,none\nMonday,1,28-01-13,60,46,590,203,255,none\nTuesday,2,29-01-13,79,38,535,141,287,none\nWednesday,3,30-01-13,80,40,397,204,382,none\nThursday,4,31-01-13,77,45,508,181,503,none\nFriday,5,01-02-13,71,59,731,236,419,none\nSaturday,6,02-02-13,89,62,602,213,329,none\nSunday,7,03-02-13,90,59,536,224,627,none\nMonday,1,04-02-13,81,49,518,127,429,none\nTuesday,2,05-02-13,93,54,331,171,328,none\nWednesday,3,06-02-13,65,34,406,146,435,none\nThursday,4,07-02-13,61,50,430,160,376,none\nFriday,5,08-02-13,112,70,601,267,581,none\nSaturday,6,09-02-13,103,58,411,141,488,none\nSunday,7,10-02-13,113,60,440,187,387,none\nMonday,1,11-02-13,63,43,334,209,236,none\nTuesday,2,12-02-13,72,49,424,184,460,none\nWednesday,3,13-02-13,93,61,599,218,388,promotion\nThursday,4,14-02-13,63,53,371,118,226,none\nFriday,5,15-02-13,67,50,730,195,523,promotion\nSaturday,6,16-02-13,66,71,820,225,568,none\nSunday,7,17-02-13,83,69,611,125,535,none\nMonday,1,18-02-13,84,56,549,179,320,promotion\nTuesday,2,19-02-13,77,43,405,206,310,none\nWednesday,3,20-02-13,69,59,431,202,509,none\nThursday,4,21-02-13,94,45,632,260,200,promotion\nFriday,5,22-02-13,93,44,689,159,420,none\nSaturday,6,23-02-13,70,70,612,244,258,none\nSunday,7,24-02-13,58,51,599,203,331,none\nMonday,1,25-02-13,63,57,502,143,330,none\nTuesday,2,26-02-13,68,34,411,99,282,none\nWednesday,3,27-02-13,66,56,551,159,445,none\nThursday,4,28-02-13,48,50,470,165,248,none\nFriday,5,01-03-13,73,56,622,119,355,none\nSaturday,6,02-03-13,120,84,597,247,506,promotion\nSunday,7,03-03-13,122,52,679,139,506,promotion\nMonday,1,04-03-13,55,42,352,156,297,none\nTuesday,2,05-03-13,84,46,513,161,251,none\nWednesday,3,06-03-13,75,52,365,100,357,none\nThursday,4,07-03-13,44,45,392,126,317,none\nFriday,5,08-03-13,87,49,653,241,455,none\nSaturday,6,09-03-13,128,48,674,168,630,none\nSunday,7,10-03-13,103,59,762,206,481,none\nMonday,1,11-03-13,62,54,356,137,231,none\nTuesday,2,12-03-13,92,50,605,171,388,none\nWednesday,3,13-03-13,83,52,527,150,485,none\nThursday,4,14-03-13,78,38,439,92,285,none\nFriday,5,15-03-13,67,47,639,246,444,none\nSaturday,6,16-03-13,116,51,557,291,381,none\nSunday,7,17-03-13,117,57,718,269,440,promotion\nMonday,1,18-03-13,57,45,520,140,186,none\nTuesday,2,19-03-13,71,30,343,130,255,none\nWednesday,3,20-03-13,110,43,261,129,304,none\nThursday,4,21-03-13,84,37,420,179,462,none\nFriday,5,22-03-13,81,44,515,224,483,none\nSaturday,6,23-03-13,108,68,793,235,375,none\nSunday,7,24-03-13,87,46,585,281,455,none\nMonday,1,25-03-13,80,46,308,117,374,none\nTuesday,2,26-03-13,61,37,440,196,322,none\nWednesday,3,27-03-13,95,23,529,142,231,none\nThursday,4,28-03-13,61,42,359,68,352,none\nFriday,5,29-03-13,78,41,528,91,460,none\nSaturday,6,30-03-13,88,33,817,271,225,none\nSunday,7,31-03-13,78,77,515,172,300,none\nMonday,1,01-04-13,75,37,411,166,335,none\nTuesday,2,02-04-13,73,41,417,68,262,none\nWednesday,3,03-04-13,76,54,497,215,348,none\nThursday,4,04-04-13,83,46,417,191,500,none\nFriday,5,05-04-13,101,72,519,289,439,none\nSaturday,6,06-04-13,94,74,552,247,333,none\nSunday,7,07-04-13,89,53,487,253,435,none\nMonday,1,08-04-13,95,54,594,147,282,promotion\nTuesday,2,09-04-13,64,63,284,147,254,none\nWednesday,3,10-04-13,75,36,522,149,370,none\nThursday,4,11-04-13,88,39,320,212,263,none\nFriday,5,12-04-13,106,69,445,119,433,none\nSaturday,6,13-04-13,101,74,653,215,444,none\nSunday,7,14-04-13,89,52,633,261,518,none\nMonday,1,15-04-13,76,44,491,140,300,none\nTuesday,2,16-04-13,74,44,599,164,396,none\nWednesday,3,17-04-13,67,52,410,231,295,none\nThursday,4,18-04-13,87,38,564,305,339,none\nFriday,5,19-04-13,107,45,609,288,412,none\nSaturday,6,20-04-13,94,75,632,272,444,none\nSunday,7,21-04-13,77,53,606,257,313,none\nMonday,1,22-04-13,73,36,360,180,475,none\nTuesday,2,23-04-13,51,47,631,147,253,none\nWednesday,3,24-04-13,72,44,460,232,391,none\nThursday,4,25-04-13,59,43,285,162,292,none\nFriday,5,26-04-13,94,78,685,245,621,none\nSaturday,6,27-04-13,79,68,400,252,385,none\nSunday,7,28-04-13,64,61,757,216,301,none\nMonday,1,29-04-13,87,43,405,143,309,none\nTuesday,2,30-04-13,72,40,403,188,294,none\nWednesday,3,01-05-13,71,61,467,178,273,none\nThursday,4,02-05-13,57,49,418,251,295,none\nFriday,5,03-05-13,100,44,434,205,467,none\nSaturday,6,04-05-13,84,98,622,346,366,none\nSunday,7,05-05-13,140,41,635,226,372,none\nMonday,1,06-05-13,83,46,588,159,357,none\nTuesday,2,07-05-13,118,45,461,201,183,none\nWednesday,3,08-05-13,73,43,463,277,257,none\nThursday,4,09-05-13,77,54,552,254,111,none\nFriday,5,10-05-13,95,45,530,248,491,none\nSaturday,6,11-05-13,90,52,431,303,525,none\nSunday,7,12-05-13,96,58,395,362,388,none\nMonday,1,13-05-13,90,42,391,193,273,none\nTuesday,2,14-05-13,53,52,432,297,248,none\nWednesday,3,15-05-13,79,40,581,182,408,none\nThursday,4,16-05-13,66,55,525,195,248,none\nFriday,5,17-05-13,82,50,740,272,276,none\nSaturday,6,18-05-13,115,60,710,405,353,none\nSunday,7,19-05-13,61,41,433,300,326,none\nMonday,1,20-05-13,96,45,394,293,516,none\nTuesday,2,21-05-13,64,48,363,194,287,none\nWednesday,3,22-05-13,77,68,611,145,506,promotion\nThursday,4,23-05-13,80,44,359,153,321,none\nFriday,5,24-05-13,93,57,415,194,424,none\nSaturday,6,25-05-13,112,61,568,377,565,none\nSunday,7,26-05-13,100,60,595,257,452,none\nMonday,1,27-05-13,81,47,504,167,418,none\nTuesday,2,28-05-13,79,59,460,231,283,none\nWednesday,3,29-05-13,66,54,521,254,475,none\nThursday,4,30-05-13,45,59,547,173,419,none\nFriday,5,31-05-13,82,59,305,98,294,none\nSaturday,6,01-06-13,93,64,631,297,394,none\nSunday,7,02-06-13,102,59,612,322,456,none\nMonday,1,03-06-13,45,36,417,313,282,none\nTuesday,2,04-06-13,58,50,385,198,263,none\nWednesday,3,05-06-13,95,43,373,185,438,none\nThursday,4,06-06-13,61,30,513,235,311,none\nFriday,5,07-06-13,56,66,572,423,326,none\nSaturday,6,08-06-13,77,76,601,379,617,none\nSunday,7,09-06-13,77,66,579,327,315,none\nMonday,1,10-06-13,83,43,502,112,330,none\nTuesday,2,11-06-13,83,56,609,323,422,promotion\nWednesday,3,12-06-13,69,32,543,258,312,none\nThursday,4,13-06-13,61,52,322,289,294,none\nFriday,5,14-06-13,76,85,534,385,353,none\nSaturday,6,15-06-13,136,66,844,293,398,none\nSunday,7,16-06-13,75,65,604,219,398,none\nMonday,1,17-06-13,89,40,452,211,321,none\nTuesday,2,18-06-13,43,40,458,299,344,none\nWednesday,3,19-06-13,81,38,489,202,225,none\nThursday,4,20-06-13,61,42,455,237,365,none\nFriday,5,21-06-13,92,68,488,269,436,promotion\nSaturday,6,22-06-13,117,75,477,352,424,none\nSunday,7,23-06-13,86,79,681,292,432,none\nMonday,1,24-06-13,78,61,430,212,381,none\nTuesday,2,25-06-13,81,61,440,251,327,none\nWednesday,3,26-06-13,78,51,303,265,195,none\nThursday,4,27-06-13,79,43,429,234,469,none\nFriday,5,28-06-13,98,67,632,184,495,none\nSaturday,6,29-06-13,80,62,791,204,402,none\nSunday,7,30-06-13,113,82,590,147,489,promotion\nMonday,1,01-07-13,76,52,504,347,366,none\nTuesday,2,02-07-13,80,46,546,160,291,none\nWednesday,3,03-07-13,55,47,369,226,359,none\nThursday,4,04-07-13,77,51,393,247,394,none\nFriday,5,05-07-13,113,81,848,437,327,promotion\nSaturday,6,06-07-13,90,58,550,353,357,none\nSunday,7,07-07-13,98,53,885,244,369,none\nMonday,1,08-07-13,60,53,452,279,238,none\nTuesday,2,09-07-13,68,37,562,281,412,none\nWednesday,3,10-07-13,70,55,483,227,506,none\nThursday,4,11-07-13,76,29,393,316,445,none\nFriday,5,12-07-13,99,52,572,224,327,none\nSaturday,6,13-07-13,93,75,580,295,563,none\nSunday,7,14-07-13,123,71,561,389,566,promotion\nMonday,1,15-07-13,77,55,564,172,308,none\nTuesday,2,16-07-13,79,41,379,199,302,none\nWednesday,3,17-07-13,82,40,411,239,236,none\nThursday,4,18-07-13,85,56,448,221,412,none\nFriday,5,19-07-13,90,77,611,411,442,none\nSaturday,6,20-07-13,65,56,613,303,446,none\nSunday,7,21-07-13,114,66,816,149,409,none\nMonday,1,22-07-13,96,54,311,135,365,none\nTuesday,2,23-07-13,80,62,268,284,434,none\nWednesday,3,24-07-13,67,43,629,265,399,none\nThursday,4,25-07-13,76,52,556,265,421,none\nFriday,5,26-07-13,101,52,640,224,494,none\nSaturday,6,27-07-13,136,76,621,511,751,promotion\nSunday,7,28-07-13,93,48,523,339,381,none\nMonday,1,29-07-13,83,61,469,345,402,none\nTuesday,2,30-07-13,77,46,453,250,420,none\nWednesday,3,31-07-13,70,41,506,198,227,none\nThursday,4,01-08-13,74,44,520,268,358,none\nFriday,5,02-08-13,96,40,507,307,289,none\nSaturday,6,03-08-13,78,47,785,277,548,none\nSunday,7,04-08-13,32,58,478,307,366,none\nMonday,1,05-08-13,47,41,391,239,432,none\nTuesday,2,06-08-13,58,50,307,277,284,none\nWednesday,3,07-08-13,67,56,437,198,214,none\nThursday,4,08-08-13,77,62,462,389,418,promotion\nFriday,5,09-08-13,90,54,586,186,270,none\nSaturday,6,10-08-13,123,81,576,291,653,none\nSunday,7,11-08-13,81,58,752,317,371,none\nMonday,1,12-08-13,73,29,309,257,278,none\nTuesday,2,13-08-13,85,44,503,192,302,none\nWednesday,3,14-08-13,63,48,361,263,273,none\nThursday,4,15-08-13,66,57,608,260,433,promotion\nFriday,5,16-08-13,86,94,274,351,447,none\nSaturday,6,17-08-13,87,65,650,439,349,none\nSunday,7,18-08-13,78,79,639,294,570,none\nMonday,1,19-08-13,67,43,370,258,272,none\nTuesday,2,20-08-13,60,51,386,179,409,none\nWednesday,3,21-08-13,95,72,491,326,356,promotion\nThursday,4,22-08-13,88,41,551,223,431,none\nFriday,5,23-08-13,68,61,548,191,424,none\nSaturday,6,24-08-13,92,54,505,349,359,none\nSunday,7,25-08-13,72,46,522,426,259,none\nMonday,1,26-08-13,75,45,366,301,359,none\nTuesday,2,27-08-13,73,48,617,271,180,promotion\nWednesday,3,28-08-13,62,54,507,261,349,promotion\nThursday,4,29-08-13,52,35,420,183,345,none\nFriday,5,30-08-13,50,61,427,282,386,none\nSaturday,6,31-08-13,118,55,547,173,519,none\nSunday,7,01-09-13,108,46,549,316,576,none\nMonday,1,02-09-13,66,32,435,162,339,none\nTuesday,2,03-09-13,56,47,693,214,208,promotion\nWednesday,3,04-09-13,65,46,371,219,428,none\nThursday,4,05-09-13,42,51,522,231,239,none\nFriday,5,06-09-13,84,63,336,300,256,none\nSaturday,6,07-09-13,79,51,691,357,405,none\nSunday,7,08-09-13,88,41,633,177,340,none\nMonday,1,09-09-13,64,49,539,249,331,none\nTuesday,2,10-09-13,93,47,402,138,275,none\nWednesday,3,11-09-13,73,59,460,234,315,none\nThursday,4,12-09-13,80,42,603,274,198,promotion\nFriday,5,13-09-13,95,50,626,187,589,none\nSaturday,6,14-09-13,89,59,725,184,394,none\nSunday,7,15-09-13,98,79,359,316,415,promotion\nMonday,1,16-09-13,67,48,459,247,341,none\nTuesday,2,17-09-13,82,40,364,203,258,none\nWednesday,3,18-09-13,70,48,310,230,305,none\nThursday,4,19-09-13,75,32,509,216,257,none\nFriday,5,20-09-13,91,46,387,237,477,none\nSaturday,6,21-09-13,109,69,460,199,353,none\nSunday,7,22-09-13,91,53,665,161,209,none\nMonday,1,23-09-13,59,42,647,216,394,promotion\nTuesday,2,24-09-13,69,47,437,208,267,none\nWednesday,3,25-09-13,81,52,247,197,359,none\nThursday,4,26-09-13,81,53,397,200,448,none\nFriday,5,27-09-13,69,46,598,157,329,none\nSaturday,6,28-09-13,68,44,702,330,475,none\nSunday,7,29-09-13,89,57,506,207,374,none\nMonday,1,30-09-13,94,46,348,237,438,none\nTuesday,2,01-10-13,79,49,504,87,392,none\nWednesday,3,02-10-13,71,50,528,232,300,none\nThursday,4,03-10-13,75,66,433,342,288,none\nFriday,5,04-10-13,98,35,743,167,413,none\nSaturday,6,05-10-13,104,91,777,191,534,none\nSunday,7,06-10-13,110,53,796,217,378,none\nMonday,1,07-10-13,59,37,551,153,339,none\nTuesday,2,08-10-13,58,41,633,192,375,none\nWednesday,3,09-10-13,86,58,507,172,253,none\nThursday,4,10-10-13,53,51,539,159,470,none\nFriday,5,11-10-13,76,48,675,207,319,none\nSaturday,6,12-10-13,102,69,624,397,432,promotion\nSunday,7,13-10-13,92,75,726,175,441,none\nMonday,1,14-10-13,75,39,480,141,378,none\nTuesday,2,15-10-13,85,60,601,166,394,none\nWednesday,3,16-10-13,60,51,497,191,389,none\nThursday,4,17-10-13,99,41,453,191,349,none\nFriday,5,18-10-13,101,57,452,176,325,none\nSaturday,6,19-10-13,117,98,688,238,373,promotion\nSunday,7,20-10-13,100,81,630,325,448,promotion\nMonday,1,21-10-13,51,50,648,216,324,none\nTuesday,2,22-10-13,84,39,596,213,412,promotion\nWednesday,3,23-10-13,73,46,304,264,348,none\nThursday,4,24-10-13,70,49,514,50,228,none\nFriday,5,25-10-13,99,57,413,184,432,none\nSaturday,6,26-10-13,102,71,564,213,399,none\nSunday,7,27-10-13,63,42,701,261,462,none\nMonday,1,28-10-13,59,42,497,153,204,none\nTuesday,2,29-10-13,66,24,456,232,273,none\nWednesday,3,30-10-13,74,42,444,115,308,none\nThursday,4,31-10-13,66,43,443,221,305,none\nFriday,5,01-11-13,48,79,512,167,446,none\nSaturday,6,02-11-13,73,55,884,243,343,none\nSunday,7,03-11-13,106,62,348,199,380,none\nMonday,1,04-11-13,80,57,485,111,443,none\nTuesday,2,05-11-13,84,38,665,182,363,none\nWednesday,3,06-11-13,67,46,730,169,328,none\nThursday,4,07-11-13,100,41,636,151,478,promotion\nFriday,5,08-11-13,113,80,543,230,410,none\nSaturday,6,09-11-13,114,67,674,206,545,none\nSunday,7,10-11-13,96,40,568,203,382,none\nMonday,1,11-11-13,93,51,446,180,364,none\nTuesday,2,12-11-13,101,34,703,154,346,none\nWednesday,3,13-11-13,79,52,512,123,394,none\nThursday,4,14-11-13,72,53,497,167,302,none\nFriday,5,15-11-13,96,51,803,160,365,none\nSaturday,6,16-11-13,112,63,855,166,554,none\nSunday,7,17-11-13,121,58,694,175,405,none\nMonday,1,18-11-13,73,44,526,197,423,none\nTuesday,2,19-11-13,77,49,513,72,355,none\nWednesday,3,20-11-13,77,41,388,99,289,none\nThursday,4,21-11-13,79,52,443,149,221,none\nFriday,5,22-11-13,87,44,692,169,439,none\nSaturday,6,23-11-13,141,88,645,340,771,promotion\nSunday,7,24-11-13,82,48,575,203,446,none\nMonday,1,25-11-13,64,52,430,200,451,promotion\nTuesday,2,26-11-13,73,58,489,152,275,none\nWednesday,3,27-11-13,85,65,538,111,426,promotion\nThursday,4,28-11-13,81,57,409,104,403,none\nFriday,5,29-11-13,97,54,647,182,412,none\nSaturday,6,30-11-13,116,51,794,183,399,none\nSunday,7,01-12-13,81,29,694,220,367,none\nMonday,1,02-12-13,92,46,358,126,404,none\nTuesday,2,03-12-13,78,41,342,154,428,none\nWednesday,3,04-12-13,71,51,407,155,195,none\nThursday,4,05-12-13,59,61,360,164,336,none\nFriday,5,06-12-13,98,56,722,311,441,none\nSaturday,6,07-12-13,99,71,684,202,217,none\nSunday,7,08-12-13,84,61,534,178,561,none\nMonday,1,09-12-13,86,52,425,103,317,none\nTuesday,2,10-12-13,65,54,557,163,446,promotion\nWednesday,3,11-12-13,67,36,418,148,373,none\nThursday,4,12-12-13,76,53,476,186,346,none\nFriday,5,13-12-13,103,66,538,201,391,none\nSaturday,6,14-12-13,72,61,681,214,449,none\nSunday,7,15-12-13,82,61,515,259,529,none\nMonday,1,16-12-13,84,46,322,148,318,none\nTuesday,2,17-12-13,65,43,487,195,300,none\nWednesday,3,18-12-13,66,51,478,181,317,none\nThursday,4,19-12-13,93,57,704,224,564,promotion\nFriday,5,20-12-13,90,62,628,237,408,none\nSaturday,6,21-12-13,86,65,613,147,310,none\nSunday,7,22-12-13,100,50,532,175,535,none\nMonday,1,23-12-13,58,27,329,133,341,none\nTuesday,2,24-12-13,84,49,476,231,357,none\nWednesday,3,25-12-13,84,63,591,98,286,promotion\nThursday,4,26-12-13,76,47,455,131,337,none\nFriday,5,27-12-13,96,50,270,149,384,none\nSaturday,6,28-12-13,101,76,658,206,518,none\nSunday,7,29-12-13,114,43,514,180,493,none\nMonday,1,30-12-13,85,51,438,157,390,none\nTuesday,2,31-12-13,71,51,506,181,363,none\nWednesday,3,01-01-14,85,52,475,146,331,none\nThursday,4,02-01-14,56,47,461,129,409,none\nFriday,5,03-01-14,86,71,752,266,536,none\nSaturday,6,04-01-14,51,79,354,282,512,none\nSunday,7,05-01-14,75,64,466,205,288,none\nMonday,1,06-01-14,84,62,475,177,379,none\nTuesday,2,07-01-14,84,42,408,126,445,none\nWednesday,3,08-01-14,85,31,653,178,387,none\nThursday,4,09-01-14,78,56,620,203,326,promotion\nFriday,5,10-01-14,51,51,661,128,375,none\nSaturday,6,11-01-14,122,85,644,235,719,promotion\nSunday,7,12-01-14,123,65,556,187,405,none\nMonday,1,13-01-14,71,43,412,70,478,none\nTuesday,2,14-01-14,80,46,375,198,302,none\nWednesday,3,15-01-14,73,33,671,167,513,promotion\nThursday,4,16-01-14,62,38,508,98,332,none\nFriday,5,17-01-14,87,52,525,185,406,none\nSaturday,6,18-01-14,88,71,661,184,609,none\nSunday,7,19-01-14,95,53,441,152,456,none\nMonday,1,20-01-14,89,39,542,174,491,none\nTuesday,2,21-01-14,56,39,393,148,328,none\nWednesday,3,22-01-14,111,51,461,180,218,promotion\nThursday,4,23-01-14,63,43,426,172,459,none\nFriday,5,24-01-14,85,62,576,150,466,none\nSaturday,6,25-01-14,101,43,714,216,419,none\nSunday,7,26-01-14,93,84,407,238,382,none\nMonday,1,27-01-14,94,54,524,224,372,none\nTuesday,2,28-01-14,78,43,425,163,327,none\nWednesday,3,29-01-14,100,50,487,190,343,none\nThursday,4,30-01-14,77,62,459,181,443,none\nFriday,5,31-01-14,70,62,500,267,505,none\nSaturday,6,01-02-14,98,54,754,186,603,none\nSunday,7,02-02-14,92,70,572,209,506,none\nMonday,1,03-02-14,80,71,555,166,284,none\nTuesday,2,04-02-14,58,41,554,176,263,none\nWednesday,3,05-02-14,90,49,407,75,443,none\nThursday,4,06-02-14,79,49,517,102,320,none\nFriday,5,07-02-14,73,44,397,136,507,none\nSaturday,6,08-02-14,87,72,752,180,568,none\nSunday,7,09-02-14,94,68,546,214,582,none\nMonday,1,10-02-14,77,37,462,136,356,none\nTuesday,2,11-02-14,60,57,422,160,376,none\nWednesday,3,12-02-14,99,50,548,204,365,promotion\nThursday,4,13-02-14,63,44,484,128,397,none\nFriday,5,14-02-14,89,77,660,176,398,none\nSaturday,6,15-02-14,121,63,690,125,493,none\nSunday,7,16-02-14,106,66,340,142,374,none\nMonday,1,17-02-14,50,53,495,129,268,promotion\nTuesday,2,18-02-14,47,34,564,173,219,none\nWednesday,3,19-02-14,63,51,442,193,397,none\nThursday,4,20-02-14,77,51,324,151,392,none\nFriday,5,21-02-14,111,40,510,205,493,none\nSaturday,6,22-02-14,67,73,288,200,561,none\nSunday,7,23-02-14,116,45,458,205,506,none\nMonday,1,24-02-14,80,58,680,178,330,none\nTuesday,2,25-02-14,90,50,490,205,399,none\nWednesday,3,26-02-14,79,39,368,165,282,none\nThursday,4,27-02-14,87,24,525,117,428,none\nFriday,5,28-02-14,90,51,464,240,495,none\nSaturday,6,01-03-14,93,86,684,95,405,none\nSunday,7,02-03-14,87,57,818,217,507,none\nMonday,1,03-03-14,97,59,561,167,500,promotion\nTuesday,2,04-03-14,70,38,509,174,361,none\nWednesday,3,05-03-14,72,64,687,178,401,promotion\nThursday,4,06-03-14,69,66,330,162,320,none\nFriday,5,07-03-14,113,64,679,137,320,none\nSaturday,6,08-03-14,100,64,707,253,368,none\nSunday,7,09-03-14,80,64,593,187,299,none\nMonday,1,10-03-14,94,54,505,177,526,promotion\nTuesday,2,11-03-14,78,46,324,126,471,none\nWednesday,3,12-03-14,47,65,562,123,280,none\nThursday,4,13-03-14,96,71,746,162,468,promotion\nFriday,5,14-03-14,89,59,776,276,516,none\nSaturday,6,15-03-14,72,88,596,209,299,none\nSunday,7,16-03-14,75,60,534,227,366,none\nMonday,1,17-03-14,96,38,579,147,486,promotion\nTuesday,2,18-03-14,75,44,552,108,254,none\nWednesday,3,19-03-14,73,46,651,178,448,none\nThursday,4,20-03-14,56,29,414,169,453,none\nFriday,5,21-03-14,74,40,752,165,471,none\nSaturday,6,22-03-14,116,53,760,243,375,none\nSunday,7,23-03-14,80,61,691,246,492,none\nMonday,1,24-03-14,78,58,479,144,245,none\nTuesday,2,25-03-14,59,51,582,197,362,none\nWednesday,3,26-03-14,58,45,519,206,412,none\nThursday,4,27-03-14,80,55,461,194,286,none\nFriday,5,28-03-14,122,45,561,201,548,none\nSaturday,6,29-03-14,98,42,581,186,434,none\nSunday,7,30-03-14,97,60,519,127,397,promotion\nMonday,1,31-03-14,74,53,446,206,346,none\nTuesday,2,01-04-14,85,38,420,203,352,none\nWednesday,3,02-04-14,76,57,378,186,262,none\nThursday,4,03-04-14,74,53,530,164,382,none\nFriday,5,04-04-14,108,66,522,224,465,none\nSaturday,6,05-04-14,109,57,698,286,499,none\nSunday,7,06-04-14,96,46,535,257,531,none\nMonday,1,07-04-14,78,54,541,202,440,none\nTuesday,2,08-04-14,61,41,435,162,205,none\nWednesday,3,09-04-14,52,34,437,219,348,none\nThursday,4,10-04-14,88,43,529,182,306,none\nFriday,5,11-04-14,107,51,603,199,519,none\nSaturday,6,12-04-14,94,53,520,181,592,none\nSunday,7,13-04-14,123,42,624,256,288,none\nMonday,1,14-04-14,90,58,398,175,258,none\nTuesday,2,15-04-14,40,48,597,247,214,none\nWednesday,3,16-04-14,86,28,516,203,307,none\nThursday,4,17-04-14,89,57,397,183,409,none\nFriday,5,18-04-14,80,59,548,218,224,none\nSaturday,6,19-04-14,109,66,499,291,577,none\nSunday,7,20-04-14,86,46,545,265,519,none\nMonday,1,21-04-14,85,65,610,115,410,none\nTuesday,2,22-04-14,71,56,448,320,135,none\nWednesday,3,23-04-14,100,35,530,215,339,none\nThursday,4,24-04-14,83,46,421,210,338,none\nFriday,5,25-04-14,99,44,424,177,567,none\nSaturday,6,26-04-14,110,37,603,311,633,none\nSunday,7,27-04-14,125,44,761,147,344,none\nMonday,1,28-04-14,73,59,529,173,297,none\nTuesday,2,29-04-14,85,44,456,143,438,none\nWednesday,3,30-04-14,82,31,488,93,396,none\nThursday,4,01-05-14,65,52,552,270,379,promotion\nFriday,5,02-05-14,112,66,523,136,324,none\nSaturday,6,03-05-14,122,49,690,449,279,none\nSunday,7,04-05-14,86,36,670,334,275,none\nMonday,1,05-05-14,75,43,460,202,174,none\nTuesday,2,06-05-14,98,26,456,205,175,none\nWednesday,3,07-05-14,73,57,529,302,271,none\nThursday,4,08-05-14,77,39,397,288,401,none\nFriday,5,09-05-14,90,64,559,249,371,none\nSaturday,6,10-05-14,150,77,718,271,505,none\nSunday,7,11-05-14,81,67,629,184,377,none\nMonday,1,12-05-14,77,31,401,120,421,none\nTuesday,2,13-05-14,105,44,484,196,338,none\nWednesday,3,14-05-14,101,40,686,231,322,promotion\nThursday,4,15-05-14,87,45,545,128,488,promotion\nFriday,5,16-05-14,90,47,250,235,243,none\nSaturday,6,17-05-14,94,66,649,357,562,promotion\nSunday,7,18-05-14,106,60,466,267,458,none\nMonday,1,19-05-14,70,67,275,260,308,none\nTuesday,2,20-05-14,86,45,364,243,446,none\nWednesday,3,21-05-14,78,59,627,313,411,promotion\nThursday,4,22-05-14,83,54,447,244,295,none\nFriday,5,23-05-14,106,50,837,159,370,none\nSaturday,6,24-05-14,118,41,296,145,346,none\nSunday,7,25-05-14,97,59,556,255,240,none\nMonday,1,26-05-14,106,28,606,181,345,none\nTuesday,2,27-05-14,75,48,480,145,418,none\nWednesday,3,28-05-14,82,46,484,279,444,promotion\nThursday,4,29-05-14,77,42,568,150,232,none\nFriday,5,30-05-14,107,54,659,280,420,promotion\nSaturday,6,31-05-14,90,75,738,314,312,promotion\nSunday,7,01-06-14,114,42,706,325,494,none\nMonday,1,02-06-14,45,34,374,303,326,none\nTuesday,2,03-06-14,85,48,451,323,431,none\nWednesday,3,04-06-14,52,54,551,323,290,none\nThursday,4,05-06-14,90,56,504,271,401,none\nFriday,5,06-06-14,112,86,804,440,357,none\nSaturday,6,07-06-14,92,70,602,281,460,none\nSunday,7,08-06-14,93,59,517,341,400,none\nMonday,1,09-06-14,79,31,445,210,448,none\nTuesday,2,10-06-14,58,34,526,288,458,none\nWednesday,3,11-06-14,103,43,449,288,443,none\nThursday,4,12-06-14,75,50,360,264,371,none\nFriday,5,13-06-14,100,51,552,302,497,none\nSaturday,6,14-06-14,106,95,896,213,555,promotion\nSunday,7,15-06-14,115,66,512,318,536,none\nMonday,1,16-06-14,68,33,453,218,359,none\nTuesday,2,17-06-14,89,39,404,333,340,none\nWednesday,3,18-06-14,89,59,311,237,329,none\nThursday,4,19-06-14,83,48,403,181,494,none\nFriday,5,20-06-14,108,79,575,347,283,none\nSaturday,6,21-06-14,57,66,794,400,446,none\nSunday,7,22-06-14,139,75,693,320,487,none\nMonday,1,23-06-14,52,47,430,212,291,none\nTuesday,2,24-06-14,62,46,447,196,379,none\nWednesday,3,25-06-14,91,61,514,244,302,none\nThursday,4,26-06-14,82,37,375,207,380,none\nFriday,5,27-06-14,84,50,376,318,470,none\nSaturday,6,28-06-14,88,66,640,253,405,none\nSunday,7,29-06-14,99,62,568,288,497,none\nMonday,1,30-06-14,79,51,398,285,342,none\nTuesday,2,01-07-14,88,32,502,197,310,none\nWednesday,3,02-07-14,59,51,471,339,338,none\nThursday,4,03-07-14,61,32,493,225,484,none\nFriday,5,04-07-14,81,66,590,386,483,none\nSaturday,6,05-07-14,124,56,693,393,219,none\nSunday,7,06-07-14,107,69,1152,420,499,promotion\nMonday,1,07-07-14,52,52,526,247,246,none\nTuesday,2,08-07-14,69,36,490,336,448,none\nWednesday,3,09-07-14,96,31,481,125,252,promotion\nThursday,4,10-07-14,89,72,609,252,402,promotion\nFriday,5,11-07-14,54,60,468,195,532,none\nSaturday,6,12-07-14,133,38,535,322,521,none\nSunday,7,13-07-14,105,48,634,265,324,none\nMonday,1,14-07-14,63,49,537,378,330,none\nTuesday,2,15-07-14,104,34,541,370,314,none\nWednesday,3,16-07-14,81,45,338,243,352,none\nThursday,4,17-07-14,42,38,396,317,395,none\nFriday,5,18-07-14,116,66,538,335,334,none\nSaturday,6,19-07-14,82,56,555,407,490,none\nSunday,7,20-07-14,103,57,590,344,562,none\nMonday,1,21-07-14,85,57,490,238,257,none\nTuesday,2,22-07-14,58,43,435,224,398,none\nWednesday,3,23-07-14,63,48,479,311,388,none\nThursday,4,24-07-14,84,53,449,281,316,none\nFriday,5,25-07-14,119,56,743,298,135,none\nSaturday,6,26-07-14,107,54,438,191,458,none\nSunday,7,27-07-14,122,69,662,298,500,none\nMonday,1,28-07-14,61,45,406,305,396,none\nTuesday,2,29-07-14,77,49,392,244,316,none\nWednesday,3,30-07-14,59,50,554,172,440,none\nThursday,4,31-07-14,90,68,561,305,272,promotion\nFriday,5,01-08-14,89,59,671,290,421,none\nSaturday,6,02-08-14,74,47,707,465,406,none\nSunday,7,03-08-14,87,53,451,412,561,none\nMonday,1,04-08-14,85,59,435,262,428,none\nTuesday,2,05-08-14,96,52,533,254,539,promotion\nWednesday,3,06-08-14,80,35,295,299,428,none\nThursday,4,07-08-14,64,31,477,146,443,none\nFriday,5,08-08-14,80,90,559,468,486,promotion\nSaturday,6,09-08-14,105,73,789,336,504,none\nSunday,7,10-08-14,115,54,714,235,441,none\nMonday,1,11-08-14,95,49,548,304,308,none\nTuesday,2,12-08-14,81,32,353,200,347,none\nWednesday,3,13-08-14,73,63,445,274,317,none\nThursday,4,14-08-14,95,56,401,237,416,none\nFriday,5,15-08-14,100,67,483,328,475,none\nSaturday,6,16-08-14,98,47,655,460,578,promotion\nSunday,7,17-08-14,102,49,559,247,450,none\nMonday,1,18-08-14,85,55,571,209,249,none\nTuesday,2,19-08-14,79,51,530,280,334,none\nWednesday,3,20-08-14,84,64,482,146,294,none\nThursday,4,21-08-14,88,26,429,186,435,none\nFriday,5,22-08-14,92,32,578,278,478,none\nSaturday,6,23-08-14,127,78,701,311,418,none\nSunday,7,24-08-14,126,86,560,551,546,promotion\nMonday,1,25-08-14,78,52,623,327,445,none\nTuesday,2,26-08-14,69,68,502,212,499,none\nWednesday,3,27-08-14,90,40,490,333,393,none\nThursday,4,28-08-14,86,52,552,277,365,none\nFriday,5,29-08-14,99,45,576,353,442,promotion\nSaturday,6,30-08-14,74,59,886,250,615,none\nSunday,7,31-08-14,137,58,698,385,581,promotion\nMonday,1,01-09-14,90,52,351,221,229,none\nTuesday,2,02-09-14,108,71,555,212,322,promotion\nWednesday,3,03-09-14,82,46,634,133,333,none\nThursday,4,04-09-14,82,34,445,211,214,none\nFriday,5,05-09-14,124,82,759,204,426,none\nSaturday,6,06-09-14,90,80,465,312,355,none\nSunday,7,07-09-14,85,42,511,298,303,none\nMonday,1,08-09-14,70,40,467,140,219,none\nTuesday,2,09-09-14,94,40,681,210,458,none\nWednesday,3,10-09-14,51,46,570,172,347,none\nThursday,4,11-09-14,96,52,294,173,214,none\nFriday,5,12-09-14,105,60,788,157,416,none\nSaturday,6,13-09-14,90,95,422,189,443,none\nSunday,7,14-09-14,72,62,576,206,486,none\nMonday,1,15-09-14,86,29,575,288,335,none\nTuesday,2,16-09-14,99,37,397,200,360,none\nWednesday,3,17-09-14,68,55,395,198,442,none\nThursday,4,18-09-14,43,48,441,251,420,none\nFriday,5,19-09-14,104,68,697,306,334,none\nSaturday,6,20-09-14,142,87,555,230,638,promotion\nSunday,7,21-09-14,113,54,656,216,601,none\nMonday,1,22-09-14,68,63,494,256,422,none\nTuesday,2,23-09-14,67,60,370,270,374,none\nWednesday,3,24-09-14,96,43,590,248,281,none\nThursday,4,25-09-14,95,34,501,180,526,none\nFriday,5,26-09-14,102,53,514,279,338,none\nSaturday,6,27-09-14,99,49,769,275,537,none\nSunday,7,28-09-14,81,64,697,138,293,none\nMonday,1,29-09-14,106,43,600,161,435,none\nTuesday,2,30-09-14,66,40,626,125,361,none\nWednesday,3,01-10-14,87,48,440,193,306,none\nThursday,4,02-10-14,76,29,535,240,402,none\nFriday,5,03-10-14,96,71,631,267,274,none\nSaturday,6,04-10-14,102,77,444,230,687,none\nSunday,7,05-10-14,113,53,689,272,617,none\nMonday,1,06-10-14,84,61,585,148,397,none\nTuesday,2,07-10-14,75,51,482,176,241,none\nWednesday,3,08-10-14,74,22,376,184,461,none\nThursday,4,09-10-14,62,65,625,206,253,none\nFriday,5,10-10-14,86,63,514,225,329,none\nSaturday,6,11-10-14,85,58,540,269,491,none\nSunday,7,12-10-14,80,76,385,223,233,none\nMonday,1,13-10-14,63,52,637,209,317,none\nTuesday,2,14-10-14,65,49,590,147,366,none\nWednesday,3,15-10-14,70,45,532,259,346,none\nThursday,4,16-10-14,103,28,569,314,433,none\nFriday,5,17-10-14,117,45,527,128,567,none\nSaturday,6,18-10-14,134,84,941,196,461,none\nSunday,7,19-10-14,79,63,658,196,459,promotion\nMonday,1,20-10-14,106,42,584,141,303,none\nTuesday,2,21-10-14,90,44,508,219,233,none\nWednesday,3,22-10-14,78,53,403,243,442,none\nThursday,4,23-10-14,82,46,754,202,376,none\nFriday,5,24-10-14,110,78,403,223,543,none\nSaturday,6,25-10-14,95,72,582,253,323,none\nSunday,7,26-10-14,93,69,554,271,343,none\nMonday,1,27-10-14,74,46,488,194,347,none\nTuesday,2,28-10-14,100,50,528,120,411,none\nWednesday,3,29-10-14,59,37,290,123,301,none\nThursday,4,30-10-14,82,41,414,235,482,none\nFriday,5,31-10-14,89,82,552,234,426,none\nSaturday,6,01-11-14,119,71,840,233,758,none\nSunday,7,02-11-14,123,68,860,174,427,none\nMonday,1,03-11-14,97,38,377,119,433,none\nTuesday,2,04-11-14,74,58,358,165,214,none\nWednesday,3,05-11-14,49,49,613,175,306,none\nThursday,4,06-11-14,97,39,636,109,316,none\nFriday,5,07-11-14,76,69,614,209,521,none\nSaturday,6,08-11-14,124,78,803,256,404,none\nSunday,7,09-11-14,59,64,722,182,329,none\nMonday,1,10-11-14,75,54,456,120,253,none\nTuesday,2,11-11-14,72,33,473,147,295,promotion\nWednesday,3,12-11-14,97,62,489,155,210,none\nThursday,4,13-11-14,87,38,425,166,298,none\nFriday,5,14-11-14,96,71,602,217,376,none\nSaturday,6,15-11-14,125,88,744,267,500,none\nSunday,7,16-11-14,87,89,649,193,391,none\nMonday,1,17-11-14,69,44,549,151,289,none\nTuesday,2,18-11-14,84,47,497,164,214,none\nWednesday,3,19-11-14,90,33,584,106,279,none\nThursday,4,20-11-14,62,46,512,85,222,none\nFriday,5,21-11-14,66,64,720,147,535,none\nSaturday,6,22-11-14,115,66,745,316,494,none\nSunday,7,23-11-14,69,53,594,144,591,none\nMonday,1,24-11-14,93,46,465,186,435,promotion\nTuesday,2,25-11-14,77,49,432,151,356,none\nWednesday,3,26-11-14,102,61,558,132,331,none\nThursday,4,27-11-14,63,52,561,92,278,none\nFriday,5,28-11-14,87,55,601,201,471,none\nSaturday,6,29-11-14,93,56,746,135,332,none\nSunday,7,30-11-14,77,53,401,190,518,none\nMonday,1,01-12-14,92,56,629,104,409,none\nTuesday,2,02-12-14,83,55,464,128,396,none\nWednesday,3,03-12-14,71,46,465,142,236,none\nThursday,4,04-12-14,52,56,385,201,328,none\nFriday,5,05-12-14,98,63,582,198,444,none\nSaturday,6,06-12-14,114,52,657,277,392,none\nSunday,7,07-12-14,87,50,685,129,488,none\nMonday,1,08-12-14,70,54,577,149,228,none\nTuesday,2,09-12-14,110,68,716,239,444,promotion\nWednesday,3,10-12-14,86,49,533,220,345,none\nThursday,4,11-12-14,55,63,566,239,306,none\nFriday,5,12-12-14,106,64,686,96,410,none\nSaturday,6,13-12-14,143,93,594,342,639,none\nSunday,7,14-12-14,100,67,707,243,277,none\nMonday,1,15-12-14,101,39,423,167,305,none\nTuesday,2,16-12-14,76,63,581,202,347,none\nWednesday,3,17-12-14,38,40,489,96,433,none\nThursday,4,18-12-14,90,52,387,129,378,none\nFriday,5,19-12-14,80,68,532,236,441,none\nSaturday,6,20-12-14,140,62,657,292,473,none\nSunday,7,21-12-14,68,49,391,148,563,none\nMonday,1,22-12-14,73,71,381,171,368,none\nTuesday,2,23-12-14,71,43,419,155,352,none\nWednesday,3,24-12-14,55,28,647,206,305,none\nThursday,4,25-12-14,90,51,650,115,216,none\nFriday,5,26-12-14,88,67,663,182,375,none\nSaturday,6,27-12-14,113,74,600,207,528,none\nSunday,7,28-12-14,88,82,601,156,427,none\nMonday,1,29-12-14,115,48,555,252,338,promotion\nTuesday,2,30-12-14,84,43,555,160,339,none\nWednesday,3,31-12-14,87,64,337,151,322,none\nThursday,4,01-01-15,88,39,281,155,407,none\nFriday,5,02-01-15,97,35,575,131,420,none\nSaturday,6,03-01-15,104,48,742,175,536,none\nSunday,7,04-01-15,85,76,394,196,530,none\nMonday,1,05-01-15,87,52,335,116,362,none\nTuesday,2,06-01-15,82,43,458,146,295,none\nWednesday,3,07-01-15,96,49,400,173,246,none\nThursday,4,08-01-15,94,43,570,185,445,none\nFriday,5,09-01-15,90,54,526,186,400,none\nSaturday,6,10-01-15,111,64,641,198,570,none\nSunday,7,11-01-15,109,67,614,163,451,none\nMonday,1,12-01-15,76,51,410,166,411,none\nTuesday,2,13-01-15,75,50,484,143,408,none\nWednesday,3,14-01-15,68,41,596,132,345,none\nThursday,4,15-01-15,81,41,491,167,282,none\nFriday,5,16-01-15,110,38,668,148,476,none\nSaturday,6,17-01-15,129,66,759,190,309,none\nSunday,7,18-01-15,79,58,580,208,284,none\nMonday,1,19-01-15,76,41,381,125,410,none\nTuesday,2,20-01-15,60,40,322,111,294,none\nWednesday,3,21-01-15,87,38,559,119,375,none\nThursday,4,22-01-15,80,52,466,154,315,none\nFriday,5,23-01-15,98,73,778,187,417,none\nSaturday,6,24-01-15,92,56,702,215,448,none\nSunday,7,25-01-15,109,75,325,193,511,none\nMonday,1,26-01-15,58,63,443,188,346,none\nTuesday,2,27-01-15,62,35,423,169,319,none\nWednesday,3,28-01-15,83,39,373,173,411,none\nThursday,4,29-01-15,51,57,568,100,463,none\nFriday,5,30-01-15,74,48,629,185,421,none\nSaturday,6,31-01-15,116,59,595,184,434,none\nSunday,7,01-02-15,107,72,412,175,424,none\nMonday,1,02-02-15,63,40,513,165,375,none\nTuesday,2,03-02-15,86,37,308,138,312,none\nWednesday,3,04-02-15,87,61,521,167,337,none\nThursday,4,05-02-15,72,42,578,132,448,none\nFriday,5,06-02-15,89,77,883,189,356,none\nSaturday,6,07-02-15,110,67,713,219,397,none\nSunday,7,08-02-15,90,53,845,205,319,none\nMonday,1,09-02-15,96,43,499,179,229,none\nTuesday,2,10-02-15,53,54,565,240,435,none\nWednesday,3,11-02-15,71,50,463,150,429,none\nThursday,4,12-02-15,107,67,576,119,376,promotion\nFriday,5,13-02-15,89,41,594,205,428,none\nSaturday,6,14-02-15,105,64,764,208,712,none\nSunday,7,15-02-15,117,54,387,166,303,none\nMonday,1,16-02-15,86,54,525,151,392,none\nTuesday,2,17-02-15,84,43,629,140,406,none\nWednesday,3,18-02-15,88,46,530,119,387,none\nThursday,4,19-02-15,102,52,460,216,486,none\nFriday,5,20-02-15,108,70,684,170,518,promotion\nSaturday,6,21-02-15,146,56,634,180,393,none\nSunday,7,22-02-15,75,31,459,124,453,none\nMonday,1,23-02-15,104,35,552,114,334,none\nTuesday,2,24-02-15,83,53,538,145,358,promotion\nWednesday,3,25-02-15,55,45,621,191,257,none\nThursday,4,26-02-15,102,53,365,190,333,none\nFriday,5,27-02-15,101,77,584,229,335,none\nSaturday,6,28-02-15,115,73,670,225,389,none\nSunday,7,01-03-15,100,54,648,217,522,none\nMonday,1,02-03-15,116,47,492,193,247,none\nTuesday,2,03-03-15,37,41,419,199,453,none\nWednesday,3,04-03-15,85,47,393,166,241,none\nThursday,4,05-03-15,61,54,568,123,342,none\nFriday,5,06-03-15,124,66,674,235,492,none\nSaturday,6,07-03-15,101,84,581,221,539,none\nSunday,7,08-03-15,77,77,614,237,208,none\nMonday,1,09-03-15,98,43,451,165,426,promotion\nTuesday,2,10-03-15,83,49,508,145,448,none\nWednesday,3,11-03-15,51,48,397,127,317,none\nThursday,4,12-03-15,70,54,492,171,597,none\nFriday,5,13-03-15,78,49,463,150,244,none\nSaturday,6,14-03-15,119,89,751,153,412,none\nSunday,7,15-03-15,77,48,475,212,458,none\nMonday,1,16-03-15,78,54,597,297,380,promotion\nTuesday,2,17-03-15,52,77,448,213,397,promotion\nWednesday,3,18-03-15,71,57,470,230,164,none\nThursday,4,19-03-15,88,43,530,194,416,none\nFriday,5,20-03-15,108,89,585,250,558,none\nSaturday,6,21-03-15,94,72,696,276,319,none\nSunday,7,22-03-15,97,46,708,174,389,none\nMonday,1,23-03-15,54,45,380,161,330,none\nTuesday,2,24-03-15,67,48,435,138,272,none\nWednesday,3,25-03-15,67,63,348,199,251,none\nThursday,4,26-03-15,78,56,523,118,367,none\nFriday,5,27-03-15,94,66,726,149,608,none\nSaturday,6,28-03-15,109,64,686,244,609,none\nSunday,7,29-03-15,127,56,257,236,446,none\nMonday,1,30-03-15,93,55,547,234,427,promotion\nTuesday,2,31-03-15,81,39,361,122,429,none\nWednesday,3,01-04-15,74,41,457,297,259,none\nThursday,4,02-04-15,73,40,482,199,359,none\nFriday,5,03-04-15,88,41,744,222,584,none\nSaturday,6,04-04-15,84,36,711,326,481,none\nSunday,7,05-04-15,71,53,541,294,564,none\nMonday,1,06-04-15,48,33,584,211,232,none\nTuesday,2,07-04-15,92,44,576,212,441,none\nWednesday,3,08-04-15,74,71,427,194,520,none\nThursday,4,09-04-15,96,54,521,140,316,none\nFriday,5,10-04-15,94,72,324,239,385,none\nSaturday,6,11-04-15,87,38,701,291,503,none\nSunday,7,12-04-15,86,61,658,183,540,none\nMonday,1,13-04-15,48,44,485,196,269,none\nTuesday,2,14-04-15,67,50,575,145,309,none\nWednesday,3,15-04-15,70,55,354,104,212,none\nThursday,4,16-04-15,73,48,616,188,357,none\nFriday,5,17-04-15,98,68,657,243,431,none\nSaturday,6,18-04-15,110,72,760,330,431,none\nSunday,7,19-04-15,112,53,481,231,553,none\nMonday,1,20-04-15,64,48,559,206,454,none\nTuesday,2,21-04-15,86,47,362,277,484,none\nWednesday,3,22-04-15,84,65,511,200,342,none\nThursday,4,23-04-15,83,57,682,301,308,none\nFriday,5,24-04-15,137,68,517,240,398,none\nSaturday,6,25-04-15,85,67,705,175,407,none\nSunday,7,26-04-15,112,66,465,237,393,none\nMonday,1,27-04-15,71,69,435,162,280,none\nTuesday,2,28-04-15,90,52,444,160,291,none\nWednesday,3,29-04-15,81,62,488,253,542,none\nThursday,4,30-04-15,77,39,328,252,275,none\nFriday,5,01-05-15,85,47,571,290,533,none\nSaturday,6,02-05-15,111,58,760,171,451,none\nSunday,7,03-05-15,84,58,691,326,553,promotion\nMonday,1,04-05-15,77,37,320,244,262,none\nTuesday,2,05-05-15,90,48,556,223,412,none\nWednesday,3,06-05-15,83,51,501,183,341,none\nThursday,4,07-05-15,97,44,699,153,290,none\nFriday,5,08-05-15,96,45,744,313,480,none\nSaturday,6,09-05-15,113,58,917,351,323,none\nSunday,7,10-05-15,51,52,678,330,268,none\nMonday,1,11-05-15,92,50,487,282,615,promotion\nTuesday,2,12-05-15,107,45,444,287,286,none\nWednesday,3,13-05-15,59,51,598,262,177,none\nThursday,4,14-05-15,107,64,642,221,395,none\nFriday,5,15-05-15,138,60,704,174,160,none\nSaturday,6,16-05-15,103,66,487,283,543,none\nSunday,7,17-05-15,108,65,277,310,251,none\nMonday,1,18-05-15,67,51,300,249,460,none\nTuesday,2,19-05-15,73,57,340,236,413,none\nWednesday,3,20-05-15,74,44,423,65,278,none\nThursday,4,21-05-15,54,22,457,266,303,none\nFriday,5,22-05-15,108,57,665,334,303,none\nSaturday,6,23-05-15,165,69,736,312,550,promotion\nSunday,7,24-05-15,82,68,768,185,570,none\nMonday,1,25-05-15,102,61,569,159,366,promotion\nTuesday,2,26-05-15,91,49,391,97,261,none\nWednesday,3,27-05-15,94,30,543,219,506,none\nThursday,4,28-05-15,90,50,546,212,380,none\nFriday,5,29-05-15,110,65,759,263,489,none\nSaturday,6,30-05-15,99,54,716,419,429,none\nSunday,7,31-05-15,81,56,525,233,621,none\nMonday,1,01-06-15,56,29,398,336,261,none\nTuesday,2,02-06-15,58,37,428,259,510,none\nWednesday,3,03-06-15,63,39,539,244,350,none\nThursday,4,04-06-15,88,43,623,316,314,none\nFriday,5,05-06-15,115,75,750,290,367,none\nSaturday,6,06-06-15,89,87,684,434,459,none\nSunday,7,07-06-15,118,55,563,181,462,none\nMonday,1,08-06-15,97,53,394,316,386,none\nTuesday,2,09-06-15,115,39,553,326,499,promotion\nWednesday,3,10-06-15,55,52,453,241,300,none\nThursday,4,11-06-15,89,59,515,281,373,none\nFriday,5,12-06-15,95,63,625,274,370,none\nSaturday,6,13-06-15,118,42,638,260,634,none\nSunday,7,14-06-15,113,67,477,227,349,none\nMonday,1,15-06-15,82,54,548,276,369,none\nTuesday,2,16-06-15,65,37,622,351,228,promotion\nWednesday,3,17-06-15,81,61,548,381,440,none\nThursday,4,18-06-15,104,49,519,271,323,none\nFriday,5,19-06-15,115,57,621,331,521,none\nSaturday,6,20-06-15,136,61,600,377,480,none\nSunday,7,21-06-15,118,67,595,241,349,none\nMonday,1,22-06-15,59,61,551,249,318,none\nTuesday,2,23-06-15,80,54,505,276,395,none\nWednesday,3,24-06-15,101,59,469,186,201,none\nThursday,4,25-06-15,76,35,501,157,325,none\nFriday,5,26-06-15,90,64,602,317,434,none\nSaturday,6,27-06-15,106,41,390,251,451,none\nSunday,7,28-06-15,116,64,364,232,365,none\nMonday,1,29-06-15,85,46,395,283,291,none\nTuesday,2,30-06-15,65,62,442,361,329,none\nWednesday,3,01-07-15,85,25,397,256,394,none\nThursday,4,02-07-15,76,39,415,368,233,none\nFriday,5,03-07-15,114,61,514,269,476,none\nSaturday,6,04-07-15,121,58,814,162,306,none\nSunday,7,05-07-15,126,59,606,337,555,promotion\nMonday,1,06-07-15,74,45,699,290,359,none\nTuesday,2,07-07-15,73,57,645,238,368,none\nWednesday,3,08-07-15,79,56,521,252,344,none\nThursday,4,09-07-15,81,50,383,274,378,none\nFriday,5,10-07-15,74,66,564,421,621,none\nSaturday,6,11-07-15,126,64,868,477,505,promotion\nSunday,7,12-07-15,107,38,640,354,482,none\nMonday,1,13-07-15,97,44,366,233,313,none\nTuesday,2,14-07-15,91,69,525,308,167,none\nWednesday,3,15-07-15,67,36,373,258,402,none\nThursday,4,16-07-15,61,42,581,232,491,none\nFriday,5,17-07-15,114,60,627,387,734,promotion\nSaturday,6,18-07-15,105,33,635,214,516,none\nSunday,7,19-07-15,113,72,688,302,397,none\nMonday,1,20-07-15,97,46,388,205,388,none\nTuesday,2,21-07-15,65,42,473,220,469,none\nWednesday,3,22-07-15,94,44,475,232,514,none\nThursday,4,23-07-15,65,54,487,265,287,none\nFriday,5,24-07-15,130,56,678,301,543,none\nSaturday,6,25-07-15,114,73,623,361,534,none\nSunday,7,26-07-15,82,61,526,514,386,none\nMonday,1,27-07-15,92,48,615,211,344,none\nTuesday,2,28-07-15,51,50,493,307,338,none\nWednesday,3,29-07-15,94,69,473,386,378,promotion\nThursday,4,30-07-15,87,43,334,229,441,none\nFriday,5,31-07-15,47,55,538,293,272,none\nSaturday,6,01-08-15,109,81,538,417,556,none\nSunday,7,02-08-15,77,54,340,356,251,none\nMonday,1,03-08-15,69,59,595,261,309,promotion\nTuesday,2,04-08-15,67,48,612,200,466,none\nWednesday,3,05-08-15,69,47,544,320,438,none\nThursday,4,06-08-15,103,61,460,212,375,none\nFriday,5,07-08-15,86,46,749,416,404,promotion\nSaturday,6,08-08-15,61,67,662,326,417,none\nSunday,7,09-08-15,114,73,526,357,403,none\nMonday,1,10-08-15,43,46,419,259,311,none\nTuesday,2,11-08-15,86,33,380,308,446,none\nWednesday,3,12-08-15,57,39,398,161,356,none\nThursday,4,13-08-15,74,45,539,246,504,none\nFriday,5,14-08-15,122,56,496,241,422,none\nSaturday,6,15-08-15,119,59,470,249,401,none\nSunday,7,16-08-15,95,57,570,248,406,none\nMonday,1,17-08-15,100,55,643,105,470,promotion\nTuesday,2,18-08-15,91,56,656,295,428,none\nWednesday,3,19-08-15,64,48,702,239,225,none\nThursday,4,20-08-15,81,53,534,221,308,none\nFriday,5,21-08-15,100,76,787,275,277,none\nSaturday,6,22-08-15,71,69,913,272,404,none\nSunday,7,23-08-15,88,85,846,265,607,none\nMonday,1,24-08-15,74,53,588,261,323,promotion\nTuesday,2,25-08-15,103,58,474,382,526,none\nWednesday,3,26-08-15,66,37,669,316,442,promotion\nThursday,4,27-08-15,87,51,381,304,348,none\nFriday,5,28-08-15,81,67,657,441,432,promotion\nSaturday,6,29-08-15,144,65,695,378,611,none\nSunday,7,30-08-15,147,74,692,335,343,none\nMonday,1,31-08-15,84,57,484,208,430,none\nTuesday,2,01-09-15,78,37,415,272,386,none\nWednesday,3,02-09-15,91,50,577,159,403,none\nThursday,4,03-09-15,72,65,536,131,390,none\nFriday,5,04-09-15,92,42,686,333,409,none\nSaturday,6,05-09-15,104,74,619,191,545,none\nSunday,7,06-09-15,116,51,785,277,525,none\nMonday,1,07-09-15,66,47,593,134,316,none\nTuesday,2,08-09-15,54,48,584,235,230,none\nWednesday,3,09-09-15,71,29,576,211,304,none\nThursday,4,10-09-15,101,38,656,124,500,none\nFriday,5,11-09-15,132,61,640,228,254,none\nSaturday,6,12-09-15,115,72,777,299,334,none\nSunday,7,13-09-15,108,66,762,251,509,none\nMonday,1,14-09-15,76,38,543,139,417,none\nTuesday,2,15-09-15,129,42,746,243,463,promotion\nWednesday,3,16-09-15,70,36,434,182,206,none\nThursday,4,17-09-15,81,59,399,222,460,none\nFriday,5,18-09-15,109,29,621,176,453,none\nSaturday,6,19-09-15,117,77,818,377,495,promotion\nSunday,7,20-09-15,88,76,651,244,451,none\nMonday,1,21-09-15,153,62,545,258,528,promotion\nTuesday,2,22-09-15,78,46,510,202,410,none\nWednesday,3,23-09-15,79,53,618,250,351,none\nThursday,4,24-09-15,101,57,532,205,368,none\nFriday,5,25-09-15,98,51,445,143,547,none\nSaturday,6,26-09-15,94,79,977,315,557,none\nSunday,7,27-09-15,109,62,718,140,431,none\nMonday,1,28-09-15,70,44,462,217,272,none\nTuesday,2,29-09-15,64,44,369,143,260,none\nWednesday,3,30-09-15,77,63,636,174,253,none\nThursday,4,01-10-15,86,44,602,267,436,none\nFriday,5,02-10-15,138,71,635,260,387,promotion\nSaturday,6,03-10-15,113,63,966,268,634,none\nSunday,7,04-10-15,85,68,682,208,450,none\nMonday,1,05-10-15,71,39,653,262,424,none\nTuesday,2,06-10-15,69,49,275,150,456,none\nWednesday,3,07-10-15,81,40,849,243,348,none\nThursday,4,08-10-15,70,32,534,230,246,none\nFriday,5,09-10-15,92,91,719,216,685,promotion\nSaturday,6,10-10-15,105,71,567,338,475,none\nSunday,7,11-10-15,141,38,515,290,453,none\nMonday,1,12-10-15,50,33,470,234,372,none\nTuesday,2,13-10-15,61,44,539,228,354,none\nWednesday,3,14-10-15,115,45,537,176,448,promotion\nThursday,4,15-10-15,82,42,549,192,492,none\nFriday,5,16-10-15,124,84,573,261,463,none\nSaturday,6,17-10-15,70,76,566,349,686,none\nSunday,7,18-10-15,119,82,584,269,543,none\nMonday,1,19-10-15,69,37,589,187,298,none\nTuesday,2,20-10-15,67,38,650,161,337,none\nWednesday,3,21-10-15,90,47,386,203,316,none\nThursday,4,22-10-15,94,64,506,235,316,none\nFriday,5,23-10-15,108,45,826,275,361,none\nSaturday,6,24-10-15,63,82,689,275,370,none\nSunday,7,25-10-15,86,87,465,280,528,none\nMonday,1,26-10-15,91,62,386,110,379,none\nTuesday,2,27-10-15,97,44,310,137,287,none\nWednesday,3,28-10-15,69,54,358,176,531,none\nThursday,4,29-10-15,93,61,581,73,303,none\nFriday,5,30-10-15,91,77,348,258,345,none\nSaturday,6,31-10-15,135,52,441,221,340,none\nSunday,7,01-11-15,88,80,981,123,417,promotion\nMonday,1,02-11-15,74,39,462,149,286,none\nTuesday,2,03-11-15,81,48,403,138,419,none\nWednesday,3,04-11-15,82,37,452,160,234,none\nThursday,4,05-11-15,73,38,514,223,344,none\nFriday,5,06-11-15,83,57,533,245,469,none\nSaturday,6,07-11-15,105,73,612,166,333,none\nSunday,7,08-11-15,75,62,646,189,473,promotion\nMonday,1,09-11-15,118,46,523,247,336,promotion\nTuesday,2,10-11-15,85,66,713,172,404,none\nWednesday,3,11-11-15,96,38,628,157,237,none\nThursday,4,12-11-15,92,52,485,142,271,none\nFriday,5,13-11-15,96,48,614,169,265,none\nSaturday,6,14-11-15,132,35,792,147,580,none\nSunday,7,15-11-15,115,32,529,83,425,none\nMonday,1,16-11-15,96,66,472,172,480,promotion\nTuesday,2,17-11-15,95,45,699,165,572,none\nWednesday,3,18-11-15,59,59,375,180,436,none\nThursday,4,19-11-15,86,63,384,115,369,none\nFriday,5,20-11-15,89,73,490,224,570,none\nSaturday,6,21-11-15,113,74,799,308,470,none\nSunday,7,22-11-15,105,44,564,225,269,none\nMonday,1,23-11-15,88,40,285,179,318,none\nTuesday,2,24-11-15,74,41,355,124,332,none\nWednesday,3,25-11-15,75,52,467,176,461,none\nThursday,4,26-11-15,97,53,554,122,344,none\nFriday,5,27-11-15,83,67,559,191,391,none\nSaturday,6,28-11-15,113,86,596,197,389,none\nSunday,7,29-11-15,104,63,532,196,626,none\nMonday,1,30-11-15,82,54,329,145,659,none\nTuesday,2,01-12-15,85,52,361,148,368,none\nWednesday,3,02-12-15,59,61,398,156,358,none\nThursday,4,03-12-15,87,47,345,165,439,none\nFriday,5,04-12-15,109,61,678,192,624,none\nSaturday,6,05-12-15,115,51,535,156,547,none\nSunday,7,06-12-15,109,45,734,199,453,promotion\nMonday,1,07-12-15,129,72,846,185,384,promotion\nTuesday,2,08-12-15,79,48,467,188,317,none\nWednesday,3,09-12-15,85,51,562,134,434,none\nThursday,4,10-12-15,99,48,728,164,445,none\nFriday,5,11-12-15,156,70,907,274,504,promotion\nSaturday,6,12-12-15,53,58,590,262,423,none\nSunday,7,13-12-15,78,77,707,185,490,none\nMonday,1,14-12-15,72,70,404,198,380,none\nTuesday,2,15-12-15,107,60,444,174,272,none\nWednesday,3,16-12-15,77,48,598,155,419,none\nThursday,4,17-12-15,65,54,638,107,341,none\nFriday,5,18-12-15,103,63,402,154,327,none\nSaturday,6,19-12-15,148,76,589,168,439,none\nSunday,7,20-12-15,106,62,657,151,477,none\nMonday,1,21-12-15,73,46,570,156,238,none\nTuesday,2,22-12-15,103,53,420,134,420,none\nWednesday,3,23-12-15,72,44,505,156,375,none\nThursday,4,24-12-15,87,46,564,237,405,none\nFriday,5,25-12-15,90,37,838,240,581,none\nSaturday,6,26-12-15,113,46,445,255,488,none\nSunday,7,27-12-15,136,49,700,265,483,none\nMonday,1,28-12-15,77,45,605,69,408,none\nTuesday,2,29-12-15,93,56,508,185,452,none\nWednesday,3,30-12-15,62,28,458,141,356,none\nThursday,4,31-12-15,65,56,491,190,446,none" }, { "path": "Data/bank.csv", "content": 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}, { "path": "Data/clscredit.csv", "content": "agegp,income,student,creditrating,buypc\nYouth,High,No,Fair,No\nYouth,High,No,Excellent,No\nMiddle ,High,No,Fair,Yes\nSenior,Medium,No,Fair,Yes\nSenior,Low,Yes,Fair,Yes\nMiddle ,Low,Yes,Excellent,No\nYouth,Low,Yes,Excellent,Yes\nYouth,Low,Yes,Excellent,Yes\nSenior,Medium,No,Fair,No\nYouth,Low,Yes,Fair,Yes\nMiddle ,Medium,Yes,Fair,Yes\nMiddle ,Medium,Yes,Excellent,Yes\nMiddle ,Medium,No,Excellent,Yes\nSenior,Medium,No,Excellent,No" }, { "path": "Data/clsplay.csv", "content": "day,outlook,temperature,humidity,wind,play\nD1,Sunny,Hot,High,Weak,No\nD2,Sunny,Hot,High,Strong,No\nD3,Overcast,Hot,High,Weak,Yes\nD4,Rain,Mild,High,Weak,Yes\nD5,Rain,Cool,Normal,Weak,Yes\nD6,Rain,Cool,Normal,Strong,No\nD7,Overcast,Cool,Normal,Strong,Yes\nD8,Sunny,Mild,High,Weak,No\nD9,Sunny,Cool,Normal,Weak,Yes\nD10,Rain,Mild,Normal,Weak,Yes\nD11,Sunny,Mild,Normal,Strong,Yes\nD12,Overcast,Mild,High,Strong,Yes\nD13,Overcast,Hot,Normal,Weak,Yes\nD14,Rain,Mild,High,Strong,No" }, { "path": "Data/clust_custseg.csv", "content": 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,1327,25,19.8,1,5,0,0,15,0,1,1,1,10\n\"1977\",373,25,19.8,1,4,0,0,14,0,2,1,1,8\n\"1978\",3527,25,19.9,1,4,0,0,12,0,2,1,1,5\n\"1979\",9905,25,19.9,1,2,0,0,9,1,2,1,1,8\n\"1980\",6797,25,19.9,1,4,0,0,12,1,2,1,1,6\n\"1981\",7011,25,19.9,1,3,0,0,15,1,1,1,1,8\n\"1982\",127,25,19.9,1,2,0,0,12,0,1,1,1,8\n\"1983\",185,25,20,1,2,0,0,15,0,0,1,1,6\n\"1984\",3635,25,20,1,2,0,0,12,0,1,1,1,6\n\"1985\",4965,25,20,1,4,0,0,11,0,0,1,1,5\n\"1986\",1106,25,20,1,1,0,1,14,0,3,1,1,10\n\"1987\",9389,25,20,1,2,0,0,14,1,2,1,1,5\n\"1988\",9741,25,20.1,1,3,0,0,11,1,2,1,1,9\n\"1989\",8825,25,20.1,1,1,0,0,12,1,2,1,1,7\n\"1990\",7405,25,20.1,1,2,0,0,11,1,1,1,1,9\n\"1991\",181,25,20.2,1,5,0,0,12,0,1,1,1,8\n\"1992\",10141,25,20.2,1,2,0,0,10,1,1,1,1,8\n\"1993\",8417,25,20.2,1,5,0,0,9,1,2,1,1,9\n\"1994\",6065,25,20.2,1,2,0,0,12,1,0,1,1,7\n\"1995\",2449,25,20.2,1,2,0,1,12,0,4,1,1,2\n\"1996\",3179,25,20.3,1,3,0,0,10,0,0,1,1,6\n\"1997\",4834,25,20.3,1,3,0,1,11,0,2,1,1,9\n\"1998\",1663,25,20.3,1,1,0,0,15,0,2,1,1,10\n\"1999\",9691,25,20.3,1,5,0,0,13,1,2,1,1,9\n\"2000\",10271,25,20.4,1,2,0,0,10,1,2,1,1,8\n\"2001\",3105,25,20.5,1,2,0,1,13,0,8,1,1,0\n\"2002\",6189,25,20.5,1,3,0,0,13,1,0,1,1,5\n\"2003\",10289,25,20.5,1,3,0,0,10,1,1,1,1,8\n\"2004\",5745,25,20.5,1,2,0,0,15,1,2,1,1,9\n\"2005\",855,25,20.6,1,4,0,0,11,0,2,1,1,10\n\"2006\",2089,25,20.6,1,2,0,0,11,0,2,1,1,10\n\"2007\",6339,25,20.6,1,1,0,0,12,1,1,1,1,8\n\"2008\",4273,25,20.6,1,5,0,0,13,0,1,1,1,6\n\"2009\",5777,25,20.6,1,4,0,0,12,1,1,1,1,6\n\"2010\",10579,25,20.6,1,4,0,0,12,1,1,1,1,6\n\"2011\",7713,25,20.6,1,1,0,0,12,1,1,1,1,5\n\"2012\",3955,25,20.7,1,5,0,0,13,0,2,1,1,7\n\"2013\",7065,25,20.7,1,5,0,0,9,1,2,1,1,5\n\"2014\",2959,25,20.7,1,1,0,0,9,0,1,1,1,9\n\"2015\",2953,25,20.7,1,3,0,0,9,0,0,1,1,7\n\"2016\",53,25,20.7,1,5,0,0,13,0,1,1,1,9\n\"2017\",8859,25,20.7,1,2,0,0,13,1,0,1,1,9\n\"2018\",237,25,20.7,1,2,0,0,12,0,1,1,1,10\n\"2019\",2335,25,20.8,1,1,0,0,9,0,2,1,1,8\n\"2020\",10165,25,20.8,1,3,0,0,10,1,2,1,1,7\n\"2021\",4663,25,20.8,1,2,0,1,12,0,8,1,1,4\n\"2022\",1539,25,20.8,1,5,0,0,14,0,2,1,1,10\n\"2023\",8453,25,20.8,1,4,0,0,11,1,2,1,1,9\n\"2024\",2533,25,20.8,1,3,0,0,15,0,2,1,1,9\n\"2025\",9151,25,20.9,1,3,0,1,14,1,7,1,1,3\n\"2026\",3801,25,21,1,2,0,0,15,0,1,1,1,10\n\"2027\",4377,25,21,1,2,0,0,13,0,0,1,1,8\n\"2028\",1993,25,21,1,1,0,0,14,0,1,1,1,7\n\"2029\",1251,25,21,1,5,0,0,12,0,1,1,1,10\n\"2030\",1625,25,21,1,4,0,0,11,0,2,1,1,5\n\"2031\",273,25,21,1,3,0,0,12,0,2,1,1,10\n\"2032\",4601,25,21.1,1,2,0,0,14,0,0,1,1,5\n\"2033\",6675,25,21.1,1,5,0,0,9,1,2,1,1,5\n\"2034\",2613,25,21.1,1,2,0,0,11,0,0,1,1,7\n\"2035\",239,25,21.1,1,5,0,0,10,0,2,1,1,10\n\"2036\",5407,25,21.2,1,3,0,0,12,0,0,1,1,8\n\"2037\",4177,25,21.2,1,1,0,0,11,0,0,1,1,5\n\"2038\",7311,25,21.2,1,3,0,0,9,1,2,1,1,5\n\"2039\",521,25,21.2,1,5,0,0,15,0,2,1,1,8\n\"2040\",3345,25,21.2,1,4,0,0,9,0,1,1,1,6\n\"2041\",9475,25,21.3,1,5,0,0,11,1,0,1,1,10\n\"2042\",4845,25,21.3,1,3,0,0,15,0,0,1,1,5\n\"2043\",2895,25,21.3,1,1,0,0,12,0,2,1,1,5\n\"2044\",2269,25,21.3,1,5,0,0,9,0,1,1,1,7\n\"2045\",4505,25,21.3,1,3,0,0,13,0,0,1,1,5\n\"2046\",5993,25,21.4,1,5,0,0,12,1,2,1,1,10\n\"2047\",7031,25,21.4,1,2,0,0,11,1,0,1,1,8\n\"2048\",9299,25,21.4,1,4,0,0,12,1,1,1,1,7\n\"2049\",105,25,21.4,1,1,0,0,11,0,1,1,1,6\n\"2050\",161,25,21.4,1,4,0,0,11,0,0,1,1,10\n\"2051\",3561,25,21.4,1,4,0,0,13,0,0,1,1,9\n\"2052\",1988,25,21.4,1,1,0,1,11,0,6,1,1,6\n\"2053\",7205,25,21.4,1,2,0,0,14,1,1,1,1,9\n\"2054\",8543,25,21.4,1,4,0,0,11,1,1,1,1,6\n\"2055\",8271,25,21.5,1,5,0,0,12,1,0,1,1,9\n\"2056\",8051,25,21.5,1,4,0,0,10,1,1,1,1,10\n\"2057\",1647,25,21.6,1,5,0,0,11,0,1,1,1,10\n\"2058\",8056,25,21.7,1,1,0,1,15,1,7,1,1,3\n\"2059\",9137,25,21.7,1,2,0,0,14,1,1,1,1,9\n\"2060\",7385,25,21.7,1,5,0,0,13,1,0,1,1,8\n\"2061\",2469,25,21.7,1,3,0,0,14,0,2,1,1,9\n\"2062\",2749,25,21.7,1,1,0,0,12,0,0,1,1,8\n\"2063\",8741,25,21.8,1,3,0,0,14,1,2,1,1,9\n\"2064\",2291,25,21.8,1,5,0,0,10,0,1,1,1,9\n\"2065\",8207,25,21.8,1,5,0,0,10,1,2,1,1,5\n\"2066\",1035,25,21.8,1,4,0,0,9,0,1,1,1,9\n\"2067\",8559,25,21.8,1,5,0,0,13,1,1,1,1,7\n\"2068\",5449,25,21.8,1,3,0,0,12,1,2,1,1,7\n\"2069\",8991,25,21.9,1,4,0,0,13,1,1,1,1,8\n\"2070\",9639,25,22,1,3,0,0,12,1,2,1,1,9\n\"2071\",6301,25,22,1,4,0,0,9,1,1,1,1,7\n\"2072\",7861,25,22,1,4,0,0,12,1,0,1,1,8\n\"2073\",10619,25,22,1,5,0,0,14,1,0,1,1,5\n\"2074\",4871,25,22,1,3,0,0,11,0,0,1,1,6\n\"2075\",7219,25,22,1,3,0,0,12,1,0,1,1,6\n\"2076\",91,25,22,1,5,0,0,12,0,2,1,1,8\n\"2077\",2687,25,22,1,5,0,0,10,0,2,1,1,6\n\"2078\",2785,25,22,1,3,0,0,10,0,1,1,1,6\n\"2079\",3185,25,22.1,1,3,0,0,12,0,1,1,1,7\n\"2080\",1985,25,22.1,1,3,0,0,12,0,1,1,1,7\n\"2081\",1883,25,22.1,1,4,0,0,12,0,1,1,1,5\n\"2082\",10657,25,22.1,1,3,0,0,13,1,0,1,1,5\n\"2083\",833,25,22.1,1,1,0,0,13,0,1,1,1,8\n\"2084\",9615,25,22.1,1,2,0,0,14,1,0,1,1,8\n\"2085\",539,25,22.1,1,5,0,0,13,0,1,1,1,5\n\"2086\",4139,25,22.2,1,4,0,0,12,0,1,1,1,6\n\"2087\",2511,25,22.2,1,5,0,0,11,0,0,1,1,8\n\"2088\",9305,25,22.2,1,1,0,0,12,1,0,1,1,9\n\"2089\",3783,25,22.3,1,5,0,0,15,0,2,1,1,7\n\"2090\",3735,25,22.3,1,2,0,0,12,0,0,1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}, { "path": "Data/denco.csv", "content": "custname,region,partnum,revenue,cost,margin\n3M COMPANY,01-East,727032005,24097.50,19851.82,4245.68\n4-STATE SUPPLY,01-East,735602000,156200.00,52381.38,103818.62\n4-STATE SUPPLY,01-East,777143000,34927.20,15382.08,19545.12\n4-STATE SUPPLY,01-East,777142000,21989.40,12562.50,9426.90\n4-STATE SUPPLY,01-East,735750000,12487.00,3686.91,8800.09\nA G C INC.,01-East,791104479,78642.00,18147.40,60494.60\nA G C INC.,01-East,791106280,3340.46,7614.14,-4273.68\nA P S TECHNOLOGY,01-East,764521000,333.00,13.27,319.73\nA W CHESTERTON COMPANY,01-East,770560000,2937.60,258.27,2679.33\nA W CHESTERTON COMPANY,01-East,770568000,1029.00,89.50,939.50\nA&F WHOLESALE INSULATION,01-East,770264000,95134.50,41677.18,53457.32\nA&F WHOLESALE INSULATION,01-East,735602000,89103.50,26645.65,62457.85\nA&F WHOLESALE INSULATION,01-East,764861000,70882.00,30089.92,40792.08\nA&F WHOLESALE INSULATION,01-East,764521000,70611.00,22683.19,47927.81\nA&F WHOLESALE INSULATION,01-East,770220200,69066.00,21411.45,47654.55\nA&F WHOLESALE INSULATION,01-East,724281200,45570.00,24306.87,21263.13\nA&F WHOLESALE INSULATION,01-East,733151500,21384.00,5113.93,16270.07\nA&F WHOLESALE INSULATION,01-East,764901000,12155.00,3649.65,8505.35\nA&F WHOLESALE INSULATION,01-East,770380000,12140.00,2314.54,9825.46\nA&F WHOLESALE INSULATION,01-East,735602110,11028.00,3418.00,7610.00\nA&F WHOLESALE INSULATION,01-East,770200000,10953.00,2403.03,8549.97\nA&F WHOLESALE INSULATION,01-East,737271000,6025.70,9870.84,-3845.14\nA&F WHOLESALE INSULATION,01-East,770540000,4897.38,474.88,4422.50\nA&F WHOLESALE INSULATION,01-East,733525600,4692.50,868.02,3824.48\nA&F WHOLESALE INSULATION,01-East,764821000,4390.00,1356.36,3033.64\nA&F WHOLESALE INSULATION,01-East,738521000,2373.12,1601.27,771.85\nA&F WHOLESALE INSULATION,01-East,739561200,1628.80,659.65,969.15\nA&F WHOLESALE INSULATION,01-East,764781000,1594.00,408.90,1185.10\nA&F WHOLESALE INSULATION,01-East,770512000,1192.55,88.67,1103.88\nA&F WHOLESALE INSULATION,01-East,733211050,968.00,223.64,744.36\nA&F WHOLESALE INSULATION,01-East,734851000,860.58,182.80,677.78\nA&F WHOLESALE INSULATION,01-East,739541200,829.44,256.39,573.05\nA&F WHOLESALE INSULATION,01-East,764902000,606.00,191.56,414.44\nA&F WHOLESALE INSULATION,01-East,735750000,516.00,127.10,388.90\nA&F WHOLESALE INSULATION,01-East,734611100,415.05,81.32,333.73\nA&F WHOLESALE INSULATION,01-East,739561000,404.64,170.44,234.20\nA&F WHOLESALE INSULATION,01-East,734311100,308.92,35.30,273.62\nA&F WHOLESALE INSULATION,01-East,733141200,271.20,66.98,204.22\nA&F WHOLESALE INSULATION,01-East,733464500,268.00,65.06,202.94\nA&F WHOLESALE INSULATION,01-East,735752000,258.00,64.44,193.56\nA.C. MILLER CONCRETE,01-East,764481000,23006.00,1404.61,21601.39\nA.C. MILLER CONCRETE,01-East,764461006,776.00,54.50,721.50\n\"ABLE AEROSPACE, INC.\",01-East,734311100,381.48,33.70,347.78\nABSORPTION CORP.,01-East,764221000,476.00,33.66,442.34\nACCEL-RF CORPORATION,01-East,733141200,520.00,66.98,453.02\nACME SPECIALTIES INC,01-East,733545700,1079.00,133.91,945.09\nACS INDUSTRIES INC.,01-East,740382101,165294.00,57490.21,107803.79\nACS INDUSTRIES INC.,01-East,733011000,4800.00,1200.36,3599.64\nADVANCE COOLING,01-East,739781100,394.88,95.21,299.67\nADVANCE THERMAL CORP,01-East,768221130,131510.40,39169.80,92340.60\nADVANCE THERMAL CORP,01-East,733201000,18278.00,4679.78,13598.22\nADVANCE THERMAL CORP,01-East,733151500,17460.00,3704.05,13755.95\nADVANCED CLIMATE TECH,01-East,770560000,597.00,89.50,507.50\nADVANCED RADIATION CORP,01-East,734211200,3785.12,369.72,3415.40\nAECOM,01-East,770722000,1342.00,38.38,1303.62\nAECOM,01-East,770930000,1054.50,23.77,1030.73\nAERO ENGINEERING & MFG,01-East,771100000,5400.00,146.93,5253.07\nAERO ENGINEERING & MFG,01-East,771102000,967.00,22.98,944.02\nAERO TUBE TECHNOLOGIES,01-East,770800000,2736.00,51.10,2684.90\nAGIS,01-East,739561100,56288.40,24606.26,31682.14\nAGIS,01-East,740063000,38955.50,12193.45,26762.05\nAGIS,01-East,764822000,15719.60,2604.25,13115.35\nAGIS,01-East,734311100,12846.96,1478.00,11368.96\nAGIS,01-East,777601000,4914.82,1366.94,3547.88\nAGIS,01-East,734900000,4603.30,1637.23,2966.07\nAGIS,01-East,764781040,3262.10,659.07,2603.03\nAGIS,01-East,764482000,2740.30,636.09,2104.21\nAGIS,01-East,739541100,2550.92,648.78,1902.14\nAGIS,01-East,734910000,2068.55,421.22,1647.33\nAGIS,01-East,764522000,1883.20,370.20,1513.00\nAGIS,01-East,764762000,1677.00,454.39,1222.61\nAGIS,01-East,743975000,1492.00,154.10,1337.90\nAGIS,01-East,739761100,1214.15,603.28,610.87\nAGIS,01-East,771170000,1119.85,75.47,1044.38\nAGIS,01-East,738521000,1110.78,573.81,536.97\nAGIS,01-East,739450000,756.22,390.35,365.87\nAGIS,01-East,739521100,690.14,139.38,550.76\nAGIS,01-East,764601000,473.55,93.00,380.55\nAGIS,01-East,740023000,463.34,118.54,344.80\nAGIS,01-East,764862000,429.15,78.55,350.60\nAGIS,01-East,733525600,427.00,65.49,361.51\nAGIS,01-East,764821000,385.40,62.62,322.78\nAGIS,01-East,793330000,288.80,53.02,235.78\nAGIS,01-East,793330074,139.00,48.23,90.77\nAGIS,01-East,734611100,131.29,26.96,104.33\nAIR LIQUIDE AT REC,01-East,734900000,203.60,37.83,165.77\nAIR PRODUCTS & CHEMICALS,01-East,764761000,508.00,30.71,477.29\nAIR PRODUCTS & CHEMICALS,01-East,764781000,454.00,20.45,433.55\nAIR PRODUCTS & CHEMICALS,01-East,764821000,430.00,15.36,414.64\nAKRO-FIREGUARD,01-East,726020019,231.50,22.61,208.89\nALBATROSS UV,01-East,764601000,326.00,13.29,312.71\nALBERTUS ENGINEERING INC.,01-East,734311100,700.00,70.60,629.40\nALCOA,01-East,724401150,28791.00,7594.88,21196.12\nALCOA,01-East,724401500,1620.00,370.66,1249.34\nALL TEMP INSULATION,01-East,735752000,23047.00,6722.23,16324.77\nALPHABET ENERGY INC,01-East,792654000,1024.00,321.90,702.10\nALPHABET ENERGY INC,01-East,792624000,666.00,86.09,579.91\nALPHABET ENERGY INC,01-East,739561200,421.12,76.67,344.45\nALPHABET ENERGY INC,01-East,739370000,364.80,138.24,226.56\nALSTOM POWER INC.,01-East,764902000,4299.75,2011.39,2288.36\nALTAIR TECHNOLOGIES,01-East,740361900,2736.00,898.74,1837.26\nALYSSA OXLEY,01-East,733423700,371.20,45.26,325.94\nAMERET LLC,01-East,764261000,424.00,22.89,401.11\nAMERICAN CONTROL & ENGRG,01-East,770560000,3018.00,172.18,2845.82\nAMERICAN CONTROL & ENGRG,01-East,777601000,366.72,72.02,294.70\nAMERICAN DESIGN &,01-East,764821000,430.00,15.36,414.64\nAMERICAN NATIONAL CARBIDE,01-East,739761100,7695.36,2572.81,5122.55\nAMERICAN POWER SERVICE,01-East,770500000,2043.00,83.60,1959.40\nAMERICAN RAILCAR,01-East,764706120,1199.52,326.91,872.61\n\"AMERISAFE, INC\",01-East,733725500,205.20,51.76,153.44\nAMERITHERM INC.,01-East,766081010,1101.00,36.37,1064.63\nAMERITHERM INC.,01-East,766121000,875.00,27.25,847.75\nANCHOR INSULATION,01-East,768144200,3070.00,526.37,2543.63\nANTHONY INTERNATIONAL,01-East,764521000,973.00,39.87,933.13\nAPCO SERVICES,01-East,770570000,3316.50,201.46,3115.04\nAPEX DISTRIBUTING INC,01-East,735602000,23968.00,5841.31,18126.69\nAPEX DISTRIBUTING,01-East,735602000,90480.00,25799.33,64680.67\nAPPLIED INDUSTRIAL TECH.,01-East,738561000,20198.72,9345.87,10852.85\nAPPLIED INDUSTRIAL TECH.,01-East,733525600,14175.00,2075.84,12099.16\nAPPLIED INDUSTRIAL TECH.,01-East,770222000,9348.15,800.79,8547.36\nAPPLIED INDUSTRIAL TECH.,01-East,764821000,3362.20,262.86,3099.34\nAPPLIED INDUSTRIAL TECH.,01-East,770522000,3337.10,205.59,3131.51\nAPPLIED INDUSTRIAL TECH.,01-East,770560000,1347.30,89.50,1257.80\nAPPLIED INDUSTRIAL TECH.,01-East,777601000,738.72,130.82,607.90\nAPPLIED INDUSTRIAL TECH.,01-East,724003000,591.15,81.82,509.33\nAPPLIED INDUSTRIAL TECH.,01-East,724281200,510.90,89.08,421.82\nAPPLIED INDUSTRIAL TECH.,01-East,764341000,406.00,47.97,358.03\nAPPLIED INDUSTRIAL TECH.,01-East,733151600,310.60,46.64,263.96\nAPPLIED TECHNICAL SERVICE,01-East,754067000,1935.00,617.82,1317.18\nAPPLIED TECHNICAL SERVICE,01-East,739450000,708.91,260.23,448.68\nAPPLIED TECHNICAL SERVICE,01-East,764521000,325.80,39.81,285.99\nAPPROVED AERONAUTICS,01-East,733464500,626.00,65.05,560.95\nAPPROVED AERONAUTICS,01-East,733444200,348.00,38.64,309.36\nARCHIE BRAY FOUNDATION,01-East,734351200,1890.75,143.35,1747.40\nAREMCO PRODUCTS INC,01-East,734000000,11289.75,1365.74,9924.01\nAREMCO PRODUCTS INC,01-East,734030000,2236.50,494.66,1741.84\nAREMCO PRODUCTS INC,01-East,734040000,1912.00,484.88,1427.12\nARI - MARMADUKE,01-East,764706120,74370.24,20268.30,54101.94\nARI - MARMADUKE,01-East,764652002,34003.20,5785.32,28217.88\nARMENA REFINING USA,01-East,764481000,1758.00,86.00,1672.00\nASHLAND ADVANCED,01-East,766689000,19660.00,2386.27,17273.73\nASK CHEMICALS HI-TECH LLC,01-East,732663000,215136.00,33045.76,182090.24\nASK CHEMICALS HI-TECH LLC,01-East,732670000,11329.90,4547.46,6782.44\nASK CHEMICALS HI-TECH LLC,01-East,732675000,6236.33,2049.38,4186.95\nASK CHEMICALS HI-TECH LLC,01-East,732650000,4881.61,1944.67,2936.94\nASK CHEMICALS HI-TECH LLC,01-East,732662000,4792.50,1755.06,3037.44\nASK CHEMICALS HI-TECH LLC,01-East,732630000,4683.15,1258.91,3424.24\nASK CHEMICALS HI-TECH LLC,01-East,733141200,265.60,133.96,131.64\nASPEN PRODUCTS GROUP,01-East,739390000,364.64,161.76,202.88\nATLANTECH DISTRIBUTION,01-East,735602000,77355.00,29396.27,47958.73\nATLANTECH DISTRIBUTION,01-East,739741100,31786.40,32062.53,-276.13\nATLANTECH DISTRIBUTION,01-East,735752000,10092.00,4207.32,5884.68\nATLANTECH DISTRIBUTION,01-East,735602010,1800.00,1130.24,669.76\nATLANTECH DISTRIBUTION,01-East,734351200,1488.52,147.24,1341.28\nATLANTECH DISTRIBUTION,01-East,734751200,654.45,113.13,541.32\nATLANTECH DISTRIBUTION,01-East,738542000,640.32,574.08,66.24\nATLANTIC CASTING,01-East,734351200,1890.75,143.35,1747.40\n\"ATLAS HOSE & GASKET CO.,\",01-East,739521100,1320.48,120.06,1200.42\nATLAS METALIZING,01-East,733586500,1857.60,181.35,1676.25\nATLAS METALIZING,01-East,734311100,381.48,33.70,347.78\nAUBURN MANUFACTURING CO.,01-East,733464600,611.00,66.28,544.72\nAUBURN MANUFACTURING CO.,01-East,770310000,518.00,45.47,472.53\nAUBURN MANUFACTURING CO.,01-East,733151500,472.00,54.11,417.89\nAUBURN MANUFACTURING CO.,01-East,733725500,286.00,51.76,234.24\nAUTOMATIC MACHINE PRODUCT,01-East,740382101,4334.40,1351.23,2983.17\n\"AVIS INDUSTRIES, INC.\",01-East,724281900,600.00,148.99,451.01\nBAE SYSTEMS,01-East,738401000,681.48,321.12,360.36\nBAE SYSTEMS,01-East,768021000,389.00,27.49,361.51\nBAILEY POTTERY EQUIPMENT,01-East,734311100,4196.80,410.84,3785.96\nBAKER DISTRIBUTING CO.,01-East,735602000,31774.50,7934.89,23839.61\nBAKER MODULES,01-East,793560000,6660.00,2649.33,4010.67\nBALLARD MATERIAL PRODUCTS,01-East,764481000,2090.00,169.41,1920.59\nBASF CORPORATION,01-East,724071900,23791.32,3183.37,20607.95\nBASF,01-East,733141200,520.00,66.98,453.02\nBATTELLE FOR THE US DOE,01-East,777601000,386.72,72.02,314.70\nBATTELLE FOR THE US DOE,01-East,768111000,280.00,15.38,264.62\nBAY INDUSTRIES INC.,01-East,735602000,1477184.00,497108.39,980075.61\nBAY INDUSTRIES INC.,01-East,735602110,428300.00,150904.70,277395.30\nBAY INDUSTRIES INC.,01-East,764631000,244921.60,80765.31,164156.29\nBAY INDUSTRIES INC.,01-East,735750000,111685.00,45262.02,66422.98\nBAY INDUSTRIES INC.,01-East,764822000,93744.00,22126.95,71617.05\nBAY INDUSTRIES INC.,01-East,764651000,87479.04,24691.61,62787.43\nBAY INDUSTRIES INC.,01-East,764901000,80118.75,33921.91,46196.84\nBAY INDUSTRIES INC.,01-East,764522000,77203.00,22375.27,54827.73\nBAY INDUSTRIES INC.,01-East,764821000,74253.50,28926.60,45326.90\nBAY INDUSTRIES INC.,01-East,764521000,48980.00,20993.74,27986.26\nBAY INDUSTRIES INC.,01-East,764706300,41077.76,17359.48,23718.28\nBAY INDUSTRIES INC.,01-East,764602000,35085.00,11564.29,23520.71\nBAY INDUSTRIES INC.,01-East,735752000,28110.00,9784.77,18325.23\nBAY INDUSTRIES INC.,01-East,764902000,18430.00,4808.72,13621.28\nBAY INDUSTRIES INC.,01-East,735862000,16374.40,7084.09,9290.31\nBAY INDUSTRIES INC.,01-East,764261000,14530.00,6650.50,7879.50\nBAY INDUSTRIES INC.,01-East,766162000,14175.00,8969.25,5205.75\nBAY INDUSTRIES INC.,01-East,764861000,13845.00,6022.31,7822.69\nBAY INDUSTRIES INC.,01-East,766121000,13800.00,5970.23,7829.77\nBAY INDUSTRIES INC.,01-East,764862000,11307.50,4896.53,6410.97\nBAY INDUSTRIES INC.,01-East,764706200,10386.24,3579.18,6807.06\nBAY INDUSTRIES INC.,01-East,764341000,9600.00,4797.40,4802.60\nBAY INDUSTRIES INC.,01-East,764901050,8960.00,2021.74,6938.26\nBAY INDUSTRIES INC.,01-East,733151500,8720.00,2164.26,6555.74\nBAY INDUSTRIES INC.,01-East,779988827,8169.56,4272.41,3897.15\nBAY INDUSTRIES INC.,01-East,780250000,5103.36,2493.56,2609.80\nBAY INDUSTRIES INC.,01-East,780401000,4239.36,2825.72,1413.64\nBAY INDUSTRIES INC.,01-East,764601000,3175.00,1328.62,1846.38\nBAY INDUSTRIES INC.,01-East,764561000,2053.00,926.49,1126.51\nBAY INDUSTRIES INC.,01-East,766001000,1290.00,599.17,690.83\nBAY INDUSTRIES INC.,01-East,766122000,1180.00,287.62,892.38\nBAY INDUSTRIES INC.,01-East,734910000,571.00,102.70,468.30\nBAY INDUSTRIES INC.,01-East,768144210,321.70,118.28,203.42\nBAY INSULATION OF TEXAS,01-East,735750000,4740.00,1906.59,2833.41\nBAY INSULATION,01-East,739781100,558.59,327.68,230.91\n\"BENGAL SUPPLY, LLC\",01-East,735602000,27360.00,8284.44,19075.56\nBIRK MANUFACTURING,01-East,733525500,752.50,70.39,682.11\nBLACKSTONE-NEY ULTRASONIC,01-East,739641100,393.84,137.43,256.41\nBLASCH PRECISION CERAMICS,01-East,764821000,4402.00,155.37,4246.63\nBLASCH PRECISION CERAMICS,01-East,770212000,3133.00,237.35,2895.65\nBLASCH PRECISION CERAMICS,01-East,770200000,2592.00,166.40,2425.60\nBLASCH PRECISION PRODUCTS,01-East,770212000,2054.00,156.16,1897.84\nBLASCH PRECISION PRODUCTS,01-East,770220000,942.00,54.69,887.31\nBLASCH PRECISION PRODUCTS,01-East,764821000,860.00,30.72,829.28\nBLASCH PRECISION PRODUCTS,01-East,779987651,815.00,95.41,719.59\nBLICKMAN INC.,01-East,739541100,23069.76,3433.81,19635.95\nBLOOM ENERGY CORPORATION,01-East,777907250,3930.00,2688.00,1242.00\nBLOUNT BOATS INC.,01-East,768133000,2592.00,706.53,1885.47\nBLOXR CORPORATION,01-East,733525500,5840.00,563.15,5276.85\nBLOXR CORPORATION,01-East,733464600,2960.00,349.71,2610.29\nBLOXR CORPORATION,01-East,733444200,1676.00,193.20,1482.80\nBLOXR CORPORATION,01-East,733464500,1252.00,130.10,1121.90\nBODY COTE,01-East,764461006,1200.00,136.24,1063.76\nBOEING AEROSPACE COMPANY,01-East,733141200,432.00,59.97,372.03\nBOEING COMMERCIAL,01-East,733648000,197199.00,116961.57,80237.43\nBOEING COMMERCIAL,01-East,733649000,89635.00,54444.71,35190.29\nBOEING COMPANY,01-East,715000000,2635.00,304.36,2330.64\nBOEING COMPANY,01-East,737912200,692.00,86.72,605.28\nBOEING COMPANY,01-East,737902100,462.00,67.12,394.88\nBOEING COMPANY,01-East,727000140,255.20,15.84,239.36\nBOLTS METALLIZING WEST,01-East,733464500,596.00,59.04,536.96\nBOLTS METALLIZING WEST,01-East,764481000,346.00,16.94,329.06\nBOSTON ATLANTIC CORP.,01-East,738361000,601.92,164.47,437.45\nBRADCO SUPPLY INC.,01-East,735602000,13050.00,3796.01,9253.99\nBRADEN MANUFACTURING,01-East,768231000,65059.00,43104.30,21954.70\nBRADEN MANUFACTURING,01-East,768241000,56919.95,37832.36,19087.59\nBRADEN MANUFACTURING,01-East,768080000,48538.00,37090.11,11447.89\nBRADEN MANUFACTURING,01-East,771320000,44468.50,6242.28,38226.22\nBRADEN MANUFACTURING,01-East,768231012,29663.00,20710.29,8952.71\nBRADEN MANUFACTURING,01-East,768241102,20942.25,14417.91,6524.34\nBRADEN MANUFACTURING,01-East,768231200,3120.00,1794.85,1325.15\nBRADEN MANUFACTURING,01-East,768144200,975.20,213.33,761.87\nBRADEN MANUFACTURING,01-East,770220000,423.90,27.35,396.55\nBRAND ENERGY SERVICES LLC,01-East,733464600,298.00,31.31,266.69\nBRAUER SUPPLY COMPANY,01-East,735602000,414296.00,132066.58,282229.42\nBRAUER SUPPLY COMPANY,01-East,735602110,58712.00,19152.93,39559.07\nBRAUER SUPPLY COMPANY,01-East,735752000,27784.00,10316.12,17467.88\nBRAUER SUPPLY COMPANY,01-East,735750000,26081.00,10090.73,15990.27\nBRAYTON ENERGY,01-East,740361701,1130.40,628.78,501.62\nBRIGHTSOURCE CONSTRUCTION,01-East,777690062,1287662.20,981523.47,306138.73\nBRIGHTSOURCE CONSTRUCTION,01-East,777692562,167520.00,159604.68,7915.32\nBRIGHTSOURCE CONSTRUCTION,01-East,777692662,74214.00,71587.06,2626.94\nBRIGHTSOURCE CONSTRUCTION,01-East,777699062,55212.48,42084.51,13127.97\nBRIGHTSOURCE CONSTRUCTION,01-East,777691562,53806.86,41013.63,12793.23\nBRIGHTSOURCE CONSTRUCTION,01-East,777690362,48810.30,37205.76,11604.54\nBRIGHTSOURCE CONSTRUCTION,01-East,777692462,46068.00,43855.34,2212.66\nBRIGHTSOURCE CONSTRUCTION,01-East,777690862,41884.00,31925.88,9958.12\nBRIGHTSOURCE CONSTRUCTION,01-East,793550074,33637.68,25235.42,8402.26\nBRIGHTSOURCE CONSTRUCTION,01-East,777691162,26386.62,14917.77,11468.85\nBRIGHTSOURCE CONSTRUCTION,01-East,777691262,22967.01,12984.93,9982.08\nBRIGHTSOURCE CONSTRUCTION,01-East,777692762,14812.56,14256.34,556.22\nBRIGHTSOURCE CONSTRUCTION,01-East,777652674,9360.00,7134.30,2225.70\nBRIGHTSOURCE CONSTRUCTION,01-East,777691862,9332.70,6996.81,2335.89\nBRIGHTSOURCE CONSTRUCTION,01-East,734351200,9206.20,1495.44,7710.76\nBRIGHTSOURCE CONSTRUCTION,01-East,734910000,8718.50,2413.34,6305.16\nBRIGHTSOURCE CONSTRUCTION,01-East,777694062,8559.36,6524.10,2035.26\nBRIGHTSOURCE CONSTRUCTION,01-East,777690162,8506.16,6483.44,2022.72\nBRIGHTSOURCE CONSTRUCTION,01-East,777652362,7994.08,7693.90,300.18\nBRIGHTSOURCE CONSTRUCTION,01-East,777690262,7766.72,5919.46,1847.26\nBRIGHTSOURCE CONSTRUCTION,01-East,777692262,4827.45,3679.56,1147.89\nBRIGHTSOURCE CONSTRUCTION,01-East,777690462,4640.40,3537.06,1103.34\nBRIGHTSOURCE CONSTRUCTION,01-East,777652474,4491.50,3422.70,1068.80\nBRIGHTSOURCE CONSTRUCTION,01-East,779988847,4118.40,3719.38,399.02\nBRIGHTSOURCE CONSTRUCTION,01-East,777690962,4039.56,3079.12,960.44\nBRIGHTSOURCE CONSTRUCTION,01-East,777690562,3168.18,2414.84,753.34\nBRIGHTSOURCE CONSTRUCTION,01-East,777691062,2854.62,2175.86,678.76\nBRIGHTSOURCE CONSTRUCTION,01-East,777691962,2446.05,1864.35,581.70\nBRIGHTSOURCE CONSTRUCTION,01-East,777692362,2357.25,1796.85,560.40\nBRIGHTSOURCE CONSTRUCTION,01-East,777692162,2129.10,1622.88,506.22\nBRIGHTSOURCE CONSTRUCTION,01-East,777690662,2041.50,1556.12,485.38\nBRIGHTSOURCE CONSTRUCTION,01-East,777690762,1912.50,1457.76,454.74\nBRIGHTSOURCE CONSTRUCTION,01-East,777692062,1723.10,1313.36,409.74\nBRIGHTSOURCE CONSTRUCTION,01-East,777691662,1636.65,1247.43,389.22\nBRIGHTSOURCE CONSTRUCTION,01-East,777693062,1499.80,1143.20,356.60\nBRIGHTSOURCE CONSTRUCTION,01-East,777691762,1424.70,1085.82,338.88\nBRIGHTSOURCE CONSTRUCTION,01-East,777692962,1192.86,1148.00,44.86\nBRIGHTSOURCE CONSTRUCTION,01-East,777652462,1175.60,1131.46,44.14\nBRIGHTSOURCE CONSTRUCTION,01-East,777693162,1175.60,1131.46,44.14\nBRIGHTSOURCE CONSTRUCTION,01-East,777652762,1147.32,1109.07,38.25\nBRIGHTSOURCE CONSTRUCTION,01-East,777652862,1147.32,1109.07,38.25\nBRIGHTSOURCE CONSTRUCTION,01-East,777652562,1132.95,876.03,256.92\nBRIGHTSOURCE CONSTRUCTION,01-East,777695062,860.04,655.58,204.46\nBRIGHTSOURCE CONSTRUCTION,01-East,777692862,662.70,637.78,24.92\nBRIGHTSOURCE CONSTRUCTION,01-East,777693262,644.00,619.76,24.24\nBRIGHTSOURCE CONSTRUCTION,01-East,777696062,587.16,447.56,139.60\nBRIGHTSOURCE CONSTRUCTION,01-East,777697062,333.60,254.30,79.30\nBRIGHTSOURCE CONSTRUCTION,01-East,777698062,333.60,254.30,79.30\nBRUCE DIAMOND CORP,01-East,739541000,3960.00,614.63,3345.37\nBRUCE DIAMOND CORP,01-East,740103000,824.88,135.47,689.41\nBRUCE DIAMOND CORP,01-East,740123000,697.20,128.01,569.19\nBRUCKNER SUPPLY,01-East,766121000,67600.00,15986.46,51613.54\nBRUCKNER SUPPLY,01-East,770522000,49598.90,3037.44,46561.46\nBRUCKNER SUPPLY,01-East,733211000,24816.00,5532.59,19283.41\nBRUCKNER SUPPLY,01-East,764821000,18717.75,5145.39,13572.36\nBRUCKNER SUPPLY,01-East,734970000,6428.22,2695.00,3733.22\nBRUCKNER SUPPLY,01-East,734311100,3501.96,308.12,3193.84\nBRUCKNER SUPPLY,01-East,733403200,3082.64,396.68,2685.96\nBRUCKNER SUPPLY,01-East,740361701,2649.60,1894.86,754.74\nBRUCKNER SUPPLY,01-East,770540000,1356.25,94.98,1261.27\nBRUCKNER SUPPLY,01-East,740402402,815.00,193.66,621.34\nBRUCKNER SUPPLY,01-East,774751000,636.00,79.14,556.86\nBRUCKNER SUPPLY,01-East,764221000,476.00,33.66,442.34\nBRUCKNER SUPPLY,01-East,733464500,412.00,65.05,346.95\nBRUCKNER SUPPLY,01-East,774671000,408.00,48.36,359.64\nBRUCKNER SUPPLY,01-East,774901000,336.00,39.57,296.43\nBRUCKNER SUPPLY,01-East,764221100,276.00,89.64,186.36\nBRUCKNER SUPPLY,01-East,764341000,101.60,23.98,77.62\nBRYANT REFRACTORY CO.,01-East,764781040,676089.00,471699.33,204389.67\nBRYANT REFRACTORY CO.,01-East,764821000,630120.00,268156.41,361963.59\nBRYANT REFRACTORY CO.,01-East,764901000,348688.50,165932.95,182755.55\nBRYANT REFRACTORY CO.,01-East,764901050,303447.00,143134.67,160312.33\nBRYANT REFRACTORY CO.,01-East,764821030,283968.00,123668.14,160299.86\nBRYANT REFRACTORY CO.,01-East,779958162,279532.50,239408.15,40124.35\nBRYANT REFRACTORY CO.,01-East,764521000,258694.00,116027.89,142666.11\nBRYANT REFRACTORY CO.,01-East,779956871,135368.20,82522.69,52845.51\nBRYANT REFRACTORY CO.,01-East,764601000,125635.50,60782.67,64852.83\nBRYANT REFRACTORY CO.,01-East,764781000,119858.80,43501.50,76357.30\nBRYANT REFRACTORY CO.,01-East,755084012,108981.72,78419.81,30561.91\nBRYANT REFRACTORY CO.,01-East,766121000,100401.00,29406.10,70994.90\nBRYANT REFRACTORY CO.,01-East,779956872,97091.40,58656.10,38435.30\nBRYANT REFRACTORY CO.,01-East,764821073,78502.50,77091.75,1410.75\nBRYANT REFRACTORY CO.,01-East,764901073,68160.00,69984.00,-1824.00\nBRYANT REFRACTORY CO.,01-East,777902600,66942.40,30423.72,36518.68\nBRYANT REFRACTORY CO.,01-East,764861000,62798.50,29542.69,33255.81\nBRYANT REFRACTORY CO.,01-East,766122000,60473.00,18929.13,41543.87\nBRYANT REFRACTORY CO.,01-East,733211000,59539.00,12254.84,47284.16\nBRYANT REFRACTORY CO.,01-East,779958166,58110.00,40961.06,17148.94\nBRYANT REFRACTORY CO.,01-East,764522000,52800.00,34920.83,17879.17\nBRYANT REFRACTORY CO.,01-East,768231000,46204.70,8821.75,37382.95\nBRYANT REFRACTORY CO.,01-East,779957196,43989.88,33000.66,10989.22\nBRYANT REFRACTORY CO.,01-East,764821161,41437.50,41106.00,331.50\nBRYANT REFRACTORY CO.,01-East,779958163,39330.00,29511.06,9818.94\nBRYANT REFRACTORY CO.,01-East,738401000,31212.00,24325.62,6886.38\nBRYANT REFRACTORY CO.,01-East,733151500,29912.60,4793.77,25118.83\nBRYANT REFRACTORY CO.,01-East,733525600,25490.00,3701.87,21788.13\nBRYANT REFRACTORY CO.,01-East,764561000,22148.50,9722.37,12426.13\nBRYANT REFRACTORY CO.,01-East,779956873,21656.96,13171.69,8485.27\nBRYANT REFRACTORY CO.,01-East,739641100,19815.99,13268.06,6547.93\nBRYANT REFRACTORY CO.,01-East,764521061,18910.00,19809.00,-899.00\nBRYANT REFRACTORY CO.,01-East,739681100,17905.14,10064.72,7840.42\nBRYANT REFRACTORY CO.,01-East,764521073,17080.00,18060.00,-980.00\nBRYANT REFRACTORY CO.,01-East,779956889,17040.00,9791.02,7248.98\nBRYANT REFRACTORY CO.,01-East,764261000,16775.00,7495.39,9279.61\nBRYANT REFRACTORY CO.,01-East,779956888,14752.80,11805.66,2947.14\nBRYANT REFRACTORY CO.,01-East,734910000,14153.10,4031.97,10121.13\nBRYANT REFRACTORY CO.,01-East,738361000,12791.68,8913.41,3878.27\nBRYANT REFRACTORY CO.,01-East,727000140,11168.32,1409.36,9758.96\nBRYANT REFRACTORY CO.,01-East,739561000,10692.28,2993.42,7698.86\nBRYANT REFRACTORY CO.,01-East,766750050,9856.00,4894.16,4961.84\nBRYANT REFRACTORY CO.,01-East,766162000,9756.00,5063.70,4692.30\nBRYANT REFRACTORY CO.,01-East,779956498,9360.00,6315.06,3044.94\nBRYANT REFRACTORY CO.,01-East,764341000,9162.00,5108.81,4053.19\nBRYANT REFRACTORY CO.,01-East,777601000,8527.59,2466.39,6061.20\nBRYANT REFRACTORY CO.,01-East,734661300,8503.00,1654.31,6848.69\nBRYANT REFRACTORY CO.,01-East,734900000,8109.40,4531.36,3578.04\nBRYANT REFRACTORY CO.,01-East,724261200,7204.40,1336.67,5867.73\nBRYANT REFRACTORY CO.,01-East,739661100,7102.08,3744.00,3358.08\nBRYANT REFRACTORY CO.,01-East,779958168,7000.00,7375.04,-375.04\nBRYANT REFRACTORY CO.,01-East,737912200,6980.25,1777.81,5202.44\nBRYANT REFRACTORY CO.,01-East,764822000,6730.00,2235.10,4494.90\nBRYANT REFRACTORY CO.,01-East,733464500,6521.80,1037.29,5484.51\nBRYANT REFRACTORY CO.,01-East,764481000,5838.00,2022.90,3815.10\nBRYANT REFRACTORY CO.,01-East,764901061,5680.00,5968.00,-288.00\nBRYANT REFRACTORY CO.,01-East,780680000,5280.00,2004.36,3275.64\nBRYANT REFRACTORY CO.,01-East,733211050,5020.00,1118.20,3901.80\nBRYANT REFRACTORY CO.,01-East,724071900,4959.36,1027.67,3931.69\nBRYANT REFRACTORY CO.,01-East,764902000,4649.50,1762.14,2887.36\nBRYANT REFRACTORY CO.,01-East,779956874,4387.46,2381.77,2005.69\nBRYANT REFRACTORY CO.,01-East,779958171,4259.08,2514.04,1745.04\nBRYANT REFRACTORY CO.,01-East,768141000,4107.50,815.88,3291.62\nBRYANT REFRACTORY CO.,01-East,768241000,3889.75,1580.91,2308.84\nBRYANT REFRACTORY CO.,01-East,764301000,3380.00,1525.36,1854.64\nBRYANT REFRACTORY CO.,01-East,780640000,3340.92,1080.00,2260.92\nBRYANT REFRACTORY CO.,01-East,779988828,3203.59,1584.96,1618.63\nBRYANT REFRACTORY CO.,01-East,733525400,3004.50,428.50,2576.00\nBRYANT REFRACTORY CO.,01-East,777642000,2578.80,1500.95,1077.85\nBRYANT REFRACTORY CO.,01-East,739450000,2552.40,1301.16,1251.24\nBRYANT REFRACTORY CO.,01-East,739541000,2519.42,501.34,2018.08\nBRYANT REFRACTORY CO.,01-East,768221000,2496.00,459.94,2036.06\nBRYANT REFRACTORY CO.,01-East,781940000,2353.58,485.42,1868.16\nBRYANT REFRACTORY CO.,01-East,779956599,2340.00,1791.22,548.78\nBRYANT REFRACTORY CO.,01-East,733151550,2282.40,334.16,1948.24\nBRYANT REFRACTORY CO.,01-East,770540000,2277.46,184.78,2092.68\nBRYANT REFRACTORY CO.,01-East,792785000,2193.12,479.91,1713.21\nBRYANT REFRACTORY CO.,01-East,733141200,1976.40,373.85,1602.55\nBRYANT REFRACTORY CO.,01-East,770450000,1958.40,199.22,1759.18\nBRYANT REFRACTORY CO.,01-East,737871000,1906.24,279.74,1626.50\nBRYANT REFRACTORY CO.,01-East,770220000,1829.40,169.53,1659.87\nBRYANT REFRACTORY CO.,01-East,754285000,1764.56,1147.22,617.34\nBRYANT REFRACTORY CO.,01-East,791003500,1746.00,432.93,1313.07\nBRYANT REFRACTORY CO.,01-East,766001000,1450.00,399.45,1050.55\nBRYANT REFRACTORY CO.,01-East,734651200,1431.10,407.01,1024.09\nBRYANT REFRACTORY CO.,01-East,737009842,1397.68,2507.20,-1109.52\nBRYANT REFRACTORY CO.,01-East,770280000,1340.00,160.05,1179.95\nBRYANT REFRACTORY CO.,01-East,764221000,1176.00,406.16,769.84\nBRYANT REFRACTORY CO.,01-East,737009844,1166.64,756.56,410.08\nBRYANT REFRACTORY CO.,01-East,781580000,1040.00,636.71,403.29\nBRYANT REFRACTORY CO.,01-East,766021000,995.00,303.72,691.28\nBRYANT REFRACTORY CO.,01-East,737821000,989.70,334.62,655.08\nBRYANT REFRACTORY CO.,01-East,779958693,920.64,556.24,364.40\nBRYANT REFRACTORY CO.,01-East,733403200,919.60,99.17,820.43\nBRYANT REFRACTORY CO.,01-East,770200000,813.60,84.82,728.78\nBRYANT REFRACTORY CO.,01-East,770320000,778.00,111.14,666.86\nBRYANT REFRACTORY CO.,01-East,770222000,739.50,68.04,671.46\nBRYANT REFRACTORY CO.,01-East,771132000,729.70,35.80,693.90\nBRYANT REFRACTORY CO.,01-East,768750000,663.12,144.72,518.40\nBRYANT REFRACTORY CO.,01-East,733725500,637.20,199.11,438.09\nBRYANT REFRACTORY CO.,01-East,793540000,492.00,115.87,376.13\nBRYANT REFRACTORY CO.,01-East,768211000,465.20,89.76,375.44\nBRYANT REFRACTORY CO.,01-East,740023000,463.68,119.31,344.37\nBRYANT REFRACTORY CO.,01-East,734822000,457.70,160.95,296.75\nBRYANT REFRACTORY CO.,01-East,770210000,440.00,63.04,376.96\nBRYANT REFRACTORY CO.,01-East,733444100,436.60,156.66,279.94\nBRYANT REFRACTORY CO.,01-East,792620000,427.60,86.74,340.86\nBRYANT REFRACTORY CO.,01-East,792783000,414.00,90.01,323.99\nBRYANT REFRACTORY CO.,01-East,754087000,331.44,142.20,189.24\nBRYANT REFRACTORY CO.,01-East,764761000,319.20,94.43,224.77\nBRYANT REFRACTORY CO.,01-East,733151600,310.00,46.64,263.36\nBRYANT REFRACTORY CO.,01-East,734311100,291.44,33.70,257.74\nBRYANT REFRACTORY CO.,01-East,793320000,262.00,117.93,144.07\nBRYANT REFRACTORY CO.,01-East,733423600,244.80,45.08,199.72\nBRYANT REFRACTORY CO.,01-East,733423700,232.96,36.22,196.74\nBRYANT REFRACTORY CO.,01-East,768751000,232.80,48.25,184.55\nBRYANT REFRACTORY CO.,01-East,734611100,228.08,107.82,120.26\nBRYANT REFRACTORY CO.,01-East,779181000,65.91,20.63,45.28\nBUFFALO DENTAL,01-East,740063000,1898.40,356.52,1541.88\nBURKHART REFRACTORY INST.,01-East,734311100,196.00,33.70,162.30\nBURNHAM INSULATION,01-East,739541000,259.20,90.50,168.70\nBURNHAM INSULATION,01-East,764901000,100.50,16.01,84.49\nC & E RECLAMATION INC.,01-East,739390000,9509.76,5823.36,3686.40\nC & E RECLAMATION INC.,01-East,777904301,2962.50,1174.68,1787.82\nC & E RECLAMATION INC.,01-East,739370000,1138.56,414.72,723.84\nC & E RECLAMATION INC.,01-East,773501000,926.10,394.18,531.92\nC & E RECLAMATION INC.,01-East,773131000,690.90,138.16,552.74\nC & E RECLAMATION INC.,01-East,733151500,472.00,54.11,417.89\nC M FURNACE INC,01-East,738521000,15802.88,5509.46,10293.42\nC M FURNACE INC,01-East,738542000,2603.76,997.55,1606.21\nC M FURNACE INC,01-East,733525400,576.00,74.13,501.87\nCABOT CORP,01-East,724261200,826.00,98.66,727.34\nCAMERON MANUFACTURING,01-East,770372000,1078.00,44.11,1033.89\nCAPITAL HARDWARE/ELGEN,01-East,735602000,82870.00,31641.00,51229.00\nCAPITAL HARDWARE/ELGEN,01-East,735602110,15840.00,6426.23,9413.77\nCARBOLINE COMPANY,01-East,720252303,214384.50,6059.96,208324.54\nCARBOLINE COMPANY,01-East,723800000,166018.50,41260.90,124757.60\nCARBOLINE COMPANY,01-East,734000008,16038.00,6792.87,9245.13\nCARBONYX CARBON TECH.,01-East,764951600,75075.84,37675.72,37400.12\nCARBONYX CARBON TECH.,01-East,766122000,51460.00,35665.08,15794.92\nCARBONYX CARBON TECH.,01-East,764951800,32686.08,16025.28,16660.80\nCARBONYX CARBON TECH.,01-East,734361300,32175.00,9647.26,22527.74\nCARBONYX CARBON TECH.,01-East,764952100,20832.00,9180.24,11651.76\nCARBONYX CARBON TECH.,01-East,764821000,13802.00,6327.47,7474.53\nCARBONYX CARBON TECH.,01-East,764951700,12257.28,6183.20,6074.08\nCARBONYX CARBON TECH.,01-East,764951900,5617.92,2837.06,2780.86\nCARBONYX CARBON TECH.,01-East,734351200,4050.00,1255.14,2794.86\nCARBONYX CARBON TECH.,01-East,766121000,2490.00,1634.93,855.07\nCARBONYX CARBON TECH.,01-East,764822010,1273.00,591.57,681.43\nCARDINDAL FG,01-East,738521000,40364.80,16199.33,24165.47\nCARDINDAL FG,01-East,727000140,2312.00,167.14,2144.86\nCARRIER ENTERPRISE NE LLC,01-East,735602000,70800.00,23715.15,47084.85\nCARRIER ENTERPRISE NE LLC,01-East,735602010,1617.00,642.19,974.81\nCARRIER ENTERPRISE NE LLC,01-East,764821000,1073.60,122.86,950.74\nCARRIER ENTERPRISE NE LLC,01-East,764901000,265.50,32.28,233.22\nCASCADE DESIGNS,01-East,770372000,7882.00,370.85,7511.15\nCASHCO INC.,01-East,737201000,3106.56,518.48,2588.08\nCASSO SOLAR,01-East,734211200,486.92,46.76,440.16\nCBOL CORPORATION,01-East,733648000,51845.10,21521.81,30323.29\nCBOL CORPORATION,01-East,732820000,10657.92,462.12,10195.80\nCBOL CORPORATION,01-East,733151600,472.00,53.12,418.88\nCDI WORLDWIDE,01-East,768241000,1457.00,70.76,1386.24\n\"CEI ENTERPRISES, INC.\",01-East,770300000,4380.75,341.31,4039.44\n\"CEI ENTERPRISES, INC.\",01-East,734711100,1704.96,244.80,1460.16\nCENTRAL GLASS PRODUCT INC,01-East,733464600,298.00,31.31,266.69\nCERALINK INC.,01-East,779989990,422.56,132.34,290.22\nCERAMASPEED,01-East,727000440,220614.00,137628.01,82985.99\nCERAMATERIALS,01-East,764863000,603.00,256.34,346.66\nCERAMATERIALS,01-East,764827000,596.00,363.85,232.15\nCERAMIC MAGNETICS,01-East,737861000,9897.84,1089.64,8808.20\nCERAMIC MAGNETICS,01-East,775061000,589.00,430.10,158.90\nCERAMIC MAGNETICS,01-East,776201000,124.20,54.85,69.35\nCERAMIC SYSTEMS INC.,01-East,793330000,317490.00,73731.26,243758.74\nCERAMIC SYSTEMS INC.,01-East,764521000,87523.00,44020.15,43502.85\nCERAMIC SYSTEMS INC.,01-East,766121000,73390.00,37439.34,35950.66\nCERAMIC SYSTEMS INC.,01-East,793530000,49057.92,27460.84,21597.08\nCERAMIC SYSTEMS INC.,01-East,766004000,42012.00,23820.53,18191.47\nCERAMIC SYSTEMS INC.,01-East,764821000,37900.00,15587.84,22312.16\nCERAMIC SYSTEMS INC.,01-East,764822000,36120.00,14750.75,21369.25\nCERAMIC SYSTEMS INC.,01-East,766122000,35680.00,18802.49,16877.51\nCERAMIC SYSTEMS INC.,01-East,766045000,31680.00,15251.30,16428.70\nCERAMIC SYSTEMS INC.,01-East,764528100,23616.00,10288.16,13327.84\nCERAMIC SYSTEMS INC.,01-East,764601000,21800.00,9494.41,12305.59\nCERAMIC SYSTEMS INC.,01-East,766005000,21458.00,13075.45,8382.55\nCERAMIC SYSTEMS INC.,01-East,764523000,17211.00,9161.74,8049.26\nCERAMIC SYSTEMS INC.,01-East,764522000,16310.00,8215.29,8094.71\nCERAMIC SYSTEMS INC.,01-East,793280074,13026.00,13067.82,-41.82\nCERAMIC SYSTEMS INC.,01-East,764481000,12978.00,6485.36,6492.64\nCERAMIC SYSTEMS INC.,01-East,764781000,11646.00,5796.96,5849.04\nCERAMIC SYSTEMS INC.,01-East,764901000,9710.00,4187.06,5522.94\nCERAMIC SYSTEMS INC.,01-East,727000140,8413.20,2589.17,5824.03\nCERAMIC SYSTEMS INC.,01-East,764907010,8142.50,5049.63,3092.87\nCERAMIC SYSTEMS INC.,01-East,764528510,7800.00,4433.24,3366.76\nCERAMIC SYSTEMS INC.,01-East,766001000,7600.00,5036.34,2563.66\nCERAMIC SYSTEMS INC.,01-East,733525500,2190.00,780.35,1409.65\nCERAMIC SYSTEMS INC.,01-East,766081010,1458.00,654.58,803.42\nCERAMIC SYSTEMS INC.,01-East,785890000,1245.75,470.23,775.52\nCERAMIC SYSTEMS INC.,01-East,764761000,1180.00,588.16,591.84\nCERAMIC SYSTEMS INC.,01-East,766162000,994.00,675.71,318.29\nCERAMIC SYSTEMS INC.,01-East,764341000,810.00,359.80,450.20\nCERAMIC SYSTEMS INC.,01-East,764561000,288.00,167.98,120.02\nCERAMIC SYSTEMS INC.,01-East,764861000,258.75,116.27,142.48\nCERAMIC SYSTEMS INC.,01-East,764902000,75.00,31.93,43.07\nCERAMIC SYSTEMS INC.,01-East,733464500,73.00,29.52,43.48\nCERAMSOURECE INC.,01-East,770600000,3245.20,1717.90,1527.30\nCERAMSOURECE INC.,01-East,770830000,1343.20,805.51,537.69\nCERAMSOURECE INC.,01-East,770570000,377.40,201.46,175.94\nCERAMSOURECE INC.,01-East,764785000,274.00,146.72,127.28\nCHANDLER ENGINEERING,01-East,733444100,332.00,75.04,256.96\nCHEF MART RESTAURANT,01-East,735602000,25440.00,8284.44,17155.56\nCHIZ BROS INC,01-East,764348000,20269.20,13106.80,7162.40\nCHIZ BROS INC,01-East,740402402,14520.00,3675.39,10844.61\nCHIZ BROS INC,01-East,738561000,10315.20,7432.32,2882.88\nCHIZ BROS INC,01-East,779440100,3762.00,605.35,3156.65\nCHIZ BROS INC,01-East,770560000,2999.40,430.45,2568.95\nCHIZ BROS INC,01-East,734351200,2977.00,294.47,2682.53\nCHIZ BROS INC,01-East,777902700,2597.49,1141.55,1455.94\nCHIZ BROS INC,01-East,779959870,1763.20,880.96,882.24\nCHIZ BROS INC,01-East,737912200,918.00,272.84,645.16\nCHIZ BROS INC,01-East,764862000,790.80,157.11,633.69\nCHIZ BROS INC,01-East,775201000,688.00,515.80,172.20\nCHIZ BROS INC,01-East,779957054,448.40,224.48,223.92\nCHIZ BROS INC,01-East,734611100,262.58,53.92,208.66\nCHIZ BROS INC,01-East,734651200,218.16,36.75,181.41\nCHIZ BROS INC,01-East,779959135,180.00,111.60,68.40\nCHIZ BROS INC,01-East,774981100,65.50,49.04,16.46\nCHIZ BROS INC,01-East,767500000,32.70,17.45,15.25\nCHRISTY CATALYTICS LLC,01-East,734830000,246.00,44.64,201.36\nCHRISTY INDUSTRIAL SERVIC,01-East,765661000,41532.00,13318.44,28213.56\nCHRISTY INDUSTRIAL SERVIC,01-East,770330000,21974.00,3034.60,18939.40\nCHRISTY INDUSTRIAL SERVIC,01-East,780770000,18746.70,5877.30,12869.40\nCHRISTY INDUSTRIAL SERVIC,01-East,727041000,12000.00,1187.73,10812.27\nCHRISTY INDUSTRIAL SERVIC,01-East,734990000,7746.24,1978.12,5768.12\nCHRISTY INDUSTRIAL SERVIC,01-East,764821000,6360.00,3071.59,3288.41\nCHRISTY INDUSTRIAL SERVIC,01-East,780290000,6127.20,3495.24,2631.96\nCHRISTY INDUSTRIAL SERVIC,01-East,780650000,5207.80,2322.56,2885.24\nCHRISTY INDUSTRIAL SERVIC,01-East,780720000,3963.60,1115.32,2848.28\nCHRISTY INDUSTRIAL SERVIC,01-East,765651000,3840.00,1311.90,2528.10\nCHRISTY INDUSTRIAL SERVIC,01-East,754087000,2704.00,1810.42,893.58\nCHRISTY INDUSTRIAL SERVIC,01-East,779957722,2676.00,1781.98,894.02\nCHRISTY INDUSTRIAL SERVIC,01-East,754085000,2449.20,1492.72,956.48\nCHRISTY INDUSTRIAL SERVIC,01-East,777642000,1722.00,475.05,1246.95\nCHRISTY INDUSTRIAL SERVIC,01-East,733211000,1685.40,343.36,1342.04\nCHRISTY INDUSTRIAL SERVIC,01-East,764781000,1590.00,1022.26,567.74\nCHRISTY INDUSTRIAL SERVIC,01-East,737311000,1284.96,273.31,1011.65\nCHRISTY INDUSTRIAL SERVIC,01-East,734810000,938.00,750.51,187.49\nCHRISTY INDUSTRIAL SERVIC,01-East,766081010,868.00,145.46,722.54\nCHRISTY INDUSTRIAL SERVIC,01-East,738561000,755.52,372.41,383.11\nCHRISTY INDUSTRIAL SERVIC,01-East,764785000,530.00,146.72,383.28\nCHRISTY INDUSTRIAL SERVIC,01-East,777650000,356.60,285.32,71.28\nCHRISTY INDUSTRIAL SERVIC,01-East,780690000,354.96,127.26,227.70\nCHRISTY INDUSTRIAL SERVIC,01-East,764761000,204.00,61.43,142.57\nCHRISTY INDUSTRIAL SERVIC,01-East,767990000,160.50,58.98,101.52\nCHRISTY INDUSTRIAL SERVIC,01-East,766121000,95.80,54.50,41.30\nCHRISTY INDUSTRIAL SERVIC,01-East,779987255,56.00,23.18,32.82\nCHRISTY REFRACTORIES CO,01-East,764764010,87014.40,54086.18,32928.22\nCHRISTY REFRACTORIES CO,01-East,727000060,77036.00,25833.63,51202.37\nCHRISTY REFRACTORIES CO,01-East,720474000,76366.25,12958.70,63407.55\nCHRISTY REFRACTORIES CO,01-East,733182300,61281.84,31628.98,29652.86\nCHRISTY REFRACTORIES CO,01-East,764030000,54407.20,28111.03,26296.17\nCHRISTY REFRACTORIES CO,01-East,730552560,37535.00,5813.35,31721.65\nCHRISTY REFRACTORIES CO,01-East,764264056,35322.00,24085.99,11236.01\nCHRISTY REFRACTORIES CO,01-East,764301000,32951.90,19151.41,13800.49\nCHRISTY REFRACTORIES CO,01-East,777750074,32766.72,27531.00,5235.72\nCHRISTY REFRACTORIES CO,01-East,764901000,32424.30,17324.77,15099.53\nCHRISTY REFRACTORIES CO,01-East,764821000,29179.00,13658.64,15520.36\nCHRISTY REFRACTORIES CO,01-East,739741100,28341.51,16475.47,11866.04\nCHRISTY REFRACTORIES CO,01-East,733481000,28140.00,14458.84,13681.16\nCHRISTY REFRACTORIES CO,01-East,764865100,24444.00,14164.82,10279.18\nCHRISTY REFRACTORIES CO,01-East,777780074,20694.80,16416.40,4278.40\nCHRISTY REFRACTORIES CO,01-East,738521000,20080.78,16796.48,3284.30\nCHRISTY REFRACTORIES CO,01-East,734211200,12406.61,1721.98,10684.63\nCHRISTY REFRACTORIES CO,01-East,734990000,12375.36,3305.15,9070.21\nCHRISTY REFRACTORIES CO,01-East,734000000,11820.60,1517.87,10302.73\nCHRISTY REFRACTORIES CO,01-East,734173000,11627.35,3161.64,8465.71\nCHRISTY REFRACTORIES CO,01-East,766261020,10584.00,6168.11,4415.89\nCHRISTY REFRACTORIES CO,01-East,770220000,9849.00,916.61,8932.39\nCHRISTY REFRACTORIES CO,01-East,733182390,9801.15,4095.04,5706.11\nCHRISTY REFRACTORIES CO,01-East,733505300,9480.00,1612.13,7867.87\nCHRISTY REFRACTORIES CO,01-East,733721000,9028.80,2825.41,6203.39\nCHRISTY REFRACTORIES CO,01-East,732429400,8688.50,997.20,7691.30\nCHRISTY REFRACTORIES CO,01-East,770560000,8373.20,692.13,7681.07\nCHRISTY REFRACTORIES CO,01-East,764340523,7978.50,8074.53,-96.03\nCHRISTY REFRACTORIES CO,01-East,764481000,7457.10,2826.95,4630.15\nCHRISTY REFRACTORIES CO,01-East,771500000,7112.48,516.02,6596.46\nCHRISTY REFRACTORIES CO,01-East,777601000,6896.90,2040.14,4856.76\nCHRISTY REFRACTORIES CO,01-East,733182380,6706.05,2801.85,3904.20\nCHRISTY REFRACTORIES CO,01-East,733202000,5394.48,3127.98,2266.50\nCHRISTY REFRACTORIES CO,01-East,764822000,5394.40,1558.28,3836.12\nCHRISTY REFRACTORIES CO,01-East,727000025,5338.50,2502.95,2835.55\nCHRISTY REFRACTORIES CO,01-East,733464500,5305.50,812.07,4493.43\nCHRISTY REFRACTORIES CO,01-East,734162000,5286.93,2252.97,3033.96\nCHRISTY REFRACTORIES CO,01-East,754925000,5168.40,1163.38,4005.02\nCHRISTY REFRACTORIES CO,01-East,777750073,5019.58,2039.90,2979.68\nCHRISTY REFRACTORIES CO,01-East,764561000,4743.50,3489.38,1254.12\nCHRISTY REFRACTORIES CO,01-East,764781000,4439.00,2680.51,1758.49\nCHRISTY REFRACTORIES CO,01-East,768241000,4429.00,900.79,3528.21\nCHRISTY REFRACTORIES CO,01-East,734311100,4371.75,515.14,3856.61\nCHRISTY REFRACTORIES CO,01-East,764601000,4324.80,2816.66,1508.14\nCHRISTY REFRACTORIES CO,01-East,764341000,4213.70,3133.22,1080.48\nCHRISTY REFRACTORIES CO,01-East,766162000,3949.00,570.34,3378.66\nCHRISTY REFRACTORIES CO,01-East,777780073,3789.20,1618.09,2171.11\nCHRISTY REFRACTORIES CO,01-East,733525500,3247.50,476.67,2770.83\nCHRISTY REFRACTORIES CO,01-East,782070000,3241.44,1654.68,1586.76\nCHRISTY REFRACTORIES CO,01-East,764261000,2907.80,1186.32,1721.48\nCHRISTY REFRACTORIES CO,01-East,766121000,2765.60,403.00,2362.60\nCHRISTY REFRACTORIES CO,01-East,764821161,2680.00,2976.00,-296.00\nCHRISTY REFRACTORIES CO,01-East,733383000,2593.80,491.79,2102.01\nCHRISTY REFRACTORIES CO,01-East,768242007,2528.00,1017.20,1510.80\nCHRISTY REFRACTORIES CO,01-East,733211000,2513.00,532.04,1980.96\nCHRISTY REFRACTORIES CO,01-East,764521000,2504.00,1327.10,1176.90\nCHRISTY REFRACTORIES CO,01-East,733151600,2317.60,399.03,1918.57\nCHRISTY REFRACTORIES CO,01-East,764461006,2236.00,1465.52,770.48\nCHRISTY REFRACTORIES CO,01-East,740123000,2226.00,640.05,1585.95\nCHRISTY REFRACTORIES CO,01-East,739561100,2157.79,653.24,1504.55\nCHRISTY REFRACTORIES CO,01-East,779959904,2125.44,1410.70,714.74\nCHRISTY REFRACTORIES CO,01-East,770240000,1946.25,202.89,1743.36\nCHRISTY REFRACTORIES CO,01-East,739561200,1804.28,596.00,1208.28\nCHRISTY REFRACTORIES CO,01-East,740382201,1800.00,594.28,1205.72\nCHRISTY REFRACTORIES CO,01-East,768121000,1749.60,351.09,1398.51\nCHRISTY REFRACTORIES CO,01-East,768231400,1746.00,958.78,787.22\nCHRISTY REFRACTORIES CO,01-East,777730022,1729.98,1380.96,349.02\nCHRISTY REFRACTORIES CO,01-East,770200000,1629.00,168.02,1460.98\nCHRISTY REFRACTORIES CO,01-East,764761000,1530.80,821.49,709.31\nCHRISTY REFRACTORIES CO,01-East,764487500,1480.00,757.91,722.09\nCHRISTY REFRACTORIES CO,01-East,734151100,1285.86,301.83,984.03\nCHRISTY REFRACTORIES CO,01-East,740382150,1254.96,548.62,706.34\nCHRISTY REFRACTORIES CO,01-East,764523010,1139.80,923.51,216.29\nCHRISTY REFRACTORIES CO,01-East,734132000,1109.60,313.31,796.29\nCHRISTY REFRACTORIES CO,01-East,737311000,990.24,273.31,716.93\nCHRISTY REFRACTORIES CO,01-East,764529600,864.00,368.44,495.56\nCHRISTY REFRACTORIES CO,01-East,733444200,852.00,142.71,709.29\nCHRISTY REFRACTORIES CO,01-East,777642000,850.40,253.37,597.03\nCHRISTY REFRACTORIES CO,01-East,733211050,816.00,397.57,418.43\nCHRISTY REFRACTORIES CO,01-East,764342000,800.40,209.07,591.33\nCHRISTY REFRACTORIES CO,01-East,738542000,792.00,574.08,217.92\nCHRISTY REFRACTORIES CO,01-East,764523504,771.00,586.35,184.65\nCHRISTY REFRACTORIES CO,01-East,755125000,721.02,149.32,571.70\nCHRISTY REFRACTORIES CO,01-East,739641100,695.19,412.29,282.90\nCHRISTY REFRACTORIES CO,01-East,720455000,693.60,94.87,598.73\nCHRISTY REFRACTORIES CO,01-East,770230000,693.00,73.92,619.08\nCHRISTY REFRACTORIES CO,01-East,768221000,672.00,210.63,461.37\nCHRISTY REFRACTORIES CO,01-East,764262000,656.00,446.16,209.84\nCHRISTY REFRACTORIES CO,01-East,770300000,649.00,70.74,578.26\nCHRISTY REFRACTORIES CO,01-East,770392000,445.40,41.51,403.89\nCHRISTY REFRACTORIES CO,01-East,733725500,410.40,103.52,306.88\nCHRISTY REFRACTORIES CO,01-East,724121700,352.00,162.85,189.15\nCHRISTY REFRACTORIES CO,01-East,766122000,344.00,57.52,286.48\nCHRISTY REFRACTORIES CO,01-East,768122000,293.40,58.22,235.18\nCHRISTY REFRACTORIES CO,01-East,764482000,268.00,68.20,199.80\nCHRISTY REFRACTORIES CO,01-East,727063000,261.60,33.59,228.01\nCHRISTY REFRACTORIES CO,01-East,764526610,225.75,168.15,57.60\nCHRISTY REFRACTORIES CO,01-East,734711100,203.51,40.72,162.79\nCHRISTY REFRACTORIES CO,01-East,733444300,195.00,46.17,148.83\nCHRISTY REFRACTORIES CO,01-East,775201000,179.60,130.63,48.97\nCHRISTY REFRACTORIES CO,01-East,773591000,88.56,26.53,62.03\nCHRISTY REFRACTORIES CO,01-East,774981100,78.73,35.92,42.81\nCIMINI & ASSOCIATES,01-East,764261000,3460.00,183.76,3276.24\nCLAUDIUS PETERS,01-East,724003000,2434.00,295.77,2138.23\nCLIMATECH,01-East,733572750,193129.11,135159.39,57969.72\nCLIMATECH,01-East,733572800,142823.27,110203.93,32619.34\nCLIMATECH,01-East,733572600,48260.16,35261.01,12999.15\nCLIMATECH,01-East,733572900,13847.04,9975.45,3871.59\nCLINTON POTTERY,01-East,764821000,430.00,15.36,414.64\nCLYDE BERGEMANN BACHMANN,01-East,768241000,47480.50,23217.78,24262.72\nCLYDE BERGEMANN BACHMANN,01-East,733725500,10216.00,2235.97,7980.03\nCLYDE BERGEMANN BACHMANN,01-East,768221000,8053.00,3863.70,4189.30\nCLYDE BERGEMANN BACHMANN,01-East,734751200,6891.90,2329.58,4562.32\nCLYDE BERGEMANN BACHMANN,01-East,734761300,2126.30,758.78,1367.52\nCLYDE BERGEMANN BACHMANN,01-East,734651200,889.20,293.98,595.22\nCLYDE BERGEMANN BACHMANN,01-East,734711100,817.12,270.80,546.32\nCLYDE BERGEMANN BACHMANN,01-East,768222000,643.50,322.20,321.30\nCLYDE BERGEMANN BACHMANN,01-East,768242000,572.00,353.28,218.72\nCLYDE BERGEMANN BACHMANN,01-East,734611100,85.08,40.44,44.64\nCOASTAL IGNITION &,01-East,733151600,450.00,46.64,403.36\nCODY SALES & SERVICE,01-East,737912200,326.50,45.47,281.03\nCOLORADO STATE UNIVERSITY,01-East,764483000,421.00,89.22,331.78\nCOLORADO STATE UNIVERSITY,01-East,764481000,346.00,16.94,329.06\nCOLUMBIA HELICOPTER,01-East,734351200,1945.80,147.24,1798.56\nCOLUMBIA HELICOPTER,01-East,770830000,1061.00,35.87,1025.13\nCOLUMBIA HELICOPTER,01-East,764521000,666.00,26.54,639.46\nCOLUMBIA MANUFACTURING IN,01-East,734121000,455.00,83.36,371.64\nCOLUMBIA MANUFACTURING IN,01-East,734132000,422.00,78.33,343.67\nCOMMERCIAL HEATING SUPPLY,01-East,739561100,1658.16,279.96,1378.20\nCOMPANIA NACIONAL,01-East,764821029,121817.10,34700.01,87117.09\nCOMPANIA NACIONAL,01-East,727000140,51075.36,9144.99,41930.37\nCOMPANIA NACIONAL,01-East,764821000,27328.00,6880.38,20447.62\nCOMPANIA NACIONAL,01-East,733151500,23664.00,5649.40,18014.60\nCOMPANIA NACIONAL,01-East,764821027,22300.00,6375.74,15924.26\nCOMPANIA NACIONAL,01-East,734000000,22186.50,3187.33,18999.17\nCOMPANIA NACIONAL,01-East,770222000,13738.75,2151.10,11587.65\nCOMPANIA NACIONAL,01-East,770560000,12240.00,1721.79,10518.21\nCOMPANIA NACIONAL,01-East,733444300,8870.00,2720.87,6149.13\nCOMPANIA NACIONAL,01-East,764901000,6882.75,1840.40,5042.35\nCOMPANIA NACIONAL,01-East,770220000,5595.00,885.98,4709.02\nCOMPANIA NACIONAL,01-East,733151600,4140.00,932.84,3207.16\nCOMPANIA NACIONAL,01-East,770260000,3780.00,676.28,3103.72\nCOMPANIA NACIONAL,01-East,770202000,3442.50,607.59,2834.91\nCOMPANIA NACIONAL,01-East,733525500,3200.00,703.94,2496.06\nCOMPANIA NACIONAL,01-East,764521000,2784.00,851.02,1932.98\nCOMPANIA NACIONAL,01-East,733211000,2416.50,728.27,1688.23\nCOMPANIA NACIONAL,01-East,764521020,1740.00,557.56,1182.44\nCOMPANIA NACIONAL,01-East,766121040,1230.00,279.13,950.87\nCOMPANIA NACIONAL,01-East,770212000,877.50,162.38,715.12\nCOMPANIA NACIONAL,01-East,770240000,798.75,140.66,658.09\nCOMPLIANCE INSPECTION,01-East,768133000,162.25,11.04,151.21\nCONSOLIDATED FABRICATORS,01-East,768241000,3700.00,2193.63,1506.37\nCONSOLIDATED FABRICATORS,01-East,768231000,2494.00,1331.79,1162.21\nCONSTRUCTION SPECIALTIES,01-East,737016200,346350.65,84901.39,261449.26\nCONSTRUCTION SPECIALTIES,01-East,733610650,89868.12,22127.38,67740.74\nCONSTRUCTION SPECIALTIES,01-East,733151600,40960.00,6637.24,34322.76\nCONSTRUCTION SPECIALTIES,01-East,764787200,32943.10,6931.03,26012.07\nCONSTRUCTION SPECIALTIES,01-East,733184000,19572.00,4417.97,15154.03\nCONSTRUCTION SPECIALTIES,01-East,733182340,16493.40,2622.64,13870.76\nCONSTRUCTION SPECIALTIES,01-East,768222000,12898.60,2726.64,10171.96\nCONSTRUCTION SPECIALTIES,01-East,768221000,4386.20,919.89,3466.31\nCONSTRUCTION SPECIALTIES,01-East,734910000,362.00,53.09,308.91\nCONSTRUCTION SPECIALTIES,01-East,733151500,294.00,47.69,246.31\nCOORSTEK,01-East,779988831,8818.50,2227.99,6590.51\nCOORSTEK,01-East,739561000,443.52,86.19,357.33\nCOORSTEK,01-East,777642000,143.00,50.36,92.64\nCORNING GLASS WORKS,01-East,733505200,910.00,167.67,742.33\nCORNING INCORPORATED,01-East,733423600,228.48,45.08,183.40\nCORNING INCORPORATED,01-East,733444100,208.60,81.62,126.98\nCORNING SHARED SERVICES,01-East,734370000,12641200.00,2013467.19,10627732.81\nCORNING SHARED SERVICES,01-East,761924000,52394.94,12023.40,40371.54\nCORNING SHARED SERVICES,01-East,770700000,44200.00,29622.50,14577.50\nCORNING SHARED SERVICES,01-East,719500000,42090.00,6819.48,35270.52\nCORNING SHARED SERVICES,01-East,718501000,21856.00,4331.98,17524.02\nCORNING SHARED SERVICES,01-East,761073000,18480.00,7110.73,11369.27\nCORNING SHARED SERVICES,01-East,766121000,4579.50,283.25,4296.25\nCORNING SHARED SERVICES,01-East,733525500,3164.00,536.32,2627.68\nCORNING SHARED SERVICES,01-East,737321000,2504.16,1168.96,1335.20\nCORNING SHARED SERVICES,01-East,766081010,2055.30,134.32,1920.98\nCORNING SHARED SERVICES,01-East,740402301,1767.50,430.33,1337.17\nCORNING SHARED SERVICES,01-East,740283401,1564.20,265.38,1298.82\nCORNING SHARED SERVICES,01-East,740402402,1535.00,387.32,1147.68\nCORNING SHARED SERVICES,01-East,770540000,1280.83,94.98,1185.85\nCORNING SHARED SERVICES,01-East,770230000,960.75,71.08,889.67\nCORNING SHARED SERVICES,01-East,733403100,944.93,122.60,822.33\nCORNING SHARED SERVICES,01-East,770380000,916.00,51.37,864.63\nCORNING SHARED SERVICES,01-East,764781000,480.60,39.44,441.16\nCORNING SHARED SERVICES,01-East,733505200,434.00,76.44,357.56\nCORNING SHARED SERVICES,01-East,777601000,287.95,58.80,229.15\nCORNING SHARED SERVICES,01-East,734611100,278.32,54.36,223.96\nCORNING SHARED SERVICES,01-East,727063000,139.60,15.92,123.68\nCORNING SHARED SERVICES,01-East,727063016,139.60,15.84,123.76\nCORNING SHARED SERVICES,01-East,726020005,132.80,14.88,117.92\nCORNING SHARED SERVICES,01-East,726020015,132.80,16.37,116.43\nCOTRONICS INC,01-East,733586500,193643.28,29482.30,164160.98\nCOTRONICS INC,01-East,733566200,91497.60,16578.07,74919.53\nCOTRONICS INC,01-East,733543500,84780.80,20175.81,64604.99\nCOTRONICS INC,01-East,770260000,53118.45,5653.58,47464.87\nCOTRONICS INC,01-East,733543700,42262.40,7887.59,34374.81\nCOTRONICS INC,01-East,733505300,32689.00,5703.00,26986.00\nCOTRONICS INC,01-East,770222000,32621.38,3059.28,29562.10\nCOTRONICS INC,01-East,733543000,18140.20,4220.58,13919.62\nCOTRONICS INC,01-East,764461006,13803.20,2118.01,11685.19\nCOTRONICS INC,01-East,770212000,12680.20,1420.95,11259.25\nCOTRONICS INC,01-East,733586400,11583.72,1774.92,9808.80\nCOTRONICS INC,01-East,733566100,3693.60,870.72,2822.88\nCOTRONICS INC,01-East,764521000,2600.00,265.94,2334.06\nCOTRONICS INC,01-East,792620000,1776.00,409.36,1366.64\nCOTRONICS INC,01-East,770200000,390.60,40.79,349.81\nCPP GUAYMAS,01-East,764609200,19215.00,6989.64,12225.36\nCPP GUAYMAS,01-East,764609500,10293.75,3921.28,6372.47\nCPP GUAYMAS,01-East,764609100,7320.00,2685.10,4634.90\nCPP GUAYMAS,01-East,764461006,353.60,111.75,241.85\nCRC INDUSTRIES,01-East,727000140,40986.00,3966.67,37019.33\nCREST ULTRASONICS,01-East,739541100,15859.20,2048.23,13810.97\nCROWN ENGINEERING CORP,01-East,733721000,11160.00,3750.70,7409.30\nCUBE TECHNOLOGY,01-East,739641100,482.04,201.60,280.44\nCUBE TECHNOLOGY,01-East,739561000,473.76,85.22,388.54\nCUSTOM HEAT LLC,01-East,733464500,298.00,29.52,268.48\nCUSTOM MACHINE & DESIGN,01-East,739741100,2504.32,774.88,1729.44\nCYCLOTHERM OF WATERTOWN,01-East,733211050,3126.40,627.42,2498.98\nD & M HYDRAULIC,01-East,770220000,471.00,27.35,443.65\n\"D-J ENGINEERING, INC.\",01-East,764821000,894.00,31.32,862.68\n\"D-J ENGINEERING, INC.\",01-East,777642000,143.00,50.36,92.64\nD. W. MACK CO,01-East,733464400,626.00,86.74,539.26\nDALE STOVER,01-East,739641100,926.64,274.86,651.78\nDALE STOVER,01-East,734611100,178.72,26.96,151.76\nDANNER CORPORATION,01-East,725441200,388.00,65.77,322.23\nDATA CENTER RESOURCES LLC,01-East,735750110,1180.00,275.34,904.66\nDATA CENTER RESOURCES LLC,01-East,735750112,945.00,290.67,654.33\nDATA CENTER RESOURCES LLC,01-East,735751022,520.50,229.97,290.53\nDAVID H. SUTHERLAND & CO.,01-East,770800000,2980.00,63.88,2916.12\n\"DAVIS-STANDARD, LLC\",01-East,733444200,2324.00,266.53,2057.47\nDCO ENERGY,01-East,734751200,275.45,37.39,238.06\nDEKA RESEARCH,01-East,733671000,1438.08,119.66,1318.42\nDELAWARE CITY REFINING CO,01-East,770240000,13627.50,963.02,12664.48\nDELPHI AUTOMOTIVE SYSTEMS,01-East,768241000,364.25,17.69,346.56\nDELPHI CORPORATION,01-East,768211000,374.00,23.39,350.61\nDELPHI CORPORATION,01-East,768231000,262.25,13.17,249.08\nDELTA REFRACTORIES,01-East,766121610,795755.36,299738.55,496016.81\nDELTA REFRACTORIES,01-East,764821000,465073.00,194458.54,270614.46\nDELTA REFRACTORIES,01-East,764901000,384200.00,181439.71,202760.29\nDELTA REFRACTORIES,01-East,734132000,277481.50,62861.76,214619.74\nDELTA REFRACTORIES,01-East,764820600,266880.00,98426.76,168453.24\nDELTA REFRACTORIES,01-East,764901050,225320.50,103145.37,122175.13\nDELTA REFRACTORIES,01-East,792627000,172084.77,72343.11,99741.66\nDELTA REFRACTORIES,01-East,766126012,163472.00,66294.12,97177.88\nDELTA REFRACTORIES,01-East,766001510,153960.00,60498.16,93461.84\nDELTA REFRACTORIES,01-East,766121000,145086.50,56815.04,88271.46\nDELTA REFRACTORIES,01-East,766001000,116207.00,45801.74,70405.26\nDELTA REFRACTORIES,01-East,764521000,103791.00,46509.93,57281.07\nDELTA REFRACTORIES,01-East,734990000,102715.60,33396.35,69319.25\nDELTA REFRACTORIES,01-East,734830000,101136.00,33145.74,67990.26\nDELTA REFRACTORIES,01-East,766122000,91531.00,34533.84,56997.16\nDELTA REFRACTORIES,01-East,734311100,90305.48,10411.74,79893.74\nDELTA REFRACTORIES,01-East,766006110,81367.00,32669.43,48697.57\nDELTA REFRACTORIES,01-East,779989337,80878.08,33761.00,47117.08\nDELTA REFRACTORIES,01-East,764822000,76330.00,30815.91,45514.09\nDELTA REFRACTORIES,01-East,766162000,71797.00,38517.86,33279.14\nDELTA REFRACTORIES,01-East,768222512,67490.00,44723.41,22766.59\nDELTA REFRACTORIES,01-East,740262000,66066.00,16608.90,49457.10\nDELTA REFRACTORIES,01-East,764605000,60295.00,26714.38,33580.62\nDELTA REFRACTORIES,01-East,762134000,59052.84,20051.57,39001.27\nDELTA REFRACTORIES,01-East,733525500,54922.50,18597.12,36325.38\nDELTA REFRACTORIES,01-East,727000140,54130.40,18545.17,35585.23\nDELTA REFRACTORIES,01-East,733211000,52745.70,10770.95,41974.75\nDELTA REFRACTORIES,01-East,764906000,52279.00,23839.04,28439.96\nDELTA REFRACTORIES,01-East,792623000,52096.10,23145.57,28950.53\nDELTA REFRACTORIES,01-East,764861000,51811.00,21126.44,30684.56\nDELTA REFRACTORIES,01-East,764824010,50328.00,18611.93,31716.07\nDELTA REFRACTORIES,01-East,768221000,45320.00,21276.57,24043.43\nDELTA REFRACTORIES,01-East,734910000,41787.30,9887.15,31900.15\nDELTA REFRACTORIES,01-East,777601000,37631.65,10119.58,27512.07\nDELTA REFRACTORIES,01-East,766594010,36736.00,19511.34,17224.66\nDELTA REFRACTORIES,01-East,733511000,32166.25,15958.65,16207.60\nDELTA REFRACTORIES,01-East,724311400,32143.00,7277.87,24865.13\nDELTA REFRACTORIES,01-East,734851000,31144.60,7559.42,23585.18\nDELTA REFRACTORIES,01-East,764522000,30760.00,14028.57,16731.43\nDELTA REFRACTORIES,01-East,764864000,29488.00,12710.97,16777.03\nDELTA REFRACTORIES,01-East,764561000,29085.00,15046.26,14038.74\nDELTA REFRACTORIES,01-East,740023000,27484.80,6895.13,20589.67\nDELTA REFRACTORIES,01-East,764601000,26938.75,13863.54,13075.21\nDELTA REFRACTORIES,01-East,733383000,25732.08,4401.18,21330.90\nDELTA REFRACTORIES,01-East,739741100,22298.88,17009.30,5289.58\nDELTA REFRACTORIES,01-East,764481000,21552.00,8959.66,12592.34\nDELTA REFRACTORIES,01-East,766594001,21504.00,11345.38,10158.62\nDELTA REFRACTORIES,01-East,764902000,20295.00,8662.44,11632.56\nDELTA REFRACTORIES,01-East,739561100,19780.99,5318.45,14462.54\nDELTA REFRACTORIES,01-East,770430000,19175.00,3375.33,15799.67\nDELTA REFRACTORIES,01-East,766081010,18227.00,6012.01,12214.99\nDELTA REFRACTORIES,01-East,740063000,17947.44,5372.37,12575.07\nDELTA REFRACTORIES,01-East,764781000,17748.00,6533.01,11214.99\nDELTA REFRACTORIES,01-East,733510000,16785.00,9095.53,7689.47\nDELTA REFRACTORIES,01-East,764520500,16184.00,6478.20,9705.80\nDELTA REFRACTORIES,01-East,740361900,14711.04,6242.47,8468.57\nDELTA REFRACTORIES,01-East,771502000,13545.00,990.96,12554.04\nDELTA REFRACTORIES,01-East,779988828,13193.12,6248.62,6944.50\nDELTA REFRACTORIES,01-East,766161000,11412.00,6279.69,5132.31\nDELTA REFRACTORIES,01-East,734211200,11272.76,1463.76,9809.00\nDELTA REFRACTORIES,01-East,766166000,9660.00,4855.52,4804.48\nDELTA REFRACTORIES,01-East,777901000,9445.38,2118.82,7326.56\nDELTA REFRACTORIES,01-East,740283502,9350.00,3745.96,5604.04\nDELTA REFRACTORIES,01-East,764564000,9296.00,4699.99,4596.01\nDELTA REFRACTORIES,01-East,768222550,9278.10,4765.43,4512.67\nDELTA REFRACTORIES,01-East,779988827,9200.94,4212.69,4988.25\nDELTA REFRACTORIES,01-East,777642000,9198.10,3631.14,5566.96\nDELTA REFRACTORIES,01-East,779989700,8437.76,3073.90,5363.86\nDELTA REFRACTORIES,01-East,733221400,7768.84,2338.09,5430.75\nDELTA REFRACTORIES,01-East,770410000,7630.00,720.95,6909.05\nDELTA REFRACTORIES,01-East,764526020,7408.00,2988.04,4419.96\nDELTA REFRACTORIES,01-East,733444200,6712.40,1106.95,5605.45\nDELTA REFRACTORIES,01-East,779988846,6226.74,2725.74,3501.00\nDELTA REFRACTORIES,01-East,766541110,6156.00,3085.30,3070.70\nDELTA REFRACTORIES,01-East,768211000,6156.00,2947.07,3208.93\nDELTA REFRACTORIES,01-East,734351200,5254.70,2503.04,2751.66\nDELTA REFRACTORIES,01-East,777903100,4900.98,1946.28,2954.70\nDELTA REFRACTORIES,01-East,724281200,4537.00,1075.67,3461.33\nDELTA REFRACTORIES,01-East,764821030,4424.00,1776.11,2647.89\nDELTA REFRACTORIES,01-East,792620000,4160.00,2046.78,2113.22\nDELTA REFRACTORIES,01-East,771320000,4155.50,684.50,3471.00\nDELTA REFRACTORIES,01-East,733403300,3678.84,371.68,3307.16\nDELTA REFRACTORIES,01-East,770372000,3404.80,238.55,3166.25\nDELTA REFRACTORIES,01-East,739781100,3392.80,2061.29,1331.51\nDELTA REFRACTORIES,01-East,764563500,3320.00,1438.28,1881.72\nDELTA REFRACTORIES,01-East,768241000,3316.00,1472.53,1843.47\nDELTA REFRACTORIES,01-East,739641100,3024.00,2061.41,962.59\nDELTA REFRACTORIES,01-East,733586600,2563.20,378.10,2185.10\nDELTA REFRACTORIES,01-East,718500000,2523.20,431.35,2091.85\nDELTA REFRACTORIES,01-East,764821073,2370.00,2187.00,183.00\nDELTA REFRACTORIES,01-East,738521000,2330.56,1624.59,705.97\nDELTA REFRACTORIES,01-East,764901073,2295.00,2187.00,108.00\nDELTA REFRACTORIES,01-East,733464600,2133.90,443.63,1690.27\nDELTA REFRACTORIES,01-East,733721000,2092.00,652.35,1439.65\nDELTA REFRACTORIES,01-East,770850000,2090.00,94.48,1995.52\nDELTA REFRACTORIES,01-East,764261000,2040.00,918.76,1121.24\nDELTA REFRACTORIES,01-East,733464500,2024.00,313.24,1710.76\nDELTA REFRACTORIES,01-East,770510000,1967.50,130.98,1836.52\nDELTA REFRACTORIES,01-East,770810000,1909.50,53.50,1856.00\nDELTA REFRACTORIES,01-East,768127000,1700.00,285.82,1414.18\nDELTA REFRACTORIES,01-East,764952100,1425.00,555.26,869.74\nDELTA REFRACTORIES,01-East,764563000,1408.00,710.78,697.22\nDELTA REFRACTORIES,01-East,740283401,1401.40,292.43,1108.97\nDELTA REFRACTORIES,01-East,766594000,1392.00,779.69,612.31\nDELTA REFRACTORIES,01-East,770540000,1279.25,94.98,1184.27\nDELTA REFRACTORIES,01-East,733525600,1205.00,261.96,943.04\nDELTA REFRACTORIES,01-East,792600000,1104.00,177.32,926.68\nDELTA REFRACTORIES,01-East,766041000,1100.00,376.49,723.51\nDELTA REFRACTORIES,01-East,734651200,1090.75,183.74,907.01\nDELTA REFRACTORIES,01-East,766021000,1076.00,110.56,965.44\nDELTA REFRACTORIES,01-East,764561210,1066.00,512.87,553.13\nDELTA REFRACTORIES,01-East,764762000,1060.00,315.70,744.30\nDELTA REFRACTORIES,01-East,792624000,1040.00,499.67,540.33\nDELTA REFRACTORIES,01-East,733423600,963.20,171.14,792.06\nDELTA REFRACTORIES,01-East,764605001,880.00,360.78,519.22\nDELTA REFRACTORIES,01-East,738361000,761.22,617.76,143.46\nDELTA REFRACTORIES,01-East,770500000,711.00,41.80,669.20\nDELTA REFRACTORIES,01-East,770710000,657.00,16.81,640.19\nDELTA REFRACTORIES,01-East,770380000,564.00,51.37,512.63\nDELTA REFRACTORIES,01-East,777904301,518.18,234.94,283.24\nDELTA REFRACTORIES,01-East,770392000,444.00,41.51,402.49\nDELTA REFRACTORIES,01-East,792720000,429.00,85.41,343.59\nDELTA REFRACTORIES,01-East,734751200,423.95,75.10,348.85\nDELTA REFRACTORIES,01-East,764761000,416.00,122.84,293.16\nDELTA REFRACTORIES,01-East,770200000,390.60,40.79,349.81\nDELTA REFRACTORIES,01-East,734822000,370.50,67.47,303.03\nDELTA REFRACTORIES,01-East,733141200,336.00,66.98,269.02\nDELTA REFRACTORIES,01-East,777587000,317.84,18.74,299.10\nDELTA REFRACTORIES,01-East,770220000,294.00,27.35,266.65\nDELTA REFRACTORIES,01-East,770570000,246.67,21.63,225.04\nDELTA REFRACTORIES,01-East,733423700,243.20,45.26,197.94\nDELTA REFRACTORIES,01-East,733423800,243.20,48.07,195.13\nDELTA REFRACTORIES,01-East,734900000,201.96,76.72,125.24\nDELTA REFRACTORIES,01-East,779041000,120.20,30.16,90.04\nDELTA REFRACTORIES,01-East,764461006,80.00,27.94,52.06\nDENTAL CREATIONS LTD,01-East,734961000,25332.00,4643.96,20688.04\nDEPT. OF THE NAVY,01-East,724311400,2346.00,376.76,1969.24\nDERRINGER NEY,01-East,733151500,450.00,47.69,402.31\nDETECTOR TECHNOLOGY,01-East,739541200,501.76,64.10,437.66\nDFAS COLUMBUS CENTER,01-East,734010000,1720.00,180.10,1539.90\nDISTRIBUTION INT'L.,01-East,735602000,690746.90,246603.50,444143.40\nDISTRIBUTION INT'L.,01-East,735602110,64332.00,20826.61,43505.39\nDISTRIBUTION INT'L.,01-East,724071900,44762.34,6992.37,37769.97\nDISTRIBUTION INT'L.,01-East,735752000,39744.00,14795.93,24948.07\nDISTRIBUTION INT'L.,01-East,734311100,33981.20,3883.17,30098.03\nDISTRIBUTION INT'L.,01-East,739370000,20040.38,15579.44,4460.94\nDISTRIBUTION INT'L.,01-East,734121000,13972.50,3751.06,10221.44\nDISTRIBUTION INT'L.,01-East,768142074,13484.00,3496.25,9987.75\nDISTRIBUTION INT'L.,01-East,739290000,13142.30,14424.48,-1282.18\nDISTRIBUTION INT'L.,01-East,735750000,6030.00,2008.93,4021.07\nDISTRIBUTION INT'L.,01-East,735650000,5850.00,1482.97,4367.03\nDISTRIBUTION INT'L.,01-East,768144200,4354.55,447.42,3907.13\nDISTRIBUTION INT'L.,01-East,768121000,2520.00,704.55,1815.45\nDISTRIBUTION INT'L.,01-East,737851000,1983.62,1326.40,657.22\nDISTRIBUTION INT'L.,01-East,718501000,1577.00,272.12,1304.88\nDISTRIBUTION INT'L.,01-East,770830000,1390.00,71.75,1318.25\nDISTRIBUTION INT'L.,01-East,770320000,1311.20,206.07,1105.13\nDISTRIBUTION INT'L.,01-East,770580000,1297.00,89.66,1207.34\nDISTRIBUTION INT'L.,01-East,733505100,975.00,180.19,794.81\nDISTRIBUTION INT'L.,01-East,737301000,839.62,643.84,195.78\nDISTRIBUTION INT'L.,01-East,733141300,779.20,209.19,570.01\nDISTRIBUTION INT'L.,01-East,768133000,778.60,85.83,692.77\nDISTRIBUTION INT'L.,01-East,770312000,712.50,113.53,598.97\nDISTRIBUTION INT'L.,01-East,770200000,689.40,81.57,607.83\nDISTRIBUTION INT'L.,01-East,733384000,675.84,141.33,534.51\nDISTRIBUTION INT'L.,01-East,742546000,661.50,82.04,579.46\nDISTRIBUTION INT'L.,01-East,724311200,631.00,171.33,459.67\nDISTRIBUTION INT'L.,01-East,768241100,500.00,183.65,316.35\nDISTRIBUTION INT'L.,01-East,770380000,498.00,51.37,446.63\nDISTRIBUTION INT'L.,01-East,770392000,392.00,41.51,350.49\nDISTRIBUTION INT'L.,01-East,733505300,375.00,77.58,297.42\nDISTRIBUTION INT'L.,01-East,734132000,366.00,76.28,289.72\nDISTRIBUTION INT'L.,01-East,777601000,271.65,72.02,199.63\nDISTRIBUTION INT'L.,01-East,777642000,210.50,66.20,144.30\nDISTRIBUTION INT'L.,01-East,767650000,174.00,63.10,110.90\nDISTRIBUTION INT'L.,01-East,767660000,93.00,33.15,59.85\nDIVERSIFIED CONTROL INC.,01-East,738401000,2725.92,1284.48,1441.44\nDOOLEY GASKET & SEAL,01-East,733525600,9630.00,1174.44,8455.56\nDOOLEY GASKET & SEAL,01-East,733202000,3572.00,735.60,2836.40\nDOOLEY GASKET & SEAL,01-East,733201000,2690.40,563.87,2126.53\nDOOLEY GASKET & SEAL,01-East,733151600,1682.40,252.63,1429.77\nDOOLEY GASKET & SEAL,01-East,733211050,1562.80,323.03,1239.77\nDOOLEY GASKET & SEAL,01-East,733182200,1242.60,195.30,1047.30\nDOOLEY GASKET & SEAL,01-East,764521000,899.10,39.81,859.29\nDOOLEY GASKET & SEAL,01-East,764601000,610.20,26.32,583.88\nDOOLEY GASKET & SEAL,01-East,739541200,470.02,80.45,389.57\nDOT/FAA TECHNICAL CENTER,01-East,733464500,313.00,32.53,280.47\nDOW CHEMICAL COMPANY,01-East,733423800,400.00,48.07,351.93\nDRAKE WATER TECHNOLOGY,01-East,764903000,2160.00,598.17,1561.83\nDRAKE WATER TECHNOLOGY,01-East,739641100,463.32,137.43,325.89\nDS FIBERTECH CORP.,01-East,727000140,53577.60,15424.00,38153.60\nDS FIBERTECH CORP.,01-East,727063016,39641.40,11472.38,28169.02\nDS FIBERTECH CORP.,01-East,715000000,27678.75,15217.97,12460.78\nDS FIBERTECH CORP.,01-East,734311100,24370.80,3543.20,20827.60\nDS FIBERTECH CORP.,01-East,763991000,17755.20,6354.36,11400.84\nDS FIBERTECH CORP.,01-East,734173000,15138.00,3651.92,11486.08\nDS FIBERTECH CORP.,01-East,764781000,12946.20,2564.86,10381.34\nDS FIBERTECH CORP.,01-East,764821000,5780.00,1240.51,4539.49\nDS FIBERTECH CORP.,01-East,764901000,4553.40,771.80,3781.60\nDS FIBERTECH CORP.,01-East,740361900,4298.88,1797.47,2501.41\nDS FIBERTECH CORP.,01-East,764761000,4294.00,1159.35,3134.65\nDS FIBERTECH CORP.,01-East,733211050,3366.90,770.29,2596.61\nDS FIBERTECH CORP.,01-East,777901100,2348.47,935.30,1413.17\nDS FIBERTECH CORP.,01-East,733525600,2244.00,338.56,1905.44\nDS FIBERTECH CORP.,01-East,770570000,2179.95,196.14,1983.81\nDS FIBERTECH CORP.,01-East,764221000,1977.20,607.32,1369.88\nDS FIBERTECH CORP.,01-East,777642000,121.50,50.36,71.14\nDSSI,01-East,739641100,463.32,137.43,325.89\nDUNCAN BOLT,01-East,733403100,1163.15,122.60,1040.55\nDURR SYSTEMS INC.,01-East,782070000,40736.40,19967.55,20768.85\nDURR SYSTEMS INC.,01-East,780770000,24779.46,12150.48,12628.98\nDURR SYSTEMS INC.,01-East,779956216,11773.92,3633.60,8140.32\nDURR SYSTEMS INC.,01-East,780561000,5148.36,2523.48,2624.88\nDURR SYSTEMS INC.,01-East,777902892,4317.30,3650.40,666.90\nDURR SYSTEMS INC.,01-East,779956217,3769.56,970.74,2798.82\nDURR SYSTEMS INC.,01-East,777904301,2591.75,1174.68,1417.07\nDURR SYSTEMS INC.,01-East,777902891,2502.00,2115.00,387.00\nDURR SYSTEMS INC.,01-East,779952144,2355.84,1316.85,1038.99\nDURR SYSTEMS INC.,01-East,766121000,1962.00,490.47,1471.53\nDURR SYSTEMS INC.,01-East,779952145,1424.88,862.71,562.17\nDURR SYSTEMS INC.,01-East,764821000,1107.00,276.45,830.55\nDURR SYSTEMS INC.,01-East,779956599,669.60,537.36,132.24\nDURR SYSTEMS INC.,01-East,780521000,660.00,342.67,317.33\nDURR SYSTEMS INC.,01-East,779989410,652.50,441.00,211.50\nDURR SYSTEMS INC.,01-East,779989510,499.50,364.50,135.00\nDURR SYSTEMS INC.,01-East,777902890,453.60,388.80,64.80\nDXP ENTERPRISES,01-East,764262000,1237.20,135.92,1101.28\nE I DUPONT DE NEMOURS &,01-East,734900000,4144.50,863.14,3281.36\nE I DUPONT DE NEMOURS &,01-East,734950000,3340.00,565.64,2774.36\nE I DUPONT DE NEMOURS &,01-East,733444100,696.00,163.25,532.75\nE&J METAL FABRICATORS INC,01-East,764601000,5085.00,197.37,4887.63\nE&J METAL FABRICATORS INC,01-East,739741100,2471.04,657.34,1813.70\nE&J METAL FABRICATORS INC,01-East,764521000,640.00,26.60,613.40\nE. A. PATTEN COMPANY,01-East,770800000,3288.00,53.64,3234.36\nE. O. WOOD CO,01-East,735602000,80552.00,24333.91,56218.09\nE. O. WOOD CO,01-East,735602110,13750.00,4812.87,8937.13\nE. O. WOOD CO,01-East,735752000,2340.00,773.28,1566.72\nE.I.DUPONT DE NEMOURS&CO,01-East,733444100,332.00,75.04,256.96\nE.K.S. PARTS INC.,01-East,734311100,19784.32,2965.90,16818.42\nE.K.S. PARTS INC.,01-East,734900000,2632.00,1059.17,1572.83\nE.K.S. PARTS INC.,01-East,770260000,612.00,67.63,544.37\nE.K.S. PARTS INC.,01-East,770240000,611.25,67.63,543.62\nE.K.S. PARTS INC.,01-East,770210000,560.00,63.05,496.95\nE.K.S. PARTS INC.,01-East,770220000,540.00,54.70,485.30\nE.K.S. PARTS INC.,01-East,739541100,428.40,83.57,344.83\nE.K.S. PARTS INC.,01-East,739561100,398.16,93.32,304.84\nE.K.S. PARTS INC.,01-East,739451000,284.16,116.03,168.13\nE.K.S. PARTS INC.,01-East,777601000,238.16,72.02,166.14\nEAGLE INSULATION,01-East,735602000,482795.00,183732.02,299062.98\nEAGLE PICHER TECHNOLOGIES,01-East,724003000,1825.50,221.83,1603.67\nEAGLE PICHER TECHNOLOGIES,01-East,740382101,626.40,252.83,373.57\n\"EAGLE ROCK SPECIALTIES,\",01-East,766121000,875.00,27.25,847.75\n\"EAGLE ROCK SPECIALTIES,\",01-East,777601000,338.76,58.80,279.96\n\"EAGLE ROCK SPECIALTIES,\",01-East,764521000,333.00,13.27,319.73\nEAST COAST METAL,01-East,735602000,19393.50,4805.20,14588.30\nEASTERN REGIONAL CONTRACT,01-East,764821000,3168.00,737.18,2430.82\nEASTERN REGIONAL CONTRACT,01-East,739781100,2250.72,856.89,1393.83\nEASTERN REGIONAL CONTRACT,01-East,737851000,2161.60,332.22,1829.38\nEASTERN REGIONAL CONTRACT,01-East,737811000,2135.76,400.30,1735.46\nEASTERN REGIONAL CONTRACT,01-East,766121000,2052.00,653.98,1398.02\nEASTERN REGIONAL CONTRACT,01-East,764902000,1980.00,478.89,1501.11\nEASTERN REGIONAL CONTRACT,01-East,764901000,1584.00,387.46,1196.54\nEASTERN REGIONAL CONTRACT,01-East,734651200,1307.34,222.00,1085.34\nEASTERN REGIONAL CONTRACT,01-East,764602000,1176.00,314.55,861.45\nEASTERN REGIONAL CONTRACT,01-East,734822000,792.20,160.95,631.25\nEASTERN REGIONAL CONTRACT,01-East,734830000,656.00,89.29,566.71\nEASTERN REGIONAL CONTRACT,01-East,734900000,100.96,38.36,62.60\nECR INTERNATIONAL INC.,01-East,734311100,2334.64,205.40,2129.24\nEDLON PRODUCTS INC.,01-East,734211200,486.92,46.75,440.17\n\"ELECTRIC MOTION CO.,INC\",01-East,739681100,906.30,327.46,578.84\nELECTRO CHEMICAL ENGR MFG,01-East,733721000,1800.00,415.91,1384.09\nEMCORE CORPORATION,01-East,779985552,3365.12,1397.82,1967.30\nEMPIRE INSUL. SPECIALTIES,01-East,735602000,2736.00,1008.69,1727.31\nEMS LLC,01-East,764521000,666.00,26.54,639.46\nENERGYSOLUTIONS US,01-East,738521000,59647.68,37618.56,22029.12\nENERGYSOLUTIONS US,01-East,764821000,6084.00,375.72,5708.28\nENERGYSOLUTIONS US,01-East,777642000,5434.00,1913.68,3520.32\nENERGYSOLUTIONS US,01-East,779985571,386.52,111.95,274.57\nENERSYS ADVANCED SYSTEMS,01-East,733525400,2870.00,274.80,2595.20\nENERSYS ADVANCED SYSTEMS,01-East,740382201,1180.00,300.51,879.49\nENERSYS ADVANCED SYSTEMS,01-East,740402301,1107.50,219.36,888.14\nESCO TURBINE TECHNOLOGIES,01-East,764609200,224489.00,82206.88,142282.12\nESCO TURBINE TECHNOLOGIES,01-East,764609500,162258.75,64151.37,98107.38\nESCO TURBINE TECHNOLOGIES,01-East,764609100,122593.00,45426.60,77166.40\nESCO TURBINE TECHNOLOGIES,01-East,764781040,25706.00,6500.75,19205.25\nESCO TURBINE TECHNOLOGIES,01-East,764461006,4577.60,1460.64,3116.96\nESCO TURBINE TECHNOLOGIES,01-East,740402301,795.00,210.97,584.03\nESTERLINE,01-East,739541100,658.56,83.57,574.99\nESTERLINE,01-East,739452000,540.54,150.83,389.71\nEVANS / EVCO,01-East,724281200,3519.00,499.18,3019.82\nEVIE AULT,01-East,733423700,380.80,36.22,344.58\nEXOTIC METAL FORMING,01-East,740382201,6470.00,1776.11,4693.89\nEXPRESS INTEGRATED TECH.,01-East,739370000,4438.08,3179.52,1258.56\nEXPRESS INTEGRATED TECH.,01-East,739741100,4073.60,3198.08,875.52\nEXPRESS METAL FAB INC.,01-East,768241000,223909.50,121746.50,102163.00\nEXPRESS METAL FAB INC.,01-East,779958169,194821.31,102613.49,92207.82\nEXPRESS METAL FAB INC.,01-East,737912200,161657.00,56722.01,104934.99\nEXPRESS METAL FAB INC.,01-East,768221000,128412.00,64711.59,63700.41\nEXPRESS METAL FAB INC.,01-East,779956285,77422.50,47154.03,30268.47\nEXPRESS METAL FAB INC.,01-East,768231000,40912.00,20708.52,20203.48\nEXPRESS METAL FAB INC.,01-East,751348000,30465.84,19085.43,11380.41\nEXPRESS METAL FAB INC.,01-East,779958170,26324.88,21598.72,4726.16\nEXPRESS METAL FAB INC.,01-East,764821000,20181.00,8822.19,11358.81\nEXPRESS METAL FAB INC.,01-East,766122000,16014.00,5867.47,10146.53\nEXPRESS METAL FAB INC.,01-East,764901000,7984.00,3783.51,4200.49\nEXPRESS METAL FAB INC.,01-East,768222000,5175.00,2561.73,2613.27\nEXPRESS METAL FAB INC.,01-East,764861000,5107.00,2413.44,2693.56\nEXPRESS METAL FAB INC.,01-East,739370000,4438.08,2088.90,2349.18\nEXPRESS METAL FAB INC.,01-East,779956599,2580.60,2059.92,520.68\nEXPRESS METAL FAB INC.,01-East,777642000,1590.00,475.05,1114.95\nEXPRESS METAL FAB INC.,01-East,768121000,182.50,71.92,110.58\nEXPRESSIONS ART GLASS,01-East,739741100,823.68,219.12,604.56\nEXPRESSIONS ART GLASS,01-East,739561200,421.12,72.33,348.79\nEYE THINK INC.,01-East,733464600,298.00,31.31,266.69\nF & B MFG LLC,01-East,740361900,4708.80,1584.97,3123.83\nF.S. SPERRY COMPANY,01-East,727041200,84171.60,12684.45,71487.15\nF.S. SPERRY COMPANY,01-East,727041000,28350.00,11281.65,17068.35\nF.S. SPERRY COMPANY,01-East,779956843,11185.68,8680.66,2505.02\nF.S. SPERRY COMPANY,01-East,734890000,4761.87,1193.78,3568.09\nF.S. SPERRY COMPANY,01-East,779956842,4116.00,3083.85,1032.15\nF.S. SPERRY COMPANY,01-East,734990000,2776.00,552.92,2223.08\nF.S. SPERRY COMPANY,01-East,779956841,2596.10,1483.72,1112.38\nF.S. SPERRY COMPANY,01-East,779956840,2056.80,1229.02,827.78\nF.S. SPERRY COMPANY,01-East,777601000,1629.90,432.10,1197.80\nF.S. SPERRY COMPANY,01-East,764822000,980.00,153.82,826.18\nF.S. SPERRY COMPANY,01-East,779261000,519.93,115.39,404.54\nF.S. SPERRY COMPANY,01-East,734910000,499.80,104.44,395.36\nF.S. SPERRY COMPANY,01-East,764821000,390.40,61.44,328.96\nF.S. SPERRY COMPANY,01-East,770210000,285.00,31.52,253.48\nFAIR RITE PRODUCTS CORP,01-East,733511000,537.50,66.56,470.94\nFASTENAL COMPANY,01-East,770320000,1130.00,111.14,1018.86\nFASTENAL COMPANY,01-East,770310000,544.00,47.28,496.72\nFAUST ELECTRIC MOTOR,01-East,764261000,424.00,22.89,401.11\nFEDERAL MOGUL WORLDWIDE,01-East,726020047,9064.00,3209.12,5854.88\nFEDERAL MOGUL WORLDWIDE,01-East,726020007,1269.60,464.04,805.56\nFEDERAL MOGUL WORLDWIDE,01-East,726020046,423.20,148.75,274.45\nFIAS USA INC,01-East,734910000,3340.00,530.85,2809.15\nFIBER GLASS INDUSTRIES,01-East,764761000,1016.00,61.43,954.57\nFIBRECAST INC.,01-East,764901000,14985.00,8647.70,6337.30\nFIBRECAST INC.,01-East,733403300,831.60,122.54,709.06\nFIRELINE 520,01-East,764822000,29566.00,11777.94,17788.06\nFIRELINE 520,01-East,764781040,25182.00,12183.17,12998.83\nFIRELINE 520,01-East,764781000,400.00,204.45,195.55\nFIRELINE 520,01-East,764461006,244.00,82.44,161.56\nFIRELINE 520,01-East,764482000,60.00,34.10,25.90\nFIRST STATE MANUFACTURING,01-East,791104475,10850.00,4964.87,5885.13\nFLAME CONTROL COATINGS,01-East,720219100,2127.60,52.39,2075.21\nFLANDERS ELECTRIC,01-East,770500000,2043.00,83.60,1959.40\nFLANDERS ELECTRIC,01-East,739541000,1128.96,143.24,985.72\nFLANDERS ELECTRIC,01-East,739561000,947.52,170.44,777.08\nFLANDERS ELECTRIC,01-East,739641100,463.32,137.43,325.89\nFLAREGAS CORP.,01-East,770280000,2906.25,240.08,2666.17\nFLEXLINE LLC,01-East,739561100,552.72,93.32,459.40\nFLEXLINE LLC,01-East,739451000,411.84,116.03,295.81\nFLIGHT ENVIRONMENTS,01-East,724281400,12293.00,1937.89,10355.11\nFMS LOGISTICS MANAGEMENT,01-East,770510000,1880.00,82.78,1797.22\nFORESTWOOD CO. INC.,01-East,768751000,4533.84,2645.88,1887.96\nFORESTWOOD CO. INC.,01-East,768753000,4533.84,2645.88,1887.96\nFORESTWOOD CO. INC.,01-East,727063000,764.80,261.74,503.06\nFOUR STATES GASKET & RUBB,01-East,738521000,556.48,298.56,257.92\nFRANK MURKEN PRODUCTS INC,01-East,733484800,648.00,82.18,565.82\nFRIC & FRAC,01-East,739641100,482.04,201.60,280.44\nFRICCION Y TECNOLOGIA,01-East,727080000,362487.20,50308.56,312178.64\nFRICCION Y TECNOLOGIA,01-East,779987651,119319.20,16797.77,102521.43\nFRIEDMAN ELECTRIC SUPPLY,01-East,764481000,360.00,17.59,342.41\nFUELCELL ENERGY,01-East,777585000,70459.55,2551.49,67908.06\nFUELCELL ENERGY,01-East,770740000,48023.00,1811.40,46211.60\nFUELCELL ENERGY,01-East,733151500,11934.00,1383.92,10550.08\nFUELCELL ENERGY,01-East,733182010,7825.29,1761.96,6063.33\nFUELCELL ENERGY,01-East,770722000,2483.00,85.76,2397.24\nFUELCELL ENERGY,01-East,770850000,1230.00,47.24,1182.76\nFUELCELL ENERGY,01-East,734611100,1215.28,176.31,1038.97\nFUELCELL ENERGY,01-East,768221000,1014.00,51.42,962.58\nFUELCELL ENERGY,01-East,734900000,673.00,191.81,481.19\nFUJI INDUSTRIES CORP,01-East,733464500,626.00,65.05,560.95\n\"FYFE CP., LLC\",01-East,727063022,55185.60,5610.80,49574.80\nG.W. LISK CO,01-East,733464400,313.00,43.37,269.63\nGAF,01-East,764221000,486.00,33.75,452.25\nGAF,01-East,764261000,424.00,22.89,401.11\nGARLOCK SEALING TECH,01-East,733151600,8684.00,1210.04,7473.96\nGARLOCK SEALING TECH,01-East,770565000,7146.00,778.95,6367.05\nGARLOCK SEALING TECH,01-East,770262000,5249.25,892.68,4356.57\nGARLOCK SEALING TECH,01-East,770244000,3596.25,554.54,3041.71\nGARLOCK SEALING TECH,01-East,770260000,3564.00,608.66,2955.34\nGARLOCK SEALING TECH,01-East,770601000,3001.95,288.96,2712.99\nGARLOCK SEALING TECH,01-East,770524000,2196.00,277.27,1918.73\nGARLOCK SEALING TECH,01-East,770221000,1887.00,275.66,1611.34\nGARLOCK SEALING TECH,01-East,770531000,1795.00,193.36,1601.64\nGARLOCK SEALING TECH,01-East,770201000,1746.00,329.56,1416.44\nGARLOCK SEALING TECH,01-East,770301000,1542.75,256.77,1285.98\nGARLOCK SEALING TECH,01-East,770322000,1378.00,317.22,1060.78\nGARLOCK SEALING TECH,01-East,770332000,1342.50,294.77,1047.73\nGARLOCK SEALING TECH,01-East,770231000,1254.75,218.91,1035.84\nGARLOCK SEALING TECH,01-East,770211000,1175.00,214.63,960.37\nGARLOCK SEALING TECH,01-East,770571000,1076.40,100.16,976.24\nGARLOCK SEALING TECH,01-East,770501000,531.00,51.44,479.56\nGARLOCK SEALING TECH,01-East,770514000,515.00,82.78,432.22\nGARLOCK SEALING TECH,01-East,770220000,378.00,54.69,323.31\nGARLOCK SEALING TECH,01-East,770310500,364.00,90.94,273.06\nGB & G DIE GASKET CO.INC.,01-East,733671000,1438.08,119.66,1318.42\nGE ENERGY CERAMIC,01-East,733151500,472.00,54.11,417.89\nGE GLOBAL RESEARCH,01-East,764821000,447.00,15.66,431.34\n\"GEMX TECHNOLOGIES, LLC\",01-East,737550000,42352.60,55206.93,-12854.33\n\"GEMX TECHNOLOGIES, LLC\",01-East,737550600,24288.70,13046.50,11242.20\n\"GEMX TECHNOLOGIES, LLC\",01-East,779980780,21410.90,11579.26,9831.64\n\"GEMX TECHNOLOGIES, LLC\",01-East,737550200,18258.80,10457.46,7801.34\n\"GEMX TECHNOLOGIES, LLC\",01-East,779980760,7224.00,3220.70,4003.30\n\"GEMX TECHNOLOGIES, LLC\",01-East,779980750,6744.00,3006.70,3737.30\n\"GEMX TECHNOLOGIES, LLC\",01-East,737550400,4762.92,2452.91,2310.01\n\"GEMX TECHNOLOGIES, LLC\",01-East,764461006,1000.00,108.98,891.02\n\"GEMX TECHNOLOGIES, LLC\",01-East,734900000,915.54,171.02,744.52\nGENERAL GASKET,01-East,771320000,6000.00,711.76,5288.24\nGENERAL GASKET,01-East,733721000,1208.00,277.28,930.72\nGENERAL GASKET,01-East,770260000,978.75,70.33,908.42\nGENERAL GASKET,01-East,733525600,752.50,71.05,681.45\nGENERAL TOOL & SUPPLY,01-East,770120000,600.16,37.20,562.96\nGENESYS,01-East,764821000,5334.00,281.79,5052.21\nGENEX TURBINE TECHNOLOGIE,01-East,740402402,3225.00,652.05,2572.95\n\"GEROME TECHNOLOGIES, INC\",01-East,765702000,5250.00,2316.78,2933.22\nGERRESHEIMER GLASS INC.,01-East,737912200,3382.00,442.06,2939.94\nGHX,01-East,770512000,6704.50,266.00,6438.50\nGHX,01-East,770810000,5070.00,103.12,4966.88\nGHX,01-East,770830000,4040.00,123.90,3916.10\nGHX,01-East,770430000,3982.50,265.64,3716.86\nGHX,01-East,770522000,1963.00,102.80,1860.20\nGHX,01-East,742541500,1868.75,138.40,1730.35\nGHX,01-East,770520000,1812.00,96.63,1715.37\nGLASTON AMERICA INC.,01-East,771320000,6343.40,1111.55,5231.85\nGLENRO INC,01-East,771320000,24934.00,3477.02,21456.98\nGLENRO INC,01-East,739561200,2947.84,506.32,2441.52\nGLENRO INC,01-East,738521000,2183.04,884.02,1299.02\nGLENRO INC,01-East,734311100,2166.78,190.16,1976.62\nGLENRO INC,01-East,733525600,1220.00,130.98,1089.02\nGLENRO INC,01-East,739541200,501.76,64.10,437.66\nGLIDEWELL DENTAL,01-East,737301000,3230.72,1287.68,1943.04\nGMS SHEETMETAL DIVISON,01-East,724003000,1899.00,245.46,1653.54\nGONZALES MFG. CO.,01-East,766594010,100470.00,59529.97,40940.03\nGONZALES MFG. CO.,01-East,766594000,18912.00,12162.45,6749.55\nGONZALES MFG. CO.,01-East,766564001,4368.00,2615.47,1752.53\nGONZALES MFG. CO.,01-East,766541110,2052.00,1119.94,932.06\nGOVERNMENT SCIENTIFIC,01-East,734751200,1118.35,149.88,968.47\nGRAVER TECHNOLOGIES,01-East,733505300,82980.00,18781.71,64198.29\nGRAYMONT MATERIAL NY INC.,01-East,734910000,724.00,106.17,617.83\nGREAT LAKES PUMP & SEAL,01-East,737321000,7180.89,1070.70,6110.19\nGREAT LAKES PUMP & SEAL,01-East,737301000,3106.56,490.72,2615.84\nGREAT LAKES PUMP & SEAL,01-East,742520400,2495.50,338.32,2157.18\nGREAT LAKES PUMP & SEAL,01-East,737271000,1615.70,254.14,1361.56\nGREAT LAKES PUMP & SEAL,01-East,770560000,1509.00,86.09,1422.91\nGREENBRIER RAIL SERVICES,01-East,764821000,9166.00,324.89,8841.11\nGREENBRIER RAIL SERVICES,01-East,764481000,1730.00,84.70,1645.30\nGREENBRIER RAIL SERVICES,01-East,764643100,302.40,105.99,196.41\nGRESCO PRODUCTS INC,01-East,724261200,786.00,78.20,707.80\nGRESCO PRODUCTS INC,01-East,734990000,463.20,55.04,408.16\nGSA ACCOUNT PAYABLE,01-East,734311100,3524.84,309.72,3215.12\nGT MIDWEST,01-East,764222050,274964.18,158313.53,116650.65\nGT MIDWEST,01-East,768122200,264011.53,163840.64,100170.89\nGT MIDWEST,01-East,733586500,190153.44,32489.24,157664.20\nGT MIDWEST,01-East,768241800,181005.95,92634.50,88371.45\nGT MIDWEST,01-East,768550000,168251.42,91022.99,77228.43\nGT MIDWEST,01-East,733201000,166744.00,41481.70,125262.30\nGT MIDWEST,01-East,733211050,100596.40,27755.55,72840.85\nGT MIDWEST,01-East,764522000,89235.00,35205.92,54029.08\nGT MIDWEST,01-East,764781040,89080.00,34248.40,54831.60\nGT MIDWEST,01-East,764521000,66995.00,14749.83,52245.17\nGT MIDWEST,01-East,742520110,62213.00,14392.91,47820.09\nGT MIDWEST,01-East,764481000,52302.00,14568.79,37733.21\nGT MIDWEST,01-East,764762060,46008.00,19292.96,26715.04\nGT MIDWEST,01-East,768225030,34248.00,17393.80,16854.20\nGT MIDWEST,01-East,764902000,32626.00,8339.23,24286.77\nGT MIDWEST,01-East,733182200,32202.72,12769.20,19433.52\nGT MIDWEST,01-East,738561000,32136.02,14863.40,17272.62\nGT MIDWEST,01-East,724281700,30440.00,12044.48,18395.52\nGT MIDWEST,01-East,764461006,26817.00,7817.24,18999.76\nGT MIDWEST,01-East,777904000,26536.44,9292.43,17244.01\nGT MIDWEST,01-East,779980925,23097.60,7842.24,15255.36\nGT MIDWEST,01-East,770830000,20653.30,890.21,19763.09\nGT MIDWEST,01-East,734661300,19811.00,6118.74,13692.26\nGT MIDWEST,01-East,770880000,19801.50,1034.84,18766.66\nGT MIDWEST,01-East,727000140,15969.80,2019.02,13950.78\nGT MIDWEST,01-East,764822000,15855.00,3627.37,12227.63\nGT MIDWEST,01-East,768242011,15053.66,7569.71,7483.95\nGT MIDWEST,01-East,739541200,13821.44,4739.81,9081.63\nGT MIDWEST,01-East,737902100,13398.73,3312.38,10086.35\nGT MIDWEST,01-East,739452000,12972.33,5499.06,7473.27\nGT MIDWEST,01-East,739681100,10423.08,6067.36,4355.72\nGT MIDWEST,01-East,770222000,9952.80,929.07,9023.73\nGT MIDWEST,01-East,733525600,9607.50,1375.25,8232.25\nGT MIDWEST,01-East,764561000,9074.00,1675.57,7398.43\nGT MIDWEST,01-East,764902021,8125.26,4108.30,4016.96\nGT MIDWEST,01-East,733171900,6510.00,1259.24,5250.76\nGT MIDWEST,01-East,738441000,6210.00,1730.71,4479.29\nGT MIDWEST,01-East,738521000,5935.68,3512.78,2422.90\nGT MIDWEST,01-East,764262000,5900.00,1706.47,4193.53\nGT MIDWEST,01-East,764601000,5751.80,1006.66,4745.14\nGT MIDWEST,01-East,737912200,5628.85,1152.75,4476.10\nGT MIDWEST,01-East,734910000,5483.10,1294.17,4188.93\nGT MIDWEST,01-East,777601000,4343.20,1269.90,3073.30\nGT MIDWEST,01-East,779081000,3796.06,1104.76,2691.30\nGT MIDWEST,01-East,739561200,3778.04,1021.32,2756.72\nGT MIDWEST,01-East,764821000,3614.00,814.07,2799.93\nGT MIDWEST,01-East,734311100,3293.32,378.73,2914.59\nGT MIDWEST,01-East,770720000,3148.40,117.70,3030.70\nGT MIDWEST,01-East,764342000,2722.10,578.39,2143.71\nGT MIDWEST,01-East,740023000,2356.36,595.01,1761.35\nGT MIDWEST,01-East,768222200,2268.00,1320.64,947.36\nGT MIDWEST,01-East,734711100,2116.40,433.10,1683.30\nGT MIDWEST,01-East,739641100,2102.94,1154.52,948.42\nGT MIDWEST,01-East,734990000,2028.88,385.00,1643.88\nGT MIDWEST,01-East,764482000,1830.00,511.44,1318.56\nGT MIDWEST,01-East,733151500,1627.80,270.53,1357.27\nGT MIDWEST,01-East,764826800,1584.00,672.39,911.61\nGT MIDWEST,01-East,764602000,1470.00,632.49,837.51\nGT MIDWEST,01-East,764461000,1449.00,419.06,1029.94\nGT MIDWEST,01-East,724281400,1395.80,332.79,1063.01\nGT MIDWEST,01-East,739741100,1050.24,438.24,612.00\nGT MIDWEST,01-East,739781100,1007.04,380.84,626.20\nGT MIDWEST,01-East,779951213,865.50,320.61,544.89\nGT MIDWEST,01-East,739561000,627.55,172.39,455.16\nGT MIDWEST,01-East,764901000,526.00,129.15,396.85\nGT MIDWEST,01-East,733725500,412.00,103.53,308.47\nGT MIDWEST,01-East,724311900,344.50,109.89,234.61\nH.C. STARCK INC.,01-East,770210000,440.00,30.32,409.68\nH.E.I.,01-East,733464400,313.00,43.37,269.63\nHAARSLEY INCORPORATED,01-East,739741100,14158.08,3944.08,10214.00\nHAARSLEY INCORPORATED,01-East,734751200,550.90,74.78,476.12\nHAARSLEY INCORPORATED,01-East,734711100,220.36,54.10,166.26\nHAAS GROUP INTERNATIONAL,01-East,734910000,1810.00,265.43,1544.57\nHAAS GROUP INTERNATIONAL,01-East,734311100,1617.44,141.21,1476.23\nHAAS GROUP INTERNATIONAL,01-East,734751200,292.00,37.71,254.29\nHAGEMEYER NORTH AMERICA,01-East,733505200,3095.00,313.15,2781.85\nHAGEMEYER NORTH AMERICA,01-East,770500000,1075.50,51.44,1024.06\nHAGEMEYER NORTH AMERICA,01-East,733525600,752.50,71.05,681.45\nHANES SUPPLY,01-East,770220000,2025.00,168.43,1856.57\nHANYAN-HIGGINS CO INC,01-East,737301000,7064.54,1717.52,5347.02\nHANYAN-HIGGINS CO INC,01-East,734990000,2131.20,663.51,1467.69\nHANYAN-HIGGINS CO INC,01-East,766121000,1623.60,163.50,1460.10\nHANYAN-HIGGINS CO INC,01-East,734910000,1484.90,311.57,1173.33\nHANYAN-HIGGINS CO INC,01-East,739561100,1084.35,293.24,791.11\nHANYAN-HIGGINS CO INC,01-East,739561200,1073.41,289.33,784.08\nHANYAN-HIGGINS CO INC,01-East,776101000,1064.25,502.81,561.44\nHANYAN-HIGGINS CO INC,01-East,734651200,629.74,110.75,518.99\nHANYAN-HIGGINS CO INC,01-East,733511000,619.50,133.12,486.38\nHANYAN-HIGGINS CO INC,01-East,739451000,525.12,232.06,293.06\nHANYAN-HIGGINS CO INC,01-East,773191000,307.00,44.38,262.62\nHANYAN-HIGGINS CO INC,01-East,777642000,290.40,88.54,201.86\nHANYAN-HIGGINS CO INC,01-East,764821000,268.40,30.72,237.68\nHARBISON-WALKER REFR.,01-East,718501000,9945.00,1224.55,8720.45\nHARCO MANUFACTURING,01-East,732429400,6672.00,466.22,6205.78\nHARPER INTERNATIONAL CORP,01-East,779985557,91230.72,26635.06,64595.66\nHARPER INTERNATIONAL CORP,01-East,740142000,52626.88,21129.30,31497.58\nHARPER INTERNATIONAL CORP,01-East,779988973,41616.00,31612.00,10004.00\nHARPER INTERNATIONAL CORP,01-East,779985552,39179.52,12967.66,26211.86\nHARPER INTERNATIONAL CORP,01-East,775171000,11581.52,5533.02,6048.50\nHARPER INTERNATIONAL CORP,01-East,737321000,11235.84,4496.94,6738.90\nHARPER INTERNATIONAL CORP,01-East,737861000,10570.56,10581.31,-10.75\nHARPER INTERNATIONAL CORP,01-East,738542000,8649.60,4799.30,3850.30\nHARPER INTERNATIONAL CORP,01-East,770540000,7329.00,538.80,6790.20\nHARPER INTERNATIONAL CORP,01-East,779951021,6440.00,4855.12,1584.88\nHARPER INTERNATIONAL CORP,01-East,779959108,5883.92,3235.12,2648.80\nHARPER INTERNATIONAL CORP,01-East,770392000,5174.00,456.92,4717.08\nHARPER INTERNATIONAL CORP,01-East,770380000,4860.00,1282.51,3577.49\nHARPER INTERNATIONAL CORP,01-East,779951023,3570.00,2670.54,899.46\nHARPER INTERNATIONAL CORP,01-East,780690000,3393.28,1866.44,1526.84\nHARPER INTERNATIONAL CORP,01-East,737871000,3045.12,839.22,2205.90\nHARPER INTERNATIONAL CORP,01-East,770372000,2877.00,538.28,2338.72\nHARPER INTERNATIONAL CORP,01-East,737311000,2703.36,1366.56,1336.80\nHARPER INTERNATIONAL CORP,01-East,780521000,2625.28,1445.00,1180.28\nHARPER INTERNATIONAL CORP,01-East,740146000,2448.72,956.86,1491.86\nHARPER INTERNATIONAL CORP,01-East,737821000,2283.84,790.54,1493.30\nHARPER INTERNATIONAL CORP,01-East,774981100,2265.07,1025.38,1239.69\nHARPER INTERNATIONAL CORP,01-East,722413100,2230.56,207.88,2022.68\nHARPER INTERNATIONAL CORP,01-East,738561000,1856.16,1541.14,315.02\nHARPER INTERNATIONAL CORP,01-East,779958975,1696.32,933.10,763.22\nHARPER INTERNATIONAL CORP,01-East,739681100,1570.75,1204.60,366.15\nHARPER INTERNATIONAL CORP,01-East,775011000,1460.00,895.07,564.93\nHARPER INTERNATIONAL CORP,01-East,764903000,1239.00,436.84,802.16\nHARPER INTERNATIONAL CORP,01-East,770530000,1177.25,93.46,1083.79\nHARPER INTERNATIONAL CORP,01-East,737251000,1121.76,601.38,520.38\nHARPER INTERNATIONAL CORP,01-East,737261000,1119.36,559.86,559.50\nHARPER INTERNATIONAL CORP,01-East,764821000,1090.00,313.10,776.90\nHARPER INTERNATIONAL CORP,01-East,739541200,1029.12,256.38,772.74\nHARPER INTERNATIONAL CORP,01-East,775151000,961.80,425.89,535.91\nHARPER INTERNATIONAL CORP,01-East,779959993,880.64,484.42,396.22\nHARPER INTERNATIONAL CORP,01-East,738381000,763.20,473.34,289.86\nHARPER INTERNATIONAL CORP,01-East,764901000,676.20,129.15,547.05\nHARPER INTERNATIONAL CORP,01-East,734751200,617.37,112.17,505.20\nHARPER INTERNATIONAL CORP,01-East,779958963,565.44,311.04,254.40\nHARPER INTERNATIONAL CORP,01-East,766121000,493.50,75.15,418.35\nHARPER INTERNATIONAL CORP,01-East,737301000,470.40,245.36,225.04\nHARPER INTERNATIONAL CORP,01-East,738401000,457.92,281.37,176.55\nHARPER INTERNATIONAL CORP,01-East,779986495,457.92,324.72,133.20\nHARPER INTERNATIONAL CORP,01-East,738521000,409.60,597.12,-187.52\nHARPER INTERNATIONAL CORP,01-East,777642000,407.75,129.56,278.19\nHARPER INTERNATIONAL CORP,01-East,779951061,400.00,298.86,101.14\nHARPER INTERNATIONAL CORP,01-East,739561200,299.52,144.66,154.86\nHARPER INTERNATIONAL CORP,01-East,764301000,69.50,18.56,50.94\nHARPER INTERNATIONAL CORP,01-East,775111000,43.80,18.81,24.99\nHARPER INTERNATIONAL CORP,01-East,775091000,43.04,21.34,21.70\nHARVEST PRODUCTS,01-East,727000140,1950.40,130.20,1820.20\nHAWAII BIOCHAR PILOR LLC,01-East,739781100,15525.22,6881.28,8643.94\nHAWAII BIOCHAR PILOR LLC,01-East,777601000,819.84,117.60,702.24\nHAZELETT STRIP CASTING CO,01-East,733464500,31927.00,3363.85,28563.15\nHAZELETT STRIP CASTING CO,01-East,739561200,13178.88,2316.01,10862.87\nHAZELETT STRIP CASTING CO,01-East,733510000,11245.00,1572.97,9672.03\nHAZELETT STRIP CASTING CO,01-East,764521000,2586.00,106.33,2479.67\nHAZELETT STRIP CASTING CO,01-East,777601000,1546.88,288.08,1258.80\nHAZELETT STRIP CASTING CO,01-East,740402402,1010.00,217.35,792.65\nHAZELETT STRIP CASTING CO,01-East,739451000,840.32,227.59,612.73\nHAZELETT STRIP CASTING CO,01-East,739741100,823.68,219.12,604.56\nHAZELETT STRIP CASTING CO,01-East,739781100,410.72,163.84,246.88\nHEAT SHIELD INC.,01-East,764461006,404.00,27.94,376.06\nHEAVY EQUIPMENT MACHINERY,01-East,764902000,2416.00,515.33,1900.67\nHERAEUS CO,01-East,766081010,11362.00,333.93,11028.07\nHERCULES INDUSTRIES - DEN,01-East,735602000,251657.60,74086.06,177571.54\nHERCULES INDUSTRIES - DEN,01-East,735602110,18734.40,5621.39,13113.01\nHERCULES INDUSTRIES,01-East,735602110,15142.40,4550.35,10592.05\nHERCULES INDUSTRIES,01-East,735602000,13977.60,4034.75,9942.85\nHI TEMP INSULATION INC,01-East,791106278,63673.29,14854.41,48818.88\nHI TEMP INSULATION INC,01-East,779989967,21744.00,4858.10,16885.90\nHI TEMP INSULATION INC,01-East,724413000,20766.00,4683.99,16082.01\nHI TEMP INSULATION INC,01-East,779989966,19680.00,3151.97,16528.03\nHI TEMP INSULATION INC,01-East,732771000,15563.52,975.70,14587.82\nHI TEMP INSULATION INC,01-East,740283550,6712.20,1214.78,5497.42\nHI TEMP INSULATION INC,01-East,740262000,4980.80,757.75,4223.05\nHI TEMP INSULATION INC,01-East,779988243,4273.50,712.81,3560.69\nHI TEMP INSULATION INC,01-East,724401500,3145.50,263.37,2882.13\nHI TEMP INSULATION INC,01-East,733423800,2503.04,383.67,2119.37\nHI TEMP INSULATION INC,01-East,764482000,1519.00,342.73,1176.27\nHI TEMP INSULATION INC,01-East,724261400,1207.80,220.54,987.26\nHI-TECH ENTERPRISES,01-East,770800000,1953.00,30.65,1922.35\nHI-TEMP FABRICATION INC.,01-East,739561100,38141.36,9590.81,28550.55\nHI-TEMP FABRICATION INC.,01-East,737871000,36618.06,11471.18,25146.88\nHI-TEMP FABRICATION INC.,01-East,739541100,20704.31,4398.57,16305.74\nHI-TEMP FABRICATION INC.,01-East,764821000,17233.20,2691.93,14541.27\nHI-TEMP FABRICATION INC.,01-East,735750000,15510.00,6168.46,9341.54\nHI-TEMP FABRICATION INC.,01-East,737851000,13427.18,3319.72,10107.46\nHI-TEMP FABRICATION INC.,01-East,738521000,5956.64,3059.10,2897.54\nHI-TEMP FABRICATION INC.,01-East,733211050,3900.80,869.70,3031.10\nHI-TEMP FABRICATION INC.,01-East,738542000,3282.42,1421.04,1861.38\nHI-TEMP FABRICATION INC.,01-East,739561200,2289.66,578.65,1711.01\nHI-TEMP FABRICATION INC.,01-East,733423800,2174.08,403.24,1770.84\nHI-TEMP FABRICATION INC.,01-East,764901000,1932.00,322.88,1609.12\nHI-TEMP FABRICATION INC.,01-East,733151600,1863.60,279.84,1583.76\nHI-TEMP FABRICATION INC.,01-East,764822000,1825.20,285.93,1539.27\nHI-TEMP FABRICATION INC.,01-East,733444200,1725.20,285.40,1439.80\nHI-TEMP FABRICATION INC.,01-East,793330000,1294.80,212.05,1082.75\nHI-TEMP FABRICATION INC.,01-East,764902000,1005.00,161.04,843.96\nHI-TEMP FABRICATION INC.,01-East,733725500,1004.00,354.38,649.62\nHI-TEMP FABRICATION INC.,01-East,734211200,851.65,92.43,759.22\nHI-TEMP FABRICATION INC.,01-East,733423700,734.40,135.77,598.63\nHI-TEMP FABRICATION INC.,01-East,733151500,651.20,108.22,542.98\nHI-TEMP FABRICATION INC.,01-East,733528000,464.34,69.06,395.28\nHI-TEMP FABRICATION INC.,01-East,733511000,348.75,75.22,273.53\nHI-TEMP FABRICATION INC.,01-East,739661100,303.55,150.50,153.05\nHI-TEMP FABRICATION INC.,01-East,739761100,303.55,120.66,182.89\nHI-TEMP FABRICATION INC.,01-East,764781000,205.60,40.89,164.71\nHI-TEMP FABRICATION INC.,01-East,733464500,185.00,29.52,155.48\nHICKORY INDUSTRIES,01-East,733464500,313.00,32.53,280.47\nHIGBEE GASKETS & SEALING,01-East,733443000,1024.50,181.10,843.40\nHIGBEE GASKETS & SEALING,01-East,733464400,943.20,158.44,784.76\nHIGBEE GASKETS & SEALING,01-East,792760000,426.70,116.62,310.08\nHIGBEE GASKETS & SEALING,01-East,733741000,284.40,93.19,191.21\nHIGBEE GASKETS & SEALING,01-East,792702000,283.50,96.60,186.90\nHIGBEE GASKETS & SEALING,01-East,733444300,255.60,41.25,214.35\nHILLBOTTOM ENTERPRISES,01-East,764602000,678.00,26.50,651.50\nHIMED,01-East,766121000,1750.00,54.50,1695.50\nHISCO - MEXICALI,01-East,734751200,2578.35,338.40,2239.95\nHOLLOWICK,01-East,733505100,430.00,90.09,339.91\nHOMANS-CORPORATE OFFICE,01-East,764901000,1593.90,193.73,1400.17\nHOMANS-CORPORATE OFFICE,01-East,764902000,531.30,63.85,467.45\n\"HONEYWELL FM&T, LLC\",01-East,791006650,19820.40,10189.49,9630.91\n\"HONEYWELL FM&T, LLC\",01-East,791006850,12197.92,5849.37,6348.55\nHPI INC,01-East,764901000,850.50,32.29,818.21\nHUDSON STUDIOS,01-East,734900000,305.40,56.74,248.66\nHUGHES CIRCUITS INC.,01-East,733464500,3353.00,339.75,3013.25\nHUTCHINSON SEAL DE,01-East,724261200,3564.00,591.96,2972.04\nI SQUARED R ELEMENT CO.,01-East,733525400,7455.00,1131.80,6323.20\nI SQUARED R ELEMENT CO.,01-East,733211000,251.20,53.20,198.00\nI.C.M.,01-East,779958986,17289.60,10920.05,6369.55\nI.C.M.,01-East,780650000,1082.46,630.14,452.32\nI.C.M.,01-East,764823500,1070.00,331.88,738.12\nI.C.M.,01-East,770220000,423.90,27.35,396.55\nIBC ADVANCED ALLOYS,01-East,739681100,8103.24,2687.52,5415.72\nIBC ADVANCED ALLOYS,01-East,733525500,3657.50,335.87,3321.63\nIBC ADVANCED ALLOYS,01-East,739641100,2817.36,952.91,1864.45\nIBC ADVANCED ALLOYS,01-East,739541000,2304.00,324.24,1979.76\nIBC ADVANCED ALLOYS,01-East,764781000,472.00,19.72,452.28\nIBC ADVANCED ALLOYS,01-East,770220000,471.00,27.35,443.65\nIBC ADVANCED ALLOYS,01-East,734311100,381.48,33.70,347.78\nICO RALLY,01-East,724281200,2404.00,356.32,2047.68\nIDC CORPORATION,01-East,735602000,310980.00,125992.60,184987.40\nIDC CORPORATION,01-East,735602110,5112.00,2142.08,2969.92\nIDC CORPORATION,01-East,768121000,4080.00,1150.67,2929.33\nIDC CORPORATION,01-East,768221000,2448.00,822.76,1625.24\n\"IGH INTERNATIONAL, CORP.\",01-East,733511000,452.50,66.56,385.94\nINDUSTRIAL FURNACE CO,01-East,737871000,1458648.96,520390.74,938258.22\nINDUSTRIAL FURNACE CO,01-East,737321000,1092083.52,466071.35,626012.17\nINDUSTRIAL FURNACE CO,01-East,737851000,691473.60,209162.64,482310.96\nINDUSTRIAL FURNACE CO,01-East,737801000,405557.28,122187.72,283369.56\nINDUSTRIAL FURNACE CO,01-East,739781100,328027.20,162673.38,165353.82\nINDUSTRIAL FURNACE CO,01-East,737301000,299225.60,130085.71,169139.89\nINDUSTRIAL FURNACE CO,01-East,766001000,182480.00,40287.35,142192.65\nINDUSTRIAL FURNACE CO,01-East,779989700,136740.48,53446.49,83293.99\nINDUSTRIAL FURNACE CO,01-East,770930000,100415.00,4516.54,95898.46\nINDUSTRIAL FURNACE CO,01-East,739741100,79820.80,45317.06,34503.74\nINDUSTRIAL FURNACE CO,01-East,768121000,67888.80,10281.22,57607.58\nINDUSTRIAL FURNACE CO,01-East,740283502,64583.20,14042.36,50540.84\nINDUSTRIAL FURNACE CO,01-East,770430000,54175.00,6268.57,47906.43\nINDUSTRIAL FURNACE CO,01-East,739641100,52538.04,31564.07,20973.97\nINDUSTRIAL FURNACE CO,01-East,739561200,50771.20,16940.00,33831.20\nINDUSTRIAL FURNACE CO,01-East,739451000,44096.00,23206.70,20889.30\nINDUSTRIAL FURNACE CO,01-East,764952010,39760.00,8760.28,30999.72\nINDUSTRIAL FURNACE CO,01-East,734851000,37384.74,10628.05,26756.69\nINDUSTRIAL FURNACE CO,01-East,770500000,33687.00,2630.10,31056.90\nINDUSTRIAL FURNACE CO,01-East,766121000,31416.00,5073.36,26342.64\nINDUSTRIAL FURNACE CO,01-East,740283401,27106.20,7565.94,19540.26\nINDUSTRIAL FURNACE CO,01-East,724311200,24213.00,7917.07,16295.93\nINDUSTRIAL FURNACE CO,01-East,737651000,23858.01,8893.92,14964.09\nINDUSTRIAL FURNACE CO,01-East,764821000,23354.00,4503.57,18850.43\nINDUSTRIAL FURNACE CO,01-East,764521000,23294.30,5339.08,17955.22\nINDUSTRIAL FURNACE CO,01-East,779988828,20508.44,10268.03,10240.41\nINDUSTRIAL FURNACE CO,01-East,770510000,19152.50,1578.12,17574.38\nINDUSTRIAL FURNACE CO,01-East,734311100,18797.39,2516.31,16281.08\nINDUSTRIAL FURNACE CO,01-East,737651010,16857.00,11281.16,5575.84\nINDUSTRIAL FURNACE CO,01-East,768111000,12948.00,2249.18,10698.82\nINDUSTRIAL FURNACE CO,01-East,766081010,6265.00,1198.49,5066.51\nINDUSTRIAL FURNACE CO,01-East,739450000,5061.60,2511.54,2550.06\nINDUSTRIAL FURNACE CO,01-East,733444200,4970.00,1025.71,3944.29\nINDUSTRIAL FURNACE CO,01-East,718501000,4788.00,1212.21,3575.79\nINDUSTRIAL FURNACE CO,01-East,740382101,4788.00,2528.30,2259.70\nINDUSTRIAL FURNACE CO,01-East,733464500,4409.00,812.07,3596.93\nINDUSTRIAL FURNACE CO,01-East,739561100,4399.08,1472.81,2926.27\nINDUSTRIAL FURNACE CO,01-East,737032000,4131.90,2467.54,1664.36\nINDUSTRIAL FURNACE CO,01-East,764761000,3965.20,1273.04,2692.16\nINDUSTRIAL FURNACE CO,01-East,740382201,3300.00,1502.56,1797.44\nINDUSTRIAL FURNACE CO,01-East,737261000,3259.20,1119.72,2139.48\nINDUSTRIAL FURNACE CO,01-East,733464400,2470.00,792.24,1677.76\nINDUSTRIAL FURNACE CO,01-East,733444100,2250.00,1008.46,1241.54\nINDUSTRIAL FURNACE CO,01-East,738561000,2122.62,868.96,1253.66\nINDUSTRIAL FURNACE CO,01-East,727000140,1482.40,269.22,1213.18\nINDUSTRIAL FURNACE CO,01-East,764481000,1446.70,392.89,1053.81\nINDUSTRIAL FURNACE CO,01-East,733151500,1224.00,216.42,1007.58\nINDUSTRIAL FURNACE CO,01-East,764781000,1002.00,245.36,756.64\nINDUSTRIAL FURNACE CO,01-East,770400000,913.50,98.65,814.85\nINDUSTRIAL FURNACE CO,01-East,764861000,592.95,127.38,465.57\nINDUSTRIAL FURNACE CO,01-East,739452500,563.52,362.66,200.86\nINDUSTRIAL FURNACE CO,01-East,793260074,481.95,122.49,359.46\nINDUSTRIAL FURNACE CO,01-East,770210000,450.00,60.64,389.36\nINDUSTRIAL FURNACE CO,01-East,733211000,414.80,94.78,320.02\nINDUSTRIAL FURNACE CO,01-East,770392000,375.00,35.15,339.85\nINDUSTRIAL FURNACE CO,01-East,739541100,356.16,83.57,272.59\nINDUSTRIAL FURNACE CO,01-East,764827000,350.40,124.42,225.98\nINDUSTRIAL FURNACE CO,01-East,768115000,250.20,114.68,135.52\nINDUSTRIAL FURNACE CO,01-East,734711100,125.11,27.05,98.06\nINDUSTRIAL FURNACE CO,01-East,764561220,81.55,29.47,52.08\nINDUSTRIAL INSULATION,01-East,735602110,145146.00,47860.90,97285.10\nINDUSTRIAL INSULATION,01-East,735602000,50782.00,15973.99,34808.01\nINDUSTRIAL INSULATION,01-East,764821000,14208.40,1853.07,12355.33\nINDUSTRIAL INSULATION,01-East,764901000,12295.59,1652.95,10642.64\nINDUSTRIAL INSULATION,01-East,735752000,9863.00,3665.84,6197.16\nINDUSTRIAL INSULATION,01-East,735750000,7792.00,2718.27,5073.73\nINDUSTRIAL INSULATION,01-East,727000140,3906.96,367.69,3539.27\nINDUSTRIAL INSULATION,01-East,771320000,3422.30,684.50,2737.80\nINDUSTRIAL INSULATION,01-East,718501000,3409.00,942.82,2466.18\nINDUSTRIAL INSULATION,01-East,739451000,2415.84,1021.91,1393.93\nINDUSTRIAL INSULATION,01-East,734751200,2341.36,373.90,1967.46\nINDUSTRIAL INSULATION,01-East,764902000,2318.50,319.27,1999.23\nINDUSTRIAL INSULATION,01-East,766688000,2311.50,247.00,2064.50\nINDUSTRIAL INSULATION,01-East,739561100,2113.28,559.92,1553.36\nINDUSTRIAL INSULATION,01-East,764521000,2078.80,318.54,1760.26\nINDUSTRIAL INSULATION,01-East,764822000,1901.20,249.13,1652.07\nINDUSTRIAL INSULATION,01-East,739561000,1830.26,514.23,1316.03\nINDUSTRIAL INSULATION,01-East,739370000,1631.16,635.74,995.42\nINDUSTRIAL INSULATION,01-East,764261000,1114.20,206.72,907.48\nINDUSTRIAL INSULATION,01-East,764262000,733.20,134.54,598.66\nINDUSTRIAL INSULATION,01-East,739541000,719.42,143.24,576.18\nINDUSTRIAL INSULATION,01-East,764221000,695.20,169.05,526.15\nINDUSTRIAL INSULATION,01-East,718500000,572.58,85.63,486.95\nINDUSTRIAL INSULATION,01-East,739561200,536.70,144.66,392.04\nINDUSTRIAL INSULATION,01-East,764301000,522.78,102.05,420.73\nINDUSTRIAL INSULATION,01-East,764601000,437.30,66.06,371.24\nINDUSTRIAL INSULATION,01-East,738401000,418.32,321.12,97.20\nINDUSTRIAL INSULATION,01-East,764342000,380.10,68.67,311.43\nINDUSTRIAL INSULATION,01-East,764341000,370.50,70.90,299.60\nINDUSTRIAL INSULATION,01-East,738521000,325.44,143.45,181.99\nINDUSTRIAL INSULATION,01-East,739541200,319.74,64.10,255.64\nINDUSTRIAL INSULATION,01-East,739450000,295.38,130.12,165.26\nINDUSTRIAL INSULATION,01-East,777601000,290.04,72.02,218.02\nINDUSTRIAL INSULATION,01-East,739761100,284.59,120.66,163.93\nINDUSTRIAL INSULATION,01-East,739380000,263.52,227.76,35.76\nINDUSTRIAL INSULATION,01-East,764781000,246.80,40.90,205.90\nINDUSTRIAL INSULATION,01-East,764482000,191.00,34.80,156.20\nINDUSTRIAL INSULATION,01-East,735602010,150.00,94.19,55.81\nINDUSTRIAL INSULATION,01-East,764781040,123.40,20.17,103.23\nINDUSTRIAL INSULATION,01-East,764561000,65.05,11.09,53.96\nINDUSTRIAL SUPPLY CO INC,01-East,741001000,38760.27,5551.88,33208.39\nINDUSTRIAL SUPPLY CO INC,01-East,733151500,29220.20,4663.50,24556.70\nINDUSTRIAL SUPPLY CO INC,01-East,777994000,11275.92,3623.14,7652.78\nINDUSTRIAL SUPPLY CO INC,01-East,733525500,5190.00,752.87,4437.13\nINDUSTRIAL SUPPLY CO INC,01-East,764221000,3675.60,539.75,3135.85\nINDUSTRIAL SUPPLY CO INC,01-East,770540000,3010.70,179.60,2831.10\nINDUSTRIAL SUPPLY CO INC,01-East,734211200,2756.63,320.81,2435.82\nINDUSTRIAL SUPPLY CO INC,01-East,770500000,2650.05,135.04,2515.01\nINDUSTRIAL SUPPLY CO INC,01-East,733141200,1974.40,373.86,1600.54\nINDUSTRIAL SUPPLY CO INC,01-East,770560000,1347.00,89.50,1257.50\nINDUSTRIAL SUPPLY CO INC,01-East,764821000,1216.80,93.94,1122.86\nINDUSTRIAL SUPPLY CO INC,01-East,764261000,1052.00,115.22,936.78\nINDUSTRIAL SUPPLY CO INC,01-East,733151600,621.20,93.28,527.92\nINDUSTRIAL TECHNOLOGIES,01-East,780250000,78938.49,35312.18,43626.31\nINDUSTRIAL TECHNOLOGIES,01-East,780260000,31066.50,12095.64,18970.86\nINDUSTRIAL TECHNOLOGIES,01-East,779957812,22621.84,11228.10,11393.74\nINDUSTRIAL TECHNOLOGIES,01-East,779952300,7296.12,3911.80,3384.32\nINDUSTRIAL TECHNOLOGIES,01-East,780210000,7146.66,3672.66,3474.00\nINDUSTRIAL TECHNOLOGIES,01-East,780370000,7025.86,2830.10,4195.76\nINDUSTRIAL TECHNOLOGIES,01-East,780230000,6790.51,3211.39,3579.12\nINDUSTRIAL TECHNOLOGIES,01-East,784210000,3240.00,1205.18,2034.82\nINDUSTRIAL TECHNOLOGIES,01-East,784120000,3105.32,1687.08,1418.24\nINDUSTRIAL TECHNOLOGIES,01-East,777642000,1422.00,484.92,937.08\nINDUSTRIAL TECHNOLOGIES,01-East,764821000,1220.00,307.16,912.84\nINDUSTRIAL TECHNOLOGIES,01-East,779988890,111.84,7.84,104.00\nINDUSTRIAL VALVES &,01-East,770810000,4435.00,95.60,4339.40\nINDUSTRIAL VALVES &,01-East,770830000,3081.00,97.81,2983.19\nINDUSTRIAL VALVES &,01-East,770850000,2684.00,84.12,2599.88\nINEX CORPORATION,01-East,733525500,937.50,135.42,802.08\nINEX CORPORATION,01-East,733151500,651.20,108.22,542.98\nINEX CORPORATION,01-East,770210000,503.00,63.04,439.96\nINFINIA,01-East,734900000,203.60,37.82,165.78\nINLINE PRO SERVICES,01-East,739681100,10165.32,4554.00,5611.32\nINLINE PRO SERVICES,01-East,738401000,4088.88,1926.72,2162.16\nINLINE PRO SERVICES,01-East,738361000,2504.16,1235.52,1268.64\nINLINE PRO SERVICES,01-East,764902000,1005.00,64.42,940.58\nINLINE PRO SERVICES,01-East,739641100,964.08,403.20,560.88\nINSUL-FAB,01-East,768212000,31243.00,7506.78,23736.22\nINSULATION CONTRACTORS,01-East,735602000,50883.20,14865.74,36017.46\nINSULATION CONTRACTORS,01-East,735750000,624.00,190.66,433.34\nINSULATION FABRICATORS,01-East,727000140,1376.00,334.27,1041.73\nINSULATION FABRICATORS,01-East,739541000,403.20,71.62,331.58\nINSULATION MATERIALS CORP,01-East,735602000,407365.00,161133.20,246231.80\nINSULATION MATERIALS CORP,01-East,764901000,136301.50,65445.72,70855.78\nINSULATION MATERIALS CORP,01-East,735750000,100152.00,43357.37,56794.63\nINSULATION MATERIALS CORP,01-East,764821000,86587.00,31981.32,54605.68\nINSULATION MATERIALS CORP,01-East,764902000,37745.00,17978.84,19766.16\nINSULATION MATERIALS CORP,01-East,764861000,29841.00,13374.31,16466.69\nINSULATION MATERIALS CORP,01-East,764521000,28258.50,9910.75,18347.75\nINSULATION MATERIALS CORP,01-East,777993000,24542.02,10405.46,14136.56\nINSULATION MATERIALS CORP,01-East,764601000,17359.00,7840.89,9518.11\nINSULATION MATERIALS CORP,01-East,768132200,12228.00,6294.18,5933.82\nINSULATION MATERIALS CORP,01-East,764821161,9460.00,8184.00,1276.00\nINSULATION MATERIALS CORP,01-East,764822000,9132.00,3391.88,5740.12\nINSULATION MATERIALS CORP,01-East,764901061,8836.50,9623.40,-786.90\nINSULATION MATERIALS CORP,01-East,766121000,8678.50,3383.12,5295.38\nINSULATION MATERIALS CORP,01-East,768141020,8618.00,3491.84,5126.16\nINSULATION MATERIALS CORP,01-East,735602110,8322.00,3325.26,4996.74\nINSULATION MATERIALS CORP,01-East,764864000,8115.00,3544.37,4570.63\nINSULATION MATERIALS CORP,01-East,733525500,7930.00,1300.60,6629.40\nINSULATION MATERIALS CORP,01-East,764781000,5742.00,2201.81,3540.19\nINSULATION MATERIALS CORP,01-East,764602000,5252.00,2327.66,2924.34\nINSULATION MATERIALS CORP,01-East,768241000,5064.00,1684.81,3379.19\nINSULATION MATERIALS CORP,01-East,764561000,4664.50,2240.61,2423.89\nINSULATION MATERIALS CORP,01-East,734910000,4368.60,924.26,3444.34\nINSULATION MATERIALS CORP,01-East,764521061,3750.00,3195.00,555.00\nINSULATION MATERIALS CORP,01-East,764481000,3418.00,1282.10,2135.90\nINSULATION MATERIALS CORP,01-East,768142000,3407.50,1313.67,2093.83\nINSULATION MATERIALS CORP,01-East,735752000,3000.00,1367.98,1632.02\nINSULATION MATERIALS CORP,01-East,766007000,2440.00,1009.12,1430.88\nINSULATION MATERIALS CORP,01-East,768221000,2200.00,707.60,1492.40\nINSULATION MATERIALS CORP,01-East,724003000,2166.00,295.76,1870.24\nINSULATION MATERIALS CORP,01-East,765502000,1900.00,529.54,1370.46\nINSULATION MATERIALS CORP,01-East,734311100,1748.70,202.23,1546.47\nINSULATION MATERIALS CORP,01-East,734900000,1619.40,567.42,1051.98\nINSULATION MATERIALS CORP,01-East,764901050,1370.00,631.79,738.21\nINSULATION MATERIALS CORP,01-East,768222000,1320.00,429.60,890.40\nINSULATION MATERIALS CORP,01-East,734651200,1209.11,221.24,987.87\nINSULATION MATERIALS CORP,01-East,764825000,1032.00,774.63,257.37\nINSULATION MATERIALS CORP,01-East,768211000,754.00,321.79,432.21\nINSULATION MATERIALS CORP,01-East,733403200,739.20,131.34,607.86\nINSULATION MATERIALS CORP,01-East,768121000,630.00,287.66,342.34\nINSULATION MATERIALS CORP,01-East,740402402,624.00,193.66,430.34\nINSULATION MATERIALS CORP,01-East,764301000,560.00,266.94,293.06\nINSULATION MATERIALS CORP,01-East,764261000,477.00,206.04,270.96\nINSULATION MATERIALS CORP,01-East,730710500,457.00,70.96,386.04\nINSULATION MATERIALS CORP,01-East,764761000,434.00,214.99,219.01\nINSULATION MATERIALS CORP,01-East,734990000,339.36,55.29,284.07\nINSULATION MATERIALS CORP,01-East,739561200,182.46,72.33,110.13\nINSULATION MATERIALS CORP,01-East,768122000,126.00,55.92,70.08\nINSULATION MATERIALS CORP,01-East,764781050,110.00,49.38,60.62\nINSULATION MATERIALS CORP,01-East,768131000,107.00,44.16,62.84\nINSULATION SALES,01-East,735602000,1960.00,690.37,1269.63\nINSULCON BENELUX B.V.,01-East,768242701,344050.08,277840.52,66209.56\nINSULCON BENELUX B.V.,01-East,768242821,17427.76,13388.16,4039.60\nINSULSHIELD TECHNOLOGY,01-East,735990100,2480.00,1214.02,1265.98\nINSULSHIELD TECHNOLOGY,01-East,768222130,1468.80,669.40,799.40\nINTEGRAL PRODUCTS,01-East,733464600,298.00,31.31,266.69\nINTELLIFUSE TECH.,01-East,740361900,667.20,220.63,446.57\nINTERNATIONAL CRYSTAL LAB,01-East,739741100,428.48,168.32,260.16\n\"INTERNATIONAL PAINT, INC.\",01-East,723800000,939270.25,305588.85,633681.40\nINTERNATIONAL TECHNIFAB,01-East,764821000,401090.00,172793.44,228296.56\nINTERNATIONAL TECHNIFAB,01-East,735602000,364004.00,118504.42,245499.58\nINTERNATIONAL TECHNIFAB,01-East,734173000,181632.00,72254.74,109377.26\nINTERNATIONAL TECHNIFAB,01-East,764901050,172626.00,66515.38,106110.62\nINTERNATIONAL TECHNIFAB,01-East,764521000,121778.00,43745.80,78032.20\nINTERNATIONAL TECHNIFAB,01-East,764601000,82870.00,29104.12,53765.88\nINTERNATIONAL TECHNIFAB,01-East,739452000,52202.89,31721.27,20481.62\nINTERNATIONAL TECHNIFAB,01-East,764821073,51070.00,51759.00,-689.00\nINTERNATIONAL TECHNIFAB,01-East,764821161,38340.00,40176.00,-1836.00\nINTERNATIONAL TECHNIFAB,01-East,764781000,36021.60,10515.99,25505.61\nINTERNATIONAL TECHNIFAB,01-East,764901000,33448.50,15044.76,18403.74\nINTERNATIONAL TECHNIFAB,01-East,764561000,31920.00,12147.95,19772.05\nINTERNATIONAL TECHNIFAB,01-East,764521073,28120.00,23865.00,4255.00\nINTERNATIONAL TECHNIFAB,01-East,764827550,27264.00,10919.35,16344.65\nINTERNATIONAL TECHNIFAB,01-East,764861000,24194.00,5949.81,18244.19\nINTERNATIONAL TECHNIFAB,01-East,764522000,22990.00,8190.87,14799.13\nINTERNATIONAL TECHNIFAB,01-East,735750000,22483.00,8454.23,14028.77\nINTERNATIONAL TECHNIFAB,01-East,764605000,19768.00,6708.54,13059.46\nINTERNATIONAL TECHNIFAB,01-East,739561100,18976.96,5485.15,13491.81\nINTERNATIONAL TECHNIFAB,01-East,733525500,18130.00,3104.03,15025.97\nINTERNATIONAL TECHNIFAB,01-East,740063000,17701.32,7744.17,9957.15\nINTERNATIONAL TECHNIFAB,01-East,735602110,17688.00,6006.35,11681.65\nINTERNATIONAL TECHNIFAB,01-East,754095000,13113.60,5108.65,8004.95\nINTERNATIONAL TECHNIFAB,01-East,768141000,11826.00,3241.74,8584.26\nINTERNATIONAL TECHNIFAB,01-East,768142000,11826.00,3354.06,8471.94\nINTERNATIONAL TECHNIFAB,01-East,764602000,11280.00,3948.78,7331.22\nINTERNATIONAL TECHNIFAB,01-East,735752000,10797.00,3930.84,6866.16\nINTERNATIONAL TECHNIFAB,01-East,779988831,9222.00,3288.30,5933.70\nINTERNATIONAL TECHNIFAB,01-East,764521061,9120.00,7668.00,1452.00\nINTERNATIONAL TECHNIFAB,01-East,764862000,8153.50,2345.06,5808.44\nINTERNATIONAL TECHNIFAB,01-East,780640000,7968.33,1783.64,6184.69\nINTERNATIONAL TECHNIFAB,01-East,733444200,7656.40,1289.16,6367.24\nINTERNATIONAL TECHNIFAB,01-East,764902000,6495.00,2887.48,3607.52\nINTERNATIONAL TECHNIFAB,01-East,764901073,5880.00,5832.00,48.00\nINTERNATIONAL TECHNIFAB,01-East,735862000,5088.00,2467.32,2620.68\nINTERNATIONAL TECHNIFAB,01-East,733423700,4915.84,778.71,4137.13\nINTERNATIONAL TECHNIFAB,01-East,764762000,4614.40,1696.40,2918.00\nINTERNATIONAL TECHNIFAB,01-East,754487000,4462.75,1708.65,2754.10\nINTERNATIONAL TECHNIFAB,01-East,733464500,3752.00,729.52,3022.48\nINTERNATIONAL TECHNIFAB,01-East,739541100,3286.42,585.01,2701.41\nINTERNATIONAL TECHNIFAB,01-East,764901061,2940.00,2984.00,-44.00\nINTERNATIONAL TECHNIFAB,01-East,734990000,2395.52,442.34,1953.18\nINTERNATIONAL TECHNIFAB,01-East,764900005,2352.00,918.56,1433.44\nINTERNATIONAL TECHNIFAB,01-East,764562000,1831.50,717.53,1113.97\nINTERNATIONAL TECHNIFAB,01-East,724281400,1022.00,178.16,843.84\nINTERNATIONAL TECHNIFAB,01-East,754387000,927.96,385.13,542.83\nINTERNATIONAL TECHNIFAB,01-East,733383000,912.56,163.93,748.63\nINTERNATIONAL TECHNIFAB,01-East,733570000,896.40,183.35,713.05\nINTERNATIONAL TECHNIFAB,01-East,754895000,885.96,193.27,692.69\nINTERNATIONAL TECHNIFAB,01-East,724261200,702.00,98.66,603.34\nINTERNATIONAL TECHNIFAB,01-East,734414100,698.42,106.12,592.30\nINTERNATIONAL TECHNIFAB,01-East,767960000,612.04,173.18,438.86\nINTERNATIONAL TECHNIFAB,01-East,734651200,584.00,73.50,510.50\nINTERNATIONAL TECHNIFAB,01-East,777601000,493.76,130.82,362.94\nINTERNATIONAL TECHNIFAB,01-East,733211000,490.60,100.59,390.01\nINTERNATIONAL TECHNIFAB,01-East,770800000,483.00,10.22,472.78\nINTERNATIONAL TECHNIFAB,01-East,767990000,481.50,176.94,304.56\nINTERNATIONAL TECHNIFAB,01-East,770300000,325.05,35.37,289.68\nINTERNATIONAL TECHNIFAB,01-East,767970000,231.12,71.89,159.23\nINTERNATIONAL TECHNIFAB,01-East,779957257,225.00,102.09,122.91\nINTERNATIONAL TECHNIFAB,01-East,734900000,47.62,18.91,28.71\nINTERNATIONAL TURBING SYS,01-East,764481000,346.00,16.94,329.06\nINTERTEK TESTING SERVICES,01-East,781540000,15192.00,5679.70,9512.30\nINTERTEK TESTING SERVICES,01-East,779958512,4712.76,1798.24,2914.52\nINTERTEK TESTING SERVICES,01-East,766121000,1744.00,383.18,1360.82\nIPR/GDF SUEZ N.A.,01-East,735655000,1301.60,95.99,1205.61\n\"IRWIN INTERNATIONAL, INC.\",01-East,733525500,8550.00,812.55,7737.45\n\"IRWIN INTERNATIONAL, INC.\",01-East,733505200,800.00,83.83,716.17\nJ & M AIRCRAFT,01-East,770520000,958.50,41.85,916.65\nJ D SUPPLY,01-East,733721000,410.40,97.80,312.60\nJ T THORPE & SON INC,01-East,780670000,5830.35,2234.71,3595.64\nJ T THORPE & SON INC,01-East,777993000,2163.30,650.34,1512.96\nJ-THERM LLC,01-East,779905209,1643.52,0.00,1643.52\n\"J. T. THORPE & SON, INC.\",01-East,770430000,2950.00,177.09,2772.91\n\"J. T. THORPE & SON, INC.\",01-East,770720000,1010.00,27.12,982.88\nJ. T. THORPE,01-East,734830000,12312.00,1634.76,10677.24\nJ.W. DUBOIS & SONS,01-East,768241000,728.50,35.38,693.12\nJAMES AVERY CRAFTSMAN INC,01-East,737301000,1615.36,643.84,971.52\nJIMCO CORPORATION,01-East,734900000,1682.50,472.84,1209.66\n\"JM SUPPLY, INC.\",01-East,735602000,8736.00,2353.60,6382.40\nJOHNSON MATTHEY GPT,01-East,764821000,878.00,61.43,816.57\nJOHNSON MATTHEY,01-East,766121000,460.35,23.95,436.40\nJOHNSON PACKINGS & INDUST,01-East,740063000,1109.80,240.20,869.60\nJOHNSON PACKINGS & INDUST,01-East,733384000,1056.00,141.33,914.67\nJOHNSON PACKINGS & INDUST,01-East,740023000,680.40,118.54,561.86\nJOHNSON PACKINGS & INDUST,01-East,733464500,596.00,59.04,536.96\nJOHNSON PACKINGS & INDUST,01-East,733444300,302.00,41.25,260.75\nJUN KANEKO STUDIOS,01-East,734311100,808.72,70.60,738.12\n\"K&L REFRACTORIES, INC.\",01-East,733211000,2070.00,473.89,1596.11\n\"K.R. ANDERSON, INC.\",01-East,734010000,502.25,45.30,456.95\nKAISER CONSTRUCTION,01-East,735655000,676.80,47.99,628.81\nKELLER TECHNOLOGY,01-East,724311200,27985.50,6041.36,21944.14\nKELLER TECHNOLOGY,01-East,770512000,22335.30,1241.27,21094.03\nKELLER TECHNOLOGY,01-East,779989700,7872.00,1572.45,6299.55\nKELLER TECHNOLOGY,01-East,740283502,5206.08,774.57,4431.51\nKELLER TECHNOLOGY,01-East,770500000,4592.70,270.10,4322.60\nKELLER TECHNOLOGY,01-East,734851000,3644.56,731.20,2913.36\nKEN MAXWELL,01-East,733464500,298.00,29.52,268.48\nKENNAMETAL INC,01-East,766121000,1750.00,54.50,1695.50\nKENNAMETAL INC,01-East,734651200,292.00,36.75,255.25\nKENNETH CROSBY,01-East,770210000,440.00,30.32,409.68\nKENNETH INDUSTRIAL,01-East,764482000,345.60,51.14,294.46\nKEYSTONE TOOL & DIE,01-East,770500000,11452.50,498.39,10954.11\nKIEWIT POWER CONSTRUCTORS,01-East,734611100,714.88,107.84,607.04\nKINGSBURY INC.,01-East,733211050,2326.00,260.92,2065.08\nKINGSBURY INC.,01-East,733141200,496.00,59.97,436.03\nKINGSTON HOSPITAL,01-East,764521000,4020.00,265.42,3754.58\nKINGSTON HOSPITAL,01-East,734351200,1945.80,147.24,1798.56\nKINGSTON HOSPITAL,01-East,739541200,522.24,80.45,441.79\nKIRK & BLUM,01-East,789160000,16116.48,6545.22,9571.26\nKIRK & BLUM,01-East,784180000,4916.80,1773.78,3143.02\nKIRK & BLUM,01-East,764521000,3820.00,530.84,3289.16\nKMI SYSTEMS INC,01-East,764345052,4590.00,2024.64,2565.36\nKMI SYSTEMS INC,01-East,765662000,3640.00,1132.50,2507.50\nKMI SYSTEMS INC,01-East,764605000,3450.00,1293.88,2156.12\nKNOWLTON TECHNOLOGIES,01-East,723960000,50111.00,11308.04,38802.96\nKOSHII MAXELUM AMERICA,01-East,768241000,534.00,70.76,463.24\nKREEM PRODUCT CO.,01-East,734464300,5951.55,672.58,5278.97\nKREISLER INDUSTRIAL CORP,01-East,770800000,20180.00,357.66,19822.34\nKTEC EQUIPMENT & SUPPLIES,01-East,727000140,510.40,31.68,478.72\nKTEC EQUIPMENT & SUPPLIES,01-East,764301000,322.00,19.06,302.94\nLABARGE ELECTRONICS,01-East,791104277,5457.78,2598.84,2858.94\nLACO SALES INC,01-East,764482000,4979.00,430.81,4548.19\nLANE REFRACTORIES,01-East,754045000,9919.80,6217.82,3701.98\nLANE REFRACTORIES,01-East,764521000,2240.00,531.88,1708.12\nLANE REFRACTORIES,01-East,734990000,1557.12,331.76,1225.36\nLANE REFRACTORIES,01-East,766686000,1200.00,361.13,838.87\nLANE REFRACTORIES,01-East,764821000,775.00,156.55,618.45\nLANE REFRACTORIES,01-East,733151500,620.00,95.38,524.62\nLANE REFRACTORIES,01-East,764481000,296.00,67.76,228.24\nLAWRENCE ELECTRONICS CO.,01-East,733464600,313.00,34.97,278.03\nLEWIS-GOETZ & CO.,01-East,770430000,1393.75,92.06,1301.69\nLEWIS-GOETZ & CO.,01-East,770372000,973.00,44.11,928.89\n\"LIMCO AIREPAIR, INC.\",01-East,733444200,664.00,69.38,594.62\nLMB INDUSTRIAL SERVICES,01-East,779958687,22875.84,11924.97,10950.87\nLMB INDUSTRIAL SERVICES,01-East,764821000,8960.00,1731.97,7228.03\nLMB INDUSTRIAL SERVICES,01-East,784180000,7340.74,3015.44,4325.30\nLMB INDUSTRIAL SERVICES,01-East,784140000,5548.60,2226.00,3322.60\nLMB INDUSTRIAL SERVICES,01-East,780290000,5148.48,2407.82,2740.66\nLMB INDUSTRIAL SERVICES,01-East,779958690,4876.80,2603.25,2273.55\nLMB INDUSTRIAL SERVICES,01-East,784100000,4599.18,2682.89,1916.29\nLMB INDUSTRIAL SERVICES,01-East,779958688,4381.92,1942.35,2439.57\nLMB INDUSTRIAL SERVICES,01-East,779958689,4118.40,2175.90,1942.50\nLMB INDUSTRIAL SERVICES,01-East,779958691,2322.72,1039.53,1283.19\nLMB INDUSTRIAL SERVICES,01-East,780210000,2023.20,1129.62,893.58\nLMB INDUSTRIAL SERVICES,01-East,779958250,1161.36,494.76,666.60\nLMB INDUSTRIAL SERVICES,01-East,767990000,668.00,224.12,443.88\nLMB INDUSTRIAL SERVICES,01-East,781590000,412.48,193.94,218.54\nLMB INDUSTRIAL SERVICES,01-East,777642000,330.00,95.01,234.99\nLOCKHEED MARTIN,01-East,779988846,800.45,267.61,532.84\nLOCKHEED MARTIN,01-East,739370000,379.52,138.24,241.28\nLONE STAR GASKET,01-East,733464500,626.00,65.05,560.95\n\"LONE STAR INDUCTION, INC\",01-East,727000140,666.00,47.51,618.49\nLONGHORN GASKET & SUPPLY,01-East,733141200,520.00,66.98,453.02\nLUCIFER FURNACES INC,01-East,737321000,10866.24,8200.34,2665.90\nLUFKIN RUBBER & GASKET,01-East,733741000,2516.40,755.83,1760.57\nLUFKIN RUBBER & GASKET,01-East,777601000,1557.70,235.18,1322.52\nLUVATA WATERBURY INC.,01-East,733211000,912.00,94.78,817.22\nLUVATA WATERBURY INC.,01-East,733151500,450.00,47.69,402.31\nLVR INC.,01-East,718501000,5525.00,680.30,4844.70\nLVR INC.,01-East,718500000,1414.40,172.54,1241.86\nLYDALL INC TECH PAPERS DI,01-East,722413100,47235.80,4720.90,42514.90\nLYNN MANUFACTURING INC.,01-East,727000060,123198.00,53223.43,69974.57\nLYNN MANUFACTURING INC.,01-East,727000100,76549.44,55157.97,21391.47\nLYNN MANUFACTURING INC.,01-East,739741100,73474.31,39855.07,33619.24\nLYNN MANUFACTURING INC.,01-East,726000062,67575.00,31194.05,36380.95\nLYNN MANUFACTURING INC.,01-East,726020056,59598.00,26213.03,33384.97\nLYNN MANUFACTURING INC.,01-East,739781100,46746.90,26491.68,20255.22\nLYNN MANUFACTURING INC.,01-East,739761100,27596.43,14152.22,13444.21\nLYNN MANUFACTURING INC.,01-East,734761300,17930.00,4255.19,13674.81\nLYNN MANUFACTURING INC.,01-East,737321000,17078.88,6076.56,11002.32\nLYNN MANUFACTURING INC.,01-East,764902000,16014.60,2755.01,13259.59\nLYNN MANUFACTURING INC.,01-East,724003000,12163.65,5060.13,7103.52\nLYNN MANUFACTURING INC.,01-East,737311000,11640.54,3006.45,8634.09\nLYNN MANUFACTURING INC.,01-East,764821000,9824.40,1552.07,8272.33\nLYNN MANUFACTURING INC.,01-East,733525500,6192.50,909.75,5282.75\nLYNN MANUFACTURING INC.,01-East,739561200,4332.23,1273.03,3059.20\nLYNN MANUFACTURING INC.,01-East,739761101,3718.52,1733.68,1984.84\nLYNN MANUFACTURING INC.,01-East,737301000,2863.39,1134.56,1728.83\nLYNN MANUFACTURING INC.,01-East,739781101,2819.72,1142.20,1677.52\nLYNN MANUFACTURING INC.,01-East,726020030,2303.10,1092.17,1210.93\nLYNN MANUFACTURING INC.,01-East,764761000,659.20,188.86,470.34\nLYNN MANUFACTURING INC.,01-East,733464500,599.30,91.57,507.73\nLYNN MANUFACTURING INC.,01-East,764781000,558.00,118.32,439.68\nLYNN MANUFACTURING INC.,01-East,733423700,465.92,72.44,393.48\nM CUBED TECHNOLOGIES INC,01-East,734351200,917.85,69.73,848.12\nM DAVIS & SONS,01-East,734910000,362.00,53.09,308.91\nM&M INSULATION,01-East,735602000,111346.00,34972.94,76373.06\nM&M INSULATION,01-East,768221000,44820.00,9132.96,35687.04\nM&M INSULATION,01-East,735750000,6408.00,1861.36,4546.64\nM&M INSULATION,01-East,768241000,5015.00,1038.97,3976.03\nM&M INSULATION,01-East,764901000,3630.00,710.33,2919.67\nM&M INSULATION,01-East,735602110,2650.00,892.53,1757.47\nM&M INSULATION,01-East,735752000,2340.00,644.40,1695.60\nMAC ARTHUR COMPANY INC.,01-East,735602000,24000.00,8069.50,15930.50\nMAC ARTHUR COMPANY INC.,01-East,735602110,24000.00,8400.65,15599.35\nMAC ARTHUR COMPANY,01-East,735602000,41040.00,13117.04,27922.96\nMAC ARTHUR COMPANY,01-East,735602110,10800.00,3570.13,7229.87\nMAGELLAN AEROSPACE,01-East,724003000,695.50,81.82,613.68\nMAGELLAN AEROSPACE,01-East,734311100,404.36,35.30,369.06\nMALYN INDUSTRIAL CERAMICS,01-East,733525600,1435.00,130.98,1304.02\nMALYN INDUSTRIAL CERAMICS,01-East,734211200,486.92,46.75,440.17\nMARIND INDUSTRIES,01-East,727063007,1845.00,948.54,896.46\nMARIND INDUSTRIES,01-East,727063005,1599.00,804.28,794.72\nMARIND INDUSTRIES,01-East,727063016,426.40,217.28,209.12\nMARK STEEL,01-East,764901000,11025.00,2882.57,8142.43\nMARLIN ENGINEERING INC.,01-East,733464500,313.00,32.53,280.47\nMATERION ADVANCED MATLS.,01-East,764821000,10208.00,850.65,9357.35\nMATERION ADVANCED MATLS.,01-East,740283401,1647.80,292.43,1355.37\nMATERION ADVANCED MATLS.,01-East,739681100,1208.44,481.84,726.60\nMATERION ADVANCED MATLS,01-East,764821000,1908.25,156.56,1751.69\nMATERION ADVANCED MATLS,01-East,739681100,628.42,414.00,214.42\nMATERION BREWSTER LLC,01-East,733151500,2744.00,298.97,2445.03\nMATERION BREWSTER LLC,01-East,764821000,1778.05,125.25,1652.80\nMATERION BREWSTER LLC,01-East,739541000,399.74,90.50,309.24\nMATRIX INDUSTRIAL,01-East,740103000,824.88,135.47,689.41\nMATRIX SOLUTIONS,01-East,734351204,5832.00,858.19,4973.81\nMAYNARD INC.,01-East,724121700,583.00,162.85,420.15\nMCCONNELL AFB,01-East,740402402,1022.50,193.66,828.84\nMCJUNKIN RED MAN CORP.,01-East,770830000,2020.00,61.94,1958.06\nMCNEIL INC.,01-East,734351203,389757.00,68680.02,321076.98\nMCNEIL INC.,01-East,764521000,155750.00,76230.46,79519.54\nMCNEIL INC.,01-East,733525500,129525.00,31288.64,98236.36\nMCNEIL INC.,01-East,754525000,117176.24,45154.66,72021.58\nMCNEIL INC.,01-East,764821000,109310.00,38251.71,71058.29\nMCNEIL INC.,01-East,739741100,69263.36,78273.85,-9010.49\nMCNEIL INC.,01-East,734351200,67463.90,11351.75,56112.15\nMCNEIL INC.,01-East,764481000,41722.20,16752.04,24970.16\nMCNEIL INC.,01-East,733202000,38228.00,15639.90,22588.10\nMCNEIL INC.,01-East,739561100,27534.36,11563.39,15970.97\nMCNEIL INC.,01-East,764601000,24800.00,8193.87,16606.13\nMCNEIL INC.,01-East,764521061,24750.00,28755.00,-4005.00\nMCNEIL INC.,01-East,779988972,23782.50,19426.00,4356.50\nMCNEIL INC.,01-East,754085000,21894.69,9293.31,12601.38\nMCNEIL INC.,01-East,764901000,20202.64,5998.38,14204.26\nMCNEIL INC.,01-East,734910000,18828.00,6297.26,12530.74\nMCNEIL INC.,01-East,777601000,17372.64,4991.49,12381.15\nMCNEIL INC.,01-East,734311100,16660.00,3530.13,13129.87\nMCNEIL INC.,01-East,733444220,15393.60,4726.81,10666.79\nMCNEIL INC.,01-East,733566100,12686.40,3766.76,8919.64\nMCNEIL INC.,01-East,770850000,12348.80,546.16,11802.64\nMCNEIL INC.,01-East,779959708,12215.52,3923.36,8292.16\nMCNEIL INC.,01-East,739451000,12006.40,10219.16,1787.24\nMCNEIL INC.,01-East,764821161,10800.00,8928.00,1872.00\nMCNEIL INC.,01-East,733464400,10752.00,3983.12,6768.88\nMCNEIL INC.,01-East,764221000,9195.60,4957.78,4237.82\nMCNEIL INC.,01-East,764482000,8108.40,3228.29,4880.11\nMCNEIL INC.,01-East,738521000,7647.54,7956.21,-308.67\nMCNEIL INC.,01-East,733586500,7128.00,1632.17,5495.83\nMCNEIL INC.,01-East,734900000,6440.00,3356.65,3083.35\nMCNEIL INC.,01-East,734211200,6008.20,778.64,5229.56\nMCNEIL INC.,01-East,734651200,5892.90,1250.97,4641.93\nMCNEIL INC.,01-East,739452000,5708.64,4936.64,772.00\nMCNEIL INC.,01-East,734751200,4844.68,937.91,3906.77\nMCNEIL INC.,01-East,771320000,4712.58,768.70,3943.88\nMCNEIL INC.,01-East,793336074,4302.00,2707.80,1594.20\nMCNEIL INC.,01-East,727000140,4250.40,1153.24,3097.16\nMCNEIL INC.,01-East,779959709,3995.56,1557.22,2438.34\nMCNEIL INC.,01-East,793330000,3764.25,910.46,2853.79\nMCNEIL INC.,01-East,779951021,3455.60,2940.82,514.78\nMCNEIL INC.,01-East,733403100,3431.40,553.38,2878.02\nMCNEIL INC.,01-East,739541100,3358.66,1258.00,2100.66\nMCNEIL INC.,01-East,764522000,3301.00,1616.85,1684.15\nMCNEIL INC.,01-East,770500000,2845.80,167.22,2678.58\nMCNEIL INC.,01-East,764822000,2736.00,966.75,1769.25\nMCNEIL INC.,01-East,739781100,2649.79,1977.19,672.60\nMCNEIL INC.,01-East,739721000,2627.57,1840.55,787.02\nMCNEIL INC.,01-East,733545700,2588.30,771.07,1817.23\nMCNEIL INC.,01-East,740063000,2390.30,716.82,1673.48\nMCNEIL INC.,01-East,733423700,2177.28,488.86,1688.42\nMCNEIL INC.,01-East,737912200,2147.45,446.30,1701.15\nMCNEIL INC.,01-East,754465000,2084.98,963.92,1121.06\nMCNEIL INC.,01-East,777904301,2073.41,939.74,1133.67\nMCNEIL INC.,01-East,780710000,2060.52,896.00,1164.52\nMCNEIL INC.,01-East,733586400,2052.00,494.95,1557.05\nMCNEIL INC.,01-East,740402402,2051.00,604.67,1446.33\nMCNEIL INC.,01-East,739561000,2008.08,861.95,1146.13\nMCNEIL INC.,01-East,779951023,1908.00,1617.58,290.42\nMCNEIL INC.,01-East,727000440,1892.00,425.69,1466.31\nMCNEIL INC.,01-East,754926000,1665.12,985.10,680.02\nMCNEIL INC.,01-East,733505300,1650.00,425.73,1224.27\nMCNEIL INC.,01-East,768021000,1613.50,409.11,1204.39\nMCNEIL INC.,01-East,770720000,1536.80,56.55,1480.25\nMCNEIL INC.,01-East,724281200,1480.00,386.57,1093.43\nMCNEIL INC.,01-East,764861000,1449.88,454.62,995.26\nMCNEIL INC.,01-East,764761000,1325.60,628.03,697.57\nMCNEIL INC.,01-East,754065000,1130.64,527.04,603.60\nMCNEIL INC.,01-East,768121000,1094.85,219.57,875.28\nMCNEIL INC.,01-East,733505200,980.00,244.10,735.90\nMCNEIL INC.,01-East,767990000,931.86,347.38,584.48\nMCNEIL INC.,01-East,777650000,923.70,427.32,496.38\nMCNEIL INC.,01-East,734851000,912.46,197.80,714.66\nMCNEIL INC.,01-East,724281400,888.00,231.94,656.06\nMCNEIL INC.,01-East,770830000,749.70,30.97,718.73\nMCNEIL INC.,01-East,770212000,730.60,162.39,568.21\nMCNEIL INC.,01-East,734990000,715.20,191.98,523.22\nMCNEIL INC.,01-East,734810000,680.00,544.58,135.42\nMCNEIL INC.,01-East,727000025,598.00,211.14,386.86\nMCNEIL INC.,01-East,739521100,517.78,139.38,378.40\nMCNEIL INC.,01-East,724003000,478.95,81.82,397.13\nMCNEIL INC.,01-East,739390000,462.24,247.29,214.95\nMCNEIL INC.,01-East,739450000,457.92,376.73,81.19\nMCNEIL INC.,01-East,740123000,444.78,128.01,316.77\nMCNEIL INC.,01-East,768750000,339.72,73.86,265.86\nMCNEIL INC.,01-East,764781040,339.00,120.99,218.01\nMCNEIL INC.,01-East,764561000,338.75,110.89,227.86\nMCNEIL INC.,01-East,770260000,333.68,70.33,263.35\nMCNEIL INC.,01-East,770270000,321.30,68.38,252.92\nMCNEIL INC.,01-East,792702000,303.75,96.60,207.15\nMCNEIL INC.,01-East,733525400,285.00,74.13,210.87\nMCNEIL INC.,01-East,733525600,285.00,71.05,213.95\nMCNEIL INC.,01-East,739541000,239.90,90.50,149.40\nMCNEIL INC.,01-East,779957254,189.00,113.16,75.84\nMCNEIL INC.,01-East,739681100,188.53,207.00,-18.47\nMCNEIL INC.,01-East,779957258,167.04,89.70,77.34\nMCNEIL INC.,01-East,770220000,151.20,28.44,122.76\nMCNEIL INC.,01-East,777642000,121.60,50.36,71.24\nMCNEIL INC.,01-East,733211050,114.00,55.91,58.09\nMCT INDUSTRIES - GOV'T,01-East,791006850,25158.21,12064.32,13093.89\nMCT INDUSTRIES - GOV'T,01-East,791006650,17177.68,8830.89,8346.79\nMECHANICAL EQUIPMENT INC,01-East,734211200,3310.20,324.03,2986.17\nMEGADIAMOND,01-East,777585000,295.00,9.07,285.93\n\"MEGTEC SYSTEMS, INC\",01-East,754055000,4717.44,2791.82,1925.62\n\"MEGTEC SYSTEMS, INC\",01-East,734651200,1219.62,74.00,1145.62\n\"MEGTEC SYSTEMS, INC\",01-East,764522000,552.00,157.40,394.60\n\"MEGTEC SYSTEMS, INC\",01-East,754045000,284.22,188.42,95.80\nMERCER GASKET & SHIM,01-East,733202000,5107.20,1325.44,3781.76\nMERIDEN MANUFACTURING INC,01-East,764461006,1196.00,83.13,1112.87\n\"MESTEK, INC.\",01-East,724281200,500.00,89.08,410.92\nMETALCRAFT TECHNOLOGIES,01-East,764821000,1307.00,46.38,1260.62\nMETALLIZED CARBON,01-East,733725500,5204.00,1019.36,4184.64\nMETALLIZED CARBON,01-East,764521000,1933.00,79.77,1853.23\nMETAULLIC SYSTEMS DIV.,01-East,766121000,5103.50,1607.68,3495.82\nMETAULLIC SYSTEMS DIV.,01-East,777904301,1998.00,704.80,1293.20\nMETAULLIC SYSTEMS DIV.,01-East,764261000,1631.00,861.31,769.69\nMETAULLIC SYSTEMS DIV.,01-East,733511000,1087.50,665.61,421.89\nMETAULLIC SYSTEMS DIV.,01-East,764821000,962.50,355.61,606.89\nMETAULLIC SYSTEMS DIV.,01-East,733510000,561.25,397.57,163.68\nMETAULLIC SYSTEMS DIV.,01-East,764341000,516.00,287.84,228.16\nMETAULLIC SYSTEMS DIV.,01-East,772101000,222.00,57.90,164.10\nMETAULLIC SYSTEMS DIV.,01-East,733464500,130.00,59.04,70.96\nMETAULLIC SYSTEMS DIV.,01-East,771981000,103.20,31.30,71.90\nMETRO CIRCUITS,01-East,733464600,1520.00,163.88,1356.12\nMETRO SUPPLY CO.,01-East,735602000,216905.00,78191.65,138713.35\nMETRO SUPPLY CO.,01-East,735602110,9400.00,3500.26,5899.74\nMETRO SUPPLY CO.,01-East,764901000,8156.00,3151.78,5004.22\nMETRO SUPPLY CO.,01-East,735750000,6510.00,2781.80,3728.20\nMETRO SUPPLY CO.,01-East,764781000,4684.00,1859.60,2824.40\nMETRO SUPPLY CO.,01-East,764821000,3054.00,1125.98,1928.02\nMETRO SUPPLY CO.,01-East,764861000,1524.00,623.46,900.54\nMETSO PAPER USA INC.,01-East,764601000,7786.00,1806.92,5979.08\nMICROCHIP TECHNOLOGY INC.,01-East,764221000,476.00,33.66,442.34\nMICROCHIP TECHNOLOGY INC.,01-East,734211200,459.00,45.68,413.32\nMID-TEX DISTRIBUTING INC,01-East,735602110,70122.00,20528.23,49593.77\nMID-TEX DISTRIBUTING INC,01-East,735602000,18523.50,5048.33,13475.17\nMIDWEST MATERIALS CO.,01-East,768141003,354256.00,161233.53,193022.47\nMIDWEST MATERIALS CO.,01-East,735602000,210176.00,53275.68,156900.32\nMIDWEST MATERIALS CO.,01-East,768121000,90960.00,27863.51,63096.49\nMIDWEST MATERIALS CO.,01-East,768141000,15795.00,7293.85,8501.15\nMIDWEST MATERIALS CO.,01-East,735750000,9408.00,3050.56,6357.44\nMIDWEST MATERIALS CO.,01-East,768144200,2052.00,315.82,1736.18\nMIDWEST MATERIALS CO.,01-East,733151500,636.60,101.80,534.80\nMIDWEST MATERIALS CO.,01-East,733211000,614.10,201.18,412.92\nMIDWEST MATERIALS CO.,01-East,779989770,560.08,0.00,560.08\nMILAMAR COATING,01-East,779987651,1255.50,156.78,1098.72\nMITSUBISHI POWER SYSTEMS,01-East,734611100,178.72,26.96,151.76\nMODYNE,01-East,770720000,1010.00,27.12,982.88\nMONTAGUE STUDIOS,01-East,739681100,444.24,120.46,323.78\nMORAC MEXICO S.A. de C.V.,01-East,734761300,2039.40,379.39,1660.01\nMORRISON SUPPLY COMPANY,01-East,735602000,4630.00,862.96,3767.04\nMOTION INDUSTRIES,01-East,770410000,1507.50,98.81,1408.69\nMOTION INDUSTRIES,01-East,770430000,1327.50,88.55,1238.95\nMOTION INDUSTRIES,01-East,733211000,912.00,94.78,817.22\nMOTION INDUSTRIES,01-East,766121000,910.00,23.95,886.05\nMOTION INDUSTRIES,01-East,734900000,737.00,133.46,603.54\nMOTION INDUSTRIES,01-East,733464600,626.00,69.94,556.06\nMOTION INDUSTRIES,01-East,734830000,370.00,45.41,324.59\nMOTION INDUSTRIES,01-East,734151100,326.17,43.14,283.03\nMOTION INDUSTRIES,01-East,733464500,298.00,29.52,268.48\nMOUNTAINVIEW MECHANICAL,01-East,780645010,100629.60,49683.31,50946.29\nMOUNTAINVIEW MECHANICAL,01-East,784320000,16483.20,9567.28,6915.92\nMOUNTAINVIEW MECHANICAL,01-East,764523000,3864.00,787.76,3076.24\nMOUNTAINVIEW MECHANICAL,01-East,784000000,2481.84,1294.06,1187.78\nMOUNTAINVIEW MECHANICAL,01-East,764522000,2144.00,419.72,1724.28\nMOUNTAINVIEW MECHANICAL,01-East,780200000,1898.64,1140.24,758.40\nMOUNTAINVIEW MECHANICAL,01-East,739370000,1413.44,635.75,777.69\nMOUNTAINVIEW MECHANICAL,01-East,739741100,807.68,438.23,369.45\nMOUNTAINVIEW MECHANICAL,01-East,764561000,102.00,22.18,79.82\nMSSM,01-East,764900005,134320.00,52817.45,81502.55\nMSSM,01-East,764827550,65296.00,24113.55,41182.45\nMSSM,01-East,764864005,22736.00,7588.29,15147.71\nMSSM,01-East,764901011,7938.75,4360.33,3578.42\nMSSM,01-East,764827150,2310.00,986.90,1323.10\nMSSM,01-East,764902501,1569.81,824.54,745.27\nMSSM,01-East,764821073,885.50,838.35,47.15\nMSSM,01-East,764781000,312.00,157.77,154.23\nMSSM,01-East,764861000,256.50,116.90,139.60\nMSSM,01-East,764901600,64.68,32.95,31.73\nMUNNELL & SHERRILL. INC,01-East,733444200,298.00,38.64,259.36\nMUNNELL & SHERRILL. INC,01-East,733464500,275.00,32.53,242.47\nMURPHY COMPANY,01-East,739741100,1713.92,673.28,1040.64\nMUSTANG GROUP,01-East,734611100,536.16,81.10,455.06\nMYLAN TECHNOLOGIES INC.,01-East,770830200,1010.00,28.98,981.02\nN2 AERO,01-East,733648000,25416.00,10632.87,14783.13\nNATIONAL ELECTRIC,01-East,734910000,683.00,102.70,580.30\nNATIONAL GYPSUM CO,01-East,764821000,447.00,15.66,431.34\nNATIONAL PAPER CONVERTING,01-East,733525500,1330.00,130.06,1199.94\nNAWC-WD/475500D,01-East,724311400,7038.00,919.50,6118.50\nNEWTEX INDUSTRIES INC.,01-East,733832000,2430.00,385.42,2044.58\nNIAGARA CERAMICS CORP.,01-East,764822000,1464.00,123.05,1340.95\nNIAGARA SPECIALTY METALS,01-East,754924010,37941.48,7234.02,30707.46\nNIAGARA SPECIALTY METALS,01-East,754904005,32485.17,6492.81,25992.36\nNIAGARA SPECIALTY METALS,01-East,754907000,32324.33,6225.56,26098.77\nNIAGARA SPECIALTY METALS,01-East,754927000,30559.02,5579.77,24979.25\nNIAGARA SPECIALTY METALS,01-East,779957936,18014.56,5719.01,12295.55\nNIAGARA SPECIALTY METALS,01-East,754877000,10244.20,2061.01,8183.19\nNIAGARA SPECIALTY METALS,01-East,754874005,7643.28,1613.73,6029.55\nNIAGARA SPECIALTY METALS,01-East,754867000,6985.44,1453.48,5531.96\nNIAGARA SPECIALTY METALS,01-East,764821000,4970.80,555.26,4415.54\nNIAGARA SPECIALTY METALS,01-East,764901000,3507.33,400.73,3106.60\nNIAGARA SPECIALTY METALS,01-East,766121000,2869.50,239.49,2630.01\nNIAGARA SPECIALTY METALS,01-East,738381000,2534.40,2080.80,453.60\nNIPRO GLASS AMERICAS CORP,01-East,793330000,1294.50,78.04,1216.46\nNIPRO GLASS AMERICAS CORP,01-East,733464400,596.00,79.22,516.78\nNIPRO GLASS AMERICAS CORP,01-East,733464500,298.00,29.52,268.48\nNOELLE INDUSTRIES,01-East,720219100,553.20,13.33,539.87\nNORAM ENGINEERING,01-East,733182200,4719.60,585.90,4133.70\nNORTH ATLANTIC ASSOCIATES,01-East,764522000,2550.40,476.10,2074.30\nNORTH ATLANTIC ASSOCIATES,01-East,764861000,362.00,63.37,298.63\nNORTH ATLANTIC ASSOCIATES,01-East,764521000,139.20,26.59,112.61\nNORTHWEST INSULATION,01-East,735750000,17532.00,5332.02,12199.98\nNORTHWEST INSULATION,01-East,735602000,10612.00,2752.53,7859.47\nNORTHWEST INSULATION,01-East,764902000,2653.50,96.62,2556.88\nNORTHWEST INSULATION,01-East,764602000,2034.00,79.49,1954.51\nNOTHING INC.,01-East,777601000,409.92,58.80,351.12\nNOV WILSON,01-East,770300000,552.75,39.97,512.78\nNOVELIS OSWEGO,01-East,739451000,8683.52,2695.26,5988.26\nNOVELIS OSWEGO,01-East,718501000,1052.50,134.69,917.81\nNOVELIS OSWEGO,01-East,739741100,700.16,219.12,481.04\nNRG/ENERGY,01-East,764461006,404.00,27.94,376.06\nNTK AVIATION AMERICA INC.,01-East,770800000,1368.00,25.56,1342.44\nNTK AVIATION AMERICA INC.,01-East,733383000,1117.60,163.93,953.67\nNU-SOURCE INC.,01-East,733464500,4688.00,472.32,4215.68\nNUTEC BICKLEY SA DE CV,01-East,779121000,691.29,93.00,598.29\nO G SUPPLY,01-East,726000240,25704.00,2662.54,23041.46\nOE-CAL,01-East,737902100,462.00,67.12,394.88\nOECO MILWAUKIE,01-East,771132000,1217.50,35.80,1181.70\nOECO MILWAUKIE,01-East,771002000,828.50,19.30,809.20\nOHMITE MFG,01-East,733151500,900.00,95.38,804.62\nOHMITE MFG,01-East,739561200,839.68,144.66,695.02\nOLYMPIC CRANES & FABRICAT,01-East,739370000,364.80,90.82,273.98\nOMNIA SALES,01-East,740283401,9609.60,1592.30,8017.30\nONQUEST INC.,01-East,779957139,568.25,395.28,172.97\nORCON - MEXICO LLC,01-East,724261200,19484.00,2731.10,16752.90\n\"OWENS CORNING MEXICO,\",01-East,733525500,752.50,70.39,682.11\n\"OWENS CORNING SALES,LLC\",01-East,793330000,3883.50,234.12,3649.38\nOWENS CORNING,01-East,793330000,898.00,53.01,844.99\nPACIFIC INSULATION CO.,01-East,735602000,320120.00,100400.76,219719.24\nPACIFIC INSULATION CO.,01-East,768241000,253575.00,100795.31,152779.69\nPACIFIC INSULATION CO.,01-East,735602110,76640.00,25621.10,51018.90\nPACIFIC INSULATION CO.,01-East,768221000,74752.00,28829.86,45922.14\nPACIFIC INSULATION CO.,01-East,726020019,39623.25,16207.45,23415.80\nPACIFIC INSULATION CO.,01-East,735750000,17048.00,6031.09,11016.91\nPACIFIC INSULATION CO.,01-East,735752000,6642.00,2391.10,4250.90\nPACIFIC INSULATION CO.,01-East,737301000,5650.56,1472.16,4178.40\nPACIFIC INSULATION CO.,01-East,764901000,2765.29,402.18,2363.11\nPACIFIC INSULATION CO.,01-East,739641100,1102.76,549.70,553.06\nPACIFIC INSULATION CO.,01-East,739661100,1062.44,601.98,460.46\nPACIFIC INSULATION CO.,01-East,733211000,382.80,94.78,288.02\nPACIFIC INSULATION CO.,01-East,764901061,339.23,111.90,227.33\nPACIFIC INSULATION CO.,01-East,733464500,196.50,29.52,166.98\nPACIFIC NORTHWEST NATL,01-East,764221000,248.00,16.92,231.08\nPACIFIC SCIENTIFIC,01-East,733671000,1438.08,119.66,1318.42\nPAC,01-East,740123000,670.32,121.37,548.95\nPALL CORPORATION-H2,01-East,733505200,49590.00,9616.56,39973.44\nPALL CORPORATION-H2,01-East,733444200,553.80,104.06,449.74\nPALL TRINITY MICRO CORP,01-East,733505200,5538.00,917.32,4620.68\nPANDO LLC,01-East,768021000,374.00,26.84,347.16\nPANELCRAFT INC.,01-East,737902100,2642.00,405.82,2236.18\nPARAGON PACIFIC INSULATIO,01-East,735602110,38000.00,13566.49,24433.51\nPARAGON PACIFIC INSULATIO,01-East,735602000,25000.00,8405.73,16594.27\nPARAGON PACIFIC INSULATIO,01-East,735750000,13320.00,4667.93,8652.07\nPARAGON PACIFIC INSULATIO,01-East,735752000,185.00,68.40,116.60\nPARAGON PACIFIC SPI,01-East,735602110,44000.00,15708.56,28291.44\nPARAGON PACIFIC SPI,01-East,735602000,12000.00,4142.22,7857.78\nPARKER AEROSPACE,01-East,779001000,275.84,36.20,239.64\nPASQUALE DINUZZO LLC,01-East,764483000,421.00,89.22,331.78\nPATS AIRCRAFT LLC,01-East,724121700,5176.00,1302.84,3873.16\n\"PATS AIRCRAFT, LLC\",01-East,724121700,584.00,162.85,421.15\nPAWLING CORP.,01-East,764781040,4232.00,314.99,3917.01\nPENNINGTON FURNACE SUPPLY,01-East,733742000,1632.00,327.00,1305.00\nPENNINGTON FURNACE SUPPLY,01-East,733721000,300.00,69.32,230.68\nPENSAR,01-East,738521000,4815.36,1291.07,3524.29\nPENSAR,01-East,764822000,1720.00,61.52,1658.48\nPENSAR,01-East,727063016,988.80,63.34,925.46\n\"PERFORMANCE PLASTICS,INC.\",01-East,724261400,6915.60,1140.39,5775.21\n\"PERFORMANCE PLASTICS,INC.\",01-East,724401150,648.00,81.46,566.54\nPETRO-CHEM DEVELOPMENT,01-East,768133000,833.00,42.91,790.09\nPHOTONIS USA INC.,01-East,771320000,2859.00,342.25,2516.75\nPHYTOTECHNOLOGY LABORATOR,01-East,726000240,441.60,29.58,412.02\nPICA MFG. SOLUTIONS,01-East,733464500,262.00,29.52,232.48\nPIONEER GASKET,01-East,770230000,2016.00,147.83,1868.17\nPIONEER GASKET,01-East,770240000,1972.50,140.66,1831.84\nPK CONTRACTING INC,01-East,768112000,269.00,19.33,249.67\nPLANET TOOL,01-East,771504000,50115.00,3432.84,46682.16\nPLANET TOOL,01-East,771502000,10041.00,687.06,9353.94\nPLIBRICO COMPANY LLC,01-East,779987650,88963.92,25595.56,63368.36\nPLIBRICO REFRACTORY,01-East,780250000,88464.74,39839.14,48625.60\nPLIBRICO REFRACTORY,01-East,779980245,26094.78,20914.05,5180.73\nPLIBRICO REFRACTORY,01-East,754467000,11160.22,3646.79,7513.43\nPLIBRICO REFRACTORY,01-East,737271000,9920.16,8968.99,951.17\nPLIBRICO REFRACTORY,01-East,764521000,7183.60,1341.56,5842.04\nPLIBRICO REFRACTORY,01-East,770540000,4787.13,454.18,4332.95\nPLIBRICO REFRACTORY,01-East,738401000,4706.40,4281.60,424.80\nPLIBRICO REFRACTORY,01-East,779951021,4069.80,1747.83,2321.97\nPLIBRICO REFRACTORY,01-East,737861000,3485.80,1771.05,1714.75\nPLIBRICO REFRACTORY,01-East,742520400,3056.71,661.56,2395.15\nPLIBRICO REFRACTORY,01-East,779959123,3047.31,1275.66,1771.65\nPLIBRICO REFRACTORY,01-East,739450000,2656.37,1404.04,1252.33\nPLIBRICO REFRACTORY,01-East,754485000,2336.16,827.82,1508.34\nPLIBRICO REFRACTORY,01-East,779951023,2217.60,961.41,1256.19\nPLIBRICO REFRACTORY,01-East,739641100,1890.44,1099.40,791.04\nPLIBRICO REFRACTORY,01-East,754367000,1782.00,820.74,961.26\nPLIBRICO REFRACTORY,01-East,739561200,1593.86,442.66,1151.20\nPLIBRICO REFRACTORY,01-East,770512000,1558.83,88.67,1470.16\nPLIBRICO REFRACTORY,01-East,737912200,1276.00,363.80,912.20\nPLIBRICO REFRACTORY,01-East,764901000,1062.60,129.15,933.45\nPLIBRICO REFRACTORY,01-East,779959124,817.62,332.68,484.94\nPLIBRICO REFRACTORY,01-East,733444200,670.80,115.92,554.88\nPLIBRICO REFRACTORY,01-East,739781100,620.32,422.89,197.43\nPLIBRICO REFRACTORY,01-East,779959125,547.56,222.65,324.91\nPLIBRICO REFRACTORY,01-East,764821000,495.00,140.91,354.09\nPLIBRICO REFRACTORY,01-East,739561000,482.98,170.44,312.54\nPLIBRICO REFRACTORY,01-East,733464500,412.60,65.06,347.54\nPLIBRICO REFRACTORY,01-East,733211000,251.30,53.20,198.10\nPLIBRICO REFRACTORY,01-East,733211050,251.30,55.91,195.39\nPLIBRICO REFRACTORY,01-East,779987718,246.00,137.09,108.91\nPLIBRICO REFRACTORY,01-East,764482000,79.60,17.40,62.20\nPOLYMERIC EXTRUDED PROD.,01-East,764481000,360.00,17.59,342.41\nPRAXAIR INC.,01-East,739561000,362.23,85.22,277.01\nPRAXAIR,01-East,739781100,2513.66,1310.72,1202.94\nPRAXAIR,01-East,734822000,2330.00,402.38,1927.62\nPRAXAIR,01-East,738561000,1700.35,496.55,1203.80\nPRAXAIR,01-East,733464400,1222.00,165.96,1056.04\nPRAXAIR,01-East,738521000,1171.58,430.36,741.22\nPRAXAIR,01-East,777601000,996.48,189.62,806.86\nPRAXAIR,01-East,738401000,975.56,461.81,513.75\nPRAXAIR,01-East,738361000,896.18,473.35,422.83\nPRAXAIR,01-East,734211200,787.76,93.50,694.26\nPRAXAIR,01-East,733211000,780.50,106.40,674.10\nPRAXAIR,01-East,734900000,499.62,95.09,404.53\nPRAXAIR,01-East,738381000,475.20,346.80,128.40\nPRAXAIR,01-East,740063000,471.74,120.10,351.64\nPRAXAIR,01-East,764901000,425.25,16.14,409.11\nPRAXAIR,01-East,764481000,346.00,16.94,329.06\nPRAXAIR,01-East,739451000,327.74,111.56,216.18\nPRAXAIR,01-East,733464500,298.00,29.52,268.48\nPRAXAIR,01-East,739390000,268.85,82.43,186.42\nPRC-DESOTO INTERNATIONAL,01-East,720252303,6185.70,145.78,6039.92\nPRECISION ELECTRO MINERAL,01-East,770260000,3820.50,275.92,3544.58\nPRECISION FITTING & GAUGE,01-East,734990000,715.20,82.94,632.26\nPRESSCUT INDUSTRIES,01-East,733444200,664.00,69.38,594.62\nPRIME MACHINE,01-East,770520000,906.00,48.32,857.68\nPROCEDYNE CORP,01-East,737321000,74163.08,34479.25,39683.83\nPROCEDYNE CORP,01-East,727000140,60538.00,8379.39,52158.61\nPROCEDYNE CORP,01-East,754477000,51914.80,19034.55,32880.25\nPROCEDYNE CORP,01-East,754527000,30667.18,10318.04,20349.14\nPROCEDYNE CORP,01-East,764821000,29892.00,3411.25,26480.75\nPROCEDYNE CORP,01-East,739681100,17674.00,7829.95,9844.05\nPROCEDYNE CORP,01-East,734311100,14840.00,1859.92,12980.08\nPROCEDYNE CORP,01-East,739781100,8942.74,3522.77,5419.97\nPROCEDYNE CORP,01-East,754907000,8493.74,1710.68,6783.06\nPROCEDYNE CORP,01-East,764601000,8116.00,1062.88,7053.12\nPROCEDYNE CORP,01-East,764902000,6689.00,1213.22,5475.78\nPROCEDYNE CORP,01-East,779958793,6090.00,2064.75,4025.25\nPROCEDYNE CORP,01-East,764781000,5856.00,817.80,5038.20\nPROCEDYNE CORP,01-East,777642000,4122.30,1246.55,2875.75\nPROCEDYNE CORP,01-East,734711100,2309.67,515.00,1794.67\nPROCEDYNE CORP,01-East,777601000,2244.26,621.74,1622.52\nPROCEDYNE CORP,01-East,733525600,2070.00,273.08,1796.92\nPROCEDYNE CORP,01-East,739761100,1639.14,723.93,915.21\nPROCEDYNE CORP,01-East,764521000,1499.85,199.38,1300.47\nPROCEDYNE CORP,01-East,764901000,1196.00,258.30,937.70\nPROCEDYNE CORP,01-East,754427000,824.35,293.39,530.96\nPROCEDYNE CORP,01-East,739541000,345.31,71.62,273.69\nPROCEDYNE CORP,01-East,766121000,291.55,23.95,267.60\nPROCEDYNE CORP,01-East,739561200,257.60,72.33,185.27\nPROCEDYNE CORP,01-East,754447000,240.50,98.42,142.08\nPROCEDYNE CORP,01-East,779957077,74.90,34.60,40.30\nPROCESS RESEARCH PRODUCTS,01-East,777990000,4950.00,872.00,4078.00\nPTI THERMAL SOLUTIONS,01-East,764827550,268742.40,95177.28,173565.12\nPTI THERMAL SOLUTIONS,01-East,768121000,200741.00,96982.28,103758.72\nPTI THERMAL SOLUTIONS,01-East,724281410,178670.10,101168.46,77501.64\nPTI THERMAL SOLUTIONS,01-East,764821000,115398.60,43190.11,72208.49\nPTI THERMAL SOLUTIONS,01-East,764521000,110633.00,39323.38,71309.62\nPTI THERMAL SOLUTIONS,01-East,740361900,84115.20,46604.54,37510.66\nPTI THERMAL SOLUTIONS,01-East,764901000,80419.00,38626.14,41792.86\nPTI THERMAL SOLUTIONS,01-East,766121000,68140.00,26226.79,41913.21\nPTI THERMAL SOLUTIONS,01-East,739457000,59365.44,37211.37,22154.07\nPTI THERMAL SOLUTIONS,01-East,733525600,58721.75,9846.10,48875.65\nPTI THERMAL SOLUTIONS,01-East,734251300,57087.41,5801.00,51286.41\nPTI THERMAL SOLUTIONS,01-East,764900005,51624.80,21586.28,30038.52\nPTI THERMAL SOLUTIONS,01-East,779988846,48199.68,21187.84,27011.84\nPTI THERMAL SOLUTIONS,01-East,764901005,47880.00,19955.00,27925.00\nPTI THERMAL SOLUTIONS,01-East,740382201,38174.00,19672.05,18501.95\nPTI THERMAL SOLUTIONS,01-East,777601000,31598.30,8892.49,22705.81\nPTI THERMAL SOLUTIONS,01-East,780680000,17560.80,6300.48,11260.32\nPTI THERMAL SOLUTIONS,01-East,759821000,13578.84,4074.20,9504.64\nPTI THERMAL SOLUTIONS,01-East,733211050,11389.80,3292.37,8097.43\nPTI THERMAL SOLUTIONS,01-East,764902000,10772.00,5130.80,5641.20\nPTI THERMAL SOLUTIONS,01-East,766162000,10674.00,4147.92,6526.08\nPTI THERMAL SOLUTIONS,01-East,734351200,10269.36,1441.25,8828.11\nPTI THERMAL SOLUTIONS,01-East,771400000,8450.70,299.90,8150.80\nPTI THERMAL SOLUTIONS,01-East,764821161,7800.00,7440.00,360.00\nPTI THERMAL SOLUTIONS,01-East,768141000,7325.05,2447.63,4877.42\nPTI THERMAL SOLUTIONS,01-East,764822000,7133.90,2479.64,4654.26\nPTI THERMAL SOLUTIONS,01-East,766122000,6650.00,2403.85,4246.15\nPTI THERMAL SOLUTIONS,01-East,795900000,5850.00,1033.66,4816.34\nPTI THERMAL SOLUTIONS,01-East,780640000,4567.23,1618.55,2948.68\nPTI THERMAL SOLUTIONS,01-East,777904301,4091.43,1527.09,2564.34\nPTI THERMAL SOLUTIONS,01-East,779956886,3900.00,1557.21,2342.79\nPTI THERMAL SOLUTIONS,01-East,734910000,3545.40,945.11,2600.29\nPTI THERMAL SOLUTIONS,01-East,733211000,3133.10,904.47,2228.63\nPTI THERMAL SOLUTIONS,01-East,779986951,2700.00,1442.58,1257.42\nPTI THERMAL SOLUTIONS,01-East,734830000,1951.60,312.50,1639.10\nPTI THERMAL SOLUTIONS,01-East,733444200,1938.00,386.40,1551.60\nPTI THERMAL SOLUTIONS,01-East,781660000,1868.52,564.32,1304.20\nPTI THERMAL SOLUTIONS,01-East,779956887,1753.92,512.34,1241.58\nPTI THERMAL SOLUTIONS,01-East,733484900,1707.20,347.01,1360.19\nPTI THERMAL SOLUTIONS,01-East,739561000,1609.92,681.76,928.16\nPTI THERMAL SOLUTIONS,01-East,738521000,1263.36,1194.24,69.12\nPTI THERMAL SOLUTIONS,01-East,733444300,1162.80,277.02,885.78\nPTI THERMAL SOLUTIONS,01-East,739561200,1073.40,289.32,784.08\nPTI THERMAL SOLUTIONS,01-East,764482000,692.00,170.48,521.52\nPTI THERMAL SOLUTIONS,01-East,770570000,640.50,95.41,545.09\nPTI THERMAL SOLUTIONS,01-East,777903000,485.10,167.33,317.77\nPTI THERMAL SOLUTIONS,01-East,793330000,423.30,53.01,370.29\nPTI THERMAL SOLUTIONS,01-East,734990000,400.96,110.59,290.37\nPTI THERMAL SOLUTIONS,01-East,779956599,264.00,171.96,92.04\nPTI THERMAL SOLUTIONS,01-East,737902100,185.30,33.87,151.43\nPTI THERMAL SOLUTIONS,01-East,777642000,183.00,75.54,107.46\nPUGET SOUND NAVAL,01-East,733671000,2741.76,225.10,2516.66\nPYROTEK INC,01-East,726020015,25617.40,20817.63,4799.77\nPYROTEK INC,01-East,777902300,18566.10,9820.10,8746.00\nPYROTEK INC,01-East,733151520,17118.00,7773.56,9344.44\nPYROTEK INC,01-East,733525400,17020.00,7449.16,9570.84\nPYROTEK INC,01-East,771320000,16332.00,4270.57,12061.43\nPYROTEK INC,01-East,737321000,16104.71,5995.92,10108.79\nPYROTEK INC,01-East,733464500,9484.00,4458.26,5025.74\nPYROTEK INC,01-East,777903800,5287.06,1853.81,3433.25\nPYROTEK INC,01-East,764821000,4316.00,1597.23,2718.77\nPYROTEK INC,01-East,739681100,3555.36,3756.44,-201.08\nPYROTEK INC,01-East,733211000,3370.00,2084.23,1285.77\nPYROTEK INC,01-East,764901050,3042.00,1137.22,1904.78\nPYROTEK INC,01-East,777902200,2489.27,1155.09,1334.18\nPYROTEK INC,01-East,737301000,2314.50,981.45,1333.05\nPYROTEK INC,01-East,734611100,2145.68,582.00,1563.68\nPYROTEK INC,01-East,781590000,2126.52,436.37,1690.15\nPYROTEK INC,01-East,754267000,2067.12,607.32,1459.80\nPYROTEK INC,01-East,733510000,1669.75,1158.11,511.64\nPYROTEK INC,01-East,734311100,1637.68,410.82,1226.86\nPYROTEK INC,01-East,768221000,1542.40,566.08,976.32\nPYROTEK INC,01-East,770260000,1474.20,202.89,1271.31\nPYROTEK INC,01-East,776131000,1156.68,907.49,249.19\nPYROTEK INC,01-East,768021000,1128.00,599.57,528.43\nPYROTEK INC,01-East,770330000,1071.00,193.29,877.71\nPYROTEK INC,01-East,773161000,1060.29,235.29,825.00\nPYROTEK INC,01-East,777901100,1033.20,561.18,472.02\nPYROTEK INC,01-East,754885000,1006.92,181.83,825.09\nPYROTEK INC,01-East,733442000,975.00,477.95,497.05\nPYROTEK INC,01-East,766121000,865.00,272.48,592.52\nPYROTEK INC,01-East,733423700,761.60,452.56,309.04\nPYROTEK INC,01-East,733505200,710.50,327.94,382.56\nPYROTEK INC,01-East,739561000,656.64,342.83,313.81\nPYROTEK INC,01-East,737261000,650.06,279.93,370.13\nPYROTEK INC,01-East,764761000,648.00,368.58,279.42\nPYROTEK INC,01-East,768241000,432.90,159.21,273.69\nPYROTEK INC,01-East,739641100,348.48,274.86,73.62\nPYROTEK INC,01-East,772101000,308.21,94.57,213.64\nPYROTEK INC,01-East,764781000,308.00,143.12,164.88\nPYROTEK INC,01-East,739661100,294.24,300.99,-6.75\nPYROTEK INC,01-East,733151600,210.00,106.23,103.77\nPYROTEK INC,01-East,734711100,203.04,54.40,148.64\nPYROTEK INC,01-East,771991000,192.63,69.89,122.74\nPYROTEK INC,01-East,739541000,191.81,71.62,120.19\nPYROTEK INC,01-East,739561200,148.74,76.67,72.07\nPYROTEK INC,01-East,773191000,99.55,22.47,77.08\nPYROTEK INC,01-East,772051000,42.13,14.05,28.08\nQUADNA - PHOENIX,01-East,764605000,3912.00,153.87,3758.13\nQUADNA - PHOENIX,01-East,734311100,381.48,33.70,347.78\nQUADNA,01-East,737321000,7095.36,3506.88,3588.48\nQUADNA,01-East,737311000,5345.28,3612.48,1732.80\nQUADNA,01-East,764605000,4633.60,828.08,3805.52\nQUADNA,01-East,764262000,607.20,66.92,540.28\nQUADNA,01-East,734211200,413.88,46.76,367.12\nQUICK CUT GASKET & RUBBER,01-East,733464500,313.00,32.53,280.47\n\"R.L. WILLIAMS & CO., INC.\",01-East,735602000,7344.00,2044.25,5299.75\nRACING INDUSTRIES,01-East,730710500,4911.00,638.67,4272.33\nRACING INDUSTRIES,01-East,730552560,3214.00,386.21,2827.79\nRAINBOW ART GLASS INC.,01-East,733671000,3595.54,344.76,3250.78\nRAMPART SUPPLY,01-East,792610000,690.00,80.93,609.07\nRATH INCORPORATED,01-East,764261110,111760.00,38351.84,73408.16\nRATH INCORPORATED,01-East,727031200,40867.20,12028.68,28838.52\nRATH INCORPORATED,01-East,764301020,37302.50,13089.33,24213.17\nRATH INCORPORATED,01-East,764821026,35980.00,8592.99,27387.01\nRATH INCORPORATED,01-East,727000140,31579.20,12684.05,18895.15\nRATH INCORPORATED,01-East,727063000,21135.60,8166.88,12968.72\nRATH INCORPORATED,01-East,764521000,18735.00,4580.45,14154.55\nRATH INCORPORATED,01-East,727063016,14461.20,5558.54,8902.66\nRATH INCORPORATED,01-East,727063007,11025.00,4426.50,6598.50\nRATH INCORPORATED,01-East,727063018,11025.00,4447.51,6577.49\nRATH INCORPORATED,01-East,766121000,6845.00,2016.42,4828.58\nRATH INCORPORATED,01-East,764601000,2753.75,397.94,2355.81\nRATH INCORPORATED,01-East,764781000,456.00,121.22,334.78\nRATH INCORPORATED,01-East,766162000,190.50,74.74,115.76\nRAYTEQ LLC,01-East,776101000,387.00,182.84,204.16\nRAYTHEON,01-East,724261200,1572.00,156.40,1415.60\nRAYTHEON,01-East,764482000,692.00,34.10,657.90\nRAYTHEON,01-East,739561000,473.76,85.22,388.54\nRB LLC,01-East,768144200,22394.00,6662.37,15731.63\nRB LLC,01-East,768133000,7630.00,2183.01,5446.99\nRB LLC,01-East,768241100,4760.00,1248.80,3511.20\n\"RBC BEARINGS, INC.\",01-East,740361900,1401.60,457.49,944.11\nRBC INDUSTRIES,01-East,779987651,3324.00,408.97,2915.03\nRECON,01-East,733403200,996.60,99.17,897.43\nRECON,01-East,733383000,985.60,163.93,821.67\nRED-RAY MFG,01-East,734311100,1851.48,202.22,1649.26\nREDCO,01-East,733211000,958.00,106.40,851.60\nREDCO,01-East,792760000,356.72,58.31,298.41\nREFRACTORY ANCHORS INC,01-East,734351204,1836.00,278.52,1557.48\nREFRACTORY ENGINEERS INC.,01-East,777730021,7482.00,4876.80,2605.20\nREFRACTORY ENGINEERS INC.,01-East,793330000,6681.00,1827.48,4853.52\nREFRACTORY ENGINEERS INC.,01-East,793330074,5208.00,2700.80,2507.20\nREFRACTORY ENGINEERS INC.,01-East,727000440,4023.60,751.70,3271.90\nREFRACTORY ENGINEERS INC.,01-East,777720000,3142.08,2253.92,888.16\nREFRACTORY ENGINEERS INC.,01-East,777762000,956.48,2814.56,-1858.08\nREFRACTORY ENGINEERS INC.,01-East,792614000,858.00,180.97,677.03\nREFRACTORY ENGINEERS INC.,01-East,792654000,638.00,259.35,378.65\nREFRACTORY MAINTENANCE CO,01-East,718500000,29539.84,6517.91,23021.93\nREFRACTORY MAINTENANCE CO,01-East,724311400,12808.00,3065.01,9742.99\nREFRACTORY MAINTENANCE CO,01-East,737321000,7276.54,1713.12,5563.42\nREFRACTORY MAINTENANCE CO,01-East,734990000,6374.32,938.93,5435.39\nREFRACTORY MAINTENANCE CO,01-East,777901100,2646.00,982.06,1663.94\nREFRACTORY MAINTENANCE CO,01-East,777901300,2321.55,849.15,1472.40\nREFRACTORY MAINTENANCE CO,01-East,740382201,859.00,293.77,565.23\nREFRACTORY SERVICE INC.,01-East,755065000,40554.00,13761.54,26792.46\nREFRACTORY SERVICE INC.,01-East,779988827,9920.18,4882.75,5037.43\nREFRACTORY SERVICE INC.,01-East,734851000,2581.71,548.40,2033.31\nREFRACTORY SERVICE INC.,01-East,777642000,344.40,95.01,249.39\nREINHOLD INDUSTRIES INC.,01-East,733403200,1133.00,99.17,1033.83\nRESTORATION SUPPLY CO.,01-East,733151500,922.00,101.80,820.20\nREVERE COPPER PRODUCTS IN,01-East,770220000,635.40,82.04,553.36\nREX ROTO CORP,01-East,793250074,543.00,313.78,229.22\nREX ROTO CORP,01-East,733464500,152.00,29.52,122.48\nRHI CANADA INC.,01-East,737301000,2469.12,2575.36,-106.24\nRHI-MONOFRAX LTD.,01-East,734351200,1945.80,147.24,1798.56\nRHI-MONOFRAX LTD.,01-East,734251300,1176.05,88.29,1087.76\nRHI-MONOFRAX LTD.,01-East,733525500,752.50,70.39,682.11\nRHI-MONOFRAX LTD.,01-East,779081000,698.56,106.23,592.33\nRHI-MONOFRAX LTD.,01-East,734751200,584.00,75.41,508.59\nRIVE TECHNOLOGY,01-East,733444100,332.00,75.04,256.96\nROBBIE HOLLAND WELDING,01-East,764822000,894.00,31.77,862.23\nROGERS CORP,01-East,733525500,12582.00,1364.95,11217.05\nROSEMOUNT ANALYTICAL INC.,01-East,724281200,2947.00,421.87,2525.13\n\"RSG AERODESIGN, LLC\",01-East,724281200,601.00,89.08,511.92\nRTI FABRICATION,01-East,737671000,2478.20,326.50,2151.70\nS & S SUPPLIES &,01-East,771120000,8595.00,285.54,8309.46\nS & S SUPPLIES &,01-East,771100000,7880.00,244.88,7635.12\nS & S SUPPLIES &,01-East,771002000,3256.50,79.49,3177.01\nS & S SUPPLIES &,01-East,771000000,3084.00,82.45,3001.55\nS & S SUPPLIES &,01-East,734910000,362.00,53.09,308.91\nS M ENGINEERING,01-East,740382101,1742.40,549.20,1193.20\nS P I,01-East,735602000,33120.00,10467.90,22652.10\nS P I,01-East,766161000,606.00,308.46,297.54\nS W ANDERSON,01-East,735602000,45700.00,16811.46,28888.54\nSAES SMART MATERIALS,01-East,733525600,717.50,65.49,652.01\nSAES SMART MATERIALS,01-East,764342000,429.50,23.40,406.10\nSAINT GOBAIN,01-East,777601000,1933.60,360.09,1573.51\nSAINT-GOBAIN,01-East,733586600,41803.20,4004.06,37799.14\nSAINT-GOBAIN,01-East,766211000,2200.00,221.56,1978.44\nSAINT-GOBAIN,01-East,738561000,1817.28,796.32,1020.96\nSAINT-GOBAIN,01-East,766681100,627.30,52.66,574.64\nSANDELIUS INSTRUMENTS INC,01-East,766681100,612.00,49.56,562.44\nSANDIA NATIONAL LABS,01-East,793330000,431.50,26.01,405.49\nSANDIA NATIONAL LAB,01-East,738521000,3210.24,860.71,2349.53\nSANDIA NATIONAL LAB,01-East,739541000,587.52,90.50,497.02\nSANDVIK HEATING,01-East,777601000,819.84,117.60,702.24\nSANDVIK THERMAL PROCESS,01-East,715000050,275511.83,147685.91,127825.92\nSANDVIK THERMAL PROCESS,01-East,763991000,229252.32,78195.76,151056.56\nSANDVIK THERMAL PROCESS,01-East,764787510,113725.44,36139.15,77586.29\nSANDVIK THERMAL PROCESS,01-East,727000140,70621.60,20680.84,49940.76\nSANDVIK THERMAL PROCESS,01-East,764761010,15552.00,4926.87,10625.13\nSANDVIK THERMAL PROCESS,01-East,734251300,2551.70,264.29,2287.41\nSANDVIK THERMAL PROCESS,01-East,734211200,685.12,92.43,592.69\nSANDVIK THERMAL PROCESS,01-East,733525500,431.25,70.39,360.86\nSANDVIK THERMAL PROCESS,01-East,777601000,287.95,58.80,229.15\nSANDY VALLEY FASTENERS,01-East,771002000,1740.00,43.20,1696.80\nSAXON GLASS TECH INC,01-East,727063000,56.80,16.80,40.00\nSCHWEITZER-MAUDUIT INT'L,01-East,766681100,918.00,74.33,843.67\nSCHWEITZER-MAUDUIT INT'L,01-East,766686000,414.00,37.54,376.46\nSCREW CONVEYOR CORP,01-East,770720000,1061.00,29.43,1031.57\nSDK LABORATORIES,01-East,727000140,520.80,32.55,488.25\nSEALING DEVICES INC,01-East,740402402,48360.00,23428.43,24931.57\nSEALING DEVICES INC,01-East,732664000,46110.00,6590.83,39519.17\nSEALING DEVICES INC,01-East,733525500,10660.00,2968.57,7691.43\nSEALING DEVICES INC,01-East,732675000,7632.28,2932.80,4699.48\nSEALING DEVICES INC,01-East,732663000,7458.00,1146.25,6311.75\nSEALING DEVICES INC,01-East,740402450,6969.34,3158.52,3810.82\nSEALING DEVICES INC,01-East,764490000,6775.13,1907.30,4867.83\nSEALING DEVICES INC,01-East,766121000,6312.00,320.40,5991.60\nSEALING DEVICES INC,01-East,740382201,6130.00,4139.77,1990.23\nSEALING DEVICES INC,01-East,764489300,2931.84,953.49,1978.35\nSEALING DEVICES INC,01-East,777601000,1392.20,288.08,1104.12\nSEALING DEVICES INC,01-East,732630100,1062.00,375.01,686.99\nSEALING DEVICES INC,01-East,764481000,592.00,170.70,421.30\nSEALING DEVICES INC,01-East,733510000,537.50,141.73,395.77\nSEISLER MACH & FABRICATIO,01-East,733464400,626.00,86.74,539.26\nSELAS HEAT TECHNOLOGY CO.,01-East,734311100,981.30,105.90,875.40\nSELLE SUPPLY COMPANY,01-East,735602000,232198.80,68574.11,163624.69\nSELLE SUPPLY COMPANY,01-East,735752000,29569.00,9872.96,19696.04\nSELLE SUPPLY COMPANY,01-East,764601000,23310.88,5648.72,17662.16\nSELLE SUPPLY COMPANY,01-East,735750000,12683.00,4146.30,8536.70\nSELLE SUPPLY COMPANY,01-East,764521000,12285.40,3041.49,9243.91\nSELLE SUPPLY COMPANY,01-East,764561000,3540.10,908.81,2631.29\nSELLE SUPPLY COMPANY,01-East,735602110,2800.00,875.07,1924.93\nSELLE SUPPLY COMPANY,01-East,764481000,383.00,102.95,280.05\nSELTEK INT'L INC,01-East,737009817,1707.00,1535.40,171.60\nSEMCOR,01-East,734910000,1086.00,159.26,926.74\nSEMICONDUCTOR PACKAGING,01-East,764821000,1341.00,46.97,1294.03\nSEMICONDUCTOR PACKAGING,01-East,764861000,666.50,25.98,640.52\nSHANNON ENTERPRISES OF WN,01-East,764902000,12575.60,3018.00,9557.60\nSHANNON ENTERPRISES OF WN,01-East,764822000,5548.00,1289.48,4258.52\nSHANNON ENTERPRISES OF WN,01-East,764342000,1695.00,552.43,1142.57\nSHARP TOOL,01-East,733742000,1184.00,210.15,973.85\nSHARP TOOL,01-East,733741000,408.00,119.06,288.94\nSHAW ENERGY & CHEMICALS,01-East,779959409,74593.75,50403.33,24190.42\nSHAW ENERGY & CHEMICALS,01-East,779959407,50866.20,16488.93,34377.27\nSHAW ENERGY & CHEMICALS,01-East,779980286,31154.76,28339.80,2814.96\nSHAW ENERGY & CHEMICALS,01-East,779959408,25433.10,9546.09,15887.01\nSHAW ENERGY & CHEMICALS,01-East,766121000,24321.00,10954.02,13366.98\nSHAW ENERGY & CHEMICALS,01-East,779980285,22988.70,20906.10,2082.60\nSHAW ENERGY & CHEMICALS,01-East,754924010,19972.80,12785.75,7187.05\nSHAW ENERGY & CHEMICALS,01-East,779980287,13225.00,12050.00,1175.00\nSHAW ENERGY & CHEMICALS,01-East,779988139,12938.64,12043.20,895.44\nSHAW ENERGY & CHEMICALS,01-East,764821000,12240.00,5528.85,6711.15\nSHAW ENERGY & CHEMICALS,01-East,779988136,10392.32,9450.24,942.08\nSHAW ENERGY & CHEMICALS,01-East,779980284,9745.50,8855.50,890.00\nSHAW ENERGY & CHEMICALS,01-East,777902888,4935.00,4494.00,441.00\nSHAW ENERGY & CHEMICALS,01-East,779980289,4637.25,4232.25,405.00\nSHAW ENERGY & CHEMICALS,01-East,777902887,4200.00,3750.00,450.00\nSHAW ENERGY & CHEMICALS,01-East,779988145,3568.00,1008.32,2559.68\nSHAW ENERGY & CHEMICALS,01-East,779956599,2460.00,2239.02,220.98\nSHAW ENERGY & CHEMICALS,01-East,779988134,2406.72,2188.68,218.04\nSHAW ENERGY & CHEMICALS,01-East,779980290,2014.50,3285.25,-1270.75\nSHAW ENERGY & CHEMICALS,01-East,779957834,1968.75,973.83,994.92\nSHAW ENERGY & CHEMICALS,01-East,779988146,1962.99,1266.67,696.32\nSHAW ENERGY & CHEMICALS,01-East,779988147,1847.36,1251.36,596.00\nSHAW ENERGY & CHEMICALS,01-East,779988148,1784.00,717.60,1066.40\nSHAW ENERGY & CHEMICALS,01-East,779988135,1452.32,1320.86,131.46\nSHAW ENERGY & CHEMICALS,01-East,777902889,1265.28,10.72,1254.56\nSHAW ENERGY & CHEMICALS,01-East,777902886,1098.24,1689.60,-591.36\nSHAW ENERGY & CHEMICALS,01-East,777642000,840.00,475.05,364.95\nSHAW ENERGY & CHEMICALS,01-East,779988138,611.52,556.40,55.12\nSHAW ENERGY & CHEMICALS,01-East,779988137,518.10,470.80,47.30\nSILURIA TECHNOLOGY,01-East,764461006,1808.00,138.31,1669.69\nSILURIA TECHNOLOGY,01-East,764821000,860.00,30.72,829.28\nSILURIA TECHNOLOGY,01-East,764481000,346.00,16.94,329.06\nSION POWER CORP,01-East,720252303,293.25,6.68,286.57\nSKIDMORE COLLEGE,01-East,777642000,270.00,63.34,206.66\nSL PACIFIC,01-East,733211000,319.00,47.39,271.61\nSMITH - SHARPE,01-East,779957935,496.20,223.89,272.31\nSMITH - SHARPE,01-East,754305000,393.75,219.57,174.18\nSMITHS TUBULAR SYSTEMS-,01-East,745121000,21536.80,3533.51,18003.29\nSMT INTERNATIONAL LLC,01-East,733211050,479.00,55.91,423.09\nSNYDER CONTRACTORS,01-East,770220000,554.40,56.87,497.53\nSNYDER CONTRACTORS,01-East,770210000,493.00,60.63,432.37\nSNYDER CONTRACTORS,01-East,733423600,279.36,35.90,243.46\nSNYDER CONTRACTORS,01-East,777601000,271.65,72.02,199.63\nSOLAR PRODUCTS INC,01-East,734351203,106800.00,16882.55,89917.45\nSOLCOAT,01-East,734030000,12388.50,2729.61,9658.89\nSOLDAR DE CHIHUAHUA S.A.,01-East,777590000,409.92,122.86,287.06\nSOUND TECHNOLOGIES,01-East,764521000,4662.00,1114.77,3547.23\nSOUND TECHNOLOGIES,01-East,764601000,4662.00,2210.58,2451.42\nSOUTH JERSEY BOILER,01-East,734414100,776.50,89.66,686.84\nSOUTH JERSEY BOILER,01-East,734211200,229.68,22.84,206.84\nSOUTH JERSEY BOILER,01-East,734311100,190.74,16.85,173.89\nSOUTHLANDS ENGINEERING,01-East,792650000,533.00,94.66,438.34\nSOUTHWEST RESEARCH INST.,01-East,768121000,503.00,28.76,474.24\nSOUTHWEST RESEARCH,01-East,764821000,3256.00,124.05,3131.95\nSOUTHWEST RESEARCH,01-East,768121000,1257.50,71.92,1185.58\nSOUTHWEST RESEARCH,01-East,764521000,960.00,39.89,920.11\nSP &I- AUBURN MA,01-East,735602000,58200.00,23024.80,35175.20\nSP &I- AUBURN MA,01-East,735602110,35580.00,14483.84,21096.16\nSP &I- AUBURN MA,01-East,764602000,3744.00,943.60,2800.40\nSP &I- AUBURN MA,01-East,764561000,2496.00,709.66,1786.34\nSP &I- AUBURN MA,01-East,764601000,2080.00,531.44,1548.56\nSP &I- AUBURN MA,01-East,764822000,1128.00,246.10,881.90\nSP &I- AUBURN MA,01-East,764521000,1030.00,265.94,764.06\nSP &I- AUBURN MA,01-East,764522000,618.00,160.32,457.68\n\"SPACE EXPLORATION TECH,\",01-East,777998700,18150.00,15425.00,2725.00\nSPAR INC,01-East,754065000,2138.40,1282.86,855.54\nSPAR INC,01-East,764901000,1640.00,645.76,994.24\nSPAR INC,01-East,754085000,1188.00,712.53,475.47\nSPECIAL METALS INC.,01-East,733525600,43867.75,7285.27,36582.48\nSPECIAL METALS INC.,01-East,733545810,6435.00,2382.37,4052.63\nSPECIAL METALS INC.,01-East,733484800,3187.20,739.63,2447.57\nSPECIAL METALS INC.,01-East,733525500,1682.00,281.58,1400.42\nSPECIAL METALS INC.,01-East,718501000,1630.80,270.75,1360.05\nSPECIAL METALS INC.,01-East,725441100,795.40,134.51,660.89\nSPECIAL METALS INC.,01-East,764861000,320.50,12.67,307.83\nSPECIAL METALS INC.,01-East,764341000,214.75,11.99,202.76\nSPECIALTY FOUNDRY PRODUCT,01-East,766162000,3429.00,1278.06,2150.94\nSPECIALTY FOUNDRY PRODUCT,01-East,766121000,2808.00,997.54,1810.46\nSPECIALTY OPTICAL SYSTEMS,01-East,740402402,2097.50,411.01,1686.49\nSPECIALTY PROD & INSUL.,01-East,735602000,105650.00,32862.64,72787.36\nSPECIALTY PROD & INSUL.,01-East,735750000,1485.00,618.03,866.97\nSPECIALTY PROD & INSUL.,01-East,764605000,254.00,103.51,150.49\nSPECIALTY PRODUCTS&INSUL,01-East,735602110,123360.00,47441.91,75918.09\nSPECIALTY PRODUCTS&INSUL,01-East,735602000,64627.00,24117.36,40509.64\nSPECIALTY PRODUCTS&INSUL,01-East,735752000,31690.00,11747.46,19942.54\nSPECIALTY PRODUCTS&INSUL,01-East,735750000,12400.00,4653.42,7746.58\nSPECIALTY PRODUCTS&INSUL,01-East,764601000,6168.00,1532.50,4635.50\nSPECIALTY PRODUCTS&INSUL,01-East,735655000,3568.00,479.94,3088.06\nSPECIALTY PRODUCTS&INSUL,01-East,764602000,3024.00,741.90,2282.10\nSPECIALTY PRODUCTS&INSUL,01-East,764521000,2504.00,637.84,1866.16\nSPECIALTY PRODUCTS&INSUL,01-East,735862000,2160.00,1233.66,926.34\nSPECIALTY PRODUCTS&INSUL,01-East,764522000,1926.00,480.99,1445.01\nSPECIALTY PRODUCTS&INSUL,01-East,764762000,920.00,315.70,604.30\nSPECIALTY PRODUCTS,01-East,735602000,1906937.00,706984.83,1199952.17\nSPECIALTY PRODUCTS,01-East,733566300,258854.40,48955.50,209898.90\nSPECIALTY PRODUCTS,01-East,735602110,183056.00,73742.10,109313.90\nSPECIALTY PRODUCTS,01-East,768222000,86926.00,20633.48,66292.52\nSPECIALTY PRODUCTS,01-East,764602000,81655.00,35405.35,46249.65\nSPECIALTY PRODUCTS,01-East,735752000,48388.00,18143.10,30244.90\nSPECIALTY PRODUCTS,01-East,735750000,37271.00,14467.33,22803.67\nSPECIALTY PRODUCTS,01-East,764901000,35307.50,15724.18,19583.32\nSPECIALTY PRODUCTS,01-East,764522000,25061.00,10313.41,14747.59\nSPECIALTY PRODUCTS,01-East,768141020,17620.00,6472.93,11147.07\nSPECIALTY PRODUCTS,01-East,766161000,12932.00,9026.67,3905.33\nSPECIALTY PRODUCTS,01-East,768212000,12396.00,3897.21,8498.79\nSPECIALTY PRODUCTS,01-East,764601000,9808.00,3487.08,6320.92\nSPECIALTY PRODUCTS,01-East,764521000,6765.00,1993.51,4771.49\nSPECIALTY PRODUCTS,01-East,735602010,6009.00,4013.36,1995.64\nSPECIALTY PRODUCTS,01-East,764482000,5780.00,2119.50,3660.50\nSPECIALTY PRODUCTS,01-East,764902000,5560.00,1277.08,4282.92\nSPECIALTY PRODUCTS,01-East,764821000,4794.00,1044.35,3749.65\nSPECIALTY PRODUCTS,01-East,764822000,3948.00,861.38,3086.62\nSPECIALTY PRODUCTS,01-East,735655000,3596.80,485.37,3111.43\nSPECIALTY PRODUCTS,01-East,764521061,3424.00,2044.80,1379.20\nSPECIALTY PRODUCTS,01-East,735657000,3168.00,768.43,2399.57\nSPECIALTY PRODUCTS,01-East,768142000,3000.00,801.87,2198.13\nSPECIALTY PRODUCTS,01-East,766162000,2522.00,1943.34,578.66\nSPECIALTY PRODUCTS,01-East,733566200,2298.24,432.14,1866.10\nSPECIALTY PRODUCTS,01-East,764561000,1560.00,443.54,1116.46\nSPECIALTY PRODUCTS,01-East,733511000,331.88,66.56,265.32\nSPECIALTY PRODUCTS,01-East,764761000,260.00,61.42,198.58\nSPIRIT AEROSYSTEMS INC.,01-East,734211200,229.68,22.84,206.84\nSPX FLOW TECHNOLOGY,01-East,720455000,1569.60,142.31,1427.29\nST GEORGE STEEL CO,01-East,777601000,677.52,117.59,559.93\nST. GEORGE STEEL,01-East,768221000,7410.00,3660.00,3750.00\nST. GEORGE STEEL,01-East,768241000,6550.00,3425.78,3124.22\nSTANDARD INDUSTRIAL SUPPL,01-East,770722000,1409.00,42.88,1366.12\nSTATE INSULATION CORP,01-East,764901000,32690.00,10977.87,21712.13\nSTATE OF DELAWARE,01-East,768144200,2088.00,105.28,1982.72\nSTATE OF DELAWARE,01-East,771810000,207.50,48.80,158.70\nSTEBBINS ENGINEERING & MF,01-East,733525600,1875.00,213.14,1661.86\nSTEBBINS ENGINEERING & MF,01-East,733525500,1390.00,130.06,1259.94\nSTEBBINS ENGINEERING & MF,01-East,733444100,1328.00,300.17,1027.83\nSTEBBINS ENGINEERING & MF,01-East,733464500,626.00,65.05,560.95\nSTUDIO BEL VETRO,01-East,739370000,379.52,138.24,241.28\nSTULLER SETTINGS INC.,01-East,770200000,666.00,42.41,623.59\nSUMMIT CONTROLS INC.,01-East,734990000,4152.00,547.75,3604.25\nSUNDANCE DIE CUT LLC,01-East,733151500,472.00,54.11,417.89\nSUNSHINE GLASS WORKS,01-East,733464500,1207.00,121.09,1085.91\nSUNSHINE GLASS WORKS,01-East,739561000,492.48,86.19,406.29\nSUPER CONDUCTOR MTLS.,01-East,764821000,2631.00,93.06,2537.94\nSURFACE COMBUSTION INC,01-East,770540000,1293.08,89.80,1203.28\nSW ANDERSON SALES,01-East,735602000,737747.25,268120.39,469626.86\nSW ANDERSON SALES,01-East,735602110,14624.00,5684.26,8939.74\nSW ANDERSON SALES,01-East,768121000,3142.40,842.50,2299.90\nSW ANDERSON SALES,01-East,735752000,2975.00,1095.48,1879.52\nSWANSON DISTRIBUTING,01-East,733464600,924.00,101.25,822.75\nTASE/HB-MEDTEK,01-East,737902100,667.00,101.30,565.70\nTAYLOR DEVICES,01-East,764521000,320.00,13.30,306.70\n\"TAYLOR-SEIDENBACH, INC.\",01-East,735602110,38668.40,10122.46,28545.94\n\"TAYLOR-SEIDENBACH, INC.\",01-East,768241600,22410.00,7313.81,15096.19\n\"TAYLOR-SEIDENBACH, INC.\",01-East,735602000,19547.00,4995.21,14551.79\n\"TAYLOR-SEIDENBACH, INC.\",01-East,768241500,14960.00,4869.65,10090.35\n\"TAYLOR-SEIDENBACH, INC.\",01-East,735750000,1896.00,508.42,1387.58\n\"TAYLOR-SEIDENBACH, INC.\",01-East,768243600,1620.00,633.25,986.75\n\"TAYLOR-SEIDENBACH, INC.\",01-East,735752000,948.00,257.76,690.24\nTERRA INTERNATIONAL,01-East,734311100,404.36,35.30,369.06\nTEXAS STREET,01-East,734990000,3321.60,438.20,2883.40\nTEXCAST INC,01-East,734311100,3624.06,320.18,3303.88\nTEXCAST INC,01-East,733141200,1016.00,126.95,889.05\nTEXCO,01-East,718501000,5525.00,680.30,4844.70\nTEXCO,01-East,774901000,427.20,48.62,378.58\nTEXCO,01-East,774631000,412.80,46.56,366.24\nTEXTRON FINANCE SHARED,01-East,734351200,917.85,69.73,848.12\nTHE BELL GROUP,01-East,734311100,1525.92,134.81,1391.11\nTHE BELL GROUP,01-East,727000140,888.00,63.35,824.65\nTHE BELL GROUP,01-East,733721000,572.00,97.79,474.21\nTHE BERND GROUP INC.,01-East,764481000,1038.00,50.82,987.18\nTHE BERND GROUP INC.,01-East,733444100,996.00,225.13,770.87\nTHE BERND GROUP INC.,01-East,764461006,388.00,27.25,360.75\nTHE CLAREMONT SALES CORP.,01-East,739561200,10783.36,2223.42,8559.94\nTHE CLAREMONT SALES CORP.,01-East,764482000,1728.00,255.71,1472.29\nTHE CLAREMONT SALES CORP.,01-East,734990000,787.44,110.08,677.36\nTHE CLAREMONT SALES CORP.,01-East,770260000,680.40,67.63,612.77\nTHE CLAREMONT SALES CORP.,01-East,739741100,350.08,109.56,240.52\nTHE MINE SUPPLY CO,01-East,733721000,1458.00,285.33,1172.67\n\"THE N.P. GROUP, INC.\",01-East,779987650,4827.60,1183.63,3643.97\n\"THE NP GROUP, INC.\",01-East,779987650,18576.00,4142.56,14433.44\n\"THE NP GROUP, INC.\",01-East,724003000,383.50,81.82,301.68\nTHE R B BIRGE CO,01-East,733525600,752.50,71.05,681.45\n\"THE SCHAEFER GROUP, INC.\",01-East,777902700,38252.34,20221.83,18030.51\n\"THE SCHAEFER GROUP, INC.\",01-East,733211000,502.60,106.40,396.20\n\"THE SCHAEFER GROUP, INC.\",01-East,739561200,278.91,76.67,202.24\nTHE TRANE CO,01-East,733525500,717.50,65.03,652.47\nTHERMAL NORTHWEST,01-East,724311200,366.50,94.19,272.31\nTHERMAL SOLUTIONS PRODUCT,01-East,726000240,9204.00,908.61,8295.39\nTHERMAL STRUCTURES,01-East,732820000,568241.79,44616.14,523625.65\nTHERMAL STRUCTURES,01-East,732830000,149046.52,13131.66,135914.86\nTHERMAL STRUCTURES,01-East,791104295,136666.97,38406.72,98260.25\nTHERMAL STRUCTURES,01-East,732771000,51728.54,3530.39,48198.15\nTHERMAL STRUCTURES,01-East,724101400,38064.00,8884.38,29179.62\nTHERMAL STRUCTURES,01-East,724261400,33455.40,6816.25,26639.15\nTHERMAL STRUCTURES,01-East,732905000,29559.59,2196.99,27362.60\nTHERMAL STRUCTURES,01-East,724281400,27616.20,7916.28,19699.92\nTHERMAL STRUCTURES,01-East,791106479,26849.05,6343.90,20505.15\nTHERMAL STRUCTURES,01-East,733525600,23351.50,3178.11,20173.39\nTHERMAL STRUCTURES,01-East,733444300,11139.60,2083.23,9056.37\nTHERMAL STRUCTURES,01-East,724121400,10462.00,3598.11,6863.89\nTHERMAL STRUCTURES,01-East,724401150,8048.40,1212.97,6835.43\nTHERMAL STRUCTURES,01-East,779989967,5431.47,1213.51,4217.96\nTHERMAL STRUCTURES,01-East,791104480,4452.72,1110.02,3342.70\nTHERMAL STRUCTURES,01-East,791104880,975.11,180.59,794.52\nTHERMAL STRUCTURES,01-East,733383000,910.80,163.93,746.87\nTHERMAL STRUCTURES,01-East,733141300,775.60,155.94,619.66\nTHERMAL STRUCTURES,01-East,733141200,756.80,119.94,636.86\nTHERMAL STRUCTURES,01-East,734611100,161.02,27.18,133.84\nTHERMO CONTROL HEATING,01-East,764901000,2707.50,481.73,2225.77\nTHERMO FISHER ENVIR.,01-East,724281200,2346.00,332.78,2013.22\nTHERMO FISHER SCIENTIFIC,01-East,738361000,626.04,308.88,317.16\nTHERMOMETRICS CORPORATION,01-East,766081010,2202.00,72.74,2129.26\nTHERMOMETRICS CORPORATION,01-East,727000140,265.60,16.71,248.89\nTHERMTREND INC.,01-East,733403200,1133.00,99.17,1033.83\nTHERMTREND INC.,01-East,770810000,845.00,17.19,827.81\nTHORPE PRODUCTS,01-East,735602000,106490.00,33828.15,72661.85\nTHORPE PRODUCTS,01-East,735862000,9100.00,3598.17,5501.83\nTHORPE PRODUCTS,01-East,768142074,6451.50,2558.12,3893.38\nTHORPE PRODUCTS,01-East,768241100,6200.00,2277.23,3922.77\nTHORPE PRODUCTS,01-East,735602110,3740.00,1071.04,2668.96\nTHORPE PRODUCTS,01-East,735752000,2600.00,889.19,1710.81\nTHORPE PRODUCTS,01-East,735750000,2180.00,755.37,1424.63\nTHORPE TECHNOLOGIES INC.,01-East,770340000,3075.75,305.15,2770.60\nTHORPE TECHNOLOGIES INC.,01-East,770330000,2130.00,202.93,1927.07\nTHORPE TECHNOLOGIES INC.,01-East,770410000,1585.00,102.99,1482.01\nTHORPE TECHNOLOGIES INC.,01-East,770140000,1177.56,76.74,1100.82\nTHORPE TECHNOLOGIES INC.,01-East,770520000,906.00,48.32,857.68\nTIGHITCO LATINOAMERICA,01-East,733525600,5252.50,529.48,4723.02\nTIGHITCO LATINOAMERICA,01-East,724281200,4195.00,1384.90,2810.10\nTIGHITCO LATINOAMERICA,01-East,724101400,1134.00,229.59,904.41\nTIGHITCO LATINOAMERICA,01-East,724281410,894.00,339.83,554.17\nTIGHITCO LATINOAMERICA,01-East,733444300,799.20,123.75,675.45\nTIGHITCO LATINOAMERICA,01-East,733423800,310.40,38.28,272.12\nTIMSCO INTERNATIONAL,01-East,734830000,3420.00,454.10,2965.90\n\"TK HOLDINGS, INC.\",01-East,733030800,255496.50,114509.22,140987.28\nTOBIASON STUDIO,01-East,739561100,552.72,93.32,459.40\nTOLEDO ENGINEERING CO INC,01-East,777750076,8697.60,4632.00,4065.60\nTOLEDO ENGINEERING CO INC,01-East,795763000,6606.60,1950.48,4656.12\nTOLEDO ENGINEERING CO INC,01-East,795762000,4340.40,1212.38,3128.02\nTOLEDO ENGINEERING CO INC,01-East,777763000,2246.64,1797.84,448.80\nTOLEDO ENGINEERING CO INC,01-East,793260000,728.00,159.84,568.16\nTOLEDO ENGINEERING CO INC,01-East,793260074,152.00,81.66,70.34\nTORTOLITA CREATIONS,01-East,733151500,958.00,54.11,903.89\nTOWANDA METADYNE,01-East,764521000,1306.00,53.13,1252.87\nTRACHTE BUILDING SYSTEMS,01-East,764601000,1498.80,315.80,1183.00\nTRANSCO,01-East,764703110,10492.44,3665.52,6826.92\nTRANSCO,01-East,764643052,4490.64,1501.95,2988.69\nTREIBACHER SCHLEIFMITTEL,01-East,764821000,430.00,15.36,414.64\nTRI-CITY INSULATION DIST.,01-East,735602000,113975.00,41332.68,72642.32\nTRI-CITY INSULATION DIST.,01-East,735750000,1700.00,661.12,1038.88\nTRI-CITY INSULATION DIST.,01-East,764601000,592.00,105.27,486.73\nTRIDENT DISTRIBUTION,01-East,735602000,15960.00,4832.60,11127.40\nTRIDENT DISTRIBUTION,01-East,735602110,13680.00,4284.15,9395.85\nTRIDENT DISTRIBUTION,01-East,735752000,3400.00,1367.98,2032.02\nTRINITY INDUSTRIES INC,01-East,764703110,2541045.92,854580.05,1686465.87\nTRINITY INDUSTRIES INC,01-East,764633510,357725.36,95000.01,262725.35\nTRINITY INDUSTRIES INC,01-East,764706110,22374.24,7479.56,14894.68\nTRIPLEX INC,01-East,770830000,10406.00,339.14,10066.86\nTRIPLEX INC,01-East,771132000,2266.00,79.02,2186.98\nTRIUMPH INSULATION,01-East,733648000,29800976.28,18922952.69,10878023.59\nTRIUMPH INSULATION,01-East,733649000,5645789.16,3636906.92,2008882.24\nTRIUMPH INSULATION,01-East,733640000,145765.80,61147.00,84618.80\nTRULITE GLASS & ALUMINUM,01-East,739741100,12854.40,5049.60,7804.80\nTRULITE GLASS & ALUMINUM,01-East,734651200,584.00,73.50,510.50\nTTM TECHNOLOGIES INC.,01-East,733525500,1505.00,140.78,1364.22\nTURPEN & ASSOCIATES,01-East,770540000,3629.50,184.78,3444.72\nTWIN CC SUPPLY,01-East,777601000,409.92,58.80,351.12\nU S ARTIST SERVICE,01-East,777601000,386.72,72.02,314.70\n\"UNICAL AVIATION, INC.\",01-East,732820000,3494.40,147.02,3347.38\nUNICIRCUIT INC.,01-East,733464500,4828.00,484.36,4343.64\nUNICIRCUIT INC.,01-East,733525500,2190.00,211.17,1978.83\nUNILUX ADVANCED,01-East,764341000,45277.50,24555.05,20722.45\nUNILUX ADVANCED,01-East,764261000,20136.00,10782.65,9353.35\nUNITED CHIMNEY,01-East,734611100,178.72,26.96,151.76\nUNITED MATERIALS INT'L,01-East,727000140,780.00,217.28,562.72\nUNITED MECHANICAL & METAL,01-East,774191000,810.00,288.86,521.14\nUNITED MECHANICAL & METAL,01-East,774401000,726.00,220.42,505.58\nUNITED MECHANICAL & METAL,01-East,774141000,265.00,77.28,187.72\nUNIVERSITY OF ALABAMA,01-East,764221000,223.00,16.92,206.08\nUNIVERSITY OF ARKANSAS,01-East,764301000,167.50,9.28,158.22\nUNIVERSITY OF CONNECTICUT,01-East,734311100,381.48,33.70,347.78\nUPSTATE REFRACTORY SERVIC,01-East,764821000,100462.50,39653.64,60808.86\nUPSTATE REFRACTORY SERVIC,01-East,766121000,65370.40,19817.36,45553.04\nUPSTATE REFRACTORY SERVIC,01-East,764901000,36433.50,14832.31,21601.19\nUPSTATE REFRACTORY SERVIC,01-East,766126500,34384.00,7774.34,26609.66\nUPSTATE REFRACTORY SERVIC,01-East,779980240,34339.86,23057.79,11282.07\nUPSTATE REFRACTORY SERVIC,01-East,779980223,31171.14,21368.85,9802.29\nUPSTATE REFRACTORY SERVIC,01-East,766162000,25765.05,9797.80,15967.25\nUPSTATE REFRACTORY SERVIC,01-East,764821073,18000.00,14580.00,3420.00\nUPSTATE REFRACTORY SERVIC,01-East,781670000,16953.03,8281.68,8671.35\nUPSTATE REFRACTORY SERVIC,01-East,770560000,16165.00,2678.28,13486.72\nUPSTATE REFRACTORY SERVIC,01-East,777601000,15275.96,5387.01,9888.95\nUPSTATE REFRACTORY SERVIC,01-East,780290000,13764.59,5681.14,8083.45\nUPSTATE REFRACTORY SERVIC,01-East,764040000,13180.32,2743.44,10436.88\nUPSTATE REFRACTORY SERVIC,01-East,737321000,12520.80,10520.64,2000.16\nUPSTATE REFRACTORY SERVIC,01-East,739681100,11068.17,5420.70,5647.47\nUPSTATE REFRACTORY SERVIC,01-East,779959551,10623.00,3469.92,7153.08\nUPSTATE REFRACTORY SERVIC,01-East,764781040,10610.10,4147.41,6462.69\nUPSTATE REFRACTORY SERVIC,01-East,782070000,10357.20,5412.89,4944.31\nUPSTATE REFRACTORY SERVIC,01-East,733525600,9625.00,1501.94,8123.06\nUPSTATE REFRACTORY SERVIC,01-East,770530000,9145.25,1455.66,7689.59\nUPSTATE REFRACTORY SERVIC,01-East,779957701,8352.00,3949.47,4402.53\nUPSTATE REFRACTORY SERVIC,01-East,755107000,8304.08,2404.09,5899.99\nUPSTATE REFRACTORY SERVIC,01-East,733525500,8218.00,1262.38,6955.62\nUPSTATE REFRACTORY SERVIC,01-East,779951021,8200.50,4161.51,4038.99\nUPSTATE REFRACTORY SERVIC,01-East,764901073,7080.00,5832.00,1248.00\nUPSTATE REFRACTORY SERVIC,01-East,779957700,6733.80,3585.66,3148.14\nUPSTATE REFRACTORY SERVIC,01-East,733221200,6462.24,1819.95,4642.29\nUPSTATE REFRACTORY SERVIC,01-East,754495000,5897.20,2875.45,3021.75\nUPSTATE REFRACTORY SERVIC,01-East,777904301,5702.61,2584.30,3118.31\nUPSTATE REFRACTORY SERVIC,01-East,764822000,5580.00,1928.47,3651.53\nUPSTATE REFRACTORY SERVIC,01-East,733151500,5412.40,948.26,4464.14\nUPSTATE REFRACTORY SERVIC,01-East,764861000,5267.25,2390.71,2876.54\nUPSTATE REFRACTORY SERVIC,01-East,737902100,5142.80,1488.99,3653.81\nUPSTATE REFRACTORY SERVIC,01-East,733211000,4789.80,1088.20,3701.60\nUPSTATE REFRACTORY SERVIC,01-East,737912200,4772.50,1084.03,3688.47\nUPSTATE REFRACTORY SERVIC,01-East,781630000,4176.00,2233.14,1942.86\nUPSTATE REFRACTORY SERVIC,01-East,754286000,3886.85,1669.09,2217.76\nUPSTATE REFRACTORY SERVIC,01-East,779951023,3727.50,2131.95,1595.55\nUPSTATE REFRACTORY SERVIC,01-East,777903700,3692.82,1512.27,2180.55\nUPSTATE REFRACTORY SERVIC,01-East,718501000,3496.50,948.31,2548.19\nUPSTATE REFRACTORY SERVIC,01-East,764601000,3342.55,1074.13,2268.42\nUPSTATE REFRACTORY SERVIC,01-East,718500000,3196.80,858.84,2337.96\nUPSTATE REFRACTORY SERVIC,01-East,754467000,2668.08,1502.16,1165.92\nUPSTATE REFRACTORY SERVIC,01-East,764902000,2655.00,961.17,1693.83\nUPSTATE REFRACTORY SERVIC,01-East,764781000,2572.50,1009.41,1563.09\nUPSTATE REFRACTORY SERVIC,01-East,764342110,2554.50,1582.43,972.07\nUPSTATE REFRACTORY SERVIC,01-East,781590000,2523.10,1182.42,1340.68\nUPSTATE REFRACTORY SERVIC,01-East,739561200,2135.04,867.98,1267.06\nUPSTATE REFRACTORY SERVIC,01-East,739561100,2049.76,599.74,1450.02\nUPSTATE REFRACTORY SERVIC,01-East,780370000,1958.88,776.12,1182.76\nUPSTATE REFRACTORY SERVIC,01-East,733211050,1865.30,441.07,1424.23\nUPSTATE REFRACTORY SERVIC,01-East,764521000,1786.15,611.37,1174.78\nUPSTATE REFRACTORY SERVIC,01-East,764906000,1770.00,654.37,1115.63\nUPSTATE REFRACTORY SERVIC,01-East,738361000,1366.84,657.88,708.96\nUPSTATE REFRACTORY SERVIC,01-East,733510000,1239.00,283.45,955.55\nUPSTATE REFRACTORY SERVIC,01-East,734990000,1149.60,273.88,875.72\nUPSTATE REFRACTORY SERVIC,01-East,782030000,1130.40,544.53,585.87\nUPSTATE REFRACTORY SERVIC,01-East,767990000,1035.08,442.34,592.74\nUPSTATE REFRACTORY SERVIC,01-East,764601500,969.00,2036.62,-1067.62\nUPSTATE REFRACTORY SERVIC,01-East,755007000,950.76,349.89,600.87\nUPSTATE REFRACTORY SERVIC,01-East,734900000,798.04,530.64,267.40\nUPSTATE REFRACTORY SERVIC,01-East,739641100,772.20,412.29,359.91\nUPSTATE REFRACTORY SERVIC,01-East,734611100,654.89,135.90,518.99\nUPSTATE REFRACTORY SERVIC,01-East,764826200,600.00,298.76,301.24\nUPSTATE REFRACTORY SERVIC,01-East,742520110,585.50,165.28,420.22\nUPSTATE REFRACTORY SERVIC,01-East,770260000,544.50,67.63,476.87\nUPSTATE REFRACTORY SERVIC,01-East,727000140,520.00,79.18,440.82\nUPSTATE REFRACTORY SERVIC,01-East,734830000,492.00,89.29,402.71\nUPSTATE REFRACTORY SERVIC,01-East,733221400,416.16,128.48,287.68\nUPSTATE REFRACTORY SERVIC,01-East,764482000,412.20,156.62,255.58\nUPSTATE REFRACTORY SERVIC,01-East,764827061,412.00,178.43,233.57\nUPSTATE REFRACTORY SERVIC,01-East,777901300,374.78,147.04,227.74\nUPSTATE REFRACTORY SERVIC,01-East,777650000,307.90,142.44,165.46\nUPSTATE REFRACTORY SERVIC,01-East,734810000,272.20,217.84,54.36\nUPSTATE REFRACTORY SERVIC,01-East,733741000,232.40,119.06,113.34\nUPSTATE REFRACTORY SERVIC,01-East,764825000,198.00,64.56,133.44\nUPSTATE REFRACTORY SERVIC,01-East,733725500,168.00,73.67,94.33\nUPSTATE REFRACTORY SERVIC,01-East,764341000,149.53,58.39,91.14\nUPSTATE REFRACTORY SERVIC,01-East,764301000,136.65,55.67,80.98\nUPSTATE REFRACTORY SERVIC,01-East,764762000,135.20,60.58,74.62\nUPSTATE REFRACTORY SERVIC,01-East,764561000,129.50,42.00,87.50\nUPSTATE REFRACTORY SERVIC,01-East,779957258,75.60,47.96,27.64\nUPSTATE REFRACTORY SERVIC,01-East,764761000,70.40,31.86,38.54\nUPSTATE REFRACTORY SERVIC,01-East,764261000,58.20,22.90,35.30\nUS JOINER LLC,01-East,768127000,1000.00,293.56,706.44\nUS JOINER LLC,01-East,768111200,580.00,157.21,422.79\n\"US TOOL GRINDING, INC.\",01-East,739781100,1232.16,491.52,740.64\nUSDA FOREST SERVICE,01-East,738521000,556.48,298.56,257.92\nUTLX CARROTANQUES,01-East,764643052,359.55,166.88,192.67\nUTLX MANUFACTURING INC.,01-East,764643054,727058.28,339146.10,387912.18\nUTLX MANUFACTURING INC.,01-East,764643052,424628.55,197089.60,227538.95\nUTLX MANUFACTURING INC.,01-East,764752120,29726.40,7388.55,22337.85\nUTLX MANUFACTURING,01-East,764643054,851928.72,397436.56,454492.16\nUTLX MANUFACTURING,01-East,764643052,429302.70,199259.09,230043.61\nUTLX MANUFACTURING,01-East,764901000,2211.25,80.07,2131.18\nV-J ELECTRONIC ASSEMBLIES,01-East,740103000,792.96,115.03,677.93\nV-J ELECTRONIC ASSEMBLIES,01-East,740123000,670.32,121.37,548.95\nVA MEDICAL CENTER,01-East,770222000,1022.25,65.43,956.82\nVA MEDICAL CENTER,01-East,770520000,958.50,41.85,916.65\nVALERO OIL,01-East,734830000,6840.00,908.20,5931.80\n\"VAN HORN, METZ & CO\",01-East,779987651,13392.50,2310.46,11082.04\n\"VAN HORN, METZ & CO\",01-East,720219100,1208.55,45.84,1162.71\nVASTEX INTERNATIONAL,01-East,733725500,300.00,73.67,226.33\nVESUVIUS USA,01-East,764520500,20000.00,9265.58,10734.42\nVESUVIUS USA,01-East,764521000,12000.00,6382.59,5617.41\nVISHAY DALE ELECTRONICS,01-East,734030000,17464.50,3847.08,13617.42\nVISUAL COMMUNICATION,01-East,777587000,4791.26,192.16,4599.10\nVITRO AUTOMOTRIZ SA DE CV,01-East,733525600,1470.00,136.54,1333.46\nVITRO VIDRIO Y CRISTAL,01-East,779571000,22545.15,5551.20,16993.95\nW. D. EQUIPMENT CO.,01-East,738542000,13711.68,5081.61,8630.07\nW. D. EQUIPMENT CO.,01-East,764526600,3960.00,1827.94,2132.06\nW. D. EQUIPMENT CO.,01-East,739781100,2115.36,856.89,1258.47\nW. D. EQUIPMENT CO.,01-East,764526500,1832.60,645.21,1187.39\nW. D. EQUIPMENT CO.,01-East,764481000,1274.00,351.77,922.23\nW. D. EQUIPMENT CO.,01-East,764521000,1225.40,292.54,932.86\nW. D. EQUIPMENT CO.,01-East,764526520,965.25,346.76,618.49\nW. D. EQUIPMENT CO.,01-East,766081010,837.00,109.11,727.89\nW. D. EQUIPMENT CO.,01-East,739681100,793.26,361.38,431.88\nW. D. EQUIPMENT CO.,01-East,738561000,708.48,248.27,460.21\nW. D. EQUIPMENT CO.,01-East,774631000,486.00,52.77,433.23\nW. D. EQUIPMENT CO.,01-East,764523020,457.60,111.09,346.51\nW. D. EQUIPMENT CO.,01-East,774901000,453.00,59.34,393.66\nW. D. EQUIPMENT CO.,01-East,737912200,444.55,86.72,357.83\nW. D. EQUIPMENT CO.,01-East,739561000,292.82,86.19,206.63\nW. D. EQUIPMENT CO.,01-East,739661100,265.68,150.50,115.18\nW. D. EQUIPMENT CO.,01-East,764526610,114.40,52.48,61.92\nWAHL REFRACTORIES,01-East,733510000,591.00,212.59,378.41\nWAHOO,01-East,720252303,1150.50,26.51,1123.99\nWALL COLMONOY CORP,01-East,733510000,2715.00,362.96,2352.04\nWALTER MORENO,01-East,768144200,271.50,13.16,258.34\nWARDELL PUBLICATIONS INC,01-East,733671000,3071.04,351.87,2719.17\nWASHINGTON MILLS,01-East,724281200,4091.00,576.48,3514.52\nWASHINGTON MILLS,01-East,733464500,611.00,62.05,548.95\nWASHINGTON MILLS,01-East,764781000,454.00,20.45,433.55\nWASHINGTON MILLS,01-East,764901000,425.25,16.14,409.11\nWASHINGTON UNIVERISTY IN,01-East,737912200,326.50,45.47,281.03\nWATT FUEL CELL,01-East,732820000,3494.40,147.02,3347.38\n\"WELCO ELECTRONICS, INC.\",01-East,770720000,3528.00,108.47,3419.53\n\"WELCO ELECTRONICS, INC.\",01-East,771500000,2911.50,165.39,2746.11\n\"WELCO ELECTRONICS, INC.\",01-East,734890000,2365.54,397.92,1967.62\n\"WELCO ELECTRONICS, INC.\",01-East,764301000,1647.00,114.40,1532.60\nWELSH RESTAURANT EQUIP.,01-East,735602000,4852.50,1158.30,3694.20\nWENDT CORPORATION,01-East,739451000,428.48,111.56,316.92\nWESCO DISTRIBUTION INC.,01-East,739641100,463.32,137.43,325.89\nWESCO DISTRIBUTION INC.,01-East,777601000,386.42,72.02,314.40\nWESCO DISTRIBUTION INC,01-East,770210000,440.00,30.32,409.68\nWEST FRASER MILLS,01-East,770220000,15831.00,915.60,14915.40\nWEST FRASER MILLS,01-East,770222000,1080.25,68.04,1012.21\nWEST METAL WORKS,01-East,766162000,1822.00,51.84,1770.16\nWESTERN INDUSTRIAL CERAMI,01-East,764521300,1198557.20,613886.53,584670.67\nWESTERN INDUSTRIAL CERAMI,01-East,726000062,333453.00,161980.73,171472.27\nWESTERN INDUSTRIAL CERAMI,01-East,768221120,275946.04,165728.29,110217.75\nWESTERN INDUSTRIAL CERAMI,01-East,764901005,262352.00,147579.22,114772.78\nWESTERN INDUSTRIAL CERAMI,01-East,733571500,249551.25,195969.57,53581.68\nWESTERN INDUSTRIAL CERAMI,01-East,766121000,215670.00,99453.46,116216.54\nWESTERN INDUSTRIAL CERAMI,01-East,764821000,184352.00,76468.68,107883.32\nWESTERN INDUSTRIAL CERAMI,01-East,768251220,128644.92,72452.40,56192.52\nWESTERN INDUSTRIAL CERAMI,01-East,768222000,119318.00,41060.42,78257.58\nWESTERN INDUSTRIAL CERAMI,01-East,764901000,117740.00,63552.63,54187.37\nWESTERN INDUSTRIAL CERAMI,01-East,768111120,102718.53,64986.48,37732.05\nWESTERN INDUSTRIAL CERAMI,01-East,764902000,101480.50,54034.22,47446.28\nWESTERN INDUSTRIAL CERAMI,01-East,726020039,100536.75,67551.51,32985.24\nWESTERN INDUSTRIAL CERAMI,01-East,768221122,98224.00,51040.90,47183.10\nWESTERN INDUSTRIAL CERAMI,01-East,734311100,94782.29,20601.96,74180.33\nWESTERN INDUSTRIAL CERAMI,01-East,764902051,84972.00,40268.16,44703.84\nWESTERN INDUSTRIAL CERAMI,01-East,733525600,80782.50,22872.66,57909.84\nWESTERN INDUSTRIAL CERAMI,01-East,764521000,77696.00,37849.20,39846.80\nWESTERN INDUSTRIAL CERAMI,01-East,768221110,73853.24,62245.57,11607.67\nWESTERN INDUSTRIAL CERAMI,01-East,764482000,71689.60,29375.46,42314.14\nWESTERN INDUSTRIAL CERAMI,01-East,727000140,66629.52,21043.73,45585.79\nWESTERN INDUSTRIAL CERAMI,01-East,768211000,65860.00,27419.87,38440.13\nWESTERN INDUSTRIAL CERAMI,01-East,768112030,65195.56,42299.81,22895.75\nWESTERN INDUSTRIAL CERAMI,01-East,734173000,61518.60,14959.97,46558.63\nWESTERN INDUSTRIAL CERAMI,01-East,768222530,59909.41,38340.26,21569.15\nWESTERN INDUSTRIAL CERAMI,01-East,763991000,51600.24,32683.76,18916.48\nWESTERN INDUSTRIAL CERAMI,01-East,764522000,50396.00,24257.22,26138.78\nWESTERN INDUSTRIAL CERAMI,01-East,733525550,47906.00,18417.67,29488.33\nWESTERN INDUSTRIAL CERAMI,01-East,734830000,47793.10,9366.91,38426.19\nWESTERN INDUSTRIAL CERAMI,01-East,764481000,46191.60,18985.92,27205.68\nWESTERN INDUSTRIAL CERAMI,01-East,764900005,46176.00,21979.10,24196.90\nWESTERN INDUSTRIAL CERAMI,01-East,739457000,43930.80,34914.07,9016.73\nWESTERN INDUSTRIAL CERAMI,01-East,777601000,42513.97,14658.36,27855.61\nWESTERN INDUSTRIAL CERAMI,01-East,734990000,40372.80,11252.79,29120.01\nWESTERN INDUSTRIAL CERAMI,01-East,737321000,35541.12,14259.28,21281.84\nWESTERN INDUSTRIAL CERAMI,01-East,734211200,29172.39,4162.93,25009.46\nWESTERN INDUSTRIAL CERAMI,01-East,764822000,27328.00,11317.52,16010.48\nWESTERN INDUSTRIAL CERAMI,01-East,764781040,27109.00,11216.76,15892.24\nWESTERN INDUSTRIAL CERAMI,01-East,740361900,25569.60,12288.69,13280.91\nWESTERN INDUSTRIAL CERAMI,01-East,737251000,24447.02,5713.13,18733.89\nWESTERN INDUSTRIAL CERAMI,01-East,737301000,24395.28,12570.56,11824.72\nWESTERN INDUSTRIAL CERAMI,01-East,779951186,24298.56,8906.05,15392.51\nWESTERN INDUSTRIAL CERAMI,01-East,734910000,23742.90,5607.19,18135.71\nWESTERN INDUSTRIAL CERAMI,01-East,733151500,23491.20,5432.97,18058.23\nWESTERN INDUSTRIAL CERAMI,01-East,727000060,21600.00,11913.27,9686.73\nWESTERN INDUSTRIAL CERAMI,01-East,768021000,20998.00,6840.28,14157.72\nWESTERN INDUSTRIAL CERAMI,01-East,764521320,20935.00,10598.80,10336.20\nWESTERN INDUSTRIAL CERAMI,01-East,768222112,20467.00,14061.07,6405.93\nWESTERN INDUSTRIAL CERAMI,01-East,766162000,20280.00,12477.47,7802.53\nWESTERN INDUSTRIAL CERAMI,01-East,768212000,17568.00,7459.28,10108.72\nWESTERN INDUSTRIAL CERAMI,01-East,726000080,16848.00,8178.50,8669.50\nWESTERN INDUSTRIAL CERAMI,01-East,764222010,16632.00,7360.83,9271.17\nWESTERN INDUSTRIAL CERAMI,01-East,739541000,16547.60,3676.05,12871.55\nWESTERN INDUSTRIAL CERAMI,01-East,780730000,16331.29,7017.01,9314.28\nWESTERN INDUSTRIAL CERAMI,01-East,764462210,16158.24,7617.71,8540.53\nWESTERN INDUSTRIAL CERAMI,01-East,733211000,16118.60,8088.99,8029.61\nWESTERN INDUSTRIAL CERAMI,01-East,764821073,15400.00,14580.00,820.00\nWESTERN INDUSTRIAL CERAMI,01-East,733211050,15176.40,7516.61,7659.79\nWESTERN INDUSTRIAL CERAMI,01-East,733484900,14825.60,2633.09,12192.51\nWESTERN INDUSTRIAL CERAMI,01-East,734351200,13897.39,2506.68,11390.71\nWESTERN INDUSTRIAL CERAMI,01-East,733525500,13817.50,3120.09,10697.41\nWESTERN INDUSTRIAL CERAMI,01-East,740402402,13723.00,3477.58,10245.42\nWESTERN INDUSTRIAL CERAMI,01-East,779988827,13144.23,6018.14,7126.09\nWESTERN INDUSTRIAL CERAMI,01-East,738361000,12093.48,9017.70,3075.78\nWESTERN INDUSTRIAL CERAMI,01-East,739641100,12052.51,6780.55,5271.96\nWESTERN INDUSTRIAL CERAMI,01-East,738521000,11274.88,7359.09,3915.79\nWESTERN INDUSTRIAL CERAMI,01-East,734890000,11205.60,3183.42,8022.18\nWESTERN INDUSTRIAL CERAMI,01-East,724281200,10200.00,2319.40,7880.60\nWESTERN INDUSTRIAL CERAMI,01-East,727041000,10108.80,1353.81,8754.99\nWESTERN INDUSTRIAL CERAMI,01-East,715000000,10088.50,5487.52,4600.98\nWESTERN INDUSTRIAL CERAMI,01-East,740382201,9726.50,3579.19,6147.31\nWESTERN INDUSTRIAL CERAMI,01-East,733151600,8943.20,2035.31,6907.89\nWESTERN INDUSTRIAL CERAMI,01-East,727041200,8463.36,1905.36,6558.00\nWESTERN INDUSTRIAL CERAMI,01-East,764461006,7391.60,2580.56,4811.04\nWESTERN INDUSTRIAL CERAMI,01-East,734251300,6790.12,701.68,6088.44\nWESTERN INDUSTRIAL CERAMI,01-East,733586600,6652.80,1082.62,5570.18\nWESTERN INDUSTRIAL CERAMI,01-East,737032000,6193.80,3337.20,2856.60\nWESTERN INDUSTRIAL CERAMI,01-East,732620000,6008.38,1425.25,4583.13\nWESTERN INDUSTRIAL CERAMI,01-East,764521073,5600.00,6450.00,-850.00\nWESTERN INDUSTRIAL CERAMI,01-East,733505200,5150.00,1038.10,4111.90\nWESTERN INDUSTRIAL CERAMI,01-East,764901073,4960.00,5832.00,-872.00\nWESTERN INDUSTRIAL CERAMI,01-East,739741100,4916.16,3029.76,1886.40\nWESTERN INDUSTRIAL CERAMI,01-East,777642000,4739.00,1846.72,2892.28\nWESTERN INDUSTRIAL CERAMI,01-East,735750000,4416.00,1579.42,2836.58\nWESTERN INDUSTRIAL CERAMI,01-East,738561000,4321.68,2196.15,2125.53\nWESTERN INDUSTRIAL CERAMI,01-East,726000240,4248.16,2034.16,2214.00\nWESTERN INDUSTRIAL CERAMI,01-East,730710500,4212.80,501.38,3711.42\nWESTERN INDUSTRIAL CERAMI,01-East,734970000,3959.34,1809.26,2150.08\nWESTERN INDUSTRIAL CERAMI,01-East,768131000,3900.00,1324.74,2575.26\nWESTERN INDUSTRIAL CERAMI,01-East,764821161,3850.00,3720.00,130.00\nWESTERN INDUSTRIAL CERAMI,01-East,792764000,3790.80,1289.34,2501.46\nWESTERN INDUSTRIAL CERAMI,01-East,733383000,3650.24,655.72,2994.52\nWESTERN INDUSTRIAL CERAMI,01-East,768232000,3560.00,1069.79,2490.21\nWESTERN INDUSTRIAL CERAMI,01-East,733505300,3550.00,868.51,2681.49\nWESTERN INDUSTRIAL CERAMI,01-East,733464500,3513.00,885.58,2627.42\nWESTERN INDUSTRIAL CERAMI,01-East,768122000,3300.00,1398.18,1901.82\nWESTERN INDUSTRIAL CERAMI,01-East,740063000,3238.86,949.46,2289.40\nWESTERN INDUSTRIAL CERAMI,01-East,751365000,3223.20,1595.24,1627.96\nWESTERN INDUSTRIAL CERAMI,01-East,764762000,3216.00,947.10,2268.90\nWESTERN INDUSTRIAL CERAMI,01-East,733423600,3109.76,530.96,2578.80\nWESTERN INDUSTRIAL CERAMI,01-East,768221000,3040.00,1039.44,2000.56\nWESTERN INDUSTRIAL CERAMI,01-East,780630000,2946.72,1422.70,1524.02\nWESTERN INDUSTRIAL CERAMI,01-East,764521061,2800.00,3195.00,-395.00\nWESTERN INDUSTRIAL CERAMI,01-East,740283502,2666.62,413.99,2252.63\nWESTERN INDUSTRIAL CERAMI,01-East,733141300,2545.60,824.70,1720.90\nWESTERN INDUSTRIAL CERAMI,01-East,793336074,2490.00,1410.63,1079.37\nWESTERN INDUSTRIAL CERAMI,01-East,737851000,2248.10,1326.40,921.70\nWESTERN INDUSTRIAL CERAMI,01-East,768231000,1780.00,539.26,1240.74\nWESTERN INDUSTRIAL CERAMI,01-East,764781000,1692.00,736.02,955.98\nWESTERN INDUSTRIAL CERAMI,01-East,739541100,1679.32,334.28,1345.04\nWESTERN INDUSTRIAL CERAMI,01-East,768241000,1589.00,479.62,1109.38\nWESTERN INDUSTRIAL CERAMI,01-East,792730000,1400.70,375.36,1025.34\nWESTERN INDUSTRIAL CERAMI,01-East,734751200,1234.71,224.34,1010.37\nWESTERN INDUSTRIAL CERAMI,01-East,732429400,1224.10,133.82,1090.28\nWESTERN INDUSTRIAL CERAMI,01-East,764261000,1105.20,276.56,828.64\nWESTERN INDUSTRIAL CERAMI,01-East,780230000,1100.19,573.61,526.58\nWESTERN INDUSTRIAL CERAMI,01-East,734900000,1017.40,421.98,595.42\nWESTERN INDUSTRIAL CERAMI,01-East,767990000,924.48,339.72,584.76\nWESTERN INDUSTRIAL CERAMI,01-East,733403200,920.04,99.17,820.87\nWESTERN INDUSTRIAL CERAMI,01-East,738401000,828.90,642.24,186.66\nWESTERN INDUSTRIAL CERAMI,01-East,741001000,735.93,114.14,621.79\nWESTERN INDUSTRIAL CERAMI,01-East,770330000,700.25,96.65,603.60\nWESTERN INDUSTRIAL CERAMI,01-East,780610000,690.75,370.02,320.73\nWESTERN INDUSTRIAL CERAMI,01-East,780250000,637.92,311.69,326.23\nWESTERN INDUSTRIAL CERAMI,01-East,771102000,579.70,22.98,556.72\nWESTERN INDUSTRIAL CERAMI,01-East,734810000,544.40,435.66,108.74\nWESTERN INDUSTRIAL CERAMI,01-East,777904301,519.18,234.94,284.24\nWESTERN INDUSTRIAL CERAMI,01-East,733484800,510.40,102.72,407.68\nWESTERN INDUSTRIAL CERAMI,01-East,724261200,467.60,78.20,389.40\nWESTERN INDUSTRIAL CERAMI,01-East,733725500,422.00,125.43,296.57\nWESTERN INDUSTRIAL CERAMI,01-East,770200000,413.10,42.41,370.69\nWESTERN INDUSTRIAL CERAMI,01-East,738381000,396.00,346.80,49.20\nWESTERN INDUSTRIAL CERAMI,01-East,770220000,293.40,27.35,266.05\nWESTERN INDUSTRIAL CERAMI,01-East,770210000,285.00,31.52,253.48\nWESTERN INDUSTRIAL CERAMI,01-East,733423700,232.96,36.22,196.74\nWESTERN INDUSTRIAL CERAMI,01-East,733444200,214.00,34.69,179.31\nWESTERN INDUSTRIAL CERAMI,01-East,777650000,178.00,142.44,35.56\nWESTERN INDUSTRIAL CERAMI,01-East,793320074,63.25,30.73,32.52\nWHITCRAFT LLC,01-East,791104481,2590.16,477.86,2112.30\nWHITCRAFT LLC,01-East,791104260,2259.44,461.80,1797.64\nWHITE CAP CORPORATION,01-East,768211000,2620.00,224.45,2395.55\nWILT INDUSTRIES INC,01-East,737251000,16073.86,7345.62,8728.24\nWILT INDUSTRIES INC,01-East,737311000,2358.72,1806.24,552.48\nWILT INDUSTRIES INC,01-East,777601000,2136.00,432.12,1703.88\nWIREROPE WORKS INC.,01-East,770200000,1809.00,127.24,1681.76\nWIREROPE WORKS INC.,01-East,770220000,1341.00,85.31,1255.69\nWOLSELEY INDUSTRIAL GROUP,01-East,770200000,11880.00,880.92,10999.08\nWOLSELEY INDUSTRIAL 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CERAMICS INC,01-East,727000046,97611.60,44203.42,53408.18\nZIRCAR CERAMICS INC,01-East,739681100,26944.56,18804.40,8140.16\nZIRCAR CERAMICS INC,01-East,739641100,20602.80,14138.05,6464.75\nZIRCAR CERAMICS INC,01-East,739781100,14172.32,8506.60,5665.72\nZIRCAR CERAMICS INC,01-East,777901100,14090.83,5611.79,8479.04\nZIRCAR CERAMICS INC,01-East,727031000,11100.00,6421.76,4678.24\nZIRCAR CERAMICS INC,01-East,764030000,9864.00,4454.10,5409.90\nZIRCAR CERAMICS INC,01-East,734311100,9060.95,1047.22,8013.73\nZIRCAR CERAMICS INC,01-East,739761100,7207.92,4281.51,2926.41\nZIRCAR CERAMICS INC,01-East,777901300,6183.42,2426.16,3757.26\nZIRCAR CERAMICS INC,01-East,734900000,5403.78,2110.44,3293.34\nZIRCAR CERAMICS INC,01-East,777901000,3152.07,1059.41,2092.66\nZIRCAR CERAMICS INC,01-East,733525600,2625.00,409.62,2215.38\nZIRCAR CERAMICS INC,01-East,763991000,2054.40,1100.66,953.74\nZIRCAR CERAMICS INC,01-East,739781101,1877.76,856.65,1021.11\nZIRCAR CERAMICS INC,01-East,734910000,1764.20,417.74,1346.46\nZIRCAR CERAMICS INC,01-East,739661100,1522.08,936.00,586.08\nZIRCAR CERAMICS INC,01-East,738521000,1415.10,1027.48,387.62\nZIRCAR CERAMICS INC,01-East,733484900,1405.60,285.71,1119.89\nZIRCAR CERAMICS INC,01-East,727031200,1100.40,504.78,595.62\nZIRCAR CERAMICS INC,01-East,793260074,924.00,398.34,525.66\nZIRCAR CERAMICS INC,01-East,733505300,910.00,170.30,739.70\nZIRCAR CERAMICS INC,01-East,734751200,654.48,113.13,541.35\nZIRCAR CERAMICS INC,01-East,733525500,448.00,70.39,377.61\nZIRCAR CERAMICS INC,01-East,733510000,302.25,79.51,222.74\nZIRCAR REFRAC. COMPOSITES,01-East,739741100,2803.84,1784.79,1019.05\nZIRCAR REFRAC. COMPOSITES,01-East,740283502,2666.62,413.99,2252.63\nZIRCAR REFRAC. COMPOSITES,01-East,738521000,2267.63,1302.71,964.92\nZIRCAR REFRAC. COMPOSITES,01-East,738561000,1547.32,762.00,785.32\nZIRCAR REFRAC. COMPOSITES,01-East,764481000,1308.80,256.70,1052.10\nZIRCAR REFRAC. COMPOSITES,01-East,739452000,1194.65,603.33,591.32\nZIRCAR REFRAC. COMPOSITES,01-East,734311100,1028.64,101.10,927.54\nZIRCAR REFRAC. COMPOSITES,01-East,793330000,996.00,212.05,783.95\nZIRCAR REFRAC. COMPOSITES,01-East,739561200,697.54,217.00,480.54\nZIRCAR REFRAC. COMPOSITES,01-East,738542000,686.40,574.08,112.32\nZIRCAR REFRAC. COMPOSITES,01-East,780240000,528.36,94.88,433.48\nZIRCAR REFRAC. COMPOSITES,01-East,793540000,492.00,115.88,376.12\nZIRCAR REFRAC. COMPOSITES,01-East,793260074,364.00,156.68,207.32\nZIRCAR REFRAC. COMPOSITES,01-East,739561100,304.92,93.32,211.60\nZIRCAR REFRAC. COMPOSITES,01-East,733511000,287.63,66.56,221.07\nZIRCAR REFRAC. COMPOSITES,01-East,739541200,277.12,64.10,213.02\nZIRCAR REFRAC. COMPOSITES,01-East,733423700,212.16,45.26,166.90\nZIRCAR REFRAC. COMPOSITES,01-East,733464600,170.30,31.31,138.99\nZIRCAR REFRAC. COMPOSITES,01-East,734900000,107.68,38.36,69.32\nZIRCAR ZIRCONIA INC.,01-East,739541000,3687.82,829.48,2858.34\nZIRCAR ZIRCONIA INC.,01-East,754287000,795.36,281.96,513.40\n3-I INDUSTRIAL SUPPLY,02-Central,793450174,5827.50,1552.96,4274.54\n3-I INDUSTRIAL SUPPLY,02-Central,734990000,798.40,110.59,687.81\n3M COMPANY,02-Central,727032004,2514017.70,1754572.03,759445.67\n3M COMPANY,02-Central,793001000,568488.00,226065.71,342422.29\n4-STATE SUPPLY,02-Central,735602000,166880.00,55965.54,110914.46\n4-STATE SUPPLY,02-Central,735750000,11033.00,3165.27,7867.73\n4-STATE SUPPLY,02-Central,735862000,1200.00,514.02,685.98\nA O SMITH,02-Central,793000000,1706.00,114.88,1591.12\nA O SMITH,02-Central,726000240,662.40,44.38,618.02\nA W CHESTERTON COMPANY,02-Central,770210000,826.50,92.16,734.34\nA W CHESTERTON COMPANY,02-Central,770372000,660.80,53.11,607.69\n\"ABP INDUCTION, LLC\",02-Central,733671000,16167.63,2322.05,13845.58\n\"ABP INDUCTION, LLC\",02-Central,733403300,9407.20,921.11,8486.09\n\"ABP INDUCTION, LLC\",02-Central,770460000,8136.50,608.69,7527.81\n\"ABP INDUCTION, LLC\",02-Central,734311100,1414.80,134.81,1279.99\nACCELLENT ENDOSCOPY,02-Central,733211050,958.00,111.82,846.18\nACCURIDE CAMDEN,02-Central,739541000,564.48,71.62,492.86\nACME INSULATION INC,02-Central,735602000,115915.00,38635.48,77279.52\nACME INSULATION INC,02-Central,735602110,82910.00,28292.45,54617.55\nACME INSULATION INC,02-Central,735752000,29860.00,10450.74,19409.26\nACTION INDUSTRIAL GROUP,02-Central,733832000,2556.00,556.68,1999.32\nACTION INDUSTRIAL GROUP,02-Central,733721000,300.00,69.32,230.68\nADVANCE THERMAL CORP,02-Central,768221130,44990.40,13400.19,31590.21\nADVANCE THERMAL CORP,02-Central,733151500,3340.00,619.98,2720.02\nADVANCE THERMAL CORP,02-Central,768122000,1944.00,671.13,1272.87\nADVANCE THERMAL CORP,02-Central,733201000,767.60,168.85,598.75\nADVANCED TECH SERVICES,02-Central,770580000,1598.00,89.66,1508.34\nADVANCED TECH SERVICES,02-Central,739681100,462.06,207.00,255.06\nADVANCED TECH SERVICES,02-Central,739781100,410.72,163.84,246.88\nADVANCED THERMAL BATTERIE,02-Central,733464400,313.00,43.37,269.63\nADVSEAL,02-Central,770310000,518.00,45.47,472.53\nAEROJET,02-Central,733403200,1133.00,99.17,1033.83\nAEROJET,02-Central,733383000,1117.60,163.93,953.67\n\"AFTECH, LLC.\",02-Central,734311100,785.84,69.00,716.84\nAGROTEK SERVICES INC.,02-Central,777992000,2400.00,1300.68,1099.32\nAJAX-TOCCO MAGNETHERMIC,02-Central,738521000,10058.95,3501.13,6557.82\nAJAX-TOCCO MAGNETHERMIC,02-Central,733525500,2641.00,260.12,2380.88\nAJAX-TOCCO MAGNETHERMIC,02-Central,733586500,1857.60,181.35,1676.25\nAJAX-TOCCO MAGNETHERMIC,02-Central,777601000,1183.36,202.84,980.52\nAJAX-TOCCO MAGNETHERMIC,02-Central,764461006,1125.20,81.75,1043.45\nAJAX-TOCCO MAGNETHERMIC,02-Central,734711100,536.16,81.14,455.02\nAJAX-TOCCO MAGNETHERMIC,02-Central,733464500,298.00,29.52,268.48\nALBEMARLE CORPORATION,02-Central,764901000,3017.00,112.46,2904.54\nALCHEMY SOUTH,02-Central,720219100,2444.70,92.16,2352.54\nALCOA INC.,02-Central,770290000,3109.50,229.76,2879.74\nALCOA TECH CENTER,02-Central,770200000,630.00,40.79,589.21\nALCOA,02-Central,793330000,11489.70,1917.22,9572.48\nALCOA,02-Central,739390000,9116.00,4044.00,5072.00\nALCOA,02-Central,733464500,1755.00,295.20,1459.80\nALCOA,02-Central,733464600,702.00,125.26,576.74\nALCOA,02-Central,766161000,512.50,51.41,461.09\nALCOA,02-Central,739370000,379.52,138.24,241.28\nALLIANCE AIRCRAFT GROUP,02-Central,770520000,958.50,41.85,916.65\nALLIANCE REFRACTORIES LTD,02-Central,764821000,370.00,153.58,216.42\nALLIED MINERAL PRODUCTS I,02-Central,779988176,66368.00,50759.89,15608.11\nALLIED MINERAL PRODUCTS I,02-Central,779981056,28774.40,20888.76,7885.64\nALLIED MINERAL PRODUCTS I,02-Central,779981057,13920.00,10024.72,3895.28\nALLIED MINERAL PRODUCTS I,02-Central,779988169,9868.80,9190.60,678.20\nALLIED MINERAL PRODUCTS I,02-Central,770360000,8703.20,578.58,8124.62\nALLIED MINERAL PRODUCTS I,02-Central,779980081,8406.00,7577.38,828.62\nALLIED MINERAL PRODUCTS I,02-Central,737301000,5481.48,1962.90,3518.58\nALLIED MINERAL PRODUCTS I,02-Central,777902700,3687.39,1630.80,2056.59\nALLIED MINERAL PRODUCTS I,02-Central,737912200,588.20,86.72,501.48\nALLIED MINERAL PRODUCTS I,02-Central,770220000,420.90,28.44,392.46\nALLIED MINERAL PRODUCTS I,02-Central,739561000,201.60,86.19,115.41\nALPHA 1,02-Central,734311100,13695.00,1203.60,12491.40\nALPHA 1,02-Central,733444300,10748.00,1449.72,9298.28\nALPHA 1,02-Central,766121000,875.00,27.25,847.75\nAMCOR AMERICAN METALS,02-Central,726020031,985944.80,504525.07,481419.73\nAMECO USA,02-Central,727000025,166.00,14.09,151.91\n\"AMERICAN FOAM CAST, INC.\",02-Central,734173000,2814.00,429.92,2384.08\nAMERICAN MECHANICAL,02-Central,735602000,174706.00,53103.10,121602.90\nAMERICAN MECHANICAL,02-Central,735750000,7664.80,2235.41,5429.39\nAMERICAN MECHANICAL,02-Central,735602010,465.00,275.22,189.78\nAMERICRAFT MFG CO.,02-Central,733721000,286.20,48.90,237.30\n\"AMERISAFE, INC\",02-Central,735602000,130460.00,44474.32,85985.68\n\"AMERISAFE, INC\",02-Central,764901000,17545.00,4865.08,12679.92\n\"AMERISAFE, INC\",02-Central,735602110,16430.00,6037.78,10392.22\n\"AMERISAFE, INC\",02-Central,764861000,10493.00,2883.16,7609.84\n\"AMERISAFE, INC\",02-Central,764821000,8240.00,2081.71,6158.29\n\"AMERISAFE, INC\",02-Central,735750000,3192.00,1304.73,1887.27\n\"AMERISAFE, INC\",02-Central,739561200,2122.24,451.34,1670.90\n\"AMERISAFE, INC\",02-Central,764261000,1950.00,758.20,1191.80\n\"AMERISAFE, INC\",02-Central,764341000,1890.00,706.97,1183.03\n\"AMERISAFE, INC\",02-Central,724281400,800.00,178.16,621.84\n\"AMERISAFE, INC\",02-Central,733725500,205.20,51.76,153.44\n\"AMERISAFE, INC\",02-Central,764461006,84.00,27.94,56.06\nAMES TRUE TEMPER,02-Central,739390000,8386.72,3720.48,4666.24\nAMES TRUE TEMPER,02-Central,739781100,3159.04,761.68,2397.36\nAMES TRUE TEMPER,02-Central,764522000,666.00,26.72,639.28\nAMES TRUE TEMPER,02-Central,774201000,589.00,157.42,431.58\nAMES TRUE TEMPER,02-Central,776131000,445.00,260.22,184.78\nAMES TRUE TEMPER,02-Central,777601000,409.92,58.80,351.12\nAMPCO METAL,02-Central,770260000,1957.50,140.66,1816.84\nAMPCO METAL,02-Central,770220000,447.00,28.44,418.56\n\"AMSTED RAIL COMPANY, INC.\",02-Central,780770000,6915.60,1091.36,5824.24\n\"AMSTED RAIL COMPANY, INC.\",02-Central,780730000,3175.20,582.38,2592.82\nANCHOR ACQUISTION LLC,02-Central,770220000,471.00,27.35,443.65\nAPEX PIPELINE SERVICES,02-Central,768222000,4396.00,250.60,4145.40\nAPPLIED INDUSTRIAL TECH.,02-Central,733525600,14332.50,2114.76,12217.74\nAPPLIED INDUSTRIAL TECH.,02-Central,733525500,6675.00,974.77,5700.23\nAPPLIED INDUSTRIAL TECH.,02-Central,733211000,3996.20,793.13,3203.07\nAPPLIED INDUSTRIAL TECH.,02-Central,737912200,3363.45,541.47,2821.98\nAPPLIED INDUSTRIAL TECH.,02-Central,770560000,2856.30,175.59,2680.71\nAPPLIED INDUSTRIAL TECH.,02-Central,734890000,2010.70,397.92,1612.78\nAPPLIED INDUSTRIAL TECH.,02-Central,734990000,1945.48,274.67,1670.81\nAPPLIED INDUSTRIAL TECH.,02-Central,766121000,1602.80,109.00,1493.80\nAPPLIED INDUSTRIAL TECH.,02-Central,770520000,770.10,48.32,721.78\nAPPLIED INDUSTRIAL TECH.,02-Central,734611100,483.07,81.53,401.54\nAPPLIED INDUSTRIAL TECH.,02-Central,739641100,393.80,137.43,256.37\nAPPLIED INDUSTRIAL TECH.,02-Central,733511000,348.75,75.22,273.53\nAPPLIED INDUSTRIAL TECH.,02-Central,734910000,307.70,53.09,254.61\nAPPLIED POROUS TECH.,02-Central,740361900,18662.40,7942.53,10719.87\nAPPLIED POWER PRODUCTS,02-Central,733464500,596.00,59.04,536.96\nAPPLIED POWER PRODUCTS,02-Central,733510000,537.50,70.86,466.64\nAPPLIED POWER PRODUCTS,02-Central,733151600,352.00,46.64,305.36\nAPPLIED PRODUCTS INC.,02-Central,733403100,1107.25,92.79,1014.46\nARCELORMITTAL,02-Central,770560000,1509.00,86.09,1422.91\nARGOS CEMENT LLC,02-Central,770600000,3449.60,192.64,3256.96\nARI - MARMADUKE,02-Central,764706120,1645267.20,437262.08,1208005.12\nARI - MARMADUKE,02-Central,764652002,517090.56,88883.55,428207.01\nARI - MARMADUKE,02-Central,764643054,9828.00,2225.83,7602.17\nARI - MARMADUKE,02-Central,764262000,252.00,46.00,206.00\nARMIL / C F S INC,02-Central,734830000,79011.70,15630.42,63381.28\nARMIL / C F S INC,02-Central,793000000,71560.00,11121.19,60438.81\nARMIL / C F S INC,02-Central,771320000,70906.00,23024.60,47881.40\nARMIL / C F S INC,02-Central,754847000,65818.80,21558.67,44260.13\nARMIL / C F S INC,02-Central,764821000,61797.80,26521.73,35276.07\nARMIL / C F S INC,02-Central,737851000,55543.04,37139.20,18403.84\nARMIL / C F S INC,02-Central,718507000,55064.88,10624.61,44440.27\nARMIL / C F S INC,02-Central,718500000,54956.92,10579.03,44377.89\nARMIL / C F S INC,02-Central,781650000,44323.20,13715.94,30607.26\nARMIL / C F S INC,02-Central,781610000,42086.94,12695.80,29391.14\nARMIL / C F S INC,02-Central,766121000,41432.00,25148.89,16283.11\nARMIL / C F S INC,02-Central,726020015,40240.80,13806.34,26434.46\nARMIL / C F S INC,02-Central,737912200,40058.80,8777.68,31281.12\nARMIL / C F S INC,02-Central,770942000,38821.60,1709.27,37112.33\nARMIL / C F S INC,02-Central,733211000,36766.40,7828.79,28937.61\nARMIL / C F S INC,02-Central,734311100,35989.36,4435.63,31553.73\nARMIL / C F S INC,02-Central,780710000,34878.00,11542.35,23335.65\nARMIL / C F S INC,02-Central,777601000,33883.07,9918.19,23964.88\nARMIL / C F S INC,02-Central,792785000,27829.44,6335.87,21493.57\nARMIL / C F S INC,02-Central,768021000,24965.60,15819.03,9146.57\nARMIL / C F S INC,02-Central,733151500,24962.40,4148.08,20814.32\nARMIL / C F S INC,02-Central,780650000,24425.94,9111.62,15314.32\nARMIL / C F S INC,02-Central,733152500,22460.00,8546.96,13913.04\nARMIL / C F S INC,02-Central,739561200,21781.25,8020.07,13761.18\nARMIL / C F S INC,02-Central,781670000,20216.94,5615.64,14601.30\nARMIL / C F S INC,02-Central,777904301,19956.49,9045.00,10911.49\nARMIL / C F S INC,02-Central,770392000,19391.40,1683.33,17708.07\nARMIL / C F S INC,02-Central,764827505,17374.50,8180.85,9193.65\nARMIL / C F S INC,02-Central,792734000,16934.40,2960.64,13973.76\nARMIL / C F S INC,02-Central,740283401,16741.34,3346.87,13394.47\nARMIL / C F S INC,02-Central,764822000,16290.00,6997.21,9292.79\nARMIL / C F S INC,02-Central,780690000,16230.29,5784.42,10445.87\nARMIL / C F S INC,02-Central,733484900,15449.60,2898.03,12551.57\nARMIL / C F S INC,02-Central,733505300,15380.00,2650.94,12729.06\nARMIL / C F S INC,02-Central,782050000,14774.40,5317.55,9456.85\nARMIL / C F S INC,02-Central,739741100,14341.36,9133.13,5208.23\nARMIL / C F S INC,02-Central,740361900,14244.96,6724.31,7520.65\nARMIL / C F S INC,02-Central,781500000,13396.05,4390.38,9005.67\nARMIL / C F S INC,02-Central,727000140,12699.04,2323.30,10375.74\nARMIL / C F S INC,02-Central,764561000,12308.01,6558.78,5749.23\nARMIL / C F S INC,02-Central,777905413,12167.76,7064.59,5103.17\nARMIL / C F S INC,02-Central,764901000,11587.00,5145.32,6441.68\nARMIL / C F S INC,02-Central,733151600,10286.80,1668.65,8618.15\nARMIL / C F S INC,02-Central,781630000,10272.96,3215.72,7057.24\nARMIL / C F S INC,02-Central,770940000,10213.00,434.16,9778.84\nARMIL / C F S INC,02-Central,768121000,10167.00,4931.60,5235.40\nARMIL / C F S INC,02-Central,733141200,10121.20,2006.27,8114.93\nARMIL / C F S INC,02-Central,733484800,9960.00,1849.08,8110.92\nARMIL / C F S INC,02-Central,780250000,9919.62,4086.91,5832.71\nARMIL / C F S INC,02-Central,764827501,9093.18,4282.79,4810.39\nARMIL / C F S INC,02-Central,734142001,9076.50,3554.48,5522.02\nARMIL / C F S INC,02-Central,733423700,9006.40,1566.22,7440.18\nARMIL / C F S INC,02-Central,766121070,8969.29,5828.28,3141.01\nARMIL / C F S INC,02-Central,733464500,8559.00,1362.00,7197.00\nARMIL / C F S INC,02-Central,759661000,8480.82,1164.68,7316.14\nARMIL / C F S INC,02-Central,764827502,8364.33,3959.88,4404.45\nARMIL / C F S INC,02-Central,734173000,8329.30,2046.89,6282.41\nARMIL / C F S INC,02-Central,770560000,8131.10,698.95,7432.15\nARMIL / C F S INC,02-Central,768241030,8094.00,3449.84,4644.16\nARMIL / C F S INC,02-Central,764342000,7985.00,4519.40,3465.60\nARMIL / C F S INC,02-Central,764861000,7421.00,3586.64,3834.36\nARMIL / C F S INC,02-Central,734711100,7024.87,1477.76,5547.11\nARMIL / C F S INC,02-Central,777905414,7001.22,4292.57,2708.65\nARMIL / C F S INC,02-Central,733464400,6946.20,1463.62,5482.58\nARMIL / C F S INC,02-Central,733201000,6856.72,1389.02,5467.70\nARMIL / C F S INC,02-Central,733725500,6766.00,3026.40,3739.60\nARMIL / C F S INC,02-Central,739681100,6731.39,4080.42,2650.97\nARMIL / C F S INC,02-Central,768211000,6497.00,2983.93,3513.07\nARMIL / C F S INC,02-Central,738561000,6408.40,5222.97,1185.43\nARMIL / C F S INC,02-Central,770200000,6129.00,663.98,5465.02\nARMIL / C F S INC,02-Central,764481000,6096.00,3296.52,2799.48\nARMIL / C F S INC,02-Central,739541200,6025.46,1928.10,4097.36\nARMIL / C F S INC,02-Central,739370000,5952.83,3898.06,2054.77\nARMIL / C F S INC,02-Central,764821073,5930.00,5832.00,98.00\nARMIL / C F S INC,02-Central,777905415,5871.88,3320.66,2551.22\nARMIL / C F S INC,02-Central,792624000,5639.80,1216.06,4423.74\nARMIL / C F S INC,02-Central,793000010,5472.00,866.59,4605.41\nARMIL / C F S INC,02-Central,770210000,5454.00,618.39,4835.61\nARMIL / C F S INC,02-Central,733525600,5383.00,808.10,4574.90\nARMIL / C F S INC,02-Central,766081010,5310.00,2448.72,2861.28\nARMIL / C F S INC,02-Central,771330000,5293.08,1665.59,3627.49\nARMIL / C F S INC,02-Central,766122000,5250.00,2884.62,2365.38\nARMIL / C F S INC,02-Central,780210000,4860.00,1907.14,2952.86\nARMIL / C F S INC,02-Central,777642000,4602.70,1590.24,3012.46\nARMIL / C F S INC,02-Central,733211050,4600.00,1056.06,3543.94\nARMIL / C F S INC,02-Central,770930000,4599.00,160.70,4438.30\nARMIL / C F S INC,02-Central,764261000,4589.00,1605.55,2983.45\nARMIL / C F S INC,02-Central,764522000,4424.00,2108.43,2315.57\nARMIL / C F S INC,02-Central,764902000,4370.00,1926.87,2443.13\nARMIL / C F S INC,02-Central,764601000,4356.00,2115.55,2240.45\nARMIL / C F S INC,02-Central,764521000,4173.00,2124.92,2048.08\nARMIL / C F S INC,02-Central,733525400,4169.75,618.30,3551.45\nARMIL / C F S INC,02-Central,764827504,4003.58,1882.58,2121.00\nARMIL / C F S INC,02-Central,770380000,3981.20,369.51,3611.69\nARMIL / C F S INC,02-Central,770240000,3948.00,416.58,3531.42\nARMIL / C F S INC,02-Central,733423800,3872.64,689.91,3182.73\nARMIL / C F S INC,02-Central,764827503,3861.00,1610.21,2250.79\nARMIL / C F S INC,02-Central,764821161,3805.00,3720.00,85.00\nARMIL / C F S INC,02-Central,777905410,3602.74,2952.47,650.27\nARMIL / C F S INC,02-Central,734414100,3578.74,562.07,3016.67\nARMIL / C F S INC,02-Central,738521000,3532.13,3547.76,-15.63\nARMIL / C F S INC,02-Central,739561000,3374.63,1196.98,2177.65\nARMIL / C F S INC,02-Central,768221000,3370.00,1747.05,1622.95\nARMIL / C F S INC,02-Central,764781000,3240.00,1409.43,1830.57\nARMIL / C F S INC,02-Central,768750000,3197.20,805.67,2391.53\nARMIL / C F S INC,02-Central,777905411,3094.30,2850.01,244.29\nARMIL / C F S INC,02-Central,770270000,3086.13,328.80,2757.33\nARMIL / C F S INC,02-Central,733423600,2851.20,512.61,2338.59\nARMIL / C F S INC,02-Central,737321000,2835.84,856.56,1979.28\nARMIL / C F S INC,02-Central,780640000,2786.48,881.47,1905.01\nARMIL / C F S INC,02-Central,733525500,2764.50,422.37,2342.13\nARMIL / C F S INC,02-Central,739541100,2705.98,815.94,1890.04\nARMIL / C F S INC,02-Central,733403200,2680.48,361.85,2318.63\nARMIL / C F S INC,02-Central,770600000,2642.36,192.64,2449.72\nARMIL / C F S INC,02-Central,793452074,2561.90,1380.26,1181.64\nARMIL / C F S INC,02-Central,764602000,2553.00,1218.82,1334.18\nARMIL / C F S INC,02-Central,739781100,2490.66,1332.94,1157.72\nARMIL / C F S INC,02-Central,737902100,2365.40,608.41,1756.99\nARMIL / C F S INC,02-Central,793260074,2353.00,2034.81,318.19\nARMIL / C F S INC,02-Central,767501000,2352.24,3.00,2349.24\nARMIL / C F S INC,02-Central,767970110,2352.24,31.86,2320.38\nARMIL / C F S INC,02-Central,771500000,2258.03,165.39,2092.64\nARMIL / C F S INC,02-Central,780770000,2213.71,699.39,1514.32\nARMIL / C F S INC,02-Central,733586400,2208.96,182.86,2026.10\nARMIL / C F S INC,02-Central,793330000,2086.50,895.31,1191.19\nARMIL / C F S INC,02-Central,777905412,2065.13,1492.94,572.19\nARMIL / C F S INC,02-Central,734851000,1898.33,456.99,1441.34\nARMIL / C F S INC,02-Central,764482000,1826.00,1029.94,796.06\nARMIL / C F S INC,02-Central,739541000,1588.90,543.01,1045.89\nARMIL / C F S INC,02-Central,781570000,1478.00,482.68,995.32\nARMIL / C F S INC,02-Central,739390000,1434.72,577.02,857.70\nARMIL / C F S INC,02-Central,764903000,1367.35,509.66,857.69\nARMIL / C F S INC,02-Central,764301000,1320.00,762.68,557.32\nARMIL / C F S INC,02-Central,781590000,1298.24,417.88,880.36\nARMIL / C F S INC,02-Central,770510000,1292.00,96.40,1195.60\nARMIL / C F S INC,02-Central,770260000,1224.90,135.26,1089.64\nARMIL / C F S INC,02-Central,780360000,1209.20,373.85,835.35\nARMIL / C F S INC,02-Central,733832000,1206.00,385.42,820.58\nARMIL / C F S INC,02-Central,770520000,1190.70,96.64,1094.06\nARMIL / C F S INC,02-Central,764521061,1180.00,1278.00,-98.00\nARMIL / C F S INC,02-Central,770570000,1143.68,100.73,1042.95\nARMIL / C F S INC,02-Central,770220000,1125.60,109.39,1016.21\nARMIL / C F S INC,02-Central,733444200,1073.00,193.20,879.80\nARMIL / C F S INC,02-Central,770450000,1050.75,97.34,953.41\nARMIL / C F S INC,02-Central,768231300,1025.00,545.35,479.65\nARMIL / C F S INC,02-Central,768231000,1024.00,539.26,484.74\nARMIL / C F S INC,02-Central,793450174,1005.14,856.63,148.51\nARMIL / C F S INC,02-Central,740382201,976.50,300.51,675.99\nARMIL / C F S INC,02-Central,770303000,934.40,25.23,909.17\nARMIL / C F S INC,02-Central,792760000,914.14,233.24,680.90\nARMIL / C F S INC,02-Central,792620000,855.20,173.48,681.72\nARMIL / C F S INC,02-Central,764341000,792.00,458.70,333.30\nARMIL / C F S INC,02-Central,764346500,792.00,469.69,322.31\nARMIL / C F S INC,02-Central,764262000,788.00,460.00,328.00\nARMIL / C F S INC,02-Central,781580000,779.68,175.64,604.04\nARMIL / C F S INC,02-Central,771132000,766.25,39.51,726.74\nARMIL / C F S INC,02-Central,781835000,734.20,214.72,519.48\nARMIL / C F S INC,02-Central,742546000,714.60,78.36,636.24\nARMIL / C F S INC,02-Central,763991000,712.00,196.12,515.88\nARMIL / C F S INC,02-Central,734890000,700.35,198.96,501.39\nARMIL / C F S INC,02-Central,764761000,681.00,472.17,208.83\nARMIL / C F S INC,02-Central,770290000,643.00,76.59,566.41\nARMIL / C F S INC,02-Central,780310000,572.48,159.95,412.53\nARMIL / C F S INC,02-Central,764862000,565.00,250.28,314.72\nARMIL / C F S INC,02-Central,767970020,522.72,1.80,520.92\nARMIL / C F S INC,02-Central,723960000,476.50,176.46,300.04\nARMIL / C F S INC,02-Central,770280000,440.88,76.95,363.93\nARMIL / C F S INC,02-Central,739641100,408.24,274.85,133.39\nARMIL / C F S INC,02-Central,764781040,385.00,201.66,183.34\nARMIL / C F S INC,02-Central,780630000,339.52,118.56,220.96\nARMIL / C F S INC,02-Central,776201000,316.00,185.15,130.85\nARMIL / C F S INC,02-Central,793540000,307.50,144.84,162.66\nARMIL / C F S INC,02-Central,734990000,140.80,27.39,113.41\nARMIL / C F S INC,02-Central,734900000,96.92,38.36,58.56\nASHEVILLE RUBBER & GASKET,02-Central,733525600,7645.00,720.37,6924.63\nASHEVILLE RUBBER & GASKET,02-Central,734211200,946.28,92.43,853.85\nASSOCIATED SPRING,02-Central,739781100,789.76,190.42,599.34\nASSOCIATED SPRING,02-Central,739741100,411.84,109.56,302.28\nATA,02-Central,745471000,2262.00,293.30,1968.70\nATA,02-Central,764821000,1290.00,46.07,1243.93\nATA,02-Central,734311100,275.36,35.30,240.06\nATD ENGINEERING MACHINE,02-Central,739370000,2218.24,639.76,1578.48\nATI FABRICATED COMPONENTS,02-Central,766121000,68580.00,3269.84,65310.16\nATLANTECH DISTRIBUTION,02-Central,735602000,235807.20,91012.40,144794.80\nATLANTECH DISTRIBUTION,02-Central,733403200,182160.00,22729.65,159430.35\nATLANTECH DISTRIBUTION,02-Central,735602110,34265.00,13764.58,20500.42\nATLANTECH DISTRIBUTION,02-Central,764781000,20700.00,8504.97,12195.03\nATLANTECH DISTRIBUTION,02-Central,764821000,17268.85,3585.65,13683.20\nATLANTECH DISTRIBUTION,02-Central,739641100,16439.04,8585.27,7853.77\nATLANTECH DISTRIBUTION,02-Central,764301000,16344.90,4089.86,12255.04\nATLANTECH DISTRIBUTION,02-Central,733464600,14582.50,2476.53,12105.97\nATLANTECH DISTRIBUTION,02-Central,764521000,5172.30,1674.49,3497.81\nATLANTECH DISTRIBUTION,02-Central,735750000,4650.00,1927.06,2722.94\nATLANTECH DISTRIBUTION,02-Central,764601000,3858.75,993.27,2865.48\nATLANTECH DISTRIBUTION,02-Central,733151500,3794.40,753.88,3040.52\nATLANTECH DISTRIBUTION,02-Central,739450000,3249.18,1431.28,1817.90\nATLANTECH DISTRIBUTION,02-Central,733444200,2572.00,455.78,2116.22\nATLANTECH DISTRIBUTION,02-Central,764522000,2563.60,817.14,1746.46\nATLANTECH DISTRIBUTION,02-Central,764602000,2537.50,655.28,1882.22\nATLANTECH DISTRIBUTION,02-Central,739561200,2478.08,795.66,1682.42\nATLANTECH DISTRIBUTION,02-Central,733141200,1960.00,299.86,1660.14\nATLANTECH DISTRIBUTION,02-Central,764221000,1624.00,942.37,681.63\nATLANTECH DISTRIBUTION,02-Central,739370000,1439.36,1382.40,56.96\nATLANTECH DISTRIBUTION,02-Central,764561000,1175.80,185.57,990.23\nATLANTECH DISTRIBUTION,02-Central,739541200,815.36,225.00,590.36\nATLANTECH DISTRIBUTION,02-Central,733211050,502.60,111.82,390.78\nATLANTECH DISTRIBUTION,02-Central,733211000,490.60,100.59,390.01\nATLANTECH DISTRIBUTION,02-Central,735752000,465.00,205.20,259.80\nATLANTECH DISTRIBUTION,02-Central,733464400,399.00,86.74,312.26\nATLANTECH DISTRIBUTION,02-Central,733423700,244.80,45.26,199.54\nATLANTECH DISTRIBUTION,02-Central,764481000,186.00,50.82,135.18\nATLANTECH DISTRIBUTION,02-Central,734311100,181.96,17.65,164.31\nATLANTECH,02-Central,735602000,21805.00,7466.95,14338.05\nATLANTECH,02-Central,735602110,6272.00,2205.14,4066.86\nATLANTECH,02-Central,764521000,693.00,239.35,453.65\nATLANTECH,02-Central,733211050,688.00,223.64,464.36\nATLANTECH,02-Central,734151100,454.34,100.61,353.73\nATLANTIC COAST FIRE EQ.,02-Central,770380000,1880.00,112.66,1767.34\nATS CAROLINA INC,02-Central,737851000,3059.20,1326.40,1732.80\n\"ATSCOTT MFG COMPANY, INC.\",02-Central,792004000,1119.68,386.27,733.41\nAUBURN LEATHER,02-Central,733671000,16480.80,4320.72,12160.08\nAUBURN LEATHER,02-Central,733742000,265.60,81.75,183.85\nAUSCO PRODUCTS INC.,02-Central,727000140,12323.20,5524.73,6798.47\nB F GOODRICH CO,02-Central,733383000,1174.80,152.13,1022.67\nB&R ENGINEERING,02-Central,734990000,1430.40,165.88,1264.52\nBABINGTON TECHNOLOGY LLC,02-Central,739781100,35129.60,10025.56,25104.04\nBABINGTON TECHNOLOGY LLC,02-Central,739761100,4053.12,1259.80,2793.32\nBABINGTON TECHNOLOGY LLC,02-Central,739741100,3739.84,1103.54,2636.30\nBABINGTON TECHNOLOGY LLC,02-Central,733151500,1350.00,143.07,1206.93\nBAKER DISTRIBUTING CO.,02-Central,735602000,4136.00,1017.64,3118.36\nBALTIMORE RUBBER & GASKET,02-Central,777601000,1160.16,216.06,944.10\nBARON BLAKESLEE SFC,02-Central,791106668,9061.92,2376.26,6685.66\nBASF CORPORATION,02-Central,740402301,9810.00,1949.09,7860.91\nBASF CORPORATION,02-Central,777601000,7031.40,1440.36,5591.04\nBASIC RESOURCES INC.,02-Central,733202000,25444.80,11292.36,14152.44\nBASIC RESOURCES INC.,02-Central,770410000,16190.00,2030.59,14159.41\nBASIC RESOURCES INC.,02-Central,733211000,12960.00,4734.53,8225.47\nBASIC RESOURCES INC.,02-Central,734351200,12364.64,1859.63,10505.01\nBASIC RESOURCES INC.,02-Central,733464500,5855.60,1498.67,4356.93\nBASIC RESOURCES INC.,02-Central,733151500,5112.00,1947.83,3164.17\nBASIC RESOURCES INC.,02-Central,733505200,1335.00,327.93,1007.07\nBASIC RESOURCES INC.,02-Central,733484800,1264.00,287.63,976.37\nBASIC RESOURCES INC.,02-Central,770220000,1065.00,136.73,928.27\nBASIC RESOURCES INC.,02-Central,733383000,805.20,163.93,641.27\nBASIC RESOURCES INC.,02-Central,777601000,697.89,216.05,481.84\nBASIC RESOURCES INC.,02-Central,764781000,126.00,39.44,86.56\nBASIC RUBBER & PLASTIC,02-Central,738381000,5008.32,4161.60,846.72\nBASIC RUBBER & PLASTIC,02-Central,738361000,4382.28,2162.16,2220.12\nBASIC RUBBER & PLASTIC,02-Central,740103000,792.96,135.47,657.49\nBASIC RUBBER & PLASTIC,02-Central,740123000,670.32,128.01,542.31\nBASIC RUBBER & PLASTIC,02-Central,738521000,556.48,298.56,257.92\nBATTELLE,02-Central,733741000,388.00,93.19,294.81\nBAY INDUSTRIES INC.,02-Central,735602000,1217970.00,430162.17,787807.83\nBAY INDUSTRIES INC.,02-Central,735602110,226608.00,82947.78,143660.22\nBAY INDUSTRIES INC.,02-Central,735750000,106438.00,43834.14,62603.86\nBAY INDUSTRIES INC.,02-Central,735752000,6508.00,2784.58,3723.42\nBAY INDUSTRIES INC.,02-Central,739561000,4596.26,1284.15,3312.11\nBAY INDUSTRIES INC.,02-Central,735602010,1590.00,1100.90,489.10\nBAY INDUSTRIES INC.,02-Central,764561000,967.50,332.66,634.84\nBAY INDUSTRIES INC.,02-Central,733151500,632.20,95.38,536.82\nBDI,02-Central,739741100,2142.40,841.60,1300.80\nBDI,02-Central,739781100,2053.60,819.20,1234.40\nBEARING SERVICE CO.,02-Central,770222000,3124.75,198.90,2925.85\nBHC INTEGRATED SUPPORT,02-Central,735602000,2465.00,714.48,1750.52\nBLUE RIDGE HOME,02-Central,739561200,424.96,76.67,348.29\nBLUE STREAK FAB &,02-Central,764821000,2580.00,92.14,2487.86\nBLUE STREAK FAB &,02-Central,733444300,2072.00,272.10,1799.90\nBLUE STREAK FAB &,02-Central,734311100,1773.86,155.65,1618.21\nBLUE STREAK FAB &,02-Central,764821073,894.00,72.90,821.10\nBLUE STREAK FAB &,02-Central,727000140,693.60,50.14,643.46\nBOATWRIGHT DISTRIBUTION,02-Central,735602000,373070.00,137263.30,235806.70\nBOATWRIGHT DISTRIBUTION,02-Central,764261000,16129.95,3680.62,12449.33\nBOATWRIGHT DISTRIBUTION,02-Central,764301000,11012.62,3604.09,7408.53\nBOATWRIGHT DISTRIBUTION,02-Central,764341000,9705.00,3412.88,6292.12\nBOATWRIGHT DISTRIBUTION,02-Central,735750000,6580.00,3150.74,3429.26\nBOATWRIGHT DISTRIBUTION,02-Central,733403200,2760.12,297.51,2462.61\nBOATWRIGHT DISTRIBUTION,02-Central,766211000,312.00,113.05,198.95\nBODYCOTE HOT ISOSTATIC,02-Central,740283502,20501.80,2864.56,17637.24\nBODYCOTE HOT ISOSTATIC,02-Central,764762000,1600.00,151.46,1448.54\nBODYCOTE HOT ISOSTATIC,02-Central,770720000,1010.00,27.12,982.88\nBODYCOTE HOT ISOSTATIC,02-Central,764781040,427.20,39.06,388.14\nBOEING,02-Central,734311100,381.48,33.70,347.78\nBOILER TUBE OF AMERICA,02-Central,739781100,67019.57,44284.37,22735.20\nBOILER TUBE OF AMERICA,02-Central,764901000,18863.10,4118.85,14744.25\nBOILER TUBE OF AMERICA,02-Central,764821000,3362.40,698.23,2664.17\nBOILER TUBE OF AMERICA,02-Central,764901073,603.00,291.60,311.40\nBOILER TUBE OF AMERICA,02-Central,764821073,304.20,145.80,158.40\nBORBET ALABAMA INC.,02-Central,739521100,1270.08,139.38,1130.70\nBORBET ALABAMA INC.,02-Central,739541100,658.56,83.57,574.99\nBORG WARNER,02-Central,740382101,892.80,296.37,596.43\nBOVARD STUDIO INC.,02-Central,739541200,501.76,64.10,437.66\nBP-HUSKY FREFINING LLC,02-Central,734761300,23595.00,3793.90,19801.10\nBP-HUSKY FREFINING LLC,02-Central,774901000,330.00,39.57,290.43\nBP-HUSKY FREFINING LLC,02-Central,767970140,224.50,24.28,200.22\nBP-HUSKY FREFINING LLC,02-Central,767970000,69.50,16.80,52.70\nBRADEN MANUFACTURING,02-Central,768241000,153.00,102.77,50.23\nBRANHAM CORP.,02-Central,770810000,2535.00,51.57,2483.43\nBRANHAM CORP.,02-Central,770800000,684.00,12.78,671.22\nBRANHAM CORP.,02-Central,733464600,596.00,62.62,533.38\nBRAZEE STREET STUDIO,02-Central,733464500,611.00,62.05,548.95\nBRC RAILCAR SERVICE,02-Central,764703110,6301.68,843.40,5458.28\nBRC RAILCAR SERVICE,02-Central,764521000,3390.00,159.25,3230.75\nBRI SPECIALTY PRODUCTS,02-Central,733211000,680.00,236.94,443.06\nBRUCKNER SUPPLY,02-Central,718501000,117740.50,49261.35,68479.15\nBRUCKNER SUPPLY,02-Central,754926000,64634.88,47295.44,17339.44\nBRUCKNER SUPPLY,02-Central,779957280,24506.88,12225.02,12281.86\nBRUCKNER SUPPLY,02-Central,754906000,20554.56,14179.68,6374.88\nBRUCKNER SUPPLY,02-Central,785800000,15626.16,9464.63,6161.53\nBRUCKNER SUPPLY,02-Central,766121000,13312.00,3164.26,10147.74\nBRUCKNER SUPPLY,02-Central,754866000,10910.40,6664.98,4245.42\nBRUCKNER SUPPLY,02-Central,770522000,8599.50,616.79,7982.71\nBRUCKNER SUPPLY,02-Central,754826000,7916.88,4555.96,3360.92\nBRUCKNER SUPPLY,02-Central,764822000,7028.00,1877.99,5150.01\nBRUCKNER SUPPLY,02-Central,754886000,5370.96,3575.86,1795.10\nBRUCKNER SUPPLY,02-Central,785780000,5131.80,3494.62,1637.18\nBRUCKNER SUPPLY,02-Central,785740000,2974.40,1995.52,978.88\nBRUCKNER SUPPLY,02-Central,785760000,2218.80,1593.06,625.74\nBRUCKNER SUPPLY,02-Central,737301000,1915.20,736.08,1179.12\nBRUCKNER SUPPLY,02-Central,785580000,1718.40,1393.74,324.66\nBRUCKNER SUPPLY,02-Central,770510000,1705.25,96.40,1608.85\nBRUCKNER SUPPLY,02-Central,785620000,1597.20,1237.95,359.25\nBRUCKNER SUPPLY,02-Central,739380000,1046.88,720.92,325.96\nBRUCKNER SUPPLY,02-Central,777601000,889.95,248.42,641.53\nBRUCKNER SUPPLY,02-Central,777904301,740.50,234.94,505.56\nBRUNNER & LAY,02-Central,724261200,786.00,78.20,707.80\nBRUNNER & LAY,02-Central,733211000,456.00,47.39,408.61\nBRYAN TOOL & MACHINE,02-Central,739781100,1184.64,285.63,899.01\nBRYAN TOOL & MACHINE,02-Central,739380000,396.48,120.15,276.33\nBRYAN TOOL & MACHINE,02-Central,739370000,364.80,90.82,273.98\nBRYANT REFRACTORY CO.,02-Central,737321000,69822.72,18630.21,51192.51\nBRYANT REFRACTORY CO.,02-Central,739781100,16576.00,6664.66,9911.34\nBRYANT REFRACTORY CO.,02-Central,764781000,12464.00,3234.30,9229.70\nBRYANT REFRACTORY CO.,02-Central,754887000,6133.92,1424.30,4709.62\nBRYANT REFRACTORY CO.,02-Central,737912200,5621.60,1387.56,4234.04\nBRYANT REFRACTORY CO.,02-Central,779958161,2998.00,1827.70,1170.30\nBRYANT REFRACTORY CO.,02-Central,780620000,973.35,485.43,487.92\nBRYANT REFRACTORY CO.,02-Central,739761100,591.84,415.20,176.64\nBRYANT REFRACTORY CO.,02-Central,734651200,390.90,111.00,279.90\nBRYANT REFRACTORY CO.,02-Central,777642000,172.50,47.52,124.98\nBUILDING SERVICE IND,02-Central,735602110,36090.00,11244.34,24845.66\nBUILDING SERVICE IND,02-Central,735752000,19164.00,8073.27,11090.73\nBUILDING SERVICE IND,02-Central,735602000,14064.00,4283.48,9780.52\nBUILDING SERVICE IND,02-Central,735750000,1920.00,824.04,1095.96\nBULLSEYE INDUSTRIAL,02-Central,764341000,712.50,119.93,592.57\nBURKHART REFRACTORY INST.,02-Central,734211200,125.00,22.84,102.16\nC P FILMS INC.,02-Central,740402402,7262.50,1403.00,5859.50\nC T TAKAHASHI CO.,02-Central,740382101,892.80,296.37,596.43\nC.F. ROARK WELDING & ENG,02-Central,733151500,3238.00,359.49,2878.51\nCAL-CHEM CO.,02-Central,723960000,50324.00,15873.77,34450.23\nCAL-CHEM CO.,02-Central,720252303,2415.60,105.78,2309.82\nCAL-CHEM CO.,02-Central,734030000,342.00,157.54,184.46\nCARBONYX CARBON TECH.,02-Central,764951600,16343.04,8201.52,8141.52\nCARBONYX CARBON TECH.,02-Central,764951800,16343.04,8012.64,8330.40\nCARBONYX CARBON TECH.,02-Central,766121000,8630.00,5449.76,3180.24\nCARBONYX CARBON TECH.,02-Central,734351203,4950.00,1547.57,3402.43\nCARBONYX CARBON TECH.,02-Central,764952100,4032.00,1776.82,2255.18\nCARBONYX CARBON TECH.,02-Central,764951700,3064.32,1545.80,1518.52\nCARBONYX CARBON TECH.,02-Central,764902000,2250.00,957.81,1292.19\nCARBONYX CARBON TECH.,02-Central,764951900,2042.88,1031.66,1011.22\nCARDINAL FG,02-Central,739452000,9300.14,4223.37,5076.77\nCARDINAL FG,02-Central,764521000,7059.00,345.31,6713.69\nCARDINAL FG,02-Central,738501000,6251.90,4206.45,2045.45\nCARDINAL FG,02-Central,738521000,4372.48,2008.33,2364.15\nCARDINAL FG,02-Central,738601000,3430.56,1975.19,1455.37\nCARDINAL FG,02-Central,739741100,2530.88,1095.58,1435.30\nCARDINAL FG,02-Central,764901073,2220.00,437.40,1782.60\nCARDINAL FG,02-Central,764521073,2172.00,387.00,1785.00\nCARDINAL FG,02-Central,738361000,1405.44,657.88,747.56\nCARDINAL FG,02-Central,792730000,547.40,62.56,484.84\nCARDINAL FG,02-Central,768212000,374.00,23.12,350.88\nCARLISLE BRAKE & FRICTION,02-Central,734030000,1089.90,494.66,595.24\nCAROLINA GLASS & MIRROR,02-Central,733525600,717.50,65.49,652.01\nCAROLINA GLASS & MIRROR,02-Central,733511000,537.50,66.56,470.94\nCAST ALUMINUM SOLUTIONS,02-Central,733525600,752.50,71.05,681.45\nCAT-I MFG,02-Central,734311100,404.36,35.30,369.06\nCATACEL,02-Central,764221000,200.00,16.83,183.17\nCECO ABATEMENT,02-Central,779957221,20929.92,10427.93,10501.99\nCECO ABATEMENT,02-Central,764821000,2112.00,308.36,1803.64\nCECO ABATEMENT,02-Central,779952098,1188.84,589.10,599.74\nCECO ABATEMENT,02-Central,777642000,1051.50,351.26,700.24\nCECO ABATEMENT,02-Central,764902000,1014.00,191.55,822.45\nCECO ABATEMENT,02-Central,779952097,614.88,382.24,232.64\nCECO ABATEMENT,02-Central,764901000,522.50,145.30,377.20\nCECO ABATEMENT,02-Central,739781100,402.72,285.63,117.09\nCECO ABATEMENT,02-Central,738561000,340.00,124.14,215.86\nCECO ABATEMENT,02-Central,779956579,100.00,57.60,42.40\nCECO ABATEMENT,02-Central,779957600,80.00,76.30,3.70\nCENTURY ALUMINUM OF,02-Central,734311100,11650.32,1025.47,10624.85\nCERAMIC SYSTEMS INC.,02-Central,733403200,17468.00,3784.75,13683.25\nCERAMIC SYSTEMS INC.,02-Central,766121000,1182.00,653.98,528.02\nCERAMIC SYSTEMS INC.,02-Central,764341000,672.00,275.22,396.78\nCERAMIC SYSTEMS INC.,02-Central,764601000,600.00,263.16,336.84\nCERAMTEC NORTH AMERICA,02-Central,770500000,1075.50,51.44,1024.06\nCGR VALLEY PRODUCTS,02-Central,733870000,17690.65,6878.15,10812.50\nCGR VALLEY PRODUCTS,02-Central,733867100,14175.00,8303.86,5871.14\nCGR VALLEY PRODUCTS,02-Central,733586600,6000.48,730.36,5270.12\nCGR VALLEY PRODUCTS,02-Central,733511000,3255.00,526.55,2728.45\nCGR VALLEY PRODUCTS,02-Central,733211050,2368.80,428.64,1940.16\nCGR VALLEY PRODUCTS,02-Central,733721000,278.00,69.32,208.68\nCH2M HILL CONSTRUCTORS,02-Central,737301000,5085.44,2575.36,2510.08\nCH2M HILL CONSTRUCTORS,02-Central,734311100,363.44,35.30,328.14\nCHARLES JOHNSTON,02-Central,739380000,396.48,120.15,276.33\nCHASE CORPORATION-GLOBAL,02-Central,720219100,82980.00,1999.44,80980.56\nCHASE SPECIALTY COATINGS,02-Central,720219100,224201.25,5455.01,218746.24\nCHEM-CLAY CORPORATION,02-Central,777601000,386.72,72.02,314.70\nCHERNE CONTRACTING CORP,02-Central,737912200,4161.50,636.63,3524.87\nCHEVRON,02-Central,780270000,481.70,92.75,388.95\nCHIZ BROS INC,02-Central,764901000,428112.70,219814.24,208298.46\nCHIZ BROS INC,02-Central,764821000,250859.60,119423.09,131436.51\nCHIZ BROS INC,02-Central,742520150,212675.20,100384.32,112290.88\nCHIZ BROS INC,02-Central,764822000,162763.00,48373.26,114389.74\nCHIZ BROS INC,02-Central,734990000,154938.88,50417.59,104521.29\nCHIZ BROS INC,02-Central,782070000,126267.98,90079.01,36188.97\nCHIZ BROS INC,02-Central,718500000,112006.40,36944.89,75061.51\nCHIZ BROS INC,02-Central,742541500,102420.52,15380.09,87040.43\nCHIZ BROS INC,02-Central,780370000,83068.86,46496.76,36572.10\nCHIZ BROS INC,02-Central,733525600,82222.50,14677.98,67544.52\nCHIZ BROS INC,02-Central,764822110,79032.00,20653.87,58378.13\nCHIZ BROS INC,02-Central,779952165,71359.56,32699.81,38659.75\nCHIZ BROS INC,02-Central,779952102,67190.40,24858.78,42331.62\nCHIZ BROS INC,02-Central,725482900,65910.00,30617.78,35292.22\nCHIZ BROS INC,02-Central,733211050,63534.90,14076.51,49458.39\nCHIZ BROS INC,02-Central,764827550,53558.40,14313.48,39244.92\nCHIZ BROS INC,02-Central,779958876,53544.45,22331.69,31212.76\nCHIZ BROS INC,02-Central,770240000,53299.50,7049.42,46250.08\nCHIZ BROS INC,02-Central,742451500,51687.72,13383.10,38304.62\nCHIZ BROS INC,02-Central,742451300,47387.38,12269.64,35117.74\nCHIZ BROS INC,02-Central,739781100,45924.88,31668.28,14256.60\nCHIZ BROS INC,02-Central,781670000,45719.10,29981.80,15737.30\nCHIZ BROS INC,02-Central,779956835,42327.36,23428.68,18898.68\nCHIZ BROS INC,02-Central,733525500,41270.00,7054.71,34215.29\nCHIZ BROS INC,02-Central,779956823,39803.40,19593.42,20209.98\nCHIZ BROS INC,02-Central,770430000,39554.75,6021.20,33533.55\nCHIZ BROS INC,02-Central,733211000,37993.80,9216.43,28777.37\nCHIZ BROS INC,02-Central,780770000,35739.90,23434.82,12305.08\nCHIZ BROS INC,02-Central,771504000,35641.80,4673.99,30967.81\nCHIZ BROS INC,02-Central,791002500,33608.59,16835.62,16772.97\nCHIZ BROS INC,02-Central,779956826,33320.64,16103.52,17217.12\nCHIZ BROS INC,02-Central,770500000,32860.35,2752.38,30107.97\nCHIZ BROS INC,02-Central,764861000,31461.00,8243.84,23217.16\nCHIZ BROS INC,02-Central,727000025,31362.57,5448.40,25914.17\nCHIZ BROS INC,02-Central,733151550,31311.00,6767.89,24543.11\nCHIZ BROS INC,02-Central,739561200,30830.59,8899.65,21930.94\nCHIZ BROS INC,02-Central,770450000,29403.00,5077.91,24325.09\nCHIZ BROS INC,02-Central,734414100,28988.88,6882.41,22106.47\nCHIZ BROS INC,02-Central,770830000,25496.80,1307.75,24189.05\nCHIZ BROS INC,02-Central,770372000,25033.40,3217.02,21816.38\nCHIZ BROS INC,02-Central,764601000,24947.00,10283.89,14663.11\nCHIZ BROS INC,02-Central,764521000,23955.00,8507.50,15447.50\nCHIZ BROS INC,02-Central,734132000,23785.95,5497.92,18288.03\nCHIZ BROS INC,02-Central,777601000,23784.89,6719.88,17065.01\nCHIZ BROS INC,02-Central,727041200,23485.44,4180.76,19304.68\nCHIZ BROS INC,02-Central,742461100,23394.00,5539.34,17854.66\nCHIZ BROS INC,02-Central,770222000,23200.00,2810.62,20389.38\nCHIZ BROS INC,02-Central,764781040,22092.90,7133.22,14959.68\nCHIZ BROS INC,02-Central,739541020,21907.20,7671.60,14235.60\nCHIZ BROS INC,02-Central,770200000,21852.00,2326.32,19525.68\nCHIZ BROS INC,02-Central,733151500,21656.80,3569.34,18087.46\nCHIZ BROS INC,02-Central,770810000,21267.40,638.28,20629.12\nCHIZ BROS INC,02-Central,781970000,20944.44,10156.95,10787.49\nCHIZ BROS INC,02-Central,739541100,20292.71,5185.87,15106.84\nCHIZ BROS INC,02-Central,733511000,20226.89,4831.98,15394.91\nCHIZ BROS INC,02-Central,726000062,20100.00,4565.32,15534.68\nCHIZ BROS INC,02-Central,770440000,19920.00,3732.46,16187.54\nCHIZ BROS INC,02-Central,793330000,19820.00,4231.18,15588.82\nCHIZ BROS INC,02-Central,734211200,19708.64,2570.76,17137.88\nCHIZ BROS INC,02-Central,779956824,19609.92,9782.70,9827.22\nCHIZ BROS INC,02-Central,739761100,19475.76,12157.83,7317.93\nCHIZ BROS INC,02-Central,718501000,17964.80,3771.28,14193.52\nCHIZ BROS INC,02-Central,777904301,17826.50,8054.84,9771.66\nCHIZ BROS INC,02-Central,734910000,17716.70,4187.77,13528.93\nCHIZ BROS INC,02-Central,770560000,17581.20,2582.69,14998.51\nCHIZ BROS INC,02-Central,734162000,17380.80,8646.82,8733.98\nCHIZ BROS INC,02-Central,739641100,16204.86,8383.08,7821.78\nCHIZ BROS INC,02-Central,742471100,15470.56,4252.63,11217.93\nCHIZ BROS INC,02-Central,735602000,14544.00,4142.22,10401.78\nCHIZ BROS INC,02-Central,768221000,14224.00,3184.22,11039.78\nCHIZ BROS INC,02-Central,770512000,13837.95,1596.09,12241.86\nCHIZ BROS INC,02-Central,737912200,13207.00,3276.34,9930.66\nCHIZ BROS INC,02-Central,737311000,13157.76,6511.98,6645.78\nCHIZ BROS INC,02-Central,766121008,12160.00,6563.55,5596.45\nCHIZ BROS INC,02-Central,739681100,11784.96,5782.10,6002.86\nCHIZ BROS INC,02-Central,742451000,11610.00,3006.08,8603.92\nCHIZ BROS INC,02-Central,734311100,11002.52,1346.38,9656.14\nCHIZ BROS INC,02-Central,766122110,10574.40,2155.32,8419.08\nCHIZ BROS INC,02-Central,764781000,10292.40,2904.99,7387.41\nCHIZ BROS INC,02-Central,766121000,10056.00,4525.00,5531.00\nCHIZ BROS INC,02-Central,779956856,10005.12,5002.92,5002.20\nCHIZ BROS INC,02-Central,764901050,9982.40,5039.64,4942.76\nCHIZ BROS INC,02-Central,734251300,9631.99,968.29,8663.70\nCHIZ BROS INC,02-Central,779989329,9408.00,3668.03,5739.97\nCHIZ BROS INC,02-Central,733725500,9400.80,2807.33,6593.47\nCHIZ BROS INC,02-Central,770260000,9333.00,1255.14,8077.86\nCHIZ BROS INC,02-Central,764481000,9238.40,2486.12,6752.28\nCHIZ BROS INC,02-Central,734751200,8802.10,1724.70,7077.40\nCHIZ BROS INC,02-Central,771320000,8519.00,1396.26,7122.74\nCHIZ BROS INC,02-Central,770392000,8384.00,1318.81,7065.19\nCHIZ BROS INC,02-Central,733151600,8312.40,1425.15,6887.25\nCHIZ BROS INC,02-Central,764522000,8265.00,3410.29,4854.71\nCHIZ BROS INC,02-Central,779952109,8224.50,3290.04,4934.46\nCHIZ BROS INC,02-Central,770530000,8165.00,1588.85,6576.15\nCHIZ BROS INC,02-Central,764524010,8096.00,4369.55,3726.45\nCHIZ BROS INC,02-Central,739561100,8053.59,2199.06,5854.53\nCHIZ BROS INC,02-Central,733403200,7730.80,1050.74,6680.06\nCHIZ BROS INC,02-Central,733671000,7460.21,1161.03,6299.18\nCHIZ BROS INC,02-Central,739541200,7351.04,2086.40,5264.64\nCHIZ BROS INC,02-Central,780730000,7269.40,4570.41,2698.99\nCHIZ BROS INC,02-Central,781630000,7236.72,4746.28,2490.44\nCHIZ BROS INC,02-Central,770510000,7020.00,1102.38,5917.62\nCHIZ BROS INC,02-Central,764561000,6676.50,1540.60,5135.90\nCHIZ BROS INC,02-Central,738561000,6630.88,4777.92,1852.96\nCHIZ BROS INC,02-Central,766081013,6450.00,3311.23,3138.77\nCHIZ BROS INC,02-Central,779958865,6093.00,3510.58,2582.42\nCHIZ BROS INC,02-Central,785890000,5943.36,4715.44,1227.92\nCHIZ BROS INC,02-Central,768112000,5846.40,1454.90,4391.50\nCHIZ BROS INC,02-Central,782060000,5481.23,2491.40,2989.83\nCHIZ BROS INC,02-Central,764821073,5478.20,6269.40,-791.20\nCHIZ BROS INC,02-Central,764821027,5440.00,2748.10,2691.90\nCHIZ BROS INC,02-Central,764261000,5316.00,1373.61,3942.39\nCHIZ BROS INC,02-Central,795210000,5230.08,1850.32,3379.76\nCHIZ BROS INC,02-Central,739741100,5161.76,3577.55,1584.21\nCHIZ BROS INC,02-Central,770210000,4986.00,778.43,4207.57\nCHIZ BROS INC,02-Central,770710000,4952.00,134.52,4817.48\nCHIZ BROS INC,02-Central,764952010,4928.00,2365.61,2562.39\nCHIZ BROS INC,02-Central,777903100,4901.86,1946.28,2955.58\nCHIZ BROS INC,02-Central,734851000,4602.24,1186.82,3415.42\nCHIZ BROS INC,02-Central,779958676,4551.12,2412.03,2139.09\nCHIZ BROS INC,02-Central,770520000,4537.50,444.35,4093.15\nCHIZ BROS INC,02-Central,779958247,4513.60,2780.60,1733.00\nCHIZ BROS INC,02-Central,764341000,4492.00,1605.45,2886.55\nCHIZ BROS INC,02-Central,770220000,4470.00,503.15,3966.85\nCHIZ BROS INC,02-Central,782020000,4390.04,1996.84,2393.20\nCHIZ BROS INC,02-Central,733423800,4288.00,767.33,3520.67\nCHIZ BROS INC,02-Central,768131000,4272.85,1344.47,2928.38\nCHIZ BROS INC,02-Central,777730600,4194.00,1276.20,2917.80\nCHIZ BROS INC,02-Central,779957955,3987.72,2538.39,1449.33\nCHIZ BROS INC,02-Central,764824000,3900.00,1896.72,2003.28\nCHIZ BROS INC,02-Central,733403300,3863.20,490.18,3373.02\nCHIZ BROS INC,02-Central,770202000,3645.00,911.40,2733.60\nCHIZ BROS INC,02-Central,784400000,3643.20,2180.00,1463.20\nCHIZ BROS INC,02-Central,740063000,3617.03,1071.46,2545.57\nCHIZ BROS INC,02-Central,793550000,3570.00,841.18,2728.82\nCHIZ BROS INC,02-Central,764902000,3380.00,638.54,2741.46\nCHIZ BROS INC,02-Central,766261000,3310.00,1563.83,1746.17\nCHIZ BROS INC,02-Central,779958862,3308.40,1876.30,1432.10\nCHIZ BROS INC,02-Central,765661000,3200.00,1287.10,1912.90\nCHIZ BROS INC,02-Central,779987564,3174.08,1033.36,2140.72\nCHIZ BROS INC,02-Central,740142500,3147.84,2360.45,787.39\nCHIZ BROS INC,02-Central,741061000,3142.82,143.71,2999.11\nCHIZ BROS INC,02-Central,770522000,3094.00,710.36,2383.64\nCHIZ BROS INC,02-Central,766122000,2952.00,1035.45,1916.55\nCHIZ BROS INC,02-Central,742491700,2929.88,1812.66,1117.22\nCHIZ BROS INC,02-Central,733444300,2845.20,570.67,2274.53\nCHIZ BROS INC,02-Central,770540000,2719.50,628.60,2090.90\nCHIZ BROS INC,02-Central,777902700,2597.49,1141.55,1455.94\nCHIZ BROS INC,02-Central,779959183,2585.88,1801.80,784.08\nCHIZ BROS INC,02-Central,771120000,2529.60,105.38,2424.22\nCHIZ BROS INC,02-Central,751165000,2515.78,1509.46,1006.32\nCHIZ BROS INC,02-Central,764482000,2509.20,626.48,1882.72\nCHIZ BROS INC,02-Central,779958863,2345.20,1282.68,1062.52\nCHIZ BROS INC,02-Central,779952104,2289.15,1030.11,1259.04\nCHIZ BROS INC,02-Central,737301000,2287.36,490.72,1796.64\nCHIZ BROS INC,02-Central,742548000,2275.00,284.20,1990.80\nCHIZ BROS INC,02-Central,793336074,2241.00,1269.57,971.43\nCHIZ BROS INC,02-Central,766231000,2100.00,719.90,1380.10\nCHIZ BROS INC,02-Central,780760000,2091.60,411.27,1680.33\nCHIZ BROS INC,02-Central,779261000,2010.54,560.02,1450.52\nCHIZ BROS INC,02-Central,739561000,1960.27,602.39,1357.88\nCHIZ BROS INC,02-Central,770300000,1955.25,226.02,1729.23\nCHIZ BROS INC,02-Central,779988833,1925.42,859.30,1066.12\nCHIZ BROS INC,02-Central,779959180,1829.96,1222.49,607.47\nCHIZ BROS INC,02-Central,768141000,1725.00,405.21,1319.79\nCHIZ BROS INC,02-Central,770212000,1716.00,468.45,1247.55\nCHIZ BROS INC,02-Central,780680000,1671.12,427.81,1243.31\nCHIZ BROS INC,02-Central,764761000,1660.00,720.18,939.82\nCHIZ BROS INC,02-Central,734861000,1485.40,611.14,874.26\nCHIZ BROS INC,02-Central,733721000,1470.00,452.45,1017.55\nCHIZ BROS INC,02-Central,771132000,1459.40,71.60,1387.80\nCHIZ BROS INC,02-Central,779957171,1414.80,1197.99,216.81\nCHIZ BROS INC,02-Central,737211000,1336.39,289.24,1047.15\nCHIZ BROS INC,02-Central,770140000,1309.72,138.91,1170.81\nCHIZ BROS INC,02-Central,740283502,1302.40,193.64,1108.76\nCHIZ BROS INC,02-Central,733464500,1236.00,195.16,1040.84\nCHIZ BROS INC,02-Central,770600000,1231.02,271.26,959.76\nCHIZ BROS INC,02-Central,764562000,1161.30,257.01,904.29\nCHIZ BROS INC,02-Central,777642000,1153.05,360.61,792.44\nCHIZ BROS INC,02-Central,754285000,1145.20,498.79,646.41\nCHIZ BROS INC,02-Central,781800000,1124.82,596.25,528.57\nCHIZ BROS INC,02-Central,733444200,1087.00,181.35,905.65\nCHIZ BROS INC,02-Central,739370000,1086.40,967.68,118.72\nCHIZ BROS INC,02-Central,763991000,1072.80,275.16,797.64\nCHIZ BROS INC,02-Central,780690000,1032.72,254.51,778.21\nCHIZ BROS INC,02-Central,770380000,1016.40,183.87,832.53\nCHIZ BROS INC,02-Central,754265000,1012.20,465.21,546.99\nCHIZ BROS INC,02-Central,738521000,962.72,717.26,245.46\nCHIZ BROS INC,02-Central,793346074,924.00,518.98,405.02\nCHIZ BROS INC,02-Central,734900000,891.48,343.64,547.84\nCHIZ BROS INC,02-Central,734651200,872.65,147.00,725.65\nCHIZ BROS INC,02-Central,738401000,828.90,642.24,186.66\nCHIZ BROS INC,02-Central,779958864,799.50,529.02,270.48\nCHIZ BROS INC,02-Central,734890000,793.73,198.96,594.77\nCHIZ BROS INC,02-Central,726020005,745.12,167.16,577.96\nCHIZ BROS INC,02-Central,779989944,743.80,678.50,65.30\nCHIZ BROS INC,02-Central,779221000,743.66,209.48,534.18\nCHIZ BROS INC,02-Central,741620310,732.00,145.82,586.18\nCHIZ BROS INC,02-Central,767970010,716.50,5.56,710.94\nCHIZ BROS INC,02-Central,792702000,710.00,193.20,516.80\nCHIZ BROS INC,02-Central,741640310,708.00,131.71,576.29\nCHIZ BROS INC,02-Central,724311200,697.90,154.28,543.62\nCHIZ BROS INC,02-Central,734711100,672.21,135.55,536.66\nCHIZ BROS INC,02-Central,792650000,656.00,228.70,427.30\nCHIZ BROS INC,02-Central,770580000,652.00,169.46,482.54\nCHIZ BROS INC,02-Central,793320074,635.00,321.38,313.62\nCHIZ BROS INC,02-Central,779959182,623.90,507.37,116.53\nCHIZ BROS INC,02-Central,727000440,614.40,121.92,492.48\nCHIZ BROS INC,02-Central,770620100,585.00,55.28,529.72\nCHIZ BROS INC,02-Central,764762000,577.60,252.56,325.04\nCHIZ BROS INC,02-Central,777650000,535.20,285.32,249.88\nCHIZ BROS INC,02-Central,777671000,526.40,460.50,65.90\nCHIZ BROS INC,02-Central,734830000,525.00,90.82,434.18\nCHIZ BROS INC,02-Central,739290000,524.16,215.31,308.85\nCHIZ BROS INC,02-Central,741001000,522.73,87.94,434.79\nCHIZ BROS INC,02-Central,779989946,496.80,480.90,15.90\nCHIZ BROS INC,02-Central,780501000,494.40,232.71,261.69\nCHIZ BROS INC,02-Central,779959804,488.25,428.95,59.30\nCHIZ BROS INC,02-Central,779989945,472.90,461.70,11.20\nCHIZ BROS INC,02-Central,733505300,465.00,77.58,387.42\nCHIZ BROS INC,02-Central,740123000,427.14,121.37,305.77\nCHIZ BROS INC,02-Central,779959204,407.58,199.73,207.85\nCHIZ BROS INC,02-Central,754067000,397.08,125.08,272.00\nCHIZ BROS INC,02-Central,734121000,372.00,83.56,288.44\nCHIZ BROS INC,02-Central,792654000,341.00,129.68,211.32\nCHIZ BROS INC,02-Central,776131000,340.20,252.08,88.12\nCHIZ BROS INC,02-Central,779001000,338.94,84.71,254.23\nCHIZ BROS INC,02-Central,739541000,311.04,71.62,239.42\nCHIZ BROS INC,02-Central,779957933,285.93,216.60,69.33\nCHIZ BROS INC,02-Central,779987255,201.60,92.70,108.90\nCHIZ BROS INC,02-Central,776101000,131.40,70.80,60.60\nCHIZ BROS INC,02-Central,734611100,131.30,26.96,104.34\nCHIZ BROS INC,02-Central,780330000,129.45,35.90,93.55\nCHIZ BROS INC,02-Central,779181000,69.87,21.45,48.42\nCHIZ BROS INC,02-Central,773591000,44.10,13.41,30.69\nCHIZ BROS INC,02-Central,779986450,10.80,2.77,8.03\nCHRISTY CATALYTICS LLC,02-Central,777904301,259.18,117.47,141.71\nCHRISTY CATALYTICS LLC,02-Central,734830000,246.00,44.64,201.36\nCHRISTY INDUSTRIAL SERVIC,02-Central,780650000,6874.88,2379.87,4495.01\nCHRISTY INDUSTRIAL SERVIC,02-Central,781500000,5951.88,961.64,4990.24\nCHRISTY INDUSTRIAL SERVIC,02-Central,754924014,3840.00,2300.72,1539.28\nCHRISTY INDUSTRIAL SERVIC,02-Central,718501000,3075.00,808.14,2266.86\nCHRISTY INDUSTRIAL SERVIC,02-Central,765661000,2304.00,763.56,1540.44\nCHRISTY INDUSTRIAL SERVIC,02-Central,754864023,1724.80,915.78,809.02\nCHRISTY INDUSTRIAL SERVIC,02-Central,754327000,1020.96,416.10,604.86\nCHRISTY INDUSTRIAL SERVIC,02-Central,754864020,915.60,392.48,523.12\nCHRISTY INDUSTRIAL SERVIC,02-Central,739761100,853.92,361.97,491.95\nCHRISTY REFRACTORIES CO,02-Central,768242007,194216.00,75633.28,118582.72\nCHRISTY REFRACTORIES CO,02-Central,766261020,160372.00,93950.99,66421.01\nCHRISTY REFRACTORIES CO,02-Central,734990000,58344.00,10782.04,47561.96\nCHRISTY REFRACTORIES CO,02-Central,764529600,48400.00,20108.26,28291.74\nCHRISTY REFRACTORIES CO,02-Central,733505300,47145.00,8132.54,39012.46\nCHRISTY REFRACTORIES CO,02-Central,733721000,12121.20,2966.63,9154.57\nCHRISTY REFRACTORIES CO,02-Central,740382150,9979.56,4150.89,5828.67\nCHRISTY REFRACTORIES CO,02-Central,737912200,9197.00,3440.23,5756.77\nCHRISTY REFRACTORIES CO,02-Central,770570000,5444.85,477.03,4967.82\nCHRISTY REFRACTORIES CO,02-Central,734151100,4714.82,992.12,3722.70\nCHRISTY REFRACTORIES CO,02-Central,770220000,4458.00,414.56,4043.44\nCHRISTY REFRACTORIES CO,02-Central,734173000,4295.00,1169.08,3125.92\nCHRISTY REFRACTORIES CO,02-Central,768132210,3800.00,1318.32,2481.68\nCHRISTY REFRACTORIES CO,02-Central,733444200,3621.00,589.72,3031.28\nCHRISTY REFRACTORIES CO,02-Central,735602000,2990.00,966.66,2023.34\nCHRISTY REFRACTORIES CO,02-Central,764342000,2736.00,2246.52,489.48\nCHRISTY REFRACTORIES CO,02-Central,733383000,2688.40,456.38,2232.02\nCHRISTY REFRACTORIES CO,02-Central,770240000,2685.00,278.62,2406.38\nCHRISTY REFRACTORIES CO,02-Central,770260000,2025.00,210.99,1814.01\nCHRISTY REFRACTORIES CO,02-Central,770200000,1998.00,208.81,1789.19\nCHRISTY REFRACTORIES CO,02-Central,740382101,1882.44,758.49,1123.95\nCHRISTY REFRACTORIES CO,02-Central,734162000,1333.64,563.87,769.77\nCHRISTY REFRACTORIES CO,02-Central,770245000,1146.79,259.92,886.87\nCHRISTY REFRACTORIES CO,02-Central,774401000,1075.50,339.36,736.14\nCHRISTY REFRACTORIES CO,02-Central,733211000,753.90,159.61,594.29\nCHRISTY REFRACTORIES CO,02-Central,770300000,668.25,75.34,592.91\nCHRISTY REFRACTORIES CO,02-Central,733151600,621.20,106.23,514.97\nCHRISTY REFRACTORIES CO,02-Central,774311000,621.00,377.43,243.57\nCHRISTY REFRACTORIES CO,02-Central,733211050,478.60,99.39,379.21\nCHRISTY REFRACTORIES CO,02-Central,733141300,324.00,69.73,254.27\nCHRISTY REFRACTORIES CO,02-Central,737902100,287.00,67.12,219.88\nCHRISTY REFRACTORIES CO,02-Central,770210000,285.00,31.52,253.48\nCHRISTY REFRACTORIES CO,02-Central,734751200,269.00,74.78,194.22\nCHRISTY REFRACTORIES CO,02-Central,734711100,262.58,54.10,208.48\nCHRISTY REFRACTORIES CO,02-Central,764482000,172.80,34.10,138.70\nCLEMSON UNIVERSITY,02-Central,788200010,200.43,35.27,165.16\nCOLUMBUS RUBBER & GASKET,02-Central,733211000,1391.00,147.98,1243.02\nCOMPOSITE USA,02-Central,733510000,565.00,79.51,485.49\nCOMPOSITE USA,02-Central,734211200,243.46,23.38,220.08\nCONDO ELECTRIC INDUSTRIAL,02-Central,733141300,496.00,69.73,426.27\nCONDO ELECTRIC INDUSTRIAL,02-Central,733151600,450.00,46.64,403.36\nCONSOLIDATED METAL PRODUC,02-Central,740361701,579.60,318.65,260.95\nCONSUTECH SYSTEMS LLC,02-Central,777642000,7665.00,2302.55,5362.45\nCORE PIPE,02-Central,724261200,786.00,78.20,707.80\nCORMETECH INC,02-Central,770200000,630.00,40.79,589.21\nCORVID TECHNOLOGIES,02-Central,777642000,143.00,50.36,92.64\n\"CPP - CLEVELAND, INC.\",02-Central,764781000,22243.00,5719.18,16523.82\n\"CPP - CLEVELAND, INC.\",02-Central,764461006,15028.00,4749.30,10278.70\nCREATIVE ENGINEERS,02-Central,764901000,1275.75,48.42,1227.33\nCROWN BATTERY MFG. CO.,02-Central,733151500,1372.00,149.49,1222.51\nCYTEC,02-Central,723960000,98698.50,27780.73,70917.77\nCYTEC,02-Central,734000008,1701.00,362.77,1338.23\nCYTEC,02-Central,720219100,1339.20,39.99,1299.21\nCYTEC,02-Central,734000006,1182.00,159.44,1022.56\nDANSER INC,02-Central,727000140,207544.56,88726.90,118817.66\nDANSER INC,02-Central,763991000,177678.80,123197.80,54481.00\nDANSER INC,02-Central,734910000,41998.80,11524.33,30474.47\nDANSER INC,02-Central,734830000,11152.00,1517.88,9634.12\nDANSER INC,02-Central,734311100,1028.64,101.11,927.53\nDANSER INC,02-Central,770220000,690.00,55.79,634.21\nDANSER INC,02-Central,770200000,486.00,42.41,443.59\nDARK CORNER GLASS,02-Central,737321000,1551.04,214.14,1336.90\nDATA CENTER RESOURCES LLC,02-Central,735751000,2002.00,621.86,1380.14\nDATA CENTER RESOURCES LLC,02-Central,735751020,704.00,260.73,443.27\nDAVIS SALES AGENCY,02-Central,738561000,10529.92,3930.10,6599.82\nDEAS CONSTRUCTION,02-Central,739741100,385.60,168.32,217.28\nDECATUR RUBBER & GASKET,02-Central,770510000,940.00,41.39,898.61\nDECATUR RUBBER & GASKET,02-Central,770200000,666.00,42.41,623.59\nDECATUR RUBBER & GASKET,02-Central,733444200,332.00,34.69,297.31\nDELTA REFRACTORIES,02-Central,766121000,20000.00,10899.52,9100.48\nDELTA REFRACTORIES,02-Central,734950000,4540.00,1131.28,3408.72\nDELTA REFRACTORIES,02-Central,764821000,4260.00,1842.95,2417.05\nDELTA REFRACTORIES,02-Central,764901000,2712.00,516.60,2195.40\nDELTA REFRACTORIES,02-Central,766081010,1320.00,545.48,774.52\nDELTA REFRACTORIES,02-Central,740402402,815.00,193.66,621.34\nDELTA REFRACTORIES,02-Central,734851000,380.23,91.40,288.83\nDELTA REFRACTORIES,02-Central,733141300,336.00,77.97,258.03\nDELTA REFRACTORIES,02-Central,733151600,324.00,53.12,270.88\nDENTSPLY CERAMCO,02-Central,734311100,2800.24,329.75,2470.49\nDENTSPLY CERAMCO,02-Central,734211200,1344.00,137.04,1206.96\nDIAMOND POWER INT'L. INC.,02-Central,727000140,3241.60,284.13,2957.47\nDIELECTRIC SOLUTIONS LLC,02-Central,733403100,1107.25,92.79,1014.46\nDILLON SUPPLY COMPANY,02-Central,770290000,2938.50,238.94,2699.56\nDILLON SUPPLY,02-Central,770290000,3918.00,318.58,3599.42\nDISTRIBUTION INT'L.,02-Central,735602000,384858.40,133532.97,251325.43\nDISTRIBUTION INT'L.,02-Central,735602110,163798.40,61559.86,102238.54\nDISTRIBUTION INT'L.,02-Central,764521030,114990.00,66784.69,48205.31\nDISTRIBUTION INT'L.,02-Central,764521000,75605.00,28435.38,47169.62\nDISTRIBUTION INT'L.,02-Central,764522000,26152.00,9225.83,16926.17\nDISTRIBUTION INT'L.,02-Central,768111200,17712.00,6791.47,10920.53\nDISTRIBUTION INT'L.,02-Central,735750000,17623.20,7140.05,10483.15\nDISTRIBUTION INT'L.,02-Central,768111220,17064.00,8544.70,8519.30\nDISTRIBUTION INT'L.,02-Central,764906000,6336.00,2330.33,4005.67\nDISTRIBUTION INT'L.,02-Central,734830000,4554.40,768.14,3786.26\nDISTRIBUTION INT'L.,02-Central,735752000,2662.40,1031.04,1631.36\nDISTRIBUTION INT'L.,02-Central,739561100,2338.56,799.66,1538.90\nDISTRIBUTION INT'L.,02-Central,733211050,2035.60,621.21,1414.39\nDISTRIBUTION INT'L.,02-Central,733511000,1550.00,1504.42,45.58\nDISTRIBUTION INT'L.,02-Central,764601000,1224.00,425.15,798.85\nDISTRIBUTION INT'L.,02-Central,733725500,1152.00,589.40,562.60\nDISTRIBUTION INT'L.,02-Central,764602000,954.60,317.96,636.64\nDISTRIBUTION INT'L.,02-Central,734151100,832.20,186.90,645.30\nDISTRIBUTION INT'L.,02-Central,735602010,812.50,470.93,341.57\nDISTRIBUTION INT'L.,02-Central,733671000,485.86,112.55,373.31\nDISTRIBUTION INT'L.,02-Central,739741100,436.99,336.64,100.35\nDISTRIBUTION INT'L.,02-Central,739370000,388.22,276.48,111.74\nDISTRIBUTION INT'L.,02-Central,777601000,287.95,58.80,229.15\nDISTRIBUTION INT'L.,02-Central,770220000,258.60,27.35,231.25\nDISTRIBUTION INT'L.,02-Central,733211000,201.00,53.20,147.80\nDISTRIBUTION INT'L.,02-Central,764901000,183.00,64.06,118.94\nDISTRIBUTION INT'L.,02-Central,734900000,171.60,57.54,114.06\nDISTRIBUTION INT'L.,02-Central,768231000,165.30,53.93,111.37\nDIVERSIFIED INSULATION,02-Central,735602000,48384.00,16461.45,31922.55\nDIVERSIFIED INSULATION,02-Central,768241000,44998.00,12500.72,32497.28\nDIVERSIFIED INSULATION,02-Central,768231000,24155.00,6627.45,17527.55\nDIVERSIFIED INSULATION,02-Central,768750000,1394.40,149.20,1245.20\nDIVERSIFIED INSULATION,02-Central,735602110,840.00,262.52,577.48\nDIVERSIFIED INSULATION,02-Central,764901073,140.50,72.90,67.60\nDOMTAR A.W. LLC,02-Central,733525500,1460.00,140.79,1319.21\nDOUGLAS MACHINES CORP.,02-Central,733211050,3740.00,422.43,3317.57\nDUMORE ENTERPRISES LTD,02-Central,733211000,912.00,94.78,817.22\nDURR SYSTEMS INC.,02-Central,777904301,18130.00,8222.73,9907.27\nDURR SYSTEMS INC.,02-Central,784140000,8866.20,4100.52,4765.68\nDURR SYSTEMS INC.,02-Central,780250000,7309.76,3710.82,3598.94\nDURR SYSTEMS INC.,02-Central,779959184,1791.60,807.62,983.98\nDURR SYSTEMS INC.,02-Central,764821000,1375.00,344.42,1030.58\nDURR SYSTEMS INC.,02-Central,780501000,232.68,116.36,116.32\nDURR SYSTEMS INC.,02-Central,779956599,35.80,28.66,7.14\nDeANGELO,02-Central,770200000,4284.00,296.89,3987.11\nE I DUPONT DE NEMOURS &,02-Central,724071900,12620.65,1776.07,10844.58\nE I DUPONT DE NEMOURS &,02-Central,733505100,1600.00,180.18,1419.82\nE TIP INC.,02-Central,733211000,456.00,47.39,408.61\nE.I.DUPONT DE NEMOURS&CO,02-Central,724071900,8714.15,1172.75,7541.40\nE.I.DUPONT DE NEMOURS&CO,02-Central,733505100,2295.00,247.77,2047.23\nE.I.DUPONT DE NEMOURS&CO,02-Central,733464500,298.00,29.52,268.48\nE.K.S. PARTS INC.,02-Central,734900000,48688.00,2269.62,46418.38\nE.K.S. PARTS INC.,02-Central,734311100,9173.60,2022.18,7151.42\nEAST COAST METAL,02-Central,735602000,23626.00,5878.31,17747.69\n\"EB AIRFOILS, LLC\",02-Central,739641100,926.64,274.86,651.78\n\"EB AIRFOILS, LLC\",02-Central,764342000,859.00,46.80,812.20\nEBERBACH CORP,02-Central,764821000,430.00,15.36,414.64\nEBNER FURNACES INC,02-Central,777902700,34306.86,15546.90,18759.96\nECONOMY DIE & GASKET,02-Central,733464400,4382.00,607.18,3774.82\nECONOMY DIE & GASKET,02-Central,733444100,1028.00,244.86,783.14\nECONOMY DIE & GASKET,02-Central,733423600,371.20,45.08,326.12\nEDEN PARK ILLUMINATION,02-Central,739390000,2103.36,494.59,1608.77\nEFFICIENT MACHINE PRODUCT,02-Central,740382101,2635.20,845.57,1789.63\nEISENMANN CORPORATION,02-Central,779952186,202744.08,154554.19,48189.89\nEISENMANN CORPORATION,02-Central,779952185,53820.00,34419.86,19400.14\nEISENMANN CORPORATION,02-Central,764822000,30212.00,11564.35,18647.65\nEISENMANN CORPORATION,02-Central,777642000,5994.00,2719.44,3274.56\nEISENMANN CORPORATION,02-Central,764901000,3900.00,1921.71,1978.29\nEISENMANN CORPORATION,02-Central,739781100,2820.48,1142.52,1677.96\nEISENMANN CORPORATION,02-Central,739390000,2785.92,1941.12,844.80\nEISENMANN CORPORATION,02-Central,779958696,1027.20,699.73,327.47\nEISENMANN CORPORATION,02-Central,764561000,810.00,221.77,588.23\nEISENMANN CORPORATION,02-Central,789180000,693.80,231.56,462.24\nEISENMANN CORPORATION,02-Central,764601000,688.00,212.58,475.42\nEISENMANN CORPORATION,02-Central,734611100,618.24,217.44,400.80\nEISENMANN CORPORATION,02-Central,773450100,496.80,120.66,376.14\nEISENMANN CORPORATION,02-Central,764521000,456.50,146.27,310.23\nEISENMANN CORPORATION,02-Central,739451000,419.84,232.07,187.77\nEISENMANN CORPORATION,02-Central,779985087,165.60,62.88,102.72\nEISENMANN CORPORATION,02-Central,780190000,129.24,112.37,16.87\nEISENMANN CORPORATION,02-Central,771910510,124.80,42.96,81.84\nEISENMANN CORPORATION,02-Central,773450200,83.20,160.40,-77.20\nELECTRO-FERRO CENTROL SAC,02-Central,733464600,5512.00,814.17,4697.83\nELECTROCHEMICAL SYSTEMS,02-Central,738401000,655.20,140.69,514.51\nEMPIRE COMFORT SYSTEM,02-Central,726020019,94264.00,52499.09,41764.91\nEMPIRE COMFORT SYSTEM,02-Central,726020018,15045.00,7749.80,7295.20\nEMPIRE COMFORT SYSTEM,02-Central,738601000,6308.16,4890.12,1418.04\nEMPIRE COMFORT SYSTEM,02-Central,777601000,639.18,130.82,508.36\nEMPIRE COMFORT SYSTEM,02-Central,777642000,281.00,82.04,198.96\nEMPIRE COMFORT SYSTEM,02-Central,734900000,134.60,38.36,96.24\nENGINEERED PRODUCTS CO.,02-Central,735602000,27390.00,8573.85,18816.15\nENGINEERED PRODUCTS CO.,02-Central,764821000,1900.00,626.21,1273.79\nENGINETICS AEROSPACE,02-Central,740283502,1680.80,220.35,1460.45\nENGINETICS AEROSPACE,02-Central,733525400,1505.00,148.27,1356.73\nENGINETICS AEROSPACE,02-Central,733444200,696.00,77.28,618.72\nENSER CORPORATION,02-Central,740382201,4460.00,1202.06,3257.94\nENSER CORPORATION,02-Central,740402402,3172.50,628.36,2544.14\nENVIRACARBON,02-Central,734900000,431.60,76.72,354.88\nENVIRONEX INC,02-Central,764342000,893.00,45.78,847.22\nENVIRONEX INC,02-Central,764301000,670.00,37.11,632.89\n\"ENVIRONMENTAL SERVICES,\",02-Central,770560000,3306.00,258.27,3047.73\nERICO INTERNATIONAL CORP.,02-Central,770218000,23760.00,3172.23,20587.77\nERIKS MIDWEST INC.,02-Central,733211000,935.00,100.59,834.41\nESCO TURBINE TECHNOLOGIES,02-Central,764781000,75408.00,20410.65,54997.35\nESCO TURBINE TECHNOLOGIES,02-Central,764461006,61591.00,19661.20,41929.80\nESSEX GROUP,02-Central,733211000,1188.00,201.19,986.81\nESSEX GROUP,02-Central,739741100,856.96,336.64,520.32\nESTERLINE,02-Central,739781100,18046.40,7071.70,10974.70\nESTERLINE,02-Central,739741100,5075.20,1784.80,3290.40\nESTERLINE,02-Central,739561200,2439.68,455.68,1984.00\nESTERLINE,02-Central,739561000,492.48,86.19,406.29\nESTERLINE,02-Central,793000000,393.60,22.98,370.62\nESTES REFRACTORY SALES &,02-Central,770560000,5988.00,358.02,5629.98\nESTES REFRACTORY SALES &,02-Central,764901000,2350.00,640.57,1709.43\nESTES REFRACTORY SALES &,02-Central,733464500,524.00,130.10,393.90\nETS SCHAEFER,02-Central,766121132,915895.60,529020.31,386875.29\nETS SCHAEFER,02-Central,766121133,350513.60,172031.13,178482.47\nETS SCHAEFER,02-Central,764521100,322646.00,153946.03,168699.97\nETS SCHAEFER,02-Central,733151500,296345.20,50672.96,245672.24\nETS SCHAEFER,02-Central,764827010,233397.00,99022.13,134374.87\nETS SCHAEFER,02-Central,768221010,187937.00,76930.97,111006.03\nETS SCHAEFER,02-Central,764903100,94403.25,41678.95,52724.30\nETS SCHAEFER,02-Central,765665100,79582.00,32508.68,47073.32\nETS SCHAEFER,02-Central,764261100,79452.80,49608.54,29844.26\nETS SCHAEFER,02-Central,764462510,61018.80,39665.25,21353.55\nETS SCHAEFER,02-Central,764785100,44280.00,24541.31,19738.69\nETS SCHAEFER,02-Central,764265110,42515.42,23941.95,18573.47\nETS SCHAEFER,02-Central,764264052,34132.00,23787.58,10344.42\nETS SCHAEFER,02-Central,770230000,31094.70,3326.38,27768.32\nETS SCHAEFER,02-Central,764901054,30766.70,13551.74,17214.96\nETS SCHAEFER,02-Central,766121007,28680.00,16932.71,11747.29\nETS SCHAEFER,02-Central,737912200,23532.51,5005.35,18527.16\nETS SCHAEFER,02-Central,764824002,22558.40,9782.96,12775.44\nETS SCHAEFER,02-Central,764828710,21196.80,8089.04,13107.76\nETS SCHAEFER,02-Central,764829200,19960.00,8532.40,11427.60\nETS SCHAEFER,02-Central,764345053,17016.80,11141.62,5875.18\nETS SCHAEFER,02-Central,793540000,14553.00,4417.06,10135.94\nETS SCHAEFER,02-Central,764266501,14254.40,8022.87,6231.53\nETS SCHAEFER,02-Central,733464500,14049.00,2156.60,11892.40\nETS SCHAEFER,02-Central,766122006,13365.00,7737.95,5627.05\nETS SCHAEFER,02-Central,764762200,12963.60,7341.68,5621.92\nETS SCHAEFER,02-Central,734900000,12038.80,4668.56,7370.24\nETS SCHAEFER,02-Central,734851000,11342.90,2421.37,8921.53\nETS SCHAEFER,02-Central,764787102,10612.80,5963.94,4648.86\nETS SCHAEFER,02-Central,766121612,10332.00,6437.90,3894.10\nETS SCHAEFER,02-Central,764902010,8952.00,3633.52,5318.48\nETS SCHAEFER,02-Central,764261610,8578.98,4921.19,3657.79\nETS SCHAEFER,02-Central,777601000,8379.56,2237.08,6142.48\nETS SCHAEFER,02-Central,733211050,8100.90,1745.61,6355.29\nETS SCHAEFER,02-Central,733211000,7084.50,1444.03,5640.47\nETS SCHAEFER,02-Central,766122000,6265.00,4026.70,2238.30\nETS SCHAEFER,02-Central,764561200,6235.00,3326.93,2908.07\nETS SCHAEFER,02-Central,764483200,5983.20,3936.67,2046.53\nETS SCHAEFER,02-Central,764601051,5482.50,3067.08,2415.42\nETS SCHAEFER,02-Central,764821000,5222.00,2191.72,3030.28\nETS SCHAEFER,02-Central,734910000,4901.65,1155.73,3745.92\nETS SCHAEFER,02-Central,764862000,4648.35,2150.54,2497.81\nETS SCHAEFER,02-Central,764261600,4377.60,2413.56,1964.04\nETS SCHAEFER,02-Central,768221000,4272.10,1731.20,2540.90\nETS SCHAEFER,02-Central,764465010,3645.60,2400.30,1245.30\nETS SCHAEFER,02-Central,766162000,2918.35,2146.00,772.35\nETS SCHAEFER,02-Central,764864006,2236.00,1069.62,1166.38\nETS SCHAEFER,02-Central,766161100,1822.00,1050.23,771.77\nETS SCHAEFER,02-Central,764762000,1702.40,969.38,733.02\nETS SCHAEFER,02-Central,733525600,1417.50,207.59,1209.91\nETS SCHAEFER,02-Central,766081010,1207.00,918.70,288.30\nETS SCHAEFER,02-Central,764341000,1147.20,767.58,379.62\nETS SCHAEFER,02-Central,793330000,1050.00,312.16,737.84\nETS SCHAEFER,02-Central,734890000,833.42,207.69,625.73\nETS SCHAEFER,02-Central,764561000,631.50,332.66,298.84\nETS SCHAEFER,02-Central,770210000,544.00,60.63,483.37\nETS SCHAEFER,02-Central,764861000,447.20,207.82,239.38\nETS SCHAEFER,02-Central,768222000,350.80,136.62,214.18\nETS SCHAEFER,02-Central,792760000,228.63,58.31,170.32\nETS SCHAEFER,02-Central,734990000,149.72,27.65,122.07\nETS SCHAEFER,02-Central,768211000,116.50,23.39,93.11\nEUROFINS SCIENTIFIC,02-Central,727000140,1041.60,65.10,976.50\nEVERBURN MANUFACTURING CO,02-Central,726000062,646200.00,435246.78,210953.22\nEVERBURN MANUFACTURING CO,02-Central,727000060,16800.00,11913.27,4886.73\nEVERBURN MANUFACTURING CO,02-Central,726000240,5443.20,3594.43,1848.77\nEVERHARD PRODUCTS INC,02-Central,764461006,388.00,27.25,360.75\nEXOCHEM CORPORATION,02-Central,726000062,1045093.50,533368.66,511724.84\nEXOCHEM CORPORATION,02-Central,727000060,22050.00,11913.27,10136.73\nF.S. SPERRY COMPANY,02-Central,764040000,307838.40,78425.97,229412.43\nF.S. SPERRY COMPANY,02-Central,764821000,95722.00,20589.36,75132.64\nF.S. SPERRY COMPANY,02-Central,777902700,51466.46,22831.10,28635.36\nF.S. SPERRY COMPANY,02-Central,766121000,30855.00,5251.78,25603.22\nF.S. SPERRY COMPANY,02-Central,768211000,24772.00,4751.64,20020.36\nF.S. SPERRY COMPANY,02-Central,733525500,23437.50,3385.58,20051.92\nF.S. SPERRY COMPANY,02-Central,779956844,20090.40,10762.47,9327.93\nF.S. SPERRY COMPANY,02-Central,779956840,12879.36,9030.03,3849.33\nF.S. SPERRY COMPANY,02-Central,779956843,12003.24,6543.06,5460.18\nF.S. SPERRY COMPANY,02-Central,764901000,11498.75,2581.74,8917.01\nF.S. SPERRY COMPANY,02-Central,733464500,9972.00,1521.08,8450.92\nF.S. SPERRY COMPANY,02-Central,754864010,8820.00,1532.38,7287.62\nF.S. SPERRY COMPANY,02-Central,764521000,7800.00,2659.41,5140.59\nF.S. SPERRY COMPANY,02-Central,779959916,7184.16,2125.48,5058.68\nF.S. SPERRY COMPANY,02-Central,777901100,6604.86,2805.90,3798.96\nF.S. SPERRY COMPANY,02-Central,734810000,6547.20,4985.55,1561.65\nF.S. SPERRY COMPANY,02-Central,727041000,6546.00,910.46,5635.54\nF.S. SPERRY COMPANY,02-Central,780690000,6300.00,3011.32,3288.68\nF.S. SPERRY COMPANY,02-Central,718501000,6011.20,1077.51,4933.69\nF.S. SPERRY COMPANY,02-Central,766123050,5535.00,2035.46,3499.54\nF.S. SPERRY COMPANY,02-Central,738361000,5460.27,2715.75,2744.52\nF.S. SPERRY COMPANY,02-Central,727000140,4980.00,714.34,4265.66\nF.S. SPERRY COMPANY,02-Central,734651200,4601.48,812.52,3788.96\nF.S. SPERRY COMPANY,02-Central,739561200,4548.61,1093.64,3454.97\nF.S. SPERRY COMPANY,02-Central,739390000,4430.02,2035.95,2394.07\nF.S. SPERRY COMPANY,02-Central,733211000,4251.90,846.32,3405.58\nF.S. SPERRY COMPANY,02-Central,759651000,4246.76,891.40,3355.36\nF.S. SPERRY COMPANY,02-Central,779956842,4188.12,3145.53,1042.59\nF.S. SPERRY COMPANY,02-Central,781610000,4019.20,2126.99,1892.21\nF.S. SPERRY COMPANY,02-Central,779440100,3548.94,576.25,2972.69\nF.S. SPERRY COMPANY,02-Central,766081010,3465.60,581.84,2883.76\nF.S. SPERRY COMPANY,02-Central,777601000,3357.59,784.91,2572.68\nF.S. SPERRY COMPANY,02-Central,777650000,2914.50,2139.90,774.60\nF.S. SPERRY COMPANY,02-Central,779980915,2640.00,1243.98,1396.02\nF.S. SPERRY COMPANY,02-Central,754097000,2610.72,872.95,1737.77\nF.S. SPERRY COMPANY,02-Central,781670000,2610.72,1403.52,1207.20\nF.S. SPERRY COMPANY,02-Central,781550000,2198.46,673.45,1525.01\nF.S. SPERRY COMPANY,02-Central,788460000,1529.40,290.97,1238.43\nF.S. SPERRY COMPANY,02-Central,738401000,1354.97,422.06,932.91\nF.S. SPERRY COMPANY,02-Central,781570000,1300.64,386.14,914.50\nF.S. SPERRY COMPANY,02-Central,754247000,1205.24,608.32,596.92\nF.S. SPERRY COMPANY,02-Central,764903000,1152.50,364.04,788.46\nF.S. SPERRY COMPANY,02-Central,770410000,1092.25,102.99,989.26\nF.S. SPERRY COMPANY,02-Central,739661100,1006.13,468.00,538.13\nF.S. SPERRY COMPANY,02-Central,759661000,811.92,158.78,653.14\nF.S. SPERRY COMPANY,02-Central,770830000,749.70,30.97,718.73\nF.S. SPERRY COMPANY,02-Central,734611100,672.22,135.23,536.99\nF.S. SPERRY COMPANY,02-Central,767990000,668.00,224.12,443.88\nF.S. SPERRY COMPANY,02-Central,770220000,599.40,55.79,543.61\nF.S. SPERRY COMPANY,02-Central,764602000,592.00,105.98,486.02\nF.S. SPERRY COMPANY,02-Central,734950000,514.20,113.13,401.07\nF.S. SPERRY COMPANY,02-Central,754447000,340.45,190.16,150.29\nF.S. SPERRY COMPANY,02-Central,739741100,309.54,168.32,141.22\nF.S. SPERRY COMPANY,02-Central,770210000,285.00,31.52,253.48\nF.S. SPERRY COMPANY,02-Central,734173000,257.10,54.34,202.76\nF.S. SPERRY COMPANY,02-Central,781805000,238.48,122.16,116.32\nF.S. SPERRY COMPANY,02-Central,777671000,230.12,184.20,45.92\nF.S. SPERRY COMPANY,02-Central,764822000,202.80,31.77,171.03\nF.S. SPERRY COMPANY,02-Central,734861000,109.14,87.30,21.84\nFAIRMONT SUPPLY CO.,02-Central,734414100,621.20,71.72,549.48\nFAIRMONT SUPPLY,02-Central,734414100,1863.60,215.18,1648.42\nFASTENAL COMPANY,02-Central,777601000,2753.24,477.70,2275.54\nFASTENAL COMPANY,02-Central,764262000,424.00,22.31,401.69\nFASTENAL,02-Central,770380000,2844.00,173.95,2670.05\nFDP VIRGINIA INC.,02-Central,726020005,217900.80,79010.85,138889.95\nFEATHERLITE INC.,02-Central,764261000,3980.00,230.15,3749.85\nFEDERAL MOGUL CORPORATION,02-Central,739451000,411.84,116.03,295.81\nFEDERAL MOGUL WORLDWIDE,02-Central,726020047,242763.92,85795.74,156968.18\nFEDERAL MOGUL WORLDWIDE,02-Central,726020005,181306.08,63174.16,118131.92\nFEDERAL MOGUL WORLDWIDE,02-Central,726020046,75957.04,26503.38,49453.66\nFEDERAL MOGUL WORLDWIDE,02-Central,726000240,6820.80,2573.78,4247.02\nFEDERAL MOGUL WORLDWIDE,02-Central,720252301,5935.50,395.45,5540.05\nFEDERAL MOGUL WORLDWIDE,02-Central,720252303,2938.50,197.04,2741.46\nFEDERAL MOGUL WORLDWIDE,02-Central,726020007,950.40,340.30,610.10\nFERGUSON ENTERPRISES,02-Central,770200000,1332.00,84.83,1247.17\nFERRITE INTERNATIONAL,02-Central,734822000,1920.00,295.90,1624.10\nFERRITE INTERNATIONAL,02-Central,718500000,1414.40,172.54,1241.86\nFIRELINE INC,02-Central,793000500,202478.40,100346.78,102131.62\nFIRELINE INC,02-Central,726000062,86400.00,44626.20,41773.80\nFIRELINE INC,02-Central,793060330,25488.00,12407.06,13080.94\nFIRELINE INC,02-Central,793060300,21240.00,10339.22,10900.78\nFIRELINE INC,02-Central,727000140,1111.32,427.58,683.74\nFIRELINE,02-Central,793060300,151653.60,73822.01,77831.59\nFIRELINE,02-Central,726000062,98736.00,51348.41,47387.59\nFIRELINE,02-Central,793000500,95040.00,48150.58,46889.42\nFIRELINE,02-Central,726020031,2106.72,1027.50,1079.22\nFISHER SCIENTIFIC CO,02-Central,727000140,255.20,15.84,239.36\nFLEET SUPPORT SERVICES,02-Central,771002000,3282.00,86.39,3195.61\nFLEET SUPPORT SERVICES,02-Central,779021000,628.96,71.47,557.49\nFLOMET INC,02-Central,764341000,4809.50,258.58,4550.92\nFLOMET INC,02-Central,764827000,484.50,45.48,439.02\nFLORIDA PURCHASING AGENCY,02-Central,749906600,4091.93,189.64,3902.29\nFLORIDA TURBINE TECHNOLOG,02-Central,768211000,389.00,22.44,366.56\nFMI,02-Central,764262000,882.00,46.00,836.00\nFORT RECOVERY INDUSTRIES,02-Central,734311100,3379.86,296.06,3083.80\nFORT RECOVERY INDUSTRIES,02-Central,734900000,1270.40,229.09,1041.31\nFOSBEL INC / CETEK LTD.,02-Central,734830000,69329.00,9368.61,59960.39\nFOSBEL INC / CETEK LTD.,02-Central,734990000,34930.00,4838.09,30091.91\nFOSBEL INC / CETEK LTD.,02-Central,764821000,1788.00,62.62,1725.38\nFOURNIER RUBBER & SUPPLY,02-Central,770380000,964.00,61.29,902.71\nFRAS-LE NORTH AMERICA INC,02-Central,726020005,72085.68,16520.72,55564.96\nFRC SUPPORT EQUIPMENT,02-Central,764781000,534.00,39.44,494.56\nFRC SUPPORT EQUIPMENT,02-Central,779081000,224.36,55.24,169.12\nFRYE & ROWE,02-Central,737912200,326.50,45.47,281.03\nFSPA,02-Central,734900000,814.40,151.32,663.08\nFUELCELL ENERGY,02-Central,768241000,15180.00,7783.80,7396.20\nFUELCELL ENERGY,02-Central,768221000,2346.00,1202.92,1143.08\nGALA INDUSTRIES INC.,02-Central,740361701,9072.00,5038.76,4033.24\n\"GARRECO, INC.\",02-Central,734990000,4550.40,550.87,3999.53\nGASKETS INC,02-Central,733586500,4999.68,754.00,4245.68\nGASKETS INC,02-Central,733201000,2537.64,506.55,2031.09\nGENERAL ELECTRIC CO,02-Central,734414100,310.60,35.86,274.74\nGENERAL INSULATION CO,02-Central,764901000,3470.00,128.64,3341.36\nGENERAL INSULATION CO,02-Central,737912200,1997.00,268.62,1728.38\nGENERAL INSULATION CO,02-Central,734311100,808.72,70.60,738.12\nGENERAL INSULATION CO,02-Central,734611100,378.88,54.36,324.52\nGENERAL JACKSON SHOWBOAT,02-Central,768133000,947.50,55.20,892.30\nGEO. V HAMILTON INC,02-Central,735602000,86769.00,24158.90,62610.10\nGEO. V HAMILTON INC,02-Central,735750000,31176.00,11264.62,19911.38\nGEO. V HAMILTON INC,02-Central,735602110,2121.00,624.77,1496.23\nGEO. V HAMILTON INC,02-Central,735862000,1992.00,616.83,1375.17\nGEORGIA TECH,02-Central,733464400,298.00,39.61,258.39\nGERARD DANIEL WORLDWIDE,02-Central,733721000,1200.00,277.28,922.72\nGLASS CRAFTERS,02-Central,733444100,1044.00,244.86,799.14\nGLASS CRAFTERS,02-Central,739450000,926.64,251.16,675.48\nGLASS CRAFTERS,02-Central,733505200,765.00,76.44,688.56\nGLASS CRAFTERS,02-Central,739641100,463.32,137.43,325.89\nGLASS CRAFTERS,02-Central,733444200,332.00,34.69,297.31\nGLASS CRAFTERS,02-Central,733464500,298.00,29.52,268.48\nGLASSTECH INC,02-Central,733484900,11712.00,2040.91,9671.09\nGLASSTECH INC,02-Central,733505300,10770.00,1627.27,9142.73\nGLASSTECH INC,02-Central,724101200,7588.00,720.80,6867.20\nGLASSTECH INC,02-Central,734752000,2505.19,454.02,2051.17\nGLENRO INC,02-Central,739561200,7292.16,1264.34,6027.82\nGLENRO INC,02-Central,738521000,6998.40,2175.10,4823.30\nGLENRO INC,02-Central,733444300,3782.00,550.97,3231.03\nGLENRO INC,02-Central,739451000,2520.96,682.77,1838.19\nGLENRO INC,02-Central,734311100,953.70,84.26,869.44\nGLENRO INC,02-Central,733505300,620.00,77.58,542.42\nGLENRO INC,02-Central,739741100,411.84,109.56,302.28\nGLENRO INC,02-Central,739781100,410.72,163.84,246.88\nGOODMAN INDUSTRIAL,02-Central,770260000,5778.00,416.58,5361.42\nGRAFTECH,02-Central,739390000,701.12,164.86,536.26\nGRAFTECH,02-Central,764261000,424.00,22.90,401.10\nGRAYBAR ELECTRIC CO,02-Central,733464500,31560.00,3397.50,28162.50\nGRAYBAR ELECTRIC CO,02-Central,733525500,2920.00,281.57,2638.43\nGREAT LAKES CHEESE,02-Central,766685000,315.00,35.46,279.54\nGREAT LAKES CHEESE,02-Central,766681100,306.00,24.78,281.22\nGREEN BOILER TECH.,02-Central,770260000,931.50,67.63,863.87\nGREEN GLOBAL ENERGY,02-Central,739561200,421.12,72.33,348.79\nGREENING ASSOCIATES INC,02-Central,733464500,313.00,32.53,280.47\nGRIFFIN WHEEL CO,02-Central,780770000,5532.48,873.08,4659.40\nGSA ACCOUNT PAYABLE,02-Central,734311100,404.36,35.30,369.06\nGT MIDWEST,02-Central,739561200,13628.80,3703.36,9925.44\nGT MIDWEST,02-Central,764522000,12870.00,6153.19,6716.81\nGT MIDWEST,02-Central,764902000,811.40,127.71,683.69\nGT MIDWEST,02-Central,770222000,739.50,68.04,671.46\nGT MIDWEST,02-Central,739561100,704.42,186.64,517.78\nGT MIDWEST,02-Central,733151500,651.20,108.21,542.99\nGT MIDWEST,02-Central,733151600,325.60,53.12,272.48\nGUARDIAN FIBERGLASS,02-Central,764822000,9528.00,340.42,9187.58\nGUARDIAN FIBERGLASS,02-Central,764262000,1272.00,66.93,1205.07\nGUARDIAN FIBERGLASS,02-Central,739741100,823.68,219.12,604.56\nHAGEMEYER NORTH AMERICA,02-Central,792600000,798.00,99.32,698.68\n\"HAMON DELTAK, INC.\",02-Central,764900005,73972.00,32769.48,41202.52\n\"HAMON DELTAK, INC.\",02-Central,764826700,23801.60,7938.50,15863.10\n\"HAMON DELTAK, INC.\",02-Central,777642000,67.50,15.84,51.66\nHANSON BRICK PLANT 4,02-Central,764303200,7176.00,4298.84,2877.16\nHARRIS COMPANIES,02-Central,733464500,298.00,29.52,268.48\nHARRIS,02-Central,764521000,653.00,26.57,626.43\nHARROP INDUSTRIES INC,02-Central,775171000,1013.60,396.52,617.08\nHARROP INDUSTRIES INC,02-Central,773591000,757.40,185.74,571.66\nHARROP INDUSTRIES INC,02-Central,774981100,185.50,69.84,115.66\nHEARTH & HOME TECHNOLOGIE,02-Central,726000080,667931.52,347389.42,320542.10\nHEARTH & HOME TECHNOLOGIE,02-Central,764020110,186719.00,139183.58,47535.42\nHEARTH & HOME TECHNOLOGIE,02-Central,768222000,86592.00,33348.62,53243.38\nHEARTH & HOME TECHNOLOGIE,02-Central,734211200,1529.20,185.93,1343.27\nHEARTH & HOME TECHNOLOGIE,02-Central,777601000,304.80,58.80,246.00\nHEAT & SENSOR,02-Central,764461006,4900.00,551.86,4348.14\nHEAT TREATING SERVICES,02-Central,734751200,275.45,37.39,238.06\nHENKEL CHEMICAL MGMT.,02-Central,734000000,1072.50,151.61,920.89\nHENKEL CHEMICAL MGMT.,02-Central,727063000,84.00,16.80,67.20\nHERAEUS ELECTRO - NITE CO,02-Central,726000240,89326.80,37653.21,51673.59\n\"HERAEUS NOBELIGHT, LLC\",02-Central,739561100,1149.12,199.92,949.20\n\"HERAEUS NOBLELIGHT, LLC\",02-Central,739561100,552.72,93.32,459.40\nHERAEUS QUARTZ TECH,02-Central,764902000,3018.00,386.50,2631.50\nHERAEUS QUARTZ TECH,02-Central,734890000,980.49,207.69,772.80\nHERAEUS-AMERSIL INC.,02-Central,779981050,6500.16,1526.07,4974.09\nHERAEUS-AMERSIL INC.,02-Central,718501000,1021.00,134.69,886.31\nHERAEUS-AMERSIL INC.,02-Central,779988827,1021.00,610.34,410.66\nHERAEUS-AMERSIL INC.,02-Central,738521000,338.56,298.56,40.00\nHERAEUS,02-Central,726000240,198562.32,83221.87,115340.45\nHERAEUS,02-Central,793000410,1728.00,773.49,954.51\nHERAEUS,02-Central,793060300,1728.00,596.48,1131.52\nHI-TEMP FABRICATION INC.,02-Central,793330000,1132.95,185.55,947.40\nHI-TEMP FABRICATION INC.,02-Central,737912200,311.40,43.36,268.04\nHIBBING INTERNATIONAL,02-Central,726000240,12398.40,836.74,11561.66\nHITCO CARBON COMPOSITIES,02-Central,791104770,1946.64,760.90,1185.74\nHITCO CARBON COMPOSITIES,02-Central,791104760,1892.46,735.90,1156.56\n\"HOMEFIRES, AQR, INC.\",02-Central,733721000,300.00,69.32,230.68\nHONEYWELL INC ',02-Central,739370000,364.80,90.82,273.98\nHONEYWELL,02-Central,734311100,404.36,35.30,369.06\nHORIZON METALS,02-Central,739781100,1642.88,655.36,987.52\n\"HSS, LLC\",02-Central,770722000,6150.00,214.41,5935.59\nHUGHES ASSOCIATES INC.,02-Central,764522000,1332.00,53.44,1278.56\nHUGHES ASSOCIATES INC.,02-Central,738561000,1164.80,248.27,916.53\nHUNTSVILLE RUBBER & GASKE,02-Central,770510000,1097.50,48.20,1049.30\nHUNTSVILLE RUBBER & GASKE,02-Central,733151500,472.00,54.11,417.89\nHUNTSVILLE RUBBER & GASKE,02-Central,733444200,348.00,38.64,309.36\nHUSSONG MANUFACTURING,02-Central,764030000,101885.28,54115.84,47769.44\nHUSSONG MANUFACTURING,02-Central,764050000,46094.40,25181.31,20913.09\nHYDRA-FAB,02-Central,733741000,408.00,119.06,288.94\nHYPER TECH RESEARCH,02-Central,740405000,681.80,122.64,559.16\nI S A SALES & SERVICE,02-Central,765651000,443457.00,235458.12,207998.88\nI S A SALES & SERVICE,02-Central,777922000,335120.64,268462.49,66658.15\nI S A SALES & SERVICE,02-Central,765501001,321988.50,187770.50,134218.00\nI S A SALES & SERVICE,02-Central,764605020,249546.00,129502.56,120043.44\nI S A SALES & SERVICE,02-Central,740025000,174323.64,114160.31,60163.33\nI S A SALES & SERVICE,02-Central,733202000,141644.10,56726.36,84917.74\nI S A SALES & SERVICE,02-Central,764261020,127647.60,100021.45,27626.15\nI S A SALES & SERVICE,02-Central,742520400,121394.00,54999.51,66394.49\nI S A SALES & SERVICE,02-Central,727041200,94288.32,13320.71,80967.61\nI S A SALES & SERVICE,02-Central,764901050,61014.00,34578.52,26435.48\nI S A SALES & SERVICE,02-Central,764781000,57498.00,37042.12,20455.88\nI S A SALES & SERVICE,02-Central,765735000,50019.84,34873.02,15146.82\nI S A SALES & SERVICE,02-Central,777921000,47737.68,38242.28,9495.40\nI S A SALES & SERVICE,02-Central,765655000,41721.57,20164.75,21556.82\nI S A SALES & SERVICE,02-Central,764528200,28526.00,14712.44,13813.56\nI S A SALES & SERVICE,02-Central,737912200,25622.50,11043.11,14579.39\nI S A SALES & SERVICE,02-Central,777904301,25155.12,11003.61,14151.51\nI S A SALES & SERVICE,02-Central,766121000,24099.00,11583.69,12515.31\nI S A SALES & SERVICE,02-Central,764523200,22881.00,13812.89,9068.11\nI S A SALES & SERVICE,02-Central,726020037,21610.40,12315.05,9295.35\nI S A SALES & SERVICE,02-Central,742431000,18287.50,7019.48,11268.02\nI S A SALES & SERVICE,02-Central,764521073,17672.00,24252.00,-6580.00\nI S A SALES & SERVICE,02-Central,764765000,17292.00,9994.25,7297.75\nI S A SALES & SERVICE,02-Central,777907221,16907.90,8156.94,8750.96\nI S A SALES & SERVICE,02-Central,779958167,15480.00,10884.12,4595.88\nI S A SALES & SERVICE,02-Central,742461000,15168.00,3113.03,12054.97\nI S A SALES & SERVICE,02-Central,726020039,14654.50,7776.01,6878.49\nI S A SALES & SERVICE,02-Central,738071000,13502.64,10816.86,2685.78\nI S A SALES & SERVICE,02-Central,724261200,12636.00,2111.38,10524.62\nI S A SALES & SERVICE,02-Central,764827000,12495.00,6667.54,5827.46\nI S A SALES & SERVICE,02-Central,764484000,10275.00,4066.55,6208.45\nI S A SALES & SERVICE,02-Central,742451000,9881.25,2100.53,7780.72\nI S A SALES & SERVICE,02-Central,739564300,9840.00,5427.33,4412.67\nI S A SALES & SERVICE,02-Central,779988929,9728.68,5786.20,3942.48\nI S A SALES & SERVICE,02-Central,764864000,9360.00,4953.26,4406.74\nI S A SALES & SERVICE,02-Central,733211000,9040.00,4035.32,5004.68\nI S A SALES & SERVICE,02-Central,764261000,7908.60,6199.16,1709.44\nI S A SALES & SERVICE,02-Central,733831000,6300.00,4682.69,1617.31\nI S A SALES & SERVICE,02-Central,770392000,6232.00,935.56,5296.44\nI S A SALES & SERVICE,02-Central,765503112,6072.40,3308.30,2764.10\nI S A SALES & SERVICE,02-Central,764822000,5770.00,2819.03,2950.97\nI S A SALES & SERVICE,02-Central,734890000,5229.28,1626.61,3602.67\nI S A SALES & SERVICE,02-Central,777950000,5103.36,4088.26,1015.10\nI S A SALES & SERVICE,02-Central,733602010,4263.84,2193.26,2070.58\nI S A SALES & SERVICE,02-Central,777601000,3952.12,1444.85,2507.27\nI S A SALES & SERVICE,02-Central,770430000,3487.50,531.28,2956.22\nI S A SALES & SERVICE,02-Central,770230000,3360.00,284.31,3075.69\nI S A SALES & SERVICE,02-Central,764345052,3200.00,2269.86,930.14\nI S A SALES & SERVICE,02-Central,764781040,2957.00,1954.93,1002.07\nI S A SALES & SERVICE,02-Central,779988828,2910.68,1562.16,1348.52\nI S A SALES & SERVICE,02-Central,733221050,2908.00,1310.58,1597.42\nI S A SALES & SERVICE,02-Central,779988940,2901.76,2205.12,696.64\nI S A SALES & SERVICE,02-Central,770220000,2790.00,448.44,2341.56\nI S A SALES & SERVICE,02-Central,764821073,2418.00,2843.10,-425.10\nI S A SALES & SERVICE,02-Central,777907222,2340.00,1170.00,1170.00\nI S A SALES & SERVICE,02-Central,754047000,1571.04,700.91,870.13\nI S A SALES & SERVICE,02-Central,738381000,1504.80,3814.80,-2310.00\nI S A SALES & SERVICE,02-Central,764601000,1500.00,797.17,702.83\nI S A SALES & SERVICE,02-Central,764521000,1410.00,796.26,613.74\nI S A SALES & SERVICE,02-Central,770280000,1350.00,480.16,869.84\nI S A SALES & SERVICE,02-Central,742411000,1340.00,365.48,974.52\nI S A SALES & SERVICE,02-Central,764901000,1282.50,608.55,673.95\nI S A SALES & SERVICE,02-Central,770240000,1237.50,421.98,815.52\nI S A SALES & SERVICE,02-Central,733151600,992.00,345.92,646.08\nI S A SALES & SERVICE,02-Central,734311100,926.76,105.90,820.86\nI S A SALES & SERVICE,02-Central,777670000,789.60,729.84,59.76\nI S A SALES & SERVICE,02-Central,738542000,684.00,1435.20,-751.20\nI S A SALES & SERVICE,02-Central,734861000,636.60,262.44,374.16\nI S A SALES & SERVICE,02-Central,734990000,518.00,191.71,326.29\nI S A SALES & SERVICE,02-Central,733741000,496.00,372.76,123.24\nI S A SALES & SERVICE,02-Central,724261400,468.00,60.94,407.06\nI S A SALES & SERVICE,02-Central,764861000,384.00,207.82,176.18\nI S A SALES & SERVICE,02-Central,779957137,367.92,398.16,-30.24\nI S A SALES & SERVICE,02-Central,779956598,300.00,265.36,34.64\nI S A SALES & SERVICE,02-Central,733151500,284.00,101.80,182.20\nI S A SALES & SERVICE,02-Central,764522000,282.00,160.33,121.67\nI S A SALES & SERVICE,02-Central,739761100,273.60,415.20,-141.60\nI S A SALES & SERVICE,02-Central,733212000,240.00,92.84,147.16\nI S A SALES & SERVICE,02-Central,764821000,88.00,61.44,26.56\nI S A SALES & SERVICE,02-Central,767501000,30.00,19.64,10.36\nI.C.M.,02-Central,779958986,7318.08,4624.64,2693.44\nI.C.M.,02-Central,780650000,916.80,517.36,399.44\nI.C.M.,02-Central,780690000,70.36,42.42,27.94\nIBC ADVANCED ALLOYS,02-Central,739641100,964.08,403.20,560.88\n\"IDG USA, LLC\",02-Central,779301000,460.69,49.41,411.28\nIN VITRO,02-Central,733384000,2235.20,309.03,1926.17\nINDUST'L FIREBRICK OF DET,02-Central,740361900,101966.40,63434.17,38532.23\nINDUST'L FIREBRICK OF DET,02-Central,733526410,88372.50,14758.56,73613.94\nINDUST'L FIREBRICK OF DET,02-Central,739781100,86500.52,64270.10,22230.42\nINDUST'L FIREBRICK OF DET,02-Central,754267000,74259.54,31536.48,42723.06\nINDUST'L FIREBRICK OF DET,02-Central,734351200,65782.56,7388.30,58394.26\nINDUST'L FIREBRICK OF DET,02-Central,733525400,48407.50,9314.97,39092.53\nINDUST'L FIREBRICK OF DET,02-Central,779989574,39302.40,24794.02,14508.38\nINDUST'L FIREBRICK OF DET,02-Central,734121000,36691.90,9512.93,27178.97\nINDUST'L FIREBRICK OF DET,02-Central,733586500,28576.80,5110.96,23465.84\nINDUST'L FIREBRICK OF DET,02-Central,739951050,21144.00,13709.32,7434.68\nINDUST'L FIREBRICK OF DET,02-Central,734990000,19546.64,5697.16,13849.48\nINDUST'L FIREBRICK OF DET,02-Central,768141003,16660.00,4306.98,12353.02\nINDUST'L FIREBRICK OF DET,02-Central,793330000,16404.00,3787.36,12616.64\nINDUST'L FIREBRICK OF DET,02-Central,734251300,16221.20,1848.29,14372.91\nINDUST'L FIREBRICK OF DET,02-Central,770392000,16210.00,1606.69,14603.31\nINDUST'L FIREBRICK OF DET,02-Central,764521000,14670.40,3188.40,11482.00\nINDUST'L FIREBRICK OF DET,02-Central,764901000,13312.00,3475.67,9836.33\nINDUST'L FIREBRICK OF DET,02-Central,738521000,12048.87,8000.11,4048.76\nINDUST'L FIREBRICK OF DET,02-Central,764601000,11930.20,3646.00,8284.20\nINDUST'L FIREBRICK OF DET,02-Central,768221000,11210.00,2493.65,8716.35\nINDUST'L FIREBRICK OF DET,02-Central,720252303,11200.50,397.55,10802.95\nINDUST'L FIREBRICK OF DET,02-Central,777601000,10597.32,2832.39,7764.93\nINDUST'L FIREBRICK OF DET,02-Central,739681100,10263.24,5510.74,4752.50\nINDUST'L FIREBRICK OF DET,02-Central,733525500,10123.00,1603.62,8519.38\nINDUST'L FIREBRICK OF DET,02-Central,724311200,9980.80,2364.54,7616.26\nINDUST'L FIREBRICK OF DET,02-Central,733505100,8828.00,1892.03,6935.97\nINDUST'L FIREBRICK OF DET,02-Central,770560000,8768.00,798.69,7969.31\nINDUST'L FIREBRICK OF DET,02-Central,734311100,8340.42,1068.75,7271.67\nINDUST'L FIREBRICK OF DET,02-Central,740283502,8069.60,1602.57,6467.03\nINDUST'L FIREBRICK OF DET,02-Central,739641100,7915.68,4728.20,3187.48\nINDUST'L FIREBRICK OF DET,02-Central,733511000,7865.00,1967.60,5897.40\nINDUST'L FIREBRICK OF DET,02-Central,764824000,7246.80,1043.19,6203.61\nINDUST'L FIREBRICK OF DET,02-Central,768241030,6936.00,1640.18,5295.82\nINDUST'L FIREBRICK OF DET,02-Central,764902000,5994.60,1544.86,4449.74\nINDUST'L FIREBRICK OF DET,02-Central,767540000,5956.84,2198.39,3758.45\nINDUST'L FIREBRICK OF DET,02-Central,739741100,5660.48,3123.40,2537.08\nINDUST'L FIREBRICK OF DET,02-Central,733832000,5616.00,1670.05,3945.95\nINDUST'L FIREBRICK OF DET,02-Central,738561000,5370.56,2427.27,2943.29\nINDUST'L FIREBRICK OF DET,02-Central,766121000,5163.30,953.71,4209.59\nINDUST'L FIREBRICK OF DET,02-Central,738401000,4782.24,1688.24,3094.00\nINDUST'L FIREBRICK OF DET,02-Central,764821000,4760.00,1242.89,3517.11\nINDUST'L FIREBRICK OF DET,02-Central,737871000,4486.72,2611.52,1875.20\nINDUST'L FIREBRICK OF DET,02-Central,739561000,4334.12,1201.85,3132.27\nINDUST'L FIREBRICK OF DET,02-Central,740103000,4126.08,1002.00,3124.08\nINDUST'L FIREBRICK OF DET,02-Central,779957613,4045.44,1817.55,2227.89\nINDUST'L FIREBRICK OF DET,02-Central,754047000,3971.88,1452.67,2519.21\nINDUST'L FIREBRICK OF DET,02-Central,733525600,3765.00,622.77,3142.23\nINDUST'L FIREBRICK OF DET,02-Central,724281900,3428.00,1300.06,2127.94\nINDUST'L FIREBRICK OF DET,02-Central,770380000,3384.80,308.22,3076.58\nINDUST'L FIREBRICK OF DET,02-Central,770600000,3097.15,277.15,2820.00\nINDUST'L FIREBRICK OF DET,02-Central,733671000,2909.08,457.31,2451.77\nINDUST'L FIREBRICK OF DET,02-Central,738381000,2568.24,1860.54,707.70\nINDUST'L FIREBRICK OF DET,02-Central,740402402,2531.00,628.36,1902.64\nINDUST'L FIREBRICK OF DET,02-Central,766081010,2029.40,228.55,1800.85\nINDUST'L FIREBRICK OF DET,02-Central,734173000,2003.55,485.46,1518.09\nINDUST'L FIREBRICK OF DET,02-Central,754067000,1985.40,625.44,1359.96\nINDUST'L FIREBRICK OF DET,02-Central,739561200,1921.02,523.67,1397.35\nINDUST'L FIREBRICK OF DET,02-Central,766162000,1845.00,311.09,1533.91\nINDUST'L FIREBRICK OF DET,02-Central,764822000,1549.60,254.16,1295.44\nINDUST'L FIREBRICK OF DET,02-Central,734751200,1501.20,339.36,1161.84\nINDUST'L FIREBRICK OF DET,02-Central,739761100,1419.84,1245.60,174.24\nINDUST'L FIREBRICK OF DET,02-Central,754265000,1388.16,398.75,989.41\nINDUST'L FIREBRICK OF DET,02-Central,734851000,1342.79,289.20,1053.59\nINDUST'L FIREBRICK OF DET,02-Central,727000140,1340.84,146.91,1193.93\nINDUST'L FIREBRICK OF DET,02-Central,737912200,1258.50,272.83,985.67\nINDUST'L FIREBRICK OF DET,02-Central,754255000,1162.84,391.93,770.91\nINDUST'L FIREBRICK OF DET,02-Central,777580000,1147.30,52.80,1094.50\nINDUST'L FIREBRICK OF DET,02-Central,768142000,1100.00,534.58,565.42\nINDUST'L FIREBRICK OF DET,02-Central,734211200,1045.09,160.94,884.15\nINDUST'L FIREBRICK OF DET,02-Central,754295000,1044.80,271.45,773.35\nINDUST'L FIREBRICK OF DET,02-Central,764482000,1008.00,417.66,590.34\nINDUST'L FIREBRICK OF DET,02-Central,738542000,950.40,861.12,89.28\nINDUST'L FIREBRICK OF DET,02-Central,772101000,942.30,372.42,569.88\nINDUST'L FIREBRICK OF DET,02-Central,734910000,907.70,212.36,695.34\nINDUST'L FIREBRICK OF DET,02-Central,754275000,844.60,238.85,605.75\nINDUST'L FIREBRICK OF DET,02-Central,770570000,768.60,95.41,673.19\nINDUST'L FIREBRICK OF DET,02-Central,754327000,736.38,299.73,436.65\nINDUST'L FIREBRICK OF DET,02-Central,742442000,717.50,158.81,558.69\nINDUST'L FIREBRICK OF DET,02-Central,739561100,703.92,186.64,517.28\nINDUST'L FIREBRICK OF DET,02-Central,740382101,698.40,296.37,402.03\nINDUST'L FIREBRICK OF DET,02-Central,733151500,652.00,108.22,543.78\nINDUST'L FIREBRICK OF DET,02-Central,770240000,648.75,67.63,581.12\nINDUST'L FIREBRICK OF DET,02-Central,770250000,591.25,66.16,525.09\nINDUST'L FIREBRICK OF DET,02-Central,764481000,522.00,101.65,420.35\nINDUST'L FIREBRICK OF DET,02-Central,733221200,521.04,130.50,390.54\nINDUST'L FIREBRICK OF DET,02-Central,771991000,502.20,137.55,364.65\nINDUST'L FIREBRICK OF DET,02-Central,734830000,464.00,90.82,373.18\nINDUST'L FIREBRICK OF DET,02-Central,733586400,450.00,91.43,358.57\nINDUST'L FIREBRICK OF DET,02-Central,733444300,426.00,82.49,343.51\nINDUST'L FIREBRICK OF DET,02-Central,770520000,421.50,41.85,379.65\nINDUST'L FIREBRICK OF DET,02-Central,733444100,415.60,163.24,252.36\nINDUST'L FIREBRICK OF DET,02-Central,740123000,387.24,128.01,259.23\nINDUST'L FIREBRICK OF DET,02-Central,754287000,363.36,140.97,222.39\nINDUST'L FIREBRICK OF DET,02-Central,754155000,320.32,137.80,182.52\nINDUST'L FIREBRICK OF DET,02-Central,754165000,308.74,125.97,182.77\nINDUST'L FIREBRICK OF DET,02-Central,738361000,304.56,308.88,-4.32\nINDUST'L FIREBRICK OF DET,02-Central,734900000,285.72,113.48,172.24\nINDUST'L FIREBRICK OF DET,02-Central,754175000,275.30,107.86,167.44\nINDUST'L FIREBRICK OF DET,02-Central,764761000,268.00,61.43,206.57\nINDUST'L FIREBRICK OF DET,02-Central,733423800,246.40,48.07,198.33\nINDUST'L FIREBRICK OF DET,02-Central,737902100,143.45,33.56,109.89\nINDUST'L FIREBRICK OF DET,02-Central,764762000,132.00,30.29,101.71\nINDUST'L FIREBRICK OF DET,02-Central,777642000,122.00,50.36,71.64\nINDUST'L FIREBRICK OF DET,02-Central,776201000,107.44,62.94,44.50\nINDUST'L FIREBRICK OF DET,02-Central,779957013,82.32,58.88,23.44\nINDUST'L FIREBRICK OF DET,02-Central,779958032,77.84,55.70,22.14\nINDUST'L FIREBRICK OF DET,02-Central,779958914,19.88,14.28,5.60\nINDUSTRIAL FIREBRICK WHSE,02-Central,764901000,236387.20,64953.73,171433.47\nINDUSTRIAL FIREBRICK WHSE,02-Central,764821000,147035.30,38615.46,108419.84\nINDUSTRIAL FIREBRICK WHSE,02-Central,764862000,101713.85,25906.54,75807.31\nINDUSTRIAL FIREBRICK WHSE,02-Central,727041000,95916.00,9768.08,86147.92\nINDUSTRIAL FIREBRICK WHSE,02-Central,727000140,93204.96,21034.09,72170.87\nINDUSTRIAL FIREBRICK WHSE,02-Central,739390000,59573.71,43165.97,16407.74\nINDUSTRIAL FIREBRICK WHSE,02-Central,764902000,46516.80,11602.26,34914.54\nINDUSTRIAL FIREBRICK WHSE,02-Central,764602000,33241.20,8897.53,24343.67\nINDUSTRIAL FIREBRICK WHSE,02-Central,764781040,28843.20,8345.02,20498.18\nINDUSTRIAL FIREBRICK WHSE,02-Central,734351200,25167.15,2514.46,22652.69\nINDUSTRIAL FIREBRICK WHSE,02-Central,733525500,24117.50,4332.91,19784.59\nINDUSTRIAL FIREBRICK WHSE,02-Central,777601000,18975.57,5666.18,13309.39\nINDUSTRIAL FIREBRICK WHSE,02-Central,739781100,18932.82,11564.43,7368.39\nINDUSTRIAL FIREBRICK WHSE,02-Central,733510000,16731.64,4651.43,12080.21\nINDUSTRIAL FIREBRICK WHSE,02-Central,733384000,14092.32,2199.08,11893.24\nINDUSTRIAL FIREBRICK WHSE,02-Central,764040000,13352.96,2090.12,11262.84\nINDUSTRIAL FIREBRICK WHSE,02-Central,764822000,12845.60,3215.48,9630.12\nINDUSTRIAL FIREBRICK WHSE,02-Central,734311100,10969.89,1260.51,9709.38\nINDUSTRIAL FIREBRICK WHSE,02-Central,739380000,10817.09,9716.45,1100.64\nINDUSTRIAL FIREBRICK WHSE,02-Central,733211050,10109.10,2907.24,7201.86\nINDUSTRIAL FIREBRICK WHSE,02-Central,734851000,10099.11,2284.94,7814.17\nINDUSTRIAL FIREBRICK WHSE,02-Central,738521000,9170.70,6870.42,2300.28\nINDUSTRIAL FIREBRICK WHSE,02-Central,739741100,8430.72,5255.81,3174.91\nINDUSTRIAL FIREBRICK WHSE,02-Central,768122000,7994.60,1621.89,6372.71\nINDUSTRIAL FIREBRICK WHSE,02-Central,733511000,7365.16,1935.68,5429.48\nINDUSTRIAL FIREBRICK WHSE,02-Central,737912200,7213.24,2172.29,5040.95\nINDUSTRIAL FIREBRICK WHSE,02-Central,739561000,7187.02,2576.06,4610.96\nINDUSTRIAL FIREBRICK WHSE,02-Central,734751200,6803.28,1234.19,5569.09\nINDUSTRIAL FIREBRICK WHSE,02-Central,770870000,6800.00,386.06,6413.94\nINDUSTRIAL FIREBRICK WHSE,02-Central,739370000,6374.65,3605.54,2769.11\nINDUSTRIAL FIREBRICK WHSE,02-Central,734990000,5778.80,1099.38,4679.42\nINDUSTRIAL FIREBRICK WHSE,02-Central,764261000,5652.65,2029.19,3623.46\nINDUSTRIAL FIREBRICK WHSE,02-Central,733423700,5591.04,1339.78,4251.26\nINDUSTRIAL FIREBRICK WHSE,02-Central,738561000,5514.92,3613.17,1901.75\nINDUSTRIAL FIREBRICK WHSE,02-Central,733525600,5239.00,961.34,4277.66\nINDUSTRIAL FIREBRICK WHSE,02-Central,733151500,5121.60,1113.36,4008.24\nINDUSTRIAL FIREBRICK WHSE,02-Central,766121000,4563.00,2155.41,2407.59\nINDUSTRIAL FIREBRICK WHSE,02-Central,764562000,3794.40,759.33,3035.07\nINDUSTRIAL FIREBRICK WHSE,02-Central,733151600,3688.80,811.00,2877.80\nINDUSTRIAL FIREBRICK WHSE,02-Central,739641100,3527.28,2767.31,759.97\nINDUSTRIAL FIREBRICK WHSE,02-Central,770880000,3354.20,178.24,3175.96\nINDUSTRIAL FIREBRICK WHSE,02-Central,739561200,3202.44,1132.69,2069.75\nINDUSTRIAL FIREBRICK WHSE,02-Central,733211000,2948.80,851.28,2097.52\nINDUSTRIAL FIREBRICK WHSE,02-Central,793330000,2739.30,624.33,2114.97\nINDUSTRIAL FIREBRICK WHSE,02-Central,768121000,2721.10,528.41,2192.69\nINDUSTRIAL FIREBRICK WHSE,02-Central,793000000,2713.20,344.64,2368.56\nINDUSTRIAL FIREBRICK WHSE,02-Central,766162000,2661.00,448.47,2212.53\nINDUSTRIAL FIREBRICK WHSE,02-Central,764482000,2152.00,681.90,1470.10\nINDUSTRIAL FIREBRICK WHSE,02-Central,792702000,1861.50,567.27,1294.23\nINDUSTRIAL FIREBRICK WHSE,02-Central,764262000,1741.20,630.16,1111.04\nINDUSTRIAL FIREBRICK WHSE,02-Central,764601000,1722.20,449.94,1272.26\nINDUSTRIAL FIREBRICK WHSE,02-Central,770740000,1360.00,54.66,1305.34\nINDUSTRIAL FIREBRICK WHSE,02-Central,770860000,1360.00,73.56,1286.44\nINDUSTRIAL FIREBRICK WHSE,02-Central,770600000,1258.15,90.42,1167.73\nINDUSTRIAL FIREBRICK WHSE,02-Central,734810000,1184.30,1021.95,162.35\nINDUSTRIAL FIREBRICK WHSE,02-Central,734910000,1038.45,256.75,781.70\nINDUSTRIAL FIREBRICK WHSE,02-Central,733141200,950.40,239.88,710.52\nINDUSTRIAL FIREBRICK WHSE,02-Central,739290000,811.00,430.64,380.36\nINDUSTRIAL FIREBRICK WHSE,02-Central,739310000,584.76,285.84,298.92\nINDUSTRIAL FIREBRICK WHSE,02-Central,768141000,583.76,131.60,452.16\nINDUSTRIAL FIREBRICK WHSE,02-Central,739561100,566.16,199.92,366.24\nINDUSTRIAL FIREBRICK WHSE,02-Central,764762000,556.00,302.93,253.07\nINDUSTRIAL FIREBRICK WHSE,02-Central,792760000,456.97,116.62,340.35\nINDUSTRIAL FIREBRICK WHSE,02-Central,734711100,393.87,81.15,312.72\nINDUSTRIAL FIREBRICK WHSE,02-Central,739541100,324.41,83.57,240.84\nINDUSTRIAL FIREBRICK WHSE,02-Central,734900000,302.88,115.08,187.80\nINDUSTRIAL FIREBRICK WHSE,02-Central,739681101,219.02,118.53,100.49\nINDUSTRIAL FURNACE CO,02-Central,734990000,2036.16,331.76,1704.40\nINDUSTRIAL FURNACE CO,02-Central,764521000,904.80,212.75,692.05\nINDUSTRIAL INSULATION,02-Central,739781100,785.52,491.52,294.00\nINDUSTRIAL INSULATION,02-Central,739741100,525.12,219.12,306.00\nINDUSTRIAL INSULATION,02-Central,766162000,473.00,74.74,398.26\nINDUSTRIAL PARTS,02-Central,742501300,2583.00,202.76,2380.24\nINDUSTRIAL PARTS,02-Central,742501400,2370.00,272.86,2097.14\nINDUSTRIAL RUBBER & GASKE,02-Central,733511000,1075.00,133.12,941.88\nINDUSTRIAL RUBBER & GASKE,02-Central,733444200,348.00,38.64,309.36\nINDUSTRIAL SUPPLIES CO.,02-Central,793320074,2860.00,642.76,2217.24\nINSULATING SERVICES INC.,02-Central,735602000,298660.00,102949.66,195710.34\nINSULATING SERVICES INC.,02-Central,764521000,38188.00,11318.66,26869.34\nINSULATING SERVICES INC.,02-Central,735750000,8100.00,3467.69,4632.31\nINSULATING SERVICES INC.,02-Central,764901000,3767.40,839.48,2927.92\nINSULATING SERVICES INC.,02-Central,764902000,3187.80,702.40,2485.40\nINSULATING SERVICES INC.,02-Central,733151500,2249.20,365.93,1883.27\nINSULATING SERVICES INC.,02-Central,733211000,1507.80,319.23,1188.57\nINSULATING SERVICES INC.,02-Central,739561200,547.07,149.00,398.07\nINSULATING SERVICES INC.,02-Central,735862000,480.00,211.45,268.55\nINSULATION FABRICATORS,02-Central,735602000,494380.00,165599.17,328780.83\nINSULATION FABRICATORS,02-Central,764901050,298093.00,116789.80,181303.20\nINSULATION FABRICATORS,02-Central,764821000,118042.00,40525.43,77516.57\nINSULATION FABRICATORS,02-Central,764605000,98847.00,31816.07,67030.93\nINSULATION FABRICATORS,02-Central,764902000,96180.00,38740.42,57439.58\nINSULATION FABRICATORS,02-Central,764864000,88056.00,30561.89,57494.11\nINSULATION FABRICATORS,02-Central,764822000,83427.00,29303.56,54123.44\nINSULATION FABRICATORS,02-Central,768241000,78478.00,19637.84,58840.16\nINSULATION FABRICATORS,02-Central,764521000,66271.00,21169.77,45101.23\nINSULATION FABRICATORS,02-Central,764901000,59274.00,24983.69,34290.31\nINSULATION FABRICATORS,02-Central,735750000,48298.00,19321.29,28976.71\nINSULATION FABRICATORS,02-Central,735602110,45680.00,15680.98,29999.02\nINSULATION FABRICATORS,02-Central,764821161,42152.00,35637.60,6514.40\nINSULATION FABRICATORS,02-Central,764762073,36571.18,10823.00,25748.18\nINSULATION FABRICATORS,02-Central,764261000,28356.00,9874.95,18481.05\nINSULATION FABRICATORS,02-Central,764341000,26043.50,8509.39,17534.11\nINSULATION FABRICATORS,02-Central,764522000,25818.00,8011.84,17806.16\nINSULATION FABRICATORS,02-Central,766161000,23049.50,8473.13,14576.37\nINSULATION FABRICATORS,02-Central,768231000,20274.20,5018.06,15256.14\nINSULATION FABRICATORS,02-Central,766121000,19470.00,5087.63,14382.37\nINSULATION FABRICATORS,02-Central,768221000,17302.00,3573.74,13728.26\nINSULATION FABRICATORS,02-Central,764302500,15840.00,5263.83,10576.17\nINSULATION FABRICATORS,02-Central,764821073,11968.00,9914.40,2053.60\nINSULATION FABRICATORS,02-Central,764862000,11362.00,4086.32,7275.68\nINSULATION FABRICATORS,02-Central,735752000,10052.00,4153.64,5898.36\nINSULATION FABRICATORS,02-Central,739741100,9889.60,5656.16,4233.44\nINSULATION FABRICATORS,02-Central,764563000,7520.00,2734.73,4785.27\nINSULATION FABRICATORS,02-Central,764781000,7494.00,1930.37,5563.63\nINSULATION FABRICATORS,02-Central,768242000,6342.50,2504.64,3837.86\nINSULATION FABRICATORS,02-Central,735655000,6120.00,815.89,5304.11\nINSULATION FABRICATORS,02-Central,764521061,5504.00,4089.60,1414.40\nINSULATION FABRICATORS,02-Central,764562000,5425.00,1950.40,3474.60\nINSULATION FABRICATORS,02-Central,738561000,4605.12,2178.99,2426.13\nINSULATION FABRICATORS,02-Central,764901073,4592.00,4082.40,509.60\nINSULATION FABRICATORS,02-Central,733211000,4517.00,928.60,3588.40\nINSULATION FABRICATORS,02-Central,733525500,4140.00,590.62,3549.38\nINSULATION FABRICATORS,02-Central,768211000,3720.00,922.36,2797.64\nINSULATION FABRICATORS,02-Central,764521073,3440.00,2580.00,860.00\nINSULATION FABRICATORS,02-Central,764481000,2564.00,763.54,1800.46\nINSULATION FABRICATORS,02-Central,764602000,2562.00,943.60,1618.40\nINSULATION FABRICATORS,02-Central,764345052,2555.00,787.36,1767.64\nINSULATION FABRICATORS,02-Central,739681100,2370.24,1656.00,714.24\nINSULATION FABRICATORS,02-Central,770372000,1999.20,132.32,1866.88\nINSULATION FABRICATORS,02-Central,727000140,1690.00,217.28,1472.72\nINSULATION FABRICATORS,02-Central,739561000,1494.29,343.79,1150.50\nINSULATION FABRICATORS,02-Central,735602010,1336.00,922.30,413.70\nINSULATION FABRICATORS,02-Central,733151500,1311.20,203.60,1107.60\nINSULATION FABRICATORS,02-Central,733671000,1294.27,119.66,1174.61\nINSULATION FABRICATORS,02-Central,764564000,1200.00,422.63,777.37\nINSULATION FABRICATORS,02-Central,740283502,1073.60,193.64,879.96\nINSULATION FABRICATORS,02-Central,793250074,1005.00,261.49,743.51\nINSULATION FABRICATORS,02-Central,733403300,920.04,92.92,827.12\nINSULATION FABRICATORS,02-Central,779261000,915.34,155.38,759.96\nINSULATION FABRICATORS,02-Central,734611100,905.14,270.91,634.23\nINSULATION FABRICATORS,02-Central,754055000,837.76,293.27,544.49\nINSULATION FABRICATORS,02-Central,793452074,738.00,295.77,442.23\nINSULATION FABRICATORS,02-Central,727000060,688.00,167.43,520.57\nINSULATION FABRICATORS,02-Central,770240000,637.50,70.33,567.17\nINSULATION FABRICATORS,02-Central,777601000,614.88,117.60,497.28\nINSULATION FABRICATORS,02-Central,733141200,324.00,59.97,264.03\nINSULATION FABRICATORS,02-Central,734651200,314.60,74.00,240.60\nINSULATION FABRICATORS,02-Central,764781040,280.00,80.66,199.34\nINSULATION FABRICATORS,02-Central,739641100,195.12,137.43,57.69\nINSULATION FABRICATORS,02-Central,764342000,63.00,23.40,39.60\nINSULATION FABRICATORS,02-Central,777642000,57.35,15.84,41.51\nINSULATION MATERIALS CORP,02-Central,735602000,137901.50,55221.52,82679.98\nINSULATION MATERIALS CORP,02-Central,768242000,44702.00,21648.79,23053.21\nINSULATION MATERIALS CORP,02-Central,771320000,25051.90,5694.09,19357.81\nINSULATION MATERIALS CORP,02-Central,768222000,5133.00,2011.01,3121.99\nINSULATION MATERIALS CORP,02-Central,764902000,3144.00,1532.49,1611.51\nINSULATION MATERIALS CORP,02-Central,764781000,2184.00,858.69,1325.31\nINSULATION MATERIALS CORP,02-Central,766121000,1596.00,653.98,942.02\nINSULATION MATERIALS CORP,02-Central,735750000,1500.00,686.70,813.30\nINSULATION MATERIALS CORP,02-Central,764821000,1312.00,491.46,820.54\nINSULATION MATERIALS CORP,02-Central,768142000,797.50,307.45,490.05\nINSULATION MATERIALS CORP,02-Central,764901000,786.00,387.46,398.54\nINSULATION MATERIALS CORP,02-Central,768241000,396.00,154.16,241.84\nINSULATION SALES,02-Central,735602000,277321.50,89874.73,187446.77\nINSULATION SALES,02-Central,735750000,24824.40,6552.76,18271.64\nINSULATION SALES,02-Central,764341000,12499.00,4577.73,7921.27\nINSULATION SALES,02-Central,764521000,2971.00,876.32,2094.68\nINSULATION SALES,02-Central,764901000,2510.00,640.58,1869.42\nINSULATION SALES,02-Central,764301000,1906.00,762.68,1143.32\nINSULATION SALES,02-Central,764261000,1426.00,505.18,920.82\nINSULATION SALES,02-Central,735602110,1019.20,350.03,669.17\nINSULATION SALES,02-Central,764342000,889.00,327.62,561.38\nINSULATION SPECIALTIES,02-Central,727000060,776940.00,457816.86,319123.14\nINSULATION SPECIALTIES,02-Central,720290010,145165.50,84880.67,60284.83\nINSULATION SPECIALTIES,02-Central,720271000,119847.60,48995.04,70852.56\nINSULATION SPECIALTIES,02-Central,715001000,53178.00,22389.09,30788.91\nINSULATION SPECIALTIES,02-Central,763991000,19764.00,6012.04,13751.96\nINSULATION SPECIALTIES,02-Central,793060000,18338.40,6106.64,12231.76\nINSULATION SPECIALTIES,02-Central,764521000,16320.00,6382.59,9937.41\nINSULATION SPECIALTIES,02-Central,793000000,13651.20,4962.83,8688.37\nINSULATION SPECIALTIES,02-Central,727063016,11750.40,6841.28,4909.12\nINSULATION SPECIALTIES,02-Central,734030000,3312.00,989.32,2322.68\nINSULATION SPECIALTIES,02-Central,734000000,1561.50,455.59,1105.91\nINSULFAB PLASTICS INC,02-Central,733444100,8712.00,2056.41,6655.59\nINTERNATIONAL RE-ENFORCED,02-Central,727000140,45722.88,7030.76,38692.12\nIPSEN CERAMICS DIV,02-Central,766162000,876.00,37.37,838.63\nIRON CITY PIPE & SUPPLY,02-Central,733211050,456.00,49.70,406.30\nIRON CITY PIPE & SUPPLY,02-Central,734910000,362.00,53.09,308.91\n\"IRWIN INTERNATIONAL, INC.\",02-Central,733525500,5700.00,541.70,5158.30\n\"IRWIN INTERNATIONAL, INC.\",02-Central,733505200,1645.00,167.66,1477.34\n\"IRWIN INTERNATIONAL, INC.\",02-Central,733464500,626.00,65.05,560.95\nJ&J TRUCK EQUIPMENT CO.,02-Central,733464600,313.00,34.97,278.03\nJACKSONVILLE ELECTRIC .,02-Central,770510000,1975.00,96.41,1878.59\nJBC TECHNOLOGIES INC.,02-Central,768211000,294.00,140.34,153.66\nJBC TECHNOLOGIES INC.,02-Central,768222000,168.00,68.31,99.69\nJEANNETTE SPECIALTY GLASS,02-Central,740402402,2097.50,411.01,1686.49\nJOHN HOPKINS UNIVERSITY,02-Central,733221400,4689.36,770.88,3918.48\nJOHN HOPKINS UNIVERSITY,02-Central,739561200,842.24,144.66,697.58\nJONATHAN ELLIS,02-Central,777642000,135.00,31.68,103.32\nJONESBORO WINAIR CO #247,02-Central,735602000,11936.00,2707.75,9228.25\nJOY MARK,02-Central,726000242,1891816.80,1082085.28,809731.52\nJOY MARK,02-Central,726020005,315632.16,174086.81,141545.35\nJOY MARK,02-Central,727000100,66203.52,67049.03,-845.51\nJOY MARK,02-Central,727000060,22142.40,13342.86,8799.54\nKAMAN INDUSTRIAL TECHNOLO,02-Central,770210000,460.00,31.52,428.48\nKBR,02-Central,767970140,2281.50,315.58,1965.92\nKBR,02-Central,767970000,972.00,302.34,669.66\nKERBER SHEET METAL,02-Central,732821100,158941.44,10070.08,148871.36\nKERBER SHEET METAL,02-Central,732771000,27054.72,1377.90,25676.82\nKERBER SHEET METAL,02-Central,733505200,5378.00,808.77,4569.23\nKERBER SHEET METAL,02-Central,733525500,5180.00,780.36,4399.64\nKERDEA TECHNOLOGIES INC.,02-Central,733444200,348.00,38.64,309.36\nKICZAN MANUFACTURING INC.,02-Central,733444200,680.60,73.33,607.27\nKIRK & BLUM,02-Central,766594000,2980.00,162.43,2817.57\nKMI SYSTEMS INC,02-Central,727000140,135594.00,57723.22,77870.78\nKMI SYSTEMS INC,02-Central,763991000,86799.60,65626.92,21172.68\nKMI SYSTEMS INC,02-Central,765662000,81264.00,25475.24,55788.76\nKMI SYSTEMS INC,02-Central,764605000,67675.50,25889.97,41785.53\nKMI SYSTEMS INC,02-Central,764345052,52778.00,24427.62,28350.38\nKMI SYSTEMS INC,02-Central,734910000,30051.00,8318.36,21732.64\nKMI SYSTEMS INC,02-Central,727000060,21204.00,9054.09,12149.91\nKYOCERA INDUSTRIAL,02-Central,766162000,246.50,74.74,171.76\nKYOCERA INDUSTRIAL,02-Central,766121000,246.00,54.50,191.50\nLARRY COOPER,02-Central,768121000,100.00,14.38,85.62\nLAYNE HEAVY CIVIL,02-Central,734751200,584.00,75.41,508.59\nLECTRAHEAT,02-Central,764461006,3944.00,275.21,3668.79\nLEE PRECISION MACHINE,02-Central,724281900,3882.00,893.92,2988.08\nLILJA CORP,02-Central,770580000,1522.00,84.73,1437.27\nLILJA CORP,02-Central,770560000,1509.00,86.09,1422.91\nLIQTECH NA INC.,02-Central,734793020,345434.70,139724.77,205709.93\nLORD CORP,02-Central,739561100,552.72,93.32,459.40\n\"LOTUS ENGINEERING, INC.\",02-Central,764481000,706.00,34.53,671.47\nLOW COUNTRY INSULATION,02-Central,735602000,13340.00,5005.19,8334.81\nLPI TECHNICAL SERVICE,02-Central,768211000,748.00,46.78,701.22\nLSI ADL TECHNOLOGY INC.,02-Central,733403100,5648.05,523.57,5124.48\nLT ENGINEERING INC.,02-Central,768144210,1777.50,511.82,1265.68\nLVR INC.,02-Central,734900000,1617.90,474.98,1142.92\nLVR INC.,02-Central,733211000,479.00,53.20,425.80\nLVR INC.,02-Central,734311100,404.36,35.30,369.06\nLVR INC.,02-Central,777601000,386.72,72.02,314.70\nM & L MECHANICAL,02-Central,734311100,404.36,35.30,369.06\nM & L MECHANICAL,02-Central,734611100,404.36,27.18,377.18\nM. LUKAS COMPANY,02-Central,733151500,9844.20,1282.11,8562.09\nM. LUKAS COMPANY,02-Central,733211000,9764.40,1514.70,8249.70\nM. LUKAS COMPANY,02-Central,792760000,609.56,116.62,492.94\nMAC ARTHUR CO,02-Central,735602110,4395.00,1338.80,3056.20\nMAC ARTHUR CO,02-Central,735602000,2197.50,647.22,1550.28\nMACHINE TOOL,02-Central,739641100,4319.64,1750.23,2569.41\nMAGNAFLEX INDUSTRIES INC.,02-Central,764765000,5040.00,2949.37,2090.63\nMAGNAFLEX INDUSTRIES INC.,02-Central,764761000,4160.00,637.20,3522.80\nMAGNEQUENCH INTERNATIONAL,02-Central,764821000,430.00,15.36,414.64\nMAGNESITA REFRACTORIES,02-Central,763991000,1713.60,156.90,1556.70\nMATHIAS DIE COMPANY INC.,02-Central,733464500,1222.00,124.09,1097.91\nMATTHEWS INTERNATIONAL,02-Central,722413100,6623.60,342.00,6281.60\nMCALLISTER MILLS INC.,02-Central,768133500,6625.60,3025.01,3600.59\nMCALLISTER MILLS INC.,02-Central,768133400,5979.20,2666.95,3312.25\nMCAULY MFG. INC.,02-Central,726000240,220.80,14.79,206.01\nMCAULY MFG. INC.,02-Central,764261000,212.00,11.45,200.55\nMCNEIL INC.,02-Central,754925000,97242.30,54251.58,42990.72\nMCNEIL INC.,02-Central,764601000,60382.40,20387.00,39995.40\nMCNEIL INC.,02-Central,795482000,32514.10,12618.76,19895.34\nMCNEIL INC.,02-Central,727041200,23023.08,4663.08,18360.00\nMCNEIL INC.,02-Central,754495000,20599.46,7626.18,12973.28\nMCNEIL INC.,02-Central,764821000,17898.00,6295.77,11602.23\nMCNEIL INC.,02-Central,764901000,16440.88,4870.86,11570.02\nMCNEIL INC.,02-Central,788260020,14187.12,4174.14,10012.98\nMCNEIL INC.,02-Central,739781100,11600.64,6855.08,4745.56\nMCNEIL INC.,02-Central,718500000,11329.28,4568.47,6760.81\nMCNEIL INC.,02-Central,754926000,11088.24,6260.30,4827.94\nMCNEIL INC.,02-Central,754095000,10120.32,4291.26,5829.06\nMCNEIL INC.,02-Central,788200010,8919.63,2954.01,5965.62\nMCNEIL INC.,02-Central,764521000,7672.00,3828.52,3843.48\nMCNEIL INC.,02-Central,738521000,7498.38,5164.26,2334.12\nMCNEIL INC.,02-Central,766121000,6825.00,1930.61,4894.39\nMCNEIL INC.,02-Central,734311100,6664.00,1412.04,5251.96\nMCNEIL INC.,02-Central,754305000,5913.64,2371.32,3542.32\nMCNEIL INC.,02-Central,739641100,5875.20,5497.08,378.12\nMCNEIL INC.,02-Central,779957621,5778.72,2688.51,3090.21\nMCNEIL INC.,02-Central,780310000,5538.20,2132.98,3405.22\nMCNEIL INC.,02-Central,734661300,5326.20,1611.84,3714.36\nMCNEIL INC.,02-Central,784140000,4448.22,2117.66,2330.56\nMCNEIL INC.,02-Central,764341000,3050.25,1204.08,1846.17\nMCNEIL INC.,02-Central,734990000,2725.60,717.52,2008.08\nMCNEIL INC.,02-Central,734900000,2649.60,1381.03,1268.57\nMCNEIL INC.,02-Central,734211200,2603.95,327.27,2276.68\nMCNEIL INC.,02-Central,739390000,2049.44,1864.89,184.55\nMCNEIL INC.,02-Central,734910000,1836.00,616.16,1219.84\nMCNEIL INC.,02-Central,754885000,1707.36,727.32,980.04\nMCNEIL INC.,02-Central,780330000,1440.10,574.46,865.64\nMCNEIL INC.,02-Central,740402402,1414.00,434.70,979.30\nMCNEIL INC.,02-Central,754896000,1404.96,787.76,617.20\nMCNEIL INC.,02-Central,777601000,1399.16,405.67,993.49\nMCNEIL INC.,02-Central,770260000,984.83,208.29,776.54\nMCNEIL INC.,02-Central,734651200,925.36,184.50,740.86\nMCNEIL INC.,02-Central,780250000,889.27,367.58,521.69\nMCNEIL INC.,02-Central,734351200,833.00,147.24,685.76\nMCNEIL INC.,02-Central,737912200,629.18,136.42,492.76\nMCNEIL INC.,02-Central,733525500,550.00,130.06,419.94\nMCNEIL INC.,02-Central,777642000,520.00,151.86,368.14\nMCNEIL INC.,02-Central,727000140,492.80,133.71,359.09\nMCNEIL INC.,02-Central,768221000,410.40,70.76,339.64\nMCNEIL INC.,02-Central,739561000,386.35,170.44,215.91\nMCNEIL INC.,02-Central,770392000,350.00,35.15,314.85\nMCNEIL INC.,02-Central,792702000,303.75,96.60,207.15\nMCNEIL INC.,02-Central,733211000,220.00,94.78,125.22\nMCNEIL INC.,02-Central,733725500,164.20,51.76,112.44\n\"MEGTEC SYSTEMS, INC\",02-Central,754055000,79930.10,46859.42,33070.68\n\"MEGTEC SYSTEMS, INC\",02-Central,753945000,39910.72,33329.70,6581.02\n\"MEGTEC SYSTEMS, INC\",02-Central,754045000,30718.27,19842.49,10875.78\n\"MEGTEC SYSTEMS, INC\",02-Central,753965000,23602.83,18089.11,5513.72\n\"MEGTEC SYSTEMS, INC\",02-Central,764821000,11351.00,2997.87,8353.13\n\"MEGTEC SYSTEMS, INC\",02-Central,764822000,8191.20,2521.02,5670.18\n\"MEGTEC SYSTEMS, INC\",02-Central,764522000,7503.20,2114.23,5388.97\n\"MEGTEC SYSTEMS, INC\",02-Central,764901000,7266.00,2439.37,4826.63\n\"MEGTEC SYSTEMS, INC\",02-Central,770540000,6659.12,549.16,6109.96\n\"MEGTEC SYSTEMS, INC\",02-Central,734651200,6134.63,369.75,5764.88\n\"MEGTEC SYSTEMS, INC\",02-Central,738401000,5139.00,5300.06,-161.06\n\"MEGTEC SYSTEMS, INC\",02-Central,764861000,5045.60,1561.41,3484.19\n\"MEGTEC SYSTEMS, INC\",02-Central,754065000,4749.00,2758.14,1990.86\n\"MEGTEC SYSTEMS, INC\",02-Central,780650000,4320.65,2478.18,1842.47\n\"MEGTEC SYSTEMS, INC\",02-Central,779988252,3666.00,3284.19,381.81\n\"MEGTEC SYSTEMS, INC\",02-Central,764561000,3618.00,1419.33,2198.67\n\"MEGTEC SYSTEMS, INC\",02-Central,764781040,3310.00,675.44,2634.56\n\"MEGTEC SYSTEMS, INC\",02-Central,764823500,2432.20,762.56,1669.64\n\"MEGTEC SYSTEMS, INC\",02-Central,780210000,2406.18,1270.82,1135.36\n\"MEGTEC SYSTEMS, INC\",02-Central,764601000,2216.25,664.31,1551.94\n\"MEGTEC SYSTEMS, INC\",02-Central,777642000,1972.80,570.06,1402.74\n\"MEGTEC SYSTEMS, INC\",02-Central,758531000,1876.06,593.72,1282.34\n\"MEGTEC SYSTEMS, INC\",02-Central,739641100,1609.52,1493.54,115.98\n\"MEGTEC SYSTEMS, INC\",02-Central,770550000,1598.26,175.27,1422.99\n\"MEGTEC SYSTEMS, INC\",02-Central,777601000,1559.32,523.26,1036.06\n\"MEGTEC SYSTEMS, INC\",02-Central,774271000,1406.57,923.42,483.15\n\"MEGTEC SYSTEMS, INC\",02-Central,734751200,1365.15,411.29,953.86\n\"MEGTEC SYSTEMS, INC\",02-Central,774301000,1329.10,675.39,653.71\n\"MEGTEC SYSTEMS, INC\",02-Central,774401000,1168.93,514.20,654.73\n\"MEGTEC SYSTEMS, INC\",02-Central,780290000,1163.32,614.16,549.16\n\"MEGTEC SYSTEMS, INC\",02-Central,764482000,1040.00,382.84,657.16\n\"MEGTEC SYSTEMS, INC\",02-Central,742546000,1000.80,156.72,844.08\n\"MEGTEC SYSTEMS, INC\",02-Central,784320000,629.43,365.84,263.59\n\"MEGTEC SYSTEMS, INC\",02-Central,764781000,570.60,122.70,447.90\n\"MEGTEC SYSTEMS, INC\",02-Central,738361000,548.32,926.64,-378.32\n\"MEGTEC SYSTEMS, INC\",02-Central,734990000,509.32,109.82,399.50\n\"MEGTEC SYSTEMS, INC\",02-Central,754295000,509.00,217.16,291.84\n\"MEGTEC SYSTEMS, INC\",02-Central,764826500,476.80,129.32,347.48\n\"MEGTEC SYSTEMS, INC\",02-Central,738521000,465.92,597.12,-131.20\n\"MEGTEC SYSTEMS, INC\",02-Central,754725000,450.41,224.69,225.72\n\"MEGTEC SYSTEMS, INC\",02-Central,754265000,440.05,199.38,240.67\n\"MEGTEC SYSTEMS, INC\",02-Central,764521000,377.60,106.27,271.33\n\"MEGTEC SYSTEMS, INC\",02-Central,734611100,279.48,80.88,198.60\n\"MEGTEC SYSTEMS, INC\",02-Central,773801000,239.70,91.14,148.56\n\"MEGTEC SYSTEMS, INC\",02-Central,764902000,184.30,63.86,120.44\nMERAK NORTH AMERICA,02-Central,768021005,913.00,106.43,806.57\nMERAK NORTH AMERICA,02-Central,768021000,374.00,26.83,347.17\nMESSIER - BUGATTI,02-Central,779601000,23172.00,3984.62,19187.38\nMESSIER - BUGATTI,02-Central,770930000,6252.00,214.28,6037.72\nMESSIER - BUGATTI,02-Central,770710000,6192.00,134.52,6057.48\nMESSIER-BUGATTI USA LLC,02-Central,779601000,131331.67,21395.85,109935.82\nMESSIER-BUGATTI USA LLC,02-Central,770930000,6252.00,214.28,6037.72\nMESSIER-BUGATTI USA LLC,02-Central,770710000,3096.00,67.25,3028.75\nMESTEK INC.,02-Central,739541000,587.52,90.50,497.02\n\"MESTEK, FORREST CITY\",02-Central,764461006,776.00,54.49,721.51\nMETALOR ELECTROTECHNICS,02-Central,734311100,404.36,35.30,369.06\nMETALSTAMP INC.,02-Central,764902000,884.50,32.21,852.29\nMGM INDUSTRIAL SPLY,02-Central,734990000,4435.20,657.31,3777.89\nMGM INDUSTRIAL SUPPLY CO,02-Central,734990000,59808.00,8594.63,51213.37\nMHD - ROCKLAND INC.,02-Central,771102000,870.00,25.10,844.90\nMHD ROCKLAND,02-Central,771102000,7573.00,229.79,7343.21\nMI-T-M CORP,02-Central,764481000,346.00,16.94,329.06\nMICROFLEX INC,02-Central,770230000,4032.00,295.67,3736.33\nMICROFLEX INC,02-Central,770240000,937.50,67.63,869.87\nMICROWAVE RESEARCH &,02-Central,739641100,1389.96,412.29,977.67\nMICROWAVE RESEARCH &,02-Central,734711100,536.16,81.15,455.01\nMICROWAVE RESEARCH &,02-Central,734311100,190.74,16.85,173.89\nMIDSTATE INDUSTRIAL,02-Central,739641100,926.64,274.85,651.79\nMIDSTATE INDUSTRIAL,02-Central,740023000,727.44,119.31,608.13\nMIDSTATE INDUSTRIAL,02-Central,740063000,616.56,120.10,496.46\nMIDSTATE INDUSTRIAL,02-Central,739681100,444.24,120.46,323.78\nMIDWEST FABRICATION,02-Central,764601000,5568.00,225.61,5342.39\nMIDWEST SERVICE CENTER,02-Central,770200000,630.00,40.79,589.21\nMIKRO SYSTEMS INC.,02-Central,764521000,666.00,26.54,639.46\nMILLIKEN AND COMPANY,02-Central,739380000,3171.84,961.23,2210.61\nMILWAUKEE INSULATION,02-Central,777601000,1160.16,216.05,944.11\nMINTEQ INTERNATIONAL INC.,02-Central,779988964,45540.00,32996.76,12543.24\nMINTEQ INTERNATIONAL INC.,02-Central,777904400,27189.00,20387.43,6801.57\nMITSUBISHI POWER SYSTEMS,02-Central,764821000,447.00,15.66,431.34\nMITSUBISHI POWER SYSTEMS,02-Central,739641100,409.68,201.60,208.08\nMODINE MFG CO,02-Central,733586400,1857.60,182.85,1674.75\nMODINE MFG CO,02-Central,733566100,979.20,129.98,849.22\nMONESSEN HEARTH SYSTEMS,02-Central,726020042,192497.85,152220.15,40277.70\nMONESSEN HEARTH SYSTEMS,02-Central,726020041,144297.75,114581.39,29716.36\nMONESSEN HEARTH SYSTEMS,02-Central,726000247,16988.40,13271.91,3716.49\nMONESSEN HEARTH SYSTEMS,02-Central,726020019,3631.50,2736.50,895.00\nMONESSEN HEARTH SYSTEMS,02-Central,726020005,2905.20,2008.13,897.07\nMONESSEN HEARTH SYSTEMS,02-Central,726000246,807.00,605.93,201.07\nMONESSEN HEARTH SYSTEMS,02-Central,777601000,441.67,144.04,297.63\nMONESSEN HEARTH SYSTEMS,02-Central,777642000,381.60,201.44,180.16\nMORGAN AM&T,02-Central,734990000,926.40,110.07,816.33\nMOTION INDUSTRIES,02-Central,770220000,11088.00,670.50,10417.50\nMOTION INDUSTRIES,02-Central,770560000,10791.00,612.86,10178.14\nMOTION INDUSTRIES,02-Central,764821000,7463.00,263.78,7199.22\nMOTION INDUSTRIES,02-Central,737251000,5242.32,902.07,4340.25\nMOTION INDUSTRIES,02-Central,739781100,5180.96,1443.62,3737.34\nMOTION INDUSTRIES,02-Central,770540000,3629.50,184.78,3444.72\nMOTION INDUSTRIES,02-Central,771170000,3563.00,150.93,3412.07\nMOTION INDUSTRIES,02-Central,738521000,2760.96,1337.69,1423.27\nMOTION INDUSTRIES,02-Central,770200000,2592.00,166.40,2425.60\nMOTION INDUSTRIES,02-Central,734890000,2424.68,406.65,2018.03\nMOTION INDUSTRIES,02-Central,770330000,2130.00,202.93,1927.07\nMOTION INDUSTRIES,02-Central,770600000,1916.25,96.32,1819.93\nMOTION INDUSTRIES,02-Central,770522000,1868.75,88.79,1779.96\nMOTION INDUSTRIES,02-Central,770210000,1320.00,90.96,1229.04\nMOTION INDUSTRIES,02-Central,777601000,1206.56,189.62,1016.94\nMOTION INDUSTRIES,02-Central,770222000,1080.25,68.04,1012.21\nMOTION INDUSTRIES,02-Central,770230000,1065.75,71.08,994.67\nMOTION INDUSTRIES,02-Central,770260000,1035.00,67.63,967.37\nMOTION INDUSTRIES,02-Central,770720000,1010.00,27.12,982.88\nMOTION INDUSTRIES,02-Central,770240000,986.25,70.33,915.92\nMOTION INDUSTRIES,02-Central,734822000,932.00,160.95,771.05\nMOTION INDUSTRIES,02-Central,766686000,845.00,73.65,771.35\nMOTION INDUSTRIES,02-Central,734311100,404.36,35.30,369.06\nMSC INDUSTRIAL DIRECT CO.,02-Central,734211200,82684.76,8058.43,74626.33\nMSC INDUSTRIAL DIRECT CO.,02-Central,734311100,14652.44,1284.55,13367.89\nMSC INDUSTRIAL DIRECT CO.,02-Central,770530000,1707.50,93.46,1614.04\nMSC INDUSTRIAL DIRECT CO.,02-Central,735602000,618.50,43.15,575.35\nMSC INDUSTRIAL DIRECT CO.,02-Central,777601000,409.92,58.80,351.12\nMSC INDUSTRIAL DIRECT CO.,02-Central,733464500,250.40,32.53,217.87\nMULTI-METALS,02-Central,770850000,1342.00,42.06,1299.94\nMUNACO PACKING & RUBBER,02-Central,764482000,1384.00,68.19,1315.81\nNASA - MARSHALL SPACE,02-Central,739390000,350.56,82.43,268.13\nNASA GLENN RESEARCH CTR.,02-Central,739521100,1270.08,139.38,1130.70\nNASA GLENN RESEARCH CTR.,02-Central,777998000,910.00,1214.32,-304.32\nNASA GLENN RESEARCH CTR.,02-Central,740361900,700.80,228.74,472.06\nNASA GLENN RESEARCH CTR.,02-Central,739541000,564.48,71.62,492.86\nNASA GLENN RESEARCH CTR.,02-Central,740361701,529.20,318.65,210.55\nNASA GLENN RESEARCH CTR.,02-Central,777998500,300.00,400.07,-100.07\nNASA MSFC,02-Central,764221000,248.00,16.92,231.08\nNATHAN DEKKENGA,02-Central,739390000,350.56,82.43,268.13\nNATIONAL MACHINE COMPANY,02-Central,733151500,1866.00,210.01,1655.99\nNAVAL RESEARCH LABORATORY,02-Central,764521000,320.00,13.30,306.70\nNAVAL SURFACE WARFARE CTR,02-Central,739452000,2162.16,603.34,1558.82\nNAVAL SURFACE WARFARE CTR,02-Central,734851000,1518.57,274.20,1244.37\nNAVAL SURFACE WARFARE CTR,02-Central,724281400,1202.00,178.16,1023.84\nNAVAL SURFACE WARFARE CTR,02-Central,739370000,1094.40,272.46,821.94\nNAVAL SURFACE WARFARE CTR,02-Central,737912200,979.50,136.41,843.09\nNAVY TECHNOLOGY CCENTER,02-Central,780650000,1339.68,206.95,1132.73\nNEW ARTS FOUNDRY,02-Central,734910000,1730.00,309.84,1420.16\nNEW ARTS FOUNDRY,02-Central,764521000,1044.00,79.77,964.23\nNORANDAL,02-Central,770340000,9330.75,940.03,8390.72\nNORFOLK NAVAL SHIPYARD,02-Central,726020033,408.80,33.33,375.47\nNORTH AMERICAN HOGANAS,02-Central,768241000,11484.00,1273.72,10210.28\nNORTHERN INDIANA PUBLIC S,02-Central,779521000,35765.68,5128.36,30637.32\nNOVELIS OSWEGO,02-Central,733525600,752.50,71.05,681.45\nNS INDUSTRIES INC.,02-Central,733484800,1568.00,164.36,1403.64\nNS INDUSTRIES INC.,02-Central,733505200,1565.00,160.27,1404.73\nNS INDUSTRIES INC.,02-Central,733510000,1130.00,159.02,970.98\nNS INDUSTRIES INC.,02-Central,733741000,776.00,186.38,589.62\nNUCOR STEEL BIRMINGHAM,02-Central,779988964,2226.00,3599.46,-1373.46\nNUCOR-YAMATO STEEL CO.,02-Central,779980910,21250.00,9094.10,12155.90\nOHIO INDUSTRIAL SALES INC,02-Central,764461006,332306.40,123177.49,209128.91\nOHIO INDUSTRIAL SALES INC,02-Central,766121610,270797.60,137858.34,132939.26\nOHIO INDUSTRIAL SALES INC,02-Central,764602500,113360.00,57238.81,56121.19\nOHIO INDUSTRIAL SALES INC,02-Central,734761300,112661.30,20458.67,92202.63\nOHIO INDUSTRIAL SALES INC,02-Central,740402450,105710.94,67205.01,38505.93\nOHIO INDUSTRIAL SALES INC,02-Central,764821000,96600.00,43162.78,53437.22\nOHIO INDUSTRIAL SALES INC,02-Central,764901000,96064.00,50970.77,45093.23\nOHIO INDUSTRIAL SALES INC,02-Central,734251300,75399.66,16277.93,59121.73\nOHIO INDUSTRIAL SALES INC,02-Central,764901073,64448.00,77274.00,-12826.00\nOHIO INDUSTRIAL SALES INC,02-Central,734311100,47907.32,5512.35,42394.97\nOHIO INDUSTRIAL SALES INC,02-Central,764822000,47873.00,20752.33,27120.67\nOHIO INDUSTRIAL SALES INC,02-Central,718500000,47001.61,8169.94,38831.67\nOHIO INDUSTRIAL SALES INC,02-Central,733202000,46642.72,9274.66,37368.06\nOHIO INDUSTRIAL SALES INC,02-Central,733586500,45010.08,6786.00,38224.08\nOHIO INDUSTRIAL SALES INC,02-Central,739561200,44831.11,15979.42,28851.69\nOHIO INDUSTRIAL SALES INC,02-Central,766121615,43048.80,21780.71,21268.09\nOHIO INDUSTRIAL SALES INC,02-Central,764264050,41396.00,25969.68,15426.32\nOHIO INDUSTRIAL SALES INC,02-Central,764521000,37620.00,17532.30,20087.70\nOHIO INDUSTRIAL SALES INC,02-Central,733484900,36881.60,6796.39,30085.21\nOHIO INDUSTRIAL SALES INC,02-Central,733525600,36113.00,5580.13,30532.87\nOHIO INDUSTRIAL SALES INC,02-Central,740361900,31337.28,13268.59,18068.69\nOHIO INDUSTRIAL SALES INC,02-Central,764481000,30070.00,17050.52,13019.48\nOHIO INDUSTRIAL SALES INC,02-Central,727041000,28800.00,2850.55,25949.45\nOHIO INDUSTRIAL SALES INC,02-Central,764821073,23460.00,24786.00,-1326.00\nOHIO INDUSTRIAL SALES INC,02-Central,764522000,23378.00,10911.89,12466.11\nOHIO INDUSTRIAL SALES INC,02-Central,779988828,21594.00,11716.15,9877.85\nOHIO INDUSTRIAL SALES INC,02-Central,766211000,21330.00,12036.77,9293.23\nOHIO INDUSTRIAL SALES INC,02-Central,734900000,21262.62,8266.88,12995.74\nOHIO INDUSTRIAL SALES INC,02-Central,733151500,18490.40,2884.37,15606.03\nOHIO INDUSTRIAL SALES INC,02-Central,739741100,17064.87,11043.46,6021.41\nOHIO INDUSTRIAL SALES INC,02-Central,734990000,16417.32,3062.56,13354.76\nOHIO INDUSTRIAL SALES INC,02-Central,779988827,16147.51,7823.58,8323.93\nOHIO INDUSTRIAL SALES INC,02-Central,764821161,15870.00,17112.00,-1242.00\nOHIO INDUSTRIAL SALES INC,02-Central,737912200,15781.37,3402.11,12379.26\nOHIO INDUSTRIAL SALES INC,02-Central,740382201,14721.50,5054.79,9666.71\nOHIO INDUSTRIAL SALES INC,02-Central,766162000,12628.00,1818.12,10809.88\nOHIO INDUSTRIAL SALES INC,02-Central,766121000,11980.80,6975.70,5005.10\nOHIO INDUSTRIAL SALES INC,02-Central,771500000,11854.50,860.05,10994.45\nOHIO INDUSTRIAL SALES INC,02-Central,733444300,10479.40,2040.78,8438.62\nOHIO INDUSTRIAL SALES INC,02-Central,764820500,10084.80,3967.42,6117.38\nOHIO INDUSTRIAL SALES INC,02-Central,733505100,9945.00,1794.43,8150.57\nOHIO INDUSTRIAL SALES INC,02-Central,727000140,9237.12,1169.17,8067.95\nOHIO INDUSTRIAL SALES INC,02-Central,734910000,9147.00,2756.25,6390.75\nOHIO INDUSTRIAL SALES INC,02-Central,739781100,8640.48,3786.18,4854.30\nOHIO INDUSTRIAL SALES INC,02-Central,737902100,8176.75,2196.88,5979.87\nOHIO INDUSTRIAL SALES INC,02-Central,779988960,8060.00,5443.60,2616.40\nOHIO INDUSTRIAL SALES INC,02-Central,734651200,7943.55,1401.46,6542.09\nOHIO INDUSTRIAL SALES INC,02-Central,771320000,7555.00,1241.91,6313.09\nOHIO INDUSTRIAL SALES INC,02-Central,739390000,7393.59,2720.22,4673.37\nOHIO INDUSTRIAL SALES INC,02-Central,734211200,7230.51,1013.53,6216.98\nOHIO INDUSTRIAL SALES INC,02-Central,734351200,6319.32,627.57,5691.75\nOHIO INDUSTRIAL SALES INC,02-Central,764781040,6158.00,1737.90,4420.10\nOHIO INDUSTRIAL SALES INC,02-Central,733141300,5629.60,1251.33,4378.27\nOHIO INDUSTRIAL SALES INC,02-Central,764826700,5520.00,2186.16,3333.84\nOHIO INDUSTRIAL SALES INC,02-Central,740283401,5333.68,1061.54,4272.14\nOHIO INDUSTRIAL SALES INC,02-Central,739541200,5277.20,1221.72,4055.48\nOHIO INDUSTRIAL SALES INC,02-Central,764482000,5022.00,2819.15,2202.85\nOHIO INDUSTRIAL SALES INC,02-Central,733484700,5008.00,956.94,4051.06\nOHIO INDUSTRIAL SALES INC,02-Central,777901100,4403.54,1870.60,2532.94\nOHIO INDUSTRIAL SALES INC,02-Central,777601000,4204.15,1078.92,3125.23\nOHIO INDUSTRIAL SALES INC,02-Central,777904301,3887.63,1762.01,2125.62\nOHIO INDUSTRIAL SALES INC,02-Central,764781000,3873.60,1419.92,2453.68\nOHIO INDUSTRIAL SALES INC,02-Central,733725500,3812.40,1172.74,2639.66\nOHIO INDUSTRIAL SALES INC,02-Central,733211000,3709.50,768.99,2940.51\nOHIO INDUSTRIAL SALES INC,02-Central,726000240,3600.00,1549.50,2050.50\nOHIO INDUSTRIAL SALES INC,02-Central,770570000,3431.04,302.19,3128.85\nOHIO INDUSTRIAL SALES INC,02-Central,766122000,3232.00,1538.48,1693.52\nOHIO INDUSTRIAL SALES INC,02-Central,764761000,3056.00,1017.22,2038.78\nOHIO INDUSTRIAL SALES INC,02-Central,766081010,3003.00,508.35,2494.65\nOHIO INDUSTRIAL SALES INC,02-Central,764762000,2969.80,1226.11,1743.69\nOHIO INDUSTRIAL SALES INC,02-Central,740063000,2862.37,827.49,2034.88\nOHIO INDUSTRIAL SALES INC,02-Central,764341000,2824.86,1727.06,1097.80\nOHIO INDUSTRIAL SALES INC,02-Central,733151600,2776.60,511.74,2264.86\nOHIO INDUSTRIAL SALES INC,02-Central,770600000,2642.43,192.64,2449.79\nOHIO INDUSTRIAL SALES INC,02-Central,740283502,2603.04,387.29,2215.75\nOHIO INDUSTRIAL SALES INC,02-Central,737301000,2590.72,981.44,1609.28\nOHIO INDUSTRIAL SALES INC,02-Central,733211050,2405.00,503.20,1901.80\nOHIO INDUSTRIAL SALES INC,02-Central,764521073,2280.00,2580.00,-300.00\nOHIO INDUSTRIAL SALES INC,02-Central,733464500,2181.60,330.74,1850.86\nOHIO INDUSTRIAL SALES INC,02-Central,779550100,2107.04,474.56,1632.48\nOHIO INDUSTRIAL SALES INC,02-Central,733403100,1889.86,245.20,1644.66\nOHIO INDUSTRIAL SALES INC,02-Central,777642000,1884.00,731.00,1153.00\nOHIO INDUSTRIAL SALES INC,02-Central,770210000,1750.00,220.67,1529.33\nOHIO INDUSTRIAL SALES INC,02-Central,720455000,1684.00,241.84,1442.16\nOHIO INDUSTRIAL SALES INC,02-Central,733511000,1327.52,266.24,1061.28\nOHIO INDUSTRIAL SALES INC,02-Central,740361701,1325.52,947.43,378.09\nOHIO INDUSTRIAL SALES INC,02-Central,733403300,1137.40,245.08,892.32\nOHIO INDUSTRIAL SALES INC,02-Central,770450000,1102.88,101.88,1001.00\nOHIO INDUSTRIAL SALES INC,02-Central,734173000,1059.50,265.70,793.80\nOHIO INDUSTRIAL SALES INC,02-Central,741041000,1026.72,41.88,984.84\nOHIO INDUSTRIAL SALES INC,02-Central,733141200,1002.40,193.93,808.47\nOHIO INDUSTRIAL SALES INC,02-Central,738521000,976.32,430.35,545.97\nOHIO INDUSTRIAL SALES INC,02-Central,734162000,952.60,396.02,556.58\nOHIO INDUSTRIAL SALES INC,02-Central,733525500,915.00,130.06,784.94\nOHIO INDUSTRIAL SALES INC,02-Central,733201000,889.20,187.96,701.24\nOHIO INDUSTRIAL SALES INC,02-Central,733464600,825.20,139.88,685.32\nOHIO INDUSTRIAL SALES INC,02-Central,764902000,756.00,386.49,369.51\nOHIO INDUSTRIAL SALES INC,02-Central,770720000,749.70,27.12,722.58\nOHIO INDUSTRIAL SALES INC,02-Central,770310000,680.00,94.56,585.44\nOHIO INDUSTRIAL SALES INC,02-Central,740382101,664.20,252.83,411.37\nOHIO INDUSTRIAL SALES INC,02-Central,768121000,660.15,132.09,528.06\nOHIO INDUSTRIAL SALES INC,02-Central,733721000,615.60,146.70,468.90\nOHIO INDUSTRIAL SALES INC,02-Central,733423700,465.92,72.44,393.48\nOHIO INDUSTRIAL SALES INC,02-Central,733444100,426.00,150.08,275.92\nOHIO INDUSTRIAL SALES INC,02-Central,764221000,300.60,84.33,216.27\nOHIO INDUSTRIAL SALES INC,02-Central,768111000,234.90,46.14,188.76\nOHIO INDUSTRIAL SALES INC,02-Central,733423600,232.96,35.90,197.06\nOHIO INDUSTRIAL SALES INC,02-Central,764525000,198.00,103.86,94.14\nOHIO INDUSTRIAL SALES INC,02-Central,764602000,104.00,52.99,51.01\nOHIO STAT UNIV,02-Central,734900000,215.80,38.36,177.44\nOHIO STATE UNIVERSITY,02-Central,768121000,489.00,44.03,444.97\nOMI REFRACTORIES,02-Central,727041000,26956.80,2707.59,24249.21\nOMI REFRACTORIES,02-Central,761051000,15789.60,10262.62,5526.98\nOMI REFRACTORIES,02-Central,727041200,8961.84,1353.81,7608.03\nOMI REFRACTORIES,02-Central,761071000,5518.80,3234.84,2283.96\nOMI REFRACTORIES,02-Central,777642000,2986.00,734.12,2251.88\nONEX INC,02-Central,764785121,71760.00,29177.10,42582.90\nONEX INC,02-Central,764781040,7560.00,3387.84,4172.16\nONEX INC,02-Central,766542300,492.00,172.77,319.23\nONEX REFRACTORIES,02-Central,768753000,472.80,48.26,424.54\nORTON CERAMIC FOUNDATION,02-Central,777991000,1697.30,449.06,1248.24\nOVERHEAD MATERIAL,02-Central,770600000,6487.60,373.47,6114.13\nOWENS BROCKWAY,02-Central,766121000,5250.00,163.50,5086.50\nOWENS BROCKWAY,02-Central,763991000,1236.00,137.58,1098.42\nOWENS CORNING FIBERGLAS,02-Central,739641100,1928.16,806.40,1121.76\nOWENS CORNING FIBERGLAS,02-Central,793260074,1366.00,163.32,1202.68\nOWENS CORNING FIBERGLAS,02-Central,793330000,449.00,26.51,422.49\n\"OWENS CORNING SALES,LLC\",02-Central,793330000,863.00,52.03,810.97\nOWENS CORNING,02-Central,766121000,910.00,23.95,886.05\nP.R. INTERNATIONAL INC.,02-Central,734414100,720.00,103.18,616.82\nPACIFIC INDUSTRIAL,02-Central,740361701,579.60,318.65,260.95\nPACOR INC.,02-Central,733525600,9805.00,3605.55,6199.45\nPACOR INC,02-Central,733525600,27120.00,9964.05,17155.95\nPACOR INC,02-Central,733444200,298.00,38.64,259.36\nPALL CORPORATION-H2,02-Central,740361900,7284.48,4036.21,3248.27\nPALL CORPORATION-H2,02-Central,740361701,688.32,637.30,51.02\nPARAMOUNT DIE CORPORATION,02-Central,733211000,912.00,94.78,817.22\nPARAMOUNT DIE CORPORATION,02-Central,733151500,450.00,47.69,402.31\nPARSONS,02-Central,727000140,531.20,33.43,497.77\nPEACHTREE DISTRIBUTING,02-Central,735602000,178480.00,66079.28,112400.72\nPEACHTREE DISTRIBUTING,02-Central,735602110,1840.00,707.04,1132.96\nPENN MANUFACTURING,02-Central,764822000,19656.00,1779.13,17876.87\nPENN MANUFACTURING,02-Central,764862000,455.00,52.36,402.64\nPERFORMANCE CONTRACTNG-KY,02-Central,735602000,1710.00,517.78,1192.22\nPERFORMANCE FRICTION,02-Central,766162000,876.00,37.37,838.63\nPHELPS INDUSTRIAL PRODUCT,02-Central,733721000,286.00,48.90,237.10\n\"PIC GROUP, INC\",02-Central,739641100,1310.98,806.40,504.58\nPILKINGTON N. A. INC.,02-Central,734611100,378.88,54.36,324.52\nPLASMA POWER,02-Central,777601000,819.84,117.60,702.24\nPLASMA POWER,02-Central,734851000,506.19,91.40,414.79\nPLASTICOMP,02-Central,764821000,430.00,15.36,414.64\nPLIBRICO COMPANY LLC,02-Central,764901000,51183.00,11279.99,39903.01\nPLIBRICO COMPANY LLC,02-Central,764821000,42912.00,8955.18,33956.82\nPLIBRICO COMPANY LLC,02-Central,768241000,32760.00,8491.42,24268.58\nPLIBRICO COMPANY LLC,02-Central,733211000,31429.30,6455.34,24973.96\nPLIBRICO COMPANY LLC,02-Central,779987650,29751.20,8526.35,21224.85\nPLIBRICO COMPANY LLC,02-Central,792622000,13860.00,3695.53,10164.47\nPLIBRICO COMPANY LLC,02-Central,768221000,12617.50,3278.74,9338.76\nPLIBRICO COMPANY LLC,02-Central,766121000,10083.60,1826.53,8257.07\nPLIBRICO COMPANY LLC,02-Central,764601000,8969.40,2199.10,6770.30\nPLIBRICO COMPANY LLC,02-Central,734990000,4637.12,994.24,3642.88\nPLIBRICO COMPANY LLC,02-Central,770560000,3279.30,268.51,3010.79\nPLIBRICO COMPANY LLC,02-Central,793330000,3207.00,686.20,2520.80\nPLIBRICO COMPANY LLC,02-Central,793320074,3162.50,1536.25,1626.25\nPLIBRICO COMPANY LLC,02-Central,733464500,2554.50,383.75,2170.75\nPLIBRICO COMPANY LLC,02-Central,770600000,2516.22,180.83,2335.39\nPLIBRICO COMPANY LLC,02-Central,779181000,2254.23,697.15,1557.08\nPLIBRICO COMPANY LLC,02-Central,733525500,1830.00,260.12,1569.88\nPLIBRICO COMPANY LLC,02-Central,764781000,1602.00,394.42,1207.58\nPLIBRICO COMPANY LLC,02-Central,779221000,1508.92,389.56,1119.36\nPLIBRICO COMPANY LLC,02-Central,734121000,1450.80,333.83,1116.97\nPLIBRICO COMPANY LLC,02-Central,777601000,1227.21,333.66,893.55\nPLIBRICO COMPANY LLC,02-Central,764761000,1080.00,375.45,704.55\nPLIBRICO COMPANY LLC,02-Central,733403300,920.04,92.92,827.12\nPLIBRICO COMPANY LLC,02-Central,768211000,725.00,224.44,500.56\nPLIBRICO COMPANY LLC,02-Central,764461006,547.20,222.12,325.08\nPLIBRICO COMPANY LLC,02-Central,793346074,508.00,296.56,211.44\nPLIBRICO COMPANY LLC,02-Central,733423700,465.92,72.44,393.48\nPLIBRICO COMPANY LLC,02-Central,764902000,301.50,64.42,237.08\nPLIBRICO COMPANY LLC,02-Central,733741000,284.40,93.19,191.21\nPLIBRICO COMPANY LLC,02-Central,739561200,278.85,76.67,202.18\nPLIBRICO COMPANY LLC,02-Central,764521000,208.80,53.18,155.62\nPLIBRICO COMPANY LLC,02-Central,733725500,205.20,51.76,153.44\nPLIBRICO REFRACTORY,02-Central,770200000,413.10,42.41,370.69\nPOOLESVILLE LAWN EQUIP.,02-Central,733464400,611.00,82.98,528.02\nPORTER WALKER INC,02-Central,733211050,958.00,111.82,846.18\nPORTER WARNER INDUSTRIES,02-Central,766121000,48142.00,6209.76,41932.24\nPORTER WARNER INDUSTRIES,02-Central,733182200,21924.48,4331.61,17592.87\nPORTER WARNER INDUSTRIES,02-Central,739741100,15776.16,8050.50,7725.66\nPORTER WARNER INDUSTRIES,02-Central,734414100,11392.61,1473.98,9918.63\nPORTER WARNER INDUSTRIES,02-Central,739761100,9303.84,5425.98,3877.86\nPORTER WARNER INDUSTRIES,02-Central,738561000,4250.88,1489.64,2761.24\nPORTER WARNER INDUSTRIES,02-Central,770220000,2543.40,164.07,2379.33\nPORTER WARNER INDUSTRIES,02-Central,733525400,2223.00,280.23,1942.77\nPORTER WARNER INDUSTRIES,02-Central,770260000,1719.23,137.96,1581.27\nPORTER WARNER INDUSTRIES,02-Central,734311100,1388.70,151.68,1237.02\nPORTER WARNER INDUSTRIES,02-Central,770392000,1315.80,118.17,1197.63\nPORTER WARNER INDUSTRIES,02-Central,734651200,1307.40,222.00,1085.40\nPORTER WARNER INDUSTRIES,02-Central,739641100,885.60,412.29,473.31\nPORTER WARNER INDUSTRIES,02-Central,770240000,866.25,70.33,795.92\nPORTER WARNER INDUSTRIES,02-Central,770210000,792.00,60.63,731.37\nPORTER WARNER INDUSTRIES,02-Central,733464500,563.40,65.05,498.35\nPORTER WARNER INDUSTRIES,02-Central,739561200,393.98,76.67,317.31\nPORTER WARNER INDUSTRIES,02-Central,764461006,270.00,81.75,188.25\nPOTTER INDUSTRIES INC.,02-Central,766121000,1820.00,47.90,1772.10\nPOW-R-MATIC INC,02-Central,733525500,2355.00,341.24,2013.76\nPOW-R-MATIC INC,02-Central,733586400,1891.80,289.41,1602.39\nPOW-R-MATIC INC,02-Central,734414100,1085.04,140.52,944.52\nPOWER PLANT SERVICES,02-Central,764901000,28011.30,2509.12,25502.18\nPOWER PLANT SERVICES,02-Central,764821000,18510.50,1581.35,16929.15\nPOWER PLANT SERVICES,02-Central,739741100,14341.38,5792.60,8548.78\nPOWER PLANT SERVICES,02-Central,770580000,8771.50,602.97,8168.53\nPOWER PLANT SERVICES,02-Central,734900000,6134.96,1677.23,4457.73\nPOWER PLANT SERVICES,02-Central,733211000,4161.00,852.15,3308.85\nPOWER PLANT SERVICES,02-Central,770260000,3894.75,346.25,3548.50\nPOWER PLANT SERVICES,02-Central,777601000,3022.20,627.63,2394.57\nPOWER PLANT SERVICES,02-Central,737902100,2973.75,508.02,2465.73\nPOWER PLANT SERVICES,02-Central,739561100,2818.86,559.92,2258.94\nPOWER PLANT SERVICES,02-Central,770220000,2475.00,195.80,2279.20\nPOWER PLANT SERVICES,02-Central,770240000,2321.25,205.59,2115.66\nPOWER PLANT SERVICES,02-Central,739781100,2094.63,983.04,1111.59\nPOWER PLANT SERVICES,02-Central,733505300,1882.50,317.89,1564.61\nPOWER PLANT SERVICES,02-Central,780250000,1673.28,420.09,1253.19\nPOWER PLANT SERVICES,02-Central,727000140,1132.80,95.02,1037.78\nPOWER PLANT SERVICES,02-Central,738521000,454.78,143.45,311.33\nPOWER PLANT SERVICES,02-Central,737912200,294.00,43.36,250.64\nPOWER PLANT SERVICES,02-Central,777642000,64.35,25.18,39.17\nPOWER PROCESS EQUIPMENT,02-Central,724281200,4320.00,642.05,3677.95\nPOWER PROCESS EQUIPMENT,02-Central,764481000,2514.00,136.82,2377.18\nPOWER PROCESS EQUIPMENT,02-Central,733725500,1620.00,310.59,1309.41\nPOWER PROCESS EQUIPMENT,02-Central,764821000,804.00,31.32,772.68\nPOWER PROCESS EQUIPMENT,02-Central,764901000,796.00,32.02,763.98\nPOWER PROCESS EQUIPMENT,02-Central,777601000,737.86,117.60,620.26\nPPG INDUSTRIES INC.,02-Central,768221000,6040.00,342.82,5697.18\nPPG INDUSTRIES INC.,02-Central,734651200,1652.70,222.00,1430.70\nPPG INDUSTRIES INC.,02-Central,733525500,717.50,65.03,652.47\nPPG INDUSTRIES INC.,02-Central,718500000,707.20,86.27,620.93\nPPG INDUSTRIES,02-Central,770200000,666.00,42.41,623.59\nPPG INDUSTRIES,02-Central,764261000,215.00,22.89,192.11\nPRECISION ART GLASS,02-Central,739741100,1252.16,387.44,864.72\nPRECISION ART GLASS,02-Central,739451000,428.48,111.56,316.92\nPRECISION MACHINE & TOOL,02-Central,740283401,4923.60,796.14,4127.46\nPRECISION TOOLS SERVICE,02-Central,770210000,460.00,31.52,428.48\nPRESCOTECH INC,02-Central,733832010,3564.00,1178.02,2385.98\nPRESCOTECH INDUSTRIES INC,02-Central,733882000,9773.60,4684.90,5088.70\nPRESCOTECH INDUSTRIES INC,02-Central,733510000,5440.00,1095.90,4344.10\nPRIME RESINS,02-Central,734000000,1446.00,151.61,1294.39\nPROLIFT INDUS EQUIP,02-Central,764521000,973.00,39.86,933.14\nPROPULSION TECHNOLOGY,02-Central,771320000,5432.00,684.50,4747.50\nPSP/MONOTECH INDUSTRIES,02-Central,779952600,194903.70,93570.63,101333.07\nPSP/MONOTECH INDUSTRIES,02-Central,766594001,148088.00,79687.11,68400.89\nPSP/MONOTECH INDUSTRIES,02-Central,766564001,61760.00,36056.11,25703.89\nPSP/MONOTECH INDUSTRIES,02-Central,766541100,4480.00,2488.97,1991.03\nPSP/MONOTECH INDUSTRIES,02-Central,767500000,2602.50,23.37,2579.13\nPYROTEK INC,02-Central,764821000,577762.00,213509.84,364252.16\nPYROTEK INC,02-Central,771320000,411715.00,107102.72,304612.28\nPYROTEK INC,02-Central,727000030,363240.00,253912.34,109327.66\nPYROTEK INC,02-Central,733182180,321071.80,147937.54,173134.26\nPYROTEK INC,02-Central,733490500,241454.00,98968.43,142485.57\nPYROTEK INC,02-Central,727000042,207051.20,143090.93,63960.27\nPYROTEK INC,02-Central,734311100,184343.42,46279.22,138064.20\nPYROTEK INC,02-Central,764521000,163356.65,72956.26,90400.39\nPYROTEK INC,02-Central,768222000,157170.40,57720.52,99449.88\nPYROTEK INC,02-Central,766211000,150895.60,104114.30,46781.30\nPYROTEK INC,02-Central,733211050,126322.00,21146.12,105175.88\nPYROTEK INC,02-Central,764601000,108882.00,47356.95,61525.05\nPYROTEK INC,02-Central,768221000,106084.20,38807.48,67276.72\nPYROTEK INC,02-Central,734910000,98252.70,40101.43,58151.27\nPYROTEK INC,02-Central,733211000,90228.00,57457.51,32770.49\nPYROTEK INC,02-Central,768222522,88542.08,32135.64,56406.44\nPYROTEK INC,02-Central,733525500,74060.00,30378.96,43681.04\nPYROTEK INC,02-Central,739781100,59640.00,53656.92,5983.08\nPYROTEK INC,02-Central,734173000,53642.70,22562.60,31080.10\nPYROTEK INC,02-Central,733403200,53411.60,22335.62,31075.98\nPYROTEK INC,02-Central,764822000,51956.00,19326.88,32629.12\nPYROTEK INC,02-Central,733525600,47395.00,19588.87,27806.13\nPYROTEK INC,02-Central,733510000,45750.75,31000.16,14750.59\nPYROTEK INC,02-Central,724311200,44164.50,12011.15,32153.35\nPYROTEK INC,02-Central,764901050,42471.00,16054.64,26416.36\nPYROTEK INC,02-Central,733151600,41964.00,20979.70,20984.30\nPYROTEK INC,02-Central,766121000,40990.00,12232.75,28757.25\nPYROTEK INC,02-Central,764781000,40397.60,18376.19,22021.41\nPYROTEK INC,02-Central,764264054,39220.00,18512.28,20707.72\nPYROTEK INC,02-Central,764261000,36920.00,19518.42,17401.58\nPYROTEK INC,02-Central,733151500,36894.00,17946.12,18947.88\nPYROTEK INC,02-Central,771330000,36855.00,11212.46,25642.54\nPYROTEK INC,02-Central,738561000,36007.04,35669.43,337.61\nPYROTEK INC,02-Central,764524000,34237.00,15617.75,18619.25\nPYROTEK INC,02-Central,766212000,34121.00,23425.19,10695.81\nPYROTEK INC,02-Central,734762000,32572.66,9207.42,23365.24\nPYROTEK INC,02-Central,734162000,31052.30,21491.66,9560.64\nPYROTEK INC,02-Central,734351200,29598.55,3568.15,26030.40\nPYROTEK INC,02-Central,733511000,26762.25,17023.00,9739.25\nPYROTEK INC,02-Central,733543700,26241.60,8948.62,17292.98\nPYROTEK INC,02-Central,727000140,25876.80,17576.91,8299.89\nPYROTEK INC,02-Central,734211200,25627.96,7182.49,18445.47\nPYROTEK INC,02-Central,764262000,24829.00,12948.56,11880.44\nPYROTEK INC,02-Central,768121000,21031.50,9026.36,12005.14\nPYROTEK INC,02-Central,727063016,20335.92,13683.86,6652.06\nPYROTEK INC,02-Central,768211000,18764.80,8696.64,10068.16\nPYROTEK INC,02-Central,764605000,18380.00,7739.90,10640.10\nPYROTEK INC,02-Central,737321000,18037.78,9971.40,8066.38\nPYROTEK INC,02-Central,733505200,17528.00,7996.47,9531.53\nPYROTEK INC,02-Central,734970000,16486.68,8386.60,8100.08\nPYROTEK INC,02-Central,734414100,15287.12,4986.05,10301.07\nPYROTEK INC,02-Central,764823500,14972.00,5308.33,9663.67\nPYROTEK INC,02-Central,764481000,13913.00,7338.82,6574.18\nPYROTEK INC,02-Central,764221000,13124.80,9138.12,3986.68\nPYROTEK INC,02-Central,764781040,12946.00,5745.89,7200.11\nPYROTEK INC,02-Central,764761000,12903.60,7385.93,5517.67\nPYROTEK INC,02-Central,739741100,12882.38,11275.48,1606.90\nPYROTEK INC,02-Central,734251300,12869.90,1582.84,11287.06\nPYROTEK INC,02-Central,738361000,12043.47,13249.81,-1206.34\nPYROTEK INC,02-Central,733525400,12027.50,5205.31,6822.19\nPYROTEK INC,02-Central,768780000,11509.76,7094.04,4415.72\nPYROTEK INC,02-Central,740402402,10766.75,3070.72,7696.03\nPYROTEK INC,02-Central,733566400,10410.00,1888.10,8521.90\nPYROTEK INC,02-Central,733484900,10054.40,4837.31,5217.09\nPYROTEK INC,02-Central,764902000,9289.00,3588.45,5700.55\nPYROTEK INC,02-Central,734761300,9215.42,2334.84,6880.58\nPYROTEK INC,02-Central,733542005,8558.86,1821.81,6737.05\nPYROTEK INC,02-Central,742645000,6756.00,1102.08,5653.92\nPYROTEK INC,02-Central,768212000,6600.40,2985.49,3614.91\nPYROTEK INC,02-Central,733202000,6566.40,3754.28,2812.12\nPYROTEK INC,02-Central,733505700,5718.48,2784.81,2933.67\nPYROTEK INC,02-Central,764783000,5572.00,2510.16,3061.84\nPYROTEK INC,02-Central,737261000,5408.44,4955.52,452.92\nPYROTEK INC,02-Central,793000000,5369.28,3652.51,1716.77\nPYROTEK INC,02-Central,739541000,5332.01,2292.46,3039.55\nPYROTEK INC,02-Central,733444200,5297.60,2735.99,2561.61\nPYROTEK INC,02-Central,737301000,5207.62,2208.24,2999.38\nPYROTEK INC,02-Central,791003400,5004.00,1308.29,3695.71\nPYROTEK INC,02-Central,739452000,4878.72,3620.04,1258.68\nPYROTEK INC,02-Central,733484800,4112.00,2013.45,2098.55\nPYROTEK INC,02-Central,764461006,3997.80,2785.74,1212.06\nPYROTEK INC,02-Central,733501550,3986.13,2952.10,1034.03\nPYROTEK INC,02-Central,742723400,3711.15,423.94,3287.21\nPYROTEK INC,02-Central,738521000,3085.38,4288.31,-1202.93\nPYROTEK INC,02-Central,764901000,3004.50,1156.96,1847.54\nPYROTEK INC,02-Central,764762000,2851.20,1609.74,1241.46\nPYROTEK INC,02-Central,737311000,2652.25,2352.87,299.38\nPYROTEK INC,02-Central,764522000,2624.60,1167.93,1456.67\nPYROTEK INC,02-Central,764341000,2503.55,1350.81,1152.74\nPYROTEK INC,02-Central,734900000,2314.00,1518.47,795.53\nPYROTEK INC,02-Central,740382201,2266.50,881.31,1385.19\nPYROTEK INC,02-Central,777590000,1964.68,1316.08,648.60\nPYROTEK INC,02-Central,777901100,1856.61,935.30,921.31\nPYROTEK INC,02-Central,777902700,1808.10,1141.55,666.55\nPYROTEK INC,02-Central,768750000,1783.20,1851.76,-68.56\nPYROTEK INC,02-Central,733505300,1757.00,806.08,950.92\nPYROTEK INC,02-Central,770570000,1707.23,196.14,1511.09\nPYROTEK INC,02-Central,777601000,1599.68,470.36,1129.32\nPYROTEK INC,02-Central,737912200,1554.57,407.12,1147.45\nPYROTEK INC,02-Central,779986561,1512.00,577.34,934.66\nPYROTEK INC,02-Central,739541200,1363.98,512.78,851.20\nPYROTEK INC,02-Central,734712000,1361.58,603.03,758.55\nPYROTEK INC,02-Central,733212055,1333.80,871.70,462.10\nPYROTEK INC,02-Central,779988827,1314.42,601.81,712.61\nPYROTEK INC,02-Central,764602000,1200.00,524.23,675.77\nPYROTEK INC,02-Central,770850000,1045.50,47.24,998.26\nPYROTEK INC,02-Central,733464500,922.00,431.32,490.68\nPYROTEK INC,02-Central,739681100,903.96,1242.00,-338.04\nPYROTEK INC,02-Central,734611100,887.04,242.62,644.42\nPYROTEK INC,02-Central,777642000,824.00,277.80,546.20\nPYROTEK INC,02-Central,739561000,653.61,342.83,310.78\nPYROTEK INC,02-Central,770310000,600.00,94.56,505.44\nPYROTEK INC,02-Central,792730000,460.00,125.12,334.88\nPYROTEK INC,02-Central,732429300,444.80,49.22,395.58\nPYROTEK INC,02-Central,733721000,417.60,167.12,250.48\nPYROTEK INC,02-Central,764861000,344.29,142.25,202.04\nPYROTEK INC,02-Central,737902100,222.96,67.74,155.22\nPYROTEK INC,02-Central,733423700,73.60,36.22,37.38\nR E UPTEGRAFF MFG COMPANY,02-Central,733741000,2408.00,662.64,1745.36\nR E UPTEGRAFF MFG COMPANY,02-Central,779001000,1290.48,221.56,1068.92\nR K METALS LLC,02-Central,763991000,428.40,39.22,389.18\nR T P COMPANY,02-Central,734040000,2353.50,515.70,1837.80\nR. G. BREWTON INC.,02-Central,770372000,15960.00,777.75,15182.25\nR. G. BREWTON INC.,02-Central,764781000,7844.00,342.53,7501.47\nR. G. BREWTON INC.,02-Central,764821000,430.00,15.36,414.64\nRALEIGH-DURHAM RUBBER,02-Central,770200000,7217.10,706.38,6510.72\nRCG OF NORTH CAROLINA LLC,02-Central,777601000,390.00,216.05,173.95\nRED SEAL ELECTRIC,02-Central,738561000,3494.40,744.82,2749.58\nRED SEAL ELECTRIC,02-Central,733444100,348.00,81.62,266.38\nREFRACTORY ENGINEERS INC.,02-Central,764821030,305282.40,90899.92,214382.48\nREFRACTORY ENGINEERS INC.,02-Central,764040000,299149.20,89159.27,209989.93\nREFRACTORY ENGINEERS INC.,02-Central,764822000,171062.60,42509.70,128552.90\nREFRACTORY ENGINEERS INC.,02-Central,733403500,139823.10,92270.19,47552.91\nREFRACTORY ENGINEERS INC.,02-Central,734361300,122986.05,13485.23,109500.82\nREFRACTORY ENGINEERS INC.,02-Central,764525000,119544.00,78252.89,41291.11\nREFRACTORY ENGINEERS INC.,02-Central,764526514,108315.20,62167.15,46148.05\nREFRACTORY ENGINEERS INC.,02-Central,764261000,102578.00,60992.92,41585.08\nREFRACTORY ENGINEERS INC.,02-Central,733218000,93093.54,70287.85,22805.69\nREFRACTORY ENGINEERS INC.,02-Central,733218300,81717.49,60364.53,21352.96\nREFRACTORY ENGINEERS INC.,02-Central,766121000,79078.40,21826.23,57252.17\nREFRACTORY ENGINEERS INC.,02-Central,764901000,76592.00,41228.86,35363.14\nREFRACTORY ENGINEERS INC.,02-Central,739641100,61985.31,34470.58,27514.73\nREFRACTORY ENGINEERS INC.,02-Central,766162000,58596.40,41842.90,16753.50\nREFRACTORY ENGINEERS INC.,02-Central,764605001,56576.00,18711.79,37864.21\nREFRACTORY ENGINEERS INC.,02-Central,764221000,54784.80,27624.60,27160.20\nREFRACTORY ENGINEERS INC.,02-Central,733218500,51427.68,50886.00,541.68\nREFRACTORY ENGINEERS INC.,02-Central,734173000,44612.20,17631.36,26980.84\nREFRACTORY ENGINEERS INC.,02-Central,777741073,43531.38,23460.41,20070.97\nREFRACTORY ENGINEERS INC.,02-Central,733505200,42265.00,7984.27,34280.73\nREFRACTORY ENGINEERS INC.,02-Central,764521022,42018.00,24678.74,17339.26\nREFRACTORY ENGINEERS INC.,02-Central,733586500,36525.60,24968.04,11557.56\nREFRACTORY ENGINEERS INC.,02-Central,764601110,30993.60,15764.64,15228.96\nREFRACTORY ENGINEERS INC.,02-Central,734311100,29957.56,6683.98,23273.58\nREFRACTORY ENGINEERS INC.,02-Central,733403200,29821.00,16098.52,13722.48\nREFRACTORY ENGINEERS INC.,02-Central,740402402,25720.00,6576.10,19143.90\nREFRACTORY ENGINEERS INC.,02-Central,766125500,23587.20,7514.32,16072.88\nREFRACTORY ENGINEERS INC.,02-Central,765661000,22318.70,3299.80,19018.90\nREFRACTORY ENGINEERS INC.,02-Central,764521000,21120.00,10616.84,10503.16\nREFRACTORY ENGINEERS INC.,02-Central,764526513,20993.60,12796.75,8196.85\nREFRACTORY ENGINEERS INC.,02-Central,733201000,19822.70,8805.47,11017.23\nREFRACTORY ENGINEERS INC.,02-Central,768141000,18720.00,3789.88,14930.12\nREFRACTORY ENGINEERS INC.,02-Central,733211050,17952.80,8373.97,9578.83\nREFRACTORY ENGINEERS INC.,02-Central,733511000,17398.00,12455.16,4942.84\nREFRACTORY ENGINEERS INC.,02-Central,739781100,17036.43,8688.25,8348.18\nREFRACTORY ENGINEERS INC.,02-Central,764523000,16748.00,9493.17,7254.83\nREFRACTORY ENGINEERS INC.,02-Central,768221000,16126.80,6281.81,9844.99\nREFRACTORY ENGINEERS INC.,02-Central,733151600,16098.00,4586.34,11511.66\nREFRACTORY ENGINEERS INC.,02-Central,764524020,16000.00,8547.43,7452.57\nREFRACTORY ENGINEERS INC.,02-Central,764264050,15000.00,9926.50,5073.50\nREFRACTORY ENGINEERS INC.,02-Central,764827500,14595.00,4806.51,9788.49\nREFRACTORY ENGINEERS INC.,02-Central,764601020,13048.00,6997.55,6050.45\nREFRACTORY ENGINEERS INC.,02-Central,739741100,12876.32,8655.08,4221.24\nREFRACTORY ENGINEERS INC.,02-Central,740361900,11770.56,5392.44,6378.12\nREFRACTORY ENGINEERS INC.,02-Central,777710073,11758.83,4877.68,6881.15\nREFRACTORY ENGINEERS INC.,02-Central,764761000,11752.60,4991.31,6761.29\nREFRACTORY ENGINEERS INC.,02-Central,764821000,11520.00,7371.81,4148.19\nREFRACTORY ENGINEERS INC.,02-Central,734990000,11120.28,2070.37,9049.91\nREFRACTORY ENGINEERS INC.,02-Central,793330000,10795.50,2969.57,7825.93\nREFRACTORY ENGINEERS INC.,02-Central,764526810,9249.60,5386.26,3863.34\nREFRACTORY ENGINEERS INC.,02-Central,764523200,8960.00,6016.77,2943.23\nREFRACTORY ENGINEERS INC.,02-Central,766162051,8437.50,3855.77,4581.73\nREFRACTORY ENGINEERS INC.,02-Central,770560000,8323.20,688.72,7634.48\nREFRACTORY ENGINEERS INC.,02-Central,770380000,8257.60,818.38,7439.22\nREFRACTORY ENGINEERS INC.,02-Central,764264058,8140.00,8046.65,93.35\nREFRACTORY ENGINEERS INC.,02-Central,764902000,7360.00,2554.16,4805.84\nREFRACTORY ENGINEERS INC.,02-Central,793250074,7276.50,3526.89,3749.61\nREFRACTORY ENGINEERS INC.,02-Central,777601000,6797.50,1937.22,4860.28\nREFRACTORY ENGINEERS INC.,02-Central,764601000,6536.00,2947.43,3588.57\nREFRACTORY ENGINEERS INC.,02-Central,733586505,6480.00,5293.02,1186.98\nREFRACTORY ENGINEERS INC.,02-Central,777904301,6435.00,2718.36,3716.64\nREFRACTORY ENGINEERS INC.,02-Central,764481000,6271.40,3385.98,2885.42\nREFRACTORY ENGINEERS INC.,02-Central,777643000,5278.50,3156.09,2122.41\nREFRACTORY ENGINEERS INC.,02-Central,770600000,5221.90,379.38,4842.52\nREFRACTORY ENGINEERS INC.,02-Central,770810000,5089.40,147.17,4942.23\nREFRACTORY ENGINEERS INC.,02-Central,727000440,4668.80,859.09,3809.71\nREFRACTORY ENGINEERS INC.,02-Central,740382201,4612.50,1502.56,3109.94\nREFRACTORY ENGINEERS INC.,02-Central,734851000,4190.06,951.50,3238.56\nREFRACTORY ENGINEERS INC.,02-Central,793580000,4092.00,1000.02,3091.98\nREFRACTORY ENGINEERS INC.,02-Central,770300000,4034.25,465.83,3568.42\nREFRACTORY ENGINEERS INC.,02-Central,755087000,3706.12,601.66,3104.46\nREFRACTORY ENGINEERS INC.,02-Central,739841000,3535.73,939.35,2596.38\nREFRACTORY ENGINEERS INC.,02-Central,770200000,3515.40,375.22,3140.18\nREFRACTORY ENGINEERS INC.,02-Central,770880000,3354.00,178.24,3175.76\nREFRACTORY ENGINEERS INC.,02-Central,765541000,3275.00,1338.28,1936.72\nREFRACTORY ENGINEERS INC.,02-Central,764261020,3240.00,2075.70,1164.30\nREFRACTORY ENGINEERS INC.,02-Central,718501000,3155.20,544.24,2610.96\nREFRACTORY ENGINEERS INC.,02-Central,770830000,3111.00,138.58,2972.42\nREFRACTORY ENGINEERS INC.,02-Central,733151500,3046.00,817.18,2228.82\nREFRACTORY ENGINEERS INC.,02-Central,792610000,2955.00,404.66,2550.34\nREFRACTORY ENGINEERS INC.,02-Central,764781000,2847.50,867.59,1979.91\nREFRACTORY ENGINEERS INC.,02-Central,733725500,2696.60,826.25,1870.35\nREFRACTORY ENGINEERS INC.,02-Central,740123000,2671.20,768.06,1903.14\nREFRACTORY ENGINEERS INC.,02-Central,770260000,2668.50,278.62,2389.88\nREFRACTORY ENGINEERS INC.,02-Central,734651200,2571.30,517.01,2054.29\nREFRACTORY ENGINEERS INC.,02-Central,764861000,2532.50,1044.37,1488.13\nREFRACTORY ENGINEERS INC.,02-Central,734751200,2518.88,449.95,2068.93\nREFRACTORY ENGINEERS INC.,02-Central,770540000,2443.00,179.60,2263.40\nREFRACTORY ENGINEERS INC.,02-Central,733444300,2385.40,473.43,1911.97\nREFRACTORY ENGINEERS INC.,02-Central,771500000,2370.75,172.01,2198.74\nREFRACTORY ENGINEERS INC.,02-Central,770220000,2347.20,218.76,2128.44\nREFRACTORY ENGINEERS INC.,02-Central,779958984,2189.00,961.48,1227.52\nREFRACTORY ENGINEERS INC.,02-Central,766681100,2173.00,380.97,1792.03\nREFRACTORY ENGINEERS INC.,02-Central,764524010,2144.00,1081.34,1062.66\nREFRACTORY ENGINEERS INC.,02-Central,733525600,2137.00,546.14,1590.86\nREFRACTORY ENGINEERS INC.,02-Central,734810000,1998.30,1608.24,390.06\nREFRACTORY ENGINEERS INC.,02-Central,764522000,1880.00,1068.86,811.14\nREFRACTORY ENGINEERS INC.,02-Central,792785000,1756.80,479.91,1276.89\nREFRACTORY ENGINEERS INC.,02-Central,777740073,1752.50,849.00,903.50\nREFRACTORY ENGINEERS INC.,02-Central,766081013,1710.00,794.69,915.31\nREFRACTORY ENGINEERS INC.,02-Central,733464600,1674.40,1538.71,135.69\nREFRACTORY ENGINEERS INC.,02-Central,764605000,1608.00,615.46,992.54\nREFRACTORY ENGINEERS INC.,02-Central,764526610,1584.00,864.80,719.20\nREFRACTORY ENGINEERS INC.,02-Central,753967000,1553.47,470.32,1083.15\nREFRACTORY ENGINEERS INC.,02-Central,770230000,1392.30,147.83,1244.47\nREFRACTORY ENGINEERS INC.,02-Central,737912200,1334.55,260.17,1074.38\nREFRACTORY ENGINEERS INC.,02-Central,764483000,1263.40,724.82,538.58\nREFRACTORY ENGINEERS INC.,02-Central,732429400,1224.00,133.82,1090.18\nREFRACTORY ENGINEERS INC.,02-Central,777730020,1151.22,1169.83,-18.61\nREFRACTORY ENGINEERS INC.,02-Central,764461006,900.00,139.69,760.31\nREFRACTORY ENGINEERS INC.,02-Central,777731073,855.22,306.59,548.63\nREFRACTORY ENGINEERS INC.,02-Central,770392000,850.00,70.30,779.70\nREFRACTORY ENGINEERS INC.,02-Central,734970000,825.00,307.84,517.16\nREFRACTORY ENGINEERS INC.,02-Central,734890000,793.73,198.96,594.77\nREFRACTORY ENGINEERS INC.,02-Central,768121000,784.35,172.60,611.75\nREFRACTORY ENGINEERS INC.,02-Central,770290000,675.35,79.65,595.70\nREFRACTORY ENGINEERS INC.,02-Central,764561000,661.50,130.70,530.80\nREFRACTORY ENGINEERS INC.,02-Central,770210000,570.50,61.84,508.66\nREFRACTORY ENGINEERS INC.,02-Central,777730073,566.51,236.20,330.31\nREFRACTORY ENGINEERS INC.,02-Central,739561200,557.95,153.34,404.61\nREFRACTORY ENGINEERS INC.,02-Central,777590000,502.12,245.72,256.40\nREFRACTORY ENGINEERS INC.,02-Central,733525500,500.00,351.97,148.03\nREFRACTORY ENGINEERS INC.,02-Central,768211000,489.50,116.95,372.55\nREFRACTORY ENGINEERS INC.,02-Central,733211000,476.60,142.17,334.43\nREFRACTORY ENGINEERS INC.,02-Central,724261200,467.60,78.20,389.40\nREFRACTORY ENGINEERS INC.,02-Central,733423600,467.20,71.80,395.40\nREFRACTORY ENGINEERS INC.,02-Central,733505100,465.00,82.59,382.41\nREFRACTORY ENGINEERS INC.,02-Central,733221400,454.24,128.48,325.76\nREFRACTORY ENGINEERS INC.,02-Central,734162000,453.80,243.11,210.69\nREFRACTORY ENGINEERS INC.,02-Central,740361701,397.08,318.65,78.43\nREFRACTORY ENGINEERS INC.,02-Central,724281200,358.00,89.08,268.92\nREFRACTORY ENGINEERS INC.,02-Central,725441200,357.90,68.74,289.16\nREFRACTORY ENGINEERS INC.,02-Central,779261000,346.61,76.93,269.68\nREFRACTORY ENGINEERS INC.,02-Central,764781040,336.00,58.59,277.41\nREFRACTORY ENGINEERS INC.,02-Central,739561000,313.70,86.19,227.51\nREFRACTORY ENGINEERS INC.,02-Central,734611100,302.90,67.95,234.95\nREFRACTORY ENGINEERS INC.,02-Central,792783000,292.10,90.01,202.09\nREFRACTORY ENGINEERS INC.,02-Central,734711100,278.32,54.40,223.92\nREFRACTORY ENGINEERS INC.,02-Central,726000240,266.00,30.29,235.71\nREFRACTORY ENGINEERS INC.,02-Central,780770000,172.61,37.20,135.41\nREFRACTORY ENGINEERS INC.,02-Central,765502000,115.00,26.48,88.52\nREFRACTORY ENGINEERS INC.,02-Central,793260074,94.10,39.17,54.93\nREFRACTORY SERVICE INC.,02-Central,733211000,206490.40,58252.96,148237.44\nREFRACTORY SERVICE INC.,02-Central,764821000,190347.90,62915.24,127432.66\nREFRACTORY SERVICE INC.,02-Central,718500000,144103.18,26478.48,117624.70\nREFRACTORY SERVICE INC.,02-Central,727041200,83680.00,17284.99,66395.01\nREFRACTORY SERVICE INC.,02-Central,734990000,75720.72,14496.40,61224.32\nREFRACTORY SERVICE INC.,02-Central,733525600,56067.25,10205.04,45862.21\nREFRACTORY SERVICE INC.,02-Central,764822000,50181.40,16719.79,33461.61\nREFRACTORY SERVICE INC.,02-Central,733586625,48886.56,6683.05,42203.51\nREFRACTORY SERVICE INC.,02-Central,764481000,48495.50,15496.87,32998.63\nREFRACTORY SERVICE INC.,02-Central,733525500,43667.75,7778.76,35888.99\nREFRACTORY SERVICE INC.,02-Central,733151500,39769.00,8396.27,31372.73\nREFRACTORY SERVICE INC.,02-Central,734173000,37997.75,9281.96,28715.79\nREFRACTORY SERVICE INC.,02-Central,779956939,36479.65,18929.53,17550.12\nREFRACTORY SERVICE INC.,02-Central,766121000,34854.00,6559.74,28294.26\nREFRACTORY SERVICE INC.,02-Central,789360000,29590.71,18641.01,10949.70\nREFRACTORY SERVICE INC.,02-Central,738561000,29332.02,10992.72,18339.30\nREFRACTORY SERVICE INC.,02-Central,770220000,29284.80,7763.43,21521.37\nREFRACTORY SERVICE INC.,02-Central,764522000,29283.00,11490.28,17792.72\nREFRACTORY SERVICE INC.,02-Central,754085000,26585.20,12428.47,14156.73\nREFRACTORY SERVICE INC.,02-Central,779958986,26176.80,14994.63,11182.17\nREFRACTORY SERVICE INC.,02-Central,764821161,23352.00,17856.00,5496.00\nREFRACTORY SERVICE INC.,02-Central,779952105,20785.05,12663.84,8121.21\nREFRACTORY SERVICE INC.,02-Central,784260000,20321.40,8991.39,11330.01\nREFRACTORY SERVICE INC.,02-Central,762116000,19815.38,6400.61,13414.77\nREFRACTORY SERVICE INC.,02-Central,777888893,19423.32,19521.60,-98.28\nREFRACTORY SERVICE INC.,02-Central,733202009,18146.25,5924.98,12221.27\nREFRACTORY SERVICE INC.,02-Central,739561200,17979.58,4993.73,12985.85\nREFRACTORY SERVICE INC.,02-Central,764787101,17848.80,11992.82,5855.98\nREFRACTORY SERVICE INC.,02-Central,733202000,17826.56,5447.50,12379.06\nREFRACTORY SERVICE INC.,02-Central,754045000,15982.20,9252.85,6729.35\nREFRACTORY SERVICE INC.,02-Central,777888891,13680.00,13910.40,-230.40\nREFRACTORY SERVICE INC.,02-Central,779952106,13305.60,6573.62,6731.98\nREFRACTORY SERVICE INC.,02-Central,734311100,12715.48,1685.02,11030.46\nREFRACTORY SERVICE INC.,02-Central,733505200,12618.50,2480.59,10137.91\nREFRACTORY SERVICE INC.,02-Central,764602000,10302.50,3407.47,6895.03\nREFRACTORY SERVICE INC.,02-Central,780210000,9583.20,5648.12,3935.08\nREFRACTORY SERVICE INC.,02-Central,754065000,9456.00,5270.46,4185.54\nREFRACTORY SERVICE INC.,02-Central,764861000,9166.61,1506.08,7660.53\nREFRACTORY SERVICE INC.,02-Central,734651200,8845.34,626.74,8218.60\nREFRACTORY SERVICE INC.,02-Central,784200000,8035.20,4236.62,3798.58\nREFRACTORY SERVICE INC.,02-Central,784000000,7884.80,4473.26,3411.54\nREFRACTORY SERVICE INC.,02-Central,777888895,7740.00,8668.80,-928.80\nREFRACTORY SERVICE INC.,02-Central,780250000,7557.60,4189.67,3367.93\nREFRACTORY SERVICE INC.,02-Central,738521000,7525.44,4373.50,3151.94\nREFRACTORY SERVICE INC.,02-Central,777888892,7497.00,8396.64,-899.64\nREFRACTORY SERVICE INC.,02-Central,777601000,7322.21,2381.12,4941.09\nREFRACTORY SERVICE INC.,02-Central,739741100,6711.36,3201.07,3510.29\nREFRACTORY SERVICE INC.,02-Central,764902000,6432.00,1915.62,4516.38\nREFRACTORY SERVICE INC.,02-Central,770210000,6412.00,711.78,5700.22\nREFRACTORY SERVICE INC.,02-Central,770540000,6404.15,474.90,5929.25\nREFRACTORY SERVICE INC.,02-Central,770560000,6327.80,523.36,5804.44\nREFRACTORY SERVICE INC.,02-Central,733610680,6137.85,1913.05,4224.80\nREFRACTORY SERVICE INC.,02-Central,764561000,6060.00,1115.48,4944.52\nREFRACTORY SERVICE INC.,02-Central,739561100,5635.38,1493.12,4142.26\nREFRACTORY SERVICE INC.,02-Central,739781100,5538.72,2094.62,3444.10\nREFRACTORY SERVICE INC.,02-Central,777888890,5508.00,6168.96,-660.96\nREFRACTORY SERVICE INC.,02-Central,780280000,5380.80,2234.63,3146.17\nREFRACTORY SERVICE INC.,02-Central,734851000,5370.80,1156.80,4214.00\nREFRACTORY SERVICE INC.,02-Central,770260000,5336.56,557.24,4779.32\nREFRACTORY SERVICE INC.,02-Central,770600000,5032.44,361.66,4670.78\nREFRACTORY SERVICE INC.,02-Central,777888894,4590.00,5140.80,-550.80\nREFRACTORY SERVICE INC.,02-Central,777642000,4498.40,1577.26,2921.14\nREFRACTORY SERVICE INC.,02-Central,733211050,4344.00,1292.12,3051.88\nREFRACTORY SERVICE INC.,02-Central,780240000,4324.32,2467.00,1857.32\nREFRACTORY SERVICE INC.,02-Central,733834800,4311.77,2362.00,1949.77\nREFRACTORY SERVICE INC.,02-Central,764781040,4248.00,1209.94,3038.06\nREFRACTORY SERVICE INC.,02-Central,779988828,4118.91,2377.44,1741.47\nREFRACTORY SERVICE INC.,02-Central,739452000,3943.17,1602.93,2340.24\nREFRACTORY SERVICE INC.,02-Central,780770000,3883.20,1487.92,2395.28\nREFRACTORY SERVICE INC.,02-Central,754044100,3851.50,2096.59,1754.91\nREFRACTORY SERVICE INC.,02-Central,777903300,3719.10,2093.90,1625.20\nREFRACTORY SERVICE INC.,02-Central,779957414,3657.00,1951.22,1705.78\nREFRACTORY SERVICE INC.,02-Central,734910000,3630.40,849.36,2781.04\nREFRACTORY SERVICE INC.,02-Central,764461006,3609.60,1049.19,2560.41\nREFRACTORY SERVICE INC.,02-Central,780330000,3407.88,1546.32,1861.56\nREFRACTORY SERVICE INC.,02-Central,733505100,3229.00,705.75,2523.25\nREFRACTORY SERVICE INC.,02-Central,777888897,3160.00,1659.20,1500.80\nREFRACTORY SERVICE INC.,02-Central,780370000,3031.92,1285.19,1746.73\nREFRACTORY SERVICE INC.,02-Central,764901000,2760.40,579.10,2181.30\nREFRACTORY SERVICE INC.,02-Central,739561000,2737.14,775.73,1961.41\nREFRACTORY SERVICE INC.,02-Central,770290000,2701.40,318.58,2382.82\nREFRACTORY SERVICE INC.,02-Central,777888896,2673.00,2993.76,-320.76\nREFRACTORY SERVICE INC.,02-Central,770280000,2611.76,313.96,2297.80\nREFRACTORY SERVICE INC.,02-Central,780310000,2506.44,1119.74,1386.70\nREFRACTORY SERVICE INC.,02-Central,784100000,2443.50,1210.17,1233.33\nREFRACTORY SERVICE INC.,02-Central,779201000,2431.20,624.60,1806.60\nREFRACTORY SERVICE INC.,02-Central,738542000,2345.76,1430.48,915.28\nREFRACTORY SERVICE INC.,02-Central,734132000,1997.50,465.89,1531.61\nREFRACTORY SERVICE INC.,02-Central,770520000,1874.25,144.96,1729.29\nREFRACTORY SERVICE INC.,02-Central,780270000,1864.00,927.50,936.50\nREFRACTORY SERVICE INC.,02-Central,780750000,1832.40,709.38,1123.02\nREFRACTORY SERVICE INC.,02-Central,777888899,1650.00,1848.00,-198.00\nREFRACTORY SERVICE INC.,02-Central,777903000,1525.72,637.43,888.29\nREFRACTORY SERVICE INC.,02-Central,780421000,1346.40,975.87,370.53\nREFRACTORY SERVICE INC.,02-Central,779988827,1240.02,610.34,629.68\nREFRACTORY SERVICE INC.,02-Central,777585000,1135.50,51.23,1084.27\nREFRACTORY SERVICE INC.,02-Central,777888898,675.00,756.00,-81.00\nREFRACTORY SERVICE INC.,02-Central,770372000,666.40,44.11,622.29\nREFRACTORY SERVICE INC.,02-Central,770100000,568.58,42.43,526.15\nREFRACTORY SERVICE INC.,02-Central,740023000,482.66,118.54,364.12\nREFRACTORY SERVICE INC.,02-Central,764562000,473.00,84.36,388.64\nREFRACTORY SERVICE INC.,02-Central,764781000,424.80,122.67,302.13\nREFRACTORY SERVICE INC.,02-Central,768141000,394.20,81.04,313.16\nREFRACTORY SERVICE INC.,02-Central,764762000,385.80,94.71,291.09\nREFRACTORY SERVICE INC.,02-Central,733444100,330.00,163.24,166.76\nREFRACTORY SERVICE INC.,02-Central,733725500,205.20,51.76,153.44\nREFRACTORY SERVICE INC.,02-Central,768121000,195.60,44.03,151.57\nREFRACTORY SERVICE INC.,02-Central,733444300,175.60,41.25,134.35\nREFRACTORY SERVICE INC.,02-Central,779221000,119.72,30.60,89.12\nREFRACTORY SERVICE INC.,02-Central,734611100,69.58,13.59,55.99\nREFRACTORY SPECIALTIES IN,02-Central,727000060,1038585.00,797974.89,240610.11\nREFRACTORY SPECIALTIES IN,02-Central,715000000,540164.75,387080.94,153083.81\nREFRACTORY SPECIALTIES IN,02-Central,726000062,267039.00,209185.44,57853.56\nREFRACTORY SPECIALTIES IN,02-Central,727063007,220378.50,162530.21,57848.29\nREFRACTORY SPECIALTIES IN,02-Central,727000081,93066.00,69603.00,23463.00\nREFRACTORY SPECIALTIES IN,02-Central,793000000,56250.00,26089.20,30160.80\nREFRACTORY SPECIALTIES IN,02-Central,720252303,42075.00,2786.30,39288.70\nREFRACTORY SPECIALTIES IN,02-Central,764821000,41970.00,14880.41,27089.59\nREFRACTORY SPECIALTIES IN,02-Central,727000080,41535.00,35208.72,6326.28\nREFRACTORY SPECIALTIES IN,02-Central,727063008,36630.00,26446.82,10183.18\nREFRACTORY SPECIALTIES IN,02-Central,763994000,30564.00,1489.82,29074.18\nREFRACTORY SPECIALTIES IN,02-Central,792620000,27990.00,8489.23,19500.77\nREFRACTORY SPECIALTIES IN,02-Central,768750000,20155.20,15581.81,4573.39\nREFRACTORY SPECIALTIES IN,02-Central,764482000,14990.00,8191.53,6798.47\nREFRACTORY SPECIALTIES IN,02-Central,793060000,12150.00,5365.14,6784.86\nREFRACTORY SPECIALTIES IN,02-Central,727031300,11562.00,8490.28,3071.72\nREFRACTORY SPECIALTIES IN,02-Central,764521000,6670.00,2654.20,4015.80\nREFRACTORY SPECIALTIES IN,02-Central,792650000,5242.00,2082.62,3159.38\nREFRACTORY SPECIALTIES IN,02-Central,764821073,3560.00,2916.00,644.00\nREFRACTORY SPECIALTIES IN,02-Central,764521073,2668.00,2580.00,88.00\nREFRACTORY SPECIALTIES IN,02-Central,754925000,2464.00,1539.06,924.94\nREFRACTORY SPECIALTIES IN,02-Central,754927000,2464.00,1455.40,1008.60\nREFRACTORY SPECIALTIES IN,02-Central,764223010,2190.00,1627.81,562.19\nREFRACTORY SPECIALTIES IN,02-Central,733510000,1920.00,954.17,965.83\nREFRACTORY SPECIALTIES IN,02-Central,764342000,1418.00,734.52,683.48\nREFRACTORY SPECIALTIES IN,02-Central,728030000,1350.00,1337.11,12.89\nREFRACTORY SPECIALTIES IN,02-Central,733586600,1180.80,352.26,828.54\nREFRACTORY SPECIALTIES IN,02-Central,792624000,855.00,258.26,596.74\nREFRACTORY SPECIALTIES IN,02-Central,764481000,640.00,351.78,288.22\nREFRACTORY SPECIALTIES IN,02-Central,764601000,516.00,212.58,303.42\nREFRACTORY SPECIALTIES IN,02-Central,733525500,430.00,140.79,289.21\nREFRACTORY SPECIALTIES IN,02-Central,764221000,420.00,336.56,83.44\nREFRACTORY SPECIALTIES IN,02-Central,733383000,414.48,152.13,262.35\nREFRACTORY SPECIALTIES IN,02-Central,764822000,335.00,123.05,211.95\nREFRACTORY SPECIALTIES IN,02-Central,733525600,215.00,71.05,143.95\nREFRACTORY SPECIALTIES IN,02-Central,733505200,210.00,76.44,133.56\nREFRACTORY SPECIALTIES IN,02-Central,764761000,162.00,95.58,66.42\nREFRACTORY SPECIALTIES IN,02-Central,764602000,129.00,52.42,76.58\nREFRACTORY SPECIALTIES IN,02-Central,764341000,89.00,45.87,43.13\nREFRACTORY SPECIALTIES IN,02-Central,764461006,88.00,55.87,32.13\nREFRACTORY SPECIALTIES IN,02-Central,764522000,66.70,26.72,39.98\nREFRACTORY SPECIALTIES IN,02-Central,764781000,47.80,19.72,28.08\nRELIACHECK MANUFACTURING,02-Central,764482000,1080.00,52.20,1027.80\nRENAISSANCE ROOFING,02-Central,733741000,1632.00,476.26,1155.74\nRENO CONSTRUCTION CO LLC,02-Central,734910000,341.50,51.35,290.15\nRENO REFRACTORIES INC.,02-Central,734910000,703.50,104.44,599.06\nRESCO PRODUCTS INC,02-Central,733510000,1130.00,159.03,970.97\n\"RESCO PRODUCTS, INC.\",02-Central,764901000,3980.25,144.13,3836.12\n\"RESCO PRODUCTS, INC.\",02-Central,733511000,3955.00,526.55,3428.45\n\"RESCO PRODUCTS, INC.\",02-Central,770220000,2682.00,170.62,2511.38\nREX ROTO CORP,02-Central,726000062,1537696.35,1139935.62,397760.73\nREX ROTO CORP,02-Central,727031200,967602.16,699312.30,268289.86\nREX ROTO CORP,02-Central,764030000,848819.64,768942.56,79877.08\nREX ROTO CORP,02-Central,727000046,504694.88,325675.44,179019.44\nREX ROTO CORP,02-Central,793000500,246682.80,116027.75,130655.05\nREX ROTO CORP,02-Central,793000200,236180.00,108914.97,127265.03\nREX ROTO CORP,02-Central,715000000,133400.00,78042.83,55357.17\nREX ROTO CORP,02-Central,777990000,113520.00,66969.60,46550.40\nREX ROTO CORP,02-Central,734070500,82831.50,27857.47,54974.03\nREX ROTO CORP,02-Central,734351200,77972.20,16724.95,61247.25\nREX ROTO CORP,02-Central,724003000,55930.00,24720.74,31209.26\nREX ROTO CORP,02-Central,793520000,26824.00,6663.32,20160.68\nREX ROTO CORP,02-Central,733525600,21458.75,4047.27,17411.48\nREX ROTO CORP,02-Central,733721000,19130.80,4325.10,14805.70\nREX ROTO CORP,02-Central,792624000,14015.20,3031.71,10983.49\nREX ROTO CORP,02-Central,764482000,11896.90,4591.08,7305.82\nREX ROTO CORP,02-Central,733521350,11437.69,2797.97,8639.72\nREX ROTO CORP,02-Central,733525500,7778.75,1472.89,6305.86\nREX ROTO CORP,02-Central,777991000,5664.60,4041.50,1623.10\nREX ROTO CORP,02-Central,733464600,4606.50,1027.17,3579.33\nREX ROTO CORP,02-Central,734211200,4287.19,488.19,3799.00\nREX ROTO CORP,02-Central,764461006,4012.60,2118.14,1894.46\nREX ROTO CORP,02-Central,793250074,3879.50,2294.42,1585.08\nREX ROTO CORP,02-Central,764781040,3817.90,1531.40,2286.50\nREX ROTO CORP,02-Central,793320074,1885.60,1414.08,471.52\nREX ROTO CORP,02-Central,764301000,1476.20,621.51,854.69\nREX ROTO CORP,02-Central,793520074,1341.20,691.64,649.56\nREX ROTO CORP,02-Central,793060300,1132.80,530.19,602.61\nREX ROTO CORP,02-Central,764762000,879.60,440.70,438.90\nREX ROTO CORP,02-Central,764484003,765.30,288.72,476.58\nREX ROTO CORP,02-Central,764522000,740.10,237.57,502.53\nREX ROTO CORP,02-Central,764822000,702.60,184.58,518.02\nREX ROTO CORP,02-Central,734311100,561.12,141.20,419.92\nREX ROTO CORP,02-Central,733464500,463.50,91.57,371.93\nREX ROTO CORP,02-Central,768221000,395.20,68.56,326.64\nREX ROTO CORP,02-Central,793530074,335.30,127.98,207.32\nREX ROTO CORP,02-Central,766681300,217.50,99.79,117.71\nREX ROTO CORP,02-Central,793580000,179.80,60.61,119.19\nREX ROTO CORP,02-Central,777642000,121.00,50.36,70.64\nREX ROTO CORP,02-Central,766681200,65.70,44.60,21.10\nRG SMITH CO. INC,02-Central,739641100,482.04,201.60,280.44\nRHI AG,02-Central,754665000,7965.72,3914.69,4051.03\nRHI CANADA INC.,02-Central,777642000,124.00,63.34,60.66\nROGERS ENGINEERING,02-Central,733211050,24906.00,3677.48,21228.52\nROGERS ENGINEERING,02-Central,770210000,1965.00,185.53,1779.47\nS & P INSULATION,02-Central,735602000,11200.00,3362.29,7837.71\nS & S INDUSTRIES,02-Central,768250220,109882.50,65185.46,44697.04\nS & S INDUSTRIES,02-Central,764781000,75978.00,26382.83,49595.17\nS & S INDUSTRIES,02-Central,768251300,68155.00,38016.10,30138.90\nS & S INDUSTRIES,02-Central,764487510,49764.00,22189.79,27574.21\nS & S INDUSTRIES,02-Central,768250221,32150.00,19363.06,12786.94\nS & S INDUSTRIES,02-Central,768250200,18240.00,11355.98,6884.02\nS & S INDUSTRIES,02-Central,768251400,4770.00,2662.88,2107.12\nS & S INDUSTRIES,02-Central,764762000,1870.00,770.08,1099.92\nS & S INDUSTRIES,02-Central,733444200,210.00,38.64,171.36\nS.C. DEPT OF AGRICULTURE,02-Central,720219100,531.90,13.10,518.80\nS.P.KINNEY ENGINEERING,02-Central,770520000,958.50,41.85,916.65\nSAFT AMERICA INC,02-Central,733423600,380.80,35.90,344.90\nSAM DONG GEORGIA INC.,02-Central,768750000,26000.00,4824.08,21175.92\nSAM DONG GEORGIA INC.,02-Central,764521000,4710.00,398.92,4311.08\nSAM DONG GEORGIA INC.,02-Central,770830000,882.00,30.97,851.03\nSAM DONG GEORGIA INC.,02-Central,770810000,737.00,17.19,719.81\n\"SAM DONG OHIO, INC.\",02-Central,777601000,5189.76,843.69,4346.07\nSANDERS SUPPLY,02-Central,735602000,12000.00,3407.06,8592.94\nSANDY WALLIN INC.,02-Central,733464500,298.00,29.52,268.48\nSAUEREISEN INC.,02-Central,720219100,4934.10,119.51,4814.59\nSAVER SYSTEM,02-Central,727063007,5211.00,632.36,4578.64\n\"SBJ ENTERPRISES, INC.\",02-Central,735602110,384.00,89.25,294.75\nSCHAD BOILER SETTING CO.,02-Central,727041200,25364.76,5856.48,19508.28\nSCHAD BOILER SETTING CO.,02-Central,733211000,19920.00,4687.15,15232.85\nSCHAD BOILER SETTING CO.,02-Central,733151500,13228.80,2443.16,10785.64\nSCHAD BOILER SETTING CO.,02-Central,777601000,7756.36,2225.24,5531.12\nSCHAD BOILER SETTING CO.,02-Central,777902700,5900.58,2174.39,3726.19\nSCHAD BOILER SETTING CO.,02-Central,793330000,4194.00,889.38,3304.62\nSCHAD BOILER SETTING CO.,02-Central,733151600,3897.20,717.71,3179.49\nSCHAD BOILER SETTING CO.,02-Central,727000140,736.00,126.70,609.30\nSCHAD BOILER SETTING CO.,02-Central,737912200,629.25,136.42,492.83\nSCHAD BOILER SETTING CO.,02-Central,770220000,586.80,54.69,532.11\nSCHAD BOILER SETTING CO.,02-Central,764481000,586.00,224.76,361.24\nSCHAD BOILER SETTING CO.,02-Central,793540000,492.00,115.87,376.13\nSCHAD BOILER SETTING CO.,02-Central,734910000,428.30,102.70,325.60\nSCHAD BOILER SETTING CO.,02-Central,734311100,381.48,33.70,347.78\nSCHULZ XTRUDED PRODUCTS,02-Central,739681100,12013.56,5382.00,6631.56\nSCHULZ XTRUDED PRODUCTS,02-Central,738361000,3756.24,1853.28,1902.96\nSCHULZ XTRUTED PRODUCTS,02-Central,738361000,3033.72,966.76,2066.96\nSCOT FORGE CO,02-Central,766122000,167138.00,71749.76,95388.24\nSCOTT- GLENN CO.LLC,02-Central,734211200,7166.29,921.03,6245.26\nSCOTT- GLENN CO.LLC,02-Central,770240000,5919.75,616.77,5302.98\nSCOTT- GLENN CO.LLC,02-Central,770260000,1317.60,137.96,1179.64\nSCOTT- GLENN CO.LLC,02-Central,770220000,1211.40,112.66,1098.74\nSCREW CONVEYOR CORP.,02-Central,770520000,4635.00,228.66,4406.34\n\"SDI LAFARGA, LLC\",02-Central,770830000,1061.00,35.87,1025.13\nSDM & ASSOCIATES INC,02-Central,733423600,380.80,35.90,344.90\nSEALTHERM CORP,02-Central,739370000,379.52,138.24,241.28\nSELAS HEAT TECHNOLOGY CO.,02-Central,734311100,654.20,70.60,583.60\nSELECT INDUSTRIES CORP,02-Central,724034500,30247.60,11133.24,19114.36\nSELECTRODE INDUSTRIES,02-Central,727000140,531.20,33.43,497.77\nSELEE CORP,02-Central,732690000,1488451.41,727047.47,761403.94\nSELEE CORP,02-Central,732659400,113487.80,44143.66,69344.14\nSELEE CORP,02-Central,792687000,95402.38,34102.04,61300.34\nSELEE CORP,02-Central,777991000,23485.00,13696.20,9788.80\nSELEE CORP,02-Central,793000410,19737.00,8035.48,11701.52\nSELEE CORP,02-Central,792683000,19167.03,6714.58,12452.45\nSELEE CORP,02-Central,732695000,6378.56,1725.72,4652.84\nSELEE CORP,02-Central,792685000,2236.67,677.18,1559.49\nSELEE CORP,02-Central,754927000,941.16,113.41,827.75\nSELEE CORP,02-Central,740402301,742.50,210.97,531.53\nSELEE CORP,02-Central,734651200,205.80,37.00,168.80\nSERVI-SEL INC.,02-Central,734211200,243.46,23.38,220.08\nSEYMORE JOHNSON AIRFORCE,02-Central,734311100,190.74,16.85,173.89\nSGL CARBON GROUP,02-Central,764526600,1236.00,609.31,626.69\nSGL CARBON LLC- MORGANTON,02-Central,754695000,112314.50,76577.92,35736.58\nSGL CARBON LLC- MORGANTON,02-Central,779959837,22032.50,11120.03,10912.47\nSGL CARBON LLC- MORGANTON,02-Central,764821000,13160.00,3697.80,9462.20\nSGL CARBON LLC- MORGANTON,02-Central,764901000,12605.00,4192.23,8412.77\nSGL CARBON LLC- MORGANTON,02-Central,779957232,10447.50,7271.73,3175.77\nSGL CARBON LLC- MORGANTON,02-Central,754055000,9545.83,6204.57,3341.26\nSGL CARBON LLC- MORGANTON,02-Central,764526600,5016.00,2270.54,2745.46\nSGL CARBON LLC- MORGANTON,02-Central,764821161,2260.00,1488.00,772.00\nSGL CARBON LLC- MORGANTON,02-Central,764524000,2196.00,597.32,1598.68\nSGL CARBON LLC- MORGANTON,02-Central,764901061,1990.00,1492.00,498.00\nSGL CARBON LLC- MORGANTON,02-Central,764821073,1130.00,729.00,401.00\nSGL CARBON LLC- MORGANTON,02-Central,764901073,995.00,729.00,266.00\nSGL CARBON LLC- MORGANTON,02-Central,764484000,660.00,217.14,442.86\n\"SGL CARBON,LLC\",02-Central,734990000,11870.40,1455.96,10414.44\nSHARP REFRACTORIES,02-Central,770540000,5400.50,274.58,5125.92\nSHARP REFRACTORIES,02-Central,770560000,1585.00,89.50,1495.50\nSHARP REFRACTORIES,02-Central,770580000,1522.00,84.73,1437.27\nSHARP REFRACTORIES,02-Central,770330000,1015.00,96.65,918.35\nSHOALS RUBBER & GASKET CO,02-Central,733211000,456.00,47.39,408.61\nSHOOK & FLETCHER INSUL CO,02-Central,764901000,535090.15,225584.69,309505.46\nSHOOK & FLETCHER INSUL CO,02-Central,735602000,351617.50,120457.30,231160.20\nSHOOK & FLETCHER INSUL CO,02-Central,764821000,254016.00,84026.76,169989.24\nSHOOK & FLETCHER INSUL CO,02-Central,726000240,229728.00,126361.36,103366.64\nSHOOK & FLETCHER INSUL CO,02-Central,764521000,144596.00,54473.77,90122.23\nSHOOK & FLETCHER INSUL CO,02-Central,738561000,61783.68,46016.36,15767.32\nSHOOK & FLETCHER INSUL CO,02-Central,735602110,38540.00,14351.12,24188.88\nSHOOK & FLETCHER INSUL CO,02-Central,738521000,38461.44,30210.97,8250.47\nSHOOK & FLETCHER INSUL CO,02-Central,764822000,29433.60,8415.88,21017.72\nSHOOK & FLETCHER INSUL CO,02-Central,737871000,21569.60,4755.60,16814.00\nSHOOK & FLETCHER INSUL CO,02-Central,766121000,18870.00,4269.30,14600.70\nSHOOK & FLETCHER INSUL CO,02-Central,764785000,8940.00,3649.74,5290.26\nSHOOK & FLETCHER INSUL CO,02-Central,737321000,8025.60,2569.69,5455.91\nSHOOK & FLETCHER INSUL CO,02-Central,768126014,7334.80,3861.89,3472.91\nSHOOK & FLETCHER INSUL CO,02-Central,764902000,7118.75,2139.11,4979.64\nSHOOK & FLETCHER INSUL CO,02-Central,764861000,6681.75,2816.54,3865.21\nSHOOK & FLETCHER INSUL CO,02-Central,734910000,6551.00,1557.80,4993.20\nSHOOK & FLETCHER INSUL CO,02-Central,764951800,6460.00,2543.85,3916.15\nSHOOK & FLETCHER INSUL CO,02-Central,793260074,6426.00,2776.40,3649.60\nSHOOK & FLETCHER INSUL CO,02-Central,777601000,5671.05,1510.99,4160.06\nSHOOK & FLETCHER INSUL CO,02-Central,738441000,5183.04,8392.32,-3209.28\nSHOOK & FLETCHER INSUL CO,02-Central,733464500,4521.20,709.03,3812.17\nSHOOK & FLETCHER INSUL CO,02-Central,779980918,4380.00,2302.21,2077.79\nSHOOK & FLETCHER INSUL CO,02-Central,764825000,4050.00,2168.54,1881.46\nSHOOK & FLETCHER INSUL CO,02-Central,764781000,3590.20,1291.11,2299.09\nSHOOK & FLETCHER INSUL CO,02-Central,779959909,2912.40,505.05,2407.35\nSHOOK & FLETCHER INSUL CO,02-Central,768121000,2764.00,1150.68,1613.32\nSHOOK & FLETCHER INSUL CO,02-Central,779959908,2648.70,335.07,2313.63\nSHOOK & FLETCHER INSUL CO,02-Central,779980915,2640.00,1368.37,1271.63\nSHOOK & FLETCHER INSUL CO,02-Central,739561200,2481.73,979.36,1502.37\nSHOOK & FLETCHER INSUL CO,02-Central,764601000,2220.00,1062.89,1157.11\nSHOOK & FLETCHER INSUL CO,02-Central,734611100,1801.25,353.08,1448.17\nSHOOK & FLETCHER INSUL CO,02-Central,734311100,1748.70,202.23,1546.47\nSHOOK & FLETCHER INSUL CO,02-Central,770200000,1652.40,169.66,1482.74\nSHOOK & FLETCHER INSUL CO,02-Central,791006650,1420.00,679.30,740.70\nSHOOK & FLETCHER INSUL CO,02-Central,739741100,910.40,841.60,68.80\nSHOOK & FLETCHER INSUL CO,02-Central,764341000,759.00,479.74,279.26\nSHOOK & FLETCHER INSUL CO,02-Central,734900000,667.10,264.80,402.30\nSHOOK & FLETCHER INSUL CO,02-Central,739641100,590.76,412.28,178.48\nSHOOK & FLETCHER INSUL CO,02-Central,733741000,568.80,186.38,382.42\nSHOOK & FLETCHER INSUL CO,02-Central,764761000,518.40,245.72,272.68\nSHOOK & FLETCHER INSUL CO,02-Central,739390000,466.08,485.28,-19.20\nSHOOK & FLETCHER INSUL CO,02-Central,733525500,457.50,65.03,392.47\nSHOOK & FLETCHER INSUL CO,02-Central,734851000,456.23,98.90,357.33\nSHOOK & FLETCHER INSUL CO,02-Central,734651200,423.94,73.75,350.19\nSHOOK & FLETCHER INSUL CO,02-Central,768122000,414.60,174.66,239.94\nSHOOK & FLETCHER INSUL CO,02-Central,768222000,414.60,204.94,209.66\nSHOOK & FLETCHER INSUL CO,02-Central,768241000,300.30,106.14,194.16\nSHOOK & FLETCHER INSUL CO,02-Central,738542000,253.92,141.16,112.76\nSHOOK & FLETCHER INSUL CO,02-Central,733211000,239.20,47.39,191.81\nSHOOK & FLETCHER INSUL CO,02-Central,733423700,232.64,36.22,196.42\nSHOOK & FLETCHER INSUL CO,02-Central,737912200,209.75,45.47,164.28\nSHOOK & FLETCHER INSUL CO,02-Central,733511000,190.00,75.22,114.78\nSHOOK & FLETCHER INSUL CO,02-Central,764762000,92.00,31.57,60.43\nSHOOK & FLETCHER,02-Central,764602600,351103.20,129862.54,221240.66\nSHOOK & FLETCHER,02-Central,764522030,154531.80,58930.50,95601.30\nSHOOK & FLETCHER,02-Central,764901000,122386.20,51980.60,70405.60\nSHOOK & FLETCHER,02-Central,764602900,78836.54,30569.70,48266.84\nSHOOK & FLETCHER,02-Central,734651200,63376.50,26796.20,36580.30\nSHOOK & FLETCHER,02-Central,735602000,49800.00,15318.43,34481.57\nSHOOK & FLETCHER,02-Central,764529560,46598.67,18680.06,27918.61\nSHOOK & FLETCHER,02-Central,764902000,31906.70,14173.98,17732.72\nSHOOK & FLETCHER,02-Central,764563001,31729.60,12137.42,19592.18\nSHOOK & FLETCHER,02-Central,764821000,24375.00,9672.52,14702.48\nSHOOK & FLETCHER,02-Central,768241000,16376.00,5481.24,10894.76\nSHOOK & FLETCHER,02-Central,734761300,8496.40,1529.26,6967.14\nSHOOK & FLETCHER,02-Central,771320000,8311.30,1368.99,6942.31\nSHOOK & FLETCHER,02-Central,766081010,8172.00,2181.96,5990.04\nSHOOK & FLETCHER,02-Central,764521000,7004.00,2922.74,4081.26\nSHOOK & FLETCHER,02-Central,764526600,2958.40,1832.26,1126.14\nSHOOK & FLETCHER,02-Central,764822000,2523.80,1010.60,1513.20\nSHOOK & FLETCHER,02-Central,764781040,2297.00,797.07,1499.93\nSHOOK & FLETCHER,02-Central,768121000,1176.00,460.26,715.74\nSHOOK & FLETCHER,02-Central,735752000,380.00,136.80,243.20\nSHOOK & FLETCHER,02-Central,724281200,358.00,89.08,268.92\nSHOOK & FLETCHER,02-Central,739561000,301.90,85.22,216.68\nSHOOK & FLETCHER,02-Central,733211000,239.20,47.39,191.81\nSIERRA LOBO,02-Central,734910000,341.50,51.35,290.15\nSIMPSON LUMBER COMPANY,02-Central,770240000,5988.75,413.88,5574.87\nSMITH - SHARPE,02-Central,733572200,201550.00,74088.59,127461.41\nSMITH - SHARPE,02-Central,754305000,95445.00,53222.89,42222.11\nSMITH - SHARPE,02-Central,733221400,84993.60,35404.38,49589.22\nSMITH - SHARPE,02-Central,779952188,74654.40,29817.68,44836.72\nSMITH - SHARPE,02-Central,733525500,71762.12,20100.84,51661.28\nSMITH - SHARPE,02-Central,764822000,70117.60,20611.92,49505.68\nSMITH - SHARPE,02-Central,764821000,58392.00,17951.57,40440.43\nSMITH - SHARPE,02-Central,768241000,49620.50,19726.33,29894.17\nSMITH - SHARPE,02-Central,764901000,43838.50,16882.53,26955.97\nSMITH - SHARPE,02-Central,779957935,38372.80,17314.00,21058.80\nSMITH - SHARPE,02-Central,764521000,35176.40,10973.28,24203.12\nSMITH - SHARPE,02-Central,768221000,34470.50,12699.86,21770.64\nSMITH - SHARPE,02-Central,733510000,32258.00,13305.77,18952.23\nSMITH - SHARPE,02-Central,733570000,30160.80,10838.69,19322.11\nSMITH - SHARPE,02-Central,733211000,29056.50,7008.09,22048.41\nSMITH - SHARPE,02-Central,777601000,26901.08,7234.38,19666.70\nSMITH - SHARPE,02-Central,768121000,23508.60,10957.99,12550.61\nSMITH - SHARPE,02-Central,751258000,20000.00,9146.20,10853.80\nSMITH - SHARPE,02-Central,733464600,18153.40,6172.24,11981.16\nSMITH - SHARPE,02-Central,779952189,17940.00,7818.64,10121.36\nSMITH - SHARPE,02-Central,766211000,16891.50,6133.72,10757.78\nSMITH - SHARPE,02-Central,733464500,16712.00,5256.13,11455.87\nSMITH - SHARPE,02-Central,768231000,16620.65,6751.66,9868.99\nSMITH - SHARPE,02-Central,771400000,10440.00,623.76,9816.24\nSMITH - SHARPE,02-Central,739561200,10376.11,2822.34,7553.77\nSMITH - SHARPE,02-Central,764601000,10266.50,2754.30,7512.20\nSMITH - SHARPE,02-Central,758422000,9797.78,3974.95,5822.83\nSMITH - SHARPE,02-Central,764821161,9411.60,6844.80,2566.80\nSMITH - SHARPE,02-Central,764521073,8820.00,5805.00,3015.00\nSMITH - SHARPE,02-Central,739741100,8594.62,4163.22,4431.40\nSMITH - SHARPE,02-Central,764602000,8438.80,2267.29,6171.51\nSMITH - SHARPE,02-Central,734311100,8421.44,1128.11,7293.33\nSMITH - SHARPE,02-Central,733484800,7950.40,1458.72,6491.68\nSMITH - SHARPE,02-Central,754285000,7932.28,2645.19,5287.09\nSMITH - SHARPE,02-Central,764901073,7919.20,6269.40,1649.80\nSMITH - SHARPE,02-Central,764341000,7418.30,3694.78,3723.52\nSMITH - SHARPE,02-Central,739561100,7237.88,1959.32,5278.56\nSMITH - SHARPE,02-Central,780290000,6825.60,2330.16,4495.44\nSMITH - SHARPE,02-Central,734751200,6795.41,1201.85,5593.56\nSMITH - SHARPE,02-Central,764781040,6681.20,1126.46,5554.74\nSMITH - SHARPE,02-Central,764902000,6666.40,1604.22,5062.18\nSMITH - SHARPE,02-Central,764481000,6640.00,1302.98,5337.02\nSMITH - SHARPE,02-Central,789180000,6381.72,2951.85,3429.87\nSMITH - SHARPE,02-Central,748602000,6109.00,792.77,5316.23\nSMITH - SHARPE,02-Central,734651200,6051.71,1257.04,4794.67\nSMITH - SHARPE,02-Central,739541200,5823.33,1235.51,4587.82\nSMITH - SHARPE,02-Central,734900000,5620.60,2218.77,3401.83\nSMITH - SHARPE,02-Central,779956599,5531.40,3331.69,2199.71\nSMITH - SHARPE,02-Central,726020005,5069.12,1085.89,3983.23\nSMITH - SHARPE,02-Central,766681100,5020.20,1176.91,3843.29\nSMITH - SHARPE,02-Central,739661100,4833.60,3064.96,1768.64\nSMITH - SHARPE,02-Central,777642000,4794.50,1551.57,3242.93\nSMITH - SHARPE,02-Central,767960000,4500.00,2379.20,2120.80\nSMITH - SHARPE,02-Central,739761100,4260.81,3268.38,992.43\nSMITH - SHARPE,02-Central,727000140,4186.20,633.42,3552.78\nSMITH - SHARPE,02-Central,779951075,3435.94,1651.49,1784.45\nSMITH - SHARPE,02-Central,738561000,3426.63,1930.74,1495.89\nSMITH - SHARPE,02-Central,777903000,3280.90,1370.47,1910.43\nSMITH - SHARPE,02-Central,741620310,3152.50,705.71,2446.79\nSMITH - SHARPE,02-Central,766121000,3132.20,355.10,2777.10\nSMITH - SHARPE,02-Central,764821073,3069.00,2187.00,882.00\nSMITH - SHARPE,02-Central,764781000,3052.00,960.41,2091.59\nSMITH - SHARPE,02-Central,741640310,3000.00,649.42,2350.58\nSMITH - SHARPE,02-Central,764861000,2674.50,1119.17,1555.33\nSMITH - SHARPE,02-Central,738521000,2571.36,1756.38,814.98\nSMITH - SHARPE,02-Central,764521061,2370.00,1917.00,453.00\nSMITH - SHARPE,02-Central,766261000,2270.00,893.62,1376.38\nSMITH - SHARPE,02-Central,766686000,2015.00,825.98,1189.02\nSMITH - SHARPE,02-Central,741720310,1966.50,139.30,1827.20\nSMITH - SHARPE,02-Central,770400100,1958.25,259.17,1699.08\nSMITH - SHARPE,02-Central,770415000,1927.50,339.78,1587.72\nSMITH - SHARPE,02-Central,734414100,1844.56,281.04,1563.52\nSMITH - SHARPE,02-Central,733211050,1811.10,465.90,1345.20\nSMITH - SHARPE,02-Central,770381500,1645.20,245.16,1400.04\nSMITH - SHARPE,02-Central,737912200,1640.90,359.57,1281.33\nSMITH - SHARPE,02-Central,733444300,1581.00,618.69,962.31\nSMITH - SHARPE,02-Central,777904301,1555.45,704.82,850.63\nSMITH - SHARPE,02-Central,767970000,1500.00,987.68,512.32\nSMITH - SHARPE,02-Central,780710000,1416.00,426.66,989.34\nSMITH - SHARPE,02-Central,779956287,1374.40,662.27,712.13\nSMITH - SHARPE,02-Central,779956286,1312.50,590.46,722.04\nSMITH - SHARPE,02-Central,780230000,1304.40,491.66,812.74\nSMITH - SHARPE,02-Central,733444200,1296.00,439.98,856.02\nSMITH - SHARPE,02-Central,754085000,1292.80,380.01,912.79\nSMITH - SHARPE,02-Central,768211000,1173.60,561.36,612.24\nSMITH - SHARPE,02-Central,739781100,1090.96,476.05,614.91\nSMITH - SHARPE,02-Central,770570000,1088.85,95.41,993.44\nSMITH - SHARPE,02-Central,739641100,983.52,806.40,177.12\nSMITH - SHARPE,02-Central,733151500,976.80,162.33,814.47\nSMITH - SHARPE,02-Central,754267000,918.72,269.92,648.80\nSMITH - SHARPE,02-Central,754255000,880.00,447.92,432.08\nSMITH - SHARPE,02-Central,754485000,865.80,345.81,519.99\nSMITH - SHARPE,02-Central,770392000,834.00,124.52,709.48\nSMITH - SHARPE,02-Central,779956289,774.00,368.30,405.70\nSMITH - SHARPE,02-Central,770520000,735.00,96.63,638.37\nSMITH - SHARPE,02-Central,733423800,722.56,134.42,588.14\nSMITH - SHARPE,02-Central,770300000,686.96,79.94,607.02\nSMITH - SHARPE,02-Central,764561000,550.20,259.05,291.15\nSMITH - SHARPE,02-Central,770392700,524.00,83.02,440.98\nSMITH - SHARPE,02-Central,741600310,486.00,64.12,421.88\nSMITH - SHARPE,02-Central,792614000,443.40,82.57,360.83\nSMITH - SHARPE,02-Central,764762000,413.40,94.71,318.69\nSMITH - SHARPE,02-Central,770290000,397.25,79.65,317.60\nSMITH - SHARPE,02-Central,734910000,396.00,102.70,293.30\nSMITH - SHARPE,02-Central,768751000,360.00,144.75,215.25\nSMITH - SHARPE,02-Central,780531000,358.46,190.34,168.12\nSMITH - SHARPE,02-Central,770380000,350.00,61.29,288.71\nSMITH - SHARPE,02-Central,739541000,299.52,90.50,209.02\nSMITH - SHARPE,02-Central,733505200,276.50,83.83,192.67\nSMITH - SHARPE,02-Central,764482000,268.50,87.00,181.50\nSMITH - SHARPE,02-Central,779956288,225.30,138.23,87.07\nSMITH - SHARPE,02-Central,779301000,223.86,47.51,176.35\nSMITH - SHARPE,02-Central,754287000,183.24,49.12,134.12\nSMITH - SHARPE,02-Central,734211200,175.47,22.84,152.63\nSMT,02-Central,739741100,428.48,168.32,260.16\nSNYDER BENTLEY CO,02-Central,733671000,4247.04,351.87,3895.17\nSOLBERG MFG,02-Central,734311100,363.44,35.30,328.14\nSOLBERG MFG,02-Central,734211200,218.82,23.38,195.44\nSOUNDTECH INC.,02-Central,768141000,2126.60,1323.70,802.90\nSOUNDTECH INC.,02-Central,768241000,1400.00,707.62,692.38\nSOUNDTECH INC.,02-Central,768021000,840.00,549.86,290.14\nSOURLIS GLASS STUDIO,02-Central,734311100,381.48,33.70,347.78\nSOUTHERN RESEARCH INSTITU,02-Central,733464600,909.00,97.60,811.40\nSPAR INC,02-Central,764522000,15930.00,7161.10,8768.90\nSPAR INC,02-Central,764821000,10550.00,4064.40,6485.60\nSPAR INC,02-Central,764601000,7865.00,3454.39,4410.61\nSPAR INC,02-Central,764901000,5670.00,2242.00,3428.00\nSPAR INC,02-Central,754065000,5010.00,3294.04,1715.96\nSPAR INC,02-Central,764861000,3075.00,1298.90,1776.10\nSPAR INC,02-Central,766121000,1670.00,478.98,1191.02\nSPAR INC,02-Central,764781000,774.00,354.98,419.02\nSPAR INC,02-Central,764521000,590.00,265.94,324.06\nSPECIALTY CERAMICS INC,02-Central,727000060,1484488.50,1135628.20,348860.30\nSPECIALTY CERAMICS INC,02-Central,727063005,322620.00,233421.47,89198.53\nSPECIALTY CERAMICS INC,02-Central,727000081,209961.00,156301.47,53659.53\nSPECIALTY CERAMICS INC,02-Central,727063009,93750.00,67336.05,26413.95\nSPECIALTY CERAMICS INC,02-Central,793060300,66765.60,29752.60,37013.00\nSPECIALTY CERAMICS INC,02-Central,793000000,58924.80,29892.68,29032.12\nSPECIALTY CERAMICS INC,02-Central,727000080,54682.50,41736.75,12945.75\nSPECIALTY CERAMICS INC,02-Central,715000000,52000.00,48697.48,3302.52\nSPECIALTY CERAMICS INC,02-Central,763991000,34009.20,30239.36,3769.84\nSPECIALTY CERAMICS INC,02-Central,720252301,26446.50,1771.70,24674.80\nSPECIALTY CERAMICS INC,02-Central,727063007,16509.00,12203.11,4305.89\nSPECIALTY CERAMICS INC,02-Central,727000070,10254.00,7808.70,2445.30\nSPECIALTY CERAMICS INC,02-Central,768750000,6998.40,5210.02,1788.38\nSPECIALTY CERAMICS INC,02-Central,764222000,5700.00,3738.79,1961.21\nSPECIALTY CERAMICS INC,02-Central,726000062,4485.00,3625.88,859.12\nSPECIALTY CERAMICS INC,02-Central,727063000,3682.80,2721.13,961.67\nSPECIALTY CERAMICS INC,02-Central,727031300,2835.00,2057.65,777.35\nSPECIALTY CERAMICS INC,02-Central,764761000,1928.00,1137.79,790.21\nSPECIALTY CERAMICS INC,02-Central,764221000,375.00,252.42,122.58\nSPECIALTY FOUNDRY PRODUCT,02-Central,764821000,341490.00,160021.76,181468.24\nSPECIALTY FOUNDRY PRODUCT,02-Central,764521000,54280.00,29215.00,25065.00\nSPECIALTY FOUNDRY PRODUCT,02-Central,764901000,40712.50,19154.24,21558.26\nSPECIALTY FOUNDRY PRODUCT,02-Central,766121000,36180.00,13839.38,22340.62\nSPECIALTY FOUNDRY PRODUCT,02-Central,764761000,35030.00,14602.44,20427.56\nSPECIALTY FOUNDRY PRODUCT,02-Central,733211000,26068.00,9415.85,16652.15\nSPECIALTY FOUNDRY PRODUCT,02-Central,764821073,23800.00,25515.00,-1715.00\nSPECIALTY FOUNDRY PRODUCT,02-Central,733151500,21200.00,5737.53,15462.47\nSPECIALTY FOUNDRY PRODUCT,02-Central,764521061,13500.00,17253.00,-3753.00\nSPECIALTY FOUNDRY PRODUCT,02-Central,733182300,12996.00,3614.15,9381.85\nSPECIALTY FOUNDRY PRODUCT,02-Central,764905200,10950.00,5783.09,5166.91\nSPECIALTY FOUNDRY PRODUCT,02-Central,733202000,9712.80,3310.16,6402.64\nSPECIALTY FOUNDRY PRODUCT,02-Central,764781000,3690.00,1840.06,1849.94\nSPECIALTY FOUNDRY PRODUCT,02-Central,764901073,3500.00,3645.00,-145.00\nSPECIALTY FOUNDRY PRODUCT,02-Central,770290000,2799.00,229.76,2569.24\nSPECIALTY FOUNDRY PRODUCT,02-Central,777642000,2714.80,860.80,1854.00\nSPECIALTY FOUNDRY PRODUCT,02-Central,770400000,934.50,92.44,842.06\nSPECIALTY FOUNDRY PRODUCT,02-Central,770260000,675.00,70.33,604.67\nSPECIALTY FOUNDRY PRODUCT,02-Central,758431000,602.12,301.08,301.04\nSPECIALTY FOUNDRY PRODUCT,02-Central,734651200,584.00,73.50,510.50\nSPECIALTY FOUNDRY PRODUCT,02-Central,770392000,446.00,41.51,404.49\nSPECIALTY FOUNDRY PRODUCT,02-Central,767550000,93.00,74.31,18.69\nSPECIALTY FOUNDRY PRODUCT,02-Central,779958914,64.00,51.00,13.00\nSPECIALTY PROD. INSULATIO,02-Central,735602000,194866.00,71720.74,123145.26\nSPECIALTY PROD. INSULATIO,02-Central,764902000,39296.25,8453.87,30842.38\nSPECIALTY PROD. INSULATIO,02-Central,735750000,13376.00,6102.75,7273.25\nSPECIALTY PROD. INSULATIO,02-Central,735602110,10622.00,4081.19,6540.81\nSPECIALTY PRODUCTS &,02-Central,735602000,209980.00,78533.57,131446.43\nSPECIALTY PRODUCTS &,02-Central,735750000,23850.00,10541.24,13308.76\nSPECIALTY PRODUCTS &,02-Central,768242000,4395.00,1324.83,3070.17\nSPECIALTY PRODUCTS &,02-Central,735602110,3240.00,1239.07,2000.93\nSPECIALTY PRODUCTS &,02-Central,735752000,604.00,257.76,346.24\nSPECIALTY PRODUCTS &,02-Central,777642000,286.00,100.72,185.28\nSPECIALTY PRODUCTS INSUL.,02-Central,735750000,5220.00,2287.91,2932.09\nSPECIALTY PRODUCTS INSUL.,02-Central,735602000,3600.00,1344.92,2255.08\nSPECIALTY PRODUCTS INSUL,02-Central,735602000,68210.00,26294.72,41915.28\nSPECIALTY PRODUCTS INSUL,02-Central,764901000,9243.00,1990.44,7252.56\nSPECIALTY PRODUCTS INSUL,02-Central,735655000,4453.60,634.78,3818.82\nSPECIALTY PRODUCTS INSUL,02-Central,735750000,3190.00,1510.75,1679.25\nSPECIALTY PRODUCTS,02-Central,724101200,2040.00,169.69,1870.31\nSPI/MOBILE PULLEY WORKS,02-Central,754467000,5827.98,1288.05,4539.93\nSPRINGFIELD INDUSTRIES,02-Central,734080010,39329.40,16576.68,22752.72\nST. GOBAIN CONTAINERS,02-Central,793330000,431.50,26.01,405.49\nSTACK-ON PRODUCTS COMPANY,02-Central,730710500,591.50,70.96,520.54\nSTANDARD FORGED PRODUCTS,02-Central,777601000,819.84,117.59,702.25\nSTANDARD STEEL CORP,02-Central,770550000,11555.00,1042.24,10512.76\nSTANDARD STEEL CORP,02-Central,754067000,5037.60,1861.09,3176.51\nSTANDARD STEEL CORP,02-Central,770460000,3397.50,368.04,3029.46\nSTANDARD STEEL CORP,02-Central,734311100,308.92,35.30,273.62\nSTARBUCKS COFFEE COMPANY,02-Central,733202000,9538.00,1066.96,8471.04\nSTECO REFRACTORIES,02-Central,764901000,44650.00,12186.40,32463.60\nSTECO REFRACTORIES,02-Central,779988955,24850.00,15800.08,9049.92\nSTECO REFRACTORIES,02-Central,764821000,18220.00,4559.41,13660.59\nSTECO REFRACTORIES,02-Central,779980918,10080.00,3784.45,6295.55\nSTECO REFRACTORIES,02-Central,764521000,6400.00,2659.41,3740.59\nSTECO REFRACTORIES,02-Central,779980915,4521.00,2130.29,2390.71\nSTECO REFRACTORIES,02-Central,779988954,3000.00,1349.64,1650.36\nSTECO REFRACTORIES,02-Central,764901050,2350.00,631.79,1718.21\nSTECO REFRACTORIES,02-Central,779988910,360.00,156.30,203.70\nSTEEL CASE INC.,02-Central,739561100,552.72,93.32,459.40\nSTETSON - ROCK ISLAND,02-Central,735750000,6300.00,2472.13,3827.87\nSTETSON BUILDING PRODUCTS,02-Central,735602110,242304.00,85740.47,156563.53\nSTETSON BUILDING PRODUCTS,02-Central,735602000,170312.00,58681.47,111630.53\nSTETSON BUILDING PRODUCTS,02-Central,735750000,27050.00,10345.79,16704.21\nSTETSON BUILDING PRODUCTS,02-Central,774271000,281.50,117.63,163.87\nSTETSON BUILDING PRODUCTS,02-Central,774401000,159.00,55.11,103.89\nSTETSON BUILDING PRODUCTS,02-Central,734990000,150.80,27.39,123.41\nSTETSON BUILDING PRODUCTS,02-Central,764341000,66.00,11.47,54.53\nSTEWART ENGINEERS,02-Central,764602000,21360.00,1572.69,19787.31\nSTEWART ENGINEERS,02-Central,727000140,17760.00,1900.35,15859.65\nSTEWART ENGINEERS,02-Central,739681100,13595.04,4336.56,9258.48\nSTEWART ENGINEERS,02-Central,739741100,6301.44,1972.05,4329.39\nSTEWART ENGINEERS,02-Central,739781100,3021.12,856.89,2164.23\nSTEWART ENGINEERS,02-Central,739561000,2012.40,426.10,1586.30\nSTEWART ENGINEERS,02-Central,770200000,918.00,81.58,836.42\nSTEWART ENGINEERS,02-Central,739541000,479.52,71.62,407.90\nSTRAUCH CHEMICAL DISTRIBU,02-Central,727063000,35540.00,3669.70,31870.30\nSTRAUCH CHEMICAL DISTRIBU,02-Central,779987650,1264.00,175.35,1088.65\nSTRAUCH CHEMICAL DISTRIBU,02-Central,727070000,948.00,131.51,816.49\nSTRAUCH CHEMICAL DISTRIBU,02-Central,723960000,823.00,176.46,646.54\nSULLIVAN SUPPLY CO,02-Central,779221000,1096.25,147.12,949.13\nSUNCOAST CHEMICALS,02-Central,737912200,326.50,45.47,281.03\nSUPERIOR SEALS & SERVICES,02-Central,733510000,1130.00,159.03,970.97\nSUPERIOR SEALS & SERVICES,02-Central,733525500,752.50,70.39,682.11\nSURFACE COMBUSTION INC,02-Central,770560000,56319.60,4379.62,51939.98\nSURFACE COMBUSTION INC,02-Central,737902100,17593.20,4047.03,13546.17\nSURFACE COMBUSTION INC,02-Central,739561200,13230.40,2991.64,10238.76\nSURFACE COMBUSTION INC,02-Central,770580000,7839.20,598.04,7241.16\nSURFACE COMBUSTION INC,02-Central,770600000,4129.93,283.06,3846.87\nSURFACE COMBUSTION INC,02-Central,770830000,1666.00,71.75,1594.25\nSURFACE COMBUSTION INC,02-Central,733151500,1514.00,190.76,1323.24\nSURFACE COMBUSTION INC,02-Central,733141200,1136.00,193.93,942.07\nSURFACE COMBUSTION INC,02-Central,770450000,1113.00,97.34,1015.66\nSURFACE COMBUSTION INC,02-Central,770280000,491.25,80.03,411.22\nSURFACE IGNITER LLC,02-Central,737851000,2941.44,332.22,2609.22\nSUZUKI GARPHYTTAN WIRE,02-Central,738542000,1878.48,423.47,1455.01\nSWANTON WELDING&MACHINING,02-Central,733464500,298.00,29.52,268.48\nSWEP NORTH AMERICA,02-Central,734251300,4438.00,350.83,4087.17\nSWEP NORTH AMERICA,02-Central,734351200,1945.80,147.24,1798.56\nSWINDELL DRESSLER,02-Central,727000140,47096.64,23766.68,23329.96\nSWINDELL DRESSLER,02-Central,777906100,43831.44,48654.00,-4822.56\nSWINDELL DRESSLER,02-Central,766166000,18368.50,9555.02,8813.48\nSWINDELL DRESSLER,02-Central,740283502,13469.50,6830.87,6638.63\nSWINDELL DRESSLER,02-Central,765664200,2457.00,679.59,1777.41\nTAMPA RUBBER & GASKET,02-Central,733721000,286.00,48.90,237.10\nTBN ASSOCIATES INC.,02-Central,735602110,54780.00,17861.50,36918.50\n\"TECH CASTINGS, INC\",02-Central,764481000,3000.00,172.65,2827.35\n\"TECH CASTINGS, INC\",02-Central,764461006,1980.00,163.48,1816.52\n\"TECH CASTINGS, INC\",02-Central,733383000,1117.60,163.93,953.67\nTHE DOW CHEMICAL COMPANY,02-Central,764781000,386.00,20.45,365.55\n\"THE N.P. GROUP, INC.\",02-Central,779987651,1341.00,327.23,1013.77\n\"THE NP GROUP, INC.\",02-Central,779987650,4827.60,1183.63,3643.97\nTHE OHIO STATE UNIVERSITY,02-Central,768121000,489.00,44.03,444.97\n\"THE SCHAEFER GROUP, INC.\",02-Central,777902700,418042.72,209448.15,208594.57\n\"THE SCHAEFER GROUP, INC.\",02-Central,764521000,271009.90,93593.04,177416.86\n\"THE SCHAEFER GROUP, INC.\",02-Central,734173000,227085.60,92858.05,134227.55\n\"THE SCHAEFER GROUP, INC.\",02-Central,754895000,139278.74,23436.58,115842.16\n\"THE SCHAEFER GROUP, INC.\",02-Central,764821000,129143.20,22620.73,106522.47\n\"THE SCHAEFER GROUP, INC.\",02-Central,739741100,78968.16,38810.32,40157.84\n\"THE SCHAEFER GROUP, INC.\",02-Central,766121000,72856.80,8186.70,64670.10\n\"THE SCHAEFER GROUP, INC.\",02-Central,764901000,44303.80,9067.36,35236.44\n\"THE SCHAEFER GROUP, INC.\",02-Central,740402402,35462.00,8796.99,26665.01\n\"THE SCHAEFER GROUP, INC.\",02-Central,759631000,29764.24,5618.24,24146.00\n\"THE SCHAEFER GROUP, INC.\",02-Central,754925000,29283.63,4514.15,24769.48\n\"THE SCHAEFER GROUP, INC.\",02-Central,739781100,26513.89,13386.50,13127.39\n\"THE SCHAEFER GROUP, INC.\",02-Central,777903100,23799.30,12991.44,10807.86\n\"THE SCHAEFER GROUP, INC.\",02-Central,764521073,23660.00,16770.00,6890.00\n\"THE SCHAEFER GROUP, INC.\",02-Central,740382201,23601.50,7752.68,15848.82\n\"THE SCHAEFER GROUP, INC.\",02-Central,733525500,22500.00,3797.23,18702.77\n\"THE SCHAEFER GROUP, INC.\",02-Central,764521061,21600.00,19170.00,2430.00\n\"THE SCHAEFER GROUP, INC.\",02-Central,781670000,21417.50,3965.84,17451.66\n\"THE SCHAEFER GROUP, INC.\",02-Central,740361900,20231.04,8562.40,11668.64\n\"THE SCHAEFER GROUP, INC.\",02-Central,733151500,20137.20,4163.69,15973.51\n\"THE SCHAEFER GROUP, INC.\",02-Central,738561000,18292.80,8591.81,9700.99\n\"THE SCHAEFER GROUP, INC.\",02-Central,733211000,17815.40,3769.30,14046.10\n\"THE SCHAEFER GROUP, INC.\",02-Central,733151600,15162.00,3074.39,12087.61\n\"THE SCHAEFER GROUP, INC.\",02-Central,777902300,14946.96,8248.88,6698.08\n\"THE SCHAEFER GROUP, INC.\",02-Central,739761100,14154.68,8259.61,5895.07\n\"THE SCHAEFER GROUP, INC.\",02-Central,734311100,14053.47,1620.07,12433.40\n\"THE SCHAEFER GROUP, INC.\",02-Central,734910000,13561.40,4734.46,8826.94\n\"THE SCHAEFER GROUP, INC.\",02-Central,754485000,13527.50,4322.66,9204.84\n\"THE SCHAEFER GROUP, INC.\",02-Central,764461006,13456.00,3578.01,9877.99\n\"THE SCHAEFER GROUP, INC.\",02-Central,770500000,13221.00,868.11,12352.89\n\"THE SCHAEFER GROUP, INC.\",02-Central,738542000,13163.76,7279.33,5884.43\n\"THE SCHAEFER GROUP, INC.\",02-Central,777904301,12698.19,5755.90,6942.29\n\"THE SCHAEFER GROUP, INC.\",02-Central,738521000,10673.28,7692.61,2980.67\n\"THE SCHAEFER GROUP, INC.\",02-Central,764821161,10170.00,6696.00,3474.00\n\"THE SCHAEFER GROUP, INC.\",02-Central,777601000,10119.13,2635.44,7483.69\n\"THE SCHAEFER GROUP, INC.\",02-Central,777903800,9974.52,3531.06,6443.46\n\"THE SCHAEFER GROUP, INC.\",02-Central,764902000,9893.80,1414.90,8478.90\n\"THE SCHAEFER GROUP, INC.\",02-Central,733505200,9765.00,1605.30,8159.70\n\"THE SCHAEFER GROUP, INC.\",02-Central,739561200,9194.88,2493.99,6700.89\n\"THE SCHAEFER GROUP, INC.\",02-Central,718551000,8925.00,2020.33,6904.67\n\"THE SCHAEFER GROUP, INC.\",02-Central,754445000,8793.80,2864.60,5929.20\n\"THE SCHAEFER GROUP, INC.\",02-Central,764901061,8790.00,4476.00,4314.00\n\"THE SCHAEFER GROUP, INC.\",02-Central,764561000,8436.38,1539.53,6896.85\n\"THE SCHAEFER GROUP, INC.\",02-Central,727000440,8366.00,2787.29,5578.71\n\"THE SCHAEFER GROUP, INC.\",02-Central,733742000,8340.40,2142.61,6197.79\n\"THE SCHAEFER GROUP, INC.\",02-Central,764481000,7939.20,1557.20,6382.00\n\"THE SCHAEFER GROUP, INC.\",02-Central,724281200,7927.40,1848.81,6078.59\n\"THE SCHAEFER GROUP, INC.\",02-Central,739561100,7728.00,2498.93,5229.07\n\"THE SCHAEFER GROUP, INC.\",02-Central,764822000,7531.00,2118.52,5412.48\n\"THE SCHAEFER GROUP, INC.\",02-Central,758461000,6882.12,1129.71,5752.41\n\"THE SCHAEFER GROUP, INC.\",02-Central,733464500,6836.90,1072.28,5764.62\n\"THE SCHAEFER GROUP, INC.\",02-Central,733484800,6323.20,1068.40,5254.80\n\"THE SCHAEFER GROUP, INC.\",02-Central,779041000,5942.96,1476.72,4466.24\n\"THE SCHAEFER GROUP, INC.\",02-Central,779081000,5888.07,1726.24,4161.83\n\"THE SCHAEFER GROUP, INC.\",02-Central,734611100,5591.30,1123.19,4468.11\n\"THE SCHAEFER GROUP, INC.\",02-Central,764781000,4842.20,877.89,3964.31\n\"THE SCHAEFER GROUP, INC.\",02-Central,737912200,4429.81,950.73,3479.08\n\"THE SCHAEFER GROUP, INC.\",02-Central,764261000,4400.00,2518.27,1881.73\n\"THE SCHAEFER GROUP, INC.\",02-Central,764862000,4273.10,657.67,3615.43\n\"THE SCHAEFER GROUP, INC.\",02-Central,771320000,4155.65,684.50,3471.15\n\"THE SCHAEFER GROUP, INC.\",02-Central,727000140,4069.12,513.78,3555.34\n\"THE SCHAEFER GROUP, INC.\",02-Central,737301000,3568.41,1931.52,1636.89\n\"THE SCHAEFER GROUP, INC.\",02-Central,734830000,2785.80,544.92,2240.88\n\"THE SCHAEFER GROUP, INC.\",02-Central,739541200,2665.47,643.57,2021.90\n\"THE SCHAEFER GROUP, INC.\",02-Central,764601000,2591.65,554.94,2036.71\n\"THE SCHAEFER GROUP, INC.\",02-Central,734950000,2355.50,628.72,1726.78\n\"THE SCHAEFER GROUP, INC.\",02-Central,788100000,2165.50,539.27,1626.23\n\"THE SCHAEFER GROUP, INC.\",02-Central,737321000,2137.60,428.28,1709.32\n\"THE SCHAEFER GROUP, INC.\",02-Central,734990000,2119.80,410.82,1708.98\n\"THE SCHAEFER GROUP, INC.\",02-Central,764761000,1992.00,382.32,1609.68\n\"THE SCHAEFER GROUP, INC.\",02-Central,777901100,1868.37,982.06,886.31\n\"THE SCHAEFER GROUP, INC.\",02-Central,779001000,1756.23,443.13,1313.10\n\"THE SCHAEFER GROUP, INC.\",02-Central,761071000,1694.86,348.22,1346.64\n\"THE SCHAEFER GROUP, INC.\",02-Central,754565000,1683.36,675.96,1007.40\n\"THE SCHAEFER GROUP, INC.\",02-Central,734211200,1642.39,208.77,1433.62\n\"THE SCHAEFER GROUP, INC.\",02-Central,777590000,1524.42,737.16,787.26\n\"THE SCHAEFER GROUP, INC.\",02-Central,734351200,1488.52,147.24,1341.28\n\"THE SCHAEFER GROUP, INC.\",02-Central,734900000,1334.37,515.22,819.15\n\"THE SCHAEFER GROUP, INC.\",02-Central,770810000,1316.00,38.24,1277.76\n\"THE SCHAEFER GROUP, INC.\",02-Central,739641100,1229.04,806.40,422.64\n\"THE SCHAEFER GROUP, INC.\",02-Central,754905000,1004.76,161.06,843.70\n\"THE SCHAEFER GROUP, INC.\",02-Central,733141300,952.00,139.46,812.54\n\"THE SCHAEFER GROUP, INC.\",02-Central,764482000,889.60,173.30,716.30\n\"THE SCHAEFER GROUP, INC.\",02-Central,770220000,882.00,84.23,797.77\n\"THE SCHAEFER GROUP, INC.\",02-Central,734651200,823.14,148.00,675.14\n\"THE SCHAEFER GROUP, INC.\",02-Central,779201000,810.08,208.20,601.88\n\"THE SCHAEFER GROUP, INC.\",02-Central,733464600,719.00,195.20,523.80\n\"THE SCHAEFER GROUP, INC.\",02-Central,768241000,688.98,119.90,569.08\n\"THE SCHAEFER GROUP, INC.\",02-Central,770290000,675.30,79.65,595.65\n\"THE SCHAEFER GROUP, INC.\",02-Central,770300000,668.26,75.34,592.92\n\"THE SCHAEFER GROUP, INC.\",02-Central,770260000,591.30,67.63,523.67\n\"THE SCHAEFER GROUP, INC.\",02-Central,724261400,467.60,60.94,406.66\n\"THE SCHAEFER GROUP, INC.\",02-Central,741021000,460.22,50.84,409.38\n\"THE SCHAEFER GROUP, INC.\",02-Central,733444200,447.20,77.28,369.92\n\"THE SCHAEFER GROUP, INC.\",02-Central,764341000,371.60,71.96,299.64\n\"THE SCHAEFER GROUP, INC.\",02-Central,733511000,348.75,75.22,273.53\n\"THE SCHAEFER GROUP, INC.\",02-Central,770310000,338.40,47.28,291.12\n\"THE SCHAEFER GROUP, INC.\",02-Central,767990000,334.00,112.06,221.94\n\"THE SCHAEFER GROUP, INC.\",02-Central,733141200,324.00,59.97,264.03\n\"THE SCHAEFER GROUP, INC.\",02-Central,779221000,225.88,58.84,167.04\n\"THE SCHAEFER GROUP, INC.\",02-Central,764861000,146.45,25.35,121.10\n\"THE SCHAEFER GROUP, INC.\",02-Central,737902100,143.45,33.56,109.89\n\"THE SCHAEFER GROUP, INC.\",02-Central,779181000,131.82,41.26,90.56\n\"THE SCHAEFER GROUP, INC.\",02-Central,734414100,118.64,17.93,100.71\nTHERMAL PRODUCTS INC,02-Central,724003000,624481.00,192940.15,431540.85\nTHERMAL PRODUCTS INC,02-Central,735602000,613467.50,221246.44,392221.06\nTHERMAL PRODUCTS INC,02-Central,734990000,514870.40,139607.52,375262.88\nTHERMAL PRODUCTS INC,02-Central,764901000,361387.50,172172.49,189215.01\nTHERMAL PRODUCTS INC,02-Central,727063022,319374.80,100067.28,219307.52\nTHERMAL PRODUCTS INC,02-Central,764821000,313300.50,147395.44,165905.06\nTHERMAL PRODUCTS INC,02-Central,766542300,263740.80,139310.31,124430.49\nTHERMAL PRODUCTS INC,02-Central,732820000,253841.28,31236.74,222604.54\nTHERMAL PRODUCTS INC,02-Central,764901050,224289.50,103121.25,121168.25\nTHERMAL PRODUCTS INC,02-Central,727070000,214497.60,56115.91,158381.69\nTHERMAL PRODUCTS INC,02-Central,764521000,192987.75,87110.46,105877.29\nTHERMAL PRODUCTS INC,02-Central,740361900,191846.40,157548.72,34297.68\nTHERMAL PRODUCTS INC,02-Central,764781040,181402.50,71109.45,110293.05\nTHERMAL PRODUCTS INC,02-Central,734351200,176135.65,33516.29,142619.36\nTHERMAL PRODUCTS INC,02-Central,724007000,158505.09,50879.71,107625.38\nTHERMAL PRODUCTS INC,02-Central,734173000,157424.50,69485.79,87938.71\nTHERMAL PRODUCTS INC,02-Central,733403200,121704.00,25345.97,96358.03\nTHERMAL PRODUCTS INC,02-Central,768231000,110808.00,55491.65,55316.35\nTHERMAL PRODUCTS INC,02-Central,764221000,107134.00,59227.82,47906.18\nTHERMAL PRODUCTS INC,02-Central,735602110,106690.00,40147.19,66542.81\nTHERMAL PRODUCTS INC,02-Central,764906000,103590.00,49288.84,54301.16\nTHERMAL PRODUCTS INC,02-Central,733525600,86310.00,25232.68,61077.32\nTHERMAL PRODUCTS INC,02-Central,764862500,85693.00,41586.50,44106.50\nTHERMAL PRODUCTS INC,02-Central,764345052,83543.50,45394.08,38149.42\nTHERMAL PRODUCTS INC,02-Central,764822000,79607.00,37703.39,41903.61\nTHERMAL PRODUCTS INC,02-Central,733586400,75852.00,25537.21,50314.79\nTHERMAL PRODUCTS INC,02-Central,764802000,66450.00,22271.66,44178.34\nTHERMAL PRODUCTS INC,02-Central,766121000,61940.50,21819.64,40120.86\nTHERMAL PRODUCTS INC,02-Central,735750000,61089.60,36669.95,24419.65\nTHERMAL PRODUCTS INC,02-Central,733202000,59751.20,23294.20,36457.00\nTHERMAL PRODUCTS INC,02-Central,779987650,57407.04,14745.67,42661.37\nTHERMAL PRODUCTS INC,02-Central,737017020,53295.00,29872.22,23422.78\nTHERMAL PRODUCTS INC,02-Central,764762000,49240.00,9553.92,39686.08\nTHERMAL PRODUCTS INC,02-Central,764781000,49176.00,19626.15,29549.85\nTHERMAL PRODUCTS INC,02-Central,733586600,47664.00,15234.15,32429.85\nTHERMAL PRODUCTS INC,02-Central,764761000,38892.00,16666.50,22225.50\nTHERMAL PRODUCTS INC,02-Central,733201000,38703.00,15282.16,23420.84\nTHERMAL PRODUCTS INC,02-Central,764602000,36552.00,18542.73,18009.27\nTHERMAL PRODUCTS INC,02-Central,764342000,34443.00,19067.11,15375.89\nTHERMAL PRODUCTS INC,02-Central,739561100,33280.80,14929.22,18351.58\nTHERMAL PRODUCTS INC,02-Central,739741100,32352.96,22012.12,10340.84\nTHERMAL PRODUCTS INC,02-Central,732771000,30320.64,2899.79,27420.85\nTHERMAL PRODUCTS INC,02-Central,733151600,27288.00,9058.52,18229.48\nTHERMAL PRODUCTS INC,02-Central,740402402,27165.00,12342.60,14822.40\nTHERMAL PRODUCTS INC,02-Central,735752000,26426.40,15697.32,10729.08\nTHERMAL PRODUCTS INC,02-Central,764522000,26079.00,11619.02,14459.98\nTHERMAL PRODUCTS INC,02-Central,734311100,26034.32,5779.05,20255.27\nTHERMAL PRODUCTS INC,02-Central,733211050,25890.00,9877.29,16012.71\nTHERMAL PRODUCTS INC,02-Central,727000140,25059.60,10766.14,14293.46\nTHERMAL PRODUCTS INC,02-Central,764262000,25037.00,13346.23,11690.77\nTHERMAL PRODUCTS INC,02-Central,764261000,24655.00,13289.91,11365.09\nTHERMAL PRODUCTS INC,02-Central,718501000,24540.00,8163.63,16376.37\nTHERMAL PRODUCTS INC,02-Central,733151500,23956.00,8073.31,15882.69\nTHERMAL PRODUCTS INC,02-Central,733211000,23714.00,8672.49,15041.51\nTHERMAL PRODUCTS INC,02-Central,733586500,22190.40,7358.69,14831.71\nTHERMAL PRODUCTS INC,02-Central,733464600,19600.00,6409.21,13190.79\nTHERMAL PRODUCTS INC,02-Central,740283502,19575.60,3064.87,16510.73\nTHERMAL PRODUCTS INC,02-Central,764526010,18063.40,7303.72,10759.68\nTHERMAL PRODUCTS INC,02-Central,734211200,17279.16,3699.24,13579.92\nTHERMAL PRODUCTS INC,02-Central,777601000,16322.64,6549.54,9773.10\nTHERMAL PRODUCTS INC,02-Central,733403100,14766.20,2743.76,12022.44\nTHERMAL PRODUCTS INC,02-Central,733403300,14696.00,2889.92,11806.08\nTHERMAL PRODUCTS INC,02-Central,727063000,13841.20,4335.16,9506.04\nTHERMAL PRODUCTS INC,02-Central,764482000,12039.00,4876.52,7162.48\nTHERMAL PRODUCTS INC,02-Central,739781100,11816.64,8754.54,3062.10\nTHERMAL PRODUCTS INC,02-Central,738521000,11458.56,6885.66,4572.90\nTHERMAL PRODUCTS INC,02-Central,737015102,10350.00,5949.26,4400.74\nTHERMAL PRODUCTS INC,02-Central,733444300,9912.00,2703.08,7208.92\nTHERMAL PRODUCTS INC,02-Central,733721000,9616.00,5169.28,4446.72\nTHERMAL PRODUCTS INC,02-Central,741101000,9364.78,852.66,8512.12\nTHERMAL PRODUCTS INC,02-Central,733525500,9112.50,2638.06,6474.44\nTHERMAL PRODUCTS INC,02-Central,724311400,9072.70,1992.26,7080.44\nTHERMAL PRODUCTS INC,02-Central,739541100,9072.00,3265.11,5806.89\nTHERMAL PRODUCTS INC,02-Central,724261200,9036.00,4121.88,4914.12\nTHERMAL PRODUCTS INC,02-Central,739561200,8706.56,3968.07,4738.49\nTHERMAL PRODUCTS INC,02-Central,722413100,8483.20,966.96,7516.24\nTHERMAL PRODUCTS INC,02-Central,734414100,7854.52,1670.94,6183.58\nTHERMAL PRODUCTS INC,02-Central,734910000,7149.00,3072.00,4077.00\nTHERMAL PRODUCTS INC,02-Central,740382201,7020.00,4460.49,2559.51\nTHERMAL PRODUCTS INC,02-Central,793320074,6643.00,4067.70,2575.30\nTHERMAL PRODUCTS INC,02-Central,779988828,6616.80,4754.88,1861.92\nTHERMAL PRODUCTS INC,02-Central,733141300,6504.00,2075.99,4428.01\nTHERMAL PRODUCTS INC,02-Central,733832000,6012.00,3168.82,2843.18\nTHERMAL PRODUCTS INC,02-Central,764605000,5115.00,2531.34,2583.66\nTHERMAL PRODUCTS INC,02-Central,777642000,4681.00,1955.74,2725.26\nTHERMAL PRODUCTS INC,02-Central,739561000,4605.12,2142.14,2462.98\nTHERMAL PRODUCTS INC,02-Central,733725500,4480.00,2070.56,2409.44\nTHERMAL PRODUCTS INC,02-Central,730710500,3810.00,1758.69,2051.31\nTHERMAL PRODUCTS INC,02-Central,733671000,3752.77,584.08,3168.69\nTHERMAL PRODUCTS INC,02-Central,733505200,3670.00,969.05,2700.95\nTHERMAL PRODUCTS INC,02-Central,770800000,3408.00,102.17,3305.83\nTHERMAL PRODUCTS INC,02-Central,732429300,3350.00,1175.13,2174.87\nTHERMAL PRODUCTS INC,02-Central,777580000,3165.66,206.40,2959.26\nTHERMAL PRODUCTS INC,02-Central,768211000,2937.60,842.04,2095.56\nTHERMAL PRODUCTS INC,02-Central,734900000,2885.60,1968.64,916.96\nTHERMAL PRODUCTS INC,02-Central,733464500,2557.00,795.13,1761.87\nTHERMAL PRODUCTS INC,02-Central,724311200,2477.45,574.08,1903.37\nTHERMAL PRODUCTS INC,02-Central,733505300,2460.00,666.03,1793.97\nTHERMAL PRODUCTS INC,02-Central,764461006,2362.00,1147.88,1214.12\nTHERMAL PRODUCTS INC,02-Central,733141200,2328.00,641.76,1686.24\nTHERMAL PRODUCTS INC,02-Central,741061000,2223.25,132.27,2090.98\nTHERMAL PRODUCTS INC,02-Central,739521100,2123.52,480.24,1643.28\nTHERMAL PRODUCTS INC,02-Central,733423700,2112.00,525.17,1586.83\nTHERMAL PRODUCTS INC,02-Central,733423600,1964.80,494.24,1470.56\nTHERMAL PRODUCTS INC,02-Central,764481000,1903.00,770.75,1132.25\nTHERMAL PRODUCTS INC,02-Central,733511000,1877.50,659.66,1217.84\nTHERMAL PRODUCTS INC,02-Central,733484900,1872.00,489.98,1382.02\nTHERMAL PRODUCTS INC,02-Central,733525400,1847.50,555.03,1292.47\nTHERMAL PRODUCTS INC,02-Central,733403500,1677.00,472.01,1204.99\nTHERMAL PRODUCTS INC,02-Central,733490600,1568.00,477.67,1090.33\nTHERMAL PRODUCTS INC,02-Central,764564000,1568.00,653.90,914.10\nTHERMAL PRODUCTS INC,02-Central,734454200,1552.60,289.40,1263.20\nTHERMAL PRODUCTS INC,02-Central,737912200,1515.90,446.29,1069.61\nTHERMAL PRODUCTS INC,02-Central,793320000,1456.00,825.51,630.49\nTHERMAL PRODUCTS INC,02-Central,739541000,1342.08,505.24,836.84\nTHERMAL PRODUCTS INC,02-Central,768221000,1295.00,619.15,675.85\nTHERMAL PRODUCTS INC,02-Central,734851000,1290.87,274.20,1016.67\nTHERMAL PRODUCTS INC,02-Central,792624000,1080.00,399.72,680.28\nTHERMAL PRODUCTS INC,02-Central,777904301,1036.72,469.88,566.84\nTHERMAL PRODUCTS INC,02-Central,771130000,1030.20,70.35,959.85\nTHERMAL PRODUCTS INC,02-Central,739541200,803.84,321.78,482.06\nTHERMAL PRODUCTS INC,02-Central,792620000,786.00,260.22,525.78\nTHERMAL PRODUCTS INC,02-Central,793330000,760.00,208.12,551.88\nTHERMAL PRODUCTS INC,02-Central,764902000,695.00,322.08,372.92\nTHERMAL PRODUCTS INC,02-Central,724281200,680.00,154.62,525.38\nTHERMAL PRODUCTS INC,02-Central,777585000,674.28,42.69,631.59\nTHERMAL PRODUCTS INC,02-Central,764861000,654.00,311.73,342.27\nTHERMAL PRODUCTS INC,02-Central,737902100,573.30,203.22,370.08\nTHERMAL PRODUCTS INC,02-Central,793260074,529.20,285.81,243.39\nTHERMAL PRODUCTS INC,02-Central,739450000,401.76,260.24,141.52\nTHERMAL PRODUCTS INC,02-Central,733444200,320.00,69.38,250.62\nTHERMAL PRODUCTS INC,02-Central,766162000,234.75,77.76,156.99\nTHERMAL PRODUCTS INC,02-Central,734162000,228.00,158.40,69.60\nTHERMAL PRODUCTS INC,02-Central,734970000,217.92,102.61,115.31\nTHERMAL PRODUCTS INC,02-Central,733510000,210.00,79.51,130.49\nTHERMAL PRODUCTS INC,02-Central,740361701,208.80,318.65,-109.85\nTHERMAL STRUCTURES INC.,02-Central,732820000,22105.77,1848.83,20256.94\nTHERMAL STRUCTURES INC.,02-Central,768144200,4260.00,648.34,3611.66\nTHERMAL STRUCTURES,02-Central,732820000,33348.01,2625.71,30722.30\nTHERMAL STRUCTURES,02-Central,732771000,10123.68,774.60,9349.08\nTHERMAL STRUCTURES,02-Central,792730000,547.40,62.56,484.84\nTHERMAL STRUCTURES,02-Central,733423700,532.16,72.44,459.72\nTHERMAL TECHNOLOGY INC,02-Central,735602000,6506.50,2019.01,4487.49\nTHERMAL TECHNOLOGY INC,02-Central,735752000,1023.00,261.72,761.28\nTHERMALTEK INC,02-Central,777698063,19502.00,9470.06,10031.94\nTHERMALTEK INC,02-Central,779988828,9840.00,7098.11,2741.89\nTHERMALTEK INC,02-Central,779988813,7380.00,4413.78,2966.22\nTHERMALTEK INC,02-Central,779988846,1934.00,1197.34,736.66\nTHERMCRAFT INC,02-Central,727000030,116461.80,48224.58,68237.22\nTHERMCRAFT INC,02-Central,764781040,55282.50,5632.33,49650.17\nTHERMCRAFT INC,02-Central,727000042,53278.40,21650.32,31628.08\nTHERMCRAFT INC,02-Central,727063020,35694.00,14374.07,21319.93\nTHERMCRAFT INC,02-Central,738561000,10627.20,3103.42,7523.78\nTHERMCRAFT INC,02-Central,738401000,9846.35,5520.24,4326.11\nTHERMCRAFT INC,02-Central,738381000,5082.06,2302.64,2779.42\nTHERMCRAFT INC,02-Central,764762000,4687.20,645.09,4042.11\nTHERMCRAFT INC,02-Central,715000000,4424.00,1221.97,3202.03\nTHERMCRAFT INC,02-Central,733525500,3891.00,471.29,3419.71\nTHERMCRAFT INC,02-Central,738441000,2848.07,1568.25,1279.82\nTHERMCRAFT INC,02-Central,763991000,2170.80,1238.24,932.56\nTHERMCRAFT INC,02-Central,738361000,1827.36,1235.52,591.84\nTHERMCRAFT INC,02-Central,777601000,1629.18,320.44,1308.74\nTHERMCRAFT INC,02-Central,779988828,1555.00,792.48,762.52\nTHERMCRAFT INC,02-Central,740402301,875.00,219.36,655.64\nTHERMCRAFT INC,02-Central,740382101,626.40,296.37,330.03\nTHERMO FISHER SCIENTIFIC,02-Central,715000500,158226.88,39806.76,118420.12\nTHERMO FISHER SCIENTIFIC,02-Central,764481000,18702.00,4192.42,14509.58\nTHERMO-TEK LTD.,02-Central,734010000,905.00,180.10,724.90\nTHIBADO INC.,02-Central,739561200,863.87,149.00,714.87\nTHOMPSON ENAMEL,02-Central,764262000,865.00,45.31,819.69\nTHOMPSON IG,02-Central,738361000,1252.08,617.76,634.32\nTHOMPSON IG,02-Central,738521000,1112.96,597.12,515.84\nTHOMPSON IG,02-Central,734311100,808.72,70.60,738.12\nTHOMPSON IG,02-Central,734611100,189.44,27.18,162.26\nTHORPE PRODUCTS,02-Central,735602000,52544.00,16568.92,35975.08\nTHORPE PRODUCTS,02-Central,768111220,8532.00,4272.35,4259.65\nTHORPE PRODUCTS,02-Central,768121012,8112.00,3452.00,4660.00\nTHORPE PRODUCTS,02-Central,771002000,1468.00,38.59,1429.41\nTHORPE TECHNOLOGIES INC.,02-Central,770430000,1327.50,88.55,1238.95\nTHORPE TECHNOLOGIES INC.,02-Central,770340000,1025.25,101.72,923.53\nTHORPE TECHNOLOGIES INC.,02-Central,770330000,1015.00,96.65,918.35\nTIGHITCO INC.,02-Central,791107600,476434.80,247529.26,228905.54\nTIGHITCO INC.,02-Central,791107850,260962.02,78958.24,182003.78\nTIGHITCO INC.,02-Central,791107200,205808.94,76872.61,128936.33\nTIGHITCO INC.,02-Central,724281410,62655.00,29215.97,33439.03\nTIGHITCO INC.,02-Central,724261400,30795.00,10489.95,20305.05\nTIGHITCO INC.,02-Central,791107400,16957.62,6742.09,10215.53\nTIGHITCO INC.,02-Central,724101400,2486.00,932.14,1553.86\nTIGHITCO INC.,02-Central,791004100,1734.40,1027.42,706.98\nTIGHITCO INC.,02-Central,791104675,1153.67,480.25,673.42\nTIGHITCO INC.,02-Central,724311400,622.00,376.76,245.24\nTIGHITCO INC.,02-Central,733211000,327.50,47.39,280.11\nTIGHITCO INC.,02-Central,792702000,303.75,96.60,207.15\nTINKER OMEGA,02-Central,734311100,404.36,35.30,369.06\nTINKER OMEGA,02-Central,734211200,243.46,23.38,220.08\nTOLEDO ENGINEERING CO INC,02-Central,777761000,810.00,658.80,151.20\nTOLEDO ENGINEERING CO INC,02-Central,792620000,427.60,86.74,340.86\nTOLEDO ENGINEERING CO INC,02-Central,793330000,230.00,52.03,177.97\nTOSOH SMD,02-Central,766121000,2625.00,81.75,2543.25\nTRACHTE BUILDING SYSTEMS,02-Central,764601010,139008.80,25897.62,113111.18\nTRACHTE BUILDING SYSTEMS,02-Central,764601000,34479.48,7301.95,27177.53\nTRAEGERBROTHERS,02-Central,764861000,9412.00,659.08,8752.92\nTRAEGERBROTHERS,02-Central,737912200,6909.00,1273.26,5635.74\nTRAEGERBROTHERS,02-Central,734711100,308.92,54.10,254.82\nTRANSCO PRODUCTS INC,02-Central,739741100,4530.24,1205.14,3325.10\nTRANSCO PRODUCTS INC,02-Central,732429300,1310.00,94.02,1215.98\nTRANSCO PRODUCTS INC,02-Central,793330000,863.00,52.02,810.98\nTRANSCO PRODUCTS INC,02-Central,793346074,474.00,74.14,399.86\nTREMCO INC,02-Central,768217310,17000.00,2874.46,14125.54\nTREMCO INC,02-Central,768217320,17000.00,4113.35,12886.65\nTRIDENT FLUID POWER,02-Central,739761100,464.16,207.60,256.56\nTRIPLEX INC,02-Central,732820000,3669.12,168.08,3501.04\nTRIPLEX INC,02-Central,766081010,3347.00,105.39,3241.61\nTRIPLEX INC,02-Central,770580000,1598.00,89.66,1508.34\nTRISTATE INDUSTRIAL,02-Central,739561100,3870.38,773.11,3097.27\nTRISTATE INDUSTRIAL,02-Central,734910000,2998.80,626.60,2372.20\nTRISTATE INDUSTRIAL,02-Central,764262000,1230.40,135.24,1095.16\nTRISTATE INDUSTRIAL,02-Central,738561000,802.94,248.27,554.67\nTRISTATE INDUSTRIAL,02-Central,733671000,722.74,119.66,603.08\nTRISTATE INDUSTRIAL,02-Central,777601000,543.30,144.04,399.26\nTROXLER ELECTRONIC,02-Central,734311100,381.48,33.70,347.78\nTWIN COMMANDER AIRCRAFT,02-Central,770200000,666.00,42.41,623.59\nU S DEPARTMENT OF ENERGY,02-Central,764861000,1923.00,76.05,1846.95\nU S MARINE CORPS.,02-Central,777601000,386.72,72.02,314.70\nUNIMIN CORP.,02-Central,754465000,6249.54,1364.71,4884.83\nUNIMIN CORP.,02-Central,754045000,3950.64,1096.89,2853.75\nUNION TANK CAR,02-Central,764643054,4467.24,2084.66,2382.58\nUNION TANK CAR,02-Central,764643052,3235.95,1501.92,1734.03\nUNITED SEAL & RUBBER CO,02-Central,733403300,2323.20,215.46,2107.74\nUNIV. of NORTH DAKOTA,02-Central,764261000,424.00,22.90,401.10\nUNIVERSAL DYNAMICS INC.,02-Central,733741000,816.00,238.13,577.87\nUNIVERSAL REFRACTORIES,02-Central,726000240,126770.40,46592.78,80177.62\nUNIVERSAL REFRACTORIES,02-Central,726020005,110613.60,26962.98,83650.62\nUNIVERSITY ILLINOIS,02-Central,738561000,582.40,124.14,458.26\nUNIVERSITY OF ALABAMA,02-Central,792730000,547.40,62.56,484.84\nUNIVERSITY OF FLORIDA,02-Central,764762000,508.00,30.29,477.71\nUNIVERSITY OF MINN,02-Central,764601000,326.00,13.29,312.71\nUNIVERSITY OF NOTRE DAME,02-Central,764521000,320.00,13.30,306.70\nUNIVERSITY OF WISCONSIN,02-Central,793260074,328.50,39.17,289.33\nUPSTATE REFRACTORY SERVIC,02-Central,777601000,223.71,72.02,151.69\nVACUFORM INC.,02-Central,726000062,1211838.00,944838.76,266999.24\nVACUFORM INC.,02-Central,727063007,300081.00,221460.21,78620.79\nVACUFORM INC.,02-Central,764521000,133222.00,54468.93,78753.07\nVACUFORM INC.,02-Central,768022000,115520.00,64239.41,51280.59\nVACUFORM INC.,02-Central,764341000,66602.00,37208.36,29393.64\nVACUFORM INC.,02-Central,727000060,62880.00,52418.38,10461.62\nVACUFORM INC.,02-Central,793000000,43274.40,21804.26,21470.14\nVACUFORM INC.,02-Central,764347010,38884.00,18700.95,20183.05\nVACUFORM INC.,02-Central,764521073,29348.00,28380.00,968.00\nVACUFORM INC.,02-Central,764521061,21344.00,20448.00,896.00\nVACUFORM INC.,02-Central,739951051,17157.92,12256.36,4901.56\nVACUFORM INC.,02-Central,766166000,9840.00,4449.48,5390.52\nVACUFORM INC.,02-Central,764481000,6480.00,3596.55,2883.45\nVACUFORM INC.,02-Central,726000240,2700.00,2091.85,608.15\nVACUFORM INC.,02-Central,764342000,2656.00,1489.52,1166.48\nVACUFORM INC.,02-Central,734851000,1352.33,570.90,781.43\nVACUFORM INC.,02-Central,766122000,1086.00,345.15,740.85\nVACUFORM INC.,02-Central,768211000,500.00,467.78,32.22\nVACUFORM INC.,02-Central,764522000,200.10,80.16,119.94\nVACUUM OVEN,02-Central,764481000,346.00,16.94,329.06\nVAN PACKER COMPANY,02-Central,768540000,9055.20,2565.63,6489.57\nVAN PACKER COMPANY,02-Central,768530000,8962.20,2562.32,6399.88\nVAN PACKER COMPANY,02-Central,768511000,8940.00,2563.05,6376.95\nVAN PACKER COMPANY,02-Central,764522000,666.00,26.72,639.28\nVAN PACKER COMPANY,02-Central,779900912,232.00,0.00,232.00\nVAN PACKER COMPANY,02-Central,779900911,116.00,0.00,116.00\nVEITSCH-RADEX AMERICA INC,02-Central,733182300,99316.80,34104.20,65212.60\nVEITSCH-RADEX AMERICA INC,02-Central,764821000,819.00,322.51,496.49\nVEITSCH-RADEX AMERICA INC,02-Central,764781000,318.00,118.32,199.68\nVESUVIUS USA,02-Central,726020033,1841764.80,1483084.98,358679.82\nVESUVIUS USA,02-Central,726020009,1061860.80,875649.81,186210.99\nVESUVIUS USA,02-Central,726020023,225735.60,179382.66,46352.94\nVESUVIUS USA,02-Central,726000240,108527.04,75478.27,33048.77\nVESUVIUS USA,02-Central,793000000,21572.40,11281.38,10291.02\nVESUVIUS USA,02-Central,777904402,1098.00,745.52,352.48\nVESUVIUS USA,02-Central,793060300,392.40,198.83,193.57\nVIRGINIA TECH,02-Central,733464500,298.00,29.52,268.48\nVIRGINIA-CAROLINA REF INC,02-Central,762134000,232819.16,99634.79,133184.37\nVIRGINIA-CAROLINA REF INC,02-Central,762114000,36000.00,17985.68,18014.32\nVIRGINIA-CAROLINA REF INC,02-Central,718500000,4114.56,1288.26,2826.30\nVIRGINIA-CAROLINA REF INC,02-Central,764821000,115.00,30.72,84.28\nVSP TECHNOLOGIES,02-Central,770280000,4043.75,310.89,3732.86\nWAHL REFRACTORIES,02-Central,733510000,9456.00,3401.46,6054.54\nWAHL REFRACTORIES,02-Central,777901100,5280.24,1870.60,3409.64\nWAHL REFRACTORIES,02-Central,777902300,2640.12,982.01,1658.11\nWAHL REFRACTORIES,02-Central,733525500,1319.50,130.06,1189.44\nWATCO MECHANICAL SERVICES,02-Central,764643054,30406.32,3713.98,26692.34\nWATCO MECHANICAL SERVICES,02-Central,764643100,3049.20,1059.90,1989.30\nWATCO MECHANICAL,02-Central,764643054,5715.36,698.28,5017.08\nWATCO MECHANICAL,02-Central,764706120,2222.64,326.91,1895.73\nWATCO MECHANICAL,02-Central,764643052,1372.14,166.88,1205.26\n\"WELCO ELECTRONICS, INC.\",02-Central,770720000,3528.00,108.47,3419.53\n\"WELCO ELECTRONICS, INC.\",02-Central,771500000,2911.50,165.39,2746.11\nWESCO DISTRIBUTION INC.,02-Central,734890000,2769.00,623.07,2145.93\nWESCO DISTRIBUTION INC.,02-Central,768222000,1610.00,139.92,1470.08\nWESTERN INDUSTRIAL CERAMI,02-Central,739741100,16803.84,5258.78,11545.06\nWESTERN INDUSTRIAL CERAMI,02-Central,780650000,3682.80,1607.94,2074.86\nWESTERN INDUSTRIAL CERAMI,02-Central,780630000,3192.28,1541.26,1651.02\nWESTERN INDUSTRIAL CERAMI,02-Central,767990000,1848.96,679.42,1169.54\nWESTERN INDUSTRIAL CERAMI,02-Central,770810000,657.90,19.12,638.78\nWESTERN INDUSTRIAL CERAMI,02-Central,734211200,371.99,46.75,325.24\nWESTERN INDUSTRIAL CERAMI,02-Central,764902000,119.00,63.86,55.14\nWFS LTD.,02-Central,766121000,1750.00,54.50,1695.50\nWFS LTD.,02-Central,734910000,1748.50,260.23,1488.27\nWFS LTD.,02-Central,733151500,450.00,47.69,402.31\n\"WHETSTONE TECHNOLOGY, LLC\",02-Central,718501000,1768.00,269.38,1498.62\nWHIPMIX,02-Central,777642000,135.00,31.68,103.32\nWICKS AIRCRAFT SUPPLY,02-Central,733525500,752.50,70.39,682.11\nWILLIAM BONNELL CO. INC.,02-Central,733464500,596.00,59.04,536.96\nWILLIAMS INDUSTRIAL SERVI,02-Central,754087000,57127.92,26276.42,30851.50\nWILLIAMS INDUSTRIAL SERVI,02-Central,754067000,24933.00,12524.03,12408.97\nWILLIAMS INDUSTRIAL SERVI,02-Central,754327000,21562.23,9517.71,12044.52\nWILLIAMS INDUSTRIAL SERVI,02-Central,779957815,20976.24,10411.72,10564.52\nWILLIAMS INDUSTRIAL SERVI,02-Central,754107000,20880.00,8799.58,12080.42\nWILLIAMS INDUSTRIAL SERVI,02-Central,754127000,12216.36,5808.00,6408.36\nWILLIAMS INDUSTRIAL SERVI,02-Central,754077000,10839.00,5138.53,5700.47\nWILLIAMS INDUSTRIAL SERVI,02-Central,770372000,10796.80,706.61,10090.19\nWILLIAMS INDUSTRIAL SERVI,02-Central,764821000,8520.75,923.88,7596.87\nWILLIAMS INDUSTRIAL SERVI,02-Central,742641500,8120.00,1207.02,6912.98\nWILLIAMS INDUSTRIAL SERVI,02-Central,764901000,6840.00,774.92,6065.08\nWILLIAMS INDUSTRIAL SERVI,02-Central,770510000,6008.75,372.50,5636.25\nWILLIAMS INDUSTRIAL SERVI,02-Central,733403200,5672.70,627.19,5045.51\nWILLIAMS INDUSTRIAL SERVI,02-Central,770380000,5349.60,379.43,4970.17\nWILLIAMS INDUSTRIAL SERVI,02-Central,770600000,5275.56,367.58,4907.98\nWILLIAMS INDUSTRIAL SERVI,02-Central,754317000,4712.40,2018.90,2693.50\nWILLIAMS INDUSTRIAL SERVI,02-Central,734851000,4675.56,1096.78,3578.78\nWILLIAMS INDUSTRIAL SERVI,02-Central,754307000,4343.72,1976.52,2367.20\nWILLIAMS INDUSTRIAL SERVI,02-Central,739681200,4004.64,1516.92,2487.72\nWILLIAMS INDUSTRIAL SERVI,02-Central,754287000,3971.20,1669.96,2301.24\nWILLIAMS INDUSTRIAL SERVI,02-Central,779951269,3450.48,1685.00,1765.48\nWILLIAMS INDUSTRIAL SERVI,02-Central,754097000,3422.72,1559.05,1863.67\nWILLIAMS INDUSTRIAL SERVI,02-Central,727000025,3417.00,438.56,2978.44\nWILLIAMS INDUSTRIAL SERVI,02-Central,770560000,3286.20,261.68,3024.52\nWILLIAMS INDUSTRIAL SERVI,02-Central,779957702,3153.92,2359.60,794.32\nWILLIAMS INDUSTRIAL SERVI,02-Central,770580000,2228.85,174.39,2054.46\nWILLIAMS INDUSTRIAL SERVI,02-Central,739561000,2052.00,511.32,1540.68\nWILLIAMS INDUSTRIAL SERVI,02-Central,754267000,1756.80,809.76,947.04\nWILLIAMS INDUSTRIAL SERVI,02-Central,753987000,1620.96,866.36,754.60\nWILLIAMS INDUSTRIAL SERVI,02-Central,734611100,1552.02,350.48,1201.54\nWILLIAMS INDUSTRIAL SERVI,02-Central,754047000,1431.00,876.14,554.86\nWILLIAMS INDUSTRIAL SERVI,02-Central,770540000,1264.38,89.80,1174.58\nWILLIAMS INDUSTRIAL SERVI,02-Central,779957703,1150.60,781.22,369.38\nWILLIAMS INDUSTRIAL SERVI,02-Central,739564300,1066.82,247.97,818.85\nWILLIAMS INDUSTRIAL SERVI,02-Central,733403100,999.75,122.60,877.15\nWILLIAMS INDUSTRIAL SERVI,02-Central,753997000,933.52,417.54,515.98\nWILLIAMS INDUSTRIAL SERVI,02-Central,779958694,850.40,412.57,437.83\nWILLIAMS INDUSTRIAL SERVI,02-Central,754297000,771.60,313.46,458.14\nWILLIAMS INDUSTRIAL SERVI,02-Central,727000140,726.00,83.57,642.43\nWILLIAMS INDUSTRIAL SERVI,02-Central,754187000,589.44,288.50,300.94\nWILLIAMS INDUSTRIAL SERVI,02-Central,770210000,560.00,60.64,499.36\nWILLIAMS INDUSTRIAL SERVI,02-Central,764521000,319.50,39.81,279.69\nWILLIAMS INDUSTRIAL SERVI,02-Central,770220000,303.60,27.35,276.25\nWILLIAMS INDUSTRIAL SERVI,02-Central,734651200,198.50,37.00,161.50\nWILLIAMS INDUSTRIAL SERVI,02-Central,754247000,164.16,91.37,72.79\nWILLIAMS INDUSTRIAL SERVI,02-Central,754197000,159.68,70.99,88.69\nWILLIAMS INDUSTRIAL SERVI,02-Central,753967000,158.76,78.39,80.37\nWISCONSIN ALUMINUM,02-Central,777601000,1980.20,333.65,1646.55\nWOLSELEY INDUSTRIAL GROUP,02-Central,770200000,2979.00,210.45,2768.55\nWOLSELEY INDUSTRIAL GROUP,02-Central,737871000,2937.12,279.74,2657.38\nWOLSELEY INDUSTRIAL GROUP,02-Central,739541000,587.52,90.50,497.02\nWOLSELEY INDUSTRIAL GROUP,02-Central,739561000,492.48,86.19,406.29\nWOLSELEY INDUSTRIAL GROUP,02-Central,739450000,482.04,125.58,356.46\n\"WOODSAGE INDUSTRIES, LLC\",02-Central,771170000,3900.00,1094.68,2805.32\n\"WOODSAGE INDUSTRIES, LLC\",02-Central,771172000,600.00,167.93,432.07\nXPEDX,02-Central,740283502,13134.00,1989.86,11144.14\nZAR-TECH,02-Central,733151500,3766.00,667.69,3098.31\nZAR-TECH,02-Central,764902000,1336.50,483.12,853.38\nZAR-TECH,02-Central,740382101,871.20,505.66,365.54\nHARBISON-WALKER REFR.,05-House,764821020,232020.00,113512.95,118507.05\nHARBISON-WALKER REFR.,05-House,779989340,220320.00,153833.40,66486.60\nHARBISON-WALKER REFR.,05-House,764901056,204078.00,102671.60,101406.40\nHARBISON-WALKER REFR.,05-House,764521012,134010.00,73994.81,60015.19\nHARBISON-WALKER REFR.,05-House,766006116,116046.01,73307.06,42738.95\nHARBISON-WALKER REFR.,05-House,764864020,111888.00,59648.16,52239.84\nHARBISON-WALKER REFR.,05-House,764605012,76356.00,40571.52,35784.48\nHARBISON-WALKER REFR.,05-House,764901012,70625.00,35748.52,34876.48\nHARBISON-WALKER REFR.,05-House,764821400,67442.40,33444.68,33997.72\nHARBISON-WALKER REFR.,05-House,768242510,55796.00,24223.51,31572.49\nHARBISON-WALKER REFR.,05-House,766122500,54051.84,34642.22,19409.62\nHARBISON-WALKER REFR.,05-House,764906200,48816.00,24365.50,24450.50\nHARBISON-WALKER REFR.,05-House,768221012,35085.00,15599.51,19485.49\nHARBISON-WALKER REFR.,05-House,764528300,32901.12,17852.00,15049.12\nHARBISON-WALKER REFR.,05-House,764785122,32490.00,20091.24,12398.76\nHARBISON-WALKER REFR.,05-House,766001200,31320.00,24214.68,7105.32\nHARBISON-WALKER REFR.,05-House,779989336,29664.00,25212.44,4451.56\nHARBISON-WALKER REFR.,05-House,764601012,29325.00,15791.86,13533.14\nHARBISON-WALKER REFR.,05-House,764349000,26180.00,16054.61,10125.39\nHARBISON-WALKER REFR.,05-House,764261120,23870.00,13806.62,10063.38\nHARBISON-WALKER REFR.,05-House,764829220,21888.00,10783.29,11104.71\nHARBISON-WALKER REFR.,05-House,764901050,21696.00,12130.39,9565.61\nHARBISON-WALKER REFR.,05-House,764561020,19392.00,9790.40,9601.60\nHARBISON-WALKER REFR.,05-House,764345061,15912.00,9047.17,6864.83\nHARBISON-WALKER REFR.,05-House,764602512,15300.00,8117.31,7182.69\nHARBISON-WALKER REFR.,05-House,764901000,15255.00,8647.70,6607.30\nHARBISON-WALKER REFR.,05-House,768121010,13370.00,5329.90,8040.10\nHARBISON-WALKER REFR.,05-House,764862600,12144.00,5758.99,6385.01\nHARBISON-WALKER REFR.,05-House,764521000,11340.00,7166.37,4173.63\nHARBISON-WALKER REFR.,05-House,764481220,10950.00,7623.68,3326.32\nHARBISON-WALKER REFR.,05-House,766046000,9828.00,5618.93,4209.07\nHARBISON-WALKER REFR.,05-House,766162300,9100.00,6294.53,2805.47\nHARBISON-WALKER REFR.,05-House,779989420,8280.00,5396.86,2883.14\nHARBISON-WALKER REFR.,05-House,764862400,8064.00,4266.45,3797.55\nHARBISON-WALKER REFR.,05-House,764822000,7353.00,4098.36,3254.64\nHARBISON-WALKER REFR.,05-House,764781300,5544.00,3469.65,2074.35\nHARBISON-WALKER REFR.,05-House,764563100,5328.00,2482.70,2845.30\nHARBISON-WALKER REFR.,05-House,764902301,4972.00,2541.80,2430.20\nHARBISON-WALKER REFR.,05-House,764785130,4480.00,2760.76,1719.24\nHARBISON-WALKER REFR.,05-House,764821300,3420.00,1682.88,1737.12\nHARBISON-WALKER REFR.,05-House,766081200,3200.00,1519.32,1680.68\nHARBISON-WALKER REFR.,05-House,764803000,3150.00,1661.96,1488.04\nHARBISON-WALKER REFR.,05-House,764861300,2520.00,1352.90,1167.10\nHARBISON-WALKER REFR.,05-House,766081016,2304.00,1092.47,1211.53\nHARBISON-WALKER REFR.,05-House,768211000,70.00,23.39,46.61\nHARBISON-WALKER REFR.,05-House,768111000,44.00,15.38,28.62\nA. R. THOMSON LTD.,07-Canada Direct,770210000,460.00,31.52,428.48\nALLIANCE REFRACTORIES LTD,07-Canada Direct,754864016,491413.86,377394.44,114019.42\nALLIANCE REFRACTORIES LTD,07-Canada Direct,768221000,411732.00,159990.19,251741.81\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779958212,200475.00,96348.62,104126.38\nALLIANCE REFRACTORIES LTD,07-Canada Direct,766121000,199800.00,116694.75,83105.25\nALLIANCE REFRACTORIES LTD,07-Canada Direct,754844006,192535.20,133772.19,58763.01\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764821000,109520.00,45851.87,63668.13\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764901000,109520.00,47651.16,61868.84\nALLIANCE REFRACTORIES LTD,07-Canada Direct,793330000,98919.00,19866.01,79052.99\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779957821,83174.40,53602.44,29571.96\nALLIANCE REFRACTORIES LTD,07-Canada Direct,754884006,72111.60,43737.31,28374.29\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764601000,67160.00,32320.98,34839.02\nALLIANCE REFRACTORIES LTD,07-Canada Direct,754844005,62762.70,43166.04,19596.66\nALLIANCE REFRACTORIES LTD,07-Canada Direct,754904006,53410.50,27415.80,25994.70\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779959405,43036.00,44094.36,-1058.36\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779958205,37350.00,18889.20,18460.80\nALLIANCE REFRACTORIES LTD,07-Canada Direct,718500000,34160.64,10960.16,23200.48\nALLIANCE REFRACTORIES LTD,07-Canada Direct,771500000,29436.75,3612.13,25824.62\nALLIANCE REFRACTORIES LTD,07-Canada Direct,718501000,28288.00,8642.05,19645.95\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779959402,27993.60,27528.60,465.00\nALLIANCE REFRACTORIES LTD,07-Canada Direct,733211000,27060.00,10658.09,16401.91\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779958200,26298.00,11825.50,14472.50\nALLIANCE REFRACTORIES LTD,07-Canada Direct,754924013,23424.00,14577.03,8846.97\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764901073,20720.00,20412.00,308.00\nALLIANCE REFRACTORIES LTD,07-Canada Direct,795551000,20240.00,14233.08,6006.92\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779959403,19640.00,16458.84,3181.16\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779980291,18101.16,13629.55,4471.61\nALLIANCE REFRACTORIES LTD,07-Canada Direct,766081010,17920.00,9610.09,8309.91\nALLIANCE REFRACTORIES LTD,07-Canada Direct,768041000,16520.00,6998.83,9521.17\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764481000,13068.00,6871.85,6196.15\nALLIANCE REFRACTORIES LTD,07-Canada Direct,767970140,11875.00,4612.36,7262.64\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779957176,11815.20,10542.14,1273.06\nALLIANCE REFRACTORIES LTD,07-Canada Direct,768231000,10956.00,3481.07,7474.93\nALLIANCE REFRACTORIES LTD,07-Canada Direct,718551000,10608.00,3265.44,7342.56\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764221000,9720.00,6071.86,3648.14\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779959412,9540.00,4508.63,5031.37\nALLIANCE REFRACTORIES LTD,07-Canada Direct,771404000,9192.75,558.12,8634.63\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764821073,8880.00,8748.00,132.00\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779959406,8808.80,9123.56,-314.76\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779957053,8800.00,6134.68,2665.32\nALLIANCE REFRACTORIES LTD,07-Canada Direct,793260074,7960.00,3133.48,4826.52\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779956945,6612.00,5187.55,1424.45\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764521000,6600.00,3191.29,3408.71\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779957820,5856.00,4560.16,1295.84\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779958210,5662.80,4760.15,902.65\nALLIANCE REFRACTORIES LTD,07-Canada Direct,771502000,5607.00,687.06,4919.94\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779959401,5077.44,4837.88,239.56\nALLIANCE REFRACTORIES LTD,07-Canada Direct,727000025,4789.50,2901.73,1887.77\nALLIANCE REFRACTORIES LTD,07-Canada Direct,768121000,4740.00,1761.36,2978.64\nALLIANCE REFRACTORIES LTD,07-Canada Direct,734651200,3972.00,1763.90,2208.10\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779958211,3938.40,3574.84,363.56\nALLIANCE REFRACTORIES LTD,07-Canada Direct,733464500,3920.00,1180.77,2739.23\nALLIANCE REFRACTORIES LTD,07-Canada Direct,776131000,2860.00,2602.20,257.80\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779957054,2700.00,1919.82,780.18\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779956598,2643.84,1719.54,924.30\nALLIANCE REFRACTORIES LTD,07-Canada Direct,779958201,2629.60,1374.32,1255.28\nALLIANCE REFRACTORIES LTD,07-Canada Direct,770722000,2476.00,171.52,2304.48\nALLIANCE REFRACTORIES LTD,07-Canada Direct,734311100,2432.40,529.52,1902.88\nALLIANCE REFRACTORIES LTD,07-Canada Direct,777601000,1999.84,1072.97,926.87\nALLIANCE REFRACTORIES LTD,07-Canada Direct,777642000,1984.00,1013.44,970.56\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764341000,1640.00,917.40,722.60\nALLIANCE REFRACTORIES LTD,07-Canada Direct,771152000,1291.00,90.76,1200.24\nALLIANCE REFRACTORIES LTD,07-Canada Direct,770850000,1238.00,94.48,1143.52\nALLIANCE REFRACTORIES LTD,07-Canada Direct,771000000,1232.00,82.45,1149.55\nALLIANCE REFRACTORIES LTD,07-Canada Direct,776101000,1180.00,914.20,265.80\nALLIANCE REFRACTORIES LTD,07-Canada Direct,734910000,1110.00,513.48,596.52\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764861000,1070.00,507.00,563.00\nALLIANCE REFRACTORIES LTD,07-Canada Direct,771100000,719.00,44.75,674.25\nALLIANCE REFRACTORIES LTD,07-Canada Direct,771102000,719.00,45.96,673.04\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764762000,672.00,363.51,308.49\nALLIANCE REFRACTORIES LTD,07-Canada Direct,733211050,492.00,198.78,293.22\nALLIANCE REFRACTORIES LTD,07-Canada Direct,734890000,453.56,198.96,254.60\nALLIANCE REFRACTORIES LTD,07-Canada Direct,733423700,249.60,72.44,177.16\nALLIANCE REFRACTORIES LTD,07-Canada Direct,733444200,204.00,69.38,134.62\nALLIANCE REFRACTORIES LTD,07-Canada Direct,764342000,168.00,91.56,76.44\nALLIANCE REFRACTORIES LTD,07-Canada Direct,733742000,136.00,81.75,54.25\nALSIPS BUILDING PRODUCTS,07-Canada Direct,735602000,63680.00,10981.19,52698.81\nALSIPS BUILDING PRODUCTS,07-Canada Direct,735890000,20700.00,4773.62,15926.38\nAMERICAN RAILCAR,07-Canada Direct,764706120,2346.12,588.90,1757.22\nAPPLIED INDUSTRIAL,07-Canada Direct,777601000,386.72,72.02,314.70\nASHCON,07-Canada Direct,777601000,3726.60,587.95,3138.65\nATLAS FLUID,07-Canada Direct,739541100,685.44,104.83,580.61\nAVANTI AEROSPACE &,07-Canada Direct,770830000,2122.00,71.74,2050.26\nAVANTI AEROSPACE &,07-Canada Direct,724003000,1391.00,163.64,1227.36\nAVANTI AEROSPACE &,07-Canada Direct,734311100,404.36,35.30,369.06\nAVMAX SPARES,07-Canada Direct,724121200,334.00,89.77,244.23\nBROCK WHITE CONSTRUCTION,07-Canada Direct,735890000,287.50,49.73,237.77\nBROCKWHITE PRINCE GEORGE,07-Canada Direct,764821000,2136.00,375.72,1760.28\nBROCKWHITE PRINCE GEORGE,07-Canada Direct,764901000,1408.00,256.23,1151.77\nCAMBRIDGE PRO FAB INC.,07-Canada Direct,768251210,212672.25,115835.03,96837.22\nCAMBRIDGE PRO FAB INC.,07-Canada Direct,768141000,65855.00,32697.61,33157.39\nCAMBRIDGE PRO FAB INC.,07-Canada Direct,766594001,12430.00,6333.51,6096.49\nCAMBRIDGE PRO FAB INC.,07-Canada Direct,764901000,11016.00,5188.62,5827.38\nCAMBRIDGE PRO FAB INC.,07-Canada Direct,766594000,7458.00,4180.84,3277.16\nCAMBRIDGE PRO FAB INC.,07-Canada Direct,766481000,3000.00,1559.97,1440.03\nCENTURYVALLEN,07-Canada Direct,734311100,381.48,33.70,347.78\nCLAYBURN REFRACTORIES LTD,07-Canada Direct,779958209,26785.30,6696.46,20088.84\nFIBRECAST INC.,07-Canada Direct,792700000,647192.00,411296.51,235895.49\nFIBRECAST INC.,07-Canada Direct,726000240,412201.20,211353.44,200847.76\nFIBRECAST INC.,07-Canada Direct,726020019,260201.00,129375.64,130825.36\nFIBRECAST INC.,07-Canada Direct,764821000,175854.00,68816.51,107037.49\nFIBRECAST INC.,07-Canada Direct,764901050,153022.00,62219.46,90802.54\nFIBRECAST INC.,07-Canada Direct,726020015,143592.00,73663.82,69928.18\nFIBRECAST INC.,07-Canada Direct,754447000,131349.60,53512.89,77836.71\nFIBRECAST INC.,07-Canada Direct,768221000,107836.50,39985.49,67851.01\nFIBRECAST INC.,07-Canada Direct,715000000,89236.25,38120.79,51115.46\nFIBRECAST INC.,07-Canada Direct,763991000,79271.20,72806.81,6464.39\nFIBRECAST INC.,07-Canada Direct,768021000,77606.00,37999.67,39606.33\nFIBRECAST INC.,07-Canada Direct,768141000,66240.00,27006.99,39233.01\nFIBRECAST INC.,07-Canada Direct,733211000,58636.00,22105.42,36530.58\nFIBRECAST INC.,07-Canada Direct,764521000,52001.50,21780.18,30221.32\nFIBRECAST INC.,07-Canada Direct,768751000,42672.40,21585.13,21087.27\nFIBRECAST INC.,07-Canada Direct,793330000,41661.00,9061.50,32599.50\nFIBRECAST INC.,07-Canada Direct,764822000,40047.00,15897.88,24149.12\nFIBRECAST INC.,07-Canada Direct,764901000,39687.00,16589.84,23097.16\nFIBRECAST INC.,07-Canada Direct,740063000,37100.28,14199.26,22901.02\nFIBRECAST INC.,07-Canada Direct,768241000,35130.50,10583.13,24547.37\nFIBRECAST INC.,07-Canada Direct,777902700,34790.14,15927.42,18862.72\nFIBRECAST INC.,07-Canada Direct,733383500,34324.40,6235.97,28088.43\nFIBRECAST INC.,07-Canada Direct,733444210,33180.00,7791.53,25388.47\nFIBRECAST INC.,07-Canada Direct,777601000,32561.81,11606.94,20954.87\nFIBRECAST INC.,07-Canada Direct,734910000,32455.50,2124.40,30331.10\nFIBRECAST INC.,07-Canada Direct,764481000,31662.00,15427.61,16234.39\nFIBRECAST INC.,07-Canada Direct,792062000,31290.08,16393.94,14896.14\nFIBRECAST INC.,07-Canada Direct,730710500,26862.50,11292.77,15569.73\nFIBRECAST INC.,07-Canada Direct,733211050,26280.00,10374.25,15905.75\nFIBRECAST INC.,07-Canada Direct,792735000,25609.51,11117.12,14492.39\nFIBRECAST INC.,07-Canada Direct,733151500,24612.00,7615.62,16996.38\nFIBRECAST INC.,07-Canada Direct,768121000,24540.00,11060.97,13479.03\nFIBRECAST INC.,07-Canada Direct,727000140,24397.20,12993.21,11403.99\nFIBRECAST INC.,07-Canada Direct,768211000,23922.00,666.96,23255.04\nFIBRECAST INC.,07-Canada Direct,733151600,22326.00,6765.41,15560.59\nFIBRECAST INC.,07-Canada Direct,733464510,21410.00,4338.63,17071.37\nFIBRECAST INC.,07-Canada Direct,764341000,20298.00,10501.91,9796.09\nFIBRECAST INC.,07-Canada Direct,768251210,19657.00,9854.86,9802.14\nFIBRECAST INC.,07-Canada Direct,766121000,19625.00,7175.97,12449.03\nFIBRECAST INC.,07-Canada Direct,764601000,18550.50,7611.65,10938.85\nFIBRECAST INC.,07-Canada Direct,720290010,18510.00,9959.26,8550.74\nFIBRECAST INC.,07-Canada Direct,734351200,18152.70,2115.23,16037.47\nFIBRECAST INC.,07-Canada Direct,734990000,17952.00,82.95,17869.05\nFIBRECAST INC.,07-Canada Direct,768750000,17172.00,9869.56,7302.44\nFIBRECAST INC.,07-Canada Direct,754867000,16170.90,4963.92,11206.98\nFIBRECAST INC.,07-Canada Direct,779951110,16140.60,4350.52,11790.08\nFIBRECAST INC.,07-Canada Direct,764522000,15785.00,6580.39,9204.61\nFIBRECAST INC.,07-Canada Direct,764781000,15507.00,7407.42,8099.58\nFIBRECAST INC.,07-Canada Direct,734173000,14894.50,5921.36,8973.14\nFIBRECAST INC.,07-Canada Direct,734311100,14757.00,2193.78,12563.22\nFIBRECAST INC.,07-Canada Direct,733423710,13990.40,3665.51,10324.89\nFIBRECAST INC.,07-Canada Direct,770830000,13489.00,526.55,12962.45\nFIBRECAST INC.,07-Canada Direct,777902610,13350.84,6325.54,7025.30\nFIBRECAST INC.,07-Canada Direct,779988283,11700.00,6950.84,4749.16\nFIBRECAST INC.,07-Canada Direct,764342000,10630.00,5326.08,5303.92\nFIBRECAST INC.,07-Canada Direct,764605000,9814.00,3865.77,5948.23\nFIBRECAST INC.,07-Canada Direct,768031000,9570.00,4616.31,4953.69\nFIBRECAST INC.,07-Canada Direct,733725500,9040.00,4077.58,4962.42\nFIBRECAST INC.,07-Canada Direct,733464500,8683.00,1710.77,6972.23\nFIBRECAST INC.,07-Canada Direct,728030000,8294.40,21393.71,-13099.31\nFIBRECAST INC.,07-Canada Direct,779957925,7337.20,4690.84,2646.36\nFIBRECAST INC.,07-Canada Direct,764261000,6907.00,3332.45,3574.55\nFIBRECAST INC.,07-Canada Direct,793060000,6793.20,1812.60,4980.60\nFIBRECAST INC.,07-Canada Direct,770220000,6426.00,1455.79,4970.21\nFIBRECAST INC.,07-Canada Direct,732429400,6415.00,2305.22,4109.78\nFIBRECAST INC.,07-Canada Direct,777902600,6387.22,2740.88,3646.34\nFIBRECAST INC.,07-Canada Direct,734211200,5930.08,780.79,5149.29\nFIBRECAST INC.,07-Canada Direct,764781040,5783.00,2807.22,2975.78\nFIBRECAST INC.,07-Canada Direct,734830000,5437.50,1130.65,4306.85\nFIBRECAST INC.,07-Canada Direct,733403230,4917.00,662.14,4254.86\nFIBRECAST INC.,07-Canada Direct,770510000,4862.50,888.60,3973.90\nFIBRECAST INC.,07-Canada Direct,733464400,4827.00,1327.68,3499.32\nFIBRECAST INC.,07-Canada Direct,739454500,4569.60,3383.32,1186.28\nFIBRECAST INC.,07-Canada Direct,740123000,4452.00,1280.10,3171.90\nFIBRECAST INC.,07-Canada Direct,733423720,4211.20,1048.21,3162.99\nFIBRECAST INC.,07-Canada Direct,733141200,3908.00,1089.60,2818.40\nFIBRECAST INC.,07-Canada Direct,771100000,3651.00,146.94,3504.06\nFIBRECAST INC.,07-Canada Direct,766122000,3562.00,1495.64,2066.36\nFIBRECAST INC.,07-Canada Direct,764864000,3470.00,1466.67,2003.33\nFIBRECAST INC.,07-Canada Direct,766162000,3466.00,1777.02,1688.98\nFIBRECAST INC.,07-Canada Direct,777904301,3369.29,1527.08,1842.21\nFIBRECAST INC.,07-Canada Direct,770280000,3338.75,938.81,2399.94\nFIBRECAST INC.,07-Canada Direct,771320000,3133.00,684.50,2448.50\nFIBRECAST INC.,07-Canada Direct,770260000,3100.50,760.13,2340.37\nFIBRECAST INC.,07-Canada Direct,770530000,3060.00,763.04,2296.96\nFIBRECAST INC.,07-Canada Direct,739457000,2998.80,1823.43,1175.37\nFIBRECAST INC.,07-Canada Direct,733525500,2985.00,682.46,2302.54\nFIBRECAST INC.,07-Canada Direct,770520000,2859.00,611.74,2247.26\nFIBRECAST INC.,07-Canada Direct,764564000,2817.00,1172.21,1644.79\nFIBRECAST INC.,07-Canada Direct,764302000,2766.00,1375.28,1390.72\nFIBRECAST INC.,07-Canada Direct,733444200,2702.00,644.17,2057.83\nFIBRECAST INC.,07-Canada Direct,764561000,2686.00,1106.03,1579.97\nFIBRECAST INC.,07-Canada Direct,734751200,2568.60,448.68,2119.92\nFIBRECAST INC.,07-Canada Direct,770410000,2568.00,592.85,1975.15\nFIBRECAST INC.,07-Canada Direct,768250020,2520.00,846.94,1673.06\nFIBRECAST INC.,07-Canada Direct,777903100,2382.87,973.14,1409.73\nFIBRECAST INC.,07-Canada Direct,770240000,2321.25,543.74,1777.51\nFIBRECAST INC.,07-Canada Direct,792785000,2289.60,531.31,1758.29\nFIBRECAST INC.,07-Canada Direct,793000000,2214.00,626.14,1587.86\nFIBRECAST INC.,07-Canada Direct,793060300,2214.00,596.48,1617.52\nFIBRECAST INC.,07-Canada Direct,768142000,2070.00,503.10,1566.90\nFIBRECAST INC.,07-Canada Direct,770850000,2042.00,94.48,1947.52\nFIBRECAST INC.,07-Canada Direct,764820600,1944.00,700.07,1243.93\nFIBRECAST INC.,07-Canada Direct,770440000,1932.20,314.85,1617.35\nFIBRECAST INC.,07-Canada Direct,770340000,1836.00,684.06,1151.94\nFIBRECAST INC.,07-Canada Direct,739561100,1753.92,759.83,994.09\nFIBRECAST INC.,07-Canada Direct,740402402,1630.00,411.01,1218.99\nFIBRECAST INC.,07-Canada Direct,720271000,1620.00,950.75,669.25\nFIBRECAST INC.,07-Canada Direct,764221000,1490.00,945.62,544.38\nFIBRECAST INC.,07-Canada Direct,734890000,1400.70,397.93,1002.77\nFIBRECAST INC.,07-Canada Direct,770560000,1246.00,261.68,984.32\nFIBRECAST INC.,07-Canada Direct,733742000,1120.00,256.80,863.20\nFIBRECAST INC.,07-Canada Direct,792760000,1097.60,291.55,806.05\nFIBRECAST INC.,07-Canada Direct,764482000,1085.00,528.48,556.52\nFIBRECAST INC.,07-Canada Direct,726020009,1062.00,250.61,811.39\nFIBRECAST INC.,07-Canada Direct,739452000,973.98,437.16,536.82\nFIBRECAST INC.,07-Canada Direct,792702000,967.50,567.27,400.23\nFIBRECAST INC.,07-Canada Direct,770540000,887.61,369.56,518.05\nFIBRECAST INC.,07-Canada Direct,733525600,887.50,202.03,685.47\nFIBRECAST INC.,07-Canada Direct,766081010,792.00,138.03,653.97\nFIBRECAST INC.,07-Canada Direct,764461006,782.00,499.43,282.57\nFIBRECAST INC.,07-Canada Direct,733444300,770.00,230.85,539.15\nFIBRECAST INC.,07-Canada Direct,764301000,770.00,419.48,350.52\nFIBRECAST INC.,07-Canada Direct,733182390,731.03,191.39,539.64\nFIBRECAST INC.,07-Canada Direct,734900000,722.40,342.05,380.35\nFIBRECAST INC.,07-Canada Direct,727063018,690.00,254.14,435.86\nFIBRECAST INC.,07-Canada Direct,779956806,654.50,299.36,355.14\nFIBRECAST INC.,07-Canada Direct,768222000,654.00,244.03,409.97\nFIBRECAST INC.,07-Canada Direct,733721000,646.00,326.18,319.82\nFIBRECAST INC.,07-Canada Direct,768212000,630.00,231.18,398.82\nFIBRECAST INC.,07-Canada Direct,770810000,626.00,17.19,608.81\nFIBRECAST INC.,07-Canada Direct,777583000,599.80,21.50,578.30\nFIBRECAST INC.,07-Canada Direct,770210000,520.00,121.27,398.73\nFIBRECAST INC.,07-Canada Direct,724003000,478.50,81.82,396.68\nFIBRECAST INC.,07-Canada Direct,734711100,463.48,94.75,368.73\nFIBRECAST INC.,07-Canada Direct,733141300,444.00,139.46,304.54\nFIBRECAST INC.,07-Canada Direct,770400000,437.50,92.44,345.06\nFIBRECAST INC.,07-Canada Direct,739541100,420.00,83.57,336.43\nFIBRECAST INC.,07-Canada Direct,734030000,405.00,157.54,247.46\nFIBRECAST INC.,07-Canada Direct,770200000,369.00,83.20,285.80\nFIBRECAST INC.,07-Canada Direct,739541000,360.00,71.62,288.38\nFIBRECAST INC.,07-Canada Direct,741021000,354.48,44.39,310.09\nFIBRECAST INC.,07-Canada Direct,764862000,351.00,150.16,200.84\nFIBRECAST INC.,07-Canada Direct,734040000,281.00,121.56,159.44\nFIBRECAST INC.,07-Canada Direct,770372000,280.00,44.11,235.89\nFIBRECAST INC.,07-Canada Direct,779951023,253.50,114.45,139.05\nFIBRECAST INC.,07-Canada Direct,770380000,232.40,51.37,181.03\nFIBRECAST INC.,07-Canada Direct,764906000,216.00,65.44,150.56\nFIBRECAST INC.,07-Canada Direct,770392000,176.00,35.15,140.85\nFIBRECAST INC.,07-Canada Direct,764761000,174.00,95.58,78.42\nFIBRECAST INC.,07-Canada Direct,733423700,150.40,36.22,114.18\nFIBRECAST INC.,07-Canada Direct,733423800,150.40,38.28,112.12\nFIBRECAST INC.,07-Canada Direct,764527200,138.00,69.69,68.31\nFIBRECAST INC.,07-Canada Direct,764861000,121.00,51.96,69.04\nFIBRECAST INC.,07-Canada Direct,768224000,83.00,41.19,41.81\nFIBRECAST INC.,07-Canada Direct,764525000,69.00,37.60,31.40\nFIBRECAST INC.,07-Canada Direct,764602000,66.50,26.50,40.00\nFIBRECAST INC.,07-Canada Direct,734162000,53.40,41.18,12.22\nGARLOCK OF CANADA LTD.,07-Canada Direct,770565000,13015.00,1502.22,11512.78\nGARLOCK OF CANADA LTD.,07-Canada Direct,770511000,1214.75,95.48,1119.27\nGARLOCK OF CANADA LTD.,07-Canada Direct,770601000,948.15,96.32,851.83\nGARLOCK OF CANADA LTD.,07-Canada Direct,770560000,746.00,89.50,656.50\nGEMCAST,07-Canada Direct,726000062,21000.00,11670.09,9329.91\nGOODMAN INDUSTRIAL,07-Canada Direct,770260000,1957.50,140.66,1816.84\nICC INDUSTRIAL CHIMNEY CO,07-Canada Direct,777601000,287.95,58.80,229.15\nINDUST'L FIREBRICK OF DET,07-Canada Direct,733151500,1550.00,244.87,1305.13\nJOEL BERMAN GLASS STUDIOS,07-Canada Direct,734311100,404.36,35.30,369.06\nL & A METALWORKS INC.,07-Canada Direct,768242010,182008.00,81440.56,100567.44\nL & A METALWORKS INC.,07-Canada Direct,768242000,78485.00,47824.83,30660.17\nL & A METALWORKS INC.,07-Canada Direct,734751200,36897.00,14414.92,22482.08\nL & A METALWORKS INC.,07-Canada Direct,768222200,15228.00,7923.84,7304.16\nL & A METALWORKS INC.,07-Canada Direct,733725500,572.00,103.53,468.47\nL & A METALWORKS INC.,07-Canada Direct,768211000,282.00,140.34,141.66\nMAGIKIST LTD.,07-Canada Direct,733182400,7489.80,1095.03,6394.77\n\"MEGTEC SYSTEMS, INC\",07-Canada Direct,754055000,1483.44,879.81,603.63\n\"MEGTEC SYSTEMS, INC\",07-Canada Direct,764522000,552.00,157.41,394.59\n\"MEGTEC SYSTEMS, INC\",07-Canada Direct,734651200,365.85,111.00,254.85\n\"MIURA CANADA CO., LTD.\",07-Canada Direct,733151500,326.00,54.11,271.89\nMOTION INDUSTRIES CANADA,07-Canada Direct,770372000,1022.00,53.11,968.89\nNOVENCO CONSULTANTS LTD,07-Canada Direct,777904301,8515.75,2701.75,5814.00\nNOVENCO CONSULTANTS LTD,07-Canada Direct,733151500,262.00,54.11,207.89\nNOVENCO CONSULTANTS LTD,07-Canada Direct,777601000,203.26,58.80,144.46\nPILKINGTON N. A. INC.,07-Canada Direct,734751200,1662.60,226.24,1436.36\nPRAIRIE STAINED GLASS,07-Canada Direct,792730000,1094.80,125.12,969.68\nPRAIRIE STAINED GLASS,07-Canada Direct,768121000,503.00,28.76,474.24\nPRAIRIE STAINED GLASS,07-Canada Direct,734611100,178.72,26.96,151.76\nPRAIRIE STAINED GLASS,07-Canada Direct,734711100,178.72,27.04,151.68\nPRAXAIR,07-Canada Direct,764521000,320.00,13.30,306.70\nPROCOR LTD,07-Canada Direct,764633510,2197.44,456.72,1740.72\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,726000062,969660.00,714928.10,254731.90\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764601000,56508.00,8457.08,48050.92\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,727000060,45900.00,35739.81,10160.19\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764821000,45683.00,16879.02,28803.98\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,737851000,32498.56,16918.44,15580.12\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764781000,20506.40,9210.78,11295.62\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,766121000,20291.00,4966.45,15324.55\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,737321000,15601.92,9155.16,6446.76\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764521000,13109.20,5873.97,7235.23\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764901000,12577.20,4891.16,7686.04\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,734173000,11961.00,5031.15,6929.85\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,768750000,8970.00,9269.13,-299.13\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,734211200,4655.74,1300.42,3355.32\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,739781100,3398.40,3108.60,289.80\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764822000,3012.00,1119.56,1892.44\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,768750010,2700.00,2800.31,-100.31\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,733151600,1974.00,963.87,1010.13\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,733211050,1890.00,1248.62,641.38\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764821161,1700.00,1488.00,212.00\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764901061,1690.00,1492.00,198.00\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764481000,1280.00,677.64,602.36\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,734910000,767.70,318.54,449.16\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764561000,736.00,354.84,381.16\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,727000140,708.00,483.86,224.14\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764902000,507.00,193.26,313.74\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,792620000,428.00,86.74,341.26\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,764261000,344.00,183.16,160.84\nPYROTEK INC (DRUMMONDVILL,07-Canada Direct,739541000,240.48,71.62,168.86\nPYROTEK INC.,07-Canada Direct,768221000,324709.80,143235.79,181474.01\nPYROTEK INC.,07-Canada Direct,768211000,147384.00,79798.00,67586.00\nPYROTEK INC.,07-Canada Direct,764861000,136476.62,65438.45,71038.17\nPYROTEK INC.,07-Canada Direct,764821000,106955.00,39520.86,67434.14\nPYROTEK INC.,07-Canada Direct,777905414,74297.74,45869.08,28428.66\nPYROTEK INC.,07-Canada Direct,768222000,64732.80,28565.44,36167.36\nPYROTEK INC.,07-Canada Direct,768241000,44093.60,19480.87,24612.73\nPYROTEK INC.,07-Canada Direct,777905413,41344.16,25491.66,15852.50\nPYROTEK INC.,07-Canada Direct,764901000,41064.00,15971.20,25092.80\nPYROTEK INC.,07-Canada Direct,763902000,39333.60,26420.86,12912.74\nPYROTEK INC.,07-Canada Direct,768232500,37658.52,12176.01,25482.51\nPYROTEK INC.,07-Canada Direct,777901100,37361.52,18893.04,18468.48\nPYROTEK INC.,07-Canada Direct,777901000,31928.28,2224.76,29703.52\nPYROTEK INC.,07-Canada Direct,764781000,26778.00,12006.54,14771.46\nPYROTEK INC.,07-Canada Direct,777905410,18305.04,11528.70,6776.34\nPYROTEK INC.,07-Canada Direct,793330000,17764.00,4849.55,12914.45\nPYROTEK INC.,07-Canada Direct,771320000,16318.14,3052.75,13265.39\nPYROTEK INC.,07-Canada Direct,733211000,16236.00,10343.60,5892.40\nPYROTEK INC.,07-Canada Direct,764822000,14816.00,5535.17,9280.83\nPYROTEK INC.,07-Canada Direct,768121000,14674.00,7478.46,7195.54\nPYROTEK INC.,07-Canada Direct,739641100,13378.91,12690.47,688.44\nPYROTEK INC.,07-Canada Direct,792702000,11928.75,5707.65,6221.10\nPYROTEK INC.,07-Canada Direct,777903100,11139.66,5838.84,5300.82\nPYROTEK INC.,07-Canada Direct,764822020,10624.00,3985.28,6638.72\nPYROTEK INC.,07-Canada Direct,792730000,10350.00,2815.20,7534.80\nPYROTEK INC.,07-Canada Direct,777905412,9540.75,7109.20,2431.55\nPYROTEK INC.,07-Canada Direct,733151500,9487.00,4612.16,4874.84\nPYROTEK INC.,07-Canada Direct,764561000,9275.60,4369.42,4906.18\nPYROTEK INC.,07-Canada Direct,777601000,9011.92,3101.30,5910.62\nPYROTEK INC.,07-Canada Direct,764301000,7580.80,4398.96,3181.84\nPYROTEK INC.,07-Canada Direct,777905401,7533.40,4893.58,2639.82\nPYROTEK INC.,07-Canada Direct,733525500,6772.50,2784.21,3988.29\nPYROTEK INC.,07-Canada Direct,764521000,6691.20,2975.22,3715.98\nPYROTEK INC.,07-Canada Direct,792783000,5671.80,2160.29,3511.51\nPYROTEK INC.,07-Canada Direct,770372000,5551.00,636.43,4914.57\nPYROTEK INC.,07-Canada Direct,733525600,5107.50,2108.02,2999.48\nPYROTEK INC.,07-Canada Direct,792760000,4198.32,1632.68,2565.64\nPYROTEK INC.,07-Canada Direct,739741100,3904.70,3150.30,754.40\nPYROTEK INC.,07-Canada Direct,739781100,3588.96,4217.79,-628.83\nPYROTEK INC.,07-Canada Direct,739661100,3530.88,3611.91,-81.03\nPYROTEK INC.,07-Canada Direct,733510000,3315.00,2255.65,1059.35\nPYROTEK INC.,07-Canada Direct,733211050,3244.00,2180.45,1063.55\nPYROTEK INC.,07-Canada Direct,777144000,3194.40,3546.40,-352.00\nPYROTEK INC.,07-Canada Direct,792785000,2966.40,1062.62,1903.78\nPYROTEK INC.,07-Canada Direct,792764000,2464.80,859.56,1605.24\nPYROTEK INC.,07-Canada Direct,764261000,2408.00,1285.68,1122.32\nPYROTEK INC.,07-Canada Direct,733511000,2403.00,1490.32,912.68\nPYROTEK INC.,07-Canada Direct,764481000,2376.60,1255.50,1121.10\nPYROTEK INC.,07-Canada Direct,733505200,2296.00,1038.06,1257.94\nPYROTEK INC.,07-Canada Direct,777715000,1939.20,1100.80,838.40\nPYROTEK INC.,07-Canada Direct,739561200,1842.28,1038.66,803.62\nPYROTEK INC.,07-Canada Direct,764601000,1733.20,736.87,996.33\nPYROTEK INC.,07-Canada Direct,777140073,1733.16,861.75,871.41\nPYROTEK INC.,07-Canada Direct,770570000,1662.00,190.82,1471.18\nPYROTEK INC.,07-Canada Direct,718501000,1591.50,404.07,1187.43\nPYROTEK INC.,07-Canada Direct,770510000,1567.50,144.60,1422.90\nPYROTEK INC.,07-Canada Direct,738521000,1514.88,1291.08,223.80\nPYROTEK INC.,07-Canada Direct,739681100,1355.94,1863.00,-507.06\nPYROTEK INC.,07-Canada Direct,738401000,1237.68,844.14,393.54\nPYROTEK INC.,07-Canada Direct,738361000,963.87,1255.58,-291.71\nPYROTEK INC.,07-Canada Direct,737251000,910.80,300.69,610.11\nPYROTEK INC.,07-Canada Direct,764522000,833.80,370.20,463.60\nPYROTEK INC.,07-Canada Direct,764781040,678.00,292.91,385.09\nPYROTEK INC.,07-Canada Direct,739561000,602.20,344.76,257.44\nPYROTEK INC.,07-Canada Direct,768231000,558.00,251.10,306.90\nPYROTEK INC.,07-Canada Direct,733141200,464.00,267.92,196.08\nPYROTEK INC.,07-Canada Direct,739561100,341.04,199.92,141.12\nPYROTEK INC.,07-Canada Direct,738561000,183.36,124.14,59.22\nPYROTEK INC.,07-Canada Direct,764461006,80.00,55.88,24.12\nQUADRA INDUSTRIAL,07-Canada Direct,779984172,580.00,196.00,384.00\nREFRABEC INC.,07-Canada Direct,762104000,88833.72,41705.55,47128.17\nREFRABEC INC.,07-Canada Direct,718501000,16650.00,4040.67,12609.33\nREFRABEC INC.,07-Canada Direct,779951086,4386.00,2193.00,2193.00\nREFRABEC INC.,07-Canada Direct,779959870,1895.50,985.29,910.21\nREFRABEC INC.,07-Canada Direct,779957054,1504.50,753.15,751.35\nREFRACTORY ENGINEERS INC.,07-Canada Direct,734311100,1373.03,272.83,1100.20\nREFRACTORY ENGINEERS INC.,07-Canada Direct,733525500,1280.00,260.12,1019.88\nREFRACTORY ENGINEERS INC.,07-Canada Direct,734910000,851.40,261.96,589.44\nREFRACTORY ENGINEERS INC.,07-Canada Direct,733211000,239.30,53.20,186.10\nREFRACTORY ENGINEERS INC.,07-Canada Direct,779221000,112.94,29.42,83.52\nREFTECH INTERNATIONAL INC,07-Canada Direct,766121000,4840.00,1915.91,2924.09\nRHI CANADA INC.,07-Canada Direct,754164100,614952.36,391805.75,223146.61\nRHI CANADA INC.,07-Canada Direct,781580000,410399.88,244057.44,166342.44\nRHI CANADA INC.,07-Canada Direct,764821000,246768.00,104565.58,142202.42\nRHI CANADA INC.,07-Canada Direct,734910000,231168.00,111034.50,120133.50\nRHI CANADA INC.,07-Canada Direct,764901000,163410.00,72196.11,91213.89\nRHI CANADA INC.,07-Canada Direct,764601000,156392.50,73691.19,82701.31\nRHI CANADA INC.,07-Canada Direct,793330000,108495.00,25980.01,82514.99\nRHI CANADA INC.,07-Canada Direct,766121000,103703.00,59651.17,44051.83\nRHI CANADA INC.,07-Canada Direct,766261000,77990.00,44176.49,33813.51\nRHI CANADA INC.,07-Canada Direct,764781000,75300.00,32083.84,43216.16\nRHI CANADA INC.,07-Canada Direct,764861000,70604.50,31082.56,39521.94\nRHI CANADA INC.,07-Canada Direct,764481000,69819.90,35820.52,33999.38\nRHI CANADA INC.,07-Canada Direct,733464500,68190.00,19933.32,48256.68\nRHI CANADA INC.,07-Canada Direct,777601000,60993.28,33402.71,27590.57\nRHI CANADA INC.,07-Canada Direct,733211000,59064.00,23984.36,35079.64\nRHI CANADA INC.,07-Canada Direct,766122000,54960.00,34514.56,20445.44\nRHI CANADA INC.,07-Canada Direct,734990000,50399.60,19640.91,30758.69\nRHI CANADA INC.,07-Canada Direct,715000000,48645.00,14304.88,34340.12\nRHI CANADA INC.,07-Canada Direct,793000000,41344.80,17369.60,23975.20\nRHI CANADA INC.,07-Canada Direct,764521000,36570.00,18126.08,18443.92\nRHI CANADA INC.,07-Canada Direct,764821161,35610.00,38539.20,-2929.20\nRHI CANADA INC.,07-Canada Direct,770222000,35510.50,6537.16,28973.34\nRHI CANADA INC.,07-Canada Direct,779988108,30723.00,0.00,30723.00\nRHI CANADA INC.,07-Canada Direct,754204010,29702.76,11835.98,17866.78\nRHI CANADA INC.,07-Canada Direct,764561000,28485.00,14379.03,14105.97\nRHI CANADA INC.,07-Canada Direct,718501000,25613.00,7814.71,17798.29\nRHI CANADA INC.,07-Canada Direct,770220000,25329.00,4715.20,20613.80\nRHI CANADA INC.,07-Canada Direct,719500000,25164.00,8986.91,16177.09\nRHI CANADA INC.,07-Canada Direct,754754020,24024.00,12331.05,11692.95\nRHI CANADA INC.,07-Canada Direct,770230000,23625.00,5089.05,18535.95\nRHI CANADA INC.,07-Canada Direct,754727000,23338.80,8440.62,14898.18\nRHI CANADA INC.,07-Canada Direct,770240000,23145.00,4733.89,18411.11\nRHI CANADA INC.,07-Canada Direct,795900120,19720.80,6211.62,13509.18\nRHI CANADA INC.,07-Canada Direct,764901061,18000.00,17904.00,96.00\nRHI CANADA INC.,07-Canada Direct,754887000,17952.72,5094.63,12858.09\nRHI CANADA INC.,07-Canada Direct,733444200,17636.00,6268.89,11367.11\nRHI CANADA INC.,07-Canada Direct,724281200,16622.00,8534.80,8087.20\nRHI CANADA INC.,07-Canada Direct,779984659,15675.00,7015.86,8659.14\nRHI CANADA INC.,07-Canada Direct,759671000,14469.00,4988.41,9480.59\nRHI CANADA INC.,07-Canada Direct,766231000,14330.00,8519.74,5810.26\nRHI CANADA INC.,07-Canada Direct,754164005,13158.12,11430.59,1727.53\nRHI CANADA INC.,07-Canada Direct,734173000,13120.00,6467.94,6652.06\nRHI CANADA INC.,07-Canada Direct,764821073,12000.00,11664.00,336.00\nRHI CANADA INC.,07-Canada Direct,733525500,11410.00,3066.43,8343.57\nRHI CANADA INC.,07-Canada Direct,764341000,11217.00,6593.03,4623.97\nRHI CANADA INC.,07-Canada Direct,737912200,11172.00,4456.44,6715.56\nRHI CANADA INC.,07-Canada Direct,754867000,10637.81,3728.48,6909.33\nRHI CANADA INC.,07-Canada Direct,734311100,10570.43,2231.35,8339.08\nRHI CANADA INC.,07-Canada Direct,764781040,9930.00,4214.44,5715.56\nRHI CANADA INC.,07-Canada Direct,718500000,9596.16,2911.30,6684.86\nRHI CANADA INC.,07-Canada Direct,777902300,9343.32,4124.44,5218.88\nRHI CANADA INC.,07-Canada Direct,733505300,8020.00,2393.58,5626.42\nRHI CANADA INC.,07-Canada Direct,733525600,6815.00,1879.40,4935.60\nRHI CANADA INC.,07-Canada Direct,724003000,6628.20,1113.96,5514.24\nRHI CANADA INC.,07-Canada Direct,779986562,6411.00,3453.17,2957.83\nRHI CANADA INC.,07-Canada Direct,764261000,6351.00,3438.54,2912.46\nRHI CANADA INC.,07-Canada Direct,777992000,6332.16,2601.36,3730.80\nRHI CANADA INC.,07-Canada Direct,754647000,5715.36,2596.24,3119.12\nRHI CANADA INC.,07-Canada Direct,733211050,5299.00,2509.72,2789.28\nRHI CANADA INC.,07-Canada Direct,777902700,5194.98,2283.11,2911.87\nRHI CANADA INC.,07-Canada Direct,740382201,5060.00,3552.23,1507.77\nRHI CANADA INC.,07-Canada Direct,764822000,4896.00,2091.89,2804.11\nRHI CANADA INC.,07-Canada Direct,734830000,4554.00,1634.76,2919.24\nRHI CANADA INC.,07-Canada Direct,764901073,4500.00,4374.00,126.00\nRHI CANADA INC.,07-Canada Direct,770380000,3744.00,616.46,3127.54\nRHI CANADA INC.,07-Canada Direct,780280000,3448.80,1263.72,2185.08\nRHI CANADA INC.,07-Canada Direct,779984680,3225.60,1292.10,1933.50\nRHI CANADA INC.,07-Canada Direct,764482000,3061.20,1576.91,1484.29\nRHI CANADA INC.,07-Canada Direct,766162000,2908.50,1674.67,1233.83\nRHI CANADA INC.,07-Canada Direct,734851000,2896.14,1188.20,1707.94\nRHI CANADA INC.,07-Canada Direct,770560000,2888.00,433.86,2454.14\nRHI CANADA INC.,07-Canada Direct,780680000,2874.00,1093.28,1780.72\nRHI CANADA INC.,07-Canada Direct,767960000,2746.63,924.98,1821.65\nRHI CANADA INC.,07-Canada Direct,754467000,2711.52,1067.44,1644.08\nRHI CANADA INC.,07-Canada Direct,770372000,2632.00,362.77,2269.23\nRHI CANADA INC.,07-Canada Direct,770026100,2262.50,363.21,1899.29\nRHI CANADA INC.,07-Canada Direct,779989696,2258.40,941.37,1317.03\nRHI CANADA INC.,07-Canada Direct,734611100,2088.30,1107.02,981.28\nRHI CANADA INC.,07-Canada Direct,777904301,2074.00,939.75,1134.25\nRHI CANADA INC.,07-Canada Direct,770260000,2052.00,421.98,1630.02\nRHI CANADA INC.,07-Canada Direct,766081010,1892.00,929.06,962.94\nRHI CANADA INC.,07-Canada Direct,727000025,1813.50,1098.70,714.80\nRHI CANADA INC.,07-Canada Direct,779959414,1762.50,699.70,1062.80\nRHI CANADA INC.,07-Canada Direct,733505200,1620.00,458.66,1161.34\nRHI CANADA INC.,07-Canada Direct,770200000,1449.00,296.90,1152.10\nRHI CANADA INC.,07-Canada Direct,733403100,1307.20,490.39,816.81\nRHI CANADA INC.,07-Canada Direct,734211200,1293.10,302.27,990.83\nRHI CANADA INC.,07-Canada Direct,764262000,1193.00,631.52,561.48\nRHI CANADA INC.,07-Canada Direct,775171000,1115.00,534.68,580.32\nRHI CANADA INC.,07-Canada Direct,770850000,1102.00,84.12,1017.88\nRHI CANADA INC.,07-Canada Direct,775131000,1052.10,505.47,546.63\nRHI CANADA INC.,07-Canada Direct,733151600,1040.00,345.92,694.08\nRHI CANADA INC.,07-Canada Direct,733671000,974.40,225.10,749.30\nRHI CANADA INC.,07-Canada Direct,765661000,890.00,318.15,571.85\nRHI CANADA INC.,07-Canada Direct,754924010,847.08,346.12,500.96\nRHI CANADA INC.,07-Canada Direct,770392000,732.00,124.53,607.47\nRHI CANADA INC.,07-Canada Direct,764901050,720.00,315.90,404.10\nRHI CANADA INC.,07-Canada Direct,770510000,715.00,82.78,632.22\nRHI CANADA INC.,07-Canada Direct,779958220,709.31,269.80,439.51\nRHI CANADA INC.,07-Canada Direct,727063000,651.12,246.69,404.43\nRHI CANADA INC.,07-Canada Direct,727063016,651.12,245.43,405.69\nRHI CANADA INC.,07-Canada Direct,734810000,612.00,428.86,183.14\nRHI CANADA INC.,07-Canada Direct,724261200,604.00,156.40,447.60\nRHI CANADA INC.,07-Canada Direct,779959413,600.00,300.81,299.19\nRHI CANADA INC.,07-Canada Direct,764605000,530.00,258.78,271.22\nRHI CANADA INC.,07-Canada Direct,780600000,480.96,264.67,216.29\nRHI CANADA INC.,07-Canada Direct,779221000,478.92,122.40,356.52\nRHI CANADA INC.,07-Canada Direct,734751200,475.50,224.34,251.16\nRHI CANADA INC.,07-Canada Direct,792650000,436.00,228.70,207.30\nRHI CANADA INC.,07-Canada Direct,739561100,431.76,186.64,245.12\nRHI CANADA INC.,07-Canada Direct,770830000,415.00,30.97,384.03\nRHI CANADA INC.,07-Canada Direct,724311200,328.00,154.28,173.72\nRHI CANADA INC.,07-Canada Direct,733151500,304.00,108.22,195.78\nRHI CANADA INC.,07-Canada Direct,733141200,288.00,119.94,168.06\nRHI CANADA INC.,07-Canada Direct,793260074,283.50,122.49,161.01\nRHI CANADA INC.,07-Canada Direct,774981100,280.00,98.09,181.91\nRHI CANADA INC.,07-Canada Direct,727000440,248.00,111.25,136.75\nRHI CANADA INC.,07-Canada Direct,733721000,240.00,97.79,142.21\nRHI CANADA INC.,07-Canada Direct,764761000,232.00,122.86,109.14\nRHI CANADA INC.,07-Canada Direct,780620000,213.50,109.18,104.32\nRHI CANADA INC.,07-Canada Direct,777650000,203.70,142.66,61.04\nRHI CANADA INC.,07-Canada Direct,727000140,186.00,83.57,102.43\nRHI CANADA INC.,07-Canada Direct,733212000,181.50,92.84,88.66\nRHI CANADA INC.,07-Canada Direct,734711100,77.83,40.72,37.11\nRHI CANADA INC.,07-Canada Direct,734900000,71.40,56.73,14.67\nRHI CANADA INC.,07-Canada Direct,777642000,62.00,31.67,30.33\nRONSCO INC.,07-Canada Direct,764643052,8981.28,4251.43,4729.85\nRONSCO INC.,07-Canada Direct,764643100,4153.97,5511.48,-1357.51\nSPECIALTY GASKETS INC.,07-Canada Direct,764901000,20715.25,4793.13,15922.12\nSPECIALTY GASKETS INC.,07-Canada Direct,768221000,16116.00,6063.46,10052.54\nSPECIALTY GASKETS INC.,07-Canada Direct,733525600,13300.00,3352.26,9947.74\nSPECIALTY GASKETS INC.,07-Canada Direct,730710500,9284.00,3122.36,6161.64\nSPECIALTY GASKETS INC.,07-Canada Direct,764261000,4622.50,1433.86,3188.64\nSPECIALTY GASKETS INC.,07-Canada Direct,732429400,2672.00,794.28,1877.72\nSPECIALTY GASKETS INC.,07-Canada Direct,764821000,2460.00,626.21,1833.79\nSPECIALTY GASKETS INC.,07-Canada Direct,764461006,2056.00,769.82,1286.18\nSPECIALTY GASKETS INC.,07-Canada Direct,764521000,1184.50,305.83,878.67\nSPECIALTY GASKETS INC.,07-Canada Direct,733211050,952.00,478.33,473.67\nSPECIALTY GASKETS INC.,07-Canada Direct,764781000,620.00,204.45,415.55\nSPECIALTY GASKETS INC.,07-Canada Direct,733151600,576.00,212.46,363.54\nSPECIALTY GASKETS INC,07-Canada Direct,764461006,1120.00,415.62,704.38\nSTRIPE WORKS,07-Canada Direct,737851000,12236.80,5305.60,6931.20\nSTRIPE WORKS,07-Canada Direct,766162000,3075.00,311.10,2763.90\nSTRIPE WORKS,07-Canada Direct,734822000,494.00,67.47,426.53\nSTRIPE WORKS,07-Canada Direct,734211200,437.64,46.75,390.89\nTHERMAL CERAMICS,07-Canada Direct,734351200,917.85,69.73,848.12\nUNIVERSITY OF ALBERTA,07-Canada Direct,737912200,523.00,86.72,436.28\nVESUVIUS USA,07-Canada Direct,726020018,25500.00,12032.49,13467.51\nWEATHERFORD LABS,07-Canada Direct,733484800,6320.00,780.71,5539.29\nWESTERN INDUSTRIAL CERAMI,07-Canada Direct,771002000,496.40,19.30,477.10\nUNIFRAX (SHANGHAI) LTD.,09-China,735657000,14256.00,10565.89,3690.11\nUNIFRAX (SHANGHAI) LTD.,09-China,735655000,8325.20,6239.17,2086.03\nUNIFRAX (SHANGHAI) LTD.,09-China,734990000,438.72,328.65,110.07\nUNIFRAX (SHANGHAI) LTD.,09-China,734211200,31.55,23.38,8.17\nUNIFRAX AUSTRALIA PTY LTD,09-China,732820000,205.97,147.02,58.95\nUNIFRAX AUSTRALIA PTY LTD,09-China,777601000,72.02,72.02,0.00\nASHCON,9x-Export,777601000,2049.60,293.98,1755.62\nATLANTECH DISTRIBUTION,9x-Export,735602000,72051.00,27654.85,44396.15\nAVIATION PRODUCTS INTL EK,9x-Export,733423600,800.00,90.16,709.84\nBETA RESEARCH & DEV. LTD.,9x-Export,737550200,1419.60,383.16,1036.44\nBETA RESEARCH & DEV. LTD.,9x-Export,779980780,920.40,271.92,648.48\nBETA RESEARCH & DEV. LTD.,9x-Export,737550600,707.70,198.28,509.42\nBETA RESEARCH & DEV. LTD.,9x-Export,737550000,687.92,445.77,242.15\nBETA RESEARCH & DEV. LTD.,9x-Export,737550400,589.20,35.23,553.97\nBETA RESEARCH & DEV. LTD.,9x-Export,779980770,273.60,143.64,129.96\nBRADEN MANUFACTURING,9x-Export,768231012,2983.50,2084.16,899.34\nBRADEN MANUFACTURING,9x-Export,768241102,1300.50,894.78,405.72\nC.I. MILPA S.A.,9x-Export,770360000,17892.00,1301.57,16590.43\nCLYDE BERGEMANN BACHMANN,9x-Export,734761300,9568.35,3414.51,6153.84\nDURR SYSTEMS INC.,9x-Export,784320000,22943.52,11241.56,11701.96\nDURR SYSTEMS INC.,9x-Export,784360000,21715.60,10641.82,11073.78\nDURR SYSTEMS INC.,9x-Export,780690000,16376.00,8030.18,8345.82\nDURR SYSTEMS INC.,9x-Export,764821000,2706.00,675.74,2030.26\nDURR SYSTEMS INC.,9x-Export,779957846,691.25,356.38,334.87\nECOLOG INTERNATIONAL FZE,9x-Export,735602000,6300.00,1510.19,4789.81\nF.L. SMIDTH INC.,9x-Export,770600000,1554.35,96.32,1458.03\nFALEK LATINA C.I.S.A.,9x-Export,733586500,30864.96,6757.46,24107.50\nFAURECIA EXHAUST MEXICANA,9x-Export,779988364,72544.50,23827.66,48716.84\nFELTEX- EASTERN CAPE,9x-Export,734751200,41616.96,8669.21,32947.75\nFELTEX- EASTERN CAPE,9x-Export,740402402,717.50,193.66,523.84\nFELTEX- EASTERN CAPE,9x-Export,779988364,214.75,44.61,170.14\nFOSECO JAPAN LTD.,9x-Export,793000010,328593.60,100675.55,227918.05\nFRAS-LE S/A,9x-Export,726020007,29048.24,6836.88,22211.36\nFRENOSA SOCIEDAD ANONIMA,9x-Export,727000060,56025.00,18352.93,37672.07\nFRICCION Y TECNOLOGIA,9x-Export,727080000,40230.00,5100.52,35129.48\nHALCO,9x-Export,724261200,1572.00,156.40,1415.60\nINDUST'L FIREBRICK OF DET,9x-Export,793330000,1194.00,318.08,875.92\nINSULCON BENELUX B.V.,9x-Export,768242900,37701.31,25073.30,12628.01\nINTERNATIONAL AGENCIES LT,9x-Export,735602115,35208.00,9446.25,25761.75\nINTERNATIONAL AGENCIES LT,9x-Export,735602000,11736.00,3026.06,8709.94\nISOLITE INSULATING PROD.,9x-Export,764040000,75142.08,48204.12,26937.96\nISOLITE INSULATING PROD.,9x-Export,719500000,38196.00,26960.76,11235.24\nISOLITE INSULATING PROD.,9x-Export,793410000,31758.72,22684.71,9074.01\nISOLITE INSULATING PROD.,9x-Export,727041000,18195.84,10261.92,7933.92\nISOLITE INSULATING PROD.,9x-Export,777650000,6879.30,4914.06,1965.24\nISOLITE INSULATING PROD.,9x-Export,733525500,1532.50,1126.96,405.54\nISOLITE INSULATING PROD.,9x-Export,733403200,897.60,396.68,500.92\nISOLITE INSULATING PROD.,9x-Export,733423600,829.44,592.76,236.68\nISOLITE INSULATING PROD.,9x-Export,792410000,719.19,513.69,205.50\nISOLITE INSULATING PROD.,9x-Export,734810000,686.22,490.14,196.08\nISOLITE INSULATING PROD.,9x-Export,792420000,667.19,476.50,190.69\nISOLITE INSULATING PROD.,9x-Export,793320074,344.40,245.80,98.60\nISOLITE INSULATING PROD.,9x-Export,733423700,252.16,181.02,71.14\nISOLITE INSULATING PROD.,9x-Export,734851000,138.65,98.90,39.75\nISOLITE INSULATING PROD.,9x-Export,734822000,94.50,67.47,27.03\n\"ITM CO., LTD.\",9x-Export,740402402,3225.00,652.05,2572.95\n\"ITM CO., LTD.\",9x-Export,740382201,1663.00,1188.57,474.43\nKLC INTERNATIONAL LTD.,9x-Export,779181000,255.92,41.25,214.67\nLIBERTY REFRACTORY,9x-Export,734173000,66636.00,20693.52,45942.48\nLIBERTY REFRACTORY,9x-Export,777642000,22056.00,8772.00,13284.00\nMACER LTDA,9x-Export,733221400,21402.72,5139.20,16263.52\nMACER LTDA,9x-Export,733151500,6624.00,1907.68,4716.32\nMACER LTDA,9x-Export,770260000,4221.45,616.77,3604.68\nMACER LTDA,9x-Export,770280000,4200.00,615.64,3584.36\nMACER LTDA,9x-Export,770240000,3197.26,405.78,2791.48\nMACER LTDA,9x-Export,733211050,2376.00,993.92,1382.08\nMACER LTDA,9x-Export,770200000,1285.20,163.14,1122.06\nMACER LTDA,9x-Export,770220000,957.60,109.38,848.22\nMACER LTDA,9x-Export,770210000,896.00,121.26,774.74\nMCNEIL INC.,9x-Export,766041100,28032.00,16648.93,11383.07\nMCNEIL INC.,9x-Export,734851000,16920.00,6580.66,10339.34\nMCNEIL INC.,9x-Export,766163100,1451.52,995.98,455.54\nNORTH SHORE INTERNATIONAL,9x-Export,770722000,15375.00,1072.03,14302.97\nPAGLES INTERNATIONAL INC.,9x-Export,764821027,32900.00,12113.90,20786.10\nPAGLES INTERNATIONAL INC.,9x-Export,774661000,3630.00,692.62,2937.38\nPAGLES INTERNATIONAL INC.,9x-Export,774901000,3000.00,593.54,2406.46\nPAGLES INTERNATIONAL INC.,9x-Export,777642000,1080.00,316.70,763.30\nRASGAS COMPANY LIMITED,9x-Export,733444200,6976.00,850.08,6125.92\nSAINT-GOBAIN AMERICA SA,9x-Export,777780074,1842.74,400.40,1442.34\nSCHOTT FORMA VITRUM DE,9x-Export,739541000,564.48,71.62,492.86\nSHOFU INC.,9x-Export,740382201,8700.00,2377.14,6322.86\nSNS CORPORATION,9x-Export,734751200,12180.00,2634.44,9545.56\nSPAR INC,9x-Export,764601000,57631.50,37812.31,19819.19\nSPAR INC,9x-Export,764901000,42016.00,26863.48,15152.52\nSPAR INC,9x-Export,766162000,28200.00,28103.56,96.44\nSPAR INC,9x-Export,737915000,6918.75,4549.63,2369.12\nUNIFRAX (SHANGHAI) LTD.,9x-Export,733525500,972.50,703.93,268.57\nUNIFRAX (SHANGHAI) LTD.,9x-Export,734611100,910.00,673.91,236.09\nUNIFRAX (SHANGHAI) LTD.,9x-Export,734211200,770.75,570.94,199.81\nUNIFRAX (SHANGHAI) LTD.,9x-Export,793060300,91.48,68.05,23.43\nUNIFRAX AUSTRALIA PTY LTD,9x-Export,735602000,26835.60,19165.06,7670.54\nUNIFRAX AUSTRALIA PTY LTD,9x-Export,764902000,8046.00,5746.84,2299.16\nUNIFRAX AUSTRALIA PTY LTD,9x-Export,732820000,235.20,168.08,67.12\nUNIFRAX AUSTRALIA PTY LTD,9x-Export,734030000,220.50,157.54,62.96\nUNIFRAX EMISSION CONTROL,9x-Export,734751200,19817.50,3770.74,16046.76\nUNIFRAX EMISSION CONTROL,9x-Export,779988364,214.80,45.22,169.58\nUNIFRAX FRANCE,9x-Export,735602115,17419.60,12944.86,4474.74\nUNIFRAX FRANCE,9x-Export,735602116,12740.00,9410.36,3329.64\nUNIFRAX FRANCE,9x-Export,735602110,3920.00,2800.22,1119.78\nUNIFRAX FRANCE,9x-Export,735602000,1294.70,924.63,370.07\nUNIFRAX GMBH,9x-Export,793160000,37523.26,26801.84,10721.42\nUNIFRAX LTD.,9x-Export,726020007,2481.84,1694.63,787.21\nUNIFRAX LTD.,9x-Export,723960000,494.00,352.92,141.08\nUNIFRAX LTD.,9x-Export,720219100,202.95,145.47,57.48\nUNIFRAX LTD.,9x-Export,732905000,197.23,140.83,56.40\nUNIFRAX S.R.O.,9x-Export,793409000,27048.71,19320.73,7727.98\nUNIFRAX S.R.O.,9x-Export,793408000,21003.18,15002.56,6000.62\nUTSU CO. LTD JAPAN,9x-Export,734311100,6691.08,587.30,6103.78\nVATANA PHAISAL ENGNRG CO.,9x-Export,733403200,29376.60,2677.57,26699.03\nVESUVIUS MEXICO SA DE CV,9x-Export,733586600,514771.20,328196.46,186574.74\nVESUVIUS MEXICO SA DE CV,9x-Export,733571000,172566.72,125708.88,46857.84\nVESUVIUS MEXICO SA DE CV,9x-Export,733403300,145368.96,68065.19,77303.77\nVESUVIUS MEXICO SA DE CV,9x-Export,733566300,12355.20,7137.60,5217.60\nVESUVIUS MEXICO SA DE CV,9x-Export,733464900,7109.40,4933.61,2175.79\nVESUVIUS MEXICO SA DE CV,9x-Export,764781000,6874.00,3867.56,3006.44\nVESUVIUS MEXICO SA DE CV,9x-Export,733490600,4328.00,2388.36,1939.64\nVESUVIUS MEXICO SA DE CV,9x-Export,733496000,4328.00,2388.36,1939.64\nVESUVIUS MEXICO SA DE CV,9x-Export,764821000,2402.00,933.36,1468.64\nVESUVIUS MEXICO SA DE CV,9x-Export,732662000,1119.02,210.44,908.58" }, { "path": "Data/dhiraj.csv", "content": 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Insurance\"\n\"963\",423,\"Stocks\"\n\"964\",446,\"NPS\"\n\"965\",481,\"Savings Account\"\n\"966\",256,\"NRI Banking\"\n\"967\",427,\"PPF\"\n\"968\",470,\"Mobile Banking\"\n\"969\",177,\"Mutual Funds\"\n\"970\",439,\"Stocks\"\n\"971\",348,\"Mobile Banking\"\n\"972\",16,\"Credit Card\"\n\"973\",72,\"Mutual Funds\"\n\"974\",95,\"Debit Card\"\n\"975\",222,\"NPS\"\n\"976\",497,\"Mobile Banking\"\n\"977\",78,\"Stocks\"\n\"978\",310,\"NPS\"\n\"979\",447,\"Savings Account\"\n\"980\",282,\"Bonds\"\n\"981\",235,\"Credit Card\"\n\"982\",144,\"Credit Card\"\n\"983\",105,\"NRI Banking\"\n\"984\",499,\"Debit Card\"\n\"985\",80,\"Debit Card\"\n\"986\",219,\"Debit Card\"\n\"987\",98,\"Stocks\"\n\"988\",245,\"FD\"\n\"989\",86,\"General Insurance\"\n\"990\",470,\"Credit Card\"\n\"991\",66,\"Bonds\"\n\"992\",398,\"Credit Card\"\n\"993\",224,\"Credit Card\"\n\"994\",48,\"FD\"\n\"995\",96,\"NPS\"\n\"996\",471,\"FD\"\n\"997\",302,\"Stocks\"\n\"998\",325,\"General Insurance\"\n\"999\",21,\"Savings Account\"\n\"1000\",75,\"Mobile Banking\"\n" }, { "path": "Data/grades.csv", "content": "ser,student,course,batch,Internal 1 Assessment (R) (O),Internal 2 Assessment (R) (O),Assignment,Viva,Attendance,Final,Calculated Grade,Comment Faculty,Comment HOD,gender,cat,dob,backs\n1,S001,BSCDS,2016,6,6,5,5,13,33,68,,,M,GEN,8-Nov-94,1\n2,S002,BSCDS,2017,8,5,5,5,15,48,86,,,M,GEN,24-Jul-93,1\n3,S003,BSCDS,2016,8,5,8,9,13,28,71,,,M,GEN,17-Feb-95,2\n4,S004,BSCDS,2017,6,6,6,7,16,47,88,,,M,GEN,4-Mar-94,6\n5,S005,BSCDS,2016,5,7,7,8,12,39,78,,,M,GEN,20-Jun-95,4\n6,S006,BSCDS,2017,5,7,9,8,15,37,81,,,M,ST,8-May-94,4\n7,S007,BSCDS,2016,8,7,6,9,15,44,89,,,M,GEN,7-May-94,6\n8,S008,BSCDS,2017,9,9,6,9,13,33,79,,,M,GEN,15-Aug-94,0\n9,S009,BSCDS,2016,9,5,5,6,12,26,63,,,M,GEN,24-Nov-94,0\n10,S010,BSCDS,2017,8,9,5,8,19,29,78,,,F,GEN,25-Jan-94,4\n11,S011,BSCDS,2016,9,5,8,8,16,32,78,,,M,GEN,8-Jun-94,3\n12,S012,BSCDS,2017,8,8,7,7,16,49,95,,,M,OBC,24-Jun-95,5\n13,S013,BSCDS,2016,8,7,6,5,13,38,77,,,M,GEN,31-Oct-94,0\n14,S014,BSCDS,2017,7,6,7,5,15,26,66,,,F,GEN,21-Oct-95,0\n15,S015,BSCDS,2016,9,6,9,5,19,29,77,,,F,OBC,30-Jun-96,0\n16,S016,BSCDS,2017,9,7,9,9,16,29,79,Good,,F,GEN,25-Sep-94,0\n17,S017,BSCDS,2016,7,9,9,8,13,33,79,,,M,GEN,16-May-94,0\n18,S018,BSCDS,2017,7,7,9,7,13,41,84,,,M,GEN,24-Jun-95,1\n19,S019,BSCDS,2016,7,7,8,7,19,35,83,,,F,GEN,11-Dec-93,2\n20,S020,BSCDS,2017,6,5,5,8,14,35,73,,,M,GEN,30-Jun-96,1\n21,S021,MSCDS,2016,9,6,5,6,14,39,79,,,F,OBC,11-Dec-93,0\n22,S022,MSCDS,2017,9,6,8,9,18,27,77,,,F,GEN,6-Nov-95,0\n23,S023,MSCDS,2016,5,7,8,6,15,25,66,,,F,GEN,22-Dec-93,0\n24,S024,MSCDS,2017,8,9,8,6,16,46,93,,,M,SC,6-Feb-95,2\n25,S025,MSCDS,2016,6,8,7,7,15,37,80,,,M,GEN,6-Jun-94,2\n26,S026,MSCDS,2017,7,9,9,7,17,32,81,,,M,GEN,2-Jan-94,0\n27,S027,MSCDS,2016,9,6,6,8,11,44,84,,,F,GEN,8-Apr-94,0\n28,S028,MSCDS,2017,7,5,8,8,10,42,80,,,F,GEN,18-Nov-94,0\n29,S029,MSCDS,2016,6,5,7,5,20,39,82,,,F,GEN,3-Jul-95,0\n30,S030,MSCDS,2017,5,8,9,7,18,29,76,,,M,OBC,10-Jan-93,0\n31,S031,MSCDS,2016,5,6,8,6,10,32,67,,,F,GEN,19-Dec-92,1\n32,S032,MSCDS,2017,5,5,6,5,12,32,65,,,M,GEN,19-Sep-93,0\n33,S033,MSCDS,2016,5,9,6,9,15,27,71,,,F,GEN,13-Aug-94,0\n34,S034,MSCDS,2017,5,5,6,6,18,28,68,,,F,GEN,21-Jan-94,0\n35,S035,MSCDS,2016,7,7,7,9,17,38,85,,,M,GEN,25-Feb-94,0\n36,S036,MSCDS,2017,6,7,5,6,13,35,72,,,M,SC,12-Jan-96,0\n37,S037,MSCDS,2016,7,7,7,5,14,44,84,,,M,GEN,27-Jul-95,3\n38,S038,MSCDS,2017,7,9,7,8,13,27,71,,,M,GEN,12-Jan-96,0\n39,S039,MSCDS,2016,8,7,7,8,18,31,79,,,M,SC,18-Feb-95,0\n40,S040,MSCDS,2017,7,9,6,5,15,33,75,,,F,GEN,3-Mar-95,0\n41,S041,MSCDS,2016,5,6,9,7,12,40,79,,,M,GEN,30-Jun-94,0\n42,S042,MSCDS,2017,8,6,9,9,20,41,93,,,M,GEN,23-Oct-94,0\n43,S043,MSCDS,2016,6,6,7,7,15,26,67,,,M,SC,15-Nov-93,3\n44,S044,MSCDS,2017,7,7,5,6,18,29,72,,,F,GEN,11-May-93,0\n45,S045,MSCDS,2016,8,5,6,5,16,36,76,,,F,GEN,2-Apr-95,0\n46,S046,MSCDS,2017,8,8,8,7,13,49,93,,,F,GEN,15-Mar-94,0\n47,S047,MSCDS,2016,8,9,8,6,11,29,71,,,M,ST,,11\n48,S048,MSCDS,2017,7,9,9,6,12,44,87,,,M,GEN,,8\n49,S049,MSCDS,2016,7,7,7,9,19,48,97,,,M,GEN,5-Apr-91,4\n50,S050,MSCDS,2017,9,7,8,7,12,32,75,,,M,GEN,16-Sep-93,4" }, { "path": "Data/hhe.txt", "content": "CMAP\nCBAP\nCFAP\nCLAP" }, { "path": "Data/iimc1.csv", "content": "rollno,sname,class,marks\n1,sname1,M,20\n2,sname2,F,43\n3,sname3,M,23\n4,sname4,M,31.66666667\n5,sname5,F,33.16666667\n6,sname6,M,34.66666667\n7,sname7,M,36.16666667\n8,sname8,F,37.66666667\n9,sname9,M,39.16666667\n10,sname10,M,40.66666667\n11,sname11,F,42.16666667\n12,sname12,M,43.66666667" }, { "path": "Data/iimtrichy.csv", "content": "\"\",\"rollno\",\"name\",\"gender\",\"course\",\"marks1\",\"marks2\",\"grades\"\n\"1\",1,\"student1-1\",\"Female\",\"MBA\",76,57,\"B\"\n\"2\",2,\"student1-2\",\"Male\",\"BBA\",53,47,\"B\"\n\"3\",3,\"student1-3\",\"Female\",\"BBA\",50,85,\"A\"\n\"4\",4,\"student1-4\",\"Male\",\"BBA\",70,73,\"C\"\n\"5\",5,\"student1-5\",\"Female\",\"BBA\",63,66,\"B\"\n\"6\",6,\"student1-6\",\"Male\",\"BBA\",62,55,\"C\"\n\"7\",7,\"student1-7\",\"Male\",\"MBA\",68,64,\"C\"\n\"8\",8,\"student1-8\",\"Male\",\"FPM\",73,72,\"A\"\n\"9\",9,\"student1-9\",\"Female\",\"FPM\",64,58,\"A\"\n\"10\",10,\"student1-10\",\"Female\",\"BBA\",65,83,\"B\"\n\"11\",11,\"student1-11\",\"Female\",\"MBA\",52,57,\"A\"\n\"12\",12,\"student1-12\",\"Female\",\"MBA\",61,66,\"C\"\n\"13\",13,\"student1-13\",\"Female\",\"BBA\",62,57,\"A\"\n\"14\",14,\"student1-14\",\"Female\",\"BBA\",65,69,\"A\"\n\"15\",15,\"student1-15\",\"Female\",\"MBA\",60,77,\"C\"\n\"16\",16,\"student1-16\",\"Male\",\"BBA\",56,53,\"B\"\n\"17\",17,\"student1-17\",\"Female\",\"MBA\",80,60,\"B\"\n\"18\",18,\"student1-18\",\"Female\",\"MBA\",71,55,\"A\"\n\"19\",19,\"student1-19\",\"Female\",\"BBA\",65,53,\"C\"\n\"20\",20,\"student1-20\",\"Female\",\"BBA\",60,67,\"B\"\n\"21\",21,\"student1-21\",\"Female\",\"FPM\",61,61,\"B\"\n\"22\",22,\"student1-22\",\"Male\",\"BBA\",66,42,\"A\"\n\"23\",23,\"student1-23\",\"Female\",\"FPM\",58,69,\"A\"\n\"24\",24,\"student1-24\",\"Male\",\"MBA\",82,73,\"A\"\n\"25\",25,\"student1-25\",\"Female\",\"MBA\",67,54,\"B\"\n\"26\",26,\"student1-26\",\"Male\",\"BBA\",66,50,\"A\"\n\"27\",27,\"student1-27\",\"Female\",\"FPM\",66,80,\"A\"\n\"28\",28,\"student1-28\",\"Female\",\"FPM\",67,69,\"C\"\n\"29\",29,\"student1-29\",\"Female\",\"BBA\",67,62,\"A\"\n\"30\",30,\"student1-30\",\"Male\",\"BBA\",71,49,\"A\"\n\"31\",31,\"student1-31\",\"Female\",\"MBA\",66,70,\"A\"\n\"32\",32,\"student1-32\",\"Female\",\"BBA\",57,41,\"B\"\n\"33\",33,\"student1-33\",\"Male\",\"BBA\",63,50,\"A\"\n\"34\",34,\"student1-34\",\"Female\",\"MBA\",77,48,\"A\"\n\"35\",35,\"student1-35\",\"Male\",\"FPM\",72,52,\"A\"\n\"36\",36,\"student1-36\",\"Female\",\"BBA\",54,49,\"A\"\n\"37\",37,\"student1-37\",\"Male\",\"BBA\",65,56,\"C\"\n\"38\",38,\"student1-38\",\"Female\",\"BBA\",68,58,\"C\"\n\"39\",39,\"student1-39\",\"Female\",\"BBA\",65,89,\"A\"\n\"40\",40,\"student1-40\",\"Female\",\"BBA\",71,56,\"B\"\n\"41\",41,\"student1-41\",\"Female\",\"FPM\",55,55,\"C\"\n\"42\",42,\"student1-42\",\"Female\",\"BBA\",60,38,\"B\"\n\"43\",43,\"student1-43\",\"Female\",\"FPM\",62,65,\"B\"\n\"44\",44,\"student1-44\",\"Female\",\"BBA\",69,47,\"C\"\n\"45\",45,\"student1-45\",\"Female\",\"BBA\",71,54,\"B\"\n\"46\",46,\"student1-46\",\"Female\",\"FPM\",69,41,\"A\"\n\"47\",47,\"student1-47\",\"Female\",\"FPM\",61,74,\"A\"\n\"48\",48,\"student1-48\",\"Male\",\"FPM\",67,58,\"A\"\n\"49\",49,\"student1-49\",\"Female\",\"BBA\",76,58,\"A\"\n\"50\",50,\"student1-50\",\"Female\",\"MBA\",68,40,\"C\"\n\"51\",51,\"student1-51\",\"Female\",\"BBA\",61,56,\"C\"\n\"52\",52,\"student1-52\",\"Female\",\"BBA\",81,61,\"B\"\n\"53\",53,\"student1-53\",\"Male\",\"BBA\",59,59,\"A\"\n\"54\",54,\"student1-54\",\"Female\",\"BBA\",68,56,\"B\"\n\"55\",55,\"student1-55\",\"Female\",\"MBA\",57,87,\"B\"\n\"56\",56,\"student1-56\",\"Female\",\"MBA\",44,77,\"B\"\n\"57\",57,\"student1-57\",\"Female\",\"BBA\",62,56,\"A\"\n\"58\",58,\"student1-58\",\"Female\",\"BBA\",59,49,\"C\"\n\"59\",59,\"student1-59\",\"Female\",\"BBA\",65,67,\"C\"\n\"60\",60,\"student1-60\",\"Female\",\"BBA\",63,71,\"B\"\n" }, { "path": "Data/iitgfa.csv", "content": "\"\",\"Stock\",\"format(Date, \"\"%b\"\")\",\"Date\",\"Open\",\"High\",\"Low\",\"Last\",\"Close\",\"Qty\",\"Turnover\"\n\"1\",\"ICICI\",\"Apr\",2018-04-15,281.057142857143,285.942857142857,277.5,282.197619047619,282.07380952381,24050928.047619,67084.3380952381\n\"2\",\"ICICI\",\"Aug\",2018-08-16,325.792857142857,332.238095238095,322.992857142857,327.947619047619,328.290476190476,23088924.8095238,76197.1176190476\n\"3\",\"ICICI\",\"Dec\",2017-12-15,309.51,312.5475,305.78,309.5475,309.55,11178511.2,34568.218\n\"4\",\"ICICI\",\"Feb\",2018-02-14,326.652631578947,330.892105263158,321.776315789474,325.95,325.763157894737,15344700.2631579,50177.1942105263\n\"5\",\"ICICI\",\"Jan\",2018-01-15,331.390909090909,336.034090909091,328.015909090909,332.786363636364,333.006818181818,16588079.2727273,56313.9654545455\n\"6\",\"ICICI\",\"Jul\",2018-07-15,273.888636363636,277.929545454545,270.820454545455,274.738636363636,274.952272727273,22464037.6818182,62316.435\n\"7\",\"ICICI\",\"Jun\",2018-06-15,289.092857142857,291.57619047619,284.192857142857,287.569047619048,287.440476190476,20901907.6666667,60026.2723809524\n\"8\",\"ICICI\",\"Mar\",2018-03-15,295.221052631579,297.734210526316,289.986842105263,293.252631578947,293.092105263158,19461420.6315789,56697.7152631579\n\"9\",\"ICICI\",\"May\",2018-05-16,294.995454545455,299.411363636364,291.3,295.145454545455,294.904545454545,19882321.7272727,59117.6736363636\n\"10\",\"ICICI\",\"Nov\",2018-05-08,335.87619047619,339.986904761905,332.290476190476,335.591666666667,335.757142857143,18000520.1904762,61134.1404761905\n\"11\",\"ICICI\",\"Oct\",2018-04-21,298.113414634146,304.439024390244,293.108536585366,299.507317073171,299.578048780488,26530527.3658537,81204.5824390244\n\"12\",\"ICICI\",\"Sep\",2018-03-03,306,308.297435897436,300.878205128205,304.139743589744,304.057692307692,15207262.4871795,46365.8746153846\n\"13\",\"PNB\",\"Apr\",2018-04-15,97.7071428571429,98.9166666666667,96.0880952380952,97.2809523809524,97.3238095238095,22150276.952381,21787.7357142857\n\"14\",\"PNB\",\"Aug\",2018-08-16,84.3833333333333,86.002380952381,82.8452380952381,84.2047619047619,84.2714285714286,42736065.047619,36353.0195238095\n\"15\",\"PNB\",\"Dec\",2017-12-15,173.255,175.755,170.345,172.9975,173.1525,16637279.95,28811.083\n\"16\",\"PNB\",\"Feb\",2018-02-14,136.928947368421,140.473684210526,132.284210526316,135.526315789474,135.7,75735091.1578947,91027.0889473684\n\"17\",\"PNB\",\"Jan\",2018-01-15,175.222727272727,178.713636363636,170.972727272727,174.6,174.481818181818,18646075.7727273,33048.9181818182\n\"18\",\"PNB\",\"Jul\",2018-07-15,77.9954545454545,79.6340909090909,76.6522727272727,77.8636363636364,77.9545454545455,28482531.1363636,22616.3713636364\n\"19\",\"PNB\",\"Jun\",2018-06-15,85.1952380952381,86.6690476190476,83.4690476190476,84.65,84.6857142857143,22741398.2857143,19226.3271428571\n\"20\",\"PNB\",\"Mar\",2018-03-15,97.1578947368421,99.4026315789474,95.3605263157895,96.9157894736842,97.2236842105263,45559558.6842105,44665.6731578947\n\"21\",\"PNB\",\"May\",2018-05-16,86.2545454545455,87.9113636363636,84.3090909090909,85.7113636363636,85.8136363636364,25202194.1818182,20972.7672727273\n\"22\",\"PNB\",\"Nov\",2018-05-08,134.35,136.458333333333,131.232142857143,133.078571428571,133.033333333333,21876496.3333333,29859.2966666667\n\"23\",\"PNB\",\"Oct\",2018-04-21,106.35,110.412195121951,104.042682926829,107.762195121951,107.543902439024,25729571.4390244,31601.7863414634\n\"24\",\"PNB\",\"Sep\",2018-03-03,112.366666666667,113.365384615385,109.630769230769,110.915384615385,110.952564102564,19025502.1025641,16332.3797435897\n\"25\",\"SBI\",\"Apr\",2018-04-15,249.752380952381,252.221428571429,246.069047619048,248.766666666667,248.671428571429,18434845.7142857,45987.8019047619\n\"26\",\"SBI\",\"Aug\",2018-08-16,303.066666666667,307.683333333333,299.369047619048,303.697619047619,303.52380952381,29597692.4285714,90208.9980952381\n\"27\",\"SBI\",\"Dec\",2017-12-15,315.88,319.1,312.12,315.25,315.3425,15417624.7,48680.3625\n\"28\",\"SBI\",\"Feb\",2018-02-14,283.684210526316,287.576315789474,278.252631578947,282.723684210526,282.592105263158,26650593.4736842,74993.0978947368\n\"29\",\"SBI\",\"Jan\",2018-01-15,308.654545454545,312.413636363636,304.006818181818,307.918181818182,307.663636363636,21237596.2272727,65797.5290909091\n\"30\",\"SBI\",\"Jul\",2018-07-15,265.072727272727,269.202272727273,262.556818181818,266.161363636364,266.120454545455,18231484.0454545,49684.8363636364\n\"31\",\"SBI\",\"Jun\",2018-06-15,271.32380952381,274.32619047619,268.130952380952,270.919047619048,270.904761904762,17631953.9047619,47938.8947619048\n\"32\",\"SBI\",\"Mar\",2018-03-15,252.118421052632,256.231578947368,247.842105263158,251.605263157895,251.665789473684,25845394.4210526,64997.677368421\n\"33\",\"SBI\",\"May\",2018-05-16,251.856818181818,256.327272727273,248.570454545455,252.911363636364,252.909090909091,22413778,57288.5063636364\n\"34\",\"SBI\",\"Nov\",2018-05-08,307.953571428571,312.330952380952,303.082142857143,307.576190476191,307.440476190476,23616111.2142857,72652.9595238095\n\"35\",\"SBI\",\"Oct\",2018-04-21,265.273170731707,270.871951219512,261.074390243902,265.784146341463,265.90487804878,29496173.2682927,83055.2080487805\n\"36\",\"SBI\",\"Sep\",2018-03-03,276.528205128205,278.662820512821,271.726923076923,273.964102564103,274.108974358974,18695389.9487179,51688.7753846154\n" }, { "path": "Data/iris.csv", "content": "\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\",\"Species\"\n5.1,3.5,1.4,0.2,\"setosa\"\n4.9,3,1.4,0.2,\"setosa\"\n4.7,3.2,1.3,0.2,\"setosa\"\n4.6,3.1,1.5,0.2,\"setosa\"\n5,3.6,1.4,0.2,\"setosa\"\n5.4,3.9,1.7,0.4,\"setosa\"\n4.6,3.4,1.4,0.3,\"setosa\"\n5,3.4,1.5,0.2,\"setosa\"\n4.4,2.9,1.4,0.2,\"setosa\"\n4.9,3.1,1.5,0.1,\"setosa\"\n5.4,3.7,1.5,0.2,\"setosa\"\n4.8,3.4,1.6,0.2,\"setosa\"\n4.8,3,1.4,0.1,\"setosa\"\n4.3,3,1.1,0.1,\"setosa\"\n5.8,4,1.2,0.2,\"setosa\"\n5.7,4.4,1.5,0.4,\"setosa\"\n5.4,3.9,1.3,0.4,\"setosa\"\n5.1,3.5,1.4,0.3,\"setosa\"\n5.7,3.8,1.7,0.3,\"setosa\"\n5.1,3.8,1.5,0.3,\"setosa\"\n5.4,3.4,1.7,0.2,\"setosa\"\n5.1,3.7,1.5,0.4,\"setosa\"\n4.6,3.6,1,0.2,\"setosa\"\n5.1,3.3,1.7,0.5,\"setosa\"\n4.8,3.4,1.9,0.2,\"setosa\"\n5,3,1.6,0.2,\"setosa\"\n5,3.4,1.6,0.4,\"setosa\"\n5.2,3.5,1.5,0.2,\"setosa\"\n5.2,3.4,1.4,0.2,\"setosa\"\n4.7,3.2,1.6,0.2,\"setosa\"\n4.8,3.1,1.6,0.2,\"setosa\"\n5.4,3.4,1.5,0.4,\"setosa\"\n5.2,4.1,1.5,0.1,\"setosa\"\n5.5,4.2,1.4,0.2,\"setosa\"\n4.9,3.1,1.5,0.2,\"setosa\"\n5,3.2,1.2,0.2,\"setosa\"\n5.5,3.5,1.3,0.2,\"setosa\"\n4.9,3.6,1.4,0.1,\"setosa\"\n4.4,3,1.3,0.2,\"setosa\"\n5.1,3.4,1.5,0.2,\"setosa\"\n5,3.5,1.3,0.3,\"setosa\"\n4.5,2.3,1.3,0.3,\"setosa\"\n4.4,3.2,1.3,0.2,\"setosa\"\n5,3.5,1.6,0.6,\"setosa\"\n5.1,3.8,1.9,0.4,\"setosa\"\n4.8,3,1.4,0.3,\"setosa\"\n5.1,3.8,1.6,0.2,\"setosa\"\n4.6,3.2,1.4,0.2,\"setosa\"\n5.3,3.7,1.5,0.2,\"setosa\"\n5,3.3,1.4,0.2,\"setosa\"\n7,3.2,4.7,1.4,\"versicolor\"\n6.4,3.2,4.5,1.5,\"versicolor\"\n6.9,3.1,4.9,1.5,\"versicolor\"\n5.5,2.3,4,1.3,\"versicolor\"\n6.5,2.8,4.6,1.5,\"versicolor\"\n5.7,2.8,4.5,1.3,\"versicolor\"\n6.3,3.3,4.7,1.6,\"versicolor\"\n4.9,2.4,3.3,1,\"versicolor\"\n6.6,2.9,4.6,1.3,\"versicolor\"\n5.2,2.7,3.9,1.4,\"versicolor\"\n5,2,3.5,1,\"versicolor\"\n5.9,3,4.2,1.5,\"versicolor\"\n6,2.2,4,1,\"versicolor\"\n6.1,2.9,4.7,1.4,\"versicolor\"\n5.6,2.9,3.6,1.3,\"versicolor\"\n6.7,3.1,4.4,1.4,\"versicolor\"\n5.6,3,4.5,1.5,\"versicolor\"\n5.8,2.7,4.1,1,\"versicolor\"\n6.2,2.2,4.5,1.5,\"versicolor\"\n5.6,2.5,3.9,1.1,\"versicolor\"\n5.9,3.2,4.8,1.8,\"versicolor\"\n6.1,2.8,4,1.3,\"versicolor\"\n6.3,2.5,4.9,1.5,\"versicolor\"\n6.1,2.8,4.7,1.2,\"versicolor\"\n6.4,2.9,4.3,1.3,\"versicolor\"\n6.6,3,4.4,1.4,\"versicolor\"\n6.8,2.8,4.8,1.4,\"versicolor\"\n6.7,3,5,1.7,\"versicolor\"\n6,2.9,4.5,1.5,\"versicolor\"\n5.7,2.6,3.5,1,\"versicolor\"\n5.5,2.4,3.8,1.1,\"versicolor\"\n5.5,2.4,3.7,1,\"versicolor\"\n5.8,2.7,3.9,1.2,\"versicolor\"\n6,2.7,5.1,1.6,\"versicolor\"\n5.4,3,4.5,1.5,\"versicolor\"\n6,3.4,4.5,1.6,\"versicolor\"\n6.7,3.1,4.7,1.5,\"versicolor\"\n6.3,2.3,4.4,1.3,\"versicolor\"\n5.6,3,4.1,1.3,\"versicolor\"\n5.5,2.5,4,1.3,\"versicolor\"\n5.5,2.6,4.4,1.2,\"versicolor\"\n6.1,3,4.6,1.4,\"versicolor\"\n5.8,2.6,4,1.2,\"versicolor\"\n5,2.3,3.3,1,\"versicolor\"\n5.6,2.7,4.2,1.3,\"versicolor\"\n5.7,3,4.2,1.2,\"versicolor\"\n5.7,2.9,4.2,1.3,\"versicolor\"\n6.2,2.9,4.3,1.3,\"versicolor\"\n5.1,2.5,3,1.1,\"versicolor\"\n5.7,2.8,4.1,1.3,\"versicolor\"\n6.3,3.3,6,2.5,\"virginica\"\n5.8,2.7,5.1,1.9,\"virginica\"\n7.1,3,5.9,2.1,\"virginica\"\n6.3,2.9,5.6,1.8,\"virginica\"\n6.5,3,5.8,2.2,\"virginica\"\n7.6,3,6.6,2.1,\"virginica\"\n4.9,2.5,4.5,1.7,\"virginica\"\n7.3,2.9,6.3,1.8,\"virginica\"\n6.7,2.5,5.8,1.8,\"virginica\"\n7.2,3.6,6.1,2.5,\"virginica\"\n6.5,3.2,5.1,2,\"virginica\"\n6.4,2.7,5.3,1.9,\"virginica\"\n6.8,3,5.5,2.1,\"virginica\"\n5.7,2.5,5,2,\"virginica\"\n5.8,2.8,5.1,2.4,\"virginica\"\n6.4,3.2,5.3,2.3,\"virginica\"\n6.5,3,5.5,1.8,\"virginica\"\n7.7,3.8,6.7,2.2,\"virginica\"\n7.7,2.6,6.9,2.3,\"virginica\"\n6,2.2,5,1.5,\"virginica\"\n6.9,3.2,5.7,2.3,\"virginica\"\n5.6,2.8,4.9,2,\"virginica\"\n7.7,2.8,6.7,2,\"virginica\"\n6.3,2.7,4.9,1.8,\"virginica\"\n6.7,3.3,5.7,2.1,\"virginica\"\n7.2,3.2,6,1.8,\"virginica\"\n6.2,2.8,4.8,1.8,\"virginica\"\n6.1,3,4.9,1.8,\"virginica\"\n6.4,2.8,5.6,2.1,\"virginica\"\n7.2,3,5.8,1.6,\"virginica\"\n7.4,2.8,6.1,1.9,\"virginica\"\n7.9,3.8,6.4,2,\"virginica\"\n6.4,2.8,5.6,2.2,\"virginica\"\n6.3,2.8,5.1,1.5,\"virginica\"\n6.1,2.6,5.6,1.4,\"virginica\"\n7.7,3,6.1,2.3,\"virginica\"\n6.3,3.4,5.6,2.4,\"virginica\"\n6.4,3.1,5.5,1.8,\"virginica\"\n6,3,4.8,1.8,\"virginica\"\n6.9,3.1,5.4,2.1,\"virginica\"\n6.7,3.1,5.6,2.4,\"virginica\"\n6.9,3.1,5.1,2.3,\"virginica\"\n5.8,2.7,5.1,1.9,\"virginica\"\n6.8,3.2,5.9,2.3,\"virginica\"\n6.7,3.3,5.7,2.5,\"virginica\"\n6.7,3,5.2,2.3,\"virginica\"\n6.3,2.5,5,1.9,\"virginica\"\n6.5,3,5.2,2,\"virginica\"\n6.2,3.4,5.4,2.3,\"virginica\"\n5.9,3,5.1,1.8,\"virginica\"\n" }, { "path": "Data/itemlist1", "content": "I1,I2,I5\nI2,I4\nI2,I3\nI1,I2,I4\nI1,I3\nI2,I3\nI1,I3\nI1,I2,I3,I5\nI1,I2,I3\n" }, { "path": "Data/km5_c2.csv", "content": "\"\",\"Channel\",\"Region\",\"Fresh\",\"Milk\",\"Grocery\",\"Frozen\",\"Detergents_Paper\",\"Delicassen\",\"C3\",\"C5\"\n\"48\",2,3,44466,54259,55571,7782,24171,6465,1,2\n\"57\",2,3,4098,29892,26866,2616,17740,1340,1,2\n\"62\",2,3,35942,38369,59598,3254,26701,2017,1,2\n\"66\",2,3,85,20959,45828,36,24231,1423,1,2\n\"86\",2,3,16117,46197,92780,1026,40827,2944,1,2\n\"87\",2,3,22925,73498,32114,987,20070,903,1,2\n\"93\",2,3,9198,27472,32034,3232,18906,5130,1,2\n\"212\",2,1,12119,28326,39694,4736,19410,2870,1,2\n\"252\",2,1,6134,23133,33586,6746,18594,5121,1,2\n\"334\",2,2,8565,4980,67298,131,38102,1215,1,2\n" }, { "path": "Data/logr2.csv", "content": "userid,gender,age,salary,purchased\n15624510,Male,19,19000,0\n15810944,Male,35,20000,0\n15668575,Female,26,43000,0\n15603246,Female,27,57000,0\n15804002,Male,19,76000,0\n15728773,Male,27,58000,0\n15598044,Female,27,84000,0\n15694829,Female,32,150000,1\n15600575,Male,25,33000,0\n15727311,Female,35,65000,0\n15570769,Female,26,80000,0\n15606274,Female,26,52000,0\n15746139,Male,20,86000,0\n15704987,Male,32,18000,0\n15628972,Male,18,82000,0\n15697686,Male,29,80000,0\n15733883,Male,47,25000,1\n15617482,Male,45,26000,1\n15704583,Male,46,28000,1\n15621083,Female,48,29000,1\n15649487,Male,45,22000,1\n15736760,Female,47,49000,1\n15714658,Male,48,41000,1\n15599081,Female,45,22000,1\n15705113,Male,46,23000,1\n15631159,Male,47,20000,1\n15792818,Male,49,28000,1\n15633531,Female,47,30000,1\n15744529,Male,29,43000,0\n15669656,Male,31,18000,0\n15581198,Male,31,74000,0\n15729054,Female,27,137000,1\n15573452,Female,21,16000,0\n15776733,Female,28,44000,0\n15724858,Male,27,90000,0\n15713144,Male,35,27000,0\n15690188,Female,33,28000,0\n15689425,Male,30,49000,0\n15671766,Female,26,72000,0\n15782806,Female,27,31000,0\n15764419,Female,27,17000,0\n15591915,Female,33,51000,0\n15772798,Male,35,108000,0\n15792008,Male,30,15000,0\n15715541,Female,28,84000,0\n15639277,Male,23,20000,0\n15798850,Male,25,79000,0\n15776348,Female,27,54000,0\n15727696,Male,30,135000,1\n15793813,Female,31,89000,0\n15694395,Female,24,32000,0\n15764195,Female,18,44000,0\n15744919,Female,29,83000,0\n15671655,Female,35,23000,0\n15654901,Female,27,58000,0\n15649136,Female,24,55000,0\n15775562,Female,23,48000,0\n15807481,Male,28,79000,0\n15642885,Male,22,18000,0\n15789109,Female,32,117000,0\n15814004,Male,27,20000,0\n15673619,Male,25,87000,0\n15595135,Female,23,66000,0\n15583681,Male,32,120000,1\n15605000,Female,59,83000,0\n15718071,Male,24,58000,0\n15679760,Male,24,19000,0\n15654574,Female,23,82000,0\n15577178,Female,22,63000,0\n15595324,Female,31,68000,0\n15756932,Male,25,80000,0\n15726358,Female,24,27000,0\n15595228,Female,20,23000,0\n15782530,Female,33,113000,0\n15592877,Male,32,18000,0\n15651983,Male,34,112000,1\n15746737,Male,18,52000,0\n15774179,Female,22,27000,0\n15667265,Female,28,87000,0\n15655123,Female,26,17000,0\n15595917,Male,30,80000,0\n15668385,Male,39,42000,0\n15709476,Male,20,49000,0\n15711218,Male,35,88000,0\n15798659,Female,30,62000,0\n15663939,Female,31,118000,1\n15694946,Male,24,55000,0\n15631912,Female,28,85000,0\n15768816,Male,26,81000,0\n15682268,Male,35,50000,0\n15684801,Male,22,81000,0\n15636428,Female,30,116000,0\n15809823,Male,26,15000,0\n15699284,Female,29,28000,0\n15786993,Female,29,83000,0\n15709441,Female,35,44000,0\n15710257,Female,35,25000,0\n15582492,Male,28,123000,1\n15575694,Male,35,73000,0\n15756820,Female,28,37000,0\n15766289,Male,27,88000,0\n15593014,Male,28,59000,0\n15584545,Female,32,86000,0\n15675949,Female,33,149000,1\n15672091,Female,19,21000,0\n15801658,Male,21,72000,0\n15706185,Female,26,35000,0\n15789863,Male,27,89000,0\n15720943,Male,26,86000,0\n15697997,Female,38,80000,0\n15665416,Female,39,71000,0\n15660200,Female,37,71000,0\n15619653,Male,38,61000,0\n15773447,Male,37,55000,0\n15739160,Male,42,80000,0\n15689237,Male,40,57000,0\n15679297,Male,35,75000,0\n15591433,Male,36,52000,0\n15642725,Male,40,59000,0\n15701962,Male,41,59000,0\n15811613,Female,36,75000,0\n15741049,Male,37,72000,0\n15724423,Female,40,75000,0\n15574305,Male,35,53000,0\n15678168,Female,41,51000,0\n15697020,Female,39,61000,0\n15610801,Male,42,65000,0\n15745232,Male,26,32000,0\n15722758,Male,30,17000,0\n15792102,Female,26,84000,0\n15675185,Male,31,58000,0\n15801247,Male,33,31000,0\n15725660,Male,30,87000,0\n15638963,Female,21,68000,0\n15800061,Female,28,55000,0\n15578006,Male,23,63000,0\n15668504,Female,20,82000,0\n15687491,Male,30,107000,1\n15610403,Female,28,59000,0\n15741094,Male,19,25000,0\n15807909,Male,19,85000,0\n15666141,Female,18,68000,0\n15617134,Male,35,59000,0\n15783029,Male,30,89000,0\n15622833,Female,34,25000,0\n15746422,Female,24,89000,0\n15750839,Female,27,96000,1\n15749130,Female,41,30000,0\n15779862,Male,29,61000,0\n15767871,Male,20,74000,0\n15679651,Female,26,15000,0\n15576219,Male,41,45000,0\n15699247,Male,31,76000,0\n15619087,Female,36,50000,0\n15605327,Male,40,47000,0\n15610140,Female,31,15000,0\n15791174,Male,46,59000,0\n15602373,Male,29,75000,0\n15762605,Male,26,30000,0\n15598840,Female,32,135000,1\n15744279,Male,32,100000,1\n15670619,Male,25,90000,0\n15599533,Female,37,33000,0\n15757837,Male,35,38000,0\n15697574,Female,33,69000,0\n15578738,Female,18,86000,0\n15762228,Female,22,55000,0\n15614827,Female,35,71000,0\n15789815,Male,29,148000,1\n15579781,Female,29,47000,0\n15587013,Male,21,88000,0\n15570932,Male,34,115000,0\n15794661,Female,26,118000,0\n15581654,Female,34,43000,0\n15644296,Female,34,72000,0\n15614420,Female,23,28000,0\n15609653,Female,35,47000,0\n15594577,Male,25,22000,0\n15584114,Male,24,23000,0\n15673367,Female,31,34000,0\n15685576,Male,26,16000,0\n15774727,Female,31,71000,0\n15694288,Female,32,117000,1\n15603319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74372,Female,58,47000,1\n15671249,Male,46,88000,1\n15779744,Male,38,71000,0\n15624755,Female,54,26000,1\n15611430,Female,60,46000,1\n15774744,Male,60,83000,1\n15629885,Female,39,73000,0\n15708791,Male,59,130000,1\n15793890,Female,37,80000,0\n15646091,Female,46,32000,1\n15596984,Female,46,74000,0\n15800215,Female,42,53000,0\n15577806,Male,41,87000,1\n15749381,Female,58,23000,1\n15683758,Male,42,64000,0\n15670615,Male,48,33000,1\n15715622,Female,44,139000,1\n15707634,Male,49,28000,1\n15806901,Female,57,33000,1\n15775335,Male,56,60000,1\n15724150,Female,49,39000,1\n15627220,Male,39,71000,0\n15672330,Male,47,34000,1\n15668521,Female,48,35000,1\n15807837,Male,48,33000,1\n15592570,Male,47,23000,1\n15748589,Female,45,45000,1\n15635893,Male,60,42000,1\n15757632,Female,39,59000,0\n15691863,Female,46,41000,1\n15706071,Male,51,23000,1\n15654296,Female,50,20000,1\n15755018,Male,36,33000,0\n15594041,Female,49,36000,1" }, { "path": "Data/msales.csv", "content": "Merchant_Id,Annual_Sales,Merchant_Location,Zone,No_of_helpers,Daily_OPEN_hours,Years_Open,Owner's_Age,Owner's_Education,Cashier_Sales_Person_Age,Merchant_Type,Shopping_Complex_Or_Standalone\nmid,sales,loc,zone,helpers,hours,years ,oage,oedn,cage,mtype,mcat\n247,195,NJ City,E,1,20,18,66,High School,33,Owner,Standalone\n9026,417,NJ City,S,1,10,14,43,Graduation,21,Patnership,Complex\n5245,147.46,Boston,N,2,23,1,46,High School,24,Patnership,Standalone\n6644,500,Boston,W,2,24,17,59,Graduation,22,Franchise,Complex\n4497,111.19,NJ City,N,20,11,4,76,Graduation,22,Patnership,Standalone\n2571,113,NJ City,E,1,24,14,31,High School,22,Owner,Standalone\n6534,447,Boston,S,1,22,12,25,Graduation,31,Patnership,Complex\n7201,393,Boston,N,1,12,16,49,Graduation,21,Patnership,Standalone\n9051,434,NJ City,E,2,13,14,79,Graduation,19,Owner,Standalone\n4157,183,NJ City,N,3,18,6,81,High School,32,Patnership,Standalone\n3746,110,NJ City,S,2,15,21,57,High School,35,Patnership,Complex\n6386,381,Boston,S,3,17,10,50,Graduation,25,Patnership,Complex\n6094,283.85,Boston,S,15,8,2,80,High School,22,Patnership,Complex\n4090,375.26,NJ City,S,5,13,4,60,High School,28,Patnership,Complex\n9536,370,NJ City,W,3,8,18,62,Graduation,23,Franchise,Complex\n7047,414,Boston,E,2,16,7,61,Graduation,27,Owner,Standalone\n3199,244,NJ City,E,2,21,16,46,High School,33,Owner,Standalone\n3548,122,NJ City,W,2,20,6,76,High School,31,Franchise,Complex\n3694,289,NJ City,S,18,21,1,54,High School,31,Patnership,Complex\n6101,467,Boston,N,2,8,23,54,Graduation,28,Patnership,Standalone\n8857,69.28,NJ City,N,3,20,1,45,High School,30,Patnership,Standalone\n7901,379,Boston,N,2,10,19,59,Graduation,24,Patnership,Standalone\n5720,420,Boston,W,2,14,17,79,Graduation,35,Franchise,Complex\n8322,487,NJ City,S,3,20,7,67,Graduation,35,Patnership,Complex\n2789,193,NJ City,N,3,16,6,75,High School,25,Patnership,Standalone\n7970,474,Boston,S,1,20,22,28,Graduation,21,Patnership,Complex\n4478,117.18,NJ City,S,25,21,3,29,No education,19,Patnership,Complex\n2109,70.5,NJ City,N,15,19,4,81,Graduation,33,Patnership,Standalone\n8430,77.67,NJ City,S,17,18,2,32,Graduation,30,Patnership,Complex\n6569,275.81,Boston,N,12,10,2,69,No education,22,Patnership,Standalone\n4965,92.68,Boston,N,8,18,1,47,Graduation,34,Patnership,Standalone\n7817,387,Boston,N,3,8,10,54,Graduation,18,Patnership,Standalone\n1798,299,NJ City,S,2,20,11,56,High School,31,Patnership,Complex\n3433,188,NJ City,N,3,14,24,47,High School,31,Patnership,Standalone\n591,284,NJ City,E,1,18,17,39,High School,22,Owner,Standalone\n1863,238,NJ City,E,2,17,16,36,High School,29,Owner,Standalone\n4537,23.48,NJ City,N,9,13,4,80,High School,22,Patnership,Standalone\n1544,122.44,NJ City,W,12,13,3,57,No education,28,Franchise,Complex\n529,470,NJ City,N,2,20,5,51,Graduation,25,Patnership,Standalone\n8065,438,NJ City,N,1,20,7,31,Graduation,35,Patnership,Standalone\n3427,127.88,NJ City,E,2,24,1,74,High School,26,Owner,Standalone\n4624,366,NJ City,W,1,16,5,27,Graduation,21,Franchise,Complex\n1816,160,NJ City,W,2,24,7,56,High School,20,Franchise,Complex\n4232,462.12,NJ City,W,1,24,14,74,No education,24,Franchise,Complex\n3295,252,NJ City,E,2,20,9,77,High School,30,Owner,Standalone\n6953,176.16,Boston,N,1,8,2,77,No education,29,Patnership,Standalone\n9907,352,NJ City,E,1,24,18,38,Graduation,23,Owner,Standalone\n7869,499,Boston,N,2,18,7,55,Graduation,20,Patnership,Standalone\n3405,84.91,NJ City,N,13,10,4,50,Graduation,20,Patnership,Standalone\n2119,276,NJ City,E,2,23,22,48,High School,20,Owner,Standalone\n4418,220,NJ City,S,2,17,24,26,High School,27,Patnership,Complex\n5843,426,Boston,E,1,19,20,84,Graduation,29,Owner,Standalone\n3947,86.88,NJ City,E,24,9,3,84,No education,28,Owner,Standalone\n4495,434,NJ City,E,3,14,5,39,Graduation,18,Owner,Standalone\n9350,358,NJ City,S,1,21,14,47,Graduation,35,Patnership,Complex\n5950,482,Boston,S,2,18,23,72,Graduation,31,Patnership,Complex\n7564,490,Boston,W,2,13,15,58,Graduation,25,Franchise,Complex\n3541,239,NJ City,N,1,19,9,42,High School,23,Patnership,Standalone\n97,100.27,NJ City,N,18,17,4,55,High School,34,Patnership,Standalone\n3745,272.24,NJ City,N,17,10,2,33,High School,28,Patnership,Standalone\n1415,20.59,NJ City,N,24,19,3,63,No education,19,Owner,Standalone\n2198,237,NJ City,S,2,14,11,42,High School,25,Patnership,Complex\n4256,227.68,NJ City,W,8,9,3,32,No education,35,Franchise,Complex\n3942,150.48,NJ City,S,4,12,1,77,Graduation,34,Patnership,Complex\n7751,215.34,Boston,E,8,19,3,85,No education,31,Owner,Standalone\n172,318,NJ City,W,1,17,7,41,High School,20,Franchise,Complex\n370,249,NJ City,S,2,13,17,38,High School,25,Patnership,Complex\n5095,489,Boston,E,3,12,10,41,Graduation,18,Owner,Standalone\n7605,427,Boston,N,3,22,7,71,Graduation,20,Patnership,Standalone\n2056,204.08,NJ City,W,12,22,4,44,High School,34,Franchise,Complex\n3073,115,NJ City,N,1,8,17,85,High School,28,Patnership,Standalone\n8017,409,Boston,N,3,8,15,66,Graduation,31,Patnership,Standalone\n8240,29.53,NJ City,N,21,16,4,75,No education,22,Franchise,Complex\n1080,131,NJ City,W,3,18,7,27,High School,20,Franchise,Complex\n6470,77.33,Boston,S,14,10,1,33,No education,23,Patnership,Complex\n6181,95.11,Boston,N,9,13,3,59,High School,31,Patnership,Standalone\n2392,281,NJ City,W,1,11,7,79,High School,32,Franchise,Complex\n919,162,NJ City,E,3,24,12,43,High School,33,Owner,Standalone\n9770,240.75,NJ City,S,8,24,3,66,No education,27,Patnership,Complex\n6106,384,Boston,S,2,12,7,63,Graduation,24,Patnership,Complex\n3605,270,NJ City,N,1,10,23,64,High School,26,Patnership,Standalone\n8651,125.31,NJ City,E,15,18,1,83,No education,30,Owner,Standalone\n8125,234.86,NJ City,N,2,12,4,55,High School,22,Patnership,Standalone\n2904,178,NJ City,W,3,24,19,74,High School,35,Franchise,Complex\n9211,481.81,NJ City,E,1,21,4,74,High School,22,Owner,Standalone\n4538,235.56,NJ City,S,3,24,11,35,No education,18,Patnership,Complex\n2401,379,NJ City,N,3,8,5,76,Graduation,26,Patnership,Standalone\n8444,474,NJ City,W,1,22,19,41,Graduation,30,Franchise,Complex\n1376,487,NJ City,W,2,24,5,40,Graduation,33,Franchise,Complex\n6265,385,Boston,N,2,22,8,83,Graduation,34,Patnership,Standalone\n2936,119.05,NJ City,W,20,18,2,72,No education,19,Franchise,Complex\n7411,44.62,Boston,N,15,23,1,51,High School,28,Owner,Standalone\n9900,210.94,NJ City,W,4,19,3,70,Graduation,30,Franchise,Complex\n3413,267,NJ City,N,1,8,25,40,High School,22,Patnership,Standalone\n1329,406.51,NJ City,N,7,8,4,64,Graduation,19,Patnership,Standalone\n9989,71.1,NJ City,N,7,16,1,37,No education,25,Patnership,Standalone\n6303,350,Boston,E,1,11,25,26,Graduation,20,Owner,Standalone\n3453,116,NJ City,N,2,13,18,69,High School,23,Patnership,Standalone\n4374,119,NJ City,S,3,12,15,48,High School,23,Patnership,Complex\n1453,247.19,NJ City,N,4,11,2,33,High School,31,Patnership,Standalone\n8699,455,NJ City,E,2,9,18,46,Graduation,34,Owner,Standalone\n9917,416,NJ City,N,2,22,16,67,Graduation,31,Patnership,Standalone\n1247,269,NJ City,E,2,22,10,36,High School,19,Owner,Standalone\n2689,174,NJ City,N,1,9,21,27,High School,35,Patnership,Standalone\n1086,307,NJ City,S,3,22,21,81,High School,18,Patnership,Complex\n5366,496,Boston,S,2,11,23,30,Graduation,27,Patnership,Complex\n8595,426,NJ City,E,3,17,5,66,Graduation,25,Owner,Standalone\n1602,50.25,NJ City,S,1,21,1,25,Graduation,19,Patnership,Complex\n8115,200.25,NJ City,E,1,9,2,83,Graduation,22,Owner,Standalone\n1338,64.3,NJ City,N,22,14,3,33,Graduation,33,Patnership,Complex\n1242,116,NJ City,S,3,24,11,41,High School,34,Patnership,Complex\n3304,243,NJ City,W,3,13,8,38,High School,28,Franchise,Complex\n2948,265,NJ City,W,2,18,24,67,High School,32,Franchise,Complex\n79,105,Boston,E,2,16,8,85,High School,31,Owner,Standalone\n2367,174,NJ City,E,3,17,22,46,High School,24,Owner,Standalone\n5530,79.05,Boston,S,20,10,1,42,High School,21,Patnership,Complex\n5110,449,Boston,S,2,16,18,79,Graduation,30,Patnership,Complex\n1811,170,NJ City,E,3,21,14,83,High School,28,Owner,Standalone\n8650,400,NJ City,S,3,22,24,80,Graduation,32,Patnership,Complex\n1466,376.99,NJ City,S,2,21,19,42,No education,28,Patnership,Complex\n2157,101,NJ City,N,2,14,13,83,High School,28,Patnership,Standalone\n3146,247,NJ City,S,1,18,7,59,High School,19,Patnership,Complex\n6659,8.39,Boston,N,2,19,2,41,No education,25,Owner,Standalone\n1967,299.59,NJ City,E,16,14,3,28,No education,30,Owner,Standalone\n951,165,NJ City,E,2,18,20,63,High School,24,Owner,Standalone\n7642,408,Boston,S,2,13,11,32,Graduation,21,Patnership,Complex\n7013,496,Boston,N,2,10,22,41,Graduation,35,Patnership,Standalone\n754,179,NJ City,S,2,22,15,53,High School,24,Patnership,Complex\n8290,373,NJ City,S,2,23,24,52,Graduation,30,Patnership,Complex\n4363,233,NJ City,E,3,14,11,62,High School,32,Owner,Standalone\n816,154,NJ City,W,1,18,18,81,High School,30,Franchise,Complex\n1278,343.28,NJ City,S,24,15,3,46,Graduation,22,Patnership,Complex\n4689,27.31,NJ City,N,2,9,1,44,Graduation,26,Patnership,Standalone\n2814,225,NJ City,S,1,16,23,71,High School,34,Patnership,Complex\n3087,54.81,NJ City,E,2,20,4,35,Graduation,22,Owner,Standalone\n5810,481,Boston,S,3,18,19,85,Graduation,31,Patnership,Complex\n9164,386,NJ City,W,3,10,6,80,Graduation,23,Franchise,Complex\n8245,3.34,NJ City,N,9,12,3,56,High School,23,Patnership,Standalone\n5098,43.74,Boston,N,13,24,1,64,High School,35,Patnership,Complex\n1494,89.26,NJ City,S,10,23,2,59,Graduation,29,Patnership,Complex\n6677,363,Boston,N,2,22,20,62,Graduation,27,Patnership,Standalone\n1869,211,NJ City,N,1,18,20,30,High School,21,Patnership,Standalone\n513,240,NJ City,N,1,17,7,39,High School,22,Patnership,Standalone\n1093,316,NJ City,N,1,18,25,36,High School,26,Patnership,Standalone\n6120,479,Boston,W,3,9,7,45,Graduation,30,Franchise,Complex\n1474,143,NJ City,S,1,14,24,33,High School,26,Patnership,Complex\n2836,142.48,NJ City,W,21,13,1,63,High School,28,Franchise,Complex\n4744,177,NJ City,W,1,13,10,67,High School,30,Franchise,Complex\n7054,462,Boston,S,3,19,24,25,Graduation,25,Patnership,Complex\n6203,368,Boston,E,3,10,13,84,Graduation,29,Owner,Standalone\n6977,450,Boston,N,1,19,14,84,Graduation,18,Patnership,Standalone\n1423,300,NJ City,E,1,20,25,52,High School,27,Owner,Standalone\n9604,351,NJ City,W,2,20,18,26,Graduation,29,Franchise,Complex\n8246,11.98,NJ City,N,16,21,1,40,No education,35,Patnership,Complex\n9124,160.23,NJ City,W,8,19,1,47,High School,32,Franchise,Complex\n3631,127.1,NJ City,E,10,21,3,48,High School,31,Owner,Standalone\n4395,117,NJ City,E,2,22,18,40,High School,25,Owner,Standalone\n3839,35.99,NJ City,N,22,12,3,85,No education,24,Owner,Standalone\n5016,411,Boston,W,1,22,6,70,Graduation,34,Franchise,Complex\n7932,170.39,Boston,W,21,8,1,82,Graduation,31,Franchise,Complex\n5386,398.73,Boston,S,3,19,1,47,High School,28,Patnership,Complex\n4031,147,NJ City,E,3,23,20,67,High School,24,Owner,Standalone\n34,53.18,Boston,S,17,8,4,67,High School,24,Patnership,Complex\n253,203,NJ City,N,3,12,20,36,High School,19,Patnership,Standalone\n3057,474,NJ City,N,1,21,5,76,Graduation,34,Patnership,Standalone\n9505,480.59,NJ City,N,23,18,2,37,High School,32,Patnership,Standalone\n9181,435,NJ City,N,3,22,15,81,Graduation,23,Patnership,Standalone\n9126,424,NJ City,S,1,12,13,59,Graduation,19,Patnership,Complex\n7610,472,Boston,S,3,19,6,42,Graduation,34,Patnership,Complex\n1704,161,NJ City,W,1,16,18,68,High School,24,Franchise,Complex\n5949,429,Boston,N,3,21,14,64,Graduation,35,Patnership,Standalone\n8070,354,NJ City,S,3,10,12,72,Graduation,35,Patnership,Complex\n8592,422,NJ City,W,3,13,20,77,Graduation,29,Franchise,Complex\n3524,1.56,NJ City,N,11,16,2,66,No education,34,Franchise,Complex\n6745,461,Boston,N,3,21,8,78,Graduation,31,Patnership,Standalone\n3385,23.42,NJ City,N,9,22,1,41,High School,20,Patnership,Standalone\n1121,124.39,NJ City,N,16,16,4,58,No education,21,Patnership,Standalone\n311,288,NJ City,E,1,13,18,54,High School,21,Owner,Standalone\n2005,403,NJ City,N,3,11,5,59,Graduation,30,Patnership,Standalone\n1825,258,NJ City,N,2,22,10,36,High School,22,Patnership,Standalone\n1322,40,NJ City,N,17,12,4,25,No education,25,Patnership,Complex\n6366,465,Boston,S,1,15,24,50,Graduation,25,Patnership,Complex\n6449,471,Boston,N,1,20,7,29,Graduation,25,Patnership,Standalone\n4797,271,NJ City,N,1,22,16,32,High School,18,Patnership,Standalone\n4289,215,NJ City,N,2,22,14,63,High School,20,Patnership,Standalone\n9370,412,NJ City,S,3,19,23,29,Graduation,20,Patnership,Complex\n7006,497,Boston,S,3,10,24,32,Graduation,22,Patnership,Complex\n1843,106.05,NJ City,E,17,23,1,57,High School,34,Owner,Standalone\n8311,21.83,NJ City,N,22,8,4,77,High School,20,Owner,Standalone\n9633,418,NJ City,N,2,21,23,50,Graduation,22,Patnership,Standalone\n8607,422.53,NJ City,E,13,20,3,54,Graduation,28,Owner,Standalone\n7894,244.72,Boston,S,23,16,3,57,High School,20,Patnership,Complex\n2355,388.74,NJ City,E,1,17,10,47,No education,24,Owner,Standalone\n3378,456.79,NJ City,S,9,22,2,32,Graduation,34,Patnership,Complex\n5561,354,Boston,N,3,22,23,25,Graduation,26,Patnership,Standalone\n9540,472,NJ City,W,2,21,20,35,Graduation,22,Franchise,Complex\n8562,383,NJ City,S,3,8,13,35,Graduation,29,Patnership,Complex\n4734,278,NJ City,S,1,20,20,34,High School,23,Patnership,Complex\n7182,460,Boston,S,1,12,25,37,Graduation,26,Patnership,Complex\n1847,123,NJ City,E,2,16,11,57,High School,35,Owner,Standalone\n2812,149,NJ City,W,1,16,13,50,High School,26,Franchise,Complex\n5250,387,Boston,S,1,10,16,40,Graduation,28,Patnership,Complex\n1351,303,NJ City,E,1,13,10,65,High School,18,Owner,Standalone\n1206,286,NJ City,S,1,18,20,52,High School,22,Patnership,Complex\n6133,485,Boston,N,1,9,18,70,Graduation,34,Patnership,Standalone\n9113,498,NJ City,N,1,24,14,55,Graduation,34,Patnership,Standalone\n8762,382,NJ City,S,2,11,13,58,Graduation,30,Patnership,Complex\n7443,419,Boston,E,3,15,22,72,Graduation,30,Owner,Standalone\n4244,121.07,NJ City,W,24,15,4,50,No education,18,Franchise,Complex\n6699,358,Boston,E,2,12,25,68,Graduation,21,Owner,Standalone\n3756,40.89,NJ City,N,15,22,3,60,Graduation,32,Franchise,Complex\n4030,385.24,NJ City,S,3,17,3,64,High School,25,Patnership,Complex\n2687,196,NJ City,E,1,16,13,74,High School,20,Owner,Standalone\n7206,64.95,Boston,N,25,18,1,72,Graduation,19,Patnership,Complex\n4853,126,NJ City,N,2,19,13,66,High School,18,Patnership,Standalone\n6325,368,Boston,N,3,20,17,71,Graduation,22,Patnership,Standalone\n4028,121,NJ City,W,3,11,21,51,High School,31,Franchise,Complex\n4771,224,NJ City,E,1,8,23,45,High School,32,Owner,Standalone\n8479,23.19,NJ City,N,8,16,2,53,High School,23,Owner,Standalone\n2560,64.33,NJ City,N,6,19,2,40,High School,19,Franchise,Complex\n1835,168,NJ City,E,2,9,25,71,High School,23,Owner,Standalone\n1459,99.7,NJ City,E,19,9,2,28,High School,29,Owner,Standalone\n8023,402,Boston,E,3,24,20,73,Graduation,35,Owner,Standalone\n7352,416,Boston,W,3,13,24,39,Graduation,25,Franchise,Complex\n5889,44.94,Boston,N,25,16,4,45,Graduation,23,Patnership,Standalone\n9791,44.32,NJ City,N,5,19,3,50,No education,22,Owner,Standalone\n5630,35.77,Boston,N,15,8,3,58,No education,35,Patnership,Complex\n3596,19.55,NJ City,N,12,13,4,43,No education,27,Franchise,Complex\n7798,467,Boston,S,2,11,6,56,Graduation,25,Patnership,Complex\n3187,257.52,NJ City,E,3,21,20,59,No education,20,Owner,Standalone\n1314,175,NJ City,S,2,24,18,80,High School,25,Patnership,Complex\n6226,369,Boston,S,3,24,5,45,Graduation,28,Patnership,Complex\n7132,92.11,Boston,W,8,24,2,73,High School,18,Franchise,Complex\n9353,484,NJ City,N,3,9,14,40,Graduation,29,Patnership,Standalone\n867,94.19,NJ City,E,4,20,4,30,Graduation,21,Owner,Standalone\n9832,366,NJ City,W,3,22,17,29,Graduation,26,Franchise,Complex\n3755,156,NJ City,E,1,19,11,30,High School,32,Owner,Standalone\n8397,374.58,NJ City,N,6,15,1,44,Graduation,24,Patnership,Standalone\n5183,493,Boston,E,3,16,12,78,Graduation,18,Owner,Standalone\n5753,62.7,Boston,N,1,14,2,37,No education,21,Patnership,Standalone\n4557,74.82,NJ City,N,23,12,1,81,Graduation,28,Patnership,Standalone\n9662,462,NJ City,S,3,23,10,35,Graduation,26,Patnership,Complex\n9396,426,NJ City,W,2,14,13,54,Graduation,31,Franchise,Complex\n8588,448,NJ City,W,2,21,12,49,Graduation,26,Franchise,Complex\n1105,6.19,NJ City,N,13,15,4,58,High School,29,Patnership,Standalone\n1588,162,NJ City,W,2,10,14,48,High School,19,Franchise,Complex\n2664,482.34,NJ City,W,24,17,3,53,Graduation,19,Franchise,Complex\n1943,191,NJ City,E,3,11,17,78,High School,23,Owner,Standalone\n661,8.61,NJ City,N,17,17,2,53,High School,29,Patnership,Standalone\n4125,75.13,NJ City,N,20,12,2,48,Graduation,19,Patnership,Standalone\n9718,13.05,NJ City,N,14,23,1,80,High School,34,Patnership,Complex\n4885,125.37,NJ City,N,11,14,2,80,High School,22,Patnership,Standalone\n7256,208,Boston,W,15,17,2,76,No education,33,Franchise,Complex\n6695,453,Boston,E,2,15,5,72,Graduation,22,Owner,Standalone\n5767,476,Boston,E,2,16,18,37,Graduation,28,Owner,Standalone\n3791,268.34,NJ City,E,24,14,3,33,No education,30,Owner,Standalone\n2027,103,NJ City,E,1,16,20,53,High School,35,Owner,Standalone\n2621,155,NJ City,N,2,22,19,75,High School,29,Patnership,Standalone\n8568,326.99,NJ City,W,2,23,3,33,Graduation,21,Franchise,Complex\n5825,26.62,Boston,N,19,19,2,65,No education,26,Patnership,Standalone\n3962,241,NJ City,S,1,9,6,82,High School,19,Patnership,Complex\n2303,101,NJ City,E,2,13,22,79,High School,35,Owner,Standalone\n8751,394,NJ City,E,1,10,7,38,Graduation,25,Owner,Standalone\n5013,43.38,Boston,N,8,15,3,56,Graduation,19,Patnership,Standalone\n3648,114.86,NJ City,W,8,23,4,74,Graduation,23,Franchise,Complex\n6012,79.04,Boston,W,1,18,4,74,Graduation,35,Franchise,Complex\n7319,363,Boston,E,3,12,15,30,Graduation,30,Owner,Standalone\n8954,397,NJ City,S,3,8,11,57,Graduation,34,Patnership,Complex\n9229,499.39,NJ City,N,23,12,2,71,High School,33,Patnership,Standalone\n3314,5.56,NJ City,N,20,19,3,37,No education,31,Patnership,Complex\n3576,167,NJ City,W,2,17,17,70,High School,27,Franchise,Complex\n6864,472,Boston,W,1,21,13,60,Graduation,27,Franchise,Complex\n6558,462,Boston,S,3,14,16,36,Graduation,33,Patnership,Complex\n6996,380,Boston,W,1,16,5,49,Graduation,22,Franchise,Complex\n8038,361,Boston,S,1,24,14,50,Graduation,21,Patnership,Complex\n3936,101.02,NJ City,W,7,9,1,85,Graduation,23,Franchise,Complex\n8960,314.37,NJ City,W,13,10,3,61,No education,23,Franchise,Complex\n2777,202,NJ City,N,1,18,14,35,High School,34,Patnership,Standalone\n6479,482,Boston,E,1,11,22,31,Graduation,33,Owner,Standalone\n5051,4.4,Boston,N,19,8,1,40,No education,35,Owner,Standalone\n4532,66.67,NJ City,N,15,16,4,30,No education,21,Franchise,Complex\n8443,393,NJ City,E,1,13,23,83,Graduation,32,Owner,Standalone\n9943,495,NJ City,E,3,18,24,48,Graduation,34,Owner,Standalone\n6503,462,Boston,E,2,19,22,61,Graduation,31,Owner,Standalone\n4732,280,NJ City,W,1,19,9,52,High School,20,Franchise,Complex\n8350,446,NJ City,S,3,11,23,25,Graduation,22,Patnership,Complex\n1097,4.31,NJ City,N,3,12,2,44,No education,28,Patnership,Standalone\n6005,361,Boston,N,3,9,19,50,Graduation,19,Patnership,Standalone\n2442,45.04,NJ City,N,8,20,2,64,Graduation,25,Patnership,Complex\n4585,443.5,NJ City,N,23,11,2,44,High School,25,Patnership,Standalone\n7878,398,Boston,S,3,11,8,54,Graduation,24,Patnership,Complex\n5060,457,Boston,W,2,19,6,57,Graduation,33,Franchise,Complex\n2195,276,NJ City,E,2,15,12,64,High School,27,Owner,Standalone\n2333,160.43,NJ City,N,9,10,1,60,No education,33,Patnership,Standalone\n6214,409,Boston,S,2,12,11,66,Graduation,34,Patnership,Complex\n6085,480,Boston,N,3,12,20,42,Graduation,29,Patnership,Standalone\n4380,111.61,NJ City,W,10,15,1,38,Graduation,30,Franchise,Complex\n2201,407,NJ City,N,3,15,5,41,Graduation,28,Patnership,Standalone\n4384,211,NJ City,W,3,21,6,34,High School,31,Franchise,Complex\n7338,454,Boston,S,3,8,22,59,Graduation,19,Patnership,Complex\n6387,453,Boston,E,3,18,15,51,Graduation,31,Owner,Standalone\n5281,481,Boston,N,3,8,5,59,Graduation,26,Patnership,Standalone\n8100,366,NJ City,W,3,14,6,80,Graduation,30,Franchise,Complex\n6733,103.2,Boston,N,14,9,4,64,High School,26,Patnership,Standalone\n2885,116.99,NJ City,N,10,24,3,26,No education,22,Patnership,Standalone\n2393,189.2,NJ City,N,11,22,4,79,No education,32,Patnership,Standalone\n2774,389.24,NJ City,S,18,23,2,34,No education,26,Patnership,Complex\n1574,165,NJ City,S,3,11,19,73,High School,27,Patnership,Complex\n74,159,Boston,S,3,16,15,71,High School,23,Patnership,Complex\n2356,196,NJ City,W,3,17,11,42,High School,31,Franchise,Complex\n3702,314,NJ City,S,2,19,21,63,High School,21,Patnership,Complex\n7543,456.35,Boston,E,10,21,4,63,High School,30,Owner,Standalone\n2441,113,NJ City,N,1,10,19,47,High School,25,Patnership,Standalone\n773,270,NJ City,N,1,12,16,78,High School,22,Patnership,Standalone\n3259,93.28,NJ City,E,8,9,4,56,High School,29,Owner,Standalone\n8267,433,NJ City,E,2,10,10,56,Graduation,20,Owner,Standalone\n6031,27.79,Boston,N,25,8,2,30,High School,35,Owner,Standalone\n3981,216,NJ City,N,2,22,17,68,High School,31,Patnership,Standalone\n9001,462,NJ City,N,2,8,24,49,Graduation,24,Patnership,Standalone\n3697,191,NJ City,N,2,18,23,47,High School,31,Patnership,Standalone\n6917,449,Boston,N,3,14,23,41,Graduation,32,Patnership,Standalone\n6393,25.18,Boston,N,1,15,1,75,Graduation,19,Patnership,Standalone\n3127,197,NJ City,E,3,17,16,78,High School,26,Owner,Standalone\n9935,460,NJ City,E,3,18,14,65,Graduation,34,Owner,Standalone\n2141,312,NJ City,N,3,21,16,60,High School,25,Patnership,Standalone\n6466,443,Boston,S,2,20,10,32,Graduation,29,Patnership,Complex\n372,61.02,NJ City,N,11,16,4,42,Graduation,30,Franchise,Complex\n2062,350,NJ City,S,2,22,5,26,Graduation,19,Patnership,Complex\n6954,492.44,Boston,S,5,8,4,59,Graduation,32,Patnership,Complex\n4385,233,NJ City,N,3,14,14,44,High School,35,Patnership,Standalone\n4861,306.66,NJ City,N,4,16,1,39,High School,28,Patnership,Standalone\n7019,474,Boston,E,3,16,5,59,Graduation,20,Owner,Standalone\n1284,26.35,NJ City,N,25,13,4,48,Graduation,28,Franchise,Complex\n4814,441.56,NJ City,S,22,19,2,52,No education,27,Patnership,Complex\n2944,59.01,NJ City,W,16,15,4,34,High School,27,Franchise,Complex\n8449,432,NJ City,N,3,13,15,39,Graduation,21,Patnership,Standalone\n4995,470.17,Boston,E,11,20,4,83,Graduation,23,Owner,Standalone\n3220,14.47,NJ City,N,9,23,2,41,High School,21,Franchise,Complex\n1737,106.81,NJ City,N,9,23,2,74,Graduation,32,Patnership,Standalone\n4645,22.47,NJ City,N,14,16,4,26,High School,19,Patnership,Standalone\n4389,351.14,NJ City,N,25,13,3,77,Graduation,22,Patnership,Standalone\n645,191,NJ City,N,2,15,13,27,High School,20,Patnership,Standalone\n5161,88.6,Boston,N,9,10,1,56,High School,34,Patnership,Standalone\n1721,237,NJ City,N,2,21,11,58,High School,31,Patnership,Standalone\n2845,303.96,NJ City,N,16,10,3,81,High School,30,Patnership,Standalone\n462,113.62,NJ City,S,8,11,1,58,Graduation,27,Patnership,Complex\n3482,42.57,NJ City,N,23,8,1,52,No education,24,Patnership,Complex\n3829,110,NJ City,N,3,22,11,33,High School,31,Patnership,Standalone\n9610,372,NJ City,S,3,13,24,67,Graduation,35,Patnership,Complex\n4132,96.29,NJ City,W,3,15,4,32,High School,26,Franchise,Complex\n8847,453.6,NJ City,E,5,12,2,35,Graduation,35,Owner,Standalone\n6252,54.52,Boston,W,18,15,2,43,Graduation,23,Franchise,Complex\n1851,94.59,NJ City,E,18,14,4,42,Graduation,33,Owner,Standalone\n7625,455,Boston,N,1,21,23,59,Graduation,32,Patnership,Standalone\n8901,438,NJ City,N,3,10,24,76,Graduation,29,Patnership,Standalone\n5222,365,Boston,S,1,11,15,79,Graduation,19,Patnership,Complex\n3847,251,NJ City,E,2,17,8,68,High School,24,Owner,Standalone\n1372,43.02,NJ City,N,15,19,4,73,High School,32,Franchise,Complex\n8594,442,NJ City,S,1,12,21,45,Graduation,28,Patnership,Complex\n9103,500,NJ City,E,2,23,21,29,Graduation,21,Owner,Standalone\n4534,131,NJ City,S,2,21,15,80,High School,34,Patnership,Complex\n5321,4.57,Boston,N,6,11,3,29,No education,24,Patnership,Standalone\n7946,22.61,Boston,N,8,14,1,67,No education,19,Patnership,Complex\n801,74.1,NJ City,N,4,8,4,53,Graduation,33,Patnership,Standalone\n3873,314,NJ City,N,1,19,10,85,High School,34,Patnership,Standalone\n9563,91.21,NJ City,E,13,17,1,59,No education,24,Owner,Standalone\n3632,273,NJ City,W,1,19,17,31,High School,18,Franchise,Complex\n6462,82.57,Boston,S,5,17,1,47,Graduation,31,Patnership,Complex\n6904,403,Boston,W,3,18,20,26,Graduation,23,Franchise,Complex\n9781,489,NJ City,N,3,15,10,28,Graduation,29,Patnership,Standalone\n1181,228,NJ City,N,1,15,6,25,High School,34,Patnership,Standalone\n5601,166.93,Boston,N,20,8,4,64,Graduation,22,Patnership,Standalone\n7164,69.08,Boston,N,17,24,4,30,Graduation,21,Franchise,Complex\n6456,440.99,Boston,W,1,17,4,75,Graduation,22,Franchise,Complex\n9067,475,NJ City,E,2,21,17,44,Graduation,32,Owner,Standalone\n797,278,NJ City,N,2,14,22,40,High School,33,Patnership,Standalone\n90,121,Boston,S,1,21,20,73,High School,35,Patnership,Complex\n7532,97.32,Boston,W,14,11,3,27,No education,18,Franchise,Complex\n7734,426,Boston,S,3,20,7,43,Graduation,35,Patnership,Complex\n681,132,NJ City,N,2,15,25,55,High School,27,Patnership,Standalone\n7599,402,Boston,E,3,10,23,73,Graduation,35,Owner,Standalone\n8577,432,NJ City,N,3,22,21,58,Graduation,25,Patnership,Standalone\n4621,243,NJ City,N,1,10,7,67,High School,20,Patnership,Standalone\n3442,274,NJ City,S,3,13,16,67,High School,18,Patnership,Complex\n1399,155,NJ City,E,1,16,12,49,High School,20,Owner,Standalone\n6249,486,Boston,N,3,8,6,39,Graduation,22,Patnership,Standalone\n6147,371,Boston,E,2,20,13,61,Graduation,34,Owner,Standalone\n4985,35.66,Boston,N,13,19,1,72,No education,26,Patnership,Standalone\n4205,179,NJ City,N,2,10,6,73,High School,22,Patnership,Standalone\n6477,97.37,Boston,N,23,10,2,37,Graduation,18,Patnership,Standalone\n6591,398,Boston,E,1,18,9,49,Graduation,29,Owner,Standalone\n3739,270,NJ City,E,3,11,8,64,High School,24,Owner,Standalone\n8108,386,NJ City,W,2,13,15,56,Graduation,30,Franchise,Complex\n4055,248,NJ City,E,1,10,20,76,High School,33,Owner,Standalone\n4870,179,NJ City,S,1,21,21,50,High School,35,Patnership,Complex\n9495,453,NJ City,E,3,24,24,32,Graduation,31,Owner,Standalone\n5385,82.59,Boston,N,10,8,1,34,Graduation,25,Patnership,Standalone\n2468,107.79,NJ City,W,7,12,1,28,No education,33,Franchise,Complex\n8501,376,NJ City,N,2,21,24,51,Graduation,24,Patnership,Standalone\n9975,427,NJ City,E,3,17,23,38,Graduation,23,Owner,Standalone\n9762,108.97,NJ City,S,4,15,2,29,Graduation,28,Patnership,Complex\n5955,388,Boston,E,3,19,21,75,Graduation,27,Owner,Standalone\n9543,40.93,NJ City,N,9,22,1,29,Graduation,26,Owner,Standalone\n4391,255,NJ City,E,2,11,13,50,High School,28,Owner,Standalone\n5773,375,Boston,N,2,23,16,60,Graduation,30,Patnership,Standalone\n971,394.51,NJ City,E,20,10,4,50,No education,28,Owner,Standalone\n4175,196,NJ City,E,3,22,14,31,High School,29,Owner,Standalone\n7423,35.09,Boston,N,23,15,1,34,High School,29,Owner,Standalone\n1485,102,NJ City,N,1,8,20,28,High School,33,Patnership,Standalone\n6353,88.83,Boston,N,20,15,3,29,No education,20,Patnership,Standalone\n5820,382,Boston,W,3,23,11,63,Graduation,25,Franchise,Complex\n3665,96.32,NJ City,N,8,8,2,33,No education,33,Patnership,Standalone\n8551,31.49,NJ City,N,15,10,2,34,High School,31,Owner,Standalone\n1540,127,NJ City,W,3,11,24,77,High School,24,Franchise,Complex\n5999,485,Boston,E,3,8,11,32,Graduation,19,Owner,Standalone\n9487,477,NJ City,E,1,8,16,59,Graduation,23,Owner,Standalone\n8011,7.77,Boston,N,13,9,2,33,High School,24,Owner,Standalone\n1667,49.17,NJ City,E,12,8,1,25,No education,19,Owner,Standalone\n2633,220.3,NJ City,N,9,10,1,49,No education,25,Patnership,Standalone\n1636,47.13,NJ City,N,24,22,1,60,High School,26,Franchise,Complex\n4168,7.2,NJ City,N,3,11,3,41,High School,24,Franchise,Complex\n2130,208,NJ City,S,1,11,8,76,High School,24,Patnership,Complex\n162,225,NJ City,S,2,10,24,26,High School,32,Patnership,Complex\n8949,452,NJ City,N,1,11,19,43,Graduation,29,Patnership,Standalone\n2318,248,NJ City,S,1,9,9,58,High School,28,Patnership,Complex\n2849,76.33,NJ City,N,7,21,4,57,No education,33,Patnership,Standalone\n4895,285,NJ City,E,3,22,20,67,High School,28,Owner,Standalone\n845,264,NJ City,N,1,17,14,54,High School,23,Patnership,Standalone\n3792,441,NJ City,W,2,11,5,40,Graduation,25,Franchise,Complex\n7122,73.1,Boston,N,15,12,1,49,Graduation,22,Patnership,Complex\n9692,7.69,NJ City,N,6,16,1,46,No education,19,Franchise,Complex\n3634,126,NJ City,S,2,11,14,40,High School,35,Patnership,Complex\n4558,66.91,NJ City,N,9,10,3,50,High School,19,Patnership,Complex\n5212,482,Boston,W,3,21,11,55,Graduation,31,Franchise,Complex\n7152,460,Boston,W,3,15,11,58,Graduation,27,Franchise,Complex\n1518,270,NJ City,S,2,13,21,76,High School,26,Patnership,Complex\n9498,411.76,NJ City,S,2,12,3,81,Graduation,19,Patnership,Complex\n2160,246,NJ City,W,3,8,13,46,High School,19,Franchise,Complex\n5046,431,Boston,S,2,19,11,48,Graduation,25,Patnership,Complex\n3462,279,NJ City,S,1,15,14,34,High School,33,Patnership,Complex\n432,66.76,NJ City,N,13,14,1,48,Graduation,34,Franchise,Complex\n4077,216,NJ City,N,3,24,16,66,High School,33,Patnership,Standalone\n6556,372,Boston,W,2,17,17,37,Graduation,29,Franchise,Complex\n3000,312.68,NJ City,W,9,15,2,85,Graduation,21,Franchise,Complex\n6342,481,Boston,S,2,8,21,32,Graduation,20,Patnership,Complex\n3589,104,NJ City,N,1,23,12,67,High School,24,Patnership,Standalone\n2894,27.47,NJ City,N,9,18,4,43,No education,28,Patnership,Complex\n8373,440,NJ City,N,2,17,19,52,Graduation,31,Patnership,Standalone\n7771,365,Boston,E,2,13,24,73,Graduation,34,Owner,Standalone\n5633,437,Boston,N,2,13,14,59,Graduation,34,Patnership,Standalone\n8357,420,NJ City,N,2,21,25,44,Graduation,19,Patnership,Standalone\n8198,362,NJ City,S,2,21,16,61,Graduation,20,Patnership,Complex\n4388,297,NJ City,W,2,19,24,30,High School,26,Franchise,Complex\n2286,295,NJ City,S,2,14,6,43,High School,18,Patnership,Complex\n2934,121,NJ City,S,3,22,21,82,High School,27,Patnership,Complex\n7841,249.56,Boston,N,18,15,2,82,No education,34,Patnership,Standalone\n1037,150,NJ City,N,2,12,6,68,High School,29,Patnership,Standalone\n5966,412,Boston,S,1,13,19,82,Graduation,29,Patnership,Complex\n9504,118.38,NJ City,W,15,19,4,73,Graduation,34,Franchise,Complex\n8711,497,NJ City,E,2,12,19,41,Graduation,30,Owner,Standalone\n1652,194.09,NJ City,W,9,19,2,66,No education,29,Franchise,Complex\n9358,27.58,NJ City,N,13,18,2,47,High School,26,Patnership,Complex\n6383,63.95,Boston,N,4,12,3,30,No education,23,Owner,Standalone\n255,143,NJ City,E,3,21,17,45,High School,18,Owner,Standalone\n2428,290,NJ City,W,3,17,10,57,High School,30,Franchise,Complex\n9267,13.75,NJ City,N,11,9,4,32,Graduation,23,Owner,Standalone\n2371,317.82,NJ City,E,8,18,4,82,High School,34,Owner,Standalone\n6935,436,Boston,E,2,11,7,73,Graduation,19,Owner,Standalone\n5514,383.16,Boston,S,17,12,2,58,Graduation,23,Patnership,Complex\n1337,266,NJ City,N,3,18,21,51,High School,18,Patnership,Standalone\n9163,497,NJ City,E,2,18,14,85,Graduation,21,Owner,Standalone\n6500,498,Boston,W,2,17,25,40,Graduation,26,Franchise,Complex\n7975,498,Boston,E,3,18,25,35,Graduation,20,Owner,Standalone\n789,284,NJ City,N,3,11,16,28,High School,30,Patnership,Standalone\n5234,469.41,Boston,S,21,15,3,62,No education,28,Patnership,Complex\n6000,14.87,Boston,N,21,13,3,64,Graduation,20,Franchise,Complex\n7037,371,Boston,N,2,17,23,33,Graduation,24,Patnership,Standalone\n235,111,NJ City,E,3,14,21,82,High School,18,Owner,Standalone\n1750,20.82,NJ City,N,15,20,2,67,High School,21,Patnership,Complex\n2339,220,NJ City,E,3,21,17,72,High School,29,Owner,Standalone\n9216,352,NJ City,W,2,13,5,70,Graduation,22,Franchise,Complex\n7688,183.73,Boston,W,17,8,2,38,No education,34,Franchise,Complex\n3527,115.48,NJ City,E,19,12,1,73,No education,34,Owner,Standalone\n2366,82.4,NJ City,S,16,22,4,83,No education,19,Patnership,Complex\n4692,186,NJ City,W,3,20,23,33,High School,19,Franchise,Complex\n1461,240,NJ City,N,3,21,20,68,High School,26,Patnership,Standalone\n1902,111,NJ City,S,1,13,17,40,High School,21,Patnership,Complex\n6849,71.01,Boston,N,20,17,2,73,Graduation,28,Patnership,Standalone\n7262,290.46,Boston,S,7,16,2,52,No education,34,Patnership,Complex\n6982,493,Boston,S,2,23,12,37,Graduation,24,Patnership,Complex\n6379,53.2,Boston,E,12,11,3,41,High School,34,Owner,Standalone\n7700,470,Boston,W,1,13,14,27,Graduation,33,Franchise,Complex\n8363,404,NJ City,E,2,16,15,82,Graduation,28,Owner,Standalone\n631,305.67,NJ City,E,22,24,2,63,High School,29,Owner,Standalone\n2409,134,NJ City,N,1,8,20,78,High School,25,Patnership,Standalone\n7670,73.96,Boston,N,9,13,3,57,No education,25,Patnership,Complex\n8008,424,Boston,W,2,11,11,39,Graduation,34,Franchise,Complex\n8925,376,NJ City,N,1,13,9,59,Graduation,27,Patnership,Standalone\n4390,65.09,NJ City,N,9,11,3,79,High School,21,Patnership,Complex\n2891,247,NJ City,E,3,16,21,79,High School,28,Owner,Standalone\n4124,209,NJ City,W,1,13,8,43,High School,23,Franchise,Complex\n66,197,Boston,S,2,22,17,84,High School,23,Patnership,Complex\n282,168,NJ City,S,2,23,14,63,High School,28,Patnership,Complex\n6362,58.35,Boston,S,22,19,4,45,No education,35,Patnership,Complex\n2033,122.31,NJ City,N,19,18,4,67,No education,21,Patnership,Standalone\n65,268,Boston,N,2,12,11,50,High School,18,Patnership,Standalone\n7768,469,Boston,W,1,16,10,60,Graduation,31,Franchise,Complex\n3003,303,NJ City,E,3,15,10,83,High School,31,Owner,Standalone\n2723,262.36,NJ City,E,11,14,4,33,No education,27,Owner,Standalone\n4580,213,NJ City,W,2,9,25,85,High School,34,Franchise,Complex\n8821,422.86,NJ City,N,7,9,1,68,High School,32,Patnership,Standalone\n9558,451,NJ City,S,2,10,14,50,Graduation,18,Patnership,Complex\n7000,388,Boston,W,3,15,17,32,Graduation,33,Franchise,Complex\n7099,350,Boston,E,3,10,23,65,Graduation,33,Owner,Standalone\n1176,401.7,NJ City,W,24,12,2,34,Graduation,20,Franchise,Complex\n5658,460,Boston,S,2,11,19,36,Graduation,24,Patnership,Complex\n980,74.93,NJ City,W,17,15,4,46,No education,33,Franchise,Complex\n7602,388,Boston,S,3,17,6,44,Graduation,22,Patnership,Complex\n1870,232,NJ City,S,1,10,14,72,High School,23,Patnership,Complex\n9534,164.4,NJ City,S,4,24,4,42,Graduation,34,Patnership,Complex\n5058,473.81,Boston,S,22,23,1,76,Graduation,29,Patnership,Complex\n3471,108.01,NJ City,E,10,12,2,78,Graduation,25,Owner,Standalone\n1180,108,NJ City,W,3,8,8,63,High School,30,Franchise,Complex\n2486,100,NJ City,S,1,23,21,36,High School,21,Patnership,Complex\n1416,71.27,NJ City,N,7,20,3,46,Graduation,29,Franchise,Complex\n2927,355.38,NJ City,E,25,15,3,44,No education,28,Owner,Standalone\n8630,478,NJ City,S,3,15,10,60,Graduation,30,Patnership,Complex\n355,99.03,NJ City,E,1,14,4,26,High School,23,Owner,Standalone\n1678,196,NJ City,S,3,8,16,43,High School,32,Patnership,Complex\n173,58.86,NJ City,N,19,8,1,62,No education,33,Patnership,Standalone\n2623,267,NJ City,E,3,19,23,46,High School,35,Owner,Standalone\n2838,100.98,NJ City,S,22,19,3,74,Graduation,21,Patnership,Complex\n8945,15.49,NJ City,N,11,11,3,37,No education,30,Patnership,Standalone\n375,114,NJ City,E,2,11,7,63,High School,25,Owner,Standalone\n1762,149,NJ City,S,3,14,12,49,High School,26,Patnership,Complex\n5558,47.43,Boston,S,12,18,3,35,No education,25,Patnership,Complex\n7306,358,Boston,S,3,19,24,31,Graduation,33,Patnership,Complex\n8432,486.15,NJ City,W,25,14,4,70,No education,32,Franchise,Complex\n6145,447,Boston,N,3,20,11,52,Graduation,28,Patnership,Standalone\n7739,478,Boston,E,3,9,9,39,Graduation,25,Owner,Standalone\n3047,150,NJ City,E,2,19,7,84,High School,22,Owner,Standalone\n9973,448,NJ City,N,2,22,17,26,Graduation,26,Patnership,Standalone\n6491,383,Boston,E,2,17,7,47,Graduation,32,Owner,Standalone\n4802,147,NJ City,S,3,15,19,54,High School,20,Patnership,Complex\n9923,364,NJ City,E,3,14,19,25,Graduation,21,Owner,Standalone\n1356,460.64,NJ City,W,10,16,1,68,Graduation,30,Franchise,Complex\n3196,52.45,NJ City,W,4,24,1,40,High School,34,Franchise,Complex\n3135,121,NJ City,E,3,13,15,72,High School,22,Owner,Standalone\n7016,363.69,Boston,W,11,12,1,52,No education,19,Franchise,Complex\n3840,193,NJ City,W,1,22,25,41,High School,32,Franchise,Complex\n8006,350,Boston,S,2,22,8,80,Graduation,18,Patnership,Complex\n5896,415,Boston,W,1,9,14,85,Graduation,33,Franchise,Complex\n3251,30.96,NJ City,N,15,24,2,40,No education,31,Owner,Standalone\n9148,382,NJ City,W,1,17,5,69,Graduation,22,Franchise,Complex\n9376,446,NJ City,W,1,24,21,40,Graduation,34,Franchise,Complex\n6744,29.82,Boston,N,25,22,1,83,Graduation,28,Franchise,Complex\n6055,114.99,Boston,E,15,23,4,78,High School,34,Owner,Standalone\n8211,493,NJ City,E,1,21,5,34,Graduation,26,Owner,Standalone\n7801,436,Boston,N,3,24,24,37,Graduation,25,Patnership,Standalone\n4567,263,NJ City,E,1,18,18,37,High School,22,Owner,Standalone\n261,211,NJ City,N,3,18,8,76,High School,30,Patnership,Standalone\n441,303.7,NJ City,N,25,15,4,67,Graduation,27,Patnership,Standalone\n2539,108,NJ City,E,3,14,18,84,High School,23,Owner,Standalone\n3417,177,NJ City,N,1,11,20,43,High School,20,Patnership,Standalone\n6735,0.88,Boston,N,12,8,1,67,Graduation,20,Owner,Standalone\n7112,10.83,Boston,N,11,18,4,34,No education,22,Franchise,Complex\n5502,350,Boston,S,3,19,18,28,Graduation,23,Patnership,Complex\n8238,127.15,NJ City,S,9,12,2,79,Graduation,25,Patnership,Complex\n7631,104.8,Boston,E,15,17,3,75,No education,34,Owner,Standalone\n4515,120.95,NJ City,E,8,8,1,81,Graduation,32,Owner,Standalone\n7875,82.28,Boston,E,13,18,3,55,Graduation,23,Owner,Standalone\n9908,370,NJ City,W,2,10,20,43,Graduation,35,Franchise,Complex\n9057,78.95,NJ City,N,18,10,3,34,Graduation,31,Patnership,Standalone\n332,163,NJ City,W,1,14,21,61,High School,22,Franchise,Complex\n1214,138,NJ City,S,3,18,24,84,High School,34,Patnership,Complex\n4874,23.54,NJ City,N,2,21,1,41,No education,28,Patnership,Complex\n8533,443,NJ City,N,3,12,16,34,Graduation,22,Patnership,Standalone\n6162,372,Boston,S,3,18,21,40,Graduation,20,Patnership,Complex\n2786,91.49,NJ City,S,3,8,3,69,No education,31,Patnership,Complex\n1899,112,NJ City,E,1,11,13,70,High School,25,Owner,Standalone\n4920,81,NJ City,W,9,16,2,38,Graduation,23,Franchise,Complex\n4683,287,NJ City,E,2,20,16,35,High School,22,Owner,Standalone\n6842,497,Boston,S,2,10,20,53,Graduation,24,Patnership,Complex\n2738,297.1,NJ City,S,17,15,4,41,No education,20,Patnership,Complex\n3198,108,NJ City,S,3,23,15,37,High School,28,Patnership,Complex\n1388,398,NJ City,W,2,21,5,36,Graduation,24,Franchise,Complex\n7139,483,Boston,E,1,8,20,36,Graduation,20,Owner,Standalone\n1015,52.52,NJ City,E,24,13,2,68,High School,33,Owner,Standalone\n5995,60,Boston,E,17,23,2,76,High School,18,Owner,Standalone\n5374,453,Boston,S,2,24,13,51,Graduation,23,Patnership,Complex\n5723,411,Boston,E,2,21,16,78,Graduation,19,Owner,Standalone\n6765,363,Boston,N,2,14,21,60,Graduation,26,Patnership,Standalone\n5442,36.1,Boston,N,8,10,2,77,Graduation,31,Patnership,Complex\n4784,291,NJ City,W,1,9,15,46,High School,26,Franchise,Complex\n6691,192.87,Boston,E,6,12,2,70,High School,21,Owner,Standalone\n7974,405,Boston,S,2,10,6,76,Graduation,30,Patnership,Complex\n9304,13.99,NJ City,N,17,8,4,81,High School,28,Franchise,Complex\n7535,481,Boston,E,1,13,7,82,Graduation,27,Owner,Standalone\n4319,310,NJ City,E,2,22,25,35,High School,22,Owner,Standalone\n5127,43.41,Boston,N,6,17,2,78,Graduation,27,Owner,Standalone\n9957,389,NJ City,N,3,21,13,57,Graduation,19,Patnership,Standalone\n7226,75.22,Boston,S,19,11,4,68,No education,20,Patnership,Complex\n238,250,NJ City,S,1,9,14,79,High School,35,Patnership,Complex\n4043,333.93,NJ City,E,4,24,1,69,No education,27,Owner,Standalone\n2477,117,NJ City,N,3,15,10,52,High School,18,Patnership,Standalone\n3610,26.45,NJ City,N,10,11,4,29,High School,27,Patnership,Complex\n9207,125.65,NJ City,E,3,17,4,52,Graduation,35,Owner,Standalone\n3312,14.33,NJ City,N,15,18,4,78,Graduation,34,Franchise,Complex\n6062,481,Boston,S,3,24,5,52,Graduation,23,Patnership,Complex\n9134,427.38,NJ City,S,17,19,1,66,No education,35,Patnership,Complex\n3104,148,NJ City,W,3,21,19,38,High School,26,Franchise,Complex\n2418,306.33,NJ City,S,14,18,3,43,Graduation,20,Patnership,Complex\n5195,499,Boston,E,2,17,13,30,Graduation,28,Owner,Standalone\n1012,250,NJ City,W,2,11,15,48,High School,18,Franchise,Complex\n6535,11.64,Boston,N,4,17,2,42,High School,18,Owner,Standalone\n6684,320.56,Boston,W,6,11,2,57,Graduation,20,Franchise,Complex\n1617,221,NJ City,N,1,10,9,66,High School,20,Patnership,Standalone\n9581,388,NJ City,N,3,8,22,49,Graduation,34,Patnership,Standalone\n1339,276,NJ City,E,3,14,19,71,High School,35,Owner,Standalone\n1644,240,NJ City,W,2,19,18,57,High School,33,Franchise,Complex\n5376,85.98,Boston,W,25,8,2,27,Graduation,31,Franchise,Complex\n5076,413,Boston,W,2,23,23,31,Graduation,29,Franchise,Complex\n28,171,Boston,W,1,23,14,55,High School,24,Franchise,Complex\n2666,205,NJ City,S,3,16,13,63,High School,30,Patnership,Complex\n1343,162,NJ City,E,3,8,13,30,High School,24,Owner,Standalone\n2357,271,NJ City,N,3,22,23,44,High School,32,Patnership,Standalone\n7611,131.11,Boston,E,21,16,3,57,Graduation,30,Owner,Standalone\n4320,246,NJ City,W,2,17,14,26,High School,20,Franchise,Complex\n1925,5.64,NJ City,N,10,20,3,31,No education,24,Patnership,Standalone\n2913,258,NJ City,N,1,21,21,69,High School,34,Patnership,Standalone\n7898,354,Boston,S,2,17,13,57,Graduation,25,Patnership,Complex\n8490,30.16,NJ City,N,14,17,3,72,Graduation,26,Patnership,Complex\n7843,431,Boston,E,3,21,25,83,Graduation,22,Owner,Standalone\n8853,362,NJ City,N,1,10,23,76,Graduation,19,Patnership,Standalone\n2880,319,NJ City,W,1,9,11,26,High School,23,Franchise,Complex\n6653,65.96,Boston,N,22,18,2,62,No education,24,Patnership,Standalone\n5684,390,Boston,W,2,10,11,43,Graduation,31,Franchise,Complex\n1691,271,NJ City,E,2,14,13,28,High School,20,Owner,Standalone\n3164,283,NJ City,W,2,8,20,72,High School,34,Franchise,Complex\n3275,372.91,NJ City,E,24,22,2,67,No education,30,Owner,Standalone\n7056,74.04,Boston,W,15,15,4,78,Graduation,29,Franchise,Complex\n8064,414,NJ City,W,3,23,14,32,Graduation,26,Franchise,Complex\n2489,464,NJ City,N,1,20,5,34,Graduation,30,Patnership,Standalone\n4404,413,NJ City,W,3,11,5,48,Graduation,26,Franchise,Complex\n3062,67.07,NJ City,N,8,12,4,58,No education,24,Patnership,Complex\n4147,311,NJ City,E,1,13,12,45,High School,26,Owner,Standalone\n9166,408,NJ City,S,3,24,16,51,Graduation,29,Patnership,Complex\n2288,183,NJ City,W,2,23,12,68,High School,32,Franchise,Complex\n1156,129,NJ City,W,3,20,22,42,High School,20,Franchise,Complex\n9529,491,NJ City,N,2,15,10,74,Graduation,26,Patnership,Standalone\n2084,123.74,NJ City,W,9,8,2,77,No education,29,Franchise,Complex\n2321,203,NJ City,N,2,24,6,59,High School,25,Patnership,Standalone\n8541,14.22,NJ City,N,14,18,3,43,Graduation,33,Patnership,Standalone\n9648,428,NJ City,W,1,19,12,60,Graduation,23,Franchise,Complex\n9329,361,NJ City,N,3,19,5,33,Graduation,22,Patnership,Standalone\n7735,491,Boston,E,3,13,24,28,Graduation,18,Owner,Standalone\n4842,73.72,NJ City,N,13,12,2,59,Graduation,30,Patnership,Complex\n8537,382,NJ City,N,1,13,22,85,Graduation,26,Patnership,Standalone\n3534,254,NJ City,S,2,8,13,80,High School,32,Patnership,Complex\n7446,108.53,Boston,S,10,14,3,79,Graduation,18,Patnership,Complex\n8570,399.46,NJ City,S,5,11,1,25,No education,28,Patnership,Complex\n7177,101.23,Boston,N,23,16,2,61,High School,30,Patnership,Standalone\n4984,144,Boston,W,3,10,9,75,High School,26,Franchise,Complex\n8892,482,NJ City,W,2,10,20,25,Graduation,33,Franchise,Complex\n1654,312,NJ City,S,1,17,16,75,High School,23,Patnership,Complex\n3946,124.19,NJ City,S,3,21,3,46,High School,31,Patnership,Complex\n3285,117.46,NJ City,N,13,21,4,51,Graduation,20,Patnership,Standalone\n9228,445,NJ City,W,1,20,8,33,Graduation,30,Franchise,Complex\n3766,13.17,NJ City,N,10,20,2,51,High School,34,Patnership,Complex\n5185,273.55,Boston,N,5,19,3,77,High School,29,Patnership,Standalone\n7308,11.83,Boston,N,20,24,4,40,Graduation,33,Franchise,Complex\n7617,482,Boston,N,1,21,21,84,Graduation,25,Patnership,Standalone\n4880,124,NJ City,W,1,24,19,54,High School,27,Franchise,Complex\n9305,169.03,NJ City,N,7,16,2,81,No education,26,Patnership,Standalone\n7664,417,Boston,W,3,10,18,45,Graduation,19,Franchise,Complex\n5980,394.28,Boston,W,11,10,2,79,High School,20,Franchise,Complex\n5524,116.09,Boston,W,2,11,1,45,High School,33,Franchise,Complex\n3184,106.11,NJ City,W,8,9,2,70,High School,35,Franchise,Complex\n2163,282.34,NJ City,E,16,22,3,85,Graduation,20,Owner,Standalone\n1287,1.38,NJ City,N,15,24,2,56,Graduation,34,Owner,Standalone\n9117,37.27,NJ City,N,14,12,3,71,Graduation,20,Patnership,Standalone\n8780,213.13,NJ City,W,3,18,3,39,No education,32,Franchise,Complex\n9422,441,NJ City,S,1,15,16,72,Graduation,33,Patnership,Complex\n3051,191,NJ City,E,3,15,23,42,High School,18,Owner,Standalone\n9035,89.12,NJ City,E,3,18,2,75,No education,30,Owner,Standalone\n948,75.05,NJ City,W,8,12,2,46,Graduation,29,Franchise,Complex\n7669,468,Boston,N,3,23,24,26,Graduation,30,Patnership,Standalone\n3944,204,NJ City,W,1,8,12,79,High School,32,Franchise,Complex\n8930,415,NJ City,S,3,18,18,36,Graduation,29,Patnership,Complex\n9708,396,NJ City,W,3,9,9,55,Graduation,26,Franchise,Complex\n7304,30.36,Boston,N,2,16,2,64,No education,22,Franchise,Complex\n1295,173,NJ City,E,1,18,15,39,High School,34,Owner,Standalone\n3784,294,NJ City,W,3,22,15,35,High School,31,Franchise,Complex\n436,90.41,NJ City,W,3,12,4,59,High School,32,Franchise,Complex\n1553,193,NJ City,N,3,10,16,48,High School,24,Patnership,Standalone\n5697,95.3,Boston,N,19,15,1,81,Graduation,26,Patnership,Standalone\n5587,403,Boston,E,3,21,9,75,Graduation,28,Owner,Standalone\n2749,184,NJ City,N,1,14,9,47,High School,32,Patnership,Standalone\n7281,99.19,Boston,N,14,11,3,68,Graduation,33,Patnership,Standalone\n5644,89.49,Boston,W,5,8,2,26,High School,19,Franchise,Complex\n2261,51.58,NJ City,N,17,24,2,54,No education,19,Patnership,Standalone\n4432,127,NJ City,W,1,16,24,85,High School,21,Franchise,Complex\n9286,430,NJ City,S,3,10,16,35,Graduation,34,Patnership,Complex\n422,140,NJ City,S,2,10,20,32,High School,22,Patnership,Complex\n4923,145,NJ City,E,2,13,14,82,High School,19,Owner,Standalone\n6858,429,Boston,S,3,18,14,59,Graduation,20,Patnership,Complex\n8257,460,NJ City,N,2,16,19,79,Graduation,22,Patnership,Standalone\n5975,481,Boston,E,3,24,19,74,Graduation,29,Owner,Standalone\n5709,490.07,Boston,N,4,17,2,29,Graduation,31,Patnership,Standalone\n8514,367,NJ City,S,3,20,22,34,Graduation,35,Patnership,Complex\n1738,292,NJ City,S,1,19,25,49,High School,20,Patnership,Complex\n570,108,NJ City,S,3,12,10,64,High School,22,Patnership,Complex\n5296,457,Boston,W,1,15,5,66,Graduation,27,Franchise,Complex\n9121,387,NJ City,N,2,10,21,26,Graduation,22,Patnership,Standalone\n4934,147,NJ City,S,2,18,22,84,High School,21,Patnership,Complex\n7662,409,Boston,S,3,19,10,41,Graduation,22,Patnership,Complex\n3014,407.91,NJ City,S,14,16,1,31,No education,27,Patnership,Complex\n409,239,NJ City,N,2,18,9,39,High School,31,Patnership,Standalone\n1919,198,NJ City,E,1,14,11,63,High School,20,Owner,Standalone\n9072,112.83,NJ City,W,25,17,1,63,Graduation,23,Franchise,Complex\n4162,104,NJ City,S,1,21,10,55,High School,25,Patnership,Complex\n4414,230,NJ City,S,1,12,22,71,High School,34,Patnership,Complex\n891,419,NJ City,E,2,9,5,82,Graduation,25,Owner,Standalone\n2331,34.07,NJ City,N,17,16,2,58,Graduation,31,Owner,Standalone\n5300,481,Boston,W,1,14,13,58,Graduation,20,Franchise,Complex\n8938,500,NJ City,S,3,22,20,25,Graduation,18,Patnership,Complex\n9008,428,NJ City,W,1,22,11,45,Graduation,20,Franchise,Complex\n6115,453,Boston,E,1,9,23,58,Graduation,26,Owner,Standalone\n1562,289,NJ City,S,2,11,24,69,High School,32,Patnership,Complex\n9263,156.9,NJ City,E,23,9,4,77,No education,29,Owner,Standalone\n8997,50.8,NJ City,N,9,12,3,79,Graduation,30,Patnership,Standalone\n9739,452,NJ City,E,1,22,22,71,Graduation,19,Owner,Standalone\n8233,432,NJ City,N,1,21,15,75,Graduation,18,Patnership,Standalone\n931,195,NJ City,E,3,8,9,50,High School,24,Owner,Standalone\n9335,353,NJ City,E,2,20,15,76,Graduation,25,Owner,Standalone\n453,300,NJ City,N,2,14,14,53,High School,28,Patnership,Standalone\n8468,27.86,NJ City,N,10,19,2,66,No education,25,Franchise,Complex\n8976,486,NJ City,W,2,21,17,64,Graduation,34,Franchise,Complex\n7119,286,Boston,E,1,8,2,46,Graduation,20,Owner,Standalone\n5579,430.29,Boston,E,1,15,2,52,No education,21,Owner,Standalone\n8845,90.15,NJ City,N,6,11,1,84,High School,34,Patnership,Standalone\n7860,418,Boston,W,3,14,13,34,Graduation,24,Franchise,Complex\n6681,48.42,Boston,N,10,20,2,55,Graduation,24,Patnership,Standalone\n3863,230,NJ City,E,3,12,7,65,High School,29,Owner,Standalone\n2382,461.99,NJ City,S,2,21,4,29,Graduation,23,Patnership,Complex\n4457,255,NJ City,N,3,12,24,74,High School,26,Patnership,Standalone\n8675,450,NJ City,E,2,20,8,38,Graduation,18,Owner,Standalone\n1254,131,NJ City,S,3,9,13,50,High School,30,Patnership,Complex\n3269,98.19,NJ City,N,17,12,4,64,No education,26,Patnership,Standalone\n2150,1.27,NJ City,N,17,11,2,33,No education,22,Patnership,Complex\n5348,497,Boston,W,2,22,9,44,Graduation,20,Franchise,Complex\n8139,394,NJ City,E,3,17,5,73,Graduation,23,Owner,Standalone\n6452,88.36,Boston,W,11,10,4,67,No education,27,Franchise,Complex\n2522,287,NJ City,S,3,14,23,85,High School,25,Patnership,Complex\n8331,360,NJ City,E,1,24,11,60,Graduation,26,Owner,Standalone\n9859,410.17,NJ City,E,5,10,4,59,High School,35,Owner,Standalone\n1868,120.44,NJ City,W,12,8,1,42,No education,26,Franchise,Complex\n5692,493,Boston,W,1,21,15,52,Graduation,26,Franchise,Complex\n7933,6.83,Boston,N,6,22,2,54,High School,20,Patnership,Standalone\n3511,233,NJ City,E,3,18,23,82,High School,30,Owner,Standalone\n6933,472,Boston,N,2,15,19,38,Graduation,31,Patnership,Standalone\n2003,112.48,NJ City,E,7,21,2,63,No education,34,Owner,Standalone\n3924,288,NJ City,W,3,9,20,81,High School,34,Franchise,Complex\n5717,453.53,Boston,N,12,16,3,48,No education,29,Patnership,Standalone\n2305,251,NJ City,N,1,12,15,27,High School,21,Patnership,Standalone\n4878,296,NJ City,S,1,21,13,76,High School,21,Patnership,Complex\n8841,381,NJ City,N,1,14,13,29,Graduation,32,Patnership,Standalone\n4091,179,NJ City,E,1,24,7,72,High School,23,Owner,Standalone\n9970,111.07,NJ City,S,15,10,4,46,High School,24,Patnership,Complex\n2332,303,NJ City,W,1,20,22,81,High School,32,Franchise,Complex\n5329,369,Boston,N,3,21,24,37,Graduation,18,Patnership,Standalone\n4944,86.42,Boston,W,25,12,2,56,Graduation,21,Franchise,Complex\n2436,101,NJ City,W,3,17,19,78,High School,18,Franchise,Complex\n9754,454,NJ City,S,3,11,20,85,Graduation,19,Patnership,Complex\n1572,349.01,NJ City,W,4,21,2,62,Graduation,33,Franchise,Complex\n3406,234,NJ City,S,1,15,18,70,High School,34,Patnership,Complex\n7043,399,Boston,E,2,23,18,35,Graduation,18,Owner,Standalone\n6899,389,Boston,E,3,10,13,55,Graduation,24,Owner,Standalone\n6532,427.05,Boston,W,10,16,1,29,High School,31,Franchise,Complex\n9860,47.89,NJ City,W,21,13,3,59,No education,32,Franchise,Complex\n2886,204,NJ City,S,2,20,17,35,High School,33,Patnership,Complex\n8029,487,Boston,N,1,23,9,78,Graduation,28,Patnership,Standalone\n3327,73.8,NJ City,N,1,13,3,75,Graduation,33,Owner,Standalone\n6986,463,Boston,S,2,23,12,57,Graduation,19,Patnership,Complex\n3991,196,NJ City,E,3,21,22,32,High School,22,Owner,Standalone\n152,127.29,NJ City,W,12,15,2,32,No education,22,Franchise,Complex\n9458,355,NJ City,S,1,23,24,75,Graduation,24,Patnership,Complex\n103,138,NJ City,E,1,24,8,39,High School,32,Owner,Standalone\n1720,129,NJ City,W,1,11,25,74,High School,32,Franchise,Complex\n6720,381,Boston,W,3,17,22,73,Graduation,26,Franchise,Complex\n2583,132.85,NJ City,E,17,15,1,77,Graduation,22,Owner,Standalone\n955,132,NJ City,E,2,22,7,33,High School,23,Owner,Standalone\n9888,16.46,NJ City,N,25,8,3,77,Graduation,24,Franchise,Complex\n8280,388,NJ City,W,3,22,16,65,Graduation,35,Franchise,Complex\n9690,371,NJ City,S,1,17,15,38,Graduation,26,Patnership,Complex\n1522,495.07,NJ City,S,15,11,3,66,High School,27,Patnership,Complex\n4111,273,NJ City,E,2,15,22,60,High School,29,Owner,Standalone\n4811,207,NJ City,E,2,15,25,36,High School,32,Owner,Standalone\n6257,463,Boston,N,1,15,9,69,Graduation,30,Patnership,Standalone\n53,111,NY City,N,3,9,6,31,High School,23,Patnership,Standalone\n2750,98.87,NJ City,S,21,24,4,26,No education,23,Patnership,Complex\n8069,98.56,NJ City,N,19,11,1,79,No education,24,Patnership,Standalone\n8550,398,NJ City,S,3,10,17,39,Graduation,32,Patnership,Complex\n5592,406,Boston,W,2,18,19,61,Graduation,22,Franchise,Complex\n6054,94.07,Boston,S,22,23,3,37,Graduation,27,Patnership,Complex\n9479,14.1,NJ City,N,20,9,1,34,No education,20,Owner,Standalone\n4937,279,Boston,N,1,23,10,50,High School,24,Patnership,Standalone\n7927,489.69,Boston,E,13,24,1,47,High School,31,Owner,Standalone\n2420,37.81,NJ City,N,16,8,4,57,No education,34,Franchise,Complex\n5837,414,Boston,N,1,20,21,39,Graduation,20,Patnership,Standalone\n8463,376,NJ City,E,3,10,17,66,Graduation,35,Owner,Standalone\n4791,61.61,NJ City,N,11,13,1,45,Graduation,29,Owner,Standalone\n9462,451,NJ City,S,2,19,21,78,Graduation,31,Patnership,Complex\n4589,301,NJ City,N,2,16,21,84,High School,18,Patnership,Standalone\n2472,303,NJ City,W,3,8,25,44,High School,24,Franchise,Complex\n9193,33.73,NJ City,N,7,18,2,59,High School,30,Patnership,Standalone\n1913,46.17,NJ City,N,25,23,2,36,No education,32,Patnership,Standalone\n8153,398,NJ City,N,2,14,21,34,Graduation,32,Patnership,Standalone\n7321,394,Boston,N,2,22,23,51,Graduation,21,Patnership,Standalone\n7014,412,Boston,S,3,13,15,71,Graduation,32,Patnership,Complex\n8058,26.52,NJ City,N,12,20,1,84,Graduation,21,Patnership,Complex\n7481,396.9,Boston,N,4,11,1,48,No education,32,Patnership,Standalone\n3567,166,NJ City,E,2,18,21,78,High School,22,Owner,Standalone\n2911,302,NJ City,E,1,11,11,73,High School,27,Owner,Standalone\n7033,439,Boston,N,3,12,24,61,Graduation,22,Patnership,Standalone\n970,40.56,NJ City,N,18,13,1,36,High School,20,Patnership,Complex\n9960,76.95,NJ City,W,19,12,1,31,Graduation,26,Franchise,Complex\n853,177,NJ City,N,1,9,24,67,High School,34,Patnership,Standalone\n8236,414,NJ City,W,2,12,12,63,Graduation,34,Franchise,Complex\n4364,148,NJ City,W,3,23,9,70,High School,20,Franchise,Complex\n1641,21.5,NJ City,N,14,8,3,81,Graduation,22,Patnership,Standalone\n5495,112.96,Boston,E,7,15,3,54,No education,20,Owner,Standalone\n8039,472,Boston,E,3,15,18,57,Graduation,33,Owner,Standalone\n3856,261,NJ City,W,3,22,20,26,High School,26,Franchise,Complex\n1830,264.49,NJ City,S,11,10,4,40,Graduation,24,Patnership,Complex\n5740,488,Boston,W,1,8,15,59,Graduation,19,Franchise,Complex\n5793,414,Boston,N,1,17,14,54,Graduation,25,Patnership,Standalone\n1350,128,NJ City,S,1,9,9,38,High School,20,Patnership,Complex\n4850,136,NJ City,S,1,14,20,71,High School,21,Patnership,Complex\n8283,407,NJ City,E,1,22,24,35,Graduation,21,Owner,Standalone\n7548,497.46,Boston,W,3,20,4,79,Graduation,21,Franchise,Complex\n3600,280,NJ City,W,2,14,25,28,High School,23,Franchise,Complex\n6168,338.3,Boston,W,16,10,2,71,Graduation,33,Franchise,Complex\n5860,89.47,Boston,W,15,8,4,39,High School,31,Franchise,Complex\n8027,200.7,Boston,E,22,15,2,74,No education,33,Owner,Standalone\n3614,230,NJ City,S,2,19,15,85,High School,24,Patnership,Complex\n4341,165,NJ City,N,1,17,22,36,High School,29,Patnership,Standalone\n6827,34.4,Boston,N,10,12,4,49,No education,28,Owner,Standalone\n4540,197,NJ City,W,3,17,24,54,High School,29,Franchise,Complex\n1529,135,NJ City,N,1,9,22,57,High School,24,Patnership,Standalone\n3049,158,NJ City,N,3,19,15,72,High School,34,Patnership,Standalone\n8401,489,NJ City,N,2,18,6,67,Graduation,19,Patnership,Standalone\n4705,213,NJ City,N,1,8,22,80,High School,19,Patnership,Standalone\n7234,433,Boston,S,2,20,24,27,Graduation,18,Patnership,Complex\n2232,143,NJ City,W,3,14,20,51,High School,27,Franchise,Complex\n963,93.7,NJ City,E,20,13,2,69,Graduation,25,Owner,Standalone\n2980,228,NJ City,W,1,19,20,35,High School,29,Franchise,Complex\n9658,230.37,NJ City,S,2,22,4,44,High School,25,Patnership,Complex\n3007,52.42,NJ City,E,24,19,3,53,High School,19,Owner,Standalone\n7409,63.06,Boston,N,21,11,1,26,No education,21,Patnership,Standalone\n2657,238,NJ City,N,2,13,16,60,High School,21,Patnership,Standalone\n1771,268,NJ City,E,2,19,25,33,High School,31,Owner,Standalone\n22,270,NJ City,S,3,20,8,80,High School,23,Patnership,Complex\n3476,261,NJ City,W,1,24,7,67,High School,20,Franchise,Complex\n5857,499,Boston,N,2,22,14,30,Graduation,19,Patnership,Standalone\n9858,443,NJ City,S,2,15,8,79,Graduation,34,Patnership,Complex\n2607,115,NJ City,E,3,13,16,81,High School,24,Owner,Standalone\n9342,89.99,NJ City,S,22,11,4,69,Graduation,26,Patnership,Complex\n2965,348.12,NJ City,N,25,8,2,46,High School,28,Patnership,Standalone\n5756,275.21,Boston,W,1,8,4,25,No education,19,Franchise,Complex\n3653,145,NJ City,N,1,8,20,29,High School,27,Patnership,Standalone\n4358,311,NJ City,S,3,13,22,57,High School,34,Patnership,Complex\n487,136.23,NJ City,E,20,18,3,55,High School,20,Owner,Standalone\n5140,107.15,Boston,W,3,11,4,42,High School,29,Franchise,Complex\n9120,436,NJ City,W,3,20,24,46,Graduation,22,Franchise,Complex\n1505,182,NJ City,N,1,20,13,46,High School,28,Patnership,Standalone\n8048,353,NJ City,W,1,12,12,42,Graduation,34,Franchise,Complex\n4743,486,NJ City,E,1,12,5,70,Graduation,34,Owner,Standalone\n511,49.06,NJ City,E,24,15,3,44,High School,30,Owner,Standalone\n6028,228.43,Boston,W,21,20,2,80,High School,31,Franchise,Complex\n1164,36.09,NJ City,N,2,8,4,31,Graduation,29,Franchise,Complex\n9966,387,NJ City,S,3,15,23,54,Graduation,23,Patnership,Complex\n4650,326,NJ City,S,14,17,4,74,Graduation,30,Patnership,Complex\n5473,319.69,Boston,N,24,13,4,52,High School,18,Patnership,Standalone\n9010,123.47,NJ City,S,1,9,1,81,High School,18,Patnership,Complex\n4590,82.45,NJ City,S,25,23,2,69,Graduation,22,Patnership,Complex\n6388,113.32,Boston,W,9,17,2,56,High School,35,Franchise,Complex\n9674,25.33,NJ City,N,7,20,4,83,No education,29,Patnership,Complex\n6987,401,Boston,E,3,17,13,41,Graduation,34,Owner,Standalone\n1190,275,NJ City,S,3,8,16,70,High School,34,Patnership,Complex\n8488,488,NJ City,W,1,10,22,33,Graduation,21,Franchise,Complex\n3721,277,NJ City,N,3,12,9,48,High School,29,Patnership,Standalone\n4271,235,NJ City,E,3,13,17,33,High School,32,Owner,Standalone\n2328,6.62,NJ City,N,9,13,3,53,Graduation,20,Franchise,Complex\n7213,84.56,Boston,N,18,15,3,73,High School,33,Patnership,Standalone\n6658,500,Boston,S,3,22,18,82,Graduation,18,Patnership,Complex\n2511,311,NJ City,E,1,15,19,25,High School,29,Owner,Standalone\n1298,263,NJ City,S,1,17,11,67,High School,34,Patnership,Complex\n1056,250,NJ City,W,2,16,9,56,High School,34,Franchise,Complex\n189,105,NJ City,N,3,11,6,34,High School,19,Patnership,Standalone\n6349,373,Boston,N,2,18,22,35,Graduation,24,Patnership,Standalone\n7317,421,Boston,N,1,18,14,49,Graduation,22,Patnership,Standalone\n1052,252,NJ City,W,2,16,6,32,High School,35,Franchise,Complex\n623,156,NJ City,E,1,18,22,44,High School,18,Owner,Standalone\n1898,132,NJ City,S,3,20,19,84,High School,31,Patnership,Complex\n7501,423,Boston,N,3,10,15,75,Graduation,34,Patnership,Standalone\n3085,50.4,NJ City,N,12,24,1,36,High School,30,Patnership,Standalone\n8109,364,NJ City,N,2,23,25,38,Graduation,35,Patnership,Standalone\n7783,81.95,Boston,E,24,21,1,85,High School,32,Owner,Standalone\n5993,93.95,Boston,N,15,17,3,77,No education,35,Patnership,Standalone\n233,182,NJ City,N,3,16,10,48,High School,35,Patnership,Standalone\n757,389.55,NJ City,N,1,16,3,77,High School,28,Patnership,Standalone\n9465,497.92,NJ City,N,5,15,1,83,Graduation,35,Patnership,Standalone\n1165,303,NJ City,N,2,19,19,51,High School,25,Patnership,Standalone\n2580,2.99,NJ City,N,3,10,2,69,Graduation,26,Franchise,Complex\n7639,92.77,Boston,E,22,16,1,52,High School,18,Owner,Standalone\n4105,139,NJ City,N,3,15,15,26,High School,31,Patnership,Standalone\n8893,363,NJ City,N,2,11,15,59,Graduation,30,Patnership,Standalone\n7457,435,Boston,N,2,24,6,62,Graduation,31,Patnership,Standalone\n8415,497,NJ City,E,3,8,21,32,Graduation,18,Owner,Standalone\n4974,142.32,Boston,S,1,13,1,53,Graduation,19,Patnership,Complex\n6205,444,Boston,N,3,11,6,31,Graduation,20,Patnership,Standalone\n3690,0.52,NJ City,N,14,11,4,83,Graduation,19,Patnership,Complex\n2848,154,NJ City,W,1,23,19,28,High School,33,Franchise,Complex\n3355,295,NJ City,E,3,16,9,79,High School,27,Owner,Standalone\n7920,84.93,Boston,W,16,21,4,35,Graduation,32,Franchise,Complex\n8748,383,NJ City,W,1,16,5,37,Graduation,23,Franchise,Complex\n3310,316,NJ City,S,3,9,12,33,High School,35,Patnership,Complex\n5257,496,Boston,N,3,15,24,43,Graduation,21,Patnership,Standalone\n6800,383.18,Boston,W,13,8,3,53,No education,19,Franchise,Complex\n9556,462,NJ City,W,3,22,20,57,Graduation,31,Franchise,Complex\n1896,164,NJ City,W,1,17,24,43,High School,30,Franchise,Complex\n7327,113.94,Boston,E,18,13,3,26,High School,18,Owner,Standalone\n88,169,Boston,W,3,23,15,38,High School,33,Franchise,Complex\n2637,14.07,NJ City,N,22,10,2,49,Graduation,32,Patnership,Standalone\n3263,274,NJ City,E,2,8,6,30,High School,23,Owner,Standalone\n4948,78.43,Boston,W,19,20,4,79,High School,28,Franchise,Complex\n6928,499,Boston,W,1,8,24,75,Graduation,18,Franchise,Complex\n7836,453,Boston,W,1,8,5,25,Graduation,27,Franchise,Complex\n7935,429,Boston,E,3,16,20,48,Graduation,20,Owner,Standalone\n2778,17.7,NJ City,N,7,9,1,66,Graduation,19,Patnership,Complex\n9875,103.76,NJ City,E,9,10,1,74,No education,20,Owner,Standalone\n4398,59.73,NJ City,S,9,13,3,43,Graduation,20,Patnership,Complex\n6036,3.84,Boston,N,21,23,4,30,Graduation,23,Franchise,Complex\n9645,11.1,NJ City,N,13,16,1,38,Graduation,31,Patnership,Standalone\n7415,489,Boston,E,3,22,8,50,Graduation,21,Owner,Standalone\n4832,213,NJ City,W,2,12,7,39,High School,20,Franchise,Complex\n843,247,NJ City,E,2,18,13,42,High School,29,Owner,Standalone\n6664,360,Boston,W,1,18,20,56,Graduation,20,Franchise,Complex\n8566,377.11,NJ City,S,10,8,2,27,High School,33,Patnership,Complex\n6422,370,Boston,S,3,17,24,58,Graduation,31,Patnership,Complex\n86,205,Boston,S,2,10,19,79,High School,33,Patnership,Complex\n6217,461,Boston,N,3,20,14,59,Graduation,28,Patnership,Standalone\n7528,479,Boston,W,3,8,23,46,Graduation,33,Franchise,Complex\n8719,494,NJ City,E,1,22,6,77,Graduation,33,Owner,Standalone\n8469,226.39,NJ City,N,8,13,1,78,Graduation,23,Patnership,Standalone\n1438,6.67,NJ City,N,2,16,3,45,High School,24,Patnership,Complex\n8646,464,NJ City,S,2,16,17,44,Graduation,20,Patnership,Complex\n4570,187,NJ City,S,1,17,8,40,High School,35,Patnership,Complex\n2922,150,NJ City,S,2,9,12,78,High School,34,Patnership,Complex\n444,262,NJ City,W,2,14,25,82,High School,24,Franchise,Complex\n9015,265.59,NJ City,E,17,18,3,84,Graduation,34,Owner,Standalone\n5494,476,Boston,S,2,23,17,51,Graduation,35,Patnership,Complex\n2858,186,NJ City,S,1,19,10,56,High School,29,Patnership,Complex\n6165,22.51,Boston,N,21,22,4,42,Graduation,27,Patnership,Standalone\n5671,321.86,Boston,E,9,17,4,28,High School,33,Owner,Standalone\n7976,88.06,Boston,W,19,13,1,33,No education,32,Franchise,Complex\n5739,350,Boston,E,3,10,16,66,Graduation,19,Owner,Standalone\n2655,292,NJ City,E,3,15,12,45,High School,25,Owner,Standalone\n5307,22.88,Boston,N,25,10,3,48,Graduation,18,Owner,Standalone\n214,423,NJ City,S,3,22,5,25,Graduation,31,Patnership,Complex\n67,132,NJ City,E,1,20,19,63,High School,29,Owner,Standalone\n8647,38.94,NJ City,N,13,18,3,67,High School,21,Owner,Standalone\n8786,321.08,NJ City,S,21,8,2,81,No education,20,Patnership,Complex\n8890,418,NJ City,S,3,19,16,42,Graduation,27,Patnership,Complex\n7570,477,Boston,S,1,18,25,28,Graduation,32,Patnership,Complex\n3038,318,NJ City,S,2,21,23,31,High School,24,Patnership,Complex\n4085,112,NJ City,N,2,8,12,29,High School,26,Patnership,Standalone\n5984,82.51,Boston,W,22,11,3,42,No education,24,Franchise,Complex\n8624,485,NJ City,W,2,9,12,26,Graduation,32,Franchise,Complex\n5092,43.21,Boston,N,22,13,3,25,High School,25,Franchise,Complex\n3613,494.83,NJ City,N,4,11,2,41,High School,20,Patnership,Standalone\n2970,219,NJ City,S,3,22,17,37,High School,27,Patnership,Complex\n1648,7.18,NJ City,N,1,18,3,43,High School,35,Franchise,Complex\n158,188,NJ City,S,3,24,17,26,High School,35,Patnership,Complex\n4236,216,NJ City,W,2,21,9,74,High School,21,Franchise,Complex\n2656,80.92,NJ City,W,21,12,2,59,High School,32,Franchise,Complex\n8975,9.66,NJ City,N,6,19,1,62,No education,32,Owner,Standalone\n8119,472,NJ City,E,1,20,5,54,Graduation,20,Owner,Standalone\n457,10.78,NJ City,N,4,18,2,40,High School,26,Patnership,Standalone\n1090,173,NJ City,S,3,24,6,75,High School,26,Patnership,Complex\n5370,458,Boston,S,2,24,10,27,Graduation,33,Patnership,Complex\n5631,476,Boston,E,3,18,23,63,Graduation,28,Owner,Standalone\n4872,194,NJ City,W,2,10,22,82,High School,34,Franchise,Complex\n7793,16.82,Boston,N,24,17,3,28,No education,30,Patnership,Standalone\n4130,166,NJ City,S,1,9,8,73,High School,35,Patnership,Complex\n3158,293,NJ City,S,1,19,23,43,High School,35,Patnership,Complex\n3531,274,NJ City,E,3,15,7,81,High School,25,Owner,Standalone\n3794,408.87,NJ City,S,1,10,1,66,No education,20,Patnership,Complex\n8137,423,NJ City,N,1,13,19,59,Graduation,29,Patnership,Standalone\n4835,338.28,NJ City,E,15,22,4,51,No education,30,Owner,Standalone\n1137,103,NJ City,N,2,22,9,57,High School,23,Patnership,Standalone\n85,157,Boston,N,2,18,19,56,High School,22,Patnership,Standalone\n5785,23.05,Boston,N,4,18,3,29,High School,23,Patnership,Standalone\n3738,403.17,NJ City,S,5,9,4,63,Graduation,27,Patnership,Complex\n3129,27.59,NJ City,N,12,23,4,75,Graduation,31,Patnership,Standalone\n2304,269,NJ City,W,3,21,7,55,High School,28,Franchise,Complex\n7953,289.83,Boston,N,1,17,3,81,Graduation,26,Patnership,Standalone\n676,151,NJ City,W,2,14,17,67,High School,22,Franchise,Complex\n5426,433,Boston,S,3,23,8,39,Graduation,27,Patnership,Complex\n4011,106.89,NJ City,E,17,15,1,49,Graduation,18,Owner,Standalone\n8338,396,NJ City,S,2,12,7,66,Graduation,31,Patnership,Complex\n5849,154.13,Boston,N,9,8,4,33,No education,35,Patnership,Standalone\n3896,107,NJ City,W,3,19,17,66,High School,29,Franchise,Complex\n549,7.83,NJ City,N,22,9,2,61,Graduation,26,Patnership,Standalone\n3545,28.33,NJ City,N,10,19,1,34,No education,28,Patnership,Standalone\n9349,40.49,NJ City,N,5,22,4,47,High School,18,Patnership,Standalone\n1629,84.46,NJ City,N,8,20,3,75,Graduation,21,Patnership,Standalone\n7826,365,Boston,S,3,18,12,56,Graduation,34,Patnership,Complex\n3751,497,NJ City,E,3,18,5,47,Graduation,19,Owner,Standalone\n3163,130.21,NJ City,E,7,19,2,55,High School,25,Owner,Standalone\n4927,190,NJ City,E,3,22,12,66,High School,21,Owner,Standalone\n3683,130,NJ City,E,3,11,6,25,High School,35,Owner,Standalone\n1125,117.97,NJ City,N,2,17,3,28,Graduation,34,Patnership,Standalone\n4587,169,NJ City,E,3,20,21,65,High School,30,Owner,Standalone\n4740,281,NJ City,W,3,13,13,67,High School,23,Franchise,Complex\n4518,325.85,NJ City,S,2,8,3,80,Graduation,33,Patnership,Complex\n5382,374,Boston,S,3,8,14,28,Graduation,27,Patnership,Complex\n2578,293,NJ City,S,3,18,17,67,High School,20,Patnership,Complex\n9175,107.75,NJ City,E,17,24,3,56,High School,23,Owner,Standalone\n9340,125.61,NJ City,W,19,21,3,28,High School,31,Franchise,Complex\n4774,278,NJ City,S,1,9,19,77,High School,28,Patnership,Complex\n9627,264.21,NJ City,E,2,12,3,42,Graduation,24,Owner,Standalone\n9579,15.56,NJ City,N,16,17,1,39,Graduation,26,Owner,Standalone\n5569,498.03,Boston,N,23,12,1,59,High School,25,Patnership,Standalone\n6510,85.6,Boston,S,9,13,2,44,Graduation,34,Patnership,Complex\n1460,16.45,NJ City,N,11,17,4,41,No education,20,Franchise,Complex\n9186,62.28,NJ City,N,7,9,3,60,Graduation,22,Patnership,Complex\n9291,421,NJ City,E,2,12,22,27,Graduation,28,Owner,Standalone\n4044,5.62,NJ City,N,4,9,1,84,Graduation,21,Franchise,Complex\n1017,137,NJ City,N,3,8,14,63,High School,23,Patnership,Standalone\n285,123,NJ City,N,2,10,22,50,High School,19,Patnership,Standalone\n7263,103.68,Boston,E,4,15,1,36,Graduation,24,Owner,Standalone\n3165,450,NJ City,N,2,23,5,61,Graduation,20,Patnership,Standalone\n2807,146,NJ City,E,1,24,21,74,High School,18,Owner,Standalone\n7106,43.31,Boston,N,17,22,4,63,No education,19,Patnership,Complex\n7485,81.21,Boston,N,6,12,2,44,Graduation,18,Patnership,Standalone\n6670,360,Boston,S,2,23,10,68,Graduation,23,Patnership,Complex\n9547,476,NJ City,E,3,11,25,63,Graduation,27,Owner,Standalone\n9823,479,NJ City,E,2,12,7,40,Graduation,29,Owner,Standalone\n6632,115.53,Boston,W,21,24,2,30,No education,29,Franchise,Complex\n5598,384,Boston,S,1,21,9,84,Graduation,28,Patnership,Complex\n6483,42.54,Boston,N,9,17,3,55,Graduation,31,Owner,Standalone\n3677,233,NJ City,N,2,23,18,43,High School,19,Patnership,Standalone\n2187,222,NJ City,E,3,17,19,51,High School,19,Owner,Standalone\n5227,482,Boston,E,3,17,18,26,Graduation,29,Owner,Standalone\n2897,1.1,NJ City,W,18,15,3,49,No education,33,Patnership,Standalone\n4067,62.16,NJ City,N,21,16,3,39,No education,33,Owner,Standalone\n359,85.73,NJ City,E,6,15,2,66,No education,33,Owner,Standalone\n8126,415,NJ City,S,1,16,21,27,Graduation,28,Patnership,Complex\n7558,476,Boston,S,3,18,13,43,Graduation,19,Patnership,Complex\n6918,488,Boston,S,3,12,17,25,Graduation,32,Patnership,Complex\n3257,190,NJ City,N,3,24,13,49,High School,21,Patnership,Standalone\n3021,434.46,NJ City,N,16,17,4,25,Graduation,29,Patnership,Standalone\n8288,377,NJ City,W,1,9,9,60,Graduation,19,Franchise,Complex\n6017,357,Boston,N,2,23,6,85,Graduation,19,Patnership,Standalone\n4191,395,NJ City,E,2,9,5,61,Graduation,21,Owner,Standalone\n2209,244,NJ City,N,2,23,23,82,High School,28,Patnership,Standalone\n7143,355.1,Boston,E,10,15,4,81,Graduation,31,Owner,Standalone\n20,159.61,NY City,W,12,22,1,84,No education,34,Franchise,Complex\n1779,165,NJ City,E,2,10,10,32,High School,35,Owner,Standalone\n1914,114,NJ City,S,2,20,20,58,High School,27,Patnership,Complex\n9309,385.03,NJ City,N,19,23,3,79,Graduation,35,Patnership,Standalone\n8557,452,NJ City,N,2,14,12,34,Graduation,31,Patnership,Standalone\n364,39.48,NJ City,N,5,15,2,79,High School,34,Franchise,Complex\n7404,364,Boston,W,2,16,5,68,Graduation,25,Franchise,Complex\n2573,78.8,NJ City,N,24,23,2,37,No education,29,Patnership,Standalone\n6126,352,Boston,S,2,20,15,84,Graduation,34,Patnership,Complex\n8078,262.61,NJ City,S,1,13,3,46,No education,34,Patnership,Complex\n8072,429,NJ City,W,3,24,7,54,Graduation,24,Franchise,Complex\n7028,472,Boston,W,3,18,8,41,Graduation,29,Franchise,Complex\n9436,428.44,NJ City,W,15,12,2,44,High School,31,Franchise,Complex\n3748,34.58,NJ City,N,12,19,4,68,High School,27,Franchise,Complex\n9834,429,NJ City,S,2,21,25,63,Graduation,26,Patnership,Complex\n3885,100,NJ City,N,3,13,14,41,High School,21,Patnership,Standalone\n1978,146,NJ City,S,1,8,17,65,High School,23,Patnership,Complex\n4618,53.57,NJ City,S,22,19,4,40,High School,21,Patnership,Complex\n5396,383,Boston,W,3,20,17,34,Graduation,30,Franchise,Complex\n7484,355,Boston,W,1,21,9,36,Graduation,20,Franchise,Complex\n1768,30.57,NJ City,N,22,14,2,53,High School,26,Franchise,Complex\n2406,258,NJ City,S,2,15,24,25,High School,24,Patnership,Complex\n5808,399,Boston,W,2,11,21,44,Graduation,22,Franchise,Complex\n7513,357,Boston,N,2,24,17,59,Graduation,28,Patnership,Standalone\n4506,278.48,NJ City,S,17,18,2,58,Graduation,19,Patnership,Complex\n8559,372,NJ City,E,1,24,20,80,Graduation,21,Owner,Standalone\n8793,440,NJ City,N,2,12,25,77,Graduation,27,Patnership,Standalone\n3194,8.55,NJ City,N,4,23,1,33,No education,24,Patnership,Complex\n9170,440.21,NJ City,S,1,9,2,49,No education,28,Patnership,Complex\n8187,443,NJ City,E,3,15,12,44,Graduation,19,Owner,Standalone\n7960,102.2,Boston,W,6,11,3,79,High School,20,Franchise,Complex\n5042,357,Boston,S,1,13,18,39,Graduation,21,Patnership,Complex\n8294,458,NJ City,S,3,9,14,60,Graduation,31,Patnership,Complex\n5562,351,Boston,S,2,15,23,52,Graduation,21,Patnership,Complex\n1209,10.92,NJ City,N,12,20,2,78,Graduation,29,Patnership,Standalone\n8750,489,NJ City,S,2,19,22,38,Graduation,28,Patnership,Complex\n5650,449,Boston,S,1,21,14,31,Graduation,18,Patnership,Complex\n2124,208,NJ City,W,1,24,16,71,High School,20,Franchise,Complex\n8591,13.37,NJ City,N,12,20,2,37,No education,18,Owner,Standalone\n6288,430,Boston,W,3,17,9,39,Graduation,27,Franchise,Complex\n205,115,NJ City,N,3,20,7,40,High School,31,Patnership,Standalone\n7346,368,Boston,S,2,12,6,66,Graduation,24,Patnership,Complex\n5686,392,Boston,S,1,18,24,54,Graduation,21,Patnership,Complex\n4198,2.71,NJ City,S,8,12,1,53,High School,27,Patnership,Complex\n5331,117.9,Boston,E,11,13,3,80,Graduation,23,Owner,Standalone\n1405,5.33,NJ City,N,11,20,3,25,High School,26,Patnership,Standalone\n2413,143,NJ City,N,2,8,11,42,High School,25,Patnership,Standalone\n8700,46.69,NJ City,N,9,19,1,26,Graduation,18,Franchise,Complex\n3968,288,NJ City,W,1,22,11,30,High School,29,Franchise,Complex\n976,120,NJ City,W,2,19,9,51,High School,30,Franchise,Complex\n6815,87.33,Boston,E,12,15,4,85,No education,24,Owner,Standalone\n6332,384,Boston,W,1,15,17,42,Graduation,19,Franchise,Complex\n5634,481,Boston,S,2,24,6,53,Graduation,26,Patnership,Complex\n2536,113.42,NJ City,W,1,9,2,60,High School,32,Franchise,Complex\n5654,368,Boston,S,2,9,7,73,Graduation,24,Patnership,Complex\n7517,436,Boston,N,1,9,22,41,Graduation,25,Patnership,Standalone\n7872,490,Boston,W,1,18,14,54,Graduation,26,Franchise,Complex\n2492,244,NJ City,W,3,18,15,83,High School,29,Franchise,Complex\n9355,383,NJ City,E,2,9,6,48,Graduation,35,Owner,Standalone\n9967,377,NJ City,E,3,9,21,75,Graduation,25,Owner,Standalone\n4177,42.12,NJ City,N,25,24,4,51,High School,23,Patnership,Standalone\n3580,156,NJ City,W,2,11,11,27,High School,28,Franchise,Complex\n687,137,NJ City,E,1,22,14,77,High School,26,Owner,Standalone\n2823,238,NJ City,E,1,21,25,40,High School,21,Owner,Standalone\n358,112,NJ City,S,3,9,11,52,High School,18,Patnership,Complex\n4761,21.52,NJ City,N,19,17,1,46,Graduation,25,Patnership,Standalone\n3825,173,NJ City,N,2,15,24,72,High School,25,Patnership,Standalone\n6737,474.49,Boston,N,16,24,1,84,No education,27,Patnership,Standalone\n6417,359,Boston,N,3,18,25,29,Graduation,31,Patnership,Standalone\n4483,292,NJ City,E,3,16,12,83,High School,23,Owner,Standalone\n4909,158,NJ City,N,1,19,23,77,High School,26,Patnership,Standalone\n128,354.2,NJ City,W,24,21,2,74,No education,20,Franchise,Complex\n5334,440,Boston,S,1,19,7,51,Graduation,27,Patnership,Complex\n7667,354,Boston,E,3,21,18,67,Graduation,22,Owner,Standalone\n7746,406,Boston,S,2,17,13,47,Graduation,23,Patnership,Complex\n7866,465,Boston,S,2,12,11,67,Graduation,22,Patnership,Complex\n6542,71.68,Boston,N,19,15,3,39,No education,21,Patnership,Complex\n4602,43.09,NJ City,N,13,22,1,73,Graduation,19,Patnership,Complex\n2185,75.91,NJ City,N,25,11,2,51,High School,26,Patnership,Standalone\n190,262,NJ City,S,1,16,13,27,High School,24,Patnership,Complex\n8733,90.31,NJ City,N,2,16,4,54,Graduation,20,Patnership,Standalone\n818,285.31,NJ City,S,23,15,2,58,No education,35,Patnership,Complex\n1585,154,NJ City,N,3,12,7,34,High School,28,Patnership,Standalone\n3288,106.63,NJ City,W,22,14,1,42,Graduation,20,Franchise,Complex\n1104,107,NJ City,W,1,12,20,45,High School,18,Franchise,Complex\n9666,122.48,NJ City,S,25,20,2,62,Graduation,33,Patnership,Complex\n4751,267,NJ City,E,2,23,19,40,High School,18,Owner,Standalone\n6463,402,Boston,E,2,19,21,44,Graduation,20,Owner,Standalone\n2302,295,NJ City,S,3,15,11,59,High School,22,Patnership,Complex\n5865,435,Boston,N,2,15,13,74,Graduation,34,Patnership,Standalone\n8262,413,NJ City,S,3,19,16,83,Graduation,23,Patnership,Complex\n1134,449.74,NJ City,S,16,15,2,47,Graduation,21,Patnership,Complex\n4667,115,NJ City,E,3,18,20,25,High School,32,Owner,Standalone\n384,50.19,NJ City,W,12,15,3,54,Graduation,23,Franchise,Complex\n8098,437,NJ City,S,2,22,23,38,Graduation,35,Patnership,Complex\n9497,57.78,NJ City,N,18,22,4,51,No education,28,Patnership,Standalone\n4604,36.75,NJ City,N,6,12,1,76,No education,27,Franchise,Complex\n762,295.1,NJ City,S,15,21,1,26,Graduation,30,Patnership,Complex\n1893,176,NJ City,N,1,12,7,27,High School,23,Patnership,Standalone\n2244,197,NJ City,W,2,22,20,59,High School,21,Franchise,Complex\n3916,53.69,NJ City,W,6,18,2,65,High School,18,Franchise,Complex\n9730,495.28,NJ City,S,22,23,4,35,High School,29,Patnership,Complex\n3077,195,NJ City,N,1,21,12,61,High School,24,Patnership,Standalone\n4159,166,NJ City,E,2,18,12,35,High School,22,Owner,Standalone\n2202,258,NJ City,S,1,17,23,55,High School,34,Patnership,Complex\n8504,19.8,NJ City,N,4,23,3,60,No education,19,Franchise,Complex\n8349,421,NJ City,N,3,15,19,63,Graduation,22,Patnership,Standalone\n3551,310,NJ City,E,1,22,13,59,High School,23,Owner,Standalone\n804,199,NJ City,W,3,21,6,49,High School,21,Franchise,Complex\n930,214,NJ City,S,1,8,21,45,High School,32,Patnership,Complex\n5310,406,Boston,S,3,10,23,38,Graduation,19,Patnership,Complex\n9078,380,NJ City,S,2,20,8,51,Graduation,28,Patnership,Complex\n5270,435,Boston,S,1,21,21,41,Graduation,26,Patnership,Complex\n6813,84.8,Boston,N,14,13,1,62,Graduation,31,Patnership,Standalone\n4403,276,NJ City,E,3,17,20,26,High School,26,Owner,Standalone\n6118,405,Boston,S,3,10,14,29,Graduation,22,Patnership,Complex\n9840,463,NJ City,W,1,20,18,28,Graduation,23,Franchise,Complex\n900,38.64,NJ City,N,7,24,3,26,Graduation,25,Franchise,Complex\n8895,429,NJ City,E,1,22,9,60,Graduation,32,Owner,Standalone\n2419,488.54,NJ City,E,21,23,4,66,High School,26,Owner,Standalone\n8275,475,NJ City,E,3,18,14,35,Graduation,21,Owner,Standalone\n2547,290,NJ City,E,2,22,18,49,High School,21,Owner,Standalone\n274,124.82,NJ City,S,21,20,3,34,High School,20,Patnership,Complex\n99,115,NJ City,E,1,8,14,28,High School,30,Owner,Standalone\n3105,112,NJ City,N,2,14,15,80,High School,26,Patnership,Standalone\n9857,365,NJ City,N,3,23,23,63,Graduation,32,Patnership,Standalone\n1094,20.49,NJ City,N,16,17,3,36,No education,34,Patnership,Complex\n7686,406,Boston,S,1,24,10,39,Graduation,34,Patnership,Complex\n9693,122.08,NJ City,N,25,9,3,47,Graduation,35,Patnership,Standalone\n5174,70.54,Boston,N,19,11,3,57,No education,30,Patnership,Complex\n8509,473,NJ City,N,1,23,6,67,Graduation,19,Patnership,Standalone\n6469,27.71,Boston,N,2,21,1,78,High School,25,Patnership,Standalone\n5657,89.54,Boston,N,18,10,1,62,No education,32,Patnership,Standalone\n345,280,NJ City,N,3,20,12,45,High School,31,Patnership,Standalone\n4012,241,NJ City,W,3,24,20,73,High School,34,Franchise,Complex\n6783,367,Boston,E,2,8,5,67,Graduation,21,Owner,Standalone\n5362,471,Boston,S,2,10,15,81,Graduation,31,Patnership,Complex\n336,223,NJ City,W,3,13,10,76,High School,26,Franchise,Complex\n8747,496,NJ City,E,1,21,9,85,Graduation,30,Owner,Standalone\n6706,434,Boston,S,2,15,24,72,Graduation,21,Patnership,Complex\n7721,396,Boston,N,2,22,17,28,Graduation,19,Patnership,Standalone\n2989,89.42,NJ City,N,10,11,1,52,High School,25,Patnership,Standalone\n3945,304,NJ City,N,1,19,11,65,High School,23,Patnership,Standalone\n7534,494,Boston,S,1,15,9,49,Graduation,19,Patnership,Complex\n7612,405,Boston,W,3,13,5,56,Graduation,32,Franchise,Complex\n9562,387,NJ City,S,3,16,16,67,Graduation,30,Patnership,Complex\n629,64.99,NJ City,N,5,22,1,70,No education,23,Patnership,Standalone\n5729,447,Boston,N,2,17,7,36,Graduation,21,Patnership,Standalone\n5745,379,Boston,N,2,22,21,60,Graduation,31,Patnership,Standalone\n5672,477,Boston,W,2,9,16,50,Graduation,22,Franchise,Complex\n7589,369,Boston,N,2,23,11,38,Graduation,25,Patnership,Standalone\n7211,492,Boston,E,2,23,14,74,Graduation,28,Owner,Standalone\n7698,499,Boston,S,1,13,12,49,Graduation,32,Patnership,Complex\n5237,451,Boston,N,3,16,14,78,Graduation,35,Patnership,Standalone\n9449,62.58,NJ City,N,7,20,3,32,No education,22,Patnership,Standalone\n9994,461,NJ City,S,2,18,25,73,Graduation,30,Patnership,Complex\n9,166,Maine,N,3,21,24,73,High School,27,Patnership,Standalone\n4468,90.77,NJ City,W,4,12,2,60,High School,21,Franchise,Complex\n392,220,NJ City,W,1,24,22,33,High School,18,Franchise,Complex\n8304,184.78,NJ City,W,6,24,4,42,Graduation,25,Franchise,Complex\n8815,57.86,NJ City,E,1,19,2,48,High School,32,Owner,Standalone\n7813,253.67,Boston,N,22,20,3,76,High School,32,Patnership,Standalone\n9238,354,NJ City,S,3,23,23,39,Graduation,26,Patnership,Complex\n7919,56.64,Boston,E,4,19,3,49,No education,33,Owner,Standalone\n6423,402,Boston,E,3,24,10,27,Graduation,35,Owner,Standalone\n7907,123.36,Boston,E,9,8,4,53,No education,18,Owner,Standalone\n9721,354,NJ City,N,3,9,20,35,Graduation,27,Patnership,Standalone\n7095,440,Boston,E,3,15,21,66,Graduation,27,Owner,Standalone\n4255,0.22,NJ City,N,20,13,3,46,High School,29,Owner,Standalone\n188,426,NJ City,W,1,12,5,52,Graduation,27,Franchise,Complex\n1866,451.84,NJ City,S,2,8,1,25,Graduation,24,Patnership,Complex\n1162,19.46,NJ City,N,14,10,2,53,High School,35,Patnership,Complex\n8832,78.53,NJ City,W,20,24,2,65,Graduation,26,Franchise,Complex\n4623,210,NJ City,E,3,9,21,62,High School,22,Owner,Standalone\n5198,380,Boston,S,3,23,19,38,Graduation,28,Patnership,Complex\n4638,47.28,NJ City,N,18,8,1,44,Graduation,28,Patnership,Complex\n4591,213,NJ City,E,2,23,24,28,High School,33,Owner,Standalone\n4601,289,NJ City,N,2,19,6,59,High School,18,Patnership,Standalone\n3388,3.47,NJ City,N,21,13,4,36,High School,23,Franchise,Complex\n9919,374,NJ City,E,3,10,6,32,Graduation,32,Owner,Standalone\n704,284,NJ City,W,2,24,18,34,High School,35,Franchise,Complex\n2376,145,NJ City,W,1,21,12,52,High School,26,Franchise,Complex\n5271,362,Boston,E,2,14,15,44,Graduation,19,Owner,Standalone\n8961,440,NJ City,N,3,9,18,81,Graduation,19,Patnership,Standalone\n4129,156,NJ City,N,2,11,23,67,High School,22,Patnership,Standalone\n7840,104.82,Boston,W,9,23,2,72,High School,33,Franchise,Complex\n2017,260,NJ City,N,3,17,6,79,High School,26,Patnership,Standalone\n3446,237,NJ City,S,2,8,7,39,High School,28,Patnership,Complex\n4227,281,NJ City,E,2,11,15,72,High School,24,Owner,Standalone\n9699,359,NJ City,E,3,17,11,30,Graduation,20,Owner,Standalone\n9930,447,NJ City,S,1,19,20,28,Graduation,31,Patnership,Complex\n2928,120,NJ City,W,3,10,19,66,High School,28,Franchise,Complex\n124,45.1,NJ City,N,24,16,3,45,High School,27,Franchise,Complex\n27,249,Boston,E,2,10,10,84,High School,24,Owner,Standalone\n8978,361,NJ City,S,2,10,12,46,Graduation,24,Patnership,Complex\n3902,283.52,NJ City,S,5,19,2,26,No education,21,Patnership,Complex\n7997,395,Boston,N,2,12,13,60,Graduation,22,Patnership,Standalone\n6605,428.53,Boston,N,13,16,4,34,No education,33,Patnership,Standalone\n9926,432,NJ City,S,3,20,8,30,Graduation,18,Patnership,Complex\n4951,13.74,Boston,N,3,16,4,66,High School,34,Owner,Standalone\n8584,52.95,NJ City,W,25,8,3,83,High School,22,Franchise,Complex\n6768,390,Boston,W,3,19,18,80,Graduation,30,Franchise,Complex\n4214,117,NJ City,S,1,17,19,72,High School,28,Patnership,Complex\n7375,426,Boston,E,1,18,7,69,Graduation,22,Owner,Standalone\n8281,66.26,NJ City,N,14,9,3,70,High School,32,Patnership,Standalone\n3478,271,NJ City,S,3,22,9,67,High School,35,Patnership,Complex\n2728,61.25,NJ City,N,15,21,2,72,High School,24,Franchise,Complex\n2854,304,NJ City,S,2,8,15,76,High School,24,Patnership,Complex\n5694,365,Boston,S,3,15,6,37,Graduation,22,Patnership,Complex\n563,168,NJ City,E,2,21,23,68,High School,28,Owner,Standalone\n559,127,NJ City,E,1,19,21,50,High School,29,Owner,Standalone\n360,44.03,NJ City,N,23,19,3,72,Graduation,29,Franchise,Complex\n9501,478,NJ City,N,1,11,21,64,Graduation,22,Patnership,Standalone\n3470,269,NJ City,S,3,8,25,79,High School,22,Patnership,Complex\n1797,427,NJ City,N,2,18,5,53,Graduation,28,Patnership,Standalone\n2180,61.3,NJ City,N,25,12,4,72,No education,35,Franchise,Complex\n653,137,NJ City,N,2,9,13,48,High School,25,Patnership,Standalone\n6908,440.55,Boston,W,18,22,1,65,No education,21,Franchise,Complex\n1071,391.36,NJ City,E,8,14,3,37,Graduation,26,Owner,Standalone\n2190,24.33,NJ City,N,6,22,4,75,Graduation,30,Patnership,Complex\n6034,362,Boston,S,3,19,24,25,Graduation,25,Patnership,Complex\n6674,410,Boston,S,2,12,23,46,Graduation,31,Patnership,Complex\n9022,378,NJ City,S,2,14,14,48,Graduation,22,Patnership,Complex\n7386,410,Boston,S,1,23,8,84,Graduation,23,Patnership,Complex\n7088,469,Boston,W,1,17,13,37,Graduation,30,Franchise,Complex\n312,243,NJ City,W,2,16,13,59,High School,22,Franchise,Complex\n3416,252,NJ City,W,2,8,17,43,High School,28,Franchise,Complex\n3368,276,NJ City,W,1,21,8,80,High School,22,Franchise,Complex\n6627,498,Boston,E,1,10,8,76,Graduation,35,Owner,Standalone\n805,132,NJ City,N,1,16,23,58,High School,20,Patnership,Standalone\n8356,492,NJ City,W,3,15,20,43,Graduation,21,Franchise,Complex\n8694,298.9,NJ City,S,6,13,3,28,Graduation,21,Patnership,Complex\n1958,68.72,NJ City,N,2,17,3,31,No education,35,Patnership,Complex\n198,126,NJ City,S,1,11,25,31,High School,19,Patnership,Complex\n7678,7.57,Boston,N,9,12,3,72,High School,22,Patnership,Complex\n7050,371,Boston,S,1,13,13,67,Graduation,35,Patnership,Complex\n3530,124.12,NJ City,S,11,24,4,69,No education,22,Patnership,Complex\n2993,162,NJ City,N,3,16,11,28,High School,20,Patnership,Standalone\n9689,389,NJ City,N,3,8,24,55,Graduation,30,Patnership,Standalone\n6633,416,Boston,N,3,24,17,74,Graduation,30,Patnership,Standalone\n831,229,NJ City,E,3,8,14,61,High School,22,Owner,Standalone\n9778,493,NJ City,S,3,10,11,81,Graduation,30,Patnership,Complex\n9615,367,NJ City,E,1,13,23,62,Graduation,27,Owner,Standalone\n3823,356.7,NJ City,E,1,8,3,35,High School,29,Owner,Standalone\n8759,118.95,NJ City,E,11,21,2,72,No education,19,Owner,Standalone\n6431,463.72,Boston,E,15,8,1,37,No education,19,Owner,Standalone\n7378,407,Boston,S,2,9,7,49,Graduation,27,Patnership,Complex\n1942,145,NJ City,S,3,13,8,52,High School,24,Patnership,Complex\n8805,395,NJ City,N,3,21,13,84,Graduation,20,Patnership,Standalone\n3678,150,NJ City,S,1,17,6,46,High School,23,Patnership,Complex\n195,488.82,NJ City,E,13,20,3,80,Graduation,20,Owner,Standalone\n950,184,NJ City,S,1,10,14,65,High School,27,Patnership,Complex\n1504,305,NJ City,W,1,13,22,37,High School,24,Franchise,Complex\n7893,387,Boston,N,1,15,18,29,Graduation,27,Patnership,Standalone\n7964,80.63,Boston,W,12,11,3,71,No education,33,Franchise,Complex\n9099,350,NJ City,E,3,15,20,51,Graduation,33,Owner,Standalone\n2991,291,NJ City,E,2,15,19,58,High School,21,Owner,Standalone\n2808,134.03,NJ City,W,17,16,3,46,Graduation,25,Franchise,Complex\n7067,450,Boston,E,1,13,6,72,Graduation,31,Owner,Standalone\n8448,469,NJ City,W,2,8,8,77,Graduation,21,Franchise,Complex\n1427,146,NJ City,E,2,10,8,29,High School,28,Owner,Standalone\n272,152,NJ City,W,1,19,17,51,High School,19,Franchise,Complex\n5509,356,Boston,N,1,17,8,48,Graduation,21,Patnership,Standalone\n527,403,NJ City,E,2,21,5,41,Graduation,22,Owner,Standalone\n4336,268.01,NJ City,W,16,15,1,63,High School,29,Franchise,Complex\n911,288,NJ City,E,1,13,15,85,High School,21,Owner,Standalone\n1981,222,NJ City,N,1,11,22,64,High School,18,Patnership,Standalone\n2476,279,NJ City,W,2,16,13,37,High School,27,Franchise,Complex\n2565,157,NJ City,N,1,11,24,82,High School,32,Patnership,Standalone\n5238,366,Boston,S,1,11,14,56,Graduation,34,Patnership,Complex\n2653,197,NJ City,N,2,14,20,70,High School,35,Patnership,Standalone\n6841,98.8,Boston,N,13,20,1,54,High School,20,Patnership,Standalone\n2844,234,NJ City,W,3,17,7,67,High School,26,Franchise,Complex\n9681,376.28,NJ City,N,18,11,1,66,Graduation,25,Patnership,Standalone\n1203,120,NJ City,E,3,17,17,77,High School,23,Owner,Standalone\n774,24.25,NJ City,N,10,11,1,33,Graduation,25,Patnership,Complex\n1523,266,NJ City,E,2,8,25,58,High School,35,Owner,Standalone\n8409,455,NJ City,N,3,18,12,39,Graduation,21,Patnership,Standalone\n1342,297,NJ City,S,1,17,7,62,High School,30,Patnership,Complex\n1087,219.05,NJ City,E,4,24,2,84,High School,32,Owner,Standalone\n8279,430,NJ City,E,3,14,16,35,Graduation,28,Owner,Standalone\n8434,18.34,NJ City,N,2,18,4,58,High School,18,Patnership,Complex\n181,240,NJ City,N,2,12,24,59,High School,22,Patnership,Standalone\n9219,59.06,NJ City,E,15,18,4,50,Graduation,31,Owner,Standalone\n5867,397,Boston,E,1,24,6,49,Graduation,23,Owner,Standalone\n8718,459,NJ City,S,2,23,22,34,Graduation,29,Patnership,Complex\n367,196,NJ City,E,3,20,17,77,High School,25,Owner,Standalone\n3282,197,NJ City,S,1,12,9,55,High School,22,Patnership,Complex\n7162,351,Boston,S,3,18,14,66,Graduation,32,Patnership,Complex\n2915,105,NJ City,E,1,21,21,74,High School,23,Owner,Standalone\n1259,371.92,NJ City,E,13,14,2,57,No education,23,Owner,Standalone\n1029,241,NJ City,N,1,15,8,83,High School,23,Patnership,Standalone\n517,22.49,NJ City,N,3,15,3,77,High School,32,Patnership,Standalone\n3149,266,NJ City,N,1,22,16,29,High School,29,Patnership,Standalone\n5777,453,Boston,N,2,12,18,49,Graduation,20,Patnership,Standalone\n2816,411.98,NJ City,W,25,20,3,48,No education,35,Franchise,Complex\n2070,218,NJ City,S,1,16,13,35,High School,22,Patnership,Complex\n8439,371,NJ City,E,2,8,5,33,Graduation,27,Owner,Standalone\n1323,231,NJ City,E,2,22,17,60,High School,27,Owner,Standalone\n6812,375.11,Boston,W,4,10,2,47,No education,23,Franchise,Complex\n6026,75.49,Boston,S,8,23,3,49,No education,26,Patnership,Complex\n1803,69.48,NJ City,N,5,16,1,66,Graduation,25,Owner,Standalone\n6502,18.17,Boston,N,9,19,3,48,High School,26,Patnership,Complex\n8134,479,NJ City,S,1,14,13,84,Graduation,21,Patnership,Complex\n1514,113.68,NJ City,S,21,16,3,26,No education,29,Patnership,Complex\n4169,121,NJ City,N,1,18,25,41,High School,19,Patnership,Standalone\n1637,57.38,NJ City,N,1,20,1,39,No education,20,Patnership,Standalone\n2308,13.2,NJ City,N,10,16,3,30,High School,29,Franchise,Complex\n8525,83.85,NJ City,N,22,24,3,68,No education,30,Patnership,Standalone\n9801,404,NJ City,N,1,16,22,66,Graduation,26,Patnership,Standalone\n1827,389,NJ City,E,2,19,5,58,Graduation,22,Owner,Standalone\n5157,357,Boston,N,3,24,15,25,Graduation,18,Patnership,Standalone\n1586,238,NJ City,S,1,22,6,67,High School,30,Patnership,Complex\n6202,374,Boston,S,2,14,7,51,Graduation,30,Patnership,Complex\n2649,155,NJ City,N,2,13,25,32,High School,35,Patnership,Standalone\n8842,496,NJ City,S,2,15,9,56,Graduation,18,Patnership,Complex\n4434,306,NJ City,S,3,24,17,50,High School,21,Patnership,Complex\n7938,422,Boston,S,2,16,12,36,Graduation,35,Patnership,Complex\n8324,72.96,NJ City,N,18,16,1,64,No education,22,Franchise,Complex\n7573,430,Boston,N,2,15,6,60,Graduation,25,Patnership,Standalone\n5704,46.23,Boston,N,2,21,2,81,High School,33,Franchise,Complex\n4109,114,NJ City,N,2,16,17,72,High School,33,Patnership,Standalone\n5647,113.12,Boston,E,11,10,3,42,High School,31,Owner,Standalone\n437,3.16,NJ City,N,21,9,3,33,No education,22,Patnership,Standalone\n7283,465,Boston,E,2,20,13,33,Graduation,24,Owner,Standalone\n8450,474,NJ City,S,3,9,8,64,Graduation,31,Patnership,Complex\n5,185,NY City,N,2,9,25,29,High School,32,Franchise,Complex\n8498,437,NJ City,S,1,8,15,35,Graduation,24,Patnership,Complex\n8882,396,NJ City,S,1,8,7,32,Graduation,29,Patnership,Complex\n2519,282,NJ City,E,1,18,18,38,High School,30,Owner,Standalone\n7723,465,Boston,E,1,20,18,53,Graduation,18,Owner,Standalone\n1853,45.66,NJ City,N,15,24,2,49,No education,30,Patnership,Standalone\n1364,113.4,NJ City,W,17,10,3,60,No education,29,Franchise,Complex\n3938,82.32,NJ City,S,22,23,3,66,No education,19,Patnership,Complex\n4484,113.87,NJ City,W,3,18,4,32,No education,25,Franchise,Complex\n3088,291,NJ City,W,3,9,11,79,High School,34,Franchise,Complex\n1619,241,NJ City,E,2,12,7,40,High School,18,Owner,Standalone\n8888,370,NJ City,W,1,16,19,51,Graduation,22,Franchise,Complex\n3851,93.4,NJ City,E,10,16,2,38,No education,33,Owner,Standalone\n916,122.13,NJ City,W,11,11,4,55,High School,23,Franchise,Complex\n9119,396.79,NJ City,E,7,18,4,47,No education,24,Owner,Standalone\n5080,450,Boston,W,3,10,11,47,Graduation,18,Franchise,Complex\n8913,348.33,NJ City,N,12,19,4,29,Graduation,31,Patnership,Standalone\n3930,125,NJ City,S,3,19,11,34,High School,18,Patnership,Complex\n1345,131,NJ City,N,3,23,20,73,High School,22,Patnership,Standalone\n6529,493,Boston,N,2,17,10,64,Graduation,29,Patnership,Standalone\n8517,398,NJ City,N,3,8,11,60,Graduation,33,Patnership,Standalone\n2234,53.13,NJ City,S,23,15,4,82,No education,21,Patnership,Complex\n9262,91.8,NJ City,S,3,8,3,69,High School,33,Patnership,Complex\n2103,117.26,NJ City,E,6,10,1,35,Graduation,27,Owner,Standalone\n2627,180,NJ City,E,2,21,13,66,High School,30,Owner,Standalone\n8306,443,NJ City,S,1,15,24,83,Graduation,29,Patnership,Complex\n690,172,NJ City,S,1,19,19,61,High School,20,Patnership,Complex\n9639,405,NJ City,E,1,13,24,83,Graduation,31,Owner,Standalone\n6384,473,Boston,W,2,12,6,59,Graduation,25,Franchise,Complex\n7511,1.61,Boston,N,1,14,1,26,No education,23,Owner,Standalone\n9941,351,NJ City,N,2,22,8,73,Graduation,22,Patnership,Standalone\n8303,360,NJ City,E,3,24,18,73,Graduation,25,Owner,Standalone\n1261,6.98,NJ City,N,19,14,1,52,High School,19,Patnership,Standalone\n9598,362,NJ City,S,1,15,17,36,Graduation,26,Patnership,Complex\n2340,47.35,NJ City,W,22,11,1,66,Graduation,25,Franchise,Complex\n1188,130,NJ City,W,2,15,23,84,High School,29,Franchise,Complex\n5738,476,Boston,S,3,10,9,70,Graduation,22,Patnership,Complex\n7908,432,Boston,W,3,17,7,45,Graduation,22,Franchise,Complex\n3435,78.43,NJ City,E,18,21,1,43,Graduation,27,Owner,Standalone\n7161,457.67,Boston,N,18,19,2,62,Graduation,18,Patnership,Standalone\n2259,88.45,NJ City,E,13,16,4,67,Graduation,24,Owner,Standalone\n6135,5.47,Boston,N,17,11,3,35,Graduation,21,Owner,Standalone\n6895,352,Boston,E,3,10,11,81,Graduation,34,Owner,Standalone\n193,62.07,NJ City,N,1,24,1,60,High School,29,Patnership,Standalone\n196,275,NJ City,W,1,8,21,45,High School,20,Franchise,Complex\n5546,430,Boston,S,1,22,18,61,Graduation,33,Patnership,Complex\n5208,432,Boston,W,3,19,6,66,Graduation,19,Franchise,Complex\n6601,7.71,Boston,N,13,10,4,61,High School,23,Patnership,Standalone\n3811,12.44,NJ City,N,13,23,2,41,High School,19,Owner,Standalone\n719,28.8,NJ City,N,3,13,1,76,No education,19,Owner,Standalone\n5952,264,Boston,W,5,20,4,40,Graduation,22,Franchise,Complex\n2960,111,NJ City,W,2,8,23,67,High School,27,Franchise,Complex\n4854,151.76,NJ City,S,14,18,2,69,Graduation,32,Patnership,Complex\n3320,184,NJ City,W,2,8,23,36,High School,25,Franchise,Complex\n5595,475,Boston,E,2,14,17,66,Graduation,24,Owner,Standalone\n1685,272.67,NJ City,N,13,24,1,31,No education,18,Patnership,Standalone\n3730,230.25,NJ City,S,18,15,4,80,High School,24,Patnership,Complex\n5552,10.62,Boston,N,25,8,4,57,No education,27,Franchise,Complex\n5253,353,Boston,N,1,15,13,73,Graduation,35,Patnership,Standalone\n8686,382,NJ City,S,2,17,17,54,Graduation,35,Patnership,Complex\n3252,258,NJ City,W,2,15,8,38,High School,20,Franchise,Complex\n3526,112.73,NJ City,S,2,21,4,72,High School,33,Patnership,Complex\n4677,109,NJ City,N,3,17,25,68,High School,35,Patnership,Standalone\n686,218,NJ City,S,1,20,6,38,High School,28,Patnership,Complex\n1707,29.98,NJ City,N,13,21,3,37,Graduation,31,Owner,Standalone\n7572,105.9,Boston,W,12,12,1,26,Graduation,31,Franchise,Complex\n1900,51.8,NJ City,W,5,8,4,81,High School,25,Franchise,Complex\n7601,410,Boston,N,1,20,16,64,Graduation,21,Patnership,Standalone\n1952,434.62,NJ City,W,20,17,3,79,No education,21,Franchise,Complex\n2495,154,NJ City,E,3,13,24,55,High School,23,Owner,Standalone\n8335,389,NJ City,E,2,13,9,55,Graduation,21,Owner,Standalone\n3848,174,NJ City,W,1,21,20,34,High School,32,Franchise,Complex\n5419,318.49,Boston,E,4,24,3,84,High School,31,Owner,Standalone\n3140,16,NJ City,N,20,24,2,55,No education,24,Franchise,Complex\n7023,132.94,Boston,E,10,22,4,83,Graduation,27,Owner,Standalone\n6211,0.75,Boston,N,3,14,3,59,High School,32,Owner,Standalone\n6347,408.52,Boston,E,12,18,2,75,No education,31,Owner,Standalone\n2389,62.49,NJ City,N,24,14,3,82,High School,34,Patnership,Standalone\n2175,16.71,NJ City,N,16,19,2,67,Graduation,24,Owner,Standalone\n2324,28.17,NJ City,N,7,10,1,31,No education,21,Franchise,Complex\n4641,244,NJ City,N,2,20,8,76,High School,27,Patnership,Standalone\n7910,291.45,Boston,S,14,9,4,40,No education,31,Patnership,Complex\n3358,150,NJ City,S,2,20,24,69,High School,18,Patnership,Complex\n6894,79.93,Boston,S,7,18,4,70,Graduation,23,Patnership,Complex\n5920,435,Boston,W,2,24,15,54,Graduation,28,Franchise,Complex\n4310,38.86,NJ City,N,11,23,2,51,No education,31,Patnership,Complex\n5197,52.48,Boston,N,12,18,3,34,High School,18,Patnership,Standalone\n5945,6.69,Boston,N,19,23,1,43,No education,30,Patnership,Standalone\n9230,475,NJ City,S,3,10,5,28,Graduation,33,Patnership,Complex\n7049,413,Boston,N,1,18,14,41,Graduation,29,Patnership,Standalone\n9845,372,NJ City,N,3,16,25,54,Graduation,24,Patnership,Standalone\n8506,450,NJ City,S,1,12,20,38,Graduation,23,Patnership,Complex\n8452,367,NJ City,W,1,10,25,81,Graduation,29,Franchise,Complex\n40,166,NY City,W,3,24,14,27,High School,34,Franchise,Complex\n6170,46.46,Boston,N,10,10,3,78,No education,34,Patnership,Complex\n9883,438,NJ City,E,1,15,9,68,Graduation,18,Owner,Standalone\n7879,398,Boston,E,3,18,20,57,Graduation,34,Owner,Standalone\n2137,138,NJ City,N,1,14,6,40,High School,29,Patnership,Standalone\n357,135.2,NJ City,N,3,14,4,40,Graduation,34,Patnership,Standalone\n262,243.02,NJ City,S,2,24,4,49,High School,35,Patnership,Complex\n7999,46.88,Boston,N,18,10,4,78,High School,34,Owner,Standalone\n2708,272,NJ City,W,2,19,13,38,High School,34,Franchise,Complex\n7081,464,Boston,N,3,24,13,66,Graduation,30,Patnership,Standalone\n2236,63.52,NJ City,N,1,13,2,30,High School,35,Franchise,Complex\n8184,495,NJ City,W,3,12,13,60,Graduation,18,Franchise,Complex\n2346,185,NJ City,S,1,12,19,38,High School,20,Patnership,Complex\n9851,390,NJ City,E,3,23,20,71,Graduation,27,Owner,Standalone\n4569,104,NJ City,N,1,12,20,77,High School,20,Patnership,Standalone\n6555,108.25,Boston,E,21,13,1,59,Graduation,30,Owner,Standalone\n6276,466,Boston,W,1,20,25,61,Graduation,20,Franchise,Complex\n8103,455,NJ City,E,2,19,23,26,Graduation,18,Owner,Standalone\n5935,498,Boston,E,2,9,9,46,Graduation,26,Owner,Standalone\n6742,34.75,Boston,N,12,10,3,42,High School,32,Patnership,Complex\n8737,493,NJ City,N,2,14,20,35,Graduation,18,Patnership,Standalone\n3857,20.14,NJ City,N,16,12,2,55,No education,20,Patnership,Standalone\n3321,36.67,NJ City,N,11,17,4,55,Graduation,27,Patnership,Standalone\n8271,382,NJ City,E,2,20,11,67,Graduation,20,Owner,Standalone\n9464,434.77,NJ City,W,2,21,4,43,No education,32,Franchise,Complex\n6374,424,Boston,S,2,17,20,61,Graduation,27,Patnership,Complex\n9496,461,NJ City,W,3,14,18,77,Graduation,30,Franchise,Complex\n5934,408,Boston,S,1,16,21,85,Graduation,27,Patnership,Complex\n3359,100.33,NJ City,E,25,18,4,32,No education,24,Owner,Standalone\n1931,298,NJ City,E,1,17,12,26,High School,33,Owner,Standalone\n140,226,NJ City,W,1,20,17,43,High School,29,Franchise,Complex\n5279,99.62,Boston,E,18,9,2,75,No education,31,Owner,Standalone\n873,101,NJ City,N,1,22,16,51,High School,20,Patnership,Standalone\n6281,469,Boston,N,1,20,15,41,Graduation,33,Patnership,Standalone\n5247,419,Boston,E,3,12,12,52,Graduation,26,Owner,Standalone\n8201,48.51,NJ City,N,14,18,1,30,No education,33,Patnership,Standalone\n2001,227,NJ City,N,3,19,19,68,High School,20,Patnership,Standalone\n6776,439,Boston,W,2,14,8,47,Graduation,30,Franchise,Complex\n1915,290.21,NJ City,E,4,17,4,82,High School,23,Owner,Standalone\n2981,17.39,NJ City,N,24,19,2,25,No education,21,Patnership,Standalone\n6671,79.59,Boston,E,14,24,1,82,No education,19,Owner,Standalone\n2635,200,NJ City,E,1,9,9,55,High School,33,Owner,Standalone\n9484,346.37,NJ City,W,22,23,2,34,High School,29,Franchise,Complex\n3844,280,NJ City,W,3,16,17,57,High School,32,Franchise,Complex\n2240,258,NJ City,W,3,9,20,76,High School,23,Franchise,Complex\n1554,10.64,NJ City,N,6,21,1,54,Graduation,22,Patnership,Complex\n3975,57.81,NJ City,E,7,23,2,44,Graduation,34,Owner,Standalone\n1632,22.45,NJ City,N,2,15,3,54,Graduation,34,Franchise,Complex\n2528,54.07,NJ City,W,16,13,2,39,No education,18,Franchise,Complex\n1686,481.11,NJ City,S,12,12,4,56,Graduation,25,Patnership,Complex\n8141,0.37,NJ City,N,6,16,1,63,No education,21,Patnership,Standalone\n1968,491.43,NJ City,W,16,17,1,76,Graduation,27,Franchise,Complex\n5728,469,Boston,W,3,8,12,39,Graduation,32,Franchise,Complex\n8739,37.19,NJ City,N,15,14,3,52,Graduation,29,Owner,Standalone\n6122,100.32,Boston,S,23,13,2,40,No education,30,Patnership,Complex\n2493,121.74,NJ City,N,20,12,4,52,Graduation,35,Patnership,Standalone\n7444,42.32,Boston,N,16,14,3,33,High School,19,Franchise,Complex\n9273,379,NJ City,N,3,20,20,69,Graduation,21,Patnership,Standalone\n7508,34.78,Boston,N,15,23,1,85,No education,32,Franchise,Complex\n3374,183,NJ City,S,2,17,15,41,High School,19,Patnership,Complex\n6520,446,Boston,W,2,8,13,81,Graduation,35,Franchise,Complex\n9728,431,NJ City,W,1,19,5,33,Graduation,35,Franchise,Complex\n876,190,NJ City,W,2,13,11,76,High School,35,Franchise,Complex\n5802,58.87,Boston,S,10,10,2,62,Graduation,19,Patnership,Complex\n8583,100.13,NJ City,E,13,15,4,77,Graduation,32,Owner,Standalone\n1526,287.5,NJ City,S,11,22,2,75,No education,26,Patnership,Complex\n6656,434,Boston,W,3,22,20,56,Graduation,34,Franchise,Complex\n7273,474,Boston,N,3,15,5,47,Graduation,26,Patnership,Standalone\n1039,133.3,NJ City,E,22,23,3,82,High School,25,Owner,Standalone\n8844,64.87,NJ City,N,5,13,4,75,Graduation,28,Franchise,Complex\n9312,397,NJ City,W,1,24,14,31,Graduation,29,Franchise,Complex\n1335,228,NJ City,E,1,21,24,28,High School,33,Owner,Standalone\n3380,275,NJ City,W,3,10,25,79,High School,31,Franchise,Complex\n7574,356,Boston,S,1,19,20,76,Graduation,27,Patnership,Complex\n1770,91.46,NJ City,S,4,12,4,31,Graduation,22,Patnership,Complex\n4068,223,NJ City,W,3,16,24,52,High School,21,Franchise,Complex\n6622,121.96,Boston,S,4,24,3,83,High School,27,Patnership,Complex\n3556,159,NJ City,W,2,11,13,85,High School,21,Franchise,Complex\n8408,457,NJ City,W,1,24,25,56,Graduation,34,Franchise,Complex\n6835,98.95,Boston,E,10,18,4,73,High School,26,Owner,Standalone\n8007,495,Boston,E,2,17,17,69,Graduation,29,Owner,Standalone\n4818,244,NJ City,S,2,11,8,52,High School,20,Patnership,Complex\n7323,462,Boston,E,2,24,14,35,Graduation,24,Owner,Standalone\n9122,115.65,NJ City,S,13,15,4,42,No education,35,Patnership,Complex\n1533,149,NJ City,N,1,21,12,59,High School,24,Patnership,Standalone\n7330,104.79,Boston,S,12,16,3,80,High School,26,Patnership,Complex\n8819,386,NJ City,E,3,19,6,66,Graduation,18,Owner,Standalone\n4963,227,Boston,E,3,11,7,76,High School,27,Owner,Standalone\n972,177,NJ City,W,2,18,18,80,High School,18,Franchise,Complex\n590,218,NJ City,S,1,12,11,53,High School,35,Patnership,Complex\n2075,311,NJ City,E,3,10,21,68,High School,28,Owner,Standalone\n5371,117.52,Boston,E,25,12,1,38,High School,22,Owner,Standalone\n8146,437,NJ City,S,2,16,23,76,Graduation,25,Patnership,Complex\n3283,177,NJ City,E,2,20,13,85,High School,23,Owner,Standalone\n9401,416,NJ City,N,3,13,6,30,Graduation,31,Patnership,Standalone\n2188,387,NJ City,W,3,16,5,59,Graduation,32,Franchise,Complex\n1550,162,NJ City,S,3,22,14,58,High School,35,Patnership,Complex\n1565,109,NJ City,N,3,17,20,58,High School,24,Patnership,Standalone\n2698,261,NJ City,S,2,22,20,78,High School,26,Patnership,Complex\n6238,398,Boston,S,3,23,16,53,Graduation,32,Patnership,Complex\n4686,460.94,NJ City,S,21,20,3,74,Graduation,25,Patnership,Complex\n8012,469,Boston,W,3,12,18,40,Graduation,27,Franchise,Complex\n3177,359.02,NJ City,N,7,23,3,33,Graduation,32,Patnership,Standalone\n872,265,NJ City,W,1,24,20,42,High School,26,Franchise,Complex\n640,405,NJ City,W,3,22,5,62,Graduation,24,Franchise,Complex\n3203,126.13,NJ City,E,6,24,2,82,No education,24,Owner,Standalone\n9345,375,NJ City,N,3,17,17,67,Graduation,25,Patnership,Standalone\n9231,98.4,NJ City,E,13,13,2,54,Graduation,26,Owner,Standalone\n1075,151,NJ City,E,3,8,7,25,High School,24,Owner,Standalone\n1435,303,NJ City,E,2,17,24,80,High School,28,Owner,Standalone\n9466,460,NJ City,S,3,22,25,80,Graduation,32,Patnership,Complex\n7684,412,Boston,W,3,12,12,78,Graduation,33,Franchise,Complex\n2579,264,NJ City,E,1,23,14,50,High School,22,Owner,Standalone\n423,49.69,NJ City,E,23,11,3,81,Graduation,32,Owner,Standalone\n6901,40.2,Boston,N,1,8,4,27,High School,30,Patnership,Standalone\n8435,52.7,NJ City,E,7,8,2,59,No education,27,Owner,Standalone\n7524,36.25,Boston,N,1,16,1,28,Graduation,18,Franchise,Complex\n2548,173.81,NJ City,W,2,8,4,36,High School,32,Franchise,Complex\n7414,95.27,Boston,S,15,11,2,42,High School,25,Patnership,Complex\n9242,482,NJ City,S,1,24,14,58,Graduation,23,Patnership,Complex\n5236,364,Boston,W,1,18,23,63,Graduation,21,Franchise,Complex\n715,224,NJ City,E,3,9,14,69,High School,32,Owner,Standalone\n8336,490,NJ City,W,3,12,13,37,Graduation,29,Franchise,Complex\n434,317,NJ City,S,2,15,18,81,High School,30,Patnership,Complex\n9599,415.3,NJ City,E,1,15,4,65,No education,21,Owner,Standalone\n4492,463.83,NJ City,W,9,10,3,58,High School,29,Franchise,Complex\n5718,494,Boston,S,2,18,11,39,Graduation,21,Patnership,Complex\n2452,481,NJ City,W,2,9,5,68,Graduation,22,Franchise,Complex\n6857,490,Boston,N,1,19,18,75,Graduation,25,Patnership,Standalone\n4069,478,NJ City,N,3,8,5,63,Graduation,33,Patnership,Standalone\n1498,75.68,NJ City,S,6,10,3,33,High School,27,Patnership,Complex\n9252,38.5,NJ City,N,1,15,1,46,Graduation,21,Franchise,Complex\n6955,39.7,Boston,N,12,12,1,27,High School,32,Owner,Standalone\n4803,83.08,NJ City,E,1,18,2,31,Graduation,22,Owner,Standalone\n9139,391,NJ City,E,1,16,24,47,Graduation,25,Owner,Standalone\n1420,112,NJ City,W,3,18,10,25,High School,27,Franchise,Complex\n6957,436,Boston,N,2,19,19,49,Graduation,24,Patnership,Standalone\n8151,422,NJ City,E,3,24,6,36,Graduation,23,Owner,Standalone\n1373,197,NJ City,N,1,11,22,52,High School,18,Patnership,Standalone\n5368,495,Boston,W,2,20,23,60,Graduation,27,Franchise,Complex\n3659,4.45,NJ City,N,9,11,1,40,No education,18,Owner,Standalone\n5152,297.03,Boston,W,20,10,4,51,High School,26,Franchise,Complex\n1821,174,NJ City,N,3,14,10,36,High School,21,Patnership,Standalone\n3461,93.09,NJ City,N,6,11,3,79,No education,29,Patnership,Standalone\n1223,90.38,NJ City,E,25,8,4,64,No education,18,Owner,Standalone\n6734,109.65,Boston,S,13,21,4,28,No education,28,Patnership,Complex\n7473,424,Boston,N,3,16,12,42,Graduation,27,Patnership,Standalone\n9918,495,NJ City,S,2,18,19,64,Graduation,29,Patnership,Complex\n5024,367.56,Boston,W,7,22,1,31,No education,31,Franchise,Complex\n7884,430,Boston,W,3,16,9,62,Graduation,30,Franchise,Complex\n6646,463,Boston,S,1,12,16,32,Graduation,29,Patnership,Complex\n1582,114,NJ City,S,3,16,9,74,High School,34,Patnership,Complex\n7107,357,Boston,E,2,17,8,85,Graduation,22,Owner,Standalone\n2569,293,NJ City,N,3,21,16,62,High School,24,Patnership,Standalone\n6424,497,Boston,W,2,9,5,33,Graduation,19,Franchise,Complex\n9043,93.36,NJ City,E,21,16,4,56,High School,27,Owner,Standalone\n4706,153,NJ City,S,3,24,10,30,High School,25,Patnership,Complex\n4691,205,NJ City,E,2,15,13,26,High School,22,Owner,Standalone\n7464,105.22,Boston,W,15,15,2,81,Graduation,33,Franchise,Complex\n3999,188,NJ City,E,1,19,19,59,High School,33,Owner,Standalone\n5541,371,Boston,N,1,11,16,67,Graduation,34,Patnership,Standalone\n7222,384,Boston,S,3,10,19,53,Graduation,20,Patnership,Complex\n5500,394,Boston,W,1,23,15,71,Graduation,35,Franchise,Complex\n4943,272,Boston,E,2,12,15,47,High School,18,Owner,Standalone\n2465,303,NJ City,N,3,10,21,61,High School,33,Patnership,Standalone\n1211,196.93,NJ City,E,17,18,3,53,No education,28,Owner,Standalone\n2805,228,NJ City,N,1,20,11,54,High School,22,Patnership,Standalone\n5852,390,Boston,W,2,15,23,41,Graduation,23,Franchise,Complex\n8438,22.16,NJ City,N,14,13,3,74,No education,27,Patnership,Complex\n6481,38.38,Boston,N,6,13,2,48,High School,23,Patnership,Standalone\n7897,420,Boston,N,3,17,20,57,Graduation,23,Patnership,Standalone\n4790,311,NJ City,S,1,14,15,47,High School,29,Patnership,Complex\n379,201,NJ City,E,3,23,17,52,High School,21,Owner,Standalone\n5850,406,Boston,S,3,19,20,33,Graduation,34,Patnership,Complex\n605,312,NJ City,N,2,18,16,49,High School,35,Patnership,Standalone\n7455,46.34,Boston,N,14,16,1,37,Graduation,35,Owner,Standalone\n7402,476,Boston,S,2,8,21,27,Graduation,29,Patnership,Complex\n5044,100.77,Boston,W,18,23,3,28,High School,20,Franchise,Complex\n2327,6.22,NJ City,N,8,15,2,27,No education,22,Owner,Standalone\n9750,429,NJ City,S,2,13,17,34,Graduation,21,Patnership,Complex\n6182,96.03,Boston,S,18,23,4,51,No education,34,Patnership,Complex\n5369,358,Boston,N,3,9,21,70,Graduation,26,Patnership,Standalone\n6186,398,Boston,S,2,8,18,79,Graduation,33,Patnership,Complex\n4481,222,NJ City,N,2,24,7,74,High School,20,Patnership,Standalone\n905,148,NJ City,N,2,16,14,75,High School,31,Patnership,Standalone\n1788,169,NJ City,W,1,21,7,62,High School,23,Franchise,Complex\n6253,406,Boston,N,2,10,25,46,Graduation,27,Patnership,Standalone\n3867,111.31,NJ City,E,25,9,3,26,Graduation,27,Owner,Standalone\n7740,385,Boston,W,1,14,20,59,Graduation,34,Franchise,Complex\n1091,235,NJ City,E,2,22,22,84,High School,34,Owner,Standalone\n8812,487,NJ City,W,2,9,22,41,Graduation,34,Franchise,Complex\n5170,417,Boston,S,2,18,16,67,Graduation,35,Patnership,Complex\n5242,448,Boston,S,1,12,17,49,Graduation,33,Patnership,Complex\n3652,319,NJ City,W,1,16,19,36,High School,34,Franchise,Complex\n8629,87.03,NJ City,N,16,12,4,74,High School,23,Patnership,Standalone\n1576,172,NJ City,W,1,8,6,76,High School,32,Franchise,Complex\n2212,124,NJ City,W,1,8,11,46,High School,34,Franchise,Complex\n3655,24.71,NJ City,N,15,17,3,37,High School,21,Owner,Standalone\n4235,218,NJ City,E,3,18,6,25,High School,24,Owner,Standalone\n1432,88.58,NJ City,W,17,24,4,51,High School,31,Franchise,Complex\n9079,282.86,NJ City,E,10,19,2,44,High School,18,Owner,Standalone\n875,74.62,NJ City,E,6,17,3,45,No education,24,Owner,Standalone\n2533,158,NJ City,N,3,24,14,69,High School,27,Patnership,Standalone\n7994,407,Boston,S,1,8,11,43,Graduation,19,Patnership,Complex\n7797,462,Boston,N,1,12,11,52,Graduation,26,Patnership,Standalone\n5318,498,Boston,S,1,12,5,74,Graduation,25,Patnership,Complex\n7778,419,Boston,S,2,9,25,73,Graduation,34,Patnership,Complex\n3173,256,NJ City,N,3,24,19,74,High School,19,Patnership,Standalone\n9794,376,NJ City,S,2,21,17,25,Graduation,28,Patnership,Complex\n6696,341.44,Boston,W,20,12,1,80,Graduation,31,Franchise,Complex\n2727,100.26,NJ City,E,25,10,1,75,Graduation,34,Owner,Standalone\n565,273,NJ City,N,3,8,12,62,High School,21,Patnership,Standalone\n6229,91.88,Boston,N,3,18,4,52,High School,32,Patnership,Standalone\n6199,379,Boston,E,1,19,25,35,Graduation,21,Owner,Standalone\n8637,351,NJ City,N,1,19,9,29,Graduation,22,Patnership,Standalone\n7228,367,Boston,W,2,8,25,71,Graduation,26,Franchise,Complex\n6814,16.69,Boston,N,9,13,2,43,High School,18,Patnership,Complex\n1642,102,NJ City,S,1,23,23,84,High School,27,Patnership,Complex\n8919,448,NJ City,E,2,22,22,51,Graduation,19,Owner,Standalone\n8254,24.33,NJ City,N,10,17,1,53,High School,35,Patnership,Complex\n1888,100,NJ City,W,3,18,24,35,High School,31,Franchise,Complex\n4971,114,Boston,E,3,11,17,34,High School,26,Owner,Standalone\n1846,196,NJ City,S,3,24,9,73,High School,25,Patnership,Complex\n4685,208,NJ City,N,1,12,13,67,High School,20,Patnership,Standalone\n8016,403,Boston,W,2,17,17,73,Graduation,28,Franchise,Complex\n8344,380,NJ City,W,3,20,14,45,Graduation,28,Franchise,Complex\n8206,469,NJ City,S,1,14,10,59,Graduation,18,Patnership,Complex\n4161,305,NJ City,N,1,19,14,80,High School,24,Patnership,Standalone\n7507,454,Boston,E,3,11,15,59,Graduation,25,Owner,Standalone\n1712,186,NJ City,W,2,15,8,29,High School,32,Franchise,Complex\n7918,396,Boston,S,3,19,10,45,Graduation,33,Patnership,Complex\n5422,464.11,Boston,S,17,20,1,47,High School,24,Patnership,Complex\n5670,43.42,Boston,N,2,10,2,32,Graduation,27,Patnership,Complex\n1793,209,NJ City,N,2,15,12,57,High School,29,Patnership,Standalone\n5809,106.98,Boston,N,11,11,4,66,High School,27,Patnership,Standalone\n7009,387,Boston,N,2,10,25,50,Graduation,27,Patnership,Standalone\n1669,160,NJ City,N,3,18,23,85,High School,33,Patnership,Standalone\n7972,392,Boston,W,1,23,15,74,Graduation,33,Franchise,Complex\n8544,405,NJ City,W,3,9,14,46,Graduation,23,Franchise,Complex\n1468,82.89,NJ City,W,14,12,4,70,High School,18,Franchise,Complex\n5082,36.95,Boston,N,5,23,1,82,Graduation,29,Patnership,Complex\n9278,108.64,NJ City,S,18,22,4,41,No education,34,Patnership,Complex\n2917,207,NJ City,N,1,19,9,38,High School,31,Patnership,Standalone\n3900,139,NJ City,W,1,16,11,79,High School,28,Franchise,Complex\n3278,177,NJ City,S,1,14,10,52,High School,29,Patnership,Complex\n9802,465,NJ City,S,1,21,15,70,Graduation,19,Patnership,Complex\n7401,53.9,Boston,N,24,24,1,45,Graduation,22,Patnership,Standalone\n7225,458,Boston,N,3,9,9,26,Graduation,26,Patnership,Standalone\n3101,467.06,NJ City,N,2,9,3,54,No education,32,Patnership,Standalone\n5642,412,Boston,S,1,16,6,82,Graduation,33,Patnership,Complex\n1939,465,NJ City,E,1,9,5,73,Graduation,35,Owner,Standalone\n9500,471,NJ City,W,1,11,7,68,Graduation,24,Franchise,Complex\n7157,40.54,Boston,N,15,21,1,71,No education,24,Patnership,Standalone\n2055,311,NJ City,E,1,14,23,56,High School,33,Owner,Standalone\n2010,195,NJ City,S,2,17,20,36,High School,34,Patnership,Complex\n4767,112.24,NJ City,E,5,19,3,65,Graduation,23,Owner,Standalone\n6179,364,Boston,E,2,17,16,48,Graduation,25,Owner,Standalone\n6563,485,Boston,E,1,19,21,77,Graduation,21,Owner,Standalone\n7716,476,Boston,W,1,8,19,70,Graduation,31,Franchise,Complex\n9901,102.97,NJ City,N,9,12,4,61,High School,21,Patnership,Standalone\n3810,268,NJ City,S,1,16,24,26,High School,33,Patnership,Complex\n5699,474.81,Boston,E,3,22,5,44,No education,27,Owner,Standalone\n4490,164,NJ City,S,1,13,8,63,High School,32,Patnership,Complex\n6979,75.43,Boston,E,24,11,1,85,High School,19,Owner,Standalone\n6263,404,Boston,E,2,15,6,29,Graduation,23,Owner,Standalone\n9594,19.32,NJ City,N,18,13,3,53,Graduation,34,Patnership,Complex\n8936,384,NJ City,W,2,17,24,49,Graduation,31,Franchise,Complex\n8874,119.71,NJ City,S,3,17,3,82,Graduation,20,Patnership,Complex\n5925,394,Boston,N,2,24,19,53,Graduation,28,Patnership,Standalone\n751,154,NJ City,E,1,20,9,37,High School,21,Owner,Standalone\n7100,72.24,Boston,N,20,10,2,70,No education,23,Franchise,Complex\n9006,92.9,NJ City,S,1,8,3,79,Graduation,24,Patnership,Complex\n3982,149,NJ City,S,3,23,19,57,High School,26,Patnership,Complex\n7066,435,Boston,S,3,10,10,58,Graduation,24,Patnership,Complex\n3034,94.84,NJ City,S,20,24,4,34,High School,18,Patnership,Complex\n8696,352,NJ City,W,3,16,24,51,Graduation,19,Franchise,Complex\n7074,1,Boston,N,5,21,1,48,Graduation,33,Patnership,Complex\n504,418.57,NJ City,W,25,19,4,68,Graduation,26,Franchise,Complex\n3953,38.08,NJ City,N,20,18,4,41,No education,19,Patnership,Standalone\n1375,274,NJ City,E,2,16,13,40,High School,27,Owner,Standalone\n3874,116.45,NJ City,S,19,18,2,70,High School,35,Patnership,Complex\n4516,57.96,NJ City,W,1,21,3,55,High School,26,Franchise,Complex\n7609,435,Boston,N,1,21,24,65,Graduation,30,Patnership,Standalone\n2422,76.96,NJ City,S,14,24,4,59,High School,25,Patnership,Complex\n6976,325.61,Boston,W,5,8,4,44,High School,35,Franchise,Complex\n2831,221,NJ City,E,1,11,19,45,High School,30,Owner,Standalone\n5637,424,Boston,N,3,18,22,51,Graduation,34,Patnership,Standalone\n5050,65.17,Boston,N,14,14,4,32,High School,18,Patnership,Complex\n8773,377,NJ City,N,3,15,20,56,Graduation,25,Patnership,Standalone\n7580,87.85,Boston,W,18,24,3,73,No education,24,Franchise,Complex\n817,309,NJ City,N,3,22,25,62,High School,35,Patnership,Standalone\n7895,116.92,Boston,E,7,15,2,48,No education,24,Owner,Standalone\n8165,450.37,NJ City,N,4,14,3,71,No education,19,Patnership,Standalone\n2364,260,NJ City,W,3,14,21,63,High School,29,Franchise,Complex\n4298,5.72,NJ City,N,3,20,3,70,No education,33,Patnership,Complex\n6784,320.35,Boston,W,3,17,4,80,High School,33,Franchise,Complex\n7663,42.91,Boston,N,10,8,1,26,High School,23,Owner,Standalone\n8922,455,NJ City,S,2,18,16,76,Graduation,27,Patnership,Complex\n7138,436,Boston,S,3,23,14,48,Graduation,26,Patnership,Complex\n4950,108.86,Boston,S,5,10,2,51,Graduation,31,Patnership,Complex\n5743,406,Boston,E,3,21,6,31,Graduation,22,Owner,Standalone\n239,363.95,NJ City,E,21,10,3,83,No education,18,Owner,Standalone\n2222,176,NJ City,S,1,17,7,58,High School,22,Patnership,Complex\n8663,494,NJ City,E,2,11,9,84,Graduation,19,Owner,Standalone\n1292,104,NJ City,W,3,9,15,81,High School,19,Franchise,Complex\n5094,379,Boston,S,1,9,17,72,Graduation,19,Patnership,Complex\n3089,174,NJ City,N,1,12,8,78,High School,24,Patnership,Standalone\n5880,399,Boston,W,3,22,13,78,Graduation,21,Franchise,Complex\n3835,52.31,NJ City,E,4,8,3,66,High School,25,Owner,Standalone\n7911,490,Boston,E,3,9,14,29,Graduation,33,Owner,Standalone\n8628,412,NJ City,W,1,22,5,84,Graduation,22,Franchise,Complex\n1531,376.27,NJ City,E,21,23,1,84,High School,21,Owner,Standalone\n1777,162.51,NJ City,N,9,21,3,81,High School,24,Patnership,Standalone\n1800,271,NJ City,W,2,21,11,72,High School,25,Franchise,Complex\n9920,491.72,NJ City,W,7,23,1,30,No education,20,Franchise,Complex\n2488,49.2,NJ City,W,17,12,4,44,High School,29,Franchise,Complex\n3757,271,NJ City,N,1,22,8,32,High School,23,Patnership,Standalone\n1073,12.17,NJ City,N,20,16,3,85,No education,30,Patnership,Standalone\n4936,224,Boston,W,3,12,24,67,High School,28,Franchise,Complex\n7322,353.24,Boston,S,12,24,2,56,No education,20,Patnership,Complex\n7888,51.73,Boston,W,2,15,3,33,High School,26,Franchise,Complex\n286,290,NJ City,S,2,8,23,36,High School,23,Patnership,Complex\n7982,104.02,Boston,S,18,19,2,27,No education,18,Patnership,Complex\n8094,392,NJ City,S,1,15,18,33,Graduation,19,Patnership,Complex\n8617,190.61,NJ City,N,1,13,5,84,High School,22,Patnership,Standalone\n6584,341.13,Boston,W,12,14,4,49,No education,32,Franchise,Complex\n545,218,NJ City,N,2,18,6,40,High School,31,Patnership,Standalone\n9538,434,NJ City,S,1,8,20,70,Graduation,23,Patnership,Complex\n8178,57.68,NJ City,S,14,8,4,55,Graduation,18,Patnership,Complex\n8917,466,NJ City,N,2,24,23,71,Graduation,31,Patnership,Standalone\n1050,270,NJ City,S,2,18,13,51,High School,27,Patnership,Complex\n9045,396,NJ City,N,3,22,10,43,Graduation,20,Patnership,Standalone\n4681,231,NJ City,N,1,15,6,54,High School,26,Patnership,Standalone\n8640,476,NJ City,W,2,22,9,27,Graduation,29,Franchise,Complex\n9317,394,NJ City,N,3,19,18,84,Graduation,25,Patnership,Standalone\n2963,214,NJ City,E,3,9,20,74,High School,24,Owner,Standalone\n9951,414,NJ City,E,1,14,25,51,Graduation,31,Owner,Standalone\n2227,234.07,NJ City,E,20,14,1,68,High School,18,Owner,Standalone\n5138,475,Boston,S,3,24,11,46,Graduation,28,Patnership,Complex\n5285,57.88,Boston,N,15,16,2,49,No education,33,Patnership,Standalone\n4049,282,NJ City,N,1,16,10,70,High School,30,Patnership,Standalone\n4290,254,NJ City,S,3,20,7,84,High School,34,Patnership,Complex\n2712,118,NJ City,W,1,19,22,64,High School,25,Franchise,Complex\n7800,408,Boston,W,1,12,6,63,Graduation,32,Franchise,Complex\n8775,475,NJ City,E,2,12,5,47,Graduation,33,Owner,Standalone\n2097,182,NJ City,N,1,22,19,43,High School,27,Patnership,Standalone\n8599,48.16,NJ City,E,9,13,4,77,High School,34,Owner,Standalone\n6988,93.91,Boston,W,23,13,4,65,High School,26,Franchise,Complex\n8510,419,NJ City,S,1,17,14,50,Graduation,24,Patnership,Complex\n4554,268,NJ City,S,1,23,18,41,High School,32,Patnership,Complex\n6959,17.48,Boston,N,3,18,1,40,No education,23,Owner,Standalone\n439,146,NJ City,E,3,15,9,26,High School,28,Owner,Standalone\n2971,65.7,NJ City,N,20,23,1,35,High School,34,Owner,Standalone\n4123,155,NJ City,E,3,21,17,30,High School,29,Owner,Standalone\n4680,194,NJ City,W,2,20,15,55,High School,25,Franchise,Complex\n9000,445,NJ City,W,2,12,13,39,Graduation,32,Franchise,Complex\n3699,82.58,NJ City,E,9,16,3,59,Graduation,33,Owner,Standalone\n968,292,NJ City,W,1,20,9,62,High School,27,Franchise,Complex\n8091,438,NJ City,E,3,10,25,57,Graduation,26,Owner,Standalone\n779,104.21,NJ City,E,1,16,4,76,No education,33,Owner,Standalone\n6880,495,Boston,W,3,11,25,71,Graduation,32,Franchise,Complex\n7459,424,Boston,E,3,20,17,29,Graduation,24,Owner,Standalone\n8585,33.96,NJ City,N,17,20,1,83,No education,25,Patnership,Standalone\n4501,244,NJ City,N,3,10,11,26,High School,35,Patnership,Standalone\n6150,290.69,Boston,S,7,20,1,47,Graduation,33,Patnership,Complex\n9058,89.22,NJ City,S,1,11,4,33,High School,21,Patnership,Complex\n8502,42.57,NJ City,N,24,24,4,79,Graduation,34,Patnership,Complex\n57,109,Maine,N,2,22,23,73,High School,35,Patnership,Standalone\n8182,353,NJ City,S,3,13,13,82,Graduation,33,Patnership,Complex\n7367,390,Boston,E,3,19,23,41,Graduation,29,Owner,Standalone\n5651,455,Boston,E,1,22,9,68,Graduation,18,Owner,Standalone\n7959,370,Boston,E,1,14,22,32,Graduation,23,Owner,Standalone\n8542,239.72,NJ City,S,1,24,1,28,High School,29,Patnership,Complex\n7545,303.55,Boston,N,6,15,2,71,Graduation,19,Patnership,Standalone\n117,307,NJ City,N,3,17,23,35,High School,27,Patnership,Standalone\n1959,192,NJ City,E,2,14,6,59,High School,32,Owner,Standalone\n206,257.67,NJ City,S,10,20,1,43,No education,27,Patnership,Complex\n6195,459,Boston,E,3,21,8,45,Graduation,20,Owner,Standalone\n5560,87.27,Boston,W,3,18,1,41,High School,22,Franchise,Complex\n2,11.64,NY City,N,20,22,4,55,No education,23,Franchise,Standalone\n4264,292,NJ City,W,1,17,6,68,High School,28,Franchise,Complex\n4577,77.41,NJ City,N,2,14,3,59,No education,32,Patnership,Standalone\n9757,280.91,NJ City,N,18,17,1,47,High School,35,Patnership,Standalone\n7341,431,Boston,N,3,11,12,78,Graduation,32,Patnership,Standalone\n7389,268.24,Boston,N,8,24,4,78,Graduation,31,Patnership,Standalone\n3786,62.25,NJ City,N,23,11,2,27,Graduation,30,Patnership,Complex\n5015,377,Boston,E,2,24,20,41,Graduation,28,Owner,Standalone\n5551,483,Boston,E,1,24,20,27,Graduation,23,Owner,Standalone\n4925,122,NJ City,N,1,24,15,72,High School,27,Patnership,Standalone\n546,266,NJ City,S,2,24,16,53,High School,19,Patnership,Complex\n2721,421.57,NJ City,N,2,16,3,62,Graduation,18,Patnership,Standalone\n3004,209,NJ City,W,3,17,13,38,High School,25,Franchise,Complex\n1219,274,NJ City,E,2,23,14,71,High School,25,Owner,Standalone\n9421,487,NJ City,N,2,18,20,56,Graduation,20,Patnership,Standalone\n112,308,NJ City,W,3,24,16,68,High School,22,Franchise,Complex\n9655,477,NJ City,E,3,10,13,65,Graduation,19,Owner,Standalone\n6676,99.7,Boston,W,16,24,1,84,High School,33,Franchise,Complex\n7288,381,Boston,W,1,15,13,58,Graduation,34,Franchise,Complex\n9187,490,NJ City,E,1,15,22,26,Graduation,27,Owner,Standalone\n1484,112,NJ City,W,3,15,10,30,High School,28,Franchise,Complex\n6087,398,Boston,E,1,21,23,39,Graduation,20,Owner,Standalone\n5244,7.42,Boston,N,20,24,3,53,Graduation,24,Franchise,Complex\n781,121,NJ City,N,1,17,21,29,High School,19,Patnership,Standalone\n4489,155,NJ City,N,3,23,9,26,High School,29,Patnership,Standalone\n2581,299,NJ City,N,2,15,23,42,High School,34,Patnership,Standalone\n378,224,NJ City,S,2,19,23,51,High School,32,Patnership,Complex\n6557,468,Boston,N,2,17,9,74,Graduation,20,Patnership,Standalone\n2953,262.1,NJ City,N,5,18,3,81,High School,21,Patnership,Standalone\n8277,68.82,NJ City,N,3,9,4,39,Graduation,33,Patnership,Standalone\n5145,390,Boston,N,3,21,7,59,Graduation,30,Patnership,Standalone\n6925,0.7,Boston,N,1,19,1,47,High School,32,Patnership,Standalone\n3886,310,NJ City,S,1,10,16,58,High School,20,Patnership,Complex\n4464,77.47,NJ City,W,8,22,4,32,Graduation,20,Franchise,Complex\n2869,155.34,NJ City,N,2,14,5,55,High School,35,Patnership,Standalone\n6176,413.81,Boston,W,3,19,5,25,No education,35,Franchise,Complex\n1780,159,NJ City,W,3,11,18,40,High School,24,Franchise,Complex\n1347,316,NJ City,E,3,11,11,44,High School,35,Owner,Standalone\n3123,308,NJ City,E,1,17,20,80,High School,19,Owner,Standalone\n7398,384,Boston,S,3,14,20,36,Graduation,22,Patnership,Complex\n5735,497.42,Boston,E,3,20,5,40,No education,22,Owner,Standalone\n8169,459,NJ City,N,3,14,17,66,Graduation,32,Patnership,Standalone\n584,242,NJ City,W,3,13,24,59,High School,35,Franchise,Complex\n7955,81.16,Boston,E,20,8,4,85,No education,25,Owner,Standalone\n8484,355.31,NJ City,W,24,16,3,59,Graduation,25,Franchise,Complex\n9034,78.34,NJ City,S,13,23,3,57,High School,24,Patnership,Complex\n494,262,NJ City,S,3,14,18,35,High School,34,Patnership,Complex\n3044,216,NJ City,W,2,15,22,51,High School,28,Franchise,Complex\n7488,415,Boston,W,2,20,24,44,Graduation,32,Franchise,Complex\n2598,211,NJ City,S,1,16,15,84,High School,24,Patnership,Complex\n5102,15.47,Boston,N,4,24,3,43,No education,18,Patnership,Complex\n3716,383.32,NJ City,W,4,15,2,69,No education,19,Franchise,Complex\n8622,387,NJ City,S,2,15,10,73,Graduation,21,Patnership,Complex\n2470,148,NJ City,S,2,22,13,29,High School,31,Patnership,Complex\n5861,468,Boston,N,3,24,20,32,Graduation,24,Patnership,Standalone\n6428,498,Boston,W,3,22,15,51,Graduation,29,Franchise,Complex\n8454,403,NJ City,S,2,8,23,73,Graduation,28,Patnership,Complex\n6172,425.25,Boston,W,18,11,2,58,High School,22,Franchise,Complex\n2278,132,NJ City,S,1,20,6,64,High School,23,Patnership,Complex\n4260,313,NJ City,W,3,20,25,58,High School,29,Franchise,Complex\n4356,217,NJ City,W,2,24,22,44,High School,30,Franchise,Complex\n3564,115,NJ City,W,3,20,9,44,High School,30,Franchise,Complex\n5913,491,Boston,N,1,14,9,77,Graduation,20,Patnership,Standalone\n8174,424,NJ City,S,3,20,9,69,Graduation,23,Patnership,Complex\n5071,156.45,Boston,E,21,23,2,52,High School,18,Owner,Standalone\n363,184,NJ City,E,1,12,22,84,High School,27,Owner,Standalone\n113,21.74,NJ City,N,6,21,2,55,No education,35,Patnership,Standalone\n7129,368,Boston,N,1,18,22,30,Graduation,23,Patnership,Standalone\n7977,48.86,Boston,N,24,21,4,82,Graduation,29,Patnership,Standalone\n9510,496,NJ City,S,1,19,13,72,Graduation,34,Patnership,Complex\n310,57.75,NJ City,S,17,22,4,37,High School,33,Patnership,Complex\n9374,439,NJ City,S,3,8,22,30,Graduation,32,Patnership,Complex\n713,150,NJ City,N,1,10,11,66,High School,26,Patnership,Standalone\n6097,339.96,Boston,N,7,9,2,39,High School,21,Patnership,Standalone\n8273,431,NJ City,N,3,14,12,60,Graduation,34,Patnership,Standalone\n650,258.45,NJ City,S,9,22,2,78,No education,34,Patnership,Complex\n4548,126.81,NJ City,W,22,8,4,79,Graduation,19,Franchise,Complex\n5786,495,Boston,S,1,20,19,71,Graduation,19,Patnership,Complex\n95,45.45,NJ City,N,12,12,1,48,No education,25,Owner,Standalone\n896,97.24,NJ City,W,12,15,1,54,No education,18,Franchise,Complex\n6103,32.7,Boston,N,21,18,2,50,High School,19,Owner,Standalone\n889,63.97,NJ City,N,1,18,2,57,High School,20,Patnership,Standalone\n2688,317,NJ City,W,1,18,18,85,High School,22,Franchise,Complex\n7905,13.47,Boston,N,21,18,3,53,Graduation,23,Patnership,Standalone\n7557,469,Boston,N,3,20,25,52,Graduation,27,Patnership,Standalone\n414,50.18,NJ City,S,1,17,4,48,Graduation,26,Patnership,Complex\n1864,228,NJ City,W,2,19,25,47,High School,23,Franchise,Complex\n9320,355,NJ City,W,1,15,9,74,Graduation,33,Franchise,Complex\n9654,443,NJ City,S,2,17,20,77,Graduation,25,Patnership,Complex\n184,304,NJ City,W,3,13,22,53,High School,32,Franchise,Complex\n8897,484,NJ City,N,2,15,23,56,Graduation,22,Patnership,Standalone\n6278,500,Boston,S,2,22,15,37,Graduation,21,Patnership,Complex\n4408,312,NJ City,W,1,24,19,57,High School,30,Franchise,Complex\n464,55.19,NJ City,W,17,12,4,34,No education,32,Franchise,Complex\n551,242,NJ City,E,2,19,6,75,High School,20,Owner,Standalone\n6279,473,Boston,E,2,13,14,29,Graduation,23,Owner,Standalone\n2134,245.84,NJ City,S,19,22,4,64,High School,25,Patnership,Complex\n8112,473,NJ City,W,1,20,14,76,Graduation,24,Franchise,Complex\n4186,240,NJ City,S,3,12,17,30,High School,22,Patnership,Complex\n5202,393,Boston,S,3,19,19,54,Graduation,23,Patnership,Complex\n6074,386,Boston,S,3,11,20,59,Graduation,32,Patnership,Complex\n1132,197,NJ City,W,3,18,22,46,High School,34,Franchise,Complex\n6878,101.66,Boston,S,17,22,2,74,No education,25,Patnership,Complex\n9014,454,NJ City,S,2,16,22,82,Graduation,21,Patnership,Complex\n5953,21.69,Boston,N,10,9,2,41,High School,23,Patnership,Standalone\n4669,467.97,NJ City,N,11,12,3,56,High School,29,Patnership,Standalone\n411,191,NJ City,E,2,22,11,32,High School,32,Owner,Standalone\n6701,440,Boston,N,3,21,22,42,Graduation,32,Patnership,Standalone\n1034,108.97,NJ City,S,21,23,1,59,No education,19,Patnership,Complex\n3039,45.28,NJ City,N,10,15,3,55,Graduation,34,Owner,Standalone\n3828,253,NJ City,W,1,17,19,75,High School,35,Franchise,Complex\n8692,401,NJ City,W,2,11,6,78,Graduation,18,Franchise,Complex\n6991,452,Boston,E,2,16,8,30,Graduation,21,Owner,Standalone\n2506,74.43,NJ City,S,21,24,3,37,High School,34,Patnership,Complex\n5590,415,Boston,S,3,21,11,47,Graduation,22,Patnership,Complex\n6997,420,Boston,N,2,17,10,33,Graduation,21,Patnership,Standalone\n465,123.63,NJ City,N,7,12,3,53,Graduation,34,Patnership,Standalone\n683,235,NJ City,E,1,8,7,34,High School,24,Owner,Standalone\n3779,52.32,NJ City,E,3,24,4,43,No education,29,Owner,Standalone\n2809,96.8,NJ City,N,9,9,4,27,High School,29,Patnership,Standalone\n9603,93.14,NJ City,E,20,16,4,28,Graduation,23,Owner,Standalone\n473,63.76,NJ City,N,3,9,3,48,No education,33,Patnership,Standalone\n5986,416,Boston,S,3,8,21,71,Graduation,30,Patnership,Complex\n3418,276.79,NJ City,S,22,10,4,69,High School,30,Patnership,Complex\n1511,244,NJ City,E,1,9,23,84,High School,34,Owner,Standalone\n520,344.13,NJ City,W,8,14,3,80,High School,33,Franchise,Complex\n6024,456,Boston,W,3,16,6,56,Graduation,22,Franchise,Complex\n1573,183,NJ City,N,1,15,15,57,High School,32,Patnership,Standalone\n2731,483.17,NJ City,E,15,23,4,69,High School,34,Owner,Standalone\n5014,374,Boston,S,1,22,6,51,Graduation,19,Patnership,Complex\n6467,392,Boston,E,2,8,18,72,Graduation,21,Owner,Standalone\n9933,62.88,NJ City,N,9,22,1,56,Graduation,33,Patnership,Standalone\n1724,484.46,NJ City,W,2,11,5,27,No education,30,Franchise,Complex\n2799,247,NJ City,E,2,12,17,36,High School,29,Owner,Standalone\n9431,372,NJ City,E,2,14,15,52,Graduation,22,Owner,Standalone\n9905,116.82,NJ City,N,21,13,1,44,No education,18,Patnership,Standalone\n9876,484,NJ City,W,2,23,25,78,Graduation,35,Franchise,Complex\n2865,132,NJ City,N,3,15,14,30,High School,35,Patnership,Standalone\n8180,378,NJ City,W,1,20,14,26,Graduation,32,Franchise,Complex\n9277,414,NJ City,N,1,11,16,43,Graduation,35,Patnership,Standalone\n7069,33.82,Boston,N,7,21,2,36,High School,34,Patnership,Standalone\n4394,159,NJ City,S,2,22,15,37,High School,26,Patnership,Complex\n8756,421,NJ City,W,3,14,25,83,Graduation,35,Franchise,Complex\n6740,343.36,Boston,W,16,10,2,53,No education,18,Franchise,Complex\n6799,411,Boston,E,2,22,10,83,Graduation,26,Owner,Standalone\n320,193.56,NJ City,W,3,11,5,63,No education,28,Franchise,Complex\n9425,380,NJ City,N,2,19,17,58,Graduation,18,Patnership,Standalone\n6828,10.24,Boston,N,8,11,1,29,Graduation,32,Franchise,Complex\n4756,283,NJ City,W,1,23,12,33,High School,33,Franchise,Complex\n6310,181.5,Boston,S,4,23,4,78,High School,18,Patnership,Complex\n3658,310,NJ City,S,1,23,17,53,High School,32,Patnership,Complex\n1646,220,NJ City,S,1,20,16,67,High School,20,Patnership,Complex\n2343,165,NJ City,E,1,17,21,70,High School,24,Owner,Standalone\n9735,439,NJ City,E,1,16,17,43,Graduation,30,Owner,Standalone\n4423,54.87,NJ City,E,25,9,4,75,High School,27,Owner,Standalone\n8222,461,NJ City,S,2,19,21,82,Graduation,23,Patnership,Complex\n2238,363,NJ City,S,2,23,5,57,Graduation,29,Patnership,Complex\n8107,46.53,NJ City,N,5,20,1,46,High School,19,Owner,Standalone\n4528,231,NJ City,W,3,17,6,58,High School,20,Franchise,Complex\n7445,360,Boston,N,1,9,16,57,Graduation,26,Patnership,Standalone\n3888,48.2,NJ City,W,24,21,3,76,Graduation,20,Franchise,Complex\n6823,96.84,Boston,E,24,14,3,71,High School,21,Owner,Standalone\n3870,136,NJ City,S,1,15,15,46,High School,26,Patnership,Complex\n614,169,NJ City,S,2,18,12,66,High School,21,Patnership,Complex\n9990,382,NJ City,S,3,11,18,81,Graduation,18,Patnership,Complex\n3401,211,NJ City,N,2,14,16,36,High School,28,Patnership,Standalone\n5314,72.18,Boston,N,6,10,4,43,High School,27,Patnership,Complex\n4048,235,NJ City,W,3,14,24,59,High School,30,Franchise,Complex\n836,143,NJ City,W,2,13,20,36,High School,19,Franchise,Complex\n4999,404.87,Boston,E,5,20,4,52,High School,29,Owner,Standalone\n8864,125,NJ City,W,24,10,4,39,No education,19,Franchise,Complex\n11,32.81,NY City,N,18,22,1,38,No education,31,Owner,Standalone\n8766,494,NJ City,S,14,12,18,61,Graduation,33,Patnership,Complex\n4912,6.59,NJ City,N,6,23,1,80,High School,20,Franchise,Complex\n4980,131.41,Boston,W,19,20,15,63,High School,30,Franchise,Complex\n3603,59.41,NJ City,E,12,23,2,42,Graduation,33,Owner,Standalone\n5498,470,Boston,S,13,10,17,41,Graduation,32,Patnership,Complex\n8033,435,Boston,N,18,16,18,76,Graduation,27,Patnership,Standalone\n6873,356.99,Boston,N,25,10,3,71,Graduation,28,Patnership,Standalone\n8297,25.53,NJ City,N,3,23,3,65,No education,30,Patnership,Standalone\n302,237,NJ City,S,1,15,15,51,High School,30,Patnership,Complex\n7931,460,Boston,E,2,10,15,70,Graduation,29,Owner,Standalone\n348,480.25,NJ City,W,19,9,7,65,High School,23,Franchise,Complex\n9509,109.17,NJ City,N,13,21,1,28,No education,18,Patnership,Standalone\n2199,388.95,NJ City,E,15,17,7,61,High School,25,Owner,Standalone\n7031,390,Boston,E,4,19,21,33,Graduation,27,Owner,Standalone\n8645,465,NJ City,N,8,18,11,79,Graduation,30,Patnership,Standalone\n5505,126.75,Boston,N,8,16,1,46,Graduation,28,Patnership,Standalone\n5655,480,Boston,E,15,18,17,74,Graduation,34,Owner,Standalone\n2435,254.45,NJ City,E,11,23,3,78,No education,21,Owner,Standalone\n3375,140,NJ City,E,2,8,14,47,High School,21,Owner,Standalone\n6673,443,Boston,N,7,24,15,29,Graduation,35,Patnership,Standalone\n8239,407,NJ City,E,1,14,19,50,Graduation,21,Owner,Standalone\n6426,376,Boston,S,21,23,12,39,Graduation,26,Patnership,Complex\n4279,215.8,NJ City,E,4,16,1,82,High School,27,Owner,Standalone\n2910,341.76,NJ City,S,24,11,21,63,High School,20,Patnership,Complex\n554,315.23,NJ City,S,24,11,4,34,No education,21,Patnership,Complex\n4939,127.52,Boston,E,23,11,1,73,High School,25,Owner,Standalone\n9337,498,NJ City,N,18,8,23,35,Graduation,23,Patnership,Standalone\n572,124.05,NJ City,W,8,8,3,76,No education,34,Franchise,Complex\n6751,499,Boston,E,12,23,23,26,Graduation,32,Owner,Standalone\n9871,440,NJ City,E,15,14,19,36,Graduation,35,Owner,Standalone\n1703,239.26,NJ City,E,19,23,13,78,High School,29,Owner,Standalone\n7279,361,Boston,E,24,18,25,55,Graduation,35,Owner,Standalone\n697,60.82,NJ City,N,4,19,3,47,High School,24,Patnership,Standalone\n9235,112.11,NJ City,E,21,17,4,68,High School,22,Owner,Standalone\n3695,169.53,NJ City,E,21,18,5,36,No education,21,Owner,Standalone\n558,306.52,NJ City,S,22,15,4,69,Graduation,22,Patnership,Complex\n2464,335.09,NJ City,W,17,16,2,44,High School,27,Franchise,Complex\n1140,434.72,NJ City,W,9,16,8,58,High School,34,Franchise,Complex\n3624,373.77,NJ City,W,19,11,18,26,High School,23,Franchise,Complex\n9208,463,NJ City,W,19,24,18,77,Graduation,33,Franchise,Complex\n6724,360,Boston,W,9,16,25,72,Graduation,24,Franchise,Complex\n5073,441.9,Boston,N,18,23,3,55,Graduation,35,Patnership,Standalone\n3985,253.01,NJ City,N,6,18,24,34,High School,33,Patnership,Standalone\n9098,6.54,NJ City,N,9,16,2,66,No education,30,Patnership,Complex\n1499,128.87,NJ City,E,23,23,13,31,High School,21,Owner,Standalone\n1832,453.27,NJ City,W,19,11,8,43,High School,31,Franchise,Complex\n4764,169.83,NJ City,W,20,18,18,70,High School,25,Franchise,Complex\n9566,399,NJ City,S,9,21,20,61,Graduation,29,Patnership,Complex\n8348,495,NJ City,W,5,14,23,73,Graduation,30,Franchise,Complex\n5760,457,Boston,W,14,22,8,37,Graduation,24,Franchise,Complex\n154,415.51,NJ City,S,10,23,8,64,High School,20,Patnership,Complex\n5846,102.99,Boston,S,3,22,2,56,No education,31,Patnership,Complex\n1802,295.83,NJ City,S,6,22,4,77,No education,21,Patnership,Complex\n3328,295,NJ City,W,22,11,15,77,High School,21,Franchise,Complex\n3238,127.41,NJ City,S,8,19,3,59,High School,31,Patnership,Complex\n9435,385.01,NJ City,E,20,20,1,43,Graduation,18,Owner,Standalone\n628,332.95,NJ City,W,9,19,3,59,High School,18,Franchise,Complex\n2732,231,NJ City,W,1,14,11,59,High School,26,Franchise,Complex\n6613,491,Boston,N,21,16,7,63,Graduation,32,Patnership,Standalone\n2345,481.21,NJ City,N,15,11,21,73,High School,26,Patnership,Standalone\n4101,187.24,NJ City,N,5,13,23,64,High School,33,Patnership,Standalone\n1635,188,NJ City,E,2,17,17,25,High School,20,Owner,Standalone\n6137,387,Boston,N,17,22,22,56,Graduation,29,Patnership,Standalone\n2995,31.53,NJ City,N,22,18,1,75,High School,29,Owner,Standalone\n6667,376,Boston,E,8,15,11,68,Graduation,25,Owner,Standalone\n1407,10.46,NJ City,N,23,8,2,70,Graduation,32,Owner,Standalone\n325,366.65,NJ City,N,4,15,20,85,High School,29,Patnership,Standalone\n1216,441.75,NJ City,W,25,9,22,33,High School,21,Franchise,Complex\n5822,431,Boston,S,25,21,24,76,Graduation,29,Patnership,Complex\n5903,118.36,Boston,E,7,13,4,59,No education,18,Owner,Standalone\n9737,296.53,NJ City,N,16,10,1,44,No education,19,Patnership,Standalone\n7465,406,Boston,N,25,23,11,85,Graduation,20,Patnership,Standalone\n6285,357,Boston,N,9,22,20,58,Graduation,23,Patnership,Standalone\n1230,278,NJ City,S,2,15,13,67,High School,34,Patnership,Complex\n92,462.54,NJ City,W,16,12,25,81,High School,26,Franchise,Complex\n2572,383.74,NJ City,W,22,18,1,31,High School,18,Franchise,Complex\n6587,104.1,Boston,E,7,11,4,57,No education,18,Owner,Standalone\n4633,219.37,NJ City,N,14,9,16,31,High School,29,Patnership,Standalone\n1352,413.07,NJ City,W,12,18,24,52,High School,35,Franchise,Complex\n8384,471,NJ City,W,24,16,24,37,Graduation,22,Franchise,Complex\n4606,447.53,NJ City,S,24,13,4,85,High School,35,Patnership,Complex\n46,266.2,Boston,S,19,21,9,36,High School,26,Patnership,Complex\n8050,374,NJ City,S,12,22,21,44,Graduation,33,Patnership,Complex\n1385,58.04,NJ City,N,4,15,2,47,No education,32,Patnership,Standalone\n2110,410.91,NJ City,S,15,8,8,75,High School,29,Patnership,Complex\n323,204,NJ City,E,2,20,22,79,High School,27,Owner,Standalone\n3361,453.7,NJ City,N,18,13,17,27,High School,26,Patnership,Standalone\n6717,181.77,Boston,N,15,16,3,41,Graduation,24,Patnership,Standalone\n7092,359.94,Boston,W,14,19,3,31,Graduation,35,Franchise,Complex\n1949,218.78,NJ City,N,19,17,14,64,High School,32,Patnership,Standalone\n6948,370,Boston,W,5,8,13,78,Graduation,30,Franchise,Complex\n258,235.6,NJ City,S,18,22,14,40,High School,25,Patnership,Complex\n2125,114.94,NJ City,N,21,11,2,69,High School,35,Patnership,Standalone\n9102,350,NJ City,S,24,15,19,36,Graduation,26,Patnership,Complex\n7867,373,Boston,E,19,10,14,71,Graduation,31,Owner,Standalone\n8720,449,NJ City,W,21,8,25,56,Graduation,19,Franchise,Complex\n6262,438,Boston,S,21,17,18,29,Graduation,29,Patnership,Complex\n2901,408.31,NJ City,N,6,20,2,44,Graduation,32,Patnership,Standalone\n6967,378,Boston,E,12,14,25,25,Graduation,21,Owner,Standalone\n1736,317.93,NJ City,W,19,18,13,50,High School,31,Franchise,Complex\n3086,94.06,NJ City,S,3,16,3,82,No education,34,Patnership,Complex\n9841,59.6,NJ City,N,22,14,3,63,High School,20,Patnership,Standalone\n1220,84.32,NJ City,W,19,13,3,85,No education,22,Franchise,Complex\n4420,133.74,NJ City,W,20,17,3,46,High School,23,Franchise,Complex\n8106,43.09,NJ City,N,25,11,3,64,Graduation,24,Patnership,Complex\n6287,26.89,Boston,N,6,8,4,68,No education,31,Owner,Standalone\n6738,278.34,Boston,S,24,13,3,75,Graduation,31,Patnership,Complex\n9817,430,NJ City,N,12,14,11,30,Graduation,28,Patnership,Standalone\n5037,358,Boston,N,2,17,9,33,Graduation,28,Patnership,Standalone\n2410,63.54,NJ City,N,18,14,4,77,High School,19,Patnership,Complex\n8351,408,NJ City,E,14,17,15,80,Graduation,32,Owner,Standalone\n3366,79.68,NJ City,S,14,8,3,36,Graduation,22,Patnership,Complex\n7497,496,Boston,N,12,20,22,72,Graduation,23,Patnership,Standalone\n7432,67.96,Boston,N,10,18,1,61,High School,29,Franchise,Complex\n24,101.09,Maine,W,2,12,1,59,Graduation,26,Franchise,Complex\n8287,354,NJ City,E,17,14,20,30,Graduation,25,Owner,Standalone\n5320,414.52,Boston,W,2,17,1,51,High School,24,Franchise,Complex\n581,105.61,NJ City,N,7,18,2,55,No education,27,Patnership,Standalone\n2138,304.3,NJ City,S,15,18,12,40,High School,27,Patnership,Complex\n883,168.97,NJ City,E,10,21,24,49,High School,26,Owner,Standalone\n2354,351.61,NJ City,S,13,14,25,38,High School,31,Patnership,Complex\n3940,132.23,NJ City,W,16,10,18,70,High School,31,Franchise,Complex\n6875,408,Boston,E,4,24,9,51,Graduation,26,Owner,Standalone\n2228,130.29,NJ City,W,25,24,25,30,High School,26,Franchise,Complex\n5971,75.62,Boston,E,22,11,3,45,High School,28,Owner,Standalone\n8197,372.78,NJ City,N,6,8,5,61,High School,18,Patnership,Standalone\n1676,329.22,NJ City,W,10,17,22,29,High School,32,Franchise,Complex\n7145,490,Boston,N,10,9,10,44,Graduation,27,Patnership,Standalone\n9545,471,NJ City,N,7,8,19,30,Graduation,21,Patnership,Standalone\n4777,208.95,NJ City,N,18,17,16,71,High School,26,Patnership,Standalone\n4158,482.05,NJ City,S,21,22,25,46,High School,35,Patnership,Complex\n3439,100.34,NJ City,E,11,20,4,80,High School,22,Owner,Standalone\n6560,487,Boston,W,9,16,24,40,Graduation,30,Franchise,Complex\n5956,497,Boston,W,15,13,23,30,Graduation,20,Franchise,Complex\n9075,458,NJ City,E,2,18,25,81,Graduation,30,Owner,Standalone\n8445,357,NJ City,N,11,23,10,53,Graduation,32,Patnership,Standalone\n3024,410,NJ City,W,8,18,5,33,Graduation,35,Franchise,Complex\n9180,396,NJ City,W,14,17,17,49,Graduation,21,Franchise,Complex\n6050,86.78,Boston,S,3,14,1,46,No education,20,Patnership,Complex\n4615,148.33,NJ City,E,2,8,2,37,High School,20,Owner,Standalone\n5554,94.26,Boston,S,17,13,3,31,High School,28,Patnership,Complex\n6640,496,Boston,W,17,13,25,77,Graduation,29,Franchise,Complex\n4267,309.49,NJ City,E,4,19,10,32,High School,19,Owner,Standalone\n1218,300.03,NJ City,S,8,16,10,36,High School,32,Patnership,Complex\n9318,368,NJ City,S,9,21,17,43,Graduation,34,Patnership,Complex\n308,351.62,NJ City,W,5,19,22,77,High School,19,Franchise,Complex\n5231,361,Boston,E,4,9,24,45,Graduation,23,Owner,Standalone\n6618,479,Boston,S,21,24,15,75,Graduation,24,Patnership,Complex\n3258,325.46,NJ City,S,6,9,12,67,High School,22,Patnership,Complex\n5392,464,Boston,W,23,14,20,28,Graduation,31,Franchise,Complex\n9874,325.77,NJ City,S,20,20,1,26,High School,34,Patnership,Complex\n4074,311.56,NJ City,S,1,11,3,28,Graduation,29,Patnership,Complex\n9569,495,NJ City,N,5,18,15,67,Graduation,35,Patnership,Standalone\n4688,69.43,NJ City,N,25,15,1,36,No education,30,Franchise,Complex\n7738,381,Boston,S,1,21,24,67,Graduation,34,Patnership,Complex\n9997,379,NJ City,N,11,14,9,74,Graduation,28,Patnership,Standalone\n4855,97.9,NJ City,E,25,12,3,47,High School,27,Owner,Standalone\n5612,90.01,Boston,W,24,20,3,36,No education,30,Franchise,Complex\n2544,27.21,NJ City,N,25,9,2,75,Graduation,29,Franchise,Complex\n1501,381.03,NJ City,N,15,17,19,65,High School,33,Patnership,Standalone\n8777,177.88,NJ City,N,21,8,4,37,No education,24,Patnership,Standalone\n9844,119.08,NJ City,W,20,15,2,55,High School,34,Franchise,Complex\n8953,494,NJ City,N,5,18,25,48,Graduation,23,Patnership,Standalone\n5325,377,Boston,N,24,19,18,35,Graduation,19,Patnership,Standalone\n7643,461.38,Boston,E,16,20,4,43,No education,34,Owner,Standalone\n635,133.26,NJ City,E,4,17,25,38,High School,35,Owner,Standalone\n288,471.69,NJ City,W,25,14,23,75,High School,31,Franchise,Complex\n7345,39.02,Boston,N,19,15,1,42,High School,34,Patnership,Standalone\n7450,2.39,Boston,S,12,13,3,34,High School,26,Patnership,Complex\n660,85.24,NJ City,W,2,23,3,47,Graduation,23,Franchise,Complex\n8249,469,NJ City,N,17,13,7,27,Graduation,24,Patnership,Standalone\n6790,66.91,Boston,N,24,21,4,40,High School,23,Patnership,Complex\n3303,48.83,NJ City,E,1,21,2,43,Graduation,30,Owner,Standalone\n6410,490,Boston,S,19,23,21,48,Graduation,34,Patnership,Complex\n2481,402.04,NJ City,N,24,11,2,81,Graduation,18,Patnership,Standalone\n1848,408.87,NJ City,W,13,10,1,67,Graduation,29,Franchise,Complex\n6981,380,Boston,N,22,8,12,79,Graduation,28,Patnership,Standalone\n1710,432.69,NJ City,S,5,10,11,37,High School,27,Patnership,Complex\n8935,358,NJ City,E,10,18,16,57,Graduation,34,Owner,Standalone\n4625,435.63,NJ City,N,18,10,13,73,High School,25,Patnership,Standalone\n679,350.16,NJ City,E,22,23,4,36,High School,29,Owner,Standalone\n5544,376,Boston,W,13,24,5,28,Graduation,30,Franchise,Complex\n5747,426,Boston,E,17,17,25,51,Graduation,29,Owner,Standalone\n1778,442.65,NJ City,S,6,16,22,35,High School,22,Patnership,Complex\n2462,317.54,NJ City,S,20,13,17,59,High School,23,Patnership,Complex\n7732,94.49,Boston,W,4,18,2,58,High School,21,Franchise,Complex\n1366,216.51,NJ City,S,12,11,24,66,High School,29,Patnership,Complex\n4596,35.36,NJ City,N,18,24,3,48,Graduation,34,Franchise,Complex\n7478,483,Boston,S,8,18,6,32,Graduation,33,Patnership,Complex\n48,403,Boston,W,17,12,5,84,Graduation,24,Franchise,Complex\n4337,100.91,NJ City,N,7,11,1,78,No education,32,Patnership,Standalone\n3200,129.15,NJ City,W,23,9,19,66,High School,21,Franchise,Complex\n1130,145,NJ City,S,2,15,16,68,High School,30,Patnership,Complex\n7470,417,Boston,S,5,14,16,75,Graduation,21,Patnership,Complex\n3644,116.59,NJ City,W,23,21,3,44,No education,24,Franchise,Complex\n6893,429,Boston,N,24,19,15,59,Graduation,26,Patnership,Standalone\n8110,454,NJ City,S,7,10,14,44,Graduation,20,Patnership,Complex\n9094,175.93,NJ City,S,23,17,5,25,High School,25,Patnership,Complex\n6763,465,Boston,E,1,22,21,85,Graduation,33,Owner,Standalone\n9025,356,NJ City,N,24,9,15,64,Graduation,18,Patnership,Standalone\n996,490.82,NJ City,W,4,13,22,41,High School,22,Franchise,Complex\n5034,390,Boston,S,19,16,15,54,Graduation,24,Patnership,Complex\n589,489.68,NJ City,N,19,20,13,31,High School,31,Patnership,Standalone\n31,58.94,Boston,E,5,12,2,29,High School,18,Owner,Standalone\n3492,52.43,NJ City,W,23,23,1,30,Graduation,33,Franchise,Complex\n7588,430,Boston,W,11,24,18,74,Graduation,19,Franchise,Complex\n6562,66.57,Boston,N,18,24,2,50,High School,25,Patnership,Complex\n7369,448,Boston,N,13,23,23,37,Graduation,31,Patnership,Standalone\n1671,216.4,NJ City,E,9,15,14,72,High School,35,Owner,Standalone\n9112,90.6,NJ City,W,3,21,4,47,High School,27,Franchise,Complex\n9736,96.43,NJ City,W,2,14,2,63,High School,25,Franchise,Complex\n6427,418,Boston,E,11,17,9,45,Graduation,19,Owner,Standalone\n9609,500,NJ City,N,1,11,5,75,Graduation,25,Patnership,Standalone\n1202,212.07,NJ City,S,18,19,2,29,No education,27,Patnership,Complex\n5856,418,Boston,W,23,10,12,38,Graduation,24,Franchise,Complex\n3781,448.77,NJ City,N,19,18,15,76,High School,32,Patnership,Standalone\n5565,434.29,Boston,N,19,8,4,52,Graduation,21,Patnership,Standalone\n648,158.22,NJ City,W,14,21,17,68,High School,21,Franchise,Complex\n5492,474,Boston,W,17,18,20,53,Graduation,26,Franchise,Complex\n6213,409,Boston,N,18,9,13,78,Graduation,20,Patnership,Standalone\n5306,415,Boston,S,12,18,13,84,Graduation,25,Patnership,Complex\n4118,196.86,NJ City,S,20,13,7,51,High School,30,Patnership,Complex\n4399,25.98,NJ City,N,13,14,3,50,High School,29,Owner,Standalone\n2315,11.63,NJ City,N,16,11,1,59,No education,27,Owner,Standalone\n3141,360.36,NJ City,N,16,19,19,30,High School,28,Patnership,Standalone\n9341,34.57,NJ City,N,10,8,3,36,No education,18,Patnership,Standalone\n8530,316.66,NJ City,S,24,19,4,75,High School,23,Patnership,Complex\n7645,413,Boston,N,20,23,19,71,Graduation,29,Patnership,Standalone\n7900,350.67,Boston,W,3,17,1,60,High School,22,Franchise,Complex\n2375,111.79,NJ City,E,8,18,2,82,No education,19,Owner,Standalone\n8573,436.01,NJ City,N,8,20,4,83,No education,31,Patnership,Standalone\n5451,425,Boston,E,9,8,13,27,Graduation,33,Owner,Standalone\n166,244,NJ City,S,3,18,19,79,High School,35,Patnership,Complex\n1236,187.36,NJ City,W,17,13,24,56,High School,25,Franchise,Complex\n7376,486,Boston,W,4,15,20,26,Graduation,35,Franchise,Complex\n637,13.23,NJ City,N,16,15,2,35,High School,34,Patnership,Standalone\n887,264.51,NJ City,E,12,18,15,82,High School,34,Owner,Standalone\n1950,295.62,NJ City,S,5,10,14,38,High School,24,Patnership,Complex\n8247,356,NJ City,E,7,24,16,57,Graduation,19,Owner,Standalone\n6811,11.25,Boston,N,4,20,4,53,High School,26,Owner,Standalone\n6183,63.51,Boston,N,7,21,4,31,Graduation,20,Owner,Standalone\n5906,498,Boston,S,9,21,12,53,Graduation,31,Patnership,Complex\n3808,102.13,NJ City,W,9,15,2,47,High School,21,Franchise,Complex\n1354,321.13,NJ City,S,12,23,9,82,High School,34,Patnership,Complex\n8608,359,NJ City,W,12,10,24,69,Graduation,35,Franchise,Complex\n5877,25.2,Boston,N,11,18,3,75,Graduation,18,Patnership,Standalone\n9410,410,NJ City,S,20,15,18,52,Graduation,35,Patnership,Complex\n3969,117.52,NJ City,N,12,21,4,76,Graduation,24,Patnership,Standalone\n9004,397,NJ City,W,23,19,20,50,Graduation,33,Franchise,Complex\n4676,360.69,NJ City,W,20,11,16,65,High School,26,Franchise,Complex\n6964,359.99,Boston,W,13,15,3,35,High School,21,Franchise,Complex\n616,188.19,NJ City,W,6,13,22,42,High School,22,Franchise,Complex\n1706,490.79,NJ City,S,22,22,4,31,No education,29,Patnership,Complex\n5345,335.07,Boston,N,1,16,4,52,No education,19,Patnership,Standalone\n5388,95.6,Boston,W,13,22,2,64,Graduation,25,Franchise,Complex\n7027,41.06,Boston,N,1,19,4,41,High School,28,Owner,Standalone\n5787,119.27,Boston,E,18,17,4,47,Graduation,21,Owner,Standalone\n2978,137.92,NJ City,S,10,16,13,28,High School,21,Patnership,Complex\n6264,16.49,Boston,N,4,8,4,43,Graduation,30,Franchise,Complex\n4047,220.66,NJ City,E,15,18,14,85,High School,32,Owner,Standalone\n9007,271.52,NJ City,E,21,14,1,61,High School,18,Owner,Standalone\n4440,207,NJ City,W,3,20,20,70,High School,28,Franchise,Complex\n4658,218.83,NJ City,S,5,8,5,70,No education,32,Patnership,Complex\n2263,123.71,NJ City,E,25,20,4,74,High School,18,Owner,Standalone\n5287,65.14,Boston,N,3,11,1,85,High School,33,Owner,Standalone\n2912,168.73,NJ City,W,16,14,18,51,High School,18,Franchise,Complex\n1118,182.35,NJ City,S,10,11,17,32,High School,20,Patnership,Complex\n5608,467,Boston,W,18,21,23,74,Graduation,31,Franchise,Complex\n745,307.13,NJ City,N,18,18,9,80,High School,20,Patnership,Standalone\n9825,18.69,NJ City,N,7,16,1,61,Graduation,25,Patnership,Standalone\n9399,499,NJ City,E,18,16,16,62,Graduation,29,Owner,Standalone\n1307,139.32,NJ City,E,25,14,10,35,High School,34,Owner,Standalone\n5169,49.1,Boston,N,24,21,2,55,Graduation,31,Patnership,Standalone\n6190,396,Boston,S,13,11,21,29,Graduation,19,Patnership,Complex\n17,168,Boston,N,2,8,19,52,High School,28,Patnership,Standalone\n7253,484,Boston,N,19,23,8,67,Graduation,29,Patnership,Standalone\n4284,168.14,NJ City,W,9,16,25,26,High School,31,Franchise,Complex\n2864,359.71,NJ City,W,8,17,3,79,No education,23,Franchise,Complex\n435,154.37,NJ City,E,13,21,20,26,High School,19,Owner,Standalone\n3477,96.29,NJ City,N,25,24,4,61,Graduation,34,Patnership,Standalone\n3322,230,NJ City,S,2,21,22,42,High School,35,Patnership,Complex\n2861,347.57,NJ City,N,22,15,17,66,High School,29,Patnership,Standalone\n8984,378,NJ City,W,13,23,9,39,Graduation,30,Franchise,Complex\n3428,469.03,NJ City,W,13,20,15,61,High School,35,Franchise,Complex\n2611,274.84,NJ City,E,8,24,16,56,High School,29,Owner,Standalone\n5870,39.55,Boston,N,14,13,4,46,No education,35,Patnership,Complex\n9751,190.39,NJ City,E,2,11,1,75,High School,31,Owner,Standalone\n3594,48.88,NJ City,S,23,14,3,68,Graduation,25,Patnership,Complex\n1920,250.34,NJ City,W,13,11,11,48,High School,19,Franchise,Complex\n2785,280.62,NJ City,N,14,19,21,42,High School,30,Patnership,Standalone\n9423,493,NJ City,E,4,21,13,62,Graduation,21,Owner,Standalone\n5022,437,Boston,S,6,14,12,57,Graduation,18,Patnership,Complex\n3782,368.74,NJ City,S,15,16,14,29,High School,28,Patnership,Complex\n6533,350,Boston,N,4,12,11,76,Graduation,21,Patnership,Standalone\n5119,422,Boston,E,10,18,11,52,Graduation,29,Owner,Standalone\n1516,224.16,NJ City,W,6,22,23,56,High School,20,Franchise,Complex\n3740,414.25,NJ City,W,8,8,5,38,No education,30,Franchise,Complex\n476,219.27,NJ City,W,18,20,18,42,High School,32,Franchise,Complex\n8687,491,NJ City,E,10,18,10,84,Graduation,18,Owner,Standalone\n4257,294,NJ City,N,3,19,8,80,High School,28,Patnership,Standalone\n727,110.63,NJ City,E,17,10,2,38,High School,20,Owner,Standalone\n5074,477,Boston,S,11,10,25,78,Graduation,33,Patnership,Complex\n8970,397,NJ City,S,17,20,24,37,Graduation,22,Patnership,Complex\n7637,386,Boston,N,18,9,8,76,Graduation,28,Patnership,Standalone\n1806,389.28,NJ City,S,24,19,21,81,High School,25,Patnership,Complex\n2608,246.92,NJ City,W,20,12,1,35,High School,20,Franchise,Complex\n5594,400,Boston,S,14,22,18,85,Graduation,35,Patnership,Complex\n7958,426,Boston,S,13,24,8,31,Graduation,23,Patnership,Complex\n215,273.56,NJ City,E,20,16,16,51,High School,35,Owner,Standalone\n4154,318.04,NJ City,S,20,9,6,63,High School,19,Patnership,Complex\n6961,378,Boston,N,14,10,20,59,Graduation,24,Patnership,Standalone\n8309,363,NJ City,N,11,9,19,31,Graduation,28,Patnership,Standalone\n6084,452,Boston,W,4,11,17,52,Graduation,27,Franchise,Complex\n4082,93.21,NJ City,S,19,20,4,47,No education,21,Patnership,Complex\n7585,461,Boston,N,4,24,14,34,Graduation,30,Patnership,Standalone\n8158,455,NJ City,S,23,21,19,44,Graduation,22,Patnership,Complex\n1628,457.02,NJ City,W,16,10,8,57,High School,29,Franchise,Complex\n8508,95.16,NJ City,W,18,20,2,80,Graduation,31,Franchise,Complex\n6581,475,Boston,N,21,23,7,67,Graduation,22,Patnership,Standalone\n1035,347.53,NJ City,E,13,12,9,65,High School,22,Owner,Standalone\n8656,472.01,NJ City,W,2,10,3,46,High School,24,Franchise,Complex\n5462,460,Boston,S,4,8,6,42,Graduation,26,Patnership,Complex\n3965,343.72,NJ City,N,22,21,9,76,High School,26,Patnership,Standalone\n6816,411,Boston,W,25,14,18,58,Graduation,20,Franchise,Complex\n1283,227.64,NJ City,E,24,23,20,36,High School,35,Owner,Standalone\n2773,141.13,NJ City,N,4,9,4,68,High School,22,Patnership,Standalone\n5387,354,Boston,E,7,18,6,45,Graduation,24,Owner,Standalone\n488,377.9,NJ City,W,19,12,25,26,High School,18,Franchise,Complex\n8854,266.68,NJ City,S,8,21,2,34,High School,27,Patnership,Complex\n4240,113,NJ City,W,2,19,23,44,High School,28,Franchise,Complex\n4992,425.23,Boston,W,8,20,7,75,High School,21,Franchise,Complex\n9747,33.71,NJ City,N,20,15,1,49,Graduation,25,Owner,Standalone\n2671,493.63,NJ City,E,25,11,16,63,High School,33,Owner,Standalone\n9755,52.53,NJ City,E,9,9,1,56,No education,18,Owner,Standalone\n8657,447,NJ City,N,18,18,18,75,Graduation,20,Patnership,Standalone\n5453,61.38,Boston,N,18,11,1,27,No education,24,Patnership,Standalone\n8200,413,NJ City,W,19,8,17,60,Graduation,25,Franchise,Complex\n5713,392,Boston,N,12,12,6,32,Graduation,35,Patnership,Standalone\n8513,448,NJ City,N,15,11,18,72,Graduation,26,Patnership,Standalone\n5944,215.58,Boston,W,24,8,3,46,High School,23,Franchise,Complex\n6119,372,Boston,E,17,22,14,81,Graduation,27,Owner,Standalone\n5425,368,Boston,N,14,17,12,41,Graduation,25,Patnership,Standalone\n5888,454,Boston,W,24,14,21,39,Graduation,33,Franchise,Complex\n2181,293,NJ City,N,1,14,11,55,High School,26,Patnership,Standalone\n3043,328.08,NJ City,E,23,14,2,43,High School,21,Owner,Standalone\n9756,67.87,NJ City,N,20,9,2,42,Graduation,20,Franchise,Complex\n6617,370,Boston,N,17,12,20,44,Graduation,26,Patnership,Standalone\n145,398.42,NJ City,N,22,15,4,63,High School,22,Patnership,Standalone\n556,491.49,NJ City,W,13,15,25,53,High School,35,Franchise,Complex\n4171,483.38,NJ City,E,14,16,11,73,High School,33,Owner,Standalone\n3029,207.18,NJ City,N,7,17,13,63,High School,22,Patnership,Standalone\n6793,271.19,Boston,N,14,13,4,62,High School,24,Patnership,Standalone\n9572,350,NJ City,W,5,18,20,25,Graduation,18,Franchise,Complex\n9477,305.77,NJ City,N,2,20,4,62,Graduation,32,Patnership,Standalone\n8172,376,NJ City,W,18,15,8,55,Graduation,30,Franchise,Complex\n2460,206,NJ City,W,2,8,8,84,High School,30,Franchise,Complex\n6803,283.67,Boston,E,6,23,3,46,No education,23,Owner,Standalone\n1905,409.53,NJ City,N,11,23,3,83,Graduation,25,Patnership,Standalone\n6041,395.46,Boston,N,4,12,4,43,No education,34,Patnership,Standalone\n3591,315.97,NJ City,E,19,17,6,50,High School,34,Owner,Standalone\n123,146.13,NJ City,E,23,19,6,40,High School,32,Owner,Standalone\n6184,338.83,Boston,W,19,14,1,65,High School,32,Franchise,Complex\n854,308.19,NJ City,S,10,11,6,63,High School,31,Patnership,Complex\n8244,62.13,NJ City,N,11,23,2,57,Graduation,28,Franchise,Complex\n4020,208,NJ City,W,2,13,9,73,High School,27,Franchise,Complex\n4465,156.13,NJ City,N,17,11,8,25,High School,27,Patnership,Standalone\n8301,406,NJ City,N,4,23,25,47,Graduation,22,Patnership,Standalone\n6277,494,Boston,N,11,9,12,48,Graduation,28,Patnership,Standalone\n3308,24.23,NJ City,N,13,10,2,73,No education,27,Franchise,Complex\n9080,20.39,NJ City,N,23,24,4,45,No education,34,Franchise,Complex\n8994,156.72,NJ City,S,20,18,2,83,Graduation,21,Patnership,Complex\n3708,459.95,NJ City,W,8,21,16,63,High School,22,Franchise,Complex\n4956,391.21,Boston,W,7,18,16,79,High School,23,Franchise,Complex\n8036,18.03,Boston,N,18,16,2,56,No education,32,Franchise,Complex\n8836,479,NJ City,W,23,24,22,29,Graduation,25,Franchise,Complex\n8483,370,NJ City,E,6,13,9,36,Graduation,23,Owner,Standalone\n3877,136,NJ City,N,18,18,10,52,High School,19,Patnership,Standalone\n4063,108.84,NJ City,E,4,16,2,71,High School,31,Owner,Standalone\n5355,417,Boston,E,14,17,23,46,Graduation,25,Owner,Standalone\n7188,371,Boston,W,18,14,25,65,Graduation,28,Franchise,Complex\n4006,238.4,NJ City,S,17,20,20,46,High School,33,Patnership,Complex\n7266,366,Boston,S,24,12,17,65,Graduation,26,Patnership,Complex\n4268,203.42,NJ City,W,5,21,7,70,High School,26,Franchise,Complex\n9528,158.6,NJ City,W,18,12,1,59,Graduation,31,Franchise,Complex\n5118,494,Boston,S,22,18,24,69,Graduation,22,Patnership,Complex\n7328,10.41,Boston,N,8,17,4,58,No education,29,Franchise,Complex\n420,321.2,NJ City,W,20,9,7,65,High School,20,Franchise,Complex\n4402,51.01,NJ City,S,21,19,4,76,High School,32,Patnership,Complex\n2873,432.61,NJ City,N,23,22,13,42,High School,22,Patnership,Standalone\n9659,388,NJ City,E,17,20,23,57,Graduation,27,Owner,Standalone\n8292,427,NJ City,W,16,24,24,53,Graduation,19,Franchise,Complex\n9217,9.59,NJ City,N,14,8,1,60,High School,25,Patnership,Standalone\n1702,188.98,NJ City,S,13,10,17,52,High School,30,Patnership,Complex\n142,149.03,NJ City,S,7,21,10,77,High School,25,Patnership,Complex\n4509,18.93,NJ City,N,3,13,2,41,Graduation,18,Patnership,Standalone\n7759,460,Boston,E,25,9,7,36,Graduation,20,Owner,Standalone\n8274,86.64,NJ City,S,11,23,3,80,Graduation,24,Patnership,Complex\n6320,70.52,Boston,N,6,21,2,38,No education,32,Franchise,Complex\n4720,174.44,NJ City,W,19,15,16,38,High School,22,Franchise,Complex\n844,206.46,NJ City,W,22,20,3,85,High School,33,Franchise,Complex\n512,207.26,NJ City,W,7,23,18,78,High School,33,Franchise,Complex\n2029,68.29,NJ City,N,8,19,2,46,High School,33,Patnership,Standalone\n5788,358,Boston,W,18,10,10,79,Graduation,32,Franchise,Complex\n5430,94.78,Boston,S,22,18,1,26,Graduation,32,Patnership,Complex\n8044,42.41,NJ City,N,10,20,4,42,High School,19,Franchise,Complex\n4822,1.41,NJ City,N,10,10,3,31,High School,22,Patnership,Complex\n2659,171,NJ City,E,2,9,13,29,High School,25,Owner,Standalone\n2711,296.31,NJ City,E,8,16,8,65,High School,35,Owner,Standalone\n5008,459,Boston,W,24,13,11,47,Graduation,24,Franchise,Complex\n2205,176.7,NJ City,N,6,14,1,81,Graduation,19,Patnership,Standalone\n1033,356.66,NJ City,N,7,23,22,31,High School,28,Patnership,Standalone\n7045,474.63,Boston,N,23,21,1,78,High School,32,Patnership,Standalone\n3517,488.37,NJ City,N,9,19,15,29,High School,23,Patnership,Standalone\n8688,112.47,NJ City,W,16,22,1,79,Graduation,32,Franchise,Complex\n186,215.17,NJ City,S,19,21,25,83,High School,20,Patnership,Complex\n9602,449,NJ City,S,7,12,23,49,Graduation,30,Patnership,Complex\n9531,385,NJ City,E,23,13,20,43,Graduation,26,Owner,Standalone\n5593,472,Boston,N,13,11,22,39,Graduation,19,Patnership,Standalone\n9088,258.55,NJ City,W,13,20,3,68,High School,21,Franchise,Complex\n7383,95.75,Boston,E,13,9,1,31,Graduation,31,Owner,Standalone\n5927,449,Boston,E,22,10,8,32,Graduation,24,Owner,Standalone\n9457,453,NJ City,N,19,19,16,43,Graduation,32,Patnership,Standalone\n5926,497,Boston,S,21,14,22,44,Graduation,27,Patnership,Complex\n8369,399,NJ City,N,13,8,19,43,Graduation,23,Patnership,Standalone\n39,234,NJ City,E,3,10,17,29,High School,33,Owner,Standalone\n3219,231,NJ City,E,1,19,7,25,High School,23,Owner,Standalone\n1729,20.55,NJ City,N,23,8,4,75,High School,29,Patnership,Standalone\n3323,439.18,NJ City,E,5,10,24,80,High School,23,Owner,Standalone\n9414,480.62,NJ City,S,1,23,4,79,Graduation,28,Patnership,Complex\n571,141.07,NJ City,E,11,8,10,58,High School,23,Owner,Standalone\n9046,106.75,NJ City,S,25,16,3,61,High School,34,Patnership,Complex\n8068,36.59,NJ City,N,2,11,2,52,High School,21,Franchise,Complex\n2093,383.54,NJ City,N,18,23,3,59,No education,31,Patnership,Standalone\n8241,126.08,NJ City,N,6,10,4,67,Graduation,32,Patnership,Standalone\n7051,124.05,Boston,E,8,14,4,50,High School,25,Owner,Standalone\n7364,443,Boston,W,19,10,8,68,Graduation,21,Franchise,Complex\n5589,18.39,Boston,N,14,24,4,65,Graduation,33,Patnership,Standalone\n7818,420,Boston,S,4,11,22,55,Graduation,34,Patnership,Complex\n2531,450.67,NJ City,E,22,10,14,63,High School,28,Owner,Standalone\n3480,375.15,NJ City,W,16,18,11,26,High School,25,Franchise,Complex\n1222,254,NJ City,S,2,22,7,66,High School,20,Patnership,Complex\n2159,160.88,NJ City,E,16,21,14,84,High School,19,Owner,Standalone\n8431,445,NJ City,E,19,16,15,83,Graduation,34,Owner,Standalone\n1726,96.97,NJ City,S,20,20,1,55,High School,20,Patnership,Complex\n6848,207.55,Boston,W,23,15,13,46,Graduation,27,Franchise,Complex\n419,351.89,NJ City,E,15,21,18,26,High School,34,Owner,Standalone\n7392,208.83,Boston,W,24,9,13,37,Graduation,27,Franchise,Complex\n3761,371.15,NJ City,N,19,24,24,44,High School,21,Patnership,Standalone\n7576,285.66,Boston,W,9,22,19,62,Graduation,27,Franchise,Complex\n2116,98.42,NJ City,W,16,8,3,37,High School,34,Franchise,Complex\n7176,71.52,Boston,N,7,12,2,60,Graduation,27,Franchise,Complex\n2566,240,NJ City,S,1,16,7,77,High School,20,Patnership,Complex\n8959,122.13,NJ City,E,9,18,2,73,High School,25,Owner,Standalone\n1627,196,NJ City,E,1,21,22,81,High School,27,Owner,Standalone\n646,464.58,NJ City,S,12,19,1,58,High School,20,Patnership,Complex\n4565,406.35,NJ City,N,8,11,4,73,No education,35,Patnership,Standalone\n9090,332.75,NJ City,S,25,24,17,68,Graduation,32,Patnership,Complex\n6792,194.79,Boston,W,20,13,13,80,Graduation,32,Franchise,Complex\n1098,495.57,NJ City,S,25,8,13,80,High School,29,Patnership,Complex\n3131,392.09,NJ City,E,18,19,17,33,High School,35,Owner,Standalone\n6642,406.89,Boston,S,15,24,24,79,Graduation,31,Patnership,Complex\n2023,11.37,NJ City,N,10,16,4,70,High School,25,Owner,Standalone\n8385,325.5,NJ City,N,10,11,21,34,Graduation,30,Patnership,Standalone\n6580,333.56,Boston,W,10,11,3,38,High School,33,Franchise,Complex\n9407,279.25,NJ City,E,11,16,5,72,No education,25,Owner,Standalone\n5967,454.97,Boston,E,12,19,21,83,Graduation,20,Owner,Standalone\n1590,25.79,NJ City,N,25,11,4,78,Graduation,33,Patnership,Complex\n598,143.5,NJ City,S,20,23,12,32,High School,23,Patnership,Complex\n9839,174.64,NJ City,E,15,22,8,27,Graduation,19,Owner,Standalone\n3827,468.27,NJ City,E,16,12,12,26,High School,19,Owner,Standalone\n8778,225.9,NJ City,S,6,18,7,85,Graduation,28,Patnership,Complex\n9307,244.8,NJ City,E,7,17,13,81,Graduation,29,Owner,Standalone\n1112,370.26,NJ City,W,1,11,3,42,No education,26,Franchise,Complex\n2758,43.35,NJ City,N,15,20,3,79,High School,35,Patnership,Complex\n9882,73.35,NJ City,N,15,24,4,58,Graduation,23,Patnership,Complex\n2650,51.43,NJ City,S,23,24,3,85,High School,19,Patnership,Complex\n9247,15.98,NJ City,N,10,10,3,44,High School,21,Owner,Standalone\n3561,310.23,NJ City,N,13,22,8,85,High School,26,Patnership,Standalone\n7114,24.42,Boston,N,25,16,3,38,High School,25,Patnership,Complex\n8399,444.96,NJ City,E,19,8,13,78,Graduation,31,Owner,Standalone\n7626,186.25,Boston,S,13,18,13,43,Graduation,28,Patnership,Complex\n5356,396.32,Boston,W,18,20,21,72,Graduation,27,Franchise,Complex\n3445,197.66,NJ City,N,24,13,22,45,High School,19,Patnership,Standalone\n6528,460.2,Boston,W,8,21,16,78,Graduation,19,Franchise,Complex\n4172,115.67,NJ City,W,10,17,2,62,No education,28,Franchise,Complex\n9255,178.46,NJ City,E,17,16,23,36,Graduation,27,Owner,Standalone\n7943,54.63,Boston,E,4,10,1,31,No education,31,Owner,Standalone\n2165,40.62,NJ City,N,19,19,2,64,No education,19,Patnership,Standalone\n1257,58.11,NJ City,N,23,10,1,31,Graduation,20,Patnership,Standalone\n1670,70.01,NJ City,N,21,10,4,61,No education,20,Patnership,Complex\n9587,449,NJ City,E,4,10,12,63,Graduation,28,Owner,Standalone\n6363,14.71,Boston,N,6,18,2,31,Graduation,25,Owner,Standalone\n8529,461.58,NJ City,N,9,15,21,61,Graduation,35,Patnership,Standalone\n8941,75.83,NJ City,N,19,14,1,41,High School,31,Patnership,Standalone\n7242,452.6,Boston,S,10,11,8,51,Graduation,34,Patnership,Complex\n3,150.95,Maine,E,23,11,17,76,High School,34,Patnership,Complex\n1625,416.59,NJ City,N,6,16,24,84,High School,29,Patnership,Standalone\n8931,220.43,NJ City,E,16,9,17,41,Graduation,26,Owner,Standalone\n1916,310.17,NJ City,W,15,17,23,41,High School,24,Franchise,Complex\n9964,475.54,NJ City,W,12,22,18,25,Graduation,19,Franchise,Complex\n969,249.67,NJ City,N,13,16,18,42,High School,23,Patnership,Standalone\n664,171.82,NJ City,W,15,20,17,70,High School,25,Franchise,Complex\n2974,138.97,NJ City,S,19,19,18,83,High School,28,Patnership,Complex\n8731,303.95,NJ City,E,19,21,11,73,Graduation,25,Owner,Standalone\n5463,340.64,Boston,E,22,9,25,35,Graduation,30,Owner,Standalone\n1003,367.88,NJ City,E,22,21,4,43,High School,26,Owner,Standalone\n9049,141.56,NJ City,N,22,19,14,81,Graduation,30,Patnership,Standalone\n9352,459.89,NJ City,W,3,15,12,40,Graduation,33,Franchise,Complex\n120,20.24,NJ City,N,1,19,4,55,Graduation,33,Franchise,Complex\n8856,348.7,NJ City,W,6,14,2,79,Graduation,20,Franchise,Complex\n8554,72.91,NJ City,N,12,16,1,67,High School,28,Patnership,Complex\n9483,390.29,NJ City,E,12,15,13,81,Graduation,23,Owner,Standalone\n4807,450.25,NJ City,E,19,18,16,43,High School,29,Owner,Standalone\n8752,432.54,NJ City,W,25,22,23,71,Graduation,34,Franchise,Complex\n2057,230,NJ City,N,3,14,15,38,High School,33,Patnership,Standalone\n5121,441.33,Boston,N,1,23,4,69,Graduation,21,Patnership,Standalone\n167,1.37,NJ City,N,8,15,2,85,No education,23,Owner,Standalone\n6059,167.3,Boston,E,19,9,9,42,Graduation,28,Owner,Standalone\n8728,492.68,NJ City,W,12,11,18,58,Graduation,31,Franchise,Complex\n4783,212.61,NJ City,E,22,9,2,85,High School,24,Owner,Standalone\n4738,297.67,NJ City,S,10,9,13,36,High School,27,Patnership,Complex\n3525,19.95,NJ City,N,8,11,1,42,Graduation,20,Patnership,Standalone\n7235,258.67,Boston,E,10,19,25,59,Graduation,33,Owner,Standalone\n691,56.89,NJ City,E,12,9,4,78,High School,29,Owner,Standalone\n8131,418.69,NJ City,E,18,19,10,33,Graduation,23,Owner,Standalone\n2325,101.43,NJ City,N,20,16,3,51,Graduation,30,Patnership,Standalone\n4133,308.69,NJ City,N,22,10,4,46,No education,35,Patnership,Standalone\n3546,218.6,NJ City,S,4,15,13,76,High School,28,Patnership,Complex\n5609,102.73,Boston,N,11,11,1,32,No education,30,Patnership,Standalone\n2523,299.59,NJ City,E,13,21,6,29,High School,28,Owner,Standalone\n6634,328.09,Boston,S,16,15,14,54,Graduation,32,Patnership,Complex\n2638,477.64,NJ City,S,23,24,25,73,High School,26,Patnership,Complex\n2484,429.39,NJ City,W,18,15,23,47,High School,28,Franchise,Complex\n9669,353.34,NJ City,N,9,20,22,66,Graduation,34,Patnership,Standalone\n5117,393.34,Boston,N,11,10,13,64,Graduation,26,Patnership,Standalone\n7583,332.92,Boston,E,20,21,8,79,Graduation,18,Owner,Standalone\n5508,202.62,Boston,W,23,15,25,46,Graduation,23,Franchise,Complex\n8163,3,NJ City,N,4,13,1,29,Graduation,23,Owner,Standalone\n4306,23.59,NJ City,N,4,18,1,31,High School,32,Patnership,Complex\n8339,494.07,NJ City,E,9,12,20,32,Graduation,29,Owner,Standalone\n4167,34.57,NJ City,N,15,19,1,83,Graduation,24,Owner,Standalone\n9765,411.81,NJ City,N,15,24,6,26,Graduation,35,Patnership,Standalone\n6271,389.37,Boston,E,25,9,13,35,Graduation,24,Owner,Standalone\n4248,310.06,NJ City,W,11,23,12,51,High School,27,Franchise,Complex\n5390,480.78,Boston,S,13,21,22,37,Graduation,31,Patnership,Complex\n3193,466.5,NJ City,N,24,21,16,31,High School,32,Patnership,Standalone\n3643,353.84,NJ City,E,24,15,11,50,High School,34,Owner,Standalone\n7786,82.39,Boston,S,15,22,4,73,High School,27,Patnership,Complex\n4307,202,NJ City,E,2,19,20,64,High School,23,Owner,Standalone\n8148,418.62,NJ City,W,12,24,9,65,Graduation,31,Franchise,Complex\n2162,311.94,NJ City,S,10,20,11,68,High School,21,Patnership,Complex\n9398,140.04,NJ City,S,3,20,10,29,Graduation,28,Patnership,Complex\n5900,252.59,Boston,W,5,13,20,38,Graduation,34,Franchise,Complex\n1412,3.32,NJ City,N,1,19,2,84,No education,20,Franchise,Complex\n4674,6.38,NJ City,N,4,12,4,79,Graduation,25,Patnership,Complex\n2083,197.71,NJ City,E,16,24,21,76,High School,33,Owner,Standalone\n9280,385.8,NJ City,W,17,9,18,70,Graduation,19,Franchise,Complex\n8318,102.86,NJ City,S,14,8,1,75,No education,32,Patnership,Complex\n9271,152.67,NJ City,E,8,18,20,49,Graduation,34,Owner,Standalone\n105,172.22,NJ City,N,16,16,1,83,Graduation,20,Patnership,Standalone\n2933,435.35,NJ City,N,11,14,8,63,High School,29,Patnership,Standalone\n8199,154.47,NJ City,E,18,8,20,65,Graduation,21,Owner,Standalone\n5981,194.93,Boston,N,25,23,13,26,Graduation,32,Patnership,Standalone\n994,343.29,NJ City,S,13,22,18,30,High School,28,Patnership,Complex\n7475,209.13,Boston,E,20,11,21,73,Graduation,28,Owner,Standalone\n2115,53.3,NJ City,E,21,21,2,68,Graduation,24,Owner,Standalone\n8995,11.91,NJ City,N,23,22,4,56,High School,18,Owner,Standalone\n6870,14.64,Boston,N,7,18,4,68,Graduation,26,Patnership,Complex\n271,275.4,NJ City,E,20,13,2,74,High School,26,Owner,Standalone\n7981,281.48,Boston,N,5,18,18,77,Graduation,21,Patnership,Standalone\n7765,153.52,Boston,N,7,21,11,47,Graduation,26,Patnership,Standalone\n6501,71.44,Boston,N,18,24,1,64,Graduation,27,Patnership,Standalone\n2699,60.47,NJ City,N,1,16,1,76,No education,34,Owner,Standalone\n2992,238.66,NJ City,W,25,19,13,27,High School,34,Franchise,Complex\n7644,171.2,Boston,W,17,23,2,62,Graduation,26,Franchise,Complex\n1393,224,NJ City,N,2,18,18,49,High School,18,Patnership,Standalone\n8794,367.79,NJ City,S,19,11,11,57,Graduation,19,Patnership,Complex\n1823,457.16,NJ City,E,12,21,10,37,High School,21,Owner,Standalone\n4027,416.06,NJ City,E,5,16,18,29,High School,29,Owner,Standalone\n3434,396.56,NJ City,S,13,10,13,81,High School,34,Patnership,Complex\n7988,444.47,Boston,W,14,21,11,30,Graduation,21,Franchise,Complex\n5526,490.84,Boston,S,3,24,3,48,Graduation,31,Patnership,Complex\n9922,164.11,NJ City,S,17,22,6,25,Graduation,22,Patnership,Complex\n1095,150.43,NJ City,E,5,20,19,66,High School,26,Owner,Standalone\n6822,198.98,Boston,S,9,22,17,44,Graduation,22,Patnership,Complex\n5112,221.8,Boston,W,21,16,16,78,Graduation,32,Franchise,Complex\n381,135.02,NJ City,N,1,21,3,39,Graduation,31,Patnership,Standalone\n9617,299.26,NJ City,N,16,22,21,73,Graduation,32,Patnership,Standalone\n4058,467.01,NJ City,S,24,12,22,25,High School,26,Patnership,Complex\n4431,367.38,NJ City,E,23,18,17,83,High School,18,Owner,Standalone\n9984,419.13,NJ City,W,18,24,19,61,Graduation,21,Franchise,Complex\n3169,190,NJ City,N,1,17,18,40,High School,28,Patnership,Standalone\n7909,184.38,Boston,N,7,16,11,59,Graduation,21,Patnership,Standalone\n6614,141.13,Boston,S,5,8,18,62,Graduation,23,Patnership,Complex\n2034,197.58,NJ City,S,9,24,20,27,High School,32,Patnership,Complex\n4815,54.6,NJ City,E,15,19,3,62,Graduation,27,Owner,Standalone\n696,106.9,NJ City,W,24,19,2,64,Graduation,20,Franchise,Complex\n9962,267.36,NJ City,S,15,21,2,62,No education,20,Patnership,Complex\n9565,414.84,NJ City,N,2,12,6,62,Graduation,32,Patnership,Standalone\n6930,62.24,Boston,N,24,19,4,43,Graduation,32,Patnership,Complex\n499,433.43,NJ City,E,19,13,8,77,High School,26,Owner,Standalone\n2966,392.92,NJ City,S,6,16,19,46,High School,24,Patnership,Complex\n3764,245.42,NJ City,W,7,16,16,77,High School,21,Franchise,Complex\n9441,132.65,NJ City,N,17,24,12,43,Graduation,33,Patnership,Standalone\n1542,482.68,NJ City,S,13,15,2,76,Graduation,35,Patnership,Complex\n8947,478.26,NJ City,E,25,15,11,69,Graduation,22,Owner,Standalone\n1320,98.61,NJ City,W,10,11,1,30,Graduation,18,Franchise,Complex\n5667,429.89,Boston,E,2,14,10,39,Graduation,20,Owner,Standalone\n1053,145,NJ City,N,1,18,16,63,High School,27,Patnership,Standalone\n3264,304.88,NJ City,W,14,8,1,49,Graduation,18,Franchise,Complex\n5791,337.42,Boston,E,5,20,1,45,High School,27,Owner,Standalone\n2275,186.13,NJ City,E,23,14,21,47,High School,29,Owner,Standalone\n8870,328.48,NJ City,S,19,14,12,30,Graduation,26,Patnership,Complex\n6663,170.51,Boston,E,16,16,13,76,Graduation,32,Owner,Standalone\n8996,478.59,NJ City,W,6,19,22,81,Graduation,22,Franchise,Complex\n2326,54.52,NJ City,S,9,20,2,43,High School,22,Patnership,Complex\n9157,41.57,NJ City,N,13,9,3,42,High School,20,Patnership,Standalone\n6604,357.89,Boston,W,16,21,16,79,Graduation,35,Franchise,Complex\n997,211.13,NJ City,N,17,20,24,57,High School,21,Patnership,Standalone\n4859,280.27,NJ City,E,19,11,13,52,High School,26,Owner,Standalone\n6923,141.6,Boston,E,23,16,14,31,Graduation,33,Owner,Standalone\n4994,205.41,Boston,S,22,8,14,54,High School,24,Patnership,Complex\n5456,92.45,Boston,W,3,20,2,33,No education,22,Franchise,Complex\n9856,63.45,NJ City,N,22,17,3,69,High School,30,Franchise,Complex\n8082,347.71,NJ City,S,3,10,3,74,Graduation,31,Patnership,Complex\n1205,342.58,NJ City,N,23,23,6,38,High School,26,Patnership,Standalone\n8173,178.85,NJ City,N,1,17,14,76,Graduation,34,Patnership,Standalone\n7556,269.57,Boston,W,7,16,22,33,Graduation,35,Franchise,Complex\n3871,437.86,NJ City,E,25,17,3,27,High School,22,Owner,Standalone\n9224,113.08,NJ City,W,22,21,1,60,No education,28,Franchise,Complex\n6189,327.25,Boston,N,1,22,25,51,Graduation,27,Patnership,Standalone\n299,108.42,NJ City,E,24,14,2,37,No education,22,Owner,Standalone\n6125,83.67,Boston,N,15,14,4,66,No education,31,Patnership,Standalone\n933,399.27,NJ City,N,13,21,1,39,Graduation,18,Patnership,Standalone\n397,222.39,NJ City,N,17,23,18,58,High School,26,Patnership,Standalone\n2946,164.62,NJ City,S,16,20,4,74,Graduation,28,Patnership,Complex\n4846,84.43,NJ City,S,9,11,1,71,High School,32,Patnership,Complex\n3468,214.29,NJ City,W,7,19,19,48,High School,28,Franchise,Complex\n7377,177.43,Boston,N,19,15,6,61,Graduation,25,Patnership,Standalone\n8633,417.91,NJ City,N,24,9,23,60,Graduation,28,Patnership,Standalone\n6007,156.99,Boston,E,14,12,3,79,High School,28,Owner,Standalone\n3573,103,NJ City,N,1,19,24,41,High School,28,Patnership,Standalone\n5626,412.55,Boston,S,8,21,22,39,Graduation,24,Patnership,Complex\n9544,85.35,NJ City,W,14,9,2,63,High School,35,Franchise,Complex\n799,4.24,NJ City,N,2,11,4,65,High School,35,Owner,Standalone\n1146,102.03,NJ City,S,6,22,2,65,Graduation,34,Patnership,Complex\n4793,221.99,NJ City,N,25,8,23,32,High School,31,Patnership,Standalone\n2937,417.51,NJ City,N,6,23,6,47,High School,18,Patnership,Standalone\n156,288,NJ City,W,2,8,21,38,High School,32,Franchise,Complex\n7238,178.87,Boston,S,22,24,14,42,Graduation,29,Patnership,Complex\n3806,46.77,NJ City,N,4,23,4,35,No education,27,Patnership,Complex\n9253,176.35,NJ City,N,16,22,24,50,Graduation,27,Patnership,Standalone\n5207,223.63,Boston,E,1,12,22,73,Graduation,35,Owner,Standalone\n8904,387.32,NJ City,W,25,10,8,44,Graduation,29,Franchise,Complex\n2846,160.57,NJ City,S,25,15,13,60,High School,21,Patnership,Complex\n5855,9.23,Boston,N,2,12,2,80,No education,26,Owner,Standalone\n8293,44.17,NJ City,N,9,22,1,84,High School,29,Patnership,Standalone\n1948,177.94,NJ City,W,24,23,19,58,High School,19,Franchise,Complex\n6200,471.73,Boston,W,8,8,6,56,Graduation,34,Franchise,Complex\n7102,479.81,Boston,S,2,11,25,44,Graduation,26,Patnership,Complex\n9560,413.33,NJ City,W,22,24,23,77,Graduation,33,Franchise,Complex\n289,23.31,NJ City,N,22,8,2,28,High School,35,Patnership,Standalone\n3340,51.34,NJ City,W,18,23,4,60,High School,22,Franchise,Complex\n3696,183.34,NJ City,W,24,15,1,30,Graduation,22,Franchise,Complex\n842,204.94,NJ City,S,16,21,21,54,High School,22,Patnership,Complex\n543,364.26,NJ City,E,6,12,1,71,Graduation,29,Owner,Standalone\n6995,6.32,Boston,N,19,23,1,74,No education,30,Owner,Standalone\n1674,482.07,NJ City,S,19,13,23,27,High School,35,Patnership,Complex\n8237,284.63,NJ City,N,15,19,18,49,Graduation,24,Patnership,Standalone\n3016,217.14,NJ City,W,24,10,10,41,High School,29,Franchise,Complex\n907,499.06,NJ City,E,25,23,11,35,High School,18,Owner,Standalone\n6215,110.88,Boston,E,11,9,2,38,No education,19,Owner,Standalone\n7767,454.88,Boston,E,20,24,13,27,Graduation,35,Owner,Standalone\n9294,428.76,NJ City,S,20,23,5,47,Graduation,20,Patnership,Complex\n9201,17.53,NJ City,N,9,12,2,29,Graduation,26,Patnership,Standalone\n4294,344.87,NJ City,S,24,17,23,47,High School,26,Patnership,Complex\n4170,32.08,NJ City,N,5,24,3,71,Graduation,27,Patnership,Complex\n3838,369.37,NJ City,S,23,20,25,85,High School,29,Patnership,Complex\n9204,476.74,NJ City,W,19,17,6,85,Graduation,18,Franchise,Complex\n767,25.36,NJ City,N,6,18,4,59,No education,20,Owner,Standalone\n988,348.11,NJ City,W,5,10,16,44,High School,25,Franchise,Complex\n2919,445.08,NJ City,E,9,22,9,69,High School,26,Owner,Standalone\n8666,10.01,NJ City,N,8,20,1,57,No education,34,Patnership,Complex\n956,475.18,NJ City,W,19,8,18,33,High School,22,Franchise,Complex\n4349,466.63,NJ City,N,13,8,19,34,High School,30,Patnership,Standalone\n4736,216.67,NJ City,W,12,16,25,30,High School,31,Franchise,Complex\n6128,414.55,Boston,W,11,17,21,52,Graduation,33,Franchise,Complex\n3280,320.58,NJ City,W,16,16,13,47,High School,19,Franchise,Complex\n8801,490.97,NJ City,N,20,18,20,39,Graduation,22,Patnership,Standalone\n9031,468.22,NJ City,E,2,15,17,63,Graduation,27,Owner,Standalone\n6754,237.13,Boston,S,1,16,10,84,Graduation,31,Patnership,Complex\n906,321.16,NJ City,S,6,8,1,27,Graduation,28,Patnership,Complex\n8863,49.55,NJ City,E,20,21,1,69,High School,27,Owner,Standalone\n8194,73.98,NJ City,S,13,18,3,58,High School,31,Patnership,Complex\n3963,283.42,NJ City,E,12,11,22,30,High School,22,Owner,Standalone\n4745,134.34,NJ City,N,16,17,19,28,High School,32,Patnership,Standalone\n9824,209.64,NJ City,W,7,20,7,27,Graduation,34,Franchise,Complex\n5571,337.47,Boston,E,13,21,25,39,Graduation,31,Owner,Standalone\n6588,201.39,Boston,W,6,23,22,39,Graduation,24,Franchise,Complex\n9551,25.53,NJ City,N,13,17,1,79,No education,34,Owner,Standalone\n7381,331.25,Boston,N,19,17,1,32,High School,31,Patnership,Standalone\n5338,242.94,Boston,S,9,21,4,38,High School,22,Patnership,Complex\n3241,211.02,NJ City,N,25,13,21,49,High School,35,Patnership,Standalone\n3931,222.72,NJ City,E,19,24,8,35,High School,26,Owner,Standalone\n7649,289.34,Boston,N,3,10,7,66,Graduation,28,Patnership,Standalone\n3300,295.34,NJ City,W,20,13,23,60,High School,23,Franchise,Complex\n4657,339.19,NJ City,N,25,16,3,44,High School,34,Patnership,Standalone\n7353,31.08,Boston,N,10,22,4,79,Graduation,31,Patnership,Standalone\n393,119.07,NJ City,N,15,18,3,42,Graduation,25,Patnership,Standalone\n3092,331.85,NJ City,W,7,17,19,47,High School,23,Franchise,Complex\n5606,223.63,Boston,S,11,19,19,70,Graduation,35,Patnership,Complex\n5413,439.44,Boston,N,7,13,8,78,Graduation,23,Patnership,Standalone\n8040,439.01,Boston,W,18,12,15,72,Graduation,28,Franchise,Complex\n3490,169.48,NJ City,S,17,15,20,41,High School,32,Patnership,Complex\n7456,239.19,Boston,W,11,15,16,49,Graduation,31,Franchise,Complex\n2146,474.25,NJ City,S,22,9,17,79,High School,23,Patnership,Complex\n380,66.62,NJ City,N,18,23,3,72,No education,32,Franchise,Complex\n7714,391.7,Boston,S,7,22,6,83,Graduation,20,Patnership,Complex\n6006,319.38,Boston,S,21,23,17,60,Graduation,31,Patnership,Complex\n1894,454.29,NJ City,S,6,14,19,65,High School,24,Patnership,Complex\n3059,274.33,NJ City,E,21,13,5,78,No education,20,Owner,Standalone\n636,253.35,NJ City,W,14,14,15,36,High School,24,Franchise,Complex\n810,17.95,NJ City,N,20,19,4,60,Graduation,28,Patnership,Complex\n710,147.12,NJ City,S,24,22,14,45,High School,30,Patnership,Complex\n4228,342.74,NJ City,W,10,11,6,64,High School,33,Franchise,Complex\n5951,355.14,Boston,E,23,23,18,59,Graduation,31,Owner,Standalone\n4527,42.02,NJ City,N,24,19,4,50,Graduation,28,Owner,Standalone\n8549,103.18,NJ City,N,2,10,2,54,No education,35,Patnership,Standalone\n9723,410.19,NJ City,E,12,18,7,79,Graduation,27,Owner,Standalone\n7165,400.32,Boston,N,18,12,25,78,Graduation,24,Patnership,Standalone\n877,304.76,NJ City,N,10,13,25,50,High School,30,Patnership,Standalone\n1906,369.64,NJ City,S,12,8,21,32,High School,27,Patnership,Complex\n7331,317.66,Boston,E,19,8,13,27,Graduation,26,Owner,Standalone\n4801,117.09,NJ City,N,3,23,3,59,High School,30,Patnership,Standalone\n3821,421.89,NJ City,N,16,10,2,66,No education,33,Patnership,Standalone\n6707,321.71,Boston,E,12,15,21,81,Graduation,19,Owner,Standalone\n5265,182.72,Boston,N,8,11,6,44,Graduation,21,Patnership,Standalone\n9033,278.59,NJ City,N,25,14,11,25,Graduation,29,Patnership,Standalone\n2210,419.17,NJ City,S,21,23,3,69,No education,20,Patnership,Complex\n2782,156.08,NJ City,S,11,9,21,47,High School,19,Patnership,Complex\n6547,139.93,Boston,E,16,23,2,85,High School,31,Owner,Standalone\n5563,462.38,Boston,E,24,14,3,38,High School,21,Owner,Standalone\n6161,151.44,Boston,N,16,22,9,52,Graduation,24,Patnership,Standalone\n2685,287.67,NJ City,N,8,10,19,45,High School,31,Patnership,Standalone\n9665,306.73,NJ City,N,5,13,14,84,Graduation,30,Patnership,Standalone\n599,48.54,NJ City,E,6,24,1,82,No education,27,Owner,Standalone\n2512,171.67,NJ City,W,20,24,13,53,High School,18,Franchise,Complex\n2204,321.12,NJ City,W,13,19,24,32,High School,24,Franchise,Complex\n538,488.76,NJ City,S,4,23,20,36,High School,21,Patnership,Complex\n8535,15.43,NJ City,N,6,17,2,71,Graduation,29,Owner,Standalone\n8395,412.16,NJ City,E,9,16,15,35,Graduation,33,Owner,Standalone\n2126,81.96,NJ City,S,3,24,1,77,No education,29,Patnership,Complex\n2431,341.47,NJ City,E,10,8,15,65,High School,30,Owner,Standalone\n9911,314.37,NJ City,E,7,9,11,49,Graduation,22,Owner,Standalone\n9237,237.67,NJ City,N,7,23,6,54,Graduation,22,Patnership,Standalone\n4223,347.33,NJ City,E,9,20,3,42,No education,30,Owner,Standalone\n1421,102.34,NJ City,N,2,14,4,77,No education,28,Patnership,Standalone\n3347,19.41,NJ City,N,12,19,3,72,No education,35,Owner,Standalone\n4838,127,NJ City,S,1,11,23,31,High School,30,Patnership,Complex\n5939,447.95,Boston,E,2,13,22,55,Graduation,34,Owner,Standalone\n9702,152.98,NJ City,S,17,19,7,74,Graduation,31,Patnership,Complex\n6149,248.08,Boston,N,23,18,23,35,Graduation,32,Patnership,Standalone\n815,460.59,NJ City,E,11,17,5,30,No education,22,Owner,Standalone\n1714,71.92,NJ City,N,8,15,1,35,High School,29,Patnership,Complex\n6879,419.98,Boston,E,14,8,12,53,Graduation,33,Owner,Standalone\n6025,470.01,Boston,N,11,16,19,56,Graduation,25,Patnership,Standalone\n6457,47.21,Boston,N,25,9,2,75,High School,30,Patnership,Standalone\n4200,300.63,NJ City,W,4,12,15,61,High School,32,Franchise,Complex\n5381,154.67,Boston,N,10,21,7,59,Graduation,29,Patnership,Standalone\n9233,287.59,NJ City,N,6,11,21,26,Graduation,29,Patnership,Standalone\n1598,10.83,NJ City,N,3,24,1,51,No education,33,Patnership,Complex\n2317,220.95,NJ City,N,17,10,15,41,High School,19,Patnership,Standalone\n800,226.39,NJ City,W,16,23,16,71,High School,34,Franchise,Complex\n7709,291.84,Boston,N,21,20,23,47,Graduation,29,Patnership,Standalone\n5898,298.66,Boston,S,15,12,15,53,Graduation,30,Patnership,Complex\n5340,370.57,Boston,W,25,9,3,52,Graduation,34,Franchise,Complex\n5103,413.8,Boston,E,15,18,10,42,Graduation,19,Owner,Standalone\n4856,390.65,NJ City,W,7,9,24,47,High School,18,Franchise,Complex\n5215,426.86,Boston,E,21,24,22,69,Graduation,35,Owner,Standalone\n3520,290.15,NJ City,W,20,8,16,84,High School,24,Franchise,Complex\n4184,379.2,NJ City,W,6,19,25,62,High School,26,Franchise,Complex\n7441,321.45,Boston,N,23,9,24,39,Graduation,28,Patnership,Standalone\n1849,417.73,NJ City,N,5,24,22,59,High School,18,Patnership,Standalone\n7224,46.98,Boston,N,2,24,3,37,Graduation,25,Franchise,Complex\n2914,99.84,NJ City,S,20,19,2,44,High School,18,Patnership,Complex\n8785,370.61,NJ City,N,19,16,21,55,Graduation,20,Patnership,Standalone\n4836,473.48,NJ City,W,24,20,14,82,High School,30,Franchise,Complex\n3116,281.7,NJ City,W,8,16,23,68,High School,35,Franchise,Complex\n9482,316.22,NJ City,S,7,12,2,26,No education,18,Patnership,Complex\n9326,217.37,NJ City,S,11,12,7,38,Graduation,31,Patnership,Complex\n9209,328.44,NJ City,N,15,23,4,61,No education,30,Patnership,Standalone\n1782,359.41,NJ City,S,1,9,2,70,Graduation,26,Patnership,Complex\n8226,446,NJ City,S,14,23,17,28,Graduation,20,Patnership,Complex\n7094,449.8,Boston,S,3,15,3,78,No education,30,Patnership,Complex\n752,183.43,NJ City,W,3,9,5,68,No education,31,Franchise,Complex\n1615,266.65,NJ City,E,21,12,14,26,High School,20,Owner,Standalone\n1154,265.85,NJ City,S,2,18,5,70,No education,25,Patnership,Complex\n3012,396.81,NJ City,W,18,18,23,67,High School,33,Franchise,Complex\n9393,298.15,NJ City,N,10,16,2,25,Graduation,29,Patnership,Standalone\n2025,248.03,NJ City,N,6,23,5,31,Graduation,27,Patnership,Standalone\n721,55.76,NJ City,N,18,13,4,66,High School,32,Patnership,Standalone\n7472,345.3,Boston,W,6,9,11,42,Graduation,24,Franchise,Complex\n9488,104.29,NJ City,W,24,21,3,79,No education,19,Franchise,Complex\n1634,148.97,NJ City,S,19,8,24,58,High School,31,Patnership,Complex\n3860,256.76,NJ City,W,14,15,19,55,High School,22,Franchise,Complex\n3054,73.55,NJ City,N,2,23,1,54,Graduation,20,Patnership,Complex\n1517,231.02,NJ City,N,2,17,2,29,No education,19,Patnership,Standalone\n1450,457.02,NJ City,S,16,10,2,78,High School,31,Patnership,Complex\n4608,100,NJ City,W,3,9,9,81,High School,33,Franchise,Complex\n6033,368.37,Boston,N,15,18,19,67,Graduation,20,Patnership,Standalone\n3536,474.7,NJ City,W,6,15,23,58,High School,18,Franchise,Complex\n1239,101.68,NJ City,E,17,24,4,49,Graduation,28,Owner,Standalone\n608,130.25,NJ City,W,19,8,5,45,No education,28,Franchise,Complex\n6941,454.36,Boston,N,2,12,17,67,Graduation,35,Patnership,Standalone\n3569,313.35,NJ City,N,8,19,1,81,No education,27,Patnership,Standalone\n1756,392.65,NJ City,W,25,15,12,84,High School,26,Franchise,Complex\n7733,198.78,Boston,N,24,24,18,32,Graduation,27,Patnership,Standalone\n5722,306.42,Boston,S,11,12,16,38,Graduation,26,Patnership,Complex\n5055,419.25,Boston,E,24,8,8,67,Graduation,25,Owner,Standalone\n2606,301,NJ City,S,19,11,4,80,No education,20,Patnership,Complex\n3772,343.62,NJ City,W,20,8,6,58,High School,34,Franchise,Complex\n2996,188.76,NJ City,W,23,24,22,77,High School,26,Franchise,Complex\n1321,263.17,NJ City,N,24,14,23,55,High School,33,Patnership,Standalone\n3128,202.79,NJ City,W,23,20,24,36,High School,18,Franchise,Complex\n4270,326.48,NJ City,S,10,15,21,49,High School,31,Patnership,Complex\n2909,366.44,NJ City,N,21,19,5,50,No education,27,Patnership,Standalone\n864,347.39,NJ City,W,13,15,21,46,High School,25,Franchise,Complex\n4371,441.01,NJ City,E,6,19,7,33,High School,23,Owner,Standalone\n8851,119.79,NJ City,E,24,11,1,85,High School,23,Owner,Standalone\n3207,29.28,NJ City,N,22,12,2,36,Graduation,34,Owner,Standalone\n6919,130.17,Boston,E,20,20,8,75,Graduation,30,Owner,Standalone\n1022,97.88,NJ City,S,17,13,3,57,No education,24,Patnership,Complex\n5976,176.9,Boston,W,16,17,18,60,Graduation,20,Franchise,Complex\n6789,240.99,Boston,N,23,12,7,72,Graduation,35,Patnership,Standalone\n5858,362.73,Boston,S,18,16,13,40,Graduation,19,Patnership,Complex\n1178,308.93,NJ City,S,24,12,18,80,High School,33,Patnership,Complex\n9298,28.91,NJ City,N,16,20,4,79,High School,32,Patnership,Complex\n2599,218.02,NJ City,E,23,9,24,32,High School,23,Owner,Standalone\n7533,183.11,Boston,N,20,21,6,50,Graduation,21,Patnership,Standalone\n540,207.22,NJ City,W,19,11,15,32,High School,21,Franchise,Complex\n4968,370,Boston,W,13,15,10,27,High School,19,Franchise,Complex\n3686,279.07,NJ City,S,17,20,8,37,High School,23,Patnership,Complex\n8701,314.85,NJ City,N,12,12,21,84,Graduation,27,Patnership,Standalone\n9652,287.6,NJ City,W,20,12,4,38,High School,19,Franchise,Complex\n3151,2.93,NJ City,E,15,13,1,54,High School,29,Owner,Standalone\n8005,123.5,Boston,N,13,12,2,81,High School,23,Patnership,Standalone\n6661,30.11,Boston,N,11,8,2,42,High School,30,Patnership,Standalone\n471,409.74,NJ City,E,18,21,7,40,High School,33,Owner,Standalone\n3139,82.35,NJ City,E,6,19,3,58,High School,35,Owner,Standalone\n8104,308.03,NJ City,W,3,16,15,32,Graduation,25,Franchise,Complex\n9475,29.8,NJ City,N,1,13,3,52,High School,27,Owner,Standalone\n5391,333.15,Boston,E,3,10,8,28,Graduation,27,Owner,Standalone\n7980,6.13,Boston,N,6,14,1,51,Graduation,28,Franchise,Complex\n6761,119.05,Boston,N,19,19,3,84,No education,31,Patnership,Standalone\n139,293.78,NJ City,E,12,18,16,45,High School,35,Owner,Standalone\n9865,63.69,NJ City,N,2,22,4,80,High School,29,Patnership,Standalone\n4189,47.2,NJ City,N,12,17,1,59,High School,30,Patnership,Standalone\n1921,32.27,NJ City,N,7,11,1,84,High School,24,Patnership,Standalone\n9667,244.41,NJ City,E,12,21,22,85,Graduation,19,Owner,Standalone\n7065,68.16,Boston,N,2,21,2,70,Graduation,18,Patnership,Standalone\n8526,484.02,NJ City,S,17,8,14,62,Graduation,22,Patnership,Complex\n7215,459.25,Boston,E,7,12,21,40,Graduation,31,Owner,Standalone\n7682,120.04,Boston,S,19,14,1,52,No education,29,Patnership,Complex\n1212,305.7,NJ City,W,20,15,25,48,High School,22,Franchise,Complex\n8111,220.82,NJ City,E,24,20,14,29,Graduation,32,Owner,Standalone\n3156,230,NJ City,W,1,15,20,48,High School,19,Franchise,Complex\n7764,495.89,Boston,W,11,20,11,42,Graduation,21,Franchise,Complex\n2015,428.63,NJ City,E,8,9,11,49,High School,31,Owner,Standalone\n8610,329.26,NJ City,S,16,20,14,32,Graduation,35,Patnership,Complex\n1515,110.57,NJ City,E,21,23,2,77,Graduation,26,Owner,Standalone\n8875,326,NJ City,E,15,23,18,41,Graduation,21,Owner,Standalone\n9085,452.96,NJ City,N,9,9,4,36,High School,19,Patnership,Standalone\n8062,433.12,NJ City,S,13,11,8,32,Graduation,28,Patnership,Complex\n5135,407.62,Boston,E,22,18,14,76,Graduation,22,Owner,Standalone\n4152,146.28,NJ City,W,15,13,25,41,High School,23,Franchise,Complex\n2705,322.27,NJ City,N,20,10,16,57,High School,21,Patnership,Standalone\n3316,182,NJ City,W,1,9,6,36,High School,35,Franchise,Complex\n7339,380.54,Boston,E,5,10,2,76,High School,23,Owner,Standalone\n813,65.69,NJ City,N,17,11,4,49,Graduation,25,Patnership,Standalone\n1723,246.59,NJ City,E,10,23,15,27,High School,29,Owner,Standalone\n6772,260.01,Boston,W,4,8,20,28,Graduation,18,Franchise,Complex\n7789,72.31,Boston,N,11,19,1,61,High School,33,Patnership,Standalone\n2988,283.49,NJ City,W,4,23,7,26,High School,20,Franchise,Complex\n539,374.05,NJ City,E,6,10,11,30,High School,31,Owner,Standalone\n8674,291.55,NJ City,S,24,9,16,49,Graduation,31,Patnership,Complex\n1313,195.35,NJ City,N,17,10,5,71,No education,34,Patnership,Standalone\n2114,39.61,NJ City,N,7,8,1,53,No education,24,Patnership,Complex\n2007,4.5,NJ City,N,17,20,3,26,Graduation,31,Owner,Standalone\n9454,309.72,NJ City,S,21,8,17,33,Graduation,18,Patnership,Complex\n9866,26.99,NJ City,N,12,15,3,71,No education,20,Patnership,Complex\n2073,446.72,NJ City,N,21,13,3,26,Graduation,24,Patnership,Standalone\n7876,446.66,Boston,W,23,19,14,65,Graduation,35,Franchise,Complex\n9623,334.87,NJ City,E,17,10,13,37,Graduation,23,Owner,Standalone\n587,54.76,NJ City,E,16,10,3,37,No education,24,Owner,Standalone\n1944,330.16,NJ City,W,11,9,15,58,High School,35,Franchise,Complex\n1783,419.73,NJ City,E,11,15,2,67,High School,31,Owner,Standalone\n3488,238.87,NJ City,W,18,13,6,78,High School,23,Franchise,Complex\n3201,470.95,NJ City,N,4,21,10,42,High School,31,Patnership,Standalone\n8738,7.19,NJ City,N,12,9,3,66,No education,26,Patnership,Complex\n4303,477.58,NJ City,E,15,18,1,61,High School,19,Owner,Standalone\n9456,396.39,NJ City,W,21,16,7,46,Graduation,33,Franchise,Complex\n9418,463.02,NJ City,S,12,16,6,85,Graduation,22,Patnership,Complex\n5213,100.03,Boston,N,22,8,2,83,No education,21,Patnership,Standalone\n4215,64.89,NJ City,N,11,16,3,72,Graduation,19,Owner,Standalone\n9898,131.6,NJ City,S,15,19,13,35,Graduation,31,Patnership,Complex\n5283,105.16,Boston,E,13,13,1,38,Graduation,19,Owner,Standalone\n1708,169,NJ City,W,1,23,18,37,High School,33,Franchise,Complex\n1262,211.28,NJ City,S,9,11,8,59,High School,25,Patnership,Complex\n6515,247.71,Boston,E,22,8,18,43,Graduation,26,Owner,Standalone\n1363,40.79,NJ City,N,21,12,3,80,High School,18,Owner,Standalone\n4155,311,NJ City,E,2,10,12,59,High School,35,Owner,Standalone\n5983,178.83,Boston,E,25,9,14,51,Graduation,28,Owner,Standalone\n6608,258.85,Boston,W,1,23,9,57,Graduation,32,Franchise,Complex\n6247,103.42,Boston,E,23,11,4,84,High School,24,Owner,Standalone\n1044,336.33,NJ City,W,12,9,23,56,High School,27,Franchise,Complex\n823,3.53,NJ City,N,21,21,1,74,High School,30,Owner,Standalone\n6030,73.09,Boston,N,10,22,1,51,Graduation,35,Patnership,Complex\n8722,302.51,NJ City,S,7,12,8,52,Graduation,25,Patnership,Complex\n8993,167,NJ City,N,14,14,3,69,No education,23,Patnership,Standalone\n9174,196.15,NJ City,S,2,8,3,42,Graduation,25,Patnership,Complex\n3122,497.64,NJ City,S,22,21,7,73,High School,20,Patnership,Complex\n9597,337.12,NJ City,N,13,21,10,49,Graduation,28,Patnership,Standalone\n1813,278,NJ City,N,2,17,20,46,High School,33,Patnership,Standalone\n8202,224.29,NJ City,S,6,12,20,28,Graduation,18,Patnership,Complex\n3498,167.99,NJ City,S,23,15,2,77,Graduation,24,Patnership,Complex\n5864,310.7,Boston,W,12,24,16,30,Graduation,27,Franchise,Complex\n5596,200.83,Boston,W,9,21,24,52,Graduation,20,Franchise,Complex\n8121,101.23,NJ City,N,12,20,2,73,Graduation,29,Patnership,Standalone\n2716,246.7,NJ City,W,24,19,3,83,High School,23,Franchise,Complex\n8638,108.47,NJ City,S,1,15,4,84,High School,33,Patnership,Complex\n5450,260.36,Boston,S,8,13,9,82,Graduation,28,Patnership,Complex\n7506,152.72,Boston,S,13,10,9,70,Graduation,27,Patnership,Complex\n309,283.52,NJ City,N,14,20,3,83,Graduation,32,Patnership,Standalone\n4212,112.23,NJ City,W,6,16,4,83,Graduation,24,Franchise,Complex\n5133,287.37,Boston,N,3,15,4,38,Graduation,20,Patnership,Standalone\n8658,277.92,NJ City,S,16,8,12,28,Graduation,22,Patnership,Complex\n3349,114.4,NJ City,N,18,8,3,37,High School,29,Patnership,Standalone\n993,1.72,NJ City,N,9,13,4,52,Graduation,24,Patnership,Standalone\n2667,250.98,NJ City,E,5,16,19,41,High School,33,Owner,Standalone\n1520,364.28,NJ City,W,24,17,9,40,High School,31,Franchise,Complex\n1997,55.65,NJ City,N,18,24,1,58,No education,19,Patnership,Standalone\n521,127.93,NJ City,N,12,18,11,55,High School,30,Patnership,Standalone\n9877,226.3,NJ City,N,17,8,10,34,Graduation,32,Patnership,Standalone\n45,402.83,Boston,N,21,15,21,75,High School,30,Patnership,Standalone\n8648,198.21,NJ City,W,20,24,24,77,Graduation,18,Franchise,Complex\n9039,131.71,NJ City,E,7,19,18,74,Graduation,35,Owner,Standalone\n3555,153.38,NJ City,E,5,12,2,48,Graduation,32,Owner,Standalone\n4072,480.11,NJ City,W,12,19,9,72,High School,19,Franchise,Complex\n5087,477.96,Boston,E,11,23,21,61,Graduation,31,Owner,Standalone\n102,478.99,NJ City,S,7,10,24,44,High School,28,Patnership,Complex\n5891,157.7,Boston,E,10,16,25,38,Graduation,34,Owner,Standalone\n3423,98.7,NJ City,E,20,9,2,34,Graduation,24,Owner,Standalone\n8586,306.44,NJ City,S,2,13,13,69,Graduation,34,Patnership,Complex\n151,43.87,NJ City,N,23,22,3,66,High School,20,Owner,Standalone\n9884,9.66,NJ City,N,1,24,4,25,No education,18,Franchise,Complex\n3119,187.68,NJ City,E,23,19,15,38,High School,22,Owner,Standalone\n5768,85.89,Boston,W,20,10,3,67,No education,34,Franchise,Complex\n3309,74.32,NJ City,N,23,23,1,60,Graduation,35,Patnership,Standalone\n5330,449.8,Boston,S,22,24,15,35,Graduation,23,Patnership,Complex\n8729,490.09,NJ City,N,1,9,23,77,Graduation,29,Patnership,Standalone\n5795,107.41,Boston,E,12,22,2,34,No education,25,Owner,Standalone\n9250,457.86,NJ City,S,19,10,16,58,Graduation,28,Patnership,Complex\n9784,174.08,NJ City,W,4,10,21,66,Graduation,33,Franchise,Complex\n4610,137,NJ City,S,3,12,23,27,High School,21,Patnership,Complex\n9248,318.13,NJ City,W,24,17,16,62,Graduation,19,Franchise,Complex\n4000,127.02,NJ City,W,3,14,1,70,High School,21,Franchise,Complex\n4014,184.54,NJ City,S,21,17,21,27,High School,28,Patnership,Complex\n524,378.17,NJ City,W,13,15,24,40,High School,20,Franchise,Complex\n3084,84.86,NJ City,W,3,15,1,25,Graduation,33,Franchise,Complex\n7638,100,Boston,S,24,24,4,29,Graduation,29,Patnership,Complex\n6107,333.22,Boston,E,14,17,18,77,Graduation,26,Owner,Standalone\n4018,334.46,NJ City,S,20,21,2,71,High School,32,Patnership,Complex\n9985,202.74,NJ City,N,9,9,24,72,Graduation,27,Patnership,Standalone\n953,315.62,NJ City,N,21,19,16,73,High School,20,Patnership,Standalone\n4451,40.55,NJ City,N,9,21,4,37,No education,28,Owner,Standalone\n6389,51.26,Boston,N,11,9,1,40,No education,32,Patnership,Standalone\n4940,408.06,Boston,W,4,14,9,40,High School,26,Franchise,Complex\n6295,467.18,Boston,E,12,18,3,30,High School,25,Owner,Standalone\n2920,414.54,NJ City,W,20,11,5,44,High School,32,Franchise,Complex\n833,285.75,NJ City,N,12,23,11,40,High School,28,Patnership,Standalone\n4612,118.29,NJ City,W,9,10,4,31,High School,34,Franchise,Complex\n7870,430.93,Boston,S,1,10,8,49,Graduation,35,Patnership,Complex\n2856,140.83,NJ City,W,20,9,12,83,High School,20,Franchise,Complex\n6222,8.95,Boston,N,17,23,2,72,Graduation,29,Patnership,Complex\n1577,29.01,NJ City,N,23,11,3,63,No education,28,Patnership,Standalone\n9616,235.05,NJ City,W,11,24,22,51,Graduation,29,Franchise,Complex\n6926,199.82,Boston,S,17,22,21,49,Graduation,32,Patnership,Complex\n1715,169.94,NJ City,E,19,14,2,40,No education,32,Owner,Standalone\n2260,472.92,NJ City,W,20,24,23,77,High School,24,Franchise,Complex\n6989,358.53,Boston,N,12,23,7,83,Graduation,25,Patnership,Standalone\n4401,100,NJ City,N,1,18,18,60,High School,31,Patnership,Standalone\n470,472.36,NJ City,S,24,9,15,28,High School,18,Patnership,Complex\n7197,494.02,Boston,N,20,18,2,49,Graduation,25,Patnership,Standalone\n3270,165.57,NJ City,S,18,24,22,26,High School,31,Patnership,Complex\n4762,375.32,NJ City,S,25,24,11,29,High School,32,Patnership,Complex\n81,356.36,Boston,N,23,12,5,35,Graduation,29,Patnership,Standalone\n5027,297.32,Boston,E,1,12,25,65,Graduation,31,Owner,Standalone\n1934,296.35,NJ City,S,23,15,1,50,No education,22,Patnership,Complex\n5624,34.39,Boston,N,20,14,2,65,No education,31,Franchise,Complex\n2943,155,NJ City,E,1,21,17,69,High School,29,Owner,Standalone\n3221,152.57,NJ City,N,25,18,15,35,High School,33,Patnership,Standalone\n4176,121.77,NJ City,W,16,24,1,38,Graduation,32,Franchise,Complex\n1567,469.5,NJ City,E,6,10,23,79,High School,32,Owner,Standalone\n7776,196.94,Boston,W,21,10,3,32,Graduation,32,Franchise,Complex\n8981,56.54,NJ City,N,14,24,4,57,No education,23,Patnership,Standalone\n1719,111.58,NJ City,E,13,18,1,54,Graduation,23,Owner,Standalone\n449,369.04,NJ City,N,17,11,18,59,High School,18,Patnership,Standalone\n1659,296.01,NJ City,E,21,21,19,42,High School,27,Owner,Standalone\n3284,80.36,NJ City,W,1,18,2,68,No education,23,Franchise,Complex\n5332,318,Boston,W,14,23,23,82,Graduation,19,Franchise,Complex\n7868,5.64,Boston,N,12,16,1,74,No education,21,Franchise,Complex\n8746,450.48,NJ City,S,15,14,12,60,Graduation,20,Patnership,Complex\n5830,385.57,Boston,S,24,13,22,58,Graduation,21,Patnership,Complex\n523,461.93,NJ City,E,6,20,23,31,High School,18,Owner,Standalone\n4387,68.47,NJ City,N,22,10,4,43,High School,19,Owner,Standalone\n6001,373.78,Boston,N,21,19,7,29,Graduation,21,Patnership,Standalone\n4779,379.83,NJ City,E,25,22,11,85,High School,19,Owner,Standalone\n1903,56.97,NJ City,E,8,17,4,77,High School,27,Owner,Standalone\n2249,81.14,NJ City,N,3,17,2,74,No education,22,Patnership,Standalone\n4446,484.27,NJ City,S,15,20,20,71,High School,26,Patnership,Complex\n7108,456.09,Boston,W,21,9,6,37,Graduation,22,Franchise,Complex\n4491,41.45,NJ City,N,16,16,1,50,Graduation,23,Owner,Standalone\n2434,132.95,NJ City,S,17,15,5,80,High School,22,Patnership,Complex\n642,291,NJ City,S,2,16,20,78,High School,21,Patnership,Complex\n2458,59.17,NJ City,S,19,18,4,25,High School,21,Patnership,Complex\n8282,363.46,NJ City,S,14,13,12,53,Graduation,19,Patnership,Complex\n5427,396.65,Boston,E,23,24,17,36,Graduation,18,Owner,Standalone\n9979,324.52,NJ City,E,11,16,25,74,Graduation,29,Owner,Standalone\n7737,295.78,Boston,N,16,15,23,66,Graduation,30,Patnership,Standalone\n1502,451.77,NJ City,S,12,19,23,27,High School,18,Patnership,Complex\n5957,262.64,Boston,N,23,14,12,44,Graduation,25,Patnership,Standalone\n9630,190.46,NJ City,S,17,14,14,45,Graduation,19,Patnership,Complex\n9192,457.16,NJ City,W,20,8,15,31,Graduation,19,Franchise,Complex\n5083,332.55,Boston,E,2,16,7,37,Graduation,29,Owner,Standalone\n6338,474.01,Boston,S,9,23,20,78,Graduation,20,Patnership,Complex\n7584,57.68,Boston,W,24,19,2,82,Graduation,28,Franchise,Complex\n7412,250.81,Boston,W,23,24,23,34,Graduation,33,Franchise,Complex\n2740,11.06,NJ City,N,22,20,4,40,High School,18,Franchise,Complex\n3578,155.1,NJ City,S,21,24,4,44,No education,28,Patnership,Complex\n6951,230.24,Boston,E,16,8,5,80,Graduation,35,Owner,Standalone\n6304,381.41,Boston,W,23,12,16,78,Graduation,32,Franchise,Complex\n9836,327.13,NJ City,W,1,18,17,68,Graduation,20,Franchise,Complex\n3976,377.08,NJ City,W,11,13,10,84,High School,35,Franchise,Complex\n9789,29.82,NJ City,N,1,19,2,32,Graduation,28,Patnership,Standalone\n2139,439.44,NJ City,E,13,15,15,35,High School,18,Owner,Standalone\n2588,233.21,NJ City,W,17,9,16,66,High School,19,Franchise,Complex\n2636,264.27,NJ City,W,11,23,23,41,High School,22,Franchise,Complex\n1910,184.33,NJ City,S,11,12,11,82,High School,28,Patnership,Complex\n4016,417.43,NJ City,W,21,19,19,45,High School,18,Franchise,Complex\n857,226.38,NJ City,N,16,13,13,85,High School,29,Patnership,Standalone\n6435,195.49,Boston,E,7,8,19,55,Graduation,30,Owner,Standalone\n6647,391.35,Boston,E,20,16,7,71,Graduation,33,Owner,Standalone\n9740,288.32,NJ City,W,25,15,12,65,Graduation,18,Franchise,Complex\n5349,374.51,Boston,N,17,9,16,57,Graduation,31,Patnership,Standalone\n9993,287.2,NJ City,N,1,8,8,25,Graduation,24,Patnership,Standalone\n3121,11.59,NJ City,N,12,16,1,49,High School,21,Patnership,Standalone\n613,494.51,NJ City,N,4,12,18,51,High School,24,Patnership,Standalone\n600,420.46,NJ City,W,15,10,19,29,High School,34,Franchise,Complex\n3192,33.68,NJ City,N,8,18,2,37,Graduation,26,Franchise,Complex\n1833,445.21,NJ City,N,25,23,25,55,High School,26,Patnership,Standalone\n8088,358.21,NJ City,W,7,8,7,85,Graduation,29,Franchise,Complex\n1221,438.1,NJ City,N,21,20,20,41,High School,18,Patnership,Standalone\n7115,415.8,Boston,E,23,23,15,73,Graduation,31,Owner,Standalone\n3563,254.69,NJ City,E,10,14,24,55,High School,26,Owner,Standalone\n2781,424.45,NJ City,N,4,19,16,61,High School,18,Patnership,Standalone\n2000,453.15,NJ City,W,11,21,25,59,High School,34,Franchise,Complex\n1316,72.31,NJ City,N,10,20,3,46,No education,18,Franchise,Complex\n7086,152.76,Boston,S,8,17,13,44,Graduation,30,Patnership,Complex\n8076,168.06,NJ City,W,23,19,1,52,Graduation,25,Franchise,Complex\n4787,110.09,NJ City,E,3,24,2,68,No education,35,Owner,Standalone\n4666,335.39,NJ City,S,2,24,4,67,High School,23,Patnership,Complex\n4242,203.23,NJ City,S,14,13,15,41,High School,22,Patnership,Complex\n1268,344.87,NJ City,W,13,10,1,57,No education,27,Franchise,Complex\n6490,106.49,Boston,S,24,17,2,51,High School,19,Patnership,Complex\n7616,265.33,Boston,W,10,17,21,83,Graduation,18,Franchise,Complex\n9459,465.23,NJ City,E,19,20,12,52,Graduation,34,Owner,Standalone\n2206,150.45,NJ City,S,16,22,9,31,High School,33,Patnership,Complex\n3892,89.44,NJ City,W,24,19,3,58,High School,29,Franchise,Complex\n3125,107,NJ City,N,2,16,19,68,High School,23,Patnership,Standalone\n8555,154.75,NJ City,E,7,10,10,73,Graduation,25,Owner,Standalone\n4530,456.5,NJ City,S,20,23,8,74,High School,18,Patnership,Complex\n7780,404.55,Boston,W,10,8,22,60,Graduation,18,Franchise,Complex\n2068,94.88,NJ City,W,21,8,1,46,High School,22,Franchise,Complex\n8235,465.97,NJ City,E,4,23,23,54,Graduation,29,Owner,Standalone\n2494,491.05,NJ City,S,25,13,1,49,High School,32,Patnership,Complex\n3025,121.41,NJ City,N,4,9,4,42,High School,27,Patnership,Standalone\n5201,321.81,Boston,N,16,20,21,54,Graduation,25,Patnership,Standalone\n7565,482,Boston,N,12,20,1,61,No education,20,Patnership,Standalone\n6861,180.93,Boston,N,19,18,6,59,Graduation,18,Patnership,Standalone\n8942,469.33,NJ City,S,25,22,16,49,Graduation,32,Patnership,Complex\n2695,355.07,NJ City,E,16,12,7,66,High School,29,Owner,Standalone\n821,410.18,NJ City,N,8,15,16,62,High School,26,Patnership,Standalone\n6371,203.67,Boston,E,12,16,5,43,No education,27,Owner,Standalone\n5940,101.53,Boston,W,16,11,1,43,Graduation,34,Franchise,Complex\n3222,81.09,NJ City,S,8,17,2,28,Graduation,34,Patnership,Complex\n2014,55.51,NJ City,S,14,20,4,80,High School,30,Patnership,Complex\n5586,22.26,Boston,N,18,17,4,46,Graduation,30,Patnership,Complex\n9019,348.23,NJ City,E,24,10,22,67,Graduation,24,Owner,Standalone\n2863,378.58,NJ City,E,22,14,20,68,High School,20,Owner,Standalone\n622,98.16,NJ City,S,2,20,1,69,High School,35,Patnership,Complex\n6177,114.07,Boston,N,4,12,2,66,Graduation,25,Patnership,Standalone\n9076,482.17,NJ City,W,21,11,21,69,Graduation,25,Franchise,Complex\n8266,305.32,NJ City,S,10,16,15,72,Graduation,20,Patnership,Complex\n9064,404.8,NJ City,W,3,20,25,41,Graduation,28,Franchise,Complex\n4199,75.75,NJ City,E,22,22,4,38,No education,25,Owner,Standalone\n1083,154.2,NJ City,E,21,17,7,51,High School,26,Owner,Standalone\n4588,352.86,NJ City,W,17,20,15,56,High School,34,Franchise,Complex\n1767,391.67,NJ City,E,23,13,15,51,High School,35,Owner,Standalone\n8223,261.41,NJ City,E,22,15,17,44,Graduation,21,Owner,Standalone\n6044,213,Boston,W,25,18,1,50,No education,22,Franchise,Complex\n8024,174.75,Boston,W,16,8,18,74,Graduation,19,Franchise,Complex\n6499,335.88,Boston,E,2,9,13,25,Graduation,26,Owner,Standalone\n5770,309.2,Boston,S,11,22,10,81,Graduation,25,Patnership,Complex\n7087,80.46,Boston,E,21,21,1,63,High School,25,Owner,Standalone\n9703,120.32,NJ City,E,3,10,2,40,High School,29,Owner,Standalone\n8891,496.08,NJ City,E,16,14,15,82,Graduation,34,Owner,Standalone\n1690,347.48,NJ City,S,23,14,21,59,High School,26,Patnership,Complex\n7476,128.29,Boston,W,17,11,7,34,Graduation,18,Franchise,Complex\n183,233.96,NJ City,E,22,18,17,39,High School,23,Owner,Standalone\n106,319.15,NJ City,S,13,17,2,25,High School,35,Patnership,Complex\n1308,67.73,NJ City,N,9,14,3,70,Graduation,22,Franchise,Complex\n9595,8.22,NJ City,N,6,18,2,67,High School,20,Owner,Standalone\n3543,114.66,NJ City,E,16,10,2,50,Graduation,23,Owner,Standalone\n5210,53.05,Boston,S,13,10,1,48,No education,31,Patnership,Complex\n4153,115.31,NJ City,N,9,10,3,41,High School,32,Patnership,Standalone\n1228,189,NJ City,W,2,13,22,43,High School,30,Franchise,Complex\n2761,490.65,NJ City,N,19,20,5,43,High School,34,Patnership,Standalone\n8313,172.88,NJ City,N,22,14,1,71,Graduation,27,Patnership,Standalone\n4933,181.4,NJ City,N,21,17,22,44,High School,31,Patnership,Standalone\n9129,286.33,NJ City,N,23,22,20,41,Graduation,23,Patnership,Standalone\n6482,20.4,Boston,N,17,20,1,46,No education,21,Patnership,Complex\n5409,176.8,Boston,N,10,10,4,79,Graduation,21,Patnership,Standalone\n4851,10.21,NJ City,N,19,10,4,34,Graduation,29,Owner,Standalone\n110,17.25,NJ City,N,1,9,1,60,No education,26,Patnership,Complex\n1804,257.93,NJ City,W,15,13,22,25,High School,23,Franchise,Complex\n4350,208.97,NJ City,S,15,20,18,69,High School,22,Patnership,Complex\n4541,257,NJ City,N,1,15,18,61,High School,27,Patnership,Standalone\n5537,256.87,Boston,N,4,22,19,78,Graduation,28,Patnership,Standalone\n880,126.86,NJ City,W,11,14,4,78,High School,23,Franchise,Complex\n5377,289.83,Boston,N,18,20,24,37,Graduation,20,Patnership,Standalone\n8156,167.94,NJ City,W,25,15,24,54,Graduation,20,Franchise,Complex\n3208,122.89,NJ City,W,24,12,1,39,High School,28,Franchise,Complex\n9588,265.66,NJ City,W,21,17,5,85,Graduation,21,Franchise,Complex\n9771,271.4,NJ City,E,7,10,4,80,Graduation,18,Owner,Standalone\n4224,164.39,NJ City,W,23,17,17,30,High School,28,Franchise,Complex\n4250,222.35,NJ City,S,13,15,15,28,High School,24,Patnership,Complex\n426,389.1,NJ City,S,10,18,16,32,High School,28,Patnership,Complex\n6585,451.05,Boston,N,15,8,22,33,Graduation,21,Patnership,Standalone\n7806,363.17,Boston,S,23,8,23,79,Graduation,22,Patnership,Complex\n8774,169.04,NJ City,S,17,8,12,35,Graduation,20,Patnership,Complex\n1640,79.06,NJ City,W,9,16,1,26,No education,28,Franchise,Complex\n7772,82.86,Boston,W,5,20,2,78,No education,31,Franchise,Complex\n1946,136.37,NJ City,S,6,18,2,81,No education,33,Patnership,Complex\n9891,378.76,NJ City,E,8,20,2,25,Graduation,19,Owner,Standalone\n9683,16.49,NJ City,N,7,22,2,83,No education,31,Owner,Standalone\n3432,272.73,NJ City,W,22,17,21,38,High School,22,Franchise,Complex\n5467,484.81,Boston,E,20,24,19,36,Graduation,35,Owner,Standalone\n9974,120.86,NJ City,S,15,8,1,25,No education,32,Patnership,Complex\n826,97.56,NJ City,S,6,17,2,54,High School,25,Patnership,Complex\n33,122.01,Boston,N,3,12,4,53,Graduation,25,Patnership,Standalone\n6713,445.32,Boston,N,2,15,12,82,Graduation,27,Patnership,Standalone\n91,87.62,Boston,E,17,18,2,63,High School,20,Owner,Standalone\n4424,295.17,NJ City,W,6,20,11,77,High School,28,Franchise,Complex\n6480,191.7,Boston,W,17,10,21,28,Graduation,29,Franchise,Complex\n2745,154,NJ City,N,1,20,18,79,High School,26,Patnership,Standalone\n4806,117.6,NJ City,S,18,9,3,26,Graduation,28,Patnership,Complex\n281,126.13,NJ City,N,3,18,4,54,No education,29,Patnership,Standalone\n6380,260.23,Boston,W,2,20,15,71,Graduation,27,Franchise,Complex\n4888,50.64,NJ City,W,19,19,3,35,High School,25,Franchise,Complex\n3816,301.75,NJ City,W,13,15,19,38,High School,32,Franchise,Complex\n8371,250.55,NJ City,E,21,16,13,64,Graduation,24,Owner,Standalone\n3341,304.33,NJ City,N,11,8,9,60,High School,27,Patnership,Standalone\n4986,227.78,Boston,S,20,8,12,61,High School,27,Patnership,Complex\n1266,28.23,NJ City,N,1,16,3,81,Graduation,22,Patnership,Complex\n8669,132.38,NJ City,N,25,11,25,52,Graduation,31,Patnership,Standalone\n865,470.7,NJ City,N,2,10,4,74,High School,26,Patnership,Standalone\n279,82.64,NJ City,E,13,22,1,52,Graduation,18,Owner,Standalone\n2784,137.36,NJ City,W,3,12,1,27,Graduation,27,Franchise,Complex\n6139,176.84,Boston,E,3,14,16,80,Graduation,31,Owner,Standalone\n7536,124.95,Boston,W,6,20,2,67,Graduation,19,Franchise,Complex\n3728,216,NJ City,W,3,22,8,62,High School,18,Franchise,Complex\n2929,46.69,NJ City,N,9,20,3,47,High School,20,Patnership,Standalone\n1319,339.57,NJ City,E,24,11,13,74,High School,30,Owner,Standalone\n8899,387.9,NJ City,E,3,24,7,63,Graduation,25,Owner,Standalone\n3744,367.73,NJ City,W,5,15,1,75,Graduation,23,Franchise,Complex\n7940,276.85,Boston,W,16,11,9,46,Graduation,27,Franchise,Complex\n1449,406.06,NJ City,N,20,23,19,51,High School,24,Patnership,Standalone\n7362,77.88,Boston,S,24,9,4,48,Graduation,35,Patnership,Complex\n9264,73.04,NJ City,N,4,21,2,85,Graduation,19,Franchise,Complex\n6224,406.57,Boston,W,19,15,14,75,Graduation,21,Franchise,Complex\n1396,60.21,NJ City,W,19,15,4,43,High School,29,Franchise,Complex\n643,141.9,NJ City,E,8,14,5,51,High School,28,Owner,Standalone\n1386,134.64,NJ City,S,5,14,20,67,High School,21,Patnership,Complex\n5814,207.95,Boston,S,17,8,6,40,Graduation,26,Patnership,Complex\n6256,87.63,Boston,W,12,18,2,47,High School,32,Franchise,Complex\n1932,0.74,NJ City,N,3,18,3,38,Graduation,22,Franchise,Complex\n3890,460.34,NJ City,S,13,13,10,37,High School,35,Patnership,Complex\n1016,67.84,NJ City,N,13,10,4,72,No education,29,Franchise,Complex\n6839,146.69,Boston,E,15,18,24,53,Graduation,19,Owner,Standalone\n1985,355.85,NJ City,N,14,20,2,52,No education,28,Patnership,Standalone\n8707,137.53,NJ City,E,22,15,2,42,High School,28,Owner,Standalone\n6741,169.07,Boston,N,8,10,24,67,Graduation,35,Patnership,Standalone\n270,305.74,NJ City,S,15,12,19,58,High School,18,Patnership,Complex\n6909,424.88,Boston,N,8,21,22,73,Graduation,24,Patnership,Standalone\n445,94.98,NJ City,N,10,9,2,46,High School,18,Patnership,Standalone\n1000,48.86,NJ City,W,22,8,4,63,High School,32,Franchise,Complex\n3134,78.67,NJ City,S,16,17,4,48,No education,18,Patnership,Complex\n7418,392.43,Boston,S,25,20,20,72,Graduation,21,Patnership,Complex\n9198,143.43,NJ City,S,18,10,9,31,Graduation,22,Patnership,Complex\n9212,22,NJ City,N,24,21,4,54,No education,24,Franchise,Complex\n2082,140.95,NJ City,S,21,12,16,31,High School,20,Patnership,Complex\n5048,32.09,Boston,N,20,11,3,71,No education,21,Franchise,Complex\n7311,118.45,Boston,E,3,23,2,34,Graduation,34,Owner,Standalone\n4931,170.24,NJ City,E,10,20,13,53,High School,21,Owner,Standalone\n5226,390.78,Boston,S,23,19,4,74,Graduation,30,Patnership,Complex\n3076,105.37,NJ City,W,23,16,2,55,High School,34,Franchise,Complex\n9928,225.17,NJ City,W,2,10,17,40,Graduation,28,Franchise,Complex\n4866,62.56,NJ City,N,11,12,3,55,Graduation,30,Patnership,Complex\n4145,179.1,NJ City,N,14,21,11,27,High School,24,Patnership,Standalone\n615,176.19,NJ City,E,7,22,21,77,High School,29,Owner,Standalone\n4477,494.94,NJ City,N,5,12,10,65,High School,34,Patnership,Standalone\n4144,402.19,NJ City,W,5,22,23,34,High School,32,Franchise,Complex\n9940,63.39,NJ City,N,2,9,2,77,High School,20,Franchise,Complex\n5828,465.71,Boston,W,19,15,3,30,No education,25,Franchise,Complex\n4021,140.05,NJ City,N,12,22,22,75,High School,35,Patnership,Standalone\n2916,266.29,NJ City,W,20,21,6,51,High School,18,Franchise,Complex\n1023,294.59,NJ City,E,19,12,11,40,High School,33,Owner,Standalone\n878,320.14,NJ City,S,8,14,2,35,No education,20,Patnership,Complex\n8896,78.39,NJ City,W,8,9,4,43,High School,33,Franchise,Complex\n7430,96.33,Boston,S,7,20,3,83,No education,19,Patnership,Complex\n4568,39.65,NJ City,N,25,17,1,36,No education,30,Franchise,Complex\n4142,151,NJ City,S,2,8,20,38,High School,19,Patnership,Complex\n9893,437.36,NJ City,N,7,13,3,70,No education,24,Patnership,Standalone\n1886,57.94,NJ City,S,25,22,4,51,No education,22,Patnership,Complex\n6154,461.29,Boston,S,4,11,1,31,High School,22,Patnership,Complex\n5540,116.18,Boston,W,14,16,2,73,No education,26,Franchise,Complex\n3552,83.23,NJ City,W,21,11,2,55,Graduation,27,Franchise,Complex\n3022,368.32,NJ City,S,8,18,20,85,High School,28,Patnership,Complex\n5375,44.04,Boston,N,11,19,3,77,No education,32,Owner,Standalone\n2337,187.76,NJ City,N,15,24,8,64,High School,31,Patnership,Standalone\n8407,388.98,NJ City,E,19,16,3,55,High School,23,Owner,Standalone\n1658,496.6,NJ City,S,6,20,24,65,High School,35,Patnership,Complex\n4597,412.77,NJ City,N,20,23,17,43,High School,21,Patnership,Standalone\n7542,280.36,Boston,S,13,16,3,64,Graduation,34,Patnership,Complex\n8472,404.96,NJ City,W,19,20,10,35,Graduation,21,Franchise,Complex\n7057,73.53,Boston,N,17,13,2,47,High School,33,Patnership,Standalone\n9297,190.12,NJ City,N,24,11,18,33,Graduation,31,Patnership,Standalone\n5380,249.98,Boston,W,23,24,14,47,Graduation,32,Franchise,Complex\n9600,181.2,NJ City,W,23,14,24,48,Graduation,31,Franchise,Complex\n467,46.16,NJ City,N,25,21,2,38,No education,23,Owner,Standalone\n506,266,NJ City,S,3,14,18,45,High School,18,Patnership,Complex\n1820,113.24,NJ City,W,8,22,2,32,No education,28,Franchise,Complex\n9021,404.23,NJ City,N,16,11,14,68,Graduation,26,Patnership,Standalone\n3994,255.94,NJ City,S,14,16,6,85,High School,30,Patnership,Complex\n1374,210.6,NJ City,S,15,24,8,45,High School,34,Patnership,Complex\n7874,471.73,Boston,S,15,13,3,57,No education,18,Patnership,Complex\n9110,362.15,NJ City,S,10,12,2,26,No education,18,Patnership,Complex\n2316,289.59,NJ City,W,9,24,23,35,High School,34,Franchise,Complex\n2710,164.66,NJ City,S,12,17,6,76,High School,18,Patnership,Complex\n5260,79.62,Boston,W,19,23,1,34,High School,32,Franchise,Complex\n7861,249.34,Boston,N,24,24,19,26,Graduation,26,Patnership,Standalone\n8332,106.68,NJ City,W,23,19,1,79,High School,29,Franchise,Complex\n2016,41.65,NJ City,N,20,18,1,64,Graduation,27,Franchise,Complex\n7097,48.29,Boston,N,2,14,3,60,No education,25,Patnership,Standalone\n4572,237.89,NJ City,W,2,8,4,70,Graduation,22,Franchise,Complex\n3496,343.24,NJ City,W,5,24,6,33,High School,23,Franchise,Complex\n4476,90.41,NJ City,W,25,12,4,33,Graduation,34,Franchise,Complex\n8560,16.95,NJ City,N,11,24,2,70,High School,27,Franchise,Complex\n8765,360.61,NJ City,N,2,9,12,30,Graduation,21,Patnership,Standalone\n2388,471.84,NJ City,W,17,24,13,57,High School,34,Franchise,Complex\n6293,198.09,Boston,N,25,12,21,74,Graduation,31,Patnership,Standalone\n4034,52,NJ City,S,10,18,3,36,No education,19,Patnership,Complex\n1998,71.3,NJ City,N,14,21,4,78,Graduation,33,Patnership,Complex\n2241,430.29,NJ City,N,15,13,19,35,High School,21,Patnership,Standalone\n1560,217.62,NJ City,W,14,18,8,36,High School,35,Franchise,Complex\n2755,88.27,NJ City,E,5,23,4,57,High School,18,Owner,Standalone\n5669,468.41,Boston,N,18,15,8,54,Graduation,31,Patnership,Standalone\n798,47.03,NJ City,N,23,22,1,73,Graduation,27,Patnership,Complex\n6544,95.72,Boston,W,25,9,2,81,High School,31,Franchise,Complex\n8440,170.17,NJ City,W,23,9,1,77,High School,19,Franchise,Complex\n1055,273.51,NJ City,E,5,8,1,77,No education,19,Owner,Standalone\n604,20.31,NJ City,N,5,11,1,59,High School,20,Franchise,Complex\n9392,489.06,NJ City,W,17,17,14,52,Graduation,32,Franchise,Complex\n5428,42.13,Boston,N,3,22,1,46,High School,19,Franchise,Complex\n9853,6.71,NJ City,N,19,8,2,35,High School,22,Patnership,Standalone\n2752,69.08,NJ City,N,1,17,1,84,High School,28,Franchise,Complex\n2860,478.6,NJ City,W,12,21,21,42,High School,28,Franchise,Complex\n979,33.4,NJ City,N,8,22,4,62,High School,30,Owner,Standalone\n3700,71.44,NJ City,N,4,20,2,40,High School,29,Franchise,Complex\n1358,167.38,NJ City,S,6,23,3,40,No education,33,Patnership,Complex\n6232,203.58,Boston,W,19,19,3,51,High School,32,Franchise,Complex\n6914,107.97,Boston,S,7,21,2,74,No education,19,Patnership,Complex\n2294,441.33,NJ City,S,8,16,17,64,High School,24,Patnership,Complex\n4932,267.17,NJ City,W,10,8,4,81,Graduation,24,Franchise,Complex\n14,180.44,Boston,S,20,17,23,50,High School,19,Patnership,Complex\n3933,216,NJ City,N,2,10,17,25,High School,34,Patnership,Standalone\n677,382.63,NJ City,N,25,21,1,78,No education,30,Patnership,Standalone\n9978,441.83,NJ City,S,12,17,10,68,Graduation,28,Patnership,Complex\n1845,354.35,NJ City,N,9,8,22,61,High School,30,Patnership,Standalone\n3959,466.86,NJ City,E,12,15,14,37,High School,25,Owner,Standalone\n9783,261.58,NJ City,E,24,10,11,57,Graduation,35,Owner,Standalone\n9220,58.54,NJ City,W,19,12,4,69,High School,18,Franchise,Complex\n4609,109.97,NJ City,N,7,16,3,58,High School,22,Patnership,Standalone\n4964,99.19,Boston,W,10,24,3,40,No education,22,Franchise,Complex\n3639,373.44,NJ City,E,10,9,3,52,Graduation,34,Owner,Standalone\n7391,176.71,Boston,E,12,22,7,63,Graduation,23,Owner,Standalone\n531,4.02,NJ City,N,4,21,2,38,Graduation,32,Owner,Standalone\n2104,132.43,NJ City,W,12,12,15,80,High School,33,Franchise,Complex\n2108,93.88,NJ City,W,22,11,2,73,No education,23,Franchise,Complex\n8791,305.52,NJ City,E,13,23,10,84,Graduation,31,Owner,Standalone\n9826,160.17,NJ City,S,4,16,10,55,Graduation,19,Patnership,Complex\n8477,88.3,NJ City,N,21,23,4,46,No education,26,Patnership,Standalone\n4520,346.52,NJ City,W,11,8,11,56,High School,24,Franchise,Complex\n5268,475.89,Boston,W,23,14,14,71,Graduation,21,Franchise,Complex\n438,189.77,NJ City,S,9,9,5,48,Graduation,29,Patnership,Complex\n3769,297.31,NJ City,N,24,20,10,76,High School,19,Patnership,Standalone\n9688,48.13,NJ City,W,25,15,2,71,High School,26,Franchise,Complex\n8122,307.7,NJ City,S,22,23,7,39,Graduation,23,Patnership,Complex\n8460,267.18,NJ City,W,14,17,5,82,Graduation,28,Franchise,Complex\n2982,163.99,NJ City,S,8,20,5,40,Graduation,26,Patnership,Complex\n1286,233,NJ City,S,2,17,20,33,High School,23,Patnership,Complex\n6447,383.85,Boston,E,25,13,8,57,Graduation,29,Owner,Standalone\n4869,270,NJ City,N,3,12,15,78,High School,27,Patnership,Standalone\n2652,126.57,NJ City,W,5,10,4,66,Graduation,25,Franchise,Complex\n4517,222.58,NJ City,N,18,13,12,73,High School,27,Patnership,Standalone\n7555,330.66,Boston,E,19,20,10,80,Graduation,28,Owner,Standalone\n8581,487.07,NJ City,N,15,18,12,75,Graduation,25,Patnership,Standalone\n2233,311.02,NJ City,N,19,17,13,54,High School,29,Patnership,Standalone\n3372,464.08,NJ City,W,21,15,10,70,High School,24,Franchise,Complex\n9050,212.14,NJ City,S,20,14,10,26,Graduation,34,Patnership,Complex\n9822,195.57,NJ City,S,14,24,22,49,Graduation,28,Patnership,Complex\n6808,390.44,Boston,W,4,12,4,82,High School,35,Franchise,Complex\n3002,158.02,NJ City,S,10,8,21,72,High School,29,Patnership,Complex\n3237,493.77,NJ City,N,20,22,8,25,High School,19,Patnership,Standalone\n8989,217.68,NJ City,N,8,24,17,44,Graduation,18,Patnership,Standalone\n7489,451.75,Boston,N,9,23,14,44,Graduation,18,Patnership,Standalone\n9450,405.08,NJ City,S,11,12,16,83,Graduation,24,Patnership,Complex\n8955,260.58,NJ City,E,8,12,17,35,Graduation,30,Owner,Standalone\n7373,17.65,Boston,N,13,9,4,67,No education,31,Patnership,Standalone\n9419,242.41,NJ City,E,10,8,4,31,No education,19,Owner,Standalone\n6960,90.8,Boston,W,20,8,2,77,Graduation,35,Franchise,Complex\n6246,78.02,Boston,S,20,17,3,54,Graduation,30,Patnership,Complex\n7454,483.77,Boston,S,14,22,12,69,Graduation,24,Patnership,Complex\n1442,234.17,NJ City,S,18,19,4,41,No education,27,Patnership,Complex\n2078,6.76,NJ City,N,1,24,4,36,No education,27,Patnership,Complex\n6344,250.31,Boston,W,2,23,22,75,Graduation,22,Franchise,Complex\n101,99.27,NJ City,N,8,10,3,61,No education,22,Patnership,Standalone\n6690,253.14,Boston,S,16,15,25,32,Graduation,35,Patnership,Complex\n9130,244.31,NJ City,S,4,18,2,29,High School,24,Patnership,Complex\n9814,40.1,NJ City,N,2,19,3,61,High School,23,Patnership,Complex\n6038,349.36,Boston,S,9,8,5,67,No education,34,Patnership,Complex\n3881,314.82,NJ City,N,21,19,22,48,High School,28,Patnership,Standalone\n2277,123.21,NJ City,N,13,18,1,78,Graduation,23,Patnership,Standalone\n4634,198.25,NJ City,S,10,19,22,74,High School,33,Patnership,Complex\n633,304.99,NJ City,N,22,12,13,82,High School,26,Patnership,Standalone\n252,84.13,NJ City,W,1,16,1,53,Graduation,25,Franchise,Complex\n2553,473.26,NJ City,N,14,13,7,35,High School,19,Patnership,Standalone\n8680,27.23,NJ City,N,18,12,4,26,High School,35,Franchise,Complex\n1677,239.95,NJ City,N,6,18,4,46,Graduation,34,Patnership,Standalone\n6846,95.49,Boston,S,20,22,3,74,Graduation,18,Patnership,Complex\n5144,58.27,Boston,W,14,11,3,81,No education,18,Franchise,Complex\n8523,90.03,NJ City,E,18,16,4,49,Graduation,22,Owner,Standalone\n7703,441.66,Boston,E,19,8,14,35,Graduation,24,Owner,Standalone\n6459,180.78,Boston,E,14,14,2,38,Graduation,28,Owner,Standalone\n7995,207.95,Boston,E,4,21,5,67,Graduation,34,Owner,Standalone\n9969,370.38,NJ City,N,17,16,11,62,Graduation,23,Patnership,Standalone\n6090,426.61,Boston,S,7,17,16,55,Graduation,28,Patnership,Complex\n1999,440.87,NJ City,E,19,8,22,40,High School,26,Owner,Standalone\n2129,317.46,NJ City,N,20,24,16,30,High School,18,Patnership,Standalone\n3256,411.9,NJ City,W,25,10,25,59,High School,33,Franchise,Complex\n7842,36.8,Boston,N,25,11,4,69,Graduation,29,Patnership,Complex\n7195,220.23,Boston,E,3,8,15,57,Graduation,28,Owner,Standalone\n9679,115.57,NJ City,E,15,16,3,38,High School,19,Owner,Standalone\n793,48.38,NJ City,N,13,10,2,67,High School,18,Patnership,Standalone\n7248,166.3,Boston,W,23,8,4,43,Graduation,25,Franchise,Complex\n1810,497.39,NJ City,S,5,16,17,80,High School,19,Patnership,Complex\n8166,454.56,NJ City,S,3,22,11,61,Graduation,30,Patnership,Complex\n720,477.63,NJ City,W,19,16,3,81,Graduation,22,Franchise,Complex\n5615,15.05,Boston,N,11,21,2,50,No education,25,Owner,Standalone\n5361,421.83,Boston,N,19,10,7,47,Graduation,20,Patnership,Standalone\n3616,146.81,NJ City,W,21,24,18,44,High School,25,Franchise,Complex\n2117,75.44,NJ City,N,1,8,3,85,No education,24,Patnership,Standalone\n8677,140.58,NJ City,N,14,13,5,61,High School,25,Patnership,Standalone\n5643,204.92,Boston,E,11,22,8,40,Graduation,30,Owner,Standalone\n6567,92.29,Boston,E,1,21,2,52,Graduation,28,Owner,Standalone\n912,472.2,NJ City,W,11,17,23,61,High School,22,Franchise,Complex\n7427,131.75,Boston,E,3,17,16,40,Graduation,30,Owner,Standalone\n8798,457.12,NJ City,S,15,14,3,53,No education,33,Patnership,Complex\n1293,271,NJ City,N,3,15,19,49,High School,33,Patnership,Standalone\n1907,330.22,NJ City,E,6,16,24,39,High School,34,Owner,Standalone\n3599,106.85,NJ City,E,5,24,3,30,No education,26,Owner,Standalone\n4089,299.5,NJ City,N,8,8,25,27,High School,25,Patnership,Standalone\n9038,26.03,NJ City,N,21,23,2,58,No education,23,Patnership,Complex\n5528,9.44,Boston,N,18,23,1,73,No education,29,Franchise,Complex\n2064,263.05,NJ City,W,15,22,17,60,High School,29,Franchise,Complex\n7805,50.58,Boston,N,22,16,4,46,No education,18,Patnership,Standalone\n8956,324.46,NJ City,W,11,8,16,25,Graduation,28,Franchise,Complex\n5099,439.9,Boston,E,6,9,1,80,No education,27,Owner,Standalone\n5142,345.36,Boston,S,25,24,17,61,Graduation,25,Patnership,Complex\n7784,260.89,Boston,W,14,9,12,43,Graduation,31,Franchise,Complex\n1546,20.77,NJ City,N,9,12,4,71,High School,19,Patnership,Complex\n9726,294.27,NJ City,S,24,8,23,48,Graduation,23,Patnership,Complex\n8128,286.97,NJ City,W,4,14,13,45,Graduation,25,Franchise,Complex\n2099,489.39,NJ City,E,16,21,22,77,High School,33,Owner,Standalone\n1537,391.47,NJ City,N,18,21,3,62,High School,27,Patnership,Standalone\n8609,396.13,NJ City,N,5,22,14,65,Graduation,19,Patnership,Standalone\n4187,144.73,NJ City,E,12,15,16,47,High School,35,Owner,Standalone\n3911,221.28,NJ City,E,8,20,3,61,No education,21,Owner,Standalone\n2810,213.31,NJ City,S,17,11,25,58,High School,35,Patnership,Complex\n3765,9.85,NJ City,N,3,10,2,60,Graduation,25,Patnership,Standalone\n1483,178.28,NJ City,E,13,11,21,48,High School,23,Owner,Standalone\n1991,117,NJ City,E,3,20,19,33,High School,27,Owner,Standalone\n6236,477.53,Boston,W,14,10,1,35,No education,23,Franchise,Complex\n4843,172.17,NJ City,E,5,12,16,67,High School,22,Owner,Standalone\n3799,157.24,NJ City,E,25,24,3,40,High School,31,Owner,Standalone\n1976,105,NJ City,W,2,15,17,45,High School,32,Franchise,Complex\n7782,10.1,Boston,N,8,8,3,68,Graduation,20,Patnership,Complex\n5567,231.78,Boston,E,15,11,18,84,Graduation,22,Owner,Standalone\n2678,173.88,NJ City,S,19,23,8,67,High School,32,Patnership,Complex\n474,279.25,NJ City,S,14,10,20,36,High School,35,Patnership,Complex\n3688,311.59,NJ City,W,12,21,16,76,High School,19,Franchise,Complex\n4143,78.81,NJ City,E,8,16,2,75,Graduation,27,Owner,Standalone\n7719,338.79,Boston,E,18,23,12,35,Graduation,25,Owner,Standalone\n6645,248.02,Boston,N,19,8,2,80,Graduation,33,Patnership,Standalone\n4512,283,NJ City,W,2,20,15,53,High School,33,Franchise,Complex\n8020,23.67,Boston,N,15,12,4,40,High School,31,Franchise,Complex\n5678,346.99,Boston,S,24,16,10,62,Graduation,25,Patnership,Complex\n4369,354.7,NJ City,N,15,11,22,56,High School,27,Patnership,Standalone\n7343,317.24,Boston,E,15,23,15,85,Graduation,27,Owner,Standalone\n4576,478.75,NJ City,W,6,18,2,69,High School,30,Franchise,Complex\n9793,111.71,NJ City,N,1,19,1,49,High School,22,Patnership,Standalone\n2962,214.44,NJ City,S,9,17,4,44,High School,27,Patnership,Complex\n6346,176.44,Boston,S,18,18,18,53,Graduation,25,Patnership,Complex\n6506,218.67,Boston,S,13,11,11,50,Graduation,25,Patnership,Complex\n1409,309.6,NJ City,N,6,20,5,56,No education,23,Patnership,Standalone\n734,31.56,NJ City,N,6,13,4,45,No education,20,Patnership,Complex\n84,176.3,Boston,W,23,20,3,37,Graduation,25,Franchise,Complex\n7890,455.26,Boston,S,25,21,21,28,Graduation,30,Patnership,Complex\n2038,481.57,NJ City,S,17,16,18,28,High School,22,Patnership,Complex\n9426,446.09,NJ City,S,23,13,8,75,Graduation,34,Patnership,Complex\n5018,478.2,Boston,S,20,8,13,76,Graduation,23,Patnership,Complex\n2069,124.27,NJ City,N,2,19,2,74,No education,24,Patnership,Standalone\n5662,383.67,Boston,S,18,19,5,73,High School,24,Patnership,Complex\n5457,78.56,Boston,N,8,14,4,58,Graduation,27,Patnership,Standalone\n3798,462.9,NJ City,S,17,9,21,36,High School,28,Patnership,Complex\n2949,291.47,NJ City,N,14,8,24,65,High School,21,Patnership,Standalone\n1072,62.33,NJ City,N,1,24,2,77,High School,26,Franchise,Complex\n1123,17.79,NJ City,N,7,19,3,50,High School,24,Owner,Standalone\n1656,403.97,NJ City,W,4,20,2,80,Graduation,21,Franchise,Complex\n220,223.92,NJ City,W,4,14,9,47,High School,28,Franchise,Complex\n3907,169.2,NJ City,E,4,17,16,41,High School,23,Owner,Standalone\n5784,67.54,Boston,N,23,20,2,46,Graduation,20,Franchise,Complex\n4901,442.36,NJ City,N,23,14,16,55,High School,29,Patnership,Standalone\n7525,343.04,Boston,N,4,18,13,56,Graduation,27,Patnership,Standalone\n2290,224.57,NJ City,S,4,8,17,44,High School,24,Patnership,Complex\n2348,259,NJ City,W,1,15,23,75,High School,33,Franchise,Complex\n9151,281.24,NJ City,E,13,13,9,69,Graduation,18,Owner,Standalone\n3950,79.19,NJ City,S,19,13,3,37,No education,35,Patnership,Complex\n9873,149.12,NJ City,N,22,10,12,83,Graduation,34,Patnership,Standalone\n3144,389.9,NJ City,W,10,17,5,44,Graduation,28,Franchise,Complex\n4555,292.08,NJ City,E,9,8,18,81,High School,20,Owner,Standalone\n4718,453.22,NJ City,S,11,13,9,37,High School,31,Patnership,Complex\n8612,188.31,NJ City,W,4,13,22,66,Graduation,27,Franchise,Complex\n5171,25.28,Boston,N,14,18,3,34,No education,23,Owner,Standalone\n7356,52.27,Boston,W,15,13,4,48,Graduation,29,Franchise,Complex\n8494,262.32,NJ City,S,14,8,9,61,Graduation,30,Patnership,Complex\n9448,416.55,NJ City,W,17,17,9,35,Graduation,22,Franchise,Complex\n9769,435.06,NJ City,N,20,11,14,38,Graduation,29,Patnership,Standalone\n76,71.73,Boston,N,18,18,3,53,High School,24,Franchise,Complex\n30,78.5,Boston,S,15,19,3,55,Graduation,33,Patnership,Complex\n8189,111.41,NJ City,N,24,14,4,77,No education,23,Patnership,Standalone\n9218,489.42,NJ City,S,13,22,22,64,Graduation,33,Patnership,Complex\n7496,94.06,Boston,W,20,13,1,68,No education,35,Franchise,Complex\n490,26.23,NJ City,N,9,20,3,39,High School,31,Patnership,Complex\n6969,319.56,Boston,N,4,15,20,48,Graduation,20,Patnership,Standalone\n2787,216.56,NJ City,E,14,9,23,74,High School,30,Owner,Standalone\n3707,233.89,NJ City,E,14,17,1,66,No education,21,Owner,Standalone\n7292,402.75,Boston,W,13,11,6,60,Graduation,29,Franchise,Complex\n2776,123.92,NJ City,W,8,10,3,72,High School,25,Franchise,Complex\n4953,239.62,Boston,N,25,18,7,60,High School,24,Patnership,Standalone\n2881,338.38,NJ City,N,7,9,25,64,High School,31,Patnership,Standalone\n3743,275.4,NJ City,E,19,14,21,79,High School,22,Owner,Standalone\n2835,109.97,NJ City,E,1,9,3,76,Graduation,30,Owner,Standalone\n9672,407.59,NJ City,W,21,15,7,58,Graduation,32,Franchise,Complex\n9284,357.91,NJ City,W,3,22,18,47,Graduation,22,Franchise,Complex\n3319,323.55,NJ City,E,9,22,1,26,High School,27,Owner,Standalone\n3880,164.12,NJ City,W,22,17,10,53,High School,22,Franchise,Complex\n3497,320.94,NJ City,N,18,10,4,66,No education,30,Patnership,Standalone\n7665,211.66,Boston,N,7,15,13,63,Graduation,27,Patnership,Standalone\n5656,498.43,Boston,W,8,13,8,52,Graduation,30,Franchise,Complex\n1074,253.17,NJ City,S,11,20,9,27,High School,21,Patnership,Complex\n9503,443.62,NJ City,E,7,20,18,44,Graduation,28,Owner,Standalone\n1592,488.82,NJ City,W,23,24,7,84,High School,27,Franchise,Complex\n3906,337.42,NJ City,S,19,23,9,47,High School,29,Patnership,Complex\n5081,470.57,Boston,N,24,14,17,53,Graduation,35,Patnership,Standalone\n7422,65.74,Boston,N,13,12,3,65,Graduation,27,Patnership,Complex\n9541,90.65,NJ City,N,22,21,4,35,High School,24,Patnership,Standalone\n9097,424.22,NJ City,N,9,11,19,56,Graduation,29,Patnership,Standalone\n3522,197.38,NJ City,S,18,14,25,54,High School,18,Patnership,Complex\n1253,413.18,NJ City,N,16,14,17,66,High School,34,Patnership,Standalone\n9546,275.66,NJ City,S,21,8,12,77,Graduation,22,Patnership,Complex\n3460,90.37,NJ City,W,20,23,2,60,High School,29,Franchise,Complex\n6992,10.85,Boston,N,24,15,1,43,No education,35,Franchise,Complex\n4062,35.5,NJ City,N,10,19,4,67,Graduation,31,Patnership,Complex\n6862,283.15,Boston,S,22,10,14,45,Graduation,25,Patnership,Complex\n3307,329.09,NJ City,E,12,13,11,38,High School,34,Owner,Standalone\n8814,30.36,NJ City,N,14,9,1,64,Graduation,24,Patnership,Complex\n6377,261.24,Boston,N,21,11,14,80,Graduation,24,Patnership,Standalone\n3430,181.05,NJ City,S,25,16,24,60,High School,18,Patnership,Complex\n4141,268.63,NJ City,N,15,23,17,76,High School,27,Patnership,Standalone\n334,43.16,NJ City,N,9,12,3,61,High School,22,Patnership,Complex\n1929,34.31,NJ City,N,16,22,2,60,Graduation,24,Patnership,Standalone\n8712,125.4,NJ City,W,7,14,3,51,Graduation,27,Franchise,Complex\n6820,40.12,Boston,N,21,23,2,32,High School,34,Franchise,Complex\n5515,287.24,Boston,E,19,14,8,53,Graduation,24,Owner,Standalone\n8314,242.61,NJ City,S,15,21,18,39,Graduation,35,Patnership,Complex\n2733,24.65,NJ City,N,23,15,3,65,Graduation,27,Patnership,Standalone\n4088,358.62,NJ City,W,20,22,8,54,High School,24,Franchise,Complex\n19,107.16,NJ City,E,14,17,4,48,High School,19,Owner,Standalone\n2986,393.23,NJ City,S,8,20,18,63,High School,32,Patnership,Complex\n2832,195.75,NJ City,W,1,17,4,41,Graduation,33,Franchise,Complex\n3922,418.5,NJ City,S,25,21,23,52,High School,18,Patnership,Complex\n4335,149,NJ City,E,3,22,12,25,High School,20,Owner,Standalone\n4875,112.28,NJ City,E,13,10,3,39,Graduation,33,Owner,Standalone\n9620,359.08,NJ City,W,16,19,10,26,Graduation,28,Franchise,Complex\n5902,466.4,Boston,S,20,12,15,85,Graduation,25,Patnership,Complex\n5812,449.86,Boston,W,17,14,20,58,Graduation,18,Franchise,Complex\n4647,142.06,NJ City,E,4,19,19,66,High School,34,Owner,Standalone\n5991,479.84,Boston,E,3,24,3,47,Graduation,26,Owner,Standalone\n6419,431.44,Boston,E,24,8,15,54,Graduation,35,Owner,Standalone\n6759,499.65,Boston,E,4,13,14,28,Graduation,34,Owner,Standalone\n3160,208,NJ City,W,2,18,17,67,High School,33,Franchise,Complex\n4293,135,NJ City,N,1,17,16,27,High School,26,Patnership,Standalone\n6270,478.44,Boston,S,15,23,22,52,Graduation,34,Patnership,Complex\n6448,377.47,Boston,W,4,10,15,85,Graduation,31,Franchise,Complex\n3584,259.39,NJ City,W,11,16,2,41,No education,31,Franchise,Complex\n6704,485.78,Boston,W,24,9,1,76,No education,25,Franchise,Complex\n3419,324.93,NJ City,E,20,18,16,73,High School,32,Owner,Standalone\n2081,316.47,NJ City,N,20,10,8,57,High School,27,Patnership,Standalone\n1173,23.66,NJ City,N,21,19,2,55,Graduation,25,Patnership,Standalone\n8346,359.85,NJ City,S,3,10,11,29,Graduation,22,Patnership,Complex\n1066,295.2,NJ City,S,8,21,12,32,High School,19,Patnership,Complex\n3977,234.6,NJ City,N,13,8,5,63,No education,23,Patnership,Standalone\n8312,168.26,NJ City,W,15,14,17,58,Graduation,18,Franchise,Complex\n6494,393.23,Boston,S,19,17,4,43,No education,20,Patnership,Complex\n671,121.85,NJ City,E,19,18,4,30,No education,28,Owner,Standalone\n3544,192.77,NJ City,W,12,23,2,38,High School,32,Franchise,Complex\n2610,309.41,NJ City,S,19,22,19,31,High School,21,Patnership,Complex\n9524,398.94,NJ City,W,13,8,11,76,Graduation,19,Franchise,Complex\n8054,121.51,NJ City,S,9,21,1,38,No education,31,Patnership,Complex\n7137,47.39,Boston,N,2,18,2,29,Graduation,22,Patnership,Standalone\n3864,462.39,NJ City,W,10,9,21,82,High School,19,Franchise,Complex\n6847,175.94,Boston,E,1,14,15,54,Graduation,31,Owner,Standalone\n2297,298.52,NJ City,N,13,8,25,55,High School,29,Patnership,Standalone\n9371,363.73,NJ City,E,17,23,18,32,Graduation,33,Owner,Standalone\n508,319.9,NJ City,W,4,12,24,40,High School,32,Franchise,Complex\n1871,297.59,NJ City,E,16,19,10,38,High School,26,Owner,Standalone\n1157,443.85,NJ City,N,8,19,23,26,High School,23,Patnership,Standalone\n9068,355.42,NJ City,W,6,21,17,60,Graduation,24,Franchise,Complex\n5511,218.39,Boston,E,2,12,14,81,Graduation,27,Owner,Standalone\n4556,13.47,NJ City,N,12,17,4,76,No education,28,Franchise,Complex\n3607,107.59,NJ City,E,18,20,4,77,High School,24,Owner,Standalone\n2647,411.9,NJ City,E,4,9,22,31,High School,35,Owner,Standalone\n5116,348.58,Boston,W,9,9,25,35,Graduation,18,Franchise,Complex\n8181,192.75,NJ City,N,11,18,10,30,Graduation,30,Patnership,Standalone\n2938,259.64,NJ City,S,2,13,3,61,High School,30,Patnership,Complex\n9017,415.52,NJ City,N,4,23,16,43,Graduation,22,Patnership,Standalone\n927,470.83,NJ City,E,5,9,18,45,High School,19,Owner,Standalone\n8987,168.63,NJ City,E,8,13,5,57,No education,21,Owner,Standalone\n8183,276.58,NJ City,E,17,23,1,66,No education,22,Owner,Standalone\n8779,276.1,NJ City,E,25,13,24,33,Graduation,18,Owner,Standalone\n2306,91.54,NJ City,S,24,17,1,83,No education,28,Patnership,Complex\n8518,469.62,NJ City,S,15,21,3,81,High School,28,Patnership,Complex\n2798,180.18,NJ City,S,8,10,22,26,High School,27,Patnership,Complex\n1457,19.87,NJ City,N,21,18,1,81,No education,19,Patnership,Standalone\n4013,60.9,NJ City,N,22,23,2,41,No education,21,Patnership,Standalone\n4794,148.52,NJ City,S,13,19,18,51,High School,25,Patnership,Complex\n2843,484.48,NJ City,E,23,17,21,50,High School,34,Owner,Standalone\n8754,272.05,NJ City,S,19,14,16,47,Graduation,20,Patnership,Complex\n8252,297.61,NJ City,W,16,15,8,58,Graduation,22,Franchise,Complex\n4847,89.62,NJ City,E,8,20,1,77,No education,28,Owner,Standalone\n4629,33.01,NJ City,N,16,21,2,44,Graduation,34,Patnership,Standalone\n9016,479.15,NJ City,W,8,8,12,85,Graduation,22,Franchise,Complex\n5801,230.32,Boston,N,23,9,17,61,Graduation,35,Patnership,Standalone\n8534,263.17,NJ City,S,6,23,21,52,Graduation,26,Patnership,Complex\n4757,102.27,NJ City,N,11,13,3,55,No education,18,Patnership,Standalone\n8671,480.25,NJ City,E,5,19,17,51,Graduation,18,Owner,Standalone\n2759,285.83,NJ City,E,11,22,19,47,High School,23,Owner,Standalone\n9748,393.26,NJ City,W,19,9,3,82,High School,25,Franchise,Complex\n4668,180.13,NJ City,W,7,10,24,57,High School,22,Franchise,Complex\n7815,156.19,Boston,E,2,17,21,47,Graduation,35,Owner,Standalone\n2802,204.46,NJ City,S,16,12,25,57,High School,30,Patnership,Complex\n4593,367.72,NJ City,N,13,21,20,54,High School,35,Patnership,Standalone\n1742,151.5,NJ City,S,23,19,16,76,High School,31,Patnership,Complex\n2035,374.41,NJ City,E,18,19,10,40,High School,22,Owner,Standalone\n9104,421.72,NJ City,W,25,18,19,32,Graduation,34,Franchise,Complex\n718,332.41,NJ City,S,19,12,23,51,High School,26,Patnership,Complex\n8459,137.94,NJ City,E,5,20,20,35,Graduation,23,Owner,Standalone\n4460,467.39,NJ City,W,4,11,15,63,High School,26,Franchise,Complex\n1390,228.29,NJ City,S,12,9,13,30,High School,23,Patnership,Complex\n3443,177.39,NJ City,E,13,15,14,28,High School,27,Owner,Standalone\n6978,351.13,Boston,S,9,20,17,41,Graduation,32,Patnership,Complex\n2797,449.24,NJ City,N,18,14,17,31,High School,27,Patnership,Standalone\n5072,188.07,Boston,W,24,16,19,54,Graduation,28,Franchise,Complex\n8659,349.68,NJ City,E,11,23,8,54,Graduation,29,Owner,Standalone\n2404,63.7,NJ City,N,7,12,1,59,High School,28,Franchise,Complex\n3205,69.39,NJ City,N,17,17,1,83,High School,29,Patnership,Standalone\n2221,128.53,NJ City,N,6,16,17,76,High School,33,Patnership,Standalone\n8631,393.14,NJ City,E,9,14,18,52,Graduation,21,Owner,Standalone\n1264,104.89,NJ City,W,13,19,1,69,High School,27,Franchise,Complex\n1096,223.37,NJ City,W,9,23,12,63,High School,28,Franchise,Complex\n6078,70.55,Boston,N,7,12,2,27,Graduation,24,Patnership,Complex\n9861,311.11,NJ City,N,25,15,7,36,Graduation,31,Patnership,Standalone\n6056,102.16,Boston,W,18,13,4,33,No education,26,Franchise,Complex\n7128,481.29,Boston,W,12,10,19,64,Graduation,27,Franchise,Complex\n3997,240.18,NJ City,N,23,8,17,77,High School,35,Patnership,Standalone\n7250,456.82,Boston,S,3,16,1,55,No education,30,Patnership,Complex\n2338,122.7,NJ City,S,24,13,3,78,High School,26,Patnership,Complex\n9988,223.33,NJ City,W,15,11,9,77,Graduation,33,Franchise,Complex\n8392,0.22,NJ City,N,12,22,2,72,High School,21,Franchise,Complex\n2255,418.17,NJ City,E,21,14,22,54,High School,21,Owner,Standalone\n232,257.53,NJ City,W,2,22,2,27,High School,25,Franchise,Complex\n5750,16.33,Boston,N,18,11,3,78,No education,26,Patnership,Complex\n8916,141.7,NJ City,W,13,23,8,72,Graduation,22,Franchise,Complex\n2164,172.47,NJ City,W,25,13,7,32,High School,28,Franchise,Complex\n2871,88.03,NJ City,E,6,16,4,80,Graduation,32,Owner,Standalone\n1539,233,NJ City,E,1,17,6,50,High School,33,Owner,Standalone\n8353,332.03,NJ City,N,3,23,1,29,High School,24,Patnership,Standalone\n8497,200.28,NJ City,N,1,10,20,80,Graduation,31,Patnership,Standalone\n3080,19.17,NJ City,N,22,10,4,53,No education,27,Franchise,Complex\n1862,137.64,NJ City,S,13,24,9,71,High School,33,Patnership,Complex\n4116,96.11,NJ City,W,7,18,3,44,Graduation,19,Franchise,Complex\n7117,318.42,Boston,N,7,10,15,70,Graduation,20,Patnership,Standalone\n3305,108.29,NJ City,N,12,22,2,38,No education,26,Patnership,Standalone\n1403,113.41,NJ City,E,21,19,4,65,No education,30,Owner,Standalone\n2726,260.7,NJ City,S,9,8,11,35,High School,18,Patnership,Complex\n6206,188.54,Boston,S,1,11,20,29,Graduation,27,Patnership,Complex\n6194,117.66,Boston,S,17,15,4,37,No education,22,Patnership,Complex\n9646,481.79,NJ City,S,1,8,9,69,Graduation,20,Patnership,Complex\n5516,217.36,Boston,W,1,13,25,55,Graduation,30,Franchise,Complex\n55,284.64,NJ City,E,11,22,3,60,High School,25,Owner,Standalone\n1951,313,NJ City,E,13,15,16,34,High School,23,Owner,Standalone\n4586,427.5,NJ City,S,18,21,25,71,High School,34,Patnership,Complex\n264,23.93,NJ City,N,16,24,2,33,Graduation,31,Franchise,Complex\n7802,376.63,Boston,S,4,13,12,52,Graduation,26,Patnership,Complex\n4441,248.58,NJ City,N,22,23,21,64,High School,24,Patnership,Standalone\n7916,466.41,Boston,W,9,23,23,63,Graduation,23,Franchise,Complex\n6781,406.9,Boston,N,2,16,3,38,High School,26,Patnership,Standalone\n1928,460.98,NJ City,W,10,22,18,52,High School,25,Franchise,Complex\n3776,56.54,NJ City,W,4,17,3,47,No education,18,Franchise,Complex\n7924,492.52,Boston,W,1,19,7,75,Graduation,34,Franchise,Complex\n8427,237.4,NJ City,E,10,11,25,74,Graduation,32,Owner,Standalone\n6049,99.12,Boston,N,12,13,2,27,High School,28,Patnership,Standalone\n1607,488.52,NJ City,E,19,22,21,54,High School,28,Owner,Standalone\n6504,90.77,Boston,W,14,24,1,44,Graduation,25,Franchise,Complex\n3804,26.75,NJ City,N,7,18,2,67,Graduation,18,Franchise,Complex\n5101,3.8,Boston,N,9,15,1,79,High School,32,Patnership,Standalone\n8950,147.73,NJ City,S,20,19,16,73,Graduation,31,Patnership,Complex\n9416,404.78,NJ City,W,21,18,23,78,Graduation,24,Franchise,Complex\n3360,108.79,NJ City,W,25,8,4,26,Graduation,28,Franchise,Complex\n6629,179.91,Boston,N,14,8,21,32,Graduation,33,Patnership,Standalone\n3081,239.75,NJ City,N,11,12,3,62,Graduation,34,Patnership,Standalone\n9626,106.83,NJ City,S,10,9,4,44,No education,20,Patnership,Complex\n2008,320.6,NJ City,W,13,15,25,73,High School,27,Franchise,Complex\n6331,75.68,Boston,E,17,24,3,50,High School,25,Owner,Standalone\n5725,289.07,Boston,N,13,20,9,39,Graduation,21,Patnership,Standalone\n3396,195.7,NJ City,W,11,23,13,39,High School,33,Franchise,Complex\n1935,242.86,NJ City,E,21,8,24,54,High School,22,Owner,Standalone\n77,316,Boston,N,1,8,15,83,High School,29,Patnership,Standalone\n4470,39.01,NJ City,N,17,20,4,60,Graduation,33,Patnership,Complex\n7121,50.47,Boston,N,2,22,3,67,No education,29,Patnership,Standalone\n7856,82.52,Boston,W,1,12,4,53,No education,20,Franchise,Complex\n4913,180.2,NJ City,N,8,15,2,69,No education,19,Patnership,Standalone\n5566,259.77,Boston,S,10,18,15,40,Graduation,25,Patnership,Complex\n3392,403.36,NJ City,W,20,19,11,61,High School,25,Franchise,Complex\n58,155.3,Boston,S,12,24,18,33,High School,31,Patnership,Complex\n2271,469.66,NJ City,E,18,9,10,69,High School,29,Owner,Standalone\n216,305.51,NJ City,W,7,11,5,31,Graduation,28,Franchise,Complex\n7581,51.18,Boston,N,25,20,3,37,Graduation,26,Patnership,Standalone\n2225,436.47,NJ City,N,25,23,25,39,High School,20,Patnership,Standalone\n6185,112.96,Boston,N,14,19,2,33,No education,18,Patnership,Standalone\n69,400.58,Maine,N,21,8,11,66,High School,31,Patnership,Standalone\n1775,53.45,NJ City,E,5,14,2,75,No education,33,Owner,Standalone\n522,218,NJ City,S,2,14,25,82,High School,28,Patnership,Complex\n8580,63.3,NJ City,N,22,9,1,43,Graduation,26,Franchise,Complex\n922,144.62,NJ City,S,12,24,4,40,High School,21,Patnership,Complex\n1030,9.78,NJ City,N,12,21,4,27,High School,18,Patnership,Complex\n3069,472.87,NJ City,N,21,24,7,65,High School,32,Patnership,Standalone\n3974,127.17,NJ City,S,15,10,2,31,No education,18,Patnership,Complex\n8866,84.84,NJ City,S,11,15,3,75,High School,29,Patnership,Complex\n1814,9.36,NJ City,N,10,24,1,44,No education,33,Patnership,Complex\n706,126.46,NJ City,S,2,11,3,57,High School,24,Patnership,Complex\n5466,159.47,Boston,S,24,11,9,76,Graduation,34,Patnership,Complex\n4217,221.54,NJ City,N,14,15,5,67,No education,31,Patnership,Standalone\n9041,179.97,NJ City,N,8,18,9,82,Graduation,31,Patnership,Standalone\n119,102.64,NJ City,E,12,13,2,47,No education,34,Owner,Standalone\n7827,313.82,Boston,E,19,11,8,82,Graduation,18,Owner,Standalone\n4017,253.55,NJ City,N,6,15,14,35,High School,23,Patnership,Standalone\n5150,461.66,Boston,S,12,12,25,38,Graduation,22,Patnership,Complex\n738,77.62,NJ City,S,23,8,3,73,Graduation,19,Patnership,Complex\n4603,201.22,NJ City,E,10,16,18,40,High School,25,Owner,Standalone\n2503,360.74,NJ City,E,19,8,25,43,High School,35,Owner,Standalone\n4494,388.76,NJ City,S,22,8,14,76,High School,21,Patnership,Complex\n1443,484.02,NJ City,E,22,23,18,76,High School,28,Owner,Standalone\n3513,471.63,NJ City,N,24,13,25,61,High School,21,Patnership,Standalone\n8567,191.16,NJ City,E,14,11,18,74,Graduation,24,Owner,Standalone\n8380,141.46,NJ City,W,15,15,17,26,Graduation,21,Franchise,Complex\n9946,108.21,NJ City,S,3,20,1,44,High School,30,Patnership,Complex\n223,264.33,NJ City,E,16,17,4,42,High School,26,Owner,Standalone\n6367,51.88,Boston,E,1,17,3,38,High School,22,Owner,Standalone\n6075,304.79,Boston,E,13,21,5,58,Graduation,23,Owner,Standalone\n7329,138.27,Boston,N,2,20,5,75,Graduation,22,Patnership,Standalone\n7136,338.23,Boston,W,24,18,10,55,Graduation,20,Franchise,Complex\n1400,185.83,NJ City,W,16,13,13,54,High School,26,Franchise,Complex\n7039,395.75,Boston,E,5,17,17,34,Graduation,22,Owner,Standalone\n4346,239,NJ City,S,2,22,18,40,High School,33,Patnership,Complex\n5052,320.35,Boston,W,13,17,22,51,Graduation,23,Franchise,Complex\n9522,90.75,NJ City,S,22,16,3,79,Graduation,28,Patnership,Complex\n4652,264,NJ City,W,1,11,18,26,High School,28,Franchise,Complex\n5904,434.52,Boston,W,19,22,25,46,Graduation,33,Franchise,Complex\n3352,102,NJ City,W,3,13,20,69,High School,19,Franchise,Complex\n8877,146.96,NJ City,N,2,8,7,83,Graduation,29,Patnership,Standalone\n8398,207.01,NJ City,S,5,11,17,25,Graduation,28,Patnership,Complex\n2397,39.5,NJ City,N,19,16,4,55,Graduation,29,Patnership,Standalone\n5379,417.83,Boston,E,23,24,7,77,Graduation,30,Owner,Standalone\n178,381.88,NJ City,S,7,21,23,77,High School,26,Patnership,Complex\n1325,412.02,NJ City,N,25,23,11,75,High School,30,Patnership,Standalone\n5618,287.98,Boston,S,2,16,17,82,Graduation,29,Patnership,Complex\n8698,101.08,NJ City,S,15,22,4,71,High School,25,Patnership,Complex\n4792,354.85,NJ City,W,24,17,5,45,High School,32,Franchise,Complex\n7990,106.47,Boston,S,13,16,2,75,High School,31,Patnership,Complex\n2509,200.76,NJ City,N,13,24,18,55,High School,22,Patnership,Standalone\n1189,152.84,NJ City,N,9,19,5,50,High School,27,Patnership,Standalone\n848,160,NJ City,W,1,15,7,73,High School,31,Franchise,Complex\n2076,350.74,NJ City,W,13,21,18,63,High School,31,Franchise,Complex\n5219,432.34,Boston,E,6,15,11,52,Graduation,25,Owner,Standalone\n9642,92.93,NJ City,S,19,16,4,36,Graduation,28,Patnership,Complex\n5759,99.53,Boston,E,22,20,1,81,No education,30,Owner,Standalone\n6721,451.02,Boston,N,15,17,20,25,Graduation,31,Patnership,Standalone\n7654,416.13,Boston,S,25,12,14,69,Graduation,32,Patnership,Complex\n1462,320.66,NJ City,S,7,16,25,37,High School,25,Patnership,Complex\n4632,256,NJ City,W,2,15,24,82,High School,34,Franchise,Complex\n1429,185.16,NJ City,N,16,14,8,29,High School,20,Patnership,Standalone\n389,76.91,NJ City,N,10,12,4,69,No education,22,Patnership,Standalone\n8943,486.51,NJ City,E,7,16,19,79,Graduation,30,Owner,Standalone\n3197,372.62,NJ City,N,12,16,14,38,High School,29,Patnership,Standalone\n8761,83.34,NJ City,N,20,21,2,36,High School,28,Patnership,Standalone\n1716,149.32,NJ City,W,15,17,19,58,High School,33,Franchise,Complex\n1241,126.36,NJ City,N,22,19,4,58,No education,24,Patnership,Standalone\n902,337.39,NJ City,S,16,9,20,42,High School,30,Patnership,Complex\n240,23.38,NJ City,N,6,19,1,48,Graduation,35,Franchise,Complex\n9123,451.18,NJ City,E,10,8,20,81,Graduation,29,Owner,Standalone\n698,191.15,NJ City,S,23,17,2,35,No education,26,Patnership,Complex\n4413,84.59,NJ City,N,5,15,2,41,Graduation,25,Patnership,Standalone\n2876,91.18,NJ City,W,4,12,1,79,No education,35,Franchise,Complex\n941,164.31,NJ City,N,16,10,13,68,High School,26,Patnership,Standalone\n5090,131.94,Boston,S,17,16,16,37,Graduation,28,Patnership,Complex\n1114,106.53,NJ City,S,24,18,3,71,High School,28,Patnership,Complex\n4071,258.54,NJ City,E,4,15,13,62,High School,32,Owner,Standalone\n1882,158.2,NJ City,S,19,23,16,35,High School,20,Patnership,Complex\n9346,107.77,NJ City,S,20,19,4,53,High School,18,Patnership,Complex\n9065,286.38,NJ City,N,5,13,24,41,Graduation,27,Patnership,Standalone\n5581,380,Boston,N,19,22,19,30,Graduation,30,Patnership,Standalone\n4301,315.32,NJ City,N,4,10,13,38,High School,35,Patnership,Standalone\n2921,355.12,NJ City,N,24,23,6,28,High School,34,Patnership,Standalone\n5841,84.54,Boston,N,21,11,4,51,Graduation,32,Patnership,Standalone\n9944,249.28,NJ City,W,12,10,24,40,Graduation,20,Franchise,Complex\n2387,103,NJ City,E,1,19,8,80,High School,28,Owner,Standalone\n4080,428.55,NJ City,W,25,12,14,31,High School,26,Franchise,Complex\n1841,397.93,NJ City,N,22,14,14,74,High School,21,Patnership,Standalone\n8473,195.3,NJ City,N,17,19,12,26,Graduation,22,Patnership,Standalone\n866,389.2,NJ City,S,17,18,24,68,High School,26,Patnership,Complex\n7987,299.52,Boston,E,15,13,8,47,Graduation,20,Owner,Standalone\n3437,481.88,NJ City,N,10,15,25,42,High School,30,Patnership,Standalone\n8907,474.77,NJ City,E,16,15,9,85,Graduation,23,Owner,Standalone\n2148,289.09,NJ City,W,5,16,7,73,High School,31,Franchise,Complex\n7503,238.06,Boston,E,21,11,4,29,Graduation,35,Owner,Standalone\n9981,56.37,NJ City,N,10,23,1,63,Graduation,24,Patnership,Standalone\n7406,79.79,Boston,S,10,14,1,82,No education,27,Patnership,Complex\n8130,495.19,NJ City,S,16,19,9,57,Graduation,27,Patnership,Complex\n4998,183.31,Boston,S,17,21,8,25,High School,29,Patnership,Complex\n2307,72.52,NJ City,N,11,23,4,50,Graduation,25,Owner,Standalone\n4979,306.17,Boston,E,22,8,23,43,High School,18,Owner,Standalone\n1428,273.89,NJ City,W,5,20,14,44,High School,25,Franchise,Complex\n408,274.87,NJ City,W,13,12,6,38,High School,21,Franchise,Complex\n6309,249.81,Boston,N,9,13,9,45,Graduation,22,Patnership,Standalone\n2524,140.63,NJ City,W,22,9,25,38,High School,22,Franchise,Complex\n460,74.94,NJ City,W,19,15,4,81,High School,28,Franchise,Complex\n5872,62.89,Boston,N,25,24,4,32,High School,27,Franchise,Complex\n3110,493.22,NJ City,S,4,23,4,44,No education,26,Patnership,Complex\n6834,499.81,Boston,S,10,13,1,32,Graduation,32,Patnership,Complex\n8149,168.94,NJ City,N,14,8,22,33,Graduation,22,Patnership,Standalone\n1285,216.73,NJ City,N,16,8,17,62,High School,34,Patnership,Standalone\n5744,1.5,Boston,N,21,12,4,79,No education,19,Franchise,Complex\n6413,175.52,Boston,N,24,21,20,71,Graduation,30,Patnership,Standalone\n35,107.46,Boston,E,3,14,2,43,No education,26,Owner,Standalone\n1681,395.62,NJ City,N,6,20,20,77,High School,33,Patnership,Standalone\n9265,481.09,NJ City,N,25,23,24,27,Graduation,25,Patnership,Standalone\n51,46.95,Boston,N,22,23,2,48,Graduation,25,Owner,Standalone\n9221,290.73,NJ City,N,3,24,22,83,Graduation,30,Patnership,Standalone\n9710,487.34,NJ City,S,15,11,14,56,Graduation,18,Patnership,Complex\n5273,390.75,Boston,N,1,23,13,75,Graduation,31,Patnership,Standalone\n9107,331.01,NJ City,E,19,9,15,57,Graduation,35,Owner,Standalone\n8493,478.79,NJ City,N,20,21,6,58,Graduation,18,Patnership,Standalone\n4334,278,NJ City,S,3,16,14,76,High School,18,Patnership,Complex\n230,340.7,NJ City,S,16,12,5,33,No education,35,Patnership,Complex\n7171,59.9,Boston,E,9,8,2,37,High School,18,Owner,Standalone\n2679,433.45,NJ City,E,13,18,13,61,High School,25,Owner,Standalone\n6523,156.82,Boston,E,25,13,17,74,Graduation,31,Owner,Standalone\n6973,224.42,Boston,N,10,14,5,72,High School,29,Patnership,Standalone\n7661,411.72,Boston,N,19,22,17,60,Graduation,28,Patnership,Standalone\n9056,112.94,NJ City,W,8,16,2,84,No education,31,Franchise,Complex\n6818,492.97,Boston,S,8,16,15,38,Graduation,25,Patnership,Complex\n4991,410.05,Boston,E,12,23,14,79,High School,23,Owner,Standalone\n6775,261.47,Boston,E,3,20,24,47,Graduation,31,Owner,Standalone\n5153,76.68,Boston,N,12,20,3,50,No education,27,Patnership,Standalone\n1213,152.17,NJ City,N,23,17,10,25,High School,23,Patnership,Standalone\n8758,324.92,NJ City,S,16,15,1,72,High School,21,Patnership,Complex\n8641,168.07,NJ City,N,4,22,9,75,Graduation,29,Patnership,Standalone\n4614,87.44,NJ City,S,5,20,2,43,Graduation,29,Patnership,Complex\n9906,50.24,NJ City,S,19,13,4,53,Graduation,18,Patnership,Complex\n6454,112.81,Boston,S,21,20,4,81,High School,26,Patnership,Complex\n6098,276.51,Boston,S,11,19,7,77,Graduation,19,Patnership,Complex\n4571,106.61,NJ City,E,22,23,3,25,No education,24,Owner,Standalone\n8145,48.68,NJ City,N,23,23,3,31,Graduation,32,Patnership,Standalone\n2412,300.28,NJ City,W,22,21,12,73,High School,20,Franchise,Complex\n2701,219.7,NJ City,N,5,10,5,51,High School,18,Patnership,Standalone\n2651,173.6,NJ City,E,24,16,23,32,High School,27,Owner,Standalone\n7567,393.01,Boston,E,20,13,24,68,Graduation,23,Owner,Standalone\n3249,417.17,NJ City,N,17,17,6,49,High School,27,Patnership,Standalone\n9359,450.94,NJ City,E,10,22,5,43,No education,20,Owner,Standalone\n9695,200.4,NJ City,E,9,22,12,76,Graduation,32,Owner,Standalone\n8940,16.76,NJ City,N,20,19,2,66,Graduation,21,Franchise,Complex\n4229,106,NJ City,N,1,9,19,35,High School,32,Patnership,Standalone\n1789,70.54,NJ City,N,9,21,1,28,High School,23,Patnership,Standalone\n3459,11.71,NJ City,N,14,11,1,54,Graduation,28,Owner,Standalone\n2361,148.94,NJ City,N,22,12,22,83,High School,19,Patnership,Standalone\n5085,357.49,Boston,N,10,10,1,28,Graduation,35,Patnership,Standalone\n3268,298.64,NJ City,W,22,19,17,42,High School,23,Franchise,Complex\n3407,233.44,NJ City,E,6,24,24,38,High School,30,Owner,Standalone\n9700,214.42,NJ City,W,19,10,9,55,Graduation,34,Franchise,Complex\n8215,4.88,NJ City,N,2,17,4,61,High School,33,Owner,Standalone\n7147,137.66,Boston,E,20,12,7,28,Graduation,34,Owner,Standalone\n5575,270.4,Boston,E,10,8,6,58,Graduation,18,Owner,Standalone\n8639,283.66,NJ City,E,16,21,17,28,Graduation,26,Owner,Standalone\n6163,345.1,Boston,E,4,21,1,30,High School,24,Owner,Standalone\n6723,104.78,Boston,E,14,13,2,64,Graduation,24,Owner,Standalone\n3869,115.51,NJ City,N,24,11,3,84,No education,33,Patnership,Standalone\n9800,173.39,NJ City,W,2,11,3,25,No education,35,Franchise,Complex\n7657,176.81,Boston,N,19,8,20,65,Graduation,28,Patnership,Standalone\n1147,360.81,NJ City,E,5,20,11,76,High School,27,Owner,Standalone\n7677,392.53,Boston,N,12,24,4,25,Graduation,30,Patnership,Standalone\n2039,213,NJ City,E,2,15,6,79,High School,29,Owner,Standalone\n1700,25.37,NJ City,N,11,17,1,83,No education,30,Franchise,Complex\n4665,370.8,NJ City,N,19,21,8,71,High School,26,Patnership,Standalone\n3957,185.86,NJ City,N,19,13,22,33,High School,28,Patnership,Standalone\n4699,121.08,NJ City,E,20,17,4,49,High School,32,Owner,Standalone\n6376,377.43,Boston,W,23,12,18,63,Graduation,32,Franchise,Complex\n9141,52.74,NJ City,N,8,20,4,66,Graduation,26,Patnership,Standalone\n9096,224.74,NJ City,W,18,17,9,84,Graduation,28,Franchise,Complex\n7883,115.38,Boston,E,7,16,1,59,No education,20,Owner,Standalone\n1500,364.86,NJ City,W,6,23,12,26,High School,30,Franchise,Complex\n9387,193.32,NJ City,E,4,9,22,56,Graduation,26,Owner,Standalone\n7159,267.81,Boston,E,11,19,11,78,Graduation,26,Owner,Standalone\n777,416.1,NJ City,N,16,21,17,40,High School,26,Patnership,Standalone\n6209,28.67,Boston,N,10,14,2,54,No education,19,Patnership,Standalone\n2646,385.21,NJ City,S,13,8,13,41,High School,27,Patnership,Complex\n7571,73.31,Boston,N,1,11,3,29,No education,30,Owner,Standalone\n5322,108.42,Boston,S,9,22,2,62,Graduation,32,Patnership,Complex\n6250,209.16,Boston,S,1,17,9,59,Graduation,30,Patnership,Complex\n4663,205,NJ City,E,1,8,6,74,High School,20,Owner,Standalone\n8932,31.23,NJ City,N,15,14,4,83,High School,22,Franchise,Complex\n1169,131,NJ City,N,1,15,19,39,High School,30,Patnership,Standalone\n3485,430.37,NJ City,N,11,16,3,40,No education,22,Patnership,Standalone\n3159,458.63,NJ City,E,6,20,19,59,High School,24,Owner,Standalone\n8276,178.63,NJ City,W,1,12,1,63,No education,30,Franchise,Complex\n6378,217.39,Boston,S,18,13,9,56,Graduation,28,Patnership,Complex\n6966,157.81,Boston,S,8,10,3,44,Graduation,18,Patnership,Complex\n1158,278.34,NJ City,S,13,9,5,52,Graduation,28,Patnership,Complex\n3245,67.12,NJ City,N,11,18,4,65,No education,24,Patnership,Standalone\n2092,213.43,NJ City,W,6,19,15,66,High School,18,Franchise,Complex\n5333,314.27,Boston,N,22,24,15,34,Graduation,24,Patnership,Standalone\n1909,41.39,NJ City,N,2,20,4,51,High School,19,Patnership,Standalone\n3124,82.41,NJ City,W,21,10,1,55,High School,32,Franchise,Complex\n386,241,NJ City,S,3,22,11,35,High School,26,Patnership,Complex\n5931,488.81,Boston,E,25,14,7,58,Graduation,19,Owner,Standalone\n1809,444.36,NJ City,N,4,10,17,39,High School,25,Patnership,Standalone\n4817,77.73,NJ City,N,7,22,2,75,No education,19,Patnership,Standalone\n8295,171.59,NJ City,E,22,19,5,54,Graduation,21,Owner,Standalone\n23,302.16,NY City,E,3,8,1,57,No education,28,Owner,Standalone\n7646,335.76,Boston,S,8,14,10,51,Graduation,23,Patnership,Complex\n7477,328.14,Boston,N,11,19,14,45,Graduation,25,Patnership,Standalone\n4865,396.75,NJ City,N,18,22,20,45,High School,19,Patnership,Standalone\n3618,2.15,NJ City,S,4,16,3,43,Graduation,34,Patnership,Complex\n1486,90.53,NJ City,S,10,24,4,73,High School,30,Patnership,Complex\n6927,217.89,Boston,E,3,22,23,85,Graduation,29,Owner,Standalone\n4719,53.85,NJ City,E,9,12,2,51,Graduation,35,Owner,Standalone\n2900,414.38,NJ City,W,9,17,2,82,No education,27,Franchise,Complex\n7325,286.85,Boston,N,9,12,17,76,Graduation,25,Patnership,Standalone\n2179,125.62,NJ City,E,12,10,1,81,High School,23,Owner,Standalone\n7693,322.52,Boston,N,3,17,3,29,High School,27,Patnership,Standalone\n722,118.24,NJ City,S,17,24,2,74,No education,28,Patnership,Complex\n4902,125,NJ City,S,3,15,13,46,High School,33,Patnership,Complex\n9077,83.7,NJ City,N,8,19,3,39,No education,28,Patnership,Standalone\n3474,126.48,NJ City,S,21,13,2,27,Graduation,29,Patnership,Complex\n8009,482.54,Boston,N,13,23,21,32,Graduation,31,Patnership,Standalone\n2216,157.55,NJ City,W,24,21,2,68,No education,19,Franchise,Complex\n3118,249.13,NJ City,S,23,18,10,46,High School,28,Patnership,Complex\n6404,2.93,Boston,W,1,19,3,39,No education,32,Franchise,Complex\n8939,203.32,NJ City,E,9,13,21,35,Graduation,18,Owner,Standalone\n1566,318,NJ City,S,1,19,12,79,High School,28,Patnership,Complex\n9570,427.49,NJ City,S,10,11,14,69,Graduation,34,Patnership,Complex\n6686,56.84,Boston,S,14,17,4,36,No education,22,Patnership,Complex\n8887,89.91,NJ City,E,8,19,2,38,High School,34,Owner,Standalone\n1569,332.99,NJ City,N,21,15,3,82,Graduation,25,Patnership,Standalone\n3472,7.65,NJ City,N,24,10,1,51,High School,28,Franchise,Complex\n7284,339.8,Boston,W,23,22,9,41,Graduation,20,Franchise,Complex\n3535,318.34,NJ City,E,16,13,18,64,High School,25,Owner,Standalone\n6753,130.89,Boston,N,6,12,16,65,Graduation,20,Patnership,Standalone\n8660,450.36,NJ City,W,11,12,17,70,Graduation,33,Franchise,Complex\n701,325.83,NJ City,N,22,17,19,25,High School,25,Patnership,Standalone\n8830,66.54,NJ City,N,19,9,2,40,High School,30,Patnership,Complex\n1982,323.27,NJ City,S,11,22,10,81,High School,19,Patnership,Complex\n4945,25.16,Boston,N,13,14,2,69,High School,21,Patnership,Standalone\n6067,316.67,Boston,E,24,23,7,42,Graduation,35,Owner,Standalone\n4266,53.38,NJ City,S,16,17,3,55,Graduation,24,Patnership,Complex\n2857,379.5,NJ City,N,8,22,10,63,High School,21,Patnership,Standalone\n9571,155.62,NJ City,E,17,21,19,73,Graduation,19,Owner,Standalone\n9048,164.95,NJ City,W,8,24,4,82,Graduation,18,Franchise,Complex\n742,8.59,NJ City,N,11,16,1,37,High School,33,Patnership,Complex\n5964,198.33,Boston,W,11,9,10,41,Graduation,21,Franchise,Complex\n4828,27.64,NJ City,N,21,18,1,80,High School,28,Franchise,Complex\n3858,258,NJ City,S,13,23,1,55,Graduation,32,Patnership,Complex\n5228,444.26,Boston,W,22,23,11,61,Graduation,25,Franchise,Complex\n918,421.33,NJ City,S,12,19,12,80,High School,25,Patnership,Complex\n3130,246.01,NJ City,S,18,21,4,33,High School,26,Patnership,Complex\n2178,280.92,NJ City,S,25,17,25,58,High School,26,Patnership,Complex\n3608,166.15,NJ City,W,3,20,1,41,No education,23,Franchise,Complex\n2018,209.28,NJ City,S,9,18,13,34,High School,31,Patnership,Complex\n2408,99.59,NJ City,W,5,22,3,54,No education,35,Franchise,Complex\n2086,453.56,NJ City,S,13,24,16,28,High School,31,Patnership,Complex\n4046,262,NJ City,S,2,15,21,41,High School,27,Patnership,Complex\n5435,107.31,Boston,E,6,16,4,48,No education,22,Owner,Standalone\n8764,121.84,NJ City,W,12,15,2,71,High School,32,Franchise,Complex\n7387,166.13,Boston,E,16,16,19,51,Graduation,18,Owner,Standalone\n1897,80.32,NJ City,N,7,24,1,30,High School,35,Patnership,Standalone\n4065,273.32,NJ City,N,4,19,19,33,High School,23,Patnership,Standalone\n8285,304.41,NJ City,N,9,9,7,26,Graduation,31,Patnership,Standalone\n2196,279.05,NJ City,W,10,19,3,41,Graduation,33,Franchise,Complex\n3729,465.5,NJ City,N,20,19,21,54,High School,33,Patnership,Standalone\n1006,430.87,NJ City,S,14,15,14,43,High School,30,Patnership,Complex\n6408,367.14,Boston,W,24,20,22,53,Graduation,24,Franchise,Complex\n3872,210.22,NJ City,W,24,8,4,51,No education,28,Franchise,Complex\n2140,376.84,NJ City,W,20,23,15,57,High School,18,Franchise,Complex\n306,143.04,NJ City,S,23,8,16,60,High School,31,Patnership,Complex\n3598,239.36,NJ City,S,14,9,18,57,High School,29,Patnership,Complex\n3226,233,NJ City,S,2,14,6,56,High School,20,Patnership,Complex\n1019,37.27,NJ City,N,10,19,1,55,No education,32,Owner,Standalone\n8138,136.99,NJ City,S,1,20,11,48,Graduation,35,Patnership,Complex\n9961,3.44,NJ City,N,14,16,1,60,High School,29,Patnership,Standalone\n2796,346.39,NJ City,W,13,23,17,36,High School,18,Franchise,Complex\n6610,198.7,Boston,S,19,8,24,46,Graduation,30,Patnership,Complex\n3216,294.87,NJ City,W,4,18,6,41,High School,19,Franchise,Complex\n874,481.44,NJ City,S,7,10,7,75,High School,28,Patnership,Complex\n8868,231.87,NJ City,W,10,13,24,50,Graduation,24,Franchise,Complex\n7811,400.85,Boston,E,11,8,9,52,Graduation,19,Owner,Standalone\n9872,469.37,NJ City,W,4,19,2,54,No education,22,Franchise,Complex\n2702,90.37,NJ City,S,2,23,2,70,No education,32,Patnership,Complex\n4712,348.07,NJ City,W,20,18,5,29,No education,21,Franchise,Complex\n926,390.71,NJ City,S,22,18,12,37,High School,28,Patnership,Complex\n7814,403.78,Boston,S,4,19,7,81,Graduation,21,Patnership,Complex\n3692,225.83,NJ City,W,19,18,14,79,High School,18,Franchise,Complex\n9696,157.26,NJ City,W,18,24,14,53,Graduation,33,Franchise,Complex\n8831,187.27,NJ City,E,3,16,12,48,Graduation,26,Owner,Standalone\n4935,110.52,NJ City,E,21,15,4,34,Graduation,21,Owner,Standalone\n6974,387.08,Boston,S,22,22,2,33,No education,29,Patnership,Complex\n2591,161,NJ City,E,3,18,22,44,High School,18,Owner,Standalone\n840,451.2,NJ City,W,8,8,2,28,Graduation,28,Franchise,Complex\n5009,451.35,Boston,N,22,19,14,82,Graduation,19,Patnership,Standalone\n6819,206.62,Boston,E,5,15,1,42,Graduation,35,Owner,Standalone\n9239,4.33,NJ City,N,22,18,3,42,No education,25,Owner,Standalone\n5063,45.98,Boston,N,15,13,2,70,No education,30,Owner,Standalone\n3785,58.1,NJ City,N,5,19,4,38,No education,31,Patnership,Standalone\n9555,14.64,NJ City,N,15,18,1,84,Graduation,23,Owner,Standalone\n4898,356.12,NJ City,S,20,8,15,74,High School,31,Patnership,Complex\n8985,15.23,NJ City,N,7,20,1,83,Graduation,24,Patnership,Standalone\n2066,16.96,NJ City,N,20,20,4,68,No education,34,Patnership,Complex\n133,235,NJ City,N,1,21,25,69,High School,30,Patnership,Standalone\n9818,55.75,NJ City,S,17,14,3,56,No education,23,Patnership,Complex\n3491,480.29,NJ City,E,15,10,1,46,No education,23,Owner,Standalone\n3154,127.9,NJ City,S,17,22,2,34,High School,21,Patnership,Complex\n1299,173.68,NJ City,E,18,19,24,56,High School,18,Owner,Standalone\n3620,100.38,NJ City,W,19,21,4,56,No education,27,Franchise,Complex\n2194,89.67,NJ City,S,6,15,4,26,High School,22,Patnership,Complex\n2604,344.83,NJ City,W,4,22,5,60,Graduation,32,Franchise,Complex\n2853,131.92,NJ City,N,11,15,3,71,Graduation,34,Patnership,Standalone\n6361,366.5,Boston,N,1,13,21,79,Graduation,21,Patnership,Standalone\n1772,221.45,NJ City,W,13,9,23,26,High School,22,Franchise,Complex\n2230,138.95,NJ City,S,1,10,3,75,High School,22,Patnership,Complex\n135,103.04,NJ City,E,10,22,1,41,Graduation,33,Owner,Standalone\n1860,69.5,NJ City,N,17,19,3,63,Graduation,30,Franchise,Complex\n3597,228.48,NJ City,N,5,16,7,65,High School,27,Patnership,Standalone\n6710,376.24,Boston,S,18,14,17,58,Graduation,27,Patnership,Complex\n3628,446.69,NJ City,W,14,11,16,56,High School,35,Franchise,Complex\n1092,460.38,NJ City,W,12,23,10,62,High School,23,Franchise,Complex\n8781,360.66,NJ City,N,24,21,18,62,Graduation,31,Patnership,Standalone\n2215,246.29,NJ City,E,11,10,13,60,High School,28,Owner,Standalone\n6290,186.37,Boston,S,24,10,10,36,Graduation,19,Patnership,Complex\n5061,329.57,Boston,N,23,19,9,26,Graduation,34,Patnership,Standalone\n5114,302.57,Boston,S,4,13,17,80,Graduation,29,Patnership,Complex\n3717,6.62,NJ City,N,2,13,3,68,Graduation,27,Patnership,Standalone\n4181,286,NJ City,N,2,23,21,51,High School,23,Patnership,Standalone\n932,439.06,NJ City,W,4,20,15,35,High School,32,Franchise,Complex\n9724,232.01,NJ City,W,2,20,25,45,Graduation,27,Franchise,Complex\n9879,175.64,NJ City,E,17,10,18,63,Graduation,20,Owner,Standalone\n8606,172.32,NJ City,S,20,14,4,25,No education,32,Patnership,Complex\n1237,416.73,NJ City,N,5,24,24,65,High School,28,Patnership,Standalone\n9887,140.48,NJ City,E,9,16,10,43,Graduation,18,Owner,Standalone\n2089,377.17,NJ City,N,24,11,20,25,High School,25,Patnership,Standalone\n382,77.84,NJ City,S,13,20,3,29,High School,26,Patnership,Complex\n1482,178.11,NJ City,S,20,21,6,26,High School,27,Patnership,Complex\n6780,241.33,Boston,W,9,15,11,77,Graduation,33,Franchise,Complex\n1818,51.95,NJ City,S,24,17,2,67,Graduation,22,Patnership,Complex\n260,245.26,NJ City,W,25,19,14,75,High School,24,Franchise,Complex\n8171,445.64,NJ City,E,18,20,13,37,Graduation,23,Owner,Standalone\n6395,258.32,Boston,E,23,24,22,51,Graduation,30,Owner,Standalone\n9092,205.51,NJ City,W,20,17,16,74,Graduation,25,Franchise,Complex\n4311,442.73,NJ City,E,13,21,22,76,High School,24,Owner,Standalone\n10000,159,NJ City,W,16,24,12,59,Graduation,35,Franchise,Complex\n8299,330.28,NJ City,E,11,21,5,68,High School,33,Owner,Standalone\n2136,307.01,NJ City,W,16,14,18,68,High School,22,Franchise,Complex\n4075,315,NJ City,E,1,24,12,49,High School,33,Owner,Standalone\n3846,263.78,NJ City,S,4,22,22,81,High School,32,Patnership,Complex\n2446,166.09,NJ City,S,12,20,20,63,High School,29,Patnership,Complex\n7243,299.7,Boston,E,6,21,15,67,Graduation,22,Owner,Standalone\n7316,207.18,Boston,W,3,22,3,29,No education,18,Franchise,Complex\n8615,472.84,NJ City,E,17,20,19,46,Graduation,18,Owner,Standalone\n6420,83.73,Boston,W,22,11,2,53,Graduation,18,Franchise,Complex\n1699,103.95,NJ City,E,6,18,2,85,High School,30,Owner,Standalone\n7101,250.54,Boston,N,17,23,6,26,Graduation,34,Patnership,Standalone\n9927,436.79,NJ City,E,13,15,4,27,Graduation,31,Owner,Standalone\n2174,468.13,NJ City,S,11,9,10,33,High School,27,Patnership,Complex\n2640,429.66,NJ City,W,13,18,12,57,High School,33,Franchise,Complex\n8442,389.1,NJ City,S,12,13,17,71,Graduation,31,Patnership,Complex\n9179,297.11,NJ City,E,20,24,7,36,Graduation,25,Owner,Standalone\n4225,499.4,NJ City,N,14,8,24,68,High School,28,Patnership,Standalone\n7091,438.74,Boston,E,14,17,17,78,Graduation,25,Owner,Standalone\n293,20.49,NJ City,N,24,11,1,54,No education,34,Patnership,Standalone\n5679,230.27,Boston,E,20,8,14,28,Graduation,23,Owner,Standalone\n7881,187.2,Boston,N,24,11,24,80,Graduation,32,Patnership,Standalone\n9983,302.9,NJ City,E,15,24,16,47,Graduation,22,Owner,Standalone\n7676,317.61,Boston,W,3,15,20,48,Graduation,30,Franchise,Complex\n9744,7.73,NJ City,N,7,22,2,28,Graduation,28,Franchise,Complex\n4037,480.56,NJ City,N,10,17,22,54,High School,20,Patnership,Standalone\n5661,356.8,Boston,N,6,19,17,65,Graduation,19,Patnership,Standalone\n4958,229.5,Boston,S,17,11,15,33,High School,26,Patnership,Complex\n822,322.28,NJ City,S,17,22,19,49,High School,24,Patnership,Complex\n6313,226.89,Boston,N,6,17,4,77,High School,26,Patnership,Standalone\n4709,151.29,NJ City,N,12,8,20,41,High School,25,Patnership,Standalone\n2549,238.41,NJ City,N,21,15,4,80,No education,25,Patnership,Standalone\n3464,86.31,NJ City,W,8,10,3,30,No education,22,Franchise,Complex\n3020,169.5,NJ City,W,12,16,12,49,High School,28,Franchise,Complex\n3010,63.17,NJ City,N,9,8,4,67,High School,22,Patnership,Complex\n9532,58.82,NJ City,W,9,8,2,69,High School,25,Franchise,Complex\n1758,403.92,NJ City,S,20,14,24,50,High School,22,Patnership,Complex\n9924,470.37,NJ City,W,3,20,21,58,Graduation,29,Franchise,Complex\n6434,319.36,Boston,S,11,9,7,28,Graduation,26,Patnership,Complex\n2822,272.68,NJ City,S,20,21,4,66,No education,19,Patnership,Complex\n350,232.05,NJ City,S,6,14,22,75,High School,24,Patnership,Complex\n1579,240.23,NJ City,E,18,21,19,84,High School,35,Owner,Standalone\n5404,363.74,Boston,W,12,22,15,54,Graduation,34,Franchise,Complex\n5838,217.17,Boston,S,24,9,6,85,Graduation,28,Patnership,Complex\n3061,47.13,NJ City,N,11,20,1,37,High School,21,Patnership,Standalone\n4531,6.65,NJ City,N,2,9,1,25,High School,20,Owner,Standalone\n4226,347.13,NJ City,S,19,12,14,78,High School,25,Patnership,Complex\n54,212.17,Maine,S,4,22,22,60,High School,26,Patnership,Complex\n5932,309.73,Boston,W,17,14,12,45,Graduation,25,Franchise,Complex\n8402,143.25,NJ City,S,7,24,1,39,No education,19,Patnership,Complex\n6460,352.3,Boston,W,9,11,24,58,Graduation,23,Franchise,Complex\n2662,161.64,NJ City,S,9,22,3,27,High School,22,Patnership,Complex\n9514,248.08,NJ City,S,23,16,21,69,Graduation,26,Patnership,Complex\n6767,343.25,Boston,E,11,18,11,82,Graduation,33,Owner,Standalone\n3447,174.36,NJ City,E,12,17,25,49,High School,21,Owner,Standalone\n5840,108.91,Boston,W,6,16,1,34,No education,23,Franchise,Complex\n127,274.88,NJ City,E,8,12,1,84,High School,24,Owner,Standalone\n6860,345.22,Boston,W,7,16,23,54,Graduation,19,Franchise,Complex\n685,455.08,NJ City,N,13,20,5,26,High School,23,Patnership,Standalone\n1698,6.88,NJ City,N,5,24,3,27,Graduation,35,Patnership,Complex\n7270,370.86,Boston,S,8,15,17,80,Graduation,19,Patnership,Complex\n6898,234.26,Boston,S,1,20,16,62,Graduation,32,Patnership,Complex\n5448,335.39,Boston,W,20,21,13,31,Graduation,32,Franchise,Complex\n1171,424.19,NJ City,E,9,18,9,35,High School,29,Owner,Standalone\n9270,378.94,NJ City,S,13,18,24,63,Graduation,29,Patnership,Complex\n475,309.95,NJ City,E,14,22,22,53,High School,29,Owner,Standalone\n3951,5.11,NJ City,N,19,16,2,32,Graduation,26,Owner,Standalone\n5299,426.81,Boston,E,11,22,11,46,Graduation,22,Owner,Standalone\n2840,278.56,NJ City,W,13,23,6,61,High School,32,Franchise,Complex\n6939,478.98,Boston,E,12,8,5,71,Graduation,32,Owner,Standalone\n8400,332.37,NJ City,W,25,14,2,28,Graduation,26,Franchise,Complex\n7073,314.69,Boston,N,16,19,23,37,Graduation,28,Patnership,Standalone\n126,425.63,NJ City,S,20,15,11,60,High School,20,Patnership,Complex\n7044,34.32,Boston,N,16,17,2,59,Graduation,26,Franchise,Complex\n1493,225.12,NJ City,N,5,24,22,76,High School,23,Patnership,Standalone\n2736,442.98,NJ City,W,8,14,18,50,High School,30,Franchise,Complex\n9256,257.29,NJ City,W,10,22,24,76,Graduation,25,Franchise,Complex\n1175,274.33,NJ City,E,16,20,20,31,High School,33,Owner,Standalone\n8802,270.1,NJ City,S,18,10,11,30,Graduation,27,Patnership,Complex\n2094,303.65,NJ City,S,8,14,4,44,Graduation,22,Patnership,Complex\n9439,66.98,NJ City,N,15,17,1,66,High School,32,Owner,Standalone\n4447,363.01,NJ City,E,24,15,22,52,High School,30,Owner,Standalone\n5475,448.69,Boston,E,14,8,19,42,Graduation,34,Owner,Standalone\n2118,331.99,NJ City,S,20,24,10,26,High School,31,Patnership,Complex\n4749,226.38,NJ City,N,6,12,2,28,Graduation,21,Patnership,Standalone\n7914,154.57,Boston,S,16,15,13,54,Graduation,26,Patnership,Complex\n5969,45.37,Boston,N,17,14,2,73,No education,27,Patnership,Standalone\n791,108.19,NJ City,E,23,16,1,68,No education,35,Owner,Standalone\n2247,380.66,NJ City,E,6,20,14,78,High School,34,Owner,Standalone\n1179,330.88,NJ City,E,19,14,10,63,High School,25,Owner,Standalone\n8084,301.79,NJ City,W,10,8,13,80,Graduation,33,Franchise,Complex\n7436,432.01,Boston,W,8,22,21,37,Graduation,35,Franchise,Complex\n7471,378.1,Boston,E,5,9,16,42,Graduation,26,Owner,Standalone\n2956,50.55,NJ City,W,22,17,2,78,High School,25,Franchise,Complex\n3397,48.14,NJ City,N,20,19,1,34,High School,26,Patnership,Standalone\n3606,262.07,NJ City,S,22,10,4,27,Graduation,35,Patnership,Complex\n6489,164.55,Boston,N,17,8,12,41,Graduation,35,Patnership,Standalone\n8467,26.19,NJ City,N,12,14,3,40,High School,35,Owner,Standalone\n2872,469.51,NJ City,W,6,17,10,84,High School,20,Franchise,Complex\n7058,420.72,Boston,S,5,15,19,43,Graduation,19,Patnership,Complex\n9215,192.44,NJ City,E,13,11,10,85,Graduation,25,Owner,Standalone\n7144,37.45,Boston,N,6,22,2,56,High School,34,Franchise,Complex\n243,52.42,NJ City,E,20,15,2,67,Graduation,19,Owner,Standalone\n6316,53.08,Boston,W,20,11,4,30,High School,27,Franchise,Complex\n3891,11.45,NJ City,N,20,10,4,54,Graduation,30,Owner,Standalone\n6402,488.55,Boston,S,3,16,20,77,Graduation,31,Patnership,Complex\n5602,352.84,Boston,S,9,14,14,25,Graduation,26,Patnership,Complex\n7030,165.02,Boston,S,16,18,11,50,Graduation,26,Patnership,Complex\n2074,109.8,NJ City,S,19,10,2,54,High School,26,Patnership,Complex\n7083,195.18,Boston,E,14,22,6,57,Graduation,29,Owner,Standalone\n1296,333.67,NJ City,W,8,23,6,84,High School,24,Franchise,Complex\n9323,269.11,NJ City,E,16,23,9,53,Graduation,30,Owner,Standalone\n2253,105.05,NJ City,N,2,9,1,44,Graduation,21,Patnership,Standalone\n6319,258.56,Boston,E,14,14,15,70,Graduation,18,Owner,Standalone\n1875,128.16,NJ City,E,22,18,9,26,High School,29,Owner,Standalone\n3206,470.11,NJ City,S,11,16,1,81,No education,22,Patnership,Complex\n9843,296.74,NJ City,E,10,13,3,51,Graduation,33,Owner,Standalone\n2874,257.6,NJ City,S,12,23,6,26,High School,31,Patnership,Complex\n6233,341.52,Boston,N,18,24,1,39,No education,21,Patnership,Standalone\n4877,433.94,NJ City,N,8,22,13,49,High School,34,Patnership,Standalone\n1580,83.39,NJ City,W,19,18,4,34,No education,25,Franchise,Complex\n9319,245.03,NJ City,E,1,11,25,56,Graduation,22,Owner,Standalone\n6392,268.94,Boston,W,14,11,10,47,Graduation,21,Franchise,Complex\n1689,458.87,NJ City,N,17,15,4,25,Graduation,32,Patnership,Standalone\n5424,237.07,Boston,W,11,22,24,72,Graduation,35,Franchise,Complex\n1697,123.46,NJ City,N,10,10,3,36,No education,32,Patnership,Standalone\n6152,127.88,Boston,W,10,13,2,37,No education,18,Franchise,Complex\n7125,158.59,Boston,N,23,20,24,80,Graduation,35,Patnership,Standalone\n8315,78.16,NJ City,E,24,12,4,76,No education,21,Owner,Standalone\n2642,439.3,NJ City,S,22,13,4,60,No education,33,Patnership,Complex\n1182,293.57,NJ City,S,10,18,7,48,High School,34,Patnership,Complex\n9691,476.08,NJ City,E,11,15,10,26,Graduation,30,Owner,Standalone\n574,75.3,NJ City,S,10,19,3,41,High School,18,Patnership,Complex\n4286,158.12,NJ City,S,7,21,7,56,High School,33,Patnership,Complex\n2801,322.86,NJ City,N,23,9,7,60,High School,34,Patnership,Standalone\n1472,259.17,NJ City,W,13,19,21,38,High School,24,Franchise,Complex\n5702,48.63,Boston,S,1,19,4,71,No education,35,Patnership,Complex\n9111,103.4,NJ City,E,4,15,3,82,Graduation,29,Owner,Standalone\n6464,403.26,Boston,W,11,9,22,52,Graduation,30,Franchise,Complex\n8543,40.84,NJ City,N,15,14,2,35,No education,23,Owner,Standalone\n4770,100.28,NJ City,S,21,20,3,60,Graduation,26,Patnership,Complex\n6140,108.26,Boston,W,23,11,1,28,No education,26,Franchise,Complex\n3913,270.08,NJ City,N,13,11,11,35,High School,33,Patnership,Standalone\n632,101,NJ City,W,1,13,14,85,High School,22,Franchise,Complex\n9758,451.61,NJ City,S,12,9,12,49,Graduation,28,Patnership,Complex\n8456,194.22,NJ City,W,9,13,14,39,Graduation,31,Franchise,Complex\n8080,300.54,NJ City,W,20,17,17,68,Graduation,30,Franchise,Complex\n5481,368.46,Boston,N,3,20,21,29,Graduation,25,Patnership,Standalone\n567,333.33,NJ City,E,13,22,11,39,High School,34,Owner,Standalone\n1741,71.08,NJ City,N,23,22,4,55,High School,21,Patnership,Standalone\n7413,494.21,Boston,N,23,19,15,81,Graduation,20,Patnership,Standalone\n2246,10.63,NJ City,N,22,24,2,59,No education,26,Patnership,Complex\n2142,467.12,NJ City,S,14,20,11,63,High School,26,Patnership,Complex\n3836,246.95,NJ City,W,13,9,22,43,High School,30,Franchise,Complex\n7290,436.51,Boston,S,21,13,18,35,Graduation,29,Patnership,Complex\n4045,134.49,NJ City,N,16,23,1,55,High School,30,Patnership,Standalone\n7172,16.87,Boston,N,16,19,1,38,No education,18,Franchise,Complex\n3602,54.31,NJ City,S,19,22,1,29,No education,27,Patnership,Complex\n4899,295,NJ City,E,2,20,7,26,High School,33,Owner,Standalone\n3190,145.13,NJ City,S,20,11,14,32,High School,30,Patnership,Complex\n8316,433.84,NJ City,W,18,16,18,73,Graduation,34,Franchise,Complex\n8097,450.19,NJ City,N,13,18,6,71,Graduation,21,Patnership,Standalone\n424,326.9,NJ City,W,20,14,16,25,High School,21,Franchise,Complex\n2818,321.62,NJ City,S,11,15,25,42,High School,30,Patnership,Complex\n3217,79.94,NJ City,N,11,11,4,32,High School,28,Patnership,Standalone\n9516,82.24,NJ City,W,7,11,3,82,Graduation,27,Franchise,Complex\n630,358.5,NJ City,S,15,18,15,82,High School,27,Patnership,Complex\n3989,284.15,NJ City,N,12,8,20,73,High School,19,Patnership,Standalone\n3142,388.98,NJ City,S,4,12,14,70,High School,21,Patnership,Complex\n2629,465.23,NJ City,N,7,11,23,61,High School,20,Patnership,Standalone\n8229,465.99,NJ City,N,14,15,3,77,Graduation,26,Patnership,Standalone\n6651,145.57,Boston,E,15,11,23,70,Graduation,34,Owner,Standalone\n6300,224.98,Boston,W,10,13,21,73,Graduation,34,Franchise,Complex\n9146,390.98,NJ City,S,2,14,7,34,Graduation,33,Patnership,Complex\n7494,222.36,Boston,S,11,9,11,77,Graduation,22,Patnership,Complex\n3654,318.45,NJ City,S,21,23,2,43,Graduation,28,Patnership,Complex\n8347,341.21,NJ City,E,3,15,12,60,Graduation,18,Owner,Standalone\n659,293.83,NJ City,E,24,15,10,69,High School,21,Owner,Standalone\n7007,364,Boston,E,16,22,5,45,No education,29,Owner,Standalone\n9063,227.18,NJ City,E,11,12,3,80,Graduation,35,Owner,Standalone\n6134,118.22,Boston,S,19,23,3,30,No education,26,Patnership,Complex\n915,127.45,NJ City,E,20,13,1,46,Graduation,32,Owner,Standalone\n2677,164.09,NJ City,N,9,16,6,50,High School,29,Patnership,Standalone\n2540,362.99,NJ City,W,12,18,13,45,High School,29,Franchise,Complex\n9024,291.94,NJ City,W,24,9,20,58,Graduation,25,Franchise,Complex\n6980,397,Boston,W,9,18,20,76,Graduation,33,Franchise,Complex\n1327,269.81,NJ City,E,7,23,22,60,High School,32,Owner,Standalone\n4106,35.51,NJ City,N,11,24,3,47,No education,34,Patnership,Complex\n7889,188.15,Boston,N,17,24,9,62,Graduation,22,Patnership,Standalone\n7349,420.18,Boston,N,17,9,12,65,Graduation,27,Patnership,Standalone\n2628,92.87,NJ City,W,14,22,2,52,Graduation,22,Franchise,Complex\n1879,378.21,NJ City,E,10,22,5,58,High School,25,Owner,Standalone\n4084,185.24,NJ City,W,21,20,25,51,High School,22,Franchise,Complex\n9332,229.29,NJ City,W,2,17,5,29,No education,20,Franchise,Complex\n1041,315.39,NJ City,N,17,8,6,81,High School,32,Patnership,Standalone\n4054,307.76,NJ City,S,2,11,4,33,High School,25,Patnership,Complex\n3519,61.97,NJ City,N,1,10,3,56,Graduation,34,Owner,Standalone\n9720,5.83,NJ City,N,2,8,3,54,Graduation,33,Franchise,Complex\n1608,223,NJ City,W,2,9,7,48,High School,27,Franchise,Complex\n2497,128.81,NJ City,N,24,14,18,57,High School,22,Patnership,Standalone\n8852,219.03,NJ City,W,14,14,7,50,Graduation,18,Franchise,Complex\n6029,298.41,Boston,N,15,12,1,41,No education,32,Patnership,Standalone\n3071,267.8,NJ City,E,15,22,22,40,High School,31,Owner,Standalone\n5818,181.28,Boston,S,3,15,7,65,Graduation,33,Patnership,Complex\n5988,257.65,Boston,W,1,16,8,75,Graduation,20,Franchise,Complex\n402,329.46,NJ City,S,21,8,20,74,High School,20,Patnership,Complex\n7089,0.26,Boston,N,7,21,4,57,Graduation,23,Patnership,Standalone\n3611,478.64,NJ City,E,17,23,24,38,High School,19,Owner,Standalone\n6596,171.56,Boston,W,24,17,22,59,Graduation,27,Franchise,Complex\n712,349.37,NJ City,W,18,17,6,49,High School,31,Franchise,Complex\n237,256.26,NJ City,N,9,18,6,51,High School,31,Patnership,Standalone\n9254,495.62,NJ City,S,9,21,19,47,Graduation,25,Patnership,Complex\n3875,258.13,NJ City,E,11,15,14,65,High School,27,Owner,Standalone\n8218,150.05,NJ City,S,4,12,23,63,Graduation,33,Patnership,Complex\n1271,232.62,NJ City,E,6,14,6,72,High School,25,Owner,Standalone\n3410,22.52,NJ City,N,5,11,3,69,No education,21,Patnership,Complex\n7526,102.5,Boston,S,24,24,1,32,No education,22,Patnership,Complex\n9395,242.99,NJ City,E,25,18,21,41,Graduation,33,Owner,Standalone\n2717,307.12,NJ City,N,21,11,19,41,High School,34,Patnership,Standalone\n9471,336.95,NJ City,E,12,14,6,32,Graduation,26,Owner,Standalone\n9362,210.92,NJ City,S,11,22,9,25,Graduation,31,Patnership,Complex\n3583,95.82,NJ City,E,12,14,4,67,High School,24,Owner,Standalone\n160,220.01,NJ City,W,6,24,10,74,High School,19,Franchise,Complex\n2707,161.55,NJ City,E,12,14,18,71,High School,23,Owner,Standalone\n515,420.27,NJ City,E,21,8,9,65,High School,19,Owner,Standalone\n692,418.91,NJ City,W,4,24,23,80,High School,25,Franchise,Complex\n4727,294.46,NJ City,E,6,24,10,45,High School,21,Owner,Standalone\n9519,1.34,NJ City,N,3,16,2,38,Graduation,19,Owner,Standalone\n8210,390.66,NJ City,S,17,13,10,79,Graduation,28,Patnership,Complex\n4955,41.8,Boston,N,6,24,1,35,No education,23,Owner,Standalone\n4066,229.84,NJ City,S,25,17,6,34,High School,21,Patnership,Complex\n7608,199.47,Boston,W,12,20,24,43,Graduation,20,Franchise,Complex\n9986,165.63,NJ City,S,1,24,4,38,No education,23,Patnership,Complex\n6890,153.32,Boston,S,12,9,1,62,No education,27,Patnership,Complex\n3068,44.03,NJ City,N,24,9,1,54,No education,28,Franchise,Complex\n7835,25.53,Boston,N,5,15,2,44,No education,23,Owner,Standalone\n7551,400.49,Boston,E,19,24,4,35,Graduation,23,Owner,Standalone\n5125,129.95,Boston,N,19,23,1,66,High School,33,Patnership,Standalone\n8196,308.66,NJ City,W,24,16,7,35,Graduation,18,Franchise,Complex\n1277,162,NJ City,N,13,23,20,25,High School,22,Patnership,Standalone\n7679,395.84,Boston,E,24,11,10,82,Graduation,23,Owner,Standalone\n7495,51.28,Boston,E,8,10,4,27,High School,19,Owner,Standalone\n3802,237,NJ City,S,2,15,19,58,High School,27,Patnership,Complex\n9290,339.46,NJ City,S,21,23,3,55,No education,34,Patnership,Complex\n461,60.74,NJ City,N,13,12,3,39,No education,21,Patnership,Standalone\n7029,393.1,Boston,N,24,18,3,47,Graduation,29,Patnership,Standalone\n116,339.76,NJ City,W,23,13,2,37,No education,24,Franchise,Complex\n7839,132.09,Boston,E,19,12,20,55,Graduation,19,Owner,Standalone\n4903,79.65,NJ City,E,18,15,2,41,High School,32,Owner,Standalone\n3009,424.1,NJ City,N,5,22,13,61,High School,28,Patnership,Standalone\n6451,242.73,Boston,E,4,13,25,33,Graduation,22,Owner,Standalone\n1930,5.8,NJ City,N,8,24,2,62,High School,29,Patnership,Complex\n1984,56.5,NJ City,W,18,16,1,65,High School,29,Franchise,Complex\n5604,478.2,Boston,W,8,18,8,55,Graduation,25,Franchise,Complex\n8965,387.49,NJ City,N,12,22,11,33,Graduation,33,Patnership,Standalone\n9299,442.48,NJ City,E,8,21,17,49,Graduation,28,Owner,Standalone\n2514,181.04,NJ City,S,25,24,6,76,High School,24,Patnership,Complex\n8898,223.64,NJ City,S,25,19,9,75,Graduation,35,Patnership,Complex\n8413,477,NJ City,N,23,8,1,74,High School,28,Patnership,Standalone\n1368,35.27,NJ City,N,20,11,4,44,Graduation,27,Franchise,Complex\n227,499.36,NJ City,E,19,20,12,29,High School,30,Owner,Standalone\n49,9.25,Boston,N,3,9,2,33,High School,25,Patnership,Standalone\n5455,387.07,Boston,E,18,8,15,57,Graduation,23,Owner,Standalone\n5123,110.17,Boston,E,14,16,3,81,No education,23,Owner,Standalone\n4452,25.56,NJ City,N,25,18,1,39,Graduation,31,Franchise,Complex\n3345,419.07,NJ City,N,15,18,20,62,High School,32,Patnership,Standalone\n6787,199.39,Boston,E,20,18,22,80,Graduation,29,Owner,Standalone\n2293,134.4,NJ City,N,9,9,2,34,High School,23,Patnership,Standalone\n5899,463.08,Boston,E,9,11,6,40,Graduation,31,Owner,Standalone\n493,387.41,NJ City,N,6,23,22,83,High School,19,Patnership,Standalone\n3899,150.65,NJ City,E,4,13,16,73,High School,32,Owner,Standalone\n6711,142.66,Boston,E,21,17,23,48,Graduation,34,Owner,Standalone\n8902,313.79,NJ City,S,17,23,1,45,High School,24,Patnership,Complex\n8393,379.4,NJ City,N,20,11,22,84,Graduation,19,Patnership,Standalone\n1290,191.04,NJ City,S,1,18,2,57,Graduation,32,Patnership,Complex\n7269,410.4,Boston,N,16,8,13,67,Graduation,25,Patnership,Standalone\n9032,471.14,NJ City,W,2,18,17,65,Graduation,26,Franchise,Complex\n9322,382.32,NJ City,S,22,19,3,66,High School,31,Patnership,Complex\n6465,56,Boston,N,13,18,3,65,Graduation,32,Patnership,Standalone\n204,49.59,NJ City,W,24,8,2,29,Graduation,26,Franchise,Complex\n560,14.73,NJ City,N,16,12,4,74,No education,33,Franchise,Complex\n2383,343.43,NJ City,E,13,22,10,37,High School,24,Owner,Standalone\n8538,36.66,NJ City,N,16,22,1,80,Graduation,34,Patnership,Complex\n9992,394.74,NJ City,W,24,8,4,30,No education,33,Franchise,Complex\n7690,316.51,Boston,S,15,19,4,35,High School,26,Patnership,Complex\n7706,331.84,Boston,S,9,17,17,55,Graduation,21,Patnership,Complex\n2186,449.37,NJ City,S,8,15,16,51,High School,30,Patnership,Complex\n4798,133.81,NJ City,S,15,21,20,50,High School,21,Patnership,Complex\n7773,435.02,Boston,N,11,18,2,44,Graduation,22,Patnership,Standalone\n9489,459.71,NJ City,N,10,12,25,58,Graduation,31,Patnership,Standalone\n5936,336.83,Boston,W,7,22,2,76,No education,22,Franchise,Complex\n2584,433.78,NJ City,W,23,13,5,63,High School,32,Franchise,Complex\n4315,229.4,NJ City,E,15,11,12,76,High School,21,Owner,Standalone\n2009,105.26,NJ City,N,16,23,1,70,No education,32,Patnership,Standalone\n1148,251.5,NJ City,W,5,16,25,48,High School,34,Franchise,Complex\n8846,481.03,NJ City,S,13,15,9,41,Graduation,26,Patnership,Complex\n7727,417.43,Boston,E,14,18,25,67,Graduation,32,Owner,Standalone\n1955,114,NJ City,E,3,9,14,77,High School,35,Owner,Standalone\n4372,34.89,NJ City,N,24,22,3,40,High School,20,Franchise,Complex\n3394,338.94,NJ City,S,6,12,19,70,High School,18,Patnership,Complex\n3710,300.41,NJ City,S,4,8,18,56,High School,24,Patnership,Complex\n8634,16.9,NJ City,N,25,20,1,27,Graduation,27,Patnership,Complex\n7655,71.95,Boston,N,2,16,1,29,No education,28,Owner,Standalone\n7500,317.89,Boston,W,10,20,15,49,Graduation,21,Franchise,Complex\n5477,252.24,Boston,N,16,12,19,27,Graduation,26,Patnership,Standalone\n829,266.93,NJ City,N,5,18,15,40,High School,33,Patnership,Standalone\n8087,2.94,NJ City,E,23,12,3,74,No education,33,Owner,Standalone\n7758,370.98,Boston,S,10,23,18,70,Graduation,25,Patnership,Complex\n3260,193.05,NJ City,W,13,20,13,77,High School,28,Franchise,Complex\n2170,423.64,NJ City,S,17,9,1,79,High School,19,Patnership,Complex\n1199,475.06,NJ City,E,8,22,25,57,High School,21,Owner,Standalone\n6837,266.2,Boston,N,17,19,23,64,Graduation,27,Patnership,Standalone\n1159,187.76,NJ City,E,10,24,6,36,High School,29,Owner,Standalone\n1265,399.48,NJ City,N,7,24,20,54,High School,26,Patnership,Standalone\n6180,150.9,Boston,W,25,24,18,74,Graduation,23,Franchise,Complex\n338,5.02,NJ City,N,22,24,4,51,No education,25,Patnership,Complex\n6450,7.76,Boston,N,20,8,1,31,Graduation,35,Patnership,Complex\n8116,178.72,NJ City,W,4,12,4,52,High School,18,Franchise,Complex\n9656,340.56,NJ City,W,15,15,19,61,Graduation,33,Franchise,Complex\n8071,414.13,NJ City,E,10,11,4,32,High School,26,Owner,Standalone\n5168,281.71,Boston,W,20,17,11,34,Graduation,33,Franchise,Complex\n7554,28.09,Boston,N,5,9,1,74,Graduation,35,Patnership,Complex\n7293,183.98,Boston,N,23,14,3,61,Graduation,20,Patnership,Standalone\n256,431.55,NJ City,W,8,23,8,60,High School,20,Franchise,Complex\n416,86.35,NJ City,W,19,8,1,75,No education,19,Franchise,Complex\n2529,391.83,NJ City,N,13,24,24,50,High School,35,Patnership,Standalone\n4302,281.38,NJ City,S,10,23,8,60,High School,24,Patnership,Complex\n4957,215.15,Boston,N,11,23,8,29,High School,19,Patnership,Standalone\n3095,78.17,NJ City,E,7,12,4,46,No education,25,Owner,Standalone\n221,387.03,NJ City,N,21,16,1,44,No education,22,Patnership,Standalone\n7653,59.89,Boston,N,15,9,2,29,Graduation,30,Patnership,Standalone\n3878,14.23,NJ City,N,21,8,2,83,No education,31,Patnership,Complex\n5461,229.25,Boston,N,1,18,3,68,High School,24,Patnership,Standalone\n3604,69.51,NJ City,N,10,19,4,80,High School,24,Franchise,Complex\n8755,180.84,NJ City,E,11,13,16,30,Graduation,26,Owner,Standalone\n8161,358.08,NJ City,N,9,19,3,67,High School,33,Patnership,Standalone\n2827,319.82,NJ City,E,19,16,4,76,High School,30,Owner,Standalone\n3015,360.46,NJ City,E,17,20,1,33,Graduation,19,Owner,Standalone\n7061,314.98,Boston,N,21,21,11,72,Graduation,24,Patnership,Standalone\n674,167.04,NJ City,S,20,16,4,55,No education,20,Patnership,Complex\n4730,34.19,NJ City,N,21,23,4,67,No education,23,Patnership,Complex\n3630,59.83,NJ City,S,13,18,4,40,Graduation,34,Patnership,Complex\n6485,297.45,Boston,N,16,10,14,45,Graduation,28,Patnership,Standalone\n3100,8.79,NJ City,N,10,11,2,69,High School,23,Franchise,Complex\n3451,308.52,NJ City,E,15,15,5,52,High School,21,Owner,Standalone\n146,50.69,NJ City,S,18,10,4,75,No education,25,Patnership,Complex\n6791,331.31,Boston,E,16,11,21,81,Graduation,19,Owner,Standalone\n6142,66.34,Boston,N,25,24,1,50,High School,27,Patnership,Complex\n4919,464.61,NJ City,E,25,21,21,28,High School,18,Owner,Standalone\n8032,219.31,Boston,W,12,12,1,48,High School,31,Franchise,Complex\n6292,497.54,Boston,W,14,11,24,30,Graduation,18,Franchise,Complex\n3126,185.4,NJ City,S,13,14,9,50,High School,33,Patnership,Complex\n1446,463.19,NJ City,S,20,9,20,64,High School,28,Patnership,Complex\n1115,354.42,NJ City,E,5,15,25,59,High School,29,Owner,Standalone\n385,418.97,NJ City,N,14,18,25,36,High School,24,Patnership,Standalone\n4338,33.23,NJ City,N,18,21,4,71,Graduation,24,Patnership,Complex\n9012,160.51,NJ City,W,3,18,19,78,Graduation,33,Franchise,Complex\n6806,112.96,Boston,S,17,13,4,75,No education,32,Patnership,Complex\n8437,86.48,NJ City,N,24,22,1,34,High School,24,Patnership,Standalone\n7915,399.61,Boston,E,2,11,12,57,Graduation,31,Owner,Standalone\n4086,302.97,NJ City,S,10,18,18,61,High School,22,Patnership,Complex\n6294,164.52,Boston,S,24,9,10,80,Graduation,28,Patnership,Complex\n36,407.36,NJ City,W,9,20,5,43,Graduation,33,Franchise,Complex\n8859,193.63,NJ City,E,23,13,23,26,Graduation,26,Owner,Standalone\n4496,253.14,NJ City,W,8,21,2,49,No education,33,Franchise,Complex\n4628,489.4,NJ City,W,16,15,16,52,High School,21,Franchise,Complex\n6370,2.99,Boston,N,24,8,4,76,High School,32,Patnership,Complex\n6586,379.37,Boston,S,24,10,1,40,High School,30,Patnership,Complex\n5483,399.16,Boston,E,23,19,11,39,Graduation,31,Owner,Standalone\n9678,111.04,NJ City,S,2,8,4,82,Graduation,28,Patnership,Complex\n7276,64.97,Boston,N,21,23,2,29,High School,22,Franchise,Complex\n7227,11.27,Boston,N,13,8,1,57,Graduation,21,Owner,Standalone\n7179,141.66,Boston,E,20,13,2,77,Graduation,21,Owner,Standalone\n1776,162.82,NJ City,W,13,14,23,38,High School,35,Franchise,Complex\n1153,493.86,NJ City,N,20,18,16,27,High School,21,Patnership,Standalone\n6260,462.72,Boston,W,4,13,25,36,Graduation,20,Franchise,Complex\n9232,446.88,NJ City,W,21,18,4,51,High School,25,Franchise,Complex\n6068,230.24,Boston,W,23,18,9,60,Graduation,26,Franchise,Complex\n5998,394.52,Boston,S,25,21,10,79,Graduation,32,Patnership,Complex\n5930,24.14,Boston,N,17,21,1,73,No education,22,Patnership,Complex\n7388,17.18,Boston,N,9,14,4,44,No education,34,Franchise,Complex\n7371,248.91,Boston,E,10,19,5,85,Graduation,20,Owner,Standalone\n3147,450.88,NJ City,E,24,15,25,40,High School,24,Owner,Standalone\n3854,422.95,NJ City,S,18,12,5,34,No education,29,Patnership,Complex\n9863,120.76,NJ City,E,13,13,2,27,No education,34,Owner,Standalone\n6830,199.9,Boston,S,5,13,4,42,No education,29,Patnership,Complex\n3932,455.38,NJ City,W,7,14,19,45,High School,19,Franchise,Complex\n1336,205.97,NJ City,W,14,10,9,85,High School,30,Franchise,Complex\n8326,244.62,NJ City,S,12,17,18,68,Graduation,35,Patnership,Complex\n736,209.68,NJ City,W,19,18,9,41,High School,27,Franchise,Complex\n2135,195.77,NJ City,E,25,12,1,60,No education,18,Owner,Standalone\n13,73.48,Boston,N,25,15,2,70,High School,26,Patnership,Standalone\n7170,459.81,Boston,S,2,19,18,31,Graduation,22,Patnership,Complex\n2690,448.31,NJ City,S,17,10,25,76,High School,29,Patnership,Complex\n3469,493.75,NJ City,N,25,14,12,67,High School,31,Patnership,Standalone\n920,460.9,NJ City,W,8,11,3,32,No education,22,Franchise,Complex\n9333,365.9,NJ City,N,18,14,13,29,Graduation,26,Patnership,Standalone\n6715,105.75,Boston,E,4,20,1,30,High School,31,Owner,Standalone\n5979,28.32,Boston,N,8,14,2,85,Graduation,20,Owner,Standalone\n5039,238.38,Boston,E,12,18,25,54,Graduation,30,Owner,Standalone\n4333,69.18,NJ City,N,13,11,3,58,High School,20,Patnership,Standalone\n8865,299.85,NJ City,N,16,11,5,48,Graduation,20,Patnership,Standalone\n7351,204.31,Boston,E,25,22,11,80,Graduation,26,Owner,Standalone\n3883,33.55,NJ City,N,23,24,1,38,High School,33,Owner,Standalone\n7514,403.09,Boston,S,10,8,14,36,Graduation,28,Patnership,Complex\n4640,159.25,NJ City,W,16,15,23,48,High School,28,Franchise,Complex\n8937,175.18,NJ City,N,5,24,22,80,Graduation,22,Patnership,Standalone\n2672,362.69,NJ City,W,4,17,22,35,High School,19,Franchise,Complex\n9827,473.36,NJ City,E,8,9,20,84,Graduation,34,Owner,Standalone\n3030,157,NJ City,S,1,24,23,37,High School,20,Patnership,Complex\n8516,77.68,NJ City,W,12,18,3,71,No education,19,Franchise,Complex\n7729,92.63,Boston,N,7,16,1,79,High School,35,Patnership,Standalone\n1201,293.82,NJ City,N,14,10,11,76,High School,23,Patnership,Standalone\n4524,53.69,NJ City,W,2,20,1,51,Graduation,28,Franchise,Complex\n8973,218.68,NJ City,N,1,22,16,35,Graduation,26,Patnership,Standalone\n4339,319.45,NJ City,E,6,22,15,52,High School,18,Owner,Standalone\n3529,122.61,NJ City,N,2,15,1,67,High School,30,Patnership,Standalone\n9759,67.36,NJ City,N,24,24,3,40,Graduation,30,Owner,Standalone\n8234,186.59,NJ City,S,24,18,4,31,No education,18,Patnership,Complex\n8457,492.96,NJ City,N,2,24,19,53,Graduation,18,Patnership,Standalone\n147,156.92,NJ City,E,21,14,4,65,Graduation,18,Owner,Standalone\n3463,474.63,NJ City,E,9,9,2,47,High School,34,Owner,Standalone\n5229,441.54,Boston,N,21,12,14,32,Graduation,29,Patnership,Standalone\n5885,387.98,Boston,N,5,14,21,53,Graduation,24,Patnership,Standalone\n5164,260.41,Boston,W,2,20,24,83,Graduation,31,Franchise,Complex\n2368,194.69,NJ City,W,10,8,3,61,High School,31,Franchise,Complex\n2779,297.08,NJ City,E,7,24,21,70,High School,26,Owner,Standalone\n9149,55.78,NJ City,N,16,24,1,52,No education,34,Patnership,Standalone\n6061,292.12,Boston,N,19,18,21,56,Graduation,27,Patnership,Standalone\n1141,261.82,NJ City,N,16,22,9,81,High School,19,Patnership,Standalone\n3376,173.76,NJ City,W,4,23,4,31,High School,24,Franchise,Complex\n6648,467.49,Boston,W,3,22,15,43,Graduation,31,Franchise,Complex\n917,323.66,NJ City,N,25,23,10,39,High School,21,Patnership,Standalone\n2403,213,NJ City,E,1,16,18,61,High School,30,Owner,Standalone\n9437,307.66,NJ City,N,2,15,21,36,Graduation,18,Patnership,Standalone\n9158,69.6,NJ City,N,23,8,1,69,No education,23,Patnership,Complex\n4622,96.62,NJ City,S,16,8,2,50,No education,27,Patnership,Complex\n2313,497.94,NJ City,N,22,8,20,33,High School,27,Patnership,Standalone\n9849,451.12,NJ City,N,9,8,3,36,Graduation,25,Patnership,Standalone\n3425,163.86,NJ City,N,5,13,17,53,High School,33,Patnership,Standalone\n5089,146.13,Boston,N,25,10,8,28,Graduation,20,Patnership,Standalone\n7848,235.42,Boston,W,16,20,10,26,Graduation,29,Franchise,Complex\n1289,306.32,NJ City,N,13,17,11,45,High School,20,Patnership,Standalone\n6576,360.53,Boston,W,20,22,13,30,Graduation,23,Franchise,Complex\n7816,21.74,Boston,N,1,12,3,65,High School,28,Franchise,Complex\n9138,353.37,NJ City,S,3,24,23,43,Graduation,22,Patnership,Complex\n5235,307.75,Boston,E,6,9,9,44,Graduation,29,Owner,Standalone\n6836,444.5,Boston,W,12,11,1,45,No education,21,Franchise,Complex\n4626,378.73,NJ City,S,7,15,15,71,High School,32,Patnership,Complex\n3232,7.47,NJ City,N,7,23,1,41,High School,34,Franchise,Complex\n2811,414.32,NJ City,E,5,21,20,26,High School,27,Owner,Standalone\n4486,395.12,NJ City,S,13,11,9,66,High School,21,Patnership,Complex\n7937,321.46,Boston,N,21,15,21,77,Graduation,34,Patnership,Standalone\n8948,389.03,NJ City,W,16,8,15,45,Graduation,22,Franchise,Complex\n2550,225.85,NJ City,S,18,11,19,40,High School,21,Patnership,Complex\n5625,50.18,Boston,N,18,15,3,49,Graduation,30,Patnership,Standalone\n7142,104.42,Boston,S,15,23,4,51,No education,20,Patnership,Complex\n8632,285.45,NJ City,W,17,14,10,51,Graduation,19,Franchise,Complex\n6341,186.69,Boston,N,23,16,22,71,Graduation,31,Patnership,Standalone\n1079,343.36,NJ City,E,24,12,10,67,High School,28,Owner,Standalone\n6207,30.21,Boston,N,13,8,3,26,Graduation,32,Owner,Standalone\n8872,423.91,NJ City,W,3,17,11,35,Graduation,35,Franchise,Complex\n7594,416.37,Boston,S,22,23,12,34,Graduation,27,Patnership,Complex\n6844,176.38,Boston,W,20,17,4,36,High School,31,Franchise,Complex\n295,414.48,NJ City,E,7,19,6,52,High School,21,Owner,Standalone\n5358,189.26,Boston,S,20,21,21,33,Graduation,21,Patnership,Complex\n9054,79.93,NJ City,S,2,15,2,65,Graduation,32,Patnership,Complex\n1965,15,NJ City,N,2,18,2,69,Graduation,26,Patnership,Standalone\n7512,378.18,Boston,W,17,11,14,82,Graduation,18,Franchise,Complex\n7426,129.36,Boston,S,18,21,16,32,Graduation,32,Patnership,Complex\n9463,212.85,NJ City,E,25,16,9,64,Graduation,29,Owner,Standalone\n3262,295.21,NJ City,S,25,16,8,56,High School,35,Patnership,Complex\n5771,207.07,Boston,E,15,9,12,80,Graduation,23,Owner,Standalone\n8059,217.16,NJ City,E,16,20,19,28,Graduation,18,Owner,Standalone\n3760,186,NJ City,W,3,19,23,82,High School,28,Franchise,Complex\n8569,221.97,NJ City,N,17,18,24,48,Graduation,33,Patnership,Standalone\n6769,430.3,Boston,N,18,21,4,66,High School,32,Patnership,Standalone\n6509,437.98,Boston,N,23,11,24,32,Graduation,18,Patnership,Standalone\n482,208.04,NJ City,S,21,19,18,68,High School,19,Patnership,Complex\n3850,117.73,NJ City,S,21,12,4,72,High School,30,Patnership,Complex\n2372,7.2,NJ City,N,25,16,2,31,No education,33,Franchise,Complex\n8461,369.09,NJ City,N,23,8,10,67,Graduation,20,Patnership,Standalone\n6123,271.3,Boston,E,15,11,9,69,Graduation,29,Owner,Standalone\n1111,39.89,NJ City,N,16,14,3,65,High School,23,Owner,Standalone\n8342,410.8,NJ City,S,11,11,5,76,No education,29,Patnership,Complex\n9133,191.35,NJ City,N,13,22,25,47,Graduation,25,Patnership,Standalone\n3574,440.57,NJ City,S,12,22,6,48,High School,23,Patnership,Complex\n3365,108,NJ City,N,2,19,12,49,High School,22,Patnership,Standalone\n1936,43.34,NJ City,N,6,22,3,48,High School,26,Franchise,Complex\n8455,389.68,NJ City,E,11,22,18,77,Graduation,30,Owner,Standalone\n882,210.5,NJ City,S,4,11,12,65,High School,31,Patnership,Complex\n2467,24.55,NJ City,N,2,14,1,85,High School,31,Owner,Standalone\n5272,2.2,Boston,W,11,22,2,31,High School,31,Franchise,Complex\n547,333.19,NJ City,E,14,9,19,66,High School,34,Owner,Standalone\n5732,114.1,Boston,W,13,19,2,75,No education,34,Franchise,Complex\n4400,187.06,NJ City,W,4,19,2,52,No education,27,Franchise,Complex\n2577,320.78,NJ City,N,4,17,14,84,High School,26,Patnership,Standalone\n1645,8.04,NJ City,N,21,13,3,59,High School,32,Patnership,Standalone\n7124,245.26,Boston,W,1,17,19,83,Graduation,29,Franchise,Complex\n3379,175.67,NJ City,E,9,22,13,26,High School,35,Owner,Standalone\n228,142.79,NJ City,W,17,23,7,72,High School,35,Franchise,Complex\n3277,117.04,NJ City,N,20,9,2,25,High School,18,Patnership,Standalone\n3635,160.91,NJ City,E,17,21,23,55,High School,34,Owner,Standalone\n5258,128.81,Boston,S,22,19,9,31,Graduation,21,Patnership,Complex\n7951,101.35,Boston,E,6,24,2,66,High School,26,Owner,Standalone\n72,203.31,Maine,W,18,16,22,65,High School,20,Franchise,Complex\n5275,93.27,Boston,E,18,21,4,42,High School,20,Owner,Standalone\n2682,191.31,NJ City,S,16,17,2,69,Graduation,27,Patnership,Complex\n7452,97.05,Boston,W,22,9,1,82,Graduation,21,Franchise,Complex\n9772,121.16,NJ City,W,14,22,3,70,High School,35,Franchise,Complex\n4083,101.84,NJ City,E,20,12,4,83,Graduation,23,Owner,Standalone\n2841,257.78,NJ City,N,22,9,15,72,High School,21,Patnership,Standalone\n6242,10.12,Boston,N,10,20,4,57,No education,31,Patnership,Complex\n4993,146.59,Boston,N,21,16,13,75,High School,22,Patnership,Standalone\n3961,465.25,NJ City,N,18,12,7,58,High School,29,Patnership,Standalone\n7193,278.37,Boston,N,25,22,6,32,Graduation,27,Patnership,Standalone\n8354,382.14,NJ City,S,11,14,24,68,Graduation,21,Patnership,Complex\n8589,126.78,NJ City,N,25,10,3,31,Graduation,31,Patnership,Standalone\n6468,77.25,Boston,W,16,19,3,56,Graduation,23,Franchise,Complex\n4179,110.97,NJ City,E,23,20,3,46,Graduation,28,Owner,Standalone\n155,300.76,NJ City,E,13,13,3,80,No education,18,Owner,Standalone\n7361,155.59,Boston,N,23,16,19,71,Graduation,26,Patnership,Standalone\n856,155.87,NJ City,W,25,24,23,62,High School,33,Franchise,Complex\n9136,428.11,NJ City,W,7,12,7,52,Graduation,22,Franchise,Complex\n870,100.88,NJ City,S,13,22,4,85,Graduation,19,Patnership,Complex\n5523,287.76,Boston,E,22,15,14,30,Graduation,33,Owner,Standalone\n9325,86.14,NJ City,N,1,12,4,64,High School,18,Patnership,Standalone\n9472,471.02,NJ City,W,9,11,11,47,Graduation,35,Franchise,Complex\n2737,311.59,NJ City,N,17,11,17,37,High School,29,Patnership,Standalone\n6355,415.36,Boston,E,17,8,17,73,Graduation,23,Owner,Standalone\n2143,91.16,NJ City,E,14,16,3,67,High School,29,Owner,Standalone\n2601,326.98,NJ City,N,22,22,18,37,High School,34,Patnership,Standalone\n904,8.18,NJ City,N,11,23,2,82,High School,22,Franchise,Complex\n4355,483.67,NJ City,E,21,9,24,73,High School,25,Owner,Standalone\n3996,379.36,NJ City,W,10,23,7,73,High School,30,Franchise,Complex\n7196,179.97,Boston,W,20,18,10,50,Graduation,23,Franchise,Complex\n6239,134.53,Boston,E,4,9,22,83,Graduation,31,Owner,Standalone\n8265,390.05,NJ City,N,5,14,14,79,Graduation,26,Patnership,Standalone\n1108,467.62,NJ City,W,4,21,14,42,High School,24,Franchise,Complex\n6345,8.62,Boston,N,22,9,4,45,Graduation,20,Patnership,Standalone\n5700,11.38,Boston,N,16,8,4,40,Graduation,28,Franchise,Complex\n5470,363.32,Boston,S,6,24,18,58,Graduation,19,Patnership,Complex\n9295,18.28,NJ City,N,25,13,3,75,High School,20,Owner,Standalone\n9625,402.76,NJ City,N,18,17,15,85,Graduation,26,Patnership,Standalone\n2958,405.8,NJ City,S,12,19,22,73,High School,34,Patnership,Complex\n4704,369.07,NJ City,W,17,20,12,83,High School,30,Franchise,Complex\n6002,268.71,Boston,S,20,8,20,45,Graduation,19,Patnership,Complex\n7084,430.61,Boston,W,3,21,19,32,Graduation,31,Franchise,Complex\n6929,140.54,Boston,N,5,18,6,79,Graduation,19,Patnership,Standalone\n5504,315.31,Boston,W,24,24,21,65,Graduation,18,Franchise,Complex\n8620,128.57,NJ City,W,10,8,11,65,Graduation,27,Franchise,Complex\n9564,374.29,NJ City,W,7,15,24,50,Graduation,26,Franchise,Complex\n627,298.77,NJ City,E,6,11,18,62,High School,35,Owner,Standalone\n1831,469.72,NJ City,E,25,23,4,83,High School,28,Owner,Standalone\n9518,181.31,NJ City,S,25,15,4,71,No education,35,Patnership,Complex\n8298,410.26,NJ City,S,14,24,8,41,Graduation,35,Patnership,Complex\n4900,124.1,NJ City,W,16,14,2,51,High School,34,Franchise,Complex\n5407,138.23,Boston,E,18,18,24,62,Graduation,33,Owner,Standalone\n3588,121.75,NJ City,W,23,9,3,79,Graduation,28,Franchise,Complex\n1061,4.22,NJ City,N,14,20,4,66,No education,19,Patnership,Standalone\n892,377.64,NJ City,W,23,24,1,46,High School,22,Franchise,Complex\n4052,466.95,NJ City,W,7,8,20,28,High School,29,Franchise,Complex\n8030,35.58,Boston,N,7,21,2,36,No education,22,Patnership,Complex\n824,400.23,NJ City,W,21,24,14,51,High School,24,Franchise,Complex\n6687,241.83,Boston,E,10,20,17,56,Graduation,18,Owner,Standalone\n6643,390.33,Boston,E,25,22,17,71,Graduation,28,Owner,Standalone\n2748,92.2,NJ City,W,1,16,4,44,Graduation,32,Franchise,Complex\n1276,255.07,NJ City,W,21,20,3,29,High School,28,Franchise,Complex\n7032,485.4,Boston,W,17,19,9,27,Graduation,22,Franchise,Complex\n9086,375.14,NJ City,S,8,9,2,78,No education,30,Patnership,Complex\n2463,133.93,NJ City,E,16,16,10,80,High School,33,Owner,Standalone\n5783,205.7,Boston,E,25,14,7,26,Graduation,34,Owner,Standalone\n4430,481.45,NJ City,S,24,11,17,67,High School,31,Patnership,Complex\n9583,273.33,NJ City,E,12,10,3,54,High School,33,Owner,Standalone\n5282,391.21,Boston,S,18,16,8,59,Graduation,26,Patnership,Complex\n5487,482.74,Boston,E,9,24,8,59,Graduation,31,Owner,Standalone\n4584,101.38,NJ City,W,20,17,2,53,Graduation,23,Franchise,Complex\n7180,291.53,Boston,W,7,23,17,81,Graduation,30,Franchise,Complex\n225,293.52,NJ City,N,25,23,7,59,High School,20,Patnership,Standalone\n812,153,NJ City,W,1,16,7,26,High School,35,Franchise,Complex\n885,111.82,NJ City,N,7,12,2,49,Graduation,19,Patnership,Standalone\n7302,151.07,Boston,S,1,17,22,47,Graduation,23,Patnership,Complex\n9909,154.66,NJ City,N,13,8,19,58,Graduation,28,Patnership,Standalone\n5482,495.53,Boston,S,8,16,19,85,Graduation,32,Patnership,Complex\n8101,277.25,NJ City,N,20,15,25,32,Graduation,26,Patnership,Standalone\n9954,46.06,NJ City,N,15,18,3,28,Graduation,19,Patnership,Complex\n5730,313.7,Boston,S,9,10,17,80,Graduation,25,Patnership,Complex\n981,327.42,NJ City,N,20,23,7,50,High School,33,Patnership,Standalone\n8,2.68,NY City,W,17,11,2,76,No education,31,Franchise,Complex\n9842,408.85,NJ City,S,7,19,14,83,Graduation,31,Patnership,Complex\n7127,164.24,Boston,E,1,8,14,36,Graduation,19,Owner,Standalone\n3734,372.39,NJ City,S,22,16,3,82,No education,23,Patnership,Complex\n7831,14.29,Boston,N,18,16,1,84,High School,24,Owner,Standalone\n4263,17.48,NJ City,N,8,16,3,36,Graduation,33,Owner,Standalone\n7287,107.51,Boston,E,8,15,4,49,Graduation,30,Owner,Standalone\n2237,105.82,NJ City,N,22,8,2,45,No education,19,Patnership,Standalone\n7447,219.86,Boston,E,3,24,12,65,Graduation,25,Owner,Standalone\n6636,483.47,Boston,W,25,10,17,41,Graduation,23,Franchise,Complex\n4760,47.86,NJ City,W,22,11,4,70,No education,33,Franchise,Complex\n9921,235.93,NJ City,N,3,20,15,70,Graduation,30,Patnership,Standalone\n2101,466.64,NJ City,N,24,23,21,37,High School,24,Patnership,Standalone\n9446,482.6,NJ City,S,21,15,12,76,Graduation,30,Patnership,Complex\n6020,242.03,Boston,W,3,17,15,58,Graduation,27,Franchise,Complex\n3005,95.8,NJ City,N,12,10,1,50,No education,28,Patnership,Standalone\n3409,209.45,NJ City,N,18,10,15,35,High School,26,Patnership,Standalone\n2791,347.62,NJ City,E,19,20,4,25,High School,18,Owner,Standalone\n7174,73.53,Boston,N,1,19,4,41,High School,27,Patnership,Complex\n8308,358.17,NJ City,W,21,10,11,67,Graduation,31,Franchise,Complex\n6689,395.76,Boston,N,23,10,6,37,Graduation,20,Patnership,Standalone\n8952,229.12,NJ City,W,9,9,25,79,Graduation,28,Franchise,Complex\n3023,70.84,NJ City,N,23,21,3,27,No education,19,Owner,Standalone\n975,483.82,NJ City,E,10,10,25,64,High School,35,Owner,Standalone\n361,214.32,NJ City,N,12,24,20,73,High School,22,Patnership,Standalone\n1603,334.84,NJ City,E,24,10,20,52,High School,34,Owner,Standalone\n5167,393.37,Boston,E,1,10,20,29,Graduation,35,Owner,Standalone\n3980,336.12,NJ City,W,4,21,9,41,High School,32,Franchise,Complex\n1250,315.7,NJ City,S,10,18,23,35,High School,35,Patnership,Complex\n4967,356.71,Boston,E,4,14,4,75,No education,24,Owner,Standalone\n5716,320.2,Boston,W,12,22,4,25,High School,25,Franchise,Complex\n1317,82.75,NJ City,N,23,18,3,41,Graduation,26,Patnership,Standalone\n5126,261.19,Boston,S,7,12,2,33,No education,25,Patnership,Complex\n8579,346.77,NJ City,E,8,24,8,34,Graduation,27,Owner,Standalone\n8251,161.1,NJ City,E,2,12,4,53,High School,34,Owner,Standalone\n8053,446.98,NJ City,N,21,10,21,32,Graduation,29,Patnership,Standalone\n1109,134.47,NJ City,N,20,18,19,25,High School,34,Patnership,Standalone\n8367,68.9,NJ City,N,17,15,1,45,Graduation,25,Owner,Standalone\n5088,334.8,Boston,W,20,22,13,45,Graduation,30,Franchise,Complex\n9731,274.8,NJ City,E,15,13,2,39,No education,32,Owner,Standalone\n2087,258.26,NJ City,E,9,21,14,82,High School,32,Owner,Standalone\n3143,11.71,NJ City,N,20,14,3,84,No education,24,Owner,Standalone\n89,139.1,Boston,N,19,16,17,74,High School,34,Patnership,Standalone\n5685,307.75,Boston,N,2,24,8,59,Graduation,24,Patnership,Standalone\n4772,291,NJ City,W,3,11,12,76,High School,31,Franchise,Complex\n1769,372.41,NJ City,N,25,12,21,72,High School,24,Patnership,Standalone\n2214,202.27,NJ City,S,17,16,8,66,High School,22,Patnership,Complex\n8771,31.19,NJ City,N,1,22,2,60,No education,30,Owner,Standalone\n5446,186.3,Boston,S,13,14,11,51,Graduation,24,Patnership,Complex\n7846,423.45,Boston,S,11,14,9,70,Graduation,21,Patnership,Complex\n9470,161.54,NJ City,S,7,10,24,30,Graduation,30,Patnership,Complex\n6746,186.23,Boston,S,17,15,24,77,Graduation,20,Patnership,Complex\n8203,151.92,NJ City,E,23,14,3,59,High School,35,Owner,Standalone\n8837,63.44,NJ City,N,11,17,4,36,No education,24,Patnership,Standalone\n400,464.37,NJ City,W,24,17,1,45,High School,25,Franchise,Complex\n3837,59.17,NJ City,N,16,13,1,65,Graduation,23,Patnership,Standalone\n3737,239.1,NJ City,N,8,16,14,34,High School,31,Patnership,Standalone\n5506,305.14,Boston,S,23,12,3,55,High School,22,Patnership,Complex\n768,163.18,NJ City,W,22,21,7,39,High School,25,Franchise,Complex\n2373,471.64,NJ City,N,15,16,23,59,High School,28,Patnership,Standalone\n4573,429.57,NJ City,N,6,8,15,68,High School,19,Patnership,Standalone\n8602,44.92,NJ City,N,24,13,2,44,High School,18,Patnership,Complex\n5230,428.95,Boston,S,18,8,12,60,Graduation,30,Patnership,Complex\n3441,223.27,NJ City,N,9,13,11,25,High School,32,Patnership,Standalone\n6042,160.86,Boston,S,14,15,4,38,Graduation,19,Patnership,Complex\n4812,198,NJ City,W,2,11,18,32,High School,22,Franchise,Complex\n8753,149.11,NJ City,N,7,18,25,28,Graduation,30,Patnership,Standalone\n3841,10.52,NJ City,N,19,24,4,54,High School,30,Patnership,Standalone\n7833,37.34,Boston,N,24,9,2,29,Graduation,19,Patnership,Standalone\n3813,188.15,NJ City,N,23,19,6,33,High School,28,Patnership,Standalone\n5354,257.37,Boston,S,3,15,8,76,Graduation,24,Patnership,Complex\n7912,205.46,Boston,W,22,14,20,39,Graduation,30,Franchise,Complex\n5309,82.07,Boston,N,1,11,1,74,No education,29,Patnership,Standalone\n9259,199.43,NJ City,E,23,9,9,53,Graduation,21,Owner,Standalone\n667,0.12,NJ City,N,22,12,1,77,High School,31,Owner,Standalone\n8157,194.35,NJ City,N,25,18,10,85,Graduation,28,Patnership,Standalone\n6757,488,Boston,N,11,18,6,82,Graduation,33,Patnership,Standalone\n6498,309.64,Boston,S,12,11,5,29,Graduation,20,Patnership,Complex\n2520,51.55,NJ City,W,6,15,2,59,Graduation,18,Franchise,Complex\n8988,68.17,NJ City,N,12,11,4,76,Graduation,29,Franchise,Complex\n2287,380.16,NJ City,E,5,22,23,43,High School,34,Owner,Standalone\n8736,149.62,NJ City,W,17,23,3,49,Graduation,26,Franchise,Complex\n6692,487.24,Boston,W,17,21,19,49,Graduation,18,Franchise,Complex\n8790,296.69,NJ City,S,11,9,21,70,Graduation,28,Patnership,Complex\n5859,361.87,Boston,E,24,15,18,40,Graduation,22,Owner,Standalone\n8587,320.08,NJ City,E,14,13,2,83,High School,18,Owner,Standalone\n1397,190,NJ City,N,1,8,17,37,High School,32,Patnership,Standalone\n2959,116.12,NJ City,E,2,22,2,47,High School,32,Owner,Standalone\n3558,55.88,NJ City,S,21,16,2,71,Graduation,26,Patnership,Complex\n5105,266.49,Boston,N,1,12,24,57,Graduation,35,Patnership,Standalone\n6824,14.98,Boston,N,11,18,4,40,No education,26,Franchise,Complex\n6113,183.37,Boston,N,11,21,12,67,Graduation,25,Patnership,Standalone\n1138,476.8,NJ City,S,15,10,6,65,High School,32,Patnership,Complex\n578,400.4,NJ City,S,15,11,25,67,High School,25,Patnership,Complex\n8333,152.2,NJ City,N,18,18,10,30,Graduation,31,Patnership,Standalone\n7487,9.43,Boston,N,11,8,4,61,No education,25,Owner,Standalone\n2219,126.87,NJ City,E,11,16,4,66,No education,34,Owner,Standalone\n9455,346.86,NJ City,E,18,11,3,48,No education,31,Owner,Standalone\n8770,369.28,NJ City,S,15,8,18,74,Graduation,18,Patnership,Complex\n3685,278.5,NJ City,N,20,8,8,63,High School,28,Patnership,Standalone\n7425,179.24,Boston,N,7,21,20,73,Graduation,27,Patnership,Standalone\n4051,126,NJ City,E,2,11,8,34,High School,19,Owner,Standalone\n7490,209.25,Boston,S,5,15,21,72,Graduation,30,Patnership,Complex\n5868,418.61,Boston,W,16,19,2,48,Graduation,34,Franchise,Complex\n8396,491.61,NJ City,W,2,18,10,62,Graduation,28,Franchise,Complex\n6702,366.82,Boston,S,10,18,24,65,Graduation,20,Patnership,Complex\n7416,165.92,Boston,W,16,14,21,30,Graduation,28,Franchise,Complex\n8717,200.45,NJ City,N,25,20,11,85,Graduation,28,Patnership,Standalone\n8386,163.98,NJ City,S,8,13,20,50,Graduation,33,Patnership,Complex\n3132,13.72,NJ City,N,19,8,2,55,Graduation,21,Franchise,Complex\n1584,341.93,NJ City,W,17,15,19,77,High School,25,Franchise,Complex\n6971,436.62,Boston,E,2,24,3,39,No education,18,Owner,Standalone\n9725,433.3,NJ City,N,20,16,13,27,Graduation,28,Patnership,Standalone\n8886,396.52,NJ City,S,1,24,11,33,Graduation,27,Patnership,Complex\n2931,464.57,NJ City,E,18,19,7,59,High School,24,Owner,Standalone\n8142,192.1,NJ City,S,22,8,13,33,Graduation,19,Patnership,Complex\n3289,172.01,NJ City,N,1,11,2,38,High School,24,Patnership,Standalone\n7021,195.13,Boston,N,4,17,19,46,Graduation,24,Patnership,Standalone\n203,487.82,NJ City,E,24,14,19,56,High School,29,Owner,Standalone\n899,309.79,NJ City,E,15,17,10,50,High School,30,Owner,Standalone\n5742,285.48,Boston,S,10,10,12,56,Graduation,20,Patnership,Complex\n1380,128,NJ City,W,3,15,19,56,High School,23,Franchise,Complex\n3956,328.87,NJ City,W,14,14,1,38,No education,24,Franchise,Complex\n705,420.55,NJ City,N,23,19,1,60,Graduation,22,Patnership,Standalone\n1945,434.32,NJ City,N,10,9,13,62,High School,25,Patnership,Standalone\n1131,319.76,NJ City,E,25,22,12,56,High School,21,Owner,Standalone\n8002,111.94,Boston,S,21,11,3,53,High School,26,Patnership,Complex\n8129,114.59,NJ City,N,11,18,2,55,No education,23,Patnership,Standalone\n5959,132.22,Boston,E,6,10,16,82,Graduation,29,Owner,Standalone\n4386,230.56,NJ City,S,12,15,19,28,High School,26,Patnership,Complex\n5824,160.19,Boston,W,6,19,1,69,High School,20,Franchise,Complex\n7819,142.21,Boston,E,22,15,23,31,Graduation,33,Owner,Standalone\n1731,74.68,NJ City,E,18,15,4,32,Graduation,21,Owner,Standalone\n6680,496.49,Boston,W,9,19,21,37,Graduation,31,Franchise,Complex\n5680,296.49,Boston,W,8,10,22,56,Graduation,23,Franchise,Complex\n5572,125.39,Boston,W,2,9,3,51,High School,34,Franchise,Complex\n2714,169.21,NJ City,S,21,20,5,60,No education,27,Patnership,Complex\n6091,62.92,Boston,N,22,14,4,65,High School,21,Owner,Standalone\n50,485.15,Boston,S,14,21,3,26,No education,30,Patnership,Complex\n9892,157.26,NJ City,W,3,21,7,39,Graduation,29,Franchise,Complex\n7903,139.97,Boston,E,5,17,15,32,Graduation,19,Owner,Standalone\n9912,303.57,NJ City,W,19,19,11,44,Graduation,32,Franchise,Complex\n9820,138.73,NJ City,W,2,23,4,76,High School,21,Franchise,Complex\n7744,289.97,Boston,W,5,20,2,64,High School,26,Franchise,Complex\n4475,195,NJ City,E,2,13,15,76,High School,31,Owner,Standalone\n2362,205,NJ City,S,1,19,19,77,High School,32,Patnership,Complex\n9339,379.16,NJ City,E,11,18,9,33,Graduation,24,Owner,Standalone\n2426,177.56,NJ City,S,12,18,6,44,High School,33,Patnership,Complex\n3272,462.16,NJ City,W,18,14,7,73,High School,28,Franchise,Complex\n830,187.76,NJ City,S,7,18,18,64,High School,19,Patnership,Complex\n4087,114.28,NJ City,E,25,11,4,85,High School,29,Owner,Standalone\n6237,112.49,Boston,N,13,12,1,46,Graduation,23,Patnership,Standalone\n5297,20.72,Boston,N,3,15,1,34,No education,24,Patnership,Standalone\n9415,250.51,NJ City,E,10,8,19,52,Graduation,33,Owner,Standalone\n9727,41.59,NJ City,N,12,24,4,62,High School,21,Owner,Standalone\n869,91.64,NJ City,N,17,8,2,33,No education,30,Patnership,Standalone\n4281,216.44,NJ City,N,23,23,13,77,High School,33,Patnership,Standalone\n7598,475.77,Boston,S,23,19,22,68,Graduation,20,Patnership,Complex\n6825,106.79,Boston,N,3,15,2,81,Graduation,24,Patnership,Standalone\n3093,302.96,NJ City,N,12,8,18,44,High School,31,Patnership,Standalone\n6088,177.75,Boston,W,14,9,12,63,Graduation,34,Franchise,Complex\n111,137.49,NJ City,E,13,13,1,71,Graduation,32,Owner,Standalone\n2438,118.44,NJ City,S,14,8,1,80,No education,26,Patnership,Complex\n6043,484.66,Boston,E,25,19,4,83,High School,24,Owner,Standalone\n7720,346.22,Boston,W,18,21,25,25,Graduation,18,Franchise,Complex\n3680,184.23,NJ City,W,17,21,10,75,High School,34,Franchise,Complex\n8152,14.63,NJ City,N,4,22,3,42,High School,32,Franchise,Complex\n3503,461.85,NJ City,E,14,22,21,29,High School,26,Owner,Standalone\n7344,45.1,Boston,N,17,10,4,54,Graduation,23,Franchise,Complex\n4166,409.26,NJ City,S,11,14,24,39,High School,33,Patnership,Complex\n8042,274.83,Boston,S,18,19,19,73,Graduation,23,Patnership,Complex\n7504,148.43,Boston,W,12,22,10,76,Graduation,28,Franchise,Complex\n668,306.34,NJ City,W,16,11,11,79,High School,30,Franchise,Complex\n1807,430.72,NJ City,E,20,15,25,40,High School,34,Owner,Standalone\n1792,47.02,NJ City,N,4,18,1,42,High School,23,Franchise,Complex\n1786,314.04,NJ City,S,11,19,25,69,High School,23,Patnership,Complex\n9810,258.69,NJ City,S,24,20,14,70,Graduation,34,Patnership,Complex\n5878,252.44,Boston,S,10,10,21,37,Graduation,21,Patnership,Complex\n8193,140.03,NJ City,N,1,13,14,46,Graduation,35,Patnership,Standalone\n8908,334.13,NJ City,W,21,21,4,28,High School,18,Franchise,Complex\n2893,483.73,NJ City,N,11,17,25,57,High School,19,Patnership,Standalone\n4988,451.13,Boston,W,5,14,25,49,High School,28,Franchise,Complex\n8855,306.54,NJ City,E,23,15,18,76,Graduation,30,Owner,Standalone\n8924,260.15,NJ City,W,11,22,2,80,No education,20,Franchise,Complex\n825,384.36,NJ City,N,20,15,25,30,High School,35,Patnership,Standalone\n6743,174.44,Boston,E,18,16,25,44,Graduation,21,Owner,Standalone\n716,213.27,NJ City,W,16,23,2,50,No education,35,Franchise,Complex\n2323,316.68,NJ City,E,9,9,14,67,High School,33,Owner,Standalone\n8678,264.24,NJ City,S,22,16,7,83,Graduation,30,Patnership,Complex\n3861,230.68,NJ City,N,22,23,15,47,High School,28,Patnership,Standalone\n3919,2.01,NJ City,E,15,23,4,43,High School,23,Owner,Standalone\n1161,428.88,NJ City,N,8,19,8,74,High School,18,Patnership,Standalone\n2045,115.55,NJ City,N,24,15,3,77,No education,33,Patnership,Standalone\n9244,317.24,NJ City,W,2,15,21,25,Graduation,28,Franchise,Complex\n8649,19.11,NJ City,N,5,13,4,34,Graduation,21,Patnership,Standalone\n296,349.53,NJ City,W,7,15,5,72,No education,19,Franchise,Complex\n6487,140.59,Boston,E,6,14,24,27,Graduation,20,Owner,Standalone\n959,168.48,NJ City,E,15,13,22,77,High School,25,Owner,Standalone\n6524,410.51,Boston,W,10,21,13,73,Graduation,27,Franchise,Complex\n1808,46.03,NJ City,N,24,22,4,48,No education,33,Franchise,Complex\n4507,104,NJ City,E,2,11,15,64,High School,24,Owner,Standalone\n6531,281.32,Boston,E,19,9,14,54,Graduation,21,Owner,Standalone\n8099,192.31,NJ City,E,22,16,11,62,Graduation,26,Owner,Standalone\n7178,141.49,Boston,S,1,13,20,43,Graduation,19,Patnership,Complex\n9753,442.38,NJ City,N,11,20,23,54,Graduation,33,Patnership,Standalone\n2320,80.92,NJ City,W,19,17,4,81,High School,22,Franchise,Complex\n2888,132,NJ City,W,3,21,10,60,High School,32,Franchise,Complex\n7672,37.83,Boston,N,6,17,2,44,High School,26,Franchise,Complex\n4285,472.12,NJ City,N,21,21,19,66,High School,26,Patnership,Standalone\n7011,355.62,Boston,E,20,12,4,33,Graduation,35,Owner,Standalone\n7961,319.14,Boston,N,4,11,19,48,Graduation,31,Patnership,Standalone\n9963,190.08,NJ City,E,14,9,24,77,Graduation,19,Owner,Standalone\n8944,2.72,NJ City,W,22,22,4,41,High School,19,Franchise,Complex\n8858,84.32,NJ City,S,19,17,2,66,No education,34,Patnership,Complex\n4466,145.6,NJ City,S,2,18,3,63,No education,31,Patnership,Complex\n7527,423.89,Boston,E,24,24,19,66,Graduation,25,Owner,Standalone\n7618,145.03,Boston,S,1,10,1,72,High School,19,Patnership,Complex\n481,426.56,NJ City,N,14,17,18,84,High School,26,Patnership,Standalone\n5075,320.89,Boston,E,16,22,19,28,Graduation,32,Owner,Standalone\n1666,245.25,NJ City,S,10,18,3,61,High School,28,Patnership,Complex\n7701,214.97,Boston,N,17,22,13,50,Graduation,19,Patnership,Standalone\n1215,255,NJ City,E,3,19,7,28,High School,31,Owner,Standalone\n1536,212.57,NJ City,W,16,24,12,45,High School,24,Franchise,Complex\n6616,333.81,Boston,W,20,12,18,68,Graduation,25,Franchise,Complex\n8920,139.59,NJ City,W,17,16,5,81,High School,19,Franchise,Complex\n4321,196.77,NJ City,N,22,10,7,54,High School,24,Patnership,Standalone\n5108,72.75,Boston,N,13,9,2,66,No education,26,Franchise,Complex\n5701,318.12,Boston,N,8,24,10,68,Graduation,32,Patnership,Standalone\n44,427.42,Boston,W,25,23,18,53,High School,29,Franchise,Complex\n9632,11.14,NJ City,N,10,8,1,77,No education,33,Franchise,Complex\n3231,443.56,NJ City,E,12,13,13,25,High School,25,Owner,Standalone\n3403,183.38,NJ City,E,23,20,22,78,High School,18,Owner,Standalone\n8272,232.89,NJ City,W,25,10,23,27,Graduation,32,Franchise,Complex\n518,355.98,NJ City,S,22,10,22,29,High School,28,Patnership,Complex\n5317,254.13,Boston,N,13,20,14,27,Graduation,35,Patnership,Standalone\n8259,256.35,NJ City,E,5,11,2,48,Graduation,26,Owner,Standalone\n4197,145.65,NJ City,N,4,11,10,61,High School,22,Patnership,Standalone\n3117,143.46,NJ City,N,18,19,8,82,High School,22,Patnership,Standalone\n4429,39.07,NJ City,N,18,9,1,80,High School,31,Patnership,Standalone\n6897,374.35,Boston,N,18,15,24,41,Graduation,30,Patnership,Standalone\n8248,45.84,NJ City,N,19,9,2,77,High School,18,Franchise,Complex\n4600,493.01,NJ City,W,15,18,5,25,High School,18,Franchise,Complex\n4262,290.93,NJ City,S,24,21,25,41,High School,24,Patnership,Complex\n7929,309.99,Boston,N,12,16,14,69,Graduation,19,Patnership,Standalone\n1311,93.61,NJ City,E,24,19,4,39,Graduation,24,Owner,Standalone\n2022,260.41,NJ City,S,24,14,20,53,High School,31,Patnership,Complex\n756,397.24,NJ City,W,5,16,17,56,High School,35,Franchise,Complex\n9428,435.18,NJ City,W,24,9,2,67,No education,26,Franchise,Complex\n7491,108.15,Boston,E,8,14,3,41,Graduation,25,Owner,Standalone\n4327,94.86,NJ City,E,20,13,4,35,High School,34,Owner,Standalone\n7085,23.05,Boston,N,22,21,2,57,No education,24,Patnership,Standalone\n6010,104.46,Boston,S,14,18,3,70,High School,19,Patnership,Complex\n7244,408.48,Boston,W,8,16,13,82,Graduation,23,Franchise,Complex\n8769,489.76,NJ City,N,15,13,19,77,Graduation,28,Patnership,Standalone\n1774,423.41,NJ City,S,5,21,15,31,High School,25,Patnership,Complex\n2720,358.39,NJ City,W,12,15,18,76,High School,33,Franchise,Complex\n7462,50.26,Boston,S,6,12,4,34,High School,21,Patnership,Complex\n6668,248.99,Boston,W,1,8,24,71,Graduation,27,Franchise,Complex\n1379,128.75,NJ City,E,22,10,10,81,High School,31,Owner,Standalone\n5520,75.76,Boston,W,5,11,4,73,Graduation,27,Franchise,Complex\n7372,73,Boston,N,21,21,2,74,High School,34,Franchise,Complex\n3444,290,NJ City,W,3,12,13,42,High School,31,Franchise,Complex\n5711,358.61,Boston,E,22,11,11,76,Graduation,24,Owner,Standalone\n7236,326.08,Boston,W,22,13,9,74,Graduation,31,Franchise,Complex\n56,6.86,NY City,N,3,23,3,74,No education,19,Franchise,Complex\n9288,458.08,NJ City,W,16,23,24,49,Graduation,20,Franchise,Complex\n5721,336.95,Boston,N,25,18,10,43,Graduation,23,Patnership,Standalone\n6178,270.67,Boston,S,25,10,4,52,High School,28,Patnership,Complex\n387,6.44,NJ City,N,18,12,2,74,Graduation,32,Owner,Standalone\n7695,423.15,Boston,E,1,19,7,28,Graduation,27,Owner,Standalone\n6624,249.44,Boston,W,9,23,12,27,Graduation,25,Franchise,Complex\n3053,10.83,NJ City,N,8,22,4,85,No education,28,Patnership,Standalone\n3204,105.72,NJ City,W,16,24,1,31,Graduation,18,Franchise,Complex\n4188,235.48,NJ City,W,11,18,25,29,High School,20,Franchise,Complex\n9682,98.56,NJ City,S,3,13,3,53,High School,20,Patnership,Complex\n5973,145.85,Boston,N,21,8,18,66,Graduation,34,Patnership,Standalone\n6060,455.26,Boston,W,5,12,19,81,Graduation,24,Franchise,Complex\n7296,43.62,Boston,N,13,24,1,80,Graduation,32,Franchise,Complex\n7079,404.85,Boston,E,14,8,2,30,No education,30,Owner,Standalone\n8496,246.53,NJ City,W,14,21,19,27,Graduation,34,Franchise,Complex\n6518,47.67,Boston,S,2,9,3,39,No education,35,Patnership,Complex\n8154,211.08,NJ City,S,4,11,8,40,Graduation,35,Patnership,Complex\n6782,195.07,Boston,S,4,16,2,51,No education,18,Patnership,Complex\n9797,494.44,NJ City,N,23,22,4,48,No education,31,Patnership,Standalone\n9767,190.1,NJ City,E,14,8,16,75,Graduation,22,Owner,Standalone\n7838,276.29,Boston,S,6,14,19,51,Graduation,19,Patnership,Complex\n6164,179.08,Boston,W,9,13,2,43,No education,20,Franchise,Complex\n9161,63.12,NJ City,N,10,23,2,69,No education,33,Patnership,Standalone\n9373,382.85,NJ City,N,11,16,3,41,High School,34,Patnership,Standalone\n536,483.22,NJ City,W,25,23,8,37,High School,26,Franchise,Complex\n7298,478.34,Boston,S,23,8,5,27,No education,25,Patnership,Complex\n2482,440.74,NJ City,S,21,23,24,76,High School,30,Patnership,Complex\n9949,16.72,NJ City,N,21,23,2,58,High School,31,Patnership,Standalone\n4435,395.97,NJ City,E,25,15,8,79,High School,29,Owner,Standalone\n9413,333.69,NJ City,N,22,14,9,78,Graduation,24,Patnership,Standalone\n5614,369.89,Boston,S,11,9,6,36,Graduation,27,Patnership,Complex\n3670,90.73,NJ City,S,25,12,1,43,High School,33,Patnership,Complex\n301,25.01,NJ City,N,2,9,1,69,High School,19,Patnership,Standalone\n5134,96.7,Boston,S,16,17,4,63,High School,29,Patnership,Complex\n9093,497.19,NJ City,N,22,18,22,29,Graduation,24,Patnership,Standalone\n194,87.41,NJ City,S,22,19,1,73,No education,35,Patnership,Complex\n3651,163.15,NJ City,E,25,10,16,70,High School,23,Owner,Standalone\n4362,462.38,NJ City,S,25,12,5,85,Graduation,22,Patnership,Complex\n8132,351.73,NJ City,W,3,14,19,60,Graduation,35,Franchise,Complex\n2756,121.09,NJ City,W,7,14,1,44,No education,18,Franchise,Complex\n1361,192.43,NJ City,N,6,18,3,65,No education,21,Patnership,Standalone\n2730,263.22,NJ City,S,7,15,16,84,High School,32,Patnership,Complex\n9512,153.07,NJ City,W,21,17,13,66,Graduation,30,Franchise,Complex\n8022,473.53,Boston,S,24,11,18,77,Graduation,23,Patnership,Complex\n5100,231.24,Boston,W,17,12,19,49,Graduation,29,Franchise,Complex\n7996,168.69,Boston,W,22,9,4,49,High School,24,Franchise,Complex\n4769,119.35,NJ City,N,7,24,2,60,No education,29,Patnership,Standalone\n5813,119.99,Boston,N,20,19,4,70,No education,34,Patnership,Standalone\n1649,468.52,NJ City,N,18,18,17,29,High School,20,Patnership,Standalone\n6942,467.93,Boston,S,4,16,11,64,Graduation,34,Patnership,Complex\n305,50.11,NJ City,N,23,18,2,75,No education,34,Patnership,Standalone\n6856,401.9,Boston,W,15,10,1,68,High School,35,Franchise,Complex\n4754,268,NJ City,S,2,11,16,39,High School,29,Patnership,Complex\n489,32.77,NJ City,N,25,14,1,50,Graduation,23,Patnership,Standalone\n7906,173.99,Boston,S,11,11,10,63,Graduation,29,Patnership,Complex\n6104,251.91,Boston,W,22,14,10,70,Graduation,33,Franchise,Complex\n978,30.42,NJ City,N,14,9,3,47,Graduation,19,Patnership,Complex\n6874,314.35,Boston,S,14,12,23,66,Graduation,26,Patnership,Complex\n3302,51.28,NJ City,S,16,10,1,74,No education,27,Patnership,Complex\n9996,283.37,NJ City,W,11,8,4,70,Graduation,29,Franchise,Complex\n4448,261.08,NJ City,W,25,19,11,36,High School,22,Franchise,Complex\n8527,361.45,NJ City,E,3,10,20,43,Graduation,19,Owner,Standalone\n6998,312.85,Boston,S,18,17,8,35,Graduation,18,Patnership,Complex\n861,125.8,NJ City,N,23,9,2,56,Graduation,33,Patnership,Standalone\n2658,162.38,NJ City,S,25,18,17,51,High School,26,Patnership,Complex\n9338,70.02,NJ City,N,18,19,1,52,No education,22,Patnership,Complex\n4002,110,NJ City,S,3,22,9,61,High School,30,Patnership,Complex\n6641,472.49,Boston,N,15,21,6,44,Graduation,25,Patnership,Standalone\n7857,351.56,Boston,N,2,13,6,36,Graduation,18,Patnership,Standalone\n3986,90,NJ City,S,18,13,1,37,No education,27,Patnership,Complex\n989,280.97,NJ City,N,5,11,15,29,High School,24,Patnership,Standalone\n7437,252.99,Boston,N,11,11,13,66,Graduation,32,Patnership,Standalone\n4112,134.1,NJ City,W,9,11,14,26,High School,35,Franchise,Complex\n552,461.11,NJ City,W,14,21,22,30,High School,31,Franchise,Complex\n3663,358.85,NJ City,E,23,21,5,40,Graduation,25,Owner,Standalone\n1492,306,NJ City,W,2,15,15,36,High School,20,Franchise,Complex\n3458,256.03,NJ City,S,24,8,11,74,High School,27,Patnership,Complex\n1859,290.81,NJ City,E,14,15,21,65,High School,21,Owner,Standalone\n5132,283.83,Boston,W,11,18,25,26,Graduation,32,Franchise,Complex\n6360,177.49,Boston,W,21,17,10,41,Graduation,22,Franchise,Complex\n2352,453.64,NJ City,W,13,23,1,36,Graduation,23,Franchise,Complex\n5617,181.62,Boston,N,5,17,11,66,Graduation,28,Patnership,Standalone\n1512,157,NJ City,W,3,20,20,59,High School,30,Franchise,Complex\n1424,360.73,NJ City,W,25,21,5,33,No education,34,Franchise,Complex\n6559,129.15,Boston,E,12,15,18,38,Graduation,20,Owner,Standalone\n641,251.45,NJ City,N,24,17,3,35,No education,23,Patnership,Standalone\n618,227.62,NJ City,S,24,14,11,54,High School,19,Patnership,Complex\n6486,408.39,Boston,S,6,13,11,74,Graduation,20,Patnership,Complex\n5556,175.4,Boston,W,6,23,1,59,Graduation,35,Franchise,Complex\n8604,22.22,NJ City,N,1,10,3,84,Graduation,18,Franchise,Complex\n7692,228.02,Boston,W,6,24,4,73,Graduation,25,Franchise,Complex\n1444,152.98,NJ City,W,24,8,16,60,High School,19,Franchise,Complex\n6458,155.29,Boston,S,25,9,11,63,Graduation,35,Patnership,Complex\n236,2.12,NJ City,W,20,14,2,59,No education,29,Franchise,Complex\n7257,320.77,Boston,N,23,16,15,59,Graduation,34,Patnership,Standalone\n8982,245.72,NJ City,S,8,11,19,53,Graduation,21,Patnership,Complex\n7336,35.19,Boston,N,12,21,4,71,High School,35,Franchise,Complex\n1746,249.13,NJ City,S,9,13,19,41,High School,28,Patnership,Complex\n569,181.29,NJ City,N,12,21,13,28,High School,33,Patnership,Standalone\n1594,446.11,NJ City,S,2,15,4,50,High School,30,Patnership,Complex\n5454,44.44,Boston,N,14,9,1,36,Graduation,18,Patnership,Complex\n8883,415.49,NJ City,E,24,23,16,76,Graduation,33,Owner,Standalone\n3815,297.18,NJ City,E,9,15,22,55,High School,30,Owner,Standalone\n9722,101.53,NJ City,S,12,10,1,63,No education,33,Patnership,Complex\n7173,229.68,Boston,N,18,13,10,55,Graduation,28,Patnership,Standalone\n25,332.8,Boston,N,18,11,18,75,High School,33,Patnership,Standalone\n6505,115.36,Boston,N,1,19,3,68,High School,27,Patnership,Standalone\n5636,298.8,Boston,W,5,24,6,82,Graduation,34,Franchise,Complex\n3954,186.52,NJ City,S,15,11,18,39,High School,27,Patnership,Complex\n4253,302,NJ City,N,3,14,13,67,High School,21,Patnership,Standalone\n5800,328.63,Boston,W,12,19,7,58,Graduation,28,Franchise,Complex\n6859,477.52,Boston,E,18,13,23,63,Graduation,30,Owner,Standalone\n8458,171.03,NJ City,S,4,11,17,33,Graduation,28,Patnership,Complex\n2558,79.93,NJ City,S,1,22,4,72,No education,27,Patnership,Complex\n2686,474.3,NJ City,S,14,9,20,42,High School,26,Patnership,Complex\n4213,291.18,NJ City,N,7,22,21,68,High School,30,Patnership,Standalone\n6174,287.69,Boston,S,15,8,13,79,Graduation,28,Patnership,Complex\n5559,256.9,Boston,E,25,19,22,55,Graduation,23,Owner,Standalone\n2264,58.41,NJ City,W,8,23,3,58,No education,32,Franchise,Complex\n5853,119.25,Boston,N,19,21,4,56,Graduation,32,Patnership,Standalone\n7395,58.45,Boston,E,16,16,3,55,Graduation,32,Owner,Standalone\n573,98.05,NJ City,N,4,10,3,47,Graduation,22,Patnership,Standalone\n2751,276.58,NJ City,E,24,18,13,57,High School,28,Owner,Standalone\n986,92.09,NJ City,S,1,18,1,33,No education,22,Patnership,Complex\n4073,474.92,NJ City,N,15,8,13,35,High School,28,Patnership,Standalone\n1062,196,NJ City,S,1,18,23,26,High School,21,Patnership,Complex\n4360,272.98,NJ City,W,11,15,25,81,High School,29,Franchise,Complex\n5914,19.98,Boston,N,10,14,1,30,High School,18,Patnership,Complex\n526,92.82,NJ City,S,15,19,4,75,High School,29,Patnership,Complex\n1812,357.48,NJ City,W,14,21,12,41,High School,26,Franchise,Complex\n5137,44.49,Boston,N,23,23,2,75,High School,25,Patnership,Standalone\n1198,104,NJ City,S,1,9,12,44,High School,24,Patnership,Complex\n1760,302.06,NJ City,W,7,21,24,42,High School,33,Franchise,Complex\n8268,341.25,NJ City,W,4,8,2,65,Graduation,19,Franchise,Complex\n5122,55.81,Boston,S,3,19,1,69,High School,27,Patnership,Complex\n936,2.09,NJ City,W,13,17,3,80,Graduation,29,Franchise,Complex\n9059,188.26,NJ City,E,9,17,16,78,Graduation,24,Owner,Standalone\n9622,118.79,NJ City,S,17,23,1,49,High School,32,Patnership,Complex\n6289,446.69,Boston,N,18,13,20,36,Graduation,26,Patnership,Standalone\n2925,223.29,NJ City,N,18,21,17,28,High School,35,Patnership,Standalone\n5350,257.14,Boston,S,11,17,2,41,High School,30,Patnership,Complex\n8135,342.63,NJ City,E,17,22,16,37,Graduation,19,Owner,Standalone\n1957,442.33,NJ City,N,17,19,23,76,High School,18,Patnership,Standalone\n5548,88.3,Boston,W,18,18,4,68,High School,29,Franchise,Complex\n4038,379.81,NJ City,S,15,11,22,62,High School,20,Patnership,Complex\n5512,170.67,Boston,W,24,23,3,85,High School,23,Franchise,Complex\n7804,121.31,Boston,W,21,12,2,70,High School,30,Franchise,Complex\n1246,409.05,NJ City,S,23,22,13,58,High School,34,Patnership,Complex\n2824,41.56,NJ City,N,3,18,1,35,High School,25,Franchise,Complex\n2310,481.8,NJ City,S,5,23,16,85,High School,20,Patnership,Complex\n6058,227.66,Boston,S,12,16,2,70,High School,24,Patnership,Complex\n3036,401.65,NJ City,W,15,20,25,65,High School,31,Franchise,Complex\n903,134.49,NJ City,E,7,12,12,33,High School,34,Owner,Standalone\n3083,50.25,NJ City,E,1,17,3,54,No education,25,Owner,Standalone\n8388,7.22,NJ City,N,9,17,2,36,Graduation,25,Franchise,Complex\n1791,233.44,NJ City,E,14,22,20,55,High School,31,Owner,Standalone\n246,165.52,NJ City,S,25,17,18,61,High School,32,Patnership,Complex\n2562,21.57,NJ City,N,24,21,3,45,Graduation,29,Patnership,Complex\n3749,482.62,NJ City,N,6,14,19,85,High School,30,Patnership,Standalone\n2350,142.02,NJ City,S,12,17,5,77,High School,31,Patnership,Complex\n9671,409.42,NJ City,E,10,21,6,44,Graduation,22,Owner,Standalone\n7705,283.28,Boston,N,20,13,10,33,Graduation,28,Patnership,Standalone\n9013,43.15,NJ City,N,4,19,1,34,High School,19,Patnership,Standalone\n2596,451.9,NJ City,W,25,17,18,25,High School,19,Franchise,Complex\n4093,464.59,NJ City,N,18,18,6,79,High School,29,Patnership,Standalone\n6130,224.98,Boston,S,23,23,21,27,Graduation,22,Patnership,Complex\n5480,152.77,Boston,W,25,16,18,74,Graduation,23,Franchise,Complex\n9714,303.31,NJ City,S,8,14,6,27,Graduation,33,Patnership,Complex\n3210,71.39,NJ City,N,14,13,2,51,Graduation,29,Patnership,Complex\n9596,386.69,NJ City,W,14,9,24,82,Graduation,24,Franchise,Complex\n6947,290.9,Boston,E,9,12,2,52,No education,24,Owner,Standalone\n8216,46.33,NJ City,N,24,10,2,42,No education,30,Franchise,Complex\n3001,411.79,NJ City,N,9,18,6,57,High School,22,Patnership,Standalone\n7828,105.77,Boston,W,11,19,3,37,High School,19,Franchise,Complex\n2766,201.37,NJ City,S,16,24,12,65,High School,29,Patnership,Complex\n8066,448.53,NJ City,S,7,9,9,30,Graduation,23,Patnership,Complex\n7774,281.3,Boston,S,3,18,13,67,Graduation,27,Patnership,Complex\n2850,242,NJ City,S,3,16,18,58,High School,30,Patnership,Complex\n8026,350.37,Boston,S,2,22,1,45,High School,23,Patnership,Complex\n6739,120.48,Boston,E,17,19,3,85,High School,34,Owner,Standalone\n5922,176.61,Boston,S,20,24,5,69,Graduation,19,Patnership,Complex\n138,227.21,NJ City,S,22,18,24,27,High School,28,Patnership,Complex\n863,75.4,NJ City,E,1,15,1,61,No education,30,Owner,Standalone\n7212,236.72,Boston,W,5,14,10,27,Graduation,19,Franchise,Complex\n3937,75.64,NJ City,N,5,12,3,31,High School,25,Patnership,Standalone\n7191,271.73,Boston,E,3,21,1,78,Graduation,24,Owner,Standalone\n3521,78.57,NJ City,N,7,10,4,35,No education,18,Patnership,Standalone\n1665,9.49,NJ City,N,14,11,3,84,Graduation,23,Patnership,Standalone\n3801,190.16,NJ City,N,8,8,13,41,High School,33,Patnership,Standalone\n9114,51.17,NJ City,S,9,17,2,35,Graduation,25,Patnership,Complex\n1043,30.09,NJ City,N,12,21,2,66,No education,22,Owner,Standalone\n9095,39.95,NJ City,N,4,18,3,69,No education,27,Owner,Standalone\n5886,107.11,Boston,S,5,11,4,60,Graduation,34,Patnership,Complex\n8910,11.73,NJ City,N,7,20,2,70,Graduation,26,Patnership,Complex\n5360,341.95,Boston,W,13,14,3,70,No education,25,Franchise,Complex\n4406,147.92,NJ City,S,17,8,2,59,No education,18,Patnership,Complex\n2203,90.08,NJ City,E,1,13,2,43,High School,26,Owner,Standalone\n6100,148.44,Boston,W,7,10,22,43,Graduation,28,Franchise,Complex\n5901,269.07,Boston,N,16,11,2,35,Graduation,21,Patnership,Standalone\n7385,237.61,Boston,N,23,13,24,52,Graduation,26,Patnership,Standalone\n6013,13.21,Boston,N,12,18,4,62,High School,31,Patnership,Standalone\n1252,338.79,NJ City,W,18,20,20,26,High School,19,Franchise,Complex\n6910,236.96,Boston,S,13,11,21,50,Graduation,33,Patnership,Complex\n3091,369.61,NJ City,E,24,21,16,75,High School,29,Owner,Standalone\n1887,489.08,NJ City,E,9,9,9,30,High School,27,Owner,Standalone\n6662,87.28,Boston,S,20,22,3,64,No education,31,Patnership,Complex\n8414,85.27,NJ City,S,20,13,1,26,No education,23,Patnership,Complex\n923,19.46,NJ City,N,22,22,3,30,No education,19,Owner,Standalone\n9575,97.86,NJ City,E,15,22,3,73,No education,21,Owner,Standalone\n5963,301.68,Boston,E,17,21,5,41,No education,24,Owner,Standalone\n9037,425.28,NJ City,N,7,20,25,74,Graduation,33,Patnership,Standalone\n846,303.53,NJ City,S,6,18,18,84,High School,25,Patnership,Complex\n5104,112.01,Boston,W,16,22,2,82,High School,25,Franchise,Complex\n6593,302.44,Boston,N,11,8,4,80,No education,29,Patnership,Standalone\n7591,240.53,Boston,E,13,12,23,41,Graduation,34,Owner,Standalone\n3812,182.33,NJ City,W,6,21,9,45,High School,28,Franchise,Complex\n7017,68.24,Boston,N,21,24,4,80,Graduation,19,Patnership,Standalone\n3917,365.72,NJ City,N,11,17,10,64,High School,26,Patnership,Standalone\n5778,254.3,Boston,S,9,22,21,40,Graduation,30,Patnership,Complex\n7075,232.33,Boston,E,6,9,6,54,Graduation,30,Owner,Standalone\n1759,87.73,NJ City,E,6,12,3,75,High School,28,Owner,Standalone\n2051,198.49,NJ City,E,15,21,23,64,High School,35,Owner,Standalone\n4910,91.91,NJ City,S,8,12,1,31,No education,34,Patnership,Complex\n4343,126.03,NJ City,E,3,19,4,71,No education,29,Owner,Standalone\n7111,56.96,Boston,E,8,20,3,53,High School,21,Owner,Standalone\n1028,258.07,NJ City,W,20,19,8,57,High School,22,Franchise,Complex\n3915,357.89,NJ City,E,20,24,22,71,High School,18,Owner,Standalone\n7310,163.08,Boston,S,20,12,15,55,Graduation,31,Patnership,Complex\n3904,36.79,NJ City,N,15,23,4,46,High School,30,Franchise,Complex\n8593,260.45,NJ City,N,10,14,3,41,High School,30,Patnership,Standalone\n6798,35.78,Boston,N,9,16,1,76,Graduation,22,Patnership,Complex\n8614,48,NJ City,S,25,17,3,65,High School,33,Patnership,Complex\n15,52.26,Boston,E,11,10,3,83,Graduation,19,Owner,Standalone\n3325,45.78,NJ City,N,8,21,1,81,High School,23,Patnership,Standalone\n9913,461.86,NJ City,N,25,22,8,61,Graduation,25,Patnership,Standalone\n315,230,NJ City,E,3,8,19,30,High School,30,Owner,Standalone\n6160,145.8,Boston,W,23,8,10,54,Graduation,32,Franchise,Complex\n354,259.66,NJ City,S,8,8,1,32,Graduation,27,Patnership,Complex\n2242,198.63,NJ City,S,7,8,20,78,High School,23,Patnership,Complex\n2987,387.73,NJ City,E,12,8,19,51,High School,31,Owner,Standalone\n1527,437.2,NJ City,E,21,8,17,49,High School,24,Owner,Standalone\n6495,233.81,Boston,E,14,17,3,60,Graduation,33,Owner,Standalone\n5780,269.52,Boston,W,7,9,5,73,No education,33,Franchise,Complex\n1988,442.49,NJ City,W,10,9,12,72,High School,27,Franchise,Complex\n1341,149.65,NJ City,N,6,14,18,46,High School,18,Patnership,Standalone\n1583,444.51,NJ City,E,14,19,20,80,High School,32,Owner,Standalone\n291,314.07,NJ City,E,19,8,1,75,Graduation,31,Owner,Standalone\n897,90.21,NJ City,N,16,17,1,59,Graduation,27,Patnership,Standalone\n8359,38.63,NJ City,N,3,22,3,32,High School,26,Owner,Standalone\n1966,134.46,NJ City,S,18,24,4,61,High School,24,Patnership,Complex\n4525,53.55,NJ City,N,11,18,4,40,High School,34,Patnership,Standalone\n1226,382.84,NJ City,S,17,17,2,74,No education,30,Patnership,Complex\n8684,407.41,NJ City,W,16,19,19,76,Graduation,21,Franchise,Complex\n7358,51.89,Boston,S,15,18,4,73,No education,22,Patnership,Complex\n862,233.29,NJ City,S,21,16,3,73,High School,22,Patnership,Complex\n8176,242.52,NJ City,W,14,24,25,39,Graduation,22,Franchise,Complex\n5806,196.54,Boston,S,20,12,14,63,Graduation,28,Patnership,Complex\n4544,23.17,NJ City,N,8,11,2,78,No education,33,Franchise,Complex\n412,352.07,NJ City,W,12,14,3,38,High School,29,Franchise,Complex\n6527,8.05,Boston,N,12,14,2,47,No education,30,Owner,Standalone\n3384,39.89,NJ City,N,1,13,4,71,Graduation,27,Franchise,Complex\n7366,155.59,Boston,S,14,18,15,27,Graduation,18,Patnership,Complex\n2258,77.29,NJ City,S,13,20,4,80,No education,28,Patnership,Complex\n7757,12.19,Boston,N,4,11,4,72,No education,19,Patnership,Standalone\n3351,249.55,NJ City,E,14,11,8,37,High School,19,Owner,Standalone\n6666,150.07,Boston,S,1,22,22,67,Graduation,25,Patnership,Complex\n5364,225.31,Boston,W,14,11,11,41,Graduation,32,Franchise,Complex\n2127,99.3,NJ City,E,6,21,3,78,Graduation,32,Owner,Standalone\n2417,471.28,NJ City,N,8,11,6,79,High School,18,Patnership,Standalone\n8090,189.99,NJ City,S,1,13,12,48,Graduation,22,Patnership,Complex\n9904,449.54,NJ City,W,24,19,23,26,Graduation,19,Franchise,Complex\n168,235.37,NJ City,W,9,17,23,75,High School,26,Franchise,Complex\n7005,495.37,Boston,N,6,16,11,79,Graduation,32,Patnership,Standalone\n298,368.61,NJ City,S,22,17,18,70,High School,26,Patnership,Complex\n7509,149.87,Boston,N,5,21,21,52,Graduation,31,Patnership,Standalone\n8511,253.38,NJ City,E,10,8,11,65,Graduation,25,Owner,Standalone\n1911,273.17,NJ City,E,3,8,5,53,Graduation,25,Owner,Standalone\n61,437.29,Boston,N,21,20,5,58,High School,34,Patnership,Standalone\n6907,352.42,Boston,E,16,16,1,56,High School,19,Owner,Standalone\n8880,215.39,NJ City,W,1,10,8,51,Graduation,28,Franchise,Complex\n2564,497.75,NJ City,W,23,21,12,82,High School,33,Franchise,Complex\n7342,18.67,Boston,N,18,12,1,56,High School,32,Patnership,Complex\n4564,112.98,NJ City,W,13,11,4,32,High School,29,Franchise,Complex\n1458,53.14,NJ City,S,21,22,4,62,Graduation,19,Patnership,Complex\n2771,468.89,NJ City,E,14,12,21,39,High School,33,Owner,Standalone\n2314,463.1,NJ City,S,17,19,5,65,High School,18,Patnership,Complex\n555,197.87,NJ City,E,10,9,14,63,High School,35,Owner,Standalone\n8803,401.43,NJ City,E,10,12,17,41,Graduation,21,Owner,Standalone\n5176,445.21,Boston,W,18,12,6,43,Graduation,32,Franchise,Complex\n3506,453.03,NJ City,S,25,19,16,65,High School,23,Patnership,Complex\n9372,315.93,NJ City,W,24,23,7,27,Graduation,22,Franchise,Complex\n4960,413.09,Boston,W,8,19,3,33,High School,30,Franchise,Complex\n7335,281.15,Boston,E,21,20,14,64,Graduation,22,Owner,Standalone\n7754,304.52,Boston,S,2,16,5,51,No education,18,Patnership,Complex\n5313,202.39,Boston,N,11,21,11,75,Graduation,22,Patnership,Standalone\n1855,6.4,NJ City,N,14,13,1,54,High School,24,Owner,Standalone\n9734,126.83,NJ City,S,16,14,3,33,No education,18,Patnership,Complex\n2243,203.57,NJ City,E,7,12,2,60,No education,18,Owner,Standalone\n8878,265.99,NJ City,S,16,14,25,50,Graduation,19,Patnership,Complex\n6750,386.42,Boston,S,14,20,10,72,Graduation,24,Patnership,Complex\n63,168.98,Boston,E,13,24,8,78,High School,19,Owner,Standalone\n726,382.83,NJ City,S,10,12,15,60,High School,18,Patnership,Complex\n7796,425.31,Boston,W,6,8,7,50,Graduation,26,Franchise,Complex\n1571,481.59,NJ City,E,14,9,5,29,No education,27,Owner,Standalone\n8037,304.45,Boston,N,22,21,10,66,Graduation,26,Patnership,Standalone\n7026,322.95,Boston,S,7,20,6,68,Graduation,35,Patnership,Complex\n7630,362.1,Boston,S,24,17,9,67,Graduation,30,Patnership,Complex\n2030,246.34,NJ City,S,19,20,16,29,High School,34,Patnership,Complex\n788,391.94,NJ City,W,3,11,5,63,No education,26,Franchise,Complex\n3763,313.03,NJ City,E,14,22,21,30,High School,27,Owner,Standalone\n5146,106.29,Boston,S,10,21,4,33,High School,34,Patnership,Complex\n1470,17.15,NJ City,N,4,9,2,50,Graduation,19,Patnership,Complex\n4032,204,NJ City,W,3,24,6,81,High School,31,Franchise,Complex\n7864,480.47,Boston,W,11,10,19,30,Graduation,18,Franchise,Complex\n1722,162.59,NJ City,S,24,14,11,71,High School,21,Patnership,Complex\n2554,469.11,NJ City,S,23,18,4,65,High School,20,Patnership,Complex\n276,189.09,NJ City,W,3,24,4,34,Graduation,22,Franchise,Complex\n2349,338.28,NJ City,N,13,16,18,83,High School,20,Patnership,Standalone\n4989,211.96,Boston,N,21,11,4,81,Graduation,35,Patnership,Standalone\n5965,396.53,Boston,N,1,17,10,70,Graduation,18,Patnership,Standalone\n5398,56.17,Boston,S,11,14,1,38,High School,19,Patnership,Complex\n5871,407.6,Boston,E,12,12,24,66,Graduation,33,Owner,Standalone\n2692,167.32,NJ City,W,20,11,24,52,High School,32,Franchise,Complex\n7479,345.59,Boston,E,23,24,24,32,Graduation,27,Owner,Standalone\n9934,399.84,NJ City,S,24,15,2,49,High School,28,Patnership,Complex\n8421,245.15,NJ City,N,7,24,6,47,Graduation,27,Patnership,Standalone\n6730,389.98,Boston,S,18,15,9,84,Graduation,22,Patnership,Complex\n992,354.49,NJ City,W,23,9,6,48,High School,20,Franchise,Complex\n2429,215.17,NJ City,N,9,18,20,31,High School,23,Patnership,Standalone\n9053,273.02,NJ City,N,18,23,20,72,Graduation,22,Patnership,Standalone\n6571,278.2,Boston,E,2,23,8,57,Graduation,32,Owner,Standalone\n3455,58.24,NJ City,E,2,15,4,52,No education,31,Owner,Standalone\n2541,6.67,NJ City,N,9,20,3,66,Graduation,23,Patnership,Standalone\n6990,80.34,Boston,S,17,13,1,28,Graduation,34,Patnership,Complex\n6729,123.72,Boston,N,19,16,3,40,Graduation,22,Patnership,Standalone\n5917,49.26,Boston,N,3,11,2,42,High School,30,Patnership,Standalone\n9251,429.53,NJ City,E,16,12,20,44,Graduation,32,Owner,Standalone\n1692,229.96,NJ City,W,7,11,8,41,High School,30,Franchise,Complex\n3178,383.21,NJ City,S,17,20,1,27,High School,26,Patnership,Complex\n6845,412.11,Boston,N,24,8,24,78,Graduation,28,Patnership,Standalone\n6155,388.37,Boston,E,6,9,19,68,Graduation,28,Owner,Standalone\n6368,133.37,Boston,W,1,19,24,25,Graduation,30,Franchise,Complex\n7845,322.88,Boston,N,7,22,4,84,Graduation,19,Patnership,Standalone\n8601,100.78,NJ City,N,19,14,2,26,Graduation,18,Patnership,Standalone\n4273,350.77,NJ City,N,19,13,20,60,High School,33,Patnership,Standalone\n7632,494.21,Boston,W,1,8,20,74,Graduation,35,Franchise,Complex\n9293,296.04,NJ City,N,19,17,2,83,No education,29,Patnership,Standalone\n7134,296.06,Boston,S,7,13,17,53,Graduation,29,Patnership,Complex\n6655,275.09,Boston,E,1,18,20,71,Graduation,19,Owner,Standalone\n4122,434.87,NJ City,S,16,8,14,74,High School,25,Patnership,Complex\n3595,212.86,NJ City,E,11,21,6,51,High School,31,Owner,Standalone\n3512,305.22,NJ City,W,20,21,21,47,High School,23,Franchise,Complex\n1082,431.94,NJ City,S,24,15,15,43,High School,21,Patnership,Complex\n5703,408.95,Boston,E,7,19,17,43,Graduation,24,Owner,Standalone\n2994,458.12,NJ City,S,5,13,21,37,High School,25,Patnership,Complex\n9411,16.13,NJ City,N,21,21,2,53,Graduation,33,Owner,Standalone\n6944,388.78,Boston,W,18,17,14,39,Graduation,27,Franchise,Complex\n1065,438.49,NJ City,N,11,22,12,64,High School,26,Patnership,Standalone\n5162,364.55,Boston,S,20,18,25,33,Graduation,34,Patnership,Complex\n9060,321.77,NJ City,W,2,13,9,63,Graduation,18,Franchise,Complex\n2552,230.17,NJ City,W,12,13,19,79,High School,28,Franchise,Complex\n1145,112.33,NJ City,N,7,12,4,78,No education,21,Patnership,Standalone\n4209,250.64,NJ City,N,10,8,2,53,Graduation,21,Patnership,Standalone\n349,53.01,NJ City,N,16,14,4,60,High School,35,Patnership,Standalone\n4780,431.56,NJ City,W,19,8,4,47,High School,26,Franchise,Complex\n3691,442.21,NJ City,E,23,16,21,33,High School,29,Owner,Standalone\n2768,83.81,NJ City,W,14,17,4,26,No education,31,Franchise,Complex\n6138,186.51,Boston,S,14,24,13,49,Graduation,30,Patnership,Complex\n4917,211.96,NJ City,N,13,19,11,80,High School,31,Patnership,Standalone\n5811,14.69,Boston,N,19,22,1,76,Graduation,24,Owner,Standalone\n9314,193.69,NJ City,S,9,10,5,83,No education,31,Patnership,Complex\n8520,349.85,NJ City,W,3,19,12,32,Graduation,34,Franchise,Complex\n5109,263.55,Boston,N,16,16,24,35,Graduation,27,Patnership,Standalone\n6261,351.67,Boston,N,10,13,2,81,Graduation,33,Patnership,Standalone\n7681,49.01,Boston,N,25,14,4,27,High School,30,Patnership,Standalone\n1297,481.73,NJ City,N,6,20,5,61,High School,32,Patnership,Standalone\n8073,69.82,NJ City,N,2,23,3,74,Graduation,24,Patnership,Standalone\n2020,227,NJ City,W,2,20,23,82,High School,28,Franchise,Complex\n4239,412.1,NJ City,E,16,18,25,65,High School,35,Owner,Standalone\n3337,287.84,NJ City,N,12,13,18,81,High School,34,Patnership,Standalone\n7105,69.48,Boston,N,13,8,2,43,High School,35,Patnership,Standalone\n6697,62.5,Boston,N,2,9,3,47,High School,31,Patnership,Standalone\n9902,78.74,NJ City,S,12,17,1,34,No education,29,Patnership,Complex\n3494,102.57,NJ City,S,14,11,4,57,No education,35,Patnership,Complex\n6144,489.94,Boston,W,11,16,25,50,Graduation,27,Franchise,Complex\n62,291.91,Boston,S,16,15,23,47,High School,29,Patnership,Complex\n966,452.72,NJ City,S,5,8,2,66,Graduation,22,Patnership,Complex\n8547,252.73,NJ City,E,23,11,19,42,Graduation,29,Owner,Standalone\n9205,231.15,NJ City,N,4,16,15,51,Graduation,20,Patnership,Standalone\n9589,227.81,NJ City,N,17,20,15,60,Graduation,25,Patnership,Standalone\n8227,310.67,NJ City,E,13,13,14,51,Graduation,26,Owner,Standalone\n9914,136.42,NJ City,S,21,16,25,81,Graduation,33,Patnership,Complex\n6282,83.92,Boston,S,2,17,4,73,Graduation,18,Patnership,Complex\n4982,257.65,Boston,S,24,16,18,57,High School,22,Patnership,Complex\n2396,137.79,NJ City,W,6,19,25,57,High School,27,Franchise,Complex\n4259,295.27,NJ City,E,8,21,11,50,High School,24,Owner,Standalone\n2193,131.09,NJ City,N,13,21,2,38,Graduation,22,Patnership,Standalone\n3987,488.54,NJ City,E,21,18,19,43,High School,34,Owner,Standalone\n9408,234.65,NJ City,W,12,15,17,60,Graduation,31,Franchise,Complex\n4412,385.45,NJ City,W,19,22,3,57,No education,20,Franchise,Complex\n2248,22.27,NJ City,N,25,11,1,82,High School,22,Franchise,Complex\n5584,310.51,Boston,W,9,16,21,28,Graduation,23,Franchise,Complex\n2803,224.97,NJ City,E,17,18,25,48,High School,32,Owner,Standalone\n492,257.71,NJ City,W,13,16,13,66,High School,24,Franchise,Complex\n2377,449.43,NJ City,N,12,24,3,84,High School,27,Patnership,Standalone\n6356,164.56,Boston,W,21,9,21,35,Graduation,30,Franchise,Complex\n7063,182.52,Boston,E,22,8,23,46,Graduation,23,Owner,Standalone\n1346,222,NJ City,S,2,21,16,25,High School,31,Patnership,Complex\n5992,488.12,Boston,W,23,15,20,56,Graduation,28,Franchise,Complex\n2713,82.28,NJ City,N,11,18,3,35,High School,19,Patnership,Standalone\n4115,433.76,NJ City,E,13,9,17,62,High School,26,Owner,Standalone\n949,70.92,NJ City,N,11,16,3,53,High School,23,Patnership,Standalone\n4789,58.31,NJ City,N,7,10,4,52,High School,31,Patnership,Standalone\n2457,132,NJ City,N,2,16,19,43,High School,25,Patnership,Standalone\n8499,414.85,NJ City,E,16,23,11,53,Graduation,23,Owner,Standalone\n8972,126.09,NJ City,W,22,13,2,75,No education,27,Franchise,Complex\n4078,48.64,NJ City,S,7,14,4,70,High School,25,Patnership,Complex\n2575,498.53,NJ City,E,18,17,15,56,High School,32,Owner,Standalone\n3560,151.56,NJ City,W,13,14,20,28,High School,18,Franchise,Complex\n3585,403.61,NJ City,N,21,13,6,82,High School,31,Patnership,Standalone\n5269,419.69,Boston,N,5,24,18,34,Graduation,18,Patnership,Standalone\n6649,413.99,Boston,N,12,17,11,37,Graduation,22,Patnership,Standalone\n10,142.72,NJ City,S,24,15,25,31,High School,22,Patnership,Complex\n2047,2.73,NJ City,E,9,15,1,62,High School,29,Owner,Standalone\n8816,28.38,NJ City,N,1,11,4,59,No education,24,Franchise,Complex\n9830,365.77,NJ City,S,13,20,17,69,Graduation,30,Patnership,Complex\n7820,456.83,Boston,W,6,10,20,43,Graduation,33,Franchise,Complex\n5691,499.75,Boston,E,16,20,7,39,Graduation,33,Owner,Standalone\n4864,188.72,NJ City,W,7,15,7,26,High School,28,Franchise,Complex\n5557,318.07,Boston,N,16,19,15,48,Graduation,28,Patnership,Standalone\n9760,137.54,NJ City,W,2,17,25,63,Graduation,21,Franchise,Complex\n5288,241.54,Boston,W,25,21,8,74,Graduation,29,Franchise,Complex\n2668,304.28,NJ City,W,6,19,25,57,High School,29,Franchise,Complex\n9154,150.57,NJ City,S,2,18,7,46,Graduation,20,Patnership,Complex\n8426,73.82,NJ City,N,21,8,4,29,No education,19,Patnership,Complex\n2011,239.86,NJ City,E,4,23,1,53,High School,33,Owner,Standalone\n5875,372.76,Boston,E,13,12,15,71,Graduation,29,Owner,Standalone\n682,299.64,NJ City,S,16,12,5,81,High School,33,Patnership,Complex\n6921,168.61,Boston,N,13,9,18,36,Graduation,29,Patnership,Standalone\n6492,160.06,Boston,W,24,19,19,76,Graduation,31,Franchise,Complex\n4886,75.7,NJ City,S,21,9,2,69,No education,22,Patnership,Complex\n9269,166.3,NJ City,N,8,13,2,79,No education,22,Patnership,Standalone\n7620,286.89,Boston,W,3,11,23,30,Graduation,34,Franchise,Complex\n2400,368.36,NJ City,W,16,17,1,35,Graduation,35,Franchise,Complex\n9611,470.31,NJ City,E,10,10,7,73,Graduation,21,Owner,Standalone\n9438,214.04,NJ City,S,22,16,20,83,Graduation,28,Patnership,Complex\n8305,308.02,NJ City,N,24,16,14,72,Graduation,26,Patnership,Standalone\n275,162.77,NJ City,E,5,13,25,41,High School,21,Owner,Standalone\n8045,81.45,NJ City,N,9,9,3,61,No education,30,Patnership,Standalone\n1624,411.69,NJ City,W,7,21,20,46,High School,23,Franchise,Complex\n7619,443,Boston,E,11,20,16,66,Graduation,22,Owner,Standalone\n1447,168.64,NJ City,E,25,19,1,53,High School,19,Owner,Standalone\n5266,376.56,Boston,S,10,21,16,49,Graduation,21,Patnership,Complex\n2111,269,NJ City,E,2,13,15,76,High School,20,Owner,Standalone\n3905,189.53,NJ City,N,19,17,25,80,High School,31,Patnership,Standalone\n3704,125.04,NJ City,W,2,14,1,65,No education,26,Franchise,Complex\n3523,30.33,NJ City,N,23,24,3,70,High School,24,Owner,Standalone\n6758,436.98,Boston,S,15,8,4,40,No education,25,Patnership,Complex\n8334,463.92,NJ City,S,14,18,10,50,Graduation,20,Patnership,Complex\n2516,476.91,NJ City,W,13,13,22,62,High School,19,Franchise,Complex\n9653,143.45,NJ City,N,9,19,13,77,Graduation,31,Patnership,Standalone\n4863,449.67,NJ City,E,8,8,10,68,High School,31,Owner,Standalone\n4026,44.76,NJ City,N,17,10,2,71,Graduation,25,Patnership,Complex\n3586,495.79,NJ City,S,22,14,14,49,High School,30,Patnership,Complex\n2826,61.08,NJ City,N,11,8,2,44,Graduation,33,Patnership,Complex\n2451,155.23,NJ City,E,10,20,21,50,High School,31,Owner,Standalone\n6284,99.88,Boston,W,13,18,4,62,No education,28,Franchise,Complex\n5933,70.94,Boston,N,8,20,4,33,No education,24,Patnership,Standalone\n5259,310.52,Boston,E,13,23,1,53,Graduation,18,Owner,Standalone\n850,73.33,NJ City,N,24,13,3,70,High School,28,Patnership,Complex\n4498,305.51,NJ City,S,18,12,13,78,High School,26,Patnership,Complex\n5616,281.08,Boston,W,22,17,15,53,Graduation,22,Franchise,Complex\n6,418.18,Maine,S,9,16,6,49,High School,18,Patnership,Complex\n6151,308.25,Boston,E,10,21,20,76,Graduation,18,Owner,Standalone\n4100,368.71,NJ City,W,5,24,3,82,No education,30,Franchise,Complex\n5664,54.89,Boston,W,10,14,1,56,Graduation,35,Franchise,Complex\n7568,339.02,Boston,W,10,24,16,44,Graduation,24,Franchise,Complex\n7460,175.84,Boston,W,3,11,25,25,Graduation,26,Franchise,Complex\n7251,22.22,Boston,N,20,21,3,42,Graduation,20,Owner,Standalone\n5408,488.91,Boston,W,10,19,15,49,Graduation,21,Franchise,Complex\n8085,146.29,NJ City,N,23,12,23,36,Graduation,20,Patnership,Standalone\n5255,169.76,Boston,E,15,15,11,32,Graduation,33,Owner,Standalone\n938,154.98,NJ City,S,24,22,22,69,High School,22,Patnership,Complex\n5136,10.85,Boston,N,3,9,2,68,Graduation,31,Franchise,Complex\n9385,114.73,NJ City,N,11,16,3,44,High School,18,Patnership,Standalone\n3797,189,NJ City,N,2,18,8,70,High School,28,Patnership,Standalone\n5490,325.8,Boston,S,4,20,18,73,Graduation,18,Patnership,Complex\n93,114.35,NJ City,N,22,15,3,78,Graduation,20,Patnership,Standalone\n5154,264.46,Boston,S,3,18,12,29,Graduation,24,Patnership,Complex\n3436,190.75,NJ City,W,4,24,11,80,High School,27,Franchise,Complex\n990,182.98,NJ City,S,10,20,20,59,High School,18,Patnership,Complex\n3626,212.7,NJ City,S,23,17,7,30,High School,32,Patnership,Complex\n5583,215.24,Boston,E,4,20,1,53,Graduation,33,Owner,Standalone\n2399,79.92,NJ City,E,8,18,2,25,No education,20,Owner,Standalone\n4723,276.17,NJ City,E,18,11,18,50,High School,33,Owner,Standalone\n9612,296.32,NJ City,W,7,21,13,38,Graduation,21,Franchise,Complex\n4287,73.74,NJ City,N,9,20,1,39,Graduation,26,Owner,Standalone\n3373,109.07,NJ City,N,4,13,3,80,High School,20,Patnership,Standalone\n2739,321.02,NJ City,E,21,14,25,25,High School,25,Owner,Standalone\n2498,305.01,NJ City,S,7,8,7,73,High School,23,Patnership,Complex\n910,251,NJ City,S,2,11,17,83,High School,22,Patnership,Complex\n5264,407.19,Boston,W,18,13,24,53,Graduation,26,Franchise,Complex\n1126,305.04,NJ City,S,5,17,14,71,High School,18,Patnership,Complex\n8818,120.67,NJ City,S,23,18,1,81,High School,29,Patnership,Complex\n4607,364.59,NJ City,E,25,13,7,73,High School,31,Owner,Standalone\n5507,476.68,Boston,E,23,8,16,69,Graduation,21,Owner,Standalone\n2590,62.04,NJ City,N,22,9,4,83,High School,28,Patnership,Complex\n3901,115.46,NJ City,N,22,11,3,55,High School,32,Patnership,Standalone\n5443,181.1,Boston,E,12,11,14,26,Graduation,32,Owner,Standalone\n4411,319.06,NJ City,E,10,21,9,76,High School,31,Owner,Standalone\n8839,415.16,NJ City,E,18,21,14,51,Graduation,19,Owner,Standalone\n2597,381.4,NJ City,N,6,19,25,71,High School,22,Patnership,Standalone\n6111,307.18,Boston,E,9,15,9,79,Graduation,27,Owner,Standalone\n7606,1.7,Boston,N,5,14,2,40,High School,33,Patnership,Complex\n4354,121.98,NJ City,S,7,14,2,47,High School,31,Patnership,Complex\n4976,251.73,Boston,W,13,20,7,62,High School,33,Franchise,Complex\n7666,14.62,Boston,N,24,22,3,58,High School,34,Patnership,Complex\n273,477.72,NJ City,N,5,14,2,52,Graduation,31,Patnership,Standalone\n3973,340.69,NJ City,N,9,8,10,31,High School,32,Patnership,Standalone\n534,335.13,NJ City,S,13,21,24,49,High School,34,Patnership,Complex\n9867,341.86,NJ City,E,20,11,15,79,Graduation,35,Owner,Standalone\n7711,8.97,Boston,N,18,13,2,74,High School,24,Owner,Standalone\n2616,87.73,NJ City,W,13,13,1,27,Graduation,33,Franchise,Complex\n3412,282.13,NJ City,W,14,10,19,25,High School,32,Franchise,Complex\n9116,265.76,NJ City,W,11,19,10,40,Graduation,21,Franchise,Complex\n1819,216.88,NJ City,E,7,16,12,52,High School,28,Owner,Standalone\n9391,108.57,NJ City,E,14,19,3,58,High School,25,Owner,Standalone\n8095,16.53,NJ City,N,12,22,1,69,High School,21,Owner,Standalone\n1001,434.03,NJ City,N,14,19,25,81,High School,18,Patnership,Standalone\n3656,240.89,NJ City,W,14,11,22,67,High School,30,Franchise,Complex\n5181,210.75,Boston,N,8,18,12,36,Graduation,32,Patnership,Standalone\n759,214.5,NJ City,E,19,18,17,59,High School,33,Owner,Standalone\n9971,375.27,NJ City,E,19,17,10,62,Graduation,18,Owner,Standalone\n132,275.59,NJ City,W,14,19,4,30,Graduation,32,Franchise,Complex\n1924,8.58,NJ City,N,7,21,1,81,High School,33,Franchise,Complex\n4858,330.01,NJ City,S,13,16,9,26,High School,33,Patnership,Complex\n9234,395.92,NJ City,S,5,23,8,43,Graduation,27,Patnership,Complex\n4883,394.98,NJ City,E,21,12,9,42,High School,35,Owner,Standalone\n4292,292,NJ City,W,2,19,18,65,High School,19,Franchise,Complex\n1163,286.52,NJ City,E,18,20,1,79,No education,21,Owner,Standalone\n8270,368.23,NJ City,S,12,15,16,38,Graduation,21,Patnership,Complex\n6299,31.52,Boston,N,1,20,4,77,No education,26,Owner,Standalone\n7354,7.84,Boston,N,16,21,2,43,High School,29,Patnership,Complex\n6568,122.34,Boston,W,8,23,1,65,High School,30,Franchise,Complex\n5668,75.07,Boston,W,14,24,4,70,High School,26,Franchise,Complex\n1876,220.37,NJ City,W,24,17,11,28,High School,26,Franchise,Complex\n2725,425.76,NJ City,N,14,8,11,33,High School,35,Patnership,Standalone\n2133,142.09,NJ City,N,4,11,24,70,High School,30,Patnership,Standalone\n4821,344.38,NJ City,N,5,15,15,41,High School,23,Patnership,Standalone\n1933,99.93,NJ City,N,5,18,1,33,High School,28,Patnership,Standalone\n8783,326.35,NJ City,E,24,8,25,43,Graduation,21,Owner,Standalone\n2877,444.74,NJ City,N,4,9,19,64,High School,32,Patnership,Standalone\n1467,339.93,NJ City,E,21,23,20,59,High School,30,Owner,Standalone\n5047,172.61,Boston,E,20,11,15,79,Graduation,20,Owner,Standalone\n6298,376.35,Boston,S,10,15,11,42,Graduation,23,Patnership,Complex\n8411,82.43,NJ City,E,11,12,3,45,No education,25,Owner,Standalone\n8787,298.9,NJ City,E,20,17,16,45,Graduation,31,Owner,Standalone\n8302,360.01,NJ City,S,5,12,25,78,Graduation,31,Patnership,Complex\n9663,176.19,NJ City,E,6,9,7,58,Graduation,35,Owner,Standalone\n8102,132.86,NJ City,S,6,22,22,83,Graduation,33,Patnership,Complex\n9106,441.81,NJ City,S,13,13,3,38,High School,21,Patnership,Complex\n7024,33.36,Boston,N,2,19,1,32,High School,29,Franchise,Complex\n4163,195.09,NJ City,E,24,16,8,45,High School,34,Owner,Standalone\n2440,245.42,NJ City,W,8,17,16,73,High School,23,Franchise,Complex\n9315,47.56,NJ City,E,12,10,4,76,Graduation,28,Owner,Standalone\n4834,258.3,NJ City,S,5,13,23,63,High School,35,Patnership,Complex\n1255,466.34,NJ City,E,6,11,23,74,High School,34,Owner,Standalone\n322,432.33,NJ City,S,24,11,4,53,High School,32,Patnership,Complex\n717,482.63,NJ City,N,23,12,14,43,High School,28,Patnership,Standalone\n8362,167.14,NJ City,S,7,18,2,30,High School,24,Patnership,Complex\n3040,152.3,NJ City,W,16,15,17,28,High School,34,Franchise,Complex\n639,73.63,NJ City,N,4,8,2,34,Graduation,20,Owner,Standalone\n784,383.07,NJ City,W,14,14,22,57,High School,30,Franchise,Complex\n1149,448.05,NJ City,N,6,15,19,28,High School,29,Patnership,Standalone\n5727,340.73,Boston,E,15,11,12,84,Graduation,24,Owner,Standalone\n7621,30.67,Boston,N,2,20,4,43,High School,32,Patnership,Standalone\n5158,223.32,Boston,S,15,16,1,49,High School,33,Patnership,Complex\n5472,430.28,Boston,W,16,22,2,71,Graduation,31,Franchise,Complex\n619,240.47,NJ City,E,1,22,2,60,High School,34,Owner,Standalone\n4906,299.06,NJ City,S,4,23,12,63,High School,28,Patnership,Complex\n4221,140.94,NJ City,N,19,21,7,65,High School,34,Patnership,Standalone\n6934,426.4,Boston,S,6,21,8,36,Graduation,24,Patnership,Complex\n7403,31.92,Boston,N,8,22,3,72,No education,24,Owner,Standalone\n9807,328.65,NJ City,E,7,19,1,58,Graduation,22,Owner,Standalone\n4682,270.39,NJ City,S,19,23,17,69,High School,25,Patnership,Complex\n9200,135.65,NJ City,W,14,19,21,37,Graduation,33,Franchise,Complex\n5690,379.5,Boston,S,13,20,22,68,Graduation,30,Patnership,Complex\n9197,245.79,NJ City,N,15,16,13,32,Graduation,25,Patnership,Standalone\n5564,451.98,Boston,W,4,11,20,65,Graduation,19,Franchise,Complex\n9716,47.32,NJ City,W,21,23,4,59,No education,21,Franchise,Complex\n2765,182.66,NJ City,N,23,18,20,85,High School,35,Patnership,Standalone\n2012,108.64,NJ City,W,15,15,1,46,No education,23,Franchise,Complex\n2594,244.21,NJ City,S,23,13,10,26,High School,18,Patnership,Complex\n3297,387.09,NJ City,N,20,19,3,66,Graduation,35,Patnership,Standalone\n5232,439.48,Boston,W,18,17,21,70,Graduation,24,Franchise,Complex\n4893,6.05,NJ City,N,1,18,1,30,Graduation,21,Patnership,Standalone\n5389,233.03,Boston,N,4,20,17,79,Graduation,29,Patnership,Standalone\n3048,341.63,NJ City,W,25,9,12,74,High School,28,Franchise,Complex\n6826,25.34,Boston,N,5,14,4,81,High School,25,Patnership,Complex\n2239,401.84,NJ City,E,23,16,14,67,High School,25,Owner,Standalone\n5041,362.58,Boston,N,1,13,4,56,High School,26,Patnership,Standalone\n6807,371.67,Boston,E,15,20,4,56,Graduation,25,Owner,Standalone\n8114,82.43,NJ City,S,8,10,2,46,No education,21,Patnership,Complex\n3172,432.46,NJ City,W,3,22,3,56,High School,29,Franchise,Complex\n9167,454.5,NJ City,E,25,15,19,46,Graduation,27,Owner,Standalone\n2715,36.01,NJ City,N,4,18,4,52,Graduation,32,Owner,Standalone\n3852,333.16,NJ City,W,17,24,8,46,High School,35,Franchise,Complex\n2794,320.31,NJ City,S,15,11,24,59,High School,19,Patnership,Complex\n1730,192.17,NJ City,S,17,24,25,42,High School,30,Patnership,Complex\n3978,94.17,NJ City,S,17,9,4,83,Graduation,29,Patnership,Complex\n4514,252.59,NJ City,S,18,16,2,84,No education,24,Patnership,Complex\n8188,136.77,NJ City,W,8,17,11,81,Graduation,34,Franchise,Complex\n4437,5.51,NJ City,N,6,22,3,34,Graduation,32,Patnership,Standalone\n3921,345.14,NJ City,N,25,24,24,78,High School,23,Patnership,Standalone\n443,346.23,NJ City,E,7,16,24,27,High School,34,Owner,Standalone\n3318,186.73,NJ City,S,17,19,19,32,High School,34,Patnership,Complex\n1273,317.92,NJ City,N,14,14,8,30,High School,31,Patnership,Standalone\n5465,17.42,Boston,N,11,13,4,40,No education,32,Patnership,Standalone\n2724,206.86,NJ City,W,16,15,11,83,High School,29,Franchise,Complex\n5741,422.79,Boston,N,8,19,8,48,Graduation,20,Patnership,Standalone\n3261,156,NJ City,N,3,14,20,75,High School,34,Patnership,Standalone\n9144,385.68,NJ City,W,4,9,10,37,Graduation,29,Franchise,Complex\n130,135.99,NJ City,S,3,24,3,31,High School,28,Patnership,Complex\n6938,255.47,Boston,S,16,18,23,46,Graduation,34,Patnership,Complex\n4962,485.51,Boston,S,19,16,14,75,High School,24,Patnership,Complex\n6348,11.42,Boston,N,1,23,3,45,Graduation,19,Franchise,Complex\n6797,271.73,Boston,N,10,19,6,56,Graduation,27,Patnership,Standalone\n3718,161.55,NJ City,S,15,21,2,83,High School,21,Patnership,Complex\n4245,270.07,NJ City,N,22,17,14,75,High School,23,Patnership,Standalone\n5706,67.11,Boston,N,1,9,4,30,Graduation,19,Patnership,Complex\n1040,141.46,NJ City,W,20,12,9,48,High School,29,Franchise,Complex\n4308,148.73,NJ City,W,13,8,25,80,High School,26,Franchise,Complex\n7516,208.72,Boston,W,22,9,25,73,Graduation,20,Franchise,Complex\n9833,489.17,NJ City,N,9,16,18,79,Graduation,32,Patnership,Standalone\n1081,176.96,NJ City,N,19,21,5,35,High School,19,Patnership,Standalone\n6070,155.07,Boston,S,4,20,18,25,Graduation,28,Patnership,Complex\n7010,153.34,Boston,S,12,17,9,25,Graduation,29,Patnership,Complex\n2734,52.83,NJ City,S,7,18,1,32,High School,25,Patnership,Complex\n9982,444.61,NJ City,S,25,8,1,46,High School,32,Patnership,Complex\n5028,350.42,Boston,W,1,10,9,68,Graduation,29,Franchise,Complex\n3568,489.44,NJ City,W,14,24,20,29,High School,33,Franchise,Complex\n4409,36.41,NJ City,N,24,19,4,70,No education,25,Patnership,Standalone\n1509,304.84,NJ City,N,14,16,5,67,Graduation,23,Patnership,Standalone\n655,353.57,NJ City,E,8,22,4,48,High School,18,Owner,Standalone\n5120,332.52,Boston,W,23,8,17,76,Graduation,33,Franchise,Complex\n1059,188.69,NJ City,E,2,9,3,50,Graduation,21,Owner,Standalone\n5239,69.77,Boston,N,23,11,4,61,High School,23,Owner,Standalone\n1117,57.84,NJ City,N,10,17,2,37,High School,24,Patnership,Standalone\n3152,150.8,NJ City,W,24,14,23,72,High School,19,Franchise,Complex\n9199,198.82,NJ City,E,9,18,14,48,Graduation,34,Owner,Standalone\n2067,30.6,NJ City,N,3,18,4,79,Graduation,20,Owner,Standalone\n2072,494.76,NJ City,W,8,11,8,28,High School,34,Franchise,Complex\n1234,27.31,NJ City,N,3,23,2,31,High School,28,Patnership,Complex\n2834,106.98,NJ City,S,13,17,3,43,No education,24,Patnership,Complex\n5884,385.25,Boston,W,21,10,13,80,Graduation,24,Franchise,Complex\n937,182.28,NJ City,N,20,9,2,58,High School,23,Patnership,Standalone\n6327,135.15,Boston,E,3,9,8,79,Graduation,23,Owner,Standalone\n3967,241.42,NJ City,E,18,19,12,37,High School,19,Owner,Standalone\n5622,464.91,Boston,S,22,13,3,46,Graduation,30,Patnership,Complex\n8563,90.98,NJ City,E,20,22,1,79,High School,30,Owner,Standalone\n1954,155.72,NJ City,S,15,15,14,42,High School,20,Patnership,Complex\n8204,222.04,NJ City,W,16,20,21,54,Graduation,25,Franchise,Complex\n2167,164,NJ City,E,3,12,9,38,High School,21,Owner,Standalone\n4543,84.28,NJ City,E,16,14,2,38,High School,35,Owner,Standalone\n4687,406.1,NJ City,E,15,24,9,49,High School,26,Owner,Standalone\n9150,194.15,NJ City,S,19,10,4,44,Graduation,20,Patnership,Complex\n597,482.37,NJ City,N,25,11,8,61,High School,22,Patnership,Standalone\n928,135.92,NJ City,W,16,17,2,73,High School,33,Franchise,Complex\n9474,346.19,NJ City,S,6,17,15,79,Graduation,27,Patnership,Complex\n8662,322.78,NJ City,S,13,15,9,38,Graduation,28,Patnership,Complex\n9101,396.88,NJ City,N,15,22,8,67,Graduation,32,Patnership,Standalone\n3032,113.77,NJ City,W,3,22,1,68,No education,21,Franchise,Complex\n2645,288.28,NJ City,N,10,15,21,55,High School,33,Patnership,Standalone\n765,346.95,NJ City,N,25,18,13,81,High School,30,Patnership,Standalone\n1510,122.17,NJ City,S,7,16,3,26,High School,23,Patnership,Complex\n8418,304.24,NJ City,S,22,11,21,84,Graduation,22,Patnership,Complex\n3703,120.97,NJ City,E,6,13,4,26,High School,31,Owner,Standalone\n37,162,NY City,N,1,22,11,27,High School,27,Patnership,Standalone\n2485,422.42,NJ City,N,8,19,6,40,High School,22,Patnership,Standalone\n5641,92.68,Boston,N,5,13,1,56,High School,28,Patnership,Standalone\n2563,381.69,NJ City,E,21,20,11,46,High School,28,Owner,Standalone\n4092,497.56,NJ City,W,18,21,13,78,High School,34,Franchise,Complex\n1369,88.3,NJ City,N,21,8,4,54,High School,28,Patnership,Standalone\n7110,11.07,Boston,N,15,19,4,80,Graduation,35,Patnership,Complex\n8828,486.54,NJ City,W,15,24,4,76,No education,35,Franchise,Complex\n9236,375.36,NJ City,W,12,12,14,81,Graduation,33,Franchise,Complex\n4750,349.76,NJ City,S,18,9,25,62,High School,18,Patnership,Complex\n4007,95.75,NJ City,E,18,16,3,28,No education,29,Owner,Standalone\n4419,39.78,NJ City,N,9,9,2,43,Graduation,34,Owner,Standalone\n2251,446.57,NJ City,E,8,21,16,65,High School,31,Owner,Standalone\n1799,408.33,NJ City,E,9,18,2,54,No education,23,Owner,Standalone\n4295,225.85,NJ City,E,17,21,18,64,High School,19,Owner,Standalone\n5093,182.44,Boston,N,16,9,12,77,Graduation,24,Patnership,Standalone\n6228,123.16,Boston,W,20,19,1,58,Graduation,29,Franchise,Complex\n5513,473.96,Boston,N,8,22,3,67,No education,27,Patnership,Standalone\n3910,438.19,NJ City,S,10,23,18,72,High School,22,Patnership,Complex\n4907,70.04,NJ City,N,21,18,4,42,No education,30,Owner,Standalone\n8809,391.28,NJ City,N,14,18,15,83,Graduation,30,Patnership,Standalone\n4459,319.82,NJ City,E,18,14,3,80,High School,22,Owner,Standalone\n7040,266.58,Boston,W,9,9,12,68,Graduation,19,Franchise,Complex\n9804,118.96,NJ City,W,9,10,1,35,Graduation,34,Franchise,Complex\n180,284.28,NJ City,W,5,16,18,34,High School,31,Franchise,Complex\n9763,385.92,NJ City,E,2,21,24,62,Graduation,18,Owner,Standalone\n9697,120.39,NJ City,N,12,22,1,26,High School,32,Patnership,Standalone\n1451,116.47,NJ City,E,24,12,2,32,No education,29,Owner,Standalone\n4472,18.22,NJ City,N,15,10,2,28,No education,20,Franchise,Complex\n5043,111.31,Boston,E,3,19,4,60,Graduation,35,Owner,Standalone\n5764,41.14,Boston,N,7,20,4,59,High School,32,Franchise,Complex\n2224,124.24,NJ City,W,1,23,1,66,High School,24,Franchise,Complex\n4342,271.71,NJ City,S,5,12,1,52,High School,35,Patnership,Complex\n3060,497.51,NJ City,W,15,8,16,48,High School,32,Franchise,Complex\n6444,321.62,Boston,W,11,15,9,45,Graduation,18,Franchise,Complex\n8485,438.75,NJ City,N,15,10,15,57,Graduation,33,Patnership,Standalone\n1160,191.08,NJ City,W,25,17,20,47,High School,24,Franchise,Complex\n7468,497.84,Boston,W,11,22,23,68,Graduation,24,Franchise,Complex\n3162,434.33,NJ City,S,5,17,24,32,High School,33,Patnership,Complex\n8123,239.27,NJ City,E,5,17,14,29,Graduation,27,Owner,Standalone\n9379,197.76,NJ City,E,22,20,14,81,Graduation,27,Owner,Standalone\n2788,390.49,NJ City,W,10,23,15,84,High School,28,Franchise,Complex\n2147,155,NJ City,E,3,20,12,29,High School,32,Owner,Standalone\n4019,225.59,NJ City,E,20,10,3,58,No education,23,Owner,Standalone\n944,472.37,NJ City,W,21,15,6,32,High School,30,Franchise,Complex\n1990,477.53,NJ City,S,5,13,10,43,High School,32,Patnership,Complex\n3674,138.98,NJ City,S,6,21,5,35,No education,20,Patnership,Complex\n2028,128.13,NJ City,W,16,8,16,75,High School,30,Franchise,Complex\n7651,464.18,Boston,E,14,12,18,60,Graduation,30,Owner,Standalone\n4351,201.95,NJ City,E,21,15,1,80,High School,20,Owner,Standalone\n3479,37.45,NJ City,N,21,15,3,47,No education,23,Owner,Standalone\n7650,63.7,Boston,N,14,21,4,83,Graduation,25,Patnership,Complex\n6727,141.32,Boston,E,10,11,17,77,Graduation,23,Owner,Standalone\n3183,121.4,NJ City,E,14,19,4,46,Graduation,31,Owner,Standalone\n7207,105.49,Boston,E,21,24,3,52,High School,23,Owner,Standalone\n9850,371.21,NJ City,S,14,14,9,84,Graduation,34,Patnership,Complex\n2665,97.22,NJ City,N,3,11,4,69,High School,35,Patnership,Standalone\n4502,27.12,NJ City,N,12,17,1,48,No education,28,Patnership,Complex\n468,230.17,NJ City,W,10,11,8,38,High School,23,Franchise,Complex\n6931,46.25,Boston,N,22,14,3,70,High School,28,Owner,Standalone\n541,448.27,NJ City,N,23,9,12,52,High School,18,Patnership,Standalone\n7275,46.62,Boston,N,2,19,2,82,Graduation,27,Owner,Standalone\n371,15.85,NJ City,N,2,22,1,66,No education,28,Owner,Standalone\n7363,279.79,Boston,E,4,20,9,56,Graduation,30,Owner,Standalone\n4679,359.99,NJ City,E,23,11,6,79,High School,33,Owner,Standalone\n208,414.39,NJ City,W,13,8,15,65,High School,23,Franchise,Complex\n1992,278,NJ City,W,3,8,10,56,High School,21,Franchise,Complex\n2967,352.75,NJ City,E,11,23,9,42,High School,18,Owner,Standalone\n9388,370.58,NJ City,W,1,22,3,33,High School,23,Franchise,Complex\n3590,152.77,NJ City,S,8,18,8,52,High School,29,Patnership,Complex\n1559,60.42,NJ City,N,19,23,3,50,No education,23,Owner,Standalone\n319,262.09,NJ City,E,13,22,11,72,High School,25,Owner,Standalone\n4450,134.34,NJ City,S,14,10,21,58,High School,32,Patnership,Complex\n7018,94.48,Boston,S,14,8,1,31,High School,34,Patnership,Complex\n6116,195.84,Boston,W,5,12,2,72,No education,34,Franchise,Complex\n2644,400.81,NJ City,W,11,10,23,59,High School,26,Franchise,Complex\n5124,474.01,Boston,W,14,23,21,52,Graduation,28,Franchise,Complex\n2218,36.02,NJ City,N,8,24,3,33,High School,34,Patnership,Complex\n1021,237.62,NJ City,N,8,20,3,83,High School,25,Patnership,Standalone\n6838,358.22,Boston,S,6,23,19,65,Graduation,31,Patnership,Complex\n6965,492.5,Boston,N,21,18,14,76,Graduation,34,Patnership,Standalone\n4282,91.05,NJ City,S,6,22,3,33,High School,30,Patnership,Complex\n1764,168.53,NJ City,W,23,16,17,67,High School,29,Franchise,Complex\n8772,295.55,NJ City,W,1,13,7,31,Graduation,33,Franchise,Complex\n3414,404.76,NJ City,S,7,17,1,35,Graduation,25,Patnership,Complex\n4582,471.35,NJ City,S,12,21,20,31,High School,22,Patnership,Complex\n5762,175.67,Boston,S,23,23,3,72,No education,25,Patnership,Complex\n2291,324.87,NJ City,E,23,24,23,34,High School,22,Owner,Standalone\n9852,274.84,NJ City,W,24,14,6,30,Graduation,20,Franchise,Complex\n7318,352.93,Boston,S,16,10,6,73,Graduation,28,Patnership,Complex\n4283,151.31,NJ City,E,10,22,25,39,High School,33,Owner,Standalone\n3876,324.53,NJ City,W,13,17,6,38,High School,25,Franchise,Complex\n4655,356.2,NJ City,E,4,19,10,36,High School,26,Owner,Standalone\n5224,301.78,Boston,W,2,14,5,35,High School,28,Franchise,Complex\n4535,375.21,NJ City,E,15,23,20,59,High School,26,Owner,Standalone\n9972,466.19,NJ City,W,11,21,4,38,Graduation,23,Franchise,Complex\n1371,241.1,NJ City,E,24,18,7,38,High School,34,Owner,Standalone\n5017,435.87,Boston,N,14,17,3,60,High School,31,Patnership,Standalone\n7973,283.15,Boston,N,10,16,22,63,Graduation,24,Patnership,Standalone\n8446,88.21,NJ City,S,23,11,1,76,High School,33,Patnership,Complex\n2173,306.99,NJ City,N,15,21,20,62,High School,24,Patnership,Standalone\n335,140.19,NJ City,E,12,8,2,83,No education,20,Owner,Standalone\n6166,162.83,Boston,S,4,14,11,82,Graduation,21,Patnership,Complex\n2806,413,NJ City,S,16,21,19,81,High School,25,Patnership,Complex\n3145,213.45,NJ City,N,24,10,6,45,High School,30,Patnership,Standalone\n672,283.15,NJ City,W,17,22,9,77,High School,32,Franchise,Complex\n2208,432.8,NJ City,W,20,13,24,84,High School,23,Franchise,Complex\n1795,240.45,NJ City,E,24,18,17,54,High School,34,Owner,Standalone\n1167,303,NJ City,E,3,14,6,26,High School,24,Owner,Standalone\n3633,215.28,NJ City,N,12,24,23,25,High School,28,Patnership,Standalone\n4539,241.19,NJ City,E,7,13,24,77,High School,19,Owner,Standalone\n6539,133.42,Boston,E,1,17,4,41,No education,29,Owner,Standalone\n2043,37.84,NJ City,N,2,9,3,38,Graduation,30,Owner,Standalone\n8826,489.83,NJ City,S,19,12,21,54,Graduation,19,Patnership,Complex\n6508,51.7,Boston,W,24,20,1,58,High School,21,Franchise,Complex\n2507,482.68,NJ City,E,25,21,21,28,High School,35,Owner,Standalone\n3052,108.92,NJ City,W,6,10,4,84,High School,25,Franchise,Complex\n5436,76.06,Boston,W,23,13,3,82,Graduation,31,Franchise,Complex\n894,446.73,NJ City,S,25,24,10,77,High School,21,Patnership,Complex\n5652,400.73,Boston,W,19,20,22,41,Graduation,30,Franchise,Complex\n3803,57.27,NJ City,E,20,11,2,69,No education,18,Owner,Standalone\n6358,410.66,Boston,S,19,11,25,25,Graduation,20,Patnership,Complex\n8963,10.21,NJ City,N,10,15,4,33,No education,24,Owner,Standalone\n2525,172.44,NJ City,N,22,24,2,27,No education,21,Patnership,Standalone\n943,54.97,NJ City,E,9,20,3,43,High School,32,Owner,Standalone\n7968,88.91,Boston,W,24,23,2,80,Graduation,35,Franchise,Complex\n1183,348.92,NJ City,E,25,16,11,30,High School,26,Owner,Standalone\n6359,263.44,Boston,E,2,18,5,36,No education,27,Owner,Standalone\n8545,447.28,NJ City,N,19,22,17,40,Graduation,28,Patnership,Standalone\n52,103.36,NJ City,W,7,22,2,59,High School,23,Franchise,Complex\n7822,447.18,Boston,S,24,20,7,84,Graduation,32,Patnership,Complex\n8797,116.54,NJ City,N,17,10,3,29,High School,31,Patnership,Standalone\n2450,339.62,NJ City,S,9,19,4,81,No education,31,Patnership,Complex\n1051,312.07,NJ City,E,15,17,20,57,High School,29,Owner,Standalone\n390,427.59,NJ City,S,16,18,3,63,Graduation,27,Patnership,Complex\n1923,426.66,NJ City,E,23,16,4,56,Graduation,30,Owner,Standalone\n7486,117.75,Boston,S,8,17,4,54,High School,25,Patnership,Complex\n7561,283.61,Boston,N,17,12,7,38,Graduation,21,Patnership,Standalone\n501,201.71,NJ City,N,21,23,6,27,High School,25,Patnership,Standalone\n2945,391.9,NJ City,N,14,8,18,59,High School,34,Patnership,Standalone\n2852,84.3,NJ City,W,14,11,1,60,No education,32,Franchise,Complex\n7149,71.23,Boston,N,5,8,1,52,Graduation,29,Patnership,Standalone\n129,328.94,NJ City,N,15,15,10,84,High School,29,Patnership,Standalone\n4892,435.01,NJ City,W,20,13,9,82,High School,35,Franchise,Complex\n9526,56.16,NJ City,S,8,22,2,34,High School,30,Patnership,Complex\n4361,114.74,NJ City,N,9,21,4,68,No education,24,Patnership,Standalone\n1605,11.85,NJ City,N,7,8,2,42,Graduation,30,Patnership,Standalone\n8253,18.44,NJ City,N,11,23,2,44,Graduation,29,Patnership,Standalone\n3215,12.98,NJ City,N,18,22,4,29,No education,30,Owner,Standalone\n7433,411.39,Boston,N,11,14,17,70,Graduation,29,Patnership,Standalone\n8792,353.72,NJ City,W,18,13,16,59,Graduation,29,Franchise,Complex\n1334,481.22,NJ City,S,5,23,23,85,High School,24,Patnership,Complex\n7658,310.09,Boston,S,9,10,14,45,Graduation,24,Patnership,Complex\n9382,25.91,NJ City,N,25,18,2,38,High School,25,Patnership,Complex\n3889,226.67,NJ City,N,19,21,24,34,High School,27,Patnership,Standalone\n1328,106.89,NJ City,W,3,20,1,50,No education,31,Franchise,Complex\n1244,473.76,NJ City,W,20,14,17,35,High School,19,Franchise,Complex\n3918,215.39,NJ City,S,11,12,17,81,High School,30,Patnership,Complex\n9044,468.74,NJ City,W,17,11,2,30,No education,34,Franchise,Complex\n5199,422.52,Boston,E,14,18,10,25,Graduation,32,Owner,Standalone\n7668,215.22,Boston,W,5,22,13,39,Graduation,34,Franchise,Complex\n8725,354.12,NJ City,N,14,15,16,80,Graduation,18,Patnership,Standalone\n9173,298.05,NJ City,N,7,23,5,44,No education,25,Patnership,Standalone\n6046,164.56,Boston,S,1,16,1,36,High School,29,Patnership,Complex\n9261,163,NJ City,N,15,17,23,28,Graduation,28,Patnership,Standalone\n1815,195.21,NJ City,E,22,21,1,64,Graduation,32,Owner,Standalone\n3636,412.84,NJ City,W,24,22,7,32,High School,24,Franchise,Complex\n1953,324.44,NJ City,N,8,18,3,49,Graduation,25,Patnership,Standalone\n755,5.82,NJ City,N,4,15,4,56,No education,31,Owner,Standalone\n3726,384.97,NJ City,S,22,11,23,50,High School,20,Patnership,Complex\n606,30.58,NJ City,N,2,19,4,36,Graduation,24,Patnership,Complex\n6537,195.13,Boston,N,8,13,7,60,Graduation,35,Patnership,Standalone\n1587,83.41,NJ City,E,10,9,2,31,Graduation,21,Owner,Standalone\n7696,476.87,Boston,W,21,17,2,40,High School,35,Franchise,Complex\n1850,18.78,NJ City,N,12,19,2,79,No education,28,Patnership,Complex\n5674,445.96,Boston,S,2,10,11,59,Graduation,18,Patnership,Complex\n1763,395.76,NJ City,E,21,24,25,51,High School,32,Owner,Standalone\n8124,88.72,NJ City,W,1,23,2,66,Graduation,33,Franchise,Complex\n4426,226.15,NJ City,S,17,11,8,84,High School,34,Patnership,Complex\n1683,142.1,NJ City,E,23,14,7,80,High School,22,Owner,Standalone\n8224,52.63,NJ City,W,15,23,2,78,High School,33,Franchise,Complex\n6438,244.62,Boston,S,1,15,21,45,Graduation,26,Patnership,Complex\n8423,111.09,NJ City,E,21,18,2,45,No education,18,Owner,Standalone\n9143,290.45,NJ City,E,10,19,20,60,Graduation,21,Owner,Standalone\n9585,482.53,NJ City,N,19,21,5,32,Graduation,29,Patnership,Standalone\n7439,323.75,Boston,E,25,16,20,84,Graduation,35,Owner,Standalone\n7777,314.46,Boston,N,2,15,8,52,Graduation,31,Patnership,Standalone\n6553,395.2,Boston,N,9,22,9,82,Graduation,33,Patnership,Standalone\n5280,75.29,Boston,W,19,23,4,71,Graduation,33,Franchise,Complex\n8118,433.08,NJ City,S,15,10,14,34,Graduation,31,Patnership,Complex\n8873,293.08,NJ City,N,23,15,9,30,Graduation,35,Patnership,Standalone\n4839,414.94,NJ City,E,7,8,19,83,High School,32,Owner,Standalone\n3031,277.91,NJ City,E,16,16,23,81,High School,27,Owner,Standalone\n3111,443.88,NJ City,E,24,15,8,58,High School,32,Owner,Standalone\n8300,343.76,NJ City,W,14,8,21,43,Graduation,23,Franchise,Complex\n7340,275.42,Boston,W,8,20,18,72,Graduation,19,Franchise,Complex\n5766,330.34,Boston,S,4,22,24,65,Graduation,34,Patnership,Complex\n4330,435.19,NJ City,S,7,8,20,84,High School,22,Patnership,Complex\n2250,384.3,NJ City,S,23,20,4,67,Graduation,29,Patnership,Complex\n6418,448.41,Boston,S,23,8,20,57,Graduation,26,Patnership,Complex\n9864,90.91,NJ City,W,1,21,4,52,Graduation,23,Franchise,Complex\n377,115.04,NJ City,N,14,9,4,53,No education,24,Patnership,Standalone\n4249,151.4,NJ City,N,8,16,22,38,High School,26,Patnership,Standalone\n6398,29.22,Boston,N,22,24,4,75,No education,21,Patnership,Complex\n6352,243.3,Boston,W,9,15,6,77,Graduation,31,Franchise,Complex\n2425,108.83,NJ City,N,11,20,2,77,High School,26,Patnership,Standalone\n421,216.69,NJ City,N,14,17,6,36,High School,30,Patnership,Standalone\n6766,236.3,Boston,S,21,12,21,41,Graduation,20,Patnership,Complex\n2955,241,NJ City,E,2,14,12,74,High School,23,Owner,Standalone\n3510,206,NJ City,S,2,17,20,38,High School,29,Patnership,Complex\n1168,232.12,NJ City,W,21,22,25,27,High School,33,Franchise,Complex\n1144,309.86,NJ City,W,19,11,18,49,High School,23,Franchise,Complex\n5790,460.98,Boston,S,14,16,16,70,Graduation,24,Patnership,Complex\n4598,413.75,NJ City,S,17,10,20,56,High School,22,Patnership,Complex\n3108,47.75,NJ City,W,11,13,3,58,Graduation,32,Franchise,Complex\n1620,57.99,NJ City,W,14,9,3,84,Graduation,22,Franchise,Complex\n9896,427.9,NJ City,W,15,12,3,42,No education,20,Franchise,Complex\n4375,153.74,NJ City,E,19,12,21,68,High School,24,Owner,Standalone\n1994,293,NJ City,S,2,22,20,78,High School,22,Patnership,Complex\n2285,82.22,NJ City,N,4,17,3,26,No education,25,Patnership,Standalone\n1107,200.2,NJ City,E,4,14,17,29,High School,20,Owner,Standalone\n786,267.4,NJ City,S,6,14,10,25,High School,32,Patnership,Complex\n6484,3.34,Boston,N,13,24,2,72,High School,27,Franchise,Complex\n2042,139.48,NJ City,S,23,14,25,26,High School,18,Patnership,Complex\n8840,452.19,NJ City,W,6,21,6,60,Graduation,26,Franchise,Complex\n7299,9.69,Boston,N,15,9,4,76,Graduation,29,Owner,Standalone\n5315,198.11,Boston,E,17,20,15,60,Graduation,29,Owner,Standalone\n6108,301.64,Boston,W,6,12,1,28,Graduation,19,Franchise,Complex\n8914,87.31,NJ City,S,10,11,2,62,High School,29,Patnership,Complex\n8382,143.67,NJ City,S,14,14,5,72,Graduation,23,Patnership,Complex\n744,267.66,NJ City,W,24,16,2,84,Graduation,18,Franchise,Complex\n8979,137.95,NJ City,E,16,23,24,58,Graduation,21,Owner,Standalone\n8964,204.81,NJ City,W,8,20,2,81,Graduation,26,Franchise,Complex\n1479,271.89,NJ City,E,19,20,21,66,High School,18,Owner,Standalone\n4871,138.71,NJ City,E,13,10,23,76,High School,25,Owner,Standalone\n5254,360.24,Boston,S,11,19,10,79,Graduation,27,Patnership,Complex\n8850,423.06,NJ City,S,16,10,6,69,Graduation,29,Patnership,Complex\n6602,349.26,Boston,S,16,24,6,79,Graduation,35,Patnership,Complex\n3078,256.5,NJ City,S,25,18,5,78,Graduation,30,Patnership,Complex\n4254,29.91,NJ City,N,22,12,2,53,Graduation,21,Patnership,Complex\n5021,36.54,Boston,N,12,23,2,69,No education,21,Patnership,Standalone\n7652,1.3,Boston,N,18,23,1,62,No education,34,Franchise,Complex\n2021,37.86,NJ City,N,8,13,4,65,No education,20,Patnership,Standalone\n9592,341.14,NJ City,W,23,12,24,78,Graduation,21,Franchise,Complex\n3235,173.55,NJ City,E,10,13,3,68,High School,31,Owner,Standalone\n6956,394.09,Boston,W,16,24,1,70,No education,18,Franchise,Complex\n159,122.92,NJ City,E,25,11,1,78,Graduation,31,Owner,Standalone\n3018,397.59,NJ City,S,8,19,18,34,High School,28,Patnership,Complex\n8749,41.2,NJ City,N,24,12,2,31,High School,23,Patnership,Standalone\n9283,214.53,NJ City,E,5,9,11,65,Graduation,32,Owner,Standalone\n1937,275.21,NJ City,N,14,18,19,81,High School,18,Patnership,Standalone\n5536,19.81,Boston,N,4,16,1,75,High School,33,Franchise,Complex\n2741,300.46,NJ City,N,13,20,16,46,High School,22,Patnership,Standalone\n507,59.33,NJ City,E,6,20,4,79,Graduation,33,Owner,Standalone\n1591,153.63,NJ City,E,10,11,2,45,High School,23,Owner,Standalone\n1185,193.27,NJ City,N,21,14,3,33,Graduation,31,Patnership,Standalone\n1784,438.7,NJ City,W,10,17,18,30,High School,35,Franchise,Complex\n3336,348.05,NJ City,W,24,15,14,76,High School,32,Franchise,Complex\n2866,258.12,NJ City,S,8,8,5,25,High School,34,Patnership,Complex\n8881,215.35,NJ City,N,20,21,17,47,Graduation,26,Patnership,Standalone\n4748,86.92,NJ City,W,9,20,3,85,No education,23,Franchise,Complex\n5312,483.97,Boston,W,10,23,1,78,No education,27,Franchise,Complex\n7770,390.76,Boston,S,1,14,10,50,Graduation,35,Patnership,Complex\n5970,263.27,Boston,S,13,11,3,83,Graduation,18,Patnership,Complex\n3949,229.41,NJ City,N,23,18,7,41,High School,26,Patnership,Standalone\n2624,311.17,NJ City,W,6,16,9,26,High School,24,Franchise,Complex\n5056,463.72,Boston,W,14,24,23,42,Graduation,26,Franchise,Complex\n9513,446.18,NJ City,N,13,12,3,63,Graduation,30,Patnership,Standalone\n6512,216.58,Boston,W,21,15,19,61,Graduation,23,Franchise,Complex\n2449,394.5,NJ City,N,5,21,4,71,High School,29,Patnership,Standalone\n4844,444.99,NJ City,W,10,10,5,41,No education,21,Franchise,Complex\n7156,154.46,Boston,W,8,17,21,72,Graduation,34,Franchise,Complex\n114,190.29,NJ City,S,25,24,4,41,Graduation,35,Patnership,Complex\n2924,397.52,NJ City,W,19,11,8,70,High School,34,Franchise,Complex\n2942,413.97,NJ City,S,3,16,1,80,No education,30,Patnership,Complex\n5714,374.74,Boston,S,16,12,13,71,Graduation,32,Patnership,Complex\n7209,204.47,Boston,N,23,13,17,48,Graduation,25,Patnership,Standalone\n4561,291.73,NJ City,N,8,19,8,27,High School,21,Patnership,Standalone\n6099,3.56,Boston,N,5,19,3,46,Graduation,35,Owner,Standalone\n6167,355.17,Boston,E,4,8,21,64,Graduation,21,Owner,Standalone\n4840,175.79,NJ City,W,13,19,22,61,High School,19,Franchise,Complex\n8323,269.07,NJ City,E,4,19,22,44,Graduation,32,Owner,Standalone\n472,348.36,NJ City,W,7,13,1,75,High School,27,Franchise,Complex\n3064,280.27,NJ City,W,12,21,9,62,High School,24,Franchise,Complex\n2556,243.61,NJ City,W,22,20,2,28,Graduation,21,Franchise,Complex\n6946,358.26,Boston,S,18,9,9,38,Graduation,35,Patnership,Complex\n3657,53.86,NJ City,N,14,23,3,79,Graduation,25,Patnership,Standalone\n4702,465.29,NJ City,S,14,11,3,85,High School,30,Patnership,Complex\n6076,294.75,Boston,W,25,10,24,78,Graduation,34,Franchise,Complex\n3547,461.2,NJ City,E,16,9,16,59,High School,25,Owner,Standalone\n3346,266.29,NJ City,S,10,18,4,48,High School,30,Patnership,Complex\n2878,186.48,NJ City,S,13,16,3,37,High School,19,Patnership,Complex\n5444,48.31,Boston,W,21,8,2,44,No education,25,Franchise,Complex\n9936,455.42,NJ City,W,6,20,4,25,Graduation,23,Franchise,Complex\n5030,86.48,Boston,S,12,16,4,80,No education,32,Patnership,Complex\n5178,374.8,Boston,S,17,14,11,58,Graduation,35,Patnership,Complex\n9738,294.02,NJ City,S,12,20,20,79,Graduation,24,Patnership,Complex\n485,167.37,NJ City,N,14,20,19,27,High School,27,Patnership,Standalone\n3592,185.16,NJ City,W,19,10,7,77,High School,35,Franchise,Complex\n3539,153.08,NJ City,E,17,17,13,73,High School,32,Owner,Standalone\n2024,407.66,NJ City,W,21,21,6,70,High School,22,Franchise,Complex\n8167,217.58,NJ City,E,5,15,25,59,Graduation,19,Owner,Standalone\n592,108.01,NJ City,W,19,17,4,66,High School,33,Franchise,Complex\n3712,228.24,NJ City,W,11,19,1,40,High School,23,Franchise,Complex\n9713,109.77,NJ City,N,6,9,1,33,No education,20,Patnership,Standalone\n6755,18.82,Boston,N,8,9,3,76,No education,22,Owner,Standalone\n8159,410.92,NJ City,E,7,18,2,49,No education,18,Owner,Standalone\n4983,429.12,Boston,E,6,24,11,40,High School,23,Owner,Standalone\n772,210.35,NJ City,W,7,17,20,72,High School,21,Franchise,Complex\n2046,178,NJ City,S,3,12,12,52,High School,21,Patnership,Complex\n1027,323.03,NJ City,E,7,19,10,43,High School,26,Owner,Standalone\n403,474.13,NJ City,E,17,18,1,85,High School,20,Owner,Standalone\n940,396.2,NJ City,W,16,16,8,64,High School,31,Franchise,Complex\n6843,173,Boston,E,5,21,2,52,Graduation,21,Owner,Standalone\n5294,38.59,Boston,N,10,21,3,46,No education,23,Patnership,Complex\n5151,17.12,Boston,N,2,8,2,67,Graduation,25,Owner,Standalone\n2171,269,NJ City,E,3,21,6,48,High School,31,Owner,Standalone\n1431,228.82,NJ City,E,6,11,10,81,High School,32,Owner,Standalone\n6330,80.08,Boston,S,2,15,1,26,Graduation,35,Patnership,Complex\n8860,268.25,NJ City,W,14,24,1,52,High School,20,Franchise,Complex\n5574,128.99,Boston,S,14,8,15,45,Graduation,26,Patnership,Complex\n9732,418.38,NJ City,W,17,18,17,76,Graduation,32,Franchise,Complex\n5724,239.79,Boston,W,2,14,11,75,Graduation,19,Franchise,Complex\n952,250,NJ City,W,2,22,22,64,High School,24,Franchise,Complex\n7231,159.69,Boston,E,5,14,22,53,Graduation,32,Owner,Standalone\n480,377.89,NJ City,W,19,21,19,72,High School,23,Franchise,Complex\n391,406.72,NJ City,E,8,23,16,47,High School,30,Owner,Standalone\n9214,208.43,NJ City,S,16,8,18,31,Graduation,34,Patnership,Complex\n1085,333.93,NJ City,N,2,11,1,28,No education,23,Patnership,Standalone\n7060,200.79,Boston,W,2,21,18,78,Graduation,32,Franchise,Complex\n7428,169.07,Boston,W,9,19,4,36,Graduation,32,Franchise,Complex\n4546,414.03,NJ City,S,5,22,2,59,High School,22,Patnership,Complex\n2890,328.79,NJ City,S,24,17,15,41,High School,27,Patnership,Complex\n9677,394.58,NJ City,N,23,10,18,84,Graduation,23,Patnership,Standalone\n2036,475.89,NJ City,W,22,18,21,77,High School,23,Franchise,Complex\n2379,485.84,NJ City,E,18,21,6,58,High School,30,Owner,Standalone\n1535,284.27,NJ City,E,11,20,19,70,High School,33,Owner,Standalone\n5916,234.53,Boston,W,15,17,22,27,Graduation,19,Franchise,Complex\n3673,64.77,NJ City,N,22,20,4,52,High School,21,Patnership,Standalone\n5529,261.5,Boston,N,10,19,12,72,Graduation,24,Patnership,Standalone\n5897,65.84,Boston,N,15,22,4,58,No education,22,Patnership,Standalone\n2615,67.18,NJ City,N,9,22,1,62,No education,18,Owner,Standalone\n8231,315.11,NJ City,E,20,17,25,57,Graduation,21,Owner,Standalone\n4029,117.81,NJ City,N,14,12,1,71,Graduation,27,Patnership,Standalone\n3398,290.27,NJ City,S,11,8,9,39,High School,20,Patnership,Complex\n8556,232.28,NJ City,W,25,16,24,77,Graduation,35,Franchise,Complex\n6156,142.22,Boston,W,24,9,15,46,Graduation,33,Franchise,Complex\n9773,355.09,NJ City,N,22,24,11,65,Graduation,32,Patnership,Standalone\n8539,151.56,NJ City,E,4,22,22,68,Graduation,28,Owner,Standalone\n5393,296.12,Boston,N,14,22,1,75,No education,18,Patnership,Standalone\n7852,403.41,Boston,W,21,9,1,58,High School,35,Franchise,Complex\n4328,251,NJ City,W,3,15,6,49,High School,22,Franchise,Complex\n8618,450.43,NJ City,S,7,24,6,82,Graduation,31,Patnership,Complex\n3505,80.76,NJ City,N,10,12,4,65,High School,31,Patnership,Standalone\n8849,305.15,NJ City,N,22,16,3,47,No education,22,Patnership,Standalone\n8667,456.62,NJ City,E,12,18,11,33,Graduation,31,Owner,Standalone\n7699,52.41,Boston,E,3,8,2,78,High School,22,Owner,Standalone\n3741,52.08,NJ City,N,8,23,4,31,Graduation,25,Patnership,Standalone\n1367,24.45,NJ City,N,23,13,2,39,No education,31,Owner,Standalone\n5067,250.04,Boston,E,3,17,2,60,Graduation,35,Owner,Standalone\n3771,28.17,NJ City,N,1,8,1,60,Graduation,26,Owner,Standalone\n4265,217.24,NJ City,N,25,16,11,42,High School,24,Patnership,Standalone\n8476,95.26,NJ City,W,8,21,1,43,High School,23,Franchise,Complex\n2002,180.97,NJ City,S,16,24,22,31,High School,19,Patnership,Complex\n8278,456.08,NJ City,S,7,24,25,74,Graduation,26,Patnership,Complex\n2542,468.64,NJ City,S,25,9,1,82,High School,20,Patnership,Complex\n265,273,NJ City,N,3,22,24,39,High School,20,Patnership,Standalone\n8923,301.71,NJ City,E,17,16,18,75,Graduation,31,Owner,Standalone\n7985,188.24,Boston,N,12,20,4,40,No education,20,Patnership,Standalone\n2973,231.6,NJ City,N,16,19,8,54,High School,35,Patnership,Standalone\n4876,296.47,NJ City,W,22,11,3,84,High School,33,Franchise,Complex\n1507,311.62,NJ City,E,12,12,7,81,High School,30,Owner,Standalone\n4322,190.51,NJ City,S,25,9,20,79,High School,18,Patnership,Complex\n4788,64.68,NJ City,N,11,16,1,79,Graduation,30,Franchise,Complex\n4726,121.7,NJ City,S,20,14,4,29,High School,18,Patnership,Complex\n8086,259.75,NJ City,S,1,13,15,44,Graduation,22,Patnership,Complex\n8697,165.4,NJ City,N,10,12,14,62,Graduation,25,Patnership,Standalone\n9517,18.31,NJ City,N,10,9,1,82,High School,19,Patnership,Standalone\n7858,340.59,Boston,S,14,15,11,36,Graduation,22,Patnership,Complex\n2508,340.74,NJ City,W,4,22,1,33,Graduation,30,Franchise,Complex\n3115,449.85,NJ City,E,7,15,20,48,High School,33,Owner,Standalone\n6698,488.61,Boston,S,6,23,6,66,Graduation,23,Patnership,Complex\n537,285.97,NJ City,N,14,15,4,58,Graduation,28,Patnership,Standalone\n6158,262.24,Boston,S,18,15,6,74,Graduation,25,Patnership,Complex\n5372,437.82,Boston,W,19,10,4,84,No education,21,Franchise,Complex\n2903,414.52,NJ City,E,13,18,5,66,No education,23,Owner,Standalone\n4095,10.57,NJ City,N,16,8,4,63,Graduation,25,Owner,Standalone\n2052,91.25,NJ City,W,14,22,2,31,Graduation,34,Franchise,Complex\n2703,230,NJ City,E,3,13,15,81,High School,32,Owner,Standalone\n2780,122,NJ City,W,1,17,16,81,High School,24,Franchise,Complex\n1600,282.7,NJ City,W,6,14,11,29,High School,25,Franchise,Complex\n2744,317.11,NJ City,W,14,19,20,42,High School,28,Franchise,Complex\n176,134.83,NJ City,W,19,19,1,78,No education,32,Franchise,Complex\n8998,304.53,NJ City,S,12,19,9,79,Graduation,21,Patnership,Complex\n6719,193.7,Boston,E,18,19,3,43,No education,21,Owner,Standalone\n4768,197.28,NJ City,W,18,12,21,55,High School,25,Franchise,Complex\n9745,150.65,NJ City,N,12,18,11,42,Graduation,20,Patnership,Standalone\n2095,283.72,NJ City,E,5,23,4,84,High School,26,Owner,Standalone\n1877,14.69,NJ City,N,4,24,2,65,No education,29,Patnership,Standalone\n1679,303.67,NJ City,E,13,14,11,55,High School,20,Owner,Standalone\n5206,443.87,Boston,S,6,13,15,38,Graduation,35,Patnership,Complex\n1281,202.65,NJ City,N,6,22,19,68,High School,35,Patnership,Standalone\n4891,63.46,NJ City,N,6,20,1,83,High School,26,Owner,Standalone\n7278,379.02,Boston,S,18,14,21,79,Graduation,28,Patnership,Complex\n9686,140.91,NJ City,S,9,9,11,44,Graduation,35,Patnership,Complex\n6678,333.34,Boston,S,6,11,5,32,Graduation,22,Patnership,Complex\n5545,159.23,Boston,N,3,18,13,64,Graduation,20,Patnership,Standalone\n5547,448.04,Boston,E,10,18,14,32,Graduation,34,Owner,Standalone\n9087,235.12,NJ City,E,21,16,20,51,Graduation,20,Owner,Standalone\n5894,92.38,Boston,S,10,21,1,41,No education,30,Patnership,Complex\n3313,11.48,NJ City,N,9,12,3,26,High School,23,Patnership,Standalone\n2815,213,NJ City,E,1,21,13,68,High School,33,Owner,Standalone\n207,105.2,NJ City,E,1,17,4,61,Graduation,29,Owner,Standalone\n3714,247,NJ City,S,3,16,19,47,High School,23,Patnership,Complex\n5096,256.28,Boston,W,25,18,25,58,Graduation,24,Franchise,Complex\n2386,280,NJ City,S,16,11,16,48,High School,29,Patnership,Complex\n1532,412.56,NJ City,W,24,12,23,47,High School,19,Franchise,Complex\n700,293.84,NJ City,W,16,12,6,38,High School,32,Franchise,Complex\n4102,432.68,NJ City,S,16,18,20,30,High School,23,Patnership,Complex\n7563,52.65,Boston,E,14,9,2,39,Graduation,35,Owner,Standalone\n7549,119.08,Boston,N,8,11,3,76,High School,26,Patnership,Standalone\n4445,408,NJ City,N,22,14,13,36,High School,28,Patnership,Standalone\n1622,453.45,NJ City,S,11,22,10,83,High School,18,Patnership,Complex\n2475,114.74,NJ City,E,15,20,2,57,Graduation,25,Owner,Standalone\n8900,416.79,NJ City,W,12,8,15,76,Graduation,32,Franchise,Complex\n6442,388.39,Boston,S,4,9,10,83,Graduation,32,Patnership,Complex\n5997,413.39,Boston,N,11,21,21,41,Graduation,22,Patnership,Standalone\n9492,64.24,NJ City,N,22,16,3,78,Graduation,32,Franchise,Complex\n9440,319.54,NJ City,W,25,16,16,27,Graduation,30,Franchise,Complex\n4733,243.45,NJ City,N,21,11,23,55,High School,28,Patnership,Standalone\n3928,464.88,NJ City,W,10,20,8,29,High School,34,Franchise,Complex\n2019,341.69,NJ City,E,16,11,20,84,High School,31,Owner,Standalone\n3783,76.28,NJ City,E,13,16,3,43,Graduation,28,Owner,Standalone\n8358,20.55,NJ City,N,15,20,4,68,Graduation,24,Patnership,Complex\n662,263,NJ City,S,3,12,10,37,High School,21,Patnership,Complex\n8673,241.3,NJ City,N,10,24,6,56,Graduation,33,Patnership,Standalone\n5510,168.31,Boston,S,11,20,8,65,Graduation,19,Patnership,Complex\n3960,18.65,NJ City,N,4,9,4,60,Graduation,26,Franchise,Complex\n5395,307.36,Boston,E,24,12,21,73,Graduation,20,Owner,Standalone\n9525,427.86,NJ City,N,18,8,15,50,Graduation,28,Patnership,Standalone\n3227,395.32,NJ City,E,16,24,17,67,High School,31,Owner,Standalone\n3668,103.42,NJ City,W,20,10,1,80,No education,23,Franchise,Complex\n9537,496.68,NJ City,N,16,8,2,44,Graduation,32,Patnership,Standalone\n8031,473.93,Boston,E,2,24,23,83,Graduation,28,Owner,Standalone\n6958,212.97,Boston,S,3,11,24,57,Graduation,19,Patnership,Complex\n1631,201.15,NJ City,E,23,11,23,35,High School,23,Owner,Standalone\n7312,10.68,Boston,N,12,8,3,53,High School,34,Franchise,Complex\n7760,348.7,Boston,W,9,10,1,72,No education,29,Franchise,Complex\n2500,184.85,NJ City,W,6,11,3,64,High School,32,Franchise,Complex\n8429,324.79,NJ City,N,8,19,13,30,Graduation,34,Patnership,Standalone\n3112,431.08,NJ City,W,18,13,4,52,High School,35,Franchise,Complex\n4094,149.52,NJ City,S,12,12,11,63,High School,35,Patnership,Complex\n3326,148.97,NJ City,S,13,15,18,38,High School,28,Patnership,Complex\n5682,239.7,Boston,S,16,13,13,69,Graduation,30,Patnership,Complex\n5978,288.03,Boston,S,2,19,16,48,Graduation,33,Patnership,Complex\n5817,73.56,Boston,N,11,17,1,85,Graduation,20,Patnership,Standalone\n5610,345.41,Boston,S,22,21,17,85,Graduation,23,Patnership,Complex\n6251,145.63,Boston,E,20,18,7,85,Graduation,33,Owner,Standalone\n6255,177.24,Boston,E,19,18,12,39,Graduation,30,Owner,Standalone\n3671,89.76,NJ City,E,2,15,4,52,No education,19,Owner,Standalone\n1530,497.63,NJ City,S,10,9,13,82,High School,20,Patnership,Complex\n6051,126.87,Boston,E,10,21,2,36,Graduation,23,Owner,Standalone\n2168,483.01,NJ City,W,17,23,4,41,No education,31,Franchise,Complex\n1248,33.29,NJ City,N,12,10,2,29,Graduation,23,Franchise,Complex\n5458,446.01,Boston,S,24,11,18,78,Graduation,29,Patnership,Complex\n6326,496.44,Boston,S,5,16,8,34,Graduation,33,Patnership,Complex\n4131,313.4,NJ City,E,24,15,8,29,High School,20,Owner,Standalone\n6619,24.75,Boston,N,2,17,4,80,High School,34,Owner,Standalone\n388,174,NJ City,W,1,15,10,33,High School,30,Franchise,Complex\n5534,15.59,Boston,N,22,17,4,74,No education,21,Patnership,Complex\n625,224.91,NJ City,N,15,22,16,47,High School,20,Patnership,Standalone\n8092,417.14,NJ City,W,9,10,3,35,High School,24,Franchise,Complex\n2586,366.17,NJ City,S,24,12,25,64,High School,21,Patnership,Complex\n5035,251.61,Boston,E,21,17,8,35,Graduation,25,Owner,Standalone\n9089,350.56,NJ City,N,4,17,21,39,Graduation,22,Patnership,Standalone\n5491,355.02,Boston,E,15,15,22,25,Graduation,23,Owner,Standalone\n8242,100.92,NJ City,S,7,22,2,85,High School,34,Patnership,Complex\n2879,9.04,NJ City,N,21,10,1,66,No education,32,Owner,Standalone\n6324,216.9,Boston,W,22,21,6,85,Graduation,34,Franchise,Complex\n879,353.24,NJ City,E,1,23,2,45,Graduation,20,Owner,Standalone\n5698,9.29,Boston,N,17,18,4,84,High School,27,Patnership,Complex\n8225,22.53,NJ City,N,7,23,1,46,No education,23,Patnership,Standalone\n1150,477.06,NJ City,S,14,17,5,53,High School,35,Patnership,Complex\n4904,445.16,NJ City,W,10,21,16,64,High School,20,Franchise,Complex\n9591,195.78,NJ City,E,16,14,14,45,Graduation,24,Owner,Standalone\n3153,259.83,NJ City,N,12,11,13,67,High School,32,Patnership,Standalone\n5518,399.96,Boston,S,8,18,10,54,Graduation,31,Patnership,Complex\n491,84.38,NJ City,E,7,19,1,40,No education,29,Owner,Standalone\n2402,55.65,NJ City,S,1,11,3,63,No education,28,Patnership,Complex\n4776,263.81,NJ City,W,7,9,9,73,High School,31,Franchise,Complex\n7691,344.67,Boston,E,25,15,1,62,No education,25,Owner,Standalone\n6196,219.27,Boston,W,14,23,25,55,Graduation,29,Franchise,Complex\n7219,233.82,Boston,E,15,21,19,71,Graduation,22,Owner,Standalone\n3236,427.74,NJ City,W,18,19,10,25,High School,27,Franchise,Complex\n6334,304.52,Boston,S,23,22,21,49,Graduation,28,Patnership,Complex\n9792,106.6,NJ City,W,16,21,3,28,Graduation,35,Franchise,Complex\n5937,40.43,Boston,N,14,9,4,59,Graduation,28,Patnership,Standalone\n7133,292.18,Boston,N,15,19,5,27,No education,23,Patnership,Standalone\n3046,270.69,NJ City,S,19,19,18,39,High School,27,Patnership,Complex\n7189,252.48,Boston,N,5,16,14,26,Graduation,30,Patnership,Standalone\n3909,320.58,NJ City,N,22,17,6,59,High School,35,Patnership,Standalone\n6339,176.01,Boston,E,6,19,1,43,Graduation,34,Owner,Standalone\n7451,443.72,Boston,E,19,18,3,72,No education,28,Owner,Standalone\n3826,290.04,NJ City,S,15,12,13,33,High School,25,Patnership,Complex\n7220,380.05,Boston,W,24,24,2,30,No education,18,Franchise,Complex\n6902,34.74,Boston,N,24,19,2,62,No education,29,Patnership,Complex\n1891,353.03,NJ City,E,10,18,18,68,High School,28,Owner,Standalone\n8360,319.34,NJ City,W,12,15,12,85,Graduation,23,Franchise,Complex\n8776,470.74,NJ City,W,7,14,4,68,High School,32,Franchise,Complex\n2918,29.23,NJ City,N,2,24,1,41,No education,20,Patnership,Complex\n3731,64.28,NJ City,N,25,24,3,39,No education,22,Owner,Standalone\n2272,32.68,NJ City,N,20,14,1,32,High School,18,Franchise,Complex\n7286,237.56,Boston,S,25,15,6,70,Graduation,34,Patnership,Complex\n1796,412.02,NJ City,W,24,17,22,47,High School,21,Franchise,Complex\n182,165.68,NJ City,S,18,24,3,34,No education,19,Patnership,Complex\n7397,384.13,Boston,N,14,23,20,67,Graduation,35,Patnership,Standalone\n442,167.38,NJ City,S,23,22,14,73,High School,25,Patnership,Complex\n7795,233.45,Boston,E,24,20,13,57,Graduation,24,Owner,Standalone\n5972,491.57,Boston,W,22,14,2,81,No education,33,Franchise,Complex\n7722,387.69,Boston,S,11,8,10,32,Graduation,26,Patnership,Complex\n1020,451.39,NJ City,W,20,24,25,74,High School,35,Franchise,Complex\n2282,209,NJ City,S,1,16,7,71,High School,32,Patnership,Complex\n1892,30.61,NJ City,N,9,13,2,34,No education,29,Franchise,Complex\n7552,225.83,Boston,W,25,21,20,63,Graduation,19,Franchise,Complex\n9210,58.17,NJ City,S,8,15,4,43,Graduation,25,Patnership,Complex\n4713,415.38,NJ City,N,18,15,2,76,Graduation,35,Patnership,Standalone\n9052,215.45,NJ City,W,3,10,9,63,Graduation,20,Franchise,Complex\n1895,356.13,NJ City,E,25,20,14,54,High School,23,Owner,Standalone\n2704,140.43,NJ City,W,11,8,1,37,High School,32,Franchise,Complex\n9327,499.27,NJ City,E,17,15,14,57,Graduation,29,Owner,Standalone\n1204,490.1,NJ City,W,5,19,1,75,High School,31,Franchise,Complex\n7277,116.72,Boston,N,23,19,4,34,No education,25,Patnership,Standalone\n3344,144.41,NJ City,W,12,13,12,39,High School,35,Franchise,Complex\n8600,460.34,NJ City,W,2,22,14,37,Graduation,24,Franchise,Complex\n3170,267.1,NJ City,S,4,12,14,76,High School,24,Patnership,Complex\n7671,319.67,Boston,E,13,17,2,68,Graduation,21,Owner,Standalone\n5646,197.39,Boston,S,23,11,13,65,Graduation,27,Patnership,Complex\n8695,417.74,NJ City,E,7,13,4,51,High School,18,Owner,Standalone\n4619,484.96,NJ City,E,12,24,16,77,High School,31,Owner,Standalone\n8018,351.23,Boston,S,10,19,5,77,No education,26,Patnership,Complex\n2619,249.18,NJ City,E,14,14,6,31,High School,35,Owner,Standalone\n4471,460.16,NJ City,E,16,16,14,62,High School,33,Owner,Standalone\n2898,150.34,NJ City,S,6,10,23,36,High School,26,Patnership,Complex\n9848,369.46,NJ City,W,10,18,2,84,No education,19,Franchise,Complex\n7104,392.6,Boston,W,20,9,7,66,Graduation,24,Franchise,Complex\n4759,385.65,NJ City,E,15,22,25,51,High School,21,Owner,Standalone\n7607,92.83,Boston,E,6,15,2,70,No education,21,Owner,Standalone\n7569,433.72,Boston,N,5,20,21,43,Graduation,21,Patnership,Standalone\n7969,150.81,Boston,N,22,22,10,79,Graduation,22,Patnership,Standalone\n7123,122.02,Boston,E,15,8,2,34,High School,34,Owner,Standalone\n1170,48.42,NJ City,S,20,24,4,34,Graduation,25,Patnership,Complex\n2790,260.16,NJ City,S,22,23,21,34,High School,23,Patnership,Complex\n1753,47.1,NJ City,N,18,20,3,48,High School,26,Patnership,Standalone\n7498,264.32,Boston,S,2,18,14,64,Graduation,32,Patnership,Complex\n2884,426.09,NJ City,W,23,22,19,84,High School,32,Franchise,Complex\n2385,479.43,NJ City,N,7,21,6,83,High School,29,Patnership,Standalone\n9894,245.46,NJ City,S,22,20,19,55,Graduation,35,Patnership,Complex\n2631,480.04,NJ City,E,24,24,15,53,High School,22,Owner,Standalone\n1471,419.31,NJ City,E,21,24,8,75,High School,34,Owner,Standalone\n383,48.59,NJ City,E,25,14,3,74,No education,23,Owner,Standalone\n6092,221.93,Boston,W,22,13,5,85,No education,19,Franchise,Complex\n4064,145.16,NJ City,W,10,24,9,66,High School,23,Franchise,Complex\n2617,208,NJ City,N,1,10,15,58,High School,34,Patnership,Standalone\n440,157,NJ City,W,3,15,7,57,High School,35,Franchise,Complex\n9694,234.63,NJ City,S,24,22,6,32,Graduation,20,Patnership,Complex\n9854,205,NJ City,S,16,19,6,65,Graduation,35,Patnership,Complex\n1761,51.71,NJ City,N,9,20,4,48,Graduation,30,Patnership,Standalone\n5278,188.03,Boston,S,23,19,2,54,High School,25,Patnership,Complex\n9451,224.51,NJ City,E,11,24,18,41,Graduation,27,Owner,Standalone\n8492,296.54,NJ City,W,8,13,3,45,No education,20,Franchise,Complex\n131,356.33,NJ City,E,8,21,11,56,High School,18,Owner,Standalone\n1657,246.56,NJ City,N,20,22,23,38,High School,31,Patnership,Standalone\n2319,196.39,NJ City,E,25,10,9,28,High School,29,Owner,Standalone\n500,212.94,NJ City,W,11,8,17,68,High School,20,Franchise,Complex\n7853,471.38,Boston,N,13,24,7,83,Graduation,26,Patnership,Standalone\n4579,224.27,NJ City,E,24,13,1,83,High School,27,Owner,Standalone\n1251,183.65,NJ City,E,24,10,11,31,High School,25,Owner,Standalone\n3538,383.2,NJ City,S,24,23,12,78,High School,34,Patnership,Complex\n6234,41.96,Boston,N,23,8,2,51,Graduation,25,Patnership,Complex\n3465,435.71,NJ City,N,15,16,6,80,High School,32,Patnership,Standalone\n9707,288.62,NJ City,E,3,22,24,77,Graduation,27,Owner,Standalone\n7917,134.43,Boston,N,24,12,17,48,Graduation,20,Patnership,Standalone\n1664,173.57,NJ City,W,14,8,5,43,No education,34,Franchise,Complex\n7502,476.83,Boston,S,6,9,21,79,Graduation,35,Patnership,Complex\n7223,256.01,Boston,E,2,22,18,58,Graduation,25,Owner,Standalone\n6660,8.21,Boston,N,22,24,4,79,Graduation,34,Franchise,Complex\n4799,17.88,NJ City,N,22,23,4,46,No education,28,Owner,Standalone\n5412,270.99,Boston,W,15,17,12,36,Graduation,32,Franchise,Complex\n174,61.53,NJ City,N,23,9,2,83,Graduation,18,Patnership,Complex\n834,194,NJ City,S,2,13,17,52,High School,24,Patnership,Complex\n6785,238.23,Boston,N,21,15,12,70,Graduation,18,Patnership,Standalone\n3623,477.22,NJ City,E,5,9,25,36,High School,25,Owner,Standalone\n6171,495.46,Boston,E,12,23,11,71,Graduation,19,Owner,Standalone\n5066,141.81,Boston,S,22,19,2,76,No education,33,Patnership,Complex\n2298,499.52,NJ City,S,10,15,13,80,High School,30,Patnership,Complex\n3649,147.6,NJ City,N,13,22,12,78,High School,27,Patnership,Standalone\n852,137,NJ City,W,3,14,6,25,High School,19,Franchise,Complex\n2555,198.21,NJ City,E,24,20,4,43,No education,24,Owner,Standalone\n6611,189.69,Boston,E,3,13,4,81,No education,35,Owner,Standalone\n8503,175.54,NJ City,E,14,9,18,50,Graduation,19,Owner,Standalone\n5873,466.94,Boston,N,6,23,17,76,Graduation,28,Patnership,Standalone\n4208,134.86,NJ City,W,19,11,1,28,No education,19,Franchise,Complex\n760,163.78,NJ City,W,25,10,3,63,High School,25,Franchise,Complex\n1437,413.77,NJ City,N,25,11,8,36,High School,34,Patnership,Standalone\n1395,330.61,NJ City,E,8,15,3,56,Graduation,29,Owner,Standalone\n6760,203.91,Boston,W,17,19,11,85,Graduation,34,Franchise,Complex\n8361,119.51,NJ City,N,3,17,4,76,Graduation,28,Patnership,Standalone\n5960,422.8,Boston,W,12,21,24,52,Graduation,21,Franchise,Complex\n2295,246,NJ City,E,1,15,23,45,High School,21,Owner,Standalone\n8929,120.05,NJ City,N,4,8,4,45,High School,24,Patnership,Standalone\n1013,87.17,NJ City,N,6,15,2,67,No education,24,Patnership,Standalone\n9469,211.8,NJ City,N,23,12,4,73,High School,19,Patnership,Standalone\n9461,305.26,NJ City,N,2,10,24,67,Graduation,25,Patnership,Standalone\n1387,133.21,NJ City,E,21,23,3,36,High School,21,Owner,Standalone\n9227,202.1,NJ City,E,8,8,5,31,No education,20,Owner,Standalone\n6022,170.64,Boston,S,24,16,24,75,Graduation,33,Patnership,Complex\n4947,467.35,Boston,E,19,15,18,25,High School,20,Owner,Standalone\n2040,327.6,NJ City,W,14,11,4,81,Graduation,29,Franchise,Complex\n934,292.86,NJ City,S,7,13,10,26,High School,24,Patnership,Complex\n9354,135.77,NJ City,S,8,17,4,39,Graduation,30,Patnership,Complex\n4848,316.02,NJ City,W,21,24,5,34,Graduation,25,Franchise,Complex\n4521,415.87,NJ City,N,25,19,9,85,High School,21,Patnership,Standalone\n5794,382.98,Boston,S,20,12,1,34,High School,19,Patnership,Complex\n6867,455.27,Boston,E,15,17,13,56,Graduation,26,Owner,Standalone\n4025,134,NJ City,N,1,21,12,30,High School,24,Patnership,Standalone\n3393,475.85,NJ City,N,4,21,21,59,High School,29,Patnership,Standalone\n4104,159.14,NJ City,W,25,10,14,38,High School,20,Franchise,Complex\n7118,255.03,Boston,S,16,14,12,80,Graduation,32,Patnership,Complex\n7109,53.26,Boston,N,17,12,2,63,No education,22,Patnership,Standalone\n6385,242.53,Boston,N,10,19,24,38,Graduation,27,Patnership,Standalone\n2979,87.3,NJ City,E,21,22,3,57,Graduation,33,Owner,Standalone\n1693,9.07,NJ City,N,5,14,1,64,High School,33,Patnership,Standalone\n1873,56.74,NJ City,N,1,10,4,33,High School,21,Patnership,Standalone\n6297,460.85,Boston,N,16,12,4,66,Graduation,26,Patnership,Standalone\n7991,57.71,Boston,E,6,18,1,67,No education,18,Owner,Standalone\n4140,205.35,NJ City,W,19,13,7,45,High School,25,Franchise,Complex\n5367,384.03,Boston,E,18,13,2,70,Graduation,34,Owner,Standalone\n2997,126.26,NJ City,N,2,17,4,71,Graduation,32,Patnership,Standalone\n8185,190.99,NJ City,N,24,12,20,69,Graduation,25,Patnership,Standalone\n7420,204.31,Boston,W,18,21,24,46,Graduation,31,Franchise,Complex\n9434,23.66,NJ City,N,25,21,3,53,No education,20,Patnership,Complex\n9942,278.11,NJ City,S,13,22,5,31,Graduation,20,Patnership,Complex\n9768,212.98,NJ City,W,2,10,8,35,Graduation,29,Franchise,Complex\n9301,188.49,NJ City,N,22,21,9,66,Graduation,26,Patnership,Standalone\n7,324.73,NJ City,E,5,21,24,44,High School,32,Owner,Standalone\n562,391.57,NJ City,S,17,10,8,33,High School,20,Patnership,Complex\n7382,299.85,Boston,S,9,18,16,84,Graduation,24,Patnership,Complex\n2612,471.35,NJ City,W,10,10,13,54,High School,27,Franchise,Complex\n2746,400.66,NJ City,S,22,12,18,26,High School,33,Patnership,Complex\n8951,429.12,NJ City,E,24,15,5,79,No education,26,Owner,Standalone\n248,144.01,NJ City,W,19,18,13,46,High School,22,Franchise,Complex\n4646,223.04,NJ City,S,1,11,2,33,No education,18,Patnership,Complex\n1106,150.47,NJ City,S,22,12,9,33,High School,19,Patnership,Complex\n5292,197.62,Boston,W,24,23,14,31,Graduation,32,Franchise,Complex\n2614,348.01,NJ City,S,3,22,1,64,High School,33,Patnership,Complex\n8804,182.15,NJ City,W,17,9,1,38,No education,30,Franchise,Complex\n2543,340.28,NJ City,E,12,22,19,64,High School,32,Owner,Standalone\n1070,453.51,NJ City,S,9,12,16,27,High School,35,Patnership,Complex\n7072,299.02,Boston,W,16,12,5,43,High School,32,Franchise,Complex\n4673,405.79,NJ City,N,19,11,2,70,No education,29,Patnership,Standalone\n6606,479.04,Boston,S,3,23,23,69,Graduation,30,Patnership,Complex\n4594,310.32,NJ City,S,16,18,18,42,High School,27,Patnership,Complex\n5961,297.97,Boston,N,2,10,6,73,Graduation,23,Patnership,Standalone\n4613,177.46,NJ City,N,14,13,2,56,No education,28,Patnership,Standalone\n9890,277.06,NJ City,S,20,12,9,76,Graduation,20,Patnership,Complex\n1256,228.76,NJ City,W,7,19,25,58,High School,35,Franchise,Complex\n8616,68.11,NJ City,N,20,13,2,60,Graduation,25,Franchise,Complex\n2706,7.6,NJ City,N,25,15,4,38,Graduation,30,Patnership,Complex\n2926,261.82,NJ City,S,9,12,6,41,High School,32,Patnership,Complex\n7950,416.05,Boston,S,10,21,5,56,Graduation,34,Patnership,Complex\n8760,349.14,NJ City,W,20,22,5,71,Graduation,23,Franchise,Complex\n3243,492.65,NJ City,E,5,21,17,58,High School,25,Owner,Standalone\n3075,439.36,NJ City,E,16,22,1,72,Graduation,27,Owner,Standalone\n2335,207.52,NJ City,E,11,15,22,57,High School,19,Owner,Standalone\n1838,162.15,NJ City,S,14,8,21,30,High School,25,Patnership,Complex\n9405,87.26,NJ City,N,1,19,3,40,Graduation,30,Patnership,Standalone\n1068,431.37,NJ City,W,13,9,11,72,High School,33,Franchise,Complex\n5660,82.31,Boston,W,16,19,1,68,No education,25,Franchise,Complex\n7925,116.76,Boston,N,14,20,1,80,No education,31,Patnership,Standalone\n6082,152,Boston,S,18,19,24,79,Graduation,34,Patnership,Complex\n9980,312.24,NJ City,W,5,16,20,34,Graduation,26,Franchise,Complex\n3137,228.52,NJ City,N,23,8,25,72,High School,20,Patnership,Standalone\n8980,246.25,NJ City,W,10,24,12,80,Graduation,27,Franchise,Complex\n4693,311.81,NJ City,N,7,12,16,76,High School,31,Patnership,Standalone\n838,246.24,NJ City,S,16,24,17,29,High School,31,Patnership,Complex\n177,395.62,NJ City,N,17,9,16,63,High School,22,Patnership,Standalone\n8822,377.71,NJ City,S,5,23,3,29,No education,33,Patnership,Complex\n217,24.91,NJ City,N,22,24,1,54,High School,24,Patnership,Standalone\n7707,235.25,Boston,E,11,18,10,52,Graduation,35,Owner,Standalone\n1744,422.18,NJ City,W,21,14,13,44,High School,31,Franchise,Complex\n2421,433.07,NJ City,N,14,20,11,49,High School,19,Patnership,Standalone\n5765,332.31,Boston,N,11,8,9,43,Graduation,33,Patnership,Standalone\n6786,470.66,Boston,S,18,9,7,79,Graduation,31,Patnership,Complex\n4583,211.32,NJ City,E,24,10,1,29,No education,20,Owner,Standalone\n6390,257.33,Boston,S,7,8,24,74,Graduation,25,Patnership,Complex\n3429,94.48,NJ City,N,23,24,1,61,Graduation,19,Patnership,Standalone\n3679,305.23,NJ City,E,14,17,20,76,High School,26,Owner,Standalone\n945,8.12,NJ City,N,16,24,2,45,Graduation,33,Patnership,Standalone\n7899,459.52,Boston,E,4,14,7,79,Graduation,29,Owner,Standalone\n3842,473.27,NJ City,S,16,12,3,61,No education,26,Patnership,Complex\n6999,219.87,Boston,E,11,20,1,27,Graduation,33,Owner,Standalone\n7301,150.74,Boston,N,22,16,6,72,Graduation,31,Patnership,Standalone\n7604,317.9,Boston,W,10,18,6,45,Graduation,20,Franchise,Complex\n6023,327.53,Boston,E,21,14,13,48,Graduation,32,Owner,Standalone\n2120,116,NJ City,W,1,19,9,63,High School,28,Franchise,Complex\n7483,245.61,Boston,E,16,20,13,79,Graduation,20,Owner,Standalone\n4700,26.74,NJ City,N,21,14,3,53,No education,30,Franchise,Complex\n4458,315.69,NJ City,S,18,23,4,31,Graduation,18,Patnership,Complex\n7660,272.81,Boston,W,19,19,25,40,Graduation,29,Franchise,Complex\n5599,269.6,Boston,E,19,23,18,76,Graduation,32,Owner,Standalone\n185,471.54,NJ City,N,10,15,8,39,High School,26,Patnership,Standalone\n3722,378.64,NJ City,S,15,15,14,68,High School,24,Patnership,Complex\n3107,384.41,NJ City,E,3,12,3,38,No education,33,Owner,Standalone\n6080,321.5,Boston,W,8,15,12,78,Graduation,34,Franchise,Complex\n4134,55.55,NJ City,S,14,16,1,25,Graduation,35,Patnership,Complex\n1310,170.23,NJ City,S,8,8,8,70,High School,21,Patnership,Complex\n2559,126,NJ City,E,1,16,18,83,High School,18,Owner,Standalone\n2363,32.34,NJ City,N,18,24,3,43,No education,31,Owner,Standalone\n5359,450.14,Boston,E,14,18,5,70,High School,23,Owner,Standalone\n8083,171.44,NJ City,E,13,24,2,85,High School,28,Owner,Standalone\n8093,38.27,NJ City,N,25,11,4,31,No education,35,Patnership,Standalone\n7522,6.19,Boston,N,6,14,4,47,High School,35,Patnership,Complex\n1570,335.03,NJ City,S,6,18,4,53,High School,18,Patnership,Complex\n3339,121.07,NJ City,E,9,18,1,30,Graduation,25,Owner,Standalone\n8745,273.29,NJ City,N,8,18,7,45,Graduation,25,Patnership,Standalone\n2365,470.22,NJ City,N,14,11,23,67,High School,19,Patnership,Standalone\n795,385.53,NJ City,E,25,24,8,36,High School,24,Owner,Standalone\n6626,465.67,Boston,S,19,16,4,59,No education,29,Patnership,Complex\n3364,120.24,NJ City,W,10,21,1,59,High School,32,Franchise,Complex\n2527,192.73,NJ City,E,12,14,23,55,High School,24,Owner,Standalone\n8789,124.99,NJ City,N,19,17,3,77,No education,33,Patnership,Standalone\n9777,26.92,NJ City,N,11,23,4,82,Graduation,32,Patnership,Standalone\n5336,82.91,Boston,W,8,20,3,47,No education,29,Franchise,Complex\n7337,135.23,Boston,N,17,23,15,26,Graduation,35,Patnership,Standalone\n2968,450.97,NJ City,W,19,21,14,55,High School,21,Franchise,Complex\n1972,382.58,NJ City,W,4,16,22,50,High School,28,Franchise,Complex\n3230,264.73,NJ City,S,14,19,13,54,High School,34,Patnership,Complex\n3179,235.71,NJ City,E,15,22,13,38,High School,28,Owner,Standalone\n5573,250.05,Boston,N,3,14,7,37,Graduation,19,Patnership,Standalone\n5632,145.37,Boston,W,18,22,25,31,Graduation,32,Franchise,Complex\n6439,39.52,Boston,N,22,13,1,28,High School,24,Owner,Standalone\n7680,455.16,Boston,W,23,10,2,28,Graduation,23,Franchise,Complex\n2480,196.85,NJ City,W,10,16,12,59,High School,34,Franchise,Complex\n5414,332.02,Boston,S,11,20,8,30,Graduation,23,Patnership,Complex\n5204,444.67,Boston,W,10,23,6,68,Graduation,35,Franchise,Complex\n9715,89.38,NJ City,E,15,16,4,60,High School,22,Owner,Standalone\n3106,263.67,NJ City,S,19,15,10,55,High School,34,Patnership,Complex\n1036,317.26,NJ City,W,19,17,22,44,High School,32,Franchise,Complex\n985,168,NJ City,N,3,15,20,25,High School,27,Patnership,Standalone\n6235,295.33,Boston,E,18,13,16,72,Graduation,20,Owner,Standalone\n808,394.77,NJ City,W,21,23,20,41,High School,27,Franchise,Complex\n6373,288.29,Boston,N,19,23,6,48,Graduation,18,Patnership,Standalone\n3698,162.16,NJ City,S,24,16,21,81,High School,22,Patnership,Complex\n7155,447.02,Boston,E,21,20,20,69,Graduation,32,Owner,Standalone\n4918,167.39,NJ City,S,23,21,9,52,High School,28,Patnership,Complex\n7544,273.78,Boston,W,22,19,9,58,Graduation,19,Franchise,Complex\n8521,336.72,NJ City,N,2,21,13,32,Graduation,27,Patnership,Standalone\n5640,223.8,Boston,W,13,24,7,35,Graduation,18,Franchise,Complex\n1970,295.56,NJ City,S,14,16,17,44,High School,18,Patnership,Complex\n5688,172.77,Boston,W,10,22,18,26,Graduation,23,Franchise,Complex\n5493,155.23,Boston,N,19,24,3,36,Graduation,20,Patnership,Standalone\n675,8.61,NJ City,N,15,21,2,28,Graduation,22,Owner,Standalone\n6525,205.63,Boston,N,17,23,20,39,Graduation,25,Patnership,Standalone\n4393,422.42,NJ City,N,6,10,9,82,High School,18,Patnership,Standalone\n3224,62.24,NJ City,N,19,9,1,34,No education,34,Franchise,Complex\n3276,129,NJ City,W,3,10,12,44,High School,35,Franchise,Complex\n2274,121,NJ City,S,2,19,4,74,Graduation,35,Patnership,Complex\n9020,220.08,NJ City,W,23,18,3,76,No education,32,Franchise,Complex\n5111,171.56,Boston,E,19,19,21,60,Graduation,26,Owner,Standalone\n6621,28.98,Boston,N,7,14,2,47,Graduation,30,Patnership,Standalone\n3754,318.53,NJ City,S,10,19,25,47,High School,33,Patnership,Complex\n7380,298.56,Boston,W,12,12,11,50,Graduation,23,Franchise,Complex\n6204,86.47,Boston,W,2,18,4,67,Graduation,19,Franchise,Complex\n8428,334.49,NJ City,W,25,14,7,73,Graduation,27,Franchise,Complex\n2065,82.27,NJ City,N,2,22,4,50,High School,33,Patnership,Standalone\n7198,387.82,Boston,S,2,17,4,65,High School,26,Patnership,Complex\n5187,260.88,Boston,E,5,10,15,27,Graduation,19,Owner,Standalone\n9953,12.87,NJ City,N,24,23,1,25,No education,24,Patnership,Standalone\n9109,368.94,NJ City,N,2,16,1,74,High School,20,Patnership,Standalone\n2416,113.72,NJ City,W,14,17,3,84,High School,28,Franchise,Complex\n458,130.83,NJ City,S,17,23,10,33,High School,32,Patnership,Complex\n6267,195.85,Boston,E,1,22,23,55,Graduation,20,Owner,Standalone\n5343,302.37,Boston,E,18,14,7,38,Graduation,25,Owner,Standalone\n6305,470.08,Boston,N,10,24,24,71,Graduation,22,Patnership,Standalone\n5429,329.02,Boston,N,3,20,16,71,Graduation,22,Patnership,Standalone\n9742,290.71,NJ City,S,23,21,22,56,Graduation,24,Patnership,Complex\n7264,335.41,Boston,W,9,15,17,78,Graduation,30,Franchise,Complex\n8378,360.59,NJ City,S,11,19,17,53,Graduation,27,Patnership,Complex\n6886,48.04,Boston,S,4,19,3,52,High School,23,Patnership,Complex\n4128,268.73,NJ City,W,8,10,14,78,High School,33,Franchise,Complex\n5411,481.2,Boston,E,6,15,14,66,Graduation,23,Owner,Standalone\n2053,320.84,NJ City,N,24,20,1,36,High School,30,Patnership,Standalone\n6962,497.81,Boston,S,24,12,15,59,Graduation,21,Patnership,Complex\n1745,144.68,NJ City,N,17,20,22,63,High School,33,Patnership,Standalone\n1503,287.28,NJ City,E,5,11,24,25,High School,19,Owner,Standalone\n3807,66.38,NJ City,N,5,18,2,75,Graduation,18,Owner,Standalone\n4510,352.07,NJ City,S,17,23,22,32,High School,23,Patnership,Complex\n5943,208.4,Boston,E,13,15,1,57,Graduation,31,Owner,Standalone\n9091,353.11,NJ City,E,15,15,17,71,Graduation,31,Owner,Standalone\n6323,219.35,Boston,E,1,23,12,37,Graduation,27,Owner,Standalone\n7863,60.36,Boston,E,10,23,2,79,Graduation,28,Owner,Standalone\n5577,55.13,Boston,N,19,17,3,55,Graduation,25,Patnership,Standalone\n1519,484.26,NJ City,E,21,14,12,67,High School,25,Owner,Standalone\n7647,146.64,Boston,E,18,13,25,65,Graduation,20,Owner,Standalone\n6169,494.76,Boston,N,11,11,19,42,Graduation,33,Patnership,Standalone\n2453,311,NJ City,N,1,18,25,25,High School,24,Patnership,Standalone\n5881,335.31,Boston,N,5,24,13,46,Graduation,33,Patnership,Standalone\n5503,302.76,Boston,E,4,10,14,40,Graduation,21,Owner,Standalone\n7956,275.1,Boston,W,2,17,10,84,Graduation,31,Franchise,Complex\n7467,443.89,Boston,E,18,19,10,65,Graduation,27,Owner,Standalone\n1612,193.04,NJ City,W,4,14,24,31,High School,34,Franchise,Complex\n157,100,NJ City,N,3,24,19,82,High School,34,Patnership,Standalone\n4868,272.26,NJ City,W,12,17,11,28,High School,25,Franchise,Complex\n9938,374.4,NJ City,S,11,22,16,77,Graduation,24,Patnership,Complex\n7560,95.95,Boston,W,4,11,1,42,Graduation,23,Franchise,Complex\n6254,473.13,Boston,S,17,21,23,77,Graduation,34,Patnership,Complex\n778,40.95,NJ City,N,5,11,4,76,High School,30,Patnership,Complex\n1610,54.15,NJ City,S,16,19,1,40,No education,35,Patnership,Complex\n9266,278.85,NJ City,S,17,9,5,26,No education,35,Patnership,Complex\n783,14.62,NJ City,N,5,16,2,72,Graduation,22,Owner,Standalone\n70,452,NJ City,S,15,12,23,54,High School,27,Patnership,Complex\n4867,174.58,NJ City,E,11,19,23,26,High School,24,Owner,Standalone\n5862,15.76,Boston,N,9,14,3,35,Graduation,27,Patnership,Complex\n6032,202.57,Boston,W,13,15,13,34,Graduation,29,Franchise,Complex\n1383,85.99,NJ City,E,1,18,1,66,Graduation,25,Owner,Standalone\n8120,141.17,NJ City,W,19,15,7,38,Graduation,22,Franchise,Complex\n2284,241.93,NJ City,W,17,22,1,31,High School,26,Franchise,Complex\n2983,486.81,NJ City,E,5,9,5,64,High School,18,Owner,Standalone\n1002,102.06,NJ City,S,9,20,2,48,Graduation,23,Patnership,Complex\n1225,392.04,NJ City,N,19,10,18,83,High School,31,Patnership,Standalone\n6425,255.67,Boston,N,3,11,10,61,Graduation,18,Patnership,Standalone\n8727,449.93,NJ City,E,20,22,15,46,Graduation,21,Owner,Standalone\n1880,486.35,NJ City,W,21,20,10,56,High School,34,Franchise,Complex\n4278,147.91,NJ City,S,25,9,10,42,High School,27,Patnership,Complex\n9073,14.86,NJ City,N,5,10,3,61,High School,29,Patnership,Standalone\n4008,103.77,NJ City,W,22,23,4,30,Graduation,19,Franchise,Complex\n187,329.82,NJ City,E,22,22,7,62,High School,35,Owner,Standalone\n1638,497.45,NJ City,S,13,15,8,29,High School,34,Patnership,Complex\n4211,429.32,NJ City,E,25,21,16,72,High School,32,Owner,Standalone\n1382,60.88,NJ City,N,17,18,4,34,No education,24,Patnership,Complex\n3011,268.27,NJ City,E,11,22,5,80,No education,20,Owner,Standalone\n6538,220.01,Boston,S,18,18,1,63,High School,29,Patnership,Complex\n7546,445.34,Boston,S,23,17,12,54,Graduation,23,Patnership,Complex\n9226,197.42,NJ City,S,3,15,5,76,High School,30,Patnership,Complex\n2570,406.68,NJ City,S,14,18,23,44,High School,18,Patnership,Complex\n6638,248.04,Boston,S,12,20,8,57,Graduation,18,Patnership,Complex\n5002,447.1,Boston,S,5,17,9,85,Graduation,25,Patnership,Complex\n1473,424.97,NJ City,N,13,16,23,77,High School,35,Patnership,Standalone\n3725,224,NJ City,N,2,11,21,58,High School,31,Patnership,Standalone\n9225,161.92,NJ City,N,10,18,22,73,Graduation,20,Patnership,Standalone\n2006,330.08,NJ City,S,10,15,15,74,High School,32,Patnership,Complex\n3920,293.39,NJ City,W,10,23,23,66,High School,27,Franchise,Complex\n7799,333.5,Boston,E,22,23,1,51,No education,35,Owner,Standalone\n7103,342.62,Boston,E,9,12,23,36,Graduation,27,Owner,Standalone\n3862,475.27,NJ City,S,19,24,19,29,High School,24,Patnership,Complex\n6003,198.84,Boston,E,14,13,8,77,Graduation,25,Owner,Standalone\n3389,271.67,NJ City,N,5,12,25,31,High School,29,Patnership,Standalone\n1696,20.28,NJ City,N,9,17,4,76,High School,25,Franchise,Complex\n8683,199.14,NJ City,E,15,23,9,73,Graduation,25,Owner,Standalone\n4113,342.03,NJ City,N,13,24,13,81,High School,20,Patnership,Standalone\n8635,275.51,NJ City,E,12,24,17,45,Graduation,32,Owner,Standalone\n8000,475.13,Boston,W,1,21,17,44,Graduation,32,Franchise,Complex\n2013,75.42,NJ City,N,13,24,2,26,Graduation,20,Patnership,Standalone\n2279,341.53,NJ City,E,2,19,2,39,No education,33,Owner,Standalone\n617,174.33,NJ City,N,22,23,21,63,High School,31,Patnership,Standalone\n1639,245.62,NJ City,E,21,23,1,82,High School,18,Owner,Standalone\n8179,211.11,NJ City,E,2,15,12,33,Graduation,33,Owner,Standalone\n502,11.2,NJ City,N,24,18,2,77,High School,30,Patnership,Complex\n6700,314.06,Boston,W,10,22,6,35,Graduation,24,Franchise,Complex\n6273,210.03,Boston,N,25,10,19,53,Graduation,20,Patnership,Standalone\n5400,291.64,Boston,W,1,24,6,67,Graduation,34,Franchise,Complex\n6983,213.56,Boston,E,6,17,21,27,Graduation,31,Owner,Standalone\n231,145.08,NJ City,E,15,14,11,35,High School,30,Owner,Standalone\n2889,291.03,NJ City,N,9,21,9,40,High School,29,Patnership,Standalone\n7808,478.2,Boston,W,14,8,11,63,Graduation,21,Franchise,Complex\n9628,70.96,NJ City,N,3,12,1,58,High School,31,Franchise,Complex\n702,66.69,NJ City,N,22,15,1,78,Graduation,30,Patnership,Complex\n924,98.33,NJ City,W,24,15,2,48,Graduation,21,Franchise,Complex\n4117,29.47,NJ City,N,16,15,3,67,High School,28,Patnership,Standalone\n2128,366.04,NJ City,W,21,12,13,55,High School,31,Franchise,Complex\n6726,327.59,Boston,S,13,15,25,83,Graduation,33,Patnership,Complex\n1963,473.56,NJ City,E,7,18,20,56,High School,29,Owner,Standalone\n4039,167.29,NJ City,E,4,11,17,76,High School,24,Owner,Standalone\n8195,264.87,NJ City,E,10,11,20,63,Graduation,33,Owner,Standalone\n2444,301.5,NJ City,W,7,24,4,35,No education,32,Franchise,Complex\n6883,211.42,Boston,E,10,15,8,82,Graduation,30,Owner,Standalone\n3487,272.79,NJ City,E,19,18,1,81,High School,33,Owner,Standalone\n8653,7.94,NJ City,N,18,14,1,47,High School,35,Patnership,Standalone\n4678,402.68,NJ City,S,3,22,5,56,High School,28,Patnership,Complex\n431,412.49,NJ City,E,4,15,17,40,High School,25,Owner,Standalone\n5006,44.29,Boston,N,25,16,3,83,No education,18,Patnership,Complex\n4996,138.13,Boston,W,15,14,7,48,High School,32,Franchise,Complex\n9785,85.45,NJ City,N,9,20,1,79,No education,21,Patnership,Standalone\n3042,24.94,NJ City,N,15,13,4,80,Graduation,30,Patnership,Complex\n100,282.19,NJ City,W,24,15,15,55,High School,21,Franchise,Complex\n290,476.65,NJ City,S,16,17,25,48,High School,34,Patnership,Complex\n4325,335.74,NJ City,N,5,19,16,71,High School,29,Patnership,Standalone\n6809,147.9,Boston,N,18,13,18,81,Graduation,33,Patnership,Standalone\n5580,19.25,Boston,N,8,19,2,78,Graduation,35,Franchise,Complex\n5977,58.85,Boston,N,19,8,2,46,High School,25,Patnership,Standalone\n8524,211.04,NJ City,W,1,15,17,71,Graduation,18,Franchise,Complex\n2821,238.79,NJ City,N,14,14,15,48,High School,22,Patnership,Standalone\n1136,8.24,NJ City,N,6,22,2,68,No education,34,Franchise,Complex\n7204,378.53,Boston,W,17,9,14,59,Graduation,27,Franchise,Complex\n4057,497.18,NJ City,N,13,17,20,38,High School,22,Patnership,Standalone\n1191,299.67,NJ City,E,12,20,4,62,Graduation,27,Owner,Standalone\n9272,461.96,NJ City,W,24,22,9,33,Graduation,27,Franchise,Complex\n7992,38.81,Boston,N,2,10,2,77,Graduation,21,Franchise,Complex\n8977,94.38,NJ City,N,1,24,3,74,High School,30,Patnership,Standalone\n6018,285.86,Boston,S,17,11,9,57,Graduation,18,Patnership,Complex\n6975,373.82,Boston,E,19,20,7,71,Graduation,34,Owner,Standalone\n2059,262,NJ City,E,2,18,7,45,High School,21,Owner,Standalone\n5915,193.32,Boston,E,23,15,13,62,Graduation,18,Owner,Standalone\n7529,103,Boston,N,24,20,1,75,No education,19,Patnership,Standalone\n3244,230.41,NJ City,W,16,13,14,60,High School,24,Franchise,Complex\n9947,102.06,NJ City,E,12,24,3,39,No education,22,Owner,Standalone\n1867,76.65,NJ City,E,3,17,3,33,High School,18,Owner,Standalone\n5243,365.45,Boston,E,3,19,4,69,No education,35,Owner,Standalone\n2895,365.13,NJ City,E,20,8,25,66,High School,30,Owner,Standalone\n8337,355.1,NJ City,N,12,16,16,46,Graduation,30,Patnership,Standalone\n6637,366.93,Boston,N,24,24,11,34,Graduation,26,Patnership,Standalone\n5007,335.27,Boston,E,2,20,4,81,Graduation,23,Owner,Standalone\n3817,77.66,NJ City,N,15,19,3,61,High School,26,Patnership,Standalone\n3161,26.31,NJ City,N,15,24,3,34,No education,23,Patnership,Standalone\n612,407.79,NJ City,W,23,11,2,59,Graduation,29,Franchise,Complex\n5923,73.51,Boston,N,7,11,2,82,High School,28,Owner,Standalone\n7775,410.04,Boston,E,16,20,24,73,Graduation,20,Owner,Standalone\n1476,435.05,NJ City,W,19,15,1,56,Graduation,24,Franchise,Complex\n5468,249.54,Boston,W,15,21,21,28,Graduation,28,Franchise,Complex\n9948,98.85,NJ City,W,22,22,2,38,Graduation,25,Franchise,Complex\n3211,345.05,NJ City,E,17,11,14,34,High School,29,Owner,Standalone\n3995,334.06,NJ City,E,22,13,9,48,High School,30,Owner,Standalone\n557,308.66,NJ City,N,8,15,18,43,High School,32,Patnership,Standalone\n2183,392.52,NJ City,E,7,21,24,41,High School,29,Owner,Standalone\n1304,217.92,NJ City,W,15,13,1,78,No education,30,Franchise,Complex\n4382,58.98,NJ City,S,22,11,1,53,No education,29,Patnership,Complex\n7303,68.54,Boston,N,5,10,1,38,High School,34,Owner,Standalone\n6865,39.31,Boston,N,22,22,1,64,High School,21,Patnership,Standalone\n1129,182.1,NJ City,N,8,18,2,37,High School,26,Patnership,Standalone\n8710,47.73,NJ City,S,2,19,3,77,High School,18,Patnership,Complex\n2177,148.94,NJ City,N,1,8,5,46,No education,26,Patnership,Standalone\n9292,87.24,NJ City,W,17,13,3,46,High School,29,Franchise,Complex\n9925,18.23,NJ City,N,22,14,3,72,High School,25,Patnership,Standalone\n6774,335.73,Boston,S,21,19,14,30,Graduation,18,Patnership,Complex\n5488,306.79,Boston,W,7,21,1,27,High School,32,Franchise,Complex\n9159,412.82,NJ City,E,24,12,6,64,Graduation,23,Owner,Standalone\n9631,473.88,NJ City,E,22,20,16,83,Graduation,31,Owner,Standalone\n1353,249,NJ City,N,2,18,23,56,High School,24,Patnership,Standalone\n9460,269.16,NJ City,W,8,9,2,67,High School,29,Franchise,Complex\n3109,391.28,NJ City,N,8,22,17,75,High School,25,Patnership,Standalone\n1564,116,NJ City,W,2,9,25,81,High School,34,Franchise,Complex\n1973,178.97,NJ City,N,11,19,21,80,High School,22,Patnership,Standalone\n5627,120.24,Boston,E,7,17,2,26,No education,25,Owner,Standalone\n7148,17.37,Boston,N,14,17,2,27,No education,20,Franchise,Complex\n3195,449.18,NJ City,E,15,8,22,46,High School,34,Owner,Standalone\n6105,459.56,Boston,N,1,22,15,76,Graduation,27,Patnership,Standalone\n1133,243.74,NJ City,N,15,22,16,73,High School,27,Patnership,Standalone\n3612,60.37,NJ City,N,22,14,4,70,Graduation,35,Franchise,Complex\n9752,168.58,NJ City,W,1,15,14,57,Graduation,21,Franchise,Complex\n278,424.15,NJ City,S,23,24,8,83,High School,23,Patnership,Complex\n5192,275.66,Boston,W,9,23,6,46,Graduation,32,Franchise,Complex\n7715,52.69,Boston,E,2,23,3,45,No education,35,Owner,Standalone\n82,155.86,Boston,S,16,24,20,27,High School,32,Patnership,Complex\n3311,305.58,NJ City,E,14,13,20,69,High School,24,Owner,Standalone\n6336,467.85,Boston,W,6,8,8,56,Graduation,28,Franchise,Complex\n5059,168.71,Boston,E,7,19,8,69,Graduation,32,Owner,Standalone\n6065,318.06,Boston,N,22,20,18,75,Graduation,29,Patnership,Standalone\n6396,379.65,Boston,W,4,21,19,34,Graduation,21,Franchise,Complex\n7160,51.91,Boston,W,13,16,1,48,No education,18,Franchise,Complex\n427,295.75,NJ City,E,12,22,9,33,High School,24,Owner,Standalone\n3941,421.07,NJ City,N,7,18,2,62,No education,23,Patnership,Standalone\n163,449.89,NJ City,E,1,20,1,34,High School,20,Owner,Standalone\n7752,375.68,Boston,W,4,16,12,28,Graduation,26,Franchise,Complex\n4340,24.74,NJ City,N,18,12,3,53,No education,19,Franchise,Complex\n6275,3.4,Boston,N,15,18,2,61,No education,23,Owner,Standalone\n313,99.91,NJ City,N,22,22,4,48,High School,33,Patnership,Standalone\n9404,297.41,NJ City,W,18,10,24,42,Graduation,35,Franchise,Complex\n1049,426.83,NJ City,N,8,13,3,69,No education,21,Patnership,Standalone\n1960,1.41,NJ City,W,4,19,1,71,High School,27,Franchise,Complex\n3082,16.16,NJ City,N,9,22,1,77,High School,27,Patnership,Complex\n7350,337.99,Boston,S,3,8,1,47,Graduation,24,Patnership,Complex\n1194,234.8,NJ City,S,11,8,3,37,Graduation,23,Patnership,Complex\n3681,8.99,NJ City,N,9,13,1,50,Graduation,23,Patnership,Standalone\n6906,344.43,Boston,S,12,18,19,69,Graduation,32,Patnership,Complex\n6952,94.37,Boston,W,7,19,2,34,High School,26,Franchise,Complex\n6329,71.51,Boston,N,1,8,2,52,No education,25,Patnership,Standalone\n4716,70.79,NJ City,N,8,14,1,25,Graduation,32,Franchise,Complex\n8690,464.65,NJ City,S,11,8,8,30,Graduation,35,Patnership,Complex\n3750,9.58,NJ City,N,9,15,4,79,Graduation,28,Patnership,Complex\n3175,179.47,NJ City,E,16,12,6,34,High School,33,Owner,Standalone\n267,98.98,NJ City,E,14,13,1,68,Graduation,25,Owner,Standalone\n8730,327.43,NJ City,S,14,19,24,72,Graduation,31,Patnership,Complex\n6493,339.73,Boston,N,11,19,22,26,Graduation,18,Patnership,Standalone\n8990,307.61,NJ City,S,9,11,18,40,Graduation,26,Patnership,Complex\n5708,116.85,Boston,W,20,24,2,79,No education,34,Franchise,Complex\n2252,179.37,NJ City,W,6,12,15,68,High School,22,Franchise,Complex\n5734,176.18,Boston,S,7,12,19,84,Graduation,32,Patnership,Complex\n2334,102.23,NJ City,S,5,24,2,45,Graduation,29,Patnership,Complex\n7823,439.38,Boston,E,25,14,15,65,Graduation,32,Owner,Standalone\n5433,421.26,Boston,N,10,10,18,39,Graduation,31,Patnership,Standalone\n9300,158.99,NJ City,W,8,10,3,26,Graduation,19,Franchise,Complex\n5128,359.36,Boston,W,11,19,11,78,Graduation,24,Franchise,Complex\n7745,313,Boston,N,24,8,24,36,Graduation,35,Patnership,Standalone\n6889,442.12,Boston,N,3,22,9,71,Graduation,34,Patnership,Standalone\n5648,41.72,Boston,N,9,24,3,39,No education,29,Franchise,Complex\n7952,498.37,Boston,W,13,20,5,84,No education,19,Franchise,Complex\n7025,129.46,Boston,N,16,16,17,47,Graduation,34,Patnership,Standalone\n5188,456.46,Boston,W,22,20,11,64,Graduation,22,Franchise,Complex\n477,116.76,NJ City,N,3,13,4,43,Graduation,31,Patnership,Standalone\n8150,17.81,NJ City,N,24,9,1,56,No education,26,Patnership,Complex\n2060,433.09,NJ City,W,12,15,18,46,High School,22,Franchise,Complex\n8835,332.21,NJ City,E,17,22,4,54,Graduation,35,Owner,Standalone\n2833,444.41,NJ City,N,23,18,17,64,High School,35,Patnership,Standalone\n3509,493.89,NJ City,N,5,8,5,52,No education,25,Patnership,Standalone\n4949,433.86,Boston,N,23,19,11,27,High School,35,Patnership,Standalone\n8212,402.02,NJ City,W,20,10,6,44,Graduation,21,Franchise,Complex\n6315,123.79,Boston,E,4,24,4,27,Graduation,28,Owner,Standalone\n6157,167.49,Boston,N,20,22,8,49,Graduation,33,Patnership,Standalone\n7166,302.66,Boston,S,25,13,2,51,No education,18,Patnership,Complex\n6124,78.29,Boston,W,4,9,1,43,High School,22,Franchise,Complex\n3335,389.75,NJ City,E,19,14,15,54,High School,35,Owner,Standalone\n8034,6.24,Boston,N,4,14,2,63,Graduation,28,Patnership,Complex\n1754,25.1,NJ City,N,24,23,1,54,No education,28,Patnership,Complex\n5497,348.48,Boston,N,18,20,2,41,High School,22,Patnership,Standalone\n3168,361.19,NJ City,W,9,11,2,41,Graduation,20,Franchise,Complex\n2696,212.96,NJ City,W,22,23,25,74,High School,23,Franchise,Complex\n2161,388.54,NJ City,N,21,16,19,31,High School,24,Patnership,Standalone\n5553,94.95,Boston,N,8,16,4,44,Graduation,21,Patnership,Standalone\n9369,218.34,NJ City,N,16,20,20,48,Graduation,19,Patnership,Standalone\n656,235.52,NJ City,W,9,22,18,51,High School,25,Franchise,Complex\n7466,186.79,Boston,S,10,15,12,40,Graduation,27,Patnership,Complex\n7683,129.42,Boston,E,11,8,19,36,Graduation,32,Owner,Standalone\n5910,478.41,Boston,S,7,22,10,42,Graduation,35,Patnership,Complex\n5947,203.56,Boston,E,7,24,25,77,Graduation,29,Owner,Standalone\n4938,433.51,Boston,S,16,18,17,67,High School,18,Patnership,Complex\n7850,165.93,Boston,S,15,24,23,72,Graduation,19,Patnership,Complex\n5346,404.46,Boston,S,12,9,13,47,Graduation,23,Patnership,Complex\n4127,142.57,NJ City,E,8,21,9,82,High School,23,Owner,Standalone\n7158,207.53,Boston,S,16,13,24,71,Graduation,22,Patnership,Complex\n9952,475.18,NJ City,W,5,22,12,83,Graduation,19,Franchise,Complex\n2473,376.01,NJ City,N,7,21,12,77,High School,28,Patnership,Standalone\n9223,44.79,NJ City,N,7,21,1,83,High School,26,Owner,Standalone\n5471,235.03,Boston,E,13,20,20,45,Graduation,19,Owner,Standalone\n7896,260.37,Boston,W,16,15,10,52,Graduation,23,Franchise,Complex\n611,327.35,NJ City,E,6,9,1,70,No education,22,Owner,Standalone\n4135,278.55,NJ City,E,24,22,9,50,High School,33,Owner,Standalone\n5342,338.81,Boston,S,12,9,1,36,No education,32,Patnership,Complex\n3984,308.72,NJ City,W,21,22,2,76,Graduation,24,Franchise,Complex\n5196,61.76,Boston,N,15,14,3,45,Graduation,21,Franchise,Complex\n8969,85.95,NJ City,N,2,13,1,74,No education,21,Patnership,Standalone\n259,428.94,NJ City,E,16,24,14,55,High School,25,Owner,Standalone\n2545,11.36,NJ City,N,22,10,4,41,High School,22,Patnership,Standalone\n6552,27.06,Boston,N,8,8,3,50,Graduation,26,Franchise,Complex\n7730,218.42,Boston,S,24,23,24,85,Graduation,18,Patnership,Complex\n3793,136.5,NJ City,N,24,20,20,61,High School,27,Patnership,Standalone\n6136,85.23,Boston,W,11,16,3,69,High School,20,Franchise,Complex\n5578,269.78,Boston,S,19,22,9,42,Graduation,32,Patnership,Complex\n4070,435.84,NJ City,S,1,10,3,48,No education,26,Patnership,Complex\n638,82.2,NJ City,S,15,8,1,70,No education,25,Patnership,Complex\n8676,106.28,NJ City,W,17,8,3,51,Graduation,25,Franchise,Complex\n9202,168.94,NJ City,S,18,16,15,78,Graduation,28,Patnership,Complex\n7233,407.41,Boston,N,23,9,19,76,Graduation,18,Patnership,Standalone\n3515,320.01,NJ City,E,15,8,13,47,High School,20,Owner,Standalone\n2311,290.83,NJ City,E,12,23,24,29,High School,30,Owner,Standalone\n6543,130.56,Boston,E,10,8,10,68,Graduation,32,Owner,Standalone\n73,394.3,Boston,N,7,18,5,63,High School,31,Patnership,Standalone\n5347,122.88,Boston,E,25,11,3,81,High School,26,Owner,Standalone\n2634,254.03,NJ City,S,6,9,1,67,Graduation,29,Patnership,Complex\n9183,85.31,NJ City,E,21,15,4,51,Graduation,35,Owner,Standalone\n2423,158.77,NJ City,E,11,19,13,70,High School,25,Owner,Standalone\n8049,102.16,NJ City,N,18,17,4,78,Graduation,28,Patnership,Standalone\n4553,190.65,NJ City,N,10,8,15,62,High School,31,Patnership,Standalone\n8561,122.48,NJ City,N,4,8,2,82,No education,26,Patnership,Standalone\n9846,100.39,NJ City,S,10,9,2,35,Graduation,32,Patnership,Complex\n748,62.77,NJ City,N,2,13,1,39,High School,35,Franchise,Complex\n454,159.46,NJ City,S,19,16,12,50,High School,34,Patnership,Complex\n8685,492.41,NJ City,N,19,13,9,70,Graduation,26,Patnership,Standalone\n2969,389.41,NJ City,N,11,14,2,68,No education,19,Patnership,Standalone\n4379,21.6,NJ City,N,10,12,1,43,No education,33,Owner,Standalone\n5439,332.74,Boston,E,21,18,14,75,Graduation,26,Owner,Standalone\n8704,319.67,NJ City,W,14,14,9,38,Graduation,30,Franchise,Complex\n4160,435.81,NJ City,W,9,8,14,31,High School,35,Franchise,Complex\n2648,101.95,NJ City,W,19,9,2,80,No education,32,Franchise,Complex\n6937,473.66,Boston,N,21,19,5,44,High School,23,Patnership,Standalone\n7326,335.83,Boston,S,3,21,18,25,Graduation,32,Patnership,Complex\n3466,495.64,NJ City,S,15,8,7,51,High School,32,Patnership,Complex\n2411,279.71,NJ City,E,7,13,23,74,High School,22,Owner,Standalone\n5418,440.04,Boston,S,11,18,22,51,Graduation,24,Patnership,Complex\n3571,265.78,NJ City,E,25,18,12,38,High School,33,Owner,Standalone\n6519,99.8,Boston,E,11,17,1,80,Graduation,30,Owner,Standalone\n5189,369.03,Boston,N,9,23,14,34,Graduation,33,Patnership,Standalone\n9809,340.31,NJ City,N,12,23,10,40,Graduation,30,Patnership,Standalone\n8703,123.95,NJ City,E,14,24,2,71,Graduation,28,Owner,Standalone\n7407,137.1,Boston,E,14,9,6,71,Graduation,30,Owner,Standalone\n8164,189.56,NJ City,W,10,14,17,46,Graduation,28,Franchise,Complex\n7587,101.59,Boston,E,6,21,3,31,Graduation,32,Owner,Standalone\n6436,390.33,Boston,W,19,19,19,30,Graduation,23,Franchise,Complex\n1613,310.7,NJ City,N,7,19,4,67,No education,24,Patnership,Standalone\n5305,4.6,Boston,N,16,15,3,40,High School,32,Patnership,Standalone\n7463,402.64,Boston,E,9,17,9,47,Graduation,21,Owner,Standalone\n1826,454.1,NJ City,S,6,24,23,69,High School,19,Patnership,Complex\n7593,339.59,Boston,N,5,8,4,71,Graduation,24,Patnership,Standalone\n6650,181.96,Boston,S,20,18,1,80,No education,34,Patnership,Complex\n3367,249.91,NJ City,E,9,22,20,53,High School,32,Owner,Standalone\n287,463.54,NJ City,E,19,17,2,83,No education,22,Owner,Standalone\n1752,44.35,NJ City,N,6,16,2,53,Graduation,28,Franchise,Complex\n3191,135.15,NJ City,E,8,12,15,37,High School,20,Owner,Standalone\n1142,448.02,NJ City,S,23,8,5,71,No education,24,Patnership,Complex\n9476,217.52,NJ City,W,20,21,22,54,Graduation,29,Franchise,Complex\n6682,345.91,Boston,S,21,13,17,40,Graduation,35,Patnership,Complex\n3334,484.74,NJ City,S,25,18,24,31,High School,30,Patnership,Complex\n7200,457.66,Boston,W,10,24,12,79,Graduation,35,Franchise,Complex\n9780,122.13,NJ City,W,10,16,2,44,Graduation,30,Franchise,Complex\n1357,208.79,NJ City,N,6,23,4,64,High School,24,Patnership,Standalone\n7865,302.3,Boston,N,24,17,16,51,Graduation,28,Patnership,Standalone\n6675,400.47,Boston,E,11,24,21,83,Graduation,23,Owner,Standalone\n1207,270.12,NJ City,E,8,23,25,63,High School,18,Owner,Standalone\n118,168.85,NJ City,S,16,12,19,64,High School,34,Patnership,Complex\n4694,235.2,NJ City,S,15,14,20,26,High School,24,Patnership,Complex\n9303,279.93,NJ City,E,20,22,14,57,Graduation,28,Owner,Standalone\n6570,342.19,Boston,S,24,12,20,49,Graduation,30,Patnership,Complex\n64,455.93,Boston,W,23,9,17,33,High School,32,Franchise,Complex\n1705,231.72,NJ City,N,10,16,8,54,High School,35,Patnership,Standalone\n1618,468.26,NJ City,S,25,22,17,66,High School,21,Patnership,Complex\n2526,259.23,NJ City,S,13,12,21,30,High School,23,Patnership,Complex\n7390,371.2,Boston,S,8,19,8,66,Graduation,27,Patnership,Complex\n6887,343.86,Boston,E,25,14,16,84,Graduation,26,Owner,Standalone\n1128,316.82,NJ City,W,10,22,13,26,High School,24,Franchise,Complex\n8966,408.6,NJ City,S,3,20,18,36,Graduation,19,Patnership,Complex\n7945,392.19,Boston,N,4,21,13,54,Graduation,27,Patnership,Standalone\n1217,242.23,NJ City,N,15,22,17,45,High School,29,Patnership,Standalone\n5434,406.72,Boston,S,6,14,1,33,High School,25,Patnership,Complex\n8269,408.34,NJ City,N,19,17,8,63,Graduation,28,Patnership,Standalone\n1064,265.09,NJ City,W,4,16,13,29,High School,23,Franchise,Complex\n787,418.57,NJ City,E,18,11,8,73,High School,22,Owner,Standalone\n6173,424.77,Boston,N,14,24,14,59,Graduation,35,Patnership,Standalone\n837,464.02,NJ City,N,11,12,17,42,High School,33,Patnership,Standalone\n8258,116.31,NJ City,S,5,22,2,34,No education,23,Patnership,Complex\n4299,90.51,NJ City,E,9,24,3,61,Graduation,25,Owner,Standalone\n9855,251.71,NJ City,E,23,20,13,35,Graduation,20,Owner,Standalone\n7873,94.99,Boston,N,13,22,1,58,High School,24,Patnership,Standalone\n1489,124.34,NJ City,N,5,10,2,30,High School,25,Patnership,Standalone\n368,383.01,NJ City,W,15,16,8,26,High School,32,Franchise,Complex\n3008,56.69,NJ City,W,4,17,2,38,No education,27,Franchise,Complex\n4845,346.18,NJ City,N,20,8,25,39,High School,24,Patnership,Standalone\n3214,425.96,NJ City,S,4,23,21,64,High School,25,Patnership,Complex\n3058,184,NJ City,S,1,19,9,73,High School,28,Patnership,Complex\n7163,78.13,Boston,E,25,14,1,34,No education,32,Owner,Standalone\n2309,85.26,NJ City,N,12,21,3,55,No education,30,Patnership,Standalone\n3113,310.37,NJ City,N,24,13,25,70,High School,20,Patnership,Standalone\n9363,214.8,NJ City,E,4,20,11,32,Graduation,32,Owner,Standalone\n535,35.77,NJ City,N,11,16,1,25,High School,33,Owner,Standalone\n6833,180.41,Boston,N,13,22,1,83,No education,25,Patnership,Standalone\n6911,210.01,Boston,E,6,20,22,62,Graduation,27,Owner,Standalone\n1993,116.24,NJ City,N,16,19,4,71,High School,35,Patnership,Standalone\n6312,140.16,Boston,W,20,12,10,69,Graduation,27,Franchise,Complex\n9240,461.56,NJ City,W,13,13,5,70,Graduation,25,Franchise,Complex\n9313,32.56,NJ City,N,8,19,2,52,High School,33,Patnership,Standalone\n4041,436.72,NJ City,N,10,24,18,64,High School,22,Patnership,Standalone\n2301,242.25,NJ City,N,6,19,11,40,High School,31,Patnership,Standalone\n331,123.5,NJ City,E,13,21,3,58,High School,20,Owner,Standalone\n8507,350.48,NJ City,E,6,19,22,33,Graduation,32,Owner,Standalone\n2384,192.66,NJ City,W,10,12,10,72,High School,33,Franchise,Complex\n9507,144.96,NJ City,E,7,22,13,36,Graduation,26,Owner,Standalone\n8486,363.08,NJ City,S,14,13,11,46,Graduation,19,Patnership,Complex\n7168,238.78,Boston,W,4,23,15,70,Graduation,21,Franchise,Complex\n199,326.07,NJ City,E,1,19,2,75,High School,32,Owner,Standalone\n2722,488.48,NJ City,S,17,14,16,38,High School,20,Patnership,Complex\n607,5.12,NJ City,N,2,24,1,53,High School,33,Owner,Standalone\n9862,42.88,NJ City,N,4,12,2,50,High School,27,Patnership,Complex\n1463,482.67,NJ City,E,18,12,4,80,No education,27,Owner,Standalone\n38,41.18,Maine,N,2,23,2,33,No education,30,Patnership,Complex\n1616,231.16,NJ City,W,14,16,10,73,High School,21,Franchise,Complex\n7449,319.74,Boston,N,23,8,14,41,Graduation,25,Patnership,Standalone\n26,28.93,Boston,N,16,16,3,31,No education,35,Patnership,Complex\n1668,85.54,NJ City,W,14,12,1,55,Graduation,33,Franchise,Complex\n484,393.99,NJ City,W,8,19,18,26,High School,27,Franchise,Complex\n1672,19.75,NJ City,N,6,12,1,54,High School,28,Franchise,Complex\n9249,439.34,NJ City,N,8,23,5,54,Graduation,20,Patnership,Standalone\n5677,418.19,Boston,N,20,18,19,78,Graduation,18,Patnership,Standalone\n3966,498.24,NJ City,S,16,11,7,80,High School,22,Patnership,Complex\n3925,34.22,NJ City,N,25,17,3,30,High School,19,Patnership,Standalone\n2795,15.71,NJ City,N,14,22,2,64,No education,32,Owner,Standalone\n429,202.91,NJ City,N,14,8,16,48,High School,18,Patnership,Standalone\n9380,393.42,NJ City,W,21,9,18,80,Graduation,28,Franchise,Complex\n7575,264.98,Boston,E,6,14,9,34,Graduation,27,Owner,Standalone\n6350,92.92,Boston,S,5,15,3,77,No education,26,Patnership,Complex\n5779,103.49,Boston,E,5,21,3,60,High School,20,Owner,Standalone\n4305,401.51,NJ City,N,13,20,10,32,High School,25,Patnership,Standalone\n5831,60.66,Boston,N,7,20,1,69,No education,18,Owner,Standalone\n3448,139.61,NJ City,W,4,23,19,49,High School,29,Franchise,Complex\n9155,387.69,NJ City,E,24,23,7,83,Graduation,35,Owner,Standalone\n3399,207.83,NJ City,E,22,9,24,51,High School,28,Owner,Standalone\n7941,197.66,Boston,N,24,17,24,48,Graduation,34,Patnership,Standalone\n7748,211.89,Boston,W,4,8,21,60,Graduation,20,Franchise,Complex\n9675,47.93,NJ City,E,6,16,3,78,Graduation,25,Owner,Standalone\n7154,41.85,Boston,N,14,17,4,28,No education,23,Patnership,Complex\n9798,88.18,NJ City,S,22,20,3,56,Graduation,24,Patnership,Complex\n849,289.84,NJ City,N,20,9,18,54,High School,25,Patnership,Standalone\n2510,111.95,NJ City,S,23,9,4,82,No education,19,Patnership,Complex\n316,398.81,NJ City,W,21,15,16,42,High School,26,Franchise,Complex\n1398,332.92,NJ City,S,21,17,8,45,High School,29,Patnership,Complex\n2954,327.44,NJ City,S,19,11,6,31,High School,22,Patnership,Complex\n8991,489.46,NJ City,E,15,23,22,35,Graduation,35,Owner,Standalone\n6590,378.61,Boston,S,18,21,22,73,Graduation,24,Patnership,Complex\n300,368.91,NJ City,W,12,19,14,73,High School,20,Franchise,Complex\n8228,241.98,NJ City,W,18,18,19,47,Graduation,22,Franchise,Complex\n8377,268.62,NJ City,N,19,11,17,55,Graduation,35,Patnership,Standalone\n7153,228.1,Boston,N,17,14,20,27,Graduation,33,Patnership,Standalone\n6970,456.08,Boston,S,8,23,17,37,Graduation,21,Patnership,Complex\n2091,297.95,NJ City,E,9,8,17,38,High School,33,Owner,Standalone\n4461,417.56,NJ City,N,14,22,23,57,High School,35,Patnership,Standalone\n5172,383.91,Boston,W,25,19,11,80,Graduation,23,Franchise,Complex\n2964,221.03,NJ City,W,23,23,1,34,Graduation,25,Franchise,Complex\n6146,245.91,Boston,S,25,15,5,66,No education,25,Patnership,Complex\n1078,197.83,NJ City,S,25,10,22,68,High School,29,Patnership,Complex\n9521,340.39,NJ City,N,20,11,14,28,Graduation,35,Patnership,Standalone\n1961,100.35,NJ City,N,13,15,2,55,No education,31,Patnership,Standalone\n2592,469.22,NJ City,W,15,16,2,68,Graduation,30,Franchise,Complex\n4804,157.92,NJ City,W,6,9,14,31,High School,25,Franchise,Complex\n3055,482.01,NJ City,E,16,22,20,83,High School,34,Owner,Standalone\n4376,242.4,NJ City,W,11,20,6,83,High School,28,Franchise,Complex\n763,366.57,NJ City,E,22,22,13,67,High School,33,Owner,Standalone\n3660,359.77,NJ City,W,18,11,17,84,High School,22,Franchise,Complex\n6497,106.48,Boston,N,14,11,2,58,No education,20,Patnership,Standalone\n9082,457.31,NJ City,S,7,21,13,44,Graduation,27,Patnership,Complex\n6198,468.91,Boston,S,16,18,23,29,Graduation,26,Patnership,Complex\n8464,232.81,NJ City,W,21,22,5,26,High School,23,Franchise,Complex\n2445,236.4,NJ City,N,5,20,14,37,High School,31,Patnership,Standalone\n4529,232.06,NJ City,N,11,8,24,77,High School,32,Patnership,Standalone\n6693,50.9,Boston,N,13,13,3,76,Graduation,33,Patnership,Standalone\n7629,44.75,Boston,N,1,15,2,44,Graduation,32,Patnership,Standalone\n4819,364.1,NJ City,E,11,19,4,31,High School,29,Owner,Standalone\n3301,103.23,NJ City,N,4,13,1,72,High School,32,Patnership,Standalone\n5499,113.95,Boston,E,2,11,4,39,Graduation,19,Owner,Standalone\n4439,128.56,NJ City,E,16,9,3,47,No education,26,Owner,Standalone\n8480,497.71,NJ City,W,13,14,18,48,Graduation,29,Franchise,Complex\n5989,178.26,Boston,N,14,8,18,28,Graduation,32,Patnership,Standalone\n3250,99.82,NJ City,S,4,8,1,74,High School,20,Patnership,Complex\n7408,422.99,Boston,W,21,20,14,72,Graduation,25,Franchise,Complex\n3701,431.85,NJ City,N,22,22,22,55,High School,31,Patnership,Standalone\n5004,330.58,Boston,W,24,11,24,55,Graduation,30,Franchise,Complex\n7267,222.35,Boston,E,15,24,23,80,Graduation,18,Owner,Standalone\n6077,486.98,Boston,N,19,11,9,85,Graduation,26,Patnership,Standalone\n9635,154.1,NJ City,E,6,10,8,73,Graduation,26,Owner,Standalone\n5319,413.56,Boston,E,15,16,9,82,Graduation,30,Owner,Standalone\n4915,1.05,NJ City,N,8,11,4,57,High School,31,Owner,Standalone\n6892,2.88,Boston,W,24,12,3,40,High School,24,Franchise,Complex\n3903,7.47,NJ City,N,14,21,1,69,Graduation,28,Owner,Standalone\n9576,148.26,NJ City,W,3,13,6,45,Graduation,21,Franchise,Complex\n6536,420.15,Boston,W,24,11,13,50,Graduation,34,Franchise,Complex\n4473,14.36,NJ City,N,18,16,3,65,Graduation,32,Patnership,Standalone\n6545,328.55,Boston,N,8,10,23,26,Graduation,33,Patnership,Standalone\n7249,325.89,Boston,N,19,19,13,80,Graduation,25,Patnership,Standalone\n5815,98.61,Boston,E,25,19,3,70,High School,20,Owner,Standalone\n5295,59.43,Boston,E,4,21,3,62,Graduation,20,Owner,Standalone\n8613,130.53,NJ City,N,24,12,12,78,Graduation,30,Patnership,Standalone\n6876,233.89,Boston,W,5,23,5,38,Graduation,33,Franchise,Complex\n5445,425.28,Boston,N,13,16,13,51,Graduation,20,Patnership,Standalone\n6382,372.68,Boston,S,13,14,7,62,Graduation,29,Patnership,Complex\n3735,424.7,NJ City,E,21,13,4,30,Graduation,30,Owner,Standalone\n4009,468.64,NJ City,N,12,20,16,31,High School,25,Patnership,Standalone\n5311,323.89,Boston,E,25,24,20,53,Graduation,30,Owner,Standalone\n4241,161.25,NJ City,N,10,10,7,62,High School,24,Patnership,Standalone\n8605,327.66,NJ City,N,10,19,25,34,Graduation,34,Patnership,Standalone\n5823,488.58,Boston,E,12,8,16,71,Graduation,28,Owner,Standalone\n4499,106.15,NJ City,E,14,24,1,38,No education,20,Owner,Standalone\n9508,30.33,NJ City,N,10,15,2,42,High School,24,Franchise,Complex\n3342,257.32,NJ City,S,21,20,19,61,High School,27,Patnership,Complex\n4060,449.48,NJ City,W,16,18,5,40,High School,31,Franchise,Complex\n376,88.29,NJ City,W,6,24,4,77,High School,34,Franchise,Complex\n8422,186.99,NJ City,S,3,18,3,54,High School,31,Patnership,Complex\n8491,461.97,NJ City,E,9,17,22,71,Graduation,20,Owner,Standalone\n245,65.11,NJ City,N,22,15,1,82,No education,25,Patnership,Standalone\n4500,305.4,NJ City,W,19,17,5,59,Graduation,27,Franchise,Complex\n4192,408.05,NJ City,W,4,10,23,79,High School,20,Franchise,Complex\n4831,154.77,NJ City,E,19,9,7,77,High School,25,Owner,Standalone\n9009,197.9,NJ City,N,17,16,21,72,Graduation,27,Patnership,Standalone\n7559,160.54,Boston,E,7,9,3,54,No education,34,Owner,Standalone\n1294,115.72,NJ City,S,4,10,3,62,High School,28,Patnership,Complex\n6795,176.96,Boston,E,16,9,7,81,Graduation,25,Owner,Standalone\n4563,59.91,NJ City,E,17,15,1,53,Graduation,31,Owner,Standalone\n1464,120.75,NJ City,W,12,21,2,43,Graduation,26,Franchise,Complex\n197,118.58,NJ City,N,3,20,1,46,No education,35,Patnership,Standalone\n8796,227.5,NJ City,W,4,10,21,40,Graduation,22,Franchise,Complex\n4185,337.75,NJ City,N,16,18,19,61,High School,25,Patnership,Standalone\n2567,333,NJ City,E,2,17,4,51,No education,26,Owner,Standalone\n5695,34.62,Boston,N,25,15,3,75,High School,25,Owner,Standalone\n9573,129.65,NJ City,N,23,16,20,68,Graduation,22,Patnership,Standalone\n8160,49.31,NJ City,W,11,16,2,62,Graduation,29,Franchise,Complex\n3369,192.02,NJ City,N,20,16,16,82,High School,32,Patnership,Standalone\n4690,499.85,NJ City,S,1,17,5,78,High School,35,Patnership,Complex\n6550,326.28,Boston,S,19,13,25,84,Graduation,32,Patnership,Complex\n3475,48.38,NJ City,E,17,23,2,50,High School,21,Owner,Standalone\n9108,208.22,NJ City,W,21,24,22,64,Graduation,27,Franchise,Complex\n7309,408.08,Boston,N,15,15,1,68,High School,21,Patnership,Standalone\n7347,209.2,Boston,E,7,9,18,80,Graduation,19,Owner,Standalone\n3948,93.45,NJ City,W,23,16,1,53,Graduation,24,Franchise,Complex\n9676,495.54,NJ City,W,7,18,6,58,Graduation,26,Franchise,Complex\n7230,401.46,Boston,S,15,11,5,71,Graduation,27,Patnership,Complex\n8319,240.39,NJ City,E,7,24,25,63,Graduation,32,Owner,Standalone\n4637,477.95,NJ City,N,1,13,5,55,No education,20,Patnership,Standalone\n3752,304.41,NJ City,W,19,11,18,36,High School,30,Franchise,Complex\n1481,191.98,NJ City,N,21,24,12,36,High School,33,Patnership,Standalone\n7566,411.02,Boston,S,4,18,22,36,Graduation,23,Patnership,Complex\n5323,343.86,Boston,E,22,17,1,43,High School,28,Owner,Standalone\n6394,307.96,Boston,S,16,9,10,69,Graduation,27,Patnership,Complex\n2131,222.8,NJ City,E,10,17,19,41,High School,34,Owner,Standalone\n1355,374.28,NJ City,E,18,12,5,57,No education,19,Owner,Standalone\n9127,349.85,NJ City,E,3,23,9,42,Graduation,25,Owner,Standalone\n3079,464.59,NJ City,E,9,20,2,79,High School,26,Owner,Standalone\n5357,479.89,Boston,N,5,10,6,32,Graduation,35,Patnership,Standalone\n5113,316.63,Boston,N,3,23,19,41,Graduation,24,Patnership,Standalone\n5328,10.92,Boston,N,2,18,2,42,Graduation,26,Franchise,Complex\n6446,441.55,Boston,S,15,21,1,53,No education,23,Patnership,Complex\n8788,171.97,NJ City,W,22,11,12,76,Graduation,31,Franchise,Complex\n6573,347.09,Boston,N,21,13,22,57,Graduation,28,Patnership,Standalone\n4857,429.32,NJ City,N,7,23,3,60,Graduation,24,Patnership,Standalone\n673,473.91,NJ City,N,4,10,8,67,High School,22,Patnership,Standalone\n4463,402.01,NJ City,E,16,13,22,42,High School,20,Owner,Standalone\n4433,292.43,NJ City,N,8,10,16,73,High School,31,Patnership,Standalone\n4741,57.79,NJ City,N,24,22,2,52,High School,25,Patnership,Standalone\n8603,64.15,NJ City,N,24,12,3,56,No education,31,Owner,Standalone\n6478,413.52,Boston,S,13,16,3,55,High School,24,Patnership,Complex\n9649,222.61,NJ City,N,16,8,7,48,Graduation,24,Patnership,Standalone\n3006,118.48,NJ City,S,20,15,1,54,Graduation,35,Patnership,Complex\n6071,143.24,Boston,E,14,17,25,85,Graduation,23,Owner,Standalone\n3298,27.67,NJ City,N,4,21,3,77,High School,20,Patnership,Complex\n5045,460.36,Boston,N,21,12,2,53,No education,23,Patnership,Standalone\n8946,191.66,NJ City,S,25,19,7,47,Graduation,28,Patnership,Complex\n961,56.05,NJ City,N,7,9,1,43,High School,19,Patnership,Standalone\n369,218.92,NJ City,N,7,14,23,32,High School,27,Patnership,Standalone\n5033,491.83,Boston,N,3,24,2,40,No education,33,Patnership,Standalone\n6008,463.83,Boston,W,14,24,15,29,Graduation,33,Franchise,Complex\n8296,191.87,NJ City,W,23,19,18,82,Graduation,26,Franchise,Complex\n5774,407.27,Boston,S,24,8,17,35,Graduation,29,Patnership,Complex\n294,283.91,NJ City,S,20,17,4,73,Graduation,18,Patnership,Complex\n7794,438.54,Boston,S,19,12,3,73,Graduation,21,Patnership,Complex\n4879,109.86,NJ City,E,21,20,4,77,High School,19,Owner,Standalone\n7832,170.04,Boston,W,14,11,4,62,No education,26,Franchise,Complex\n5423,281.73,Boston,E,12,12,8,51,Graduation,31,Owner,Standalone\n8726,444.57,NJ City,S,21,18,7,61,Graduation,32,Patnership,Complex\n5892,458.09,Boston,W,11,17,6,51,Graduation,35,Franchise,Complex\n9176,379.03,NJ City,W,6,11,4,46,No education,24,Franchise,Complex\n6924,393.71,Boston,W,8,10,21,75,Graduation,24,Franchise,Complex\n6985,278.17,Boston,N,18,14,24,26,Graduation,21,Patnership,Standalone\n1922,38.97,NJ City,N,21,15,3,69,No education,32,Patnership,Complex\n680,433.81,NJ City,W,14,15,9,50,High School,21,Franchise,Complex\n9011,96.59,NJ City,E,8,15,2,38,No education,34,Owner,Standalone\n7169,36.61,Boston,N,16,15,1,45,No education,26,Patnership,Standalone\n9638,314.18,NJ City,S,21,14,17,31,Graduation,20,Patnership,Complex\n8824,55.54,NJ City,W,12,15,2,82,High School,29,Franchise,Complex\n4190,365.26,NJ City,S,7,21,24,33,High School,32,Patnership,Complex\n3353,151.81,NJ City,N,19,8,16,72,High School,26,Patnership,Standalone\n5012,417.91,Boston,W,3,11,4,78,No education,31,Franchise,Complex\n2281,264.21,NJ City,N,19,21,8,83,High School,22,Patnership,Standalone\n5070,344.97,Boston,S,13,12,6,44,Graduation,33,Patnership,Complex\n4438,223.69,NJ City,S,21,9,22,30,High School,23,Patnership,Complex\n9705,79.41,NJ City,N,14,12,4,26,Graduation,32,Patnership,Standalone\n6333,414.79,Boston,N,16,19,8,30,Graduation,21,Patnership,Standalone\n6117,480.19,Boston,N,21,13,17,64,Graduation,34,Patnership,Standalone\n9042,27.63,NJ City,N,12,20,2,68,Graduation,30,Patnership,Complex\n6603,317.03,Boston,E,25,17,10,68,Graduation,18,Owner,Standalone\n4454,317.64,NJ City,S,5,20,8,30,High School,29,Patnership,Complex\n3537,287.57,NJ City,N,14,22,2,41,Graduation,35,Patnership,Standalone\n2106,81.32,NJ City,S,12,15,1,85,Graduation,26,Patnership,Complex\n3788,23.45,NJ City,N,13,9,3,77,No education,35,Franchise,Complex\n3666,158.97,NJ City,S,13,13,8,63,High School,33,Patnership,Complex\n2182,193.21,NJ City,S,25,9,21,63,High School,28,Patnership,Complex\n7076,475.06,Boston,W,4,22,3,72,No education,32,Franchise,Complex\n4233,380.44,NJ City,N,5,9,6,52,High School,31,Patnership,Standalone\n7379,327.83,Boston,E,8,8,4,65,No education,28,Owner,Standalone\n8768,212.83,NJ City,W,11,9,9,34,Graduation,19,Franchise,Complex\n4533,168.6,NJ City,N,10,16,9,82,High School,25,Patnership,Standalone\n1495,382.95,NJ City,E,8,8,17,68,High School,22,Owner,Standalone\n8713,106,NJ City,N,8,8,3,71,High School,22,Patnership,Standalone\n6400,44.49,Boston,N,20,23,1,55,High School,27,Franchise,Complex\n3348,71.07,NJ City,N,9,9,3,54,Graduation,35,Franchise,Complex\n3970,410.46,NJ City,S,13,11,14,36,High School,18,Patnership,Complex\n9367,190.81,NJ City,E,11,22,7,56,Graduation,20,Owner,Standalone\n7272,265.82,Boston,W,8,21,10,83,Graduation,30,Franchise,Complex\n5942,340.16,Boston,S,11,22,4,50,No education,30,Patnership,Complex\n5755,495.67,Boston,E,23,21,3,44,High School,34,Owner,Standalone\n344,12.66,NJ City,N,10,21,4,55,No education,28,Franchise,Complex\n1232,440.17,NJ City,W,19,22,11,43,High School,35,Franchise,Complex\n6210,439.39,Boston,S,1,15,16,53,Graduation,18,Patnership,Complex\n8665,44.88,NJ City,N,3,21,3,59,High School,34,Patnership,Standalone\n1024,204.34,NJ City,W,4,12,1,65,High School,35,Franchise,Complex\n4482,178.08,NJ City,S,14,13,14,74,High School,28,Patnership,Complex\n9222,356.56,NJ City,S,13,8,19,76,Graduation,23,Patnership,Complex\n7417,333.17,Boston,N,4,16,2,54,High School,23,Patnership,Standalone\n3912,104.07,NJ City,W,5,19,2,39,Graduation,28,Franchise,Complex\n107,412.4,NJ City,E,21,13,16,69,High School,23,Owner,Standalone\n6230,220.53,Boston,S,14,12,2,50,No education,32,Patnership,Complex\n2344,119.79,NJ City,W,1,9,2,52,High School,34,Franchise,Complex\n7830,18.1,Boston,N,20,10,3,58,Graduation,34,Patnership,Complex\n9828,284.45,NJ City,W,7,20,20,69,Graduation,31,Franchise,Complex\n8489,347.45,NJ City,N,14,11,12,81,Graduation,23,Patnership,Standalone\n4627,17.76,NJ City,N,5,13,4,27,High School,26,Owner,Standalone\n3637,388.69,NJ City,N,10,12,14,73,High School,34,Patnership,Standalone\n6896,8.46,Boston,N,18,23,2,55,No education,22,Franchise,Complex\n4275,241.65,NJ City,E,24,13,2,53,Graduation,32,Owner,Standalone\n2684,326.85,NJ City,W,18,14,10,58,High School,31,Franchise,Complex\n2535,243.87,NJ City,E,8,21,12,59,High School,32,Owner,Standalone\n585,391.41,NJ City,N,21,8,4,49,Graduation,33,Patnership,Standalone\n731,132.94,NJ City,E,8,18,20,54,High School,32,Owner,Standalone\n9361,115.47,NJ City,N,19,16,4,76,High School,18,Patnership,Standalone\n4833,385.7,NJ City,N,6,18,1,84,Graduation,19,Patnership,Standalone\n6197,201.31,Boston,N,18,15,5,45,No education,18,Patnership,Standalone\n3723,15.59,NJ City,N,16,17,2,64,Graduation,34,Owner,Standalone\n2474,295.67,NJ City,S,6,17,21,84,High School,34,Patnership,Complex\n3090,24.9,NJ City,N,15,15,1,64,Graduation,35,Patnership,Complex\n5715,487.35,Boston,E,8,24,4,72,Graduation,23,Owner,Standalone\n5726,446.35,Boston,S,12,16,8,43,Graduation,25,Patnership,Complex\n7140,196.65,Boston,W,5,22,1,29,Graduation,33,Franchise,Complex\n5084,288.84,Boston,W,16,12,12,36,Graduation,18,Franchise,Complex\n582,238.42,NJ City,S,23,14,13,78,High School,33,Patnership,Complex\n1597,159.37,NJ City,N,13,15,15,25,High School,23,Patnership,Standalone\n9813,380.51,NJ City,N,20,17,3,29,Graduation,33,Patnership,Standalone\n6872,335.99,Boston,W,9,8,9,35,Graduation,26,Franchise,Complex\n5924,324.02,Boston,W,5,9,12,81,Graduation,18,Franchise,Complex\n5962,416.49,Boston,S,1,14,8,48,Graduation,29,Patnership,Complex\n881,452.94,NJ City,N,24,11,7,65,High School,35,Patnership,Standalone\n7384,230.16,Boston,W,13,9,14,59,Graduation,25,Franchise,Complex\n2975,131.73,NJ City,E,13,23,8,48,High School,31,Owner,Standalone\n1728,210.52,NJ City,W,15,8,23,39,High School,35,Franchise,Complex\n1839,229.47,NJ City,E,23,18,24,26,High School,29,Owner,Standalone\n3601,495.9,NJ City,N,10,14,17,33,High School,31,Patnership,Standalone\n3645,466.46,NJ City,N,10,24,4,48,Graduation,26,Patnership,Standalone\n8056,132.3,NJ City,W,22,18,15,75,Graduation,30,Franchise,Complex\n4314,380.86,NJ City,S,18,16,17,52,High School,29,Patnership,Complex\n1465,73.49,NJ City,N,13,18,3,43,High School,22,Patnership,Standalone\n8320,271.03,NJ City,W,6,22,13,68,Graduation,22,Franchise,Complex\n609,317.77,NJ City,N,24,17,1,27,Graduation,28,Patnership,Standalone\n292,119.04,NJ City,W,4,20,1,40,High School,29,Franchise,Complex\n9206,385.15,NJ City,S,9,12,13,34,Graduation,27,Patnership,Complex\n9018,293.18,NJ City,S,23,11,17,53,Graduation,27,Patnership,Complex\n5365,490.57,Boston,N,16,12,23,70,Graduation,25,Patnership,Standalone\n5399,453.94,Boston,E,14,19,3,73,No education,32,Owner,Standalone\n5246,43.38,Boston,N,15,14,2,38,No education,24,Patnership,Complex\n303,2.32,NJ City,E,20,12,4,54,Graduation,18,Owner,Standalone\n5479,309.04,Boston,E,11,19,20,50,Graduation,19,Owner,Standalone\n3440,144.41,NJ City,W,14,13,2,72,No education,33,Franchise,Complex\n1291,426.73,NJ City,E,15,22,23,28,High School,33,Owner,Standalone\n7003,403.44,Boston,E,21,11,22,40,Graduation,24,Owner,Standalone\n6565,94.51,Boston,N,15,16,3,29,High School,27,Patnership,Standalone\n8470,335.84,NJ City,S,10,20,21,35,Graduation,19,Patnership,Complex\n6854,409.68,Boston,S,6,23,13,73,Graduation,32,Patnership,Complex\n3279,354.6,NJ City,E,23,23,7,45,High School,21,Owner,Standalone\n3778,109,NJ City,S,1,20,9,63,High School,18,Patnership,Complex\n7790,468.49,Boston,S,5,16,10,59,Graduation,29,Patnership,Complex\n8741,472.8,NJ City,N,15,24,2,78,No education,34,Patnership,Standalone\n1200,348.72,NJ City,W,17,24,16,45,High School,27,Franchise,Complex\n7590,402.56,Boston,S,12,18,14,62,Graduation,30,Patnership,Complex\n5905,107.34,Boston,N,18,22,2,55,High School,21,Patnership,Standalone\n1983,443.45,NJ City,E,20,21,10,49,High School,25,Owner,Standalone\n4654,262,NJ City,S,3,17,12,40,High School,32,Patnership,Complex\n7187,109.99,Boston,E,3,13,3,65,No education,27,Owner,Standalone\n5341,137.39,Boston,N,10,16,24,32,Graduation,35,Patnership,Standalone\n6564,104.75,Boston,W,14,12,2,37,Graduation,35,Franchise,Complex\n9433,281.48,NJ City,N,9,8,25,58,Graduation,19,Patnership,Standalone\n3174,310,NJ City,S,1,16,10,80,High School,19,Patnership,Complex\n5193,457.02,Boston,N,16,8,10,71,Graduation,33,Patnership,Standalone\n4887,492.48,NJ City,E,9,17,16,79,High School,25,Owner,Standalone\n6877,176.47,Boston,N,7,22,19,41,Graduation,30,Patnership,Standalone\n3287,369.87,NJ City,E,13,14,16,30,High School,23,Owner,Standalone\n6652,37.03,Boston,N,6,21,1,75,High School,18,Franchise,Complex\n7396,242.59,Boston,W,4,14,7,62,Graduation,18,Franchise,Complex\n2166,20.78,NJ City,N,6,21,2,34,Graduation,25,Patnership,Complex\n2329,172.34,NJ City,N,7,22,13,31,High School,28,Patnership,Standalone\n5821,204.98,Boston,N,24,13,4,61,High School,21,Patnership,Standalone\n9835,107.08,NJ City,E,4,11,1,58,High School,28,Owner,Standalone\n2370,317.21,NJ City,S,16,13,9,36,High School,30,Patnership,Complex\n7001,99.21,Boston,N,17,21,3,66,No education,22,Patnership,Standalone\n895,84.54,NJ City,E,15,16,3,51,High School,18,Owner,Standalone\n4862,160.45,NJ City,S,13,9,11,76,High School,26,Patnership,Complex\n5308,341.25,Boston,W,8,20,5,66,High School,21,Franchise,Complex\n9796,14.84,NJ City,N,23,10,3,51,High School,21,Franchise,Complex\n2098,485.46,NJ City,S,17,9,23,45,High School,22,Patnership,Complex\n2585,14.5,NJ City,N,1,9,3,52,No education,33,Patnership,Standalone\n9808,274.6,NJ City,W,8,20,10,54,Graduation,25,Franchise,Complex\n2443,303,NJ City,E,1,12,6,56,High School,25,Owner,Standalone\n1755,198.05,NJ City,E,10,17,8,81,High School,28,Owner,Standalone\n498,134.84,NJ City,S,16,18,13,54,High School,28,Patnership,Complex\n3955,230.87,NJ City,E,7,11,9,32,High School,24,Owner,Standalone\n1392,296.67,NJ City,W,1,23,4,59,Graduation,35,Franchise,Complex\n7623,121.32,Boston,E,7,13,4,69,Graduation,30,Owner,Standalone\n5351,144.71,Boston,E,9,18,4,50,No education,24,Owner,Standalone\n9790,301.05,NJ City,S,13,14,21,78,Graduation,24,Patnership,Complex\n7297,389.24,Boston,N,12,15,12,36,Graduation,23,Patnership,Standalone\n1872,246.83,NJ City,W,17,21,4,42,Graduation,29,Franchise,Complex\n7541,285.49,Boston,N,24,24,6,27,Graduation,29,Patnership,Standalone\n5611,235.02,Boston,E,3,20,20,33,Graduation,28,Owner,Standalone\n8811,113.22,NJ City,E,19,14,3,60,Graduation,22,Owner,Standalone\n2226,72.33,NJ City,N,9,23,4,35,Graduation,32,Patnership,Complex\n5736,490.23,Boston,W,21,22,3,32,Graduation,26,Franchise,Complex\n4551,372.08,NJ City,E,12,16,20,64,High School,24,Owner,Standalone\n847,131.81,NJ City,E,15,21,13,59,High School,26,Owner,Standalone\n6665,395.66,Boston,N,21,24,25,34,Graduation,31,Patnership,Standalone\n8175,228.72,NJ City,E,6,20,12,68,Graduation,26,Owner,Standalone\n2700,335.3,NJ City,W,21,15,14,43,High School,26,Franchise,Complex\n201,438.49,NJ City,N,23,16,25,36,High School,29,Patnership,Standalone\n5053,393.29,Boston,N,14,24,10,34,Graduation,31,Patnership,Standalone\n5851,78.32,Boston,E,2,13,3,31,High School,26,Owner,Standalone\n4234,201.37,NJ City,S,25,17,9,33,High School,31,Patnership,Complex\n5597,477.47,Boston,N,23,20,1,27,No education,35,Patnership,Standalone\n1912,59.77,NJ City,W,22,8,4,72,High School,23,Franchise,Complex\n9409,99.07,NJ City,N,2,18,3,43,High School,25,Patnership,Standalone\n898,436.15,NJ City,S,20,11,14,77,High School,25,Patnership,Complex\n2414,417.55,NJ City,S,8,11,2,38,No education,30,Patnership,Complex\n6430,413.12,Boston,S,13,22,16,79,Graduation,27,Patnership,Complex\n4324,261.67,NJ City,W,8,17,16,43,High School,28,Franchise,Complex\n3833,333.97,NJ City,N,7,18,4,61,No education,31,Patnership,Standalone\n8885,387.92,NJ City,N,8,24,25,42,Graduation,27,Patnership,Standalone\n143,225.18,NJ City,E,24,10,23,25,High School,35,Owner,Standalone\n1962,103.41,NJ City,S,19,12,4,51,Graduation,29,Patnership,Complex\n860,43.38,NJ City,N,21,23,2,26,No education,21,Franchise,Complex\n9195,79.3,NJ City,E,8,12,1,36,Graduation,28,Owner,Standalone\n5522,52.96,Boston,S,5,18,1,80,No education,28,Patnership,Complex\n5223,9.96,Boston,N,22,8,2,76,Graduation,23,Owner,Standalone\n8912,430.94,NJ City,W,4,15,14,46,Graduation,32,Franchise,Complex\n7135,484.44,Boston,E,22,12,20,63,Graduation,32,Owner,Standalone\n9582,234.79,NJ City,S,12,13,7,56,Graduation,18,Patnership,Complex\n8926,438.23,NJ City,S,17,24,6,45,Graduation,25,Patnership,Complex\n3370,449.01,NJ City,S,13,13,17,26,High School,23,Patnership,Complex\n6779,398.6,Boston,E,20,10,4,55,No education,27,Owner,Standalone\n8626,241.88,NJ City,S,20,14,17,52,Graduation,31,Patnership,Complex\n7954,298.58,Boston,S,14,11,11,62,Graduation,24,Patnership,Complex\n3037,154.42,NJ City,N,18,17,16,60,High School,21,Patnership,Standalone\n8364,376.46,NJ City,W,9,10,13,60,Graduation,22,Franchise,Complex\n9344,389.31,NJ City,W,20,8,12,38,Graduation,23,Franchise,Complex\n2643,396.57,NJ City,E,5,18,5,34,Graduation,35,Owner,Standalone\n2256,225.47,NJ City,W,16,8,21,79,High School,22,Franchise,Complex\n6705,337.09,Boston,N,5,10,8,62,Graduation,30,Patnership,Standalone\n1272,278.64,NJ City,W,11,8,22,85,High School,34,Franchise,Complex\n2381,425.35,NJ City,N,25,12,3,59,No education,29,Patnership,Standalone\n7442,471.97,Boston,S,5,17,16,28,Graduation,20,Patnership,Complex\n7877,212,Boston,N,17,10,14,85,Graduation,32,Patnership,Standalone\n8047,450.08,NJ City,E,20,20,25,45,Graduation,29,Owner,Standalone\n6474,379.73,Boston,S,25,13,16,31,Graduation,27,Patnership,Complex\n356,10.01,NJ City,N,11,19,4,54,No education,18,Franchise,Complex\n4916,49.49,NJ City,W,5,12,2,60,No education,22,Franchise,Complex\n3167,15.77,NJ City,N,6,11,2,81,No education,32,Owner,Standalone\n579,292.4,NJ City,E,12,17,22,57,High School,30,Owner,Standalone\n9910,482.88,NJ City,S,16,12,5,40,High School,30,Patnership,Complex\n8261,117.51,NJ City,N,7,10,3,54,No education,21,Patnership,Standalone\n5746,298.24,Boston,S,4,14,1,78,High School,21,Patnership,Complex\n8536,66.02,NJ City,N,9,18,3,76,High School,20,Franchise,Complex\n6912,22.72,Boston,N,19,12,3,26,Graduation,29,Franchise,Complex\n9816,56.04,NJ City,W,10,17,4,35,Graduation,32,Franchise,Complex\n5751,101.17,Boston,E,21,19,3,56,Graduation,34,Owner,Standalone\n8644,160.29,NJ City,W,9,16,4,44,High School,19,Franchise,Complex\n9071,117.15,NJ City,E,12,19,4,56,No education,33,Owner,Standalone\n327,162.09,NJ City,E,4,12,11,69,High School,31,Owner,Standalone\n7210,19.91,Boston,N,18,11,3,29,High School,32,Patnership,Complex\n3242,173.66,NJ City,S,15,15,10,48,High School,33,Patnership,Complex\n1333,163,NJ City,N,3,16,7,45,High School,28,Patnership,Standalone\n1384,259.72,NJ City,W,9,16,5,38,High School,19,Franchise,Complex\n2862,60.73,NJ City,N,10,20,4,25,Graduation,33,Patnership,Complex\n3742,242.72,NJ City,S,20,13,12,81,High School,33,Patnership,Complex\n3266,118.23,NJ City,S,4,8,1,25,No education,33,Patnership,Complex\n9432,5.02,NJ City,N,14,17,2,74,Graduation,27,Franchise,Complex\n4766,291.33,NJ City,S,13,12,10,57,High School,28,Patnership,Complex\n2155,253.77,NJ City,E,10,20,6,63,High School,27,Owner,Standalone\n9607,450.41,NJ City,E,6,14,1,70,High School,28,Owner,Standalone\n3324,37.21,NJ City,N,21,18,3,35,Graduation,29,Franchise,Complex\n7077,250.83,Boston,N,23,9,5,54,Graduation,22,Patnership,Standalone\n8693,57.21,NJ City,N,14,8,4,43,No education,34,Patnership,Standalone\n9493,209.46,NJ City,N,5,19,4,36,High School,19,Patnership,Standalone\n3553,56.93,NJ City,N,19,8,1,83,High School,19,Patnership,Standalone\n859,321.84,NJ City,E,16,9,18,54,High School,33,Owner,Standalone\n6322,222.87,Boston,S,12,19,17,81,Graduation,27,Patnership,Complex\n5525,282.35,Boston,N,24,10,23,57,Graduation,33,Patnership,Standalone\n2189,262.7,NJ City,N,25,16,4,49,No education,29,Patnership,Standalone\n8144,12.48,NJ City,N,6,16,3,37,No education,24,Franchise,Complex\n3865,464.38,NJ City,N,10,23,13,55,High School,33,Patnership,Standalone\n6421,445.77,Boston,N,6,12,11,26,Graduation,30,Patnership,Standalone\n2145,399.78,NJ City,N,11,22,10,40,High School,29,Patnership,Standalone\n7769,151.24,Boston,N,3,8,7,45,Graduation,26,Patnership,Standalone\n8879,450.22,NJ City,E,8,11,17,40,Graduation,24,Owner,Standalone\n4522,365.62,NJ City,S,25,15,17,56,High School,32,Patnership,Complex\n6905,269.32,Boston,N,17,17,5,44,No education,32,Patnership,Standalone\n6433,290.98,Boston,N,6,12,19,66,Graduation,34,Patnership,Standalone\n1568,206,NJ City,W,1,16,22,75,High School,23,Franchise,Complex\n9190,34.22,NJ City,N,18,20,4,52,High School,28,Patnership,Complex\n9172,480.36,NJ City,W,25,13,12,75,Graduation,24,Franchise,Complex\n3332,269.02,NJ City,W,8,18,15,31,High School,25,Franchise,Complex\n1127,428.31,NJ City,E,24,21,13,51,High School,22,Owner,Standalone\n1904,440.63,NJ City,W,23,9,20,73,High School,27,Franchise,Complex\n4523,133.66,NJ City,E,25,9,19,45,High School,18,Owner,Standalone\n6597,435.54,Boston,N,2,24,20,29,Graduation,19,Patnership,Standalone\n5186,248.44,Boston,S,21,21,23,48,Graduation,29,Patnership,Complex\n3641,458.36,NJ City,N,7,21,13,50,High School,29,Patnership,Standalone\n9182,471.71,NJ City,S,4,16,6,69,Graduation,30,Patnership,Complex\n6159,303.63,Boston,E,23,15,19,41,Graduation,26,Owner,Standalone\n1727,92.02,NJ City,E,9,12,3,41,No education,22,Owner,Standalone\n268,261.42,NJ City,W,17,11,25,33,High School,19,Franchise,Complex\n2551,85.06,NJ City,E,3,15,1,56,High School,27,Owner,Standalone\n7034,358.7,Boston,S,6,11,14,76,Graduation,26,Patnership,Complex\n2432,349.36,NJ City,W,7,9,25,58,High School,27,Franchise,Complex\n2269,145.22,NJ City,N,16,24,16,66,High School,31,Patnership,Standalone\n7365,90.22,Boston,N,22,8,1,32,Graduation,26,Patnership,Standalone\n5301,105.76,Boston,N,1,11,2,81,Graduation,30,Patnership,Standalone\n6188,186.48,Boston,W,8,22,12,35,Graduation,19,Franchise,Complex\n1302,235,NJ City,S,1,11,23,38,High School,27,Patnership,Complex\n1578,402.71,NJ City,S,18,24,11,85,High School,32,Patnership,Complex\n8574,337.81,NJ City,S,17,8,2,66,Graduation,34,Patnership,Complex\n7936,450.57,Boston,W,21,9,4,65,High School,23,Franchise,Complex\n4924,234.67,NJ City,W,7,9,11,85,High School,31,Franchise,Complex\n5143,67.89,Boston,N,13,15,3,76,High School,31,Owner,Standalone\n3240,173.11,NJ City,W,18,21,22,70,High School,29,Franchise,Complex\n169,231.65,NJ City,N,12,10,8,51,High School,31,Patnership,Standalone\n5761,427.8,Boston,N,20,8,1,51,High School,30,Patnership,Standalone\n3415,399.54,NJ City,E,15,10,6,62,High School,31,Owner,Standalone\n7702,47.76,Boston,S,3,16,4,31,High School,29,Patnership,Complex\n6335,144.87,Boston,E,6,20,4,28,No education,21,Owner,Standalone\n4365,26.95,NJ City,N,23,17,2,34,Graduation,24,Patnership,Standalone\n9147,410.44,NJ City,E,11,10,1,82,Graduation,22,Owner,Standalone\n6369,467.17,Boston,N,15,13,4,27,Graduation,24,Patnership,Standalone\n5240,447.06,Boston,W,15,14,12,53,Graduation,27,Franchise,Complex\n343,313,NJ City,E,2,18,6,41,High School,30,Owner,Standalone\n7807,435.99,Boston,E,5,22,1,53,High School,23,Owner,Standalone\n3554,211.92,NJ City,S,8,12,25,27,High School,30,Patnership,Complex\n9987,495.22,NJ City,E,7,13,17,52,Graduation,25,Owner,Standalone\n1031,131.51,NJ City,E,14,10,19,41,High School,27,Owner,Standalone\n5719,212.94,Boston,E,4,19,17,31,Graduation,18,Owner,Standalone\n4550,477.76,NJ City,S,4,10,13,38,High School,21,Patnership,Complex\n2691,236.17,NJ City,E,25,22,6,53,High School,26,Owner,Standalone\n6127,175.48,Boston,E,6,17,13,58,Graduation,29,Owner,Standalone\n3830,74.28,NJ City,S,1,18,2,60,No education,33,Patnership,Complex\n8170,71.05,NJ City,N,13,10,1,65,High School,35,Patnership,Complex\n2096,194.97,NJ City,W,4,15,18,27,High School,21,Franchise,Complex\n8578,463.72,NJ City,S,7,12,17,52,Graduation,30,Patnership,Complex\n9241,51.23,NJ City,N,9,15,3,36,High School,31,Patnership,Standalone\n4479,64.88,NJ City,N,21,17,1,70,Graduation,26,Owner,Standalone\n5010,262.59,Boston,S,11,10,9,44,Graduation,28,Patnership,Complex\n4505,241.26,NJ City,N,22,10,22,33,High School,35,Patnership,Standalone\n466,378.14,NJ City,S,14,15,10,66,High School,20,Patnership,Complex\n7547,18.03,Boston,N,16,22,4,67,No education,34,Owner,Standalone\n1733,392.43,NJ City,N,11,16,23,65,High School,33,Patnership,Standalone\n1436,198.97,NJ City,W,7,19,13,30,High School,20,Franchise,Complex\n8060,173.62,NJ City,W,5,19,7,39,Graduation,18,Franchise,Complex\n1661,405.77,NJ City,N,18,19,3,59,No education,34,Patnership,Standalone\n7922,161.76,Boston,S,15,18,7,85,Graduation,24,Patnership,Complex\n4036,463.32,NJ City,W,19,18,16,67,High School,27,Franchise,Complex\n5406,73.97,Boston,S,17,11,2,37,Graduation,32,Patnership,Complex\n5911,248.59,Boston,E,21,8,13,59,Graduation,31,Owner,Standalone\n210,205,NJ City,S,3,10,9,82,High School,28,Patnership,Complex\n6725,251.54,Boston,N,19,9,16,62,Graduation,20,Patnership,Standalone\n6994,282.8,Boston,S,21,23,17,47,Graduation,20,Patnership,Complex\n509,429.34,NJ City,N,13,22,6,73,High School,32,Patnership,Standalone\n5476,288.56,Boston,W,15,8,20,63,Graduation,24,Franchise,Complex\n330,44.9,NJ City,N,6,8,2,56,Graduation,35,Patnership,Complex\n8096,444.62,NJ City,W,6,18,16,84,Graduation,25,Franchise,Complex\n9929,373.2,NJ City,N,4,11,9,70,Graduation,27,Patnership,Standalone\n6223,139.83,Boston,E,22,19,1,35,High School,28,Owner,Standalone\n5639,468.8,Boston,E,17,10,18,66,Graduation,20,Owner,Standalone\n4296,350.21,NJ City,W,13,24,8,48,High School,33,Franchise,Complex\n6016,430.39,Boston,W,3,10,11,76,Graduation,22,Franchise,Complex\n6021,37.56,Boston,N,4,21,4,29,Graduation,30,Patnership,Standalone\n8352,384.72,NJ City,W,1,20,21,58,Graduation,30,Franchise,Complex\n1865,185.72,NJ City,N,10,18,18,75,High School,22,Patnership,Standalone\n3056,59.51,NJ City,W,6,16,4,40,No education,31,Franchise,Complex\n3041,250.17,NJ City,N,9,17,22,53,High School,20,Patnership,Standalone\n4258,429.76,NJ City,S,9,16,14,33,High School,30,Patnership,Complex\n4908,53.11,NJ City,W,7,13,3,31,Graduation,34,Franchise,Complex\n5410,229.54,Boston,S,10,10,13,46,Graduation,19,Patnership,Complex\n9916,179.84,NJ City,W,14,17,1,26,High School,24,Franchise,Complex\n3855,437.3,NJ City,E,10,8,24,73,High School,22,Owner,Standalone\n5605,312.11,Boston,N,14,14,14,69,Graduation,28,Patnership,Standalone\n7595,418.75,Boston,E,7,23,12,62,Graduation,28,Owner,Standalone\n83,305.49,Boston,E,11,20,22,76,High School,29,Owner,Standalone\n6066,211.73,Boston,S,13,21,24,47,Graduation,24,Patnership,Complex\n4156,2.63,NJ City,W,21,18,4,60,High School,35,Franchise,Complex\n1687,376.18,NJ City,E,2,21,1,43,High School,35,Owner,Standalone\n469,237.41,NJ City,N,8,18,2,36,High School,20,Patnership,Standalone\n496,5.99,NJ City,N,10,8,1,70,High School,33,Franchise,Complex\n6015,232.42,Boston,E,12,9,22,61,Graduation,31,Owner,Standalone\n1139,49.43,NJ City,E,21,9,4,60,No education,30,Owner,Standalone\n8117,43.44,NJ City,N,7,18,4,44,No education,29,Patnership,Standalone\n2061,267.47,NJ City,N,13,22,10,72,High School,18,Patnership,Standalone\n2754,80.87,NJ City,S,17,9,2,55,Graduation,24,Patnership,Complex\n4724,112.5,NJ City,W,2,10,2,40,No education,35,Franchise,Complex\n9743,386.94,NJ City,E,2,10,17,51,Graduation,24,Owner,Standalone\n7203,338.91,Boston,E,19,24,8,36,Graduation,18,Owner,Standalone\n211,430.58,NJ City,E,20,8,10,43,High School,35,Owner,Standalone\n7405,175.26,Boston,N,15,15,8,31,Graduation,23,Patnership,Standalone\n2951,28.15,NJ City,N,13,12,2,73,No education,21,Owner,Standalone\n9279,172.41,NJ City,E,23,16,8,73,Graduation,29,Owner,Standalone\n9527,243.22,NJ City,E,7,15,18,45,Graduation,21,Owner,Standalone\n5538,418.25,Boston,S,7,19,20,44,Graduation,26,Patnership,Complex\n4575,295.28,NJ City,E,7,19,19,29,High School,35,Owner,Standalone\n7126,454.26,Boston,S,11,13,24,64,Graduation,24,Patnership,Complex\n8861,330.34,NJ City,N,11,13,9,26,Graduation,31,Patnership,Standalone\n8974,229.07,NJ City,S,13,14,25,73,Graduation,27,Patnership,Complex\n1315,375.14,NJ City,E,25,13,10,71,High School,33,Owner,Standalone\n9749,244.71,NJ City,N,13,20,25,30,Graduation,29,Patnership,Standalone\n1411,27.3,NJ City,N,8,10,2,73,High School,35,Owner,Standalone\n6551,401.71,Boston,E,20,22,15,72,Graduation,33,Owner,Standalone\n7499,218.26,Boston,E,20,22,22,37,Graduation,32,Owner,Standalone\n6227,338.36,Boston,E,6,16,19,65,Graduation,20,Owner,Standalone\n9027,474.61,NJ City,E,23,20,4,42,Graduation,20,Owner,Standalone\n5469,134.54,Boston,N,22,22,21,71,Graduation,31,Patnership,Standalone\n2461,440,NJ City,N,7,23,14,49,High School,35,Patnership,Standalone\n7435,282.4,Boston,E,5,24,23,62,Graduation,19,Owner,Standalone\n365,399.81,NJ City,N,15,13,9,42,High School,31,Patnership,Standalone\n9805,180.81,NJ City,N,24,23,15,78,Graduation,25,Patnership,Standalone\n2079,108.23,NJ City,E,8,11,3,84,Graduation,34,Owner,Standalone\n8317,474.31,NJ City,N,7,10,17,37,Graduation,32,Patnership,Standalone\n3381,382.33,NJ City,N,6,13,17,27,High School,28,Patnership,Standalone\n753,7.52,NJ City,N,25,11,4,84,Graduation,35,Patnership,Standalone\n8715,480.64,NJ City,E,6,12,7,38,Graduation,26,Owner,Standalone\n9055,53.14,NJ City,E,7,24,4,82,High School,31,Owner,Standalone\n6240,442.94,Boston,W,12,24,4,38,Graduation,19,Franchise,Complex\n2398,141.05,NJ City,S,24,10,20,81,High School,28,Patnership,Complex\n7540,87.83,Boston,W,6,19,1,63,High School,34,Franchise,Complex\n6193,225.56,Boston,N,22,14,25,69,Graduation,34,Patnership,Standalone\n8014,309.37,Boston,S,20,21,3,50,High School,35,Patnership,Complex\n7205,130.61,Boston,N,24,24,17,75,Graduation,29,Patnership,Standalone\n9897,164.87,NJ City,N,19,23,18,35,Graduation,27,Patnership,Standalone\n6243,477.22,Boston,E,5,14,11,43,Graduation,35,Owner,Standalone\n8825,217.82,NJ City,N,13,24,17,65,Graduation,29,Patnership,Standalone\n802,449.62,NJ City,S,25,8,18,77,High School,23,Patnership,Complex\n2632,213.14,NJ City,W,9,17,21,85,High School,30,Franchise,Complex\n9803,214.32,NJ City,E,10,8,1,84,No education,30,Owner,Standalone\n9779,164.35,NJ City,E,1,9,9,44,Graduation,28,Owner,Standalone\n1227,471.59,NJ City,E,21,9,3,82,Graduation,28,Owner,Standalone\n5954,299,Boston,S,5,21,18,68,Graduation,20,Patnership,Complex\n59,128.43,Boston,E,25,23,21,60,High School,34,Owner,Standalone\n2220,319.1,NJ City,W,1,24,5,54,Graduation,20,Franchise,Complex\n2875,371.24,NJ City,E,6,22,6,55,High School,31,Owner,Standalone\n5874,245.67,Boston,S,15,13,23,78,Graduation,28,Patnership,Complex\n352,130.07,NJ City,W,16,19,10,67,High School,21,Franchise,Complex\n9494,107.66,NJ City,S,20,17,2,78,No education,31,Patnership,Complex\n7741,475.46,Boston,N,14,9,24,55,Graduation,18,Patnership,Standalone\n8799,338.1,NJ City,E,24,10,16,77,Graduation,27,Owner,Standalone\n347,466.67,NJ City,E,11,21,1,62,No education,32,Owner,Standalone\n8668,359.4,NJ City,W,5,23,23,26,Graduation,25,Franchise,Complex\n5274,205.33,Boston,S,22,13,15,52,Graduation,30,Patnership,Complex\n5985,390.55,Boston,N,8,10,24,69,Graduation,25,Patnership,Standalone\n5649,410.21,Boston,N,5,9,14,27,Graduation,27,Patnership,Standalone\n406,281.21,NJ City,S,9,11,4,68,High School,30,Patnership,Complex\n9336,492.91,NJ City,W,21,17,19,50,Graduation,23,Franchise,Complex\n2829,265.91,NJ City,N,16,20,10,79,High School,30,Patnership,Standalone\n946,40.01,NJ City,N,19,17,4,43,High School,28,Patnership,Complex\n8576,492.11,NJ City,W,15,18,1,40,No education,24,Franchise,Complex\n2694,215.76,NJ City,S,23,19,22,59,High School,33,Patnership,Complex\n1402,35.74,NJ City,N,16,22,2,30,High School,24,Patnership,Complex\n2360,234.84,NJ City,W,1,17,4,78,No education,28,Franchise,Complex\n6728,346.21,Boston,W,3,13,17,62,Graduation,31,Franchise,Complex\n2867,75.73,NJ City,E,6,8,2,68,No education,30,Owner,Standalone\n6455,123.05,Boston,E,24,16,2,43,No education,29,Owner,Standalone\n4392,252,NJ City,W,3,8,25,76,High School,32,Franchise,Complex\n1193,302,NJ City,N,1,17,25,40,High School,29,Patnership,Standalone\n3202,140.76,NJ City,S,13,19,25,49,High School,29,Patnership,Complex\n7295,54.82,Boston,E,17,19,4,83,No education,24,Owner,Standalone\n3853,173,NJ City,N,1,8,16,67,High School,28,Patnership,Standalone\n1874,458.34,NJ City,S,11,15,18,38,High School,26,Patnership,Complex\n9606,155.57,NJ City,S,2,21,25,29,Graduation,28,Patnership,Complex\n42,10.15,Boston,N,6,20,1,65,Graduation,19,Patnership,Complex\n5836,184.64,Boston,W,4,24,24,61,Graduation,25,Franchise,Complex\n4035,460.41,NJ City,E,24,9,12,26,High School,23,Owner,Standalone\n1549,211.57,NJ City,N,8,16,25,72,High School,21,Patnership,Standalone\n7393,383.75,Boston,N,25,18,18,26,Graduation,24,Patnership,Standalone\n1306,245.24,NJ City,S,19,22,24,75,High School,25,Patnership,Complex\n9142,417.24,NJ City,S,3,11,12,37,Graduation,23,Patnership,Complex\n9868,467.67,NJ City,W,13,18,6,30,Graduation,19,Franchise,Complex\n561,355.27,NJ City,N,4,20,10,53,High School,22,Patnership,Standalone\n4729,449.87,NJ City,N,17,13,15,39,High School,32,Patnership,Standalone\n2156,120.22,NJ City,W,15,9,3,82,No education,19,Franchise,Complex\n7113,490.4,Boston,N,18,19,8,72,Graduation,19,Patnership,Standalone\n4467,5.12,NJ City,N,10,24,3,59,Graduation,28,Owner,Standalone\n6522,212.15,Boston,S,14,15,7,41,Graduation,26,Patnership,Complex\n9386,172.4,NJ City,S,16,9,12,39,Graduation,32,Patnership,Complex\n6308,384.19,Boston,W,20,9,1,30,No education,24,Franchise,Complex\n1980,44.17,NJ City,N,8,16,3,30,Graduation,29,Franchise,Complex\n1695,100.77,NJ City,E,8,24,3,78,Graduation,26,Owner,Standalone\n3382,303.5,NJ City,S,20,12,12,72,High School,25,Patnership,Complex\n6657,123.22,Boston,N,6,22,2,27,Graduation,27,Patnership,Standalone\n7080,220.25,Boston,W,24,17,5,62,Graduation,22,Franchise,Complex\n6884,165.29,Boston,W,23,19,7,79,Graduation,23,Franchise,Complex\n9258,409.38,NJ City,S,9,23,20,70,Graduation,29,Patnership,Complex\n2407,90.88,NJ City,E,24,18,3,68,High School,28,Owner,Standalone\n6511,453.34,Boston,E,24,9,22,78,Graduation,23,Owner,Standalone\n6079,146.92,Boston,E,13,9,14,46,Graduation,18,Owner,Standalone\n1528,242.05,NJ City,W,5,15,14,62,High School,24,Franchise,Complex\n2763,468.1,NJ City,E,10,11,20,26,High School,30,Owner,Standalone\n1680,170.03,NJ City,W,8,17,15,69,High School,32,Franchise,Complex\n9774,330.63,NJ City,S,24,13,3,39,Graduation,19,Patnership,Complex\n5883,238.34,Boston,E,20,19,20,56,Graduation,21,Owner,Standalone\n8067,338.01,NJ City,E,12,16,16,64,Graduation,24,Owner,Standalone\n8500,374.49,NJ City,W,4,8,14,73,Graduation,30,Franchise,Complex\n3067,299.99,NJ City,E,23,8,23,60,High School,32,Owner,Standalone\n3533,48.27,NJ City,N,20,21,3,33,No education,29,Patnership,Standalone\n1524,34.7,NJ City,N,22,8,2,28,Graduation,18,Franchise,Complex\n5757,288.22,Boston,N,25,11,16,63,Graduation,21,Patnership,Standalone\n2347,400.3,NJ City,E,18,17,13,25,High School,30,Owner,Standalone\n9955,210.8,NJ City,E,20,10,18,29,Graduation,26,Owner,Standalone\n3072,208.62,NJ City,W,11,19,1,73,Graduation,27,Franchise,Complex\n6286,242.9,Boston,S,9,9,21,70,Graduation,26,Patnership,Complex\n9634,163.21,NJ City,S,14,23,23,85,Graduation,34,Patnership,Complex\n7939,206.76,Boston,E,12,16,14,58,Graduation,28,Owner,Standalone\n3402,385.11,NJ City,S,20,18,15,25,High School,18,Patnership,Complex\n229,223.24,NJ City,N,11,10,8,39,High School,33,Patnership,Standalone\n8813,178.43,NJ City,N,23,22,14,63,Graduation,29,Patnership,Standalone\n1418,470.57,NJ City,S,17,21,18,64,High School,27,Patnership,Complex\n9684,499.93,NJ City,W,11,13,24,70,Graduation,21,Franchise,Complex\n41,140,Maine,N,2,22,23,44,High School,30,Patnership,Standalone\n796,76.79,NJ City,W,7,20,3,34,High School,32,Franchise,Complex\n9276,415.96,NJ City,W,3,20,2,43,Graduation,31,Franchise,Complex\n4661,439.45,NJ City,N,7,18,6,79,High School,18,Patnership,Standalone\n2292,158.1,NJ City,W,18,24,19,36,High School,21,Franchise,Complex\n3831,173.63,NJ City,E,11,22,11,36,High School,22,Owner,Standalone\n7469,473.74,Boston,N,24,9,17,46,Graduation,29,Patnership,Standalone\n9641,193.24,NJ City,N,7,20,10,33,Graduation,21,Patnership,Standalone\n5036,221.16,Boston,W,8,23,2,29,No education,32,Franchise,Complex\n7062,457.84,Boston,S,19,19,17,30,Graduation,32,Patnership,Complex\n8564,126.42,NJ City,W,11,18,4,78,No education,35,Franchise,Complex\n5752,72.94,Boston,N,13,21,4,81,High School,26,Franchise,Complex\n6453,103.84,Boston,N,24,11,2,77,Graduation,30,Patnership,Standalone\n1842,454.4,NJ City,S,13,21,24,29,High School,19,Patnership,Complex\n6296,368.35,Boston,W,12,10,21,59,Graduation,34,Franchise,Complex\n4701,167.48,NJ City,N,24,13,1,33,Graduation,33,Patnership,Standalone\n828,151.12,NJ City,W,4,15,18,28,High School,28,Franchise,Complex\n771,372.88,NJ City,E,13,14,18,61,High School,27,Owner,Standalone\n5091,310.97,Boston,E,12,18,4,56,Graduation,20,Owner,Standalone\n5220,246.7,Boston,W,20,24,11,42,Graduation,28,Franchise,Complex\n3120,499.63,NJ City,W,24,13,19,55,High School,27,Franchise,Complex\n9084,243.3,NJ City,W,16,10,14,39,Graduation,25,Franchise,Complex\n5990,399.44,Boston,S,23,15,25,60,Graduation,28,Patnership,Complex\n4703,274.88,NJ City,E,6,20,16,28,High School,18,Owner,Standalone\n4096,253.76,NJ City,W,22,21,6,51,High School,30,Franchise,Complex\n5792,262.12,Boston,W,16,11,21,64,Graduation,24,Franchise,Complex\n1986,141.68,NJ City,S,25,17,11,80,High School,22,Patnership,Complex\n4329,421.83,NJ City,N,18,16,9,37,High School,20,Patnership,Standalone\n2813,40.48,NJ City,N,9,23,3,50,No education,33,Patnership,Standalone\n770,34.94,NJ City,N,25,16,2,76,No education,20,Patnership,Complex\n967,210.66,NJ City,E,19,13,22,78,High School,28,Owner,Standalone\n5539,98.88,Boston,E,12,16,2,62,High School,22,Owner,Standalone\n9403,430.01,NJ City,E,1,13,4,84,High School,33,Owner,Standalone\n9741,70.83,NJ City,N,12,10,4,35,Graduation,30,Patnership,Standalone\n2192,223.89,NJ City,W,21,23,16,51,High School,33,Franchise,Complex\n4107,127.28,NJ City,E,25,8,3,38,Graduation,28,Owner,Standalone\n3689,372.84,NJ City,N,5,19,20,46,High School,19,Patnership,Standalone\n6546,217.69,Boston,S,6,24,23,28,Graduation,22,Patnership,Complex\n1975,395.72,NJ City,E,15,22,4,30,High School,29,Owner,Standalone\n7717,158.59,Boston,N,7,9,23,65,Graduation,27,Patnership,Standalone\n3662,259,NJ City,S,1,20,9,48,High School,21,Patnership,Complex\n5683,475.44,Boston,E,3,11,16,83,Graduation,31,Owner,Standalone\n2905,385.83,NJ City,N,12,20,3,80,High School,35,Patnership,Standalone\n5666,316.68,Boston,S,20,17,20,41,Graduation,29,Patnership,Complex\n6321,178.84,Boston,N,12,16,24,35,Graduation,32,Patnership,Standalone\n7410,396.64,Boston,S,22,19,2,71,Graduation,30,Patnership,Complex\n8003,48.29,Boston,E,24,14,4,84,No education,34,Owner,Standalone\n9194,349.61,NJ City,S,4,19,1,85,No education,25,Patnership,Complex\n7779,314.46,Boston,E,8,22,24,30,Graduation,24,Owner,Standalone\n5879,446.87,Boston,E,13,20,17,62,Graduation,30,Owner,Standalone\n161,333.62,NJ City,N,20,21,24,77,High School,32,Patnership,Standalone\n987,37.91,NJ City,N,19,10,3,75,Graduation,22,Owner,Standalone\n7962,142.88,Boston,S,21,24,9,50,Graduation,20,Patnership,Complex\n1941,89.21,NJ City,N,5,21,4,35,Graduation,29,Patnership,Standalone\n7431,13.78,Boston,N,10,13,1,42,Graduation,27,Owner,Standalone\n2026,339.43,NJ City,S,7,8,8,70,High School,28,Patnership,Complex\n6218,377.19,Boston,S,17,20,3,44,No education,25,Patnership,Complex\n1785,304.64,NJ City,N,24,16,6,74,High School,25,Patnership,Standalone\n2793,186.6,NJ City,N,16,9,8,53,High School,29,Patnership,Standalone\n8208,39.45,NJ City,N,22,23,1,73,Graduation,30,Franchise,Complex\n7245,177.05,Boston,N,21,8,16,36,Graduation,28,Patnership,Standalone\n807,41.75,NJ City,N,25,9,3,65,Graduation,32,Owner,Standalone\n2427,210.2,NJ City,E,7,21,6,78,High School,28,Owner,Standalone\n947,190.35,NJ City,E,8,13,2,81,No education,21,Owner,Standalone\n7214,409.19,Boston,S,13,23,18,64,Graduation,23,Patnership,Complex\n1987,408.88,NJ City,E,3,15,4,49,High School,20,Owner,Standalone\n7334,242.54,Boston,S,15,11,11,43,Graduation,26,Patnership,Complex\n4644,225.52,NJ City,W,9,9,16,33,High School,22,Franchise,Complex\n1956,328.39,NJ City,W,19,23,11,48,High School,23,Franchise,Complex\n4001,75.37,NJ City,N,3,22,3,53,No education,26,Patnership,Standalone\n1908,143.15,NJ City,W,25,11,5,71,Graduation,30,Franchise,Complex\n1548,436.37,NJ City,W,12,23,8,34,High School,26,Franchise,Complex\n1120,333.35,NJ City,W,23,19,15,73,High School,21,Franchise,Complex\n5211,407.16,Boston,E,7,17,25,60,Graduation,19,Owner,Standalone\n7579,266.25,Boston,E,8,8,19,53,Graduation,20,Owner,Standalone\n983,446.46,NJ City,E,16,13,11,27,High School,25,Owner,Standalone\n4456,47.54,NJ City,W,16,20,2,72,High School,25,Franchise,Complex\n809,481.65,NJ City,N,4,21,21,67,High School,18,Patnership,Standalone\n4201,66.63,NJ City,N,9,12,4,45,High School,22,Patnership,Standalone\n5378,370.24,Boston,S,25,9,11,51,Graduation,25,Patnership,Complex\n4396,12.17,NJ City,N,16,21,2,32,High School,23,Franchise,Complex\n8394,298.23,NJ City,S,17,17,11,45,Graduation,24,Patnership,Complex\n7070,366.82,Boston,S,8,18,13,25,Graduation,32,Patnership,Complex\n4079,25.04,NJ City,N,11,13,1,84,No education,28,Owner,Standalone\n4763,363.85,NJ City,E,4,23,18,28,High School,26,Owner,Standalone\n1940,354.09,NJ City,W,17,24,18,60,High School,32,Franchise,Complex\n115,463.9,NJ City,E,19,13,2,49,High School,34,Owner,Standalone\n4436,341.6,NJ City,W,18,22,5,52,No education,34,Franchise,Complex\n1861,415.31,NJ City,N,14,20,16,68,High School,21,Patnership,Standalone\n8597,13.82,NJ City,N,10,19,2,42,No education,32,Patnership,Standalone\n4882,242.6,NJ City,S,20,18,23,32,High School,25,Patnership,Complex\n209,54.51,NJ City,N,7,15,3,55,No education,26,Patnership,Standalone\n9520,476.37,NJ City,W,16,8,6,84,Graduation,32,Franchise,Complex\n6993,376.34,Boston,N,6,17,5,75,Graduation,26,Patnership,Standalone\n2254,440.13,NJ City,S,5,22,2,55,High School,28,Patnership,Complex\n8918,105.14,NJ City,S,24,18,1,35,No education,19,Patnership,Complex\n7641,102.77,Boston,N,16,11,2,84,Graduation,25,Patnership,Standalone\n8636,177.16,NJ City,W,14,9,20,62,Graduation,31,Franchise,Complex\n9486,207.51,NJ City,S,22,10,22,67,Graduation,21,Patnership,Complex\n1662,263.6,NJ City,S,12,15,9,81,High School,26,Patnership,Complex\n2172,186.27,NJ City,W,4,15,2,56,Graduation,24,Franchise,Complex\n1947,276.92,NJ City,E,19,17,17,62,High School,24,Owner,Standalone\n5799,103.09,Boston,E,10,16,3,59,Graduation,34,Owner,Standalone\n7766,486.3,Boston,S,9,14,8,38,Graduation,27,Patnership,Complex\n6340,413.5,Boston,W,9,18,1,71,High School,25,Franchise,Complex\n525,167.95,NJ City,N,2,23,1,74,Graduation,20,Patnership,Standalone\n9260,26.28,NJ City,N,20,15,1,64,No education,24,Franchise,Complex\n9467,310.77,NJ City,E,5,18,9,47,Graduation,23,Owner,Standalone\n8495,364.38,NJ City,E,5,24,14,65,Graduation,29,Owner,Standalone\n8389,84.16,NJ City,N,10,12,4,80,High School,19,Patnership,Standalone\n4050,150.99,NJ City,S,21,18,17,73,High School,33,Patnership,Complex\n8365,29.58,NJ City,N,17,9,2,30,High School,32,Patnership,Standalone\n6579,245.36,Boston,E,4,18,24,34,Graduation,26,Owner,Standalone\n1837,348.1,NJ City,N,25,10,1,60,High School,19,Patnership,Standalone\n6615,221.72,Boston,E,2,24,6,29,Graduation,31,Owner,Standalone\n5184,204.64,Boston,W,15,8,22,72,Graduation,21,Franchise,Complex\n448,400.23,NJ City,W,18,13,24,62,High School,31,Franchise,Complex\n2004,209.22,NJ City,W,12,20,17,25,High School,31,Franchise,Complex\n4204,269.42,NJ City,W,10,19,24,67,High School,22,Franchise,Complex\n9390,455.03,NJ City,S,1,17,18,72,Graduation,25,Patnership,Complex\n5832,321.89,Boston,W,13,13,10,32,Graduation,29,Franchise,Complex\n7622,393.88,Boston,S,15,8,15,40,Graduation,31,Patnership,Complex\n398,347.48,NJ City,S,12,18,4,28,No education,27,Patnership,Complex\n9368,59.6,NJ City,W,16,11,3,74,No education,21,Franchise,Complex\n8672,484.54,NJ City,W,2,13,24,77,Graduation,19,Franchise,Complex\n4146,389.16,NJ City,S,11,24,12,50,High School,29,Patnership,Complex\n2063,369.38,NJ City,E,4,9,2,29,No education,27,Owner,Standalone\n8894,272.05,NJ City,S,23,21,12,41,Graduation,34,Patnership,Complex\n5829,192.01,Boston,N,22,9,11,83,Graduation,32,Patnership,Standalone\n75,382.05,Boston,E,24,16,22,45,High School,34,Owner,Standalone\n9523,73.42,NJ City,N,21,9,4,60,High School,22,Owner,Standalone\n1717,271.4,NJ City,N,5,18,16,85,High School,34,Patnership,Standalone\n3882,75.49,NJ City,S,2,9,1,61,Graduation,21,Patnership,Complex\n8462,74.75,NJ City,S,22,11,1,51,No education,26,Patnership,Complex\n5946,337.15,Boston,S,13,17,15,62,Graduation,28,Patnership,Complex\n4040,305,NJ City,W,3,21,18,48,High School,33,Franchise,Complex\n9687,432.29,NJ City,E,10,20,7,32,Graduation,31,Owner,Standalone\n8598,206.11,NJ City,S,20,12,21,46,Graduation,30,Patnership,Complex\n7592,42.02,Boston,N,25,15,4,50,No education,28,Franchise,Complex\n9549,150,NJ City,N,12,11,8,60,Graduation,30,Patnership,Standalone\n7071,246.58,Boston,E,15,13,3,62,Graduation,18,Owner,Standalone\n5441,182.08,Boston,N,21,14,25,29,Graduation,35,Patnership,Standalone\n8205,101.72,NJ City,N,11,20,2,36,Graduation,26,Patnership,Standalone\n6712,407.95,Boston,W,5,12,19,66,Graduation,25,Franchise,Complex\n8481,203.44,NJ City,N,8,18,17,39,Graduation,30,Patnership,Standalone\n5848,424.09,Boston,W,13,24,16,28,Graduation,35,Franchise,Complex\n8884,152.48,NJ City,W,15,9,21,40,Graduation,24,Franchise,Complex\n5996,322.92,Boston,W,14,19,24,83,Graduation,19,Franchise,Complex\n5327,155.12,Boston,E,15,21,25,56,Graduation,29,Owner,Standalone\n9162,214.74,NJ City,S,11,23,15,30,Graduation,22,Patnership,Complex\n4739,435.31,NJ City,E,17,14,22,83,High School,25,Owner,Standalone\n2447,165.59,NJ City,E,24,22,13,39,High School,24,Owner,Standalone\n516,24.75,NJ City,N,18,11,1,31,Graduation,21,Franchise,Complex\n5065,326.77,Boston,N,12,18,15,61,Graduation,22,Patnership,Standalone\n4581,457.87,NJ City,N,6,18,22,78,High School,32,Patnership,Standalone\n9289,472.36,NJ City,N,8,15,25,34,Graduation,34,Patnership,Standalone\n6748,171.63,Boston,W,6,14,24,81,Graduation,35,Franchise,Complex\n7659,496.21,Boston,E,8,10,2,40,Graduation,31,Owner,Standalone\n5062,477.12,Boston,S,12,23,22,39,Graduation,21,Patnership,Complex\n977,374.44,NJ City,N,14,14,11,27,High School,23,Patnership,Standalone\n8307,297.64,NJ City,E,20,9,25,73,Graduation,34,Owner,Standalone\n820,190.53,NJ City,W,20,20,23,78,High School,23,Franchise,Complex\n418,357.1,NJ City,S,8,21,4,83,High School,27,Patnership,Complex\n2265,61.76,NJ City,N,7,16,1,68,Graduation,18,Patnership,Standalone\n9383,105.18,NJ City,E,4,16,3,82,No education,25,Owner,Standalone\n6019,63.1,Boston,N,22,22,3,84,High School,19,Owner,Standalone\n4183,188.3,NJ City,E,17,11,12,79,High School,28,Owner,Standalone\n3033,497.3,NJ City,N,13,22,20,35,High School,26,Patnership,Standalone\n9711,279.3,NJ City,E,7,19,25,30,Graduation,35,Owner,Standalone\n2772,2.56,NJ City,W,14,24,1,49,Graduation,18,Franchise,Complex\n3579,248.64,NJ City,E,14,18,12,54,High School,29,Owner,Standalone\n8213,98.64,NJ City,N,13,10,4,25,No education,24,Patnership,Standalone\n9998,163.82,NJ City,S,5,22,1,31,No education,24,Patnership,Complex\n3225,123.95,NJ City,N,3,15,4,52,Graduation,23,Patnership,Standalone\n4485,368.7,NJ City,N,11,19,24,38,High School,28,Patnership,Standalone\n7315,304.62,Boston,E,21,23,11,31,Graduation,20,Owner,Standalone\n1151,268.11,NJ City,E,7,13,25,35,High School,20,Owner,Standalone\n6747,147.25,Boston,E,19,22,15,25,Graduation,32,Owner,Standalone\n8013,317.35,Boston,N,9,24,25,73,Graduation,21,Patnership,Standalone\n9542,140.29,NJ City,S,6,13,6,53,Graduation,26,Patnership,Complex\n8260,254.77,NJ City,W,1,9,17,77,Graduation,23,Franchise,Complex\n3528,280.88,NJ City,W,14,15,14,32,High School,23,Franchise,Complex\n7880,396.33,Boston,W,5,10,8,61,Graduation,20,Franchise,Complex\n200,251.59,NJ City,W,21,15,12,56,High School,21,Franchise,Complex\n5478,100.76,Boston,S,6,23,3,61,Graduation,23,Patnership,Complex\n9365,325.5,NJ City,N,23,10,6,81,Graduation,21,Patnership,Standalone\n7871,126.17,Boston,E,15,18,4,34,No education,31,Owner,Standalone\n9950,203.66,NJ City,S,23,18,14,58,Graduation,23,Patnership,Complex\n769,326.34,NJ City,N,18,24,23,68,High School,33,Patnership,Standalone\n224,102.43,NJ City,W,16,15,4,75,No education,24,Franchise,Complex\n1235,116,NJ City,E,3,17,14,51,High School,28,Owner,Standalone\n6578,351.54,Boston,S,17,8,25,61,Graduation,18,Patnership,Complex\n5166,400.42,Boston,S,16,19,1,58,Graduation,27,Patnership,Complex\n839,107.32,NJ City,E,20,20,2,36,No education,32,Owner,Standalone\n6600,10.22,Boston,N,4,13,2,62,Graduation,31,Franchise,Complex\n7520,415.03,Boston,W,13,21,24,85,Graduation,20,Franchise,Complex\n8004,416.36,Boston,W,22,11,2,36,Graduation,30,Franchise,Complex\n5252,307.69,Boston,W,14,11,23,72,Graduation,28,Franchise,Complex\n1401,331.56,NJ City,N,10,15,20,67,High School,18,Patnership,Standalone\n3542,277.17,NJ City,S,6,19,17,46,High School,22,Patnership,Complex\n7141,492.73,Boston,N,10,16,5,69,High School,25,Patnership,Standalone\n8376,351.13,NJ City,W,11,11,14,54,Graduation,23,Franchise,Complex\n3228,378.86,NJ City,W,16,9,16,31,High School,31,Franchise,Complex\n6461,407.93,Boston,N,16,21,1,57,No education,22,Patnership,Standalone\n9580,341.32,NJ City,W,13,15,17,81,Graduation,28,Franchise,Complex\n9356,167.29,NJ City,W,21,20,21,26,Graduation,30,Franchise,Complex\n6900,102.51,Boston,W,2,10,1,50,Graduation,24,Franchise,Complex\n3650,11.25,NJ City,N,6,11,4,55,No education,24,Patnership,Complex\n1365,29.61,NJ City,N,23,23,3,41,Graduation,25,Patnership,Standalone\n4114,328.21,NJ City,S,14,19,3,54,High School,23,Patnership,Complex\n6069,167,Boston,N,6,10,11,41,Graduation,21,Patnership,Standalone\n1378,289.26,NJ City,S,7,8,22,79,High School,29,Patnership,Complex\n4194,247.02,NJ City,S,13,19,16,58,High School,29,Patnership,Complex\n121,228.92,NJ City,N,24,12,5,69,High School,29,Patnership,Standalone\n4443,42.3,NJ City,N,20,15,4,78,Graduation,28,Owner,Standalone\n3627,19.5,NJ City,N,7,23,3,36,Graduation,23,Owner,Standalone\n764,468.84,NJ City,W,14,22,23,60,High School,35,Franchise,Complex\n1331,479.08,NJ City,E,4,18,23,57,High School,20,Owner,Standalone\n7254,66.29,Boston,N,25,11,4,79,Graduation,26,Patnership,Complex\n1732,460.93,NJ City,W,16,11,11,57,High School,30,Franchise,Complex\n5613,105.16,Boston,N,18,22,2,66,Graduation,28,Patnership,Standalone\n9023,236.92,NJ City,E,18,17,19,47,Graduation,19,Owner,Standalone\n9881,362.43,NJ City,N,18,16,7,73,Graduation,21,Patnership,Standalone\n413,49.9,NJ City,N,12,21,1,27,No education,23,Patnership,Standalone\n7728,116.87,Boston,W,19,10,1,31,Graduation,28,Franchise,Complex\n3371,206.05,NJ City,E,4,21,3,58,No education,29,Owner,Standalone\n8286,354.82,NJ City,S,21,10,4,44,Graduation,30,Patnership,Complex\n914,64.16,NJ City,N,25,20,4,75,No education,18,Patnership,Complex\n6773,83.16,Boston,N,4,20,3,36,No education,27,Patnership,Standalone\n6328,80.25,Boston,W,3,20,1,31,High School,18,Franchise,Complex\n3572,176.77,NJ City,W,24,18,13,39,High School,23,Franchise,Complex\n6089,58.2,Boston,N,22,9,2,60,No education,34,Patnership,Standalone\n219,284.08,NJ City,E,10,18,18,78,High School,24,Owner,Standalone\n6801,94.73,Boston,N,12,14,4,35,Graduation,25,Patnership,Standalone\n9829,54.19,NJ City,N,3,16,4,32,High School,23,Patnership,Standalone\n5057,222.29,Boston,N,20,9,20,68,Graduation,33,Patnership,Standalone\n3884,338.86,NJ City,W,17,16,6,40,High School,22,Franchise,Complex\n6437,434.98,Boston,N,13,20,3,48,No education,18,Patnership,Standalone\n714,49.82,NJ City,S,4,10,4,45,Graduation,24,Patnership,Complex\n7947,103.38,Boston,E,2,24,4,79,Graduation,19,Owner,Standalone\n610,341.23,NJ City,S,24,20,7,58,High School,33,Patnership,Complex\n4873,25.15,NJ City,N,11,22,3,46,High School,20,Patnership,Standalone\n9311,419.65,NJ City,E,13,16,24,73,Graduation,26,Owner,Standalone\n4508,30.12,NJ City,N,6,19,3,58,No education,31,Franchise,Complex\n2289,334.82,NJ City,N,17,19,22,53,High School,25,Patnership,Standalone\n192,126.66,NJ City,W,4,10,1,72,Graduation,31,Franchise,Complex\n9491,499.17,NJ City,E,25,20,21,66,Graduation,25,Owner,Standalone\n9105,52.96,NJ City,N,16,10,4,64,Graduation,19,Patnership,Standalone\n6731,110.03,Boston,E,2,18,2,50,No education,30,Owner,Standalone\n8721,374.74,NJ City,N,21,22,2,77,Graduation,31,Patnership,Standalone\n8905,308.05,NJ City,N,9,9,21,51,Graduation,22,Patnership,Standalone\n5582,143.96,Boston,S,14,11,21,83,Graduation,20,Patnership,Complex\n7742,146.6,Boston,S,19,11,4,76,No education,18,Patnership,Complex\n9429,362.7,NJ City,N,23,21,18,69,Graduation,27,Patnership,Standalone\n3789,98.91,NJ City,N,20,13,2,74,Graduation,24,Patnership,Standalone\n3421,428.73,NJ City,N,17,17,9,43,High School,35,Patnership,Standalone\n4921,354.15,NJ City,N,5,13,1,25,High School,31,Patnership,Standalone\n2031,368.24,NJ City,E,25,18,17,46,High School,20,Owner,Standalone\n8230,386.39,NJ City,S,22,21,6,65,Graduation,32,Patnership,Complex\n1673,340.95,NJ City,N,17,13,18,40,High School,20,Patnership,Standalone\n1099,394.44,NJ City,E,21,11,20,26,High School,29,Owner,Standalone\n7704,316.16,Boston,W,15,15,10,66,Graduation,21,Franchise,Complex\n1042,246.73,NJ City,S,21,11,9,72,High School,31,Patnership,Complex\n2515,35.35,NJ City,N,21,13,1,37,High School,30,Owner,Standalone\n280,156.27,NJ City,W,22,21,10,33,High School,27,Franchise,Complex\n8155,415.54,NJ City,E,1,19,7,45,Graduation,22,Owner,Standalone\n1751,444.5,NJ City,E,3,10,2,64,No education,21,Owner,Standalone\n5731,461.58,Boston,E,25,9,4,75,High School,21,Owner,Standalone\n6283,460.9,Boston,E,8,15,16,85,Graduation,20,Owner,Standalone\n1541,360.73,NJ City,N,13,19,16,62,High School,28,Patnership,Standalone\n5895,403.73,Boston,E,22,24,1,82,Graduation,28,Owner,Standalone\n7648,246.96,Boston,W,15,9,22,67,Graduation,29,Franchise,Complex\n3780,164.32,NJ City,W,14,14,20,54,High School,32,Franchise,Complex\n7247,22.02,Boston,N,3,11,4,31,No education,33,Owner,Standalone\n8986,139.61,NJ City,S,11,9,7,49,Graduation,26,Patnership,Complex\n8957,288.83,NJ City,N,12,9,23,37,Graduation,19,Patnership,Standalone\n2792,241.1,NJ City,W,15,24,10,79,High School,21,Franchise,Complex\n1604,363.21,NJ City,W,23,22,11,82,High School,20,Franchise,Complex\n3290,41.68,NJ City,N,8,15,3,29,No education,26,Patnership,Complex\n9152,295.92,NJ City,W,25,16,11,27,Graduation,20,Franchise,Complex\n5665,469.5,Boston,N,11,21,4,29,High School,26,Patnership,Standalone\n1878,118.1,NJ City,S,15,22,3,44,Graduation,28,Patnership,Complex\n9384,428.01,NJ City,W,5,9,13,46,Graduation,19,Franchise,Complex\n2681,370.77,NJ City,N,11,13,7,54,High School,19,Patnership,Standalone\n6221,82.78,Boston,N,17,22,4,76,No education,26,Patnership,Standalone\n5402,446.21,Boston,S,10,14,18,42,Graduation,29,Patnership,Complex\n5437,471.8,Boston,N,16,18,14,52,Graduation,31,Patnership,Standalone\n1046,212.54,NJ City,S,12,15,8,32,High School,19,Patnership,Complex\n6598,368.35,Boston,S,6,12,3,79,High School,33,Patnership,Complex\n4276,247.44,NJ City,W,5,15,23,37,High School,31,Franchise,Complex\n4022,184.54,NJ City,S,19,11,18,58,High School,24,Patnership,Complex\n6672,267.27,Boston,W,7,24,18,49,Graduation,33,Franchise,Complex\n1410,296.5,NJ City,S,15,9,23,52,High School,21,Patnership,Complex\n4317,131.32,NJ City,N,22,8,15,52,High School,33,Patnership,Standalone\n553,426.47,NJ City,N,21,19,19,55,High School,22,Patnership,Standalone\n9145,350.18,NJ City,N,9,10,16,51,Graduation,31,Patnership,Standalone\n4345,150.55,NJ City,N,19,8,15,34,High School,34,Patnership,Standalone\n9561,161.03,NJ City,N,10,11,24,51,Graduation,30,Patnership,Standalone\n374,364.63,NJ City,S,11,18,24,31,High School,26,Patnership,Complex\n8370,362.74,NJ City,S,4,14,6,35,Graduation,25,Patnership,Complex\n3493,477.64,NJ City,N,18,13,19,38,High School,30,Patnership,Standalone\n2054,322.32,NJ City,S,18,21,17,58,High School,33,Patnership,Complex\n9420,198.7,NJ City,W,18,12,24,82,Graduation,22,Franchise,Complex\n723,488.11,NJ City,E,8,21,6,29,High School,34,Owner,Standalone\n8565,435.55,NJ City,N,20,19,24,29,Graduation,26,Patnership,Standalone\n2530,9.12,NJ City,N,17,12,1,80,High School,23,Patnership,Complex\n9443,376.47,NJ City,E,22,24,13,68,Graduation,33,Owner,Standalone\n6718,444.29,Boston,S,5,8,3,38,High School,28,Patnership,Complex\n3229,140.98,NJ City,N,11,22,7,80,High School,28,Patnership,Standalone\n2669,98.56,NJ City,N,21,14,3,84,No education,33,Patnership,Standalone\n2415,28.06,NJ City,N,15,9,3,38,Graduation,22,Owner,Standalone\n9837,222.97,NJ City,N,17,19,14,34,Graduation,19,Patnership,Standalone\n1224,311.49,NJ City,W,11,8,24,71,High School,24,Franchise,Complex\n803,124.72,NJ City,E,21,10,4,72,No education,18,Owner,Standalone\n6514,56.76,Boston,S,7,18,1,82,High School,31,Patnership,Complex\n3518,174,NJ City,S,3,20,18,52,High School,25,Patnership,Complex\n7048,213.17,Boston,W,15,13,17,44,Graduation,19,Franchise,Complex\n2121,281.6,NJ City,N,3,11,3,47,Graduation,31,Patnership,Standalone\n3625,423.75,NJ City,N,16,11,24,38,High School,19,Patnership,Standalone\n4206,264.79,NJ City,S,25,12,10,74,High School,33,Patnership,Complex\n7640,277.36,Boston,W,21,9,1,68,No education,25,Franchise,Complex\n1054,382.97,NJ City,S,15,17,24,76,High School,21,Patnership,Complex\n964,341.15,NJ City,W,14,10,20,43,High School,28,Franchise,Complex\n6429,446.79,Boston,N,20,17,4,68,Graduation,34,Patnership,Standalone\n3255,142,NJ City,E,1,17,14,84,High School,29,Owner,Standalone\n342,247.12,NJ City,S,19,15,7,52,High School,29,Patnership,Complex\n8217,448.87,NJ City,N,25,18,5,57,Graduation,25,Patnership,Standalone\n4097,68.96,NJ City,N,4,18,1,53,No education,35,Patnership,Standalone\n1101,37.73,NJ City,N,25,24,4,49,Graduation,29,Patnership,Standalone\n741,393.11,NJ City,N,22,12,7,44,High School,21,Patnership,Standalone\n684,125.36,NJ City,W,20,19,3,31,Graduation,32,Franchise,Complex\n9903,121.69,NJ City,E,11,12,1,45,Graduation,26,Owner,Standalone\n2353,69.92,NJ City,N,10,19,2,25,High School,20,Patnership,Standalone\n5929,273.99,Boston,N,9,12,15,68,Graduation,35,Patnership,Standalone\n304,387.86,NJ City,W,25,8,15,32,High School,34,Franchise,Complex\n5179,13.67,Boston,N,3,10,1,75,High School,26,Owner,Standalone\n5079,30.51,Boston,N,9,10,1,25,Graduation,26,Owner,Standalone\n8515,200.62,NJ City,E,16,20,24,64,Graduation,22,Owner,Standalone\n4415,102.38,NJ City,E,23,9,2,50,No education,23,Owner,Standalone\n8343,28.75,NJ City,N,12,9,1,78,Graduation,31,Owner,Standalone\n4954,37.64,Boston,N,21,22,4,77,High School,33,Patnership,Complex\n5284,8.1,Boston,N,19,10,2,49,High School,24,Franchise,Complex\n4422,409.16,NJ City,S,5,20,16,32,High School,35,Patnership,Complex\n7993,115.81,Boston,N,14,11,4,83,High School,20,Patnership,Standalone\n3473,104.1,NJ City,N,6,13,3,85,No education,34,Patnership,Standalone\n3350,363.4,NJ City,S,11,9,18,71,High School,34,Patnership,Complex\n2483,286.56,NJ City,E,23,21,15,32,High School,23,Owner,Standalone\n6932,207.5,Boston,W,18,20,3,67,No education,30,Franchise,Complex\n318,447.11,NJ City,S,9,13,16,26,High School,34,Patnership,Complex\n4004,163,NJ City,W,3,13,21,83,High School,24,Franchise,Complex\n7131,223.5,Boston,E,3,24,12,67,Graduation,21,Owner,Standalone\n7736,487.17,Boston,W,14,11,5,61,No education,35,Franchise,Complex\n9709,213.02,NJ City,N,2,22,19,58,Graduation,26,Patnership,Standalone\n5543,113.97,Boston,E,18,22,4,28,No education,26,Owner,Standalone\n222,306.29,NJ City,S,13,22,23,43,High School,24,Patnership,Complex\n1545,433.03,NJ City,N,13,19,3,29,Graduation,26,Patnership,Standalone\n2268,306.83,NJ City,W,20,17,19,42,High School,26,Franchise,Complex\n6869,222.34,Boston,N,19,16,8,75,Graduation,31,Patnership,Standalone\n4852,442.69,NJ City,W,13,14,10,57,High School,24,Franchise,Complex\n8321,328.34,NJ City,N,22,13,24,35,Graduation,21,Patnership,Standalone\n568,197.64,NJ City,W,15,12,4,44,High School,31,Franchise,Complex\n6574,375.24,Boston,S,17,20,1,40,High School,27,Patnership,Complex\n1348,398.02,NJ City,W,7,20,10,29,High School,20,Franchise,Complex\n1926,445.4,NJ City,S,12,16,9,82,High School,28,Patnership,Complex\n2391,206.65,NJ City,E,22,24,22,70,High School,27,Owner,Standalone\n2471,316.88,NJ City,E,5,21,3,68,No education,20,Owner,Standalone\n7851,329.06,Boston,E,4,8,19,63,Graduation,32,Owner,Standalone\n6027,257.06,Boston,E,4,24,14,62,Graduation,35,Owner,Standalone\n1766,118.37,NJ City,S,16,11,3,72,No education,35,Patnership,Complex\n2283,341.53,NJ City,E,18,15,17,45,High School,24,Owner,Standalone\n5339,307.69,Boston,E,8,21,24,63,Graduation,25,Owner,Standalone\n5928,392.29,Boston,W,14,17,24,55,Graduation,30,Franchise,Complex\n2296,465.32,NJ City,W,7,20,13,63,High School,28,Franchise,Complex\n1979,244.12,NJ City,E,8,10,23,52,High School,31,Owner,Standalone\n4304,131.89,NJ City,W,12,17,17,81,High School,26,Franchise,Complex\n7192,440.36,Boston,W,17,19,9,29,Graduation,19,Franchise,Complex\n3354,46.67,NJ City,N,14,14,1,58,Graduation,26,Patnership,Complex\n6110,414.4,Boston,S,11,19,2,49,No education,20,Patnership,Complex\n9939,322.31,NJ City,E,14,15,7,57,Graduation,26,Owner,Standalone\n6045,343.69,Boston,N,24,8,20,30,Graduation,19,Patnership,Standalone\n148,275.72,NJ City,W,5,12,14,61,High School,18,Franchise,Complex\n9535,88.6,NJ City,E,24,17,1,32,High School,31,Owner,Standalone\n583,450.83,NJ City,E,21,16,16,50,High School,30,Owner,Standalone\n728,1.9,NJ City,N,25,10,4,71,No education,19,Franchise,Complex\n1901,68.77,NJ City,N,24,14,4,54,No education,31,Patnership,Standalone\n6810,295.86,Boston,S,10,8,7,31,Graduation,25,Patnership,Complex\n5659,140.18,Boston,E,18,12,23,49,Graduation,35,Owner,Standalone\n2231,468.71,NJ City,E,4,17,13,36,High School,34,Owner,Standalone\n1725,144.32,NJ City,N,15,14,8,30,High School,31,Patnership,Standalone\n6984,152.64,Boston,W,23,16,13,64,Graduation,18,Franchise,Complex\n80,136,Boston,W,1,23,20,54,High School,29,Franchise,Complex\n8810,334.82,NJ City,S,22,21,4,42,No education,35,Patnership,Complex\n324,64.28,NJ City,N,4,23,2,36,Graduation,27,Franchise,Complex\n4664,68.67,NJ City,N,17,20,4,39,No education,33,Franchise,Complex\n8417,274.23,NJ City,N,18,9,8,65,Graduation,19,Patnership,Standalone\n2837,29.68,NJ City,N,13,17,2,54,No education,27,Patnership,Standalone\n9468,326.35,NJ City,W,23,21,8,59,Graduation,28,Franchise,Complex\n1025,263.01,NJ City,N,20,8,6,43,High School,24,Patnership,Standalone\n8621,355.91,NJ City,N,21,21,1,75,No education,27,Patnership,Standalone\n4574,188,NJ City,S,3,21,11,70,High School,21,Patnership,Complex\n5261,432.52,Boston,N,18,8,11,30,Graduation,18,Patnership,Standalone\n4216,299.66,NJ City,W,7,19,7,25,High School,29,Franchise,Complex\n8744,122.36,NJ City,W,11,14,2,62,No education,24,Franchise,Complex\n3155,345.18,NJ City,E,12,8,16,28,High School,27,Owner,Standalone\n3550,119.58,NJ City,S,9,13,4,70,High School,27,Patnership,Complex\n12,269,Maine,W,3,23,14,42,High School,34,Franchise,Complex\n9316,101.86,NJ City,W,10,9,4,27,High School,35,Franchise,Complex\n1828,106.05,NJ City,W,1,14,2,26,High School,29,Franchise,Complex\n4747,100.64,NJ City,E,8,20,4,67,High School,29,Owner,Standalone\n5248,485.64,Boston,W,23,9,6,40,Graduation,27,Franchise,Complex\n7821,4.81,Boston,N,3,21,2,64,Graduation,22,Patnership,Standalone\n401,137.46,NJ City,N,13,19,5,80,No education,34,Patnership,Standalone\n7834,306.82,Boston,S,18,21,11,79,Graduation,20,Patnership,Complex\n4808,293.79,NJ City,W,23,21,23,69,High School,21,Franchise,Complex\n4911,214.78,NJ City,E,15,19,21,48,High School,20,Owner,Standalone\n5893,290.47,Boston,N,19,12,12,64,Graduation,34,Patnership,Standalone\n5303,23.19,Boston,N,24,17,2,47,No education,32,Owner,Standalone\n9746,133.98,NJ City,S,23,21,15,70,Graduation,21,Patnership,Complex\n3501,125.16,NJ City,N,6,15,4,38,Graduation,25,Patnership,Standalone\n3188,446.49,NJ City,W,1,22,4,74,No education,29,Franchise,Complex\n3770,408.08,NJ City,S,6,18,6,48,High School,24,Patnership,Complex\n7762,374.9,Boston,S,9,12,17,76,Graduation,30,Patnership,Complex\n4366,269,NJ City,S,2,17,7,33,High School,34,Patnership,Complex\n4010,195.97,NJ City,S,23,20,3,44,No education,35,Patnership,Complex\n4651,270.4,NJ City,E,4,16,14,37,High School,19,Owner,Standalone\n6488,218.04,Boston,W,16,19,23,52,Graduation,25,Franchise,Complex\n6530,439.56,Boston,S,19,8,12,49,Graduation,29,Patnership,Complex\n8558,91.14,NJ City,S,16,12,1,41,No education,35,Patnership,Complex\n4715,378.07,NJ City,E,9,19,21,55,High School,25,Owner,Standalone\n6607,96.86,Boston,E,1,18,3,80,High School,27,Owner,Standalone\n7167,342.09,Boston,E,21,10,8,32,Graduation,23,Owner,Standalone\n1829,82.39,NJ City,N,17,11,2,29,No education,25,Patnership,Standalone\n6850,213.31,Boston,S,21,17,8,59,Graduation,32,Patnership,Complex\n9717,118.83,NJ City,N,9,24,1,60,Graduation,25,Patnership,Standalone\n9156,487.76,NJ City,W,8,13,3,64,Graduation,27,Franchise,Complex\n3809,65.85,NJ City,N,19,19,1,49,No education,19,Patnership,Standalone\n5440,396.43,Boston,W,12,21,22,50,Graduation,19,Franchise,Complex\n1011,459.09,NJ City,E,3,19,1,56,Graduation,26,Owner,Standalone\n7246,186.93,Boston,S,7,24,10,37,Graduation,33,Patnership,Complex\n134,466.99,NJ City,S,24,24,1,72,No education,30,Patnership,Complex\n6913,204.3,Boston,N,4,16,16,81,Graduation,33,Patnership,Standalone\n2184,12.71,NJ City,N,3,10,2,38,Graduation,29,Franchise,Complex\n1623,109.63,NJ City,E,20,20,2,70,Graduation,21,Owner,Standalone\n353,487.12,NJ City,N,20,13,8,74,High School,24,Patnership,Standalone\n8057,298.16,NJ City,N,6,16,16,27,Graduation,19,Patnership,Standalone\n4513,297.22,NJ City,N,18,15,3,63,High School,26,Patnership,Standalone\n326,98.41,NJ City,S,23,11,4,47,No education,26,Patnership,Complex\n9028,221.6,NJ City,W,6,23,10,60,Graduation,21,Franchise,Complex\n4781,204,NJ City,N,1,16,22,71,High School,25,Patnership,Standalone\n5588,403.12,Boston,W,14,24,19,51,Graduation,35,Franchise,Complex\n4178,241,NJ City,S,4,18,24,29,High School,26,Patnership,Complex\n7291,478.77,Boston,E,3,11,15,44,Graduation,21,Owner,Standalone\n9444,389.26,NJ City,W,9,22,25,73,Graduation,30,Franchise,Complex\n2466,183,NJ City,S,1,10,15,40,High School,32,Patnership,Complex\n5531,169.69,Boston,E,9,23,2,72,No education,20,Owner,Standalone\n9347,311.11,NJ City,E,1,17,5,55,No education,30,Owner,Standalone\n8571,82.47,NJ City,E,19,14,3,54,Graduation,23,Owner,Standalone\n7241,422.38,Boston,N,12,8,21,67,Graduation,19,Patnership,Standalone\n2223,316.31,NJ City,E,22,24,25,65,High School,18,Owner,Standalone\n5190,438.69,Boston,S,11,24,4,66,Graduation,23,Patnership,Complex\n1422,471.94,NJ City,S,11,8,20,63,High School,20,Patnership,Complex\n9999,357.37,NJ City,E,5,21,17,41,Graduation,35,Owner,Standalone\n7314,387.14,Boston,S,8,11,7,73,Graduation,33,Patnership,Complex\n888,132.91,NJ City,W,23,10,14,66,High School,26,Franchise,Complex\n8664,243.25,NJ City,W,19,8,17,69,Graduation,23,Franchise,Complex\n973,230.45,NJ City,N,10,21,15,37,High School,30,Patnership,Standalone\n4442,131.86,NJ City,S,11,17,22,75,High School,24,Patnership,Complex\n3843,114.79,NJ City,E,8,11,4,55,Graduation,28,Owner,Standalone\n6472,119.41,Boston,W,16,19,1,72,High School,29,Franchise,Complex\n4714,220.37,NJ City,S,4,24,6,49,High School,28,Patnership,Complex\n6507,274.74,Boston,E,20,8,16,70,Graduation,21,Owner,Standalone\n4238,143.74,NJ City,S,6,9,19,62,High School,34,Patnership,Complex\n3356,291.13,NJ City,W,13,18,1,77,No education,27,Franchise,Complex\n5619,332.53,Boston,E,1,15,4,42,Graduation,34,Owner,Standalone\n2245,253.41,NJ City,N,19,24,11,27,High School,22,Patnership,Standalone\n2595,439.02,NJ City,E,12,22,20,56,High School,29,Owner,Standalone\n1497,288,NJ City,N,3,22,17,43,High School,32,Patnership,Standalone\n658,150.14,NJ City,S,17,11,11,48,High School,20,Patnership,Complex\n8243,2.11,NJ City,E,10,20,2,65,No education,29,Owner,Standalone\n2639,80.76,NJ City,E,5,18,4,37,No education,29,Owner,Standalone\n4098,497.98,NJ City,S,20,11,17,33,High School,28,Patnership,Complex\n3452,20.23,NJ City,N,1,9,4,76,No education,28,Franchise,Complex\n7237,73.09,Boston,N,6,19,1,64,High School,28,Patnership,Standalone\n841,59.49,NJ City,N,13,20,3,53,High School,26,Patnership,Standalone\n5209,115.19,Boston,N,21,11,3,51,High School,33,Patnership,Standalone\n3489,268.06,NJ City,N,20,8,11,77,High School,32,Patnership,Standalone\n8962,392.63,NJ City,S,1,10,14,25,Graduation,31,Patnership,Complex\n9838,439.83,NJ City,S,7,24,17,63,Graduation,24,Patnership,Complex\n1155,268.85,NJ City,E,20,16,17,46,High School,24,Owner,Standalone\n7186,52.2,Boston,S,12,13,3,65,High School,22,Patnership,Complex\n3050,32.16,NJ City,N,13,9,1,62,No education,18,Patnership,Complex\n1330,426.64,NJ City,S,22,19,16,75,High School,22,Patnership,Complex\n3669,300.11,NJ City,N,23,13,1,78,Graduation,33,Patnership,Standalone\n6630,1.53,Boston,N,14,21,2,60,Graduation,27,Patnership,Complex\n6306,59.37,Boston,S,15,9,3,76,Graduation,35,Patnership,Complex\n2899,154.06,NJ City,E,10,19,17,25,High School,32,Owner,Standalone\n913,413.38,NJ City,N,8,11,13,33,High School,35,Patnership,Standalone\n2908,383.94,NJ City,W,15,12,12,59,High School,29,Franchise,Complex\n1089,83.95,NJ City,N,21,20,3,32,Graduation,23,Patnership,Standalone\n6575,288.65,Boston,E,7,14,1,72,No education,29,Owner,Standalone\n170,269.46,NJ City,S,24,14,2,79,No education,35,Patnership,Complex\n2405,61.27,NJ City,N,11,13,3,45,No education,28,Patnership,Standalone\n1749,62.63,NJ City,N,12,17,4,60,Graduation,28,Patnership,Standalone\n5797,446,Boston,N,5,14,21,56,Graduation,27,Patnership,Standalone\n7743,136.15,Boston,E,21,23,9,58,Graduation,18,Owner,Standalone\n3687,420.74,NJ City,E,24,17,7,30,High School,18,Owner,Standalone\n4695,198.81,NJ City,E,12,18,3,48,Graduation,20,Owner,Standalone\n5555,376.19,Boston,E,3,12,1,68,No education,34,Owner,Standalone\n3787,126.31,NJ City,E,12,22,4,80,High School,20,Owner,Standalone\n2882,310.11,NJ City,S,20,13,15,68,High School,34,Patnership,Complex\n7603,357.86,Boston,E,11,15,22,78,Graduation,21,Owner,Standalone\n2454,139.64,NJ City,S,17,14,6,38,High School,22,Patnership,Complex\n3483,63.56,NJ City,N,6,18,1,68,Graduation,32,Owner,Standalone\n9608,321.35,NJ City,W,20,16,18,69,Graduation,22,Franchise,Complex\n5432,225.08,Boston,W,23,16,24,31,Graduation,23,Franchise,Complex\n7294,90.11,Boston,S,17,15,3,43,High School,30,Patnership,Complex\n8724,312.33,NJ City,W,14,22,22,59,Graduation,29,Franchise,Complex\n2972,431.88,NJ City,W,23,11,22,44,High School,22,Franchise,Complex\n9577,276.66,NJ City,N,13,21,12,47,Graduation,26,Patnership,Standalone\n6093,398.85,Boston,N,21,24,1,46,Graduation,35,Patnership,Standalone\n1067,182.89,NJ City,E,12,22,14,67,High School,32,Owner,Standalone\n4922,183.4,NJ City,S,12,23,9,66,High School,32,Patnership,Complex\n3676,261.96,NJ City,W,4,17,23,44,High School,19,Franchise,Complex\n4165,263.26,NJ City,N,20,11,24,37,High School,22,Patnership,Standalone\n1538,118.48,NJ City,S,17,18,3,27,No education,32,Patnership,Complex\n3819,276.83,NJ City,E,14,17,13,77,High School,23,Owner,Standalone\n8512,27.21,NJ City,N,12,10,4,43,High School,32,Franchise,Complex\n9128,390.02,NJ City,W,18,17,20,32,Graduation,23,Franchise,Complex\n1305,35.41,NJ City,N,12,11,2,46,Graduation,33,Patnership,Standalone\n3705,122.96,NJ City,N,10,16,2,44,Graduation,24,Patnership,Standalone\n1119,14.96,NJ City,N,4,10,3,76,Graduation,25,Owner,Standalone\n1184,41.78,NJ City,N,23,15,3,59,No education,19,Franchise,Complex\n8867,103.54,NJ City,E,7,20,2,39,No education,20,Owner,Standalone\n5689,421.79,Boston,N,24,17,3,47,High School,34,Patnership,Standalone\n8643,59.83,NJ City,E,15,18,2,66,Graduation,25,Owner,Standalone\n1647,346.1,NJ City,E,4,11,15,66,High School,28,Owner,Standalone\n3988,287,NJ City,W,2,24,14,81,High School,23,Franchise,Complex\n2961,299,NJ City,N,3,10,7,57,High School,24,Patnership,Standalone\n6083,264.29,Boston,E,12,15,24,42,Graduation,18,Owner,Standalone\n7921,439.33,Boston,N,13,15,13,61,Graduation,20,Patnership,Standalone\n6441,296.97,Boston,N,1,8,11,38,Graduation,31,Patnership,Standalone\n8691,271.48,NJ City,E,10,22,4,50,Graduation,24,Owner,Standalone\n122,37.87,NJ City,N,3,17,1,33,No education,19,Patnership,Complex\n4352,428.08,NJ City,W,20,12,1,80,No education,27,Franchise,Complex\n9047,131.2,NJ City,E,5,16,7,63,Graduation,22,Owner,Standalone\n4493,26.34,NJ City,N,16,9,1,54,No education,34,Patnership,Standalone\n6153,249.7,Boston,N,1,13,6,54,Graduation,21,Patnership,Standalone\n3774,306.64,NJ City,S,3,15,4,27,Graduation,25,Patnership,Complex\n3136,286.48,NJ City,W,23,18,19,68,High School,18,Franchise,Complex\n4800,59.91,NJ City,W,1,10,3,61,Graduation,35,Franchise,Complex\n5416,113.23,Boston,W,19,12,3,68,High School,21,Franchise,Complex\n9847,152.84,NJ City,E,9,19,12,45,Graduation,32,Owner,Standalone\n4981,211.42,Boston,N,16,15,1,57,High School,24,Patnership,Standalone\n3438,167.15,NJ City,S,11,24,5,37,Graduation,25,Patnership,Complex\n4180,183.06,NJ City,W,22,23,5,66,High School,34,Franchise,Complex\n1630,80.1,NJ City,S,22,19,3,44,High School,33,Patnership,Complex\n6802,64.9,Boston,N,24,20,3,42,High School,18,Patnership,Complex\n8582,177.91,NJ City,S,7,16,16,36,Graduation,30,Patnership,Complex\n5994,299.96,Boston,S,8,9,21,75,Graduation,18,Patnership,Complex\n4246,40.11,NJ City,N,25,21,4,37,High School,21,Patnership,Complex\n5623,240.07,Boston,E,9,23,22,50,Graduation,20,Owner,Standalone\n8391,170.25,NJ City,E,10,14,8,42,Graduation,30,Owner,Standalone\n4367,281.53,NJ City,E,8,22,9,34,High School,18,Owner,Standalone\n9657,292.5,NJ City,N,11,16,20,36,Graduation,25,Patnership,Standalone\n7886,404.35,Boston,S,10,21,17,84,Graduation,34,Patnership,Complex\n995,162.43,NJ City,E,9,23,1,78,No education,33,Owner,Standalone\n8416,341.56,NJ City,W,21,24,12,59,Graduation,22,Franchise,Complex\n670,413.36,NJ City,S,25,24,12,42,High School,27,Patnership,Complex\n6554,455.89,Boston,S,2,15,17,49,Graduation,29,Patnership,Complex\n3331,114.24,NJ City,E,10,21,4,55,High School,20,Owner,Standalone\n5484,352.23,Boston,W,3,18,3,55,Graduation,26,Franchise,Complex\n2144,204.99,NJ City,W,22,23,24,48,High School,22,Franchise,Complex\n3570,479.96,NJ City,S,2,22,5,71,Graduation,27,Patnership,Complex\n5696,392.75,Boston,W,10,14,8,69,Graduation,31,Franchise,Complex\n1324,306.2,NJ City,W,11,24,11,52,High School,31,Franchise,Complex\n6517,125.34,Boston,N,7,16,4,26,High School,35,Patnership,Standalone\n575,263,NJ City,E,3,23,20,67,High School,24,Owner,Standalone\n5591,321.27,Boston,E,13,20,10,41,Graduation,21,Owner,Standalone\n5816,425.58,Boston,W,11,20,17,80,Graduation,34,Franchise,Complex\n1790,394.4,NJ City,S,5,15,3,55,No education,33,Patnership,Complex\n7749,186.2,Boston,N,12,13,7,33,Graduation,18,Patnership,Standalone\n4672,133.1,NJ City,W,10,8,16,56,High School,25,Franchise,Complex\n1135,473.6,NJ City,E,11,14,25,60,High School,19,Owner,Standalone\n4975,127.93,Boston,E,20,15,1,26,High School,33,Owner,Standalone\n7012,118.42,Boston,W,24,22,4,42,High School,30,Franchise,Complex\n5781,96.81,Boston,N,10,14,3,48,Graduation,30,Patnership,Standalone\n234,342.2,NJ City,S,9,21,1,27,Graduation,32,Patnership,Complex\n1231,9.31,NJ City,N,20,18,4,40,High School,33,Owner,Standalone\n1844,67.62,NJ City,N,7,17,4,55,No education,26,Franchise,Complex\n8081,362.2,NJ City,N,15,15,22,81,Graduation,23,Patnership,Standalone\n3166,165.51,NJ City,S,18,22,23,35,High School,19,Patnership,Complex\n2902,411.96,NJ City,S,13,16,22,42,High School,24,Patnership,Complex\n2998,84.51,NJ City,S,19,23,3,36,High School,26,Patnership,Complex\n1448,435.58,NJ City,W,15,15,2,65,No education,24,Franchise,Complex\n1480,178.79,NJ City,W,6,10,13,51,High School,35,Franchise,Complex\n9324,388.09,NJ City,W,11,21,6,58,Graduation,22,Franchise,Complex\n5077,220.71,Boston,N,9,23,10,46,Graduation,18,Patnership,Standalone\n1663,46.62,NJ City,N,19,13,1,80,High School,20,Owner,Standalone\n1781,453.31,NJ City,N,13,9,3,74,No education,21,Patnership,Standalone\n7537,151.42,Boston,N,8,22,3,35,High School,34,Patnership,Standalone\n3063,84.04,NJ City,E,5,13,1,31,Graduation,30,Owner,Standalone\n8682,98.82,NJ City,S,24,20,4,64,Graduation,18,Patnership,Complex\n962,43.52,NJ City,N,19,11,3,26,No education,27,Patnership,Complex\n2673,340.96,NJ City,N,13,14,2,40,Graduation,27,Patnership,Standalone\n8374,305.9,NJ City,S,20,19,4,72,High School,23,Patnership,Complex\n4359,233.54,NJ City,E,17,13,12,43,High School,21,Owner,Standalone\n8366,414.84,NJ City,S,25,8,4,37,No education,20,Patnership,Complex\n9651,185.63,NJ City,E,25,18,19,33,Graduation,19,Owner,Standalone\n6274,193.07,Boston,S,3,11,15,32,Graduation,25,Patnership,Complex\n3795,186,NJ City,E,2,13,22,36,High School,34,Owner,Standalone\n6476,409.84,Boston,W,15,11,24,62,Graduation,29,Franchise,Complex\n8052,433.07,NJ City,W,15,16,14,34,Graduation,27,Franchise,Complex\n8475,319.48,NJ City,E,25,12,12,33,Graduation,27,Owner,Standalone\n8372,279.99,NJ City,W,9,18,6,58,Graduation,30,Franchise,Complex\n1,205.93,NJ City,N,16,11,12,75,High School,34,Owner,Complex\n6840,339.05,Boston,W,6,12,18,81,Graduation,32,Franchise,Complex\n1406,352.87,NJ City,S,19,18,8,26,High School,35,Patnership,Complex\n8708,458.34,NJ City,W,22,20,7,35,Graduation,18,Franchise,Complex\n4595,30.32,NJ City,N,8,21,3,53,No education,34,Owner,Standalone\n3979,123.26,NJ City,E,3,15,3,82,High School,21,Owner,Standalone\n3516,436.84,NJ City,W,2,10,3,49,Graduation,26,Franchise,Complex\n2448,51.41,NJ City,W,2,24,4,49,Graduation,28,Franchise,Complex\n430,9.38,NJ City,N,9,10,4,81,High School,26,Patnership,Complex\n6216,120.31,Boston,W,7,11,1,36,Graduation,18,Franchise,Complex\n6141,434.1,Boston,N,11,23,15,37,Graduation,22,Patnership,Standalone\n2977,100.32,NJ City,N,4,21,3,26,High School,19,Patnership,Standalone\n1270,232,NJ City,S,2,9,15,81,High School,28,Patnership,Complex\n7673,244.94,Boston,N,16,15,2,45,No education,27,Patnership,Standalone\n1174,15.83,NJ City,N,20,16,3,65,High School,32,Patnership,Complex\n351,18.98,NJ City,N,15,21,1,62,Graduation,29,Owner,Standalone\n1414,212,NJ City,S,3,8,11,46,High School,34,Patnership,Complex\n5097,95.69,Boston,N,14,17,3,71,Graduation,19,Patnership,Standalone\n5276,458.2,Boston,W,8,11,8,74,Graduation,20,Franchise,Complex\n4469,237.24,NJ City,N,4,10,13,49,High School,35,Patnership,Standalone\n8328,197.24,NJ City,W,25,18,22,72,Graduation,21,Franchise,Complex\n9650,440.28,NJ City,S,22,15,8,51,Graduation,21,Patnership,Complex\n2105,385.16,NJ City,N,9,8,23,81,High School,25,Patnership,Standalone\n9306,33.82,NJ City,N,3,15,4,66,Graduation,24,Patnership,Complex\n1274,342.62,NJ City,S,22,18,3,82,No education,33,Patnership,Complex\n6709,132.07,Boston,N,21,16,10,28,Graduation,19,Patnership,Standalone\n9880,375.07,NJ City,W,14,16,3,76,High School,31,Franchise,Complex\n8806,8.59,NJ City,N,9,20,3,34,High School,18,Patnership,Complex\n4449,147.96,NJ City,N,18,11,13,33,High School,35,Patnership,Standalone\n5827,304.62,Boston,E,7,10,25,67,Graduation,25,Owner,Standalone\n78,285.14,Boston,S,7,11,14,73,High School,29,Patnership,Complex\n958,200,NJ City,S,1,22,10,69,High School,26,Patnership,Complex\n2775,141,NJ City,E,3,11,17,47,High School,29,Owner,Standalone\n6301,240.37,Boston,N,11,16,17,82,Graduation,23,Patnership,Standalone\n3185,37.32,NJ City,N,14,16,4,48,No education,32,Patnership,Standalone\n7208,394.41,Boston,W,2,24,18,44,Graduation,20,Franchise,Complex\n6628,19.37,Boston,N,20,21,2,76,High School,31,Franchise,Complex\n8256,316.81,NJ City,W,24,13,14,58,Graduation,25,Franchise,Complex\n3234,234.43,NJ City,S,4,14,14,72,High School,24,Patnership,Complex\n8575,253.09,NJ City,E,7,21,19,78,Graduation,22,Owner,Standalone\n9397,302.68,NJ City,N,23,10,9,70,Graduation,22,Patnership,Standalone\n7755,301.67,Boston,E,6,18,5,63,Graduation,33,Owner,Standalone\n2176,452.77,NJ City,W,2,24,3,34,High School,33,Franchise,Complex\n1088,205.76,NJ City,W,13,19,9,40,High School,29,Franchise,Complex\n9685,362.5,NJ City,N,17,18,21,29,Graduation,33,Patnership,Standalone\n2088,285.88,NJ City,W,2,21,2,65,Graduation,28,Franchise,Complex\n5486,423.78,Boston,S,2,18,7,28,Graduation,19,Patnership,Complex\n3450,79.46,NJ City,S,18,10,1,59,Graduation,27,Patnership,Complex\n3181,372.89,NJ City,N,14,20,18,47,High School,24,Patnership,Standalone\n7825,462.6,Boston,N,22,18,5,33,High School,25,Patnership,Standalone\n4444,62.66,NJ City,N,11,11,1,25,High School,31,Franchise,Complex\n6943,281.14,Boston,E,16,14,3,29,High School,35,Owner,Standalone\n1881,28.32,NJ City,N,15,14,3,38,Graduation,24,Patnership,Standalone\n4928,339.83,NJ City,W,13,17,5,78,No education,19,Franchise,Complex\n1743,108.94,NJ City,E,10,12,3,39,Graduation,25,Owner,Standalone\n218,332.2,NJ City,S,25,22,14,35,High School,33,Patnership,Complex\n8528,195.98,NJ City,W,4,12,2,53,No education,27,Franchise,Complex\n7064,270.96,Boston,W,10,23,24,48,Graduation,24,Franchise,Complex\n2683,348.2,NJ City,E,7,17,22,52,High School,33,Owner,Standalone\n3070,418.53,NJ City,S,15,20,5,46,High School,19,Patnership,Complex\n7218,259.22,Boston,S,14,22,19,43,Graduation,31,Patnership,Complex\n4737,279.92,NJ City,N,7,18,15,43,High School,31,Patnership,Standalone\n6635,412.53,Boston,E,1,19,25,45,Graduation,33,Owner,Standalone\n8019,306.88,Boston,E,5,21,9,37,Graduation,31,Owner,Standalone\n3709,480.04,NJ City,N,4,20,10,75,High School,30,Patnership,Standalone\n7636,273.83,Boston,W,25,8,1,41,High School,20,Franchise,Complex\n2479,423.28,NJ City,E,24,20,7,74,High School,28,Owner,Standalone\n1787,471,NJ City,E,23,11,25,41,High School,29,Owner,Standalone\n6208,350.9,Boston,W,11,13,17,42,Graduation,18,Franchise,Complex\n3990,315.33,NJ City,S,11,22,7,75,High School,20,Patnership,Complex\n1243,148.27,NJ City,E,18,9,7,64,High School,31,Owner,Standalone\n456,159.75,NJ City,W,21,10,9,71,High School,24,Franchise,Complex\n6950,346.9,Boston,S,16,23,18,52,Graduation,24,Patnership,Complex\n6752,471.68,Boston,W,15,24,16,45,Graduation,28,Franchise,Complex\n1477,43.05,NJ City,N,10,20,1,64,High School,21,Patnership,Standalone\n5863,92.15,Boston,E,8,11,3,57,High School,22,Owner,Standalone\n149,191.77,NJ City,N,8,17,19,62,High School,19,Patnership,Standalone\n4261,26.42,NJ City,N,21,24,3,51,High School,27,Patnership,Standalone\n8425,347.39,NJ City,N,6,22,8,43,Graduation,30,Patnership,Standalone\n1005,292.39,NJ City,N,10,9,18,39,High School,20,Patnership,Standalone\n2923,71.3,NJ City,N,6,12,2,74,High School,28,Owner,Standalone\n8740,230.59,NJ City,W,2,23,5,44,High School,19,Franchise,Complex\n4033,465.97,NJ City,N,7,22,7,72,High School,29,Patnership,Standalone\n9870,325.42,NJ City,S,24,15,10,39,Graduation,28,Patnership,Complex\n7792,135.36,Boston,W,12,11,8,49,Graduation,29,Franchise,Complex\n9378,143.36,NJ City,S,18,24,22,83,Graduation,24,Patnership,Complex\n8471,380.5,NJ City,E,19,18,11,57,Graduation,32,Owner,Standalone\n6009,364.62,Boston,N,23,22,9,70,Graduation,21,Patnership,Standalone\n740,109,NJ City,W,2,11,25,67,High School,27,Franchise,Complex\n9706,57.84,NJ City,S,15,21,3,74,High School,28,Patnership,Complex\n4616,118.6,NJ City,W,23,22,4,73,No education,23,Franchise,Complex\n9590,419.31,NJ City,S,18,24,5,28,No education,27,Patnership,Complex\n7320,41.39,Boston,N,17,9,2,72,Graduation,21,Franchise,Complex\n4059,331.43,NJ City,E,17,18,14,42,High School,26,Owner,Standalone\n6805,196.03,Boston,N,5,17,25,44,Graduation,35,Patnership,Standalone\n7934,397.09,Boston,S,24,23,10,77,Graduation,33,Patnership,Complex\n3426,346.5,NJ City,S,19,17,18,54,High School,33,Patnership,Complex\n7199,393.7,Boston,E,20,18,3,42,No education,25,Owner,Standalone\n9070,116.18,NJ City,S,14,10,2,63,High School,22,Patnership,Complex\n3562,70.05,NJ City,N,2,21,2,67,High School,24,Patnership,Complex\n212,186.64,NJ City,W,17,16,22,70,High School,20,Franchise,Complex\n8623,193.87,NJ City,E,17,24,13,31,Graduation,22,Owner,Standalone\n3593,128.85,NJ City,N,22,18,15,62,High School,26,Patnership,Standalone\n1735,444.99,NJ City,E,12,23,8,66,High School,24,Owner,Standalone\n6225,78.69,Boston,N,15,24,2,60,Graduation,18,Patnership,Standalone\n8838,130.91,NJ City,S,24,13,14,50,Graduation,26,Patnership,Complex\n8642,393.35,NJ City,S,4,9,24,36,Graduation,32,Patnership,Complex\n4696,416.27,NJ City,W,9,20,24,25,High School,35,Franchise,Complex\n4526,280,NJ City,S,2,18,25,74,High School,30,Patnership,Complex\n9511,9.18,NJ City,N,12,13,4,28,High School,20,Owner,Standalone\n5025,33.73,Boston,N,16,9,4,82,Graduation,19,Patnership,Standalone\n6794,210.95,Boston,S,7,21,2,80,No education,18,Patnership,Complex\n8833,191.11,NJ City,N,23,8,16,76,Graduation,19,Patnership,Standalone\n8143,486.9,NJ City,E,19,17,23,75,Graduation,23,Owner,Standalone\n399,476.07,NJ City,E,11,23,15,32,High School,35,Owner,Standalone\n5854,99.58,Boston,S,12,22,3,31,High School,19,Patnership,Complex\n1389,55.17,NJ City,N,22,22,3,66,Graduation,35,Patnership,Standalone\n5147,195.95,Boston,E,19,24,21,76,Graduation,24,Owner,Standalone\n5585,355.04,Boston,N,23,8,15,74,Graduation,28,Patnership,Standalone\n7474,450.61,Boston,S,8,11,23,36,Graduation,26,Patnership,Complex\n9168,48.58,NJ City,W,18,15,4,48,Graduation,32,Franchise,Complex\n7282,56.24,Boston,S,5,18,1,42,High School,20,Patnership,Complex\n5267,474.61,Boston,E,16,24,20,71,Graduation,25,Owner,Standalone\n8889,114.32,NJ City,N,5,8,3,50,Graduation,28,Patnership,Standalone\n999,443.44,NJ City,E,5,10,23,48,High School,28,Owner,Standalone\n4397,364.73,NJ City,N,20,20,16,37,High School,19,Patnership,Standalone\n3386,308.76,NJ City,S,8,20,5,27,No education,31,Patnership,Complex\n1469,90.14,NJ City,N,21,16,1,71,No education,27,Patnership,Standalone\n942,111.12,NJ City,S,4,14,4,33,Graduation,21,Patnership,Complex\n8051,175.31,NJ City,E,23,17,23,60,Graduation,28,Owner,Standalone\n1430,32.98,NJ City,N,19,20,1,73,No education,34,Patnership,Complex\n4884,104.16,NJ City,W,5,10,4,50,Graduation,32,Franchise,Complex\n5570,424.78,Boston,S,25,23,7,77,Graduation,35,Patnership,Complex\n8136,463.39,NJ City,W,18,11,24,37,Graduation,19,Franchise,Complex\n2770,255.39,NJ City,S,19,16,10,66,High School,30,Patnership,Complex\n602,64.87,NJ City,N,20,12,1,41,No education,31,Patnership,Complex\n1857,134.85,NJ City,N,25,24,23,69,High School,25,Patnership,Standalone\n7052,20.71,Boston,N,5,19,3,64,No education,20,Franchise,Complex\n4959,321.14,Boston,E,16,24,18,84,High School,24,Owner,Standalone\n3587,126.73,NJ City,E,21,23,2,58,No education,18,Owner,Standalone\n6936,196.44,Boston,W,15,21,4,67,Graduation,23,Franchise,Complex\n8531,297.99,NJ City,E,1,18,25,42,Graduation,26,Owner,Standalone\n1496,157.94,NJ City,W,2,19,3,35,No education,19,Franchise,Complex\n7761,179.13,Boston,N,3,23,19,75,Graduation,27,Patnership,Standalone\n5233,288.08,Boston,N,11,9,15,34,Graduation,28,Patnership,Standalone\n165,219.52,NJ City,N,8,20,8,32,High School,25,Patnership,Standalone\n855,377.84,NJ City,E,19,21,8,60,High School,26,Owner,Standalone\n269,363.14,NJ City,N,20,10,24,47,High School,27,Patnership,Standalone\n9899,221.1,NJ City,E,9,10,13,54,Graduation,27,Owner,Standalone\n1426,77.32,NJ City,S,22,21,1,79,High School,33,Patnership,Complex\n6375,421.51,Boston,E,12,15,20,36,Graduation,35,Owner,Standalone\n5845,130.7,Boston,N,10,24,15,38,Graduation,21,Patnership,Standalone\n3274,183,NJ City,S,2,18,7,78,High School,22,Patnership,Complex\n3180,367.8,NJ City,W,10,12,12,68,High School,19,Franchise,Complex\n794,368.69,NJ City,S,5,19,1,41,No education,21,Patnership,Complex\n5191,333.39,Boston,E,13,8,23,65,Graduation,21,Owner,Standalone\n3943,383.13,NJ City,E,19,11,20,51,High School,18,Owner,Standalone\n2847,67.13,NJ City,N,13,14,4,71,Graduation,23,Owner,Standalone\n2950,88.28,NJ City,S,1,23,1,42,High School,20,Patnership,Complex\n4108,341.21,NJ City,W,13,12,14,70,High School,33,Franchise,Complex\n4987,11.34,Boston,N,19,24,2,54,High School,34,Owner,Standalone\n8140,440.81,NJ City,W,22,19,8,56,Graduation,27,Franchise,Complex\n5304,442.82,Boston,W,11,19,6,50,Graduation,30,Franchise,Complex\n9453,135.46,NJ City,N,8,8,15,29,Graduation,20,Patnership,Standalone\n782,463.38,NJ City,S,15,22,13,64,High School,18,Patnership,Complex\n9115,175.21,NJ City,E,9,17,18,62,Graduation,19,Owner,Standalone\n8075,143.82,NJ City,E,5,22,2,47,No education,35,Owner,Standalone\n7578,152.63,Boston,S,14,13,12,47,Graduation,33,Patnership,Complex\n7518,387.85,Boston,S,6,18,16,37,Graduation,34,Patnership,Complex\n4357,4.66,NJ City,N,22,15,4,81,High School,33,Patnership,Standalone\n5982,431.23,Boston,S,24,17,18,64,Graduation,31,Patnership,Complex\n3333,8.24,NJ City,N,22,14,4,37,Graduation,35,Patnership,Standalone\n9364,354.85,NJ City,W,20,13,12,84,Graduation,21,Franchise,Complex\n7202,386.98,Boston,S,4,10,10,70,Graduation,34,Patnership,Complex\n4126,437.16,NJ City,S,5,15,23,74,High School,29,Patnership,Complex\n8325,131.64,NJ City,N,12,8,25,38,Graduation,29,Patnership,Standalone\n6272,98.16,Boston,W,11,23,4,46,No education,33,Franchise,Complex\n8255,36.44,NJ City,N,4,20,3,41,No education,19,Owner,Standalone\n5847,302.95,Boston,E,25,15,17,57,Graduation,21,Owner,Standalone\n2906,132.79,NJ City,S,5,9,13,31,High School,32,Patnership,Complex\n730,137.7,NJ City,S,14,21,2,43,High School,20,Patnership,Complex\n175,303.86,NJ City,E,22,12,2,41,High School,26,Owner,Standalone\n2456,238,NJ City,W,1,10,14,54,High School,34,Franchise,Complex\n6716,342.37,Boston,W,21,13,5,64,No education,32,Franchise,Complex\n241,109,NJ City,N,3,20,24,26,High School,30,Patnership,Standalone\n3138,471.9,NJ City,S,8,20,24,70,High School,31,Patnership,Complex\n2984,182.36,NJ City,W,16,14,4,74,No education,25,Franchise,Complex\n5968,458.15,Boston,W,18,15,5,52,High School,18,Franchise,Complex\n9782,304.63,NJ City,S,15,23,20,50,Graduation,26,Patnership,Complex\n2517,302.74,NJ City,N,25,17,25,44,High School,23,Patnership,Standalone\n3929,303.73,NJ City,N,13,14,7,53,High School,33,Patnership,Standalone\n699,232.8,NJ City,E,22,16,14,39,High School,24,Owner,Standalone\n7965,391.69,Boston,N,15,19,19,85,Graduation,24,Patnership,Standalone\n5517,166.29,Boston,N,13,8,15,75,Graduation,23,Patnership,Standalone\n7710,310.5,Boston,S,19,8,6,58,Graduation,27,Patnership,Complex\n2546,498.11,NJ City,S,25,22,8,82,High School,32,Patnership,Complex\n1077,23.24,NJ City,N,5,24,1,73,Graduation,32,Patnership,Standalone\n2032,50.44,NJ City,W,12,16,1,70,High School,29,Franchise,Complex\n8911,65.45,NJ City,N,8,16,4,76,High School,28,Owner,Standalone\n9729,95.05,NJ City,N,10,10,4,48,Graduation,29,Patnership,Standalone\n651,108,NJ City,E,2,23,22,47,High School,29,Owner,Standalone\n1653,84.67,NJ City,N,1,11,3,40,Graduation,34,Patnership,Standalone\n4103,133.88,NJ City,E,12,16,14,82,High School,32,Owner,Standalone\n4,93.99,NJ City,W,19,9,3,42,High School,29,Owner,Standalone\n2593,20.7,NJ City,N,4,15,2,26,High School,20,Patnership,Standalone\n6268,176.2,Boston,W,12,8,20,40,Graduation,33,Franchise,Complex\n7255,379.6,Boston,E,23,15,21,59,Graduation,21,Owner,Standalone\n2887,357.86,NJ City,E,12,13,19,38,High School,23,Owner,Standalone\n2532,466.53,NJ City,W,4,10,10,29,High School,19,Franchise,Complex\n3706,489.87,NJ City,S,14,13,9,84,High School,30,Patnership,Complex\n4889,344.2,NJ City,N,25,14,2,70,No education,26,Patnership,Standalone\n4930,383.09,NJ City,S,16,24,11,30,High School,34,Patnership,Complex\n3182,35.32,NJ City,N,19,12,2,31,No education,19,Patnership,Complex\n2122,85.83,NJ City,S,25,19,1,27,High School,26,Patnership,Complex\n5203,50.6,Boston,E,19,21,3,66,High School,33,Owner,Standalone\n8168,426.91,NJ City,W,23,24,23,53,Graduation,24,Franchise,Complex\n8035,481.89,Boston,E,24,23,14,82,Graduation,34,Owner,Standalone\n5200,402.62,Boston,W,2,21,2,37,High School,33,Franchise,Complex\n8915,454.71,NJ City,E,23,22,1,73,No education,23,Owner,Standalone\n5987,133.74,Boston,E,19,17,13,64,Graduation,34,Owner,Standalone\n4503,22.83,NJ City,N,2,18,2,62,Graduation,25,Owner,Standalone\n2661,145,NJ City,N,1,18,12,47,High School,29,Patnership,Standalone\n4519,330.93,NJ City,E,8,9,6,33,High School,23,Owner,Standalone\n7862,225.62,Boston,S,25,14,9,38,Graduation,26,Patnership,Complex\n2496,247.52,NJ City,W,11,11,4,82,Graduation,22,Franchise,Complex\n3557,494.88,NJ City,N,12,23,6,33,High School,25,Patnership,Standalone\n7785,453.09,Boston,N,8,19,13,50,Graduation,24,Patnership,Standalone\n3329,166.55,NJ City,N,8,16,10,25,High School,19,Patnership,Standalone\n2760,383.2,NJ City,W,19,16,19,71,High School,31,Franchise,Complex\n7718,112.62,Boston,S,22,23,3,29,No education,30,Patnership,Complex\n6583,356.85,Boston,E,4,18,8,80,Graduation,35,Owner,Standalone\n8340,28.12,NJ City,N,1,18,4,46,Graduation,27,Franchise,Complex\n693,78.38,NJ City,N,24,10,3,63,Graduation,28,Patnership,Standalone\n4313,181.57,NJ City,N,7,9,2,54,No education,33,Patnership,Standalone\n8800,123.32,NJ City,W,23,12,4,85,High School,28,Franchise,Complex\n4373,233.03,NJ City,N,12,14,19,85,High School,26,Patnership,Standalone\n2041,135.52,NJ City,N,19,12,11,67,High School,28,Patnership,Standalone\n9302,295.3,NJ City,S,16,11,25,50,Graduation,23,Patnership,Complex\n1260,115.5,NJ City,W,18,15,3,68,Graduation,24,Franchise,Complex\n8848,10.83,NJ City,N,5,20,2,74,High School,18,Franchise,Complex\n125,264,NJ City,N,2,19,15,70,High School,26,Patnership,Standalone\n3897,313.68,NJ City,N,25,21,3,27,Graduation,29,Patnership,Standalone\n2151,434.5,NJ City,E,20,17,12,40,High School,27,Owner,Standalone\n3467,418.49,NJ City,E,17,12,7,83,High School,30,Owner,Standalone\n1854,219.05,NJ City,S,12,16,1,40,Graduation,20,Patnership,Complex\n9165,391.49,NJ City,N,19,18,15,48,Graduation,18,Patnership,Standalone\n3998,166.16,NJ City,S,7,16,8,43,High School,25,Patnership,Complex\n3248,98.78,NJ City,W,21,21,1,58,No education,22,Franchise,Complex\n4309,433.41,NJ City,N,6,23,25,73,High School,29,Patnership,Standalone\n5205,137.98,Boston,N,12,10,4,36,Graduation,20,Patnership,Standalone\n8909,83.69,NJ City,N,9,8,2,31,No education,22,Patnership,Standalone\n689,58.02,NJ City,N,18,8,4,37,No education,22,Patnership,Standalone\n1614,352.6,NJ City,S,23,20,10,68,High School,27,Patnership,Complex\n5175,414.88,Boston,E,25,14,23,28,Graduation,18,Owner,Standalone\n8522,276.49,NJ City,S,11,14,1,73,No education,23,Patnership,Complex\n9787,233.6,NJ City,E,6,16,20,61,Graduation,33,Owner,Standalone\n893,408.8,NJ City,N,25,13,13,62,High School,35,Patnership,Standalone\n5798,37.31,Boston,N,19,24,3,59,No education,28,Patnership,Complex\n5629,270.73,Boston,N,22,13,23,39,Graduation,27,Patnership,Standalone\n3834,416.12,NJ City,S,13,22,19,73,High School,24,Patnership,Complex\n6881,16.84,Boston,N,20,13,1,48,No education,29,Patnership,Standalone\n9915,73.75,NJ City,N,18,18,1,77,Graduation,26,Owner,Standalone\n1995,162.72,NJ City,E,8,18,12,61,High School,23,Owner,Standalone\n5474,66.97,Boston,N,8,23,2,69,No education,29,Patnership,Complex\n8310,132.35,NJ City,S,5,24,18,64,Graduation,21,Patnership,Complex\n9074,456.45,NJ City,S,17,17,16,76,Graduation,33,Patnership,Complex\n5737,45.08,Boston,N,24,14,2,61,High School,32,Patnership,Standalone\n9553,376.48,NJ City,N,5,13,19,28,Graduation,19,Patnership,Standalone\n3621,16.33,NJ City,N,20,11,3,38,Graduation,35,Patnership,Standalone\n2049,212.46,NJ City,N,16,19,3,31,Graduation,25,Patnership,Standalone\n3296,48.27,NJ City,W,10,19,1,34,No education,19,Franchise,Complex\n1552,365.07,NJ City,W,7,10,5,30,High School,32,Franchise,Complex\n4914,144.16,NJ City,S,20,24,16,80,High School,34,Patnership,Complex\n577,144.45,NJ City,N,14,18,13,75,High School,34,Patnership,Standalone\n1506,381.87,NJ City,S,23,16,7,42,High School,22,Patnership,Complex\n1047,245.43,NJ City,E,4,24,11,28,High School,27,Owner,Standalone\n9481,120.22,NJ City,N,17,12,4,41,High School,23,Patnership,Standalone\n6516,62.66,Boston,N,8,20,2,26,Graduation,24,Franchise,Complex\n749,232.1,NJ City,N,17,23,3,35,No education,24,Patnership,Standalone\n2680,458.7,NJ City,W,25,17,8,77,High School,20,Franchise,Complex\n6863,20.47,Boston,N,7,20,4,38,No education,32,Owner,Standalone\n851,480.87,NJ City,E,21,20,14,61,High School,35,Owner,Standalone\n4405,169.41,NJ City,N,2,16,5,26,High School,27,Patnership,Standalone\n1282,479.94,NJ City,S,15,19,1,74,High School,31,Patnership,Complex\n2439,222.64,NJ City,E,6,14,13,79,High School,20,Owner,Standalone\n1547,388.53,NJ City,E,5,15,17,68,High School,29,Owner,Standalone\n3879,75.66,NJ City,E,22,17,4,59,Graduation,18,Owner,Standalone\n410,277.68,NJ City,S,24,12,7,75,High School,28,Patnership,Complex\n7989,139.91,Boston,N,3,24,9,63,Graduation,31,Patnership,Standalone\n6039,113.41,Boston,E,25,14,4,57,Graduation,23,Owner,Standalone\n4237,392.44,NJ City,N,4,19,11,44,High School,30,Patnership,Standalone\n6473,393.1,Boston,N,25,24,8,25,Graduation,25,Patnership,Standalone\n1765,190.81,NJ City,N,6,23,25,71,High School,24,Patnership,Standalone\n5941,391.41,Boston,N,11,19,21,57,Graduation,24,Patnership,Standalone\n9188,488.54,NJ City,W,18,18,21,26,Graduation,35,Franchise,Complex\n663,438.86,NJ City,E,14,23,20,27,High School,33,Owner,Standalone\n909,440.62,NJ City,N,13,22,17,31,High School,33,Patnership,Standalone\n7239,235.33,Boston,E,12,8,1,67,Graduation,25,Owner,Standalone\n1192,277.18,NJ City,W,11,9,4,53,High School,22,Franchise,Complex\n9394,159.01,NJ City,S,8,15,18,42,Graduation,29,Patnership,Complex\n2842,480.04,NJ City,S,16,13,18,33,High School,22,Patnership,Complex\n479,297,NJ City,E,2,18,19,45,High School,28,Owner,Standalone\n3759,433.1,NJ City,E,11,9,25,35,High School,20,Owner,Standalone\n1010,394.77,NJ City,S,8,19,23,70,High School,30,Patnership,Complex\n4269,219.28,NJ City,N,19,18,7,73,High School,33,Patnership,Standalone\n9719,459.99,NJ City,E,8,13,13,69,Graduation,18,Owner,Standalone\n9764,103.49,NJ City,W,17,19,4,35,No education,30,Franchise,Complex\n2071,276.63,NJ City,E,7,11,22,70,High School,22,Owner,Standalone\n9125,470.26,NJ City,N,25,22,3,54,No education,35,Patnership,Standalone\n2828,170.93,NJ City,W,15,11,25,37,High School,34,Franchise,Complex\n5139,237.29,Boston,E,8,11,19,85,Graduation,21,Owner,Standalone\n5353,192.7,Boston,N,21,24,23,25,Graduation,28,Patnership,Standalone\n8390,294.51,NJ City,S,16,21,2,77,No education,28,Patnership,Complex\n6244,377.31,Boston,W,24,18,15,58,Graduation,32,Franchise,Complex\n3343,476.18,NJ City,E,5,15,21,34,High School,25,Owner,Standalone\n5148,385.01,Boston,W,8,17,24,30,Graduation,30,Franchise,Complex\n5218,392.1,Boston,S,7,9,3,32,High School,23,Patnership,Complex\n6040,381.23,Boston,W,15,18,16,25,Graduation,35,Franchise,Complex\n644,311.36,NJ City,W,18,18,10,71,High School,18,Franchise,Complex\n2217,19.5,NJ City,N,16,13,3,26,Graduation,29,Patnership,Standalone\n483,409.13,NJ City,E,24,11,13,44,High School,34,Owner,Standalone\n6762,444.49,Boston,S,9,24,7,51,Graduation,19,Patnership,Complex\n5621,33.28,Boston,N,8,23,4,28,No education,31,Patnership,Standalone\n4969,209.1,Boston,N,9,15,24,46,High School,21,Patnership,Standalone\n6037,416.46,Boston,N,3,17,8,51,Graduation,28,Patnership,Standalone\n4636,376.16,NJ City,W,12,21,11,35,High School,24,Franchise,Complex\n4316,393.35,NJ City,W,4,19,2,42,No education,21,Franchise,Complex\n250,439.52,NJ City,S,24,8,7,45,High School,32,Patnership,Complex\n2505,83.57,NJ City,N,25,16,2,68,Graduation,27,Patnership,Standalone\n2123,80.3,NJ City,E,5,8,4,45,No education,29,Owner,Standalone\n9137,150.06,NJ City,N,20,14,21,78,Graduation,35,Patnership,Standalone\n7597,97.53,Boston,N,7,16,2,54,High School,22,Patnership,Standalone\n9424,455.23,NJ City,W,1,16,12,26,Graduation,34,Franchise,Complex\n9040,340.41,NJ City,W,4,16,2,63,High School,27,Franchise,Complex\n621,335.31,NJ City,N,20,24,8,51,High School,32,Patnership,Standalone\n5289,164.23,Boston,N,24,13,1,65,Graduation,24,Patnership,Standalone\n9036,0.94,NJ City,N,4,20,1,67,Graduation,28,Franchise,Complex\n603,138.14,NJ City,E,14,19,1,27,Graduation,33,Owner,Standalone\n9673,299.94,NJ City,N,10,20,20,50,Graduation,21,Patnership,Standalone\n2341,101.93,NJ City,N,7,14,1,55,High School,30,Patnership,Standalone\n7194,230.22,Boston,S,7,22,24,75,Graduation,35,Patnership,Complex\n6771,250.75,Boston,E,3,14,3,83,Graduation,21,Owner,Standalone\n3431,342.87,NJ City,E,22,20,17,84,High School,33,Owner,Standalone\n3640,470.04,NJ City,W,6,12,22,59,High School,29,Franchise,Complex\n1102,342.69,NJ City,S,18,19,2,43,High School,24,Patnership,Complex\n96,494.33,NJ City,W,7,17,10,41,High School,27,Franchise,Complex\n249,495.2,NJ City,N,8,15,1,62,Graduation,19,Patnership,Standalone\n5003,400.73,Boston,E,7,14,23,58,Graduation,34,Owner,Standalone\n8763,112.04,NJ City,E,3,10,1,77,Graduation,35,Owner,Standalone\n5890,29.12,Boston,N,25,12,3,46,High School,29,Patnership,Complex\n8041,129.77,Boston,N,17,21,23,63,Graduation,27,Patnership,Standalone\n6688,499.76,Boston,W,25,20,20,30,Graduation,26,Franchise,Complex\n5775,256.95,Boston,E,18,10,19,40,Graduation,34,Owner,Standalone\n2213,179.11,NJ City,N,9,23,3,46,No education,33,Patnership,Standalone\n6561,370.42,Boston,N,5,18,7,53,Graduation,19,Patnership,Standalone\n8250,438.22,NJ City,S,12,12,25,51,Graduation,22,Patnership,Complex\n929,207.38,NJ City,N,18,18,5,78,No education,31,Patnership,Standalone\n649,289.02,NJ City,N,11,12,18,75,High School,22,Patnership,Standalone\n6623,365.7,Boston,E,5,8,21,79,Graduation,23,Owner,Standalone\n6916,384.71,Boston,W,25,22,23,25,Graduation,34,Franchise,Complex\n3682,110.36,NJ City,S,1,11,3,55,High School,31,Patnership,Complex\n4742,443.89,NJ City,S,7,9,2,40,No education,35,Patnership,Complex\n6409,62.9,Boston,N,15,15,4,26,High School,18,Patnership,Standalone\n2491,397.9,NJ City,E,4,23,1,43,High School,35,Owner,Standalone\n9761,375.25,NJ City,N,24,18,21,77,Graduation,33,Patnership,Standalone\n1116,381.07,NJ City,W,14,17,4,79,Graduation,20,Franchise,Complex\n761,441.32,NJ City,N,21,24,15,26,High School,20,Patnership,Standalone\n2985,92.83,NJ City,N,4,19,4,70,Graduation,31,Patnership,Standalone\n3383,442.63,NJ City,E,25,17,21,44,High School,32,Owner,Standalone\n7627,294.96,Boston,E,5,13,4,70,High School,32,Owner,Standalone\n8670,408.72,NJ City,S,22,10,10,58,Graduation,32,Patnership,Complex\n2270,493.73,NJ City,S,25,13,24,81,High School,26,Patnership,Complex\n1280,191.44,NJ City,W,7,9,15,70,High School,18,Franchise,Complex\n7844,275.54,Boston,W,8,9,11,84,Graduation,26,Franchise,Complex\n7829,237.5,Boston,N,21,24,8,53,Graduation,32,Patnership,Standalone\n7697,249.45,Boston,N,1,15,10,28,Graduation,24,Patnership,Standalone\n3246,159.98,NJ City,S,8,19,2,41,Graduation,21,Patnership,Complex\n5194,233.56,Boston,S,19,9,8,27,Graduation,32,Patnership,Complex\n5501,239.22,Boston,N,3,20,21,56,Graduation,21,Patnership,Standalone\n5464,198.85,Boston,W,8,20,20,60,Graduation,35,Franchise,Complex\n4081,127.6,NJ City,N,10,21,1,45,High School,22,Patnership,Standalone\n8074,293.58,NJ City,S,23,8,11,36,Graduation,32,Patnership,Complex\n7713,285.88,Boston,N,21,21,20,25,Graduation,28,Patnership,Standalone\n4332,121.69,NJ City,W,16,11,4,76,Graduation,18,Franchise,Complex\n9243,53.99,NJ City,E,19,13,4,83,Graduation,19,Owner,Standalone\n4810,355.17,NJ City,S,19,20,18,26,High School,19,Patnership,Complex\n6853,106.59,Boston,N,7,21,4,53,High School,34,Patnership,Standalone\n3390,119.39,NJ City,S,21,10,3,75,Graduation,26,Patnership,Complex\n7059,100.92,Boston,E,1,21,1,46,Graduation,21,Owner,Standalone\n3753,423.92,NJ City,N,22,22,14,45,High School,31,Patnership,Standalone\n4966,157.92,Boston,S,11,23,23,48,High School,35,Patnership,Complex\n6592,390.68,Boston,W,8,13,18,46,Graduation,33,Franchise,Complex\n9530,124.58,NJ City,S,8,18,1,38,No education,29,Patnership,Complex\n8433,498.36,NJ City,N,3,22,5,35,Graduation,35,Patnership,Standalone\n9485,97.92,NJ City,N,16,15,1,31,No education,35,Patnership,Standalone\n935,306,NJ City,E,1,23,12,62,High School,28,Owner,Standalone\n7055,207.42,Boston,E,20,15,10,29,Graduation,30,Owner,Standalone\n5438,437.04,Boston,S,4,13,24,82,Graduation,32,Patnership,Complex\n5607,137.54,Boston,E,18,8,10,34,Graduation,24,Owner,Standalone\n455,192.53,NJ City,E,14,19,15,47,High School,30,Owner,Standalone\n5710,179.33,Boston,S,14,20,4,36,High School,23,Patnership,Complex\n8742,457.66,NJ City,S,25,12,6,77,Graduation,30,Patnership,Complex\n4617,244.2,NJ City,N,11,14,7,84,High School,22,Patnership,Standalone\n6513,17.63,Boston,N,6,22,4,62,Graduation,18,Patnership,Standalone\n4841,146.44,NJ City,N,18,19,16,66,High School,31,Patnership,Standalone\n4219,308.11,NJ City,E,7,17,6,76,High School,25,Owner,Standalone\n428,320.44,NJ City,W,5,17,6,73,High School,29,Franchise,Complex\n6475,27.68,Boston,N,13,10,1,35,High School,29,Owner,Standalone\n1172,174.45,NJ City,W,14,9,11,41,High School,26,Franchise,Complex\n3926,389.92,NJ City,S,23,12,5,36,No education,33,Patnership,Complex\n5948,234.85,Boston,W,15,8,21,74,Graduation,20,Franchise,Complex\n9473,399.14,NJ City,N,3,12,10,29,Graduation,20,Patnership,Standalone\n9931,382.82,NJ City,E,18,20,17,36,Graduation,26,Owner,Standalone\n2630,66.92,NJ City,N,12,15,2,61,No education,31,Patnership,Complex\n3028,341.58,NJ City,W,11,20,18,63,High School,32,Franchise,Complex\n7539,216.71,Boston,E,8,14,5,61,Graduation,18,Owner,Standalone\n5290,355.96,Boston,S,10,11,15,84,Graduation,22,Patnership,Complex\n6540,184.3,Boston,W,23,20,15,73,Graduation,28,Franchise,Complex\n3066,465.62,NJ City,S,19,18,9,76,High School,31,Patnership,Complex\n746,450.09,NJ City,S,22,19,7,50,High School,30,Patnership,Complex\n982,343.63,NJ City,S,17,14,5,34,High School,21,Patnership,Complex\n4326,399.42,NJ City,S,17,9,16,60,High School,33,Patnership,Complex\n3404,495.42,NJ City,W,6,22,23,44,High School,32,Franchise,Complex\n8419,129.72,NJ City,E,23,8,5,39,High School,30,Owner,Standalone\n7930,316.98,Boston,S,5,17,1,28,High School,20,Patnership,Complex\n3758,283.3,NJ City,S,10,23,25,63,High School,33,Patnership,Complex\n2342,249.86,NJ City,S,21,14,22,28,High School,23,Patnership,Complex\n5384,305.17,Boston,W,2,16,14,70,Graduation,23,Franchise,Complex\n1425,445.05,NJ City,N,10,17,10,42,High School,35,Patnership,Standalone\n4765,227.31,NJ City,N,21,11,9,77,High School,35,Patnership,Standalone\n7221,199.21,Boston,N,20,23,15,57,Graduation,20,Patnership,Standalone\n7184,338.56,Boston,W,13,23,1,63,No education,30,Franchise,Complex\n1822,144.87,NJ City,S,5,18,2,49,High School,28,Patnership,Complex\n3958,128.65,NJ City,S,8,11,6,77,High School,35,Patnership,Complex\n4731,19.55,NJ City,N,2,12,3,38,Graduation,21,Owner,Standalone\n6736,181.23,Boston,W,25,11,16,31,Graduation,27,Franchise,Complex\n7837,243.09,Boston,N,9,12,21,76,Graduation,20,Patnership,Standalone\n8345,426.56,NJ City,N,6,11,14,37,Graduation,26,Patnership,Standalone\n8113,402.37,NJ City,N,16,18,19,26,Graduation,31,Patnership,Standalone\n6625,430.08,Boston,N,13,18,5,62,High School,35,Patnership,Standalone\n2478,342.88,NJ City,S,21,13,17,54,High School,34,Patnership,Complex\n3484,48.38,NJ City,W,23,9,3,33,High School,26,Franchise,Complex\n3281,164,NJ City,N,2,18,12,50,High School,31,Patnership,Standalone\n497,473.25,NJ City,N,6,21,21,41,High School,28,Patnership,Standalone\n415,350.13,NJ City,E,8,19,8,72,High School,32,Owner,Standalone\n5049,292.85,Boston,N,21,20,19,74,Graduation,24,Patnership,Standalone\n7613,439.17,Boston,N,18,12,4,77,No education,29,Patnership,Standalone\n1711,261.15,NJ City,E,17,16,9,73,High School,27,Owner,Standalone\n8436,120.91,NJ City,W,15,21,1,25,Graduation,29,Franchise,Complex\n9296,198.5,NJ City,W,24,12,22,59,Graduation,34,Franchise,Complex\n3713,74.05,NJ City,N,17,21,2,69,No education,18,Patnership,Standalone\n9445,273.38,NJ City,N,1,22,9,54,Graduation,22,Patnership,Standalone\n7042,478.17,Boston,S,5,23,1,52,High School,35,Patnership,Complex\n8714,114.85,NJ City,S,18,11,2,31,No education,32,Patnership,Complex\n8341,0.88,NJ City,N,16,9,2,41,High School,22,Patnership,Standalone\n5645,383.39,Boston,N,2,9,2,81,No education,34,Patnership,Standalone\n5803,7.31,Boston,N,12,8,3,26,High School,26,Owner,Standalone\n9618,162.76,NJ City,S,13,19,24,77,Graduation,25,Patnership,Complex\n9083,76.93,NJ City,E,1,9,2,26,No education,35,Owner,Standalone\n433,348.18,NJ City,N,21,22,13,35,High School,24,Patnership,Standalone\n2990,7.19,NJ City,N,6,21,4,26,No education,22,Patnership,Complex\n1852,29.44,NJ City,N,7,19,4,51,High School,34,Franchise,Complex\n4722,10.03,NJ City,N,13,15,1,58,Graduation,24,Patnership,Complex\n7983,303.35,Boston,E,13,21,18,51,Graduation,33,Owner,Standalone\n9660,85.4,NJ City,W,14,9,4,64,Graduation,29,Franchise,Complex\n9321,253.93,NJ City,N,7,18,20,47,Graduation,34,Patnership,Standalone\n2336,185.15,NJ City,W,6,18,14,38,High School,23,Franchise,Complex\n1288,261,NJ City,W,2,22,21,34,High School,19,Franchise,Complex\n5909,149.63,Boston,N,3,8,22,38,Graduation,30,Patnership,Standalone\n3102,256.55,NJ City,S,12,11,8,44,High School,34,Patnership,Complex\n3330,431.3,NJ City,S,12,11,24,82,High School,30,Patnership,Complex\n5420,193.01,Boston,W,15,24,5,65,No education,22,Franchise,Complex\n8186,488.45,NJ City,S,14,22,22,32,Graduation,25,Patnership,Complex\n2868,435.94,NJ City,W,9,17,19,79,High School,33,Franchise,Complex\n8441,338.49,NJ City,N,21,15,11,76,Graduation,32,Patnership,Standalone\n7515,4.41,Boston,N,14,19,1,59,Graduation,31,Owner,Standalone\n8383,306.68,NJ City,E,9,20,7,59,Graduation,25,Owner,Standalone\n3114,297.11,NJ City,S,4,22,5,30,Graduation,24,Patnership,Complex\n9593,141.12,NJ City,N,9,19,3,64,No education,30,Patnership,Standalone\n307,425.5,NJ City,E,13,24,21,68,High School,35,Owner,Standalone\n7803,33.5,Boston,N,8,10,2,83,Graduation,34,Owner,Standalone\n1152,346.29,NJ City,W,23,13,6,52,High School,34,Franchise,Complex\n1312,207.5,NJ City,W,5,12,23,46,High School,25,Franchise,Complex\n9287,213.14,NJ City,E,9,15,24,64,Graduation,20,Owner,Standalone\n337,388.85,NJ City,N,6,20,20,71,High School,33,Patnership,Standalone\n6722,123.5,Boston,S,6,24,2,37,No education,30,Patnership,Complex\n9554,216.86,NJ City,S,12,17,11,39,Graduation,33,Patnership,Complex\n3887,497.87,NJ City,E,17,21,15,67,High School,20,Owner,Standalone\n2534,206.72,NJ City,S,17,14,23,27,High School,22,Patnership,Complex\n7913,382.19,Boston,N,19,10,18,66,Graduation,31,Patnership,Standalone\n505,19.86,NJ City,N,18,18,2,72,High School,21,Patnership,Standalone\n4099,405.38,NJ City,E,23,16,7,64,High School,22,Owner,Standalone\n9191,200.68,NJ City,E,9,20,20,85,Graduation,34,Owner,Standalone\n4370,454.96,NJ City,S,11,8,11,43,High School,27,Patnership,Complex\n1561,165.57,NJ City,N,7,13,19,50,High School,33,Patnership,Standalone\n2058,15.98,NJ City,N,8,14,1,35,Graduation,26,Patnership,Complex\n766,330.19,NJ City,S,19,24,15,45,High School,22,Patnership,Complex\n1269,407.02,NJ City,N,7,10,8,82,High School,25,Patnership,Standalone\n8732,163.5,NJ City,W,17,24,12,52,Graduation,28,Franchise,Complex\n8654,234.57,NJ City,S,20,24,24,41,Graduation,29,Patnership,Complex\n6219,27.97,Boston,N,23,16,2,62,Graduation,22,Owner,Standalone\n4231,169.88,NJ City,E,4,13,22,27,High School,26,Owner,Standalone\n8162,365.1,NJ City,S,4,14,12,48,Graduation,21,Patnership,Complex\n6851,436.81,Boston,E,10,8,2,77,No education,25,Owner,Standalone\n1300,169.96,NJ City,W,6,9,16,77,High School,29,Franchise,Complex\n7849,336.06,Boston,N,5,14,24,66,Graduation,18,Patnership,Standalone\n8105,27.64,NJ City,N,1,10,1,35,No education,28,Patnership,Standalone\n9348,319.69,NJ City,W,10,11,19,53,Graduation,18,Franchise,Complex\n5705,12.78,Boston,N,7,13,3,25,No education,28,Patnership,Standalone\n7300,214.25,Boston,W,18,8,9,63,Graduation,22,Franchise,Complex\n5958,412.59,Boston,S,12,22,14,48,Graduation,20,Patnership,Complex\n153,364.51,NJ City,N,17,17,25,84,High School,34,Patnership,Standalone\n3672,210.47,NJ City,W,19,10,18,54,High School,30,Franchise,Complex\n3213,349.9,NJ City,N,10,22,19,83,High School,33,Patnership,Standalone\n4427,228.57,NJ City,E,13,10,24,53,High School,33,Owner,Standalone\n3582,137,NJ City,S,3,10,14,48,High School,22,Patnership,Complex\n5748,94.87,Boston,W,3,15,1,38,Graduation,26,Franchise,Complex\n68,259.54,NY City,W,16,22,20,31,High School,18,Franchise,Complex\n4136,119,NJ City,W,3,8,18,29,High School,24,Franchise,Complex\n1249,300.64,NJ City,N,18,14,2,58,High School,20,Patnership,Standalone\n4182,173.87,NJ City,S,25,18,14,41,High School,25,Patnership,Complex\n3820,443.92,NJ City,W,17,17,8,60,High School,26,Franchise,Complex\n4753,493.49,NJ City,N,7,15,10,65,High School,25,Patnership,Standalone\n2154,395.62,NJ City,S,15,23,9,37,High School,35,Patnership,Complex\n6191,239.83,Boston,E,7,17,12,50,Graduation,22,Owner,Standalone\n7550,473.93,Boston,S,16,22,17,71,Graduation,35,Patnership,Complex\n8191,44.3,NJ City,N,15,9,1,64,High School,24,Owner,Standalone\n3581,26.8,NJ City,N,10,15,2,39,No education,28,Patnership,Standalone\n6831,302.68,Boston,E,16,8,21,26,Graduation,23,Owner,Standalone\n6443,342.31,Boston,E,17,21,14,29,Graduation,21,Owner,Standalone\n5373,32.27,Boston,N,12,18,2,79,Graduation,35,Patnership,Standalone\n8025,4.48,Boston,N,11,12,3,60,Graduation,34,Patnership,Standalone\n2568,365.46,NJ City,W,25,12,11,82,High School,21,Franchise,Complex\n2718,439.72,NJ City,S,4,18,22,81,High School,19,Patnership,Complex\n4207,205,NJ City,E,2,13,14,55,High School,25,Owner,Standalone\n1757,442.62,NJ City,N,15,13,21,85,High School,22,Patnership,Standalone\n5086,374.04,Boston,S,14,18,5,80,High School,31,Patnership,Complex\n4042,165.55,NJ City,S,24,23,25,74,High School,32,Patnership,Complex\n4827,215,NJ City,E,3,23,8,39,High School,22,Owner,Standalone\n340,356.78,NJ City,W,12,24,4,83,High School,22,Franchise,Complex\n1556,136.97,NJ City,W,15,13,6,55,High School,22,Franchise,Complex\n6548,11.05,Boston,N,15,13,2,45,No education,33,Franchise,Complex\n6248,133.33,Boston,W,7,15,6,38,Graduation,32,Franchise,Complex\n2941,256.48,NJ City,N,24,16,23,68,High School,23,Patnership,Standalone\n9375,301.74,NJ City,E,1,23,17,69,Graduation,25,Owner,Standalone\n2048,284.43,NJ City,W,1,18,1,44,No education,26,Franchise,Complex\n2100,25.76,NJ City,N,1,15,1,34,Graduation,26,Franchise,Complex\n2753,207.69,NJ City,N,19,18,24,62,High School,33,Patnership,Standalone\n3189,199,NJ City,N,2,9,23,62,High School,21,Patnership,Standalone\n1377,26.86,NJ City,N,10,17,3,68,Graduation,32,Patnership,Standalone\n486,273,NJ City,S,1,10,11,56,High School,20,Patnership,Complex\n5324,426.9,Boston,W,8,13,7,82,Graduation,32,Franchise,Complex\n5165,492.48,Boston,N,17,19,24,34,Graduation,29,Patnership,Standalone\n9257,403.91,NJ City,N,14,21,17,49,Graduation,18,Patnership,Standalone\n3457,227.15,NJ City,N,7,11,17,55,High School,27,Patnership,Standalone\n164,470.5,NJ City,W,7,10,19,84,High School,28,Franchise,Complex\n6415,311.92,Boston,E,13,15,14,42,Graduation,35,Owner,Standalone\n6405,286.25,Boston,N,22,20,7,42,Graduation,25,Patnership,Standalone\n7429,101.8,Boston,N,11,12,3,52,High School,31,Patnership,Standalone\n1824,52.65,NJ City,W,9,20,2,85,Graduation,27,Franchise,Complex\n9131,303.79,NJ City,E,22,20,20,41,Graduation,28,Owner,Standalone\n3894,125.95,NJ City,S,10,13,1,33,Graduation,32,Patnership,Complex\n4381,365.35,NJ City,N,21,24,2,31,High School,19,Patnership,Standalone\n5397,306.05,Boston,N,24,12,18,82,Graduation,21,Patnership,Standalone\n3822,399.7,NJ City,S,23,24,4,37,Graduation,27,Patnership,Complex\n7419,441.57,Boston,E,15,21,5,34,Graduation,25,Owner,Standalone\n4711,174.86,NJ City,E,6,17,2,52,High School,19,Owner,Standalone\n694,205,NJ City,S,3,16,20,27,High School,28,Patnership,Complex\n2262,415.62,NJ City,S,13,20,25,27,High School,29,Patnership,Complex\n7324,470.66,Boston,W,18,10,20,79,Graduation,19,Franchise,Complex\n3094,318.92,NJ City,S,23,19,25,79,High School,27,Patnership,Complex\n9644,314.66,NJ City,W,23,23,17,25,Graduation,28,Franchise,Complex\n4708,445.62,NJ City,W,17,13,21,33,High School,23,Franchise,Complex\n5882,38.95,Boston,N,4,9,2,56,No education,27,Patnership,Complex\n8679,357.9,NJ City,E,1,24,9,67,Graduation,29,Owner,Standalone\n1748,360.21,NJ City,W,20,21,1,61,No education,33,Franchise,Complex\n4825,494.81,NJ City,N,7,12,1,72,High School,20,Patnership,Standalone\n1817,324.79,NJ City,N,25,13,10,39,High School,21,Patnership,Standalone\n6679,426.94,Boston,E,17,19,18,55,Graduation,22,Owner,Standalone\n4488,282.62,NJ City,W,23,14,6,25,High School,32,Franchise,Complex\n6354,255.91,Boston,S,7,20,11,27,Graduation,23,Patnership,Complex\n2158,50.03,NJ City,S,6,22,3,31,High School,33,Patnership,Complex\n6414,391.82,Boston,S,15,15,16,46,Graduation,18,Patnership,Complex\n586,328.22,NJ City,S,11,21,12,71,High School,31,Patnership,Complex\n729,187.96,NJ City,N,16,20,10,57,High School,30,Patnership,Standalone\n9733,283.66,NJ City,N,9,18,9,82,Graduation,18,Patnership,Standalone\n9586,173.83,NJ City,S,20,14,16,32,Graduation,24,Patnership,Complex\n8999,241.42,NJ City,E,10,9,10,75,Graduation,25,Owner,Standalone\n4243,460.97,NJ City,E,6,17,24,57,High School,31,Owner,Standalone\n5805,159.35,Boston,N,8,16,22,59,Graduation,25,Patnership,Standalone\n954,115.64,NJ City,S,18,13,3,55,Graduation,30,Patnership,Complex\n6714,193.48,Boston,S,23,16,15,26,Graduation,31,Patnership,Complex\n242,475.22,NJ City,S,8,10,13,74,High School,30,Patnership,Complex\n8921,370.12,NJ City,N,19,19,8,70,Graduation,26,Patnership,Standalone\n4824,96.43,NJ City,W,22,16,3,67,Graduation,25,Franchise,Complex\n9636,332.37,NJ City,W,21,13,8,76,Graduation,24,Franchise,Complex\n1557,452.53,NJ City,N,3,16,2,72,Graduation,22,Patnership,Standalone\n3299,278,NJ City,E,2,9,10,81,High School,33,Owner,Standalone\n9308,11.86,NJ City,N,19,14,1,40,No education,22,Franchise,Complex\n5177,329.41,Boston,N,22,11,10,71,Graduation,35,Patnership,Standalone\n7694,170.75,Boston,S,16,16,19,63,Graduation,33,Patnership,Complex\n6888,192.15,Boston,W,22,23,5,60,Graduation,29,Franchise,Complex\n5326,293.9,Boston,S,14,13,12,35,Graduation,27,Patnership,Complex\n7967,313.95,Boston,E,24,16,18,37,Graduation,19,Owner,Standalone\n6852,219.15,Boston,W,15,15,4,50,Graduation,33,Franchise,Complex\n87,485.04,Boston,E,9,23,9,49,High School,26,Owner,Standalone\n871,376.68,NJ City,E,7,14,16,76,High School,23,Owner,Standalone\n9177,60.3,NJ City,N,1,14,3,77,Graduation,20,Patnership,Standalone\n5844,310.65,Boston,W,5,12,8,28,Graduation,34,Franchise,Complex\n2330,458.43,NJ City,S,23,11,1,67,No education,29,Patnership,Complex\n550,145.05,NJ City,S,23,10,20,35,High School,33,Patnership,Complex\n9442,264.02,NJ City,S,9,10,20,41,Graduation,28,Patnership,Complex\n1701,106.89,NJ City,N,22,10,4,57,Graduation,32,Patnership,Standalone\n4547,166.98,NJ City,E,21,22,10,49,High School,27,Owner,Standalone\n8043,299.64,Boston,E,3,20,15,34,Graduation,34,Owner,Standalone\n2469,164.81,NJ City,N,8,19,15,47,High School,32,Patnership,Standalone\n1834,369.91,NJ City,S,20,16,23,36,High School,21,Patnership,Complex\n3622,189.66,NJ City,S,20,12,8,71,High School,18,Patnership,Complex\n144,37.4,NJ City,N,20,9,1,53,Graduation,22,Franchise,Complex\n5344,391.07,Boston,W,21,13,14,27,Graduation,34,Franchise,Complex\n1434,243.44,NJ City,S,25,23,19,49,High School,31,Patnership,Complex\n9381,487.77,NJ City,N,16,9,8,57,Graduation,30,Patnership,Standalone\n9704,443.11,NJ City,W,12,22,15,82,Graduation,30,Franchise,Complex\n5807,220.96,Boston,E,9,12,11,45,Graduation,30,Owner,Standalone\n8133,238.33,NJ City,N,8,12,13,42,Graduation,31,Patnership,Standalone\n4752,82.48,NJ City,W,20,23,3,75,Graduation,32,Franchise,Complex\n3927,366.1,NJ City,E,10,18,19,64,High School,23,Owner,Standalone\n1440,159.35,NJ City,W,13,23,19,46,High School,31,Franchise,Complex\n4659,312.24,NJ City,E,21,17,16,37,High School,30,Owner,Standalone\n7305,134.08,Boston,N,3,13,21,67,Graduation,27,Patnership,Standalone\n6708,377.39,Boston,W,23,8,8,82,Graduation,26,Franchise,Complex\n747,335.12,NJ City,E,11,16,20,78,High School,35,Owner,Standalone\n9100,162.33,NJ City,W,1,17,3,27,High School,23,Franchise,Complex\n3773,89.7,NJ City,N,19,13,1,83,No education,23,Patnership,Standalone\n8933,159.26,NJ City,N,9,22,2,34,No education,29,Patnership,Standalone\n5417,92.08,Boston,N,25,24,2,36,No education,23,Patnership,Standalone\n9574,5.52,NJ City,N,3,23,3,82,High School,19,Patnership,Complex\n6014,106.25,Boston,S,13,14,4,76,No education,34,Patnership,Complex\n4148,365.13,NJ City,W,12,21,9,66,High School,32,Franchise,Complex\n5040,120.71,Boston,W,8,21,2,62,Graduation,30,Franchise,Complex\n4174,121.14,NJ City,S,17,17,3,72,High School,31,Patnership,Complex\n2359,60.14,NJ City,E,17,9,2,31,High School,27,Owner,Standalone\n7004,201.6,Boston,W,10,9,17,30,Graduation,24,Franchise,Complex\n1048,376.36,NJ City,W,20,21,8,85,High School,33,Franchise,Complex\n7979,449.93,Boston,E,8,22,9,66,Graduation,28,Owner,Standalone\n921,424.46,NJ City,N,13,21,8,64,High School,27,Patnership,Standalone\n2112,331.2,NJ City,W,14,18,1,48,Graduation,29,Franchise,Complex\n5782,69.07,Boston,N,16,18,3,69,High School,28,Patnership,Complex\n1360,173.44,NJ City,W,15,14,21,66,High School,20,Franchise,Complex\n9680,116.44,NJ City,W,1,22,1,26,No education,33,Franchise,Complex\n2152,357.94,NJ City,W,19,20,16,66,High School,31,Franchise,Complex\n1989,337.41,NJ City,N,20,19,2,49,Graduation,32,Patnership,Standalone\n1258,474.52,NJ City,S,7,18,18,77,High School,32,Patnership,Complex\n1709,317,NJ City,N,1,22,13,84,High School,19,Patnership,Standalone\n3824,154.63,NJ City,W,7,12,18,79,High School,28,Franchise,Complex\n7519,312.11,Boston,E,17,8,9,25,Graduation,21,Owner,Standalone\n2378,484.4,NJ City,S,25,21,11,51,High School,26,Patnership,Complex\n542,175.87,NJ City,S,16,11,15,69,High School,19,Patnership,Complex\n2930,293.38,NJ City,S,9,8,4,81,No education,30,Patnership,Complex\n1195,16.86,NJ City,N,14,14,4,67,High School,28,Owner,Standalone\n8655,371.8,NJ City,E,19,19,13,74,Graduation,28,Owner,Standalone\n8055,193.38,NJ City,E,9,15,12,68,Graduation,24,Owner,Standalone\n4670,300,NJ City,S,1,22,6,61,High School,26,Patnership,Complex\n1009,170,NJ City,N,3,17,19,78,High School,32,Patnership,Standalone\n708,15.69,NJ City,N,22,13,3,26,Graduation,23,Franchise,Complex\n7333,237.38,Boston,N,3,16,12,32,Graduation,20,Patnership,Standalone\n2589,190.36,NJ City,N,6,24,13,47,High School,20,Patnership,Standalone\n6129,445.9,Boston,N,24,9,12,84,Graduation,34,Patnership,Standalone\n6922,326.16,Boston,S,25,15,18,39,Graduation,23,Patnership,Complex\n1267,142.67,NJ City,E,6,16,23,63,High School,25,Owner,Standalone\n9360,126.97,NJ City,W,13,22,1,43,Graduation,29,Franchise,Complex\n4786,90.34,NJ City,S,23,22,4,35,High School,19,Patnership,Complex\n9559,67.88,NJ City,N,12,13,1,27,High School,22,Owner,Standalone\n9584,225.14,NJ City,W,3,18,21,42,Graduation,23,Franchise,Complex\n2102,376.92,NJ City,S,15,16,10,30,High School,19,Patnership,Complex\n8661,104.17,NJ City,N,19,10,3,35,Graduation,33,Patnership,Standalone\n451,265.54,NJ City,E,25,12,22,81,High School,29,Owner,Standalone\n7634,425.55,Boston,S,11,16,14,67,Graduation,34,Patnership,Complex\n1938,300,NJ City,S,3,21,22,36,High School,34,Patnership,Complex\n1433,210.82,NJ City,N,4,12,20,39,High School,19,Patnership,Standalone\n4562,68.37,NJ City,N,6,18,3,57,No education,18,Patnership,Complex\n7260,153.73,Boston,W,9,8,23,66,Graduation,19,Franchise,Complex\n3507,252.55,NJ City,E,21,20,13,38,High School,30,Owner,Standalone\n1740,305.63,NJ City,W,3,10,4,53,Graduation,19,Franchise,Complex\n7078,151.91,Boston,S,12,17,2,47,High School,34,Patnership,Complex\n1032,389.99,NJ City,W,17,10,15,33,High School,31,Franchise,Complex\n1650,197.11,NJ City,S,12,9,18,28,High School,32,Patnership,Complex\n8406,219.5,NJ City,S,6,17,8,72,Graduation,24,Patnership,Complex\n9427,79.63,NJ City,E,8,8,1,47,High School,23,Owner,Standalone\n7348,11.59,Boston,N,5,17,1,39,High School,24,Franchise,Complex\n6968,17.71,Boston,N,18,21,2,25,No education,34,Franchise,Complex\n8820,354.28,NJ City,W,6,12,14,32,Graduation,33,Franchise,Complex\n519,206.82,NJ City,E,11,9,12,26,High School,19,Owner,Standalone\n2587,495.04,NJ City,E,8,24,3,54,High School,35,Owner,Standalone\n1060,114.95,NJ City,W,11,19,2,43,High School,22,Franchise,Complex\n5869,242.89,Boston,N,11,14,1,55,High School,20,Patnership,Standalone\n7628,387.17,Boston,W,5,24,16,74,Graduation,20,Franchise,Complex\n7624,379.57,Boston,W,14,10,24,80,Graduation,23,Franchise,Complex\n3186,269.93,NJ City,S,5,21,14,42,High School,31,Patnership,Complex\n3411,492.01,NJ City,E,9,9,20,84,High School,19,Owner,Standalone\n3720,435.96,NJ City,W,14,22,3,47,Graduation,24,Franchise,Complex\n3362,177.12,NJ City,S,6,23,21,64,High School,34,Patnership,Complex\n8147,256.96,NJ City,E,15,22,13,60,Graduation,24,Owner,Standalone\n8876,89.66,NJ City,W,24,20,3,29,No education,31,Franchise,Complex\n9330,385.98,NJ City,S,21,14,19,85,Graduation,27,Patnership,Complex\n2675,477.55,NJ City,E,5,10,10,48,High School,23,Owner,Standalone\n4758,329.01,NJ City,S,11,23,13,53,High School,27,Patnership,Complex\n1694,274,NJ City,S,3,19,18,75,High School,21,Patnership,Complex\n5918,254.88,Boston,S,19,9,23,76,Graduation,34,Patnership,Complex\n1575,28.05,NJ City,N,14,10,1,31,Graduation,30,Owner,Standalone\n6915,128.7,Boston,E,19,14,9,43,Graduation,19,Owner,Standalone\n4611,320.46,NJ City,E,10,19,23,38,High School,27,Owner,Standalone\n4348,5.4,NJ City,N,22,10,3,42,High School,19,Franchise,Complex\n8387,180.24,NJ City,E,7,8,1,83,No education,20,Owner,Standalone\n5019,115.89,Boston,E,1,11,2,63,Graduation,33,Owner,Standalone\n6175,376.26,Boston,E,20,13,4,49,High School,32,Owner,Standalone\n7265,497.06,Boston,N,4,9,19,44,Graduation,34,Patnership,Standalone\n3800,219.6,NJ City,W,14,11,19,29,High School,19,Franchise,Complex\n8540,472.7,NJ City,W,10,24,2,76,No education,33,Franchise,Complex\n5758,194.88,Boston,S,1,13,4,47,High School,29,Patnership,Complex\n6011,27.29,Boston,N,12,22,1,70,No education,35,Owner,Standalone\n3715,250,NJ City,E,6,21,8,60,High School,23,Owner,Standalone\n2767,432.96,NJ City,E,5,23,15,79,High School,26,Owner,Standalone\n2800,306.37,NJ City,W,9,21,14,69,High School,30,Franchise,Complex\n6595,90.73,Boston,E,8,22,4,33,High School,33,Owner,Standalone\n6749,319.21,Boston,N,22,12,10,36,Graduation,24,Patnership,Standalone\n7957,110.27,Boston,N,14,23,3,52,High School,27,Patnership,Standalone\n7229,107.08,Boston,N,10,8,3,51,No education,28,Patnership,Standalone\n6764,30.93,Boston,N,21,13,4,33,No education,35,Franchise,Complex\n5023,152.91,Boston,E,20,18,8,62,Graduation,29,Owner,Standalone\n3098,434.32,NJ City,S,4,10,4,62,No education,24,Patnership,Complex\n9030,313.89,NJ City,S,11,19,4,36,Graduation,29,Patnership,Complex\n7090,16.68,Boston,N,21,11,3,40,High School,32,Patnership,Complex\n1856,311.95,NJ City,W,21,9,10,69,High School,23,Franchise,Complex\n6307,446.85,Boston,E,15,22,20,30,Graduation,21,Owner,Standalone\n3790,130.83,NJ City,S,18,8,7,76,High School,26,Patnership,Complex\n1113,83.27,NJ City,N,10,15,3,49,Graduation,18,Patnership,Standalone\n4247,480.28,NJ City,E,8,24,19,63,High School,22,Owner,Standalone\n4785,443.21,NJ City,N,6,13,16,75,High School,26,Patnership,Standalone\n8405,162.35,NJ City,N,16,16,11,60,Graduation,25,Patnership,Standalone\n3223,388.49,NJ City,E,11,11,4,53,High School,18,Owner,Standalone\n1057,256.42,NJ City,N,22,14,5,61,High School,32,Patnership,Standalone\n2602,264.27,NJ City,S,19,16,3,65,High School,21,Patnership,Complex\n9328,56.05,NJ City,W,18,10,4,57,High School,23,Franchise,Complex\n6245,44.53,Boston,N,19,14,4,40,No education,28,Patnership,Standalone\n7510,146.46,Boston,S,19,19,6,71,Graduation,20,Patnership,Complex\n7577,372.6,Boston,N,19,11,17,59,Graduation,29,Patnership,Standalone\n4347,231.53,NJ City,E,11,15,18,78,High School,23,Owner,Standalone\n329,248,NJ City,N,3,12,25,40,High School,28,Patnership,Standalone\n5663,1.4,Boston,N,5,24,3,58,No education,27,Owner,Standalone\n5262,372.33,Boston,S,19,8,1,41,Graduation,19,Patnership,Complex\n2050,7.82,NJ City,N,25,9,1,49,High School,18,Patnership,Complex\n750,299.57,NJ City,S,6,14,10,40,High School,30,Patnership,Complex\n4417,344.78,NJ City,N,2,8,2,34,High School,25,Patnership,Standalone\n5804,290.92,Boston,W,6,16,12,32,Graduation,26,Franchise,Complex\n7041,98.07,Boston,N,8,22,2,51,Graduation,33,Patnership,Standalone\n6566,317.86,Boston,S,3,23,18,29,Graduation,30,Patnership,Complex\n886,141.69,NJ City,S,15,24,23,78,High School,30,Patnership,Complex\n3265,235,NJ City,N,2,21,6,63,High School,27,Patnership,Standalone\n5675,149.73,Boston,E,5,15,6,38,Graduation,20,Owner,Standalone\n141,287,NJ City,N,3,9,13,76,High School,28,Patnership,Standalone\n2504,8.41,NJ City,N,7,17,2,83,No education,27,Franchise,Complex\n5401,300.5,Boston,N,1,15,22,49,Graduation,26,Patnership,Standalone\n2883,239.37,NJ City,E,24,13,24,31,High School,24,Owner,Standalone\n109,157.06,NJ City,N,22,22,18,57,High School,30,Patnership,Standalone\n532,462.48,NJ City,W,4,15,22,79,High School,29,Franchise,Complex\n5001,91.78,Boston,N,16,16,4,66,Graduation,27,Patnership,Standalone\n1589,349.38,NJ City,N,19,10,12,49,High School,29,Patnership,Standalone\n2374,15.15,NJ City,N,16,14,4,78,High School,25,Patnership,Complex\n446,183.76,NJ City,S,16,13,5,54,No education,33,Patnership,Complex\n3292,308.17,NJ City,W,10,19,2,38,High School,26,Franchise,Complex\n593,488.2,NJ City,N,13,21,14,33,High School,18,Patnership,Standalone\n3218,44.8,NJ City,N,14,12,4,67,No education,21,Patnership,Complex\n4277,360.19,NJ City,N,23,12,13,66,High School,28,Patnership,Standalone\n594,76.95,NJ City,S,25,21,2,70,Graduation,28,Patnership,Complex\n1456,390.19,NJ City,W,15,9,22,46,High School,25,Franchise,Complex\n2424,101.13,NJ City,W,1,16,3,65,Graduation,35,Franchise,Complex\n4648,89.14,NJ City,W,2,23,4,64,High School,26,Franchise,Complex\n3495,115.93,NJ City,E,13,16,4,61,Graduation,19,Owner,Standalone\n4416,65.1,NJ City,N,12,12,2,78,Graduation,20,Franchise,Complex\n7258,436.68,Boston,S,25,13,24,73,Graduation,32,Patnership,Complex\n5834,307.71,Boston,S,8,10,10,66,Graduation,28,Patnership,Complex\n136,234.76,NJ City,W,16,23,10,52,High School,19,Franchise,Complex\n4121,196.17,NJ City,N,7,20,10,38,High School,25,Patnership,Standalone\n4511,459.12,NJ City,E,12,11,22,80,High School,33,Owner,Standalone\n8412,53.02,NJ City,W,6,24,3,34,Graduation,23,Franchise,Complex\n5754,286.99,Boston,S,7,14,10,60,Graduation,25,Patnership,Complex\n1885,430.96,NJ City,N,7,9,23,65,High School,32,Patnership,Standalone\n9775,495.41,NJ City,E,6,17,25,43,Graduation,34,Owner,Standalone\n5403,403.14,Boston,E,5,15,18,55,Graduation,30,Owner,Standalone\n3895,285.32,NJ City,E,5,14,6,58,High School,18,Owner,Standalone\n1883,135.02,NJ City,E,12,17,19,69,High School,34,Owner,Standalone\n5638,354.78,Boston,S,23,23,9,51,Graduation,35,Patnership,Complex\n9795,79.33,NJ City,E,23,24,3,38,Graduation,22,Owner,Standalone\n5106,42.96,Boston,N,5,21,4,80,Graduation,30,Patnership,Complex\n9878,196.36,NJ City,S,5,17,19,69,Graduation,23,Patnership,Complex\n4331,194.55,NJ City,E,5,19,8,83,High School,27,Owner,Standalone\n6096,164.66,Boston,W,2,18,17,47,Graduation,25,Franchise,Complex\n5833,420.39,Boston,N,2,11,7,83,Graduation,24,Patnership,Standalone\n4202,225.26,NJ City,S,14,9,9,38,High School,19,Patnership,Complex\n8061,386.36,NJ City,N,20,9,21,77,Graduation,24,Patnership,Standalone\n2613,150,NJ City,N,3,24,21,65,High School,22,Patnership,Standalone\n2499,10.75,NJ City,N,17,8,2,74,Graduation,29,Owner,Standalone\n6403,412.73,Boston,E,18,13,14,30,Graduation,35,Owner,Standalone\n7750,117.94,Boston,S,24,14,4,35,High School,27,Patnership,Complex\n8823,237.37,NJ City,E,23,14,24,69,Graduation,25,Owner,Standalone\n8028,252.99,Boston,W,5,13,13,36,Graduation,19,Franchise,Complex\n3027,375.16,NJ City,E,23,9,16,43,High School,31,Owner,Standalone\n3391,111.05,NJ City,E,5,9,4,58,High School,18,Owner,Standalone\n7038,267.11,Boston,S,4,18,6,43,Graduation,31,Patnership,Complex\n9956,253.13,NJ City,W,8,21,19,85,Graduation,21,Franchise,Complex\n1508,203.33,NJ City,W,22,17,14,66,High School,20,Franchise,Complex\n4721,119,NJ City,N,2,13,7,35,High School,21,Patnership,Standalone\n9539,353.93,NJ City,E,16,15,5,43,No education,35,Owner,Standalone\n1445,168,NJ City,N,1,21,10,57,High School,33,Patnership,Standalone\n8505,112.58,NJ City,N,21,23,3,40,Graduation,25,Patnership,Standalone\n3500,402.13,NJ City,W,18,21,15,49,High School,22,Franchise,Complex\n4973,244.02,Boston,N,23,16,14,75,High School,20,Patnership,Standalone\n8590,487.01,NJ City,S,9,11,11,44,Graduation,32,Patnership,Complex\n733,450.79,NJ City,N,5,20,20,41,High School,28,Patnership,Standalone\n150,268.21,NJ City,S,18,11,17,43,High School,19,Patnership,Complex\n2609,172.98,NJ City,N,8,8,20,28,High School,18,Patnership,Standalone\n1747,294.54,NJ City,E,4,16,16,80,High School,33,Owner,Standalone\n1884,150.3,NJ City,W,23,15,11,72,High School,33,Franchise,Complex\n9499,447.41,NJ City,E,9,20,3,26,High School,25,Owner,Standalone\n4300,323.02,NJ City,W,5,10,24,46,High School,21,Franchise,Complex\n8782,448.51,NJ City,S,23,13,11,74,Graduation,34,Patnership,Complex\n4318,10.02,NJ City,N,9,21,4,81,High School,24,Patnership,Complex\n6612,36.76,Boston,N,10,16,2,29,Graduation,19,Franchise,Complex\n4735,362.87,NJ City,E,15,14,1,54,High School,29,Owner,Standalone\n7421,119.12,Boston,N,25,22,2,58,No education,28,Patnership,Standalone\n3577,27.01,NJ City,N,12,10,1,85,High School,19,Patnership,Standalone\n9196,427.81,NJ City,W,13,16,12,72,Graduation,23,Franchise,Complex\n6121,78.01,Boston,N,21,20,4,80,High School,25,Patnership,Standalone\n6399,178.98,Boston,E,16,10,4,50,Graduation,31,Owner,Standalone\n7505,365.35,Boston,N,24,16,25,68,Graduation,35,Patnership,Standalone\n5291,428.93,Boston,E,2,18,18,84,Graduation,20,Owner,Standalone\n9799,276.96,NJ City,E,14,16,1,57,High School,18,Owner,Standalone\n775,63.72,NJ City,N,21,8,3,25,High School,34,Owner,Standalone\n6756,314.98,Boston,W,1,10,18,38,Graduation,20,Franchise,Complex\n8474,40.05,NJ City,N,22,13,3,39,No education,33,Patnership,Complex\n9932,260.53,NJ City,W,16,12,25,37,Graduation,22,Franchise,Complex\n6871,414.39,Boston,E,8,24,8,85,Graduation,25,Owner,Standalone\n7374,53.24,Boston,S,13,9,2,64,Graduation,22,Patnership,Complex\n2719,316.02,NJ City,E,1,8,2,55,High School,26,Owner,Standalone\n8403,224.16,NJ City,E,23,18,22,35,Graduation,35,Owner,Standalone\n9640,102.07,NJ City,W,23,14,4,58,High School,23,Franchise,Complex\n7357,347.9,Boston,N,1,20,9,65,Graduation,32,Patnership,Standalone\n8329,80.09,NJ City,N,10,8,2,78,High School,22,Patnership,Standalone\n9629,494.44,NJ City,N,25,23,5,80,No education,31,Patnership,Standalone\n5452,353.79,Boston,W,5,11,5,45,High School,31,Franchise,Complex\n2312,85.02,NJ City,W,1,16,3,55,No education,24,Franchise,Complex\n1688,1.36,NJ City,N,20,15,3,45,No education,25,Franchise,Complex\n5600,225.58,Boston,W,1,11,11,78,Graduation,27,Franchise,Complex\n3638,186,NJ City,S,2,13,19,52,High School,35,Patnership,Complex\n8519,66.42,NJ City,N,9,20,2,26,No education,20,Owner,Standalone\n8807,108.01,NJ City,E,7,12,1,39,No education,26,Owner,Standalone\n8869,292.7,NJ City,N,13,12,7,68,Graduation,19,Patnership,Standalone\n3306,262.93,NJ City,S,25,22,22,35,High School,32,Patnership,Complex\n564,44.83,NJ City,N,8,22,1,57,Graduation,29,Franchise,Complex\n2907,264.2,NJ City,E,12,12,18,52,High School,34,Owner,Standalone\n9621,341.24,NJ City,N,5,16,16,29,Graduation,20,Patnership,Standalone\n7261,399.33,Boston,N,11,14,2,60,High School,25,Patnership,Standalone\n957,354.03,NJ City,N,23,8,21,61,High School,31,Patnership,Standalone\n1840,182.6,NJ City,W,8,13,22,31,High School,35,Franchise,Complex\n7892,197.82,Boston,W,6,13,11,42,Graduation,34,Franchise,Complex\n4639,460.22,NJ City,E,10,14,16,72,High School,25,Owner,Standalone\n1836,116.36,NJ City,W,10,17,3,62,Graduation,32,Franchise,Complex\n137,17.78,NJ City,N,17,19,3,63,No education,28,Patnership,Standalone\n3642,18.03,NJ City,N,8,12,4,35,Graduation,28,Patnership,Complex\n8834,159.76,NJ City,S,6,8,25,54,Graduation,30,Patnership,Complex\n9003,391.9,NJ City,E,7,10,2,53,Graduation,30,Owner,Standalone\n1858,236.4,NJ City,S,13,14,20,39,High School,23,Patnership,Complex\n5449,285.42,Boston,N,16,21,8,85,Graduation,32,Patnership,Standalone\n9886,66.54,NJ City,N,13,15,2,67,High School,31,Patnership,Complex\n2169,104.06,NJ City,N,3,18,2,79,Graduation,25,Patnership,Standalone\n1581,424.09,NJ City,N,10,11,1,85,Graduation,29,Patnership,Standalone\n1534,98.88,NJ City,S,18,20,3,31,High School,30,Patnership,Complex\n4881,337.02,NJ City,N,9,22,15,53,High School,27,Patnership,Standalone\n739,233.51,NJ City,E,12,21,21,78,High School,27,Owner,Standalone\n396,317.65,NJ City,W,15,18,9,67,High School,32,Franchise,Complex\n6778,221.31,Boston,S,15,10,23,78,Graduation,19,Patnership,Complex\n8089,379.75,NJ City,N,21,9,11,62,Graduation,34,Patnership,Standalone\n3026,221.86,NJ City,S,1,9,1,48,No education,33,Patnership,Complex\n6343,252.79,Boston,E,10,13,24,27,Graduation,22,Owner,Standalone\n8221,362.7,NJ City,N,9,16,13,33,Graduation,32,Patnership,Standalone\n2149,366.27,NJ City,N,21,9,24,82,High School,33,Patnership,Standalone\n4487,208.2,NJ City,E,5,16,10,27,High School,35,Owner,Standalone\n1332,4.87,NJ City,N,5,22,2,47,Graduation,18,Franchise,Complex\n4193,196.93,NJ City,N,20,21,9,71,High School,27,Patnership,Standalone\n620,91.49,NJ City,W,7,22,4,54,No education,19,Franchise,Complex\n18,425.42,Boston,S,24,20,25,64,High School,27,Patnership,Complex\n4970,50.83,Boston,S,3,17,4,32,No education,30,Patnership,Complex\n1245,138.13,NJ City,N,5,18,22,37,High School,30,Patnership,Standalone\n9895,2.12,NJ City,E,4,23,3,41,High School,35,Owner,Standalone\n3805,260.27,NJ City,N,14,22,6,80,High School,20,Patnership,Standalone\n6407,134.51,Boston,E,14,13,9,69,Graduation,26,Owner,Standalone\n1413,47.27,NJ City,N,13,18,3,60,Graduation,32,Patnership,Standalone\n1563,324.2,NJ City,E,13,14,1,65,Graduation,32,Owner,Standalone\n4941,291.44,Boston,N,18,19,25,41,High School,33,Patnership,Standalone\n4643,344.5,NJ City,E,25,15,19,47,High School,20,Owner,Standalone\n1076,394.87,NJ City,W,19,10,17,33,High School,24,Franchise,Complex\n2743,380.95,NJ City,E,14,9,9,39,High School,19,Owner,Standalone\n2820,299.21,NJ City,W,12,13,8,41,High School,32,Franchise,Complex\n758,455.76,NJ City,S,4,13,1,31,No education,28,Patnership,Complex\n6521,352.98,Boston,N,11,21,19,65,Graduation,22,Patnership,Standalone\n2839,59.39,NJ City,E,14,17,4,41,High School,24,Owner,Standalone\n580,498.23,NJ City,W,20,22,15,40,High School,18,Franchise,Complex\n339,156.54,NJ City,E,18,9,20,56,High School,34,Owner,Standalone\n6057,85.83,Boston,N,25,11,2,47,Graduation,20,Patnership,Standalone\n9081,259.7,NJ City,N,22,24,24,45,Graduation,29,Patnership,Standalone\n3395,42.21,NJ City,N,10,18,3,72,No education,30,Owner,Standalone\n4110,351.59,NJ City,S,10,13,23,85,High School,28,Patnership,Complex\n3747,161.25,NJ City,E,14,19,10,71,High School,19,Owner,Standalone\n9140,494.16,NJ City,W,15,15,24,81,Graduation,33,Franchise,Complex\n5653,203.12,Boston,N,16,11,2,46,High School,23,Patnership,Standalone\n7944,316.9,Boston,W,8,22,20,62,Graduation,18,Franchise,Complex\n3422,23.62,NJ City,N,21,18,1,83,No education,35,Patnership,Complex\n9366,157.35,NJ City,S,8,11,20,25,Graduation,32,Patnership,Complex\n3099,17.02,NJ City,N,4,18,3,70,Graduation,20,Owner,Standalone\n3338,327.45,NJ City,S,14,15,25,29,High School,31,Patnership,Complex\n4474,273.05,NJ City,S,21,14,20,49,High School,22,Patnership,Complex\n7904,310.24,Boston,W,13,14,20,34,Graduation,28,Franchise,Complex\n9977,302.97,NJ City,N,21,23,4,85,No education,33,Patnership,Standalone\n1240,39.11,NJ City,N,14,11,3,58,High School,24,Franchise,Complex\n1303,173,NJ City,E,3,16,10,70,High School,19,Owner,Standalone\n3619,73.37,NJ City,N,15,9,3,47,High School,24,Owner,Standalone\n7687,50.19,Boston,E,14,22,2,52,High School,34,Owner,Standalone\n4890,30.03,NJ City,N,20,21,1,78,Graduation,19,Patnership,Complex\n7232,21.54,Boston,N,19,10,4,57,No education,27,Franchise,Complex\n8451,390.09,NJ City,E,24,22,3,43,Graduation,29,Owner,Standalone\n9275,48.62,NJ City,E,19,22,3,38,No education,29,Owner,Standalone\n6095,235.33,Boston,E,23,13,21,71,Graduation,25,Owner,Standalone\n8817,149.92,NJ City,N,20,13,22,55,Graduation,18,Patnership,Standalone\n4542,59.22,NJ City,S,25,14,3,72,Graduation,29,Patnership,Complex\n362,392.8,NJ City,S,5,15,16,75,High School,32,Patnership,Complex\n9400,269.04,NJ City,W,15,19,5,65,High School,27,Franchise,Complex\n1490,44.72,NJ City,N,3,22,4,67,No education,34,Patnership,Complex\n2892,279.83,NJ City,W,12,22,24,43,High School,35,Franchise,Complex\n4164,186.67,NJ City,W,15,24,16,71,High School,27,Franchise,Complex\n7438,393.92,Boston,S,24,17,21,51,Graduation,33,Patnership,Complex\n7948,101.25,Boston,W,9,10,1,82,High School,24,Franchise,Complex\n3133,195.92,NJ City,N,6,14,25,52,High School,28,Patnership,Standalone\n1439,209.14,NJ City,E,25,12,16,56,High School,35,Owner,Standalone\n3357,274.66,NJ City,N,11,13,10,49,High School,33,Patnership,Standalone\n6048,4.35,Boston,N,14,16,1,39,Graduation,31,Franchise,Complex\n4053,496.53,NJ City,N,18,16,16,84,High School,19,Patnership,Standalone\n9062,261.69,NJ City,S,20,23,11,63,Graduation,35,Patnership,Complex\n4024,469.5,NJ City,W,24,17,1,38,High School,33,Franchise,Complex\n7810,192.66,Boston,S,17,19,15,49,Graduation,24,Patnership,Complex\n4592,335.85,NJ City,W,10,14,10,65,High School,27,Franchise,Complex\n9184,370.79,NJ City,W,20,15,10,51,Graduation,29,Franchise,Complex\n7098,122.3,Boston,S,17,18,3,44,Graduation,21,Patnership,Complex\n8063,228.8,NJ City,E,6,15,17,44,Graduation,30,Owner,Standalone\n8291,439.23,NJ City,E,25,24,3,68,No education,32,Owner,Standalone\n1555,470.9,NJ City,E,14,16,25,67,High School,25,Owner,Standalone\n3609,273.32,NJ City,N,14,15,9,75,High School,27,Patnership,Standalone\n4150,28.57,NJ City,N,19,16,3,27,High School,35,Patnership,Complex\n2735,111.14,NJ City,E,6,11,1,37,No education,33,Owner,Standalone\n3486,269.55,NJ City,S,18,24,9,43,High School,29,Patnership,Complex\n8743,352.98,NJ City,E,9,17,22,26,Graduation,21,Owner,Standalone\n3377,452.97,NJ City,N,16,19,19,68,High School,28,Patnership,Standalone\n5733,151.34,Boston,N,24,11,10,45,Graduation,18,Patnership,Standalone\n8706,137.25,NJ City,S,10,19,3,33,Graduation,35,Patnership,Complex\n3646,0.48,NJ City,N,1,12,4,31,High School,18,Patnership,Complex\n2618,369.27,NJ City,S,18,9,21,77,High School,32,Patnership,Complex\n6318,199.12,Boston,S,13,11,25,69,Graduation,33,Patnership,Complex\n417,199.89,NJ City,N,19,19,12,42,High School,30,Patnership,Standalone\n544,50.52,NJ City,W,13,12,2,80,High School,34,Franchise,Complex\n5842,479.73,Boston,S,13,20,13,68,Graduation,35,Patnership,Complex\n9959,1.18,NJ City,N,14,13,2,58,No education,23,Owner,Standalone\n709,230.03,NJ City,N,25,22,13,85,High School,20,Patnership,Standalone\n6241,33.19,Boston,N,9,24,2,34,High School,31,Patnership,Standalone\n8077,182.66,NJ City,N,23,18,6,77,Graduation,23,Patnership,Standalone\n3983,93.63,NJ City,E,17,19,2,47,No education,23,Owner,Standalone\n7855,45.39,Boston,N,10,19,3,31,High School,24,Owner,Standalone\n7633,459.38,Boston,N,3,15,4,31,High School,20,Patnership,Standalone\n8482,72.98,NJ City,N,2,17,3,82,High School,32,Patnership,Complex\n7963,455.62,Boston,E,22,15,3,63,High School,27,Owner,Standalone\n7538,383.51,Boston,S,24,22,6,49,Graduation,23,Patnership,Complex\n9619,301.39,NJ City,E,17,20,9,84,Graduation,23,Owner,Standalone\n8420,487.84,NJ City,W,25,9,25,43,Graduation,30,Franchise,Complex\n1684,470.95,NJ City,W,24,15,20,26,High School,35,Franchise,Complex\n1488,292.57,NJ City,W,15,17,21,36,High School,35,Franchise,Complex\n3868,83.84,NJ City,W,16,24,1,41,High School,25,Franchise,Complex\n6365,245.55,Boston,N,3,17,7,84,Graduation,34,Patnership,Standalone\n2947,262.81,NJ City,E,6,16,23,84,High School,30,Owner,Standalone\n5383,262.09,Boston,E,11,20,4,36,High School,30,Owner,Standalone\n790,148,NJ City,S,2,20,20,25,High School,31,Patnership,Complex\n254,0.67,NJ City,N,11,17,1,32,No education,33,Patnership,Complex\n4698,167.63,NJ City,S,5,22,15,84,High School,20,Patnership,Complex\n4222,101.47,NJ City,S,22,14,1,29,High School,23,Patnership,Complex\n666,243,NJ City,S,1,9,10,33,High School,22,Patnership,Complex\n2044,307,NJ City,W,3,8,18,80,High School,28,Franchise,Complex\n2080,7.8,NJ City,N,8,23,1,61,High School,31,Franchise,Complex\n3273,356.62,NJ City,N,6,15,24,44,High School,25,Patnership,Standalone\n8424,267.68,NJ City,W,2,13,2,47,Graduation,24,Franchise,Complex\n9452,338.72,NJ City,W,7,14,24,30,Graduation,26,Franchise,Complex\n5459,453.82,Boston,E,7,14,18,63,Graduation,27,Owner,Standalone\n2455,222.85,NJ City,E,25,12,14,57,High School,27,Owner,Standalone\n2433,292.41,NJ City,N,6,22,13,63,High School,22,Patnership,Standalone\n7185,355.27,Boston,N,14,18,7,57,Graduation,32,Patnership,Standalone\n2859,26.68,NJ City,N,5,21,4,72,Graduation,24,Owner,Standalone\n2459,102.98,NJ City,E,25,21,4,27,No education,28,Owner,Standalone\n7440,338.88,Boston,W,3,8,9,65,Graduation,25,Franchise,Complex\n7190,154.56,Boston,S,1,24,21,66,Graduation,28,Patnership,Complex\n8379,321.32,NJ City,E,10,16,13,33,Graduation,19,Owner,Standalone\n3777,244,NJ City,N,3,11,11,40,High School,30,Patnership,Standalone\n7332,6.7,Boston,N,5,14,2,46,Graduation,31,Franchise,Complex\n3684,437.2,NJ City,W,8,11,2,78,Graduation,32,Franchise,Complex\n5293,470.02,Boston,N,3,10,4,82,High School,30,Patnership,Standalone\n7175,375.74,Boston,E,21,23,8,33,Graduation,25,Owner,Standalone\n3972,405.72,NJ City,W,17,10,1,42,Graduation,25,Franchise,Complex\n3424,293,NJ City,W,3,19,8,69,High School,27,Franchise,Complex\n7480,275.15,Boston,W,19,16,5,78,High School,33,Franchise,Complex\n8709,379.18,NJ City,N,11,20,14,56,Graduation,31,Patnership,Standalone\n1004,262.09,NJ City,W,23,20,17,82,High School,21,Franchise,Complex\n5054,45.8,Boston,N,4,8,2,31,No education,33,Patnership,Complex\n7685,469.12,Boston,N,5,8,3,70,No education,27,Patnership,Standalone\n6266,421.81,Boston,S,1,13,4,34,No education,28,Patnership,Complex\n6788,72.47,Boston,N,21,20,4,65,No education,29,Franchise,Complex\n1964,280.24,NJ City,W,25,10,19,71,High School,20,Franchise,Complex\n3239,452.65,NJ City,E,9,16,23,26,High School,33,Owner,Standalone\n6109,415.25,Boston,N,22,21,4,51,High School,28,Patnership,Standalone\n108,237.69,NJ City,W,18,20,17,80,High School,32,Franchise,Complex\n9945,165,NJ City,N,13,22,14,51,Graduation,26,Patnership,Standalone\n3045,465.03,NJ City,N,13,20,22,56,High School,24,Patnership,Standalone\n9701,208.05,NJ City,N,7,23,9,81,Graduation,20,Patnership,Standalone\n6364,95.93,Boston,W,19,8,4,84,High School,20,Franchise,Complex\n6683,494.72,Boston,E,24,14,9,79,Graduation,30,Owner,Standalone\n6201,123.97,Boston,N,9,18,2,48,Graduation,35,Patnership,Standalone\n2620,385.01,NJ City,W,10,17,23,58,High School,19,Franchise,Complex\n4297,96.67,NJ City,N,23,22,1,80,High School,18,Patnership,Standalone\n4642,340.09,NJ City,S,6,12,6,43,High School,31,Patnership,Complex\n3566,436.53,NJ City,S,21,22,22,63,High School,30,Patnership,Complex\n8934,1.66,NJ City,N,13,19,1,50,Graduation,21,Patnership,Complex\n5693,179.12,Boston,N,8,14,21,28,Graduation,19,Patnership,Standalone\n1521,167.6,NJ City,N,10,14,6,26,High School,20,Patnership,Standalone\n7971,343.3,Boston,E,3,17,18,45,Graduation,29,Owner,Standalone\n4599,200.92,NJ City,E,9,21,15,34,High School,23,Owner,Standalone\n2390,128.91,NJ City,S,4,19,1,59,No education,26,Patnership,Complex\n8627,269.03,NJ City,E,17,9,24,44,Graduation,24,Owner,Standalone\n7582,326.31,Boston,S,24,8,15,41,Graduation,29,Patnership,Complex\n2622,47.92,NJ City,S,14,10,3,84,Graduation,31,Patnership,Complex\n5866,436.57,Boston,S,22,12,23,37,Graduation,20,Patnership,Complex\n9069,397.03,NJ City,N,17,23,4,64,Graduation,21,Patnership,Standalone\n5673,363.76,Boston,N,16,24,16,38,Graduation,21,Patnership,Standalone\n7763,42.06,Boston,N,16,18,3,78,No education,32,Owner,Standalone\n984,433.51,NJ City,W,22,10,6,29,High School,26,Franchise,Complex\n263,308.71,NJ City,E,3,23,2,69,No education,19,Owner,Standalone\n4138,29.71,NJ City,N,21,21,4,56,High School,35,Patnership,Complex\n5576,54.49,Boston,W,1,23,2,61,No education,19,Franchise,Complex\n4775,476.12,NJ City,E,3,13,5,79,No education,25,Owner,Standalone\n9786,416.32,NJ City,S,2,10,9,84,Graduation,29,Patnership,Complex\n4552,127.22,NJ City,W,20,18,1,68,High School,32,Franchise,Complex\n6891,431.19,Boston,E,25,11,24,83,Graduation,34,Owner,Standalone\n1143,219.11,NJ City,E,18,10,5,60,Graduation,20,Owner,Standalone\n4725,274,NJ City,N,1,10,18,36,High School,35,Patnership,Standalone\n4137,57.72,NJ City,N,8,13,3,57,Graduation,18,Patnership,Standalone\n4455,462.22,NJ City,E,11,10,1,40,Graduation,35,Owner,Standalone\n3736,49.74,NJ City,W,25,8,2,58,High School,18,Franchise,Complex\n743,490.1,NJ City,E,5,14,24,56,High School,32,Owner,Standalone\n8552,131.56,NJ City,W,7,10,21,49,Graduation,20,Franchise,Complex\n3565,154.26,NJ City,N,20,16,16,66,High School,25,Patnership,Standalone\n7008,284.01,Boston,W,10,15,17,26,Graduation,31,Franchise,Complex\n5681,7.45,Boston,N,10,9,4,79,No education,21,Patnership,Standalone\n1279,339.53,NJ City,E,11,21,17,35,High School,31,Owner,Standalone\n4977,221.04,Boston,N,21,14,13,56,High School,21,Patnership,Standalone\n2235,17.12,NJ City,N,4,10,3,61,Graduation,18,Owner,Standalone\n9213,207.61,NJ City,N,22,14,23,53,Graduation,18,Patnership,Standalone\n6397,190.95,Boston,N,21,8,4,38,High School,20,Patnership,Standalone\n647,27.23,NJ City,N,8,17,4,62,No education,26,Owner,Standalone\n9132,55.66,NJ City,W,25,12,1,61,Graduation,24,Franchise,Complex\n5796,123.94,Boston,W,16,16,2,31,Graduation,24,Franchise,Complex\n8355,376.71,NJ City,E,2,17,7,50,Graduation,35,Owner,Standalone\n7394,6.21,Boston,N,20,14,4,35,No education,35,Patnership,Complex\n4620,219.06,NJ City,W,10,21,8,84,High School,31,Franchise,Complex\n3661,145.25,NJ City,N,18,21,5,39,High School,32,Patnership,Standalone\n4280,52.22,NJ City,W,11,15,4,31,No education,31,Franchise,Complex\n2207,385.5,NJ City,E,20,18,9,77,High School,19,Owner,Standalone\n3233,202.06,NJ City,N,11,15,6,50,High School,28,Patnership,Standalone\n321,66.77,NJ City,N,10,13,3,46,Graduation,18,Patnership,Standalone\n2077,398.03,NJ City,N,9,12,11,46,High School,31,Patnership,Standalone\n4816,29.11,NJ City,N,9,22,4,55,High School,26,Franchise,Complex\n735,448.04,NJ City,E,19,21,23,69,High School,24,Owner,Standalone\n1601,449.13,NJ City,N,10,18,21,70,High School,28,Patnership,Standalone\n9811,186.85,NJ City,E,21,21,13,27,Graduation,29,Owner,Standalone\n7725,266.51,Boston,N,9,24,20,56,Graduation,22,Patnership,Standalone\n1069,112.7,NJ City,N,23,20,1,52,High School,27,Patnership,Standalone\n6053,126.71,Boston,N,12,22,2,73,No education,19,Patnership,Standalone\n5628,76.05,Boston,W,13,13,4,82,Graduation,22,Franchise,Complex\n7978,97.78,Boston,S,18,13,4,85,High School,18,Patnership,Complex\n8375,0.93,NJ City,N,25,11,2,62,No education,32,Owner,Standalone\n7986,345.33,Boston,S,7,12,14,58,Graduation,21,Patnership,Complex\n695,181.23,NJ City,E,17,18,10,44,High School,24,Owner,Standalone\n4990,322.77,Boston,S,6,15,4,71,High School,20,Patnership,Complex\n4425,307,NJ City,N,2,17,22,42,High School,35,Patnership,Standalone\n5130,58.05,Boston,S,18,20,1,74,Graduation,24,Patnership,Complex\n366,434.25,NJ City,S,10,23,20,75,High School,20,Patnership,Complex\n478,290.06,NJ City,S,20,15,7,51,High School,34,Patnership,Complex\n9502,236.16,NJ City,S,11,13,5,26,High School,25,Patnership,Complex\n7787,390.22,Boston,E,4,12,24,45,Graduation,32,Owner,Standalone\n6654,187.45,Boston,S,5,19,3,35,Graduation,24,Patnership,Complex\n171,370.97,NJ City,E,25,10,9,49,High School,35,Owner,Standalone\n2395,253.76,NJ City,E,18,13,4,85,High School,20,Owner,Standalone\n1394,35.59,NJ City,N,2,19,3,54,No education,20,Patnership,Complex\n5519,136.16,Boston,E,8,23,11,36,Graduation,31,Owner,Standalone\n8548,400.93,NJ City,W,3,13,4,52,High School,31,Franchise,Complex\n7712,242.2,Boston,W,22,16,16,62,Graduation,29,Franchise,Complex\n9118,195.84,NJ City,S,6,11,22,43,Graduation,22,Patnership,Complex\n7902,93.01,Boston,S,19,24,4,81,Graduation,31,Patnership,Complex\n5789,14.55,Boston,N,16,8,1,40,No education,24,Patnership,Standalone\n7530,196.82,Boston,S,17,24,13,83,Graduation,26,Patnership,Complex\n5912,240.14,Boston,W,17,20,2,30,No education,19,Franchise,Complex\n191,459.08,NJ City,E,6,9,10,84,High School,32,Owner,Standalone\n5149,371.75,Boston,N,3,10,24,69,Graduation,31,Patnership,Standalone\n5159,209.01,Boston,E,16,24,11,53,Graduation,19,Owner,Standalone\n6620,49.34,Boston,W,7,21,4,71,No education,27,Franchise,Complex\n9869,380.5,NJ City,N,21,17,19,34,Graduation,24,Patnership,Standalone\n2191,47.9,NJ City,E,9,18,1,57,High School,34,Owner,Standalone\n9171,208.18,NJ City,E,25,22,1,81,Graduation,29,Owner,Standalone\n1609,44.63,NJ City,N,20,15,1,32,High School,28,Patnership,Standalone\n1525,7.11,NJ City,N,15,19,4,68,High School,29,Patnership,Standalone\n1596,283.6,NJ City,W,17,10,13,57,High School,30,Franchise,Complex\n7966,246.22,Boston,S,3,17,13,55,Graduation,19,Patnership,Complex\n1340,338.15,NJ City,W,12,23,14,77,High School,33,Franchise,Complex\n7885,180.54,Boston,N,6,13,11,31,Graduation,33,Patnership,Standalone\n3664,498.49,NJ City,W,19,23,15,54,High School,20,Franchise,Complex\n1441,235.41,NJ City,N,14,21,6,57,High School,19,Patnership,Standalone\n9968,147.07,NJ City,W,11,13,22,35,Graduation,20,Franchise,Complex\n9506,117.15,NJ City,S,10,19,4,34,No education,20,Patnership,Complex\n4377,169.73,NJ City,N,20,12,14,27,High School,26,Patnership,Standalone\n6882,5.54,Boston,N,9,14,1,74,Graduation,24,Patnership,Complex\n8553,150.3,NJ City,N,25,22,21,82,Graduation,20,Patnership,Standalone\n1058,96.01,NJ City,S,8,14,3,49,No education,24,Patnership,Complex\n2107,322.55,NJ City,E,25,17,8,47,High School,25,Owner,Standalone\n4778,218,NJ City,S,3,17,23,72,High School,32,Patnership,Complex\n8209,388.3,NJ City,N,21,10,15,67,Graduation,21,Patnership,Standalone\n7216,205.42,Boston,W,6,8,8,30,Graduation,18,Franchise,Complex\n7448,466.56,Boston,W,21,21,14,42,Graduation,29,Franchise,Complex\n4120,196,NJ City,W,18,20,13,64,High School,21,Franchise,Complex\n4630,254.82,NJ City,S,25,14,3,28,High School,21,Patnership,Complex\n2952,11.31,NJ City,N,18,17,4,74,Graduation,28,Franchise,Complex\n737,203,NJ City,N,2,21,6,64,High School,22,Patnership,Standalone\n2276,469.93,NJ City,W,13,17,6,73,High School,26,Franchise,Complex\n3103,234,NJ City,E,2,10,19,74,High School,34,Owner,Standalone\n7847,52.1,Boston,E,4,24,4,44,No education,18,Owner,Standalone\n3952,161.07,NJ City,W,23,10,5,66,High School,20,Franchise,Complex\n226,21.35,NJ City,N,14,24,4,26,High School,35,Patnership,Complex\n9430,138.19,NJ City,S,13,11,7,63,Graduation,29,Patnership,Complex\n665,112.47,NJ City,N,5,24,3,85,No education,35,Patnership,Standalone\n3291,84.28,NJ City,E,5,18,2,57,Graduation,23,Owner,Standalone\n8487,354.85,NJ City,E,25,15,16,34,Graduation,34,Owner,Standalone\n9668,295.56,NJ City,W,25,8,19,61,Graduation,32,Franchise,Complex\n43,394.95,Boston,E,12,13,25,81,High School,25,Owner,Standalone\n1309,249.31,NJ City,N,8,11,2,66,High School,23,Patnership,Standalone\n5763,260.5,Boston,E,7,17,5,29,Graduation,33,Owner,Standalone\n8220,401.66,NJ City,W,21,15,16,81,Graduation,23,Franchise,Complex\n9377,495.24,NJ City,N,12,9,18,57,Graduation,29,Patnership,Standalone\n5249,373.95,Boston,N,17,19,20,60,Graduation,24,Patnership,Standalone\n3617,427.12,NJ City,N,19,20,15,45,High School,27,Patnership,Standalone\n6639,347.11,Boston,E,18,23,21,53,Graduation,34,Owner,Standalone\n7523,336.93,Boston,E,7,22,2,59,No education,32,Owner,Standalone\n2037,262,NJ City,N,3,17,7,55,High School,19,Patnership,Standalone\n9647,428.61,NJ City,E,7,19,23,42,Graduation,34,Owner,Standalone\n4671,495.35,NJ City,E,25,16,19,76,High School,27,Owner,Standalone\n827,290.21,NJ City,E,4,22,22,58,High School,20,Owner,Standalone\n6432,199.16,Boston,W,6,8,17,69,Graduation,30,Franchise,Complex\n5405,56.95,Boston,N,18,22,2,83,No education,22,Patnership,Standalone\n7259,248.44,Boston,E,8,22,22,49,Graduation,28,Owner,Standalone\n5277,125.3,Boston,N,9,9,4,26,Graduation,19,Patnership,Standalone\n404,101.62,NJ City,W,20,12,2,80,No education,25,Franchise,Complex\n7562,488.01,Boston,S,5,16,22,35,Graduation,20,Patnership,Complex\n2090,311,NJ City,S,2,18,14,39,High School,18,Patnership,Complex\n8829,236.03,NJ City,N,21,8,17,37,Graduation,22,Patnership,Standalone\n8001,88.33,Boston,N,7,19,2,34,Graduation,35,Patnership,Standalone\n1166,346.97,NJ City,S,16,9,7,43,High School,35,Patnership,Complex\n6572,338.79,Boston,W,22,14,24,60,Graduation,19,Franchise,Complex\n1660,319.67,NJ City,W,12,14,21,38,High School,18,Franchise,Complex\n6081,263.68,Boston,N,25,11,21,80,Graduation,25,Patnership,Standalone\n2626,47.63,NJ City,S,10,24,2,85,High School,23,Patnership,Complex\n7854,228.51,Boston,S,5,16,21,62,Graduation,33,Patnership,Complex\n1008,5.16,NJ City,N,19,15,4,49,Graduation,22,Franchise,Complex\n4462,70.14,NJ City,N,18,22,3,55,High School,31,Patnership,Complex\n5921,342.83,Boston,N,9,8,13,56,Graduation,19,Patnership,Standalone\n60,187.42,Boston,W,8,19,16,53,High School,32,Franchise,Complex\n5026,203.07,Boston,S,25,17,8,49,Graduation,30,Patnership,Complex\n8289,108.47,NJ City,N,8,8,3,53,Graduation,25,Patnership,Standalone\n213,9.15,NJ City,N,9,18,3,75,Graduation,20,Patnership,Standalone\n6541,126.38,Boston,N,20,23,1,43,High School,35,Patnership,Standalone\n1177,142,NJ City,N,3,24,12,50,High School,19,Patnership,Standalone\n9490,147.78,NJ City,S,23,9,16,56,Graduation,28,Patnership,Complex\n5068,354.1,Boston,W,10,10,15,66,Graduation,21,Franchise,Complex\n3675,215.74,NJ City,E,22,9,2,68,Graduation,27,Owner,Standalone\n3647,374.4,NJ City,E,8,19,25,36,High School,32,Owner,Standalone\n9637,206.44,NJ City,N,2,24,18,54,Graduation,33,Patnership,Standalone\n283,241,NJ City,E,1,10,16,27,High School,21,Owner,Standalone\n5676,100.47,Boston,W,9,9,3,83,Graduation,31,Franchise,Complex\n5363,216.11,Boston,E,11,20,16,48,Graduation,21,Owner,Standalone\n7035,225.27,Boston,E,7,22,7,72,Graduation,31,Owner,Standalone\n4559,487.7,NJ City,E,14,19,10,61,High School,33,Owner,Standalone\n4015,70.09,NJ City,N,18,22,3,61,High School,31,Owner,Standalone\n7285,148.53,Boston,N,5,14,21,36,Graduation,22,Patnership,Standalone\n5521,340.24,Boston,N,25,16,18,52,Graduation,27,Patnership,Standalone\n9806,404.17,NJ City,S,9,14,24,44,Graduation,31,Patnership,Complex\n2830,342.93,NJ City,S,16,21,9,55,High School,27,Patnership,Complex\n8808,409.54,NJ City,W,9,21,3,52,Graduation,26,Franchise,Complex\n7307,375.79,Boston,E,11,11,1,49,No education,35,Owner,Standalone\n2582,391.96,NJ City,S,12,22,20,66,High School,26,Patnership,Complex\n4707,179.52,NJ City,E,10,15,25,74,High School,28,Owner,Standalone\n9812,218.84,NJ City,W,10,17,19,46,Graduation,28,Franchise,Complex\n3796,52.58,NJ City,W,14,14,4,31,High School,30,Franchise,Complex\n7689,73.78,Boston,N,3,24,1,77,Graduation,23,Patnership,Standalone\n2280,176.67,NJ City,W,9,9,18,73,High School,18,Franchise,Complex\n2819,132.38,NJ City,E,23,14,23,42,High School,25,Owner,Standalone\n5776,298.21,Boston,W,25,8,15,31,Graduation,28,Franchise,Complex\n5919,101.65,Boston,E,12,21,2,43,Graduation,22,Owner,Standalone\n7355,488.66,Boston,E,8,17,5,31,No education,32,Owner,Standalone\n6577,396.22,Boston,N,8,14,5,83,High School,33,Patnership,Standalone\n776,71.57,NJ City,N,9,12,1,80,No education,31,Franchise,Complex\n1063,265.06,NJ City,E,16,21,11,63,High School,31,Owner,Standalone\n7002,324.85,Boston,S,25,24,15,62,Graduation,23,Patnership,Complex\n884,284.8,NJ City,W,25,9,1,57,No education,32,Franchise,Complex\n1007,367.99,NJ City,E,20,24,1,55,No education,23,Owner,Standalone\n1675,250.84,NJ City,E,5,19,17,53,High School,33,Owner,Standalone\n5155,287.76,Boston,E,5,16,10,61,Graduation,32,Owner,Standalone\n9815,392.18,NJ City,E,20,20,2,85,No education,29,Owner,Standalone\n297,103.27,NJ City,N,18,18,3,83,Graduation,31,Patnership,Standalone\n3508,61.38,NJ City,N,1,24,4,66,High School,24,Franchise,Complex\n1611,105,NJ City,E,3,8,3,72,Graduation,27,Owner,Standalone\n2437,8.34,NJ City,N,16,24,4,65,High School,20,Patnership,Standalone\n5217,154.41,Boston,N,16,19,6,63,Graduation,21,Patnership,Standalone\n8681,401.5,NJ City,N,9,12,1,48,No education,30,Patnership,Standalone\n6694,163.77,Boston,S,10,22,18,58,Graduation,28,Patnership,Complex\n9351,374.26,NJ City,E,13,19,10,50,Graduation,25,Owner,Standalone\n678,167.65,NJ City,S,15,18,21,76,High School,31,Patnership,Complex\n8079,334.87,NJ City,E,1,17,18,62,Graduation,30,Owner,Standalone\n9135,186.65,NJ City,E,17,22,1,40,Graduation,24,Owner,Standalone\n2257,436.17,NJ City,N,7,16,9,77,High School,29,Patnership,Standalone\n510,294.96,NJ City,S,3,23,2,66,Graduation,28,Patnership,Complex\n7068,391.27,Boston,W,2,24,17,62,Graduation,22,Franchise,Complex\n328,167.52,NJ City,W,15,10,19,53,High School,22,Franchise,Complex\n9643,480.76,NJ City,E,8,18,20,84,Graduation,18,Owner,Standalone\n9402,91.23,NJ City,S,5,23,3,85,Graduation,21,Patnership,Complex\n6280,177.23,Boston,W,5,23,19,52,Graduation,33,Franchise,Complex\n1404,355.16,NJ City,W,23,13,10,62,High School,33,Franchise,Complex\n3814,23.49,NJ City,N,2,12,2,85,High School,34,Patnership,Complex\n5550,153.14,Boston,S,13,18,5,54,Graduation,34,Patnership,Complex\n6372,360.38,Boston,W,11,13,19,52,Graduation,34,Franchise,Complex\n528,306.76,NJ City,W,25,19,16,27,High School,28,Franchise,Complex\n4353,335.84,NJ City,N,7,14,4,34,Graduation,23,Patnership,Standalone\n2999,316.53,NJ City,E,15,9,20,30,High School,32,Owner,Standalone\n2132,53.36,NJ City,W,23,14,2,34,No education,33,Franchise,Complex\n9712,295.63,NJ City,W,7,14,12,67,Graduation,28,Franchise,Complex\n7313,446.82,Boston,N,3,12,10,70,Graduation,35,Patnership,Standalone\n2430,333.05,NJ City,S,14,16,11,29,High School,30,Patnership,Complex\n8906,392.2,NJ City,S,22,19,6,41,Graduation,27,Patnership,Complex\n4717,134,NJ City,N,1,21,15,57,High School,26,Patnership,Standalone\n4813,272.23,NJ City,N,8,16,10,83,High School,29,Patnership,Standalone\n6381,193.17,Boston,N,5,18,12,48,Graduation,28,Patnership,Standalone\n2513,81.54,NJ City,N,12,16,4,61,No education,35,Patnership,Standalone\n2574,76.77,NJ City,S,14,19,4,25,Graduation,29,Patnership,Complex\n9568,237.79,NJ City,W,9,23,4,38,High School,31,Franchise,Complex\n1026,462.12,NJ City,S,14,10,13,85,High School,22,Patnership,Complex\n2518,212.94,NJ City,S,18,16,12,33,High School,27,Patnership,Complex\n3732,410.05,NJ City,W,11,23,9,74,High School,28,Franchise,Complex\n7280,387.24,Boston,W,13,19,20,62,Graduation,31,Franchise,Complex\n3939,297.41,NJ City,E,5,10,24,68,High School,21,Owner,Standalone\n2660,105.15,NJ City,W,23,19,2,39,No education,20,Franchise,Complex\n2764,82.04,NJ City,W,24,18,2,42,High School,18,Franchise,Complex\n8466,61.45,NJ City,N,18,22,4,33,Graduation,32,Patnership,Complex\n4076,250.93,NJ City,W,20,11,11,70,High School,25,Franchise,Complex\n1208,448.26,NJ City,W,13,18,15,30,High School,24,Franchise,Complex\n1918,269.7,NJ City,S,17,14,17,26,High School,21,Patnership,Complex\n6496,81.75,Boston,W,12,20,1,36,High School,18,Franchise,Complex\n2729,450.52,NJ City,N,6,10,4,31,No education,28,Patnership,Standalone\n6148,333.75,Boston,W,21,23,25,43,Graduation,20,Franchise,Complex\n9937,33.04,NJ City,N,22,8,1,68,High School,20,Patnership,Standalone\n7923,28.76,Boston,N,3,12,2,79,Graduation,33,Owner,Standalone\n1718,232.64,NJ City,S,20,24,14,44,High School,28,Patnership,Complex\n9245,287.98,NJ City,N,14,16,22,70,Graduation,18,Patnership,Standalone\n3859,142,NJ City,E,3,22,16,54,High School,21,Owner,Standalone\n1773,468.39,NJ City,N,24,20,3,35,Graduation,26,Patnership,Standalone\n8652,322.5,NJ City,W,13,18,22,67,Graduation,34,Franchise,Complex\n9550,246.6,NJ City,S,16,22,5,73,High School,29,Patnership,Complex\n425,200,NJ City,N,1,17,22,53,High School,34,Patnership,Standalone\n8177,315.62,NJ City,N,5,13,2,47,No education,20,Patnership,Standalone\n8784,495.03,NJ City,W,17,18,23,34,Graduation,26,Franchise,Complex\n3096,124.32,NJ City,W,16,15,3,61,Graduation,20,Franchise,Complex\n576,243.98,NJ City,W,25,23,1,84,Graduation,31,Franchise,Complex\n707,106.43,NJ City,E,24,14,3,62,No education,23,Owner,Standalone\n9160,120.09,NJ City,W,23,24,2,51,High School,35,Franchise,Complex\n8827,63.22,NJ City,N,9,21,4,36,High School,24,Owner,Standalone\n2783,65.91,NJ City,N,22,24,3,47,No education,28,Owner,Standalone\n1621,435.72,NJ City,N,19,11,12,79,High School,21,Patnership,Standalone\n5489,74.27,Boston,N,22,23,3,70,No education,29,Patnership,Standalone\n939,240.85,NJ City,E,10,18,7,67,High School,31,Owner,Standalone\n1233,1.78,NJ City,N,6,13,4,43,Graduation,26,Patnership,Standalone\n4578,54.1,NJ City,S,24,22,3,53,Graduation,18,Patnership,Complex\n1513,155.3,NJ City,N,10,16,3,41,High School,20,Patnership,Standalone\n8992,152.82,NJ City,W,14,13,24,55,Graduation,33,Franchise,Complex\n6291,424.23,Boston,E,14,19,8,44,Graduation,25,Owner,Standalone\n5535,436.92,Boston,E,5,11,22,36,Graduation,19,Owner,Standalone\n7926,231,Boston,S,12,10,16,50,Graduation,24,Patnership,Complex\n7151,434.91,Boston,E,22,14,18,55,Graduation,21,Owner,Standalone\n2674,130.94,NJ City,S,10,18,21,54,High School,31,Patnership,Complex\n3148,450.9,NJ City,W,22,23,17,61,High School,33,Franchise,Complex\n1977,257.49,NJ City,N,13,9,13,31,High School,20,Patnership,Standalone\n5078,314.89,Boston,S,12,12,15,33,Graduation,24,Patnership,Complex\n991,336.02,NJ City,E,15,22,5,76,High School,30,Owner,Standalone\n9885,161.06,NJ City,N,10,20,16,64,Graduation,34,Patnership,Standalone\n9965,61.19,NJ City,N,25,11,1,33,No education,28,Patnership,Standalone\n7615,309.51,Boston,E,3,14,15,38,Graduation,23,Owner,Standalone\n5707,448.41,Boston,E,3,10,6,26,Graduation,21,Owner,Standalone\n6311,161.91,Boston,E,25,11,10,78,Graduation,30,Owner,Standalone\n1593,227.26,NJ City,N,13,8,20,66,High School,32,Patnership,Standalone\n1452,161.66,NJ City,W,6,22,9,35,High School,24,Franchise,Complex\n3540,393.68,NJ City,W,12,20,8,56,High School,26,Franchise,Complex\n7859,274.71,Boston,E,3,11,9,39,Graduation,25,Owner,Standalone\n5533,195.44,Boston,N,7,19,15,39,Graduation,26,Patnership,Standalone\n9613,37.68,NJ City,N,12,21,2,57,High School,26,Patnership,Standalone\n4897,184.22,NJ City,N,15,21,6,71,High School,35,Patnership,Standalone\n5163,205.43,Boston,E,9,14,22,82,Graduation,34,Owner,Standalone\n9389,203.45,NJ City,N,12,9,13,63,Graduation,21,Patnership,Standalone\n2825,327.04,NJ City,N,14,8,12,58,High School,27,Patnership,Standalone\n5064,446.45,Boston,W,22,11,6,34,Graduation,34,Franchise,Complex\n6114,160.35,Boston,S,23,17,13,60,Graduation,19,Patnership,Complex\n6220,402.38,Boston,W,2,13,3,48,High School,24,Franchise,Complex\n7596,275.57,Boston,W,25,14,23,81,Graduation,20,Franchise,Complex\n8968,280.82,NJ City,W,7,8,18,33,Graduation,35,Franchise,Complex\n8021,143.51,Boston,N,15,11,5,58,No education,19,Patnership,Standalone\n6631,346.9,Boston,E,3,10,25,68,Graduation,30,Owner,Standalone\n6192,436.19,Boston,W,8,12,18,49,Graduation,33,Franchise,Complex\n6594,161.33,Boston,S,7,20,15,66,Graduation,28,Patnership,Complex\n595,394.15,NJ City,E,21,18,9,71,High School,35,Owner,Standalone\n395,224.57,NJ City,E,25,19,5,38,No education,25,Owner,Standalone\n7274,128.8,Boston,S,17,21,16,40,Graduation,22,Patnership,Complex\n6732,55.33,Boston,W,23,16,2,39,Graduation,24,Franchise,Complex\n9478,324.69,NJ City,S,3,15,17,70,Graduation,28,Patnership,Complex\n6868,125.5,Boston,W,10,17,1,66,High School,35,Franchise,Complex\n4796,18.25,NJ City,N,1,8,1,72,No education,18,Franchise,Complex\n9991,311.91,NJ City,E,10,14,1,44,High School,26,Owner,Standalone\n1643,164.68,NJ City,E,22,13,25,51,High School,30,Owner,Standalone\n4312,110.83,NJ City,W,24,12,2,46,High School,31,Franchise,Complex\n1362,355.18,NJ City,S,16,18,6,53,High School,24,Patnership,Complex\n98,428.65,NJ City,S,24,15,19,35,High School,22,Patnership,Complex\n3504,106.03,NJ City,W,5,23,4,78,Graduation,22,Franchise,Complex\n588,33.88,NJ City,N,2,12,2,27,Graduation,23,Franchise,Complex\n9976,7.06,NJ City,N,22,23,2,51,High School,30,Franchise,Complex\n7022,131.93,Boston,S,7,9,2,63,No education,33,Patnership,Complex\n6102,118.2,Boston,S,18,19,1,39,Graduation,27,Patnership,Complex\n4605,314.34,NJ City,N,8,12,2,70,Graduation,19,Patnership,Standalone\n3971,198.81,NJ City,E,13,10,4,46,No education,19,Owner,Standalone\n8702,241.26,NJ City,S,4,10,1,47,No education,21,Patnership,Complex\n3733,233.88,NJ City,N,12,13,25,75,High School,26,Patnership,Standalone\n4545,264.76,NJ City,N,24,8,25,46,High School,32,Patnership,Standalone\n2487,478.33,NJ City,E,18,16,24,71,High School,31,Owner,Standalone\n394,21.31,NJ City,N,1,11,1,25,High School,35,Patnership,Complex\n6855,5.22,Boston,N,24,16,1,81,Graduation,27,Owner,Standalone\n6052,356.44,Boston,W,16,11,10,61,Graduation,22,Franchise,Complex\n6406,34.7,Boston,N,25,15,2,39,High School,18,Patnership,Complex\n624,373.09,NJ City,W,17,8,22,73,High School,30,Franchise,Complex\n4151,448.33,NJ City,E,19,14,12,70,High School,21,Owner,Standalone\n32,281.83,Boston,W,21,12,23,66,High School,30,Franchise,Complex\n6401,96.07,Boston,N,23,11,3,80,No education,18,Patnership,Standalone\n2939,261.35,NJ City,E,9,10,15,81,High School,31,Owner,Standalone\n9153,420.41,NJ City,N,7,18,19,64,Graduation,26,Patnership,Standalone\n7756,65.02,Boston,N,25,14,3,33,High School,34,Franchise,Complex\n3667,184.73,NJ City,E,10,12,17,52,High School,32,Owner,Standalone\n6777,42.68,Boston,N,11,15,2,50,Graduation,29,Patnership,Standalone\n8232,376.42,NJ City,W,23,20,16,54,Graduation,21,Franchise,Complex\n2817,286.17,NJ City,N,9,15,3,54,Graduation,20,Patnership,Standalone\n9447,46.75,NJ City,N,17,13,3,51,Graduation,25,Owner,Standalone\n3176,499.81,NJ City,W,14,14,22,72,High School,24,Franchise,Complex\n6445,193,Boston,N,22,8,18,33,Graduation,23,Patnership,Standalone\n8795,290.54,NJ City,E,14,21,11,31,Graduation,32,Owner,Standalone\n8381,209.32,NJ City,N,16,12,24,71,Graduation,28,Patnership,Standalone\n832,172.02,NJ City,W,17,12,24,42,High School,23,Franchise,Complex\n8734,122.52,NJ City,S,6,12,4,51,High School,32,Patnership,Complex\n251,416.86,NJ City,E,10,13,20,81,High School,34,Owner,Standalone\n5485,32,Boston,N,20,9,3,40,High School,24,Patnership,Standalone\n9268,105.84,NJ City,W,3,20,2,62,High School,21,Franchise,Complex\n3934,229.28,NJ City,S,13,13,17,64,High School,35,Patnership,Complex\n2576,405.61,NJ City,W,3,12,5,47,No education,35,Franchise,Complex\n21,181.33,Maine,N,9,15,2,76,Graduation,18,Patnership,Standalone\n3294,496.65,NJ City,S,17,24,20,39,High School,32,Patnership,Complex\n2870,339.21,NJ City,S,8,23,9,75,High School,34,Patnership,Complex\n4635,302.72,NJ City,E,17,13,24,85,High School,18,Owner,Standalone\n8843,74.77,NJ City,E,15,9,3,30,No education,24,Owner,Standalone\n7809,492.58,Boston,N,10,10,24,85,Graduation,28,Patnership,Standalone\n9357,26.91,NJ City,N,2,11,2,73,Graduation,21,Patnership,Standalone\n6903,54.62,Boston,E,6,21,1,43,Graduation,34,Owner,Standalone\n8716,121.86,NJ City,W,6,11,1,63,High School,22,Franchise,Complex\n9578,404.81,NJ City,S,8,24,2,82,No education,26,Patnership,Complex\n407,271,NJ City,E,2,15,7,28,High School,31,Owner,Standalone\n7183,13.26,Boston,N,13,9,2,49,High School,24,Owner,Standalone\n5839,175.38,Boston,E,9,24,4,26,High School,35,Owner,Standalone\n9661,378.03,NJ City,N,10,9,18,35,Graduation,30,Patnership,Standalone\n8757,313.08,NJ City,N,10,13,17,29,Graduation,34,Patnership,Standalone\n4061,293.77,NJ City,N,12,16,2,48,No education,24,Patnership,Standalone\n1370,347.05,NJ City,S,4,10,1,54,No education,22,Patnership,Complex\n8010,451.79,Boston,S,24,18,11,73,Graduation,22,Patnership,Complex\n7724,46.34,Boston,N,1,19,1,61,No education,20,Franchise,Complex\n8015,258.87,Boston,E,20,11,18,56,Graduation,24,Owner,Standalone\n4196,85.61,NJ City,W,19,13,3,68,No education,33,Franchise,Complex\n1417,312.76,NJ City,N,7,19,2,73,High School,18,Patnership,Standalone\n5298,257.46,Boston,S,11,16,23,76,Graduation,34,Patnership,Complex\n4023,324.72,NJ City,E,15,18,18,67,High School,33,Owner,Standalone\n8219,465.62,NJ City,E,16,15,7,50,Graduation,27,Owner,Standalone\n7268,57.47,Boston,W,23,21,2,84,No education,31,Franchise,Complex\n5532,116.75,Boston,W,7,13,4,79,Graduation,23,Franchise,Complex\n2358,8.28,NJ City,N,9,8,2,71,Graduation,21,Patnership,Complex\n8264,389.83,NJ City,W,10,23,22,50,Graduation,26,Franchise,Complex\n7791,497.98,Boston,E,16,13,23,47,Graduation,31,Owner,Standalone\n5214,490.41,Boston,S,6,8,24,65,Graduation,24,Patnership,Complex\n652,158.51,NJ City,W,7,10,11,80,High School,23,Franchise,Complex\n5549,40.33,Boston,N,21,11,1,48,No education,31,Patnership,Standalone\n7531,276.64,Boston,E,3,19,23,31,Graduation,35,Owner,Standalone\n6259,106.87,Boston,E,19,23,4,52,High School,33,Owner,Standalone\n3893,82.51,NJ City,N,18,23,4,73,No education,22,Patnership,Standalone\n2940,284.07,NJ City,W,10,23,8,40,High School,19,Franchise,Complex\n6258,222.42,Boston,S,19,23,24,48,Graduation,32,Patnership,Complex\n5069,358.88,Boston,N,5,11,12,35,Graduation,28,Patnership,Standalone\n965,145.23,NJ City,N,9,15,4,28,No education,27,Patnership,Standalone\n2153,389.81,NJ City,N,10,15,13,67,High School,27,Patnership,Standalone\n732,60.48,NJ City,N,17,8,4,55,Graduation,31,Franchise,Complex\n4428,277,NJ City,W,2,12,9,76,High School,20,Franchise,Complex\n6945,473.74,Boston,N,19,19,8,48,Graduation,31,Patnership,Standalone\n7887,117.11,Boston,E,18,13,2,49,Graduation,18,Owner,Standalone\n4005,183.47,NJ City,N,8,20,16,34,High School,25,Patnership,Standalone\n3866,460.27,NJ City,S,21,12,7,51,High School,23,Patnership,Complex\n8767,327.85,NJ City,E,1,23,12,82,Graduation,23,Owner,Standalone\n6963,180.03,Boston,E,8,23,12,65,Graduation,33,Owner,Standalone\n4946,459.13,Boston,S,20,19,14,51,High School,22,Patnership,Complex\n463,286.14,NJ City,E,8,10,14,34,High School,25,Owner,Standalone\n5974,6.81,Boston,N,5,12,4,60,High School,29,Patnership,Complex\n7600,437.13,Boston,W,24,12,16,77,Graduation,19,Franchise,Complex\n7788,199.53,Boston,W,7,11,13,79,Graduation,32,Franchise,Complex\n6589,104.93,Boston,N,5,10,3,35,High School,18,Patnership,Standalone\n1186,348.67,NJ City,S,17,16,5,40,High School,29,Patnership,Complex\n6112,78.97,Boston,W,21,24,3,76,High School,34,Franchise,Complex\n16,240.1,Boston,W,11,24,22,65,High School,31,Franchise,Complex\n8478,62.51,NJ City,N,16,15,4,75,Graduation,31,Patnership,Complex\n341,255.07,NJ City,N,13,17,18,39,High School,31,Patnership,Standalone\n4896,67.66,NJ City,N,15,23,2,36,Graduation,28,Franchise,Complex\n2394,422.26,NJ City,S,10,17,19,38,High School,25,Patnership,Complex\n8410,453.47,NJ City,S,3,13,19,28,Graduation,23,Patnership,Complex\n8572,205.67,NJ City,W,20,20,24,31,Graduation,31,Franchise,Complex\n6866,138.22,Boston,S,25,21,5,35,No education,22,Patnership,Complex\n6269,378.29,Boston,N,25,24,19,39,Graduation,24,Patnership,Standalone\n5687,474.51,Boston,E,21,19,25,65,Graduation,34,Owner,Standalone\n9958,253.33,NJ City,S,20,8,10,41,Graduation,28,Patnership,Complex\n6609,334.21,Boston,N,23,11,17,30,Graduation,22,Patnership,Standalone\n5107,379.66,Boston,E,21,11,5,70,High School,31,Owner,Standalone\n3845,351.87,NJ City,N,15,12,17,35,High School,31,Patnership,Standalone\n8971,273.42,NJ City,E,19,21,7,36,Graduation,30,Owner,Standalone\n3035,394.56,NJ City,E,18,16,1,60,No education,29,Owner,Standalone\n7949,149.86,Boston,N,10,18,9,30,Graduation,28,Patnership,Standalone\n8368,243.35,NJ City,W,13,18,24,76,Graduation,32,Franchise,Complex\n5251,303.34,Boston,E,25,11,6,28,Graduation,29,Owner,Standalone\n4755,346.05,NJ City,E,24,14,4,56,Graduation,23,Owner,Standalone\n7096,459.49,Boston,W,1,14,19,46,Graduation,18,Franchise,Complex\n4675,71.68,NJ City,N,6,18,1,51,High School,25,Owner,Standalone\n3549,175.39,NJ City,N,7,24,19,47,High School,19,Patnership,Standalone\n6004,309.2,Boston,W,14,17,14,84,Graduation,33,Franchise,Complex\n5182,241.53,Boston,S,17,10,9,52,Graduation,19,Patnership,Complex\n2557,114,NJ City,N,2,15,8,75,High School,34,Patnership,Standalone\n9624,220.47,NJ City,W,11,11,13,31,Graduation,20,Franchise,Complex\n5568,226.59,Boston,W,15,19,16,56,Graduation,31,Franchise,Complex\n5032,348.8,Boston,W,19,19,25,64,Graduation,21,Franchise,Complex\n1045,110.61,NJ City,N,13,12,1,26,High School,34,Patnership,Standalone\n3150,251.81,NJ City,S,23,20,7,63,High School,26,Patnership,Complex\n5011,189.16,Boston,E,10,9,1,70,High School,27,Owner,Standalone\n7998,182.41,Boston,S,1,11,5,50,Graduation,24,Patnership,Complex\n2300,304.62,NJ City,W,22,10,20,31,High School,21,Franchise,Complex\n8927,47.28,NJ City,N,4,9,1,69,No education,24,Owner,Standalone\n6526,387.11,Boston,S,9,10,10,32,Graduation,21,Patnership,Complex\n257,439.39,NJ City,N,14,9,6,76,High School,34,Patnership,Standalone\n1974,79.16,NJ City,S,25,21,2,40,Graduation,19,Patnership,Complex\n1805,124,NJ City,N,3,11,10,29,High School,30,Patnership,Standalone\n71,436.56,NY City,E,24,8,11,41,High School,23,Owner,Standalone\n960,153.85,NJ City,W,6,11,17,82,High School,25,Franchise,Complex\n908,315.67,NJ City,W,15,22,8,41,High School,34,Franchise,Complex\n3013,337.2,NJ City,N,11,22,8,35,High School,24,Patnership,Standalone\n3499,420.47,NJ City,E,6,24,11,60,High School,21,Owner,Standalone\n7731,220.06,Boston,E,7,23,16,82,Graduation,25,Owner,Standalone\n8862,170.91,NJ City,S,6,10,22,62,Graduation,33,Patnership,Complex\n4421,258.36,NJ City,N,21,16,21,60,High School,28,Patnership,Standalone\n2851,456.39,NJ City,E,18,16,15,59,High School,20,Owner,Standalone\n3719,166.67,NJ City,E,6,22,11,75,High School,32,Owner,Standalone\n5542,204.5,Boston,S,13,11,16,30,Graduation,30,Patnership,Complex\n868,147,NJ City,W,3,8,6,74,High School,34,Franchise,Complex\n5907,499.17,Boston,E,6,9,15,30,Graduation,33,Owner,Standalone\n1014,152.89,NJ City,S,12,16,2,30,Graduation,27,Patnership,Complex\n6821,164.7,Boston,N,3,21,18,46,Graduation,18,Patnership,Standalone\n1454,183.27,NJ City,S,7,15,25,47,High School,33,Patnership,Complex\n4139,109.24,NJ City,E,12,12,2,71,No education,23,Owner,Standalone\n4368,433.7,NJ City,W,11,13,6,38,High School,28,Franchise,Complex\n835,479.58,NJ City,E,8,8,4,74,High School,26,Owner,Standalone\n5286,335.28,Boston,S,14,24,12,44,Graduation,33,Patnership,Complex\n9515,217.39,NJ City,E,1,22,6,79,Graduation,32,Owner,Standalone\n725,430,NJ City,N,10,16,6,69,High School,31,Patnership,Standalone\n8625,463.43,NJ City,N,15,21,18,68,Graduation,31,Patnership,Standalone\n3775,7.02,NJ City,N,7,20,4,64,High School,24,Owner,Standalone\n9331,163.97,NJ City,E,2,13,5,27,High School,28,Owner,Standalone\n4929,93.78,NJ City,N,14,9,3,27,Graduation,23,Patnership,Standalone\n9819,495.08,NJ City,E,24,17,19,80,Graduation,31,Owner,Standalone\n9548,381.73,NJ City,W,3,8,9,31,Graduation,35,Franchise,Complex\n4849,126.6,NJ City,N,6,22,2,39,High School,32,Patnership,Standalone\n4119,41.52,NJ City,N,11,12,4,83,Graduation,33,Owner,Standalone\n4378,481.39,NJ City,S,4,8,10,40,High School,21,Patnership,Complex\n792,378.48,NJ City,W,21,20,12,82,High School,29,Franchise,Complex\n7781,151.94,Boston,N,2,22,24,84,Graduation,29,Patnership,Standalone\n9601,52.71,NJ City,N,16,12,3,34,High School,19,Patnership,Standalone\n7020,24.42,Boston,N,1,14,4,68,Graduation,25,Franchise,Complex\n3575,33.92,NJ City,N,20,13,4,67,No education,33,Owner,Standalone\n5938,405.96,Boston,S,25,24,6,52,Graduation,20,Patnership,Complex\n4978,235,Boston,S,3,13,16,38,High School,27,Patnership,Complex\n4805,322.09,NJ City,N,15,10,14,60,High School,25,Patnership,Standalone\n4220,122.78,NJ City,W,8,18,1,75,No education,29,Franchise,Complex\n1419,76.86,NJ City,E,9,12,4,45,Graduation,29,Owner,Standalone\n890,102.51,NJ City,S,17,17,2,41,No education,27,Patnership,Complex\n3456,417.1,NJ City,W,20,19,6,62,High School,27,Franchise,Complex\n9189,122.19,NJ City,N,2,12,3,81,Graduation,18,Patnership,Standalone\n4773,321.97,NJ City,N,23,22,5,41,Graduation,30,Patnership,Standalone\n3818,1.69,NJ City,N,6,21,3,40,No education,22,Patnership,Complex\n7146,411.24,Boston,S,11,20,13,85,Graduation,29,Patnership,Complex\n4323,120.2,NJ City,E,9,16,3,50,Graduation,20,Owner,Standalone\n4631,432.7,NJ City,E,16,13,18,49,High School,19,Owner,Standalone\n4823,473.48,NJ City,E,23,20,1,85,No education,21,Owner,Standalone\n314,87.05,NJ City,S,11,22,2,72,No education,33,Patnership,Complex\n8546,365.2,NJ City,S,24,18,4,36,No education,27,Patnership,Complex\n3908,211.56,NJ City,W,5,15,12,81,High School,30,Franchise,Complex\n4251,415.29,NJ City,E,21,11,15,53,High School,23,Owner,Standalone\n9552,149.96,NJ City,W,7,23,15,79,Graduation,30,Franchise,Complex\n7675,127.82,Boston,E,17,22,2,82,High School,19,Owner,Standalone\n1599,318.53,NJ City,E,12,8,10,51,High School,23,Owner,Standalone\n2351,312,NJ City,E,2,22,21,78,High School,27,Owner,Standalone\n4809,290.74,NJ City,N,14,17,6,40,High School,18,Patnership,Standalone\n450,397.16,NJ City,S,15,11,25,64,High School,18,Patnership,Complex\n4274,491.52,NJ City,S,19,12,9,36,High School,21,Patnership,Complex\n3559,395.75,NJ City,E,4,16,3,80,High School,27,Owner,Standalone\n5256,244.04,Boston,W,17,11,12,33,Graduation,27,Franchise,Complex\n9605,61.88,NJ City,N,1,12,3,54,No education,22,Patnership,Standalone\n2299,136,NJ City,E,3,8,24,73,High School,18,Owner,Standalone\n3271,206.63,NJ City,E,21,18,20,72,High School,18,Owner,Standalone\n2976,496.86,NJ City,W,15,22,12,51,High School,33,Franchise,Complex\n4383,178.44,NJ City,E,14,8,9,29,High School,34,Owner,Standalone\n3762,269,NJ City,S,2,23,19,67,High School,23,Patnership,Complex\n1318,11.63,NJ City,N,11,17,2,47,High School,32,Patnership,Complex\n6063,432.39,Boston,E,23,18,7,41,Graduation,34,Owner,Standalone\n4272,403.27,NJ City,W,15,20,16,62,High School,21,Franchise,Complex\n9061,123.21,NJ City,N,3,16,2,36,High School,35,Patnership,Standalone\n9005,299.61,NJ City,N,2,23,22,47,Graduation,25,Patnership,Standalone\n1543,12.15,NJ City,N,9,9,2,77,High School,26,Owner,Standalone\n7130,477.24,Boston,S,13,17,16,28,Graduation,27,Patnership,Complex\n6047,24.52,Boston,N,15,17,4,63,No education,22,Owner,Standalone\n6416,187.33,Boston,W,12,8,19,73,Graduation,31,Franchise,Complex\n819,199.56,NJ City,E,10,24,2,31,Graduation,30,Owner,Standalone\n4997,111,Boston,N,1,17,23,52,High School,34,Patnership,Standalone\n266,347.18,NJ City,S,14,17,3,43,No education,21,Patnership,Complex\n1633,11.78,NJ City,N,13,16,1,66,High School,32,Patnership,Standalone\n3724,202.53,NJ City,W,20,15,1,31,High School,33,Franchise,Complex\n724,201.65,NJ City,W,25,21,14,44,High School,25,Franchise,Complex\n5887,260.81,Boston,E,24,9,13,65,Graduation,34,Owner,Standalone\n9776,426.17,NJ City,W,3,9,21,30,Graduation,33,Franchise,Complex\n5431,469.7,Boston,E,7,21,18,51,Graduation,27,Owner,Standalone\n3615,61.88,NJ City,N,12,17,4,76,Graduation,29,Owner,Standalone\n6086,327.55,Boston,S,13,12,11,52,Graduation,22,Patnership,Complex\n9995,468.77,NJ City,E,1,14,22,82,Graduation,26,Owner,Standalone\n3247,64.71,NJ City,N,21,24,3,81,High School,34,Owner,Standalone\n1349,31.99,NJ City,N,15,8,1,29,No education,30,Patnership,Standalone\n7036,188.41,Boston,W,8,19,7,50,Graduation,21,Franchise,Complex\n7252,151.39,Boston,W,14,15,15,69,Graduation,20,Franchise,Complex\n5769,429.6,Boston,N,15,20,5,83,Graduation,24,Patnership,Standalone\n7614,52.68,Boston,S,14,18,2,33,Graduation,33,Patnership,Complex\n8596,192.54,NJ City,W,3,22,13,57,Graduation,30,Franchise,Complex\n925,237.82,NJ City,N,23,13,10,40,High School,25,Patnership,Standalone\n8327,92.55,NJ City,E,10,23,2,47,No education,34,Owner,Standalone\n5876,19.74,Boston,N,23,19,2,79,No education,25,Franchise,Complex\n3914,499.6,NJ City,S,14,18,8,85,High School,33,Patnership,Complex\n9417,430.68,NJ City,N,24,17,7,25,Graduation,22,Patnership,Standalone\n2229,160.86,NJ City,N,9,17,21,84,High School,21,Patnership,Standalone\n4653,424.5,NJ City,N,25,8,9,85,High School,19,Patnership,Standalone\n346,331.36,NJ City,S,12,14,10,72,High School,34,Patnership,Complex\n3514,298.42,NJ City,S,8,20,16,81,High School,30,Patnership,Complex\n9406,369.02,NJ City,S,5,11,4,51,High School,18,Patnership,Complex\n2762,48.91,NJ City,S,22,10,1,67,No education,31,Patnership,Complex\n2670,275.7,NJ City,S,8,24,4,48,Graduation,32,Patnership,Complex\n4003,176,NJ City,E,3,20,21,66,High School,34,Owner,Standalone\n4656,169.85,NJ City,W,12,14,2,27,Graduation,25,Franchise,Complex\n1739,40.06,NJ City,N,20,19,3,55,No education,26,Owner,Standalone\n1100,343.79,NJ City,W,15,10,7,36,High School,24,Franchise,Complex\n6391,451.38,Boston,E,13,13,25,66,Graduation,26,Owner,Standalone\n5835,287.87,Boston,E,18,15,24,57,Graduation,28,Owner,Standalone\n277,120.5,NJ City,N,10,16,3,27,High School,21,Patnership,Standalone\n4961,485.2,Boston,N,19,16,24,56,High School,19,Patnership,Standalone\n9246,168.01,NJ City,S,7,16,24,45,Graduation,34,Patnership,Complex\n3502,191.74,NJ City,S,15,10,7,44,High School,26,Patnership,Complex\n5216,243.73,Boston,W,25,11,10,64,Graduation,22,Franchise,Complex\n6073,292.77,Boston,N,22,11,5,60,High School,28,Patnership,Standalone\n5415,214.14,Boston,E,5,22,10,58,Graduation,35,Owner,Standalone\n2502,62.66,NJ City,N,6,9,3,51,Graduation,24,Patnership,Complex\n8465,499.86,NJ City,N,19,9,24,64,Graduation,23,Patnership,Standalone\n7928,212.55,Boston,W,1,14,16,68,Graduation,25,Franchise,Complex\n4549,348.94,NJ City,N,18,17,23,80,High School,28,Patnership,Standalone\n7674,80.2,Boston,S,18,24,1,56,Graduation,20,Patnership,Complex\n5241,167.76,Boston,N,7,14,11,40,Graduation,35,Patnership,Standalone\n3363,299.35,NJ City,E,4,17,3,30,Graduation,29,Owner,Standalone\n6972,107.46,Boston,W,8,16,2,46,Graduation,19,Franchise,Complex\n7181,239.96,Boston,N,19,12,18,80,Graduation,19,Patnership,Standalone\n5180,499.33,Boston,W,19,13,25,72,Graduation,32,Franchise,Complex\n669,60.06,NJ City,N,15,19,3,32,Graduation,21,Patnership,Standalone\n5749,42.63,Boston,N,13,21,2,56,High School,35,Patnership,Standalone\n3727,365.06,NJ City,E,7,24,4,50,High School,22,Owner,Standalone\n4697,159.24,NJ City,N,11,21,12,29,High School,25,Patnership,Standalone\n1263,359.67,NJ City,E,18,23,24,59,High School,20,Owner,Standalone\n3254,245.67,NJ City,S,12,18,8,50,High School,26,Patnership,Complex\n9282,330.86,NJ City,S,15,20,3,73,Graduation,31,Patnership,Complex\n1927,418.02,NJ City,E,21,15,17,55,High School,30,Owner,Standalone\n1103,247.67,NJ City,E,20,17,24,58,High School,29,Owner,Standalone\n5031,321.81,Boston,E,15,19,15,62,Graduation,19,Owner,Standalone\n495,218,NJ City,E,3,19,7,40,High School,26,Owner,Standalone\n1196,113.01,NJ City,W,14,19,4,39,No education,32,Franchise,Complex\n3935,498.08,NJ City,E,10,22,16,38,High School,20,Owner,Standalone\n5337,47.47,Boston,N,12,22,1,60,Graduation,31,Patnership,Standalone\n1455,140,NJ City,E,9,23,13,62,High School,35,Owner,Standalone\n7120,162.68,Boston,W,7,14,16,27,Graduation,32,Franchise,Complex\n3267,398.92,NJ City,E,12,14,14,64,High School,35,Owner,Standalone\n7217,295.94,Boston,N,20,17,18,63,Graduation,29,Patnership,Standalone\n6317,304.77,Boston,N,3,20,6,63,Graduation,21,Patnership,Standalone\n5335,415.83,Boston,E,18,18,9,56,Graduation,33,Owner,Standalone\n459,77.82,NJ City,E,22,24,4,34,Graduation,34,Owner,Standalone\n6337,185.03,Boston,N,10,8,23,66,Graduation,26,Patnership,Standalone\n2267,471.18,NJ City,E,19,24,18,73,High School,26,Owner,Standalone\n4894,302.66,NJ City,S,3,11,5,44,High School,24,Patnership,Complex\n3408,332.39,NJ City,W,15,19,8,46,High School,29,Franchise,Complex\n596,38.3,NJ City,N,13,11,2,41,No education,25,Franchise,Complex\n7984,170.92,Boston,W,18,22,19,61,Graduation,27,Franchise,Complex\n1551,233.81,NJ City,E,24,13,14,28,High School,31,Owner,Standalone\n4407,148.72,NJ City,E,16,18,11,69,High School,32,Owner,Standalone\n1238,342.46,NJ City,S,21,22,11,34,High School,32,Patnership,Complex\n5000,338.2,Boston,W,22,18,9,70,Graduation,33,Franchise,Complex\n6796,370.11,Boston,W,9,21,9,35,Graduation,21,Franchise,Complex\n4566,483.5,NJ City,S,12,21,18,41,High School,28,Patnership,Complex\n9281,201.03,NJ City,N,22,14,3,44,No education,18,Patnership,Standalone\n7882,343.77,Boston,S,6,12,5,73,High School,35,Patnership,Complex\n5527,421.19,Boston,E,8,23,23,79,Graduation,19,Owner,Standalone\n2521,404.18,NJ City,N,5,8,24,60,High School,31,Patnership,Standalone\n8903,134.49,NJ City,E,19,9,8,43,Graduation,28,Owner,Standalone\n373,188,NJ City,N,21,16,7,67,High School,33,Patnership,Standalone\n3532,247,NJ City,W,1,23,22,28,High School,27,Franchise,Complex\n7492,124.3,Boston,W,20,12,3,49,High School,34,Franchise,Complex\n5447,173.95,Boston,E,3,19,24,77,Graduation,25,Owner,Standalone\n2697,67.65,NJ City,N,2,18,4,53,Graduation,33,Patnership,Standalone\n2769,197.13,NJ City,N,11,22,13,55,High School,33,Patnership,Standalone\n5421,414.46,Boston,N,17,8,5,31,Graduation,29,Patnership,Standalone\n9533,447.44,NJ City,N,25,10,23,79,Graduation,24,Patnership,Standalone\n6949,199.91,Boston,N,20,15,20,25,Graduation,24,Patnership,Standalone\n7116,298.41,Boston,W,15,16,19,37,Graduation,25,Franchise,Complex\n9567,264.32,NJ City,E,18,24,16,25,Graduation,24,Owner,Standalone\n7461,153.21,Boston,N,23,16,2,56,Graduation,24,Patnership,Standalone\n1475,416.02,NJ City,E,13,23,12,56,High School,23,Owner,Standalone\n3449,185.05,NJ City,N,6,9,24,37,High School,29,Patnership,Standalone\n5141,274.9,Boston,N,20,10,6,61,Graduation,19,Patnership,Standalone\n9412,116.08,NJ City,W,18,23,1,76,High School,18,Franchise,Complex\n2538,94.17,NJ City,S,1,14,2,46,Graduation,22,Patnership,Complex\n3317,271.74,NJ City,N,6,11,8,52,High School,25,Patnership,Standalone\n6231,358.5,Boston,E,11,21,23,51,Graduation,21,Owner,Standalone\n5603,385.18,Boston,E,11,13,24,37,Graduation,29,Owner,Standalone\n6817,92.95,Boston,N,19,21,2,66,High School,22,Patnership,Standalone\n2380,140.11,NJ City,W,14,11,18,74,High School,34,Franchise,Complex\n8611,444.22,NJ City,E,2,13,13,38,Graduation,35,Owner,Standalone\n6703,198.27,Boston,E,12,11,23,68,Graduation,23,Owner,Standalone\n3065,443,NJ City,N,19,22,2,55,No education,27,Patnership,Standalone\n1391,196,NJ City,E,3,23,25,83,High School,21,Owner,Standalone\n3420,248.71,NJ City,W,12,12,9,25,High School,30,Franchise,Complex\n4173,299.33,NJ City,N,15,24,17,65,High School,29,Patnership,Standalone\n1595,91.5,NJ City,E,13,16,1,30,No education,30,Owner,Standalone\n9831,264.67,NJ City,E,7,14,4,79,Graduation,34,Owner,Standalone\n1381,117.74,NJ City,N,15,14,4,67,High School,35,Patnership,Standalone\n806,175.12,NJ City,S,5,14,1,79,No education,18,Patnership,Complex\n7082,307.44,Boston,S,19,17,1,59,No education,28,Patnership,Complex\n2322,96.25,NJ City,S,17,10,1,28,Graduation,28,Patnership,Complex\n333,195.19,NJ City,N,11,20,4,83,Graduation,34,Patnership,Standalone\n9670,230.48,NJ City,S,23,22,15,53,Graduation,28,Patnership,Complex\n2501,278.94,NJ City,N,22,17,17,43,High School,18,Patnership,Standalone\n8735,84.06,NJ City,E,12,11,1,31,No education,29,Owner,Standalone\n2855,450.96,NJ City,E,13,9,12,34,High School,33,Owner,Standalone\n2709,168.77,NJ City,N,14,16,8,81,High School,20,Patnership,Standalone\n8404,186.88,NJ City,W,10,14,15,60,Graduation,22,Franchise,Complex\n2600,434.99,NJ City,W,14,22,5,43,No education,18,Franchise,Complex\n4056,206.03,NJ City,W,7,11,13,59,High School,31,Franchise,Complex\n9178,406.25,NJ City,S,9,8,4,59,High School,32,Patnership,Complex\n514,79.92,NJ City,S,11,23,1,43,High School,35,Patnership,Complex\n4252,397.85,NJ City,W,14,11,15,85,High School,32,Franchise,Complex\n3157,117.53,NJ City,N,16,18,2,79,High School,22,Patnership,Standalone\n1038,255.38,NJ City,S,18,11,2,53,Graduation,19,Patnership,Complex\n2663,314.67,NJ City,E,16,9,15,28,High School,31,Owner,Standalone\n3992,93.3,NJ City,W,25,15,2,55,No education,24,Franchise,Complex\n548,383.75,NJ City,W,6,12,18,36,High School,18,Franchise,Complex\n5225,180.29,Boston,N,18,9,19,84,Graduation,31,Patnership,Standalone\n6314,43.28,Boston,N,1,20,2,61,No education,29,Patnership,Complex\n6549,213.25,Boston,N,4,17,18,84,Graduation,24,Patnership,Standalone\n2757,88.97,NJ City,N,22,22,1,81,Graduation,28,Patnership,Standalone\n2603,320.99,NJ City,E,25,9,20,27,High School,29,Owner,Standalone\n8723,405.84,NJ City,E,21,22,6,53,Graduation,28,Owner,Standalone\n711,87.82,NJ City,E,23,20,4,65,Graduation,29,Owner,Standalone\n5115,441.74,Boston,E,24,14,19,34,Graduation,19,Owner,Standalone\n6804,430.6,Boston,W,16,21,2,29,Graduation,30,Franchise,Complex\n703,194.03,NJ City,E,13,17,12,63,High School,24,Owner,Standalone\n8619,458.58,NJ City,E,12,10,21,84,Graduation,33,Owner,Standalone\n1651,83.3,NJ City,E,22,16,3,80,High School,23,Owner,Standalone\n9889,490.33,NJ City,N,18,22,8,55,Graduation,20,Patnership,Standalone\n9002,192.08,NJ City,S,5,20,15,43,Graduation,31,Patnership,Complex\n2896,345.65,NJ City,W,11,23,7,74,High School,34,Franchise,Complex\n4820,103.65,NJ City,W,24,16,3,71,No education,25,Franchise,Complex\n4860,22.92,NJ City,N,13,19,2,28,Graduation,18,Franchise,Complex\n4972,54.36,Boston,W,14,14,1,29,High School,30,Franchise,Complex\n7635,334.06,Boston,E,8,15,17,61,Graduation,31,Owner,Standalone\n9310,339.53,NJ City,S,9,15,21,36,Graduation,33,Patnership,Complex\n6412,238.57,Boston,W,17,21,17,64,Graduation,26,Franchise,Complex\n6920,491.6,Boston,W,4,23,5,62,No education,26,Franchise,Complex\n4480,266.09,NJ City,W,15,18,18,47,High School,35,Franchise,Complex\n3454,307.48,NJ City,S,19,13,7,52,High School,19,Patnership,Complex\n1487,235,NJ City,E,3,10,21,68,High School,19,Owner,Standalone\n5173,225.59,Boston,N,5,15,3,69,High School,24,Patnership,Standalone\n3017,412.77,NJ City,N,23,19,13,74,High School,23,Patnership,Standalone\n6471,362.9,Boston,E,22,23,1,52,Graduation,20,Owner,Standalone\n1084,22.59,NJ City,N,20,23,1,30,High School,33,Franchise,Complex\n8284,332.07,NJ City,W,3,11,23,77,Graduation,27,Franchise,Complex\n6072,57.9,Boston,W,9,11,4,39,Graduation,31,Franchise,Complex\n6131,416.4,Boston,E,25,12,15,57,Graduation,32,Owner,Standalone\n6357,52.34,Boston,N,23,23,1,31,Graduation,22,Patnership,Standalone\n8532,86.89,NJ City,W,13,14,1,80,Graduation,24,Franchise,Complex\n5038,476.17,Boston,S,22,13,13,69,Graduation,21,Patnership,Complex\n1210,252.06,NJ City,S,6,13,24,62,High School,35,Patnership,Complex\n4662,191.95,NJ City,S,10,15,23,27,High School,26,Patnership,Complex\n566,106.4,NJ City,S,7,13,2,39,No education,21,Patnership,Complex\n9185,248.11,NJ City,N,21,21,5,36,No education,29,Patnership,Standalone\n5029,226.12,Boston,N,4,12,11,75,Graduation,34,Patnership,Standalone\n7289,80.79,Boston,N,16,10,2,55,No education,31,Patnership,Standalone\n6940,119.84,Boston,W,3,8,3,29,High School,20,Franchise,Complex\n4504,177.24,NJ City,W,23,18,12,63,High School,33,Franchise,Complex\n5460,167.43,Boston,W,24,10,24,81,Graduation,20,Franchise,Complex\n447,235.92,NJ City,E,16,17,1,33,Graduation,31,Owner,Standalone\n4195,206,NJ City,E,3,8,24,84,High School,33,Owner,Standalone\n7240,64.89,Boston,N,8,18,4,68,High School,23,Franchise,Complex\n8871,114.09,NJ City,E,23,20,3,85,Graduation,30,Owner,Standalone\n6351,79.36,Boston,E,20,20,3,64,Graduation,23,Owner,Standalone\n3711,349.2,NJ City,E,12,9,23,69,High School,19,Owner,Standalone\n2747,292.92,NJ City,E,13,12,24,26,High School,23,Owner,Standalone\n2957,293.48,NJ City,N,25,17,15,72,High School,27,Patnership,Standalone\n858,229.04,NJ City,S,16,18,10,37,High School,35,Patnership,Complex\n657,71.11,NJ City,N,21,17,4,63,Graduation,22,Patnership,Standalone\n3832,12.93,NJ City,N,1,15,1,71,High School,20,Franchise,Complex\n1917,27.76,NJ City,N,15,12,2,85,Graduation,21,Patnership,Standalone\n6582,46.95,Boston,N,4,23,2,57,Graduation,30,Patnership,Complex\n5316,412.13,Boston,W,8,22,13,34,Graduation,34,Franchise,Complex\n179,90.72,NJ City,E,15,8,3,39,No education,19,Owner,Standalone\n6885,167.22,Boston,N,5,22,12,40,Graduation,23,Patnership,Standalone\n2676,243.35,NJ City,W,16,20,15,38,High School,22,Franchise,Complex\n2935,169.46,NJ City,E,19,8,8,80,High School,32,Owner,Standalone\n7434,82.81,Boston,S,3,23,2,51,Graduation,28,Patnership,Complex\n4410,12.4,NJ City,N,19,9,3,77,Graduation,32,Patnership,Complex\n7046,251.47,Boston,S,16,9,9,51,Graduation,35,Patnership,Complex\n7424,447.19,Boston,W,4,24,19,31,Graduation,28,Franchise,Complex\n4453,379.93,NJ City,N,12,9,2,60,High School,23,Patnership,Standalone\n4218,232.61,NJ City,S,16,14,1,50,Graduation,34,Patnership,Complex\n7726,393.15,Boston,S,9,11,22,58,Graduation,26,Patnership,Complex\n8967,116.28,NJ City,E,9,12,4,36,Graduation,28,Owner,Standalone\n1491,131.14,NJ City,E,5,19,18,59,High School,33,Owner,Standalone\n1969,169.49,NJ City,N,14,10,11,65,High School,35,Patnership,Standalone\n3964,170.23,NJ City,W,17,21,3,82,High School,20,Franchise,Complex\n7586,43.9,Boston,N,12,11,1,80,No education,35,Patnership,Complex\n2561,408.66,NJ City,N,15,22,21,70,High School,19,Patnership,Standalone\n5908,258.37,Boston,W,24,12,15,66,Graduation,28,Franchise,Complex\n3993,266.4,NJ City,N,8,20,2,41,Graduation,20,Patnership,Standalone\n3293,304.58,NJ City,N,16,8,10,75,High School,27,Patnership,Standalone\n4829,185.06,NJ City,N,22,16,21,75,High School,31,Patnership,Standalone\n6669,121.66,Boston,N,18,19,4,83,Graduation,35,Patnership,Standalone\n5160,347.67,Boston,W,1,10,11,29,Graduation,18,Franchise,Complex\n1110,479.73,NJ City,S,17,18,12,28,High School,35,Patnership,Complex\n7656,79.86,Boston,W,4,15,2,81,Graduation,24,Franchise,Complex\n5221,61.35,Boston,N,14,10,1,64,High School,22,Patnership,Standalone\n780,108.7,NJ City,W,18,11,1,48,Graduation,27,Franchise,Complex\n9788,128.06,NJ City,W,24,24,1,79,No education,18,Franchise,Complex\n4837,264.46,NJ City,N,24,22,15,61,High School,29,Patnership,Standalone\n1801,466.04,NJ City,N,16,23,18,45,High School,30,Patnership,Standalone\n7812,26.36,Boston,N,9,18,4,84,Graduation,18,Franchise,Complex\n7015,258.79,Boston,E,22,22,8,57,Graduation,23,Owner,Standalone\n3286,201.03,NJ City,S,3,23,3,54,High School,27,Patnership,Complex\n1408,69.53,NJ City,N,17,14,1,31,High School,29,Franchise,Complex\n7521,147.1,Boston,N,21,23,3,69,Graduation,23,Patnership,Standalone\n654,73.7,NJ City,N,9,17,1,57,Graduation,33,Patnership,Complex\n1229,351.47,NJ City,N,16,8,17,39,High School,18,Patnership,Standalone\n3767,452.07,NJ City,E,13,22,17,62,High School,25,Owner,Standalone\n4795,418.83,NJ City,E,16,18,18,77,High School,30,Owner,Standalone\n3019,88.86,NJ City,E,15,15,3,61,High School,24,Owner,Standalone\n5352,391.36,Boston,W,8,12,15,40,Graduation,25,Franchise,Complex\n1558,417.63,NJ City,S,12,16,9,74,High School,32,Patnership,Complex\n1124,390.37,NJ City,W,21,18,18,76,High School,26,Franchise,Complex\n530,424.11,NJ City,S,24,16,21,37,High School,34,Patnership,Complex\n8127,252.31,NJ City,E,23,14,24,37,Graduation,30,Owner,Standalone\n7053,71.36,Boston,N,9,20,2,65,Graduation,24,Patnership,Standalone\n2804,470.58,NJ City,W,7,20,25,72,High School,30,Franchise,Complex\n94,273.94,NJ City,S,15,20,3,37,High School,31,Patnership,Complex\n5772,96.14,Boston,W,20,14,1,39,Graduation,25,Franchise,Complex\n2932,495.22,NJ City,W,25,15,13,62,High School,30,Franchise,Complex\n1713,451.7,NJ City,N,4,17,22,25,High School,32,Patnership,Standalone\n9029,329.68,NJ City,N,6,21,4,57,No education,29,Patnership,Standalone\n2537,48.61,NJ City,N,2,9,1,46,No education,35,Patnership,Standalone\n8453,20.85,NJ City,N,9,18,2,30,No education,30,Patnership,Standalone\n1326,30.36,NJ City,N,12,18,2,26,Graduation,31,Patnership,Complex\n7891,138.46,Boston,E,19,19,20,53,Graduation,27,Owner,Standalone\n9334,109.28,NJ City,S,24,15,4,60,High School,20,Patnership,Complex\n1996,347.02,NJ City,W,16,9,13,38,High School,31,Franchise,Complex\n3253,110.84,NJ City,N,25,9,4,35,High School,30,Patnership,Standalone\n4203,14.24,NJ City,N,7,20,1,48,Graduation,27,Owner,Standalone\n2641,303.7,NJ City,N,13,16,10,31,High School,27,Patnership,Standalone\n634,275.65,NJ City,S,9,9,15,60,High School,20,Patnership,Complex\n2200,391.11,NJ City,W,8,11,22,59,High School,25,Franchise,Complex\n8705,36.96,NJ City,N,16,9,1,56,No education,29,Patnership,Standalone\n5819,328.44,Boston,E,12,16,3,53,No education,22,Owner,Standalone\n6064,95.96,Boston,W,22,14,4,71,High School,19,Franchise,Complex\n1734,155.46,NJ City,S,25,22,4,39,Graduation,26,Patnership,Complex\n6599,492.82,Boston,E,3,11,11,50,Graduation,30,Owner,Standalone\n1301,417.53,NJ City,N,18,13,23,33,High School,31,Patnership,Standalone\n5496,327.01,Boston,W,6,13,18,52,Graduation,24,Franchise,Complex\n2266,482.22,NJ City,S,19,22,9,46,High School,21,Patnership,Complex\n4830,490.19,NJ City,S,17,13,25,27,High School,33,Patnership,Complex\n7708,197.03,Boston,W,12,9,21,58,Graduation,24,Franchise,Complex\n4560,45.69,NJ City,N,4,16,4,76,Graduation,35,Franchise,Complex\n4782,422.82,NJ City,S,23,14,7,80,High School,20,Patnership,Complex\n8958,295.01,NJ City,S,18,21,4,61,Graduation,21,Patnership,Complex\n3315,185.28,NJ City,E,4,10,22,71,High School,18,Owner,Standalone\n4660,122.11,NJ City,W,7,22,3,68,High School,20,Franchise,Complex\n1889,39.84,NJ City,N,10,12,2,38,No education,26,Patnership,Standalone\n974,311.05,NJ City,S,19,10,16,31,High School,19,Patnership,Complex\n7824,334.66,Boston,W,22,16,20,40,Graduation,33,Franchise,Complex\n4926,219,NJ City,S,3,10,13,53,High School,30,Patnership,Complex\n6685,53.91,Boston,N,13,16,3,75,High School,26,Patnership,Standalone\n4710,292.99,NJ City,S,9,18,3,39,Graduation,27,Patnership,Complex\n9203,447.17,NJ City,E,18,18,4,81,No education,25,Owner,Standalone\n8046,366.52,NJ City,S,6,13,1,46,Graduation,27,Patnership,Complex\n6440,287.86,Boston,W,5,14,23,40,Graduation,27,Franchise,Complex\n244,151.04,NJ City,W,24,22,18,81,High School,26,Franchise,Complex\n317,312.21,NJ City,N,15,17,1,38,No education,31,Patnership,Standalone\n4344,407.67,NJ City,W,10,17,13,59,High School,32,Franchise,Complex\n533,390.97,NJ City,N,10,18,14,34,High School,18,Patnership,Standalone\n3212,336.11,NJ City,W,17,12,20,59,High School,23,Franchise,Complex\n2211,337.32,NJ City,E,1,15,3,42,Graduation,20,Owner,Standalone\n4649,302.92,NJ City,N,13,12,16,63,High School,29,Patnership,Standalone\n9698,15.98,NJ City,N,14,16,4,84,No education,21,Patnership,Complex\n1655,480.3,NJ City,E,18,8,20,49,High School,22,Owner,Standalone\n5394,26.45,Boston,N,20,18,4,52,Graduation,35,Patnership,Complex\n1890,243.07,NJ City,S,21,8,23,71,High School,19,Patnership,Complex\n785,424.61,NJ City,N,10,10,1,62,No education,28,Patnership,Standalone\n452,298.39,NJ City,W,9,13,16,82,High School,18,Franchise,Complex\n9557,311.15,NJ City,N,24,11,25,80,Graduation,34,Patnership,Standalone\n5131,343.3,Boston,E,10,14,24,49,Graduation,21,Owner,Standalone\n6035,499.88,Boston,E,22,11,2,74,No education,27,Owner,Standalone\n4149,43.8,NJ City,N,15,18,1,61,Graduation,30,Patnership,Standalone\n7271,327.63,Boston,E,25,13,21,40,Graduation,22,Owner,Standalone\n5263,102.89,Boston,E,6,16,4,62,High School,35,Owner,Standalone\n4288,201.89,NJ City,W,4,23,21,85,High School,35,Franchise,Complex\n1682,333.76,NJ City,S,23,23,3,30,No education,24,Patnership,Complex\n8928,253.05,NJ City,W,17,8,10,49,Graduation,32,Franchise,Complex\n814,478.98,NJ City,S,13,22,25,46,High School,25,Patnership,Complex\n7360,39.21,Boston,N,15,18,2,28,High School,30,Franchise,Complex\n29,349.03,Boston,N,4,17,20,32,High School,23,Patnership,Standalone\n5712,361.21,Boston,W,17,8,21,42,Graduation,22,Franchise,Complex\n6411,405.54,Boston,E,1,20,19,25,Graduation,28,Owner,Standalone\n3923,190.08,NJ City,E,12,21,21,67,High School,20,Owner,Standalone\n4746,227.79,NJ City,S,8,10,21,78,High School,22,Patnership,Complex\n5156,26.63,Boston,N,7,12,1,41,No education,19,Franchise,Complex\n3481,292.16,NJ City,N,13,21,11,77,High School,21,Patnership,Standalone\n7150,305.97,Boston,S,15,17,22,53,Graduation,26,Patnership,Complex\n601,161.32,NJ City,N,24,10,2,82,High School,19,Patnership,Standalone\n9614,263.38,NJ City,S,6,18,18,38,Graduation,29,Patnership,Complex\n1359,0.3,NJ City,N,16,19,4,73,Graduation,33,Owner,Standalone\n2490,259,NJ City,S,3,22,6,46,High School,18,Patnership,Complex\n8330,48.85,NJ City,S,13,18,1,37,No education,27,Patnership,Complex\n2197,124.59,NJ City,N,22,17,3,77,High School,31,Patnership,Standalone\n104,196.29,NJ City,W,4,24,25,67,High School,21,Franchise,Complex\n7370,383.6,Boston,S,3,12,16,55,Graduation,28,Patnership,Complex\n8447,42.57,NJ City,N,23,15,1,38,No education,34,Owner,Standalone\n1344,495.74,NJ City,W,6,23,19,49,High School,34,Franchise,Complex\n3693,62.24,NJ City,N,8,19,2,81,Graduation,25,Patnership,Standalone\n1018,71.84,NJ City,N,11,16,2,54,High School,33,Patnership,Complex\n2605,13.65,NJ City,N,6,9,4,40,High School,20,Patnership,Standalone\n3849,297.52,NJ City,N,10,17,17,77,High School,28,Patnership,Standalone\n405,467.63,NJ City,N,18,16,15,28,High School,24,Patnership,Standalone\n7553,8.06,Boston,N,12,21,2,81,No education,19,Patnership,Standalone\n7482,13.72,Boston,N,11,21,3,49,Graduation,27,Patnership,Complex\n8207,284.41,NJ City,E,13,15,5,34,No education,27,Owner,Standalone\n3171,351.54,NJ City,E,19,20,21,72,High School,31,Owner,Standalone\n688,402.29,NJ City,W,25,14,8,27,High School,24,Franchise,Complex\n5020,139.89,Boston,W,25,11,9,79,Graduation,32,Franchise,Complex\n4942,289.12,Boston,S,11,11,17,47,High School,18,Patnership,Complex\n2113,463.62,NJ City,N,17,13,13,29,High School,21,Patnership,Standalone\n9169,107.16,NJ City,N,9,15,4,48,High School,23,Patnership,Standalone\n6212,118.9,Boston,W,5,24,1,61,No education,27,Franchise,Complex\n3629,12.77,NJ City,N,20,13,4,31,No education,19,Patnership,Standalone\n4826,358.19,NJ City,S,10,19,15,68,High School,28,Patnership,Complex\n8689,435.57,NJ City,N,24,9,22,80,Graduation,18,Patnership,Standalone\n7493,305.23,Boston,N,14,22,18,67,Graduation,30,Patnership,Standalone\n1971,118,NJ City,E,1,12,17,68,High School,28,Owner,Standalone\n5826,396.97,Boston,S,18,12,14,54,Graduation,25,Patnership,Complex\n9480,414.59,NJ City,W,2,16,7,49,Graduation,20,Franchise,Complex\n5005,204.97,Boston,N,19,8,3,83,High School,32,Patnership,Standalone\n9285,0.86,NJ City,N,14,22,3,30,Graduation,29,Patnership,Standalone\n7747,419.17,Boston,E,10,20,21,54,Graduation,19,Owner,Standalone\n3400,225.99,NJ City,W,21,18,8,28,High School,30,Franchise,Complex\n5129,380.47,Boston,N,14,8,25,42,Graduation,25,Patnership,Standalone\n3387,367.35,NJ City,E,4,21,11,81,High School,24,Owner,Standalone\n998,70.75,NJ City,N,11,9,2,82,No education,32,Patnership,Complex\n7093,194.19,Boston,N,13,14,9,84,Graduation,32,Patnership,Standalone\n7368,136.21,Boston,W,1,16,19,77,Graduation,31,Franchise,Complex\n4230,37.89,NJ City,N,3,8,4,66,Graduation,30,Patnership,Complex\n1626,163.63,NJ City,S,18,22,25,59,High School,33,Patnership,Complex\n1122,314.96,NJ City,S,5,19,14,85,High School,22,Patnership,Complex\n2742,81.05,NJ City,S,8,11,1,61,Graduation,29,Patnership,Complex\n3209,302.48,NJ City,N,23,9,4,66,No education,26,Patnership,Standalone\n1275,298.19,NJ City,E,10,12,6,36,High School,28,Owner,Standalone\n9664,435.61,NJ City,W,2,11,9,63,Graduation,24,Franchise,Complex\n47,94.1,Boston,E,6,16,2,43,No education,31,Owner,Standalone\n202,50.09,NJ City,S,20,24,1,74,High School,32,Patnership,Complex\n9066,143.72,NJ City,S,3,12,25,25,Graduation,31,Patnership,Complex\n6187,263.65,Boston,E,8,21,4,33,High School,33,Owner,Standalone\n4905,325,NJ City,N,21,12,6,49,High School,23,Patnership,Standalone\n4210,316.46,NJ City,S,15,9,13,59,High School,25,Patnership,Complex\n7359,95.93,Boston,E,16,11,3,84,Graduation,26,Owner,Standalone\n8983,402.87,NJ City,E,25,18,11,78,Graduation,18,Owner,Standalone\n7399,257.17,Boston,E,7,20,25,69,Graduation,20,Owner,Standalone\n9821,64.34,NJ City,N,10,18,3,55,No education,35,Patnership,Standalone\n7453,162.93,Boston,N,8,8,7,62,Graduation,26,Patnership,Standalone\n4728,263.83,NJ City,W,13,16,11,42,High School,30,Franchise,Complex\n9274,414.83,NJ City,S,23,14,15,66,Graduation,35,Patnership,Complex\n2369,4.68,NJ City,N,14,22,3,51,No education,18,Patnership,Standalone\n8192,126.48,NJ City,W,16,16,3,36,No education,32,Franchise,Complex\n2625,40.33,NJ City,N,2,18,2,42,Graduation,27,Patnership,Standalone\n6829,477.77,Boston,N,14,22,9,39,Graduation,33,Patnership,Standalone\n8190,199.26,NJ City,S,6,10,13,57,Graduation,33,Patnership,Complex\n7458,410.57,Boston,S,15,11,11,74,Graduation,21,Patnership,Complex\n8263,484.48,NJ City,E,6,16,2,47,High School,19,Owner,Standalone\n6132,276.31,Boston,W,17,24,10,59,Graduation,27,Franchise,Complex\n8214,277.73,NJ City,S,4,11,15,35,Graduation,18,Patnership,Complex\n626,123,NJ City,S,1,23,15,45,High School,27,Patnership,Complex\n2654,472.16,NJ City,S,12,17,16,42,High School,35,Patnership,Complex\n6832,486.14,Boston,W,8,9,10,67,Graduation,33,Franchise,Complex\n1606,388.74,NJ City,S,9,12,13,46,High School,23,Patnership,Complex\n1794,163.27,NJ City,S,13,19,25,25,High School,29,Patnership,Complex\n3097,183.45,NJ City,N,18,23,5,32,High School,35,Patnership,Standalone\n7753,36.91,Boston,N,16,18,2,42,High School,24,Patnership,Standalone\n1197,314.67,NJ City,N,15,14,13,84,High School,23,Patnership,Standalone\n9766,416,NJ City,S,9,23,22,36,Graduation,19,Patnership,Complex\n3898,183.83,NJ City,S,13,22,15,30,High School,22,Patnership,Complex\n4291,23.78,NJ City,N,21,20,2,53,High School,21,Owner,Standalone\n6770,29.81,Boston,N,7,21,4,77,No education,22,Patnership,Complex\n6302,4.99,Boston,N,18,11,1,80,No education,27,Patnership,Complex\n5635,8.26,Boston,N,18,21,2,68,High School,31,Owner,Standalone\n3074,473.93,NJ City,S,11,15,16,57,High School,19,Patnership,Complex\n7400,256.82,Boston,W,2,20,9,62,Graduation,27,Franchise,Complex\n503,31.38,NJ City,N,25,8,4,79,No education,32,Owner,Standalone\n2693,179,NJ City,N,1,13,22,76,High School,20,Patnership,Standalone\n9343,373.47,NJ City,E,2,22,15,58,Graduation,28,Owner,Standalone\n5302,62.78,Boston,N,3,21,2,71,High School,23,Patnership,Complex\n3768,425.43,NJ City,W,23,16,13,31,High School,24,Franchise,Complex\n7942,161.77,Boston,S,13,13,1,56,High School,23,Patnership,Complex\n811,453.14,NJ City,E,23,8,7,38,High School,23,Owner,Standalone\n1187,196.21,NJ City,E,5,22,3,70,No education,22,Owner,Standalone\n901,81.19,NJ City,N,24,17,3,60,High School,27,Patnership,Standalone\n4684,11.69,NJ City,N,1,21,4,78,High School,35,Franchise,Complex\n284,19.65,NJ City,N,9,11,2,68,No education,33,Franchise,Complex\n4536,278,NJ City,W,1,14,20,31,High School,18,Franchise,Complex\n5620,442.51,Boston,W,6,19,5,51,High School,18,Franchise,Complex\n6143,219.34,Boston,E,23,23,2,74,No education,21,Owner,Standalone\n2085,308.46,NJ City,N,25,8,6,81,High School,22,Patnership,Standalone\n1478,125,NJ City,S,2,11,14,26,High School,31,Patnership,Complex\n2273,91.63,NJ City,N,21,23,3,33,No education,18,Patnership,Standalone\n4952,91.91,Boston,W,14,11,2,28,No education,25,Franchise,Complex" }, { "path": "Data/mtcars.csv", "content": "\"\",\"mpg\",\"cyl\",\"disp\",\"hp\",\"drat\",\"wt\",\"qsec\",\"vs\",\"am\",\"gear\",\"carb\"\n\"Mazda RX4\",21,6,160,110,3.9,2.62,16.46,0,1,4,4\n\"Mazda RX4 Wag\",21,6,160,110,3.9,2.875,17.02,0,1,4,4\n\"Datsun 710\",22.8,4,108,93,3.85,2.32,18.61,1,1,4,1\n\"Hornet 4 Drive\",21.4,6,258,110,3.08,3.215,19.44,1,0,3,1\n\"Hornet Sportabout\",18.7,8,360,175,3.15,3.44,17.02,0,0,3,2\n\"Valiant\",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1\n\"Duster 360\",14.3,8,360,245,3.21,3.57,15.84,0,0,3,4\n\"Merc 240D\",24.4,4,146.7,62,3.69,3.19,20,1,0,4,2\n\"Merc 230\",22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2\n\"Merc 280\",19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4\n\"Merc 280C\",17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4\n\"Merc 450SE\",16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3\n\"Merc 450SL\",17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3\n\"Merc 450SLC\",15.2,8,275.8,180,3.07,3.78,18,0,0,3,3\n\"Cadillac Fleetwood\",10.4,8,472,205,2.93,5.25,17.98,0,0,3,4\n\"Lincoln Continental\",10.4,8,460,215,3,5.424,17.82,0,0,3,4\n\"Chrysler Imperial\",14.7,8,440,230,3.23,5.345,17.42,0,0,3,4\n\"Fiat 128\",32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1\n\"Honda Civic\",30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2\n\"Toyota Corolla\",33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1\n\"Toyota Corona\",21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1\n\"Dodge Challenger\",15.5,8,318,150,2.76,3.52,16.87,0,0,3,2\n\"AMC Javelin\",15.2,8,304,150,3.15,3.435,17.3,0,0,3,2\n\"Camaro Z28\",13.3,8,350,245,3.73,3.84,15.41,0,0,3,4\n\"Pontiac Firebird\",19.2,8,400,175,3.08,3.845,17.05,0,0,3,2\n\"Fiat X1-9\",27.3,4,79,66,4.08,1.935,18.9,1,1,4,1\n\"Porsche 914-2\",26,4,120.3,91,4.43,2.14,16.7,0,1,5,2\n\"Lotus Europa\",30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2\n\"Ford Pantera L\",15.8,8,351,264,4.22,3.17,14.5,0,1,5,4\n\"Ferrari Dino\",19.7,6,145,175,3.62,2.77,15.5,0,1,5,6\n\"Maserati Bora\",15,8,301,335,3.54,3.57,14.6,0,1,5,8\n\"Volvo 142E\",21.4,4,121,109,4.11,2.78,18.6,1,1,4,2\n" }, { "path": "Data/mtcars1.csv", "content": "\"mpg\",\"cyl\",\"disp\",\"hp\",\"drat\",\"wt\",\"qsec\",\"vs\",\"am\",\"gear\",\"carb\"\n21,6,160,110,3.9,2.62,16.46,0,1,4,4\n21,6,160,110,3.9,2.875,17.02,0,1,4,4\n22.8,4,108,93,3.85,2.32,18.61,1,1,4,1\n21.4,6,258,110,3.08,3.215,19.44,1,0,3,1\n18.7,8,360,175,3.15,3.44,17.02,0,0,3,2\n18.1,6,225,105,2.76,3.46,20.22,1,0,3,1\n14.3,8,360,245,3.21,3.57,15.84,0,0,3,4\n24.4,4,146.7,62,3.69,3.19,20,1,0,4,2\n22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2\n19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4\n17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4\n16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3\n17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3\n15.2,8,275.8,180,3.07,3.78,18,0,0,3,3\n10.4,8,472,205,2.93,5.25,17.98,0,0,3,4\n10.4,8,460,215,3,5.424,17.82,0,0,3,4\n14.7,8,440,230,3.23,5.345,17.42,0,0,3,4\n32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1\n30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2\n33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1\n21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1\n15.5,8,318,150,2.76,3.52,16.87,0,0,3,2\n15.2,8,304,150,3.15,3.435,17.3,0,0,3,2\n13.3,8,350,245,3.73,3.84,15.41,0,0,3,4\n19.2,8,400,175,3.08,3.845,17.05,0,0,3,2\n27.3,4,79,66,4.08,1.935,18.9,1,1,4,1\n26,4,120.3,91,4.43,2.14,16.7,0,1,5,2\n30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2\n15.8,8,351,264,4.22,3.17,14.5,0,1,5,4\n19.7,6,145,175,3.62,2.77,15.5,0,1,5,6\n15,8,301,335,3.54,3.57,14.6,0,1,5,8\n21.4,4,121,109,4.11,2.78,18.6,1,1,4,2\n" }, { "path": "Data/myitems1.csv", "content": "\"\",\"transactionID\",\"items\"\n\"1\",3,\"milk\"\n\"2\",2,\"milk\"\n\"3\",2,\"bread\"\n\"4\",6,\"bread\"\n\"5\",5,\"milk\"\n\"6\",4,\"bread\"\n\"7\",9,\"butter\"\n\"8\",3,\"eggs\"\n\"9\",6,\"eggs\"\n\"10\",4,\"milk\"\n\"11\",3,\"milk\"\n\"12\",9,\"milk\"\n\"13\",4,\"bread\"\n\"14\",1,\"milk\"\n\"15\",10,\"eggs\"\n\"16\",4,\"milk\"\n\"17\",4,\"butter\"\n\"18\",7,\"milk\"\n\"19\",8,\"eggs\"\n\"20\",5,\"milk\"\n\"21\",10,\"bread\"\n\"22\",1,\"eggs\"\n\"23\",7,\"bread\"\n\"24\",9,\"milk\"\n\"25\",4,\"milk\"\n\"26\",10,\"eggs\"\n\"27\",5,\"eggs\"\n\"28\",10,\"milk\"\n\"29\",4,\"milk\"\n\"30\",6,\"bread\"\n\"31\",9,\"bread\"\n\"32\",7,\"butter\"\n\"33\",5,\"bread\"\n\"34\",6,\"butter\"\n\"35\",10,\"eggs\"\n\"36\",9,\"eggs\"\n\"37\",3,\"milk\"\n\"38\",9,\"milk\"\n\"39\",3,\"milk\"\n\"40\",4,\"milk\"\n\"41\",6,\"bread\"\n\"42\",2,\"milk\"\n\"43\",6,\"bread\"\n\"44\",10,\"butter\"\n\"45\",4,\"milk\"\n\"46\",4,\"butter\"\n\"47\",1,\"bread\"\n\"48\",9,\"butter\"\n\"49\",6,\"milk\"\n\"50\",9,\"butter\"\n" }, { "path": "Data/myrules1.csv", "content": "\"\",\"rules\",\"support\",\"confidence\",\"lift\",\"count\"\n\"1\",\"{I4} => {I2}\",0.222222222222222,1,1.28571428571429,2\n\"2\",\"{I5} => {I1}\",0.222222222222222,1,1.5,2\n\"3\",\"{I5} => {I2}\",0.222222222222222,1,1.28571428571429,2\n\"4\",\"{I3} => {I1}\",0.444444444444444,0.666666666666667,1,4\n\"5\",\"{I1} => {I3}\",0.444444444444444,0.666666666666667,1,4\n\"6\",\"{I3} => {I2}\",0.444444444444444,0.666666666666667,0.857142857142857,4\n\"7\",\"{I2} => {I3}\",0.444444444444444,0.571428571428571,0.857142857142857,4\n\"8\",\"{I1} => {I2}\",0.444444444444444,0.666666666666667,0.857142857142857,4\n\"9\",\"{I2} => {I1}\",0.444444444444444,0.571428571428571,0.857142857142857,4\n\"10\",\"{I1,I5} => {I2}\",0.222222222222222,1,1.28571428571429,2\n\"11\",\"{I2,I5} => {I1}\",0.222222222222222,1,1.5,2\n\"12\",\"{I1,I2} => {I5}\",0.222222222222222,0.5,2.25,2\n\"13\",\"{I1,I3} => {I2}\",0.222222222222222,0.5,0.642857142857143,2\n\"14\",\"{I2,I3} => {I1}\",0.222222222222222,0.5,0.75,2\n\"15\",\"{I1,I2} => {I3}\",0.222222222222222,0.5,0.75,2\n" }, { "path": "Data/node1.csv", "content": "\"\",\"gender\",\"married\",\"play\"\n\"1\",\"Female\",\"Single\",\"NotPlay\"\n\"2\",\"Male\",\"Single\",\"NotPlay\"\n\"3\",\"Male\",\"Single\",\"NotPlay\"\n\"4\",\"Male\",\"Single\",\"NotPlay\"\n\"5\",\"Male\",\"Married\",\"Play\"\n\"6\",\"Male\",\"Single\",\"NotPlay\"\n\"7\",\"Female\",\"Single\",\"NotPlay\"\n\"8\",\"Female\",\"Single\",\"NotPlay\"\n\"9\",\"Male\",\"Single\",\"NotPlay\"\n\"10\",\"Female\",\"Single\",\"NotPlay\"\n\"11\",\"Male\",\"Single\",\"Play\"\n\"12\",\"Female\",\"Single\",\"NotPlay\"\n\"13\",\"Female\",\"Single\",\"NotPlay\"\n\"14\",\"Male\",\"Married\",\"Play\"\n\"15\",\"Female\",\"Single\",\"NotPlay\"\n\"16\",\"Male\",\"Married\",\"Play\"\n\"17\",\"Female\",\"Single\",\"NotPlay\"\n\"18\",\"Female\",\"Single\",\"NotPlay\"\n\"19\",\"Female\",\"Single\",\"NotPlay\"\n\"20\",\"Female\",\"Single\",\"NotPlay\"\n\"21\",\"Female\",\"Single\",\"NotPlay\"\n\"22\",\"Female\",\"Single\",\"NotPlay\"\n\"23\",\"Female\",\"Single\",\"NotPlay\"\n\"24\",\"Female\",\"Single\",\"NotPlay\"\n\"25\",\"Female\",\"Single\",\"NotPlay\"\n\"26\",\"Male\",\"Married\",\"Play\"\n\"27\",\"Female\",\"Single\",\"NotPlay\"\n\"28\",\"Male\",\"Married\",\"Play\"\n\"29\",\"Male\",\"Married\",\"Play\"\n\"30\",\"Female\",\"Single\",\"NotPlay\"\n" }, { "path": "Data/pumba.csv", "content": "rollno,sname,age,course,gender,marks,married\nS-1,Student1- xyz,45,MTech,M,57,FALSE\nS-2,Student2- xyz,47,BTech,M,58,FALSE\nS-3,Student3- xyz,26,BTech,M,67,FALSE\nS-4,Student4- xyz,29,BTech,M,63,FALSE\nS-5,Student5- xyz,24,Phd,F,57,FALSE\nS-6,Student6- xyz,38,Phd,M,59,FALSE\nS-7,Student7- xyz,41,BTech,M,64,FALSE\nS-8,Student8- xyz,28,MTech,F,61,FALSE\nS-9,Student9- xyz,35,MTech,M,69,FALSE\nS-10,Student10- xyz,22,MTech,M,56,FALSE\nS-11,Student11- xyz,33,MTech,M,60,FALSE\nS-12,Student12- xyz,32,Phd,M,64,FALSE\nS-13,Student13- xyz,44,BTech,M,63,FALSE\nS-14,Student14- xyz,36,BTech,F,57,FALSE\nS-15,Student15- xyz,36,Phd,M,62,FALSE\nS-16,Student16- xyz,34,MTech,M,65,FALSE\nS-17,Student17- xyz,48,MTech,F,58,TRUE\nS-18,Student18- xyz,44,BTech,M,56,FALSE\nS-19,Student19- xyz,44,MTech,F,65,FALSE\nS-20,Student20- xyz,39,BTech,F,63,FALSE\nS-21,Student21- xyz,21,MTech,M,62,FALSE\nS-22,Student22- xyz,25,BTech,F,62,FALSE\nS-23,Student23- xyz,43,BTech,F,53,FALSE\nS-24,Student24- xyz,45,BTech,M,61,FALSE\nS-25,Student25- xyz,30,BTech,F,56,FALSE\nS-26,Student26- xyz,31,Phd,F,65,FALSE\nS-27,Student27- xyz,29,MTech,M,58,FALSE\nS-28,Student28- xyz,39,BTech,M,65,FALSE\nS-29,Student29- xyz,36,MTech,M,65,FALSE\nS-30,Student30- xyz,23,BTech,M,65,TRUE\n" }, { "path": "Data/rules.csv", "content": "\"\",\"lhs\",\"\",\"rhs\",\"support\",\"confidence\",\"lift\",\"count\"\n\"[1]\",\"{whole milk}\",\"=>\",\"{hamburger meat}\",0.0147432638535841,0.057699960206924,1.73541011815015,145\n\"[2]\",\"{whole milk}\",\"=>\",\"{hygiene articles}\",0.0128113879003559,0.0501392757660167,1.52197462086041,126\n\"[3]\",\"{whole milk}\",\"=>\",\"{sugar}\",0.0150482968988307,0.0588937524870672,1.73939956669762,148\n\"[4]\",\"{whole milk}\",\"=>\",\"{long life bakery product}\",0.0135231316725979,0.052924791086351,1.41444380525615,133\n\"[5]\",\"{whole milk}\",\"=>\",\"{dessert}\",0.0137264870360956,0.0537206526064465,1.44751402297096,135\n" }, { "path": "Data/rulesR.csv", "content": "\"\",\"rules\",\"support\",\"confidence\",\"lift\",\"count\"\n\"1\",\"{I4} => {I2}\",0.222222222222222,1,1.28571428571429,2\n\"2\",\"{I5} => {I1}\",0.222222222222222,1,1.5,2\n\"3\",\"{I5} => {I2}\",0.222222222222222,1,1.28571428571429,2\n\"4\",\"{I3} => {I1}\",0.444444444444444,0.666666666666667,1,4\n\"5\",\"{I1} => {I3}\",0.444444444444444,0.666666666666667,1,4\n\"6\",\"{I3} => {I2}\",0.444444444444444,0.666666666666667,0.857142857142857,4\n\"7\",\"{I2} => {I3}\",0.444444444444444,0.571428571428571,0.857142857142857,4\n\"8\",\"{I1} => {I2}\",0.444444444444444,0.666666666666667,0.857142857142857,4\n\"9\",\"{I2} => {I1}\",0.444444444444444,0.571428571428571,0.857142857142857,4\n\"10\",\"{I1,I5} => {I2}\",0.222222222222222,1,1.28571428571429,2\n\"11\",\"{I2,I5} => {I1}\",0.222222222222222,1,1.5,2\n\"12\",\"{I1,I2} => {I5}\",0.222222222222222,0.5,2.25,2\n\"13\",\"{I1,I3} => {I2}\",0.222222222222222,0.5,0.642857142857143,2\n\"14\",\"{I2,I3} => {I1}\",0.222222222222222,0.5,0.75,2\n\"15\",\"{I1,I2} => {I3}\",0.222222222222222,0.5,0.75,2\n" }, { "path": "Data/salesslr.csv", "content": "X,Y\n1.7,3.7\n1.6,3.9\n2.8,6.7\n5.6,9.5\n1.3,3.4\n2.2,5.6\n1.3,3.7\n1.1,2.7\n3.2,5.5\n1.5,2.9\n5.2,10.7\n4.6,7.6\n5.8,11.8\n3,4.1" }, { "path": "Data/slr1.csv", "content": "X,Y\n1.7,3.7\n1.6,3.9\n2.8,6.7\n5.6,9.5\n1.3,3.4\n2.2,5.6\n1.3,3.7\n1.1,2.7\n3.2,5.5\n1.5,2.9\n5.2,10.7\n4.6,7.6\n5.8,11.8\n3,4.1" }, { "path": "Data/student.csv", "content": "rollno,name,age\n1,student1,23\n2,student2,24\n3,student3,25\n4,student4,26\n5,student5,27\n6,student6,28\n7,student7,29\n8,student8,30\n9,student9,31\n10,student10,32\n11,student11,33\n12,student12,34\n13,student13,35\n14,student14,36\n15,student15,37\n" }, { "path": "Data/studentdata2.csv", "content": "1,\"BSCDS\",2016,87,92,79,42,92,,\n2,\"BSCDS\",2017,96,102,89,77,104,,\n3,\"BSCDS\",2016,87,133,89,61,99,,\n4,\"BSCDS\",2017,90,82,71,67,100,,\n5,\"BSCDS\",2016,63,102,83,51,108,,\n6,\"BSCDS\",2017,74,127,68,77,116,,\n7,\"BSCDS\",2016,102,95,60,93,126,,\n8,\"BSCDS\",2017,62,79,63,59,102,,\n9,\"BSCDS\",2016,84,83,74,69,126,,\n10,\"BSCDS\",2017,86,92,93,93,98,,\n11,\"BSCDS\",2016,95,83,76,81,117,,\n12,\"BSCDS\",2017,75,95,67,93,135,,\n13,\"BSCDS\",2016,82,92,77,70,110,,\n14,\"BSCDS\",2017,54,96,82,68,103,,\n15,\"BSCDS\",2016,85,108,62,64,110,,\n16,\"BSCDS\",2017,70,112,64,87,128,,\n17,\"BSCDS\",2016,76,134,56,55,99,,\n18,\"BSCDS\",2017,62,99,62,74,124,,\n19,\"BSCDS\",2016,86,94,66,69,123,,\n20,\"BSCDS\",2017,90,129,72,97,113,,\n21,\"MSCDS\",2016,75,134,89,63,110,,\n22,\"MSCDS\",2017,107,101,84,52,105,,\n23,\"MSCDS\",2016,90,93,78,73,106,,\n24,\"MSCDS\",2017,94,64,69,70,116,,\n25,\"MSCDS\",2016,111,128,75,60,131,,\n26,\"MSCDS\",2017,70,83,80,72,108,,\n27,\"MSCDS\",2016,92,78,70,67,96,,\n28,\"MSCDS\",2017,95,161,86,62,103,,\n29,\"MSCDS\",2016,80,105,86,76,113,,\n30,\"MSCDS\",2017,85,99,63,66,107,,\n31,\"MSCDS\",2016,65,45,68,48,108,,\n32,\"MSCDS\",2017,87,98,87,38,108,,\n33,\"MSCDS\",2016,69,120,58,63,96,,\n34,\"MSCDS\",2017,92,108,79,87,108,,\n35,\"MSCDS\",2016,68,118,77,80,119,,\n36,\"MSCDS\",2017,85,140,107,85,117,,\n37,\"MSCDS\",2016,67,123,48,82,115,,\n38,\"MSCDS\",2017,76,90,67,74,106,,\n39,\"MSCDS\",2016,57,114,82,52,108,,\n40,\"MSCDS\",2017,82,96,92,72,98,,\n41,\"MSCDS\",2016,95,89,83,89,109,,\n42,\"MSCDS\",2017,83,43,61,29,113,,\n43,\"MSCDS\",2016,54,84,97,61,127,,\n44,\"MSCDS\",2017,71,110,57,83,120,,\n45,\"MSCDS\",2016,105,143,64,76,105,,\n46,\"MSCDS\",2017,107,110,75,79,114,,\n47,\"MSCDS\",2016,62,112,97,75,99,,\n48,\"MSCDS\",2017,74,81,80,76,119,,\n49,\"MSCDS\",2016,85,103,82,70,120,,\n50,\"MSCDS\",2017,85,58,92,59,131,,\n51,\"PGDDS\",2016,71,110,85,55,114,,\n52,\"PGDDS\",2017,66,114,63,70,105,,\n53,\"PGDDS\",2016,77,104,82,65,111,,\n54,\"PGDDS\",2017,95,114,74,59,105,,\n55,\"PGDDS\",2016,80,106,93,75,102,,\n56,\"PGDDS\",2017,70,115,73,72,112,,\n57,\"PGDDS\",2016,72,137,60,88,101,,\n58,\"PGDDS\",2017,104,122,80,57,112,,\n59,\"PGDDS\",2016,73,101,79,64,114,,\n60,\"PGDDS\",2017,91,78,75,69,109,,\n61,\"PGDDS\",2016,102,108,64,53,108,,\n62,\"PGDDS\",2017,87,92,67,67,104,,\n63,\"PGDDS\",2016,74,130,75,62,99,,\n64,\"PGDDS\",2017,79,68,85,44,118,,\n65,\"PGDDS\",2016,73,92,71,67,110,,\n66,\"BTCSDA\",2017,98,108,66,64,118,,\n67,\"BTCSDA\",2016,78,97,48,84,98,,\n68,\"BTCSDA\",2017,103,88,80,63,112,,\n69,\"BTCSDA\",2016,72,94,79,77,117,,\n70,\"BTCSDA\",2017,70,113,88,72,110,,\n71,\"BTCSDA\",2016,82,118,90,55,108,,\n72,\"BTCSDA\",2017,80,113,74,74,102,,\n73,\"BTCSDA\",2016,72,145,88,69,131,,\n74,\"BTCSDA\",2017,56,123,75,72,118,,\n75,\"BTCSDA\",2016,80,106,68,78,128,,\n76,\"BTCSDA\",2017,69,87,67,71,111,,\n77,\"BTCSDA\",2016,96,158,72,89,104,,\n78,\"BTCSDA\",2017,66,114,77,71,95,,\n79,\"BTCSDA\",2016,97,86,75,55,104,,\n80,\"BTCSDA\",2017,70,104,77,79,112,,\n81,\"BTCSDA\",2016,84,94,77,55,120,,\n82,\"BTCSDA\",2017,93,95,82,47,113,,\n83,\"BTCSDA\",2016,72,81,72,60,98,,\n84,\"BTCSDA\",2017,88,102,91,61,117,,\n85,\"BTCSDA\",2016,79,106,79,63,93,,\n86,\"BTCSDA\",2017,63,78,63,89,106,,\n87,\"BTCSDA\",2016,76,35,72,90,107,,\n88,\"BTCSDA\",2017,104,95,80,79,91,,\n89,\"BTCSDA\",2016,77,101,71,55,119,,\n90,\"BTCSDA\",2017,68,112,78,70,104,,\n91,\"BTCSDA\",2016,79,101,79,76,107,,\n92,\"BTCSDA\",2017,85,108,88,68,124,,\n93,\"BTCSDA\",2016,94,78,68,72,116,,\n94,\"BTCSDA\",2017,97,114,75,59,109,,\n95,\"BTCSDA\",2016,82,114,69,55,115,,\n96,\"BTCSDA\",2017,97,105,64,87,114,,\n97,\"BTCSDA\",2016,69,127,57,73,127,,\n98,\"BTCSDA\",2017,45,108,77,65,113,,\n99,\"BTCSDA\",2016,73,105,81,74,115,,\n100,\"BTCSDA\",2017,72,105,81,95,113,,\n" }, { "path": "Data/studentdata3.txt", "content": "------------------------------------------------------------------------------------------------------------------------------------------\n| rollno | course | batch | Lesson 1 | Lesson 2 | Quiz | Project | Final | Comment | Comment |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 1 | BSCDS | 2016 | 87 | 92 | 79 | 42 | 92 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 2 | BSCDS | 2017 | 96 | 102 | 89 | 77 | 104 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 3 | BSCDS | 2016 | 87 | 133 | 89 | 61 | 99 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 4 | BSCDS | 2017 | 90 | 82 | 71 | 67 | 100 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 5 | BSCDS | 2016 | 63 | 102 | 83 | 51 | 108 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 6 | BSCDS | 2017 | 74 | 127 | 68 | 77 | 116 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 7 | BSCDS | 2016 | 102 | 95 | 60 | 93 | 126 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 8 | BSCDS | 2017 | 62 | 79 | 63 | 59 | 102 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 9 | BSCDS | 2016 | 84 | 83 | 74 | 69 | 126 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 10 | BSCDS | 2017 | 86 | 92 | 93 | 93 | 98 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 11 | BSCDS | 2016 | 95 | 83 | 76 | 81 | 117 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 12 | BSCDS | 2017 | 75 | 95 | 67 | 93 | 135 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 13 | BSCDS | 2016 | 82 | 92 | 77 | 70 | 110 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 14 | BSCDS | 2017 | 54 | 96 | 82 | 68 | 103 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 15 | BSCDS | 2016 | 85 | 108 | 62 | 64 | 110 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 16 | BSCDS | 2017 | 70 | 112 | 64 | 87 | 128 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 17 | BSCDS | 2016 | 76 | 134 | 56 | 55 | 99 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 18 | BSCDS | 2017 | 62 | 99 | 62 | 74 | 124 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 19 | BSCDS | 2016 | 86 | 94 | 66 | 69 | 123 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 20 | BSCDS | 2017 | 90 | 129 | 72 | 97 | 113 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 21 | MSCDS | 2016 | 75 | 134 | 89 | 63 | 110 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 22 | MSCDS | 2017 | 107 | 101 | 84 | 52 | 105 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 23 | MSCDS | 2016 | 90 | 93 | 78 | 73 | 106 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 24 | MSCDS | 2017 | 94 | 64 | 69 | 70 | 116 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 25 | MSCDS | 2016 | 111 | 128 | 75 | 60 | 131 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 26 | MSCDS | 2017 | 70 | 83 | 80 | 72 | 108 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 27 | MSCDS | 2016 | 92 | 78 | 70 | 67 | 96 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 28 | MSCDS | 2017 | 95 | 161 | 86 | 62 | 103 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 29 | MSCDS | 2016 | 80 | 105 | 86 | 76 | 113 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 30 | MSCDS | 2017 | 85 | 99 | 63 | 66 | 107 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 31 | MSCDS | 2016 | 65 | 45 | 68 | 48 | 108 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 32 | MSCDS | 2017 | 87 | 98 | 87 | 38 | 108 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 33 | MSCDS | 2016 | 69 | 120 | 58 | 63 | 96 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 34 | MSCDS | 2017 | 92 | 108 | 79 | 87 | 108 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 35 | MSCDS | 2016 | 68 | 118 | 77 | 80 | 119 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 36 | MSCDS | 2017 | 85 | 140 | 107 | 85 | 117 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 37 | MSCDS | 2016 | 67 | 123 | 48 | 82 | 115 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 38 | MSCDS | 2017 | 76 | 90 | 67 | 74 | 106 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 39 | MSCDS | 2016 | 57 | 114 | 82 | 52 | 108 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 40 | MSCDS | 2017 | 82 | 96 | 92 | 72 | 98 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 41 | MSCDS | 2016 | 95 | 89 | 83 | 89 | 109 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 42 | MSCDS | 2017 | 83 | 43 | 61 | 29 | 113 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 43 | MSCDS | 2016 | 54 | 84 | 97 | 61 | 127 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 44 | MSCDS | 2017 | 71 | 110 | 57 | 83 | 120 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 45 | MSCDS | 2016 | 105 | 143 | 64 | 76 | 105 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 46 | MSCDS | 2017 | 107 | 110 | 75 | 79 | 114 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 47 | MSCDS | 2016 | 62 | 112 | 97 | 75 | 99 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 48 | MSCDS | 2017 | 74 | 81 | 80 | 76 | 119 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 49 | MSCDS | 2016 | 85 | 103 | 82 | 70 | 120 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 50 | MSCDS | 2017 | 85 | 58 | 92 | 59 | 131 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 51 | PGDDS | 2016 | 71 | 110 | 85 | 55 | 114 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 52 | PGDDS | 2017 | 66 | 114 | 63 | 70 | 105 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 53 | PGDDS | 2016 | 77 | 104 | 82 | 65 | 111 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 54 | PGDDS | 2017 | 95 | 114 | 74 | 59 | 105 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 55 | PGDDS | 2016 | 80 | 106 | 93 | 75 | 102 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 56 | PGDDS | 2017 | 70 | 115 | 73 | 72 | 112 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 57 | PGDDS | 2016 | 72 | 137 | 60 | 88 | 101 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 58 | PGDDS | 2017 | 104 | 122 | 80 | 57 | 112 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 59 | PGDDS | 2016 | 73 | 101 | 79 | 64 | 114 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 60 | PGDDS | 2017 | 91 | 78 | 75 | 69 | 109 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 61 | PGDDS | 2016 | 102 | 108 | 64 | 53 | 108 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 62 | PGDDS | 2017 | 87 | 92 | 67 | 67 | 104 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 63 | PGDDS | 2016 | 74 | 130 | 75 | 62 | 99 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 64 | PGDDS | 2017 | 79 | 68 | 85 | 44 | 118 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 65 | PGDDS | 2016 | 73 | 92 | 71 | 67 | 110 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 66 | BTCSDA | 2017 | 98 | 108 | 66 | 64 | 118 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 67 | BTCSDA | 2016 | 78 | 97 | 48 | 84 | 98 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 68 | BTCSDA | 2017 | 103 | 88 | 80 | 63 | 112 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 69 | BTCSDA | 2016 | 72 | 94 | 79 | 77 | 117 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 70 | BTCSDA | 2017 | 70 | 113 | 88 | 72 | 110 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 71 | BTCSDA | 2016 | 82 | 118 | 90 | 55 | 108 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 72 | BTCSDA | 2017 | 80 | 113 | 74 | 74 | 102 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 73 | BTCSDA | 2016 | 72 | 145 | 88 | 69 | 131 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 74 | BTCSDA | 2017 | 56 | 123 | 75 | 72 | 118 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 75 | BTCSDA | 2016 | 80 | 106 | 68 | 78 | 128 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 76 | BTCSDA | 2017 | 69 | 87 | 67 | 71 | 111 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 77 | BTCSDA | 2016 | 96 | 158 | 72 | 89 | 104 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 78 | BTCSDA | 2017 | 66 | 114 | 77 | 71 | 95 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 79 | BTCSDA | 2016 | 97 | 86 | 75 | 55 | 104 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 80 | BTCSDA | 2017 | 70 | 104 | 77 | 79 | 112 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 81 | BTCSDA | 2016 | 84 | 94 | 77 | 55 | 120 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 82 | BTCSDA | 2017 | 93 | 95 | 82 | 47 | 113 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 83 | BTCSDA | 2016 | 72 | 81 | 72 | 60 | 98 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 84 | BTCSDA | 2017 | 88 | 102 | 91 | 61 | 117 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 85 | BTCSDA | 2016 | 79 | 106 | 79 | 63 | 93 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 86 | BTCSDA | 2017 | 63 | 78 | 63 | 89 | 106 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 87 | BTCSDA | 2016 | 76 | 35 | 72 | 90 | 107 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 88 | BTCSDA | 2017 | 104 | 95 | 80 | 79 | 91 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 89 | BTCSDA | 2016 | 77 | 101 | 71 | 55 | 119 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 90 | BTCSDA | 2017 | 68 | 112 | 78 | 70 | 104 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 91 | BTCSDA | 2016 | 79 | 101 | 79 | 76 | 107 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 92 | BTCSDA | 2017 | 85 | 108 | 88 | 68 | 124 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 93 | BTCSDA | 2016 | 94 | 78 | 68 | 72 | 116 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 94 | BTCSDA | 2017 | 97 | 114 | 75 | 59 | 109 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 95 | BTCSDA | 2016 | 82 | 114 | 69 | 55 | 115 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 96 | BTCSDA | 2017 | 97 | 105 | 64 | 87 | 114 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 97 | BTCSDA | 2016 | 69 | 127 | 57 | 73 | 127 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 98 | BTCSDA | 2017 | 45 | 108 | 77 | 65 | 113 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 99 | BTCSDA | 2016 | 73 | 105 | 81 | 74 | 115 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n| 100 | BTCSDA | 2017 | 72 | 105 | 81 | 95 | 113 | | |\n------------------------------------------------------------------------------------------------------------------------------------------\n" }, { "path": "Data/studentdata4.csv", "content": "ser,student,course,batch,Internal 1 Assessment (R) (O),Internal 2 Assessment (R) (O),Assignment,Viva,Attendance,Final,Calculated Grade,Comment Faculty,Comment HOD,Gender,Cat,Date of Birth,Backs\n1,S001,BSCDS,2016,7,9,7,5,18,32,78,,,M,GEN,8-Nov-94,1\n2,S002,BSCDS,2017,9,5,8,5,15,31,73,,,M,GEN,24-Jul-93,1\n3,S003,BSCDS,2016,8,6,5,6,11,43,79,,,M,GEN,17-Feb-95,2\n4,S004,BSCDS,2017,8,5,5,8,14,32,72,,,M,GEN,4-Mar-94,6\n5,S005,BSCDS,2016,5,9,9,5,16,49,93,,,M,GEN,20-Jun-95,4\n6,S006,BSCDS,2017,6,6,7,8,18,45,90,,,M,ST,8-May-94,4\n7,S007,BSCDS,2016,5,9,6,6,17,37,80,,,M,GEN,7-May-94,6\n8,S008,BSCDS,2017,6,6,5,5,17,31,70,,,M,GEN,15-Aug-94,0\n9,S009,BSCDS,2016,6,5,8,8,18,30,75,,,M,GEN,24-Nov-94,0\n10,S010,BSCDS,2017,9,6,7,6,14,41,83,,,F,GEN,25-Jan-94,4\n11,S011,BSCDS,2016,5,9,6,7,12,39,78,,,M,GEN,8-Jun-94,3\n12,S012,BSCDS,2017,8,8,7,7,17,30,77,,,M,OBC,24-Jun-95,5\n13,S013,BSCDS,2016,8,7,9,6,16,28,74,,,M,GEN,31-Oct-94,0\n14,S014,BSCDS,2017,8,7,5,9,20,36,85,,,F,GEN,21-Oct-95,0\n15,S015,BSCDS,2016,8,8,6,6,10,40,78,,,F,OBC,30-Jun-96,0\n16,S016,BSCDS,2017,9,7,6,6,20,42,90,Good,,F,GEN,25-Sep-94,0\n17,S017,BSCDS,2016,8,6,7,5,18,41,85,,,M,GEN,16-May-94,0\n18,S018,BSCDS,2017,9,8,6,7,14,38,82,,,M,GEN,24-Jun-95,1\n19,S019,BSCDS,2016,5,7,6,5,19,40,82,,,F,GEN,11-Dec-93,2\n20,S020,BSCDS,2017,9,5,5,8,20,42,89,,,M,GEN,30-Jun-96,1\n21,S021,MSCDS,2016,8,7,6,5,12,44,82,,,F,OBC,11-Dec-93,0\n22,S022,MSCDS,2017,7,7,5,5,18,30,72,,,F,GEN,6-Nov-95,0\n23,S023,MSCDS,2016,8,5,5,7,19,27,71,,,F,GEN,22-Dec-93,0\n24,S024,MSCDS,2017,5,8,7,7,18,25,70,,,M,SC,6-Feb-95,2\n25,S025,MSCDS,2016,5,5,5,8,10,47,80,,,M,GEN,6-Jun-94,2\n26,S026,MSCDS,2017,7,8,6,6,19,25,71,,,M,GEN,2-Jan-94,0\n27,S027,MSCDS,2016,9,9,5,7,13,40,83,,,F,GEN,8-Apr-94,0\n28,S028,MSCDS,2017,6,9,9,5,16,29,74,,,F,GEN,18-Nov-94,0\n29,S029,MSCDS,2016,7,5,7,5,13,26,63,,,F,GEN,3-Jul-95,0\n30,S030,MSCDS,2017,6,7,9,6,11,38,77,,,M,OBC,10-Jan-93,0\n31,S031,MSCDS,2016,7,5,5,8,18,39,82,,,F,GEN,19-Dec-92,1\n32,S032,MSCDS,2017,9,5,8,6,17,27,72,,,M,GEN,19-Sep-93,0\n33,S033,MSCDS,2016,7,8,7,8,18,46,94,,,F,GEN,13-Aug-94,0\n34,S034,MSCDS,2017,8,8,8,5,14,33,76,,,F,GEN,21-Jan-94,0\n35,S035,MSCDS,2016,8,5,8,6,19,25,71,,,M,GEN,25-Feb-94,0\n36,S036,MSCDS,2017,6,9,5,9,10,35,74,,,M,SC,12-Jan-96,0\n37,S037,MSCDS,2016,5,7,5,7,20,37,81,,,M,GEN,27-Jul-95,3\n38,S038,MSCDS,2017,8,7,9,7,16,25,72,,,M,GEN,12-Jan-96,0\n39,S039,MSCDS,2016,9,8,9,8,11,28,73,,,M,SC,18-Feb-95,0\n40,S040,MSCDS,2017,5,6,7,5,17,38,78,,,F,GEN,3-Mar-95,0\n41,S041,MSCDS,2016,6,6,6,5,14,49,86,,,M,GEN,30-Jun-94,0\n42,S042,MSCDS,2017,6,8,8,9,12,26,69,,,M,GEN,23-Oct-94,0\n43,S043,MSCDS,2016,6,8,9,8,15,47,93,,,M,SC,15-Nov-93,3\n44,S044,MSCDS,2017,8,5,7,7,19,46,92,,,F,GEN,11-May-93,0\n45,S045,MSCDS,2016,7,6,5,9,11,26,64,,,F,GEN,2-Apr-95,0\n46,S046,MSCDS,2017,9,6,9,9,18,42,93,,,F,GEN,15-Mar-94,0\n47,S047,MSCDS,2016,6,8,6,6,20,26,72,,,M,ST,,11\n48,S048,MSCDS,2017,6,5,9,5,12,46,83,,,M,GEN,,8\n49,S049,MSCDS,2016,6,8,5,8,18,48,93,,,M,GEN,5-Apr-91,4\n50,S050,MSCDS,2017,7,7,6,8,15,44,87,,,M,GEN,16-Sep-93,4" }, { "path": "Data/students3.csv", "content": "\"\",\"rollno\",\"name\",\"gender\",\"age\",\"course\",\"marks\",\"married\"\n\"1\",\"S-1\",\"Student1 - surname\",\"M\",32,\"BTech\",58,FALSE\n\"2\",\"S-2\",\"Student2 - surname\",\"M\",22,\"BTech\",37,FALSE\n\"3\",\"S-3\",\"Student3 - surname\",\"M\",31,\"BTech\",52,FALSE\n\"4\",\"S-4\",\"Student4 - surname\",\"M\",28,\"MTech\",80,FALSE\n\"5\",\"S-5\",\"Student5 - surname\",\"M\",25,\"BTech\",43,FALSE\n\"6\",\"S-6\",\"Student6 - surname\",\"F\",23,\"MTech\",42,FALSE\n\"7\",\"S-7\",\"Student7 - surname\",\"M\",31,\"Phd\",61,FALSE\n\"8\",\"S-8\",\"Student8 - surname\",\"F\",31,\"BTech\",57,TRUE\n\"9\",\"S-9\",\"Student9 - surname\",\"M\",28,\"BTech\",53,TRUE\n\"10\",\"S-10\",\"Student10 - surname\",\"F\",29,\"BTech\",73,TRUE\n\"11\",\"S-11\",\"Student11 - surname\",\"M\",37,\"BTech\",26,FALSE\n\"12\",\"S-12\",\"Student12 - surname\",\"M\",23,\"MTech\",68,FALSE\n\"13\",\"S-13\",\"Student13 - 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2010\"\n\"305\",\"6\",\"22\",\"10\",\"1\",\"0\",\"60.00\",\"4\",\"caught\",\"3\",\"v South Africa\",\"Durban\",\"26 Dec 2010\"\n\"306\",\"146\",\"465\",\"314\",\"17\",\"2\",\"46.49\",\"4\",\"bowled\",\"2\",\"v South Africa\",\"Cape Town\",\"2 Jan 2011\"\n\"307\",\"14*\",\"146\",\"91\",\"0\",\"0\",\"15.38\",\"4\",\"not out\",\"4\",\"v South Africa\",\"Cape Town\",\"2 Jan 2011\"\n\"308\",\"34\",\"90\",\"58\",\"6\",\"0\",\"58.62\",\"4\",\"caught\",\"2\",\"v England\",\"Lord's\",\"21 Jul 2011\"\n\"309\",\"12\",\"84\",\"68\",\"1\",\"0\",\"17.64\",\"5\",\"lbw\",\"4\",\"v England\",\"Lord's\",\"21 Jul 2011\"\n\"310\",\"16\",\"33\",\"34\",\"3\",\"0\",\"47.05\",\"4\",\"caught\",\"2\",\"v England\",\"Nottingham\",\"29 Jul 2011\"\n\"311\",\"56\",\"136\",\"86\",\"8\",\"0\",\"65.11\",\"4\",\"lbw\",\"4\",\"v England\",\"Nottingham\",\"29 Jul 2011\"\n\"312\",\"1\",\"10\",\"8\",\"0\",\"0\",\"12.50\",\"4\",\"caught\",\"1\",\"v England\",\"Birmingham\",\"10 Aug 2011\"\n\"313\",\"40\",\"92\",\"60\",\"8\",\"0\",\"66.66\",\"4\",\"run out\",\"3\",\"v England\",\"Birmingham\",\"10 Aug 2011\"\n\"314\",\"23\",\"65\",\"34\",\"4\",\"0\",\"67.64\",\"4\",\"caught\",\"2\",\"v England\",\"The Oval\",\"18 Aug 2011\"\n\"315\",\"91\",\"235\",\"172\",\"11\",\"0\",\"52.90\",\"4\",\"lbw\",\"3\",\"v England\",\"The Oval\",\"18 Aug 2011\"\n\"316\",\"7\",\"25\",\"18\",\"1\",\"0\",\"38.88\",\"4\",\"lbw\",\"2\",\"v West Indies\",\"Delhi\",\"6 Nov 2011\"\n\"317\",\"76\",\"200\",\"148\",\"10\",\"0\",\"51.35\",\"4\",\"lbw\",\"4\",\"v West Indies\",\"Delhi\",\"6 Nov 2011\"\n\"318\",\"38\",\"74\",\"71\",\"5\",\"0\",\"53.52\",\"4\",\"caught\",\"1\",\"v West Indies\",\"Kolkata\",\"14 Nov 2011\"\n\"319\",\"94\",\"219\",\"153\",\"8\",\"2\",\"61.43\",\"4\",\"caught\",\"2\",\"v West Indies\",\"Mumbai\",\"22 Nov 2011\"\n\"320\",\"3\",\"6\",\"7\",\"0\",\"0\",\"42.85\",\"4\",\"caught\",\"4\",\"v West Indies\",\"Mumbai\",\"22 Nov 2011\"\n\"321\",\"73\",\"151\",\"98\",\"8\",\"1\",\"74.48\",\"4\",\"bowled\",\"2\",\"v Australia\",\"Melbourne\",\"26 Dec 2011\"\n\"322\",\"32\",\"73\",\"46\",\"4\",\"0\",\"69.56\",\"4\",\"caught\",\"4\",\"v Australia\",\"Melbourne\",\"26 Dec 2011\"\n\"323\",\"41\",\"133\",\"89\",\"8\",\"0\",\"46.06\",\"4\",\"bowled\",\"1\",\"v Australia\",\"Sydney\",\"3 Jan 2012\"\n\"324\",\"80\",\"213\",\"141\",\"9\",\"0\",\"56.73\",\"4\",\"caught\",\"3\",\"v Australia\",\"Sydney\",\"3 Jan 2012\"\n\"325\",\"15\",\"46\",\"25\",\"3\",\"0\",\"60.00\",\"4\",\"lbw\",\"1\",\"v Australia\",\"Perth\",\"13 Jan 2012\"\n\"326\",\"8\",\"22\",\"16\",\"1\",\"0\",\"50.00\",\"4\",\"lbw\",\"3\",\"v Australia\",\"Perth\",\"13 Jan 2012\"\n\"327\",\"25\",\"94\",\"69\",\"3\",\"0\",\"36.23\",\"4\",\"caught\",\"2\",\"v Australia\",\"Adelaide\",\"24 Jan 2012\"\n\"328\",\"13\",\"46\",\"34\",\"2\",\"0\",\"38.23\",\"4\",\"caught\",\"4\",\"v Australia\",\"Adelaide\",\"24 Jan 2012\"\n\"329\",\"19\",\"86\",\"62\",\"2\",\"0\",\"30.64\",\"4\",\"bowled\",\"1\",\"v New Zealand\",\"Hyderabad (Deccan)\",\"23 Aug 2012\"\n\"330\",\"17\",\"61\",\"50\",\"3\",\"0\",\"34.00\",\"4\",\"bowled\",\"2\",\"v New Zealand\",\"Bengaluru\",\"31 Aug 2012\"\n\"331\",\"27\",\"78\",\"34\",\"5\",\"0\",\"79.41\",\"4\",\"bowled\",\"4\",\"v New Zealand\",\"Bengaluru\",\"31 Aug 2012\"\n\"332\",\"13\",\"25\",\"18\",\"2\",\"0\",\"72.22\",\"4\",\"caught\",\"1\",\"v England\",\"Ahmedabad\",\"15 Nov 2012\"\n\"333\",\"DNB\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"4\",\"v England\",\"Ahmedabad\",\"15 Nov 2012\"\n\"334\",\"8\",\"14\",\"12\",\"1\",\"0\",\"66.66\",\"4\",\"bowled\",\"1\",\"v England\",\"Mumbai\",\"23 Nov 2012\"\n\"335\",\"8\",\"28\",\"19\",\"2\",\"0\",\"42.10\",\"4\",\"lbw\",\"3\",\"v England\",\"Mumbai\",\"23 Nov 2012\"\n\"336\",\"76\",\"202\",\"155\",\"13\",\"0\",\"49.03\",\"4\",\"caught\",\"1\",\"v England\",\"Kolkata\",\"5 Dec 2012\"\n\"337\",\"5\",\"17\",\"6\",\"1\",\"0\",\"83.33\",\"4\",\"caught\",\"3\",\"v England\",\"Kolkata\",\"5 Dec 2012\"\n\"338\",\"2\",\"25\",\"13\",\"0\",\"0\",\"15.38\",\"4\",\"bowled\",\"2\",\"v England\",\"Nagpur\",\"13 Dec 2012\"\n\"339\",\"81\",\"257\",\"159\",\"7\",\"0\",\"50.94\",\"4\",\"bowled\",\"2\",\"v Australia\",\"Chennai\",\"22 Feb 2013\"\n\"340\",\"13*\",\"10\",\"10\",\"0\",\"2\",\"130.00\",\"4\",\"not out\",\"4\",\"v Australia\",\"Chennai\",\"22 Feb 2013\"\n\"341\",\"7\",\"32\",\"15\",\"1\",\"0\",\"46.66\",\"4\",\"caught\",\"2\",\"v Australia\",\"Hyderabad (Deccan)\",\"2 Mar 2013\"\n\"342\",\"37\",\"125\",\"81\",\"5\",\"0\",\"45.67\",\"4\",\"caught\",\"2\",\"v Australia\",\"Mohali\",\"14 Mar 2013\"\n\"343\",\"21\",\"71\",\"23\",\"2\",\"0\",\"91.30\",\"4\",\"run out\",\"4\",\"v Australia\",\"Mohali\",\"14 Mar 2013\"\n\"344\",\"32\",\"99\",\"53\",\"5\",\"0\",\"60.37\",\"4\",\"lbw\",\"2\",\"v Australia\",\"Delhi\",\"22 Mar 2013\"\n\"345\",\"1\",\"8\",\"5\",\"0\",\"0\",\"20.00\",\"4\",\"lbw\",\"4\",\"v Australia\",\"Delhi\",\"22 Mar 2013\"\n\"346\",\"10\",\"41\",\"24\",\"2\",\"0\",\"41.66\",\"4\",\"lbw\",\"2\",\"v West Indies\",\"Kolkata\",\"6 Nov 2013\"\n\"347\",\"74\",\"150\",\"118\",\"12\",\"0\",\"62.71\",\"4\",\"caught\",\"2\",\"v West Indies\",\"Mumbai\",\"14 Nov 2013\"\n" }, { "path": "Data/ximb.csv", "content": "\"\",\"gender\",\"spl\",\"age\",\"experience\",\"grade\",\"placement\"\n\"1\",\"M\",\"Finance\",28.77,5.37,\"A\",\"no\"\n\"2\",\"F\",\"Marketing\",27.73,3.71,\"B\",\"yes\"\n\"3\",\"M\",\"Finance\",29.46,4.55,\"B\",\"yes\"\n\"4\",\"M\",\"HR\",29.28,4.35,\"A\",\"yes\"\n\"5\",\"M\",\"Finance\",22.44,4.75,\"B\",\"yes\"\n\"6\",\"M\",\"HR\",28.89,4.59,\"D\",\"yes\"\n\"7\",\"F\",\"HR\",27.44,3.22,\"A\",\"yes\"\n\"8\",\"M\",\"Finance\",25,5.76,\"A\",\"no\"\n\"9\",\"F\",\"Marketing\",23.96,2.37,\"C\",\"yes\"\n\"10\",\"M\",\"Finance\",23.65,3.68,\"A\",\"no\"\n\"11\",\"M\",\"HR\",25.21,4.17,\"C\",\"no\"\n\"12\",\"M\",\"HR\",22.26,4.56,\"A\",\"yes\"\n\"13\",\"M\",\"Finance\",29.59,4.42,\"B\",\"yes\"\n\"14\",\"M\",\"HR\",25.43,3.28,\"A\",\"yes\"\n\"15\",\"M\",\"Finance\",29.47,4.04,\"B\",\"yes\"\n\"16\",\"F\",\"Marketing\",24.67,4.16,\"D\",\"no\"\n\"17\",\"F\",\"Finance\",22.99,4,\"B\",\"yes\"\n\"18\",\"F\",\"Finance\",26.06,5.1,\"C\",\"yes\"\n\"19\",\"F\",\"Finance\",24.96,4.84,\"C\",\"yes\"\n\"20\",\"F\",\"Finance\",23.46,3.89,\"C\",\"yes\"\n\"21\",\"M\",\"Finance\",25.68,3.9,\"C\",\"no\"\n\"22\",\"M\",\"HR\",27.62,2.59,\"B\",\"yes\"\n\"23\",\"M\",\"Finance\",22.37,3.96,\"A\",\"yes\"\n\"24\",\"M\",\"HR\",27.86,3.24,\"B\",\"yes\"\n\"25\",\"M\",\"Finance\",22.26,3.79,\"B\",\"yes\"\n\"26\",\"M\",\"HR\",28.73,4.15,\"C\",\"yes\"\n\"27\",\"M\",\"HR\",24.78,3.35,\"C\",\"yes\"\n\"28\",\"F\",\"Marketing\",29.89,2.12,\"B\",\"no\"\n\"29\",\"F\",\"Finance\",23.39,4.38,\"D\",\"yes\"\n\"30\",\"M\",\"Finance\",26.21,3.64,\"B\",\"yes\"\n\"31\",\"M\",\"HR\",22.99,2.99,\"C\",\"no\"\n\"32\",\"M\",\"Finance\",23.54,2.72,\"C\",\"yes\"\n\"33\",\"M\",\"Finance\",23.18,5.56,\"B\",\"yes\"\n\"34\",\"F\",\"HR\",25.22,4.44,\"B\",\"no\"\n\"35\",\"F\",\"HR\",22.24,4.09,\"D\",\"yes\"\n\"36\",\"F\",\"Marketing\",21.47,2.77,\"B\",\"yes\"\n\"37\",\"F\",\"Marketing\",25.59,5.09,\"A\",\"yes\"\n\"38\",\"M\",\"Finance\",25.97,5.56,\"A\",\"yes\"\n\"39\",\"F\",\"Marketing\",25.29,4.38,\"A\",\"yes\"\n\"40\",\"F\",\"Finance\",24.02,3.39,\"D\",\"yes\"\n\"41\",\"F\",\"Marketing\",24.14,4.85,\"B\",\"yes\"\n\"42\",\"M\",\"HR\",26.92,4.39,\"D\",\"no\"\n\"43\",\"M\",\"HR\",23.34,3.35,\"B\",\"yes\"\n\"44\",\"M\",\"HR\",24.55,3.26,\"A\",\"no\"\n\"45\",\"F\",\"Finance\",26.83,5.49,\"A\",\"yes\"\n\"46\",\"M\",\"HR\",21.75,4.39,\"B\",\"no\"\n\"47\",\"M\",\"Finance\",21.93,3.55,\"A\",\"yes\"\n\"48\",\"F\",\"Marketing\",25.63,2.44,\"C\",\"no\"\n\"49\",\"M\",\"Finance\",27.48,3.01,\"B\",\"yes\"\n\"50\",\"M\",\"Finance\",21.79,3.44,\"B\",\"yes\"\n\"51\",\"M\",\"Marketing\",27.06,2.36,\"C\",\"yes\"\n\"52\",\"M\",\"Finance\",28.34,2.62,\"C\",\"yes\"\n\"53\",\"M\",\"HR\",23.54,4.7,\"C\",\"no\"\n\"54\",\"F\",\"Finance\",21.3,4.09,\"A\",\"yes\"\n\"55\",\"F\",\"Marketing\",21.23,2.85,\"B\",\"yes\"\n\"56\",\"M\",\"Finance\",24.99,4.48,\"A\",\"yes\"\n\"57\",\"F\",\"HR\",25.13,1.91,\"A\",\"no\"\n\"58\",\"M\",\"Finance\",27.09,6.32,\"B\",\"yes\"\n\"59\",\"F\",\"HR\",21.39,5.51,\"B\",\"no\"\n\"60\",\"F\",\"HR\",21.58,5.38,\"A\",\"yes\"\n\"61\",\"F\",\"Finance\",26.83,4.74,\"B\",\"yes\"\n\"62\",\"M\",\"Marketing\",29.27,3.53,\"C\",\"yes\"\n\"63\",\"F\",\"Finance\",26.83,4.74,\"A\",\"yes\"\n\"64\",\"M\",\"HR\",22.69,3.72,\"C\",\"no\"\n\"65\",\"F\",\"HR\",22.36,4.01,\"A\",\"yes\"\n\"66\",\"F\",\"Marketing\",23.76,4.6,\"A\",\"yes\"\n\"67\",\"F\",\"Finance\",25.51,4.05,\"D\",\"yes\"\n\"68\",\"M\",\"Finance\",26.08,4.4,\"C\",\"yes\"\n\"69\",\"F\",\"Finance\",23.41,4.8,\"A\",\"yes\"\n\"70\",\"M\",\"HR\",24.31,3.01,\"B\",\"yes\"\n\"71\",\"M\",\"Finance\",25.72,3.49,\"A\",\"no\"\n\"72\",\"M\",\"Marketing\",22.35,3.35,\"A\",\"yes\"\n\"73\",\"M\",\"HR\",23.1,3.03,\"C\",\"yes\"\n\"74\",\"M\",\"Finance\",26.93,3.17,\"B\",\"yes\"\n\"75\",\"M\",\"HR\",21.92,5.08,\"A\",\"yes\"\n\"76\",\"F\",\"Marketing\",23.7,4.58,\"C\",\"no\"\n\"77\",\"M\",\"Marketing\",23.69,4.47,\"C\",\"yes\"\n\"78\",\"M\",\"Marketing\",27.24,4.9,\"D\",\"yes\"\n\"79\",\"M\",\"Marketing\",26.08,4.08,\"A\",\"no\"\n\"80\",\"M\",\"Finance\",23.76,4.75,\"A\",\"yes\"\n\"81\",\"F\",\"HR\",22.89,3.86,\"D\",\"yes\"\n\"82\",\"F\",\"Finance\",22.52,5.11,\"A\",\"yes\"\n\"83\",\"M\",\"Finance\",26.82,5.27,\"A\",\"no\"\n\"84\",\"M\",\"Marketing\",25.16,2.77,\"B\",\"no\"\n\"85\",\"M\",\"HR\",25.73,3.91,\"A\",\"yes\"\n\"86\",\"F\",\"HR\",27.68,4.28,\"B\",\"yes\"\n\"87\",\"M\",\"Marketing\",26.4,3.29,\"B\",\"yes\"\n\"88\",\"M\",\"HR\",26.36,4.31,\"A\",\"yes\"\n\"89\",\"F\",\"HR\",29.26,5.63,\"B\",\"yes\"\n\"90\",\"M\",\"HR\",25.29,3.29,\"A\",\"yes\"\n\"91\",\"M\",\"Marketing\",24.93,2.67,\"B\",\"no\"\n\"92\",\"F\",\"Finance\",21.23,2.79,\"A\",\"yes\"\n\"93\",\"M\",\"HR\",27.19,5.11,\"A\",\"yes\"\n\"94\",\"F\",\"Finance\",29.34,3.01,\"A\",\"yes\"\n\"95\",\"M\",\"Marketing\",27.96,4.8,\"B\",\"yes\"\n\"96\",\"F\",\"Finance\",25.36,4.36,\"B\",\"yes\"\n\"97\",\"M\",\"Finance\",24.07,4.66,\"B\",\"no\"\n\"98\",\"M\",\"Marketing\",22.74,2.39,\"A\",\"yes\"\n\"99\",\"M\",\"Finance\",22.4,2.92,\"D\",\"yes\"\n\"100\",\"M\",\"Finance\",24.78,3.45,\"B\",\"yes\"\n" }, { "path": "Unsorted/CLT.R", "content": "#CLT\nsdm.sim <- function(n,src.dist=NULL,param1=NULL,param2=NULL) {\n r <- 10000 # Number of replications/samples - DO NOT ADJUST\n # This produces a matrix of observations with \n # n columns and r rows. Each row is one sample:\n my.samples <- switch(src.dist,\n \"E\" = matrix(rexp(n*r,param1),r),\n \"N\" = matrix(rnorm(n*r,param1,param2),r),\n \"U\" = matrix(runif(n*r,param1,param2),r),\n \"P\" = matrix(rpois(n*r,param1),r),\n \"C\" = matrix(rcauchy(n*r,param1,param2),r),\n \"B\" = matrix(rbinom(n*r,param1,param2),r),\n \"G\" = matrix(rgamma(n*r,param1,param2),r),\n \"X\" = matrix(rchisq(n*r,param1),r),\n \"T\" = matrix(rt(n*r,param1),r))\n all.sample.sums <- apply(my.samples,1,sum)\n all.sample.means <- apply(my.samples,1,mean) \n all.sample.vars <- apply(my.samples,1,var) \n par(mfrow=c(2,2))\n hist(my.samples[1,],col=\"gray\",main=\"Distribution of One Sample\")\n hist(all.sample.sums,col=\"gray\",main=\"Sampling Distributionnof\n the Sum\")\n hist(all.sample.means,col=\"gray\",main=\"Sampling Distributionnof the Mean\")\n hist(all.sample.vars,col=\"gray\",main=\"Sampling Distributionnof\n the Variance\")\n}\nsdm.sim(50,src.dist=\"E\",param1=1)\nset.seed(13)\nfg <- sample(x=c(0,1),size=8*10,replace=TRUE,prob=c(.20,.80))\nfgm <- matrix(fg,nrow=10)\nfgmd <- cbind(fgm,apply(fgm,1,mean)*100)\nfgmd\n\n" }, { "path": "Unsorted/R-Exercise.R", "content": "#..............................Exercise on R..............................#\n\n# Q.1\nsessionInfo()\n# Clear environment\nrm(list = ls())\n# Q.2\nabc<- 10\n\n# Q.3\na<-c(1,2,3)\nis.numeric(a)\nb<-c(\"aa\",\"bb\",\"cc\")\nis.character(b)\nc<- c(TRUE, FALSE, TRUE)\nis.logical(c)\n\n# Q.4\nls()\n\n# Q.5\nx<-c(4,6,5,4,7,2,9,3)\n# Q.5.a\nlength(x)\nmean(x)\nsum(x)\nmax(x)\nmin(x)\nvar(x)\n# Q.5.b\nx[3]\nx[c(1, 3, 5, 7)]\nx[2:6] \n\n# Q.6\ny<-matrix(1:24,6,4)\n\n# Q.7\ndf<-data.frame(StoreID=c(111,208,113,408),\n Tenure=c(25,34,28,52),\n StoreType=c(\"Type1\",\"Type2\",\"Type3\",\"Type4\"),\n Status=c(\"Poor\",\"Improved\",\"Good\",\"Excellent\"))\n\n# Q.8.a\ndf[,c(1,2)]\n# Q.8.b\ndf[,c(3,4)]\n# Q.8.c\nstoredata$Tenure\n\n# Q.9\ncategory<- c(\"A\", \"B\", \"C\", \"D\")\ncategory <- factor(category)\n\nstatus <- c(1, 2, 3, 4)\nstatus <- factor(status, levels=c(1,2,3,4), labels=c(\"Poor\", \"Improved\", \"Excellent\", \"Super\"))\nstatus\n\noutcome <- c(1, 3, 2, 4, 3, 1, 1)\noutcome <- factor(outcome, ordered=TRUE,\n levels=c(1,2,3,4), \n labels=c(\"Poor\", \"Average\", \"Good\", \"Excellent\"))\noutcome\n\n# Q.10\nh<- c(5,26,19,88)\nj<- matrix(c(1:15),5,3)\nk<- c(\"one\",\"two\",\"three\",\"four\")\nmylist<- list(title=\"My First List\", ages=h,j,k) \nprint(mylist)\n\nmylist[[2]] \nmylist[[\"ages\"]]\nmylist[[2]][2]\nmylist[[3]][2,2]\nmylist[[3]][3,2]\n\n" }, { "path": "Unsorted/RCommander.R.R", "content": "\nlibrary(abind, pos=18)\nlibrary(e1071, pos=19)\nnumSummary(df2[,\"a\", drop=FALSE], statistics=c(\"mean\", \"sd\", \"IQR\", \"quantiles\", \"skewness\"), quantiles=c(0,.25,.5,.75,1), type=\"2\")\n\nset.seed(52410)\nqnorm(c(.4), mean=0, sd=1, lower.tail=TRUE)\nsummary(df)\nwith(df2, Hist(excel, groups=course, \n scale=\"frequency\", breaks=\"Sturges\", col=\"darkgray\", \n xlab=\"X\", ylab=\"Y\", main=\"Title\"))\nlocal({\n .Table <- xtabs(~city+course, data=df2)\n cat(\"\\nFrequency table:\\n\")\n print(.Table)\n .Test <- chisq.test(.Table, correct=FALSE)\n print(.Test)\n})\nlocal({\n .Table <- xtabs(~gender+hostel+city, data=df2)\n cat(\"\\nFrequency table:\\n\")\n print(.Table) \n})\nDataset <- \n read.table(\"E:/rWork/rdata/Student Attnd Marks.csv\", \n header=TRUE, sep=\"\", na.strings=\"NA\", dec=\".\", \n strip.white=TRUE)\nDataset <- read.table(\"E:/rWork/rdata/dbacs4.csv\", \n header=TRUE, sep=\"\", na.strings=\"NA\", dec=\".\", \n strip.white=TRUE)\nDataset <- \n read.table(\"E:/rWork/rProjects/dspgmsc2017/data/student.csv\",\n header=TRUE, sep=\"\", na.strings=\"NA\", dec=\".\", \n strip.white=TRUE)\nDataset <- \n read.table(\"E:/rWork/rProjects/dspgmsc2017/data/student.csv\",\n header=TRUE, sep=\"\", na.strings=\"NA\", dec=\".\", \n strip.white=TRUE)\nsummary(student)\nlocal({\n .Table <- xtabs(~batch+city, data=student)\n cat(\"\\nFrequency table:\\n\")\n print(.Table)\n .Test <- chisq.test(.Table, correct=FALSE)\n print(.Test)\n})\nlocal({\n .Table <- with(student, table(gender))\n cat(\"\\ncounts:\\n\")\n print(.Table)\n cat(\"\\npercentages:\\n\")\n print(round(100*.Table/sum(.Table), 2))\n})\nsapply(student, function(x)(sum(is.na(x)))) \n # NA counts\ncor(student[,c(\"cbnst\",\"cgpa\")], use=\"complete\")\nnormalityTest(~java, test=\"shapiro.test\", \n data=student)\nwith(student, (t.test(age, alternative='less', mu=30, \n conf.level=.95)))\n\n" }, { "path": "Unsorted/basiclm1.R", "content": "#Linear Regression \n#https://msperlin.github.io/pafdR/models.html\n\nset.seed(10)\n\n# number of time periods\nnT <- 1000\n\n# set parameters\nmy.intercept <- 0.5\nmy.beta <- 1.5\n\n# simulate\nx <- rnorm(nT)\ny <- my.intercept + my.beta*x + rnorm(nT)\n\n# set df\ndf <- data.frame(y, x)\n\n# estimate model----\nmy.lm <- lm(data = df, \n formula = y ~ x )\n\nlibrary(car)\n\n# set test matrix ----\ntest.matrix <- matrix(c(my.intercept, # alpha test value\n my.beta)) # beta test value\n\n# hypothesis matrix ----\nhyp.mat <- matrix(c(1,0,\n 0,1),nrow = 2)\nsummary(lm(y ~ x))\n\n# do test ----\nmy.waldtest <- linearHypothesis(my.lm, \n hypothesis.matrix = hyp.mat, \n rhs = test.matrix)\n\n# print result\nprint(my.waldtest)\n\n#Hypothesis Tests ----\nlibrary(lmtest)\n\n# Breush Pagan test 1 - Serial correlation\n# Null Hypothesis: No serial correlation in residual\nprint(bgtest(my.lm, order = 5))\n\n# Breush Pagan test 2 - Homocesdasticity of residuals\n# Null Hypothesis: homocesdasticity (constant variance of residuals)\nprint(ncvTest(my.lm))\n\n# Durbin Watson test - Serial correlation\n# Null Hypothesis: No serial correlation in residual\nprint(dwtest(my.lm))\n\n# Shapiro test - Normality\n# Null Hypothesis: Data is normally distributed\nprint(shapiro.test(my.lm$residuals))\n\n\nlibrary(gvlma)\n\n# global validation of model----\ngvmodel <- gvlma(my.lm) \n\n# print result\nsummary(gvmodel)\n" }, { "path": "Unsorted/binomial.R", "content": "# Binomial\nchoose(5,2)\nchoose(5, 0:5)\nfor (n in 0:10) print(choose(n, 0:n))\ndbinom(2, size=5, prob=1/6) \nchoose(5,2) * (1/6)^2 * (5/6)^3 \nprobs = dbinom(0:5, size=5, prob=1/6) \ndata.frame(0:5, probs)\nsum(probs) \nplot(0:5, probs, type=\"h\", xlim=c(0,5), ylim=c(0,.5))\npoints(0:5, probs, pch=16, cex=2) \nprobs2 = dbinom(0:100, size=100, prob=1/2) \ndata.frame(0:100, probs2) \nround(data.frame(0:100, probs2), digits=5) \nplot(0:100, probs2, type=\"h\", xlim=c(0,100), ylim=c(0,.1))\npoints(0:100, probs2, pch=16, cex=.5) \nsum(dbinom(59:100, size=100, prob=1/2)) \n" }, { "path": "Unsorted/boxplot.R", "content": "# Box Plot \n# http://www.statmethods.net/graphs/boxplot.html\n\n# Boxplot of MPG by Car Cylinders \n# Reset Frame/ Plot area\npar(mfrow=c(1,1))\ndev.off()\n\nboxplot(mpg~cyl,data=mtcars, main=\"Car Mileage Data\", xlab=\"Number of Cylinders\", ylab=\"Miles Per Gallon\")\n\n# Notched Boxplot of Tooth Growth Against 2 Crossed Factors\n# boxes colored for ease of interpretation \nboxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, \n col=(c(\"gold\",\"darkgreen\")),\n main=\"Tooth Growth\", xlab=\"Suppliment and Dose\")\n\n# Violin Plots\nlibrary(vioplot)\nx1 <- mtcars$mpg[mtcars$cyl==4]\nx2 <- mtcars$mpg[mtcars$cyl==6]\nx3 <- mtcars$mpg[mtcars$cyl==8]\nvioplot(x1, x2, x3, names=c(\"4 cyl\", \"6 cyl\", \"8 cyl\"), \n col=\"gold\")\ntitle(\"Violin Plots of Miles Per Gallon\")\n\n# Example of a Bagplot\nlibrary(aplpack)\nattach(mtcars)\nbagplot(wt,mpg, xlab=\"Car Weight\", ylab=\"Miles Per Gallon\",\n main=\"Bagplot Example\")\n" }, { "path": "Unsorted/c.R", "content": "library(foreign)\ndb = file.choose()\ndataset = read.spss(db,to.data.frame=TRUE)\n\nx=c(1,3,4,6,8,9,11,14)\ny=c(1,2,2,4,6,8,8,9)\nfit1 = lm(x~y)\nsummary(fit1)\n" }, { "path": "Unsorted/central1.R", "content": "# Central Tendencies\n\nx = ceiling(rnorm(100, 50, 10)) # 100 random nos, mean=50,sd=10\nx\nhist(x)\ndensity(x)\nplot(density(x), xlab='Marks', ylab='Relative %', \n main='Distribution of Marks')\nabline(v=mean(x), col='red', lty=1)\nabline(v=median(x), col='green', lty=2)\nabline(v=mode(x)) # error\nlibrary(modeest)\nm =mlv(x, method = \"mfv\")\nm$M\nabline(v=m$M, col='blue' , lty=3)\n?abline\ntext(c(mean(x), median(x), m$M ),c(0.01,0.02,0.03), \n col=c('red','green','blue'), cex=.5, labels= c('Mean', 'Median', 'Mode'))\n?text\ntext=c(mean(x), median(x), m$M)\ntext\nmtext(text=c(mean(x), median(x), m$M), side=1,col=c('red','green','blue')\n ,cex=0.5,at= c(mean(x), median(x), m$M ))\n?mtext\n\n" }, { "path": "Unsorted/colstats1.R", "content": "#Column Stats\n\n# colStats {timeSeries}\n# \n# colStats\t calculates column statistics,\n# colSums\t calculates column sums,\n# colMeans\t calculates column means,\n# colSds\t calculates column standard deviations,\n# colVars\t calculates column variances,\n# colSkewness\t calculates column skewness,\n# colKurtosis\t calculates column kurtosis,\n# colMaxs\t calculates maximum values in each column,\n# colMins\t calculates minimum values in each column,\n# colProds\t computes product of all values in each column,\n# colQuantiles\t computes quantiles of each column.\n\nlibrary(timeSeries)\nmtcars\ncolQuantiles(mtcars)\ncolSkewness(mtcars)\n?colQuantiles\ncolStats(mtcars,mean)\n" }, { "path": "Unsorted/complextables.R", "content": "# Complex Tables\n# http://r-exercises.com/2016/04/26/complex-tables-exercises/\n# http://r-exercises.com/2016/04/26/complex-tables-solutions/\n\n# Exercise 1 : In order to demonstrate the ftable() function’s capabilities, input the Titanic data from R:\ndata(Titanic)\nTitanic\n#For the first exercise, create a basic flat contingency table from the Titanic data, using the ftable() function.\nftable(Titanic)\n\n#Exercise 2 : The row.vars argument specifies the table variables that will format as table rows. row.vars= is definable with variable numbers, or the variable names.\n# Therefore, use row.vars= to specify the variable, Class, as the row variables.\nftable(Titanic, row.vars = 1)\nftable(Titanic, row.vars = \"Class\")\n# Exercise 3 : Combine the row.vars and col.vars arguments to specify Class & Sex as the row variables, and Survived as the column variable.\n#row.vars and col.vars are definable with variable numbers, or the variable names.\nftable(Titanic, row.vars = 1:2, col.vars = 4)\nftable(Titanic, row.vars = c(\"Class\", \"Sex\"), col.vars = \"Survived\")\n\n#Exercise 4\n#With the parameters from the ftable used in Exercise 3, reverse the order of the Class and Sex columns.\nftable(Titanic, row.vars = 2:1, col.vars = 4)\nftable(Titanic, row.vars = c(\"Sex\", \"Class\"), col.vars = \"Survived\")\n\n\n#Exercise 5\n#Next, using the ftable() code from Exercise 4, specify Age as the column variable.\nftable(Titanic, row.vars = 1:2, col.vars = 3)\n#As you can see from this exercise, ftable() allows for the formatting of data for different areas of inquiry.\nftable(Titanic, row.vars = c(\"Sex\", \"Class\"),col.vars = \"Age\")\n\n#Exercise 6 : Using the data.frame() function will coerce ftable columns into rows. To demonstrate this, place the ftable() from Exercise 5, within the data.frame() function.\ndata.frame(ftable(Titanic, row.vars = 1:2, col.vars = 3))\ndata.frame(ftable(Titanic, row.vars = c(\"Sex\", \"Class\"),col.vars = \"Age\"))\n\n#Exercise 7 :Function ftable.formula provides a formula interface, (a data = . argument), for creating flat contingency tables.\n#For example: ftable(Survived ~ ., data = Titanic)\n# Use the formula interface for ftable() to display the quantities in the Titanic data for Male/Female passengers, by Class and Age.\nftable(Sex ~ Class + Age, data = Titanic)\n\n# Exercise 8 : The ftable() function creates an object of class ftable. In order to demonstrate this, save the results of the ftable formula from Exercise 5 as an ftable variable called titanicStats.\ntitanicStats <- ftable(Sex ~ Class + Age, data = Titanic)\ntitanicStats\n\n# Exercise 9 : Using the write.table() function, write the ftable, titanicStats, to a file. Make sure your working directory is set to a folder where you can find the resulting file. Name the file, “table1”.\nwrite.ftable(titanicStats, \"table1\")\n\n# Exercise 10 : read.ftable() reads in a flat-like contingency table from a file.\n# Using read.ftable(), read the file, “table1”, into an R language environment variable called data1\ndata1 <- read.ftable(\"table1\")\ndata1\ndf = data.frame(data1)\ndf\n" }, { "path": "Unsorted/cor1.R", "content": "# Covariance and Correlation\n# \n#\n#Covariance----\n\n#Correlation----\n \nlibrary(Hmisc)\nsplom(Orange) # Correlation Plot\nwith(Orange, cor(age,circumference))\n#0.91\n#\n#Correlation Test ----\n\ncor.test(Orange$age, Orange$circumference)\n\nHmisc::rcorr(Orange$age, Orange$circumference)\nrcorr(as.matrix( mtcars))\n" }, { "path": "Unsorted/crossfold1.R", "content": "#Steps\nlibrary(MASS, quietly=T)\nlibrary(caret)\nset.seed(123)\ndf = iris\nhelp(iris)\nstr(df)\ndim(df)\nhead(df)\nsummary(df)\napply(df, 2, function(x) length(unique(x)))\n\n#Create train & test data\nlibrary(caTools)\nlibrary(caret)\n(ind = createDataPartition(df$Species, p = 2/3, list=F))\n(trainDF = df[ind,]) \n(testDF = df[-ind,]) \n\nControlParameters = trainControl(method='cv', number=5,\n savePredictions =T, classProbs=T)\nControlParameters\n\n(parameterGrid = expand.grid(mtry= c(2,3,4)))\nmethod='cv'\nnumber=5\nclassProbs=T\n\nmodelRandom = train(Species ~ . , data = trainDF, \n method='rf', trControl = ControlParameters, \n tuneGrid = parameterGrid) \n#preProcess = c('center', 'scale')\nmodelRandom\n\nnames(getModelInfo())\n\n(predictions = predict(modelRandom, testDF))\n\n(t = table(predictions= predictions, actual=testDF$Species))\n\nmodelRandom = train(Species ~ . , data = trainDF, \n method='rf', trControl = ControlParameters, \n tuneGrid = parameterGrid) \n\n" }, { "path": "Unsorted/cut1.R", "content": "#cut\n?cut # divides the range of x into intervals & codes the values in x \n# according to which interval they fall\ncut(x, breaks, labels = NULL,\n include.lowest = FALSE, right = TRUE, dig.lab = 3,\n ordered_result = FALSE, ...)\n\n\nset.seed(1234)\nmarks = ceiling(rnorm(100,60,15))\nmarks\nmean(marks)\nrange(marks)\n(bks = seq(20,80,1))\n(bks = seq(20,80,5)) # 12 interval\n# Slabs : 20-25, 25-30 ....\n\n(bks = seq(20,80,10)) # 6 intervals\n# Slabs : 20-30, 30-40 ....\n\nsort(marks)\nclassint = cut(marks, breaks=bks)\nhead(classint)\nhead(marks)\n# 60 is enclosed in (50-60]\ntable(classint) # Freq Table\ncbind(table(classint)) # column format view \n\n\n# without giving manual breaks\ncut(marks, breaks=6)\n#(23.9-32.5] - not a clean break\n#right=TRUE : interval close on right & open on left : Default\n#right (0-10] : value of 10 included in this instead of (10-20]\n\n# include.lowest =FALSE (default)\n# x[i] = lowest when right=FALSE\n# x[i] = highest when right=TRUE\n# 0 2 5 5.1 10 : x[i] = 1 when right=F [0,5)\n# \n# 0 2 5 5.1 10 : x[i] = 10 when right=T (5-10]\nx1 = c(0,2,5.1,3,10,14.5, 15,16,20)\nx1\ncut(x1,breaks=4, include.lowest=T ) # not good\n#Levels: [-0.02,5] (5,10] (10,15] (15,20]\n\ncut(x1,breaks=4, include.lowest=T) # not good\n#Levels: [-0.02,5] (5,10] (10,15] (15,20]\n\n\n(bks2 = seq(0,20,5))\n\ncut(x1,breaks=bks2, include.lowest=T ) # not correct\n#Levels: sq[0,5] (5,10] (10,15] (15,20]\nx1 \n# 0.0 2.0 5.1 3.0 10.0 14.5 15.0 16.0 20.0\n\ncut(x1,breaks=bks2, include.lowest=T, right=F ) # correct\n#Levels: s[0,5) [5,10) [10,15) s[15,20]s\n#s[0,5)[0,5)[5,10) [0,5)[10,15)[10,15)[15,20][15,20] s[15,20]s\n\n#understand these steps\n\n#when u do right=TRUE, intervals closed on right. \n#max value included in last interval, but min value may be lost\n#therefore include.lowest=T required to have it in first interval\n\nx1\n(xc1= cut(x1,breaks=bks2, include.lowest=T, right=T ) ) # correct\n#s[0,5] (5,10] (10,15] (15,20]s\n#[0,5] [0,5](5,10][0,5] (5,10](10,15](10,15](15,20] (15,20]\n\n# vice-versa works as include.highest now\n(xc2= cut(x1,breaks=bks2, include.lowest=F, right=F )) # not correct\n# NA value for max -> correct it\n(xc3= cut(x1,breaks=bks2, include.lowest=T, right=F )) # correct\n#Levels: (0,5]s (5,10] (10,15] s[15,20]s \n#[0,5)[0,5)[5,10)[0,5)[10,15)[10,15)[15,20][15,20][15,20]\n\ntable(xc1,useNA='ifany')\ntable(xc2, useNA='ifany')\ntable(xc3, useNA='ifany')\n?table\n\n\n# labels to classes\nmarks\n(bks = seq(20,80,10)) # 6 intervals\nclassint = cut(marks, breaks=bks)\nbks\nlength(bks)\n(classint = cut(marks, breaks=bks)) # intervals\ntable(classint)\n(classint = cut(marks, breaks=bks, labels=F)) # integers\ntable(classint)\n(classint = cut(marks, breaks=bks, labels=LETTERS[1:length(bks)-1])) # integers\ntable(classint)\n?cut\n(classint = cut(marks, breaks=bks, ordered_result = T,\n labels=LETTERS[1:length(bks)-1])) # integers\ntable(classint)\nplot(classint)\n\n#dig.lab\n## sometimes the default dig.lab is not enough to be avoid confusion:\naaa <- c(1,2,3,4,5,2,3,4,5,6,7)\ncut(aaa, 3)\ncut(aaa, 3, dig.lab = 4, ordered = TRUE)\n\n## Label construction:\ny <- stats::rnorm(100)\ntable(cut(y, breaks = pi/3*(-3:3)))\ntable(cut(y, breaks = pi/3*(-3:3), dig.lab = 1))\ntable(cut(y, breaks = pi/3*(-3:3), dig.lab = 2))\ntable(cut(y, breaks = pi/3*(-3:3), dig.lab = 3))\ntable(cut(y, breaks = pi/3*(-3:3), dig.lab = 4))\n\n\n## sometimes the default dig.lab is not enough to be avoid confusion:\naaa <- c(1,2,3,4,5,2,3,4,5,6,7)\ncut(aaa, 3)\ncut(aaa, 3, dig.lab = 4, ordered = TRUE)\n\n\n\n## generate data for clinical trial example----------\nclinical.trial <-\n data.frame(patient = 1:100, \n age = rnorm(100, mean = 60, sd = 8),\n year.enroll = sample(paste(\"19\", 85:99, sep = \"\"),\n 100, replace = TRUE))\nsummary(clinical.trial)\n## basic usage of cut with a numeric variable\nc1 <- cut(clinical.trial$age, breaks = 4)\ntable(c1)\n\nc1\n\n## year.enroll is a factor, so must convert to numeric first!\nc2 <- cut(as.numeric(as.character(clinical.trial$year.enroll)),\n breaks = 3)\ntable(c2)\nc2\n\n\n\n# Missing values due to close on Right or Left\nset.seed(1234)\n\nmarks = ceiling(rnorm(100,60,15))\nmarks\nmean(marks)\nrange(marks)\ntable(marks)\nmarks[c(1,2,3,4,5)] = c(0,10,11,99,100)\nhead(marks,n=5)\n(bks = seq(0,100,10)) # 10 intervals\n\n# Slabs : 0-10,20-30 ....\nclassint1 = cut(marks, breaks=bks) # default right=T\nhead(classint1,n=5)\nhead(marks,n=5)\ntable(classint1)\ntable(classint1, useNA='ifany')\n\nclassint2 = cut(marks, breaks=bks,right=F) # default right=T\nhead(classint2,n=5)\nhead(marks,n=5)\ntable(classint2, useNA='ifany')\n\n# correct the problem of NA\nclassint1a = cut(marks, breaks=bks, include.lowest = T) # default right=T\nas.character(head(classint1a,n=5))\nhead(marks,n=5)\ntable(classint1a,useNA='ifany')\n\n\nclassint2a = cut(marks, breaks=bks, include.lowest=T, right=F) # default right=T\nas.character(head(classint2a,n=5))\nhead(marks,n=5)\ntable(classint2a,useNA='ifany')\n\nLETTERS[1:5]; letters[1:8]; month.abb[1:5]\n#Giving Labels\nlength(bks)\n#levels(classint)\nLETTERS[1:length(bks)-1]\nclassint1b = cut(marks, breaks=bks, include.lowest = T, \n labels=LETTERS[1:length(bks)-1]) # default right=T\nas.character(head(classint1b,n=5))\nhead(marks,n=5)\ntable(classint1b,useNA='ifany')\n\n\n\n\n# Ordered_labels\nclassint1c = cut(marks, breaks=bks, include.lowest = T,\n labels=c('B','C','F','A','G','P','L','H','K','O')) # default right=T\nas.character(head(classint1b,n=5))\nhead(marks,n=5)\ntable(classint1c,useNA='ifany')\n\n\n\n\n\n\n# classint1d = cut(marks, breaks=bks, include.lowest = T, ordered_result = T,\n# labels=c('1','3','5','8','4','6','7','10','9','2')) # default right=T\n# as.character(head(classint1b,n=5))\n# head(marks,n=5)\n# table(classint1d,useNA='ifany')\n\n\n\n\n\n# try for dig.lab =3\nclassint5 = cut(marks, breaks=10, dig.lab=2)\nclassint5\ntable(classint5)\n" }, { "path": "Unsorted/cutprety1.R", "content": "# Equally spaced values - pretty\n\nset.seed(1234)\nmarks = ceiling(rnorm(100,60,15))\n\n?pretty\npretty(marks,10)\n\nmarks2 = ceiling(runif(100,20,90))\npretty(marks2,4) #4+1 Interval\npretty(marks2,5) #5+1 Interval\n\nmarks2 = ceiling(runif(100,25,90))\npretty4 = pretty(marks2,4) #4+1 Interval\npretty(marks2,5) #5+1 Interval\n\ncut4 = cut(marks2, pretty4 )\ntable(cut4)\ncbind(table(cut4))\n\ncut4L = cut(marks2, pretty4, labels=c(\"L2040\", \"L4060\",\"L6080\", \"L80100\") )\ntable(cut4L)\ncbind(table(cut4L))\npie(cbind(table(cut4L)), labels=c(\"L2040\", \"L4060\",\"L6080\", \"L80100\"))\nbarplot(cbind(table(cut4L)), names.arg = c(\"L2040\", \"L4060\",\"L6080\", \"L80100\"), beside = T)\n" }, { "path": "Unsorted/datalevels.R", "content": "#Types of Data\n\n# Create the function.\ngetmode <- function(v) {\n uniqv <- unique(v)\n uniqv[which.max(tabulate(match(v, uniqv)))]\n}\n\n# Nominal\ntshirt = c('red', 'blue', rep('green',3), rep('black',3))\ntshirt\nmean(tshirt)\nmedian(tshirt)\ntshirt = c(tshirt, rep('yellow',2))\ntshirt\nmedian(tshirt)\ntable(tshirt)\ngetmode(tshirt) # first color in entry\n\n# Ordinal\ngrades = c(rep('Good',2), rep('Poor',4), rep('Excellent'))\ngrades\nmean(grades)\nmedian(grades)\ngetmode(grades)\n\nf1 = factor(grades, levels=c( 'Good','Poor','Excellent'), ordered=F)\nf1\nf2 = factor(grades, levels=c('Poor', 'Good', 'Excellent'), ordered=T)\nf2\n\nmean(f2)\nf2\nmedian(f2)# not working - it is Good\ngetmode(f2)\nsummary(f2)\n\n# Interval\n\n\n\n# Ratio\n\n" }, { "path": "Unsorted/dbconnection.R", "content": "library(DBI)\ncon <- dbConnect(odbc::odbc(), \"ds\")\ndata <- sqlQuery( con , paste (\"select * from student\"))\ndbListTables(con)\n\n# List tables beginning with f\ndbListTables(con, table_name = \"f%\")\n\n# List all fields in the 'flights' database\ndbListFields(con, \"Students\")\n\nStudents <- dbReadTable(con, \"Students\")\nStudents\n\nlibrary(tibble)\nas_tibble(Students)\n\n\ndbWriteTable(con, \"Studentnew\", as.data.frame(Students))\n" }, { "path": "Unsorted/dec17.R", "content": "paste(\"a\", \"b\", se = \":\")\nx= 1:5\ny= NULL\nis.null(x)\nf <- function(a, b) a^2\nf(2)\npaste(\"a\", \"b\", sep = \":\")\npaste(\"a\", \"b\", se = \":\")\nSys.Date()\nclass(as.Date('1970-01-02'))\n\nx = 1:5\napply(x, FUN=sqrt)\n?apply\ndata(package = .packages(all.available = TRUE))\npaste('Data', 'Science', 'from', 'MUIT', sep='-')\n\nmonth.abb[1:12]\n(sd = paste('01',month.abb[1:6],2016,sep='-'))\nsd1 = as.Date(sd,'%d-%b-%Y')\nsaledate = rep(sd1,times=4)\nsaledate\ndept = rep(c('Dept1','Dept2'), each=12)\n(dept = factor(dept, ordered=T, levels=c('Dept2', 'Dept1')))\n(city = rep(c('Delhi','Noida'), times=2, each=6))\nset.seed(1234)\nsaleamt = ceiling(runif(24, 100, 200))\nset.seed(1234)\nadvamt = ceiling(runif(24, 25,40))\ndf = data.frame(saledate, dept=as.factor(dept), city=as.factor(city), saleamt, advamt)\ndf\nwith(df, boxplot(saleamt ~ dept))\nwith(df, boxplot(saleamt ~ dept + city))\n\nagdeptcity = aggregate(df$saleamt, by=list(dept, city), mean)\npie(agdeptcity$x, labels=paste(agdeptcity$Group.1, agdeptcity$Group.2, sep='-'))\n\n\n(t1 = xtabs(saleamt ~ dept + city, data=df))\nmargin.table(t1,c(2))\naddmargins(t1,c(1,2), FUN=list(list(sd,mean), sum))\nprop.table(t1)*100\n\n(df2= subset(df, city='noida', select=c(dept, saleamt,advamt)))\n\nplot(y=df2$saleamt, x=df2$advamt)\nabline(lm(df2$saleamt ~ df2$advamt))\ncor(df2$saleamt, df2$advamt)\ndf2\ndf2[order(saleamt, -advamt),]\nwith(df2,plot(x=dept, y=saleamt))\nwithin(df2, profit <- saleamt - advamt)\ndf2\n(mat2 = as.matrix(df2[2:3]))\nsweep(df2[2:3],2,1,'+')\n\nreshape2::melt(df2, id='dept')\n\n\n\nmatrix1 = as.matrix(airquality)\n\nmatrix1\ncolSums(is.na(matrix1))\nmean(matrix1[,1],na.rm=T)\n\ncolSums(is.na(matrix1))\n\nsapply(matrix1, function(x)all(is.na(x)))\n\nwhich(is.na(matrix1))\nmatrix1[which(is.na(matrix1[,1])),1]\nlength(matrix1[which(is.na(matrix1[,1])),1])\nmatrix1[which(is.na(matrix1[,2])),2]\nlength(matrix1[which(is.na(matrix1[,2])),2])\n\nlength(matrix1[which(is.na(matrix1[,1]))])\nlength(matrix1[which(is.na(matrix1[,2]))])\nmatrix1[which(is.na(matrix1[,2]))]\nwhich(is.na(matrix1[,2]))\nwhich(is.na(matrix1[,2]))\n\n\nceiling(mean(matrix1[,1],na.rm=T))\nceiling(mean(matrix1[,2],na.rm=T))\nmatrix1[which(is.na(matrix1[,1])),1] = ceiling(mean(matrix1[,1],na.rm=T))\nmatrix1[which(is.na(matrix1[,2])),2] = ceiling(mean(matrix1[,2],na.rm=T))\ncolSums(is.na(matrix1))\n\nmatrix1[which(is.na(matrix1[,2])),2]\nmatrix1[c(5,6,11,27,96,98)]\nwhich(is.na(matrix1[,2]))\n\napply(matrix1, 2, mean)\nsapply(matrix1[1:10], log)\nmatrix1\napply(matrix1[1:10], 2, log)\n?lapply\n\n\n\n# List\n(myList = list(nostudents = 1:10, school = 'Data Science', \n course=c('PG','MSc')))\nlength(myList$course)\n\n#myArray\nset.seed(1234)\n(x=ceiling(rnorm(2*3*5*4,50,10)))\n\ndepts=c('D1','D2')\ncourses=c('C1','C2','C3')\nstudents=c('S1','S2','S3','S4','S5')\nsubjects=c('Sb1', 'Sb2', 'Sb3', 'Sb4')\nmyArray = array(x, dim = c(5,4,3,2), dimnames = list(students, subjects, courses,depts))\n\n\nmyArray\n\nsum(myArray)\nlapply(myArray,c(4), mean)\n?apply\napply(myArray, c(2), function(x) max(x))\napply(myArray, c(4), function(x) mean(x))\napply(myArray, c(1,3,4), function(x) sum(x))\napply(myArray, c(2), function(x) sd(x))\n\n\n(myList = list(rollno = 100:120, school = 'Data Science', \n course=c('PG','MSc')))\n\nlength(myList$rollno)\n\nx = 11:20\nfor (i in c(5,2,7)) {\n print(x[i]^2)\n}\n\nfunc1 = function(x) sqrt(x)\nfunc1(x)\n" }, { "path": "Unsorted/dec17b.R", "content": "\narray1 = array(1:12, dim=c(2,3,2) )\ndimnames1 = list(letters[1:2],\n LETTERS[1:3], c('One','Two') )\ndimnames(array1) = dimnames1\narray1\n\nmat1 = matrix(1:24, nrow=4)\ndimnames2 = list(letters[1:4], LETTERS[1:6])\ndimnames(mat1) = dimnames2\nmat1\nmat1[4]= NA\n\napply(array1, 1, FUN=sum)\napply(array1, c(2,3), FUN=sum)\n\napply(mat1, 1, FUN=mean)\napply(mat1, MARGIN=2, FUN=median, na.rm=T)\n\nrowSums(array1, dims=3)\ncolSums(array1, dims=1)\ncolSums(array1, dims=2)\n\ncolSums(mat1,dims=1, na.rm=T)\ncolSums(mat1, na.rm=T)\n\nrowSums(mat1,dims=1, na.rm=T)\n\n\na <- c(1,1,1,1,2,2,2,2,2)\nb <- c(10,12,15,12,NA,30,42,38,40)\n(s=split(b,a))\nlapply(s,mean, na.rm=T)\n" }, { "path": "Unsorted/demo1.R", "content": "# Demos\n# http://r-pkgs.had.co.nz/demo.html\n\n#Show all available demos: demo()\ndemo()\n\nlibrary(graphics)\ndemo()\n\nlibrary(base)\ndemo()\n\nlibrary(grDevices)\ndemo()\n\nlibrary(stats)\ndemo()\n\ndemo(package = .packages(all.available = TRUE))\n\n#Show all demos in a package: demo(package = \"httr\")\n\ndemo(package = \"httr\")\n\n#Run a specific demo: demo(\"oauth1-twitter\", package = \"httr\").\ndemo(\"oauth1-twitter\", package = \"httr\")\n\n#Find a demo: system.file(\"demo\", \"oauth1-twitter.R\", package = \"httr\")\nsystem.file(\"demo\", \"oauth1-twitter.R\", package = \"httr\").\n\n#https://cran.r-project.org/web/packages/TeachingDemos/TeachingDemos.pdf\nlibrary(TeachingDemos)\nci.examp()\nplot.dice( expand.grid(1:6,1:6), layout=c(6,6) )\n\n\ncal(2011)\n" }, { "path": "Unsorted/density2.R", "content": "# Density\n\nset.seed(1234)\nx = ceiling(rnorm(100,50,15))\nsummary(x)\nhead(x)\nhist(x)\n\nwrite(x, file='./file/sampledata.txt', ncolumns = 1)\n# write to a file to copy to execl\n# \nhist(x, freq=F)\nlines(density(x), lwd=2)\n\n\nplot(density(x))\nrug(x) # jitters at points\nabline(v=x) # vertical lines\n\n#another method\nplot(density(x)$x, density(x)$y)\nrug(density(x)$x)\ndensity(x)$x\n" }, { "path": "Unsorted/descstatsgraphs1.R", "content": "\n?pastecs::stat.desc(mtcars)\n?fivenum\nlibrary(DescTools)\ndata(\"iris\")\nDesc(iris, plotit = T)\n\ntabplot::tableplot(iris, sortCol=Species)\ntabplot::tableplot(mtcars, sortCol=am)\n\n\nf_summary <- function(data_to_plot)\n{\n ## univariate data summary\n require(nortest)\n #data <- as.numeric(scan (\"data.txt\")) #commenting out by mike\n data <- na.omit(as.numeric(as.character(data_to_plot))) #added by mike\n dataFull <- as.numeric(as.character(data_to_plot))\n \n # first job is to save the graphics parameters currently used\n def.par <- par(no.readonly = TRUE)\n par(\"plt\" = c(.2,.95,.2,.8))\n layout( matrix(c(1,1,2,2,1,1,2,2,4,5,8,8,6,7,9,10,3,3,9,10), 5, 4, byrow = TRUE))\n \n #histogram on the top left\n h <- hist(data, breaks = \"Sturges\", plot = FALSE)\n xfit<-seq(min(data),max(data),length=100)\n yfit<-yfit<-dnorm(xfit,mean=mean(data),sd=sd(data))\n yfit <- yfit*diff(h$mids[1:2])*length(data)\n plot (h, axes = TRUE, main = paste(deparse(substitute(data_to_plot))), cex.main=2, xlab=NA)\n lines(xfit, yfit, col=\"blue\", lwd=2)\n leg1 <- paste(\"mean = \", round(mean(data), digits = 4))\n leg2 <- paste(\"sd = \", round(sd(data),digits = 4))\n count <- paste(\"count = \", sum(!is.na(dataFull)))\n missing <- paste(\"missing = \", sum(is.na(dataFull)))\n legend(x = \"topright\", c(leg1,leg2,count,missing), bty = \"n\")\n \n ## normal qq plot\n qqnorm(data, bty = \"n\", pch = 20)\n qqline(data)\n p <- ad.test(data)\n leg <- paste(\"Anderson-Darling p = \", round(as.numeric(p[2]), digits = 4))\n legend(x = \"topleft\", leg, bty = \"n\")\n \n ## boxplot (bottom left)\n boxplot(data, horizontal = TRUE)\n leg1 <- paste(\"median = \", round(median(data), digits = 4))\n lq <- quantile(data, 0.25)\n leg2 <- paste(\"25th percentile = \", round(lq,digits = 4))\n uq <- quantile(data, 0.75)\n leg3 <- paste(\"75th percentile = \", round(uq,digits = 4))\n legend(x = \"top\", leg1, bty = \"n\")\n legend(x = \"bottom\", paste(leg2, leg3, sep = \"; \"), bty = \"n\")\n \n ## the various histograms with different bins\n h2 <- hist(data, breaks = (0:20 * (max(data) - min (data))/20)+min(data), plot = FALSE)\n plot (h2, axes = TRUE, main = \"20 bins\")\n \n h3 <- hist(data, breaks = (0:10 * (max(data) - min (data))/10)+min(data), plot = FALSE)\n plot (h3, axes = TRUE, main = \"10 bins\")\n \n h4 <- hist(data, breaks = (0:8 * (max(data) - min (data))/8)+min(data), plot = FALSE)\n plot (h4, axes = TRUE, main = \"8 bins\")\n \n h5 <- hist(data, breaks = (0:6 * (max(data) - min (data))/6)+min(data), plot = FALSE)\n plot (h5, axes = TRUE,main = \"6 bins\")\n \n ## the time series, ACF and PACF\n plot (data, main = \"Time series\", pch = 20, ylab = paste(deparse(substitute(data_to_plot))))\n acf(data, lag.max = 20)\n pacf(data, lag.max = 20)\n \n ## reset the graphics display to default\n par(def.par)\n \n #original code for f_summary by respiratoryclub\n \n}\n\nf_summary(mtcars$mpg)\n\nlibrary(PerformanceAnalytics)\nchart.Correlation(iris[,1:4],col=iris$Species)\n\nlibrary(Hmisc)\n\n" }, { "path": "Unsorted/dplyr1.R", "content": "#dplyr\n\nlibrary(dplyr)\n\n# set rnd df\nset.seed(10)\nN <- 5\nmy.df <- data.frame(COL1 = runif(N), \n COL2 = runif(N), \n G = runif(N),\n B = runif(N))\n\n# select columns with dplyr::select\nmy.temp.df <- select(my.df, COL1, G)\nprint(my.temp.df)\n" }, { "path": "Unsorted/ds1.R", "content": "# Data Structures\n# exploring data structures, using data entry, importing data, annotating dataset\n# data types - numeric, character, logical, complex, raw\n# Vectors\na = c(1,2,5,6,-2,4)\nb = c(\"one\", \"two\", 'three')\nc <- c(TRUE, FALSE, T,F,TRUE,FALSE)\na ; b; c\na[3] ; a[c(1,3,5)] ; a[c(2:6)]\n\n# Matrices\n# 2 D arrray where each element as same datatype\ny = matrix(1:20,nrow=5,ncol=4)\ny\ncells = c(1,26,24,68)\nrnames = c('R1','R2')\ncnames = c('C1','C2')\n# filled by coln first\nmymatrix = matrix(cells,nrow=2,ncol=2,byrow=F,dimnames=list(rnames,cnames))\nmymatrix\n# filled by row first\nmymatrix = matrix(cells,nrow=2,ncol=2,byrow=T,dimnames=list(rnames,cnames))\nmymatrix\nmymatrix[1,2] # first row, second coln\nx = matrix(1:10,nrow=2)\nx\nx[2,] ; x[,2];x[1,4] ;x[1,c(4,5)]\n\n#Arrays\n# similar to matrices but can have more than 2 dim\n# myarray= array(vector,dim,dimnames)\ndim1 = c('A1','A2')\ndim2 = c('B1','B2','B3')\ndim3 = c('C1','C2','C3','C4')\nz = array(1:24,c(2,3,4))\nz\nz = array(1:24,c(2,3,4),dimnames=list(dim1,dim2,dim3))\nz\n\n# data frame\n# different colns contain different modes of data (numeric, character)\n# mydata = data.frame(col1,col2,col3)\npatientid = c(1,2,3,4)\nage = c(25,34,28,52)\ndiabetes = c('Type1','Type2','Type1','Type1')\nstatus = c('Poor','Improved','Excellent','Poor')\npatientdata = data.frame(patientid,age,diabetes,status)\npatientdata\npatientdata[1:2]\npatientdata[c('diabetes','status')]\npatientdata$age\ntable(patientdata$diabetes,patientdata$status)\ntable(patientdata$diabetes,patientdata$status,patientdata$age)\n\n#attach, detach, with\nsummary(mtcars$mpg)\nsummary(patientdata)\nplot(mtcars$mpg, mtcars$disp)\nplot(mtcars$mpg, mtcars$wt)\n# another way of writing\nattach(mtcars)\nsummary(mpg)\nplot(mpg, disp)\nplot(mpg,wt)\ndetach(mtcars)\n# with\nwith(mtcars, {\n summary(mpg,disp,wt)\n plot(mpg,disp)\n plot(mpg,wt)\n} )\n# special assignment : with recommended over attach\nwith(mtcars, {\n nokeepstats = summary(mpg)\n keepstats <<- summary(mpg)\n} )\nnokeepstats # no output\nkeepstats\n\n# case identifiers : identify each row with rowname option\npatientdata = data.frame(patientid,age,diabetes,status,row.names = patientid)\npatientdata\nsummary(patientdata)\n\n# factors\ndiabetes = c('Type1','Type2','Type1','Type1')\ndiabetes = factor(diabetes)\ndiabetes\nstatus = c('Poor','Improved','Excellent','Poor')\nstatus = factor(status, ordered=T)\nstatus\npatientdata = data.frame(patientid,age,diabetes,status)\n\nsummary(patientdata)\nstatus = factor(status, ordered=T,levels=c('Poor','Improved','Excellent'))\nstatus\n\npatientdata = data.frame(patientid,age,diabetes,status)\npatientdata\nstr(patientdata)\n\n#Lists : most complex - ordered collection of objects\n# combinatino of matrices, data frames, other lists\n#mylist = list(object1, object2)\n\ng = 'My first list'\nh = c(25,26,18,39)\nj = matrix(1:10,nrow=5)\nk = c(\"one\",'two','Three')\nmylist = list(title=g,ages=h,j,k)\nmylist\nmylist[[2]]\nmylist[['ages']]\n" }, { "path": "Unsorted/env1.R", "content": "# Session Info\n\n\nsessionInfo()\ntoLatex(sessionInfo(), locale = FALSE)\n\n\n\n#http://adv-r.had.co.nz/memory.html\n\nlibrary(pryr)\nmem_used()\n\n\nlibrary(pryr)\nobject_size(1:10)\n## 88 B\nobject_size(mean)\n## 832 B\nobject_size(mtcars)\n## 6.74 kB\n\nrm(list=ls())\nsizes <- sapply(0:50, function(n) object_size(seq_len(n)))\nplot(0:50, sizes, xlab = \"Length\", ylab = \"Size (bytes)\", \n type = \"s\")\n\n\nobject_size(numeric())\n## 40 B\nobject_size(logical())\n## 40 B\nobject_size(raw())\n## 40 B\nobject_size(list())\n## 40 B\n\nplot(0:50, sizes - 40, xlab = \"Length\", \n ylab = \"Bytes excluding overhead\", type = \"n\")\nabline(h = 0, col = \"grey80\")\nabline(h = c(8, 16, 32, 48, 64, 128), col = \"grey80\")\nabline(a = 0, b = 4, col = \"grey90\", lwd = 4)\nlines(sizes - 40, type = \"s\")\n\nx <- 1:1e6\nobject_size(x)\n## 4 MB\ny <- list(x, x, x)\nobject_size(y)\n## 4 MB\n\n\nsort( sapply(ls(),function(x){object.size(get(x))})) \n\n\n#https://stackoverflow.com/questions/1395270/determining-memory-usage-of-objects\n\nmemory.profile()\nlsos()\ngc\nmemory.size()\nobject.size(1)\n#32 bytes\nobject.size(\"Hello world!\")\n#72 bytes\n#object.size(audioscrobbler)\n#39374504 bytes" }, { "path": "Unsorted/examB.R", "content": "#Array -------\nset.seed(1234)\n(x=round(runif(2*3*5,2,5)))\n(elections = array(x, dim=c(5,3,2)))\n(state = c('gujrat','hp'))\n(party = c('bjp','congress', 'aap'))\n(district = paste('D',1:5,sep=''))\n(dimnames(elections)= list(district, party, state))\nelections\n\n#summarise the elections\n#statewise\napply(elections,3, sum)\n#partywise\napply(elections,2, sum)\n#state and partywise\n(t1 = apply(elections,c(2,3), sum))\naddmargins(t1,c(1,2),FUN=list(mean, list(sd,sum)))\n#prop in each state\nprop.table(t1,2)\n\n\n# Built in Data Set\ndata(\"iris\")\nstr(iris) \ndim(iris)\ndf2 = iris\n\nml = sample(50:100,4,replace=F) \ndf2[ml,2] = NA\n#list out the column 1 & 3 of complete cases\ncolSums(is.na(df2[1:4]))\n\nsum(complete.cases(df2))\nsum(!complete.cases(df2))\n\ndf2[complete.cases(df2),][c(1:5),c(1,3)]\n#replace missing values in column 2 with mean of column1\ndf2[is.na(df2[2]),2]\nmean(df2$Sepal.Length)\ndf2[is.na(df2[2]),2] = mean(df2$Sepal.Length)\n#check if values have been changed\ncolSums(is.na(df2[1:4]))\n\nsi = function(principal, time, rate=.10) \n return(principal * time * rate )\nsi(1000, 2)\nsi(1000, 2,.20)\n\n#Count Even Nos\ncount=0\nfor (i in c(2,5,3,9,8,11,6)) {\n if (i%%2 == 0) {\n count <<- count + 1\n# print(count)\n }\n}\nprint(count)\n\nx = c(2,5,3,9,8,11,6)\nsum(x%%2==0)\n\n\ndata(Arthritis, package='vcd')\nsummary(Arthritis)\nstr(Arthritis)\ntable(Arthritis$Treatment, Arthritis$Improved, Arthritis$Sex)\nftable(Arthritis$Treatment ~ Arthritis$Improved + Arthritis$Sex )\ntable(Arthritis$Improved)\n" }, { "path": "Unsorted/extra.R", "content": "\n\n#1\nsales = c(10000, 13000, 12000, 15000)\nnames(sales) = paste('Q',1:4,sep='')\nsales\nbarplot(sales)\nbarplot(sales, horiz=T)\npie(sales)\nsales[sales > 11000]\nsales[ names(sales) == 'Q3']\n\n# 1\n# Attendance\nattnd05Sep17 = sample(c('A','P'), size=30, replace=T, prob = c(0.3,0.7)) \ntable(attnd05Sep17)\n\n# Cricket Players\nset.seed(1000)\n(p1 = ceiling(runif(10,50,100)))\nset.seed(1100)\n(p2 = ceiling(runif(10,50,100)))\nset.seed(1200)\n(p3 = ceiling(runif(10,50,100)))\nset.seed(1300)\n(p4 = ceiling(runif(10,50,100)))\nset.seed(1400)\n(p5 = ceiling(runif(10,50,100)))\nscores = matrix(c(p1,p2,p3,p4,p5), byrow=F,ncol=5,\n dimnames=list(paste('M',1:10,sep=''),paste('P',1:5,sep='')))\nscores\ncolMeans(scores)\nrowMeans(scores)\n\n\n#2 \n\n\n# \nguests =c('Achal','Apoorva', 'Lalit', 'Goldie')\nhosts = data.frame(course=c('BSc','LLB','MSc'), \n school=c('Animation', 'Law','DS'),\n nos =c(50, 20, 24))\nhosts$male = round(hosts$nos * .8)\nhosts$female = hosts$nos - hosts$male\nhosts\nbills = matrix(c(100,300, 400, 500, 230,\n 500,200,150, 400, 700),\n nrow=2, byrow=T,\n dimnames=list(c('decoration','food'),\n paste('B',1:5,sep='')))\nbills \nfreshers=list(guests=guests, hosts=hosts, bills=bills)\nfreshers\nsum(freshers$bills[1,])\nsum(freshers$bills[2,])\nfreshers$hosts$male\nfreshers$hosts$female\nlength(freshers$guests)\n" }, { "path": "Unsorted/fd1.R", "content": "# Frequency Distribution\n\n#Discrete Cat Data\nattend = c('A','P','P','A','P','A')\ntable(attend)\ncbind(table(attend)) # A & P on left side\n\n#Discrete Numeric Data\n\ncolor=c('Blue','Green','Magenta','Green','Black','Blue','Black')\nx2 = table(color)\nx2\nx2a = cbind(x2)\nx2a\nhist(x2a) # not relevant \nbarplot(x2a) # not good\nbarplot(x2a, beside=T) # Better\nunique(color) ; length(unique(color))\nbarplot(x2a, beside=T, col=rainbow(length(unique(color))))\npie(x2a)\n\n#Continuous Data \nset.seed(1234)\nx3 = runif(100,0,150) # 0 to 150 marks range, 100 values \nx3\nx3 = ceiling(x3)\nx3\nrange(x3)\n# Divide range into step of 15 ie 10 levels\nbreaks = seq(0,150,by=15)\nbreaks\nx3[1] = 60\nx3[2] = 75\nx3.cut = cut(x3, breaks)\nx3.cut = cut(x3, breaks, labels=letters[1:10])\nx3.cut = cut(x3, breaks, labels=letters[1:length(breaks)])\n\nlength(breaks)\nx3.cut\n?cut\nx3a = table(x3.cut)\nx3a\nx3b = cbind(x3a)\nx3b\nhist(x3b)\npie(x3b)\nbarplot(x3b, beside=T)\nbarplot(x3b, beside=T, names.arg =rownames(x3b))\nplot(x3b)\n" }, { "path": "Unsorted/fd2.R", "content": "# Frequency 2\n# Relative Frequency\n#http://www.r-tutor.com/elementary-statistics/qualitative-data/relative-frequency-distribution-qualitative-data\n\n#Eg1\nlibrary(MASS) # load the MASS package \nschool = painters$School # the painter schools \nschool.freq = table(school) # apply the table function\nschool\nschool.freq\nhead(painters)\nnrow(painters) # no of rows/ records\nschool.relfreq = school.freq / nrow(painters)\nschool.relfreq\ncbind(school.relfreq) \nround(cbind(school.relfreq),2)\nschool2 = data.frame(round(cbind(school.relfreq),2))\nhist(school2$school.relfreq)\n\n#Cumulative Relative Frequency\nschool2$cum = cumsum(as.numeric(school2$school.relfreq))\nschool2\nplot(school2$cum, type='l')\n\n\n\n#Eg2 https://cnx.org/contents/yTlmgmzh@4/Frequency-Relative-Frequency-a\n# Entering the data : 20 records\nhours.worked = c(5, 6, 3, 3, 2, 4, 7, 5, 2, 3, 5, 6, \n 5, 4, 4, 3, 5, 2, 5, 3)\n\n# A general frequency table\ntable(hours.worked)\n## hours.worked\n## 2 3 4 5 6 7 \n## 3 5 3 6 2 1\n\n# Relative frequency table\nlength(hours.worked)\ntable(hours.worked)/length(hours.worked)\n## hours.worked\n## 2 3 4 5 6 7 \n## 0.15 0.25 0.15 0.30 0.10 0.05\n\n# To get cumulative frequencies, we need to put\n# the hours into different intervals\nx = table(cut(hours.worked, breaks = c(1:7)))\nx\n# Cumulative frequencies\ncumsum(x)\n## (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] \n## 3 8 11 17 19 20\n\n# Cumulative relative frequencies\ncumsum(x)/length(hours.worked)\n## (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] \n## 0.15 0.40 0.55 0.85 0.95 1.00\n" }, { "path": "Unsorted/fd3.R", "content": "#FD3\n\n\n\n\n\n# cum relative Freq Graph\nduration = faithful$eruptions \nbreaks = seq(1.5, 5.5, by=0.5) \nduration.cut = cut(duration, breaks, right=FALSE) #closed on left\nduration.freq = table(duration.cut) \nduration.cumfreq = cumsum(duration.freq) \nduration.cumfreq\nduration.cumrelfreq = duration.cumfreq / nrow(faithful)\n\ncumrelfreq0 = c(0, duration.cumrelfreq) \ncumrelfreq0\nplot(breaks, cumrelfreq0, \n main=\"Old Faithful Eruptions\", # main title \n xlab=\"Duration minutes\", \n ylab=\"Cumulative eruption proportion\") \nlines(breaks, cumrelfreq0) # join the points\n\n\n#Eg3\nFn = ecdf(duration) \n?ecdf\nplot(Fn, \n main=\"Old Faithful Eruptions\", \n xlab=\"Duration minutes\", \n ylab=\"Cumulative eruption proportion\")\n" }, { "path": "Unsorted/fd4.R", "content": "# Freq Distribution\n" }, { "path": "Unsorted/fd5-means.R", "content": "# Table of Value - Mean Median Mode\n\n# Observation Data -------------\nmarks = c(43, 60, 37, 48, 65, 48, 57, 78, 31, 59, 50)\nmarks1 = scan() \n# copy column of values from Excel \n#and paste & enter after the last value\nmarks1\n\nmean(marks) # 52.36\nmedian(marks) # 50\n\nestimate_mode <- function(x) {\n d <- density(x)\n d$x[which.max(d$y)]\n}\nestimate_mode(marks)\n#50.89\n\n# Quantile, Percentile, --------\nquantile(marks)\nsort(marks)\nquantile(marks, c(0,.1,.2,.25, .4, .5, .75, .9, 1))\nquantile(marks, c(0,.09,.18,.27, .36, .45, .55, .64, .73, .82, .91, 1))\nIQR(marks)\nquantile(marks, c(.25,.75))\n(iqr = quantile(marks, .75) - quantile(marks,.25))\n\nsummary(marks)\n\n\n# Discrete Data\n\n#marks2 = scan()\n\nmarks2a = c(3.5, 4.5, 5.5, 6.5, 7.5,8.5, 9.5)\nfreq2 = c(3,7,22,60,85,32,8)\ncbind(marks2a, freq) # see how it looks - excel\ndf2 = data.frame(marks2a, freq2)\ndf2\ndf2$marksfreq = df2$marks2a * df2$freq2\ndf2\nsum(df2$marksfreq)\nmeanmarks2a = sum(df2$marksfreq)/ sum(df2$freq2)\nmeanmarks2a\nmidfreq = sum(freq2)/2\ndf2$cumfreq = cumsum(df2$freq2)\ndf2\n# Median\ndf2$marks2a[df2$cumfreq > midfreq] # X which are more than mid value\n# use min to select least value\nmedian2 = min(df2$marks2a[df2$cumfreq > midfreq]) # \nmedian2\n\n#mode\nmax(df2$freq2)\nwhich(df2$freq2 == max(df2$freq2)) # index No\ndf2$marks2[5]\n\nmode2 = df2$marks2[ which(df2$freq2 == max(df2$freq2))]\nmode2\n\n# real data is not like this - it is observations\n#create data from the table given\nx2a = rep(marks2a, freq2)\nx2a # total obsverations\ntable(x2a)\ncbind(table(x2a)) # Looks like the orginal table\nmean(x2a)\nmedian(x2a)\nestimate_mode(x2a)\n\n\n# Discrete Data - Cat Labels -------------\n?scan\n#marks2b = scan(what = character())\nmarks2b =c (\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\") \nfreq2\nx2b = rep(marks2b, freq2)\nx2b\ntable(x2b)\ncbind(table(x2b)) # looks similar\n\nmean(x2b) # will not work becoz data is not numeric\n# Median - E\nmedian(x2b) # mid value if arranged in series\nlength(x2b)\nmid = ceiling(length(x2b)/2) \nmid\nx2b[mid]\norder(x2b)[mid]\nx2b[order(x2b)[mid]]\n\n# Mode - max freq\n\nestimate_mode(x2b) # does not work\ndensity(x2b) # because u cannot calc density \n\n#simples \ntable(x2b)\nrownames(table(x2b))\ncolnames(table(x2b)) # does not work\n\nmax(table(x2b))\nmode2b1 = rownames(table(x2b))[which(table(x2b) == max(table(x2b)))]\nmode2b1\n\n#Method3\nfreq <- tapply(x2b,x2b,length)\ntable(x2b)\nnames(freq)[which.max(freq)]\n\n\n# continuous Data\n\n#class = scan(what='character')\nclass3 =c(\"0-10\",\"10-20\",\"20-30\",\"30-40\",\"40-50\",\"50-60\", \"60-70\")\nclass3\n#freq3 = scan()\nfreq3 = c(3, 7, 22, 60, 85, 32, 8)\nas.numeric(unlist(strsplit(class3,\"-\"))) # long method\nmidx3 = c(5,15,25,35,45,55,65)\ncbind(class3, midx3, freq3) #midx3 is also character\ndf3 = data.frame(class3, midx3, freq3)\ndf3\ndf3$midxfreq = df3$midx3 * df3$freq3\ndf3\n\n# and so..on like previous eg\n\n# create observation data from contiuous series\nx3 = rep(midx3, freq3)\nx3\nsum(x3)\ntable(x3)\ncbind(table(x3)) # looks like original table\n\nmean(x3)\nmedian(x3)\nestimate_mode(x3)\n" }, { "path": "Unsorted/googleS.R", "content": "# Google Places\n#https://stackoverflow.com/questions/28026897/google-place-with-r\n\n\nlibrary(googleway)\n\n# NOT RUN {\n## query restaurants in Melbourne (will return 20 results)\napi_key <- 'your_api_key'\n\nres <- google_places(search_string = \"Restaurants in Melbourne, Australia\", key = api_key)\n\n\n## melbourne, AU \ndf_places <- google_places(search_string = \"cafe\", location = c(-37.81827, 144.9671), key = key)\n\ndf_places$results$name\n## use the 'next_page_token' from the previous search to get the next 20 results\nres_next <- google_places(search_string = \"Restaurants in Melbourne, Australia\", page_token = res$next_page_token, key = api_key)\n\n## search for a specific place type\ngoogle_places(location = c(-37.817839,144.9673254),place_type = \"bicycle_store\", radius = 20000, key = api_key)\n\n## search for places that are open at the time of query\ngoogle_places(search_string = \"Bicycle shop, Melbourne, Australia\", open_now = TRUE, key = api_key)\n\n# }" }, { "path": "Unsorted/knitr.R", "content": "# Code and Output\n\nlibrary(\"knitr\")\nknit2html(\"./a1/as1.R\")\n" }, { "path": "Unsorted/kurtosis.R", "content": "#Kurtosis\n#describes the tail shape of the data distribution\n# The normal distribution has zero kurtosis and \n# thus the standard tail shape. \n# It is said to be mesokurtic. \n# Negative kurtosis would indicate a thin-tailed data distribution, \n# and is said to be platykurtic. \n# Positive kurtosis would indicate a fat-tailed distribution, \n# and is said to be leptokurtic.\n\nset.seed(1234)\n(marks = ceiling(rnorm(100,50,20)))\nx=marks\nlibrary(e1071) # load e1071 \nhist(x)\nplot(density(x))\ne1071::kurtosis(x) # -0.38 -vely Thin Tail\n\n\n\n\nx1= c(2, 5, -1, 3, 4, 5, 0, 2)\nkurt(x1)\n\n\n\nlibrary(e1071) # load e1071 \nduration = faithful$eruptions # eruption durations\nplot(density(duration))\nkurtosis(duration) # apply the kurtosis function \n#[1] -1.5116 -ve : thin Tail" }, { "path": "Unsorted/lm-sales.R", "content": "# Linear Regression \n# Area(X) - Sales(Y)\n# Create Data\nX = c(1.7,1.6,2.8,5.6,1.3,2.2,1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )\nY = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )\n\n# Summary\nlength(X)\nsum(X); mean(X)\nsum(Y); mean(Y)\n\npar(mfrow=c(1,1))\n# Covariance\ncov(X,Y)\n\n# Correlation\ncor(X,Y)\nplot(X,Y, main=' Plot between X and Y')\nabline(lm(Y ~ X), col='red')\n\n# Correlation Test\ncor.test(X,Y)\n\n\n(df = data.frame(X,Y))\n(xm = mean(X))\n(ym = mean(Y))\ndf\n# Calculate diff between X and xm and similarly for Y\n(df$x = round(X - xm,2))\ntransform(df, y = round(Y - ym,2))\ndf # only x\n\ndf= transform(df, y = round(Y - ym,2)) # save into df\n\n(df$xy = round(df$x * df$y,2))\ndf\n# temporarily display - does not add column permanently\nplyr::mutate(df, x2 = x ^ 2) \ndf\ndf = plyr::mutate(df, x2 = x ^ 2) \ndf\n\ncolSums(df)\n(SSXY = with(df, sum(xy)))\n(SSX2 = sum(df$x2))\n\n(b1 = round(SSXY/ SSX2,2))\n(b0 = ym - b1 * xm)\n# Predicted Values based on coefficients\n(Yp = round(b0 + b1 * X,2))\n(df$Yp = Yp)\ndf\n\n# Errors : SST = SSE (predict ~ actual) + SSR (predict ~ meany) + \n(df$SSe = round((Y - Yp)^2,2))\ndf\n(df$SSr = round((Yp - ym)^2,2))\ndf\n(SSE = sum(df$SSe)) # Unexplained Variation\n(SSR = sum(df$SSr)) # Regression Sum of Squares\n(SST = SSE + SSR) # Total Sum of Squares should be min\n\n#Coeff of Determination\n(r2 = round(SSR/ SST,3))\n\n#Variability due to other factors \n(1 - r2) # unexplained Variation\n\n#Std Error of Estimate : Residual Std Error\nn = length(X)\n(Syx = round(sqrt(SSE/(n - 2)),3))\n#Variability of Actual Values of Y (Yi) from Yp\n\n#Residuals\nresidue = Y-Yp\nsummary(residue)\n\nfit=lm(formula = Y ~ X)\nsummary(fit)\npar(mar=c(5,4,4,2))\nplot(Y, X,col = \"blue\",main = \"Area & Sales\", abline(lm(x~y)),cex = 1.3, pch =\n 16,xlab = \"Area in sq ft\",ylab = \"Sales in 1000 Rs\")\n\nlayout(matrix(c(1,2,3,4),2,2))\nplot(fit)\n\nplot(fit, which=1:4)\n\nlmtest::dwtest(fit)\ncar::durbinWatsonTest(fit)\n\naov(fit)\n" }, { "path": "Unsorted/lm1-sales.R", "content": "# Linear Modeling : Sales\n# \n# \nX = c(1.7,1.6,2.8,5.6,1.3,2.2, 1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )\nY = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7, 2.7,5.5,2.9,10.7,7.6,11.8,4.1 )\n\ncor.test(X,Y)\n#Plot\nplot(x=X,y=Y, main=' Plot between X and Y')\nabline(lm(Y ~ X), col='blue')\ntext(x=X,y=Y,labels=paste0(X,Y),cex=.7, col='red')\n?text\nfit =lm(Y ~ X)\nsummary(fit)\n\ndf =data.frame(X,Y)\ndf\ndf$x = round(X - mean(X),2)\ndf$y = round(Y - mean(Y),2)\ndf\ncolMeans(df)\ndf$xy = round(df$x * df$y,2)\ndf$x2 = round(df$x^2,2)\nSSxy = sum(df$xy)\nSSx2 = sum(df$x2)\n(b1 = SSxy / SSx2)\n(b0 = mean(Y) - b1 * mean(X))\n(df$Yh = round(fitted(fit),2))\ndf\nplot(df$Yh, X)\n(df$residuals = round(df$Y - df$Yh, 2))\nsummary(df$residuals)\nsummary(fit)\ndf$SSr = round((df$Yh - mean(Y))^2, 2)\ndf$SSe = round((df$Y - df$Yh) ^ 2, 2)\ndf\ncolSums(df)\n(SSR = round(sum(df$SSr),2))\n(SSE = round(sum(df$SSe),2))\n(R2 = round(sum(df$SSr)/ (sum(df$SSe) + sum(df$SSr)),4))\ndf\nn=length(X)\n(stderror = sqrt(sum(df$SSe)/(n-2)))\n\np = 1 # No of IV Variables\n(MSR = SSR/p)\n(MSE = SSE/(n-p-1))\n(Fstats = MSR/MSE)\nsummary(fit)\n\n\n\n\n\n\n" }, { "path": "Unsorted/lm1.R", "content": "# Regression - Car Data Set\n# http://r-statistics.co/Linear-Regression.html\n# \npar(mfrow=c(1, 1))\n?cars\nplot(x=cars$speed, y=cars$dist)\nscatter.smooth(x=cars$speed, y=cars$dist, main=\"Dist ~ Speed\") # scatterplot\n\npar(mfrow=c(1, 2)) # divide graph area in 2 columns\nboxplot.stats(cars$dist)\nboxplot(cars$speed, main=\"Speed\", sub=paste(\"Outlier rows: \", \n boxplot.stats(cars$speed)$out)) # box plot for 'speed'\nboxplot(cars$dist, main=\"Distance\", sub=paste(\"Outlier rows: \", \n boxplot.stats(cars$dist)$out)) # box plot for 'distance'\n\n\n# Check for normality of x \nlibrary(e1071)\npar(mfrow=c(1, 2)) # divide graph area in 2 columns\nplot(density(cars$speed), main=\"Density Plot: Speed\", \n ylab=\"Frequency\", sub=paste(\"Skewness:\", round(e1071::skewness(cars$speed), 2))) \n# density plot for 'speed'\npolygon(density(cars$speed), col=\"red\")\nplot(density(cars$dist), main=\"Density Plot: Distance\", \n ylab=\"Frequency\", sub=paste(\"Skewness:\", round(e1071::skewness(cars$dist), 2))) \n# density plot for 'dist'\npolygon(density(cars$dist), col=\"red\")\n\n\n# correlation\ncor(cars$speed, cars$dist) # calculate correlation between speed and distance\n\n\n#LM\nlinearMod <- lm(dist ~ speed, data=cars) # build linear regression model on full data\nprint(linearMod)\n#dist = Intercept + (β ∗ speed) #=> dist = −17.579 + 3.932∗speed\nsummary(linearMod)\n\n#AIC and BIC :For model comparison, the model with the lowest AIC and BIC score is preferred.\nAIC(linearMod) # AIC => 419.1569\nBIC(linearMod) # BIC => 424.8929\n?AIC\n\n\n\n# Predicting LM\n# Create Training and Test data -\nset.seed(100) # setting seed to reproduce results of random sampling\ntrainingRowIndex <- sample(1:nrow(cars), 0.8*nrow(cars)) # row indices for training data\ntrainingData <- cars[trainingRowIndex, ] # model training data\ntestData <- cars[-trainingRowIndex, ] # test data\n\n# Build the model on training data -\nlmMod <- lm(dist ~ speed, data=trainingData) # build the model\ndistPred <- predict(lmMod, testData) # predict distance\n\nsummary (lmMod) # model summary\n\nAIC (lmMod)\n\nactuals_preds <- data.frame(cbind(actuals=testData$dist, predicteds=distPred)) # make actuals_predicteds dataframe.\n(correlation_accuracy <- cor(actuals_preds)) # 82.7%\nhead(actuals_preds)\n\nactuals_preds\napply(actuals_preds, 1, min)\napply(actuals_preds, 1, max)\n(min_max_accuracy <- mean(apply(actuals_preds, 1, min) / apply(actuals_preds, 1, max)) )\n# => (38%) 58.42%, min_max accuracy\n(mape <- mean(abs((actuals_preds$predicteds - actuals_preds$actuals))/actuals_preds$actuals) )\n# => 69% (48.38%), mean absolute percentage deviation\n# \n\n#K Fold\nlibrary(DAAG)\ncvResults <- suppressWarnings(CVlm(data=cars, form.lm=dist ~ speed, m=5, dots=FALSE, seed=29, legend.pos=\"topleft\", printit=FALSE, main=\"Small symbols are predicted values while bigger ones are actuals.\")); # performs the CV\nattr(cvResults, 'ms') # => 251.2783 mean squared error\n\n\n# Assumptions\nmod <- lm(dist ~ speed, data=cars)\nmean(mod$residuals)\n\npar(mfrow=c(2,2)) # set 2 rows and 2 column plot layout\nmod_1 <- lm(mpg ~ disp, data=mtcars) # linear model\nplot(mod_1)\n\nmod <- lm(dist ~ speed, data=cars[1:20, ]) # linear model\nplot(mod)\n\npar(mfrow=c(1,1))\n#Auto Correlation\n# Method 1: Visualise with acf plot\nlibrary(ggplot2)\ndata(economics)\nlmMod <- lm(pce ~ pop, data=economics)\nacf(lmMod$residuals) # highly autocorrelated from the picture.\n\n\n#Assumption 3\n#Homoscedasticity of residuals or equal variance\npar(mfrow=c(2,2)) # set 2 rows and 2 column plot layout\nmod_1 <- lm(mpg ~ disp, data=mtcars) # linear model\nplot(mod_1)\n\n\n\n#Assumption 4 :No autocorrelation of residuals\npar(mfrow=c(1,1))\n# Method 1: Visualise with acf plot\nlibrary(ggplot2)\ndata(economics)\nlmMod <- lm(pce ~ pop, data=economics)\nacf(lmMod$residuals) # highly autocorrelated from the picture.\n\n# Method 2: Runs test to test for randomness\nlawstat::runs.test(lmMod$residuals)\n#=> Runs Test - Two sided\n#=> data: lmMod$residuals\n#=> Standardized Runs Statistic = -23.812, p-value < 2.2e-16\n\n# Method 3: Durbin-Watson test\nlmtest::dwtest(lmMod)\n#=> Durbin-Watson test\n\n#=> data: lmMod\n#=> DW = 0.0021559, p-value < 2.2e-16\n#=> alternative hypothesis: true autocorrelation is greater than 0\n#\n# Rectify\nlibrary(DataCombine)\necon_data <- data.frame(economics, resid_mod1=lmMod$residuals)\necon_data_1 <- slide(econ_data, Var=\"resid_mod1\", NewVar = \"lag1\", slideBy = -1)\necon_data_2 <- na.omit(econ_data_1)\nlmMod2 <- lm(pce ~ pop + lag1, data=econ_data_2)\n\n#Check 1\n\nacf(lmMod2$residuals)\n\n#Check 2\nlibrary(lawstat)\nlawstat::runs.test(lmMod2$residuals) # runs test\n\n#Check 3\n\nlmtest::dwtest(lmMod2)\n\n\n#Assumption 5\nmod.lm <- lm(dist ~ speed, data=cars)\ncor.test(cars$speed, mod.lm$residuals) # do correlation test \n\n#Assumption 6\ndim(cars)\n\n#Assumption 7 \nvar(cars$speed)\n\n\n#Assumptions 9\nlibrary(car)\nmod2 <- lm(mpg ~ ., data=mtcars)\nvif(mod2)\n\nlibrary(corrplot)\ncorrplot(cor(mtcars[, -1]))\n\nmod <- lm(mpg ~ cyl + gear, data=mtcars)\nvif(mod)\n\n\n#Assumption 10 \npar(mfrow=c(2,2))\nmod <- lm(dist ~ speed, data=cars)\nplot(mod)\n\n\n#Check Auto\npar(mfrow=c(2,2)) # draw 4 plots in same window\nmod <- lm(dist ~ speed, data=cars)\nlibrary(gvlma)\ngvlma::gvlma(mod)\n\nsummary(gvlma::gvlma(mod))\n\nplot(mod)\n\nmod <- lm(dist ~ speed, data=cars[-c(23, 35, 49), ])\ngvlma::gvlma(mod)\nplot(mod)\n\ninfluence.measures(mod)\n" }, { "path": "Unsorted/miscscripts.R", "content": ".libPaths(\"E:/rWork/rLibrary\")\n.First = function() cat(\"\\n Welcome Dhiraj to R \")\n.Last = function() cat(\"\\n Thanks for working in R\")\ninstall.packages(\"mosaic\")\nlibrary(mosaic)\n#require(mosaic)\nSys.setenv(https_proxy=\"\")\nSys.setenv(http_proxy=\"\")\nSys.getenv(\"http_proxy\")\noptions(digits=3)\n" }, { "path": "Unsorted/nd1.R", "content": "# Stats Questions\n# Normal Distribution\n\n# Q1: 95% of students at school weigh between 62 kg and 90 kg.\n# Assuming this data is normally distributed,\n# what are the mean and standard deviation?\n#pnorm, qnorm\n#(z1 = for p = .025); z2 for p2 =(.95+.025)) \nx1=62; y1=90\n#Linear Equations\n#z1 * sd + m = x1\n#z2 * sd + m = x2\nlibrary(matlib) \n#https://cran.r-project.org/web/packages/matlib/vignettes/linear-equations.html\n#\n#Eg1a\n(z1 = qnorm(p=.025)); (z2=qnorm(p=.975))\n(A <- matrix(c(z1,1, z2, 1), ncol=2, byrow=T))\n(b <- c(62,95))\ncbind(A,b)\nshowEqn(A, b)\nplotEqn(A,b)\nplotEqn(A,b, xlim=c(-10, 10))\nabline(v=8, h=78)\n(x <- MASS::ginv(A) %*% b)\npoints(x[1], x[2], pch=15) # approx Solution\nsolve(A, b)\n#8.418522 78.500000\n# Ans mean-76, mean-7\n# Proba values from Table equivalent to this command Z -> P\nround(pnorm(c(-3,0,3)),4)\n#0.0013 0.5000 0.9987\n\n# Probability to Z :P -> Z\nround(qnorm(c(0.0013, 0.5000, 0.9987)),3)\n#[1] -3.011 0.000 3.011\n\n# 95% students betw 62 and 90 : area lies in center : 2.5% on each side\n\n\n\n\n# Function to calculate p values (area)\nfuncZ <- function(x1,mean1=0, sd1=1) {\n (exp(-(1/2)* ((x1- mean1)/sd1)^2) / (sd1 * (2 * pi)^.5))\n}\nfuncZ(x1=-3)\n#Table of Z\n(zvalues = seq(-3,2.99,by=.01))\n(pvalues = pnorm(zvalues))\n(mztable = matrix(round(pvalues,3), ncol=10, byrow=T) )\nrownames(mztable) = c(-3,3,by=.1)\ndim(mztable)\n\nround(pnorm(zvalues),4)\n\n#Q2: 99.7% of the components have lengths between 1.176 cm and 1.224 cm.\n#Assuming this data is normally distributed, what are the mean and standard deviation?\n(z2a=qnorm(p=(1-.997)/2))\n(z2b=qnorm(p=.997+(1-.997)/2))\nx2a=1.176; x2b=1.224\n(A <- matrix(c(z2a,1, z2b, 1), ncol=2, byrow=T))\n(b <- c(x2a,x2b))\ncbind(A,b)\nshowEqn(A, b)\nplotEqn(A,b)\nplotEqn(A,b, xlim=c(-3, 3))\n#abline(v=8, h=78)\n(x <- MASS::ginv(A) %*% b)\npoints(x[1], x[2], pch=15) # approx Solution\nsolve(A, b)\n#[1] 0.008086967 1.200000000\n\n#Q3: 68% of the marks in a test are between 51 and 64\nperc=.68\n(z3a=qnorm(p=(1-perc)/2))\n(z3b=qnorm(p=perc+(1-perc)/2))\nx3a=51; x3b=64\n(A <- matrix(c(z3a,1, z3b, 1), ncol=2, byrow=T))\n(b <- c(x3a,x3b))\ncbind(A,b)\nshowEqn(A, b)\nplotEqn(A,b)\nplotEqn(A,b, xlim=c(-5, 10), ylim=c(0,100))\n#abline(v=8, h=78)\n(x <- MASS::ginv(A) %*% b)\npoints(x[1], x[2], pch=15) # approx Solution\nsolve(A, b)\n#[1] 6.536225 57.500000\n\n#Q5 : x1= 250 ; mean=255; sd=2.5 Tea packets\nx1= 250 ; mean=255; sd=2.5\n(pnorm(x1, mean, sd)) * 100\n# Perc 2.275013 % are underweight ie less than 250\n\n#Q6 : x1 = 50 ; mean= 42 ; sd=8 : Scores what percentage of students pass the test\nx1 = 50 ; mean= 42 ; sd=8\n1-(p1=(pnorm(q=x1, mean, sd)) )\n(p1=(pnorm(q=50, mean, sd,lower.tail = F)) ) * 100\n# 10% of students pass the exam\n# # alternative\n((50-42)/8)\n#1sd -> 68%; #bal - 32% ; #one side= 32%/2= 16%\n#\n\n#Q6: mean=3.1 ; sd=0.005 ; perc=99.7 : Machine Parts: Limits ?\nmean=3.1 ; sd=0.005 ; perc=.997\n# q values ?\n(z1 = qnorm(p=(1-perc)/2, mean, sd))\n(z2 = qnorm(p=perc+ (1-perc)/2, mean, sd))\n#limits are 3.08, 3.11\n\n#Q7: Temp : x1=39; x2=42; mean=36; sd= 3 ; find perc between\nx1=39; x2=42; mean=36; sd= 3\nperc = abs(pnorm(q=x1,mean,sd) - pnorm(q=x2,mean,sd)) # 14%\n(days = perc * 30) # jun is of 30 days- 4\n\n#Q8 : Height: x1=1.4; x2=1.6; mean=1.7; sd= 0.2 ; n= 400\nx1=1.4; x2=1.6; mean=1.7; sd= 0.2 ; n = 400\n(perc = abs(pnorm(q=x1,mean,sd) - pnorm(q=x2,mean,sd))) # 24%\n(nos = perc * n) # jun is of 30 days- 4\n#96\n\n#Q9 : Rainfall : x1=8.5 ; mean=10; sd=1.5; n=31\nx1=8.5 ; mean=10; sd=1.5; n =31\n(perc = (pnorm(q=x1,mean,sd, lower.tail = T)))\n(days = round(perc * n))\n# 5 days\n\n#Q10 : : x1=22 ; x2=57 ; mean= 43; sd=14; n=5000 ; Population - Age\nx1=22 ; x2=57 ; mean=43; sd=14; n =5000\n(perc = abs(pnorm(q=x1,mean,sd) - pnorm(q=x2,mean,sd))) # .77\n(nos = perc * n) # 3872\n\n\n\n#Quiz2\n#Q1: find P(0 < Z ≤ 1)\npnorm(q=0)- pnorm(q=1)\n\n#Q2 : P(Z ≤ 2)\npnorm(q=2,lower.tail = T)\n\n#Q3: P(-1.65 < Z ≤ 1.93)\npnorm(q=-1.65)- pnorm(q=1.93)\n\n#Q4 : P(0.85 < Z ≤ 2.23)\npnorm(q=0.85)- pnorm(q=2.23)\n\n#Q5: P(Z > 1.75)\n1- pnorm(q=1.75, lower.tail = T)\npnorm(q=1.75, lower.tail = F)\n\n#Q6: P(Z ≤ -0.69)\npnorm(q=-0.69, lower.tail = T)\n\n\n#Q7 : P(-1.27 < Z ≤ 0)\n(perc = pnorm(q=-1.27) - pnorm(q=0))\n\n#Q8: P(Z > -2.64)\n1- pnorm(q=-2.64, lower.tail = T)\npnorm(q=-2.64, lower.tail = F)\n\n#Q9: P(Z ≤ 0.96)\npnorm(q=0.96, lower.tail = T)\n\n#Q10 : P(-2.31 < Z ≤ 0.82)\n(perc = pnorm(q=-2.31) - pnorm(q=0.82))\n\n\n#Quiz 3\n\n#Q1 : mean=200 ; sd=40; x1>=250 ; P(x>250)\nmean=200 ; sd=40; x1=250\n1-pnorm(q=x1, mean, sd)\npnorm(q=x1, mean, sd, lower.tail = F)\n" }, { "path": "Unsorted/normal.R", "content": "#normal Distribution \ncurve(dnorm, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = \"z\", ylab=\"f(z)\")\n\n#Change this value if you’d like to do a graph for another value of z, where3 < z < 3.\nz = -1.24\nx = c(-3, seq(-3, z, by=.001), z)\ny = c(0, dnorm(seq(-3, z, by=.001)), 0)\npolygon(x, y, col=\"red\")\npnorm(1.24) - pnorm(0)\n\ncurve(dnorm, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = \"z\", ylab=\"f(z)\")\nplot.new()\n#• Change these values if you’d like to do a graph for other values of zleft or\n#zright, where both values are between \u0001 3 and 3.\nzleft = 0\nzright = 1.24\nx = c(zleft, seq(zleft, zright, by=.001), zright)\ny = c(0, dnorm(seq(zleft, zright, by=.001)), 0)\npolygon(x, y, col=\"red\")\n#--------------\n\n#Find P Z( ) > \u00011.24 #• Graph and shade for the above problem.\n1 - pnorm(-1.24)\nplot.new()\ncurve(dnorm, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = \"z\", ylab=\"f(z)\")\nz = -1.24\nx = c(z, seq(z, 3, by=.001), 3)\ny = c(0, dnorm(seq(z, 3, by=.001)), 0)\npolygon(x, y, col=\"red\")\n\nqnorm(0.85) \nplot.new()\ncurve(dnorm, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = \"z\", ylab=\"f(z)\")\n#• Change this value if you’d like to do another quantile.\n\nprob = 0.85\n\nx = c(-3, seq(-3, qnorm(prob), by=.001), qnorm(prob))\ny = c(0, dnorm(seq(-3, qnorm(prob), by=.001)), 0)\npolygon(x, y, col=\"red\")\n#• 85% of the total area under the curve is shaded red. (This includes the tiny\n piece to the left of z = \u0001 3; our figure cuts it off.) \npnorm(95, mean=100, sd=15) - pnorm(80, mean=100, sd=15)\n1 - pnorm(125, mean=100, sd=15)\n\nqnorm(0.75, mean=100, sd=15) \n" }, { "path": "Unsorted/paneldata1.R", "content": "#Panel Data\n\nset.seed(25)\n\n# number of obs for each case\n(nT <- 5)\n\n# set number of groups\n(N <- 12)\n\n# set possible cases\n(possible.cases <- LETTERS[1:N])\n\n# set parameters\n(my.alphas <- seq(-10,10,length.out = N))\n(my.beta <- 1.5)\n\n# set indep var (x) and dates\nrep(nT,N)\nsapply(5,rnorm)\n\n(indep.var <- sapply(rep(nT,N), rnorm))\n(my.dates <- Sys.Date() + 1:nT)\n\n# create response matrix (y)\n(response.matrix <- matrix(rep(my.alphas,nT), nrow = nT, \n byrow = TRUE) + indep.var*my.beta + sapply(rep(nT,N),rnorm, sd = 0.25) )\n\n# set df\n(sim.df <- data.frame(G = as.character(sapply(possible.cases, \n rep, times=nT )), dates = rep(my.dates, times=N),\n y = as.numeric(response.matrix), x = as.numeric(indep.var), \n stringsAsFactors = FALSE))\n\n# print result\nprint(str(sim.df))\n\n#Plot\nlibrary(ggplot2)\n#one Plot\n(dataA = sim.df[sim.df$G=='A',])\nplot(x ~ y, dataA)\n\n#Multiple Plots\np <- ggplot(sim.df, aes(x=x, y=y))\np <- p + geom_point()\np <- p + facet_wrap(~G)\nprint(p)\n\n\nlibrary(plm)\n\n# estimate panel data model with fixed effects\nmy.pdm <- plm(data = sim.df, \n formula = y ~ x, \n model = 'within',\n index = c('G','dates'))\n\n# print result\nprint(summary(my.pdm,))\nprint(fixef(my.pdm))\n\n\n\n#Case Study\nlibrary(plm)\n\n# data from Grunfeld\ndata(\"Grunfeld\")\n\n# print it\nprint(str(Grunfeld))\nhead(Grunfeld)\n\n\n\nmy.fct <- function(df) {\n # Estimates a linear model from Grunfeld data\n # Args: # df - dataframe from Grunfeld\n #Returns: # lm object\n my.model <- lm(data = df, formula = inv ~ value + capital)\n return(my.model)\n}\nsummary(Grunfeld)\nhead(Grunfeld)\nGrunfeld$firm\n# estimate model for each firm\nmy.l <- by(Grunfeld, INDICES = Grunfeld$firm, FUN = my.fct)\n\n# print result\nmy.coefs <- sapply(my.l, coef)\nprint(my.coefs)\n\n# test if all coef are the same across firms\nmy.pooltest <- pooltest(inv~value+capital, \n data = Grunfeld, \n model = \"pooling\")\n\n# print result\nprint(my.pooltest)\n\n\n# set options for Hausman test\nmy.formula <- inv ~ value + capital\nmy.index <- c('firm','year')\n\n# do Hausman test\nmy.hausman.test <- phtest(x = my.formula, \n data = Grunfeld,\n model = c('within', 'random'),\n index = my.index)\n\n# print result\nprint(my.hausman.test)\n\n\n# set panel data model with random effects\nmy.model <- 'random'\nmy.formula <- inv ~ value + capital\nmy.index <- c('firm','year')\n\n# estimate it\nmy.pdm.random <- plm(data = Grunfeld, \n formula = my.formula, \n model = my.model,\n index = my.index)\n\n# print result\nprint(summary(my.pdm.random))\n\n\nlibrary(systemfit)\n\n# set pdataframe\n(p.Grunfeld <- pdata.frame(Grunfeld, c( \"firm\", \"year\" )))\n\n# estimate sur\nmy.SUR <- systemfit(formula = inv ~value + capital,\n method = \"SUR\", data = p.Grunfeld)\nprint(my.SUR)\n" }, { "path": "Unsorted/plot1.R", "content": "\n\n# collect the values together, and assign them to a variable called y\nc(6,10,10,17,7,12,7,11,6,16,3,8,13,8,7,12,6,5,10,9) -> y\nhist( y ) -> h # do a histogram of y and assign its info to h\nh$counts <- cumsum(h$counts) # replace the cell freq.s by cumulative freq.s\nplot( h ) # plot a cumulative histogram of y\n\n\nplot(sort(y), 1:length(y)) # plot rank on value of y\n\nplot(y, rank(y))\n\nn <- length(y)\np <- (.5:n)/n\nplot(sort(y),p)\npoints(sort(y),1-p)\nabline(v=median(y))\n" }, { "path": "Unsorted/practise-dec17c.R", "content": "# Practise\n#create random 10 dates between 01 and 30\n#month - jan \n#with random years 1990 and 1994\n# save in date of birth column\nset.seed(1234)\n(day = round(runif(10, 1, 30),0)) \n(month = 'Jan')\n(year = sample(x=1990:2000,10,replace=T)) \n(dob1= paste(year,month,day,sep='-'))\n(dob = as.Date(dob1,'%Y-%b-%d'))\nclass(dob) \n#vector of student1 to student2 and give sname\n(sname = paste('student',1:10, sep=''))\n(marks = ceiling(runif(10,50,100)))\n#put student1 to student5 to Dept1, rest in Dept2\n(dept = rep(c('dept1','dept2'),each=5))\n#Every alternate student gender as M or F\n(gender = rep(c('M','F'),5))\n(df1=data.frame(sname, dept, gender, dob,marks))\n\n#calculate age column as on date; round of the age\n(df1$agedays = Sys.Date() - df1$dob)\n(df1$age = round(as.numeric(df1$agedays)/365,0))\n#create a column specify Grades A to C as follows\n#A > 80, B : 60-79 and C < 60\n\ndf1$grade = ifelse(df1$marks >= 80, 'Pass', 'Fail')\ndf1\n#aggregate data \naggregate(df1$age, by=list(dept, gender), mean)\n#draw a pie graph for grades\npie(table(df1$grade))\n#plot age vs marks \nplot(x=age , y=marks, data=df1)\n#draw fit line for the plot above\nabline(lm(marks ~ age, data=df1))\ndf1\n\n#Array -------\n# Two states hold elections gujrat, hp\n# They have 3 parties - bjp, congress, aap\n# Each state has 5 districts\n# put values between 2 to 5 to each district of state for each party\n# on random basis. use set.seed(1234)\n(x=round(runif(2*3*5,2,5)))\n(elections = array(x, dim=c(5,3,2)))\n(state = c('gujrat','hp'))\n(party = c('bjp','congress', 'aap'))\n(district = paste('D',1:5,sep=''))\n(dimnames(elections)= list(district, party, state))\nelections\n\n#summarise the elections\n#statewise\napply(elections,3, sum)\n#partywise\napply(elections,2, sum)\n#state and partywise\n(t1 = apply(elections,c(2,3), sum))\naddmargins(t1,c(1,2),FUN=list(mean, list(sd,sum)))\n#prop in each state\nprop.table(t1,2)\n\n\n# Built in Data Set\ndata(\"iris\")\nstr(iris) \ndim(iris)\ndf2 = iris\n#introduce NA values at (R,C) - (1,2), (2,4),(2,3),(1,5)\n#select 4 random locations between 50 & 100 in Coln 2 using\n#sample command and put NA missing values\nml = sample(50:100,4,replace=F) \ndf2[ml,2] = NA\n#list out the column 1 & 3 of complete cases\ncolSums(is.na(df2[1:4]))\n\nsum(complete.cases(df2))\nsum(!complete.cases(df2))\n\ndf2[complete.cases(df2),][c(1:5),c(1,3)]\n#replace missing values in column 2 with mean of column1\ndf2[is.na(df2[2]),2]\nmean(df2$Sepal.Length)\ndf2[is.na(df2[2]),2] = mean(df2$Sepal.Length)\n#check if values have been changed\ncolSums(is.na(df2[1:4]))\n\n\n#use data set mtcars \n#Find properties of dataset : structure, dimensions\n#\ndata(mtcars)\nstr(mtcars)\ndim(mtcars)\n#plot mpg vs wt \nplot(mpg ~ wt, data=mtcars)\n#draw abline for above graph\nabline(lm(mpg ~ wt, data=mtcars))\n#draw histogram of mpg with default settings\nhist(mtcars$mpg)\n#list out variables names of mtcars\nnames(mtcars)\n#copy mtcars into another data frame df3 and use this for further calc\ndf3=mtcars\n#convert am to factors\ndf3$am =factor(df3$am)\n#aggregate DF by am with mean values of cols 1 to 5\naggregate(df3[1:5], by=list(df3$am), mean)\n#list Df rows 25 to 30, and colns 5:8\ndf3[25:30,5:8]\n#list DF with am=0 & cyl=4\ndf3[df3$am==0 & df3$cyl==4,1:4]\n#List the DF in order cyl (increasing) & mpg(decreasing) - only 1st 2 column\ndf3[order(df3$cyl, -df3$mpg),1:2]\n\n\n#Eg1: Freq Distribution #faithful - durations\nduration = faithful$eruptions\nrange(duration)\n#1.6 5.1\nbreaks = seq(1.5, 5.5, by=0.5)\nbreaks\nduration.cut = cut(duration, breaks, right=FALSE)\nduration.freq = table(duration.cut)\ncbind(duration.freq)\n#duration.freq [1.5,2) 51 [2,2.5) 41\nhist(duration,labels=T)\n\n#Descriptive \n# Quantile Values fo Temp variable of airqualty Data Set\n# \nquantile(airquality$Temp)\n" }, { "path": "Unsorted/practise.R", "content": "student = read.csv(\"student.csv\",stringsAsFactors = F)\nstudent\nlibrary(\"dplyr\")\nmutate(student, avgmarks = JAVA/2)\nstr(student)\nmy_data <- as_data_frame(student)\nstr(my_data)\nstudent = read.csv(\"student.csv\", header=T, na.strings=c(\"\",\"NA\"), stringsAsFactors = F)\nstudent$ser = NULL\nhead(student)\nrow.names(student) = student$rollno\n#------------\nx <- c(a = 1, b = 2,c=3,d=4)\nclass(x)\nstr(x)\nx\ny=c(1,2,3)\nseq(5,11,along.with = y)\n\n# first code --------------------------------------------------------------\n\n# second code ------------------------------------------\n\n# abc ---------------------------------------------------------------------\n\n\n\n# 19 Nov 16 Split and combine\n\ncyl=c(4,4,8,6,8,4,6,4)\nmpg=c(36,38,21,28,19,35,25,32)\nhp=c(100,93,340,258,365,97,240,103)\nbrand=c(\"Ford\",\"Honda\",\"Nissan\",\"Nissan\",\"Honda\",\"Honda\",\"Ford\",\"Ford\")\n\nmyCars = data.frame(cyl,mpg,hp,brand)\nmyCars$cyl = factor(myCars$cyl)\n# str(myCars)\nmyCars\n# split on factors\nsplitCars = split(myCars, myCars$cyl)\nsplitCars\n\n#Linear Problems\nres <- c(2,2,3,3)\nmat <- matrix (c(14, 8, 16, 6, 5, 3, 7, 6, 5, 4, 3, 1, 2, 4, 7, 9), ncol=4)\nsolve (mat)\n\n# 20 Nov 16\nour.data = scan()\n" }, { "path": "Unsorted/rattle1.R", "content": "# rattle\nlibrary(rattle)\n#data(weather) \n#http://rattle.togaware.com/weatherAUS.csv\n#\nrattle()\n#http://archive.ics.uci.edu/ml/datasets/Heart+Disease\n#https://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/heart_for_rattle.txt\n#above data link\nheart = read.table('./data/heart_for_rattle.txt', header=T)\nsummary(heart)\n\n\nfname <- \"file:///E:/rWork/rDataSets/nyc.csv\" \ncrs$dataset <- read.csv(fname,\n na.strings=c(\".\", \"NA\", \"\", \"?\"),\n strip.white=TRUE, encoding=\"UTF-8\")\nstr(crs$dataset)\ncrs$dataset$X\ncrs$y = data.frame(a=1:10, b=10:19)\ncrs$y\nrattle(system.file(\"csv\", \"weather.csv\", package = \"rattle\"))\neapply(crs,FUN=summary)\n\n\n# Using Rattle\n# Generate the table data for plotting.\nds <- table(crs$dataset[crs$sample,]$sex, crs$dataset[crs$sample,]$disease)\n\n# Sort the entries.\nord <- order(apply(ds, 1, sum), decreasing=TRUE)\n\nmosaicplot(ds[ord,], main=\"Mosaic of sex (sample)\nby disease\", sub=\"Rattle 2017-Oct-16 06:51:21 dupad\", color=colorspace::rainbow_hcl(3)[-1], cex=0.7, xlab=\"sex\", ylab=\"disease\")\n" }, { "path": "Unsorted/rattle2.R", "content": "\nrattle(system.file(\"csv\", \"weather.csv\", package = \"rattle\"))\n" }, { "path": "Unsorted/rcdr1.R", "content": "# using R Commander\nlibrary(Rcmdr)\n\n# Entering Data\nattach(mtcars)\nload('./data/du3.Rdata')\nwith(df2, Hist(excel, groups=course, \n scale=\"frequency\", breaks=\"Sturges\", col=\"darkgray\"))\n\nwith(df2, Hist(age, groups=course, \n scale=\"frequency\", breaks=\"Sturges\", col=\"darkgray\"))\nxtabs(~gender+hostel+city, data=df2)\n\nt.test(student$age, alternative='less', mu=30, \n conf.level=.95)\n\nsummary(women[,'height'])\nboxplot(age ~ gender, data=student)\nRcmdr> Boxplot(age ~ gender, data = student)\n" }, { "path": "Unsorted/rjava.R", "content": "# Java in R\n# \n\nSys.getenv(\"PATH\")\nSys.getenv(\"JAVA_HOME\")\n#JAVA_HOME path to the jre folder inside \"jdk1.8.0_121\"\n#\n\ninstall.packages('rJava')\nlibrary(rJava)\n\n#https://support.microsoft.com/en-us/help/3103813/qa-when-i-try-to-load-the-rjava-package-using-the-library-command--i-g\n# Linux\n# https://stackoverflow.com/questions/3311940/r-rjava-package-install-failing\n# http://www.oracle.com/technetwork/java/javase/downloads/jre8-downloads-2133155.html\n# \n# \nSys.setenv(JAVA_HOME='C:\\\\Program Files\\\\Java\\\\jre1.8.0_144\\\\bin')\n\nremove.packages('rJava')\n?remove\n.rs.restartR()\n\n#https://stackoverflow.com/questions/43788788/another-rjava-library-install-error-rjava-rdb-is-corrupt\n#" }, { "path": "Unsorted/rle1.R", "content": "?rle\nrle(x=1:10)\nc( 22, 0, 0, 20, 0, 50, 23, 1, 2, 21, 0) -> y\ny\nrle(y)\ndist=rle(sort(y))\nm=dist$lengths==max(dist$lengths) # note the most common value\nm\ndist$values[which(m)] # give the modal value in variable y \ndist$lengths[which(m)] # give the frequency of that value\n\nx <- rev(rep(6:10, 1:5))\nx\nrle(x)\n" }, { "path": "Unsorted/sample1.R", "content": "#Sampling\nx = 1:3\nsample(x)\n\nsample(1:3, size=3, replace=FALSE) # same as previous line\nsample(1:3, size=3, replace=TRUE) # no may get repeated\n\n# 4 Val\nsample(c(1,2,3,4,5,6), size=4, replace=TRUE)\n\n# 10 Values with ratio of probabilities\nsample(1:2, size=10, prob=c(1,3), replace=TRUE)\n\n\nsample(1:3, size=1000, replace=TRUE, prob=c(.30,.60,.10))\ntable(sample(1:3, size=1000, replace=TRUE, prob=c(.30,.60,.10)))\n\nbarplot(table(sample(1:3, size=1000, replace=TRUE, prob=c(.30,.60,.10))))\n\n" }, { "path": "Unsorted/sample2.R", "content": "#Sample\n?sample\n\n#Simple\nx = c('A', 'B', 'C', 'D', 'E')\nsample(x, 2)\nsample(x, 2)\nsample(x, 4, replace=TRUE) # repeats samples\n\n# with Probability\n\nprobability = c(0.1, 0.15, 0.2, 0.25, 0.3 )\nsum(probability)\n#They need not sum to one, but they should be nonnegative and not all zero.\n#probability of choosing the next item is proportional to the probabilities amongst the remaining items. The number of nonzero weights must be at least size in this case.\n\nsample(x, 3, prob=probability)\nx\n\n# Bernoillis Trial\nsample(c(0,1), 100, replace = TRUE)\n\n# Data as Data Frame\nmtcars\n(mtcars1 = mtcars[c(1:4)]) # reduce the no of cols\nstr(mtcars1)\n(mtcars2 = sample(mtcars1, 1, replace=FALSE)) # Select either colns\n(mtcars3 = mtcars1[sample(1:nrow(mtcars1), 3, replace=FALSE),]) #select any 3 rows\n\n# \n(m = matrix(1:24, nrow=6))\nsample(m, 3) # any value from the matrix cell not row\nnrow(m)\nm[sample(1:nrow(m), 3),] # select any row of matrix\n\n# Give probability to each row\npm = c(0.1, 0.15, 0.2, 0.25, 0.3, 0.2 )\nsum(pm) # incorrect but still worked\nm[sample(1:nrow(m), 3, prob= pm),]\nm\n\n\n\n#Multiple Samples stored in a file\nidorg = c(1,2,3,4,5)\nx = c(14,20,21,16,17)\ny = c(31,21,20,50,13)\ndataset = cbind (idorg,x,y)\ndataset\n\nfor (i in 1:4) {\n attempt = dataset[sample(1:nrow(dataset), 3, replace=FALSE),]\n print(attempt)\n }\n\n# print to a file\nfor (i in 1:4) {\n attempt <- dataset[sample(1:nrow(dataset), 3, replace=FALSE),]\n write.table(attempt, sprintf( \"./data/dataset[%d].csv\", i ), sep='\\t')\n}\n\n\n\n# List\ng =\"My First List\"\nh = c(25, 26,18,39)\nj = matrix(1:10,nrow=2)\nk = c('one','two','three')\nl = data.frame(formno = c(17010, 17045, 17012), \n name=c('Achal', 'Apoorva', 'Goldie'), \n gender=c('M','F','M'), stringsAsFactors = F)\ng;h;j;k;l\nmylist = list(title=g, ages=h, j, h,students=l)\nmylist # full list\nsample(mylist,2) # anytwo objects from the list\nsample(mylist$students ,2 ) # any two cols of DF\nmylist$students[sample(1:nrow(mylist$students),2), 1] # from col1\nmylist$students[sample(1:nrow(mylist$students),2), 2] # from col2\nmylist$students[sample(1:nrow(mylist$students),2), 3] # from col3\n\nsample(mylist$ages ,2 ) # any two values of numeric vector age\n\n\n\n\n# Sample based on conditional value\nx = 1:10\nx\nsample(x[x > 8]) # length 2\nsample(x[x > 9]) # oops -- length 10! # incorrect value\nsample(x[x > 10]) # error!\n\ntry(sample(x[x > 10])) #no output\n\n?try\n## This is safer:\nresample <- function(x) x[sample.int(length(x))]\nresample(x[x > 8]) # length 2\nresample(x[x > 9]) # length 1\nresample(x[x > 10]) # length 0\n\n?sample.int\n#sample.int is a bare interface in which both n and size must be supplied as integers.\n\n\nsample.int(1e5, 12, replace = TRUE)\nsample.int(1e5, 12) # not that there is much chance of duplicates\n\n# Extras\n\nlibrary(data.table)\nset.seed(10)\n\nmtcars <- data.table(mtcars)\nmtcars[sample(.N, 6)]\n\n\nlibrary(dplyr)\ndf= mtcars\nsample_n(df, 10)\n\n\n\n#install.packages('kimisc')\n\nlibrary(kimisc)\nexample(sample.rows)\n\nset.seed(42)\n\nsample.rows(data.frame(a=c(1,2,3), b=c(4,5,6),\n row.names=c('a', 'b', 'c')), 10, replace=TRUE)\nsample.rows(mtcars,5)\n\n\n# \nlibrary(\"tibble\") \na = mtcars[sample(1:nrow(mtcars), 3),]\na\n\n\n# Library kimisc\nlibrary(kimisc)\nexample(sample.rows)\nset.seed(12)\ndf3= data.frame(a=c(1,2,3), b=c(4,5,6), \n row.names = c('a','b','c'))\nsample.rows(df3, 10, replace=TRUE) # show how many times repeated\n " }, { "path": "Unsorted/scripting1.R", "content": "# Looping\n#ex1\n\ni <- 0\nrepeat{\n i <- i + 2\n print(i)\n if(i == 10) {\n break\n }\n}\n\n#ex2\nmsg <- c(\"Hello\")\ni <- 1\nrepeat {\n i <- i + 1\n print(c(msg,i))\n if(i > 5) {\n break\n }\n}\n\n# Ex3\ni <- 1\nwhile(i < 8) {\n print(i)\n i <- i + 2\n}\n\n# Ex4\nmsg <- c(\"Hello\")\ni <- 1\n\nwhile (i < 7) {\n print(msg)\n i = i + 1\n}\n\n#ex5\nx <- c(7, 4, 3, 8, 9, 25)\nfor(i in 1:4) {\n print(x[i])\n}\n\n#ex6\ny <- c(\"q\", \"w\", \"e\", \"r\", \"z\", \"c\")\nfor(letter in y) {\n print(letter)\n}\n\n#ex7\ni <- 1\nwhile(i < 5) {\n i <- i + 1\n if (i == 3) break\n print(i)\n}\n\n#ex8\nfor (a in 1:3)\n{\n for (b in 1:3)\n {\n print(c(a, b))\n if (b == 2) break\n }\n}\n\n#ex9\ni <- 1\nwhile(i < 5) {\n i <- i + 1\n if (i == 3) {\n next\n }\n print(i)\n}\n\n#ex10\ni <- 1:5\nfor (val in i) {\n if (val == 3){\n next\n }\n print(val)\n}" }, { "path": "Unsorted/skewness1.R", "content": "# Skewness\n#measure of symmetry. As a rule, negative skewness indicates \n#that the mean of the data values is less than the median, and the data distribution is left-skewed. Positive skewness would indicate that the mean of the data values is larger than the median, \n#and the data distribution is right-skewed.\n\n\n\nset.seed(1234)\n(marks = ceiling(rnorm(100,50,20)))\nx=marks\nlibrary(e1071) # load e1071 \nhist(x)\nplot(density(x))\ne1071::skewness(x) #0.5884632 +vely \n\n# Mean Median Mode\n(m1 = mean(x))\n(m2 = median(x))\n(m3 = modeest::mlv(x, method = \"mfv\")) # this is not atomic value\n#str(m3)\ncentraltendency= c(MEAN= m1,MEDIAN= m2,MODE= m3$M)\nsort(centraltendency)\n\n# Other Packages\nlibrary(moments)\nmoments::skewness(x) #0.5974018\n\n\n\nlibrary(e1071) # load e1071 \nduration = faithful$eruptions # eruption durations \nskewness(duration) # apply the skewness function \n\nlibrary(fBasics) # skewness part of timeDate package\ntimeDate::skewness(x)\n?skewness # see where all help is available\ntimeDate::skewness(x, na.rm = FALSE, method = c(\"moment\", \"fisher\"))\n\n# Psych Package\nlibrary(psych)\npsych::describe(x)\npsych::describeBy(mtcars,group= mtcars$am)\n\n\n# Skewness of multiple Columns\n?colSkewness\ntimeSeries::colSkewness(mtcars, na.rm = FALSE)\n\n\n\n# Generate Various Directions\nN <- 10000\nx <- rnbinom(N, 10, .5)\nhist(x, \n xlim=c(min(x),max(x)), probability=T, nclass=max(x)-min(x)+1, \n col='lightblue', xlab=' ', ylab=' ', axes=F,\n main='Positive Skewed')\nlines(density(x,bw=1), col='red', lwd=3)\nskewness(x)\n\n\nndata = rbeta(10000,5,2)\npdata = rbeta(10000,2,5)\ndata = rbeta(10000,5,5)\nhead(ndata); head(data) ; head(ndata)\nhist(pdata)\nhist(data)\nhist(ndata)\n\nskewness(ndata)\nskewness(data)\nskewness(pdata)\n\n# all 3 together\ntimeSeries::colSkewness(cbind(ndata,data,pdata), na.rm = FALSE)\n\nlibrary(asbio)\nasbio::skew(x)\n\nexp<-rexp(10000)\nexp\nskew(exp)\nkurt(exp)\n" }, { "path": "Unsorted/skewness2.R", "content": "#skewness2\n\n\nlibrary(fGarch)\n# dsnorm(x, mean = 0, sd = 1, xi = 1.5, log = FALSE)\n# psnorm(q, mean = 0, sd = 1, xi = 1.5)\n# qsnorm(p, mean = 0, sd = 1, xi = 1.5)\n# rsnorm(n, mean = 0, sd = 1, xi = 1.5)\n\npar(mfrow = c(2, 2))\nset.seed(1953)\nr = rsnorm(n = 1000)\nplot(r, type = \"l\", main = \"snorm\", col = \"steelblue\")\n\n# Plot empirical density and compare with true density:\nhist(r, n = 25, probability = TRUE, border = \"white\", col = \"steelblue\")\nbox()\nx = seq(min(r), max(r), length = 201)\nlines(x, dsnorm(x), lwd = 2)\n\n# Plot df and compare with true df:\nplot(sort(r), (1:1000/1000), main = \"Probability\", col = \"steelblue\",\n ylab = \"Probability\")\nlines(x, psnorm(x), lwd = 2)\n\n# Compute quantiles:\nround(qsnorm(psnorm(q = seq(-1, 5, by = 1))), digits = 6)\n\n" }, { "path": "Unsorted/skewness3.R", "content": "#Skewness 3\nlibrary(e1071)\ndata(airquality)\nozone <- airquality$Ozone\nozone <- ozone[!is.na(ozone)]\nhist(ozone, col = \"tomato\")\nskewness(ozone)\n" }, { "path": "Unsorted/smpdist1.R", "content": "# Sampling Distribution ####\nplot.new()\n\n# Generate Random Nos from ND ####\nrnorm(100)\nrnorm(100)*10\nround(rnorm(100)*10)\nx=round(rnorm(100)*10)\nmeanx = mean(x)\nsdx = sd(x)\n# plot ####\nplot(density(x))\n\n# dnorm() returns the probability density function or pdf.####\ndnorm(1)\ndnorm(-2)\ndnorm(3)\n#height of the normal curve at some value along the x-axis. ####\ndnorm(1,mean=meanx,sd=sdx)\ndnorm(10,mean=meanx,sd=sdx)\ndnorm(10,mean=meanx,sd=sdx)\nabline(h=dnorm(-10,mean=meanx,sd=sdx))\nabline(h=dnorm(20,mean=meanx,sd=sdx))\n\n# Cumulative Freq / Area under the curve\n# default - lower tail. area from left side\npnorm(0)\npnorm(0,mean=meanx, sd=sdx)\npnorm(10,mean=meanx, sd=sdx)\npnorm(100,mean=meanx, sd=sdx)\npnorm(30,mean=meanx, sd=sdx)\n\nabline(v=30,col=4,lwd=2)\nabline(v=-25,col=4,lwd=2)\nabline(v=meanx - 1 * sdx,col=4,lwd=3)\n# Upper Tail\npnorm(15,mean=meanx, sd=sdx,lower.tail = F)\nabline(v=15,col=6,lwd=2) # area to the right of 15\n\nplot.new()\nplot(density(x))\n\n#Quantiles\nqnorm(.95)\nqnorm(.10) # 10% values\nabline(v=qnorm(.95,mean=meanx,sd=sdx),col=\"red\",lwd=4)\n# 95% values upto this point from left\n# 60% values from right\nqnorm(.60,mean=meanx,sd=sdx,lower.tail = F)\nabline(v=qnorm(.60,mean=meanx,sd=sdx,lower.tail = F),col=\"red\",lwd=5)\nqnorm(c(.025,.975),mean=meanx,sd=sdx)\nabline(v=qnorm(c(.025,.975),mean=meanx,sd=sdx),col=\"blue\",lwd=3)\n" }, { "path": "Unsorted/summary1.R", "content": "# Summary ----------\n\n\nset.seed(1234)\nx = ceiling(rnorm(100, 50, 10)) # 100 random nos mean-50, Sd-10\nx\n\nsummary(x)\n\n# Five Number Summary ---------\n#lies in the lack of universal agreement on how the 1st and 3rd quartiles should be calculated.\n#https://www.r-bloggers.com/exploratory-data-analysis-the-5-number-summary-two-different-methods-in-r/\n \nfivenum(x)\n\nx = seq(1, 9, by = 2)\nx # Odd Nos\nfivenum(x)\nsummary(x) # same output\n\ny = seq(1, 11, by = 2)\ny # Even Nos\nfivenum(y) # no control for digits\nsummary(y) #Different for 1Q, 3Q\nsummary(x, digit=2)\ny\n(Q1 = y[1] + (length(y) - 1 ) * .25)\n# This position is not a integer, so cannot extract it\n\n#*fivenum() does not have an argument for controlling the number of decimal places in its output, while summary() has the “digits” option for doing so. You may need to invoke this option in summary() to get more decimal places to when comparing its output with fivenum()’s output.\n\n?fivenum\nfivenum(c(rnorm(100), -1:1/0))\n" }, { "path": "Unsorted/sumstats1.R", "content": "# Summary Stats\n#http://sphweb.bumc.bu.edu/otlt/mph-modules/bs/r/r2_summarystats-graphs/R2_SummaryStats-Graphs_print.html\n\nairquality[1:5,]\nmean(airquality$Temp)\nmedian(airquality$Temp)\nvar(airquality$Wind)\nattach(airquality)\nvar(Wind)\n\nsummary(airquality)\n\n\npar(mar=c(3,3,2,2), mfrow=c(1,1))\n#Fit normal curve over data (use dnorm & curve instead of curve)\nhist(airquality$Temp, prob=T, main='Temperature')\ncurve(dnorm(x, mean= mean(airquality$Temp), sd = sqrt(var(airquality$Temp))), col='darkblue', lwd=2, add=TRUE)\n?dnorm\n\n\nqqnorm(airquality$Temp)\nqqline(airquality$Temp)\nboxplot(airquality[, 1:4])\n\n\n\nx <- rnorm(1000)\nhist(x, freq = FALSE, col = \"grey\")\ncurve(dnorm, col = 2, add = TRUE)\n\nlibrary(lessR)\n# generate 100 random normal data values\ny <- rnorm(100)\n# normal curve and general density curves superimposed over histogram\n# all defaults\nhist(y)\ncolor.density(y)\nHistogram(y)\n\n\n\nlibrary(e1071) # For the \"skewness\" and \"kurtosis\" functions\nn <- 1000\nx <- rnorm(n)\nqqnorm(x, main=paste(\"kurtosis =\", round(kurtosis(x), digits=2),\n \"(gaussian)\"))\nqqline(x, col=\"red\")\nop <- par(fig=c(.02,.5,.5,.98), new=TRUE)\nhist(x, probability=T,\n col=\"light blue\", xlab=\"\", ylab=\"\", main=\"\", axes=F)\nlines(density(x), col=\"red\", lwd=2)\nbox()\npar(op)\n" }, { "path": "Unsorted/ttest1.R", "content": "# Distributions\n\n# t-test\n?t.test\nattach(CO2)\nt.test(CO2$uptake, mu=30)\ntstats = t.test(uptake, mu=30)\ntstats$statistic\n\nCO2$uptake\n(m=mean(CO2$uptake))\n(s= sd(CO2$uptake))\n(N = length(CO2$uptake))\nmu=30\n(tcalc = (m - mu)/(s * sqrt(N)))\n\n?qt\nqt(p=.975, df=N-1)\n\n\na = c(65, 78, 88, 55, 48, 95, 66, 57, 79, 81)\n\nt.test (a, mu=75)\n" }, { "path": "Unsorted/vaibhavi.R", "content": "# vaibhav\n# \nlibrary(ggplot2)\nuniDataCateg<- read.csv(\"bic.csv\",header = TRUE)\nggplot(uniDataCateg, aes(x = values, y = perc)) + geom_bar(stat = \"identity\")+ facet_wrap(~ variable, scales = \"free\")\nstr(uniDataCateg)\nbarplot(uniDataCateg$perc)\n\n\n\ndat <- read.table(text = \" ONE TWO THREE\n1 23 234 324\n 2 34 534 12\n 3 56 324 124\n 4 34 234 124\n 5 123 534 654\",sep = \"\",header = TRUE)\ndat\nlibrary(reshape2)\n#Add an id variable for the filled regions\ndatm <- melt(cbind(dat, ind = rownames(dat)), id.vars = c('ind'))\ndatm\nlibrary(scales)\nggplot(datm,aes(x = variable, y = value,fill = ind)) + \n geom_bar(position = \"fill\",stat = \"identity\") + \n scale_y_continuous(labels = percent_format())\n\n\n\nyour_matrix<-( \n rbind(\n c(10,20), \n c(20,30), \n c(40,50)\n )\n)\nyour_matrix\nbarplot(prop.table(your_matrix, 2) )\n\nstr(uniDataCateg)\ndf1 = as.matrix(uniDataCateg[,c('Perc_churn','diff')])\nhead(df1)\nbarplot(prop.table(df1,1))\n\n\nlibrary(cowplot)\n# Bar plot\nbp <- ggplot(diamonds, aes(clarity, fill = cut)) +\n geom_bar() +\n theme(axis.text.x = element_text(angle=70, vjust=0.5))\nbp\ndiamonds\nbp2 = ggplot(diamonds, aes(clarity, fill = cut)) + geom_bar()\nbp2\n\nstr(Students)\n\nlibrary(Lock5Data)\ndata(SleepStudy)\ndata(student)\nheart = read.table(\"heart-rate\", header = TRUE)\nstudents = read.csv(\"students.csv\")\n\n\nhttps://rpubs.com/escott8908/RGC_Ch3_Gar_Graphs\nlibrary(gcookbook)\nlibrary(ggplot2)\nlibrary(plyr) #Is this automatically loaded with ggplot2?\nlibrary(dplyr)\n\ncabbage_exp\nggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) + \n geom_bar(stat='identity')\n" }, { "path": "_config.yml", "content": "theme: jekyll-theme-leap-day" }, { "path": "all_letters.csv", "content": "\"\",\"x\"\n\"1\",\"a\"\n\"2\",\"A\"\n\"3\",\"b\"\n\"4\",\"B\"\n\"5\",\"c\"\n\"6\",\"C\"\n\"7\",\"d\"\n\"8\",\"D\"\n\"9\",\"e\"\n\"10\",\"E\"\n\"11\",\"f\"\n\"12\",\"F\"\n\"13\",\"g\"\n\"14\",\"G\"\n\"15\",\"h\"\n\"16\",\"H\"\n\"17\",\"i\"\n\"18\",\"I\"\n\"19\",\"j\"\n\"20\",\"J\"\n\"21\",\"k\"\n\"22\",\"K\"\n\"23\",\"l\"\n\"24\",\"L\"\n\"25\",\"m\"\n\"26\",\"M\"\n\"27\",\"n\"\n\"28\",\"N\"\n\"29\",\"o\"\n\"30\",\"O\"\n\"31\",\"p\"\n\"32\",\"P\"\n\"33\",\"q\"\n\"34\",\"Q\"\n\"35\",\"r\"\n\"36\",\"R\"\n\"37\",\"s\"\n\"38\",\"S\"\n\"39\",\"t\"\n\"40\",\"T\"\n\"41\",\"u\"\n\"42\",\"U\"\n\"43\",\"v\"\n\"44\",\"V\"\n\"45\",\"w\"\n\"46\",\"W\"\n\"47\",\"x\"\n\"48\",\"X\"\n\"49\",\"y\"\n\"50\",\"Y\"\n\"51\",\"z\"\n\"52\",\"Z\"\n" }, { "path": "analytics.Rproj", "content": "Version: 1.0\n\nRestoreWorkspace: Default\nSaveWorkspace: Default\nAlwaysSaveHistory: Default\n\nEnableCodeIndexing: Yes\nUseSpacesForTab: Yes\nNumSpacesForTab: 2\nEncoding: UTF-8\n\nRnwWeave: Sweave\nLaTeX: pdfLaTeX\n" }, { "path": "blank.R", "content": "#Blank" }, { "path": "cacert.pem", "content": "##\n## Bundle of CA Root Certificates\n##\n## Certificate data from Mozilla as of: Wed Dec 5 04:12:10 2018 GMT\n##\n## This is a bundle of X.509 certificates of public Certificate Authorities\n## (CA). These were automatically extracted from Mozilla's root certificates\n## file (certdata.txt). This file can be found in the mozilla source tree:\n## https://hg.mozilla.org/releases/mozilla-release/raw-file/default/security/nss/lib/ckfw/builtins/certdata.txt\n##\n## It contains the certificates in PEM format and therefore\n## can be directly used with curl / libcurl / php_curl, or with\n## an Apache+mod_ssl webserver for SSL client authentication.\n## Just configure this file as the SSLCACertificateFile.\n##\n## Conversion done with mk-ca-bundle.pl version 1.27.\n## SHA256: 35b415062acb8c2c27607083b5b3bec8f4ff57463c9b9f06db3e8df3ea895592\n##\n\n\nGlobalSign Root CA\n==================\n-----BEGIN CERTIFICATE-----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\nX4XSQRjbgbMEHMUfpIBvFSDJ3gyICh3WZlXi/EjJKSZp4A==\n-----END CERTIFICATE-----\n\nGlobalSign Root CA - R2\n=======================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nVerisign Class 3 Public Primary Certification Authority - G3\n============================================================\n-----BEGIN CERTIFICATE-----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\nt20Xj50egWTh/sVFuq1ruQp6Tk9LhO5L8X3dEQ==\n-----END CERTIFICATE-----\n\nEntrust.net Premium 2048 Secure Server CA\n=========================================\n-----BEGIN CERTIFICATE-----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\nnNFMFY3h7CI3zJpDC5fcgJCNs2ebb0gIFVbPv/ErfF6adulZkMV8gzURZVE=\n-----END CERTIFICATE-----\n\nBaltimore CyberTrust Root\n=========================\n-----BEGIN CERTIFICATE-----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\nRCwBXbsdtTLSR9I4LtD+gdwyah617jzV/OeBHRnDJELqYzmp\n-----END CERTIFICATE-----\n\nAddTrust External Root\n======================\n-----BEGIN CERTIFICATE-----\nMIIENjCCAx6gAwIBAgIBATANBgkqhkiG9w0BAQUFADBvMQswCQYDVQQGEwJTRTEUMBIGA1UEChML\nQWRkVHJ1c3QgQUIxJjAkBgNVBAsTHUFkZFRydXN0IEV4dGVybmFsIFRUUCBOZXR3b3JrMSIwIAYD\nVQQDExlBZGRUcnVzdCBFeHRlcm5hbCBDQSBSb290MB4XDTAwMDUzMDEwNDgzOFoXDTIwMDUzMDEw\nNDgzOFowbzELMAkGA1UEBhMCU0UxFDASBgNVBAoTC0FkZFRydXN0IEFCMSYwJAYDVQQLEx1BZGRU\ncnVzdCBFeHRlcm5hbCBUVFAgTmV0d29yazEiMCAGA1UEAxMZQWRkVHJ1c3QgRXh0ZXJuYWwgQ0Eg\nUm9vdDCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoCggEBALf3GjPm8gAELTngTlvtH7xsD821\n+iO2zt6bETOXpClMfZOfvUq8k+0DGuOPz+VtUFrWlymUWoCwSXrbLpX9uMq/NzgtHj6RQa1wVsfw\nTz/oMp50ysiQVOnGXw94nZpAPA6sYapeFI+eh6FqUNzXmk6vBbOmcZSccbNQYArHE504B4YCqOmo\naSYYkKtMsE8jqzpPhNjfzp/haW+710LXa0Tkx63ubUFfclpxCDezeWWkWaCUN/cALw3CknLa0Dhy\n2xSoRcRdKn23tNbE7qzNE0S3ySvdQwAl+mG5aWpYIxG3pzOPVnVZ9c0p10a3CitlttNCbxWyuHv7\n7+ldU9U0WicCAwEAAaOB3DCB2TAdBgNVHQ4EFgQUrb2YejS0Jvf6xCZU7wO94CTLVBowCwYDVR0P\nBAQDAgEGMA8GA1UdEwEB/wQFMAMBAf8wgZkGA1UdIwSBkTCBjoAUrb2YejS0Jvf6xCZU7wO94CTL\nVBqhc6RxMG8xCzAJBgNVBAYTAlNFMRQwEgYDVQQKEwtBZGRUcnVzdCBBQjEmMCQGA1UECxMdQWRk\nVHJ1c3QgRXh0ZXJuYWwgVFRQIE5ldHdvcmsxIjAgBgNVBAMTGUFkZFRydXN0IEV4dGVybmFsIENB\nIFJvb3SCAQEwDQYJKoZIhvcNAQEFBQADggEBALCb4IUlwtYj4g+WBpKdQZic2YR5gdkeWxQHIzZl\nj7DYd7usQWxHYINRsPkyPef89iYTx4AWpb9a/IfPeHmJIZriTAcKhjW88t5RxNKWt9x+Tu5w/Rw5\n6wwCURQtjr0W4MHfRnXnJK3s9EK0hZNwEGe6nQY1ShjTK3rMUUKhemPR5ruhxSvCNr4TDea9Y355\ne6cJDUCrat2PisP29owaQgVR1EX1n6diIWgVIEM8med8vSTYqZEXc4g/VhsxOBi0cQ+azcgOno4u\nG+GMmIPLHzHxREzGBHNJdmAPx/i9F4BrLunMTA5amnkPIAou1Z5jJh5VkpTYghdae9C8x49OhgQ=\n-----END CERTIFICATE-----\n\nEntrust Root Certification Authority\n====================================\n-----BEGIN CERTIFICATE-----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\ntHuu2guQOHXvgR1m0vdXcDazv/wor3ElhVsT/h5/WrQ8\n-----END CERTIFICATE-----\n\nGeoTrust Global CA\n==================\n-----BEGIN CERTIFICATE-----\nMIIDVDCCAjygAwIBAgIDAjRWMA0GCSqGSIb3DQEBBQUAMEIxCzAJBgNVBAYTAlVTMRYwFAYDVQQK\nEw1HZW9UcnVzdCBJbmMuMRswGQYDVQQDExJHZW9UcnVzdCBHbG9iYWwgQ0EwHhcNMDIwNTIxMDQw\nMDAwWhcNMjIwNTIxMDQwMDAwWjBCMQswCQYDVQQGEwJVUzEWMBQGA1UEChMNR2VvVHJ1c3QgSW5j\nLjEbMBkGA1UEAxMSR2VvVHJ1c3QgR2xvYmFsIENBMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIB\nCgKCAQEA2swYYzD99BcjGlZ+W988bDjkcbd4kdS8odhM+KhDtgPpTSEHCIjaWC9mOSm9BXiLnTjo\nBbdqfnGk5sRgprDvgOSJKA+eJdbtg/OtppHHmMlCGDUUna2YRpIuT8rxh0PBFpVXLVDviS2Aelet\n8u5fa9IAjbkU+BQVNdnARqN7csiRv8lVK83Qlz6cJmTM386DGXHKTubU1XupGc1V3sjs0l44U+Vc\nT4wt/lAjNvxm5suOpDkZALeVAjmRCw7+OC7RHQWa9k0+bw8HHa8sHo9gOeL6NlMTOdReJivbPagU\nvTLrGAMoUgRx5aszPeE4uwc2hGKceeoWMPRfwCvocWvk+QIDAQABo1MwUTAPBgNVHRMBAf8EBTAD\nAQH/MB0GA1UdDgQWBBTAephojYn7qwVkDBF9qn1luMrMTjAfBgNVHSMEGDAWgBTAephojYn7qwVk\nDBF9qn1luMrMTjANBgkqhkiG9w0BAQUFAAOCAQEANeMpauUvXVSOKVCUn5kaFOSPeCpilKInZ57Q\nzxpeR+nBsqTP3UEaBU6bS+5Kb1VSsyShNwrrZHYqLizz/Tt1kL/6cdjHPTfStQWVYrmm3ok9Nns4\nd0iXrKYgjy6myQzCsplFAMfOEVEiIuCl6rYVSAlk6l5PdPcFPseKUgzbFbS9bZvlxrFUaKnjaZC2\nmqUPuLk/IH2uSrW4nOQdtqvmlKXBx4Ot2/Unhw4EbNX/3aBd7YdStysVAq45pmp06drE57xNNB6p\nXE0zX5IJL4hmXXeXxx12E6nV5fEWCRE11azbJHFwLJhWC9kXtNHjUStedejV0NxPNO3CBWaAocvm\nMw==\n-----END CERTIFICATE-----\n\nGeoTrust Universal CA\n=====================\n-----BEGIN CERTIFICATE-----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\nP/rmMuGNG2+k5o7Y+SlIis5z/iw=\n-----END CERTIFICATE-----\n\nGeoTrust Universal CA 2\n=======================\n-----BEGIN CERTIFICATE-----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\nX36vWkzaH6byHCx+rgIW0lbQL1dTR+iS\n-----END CERTIFICATE-----\n\nComodo AAA Services root\n========================\n-----BEGIN 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CERTIFICATE-----\n\nQuoVadis Root CA\n================\n-----BEGIN CERTIFICATE-----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\n5nrQNiOKSnQ2+Q==\n-----END CERTIFICATE-----\n\nQuoVadis Root CA 2\n==================\n-----BEGIN CERTIFICATE-----\nMIIFtzCCA5+gAwIBAgICBQkwDQYJKoZIhvcNAQEFBQAwRTELMAkGA1UEBhMCQk0xGTAXBgNVBAoT\nEFF1b1ZhZGlzIExpbWl0ZWQxGzAZBgNVBAMTElF1b1ZhZGlzIFJvb3QgQ0EgMjAeFw0wNjExMjQx\nODI3MDBaFw0zMTExMjQxODIzMzNaMEUxCzAJBgNVBAYTAkJNMRkwFwYDVQQKExBRdW9WYWRpcyBM\naW1pdGVkMRswGQYDVQQDExJRdW9WYWRpcyBSb290IENBIDIwggIiMA0GCSqGSIb3DQEBAQUAA4IC\nDwAwggIKAoICAQCaGMpLlA0ALa8DKYrwD4HIrkwZhR0In6spRIXzL4GtMh6QRr+jhiYaHv5+HBg6\nXJxgFyo6dIMzMH1hVBHL7avg5tKifvVrbxi3Cgst/ek+7wrGsxDp3MJGF/hd/aTa/55JWpzmM+Yk\nlvc/ulsrHHo1wtZn/qtmUIttKGAr79dgw8eTvI02kfN/+NsRE8Scd3bBrrcCaoF6qUWD4gXmuVbB\nlDePSHFjIuwXZQeVikvfj8ZaCuWw419eaxGrDPmF60Tp+ARz8un+XJiM9XOva7R+zdRcAitMOeGy\nlZUtQofX1bOQQ7dsE/He3fbE+Ik/0XX1ksOR1YqI0JDs3G3eicJlcZaLDQP9nL9bFqyS2+r+eXyt\n66/3FsvbzSUr5R/7mp/iUcw6UwxI5g69ybR2BlLmEROFcmMDBOAENisgGQLodKcftslWZvB1Jdxn\nwQ5hYIizPtGo/KPaHbDRsSNU30R2be1B2MGyIrZTHN81Hdyhdyox5C315eXbyOD/5YDXC2Og/zOh\nD7osFRXql7PSorW+8oyWHhqPHWykYTe5hnMz15eWniN9gqRMgeKh0bpnX5UHoycR7hYQe7xFSkyy\nBNKr79X9DFHOUGoIMfmR2gyPZFwDwzqLID9ujWc9Otb+fVuIyV77zGHcizN300QyNQliBJIWENie\nJ0f7OyHj+OsdWwIDAQABo4GwMIGtMA8GA1UdEwEB/wQFMAMBAf8wCwYDVR0PBAQDAgEGMB0GA1Ud\nDgQWBBQahGK8SEwzJQTU7tD2A8QZRtGUazBuBgNVHSMEZzBlgBQahGK8SEwzJQTU7tD2A8QZRtGU\na6FJpEcwRTELMAkGA1UEBhMCQk0xGTAXBgNVBAoTEFF1b1ZhZGlzIExpbWl0ZWQxGzAZBgNVBAMT\nElF1b1ZhZGlzIFJvb3QgQ0EgMoICBQkwDQYJKoZIhvcNAQEFBQADggIBAD4KFk2fBluornFdLwUv\nZ+YTRYPENvbzwCYMDbVHZF34tHLJRqUDGCdViXh9duqWNIAXINzng/iN/Ae42l9NLmeyhP3ZRPx3\nUIHmfLTJDQtyU/h2BwdBR5YM++CCJpNVjP4iH2BlfF/nJrP3MpCYUNQ3cVX2kiF495V5+vgtJodm\nVjB3pjd4M1IQWK4/YY7yarHvGH5KWWPKjaJW1acvvFYfzznB4vsKqBUsfU16Y8Zsl0Q80m/DShcK\n+JDSV6IZUaUtl0HaB0+pUNqQjZRG4T7wlP0QADj1O+hA4bRuVhogzG9Yje0uRY/W6ZM/57Es3zrW\nIozchLsib9D45MY56QSIPMO661V6bYCZJPVsAfv4l7CUW+v90m/xd2gNNWQjrLhVoQPRTUIZ3Ph1\nWVaj+ahJefivDrkRoHy3au000LYmYjgahwz46P0u05B/B5EqHdZ+XIWDmbA4CD/pXvk1B+TJYm5X\nf6dQlfe6yJvmjqIBxdZmv3lh8zwc4bmCXF2gw+nYSL0ZohEUGW6yhhtoPkg3Goi3XZZenMfvJ2II\n4pEZXNLxId26F0KCl3GBUzGpn/Z9Yr9y4aOTHcyKJloJONDO1w2AFrR4pTqHTI2KpdVGl/IsELm8\nVCLAAVBpQ570su9t+Oza8eOx79+Rj1QqCyXBJhnEUhAFZdWCEOrCMc0u\n-----END CERTIFICATE-----\n\nQuoVadis Root CA 3\n==================\n-----BEGIN CERTIFICATE-----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\nqZ5hHY8y2o4M1nQ+ewkk2gF3R8Q7zTSMmfXK4SVhM7JZG+Ju1zdXtg2pEto=\n-----END CERTIFICATE-----\n\nSecurity Communication Root CA\n==============================\n-----BEGIN CERTIFICATE-----\nMIIDWjCCAkKgAwIBAgIBADANBgkqhkiG9w0BAQUFADBQMQswCQYDVQQGEwJKUDEYMBYGA1UEChMP\nU0VDT00gVHJ1c3QubmV0MScwJQYDVQQLEx5TZWN1cml0eSBDb21tdW5pY2F0aW9uIFJvb3RDQTEw\nHhcNMDMwOTMwMDQyMDQ5WhcNMjMwOTMwMDQyMDQ5WjBQMQswCQYDVQQGEwJKUDEYMBYGA1UEChMP\nU0VDT00gVHJ1c3QubmV0MScwJQYDVQQLEx5TZWN1cml0eSBDb21tdW5pY2F0aW9uIFJvb3RDQTEw\nggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQCzs/5/022x7xZ8V6UMbXaKL0u/ZPtM7orw\n8yl89f/uKuDp6bpbZCKamm8sOiZpUQWZJtzVHGpxxpp9Hp3dfGzGjGdnSj74cbAZJ6kJDKaVv0uM\nDPpVmDvY6CKhS3E4eayXkmmziX7qIWgGmBSWh9JhNrxtJ1aeV+7AwFb9Ms+k2Y7CI9eNqPPYJayX\n5HA49LY6tJ07lyZDo6G8SVlyTCMwhwFY9k6+HGhWZq/NQV3Is00qVUarH9oe4kA92819uZKAnDfd\nDJZkndwi92SL32HeFZRSFaB9UslLqCHJxrHty8OVYNEP8Ktw+N/LTX7s1vqr2b1/VPKl6Xn62dZ2\nJChzAgMBAAGjPzA9MB0GA1UdDgQWBBSgc0mZaNyFW2XjmygvV5+9M7wHSDALBgNVHQ8EBAMCAQYw\nDwYDVR0TAQH/BAUwAwEB/zANBgkqhkiG9w0BAQUFAAOCAQEAaECpqLvkT115swW1F7NgE+vGkl3g\n0dNq/vu+m22/xwVtWSDEHPC32oRYAmP6SBbvT6UL90qY8j+eG61Ha2POCEfrUj94nK9NrvjVT8+a\nmCoQQTlSxN3Zmw7vkwGusi7KaEIkQmywszo+zenaSMQVy+n5Bw+SUEmK3TGXX8npN6o7WWWXlDLJ\ns58+OmJYxUmtYg5xpTKqL8aJdkNAExNnPaJUJRDL8Try2frbSVa7pv6nQTXD4IhhyYjH3zYQIphZ\n6rBK+1YWc26sTfcioU+tHXotRSflMMFe8toTyyVCUZVHA4xsIcx0Qu1T/zOLjw9XARYvz6buyXAi\nFL39vmwLAw==\n-----END CERTIFICATE-----\n\nSonera Class 2 Root CA\n======================\n-----BEGIN CERTIFICATE-----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\nllpwrN9M\n-----END CERTIFICATE-----\n\nXRamp Global CA Root\n====================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nGo Daddy Class 2 CA\n===================\n-----BEGIN CERTIFICATE-----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\nvZ8=\n-----END CERTIFICATE-----\n\nStarfield Class 2 CA\n====================\n-----BEGIN CERTIFICATE-----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\nQBFGmh95DmK/D5fs4C8fF5Q=\n-----END CERTIFICATE-----\n\nTaiwan GRCA\n===========\n-----BEGIN CERTIFICATE-----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\n+fwC00fmcc4QAu4njIT/rEUNE1yDMuAlpYYsfPQS\n-----END CERTIFICATE-----\n\nDigiCert Assured ID Root CA\n===========================\n-----BEGIN CERTIFICATE-----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\n+o0bJW1sj6W3YQGx0qMmoRBxna3iw/nDmVG3KwcIzi7mULKn+gpFL6Lw8g==\n-----END CERTIFICATE-----\n\nDigiCert Global Root CA\n=======================\n-----BEGIN CERTIFICATE-----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\nCAUw7C29C79Fv1C5qfPrmAESrciIxpg0X40KPMbp1ZWVbd4=\n-----END CERTIFICATE-----\n\nDigiCert High Assurance EV Root CA\n==================================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nCertplus Class 2 Primary CA\n===========================\n-----BEGIN CERTIFICATE-----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\nl7+ijrRU\n-----END CERTIFICATE-----\n\nDST Root CA X3\n==============\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nSwissSign Gold CA - G2\n======================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nSwissSign Silver CA - G2\n========================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nGeoTrust Primary Certification Authority\n========================================\n-----BEGIN CERTIFICATE-----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\n1riR/aYNKxoUAT6A8EKglQdebc3MS6RFjasS6LPeWuWgfOgPIh1a6Vk=\n-----END CERTIFICATE-----\n\nthawte Primary Root CA\n======================\n-----BEGIN CERTIFICATE-----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\nz7z9M/P2C2F+fpErgUfCJzDupxBdN49cOSvkBPB7jVaMaA==\n-----END CERTIFICATE-----\n\nVeriSign Class 3 Public Primary Certification Authority - G5\n============================================================\n-----BEGIN CERTIFICATE-----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\nZV8NhnacRHr2lVz2XTIIM6RUthg/aFzyQkqFOFSDX9HoLPKsEdao7WNq\n-----END CERTIFICATE-----\n\nSecureTrust CA\n==============\n-----BEGIN CERTIFICATE-----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\n3ItHuuG51WLQoqD0ZwV4KWMabwTW+MZMo5qxN7SN5ShLHZ4swrhovO0C7jE=\n-----END CERTIFICATE-----\n\nSecure Global CA\n================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nCOMODO Certification Authority\n==============================\n-----BEGIN CERTIFICATE-----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\n+8cFduPYSo38NBejxiEovjBFMR7HeL5YYTisO+IBZQ==\n-----END CERTIFICATE-----\n\nNetwork Solutions Certificate Authority\n=======================================\n-----BEGIN CERTIFICATE-----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\nydi8NmdspZS11My5vWo1ViHe2MPr+8ukYEywVaCge1ey\n-----END CERTIFICATE-----\n\nCOMODO ECC Certification Authority\n==================================\n-----BEGIN CERTIFICATE-----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\nU/7dIOA1mjbRxwG55tzd8/8dLDoWV9mSOdY=\n-----END CERTIFICATE-----\n\nOISTE WISeKey Global Root GA CA\n===============================\n-----BEGIN CERTIFICATE-----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\nokxSdsARo27mHbrjWr42U8U+dY+GaSlYU7Wcu2+fXMUY7N0v4ZjJ/L7fCg0=\n-----END CERTIFICATE-----\n\nCertigna\n========\n-----BEGIN CERTIFICATE-----\nMIIDqDCCApCgAwIBAgIJAP7c4wEPyUj/MA0GCSqGSIb3DQEBBQUAMDQxCzAJBgNVBAYTAkZSMRIw\nEAYDVQQKDAlEaGlteW90aXMxETAPBgNVBAMMCENlcnRpZ25hMB4XDTA3MDYyOTE1MTMwNVoXDTI3\nMDYyOTE1MTMwNVowNDELMAkGA1UEBhMCRlIxEjAQBgNVBAoMCURoaW15b3RpczERMA8GA1UEAwwI\nQ2VydGlnbmEwggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDIaPHJ1tazNHUmgh7stL7q\nXOEm7RFHYeGifBZ4QCHkYJ5ayGPhxLGWkv8YbWkj4Sti993iNi+RB7lIzw7sebYs5zRLcAglozyH\nGxnygQcPOJAZ0xH+hrTy0V4eHpbNgGzOOzGTtvKg0KmVEn2lmsxryIRWijOp5yIVUxbwzBfsV1/p\nogqYCd7jX5xv3EjjhQsVWqa6n6xI4wmy9/Qy3l40vhx4XUJbzg4ij02Q130yGLMLLGq/jj8UEYkg\nDncUtT2UCIf3JR7VsmAA7G8qKCVuKj4YYxclPz5EIBb2JsglrgVKtOdjLPOMFlN+XPsRGgjBRmKf\nIrjxwo1p3Po6WAbfAgMBAAGjgbwwgbkwDwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4EFgQUGu3+QTmQ\ntCRZvgHyUtVF9lo53BEwZAYDVR0jBF0wW4AUGu3+QTmQtCRZvgHyUtVF9lo53BGhOKQ2MDQxCzAJ\nBgNVBAYTAkZSMRIwEAYDVQQKDAlEaGlteW90aXMxETAPBgNVBAMMCENlcnRpZ25hggkA/tzjAQ/J\nSP8wDgYDVR0PAQH/BAQDAgEGMBEGCWCGSAGG+EIBAQQEAwIABzANBgkqhkiG9w0BAQUFAAOCAQEA\nhQMeknH2Qq/ho2Ge6/PAD/Kl1NqV5ta+aDY9fm4fTIrv0Q8hbV6lUmPOEvjvKtpv6zf+EwLHyzs+\nImvaYS5/1HI93TDhHkxAGYwP15zRgzB7mFncfca5DClMoTOi62c6ZYTTluLtdkVwj7Ur3vkj1klu\nPBS1xp81HlDQwY9qcEQCYsuuHWhBp6pX6FOqB9IG9tUUBguRA3UsbHK1YZWaDYu5Def131TN3ubY\n1gkIl2PlwS6wt0QmwCbAr1UwnjvVNioZBPRcHv/PLLf/0P2HQBHVESO7SMAhqaQoLf0V+LBOK/Qw\nWyH8EZE0vkHve52Xdf+XlcCWWC/qu0bXu+TZLg==\n-----END CERTIFICATE-----\n\nDeutsche Telekom Root CA 2\n==========================\n-----BEGIN CERTIFICATE-----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\nCm26OWMohpLzGITY+9HPBVZkVw==\n-----END CERTIFICATE-----\n\nCybertrust Global Root\n======================\n-----BEGIN CERTIFICATE-----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\nWL1WMRJOEcgh4LMRkWXbtKaIOM5V\n-----END CERTIFICATE-----\n\nePKI Root Certification Authority\n=================================\n-----BEGIN CERTIFICATE-----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\nBCEIZ43ygknQW/2xzQ+DhNQ+IIX3Sj0rnP0qCglN6oH4EZw=\n-----END CERTIFICATE-----\n\ncertSIGN ROOT CA\n================\n-----BEGIN CERTIFICATE-----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\nTogVZ96edhBiIL5VaZVDADlN9u6wWk5JRFRYX0KD\n-----END CERTIFICATE-----\n\nGeoTrust Primary Certification Authority - G3\n=============================================\n-----BEGIN CERTIFICATE-----\nMIID/jCCAuagAwIBAgIQFaxulBmyeUtB9iepwxgPHzANBgkqhkiG9w0BAQsFADCBmDELMAkGA1UE\nBhMCVVMxFjAUBgNVBAoTDUdlb1RydXN0IEluYy4xOTA3BgNVBAsTMChjKSAyMDA4IEdlb1RydXN0\nIEluYy4gLSBGb3IgYXV0aG9yaXplZCB1c2Ugb25seTE2MDQGA1UEAxMtR2VvVHJ1c3QgUHJpbWFy\neSBDZXJ0aWZpY2F0aW9uIEF1dGhvcml0eSAtIEczMB4XDTA4MDQwMjAwMDAwMFoXDTM3MTIwMTIz\nNTk1OVowgZgxCzAJBgNVBAYTAlVTMRYwFAYDVQQKEw1HZW9UcnVzdCBJbmMuMTkwNwYDVQQLEzAo\nYykgMjAwOCBHZW9UcnVzdCBJbmMuIC0gRm9yIGF1dGhvcml6ZWQgdXNlIG9ubHkxNjA0BgNVBAMT\nLUdlb1RydXN0IFByaW1hcnkgQ2VydGlmaWNhdGlvbiBBdXRob3JpdHkgLSBHMzCCASIwDQYJKoZI\nhvcNAQEBBQADggEPADCCAQoCggEBANziXmJYHTNXOTIz+uvLh4yn1ErdBojqZI4xmKU4kB6Yzy5j\nK/BGvESyiaHAKAxJcCGVn2TAppMSAmUmhsalifD614SgcK9PGpc/BkTVyetyEH3kMSj7HGHmKAdE\nc5IiaacDiGydY8hS2pgn5whMcD60yRLBxWeDXTPzAxHsatBT4tG6NmCUgLthY2xbF37fQJQeqw3C\nIShwiP/WJmxsYAQlTlV+fe+/lEjetx3dcI0FX4ilm/LC7urRQEFtYjgdVgbFA0dRIBn8exALDmKu\ndlW/X3e+PkkBUz2YJQN2JFodtNuJ6nnltrM7P7pMKEF/BqxqjsHQ9gUdfeZChuOl1UcCAwEAAaNC\nMEAwDwYDVR0TAQH/BAUwAwEB/zAOBgNVHQ8BAf8EBAMCAQYwHQYDVR0OBBYEFMR5yo6hTgMdHNxr\n2zFblD4/MH8tMA0GCSqGSIb3DQEBCwUAA4IBAQAtxRPPVoB7eni9n64smefv2t+UXglpp+duaIy9\ncr5HqQ6XErhK8WTTOd8lNNTBzU6B8A8ExCSzNJbGpqow32hhc9f5joWJ7w5elShKKiePEI4ufIbE\nAp7aDHdlDkQNkv39sxY2+hENHYwOB4lqKVb3cvTdFZx3NWZXqxNT2I7BQMXXExZacse3aQHEerGD\nAWh9jUGhlBjBJVz88P6DAod8DQ3PLghcSkANPuyBYeYk28rgDi0Hsj5W3I31QYUHSJsMC8tJP33s\nt/3LjWeJGqvtux6jAAgIFyqCXDFdRootD4abdNlF+9RAsXqqaC2Gspki4cErx5z481+oghLrGREt\n-----END CERTIFICATE-----\n\nthawte Primary Root CA - G2\n===========================\n-----BEGIN CERTIFICATE-----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\nrr0TKUQNJ1uo52icEvdYPy5yAlejj6EULg==\n-----END CERTIFICATE-----\n\nthawte Primary Root CA - G3\n===========================\n-----BEGIN CERTIFICATE-----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\ner/YB1IsYvdwY9k5vG8cwnncdimvzsUsZAReiDZuMdRAGmI0Nj81Aa6sY6A=\n-----END CERTIFICATE-----\n\nGeoTrust Primary Certification Authority - G2\n=============================================\n-----BEGIN CERTIFICATE-----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\nnpaqBA+K\n-----END CERTIFICATE-----\n\nVeriSign Universal Root Certification Authority\n===============================================\n-----BEGIN CERTIFICATE-----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\nmJO37M2CYfE45k+XmCpajQ==\n-----END CERTIFICATE-----\n\nVeriSign Class 3 Public Primary Certification Authority - G4\n============================================================\n-----BEGIN CERTIFICATE-----\nMIIDhDCCAwqgAwIBAgIQL4D+I4wOIg9IZxIokYesszAKBggqhkjOPQQDAzCByjELMAkGA1UEBhMC\nVVMxFzAVBgNVBAoTDlZlcmlTaWduLCBJbmMuMR8wHQYDVQQLExZWZXJpU2lnbiBUcnVzdCBOZXR3\nb3JrMTowOAYDVQQLEzEoYykgMjAwNyBWZXJpU2lnbiwgSW5jLiAtIEZvciBhdXRob3JpemVkIHVz\nZSBvbmx5MUUwQwYDVQQDEzxWZXJpU2lnbiBDbGFzcyAzIFB1YmxpYyBQcmltYXJ5IENlcnRpZmlj\nYXRpb24gQXV0aG9yaXR5IC0gRzQwHhcNMDcxMTA1MDAwMDAwWhcNMzgwMTE4MjM1OTU5WjCByjEL\nMAkGA1UEBhMCVVMxFzAVBgNVBAoTDlZlcmlTaWduLCBJbmMuMR8wHQYDVQQLExZWZXJpU2lnbiBU\ncnVzdCBOZXR3b3JrMTowOAYDVQQLEzEoYykgMjAwNyBWZXJpU2lnbiwgSW5jLiAtIEZvciBhdXRo\nb3JpemVkIHVzZSBvbmx5MUUwQwYDVQQDEzxWZXJpU2lnbiBDbGFzcyAzIFB1YmxpYyBQcmltYXJ5\nIENlcnRpZmljYXRpb24gQXV0aG9yaXR5IC0gRzQwdjAQBgcqhkjOPQIBBgUrgQQAIgNiAASnVnp8\nUtpkmw4tXNherJI9/gHmGUo9FANL+mAnINmDiWn6VMaaGF5VKmTeBvaNSjutEDxlPZCIBIngMGGz\nrl0Bp3vefLK+ymVhAIau2o970ImtTR1ZmkGxvEeA3J5iw/mjgbIwga8wDwYDVR0TAQH/BAUwAwEB\n/zAOBgNVHQ8BAf8EBAMCAQYwbQYIKwYBBQUHAQwEYTBfoV2gWzBZMFcwVRYJaW1hZ2UvZ2lmMCEw\nHzAHBgUrDgMCGgQUj+XTGoasjY5rw8+AatRIGCx7GS4wJRYjaHR0cDovL2xvZ28udmVyaXNpZ24u\nY29tL3ZzbG9nby5naWYwHQYDVR0OBBYEFLMWkf3upm7ktS5Jj4d4gYDs5bG1MAoGCCqGSM49BAMD\nA2gAMGUCMGYhDBgmYFo4e1ZC4Kf8NoRRkSAsdk1DPcQdhCPQrNZ8NQbOzWm9kA3bbEhCHQ6qQgIx\nAJw9SDkjOVgaFRJZap7v1VmyHVIsmXHNxynfGyphe3HR3vPA5Q06Sqotp9iGKt0uEA==\n-----END CERTIFICATE-----\n\nNetLock Arany (Class Gold) Főtanúsítvány\n========================================\n-----BEGIN CERTIFICATE-----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\ndZWAUWpLMKawYqGT8ZvYzsRjdT9ZR7E=\n-----END CERTIFICATE-----\n\nStaat der Nederlanden Root CA - G2\n==================================\n-----BEGIN CERTIFICATE-----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\n66+KAQ==\n-----END CERTIFICATE-----\n\nHongkong Post Root CA 1\n=======================\n-----BEGIN CERTIFICATE-----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\nc4afU9hDDl3WY4JxHYB0yvbiAmvZWg==\n-----END CERTIFICATE-----\n\nSecureSign RootCA11\n===================\n-----BEGIN CERTIFICATE-----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\nlgeLKBObjBmNQSdJQO7e5iNEOdyhIta6A/I=\n-----END CERTIFICATE-----\n\nMicrosec e-Szigno Root CA 2009\n==============================\n-----BEGIN CERTIFICATE-----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\nLXpUq3DDfSJlgnCW\n-----END CERTIFICATE-----\n\nGlobalSign Root CA - R3\n=======================\n-----BEGIN 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CERTIFICATE-----\n\nAutoridad de Certificacion Firmaprofesional CIF A62634068\n=========================================================\n-----BEGIN 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CERTIFICATE-----\n\nIzenpe.com\n==========\n-----BEGIN CERTIFICATE-----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\nWrOZyGlsQyYBNWNgVYkDOnXYukrZVP/u3oDYLdE41V4tC5h9Pmzb/CaIxw==\n-----END CERTIFICATE-----\n\nChambers of Commerce Root - 2008\n================================\n-----BEGIN 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CERTIFICATE-----\n\nGlobal Chambersign Root - 2008\n==============================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nGo Daddy Root Certificate Authority - G2\n========================================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nStarfield Root Certificate Authority - G2\n=========================================\n-----BEGIN CERTIFICATE-----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\nc2T5NnReJaH1ZgUufzkVqSr7UIuOhWn0\n-----END CERTIFICATE-----\n\nStarfield Services Root Certificate Authority - G2\n==================================================\n-----BEGIN CERTIFICATE-----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\nYyWf/Wi3MOxw+3WKt21gZ7IeyLnp2KhvAotnDU0mV3HaIPzBSlCNsSi6\n-----END CERTIFICATE-----\n\nAffirmTrust Commercial\n======================\n-----BEGIN 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CERTIFICATE-----\n\nAffirmTrust Networking\n======================\n-----BEGIN 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CERTIFICATE-----\n\nHellenic Academic and Research Institutions RootCA 2011\n=======================================================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nActalis Authentication Root CA\n==============================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nTrustis FPS Root CA\n===================\n-----BEGIN CERTIFICATE-----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\niB6XzCGcKQENZetX2fNXlrtIzYE=\n-----END CERTIFICATE-----\n\nBuypass Class 2 Root CA\n=======================\n-----BEGIN CERTIFICATE-----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\nrJgWVqA=\n-----END CERTIFICATE-----\n\nBuypass Class 3 Root CA\n=======================\n-----BEGIN CERTIFICATE-----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\nCp/HuZc=\n-----END CERTIFICATE-----\n\nT-TeleSec GlobalRoot Class 3\n============================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nEE Certification Centre Root CA\n===============================\n-----BEGIN CERTIFICATE-----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\ndcGWxZ0=\n-----END CERTIFICATE-----\n\nD-TRUST Root Class 3 CA 2 2009\n==============================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nD-TRUST Root Class 3 CA 2 EV 2009\n=================================\n-----BEGIN CERTIFICATE-----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\nw9y4AyHqnxbxLFS1\n-----END CERTIFICATE-----\n\nCA Disig Root R2\n================\n-----BEGIN CERTIFICATE-----\nMIIFaTCCA1GgAwIBAgIJAJK4iNuwisFjMA0GCSqGSIb3DQEBCwUAMFIxCzAJBgNVBAYTAlNLMRMw\nEQYDVQQHEwpCcmF0aXNsYXZhMRMwEQYDVQQKEwpEaXNpZyBhLnMuMRkwFwYDVQQDExBDQSBEaXNp\nZyBSb290IFIyMB4XDTEyMDcxOTA5MTUzMFoXDTQyMDcxOTA5MTUzMFowUjELMAkGA1UEBhMCU0sx\nEzARBgNVBAcTCkJyYXRpc2xhdmExEzARBgNVBAoTCkRpc2lnIGEucy4xGTAXBgNVBAMTEENBIERp\nc2lnIFJvb3QgUjIwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQCio8QACdaFXS1tFPbC\nw3OeNcJxVX6B+6tGUODBfEl45qt5WDza/3wcn9iXAng+a0EE6UG9vgMsRfYvZNSrXaNHPWSb6Wia\nxswbP7q+sos0Ai6YVRn8jG+qX9pMzk0DIaPY0jSTVpbLTAwAFjxfGs3Ix2ymrdMxp7zo5eFm1tL7\nA7RBZckQrg4FY8aAamkw/dLukO8NJ9+flXP04SXabBbeQTg06ov80egEFGEtQX6sx3dOy1FU+16S\nGBsEWmjGycT6txOgmLcRK7fWV8x8nhfRyyX+hk4kLlYMeE2eARKmK6cBZW58Yh2EhN/qwGu1pSqV\ng8NTEQxzHQuyRpDRQjrOQG6Vrf/GlK1ul4SOfW+eioANSW1z4nuSHsPzwfPrLgVv2RvPN3YEyLRa\n5Beny912H9AZdugsBbPWnDTYltxhh5EF5EQIM8HauQhl1K6yNg3ruji6DOWbnuuNZt2Zz9aJQfYE\nkoopKW1rOhzndX0CcQ7zwOe9yxndnWCywmZgtrEE7snmhrmaZkCo5xHtgUUDi/ZnWejBBhG93c+A\nAk9lQHhcR1DIm+YfgXvkRKhbhZri3lrVx/k6RGZL5DJUfORsnLMOPReisjQS1n6yqEm70XooQL6i\nFh/f5DcfEXP7kAplQ6INfPgGAVUzfbANuPT1rqVCV3w2EYx7XsQDnYx5nQIDAQABo0IwQDAPBgNV\nHRMBAf8EBTADAQH/MA4GA1UdDwEB/wQEAwIBBjAdBgNVHQ4EFgQUtZn4r7CU9eMg1gqtzk5WpC5u\nQu0wDQYJKoZIhvcNAQELBQADggIBACYGXnDnZTPIgm7ZnBc6G3pmsgH2eDtpXi/q/075KMOYKmFM\ntCQSin1tERT3nLXK5ryeJ45MGcipvXrA1zYObYVybqjGom32+nNjf7xueQgcnYqfGopTpti72TVV\nsRHFqQOzVju5hJMiXn7B9hJSi+osZ7z+Nkz1uM/Rs0mSO9MpDpkblvdhuDvEK7Z4bLQjb/D907Je\ndR+Zlais9trhxTF7+9FGs9K8Z7RiVLoJ92Owk6Ka+elSLotgEqv89WBW7xBci8QaQtyDW2QOy7W8\n1k/BfDxujRNt+3vrMNDcTa/F1balTFtxyegxvug4BkihGuLq0t4SOVga/4AOgnXmt8kHbA7v/zjx\nmHHEt38OFdAlab0inSvtBfZGR6ztwPDUO+Ls7pZbkBNOHlY667DvlruWIxG68kOGdGSVyCh13x01\nutI3gzhTODY7z2zp+WsO0PsE6E9312UBeIYMej4hYvF/Y3EMyZ9E26gnonW+boE+18DrG5gPcFw0\nsorMwIUY6256s/daoQe/qUKS82Ail+QUoQebTnbAjn39pCXHR+3/H3OszMOl6W8KjptlwlCFtaOg\nUxLMVYdh84GuEEZhvUQhuMI9dM9+JDX6HAcOmz0iyu8xL4ysEr3vQCj8KWefshNPZiTEUxnpHikV\n7+ZtsH8tZ/3zbBt1RqPlShfppNcL\n-----END CERTIFICATE-----\n\nACCVRAIZ1\n=========\n-----BEGIN CERTIFICATE-----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\nEfbRD0tVNEYqi4Y7\n-----END CERTIFICATE-----\n\nTWCA Global Root CA\n===================\n-----BEGIN CERTIFICATE-----\nMIIFQTCCAymgAwIBAgICDL4wDQYJKoZIhvcNAQELBQAwUTELMAkGA1UEBhMCVFcxEjAQBgNVBAoT\nCVRBSVdBTi1DQTEQMA4GA1UECxMHUm9vdCBDQTEcMBoGA1UEAxMTVFdDQSBHbG9iYWwgUm9vdCBD\nQTAeFw0xMjA2MjcwNjI4MzNaFw0zMDEyMzExNTU5NTlaMFExCzAJBgNVBAYTAlRXMRIwEAYDVQQK\nEwlUQUlXQU4tQ0ExEDAOBgNVBAsTB1Jvb3QgQ0ExHDAaBgNVBAMTE1RXQ0EgR2xvYmFsIFJvb3Qg\nQ0EwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQCwBdvI64zEbooh745NnHEKH1Jw7W2C\nnJfF10xORUnLQEK1EjRsGcJ0pDFfhQKX7EMzClPSnIyOt7h52yvVavKOZsTuKwEHktSz0ALfUPZV\nr2YOy+BHYC8rMjk1Ujoog/h7FsYYuGLWRyWRzvAZEk2tY/XTP3VfKfChMBwqoJimFb3u/Rk28OKR\nQ4/6ytYQJ0lM793B8YVwm8rqqFpD/G2Gb3PpN0Wp8DbHzIh1HrtsBv+baz4X7GGqcXzGHaL3SekV\ntTzWoWH1EfcFbx39Eb7QMAfCKbAJTibc46KokWofwpFFiFzlmLhxpRUZyXx1EcxwdE8tmx2RRP1W\nKKD+u4ZqyPpcC1jcxkt2yKsi2XMPpfRaAok/T54igu6idFMqPVMnaR1sjjIsZAAmY2E2TqNGtz99\nsy2sbZCilaLOz9qC5wc0GZbpuCGqKX6mOL6OKUohZnkfs8O1CWfe1tQHRvMq2uYiN2DLgbYPoA/p\nyJV/v1WRBXrPPRXAb94JlAGD1zQbzECl8LibZ9WYkTunhHiVJqRaCPgrdLQABDzfuBSO6N+pjWxn\nkjMdwLfS7JLIvgm/LCkFbwJrnu+8vyq8W8BQj0FwcYeyTbcEqYSjMq+u7msXi7Kx/mzhkIyIqJdI\nzshNy/MGz19qCkKxHh53L46g5pIOBvwFItIm4TFRfTLcDwIDAQABoyMwITAOBgNVHQ8BAf8EBAMC\nAQYwDwYDVR0TAQH/BAUwAwEB/zANBgkqhkiG9w0BAQsFAAOCAgEAXzSBdu+WHdXltdkCY4QWwa6g\ncFGn90xHNcgL1yg9iXHZqjNB6hQbbCEAwGxCGX6faVsgQt+i0trEfJdLjbDorMjupWkEmQqSpqsn\nLhpNgb+E1HAerUf+/UqdM+DyucRFCCEK2mlpc3INvjT+lIutwx4116KD7+U4x6WFH6vPNOw/KP4M\n8VeGTslV9xzU2KV9Bnpv1d8Q34FOIWWxtuEXeZVFBs5fzNxGiWNoRI2T9GRwoD2dKAXDOXC4Ynsg\n/eTb6QihuJ49CcdP+yz4k3ZB3lLg4VfSnQO8d57+nile98FRYB/e2guyLXW3Q0iT5/Z5xoRdgFlg\nlPx4mI88k1HtQJAH32RjJMtOcQWh15QaiDLxInQirqWm2BJpTGCjAu4r7NRjkgtevi92a6O2JryP\nA9gK8kxkRr05YuWW6zRjESjMlfGt7+/cgFhI6Uu46mWs6fyAtbXIRfmswZ/ZuepiiI7E8UuDEq3m\ni4TWnsLrgxifarsbJGAzcMzs9zLzXNl5fe+epP7JI8Mk7hWSsT2RTyaGvWZzJBPqpK5jwa19hAM8\nEHiGG3njxPPyBJUgriOCxLM6AGK/5jYk4Ve6xx6QddVfP5VhK8E7zeWzaGHQRiapIVJpLesux+t3\nzqY6tQMzT3bR51xUAV3LePTJDL/PEo4XLSNolOer/qmyKwbQBM0=\n-----END CERTIFICATE-----\n\nTeliaSonera Root CA v1\n======================\n-----BEGIN CERTIFICATE-----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\nWWFUyBy6ysHK4bkgTI86k4mloMy/0/Z1pHWWbVY=\n-----END CERTIFICATE-----\n\nE-Tugra Certification Authority\n===============================\n-----BEGIN CERTIFICATE-----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\nC7TbO6Orb1wdtn7os4I07QZcJA==\n-----END CERTIFICATE-----\n\nT-TeleSec GlobalRoot Class 2\n============================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nAtos TrustedRoot 2011\n=====================\n-----BEGIN CERTIFICATE-----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\n3mB/ufNPRJLvKrcYPqcZ2Qt9sTdBQrC6YB3y/gkRsPCHe6ed\n-----END CERTIFICATE-----\n\nQuoVadis Root CA 1 G3\n=====================\n-----BEGIN CERTIFICATE-----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\nhMJKzRwuJIczYOXD\n-----END CERTIFICATE-----\n\nQuoVadis Root CA 2 G3\n=====================\n-----BEGIN CERTIFICATE-----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\nO3jtZsSOeWmD3n+M\n-----END CERTIFICATE-----\n\nQuoVadis Root CA 3 G3\n=====================\n-----BEGIN CERTIFICATE-----\nMIIFYDCCA0igAwIBAgIULvWbAiin23r/1aOp7r0DoM8Sah0wDQYJKoZIhvcNAQELBQAwSDELMAkG\nA1UEBhMCQk0xGTAXBgNVBAoTEFF1b1ZhZGlzIExpbWl0ZWQxHjAcBgNVBAMTFVF1b1ZhZGlzIFJv\nb3QgQ0EgMyBHMzAeFw0xMjAxMTIyMDI2MzJaFw00MjAxMTIyMDI2MzJaMEgxCzAJBgNVBAYTAkJN\nMRkwFwYDVQQKExBRdW9WYWRpcyBMaW1pdGVkMR4wHAYDVQQDExVRdW9WYWRpcyBSb290IENBIDMg\nRzMwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQCzyw4QZ47qFJenMioKVjZ/aEzHs286\nIxSR/xl/pcqs7rN2nXrpixurazHb+gtTTK/FpRp5PIpM/6zfJd5O2YIyC0TeytuMrKNuFoM7pmRL\nMon7FhY4futD4tN0SsJiCnMK3UmzV9KwCoWdcTzeo8vAMvMBOSBDGzXRU7Ox7sWTaYI+FrUoRqHe\n6okJ7UO4BUaKhvVZR74bbwEhELn9qdIoyhA5CcoTNs+cra1AdHkrAj80//ogaX3T7mH1urPnMNA3\nI4ZyYUUpSFlob3emLoG+B01vr87ERRORFHAGjx+f+IdpsQ7vw4kZ6+ocYfx6bIrc1gMLnia6Et3U\nVDmrJqMz6nWB2i3ND0/kA9HvFZcba5DFApCTZgIhsUfei5pKgLlVj7WiL8DWM2fafsSntARE60f7\n5li59wzweyuxwHApw0BiLTtIadwjPEjrewl5qW3aqDCYz4ByA4imW0aucnl8CAMhZa634RylsSqi\nMd5mBPfAdOhx3v89WcyWJhKLhZVXGqtrdQtEPREoPHtht+KPZ0/l7DxMYIBpVzgeAVuNVejH38DM\ndyM0SXV89pgR6y3e7UEuFAUCf+D+IOs15xGsIs5XPd7JMG0QA4XN8f+MFrXBsj6IbGB/kE+V9/Yt\nrQE5BwT6dYB9v0lQ7e/JxHwc64B+27bQ3RP+ydOc17KXqQIDAQABo0IwQDAPBgNVHRMBAf8EBTAD\nAQH/MA4GA1UdDwEB/wQEAwIBBjAdBgNVHQ4EFgQUxhfQvKjqAkPyGwaZXSuQILnXnOQwDQYJKoZI\nhvcNAQELBQADggIBADRh2Va1EodVTd2jNTFGu6QHcrxfYWLopfsLN7E8trP6KZ1/AvWkyaiTt3px\nKGmPc+FSkNrVvjrlt3ZqVoAh313m6Tqe5T72omnHKgqwGEfcIHB9UqM+WXzBusnIFUBhynLWcKzS\nt/Ac5IYp8M7vaGPQtSCKFWGafoaYtMnCdvvMujAWzKNhxnQT5WvvoxXqA/4Ti2Tk08HS6IT7SdEQ\nTXlm66r99I0xHnAUrdzeZxNMgRVhvLfZkXdxGYFgu/BYpbWcC/ePIlUnwEsBbTuZDdQdm2NnL9Du\nDcpmvJRPpq3t/O5jrFc/ZSXPsoaP0Aj/uHYUbt7lJ+yreLVTubY/6CD50qi+YUbKh4yE8/nxoGib\nIh6BJpsQBJFxwAYf3KDTuVan45gtf4Od34wrnDKOMpTwATwiKp9Dwi7DmDkHOHv8XgBCH/MyJnmD\nhPbl8MFREsALHgQjDFSlTC9JxUrRtm5gDWv8a4uFJGS3iQ6rJUdbPM9+Sb3H6QrG2vd+DhcI00iX\n0HGS8A85PjRqHH3Y8iKuu2n0M7SmSFXRDw4m6Oy2Cy2nhTXN/VnIn9HNPlopNLk9hM6xZdRZkZFW\ndSHBd575euFgndOtBBj0fOtek49TSiIp+EgrPk2GrFt/ywaZWWDYWGWVjUTR939+J399roD1B0y2\nPpxxVJkES/1Y+Zj0\n-----END CERTIFICATE-----\n\nDigiCert Assured ID Root G2\n===========================\n-----BEGIN CERTIFICATE-----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\nIhNzbM8m9Yop5w==\n-----END CERTIFICATE-----\n\nDigiCert Assured ID Root G3\n===========================\n-----BEGIN CERTIFICATE-----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\n1vUhZscv6pZjamVFkpUBtA==\n-----END CERTIFICATE-----\n\nDigiCert Global Root G2\n=======================\n-----BEGIN CERTIFICATE-----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\nMrY=\n-----END CERTIFICATE-----\n\nDigiCert Global Root G3\n=======================\n-----BEGIN CERTIFICATE-----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\nVOKa5Vt8sycX\n-----END CERTIFICATE-----\n\nDigiCert Trusted Root G4\n========================\n-----BEGIN CERTIFICATE-----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\n82Z+\n-----END CERTIFICATE-----\n\nCOMODO RSA Certification Authority\n==================================\n-----BEGIN CERTIFICATE-----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\nLaZRfyHBNVOFBkpdn627G190\n-----END CERTIFICATE-----\n\nUSERTrust RSA Certification Authority\n=====================================\n-----BEGIN CERTIFICATE-----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\nQ+6IHdfGjjxDah2nGN59PRbxYvnKkKj9\n-----END CERTIFICATE-----\n\nUSERTrust ECC Certification Authority\n=====================================\n-----BEGIN CERTIFICATE-----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\n9YO6bVi9JNlWSOrvxKJGgYhqOkbRqZtNyWHa0V1Xahg=\n-----END CERTIFICATE-----\n\nGlobalSign ECC Root CA - R4\n===========================\n-----BEGIN CERTIFICATE-----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\nJzWbVsaj8kfSt24bAgAXqmemFZHe+pTsewv4n4Q=\n-----END CERTIFICATE-----\n\nGlobalSign ECC Root CA - R5\n===========================\n-----BEGIN CERTIFICATE-----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\nyFz9SO8NdCKoCOJuxUnOxwy8p2Fp8fc74SrL+SvzZpA3\n-----END CERTIFICATE-----\n\nStaat der Nederlanden Root CA - G3\n==================================\n-----BEGIN CERTIFICATE-----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\ntzDfqu1XhUisHWrdOWnk4Xl4vs4Fv6EM94B7IWcnMFk=\n-----END CERTIFICATE-----\n\nStaat der Nederlanden EV Root CA\n================================\n-----BEGIN CERTIFICATE-----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\neUN51q1veieQA6TqJIc/2b3Z6fJfUEkc7uzXLg==\n-----END CERTIFICATE-----\n\nIdenTrust Commercial Root CA 1\n==============================\n-----BEGIN CERTIFICATE-----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\ncGzM7vRX+Bi6hG6H\n-----END CERTIFICATE-----\n\nIdenTrust Public Sector Root CA 1\n=================================\n-----BEGIN CERTIFICATE-----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\n3Wl9af0AVqW3rLatt8o+Ae+c\n-----END CERTIFICATE-----\n\nEntrust Root Certification Authority - G2\n=========================================\n-----BEGIN CERTIFICATE-----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\ne4pIb4tF9g==\n-----END CERTIFICATE-----\n\nEntrust Root Certification Authority - EC1\n==========================================\n-----BEGIN CERTIFICATE-----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\nkmhtc9hvJqwhAriZtyZBWyVgrtBIGu4G\n-----END CERTIFICATE-----\n\nCFCA EV ROOT\n============\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nCertinomis - Root CA\n====================\n-----BEGIN CERTIFICATE-----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\nhkIGuUE=\n-----END CERTIFICATE-----\n\nOISTE WISeKey Global Root GB CA\n===============================\n-----BEGIN CERTIFICATE-----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\nNc1MaRVUGpCY3useX8p3x8uOPUNpnJpY0CQ73xtAln41rYHHTnG6iBM=\n-----END CERTIFICATE-----\n\nSZAFIR ROOT CA2\n===============\n-----BEGIN CERTIFICATE-----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\n+/NNIxuZMzSgLvWpCz/UXeHPhJ/iGcJfitYgHuNztw==\n-----END CERTIFICATE-----\n\nCertum Trusted Network CA 2\n===========================\n-----BEGIN CERTIFICATE-----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\nzAYspsbiDrW5viSP\n-----END CERTIFICATE-----\n\nHellenic Academic and Research Institutions RootCA 2015\n=======================================================\n-----BEGIN CERTIFICATE-----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\np/UsQu0yrbYhnr68\n-----END CERTIFICATE-----\n\nHellenic Academic and Research Institutions ECC RootCA 2015\n===========================================================\n-----BEGIN CERTIFICATE-----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\ndc5FZXIhF8siQQ6ME5g4mlRtm8rifOoCWCKR\n-----END CERTIFICATE-----\n\nISRG Root X1\n============\n-----BEGIN CERTIFICATE-----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\nm+kXQ99b21/+jh5Xos1AnX5iItreGCc=\n-----END CERTIFICATE-----\n\nAC RAIZ FNMT-RCM\n================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nAmazon Root CA 1\n================\n-----BEGIN CERTIFICATE-----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\nxJNDd2ZhwLnoQdeXeGADbkpyrqXRfboQnoZsG4q5WTP468SQvvG5\n-----END CERTIFICATE-----\n\nAmazon Root CA 2\n================\n-----BEGIN CERTIFICATE-----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\nJUmTtt8AF9pYfl3uxRuw0dFfIRDH+fO6AgonB8Xx1sfT4PsJYGw=\n-----END CERTIFICATE-----\n\nAmazon Root CA 3\n================\n-----BEGIN CERTIFICATE-----\nMIIBtjCCAVugAwIBAgITBmyf1XSXNmY/Owua2eiedgPySjAKBggqhkjOPQQDAjA5MQswCQYDVQQG\nEwJVUzEPMA0GA1UEChMGQW1hem9uMRkwFwYDVQQDExBBbWF6b24gUm9vdCBDQSAzMB4XDTE1MDUy\nNjAwMDAwMFoXDTQwMDUyNjAwMDAwMFowOTELMAkGA1UEBhMCVVMxDzANBgNVBAoTBkFtYXpvbjEZ\nMBcGA1UEAxMQQW1hem9uIFJvb3QgQ0EgMzBZMBMGByqGSM49AgEGCCqGSM49AwEHA0IABCmXp8ZB\nf8ANm+gBG1bG8lKlui2yEujSLtf6ycXYqm0fc4E7O5hrOXwzpcVOho6AF2hiRVd9RFgdszflZwjr\nZt6jQjBAMA8GA1UdEwEB/wQFMAMBAf8wDgYDVR0PAQH/BAQDAgGGMB0GA1UdDgQWBBSrttvXBp43\nrDCGB5Fwx5zEGbF4wDAKBggqhkjOPQQDAgNJADBGAiEA4IWSoxe3jfkrBqWTrBqYaGFy+uGh0Psc\neGCmQ5nFuMQCIQCcAu/xlJyzlvnrxir4tiz+OpAUFteMYyRIHN8wfdVoOw==\n-----END CERTIFICATE-----\n\nAmazon Root CA 4\n================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nLuxTrust Global Root 2\n======================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nTUBITAK Kamu SM SSL Kok Sertifikasi - Surum 1\n=============================================\n-----BEGIN CERTIFICATE-----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\nq5Rm+K37DwhuJi1/FwcJsoz7UMCflo3Ptv0AnVoUmr8CRPXBwp8iXqIPoeM=\n-----END CERTIFICATE-----\n\nGDCA TrustAUTH R5 ROOT\n======================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nTrustCor RootCert CA-1\n======================\n-----BEGIN CERTIFICATE-----\nMIIEMDCCAxigAwIBAgIJANqb7HHzA7AZMA0GCSqGSIb3DQEBCwUAMIGkMQswCQYDVQQGEwJQQTEP\nMA0GA1UECAwGUGFuYW1hMRQwEgYDVQQHDAtQYW5hbWEgQ2l0eTEkMCIGA1UECgwbVHJ1c3RDb3Ig\nU3lzdGVtcyBTLiBkZSBSLkwuMScwJQYDVQQLDB5UcnVzdENvciBDZXJ0aWZpY2F0ZSBBdXRob3Jp\ndHkxHzAdBgNVBAMMFlRydXN0Q29yIFJvb3RDZXJ0IENBLTEwHhcNMTYwMjA0MTIzMjE2WhcNMjkx\nMjMxMTcyMzE2WjCBpDELMAkGA1UEBhMCUEExDzANBgNVBAgMBlBhbmFtYTEUMBIGA1UEBwwLUGFu\nYW1hIENpdHkxJDAiBgNVBAoMG1RydXN0Q29yIFN5c3RlbXMgUy4gZGUgUi5MLjEnMCUGA1UECwwe\nVHJ1c3RDb3IgQ2VydGlmaWNhdGUgQXV0aG9yaXR5MR8wHQYDVQQDDBZUcnVzdENvciBSb290Q2Vy\ndCBDQS0xMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAv463leLCJhJrMxnHQFgKq1mq\njQCj/IDHUHuO1CAmujIS2CNUSSUQIpidRtLByZ5OGy4sDjjzGiVoHKZaBeYei0i/mJZ0PmnK6bV4\npQa81QBeCQryJ3pS/C3Vseq0iWEk8xoT26nPUu0MJLq5nux+AHT6k61sKZKuUbS701e/s/OojZz0\nJEsq1pme9J7+wH5COucLlVPat2gOkEz7cD+PSiyU8ybdY2mplNgQTsVHCJCZGxdNuWxu72CVEY4h\ngLW9oHPY0LJ3xEXqWib7ZnZ2+AYfYW0PVcWDtxBWcgYHpfOxGgMFZA6dWorWhnAbJN7+KIor0Gqw\n/Hqi3LJ5DotlDwIDAQABo2MwYTAdBgNVHQ4EFgQU7mtJPHo/DeOxCbeKyKsZn3MzUOcwHwYDVR0j\nBBgwFoAU7mtJPHo/DeOxCbeKyKsZn3MzUOcwDwYDVR0TAQH/BAUwAwEB/zAOBgNVHQ8BAf8EBAMC\nAYYwDQYJKoZIhvcNAQELBQADggEBACUY1JGPE+6PHh0RU9otRCkZoB5rMZ5NDp6tPVxBb5UrJKF5\nmDo4Nvu7Zp5I/5CQ7z3UuJu0h3U/IJvOcs+hVcFNZKIZBqEHMwwLKeXx6quj7LUKdJDHfXLy11yf\nke+Ri7fc7Waiz45mO7yfOgLgJ90WmMCV1Aqk5IGadZQ1nJBfiDcGrVmVCrDRZ9MZyonnMlo2HD6C\nqFqTvsbQZJG2z9m2GM/bftJlo6bEjhcxwft+dtvTheNYsnd6djtsL1Ac59v2Z3kf9YKVmgenFK+P\n3CghZwnS1k1aHBkcjndcw5QkPTJrS37UeJSDvjdNzl/HHk484IkzlQsPpTLWPFp5LBk=\n-----END CERTIFICATE-----\n\nTrustCor RootCert CA-2\n======================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nTrustCor ECA-1\n==============\n-----BEGIN CERTIFICATE-----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\njaoITmJF4PkL0uDgPFveXHEnJcLmA4GLEFPjx1WitJ/X5g==\n-----END CERTIFICATE-----\n\nSSL.com Root Certification Authority RSA\n========================================\n-----BEGIN CERTIFICATE-----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\nWuuA8ShYIc2wBlX7Jz9TkHCpBB5XJ7k=\n-----END CERTIFICATE-----\n\nSSL.com Root Certification Authority ECC\n========================================\n-----BEGIN CERTIFICATE-----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\n5Wajs6O7pdWLjwkspl1+4vAHCGht0nxpbl/f5Wpl\n-----END CERTIFICATE-----\n\nSSL.com EV Root Certification Authority RSA R2\n==============================================\n-----BEGIN CERTIFICATE-----\nMIIF6zCCA9OgAwIBAgIIVrYpzTS8ePYwDQYJKoZIhvcNAQELBQAwgYIxCzAJBgNVBAYTAlVTMQ4w\nDAYDVQQIDAVUZXhhczEQMA4GA1UEBwwHSG91c3RvbjEYMBYGA1UECgwPU1NMIENvcnBvcmF0aW9u\nMTcwNQYDVQQDDC5TU0wuY29tIEVWIFJvb3QgQ2VydGlmaWNhdGlvbiBBdXRob3JpdHkgUlNBIFIy\nMB4XDTE3MDUzMTE4MTQzN1oXDTQyMDUzMDE4MTQzN1owgYIxCzAJBgNVBAYTAlVTMQ4wDAYDVQQI\nDAVUZXhhczEQMA4GA1UEBwwHSG91c3RvbjEYMBYGA1UECgwPU1NMIENvcnBvcmF0aW9uMTcwNQYD\nVQQDDC5TU0wuY29tIEVWIFJvb3QgQ2VydGlmaWNhdGlvbiBBdXRob3JpdHkgUlNBIFIyMIICIjAN\nBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAjzZlQOHWTcDXtOlG2mvqM0fNTPl9fb69LT3w23jh\nhqXZuglXaO1XPqDQCEGD5yhBJB/jchXQARr7XnAjssufOePPxU7Gkm0mxnu7s9onnQqG6YE3Bf7w\ncXHswxzpY6IXFJ3vG2fThVUCAtZJycxa4bH3bzKfydQ7iEGonL3Lq9ttewkfokxykNorCPzPPFTO\nZw+oz12WGQvE43LrrdF9HSfvkusQv1vrO6/PgN3B0pYEW3p+pKk8OHakYo6gOV7qd89dAFmPZiw+\nB6KjBSYRaZfqhbcPlgtLyEDhULouisv3D5oi53+aNxPN8k0TayHRwMwi8qFG9kRpnMphNQcAb9Zh\nCBHqurj26bNg5U257J8UZslXWNvNh2n4ioYSA0e/ZhN2rHd9NCSFg83XqpyQGp8hLH94t2S42Oim\n9HizVcuE0jLEeK6jj2HdzghTreyI/BXkmg3mnxp3zkyPuBQVPWKchjgGAGYS5Fl2WlPAApiiECto\nRHuOec4zSnaqW4EWG7WK2NAAe15itAnWhmMOpgWVSbooi4iTsjQc2KRVbrcc0N6ZVTsj9CLg+Slm\nJuwgUHfbSguPvuUCYHBBXtSuUDkiFCbLsjtzdFVHB3mBOagwE0TlBIqulhMlQg+5U8Sb/M3kHN48\n+qvWBkofZ6aYMBzdLNvcGJVXZsb/XItW9XcCAwEAAaNjMGEwDwYDVR0TAQH/BAUwAwEB/zAfBgNV\nHSMEGDAWgBT5YLvU49U09rj1BoAlp3PbRmmonjAdBgNVHQ4EFgQU+WC71OPVNPa49QaAJadz20Zp\nqJ4wDgYDVR0PAQH/BAQDAgGGMA0GCSqGSIb3DQEBCwUAA4ICAQBWs47LCp1Jjr+kxJG7ZhcFUZh1\n++VQLHqe8RT6q9OKPv+RKY9ji9i0qVQBDb6Thi/5Sm3HXvVX+cpVHBK+Rw82xd9qt9t1wkclf7nx\nY/hoLVUE0fKNsKTPvDxeH3jnpaAgcLAExbf3cqfeIg29MyVGjGSSJuM+LmOW2puMPfgYCdcDzH2G\nguDKBAdRUNf/ktUM79qGn5nX67evaOI5JpS6aLe/g9Pqemc9YmeuJeVy6OLk7K4S9ksrPJ/psEDz\nOFSz/bdoyNrGj1E8svuR3Bznm53htw1yj+KkxKl4+esUrMZDBcJlOSgYAsOCsp0FvmXtll9ldDz7\nCTUue5wT/RsPXcdtgTpWD8w74a8CLyKsRspGPKAcTNZEtF4uXBVmCeEmKf7GUmG6sXP/wwyc5Wxq\nlD8UykAWlYTzWamsX0xhk23RO8yilQwipmdnRC652dKKQbNmC1r7fSOl8hqw/96bg5Qu0T/fkreR\nrwU7ZcegbLHNYhLDkBvjJc40vG93drEQw/cFGsDWr3RiSBd3kmmQYRzelYB0VI8YHMPzA9C/pEN1\nhlMYegouCRw2n5H9gooiS9EOUCXdywMMF8mDAAhONU2Ki+3wApRmLER/y5UnlhetCTCstnEXbosX\n9hwJ1C07mKVx01QT2WDz9UtmT/rx7iASjbSsV7FFY6GsdqnC+w==\n-----END CERTIFICATE-----\n\nSSL.com EV Root Certification Authority ECC\n===========================================\n-----BEGIN CERTIFICATE-----\nMIIClDCCAhqgAwIBAgIILCmcWxbtBZUwCgYIKoZIzj0EAwIwfzELMAkGA1UEBhMCVVMxDjAMBgNV\nBAgMBVRleGFzMRAwDgYDVQQHDAdIb3VzdG9uMRgwFgYDVQQKDA9TU0wgQ29ycG9yYXRpb24xNDAy\nBgNVBAMMK1NTTC5jb20gRVYgUm9vdCBDZXJ0aWZpY2F0aW9uIEF1dGhvcml0eSBFQ0MwHhcNMTYw\nMjEyMTgxNTIzWhcNNDEwMjEyMTgxNTIzWjB/MQswCQYDVQQGEwJVUzEOMAwGA1UECAwFVGV4YXMx\nEDAOBgNVBAcMB0hvdXN0b24xGDAWBgNVBAoMD1NTTCBDb3Jwb3JhdGlvbjE0MDIGA1UEAwwrU1NM\nLmNvbSBFViBSb290IENlcnRpZmljYXRpb24gQXV0aG9yaXR5IEVDQzB2MBAGByqGSM49AgEGBSuB\nBAAiA2IABKoSR5CYG/vvw0AHgyBO8TCCogbR8pKGYfL2IWjKAMTH6kMAVIbc/R/fALhBYlzccBYy\n3h+Z1MzFB8gIH2EWB1E9fVwHU+M1OIzfzZ/ZLg1KthkuWnBaBu2+8KGwytAJKaNjMGEwHQYDVR0O\nBBYEFFvKXuXe0oGqzagtZFG22XKbl+ZPMA8GA1UdEwEB/wQFMAMBAf8wHwYDVR0jBBgwFoAUW8pe\n5d7SgarNqC1kUbbZcpuX5k8wDgYDVR0PAQH/BAQDAgGGMAoGCCqGSM49BAMCA2gAMGUCMQCK5kCJ\nN+vp1RPZytRrJPOwPYdGWBrssd9v+1a6cGvHOMzosYxPD/fxZ3YOg9AeUY8CMD32IygmTMZgh5Mm\nm7I1HrrW9zzRHM76JTymGoEVW/MSD2zuZYrJh6j5B+BimoxcSg==\n-----END CERTIFICATE-----\n\nGlobalSign Root CA - R6\n=======================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n\nOISTE WISeKey Global Root GC CA\n===============================\n-----BEGIN CERTIFICATE-----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\n-----END CERTIFICATE-----\n" }, { "path": "car.data", "content": 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h,high,4,more,big,high,acc\nhigh,high,5more,2,small,low,unacc\nhigh,high,5more,2,small,med,unacc\nhigh,high,5more,2,small,high,unacc\nhigh,high,5more,2,med,low,unacc\nhigh,high,5more,2,med,med,unacc\nhigh,high,5more,2,med,high,unacc\nhigh,high,5more,2,big,low,unacc\nhigh,high,5more,2,big,med,unacc\nhigh,high,5more,2,big,high,unacc\nhigh,high,5more,4,small,low,unacc\nhigh,high,5more,4,small,med,unacc\nhigh,high,5more,4,small,high,acc\nhigh,high,5more,4,med,low,unacc\nhigh,high,5more,4,med,med,acc\nhigh,high,5more,4,med,high,acc\nhigh,high,5more,4,big,low,unacc\nhigh,high,5more,4,big,med,acc\nhigh,high,5more,4,big,high,acc\nhigh,high,5more,more,small,low,unacc\nhigh,high,5more,more,small,med,unacc\nhigh,high,5more,more,small,high,acc\nhigh,high,5more,more,med,low,unacc\nhigh,high,5more,more,med,med,acc\nhigh,high,5more,more,med,high,acc\nhigh,high,5more,more,big,low,unacc\nhigh,high,5more,more,big,med,acc\nhigh,high,5more,more,big,high,acc\nhigh,med,2,2,small,low,unacc\nhigh,med,2,2,small,med,unacc\nhigh,med,2,2,small,high,unacc\nhigh,med,2,2,med,low,unacc\nhigh,med,2,2,med,med,unacc\nhigh,med,2,2,med,high,unacc\nhigh,med,2,2,big,low,unacc\nhigh,med,2,2,big,med,unacc\nhigh,med,2,2,big,high,unacc\nhigh,med,2,4,small,low,unacc\nhigh,med,2,4,small,med,unacc\nhigh,med,2,4,small,high,acc\nhigh,med,2,4,med,low,unacc\nhigh,med,2,4,med,med,unacc\nhigh,med,2,4,med,high,acc\nhigh,med,2,4,big,low,unacc\nhigh,med,2,4,big,med,acc\nhigh,med,2,4,big,high,acc\nhigh,med,2,more,small,low,unacc\nhigh,med,2,more,small,med,unacc\nhigh,med,2,more,small,high,unacc\nhigh,med,2,more,med,low,unacc\nhigh,med,2,more,med,med,unacc\nhigh,med,2,more,med,high,acc\nhigh,med,2,more,big,low,unacc\nhigh,med,2,more,big,med,acc\nhigh,med,2,more,big,high,acc\nhigh,med,3,2,small,low,unacc\nhigh,med,3,2,small,med,unacc\nhigh,med,3,2,small,high,unacc\nhigh,med,3,2,med,low,unacc\nhigh,med,3,2,med,med,unacc\nhigh,med,3,2,med,high,unacc\nhigh,med,3,2,big,low,unacc\nhigh,med,3,2,big,med,unacc\nhigh,med,3,2,big,high,unacc\nhigh,med,3,4,small,low,unacc\nhigh,med,3,4,small,med,unacc\nhigh,med,3,4,small,high,acc\nhigh,med,3,4,med,low,unacc\nhigh,med,3,4,med,med,unacc\nhigh,med,3,4,med,high,acc\nhigh,med,3,4,big,low,unacc\nhigh,med,3,4,big,med,acc\nhigh,med,3,4,big,high,acc\nhigh,med,3,more,small,low,unacc\nhigh,med,3,more,small,med,unacc\nhigh,med,3,more,small,high,acc\nhigh,med,3,more,med,low,unacc\nhigh,med,3,more,med,med,acc\nhigh,med,3,more,med,high,acc\nhigh,med,3,more,big,low,unacc\nhigh,med,3,more,big,med,acc\nhigh,med,3,more,big,high,acc\nhigh,med,4,2,small,low,unacc\nhigh,med,4,2,small,med,unacc\nhigh,med,4,2,small,high,unacc\nhigh,med,4,2,med,low,unacc\nhigh,med,4,2,med,med,unacc\nhigh,med,4,2,med,high,unacc\nhigh,med,4,2,big,low,unacc\nhigh,med,4,2,big,med,unacc\nhigh,med,4,2,big,high,unacc\nhigh,med,4,4,small,low,unacc\nhigh,med,4,4,small,med,unacc\nhigh,med,4,4,small,high,acc\nhigh,med,4,4,med,low,unacc\nhigh,med,4,4,med,med,acc\nhigh,med,4,4,med,high,acc\nhigh,med,4,4,big,low,unacc\nhigh,med,4,4,big,med,acc\nhigh,med,4,4,big,high,acc\nhigh,med,4,more,small,low,unacc\nhigh,med,4,more,small,med,unacc\nhigh,med,4,more,small,high,acc\nhigh,med,4,more,med,low,unacc\nhigh,med,4,more,med,med,acc\nhigh,med,4,more,med,high,acc\nhigh,med,4,more,big,low,unacc\nhigh,med,4,more,big,med,acc\nhigh,med,4,more,big,high,acc\nhigh,med,5more,2,small,low,unacc\nhigh,med,5more,2,small,med,unacc\nhigh,med,5more,2,small,high,unacc\nhigh,med,5more,2,med,low,unacc\nhigh,med,5more,2,med,med,unacc\nhigh,med,5more,2,med,high,unacc\nhigh,med,5more,2,big,low,unacc\nhigh,med,5more,2,big,med,unacc\nhigh,med,5more,2,big,high,unacc\nhigh,med,5more,4,small,low,unacc\nhigh,med,5more,4,small,med,unacc\nhigh,med,5more,4,small,high,acc\nhigh,med,5more,4,med,low,unacc\nhigh,med,5more,4,med,med,acc\nhigh,med,5more,4,med,high,acc\nhigh,med,5more,4,big,low,unacc\nhigh,med,5more,4,big,med,acc\nhigh,med,5more,4,big,high,acc\nhigh,med,5more,more,small,low,unacc\nhigh,med,5more,more,small,med,unacc\nhigh,med,5more,more,small,high,acc\nhigh,med,5more,more,med,low,unacc\nhigh,med,5more,more,med,med,acc\nhigh,med,5more,more,med,high,acc\nhigh,med,5more,more,big,low,unacc\nhigh,med,5more,more,big,med,acc\nhigh,med,5more,more,big,high,acc\nhigh,low,2,2,small,low,unacc\nhigh,low,2,2,small,med,unacc\nhigh,low,2,2,small,high,unacc\nhigh,low,2,2,med,low,unacc\nhigh,low,2,2,med,med,unacc\nhigh,low,2,2,med,high,unacc\nhigh,low,2,2,big,low,unacc\nhigh,low,2,2,big,med,unacc\nhigh,low,2,2,big,high,unacc\nhigh,low,2,4,small,low,unacc\nhigh,low,2,4,small,med,unacc\nhigh,low,2,4,small,high,acc\nhigh,low,2,4,med,low,unacc\nhigh,low,2,4,med,med,unacc\nhigh,low,2,4,med,high,acc\nhigh,low,2,4,big,low,unacc\nhigh,low,2,4,big,med,acc\nhigh,low,2,4,big,high,acc\nhigh,low,2,more,small,low,unacc\nhigh,low,2,more,small,med,unacc\nhigh,low,2,more,small,high,unacc\nhigh,low,2,more,med,low,unacc\nhigh,low,2,more,med,med,unacc\nhigh,low,2,more,med,high,acc\nhigh,low,2,more,big,low,unacc\nhigh,low,2,more,big,med,acc\nhigh,low,2,more,big,high,acc\nhigh,low,3,2,small,low,unacc\nhigh,low,3,2,small,med,unacc\nhigh,low,3,2,small,high,unacc\nhigh,low,3,2,med,low,unacc\nhigh,low,3,2,med,med,unacc\nhigh,low,3,2,med,high,unacc\nhigh,low,3,2,big,low,unacc\nhigh,low,3,2,big,med,unacc\nhigh,low,3,2,big,high,unacc\nhigh,low,3,4,small,low,unacc\nhigh,low,3,4,small,med,unacc\nhigh,low,3,4,small,high,acc\nhigh,low,3,4,med,low,unacc\nhigh,low,3,4,med,med,unacc\nhigh,low,3,4,med,high,acc\nhigh,low,3,4,big,low,unacc\nhigh,low,3,4,big,med,acc\nhigh,low,3,4,big,high,acc\nhigh,low,3,more,small,low,unacc\nhigh,low,3,more,small,med,unacc\nhigh,low,3,more,small,high,acc\nhigh,low,3,more,med,low,unacc\nhigh,low,3,more,med,med,acc\nhigh,low,3,more,med,high,acc\nhigh,low,3,more,big,low,unacc\nhigh,low,3,more,big,med,acc\nhigh,low,3,more,big,high,acc\nhigh,low,4,2,small,low,unacc\nhigh,low,4,2,small,med,unacc\nhigh,low,4,2,small,high,unacc\nhigh,low,4,2,med,low,unacc\nhigh,low,4,2,med,med,unacc\nhigh,low,4,2,med,high,unacc\nhigh,low,4,2,big,low,unacc\nhigh,low,4,2,big,med,unacc\nhigh,low,4,2,big,high,unacc\nhigh,low,4,4,small,low,unacc\nhigh,low,4,4,small,med,unacc\nhigh,low,4,4,small,high,acc\nhigh,low,4,4,med,low,unacc\nhigh,low,4,4,med,med,acc\nhigh,low,4,4,med,high,acc\nhigh,low,4,4,big,low,unacc\nhigh,low,4,4,big,med,acc\nhigh,low,4,4,big,high,acc\nhigh,low,4,more,small,low,unacc\nhigh,low,4,more,small,med,unacc\nhigh,low,4,more,small,high,acc\nhigh,low,4,more,med,low,unacc\nhigh,low,4,more,med,med,acc\nhigh,low,4,more,med,high,acc\nhigh,low,4,more,big,low,unacc\nhigh,low,4,more,big,med,acc\nhigh,low,4,more,big,high,acc\nhigh,low,5more,2,small,low,unacc\nhigh,low,5more,2,small,med,unacc\nhigh,low,5more,2,small,high,unacc\nhigh,low,5more,2,med,low,unacc\nhigh,low,5more,2,med,med,unacc\nhigh,low,5more,2,med,high,unacc\nhigh,low,5more,2,big,low,unacc\nhigh,low,5more,2,big,med,unacc\nhigh,low,5more,2,big,high,unacc\nhigh,low,5more,4,small,low,unacc\nhigh,low,5more,4,small,med,unacc\nhigh,low,5more,4,small,high,acc\nhigh,low,5more,4,med,low,unacc\nhigh,low,5more,4,med,med,acc\nhigh,low,5more,4,med,high,acc\nhigh,low,5more,4,big,low,unacc\nhigh,low,5more,4,big,med,acc\nhigh,low,5more,4,big,high,acc\nhigh,low,5more,more,small,low,unacc\nhigh,low,5more,more,small,med,unacc\nhigh,low,5more,more,small,high,acc\nhigh,low,5more,more,med,low,unacc\nhigh,low,5more,more,med,med,acc\nhigh,low,5more,more,med,high,acc\nhigh,low,5more,more,big,low,unacc\nhigh,low,5more,more,big,med,acc\nhigh,low,5more,more,big,high,acc\nmed,vhigh,2,2,small,low,unacc\nmed,vhigh,2,2,small,med,unacc\nmed,vhigh,2,2,small,high,unacc\nmed,vhigh,2,2,med,low,unacc\nmed,vhigh,2,2,med,med,unacc\nmed,vhigh,2,2,med,high,unacc\nmed,vhigh,2,2,big,low,unacc\nmed,vhigh,2,2,big,med,unacc\nmed,vhigh,2,2,big,high,unacc\nmed,vhigh,2,4,small,low,unacc\nmed,vhigh,2,4,small,med,unacc\nmed,vhigh,2,4,small,high,acc\nmed,vhigh,2,4,med,low,unacc\nmed,vhigh,2,4,med,med,unacc\nmed,vhigh,2,4,med,high,acc\nmed,vhigh,2,4,big,low,unacc\nmed,vhigh,2,4,big,med,acc\nmed,vhigh,2,4,big,high,acc\nmed,vhigh,2,more,small,low,unacc\nmed,vhigh,2,more,small,med,unacc\nmed,vhigh,2,more,small,high,unacc\nmed,vhigh,2,more,med,low,unacc\nmed,vhigh,2,more,med,med,unacc\nmed,vhigh,2,more,med,high,acc\nmed,vhigh,2,more,big,low,unacc\nmed,vhigh,2,more,big,med,acc\nmed,vhigh,2,more,big,high,acc\nmed,vhigh,3,2,small,low,unacc\nmed,vhigh,3,2,small,med,unacc\nmed,vhigh,3,2,small,high,unacc\nmed,vhigh,3,2,med,low,unacc\nmed,vhigh,3,2,med,med,unacc\nmed,vhigh,3,2,med,high,unacc\nmed,vhigh,3,2,big,low,unacc\nmed,vhigh,3,2,big,med,unacc\nmed,vhigh,3,2,big,high,unacc\nmed,vhigh,3,4,small,low,unacc\nmed,vhigh,3,4,small,med,unacc\nmed,vhigh,3,4,small,high,acc\nmed,vhigh,3,4,med,low,unacc\nmed,vhigh,3,4,med,med,unacc\nmed,vhigh,3,4,med,high,acc\nmed,vhigh,3,4,big,low,unacc\nmed,vhigh,3,4,big,med,acc\nmed,vhigh,3,4,big,high,acc\nmed,vhigh,3,more,small,low,unacc\nmed,vhigh,3,more,small,med,unacc\nmed,vhigh,3,more,small,high,acc\nmed,vhigh,3,more,med,low,unacc\nmed,vhigh,3,more,med,med,acc\nmed,vhigh,3,more,med,high,acc\nmed,vhigh,3,more,big,low,unacc\nmed,vhigh,3,more,big,med,acc\nmed,vhigh,3,more,big,high,acc\nmed,vhigh,4,2,small,low,unacc\nmed,vhigh,4,2,small,med,unacc\nmed,vhigh,4,2,small,high,unacc\nmed,vhigh,4,2,med,low,unacc\nmed,vhigh,4,2,med,med,unacc\nmed,vhigh,4,2,med,high,unacc\nmed,vhigh,4,2,big,low,unacc\nmed,vhigh,4,2,big,med,unacc\nmed,vhigh,4,2,big,high,unacc\nmed,vhigh,4,4,small,low,unacc\nmed,vhigh,4,4,small,med,unacc\nmed,vhigh,4,4,small,high,acc\nmed,vhigh,4,4,med,low,unacc\nmed,vhigh,4,4,med,med,acc\nmed,vhigh,4,4,med,high,acc\nmed,vhigh,4,4,big,low,unacc\nmed,vhigh,4,4,big,med,acc\nmed,vhigh,4,4,big,high,acc\nmed,vhigh,4,more,small,low,unacc\nmed,vhigh,4,more,small,med,unacc\nmed,vhigh,4,more,small,high,acc\nmed,vhigh,4,more,med,low,unacc\nmed,vhigh,4,more,med,med,acc\nmed,vhigh,4,more,med,high,acc\nmed,vhigh,4,more,big,low,unacc\nmed,vhigh,4,more,big,med,acc\nmed,vhigh,4,more,big,high,acc\nmed,vhigh,5more,2,small,low,unacc\nm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acc\nlow,high,3,4,small,med,acc\nlow,high,3,4,small,high,acc\nlow,high,3,4,med,low,unacc\nlow,high,3,4,med,med,acc\nlow,high,3,4,med,high,acc\nlow,high,3,4,big,low,unacc\nlow,high,3,4,big,med,acc\nlow,high,3,4,big,high,vgood\nlow,high,3,more,small,low,unacc\nlow,high,3,more,small,med,acc\nlow,high,3,more,small,high,acc\nlow,high,3,more,med,low,unacc\nlow,high,3,more,med,med,acc\nlow,high,3,more,med,high,vgood\nlow,high,3,more,big,low,unacc\nlow,high,3,more,big,med,acc\nlow,high,3,more,big,high,vgood\nlow,high,4,2,small,low,unacc\nlow,high,4,2,small,med,unacc\nlow,high,4,2,small,high,unacc\nlow,high,4,2,med,low,unacc\nlow,high,4,2,med,med,unacc\nlow,high,4,2,med,high,unacc\nlow,high,4,2,big,low,unacc\nlow,high,4,2,big,med,unacc\nlow,high,4,2,big,high,unacc\nlow,high,4,4,small,low,unacc\nlow,high,4,4,small,med,acc\nlow,high,4,4,small,high,acc\nlow,high,4,4,med,low,unacc\nlow,high,4,4,med,med,acc\nlow,high,4,4,med,high,vgood\nlow,high,4,4,big,low,unacc\nlow,high,4,4,big,med,acc\nlow,high,4,4,big,high,vgood\nlow,high,4,more,small,low,unacc\nlow,high,4,more,small,med,acc\nlow,high,4,more,small,high,acc\nlow,high,4,more,med,low,unacc\nlow,high,4,more,med,med,acc\nlow,high,4,more,med,high,vgood\nlow,high,4,more,big,low,unacc\nlow,high,4,more,big,med,acc\nlow,high,4,more,big,high,vgood\nlow,high,5more,2,small,low,unacc\nlow,high,5more,2,small,med,unacc\nlow,high,5more,2,small,high,unacc\nlow,high,5more,2,med,low,unacc\nlow,high,5more,2,med,med,unacc\nlow,high,5more,2,med,high,unacc\nlow,high,5more,2,big,low,unacc\nlow,high,5more,2,big,med,unacc\nlow,high,5more,2,big,high,unacc\nlow,high,5more,4,small,low,unacc\nlow,high,5more,4,small,med,acc\nlow,high,5more,4,small,high,acc\nlow,high,5more,4,med,low,unacc\nlow,high,5more,4,med,med,acc\nlow,high,5more,4,med,high,vgood\nlow,high,5more,4,big,low,unacc\nlow,high,5more,4,big,med,acc\nlow,high,5more,4,big,high,vgood\nlow,high,5more,more,small,low,unacc\nlow,high,5more,more,small,med,acc\nlow,high,5more,more,small,high,acc\nlow,high,5more,more,med,low,unacc\nlow,high,5more,more,med,med,acc\nlow,high,5more,more,med,high,vgood\nlow,high,5more,more,big,low,unacc\nlow,high,5more,more,big,med,acc\nlow,high,5more,more,big,high,vgood\nlow,med,2,2,small,low,unacc\nlow,med,2,2,small,med,unacc\nlow,med,2,2,small,high,unacc\nlow,med,2,2,med,low,unacc\nlow,med,2,2,med,med,unacc\nlow,med,2,2,med,high,unacc\nlow,med,2,2,big,low,unacc\nlow,med,2,2,big,med,unacc\nlow,med,2,2,big,high,unacc\nlow,med,2,4,small,low,unacc\nlow,med,2,4,small,med,acc\nlow,med,2,4,small,high,good\nlow,med,2,4,med,low,unacc\nlow,med,2,4,med,med,acc\nlow,med,2,4,med,high,good\nlow,med,2,4,big,low,unacc\nlow,med,2,4,big,med,good\nlow,med,2,4,big,high,vgood\nlow,med,2,more,small,low,unacc\nlow,med,2,more,small,med,unacc\nlow,med,2,more,small,high,unacc\nlow,med,2,more,med,low,unacc\nlow,med,2,more,med,med,acc\nlow,med,2,more,med,high,good\nlow,med,2,more,big,low,unacc\nlow,med,2,more,big,med,good\nlow,med,2,more,big,high,vgood\nlow,med,3,2,small,low,unacc\nlow,med,3,2,small,med,unacc\nlow,med,3,2,small,high,unacc\nlow,med,3,2,med,low,unacc\nlow,med,3,2,med,med,unacc\nlow,med,3,2,med,high,unacc\nlow,med,3,2,big,low,unacc\nlow,med,3,2,big,med,unacc\nlow,med,3,2,big,high,unacc\nlow,med,3,4,small,low,unacc\nlow,med,3,4,small,med,acc\nlow,med,3,4,small,high,good\nlow,med,3,4,med,low,unacc\nlow,med,3,4,med,med,acc\nlow,med,3,4,med,high,good\nlow,med,3,4,big,low,unacc\nlow,med,3,4,big,med,good\nlow,med,3,4,big,high,vgood\nlow,med,3,more,small,low,unacc\nlow,med,3,more,small,med,acc\nlow,med,3,more,small,high,good\nlow,med,3,more,med,low,unacc\nlow,med,3,more,med,med,good\nlow,med,3,more,med,high,vgood\nlow,med,3,more,big,low,unacc\nlow,med,3,more,big,med,good\nlow,med,3,more,big,high,vgood\nlow,med,4,2,small,low,unacc\nlow,med,4,2,small,med,unacc\nlow,med,4,2,small,high,unacc\nlow,med,4,2,med,low,unacc\nlow,med,4,2,med,med,unacc\nlow,med,4,2,med,high,unacc\nlow,med,4,2,big,low,unacc\nlow,med,4,2,big,med,unacc\nlow,med,4,2,big,high,unacc\nlow,med,4,4,small,low,unacc\nlow,med,4,4,small,med,acc\nlow,med,4,4,small,high,good\nlow,med,4,4,med,low,unacc\nlow,med,4,4,med,med,good\nlow,med,4,4,med,high,vgood\nlow,med,4,4,big,low,unacc\nlow,med,4,4,big,med,good\nlow,med,4,4,big,high,vgood\nlow,med,4,more,small,low,unacc\nlow,med,4,more,small,med,acc\nlow,med,4,more,small,high,good\nlow,med,4,more,med,low,unacc\nlow,med,4,more,med,med,good\nlow,med,4,more,med,high,vgood\nlow,med,4,more,big,low,unacc\nlow,med,4,more,big,med,good\nlow,med,4,more,big,high,vgood\nlow,med,5more,2,small,low,unacc\nlow,med,5more,2,small,med,unacc\nlow,med,5more,2,small,high,unacc\nlow,med,5more,2,med,low,unacc\nlow,med,5more,2,med,med,unacc\nlow,med,5more,2,med,high,unacc\nlow,med,5more,2,big,low,unacc\nlow,med,5more,2,big,med,unacc\nlow,med,5more,2,big,high,unacc\nlow,med,5more,4,small,low,unacc\nlow,med,5more,4,small,med,acc\nlow,med,5more,4,small,high,good\nlow,med,5more,4,med,low,unacc\nlow,med,5more,4,med,med,good\nlow,med,5more,4,med,high,vgood\nlow,med,5more,4,big,low,unacc\nlow,med,5more,4,big,med,good\nlow,med,5more,4,big,high,vgood\nlow,med,5more,more,small,low,unacc\nlow,med,5more,more,small,med,acc\nlow,med,5more,more,small,high,good\nlow,med,5more,more,med,low,unacc\nlow,med,5more,more,med,med,good\nlow,med,5more,more,med,high,vgood\nlow,med,5more,more,big,low,unacc\nlow,med,5more,more,big,med,good\nlow,med,5more,more,big,high,vgood\nlow,low,2,2,small,low,unacc\nlow,low,2,2,small,med,unacc\nlow,low,2,2,small,high,unacc\nlow,low,2,2,med,low,unacc\nlow,low,2,2,med,med,unacc\nlow,low,2,2,med,high,unacc\nlow,low,2,2,big,low,unacc\nlow,low,2,2,big,med,unacc\nlow,low,2,2,big,high,unacc\nlow,low,2,4,small,low,unacc\nlow,low,2,4,small,med,acc\nlow,low,2,4,small,high,good\nlow,low,2,4,med,low,unacc\nlow,low,2,4,med,med,acc\nlow,low,2,4,med,high,good\nlow,low,2,4,big,low,unacc\nlow,low,2,4,big,med,good\nlow,low,2,4,big,high,vgood\nlow,low,2,more,small,low,unacc\nlow,low,2,more,small,med,unacc\nlow,low,2,more,small,high,unacc\nlow,low,2,more,med,low,unacc\nlow,low,2,more,med,med,acc\nlow,low,2,more,med,high,good\nlow,low,2,more,big,low,unacc\nlow,low,2,more,big,med,good\nlow,low,2,more,big,high,vgood\nlow,low,3,2,small,low,unacc\nlow,low,3,2,small,med,unacc\nlow,low,3,2,small,high,unacc\nlow,low,3,2,med,low,unacc\nlow,low,3,2,med,med,unacc\nlow,low,3,2,med,high,unacc\nlow,low,3,2,big,low,unacc\nlow,low,3,2,big,med,unacc\nlow,low,3,2,big,high,unacc\nlow,low,3,4,small,low,unacc\nlow,low,3,4,small,med,acc\nlow,low,3,4,small,high,good\nlow,low,3,4,med,low,unacc\nlow,low,3,4,med,med,acc\nlow,low,3,4,med,high,good\nlow,low,3,4,big,low,unacc\nlow,low,3,4,big,med,good\nlow,low,3,4,big,high,vgood\nlow,low,3,more,small,low,unacc\nlow,low,3,more,small,med,acc\nlow,low,3,more,small,high,good\nlow,low,3,more,med,low,unacc\nlow,low,3,more,med,med,good\nlow,low,3,more,med,high,vgood\nlow,low,3,more,big,low,unacc\nlow,low,3,more,big,med,good\nlow,low,3,more,big,high,vgood\nlow,low,4,2,small,low,unacc\nlow,low,4,2,small,med,unacc\nlow,low,4,2,small,high,unacc\nlow,low,4,2,med,low,unacc\nlow,low,4,2,med,med,unacc\nlow,low,4,2,med,high,unacc\nlow,low,4,2,big,low,unacc\nlow,low,4,2,big,med,unacc\nlow,low,4,2,big,high,unacc\nlow,low,4,4,small,low,unacc\nlow,low,4,4,small,med,acc\nlow,low,4,4,small,high,good\nlow,low,4,4,med,low,unacc\nlow,low,4,4,med,med,good\nlow,low,4,4,med,high,vgood\nlow,low,4,4,big,low,unacc\nlow,low,4,4,big,med,good\nlow,low,4,4,big,high,vgood\nlow,low,4,more,small,low,unacc\nlow,low,4,more,small,med,acc\nlow,low,4,more,small,high,good\nlow,low,4,more,med,low,unacc\nlow,low,4,more,med,med,good\nlow,low,4,more,med,high,vgood\nlow,low,4,more,big,low,unacc\nlow,low,4,more,big,med,good\nlow,low,4,more,big,high,vgood\nlow,low,5more,2,small,low,unacc\nlow,low,5more,2,small,med,unacc\nlow,low,5more,2,small,high,unacc\nlow,low,5more,2,med,low,unacc\nlow,low,5more,2,med,med,unacc\nlow,low,5more,2,med,high,unacc\nlow,low,5more,2,big,low,unacc\nlow,low,5more,2,big,med,unacc\nlow,low,5more,2,big,high,unacc\nlow,low,5more,4,small,low,unacc\nlow,low,5more,4,small,med,acc\nlow,low,5more,4,small,high,good\nlow,low,5more,4,med,low,unacc\nlow,low,5more,4,med,med,good\nlow,low,5more,4,med,high,vgood\nlow,low,5more,4,big,low,unacc\nlow,low,5more,4,big,med,good\nlow,low,5more,4,big,high,vgood\nlow,low,5more,more,small,low,unacc\nlow,low,5more,more,small,med,acc\nlow,low,5more,more,small,high,good\nlow,low,5more,more,med,low,unacc\nlow,low,5more,more,med,med,good\nlow,low,5more,more,med,high,vgood\nlow,low,5more,more,big,low,unacc\nlow,low,5more,more,big,med,good\nlow,low,5more,more,big,high,vgood\n" }, { "path": "data/Dataset1-Media-Example-EDGES.csv", "content": "from,to,weight,type\ns01,s02,10,hyperlink\ns01,s02,12,hyperlink\ns01,s03,22,hyperlink\ns01,s04,21,hyperlink\ns04,s11,22,mention\ns05,s15,21,mention\ns06,s17,21,mention\ns08,s09,11,mention\ns08,s09,12,mention\ns03,s04,22,hyperlink\ns04,s03,23,hyperlink\ns01,s15,20,mention\ns15,s01,11,hyperlink\ns15,s01,11,hyperlink\ns16,s17,21,mention\ns16,s06,23,hyperlink\ns06,s16,21,hyperlink\ns09,s10,21,mention\ns08,s07,21,mention\ns07,s08,22,mention\ns07,s10,21,hyperlink\ns05,s02,21,hyperlink\ns02,s03,21,hyperlink\ns02,s01,23,hyperlink\ns03,s01,21,hyperlink\ns12,s13,22,hyperlink\ns12,s14,22,mention\ns14,s13,21,mention\ns13,s12,21,hyperlink\ns05,s09,2,hyperlink\ns02,s10,5,hyperlink\ns03,s12,1,hyperlink\ns04,s06,1,mention\ns10,s03,2,hyperlink\ns03,s10,2,mention\ns04,s12,3,hyperlink\ns13,s17,1,mention\ns06,s06,1,hyperlink\ns14,s11,1,mention\ns03,s11,1,hyperlink\ns12,s06,2,mention\ns04,s17,2,mention\ns17,s04,4,hyperlink\ns08,s03,2,hyperlink\ns03,s08,4,hyperlink\ns07,s14,4,mention\ns15,s06,4,hyperlink\ns15,s04,1,hyperlink\ns05,s01,1,mention\ns02,s09,1,hyperlink\ns03,s05,1,hyperlink\ns07,s03,1,mention\n" }, { "path": "data/Dataset1-Media-Example-NODES.csv", "content": "id,media,media.type,type.label,audience.size\ns01,NY Times,1,Newspaper,20\ns02,Washington Post,1,Newspaper,25\ns03,Wall Street Journal,1,Newspaper,30\ns04,USA Today,1,Newspaper,32\ns05,LA Times,1,Newspaper,20\ns06,New York Post,1,Newspaper,50\ns07,CNN,2,TV,56\ns08,MSNBC,2,TV,34\ns09,FOX News,2,TV,60\ns10,ABC,2,TV,23\ns11,BBC,2,TV,34\ns12,Yahoo News,3,Online,33\ns13,Google News,3,Online,23\ns14,Reuters.com,3,Online,12\ns15,NYTimes.com,3,Online,24\ns16,WashingtonPost.com,3,Online,28\ns17,AOL.com,3,Online,33\n" }, { "path": "data/Dataset2-Media-User-Example-EDGES.csv", "content": ",U01,U02,U03,U04,U05,U06,U07,U08,U09,U10,U11,U12,U13,U14,U15,U16,U17,U18,U19,U20\ns01,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ns02,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ns03,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0\ns04,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0\ns05,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0\ns06,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0\ns07,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0\ns08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0\ns09,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1\ns10,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0\n" }, { "path": "data/Dataset2-Media-User-Example-NODES.csv", "content": "id,media,media.type,media.name,audience.size\ns01,NYT,1,Newspaper,20\ns02,WaPo,1,Newspaper,25\ns03,WSJ,1,Newspaper,30\ns04,USAT,1,Newspaper,32\ns05,LATimes,1,Newspaper,20\ns06,CNN,2,TV,56\ns07,MSNBC,2,TV,34\ns08,FOX ,2,TV,60\ns09,ABC,2,TV,23\ns10,BBC,2,TV,34\nU01,John,NA,NA,NA\nU02,Mary,NA,NA,NA\nU03,Paul,NA,NA,NA\nU04,Ted,NA,NA,NA\nU05,Tom,NA,NA,NA\nU06,Kate,NA,NA,NA\nU07,Ed,NA,NA,NA\nU08,Anna,NA,NA,NA\nU09,Dan,NA,NA,NA\nU10,Nancy,NA,NA,NA\nU11,Sandra,NA,NA,NA\nU12,Ronda,NA,NA,NA\nU13,Sheila,NA,NA,NA\nU14,Jim,NA,NA,NA\nU15,Jill,NA,NA,NA\nU16,Jo,NA,NA,NA\nU17,Brian,NA,NA,NA\nU18,Jason,NA,NA,NA\nU19,Lisa,NA,NA,NA\nU20,Dave,NA,NA,NA\n" }, { "path": "data/ItemList.csv", "content": "\"\",\"itemList\"\n\"1\",\"newspapers\"\n\"2\",\"shopping bags\"\n\"3\",\"chewing gum\"\n\"4\",\"bottled beer\"\n\"5\",\"sugar\"\n\"6\",\"root vegetables\"\n\"7\",\"domestic eggs\"\n\"8\",\"bottled beer\"\n\"9\",\"fruit/vegetable juice\"\n\"10\",\"sugar\"\n\"11\",\"waffles\"\n\"12\",\"coffee\"\n\"13\",\"tropical fruit,sausage,popcorn,ham\"\n\"14\",\"bottled water\"\n\"15\",\"butter milk\"\n\"16\",\"whole milk\"\n\"17\",\"curd cheese\"\n\"18\",\"rolls/buns\"\n\"19\",\"white bread\"\n\"20\",\"pastry\"\n\"21\",\"tropical fruit\"\n\"22\",\"pastry\"\n\"23\",\"rolls/buns\"\n\"24\",\"frankfurter\"\n\"25\",\"fruit/vegetable juice,citrus fruit,frankfurter,other vegetables\"\n\"26\",\"other vegetables,butter,domestic eggs,yogurt,canned beer,curd\"\n\"27\",\"dessert\"\n\"28\",\"other vegetables\"\n\"29\",\"specialty bar\"\n\"30\",\"ham\"\n\"31\",\"tropical fruit\"\n\"32\",\"finished products\"\n\"33\",\"rolls/buns\"\n\"34\",\"rolls/buns\"\n\"35\",\"beef\"\n\"36\",\"liquor (appetizer),frozen potato products,bathroom cleaner\"\n\"37\",\"detergent\"\n\"38\",\"rolls/buns,specialty bar,other vegetables,candy,whole milk,pork,brown bread\"\n\"39\",\"brown bread,other vegetables,frozen meals,oil\"\n\"40\",\"root vegetables\"\n\"41\",\"whole milk\"\n\"42\",\"hamburger meat\"\n\"43\",\"citrus fruit\"\n\"44\",\"sausage\"\n\"45\",\"frankfurter\"\n\"46\",\"bottled water\"\n\"47\",\"other vegetables\"\n\"48\",\"pork\"\n\"49\",\"soda\"\n\"50\",\"canned beer\"\n\"51\",\"beverages\"\n\"52\",\"whole milk,chocolate marshmallow,berries,chocolate\"\n\"53\",\"snack products\"\n\"54\",\"chocolate,frankfurter,processed cheese,tropical fruit\"\n\"55\",\"frankfurter,butter milk,soda\"\n\"56\",\"packaged fruit/vegetables\"\n\"57\",\"sausage\"\n\"58\",\"whole milk\"\n\"59\",\"tropical fruit\"\n\"60\",\"berries\"\n\"61\",\"frankfurter\"\n\"62\",\"misc. beverages\"\n\"63\",\"rolls/buns\"\n\"64\",\"dishes,tropical fruit,bottled water,bottled water\"\n\"65\",\"chicken\"\n\"66\",\"citrus fruit\"\n\"67\",\"red/blush wine\"\n\"68\",\"berries\"\n\"69\",\"whole milk\"\n\"70\",\"abrasive cleaner\"\n\"71\",\"sausage\"\n\"72\",\"onions\"\n\"73\",\"packaged fruit/vegetables\"\n\"74\",\"popcorn\"\n\"75\",\"bottled beer\"\n\"76\",\"canned beer\"\n\"77\",\"whole milk\"\n\"78\",\"root vegetables\"\n\"79\",\"frankfurter\"\n\"80\",\"specialty chocolate\"\n\"81\",\"sausage\"\n\"82\",\"sausage\"\n\"83\",\"curd,chocolate\"\n\"84\",\"chocolate\"\n\"85\",\"whipped/sour cream\"\n\"86\",\"fruit/vegetable juice\"\n\"87\",\"photo/film\"\n\"88\",\"beverages\"\n\"89\",\"domestic eggs\"\n\"90\",\"salty snack\"\n\"91\",\"citrus fruit\"\n\"92\",\"margarine\"\n\"93\",\"whole milk\"\n\"94\",\"other vegetables\"\n\"95\",\"pork\"\n\"96\",\"root vegetables\"\n\"97\",\"rolls/buns\"\n\"98\",\"fruit/vegetable juice\"\n\"99\",\"canned beer\"\n\"100\",\"chicken,newspapers\"\n\"101\",\"root vegetables\"\n\"102\",\"herbs\"\n\"103\",\"frankfurter\"\n\"104\",\"whole milk\"\n\"105\",\"other vegetables\"\n\"106\",\"grapes\"\n\"107\",\"rolls/buns\"\n\"108\",\"finished products\"\n\"109\",\"shopping bags\"\n\"110\",\"sausage\"\n\"111\",\"soda\"\n\"112\",\"whole milk\"\n\"113\",\"pot plants,tropical fruit,sausage,misc. beverages\"\n\"114\",\"frozen potato products\"\n\"115\",\"frankfurter\"\n\"116\",\"citrus fruit\"\n\"117\",\"other vegetables,yogurt,canned beer,ham\"\n\"118\",\"sausage\"\n\"119\",\"yogurt,flour,berries,detergent\"\n\"120\",\"pip fruit\"\n\"121\",\"packaged fruit/vegetables,rolls/buns,soda,whole milk\"\n\"122\",\"sausage\"\n\"123\",\"sausage\"\n\"124\",\"pastry\"\n\"125\",\"cereals\"\n\"126\",\"frankfurter\"\n\"127\",\"whole milk,shopping bags\"\n\"128\",\"chocolate\"\n\"129\",\"ham\"\n\"130\",\"whole milk\"\n\"131\",\"fish\"\n\"132\",\"hamburger meat\"\n\"133\",\"beef\"\n\"134\",\"oil\"\n\"135\",\"rolls/buns\"\n\"136\",\"pasta\"\n\"137\",\"oil\"\n\"138\",\"domestic eggs\"\n\"139\",\"berries\"\n\"140\",\"hamburger meat\"\n\"141\",\"root vegetables\"\n\"142\",\"misc. beverages\"\n\"143\",\"hamburger meat\"\n\"144\",\"root vegetables\"\n\"145\",\"hamburger meat\"\n\"146\",\"rolls/buns\"\n\"147\",\"rolls/buns\"\n\"148\",\"margarine\"\n\"149\",\"curd\"\n\"150\",\"turkey\"\n\"151\",\"curd\"\n\"152\",\"red/blush wine\"\n\"153\",\"pastry\"\n\"154\",\"frankfurter\"\n\"155\",\"soda\"\n\"156\",\"white bread\"\n\"157\",\"bottled water\"\n\"158\",\"whole milk\"\n\"159\",\"rolls/buns\"\n\"160\",\"tropical fruit\"\n\"161\",\"coffee\"\n\"162\",\"butter milk\"\n\"163\",\"sausage\"\n\"164\",\"herbs\"\n\"165\",\"coffee\"\n\"166\",\"beef,rolls/buns,other vegetables\"\n\"167\",\"citrus fruit\"\n\"168\",\"other vegetables\"\n\"169\",\"whole milk\"\n\"170\",\"grapes\"\n\"171\",\"tropical fruit\"\n\"172\",\"whole milk\"\n\"173\",\"other vegetables\"\n\"174\",\"soda\"\n\"175\",\"tropical fruit\"\n\"176\",\"flower soil/fertilizer\"\n\"177\",\"sparkling wine\"\n\"178\",\"fruit/vegetable juice\"\n\"179\",\"waffles,sugar,rolls/buns\"\n\"180\",\"whole milk\"\n\"181\",\"other vegetables\"\n\"182\",\"other vegetables,coffee\"\n\"183\",\"oil\"\n\"184\",\"sausage\"\n\"185\",\"citrus fruit,tropical fruit,beef,waffles\"\n\"186\",\"fruit/vegetable juice\"\n\"187\",\"pip fruit\"\n\"188\",\"citrus fruit\"\n\"189\",\"sugar\"\n\"190\",\"newspapers,soda,packaged fruit/vegetables,whole milk,whole milk,whole milk\"\n\"191\",\"bottled beer\"\n\"192\",\"curd cheese\"\n\"193\",\"canned beer\"\n\"194\",\"whole milk\"\n\"195\",\"whipped/sour cream\"\n\"196\",\"baking powder\"\n\"197\",\"whole milk\"\n\"198\",\"frankfurter\"\n\"199\",\"specialty bar\"\n" }, { "path": "data/MBA.csv", "content": "ID,Products\n1,Product A\n1,Product B\n1,Product C\n1,Product I\n2,Product E\n2,Product F\n2,Product H\n2,Product I\n2,Product J\n2,Product K\n3,Product A\n3,Product I\n3,Product J\n3,Product L\n3,Product M\n4,Product D\n4,Product E\n4,Product F\n4,Product G\n4,Product H\n5,Product D\n5,Product E\n5,Product F\n5,Product G\n5,Product H\n5,Product N\n6,Product A\n6,Product B\n6,Product C\n6,Product I\n6,Product O\n7,Product B\n7,Product C\n7,Product L\n7,Product M\n7,Product O\n8,Product D\n8,Product E\n8,Product F\n8,Product G\n8,Product H\n8,Product I\n8,Product J\n8,Product K\n8,Product O\n9,Product C\n9,Product F\n9,Product H\n9,Product I\n9,Product J\n9,Product L\n9,Product M\n10,Product A\n10,Product B\n10,Product C\n10,Product I\n10,Product O\n11,Product E\n11,Product H\n11,Product L\n12,Product A\n12,Product B\n12,Product C\n12,Product I\n12,Product M\n13,Product A\n13,Product F\n13,Product G\n13,Product I\n13,Product J\n13,Product L\n14,Product A\n14,Product B\n14,Product C\n14,Product I\n15,Product E\n15,Product F\n15,Product G\n15,Product H\n15,Product I\n15,Product J\n15,Product K\n" }, { "path": "data/MBArules.csv", "content": "\"rules\",\"support\",\"confidence\",\"lift\",\"count\"\n\"1\",\"{Product N} => {Product D}\",0.0666666666666667,1,5,1\n\"2\",\"{Product N} => {Product G}\",0.0666666666666667,1,3,1\n\"3\",\"{Product N} => {Product E}\",0.0666666666666667,1,2.5,1\n\"4\",\"{Product N} => {Product H}\",0.0666666666666667,1,2.14285714285714,1\n\"5\",\"{Product N} => {Product F}\",0.0666666666666667,1,2.14285714285714,1\n\"6\",\"{Product D} => {Product G}\",0.2,1,3,3\n\"7\",\"{Product D} => {Product E}\",0.2,1,2.5,3\n\"8\",\"{Product D} => {Product H}\",0.2,1,2.14285714285714,3\n\"9\",\"{Product D} => {Product F}\",0.2,1,2.14285714285714,3\n\"10\",\"{Product K} => {Product E}\",0.2,1,2.5,3\n\"11\",\"{Product K} => {Product J}\",0.2,1,2.5,3\n\"12\",\"{Product K} => {Product H}\",0.2,1,2.14285714285714,3\n\"13\",\"{Product K} => {Product F}\",0.2,1,2.14285714285714,3\n\"14\",\"{Product K} => {Product I}\",0.2,1,1.36363636363636,3\n\"15\",\"{Product B} => {Product A}\",0.333333333333333,0.833333333333333,1.78571428571429,5\n\"16\",\"{Product B} => {Product C}\",0.4,1,2.14285714285714,6\n\"17\",\"{Product C} => {Product B}\",0.4,0.857142857142857,2.14285714285714,6\n\"18\",\"{Product B} => {Product I}\",0.333333333333333,0.833333333333333,1.13636363636364,5\n\"19\",\"{Product G} => {Product E}\",0.266666666666667,0.8,2,4\n\"20\",\"{Product G} => {Product H}\",0.266666666666667,0.8,1.71428571428571,4\n\"21\",\"{Product G} => {Product F}\",0.333333333333333,1,2.14285714285714,5\n\"22\",\"{Product A} => {Product I}\",0.466666666666667,1,1.36363636363636,7\n\"23\",\"{Product C} => {Product I}\",0.4,0.857142857142857,1.16883116883117,6\n\"24\",\"{Product E} => {Product H}\",0.4,1,2.14285714285714,6\n\"25\",\"{Product H} => {Product E}\",0.4,0.857142857142857,2.14285714285714,6\n\"26\",\"{Product E} => {Product F}\",0.333333333333333,0.833333333333333,1.78571428571429,5\n\"27\",\"{Product J} => {Product F}\",0.333333333333333,0.833333333333333,1.78571428571429,5\n\"28\",\"{Product J} => {Product I}\",0.4,1,1.36363636363636,6\n\"29\",\"{Product H} => {Product F}\",0.4,0.857142857142857,1.83673469387755,6\n\"30\",\"{Product F} => {Product H}\",0.4,0.857142857142857,1.83673469387755,6\n\"31\",\"{Product D,Product N} => {Product G}\",0.0666666666666667,1,3,1\n\"32\",\"{Product G,Product N} => {Product D}\",0.0666666666666667,1,5,1\n\"33\",\"{Product D,Product N} => {Product E}\",0.0666666666666667,1,2.5,1\n\"34\",\"{Product E,Product N} => {Product D}\",0.0666666666666667,1,5,1\n\"35\",\"{Product D,Product N} => {Product H}\",0.0666666666666667,1,2.14285714285714,1\n\"36\",\"{Product H,Product N} => {Product D}\",0.0666666666666667,1,5,1\n\"37\",\"{Product D,Product N} => {Product F}\",0.0666666666666667,1,2.14285714285714,1\n\"38\",\"{Product F,Product N} => {Product D}\",0.0666666666666667,1,5,1\n\"39\",\"{Product G,Product N} => {Product E}\",0.0666666666666667,1,2.5,1\n\"40\",\"{Product E,Product N} => {Product G}\",0.0666666666666667,1,3,1\n\"41\",\"{Product G,Product N} => {Product H}\",0.0666666666666667,1,2.14285714285714,1\n\"42\",\"{Product H,Product N} => {Product G}\",0.0666666666666667,1,3,1\n\"43\",\"{Product G,Product N} => {Product F}\",0.0666666666666667,1,2.14285714285714,1\n\"44\",\"{Product F,Product N} => {Product G}\",0.0666666666666667,1,3,1\n\"45\",\"{Product E,Product N} => {Product H}\",0.0666666666666667,1,2.14285714285714,1\n\"46\",\"{Product H,Product N} => {Product E}\",0.0666666666666667,1,2.5,1\n\"47\",\"{Product E,Product N} => {Product F}\",0.0666666666666667,1,2.14285714285714,1\n\"48\",\"{Product F,Product N} => {Product 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"id,diagnosis_result,radius,texture,perimeter,area,smoothness,compactness,symmetry,fractal_dimension\r\n1,M,23,12,151,954,0.143,0.278,0.242,0.079\r\n2,B,9,13,133,1326,0.143,0.079,0.181,0.057\r\n3,M,21,27,130,1203,0.125,0.16,0.207,0.06\r\n4,M,14,16,78,386,0.07,0.284,0.26,0.097\r\n5,M,9,19,135,1297,0.141,0.133,0.181,0.059\r\n6,B,25,25,83,477,0.128,0.17,0.209,0.076\r\n7,M,16,26,120,1040,0.095,0.109,0.179,0.057\r\n8,M,15,18,90,578,0.119,0.165,0.22,0.075\r\n9,M,19,24,88,520,0.127,0.193,0.235,0.074\r\n10,M,25,11,84,476,0.119,0.24,0.203,0.082\r\n11,M,24,21,103,798,0.082,0.067,0.153,0.057\r\n12,M,17,15,104,781,0.097,0.129,0.184,0.061\r\n13,B,14,15,132,1123,0.097,0.246,0.24,0.078\r\n14,M,12,22,104,783,0.084,0.1,0.185,0.053\r\n15,M,12,13,94,578,0.113,0.229,0.207,0.077\r\n16,M,22,19,97,659,0.114,0.16,0.23,0.071\r\n17,M,10,16,95,685,0.099,0.072,0.159,0.059\r\n18,M,15,14,108,799,0.117,0.202,0.216,0.074\r\n19,M,20,14,130,1260,0.098,0.103,0.158,0.054\r\n20,B,17,11,87,566,0.098,0.081,0.189,0.058\r\n21,B,16,14,86,520,0.108,0.127,0.197,0.068\r\n22,B,17,24,60,274,0.102,0.065,0.182,0.069\r\n23,M,20,27,103,704,0.107,0.214,0.252,0.07\r\n24,M,19,12,137,1404,0.094,0.102,0.177,0.053\r\n25,M,9,13,110,905,0.112,0.146,0.2,0.063\r\n26,M,19,27,116,913,0.119,0.228,0.304,0.074\r\n27,M,10,24,97,645,0.105,0.187,0.225,0.069\r\n28,M,16,24,122,1094,0.094,0.107,0.17,0.057\r\n29,M,15,15,102,732,0.108,0.17,0.193,0.065\r\n30,M,11,16,115,955,0.098,0.116,0.174,0.061\r\n31,M,11,22,125,1088,0.106,0.189,0.218,0.062\r\n32,M,23,26,78,441,0.111,0.152,0.23,0.078\r\n33,M,20,18,113,899,0.12,0.15,0.225,0.064\r\n34,M,11,21,128,1162,0.094,0.172,0.185,0.063\r\n35,M,16,23,107,807,0.104,0.156,0.2,0.065\r\n36,M,10,13,110,870,0.096,0.134,0.19,0.057\r\n37,M,18,12,94,633,0.098,0.11,0.189,0.061\r\n38,B,21,11,83,524,0.09,0.038,0.147,0.059\r\n39,M,11,15,96,699,0.094,0.051,0.157,0.055\r\n40,M,10,14,88,559,0.102,0.126,0.172,0.064\r\n41,M,24,16,86,563,0.082,0.06,0.178,0.056\r\n42,M,19,27,72,371,0.123,0.122,0.19,0.069\r\n43,M,11,11,128,1104,0.091,0.219,0.231,0.063\r\n44,M,15,21,87,545,0.104,0.144,0.197,0.068\r\n45,M,10,15,85,532,0.097,0.105,0.175,0.062\r\n46,M,18,11,124,1076,0.11,0.169,0.191,0.06\r\n47,B,22,12,52,202,0.086,0.059,0.177,0.065\r\n48,M,20,14,86,535,0.116,0.123,0.213,0.068\r\n49,B,20,21,78,449,0.103,0.091,0.168,0.06\r\n50,B,25,11,87,561,0.088,0.077,0.181,0.057\r\n51,B,19,25,75,428,0.086,0.05,0.15,0.059\r\n52,B,19,22,87,572,0.077,0.061,0.135,0.06\r\n53,B,25,15,76,438,0.083,0.048,0.187,0.061\r\n54,M,14,26,120,1033,0.115,0.149,0.209,0.063\r\n55,M,18,25,97,713,0.091,0.071,0.162,0.057\r\n56,B,18,13,73,409,0.095,0.055,0.192,0.059\r\n57,M,10,19,126,1152,0.105,0.127,0.192,0.06\r\n58,M,17,20,96,657,0.114,0.137,0.203,0.068\r\n59,B,22,15,83,527,0.081,0.038,0.182,0.055\r\n60,B,23,26,54,225,0.098,0.053,0.168,0.072\r\n61,B,15,18,65,312,0.113,0.081,0.274,0.07\r\n62,B,25,15,55,222,0.124,0.09,0.183,0.068\r\n63,M,12,22,96,646,0.105,0.201,0.195,0.073\r\n64,B,24,17,59,261,0.077,0.088,0.234,0.07\r\n65,M,16,19,83,499,0.112,0.126,0.191,0.066\r\n66,M,11,21,97,668,0.117,0.148,0.195,0.067\r\n67,B,12,13,60,269,0.104,0.078,0.172,0.069\r\n68,B,18,12,72,394,0.081,0.047,0.152,0.057\r\n69,B,16,17,59,251,0.107,0.141,0.211,0.08\r\n70,B,17,21,81,503,0.098,0.052,0.159,0.057\r\n71,M,21,18,124,1130,0.09,0.103,0.158,0.055\r\n72,B,9,26,59,244,0.098,0.153,0.19,0.09\r\n73,M,21,12,114,929,0.107,0.183,0.193,0.065\r\n74,M,22,25,90,584,0.101,0.128,0.166,0.066\r\n75,B,18,13,79,471,0.092,0.068,0.172,0.059\r\n76,M,21,18,104,818,0.092,0.084,0.18,0.054\r\n77,B,10,17,88,559,0.129,0.105,0.24,0.066\r\n78,M,11,21,120,1006,0.107,0.215,0.215,0.067\r\n79,M,16,18,144,1245,0.129,0.345,0.291,0.081\r\n80,B,22,16,83,506,0.099,0.095,0.172,0.06\r\n81,B,10,18,74,402,0.11,0.094,0.184,0.07\r\n82,B,17,21,86,520,0.108,0.154,0.194,0.069\r\n83,M,10,15,172,1878,0.106,0.267,0.183,0.068\r\n84,M,20,14,129,1132,0.122,0.179,0.163,0.072\r\n85,B,25,21,77,443,0.097,0.072,0.208,0.06\r\n86,M,14,13,121,1075,0.099,0.105,0.213,0.06\r\n87,M,19,26,94,648,0.094,0.099,0.208,0.056\r\n88,M,19,11,122,1076,0.09,0.121,0.195,0.056\r\n89,B,11,11,80,466,0.088,0.094,0.193,0.064\r\n90,B,12,23,96,652,0.113,0.134,0.212,0.063\r\n91,B,23,27,95,663,0.09,0.086,0.169,0.059\r\n92,M,10,12,100,728,0.092,0.104,0.172,0.061\r\n93,B,14,14,85,552,0.074,0.051,0.139,0.053\r\n94,B,10,17,87,555,0.102,0.082,0.164,0.057\r\n95,M,22,26,100,706,0.104,0.155,0.186,0.063\r\n96,M,23,16,132,1264,0.091,0.131,0.21,0.056\r\n97,B,22,14,78,451,0.105,0.071,0.19,0.066\r\n98,B,19,27,62,295,0.102,0.053,0.135,0.069\r\n99,B,21,24,74,413,0.09,0.075,0.162,0.066\r\n100,M,16,27,94,643,0.098,0.114,0.188,0.064\r\n" }, { "path": "data/Rules_20.csv", "content": "\"confidence\",\"support\"\n" }, { "path": "data/StudentPassFail.csv", "content": "studentid,comnskills,englishspk,acadperf,relocwilling,eyecontact,extraco,gender,select\ns01,1,1,1,1,1,1,1,1\ns02,1,1,1,1,1,1,1,1\ns03,0,0,0,0,0,0,0,0\ns04,0,1,0,0,0,0,0,0\ns05,1,1,1,0,1,1,1,1\ns06,1,1,1,1,0,0,0,0\ns07,0,0,0,0,0,0,0,0\ns08,1,1,1,1,0,0,0,1\ns09,1,1,1,1,1,1,1,1\ns10,0,1,0,0,0,0,0,0\ns11,0,1,0,1,0,0,0,0\ns12,0,0,0,0,0,0,0,0\ns13,1,1,1,1,1,1,1,1\ns14,1,1,0,1,1,0,0,0\ns15,1,1,1,0,1,0,0,1\ns16,1,1,0,0,1,1,1,1\ns17,1,0,0,1,1,1,1,0\ns18,0,0,0,0,1,1,0,0\ns19,0,0,0,0,0,0,1,0\ns20,1,0,1,1,1,1,1,1\ns21,1,1,0,1,1,1,1,0\ns22,1,1,1,0,1,1,1,1\ns23,1,1,1,1,0,0,1,1\ns24,1,1,1,1,1,1,0,0\ns25,0,0,0,1,0,0,0,0\ns26,0,1,1,1,0,0,0,0\ns27,0,0,1,0,0,0,0,0\ns28,1,1,1,1,0,1,1,1\ns29,1,1,1,1,1,0,1,1\ns30,1,1,1,1,0,1,1,1\ns31,1,1,1,0,1,1,1,1\ns32,1,0,0,1,1,1,1,0\ns33,0,0,0,0,0,0,1,0\ns34,1,1,1,1,1,1,1,1\ns35,1,1,1,1,1,1,1,1\ns36,0,1,0,0,1,1,1,0\ns37,0,0,0,0,0,0,0,0\ns38,0,0,0,0,0,0,0,0\ns39,1,1,0,1,1,1,1,1\ns40,0,0,0,0,0,0,0,0\ns41,1,0,1,1,1,1,1,1\ns42,1,0,0,0,0,0,0,0\ns43,0,0,0,0,1,0,0,0\ns44,0,1,0,0,0,0,1,0\ns45,1,1,0,1,1,1,1,1\ns46,1,1,1,0,1,0,1,0\ns47,0,0,0,0,0,0,0,0\ns48,0,0,1,0,0,1,0,0\ns49,0,1,1,0,1,1,1,0\ns50,0,0,0,0,0,1,0,0" }, { "path": "data/StudentTid1.csv", "content": "\"tid,comnskills,englishspk,acadperf,relocwilling,eyecontact,extraco,gender\"\n\"s1,Yes,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"s2,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"s3,No,No,No,No,No,No,No,No\"\n\"s4,No,Yes,No,No,No,No,No,No\"\n\"s5,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes\"\n\"s6,Yes,Yes,Yes,Yes,No,No,No,No\"\n\"s7,No,No,No,No,No,No,No,No\"\n\"s8,Yes,Yes,Yes,Yes,No,No,No,Yes\"\n\"s9,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"s10,No,Yes,No,No,No,No,No,No\"\n\"s11,No,Yes,No,Yes,No,No,No,No\"\n\"s12,No,No,No,No,No,No,No,No\"\n\"s13,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"s14,Yes,Yes,No,Yes,Yes,No,No,No\"\n\"s15,Yes,Yes,Yes,No,Yes,No,No,Yes\"\n\"s16,Yes,Yes,No,No,Yes,Yes,Yes,Yes\"\n\"s17,Yes,No,No,Yes,Yes,Yes,Yes,No\"\n\"s18,No,No,No,No,Yes,Yes,No,No\"\n\"s19,No,No,No,No,No,No,Yes,No\"\n\"s20,Yes,No,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"s21,Yes,Yes,No,Yes,Yes,Yes,Yes,No\"\n\"s22,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes\"\n\"s23,Yes,Yes,Yes,Yes,No,No,Yes,Yes\"\n\"s24,Yes,Yes,Yes,Yes,Yes,Yes,No,No\"\n\"s25,No,No,No,Yes,No,No,No,No\"\n\"s26,No,Yes,Yes,Yes,No,No,No,No\"\n\"s27,No,No,Yes,No,No,No,No,No\"\n\"s28,Yes,Yes,Yes,Yes,No,Yes,Yes,Yes\"\n\"s29,Yes,Yes,Yes,Yes,Yes,No,Yes,Yes\"\n\"s30,Yes,Yes,Yes,Yes,No,Yes,Yes,Yes\"\n\"s31,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes\"\n\"s32,Yes,No,No,Yes,Yes,Yes,Yes,No\"\n\"s33,No,No,No,No,No,No,Yes,No\"\n\"s34,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"s35,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"s36,No,Yes,No,No,Yes,Yes,Yes,No\"\n\"s37,No,No,No,No,No,No,No,No\"\n\"s38,No,No,No,No,No,No,No,No\"\n\"s39,Yes,Yes,No,Yes,Yes,Yes,Yes,Yes\"\n\"s40,No,No,No,No,No,No,No,No\"\n\"s41,Yes,No,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"s42,Yes,No,No,No,No,No,No,No\"\n\"s43,No,No,No,No,Yes,No,No,No\"\n\"s44,No,Yes,No,No,No,No,Yes,No\"\n\"s45,Yes,Yes,No,Yes,Yes,Yes,Yes,Yes\"\n\"s46,Yes,Yes,Yes,No,Yes,No,Yes,No\"\n\"s47,No,No,No,No,No,No,No,No\"\n\"s48,No,No,Yes,No,No,Yes,No,No\"\n\"s49,No,Yes,Yes,No,Yes,Yes,Yes,No\"\n\"s50,No,No,No,No,No,Yes,No,No\"" }, { "path": "data/StudentTid2.csv", "content": "\"studentch,bigdata,wmc,cc,pr,adbms,crypto\"\n\"c1,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c2,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c3,No,No,No,No,No,No\"\n\"c4,No,Yes,No,No,No,No\"\n\"c5,Yes,Yes,Yes,No,Yes,Yes\"\n\"c6,Yes,Yes,Yes,Yes,No,No\"\n\"c7,No,No,No,No,No,No\"\n\"c8,Yes,Yes,Yes,Yes,No,No\"\n\"c9,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c10,No,Yes,No,No,No,No\"\n\"c11,No,Yes,No,Yes,No,No\"\n\"c12,No,No,No,No,No,No\"\n\"c13,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c14,Yes,Yes,No,Yes,Yes,No\"\n\"c15,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c16,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c17,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c18,No,No,No,No,No,No\"\n\"c19,No,No,No,No,No,No\"\n\"c20,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c21,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c22,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c23,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c24,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c25,No,No,No,No,No,No\"\n\"c26,No,Yes,Yes,Yes,No,No\"\n\"c27,No,No,Yes,No,No,No\"\n\"c28,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c29,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c30,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c31,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c32,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c33,No,No,No,No,No,No\"\n\"c34,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c35,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c36,No,Yes,No,No,Yes,Yes\"\n\"c37,No,No,No,No,No,No\"\n\"c38,No,No,No,No,No,No\"\n\"c39,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c40,No,No,No,No,No,No\"\n\"c41,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c42,Yes,No,No,No,No,No\"\n\"c43,No,No,No,No,No,No\"\n\"c44,No,No,No,No,No,No\"\n\"c45,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c46,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c47,No,No,No,No,No,No\"\n\"c48,No,No,No,No,No,No\"\n\"c49,Yes,Yes,Yes,Yes,Yes,Yes\"\n\"c50,No,No,No,No,No,No\"" }, { "path": "data/ar14.csv", "content": "graduation,attendance,assignment,unittest,result\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor\nGood,Good,Good,Good,Good\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor\nGood,Good,Good,Good,Good\nGood,Good,Good,Good,Good\nAvg,Avg,Avg,Avg,Avg\nPoor,Poor,Poor,Poor,Poor" }, { "path": "data/ar14b.csv", "content": "graduation,attendance,assignment,unittest,result\n87,57,68,65,73\n66,87,86,64,81\n51,82,62,95,55\n85,94,81,64,54\n84,84,63,79,76\n66,95,51,79,85\n55,95,71,60,79\n65,65,78,82,61\n84,67,53,94,65\n71,87,87,81,63\n50,81,65,67,63\n92,85,69,80,76\n58,53,93,91,94\n71,83,73,87,51\n74,70,82,64,89\n56,93,65,82,70\n92,90,91,58,56\n72,50,81,79,80\n76,64,73,54,92\n57,93,95,72,73\n63,62,60,91,64\n78,70,61,66,72\n64,77,87,88,70\n92,87,83,67,72\n65,54,76,55,53\n89,51,76,55,56\n69,84,74,84,62\n88,70,64,68,91\n54,94,95,72,77" }, { "path": "data/dar1.csv", "content": "java,cpp,dbms,dwm,cbnst,os,grade\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,No,No,Yes,No,No,Fail\nNo,Yes,No,No,No,No,Fail\nYes,Yes,Yes,No,Yes,Yes,Pass\nYes,Yes,Yes,Yes,No,No,Pass\nNo,No,No,No,No,Yes,Fail\nYes,Yes,Yes,Yes,No,No,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,No,No,Fail\nNo,Yes,No,Yes,No,No,Fail\nNo,No,No,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,No,Yes,Yes,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,No,No,Fail\nNo,No,No,No,No,No,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,No,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,No,Yes,No,No,No,Fail\nNo,Yes,Yes,Yes,No,No,Pass\nNo,No,Yes,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,Yes,Yes,Pass\nNo,No,No,No,Yes,No,Fail\nNo,No,Yes,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,No,No,Yes,No,No,Fail\nNo,Yes,No,No,No,No,Fail\nNo,No,No,Yes,Yes,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,No,Yes,No,No,No,Fail\nNo,No,No,Yes,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,No,No,No,No,Yes,Fail" }, { "path": "data/dar1w.csv", "content": "\"java\",\"cpp\",\"dbms\",\"dwm\",\"cbnst\",\"os\",\"grade\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Fail\"\n\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Pass\"\n\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Fail\"\n\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Fail\"\n\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Fail\"\n\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Fail\"\n\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Pass\"\n\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Fail\"\n\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Fail\"\n\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Fail\"\n\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Fail\"\n\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Fail\"\n\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Pass\"\n\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Fail\"\n" }, { "path": "data/dar1w.csv.arff", "content": "@relation dar1w\n\n@attribute java {Yes,No}\n@attribute cpp {Yes,No}\n@attribute dbms {Yes,No}\n@attribute dwm {Yes,No}\n@attribute cbnst {Yes,No}\n@attribute os {Yes,No}\n@attribute grade {Pass,Fail}\n\n@data\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,No,No,Yes,No,No,Fail\nNo,Yes,No,No,No,No,Fail\nYes,Yes,Yes,No,Yes,Yes,Pass\nYes,Yes,Yes,Yes,No,No,Pass\nNo,No,No,No,No,Yes,Fail\nYes,Yes,Yes,Yes,No,No,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,No,No,Fail\nNo,Yes,No,Yes,No,No,Fail\nNo,No,No,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,No,Yes,Yes,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,No,No,Fail\nNo,No,No,No,No,No,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,No,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,No,Yes,No,No,No,Fail\nNo,Yes,Yes,Yes,No,No,Pass\nNo,No,Yes,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,Yes,Yes,Pass\nNo,No,No,No,Yes,No,Fail\nNo,No,Yes,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,Yes,No,No,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,No,No,Yes,No,No,Fail\nNo,Yes,No,No,No,No,Fail\nNo,No,No,Yes,Yes,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,No,Yes,No,No,No,Fail\nNo,No,No,Yes,No,No,Fail\nYes,Yes,Yes,Yes,Yes,Yes,Pass\nNo,No,No,No,No,Yes,Fail\n" }, { "path": "data/dar2.csv", "content": "studentid,comnskills,englishspk,acadperf,relocwilling,eyecontact,extraco,gender,select\ns01,1,1,1,1,1,1,1,1\ns02,1,1,1,1,1,1,1,1\ns03,0,0,0,0,0,0,0,0\ns04,0,1,0,0,0,0,0,0\ns05,1,1,1,0,1,1,1,1\ns06,1,1,1,1,0,0,0,0\ns07,0,0,0,0,0,0,0,0\ns08,1,1,1,1,0,0,0,1\ns09,1,1,1,1,1,1,1,1\ns10,0,1,0,0,0,0,0,0\ns11,0,1,0,1,0,0,0,0\ns12,0,0,0,0,0,0,0,0\ns13,1,1,1,1,1,1,1,1\ns14,1,1,0,1,1,0,0,0\ns15,1,1,1,0,1,0,0,1\ns16,1,1,0,0,1,1,1,1\ns17,1,0,0,1,1,1,1,0\ns18,0,0,0,0,1,1,0,0\ns19,0,0,0,0,0,0,1,0\ns20,1,0,1,1,1,1,1,1\ns21,1,1,0,1,1,1,1,0\ns22,1,1,1,0,1,1,1,1\ns23,1,1,1,1,0,0,1,1\ns24,1,1,1,1,1,1,0,0\ns25,0,0,0,1,0,0,0,0\ns26,0,1,1,1,0,0,0,0\ns27,0,0,1,0,0,0,0,0\ns28,1,1,1,1,0,1,1,1\ns29,1,1,1,1,1,0,1,1\ns30,1,1,1,1,0,1,1,1\ns31,1,1,1,0,1,1,1,1\ns32,1,0,0,1,1,1,1,0\ns33,0,0,0,0,0,0,1,0\ns34,1,1,1,1,1,1,1,1\ns35,1,1,1,1,1,1,1,1\ns36,0,1,0,0,1,1,1,0\ns37,0,0,0,0,0,0,0,0\ns38,0,0,0,0,0,0,0,0\ns39,1,1,0,1,1,1,1,1\ns40,0,0,0,0,0,0,0,0\ns41,1,0,1,1,1,1,1,1\ns42,1,0,0,0,0,0,0,0\ns43,0,0,0,0,1,0,0,0\ns44,0,1,0,0,0,0,1,0\ns45,1,1,0,1,1,1,1,1\ns46,1,1,1,0,1,0,1,0\ns47,0,0,0,0,0,0,0,0\ns48,0,0,1,0,0,1,0,0\ns49,0,1,1,0,1,1,1,0\ns50,0,0,0,0,0,1,0,0" }, { "path": "data/dar3.csv", "content": "bigdata,mobcomp,cloudcomp,patternrecogn,adbms,crypto\nYes,Yes,Yes,,,\nYes,,,,Yes,Yes\n,Yes,Yes,Yes,,\n,Yes,Yes,Yes,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\n,Yes,Yes,Yes,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\n,Yes,,Yes,Yes,\n,Yes,,Yes,Yes,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\nYes,Yes,,Yes,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\n,,,Yes,Yes,Yes\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\nYes,Yes,,,,Yes\nYes,,,,Yes,Yes\n,,,Yes,Yes,Yes\nYes,Yes,Yes,,,\n,Yes,Yes,Yes,,\n,Yes,Yes,Yes,,\nYes,Yes,Yes,,,\nYes,Yes,,,,Yes\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\n,,,,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\n,Yes,,,Yes,Yes\n,,,,,\n,,,,,\nYes,Yes,Yes,,,\n,,,,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,Yes,Yes,Yes\nYes,Yes,Yes,Yes,Yes,Yes\n,Yes,Yes,Yes,,\n,Yes,Yes,Yes,,\nYes,Yes,Yes,,,\nYes,Yes,Yes,,," }, { "path": "data/dar3a.csv", "content": "\"s01,bigdata,mobcomp,cloudcomp\"\n\"s02,mobcomp,patternrecogn,adbms\"\n\"s03,mobcomp,patternrecogn,adbms\"\n\"s04,bigdata,mobcomp,cloudcomp\"\n\"s05,bigdata,mobcomp,cloudcomp\"\n\"s06,bigdata,mobcomp,patternrecogn\"\n\"s07,bigdata,mobcomp,cloudcomp\"\n\"s08,bigdata,mobcomp,cloudcomp\"\n\"s09,bigdata,mobcomp,cloudcomp\"\n\"s10,patternrecogn,adbms,crypto\"\n\"s11,bigdata,mobcomp,cloudcomp\"\n\"s12,bigdata,adbms,crypto\"\n\"s13,bigdata,mobcomp,cloudcomp\"\n\"s14,bigdata,mobcomp,cloudcomp\"\n\"s15,bigdata,mobcomp,crypto\"\n\"s16,bigdata,adbms,crypto\"\n\"s17,patternrecogn,adbms,crypto\"\n\"s18,bigdata,mobcomp,cloudcomp\"\n\"s19,mobcomp,cloudcomp,patternrecogn\"\n\"s20,mobcomp,cloudcomp,patternrecogn\"\n\"s21,bigdata,mobcomp,cloudcomp\"\n\"s22,bigdata,mobcomp,crypto\"\n\"s23,mobcomp,cloudcomp,patternrecogn\"\n\"s24,bigdata,mobcomp,cloudcomp\"\n\"s25,bigdata,mobcomp,cloudcomp\"\n\"s26,bigdata,mobcomp,cloudcomp\"\n\"s27,cloudcomp,patternrecogn,adbms\"\n\"s28,bigdata,mobcomp,cloudcomp\"\n\"s29,bigdata,mobcomp,cloudcomp\"\n\"s30,mobcomp,adbms,crypto\"\n\"s31,cloudcomp,patternrecogn,adbms\"\n\"s32,cloudcomp,patternrecogn,adbms\"\n\"s33,bigdata,mobcomp,cloudcomp\"\n\"s34,mobcomp,cloudcomp,patternrecogn\"\n\"s35,cloudcomp,patternrecogn,adbms\"\n\"s36,bigdata,mobcomp,cloudcomp\"\n\"s37,bigdata,mobcomp,cloudcomp\"\n\"s38,bigdata,mobcomp,cloudcomp\"\n\"s39,bigdata,mobcomp,cloudcomp\"\n\"s40,bigdata,mobcomp,cloudcomp\"\n\"s41,bigdata,mobcomp,crypto\"\n\"s42,mobcomp,cloudcomp,patternrecogn\"\n\"s43,mobcomp,cloudcomp,patternrecogn\"\n\"s44,bigdata,mobcomp,cloudcomp\"\n\"s45,bigdata,mobcomp,cloudcomp\"\n\"s46,bigdata,mobcomp,cloudcomp\"\n\"s47,bigdata,mobcomp,cloudcomp\"\n\"s48,mobcomp,cloudcomp,patternrecogn\"\n\"s49,bigdata,mobcomp,cloudcomp\"\n\"s50,bigdata,mobcomp,cloudcomp\"" }, { "path": "data/dar3b.csv", "content": "studentch,elective1,elective2,elective3,elective4,elective5,elective6\nc01,1,1,1,1,1,1\nc02,1,1,1,1,1,1\nc03,0,0,0,0,0,0\nc04,0,1,0,0,0,0\nc05,1,1,1,0,1,1\nc06,1,1,1,1,0,0\nc07,0,0,0,0,0,0\nc08,1,1,1,1,0,0\nc09,1,1,1,1,1,1\nc10,0,1,0,0,0,0\nc11,0,1,0,1,0,0\nc12,0,0,0,0,0,0\nc13,1,1,1,1,1,1\nc14,1,1,0,1,1,0\nc15,1,1,1,1,1,1\nc16,1,1,1,1,1,1\nc17,1,1,1,1,1,1\nc18,0,0,0,0,0,0\nc19,0,0,0,0,0,0\nc20,1,1,1,1,1,1\nc21,1,1,1,1,1,1\nc22,1,1,1,1,1,1\nc23,1,1,1,1,1,1\nc24,1,1,1,1,1,1\nc25,0,0,0,0,0,0\nc26,0,1,1,1,0,0\nc27,0,0,1,0,0,0\nc28,1,1,1,1,1,1\nc29,1,1,1,1,1,1\nc30,1,1,1,1,1,1\nc31,1,1,1,1,1,1\nc32,1,1,1,1,1,1\nc33,0,0,0,0,0,0\nc34,1,1,1,1,1,1\nc35,1,1,1,1,1,1\nc36,0,1,0,0,1,1\nc37,0,0,0,0,0,0\nc38,0,0,0,0,0,0\nc39,1,1,1,1,1,1\nc40,0,0,0,0,0,0\nc41,1,1,1,1,1,1\nc42,1,0,0,0,0,0\nc43,0,0,0,0,0,0\nc44,0,0,0,0,0,0\nc45,1,1,1,1,1,1\nc46,1,1,1,1,1,1\nc47,0,0,0,0,0,0\nc48,0,0,0,0,0,0\nc49,1,1,1,1,1,1\nc50,0,0,0,0,0,0" }, { "path": "data/data1.R", "content": "# Importing and Sorting Data\ntrain = read.csv('data/train.csv')\ntest = read.csv('data/test.csv')\nstr(train) # has purchase coln extra\nstr(test)\nsummary(train)\nsummary(test)\nhead(train)\nhead(train)\ny_train = train$Purchase\nx_train = train$Occupation\nx_test = test$Occupation\n\n" }, { "path": "data/dateformat1.R", "content": "# Date Formats\n#https://www.r-bloggers.com/date-formats-in-r/\n#http://www.statmethods.net/input/dates.html\n\n\n# use as.Date( ) to convert strings to dates \nmydates <- as.Date(c(\"2007-06-22\", \"2004-02-13\"))\n# number of days between 6/22/07 and 2/13/04 \ndays <- mydates[1] - mydates[2]\nSys.Date() #returns today's date. \ndate() #returns the current date and time.\n\nas.Date(dates,\n format = \"%m/%d/%y\")" }, { "path": "data/groceries.csv", "content": "Member_number,Date,itemDescription\n1619052826499,10/22/2012,citrus fruit\n1679031673299,10/03/2010,tropical fruit\n1634042851699,07/07/2011,whole milk\n1656022021999,03/13/2010,pip fruit\n1612011328599,09/07/2013,other vegetables\n1660042076699,06/06/2010,whole milk\n1634062916299,02/15/2014,abrasive cleaner\n1616070832799,12/30/2012,rolls/buns\n1655052493299,10/24/2011,other vegetables\n1615111911399,12/15/2011,liquor (appetizer)\n1651110523499,11/15/2013,pot plants\n1659030116999,12/17/2012,whole milk\n1606041259699,08/19/2010,tropical fruit\n1626091280899,07/20/2010,chocolate\n1689081758199,04/10/2013,citrus fruit\n1640041714599,03/05/2014,curd\n1630051469899,10/22/2012,dishes\n1681021693299,05/31/2013,beef\n1627050218699,08/16/2014,frankfurter\n1647060986799,06/19/2013,chicken\n1612011328599,09/07/2013,butter\n1611032908799,12/03/2012,fruit/vegetable juice\n1657011295699,10/09/2013,packaged fruit/vegetables\n1640041714599,03/05/2014,chocolate\n1616070832799,12/30/2012,specialty bar\n1616070832799,12/30/2012,other vegetables\n1627050218699,08/16/2014,butter milk\n1626042228999,01/17/2010,whole milk\n1651110523499,11/15/2013,tropical fruit\n1647060986799,06/19/2013,newspapers\n1630051469899,10/22/2012,tropical fruit\n1681021693299,05/31/2013,rolls/buns\n1685101155599,08/04/2010,waffles\n1630051469899,10/22/2012,bottled water\n1656012667099,03/11/2010,yogurt\n1651110523499,11/15/2013,sausage\n1688081821499,10/20/2010,other vegetables\n1617081511799,11/23/2014,brown bread\n1692012598499,01/30/2011,newspapers\n1655052493299,10/24/2011,yogurt\n1670051155999,10/25/2012,hamburger meat\n1677121935999,10/28/2010,bottled water\n1668070593399,02/05/2013,root vegetables\n1604042540799,08/09/2012,sugar\n1624090693999,02/07/2014,pork\n1640092051699,01/28/2011,whipped/sour cream\n1615041962899,08/01/2011,beef\n1609040411099,08/01/2014,pastry\n1604032908399,10/12/2013,fruit/vegetable juice\n1655052493299,10/24/2011,canned beer\n1618072441999,05/29/2012,root vegetables\n1681031185999,03/25/2013,citrus fruit\n1649121242599,07/16/2010,sausage\n1612022797599,06/03/2011,specialty bar\n1689081758199,04/10/2013,tropical 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products\n1661050632799,06/10/2013,beef\n1684082440799,12/26/2014,flower soil/fertilizer\n1692012598499,01/30/2011,whole milk\n1610112630299,11/28/2010,frankfurter\n1683082574099,10/12/2014,tropical fruit\n1612011328599,09/07/2013,canned beer\n1613112717499,08/11/2014,finished products\n1643030485499,11/19/2011,beverages\n1669012238999,09/18/2011,hamburger meat\n1607030587099,04/01/2011,whole milk\n1649061207499,07/13/2013,finished products\n1611032908799,12/03/2012,other vegetables\n1612100987299,12/14/2010,ham\n1608102521399,02/15/2011,white bread\n1645111803599,10/18/2013,other vegetables\n1674021053099,04/12/2010,curd\n1606041259699,08/19/2010,popcorn\n1692012598499,07/26/2011,bottled beer\n1612011328599,09/07/2013,curd\n1689081758199,04/10/2013,waffles\n1692012598499,01/30/2011,whole milk\n1648051560299,04/14/2010,whole milk\n1612012216399,01/19/2011,dessert\n1679091789699,02/19/2013,sausage\n1681021693299,05/31/2013,other vegetables\n1600122428599,08/26/2012,shopping bags\n1606041259699,08/19/2010,ham\n1612020387199,02/11/2014,other vegetables\n1648092066299,04/06/2012,other vegetables\n1684032180399,02/13/2013,soda\n1647080809499,11/11/2010,herbs\n" }, { "path": "data/mushrooms.csv", "content": 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g\ne,x,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g\np,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u\ne,x,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g\ne,f,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g\ne,x,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g\np,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g\ne,f,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g\np,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u\ne,f,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g\ne,x,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g\ne,x,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,f,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g\ne,f,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g\ne,x,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g\ne,x,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g\ne,x,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g\ne,f,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g\ne,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m\ne,f,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g\ne,x,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,f,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g\ne,x,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\np,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u\ne,x,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g\ne,x,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g\ne,f,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g\ne,f,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m\ne,x,f,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d\ne,f,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g\ne,f,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g\ne,f,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g\ne,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g\ne,x,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g\ne,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g\ne,f,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g\ne,x,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g\ne,x,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g\ne,x,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g\ne,x,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g\ne,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m\ne,x,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g\np,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u\ne,f,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g\np,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u\ne,f,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g\ne,f,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g\np,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g\ne,x,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g\np,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u\ne,x,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g\ne,f,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g\np,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g\ne,x,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,x,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,x,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g\ne,x,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\np,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u\ne,f,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g\ne,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g\ne,x,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g\ne,f,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g\ne,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m\ne,f,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g\ne,f,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g\ne,f,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g\np,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u\ne,x,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g\ne,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g\ne,f,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g\ne,f,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g\ne,x,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g\ne,f,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g\ne,x,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g\ne,x,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\np,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g\ne,x,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g\np,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u\ne,f,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g\ne,f,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g\ne,x,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g\ne,x,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g\ne,f,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g\ne,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g\ne,f,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g\ne,x,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g\np,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g\ne,f,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g\np,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g\ne,f,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,x,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g\np,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u\ne,f,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g\np,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u\ne,x,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g\ne,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p\ne,f,f,w,f,n,f,w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u,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\np,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u\ne,x,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\np,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g\ne,x,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,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c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\np,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,x,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,f,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\np,b,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\np,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\ne,f,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,k,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\ne,f,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,k,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,x,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,k,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,x,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,b,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,x,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\np,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,x,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,k,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w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f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\np,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d\ne,f,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\np,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,f,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,f,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,x,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,x,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,b,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,k,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,k,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,b,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\np,b,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,f,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\ne,k,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,k,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\np,b,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,y,y,f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\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\ne,k,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,f,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,f,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\np,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\ne,k,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\ne,k,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,c,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\np,b,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u\np,b,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\ne,k,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,k,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\ne,x,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d\ne,f,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\ne,x,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,f,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\ne,x,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\ne,k,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\ne,x,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,x,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,f,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,b,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,x,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,f,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\ne,f,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,x,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\ne,k,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\np,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,k,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\ne,k,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,f,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,k,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\ne,k,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,x,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,x,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,b,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\ne,k,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,k,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,x,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\np,k,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\ne,k,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,k,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\ne,k,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,b,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\ne,k,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,b,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\ne,k,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\np,k,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\ne,f,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,k,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,x,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,k,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,k,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,k,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,f,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\ne,x,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\ne,x,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\ne,f,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\ne,x,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l\np,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\ne,x,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\ne,x,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l\ne,f,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,k,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,f,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,b,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p\ne,f,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,b,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,x,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\ne,f,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,k,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,x,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\np,f,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,x,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,f,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,x,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,x,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\np,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d\np,x,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\ne,f,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,k,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\np,b,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,x,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,f,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,x,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,f,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,x,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,f,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,k,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,k,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\ne,k,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\np,b,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\np,b,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,b,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\np,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,x,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,f,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,b,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,f,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\ne,x,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,x,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,x,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\np,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\ne,k,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\ne,k,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\np,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d\np,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l\np,b,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,f,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,k,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\ne,k,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,x,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\np,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,k,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\ne,k,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,k,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\np,c,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d\ne,x,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\ne,k,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\ne,x,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,b,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\np,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\ne,f,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\ne,f,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,x,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\ne,x,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,k,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,k,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\ne,k,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,b,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\ne,f,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,f,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\np,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,x,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\np,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,x,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\ne,f,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,b,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\np,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,b,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,x,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\ne,f,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\ne,x,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,b,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,x,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\np,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\ne,f,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,x,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\ne,x,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\ne,f,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d\ne,k,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,k,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,f,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,b,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,f,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,x,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,k,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,k,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,x,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\ne,x,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,b,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,k,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,k,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\ne,f,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\ne,f,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,k,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,x,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\np,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\np,x,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,f,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\ne,k,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,k,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\ne,f,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\ne,f,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\np,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\ne,x,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\ne,f,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l\ne,x,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,f,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,k,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\ne,f,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,k,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\ne,f,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\ne,f,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,f,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,b,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,k,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\np,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\ne,f,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\ne,x,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\np,k,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\ne,k,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,f,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\np,b,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,f,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\ne,x,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\ne,k,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d\ne,x,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\ne,k,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,k,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,k,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\np,b,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,b,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,x,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,x,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\np,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,f,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,x,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\ne,k,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\np,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,f,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\np,b,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,x,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,x,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\ne,x,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,b,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\np,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\np,b,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,f,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,x,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\np,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,f,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\ne,k,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\ne,x,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\ne,k,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\ne,f,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\ne,x,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\np,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,x,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,f,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\ne,k,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,b,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d\np,b,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,x,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,k,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\ne,x,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,f,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\ne,f,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d\np,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,x,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,b,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,k,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,x,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\np,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,k,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,x,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\ne,f,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\np,f,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\ne,x,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,x,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,x,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\ne,k,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,x,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\np,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\np,b,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l\np,b,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,x,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,b,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,k,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,f,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,b,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,f,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\np,b,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,f,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\ne,x,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,k,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\np,b,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\ne,x,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,f,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,k,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,x,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,f,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\np,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,k,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,x,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d\np,b,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d\ne,k,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,f,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,x,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\np,x,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,f,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,f,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\np,k,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,f,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p\np,x,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,f,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,f,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,x,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,x,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\np,f,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,f,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,f,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,f,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,f,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,x,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,k,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,x,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l\np,f,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,f,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,k,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\np,f,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,f,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,f,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,f,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,f,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,f,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,x,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\np,f,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\np,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,f,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,x,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\ne,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l\np,f,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\ne,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l\np,f,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,f,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\np,f,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,f,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,x,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,f,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\np,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,f,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,f,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,f,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,f,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,f,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\np,x,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,x,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,f,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,f,y,n,f,y,f,c,n,b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}, { "path": "data/splitData1.R", "content": "# Splitting Data into 2 or more frames\ndata(mtcars)\nmtcars\n## 75% of the sample size\nsmp_size <- floor(0.75 * nrow(mtcars))\nsmp_size\n## set the seed to make your partition reproductible\nset.seed(123)\ntrain_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)\ntrain_ind\ntrain <- mtcars[train_ind, ]\ntrain\ntest <- mtcars[-train_ind, ]\n\n\n# Another Code ---------\nbound <- floor((nrow(df)/4)*3) #define % of training and test set\n\ndf <- df[sample(nrow(df)), ] #sample rows \ndf.train <- df[1:bound, ] #get training set\ndf.test <- df[(bound+1):nrow(df), ] #get test set\n\n\n\n" }, { "path": "data/student1.csv", "content": "rollno,feepaid,gender,cat,dob,class10,class12,sem1,sem2,attnd,btechmarks,batch,br,btechfinal,city,java,dbms,dwm,vlsi,cpp,cbnst,finalgrade\n102001,35800,F,GEN,8-Nov-94,57,First,61,63,0.80,66,10-14,EC ,Pass,Gurgaon,77,56,62,42,64,56,A\n102002,35800,M,GEN,24-Jul-93,78,First,67,73,0.71,69,10-14,CS,Pass,Gurgaon,92,45,63,63,62,40,A\n102003,35800,M,GEN,17-Feb-95,77,Third,50,72,0.11,34,11-15,EC ,Fail,Dehra Dun,48,42,43,47,32,48,C\n102004,35800,M,GEN,4-Mar-94,59,Third,67,58,0.15,36,11-15,CS,Fail,Delhi,47,31,55,49,46,50,C\n102007,35800,M,GEN,20-Jun-95,68,First,53,68,0.64,65,10-14,EC ,Pass,Almora,65,50,53,50,61,58,A\n102008,35800,M,GEN,8-May-94,67,First,51,78,0.64,62,10-14,EC ,Pass,Haldwani,67,54,47,65,50,58,A\n102009,35800,M,ST,7-May-94,70,Second,75,66,0.33,35,11-15,CS,Fail,Delhi,65,40,31,65,42,49,C\n102011,35800,M,GEN,15-Aug-94,66,Second,52,77,0.63,64,10-14,CS,Pass,Haldwani,84,65,58,51,64,50,B\n102013,30800,M,GEN,24-Nov-94,61,First,67,66,0.66,68,11-15,CS,Pass,Haldwani,71,64,42,53,40,56,A\n102014,35800,M,GEN,25-Jan-94,60,Second,63,68,0.54,58,11-15,EC ,Pass,Gurgaon,77,65,58,44,65,50,B\n102015,35800,F,GEN,8-Jun-94,68,Second,73,79,0.56,57,10-14,CS,Pass,Dehra Dun,55,46,49,54,56,46,B\n102016,35800,M,GEN,24-Jun-95,80,Second,69,52,0.34,45,10-14,EC ,Pass,Rudrapur,49,46,49,55,49,50,B\n102017,35800,M,OBC,31-Oct-94,62,First,76,54,0.72,70,11-15,EC ,Pass,Noida,98,78,88,42,81,41,A\n102018,3800,M,GEN,21-Oct-95,60,First,75,66,0.69,64,11-15,CS,Pass,Almora,58,54,50,65,43,64,A\n102019,3800,F,GEN,30-Jun-96,63,First,69,65,0.85,67,10-14,EC ,Pass,Haldwani,77,75,88,81,83,42,A\n102020,26200,F,OBC,25-Sep-94,54,First,68,78,0.81,69,10-14,EC ,Pass,Haldwani,42,65,64,43,60,63,A\n102021,35800,F,GEN,16-May-94,53,First,75,65,0.76,78,11-15,CS,Pass,Noida,93,84,94,70,86,40,A\n102022,35800,M,GEN,24-Jun-95,75,Second,55,75,0.27,36,10-14,CS,Fail,Haldwani,44,49,34,47,55,48,C\n102023,35800,M,GEN,11-Dec-93,76,Second,67,51,0.30,45,11-15,EC ,Pass,Haldwani,49,46,49,49,50,55,B\n102024,35800,F,GEN,30-Jun-96,57,First,62,77,0.81,64,10-14,CS,Pass,Haldwani,71,68,75,50,66,50,A\n102025,35800,M,GEN,11-Dec-93,73,First,75,72,0.90,79,11-15,CS,Pass,Haldwani,88,89,95,91,87,47,A\n102026,35800,F,OBC,6-Nov-95,53,First,63,65,0.80,63,10-14,CS,Pass,Haldwani,68,65,71,44,71,50,A\n102027,35800,F,GEN,22-Dec-93,73,First,74,58,0.79,62,10-14,EC ,Pass,Delhi,70,61,68,58,76,50,A\n102028,35800,F,GEN,6-Feb-95,69,First,51,64,0.74,64,10-14,CS,Pass,Noida,65,50,53,50,61,58,A\n102029,35800,M,SC,6-Jun-94,71,Second,80,75,0.32,48,11-15,EC ,Pass,Gurgaon,49,46,49,46,50,55,B\n102030,35800,M,GEN,2-Jan-94,62,First,66,76,0.64,61,11-15,EC ,Pass,Haldwani,65,77,71,58,72,43,A\n102031,35800,M,GEN,8-Apr-94,65,Second,76,75,0.58,54,10-14,CS,Pass,Haldwani,49,46,49,54,56,62,B\n102032,35800,F,GEN,18-Nov-94,70,First,58,51,0.72,68,11-15,CS,Pass,Delhi,62,62,69,63,63,42,A\n102063,35800,F,GEN,3-Jul-95,74,First,53,65,0.90,72,10-14,EC ,Pass,Haldwani,40,67,47,40,61,62,A\n102034,35800,F,GEN,10-Jan-93,52,First,77,60,0.81,68,11-15,CS,Pass,Haldwani,74,74,83,56,62,56,A\n102035,35800,M,OBC,19-Dec-92,57,First,77,60,0.81,63,10-14,EC ,Pass,Delhi,76,58,50,65,50,64,A\n102036,35800,F,GEN,19-Sep-93,68,First,61,75,0.73,68,10-14,CS,Pass,Delhi,89,58,63,70,83,41,A\n102037,35800,M,GEN,13-Aug-94,55,Third,74,75,0.22,38,11-15,CS,Fail,Noida,49,46,49,23,46,50,C\n102038,35800,F,GEN,21-Jan-94,63,First,60,53,0.92,76,11-15,CS,Pass,Noida,87,94,78,85,76,45,A\n102039,35800,F,GEN,25-Feb-94,61,First,58,55,0.73,75,10-14,CS,Pass,Dehra Dun,82,64,72,83,73,43,B\n102040,35800,M,GEN,12-Jan-96,66,Second,56,61,0.60,56,10-14,CS,Pass,Haldwani,89,77,54,56,62,45,A\n102041,35800,M,SC,27-Jul-95,66,Second,79,67,0.25,42,10-14,EC ,Pass,Haldwani,49,42,46,49,46,50,B\n102042,35800,M,GEN,12-Jan-96,58,Second,65,80,0.22,39,11-15,EC ,Fail,Kashipur,33,55,45,46,55,31,C\n102043,35800,M,GEN,18-Feb-95,71,First,66,63,0.64,67,10-14,EC ,Pass,Delhi,47,49,63,45,56,62,A\n102044,35800,M,SC,3-Mar-95,63,Second,63,53,0.33,57,10-14,CS,Pass,Haldwani,55,46,49,65,58,56,B\n102045,35800,F,GEN,30-Jun-94,51,First,60,60,0.87,80,11-15,CS,Pass,Delhi,96,96,98,76,81,47,A\n102047,35800,M,GEN,23-Oct-94,80,Second,54,53,0.52,54,11-15,CS,Pass,Haldwani,49,55,46,54,64,50,A\n102048,35800,M,GEN,15-Nov-93,77,Third,54,66,0.08,35,10-14,EC ,Fail,Noida,42,47,65,35,41,49,C\n102049,35800,M,SC,11-May-93,50,Third,61,55,0.09,35,10-14,EC ,Fail,Noida,65,65,48,35,45,35,C\n102058,35800,F,GEN,2-Apr-95,67,First,75,68,0.83,71,10-14,EC ,Pass,Haldwani,62,86,79,40,76,47,A\n103023,35800,F,GEN,15-Mar-94,65,First,77,62,0.90,70,11-15,EC ,Pass,Noida,93,74,81,73,80,43,A\n102050,500,F,GEN,1-Jul-96,51,Third,52,58,0.04,33,10-14,EC ,Fail,Haldwani,39,65,43,65,50,36,C\n102051,500,M,ST,1-Aug-96,60,Third,76,59,0.04,32,10-14,EC ,Fail,Haldwani,30,46,34,65,32,35,C\n102052,35800,M,GEN,5-Apr-91,71,First,56,55,0.94,68,10-14,EC ,Pass,Haldwani,85,72,67,40,72,56,A\n102033,35800,M,GEN,16-Sep-93,72,Third,62,69,0.13,36,11-15,CS,Fail,Dehra Dun,44,45,55,35,42,43,C" }, { "path": "data/studentdata.R", "content": "# Student Data Prepare\n# Split into train and test of Student \nstudent = read.csv('data/student1.csv')\nstr(student)\nstudent$dob = as.Date(student$dob, format = \"%d-%b-%y\")\nstr(student)\nstudent$age = round(as.numeric((Sys.Date() - student$dob)/365 ))\nhead(student)\n\n## 75% of the sample size as train Data\nsmp_size <- floor(0.80 * nrow(student))\nsmp_size\n## set the seed to make your partition reproductible\nset.seed(123)\ntrain_ind <- sample(seq_len(nrow(student)), size = smp_size)\ntrain_ind\ntrain1 <- student[train_ind, ]\nhead(train1)\nstr(train1)\ntest1 <- student[-train_ind, ] # rest of the data use as test\nhead(test1)\nstr(test1)\nnrow(train1);nrow(test1)\n# want to train the data on basis of age, class10, sem1 marks\n\nx_train = train1[,c('age','class10','sem1')]\nhead(x_train)\ny_train = train1$btechmarks\nhead(y_train)\nx_test = train1[,c('age','class10','sem1')]\nhead(x_test)\n" }, { "path": "data/studentdata.csv", "content": 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}, { "path": "download/fms.txt", "content": "The Faculty of Management Studies focuses on management education more than just business management. The commitment is thought leadership with a deep understanding of business. The approach to pedagogy combines fieldwork, case studies and instrumented feedback with a strong emphasis on concepts and theory. The intent is to encourage intellectual curiosity and open minds to the adventure of ideas.\r\r\n\r\r\nBut much about the school is not just about what is taught within its confines. FMS has the unique privilege to be part of one of the premier universities world – the University of Delhi - with some of the finest departments in Economics, Law, Sociology, Commerce and Operations Research. Our collaborative approach involves inputs from various departments which gives the benefit of a much wider view and deeper understanding. This is indicated by the success of our alumni who are in positions of leadership in industries & governments across the world. The network of more than 10000 alumni gives students unmatched access to information, mentors and careers.\r\r\n\r\r\nFMS rewards initiative, novelty and thinking outside the box. Our students are individualists of enormous intellectual energy with a talent for collaboration and teamwork. We are a diverse lot, possessing flair and dynamism that develops in a metropolis like Delhi and thrives on challenges both inside and outside the classroom.\r\r\n\r\r\nCorporate recruiters value our graduates for their intellectual abilities, their collaborative mind-set, their individuality and their ability to hit the ground running.\r\r\n\r\r\nWe invite you to discover FMS for yourself." }, { "path": "download/iris.csv", "content": "\"\",\"Sepal.Length\",\"Sepal.Width\",\"Petal.Length\",\"Petal.Width\",\"Species\"\n\"1\",5.1,3.5,1.4,0.2,\"setosa\"\n\"2\",4.9,3,1.4,0.2,\"setosa\"\n\"3\",4.7,3.2,1.3,0.2,\"setosa\"\n\"4\",4.6,3.1,1.5,0.2,\"setosa\"\n\"5\",5,3.6,1.4,0.2,\"setosa\"\n\"6\",5.4,3.9,1.7,0.4,\"setosa\"\n\"7\",4.6,3.4,1.4,0.3,\"setosa\"\n\"8\",5,3.4,1.5,0.2,\"setosa\"\n\"9\",4.4,2.9,1.4,0.2,\"setosa\"\n\"10\",4.9,3.1,1.5,0.1,\"setosa\"\n\"11\",5.4,3.7,1.5,0.2,\"setosa\"\n\"12\",4.8,3.4,1.6,0.2,\"setosa\"\n\"13\",4.8,3,1.4,0.1,\"setosa\"\n\"14\",4.3,3,1.1,0.1,\"setosa\"\n\"15\",5.8,4,1.2,0.2,\"setosa\"\n\"16\",5.7,4.4,1.5,0.4,\"setosa\"\n\"17\",5.4,3.9,1.3,0.4,\"setosa\"\n\"18\",5.1,3.5,1.4,0.3,\"setosa\"\n\"19\",5.7,3.8,1.7,0.3,\"setosa\"\n\"20\",5.1,3.8,1.5,0.3,\"setosa\"\n\"21\",5.4,3.4,1.7,0.2,\"setosa\"\n\"22\",5.1,3.7,1.5,0.4,\"setosa\"\n\"23\",4.6,3.6,1,0.2,\"setosa\"\n\"24\",5.1,3.3,1.7,0.5,\"setosa\"\n\"25\",4.8,3.4,1.9,0.2,\"setosa\"\n\"26\",5,3,1.6,0.2,\"setosa\"\n\"27\",5,3.4,1.6,0.4,\"setosa\"\n\"28\",5.2,3.5,1.5,0.2,\"setosa\"\n\"29\",5.2,3.4,1.4,0.2,\"setosa\"\n\"30\",4.7,3.2,1.6,0.2,\"setosa\"\n\"31\",4.8,3.1,1.6,0.2,\"setosa\"\n\"32\",5.4,3.4,1.5,0.4,\"setosa\"\n\"33\",5.2,4.1,1.5,0.1,\"setosa\"\n\"34\",5.5,4.2,1.4,0.2,\"setosa\"\n\"35\",4.9,3.1,1.5,0.2,\"setosa\"\n\"36\",5,3.2,1.2,0.2,\"setosa\"\n\"37\",5.5,3.5,1.3,0.2,\"setosa\"\n\"38\",4.9,3.6,1.4,0.1,\"setosa\"\n\"39\",4.4,3,1.3,0.2,\"setosa\"\n\"40\",5.1,3.4,1.5,0.2,\"setosa\"\n\"41\",5,3.5,1.3,0.3,\"setosa\"\n\"42\",4.5,2.3,1.3,0.3,\"setosa\"\n\"43\",4.4,3.2,1.3,0.2,\"setosa\"\n\"44\",5,3.5,1.6,0.6,\"setosa\"\n\"45\",5.1,3.8,1.9,0.4,\"setosa\"\n\"46\",4.8,3,1.4,0.3,\"setosa\"\n\"47\",5.1,3.8,1.6,0.2,\"setosa\"\n\"48\",4.6,3.2,1.4,0.2,\"setosa\"\n\"49\",5.3,3.7,1.5,0.2,\"setosa\"\n\"50\",5,3.3,1.4,0.2,\"setosa\"\n\"51\",7,3.2,4.7,1.4,\"versicolor\"\n\"52\",6.4,3.2,4.5,1.5,\"versicolor\"\n\"53\",6.9,3.1,4.9,1.5,\"versicolor\"\n\"54\",5.5,2.3,4,1.3,\"versicolor\"\n\"55\",6.5,2.8,4.6,1.5,\"versicolor\"\n\"56\",5.7,2.8,4.5,1.3,\"versicolor\"\n\"57\",6.3,3.3,4.7,1.6,\"versicolor\"\n\"58\",4.9,2.4,3.3,1,\"versicolor\"\n\"59\",6.6,2.9,4.6,1.3,\"versicolor\"\n\"60\",5.2,2.7,3.9,1.4,\"versicolor\"\n\"61\",5,2,3.5,1,\"versicolor\"\n\"62\",5.9,3,4.2,1.5,\"versicolor\"\n\"63\",6,2.2,4,1,\"versicolor\"\n\"64\",6.1,2.9,4.7,1.4,\"versicolor\"\n\"65\",5.6,2.9,3.6,1.3,\"versicolor\"\n\"66\",6.7,3.1,4.4,1.4,\"versicolor\"\n\"67\",5.6,3,4.5,1.5,\"versicolor\"\n\"68\",5.8,2.7,4.1,1,\"versicolor\"\n\"69\",6.2,2.2,4.5,1.5,\"versicolor\"\n\"70\",5.6,2.5,3.9,1.1,\"versicolor\"\n\"71\",5.9,3.2,4.8,1.8,\"versicolor\"\n\"72\",6.1,2.8,4,1.3,\"versicolor\"\n\"73\",6.3,2.5,4.9,1.5,\"versicolor\"\n\"74\",6.1,2.8,4.7,1.2,\"versicolor\"\n\"75\",6.4,2.9,4.3,1.3,\"versicolor\"\n\"76\",6.6,3,4.4,1.4,\"versicolor\"\n\"77\",6.8,2.8,4.8,1.4,\"versicolor\"\n\"78\",6.7,3,5,1.7,\"versicolor\"\n\"79\",6,2.9,4.5,1.5,\"versicolor\"\n\"80\",5.7,2.6,3.5,1,\"versicolor\"\n\"81\",5.5,2.4,3.8,1.1,\"versicolor\"\n\"82\",5.5,2.4,3.7,1,\"versicolor\"\n\"83\",5.8,2.7,3.9,1.2,\"versicolor\"\n\"84\",6,2.7,5.1,1.6,\"versicolor\"\n\"85\",5.4,3,4.5,1.5,\"versicolor\"\n\"86\",6,3.4,4.5,1.6,\"versicolor\"\n\"87\",6.7,3.1,4.7,1.5,\"versicolor\"\n\"88\",6.3,2.3,4.4,1.3,\"versicolor\"\n\"89\",5.6,3,4.1,1.3,\"versicolor\"\n\"90\",5.5,2.5,4,1.3,\"versicolor\"\n\"91\",5.5,2.6,4.4,1.2,\"versicolor\"\n\"92\",6.1,3,4.6,1.4,\"versicolor\"\n\"93\",5.8,2.6,4,1.2,\"versicolor\"\n\"94\",5,2.3,3.3,1,\"versicolor\"\n\"95\",5.6,2.7,4.2,1.3,\"versicolor\"\n\"96\",5.7,3,4.2,1.2,\"versicolor\"\n\"97\",5.7,2.9,4.2,1.3,\"versicolor\"\n\"98\",6.2,2.9,4.3,1.3,\"versicolor\"\n\"99\",5.1,2.5,3,1.1,\"versicolor\"\n\"100\",5.7,2.8,4.1,1.3,\"versicolor\"\n\"101\",6.3,3.3,6,2.5,\"virginica\"\n\"102\",5.8,2.7,5.1,1.9,\"virginica\"\n\"103\",7.1,3,5.9,2.1,\"virginica\"\n\"104\",6.3,2.9,5.6,1.8,\"virginica\"\n\"105\",6.5,3,5.8,2.2,\"virginica\"\n\"106\",7.6,3,6.6,2.1,\"virginica\"\n\"107\",4.9,2.5,4.5,1.7,\"virginica\"\n\"108\",7.3,2.9,6.3,1.8,\"virginica\"\n\"109\",6.7,2.5,5.8,1.8,\"virginica\"\n\"110\",7.2,3.6,6.1,2.5,\"virginica\"\n\"111\",6.5,3.2,5.1,2,\"virginica\"\n\"112\",6.4,2.7,5.3,1.9,\"virginica\"\n\"113\",6.8,3,5.5,2.1,\"virginica\"\n\"114\",5.7,2.5,5,2,\"virginica\"\n\"115\",5.8,2.8,5.1,2.4,\"virginica\"\n\"116\",6.4,3.2,5.3,2.3,\"virginica\"\n\"117\",6.5,3,5.5,1.8,\"virginica\"\n\"118\",7.7,3.8,6.7,2.2,\"virginica\"\n\"119\",7.7,2.6,6.9,2.3,\"virginica\"\n\"120\",6,2.2,5,1.5,\"virginica\"\n\"121\",6.9,3.2,5.7,2.3,\"virginica\"\n\"122\",5.6,2.8,4.9,2,\"virginica\"\n\"123\",7.7,2.8,6.7,2,\"virginica\"\n\"124\",6.3,2.7,4.9,1.8,\"virginica\"\n\"125\",6.7,3.3,5.7,2.1,\"virginica\"\n\"126\",7.2,3.2,6,1.8,\"virginica\"\n\"127\",6.2,2.8,4.8,1.8,\"virginica\"\n\"128\",6.1,3,4.9,1.8,\"virginica\"\n\"129\",6.4,2.8,5.6,2.1,\"virginica\"\n\"130\",7.2,3,5.8,1.6,\"virginica\"\n\"131\",7.4,2.8,6.1,1.9,\"virginica\"\n\"132\",7.9,3.8,6.4,2,\"virginica\"\n\"133\",6.4,2.8,5.6,2.2,\"virginica\"\n\"134\",6.3,2.8,5.1,1.5,\"virginica\"\n\"135\",6.1,2.6,5.6,1.4,\"virginica\"\n\"136\",7.7,3,6.1,2.3,\"virginica\"\n\"137\",6.3,3.4,5.6,2.4,\"virginica\"\n\"138\",6.4,3.1,5.5,1.8,\"virginica\"\n\"139\",6,3,4.8,1.8,\"virginica\"\n\"140\",6.9,3.1,5.4,2.1,\"virginica\"\n\"141\",6.7,3.1,5.6,2.4,\"virginica\"\n\"142\",6.9,3.1,5.1,2.3,\"virginica\"\n\"143\",5.8,2.7,5.1,1.9,\"virginica\"\n\"144\",6.8,3.2,5.9,2.3,\"virginica\"\n\"145\",6.7,3.3,5.7,2.5,\"virginica\"\n\"146\",6.7,3,5.2,2.3,\"virginica\"\n\"147\",6.3,2.5,5,1.9,\"virginica\"\n\"148\",6.5,3,5.2,2,\"virginica\"\n\"149\",6.2,3.4,5.4,2.3,\"virginica\"\n\"150\",5.9,3,5.1,1.8,\"virginica\"\n" }, { "path": "download/rowling.txt", "content": "President Faust, members of the Harvard Corporation and the Board of Overseers, members of the faculty, proud parents, and, above all, graduates.\r\r\n\r\r\nThe first thing I would like to say is ‘thank you.’ Not only has Harvard given me an extraordinary honour, but the weeks of fear and nausea I have endured at the thought of giving this commencement address have made me lose weight. A win-win situation! Now all I have to do is take deep breaths, squint at the red banners and convince myself that I am at the world’s largest Gryffindor reunion.\r\r\nDelivering a commencement address is a great responsibility; or so I thought until I cast my mind back to my own graduation. The commencement speaker that day was the distinguished British philosopher Baroness Mary Warnock. Reflecting on her speech has helped me enormously in writing this one, because it turns out that I can’t remember a single word she said. This liberating discovery enables me to proceed without any fear that I might inadvertently influence you to abandon promising careers in business, the law or politics for the giddy delights of becoming a gay wizard.\r\r\n\r\r\nYou see? If all you remember in years to come is the ‘gay wizard’ joke, I’ve come out ahead of Baroness Mary Warnock. Achievable goals: the first step to self improvement.\r\r\n\r\r\nActually, I have wracked my mind and heart for what I ought to say to you today. I have asked myself what I wish I had known at my own graduation, and what important lessons I have learned in the 21 years that have expired between that day and this.\r\r\n\r\r\nI have come up with two answers. On this wonderful day when we are gathered together to celebrate your academic success, I have decided to talk to you about the benefits of failure. And as you stand on the threshold of what is sometimes called ‘real life’, I want to extol the crucial importance of imagination.\r\r\n\r\r\nThese may seem quixotic or paradoxical choices, but please bear with me.\r\r\n\r\r\nLooking back at the 21-year-old that I was at graduation, is a slightly uncomfortable experience for the 42-year-old that she has become. Half my lifetime ago, I was striking an uneasy balance between the ambition I had for myself, and what those closest to me expected of me.\r\r\n\r\r\nI was convinced that the only thing I wanted to do, ever, was to write novels. However, my parents, both of whom came from impoverished backgrounds and neither of whom had been to college, took the view that my overactive imagination was an amusing personal quirk that would never pay a mortgage, or secure a pension. I know that the irony strikes with the force of a cartoon anvil, now.\r\r\n\r\r\nSo they hoped that I would take a vocational degree; I wanted to study English Literature. A compromise was reached that in retrospect satisfied nobody, and I went up to study Modern Languages. Hardly had my parents’ car rounded the corner at the end of the road than I ditched German and scuttled off down the Classics corridor.\r\r\n\r\r\nI cannot remember telling my parents that I was studying Classics; they might well have found out for the first time on graduation day. Of all the subjects on this planet, I think they would have been hard put to name one less useful than Greek mythology when it came to securing the keys to an executive bathroom.\r\r\n\r\r\nI would like to make it clear, in parenthesis, that I do not blame my parents for their point of view. There is an expiry date on blaming your parents for steering you in the wrong direction; the moment you are old enough to take the wheel, responsibility lies with you. What is more, I cannot criticise my parents for hoping that I would never experience poverty. They had been poor themselves, and I have since been poor, and I quite agree with them that it is not an ennobling experience. Poverty entails fear, and stress, and sometimes depression; it means a thousand petty humiliations and hardships. Climbing out of poverty by your own efforts, that is indeed something on which to pride yourself, but poverty itself is romanticised only by fools.\r\r\n\r\r\nWhat I feared most for myself at your age was not poverty, but failure.\r\r\nAt your age, in spite of a distinct lack of motivation at university, where I had spent far too long in the coffee bar writing stories, and far too little time at lectures, I had a knack for passing examinations, and that, for years, had been the measure of success in my life and that of my peers.\r\r\n\r\r\nI am not dull enough to suppose that because you are young, gifted and well-educated, you have never known hardship or heartbreak. Talent and intelligence never yet inoculated anyone against the caprice of the Fates, and I do not for a moment suppose that everyone here has enjoyed an existence of unruffled privilege and contentment.\r\r\n\r\r\nHowever, the fact that you are graduating from Harvard suggests that you are not very well-acquainted with failure. You might be driven by a fear of failure quite as much as a desire for success. Indeed, your conception of failure might not be too far from the average person’s idea of success, so high have you already flown.\r\r\n\r\r\nUltimately, we all have to decide for ourselves what constitutes failure, but the world is quite eager to give you a set of criteria if you let it. So I think it fair to say that by any conventional measure, a mere seven years after my graduation day, I had failed on an epic scale. An exceptionally short-lived marriage had imploded, and I was jobless, a lone parent, and as poor as it is possible to be in modern Britain, without being homeless. The fears that my parents had had for me, and that I had had for myself, had both come to pass, and by every usual standard, I was the biggest failure I knew.\r\r\n\r\r\nNow, I am not going to stand here and tell you that failure is fun. That period of my life was a dark one, and I had no idea that there was going to be what the press has since represented as a kind of fairy tale resolution. I had no idea then how far the tunnel extended, and for a long time, any light at the end of it was a hope rather than a reality.\r\r\n\r\r\nSo why do I talk about the benefits of failure? Simply because failure meant a stripping away of the inessential. I stopped pretending to myself that I was anything other than what I was, and began to direct all my energy into finishing the only work that mattered to me. Had I really succeeded at anything else, I might never have found the determination to succeed in the one arena I believed I truly belonged. I was set free, because my greatest fear had been realised, and I was still alive, and I still had a daughter whom I adored, and I had an old typewriter and a big idea. And so rock bottom became the solid foundation on which I rebuilt my life.\r\r\n\r\r\nYou might never fail on the scale I did, but some failure in life is inevitable. It is impossible to live without failing at something, unless you live so cautiously that you might as well not have lived at all – in which case, you fail by default.\r\r\n\r\r\nFailure gave me an inner security that I had never attained by passing examinations. Failure taught me things about myself that I could have learned no other way. I discovered that I had a strong will, and more discipline than I had suspected; I also found out that I had friends whose value was truly above the price of rubies.\r\r\n\r\r\nThe knowledge that you have emerged wiser and stronger from setbacks means that you are, ever after, secure in your ability to survive. You will never truly know yourself, or the strength of your relationships, until both have been tested by adversity. Such knowledge is a true gift, for all that it is painfully won, and it has been worth more than any qualification I ever earned.\r\r\n" }, { "path": "download/vector.R", "content": "\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n \n \n\n\n\n \n \n \n \n \n \n\n \n \n analytics/20a-vectors.R at master · dupadhyaya/analytics · GitHub\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n\n \n\n \n\n \n \n \n \n\n\n\n\n\n\n\n\n\n\n \n\n \n \n\n \n \n\n \n\n \n\n \n \n\n \n\n \n\n \n\n\n \n\n\n \n\n \n\n \n \n\n\n\n\n\n\n\n \n\n \n \n\n
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    \n analytics/15a-DS/20a-vectors.R\n
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    \n\n\n \n
    \n \n \n f003881\n \n Jul 20, 2018\n \n
    \n \"@dupadhyaya\"\n dupadhyaya\n 19 Jul\n
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    \n \n \n 1 contributor\n \n \n
    \n \n

    Users who have contributed to this file

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    \n 138 lines (91 sloc)\n \n 2.48 KB\n
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    # Vectors
    \n
    #sequence of data elements of the same basic type
    \n
    #Scalar
    \n
    #Vector----
    x = c(1, 5, 4, 9, 0)
    typeof(x) #[1] "double"
    length(x) #[1] 5
    \n
    #Vector using : operator
    x = 1:7; x #[1] 1 2 3 4 5 6 7
    y <- 2:-2; y #[1] 2 1 0 -1 -2
    \n
    #Vector using seq command
    seq(1, 3, by=0.2) # specify step size
    #[1] 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0
    seq(1, 5, length.out=4) # specify length of the vector 4parts from 5
    #[1] 1.00 2.33 3.667 5.00
    \n
    \n
    #Character/ String ----
    \n
    #Numeric ----
    \n
    \n
    #Logical----
    \n
    \n
    \n
    \n
    \n
    \n
    # Combining vectors with c()----
    \n
    \n
    \n
    \n
    #Vector Coercian ----
    x = c(1, 5.4, TRUE, "hello")
    x #[1] "1" "5.4" "TRUE" "hello"
    typeof(x) #[1] "character"
    \n
    \n
    #Vector Recycling ----
    #If two vectors are of unequal length, the shorter one will be recycled in order to match the longer vector. Recycle values of u
    \n
    u = c(10, 20, 30)
    v = c(1, 2, 3, 4, 5, 6, 7, 8, 9)
    u + v
    #[1] 11 22 33 14 25 36 17 28 39
    #1+10, 2+20, 3+30, 4+10, 5+20, 6+30, 7+10 ...
    \n
    #Vector Index ----
    u[1]
    \n
    u[-3] #negative index :strip the member whose position has the same absolute value as the negative index.
    \n
    #Numeric Index Vector ----
    s = c("aa", "bb", "cc", "dd", "ee")
    s[c(2, 3)]
    s[-c(4,5)]
    \n
    #cannot mix + & -
    s[c(-2,3)]
    \n
    #Duplicate Index
    s[c(2,3,2)]
    \n
    #Range Index
    s[c(2:5)]
    s[2:5]
    #Out of Range Index
    s[c(10)]
    \n
    #Logical Index Vector
    #Its members are TRUE if the corresponding members in the original vector are to be included in the slice, and FALSE if otherwise.
    s = c("aa", "bb", "cc", "dd", "ee") #retrieve 2nd & 4th member
    L = c(FALSE, TRUE, FALSE, TRUE, FALSE)
    s[L]
    #[1] "bb" "dd"
    \n
    x = 1:10
    x[x<5] #elements satisfying condition
    \n
    \n
    #Named Vector Members ----
    #We can assign names to vector members.
    (v = c("Mary", "Sue") )
    names(v) = c("First", "Last") #Name the members
    v
    #Retrieve the first member by its name.
    v["First"] #[1] "Mary"
    #everse the order with a character string index vector.
    v[c("Last", "First")]
    \n
    \n
    #DELETE Vector----
    (x = 1:10)
    x = NULL #delete
    x
    \n
    \n
    \n
    #Vector ARITHMETIC----
    # Create two vectors.
    v1 = c(3,8,4,5,0,11)
    v2 = c(4,11,0,8,1,2)
    \n
    (add.result = v1+v2) # Vector addition.
    (sub.result = v1-v2) # Vector subtraction.
    (multi.result = v1*v2) # Vector multiplication.
    (divi.result = v1/v2) # Vector division.
    (v1/2)
    \n
    \n
    #Sort
    v = c(3,8,4,5,0,11, -9, 304)
    sort(v)
    sort(v, decreasing=T)
    \n
    \n
    \n
    #READ values
    nums = scan() #type the numbers with space
    nums
    names = scan(what='character')
    names
    \n
    \n
    #Missing Values
    x = c(1,4,7,NA,12,19,15,21,20)
    mean(x)
    mean(x, na.rm=T)
    is.na(x)
    x[!is.na(x)] #non missing values
    \n\n
    \n \n \n \n \n \n \n
    \n\n \n\n \n\n
    \n \n \n
    \n \n \n
    \n
    \n\n\n \n
    \n\n\n \n \n\n \n\n \n
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    \n
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    \n\n
    \n Press h to open a hovercard with more details.\n
    \n\n\n \n\n\n" }, { "path": "file1.R", "content": "mtcars\nwomen\n" }, { "path": "fms.txt", "content": "The Faculty of Management Studies focuses on management education more than just business management. The commitment is thought leadership with a deep understanding of business. The approach to pedagogy combines fieldwork, case studies and instrumented feedback with a strong emphasis on concepts and theory. The intent is to encourage intellectual curiosity and open minds to the adventure of ideas.\nBut much about the school is not just about what is taught within its confines. FMS has the unique privilege to be part of one of the premier universities world – the University of Delhi - with some of the finest departments in Economics, Law, Sociology, Commerce and Operations Research. Our collaborative approach involves inputs from various departments which gives the benefit of a much wider view and deeper understanding. This is indicated by the success of our alumni who are in positions of leadership in industries & governments across the world. The network of more than 10000 alumni gives students unmatched access to information, mentors and careers.\n\nFMS rewards initiative, novelty and thinking outside the box. Our students are individualists of enormous intellectual energy with a talent for collaboration and teamwork. We are a diverse lot, possessing flair and dynamism that develops in a metropolis like Delhi and thrives on challenges both inside and outside the classroom.\nCorporate recruiters value our graduates for their intellectual abilities, their collaborative mind-set, their individuality and their ability to hit the ground running.\nWe invite you to discover FMS for yourself.\n" }, { "path": "iimc1.R", "content": "\n#vector\n\n#ND with mean 80 , sd 10, 100 values\n\nsales = rnorm(10000000, mean=80, sd=10 )\nsales \n#trunc, round, floor, ceiling\nsales = trunc(sales)\nsales\nsummary(sales)\nboxplot(sales)\nmean(sales)\nmedian(sales)\nhist(sales)\nplot(density(sales))\n#10-50\nrange(sales)\n#matrix\nm1 = matrix(100:123, ncol=2)\nm1\ncolSums(m1)\ncolMeans(m1)\nm1[,1]\ncolnames(m1) =c('A','B')\nhead(m1)\ntail(m1)\n\n#DF\n(sname = paste('Student', 1:30,sep='_'))\n(marks = trunc(runif(n=30, max=90, min=50)))\nrunif(30)\n(gender = sample(c('M','F'), replace=T, size=30, prob=c(.7,.3)))\n?runif\ndf1 = data.frame(sname, marks, gender)\nhead(df1)\n\nlibrary(dplyr)\ndf1 %>% group_by(gender) %>% summarise(mean(marks), n(), min(marks), max(marks))\n#name, marks (descending order); no gender\nnames(df1)\nstr(df1)\nsummary(df1)\n\ndf1[(order(df1$marks, decreasing = T)), c('sname', 'marks')]\ndf1[(order(df1$marks, decreasing = T)), -3]\n#use dplyr\ndf1 %>% select(sname, marks ) %>% arrange(desc(marks))\nnames(df1)\ndf1[sample(1:30, size=5),2] = NA\n#5 missing values on random location in marks column \nsum(complete.cases(df1))\nsum(!complete.cases(df1))\n\ndf1[!complete.cases(df1),2] = mean(df1$marks, na.rm=T)\nmean(df1$marks, na.rm=T)\ndf2 = df1[complete.cases(df1), ]\nsum(is.na(df1))\nsum(is.na(df2))\n\nsample(1:30, size=5)\ndf1\ndf1 %>% sample_n(3)\ndf1 %>% sample_frac(.3)\n\n# Linear Regression\n\n# Logistic Regression\n\n# \ndf = read.csv(file.choose())\nhead(df)\n" }, { "path": "mdi1.R", "content": "\n#create Vectors to be combined into DF\n(rollno = 1:30)\n(sname = paste('student',1:30,sep=''))\n(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))\n(marks1 = floor(rnorm(30,mean= 50,sd=10)))\n(marks2 = ceiling(rnorm(30,40,5)))\n(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))\nrollno; sname; gender\nmarks1 ; marks2; course\n\n#create DF\ndf1= data.frame(rollno, sname, gender, marks1, marks2, course, stringsAsFactors = F)\ndf1$gender = factor(df1$gender)\ndf1$course = factor(df1$course)\nstr(df1)\nhead(df1)\n#install.packages('dplyr')\nlibrary(dplyr)\ndf1\ndf1 %>% group_by(gender) %>% summarise(MeanM1 = mean(marks1), MeanM2 = mean(marks2), maxM2 = max(marks2))\n\nsummarise(mtcars, meanmpg = mean(mpg))\nsummarise(df1, meanM1 = mean(marks1))\n?mtcars\nmean(mtcars$mpg)\nnames(mtcars)\ntable(mtcars$gear)\ntable(mtcars$cyl, mtcars$am)\ntable(mtcars$cyl, mtcars$am, mtcars$vs)\nxtabs( ~ cyl + am + vs, data=mtcars)\nmtcars %>% group_by(gear, cyl) %>% summarise(meanMPG = mean(mpg)) %>% arrange(desc(meanMPG))\n\nmtcars %>% select(mpg,wt) %>% head(n=7)\nmtcars %>% select(mpg,wt) %>% slice(c(1,5,6))\nmtcars$mpg\nmtcars %>% select(mpg, wt) %>% slice(seq(1,32,2))\nmtcars %>% filter(mpg > 25) %>% select(mpg, wt, hp, am)\nmtcars %>% filter(mpg > 25 & hp < 60 ) %>% select(mpg, wt, hp, am)\n\nmtcars %>% sample_n(3)\nmtcars %>% sample_frac(.2)\n\n\n" }, { "path": "packages/switchr.R", "content": "#switchR for switching between libraries\n#https://cran.r-project.org/web/packages/switchr/switchr.pdf\n\nlibrary(switchr)\n\n#To switch to a different library, creating it if necessary, use the switchTo function:\n \nswitchTo(\"mynewlib\")\n#Then work normally, when you wish to switch back to your original library, use switchBack:\nswitchBack()\n\n#-----------\n.libPaths2()\n\n#.libPaths2(fulllp, exclude.site = TRUE)\n\ndefaultRepos()\n\ndep_repos('dplyr')\n\ninstall_packages(pkgs, repos, versions = NULL, verbose = FALSE, ...)\n\nlibrary_paths(swtichr)\npackages(dplyr)\n" }, { "path": "packages/useful.R", "content": "\n\n\n\n\n#library - useful\nlibrary(useful)\nk1 <- kmeans(x=iris[, 1:4], centers=3)\nplot(k1)\nplot(k1, data=iris)\n\ntoCheck <- c('BIG', 'little', 'Mixed', 'BIG WITH SPACE', 'little with space', 'MIXED with SPACE')\nfind.case(toCheck, 'upper')\nfind.case(toCheck, 'lower')\n\n\ndata(iris)\nhartiganResults <- FitKMeans(iris[, -ncol(iris)])\nPlotHartigan(hartiganResults)\n\ndata=mtcars\n#ForceDataFrame(data)\nindexToPosition(3, 2)\nindexToPosition(c(1, 4, 5, 7, 9), 3)\nindexToPosition(1:16, 4)\nindexToPosition(c(1, 3, 5, 6, 8, 10, 11, 13, 15), 5)\n\nx=mtcars\nleft(x, c = 5L)\n\ntheDF <- data.frame(A=1:10, B=11:20, C=1:10, D=11:20)\ndf <- moveToFront(theDF, c('B', 'C'))\ndf\nmoveToFront(theDF, c('C', 'B'))\nmoveToFront(theDF, c('C', 'C'))\nmoveToBack(theDF, c('C', 'C'))\nmoveToBack(theDF, c('C', 'B'))\nmoveToBack(theDF, c('C', 'C'))\n\n\nplot(sunspot.year)\nplot(sunspot.year, acf=TRUE)\n\npositionToIndex(1, 2, 2)\npositionToIndex(row=c(1, 1, 2, 1, 3), col=c(1, 2, 2, 3, 3), nrow=3)\npositionToIndex(rep(1:4, 4), rep(1:4, each=4), nrow=4)\npositionToIndex(rep(c(1, 3, 5), 3), rep(1:3, each=3), nrow=5)\n\n\ntheDF <- data.frame(A=1:10, B=1:10)\nreclass(theDF) <- 'newclass'\nclass(theDF)\ntheDF <- reclass(theDF, 'another')\nclass(theDF)\n\nright(diamonds) # displays all rows and only the last 5 columns\n\nmyData <- data.frame(Upper=LETTERS, lower=letters)\nmyData\nshift.column(data=myData, columns=\"lower\")\nshift.column(data=myData, columns=\"lower\", len=2)\nshift.column(data=myData, columns=c(\"lower\", \"Upper\"), len=3, up=T)\nshift.column(data=myData, columns=c(\"lower\", \"Upper\"), len=3, up=F)\n\n\ntheDF <- data.frame(A=1:10, B=1:10, C=1:10)\ntheDF[c(1, 4, 6), c(1)] <- NA\ntheDF[c(3, 4, 8), c(3)] <- NA\ntheDF\nsimple.impute.data.frame(theDF)\nsimple.impute.data.frame(theDF, mean)\nsimple.impute.data.frame(theDF, constant(4))\n\nsubOut(c(\"Hello\", \"(parens)\", \"Excited! Mark\"))\nsubOut(c(\"Hello\", \"(parens)\", \"Excited! Mark\"), specialChars=c(\"!\", \"(\"))\n\nsubSpecials(c(\"Hello\", \"(parens)\", \"Excited! Mark\"))\nsubSpecials(c(\"Hello\", \"(parens)\", \"Excited! Mark\"), specialChars=c(\"!\", \"(\"))\nsubSpecials(c(\"Hello\", \"(parens)\", \"Excited! Mark\"),\n c(\"This is a period. And this is an asterisk *\"), specialChars=c(\"!\", \"(\"))\nsubSpecials(c(\"Hello\", \"(parens)\", \"Excited! Mark\"),\n c(\"This is a period. And this is an asterisk *\"), specialChars=c(\"!\", \"(\", \"*\"))" }, { "path": "packages/useful2.R", "content": "#useful Functions\n\nlibrary(useful)\n\n\nbinary.flip(c(1,1,0,1,0,0,1))\n\nclassdf(mtcars, cols=c(1:5))\nclassdf(mtcars)\n\nbottomleft(mtcars, r = 5L, c = 5L)\nbottomright(mtcars, r = 5L, c = 5L)\ntopright(mtcars)\ntopleft(mtcars)\n\nfortify(acf(sunspot.year, plot=FALSE))\nfortify(pacf(sunspot.year, plot=FALSE))\n\nk1 <- kmeans(x=iris[, 1:4], centers=3)\nhold <- fortify(k1, data=iris)\nhead(hold)\nhold2 <- fortify(k1)\nhead(hold2)\n\nMapToInterval(1:10, start=0, stop=1)\nmapping(1:10, start=0, stop=1)\nmapping(0:100, start=min(mtcars$mpg), stop=max(mtcars$mpg))\n\nrequire(scales)\nvect <- c(1000, 1500, 23450, 21784, 875003780)\nmultiple(vect)\nmultiple(vect, extra=dollar)\nmultiple(vect, extra=identity)\n\nrequire(ggplot2)\ndata(diamonds)\nggplot(diamonds, aes(x=x, y=y, color=price*100)) + geom_point() + \n scale_color_gradient2(labels=multiple)\n\n\nvect <- c(mean, mode, mean)\nvect2 <- c(mean, mode, max)\nvect3 <- c(mean, mean)\ncompare.list(vect, vect)\ncompare.list(vect, vect2)\n\n\ntheDF <- data.frame(A=1:10, B=11:20, C=1:10, D=11:20)\ntheDF\ncolsToFront(theDF, c('B', 'C'))\ncolsToFront(theDF, c('C', 'B'))\ncolsToFront(theDF, c('C', 'C'))\ncolsToBack(theDF, c('C', 'C'))\ncolsToBack(theDF, c('C', 'B'))\ncolsToBack(theDF, c('C', 'C'))\n\ntheDF <- data.frame(A=1:10, B=1:10, C=1:10)\ntheDF[c(1, 4, 6), c(1)] <- NA\ntheDF\ntheDF[c(3, 4, 8), c(3)] <- NA\ntheDF\nsimple.impute(theDF, constant(4))\n\nlibrary(scales)\nvect <- c(1000, 1500, 23450, 21784, 875003780)\nmultiple_format()(vect)\nmultiple_format(extra=dollar)(vect)\nmultiple_format(extra=identity)(vect)\nmultiple_format()(mtcars$mpg)(multiple=kgs)\nclass(mtcars$mpg)\nmultiple(vect, multiple='K', digits=2, prefix='Rs')\n?multiple_format\n\nload_packages(c('ggplot2', 'dplyr'))\n\ntoCheck <- c('BIG', 'little', 'Mixed', 'BIG WITH SPACE', \n 'little with space', 'MIXED with SPACE', '17')\nnumeric.case(toCheck)\n\n\nrequire(scales)\nvect <- c(1000, 1500, 23450, 21784, 875003780)\nmultiple.comma(vect)\nmultiple.comma(vect, multiple=\"k\")\nmultiple.comma(vect, multiple=\"h\")\n\n# NOT RUN {\ntoCheck <- c('BIG', 'little', 'Mixed', 'BIG WITH SPACE', 'little with space', 'MIXED with SPACE')\nlower.case(toCheck)\n# }\n\n\n#library - useful\nlibrary(useful)\nk1 <- kmeans(x=iris[, 1:4], centers=3)\nplot(k1)\nplot(k1, data=iris)\n\ntoCheck <- c('BIG', 'little', 'Mixed', 'BIG WITH SPACE', 'little with space', 'MIXED with SPACE')\nfind.case(toCheck, 'upper')\nfind.case(toCheck, 'lower')\n\n\ndata(iris)\nhartiganResults <- FitKMeans(iris[, -ncol(iris)])\nPlotHartigan(hartiganResults)\n\ndata=mtcars\n#ForceDataFrame(data)\nindexToPosition(3, 2)\nindexToPosition(c(1, 4, 5, 7, 9), 3)\nindexToPosition(1:16, 4)\nindexToPosition(c(1, 3, 5, 6, 8, 10, 11, 13, 15), 5)\n\nx=mtcars\nleft(x, c = 5L)\n\ntheDF <- data.frame(A=1:10, B=11:20, C=1:10, D=11:20)\ndf <- moveToFront(theDF, c('B', 'C'))\ndf\nmoveToFront(theDF, c('C', 'B'))\nmoveToFront(theDF, c('C', 'C'))\nmoveToBack(theDF, c('C', 'C'))\nmoveToBack(theDF, c('C', 'B'))\nmoveToBack(theDF, c('C', 'C'))\n\n\nplot(sunspot.year)\nplot(sunspot.year, acf=TRUE)\n\npositionToIndex(1, 2, 2)\npositionToIndex(row=c(1, 1, 2, 1, 3), col=c(1, 2, 2, 3, 3), nrow=3)\npositionToIndex(rep(1:4, 4), rep(1:4, each=4), nrow=4)\npositionToIndex(rep(c(1, 3, 5), 3), rep(1:3, each=3), nrow=5)\n\n\ntheDF <- data.frame(A=1:10, B=1:10)\nreclass(theDF) <- 'newclass'\nclass(theDF)\ntheDF <- reclass(theDF, 'another')\nclass(theDF)\n\nright(diamonds) # displays all rows and only the last 5 columns\n\nmyData <- data.frame(Upper=LETTERS, lower=letters)\nmyData\nshift.column(data=myData, columns=\"lower\")\nshift.column(data=myData, columns=\"lower\", len=2)\nshift.column(data=myData, columns=c(\"lower\", \"Upper\"), len=3, up=T)\nshift.column(data=myData, columns=c(\"lower\", \"Upper\"), len=3, up=F)\n\n\ntheDF <- data.frame(A=1:10, B=1:10, C=1:10)\ntheDF[c(1, 4, 6), c(1)] <- NA\ntheDF[c(3, 4, 8), c(3)] <- NA\ntheDF\nsimple.impute.data.frame(theDF)\nsimple.impute.data.frame(theDF, mean)\nsimple.impute.data.frame(theDF, constant(4))\n\nsubOut(c(\"Hello\", \"(parens)\", \"Excited! Mark\"))\nsubOut(c(\"Hello\", \"(parens)\", \"Excited! Mark\"), specialChars=c(\"!\", \"(\"))\n\nsubSpecials(c(\"Hello\", \"(parens)\", \"Excited! Mark\"))\nsubSpecials(c(\"Hello\", \"(parens)\", \"Excited! Mark\"), specialChars=c(\"!\", \"(\"))\nsubSpecials(c(\"Hello\", \"(parens)\", \"Excited! Mark\"),\n c(\"This is a period. And this is an asterisk *\"), specialChars=c(\"!\", \"(\"))\nsubSpecials(c(\"Hello\", \"(parens)\", \"Excited! Mark\"),\n c(\"This is a period. And this is an asterisk *\"), specialChars=c(\"!\", \"(\", \"*\"))" }, { "path": "report/example1.Rmd", "content": "#Report\nHere is some data\n\n```{r mtcars}\nsummary(mtcars)\naggregate(mtcars$mpg, by=list(mtcars$cyl), mean)\n```\n\nHere is the plot\n```{r,fig.width=5, fig.height=5,echo=FALSE}\n#insert figures manually\nplot(women)\n```\n\n```{r cars}\nsummary(cars)\n```\n```{r iris}\nhead(iris)\n```\n" }, { "path": "report/knit2.R", "content": "library(knitr)\n(f = system.file(\"examples\", \"knitr-minimal.Rnw\", package = \"knitr\"))\nknit(f) # compile to tex\n\npurl(f) # tangle R code\npurl(f, documentation = 0) # extract R code only\npurl(f, documentation = 2) # also include documentation\n" }, { "path": "report/knitr-minimal.R", "content": "#' %% LyX 2.2.1 created this file. For more info, see http://www.lyx.org/.\n#' %% Do not edit unless you really know what you are doing.\n#' \\documentclass{article}\n#' \\usepackage[sc]{mathpazo}\n#' \\usepackage[T1]{fontenc}\n#' \\usepackage{geometry}\n#' \\geometry{verbose,tmargin=2.5cm,bmargin=2.5cm,lmargin=2.5cm,rmargin=2.5cm}\n#' \\setcounter{secnumdepth}{2}\n#' \\setcounter{tocdepth}{2}\n#' \\usepackage{url}\n#' \\usepackage[unicode=true,pdfusetitle,\n#' bookmarks=true,bookmarksnumbered=true,bookmarksopen=true,bookmarksopenlevel=2,\n#' breaklinks=false,pdfborder={0 0 1},backref=false,colorlinks=false]\n#' {hyperref}\n#' \\hypersetup{\n#' pdfstartview={XYZ null null 1}}\n#' \\usepackage{breakurl}\n#' \\begin{document}\n## ----setup, include=FALSE, cache=FALSE-----------------------------------\nlibrary(knitr)\n# set global chunk options\nopts_chunk$set(fig.path='figure/minimal-', fig.align='center', fig.show='hold')\noptions(formatR.arrow=TRUE,width=90)\n\n#' \n#' \\title{A Minimal Demo of knitr}\n#' \n#' \\author{Yihui Xie}\n#' \n#' \\maketitle\n#' You can test if \\textbf{knitr} works with this minimal demo. OK, let's\n#' get started with some boring random numbers:\n#' \n## ----boring-random-------------------------------------------------------\nset.seed(1121)\n(x=rnorm(20))\nmean(x);var(x)\n\n#' \n#' The first element of \\texttt{x} is \\Sexpr{x[1]}. Boring boxplots\n#' and histograms recorded by the PDF device:\n#' \n## ----boring-plots, fig.width=4, fig.height=4, out.width='.4\\\\linewidth'----\n## two plots side by side (option fig.show='hold')\npar(mar=c(4,4,.1,.1),cex.lab=.95,cex.axis=.9,mgp=c(2,.7,0),tcl=-.3,las=1)\nboxplot(x)\nhist(x,main='')\n\n#' \n#' Do the above chunks work? You should be able to compile the \\TeX{}\n#' document and get a PDF file like this one: \\url{https://github.com/yihui/knitr/releases/download/doc/knitr-minimal.pdf}.\n#' The Rnw source of this document is at \\url{https://github.com/yihui/knitr/blob/master/inst/examples/knitr-minimal.Rnw}.\n#' \\end{document}\n" }, { "path": "report/knitr-minimal.tex", "content": "%% LyX 2.2.1 created this file. For more info, see http://www.lyx.org/.\n%% Do not edit unless you really know what you are doing.\n\\documentclass{article}\\usepackage[]{graphicx}\\usepackage[]{color}\n%% maxwidth is the original width if it is less than linewidth\n%% otherwise use linewidth (to make sure the graphics do not exceed the margin)\n\\makeatletter\n\\def\\maxwidth{ %\n \\ifdim\\Gin@nat@width>\\linewidth\n \\linewidth\n \\else\n \\Gin@nat@width\n \\fi\n}\n\\makeatother\n\n\\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345}\n\\newcommand{\\hlnum}[1]{\\textcolor[rgb]{0.686,0.059,0.569}{#1}}%\n\\newcommand{\\hlstr}[1]{\\textcolor[rgb]{0.192,0.494,0.8}{#1}}%\n\\newcommand{\\hlcom}[1]{\\textcolor[rgb]{0.678,0.584,0.686}{\\textit{#1}}}%\n\\newcommand{\\hlopt}[1]{\\textcolor[rgb]{0,0,0}{#1}}%\n\\newcommand{\\hlstd}[1]{\\textcolor[rgb]{0.345,0.345,0.345}{#1}}%\n\\newcommand{\\hlkwa}[1]{\\textcolor[rgb]{0.161,0.373,0.58}{\\textbf{#1}}}%\n\\newcommand{\\hlkwb}[1]{\\textcolor[rgb]{0.69,0.353,0.396}{#1}}%\n\\newcommand{\\hlkwc}[1]{\\textcolor[rgb]{0.333,0.667,0.333}{#1}}%\n\\newcommand{\\hlkwd}[1]{\\textcolor[rgb]{0.737,0.353,0.396}{\\textbf{#1}}}%\n\\let\\hlipl\\hlkwb\n\n\\usepackage{framed}\n\\makeatletter\n\\newenvironment{kframe}{%\n \\def\\at@end@of@kframe{}%\n \\ifinner\\ifhmode%\n \\def\\at@end@of@kframe{\\end{minipage}}%\n \\begin{minipage}{\\columnwidth}%\n \\fi\\fi%\n \\def\\FrameCommand##1{\\hskip\\@totalleftmargin \\hskip-\\fboxsep\n \\colorbox{shadecolor}{##1}\\hskip-\\fboxsep\n % There is no \\\\@totalrightmargin, so:\n \\hskip-\\linewidth \\hskip-\\@totalleftmargin \\hskip\\columnwidth}%\n \\MakeFramed {\\advance\\hsize-\\width\n \\@totalleftmargin\\z@ \\linewidth\\hsize\n \\@setminipage}}%\n {\\par\\unskip\\endMakeFramed%\n \\at@end@of@kframe}\n\\makeatother\n\n\\definecolor{shadecolor}{rgb}{.97, .97, .97}\n\\definecolor{messagecolor}{rgb}{0, 0, 0}\n\\definecolor{warningcolor}{rgb}{1, 0, 1}\n\\definecolor{errorcolor}{rgb}{1, 0, 0}\n\\newenvironment{knitrout}{}{} % an empty environment to be redefined in TeX\n\n\\usepackage{alltt}\n\\usepackage[sc]{mathpazo}\n\\usepackage[T1]{fontenc}\n\\usepackage{geometry}\n\\geometry{verbose,tmargin=2.5cm,bmargin=2.5cm,lmargin=2.5cm,rmargin=2.5cm}\n\\setcounter{secnumdepth}{2}\n\\setcounter{tocdepth}{2}\n\\usepackage{url}\n\\usepackage[unicode=true,pdfusetitle,\n bookmarks=true,bookmarksnumbered=true,bookmarksopen=true,bookmarksopenlevel=2,\n breaklinks=false,pdfborder={0 0 1},backref=false,colorlinks=false]\n {hyperref}\n\\hypersetup{\n pdfstartview={XYZ null null 1}}\n\\usepackage{breakurl}\n\\IfFileExists{upquote.sty}{\\usepackage{upquote}}{}\n\\begin{document}\n\n\n\\title{A Minimal Demo of knitr}\n\n\\author{Yihui Xie}\n\n\\maketitle\nYou can test if \\textbf{knitr} works with this minimal demo. OK, let's\nget started with some boring random numbers:\n\n\\begin{knitrout}\n\\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\\color{fgcolor}\\begin{kframe}\n\\begin{alltt}\n\\hlkwd{set.seed}\\hlstd{(}\\hlnum{1121}\\hlstd{)}\n\\hlstd{(x}\\hlkwb{=}\\hlkwd{rnorm}\\hlstd{(}\\hlnum{20}\\hlstd{))}\n\\end{alltt}\n\\begin{verbatim}\n## [1] 0.1449583 0.4383221 0.1531912 1.0849426 1.9995449 -0.8118832 0.1602680\n## [8] 0.5858923 0.3600880 -0.0253084 0.1508809 0.1100824 1.3596812 -0.3269946\n## [15] -0.7163819 1.8097690 0.5084011 -0.5274603 0.1327188 -0.1559430\n\\end{verbatim}\n\\begin{alltt}\n\\hlkwd{mean}\\hlstd{(x);}\\hlkwd{var}\\hlstd{(x)}\n\\end{alltt}\n\\begin{verbatim}\n## [1] 0.3217385\n## [1] 0.5714534\n\\end{verbatim}\n\\end{kframe}\n\\end{knitrout}\n\nThe first element of \\texttt{x} is 0.1449583. Boring boxplots\nand histograms recorded by the PDF device:\n\n\\begin{knitrout}\n\\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\\color{fgcolor}\\begin{kframe}\n\\begin{alltt}\n\\hlcom{## two plots side by side (option fig.show='hold')}\n\\hlkwd{par}\\hlstd{(}\\hlkwc{mar}\\hlstd{=}\\hlkwd{c}\\hlstd{(}\\hlnum{4}\\hlstd{,}\\hlnum{4}\\hlstd{,}\\hlnum{.1}\\hlstd{,}\\hlnum{.1}\\hlstd{),}\\hlkwc{cex.lab}\\hlstd{=}\\hlnum{.95}\\hlstd{,}\\hlkwc{cex.axis}\\hlstd{=}\\hlnum{.9}\\hlstd{,}\\hlkwc{mgp}\\hlstd{=}\\hlkwd{c}\\hlstd{(}\\hlnum{2}\\hlstd{,}\\hlnum{.7}\\hlstd{,}\\hlnum{0}\\hlstd{),}\\hlkwc{tcl}\\hlstd{=}\\hlopt{-}\\hlnum{.3}\\hlstd{,}\\hlkwc{las}\\hlstd{=}\\hlnum{1}\\hlstd{)}\n\\hlkwd{boxplot}\\hlstd{(x)}\n\\hlkwd{hist}\\hlstd{(x,}\\hlkwc{main}\\hlstd{=}\\hlstr{''}\\hlstd{)}\n\\end{alltt}\n\\end{kframe}\n\n{\\centering \\includegraphics[width=.4\\linewidth]{figure/minimal-boring-plots-1} \n\\includegraphics[width=.4\\linewidth]{figure/minimal-boring-plots-2} \n\n}\n\n\n\n\\end{knitrout}\n\nDo the above chunks work? You should be able to compile the \\TeX{}\ndocument and get a PDF file like this one: \\url{https://github.com/yihui/knitr/releases/download/doc/knitr-minimal.pdf}.\nThe Rnw source of this document is at \\url{https://github.com/yihui/knitr/blob/master/inst/examples/knitr-minimal.Rnw}.\n\\end{document}\n" }, { "path": "report/mdreport1.Rmd", "content": "---\ntitle: \"report1\"\nauthor: \"dhiraj\"\ndate: \"April 3, 2018\"\noutput: html_document\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE)\n```\n\n## R Markdown\n\nThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see .\n\nWhen you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:\n\n```{r cars}\nsummary(cars)\n```\n\n## Including Plots\n\n\n```{r pressure, echo=FALSE}\n#no code printed\nplot(pressure)\n```\n\n```{r}\n#with code\nA <- c(\"Bob\", \"Tom\", \"Bill\", \"Joe\")\nB <- c(1.78, 1.86, 1.85, 1.70)\ndataframe <- data.frame(A, B)\nhead(dataframe)\n```\nCreating DF without display of code\n```{r,echo=FALSE}\n#without code\nA <- c(\"Bob\", \"Tom\", \"Bill\", \"Joe\")\nB <- c(1.78, 1.86, 1.85, 1.70)\ndataframe <- data.frame(A, B)\nhead(dataframe)\n```\n\n```{r,fig.width=5, fig.height=5,echo=FALSE}\n#insert figures manually\nplot(cars)\n```\n```{r,fig.width=5, fig.height=5,echo=FALSE,dev=\"svg\"}\n#change file type from png to svg\nplot(cars)\n```\n```{r,echo=FALSE}\n# Inserting Table\nlibrary(knitr)\nkable(dataframe,digits=1)\n```\n\n\n\nNote that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.\n" }, { "path": "report/report2.Rmd", "content": "---\ntitle: \"Test Report on Markdown\"\nauthor: \"dhiraj\"\ndate: \"April 3, 2018\"\noutput: pdf_document\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE)\n```\n\n## R Markdown\n\nThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see .\n\nWhen you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:\n\n```{r cars}\nsummary(cars)\n```\n\n## Including Plots\n\nYou can also embed plots, for example:\n\n```{r pressure, echo=FALSE}\nplot(pressure)\n```\n\nNote that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.\n" }, { "path": "report/report3.Rmd", "content": "---\ntitle: \"report3\"\nauthor: \"dhiraj\"\ndate: \"April 3, 2018\"\noutput: pdf_document\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE)\n```\n\n## R Markdown\n\nThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see .\n\nWhen you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:\n\n```{r cars}\nsummary(cars)\n```\n\n## Including Plots\n\nYou can also embed plots, for example:\n\n```{r pressure, echo=FALSE}\nplot(pressure)\n```\n\nNote that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.\n" }, { "path": "report/report4.Rmd", "content": "---\ntitle: \"pptreport\"\nauthor: \"dhiraj\"\ndate: \"April 3, 2018\"\noutput:\n slidy_presentation: default\n ioslides_presentation: default\n beamer_presentation: default\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = FALSE)\n```\n\n## R Markdown\n\nThis is an R Markdown presentation. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see .\n\nWhen you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.\n\n## Slide with Bullets\n\n- Bullet 1\n- Bullet 2\n- Bullet 3\n\n## Slide with R Output\n\n```{r cars, echo = TRUE}\nsummary(cars)\n```\n\n## Slide with Plot\n\n```{r pressure}\nplot(pressure)\n```\n\n" }, { "path": "report/report4.html", "content": "\n\n\n\n \n \n \n \n pptreport\n \n \n \n \n\n\n
    \n

    pptreport

    \n

    \ndhiraj\n

    \n

    April 3, 2018

    \n
    \n
    \n

    R Markdown

    \n

    This is an R Markdown presentation. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

    \n

    When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.

    \n
    \n
    \n

    Slide with Bullets

    \n
      \n
    • Bullet 1
      • \n
    • Bullet 2
      • \n
    • Bullet 3
      • \n
    \n
    \n
    \n

    Slide with R Output

    \n
    summary(cars)
    \n
    ##      speed           dist       \n##  Min.   : 4.0   Min.   :  2.00  \n##  1st Qu.:12.0   1st Qu.: 26.00  \n##  Median :15.0   Median : 36.00  \n##  Mean   :15.4   Mean   : 42.98  \n##  3rd Qu.:19.0   3rd Qu.: 56.00  \n##  Max.   :25.0   Max.   :120.00
    \n
    \n
    \n

    Slide with Plot

    \n

    \n
    \n\n \n \n\n\n\n" }, { "path": "report/report4.log", "content": "This is pdfTeX, Version 3.14159265-2.6-1.40.19 (MiKTeX 2.9.6630 64-bit) (preloaded format=pdflatex 2018.4.3) 3 APR 2018 14:38\nentering extended mode\n**./report4.tex\n(report4.tex\nLaTeX2e <2017-04-15>\nBabel <3.18> and hyphenation patterns for 75 language(s) loaded.\n" }, { "path": "report/reportnotes", "content": "library('rmarkdown')\nrmarkdown::render('.\\\\report\\\\sample1.R')\n\nlibrary(knitr)\nlibrary(tinytex)\nknitr::knit2pdf('.\\\\report\\\\sample1.R')" }, { "path": "report/sample1.R", "content": "#sample for reports\n\n\nmtcars\n\nwomen\n\nlibrary('knitr')\nknitr::kable(head(mtcars))\n\n#rmarkdown::render('.\\\\report\\\\sample1.R')" }, { "path": "report/sample2.R", "content": "# Sample 2\n\n\nAirPassengers\n\nairquality\n" }, { "path": "report/xbrl.Cache/aapl-20140927.xml", "content": "\n\n\n\n\n\n\n \n 76615000000\n 1240000000\n 1375000000\n 9815000000\n 33.07\n 101122000\n 443000000\n 62841000000\n 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23313000000\n -242000000\n 128000000\n 257000000\n 0\n 0.0051\n 1000000000\n 0.0105\n 0.0030\n 1500000000\n 0.0045\n 0.0051\n 1500000000\n 0.0445\n 0.0448\n 1000000000\n 0.0385\n 0.0391\n 3000000000\n 0.0345\n 0.0090\n 2500000000\n 0.0210\n 0.0053\n 2000000000\n 0.0110\n 2000000000\n 0.0100\n 0.0108\n 4000000000\n 0.0031\n 1000000000\n 0.0240\n 0.0244\n 5500000000\n 0.0285\n 0.0079\n 3000000000\n 0.0054\n 1000000000\n 385000000\n 492600000\n 1200000\n 5864840000\n 368000000\n 417000000\n 5100000\n 485000000\n 2600000000\n 295000000\n 2200000000\n 7\n 5.68\n 15500000\n 44314000\n 0.262\n 53666000000\n 6521634000\n 5.72\n 1.64\n 0.35\n 6477320000\n 745000000\n 148489000000\n -112000000\n 64304000000\n 9128000000\n 30500000000\n 8165000000\n 10528000000\n -970000000\n 170910000000\n -1080000000\n 973000000\n 50155000000\n 496000000\n 255000000\n -922000000\n 48999000000\n 287000000\n 36067000000\n 5032000000\n 1156000000\n 1616000000\n -223000000\n 626000000\n 911000000\n 160000000\n 22860000000\n -324000000\n 522000000\n 308000000\n 82000000\n 37037000000\n 0\n 2172000000\n 2253000000\n 1082000000\n -791000000\n 64000000\n 2253000000\n 1141000000\n 265000000\n 1459000000\n 136000000\n 15305000000\n 4475000000\n 10830000000\n 3100000000\n 592000000\n 106606000000\n -311000000\n 701000000\n 11212000000\n 136000000\n 22950000000\n 960000000\n 3513000000\n -426000000\n 0\n -4614000000\n 17554000000\n 13118000000\n 645000000\n 458000000\n 9334000000\n 5800000000\n -16379000000\n 35000000\n -458000000\n 530000000\n 3703000000\n 773000000\n 1084000000\n 135000000\n 0\n 816000000\n -33774000000\n 1100000000\n 1559000000\n 6757000000\n 508000000\n 351000000\n 0\n 1100000000\n 2340000000\n 20317000000\n 104130000000\n 2253000000\n 1000000000\n 189000000\n 1878000000\n 1133000000\n 4521000000\n 131000000\n 16896000000\n 118000000\n 9000000\n 1232000000\n 10676000000\n 10564000000\n -587000000\n 643000000\n 40\n P4Y\n 5\n P2Y\n 8877000\n 78.23\n 42291000\n 39415000\n 45.96\n 57.31\n 2253000000\n 3558000000\n 58980000000\n -4170000000\n 135740000\n 9000000000\n 78767000000\n 66197000000\n 25946000000\n 917000000\n 350000000\n -301000000\n 986000000\n 0.10\n 0.68\n 0.21\n 0.47\n 0.13\n 0.15\n 5706000000\n 62739000000\n 22817000000\n 91279000000\n 37883000000\n 13025000000\n 11181000000\n 3753000000\n 13462000000\n 6819000000\n 25417000000\n 8541000000\n 31980000000\n 495000000\n 20228000000\n 4025000000\n 382000000\n 4411000000\n 21483000000\n 16051000000\n -970000000\n 0\n 0\n 0\n 0\n 0\n 0\n 0\n 37037000000\n 0\n 22950000000\n 0\n 10676000000\n -444000000\n 328837000\n 0\n 0\n 2253000000\n 0\n 1232000000\n 0\n 48873000\n -143000000\n 496000000\n 419000000\n 143000000\n 0\n 891000000\n 676000000\n 12000000\n -6000000\n 0\n 0\n 903000000\n 670000000\n 2016\n 2017\n 2016\n 2044\n 2043\n 2024\n 2019\n 2018\n 2018\n 2017\n 2023\n 2021\n 2019\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Advertising Costs</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Advertising costs are expensed as incurred and\n included in selling, general and administrative expenses.\n Advertising expense was $1.2 billion, $1.1 billion and $1.0 billion\n for 2014, 2013 and 2012, respectively.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Basis of Presentation and Preparation</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The accompanying consolidated financial statements\n include the accounts of the Company. Intercompany accounts and\n transactions have been eliminated. The preparation of these\n consolidated financial statements in conformity with U.S. generally\n accepted accounting principles (&#x201C;GAAP&#x201D;) requires\n management to make estimates and assumptions that affect the\n amounts reported in these consolidated financial statements and\n accompanying notes. Actual results could differ materially from\n those estimates.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company&#x2019;s fiscal year is the 52 or\n 53-week period that ends on the last Saturday of September. The\n Company&#x2019;s fiscal years 2014, 2013 and 2012 ended on\n September&#xA0;27, 2014,&#xA0;September&#xA0;28, 2013 and\n September&#xA0;29, 2012, respectively. An additional week is\n included in the first fiscal quarter approximately every\n six&#xA0;years to realign fiscal quarters with calendar quarters.\n Fiscal years 2014 and 2013 spanned 52 weeks each. Fiscal year 2012\n spanned 53 weeks, with a 14th week included in the first quarter of\n 2012. Unless otherwise stated, references to particular years,\n quarters, months and periods refer to the Company&#x2019;s fiscal\n years ended in September and the associated quarters, months and\n periods of those fiscal years.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">During the first quarter of 2014, the Company\n adopted updated accounting standards that (i)&#xA0;required\n disclosure of additional information about the amounts reclassified\n out of accumulated other comprehensive income (&#x201C;AOCI&#x201D;)\n by component and (ii)&#xA0;required gross and net disclosures about\n offsetting assets and liabilities. The adoption of these updated\n standards only impacted the disclosures in the Notes to\n Consolidated Financial Statements; accordingly, the adoption had no\n impact on the Company&#x2019;s financial position or results of\n operations. The Company has provided these additional disclosures\n in this <font style=\"WHITE-SPACE: nowrap\">Form&#xA0;10-K</font> in\n Note 8, &#x201C;Comprehensive Income&#x201D; and Note 2,\n &#x201C;Financial Instruments,&#x201D; respectively.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The following tables show the Company&#x2019;s cash\n and <font style=\"WHITE-SPACE: nowrap\"><font style=\"WHITE-SPACE: nowrap\">available-for-sale</font></font>\n securities&#x2019; adjusted cost, gross unrealized gains, gross\n unrealized losses and fair value by significant investment category\n recorded as cash and cash equivalents or short- or long-term\n marketable securities as of September&#xA0;27, 2014 and\n September&#xA0;28, 2013 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"33%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"26\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Adjusted<br />\n Cost</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Losses</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Cash\n and<br />\n Cash<br />\n Equivalents</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">Short-Term</font><br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Long-Term<br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cash</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 1:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Money market funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mutual funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,531</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(132</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,077</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(132</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,946</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 2:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. Treasury securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,140</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">15</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(9</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,146</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">607</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,527</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. agency securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,373</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(11</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,365</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">652</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">157</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,556</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Non-U.S. government securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,925</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">69</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(69</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,925</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">204</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,721</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Certificates of deposit and time deposits</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,832</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,832</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,230</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,233</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,369</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">475</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">475</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">166</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">309</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Corporate securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">85,431</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">296</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(241</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">85,486</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,298</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">79,182</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Municipal securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">940</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">948</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">948</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mortgage- and asset-backed securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,907</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">26</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(49</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,884</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,859</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">141,023</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">417</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(379</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">141,061</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,066</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,833</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">130,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">155,332</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;418</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(511</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">155,239</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,844</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,233</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">130,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"39%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"26\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Adjusted<br />\n Cost</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Losses</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Cash\n and<br />\n Cash<br />\n Equivalents</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">Short-Term</font><br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Long-Term<br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cash</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 1:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Money market funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mutual funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,999</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,792</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,595</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 2:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. Treasury securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">27,642</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">27,619</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">431</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,554</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">19,634</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. agency securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,878</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(52</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,838</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">177</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,412</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,249</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Non-U.S. government securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,545</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">35</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(137</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,443</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">50</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">313</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,080</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Certificates of deposit and time deposits</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,344</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,344</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,264</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">844</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">236</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,998</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,998</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,835</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,163</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Corporate securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,586</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">275</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(252</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,609</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,077</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">46,532</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Municipal securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,257</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">45</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(22</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,280</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,114</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mortgage- and asset-backed securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,396</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(89</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,330</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,322</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">132,646</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">414</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(599</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">132,461</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,761</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,485</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">106,215</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">147,143</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;414</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(796</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">146,761</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,259</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;26,287</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">106,215</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Note 10 &#x2013; Commitments and Contingencies</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Accrued Warranty and Indemnification</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company offers a basic limited parts and labor\n warranty on its hardware products. The basic warranty period for\n hardware products is typically one year from the date of purchase\n by the end-user. The Company also offers a <font style=\"white-space:nowrap\">90-day</font> basic warranty for its service\n parts used to repair the Company&#x2019;s hardware products. The\n Company provides currently for the estimated cost that may be\n incurred under its basic limited product warranties at the time\n related revenue is recognized. Factors considered in determining\n appropriate accruals for product warranty obligations include the\n size of the installed base of products subject to warranty\n protection, historical and projected warranty claim rates,\n historical and projected cost-per-claim and knowledge of specific\n product failures that are outside of the Company&#x2019;s typical\n experience. The Company assesses the adequacy of its pre-existing\n warranty liabilities and adjusts the amounts as necessary based on\n actual experience and changes in future estimates.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table shows changes in the\n Company&#x2019;s accrued warranties and related costs for 2014, 2013\n and 2012 (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Beginning accrued warranty and related costs</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,967</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,638</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,240</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Cost of warranty claims</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,760</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,703</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(1,786</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accruals for product warranty</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,952</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,032</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,184</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Ending accrued warranty and related costs</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,159</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,967</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,638</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company generally does not indemnify end-users\n of its operating system and application software against legal\n claims that the software infringes third-party intellectual\n property rights. Other agreements entered into by the Company\n sometimes include indemnification provisions under which the\n Company could be subject to costs and/or damages in the event of an\n infringement claim against the Company or an indemnified\n third-party. However, the Company has not been required to make any\n significant payments resulting from such an infringement claim\n asserted against it or an indemnified third-party. In the opinion\n of management, there was not at least a reasonable possibility the\n Company may have incurred a material loss with respect to\n indemnification of end-users of its operating system or application\n software for infringement of third-party intellectual property\n rights. The Company did not record a liability for infringement\n costs related to indemnification as of September&#xA0;27, 2014 or\n September&#xA0;28, 2013.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company has entered into indemnification\n agreements with its directors and executive officers. Under these\n agreements, the Company has agreed to indemnify such individuals to\n the fullest extent permitted by law against liabilities that arise\n by reason of their status as directors or officers and to advance\n expenses incurred by such individuals in connection with related\n legal proceedings. It is not possible to determine the maximum\n potential amount of payments the Company could be required to make\n under these agreements due to the limited history of prior\n indemnification claims and the unique facts and circumstances\n involved in each claim. However, the Company maintains directors\n and officers liability insurance coverage to reduce its exposure to\n such obligations, and payments made under these agreements\n historically have not been material.</p>\n <p style=\"font-size:1px;margin-top:18px;margin-bottom:0px\">\n &#xA0;</p>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Concentrations in the Available Sources of Supply of Materials\n and Product</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Although most components essential to the\n Company&#x2019;s business are generally available from multiple\n sources, a number of components are currently obtained from single\n or limited sources. In addition, the Company competes for various\n components with other participants in the markets for mobile\n communication and media devices and personal computers. Therefore,\n many components used by the Company, including those that are\n available from multiple sources, are at times subject to\n industry-wide shortage and significant pricing fluctuations that\n could materially adversely affect the Company&#x2019;s financial\n condition and operating results.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company uses some custom components that are\n not commonly used by its competitors, and new products introduced\n by the Company often utilize custom components available from only\n one source. When a component or product uses new technologies,\n initial capacity constraints may exist until the suppliers&#x2019;\n yields have matured or manufacturing capacity has increased. If the\n Company&#x2019;s supply of components for a new or existing product\n were delayed or constrained, or if an outsourcing partner delayed\n shipments of completed products to the Company, the Company&#x2019;s\n financial condition and operating results could be materially\n adversely affected. The Company&#x2019;s business and financial\n performance could also be materially adversely affected depending\n on the time required to obtain sufficient quantities from the\n original source, or to identify and obtain sufficient quantities\n from an alternative source. Continued availability of these\n components at acceptable prices, or at all, may be affected if\n those suppliers concentrated on the production of common components\n instead of components customized to meet the Company&#x2019;s\n requirements.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company has entered into agreements for the\n supply of many components; however, there can be no guarantee that\n the Company will be able to extend or renew these agreements on\n similar terms, or at all. Therefore, the Company remains subject to\n significant risks of supply shortages and price increases that\n could materially adversely affect its financial condition and\n operating results.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Substantially all of the Company&#x2019;s hardware\n products are manufactured by outsourcing partners that are located\n primarily in Asia. A significant concentration of this\n manufacturing is currently performed by a small number of\n outsourcing partners, often in single locations. Certain of these\n outsourcing partners are the sole-sourced suppliers of components\n and manufacturers for many of the Company&#x2019;s products.\n Although the Company works closely with its outsourcing partners on\n manufacturing schedules, the Company&#x2019;s operating results\n could be adversely affected if its outsourcing partners were unable\n to meet their production commitments. The Company&#x2019;s purchase\n commitments typically cover its requirements for periods up to 150\n days.</p>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Other Off-Balance Sheet Commitments</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Operating Leases</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company leases various equipment and\n facilities, including retail space, under noncancelable operating\n lease arrangements. The Company does not currently utilize any\n other off-balance sheet financing arrangements. The major facility\n leases are typically for terms not exceeding 10 years and generally\n contain multi-year renewal options. Leases for retail space are for\n terms ranging from five to 20 years, the majority of which are for\n 10 years, and often contain multi-year renewal options. As of\n September&#xA0;27, 2014, the Company&#x2019;s total future minimum\n lease payments under noncancelable operating leases were $5.0\n billion, of which $3.6 billion related to leases for retail\n space.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Rent expense under all operating leases, including\n both cancelable and noncancelable leases, was $717 million, $645\n million and $488 million in 2014, 2013 and 2012, respectively.\n Future minimum lease payments under noncancelable operating leases\n having remaining terms in excess of one year as of\n September&#xA0;27, 2014, are as follows (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"91%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2015</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">662</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2016</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">676</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2017</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">645</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2018</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">593</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2019</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">534</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Thereafter</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,877</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,987</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"font-size:1px;margin-top:18px;margin-bottom:0px\">\n &#xA0;</p>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Other Commitments</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company utilizes several outsourcing partners\n to manufacture sub-assemblies for the Company&#x2019;s products and\n to perform final assembly and testing of finished products. These\n outsourcing partners acquire components and build product based on\n demand information supplied by the Company, which typically covers\n periods up to 150 days. The Company also obtains individual\n components for its products from a wide variety of individual\n suppliers. Consistent with industry practice, the Company acquires\n components through a combination of purchase orders, supplier\n contracts and open orders based on projected demand information.\n Where appropriate,&#xA0;the&#xA0;purchases are applied\n to&#xA0;inventory component prepayments&#xA0;that\n are&#xA0;outstanding with the&#xA0;respective supplier. As of\n September&#xA0;27, 2014, the Company had outstanding off-balance\n sheet third-party manufacturing commitments and component purchase\n commitments of $24.5 billion.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">In addition to the off-balance sheet commitments\n mentioned above, the Company had outstanding obligations of $3.4\n billion as of September&#xA0;27, 2014, which consisted mainly of\n commitments to acquire capital assets, including product tooling\n and manufacturing process equipment, and commitments related to\n advertising, R&amp;D, Internet and telecommunications services and\n other obligations.</p>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Contingencies</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company is subject to various legal proceedings\n and claims that have arisen in the ordinary course of business and\n that have not been fully adjudicated. In the opinion of management,\n there was not at least a reasonable possibility the Company may\n have incurred a material loss, or a material loss in excess of a\n recorded accrual, with respect to loss contingencies. However, the\n outcome of litigation is inherently uncertain. Therefore, although\n management considers the likelihood of such an outcome to be\n remote, if one or more of these legal matters were resolved against\n the Company in a reporting period for amounts in excess of\n management&#x2019;s expectations, the Company&#x2019;s consolidated\n financial statements for that reporting period could be materially\n adversely affected.</p>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <i>Apple Inc. v. Samsung Electronics Co., Ltd, et al.</i></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">On August&#xA0;24, 2012, a jury returned a verdict\n awarding the Company $1.05 billion in its lawsuit against Samsung\n Electronics Co., Ltd and affiliated parties in the United States\n District Court, Northern District of California, San Jose Division.\n On March&#xA0;6, 2014, the District Court entered final judgment in\n favor of the Company in the amount of approximately $930\n million.&#xA0;Because&#xA0;the award is now subject to appeal, the\n Company has not recognized the award in its results of\n operations.</p>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <i>VirnetX, Inc. v. Apple Inc. et al.</i></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">On August&#xA0;11, 2010, VirnetX, Inc. filed an\n action against the Company alleging that certain of its products\n infringed on four patents relating to network communications\n technology. On November&#xA0;6, 2012, a jury returned a verdict\n against the Company, and awarded damages of $368 million. On\n September&#xA0;16, 2014, the Court of Appeals for the Federal\n Circuit affirmed the District Court&#x2019;s decision in part,\n reversed in part, and vacated the damages award. The case has been\n remanded for further proceedings.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 18pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n <b>Note 6 &#x2013; Debt</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 6pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n <b>Commercial Paper</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 6pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">In April 2014, the Board of Directors authorized\n the Company to issue unsecured short-term promissory notes\n (&#x201C;Commercial Paper&#x201D;) pursuant to a commercial paper\n program. The Company intends to use net proceeds from the\n commercial paper program for general corporate purposes, including\n dividends and share repurchases. As of September&#xA0;27, 2014, the\n Company had $6.3 billion of Commercial Paper outstanding, with a\n weighted-average interest rate of 0.12% and maturities generally\n less than nine months.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 12pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">The following table provides a summary of cash\n flows associated with the issuance and maturities of Commercial\n Paper for 2014 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 12pt 'Times New Roman'; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 18pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n </p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"91%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Maturities less than 90 days:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Proceeds from (repayments of) commercial paper, net</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,865</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Maturities greater than 90 days:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Proceeds from commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,771</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Repayments of commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(330</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Maturities greater than 90 days, net</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,441</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 7em; TEXT-INDENT: -1em\">\n Total proceeds from issuance of commercial paper, net</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,306</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 1px 'Times New Roman'; MARGIN-TOP: 18px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n <b>Long-Term Debt</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 6pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">In the third quarter of 2014 and 2013, the Company\n issued $12.0 billion and $17.0 billion of long-term debt,\n respectively. The debt issuances included floating- and fixed-rate\n notes with varying maturities for an aggregate principal amount of\n $29.0 billion (collectively the &#x201C;Notes&#x201D;). The Notes are\n senior unsecured obligations, and interest is payable in arrears,\n quarterly for the floating-rate notes and semi-annually for the\n fixed-rate notes.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 12pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">The following table provides a summary of the\n Company&#x2019;s long-term debt as of September&#xA0;27, 2014 and\n September&#xA0;28, 2013:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 12pt 'Times New Roman'; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 18pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n </p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"68%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"6\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"6\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Effective</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Interest<br />\n Rate</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Effective</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Interest<br />\n Rate</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Floating-rate notes due 2016</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.51%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.51%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Floating-rate notes due 2017</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.31%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Floating-rate notes due 2018</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.10%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.10%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Floating-rate notes due 2019</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.54%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 0.45% notes due 2016</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.51%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.51%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 1.05% notes due 2017</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.30%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 1.00% notes due 2018</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.08%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.08%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 2.10% notes due 2019</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.53%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 2.85% notes due 2021</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.79%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 2.40% notes due 2023</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2.44%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2.44%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 3.45% notes due 2024</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.90%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 3.85% notes due 2043</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3.91%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3.91%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 4.45% notes due 2044</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4.48%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total borrowings</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">29,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">17,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Unamortized discount</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(52</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(40</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Hedge accounting fair value adjustments</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">39</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total long-term debt</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">28,987</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,960</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 12pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">The Company has entered, and may enter in the\n future, into interest rate swaps to manage interest rate risk on\n the Notes. Such swaps allow the Company to effectively convert\n fixed-rate payments into floating-rate payments or floating-rate\n payments into fixed-rate payments. In the third quarter of 2014,\n the Company entered into interest rate swaps with an aggregate\n notional amount of $9.0 billion, which effectively converted the\n fixed-rate notes due 2017, 2019, 2021 and 2024 into floating-rate\n notes. In the third quarter of 2013, the Company entered into\n interest rate swaps&#xA0;with an aggregate notional amount of $3.0\n billion, which effectively converted the floating-rate notes due\n 2016 and 2018 into fixed-rate notes.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 12pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">The effective rates for the Notes include the\n interest on the Notes, amortization of the discount and, if\n applicable, adjustments related to hedging. The Company recognized\n $381 million and $136 million of interest expense on its long-term\n debt for the years ended September&#xA0;27, 2014 and\n September&#xA0;28, 2013, respectively. The Company did not have any\n long-term debt in 2012.</p>\n <p style=\"MARGIN-BOTTOM: 0px; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 1px 'Times New Roman'; MARGIN-TOP: 12px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">Future principal payments for the Company&#x2019;s\n Notes as of September&#xA0;27, 2014, are as follows (in\n millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 12pt 'Times New Roman'; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 18pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n </p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"90%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2015</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2016</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2017</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2018</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2019</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Thereafter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">15,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">29,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 12pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">As of September&#xA0;27, 2014 and\n September&#xA0;28, 2013, the fair value of the Company&#x2019;s\n Notes, based on Level 2 inputs, was $28.5 billion and $15.9\n billion, respectively.</p>\n </div>\n 6.45\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Earnings Per Share</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Basic earnings per share is computed by dividing\n income available to common shareholders by the weighted-average\n number of shares of common stock outstanding during the period.\n Diluted earnings per share is computed by dividing income available\n to common shareholders by the weighted-average number of shares of\n common stock outstanding during the period increased to include the\n number of additional shares of common stock that would have been\n outstanding if the potentially dilutive securities had been issued.\n Potentially dilutive securities include outstanding stock options,\n shares to be purchased under the Company&#x2019;s employee stock\n purchase plan, unvested restricted stock and unvested RSUs. The\n dilutive effect of potentially dilutive securities is reflected in\n diluted earnings per share by application of the treasury stock\n method. Under the treasury stock method, an increase in the fair\n market value of the Company&#x2019;s common stock can result in a\n greater dilutive effect from potentially dilutive securities.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table shows the computation of basic\n and diluted earnings per share for 2014, 2013 and 2012 (net income\n in millions and shares in thousands):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"67%\"></td>\n <td valign=\"bottom\" width=\"6%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"6%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Numerator:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Net income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">39,510</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">37,037</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">41,733</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Denominator:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Weighted-average shares outstanding</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,085,572</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,477,320</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,543,726</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Effect of dilutive securities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">37,091</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">44,314</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">73,757</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Weighted-average diluted shares</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,122,663</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,521,634</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,617,483</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-size:1pt\">\n <td height=\"10\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Basic earnings per share</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6.49</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5.72</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6.38</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Diluted earnings per share</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6.45</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5.68</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6.31</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Potentially dilutive securities, the effect of\n which would have been antidilutive, were not significant for 2014,\n 2013 and 2012. The Company excluded these securities from the\n computation of diluted earnings per share.</p>\n </div>\n The U.S. and China were the only countries that accounted for more than 10% of the Company's net sales in 2014, 2013 and 2012\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Long-Lived Assets Including Goodwill and Other Acquired\n Intangible Assets</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company reviews property, plant and equipment,\n inventory component prepayments and certain identifiable\n intangibles, excluding goodwill, for impairment. Long-lived assets\n are reviewed for impairment whenever events or changes in\n circumstances indicate the carrying amount of an asset may not be\n recoverable. Recoverability of these assets is measured by\n comparison of their carrying amounts to future undiscounted cash\n flows the assets are expected to generate. If property, plant and\n equipment, inventory component prepayments and certain identifiable\n intangibles are considered to be impaired, the impairment to be\n recognized equals the amount by which the carrying value of the\n assets exceeds its fair value. The Company did not record any\n significant impairments during 2014, 2013 and 2012.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company does not amortize goodwill and\n intangible assets with indefinite useful lives, rather such assets\n are required to be tested for impairment at least annually or\n sooner whenever events or changes in circumstances indicate that\n the assets may be impaired. The Company performs its goodwill and\n intangible asset impairment tests in the fourth quarter of each\n year. The Company did not recognize any impairment charges related\n to goodwill or indefinite lived intangible assets during 2014, 2013\n and 2012. The Company established reporting units based on its\n current reporting structure. For purposes of testing goodwill for\n impairment, goodwill has been allocated to these reporting units to\n the extent it relates to each reporting unit. In 2014 and 2013, the\n Company&#x2019;s goodwill was allocated to the Americas and Europe\n reportable operating segments.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company amortizes its intangible assets with\n definite useful lives over their estimated useful lives and reviews\n these assets for impairment. The Company typically amortizes its\n acquired intangible assets with definite useful lives over periods\n from three to seven years.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Inventories</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Inventories are stated at the lower of cost,\n computed using the first-in, first-out method, or market. If the\n cost of the inventories exceeds their market value, provisions are\n made currently for the difference between the cost and the market\n value.</p>\n </div>\n P150D\n P4Y\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">The following table shows total assets by segment\n and reconciliation to the Consolidated Balance Sheets as of\n September&#xA0;27, 2014 and September&#xA0;28, 2013 (in\n millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"80%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Segment assets:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Americas</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,352</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,653</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Europe</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,631</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,134</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Greater China</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,172</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,943</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Japan</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,106</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,932</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Rest of Asia Pacific</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">908</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">923</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Retail</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,626</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,329</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Total segment assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24,795</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">18,914</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Corporate assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">207,044</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">188,086</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">231,839</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">207,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">A reconciliation of the provision for income taxes,\n with the amount computed by applying the statutory federal income\n tax rate (35% in 2014, 2013 and 2012) to income before provision\n for income taxes for 2014, 2013 and 2012, is as follows (dollars in\n millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Computed expected tax</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">18,719</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">17,554</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">19,517</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n State taxes, net of federal effect</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">469</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">508</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">677</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Indefinitely invested earnings of foreign subsidiaries</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(4,744</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(4,614</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(5,895</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Research and development credit, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(88</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(287</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(103</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Domestic production activities deduction</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(495</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(308</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(328</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">112</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">265</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">162</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Provision for income taxes</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,973</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,118</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">14,030</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Effective tax rate</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">26.1%</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">26.2%</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">25.2%</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n </div>\n <div>\n <div>\n &#xA0;Long-lived assets\n as of September&#xA0;27, 2014 and September&#xA0;28, 2013 are as\n follows (in millions):</div>\n <div><br class=\"Apple-interchange-newline\" />\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Long-lived assets:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n U.S.</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,108</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,399</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n China&#xA0;<sup style=\"FONT-SIZE: 10px; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,477</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,403</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Other countries</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,917</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,786</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total long-lived assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,502</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">17,588</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 6pt 'Times New Roman'; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 8px; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">China\n includes Hong Kong. Long-lived assets located in China consist\n primarily of product tooling and manufacturing process equipment\n and assets related to retail stores and related infrastructure.</p>\n </td>\n </tr>\n </table>\n </div>\n </div>\n No single customer that accounted for more than 10% of net sales in 2014, 2013 or 2012\n 15600000\n 10-K\n APPLE INC\n Yes\n AAPL\n 37091000\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\">\n <b>Note 1 &#x2013; Summary of Significant Accounting\n Policies</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Apple Inc. and its wholly-owned subsidiaries\n (collectively &#x201C;Apple&#x201D; or the &#x201C;Company&#x201D;)\n designs, manufactures and markets mobile communication and media\n devices, personal computers and portable digital music players, and\n sells a variety of related software, services, accessories,\n networking solutions and third-party digital content and\n applications. The Company sells its products worldwide through its\n retail stores, online stores and direct sales force, as well as\n through third-party cellular network carriers, wholesalers,\n retailers and value-added resellers. In addition, the Company sells\n a variety of third-party iPhone, iPad, Mac and iPod compatible\n products, including application software, and various accessories\n through its online and retail stores. The Company sells to\n consumers, small and mid-sized businesses, and education,\n enterprise and government customers.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Basis of Presentation and Preparation</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The accompanying consolidated financial statements\n include the accounts of the Company. Intercompany accounts and\n transactions have been eliminated. The preparation of these\n consolidated financial statements in conformity with U.S. generally\n accepted accounting principles (&#x201C;GAAP&#x201D;) requires\n management to make estimates and assumptions that affect the\n amounts reported in these consolidated financial statements and\n accompanying notes. Actual results could differ materially from\n those estimates.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company&#x2019;s fiscal year is the 52 or\n 53-week period that ends on the last Saturday of September. The\n Company&#x2019;s fiscal years 2014, 2013 and 2012 ended on\n September&#xA0;27, 2014,&#xA0;September&#xA0;28, 2013 and\n September&#xA0;29, 2012, respectively. An additional week is\n included in the first fiscal quarter approximately every\n six&#xA0;years to realign fiscal quarters with calendar quarters.\n Fiscal years 2014 and 2013 spanned 52 weeks each. Fiscal year 2012\n spanned 53 weeks, with a 14th week included in the first quarter of\n 2012. Unless otherwise stated, references to particular years,\n quarters, months and periods refer to the Company&#x2019;s fiscal\n years ended in September and the associated quarters, months and\n periods of those fiscal years.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">During the first quarter of 2014, the Company\n adopted updated accounting standards that (i)&#xA0;required\n disclosure of additional information about the amounts reclassified\n out of accumulated other comprehensive income (&#x201C;AOCI&#x201D;)\n by component and (ii)&#xA0;required gross and net disclosures about\n offsetting assets and liabilities. The adoption of these updated\n standards only impacted the disclosures in the Notes to\n Consolidated Financial Statements; accordingly, the adoption had no\n impact on the Company&#x2019;s financial position or results of\n operations. The Company has provided these additional disclosures\n in this <font style=\"WHITE-SPACE: nowrap\">Form&#xA0;10-K</font> in\n Note 8, &#x201C;Comprehensive Income&#x201D; and Note 2,\n &#x201C;Financial Instruments,&#x201D; respectively.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Common Stock Split</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">On June&#xA0;6, 2014, the Company effected a\n seven-for-one stock split to shareholders of record as of\n June&#xA0;2, 2014. All share and per share information has been\n retroactively adjusted to reflect the stock split.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Revenue Recognition</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Net sales consist primarily of revenue from the\n sale of hardware, software, digital content and applications,\n accessories and service and support contracts. The Company\n recognizes revenue when persuasive evidence of an arrangement\n exists, delivery has occurred, the sales price is fixed or\n determinable and collection is probable. Product is considered\n delivered to the customer once it has been shipped and title, risk\n of loss and rewards of ownership have been transferred. For most of\n the Company&#x2019;s product sales, these criteria are met at the\n time the product is shipped. For online sales to individuals, for\n some sales to education customers in the U.S., and for certain\n other sales, the Company defers revenue until the customer receives\n the product because the Company retains a portion of the risk of\n loss on these sales during transit. The Company recognizes revenue\n from the sale of hardware products, software bundled with hardware\n that is essential to the functionality of the hardware, and\n third-party digital content sold on the iTunes Store in accordance\n with general revenue recognition accounting guidance. The Company\n recognizes revenue in accordance with industry specific software\n accounting guidance for the following types of sales transactions:\n (i)&#xA0;standalone sales of software products, (ii)&#xA0;sales of\n software upgrades and (iii)&#xA0;sales of software bundled with\n hardware not essential to the functionality of the hardware.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">For the sale of most third-party products, the\n Company recognizes revenue based on the gross amount billed to\n customers because the Company establishes its own pricing for such\n products, retains related inventory risk for physical products, is\n the primary obligor to the customer and assumes the credit risk for\n amounts billed to its customers. For third-party applications sold\n through the App Store and Mac App Store and certain digital content\n sold through the iTunes Store, the Company does not determine the\n selling price of the products and is not the primary obligor to the\n customer. Therefore, the Company accounts for such sales on a net\n basis by recognizing in net sales only the commission it retains\n from each sale. The portion of the gross amount billed to customers\n that is remitted by the Company to third-party app developers and\n certain digital content owners is not reflected in the\n Company&#x2019;s Consolidated Statements of Operations.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company records deferred revenue when it\n receives payments in advance of the delivery of products or the\n performance of services. This includes amounts that have been\n deferred for unspecified and specified software upgrade rights and\n non-software services that are attached to hardware and software\n products. The Company sells gift cards redeemable at its retail and\n online stores, and also sells gift cards redeemable on the iTunes\n Store for the purchase of digital content and software. The Company\n records deferred revenue upon the sale of the card, which is\n relieved upon redemption of the card by the customer. Revenue from\n AppleCare service and support contracts is deferred and recognized\n over the service coverage periods. AppleCare service and support\n contracts typically include extended phone support, repair\n services, web-based support resources and diagnostic tools offered\n under the Company&#x2019;s standard limited warranty.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company records reductions to revenue for\n estimated commitments related to price protection and other\n customer incentive programs. For transactions involving price\n protection, the Company recognizes revenue net of the estimated\n amount to be refunded. For the Company&#x2019;s other customer\n incentive programs, the estimated cost of these programs is\n recognized at the later of the date at which the Company has sold\n the product or the date at which the program is offered. The\n Company also records reductions to revenue for expected future\n product returns based on the Company&#x2019;s historical experience.\n Revenue is recorded net of taxes collected from customers that are\n remitted to governmental authorities, with the collected taxes\n recorded as current liabilities until remitted to the relevant\n government authority.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <i>Revenue Recognition for Arrangements with Multiple\n Deliverables</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">For multi-element arrangements that include\n hardware products containing software essential to the hardware\n product&#x2019;s functionality, undelivered software elements that\n relate to the hardware product&#x2019;s essential software, and\n undelivered non-software services, the Company allocates revenue to\n all deliverables based on their relative selling prices. In such\n circumstances, the Company uses a hierarchy to determine the\n selling price to be used for allocating revenue to deliverables:\n (i)&#xA0;vendor-specific objective evidence of fair value\n (&#x201C;VSOE&#x201D;), (ii)&#xA0;third-party evidence of selling\n price (&#x201C;TPE&#x201D;) and (iii)&#xA0;best estimate of selling\n price (&#x201C;ESP&#x201D;). VSOE generally exists only when the\n Company sells the deliverable separately and is the price actually\n charged by the Company for that deliverable. ESPs reflect the\n Company&#x2019;s best estimates of what the selling prices of\n elements would be if they were sold regularly on a stand-alone\n basis. For multi-element arrangements accounted for in accordance\n with industry specific software accounting guidance, the Company\n allocates revenue to all deliverables based on the VSOE of each\n element, and if VSOE does not exist revenue is recognized when\n elements lacking VSOE are delivered.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">For sales of qualifying versions of iPhone, iPad\n and iPod touch (&#x201C;iOS devices&#x201D;), Mac and Apple TV, the\n Company has indicated it may from time to time provide future\n unspecified software upgrades and features to the essential\n software bundled with each of these hardware products free of\n charge to customers. Essential software for iOS devices includes\n iOS and related applications and for Mac includes OS X and related\n applications. The Company also provides various non-software\n services to owners of qualifying versions of iOS devices and Mac.\n The Company has identified up to three deliverables regularly\n included in arrangements involving the sale of these devices. The\n first deliverable is the hardware and software essential to the\n functionality of the hardware device delivered at the time of sale.\n The second deliverable is the embedded right included with the\n purchase of iOS devices, Mac and Apple TV to receive on a\n when-and-if-available basis, future unspecified software upgrades\n and features relating to the product&#x2019;s essential software.\n The third deliverable is the non-software services to be provided\n to qualifying versions of iOS devices and Mac. The Company\n allocates revenue between these deliverables using the relative\n selling price method. Because the Company has neither VSOE nor TPE\n for these deliverables, the allocation of revenue is based on the\n Company&#x2019;s ESPs. Revenue allocated to the delivered hardware\n and the related essential software is recognized at the time of\n sale provided the other conditions for revenue recognition have\n been met. Revenue allocated to the embedded unspecified software\n upgrade rights and the non-software services is deferred and\n recognized on a straight-line basis over the estimated period the\n software upgrades and non-software services are expected to be\n provided for each of these devices, which ranges from two to four\n years. Cost of sales related to delivered hardware and related\n essential software, including estimated warranty costs, are\n recognized at the time of sale. Costs incurred to provide\n non-software services are recognized as cost of sales as incurred,\n and engineering and sales and marketing costs are recognized as\n operating expenses as incurred.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company&#x2019;s process for determining its ESP\n for deliverables without VSOE or TPE considers multiple factors\n that may vary depending upon the unique facts and circumstances\n related to each deliverable. The Company believes its customers\n would be reluctant to buy unspecified software upgrade rights for\n the essential software included with its qualifying hardware\n products. This view is primarily based on the fact that unspecified\n software upgrade rights do not obligate the Company to provide\n upgrades at a particular time or at all, and do not specify to\n customers which upgrades or features will be delivered. The Company\n also believes its customers would be unwilling to pay a significant\n amount for access to the non-software services because other\n companies offer similar services at little or no cost to users.\n Therefore, the Company has concluded that if it were to sell\n upgrade rights or access to the non-software services on a\n standalone basis, including those rights and services attached to\n iOS devices, Mac and Apple TV, the selling prices would be\n relatively low. Key factors considered by the Company in developing\n the ESPs for software upgrade rights include prices charged by the\n Company for similar offerings, market trends in the pricing of\n Apple-branded and third-party Mac and iOS compatible software, the\n nature of the upgrade rights (e.g., unspecified versus specified)\n and the relative ESP of the upgrade rights as compared to the total\n selling price of the product. The Company may also consider\n additional factors as appropriate, including the impact of other\n products and services provided to customers, the pricing of\n competitive alternatives if they exist, product-specific business\n objectives and the length of time a particular version of a device\n has been available. When relevant, the same factors are considered\n by the Company in developing ESPs for offerings such as the\n non-software services with additional consideration given to the\n estimated cost to provide such services.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">In 2014, 2013 and 2012, the Company&#x2019;s\n combined ESPs for the unspecified software upgrade rights and the\n rights to receive the non-software services included with its\n qualifying hardware devices have ranged from $5 to $40. As of\n September&#xA0;27, 2014, the combined ESPs for Mac was $40 and for\n iPhone and iPad ranged from $15 to $25. Revenue allocated to such\n rights is deferred and recognized on a straight-line basis over the\n estimated period the rights are expected to be provided for each\n device, which ranges from two to four years.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Shipping Costs</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">For all periods presented, amounts billed to\n customers related to shipping and handling are classified as\n revenue, and the Company&#x2019;s shipping and handling costs are\n included in cost of sales.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Warranty Costs</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company generally provides for the estimated\n cost of hardware and software warranties at the time the related\n revenue is recognized. The Company assesses the adequacy of its\n pre-existing warranty liabilities and adjusts the amounts as\n necessary based on actual experience and changes in future\n estimates.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Software Development Costs</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Research and development (&#x201C;R&amp;D&#x201D;)\n costs are expensed as incurred. Development costs of computer\n software to be sold, leased, or otherwise marketed are subject to\n capitalization beginning when a product&#x2019;s technological\n feasibility has been established and ending when a product is\n available for general release to customers. In most instances, the\n Company&#x2019;s products are released soon after technological\n feasibility has been established. Costs incurred subsequent to\n achievement of technological feasibility were not significant, and\n software development costs were expensed as incurred during 2014,\n 2013 and 2012.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Advertising Costs</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Advertising costs are expensed as incurred and\n included in selling, general and administrative expenses.\n Advertising expense was $1.2 billion, $1.1 billion and $1.0 billion\n for 2014, 2013 and 2012, respectively.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Share-based Compensation</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company recognizes expense related to\n share-based payment transactions in which it receives employee\n services in exchange for (a)&#xA0;equity instruments of the Company\n or (b)&#xA0;liabilities that are based on the fair value of the\n enterprise&#x2019;s equity instruments or that may be settled by the\n issuance of such equity instruments. Share-based compensation cost\n for restricted stock and restricted stock units\n (&#x201C;RSUs&#x201D;) is measured based on the closing fair market\n value of the Company&#x2019;s common stock on the date of grant. The\n Company recognizes share-based compensation cost over the\n award&#x2019;s requisite service period on a straight-line basis for\n time-based RSUs and on a graded basis for RSUs that are contingent\n on the achievement of performance metrics. The Company recognizes a\n benefit from share-based compensation in the Consolidated\n Statements of Shareholders&#x2019; Equity if an incremental tax\n benefit is realized. In addition, the Company recognizes the\n indirect effects of share-based compensation on R&amp;D tax\n credits, foreign tax credits and domestic manufacturing deductions\n in the Consolidated Statements of Operations. Further information\n regarding share-based compensation can be found in Note 9,\n &#x201C;Benefit Plans&#x201D; of this <font style=\"WHITE-SPACE: nowrap\">Form&#xA0;10-K.</font></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Income Taxes</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The provision for income taxes is computed using\n the asset and liability method, under which deferred tax assets and\n liabilities are recognized for the expected future tax consequences\n of temporary differences between the financial reporting and tax\n bases of assets and liabilities and for operating losses and tax\n credit carryforwards. Deferred tax assets and liabilities are\n measured using the currently enacted tax rates that apply to\n taxable income in effect for the years in which those tax assets\n are expected to be realized or settled. The Company records a\n valuation allowance to reduce deferred tax assets to the amount\n that is believed more likely than not to be realized.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company recognizes the tax benefit from an\n uncertain tax position only if it is more likely than not the tax\n position will be sustained on examination by the taxing\n authorities, based on the technical merits of the position. The tax\n benefits recognized in the financial statements from such positions\n are then measured based on the largest benefit that has a greater\n than 50% likelihood of being realized upon settlement. See Note 5,\n &#x201C;Income Taxes&#x201D; of this Form 10-K for additional\n information.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Earnings Per Share</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Basic earnings per share is computed by dividing\n income available to common shareholders by the weighted-average\n number of shares of common stock outstanding during the period.\n Diluted earnings per share is computed by dividing income available\n to common shareholders by the weighted-average number of shares of\n common stock outstanding during the period increased to include the\n number of additional shares of common stock that would have been\n outstanding if the potentially dilutive securities had been issued.\n Potentially dilutive securities include outstanding stock options,\n shares to be purchased under the Company&#x2019;s employee stock\n purchase plan, unvested restricted stock and unvested RSUs. The\n dilutive effect of potentially dilutive securities is reflected in\n diluted earnings per share by application of the treasury stock\n method. Under the treasury stock method, an increase in the fair\n market value of the Company&#x2019;s common stock can result in a\n greater dilutive effect from potentially dilutive securities.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The following table shows the computation of basic\n and diluted earnings per share for 2014, 2013 and 2012 (net income\n in millions and shares in thousands):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"67%\"></td>\n <td valign=\"bottom\" width=\"6%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"6%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Numerator:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">39,510</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,037</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">41,733</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Denominator:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Weighted-average shares outstanding</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,085,572</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,477,320</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,543,726</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Effect of dilutive securities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,091</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">44,314</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">73,757</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Weighted-average diluted shares</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,122,663</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,521,634</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,617,483</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"10\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Basic earnings per share</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6.49</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5.72</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6.38</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Diluted earnings per share</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6.45</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5.68</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6.31</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">Potentially dilutive securities, the effect of\n which would have been antidilutive, were not significant for 2014,\n 2013 and 2012. The Company excluded these securities from the\n computation of diluted earnings per share.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Financial Instruments</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\">\n <i>Cash Equivalents and Marketable Securities</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">All highly liquid investments with maturities of\n three months or less at the date of purchase are classified as cash\n equivalents. The Company&#x2019;s marketable debt and equity\n securities have been classified and accounted for as <font style=\"WHITE-SPACE: nowrap\"><font style=\"WHITE-SPACE: nowrap\">available-for-sale.</font></font> Management\n determines the appropriate classification of its investments at the\n time of purchase and reevaluates the designations at each balance\n sheet date. The Company classifies its marketable debt securities\n as either short-term or long-term based on each instrument&#x2019;s\n underlying contractual maturity date. Marketable debt securities\n with maturities of 12 months or less are classified as short-term\n and marketable debt securities with maturities greater than 12\n months are classified as long-term. The Company classifies its\n marketable equity securities, including mutual funds, as either\n short-term or long-term based on the nature of each security and\n its availability for use in current operations. The Company&#x2019;s\n marketable debt and equity securities are carried at fair value,\n with the unrealized gains and losses, net of taxes, reported as a\n component of shareholders&#x2019; equity. The cost of securities\n sold is based upon the specific identification method.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <i>Derivative Financial Instruments</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company accounts for its derivative instruments\n as either assets or liabilities and carries them at fair value.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">For derivative instruments that hedge the exposure\n to variability in expected future cash flows that are designated as\n cash flow hedges, the effective portion of the gain or loss on the\n derivative instrument is reported as a component of AOCI in\n shareholders&#x2019; equity and reclassified into income in the same\n period or periods during which the hedged transaction affects\n earnings. The ineffective portion of the gain or loss on the\n derivative instrument, if any, is recognized in current income. To\n receive hedge accounting treatment, cash flow hedges must be highly\n effective in offsetting changes to expected future cash flows on\n hedged transactions. For options designated as cash flow hedges,\n changes in the time value are excluded from the assessment of hedge\n effectiveness and are recognized in income. For derivative\n instruments that hedge the exposure to changes in the fair value of\n an asset or a liability and that are designated as fair value\n hedges, both the net gain or loss on the derivative instrument as\n well as the offsetting gain or loss on the hedged item attributable\n to the hedged risk are recognized in earnings in the current\n period. For derivative instruments designated as a hedge of the\n foreign currency translation exposure of the net investment in a\n foreign operation, the net gain or loss on the effective portion of\n the derivative instrument is reported in the same manner as a\n foreign currency translation adjustment. For forward exchange\n contracts designated as net investment hedges, the Company excludes\n changes in fair value relating to changes in the forward carry\n component from its definition of effectiveness. Accordingly, any\n gains or losses related to this component are recognized in current\n income. Derivatives that do not qualify as hedges are adjusted to\n fair value through current income.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Allowance for Doubtful Accounts</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company records its allowance for doubtful\n accounts based upon its assessment of various factors. The Company\n considers historical experience, the age of the accounts receivable\n balances, credit quality of the Company&#x2019;s customers, current\n economic conditions and other factors that may affect\n customers&#x2019; ability to pay.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Inventories</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Inventories are stated at the lower of cost,\n computed using the first-in, first-out method, or market. If the\n cost of the inventories exceeds their market value, provisions are\n made currently for the difference between the cost and the market\n value.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Property, Plant and Equipment</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Property, plant and equipment are stated at cost.\n Depreciation is computed by use of the <font style=\"WHITE-SPACE: nowrap\">straight-line</font> method over the\n estimated useful lives of the assets, which for buildings is the\n lesser of 30 years or the remaining life of the underlying\n building; between two to five years for machinery and equipment,\n including product tooling and manufacturing process equipment; and\n the shorter of lease terms or ten years for leasehold improvements.\n The Company capitalizes eligible costs to acquire or develop\n internal-use software that are incurred subsequent to the\n preliminary project stage. Capitalized costs related to\n internal-use software are amortized using the straight-line method\n over the estimated useful lives of the assets, which range from\n three to five years. Depreciation and amortization expense on\n property and equipment was $6.9 billion, $5.8 billion and $2.6\n billion during 2014, 2013 and 2012, respectively.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Long-Lived Assets Including Goodwill and Other Acquired\n Intangible Assets</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company reviews property, plant and equipment,\n inventory component prepayments and certain identifiable\n intangibles, excluding goodwill, for impairment. Long-lived assets\n are reviewed for impairment whenever events or changes in\n circumstances indicate the carrying amount of an asset may not be\n recoverable. Recoverability of these assets is measured by\n comparison of their carrying amounts to future undiscounted cash\n flows the assets are expected to generate. If property, plant and\n equipment, inventory component prepayments and certain identifiable\n intangibles are considered to be impaired, the impairment to be\n recognized equals the amount by which the carrying value of the\n assets exceeds its fair value. The Company did not record any\n significant impairments during 2014, 2013 and 2012.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company does not amortize goodwill and\n intangible assets with indefinite useful lives, rather such assets\n are required to be tested for impairment at least annually or\n sooner whenever events or changes in circumstances indicate that\n the assets may be impaired. The Company performs its goodwill and\n intangible asset impairment tests in the fourth quarter of each\n year. The Company did not recognize any impairment charges related\n to goodwill or indefinite lived intangible assets during 2014, 2013\n and 2012. The Company established reporting units based on its\n current reporting structure. For purposes of testing goodwill for\n impairment, goodwill has been allocated to these reporting units to\n the extent it relates to each reporting unit. In 2014 and 2013, the\n Company&#x2019;s goodwill was allocated to the Americas and Europe\n reportable operating segments.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company amortizes its intangible assets with\n definite useful lives over their estimated useful lives and reviews\n these assets for impairment. The Company typically amortizes its\n acquired intangible assets with definite useful lives over periods\n from three to seven years.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Fair Value Measurements</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company applies fair value accounting for all\n financial assets and liabilities and non-financial assets and\n liabilities that are recognized or disclosed at fair value in the\n financial statements on a recurring basis. The Company defines fair\n value as the price that would be received from selling an asset or\n paid to transfer a liability in an orderly transaction between\n market participants at the measurement date. When determining the\n fair value measurements for assets and liabilities, which are\n required to be recorded at fair value, the Company considers the\n principal or most advantageous market in which the Company would\n transact and the market-based risk measurements or assumptions that\n market participants would use in pricing the asset or liability,\n such as risks inherent in valuation techniques, transfer\n restrictions and credit risk. Fair value is estimated by applying\n the following hierarchy, which prioritizes the inputs used to\n measure fair value into three levels and bases the categorization\n within the hierarchy upon the lowest level of input that is\n available and significant to the fair value measurement:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\"><i>Level 1</i> &#x2013; Quoted prices in active\n markets for identical assets or liabilities.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\"><i>Level 2</i> &#x2013; Observable inputs other than\n quoted prices in active markets for identical assets and\n liabilities, quoted prices for identical or similar assets or\n liabilities in inactive markets, or other inputs that are\n observable or can be corroborated by observable market data for\n substantially the full term of the assets or liabilities.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\"><i>Level 3</i> &#x2013; Inputs that are generally\n unobservable and typically reflect management&#x2019;s estimate of\n assumptions that market participants would use in pricing the asset\n or liability.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company&#x2019;s valuation techniques used to\n measure the fair value of money market funds and certain marketable\n equity securities were derived from quoted prices in active markets\n for identical assets or liabilities. The valuation techniques used\n to measure the fair value of the Company&#x2019;s debt instruments\n and all other financial instruments, all of which have\n counterparties with high credit ratings, were valued based on\n quoted market prices or model driven valuations using significant\n inputs derived from or corroborated by observable market data.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">In accordance with the fair value accounting\n requirements, companies may choose to measure eligible financial\n instruments and certain other items at fair value. The Company has\n not elected the fair value option for any eligible financial\n instruments.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Foreign Currency Translation and Remeasurement</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company translates the assets and liabilities\n of its non-U.S. dollar functional currency subsidiaries into U.S.\n dollars using exchange rates in effect at the end of each period.\n Revenue and expenses for these subsidiaries are translated using\n rates that approximate those in effect during the period. Gains and\n losses from these translations are recognized in foreign currency\n translation included in AOCI in shareholders&#x2019; equity. The\n Company&#x2019;s subsidiaries that use the U.S. dollar as their\n functional currency remeasure monetary assets and liabilities at\n exchange rates in effect at the end of each period, and\n inventories, property and nonmonetary assets and liabilities at\n historical rates. Gains and losses from these remeasurements were\n not significant during 2014, 2013 and 2012 and have been included\n in the Company&#x2019;s results of operations.</p>\n </div>\n <div>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Share-based Compensation</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company recognizes expense related to\n share-based payment transactions in which it receives employee\n services in exchange for (a)&#xA0;equity instruments of the Company\n or (b)&#xA0;liabilities that are based on the fair value of the\n enterprise&#x2019;s equity instruments or that may be settled by the\n issuance of such equity instruments. Share-based compensation cost\n for restricted stock and restricted stock units\n (&#x201C;RSUs&#x201D;) is measured based on the closing fair market\n value of the Company&#x2019;s common stock on the date of grant. The\n Company recognizes share-based compensation cost over the\n award&#x2019;s requisite service period on a straight-line basis for\n time-based RSUs and on a graded basis for RSUs that are contingent\n on the achievement of performance metrics. The Company recognizes a\n benefit from share-based compensation in the Consolidated\n Statements of Shareholders&#x2019; Equity if an incremental tax\n benefit is realized. In addition, the Company recognizes the\n indirect effects of share-based compensation on R&amp;D tax\n credits, foreign tax credits and domestic manufacturing deductions\n in the Consolidated Statements of Operations. Further information\n regarding share-based compensation can be found in Note 9,\n &#x201C;Benefit Plans&#x201D; of this <font style=\"white-space:nowrap\">Form&#xA0;10-K.</font></p>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Note 4 &#x2013; Goodwill and Other Intangible Assets</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">On July&#xA0;31, 2014, the Company completed the\n acquisitions of Beats Music, LLC, which offers a subscription\n streaming music service, and Beats Electronics, LLC, which makes\n Beats<sup style=\"font-size:85%; vertical-align:top\">&#xAE;</sup>\n headphones, speakers and audio software (collectively,\n &#x201C;Beats&#x201D;). The total purchase price consideration for\n these acquisitions was $2.6 billion, which consisted primarily of\n cash, of which $2.2 billion was allocated to goodwill, $636 million\n to acquired intangible assets and $258 million to net liabilities\n assumed. Concurrent with the close of the acquisition, the Company\n repaid $295 million of existing Beats outstanding debt to\n third-party creditors. In conjunction with the Beats acquisitions,\n the Company issued approximately 5.1&#xA0;million shares of its\n common stock to certain former equity holders of Beats. The\n restricted stock was valued at approximately $485 million based on\n the Company&#x2019;s common stock on the acquisition date. The\n majority of these shares, valued at approximately $417 million,\n will vest over time based on continued employment with Apple.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company also completed various other business\n acquisitions during 2014 for an aggregate cash consideration, net\n of cash acquired, of $957 million, of which $828 million was\n allocated to goodwill, $257 million to acquired intangible assets\n and $128 million to net liabilities assumed.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company completed various business acquisitions\n during 2013 for an aggregate cash consideration, net of cash\n acquired, of $496 million, of which $419 million was allocated to\n goodwill, $179 million to acquired intangible assets and $102\n million to net liabilities assumed.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company&#x2019;s gross carrying amount of\n goodwill was $4.6 billion and $1.6 billion as of September&#xA0;27,\n 2014 and September&#xA0;28, 2013, respectively. The Company did not\n have any goodwill impairments during 2014, 2013 or 2012.</p>\n <p style=\"font-size:1px;margin-top:12px;margin-bottom:0px\">\n &#xA0;</p>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table summarizes the components of\n gross and net intangible asset balances as of September&#xA0;27,\n 2014 and September&#xA0;28, 2013 (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"50%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"10\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"10\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Gross<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Accumulated<br />\n Amortization</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Net<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Gross<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Accumulated<br />\n Amortization</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Net<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Definite-lived and amortizable acquired intangible assets</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">7,127</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,085</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,042</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,081</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(2,002</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,079</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Indefinite-lived and non-amortizable acquired intangible assets</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total acquired intangible assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">7,227</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,085</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,142</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,181</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(2,002</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,179</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Amortization expense related to acquired intangible\n assets was $1.1 billion, $960 million and $605 million in 2014,\n 2013 and 2012, respectively. As of September&#xA0;27, 2014, the\n remaining weighted-average amortization period for acquired\n intangible assets is 3.8 years. The expected annual amortization\n expense related to acquired intangible assets as of\n September&#xA0;27, 2014, is as follows (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"90%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2015</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,204</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2016</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;1,083</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2017</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">825</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2018</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">592</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2019</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">175</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Thereafter</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">163</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,042</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Income Taxes</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The provision for income taxes is computed using\n the asset and liability method, under which deferred tax assets and\n liabilities are recognized for the expected future tax consequences\n of temporary differences between the financial reporting and tax\n bases of assets and liabilities and for operating losses and tax\n credit carryforwards. Deferred tax assets and liabilities are\n measured using the currently enacted tax rates that apply to\n taxable income in effect for the years in which those tax assets\n are expected to be realized or settled. The Company records a\n valuation allowance to reduce deferred tax assets to the amount\n that is believed more likely than not to be realized.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company recognizes the tax benefit from an\n uncertain tax position only if it is more likely than not the tax\n position will be sustained on examination by the taxing\n authorities, based on the technical merits of the position. The tax\n benefits recognized in the financial statements from such positions\n are then measured based on the largest benefit that has a greater\n than 50% likelihood of being realized upon settlement. See Note 5,\n &#x201C;Income Taxes&#x201D; of this Form 10-K for additional\n information.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">The following tables show the Company&#x2019;s\n derivative instruments at gross fair value as reflected in the\n Consolidated Balance Sheets as of September&#xA0;27, 2014 and\n September&#xA0;28, 2013 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"58%\"></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value&#xA0;of&#xA0;Derivatives</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>Designated&#xA0;as&#xA0;\n Hedge&#xA0;Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value&#xA0;of&#xA0;Derivatives&#xA0;Not</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>Designated&#xA0;\n as&#xA0;Hedge&#xA0;Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Total</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Derivative assets&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,332</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">222</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,554</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">81</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">81</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Derivative liabilities&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\" colspan=\"9\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">41</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">40</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">81</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"58%\"></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value&#xA0;of&#xA0;Derivatives</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>Designated&#xA0;as&#xA0;\n Hedge&#xA0;Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value&#xA0;of&#xA0;Derivatives&#xA0;Not</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>Designated&#xA0;\n as&#xA0;Hedge&#xA0;Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Total</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Derivative assets&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">145</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">170</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Derivative liabilities&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\" colspan=\"9\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">389</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">46</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">435</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">The\n fair value of derivative assets is measured using Level 2 fair\n value inputs and is recorded as other current assets in the\n Consolidated Balance Sheets.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">The\n fair value of derivative liabilities is measured using Level 2 fair\n value inputs and is recorded as accrued expenses in the\n Consolidated Balance Sheets.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Inventories</b></p>\n <p style=\"font-size:6pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Components</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">471</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">683</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Finished goods</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;&#xA0;1,640</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;&#xA0;1,081</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total inventories</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,111</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,764</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Note 11 &#x2013; Segment Information and Geographic Data</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company reports segment information based on\n the &#x201C;management&#x201D; approach. The management approach\n designates the internal reporting used by management for making\n decisions and assessing performance as the source of the\n Company&#x2019;s reportable segments.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company manages its business primarily on a\n geographic basis. The Company&#x2019;s reportable operating segments\n consist of the Americas, Europe, Greater China, Japan, Rest of Asia\n Pacific and Retail operations. The Americas segment includes both\n North and South America. The Europe segment includes European\n countries, as well as India, the Middle East and Africa. The\n Greater China segment includes China, Hong Kong and Taiwan. The\n Rest of Asia Pacific segment includes Australia and Asian\n countries, other than those countries included in the\n Company&#x2019;s other operating segments. The results of the\n Company&#x2019;s geographic segments do not include results of the\n Retail segment. Each operating segment provides similar hardware\n and software products and similar services. The accounting policies\n of the various segments are the same as those described in Note 1,\n &#x201C;Summary of Significant Accounting Policies.&#x201D;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company evaluates the performance of its\n operating segments based on net sales and operating income. Net\n sales for geographic segments are generally based on the location\n of customers, while Retail segment net sales are based on sales\n through the Company&#x2019;s retail stores. Operating income for\n each segment includes net sales to third parties, related cost of\n sales and operating expenses directly attributable to the segment.\n Advertising expenses are generally included in the geographic\n segment in which the expenditures are incurred. Operating income\n for each segment excludes other income and expense and certain\n expenses managed outside the operating segments. Costs excluded\n from segment operating income include various corporate expenses\n such as R&amp;D, corporate marketing expenses, share-based\n compensation expense, income taxes, various nonrecurring charges,\n other separately managed general and administrative costs and\n certain manufacturing period expenses. The Company does not include\n intercompany transfers between segments for management reporting\n purposes.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">Segment assets include receivables and inventories,\n and for the Retail segment also includes capital assets. Segment\n assets exclude corporate assets, such as cash and cash equivalents,\n short-term and long-term marketable securities, vendor non-trade\n receivables, other long-term investments, manufacturing and\n corporate facilities, product tooling and manufacturing process\n equipment, miscellaneous corporate infrastructure, goodwill and\n other acquired intangible assets. Except for the Retail segment,\n capital asset purchases for long-lived assets are not reported to\n management by segment and therefore are excluded from the\n geographic segment assets and instead included in corporate assets.\n Cash payments for capital asset purchases by the Retail segment\n were $484 million, $495 million and $858 million for 2014, 2013 and\n 2012, respectively. The Company&#x2019;s total depreciation and\n amortization was $7.9 billion, $6.8 billion and $3.3 billion in\n 2014, 2013 and 2012, respectively, of which $441 million, $382\n million and $319 million was related to the Retail segment in the\n respective years. Depreciation and amortization on segment assets\n included in the geographic segments was not significant.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">As the Company continues to expand its business,\n management believes collaboration across its online, Retail and\n indirect channels is integral to better serve its customers and\n optimize its financial results. Beginning in the first quarter of\n 2015, management will be assessing business performance and making\n decisions on a geographic basis, including the results of its\n retail stores in each respective geographic segment. Accordingly,\n the Company will be changing its reportable segments to align with\n the way the business is managed. The Company&#x2019;s reportable\n operating segments will consist of the Americas, Europe, Greater\n China, Japan and Rest of Asia Pacific operations, and the Retail\n segment will no longer be classified as a separate reportable\n segment. The historical segment data for prior periods will be\n reclassified to reflect this change beginning in the Quarterly\n Report on Form&#xA0;10-Q for the quarter ending December&#xA0;27,\n 2014.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">The following table shows information by operating\n segment for 2014, 2013 and 2012 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"73%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Americas:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">65,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">62,739</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">57,512</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24,316</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,817</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,414</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Europe:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">40,929</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,883</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">36,323</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,771</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,025</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,869</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Greater China:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">29,846</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25,417</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,533</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,016</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,541</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,843</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Japan:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,982</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,462</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,571</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,183</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,819</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,861</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Rest of Asia Pacific:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,344</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,181</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,741</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,636</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,753</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,253</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Retail:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,462</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">20,228</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">18,828</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,575</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,025</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,613</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">A reconciliation of the Company&#x2019;s segment\n operating income to the Consolidated Statements of Operations for\n 2014, 2013 and 2012, is as follows (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"73%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Segment operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">65,497</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">58,980</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">62,853</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Research and development expense&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(4,825</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(3,558</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(2,713</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Share-based compensation expense</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(2,863</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(2,253</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(1,740</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Other corporate expenses, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(5,306</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(4,170</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(3,159</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total operating income</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">52,503</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">48,999</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">55,241</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Amount excludes research and development share-based compensation\n expense.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">The following table shows total assets by segment\n and reconciliation to the Consolidated Balance Sheets as of\n September&#xA0;27, 2014 and September&#xA0;28, 2013 (in\n millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"80%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Segment assets:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Americas</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,352</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,653</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Europe</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,631</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,134</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Greater China</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,172</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,943</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Japan</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,106</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,932</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Rest of Asia Pacific</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">908</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">923</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Retail</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,626</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,329</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Total segment assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24,795</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">18,914</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Corporate assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">207,044</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">188,086</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">231,839</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">207,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The U.S. and China were the only countries that\n accounted for more than 10% of the Company&#x2019;s net sales in\n 2014, 2013 and 2012. There was no single customer that accounted\n for more than 10% of net sales in 2014, 2013 or 2012. Net sales for\n 2014, 2013 and 2012 and long-lived assets as of September&#xA0;27,\n 2014 and September&#xA0;28, 2013 are as follows (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"73%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net sales:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n U.S.</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">68,909</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">66,197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">60,949</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n China&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">30,638</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25,946</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,797</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Other countries</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">83,248</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">78,767</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">72,762</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total net sales</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">182,795</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">170,910</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">156,508</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Long-lived assets:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n U.S.</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,108</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,399</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n China&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,477</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,403</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Other countries</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,917</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,786</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total long-lived assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,502</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">17,588</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">China\n includes Hong Kong. Long-lived assets located in China consist\n primarily of product tooling and manufacturing process equipment\n and assets related to retail stores and related infrastructure.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">Information regarding net sales by product for\n 2014, 2013 and 2012, is as follows (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"73%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net Sales by Product:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n iPhone<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">101,991</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">91,279</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">78,692</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n iPad<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">30,283</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">31,980</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">30,945</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Mac&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24,079</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,483</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,221</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n iPod&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,286</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,411</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,615</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n iTunes, Software and Services&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">18,063</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,051</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,890</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Accessories&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(3)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,093</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,706</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,145</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total net sales</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">182,795</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">170,910</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">156,508</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Includes deferrals and amortization of related non-software\n services and software upgrade rights.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Includes revenue from the iTunes Store, the App Store, the Mac App\n Store, the iBooks Store, AppleCare, licensing and other\n services.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(3)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Includes sales of Apple-branded and third-party accessories for\n iPhone, iPad, Mac and iPod.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n </div>\n Large Accelerated Filer\n No\n 0.261\n 0.125\n P2Y10M24D\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Other Income and Expense</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table shows the detail of other\n income and expense for 2014, 2013 and 2012 (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Interest and dividend income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;1,795</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;1,616</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;1,088</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Interest expense</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(384</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(136</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other expense, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(431</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(324</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(566</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total other income/(expense), net</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">980</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,156</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">522</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n 59713000000\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Non-Current Liabilities</b></p>\n <p style=\"font-size:6pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred tax liabilities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">20,259</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">16,489</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other non-current liabilities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,567</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">3,719</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total other non-current liabilities</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">24,826</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">20,208</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table shows a summary of the\n share-based compensation expense included in the Consolidated\n Statements of Operations for 2014, 2013 and 2012 (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Cost of sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">450</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">350</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">265</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Research and development</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,216</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">917</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">668</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Selling, general and administrative</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,197</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">986</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">807</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total share-based compensation expense</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,863</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,253</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,740</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Revenue Recognition</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Net sales consist primarily of revenue from the\n sale of hardware, software, digital content and applications,\n accessories and service and support contracts. The Company\n recognizes revenue when persuasive evidence of an arrangement\n exists, delivery has occurred, the sales price is fixed or\n determinable and collection is probable. Product is considered\n delivered to the customer once it has been shipped and title, risk\n of loss and rewards of ownership have been transferred. For most of\n the Company&#x2019;s product sales, these criteria are met at the\n time the product is shipped. For online sales to individuals, for\n some sales to education customers in the U.S., and for certain\n other sales, the Company defers revenue until the customer receives\n the product because the Company retains a portion of the risk of\n loss on these sales during transit. The Company recognizes revenue\n from the sale of hardware products, software bundled with hardware\n that is essential to the functionality of the hardware, and\n third-party digital content sold on the iTunes Store in accordance\n with general revenue recognition accounting guidance. The Company\n recognizes revenue in accordance with industry specific software\n accounting guidance for the following types of sales transactions:\n (i)&#xA0;standalone sales of software products, (ii)&#xA0;sales of\n software upgrades and (iii)&#xA0;sales of software bundled with\n hardware not essential to the functionality of the hardware.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">For the sale of most third-party products, the\n Company recognizes revenue based on the gross amount billed to\n customers because the Company establishes its own pricing for such\n products, retains related inventory risk for physical products, is\n the primary obligor to the customer and assumes the credit risk for\n amounts billed to its customers. For third-party applications sold\n through the App Store and Mac App Store and certain digital content\n sold through the iTunes Store, the Company does not determine the\n selling price of the products and is not the primary obligor to the\n customer. Therefore, the Company accounts for such sales on a net\n basis by recognizing in net sales only the commission it retains\n from each sale. The portion of the gross amount billed to customers\n that is remitted by the Company to third-party app developers and\n certain digital content owners is not reflected in the\n Company&#x2019;s Consolidated Statements of Operations.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company records deferred revenue when it\n receives payments in advance of the delivery of products or the\n performance of services. This includes amounts that have been\n deferred for unspecified and specified software upgrade rights and\n non-software services that are attached to hardware and software\n products. The Company sells gift cards redeemable at its retail and\n online stores, and also sells gift cards redeemable on the iTunes\n Store for the purchase of digital content and software. The Company\n records deferred revenue upon the sale of the card, which is\n relieved upon redemption of the card by the customer. Revenue from\n AppleCare service and support contracts is deferred and recognized\n over the service coverage periods. AppleCare service and support\n contracts typically include extended phone support, repair\n services, web-based support resources and diagnostic tools offered\n under the Company&#x2019;s standard limited warranty.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company records reductions to revenue for\n estimated commitments related to price protection and other\n customer incentive programs. For transactions involving price\n protection, the Company recognizes revenue net of the estimated\n amount to be refunded. For the Company&#x2019;s other customer\n incentive programs, the estimated cost of these programs is\n recognized at the later of the date at which the Company has sold\n the product or the date at which the program is offered. The\n Company also records reductions to revenue for expected future\n product returns based on the Company&#x2019;s historical experience.\n Revenue is recorded net of taxes collected from customers that are\n remitted to governmental authorities, with the collected taxes\n recorded as current liabilities until remitted to the relevant\n government authority.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <i>Revenue Recognition for Arrangements with Multiple\n Deliverables</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">For multi-element arrangements that include\n hardware products containing software essential to the hardware\n product&#x2019;s functionality, undelivered software elements that\n relate to the hardware product&#x2019;s essential software, and\n undelivered non-software services, the Company allocates revenue to\n all deliverables based on their relative selling prices. In such\n circumstances, the Company uses a hierarchy to determine the\n selling price to be used for allocating revenue to deliverables:\n (i)&#xA0;vendor-specific objective evidence of fair value\n (&#x201C;VSOE&#x201D;), (ii)&#xA0;third-party evidence of selling\n price (&#x201C;TPE&#x201D;) and (iii)&#xA0;best estimate of selling\n price (&#x201C;ESP&#x201D;). VSOE generally exists only when the\n Company sells the deliverable separately and is the price actually\n charged by the Company for that deliverable. ESPs reflect the\n Company&#x2019;s best estimates of what the selling prices of\n elements would be if they were sold regularly on a stand-alone\n basis. For multi-element arrangements accounted for in accordance\n with industry specific software accounting guidance, the Company\n allocates revenue to all deliverables based on the VSOE of each\n element, and if VSOE does not exist revenue is recognized when\n elements lacking VSOE are delivered.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">For sales of qualifying versions of iPhone, iPad\n and iPod touch (&#x201C;iOS devices&#x201D;), Mac and Apple TV, the\n Company has indicated it may from time to time provide future\n unspecified software upgrades and features to the essential\n software bundled with each of these hardware products free of\n charge to customers. Essential software for iOS devices includes\n iOS and related applications and for Mac includes OS X and related\n applications. The Company also provides various non-software\n services to owners of qualifying versions of iOS devices and Mac.\n The Company has identified up to three deliverables regularly\n included in arrangements involving the sale of these devices. The\n first deliverable is the hardware and software essential to the\n functionality of the hardware device delivered at the time of sale.\n The second deliverable is the embedded right included with the\n purchase of iOS devices, Mac and Apple TV to receive on a\n when-and-if-available basis, future unspecified software upgrades\n and features relating to the product&#x2019;s essential software.\n The third deliverable is the non-software services to be provided\n to qualifying versions of iOS devices and Mac. The Company\n allocates revenue between these deliverables using the relative\n selling price method. Because the Company has neither VSOE nor TPE\n for these deliverables, the allocation of revenue is based on the\n Company&#x2019;s ESPs. Revenue allocated to the delivered hardware\n and the related essential software is recognized at the time of\n sale provided the other conditions for revenue recognition have\n been met. Revenue allocated to the embedded unspecified software\n upgrade rights and the non-software services is deferred and\n recognized on a straight-line basis over the estimated period the\n software upgrades and non-software services are expected to be\n provided for each of these devices, which ranges from two to four\n years. Cost of sales related to delivered hardware and related\n essential software, including estimated warranty costs, are\n recognized at the time of sale. Costs incurred to provide\n non-software services are recognized as cost of sales as incurred,\n and engineering and sales and marketing costs are recognized as\n operating expenses as incurred.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company&#x2019;s process for determining its ESP\n for deliverables without VSOE or TPE considers multiple factors\n that may vary depending upon the unique facts and circumstances\n related to each deliverable. The Company believes its customers\n would be reluctant to buy unspecified software upgrade rights for\n the essential software included with its qualifying hardware\n products. This view is primarily based on the fact that unspecified\n software upgrade rights do not obligate the Company to provide\n upgrades at a particular time or at all, and do not specify to\n customers which upgrades or features will be delivered. The Company\n also believes its customers would be unwilling to pay a significant\n amount for access to the non-software services because other\n companies offer similar services at little or no cost to users.\n Therefore, the Company has concluded that if it were to sell\n upgrade rights or access to the non-software services on a\n standalone basis, including those rights and services attached to\n iOS devices, Mac and Apple TV, the selling prices would be\n relatively low. Key factors considered by the Company in developing\n the ESPs for software upgrade rights include prices charged by the\n Company for similar offerings, market trends in the pricing of\n Apple-branded and third-party Mac and iOS compatible software, the\n nature of the upgrade rights (e.g., unspecified versus specified)\n and the relative ESP of the upgrade rights as compared to the total\n selling price of the product. The Company may also consider\n additional factors as appropriate, including the impact of other\n products and services provided to customers, the pricing of\n competitive alternatives if they exist, product-specific business\n objectives and the length of time a particular version of a device\n has been available. When relevant, the same factors are considered\n by the Company in developing ESPs for offerings such as the\n non-software services with additional consideration given to the\n estimated cost to provide such services.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">In 2014, 2013 and 2012, the Company&#x2019;s\n combined ESPs for the unspecified software upgrade rights and the\n rights to receive the non-software services included with its\n qualifying hardware devices have ranged from $5 to $40. As of\n September&#xA0;27, 2014, the combined ESPs for Mac was $40 and for\n iPhone and iPad ranged from $15 to $25. Revenue allocated to such\n rights is deferred and recognized on a straight-line basis over the\n estimated period the rights are expected to be provided for each\n device, which ranges from two to four years.</p>\n </div>\n <div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">Net sales for 2014, 2013 and 2012 are as follows\n (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <br class=\"Apple-interchange-newline\" />\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"73%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net sales:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n U.S.</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">68,909</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">66,197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">60,949</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n China&#xA0;<sup style=\"FONT-SIZE: 10px; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">30,638</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25,946</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,797</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Other countries</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">83,248</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">78,767</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">72,762</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total net sales</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">182,795</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">170,910</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">156,508</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td style=\"WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 1px ARIAL; LETTER-SPACING: normal; BACKGROUND-COLOR: rgb(255,255,255); TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" height=\"16\"></td>\n <td style=\"WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 1px ARIAL; LETTER-SPACING: normal; BACKGROUND-COLOR: rgb(255,255,255); TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" height=\"16\" colspan=\"4\"></td>\n <td style=\"WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 1px ARIAL; LETTER-SPACING: normal; BACKGROUND-COLOR: rgb(255,255,255); TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" height=\"16\" colspan=\"4\"></td>\n </tr>\n </table>\n </div>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">China\n includes Hong Kong.</p>\n </td>\n </tr>\n </table>\n </div>\n P3Y9M18D\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <i>Derivative Financial Instruments</i></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company accounts for its derivative instruments\n as either assets or liabilities and carries them at fair value.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">For derivative instruments that hedge the exposure\n to variability in expected future cash flows that are designated as\n cash flow hedges, the effective portion of the gain or loss on the\n derivative instrument is reported as a component of AOCI in\n shareholders&#x2019; equity and reclassified into income in the same\n period or periods during which the hedged transaction affects\n earnings. The ineffective portion of the gain or loss on the\n derivative instrument, if any, is recognized in current income. To\n receive hedge accounting treatment, cash flow hedges must be highly\n effective in offsetting changes to expected future cash flows on\n hedged transactions. For options designated as cash flow hedges,\n changes in the time value are excluded from the assessment of hedge\n effectiveness and are recognized in income. For derivative\n instruments that hedge the exposure to changes in the fair value of\n an asset or a liability and that are designated as fair value\n hedges, both the net gain or loss on the derivative instrument as\n well as the offsetting gain or loss on the hedged item attributable\n to the hedged risk are recognized in earnings in the current\n period. For derivative instruments designated as a hedge of the\n foreign currency translation exposure of the net investment in a\n foreign operation, the net gain or loss on the effective portion of\n the derivative instrument is reported in the same manner as a\n foreign currency translation adjustment. For forward exchange\n contracts designated as net investment hedges, the Company excludes\n changes in fair value relating to changes in the forward carry\n component from its definition of effectiveness. Accordingly, any\n gains or losses related to this component are recognized in current\n income. Derivatives that do not qualify as hedges are adjusted to\n fair value through current income.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Note 2 &#x2013; Financial Instruments</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\">\n <b>Cash, Cash Equivalents and Marketable Securities</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The following tables show the Company&#x2019;s cash\n and <font style=\"WHITE-SPACE: nowrap\"><font style=\"WHITE-SPACE: nowrap\">available-for-sale</font></font>\n securities&#x2019; adjusted cost, gross unrealized gains, gross\n unrealized losses and fair value by significant investment category\n recorded as cash and cash equivalents or short- or long-term\n marketable securities as of September&#xA0;27, 2014 and\n September&#xA0;28, 2013 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"33%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"26\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Adjusted<br />\n Cost</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Losses</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Cash\n and<br />\n Cash<br />\n Equivalents</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">Short-Term</font><br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Long-Term<br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cash</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 1:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Money market funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mutual funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,531</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(132</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,077</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(132</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,946</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 2:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. Treasury securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,140</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">15</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(9</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,146</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">607</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,527</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. agency securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,373</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(11</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,365</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">652</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">157</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,556</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Non-U.S. government securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,925</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">69</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(69</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,925</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">204</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,721</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Certificates of deposit and time deposits</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,832</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,832</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,230</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,233</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,369</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">475</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">475</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">166</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">309</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Corporate securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">85,431</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">296</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(241</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">85,486</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,298</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">79,182</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Municipal securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">940</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">948</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">948</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mortgage- and asset-backed securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,907</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">26</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(49</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,884</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,859</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">141,023</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">417</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(379</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">141,061</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,066</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,833</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">130,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">155,332</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;418</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(511</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">155,239</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,844</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,233</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">130,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"39%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"26\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Adjusted<br />\n Cost</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Losses</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Cash\n and<br />\n Cash<br />\n Equivalents</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">Short-Term</font><br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Long-Term<br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cash</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 1:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Money market funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mutual funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,999</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,792</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,595</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 2:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. Treasury securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">27,642</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">27,619</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">431</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,554</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">19,634</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. agency securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,878</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(52</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,838</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">177</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,412</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,249</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Non-U.S. government securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,545</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">35</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(137</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,443</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">50</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">313</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,080</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Certificates of deposit and time deposits</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,344</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,344</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,264</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">844</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">236</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,998</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,998</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,835</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,163</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Corporate securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,586</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">275</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(252</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,609</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,077</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">46,532</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Municipal securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,257</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">45</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(22</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,280</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,114</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mortgage- and asset-backed securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,396</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(89</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,330</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,322</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">132,646</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">414</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(599</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">132,461</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,761</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,485</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">106,215</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">147,143</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;414</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(796</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">146,761</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,259</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;26,287</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">106,215</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company may sell certain of its marketable\n securities prior to their stated maturities for strategic reasons\n including, but not limited to, anticipation of credit deterioration\n and duration management. The net realized gains or losses\n recognized by the Company, related to such sales, were not\n significant during 2014, 2013 and 2012. The maturities of the\n Company&#x2019;s long-term marketable securities generally range\n from one to five years.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">As of September&#xA0;27, 2014 and\n September&#xA0;28, 2013, gross unrealized losses related to\n individual securities that had been in a continuous loss position\n for 12 months or longer were not significant.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">As of September&#xA0;27, 2014, the Company\n considers the declines in market value of its marketable securities\n investment portfolio to be temporary in nature and does not\n consider any of its investments other-than-temporarily impaired.\n The Company typically invests in highly-rated securities, and its\n investment policy limits the amount of credit exposure to any one\n issuer. The policy generally requires investments to be investment\n grade, with the primary objective of minimizing the potential risk\n of principal loss. Fair values were determined for each individual\n security in the investment portfolio. When evaluating an investment\n for other-than-temporary impairment, the Company reviews factors\n such as the length of time and extent to which fair value has been\n below its cost basis, the financial condition of the issuer and any\n changes thereto, changes in market interest rates, and the\n Company&#x2019;s intent to sell, or whether it is more likely than\n not it will be required to sell, the investment before recovery of\n the investment&#x2019;s cost basis. During 2014, 2013 and 2012 the\n Company did not recognize any significant impairment charges.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Derivative Financial Instruments</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company uses derivatives to partially offset\n its business exposure to foreign currency and interest rate risk.\n The Company may enter into forward contracts, option contracts,\n swaps, or other derivative instruments to offset some of the risk\n on expected future cash flows, on net investments in certain\n foreign subsidiaries and on certain existing assets and\n liabilities. However, the Company may choose not to hedge certain\n exposures for a variety of reasons including, but not limited to,\n accounting considerations and the prohibitive economic cost of\n hedging particular exposures. There can be no assurance the hedges\n will offset more than a portion of the financial impact resulting\n from movements in foreign currency exchange or interest rates.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">To help protect gross margins from fluctuations in\n foreign currency exchange rates, certain of the Company&#x2019;s\n subsidiaries whose functional currency is the U.S. dollar hedge a\n portion of forecasted foreign currency revenue. The Company&#x2019;s\n subsidiaries whose functional currency is not the U.S. dollar and\n who sell in local currencies may hedge a portion of forecasted\n inventory purchases not denominated in the subsidiaries&#x2019;\n functional currencies. The Company typically hedges portions of its\n forecasted foreign currency exposure associated with revenue and\n inventory purchases, typically for up to 12 months.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">To help protect the net investment in a foreign\n operation from adverse changes in foreign currency exchange rates,\n the Company may enter into foreign currency forward and option\n contracts to offset the changes in the carrying amounts of these\n investments due to fluctuations in foreign currency exchange\n rates.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company may also enter into foreign currency\n forward contracts and option contracts to partially offset the\n foreign currency exchange gains and losses generated by the\n re-measurement of certain assets and liabilities denominated in\n non-functional currencies.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company may enter into interest rate swaps,\n options, or other instruments to manage interest rate risk. These\n instruments may offset a portion of changes in income or expense,\n or changes in fair value of the Company&#x2019;s long-term debt or\n investments.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company records all derivatives in the\n Consolidated Balance Sheets at fair value. The Company&#x2019;s\n accounting treatment for these instruments is based on the hedge\n designation. The effective portions of cash flow hedges are\n recorded in AOCI until the hedged item is recognized in earnings.\n Gains and losses related to changes in fair value hedges are\n recognized in earnings along with a corresponding loss or gain\n related to the change in value of the underlying hedged item. The\n effective portions of net investment hedges are recorded in other\n comprehensive income (&#x201C;OCI&#x201D;) as a part of the\n cumulative translation adjustment. The ineffective portions of cash\n flow hedges and net investment hedges are recorded in other income\n and expense. Derivatives that are not designated as hedging\n instruments are adjusted to fair value through earnings in the\n financial statement line item to which the derivative relates.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">Deferred gains and losses associated with cash flow\n hedges of foreign currency revenue are recognized as a component of\n net sales in the same period as the related revenue is recognized,\n and deferred gains and losses related to cash flow hedges of\n inventory purchases are recognized as a component of cost of sales\n in the same period as the related costs are recognized. Deferred\n gains and losses associated with cash flow hedges of interest\n income or expense are recognized as a component of other\n income/(expense), net in the same period as the related income or\n expense is recognized. The Company&#x2019;s foreign currency and\n interest rate transactions hedged with cash flow hedges as of\n September&#xA0;27, 2014 are expected to occur within 12 months and\n four years, respectively.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">Derivative instruments designated as cash flow\n hedges must be de-designated as hedges when it is probable the\n forecasted hedged transaction will not occur in the initially\n identified time period or within a subsequent two-month time\n period. Deferred gains and losses in AOCI associated with such\n derivative instruments are reclassified immediately into other\n income and expense. Any subsequent changes in fair value of such\n derivative instruments are reflected in other income and expense\n unless they are re-designated as hedges of other transactions. The\n Company did not recognize any significant net gains or losses\n related to the loss of hedge designation on discontinued cash flow\n hedges during 2014, 2013 and 2012.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The gain/loss recognized in other income and\n expense for foreign currency forward and option contracts not\n designated as hedging instruments was not significant during 2014,\n 2013 and 2012. These amounts represent the net gain or loss on the\n derivative contracts and do not include changes in the related\n exposures, which generally offset a portion of the gain or loss on\n the derivative contracts.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">The following table shows the notional amounts of\n the Company&#x2019;s outstanding derivative instruments and credit\n risk amounts associated with outstanding or unsettled derivative\n instruments as of September&#xA0;27, 2014 and September&#xA0;28,\n 2013 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"67%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"6\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"6\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Notional<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Credit&#xA0;Risk<br />\n Amounts</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Notional<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Credit&#xA0;Risk<br />\n Amounts</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Instruments designated as accounting hedges:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">42,945</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,333</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">35,013</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">159</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">89</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Instruments not designated as accounting hedges:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">38,510</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">222</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,131</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The notional amounts for outstanding derivative\n instruments provide one measure of the transaction volume\n outstanding and do not represent the amount of the Company&#x2019;s\n exposure to credit or market loss. The credit risk amounts\n represent the Company&#x2019;s gross exposure to potential\n accounting loss on derivative instruments that are outstanding or\n unsettled if all counterparties failed to perform according to the\n terms of the contract, based on <font style=\"WHITE-SPACE: nowrap\">then-current</font> currency or interest\n rates at each respective date. The Company&#x2019;s gross exposure\n on these transactions may be further mitigated by collateral\n received from certain counterparties. The Company&#x2019;s exposure\n to credit loss and market risk will vary over time as currency and\n interest rates change. Although the table above reflects the\n notional and credit risk amounts of the Company&#x2019;s derivative\n instruments, it does not reflect the gains or losses associated\n with the exposures and transactions that the instruments are\n intended to hedge. The amounts ultimately realized upon settlement\n of these financial instruments, together with the gains and losses\n on the underlying exposures, will depend on actual market\n conditions during the remaining life of the instruments.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company generally enters into master netting\n arrangements, which are designed to reduce credit risk by\n permitting net settlement of transactions with the same\n counterparty. To further limit credit risk, the Company generally\n enters into collateral security arrangements that provide for\n collateral to be received or posted when the net fair value of\n certain financial instruments fluctuates from contractually\n established thresholds. The Company presents its derivative assets\n and derivative liabilities at their gross fair values in its\n Consolidated Balance Sheets. As of September&#xA0;27, 2014, the\n Company received $2.1 billion of cash collateral related to the\n derivative instruments under its collateral security arrangements,\n which were recorded as accrued expenses in the Consolidated Balance\n Sheet.&#xA0;As of September&#xA0;28, 2013, the Company posted cash\n collateral related to the derivative instruments under its\n collateral security arrangements of $164 million, which were\n recorded as other current assets in the Consolidated Balance\n Sheet.&#xA0;The Company did not have any derivative instruments\n with credit-risk related contingent features that would require it\n to post additional collateral as of September&#xA0;27, 2014 or\n September&#xA0;28, 2013.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">Under master netting arrangements with the\n respective counterparties to the Company&#x2019;s derivative\n contracts, the&#xA0;Company is allowed to net settle transactions\n with a single net amount payable by one party to the other. As of\n September&#xA0;27, 2014 and September&#xA0;28, 2013, the potential\n effects of these rights of set-off associated with the\n Company&#x2019;s derivative contracts, including the effects of\n collateral, would be a reduction to both derivative assets and\n derivative liabilities of&#xA0;$1.6 billion and $333 million,\n respectively, resulting in net derivative liabilities of\n $549&#xA0;million and $57 million, respectively.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">The following tables show the Company&#x2019;s\n derivative instruments at gross fair value as reflected in the\n Consolidated Balance Sheets as of September&#xA0;27, 2014 and\n September&#xA0;28, 2013 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"58%\"></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value&#xA0;of&#xA0;Derivatives</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>Designated&#xA0;as&#xA0;\n Hedge&#xA0;Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value&#xA0;of&#xA0;Derivatives&#xA0;Not</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>Designated&#xA0;\n as&#xA0;Hedge&#xA0;Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Total</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Derivative assets&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,332</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">222</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,554</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">81</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">81</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Derivative liabilities&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\" colspan=\"9\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">41</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">40</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">81</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"58%\"></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"12%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value&#xA0;of&#xA0;Derivatives</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>Designated&#xA0;as&#xA0;\n Hedge&#xA0;Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value&#xA0;of&#xA0;Derivatives&#xA0;Not</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>Designated&#xA0;\n as&#xA0;Hedge&#xA0;Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Total</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Fair&#xA0;Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Derivative assets&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">145</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">170</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Derivative liabilities&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\" colspan=\"9\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">389</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">46</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">435</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">The\n fair value of derivative assets is measured using Level 2 fair\n value inputs and is recorded as other current assets in the\n Consolidated Balance Sheets.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">The\n fair value of derivative liabilities is measured using Level 2 fair\n value inputs and is recorded as accrued expenses in the\n Consolidated Balance Sheets.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The following tables show the pre-tax effect of the\n Company&#x2019;s derivative instruments designated as cash flow, net\n investment and fair value hedges on OCI and the Consolidated\n Statements of Operations for 2014, 2013 and 2012 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"48%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Gains/(Losses)&#xA0;Recognized&#xA0;in</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>OCI-Effective\n Portion</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Gains/(Losses)&#xA0;Reclassified&#xA0;from&#xA0;AOCI</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">into&#xA0;Net&#xA0;Income-Effective&#xA0;Portion</font></b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2014&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2013&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2012&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2014&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2013&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2012&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cash flow hedges:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,750</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;891</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(175</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(154</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">676</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">607</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(15</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(16</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(6</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,735</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">903</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(175</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(170</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">670</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;607</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net investment hedges:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">53</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">143</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(5</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"12\"></td>\n <td height=\"16\" colspan=\"12\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Gains/(Losses) On Derivative<br />\n Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Gains/(Losses) Related to</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Hedged Items</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fair value hedges:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">39</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(39</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company recognized a charge of $120 million,\n $301 million and $658 million in other income and expense during\n 2014, 2013 and 2012, respectively, for the ineffective portions of\n and amounts excluded from the effectiveness testing of cash flow\n hedges. The ineffective portions of and amounts excluded from the\n effectiveness testing of net investment and fair value hedges\n recognized in other income and expense were not significant during\n 2014, 2013 and 2012.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Accounts Receivable</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\">\n <i>Trade Receivables</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company has considerable trade receivables\n outstanding with its third-party cellular network carriers,\n wholesalers, retailers, value-added resellers, small and mid-sized\n businesses, and education, enterprise and government customers. The\n Company generally does not require collateral from its customers;\n however, the Company will require collateral in certain instances\n to limit credit risk. In addition, when possible, the Company\n attempts to limit credit risk on trade receivables with credit\n insurance for certain customers or by requiring third-party\n financing, loans or leases to support credit exposure. These\n credit-financing arrangements are directly between the third-party\n financing company and the end customer. As such, the Company\n generally does not assume any recourse or credit risk sharing\n related to any of these arrangements.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">As of September&#xA0;27, 2014, the Company had two\n customers that represented 10% or more of total trade receivables,\n one of which accounted for 16% and the other 13%. As of\n September&#xA0;28, 2013, the Company had two customers that\n represented 10% or more of total trade receivables, one of which\n accounted for 13% and the other 10%. The Company&#x2019;s cellular\n network carriers accounted for 72% and 68% of trade receivables as\n of September&#xA0;27, 2014 and September&#xA0;28, 2013,\n respectively. The additions and write-offs to the Company&#x2019;s\n allowance for doubtful accounts during 2014, 2013 and 2012 were not\n significant.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <i>Vendor Non-Trade Receivables</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company has non-trade receivables from certain\n of its manufacturing vendors resulting from the sale of components\n to these manufacturing vendors who manufacture sub-assemblies or\n assemble final products for the Company. The Company purchases\n these components directly from suppliers. Three of the\n Company&#x2019;s vendors accounted for 51%, 16% and 14% of total\n vendor non-trade receivables as of September&#xA0;27, 2014 and\n three of the Company&#x2019;s vendors accounted for 47%, 21% and 15%\n of total vendor non-trade receivables as of September&#xA0;28,\n 2013. The Company does not reflect the sale of these components in\n net sales and does not recognize any profits on these sales until\n the related products are sold by the Company, at which time any\n profit is recognized as a reduction of cost of sales.</p>\n </div>\n P12M\n <div>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Accrued Expenses</b></p>\n <p style=\"font-size:6pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued warranty and related costs</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,159</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,967</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued marketing and selling expenses</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;&#xA0;2,321</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;&#xA0;1,291</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued taxes</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,209</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,200</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued compensation and employee benefits</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,209</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">959</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred margin on component sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,057</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,262</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other current liabilities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">8,498</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,177</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total accrued expenses</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">18,453</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,856</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The provision for income taxes for 2014, 2013 and\n 2012, consisted of the following (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Federal:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Current</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">8,624</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">9,334</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">7,240</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">3,183</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,878</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,018</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">11,807</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">11,212</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">12,258</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n State:</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Current</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">855</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,084</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,182</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(178</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(311</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(123</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">677</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">773</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,059</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Foreign:</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Current</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,147</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,559</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,203</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(658</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(426</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(490</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,489</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,133</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">713</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Provision for income taxes</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,973</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,118</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">14,030</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">Information regarding net sales by product for\n 2014, 2013 and 2012, is as follows (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"73%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net Sales by Product:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n iPhone<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">101,991</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">91,279</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">78,692</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n iPad<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">30,283</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">31,980</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">30,945</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Mac&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24,079</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,483</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,221</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n iPod&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,286</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,411</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,615</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n iTunes, Software and Services&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">18,063</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,051</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,890</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Accessories&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(3)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,093</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,706</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,145</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total net sales</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">182,795</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">170,910</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">156,508</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Includes deferrals and amortization of related non-software\n services and software upgrade rights.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Includes revenue from the iTunes Store, the App Store, the Mac App\n Store, the iBooks Store, AppleCare, licensing and other\n services.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(3)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Includes sales of Apple-branded and third-party accessories for\n iPhone, iPad, Mac and iPod.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table summarizes the components of\n gross and net intangible asset balances as of September&#xA0;27,\n 2014 and September&#xA0;28, 2013 (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"50%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"10\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"10\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Gross<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Accumulated<br />\n Amortization</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Net<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Gross<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Accumulated<br />\n Amortization</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Net<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Definite-lived and amortizable acquired intangible assets</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">7,127</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,085</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,042</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,081</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(2,002</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,079</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Indefinite-lived and non-amortizable acquired intangible assets</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total acquired intangible assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">7,227</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,085</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,142</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,181</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(2,002</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,179</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table summarizes the components of\n gross and net intangible asset balances as of September&#xA0;27,\n 2014 and September&#xA0;28, 2013 (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"50%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"10\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"10\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Gross<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Accumulated<br />\n Amortization</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Net<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Gross<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Accumulated<br />\n Amortization</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Net<br />\n Carrying<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Definite-lived and amortizable acquired intangible assets</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">7,127</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,085</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,042</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,081</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(2,002</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,079</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Indefinite-lived and non-amortizable acquired intangible assets</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">100</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total acquired intangible assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">7,227</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,085</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,142</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,181</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(2,002</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,179</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">A summary of the Company&#x2019;s RSU activity and\n related information for 2014, 2013 and 2012, is as follows:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"64%\"></td>\n <td valign=\"bottom\" width=\"7%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"7%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"7%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Number of</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>RSUs</b></font><br />\n <font style=\"FONT-FAMILY: Arial\">(in&#xA0;thousands)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">Weighted-Average</font></b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Grant Date\n Fair</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Value Per Share</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Aggregate</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Intrinsic&#xA0;Value</b></font><br />\n <font style=\"FONT-FAMILY: Arial\">(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;24, 2011</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">101,122</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">33.07</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs granted</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,597</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">61.62</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs vested</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(44,137</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">29.32</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs cancelled</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(6,545</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">36.57</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;29, 2012</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">105,037</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">49.27</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs granted</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">39,415</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">78.23</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs vested</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(42,291</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">45.96</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs cancelled</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(8,877</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">57.31</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;28, 2013</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">93,284</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">62.24</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs granted</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">59,269</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">74.54</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs vested</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(43,111</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">57.29</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs cancelled</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(5,620</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">68.47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;27, 2014</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">103,822</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">70.98</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,460</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">The following table shows information by operating\n segment for 2014, 2013 and 2012 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"73%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Americas:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">65,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">62,739</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">57,512</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24,316</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,817</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,414</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Europe:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">40,929</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,883</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">36,323</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,771</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,025</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,869</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Greater China:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">29,846</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25,417</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,533</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,016</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,541</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,843</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Japan:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,982</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,462</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,571</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,183</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,819</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,861</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Rest of Asia Pacific:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,344</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,181</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,741</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,636</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,753</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,253</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Retail:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,462</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">20,228</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">18,828</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,575</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,025</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,613</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Shipping Costs</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">For all periods presented, amounts billed to\n customers related to shipping and handling are classified as\n revenue, and the Company&#x2019;s shipping and handling costs are\n included in cost of sales.</p>\n </div>\n The Company offers a basic limited parts and labor warranty on its hardware products. The basic warranty period for hardware products is typically one year from the date of purchase by the end-user. The Company also offers a 90-day basic warranty for its service parts used to repair the Company's hardware products. The Company provides currently for the estimated cost that may be incurred under its basic limited product warranties at the time related revenue is recognized.\n 2014-09-27\n <div>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <i>Cash Equivalents and Marketable Securities</i></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">All highly liquid investments with maturities of\n three months or less at the date of purchase are classified as cash\n equivalents. The Company&#x2019;s marketable debt and equity\n securities have been classified and accounted for as <font style=\"white-space:nowrap\"><font style=\"white-space:nowrap\">available-for-sale.</font></font> Management\n determines the appropriate classification of its investments at the\n time of purchase and reevaluates the designations at each balance\n sheet date. The Company classifies its marketable debt securities\n as either short-term or long-term based on each instrument&#x2019;s\n underlying contractual maturity date. Marketable debt securities\n with maturities of 12 months or less are classified as short-term\n and marketable debt securities with maturities greater than 12\n months are classified as long-term. The Company classifies its\n marketable equity securities, including mutual funds, as either\n short-term or long-term based on the nature of each security and\n its availability for use in current operations. The Company&#x2019;s\n marketable debt and equity securities are carried at fair value,\n with the unrealized gains and losses, net of taxes, reported as a\n component of shareholders&#x2019; equity. The cost of securities\n sold is based upon the specific identification method.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Property, Plant and Equipment</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">Property, plant and equipment are stated at cost.\n Depreciation is computed by use of the <font style=\"WHITE-SPACE: nowrap\">straight-line</font> method over the\n estimated useful lives of the assets, which for buildings is the\n lesser of 30 years or the remaining life of the underlying\n building; between two to five years for machinery and equipment,\n including product tooling and manufacturing process equipment; and\n the shorter of lease terms or ten years for leasehold improvements.\n The Company capitalizes eligible costs to acquire or develop\n internal-use software that are incurred subsequent to the\n preliminary project stage. Capitalized costs related to\n internal-use software are amortized using the straight-line method\n over the estimated useful lives of the assets, which range from\n three to five years. Depreciation and amortization expense on\n property and equipment was $6.9 billion, $5.8 billion and $2.6\n billion during 2014, 2013 and 2012, respectively.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Note 12 &#x2013; Selected Quarterly Financial Information\n (Unaudited)</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The following tables show a summary of the\n Company&#x2019;s quarterly financial information for each of the\n four quarters of 2014 and 2013 (in millions, except per share\n amounts):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"48%\"></td>\n <td valign=\"bottom\" width=\"8%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"8%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"8%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"8%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;Fourth&#xA0;Quarter&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;Third&#xA0;Quarter&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Second&#xA0;Quarter</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;First&#xA0;Quarter&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2014:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">42,123</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,432</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">45,646</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">57,594</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Gross margin</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,009</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,735</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">17,947</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,846</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,467</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,748</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,223</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,072</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Earnings per share&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Basic</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.43</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.29</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.67</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2.08</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Diluted</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.42</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.28</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.66</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2.07</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2013:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\" colspan=\"13\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,472</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">35,323</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">43,603</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,512</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Gross margin</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,871</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,024</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,349</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,060</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,512</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,900</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,547</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,078</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Earnings per share&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Basic</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.19</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.07</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.45</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.99</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Diluted</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.18</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.07</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.97</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">Basic\n and diluted earnings per share are computed independently for each\n of the quarters presented. Therefore, the sum of quarterly basic\n and diluted per share information may not equal annual basic and\n diluted earnings per share.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Allowance for Doubtful Accounts</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company records its allowance for doubtful\n accounts based upon its assessment of various factors. The Company\n considers historical experience, the age of the accounts receivable\n balances, credit quality of the Company&#x2019;s customers, current\n economic conditions and other factors that may affect\n customers&#x2019; ability to pay.</p>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Software Development Costs</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Research and development (&#x201C;R&amp;D&#x201D;)\n costs are expensed as incurred. Development costs of computer\n software to be sold, leased, or otherwise marketed are subject to\n capitalization beginning when a product&#x2019;s technological\n feasibility has been established and ending when a product is\n available for general release to customers. In most instances, the\n Company&#x2019;s products are released soon after technological\n feasibility has been established. Costs incurred subsequent to\n achievement of technological feasibility were not significant, and\n software development costs were expensed as incurred during 2014,\n 2013 and 2012.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">The following table shows the changes in AOCI by\n component for 2014 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 12pt 'Times New Roman'; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"58%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Cumulative</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Foreign</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Currency&#xA0;</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Translation</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrecognized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains/Losses</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>on Derivative</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>/Losses on</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Marketable</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Total</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;28, 2013</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(105</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(175</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(191</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(471</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Other comprehensive income/(loss) before reclassifications</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(187</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,687</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">438</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,938</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Amounts reclassified from AOCI</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">185</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(205</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(20</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Tax effect</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;50</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(333</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(82</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(365</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4.72em; TEXT-INDENT: -1em\">\n Other comprehensive income/(loss)</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(137</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,539</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;151</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,553</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;27, 2014</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(242</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,364</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(40</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,082</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The following table provides a summary of the\n Company&#x2019;s long-term debt as of September&#xA0;27, 2014 and\n September&#xA0;28, 2013:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"68%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"6\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"6\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Effective</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Interest<br />\n Rate</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Effective</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Interest<br />\n Rate</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Floating-rate notes due 2016</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.51%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.51%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Floating-rate notes due 2017</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.31%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Floating-rate notes due 2018</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.10%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.10%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Floating-rate notes due 2019</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.54%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 0.45% notes due 2016</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.51%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.51%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 1.05% notes due 2017</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.30%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 1.00% notes due 2018</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.08%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.08%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 2.10% notes due 2019</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.53%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 2.85% notes due 2021</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.79%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 2.40% notes due 2023</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2.44%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2.44%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 3.45% notes due 2024</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.90%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 3.85% notes due 2043</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3.91%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3.91%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fixed-rate 4.45% notes due 2044</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4.48%</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total borrowings</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">29,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">17,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Unamortized discount</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(52</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(40</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Hedge accounting fair value adjustments</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">39</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total long-term debt</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">28,987</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,960</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n </table>\n </div>\n P1Y4M24D\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Note 7 &#x2013; Shareholders&#x2019; Equity</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\">\n <b>Preferred and Common Stock</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">During the second quarter of 2014, the\n Company&#x2019;s shareholders approved amendments (the\n &#x201C;Amendments&#x201D;) to the Company&#x2019;s Restated Articles\n of Incorporation. The Amendments included the elimination of the\n Board of Directors&#x2019; authority to issue preferred stock and\n established a par value for the Company&#x2019;s common stock of\n $0.00001 per share.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Dividends</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company declared and paid cash dividends per\n common share during the periods presented as follows:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Dividends</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Per Share</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"><font style=\"FONT-FAMILY: Arial\">2014:</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Fourth quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,807</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Third quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,830</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Second quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,655</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n First quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,739</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.82</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,031</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"8\"></td>\n <td height=\"8\" colspan=\"4\"></td>\n <td height=\"8\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"><font style=\"FONT-FAMILY: Arial\">2013:</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Fourth quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,763</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Third quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,789</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Second quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.38</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,490</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n First quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.38</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,486</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.64</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,528</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company paid cash dividends of $0.38 per share,\n totaling $2.5 billion, during the fourth quarter of 2012. Future\n dividends are subject to declaration by the Board of Directors.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Share Repurchase Program</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">In 2012, the Company&#x2019;s Board of Directors\n authorized a program to repurchase up to $10 billion of the\n Company&#x2019;s common stock beginning in 2013. The Company&#x2019;s\n Board of Directors increased the share repurchase authorization to\n $60 billion in April 2013 and to $90 billion in April 2014. As of\n September&#xA0;27, 2014, $67.9 billion of the $90 billion had been\n utilized. The Company&#x2019;s share repurchase program does not\n obligate it to acquire any specific number of shares. Under the\n program, shares may be repurchased in privately negotiated and/or\n open market transactions, including under plans complying with Rule\n 10b5-1 under the Securities Exchange Act of 1934, as amended (the\n &#x201C;Exchange Act&#x201D;).</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company has entered into four accelerated share\n repurchase arrangements (&#x201C;ASRs&#x201D;) with financial\n institutions beginning in August 2012. In exchange for up-front\n payments, the financial institutions deliver shares of the\n Company&#x2019;s common stock during the purchase periods of each\n ASR. The total number of shares ultimately delivered, and therefore\n the average repurchase price paid per share, will be determined at\n the end of the applicable purchase period of each ASR based on the\n volume weighted-average price of the Company&#x2019;s common stock\n during that period. The shares received are retired in the periods\n they are delivered, and the <font style=\"WHITE-SPACE: nowrap\">up-front</font> payments are accounted for as\n a reduction to shareholders&#x2019; equity in the Company&#x2019;s\n Consolidated Balance Sheet in the periods the payments are made.\n The Company reflects the ASRs as a repurchase of common stock in\n the period delivered for purposes of calculating earnings per share\n and as forward contracts indexed to its own common stock. The ASRs\n met all of the applicable criteria for equity classification, and\n therefore, were not accounted for as derivative instruments.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The following table presents the Company&#x2019;s\n ASRs:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"59%\"></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Purchase<br />\n Period End<br />\n Date</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Number of<br />\n Shares<br /></b>(in&#xA0;thousands)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Average<br />\n Repurchase<br />\n Price Per<br />\n Share</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>ASR<br />\n Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n August 2014 ASR</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;&#xA0;&#xA0;&#xA0;</sup></font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">59,924</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n January 2014 ASR</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">134,247</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n April 2013 ASR</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">March&#xA0;2014</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">172,548</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(2)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">69.55</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n August 2012 ASR</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">April 2013</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">28,544</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">68.31</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,950</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n &#x201C;Number of Shares&#x201D; represents those shares delivered in\n advance of settlement and does not represent the final number of\n shares to be delivered under the ASRs. The total number of shares\n ultimately delivered, and therefore the average repurchase price\n paid per share, will be determined at the end of the applicable\n purchase period based on the volume weighted-average price of the\n Company&#x2019;s common stock during that period. The August 2014\n ASR and January 2014 ASR purchase periods will end in or before\n February 2015 and December 2014, respectively.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup></td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Includes 8.0&#xA0;million shares that were delivered and retired at\n the end of the purchase period, which concluded in the second\n quarter of 2014.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">Additionally, the Company repurchased shares of its\n common stock in the open market, which were retired upon\n repurchase, during the periods presented as follows:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"70%\"></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Number of<br />\n Shares<br /></b>(in&#xA0;thousands)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Average<br />\n Repurchase<br />\n Price Per<br />\n Share</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"><font style=\"FONT-FAMILY: Arial\">2014:</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Fourth quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">81,255</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">98.46</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Third quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">58,661</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">85.23</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Second quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">79,749</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">75.24</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n First quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">66,847</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">74.79</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">286,512</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"><font style=\"FONT-FAMILY: Arial\">2013:</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Fourth quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">73,064</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">68.43</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Third quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">62,676</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">63.82</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Second quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n First quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">135,740</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n false\n --09-27\n 2014\n 6122663000\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The following table presents the Company&#x2019;s\n ASRs:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"59%\"></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Purchase<br />\n Period End<br />\n Date</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Number of<br />\n Shares<br /></b>(in&#xA0;thousands)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Average<br />\n Repurchase<br />\n Price Per<br />\n Share</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>ASR<br />\n Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n August 2014 ASR</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;&#xA0;&#xA0;&#xA0;</sup></font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">59,924</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n January 2014 ASR</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">134,247</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n April 2013 ASR</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">March&#xA0;2014</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">172,548</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">&#xA0;(2)</sup>&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">69.55</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n August 2012 ASR</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">April 2013</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">28,544</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">68.31</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,950</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n &#x201C;Number of Shares&#x201D; represents those shares delivered in\n advance of settlement and does not represent the final number of\n shares to be delivered under the ASRs. The total number of shares\n ultimately delivered, and therefore the average repurchase price\n paid per share, will be determined at the end of the applicable\n purchase period based on the volume weighted-average price of the\n Company&#x2019;s common stock during that period. The August 2014\n ASR and January 2014 ASR purchase periods will end in or before\n February 2015 and December 2014, respectively.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(2)</sup></td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Includes 8.0&#xA0;million shares that were delivered and retired at\n the end of the purchase period, which concluded in the second\n quarter of 2014.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n </div>\n 6.49\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Foreign Currency Translation and Remeasurement</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company translates the assets and liabilities\n of its non-U.S. dollar functional currency subsidiaries into U.S.\n dollars using exchange rates in effect at the end of each period.\n Revenue and expenses for these subsidiaries are translated using\n rates that approximate those in effect during the period. Gains and\n losses from these translations are recognized in foreign currency\n translation included in AOCI in shareholders&#x2019; equity. The\n Company&#x2019;s subsidiaries that use the U.S. dollar as their\n functional currency remeasure monetary assets and liabilities at\n exchange rates in effect at the end of each period, and\n inventories, property and nonmonetary assets and liabilities at\n historical rates. Gains and losses from these remeasurements were\n not significant during 2014, 2013 and 2012 and have been included\n in the Company&#x2019;s results of operations.</p>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Note 5 &#x2013; Income Taxes</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The provision for income taxes for 2014, 2013 and\n 2012, consisted of the following (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Federal:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Current</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">8,624</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">9,334</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">7,240</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">3,183</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,878</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,018</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">11,807</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">11,212</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">12,258</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n State:</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Current</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">855</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,084</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,182</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(178</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(311</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(123</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">677</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">773</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,059</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Foreign:</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Current</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,147</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,559</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,203</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(658</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(426</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(490</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,489</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,133</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">713</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Provision for income taxes</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,973</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,118</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">14,030</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"font-size:1px;margin-top:12px;margin-bottom:0px\">\n &#xA0;</p>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The foreign provision for income taxes is based on\n foreign pre-tax earnings of $33.6 billion, $30.5 billion and\n $36.8&#xA0;billion in 2014, 2013 and 2012, respectively. The\n Company&#x2019;s consolidated financial statements provide for any\n related tax liability on undistributed earnings that the Company\n does not intend to be indefinitely reinvested outside the U.S.\n Substantially all of the Company&#x2019;s undistributed\n international earnings intended to be indefinitely reinvested in\n operations outside the U.S. were generated by subsidiaries\n organized in Ireland, which has a statutory tax rate of 12.5%. As\n of September&#xA0;27, 2014, U.S. income taxes have not been\n provided on a cumulative total of $69.7 billion of such earnings.\n The amount of unrecognized deferred tax liability related to these\n temporary differences is estimated to be approximately $23.3\n billion.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">As of September&#xA0;27, 2014 and\n September&#xA0;28, 2013, $137.1 billion and $111.3 billion,\n respectively, of the Company&#x2019;s cash, cash equivalents and\n marketable securities were held by foreign subsidiaries and are\n generally based in U.S. <font style=\"white-space:nowrap\">dollar-denominated</font> holdings. Amounts\n held by foreign subsidiaries are generally subject to U.S. income\n taxation on repatriation to the U.S.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">A reconciliation of the provision for income taxes,\n with the amount computed by applying the statutory federal income\n tax rate (35% in 2014, 2013 and 2012) to income before provision\n for income taxes for 2014, 2013 and 2012, is as follows (dollars in\n millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Computed expected tax</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">18,719</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">17,554</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">19,517</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n State taxes, net of federal effect</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">469</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">508</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">677</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Indefinitely invested earnings of foreign subsidiaries</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(4,744</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(4,614</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(5,895</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Research and development credit, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(88</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(287</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(103</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Domestic production activities deduction</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(495</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(308</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(328</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">112</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">265</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">162</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Provision for income taxes</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,973</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,118</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">14,030</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Effective tax rate</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">26.1%</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">26.2%</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">25.2%</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company&#x2019;s income taxes payable have been\n reduced by the tax benefits from employee stock plan awards. For\n stock options, the Company receives an income tax benefit\n calculated as the tax effect of the difference between the fair\n market value of the stock issued at the time of the exercise and\n the exercise price.&#xA0;For RSUs, the Company receives an income\n tax benefit upon the award&#x2019;s vesting equal to the tax effect\n of the underlying stock&#x2019;s fair market value. The Company had\n net excess tax benefits from equity awards of $706 million, $643\n million and $1.4 billion in 2014, 2013 and 2012, respectively,\n which were reflected as increases to common stock.</p>\n <p style=\"font-size:1px;margin-top:12px;margin-bottom:0px\">\n &#xA0;</p>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">As of September&#xA0;27, 2014 and\n September&#xA0;28, 2013, the significant components of the\n Company&#x2019;s deferred tax assets and liabilities were (in\n millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred tax assets:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued liabilities and other reserves</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,761</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,892</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred revenue</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,787</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,475</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Basis of capital assets and investments</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">898</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,020</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Share-based compensation</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">454</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">458</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">644</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,029</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:5.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total deferred tax assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,544</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,874</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Less valuation allowance</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred tax assets, net of valuation allowance</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,544</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,874</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred tax liabilities:</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Unremitted earnings of foreign subsidiaries</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">21,544</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">18,044</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">120</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">112</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:5.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total deferred tax liabilities</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">21,664</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">18,156</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Net deferred tax liabilities</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(15,120</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(12,282</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Deferred tax assets and liabilities reflect the\n effects of tax losses, credits and the future income tax effects of\n temporary differences between the consolidated financial statement\n carrying amounts of existing assets and liabilities and their\n respective tax bases and are measured using enacted tax rates that\n apply to taxable income in the years in which those temporary\n differences are expected to be recovered or settled.</p>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Uncertain Tax Positions</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Tax positions are evaluated in a two-step process.\n The Company first determines whether it is more likely than not\n that a tax position will be sustained upon examination. If a tax\n position meets the more-likely-than-not recognition threshold it is\n then measured to determine the amount of benefit to recognize in\n the financial statements. The tax position is measured as the\n largest amount of benefit that is greater than 50% likely of being\n realized upon ultimate settlement. The Company classifies gross\n interest and penalties and unrecognized tax benefits that are not\n expected to result in payment or receipt of cash within one year as\n non-current liabilities in the Consolidated Balance Sheets.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">As of September&#xA0;27, 2014, the total amount of\n gross unrecognized tax benefits was $4.0 billion, of which\n $1.4&#xA0;billion, if recognized, would affect the Company&#x2019;s\n effective tax rate. As of September&#xA0;28, 2013, the total amount\n of gross unrecognized tax benefits was $2.7 billion, of which $1.4\n billion, if recognized, would affect the Company&#x2019;s effective\n tax rate.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The aggregate changes in the balance of gross\n unrecognized tax benefits, which excludes interest and penalties,\n for 2014, 2013 and 2012, is as follows (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"79%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Beginning Balance</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,714</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,062</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,375</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Increases related to tax positions taken during a prior year</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,295</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">745</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">340</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Decreases related to tax positions taken during a prior year</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(280</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(118</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(107</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Increases related to tax positions taken during the current\n year</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">882</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">626</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">467</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Decreases related to settlements with taxing authorities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(574</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(592</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Decreases related to expiration of statute of limitations</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(4</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(9</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(10</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Ending Balance</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,033</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,714</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,062</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"font-size:1px;margin-top:12px;margin-bottom:0px\">\n &#xA0;</p>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company includes interest and penalties related\n to unrecognized tax benefits within the provision for income taxes.\n As of September&#xA0;27, 2014 and September&#xA0;28, 2013, the\n total amount of gross interest and penalties accrued was $630\n million and $590 million, respectively, which is classified as\n non-current liabilities in the Consolidated Balance Sheets. In\n connection with tax matters, the Company recognized interest and\n penalty expense in 2014, 2013 and 2012 of $40 million, $189 million\n and $140 million, respectively.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company is subject to taxation and files income\n tax returns in the U.S. federal jurisdiction and in many state and\n foreign jurisdictions. During the fiscal year ended\n September&#xA0;27, 2014, the U.S. Internal Revenue Service\n (&#x201C;IRS&#x201D;) concluded its review of the years 2004 through\n 2009, which resulted in the Company reducing its gross unrecognized\n tax benefits by $570 million and recognizing a tax benefit of $166\n million. The IRS is currently examining the years 2010 through\n 2012. In addition, the Company is also subject to audits by state,\n local and foreign tax authorities.&#xA0;In major states and major\n foreign jurisdictions, the years subsequent to 1996 and 2002,\n respectively, generally remain open and could be subject to\n examination by the taxing authorities.</p>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Management believes that an adequate provision has\n been made for any adjustments that may result from tax\n examinations. However, the outcome of tax audits cannot be\n predicted with certainty. If any issues addressed in the\n Company&#x2019;s tax audits are resolved in a manner not consistent\n with management&#x2019;s expectations, the Company could be required\n to adjust its provision for income taxes in the period such\n resolution occurs. Although timing of the resolution and/or closure\n of audits is not certain, the Company does not believe it is\n reasonably possible that its unrecognized tax benefits would\n materially change in the next 12 months.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The following tables show the Company&#x2019;s cash\n and <font style=\"WHITE-SPACE: nowrap\"><font style=\"WHITE-SPACE: nowrap\">available-for-sale</font></font>\n securities&#x2019; adjusted cost, gross unrealized gains, gross\n unrealized losses and fair value by significant investment category\n recorded as cash and cash equivalents or short- or long-term\n marketable securities as of September&#xA0;27, 2014 and\n September&#xA0;28, 2013 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"33%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"26\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Adjusted<br />\n Cost</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Losses</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Cash\n and<br />\n Cash<br />\n Equivalents</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">Short-Term</font><br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Long-Term<br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cash</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;10,232</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 1:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Money market funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mutual funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,531</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(132</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,077</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(132</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,946</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,546</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,400</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 2:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. Treasury securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,140</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">15</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(9</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23,146</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">607</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,527</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. agency securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,373</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(11</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,365</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">652</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">157</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,556</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Non-U.S. government securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,925</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">69</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(69</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,925</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">204</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,721</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Certificates of deposit and time deposits</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,832</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,832</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,230</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,233</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,369</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">475</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">475</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">166</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">309</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Corporate securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">85,431</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">296</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(241</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">85,486</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,298</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">79,182</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Municipal securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">940</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">948</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">948</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mortgage- and asset-backed securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,907</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">26</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(49</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,884</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,859</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">141,023</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">417</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(379</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">141,061</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,066</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,833</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">130,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">155,332</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;418</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(511</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">155,239</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,844</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,233</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">130,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 12px\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"39%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"26\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Adjusted<br />\n Cost</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Losses</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Fair</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Value</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Cash\n and<br />\n Cash<br />\n Equivalents</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">Short-Term</font><br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Long-Term<br />\n Marketable<br />\n Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cash</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;8,705</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 1:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Money market funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mutual funds</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,999</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,792</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,595</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,793</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,802</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Level 2:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. Treasury securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">27,642</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">24</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">27,619</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">431</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,554</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">19,634</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n U.S. agency securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,878</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(52</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,838</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">177</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,412</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,249</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Non-U.S. government securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,545</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">35</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(137</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,443</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">50</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">313</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,080</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Certificates of deposit and time deposits</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,344</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,344</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,264</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">844</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">236</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,998</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,998</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,835</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,163</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Corporate securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,586</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">275</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(252</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,609</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,077</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">46,532</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Municipal securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,257</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">45</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(22</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,280</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,114</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5,162</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 2em; TEXT-INDENT: -1em\">\n Mortgage- and asset-backed securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,396</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">23</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(89</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,330</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,322</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4em; TEXT-INDENT: -1em\">\n Subtotal</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">132,646</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">414</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(599</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">132,461</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,761</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">22,485</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">106,215</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">147,143</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;414</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(796</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">146,761</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,259</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;26,287</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">106,215</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">As of September&#xA0;27, 2014 and\n September&#xA0;28, 2013, the significant components of the\n Company&#x2019;s deferred tax assets and liabilities were (in\n millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred tax assets:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued liabilities and other reserves</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,761</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,892</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred revenue</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,787</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,475</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Basis of capital assets and investments</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">898</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,020</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Share-based compensation</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">454</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">458</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">644</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,029</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:5.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total deferred tax assets</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,544</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,874</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Less valuation allowance</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred tax assets, net of valuation allowance</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,544</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,874</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred tax liabilities:</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Unremitted earnings of foreign subsidiaries</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">21,544</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">18,044</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">120</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">112</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:5.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total deferred tax liabilities</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">21,664</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">18,156</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Net deferred tax liabilities</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(15,120</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(12,282</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The following table shows the computation of basic\n and diluted earnings per share for 2014, 2013 and 2012 (net income\n in millions and shares in thousands):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"67%\"></td>\n <td valign=\"bottom\" width=\"6%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"6%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Numerator:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Net income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">39,510</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,037</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">41,733</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Denominator:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Weighted-average shares outstanding</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,085,572</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,477,320</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,543,726</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Effect of dilutive securities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,091</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">44,314</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">73,757</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Weighted-average diluted shares</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,122,663</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,521,634</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,617,483</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"10\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n <td height=\"10\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Basic earnings per share</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6.49</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5.72</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6.38</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Diluted earnings per share</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6.45</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">5.68</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6.31</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">2013 and 2012, respectively. Future minimum lease\n payments under noncancelable operating leases having remaining\n terms in excess of one year as of September&#xA0;27, 2014, are as\n follows (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"91%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2015</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">662</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2016</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">676</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2017</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">645</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2018</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">593</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2019</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">534</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Thereafter</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,877</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,987</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">Future principal payments for the Company&#x2019;s\n Notes as of September&#xA0;27, 2014, are as follows (in\n millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"90%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2015</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2016</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2017</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,500</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2018</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2019</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Thereafter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">15,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">29,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The following tables show a summary of the\n Company&#x2019;s quarterly financial information for each of the\n four quarters of 2014 and 2013 (in millions, except per share\n amounts):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"48%\"></td>\n <td valign=\"bottom\" width=\"8%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"8%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"8%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"8%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;Fourth&#xA0;Quarter&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;Third&#xA0;Quarter&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Second&#xA0;Quarter</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;First&#xA0;Quarter&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2014:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">42,123</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,432</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">45,646</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">57,594</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Gross margin</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,009</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">14,735</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">17,947</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,846</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">8,467</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,748</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,223</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,072</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n <td height=\"16\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Earnings per share&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Basic</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.43</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.29</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.67</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2.08</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Diluted</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.42</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.28</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.66</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2.07</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n 2013:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\" colspan=\"13\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">37,472</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">35,323</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">43,603</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,512</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Gross margin</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,871</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,024</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,349</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">21,060</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">7,512</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,900</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">9,547</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">13,078</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Earnings per share&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Basic</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.19</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.07</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.45</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.99</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Diluted</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.18</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.07</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.97</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">Basic\n and diluted earnings per share are computed independently for each\n of the quarters presented. Therefore, the sum of quarterly basic\n and diluted per share information may not equal annual basic and\n diluted earnings per share.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Warranty Costs</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The Company generally provides for the estimated\n cost of hardware and software warranties at the time the related\n revenue is recognized. The Company assesses the adequacy of its\n pre-existing warranty liabilities and adjusts the amounts as\n necessary based on actual experience and changes in future\n estimates.</p>\n </div>\n <div>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The aggregate changes in the balance of gross\n unrecognized tax benefits, which excludes interest and penalties,\n for 2014, 2013 and 2012, is as follows (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"79%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Beginning Balance</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,714</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,062</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,375</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Increases related to tax positions taken during a prior year</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,295</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">745</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">340</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Decreases related to tax positions taken during a prior year</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(280</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(118</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(107</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Increases related to tax positions taken during the current\n year</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">882</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">626</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">467</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Decreases related to settlements with taxing authorities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(574</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(592</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Decreases related to expiration of statute of limitations</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(4</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(9</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(10</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Ending Balance</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,033</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,714</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,062</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n 0000320193\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Note 3 &#x2013; Consolidated Financial Statement Details</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following tables show the Company&#x2019;s\n consolidated balance sheet details as of September&#xA0;27, 2014\n and September&#xA0;28, 2013 (in millions):</p>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Inventories</b></p>\n <p style=\"font-size:6pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Components</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">471</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">683</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Finished goods</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;&#xA0;1,640</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;&#xA0;1,081</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total inventories</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,111</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,764</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Property, Plant and Equipment</b></p>\n <p style=\"font-size:6pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Land and buildings</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,863</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">3,309</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Machinery, equipment and internal-use software</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">29,639</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">21,242</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Leasehold improvements</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,513</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">3,968</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Gross property, plant and equipment</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">39,015</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">28,519</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accumulated depreciation and amortization</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(18,391</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(11,922</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Net property, plant and equipment</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">20,624</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">16,597</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"font-size:1px;margin-top:18px;margin-bottom:0px\">\n &#xA0;</p>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Accrued Expenses</b></p>\n <p style=\"font-size:6pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued warranty and related costs</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,159</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,967</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued marketing and selling expenses</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;&#xA0;2,321</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;&#xA0;1,291</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued taxes</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,209</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,200</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accrued compensation and employee benefits</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,209</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">959</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred margin on component sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,057</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,262</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other current liabilities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">8,498</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,177</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total accrued expenses</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">18,453</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">13,856</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Non-Current Liabilities</b></p>\n <p style=\"font-size:6pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Deferred tax liabilities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">20,259</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">16,489</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other non-current liabilities</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,567</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">3,719</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total other non-current liabilities</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">24,826</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">20,208</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Other Income and Expense</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table shows the detail of other\n income and expense for 2014, 2013 and 2012 (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Interest and dividend income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;1,795</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;1,616</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;1,088</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Interest expense</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(384</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(136</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Other expense, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(431</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(324</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(566</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total other income/(expense), net</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">980</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,156</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">522</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n 1.82\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company declared and paid cash dividends per\n common share during the periods presented as follows:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Dividends</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Per Share</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"><font style=\"FONT-FAMILY: Arial\">2014:</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Fourth quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,807</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Third quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,830</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Second quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,655</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n First quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,739</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.82</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">11,031</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"8\"></td>\n <td height=\"8\" colspan=\"4\"></td>\n <td height=\"8\" colspan=\"4\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"><font style=\"FONT-FAMILY: Arial\">2013:</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Fourth quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,763</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Third quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,789</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Second quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.38</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,490</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n First quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0.38</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,486</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1.64</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,528</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n 0.35\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">A reconciliation of the Company&#x2019;s segment\n operating income to the Consolidated Statements of Operations for\n 2014, 2013 and 2012, is as follows (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"73%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Segment operating income</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">65,497</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">58,980</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">62,853</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Research and development expense&#xA0;<sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(4,825</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(3,558</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(2,713</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Share-based compensation expense</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(2,863</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(2,253</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(1,740</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Other corporate expenses, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(5,306</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(4,170</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(3,159</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total operating income</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">52,503</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">48,999</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">55,241</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 6pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\">\n <tr>\n <td width=\"4%\">&#xA0;</td>\n <td valign=\"top\" width=\"3%\" align=\"left\"><sup style=\"FONT-SIZE: 85%; VERTICAL-ALIGN: top\">(1)</sup>&#xA0;</td>\n <td valign=\"top\" align=\"left\">\n <p style=\"FONT-SIZE: 8pt; FONT-FAMILY: Arial\" align=\"justify\">\n Amount excludes research and development share-based compensation\n expense.</p>\n </td>\n <td width=\"7%\">&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The following tables show the pre-tax effect of the\n Company&#x2019;s derivative instruments designated as cash flow, net\n investment and fair value hedges on OCI and the Consolidated\n Statements of Operations for 2014, 2013 and 2012 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"48%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Gains/(Losses)&#xA0;Recognized&#xA0;in</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b>OCI-Effective\n Portion</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Gains/(Losses)&#xA0;Reclassified&#xA0;from&#xA0;AOCI</b></font><br />\n\n <font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">into&#xA0;Net&#xA0;Income-Effective&#xA0;Portion</font></b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2014&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2013&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2012&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2014&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2013&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>&#xA0;&#xA0;&#xA0;&#xA0;2012&#xA0;&#xA0;&#xA0;&#xA0;</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cash flow hedges:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,750</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;891</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(175</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(154</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">676</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">607</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(15</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(16</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(6</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,735</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">903</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(175</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(170</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">670</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;607</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Net investment hedges:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">53</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">143</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(5</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 1pt\">\n <td height=\"16\"></td>\n <td height=\"16\" colspan=\"12\"></td>\n <td height=\"16\" colspan=\"12\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Gains/(Losses) On Derivative<br />\n Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"10\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Gains/(Losses) Related to</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Hedged Items</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Fair value hedges:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">39</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(39</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">The following table shows changes in the\n Company&#x2019;s accrued warranties and related costs for 2014, 2013\n and 2012 (in millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Beginning accrued warranty and related costs</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,967</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,638</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,240</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Cost of warranty claims</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,760</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(3,703</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(1,786</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accruals for product warranty</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,952</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,032</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,184</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Ending accrued warranty and related costs</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,159</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">2,967</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,638</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n FY\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 18pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n <b>Note 8 &#x2013; Comprehensive Income</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 6pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">Comprehensive income consists of two components,\n net income and OCI. OCI refers to revenue, expenses, and gains and\n losses that under GAAP are recorded as an element of\n shareholders&#x2019; equity but are excluded from net income. The\n Company&#x2019;s OCI consists of foreign currency translation\n adjustments from those subsidiaries not using the U.S. dollar as\n their functional currency, net deferred gains and losses on certain\n derivative instruments accounted for as cash flow hedges and\n unrealized gains and losses on marketable securities classified as\n available-for-sale.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 12pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">The following table shows the gross amounts\n reclassified from AOCI into the Consolidated Statements of\n Operations and the associated financial statement line item for\n 2014 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 12pt 'Times New Roman'; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"48%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td width=\"45%\"></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" nowrap=\"nowrap\">\n <p style=\"MARGIN-BOTTOM: 1pt; FONT-SIZE: 8pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Comprehensive Income Components</b></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\">\n <p style=\"MARGIN-BOTTOM: 1pt; FONT-SIZE: 8pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Financial Statement Line Item</b></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Unrecognized gains/losses on derivative instruments:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Revenue</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">449</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cost of sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(295</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Other income/expense, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">15</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Other income/expense, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">185</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Unrealized gains/losses on marketable securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Other income/expense, net</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(205</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\" colspan=\"3\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total amounts reclassified from AOCI</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(20</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 1px 'Times New Roman'; MARGIN-TOP: 12px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">The following table shows the changes in AOCI by\n component for 2014 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 12pt 'Times New Roman'; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"58%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Cumulative</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Foreign</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Currency&#xA0;</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Translation</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrecognized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains/Losses</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>on Derivative</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Instruments</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Unrealized</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Gains</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>/Losses on</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Marketable</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Securities</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: rgb(0,0,0) 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Total</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;28, 2013</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(105</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(175</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(191</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(471</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Other comprehensive income/(loss) before reclassifications</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(187</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,687</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">438</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,938</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Amounts reclassified from AOCI</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">0</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">185</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(205</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(20</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Tax effect</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;&#xA0;&#xA0;50</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(333</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(82</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(365</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 4.72em; TEXT-INDENT: -1em\">\n Other comprehensive income/(loss)</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(137</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,539</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;&#xA0;151</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,553</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;27, 2014</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(242</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,364</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(40</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,082</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Note 9 &#x2013; Benefit Plans</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\">\n <b>Stock Plans</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\">\n <i>2014 Employee Stock Plan</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">In the second quarter of 2014, shareholders\n approved the 2014 Employee Stock Plan (the &#x201C;2014 Plan&#x201D;)\n and terminated the Company&#x2019;s authority to grant new awards\n under the 2003 Employee Stock Plan (the &#x201C;2003 Plan&#x201D;).\n The 2014 Plan provides for broad-based equity grants to employees,\n including executive officers, and permits the granting of RSUs,\n stock grants, performance-based awards, stock options and stock\n appreciation rights, as well as cash bonus awards. RSUs granted\n under the 2014 Plan generally vest over four years, based on\n continued employment, and are settled upon vesting in shares of the\n Company&#x2019;s common stock on a one-for-one basis. Each share\n issued with respect to RSUs granted under the 2014 Plan reduces the\n number of shares available for grant under the plan by two shares.\n RSUs cancelled and shares withheld to satisfy tax withholding\n obligations increase the number of shares available for grant under\n the 2014 Plan utilizing a factor of two times the number of RSUs\n cancelled or shares withheld. Currently, all RSUs granted under the\n 2014 Plan have dividend equivalent rights (&#x201C;DERs&#x201D;),\n which entitle holders of RSUs to the same dividend value per share\n as holders of common stock. DERs are subject to the same vesting\n and other terms and conditions as the corresponding unvested RSUs.\n DERs are accumulated and paid when the underlying shares vest. Upon\n approval of the 2014 Plan, the Company reserved 385&#xA0;million\n shares plus the number of shares remaining that were reserved but\n not issued under the 2003 Plan. Shares subject to outstanding\n awards under the 2003 Plan that expire, are cancelled or otherwise\n terminate, or are withheld to satisfy tax withholding obligations\n with respect to RSUs, will also be available for awards under the\n 2014 Plan. As of September&#xA0;27, 2014, approximately\n 492.6&#xA0;million shares were reserved for future issuance under\n the 2014 Plan.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <i>2003 Employee Stock Plan</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The 2003 Plan is a shareholder approved plan that\n provided for broad-based equity grants to employees, including\n executive officers. The 2003 Plan permitted the granting of\n incentive stock options, nonstatutory stock options, RSUs, stock\n appreciation rights, stock purchase rights and performance-based\n awards. Options granted under the 2003 Plan generally expire seven\n to ten years after the grant date and generally become exercisable\n over a period of four years, based on continued employment, with\n either annual, semi-annual or quarterly vesting. RSUs granted under\n the 2003 Plan generally vest over two to four years, based on\n continued employment and are settled upon vesting in shares of the\n Company&#x2019;s common stock on a one-for-one basis. All RSUs,\n other than RSUs held by the Chief Executive Officer, granted under\n the 2003 Plan have DERs. DERs are subject to the same vesting and\n other terms and conditions as the corresponding unvested RSUs. DERs\n are accumulated and paid when the underlying shares vest. In the\n second quarter of 2014, the Company terminated the authority to\n grant new awards under the 2003 Plan.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <i>1997 Director Stock Plan</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The 1997 Director Stock Plan (the &#x201C;Director\n Plan&#x201D;) is a shareholder approved plan that (i)&#xA0;permits\n the Company to grant awards of RSUs or stock options to the\n Company&#x2019;s non-employee directors, (ii)&#xA0;provides for\n automatic initial grants of RSUs upon a non-employee director\n joining the Board of Directors and automatic annual grants of RSUs\n at each annual meeting of shareholders, and (iii)&#xA0;permits the\n Board of Directors to prospectively change the relative mixture of\n stock options and RSUs for the initial and annual award grants and\n the methodology for determining the number of shares of the\n Company&#x2019;s common stock subject to these grants without\n shareholder approval. Each share issued with respect to RSUs\n granted under the Director Plan reduces the number of shares\n available for grant under the plan by two shares. The Director Plan\n expires November&#xA0;9, 2019. All RSUs granted under the Director\n Plan are entitled to DERs. DERs are subject to the same vesting and\n other terms and conditions as the corresponding unvested RSUs. DERs\n are accumulated and paid when the underlying shares vest. As of\n September&#xA0;27, 2014, approximately 1.2&#xA0;million shares were\n reserved for future issuance under the Director Plan.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <i>Rule 10b5-1 Trading Plans</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">During the fourth quarter of 2014, Section&#xA0;16\n officers Timothy D. Cook, Luca Maestri, Daniel Riccio, Philip W.\n Schiller, D. Bruce Sewell and Jeffrey E. Williams had equity\n trading plans in place in accordance with <font style=\"WHITE-SPACE: nowrap\">Rule&#xA0;10b5-1(c)(1)</font> under the\n Exchange Act. An equity trading plan is a written document that\n pre-establishes the amounts, prices and dates (or formula for\n determining the amounts, prices and dates) of future purchases or\n sales of the Company&#x2019;s stock, including shares acquired\n pursuant to the Company&#x2019;s employee and director equity\n plans.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <i>Employee Stock Purchase Plan</i></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Employee Stock Purchase Plan (the\n &#x201C;Purchase Plan&#x201D;) is a shareholder approved plan under\n which substantially all employees may purchase the Company&#x2019;s\n common stock through payroll deductions at a price equal to 85% of\n the lower of the fair market values of the stock as of the\n beginning or the end of six-month offering periods. An\n employee&#x2019;s payroll deductions under the Purchase Plan are\n limited to 10% of the employee&#x2019;s compensation and employees\n may not purchase more than $25,000 of stock during any calendar\n year. As of September&#xA0;27, 2014, approximately 7.6&#xA0;million\n shares were reserved for future issuance under the Purchase\n Plan.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>401(k) Plan</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company&#x2019;s 401(k) Plan is a deferred\n salary arrangement under Section&#xA0;401(k) of the Internal\n Revenue Code. Under the 401(k) Plan, participating U.S. employees\n may defer a portion of their pre-tax earnings, up to the IRS annual\n contribution limit ($17,500 for calendar year 2014). The Company\n matches 50% to 100% of each employee&#x2019;s contributions,\n depending on length of service, up to a maximum 6% of the\n employee&#x2019;s eligible earnings. The Company&#x2019;s matching\n contributions to the 401(k) Plan were $163 million, $135 million\n and $114&#xA0;million in 2014, 2013 and 2012, respectively.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Restricted Stock Units</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">A summary of the Company&#x2019;s RSU activity and\n related information for 2014, 2013 and 2012, is as follows:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"64%\"></td>\n <td valign=\"bottom\" width=\"7%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"7%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"7%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Number of</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>RSUs</b></font><br />\n <font style=\"FONT-FAMILY: Arial\">(in&#xA0;thousands)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b><font style=\"WHITE-SPACE: nowrap\">Weighted-Average</font></b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Grant Date\n Fair</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Value Per Share</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Aggregate</b></font><br />\n <font style=\"FONT-FAMILY: Arial\"><b>Intrinsic&#xA0;Value</b></font><br />\n <font style=\"FONT-FAMILY: Arial\">(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;24, 2011</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">101,122</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">33.07</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs granted</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">54,597</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">61.62</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs vested</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(44,137</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">29.32</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs cancelled</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(6,545</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">36.57</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;29, 2012</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">105,037</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">49.27</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs granted</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">39,415</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">78.23</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs vested</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(42,291</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">45.96</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs cancelled</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(8,877</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">57.31</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;28, 2013</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">93,284</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">62.24</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs granted</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">59,269</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">74.54</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs vested</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(43,111</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">57.29</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n RSUs cancelled</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(5,620</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">68.47</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Balance at September&#xA0;27, 2014</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">103,822</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">70.98</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">10,460</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The fair value as of the respective vesting dates\n of RSUs was $3.4 billion, $3.1 billion and $3.3 billion for 2014,\n 2013 and 2012, respectively. The majority of RSUs that vested in\n 2014, 2013 and 2012 were net-share settled such that the Company\n withheld shares with value equivalent to the employees&#x2019;\n minimum statutory obligation for the applicable income and other\n employment taxes, and remitted the cash to the appropriate taxing\n authorities. The total shares withheld were approximately\n 15.6&#xA0;million, 15.5&#xA0;million and 16.1&#xA0;million for\n 2014, 2013 and 2012, respectively, and were based on the value of\n the RSUs on their respective vesting dates as determined by the\n Company&#x2019;s closing stock price. Total payments for the\n employees&#x2019; tax obligations to taxing authorities were\n $1.2&#xA0;billion, $1.1 billion and $1.2 billion in 2014, 2013 and\n 2012, respectively, and are reflected as a financing activity\n within the Consolidated Statements of Cash Flows.&#xA0;These\n net-share settlements had the effect of share repurchases by the\n Company as they reduced and retired the number of shares that would\n have otherwise been issued as a result of the vesting and did not\n represent an expense to the Company.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Stock Options</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company had 6.6&#xA0;million stock options\n outstanding as of September&#xA0;27, 2014, with a weighted-average\n exercise price per share of $21.99 and weighted-average remaining\n contractual term of 1.4 years, substantially all of which are\n exercisable. The aggregate intrinsic value of the stock options\n outstanding as of September&#xA0;27, 2014 was $520 million, which\n represents the value of the Company&#x2019;s closing stock price on\n the last trading day of the period in excess of the\n weighted-average exercise price multiplied by the number of options\n outstanding. Total intrinsic value of options at time of exercise\n was $1.5 billion, $1.0 billion and $2.3 billion for 2014, 2013 and\n 2012, respectively.</p>\n <p style=\"MARGIN-BOTTOM: 0px; FONT-SIZE: 1px; MARGIN-TOP: 18px\">\n &#xA0;</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Share-based Compensation</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The following table shows a summary of the\n share-based compensation expense included in the Consolidated\n Statements of Operations for 2014, 2013 and 2012 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"76%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2012</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cost of sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">450</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">350</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">265</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Research and development</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,216</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">917</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">668</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Selling, general and administrative</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,197</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">986</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">807</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total share-based compensation expense</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,863</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">2,253</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,740</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The income tax benefit related to share-based\n compensation expense was $1.0 billion, $816 million and\n $567&#xA0;million for 2014, 2013 and 2012, respectively. As of\n September&#xA0;27, 2014, the total unrecognized compensation cost\n related to outstanding stock options, RSUs and restricted stock was\n $6.3 billion, which the Company expects to recognize over a\n weighted-average period of 2.9 years.</p>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 18pt\">\n <b>Fair Value Measurements</b></p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 6pt\" align=\"justify\">The Company applies fair value accounting for all\n financial assets and liabilities and non-financial assets and\n liabilities that are recognized or disclosed at fair value in the\n financial statements on a recurring basis. The Company defines fair\n value as the price that would be received from selling an asset or\n paid to transfer a liability in an orderly transaction between\n market participants at the measurement date. When determining the\n fair value measurements for assets and liabilities, which are\n required to be recorded at fair value, the Company considers the\n principal or most advantageous market in which the Company would\n transact and the market-based risk measurements or assumptions that\n market participants would use in pricing the asset or liability,\n such as risks inherent in valuation techniques, transfer\n restrictions and credit risk. Fair value is estimated by applying\n the following hierarchy, which prioritizes the inputs used to\n measure fair value into three levels and bases the categorization\n within the hierarchy upon the lowest level of input that is\n available and significant to the fair value measurement:</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\"><i>Level 1</i> &#x2013; Quoted prices in active\n markets for identical assets or liabilities.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\"><i>Level 2</i> &#x2013; Observable inputs other than\n quoted prices in active markets for identical assets and\n liabilities, quoted prices for identical or similar assets or\n liabilities in inactive markets, or other inputs that are\n observable or can be corroborated by observable market data for\n substantially the full term of the assets or liabilities.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\"><i>Level 3</i> &#x2013; Inputs that are generally\n unobservable and typically reflect management&#x2019;s estimate of\n assumptions that market participants would use in pricing the asset\n or liability.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The Company&#x2019;s valuation techniques used to\n measure the fair value of money market funds and certain marketable\n equity securities were derived from quoted prices in active markets\n for identical assets or liabilities. The valuation techniques used\n to measure the fair value of the Company&#x2019;s debt instruments\n and all other financial instruments, all of which have\n counterparties with high credit ratings, were valued based on\n quoted market prices or model driven valuations using significant\n inputs derived from or corroborated by observable market data.</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">In accordance with the fair value accounting\n requirements, companies may choose to measure eligible financial\n instruments and certain other items at fair value. The Company has\n not elected the fair value option for any eligible financial\n instruments.</p>\n </div>\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Property, Plant and Equipment</b></p>\n <p style=\"font-size:6pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"82%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Land and buildings</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,863</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">3,309</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Machinery, equipment and internal-use software</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">29,639</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">21,242</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Leasehold improvements</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,513</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">3,968</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Gross property, plant and equipment</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">39,015</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">28,519</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Accumulated depreciation and amortization</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(18,391</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">(11,922</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">)&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Net property, plant and equipment</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">20,624</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">16,597</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 12pt\" align=\"justify\">The following table shows the gross amounts\n reclassified from AOCI into the Consolidated Statements of\n Operations and the associated financial statement line item for\n 2014 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"48%\"></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n <td width=\"45%\"></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"1%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" nowrap=\"nowrap\">\n <p style=\"MARGIN-BOTTOM: 1pt; FONT-SIZE: 8pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Comprehensive Income Components</b></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\">\n <p style=\"MARGIN-BOTTOM: 1pt; FONT-SIZE: 8pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\">\n <b>Financial Statement Line Item</b></p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Unrecognized gains/losses on derivative instruments:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Revenue</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">449</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Cost of sales</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(295</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Other income/expense, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">15</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Other income/expense, net</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">185</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Unrealized gains/losses on marketable securities</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Other income/expense, net</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(205</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\" colspan=\"3\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Total amounts reclassified from AOCI</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(20</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: #000000 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n Yes\n 6085572000\n 1295000000\n 217128000000\n -137000000\n 70537000000\n 10026000000\n 33600000000\n 330000000\n 9571000000\n 11031000000\n 1553000000\n 182795000000\n -167000000\n 76000000\n 53483000000\n 3765000000\n -36000000\n 151000000\n 52503000000\n 88000000\n 41063000000\n 4952000000\n 980000000\n 1795000000\n 2220000000\n 882000000\n 242000000\n -16000000\n 45000000000\n -431000000\n 1938000000\n 1390000000\n 495000000\n 71000000\n 39510000000\n 339000000\n 4232000000\n 2863000000\n 1158000000\n 285000000\n 1539000000\n 2863000000\n 2347000000\n 112000000\n 1460000000\n 384000000\n 18034000000\n 6041000000\n 11993000000\n 3400000000\n 574000000\n 112258000000\n -178000000\n 17500\n 739000000\n 11807000000\n -166000000\n 381000000\n 45000000000\n 1100000000\n -415000000\n -658000000\n 0\n 570000000\n -4744000000\n 18719000000\n 13973000000\n 717000000\n -185000000\n -149000000\n 1865000000\n 8624000000\n 6900000000\n -37549000000\n 50000000\n 153000000\n 6306000000\n 730000000\n 3760000000\n 677000000\n 855000000\n 163000000\n 0\n 1000000000\n -22579000000\n 4771000000\n 1200000000\n 2147000000\n 7946000000\n 469000000\n 205000000\n 365000000\n 297000000\n 6306000000\n 4441000000\n 20000000\n 1200000000\n 5938000000\n 18810000000\n 189301000000\n 2863000000\n 1500000000\n 40000000\n 3183000000\n 1489000000\n 6010000000\n 134000000\n 11960000000\n 280000000\n 4000000\n P1Y\n 735000000\n <div>\n <p style=\"margin-top:18pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\">\n <b>Common Stock Split</b></p>\n <p style=\"margin-top:6pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">On June&#xA0;6, 2014, the Company effected a\n seven-for-one stock split to shareholders of record as of\n June&#xA0;2, 2014. All share and per share information has been\n retroactively adjusted to reflect the stock split.</p>\n </div>\n 11215000000\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-TOP: 0pt\" align=\"justify\">The following table shows the notional amounts of\n the Company&#x2019;s outstanding derivative instruments and credit\n risk amounts associated with outstanding or unsettled derivative\n instruments as of September&#xA0;27, 2014 and September&#xA0;28,\n 2013 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; FONT-SIZE: 12pt; MARGIN-TOP: 0pt\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"67%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"6\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2014</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"6\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>2013</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 8pt; FONT-FAMILY: ARIAL\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Notional<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Credit&#xA0;Risk<br />\n Amounts</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Notional<br />\n Amount</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td style=\"BORDER-BOTTOM: #000000 1px solid\" valign=\"bottom\" colspan=\"2\" align=\"center\"><font style=\"FONT-FAMILY: Arial\"><b>Credit&#xA0;Risk<br />\n Amounts</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Instruments designated as accounting hedges:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">42,945</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,333</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">35,013</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">159</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Interest rate contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">12,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">89</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">3,000</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">44</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Instruments not designated as accounting hedges:</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Foreign exchange contracts</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">38,510</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">222</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">16,131</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">25</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n </table>\n </div>\n 11126000000\n -448000000\n <div>\n <p style=\"margin-top:0pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">Additionally, the Company repurchased shares of its\n common stock in the open market, which were retired upon\n repurchase, during the periods presented as follows:</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"70%\"></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"5%\"></td>\n <td></td>\n <td></td>\n <td></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:8pt\">\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Number of<br />\n Shares<br /></b>(in&#xA0;thousands)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Average<br />\n Repurchase<br />\n Price Per<br />\n Share</b></font></td>\n <td valign=\"bottom\">&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\" colspan=\"2\" align=\"center\" style=\"border-bottom:1.00px solid #000000\"><font style=\"font-family:Arial;\"><b>Amount<br /></b>(in&#xA0;millions)</font></td>\n <td valign=\"bottom\">&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"><font style=\"font-family:Arial;\">2014:</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Fourth quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">81,255</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">98.46</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">8,000</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Third quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">58,661</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">85.23</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,000</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Second quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">79,749</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">75.24</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">6,000</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n First quarter</p>\n </td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">66,847</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">74.79</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,000</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:5.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">286,512</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">24,000</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\"><font style=\"font-family:Arial;\">2013:</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Fourth quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">73,064</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">68.43</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">5,000</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Third quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">62,676</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">63.82</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,000</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Second quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n First quarter</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">0</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:5.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">135,740</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">9,000</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n P5Y\n <div>\n <p style=\"margin-top:12pt; margin-bottom:0pt; font-size:9pt; font-family:Arial\" align=\"justify\">acquired intangible assets is 3.8 years. The\n expected annual amortization expense related to acquired intangible\n assets as of September&#xA0;27, 2014, is as follows (in\n millions):</p>\n <p style=\"font-size:12pt;margin-top:0pt;margin-bottom:0pt\">\n &#xA0;</p>\n <table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" border=\"0\" style=\"BORDER-COLLAPSE:COLLAPSE; font-family:ARIAL; font-size:9pt\" align=\"center\">\n <tr>\n <td width=\"90%\"></td>\n <td valign=\"bottom\" width=\"4%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2015</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">1,204</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2016</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;1,083</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2017</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">825</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2018</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">592</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n 2019</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">175</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:1.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Thereafter</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">163</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:1.00px solid #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr bgcolor=\"#EFEFEF\" style=\"font-family:ARIAL; font-size:9pt\">\n <td valign=\"top\">\n <p style=\"margin-left:3.00em; text-indent:-1.00em; font-size:9pt; font-family:Arial\">\n Total</p>\n </td>\n <td valign=\"bottom\"><font style=\"font-size:8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"font-family:Arial;\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"font-family:Arial;\">4,042</font></td>\n <td nowrap=\"nowrap\" valign=\"bottom\"><font style=\"font-family:Arial;\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"font-size:1px;\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"border-top:3.00px double #000000\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n </div>\n 706000000\n <div>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 9pt Arial; MARGIN-TOP: 12pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" align=\"justify\">The following table provides a summary of cash\n flows associated with the issuance and maturities of Commercial\n Paper for 2014 (in millions):</p>\n <p style=\"MARGIN-BOTTOM: 0pt; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 12pt 'Times New Roman'; MARGIN-TOP: 0pt; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n <table style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL; BORDER-COLLAPSE: collapse; TEXT-TRANSFORM: none; WORD-SPACING: 0px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" align=\"center\" border=\"0\">\n <tr>\n <td width=\"91%\"></td>\n <td valign=\"bottom\" width=\"3%\"></td>\n <td></td>\n <td></td>\n <td valign=\"bottom\" width=\"2%\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Maturities less than 90 days:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Proceeds from (repayments of) commercial paper, net</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">1,865</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 1em; TEXT-INDENT: -1em\">\n Maturities greater than 90 days:</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\"></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Proceeds from commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,771</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 3em; TEXT-INDENT: -1em\">\n Repayments of commercial paper</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">(330</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">)&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 5em; TEXT-INDENT: -1em\">\n Maturities greater than 90 days, net</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">&#xA0;</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">4,441</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 1px solid\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n <tr style=\"FONT-SIZE: 9pt; FONT-FAMILY: ARIAL\" bgcolor=\"#EFEFEF\">\n <td valign=\"top\">\n <p style=\"FONT-SIZE: 9pt; FONT-FAMILY: Arial; MARGIN-LEFT: 7em; TEXT-INDENT: -1em\">\n Total proceeds from issuance of commercial paper, net</p>\n </td>\n <td valign=\"bottom\"><font style=\"FONT-SIZE: 8pt\">&#xA0;&#xA0;</font></td>\n <td valign=\"bottom\"><font style=\"FONT-FAMILY: Arial\">$</font></td>\n <td valign=\"bottom\" align=\"right\"><font style=\"FONT-FAMILY: Arial\">6,306</font></td>\n <td valign=\"bottom\" nowrap=\"nowrap\"><font style=\"FONT-FAMILY: Arial\">&#xA0;&#xA0;</font></td>\n </tr>\n <tr style=\"FONT-SIZE: 1px\">\n <td valign=\"bottom\"></td>\n <td valign=\"bottom\">&#xA0;&#xA0;</td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td valign=\"bottom\">\n <p style=\"BORDER-TOP: rgb(0,0,0) 3px double\">&#xA0;</p>\n </td>\n <td>&#xA0;</td>\n </tr>\n </table>\n <p style=\"MARGIN-BOTTOM: 0px; WHITE-SPACE: normal; TEXT-TRANSFORM: none; WORD-SPACING: 0px; COLOR: rgb(0,0,0); FONT: 1px 'Times New Roman'; MARGIN-TOP: 18px; LETTER-SPACING: normal; TEXT-INDENT: 0px; -webkit-text-stroke-width: 0px\">\n &#xA0;</p>\n </div>\n P10Y\n 1.00\n P7Y\n P9M\n 40\n 0.06\n P4Y\n 25000\n P10Y\n P20Y\n P30Y\n P5Y\n P10Y\n P5Y\n 25\n P10Y\n P4Y\n 0.50\n P3Y\n 5\n P2Y\n P5Y\n P2Y\n P3Y\n 15\n P7Y\n P2Y\n 0.85\n P6M\n 5620000\n 74.54\n 43111000\n 59269000\n 57.29\n 68.47\n 2863000000\n 4825000000\n 65497000000\n -5306000000\n 172548000\n 69.55\n 8000000\n March 2014\n 12000000000\n 286512000\n 24000000000\n 59924000\n \n \n 9000000000\n 28544000\n 68.31\n April 2013\n 1950000000\n 134247000\n \n \n 12000000000\n 83248000000\n 68909000000\n 30638000000\n 1216000000\n 450000000\n 295000000\n -15000000\n -16000000\n -120000000\n 1197000000\n -449000000\n 0.13\n 0.72\n 0.16\n 0.51\n 0.16\n 0.14\n 6093000000\n 65232000000\n 24316000000\n 101991000000\n 40929000000\n 14771000000\n 10344000000\n 3636000000\n 14982000000\n 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\n \n \n \n 0000320193\n us-gaap:CommonStockIncludingAdditionalPaidInCapitalMember\n \n \n 2011-09-24\n \n \n \n \n 0000320193\n us-gaap:RetainedEarningsMember\n \n \n 2011-09-24\n \n \n \n \n 0000320193\n us-gaap:AccumulatedOtherComprehensiveIncomeMember\n \n \n 2011-09-24\n \n \n \n \n 0000320193\n us-gaap:RestrictedStockUnitsRSUMember\n \n \n 2011-09-24\n \n \n \n \n 0000320193\n \n \n 2011-09-24\n \n \n \n iso4217:USD\n \n \n \n \n iso4217:USD\n \n \n shares\n \n \n \n \n shares\n \n \n aapl:Vendor\n \n \n aapl:Customer\n \n \n pure\n \n \n aapl:Item\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n The fair value of derivative assets is measured using Level 2 fair value inputs and is recorded as other current assets in the Consolidated Balance Sheets.\n The fair value of derivative liabilities is measured using Level 2 fair value inputs and is recorded as accrued expenses in the Consolidated Balance Sheets.\n \"Number of Shares\" represents those shares delivered in advance of settlement and does not represent the final number of shares to be delivered under the ASRs. The total number of shares ultimately delivered, and therefore the average repurchase price paid per share, will be determined at the end of the applicable purchase period based on the volume weighted-average price of the Company's common stock during that period. The August 2014 ASR and January 2014 ASR purchase periods will end in or before February 2015 and December 2014, respectively.\n Includes 8.0 million shares that were delivered and retired at the end of the purchase period, which concluded in the second quarter of 2014.\n Amount excludes research and development share-based compensation expense.\n China includes Hong Kong. Long-lived assets located in China consist primarily of product tooling and manufacturing process equipment and assets related to retail stores and related infrastructure.\n Includes deferrals and amortization of related non-software services and software upgrade rights.\n Includes revenue from the iTunes Store, the App Store, the Mac App Store, the iBooks Store, AppleCare, licensing and other services.\n Includes sales of Apple-branded and third-party accessories for iPhone, iPad, Mac and iPod.\n Basic and diluted earnings per share are computed independently for each of the quarters presented. Therefore, the sum of quarterly basic and diluted per share information may not equal annual basic and diluted earnings per share.\n China includes Hong Kong.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "report/xbrl.Cache/aapl-20140927.xsd", "content": "\n\n\n\n\n\n\n \n \n \n 101 - Document - Document and Entity Information\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 103 - Statement - CONSOLIDATED STATEMENTS OF OPERATIONS\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 104 - Statement - CONSOLIDATED STATEMENTS OF COMPREHENSIVE INCOME\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 105 - Statement - CONSOLIDATED STATEMENTS OF COMPREHENSIVE INCOME (Parenthetical)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 106 - Statement - CONSOLIDATED BALANCE SHEETS\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 107 - Statement - CONSOLIDATED BALANCE SHEETS (Parenthetical)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 108 - Statement - CONSOLIDATED STATEMENTS OF SHAREHOLDERS' EQUITY\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 109 - Statement - CONSOLIDATED STATEMENTS OF CASH FLOWS\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 110 - Disclosure - Summary of Significant Accounting Policies\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 111 - Disclosure - Financial Instruments\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 112 - Disclosure - Consolidated Financial Statement Details\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 113 - Disclosure - Goodwill and Other Intangible Assets\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 114 - Disclosure - Income Taxes\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 115 - Disclosure - Debt\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 116 - Disclosure - Shareholders' Equity\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 117 - Disclosure - Comprehensive Income\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 118 - Disclosure - Benefit Plans\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 119 - Disclosure - Commitments and Contingencies\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 120 - Disclosure - Segment Information and Geographic Data\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 121 - Disclosure - Selected Quarterly Financial Information (Unaudited)\n 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link:definitionLink\n \n \n 136 - Disclosure - Computation of Basic and Diluted Earnings Per Share (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 137 - Disclosure - Cash and Available-for-Sale Securities' Adjusted Cost, Gross Unrealized Gains, Gross Unrealized Losses and Fair Value Recorded as Cash and Cash Equivalents or Short-Term or Long-Term Marketable Securities (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 138 - Disclosure - Financial Instruments - Additional Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 139 - Disclosure - Notional Amounts of Outstanding Derivative Instruments and Credit Risk Amounts Associated with Outstanding or Unsettled Derivative Instruments (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 140 - Disclosure - Derivative Instruments at Gross Fair Value as Reflected in Consolidated Balance Sheets (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 141 - Disclosure - Pre-Tax Effect of Derivative Instruments Designated as Cash Flow, Net Investment and Fair Value Hedges (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 142 - Disclosure - Inventory (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 143 - Disclosure - Property, Plant and Equipment (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 144 - Disclosure - Accrued Expenses (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 145 - Disclosure - Non-Current Liabilities (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 146 - Disclosure - Other Income and Expense (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 147 - Disclosure - Goodwill and Other Intangible Assets - Additional Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 148 - Disclosure - Components of Gross and Net Intangible Asset Balances (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 149 - Disclosure - Expected Annual Amortization Expense Related to Acquired Intangible Assets (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 150 - Disclosure - Provision for Income Taxes (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 151 - Disclosure - Income Taxes - Additional Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 152 - Disclosure - Reconciliation of the Provision for Income Taxes (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 153 - Disclosure - Significant Components of the Company's Deferred Tax Assets and Liabilities (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 154 - Disclosure - Aggregate Changes in the Balance of Gross Unrecognized Tax Benefits, which Excludes Interest and Penalties (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 155 - Disclosure - Debt - Additional Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 156 - Disclosure - Summary of Cash Flows Associated With Issuance and Maturities of Commercial Paper (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 157 - Disclosure - Summary of Long-Term Debt (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 158 - Disclosure - Summary of Long-Term Debt (Parenthetical) (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 159 - Disclosure - Debt Instrument Future Principal Payments (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 160 - Disclosure - Shareholders' Equity - Additional Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 161 - Disclosure - Summary of Dividends Declared and Paid (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 162 - Disclosure - Accelerated Share Repurchase Arrangements (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 163 - Disclosure - Accelerated Share Repurchase Arrangements (Parenthetical) (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 164 - Disclosure - Repurchases of Common Shares in Open Market (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 165 - Disclosure - Gross Amounts Reclassified from AOCI into Consolidated Statements of Operations (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 166 - Disclosure - Change in Accumulated Other Comprehensive Income by Component (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 167 - Disclosure - Benefit Plans - Additional Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 168 - Disclosure - Restricted Stock Units Activity and Related Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 169 - Disclosure - Summary of Share-Based Compensation Expense (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 170 - Disclosure - Commitments and Contingencies - Additional Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 171 - Disclosure - Changes in Accrued Warranties and Related Costs (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 172 - Disclosure - Future Minimum Lease Payments under Noncancelable Operating Leases (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 173 - Disclosure - Segment Information and Geographic Data - Additional Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 174 - Disclosure - Summary Information by Operating Segment (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 175 - Disclosure - Reconciliation of Segment Operating Income to Consolidated Statements of Operations (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 176 - Disclosure - Total Assets by Segment and Reconciliation to Consolidated Balance Sheets (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 177 - Disclosure - Net Sales (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 178 - Disclosure - Long-Lived Assets (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 179 - Disclosure - Net Sales by Product (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n 180 - Disclosure - Summary of Quarterly Financial Information (Detail)\n link:calculationLink\n link:presentationLink\n link:definitionLink\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "report/xbrl.Cache/aapl-20140927_cal.xml", "content": "\n\n\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "report/xbrl.Cache/aapl-20140927_def.xml", "content": "\n\n\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "report/xbrl.Cache/aapl-20140927_lab.xml", "content": "\n\n\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "report/xbrl.Cache/aapl-20140927_pre.xml", "content": "\n\n\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "report/xbrl.Cache/country-2013-01-31.xsd", "content": "\n\n\n\n\n\n995410 - Document - Country Code\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "report/xbrl.Cache/currency-2014-01-31.xsd", "content": "\n\n\n\n\n995430 - Document - Currency\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n999030 - Document - Deprecated Currency Concepts\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "report/xbrl.Cache/dei-2014-01-31.xsd", "content": "\n\n\n\nThis file is the schema file for Document and Entity Information (dei).\nThe DTS starting with this schema file will not contain any linkbases.\n\n\n995200 - Document - Document Information\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information, Entities [Table]\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information, Entity Addresses [Table]\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information, Entity Contact Personnel [Table]\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information, Entity Listings [Table]\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information, Entity Phone Fax Numbers [Table]\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information, Entity Sector Industry Classifications [Table]\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information, Entity North American Industry Classifications [Table]\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995400 - Document - Entity Information, Entity Location [Table]\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n999100 - Document - Deprecated Document Entity Concepts\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "report/xbrl.Cache/exch-2014-01-31.xsd", "content": "\n\n\n\n\n995440 - Document - Exchange\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n999020 - Document - Deprecated Exchange Concepts\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "report/xbrl.Cache/invest-2013-01-31.xsd", "content": "\n\n\n\n\n\n995471 - Document - Investment (Details)\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n995472 - Document - Investment, Investment Identifier (Details)\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n995474 - Document - Investment, Deprecated Concepts\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995473 - Document - Investment, Defaults\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n995470 - Document - Investment (Tables)\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "report/xbrl.Cache/naics-2011-01-31.xsd", "content": "\n\n\n\n\nThis file is the schema file for NAICS (naics).\nThe DTS starting with this schema file will not contain any linkbases.\n\n\n\n995460 - Document - NAICS\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n Schema for non-numeric types.\n \n \n \n \n The domain item type indicates that an element is a domain or domain member. Domain item types\n may or may not have attribute abstract=\"true\".\n \n \n \n \n \n \n \n \n \n escapedItemType specializes string. There is no constraint on whether the content resulting\n from XML un-escaping is well-formed or not. It is a suitable base type for (say) a data type whose unescaped content must\n conform to an SGML DTD.\n \n \n \n \n \n \n \n \n \n xmlNodesItemType specializes escapedItemType. The unescaped content MUST be a sequence of XML\n text and well-formed XML nodes. This content constraint cannot be captured by a finite length regular\n expression.\n \n \n \n \n \n \n \n \n \n xmlItemType specializes xmlNodesItemType. The unescaped content MUST be well formed XML. This\n is a suitable base type for elements whose content must conform to a specific XML Schema or DTD. This content constraint\n cannot be captured by a finite length regular expression, other than that \"<\" must be the first non-whitespace\n character and \">\" the last non-whitespace character.\n \n \n \n \n \n \n \n \n \n textBlockItemType specializes xmlNodesItemType. The unescaped content MUST have mixed content\n containing a simple string, or a fragment of XHTML or a mixture of both.\n \n \n \n \n \n \n \n\n" }, { "path": "report/xbrl.Cache/numeric-2009-12-16.xsd", "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "report/xbrl.Cache/ref-2006-02-27.xsd", "content": " \r\r\n\r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Publisher of the reference material, such as SEC, FASB, or AICPA.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Name refers to the specific publication. For example, \"Statement of Financial Standards\", \"Statement of Position\" or \"IFRS\". It does not include the number.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Number is used to record the actual number of the specific publication. For example, the number for FAS 133 would be 133.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n The issue date of the specific reference. The format is CCYY-MM-DD.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n For a publication that uses chapters, this part should be used to capture this information. Because chapters are not necessarily numbers, this is a string.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Article refers to a statutory article in legal material.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Notes can contain reference material; use this element when the note is published as a standalone document. There is a separate element for footnotes within other references.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Section is used to capture information typically captured in sections of legislation or reference documents.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Subsection is a subsection of the section part.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Paragraph is used to refer to specific paragraphs in a document.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Subparagraph of a paragraph.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Sub component of a sub paragraph.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Subcomponent of a clause in a paragraph.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Refers to the name of an Appendix, which could be a number or text.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Example captures examples used in reference documentation; there is a separate element for Exhibits.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Page number of the reference material.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Exhibit refers to exhibits in reference documentation; examples have a separate element.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Footnote is used to reference footnotes that appear in reference information.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n In some reference material individual sentences can be referred to, and this element allows them to be referenced.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Full URI of the reference such as \"http://www.fasb.org/fas133\".\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Date or DateTime that the URI was valid, in CCYY-MM-DD format.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n" }, { "path": "report/xbrl.Cache/us-gaap-2014-01-31.xsd", "content": "\n\n\n\n\n\nThis file is the schema file for us-gaap (us-gaap).\n\nThe DTS starting with this schema file will not contain any linkbases.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n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Groups\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n778000 - Disclosure - Extraordinary and Unusual Items\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n780000 - Disclosure - Earnings Per Share\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n802000 - Disclosure - Reorganizations\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n805000 - Disclosure - Derivative Instruments and Hedging Activities\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n820000 - Disclosure - Foreign Operations and Currency Translation\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n840000 - Disclosure - Nonmonetary Transactions\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n845000 - Disclosure - Related Party Disclosures\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n870000 - Disclosure - Subsequent Events\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n910000 - Disclosure - Contractors\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n915000 - Disclosure - Development Stage Enterprises\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n940000 - Disclosure - Financial Services, Banking and Thrift\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n948000 - Disclosure - Financial Services, Mortgage Banking\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n955000 - Disclosure - Health Care Organizations\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n975000 - Disclosure - Real Estate\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n993000 - Disclosure - SEC Schedule, Article 12-09, Valuation and Qualifying Accounts\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n993200 - Disclosure - SEC Schedule, Article 12-28, Real Estate and Accumulated Depreciation\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n993400 - Disclosure - SEC Schedule, Article 12-29, Mortgage Loans on Real Estate\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n993600 - Disclosure - SEC Schedule, Article 12-15, Summary of Investments - Other than Investments in Related Parties\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n993800 - Disclosure - SEC Schedule, Article 12-16, Supplementary Insurance Information\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n994000 - Disclosure - SEC Schedule, Article 12-17, Reinsurance\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n994200 - Disclosure - SEC Schedule, Article 12-18, Supplemental Information (for Property-Casualty Insurance Underwriters)\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n108050 - Statement - Statement of Financial Position, Unclassified - Deposit Based Operations\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n943000 - Disclosure - Financial Services, Brokers and Dealers\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n985000 - Disclosure - Other Industries\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n152201 - Statement - Statement of Cash Flows, Additional Cash Flow Elements\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n152205 - Statement - Statement of Cash Flows, Supplemental Disclosures\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n104100 - Statement - Statement of Financial Position, Classified\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n124003 - Statement - Statement of Income\nlink:presentationLink\nlink:calculationLink\nlink:definitionLink\n\n\n148411 - Statement - Statement of Other Comprehensive Income\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n148412 - Statement - Statement of Other Comprehensive Income\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n148413 - Statement - Statement of Other Comprehensive Income\nlink:calculationLink\nlink:definitionLink\nlink:presentationLink\n\n\n370000 - Disclosure - Intangible Assets, Goodwill and Other, Schedule of Acquired Finite-Lived Intangible Asset by Major Class [Table]\nlink:definitionLink\n\n\n370000 - Disclosure - Intangible Assets, Goodwill and Other, Schedule of Acquired Indefinite-lived Intangible Assets by Major Class [Table]\nlink:definitionLink\n\n\n152205 - Statement - Statement of Cash Flows, Supplemental Disclosures, Noncash or Part Noncash Acquisitions [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Schedule of Assets Sold under Agreements to Repurchase [Table]\nlink:definitionLink\n\n\n940000 - Disclosure - Financial Services, Banking and Thrift, Schedule of Assets Sold under Agreements to Repurchase [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Assumptions for Fair Value as of Balance Sheet Date of Assets or Liabilities that relate to Transferor's Continuing Involvement [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Fair Value Assumption, Date of Securitization or Asset-backed Financing Arrangement, Transferor's Continuing Involvement, Servicing Assets or Liabilities [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Auction Market Preferred Securities, Stock Series [Table]\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations, Schedule of Business Acquisitions by Acquisition, Contingent Consideration [Table]\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations, Schedule of Business Acquisitions by Acquisition, Equity Interest Issued or Issuable [Table]\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations, Schedule of Business Acquisitions, by Acquisition [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Schedule of Capital Units [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Capitalized Costs Relating to Oil and Gas Producing Activities, by Geographic Area [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Liability for Catastrophe Claims [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Causes of Increase (Decrease) in Liability for Unpaid Claims and Claims Adjustment Expense [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Ceded Credit Risk [Table]\nlink:definitionLink\n\n\n250000 - Disclosure - Accounting Changes and Error Corrections, Schedule of Change in Accounting Estimate [Table]\nlink:definitionLink\n\n\n300000 - Disclosure - Cash and Cash Equivalents, Schedule of Compensating Balances [Table]\nlink:definitionLink\n\n\n275000 - Disclosure - Risks and Uncertainties, Concentration Risk [Table]\nlink:definitionLink\n\n\n152205 - Statement - Statement of Cash Flows, Supplemental Disclosures, Conversion of Stock [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Costs Incurred in Oil and Gas Property Acquisition, Exploration, and Development Activities [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Customer Securities for which Entity has Right to Sell or Repledge [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Customer Securities for which Entity has Right to Sell or Repledge [Table]\nlink:definitionLink\n\n\n152205 - Statement - Statement of Cash Flows, Supplemental Disclosures, Debt Conversion [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Schedule of Long-term Debt Instruments [Table]\nlink:definitionLink\n\n\n440000 - Disclosure - Revenue Recognition and Deferred Revenue, Deferred Revenue Arrangement, by Type [Table]\nlink:definitionLink\n\n\n805000 - Disclosure - Derivative Instruments and Hedging Activities, Derivative [Table]\nlink:definitionLink\n\n\n456000 - Disclosure - Guarantees, Schedule of Guarantor Obligations [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Development Wells Drilled [Table]\nlink:definitionLink\n\n\n775000 - Disclosure - Discontinued Operations and Disposal Groups, Income Statement, Balance Sheet and Additional Disclosures by Disposal Groups, Including Discontinued Operations [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Discounted Future Net Cash Flows Relating to Proved Oil and Gas Reserves [Table]\nlink:definitionLink\n\n\n152205 - Statement - Statement of Cash Flows, Supplemental Disclosures, Noncash or Part Noncash Divestitures [Table]\nlink:definitionLink\n\n\n152205 - Statement - Statement of Cash Flows, Supplemental Disclosures, Dividends Payable [Table]\nlink:definitionLink\n\n\n710000 - Disclosure - Compensation Related Costs, Share Based Payments, Schedule of Employee Stock Ownership Plan (ESOP) Disclosures [Table]\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting, Schedule of Revenue by Major Customers, by Reporting Segments [Table]\nlink:definitionLink\n\n\n425000 - Disclosure - Environmental Remediation Obligations, Schedule of Environmental Exit Cost [Table]\nlink:definitionLink\n\n\n250000 - Disclosure - Accounting Changes and Error Corrections, Schedule of Error Corrections and Prior Period Adjustment Restatement [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Exploratory Wells Drilled [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Schedule of Extinguishment of Debt [Table]\nlink:definitionLink\n\n\n778000 - Disclosure - Extraordinary and Unusual Items, Extraordinary Items [Table]\nlink:definitionLink\n\n\n940000 - Disclosure - Financial Services, Banking and Thrift, Schedule of Federal Home Loan Bank Advances, by Branch of FHLB Bank [Table]\nlink:definitionLink\n\n\n470000 - Disclosure - Other Liabilities, Forward Contract Indexed to Issuer's Equity [Table]\nlink:definitionLink\n\n\n802000 - Disclosure - Reorganizations, Fresh-Start Adjustments [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Gain Contingencies [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Gas and Oil Acreage [Table]\nlink:definitionLink\n\n\n370000 - Disclosure - Intangible Assets, Goodwill and Other, Schedule of Goodwill [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Schedule of Incentive Distribution Made to Managing Member or General Partner [Table]\nlink:definitionLink\n\n\n770000 - Disclosure - Income Taxes, Income Tax Contingency [Table]\nlink:definitionLink\n\n\n770000 - Disclosure - Income Taxes, Income Tax Examination [Table]\nlink:definitionLink\n\n\n770000 - Disclosure - Income Taxes, Income Tax Holiday [Table]\nlink:definitionLink\n\n\n820000 - Disclosure - Foreign Operations and Currency Translation, Schedule of Intercompany Foreign Currency Balance [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Jointly Owned Utility Plant Interests [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Liabilities for Guarantees on Long-Duration Contracts [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Liability for Future Policy Benefit, by Product Segment [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Line of Credit Facility [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Long-Duration Contracts, Assumptions, by Product and Guarantee [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Recorded Unconditional Purchase Obligation [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Loss Contingencies [Table]\nlink:definitionLink\n\n\n955000 - Disclosure - Health Care Organizations, Schedule of Malpractice Insurance [Table]\nlink:definitionLink\n\n\n770000 - Disclosure - Income Taxes, Significant Change in Unrecognized Tax Benefits is Reasonably Possible [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Net Amount at Risk, by Product and Guarantee [Table]\nlink:definitionLink\n\n\n250000 - Disclosure - Accounting Changes and Error Corrections, New Accounting Pronouncements or Change in Accounting Principle [Table]\nlink:definitionLink\n\n\n840000 - Disclosure - Nonmonetary Transactions, Nonmonetary Transaction, by Type [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Oil and Gas Delivery Commitments and Contracts [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Proved Developed and Undeveloped Oil and Gas Reserve Quantities [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Average Sales Price and Production Costs Per Unit of Production [Table]\nlink:definitionLink\n\n\n470000 - Disclosure - Other Liabilities, Option Indexed to Issuer's Equity [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Other Investment Not Readily Marketable [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Schedule of Other Ownership Interests [Table]\nlink:definitionLink\n\n\n152205 - Statement - Statement of Cash Flows, Supplemental Disclosures, Other Significant Noncash Transactions [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Participating Mortgage Loans [Table]\nlink:definitionLink\n\n\n730000 - Disclosure - Compensation Related Costs, Retirement Benefits, Schedule of Defined Benefit Plans Disclosures [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Schedule of Preferred Units [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Principal Transaction Revenue [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Productive Wells [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Long-term Contracts for Purchase of Electric Power [Table]\nlink:definitionLink\n\n\n250000 - Disclosure - Accounting Changes and Error Corrections, Schedule of Quantifying Prior Year Misstatement Corrected in Current Year Financial Statements [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Assets that Continue to be Recognized, Transferred Financial Assets and Other Financial Assets Managed Together [Table]\nlink:definitionLink\n\n\n993200 - Disclosure - SEC Schedule, Article 12-28, Real Estate and Accumulated Depreciation, SEC Schedule III, Real Estate and Accumulated Depreciation, by Property [Table]\nlink:definitionLink\n\n\n320000 - Disclosure - Receivables, Loans, Notes Receivable, and Others, Schedule of Receivables with Imputed Interest [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Registration Payment Arrangement [Table]\nlink:definitionLink\n\n\n994000 - Disclosure - SEC Schedule, Article 12-17, Reinsurance, Reinsurance Premiums for Insurance Companies, by Product Segment [Table]\nlink:definitionLink\n\n\n845000 - Disclosure - Related Party Disclosures, Schedule of Related Party Transactions, by Related Party [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Schedule of Repurchase Agreement Counterparty [Table]\nlink:definitionLink\n\n\n940000 - Disclosure - Financial Services, Banking and Thrift, Schedule of Repurchase Agreement Counterparty [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Schedule of Resale Agreement Counterparty [Table]\nlink:definitionLink\n\n\n940000 - Disclosure - Financial Services, Banking and Thrift, Schedule of Resale Agreement Counterparty [Table]\nlink:definitionLink\n\n\n370000 - Disclosure - Intangible Assets, Goodwill and Other, Schedule of Research and Development Assets Acquired Other than Through Business Combination [Table]\nlink:definitionLink\n\n\n300000 - Disclosure - Cash and Cash Equivalents, Schedule of Restricted Cash and Cash Equivalents [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Results of Operations for Oil and Gas Producing Activities, by Geographic Area [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Managing of Risks Inherent in Servicing Assets and Servicing Liabilities [Table]\nlink:definitionLink\n\n\n831000 - Disclosure - Leases, Sale Leaseback Transaction [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Schedule of Fair Value of Separate Accounts by Major Category of Investment [Table]\nlink:definitionLink\n\n\n993400 - Disclosure - SEC Schedule, Article 12-29, Mortgage Loans on Real Estate, Mortgage Loans on Real Estate Schedule [Table]\nlink:definitionLink\n\n\n994200 - Disclosure - SEC Schedule, Article 12-18, Supplemental Information (for Property-Casualty Insurance Underwriters), Supplemental Information for Property, Casualty Insurance Underwriters [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Securities Financing Transaction [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Security Owned Not Readily Marketable [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Securitization or Asset-backed Financing Arrangements, Financial Asset for which Transfer is Accounted as Sale [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Financial Instruments Owned and Pledged as Collateral [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Financial Instruments Owned and Pledged as Collateral [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Security Owned and Sold, Not yet Purchased, at Fair Value [Table]\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting, Reconciliation of Assets from Segment to Consolidated [Table]\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting, Schedule of Segment Reporting Information, by Segment [Table]\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting, Reconciliation of Other Significant Reconciling Items from Segments to Consolidated [Table]\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting, Reconciliation of Operating Profit (Loss) from Segments to Consolidated [Table]\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting, Reconciliation of Revenue from Segments to Consolidated [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Sensitivity Analysis of Fair Value, Transferor's Interests in Transferred Financial Assets [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Servicing Asset at Amortized Cost [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Servicing Assets at Fair Value [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Servicing Liabilities at Fair Value [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Servicing Liability at Amortized Cost [Table]\nlink:definitionLink\n\n\n710000 - Disclosure - Compensation Related Costs, Share Based Payments, Schedule of Share-based Compensation, Shares Authorized under Stock Option Plans, by Exercise Price Range [Table]\nlink:definitionLink\n\n\n710000 - Disclosure - Compensation Related Costs, Share Based Payments, Schedule of Share-based Compensation Arrangements by Share-based Payment Award [Table]\nlink:definitionLink\n\n\n710000 - Disclosure - Compensation Related Costs, Share Based Payments, Schedule of Share-based Goods and Nonemployee Services Transaction [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Schedule of Short-term Debt [Table]\nlink:definitionLink\n\n\n425000 - Disclosure - Environmental Remediation Obligations, Site Contingency [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Statutory Accounting Practices [Table]\nlink:definitionLink\n\n\n942000 - Disclosure - Financial Services, Brokers and Dealers, Subordinated Borrowing [Table]\nlink:definitionLink\n\n\n870000 - Disclosure - Subsequent Events, Subsequent Event [Table]\nlink:definitionLink\n\n\n200000 - Disclosure - Organization, Consolidation and Presentation of Financial Statements, Schedule of Subsidiary of Limited Liability Company or Limited Partnership [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Schedule of Subsidiary or Equity Method Investee [Table]\nlink:definitionLink\n\n\n993800 - Disclosure - SEC Schedule, Article 12-16, Supplementary Insurance Information, Supplementary Insurance Information, by Segment [Table]\nlink:definitionLink\n\n\n770000 - Disclosure - Income Taxes, Tax Credit Carryforward [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Troubled Debt Restructuring, Debtor, Current Period [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Troubled Debt Restructuring, Debtor, Subsequent Periods [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Unrecorded Unconditional Purchase Obligation [Table]\nlink:definitionLink\n\n\n778000 - Disclosure - Extraordinary and Unusual Items, Unusual or Infrequent Item [Table]\nlink:definitionLink\n\n\n275000 - Disclosure - Risks and Uncertainties, Unusual Risk or Uncertainty [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Public Utilities, Inventory [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Valuation Allowance for Impairment of Recognized Servicing Assets [Table]\nlink:definitionLink\n\n\n770000 - Disclosure - Income Taxes, Valuation Allowance [Table]\nlink:definitionLink\n\n\n993000 - Disclosure - SEC Schedule, Article 12-09, Valuation and Qualifying Accounts, Valuation and Qualifying Accounts Disclosure [Table]\nlink:definitionLink\n\n\n290000 - Disclosure - Accounting Policies, Schedule of Variable Interest Entities [Table]\nlink:definitionLink\n\n\n200000 - Disclosure - Organization, Consolidation and Presentation of Financial Statements, Schedule of Variable Interest Entities [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Weighted Average Discount Rate [Table]\nlink:definitionLink\n\n\n993200 - Disclosure - SEC Schedule, Article 12-28, Real Estate and Accumulated Depreciation, SEC Schedule III, Real Estate, Write-down or Reserve [Table]\nlink:definitionLink\n\n\n330000 - Disclosure - Investments, Debt and Equity Securities, Schedule of Available-for-sale Securities [Table]\nlink:definitionLink\n\n\n330000 - Disclosure - Investments, Debt and Equity Securities, Schedule of Held-to-maturity Securities [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Schedule of Limited Partners' Capital Account by Class [Table]\nlink:definitionLink\n\n\n330000 - Disclosure - Investments, Debt and Equity Securities, Trading Securities [Table]\nlink:definitionLink\n\n\n330000 - Disclosure - Investments, Debt and Equity Securities, Investment Income [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Investment Income [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Targeted or Tracking Stock, Stock [Table]\nlink:definitionLink\n\n\n430000 - Disclosure - Restructuring and Related Activities, Schedule of Restructuring and Related Costs [Table]\nlink:definitionLink\n\n\n831000 - Disclosure - Leases, Property Subject to or Available for Operating Lease, by Major Property Class [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, by Balance Sheet Grouping [Table]\nlink:definitionLink\n\n\n148600 - Statement - Statement of Shareholders' Equity, Statement [Table]\nlink:definitionLink\n\n\n190000 - Statement - Common Domain Members, Statement [Table]\nlink:definitionLink\n\n\n148400 - Statement - Statement of Comprehensive Income, Statement [Table]\nlink:definitionLink\n\n\n104000 - Statement - Statement of Financial Position, Classified, Statement [Table]\nlink:definitionLink\n\n\n108000 - Statement - Statement of Financial Position, Unclassified - Deposit Based Operations, Statement [Table]\nlink:definitionLink\n\n\n108200 - Statement - Statement of Financial Position, Unclassified - Insurance Based Operations, Statement [Table]\nlink:definitionLink\n\n\n112000 - Statement - Statement of Financial Position, Unclassified - Securities Based Operations, Statement [Table]\nlink:definitionLink\n\n\n124000 - Statement - Statement of Income (Including Gross Margin), Statement [Table]\nlink:definitionLink\n\n\n132001 - Statement - Statement of Income, Interest Based Revenue, Statement [Table]\nlink:definitionLink\n\n\n136000 - Statement - Statement of Income, Insurance Based Revenue, Statement [Table]\nlink:definitionLink\n\n\n140400 - Statement - Statement of Income, Securities Based Income, Statement [Table]\nlink:definitionLink\n\n\n152200 - Statement - Statement of Cash Flows, Statement [Table]\nlink:definitionLink\n\n\n160000 - Statement - Statement of Cash Flows, Deposit Based Operations, Statement [Table]\nlink:definitionLink\n\n\n164000 - Statement - Statement of Cash Flows, Insurance Based Operations, Statement [Table]\nlink:definitionLink\n\n\n168400 - Statement - Statement of Cash Flows, Securities Based Operations, Statement [Table]\nlink:definitionLink\n\n\n172600 - Statement - Statement of Cash Flows, Direct Method Operating Activities, Statement [Table]\nlink:definitionLink\n\n\n152000 - Statement - Statement of Partners' Capital, Statement [Table]\nlink:definitionLink\n\n\n110200 - Statement - Statement of Financial Position, Unclassified - Real Estate Operations, Statement [Table]\nlink:definitionLink\n\n\n110000 - Statement - Statement of Financial Position, Classified - Real Estate Operations, Statement [Table]\nlink:definitionLink\n\n\n124100 - Statement - Statement of Income, Statement [Table]\nlink:definitionLink\n\n\n144000 - Statement - Statement of Income, Real Estate, Excluding REITs, Statement [Table]\nlink:definitionLink\n\n\n145000 - Statement - Statement of Income, Real Estate Investment Trusts, Statement [Table]\nlink:definitionLink\n\n\n170000 - Statement - Statement of Cash Flows, Real Estate, Including REITs, Statement [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Estimate Not Practicable [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Option Indexed to Issuer's Equity, Equity [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Concentration of Risk [Table]\nlink:definitionLink\n\n\n730000 - Disclosure - Compensation Related Costs, Retirement Benefits, Schedule of Incremental Effects on Balance Sheet, Application of FAS 158 Recognition Provisions [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Assets Measured on Recurring Basis, Unobservable Input Reconciliation [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Liabilities Measured on Recurring Basis, Unobservable Input Reconciliation [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Class of Warrant or Right [Table]\nlink:definitionLink\n\n\n470000 - Disclosure - Other Liabilities, Class of Warrant or Right [Table]\nlink:definitionLink\n\n\n770000 - Disclosure - Income Taxes, Operating Loss Carryforwards [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Option, Qualitative Disclosures Related to Election [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Option, Quantitative Disclosures [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Subsidiary or Equity Method Investee, Sale of Stock by Subsidiary or Equity Investee [Table]\nlink:definitionLink\n\n\n955000 - Disclosure - Health Care Organizations, Health Care Trust Fund [Table]\nlink:definitionLink\n\n\n993600 - Disclosure - SEC Schedule, Article 12-15, Summary of Investments - Other than Investments in Related Parties, Summary of Investments, Other than Investments in Related Parties [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Long-term Purchase Commitment [Table]\nlink:definitionLink\n\n\n993400 - Disclosure - SEC Schedule, Article 12-29, Mortgage Loans on Real Estate, Mortgage Loans on Real Estate, Write-down or Reserve [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Class of Treasury Stock [Table]\nlink:definitionLink\n\n\n780000 - Disclosure - Earnings Per Share, Schedule of Earnings Per Share, Diluted, by Common Class, Including Two Class Method [Table]\nlink:definitionLink\n\n\n780000 - Disclosure - Earnings Per Share, Schedule of Earnings Per Share, Basic, by Common Class, Including Two Class Method [Table]\nlink:definitionLink\n\n\n780000 - Disclosure - Earnings Per Share, Schedule of Antidilutive Securities Excluded from Computation of Earnings Per Share [Table]\nlink:definitionLink\n\n\n430000 - Disclosure - Restructuring and Related Activities, Schedule of Effects on Future Earnings and Cash Flows Resulting from Exit Plan [Table]\nlink:definitionLink\n\n\n333000 - Disclosure - Investments, Equity Method and Joint Ventures, Schedule of Equity Method Investments [Table]\nlink:definitionLink\n\n\n710000 - Disclosure - Compensation Related Costs, Share Based Payments, Schedule of Employee Service Share-based Compensation, Allocation of Recognized Period Costs [Table]\nlink:definitionLink\n\n\n290000 - Disclosure - Accounting Policies, Property, Plant and Equipment [Table]\nlink:definitionLink\n\n\n360000 - Disclosure - Property, Plant, and Equipment, Property, Plant and Equipment [Table]\nlink:definitionLink\n\n\n360000 - Disclosure - Property, Plant, and Equipment, Schedule of Significant Acquisitions and Disposals [Table]\nlink:definitionLink\n\n\n360000 - Disclosure - Property, Plant, and Equipment, Schedule of Impaired Long-Lived Assets Held and Used [Table]\nlink:definitionLink\n\n\n360000 - Disclosure - Property, Plant, and Equipment, Impaired Assets to be Disposed of by Method Other than Sale [Table]\nlink:definitionLink\n\n\n360000 - Disclosure - Property, Plant, and Equipment, Schedule of Long-Lived Assets to be Abandoned [Table]\nlink:definitionLink\n\n\n360000 - Disclosure - Property, Plant, and Equipment, Schedule of Assets Disposed of by Method Other than Sale, in Period of Disposition [Table]\nlink:definitionLink\n\n\n360000 - Disclosure - Property, Plant, and Equipment, Schedule of Long Lived Assets Held-for-sale [Table]\nlink:definitionLink\n\n\n710000 - Disclosure - Compensation Related Costs, Share Based Payments, Schedule of Deferred Compensation Arrangement with Individual, Share-based Payments [Table]\nlink:definitionLink\n\n\n470000 - Disclosure - Other Liabilities, Schedule of Financial Instruments Subject to Mandatory Redemption [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Public Utility, Property, Plant and Equipment [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Projects with Exploratory Well Costs Capitalized for More than One Year [Table]\nlink:definitionLink\n\n\n940000 - Disclosure - Financial Services, Banking and Thrift, Schedule of Compliance with Regulatory Capital Requirements under Banking Regulations [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Oil and Gas In Process Activities [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Amortization Expense Per Unit of Production [Table]\nlink:definitionLink\n\n\n965000 - Disclosure - Extractive Industries, Capitalized Costs of Unproved Properties Excluded from Amortization [Table]\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations, Finite-Lived Intangible Assets Acquired as Part of Business Combination [Table]\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations, Indefinite-lived Intangible Assets Acquired as Part of Business Combination [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Schedule of Fair Value, Off-balance Sheet Risks [Table]\nlink:definitionLink\n\n\n948000 - Disclosure - Financial Services, Mortgage Banking, Schedule of Compliance with Regulatory Capital Requirements for Mortgage Companies [Table]\nlink:definitionLink\n\n\n705000 - Disclosure - Compensation Related Costs, General, Schedule of Deferred Compensation Arrangement with Individual, Excluding Share-based Payments and Postretirement Benefits, by Title of Individual and by Type of Deferred Compensation [Table]\nlink:definitionLink\n\n\n730000 - Disclosure - Compensation Related Costs, Retirement Benefits, Schedule of Deferred Compensation Arrangement with Individual, Postretirement Benefits [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Product Liability Contingency [Table]\nlink:definitionLink\n\n\n991000 - Disclosure - SEC Schedule, Article 12-04, Condensed Financial Information of Registrant, Condensed Financial Statements [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Schedule of Insured Financial Obligations with Credit Deterioration [Table]\nlink:definitionLink\n\n\n285000 - Disclosure - Interim Reporting, Schedule of Effect of Fourth Quarter Events [Table]\nlink:definitionLink\n\n\n760000 - Disclosure - Research and Development, Schedule of Research and Development Arrangement, Contract to Perform for Others [Table]\nlink:definitionLink\n\n\n470000 - Disclosure - Other Liabilities, Accelerated Share Repurchases [Table]\nlink:definitionLink\n\n\n320000 - Disclosure - Receivables, Loans, Notes Receivable, and Others, Schedule of Accounts, Notes, Loans and Financing Receivable [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Schedule of Stock by Class [Table]\nlink:definitionLink\n\n\n200000 - Disclosure - Organization, Consolidation and Presentation of Financial Statements, Consolidation, Less than Wholly Owned Subsidiary, Parent Ownership Interest, Effects of Changes, Net [Table]\nlink:definitionLink\n\n\n472000 - Disclosure - Noncontrolling Interest, Consolidation, Less than Wholly Owned Subsidiary, Parent Ownership Interest, Effects of Changes, Net [Table]\nlink:definitionLink\n\n\n805000 - Disclosure - Derivative Instruments and Hedging Activities, Schedule of Credit Derivatives [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Schedule of Capitalization [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Derecognized Transferred Financial Assets and Other Financial Assets Managed Together [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Schedule of Assets and Associated Liabilities Accounted for as Secured Borrowings [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Schedule of Capitalization, Long-term Debt [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Schedule of Capitalization, Equity [Table]\nlink:definitionLink\n\n\n472000 - Disclosure - Noncontrolling Interest, Redeemable Noncontrolling Interest, by Legal Entity [Table]\nlink:definitionLink\n\n\n480000 - Disclosure - Temporary Equity, Redeemable Noncontrolling Interest, by Legal Entity [Table]\nlink:definitionLink\n\n\n480000 - Disclosure - Temporary Equity, Temporary Equity, by Class of Stock [Table]\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting, Schedule of Revenues from External Customers and Long-Lived Assets [Table]\nlink:definitionLink\n\n\n790000 - Disclosure - Segment Reporting, Revenue from External Customers by Products and Services [Table]\nlink:definitionLink\n\n\n290000 - Disclosure - Accounting Policies, Revenue Recognition, Multiple-deliverable Arrangements [Table]\nlink:definitionLink\n\n\n440000 - Disclosure - Revenue Recognition and Deferred Revenue, Revenue Recognition, Multiple-deliverable Arrangements [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Investments, Entities that Calculate Net Asset Value Per Share [Table]\nlink:definitionLink\n\n\n865000 - Disclosure - Transfers and Servicing, Qualitative and Quantitative Information, Transferor's Continuing Involvement [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Purchase Commitment, Excluding Long-term Commitment [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Supply Commitment [Table]\nlink:definitionLink\n\n\n290000 - Disclosure - Accounting Policies, Revenue Recognition, Milestone Method [Table]\nlink:definitionLink\n\n\n250000 - Disclosure - Accounting Changes and Error Corrections, New Accounting Pronouncement, Early Adoption [Table]\nlink:definitionLink\n\n\n820000 - Disclosure - Foreign Operations and Currency Translation, Schedule of Differences between Reported Amount and Reporting Currency Denominated Amount [Table]\nlink:definitionLink\n\n\n320000 - Disclosure - Receivables, Loans, Notes Receivable, and Others, Schedule of Financing Receivables Past Due [Table]\nlink:definitionLink\n\n\n320000 - Disclosure - Receivables, Loans, Notes Receivable, and Others, Schedule of Financing Receivable, Allowance for Credit Losses [Table]\nlink:definitionLink\n\n\n290000 - Disclosure - Accounting Policies, Schedule of Impaired Financing Receivable [Table]\nlink:definitionLink\n\n\n320000 - Disclosure - Receivables, Loans, Notes Receivable, and Others, Schedule of Impaired Financing Receivable [Table]\nlink:definitionLink\n\n\n320000 - Disclosure - Receivables, Loans, Notes Receivable, and Others, Schedule of Financing Receivable, Troubled Debt Restructurings [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Schedule of Regulatory Assets [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Schedule of Regulatory Liabilities [Table]\nlink:definitionLink\n\n\n320000 - Disclosure - Receivables, Loans, Notes Receivable, and Others, Schedule of Financing Receivable, Recorded Investment, Credit Quality Indicator [Table]\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations, Business Combination, Separately Recognized Transactions [Table]\nlink:definitionLink\n\n\n285000 - Disclosure - Interim Reporting, Interim Period, Costs Not Allocable [Table]\nlink:definitionLink\n\n\n940000 - Disclosure - Financial Services, Banking and Thrift, Capital Requirements on Foreign Financial Institutions [Table]\nlink:definitionLink\n\n\n200000 - Disclosure - Organization, Consolidation and Presentation of Financial Statements, Schedule of Product Information [Table]\nlink:definitionLink\n\n\n290000 - Disclosure - Accounting Policies, Schedule of Product Information [Table]\nlink:definitionLink\n\n\n200000 - Disclosure - Organization, Consolidation and Presentation of Financial Statements, Schedule of Real Estate Properties [Table]\nlink:definitionLink\n\n\n290000 - Disclosure - Accounting Policies, Schedule of Real Estate Properties [Table]\nlink:definitionLink\n\n\n200000 - Disclosure - Organization, Consolidation and Presentation of Financial Statements, Schedule of Franchisor Disclosure [Table]\nlink:definitionLink\n\n\n290000 - Disclosure - Accounting Policies, Schedule of Franchisor Disclosure [Table]\nlink:definitionLink\n\n\n330000 - Disclosure - Investments, Debt and Equity Securities, Schedule of Other than Temporary Impairment, Credit Losses Recognized in Earnings [Table]\nlink:definitionLink\n\n\n330000 - Disclosure - Investments, Debt and Equity Securities, Schedule of Gain (Loss) on Investments [Table]\nlink:definitionLink\n\n\n730000 - Disclosure - Compensation Related Costs, Retirement Benefits, Defined Benefit Plans and Other Postretirement Benefit Plans Disclosures [Table]\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations, Business Acquisition, Pro Forma Information, Nonrecurring Adjustments [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value Measurements, Recurring and Nonrecurring [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value Measurements, Recurring and Nonrecurring, Valuation Techniques [Table]\nlink:definitionLink\n\n\n831000 - Disclosure - Leases, Schedule of Capital Leased Assets [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Schedule of Retained Earnings Adjustments [Table]\nlink:definitionLink\n\n\n250000 - Disclosure - Accounting Changes and Error Corrections, Prospective Adoption of New Accounting Pronouncements [Table]\nlink:definitionLink\n\n\n200000 - Disclosure - Organization, Consolidation and Presentation of Financial Statements, Schedule of Collaborative Arrangements and Non-collaborative Arrangement Transactions [Table]\nlink:definitionLink\n\n\n336000 - Disclosure - Investments, All Other Investments, Schedule of Cost-method Investments [Table]\nlink:definitionLink\n\n\n831000 - Disclosure - Leases, Schedule of Operating Leased Assets [Table]\nlink:definitionLink\n\n\n985000 - Disclosure - Other Industries, Products and Services [Table]\nlink:definitionLink\n\n\n778000 - Disclosure - Extraordinary and Unusual Items, Schedule of Business Insurance Recoveries [Table]\nlink:definitionLink\n\n\n370000 - Disclosure - Intangible Assets, Goodwill and Other, Schedule of Indefinite-Lived Intangible Assets [Table]\nlink:definitionLink\n\n\n370000 - Disclosure - Intangible Assets, Goodwill and Other, Schedule of Finite-Lived Intangible Assets [Table]\nlink:definitionLink\n\n\n955000 - Disclosure - Health Care Organizations, Schedule of Revenue Sources, Health Care Organization [Table]\nlink:definitionLink\n\n\n300000 - Disclosure - Cash and Cash Equivalents, Schedule of Cash and Cash Equivalents [Table]\nlink:definitionLink\n\n\n320000 - Disclosure - Receivables, Loans, Notes Receivable, and Others, Certain Loans Acquired in Transfer Not Accounted for as Debt Securities Acquired During Period [Table]\nlink:definitionLink\n\n\n985000 - Disclosure - Other Industries, Types of Commercial Aircraft [Table]\nlink:definitionLink\n\n\n985000 - Disclosure - Other Industries, Airline Destination [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Net Derivative Asset (Liability) Measured on Recurring Basis, Unobservable Input Reconciliation [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value Inputs, Assets, Quantitative Information [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value Inputs, Liabilities, Quantitative Information [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value Inputs, Instruments Classified in Shareholders' Equity, Quantitative Information [Table]\nlink:definitionLink\n\n\n815000 - Disclosure - Fair Value Measures and Disclosures, Fair Value, Instruments Classified in Shareholders' Equity Measured on Recurring Basis, Unobservable Input Reconciliation [Table]\nlink:definitionLink\n\n\n730000 - Disclosure - Compensation Related Costs, Retirement Benefits, Schedule of Multiemployer Plans [Table]\nlink:definitionLink\n\n\n805000 - Disclosure - Derivative Instruments and Hedging Activities, Trading Activities, Gain and Losses, by Type, by Income Statement Location [Table]\nlink:definitionLink\n\n\n805000 - Disclosure - Derivative Instruments and Hedging Activities, Derivative Instruments, Gain (Loss) [Table]\nlink:definitionLink\n\n\n805000 - Disclosure - Derivative Instruments and Hedging Activities, Fair Values Derivatives, Balance Sheet Location, by Derivative Contract Type [Table]\nlink:definitionLink\n\n\n993540 - Disclosure - Investments Sold Not yet Purchased, Investments Sold, Not yet Purchased [Table]\nlink:definitionLink\n\n\n993510 - Disclosure - Other than Securities Investment Holdings, Investment Holdings, Other than Securities [Table]\nlink:definitionLink\n\n\n993530 - Disclosure - Investments Federal Income Tax Note, Federal Income Tax Note [Table]\nlink:definitionLink\n\n\n993500 - Disclosure - Investment Holdings, Schedule of Investments [Table]\nlink:definitionLink\n\n\n993520 - Disclosure - Summary of Investment Holdings, Schedule of Investments [Table]\nlink:definitionLink\n\n\n993540 - Disclosure - Investments Sold Not yet Purchased, Schedule of Investments [Table]\nlink:definitionLink\n\n\n993560 - Disclosure - Open Option Contracts Written, Schedule of Investments [Table]\nlink:definitionLink\n\n\n993570 - Disclosure - Investments in and Advances to Affiliates, Schedule of Investments [Table]\nlink:definitionLink\n\n\n993510 - Disclosure - Other than Securities Investment Holdings, Schedule of Investments [Table]\nlink:definitionLink\n\n\n993530 - Disclosure - Investments Federal Income Tax Note, Schedule of Investments [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Reinsurance Retention Policy [Table]\nlink:definitionLink\n\n\n430000 - Disclosure - Restructuring and Related Activities, Impairment Effects on Earnings Per Share [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Effects of Reinsurance [Table]\nlink:definitionLink\n\n\n980000 - Disclosure - Regulated Operations, Public Utilities General Disclosures [Table]\nlink:definitionLink\n\n\n210000 - Disclosure - Balance Sheet Offsetting, Offsetting Assets [Table]\nlink:definitionLink\n\n\n210000 - Disclosure - Balance Sheet Offsetting, Offsetting Liabilities [Table]\nlink:definitionLink\n\n\n805000 - Disclosure - Derivative Instruments and Hedging Activities, Derivative Instruments and Hedging Activities Disclosures [Table]\nlink:definitionLink\n\n\n450000 - Disclosure - Commitment and Contingencies, Other Commitments [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Accumulated Other Comprehensive Income (Loss) [Table]\nlink:definitionLink\n\n\n991000 - Disclosure - SEC Schedule, Article 12-04, Condensed Financial Information of Registrant, Condensed Balance Sheet Statement [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Distributions Made to Limited Liability Company (LLC) Member [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Distributions Made to Limited Partner [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Debt Instrument Redemption [Table]\nlink:definitionLink\n\n\n991000 - Disclosure - SEC Schedule, Article 12-04, Condensed Financial Information of Registrant, Condensed Cash Flow Statement [Table]\nlink:definitionLink\n\n\n340000 - Disclosure - Inventory, Inventory, Current [Table]\nlink:definitionLink\n\n\n800000 - Disclosure - Business Combinations, Business Combination, Segment Allocation [Table]\nlink:definitionLink\n\n\n500000 - Disclosure - Equity, Reclassification out of Accumulated Other Comprehensive Income [Table]\nlink:definitionLink\n\n\n805000 - Disclosure - Derivative Instruments and Hedging Activities, Reclassification out of Accumulated Other Comprehensive Income [Table]\nlink:definitionLink\n\n\n460000 - Disclosure - Debt, Obligation with Joint and Several Liability Arrangement [Table]\nlink:definitionLink\n\n\n939000 - Disclosure - Financial Services, Federal Home Loan Banks, Federal Home Loan Bank, Advances [Table]\nlink:definitionLink\n\n\n944000 - Disclosure - Financial Services, Insurance, Liability for Claims and Claims Adjustment Expense [Table]\nlink:definitionLink\n\n\n991000 - Disclosure - SEC Schedule, Article 12-04, Condensed Financial Information of Registrant, Condensed Income Statement [Table]\nlink:definitionLink\n\n\n991000 - Disclosure - SEC Schedule, Article 12-04, Condensed Financial Information of Registrant, Condensed Statement of Comprehensive Income [Table]\nlink:definitionLink\n\n\n730000 - Disclosure - Compensation Related Costs, Retirement Benefits, Defined Contribution Plan [Table]\nlink:definitionLink\n\n\n\n\n" }, { "path": "report/xbrl.Cache/us-types-2014-01-31.xsd", "content": "\n\n\n\n\n\n This file is the schema file for us-types (us-types).\n \n The DTS starting with this schema file will not contain any linkbases.\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "report/xbrl.Cache/xbrl-instance-2003-12-31.xsd", "content": "\r\r\n\r\r\n\r\r\n\r\r\n \r\r\n \r\r\n Taxonomy schema for XBRL. This schema defines syntax relating to \r\r\n XBRL instances.\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n\r\r\n \r\r\n \r\r\n Define the attributes to be used on XBRL concept definitions\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n The periodType attribute (restricting the period for XBRL items)\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n The balance attribute (imposes calculation relationship restrictions)\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n Define the simple types used as a base for for item types\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n the monetary type serves as the datatype for those financial \r\r\n concepts in a taxonomy which denote units in a currency.\r\r\n Instance items with this type must have a unit of measure \r\r\n from the ISO 4217 namespace of currencies.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n This datatype serves as the datatype for share based \r\r\n financial concepts.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n This datatype serves as the type for dimensionless numbers \r\r\n such as percentage change, growth rates, and other ratios \r\r\n where the numerator and denominator have the same units.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n As the name implies this is a decimal value that can not take \r\r\n the value 0 - it is used as the type for the denominator of a \r\r\n fractionItemType.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n This type is used to specify the value of the \r\r\n precision attribute on numeric items. It consists \r\r\n of the union of nonNegativeInteger and \"INF\" (used \r\r\n to signify infinite precision or \"exact value\").\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n This type is used to specify the value of the decimals attribute \r\r\n on numeric items. It consists of the union of integer and \"INF\" \r\r\n (used to signify that a number is expressed to an infinite number \r\r\n of decimal places or \"exact value\").\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Attributes for all items and tuples. \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Group of attributes for tuples.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Attributes for all items.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Attributes for all numeric items (fractional and non-fractional).\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Group of attributes for non-fractional numeric items\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Group of attributes for non-numeric items\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n General numeric item types - for use on concept element definitions\r\r\n The following 3 numeric types are all based on the built-in \r\r\n data types of XML Schema.\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n XBRL domain numeric item types - for use on concept element definitions\r\r\n The following 4 numeric types are all types that have been identified as \r\r\n having particular relevance to the domain space addressed by XBRL and are \r\r\n hence included in addition to the built-in types from XML Schema.\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n The following 13 numeric types are all based on the XML Schema \r\r\n built-in types that are derived by restriction from decimal.\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n The following 17 non-numeric types are all based on the primitive built-in \r\r\n data types of XML Schema.\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n The following 5 non-numeric types are all based on the XML Schema \r\r\n built-in types that are derived by restriction and/or list from string.\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n XML Schema components contributing to the context element\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n The type for the entity element, used to describe the reporting entity.\r\r\n Note that the scheme attribute is required and cannot be empty.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n The union of the date and dateTime simple types.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n The type for the period element, used to describe the reporting date info.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Used for the scenario under which fact have been reported.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Used for an island of context to which facts can be related.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n XML Schema components contributing to the unit element\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n A collection of sibling measure elements\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Element used to represent division in units\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Element used to represent units information about numeric items\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n Elements to use for facts in instances\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Abstract item element used as head of item substitution group\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Abstract tuple element used as head of tuple substitution group\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n XBRL instance root element.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n\r\r\n" }, { "path": "report/xbrl.Cache/xbrl-linkbase-2003-12-31.xsd", "content": "\r\r\n\r\r\n\r\r\n\r\r\n \r\r\n \r\r\n XBRL simple and extended link schema constructs\r\r\n \r\r\n \r\r\n\r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Concrete element to use for documentation of \r\r\n extended links and linkbases.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Concrete locator element. The loc element is the \r\r\n XLink locator element for all extended links in XBRL.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Concrete arc for use in label extended links.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Concrete arc for use in reference extended links.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Concrete arc for use in definition extended links.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Extension of the extended link arc type for presentation arcs.\r\r\n Adds a preferredLabel attribute that documents the role attribute\r\r\n value of preferred labels (as they occur in label extended links).\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Extension of the extended link arc type for calculation arcs.\r\r\n Adds a weight attribute to track weights on contributions to \r\r\n summations.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Concrete arc for use in footnote extended links.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the label resource element.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the reference part element - for use in reference resources.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the reference resource element.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the reference resource element\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n presentation extended link element definition.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n definition extended link element definition\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n calculation extended link element definition\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n label extended link element definition\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n reference extended link element definition\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n footnote extended link element definition\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the linkbase element. Used to \r\r\n contain a set of zero or more extended link elements.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the linkbaseRef element - used \r\r\n to link to XBRL taxonomy extended links from \r\r\n taxonomy schema documents and from XBRL\r\r\n instances.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n This attribute must have the value:\r\r\n http://www.w3.org/1999/xlink/properties/linkbase\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the schemaRef element - used \r\r\n to link to XBRL taxonomy schemas from \r\r\n XBRL instances.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the roleRef element - used \r\r\n to link to resolve xlink:role attribute values to \r\r\n the roleType element declaration.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n This attribute contains the role name.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the roleRef element - used \r\r\n to link to resolve xlink:arcrole attribute values to \r\r\n the arcroleType element declaration.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n This attribute contains the arc role name.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n The element to use for human-readable definition \r\r\n of custom roles and arc roles.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Definition of the usedOn element - used\r\r\n to identify what elements may use a \r\r\n taxonomy defined role or arc role value.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n The roleType element definition - used to define custom\r\r\n role values in XBRL extended links.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n The arcroleType element definition - used to define custom\r\r\n arc role values in XBRL extended links.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n\r\r\n" }, { "path": "report/xbrl.Cache/xbrldt-2005.xsd", "content": "\r\r\n\r\r\n\r\r\n\t\r\r\n\t\t\r\r\n\t\t\t\r\r\n\t\t\t\tSource (a hypercube) contains the target (a dimension) among others.\r\r\n\t\t\t\tdefinitionArc\r\r\n\t\t\t\r\r\n\t\t\t\r\r\n\t\t\t\tSource (a dimension) has only the target (a domain) as its domain.\r\r\n\t\t\t\tdefinitionArc\r\r\n\t\t\t\r\r\n\t\t\t\r\r\n\t\t\t\tSource (a domain) contains the target (a member).\r\r\n\t\t\t\tdefinitionArc\r\r\n\t\t\t\r\r\n\t\t\t\r\r\n\t\t\t\tSource (a primary item declaration) requires a combination of dimension members of the target (hypercube) to appear in the context of the primary item.\r\r\n\t\t\t\tdefinitionArc\r\r\n\t\t\t\r\r\n\t\t\t\r\r\n\t\t\t\tSource (a primary item declaration) requires a combination of dimension members of the target (hypercube) not to appear in the context of the primary item.\r\r\n\t\t\t\tdefinitionArc\r\r\n\t\t\t\r\r\n\t\t\t\r\r\n\t\t\t\tSource (a dimension) declares that there is a default member that is the target of the arc (a member).\r\r\n\t\t\t\tdefinitionArc\r\r\n\t\t\t\r\r\n\t\t\r\r\n\t\r\r\n\t\r\r\n\t\r\r\n\t\t\r\r\n\t\t\t\r\r\n\t\t\t\r\r\n\t\t\r\r\n\t\r\r\n\t\r\r\n\t\r\r\n\t\r\r\n\t\r\r\n\t\r\r\n\t\r\r\n\t\r\r\n\r\r\n" }, { "path": "report/xbrl.Cache/xl-2003-12-31.xsd", "content": "\r\r\n\r\r\n\r\r\n\r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n A URI type with a minimum length of 1 character.\r\r\n Used on role and arcrole and href elements.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Element type to use for documentation of \r\r\n extended links and linkbases.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Abstract element to use for documentation of \r\r\n extended links and linkbases.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n XBRL simple and extended link schema constructs\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Type for the abstract title element - \r\r\n used as a title element template.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\t \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Generic title element for use in extended link documentation.\r\r\n Used on extended links, arcs, locators.\r\r\n See http://www.w3.org/TR/xlink/#title-element for details.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Generic locator type.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\t \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Abstract locator element to be used as head of locator substitution group\r\r\n for all extended link locators in XBRL.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Enumerated values for the use attribute on extended link arcs.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n basic extended link arc type - extended where necessary for specific arcs\r\r\n Extends the generic arc type by adding use, priority and order attributes.\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Abstract element to use as head of arc element substitution group.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Generic type for the resource type element\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\t \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Abstract element to use as head of resource element substitution group.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Generic extended link type\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\t \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Abstract extended link element at head of extended link substitution group.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n Type for the simple links defined in XBRL\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n The abstract element at the head of the simple link substitution group.\r\r\n \r\r\n \r\r\n \r\r\n\r\r\n\r\r\n" }, { "path": "report/xbrl.Cache/xlink-2003-12-31.xsd", "content": "\r\r\n\r\r\n\r\r\n \r\r\n \r\r\n \r\r\n XLink attribute specification\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\t \r\r\n\t \r\r\n\t Enumeration of values for the type attribute\r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\t \r\r\n\t \r\r\n\t A URI with a minimum length of 1 character.\r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n\t \r\r\n\t \r\r\n\t A URI with a minimum length of 1 character.\r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n \r\r\n \r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\t \r\r\n\t \r\r\n\t Enumeration of values for the show attribute\r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t \r\r\n\t\r\r\n\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n Enumeration of values for the actuate attribute\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\t\r\r\n\t\r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n \r\r\n\r\r\n" }, { "path": "studentdata.csv", "content": "\"rollno\",\"name\",\"dob\",\"gender\",\"course\",\"batch\",\"EXCEL\",\"MATHS\",\"PYTHON\",\"RPGM\",\"STATS\",\"age\"\n1,\"XYZ\",1994-01-01,\"M\",\"BSCDS\",2016,45,109,61,74,92,24\n2,\"XYZ\",1994-01-08,\"M\",\"BSCDS\",2017,88,121,64,87,104,24\n3,\"XYZ\",1994-01-15,\"M\",\"BSCDS\",2016,68,109,64,86,99,24\n4,\"XYZ\",1994-01-22,\"M\",\"BSCDS\",2017,77,113,68,65,100,24\n5,\"XYZ\",1994-01-29,\"F\",\"BSCDS\",2016,56,77,77,79,108,24\n6,\"XYZ\",1994-02-05,\"M\",\"BSCDS\",2017,88,93,81,62,116,24\n7,\"XYZ\",1994-02-12,\"M\",\"BSCDS\",2016,108,130,67,52,126,24\n8,\"XYZ\",1994-02-19,\"M\",\"BSCDS\",2017,67,76,46,56,102,24\n9,\"XYZ\",1994-02-26,\"M\",\"BSCDS\",2016,78,105,85,68,126,24\n10,\"XYZ\",1994-03-05,\"M\",\"BSCDS\",2017,109,108,81,92,98,24\n11,\"XYZ\",1994-03-12,\"M\",\"BSCDS\",2016,94,120,68,71,117,24\n12,\"XYZ\",1994-03-19,\"M\",\"BSCDS\",2017,109,93,64,60,135,24\n13,\"XYZ\",1994-03-26,\"M\",\"BSCDS\",2016,80,103,72,73,110,24\n14,\"XYZ\",1994-04-02,\"F\",\"BSCDS\",2017,78,65,76,78,103,24\n15,\"XYZ\",1994-04-09,\"M\",\"BSCDS\",2016,72,107,82,54,110,24\n16,\"XYZ\",1994-04-16,\"F\",\"BSCDS\",2017,102,87,89,57,128,24\n17,\"XYZ\",1994-04-23,\"M\",\"BSCDS\",2016,61,95,67,47,99,24\n18,\"XYZ\",1994-04-30,\"M\",\"BSCDS\",2017,85,76,78,55,124,24\n19,\"XYZ\",1994-05-07,\"M\",\"BSCDS\",2016,79,108,88,59,123,24\n20,\"XYZ\",1994-05-14,\"M\",\"BSCDS\",2017,114,113,66,66,113,24\n21,\"XYZ\",1994-05-21,\"M\",\"MSCDS\",2016,71,94,73,87,110,24\n22,\"XYZ\",1994-05-28,\"M\",\"MSCDS\",2017,57,137,57,81,105,24\n23,\"XYZ\",1994-06-04,\"M\",\"MSCDS\",2016,83,113,78,73,106,24\n24,\"XYZ\",1994-06-11,\"M\",\"MSCDS\",2017,79,119,69,63,116,24\n25,\"XYZ\",1994-06-18,\"M\",\"MSCDS\",2016,67,141,66,70,131,24\n26,\"XYZ\",1994-06-25,\"F\",\"MSCDS\",2017,82,87,67,77,108,24\n27,\"XYZ\",1994-07-02,\"M\",\"MSCDS\",2016,76,116,79,64,96,24\n28,\"XYZ\",1994-07-09,\"F\",\"MSCDS\",2017,70,120,53,83,103,24\n29,\"XYZ\",1994-07-16,\"F\",\"MSCDS\",2016,88,100,71,83,113,24\n30,\"XYZ\",1994-07-23,\"M\",\"MSCDS\",2017,75,106,65,56,107,24\n31,\"XYZ\",1994-07-30,\"M\",\"MSCDS\",2016,52,80,65,61,108,24\n32,\"XYZ\",1994-08-06,\"M\",\"MSCDS\",2017,40,109,80,84,108,24\n33,\"XYZ\",1994-08-13,\"M\",\"MSCDS\",2016,71,86,79,50,96,24\n34,\"XYZ\",1994-08-20,\"M\",\"MSCDS\",2017,102,116,74,75,108,23\n35,\"XYZ\",1994-08-27,\"M\",\"MSCDS\",2016,93,84,80,73,119,23\n36,\"XYZ\",1994-09-03,\"M\",\"MSCDS\",2017,98,106,69,108,117,23\n37,\"XYZ\",1994-09-10,\"M\",\"MSCDS\",2016,95,83,55,37,115,23\n38,\"XYZ\",1994-09-17,\"M\",\"MSCDS\",2017,85,95,87,60,106,23\n39,\"XYZ\",1994-09-24,\"F\",\"MSCDS\",2016,57,69,73,78,108,23\n40,\"XYZ\",1994-10-01,\"F\",\"MSCDS\",2017,83,103,71,90,98,23\n41,\"XYZ\",1994-10-08,\"M\",\"MSCDS\",2016,104,120,77,80,109,23\n42,\"XYZ\",1994-10-15,\"M\",\"MSCDS\",2017,29,104,83,53,113,23\n43,\"XYZ\",1994-10-22,\"M\",\"MSCDS\",2016,68,65,79,97,127,23\n44,\"XYZ\",1994-10-29,\"M\",\"MSCDS\",2017,97,88,84,49,120,23\n45,\"XYZ\",1994-11-05,\"M\",\"MSCDS\",2016,88,133,75,57,105,23\n46,\"XYZ\",1994-11-12,\"M\",\"MSCDS\",2017,92,136,70,69,114,23\n47,\"XYZ\",1994-11-19,\"M\",\"MSCDS\",2016,87,76,72,97,99,23\n48,\"XYZ\",1994-11-26,\"M\",\"MSCDS\",2017,88,93,83,76,119,23\n49,\"XYZ\",1994-12-03,\"M\",\"MSCDS\",2016,80,107,67,79,120,23\n50,\"XYZ\",1994-12-10,\"M\",\"MSCDS\",2017,66,106,65,91,131,23\n51,\"XYZ\",1994-12-17,\"M\",\"PGDDS\",2016,62,88,78,82,114,23\n52,\"XYZ\",1994-12-24,\"M\",\"PGDDS\",2017,81,81,81,55,105,23\n53,\"XYZ\",1994-12-31,\"M\",\"PGDDS\",2016,74,96,58,79,111,23\n54,\"XYZ\",1995-01-07,\"M\",\"PGDDS\",2017,67,120,83,69,105,23\n55,\"XYZ\",1995-01-14,\"M\",\"PGDDS\",2016,86,100,75,91,102,23\n56,\"XYZ\",1995-01-21,\"M\",\"PGDDS\",2017,82,87,73,67,112,23\n57,\"XYZ\",1995-01-28,\"M\",\"PGDDS\",2016,102,90,86,52,101,23\n58,\"XYZ\",1995-02-04,\"M\",\"PGDDS\",2017,63,132,90,76,112,23\n59,\"XYZ\",1995-02-11,\"M\",\"PGDDS\",2016,72,91,64,74,114,23\n60,\"XYZ\",1995-02-18,\"F\",\"PGDDS\",2017,79,115,85,70,109,23\n61,\"XYZ\",1995-02-25,\"F\",\"PGDDS\",2016,59,129,69,57,108,23\n62,\"XYZ\",1995-03-04,\"M\",\"PGDDS\",2017,76,109,76,60,104,23\n63,\"XYZ\",1995-03-11,\"M\",\"PGDDS\",2016,69,92,80,70,99,23\n64,\"XYZ\",1995-03-18,\"M\",\"PGDDS\",2017,48,99,76,82,118,23\n65,\"XYZ\",1995-03-25,\"M\",\"PGDDS\",2016,76,90,60,66,110,23\n66,\"XYZ\",1995-04-01,\"M\",\"BTCSDA\",2017,72,125,70,60,118,23\n67,\"XYZ\",1995-04-08,\"M\",\"BTCSDA\",2016,97,97,90,38,98,23\n68,\"XYZ\",1995-04-15,\"M\",\"BTCSDA\",2017,71,131,77,76,112,23\n69,\"XYZ\",1995-04-22,\"M\",\"BTCSDA\",2016,88,89,67,75,117,23\n70,\"XYZ\",1995-04-29,\"M\",\"BTCSDA\",2017,82,87,68,86,110,23\n71,\"XYZ\",1995-05-06,\"M\",\"BTCSDA\",2016,61,103,86,88,108,23\n72,\"XYZ\",1995-05-13,\"F\",\"BTCSDA\",2017,86,100,78,68,102,23\n73,\"XYZ\",1995-05-20,\"M\",\"BTCSDA\",2016,78,90,66,86,131,23\n74,\"XYZ\",1995-05-27,\"M\",\"BTCSDA\",2017,82,68,75,70,118,23\n75,\"XYZ\",1995-06-03,\"M\",\"BTCSDA\",2016,90,101,81,62,128,23\n76,\"XYZ\",1995-06-10,\"M\",\"BTCSDA\",2017,82,86,92,60,111,23\n77,\"XYZ\",1995-06-17,\"M\",\"BTCSDA\",2016,104,122,66,66,104,23\n78,\"XYZ\",1995-06-24,\"M\",\"BTCSDA\",2017,82,81,71,73,95,23\n79,\"XYZ\",1995-07-01,\"M\",\"BTCSDA\",2016,61,123,85,70,104,23\n80,\"XYZ\",1995-07-08,\"M\",\"BTCSDA\",2017,91,87,70,72,112,23\n81,\"XYZ\",1995-07-15,\"F\",\"BTCSDA\",2016,62,106,90,72,120,23\n82,\"XYZ\",1995-07-22,\"M\",\"BTCSDA\",2017,51,118,85,79,113,23\n83,\"XYZ\",1995-07-29,\"M\",\"BTCSDA\",2016,68,90,58,67,98,23\n84,\"XYZ\",1995-08-05,\"M\",\"BTCSDA\",2017,69,111,79,90,117,23\n85,\"XYZ\",1995-08-12,\"M\",\"BTCSDA\",2016,71,98,61,75,93,23\n86,\"XYZ\",1995-08-19,\"F\",\"BTCSDA\",2017,104,77,60,56,106,22\n87,\"XYZ\",1995-08-26,\"M\",\"BTCSDA\",2016,105,95,76,67,107,22\n88,\"XYZ\",1995-09-02,\"M\",\"BTCSDA\",2017,91,132,76,76,91,22\n89,\"XYZ\",1995-09-09,\"M\",\"BTCSDA\",2016,61,96,75,65,119,22\n90,\"XYZ\",1995-09-16,\"F\",\"BTCSDA\",2017,80,84,88,74,104,22\n91,\"XYZ\",1995-09-23,\"M\",\"BTCSDA\",2016,87,99,78,75,107,22\n92,\"XYZ\",1995-09-30,\"F\",\"BTCSDA\",2017,78,107,68,86,124,22\n93,\"XYZ\",1995-10-07,\"M\",\"BTCSDA\",2016,83,119,76,62,116,22\n94,\"XYZ\",1995-10-14,\"M\",\"BTCSDA\",2017,66,123,70,70,109,22\n95,\"XYZ\",1995-10-21,\"M\",\"BTCSDA\",2016,61,102,74,63,115,22\n96,\"XYZ\",1995-10-28,\"M\",\"BTCSDA\",2017,101,123,64,56,114,22\n97,\"XYZ\",1995-11-04,\"M\",\"BTCSDA\",2016,84,85,74,48,127,22\n98,\"XYZ\",1995-11-11,\"M\",\"BTCSDA\",2017,74,53,76,72,113,22\n99,\"XYZ\",1995-11-18,\"M\",\"BTCSDA\",2016,85,91,68,77,115,22\n100,\"XYZ\",1995-11-25,\"M\",\"BTCSDA\",2017,111,90,75,78,113,22\n" }, { "path": "ximb.txt", "content": "\" Education… that meets the needs of the society and enables the building of sustainable communities. \"\nXavier University Bhubaneswar (XUB) sees itself as a quality institute delivering quality management education to quality students from the world over. The Xavier University came into being with the Government of Odisha passing the Xavier University Act in June 2013. It inherits the legacy of XIMB (Xavier Institute of Management Bhubaneswar), a premier world class B-School in the country. The identity of Xavier University is distinguished by its philosophy of ‘Inspiring Futures’ by not just walking the well-trodden path but striking out and exploring new paths. There is a clear intent to be the first to respond to the needs of the society and enable the building of sustainable communities that inspire the future of the State and the Country serving as an active agent of change.\nThe Xavier University stands tall in its mission of inspiring future generation of students aspiring for excellent quality higher education. We offer a wide gamut of programs in Business and Management. The University aims to fulfill its mission by continuing to introduce new programmes to groom visionary, competent, committed, compassionate and value-based leaders. It empowers the students with the knowledge, skills, and long-term vision that leads to innovation, service to society and growth. The University encourages the students to cultivate learning opportunities beyond the classroom, through various Student Forums. The academic programs are designed around a \"learning by-doing\" model that encourages extensive interaction with all components of the University’s ecosystem which serves as a starting point for evolving leadership philosophy and resilient business practices. Apart from academic activities, the co-curricular activities, sports, cultural and community activities, form important parts of the life of the students.\nThe University lives up to its vision of faith and spiritual inspiration and strives to build a just and humane society. It stands by the poor in their struggle for justice, it stands for the rights of the displaced persons, it promotes compassion, ethics and care for the environment as path to bring about global peace and sustainable. These excellences we have lived through the years has become an example, a legend for all to imitate and follow. This aligns to our motto -not to benchmark but to be benchmarked.\nIn its endeavor to take up the tasks which are bold but necessary and which hitherto nobody has taken up, the Xavier City Campus of the Xavier University was inaugurated on July 7, 2014. Presently it hosts eight schools offering Doctoral, Masters and Under-graduate programmes. History was made in October 1987 with the establishment of XIMB (Xavier Institute of Management Bhubaneswar), Xavier University’s flagship management institute which, over the past 30 years has been recognized as a premier Business School in the country. It aspires to lead the country in management education by focusing on research and innovation. Our intellectual capital rankings have been in the top five in the country consistently.\nSoon after came a unique rural intervention initiative - Xavier School of Rural Management, committed to our mission of creating social value, meeting the changing needs of rural businesses, government and development organizations. The Xavier School of Human Resources Management is an exclusive course grooming people-sensitive HR leaders equipped to build sustainable organizations and equitable society. With the foundation strongly laid came the courage and strength to a new metamorphosis of a longer lasting higher level change and transformation, leading to the creation of well integrated courses with a common purpose. The University hosts a unique course of study - a School of Sustainability Management, the first of its kind in the Country and among the very few in the World, proof of continuous innovation. The University’s Xavier School of Communications is the first one in the State of Odisha and is equipped as a state-of-the-art high quality media school. The newly instituted Xavier School of Commerce is an educational initiative to provide holistic and integral career oriented undergraduate as well as postgraduate programmes, fostered in a spirit of academic excellence.\nStudying at Xavier University is an opportunity to be the light that lights the world and be the fire that lights other fires…that lights other lives.\n" } ]